A Bio-Inspired, Motion-Based Analysis of Crowd Behavior Attributes Relevance to Motion Transparency, Velocity Gradients, and Motion Patterns

PubMed Central

Raudies, Florian; Neumann, Heiko

2012-01-01

The analysis of motion crowds is concerned with the detection of potential hazards for individuals of the crowd. Existing methods analyze the statistics of pixel motion to classify non-dangerous or dangerous behavior, to detect outlier motions, or to estimate the mean throughput of people for an image region. We suggest a biologically inspired model for the analysis of motion crowds that extracts motion features indicative for potential dangers in crowd behavior. Our model consists of stages for motion detection, integration, and pattern detection that model functions of the primate primary visual cortex area (V1), the middle temporal area (MT), and the medial superior temporal area (MST), respectively. This model allows for the processing of motion transparency, the appearance of multiple motions in the same visual region, in addition to processing opaque motion. We suggest that motion transparency helps to identify “danger zones” in motion crowds. For instance, motion transparency occurs in small exit passages during evacuation. However, motion transparency occurs also for non-dangerous crowd behavior when people move in opposite directions organized into separate lanes. Our analysis suggests: The combination of motion transparency and a slow motion speed can be used for labeling of candidate regions that contain dangerous behavior. In addition, locally detected decelerations or negative speed gradients of motions are a precursor of danger in crowd behavior as are globally detected motion patterns that show a contraction toward a single point. In sum, motion transparency, image speeds, motion patterns, and speed gradients extracted from visual motion in videos are important features to describe the behavioral state of a motion crowd. PMID:23300930

Cochlear spike synchronization and neuron coincidence detection model

NASA Astrophysics Data System (ADS)

Bader, Rolf

2018-02-01

Coincidence detection of a spike pattern fed from the cochlea into a single neuron is investigated using a physical Finite-Difference model of the cochlea and a physiologically motivated neuron model. Previous studies have shown experimental evidence of increased spike synchronization in the nucleus cochlearis and the trapezoid body [Joris et al., J. Neurophysiol. 71(3), 1022-1036 and 1037-1051 (1994)] and models show tone partial phase synchronization at the transition from mechanical waves on the basilar membrane into spike patterns [Ch. F. Babbs, J. Biophys. 2011, 435135]. Still the traveling speed of waves on the basilar membrane cause a frequency-dependent time delay of simultaneously incoming sound wavefronts up to 10 ms. The present model shows nearly perfect synchronization of multiple spike inputs as neuron outputs with interspike intervals (ISI) at the periodicity of the incoming sound for frequencies from about 30 to 300 Hz for two different amounts of afferent nerve fiber neuron inputs. Coincidence detection serves here as a fusion of multiple inputs into one single event enhancing pitch periodicity detection for low frequencies, impulse detection, or increased sound or speech intelligibility due to dereverberation.

Aposematism and crypsis are not enough to explain dorsal polymorphism in the Iberian adder

NASA Astrophysics Data System (ADS)

Martínez-Freiría, Fernando; Pérez i de Lanuza, Guillem; Pimenta, António A.; Pinto, Tiago; Santos, Xavier

2017-11-01

Aposematic organisms can show phenotypic variability across their distributional ranges. The ecological advantages of this variability have been scarcely studied in vipers. We explored this issue in Vipera seoanei, a species that exhibits five geographically structured dorsal colour phenotypes across Northern Iberia: two zigzag patterned (Classic and Cantabrica), one dorsal-strip patterned (Bilineata), one even grey (Uniform), and one melanistic (Melanistic). We compared predation rates (raptors and mammals) on plasticine models resembling each colour phenotype in three localities. Visual modelling techniques were used to infer detectability (i.e. conspicuousness) of each model type for visually guided predators (i.e. diurnal raptors). We hypothesize that predation rates will be lower for the two zigzag models (aposematism hypothesis) and that models with higher detectability would show higher predation rates (detectability hypothesis). Classic and Bilineata models were the most conspicuous, while Cantabrica and Uniform were the less. Melanistic presented an intermediate conspicuousness. Predation rate was low (3.24% of models) although there was variation in attack frequency among models. Zigzag models were scarcely predated supporting the aposematic role of the zigzag pattern in European vipers to reduce predation (aposematism hypothesis). From the non-zigzag models, high predation occurred on Bilineata and Melanistic models, and low on Uniform models, partially supporting our detectability hypothesis. These results suggest particular evolutionary advantages for non-zigzag phenotypes such as better performance of Melanistic phenotypes in cold environments or better crypsis of Uniform phenotypes. Polymorphism in V. seoanei may respond to a complex number of forces acting differentially across an environmental gradient.

On Identifiability of Bias-Type Actuator-Sensor Faults in Multiple-Model-Based Fault Detection and Identification

NASA Technical Reports Server (NTRS)

Joshi, Suresh M.

2012-01-01

This paper explores a class of multiple-model-based fault detection and identification (FDI) methods for bias-type faults in actuators and sensors. These methods employ banks of Kalman-Bucy filters to detect the faults, determine the fault pattern, and estimate the fault values, wherein each Kalman-Bucy filter is tuned to a different failure pattern. Necessary and sufficient conditions are presented for identifiability of actuator faults, sensor faults, and simultaneous actuator and sensor faults. It is shown that FDI of simultaneous actuator and sensor faults is not possible using these methods when all sensors have biases.

Detection of Anomalous Insiders in Collaborative Environments via Relational Analysis of Access Logs

PubMed Central

Chen, You; Malin, Bradley

2014-01-01

Collaborative information systems (CIS) are deployed within a diverse array of environments, ranging from the Internet to intelligence agencies to healthcare. It is increasingly the case that such systems are applied to manage sensitive information, making them targets for malicious insiders. While sophisticated security mechanisms have been developed to detect insider threats in various file systems, they are neither designed to model nor to monitor collaborative environments in which users function in dynamic teams with complex behavior. In this paper, we introduce a community-based anomaly detection system (CADS), an unsupervised learning framework to detect insider threats based on information recorded in the access logs of collaborative environments. CADS is based on the observation that typical users tend to form community structures, such that users with low a nity to such communities are indicative of anomalous and potentially illicit behavior. The model consists of two primary components: relational pattern extraction and anomaly detection. For relational pattern extraction, CADS infers community structures from CIS access logs, and subsequently derives communities, which serve as the CADS pattern core. CADS then uses a formal statistical model to measure the deviation of users from the inferred communities to predict which users are anomalies. To empirically evaluate the threat detection model, we perform an analysis with six months of access logs from a real electronic health record system in a large medical center, as well as a publicly-available dataset for replication purposes. The results illustrate that CADS can distinguish simulated anomalous users in the context of real user behavior with a high degree of certainty and with significant performance gains in comparison to several competing anomaly detection models. PMID:25485309

Using forbidden ordinal patterns to detect determinism in irregularly sampled time series.

PubMed

Kulp, C W; Chobot, J M; Niskala, B J; Needhammer, C J

2016-02-01

It is known that when symbolizing a time series into ordinal patterns using the Bandt-Pompe (BP) methodology, there will be ordinal patterns called forbidden patterns that do not occur in a deterministic series. The existence of forbidden patterns can be used to identify deterministic dynamics. In this paper, the ability to use forbidden patterns to detect determinism in irregularly sampled time series is tested on data generated from a continuous model system. The study is done in three parts. First, the effects of sampling time on the number of forbidden patterns are studied on regularly sampled time series. The next two parts focus on two types of irregular-sampling, missing data and timing jitter. It is shown that forbidden patterns can be used to detect determinism in irregularly sampled time series for low degrees of sampling irregularity (as defined in the paper). In addition, comments are made about the appropriateness of using the BP methodology to symbolize irregularly sampled time series.

Modeling peripheral vision for moving target search and detection.

PubMed

Yang, Ji Hyun; Huston, Jesse; Day, Michael; Balogh, Imre

2012-06-01

Most target search and detection models focus on foveal vision. In reality, peripheral vision plays a significant role, especially in detecting moving objects. There were 23 subjects who participated in experiments simulating target detection tasks in urban and rural environments while their gaze parameters were tracked. Button responses associated with foveal object and peripheral object (PO) detection and recognition were recorded. In an urban scenario, pedestrians appearing in the periphery holding guns were threats and pedestrians with empty hands were non-threats. In a rural scenario, non-U.S. unmanned aerial vehicles (UAVs) were considered threats and U.S. UAVs non-threats. On average, subjects missed detecting 2.48 POs among 50 POs in the urban scenario and 5.39 POs in the rural scenario. Both saccade reaction time and button reaction time can be predicted by peripheral angle and entrance speed of POs. Fast moving objects were detected faster than slower objects and POs appearing at wider angles took longer to detect than those closer to the gaze center. A second-order mixed-effect model was applied to provide each subject's prediction model for peripheral target detection performance as a function of eccentricity angle and speed. About half the subjects used active search patterns while the other half used passive search patterns. An interactive 3-D visualization tool was developed to provide a representation of macro-scale head and gaze movement in the search and target detection task. An experimentally validated stochastic model of peripheral vision in realistic target detection scenarios was developed.

Process Mining for Individualized Behavior Modeling Using Wireless Tracking in Nursing Homes

PubMed Central

Fernández-Llatas, Carlos; Benedi, José-Miguel; García-Gómez, Juan M.; Traver, Vicente

2013-01-01

The analysis of human behavior patterns is increasingly used for several research fields. The individualized modeling of behavior using classical techniques requires too much time and resources to be effective. A possible solution would be the use of pattern recognition techniques to automatically infer models to allow experts to understand individual behavior. However, traditional pattern recognition algorithms infer models that are not readily understood by human experts. This limits the capacity to benefit from the inferred models. Process mining technologies can infer models as workflows, specifically designed to be understood by experts, enabling them to detect specific behavior patterns in users. In this paper, the eMotiva process mining algorithms are presented. These algorithms filter, infer and visualize workflows. The workflows are inferred from the samples produced by an indoor location system that stores the location of a resident in a nursing home. The visualization tool is able to compare and highlight behavior patterns in order to facilitate expert understanding of human behavior. This tool was tested with nine real users that were monitored for a 25-week period. The results achieved suggest that the behavior of users is continuously evolving and changing and that this change can be measured, allowing for behavioral change detection. PMID:24225907

Edge detection and localization with edge pattern analysis and inflection characterization

NASA Astrophysics Data System (ADS)

Jiang, Bo

2012-05-01

In general edges are considered to be abrupt changes or discontinuities in two dimensional image signal intensity distributions. The accuracy of front-end edge detection methods in image processing impacts the eventual success of higher level pattern analysis downstream. To generalize edge detectors designed from a simple ideal step function model to real distortions in natural images, research on one dimensional edge pattern analysis to improve the accuracy of edge detection and localization proposes an edge detection algorithm, which is composed by three basic edge patterns, such as ramp, impulse, and step. After mathematical analysis, general rules for edge representation based upon the classification of edge types into three categories-ramp, impulse, and step (RIS) are developed to reduce detection and localization errors, especially reducing "double edge" effect that is one important drawback to the derivative method. But, when applying one dimensional edge pattern in two dimensional image processing, a new issue is naturally raised that the edge detector should correct marking inflections or junctions of edges. Research on human visual perception of objects and information theory pointed out that a pattern lexicon of "inflection micro-patterns" has larger information than a straight line. Also, research on scene perception gave an idea that contours have larger information are more important factor to determine the success of scene categorization. Therefore, inflections or junctions are extremely useful features, whose accurate description and reconstruction are significant in solving correspondence problems in computer vision. Therefore, aside from adoption of edge pattern analysis, inflection or junction characterization is also utilized to extend traditional derivative edge detection algorithm. Experiments were conducted to test my propositions about edge detection and localization accuracy improvements. The results support the idea that these edge detection method improvements are effective in enhancing the accuracy of edge detection and localization.

Sample size and the detection of a hump-shaped relationship between biomass and species richness in Mediterranean wetlands

USGS Publications Warehouse

Espinar, J.L.

2006-01-01

Questions: What is the observed relationship between biomass and species richness across both spatial and temporal scales in communities of submerged annual macrophytes? Does the number of plots sampled affect detection of hump-shaped pattern? Location: Don??ana National Park, southwestern Spain. Methods: A total of 102 plots were sampled during four hydrological cycles. In each hydrological cycle, the plots were distributed randomly along an environmental flooding gradient in three contrasted microhabitats located in the transition zone just below the upper marsh. In each plot (0.5 m x 0.5 m), plant density and above- and below-ground biomass of submerged vegetation were measured. The hump-shaped model was tested by using a generalized linear model (GLM). A bootstrap procedure was used to test the effect of the number of plots on the ability to detect hump-shaped patterns. Result: The area exhibited low species density with a range of 1 - 9 species and low values of biomass with a range of 0.2 - 87.6 g-DW / 0.25 m2. When data from all years and all microhabitats were combined, the relationships between biomass and species richness showed a hump-shaped pattern. The number of plots was large enough to allow detection of the hump-shaped pattern across microhabitats but it was too small to confirm the hump-shaped pattern within each individual microhabitat. Conclusion: This study provides evidence of hump-shaped patterns across microhabitats when GLM analysis is used. In communities of submerged annual macrophytes in Mediterranean wetlands, the highest species density occurs in intermediate values of biomass. The bootstrap procedure indicates that the number of plots affects the detection of hump-shaped patterns. ?? IAVS; Opulus Press.

When Can Species Abundance Data Reveal Non-neutrality?

PubMed Central

Al Hammal, Omar; Alonso, David; Etienne, Rampal S.; Cornell, Stephen J.

2015-01-01

Species abundance distributions (SAD) are probably ecology’s most well-known empirical pattern, and over the last decades many models have been proposed to explain their shape. There is no consensus over which model is correct, because the degree to which different processes can be discerned from SAD patterns has not yet been rigorously quantified. We present a power calculation to quantify our ability to detect deviations from neutrality using species abundance data. We study non-neutral stochastic community models, and show that the presence of non-neutral processes is detectable if sample size is large enough and/or the amplitude of the effect is strong enough. Our framework can be used for any candidate community model that can be simulated on a computer, and determines both the sampling effort required to distinguish between alternative processes, and a range for the strength of non-neutral processes in communities whose patterns are statistically consistent with neutral theory. We find that even data sets of the scale of the 50 Ha forest plot on Barro Colorado Island, Panama, are unlikely to be large enough to detect deviations from neutrality caused by competitive interactions alone, though the presence of multiple non-neutral processes with contrasting effects on abundance distributions may be detectable. PMID:25793889

Modeling Temporal Behavior in Large Networks: A Dynamic Mixed-Membership Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rossi, R; Gallagher, B; Neville, J

Given a large time-evolving network, how can we model and characterize the temporal behaviors of individual nodes (and network states)? How can we model the behavioral transition patterns of nodes? We propose a temporal behavior model that captures the 'roles' of nodes in the graph and how they evolve over time. The proposed dynamic behavioral mixed-membership model (DBMM) is scalable, fully automatic (no user-defined parameters), non-parametric/data-driven (no specific functional form or parameterization), interpretable (identifies explainable patterns), and flexible (applicable to dynamic and streaming networks). Moreover, the interpretable behavioral roles are generalizable, computationally efficient, and natively supports attributes. We applied ourmore » model for (a) identifying patterns and trends of nodes and network states based on the temporal behavior, (b) predicting future structural changes, and (c) detecting unusual temporal behavior transitions. We use eight large real-world datasets from different time-evolving settings (dynamic and streaming). In particular, we model the evolving mixed-memberships and the corresponding behavioral transitions of Twitter, Facebook, IP-Traces, Email (University), Internet AS, Enron, Reality, and IMDB. The experiments demonstrate the scalability, flexibility, and effectiveness of our model for identifying interesting patterns, detecting unusual structural transitions, and predicting the future structural changes of the network and individual nodes.« less

Unsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields.

PubMed

Yousefi, Siamak; Balasubramanian, Madhusudhanan; Goldbaum, Michael H; Medeiros, Felipe A; Zangwill, Linda M; Weinreb, Robert N; Liebmann, Jeffrey M; Girkin, Christopher A; Bowd, Christopher

2016-05-01

To validate Gaussian mixture-model with expectation maximization (GEM) and variational Bayesian independent component analysis mixture-models (VIM) for detecting glaucomatous progression along visual field (VF) defect patterns (GEM-progression of patterns (POP) and VIM-POP). To compare GEM-POP and VIM-POP with other methods. GEM and VIM models separated cross-sectional abnormal VFs from 859 eyes and normal VFs from 1117 eyes into abnormal and normal clusters. Clusters were decomposed into independent axes. The confidence limit (CL) of stability was established for each axis with a set of 84 stable eyes. Sensitivity for detecting progression was assessed in a sample of 83 eyes with known progressive glaucomatous optic neuropathy (PGON). Eyes were classified as progressed if any defect pattern progressed beyond the CL of stability. Performance of GEM-POP and VIM-POP was compared to point-wise linear regression (PLR), permutation analysis of PLR (PoPLR), and linear regression (LR) of mean deviation (MD), and visual field index (VFI). Sensitivity and specificity for detecting glaucomatous VFs were 89.9% and 93.8%, respectively, for GEM and 93.0% and 97.0%, respectively, for VIM. Receiver operating characteristic (ROC) curve areas for classifying progressed eyes were 0.82 for VIM-POP, 0.86 for GEM-POP, 0.81 for PoPLR, 0.69 for LR of MD, and 0.76 for LR of VFI. GEM-POP was significantly more sensitive to PGON than PoPLR and linear regression of MD and VFI in our sample, while providing localized progression information. Detection of glaucomatous progression can be improved by assessing longitudinal changes in localized patterns of glaucomatous defect identified by unsupervised machine learning.

Discovering biclusters in gene expression data based on high-dimensional linear geometries

PubMed Central

Gan, Xiangchao; Liew, Alan Wee-Chung; Yan, Hong

2008-01-01

Background In DNA microarray experiments, discovering groups of genes that share similar transcriptional characteristics is instrumental in functional annotation, tissue classification and motif identification. However, in many situations a subset of genes only exhibits consistent pattern over a subset of conditions. Conventional clustering algorithms that deal with the entire row or column in an expression matrix would therefore fail to detect these useful patterns in the data. Recently, biclustering has been proposed to detect a subset of genes exhibiting consistent pattern over a subset of conditions. However, most existing biclustering algorithms are based on searching for sub-matrices within a data matrix by optimizing certain heuristically defined merit functions. Moreover, most of these algorithms can only detect a restricted set of bicluster patterns. Results In this paper, we present a novel geometric perspective for the biclustering problem. The biclustering process is interpreted as the detection of linear geometries in a high dimensional data space. Such a new perspective views biclusters with different patterns as hyperplanes in a high dimensional space, and allows us to handle different types of linear patterns simultaneously by matching a specific set of linear geometries. This geometric viewpoint also inspires us to propose a generic bicluster pattern, i.e. the linear coherent model that unifies the seemingly incompatible additive and multiplicative bicluster models. As a particular realization of our framework, we have implemented a Hough transform-based hyperplane detection algorithm. The experimental results on human lymphoma gene expression dataset show that our algorithm can find biologically significant subsets of genes. Conclusion We have proposed a novel geometric interpretation of the biclustering problem. We have shown that many common types of bicluster are just different spatial arrangements of hyperplanes in a high dimensional data space. An implementation of the geometric framework using the Fast Hough transform for hyperplane detection can be used to discover biologically significant subsets of genes under subsets of conditions for microarray data analysis. PMID:18433477

RT-PCR detection of Candida albicans ALS gene expression in the reconstituted human epithelium (RHE) model of oral candidiasis and in model biofilms.

PubMed

Green, Clayton B; Cheng, Georgina; Chandra, Jyotsna; Mukherjee, Pranab; Ghannoum, Mahmoud A; Hoyer, Lois L

2004-02-01

An RT-PCR assay was developed to analyse expression patterns of genes in the Candida albicans ALS (agglutinin-like sequence) family. Inoculation of a reconstituted human buccal epithelium (RHE) model of mucocutaneous candidiasis with strain SC5314 showed destruction of the epithelial layer by C. albicans and also formation of an upper fungal layer that had characteristics similar to a biofilm. RT-PCR analysis of total RNA samples extracted from C. albicans-inoculated buccal RHE showed that ALS1, ALS2, ALS3, ALS4, ALS5 and ALS9 were consistently detected over time as destruction of the RHE progressed. Detection of transcripts from ALS7, and particularly from ALS6, was more sporadic, but not associated with a strictly temporal pattern. The expression pattern of ALS genes in C. albicans cultures used to inoculate the RHE was similar to that observed in the RHE model, suggesting that contact of C. albicans with buccal RHE does little to alter ALS gene expression. RT-PCR analysis of RNA samples extracted from model denture and catheter biofilms showed similar gene expression patterns to the buccal RHE specimens. Results from the RT-PCR analysis of biofilm RNA specimens were consistent between various C. albicans strains during biofilm development and were comparable to gene expression patterns in planktonic cells. The RT-PCR assay described here will be useful for analysis of human clinical specimens and samples from other disease models. The method will provide further insight into the role of ALS genes and their encoded proteins in the diverse interactions between C. albicans and its host.

Using multilevel spatial models to understand salamander site occupancy patterns after wildfire

USGS Publications Warehouse

Chelgren, Nathan; Adams, Michael J.; Bailey, Larissa L.; Bury, R. Bruce

2011-01-01

Studies of the distribution of elusive forest wildlife have suffered from the confounding of true presence with the uncertainty of detection. Occupancy modeling, which incorporates probabilities of species detection conditional on presence, is an emerging approach for reducing observation bias. However, the current likelihood modeling framework is restrictive for handling unexplained sources of variation in the response that may occur when there are dependence structures such as smaller sampling units that are nested within larger sampling units. We used multilevel Bayesian occupancy modeling to handle dependence structures and to partition sources of variation in occupancy of sites by terrestrial salamanders (family Plethodontidae) within and surrounding an earlier wildfire in western Oregon, USA. Comparison of model fit favored a spatial N-mixture model that accounted for variation in salamander abundance over models that were based on binary detection/non-detection data. Though catch per unit effort was higher in burned areas than unburned, there was strong support that this pattern was due to a higher probability of capture for individuals in burned plots. Within the burn, the odds of capturing an individual given it was present were 2.06 times the odds outside the burn, reflecting reduced complexity of ground cover in the burn. There was weak support that true occupancy was lower within the burned area. While the odds of occupancy in the burn were 0.49 times the odds outside the burn among the five species, the magnitude of variation attributed to the burn was small in comparison to variation attributed to other landscape variables and to unexplained, spatially autocorrelated random variation. While ordinary occupancy models may separate the biological pattern of interest from variation in detection probability when all sources of variation are known, the addition of random effects structures for unexplained sources of variation in occupancy and detection probability may often more appropriately represent levels of uncertainty. ?? 2011 by the Ecological Society of America.

A system for learning statistical motion patterns.

PubMed

Hu, Weiming; Xiao, Xuejuan; Fu, Zhouyu; Xie, Dan; Tan, Tieniu; Maybank, Steve

2006-09-01

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy K-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction.

Modeling seasonal detection patterns for burrowing owl surveys

Treesearch

Quresh S. Latif; Kathleen D. Fleming; Cameron Barrows; John T. Rotenberry

2012-01-01

To guide monitoring of burrowing owls (Athene cunicularia) in the Coachella Valley, California, USA, we analyzed survey-method-specific seasonal variation in detectability. Point-based call-broadcast surveys yielded high early season detectability that then declined through time, whereas detectability on driving surveys increased through the season. Point surveys...

Behavioral and Temporal Pattern Detection Within Financial Data With Hidden Information

DTIC Science & Technology

2012-02-01

probabilistic pattern detector to monitor the pattern. 15. SUBJECT TERMS Runtime verification, Hidden data, Hidden Markov models, Formal specifications...sequences in many other fields besides financial systems [L, TV, LC, LZ ]. Rather, the technique suggested in this paper is positioned as a hybrid...operation of the pattern detector . Section 7 describes the operation of the probabilistic pattern-matching monitor, and section 8 describes three

Repetition Is the Feature Behind the Attentional Bias for Recognizing Threatening Patterns.

PubMed

Shabbir, Maryam; Zon, Adelynn M Y; Thuppil, Vivek

2018-01-01

Animals attend to what is relevant in order to behave in an effective manner and succeed in their environments. In several nonhuman species, there is an evolved bias for attending to patterns indicative of threats in the natural environment such as dangerous animals. Because skins of many dangerous animals are typically repetitive, we propose that repetition is the key feature enabling recognition of evolutionarily important threats. The current study consists of two experiments where we measured participants' reactions to pictures of male and female models wearing clothing of various repeating (leopard skin, snakeskin, and floral print) and nonrepeating (camouflage, shiny, and plain) patterns. In Experiment 1, when models wearing patterns were presented side by side with total fixation duration as the measure, the repeating floral pattern was the most provocative, with total fixation duration significantly longer than all other patterns. Leopard and snakeskin patterns had total fixation durations that were significantly longer than the plain pattern. In Experiment 2, we employed a visual-search task where participants were required to find models wearing the various patterns in a setting of a crowded airport terminal. Participants detected leopard skin pattern and repetitive floral pattern significantly faster than two of the nonpatterned clothing styles. Our experimental findings support the hypothesis that repetition of specific visual features might facilitate target detection, especially those characterizing evolutionary important threats. Our findings that intricate, but nonthreatening repeating patterns can have similar attention-grabbing properties to animal skin patterns have important implications for the fashion industry and wildlife trade.

Imbalance aware lithography hotspot detection: a deep learning approach

NASA Astrophysics Data System (ADS)

Yang, Haoyu; Luo, Luyang; Su, Jing; Lin, Chenxi; Yu, Bei

2017-03-01

With the advancement of VLSI technology nodes, light diffraction caused lithographic hotspots have become a serious problem affecting manufacture yield. Lithography hotspot detection at the post-OPC stage is imperative to check potential circuit failures when transferring designed patterns onto silicon wafers. Although conventional lithography hotspot detection methods, such as machine learning, have gained satisfactory performance, with extreme scaling of transistor feature size and more and more complicated layout patterns, conventional methodologies may suffer from performance degradation. For example, manual or ad hoc feature extraction in a machine learning framework may lose important information when predicting potential errors in ultra-large-scale integrated circuit masks. In this paper, we present a deep convolutional neural network (CNN) targeting representative feature learning in lithography hotspot detection. We carefully analyze impact and effectiveness of different CNN hyper-parameters, through which a hotspot-detection-oriented neural network model is established. Because hotspot patterns are always minorities in VLSI mask design, the training data set is highly imbalanced. In this situation, a neural network is no longer reliable, because a trained model with high classification accuracy may still suffer from high false negative results (missing hotspots), which is fatal in hotspot detection problems. To address the imbalance problem, we further apply minority upsampling and random-mirror flipping before training the network. Experimental results show that our proposed neural network model achieves highly comparable or better performance on the ICCAD 2012 contest benchmark compared to state-of-the-art hotspot detectors based on deep or representative machine leaning.

A Dictionary Approach to Electron Backscatter Diffraction Indexing.

PubMed

Chen, Yu H; Park, Se Un; Wei, Dennis; Newstadt, Greg; Jackson, Michael A; Simmons, Jeff P; De Graef, Marc; Hero, Alfred O

2015-06-01

We propose a framework for indexing of grain and subgrain structures in electron backscatter diffraction patterns of polycrystalline materials. We discretize the domain of a dynamical forward model onto a dense grid of orientations, producing a dictionary of patterns. For each measured pattern, we identify the most similar patterns in the dictionary, and identify boundaries, detect anomalies, and index crystal orientations. The statistical distribution of these closest matches is used in an unsupervised binary decision tree (DT) classifier to identify grain boundaries and anomalous regions. The DT classifies a pattern as an anomaly if it has an abnormally low similarity to any pattern in the dictionary. It classifies a pixel as being near a grain boundary if the highly ranked patterns in the dictionary differ significantly over the pixel's neighborhood. Indexing is accomplished by computing the mean orientation of the closest matches to each pattern. The mean orientation is estimated using a maximum likelihood approach that models the orientation distribution as a mixture of Von Mises-Fisher distributions over the quaternionic three sphere. The proposed dictionary matching approach permits segmentation, anomaly detection, and indexing to be performed in a unified manner with the additional benefit of uncertainty quantification.

Test pattern generation for ILA sequential circuits

NASA Technical Reports Server (NTRS)

Feng, YU; Frenzel, James F.; Maki, Gary K.

1993-01-01

An efficient method of generating test patterns for sequential machines implemented using one-dimensional, unilateral, iterative logic arrays (ILA's) of BTS pass transistor networks is presented. Based on a transistor level fault model, the method affords a unique opportunity for real-time fault detection with improved fault coverage. The resulting test sets are shown to be equivalent to those obtained using conventional gate level models, thus eliminating the need for additional test patterns. The proposed method advances the simplicity and ease of the test pattern generation for a special class of sequential circuitry.

Human connectome module pattern detection using a new multi-graph MinMax cut model.

PubMed

De, Wang; Wang, Yang; Nie, Feiping; Yan, Jingwen; Cai, Weidong; Saykin, Andrew J; Shen, Li; Huang, Heng

2014-01-01

Many recent scientific efforts have been devoted to constructing the human connectome using Diffusion Tensor Imaging (DTI) data for understanding the large-scale brain networks that underlie higher-level cognition in human. However, suitable computational network analysis tools are still lacking in human connectome research. To address this problem, we propose a novel multi-graph min-max cut model to detect the consistent network modules from the brain connectivity networks of all studied subjects. A new multi-graph MinMax cut model is introduced to solve this challenging computational neuroscience problem and the efficient optimization algorithm is derived. In the identified connectome module patterns, each network module shows similar connectivity patterns in all subjects, which potentially associate to specific brain functions shared by all subjects. We validate our method by analyzing the weighted fiber connectivity networks. The promising empirical results demonstrate the effectiveness of our method.

Deep Independence Network Analysis of Structural Brain Imaging: Application to Schizophrenia

PubMed Central

Castro, Eduardo; Hjelm, R. Devon; Plis, Sergey M.; Dinh, Laurent; Turner, Jessica A.; Calhoun, Vince D.

2016-01-01

Linear independent component analysis (ICA) is a standard signal processing technique that has been extensively used on neuroimaging data to detect brain networks with coherent brain activity (functional MRI) or covarying structural patterns (structural MRI). However, its formulation assumes that the measured brain signals are generated by a linear mixture of the underlying brain networks and this assumption limits its ability to detect the inherent nonlinear nature of brain interactions. In this paper, we introduce nonlinear independent component estimation (NICE) to structural MRI data to detect abnormal patterns of gray matter concentration in schizophrenia patients. For this biomedical application, we further addressed the issue of model regularization of nonlinear ICA by performing dimensionality reduction prior to NICE, together with an appropriate control of the complexity of the model and the usage of a proper approximation of the probability distribution functions of the estimated components. We show that our results are consistent with previous findings in the literature, but we also demonstrate that the incorporation of nonlinear associations in the data enables the detection of spatial patterns that are not identified by linear ICA. Specifically, we show networks including basal ganglia, cerebellum and thalamus that show significant differences in patients versus controls, some of which show distinct nonlinear patterns. PMID:26891483

A Study on the Model of Detecting the Variation of Geomagnetic Intensity Based on an Adapted Motion Strategy.

PubMed

Li, Hong; Liu, Mingyong; Liu, Kun; Zhang, Feihu

2017-12-25

By simulating the geomagnetic fields and analyzing thevariation of intensities, this paper presents a model for calculating the objective function ofan Autonomous Underwater Vehicle (AUV)geomagnetic navigation task. By investigating the biologically inspired strategies, the AUV successfullyreachesthe destination duringgeomagnetic navigation without using the priori geomagnetic map. Similar to the pattern of a flatworm, the proposed algorithm relies on a motion pattern to trigger a local searching strategy by detecting the real-time geomagnetic intensity. An adapted strategy is then implemented, which is biased on the specific target. The results show thereliabilityandeffectivenessofthe proposed algorithm.

Detecting Math Anxiety with a Mixture Partial Credit Model

ERIC Educational Resources Information Center

Ölmez, Ibrahim Burak; Cohen, Allan S.

2017-01-01

The purpose of this study was to investigate a new methodology for detection of differences in middle grades students' math anxiety. A mixture partial credit model analysis revealed two distinct latent classes based on homogeneities in response patterns within each latent class. Students in Class 1 had less anxiety about apprehension of math…

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yan, Guanghua, E-mail: yan@ufl.edu; Li, Jonathan; Huang, Yin

Purpose: To propose a simple model to explain the origin of ghost markers in marker-based optical tracking systems (OTS) and to develop retrospective strategies to detect and eliminate ghost markers. Methods: In marker-based OTS, ghost markers are virtual markers created due to the cross-talk between the two camera sensors, which can lead to system execution failure or inaccuracy in patient tracking. As a result, the users have to limit the number of markers and avoid certain marker configurations to reduce the chances of ghost markers. In this work, the authors propose retrospective strategies to detect and eliminate ghost markers. Themore » two camera sensors were treated as mathematical points in space. The authors identified the coplanar within limit (CWL) condition as the necessary condition for ghost marker occurrence. A simple ghost marker detection method was proposed based on the model. Ghost marker elimination was achieved through pattern matching: a ghost marker-free reference set was matched with the optical marker set observed by the OTS; unmatched optical markers were eliminated as either ghost markers or misplaced markers. The pattern matching problem was formulated as a constraint satisfaction problem (using pairwise distances as constraints) and solved with an iterative backtracking algorithm. Wildcard markers were introduced to address missing or misplaced markers. An experiment was designed to measure the sensor positions and the limit for the CWL condition. The ghost marker detection and elimination algorithms were verified with samples collected from a five-marker jig and a nine-marker anthropomorphic phantom, rotated with the treatment couch from −60° to +60°. The accuracy of the pattern matching algorithm was further validated with marker patterns from 40 patients who underwent stereotactic body radiotherapy (SBRT). For this purpose, a synthetic optical marker pattern was created for each patient by introducing ghost markers, marker position uncertainties, and marker displacement. Results: The sensor positions and the limit for the CWL condition were measured with excellent reproducibility (standard deviation ≤ 0.39 mm). The ghost marker detection algorithm had perfect detection accuracy for both the jig (1544 samples) and the anthropomorphic phantom (2045 samples). Pattern matching was successful for all samples from both phantoms as well as the 40 patient marker patterns. Conclusions: The authors proposed a simple model to explain the origin of ghost markers and identified the CWL condition as the necessary condition for ghost marker occurrence. The retrospective ghost marker detection and elimination algorithms guarantee complete ghost marker elimination while providing the users with maximum flexibility in selecting the number of markers and their configuration to meet their clinic needs.« less

PolyaPeak: Detecting Transcription Factor Binding Sites from ChIP-seq Using Peak Shape Information

PubMed Central

Wu, Hao; Ji, Hongkai

2014-01-01

ChIP-seq is a powerful technology for detecting genomic regions where a protein of interest interacts with DNA. ChIP-seq data for mapping transcription factor binding sites (TFBSs) have a characteristic pattern: around each binding site, sequence reads aligned to the forward and reverse strands of the reference genome form two separate peaks shifted away from each other, and the true binding site is located in between these two peaks. While it has been shown previously that the accuracy and resolution of binding site detection can be improved by modeling the pattern, efficient methods are unavailable to fully utilize that information in TFBS detection procedure. We present PolyaPeak, a new method to improve TFBS detection by incorporating the peak shape information. PolyaPeak describes peak shapes using a flexible Pólya model. The shapes are automatically learnt from the data using Minorization-Maximization (MM) algorithm, then integrated with the read count information via a hierarchical model to distinguish true binding sites from background noises. Extensive real data analyses show that PolyaPeak is capable of robustly improving TFBS detection compared with existing methods. An R package is freely available. PMID:24608116

Qualitative Contrast between Knowledge-Limited Mixed-State and Variable-Resources Models of Visual Change Detection

ERIC Educational Resources Information Center

Nosofsky, Robert M.; Donkin, Chris

2016-01-01

We report an experiment designed to provide a qualitative contrast between knowledge-limited versions of mixed-state and variable-resources (VR) models of visual change detection. The key data pattern is that observers often respond "same" on big-change trials, while simultaneously being able to discriminate between same and small-change…

Modeling of gene expression pattern alteration by p,p′-DDE and dieldrin in largemouth bass

PubMed Central

Garcia-Reyero, Natàlia; Barber, David; Gross, Timothy; Denslow, Nancy

2007-01-01

In this study, largemouth bass (LMB) were subchronically exposed to p,p′-DDE or dieldrin in their diet to evaluate the effect of exposure on expression of genes involved in reproduction and steroid homeostasis. Using real-time PCR, we detected a different gene expression pattern for each OCP, suggesting that they each affect LMB in a different way. We also detected a different expression pattern among sexes, suggesting that sexes are affected differently by OCPs perhaps reflecting the different adaptive responses of each sex to dysregulation caused by OCP exposure. PMID:16707152

Modeling of gene expression pattern alteration by p,p′-DDE and dieldrin in largemouth bass

USGS Publications Warehouse

Garcia-Reyero, Natalia; Barber, David; Gross, Timothy; Denslow, Nancy

2006-01-01

In this study, largemouth bass (LMB) were subchronically exposed to p,p′-DDE or dieldrin in their diet to evaluate the effect of exposure on expression of genes involved in reproduction and steroid homeostasis. Using real-time PCR, we detected a different gene expression pattern for each OCP, suggesting that they each affect LMB in a different way. We also detected a different expression pattern among sexes, suggesting that sexes are affected differently by OCPs perhaps reflecting the different adaptive responses of each sex to dysregulation caused by OCP exposure.

Method of locating underground mines fires

DOEpatents

Laage, Linneas; Pomroy, William

1992-01-01

An improved method of locating an underground mine fire by comparing the pattern of measured combustion product arrival times at detector locations with a real time computer-generated array of simulated patterns. A number of electronic fire detection devices are linked thru telemetry to a control station on the surface. The mine's ventilation is modeled on a digital computer using network analysis software. The time reguired to locate a fire consists of the time required to model the mines' ventilation, generate the arrival time array, scan the array, and to match measured arrival time patterns to the simulated patterns.

Imbalance aware lithography hotspot detection: a deep learning approach

NASA Astrophysics Data System (ADS)

Yang, Haoyu; Luo, Luyang; Su, Jing; Lin, Chenxi; Yu, Bei

2017-07-01

With the advancement of very large scale integrated circuits (VLSI) technology nodes, lithographic hotspots become a serious problem that affects manufacture yield. Lithography hotspot detection at the post-OPC stage is imperative to check potential circuit failures when transferring designed patterns onto silicon wafers. Although conventional lithography hotspot detection methods, such as machine learning, have gained satisfactory performance, with the extreme scaling of transistor feature size and layout patterns growing in complexity, conventional methodologies may suffer from performance degradation. For example, manual or ad hoc feature extraction in a machine learning framework may lose important information when predicting potential errors in ultra-large-scale integrated circuit masks. We present a deep convolutional neural network (CNN) that targets representative feature learning in lithography hotspot detection. We carefully analyze the impact and effectiveness of different CNN hyperparameters, through which a hotspot-detection-oriented neural network model is established. Because hotspot patterns are always in the minority in VLSI mask design, the training dataset is highly imbalanced. In this situation, a neural network is no longer reliable, because a trained model with high classification accuracy may still suffer from a high number of false negative results (missing hotspots), which is fatal in hotspot detection problems. To address the imbalance problem, we further apply hotspot upsampling and random-mirror flipping before training the network. Experimental results show that our proposed neural network model achieves comparable or better performance on the ICCAD 2012 contest benchmark compared to state-of-the-art hotspot detectors based on deep or representative machine leaning.

[Interactive patterns detection in family communication with adolescents].

PubMed

Gimeno Collado, Adelina; Anguera Argilaga, M Teresa; Berzosa Sanz, Amparo; Ramírez Ramírez, Luis

2006-11-01

Interactive patterns detection in family communication with adolescents. Nondistant communication is a relevant indicator for family functionality valuation. The goal of this study is to analyze this communication in order to identify specific kinds of leadership, interaction patterns and the relation between verbal and nonverbal elements in communication. The observational design exposed is an idiographic one, punctual and multidimensional, which uses field format as observation instrument. Participants were seven standardized families made up of both ancestors and an adolescent son or daughter. According to the family models analyzed, results show a predominantly democratic communication style in adults with recurrent support expressions. The sequential analysis incorporates only categories from the emitter point of view, and detects relevant sequences which show symmetric interaction between all three family members. Verbal and nonverbal channels provide complementary information. Depending on adolescents' gender different patterns in behaviour can be identified as well.

Detection biases yield misleading patterns of species persistence and colonization in fragmented landscapes

USGS Publications Warehouse

Ruiz-Gutierrez, Viviana; Zipkin, Elise F.

2011-01-01

Species occurrence patterns, and related processes of persistence, colonization and turnover, are increasingly being used to infer habitat suitability, predict species distributions, and measure biodiversity potential. The majority of these studies do not account for observational error in their analyses despite growing evidence suggesting that the sampling process can significantly influence species detection and subsequently, estimates of occurrence. We examined the potential biases of species occurrence patterns that can result from differences in detectability across species and habitat types using hierarchical multispecies occupancy models applied to a tropical bird community in an agricultural fragmented landscape. Our results suggest that detection varies widely among species and habitat types. Not incorporating detectability severely biased occupancy dynamics for many species by overestimating turnover rates, producing misleading patterns of persistence and colonization of agricultural habitats, and misclassifying species into ecological categories (i.e., forest specialists and generalists). This is of serious concern, given that most research on the ability of agricultural lands to maintain current levels of biodiversity by and large does not correct for differences in detectability. We strongly urge researchers to apply an inferential framework which explicitly account for differences in detectability to fully characterize species-habitat relationships, correctly guide biodiversity conservation in human-modified landscapes, and generate more accurate predictions of species responses to future changes in environmental conditions.

AA9int: SNP Interaction Pattern Search Using Non-Hierarchical Additive Model Set.

PubMed

Lin, Hui-Yi; Huang, Po-Yu; Chen, Dung-Tsa; Tung, Heng-Yuan; Sellers, Thomas A; Pow-Sang, Julio; Eeles, Rosalind; Easton, Doug; Kote-Jarai, Zsofia; Amin Al Olama, Ali; Benlloch, Sara; Muir, Kenneth; Giles, Graham G; Wiklund, Fredrik; Gronberg, Henrik; Haiman, Christopher A; Schleutker, Johanna; Nordestgaard, Børge G; Travis, Ruth C; Hamdy, Freddie; Neal, David E; Pashayan, Nora; Khaw, Kay-Tee; Stanford, Janet L; Blot, William J; Thibodeau, Stephen N; Maier, Christiane; Kibel, Adam S; Cybulski, Cezary; Cannon-Albright, Lisa; Brenner, Hermann; Kaneva, Radka; Batra, Jyotsna; Teixeira, Manuel R; Pandha, Hardev; Lu, Yong-Jie; Park, Jong Y

2018-06-07

The use of single nucleotide polymorphism (SNP) interactions to predict complex diseases is getting more attention during the past decade, but related statistical methods are still immature. We previously proposed the SNP Interaction Pattern Identifier (SIPI) approach to evaluate 45 SNP interaction patterns/patterns. SIPI is statistically powerful but suffers from a large computation burden. For large-scale studies, it is necessary to use a powerful and computation-efficient method. The objective of this study is to develop an evidence-based mini-version of SIPI as the screening tool or solitary use and to evaluate the impact of inheritance mode and model structure on detecting SNP-SNP interactions. We tested two candidate approaches: the 'Five-Full' and 'AA9int' method. The Five-Full approach is composed of the five full interaction models considering three inheritance modes (additive, dominant and recessive). The AA9int approach is composed of nine interaction models by considering non-hierarchical model structure and the additive mode. Our simulation results show that AA9int has similar statistical power compared to SIPI and is superior to the Five-Full approach, and the impact of the non-hierarchical model structure is greater than that of the inheritance mode in detecting SNP-SNP interactions. In summary, it is recommended that AA9int is a powerful tool to be used either alone or as the screening stage of a two-stage approach (AA9int+SIPI) for detecting SNP-SNP interactions in large-scale studies. The 'AA9int' and 'parAA9int' functions (standard and parallel computing version) are added in the SIPI R package, which is freely available at https://linhuiyi.github.io/LinHY_Software/. hlin1@lsuhsc.edu. Supplementary data are available at Bioinformatics online.

Sound transmission in the chest under surface excitation - An experimental and computational study with diagnostic applications

PubMed Central

Peng, Ying; Dai, Zoujun; Mansy, Hansen A.; Sandler, Richard H.; Balk, Robert A; Royston, Thomas. J

2014-01-01

Chest physical examination often includes performing chest percussion, which involves introducing sound stimulus to the chest wall and detecting an audible change. This approach relies on observations that underlying acoustic transmission, coupling, and resonance patterns can be altered by chest structure changes due to pathologies. More accurate detection and quantification of these acoustic alterations may provide further useful diagnostic information. To elucidate the physical processes involved, a realistic computer model of sound transmission in the chest is helpful. In the present study, a computational model was developed and validated by comparing its predictions with results from animal and human experiments which involved applying acoustic excitation to the anterior chest while detecting skin vibrations at the posterior chest. To investigate the effect of pathology on sound transmission, the computational model was used to simulate the effects of pneumothorax on sounds introduced at the anterior chest and detected at the posterior. Model predictions and experimental results showed similar trends. The model also predicted wave patterns inside the chest, which may be used to assess results of elastography measurements. Future animal and human tests may expand the predictive power of the model to include acoustic behavior for a wider range of pulmonary conditions. PMID:25001497

Detection and Alert of muscle fatigue considering a Surface Electromyography Chaotic Model

NASA Astrophysics Data System (ADS)

Herrera, V.; Romero, J. F.; Amestegui, M.

2011-03-01

This work propose a detection and alert algorithm for muscle fatigue in paraplegic patients undergoing electro-therapy sessions. The procedure is based on a mathematical chaotic model emulating physiological signals and Continuous Wavelet Transform (CWT). The chaotic model developed is based on a logistic map that provides suitable data accomplishing some physiological signal class patterns. The CWT was applied to signals generated by the model and the resulting vector was obtained through Total Wavelet Entropy (TWE). In this sense, the presented work propose a viable and practical alert and detection algorithm for muscle fatigue.

Periodicity analysis of tourist arrivals to Banda Aceh using smoothing SARIMA approach

NASA Astrophysics Data System (ADS)

Miftahuddin, Helida, Desri; Sofyan, Hizir

2017-11-01

Forecasting the number of tourist arrivals who enters a region is needed for tourism businesses, economic and industrial policies, so that the statistical modeling needs to be conducted. Banda Aceh is the capital of Aceh province more economic activity is driven by the services sector, one of which is the tourism sector. Therefore, the prediction of the number of tourist arrivals is needed to develop further policies. The identification results indicate that the data arrival of foreign tourists to Banda Aceh to contain the trend and seasonal nature. Allegedly, the number of arrivals is influenced by external factors, such as economics, politics, and the holiday season caused the structural break in the data. Trend patterns are detected by using polynomial regression with quadratic and cubic approaches, while seasonal is detected by a periodic regression polynomial with quadratic and cubic approach. To model the data that has seasonal effects, one of the statistical methods that can be used is SARIMA (Seasonal Autoregressive Integrated Moving Average). The results showed that the smoothing, a method to detect the trend pattern is cubic polynomial regression approach, with the modified model and the multiplicative periodicity of 12 months. The AIC value obtained was 70.52. While the method for detecting the seasonal pattern is a periodic regression polynomial cubic approach, with the modified model and the multiplicative periodicity of 12 months. The AIC value obtained was 73.37. Furthermore, the best model to predict the number of foreign tourist arrivals to Banda Aceh in 2017 to 2018 is SARIMA (0,1,1)(1,1,0) with MAPE is 26%.

Efficient Personalized Mispronunciation Detection of Taiwanese-Accented English Speech Based on Unsupervised Model Adaptation and Dynamic Sentence Selection

ERIC Educational Resources Information Center

Wu, Chung-Hsien; Su, Hung-Yu; Liu, Chao-Hong

2013-01-01

This study presents an efficient approach to personalized mispronunciation detection of Taiwanese-accented English. The main goal of this study was to detect frequently occurring mispronunciation patterns of Taiwanese-accented English instead of scoring English pronunciations directly. The proposed approach quickly identifies personalized…

Validating EHR clinical models using ontology patterns.

PubMed

Martínez-Costa, Catalina; Schulz, Stefan

2017-12-01

Clinical models are artefacts that specify how information is structured in electronic health records (EHRs). However, the makeup of clinical models is not guided by any formal constraint beyond a semantically vague information model. We address this gap by advocating ontology design patterns as a mechanism that makes the semantics of clinical models explicit. This paper demonstrates how ontology design patterns can validate existing clinical models using SHACL. Based on the Clinical Information Modelling Initiative (CIMI), we show how ontology patterns detect both modeling and terminology binding errors in CIMI models. SHACL, a W3C constraint language for the validation of RDF graphs, builds on the concept of "Shape", a description of data in terms of expected cardinalities, datatypes and other restrictions. SHACL, as opposed to OWL, subscribes to the Closed World Assumption (CWA) and is therefore more suitable for the validation of clinical models. We have demonstrated the feasibility of the approach by manually describing the correspondences between six CIMI clinical models represented in RDF and two SHACL ontology design patterns. Using a Java-based SHACL implementation, we found at least eleven modeling and binding errors within these CIMI models. This demonstrates the usefulness of ontology design patterns not only as a modeling tool but also as a tool for validation. Copyright © 2017 Elsevier Inc. All rights reserved.

HyDe: a Python Package for Genome-Scale Hybridization Detection.

PubMed

Blischak, Paul D; Chifman, Julia; Wolfe, Andrea D; Kubatko, Laura S

2018-03-19

The analysis of hybridization and gene flow among closely related taxa is a common goal for researchers studying speciation and phylogeography. Many methods for hybridization detection use simple site pattern frequencies from observed genomic data and compare them to null models that predict an absence of gene flow. The theory underlying the detection of hybridization using these site pattern probabilities exploits the relationship between the coalescent process for gene trees within population trees and the process of mutation along the branches of the gene trees. For certain models, site patterns are predicted to occur in equal frequency (i.e., their difference is 0), producing a set of functions called phylogenetic invariants. In this paper we introduce HyDe, a software package for detecting hybridization using phylogenetic invariants arising under the coalescent model with hybridization. HyDe is written in Python, and can be used interactively or through the command line using pre-packaged scripts. We demonstrate the use of HyDe on simulated data, as well as on two empirical data sets from the literature. We focus in particular on identifying individual hybrids within population samples and on distinguishing between hybrid speciation and gene flow. HyDe is freely available as an open source Python package under the GNU GPL v3 on both GitHub (https://github.com/pblischak/HyDe) and the Python Package Index (PyPI: https://pypi.python.org/pypi/phyde).

Visual saliency detection based on modeling the spatial Gaussianity

NASA Astrophysics Data System (ADS)

Ju, Hongbin

2015-04-01

In this paper, a novel salient object detection method based on modeling the spatial anomalies is presented. The proposed framework is inspired by the biological mechanism that human eyes are sensitive to the unusual and anomalous objects among complex background. It is supposed that a natural image can be seen as a combination of some similar or dissimilar basic patches, and there is a direct relationship between its saliency and anomaly. Some patches share high degree of similarity and have a vast number of quantity. They usually make up the background of an image. On the other hand, some patches present strong rarity and specificity. We name these patches "anomalies". Generally, anomalous patch is a reflection of the edge or some special colors and textures in an image, and these pattern cannot be well "explained" by their surroundings. Human eyes show great interests in these anomalous patterns, and will automatically pick out the anomalous parts of an image as the salient regions. To better evaluate the anomaly degree of the basic patches and exploit their nonlinear statistical characteristics, a multivariate Gaussian distribution saliency evaluation model is proposed. In this way, objects with anomalous patterns usually appear as the outliers in the Gaussian distribution, and we identify these anomalous objects as salient ones. Experiments are conducted on the well-known MSRA saliency detection dataset. Compared with other recent developed visual saliency detection methods, our method suggests significant advantages.

Hotspot detection using image pattern recognition based on higher-order local auto-correlation

NASA Astrophysics Data System (ADS)

Maeda, Shimon; Matsunawa, Tetsuaki; Ogawa, Ryuji; Ichikawa, Hirotaka; Takahata, Kazuhiro; Miyairi, Masahiro; Kotani, Toshiya; Nojima, Shigeki; Tanaka, Satoshi; Nakagawa, Kei; Saito, Tamaki; Mimotogi, Shoji; Inoue, Soichi; Nosato, Hirokazu; Sakanashi, Hidenori; Kobayashi, Takumi; Murakawa, Masahiro; Higuchi, Tetsuya; Takahashi, Eiichi; Otsu, Nobuyuki

2011-04-01

Below 40nm design node, systematic variation due to lithography must be taken into consideration during the early stage of design. So far, litho-aware design using lithography simulation models has been widely applied to assure that designs are printed on silicon without any error. However, the lithography simulation approach is very time consuming, and under time-to-market pressure, repetitive redesign by this approach may result in the missing of the market window. This paper proposes a fast hotspot detection support method by flexible and intelligent vision system image pattern recognition based on Higher-Order Local Autocorrelation. Our method learns the geometrical properties of the given design data without any defects as normal patterns, and automatically detects the design patterns with hotspots from the test data as abnormal patterns. The Higher-Order Local Autocorrelation method can extract features from the graphic image of design pattern, and computational cost of the extraction is constant regardless of the number of design pattern polygons. This approach can reduce turnaround time (TAT) dramatically only on 1CPU, compared with the conventional simulation-based approach, and by distributed processing, this has proven to deliver linear scalability with each additional CPU.

Rejecting probability summation for radial frequency patterns, not so Quick!

PubMed

Baldwin, Alex S; Schmidtmann, Gunnar; Kingdom, Frederick A A; Hess, Robert F

2016-05-01

Radial frequency (RF) patterns are used to assess how the visual system processes shape. They are thought to be detected globally. This is supported by studies that have found summation for RF patterns to be greater than what is possible if the parts were being independently detected and performance only then improved with an increasing number of cycles by probability summation between them. However, the model of probability summation employed in these previous studies was based on High Threshold Theory (HTT), rather than Signal Detection Theory (SDT). We conducted rating scale experiments to investigate the receiver operating characteristics. We find these are of the curved form predicted by SDT, rather than the straight lines predicted by HTT. This means that to test probability summation we must use a model based on SDT. We conducted a set of summation experiments finding that thresholds decrease as the number of modulated cycles increases at approximately the same rate as previously found. As this could be consistent with either additive or probability summation, we performed maximum-likelihood fitting of a set of summation models (Matlab code provided in our Supplementary material) and assessed the fits using cross validation. We find we are not able to distinguish whether the responses to the parts of an RF pattern are combined by additive or probability summation, because the predictions are too similar. We present similar results for summation between separate RF patterns, suggesting that the summation process there may be the same as that within a single RF. Copyright © 2016 Elsevier Ltd. All rights reserved.

Detection of artifacts from high energy bursts in neonatal EEG.

PubMed

Bhattacharyya, Sourya; Biswas, Arunava; Mukherjee, Jayanta; Majumdar, Arun Kumar; Majumdar, Bandana; Mukherjee, Suchandra; Singh, Arun Kumar

2013-11-01

Detection of non-cerebral activities or artifacts, intermixed within the background EEG, is essential to discard them from subsequent pattern analysis. The problem is much harder in neonatal EEG, where the background EEG contains spikes, waves, and rapid fluctuations in amplitude and frequency. Existing artifact detection methods are mostly limited to detect only a subset of artifacts such as ocular, muscle or power line artifacts. Few methods integrate different modules, each for detection of one specific category of artifact. Furthermore, most of the reference approaches are implemented and tested on adult EEG recordings. Direct application of those methods on neonatal EEG causes performance deterioration, due to greater pattern variation and inherent complexity. A method for detection of a wide range of artifact categories in neonatal EEG is thus required. At the same time, the method should be specific enough to preserve the background EEG information. The current study describes a feature based classification approach to detect both repetitive (generated from ECG, EMG, pulse, respiration, etc.) and transient (generated from eye blinking, eye movement, patient movement, etc.) artifacts. It focuses on artifact detection within high energy burst patterns, instead of detecting artifacts within the complete background EEG with wide pattern variation. The objective is to find true burst patterns, which can later be used to identify the Burst-Suppression (BS) pattern, which is commonly observed during newborn seizure. Such selective artifact detection is proven to be more sensitive to artifacts and specific to bursts, compared to the existing artifact detection approaches applied on the complete background EEG. Several time domain, frequency domain, statistical features, and features generated by wavelet decomposition are analyzed to model the proposed bi-classification between burst and artifact segments. A feature selection method is also applied to select the feature subset producing highest classification accuracy. The suggested feature based classification method is executed using our recorded neonatal EEG dataset, consisting of burst and artifact segments. We obtain 78% sensitivity and 72% specificity as the accuracy measures. The accuracy obtained using the proposed method is found to be about 20% higher than that of the reference approaches. Joint use of the proposed method with our previous work on burst detection outperforms reference methods on simultaneous burst and artifact detection. As the proposed method supports detection of a wide range of artifact patterns, it can be improved to incorporate the detection of artifacts within other seizure patterns and background EEG information as well. © 2013 Elsevier Ltd. All rights reserved.

Optical diffraction for measurements of nano-mechanical bending

NASA Astrophysics Data System (ADS)

Hermans, Rodolfo I.; Dueck, Benjamin; Ndieyira, Joseph Wafula; McKendry, Rachel A.; Aeppli, Gabriel

2016-06-01

We explore and exploit diffraction effects that have been previously neglected when modelling optical measurement techniques for the bending of micro-mechanical transducers such as cantilevers for atomic force microscopy. The illumination of a cantilever edge causes an asymmetric diffraction pattern at the photo-detector affecting the calibration of the measured signal in the popular optical beam deflection technique (OBDT). The conditions that avoid such detection artefacts conflict with the use of smaller cantilevers. Embracing diffraction patterns as data yields a potent detection technique that decouples tilt and curvature and simultaneously relaxes the requirements on the illumination alignment and detector position through a measurable which is invariant to translation and rotation. We show analytical results, numerical simulations and physiologically relevant experimental data demonstrating the utility of the diffraction patterns. We offer experimental design guidelines and quantify possible sources of systematic error in OBDT. We demonstrate a new nanometre resolution detection method that can replace OBDT, where diffraction effects from finite sized or patterned cantilevers are exploited. Such effects are readily generalized to cantilever arrays, and allow transmission detection of mechanical curvature, enabling instrumentation with simpler geometry. We highlight the comparative advantages over OBDT by detecting molecular activity of antibiotic Vancomycin.

A method for detecting and characterizing outbreaks of infectious disease from clinical reports.

PubMed

Cooper, Gregory F; Villamarin, Ricardo; Rich Tsui, Fu-Chiang; Millett, Nicholas; Espino, Jeremy U; Wagner, Michael M

2015-02-01

Outbreaks of infectious disease can pose a significant threat to human health. Thus, detecting and characterizing outbreaks quickly and accurately remains an important problem. This paper describes a Bayesian framework that links clinical diagnosis of individuals in a population to epidemiological modeling of disease outbreaks in the population. Computer-based diagnosis of individuals who seek healthcare is used to guide the search for epidemiological models of population disease that explain the pattern of diagnoses well. We applied this framework to develop a system that detects influenza outbreaks from emergency department (ED) reports. The system diagnoses influenza in individuals probabilistically from evidence in ED reports that are extracted using natural language processing. These diagnoses guide the search for epidemiological models of influenza that explain the pattern of diagnoses well. Those epidemiological models with a high posterior probability determine the most likely outbreaks of specific diseases; the models are also used to characterize properties of an outbreak, such as its expected peak day and estimated size. We evaluated the method using both simulated data and data from a real influenza outbreak. The results provide support that the approach can detect and characterize outbreaks early and well enough to be valuable. We describe several extensions to the approach that appear promising. Copyright © 2014 Elsevier Inc. All rights reserved.

A Method for Detecting and Characterizing Outbreaks of Infectious Disease from Clinical Reports

PubMed Central

Cooper, Gregory F.; Villamarin, Ricardo; Tsui, Fu-Chiang (Rich); Millett, Nicholas; Espino, Jeremy U.; Wagner, Michael M.

2014-01-01

Outbreaks of infectious disease can pose a significant threat to human health. Thus, detecting and characterizing outbreaks quickly and accurately remains an important problem. This paper describes a Bayesian framework that links clinical diagnosis of individuals in a population to epidemiological modeling of disease outbreaks in the population. Computer-based diagnosis of individuals who seek healthcare is used to guide the search for epidemiological models of population disease that explain the pattern of diagnoses well. We applied this framework to develop a system that detects influenza outbreaks from emergency department (ED) reports. The system diagnoses influenza in individuals probabilistically from evidence in ED reports that are extracted using natural language processing. These diagnoses guide the search for epidemiological models of influenza that explain the pattern of diagnoses well. Those epidemiological models with a high posterior probability determine the most likely outbreaks of specific diseases; the models are also used to characterize properties of an outbreak, such as its expected peak day and estimated size. We evaluated the method using both simulated data and data from a real influenza outbreak. The results provide support that the approach can detect and characterize outbreaks early and well enough to be valuable. We describe several extensions to the approach that appear promising. PMID:25181466

Transducer model produces facilitation from opposite-sign flanks

NASA Technical Reports Server (NTRS)

Solomon, J. A.; Watson, A. B.; Morgan, M. J.

1999-01-01

Small spots, lines and Gabor patterns can be easier to detect when they are superimposed upon similar spots, lines and Gabor patterns. Traditionally, such facilitation has been understood to be a consequence of nonlinear contrast transduction. Facilitation has also been reported to arise from non-overlapping patterns with opposite sign. We point out that this result does not preclude the traditional explanation for superimposed targets. Moreover, we find that facilitation from opposite-sign flanks is weaker than facilitation from same-sign flanks. Simulations with a transducer model produce opposite-sign facilitation.

A Robust Current Pattern for the Detection of Intraventricular Hemorrhage in Neonates Using Electrical Impedance Tomography

PubMed Central

Tang, T.; Oh, Sungho; Sadleir, R. J.

2010-01-01

We compared two 16-electrode electrical impedance tomography (EIT) current patterns on their ability to reconstruct and quantify small amounts of bleeding inside a neonatal human head using both simulated and phantom data. The current patterns used were an adjacent injection RING pattern (with electrodes located equidistantly on the equator of a sphere) and an EEG current pattern based on the 10–20 EEG electrode layout. Structures mimicking electrically important structures in the infant skull were included in a spherical numerical forward model and their effects on reconstructions were determined. The EEG pattern was found to be a better topology to localize and quantify anomalies within lateral ventricular regions. The RING electrode pattern could not reconstruct anomaly location well, as it could not distinguish different axial positions. The quantification accuracy of the RING pattern was as good as the EEG pattern in noise-free environments. However, the EEG pattern showed better quantification ability than the RING pattern when noise was added. The performance of the EEG pattern improved further with respect to the RING pattern when a fontanel was included in forward models. Significantly better resolution and contrast of reconstructed anomalies was achieved when generated from a model containing such an opening and 50 dB added noise. The EEG method was further applied to reconstruct data from a realistic neonatal head model. Overall, acceptable reconstructions and quantification results were obtained using this model and the homogeneous spherical forward model. PMID:20238166

Finite element model updating and damage detection for bridges using vibration measurement.

DOT National Transportation Integrated Search

2013-12-01

In this report, the results of a study on developing a damage detection methodology based on Statistical Pattern Recognition are : presented. This methodology uses a new damage sensitive feature developed in this study that relies entirely on modal :...

Optimization of Training Sets For Neural-Net Processing of Characteristic Patterns From Vibrating Solids

NASA Technical Reports Server (NTRS)

Decker, Arthur J. (Inventor)

2006-01-01

An artificial neural network is disclosed that processes holography generated characteristic pattern of vibrating structures along with finite-element models. The present invention provides for a folding operation for conditioning training sets for optimally training forward-neural networks to process characteristic fringe pattern. The folding pattern increases the sensitivity of the feed-forward network for detecting changes in the characteristic pattern The folding routine manipulates input pixels so as to be scaled according to the location in an intensity range rather than the position in the characteristic pattern.

Digital and optical shape representation and pattern recognition; Proceedings of the Meeting, Orlando, FL, Apr. 4-6, 1988

NASA Technical Reports Server (NTRS)

Juday, Richard D. (Editor)

1988-01-01

The present conference discusses topics in pattern-recognition correlator architectures, digital stereo systems, geometric image transformations and their applications, topics in pattern recognition, filter algorithms, object detection and classification, shape representation techniques, and model-based object recognition methods. Attention is given to edge-enhancement preprocessing using liquid crystal TVs, massively-parallel optical data base management, three-dimensional sensing with polar exponential sensor arrays, the optical processing of imaging spectrometer data, hybrid associative memories and metric data models, the representation of shape primitives in neural networks, and the Monte Carlo estimation of moment invariants for pattern recognition.

Detection experiments with humans implicate visual predation as a driver of colour polymorphism dynamics in pygmy grasshoppers

PubMed Central

2013-01-01

Background Animal colour patterns offer good model systems for studies of biodiversity and evolution of local adaptations. An increasingly popular approach to study the role of selection for camouflage for evolutionary trajectories of animal colour patterns is to present images of prey on paper or computer screens to human ‘predators’. Yet, few attempts have been made to confirm that rates of detection by humans can predict patterns of selection and evolutionary modifications of prey colour patterns in nature. In this study, we first analyzed encounters between human ‘predators’ and images of natural black, grey and striped colour morphs of the polymorphic Tetrix subulata pygmy grasshoppers presented on background images of unburnt, intermediate or completely burnt natural habitats. Next, we compared detection rates with estimates of capture probabilities and survival of free-ranging grasshoppers, and with estimates of relative morph frequencies in natural populations. Results The proportion of grasshoppers that were detected and time to detection depended on both the colour pattern of the prey and on the type of visual background. Grasshoppers were detected more often and faster on unburnt backgrounds than on 50% and 100% burnt backgrounds. Striped prey were detected less often than grey or black prey on unburnt backgrounds; grey prey were detected more often than black or striped prey on 50% burnt backgrounds; and black prey were detected less often than grey prey on 100% burnt backgrounds. Rates of detection mirrored previously reported rates of capture by humans of free-ranging grasshoppers, as well as morph specific survival in the wild. Rates of detection were also correlated with frequencies of striped, black and grey morphs in samples of T. subulata from natural populations that occupied the three habitat types used for the detection experiment. Conclusions Our findings demonstrate that crypsis is background-dependent, and implicate visual predation as an important driver of evolutionary modifications of colour polymorphism in pygmy grasshoppers. Our study provides the clearest evidence to date that using humans as ‘predators’ in detection experiments may provide reliable information on the protective values of prey colour patterns and of natural selection and microevolution of camouflage in the wild. PMID:23639215

The use of UAV to document sloping landscapes to produce digital elevation models to examine environmental degradation

NASA Astrophysics Data System (ADS)

Themistocleous, K.; Agapiou, A.; Papadavid, G.; Christoforou, M.; Hadjimitsis, D. G.

2015-10-01

This paper focuses on the use of Unmanned Aerial Vehicles (UAVs) over the study area of Pissouri in Cyprus to document the sloping landscapes of the area. The study area has been affected by overgrazing, which has led to shifts in the vegetation patterns and changing microtopography of the soil. The UAV images were used to generate digital elevation models (DEMs) to examine the changes in microtopography. Next to that orthophotos were used to detect changes in vegetation patterns. The combined data of the digital elevation models and the orthophotos will be used to detect the occurrence of catastrophic shifts and mechanisms for desertification in the study area due to overgrazing. This study is part of the "CASCADE- Catastrophic shifts in dryland" project.

Analysis of Wave Velocity Patterns in Black Cherry Trees and its Effect on Internal Decay Detection

Treesearch

Guanghui Li; Xiping Wang; Jan Wiedenbeck; Robert J. Ross

2013-01-01

In this study, we examined stress wave velocity patterns in the cross sections of black cherry trees, developed analytical models of stress wave velocity in sound healthy trees, and then tested the effectiveness of the models as a tool for tree decay diagnosis. Acoustic tomography data of the tree cross sections were collected from 12 black cherry trees at a production...

Analysis of wave velocity patterns in black cherry trees and its effect on internal decay detection

Treesearch

Guanghui Li; Xiping Wang; Hailin Feng; Jan Wiedenbeck; Robert J. Ross

2014-01-01

In this study, we examined stress wave velocity patterns in the cross sections of black cherry trees, developed analytical models of stress wave velocity in sound healthy trees, and then tested the effectiveness of the models as a tool for tree decay diagnosis. Acoustic tomography data of the tree cross sections were collected from 12 black cherry trees at a production...

Closing the Loop for Memory Prostheses: Detecting the Role of Hippocampal Neural Ensembles Using Nonlinear Models

PubMed Central

Hampson, Robert E.; Song, Dong; Chan, Rosa H.M.; Sweatt, Andrew J.; Riley, Mitchell R.; Goonawardena, Anushka V.; Marmarelis, Vasilis Z.; Gerhardt, Greg A.; Berger, Theodore W.; Deadwyler, Sam A.

2012-01-01

A major factor involved in providing closed loop feedback for control of neural function is to understand how neural ensembles encode online information critical to the final behavioral endpoint. This issue was directly assessed in rats performing a short-term delay memory task in which successful encoding of task information is dependent upon specific spatiotemporal firing patterns recorded from ensembles of CA3 and CA1 hippocampal neurons. Such patterns, extracted by a specially designed nonlinear multi-input multi-output (MIMO) nonlinear mathematical model, were used to predict successful performance online via a closed loop paradigm which regulated trial difficulty (time of retention) as a function of the “strength” of stimulus encoding. The significance of the MIMO model as a neural prosthesis has been demonstrated by substituting trains of electrical stimulation pulses to mimic these same ensemble firing patterns. This feature was used repeatedly to vary “normal” encoding as a means of understanding how neural ensembles can be “tuned” to mimic the inherent process of selecting codes of different strength and functional specificity. The capacity to enhance and tune hippocampal encoding via MIMO model detection and insertion of critical ensemble firing patterns shown here provides the basis for possible extension to other disrupted brain circuitry. PMID:22498704

Application of Knowledge Discovery in Databases Methodologies for Predictive Models for Pregnancy Adverse Events

ERIC Educational Resources Information Center

Taft, Laritza M.

2010-01-01

In its report "To Err is Human", The Institute of Medicine recommended the implementation of internal and external voluntary and mandatory automatic reporting systems to increase detection of adverse events. Knowledge Discovery in Databases (KDD) allows the detection of patterns and trends that would be hidden or less detectable if analyzed by…

Salient regions detection using convolutional neural networks and color volume

NASA Astrophysics Data System (ADS)

Liu, Guang-Hai; Hou, Yingkun

2018-03-01

Convolutional neural network is an important technique in machine learning, pattern recognition and image processing. In order to reduce the computational burden and extend the classical LeNet-5 model to the field of saliency detection, we propose a simple and novel computing model based on LeNet-5 network. In the proposed model, hue, saturation and intensity are utilized to extract depth cues, and then we integrate depth cues and color volume to saliency detection following the basic structure of the feature integration theory. Experimental results show that the proposed computing model outperforms some existing state-of-the-art methods on MSRA1000 and ECSSD datasets.

Identification of cigarette smoke inhalations from wearable sensor data using a Support Vector Machine classifier.

PubMed

Lopez-Meyer, Paulo; Tiffany, Stephen; Sazonov, Edward

2012-01-01

This study presents a subject-independent model for detection of smoke inhalations from wearable sensors capturing characteristic hand-to-mouth gestures and changes in breathing patterns during cigarette smoking. Wearable sensors were used to detect the proximity of the hand to the mouth and to acquire the respiratory patterns. The waveforms of sensor signals were used as features to build a Support Vector Machine classification model. Across a data set of 20 enrolled participants, precision of correct identification of smoke inhalations was found to be >87%, and a resulting recall >80%. These results suggest that it is possible to analyze smoking behavior by means of a wearable and non-invasive sensor system.

Accurate LC peak boundary detection for ¹⁶O/¹⁸O labeled LC-MS data.

PubMed

Cui, Jian; Petritis, Konstantinos; Tegeler, Tony; Petritis, Brianne; Ma, Xuepo; Jin, Yufang; Gao, Shou-Jiang S J; Zhang, Jianqiu Michelle

2013-01-01

In liquid chromatography-mass spectrometry (LC-MS), parts of LC peaks are often corrupted by their co-eluting peptides, which results in increased quantification variance. In this paper, we propose to apply accurate LC peak boundary detection to remove the corrupted part of LC peaks. Accurate LC peak boundary detection is achieved by checking the consistency of intensity patterns within peptide elution time ranges. In addition, we remove peptides with erroneous mass assignment through model fitness check, which compares observed intensity patterns to theoretically constructed ones. The proposed algorithm can significantly improve the accuracy and precision of peptide ratio measurements.

Accurate LC Peak Boundary Detection for 16 O/ 18 O Labeled LC-MS Data

PubMed Central

Cui, Jian; Petritis, Konstantinos; Tegeler, Tony; Petritis, Brianne; Ma, Xuepo; Jin, Yufang; Gao, Shou-Jiang (SJ); Zhang, Jianqiu (Michelle)

2013-01-01

In liquid chromatography-mass spectrometry (LC-MS), parts of LC peaks are often corrupted by their co-eluting peptides, which results in increased quantification variance. In this paper, we propose to apply accurate LC peak boundary detection to remove the corrupted part of LC peaks. Accurate LC peak boundary detection is achieved by checking the consistency of intensity patterns within peptide elution time ranges. In addition, we remove peptides with erroneous mass assignment through model fitness check, which compares observed intensity patterns to theoretically constructed ones. The proposed algorithm can significantly improve the accuracy and precision of peptide ratio measurements. PMID:24115998

Modeling Patterns of Activities using Activity Curves

PubMed Central

Dawadi, Prafulla N.; Cook, Diane J.; Schmitter-Edgecombe, Maureen

2016-01-01

Pervasive computing offers an unprecedented opportunity to unobtrusively monitor behavior and use the large amount of collected data to perform analysis of activity-based behavioral patterns. In this paper, we introduce the notion of an activity curve, which represents an abstraction of an individual’s normal daily routine based on automatically-recognized activities. We propose methods to detect changes in behavioral routines by comparing activity curves and use these changes to analyze the possibility of changes in cognitive or physical health. We demonstrate our model and evaluate our change detection approach using a longitudinal smart home sensor dataset collected from 18 smart homes with older adult residents. Finally, we demonstrate how big data-based pervasive analytics such as activity curve-based change detection can be used to perform functional health assessment. Our evaluation indicates that correlations do exist between behavior and health changes and that these changes can be automatically detected using smart homes, machine learning, and big data-based pervasive analytics. PMID:27346990

Modeling Patterns of Activities using Activity Curves.

PubMed

Dawadi, Prafulla N; Cook, Diane J; Schmitter-Edgecombe, Maureen

2016-06-01

Pervasive computing offers an unprecedented opportunity to unobtrusively monitor behavior and use the large amount of collected data to perform analysis of activity-based behavioral patterns. In this paper, we introduce the notion of an activity curve , which represents an abstraction of an individual's normal daily routine based on automatically-recognized activities. We propose methods to detect changes in behavioral routines by comparing activity curves and use these changes to analyze the possibility of changes in cognitive or physical health. We demonstrate our model and evaluate our change detection approach using a longitudinal smart home sensor dataset collected from 18 smart homes with older adult residents. Finally, we demonstrate how big data-based pervasive analytics such as activity curve-based change detection can be used to perform functional health assessment. Our evaluation indicates that correlations do exist between behavior and health changes and that these changes can be automatically detected using smart homes, machine learning, and big data-based pervasive analytics.

Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.

Neuromimetic machine vision and pattern recognition algorithms are of great interest for landscape characterization and change detection in satellite imagery in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methods to the environmental sciences, using adaptive sparse signal processing combined with machine learning. A Hebbian learning rule is used to build multispectral, multiresolution dictionaries from regional satellite normalized band difference index data. Land cover labels are automatically generated via our CoSA algorithm: Clustering of Sparse Approximations, using a clustering distance metric that combines spectral and spatial textural characteristics tomore » help separate geologic, vegetative, and hydrologie features. We demonstrate our method on example Worldview-2 satellite images of an Arctic region, and use CoSA labels to detect seasonal surface changes. In conclusion, our results suggest that neuroscience-based models are a promising approach to practical pattern recognition and change detection problems in remote sensing.« less

Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

DOE PAGES

Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; ...

2014-10-01

Neuromimetic machine vision and pattern recognition algorithms are of great interest for landscape characterization and change detection in satellite imagery in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methods to the environmental sciences, using adaptive sparse signal processing combined with machine learning. A Hebbian learning rule is used to build multispectral, multiresolution dictionaries from regional satellite normalized band difference index data. Land cover labels are automatically generated via our CoSA algorithm: Clustering of Sparse Approximations, using a clustering distance metric that combines spectral and spatial textural characteristics tomore » help separate geologic, vegetative, and hydrologie features. We demonstrate our method on example Worldview-2 satellite images of an Arctic region, and use CoSA labels to detect seasonal surface changes. In conclusion, our results suggest that neuroscience-based models are a promising approach to practical pattern recognition and change detection problems in remote sensing.« less

Adaptive Texture Synthesis for Large Scale City Modeling

NASA Astrophysics Data System (ADS)

Despine, G.; Colleu, T.

2015-02-01

Large scale city models textured with aerial images are well suited for bird-eye navigation but generally the image resolution does not allow pedestrian navigation. One solution to face this problem is to use high resolution terrestrial photos but it requires huge amount of manual work to remove occlusions. Another solution is to synthesize generic textures with a set of procedural rules and elementary patterns like bricks, roof tiles, doors and windows. This solution may give realistic textures but with no correlation to the ground truth. Instead of using pure procedural modelling we present a method to extract information from aerial images and adapt the texture synthesis to each building. We describe a workflow allowing the user to drive the information extraction and to select the appropriate texture patterns. We also emphasize the importance to organize the knowledge about elementary pattern in a texture catalogue allowing attaching physical information, semantic attributes and to execute selection requests. Roofs are processed according to the detected building material. Façades are first described in terms of principal colours, then opening positions are detected and some window features are computed. These features allow selecting the most appropriate patterns from the texture catalogue. We experimented this workflow on two samples with 20 cm and 5 cm resolution images. The roof texture synthesis and opening detection were successfully conducted on hundreds of buildings. The window characterization is still sensitive to the distortions inherent to the projection of aerial images onto the facades.

Real-time hypoglycemia detection from continuous glucose monitoring data of subjects with type 1 diabetes.

PubMed

Jensen, Morten Hasselstrøm; Christensen, Toke Folke; Tarnow, Lise; Seto, Edmund; Dencker Johansen, Mette; Hejlesen, Ole Kristian

2013-07-01

Hypoglycemia is a potentially fatal condition. Continuous glucose monitoring (CGM) has the potential to detect hypoglycemia in real time and thereby reduce time in hypoglycemia and avoid any further decline in blood glucose level. However, CGM is inaccurate and shows a substantial number of cases in which the hypoglycemic event is not detected by the CGM. The aim of this study was to develop a pattern classification model to optimize real-time hypoglycemia detection. Features such as time since last insulin injection and linear regression, kurtosis, and skewness of the CGM signal in different time intervals were extracted from data of 10 male subjects experiencing 17 insulin-induced hypoglycemic events in an experimental setting. Nondiscriminative features were eliminated with SEPCOR and forward selection. The feature combinations were used in a Support Vector Machine model and the performance assessed by sample-based sensitivity and specificity and event-based sensitivity and number of false-positives. The best model was composed by using seven features and was able to detect 17 of 17 hypoglycemic events with one false-positive compared with 12 of 17 hypoglycemic events with zero false-positives for the CGM alone. Lead-time was 14 min and 0 min for the model and the CGM alone, respectively. This optimized real-time hypoglycemia detection provides a unique approach for the diabetes patient to reduce time in hypoglycemia and learn about patterns in glucose excursions. Although these results are promising, the model needs to be validated on CGM data from patients with spontaneous hypoglycemic events.

Discrimination of geographical origin and detection of adulteration of kudzu root by fluorescence spectroscopy coupled with multi-way pattern recognition

NASA Astrophysics Data System (ADS)

Hu, Leqian; Ma, Shuai; Yin, Chunling

2018-03-01

In this work, fluorescence spectroscopy combined with multi-way pattern recognition techniques were developed for determining the geographical origin of kudzu root and detection and quantification of adulterants in kudzu root. Excitation-emission (EEM) spectra were obtained for 150 pure kudzu root samples of different geographical origins and 150 fake kudzu roots with different adulteration proportions by recording emission from 330 to 570 nm with excitation in the range of 320-480 nm, respectively. Multi-way principal components analysis (M-PCA) and multilinear partial least squares discriminant analysis (N-PLS-DA) methods were used to decompose the excitation-emission matrices datasets. 150 pure kudzu root samples could be differentiated exactly from each other according to their geographical origins by M-PCA and N-PLS-DA models. For the adulteration kudzu root samples, N-PLS-DA got better and more reliable classification result comparing with the M-PCA model. The results obtained in this study indicated that EEM spectroscopy coupling with multi-way pattern recognition could be used as an easy, rapid and novel tool to distinguish the geographical origin of kudzu root and detect adulterated kudzu root. Besides, this method was also suitable for determining the geographic origin and detection the adulteration of the other foodstuffs which can produce fluorescence.

Pipeline Processing With an Iterative, Context-Based Detection Model

DTIC Science & Technology

2015-04-19

pattern detectors , correlation detectors , subspace detectors , matched field detectors , nuclear explosion monitoring 16. SECURITY CLASSIFICATION OF: 17...38 13. 3 days of SPAO-BHZ data which is dominated by signals from nearby icequakes. .... 39 14. (Top) 94 detections produced by detector ...92532 and (bottom) 148 detections from detector 92541 produced during the first run of the framework. .................................. 40 15. The 49

VisitSense: Sensing Place Visit Patterns from Ambient Radio on Smartphones for Targeted Mobile Ads in Shopping Malls.

PubMed

Kim, Byoungjip; Kang, Seungwoo; Ha, Jin-Young; Song, Junehwa

2015-07-16

In this paper, we introduce a novel smartphone framework called VisitSense that automatically detects and predicts a smartphone user's place visits from ambient radio to enable behavioral targeting for mobile ads in large shopping malls. VisitSense enables mobile app developers to adopt visit-pattern-aware mobile advertising for shopping mall visitors in their apps. It also benefits mobile users by allowing them to receive highly relevant mobile ads that are aware of their place visit patterns in shopping malls. To achieve the goal, VisitSense employs accurate visit detection and prediction methods. For accurate visit detection, we develop a change-based detection method to take into consideration the stability change of ambient radio and the mobility change of users. It performs well in large shopping malls where ambient radio is quite noisy and causes existing algorithms to easily fail. In addition, we proposed a causality-based visit prediction model to capture the causality in the sequential visit patterns for effective prediction. We have developed a VisitSense prototype system, and a visit-pattern-aware mobile advertising application that is based on it. Furthermore, we deploy the system in the COEX Mall, one of the largest shopping malls in Korea, and conduct diverse experiments to show the effectiveness of VisitSense.

Time series modeling for syndromic surveillance.

PubMed

Reis, Ben Y; Mandl, Kenneth D

2003-01-23

Emergency department (ED) based syndromic surveillance systems identify abnormally high visit rates that may be an early signal of a bioterrorist attack. For example, an anthrax outbreak might first be detectable as an unusual increase in the number of patients reporting to the ED with respiratory symptoms. Reliably identifying these abnormal visit patterns requires a good understanding of the normal patterns of healthcare usage. Unfortunately, systematic methods for determining the expected number of (ED) visits on a particular day have not yet been well established. We present here a generalized methodology for developing models of expected ED visit rates. Using time-series methods, we developed robust models of ED utilization for the purpose of defining expected visit rates. The models were based on nearly a decade of historical data at a major metropolitan academic, tertiary care pediatric emergency department. The historical data were fit using trimmed-mean seasonal models, and additional models were fit with autoregressive integrated moving average (ARIMA) residuals to account for recent trends in the data. The detection capabilities of the model were tested with simulated outbreaks. Models were built both for overall visits and for respiratory-related visits, classified according to the chief complaint recorded at the beginning of each visit. The mean absolute percentage error of the ARIMA models was 9.37% for overall visits and 27.54% for respiratory visits. A simple detection system based on the ARIMA model of overall visits was able to detect 7-day-long simulated outbreaks of 30 visits per day with 100% sensitivity and 97% specificity. Sensitivity decreased with outbreak size, dropping to 94% for outbreaks of 20 visits per day, and 57% for 10 visits per day, all while maintaining a 97% benchmark specificity. Time series methods applied to historical ED utilization data are an important tool for syndromic surveillance. Accurate forecasting of emergency department total utilization as well as the rates of particular syndromes is possible. The multiple models in the system account for both long-term and recent trends, and an integrated alarms strategy combining these two perspectives may provide a more complete picture to public health authorities. The systematic methodology described here can be generalized to other healthcare settings to develop automated surveillance systems capable of detecting anomalies in disease patterns and healthcare utilization.

Confounder Detection in High-Dimensional Linear Models Using First Moments of Spectral Measures.

PubMed

Liu, Furui; Chan, Laiwan

2018-06-12

In this letter, we study the confounder detection problem in the linear model, where the target variable [Formula: see text] is predicted using its [Formula: see text] potential causes [Formula: see text]. Based on an assumption of a rotation-invariant generating process of the model, recent study shows that the spectral measure induced by the regression coefficient vector with respect to the covariance matrix of [Formula: see text] is close to a uniform measure in purely causal cases, but it differs from a uniform measure characteristically in the presence of a scalar confounder. Analyzing spectral measure patterns could help to detect confounding. In this letter, we propose to use the first moment of the spectral measure for confounder detection. We calculate the first moment of the regression vector-induced spectral measure and compare it with the first moment of a uniform spectral measure, both defined with respect to the covariance matrix of [Formula: see text]. The two moments coincide in nonconfounding cases and differ from each other in the presence of confounding. This statistical causal-confounding asymmetry can be used for confounder detection. Without the need to analyze the spectral measure pattern, our method avoids the difficulty of metric choice and multiple parameter optimization. Experiments on synthetic and real data show the performance of this method.

Intelligent-based Structural Damage Detection Model

NASA Astrophysics Data System (ADS)

Lee, Eric Wai Ming; Yu, Kin Fung

2010-05-01

This paper presents the application of a novel Artificial Neural Network (ANN) model for the diagnosis of structural damage. The ANN model, denoted as the GRNNFA, is a hybrid model combining the General Regression Neural Network Model (GRNN) and the Fuzzy ART (FA) model. It not only retains the important features of the GRNN and FA models (i.e. fast and stable network training and incremental growth of network structure) but also facilitates the removal of the noise embedded in the training samples. Structural damage alters the stiffness distribution of the structure and so as to change the natural frequencies and mode shapes of the system. The measured modal parameter changes due to a particular damage are treated as patterns for that damage. The proposed GRNNFA model was trained to learn those patterns in order to detect the possible damage location of the structure. Simulated data is employed to verify and illustrate the procedures of the proposed ANN-based damage diagnosis methodology. The results of this study have demonstrated the feasibility of applying the GRNNFA model to structural damage diagnosis even when the training samples were noise contaminated.

Evaluation of neovascularization patterns in an orthotopic rat glioma model with dynamic contrast-enhanced MRI.

PubMed

Xuesong, Du; Wei, Xue; Heng, Liu; Xiao, Chen; Shunan, Wang; Yu, Guo; Weiguo, Zhang

2017-09-01

Background Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been proved useful in evaluating glioma angiogenesis, but the utility in evaluating neovascularization patterns has not been reported. Purpose To evaluate in vivo real-time glioma neovascularization patterns by measuring glioma perfusion quantitatively using DCE-MRI. Material and Methods Thirty Sprague-Dawley rats were used to establish C6 orthotopic glioma model and underwent MRI and pathology detections. As MRI and pathology were performed at six time points (i.e. 4, 8, 12, 16, 20, and 24 days) post transplantation, neovascularization patterns were evaluated via DCE-MRI. Results Four neovascularization patterns were observed in glioma tissues. Sprout angiogenesis and intussusceptive microvascular growth located inside tumor, while vascular co-option and vascular mimicry were found in the tumor margin and necrotic area, respectively. Sprout angiogenesis and intussusceptive microvascular growth increased with K trans , K ep , and V p inside tumor tissue. In addition, K ep and V p were positively correlated with sprout angiogenesis and intussusceptive microvascular growth. Vascular co-option was decreased at 12 and 16 days post transplantation and correlated negatively with K trans and K ep detected in the glioma margin, respectively. Changes of vascular mimicry showed no significant statistical difference at the six time points. Conclusion Our results indicate that DCE-MRI can evaluate neovascularization patterns in a glioma model. Furthermore, DCE-MRI could be an imaging biomarker for guidance of antiangiogenic treatments in humans in the future.

Evolving optimised decision rules for intrusion detection using particle swarm paradigm

NASA Astrophysics Data System (ADS)

Sivatha Sindhu, Siva S.; Geetha, S.; Kannan, A.

2012-12-01

The aim of this article is to construct a practical intrusion detection system (IDS) that properly analyses the statistics of network traffic pattern and classify them as normal or anomalous class. The objective of this article is to prove that the choice of effective network traffic features and a proficient machine-learning paradigm enhances the detection accuracy of IDS. In this article, a rule-based approach with a family of six decision tree classifiers, namely Decision Stump, C4.5, Naive Baye's Tree, Random Forest, Random Tree and Representative Tree model to perform the detection of anomalous network pattern is introduced. In particular, the proposed swarm optimisation-based approach selects instances that compose training set and optimised decision tree operate over this trained set producing classification rules with improved coverage, classification capability and generalisation ability. Experiment with the Knowledge Discovery and Data mining (KDD) data set which have information on traffic pattern, during normal and intrusive behaviour shows that the proposed algorithm produces optimised decision rules and outperforms other machine-learning algorithm.

Object Detection in Natural Backgrounds Predicted by Discrimination Performance and Models

NASA Technical Reports Server (NTRS)

Ahumada, A. J., Jr.; Watson, A. B.; Rohaly, A. M.; Null, Cynthia H. (Technical Monitor)

1995-01-01

In object detection, an observer looks for an object class member in a set of backgrounds. In discrimination, an observer tries to distinguish two images. Discrimination models predict the probability that an observer detects a difference between two images. We compare object detection and image discrimination with the same stimuli by: (1) making stimulus pairs of the same background with and without the target object and (2) either giving many consecutive trials with the same background (discrimination) or intermixing the stimuli (object detection). Six images of a vehicle in a natural setting were altered to remove the vehicle and mixed with the original image in various proportions. Detection observers rated the images for vehicle presence. Discrimination observers rated the images for any difference from the background image. Estimated detectabilities of the vehicles were found by maximizing the likelihood of a Thurstone category scaling model. The pattern of estimated detectabilities is similar for discrimination and object detection, and is accurately predicted by a Cortex Transform discrimination model. Predictions of a Contrast- Sensitivity- Function filter model and a Root-Mean-Square difference metric based on the digital image values are less accurate. The discrimination detectabilities averaged about twice those of object detection.

Spatial filtering precedes motion detection.

PubMed

Morgan, M J

1992-01-23

When we perceive motion on a television or cinema screen, there must be some process that allows us to track moving objects over time: if not, the result would be a conflicting mass of motion signals in all directions. A possible mechanism, suggested by studies of motion displacement in spatially random patterns, is that low-level motion detectors have a limited spatial range, which ensures that they tend to be stimulated over time by the same object. This model predicts that the direction of displacement of random patterns cannot be detected reliably above a critical absolute displacement value (Dmax) that is independent of the size or density of elements in the display. It has been inferred that Dmax is a measure of the size of motion detectors in the visual pathway. Other studies, however, have shown that Dmax increases with element size, in which case the most likely interpretation is that Dmax depends on the probability of false matches between pattern elements following a displacement. These conflicting accounts are reconciled here by showing that Dmax is indeed determined by the spacing between the elements in the pattern, but only after fine detail has been removed by a physiological prefiltering stage: the filter required to explain the data has a similar size to the receptive field of neurons in the primate magnocellular pathway. The model explains why Dmax can be increased by removing high spatial frequencies from random patterns, and simplifies our view of early motion detection.

An introgressed wing pattern acts as a mating cue.

PubMed

Sánchez, Angela P; Pardo-Diaz, Carolina; Enciso-Romero, Juan; Muñoz, Astrid; Jiggins, Chris D; Salazar, Camilo; Linares, Mauricio

2015-06-01

Heliconius butterflies provide good examples of both homoploid hybrid speciation and ecological speciation. In particular, examples of adaptive introgression have been detected among the subspecies of Heliconius timareta, which acquired red color pattern elements from H. melpomene. We tested whether the introgression of red wing pattern elements into H. timareta florencia might also be associated with incipient reproductive isolation (RI) from its close relative, H. timareta subsp. nov., found in the eastern Andes. No choice experiments show a 50% reduction in mating between females of H. t. subsp. nov. and males of H .t. florencia, but not in the reciprocal direction. In choice experiments using wing models, males of H. timareta subsp. nov. approach and court red phenotypes less than their own, whereas males of H. t. florencia prefer models with a red phenotype. Intrinsic postzygotic isolation was not detected in crosses between these H. timareta races. These results suggest that a color pattern trait gained by introgression is triggering RI between H. timareta subsp. nov. and H. t. florencia. © 2015 The Author(s). Evolution © 2015 The Society for the Study of Evolution.

Detection of CMOS bridging faults using minimal stuck-at fault test sets

NASA Technical Reports Server (NTRS)

Ijaz, Nabeel; Frenzel, James F.

1993-01-01

The performance of minimal stuck-at fault test sets at detecting bridging faults are evaluated. New functional models of circuit primitives are presented which allow accurate representation of bridging faults under switch-level simulation. The effectiveness of the patterns is evaluated using both voltage and current testing.

Research on the frequency hopping bistatic sonar system

NASA Astrophysics Data System (ADS)

Liang, Guo-long; Zhang, Yao; Zhang, Guang-pu; Liu, Kai

2011-10-01

A new model for bistatic sonar system is established, in which frequency hopping (FH) signals are used for targets detection according to some rules. This model can decrease the time between adjacent signals and obtain more information in a unit time. The receiving system will receive and process the signals of different frequency respectively, according the FH pattern, for detecting and locating targets. This method can helps yield more stable and accurate outputs, using the characteristic of the FH signals, increase the ability of anti-detection and anti partial-band jamming.

A hierarchical model for estimating the spatial distribution and abundance of animals detected by continuous-time recorders

USGS Publications Warehouse

Dorazio, Robert; Karanth, K. Ullas

2017-01-01

MotivationSeveral spatial capture-recapture (SCR) models have been developed to estimate animal abundance by analyzing the detections of individuals in a spatial array of traps. Most of these models do not use the actual dates and times of detection, even though this information is readily available when using continuous-time recorders, such as microphones or motion-activated cameras. Instead most SCR models either partition the period of trap operation into a set of subjectively chosen discrete intervals and ignore multiple detections of the same individual within each interval, or they simply use the frequency of detections during the period of trap operation and ignore the observed times of detection. Both practices make inefficient use of potentially important information in the data.Model and data analysisWe developed a hierarchical SCR model to estimate the spatial distribution and abundance of animals detected with continuous-time recorders. Our model includes two kinds of point processes: a spatial process to specify the distribution of latent activity centers of individuals within the region of sampling and a temporal process to specify temporal patterns in the detections of individuals. We illustrated this SCR model by analyzing spatial and temporal patterns evident in the camera-trap detections of tigers living in and around the Nagarahole Tiger Reserve in India. We also conducted a simulation study to examine the performance of our model when analyzing data sets of greater complexity than the tiger data.BenefitsOur approach provides three important benefits: First, it exploits all of the information in SCR data obtained using continuous-time recorders. Second, it is sufficiently versatile to allow the effects of both space use and behavior of animals to be specified as functions of covariates that vary over space and time. Third, it allows both the spatial distribution and abundance of individuals to be estimated, effectively providing a species distribution model, even in cases where spatial covariates of abundance are unknown or unavailable. We illustrated these benefits in the analysis of our data, which allowed us to quantify differences between nocturnal and diurnal activities of tigers and to estimate their spatial distribution and abundance across the study area. Our continuous-time SCR model allows an analyst to specify many of the ecological processes thought to be involved in the distribution, movement, and behavior of animals detected in a spatial trapping array of continuous-time recorders. We plan to extend this model to estimate the population dynamics of animals detected during multiple years of SCR surveys.

Event Detection for Hydrothermal Plumes: A case study at Grotto Vent

NASA Astrophysics Data System (ADS)

Bemis, K. G.; Ozer, S.; Xu, G.; Rona, P. A.; Silver, D.

2012-12-01

Evidence is mounting that geologic events such as volcanic eruptions (and intrusions) and earthquakes (near and far) influence the flow rates and temperatures of hydrothermal systems. Connecting such suppositions to observations of hydrothermal output is challenging, but new ongoing time series have the potential to capture such events. This study explores using activity detection, a technique modified from computer vision, to identify pre-defined events within an extended time series recorded by COVIS (Cabled Observatory Vent Imaging Sonar) and applies it to a time series, with gaps, from Sept 2010 to the present; available measurements include plume orientation, plume rise rate, and diffuse flow area at the NEPTUNE Canada Observatory at Grotto Vent, Main Endeavour Field, Juan de Fuca Ridge. Activity detection is the process of finding a pattern (activity) in a data set containing many different types of patterns. Among many approaches proposed to model and detect activities, we have chosen a graph-based technique, Petri Nets, as they do not require training data to model the activity. They use the domain expert's knowledge to build the activity as a combination of feature states and their transitions (actions). Starting from a conceptual model of how hydrothermal plumes respond to daily tides, we have developed a Petri Net based detection algorithm that identifies deviations from the specified response. Initially we assumed that the orientation of the plume would change smoothly and symmetrically in a consistent daily pattern. However, results indicate that the rate of directional changes varies. The present Petri Net detects unusually large and rapid changes in direction or amount of bending; however inspection of Figure 1 suggests that many of the events detected may be artifacts resulting from gaps in the data or from the large temporal spacing. Still, considerable complexity overlies the "normal" tidal response pattern (the data has a dominant frequency of ~12.9 hours). We are in the process of defining several events of particular scientific interest: 1) transient behavioral changes associated with atmospheric storms, earthquakes or volcanic intrusions or eruptions, 2) mutual interaction of neighboring plumes on each other's behavior, and 3) rapid shifts in plume direction that indicate the presence of unusual currents or changes in currents. We will query the existing data to see if these relationships are ever observed as well as testing our understanding of the "normal" pattern of response to tidal currents.Figure 1. Arrows indicate plume orientation at a given time (time axis in days after 9/29/10) and stars indicate times when orientation changes rapidly.

Visual Persons Behavior Diary Generation Model based on Trajectories and Pose Estimation

NASA Astrophysics Data System (ADS)

Gang, Chen; Bin, Chen; Yuming, Liu; Hui, Li

2018-03-01

The behavior pattern of persons was the important output of the surveillance analysis. This paper focus on the generation model of visual person behavior diary. The pipeline includes the person detection, tracking, and the person behavior classify. This paper adopts the deep convolutional neural model YOLO (You Only Look Once)V2 for person detection module. Multi person tracking was based on the detection framework. The Hungarian assignment algorithm was used to the matching. The person appearance model was integrated by HSV color model and Hash code model. The person object motion was estimated by the Kalman Filter. The multi objects were matching with exist tracklets through the appearance and motion location distance by the Hungarian assignment method. A long continuous trajectory for one person was get by the spatial-temporal continual linking algorithm. And the face recognition information was used to identify the trajectory. The trajectories with identification information can be used to generate the visual diary of person behavior based on the scene context information and person action estimation. The relevant modules are tested in public data sets and our own capture video sets. The test results show that the method can be used to generate the visual person behavior pattern diary with certain accuracy.

Comparison of Computational-Model and Experimental-Example Trained Neural Networks for Processing Speckled Fringe Patterns

NASA Technical Reports Server (NTRS)

Decker, A. J.; Fite, E. B.; Thorp, S. A.; Mehmed, O.

1998-01-01

The responses of artificial neural networks to experimental and model-generated inputs are compared for detection of damage in twisted fan blades using electronic holography. The training-set inputs, for this work, are experimentally generated characteristic patterns of the vibrating blades. The outputs are damage-flag indicators or second derivatives of the sensitivity-vector-projected displacement vectors from a finite element model. Artificial neural networks have been trained in the past with computational-model-generated training sets. This approach avoids the difficult inverse calculations traditionally used to compare interference fringes with the models. But the high modeling standards are hard to achieve, even with fan-blade finite-element models.

Comparison of Computational, Model and Experimental, Example Trained Neural Networks for Processing Speckled Fringe Patterns

NASA Technical Reports Server (NTRS)

Decker, A. J.; Fite, E. B.; Thorp, S. A.; Mehmed, O.

1998-01-01

The responses of artificial neural networks to experimental and model-generated inputs are compared for detection of damage in twisted fan blades using electronic holography. The training-set inputs, for this work, are experimentally generated characteristic patterns of the vibrating blades. The outputs are damage-flag indicators or second derivatives of the sensitivity-vector-projected displacement vectors from a finite element model. Artificial neural networks have been trained in the past with computational-model- generated training sets. This approach avoids the difficult inverse calculations traditionally used to compare interference fringes with the models. But the high modeling standards are hard to achieve, even with fan-blade finite-element models.

RSAT: regulatory sequence analysis tools.

PubMed

Thomas-Chollier, Morgane; Sand, Olivier; Turatsinze, Jean-Valéry; Janky, Rekin's; Defrance, Matthieu; Vervisch, Eric; Brohée, Sylvain; van Helden, Jacques

2008-07-01

The regulatory sequence analysis tools (RSAT, http://rsat.ulb.ac.be/rsat/) is a software suite that integrates a wide collection of modular tools for the detection of cis-regulatory elements in genome sequences. The suite includes programs for sequence retrieval, pattern discovery, phylogenetic footprint detection, pattern matching, genome scanning and feature map drawing. Random controls can be performed with random gene selections or by generating random sequences according to a variety of background models (Bernoulli, Markov). Beyond the original word-based pattern-discovery tools (oligo-analysis and dyad-analysis), we recently added a battery of tools for matrix-based detection of cis-acting elements, with some original features (adaptive background models, Markov-chain estimation of P-values) that do not exist in other matrix-based scanning tools. The web server offers an intuitive interface, where each program can be accessed either separately or connected to the other tools. In addition, the tools are now available as web services, enabling their integration in programmatic workflows. Genomes are regularly updated from various genome repositories (NCBI and EnsEMBL) and 682 organisms are currently supported. Since 1998, the tools have been used by several hundreds of researchers from all over the world. Several predictions made with RSAT were validated experimentally and published.

Detecting potential anomalies in projections of rainfall trends and patterns using human observations

NASA Astrophysics Data System (ADS)

Kohfeld, K. E.; Savo, V.; Sillmann, J.; Morton, C.; Lepofsky, D.

2016-12-01

Shifting precipitation patterns are a well-documented consequence of climate change, but their spatial variability is particularly difficult to assess. While the accuracy of global models has increased, specific regional changes in precipitation regimes are not well captured by these models. Typically, researchers who wish to detect trends and patterns in climatic variables, such as precipitation, use instrumental observations. In our study, we combined observations of rainfall by subsistence-oriented communities with several metrics of rainfall estimated from global instrumental records for comparable time periods (1955 - 2005). This comparison was aimed at identifying: 1) which rainfall metrics best match human observations of changes in precipitation; 2) areas where local communities observe changes not detected by global models. The collated observations ( 3800) made by subsistence-oriented communities covered 129 countries ( 1830 localities). For comparable time periods, we saw a substantial correspondence between instrumental records and human observations (66-77%) at the same locations, regardless of whether we considered trends in general rainfall, drought, or extreme rainfall. We observed a clustering of mismatches in two specific regions, possibly indicating some climatic phenomena not completely captured by the currently available global models. Many human observations also indicated an increased unpredictability in the start, end, duration, and continuity of the rainy seasons, all of which may hamper the performance of subsistence activities. We suggest that future instrumental metrics should capture this unpredictability of rainfall. This information would be important for thousands of subsistence-oriented communities in planning, coping, and adapting to climate change.

Neuronal encoding of object and distance information: a model simulation study on naturalistic optic flow processing

PubMed Central

Hennig, Patrick; Egelhaaf, Martin

2011-01-01

We developed a model of the input circuitry of the FD1 cell, an identified motion-sensitive interneuron in the blowfly's visual system. The model circuit successfully reproduces the FD1 cell's most conspicuous property: its larger responses to objects than to spatially extended patterns. The model circuit also mimics the time-dependent responses of FD1 to dynamically complex naturalistic stimuli, shaped by the blowfly's saccadic flight and gaze strategy: the FD1 responses are enhanced when, as a consequence of self-motion, a nearby object crosses the receptive field during intersaccadic intervals. Moreover, the model predicts that these object-induced responses are superimposed by pronounced pattern-dependent fluctuations during movements on virtual test flights in a three-dimensional environment with systematic modifications of the environmental patterns. Hence, the FD1 cell is predicted to detect not unambiguously objects defined by the spatial layout of the environment, but to be also sensitive to objects distinguished by textural features. These ambiguous detection abilities suggest an encoding of information about objects—irrespective of the features by which the objects are defined—by a population of cells, with the FD1 cell presumably playing a prominent role in such an ensemble. PMID:22461769

Patterned mask inspection technology with Projection Electron Microscope (PEM) technique for 11 nm half-pitch (hp) generation EUV masks

NASA Astrophysics Data System (ADS)

Hirano, Ryoichi; Iida, Susumu; Amano, Tsuyoshi; Watanabe, Hidehiro; Hatakeyama, Masahiro; Murakami, Takeshi; Yoshikawa, Shoji; Suematsu, Kenichi; Terao, Kenji

2015-07-01

High-sensitivity EUV mask pattern defect detection is one of the major issues in order to realize the device fabrication by using the EUV lithography. We have already designed a novel Projection Electron Microscope (PEM) optics that has been integrated into a new inspection system named EBEYE-V30 ("Model EBEYE" is an EBARA's model code), and which seems to be quite promising for 16 nm hp generation EUVL Patterned mask Inspection (PI). Defect inspection sensitivity was evaluated by capturing an electron image generated at the mask by focusing onto an image sensor. The progress of the novel PEM optics performance is not only about making an image sensor with higher resolution but also about doing a better image processing to enhance the defect signal. In this paper, we describe the experimental results of EUV patterned mask inspection using the above-mentioned system. The performance of the system is measured in terms of defect detectability for 11 nm hp generation EUV mask. To improve the inspection throughput for 11 nm hp generation defect detection, it would require a data processing rate of greater than 1.5 Giga- Pixel-Per-Second (GPPS) that would realize less than eight hours of inspection time including the step-and-scan motion associated with the process. The aims of the development program are to attain a higher throughput, and enhance the defect detection sensitivity by using an adequate pixel size with sophisticated image processing resulting in a higher processing rate.

A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models

PubMed Central

2011-01-01

Background Remote homology detection is a hard computational problem. Most approaches have trained computational models by using either full protein sequences or multiple sequence alignments (MSA), including all positions. However, when we deal with proteins in the "twilight zone" we can observe that only some segments of sequences (motifs) are conserved. We introduce a novel logical representation that allows us to represent physico-chemical properties of sequences, conserved amino acid positions and conserved physico-chemical positions in the MSA. From this, Inductive Logic Programming (ILP) finds the most frequent patterns (motifs) and uses them to train propositional models, such as decision trees and support vector machines (SVM). Results We use the SCOP database to perform our experiments by evaluating protein recognition within the same superfamily. Our results show that our methodology when using SVM performs significantly better than some of the state of the art methods, and comparable to other. However, our method provides a comprehensible set of logical rules that can help to understand what determines a protein function. Conclusions The strategy of selecting only the most frequent patterns is effective for the remote homology detection. This is possible through a suitable first-order logical representation of homologous properties, and through a set of frequent patterns, found by an ILP system, that summarizes essential features of protein functions. PMID:21429187

A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models.

PubMed

Bernardes, Juliana S; Carbone, Alessandra; Zaverucha, Gerson

2011-03-23

Remote homology detection is a hard computational problem. Most approaches have trained computational models by using either full protein sequences or multiple sequence alignments (MSA), including all positions. However, when we deal with proteins in the "twilight zone" we can observe that only some segments of sequences (motifs) are conserved. We introduce a novel logical representation that allows us to represent physico-chemical properties of sequences, conserved amino acid positions and conserved physico-chemical positions in the MSA. From this, Inductive Logic Programming (ILP) finds the most frequent patterns (motifs) and uses them to train propositional models, such as decision trees and support vector machines (SVM). We use the SCOP database to perform our experiments by evaluating protein recognition within the same superfamily. Our results show that our methodology when using SVM performs significantly better than some of the state of the art methods, and comparable to other. However, our method provides a comprehensible set of logical rules that can help to understand what determines a protein function. The strategy of selecting only the most frequent patterns is effective for the remote homology detection. This is possible through a suitable first-order logical representation of homologous properties, and through a set of frequent patterns, found by an ILP system, that summarizes essential features of protein functions.

Detecting consistent patterns of directional adaptation using differential selection codon models.

PubMed

Parto, Sahar; Lartillot, Nicolas

2017-06-23

Phylogenetic codon models are often used to characterize the selective regimes acting on protein-coding sequences. Recent methodological developments have led to models explicitly accounting for the interplay between mutation and selection, by modeling the amino acid fitness landscape along the sequence. However, thus far, most of these models have assumed that the fitness landscape is constant over time. Fluctuations of the fitness landscape may often be random or depend on complex and unknown factors. However, some organisms may be subject to systematic changes in selective pressure, resulting in reproducible molecular adaptations across independent lineages subject to similar conditions. Here, we introduce a codon-based differential selection model, which aims to detect and quantify the fine-grained consistent patterns of adaptation at the protein-coding level, as a function of external conditions experienced by the organism under investigation. The model parameterizes the global mutational pressure, as well as the site- and condition-specific amino acid selective preferences. This phylogenetic model is implemented in a Bayesian MCMC framework. After validation with simulations, we applied our method to a dataset of HIV sequences from patients with known HLA genetic background. Our differential selection model detects and characterizes differentially selected coding positions specifically associated with two different HLA alleles. Our differential selection model is able to identify consistent molecular adaptations as a function of repeated changes in the environment of the organism. These models can be applied to many other problems, ranging from viral adaptation to evolution of life-history strategies in plants or animals.

Detecting Lung Diseases from Exhaled Aerosols: Non-Invasive Lung Diagnosis Using Fractal Analysis and SVM Classification

PubMed Central

Xi, Jinxiang; Zhao, Weizhong; Yuan, Jiayao Eddie; Kim, JongWon; Si, Xiuhua; Xu, Xiaowei

2015-01-01

Background Each lung structure exhales a unique pattern of aerosols, which can be used to detect and monitor lung diseases non-invasively. The challenges are accurately interpreting the exhaled aerosol fingerprints and quantitatively correlating them to the lung diseases. Objective and Methods In this study, we presented a paradigm of an exhaled aerosol test that addresses the above two challenges and is promising to detect the site and severity of lung diseases. This paradigm consists of two steps: image feature extraction using sub-regional fractal analysis and data classification using a support vector machine (SVM). Numerical experiments were conducted to evaluate the feasibility of the breath test in four asthmatic lung models. A high-fidelity image-CFD approach was employed to compute the exhaled aerosol patterns under different disease conditions. Findings By employing the 10-fold cross-validation method, we achieved 100% classification accuracy among four asthmatic models using an ideal 108-sample dataset and 99.1% accuracy using a more realistic 324-sample dataset. The fractal-SVM classifier has been shown to be robust, highly sensitive to structural variations, and inherently suitable for investigating aerosol-disease correlations. Conclusion For the first time, this study quantitatively linked the exhaled aerosol patterns with their underlying diseases and set the stage for the development of a computer-aided diagnostic system for non-invasive detection of obstructive respiratory diseases. PMID:26422016

The surface geometry of inherited joint and fracture trace patterns resulting from active and passive deformation

NASA Technical Reports Server (NTRS)

Podwysocki, M. H.; Gold, D. P.

1974-01-01

Hypothetical models are considered for detecting subsurface structure from the fracture or joint pattern, which may be influenced by the structure and propagated to the surface. Various patterns of an initially orthogonal fracture grid are modeled according to active and passive deformation mechanisms. In the active periclinal structure with a vertical axis, fracture frequency increased both over the dome and basin, and remained constant with decreasing depth to the structure. For passive periclinal features such as a reef or sand body, fracture frequency is determined by the arc of curvature and showed a reduction over the reefmound and increased over the basin.

VisitSense: Sensing Place Visit Patterns from Ambient Radio on Smartphones for Targeted Mobile Ads in Shopping Malls

PubMed Central

Kim, Byoungjip; Kang, Seungwoo; Ha, Jin-Young; Song, Junehwa

2015-01-01

In this paper, we introduce a novel smartphone framework called VisitSense that automatically detects and predicts a smartphone user’s place visits from ambient radio to enable behavioral targeting for mobile ads in large shopping malls. VisitSense enables mobile app developers to adopt visit-pattern-aware mobile advertising for shopping mall visitors in their apps. It also benefits mobile users by allowing them to receive highly relevant mobile ads that are aware of their place visit patterns in shopping malls. To achieve the goal, VisitSense employs accurate visit detection and prediction methods. For accurate visit detection, we develop a change-based detection method to take into consideration the stability change of ambient radio and the mobility change of users. It performs well in large shopping malls where ambient radio is quite noisy and causes existing algorithms to easily fail. In addition, we proposed a causality-based visit prediction model to capture the causality in the sequential visit patterns for effective prediction. We have developed a VisitSense prototype system, and a visit-pattern-aware mobile advertising application that is based on it. Furthermore, we deploy the system in the COEX Mall, one of the largest shopping malls in Korea, and conduct diverse experiments to show the effectiveness of VisitSense. PMID:26193275

Pattern formation in a model for mountain pine beetle dispersal: linking model predictions to data.

PubMed

Strohm, S; Tyson, R C; Powell, J A

2013-10-01

Pattern formation occurs in a wide range of biological systems. This pattern formation can occur in mathematical models because of diffusion-driven instability or due to the interaction between reaction, diffusion, and chemotaxis. In this paper, we investigate the spatial pattern formation of attack clusters in a system for Mountain Pine Beetle. The pattern formation (aggregation) of the Mountain Pine Beetle in order to attack susceptible trees is crucial for their survival and reproduction. We use a reaction-diffusion equation with chemotaxis to model the interaction between Mountain Pine Beetle, Mountain Pine Beetle pheromones, and susceptible trees. Mathematical analysis is utilized to discover the spacing in-between beetle attacks on the susceptible landscape. The model predictions are verified by analysing aerial detection survey data of Mountain Pine Beetle Attack from the Sawtooth National Recreation Area. We find that the distance between Mountain Pine Beetle attack clusters predicted by our model closely corresponds to the observed attack data in the Sawtooth National Recreation Area. These results clarify the spatial mechanisms controlling the transition from incipient to epidemic populations and may lead to control measures which protect forests from Mountain Pine Beetle outbreak.

Simulation of pattern and defect detection in periodic amplitude and phase structures using photorefractive four-wave mixing

NASA Astrophysics Data System (ADS)

Nehmetallah, Georges; Banerjee, Partha; Khoury, Jed

2015-03-01

The nonlinearity inherent in four-wave mixing in photorefractive (PR) materials is used for adaptive filtering. Examples include script enhancement on a periodic pattern, scratch and defect cluster enhancement, periodic pattern dislocation enhancement, etc. through intensity filtering image manipulation. Organic PR materials have large space-bandwidth product, which makes them useful in adaptive filtering techniques in quality control systems. For instance, in the case of edge enhancement, phase conjugation via four-wave mixing suppresses the low spatial frequencies of the Fourier spectrum of an aperiodic image and consequently leads to image edge enhancement. In this work, we model, numerically verify, and simulate the performance of a four wave mixing setup used for edge, defect and pattern detection in periodic amplitude and phase structures. The results show that this technique successfully detects the slightest defects clearly even with no enhancement. This technique should facilitate improvements in applications such as image display sharpness utilizing edge enhancement, production line defect inspection of fabrics, textiles, e-beam lithography masks, surface inspection, and materials characterization.

Invasive Species Distribution Modeling (iSDM): Are absence data and dispersal constraints needed to predict actual distributions?

Treesearch

Tomáš Václavík; Ross K. Meentemeyer

2009-01-01

Species distribution models (SDMs) based on statistical relationships between occurrence data and underlying environmental conditions are increasingly used to predict spatial patterns of biological invasions and prioritize locations for early detection and control of invasion outbreaks. However, invasive species distribution models (iSDMs) face special challenges...

The Manifest Association Structure of the Single-Factor Model: Insights from Partial Correlations

ERIC Educational Resources Information Center

Salgueiro, Maria de Fatima; Smith, Peter W. F.; McDonald, John W.

2008-01-01

The association structure between manifest variables arising from the single-factor model is investigated using partial correlations. The additional insights to the practitioner provided by partial correlations for detecting a single-factor model are discussed. The parameter space for the partial correlations is presented, as are the patterns of…

Detection and recognition of simple spatial forms

NASA Technical Reports Server (NTRS)

Watson, A. B.

1983-01-01

A model of human visual sensitivity to spatial patterns is constructed. The model predicts the visibility and discriminability of arbitrary two-dimensional monochrome images. The image is analyzed by a large array of linear feature sensors, which differ in spatial frequency, phase, orientation, and position in the visual field. All sensors have one octave frequency bandwidths, and increase in size linearly with eccentricity. Sensor responses are processed by an ideal Bayesian classifier, subject to uncertainty. The performance of the model is compared to that of the human observer in detecting and discriminating some simple images.

Grid-texture mechanisms in human vision: Contrast detection of regular sparse micro-patterns requires specialist templates.

PubMed

Baker, Daniel H; Meese, Tim S

2016-07-27

Previous work has shown that human vision performs spatial integration of luminance contrast energy, where signals are squared and summed (with internal noise) over area at detection threshold. We tested that model here in an experiment using arrays of micro-pattern textures that varied in overall stimulus area and sparseness of their target elements, where the contrast of each element was normalised for sensitivity across the visual field. We found a power-law improvement in performance with stimulus area, and a decrease in sensitivity with sparseness. While the contrast integrator model performed well when target elements constituted 50-100% of the target area (replicating previous results), observers outperformed the model when texture elements were sparser than this. This result required the inclusion of further templates in our model, selective for grids of various regular texture densities. By assuming a MAX operation across these noisy mechanisms the model also accounted for the increase in the slope of the psychometric function that occurred as texture density decreased. Thus, for the first time, mechanisms that are selective for texture density have been revealed at contrast detection threshold. We suggest that these mechanisms have a role to play in the perception of visual textures.

Grid-texture mechanisms in human vision: Contrast detection of regular sparse micro-patterns requires specialist templates

PubMed Central

Baker, Daniel H.; Meese, Tim S.

2016-01-01

Previous work has shown that human vision performs spatial integration of luminance contrast energy, where signals are squared and summed (with internal noise) over area at detection threshold. We tested that model here in an experiment using arrays of micro-pattern textures that varied in overall stimulus area and sparseness of their target elements, where the contrast of each element was normalised for sensitivity across the visual field. We found a power-law improvement in performance with stimulus area, and a decrease in sensitivity with sparseness. While the contrast integrator model performed well when target elements constituted 50–100% of the target area (replicating previous results), observers outperformed the model when texture elements were sparser than this. This result required the inclusion of further templates in our model, selective for grids of various regular texture densities. By assuming a MAX operation across these noisy mechanisms the model also accounted for the increase in the slope of the psychometric function that occurred as texture density decreased. Thus, for the first time, mechanisms that are selective for texture density have been revealed at contrast detection threshold. We suggest that these mechanisms have a role to play in the perception of visual textures. PMID:27460430

Clustering Of Left Ventricular Wall Motion Patterns

NASA Astrophysics Data System (ADS)

Bjelogrlic, Z.; Jakopin, J.; Gyergyek, L.

1982-11-01

A method for detection of wall regions with similar motion was presented. A model based on local direction information was used to measure the left ventricular wall motion from cineangiographic sequence. Three time functions were used to define segmental motion patterns: distance of a ventricular contour segment from the mean contour, the velocity of a segment and its acceleration. Motion patterns were clustered by the UPGMA algorithm and by an algorithm based on K-nearest neighboor classification rule.

Classifier utility modeling and analysis of hypersonic inlet start/unstart considering training data costs

NASA Astrophysics Data System (ADS)

Chang, Juntao; Hu, Qinghua; Yu, Daren; Bao, Wen

2011-11-01

Start/unstart detection is one of the most important issues of hypersonic inlets and is also the foundation of protection control of scramjet. The inlet start/unstart detection can be attributed to a standard pattern classification problem, and the training sample costs have to be considered for the classifier modeling as the CFD numerical simulations and wind tunnel experiments of hypersonic inlets both cost time and money. To solve this problem, the CFD simulation of inlet is studied at first step, and the simulation results could provide the training data for pattern classification of hypersonic inlet start/unstart. Then the classifier modeling technology and maximum classifier utility theories are introduced to analyze the effect of training data cost on classifier utility. In conclusion, it is useful to introduce support vector machine algorithms to acquire the classifier model of hypersonic inlet start/unstart, and the minimum total cost of hypersonic inlet start/unstart classifier can be obtained by the maximum classifier utility theories.

Analytical modeling and sensor monitoring for optimal processing of advanced textile structural composites by resin transfer molding

NASA Technical Reports Server (NTRS)

Loos, Alfred C.; Macrae, John D.; Hammond, Vincent H.; Kranbuehl, David E.; Hart, Sean M.; Hasko, Gregory H.; Markus, Alan M.

1993-01-01

A two-dimensional model of the resin transfer molding (RTM) process was developed which can be used to simulate the infiltration of resin into an anisotropic fibrous preform. Frequency dependent electromagnetic sensing (FDEMS) has been developed for in situ monitoring of the RTM process. Flow visualization tests were performed to obtain data which can be used to verify the sensor measurements and the model predictions. Results of the tests showed that FDEMS can accurately detect the position of the resin flow-front during mold filling, and that the model predicted flow-front patterns agreed well with the measured flow-front patterns.

A compressive sensing based secure watermark detection and privacy preserving storage framework.

PubMed

Qia Wang; Wenjun Zeng; Jun Tian

2014-03-01

Privacy is a critical issue when the data owners outsource data storage or processing to a third party computing service, such as the cloud. In this paper, we identify a cloud computing application scenario that requires simultaneously performing secure watermark detection and privacy preserving multimedia data storage. We then propose a compressive sensing (CS)-based framework using secure multiparty computation (MPC) protocols to address such a requirement. In our framework, the multimedia data and secret watermark pattern are presented to the cloud for secure watermark detection in a CS domain to protect the privacy. During CS transformation, the privacy of the CS matrix and the watermark pattern is protected by the MPC protocols under the semi-honest security model. We derive the expected watermark detection performance in the CS domain, given the target image, watermark pattern, and the size of the CS matrix (but without the CS matrix itself). The correctness of the derived performance has been validated by our experiments. Our theoretical analysis and experimental results show that secure watermark detection in the CS domain is feasible. Our framework can also be extended to other collaborative secure signal processing and data-mining applications in the cloud.

A research using hybrid RBF/Elman neural networks for intrusion detection system secure model

NASA Astrophysics Data System (ADS)

Tong, Xiaojun; Wang, Zhu; Yu, Haining

2009-10-01

A hybrid RBF/Elman neural network model that can be employed for both anomaly detection and misuse detection is presented in this paper. The IDSs using the hybrid neural network can detect temporally dispersed and collaborative attacks effectively because of its memory of past events. The RBF network is employed as a real-time pattern classification and the Elman network is employed to restore the memory of past events. The IDSs using the hybrid neural network are evaluated against the intrusion detection evaluation data sponsored by U.S. Defense Advanced Research Projects Agency (DARPA). Experimental results are presented in ROC curves. Experiments show that the IDSs using this hybrid neural network improve the detection rate and decrease the false positive rate effectively.

A neural model of the temporal dynamics of figure-ground segregation in motion perception.

PubMed

Raudies, Florian; Neumann, Heiko

2010-03-01

How does the visual system manage to segment a visual scene into surfaces and objects and manage to attend to a target object? Based on psychological and physiological investigations, it has been proposed that the perceptual organization and segmentation of a scene is achieved by the processing at different levels of the visual cortical hierarchy. According to this, motion onset detection, motion-defined shape segregation, and target selection are accomplished by processes which bind together simple features into fragments of increasingly complex configurations at different levels in the processing hierarchy. As an alternative to this hierarchical processing hypothesis, it has been proposed that the processing stages for feature detection and segregation are reflected in different temporal episodes in the response patterns of individual neurons. Such temporal epochs have been observed in the activation pattern of neurons as low as in area V1. Here, we present a neural network model of motion detection, figure-ground segregation and attentive selection which explains these response patterns in an unifying framework. Based on known principles of functional architecture of the visual cortex, we propose that initial motion and motion boundaries are detected at different and hierarchically organized stages in the dorsal pathway. Visual shapes that are defined by boundaries, which were generated from juxtaposed opponent motions, are represented at different stages in the ventral pathway. Model areas in the different pathways interact through feedforward and modulating feedback, while mutual interactions enable the communication between motion and form representations. Selective attention is devoted to shape representations by sending modulating feedback signals from higher levels (working memory) to intermediate levels to enhance their responses. Areas in the motion and form pathway are coupled through top-down feedback with V1 cells at the bottom end of the hierarchy. We propose that the different temporal episodes in the response pattern of V1 cells, as recorded in recent experiments, reflect the strength of modulating feedback signals. This feedback results from the consolidated shape representations from coherent motion patterns and the attentive modulation of responses along the cortical hierarchy. The model makes testable predictions concerning the duration and delay of the temporal episodes of V1 cell responses as well as their response variations that were caused by modulating feedback signals. Copyright 2009 Elsevier Ltd. All rights reserved.

Locating Sensors for Detecting Source-to-Target Patterns of Special Nuclear Material Smuggling: A Spatial Information Theoretic Approach

PubMed Central

Przybyla, Jay; Taylor, Jeffrey; Zhou, Xuesong

2010-01-01

In this paper, a spatial information-theoretic model is proposed to locate sensors for detecting source-to-target patterns of special nuclear material (SNM) smuggling. In order to ship the nuclear materials from a source location with SNM production to a target city, the smugglers must employ global and domestic logistics systems. This paper focuses on locating a limited set of fixed and mobile radiation sensors in a transportation network, with the intent to maximize the expected information gain and minimize the estimation error for the subsequent nuclear material detection stage. A Kalman filtering-based framework is adapted to assist the decision-maker in quantifying the network-wide information gain and SNM flow estimation accuracy. PMID:22163641

Locating sensors for detecting source-to-target patterns of special nuclear material smuggling: a spatial information theoretic approach.

PubMed

Przybyla, Jay; Taylor, Jeffrey; Zhou, Xuesong

2010-01-01

In this paper, a spatial information-theoretic model is proposed to locate sensors for detecting source-to-target patterns of special nuclear material (SNM) smuggling. In order to ship the nuclear materials from a source location with SNM production to a target city, the smugglers must employ global and domestic logistics systems. This paper focuses on locating a limited set of fixed and mobile radiation sensors in a transportation network, with the intent to maximize the expected information gain and minimize the estimation error for the subsequent nuclear material detection stage. A Kalman filtering-based framework is adapted to assist the decision-maker in quantifying the network-wide information gain and SNM flow estimation accuracy.

Detection of aquifer system compaction and land subsidence using interferometric synthetic aperture radar, Antelope Valley, Mojave Desert, California

USGS Publications Warehouse

Galloway, D.L.; Hudnut, K.W.; Ingebritsen, S.E.; Phillips, S.P.; Peltzer, G.; Rogez, F.; Rosen, P.A.

1998-01-01

Interferometric synthetic aperture radar (InSAR) has great potential to detect and quantify land subsidence caused by aquifer system compaction. InSAR maps with high spatial detail and resolution of range displacement (±10 mm in change of land surface elevation) were developed for a groundwater basin (∼103 km2) in Antelope Valley, California, using radar data collected from the ERS-1 satellite. These data allow comprehensive comparison between recent (1993–1995) subsidence patterns and those detected historically (1926–1992) by more traditional methods. The changed subsidence patterns are generally compatible with recent shifts in land and water use. The InSAR-detected patterns are generally consistent with predictions based on a coupled model of groundwater flow and aquifer system compaction. The minor inconsistencies may reflect our imperfect knowledge of the distribution and properties of compressible sediments. When used in conjunction with coincident measurements of groundwater levels and other geologic information, InSAR data may be useful for constraining parameter estimates in simulations of aquifer system compaction.

A model of human decision making in multiple process monitoring situations

NASA Technical Reports Server (NTRS)

Greenstein, J. S.; Rouse, W. B.

1982-01-01

Human decision making in multiple process monitoring situations is considered. It is proposed that human decision making in many multiple process monitoring situations can be modeled in terms of the human's detection of process related events and his allocation of attention among processes once he feels event have occurred. A mathematical model of human event detection and attention allocation performance in multiple process monitoring situations is developed. An assumption made in developing the model is that, in attempting to detect events, the human generates estimates of the probabilities that events have occurred. An elementary pattern recognition technique, discriminant analysis, is used to model the human's generation of these probability estimates. The performance of the model is compared to that of four subjects in a multiple process monitoring situation requiring allocation of attention among processes.

Detecting Payload Attacks on Programmable Logic Controllers (PLCs)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Huan

Programmable logic controllers (PLCs) play critical roles in industrial control systems (ICS). Providing hardware peripherals and firmware support for control programs (i.e., a PLC’s “payload”) written in languages such as ladder logic, PLCs directly receive sensor readings and control ICS physical processes. An attacker with access to PLC development software (e.g., by compromising an engineering workstation) can modify the payload program and cause severe physical damages to the ICS. To protect critical ICS infrastructure, we propose to model runtime behaviors of legitimate PLC payload program and use runtime behavior monitoring in PLC firmware to detect payload attacks. By monitoring themore » I/O access patterns, network access patterns, as well as payload program timing characteristics, our proposed firmware-level detection mechanism can detect abnormal runtime behaviors of malicious PLC payload. Using our proof-of-concept implementation, we evaluate the memory and execution time overhead of implementing our proposed method and find that it is feasible to incorporate our method into existing PLC firmware. In addition, our evaluation results show that a wide variety of payload attacks can be effectively detected by our proposed approach. The proposed firmware-level payload attack detection scheme complements existing bumpin- the-wire solutions (e.g., external temporal-logic-based model checkers) in that it can detect payload attacks that violate realtime requirements of ICS operations and does not require any additional apparatus.« less

Detection of Ludic Patterns in Two Triadic Motor Games and Differences in Decision Complexity

PubMed Central

Aguilar, Miguel Pic; Navarro-Adelantado, Vicente; Jonsson, Gudberg K.

2018-01-01

The triad is a particular structure in which an ambivalent social relationship takes place. This work is focused on the search of behavioral regularities in the practice of motor games in triad, which is a little known field. For the detection of behavioral patterns not visible to the naked eye, we use Theme. A chasing games model was followed, with rules, and in two different structures (A↔B↔C↔A and A → B → C → A) on four class groups (two for each structure), for a total of 84, 12, and 13 year old secondary school students, 37 girls (44%) and 47 boys (56%). The aim was to examine if the players' behavior, in relation to the triad structure, matches with any ludic behavior patterns. An observational methodology was applied, with a nomothetic, punctual and multidimensional design. The intra and inter-evaluative correlation coefficients and the generalizability theory ensured the quality of the data. A mixed behavioral role system was used (four criteria and 15 categories), and the pattern detection software Theme was applied to detect temporal regularities in the order of event occurrences. The results show that time location of motor responses in triad games was not random. In the “maze” game we detected more complex ludic patterns than the “three fields” game, which might be explained by means of structural determinants such as circulation. This research points out the decisional complexity in motor games, and it confirms the differences among triads from the point of view of motor communication. PMID:29354084

Terrestrial implications of mathematical modeling developed for space biomedical research

NASA Technical Reports Server (NTRS)

Lujan, Barbara F.; White, Ronald J.; Leonard, Joel I.; Srinivasan, R. Srini

1988-01-01

This paper summarizes several related research projects supported by NASA which seek to apply computer models to space medicine and physiology. These efforts span a wide range of activities, including mathematical models used for computer simulations of physiological control systems; power spectral analysis of physiological signals; pattern recognition models for detection of disease processes; and computer-aided diagnosis programs.

Evaluating Data Clustering Approach for Life-Cycle Facility Control

DTIC Science & Technology

2013-04-01

produce 90% matching accuracy with noise/variations up to 55%. KEYWORDS: Building Information Modelling ( BIM ), machine learning, pattern detection...reconciled to building information model elements and ultimately to an expected resource utilization schedule. The motivation for this integration is to...by interoperable data sources and building information models . Building performance modelling and simulation efforts such as those by Maile et al

Early Menarche and Gestational Diabetes Mellitus at First Live Birth.

PubMed

Shen, Yun; Hu, Hui; D Taylor, Brandie; Kan, Haidong; Xu, Xiaohui

2017-03-01

To examine the association between early menarche and gestational diabetes mellitus (GDM). Data from the National Health and Nutrition Examination Survey 2007-2012 were used to investigate the association between age at menarche and the risk of GDM at first birth among 5914 women. A growth mixture model was used to detect distinctive menarche onset patterns based on self-reported age at menarche. Logistic regression models were then used to examine the associations between menarche initiation patterns and GDM after adjusting for sociodemographic factors, family history of diabetes mellitus, lifetime greatest Body Mass Index, smoking status, and physical activity level. Among the 5914 first-time mothers, 3.4 % had self-reported GDM. We detected three groups with heterogeneous menarche onset patterns, the Early, Normal, and Late Menarche Groups. The regression model shows that compared to the Normal Menarche Group, the Early Menarche Group had 1.75 (95 % CI 1.10, 2.79) times the odds of having GDM. No statistically significant difference was observed between the Normal and the Late Menarche Group. This study suggests that early menarche may be a risk factor of GDM. Future studies are warranted to examine and confirm this finding.

Robust pattern decoding in shape-coded structured light

NASA Astrophysics Data System (ADS)

Tang, Suming; Zhang, Xu; Song, Zhan; Song, Lifang; Zeng, Hai

2017-09-01

Decoding is a challenging and complex problem in a coded structured light system. In this paper, a robust pattern decoding method is proposed for the shape-coded structured light in which the pattern is designed as grid shape with embedded geometrical shapes. In our decoding method, advancements are made at three steps. First, a multi-template feature detection algorithm is introduced to detect the feature point which is the intersection of each two orthogonal grid-lines. Second, pattern element identification is modelled as a supervised classification problem and the deep neural network technique is applied for the accurate classification of pattern elements. Before that, a training dataset is established, which contains a mass of pattern elements with various blurring and distortions. Third, an error correction mechanism based on epipolar constraint, coplanarity constraint and topological constraint is presented to reduce the false matches. In the experiments, several complex objects including human hand are chosen to test the accuracy and robustness of the proposed method. The experimental results show that our decoding method not only has high decoding accuracy, but also owns strong robustness to surface color and complex textures.

Adaptive hidden Markov model with anomaly States for price manipulation detection.

PubMed

Cao, Yi; Li, Yuhua; Coleman, Sonya; Belatreche, Ammar; McGinnity, Thomas Martin

2015-02-01

Price manipulation refers to the activities of those traders who use carefully designed trading behaviors to manually push up or down the underlying equity prices for making profits. With increasing volumes and frequency of trading, price manipulation can be extremely damaging to the proper functioning and integrity of capital markets. The existing literature focuses on either empirical studies of market abuse cases or analysis of particular manipulation types based on certain assumptions. Effective approaches for analyzing and detecting price manipulation in real time are yet to be developed. This paper proposes a novel approach, called adaptive hidden Markov model with anomaly states (AHMMAS) for modeling and detecting price manipulation activities. Together with wavelet transformations and gradients as the feature extraction methods, the AHMMAS model caters to price manipulation detection and basic manipulation type recognition. The evaluation experiments conducted on seven stock tick data from NASDAQ and the London Stock Exchange and 10 simulated stock prices by stochastic differential equation show that the proposed AHMMAS model can effectively detect price manipulation patterns and outperforms the selected benchmark models.

An evidence accumulation model for conflict detection performance in a simulated air traffic control task.

PubMed

Neal, Andrew; Kwantes, Peter J

2009-04-01

The aim of this article is to develop a formal model of conflict detection performance. Our model assumes that participants iteratively sample evidence regarding the state of the world and accumulate it over time. A decision is made when the evidence reaches a threshold that changes over time in response to the increasing urgency of the task. Two experiments were conducted to examine the effects of conflict geometry and timing on response proportions and response time. The model is able to predict the observed pattern of response times, including a nonmonotonic relationship between distance at point of closest approach and response time, as well as effects of angle of approach and relative velocity. The results demonstrate that evidence accumulation models provide a good account of performance on a conflict detection task. Evidence accumulation models are a form of dynamic signal detection theory, allowing for the analysis of response times as well as response proportions, and can be used for simulating human performance on dynamic decision tasks.

Modeling false positive detections in species occurrence data under different study designs.

PubMed

Chambert, Thierry; Miller, David A W; Nichols, James D

2015-02-01

The occurrence of false positive detections in presence-absence data, even when they occur infrequently, can lead to severe bias when estimating species occupancy patterns. Building upon previous efforts to account for this source of observational error, we established a general framework to model false positives in occupancy studies and extend existing modeling approaches to encompass a broader range of sampling designs. Specifically, we identified three common sampling designs that are likely to cover most scenarios encountered by researchers. The different designs all included ambiguous detections, as well as some known-truth data, but their modeling differed in the level of the model hierarchy at which the known-truth information was incorporated (site level or observation level). For each model, we provide the likelihood, as well as R and BUGS code needed for implementation. We also establish a clear terminology and provide guidance to help choosing the most appropriate design and modeling approach.

Change-point detection of induced and natural seismicity

NASA Astrophysics Data System (ADS)

Fiedler, B.; Holschneider, M.; Zoeller, G.; Hainzl, S.

2016-12-01

Earthquake rates are influenced by tectonic stress buildup, earthquake-induced stress changes, and transient aseismic sources. While the first two sources can be well modeled due to the fact that the source is known, transient aseismic processes are more difficult to detect. However, the detection of the associated changes of the earthquake activity is of great interest, because it might help to identify natural aseismic deformation patterns (such as slow slip events) and the occurrence of induced seismicity related to human activities. We develop a Bayesian approach to detect change-points in seismicity data which are modeled by Poisson processes. By means of a Likelihood-Ratio-Test, we proof the significance of the change of the intensity. The model is also extended to spatiotemporal data to detect the area of the transient changes. The method is firstly tested for synthetic data and then applied to observational data from central US and the Bardarbunga volcano in Iceland.

Patterns on serpentine shapes elicit visual attention in marmosets (Callithrix jacchus).

PubMed

Wombolt, Jessica R; Caine, Nancy G

2016-09-01

Given the prevalence of threatening snakes in the evolutionary history, and modern-day environments of human and nonhuman primates, sensory, and perceptual abilities that allow for quick detection of, and appropriate response to snakes are likely to have evolved. Many studies have demonstrated that primates recognize snakes faster than other stimuli, and it is suggested that the unique serpentine shape is responsible for its quick detection. However, there are many nonthreatening serpentine shapes in the environment (e.g., vines) that are not threatening; therefore, other cues must be used to distinguish threatening from benign serpentine objects. In two experiments, we systematically evaluated how common marmosets (Callithrix jacchus) visually attend to specific snake-like features. In the first experiment, we examined if skin pattern is a cue that elicits increased visual inspection of serpentine shapes by measuring the amount of time the marmosets looked into a blind before, during, and after presentation of clay models with and without patterns. The marmosets spent the most time looking at the objects, both serpentine and triangle, that were etched with scales, suggesting that something may be uniquely salient about scales in evoking attention. In contrast, they showed relatively little interest in the unpatterned serpentine and control (a triangle) stimuli. In experiment 2, we replicated and extended the results of experiment 1 by adding additional stimulus conditions. We found that patterns on a serpentine shape generated more inspection than those same patterns on a triangle shape. We were unable to confirm that a scaled pattern is unique in its ability to elicit visual interest; the scaled models elicited similar looking times as line and star patterns. Our data provide a foundation for future research to examine how snakes are detected and identified by primates. Am. J. Primatol. 78:928-936, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Gas Debris Disks: A New Way to Produce Dust Patterns

NASA Technical Reports Server (NTRS)

Kuchner, Marc J.

2012-01-01

Debris disks like those around Fomalhaut and Beta Pictoris show striking dust patterns often attributed to planets. But adding a bit of gas to our models of these disks--too little to detect-could alter this interpretation. Small amounts of gas lead to new dynamical instabilities that may mimic the narrow eccentric rings and other structures planets would create in a gas-free disk. rll discuss these phenomena and whether or not we can still use dust patterns as indicators of hidden exoplanets.

Investigating species co-occurrence patterns when species are detected imperfectly

USGS Publications Warehouse

MacKenzie, D.I.; Bailey, L.L.; Nichols, J.D.

2004-01-01

1. Over the last 30 years there has been a great deal of interest in investigating patterns of species co-occurrence across a number of locations, which has led to the development of numerous methods to determine whether there is evidence that a particular pattern may not have occurred by random chance. 2. A key aspect that seems to have been largely overlooked is the possibility that species may not always be detected at a location when present, which leads to 'false absences' in a species presence/absence matrix that may cause incorrect inferences to be made about co-occurrence patterns. Furthermore, many of the published methods for investigating patterns of species co-occurrence do not account for potential differences in the site characteristics that may partially (at least) explain non-random patterns (e.g. due to species having similar/different habitat preferences). 3. Here we present a statistical method for modelling co-occurrence patterns between species while accounting for imperfect detection and site characteristics. This method requires that multiple presence/absence surveys for the species be conducted over a reasonably short period of time at most sites. The method yields unbiased estimates of probabilities of occurrence, and is practical when the number of species is small (< 4). 4. To illustrate the method we consider data collected on two terrestrial salamander species, Plethodonjordani and members of the Plethodon glutinosus complex, collected in the Great Smoky Mountains National Park, USA. We find no evidence that the species do not occur independently at sites once site elevation has been allowed for, although we find some evidence of a statistical interaction between species in terms of detectability that we suggest may be due to changes in relative abundances.

Occupancy Estimation and Modeling : Inferring Patterns and Dynamics of Species Occurrence

USGS Publications Warehouse

MacKenzie, D.I.; Nichols, J.D.; Royle, J. Andrew; Pollock, K.H.; Bailey, L.L.; Hines, J.E.

2006-01-01

This is the first book to examine the latest methods in analyzing presence/absence data surveys. Using four classes of models (single-species, single-season; single-species, multiple season; multiple-species, single-season; and multiple-species, multiple-season), the authors discuss the practical sampling situation, present a likelihood-based model enabling direct estimation of the occupancy-related parameters while allowing for imperfect detectability, and make recommendations for designing studies using these models. It provides authoritative insights into the latest in estimation modeling; discusses multiple models which lay the groundwork for future study designs; addresses critical issues of imperfect detectibility and its effects on estimation; and explores the role of probability in estimating in detail.

Person-Fit Statistics for Joint Models for Accuracy and Speed

ERIC Educational Resources Information Center

Fox, Jean-Paul; Marianti, Sukaesi

2017-01-01

Response accuracy and response time data can be analyzed with a joint model to measure ability and speed of working, while accounting for relationships between item and person characteristics. In this study, person-fit statistics are proposed for joint models to detect aberrant response accuracy and/or response time patterns. The person-fit tests…

Sensing Urban Land-Use Patterns by Integrating Google Tensorflow and Scene-Classification Models

NASA Astrophysics Data System (ADS)

Yao, Y.; Liang, H.; Li, X.; Zhang, J.; He, J.

2017-09-01

With the rapid progress of China's urbanization, research on the automatic detection of land-use patterns in Chinese cities is of substantial importance. Deep learning is an effective method to extract image features. To take advantage of the deep-learning method in detecting urban land-use patterns, we applied a transfer-learning-based remote-sensing image approach to extract and classify features. Using the Google Tensorflow framework, a powerful convolution neural network (CNN) library was created. First, the transferred model was previously trained on ImageNet, one of the largest object-image data sets, to fully develop the model's ability to generate feature vectors of standard remote-sensing land-cover data sets (UC Merced and WHU-SIRI). Then, a random-forest-based classifier was constructed and trained on these generated vectors to classify the actual urban land-use pattern on the scale of traffic analysis zones (TAZs). To avoid the multi-scale effect of remote-sensing imagery, a large random patch (LRP) method was used. The proposed method could efficiently obtain acceptable accuracy (OA = 0.794, Kappa = 0.737) for the study area. In addition, the results show that the proposed method can effectively overcome the multi-scale effect that occurs in urban land-use classification at the irregular land-parcel level. The proposed method can help planners monitor dynamic urban land use and evaluate the impact of urban-planning schemes.

Extreme ultraviolet patterned mask inspection performance of advanced projection electron microscope system for 11nm half-pitch generation

NASA Astrophysics Data System (ADS)

Hirano, Ryoichi; Iida, Susumu; Amano, Tsuyoshi; Watanabe, Hidehiro; Hatakeyama, Masahiro; Murakami, Takeshi; Suematsu, Kenichi; Terao, Kenji

2016-03-01

Novel projection electron microscope optics have been developed and integrated into a new inspection system named EBEYE-V30 ("Model EBEYE" is an EBARA's model code) , and the resulting system shows promise for application to half-pitch (hp) 16-nm node extreme ultraviolet lithography (EUVL) patterned mask inspection. To improve the system's inspection throughput for 11-nm hp generation defect detection, a new electron-sensitive area image sensor with a high-speed data processing unit, a bright and stable electron source, and an image capture area deflector that operates simultaneously with the mask scanning motion have been developed. A learning system has been used for the mask inspection tool to meet the requirements of hp 11-nm node EUV patterned mask inspection. Defects are identified by the projection electron microscope system using the "defectivity" from the characteristics of the acquired image. The learning system has been developed to reduce the labor and costs associated with adjustment of the detection capability to cope with newly-defined mask defects. We describe the integration of the developed elements into the inspection tool and the verification of the designed specification. We have also verified the effectiveness of the learning system, which shows enhanced detection capability for the hp 11-nm node.

Selecting climate simulations for impact studies based on multivariate patterns of climate change.

PubMed

Mendlik, Thomas; Gobiet, Andreas

In climate change impact research it is crucial to carefully select the meteorological input for impact models. We present a method for model selection that enables the user to shrink the ensemble to a few representative members, conserving the model spread and accounting for model similarity. This is done in three steps: First, using principal component analysis for a multitude of meteorological parameters, to find common patterns of climate change within the multi-model ensemble. Second, detecting model similarities with regard to these multivariate patterns using cluster analysis. And third, sampling models from each cluster, to generate a subset of representative simulations. We present an application based on the ENSEMBLES regional multi-model ensemble with the aim to provide input for a variety of climate impact studies. We find that the two most dominant patterns of climate change relate to temperature and humidity patterns. The ensemble can be reduced from 25 to 5 simulations while still maintaining its essential characteristics. Having such a representative subset of simulations reduces computational costs for climate impact modeling and enhances the quality of the ensemble at the same time, as it prevents double-counting of dependent simulations that would lead to biased statistics. The online version of this article (doi:10.1007/s10584-015-1582-0) contains supplementary material, which is available to authorized users.

From trees to forest: relational complexity network and workload of air traffic controllers.

PubMed

Zhang, Jingyu; Yang, Jiazhong; Wu, Changxu

2015-01-01

In this paper, we propose a relational complexity (RC) network framework based on RC metric and network theory to model controllers' workload in conflict detection and resolution. We suggest that, at the sector level, air traffic showing a centralised network pattern can provide cognitive benefits in visual search and resolution decision which will in turn result in lower workload. We found that the network centralisation index can account for more variance in predicting perceived workload and task completion time in both a static conflict detection task (Study 1) and a dynamic one (Study 2) in addition to other aircraft-level and pair-level factors. This finding suggests that linear combination of aircraft-level or dyad-level information may not be adequate and the global-pattern-based index is necessary. Theoretical and practical implications of using this framework to improve future workload modelling and management are discussed. We propose a RC network framework to model the workload of air traffic controllers. The effect of network centralisation was examined in both a static conflict detection task and a dynamic one. Network centralisation was predictive of perceived workload and task completion time over and above other control variables.

Army Logistician. Volume 39, Issue 1, January-February 2007

DTIC Science & Technology

2007-02-01

of electronic systems using statistical methods. P& C , however, requires advanced prognostic capabilities not only to detect the early onset of...patterns. Entities operating in a P& C -enabled environment will sense and understand contextual meaning , communicate their state and mission, and act to...accessing of historical and simulation patterns; on- board prognostics capabilities; physics of failure analyses; and predictive modeling. P& C also

In vivo visualization method by absolute blood flow velocity based on speckle and fringe pattern using two-beam multipoint laser Doppler velocimetry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kyoden, Tomoaki, E-mail: kyouden@nc-toyama.ac.jp; Naruki, Shoji; Akiguchi, Shunsuke

Two-beam multipoint laser Doppler velocimetry (two-beam MLDV) is a non-invasive imaging technique able to provide an image of two-dimensional blood flow and has potential for observing cancer as previously demonstrated in a mouse model. In two-beam MLDV, the blood flow velocity can be estimated from red blood cells passing through a fringe pattern generated in the skin. The fringe pattern is created at the intersection of two beams in conventional LDV and two-beam MLDV. Being able to choose the depth position is an advantage of two-beam MLDV, and the position of a blood vessel can be identified in a three-dimensionalmore » space using this technique. Initially, we observed the fringe pattern in the skin, and the undeveloped or developed speckle pattern generated in a deeper position of the skin. The validity of the absolute velocity value detected by two-beam MLDV was verified while changing the number of layers of skin around a transparent flow channel. The absolute velocity value independent of direction was detected using the developed speckle pattern, which is created by the skin construct and two beams in the flow channel. Finally, we showed the relationship between the signal intensity and the fringe pattern, undeveloped speckle, or developed speckle pattern based on the skin depth. The Doppler signals were not detected at deeper positions in the skin, which qualitatively indicates the depth limit for two-beam MLDV.« less

Landscape characteristics influence pond occupancy by frogs after accounting for detectability

USGS Publications Warehouse

Mazerolle, M.J.; Desrochers, A.; Rochefort, L.

2005-01-01

Many investigators have hypothesized that landscape attributes such as the amount and proximity of habitat are important for amphibian spatial patterns. This has produced a number of studies focusing on the effects of landscape characteristics on amphibian patterns of occurrence in patches or ponds, most of which conclude that the landscape is important. We identified two concerns associated with these studies: one deals with their applicability to other landscape types, as most have been conducted in agricultural landscapes; the other highlights the need to account for the probability of detection. We tested the hypothesis that landscape characteristics influence spatial patterns of amphibian occurrence at ponds after accounting for the probability of detection in little-studied peatland landscapes undergoing peat mining. We also illustrated the costs of not accounting for the probability of detection by comparing our results to conventional logistic regression analyses. Results indicate that frog occurrence increased with the percent cover of ponds within 100, 250, and 1000 m, as well as the amount of forest cover within 1000 m. However, forest cover at 250 m had a negative influence on frog presence at ponds. Not accounting for the probability of detection resulted in underestimating the influence of most variables on frog occurrence, whereas a few were overestimated. Regardless, we show that conventional logistic regression can lead to different conclusions than analyses accounting for detectability. Our study is consistent with the hypothesis that landscape characteristics are important in determining the spatial patterns of frog occurrence at ponds. We strongly recommend estimating the probability of detection in field surveys, as this will increase the quality and conservation potential of models derived from such data. ?? 2005 by the Ecological Society of America.

Modeling anuran detection and site occupancy on North American Amphibian Monitoring Program (NAAMP) routes in Maryland

USGS Publications Warehouse

Weir, L.A.; Royle, J. Andrew; Nanjappa, P.; Jung, R.E.

2005-01-01

One of the most fundamental problems in monitoring animal populations is that of imperfect detection. Although imperfect detection can be modeled, studies examining patterns in occurrence often ignore detection and thus fail to properly partition variation in detection from that of occurrence. In this study, we used anuran calling survey data collected on North American Amphibian Monitoring Program routes in eastern Maryland to investigate factors that influence detection probability and site occupancy for 10 anuran species. In 2002, 17 calling survey routes in eastern Maryland were surveyed to collect environmental and species data nine or more times. To analyze these data, we developed models incorporating detection probability and site occupancy. The results suggest that, for more than half of the 10 species, detection probabilities vary most with season (i.e., day-of-year), air temperature, time, and moon illumination, whereas site occupancy may vary by the amount of palustrine forested wetland habitat. Our results suggest anuran calling surveys should document air temperature, time of night, moon illumination, observer skill, and habitat change over time, as these factors can be important to model-adjusted estimates of site occupancy. Our study represents the first formal modeling effort aimed at developing an analytic assessment framework for NAAMP calling survey data.

Testing hypotheses on distribution shifts and changes in phenology of imperfectly detectable species

USGS Publications Warehouse

Chambert, Thierry A.; Kendall, William L.; Hines, James E.; Nichols, James D.; Pedrini, Paolo; Waddle, J. Hardin; Tavecchia, Giacomo; Walls, Susan C.; Tenan, Simone

2015-01-01

With ongoing climate change, many species are expected to shift their spatial and temporal distributions. To document changes in species distribution and phenology, detection/non-detection data have proven very useful. Occupancy models provide a robust way to analyse such data, but inference is usually focused on species spatial distribution, not phenology.We present a multi-season extension of the staggered-entry occupancy model of Kendall et al. (2013, Ecology, 94, 610), which permits inference about the within-season patterns of species arrival and departure at sampling sites. The new model presented here allows investigation of species phenology and spatial distribution across years, as well as site extinction/colonization dynamics.We illustrate the model with two data sets on European migratory passerines and one data set on North American treefrogs. We show how to derive several additional phenological parameters, such as annual mean arrival and departure dates, from estimated arrival and departure probabilities.Given the extent of detection/non-detection data that are available, we believe that this modelling approach will prove very useful to further understand and predict species responses to climate change.

a Probabilistic Embedding Clustering Method for Urban Structure Detection

NASA Astrophysics Data System (ADS)

Lin, X.; Li, H.; Zhang, Y.; Gao, L.; Zhao, L.; Deng, M.

2017-09-01

Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM) to find latent features from high dimensional urban sensing data by "learning" via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China) proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.

Microbial community pattern detection in human body habitats via ensemble clustering framework.

PubMed

Yang, Peng; Su, Xiaoquan; Ou-Yang, Le; Chua, Hon-Nian; Li, Xiao-Li; Ning, Kang

2014-01-01

The human habitat is a host where microbial species evolve, function, and continue to evolve. Elucidating how microbial communities respond to human habitats is a fundamental and critical task, as establishing baselines of human microbiome is essential in understanding its role in human disease and health. Recent studies on healthy human microbiome focus on particular body habitats, assuming that microbiome develop similar structural patterns to perform similar ecosystem function under same environmental conditions. However, current studies usually overlook a complex and interconnected landscape of human microbiome and limit the ability in particular body habitats with learning models of specific criterion. Therefore, these methods could not capture the real-world underlying microbial patterns effectively. To obtain a comprehensive view, we propose a novel ensemble clustering framework to mine the structure of microbial community pattern on large-scale metagenomic data. Particularly, we first build a microbial similarity network via integrating 1920 metagenomic samples from three body habitats of healthy adults. Then a novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is proposed and applied onto the network to detect clustering pattern. Extensive experiments are conducted to evaluate the effectiveness of our model on deriving microbial community with respect to body habitat and host gender. From clustering results, we observed that body habitat exhibits a strong bound but non-unique microbial structural pattern. Meanwhile, human microbiome reveals different degree of structural variations over body habitat and host gender. In summary, our ensemble clustering framework could efficiently explore integrated clustering results to accurately identify microbial communities, and provide a comprehensive view for a set of microbial communities. The clustering results indicate that structure of human microbiome is varied systematically across body habitats and host genders. Such trends depict an integrated biography of microbial communities, which offer a new insight towards uncovering pathogenic model of human microbiome.

Microbial community pattern detection in human body habitats via ensemble clustering framework

PubMed Central

2014-01-01

Background The human habitat is a host where microbial species evolve, function, and continue to evolve. Elucidating how microbial communities respond to human habitats is a fundamental and critical task, as establishing baselines of human microbiome is essential in understanding its role in human disease and health. Recent studies on healthy human microbiome focus on particular body habitats, assuming that microbiome develop similar structural patterns to perform similar ecosystem function under same environmental conditions. However, current studies usually overlook a complex and interconnected landscape of human microbiome and limit the ability in particular body habitats with learning models of specific criterion. Therefore, these methods could not capture the real-world underlying microbial patterns effectively. Results To obtain a comprehensive view, we propose a novel ensemble clustering framework to mine the structure of microbial community pattern on large-scale metagenomic data. Particularly, we first build a microbial similarity network via integrating 1920 metagenomic samples from three body habitats of healthy adults. Then a novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is proposed and applied onto the network to detect clustering pattern. Extensive experiments are conducted to evaluate the effectiveness of our model on deriving microbial community with respect to body habitat and host gender. From clustering results, we observed that body habitat exhibits a strong bound but non-unique microbial structural pattern. Meanwhile, human microbiome reveals different degree of structural variations over body habitat and host gender. Conclusions In summary, our ensemble clustering framework could efficiently explore integrated clustering results to accurately identify microbial communities, and provide a comprehensive view for a set of microbial communities. The clustering results indicate that structure of human microbiome is varied systematically across body habitats and host genders. Such trends depict an integrated biography of microbial communities, which offer a new insight towards uncovering pathogenic model of human microbiome. PMID:25521415

Discovering Structural Regularity in 3D Geometry

PubMed Central

Pauly, Mark; Mitra, Niloy J.; Wallner, Johannes; Pottmann, Helmut; Guibas, Leonidas J.

2010-01-01

We introduce a computational framework for discovering regular or repeated geometric structures in 3D shapes. We describe and classify possible regular structures and present an effective algorithm for detecting such repeated geometric patterns in point- or mesh-based models. Our method assumes no prior knowledge of the geometry or spatial location of the individual elements that define the pattern. Structure discovery is made possible by a careful analysis of pairwise similarity transformations that reveals prominent lattice structures in a suitable model of transformation space. We introduce an optimization method for detecting such uniform grids specifically designed to deal with outliers and missing elements. This yields a robust algorithm that successfully discovers complex regular structures amidst clutter, noise, and missing geometry. The accuracy of the extracted generating transformations is further improved using a novel simultaneous registration method in the spatial domain. We demonstrate the effectiveness of our algorithm on a variety of examples and show applications to compression, model repair, and geometry synthesis. PMID:21170292

A fuzzy pattern matching method based on graph kernel for lithography hotspot detection

NASA Astrophysics Data System (ADS)

Nitta, Izumi; Kanazawa, Yuzi; Ishida, Tsutomu; Banno, Koji

2017-03-01

In advanced technology nodes, lithography hotspot detection has become one of the most significant issues in design for manufacturability. Recently, machine learning based lithography hotspot detection has been widely investigated, but it has trade-off between detection accuracy and false alarm. To apply machine learning based technique to the physical verification phase, designers require minimizing undetected hotspots to avoid yield degradation. They also need a ranking of similar known patterns with a detected hotspot to prioritize layout pattern to be corrected. To achieve high detection accuracy and to prioritize detected hotspots, we propose a novel lithography hotspot detection method using Delaunay triangulation and graph kernel based machine learning. Delaunay triangulation extracts features of hotspot patterns where polygons locate irregularly and closely one another, and graph kernel expresses inner structure of graphs. Additionally, our method provides similarity between two patterns and creates a list of similar training patterns with a detected hotspot. Experiments results on ICCAD 2012 benchmarks show that our method achieves high accuracy with allowable range of false alarm. We also show the ranking of the similar known patterns with a detected hotspot.

Applicability of mathematical modeling to problems of environmental physiology

NASA Technical Reports Server (NTRS)

White, Ronald J.; Lujan, Barbara F.; Leonard, Joel I.; Srinivasan, R. Srini

1988-01-01

The paper traces the evolution of mathematical modeling and systems analysis from terrestrial research to research related to space biomedicine and back again to terrestrial research. Topics covered include: power spectral analysis of physiological signals; pattern recognition models for detection of disease processes; and, computer-aided diagnosis programs used in conjunction with a special on-line biomedical computer library.

Modeling and measuring the visual detection of ecologically relevant motion by an Anolis lizard.

PubMed

Pallus, Adam C; Fleishman, Leo J; Castonguay, Philip M

2010-01-01

Motion in the visual periphery of lizards, and other animals, often causes a shift of visual attention toward the moving object. This behavioral response must be more responsive to relevant motion (predators, prey, conspecifics) than to irrelevant motion (windblown vegetation). Early stages of visual motion detection rely on simple local circuits known as elementary motion detectors (EMDs). We presented a computer model consisting of a grid of correlation-type EMDs, with videos of natural motion patterns, including prey, predators and windblown vegetation. We systematically varied the model parameters and quantified the relative response to the different classes of motion. We carried out behavioral experiments with the lizard Anolis sagrei and determined that their visual response could be modeled with a grid of correlation-type EMDs with a spacing parameter of 0.3 degrees visual angle, and a time constant of 0.1 s. The model with these parameters gave substantially stronger responses to relevant motion patterns than to windblown vegetation under equivalent conditions. However, the model is sensitive to local contrast and viewer-object distance. Therefore, additional neural processing is probably required for the visual system to reliably distinguish relevant from irrelevant motion under a full range of natural conditions.

Autonomous acoustic recorders reveal complex patterns in avian detection probability

USGS Publications Warehouse

Thompson, Sarah J.; Handel, Colleen M.; McNew, Lance B.

2017-01-01

Avian point‐count surveys are typically designed to occur during periods when birds are consistently active and singing, but seasonal and diurnal patterns of detection probability are often not well understood and may vary regionally or between years. We deployed autonomous acoustic recorders to assess how avian availability for detection (i.e., the probability that a bird signals its presence during a recording) varied during the breeding season with time of day, date, and weather‐related variables at multiple subarctic tundra sites in Alaska, USA, 2013–2014. A single observer processed 2,692 10‐minute recordings across 11 site‐years. We used time‐removal methods to assess availability and used generalized additive models to examine patterns of detectability (joint probability of presence, availability, and detection) for 16 common species. Despite lack of distinct dawn or dusk, most species displayed circadian vocalization patterns, with detection rates generally peaking between 0800 hours and 1200 hours but remaining high as late as 2000 hours for some species. Between 2200 hours and 0500 hours, most species’ detection rates dropped to near 0, signaling a distinctive rest period. Detectability dropped sharply for most species in early July. For all species considered, time‐removal analysis indicated nearly 100% likelihood of detection during a 10‐minute recording conducted in June, between 0500 hours and 2000 hours. This indicates that non‐detections during appropriate survey times and dates were attributable to the species’ absence or that silent birds were unlikely to initiate singing during a 10‐minute interval, whereas vocally active birds were singing very frequently. Systematic recordings revealed a gradient of species’ presence at each site, from ubiquitous to incidental. Although the total number of species detected at a site ranged from 16 to 27, we detected only 4 to 15 species on ≥5% of the site's recordings. Recordings provided an unusually detailed and consistent dataset that allowed us to identify, among other things, appropriate survey dates and times for species breeding at northern latitudes. Our results also indicated that more recordings of shorter duration (1–4 min) may be most efficient for detecting passerines.

Partitioning Detectability Components in Populations Subject to Within-Season Temporary Emigration Using Binomial Mixture Models

PubMed Central

O’Donnell, Katherine M.; Thompson, Frank R.; Semlitsch, Raymond D.

2015-01-01

Detectability of individual animals is highly variable and nearly always < 1; imperfect detection must be accounted for to reliably estimate population sizes and trends. Hierarchical models can simultaneously estimate abundance and effective detection probability, but there are several different mechanisms that cause variation in detectability. Neglecting temporary emigration can lead to biased population estimates because availability and conditional detection probability are confounded. In this study, we extend previous hierarchical binomial mixture models to account for multiple sources of variation in detectability. The state process of the hierarchical model describes ecological mechanisms that generate spatial and temporal patterns in abundance, while the observation model accounts for the imperfect nature of counting individuals due to temporary emigration and false absences. We illustrate our model’s potential advantages, including the allowance of temporary emigration between sampling periods, with a case study of southern red-backed salamanders Plethodon serratus. We fit our model and a standard binomial mixture model to counts of terrestrial salamanders surveyed at 40 sites during 3–5 surveys each spring and fall 2010–2012. Our models generated similar parameter estimates to standard binomial mixture models. Aspect was the best predictor of salamander abundance in our case study; abundance increased as aspect became more northeasterly. Increased time-since-rainfall strongly decreased salamander surface activity (i.e. availability for sampling), while higher amounts of woody cover objects and rocks increased conditional detection probability (i.e. probability of capture, given an animal is exposed to sampling). By explicitly accounting for both components of detectability, we increased congruence between our statistical modeling and our ecological understanding of the system. We stress the importance of choosing survey locations and protocols that maximize species availability and conditional detection probability to increase population parameter estimate reliability. PMID:25775182

Topological image texture analysis for quality assessment

NASA Astrophysics Data System (ADS)

Asaad, Aras T.; Rashid, Rasber Dh.; Jassim, Sabah A.

2017-05-01

Image quality is a major factor influencing pattern recognition accuracy and help detect image tampering for forensics. We are concerned with investigating topological image texture analysis techniques to assess different type of degradation. We use Local Binary Pattern (LBP) as a texture feature descriptor. For any image construct simplicial complexes for selected groups of uniform LBP bins and calculate persistent homology invariants (e.g. number of connected components). We investigated image quality discriminating characteristics of these simplicial complexes by computing these models for a large dataset of face images that are affected by the presence of shadows as a result of variation in illumination conditions. Our tests demonstrate that for specific uniform LBP patterns, the number of connected component not only distinguish between different levels of shadow effects but also help detect the infected regions as well.

Detecting misinformation and knowledge conflicts in relational data

NASA Astrophysics Data System (ADS)

Levchuk, Georgiy; Jackobsen, Matthew; Riordan, Brian

2014-06-01

Information fusion is required for many mission-critical intelligence analysis tasks. Using knowledge extracted from various sources, including entities, relations, and events, intelligence analysts respond to commander's information requests, integrate facts into summaries about current situations, augment existing knowledge with inferred information, make predictions about the future, and develop action plans. However, information fusion solutions often fail because of conflicting and redundant knowledge contained in multiple sources. Most knowledge conflicts in the past were due to translation errors and reporter bias, and thus could be managed. Current and future intelligence analysis, especially in denied areas, must deal with open source data processing, where there is much greater presence of intentional misinformation. In this paper, we describe a model for detecting conflicts in multi-source textual knowledge. Our model is based on constructing semantic graphs representing patterns of multi-source knowledge conflicts and anomalies, and detecting these conflicts by matching pattern graphs against the data graph constructed using soft co-reference between entities and events in multiple sources. The conflict detection process maintains the uncertainty throughout all phases, providing full traceability and enabling incremental updates of the detection results as new knowledge or modification to previously analyzed information are obtained. Detected conflicts are presented to analysts for further investigation. In the experimental study with SYNCOIN dataset, our algorithms achieved perfect conflict detection in ideal situation (no missing data) while producing 82% recall and 90% precision in realistic noise situation (15% of missing attributes).

Modeling Agrilus planipennis within-tree colonization patterns and development of a systematic subsampling plan

USDA-ARS?s Scientific Manuscript database

Emerald ash borer, Agrilus planipennis Fairmaire, an insect native to central Asia, was first detected in southeast Michigan in 2002, and has since killed millions of ash trees, Fraxinus spp., throughout eastern North America. Here, we use generalized linear mixed models to predict the presence or a...

A Generalized Process Model of Human Action Selection and Error and its Application to Error Prediction

DTIC Science & Technology

2014-07-01

Macmillan & Creelman , 2005). This is a quite high degree of discriminability and it means that when the decision model predicts a probability of...ROC analysis. Pattern Recognition Letters, 27(8), 861-874. Retrieved from Google Scholar. Macmillan, N. A., & Creelman , C. D. (2005). Detection

Elkhorn Slough: Detecting Eutrophication through Geospatial Modeling Applications

NASA Astrophysics Data System (ADS)

Caraballo Álvarez, I. O.; Childs, A.; Jurich, K.

2016-12-01

Elkhorn Slough in Monterey, California, has experienced substantial nutrient loading and eutrophication over the past 21 years as a result of fertilizer-rich runoff from nearby agricultural fields. This study seeks to identify and track spatial patterns of eutrophication hotspots and the correlation to land use changes, possible nutrient sources, and general climatic trends using remotely sensed and in situ data. Threats of rising sea level, subsiding marshes, and increased eutrophication hotspots demonstrate the necessity to analyze the effects of increasing nutrient loads, relative sea level changes, and sedimentation within Elkhorn Slough. The Soil & Water Assessment Tool (SWAT) model integrates specified inputs to assess nutrient and sediment loading and their sources. TerrSet's Land Change Modeler forecasts the future potential of land change transitions for various land cover classes around the slough as a result of nutrient loading, eutrophication, and increased sedimentation. TerrSet's Earth Trends Modeler provides a comprehensive analysis of image time series to rapidly assess long term eutrophication trends and detect spatial patterns of known hotspots. Results from this study will inform future coastal management practices and provide greater spatial and temporal insight into Elkhorn Slough eutrophication dynamics.

Wave-current interaction study in the Gulf of Alaska for detection of eddies by synthetic aperture radar

NASA Technical Reports Server (NTRS)

Liu, Antony K.; Peng, Chich Y.; Schumacher, James D.

1994-01-01

High resolution Esa Remote Sensing Satellite-1 (ERS-1) Synthetic Aperture Radar (SAR) images are used to detect a mesoscale eddy. Such features limit dispersal of pollock larvae and therefore likely influence recruitment of fish in the Gulf of Alaska. During high sea states and high winds, the direct surface signature of the eddy was not clearly visible, but the wave refraction in the eddy area was observed. The rays of the wave field are traced out directly from the SAR image. The ray pattern gives information on the refraction pattern and on the relative variation of the wave energy along a ray through wave current interaction. These observations are simulated by a ray-tracing model which incorporates a surface current field associated with the eddy. The numerical results of the model show that the waves are refracted and diverge in the eddy field with energy density decreasing. The model-data comparison for each ray shows the model predictions are in good agreement with the SAR data.

The Bees among Us: Modelling Occupancy of Solitary Bees

PubMed Central

MacIvor, J. Scott; Packer, Laurence

2016-01-01

Occupancy modelling has received increasing attention as a tool for differentiating between true absence and non-detection in biodiversity data. This is thought to be particularly useful when a species of interest is spread out over a large area and sampling is constrained. We used occupancy modelling to estimate the probability of three phylogenetically independent pairs of native—introduced species [Megachile campanulae (Robertson)—Megachile rotundata (Fab.), Megachile pugnata Say—Megachile centuncularis (L.), Osmia pumila Cresson—Osmia caerulescens (L.)] (Apoidea: Megachilidae) being present when repeated sampling did not always find them. Our study occurred along a gradient of urbanization and used nest boxes (bee hotels) set up over three consecutive years. Occupancy modelling discovered different patterns to those obtained by species detection and abundance-based data alone. For example, it predicted that the species that was ranked 4th in terms of detection actually had the greatest occupancy among all six species. The native M. pugnata had decreased occupancy with increasing building footprint and a similar but not significant pattern was found for the native O. pumila. Two introduced bees (M. rotundata and M. centuncularis), and one native (M. campanulae) had modelled occupancy values that increased with increasing urbanization. Occupancy probability differed among urban green space types for three of six bee species, with values for two native species (M. campanulae and O. pumila) being highest in home gardens and that for the exotic O. caerulescens being highest in community gardens. The combination of occupancy modelling with analysis of habitat variables as an augmentation to detection and abundance-based sampling is suggested to be the best way to ensure that urban habitat management results in the desired outcomes. PMID:27911954

A travel time forecasting model based on change-point detection method

NASA Astrophysics Data System (ADS)

LI, Shupeng; GUANG, Xiaoping; QIAN, Yongsheng; ZENG, Junwei

2017-06-01

Travel time parameters obtained from road traffic sensors data play an important role in traffic management practice. A travel time forecasting model is proposed for urban road traffic sensors data based on the method of change-point detection in this paper. The first-order differential operation is used for preprocessing over the actual loop data; a change-point detection algorithm is designed to classify the sequence of large number of travel time data items into several patterns; then a travel time forecasting model is established based on autoregressive integrated moving average (ARIMA) model. By computer simulation, different control parameters are chosen for adaptive change point search for travel time series, which is divided into several sections of similar state.Then linear weight function is used to fit travel time sequence and to forecast travel time. The results show that the model has high accuracy in travel time forecasting.

Mouse V1 population correlates of visual detection rely on heterogeneity within neuronal response patterns

PubMed Central

Montijn, Jorrit S; Goltstein, Pieter M; Pennartz, Cyriel MA

2015-01-01

Previous studies have demonstrated the importance of the primary sensory cortex for the detection, discrimination, and awareness of visual stimuli, but it is unknown how neuronal populations in this area process detected and undetected stimuli differently. Critical differences may reside in the mean strength of responses to visual stimuli, as reflected in bulk signals detectable in functional magnetic resonance imaging, electro-encephalogram, or magnetoencephalography studies, or may be more subtly composed of differentiated activity of individual sensory neurons. Quantifying single-cell Ca2+ responses to visual stimuli recorded with in vivo two-photon imaging, we found that visual detection correlates more strongly with population response heterogeneity rather than overall response strength. Moreover, neuronal populations showed consistencies in activation patterns across temporally spaced trials in association with hit responses, but not during nondetections. Contrary to models relying on temporally stable networks or bulk signaling, these results suggest that detection depends on transient differentiation in neuronal activity within cortical populations. DOI: http://dx.doi.org/10.7554/eLife.10163.001 PMID:26646184

Deep Learning-Based Data Forgery Detection in Automatic Generation Control

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Fengli; Li, Qinghua

Automatic Generation Control (AGC) is a key control system in the power grid. It is used to calculate the Area Control Error (ACE) based on frequency and tie-line power flow between balancing areas, and then adjust power generation to maintain the power system frequency in an acceptable range. However, attackers might inject malicious frequency or tie-line power flow measurements to mislead AGC to do false generation correction which will harm the power grid operation. Such attacks are hard to be detected since they do not violate physical power system models. In this work, we propose algorithms based on Neural Networkmore » and Fourier Transform to detect data forgery attacks in AGC. Different from the few previous work that rely on accurate load prediction to detect data forgery, our solution only uses the ACE data already available in existing AGC systems. In particular, our solution learns the normal patterns of ACE time series and detects abnormal patterns caused by artificial attacks. Evaluations on the real ACE dataset show that our methods have high detection accuracy.« less

Influence of Pattern of Missing Data on Performance of Imputation Methods: An Example Using National Data on Drug Injection in Prisons

PubMed Central

Haji-Maghsoudi, Saiedeh; Haghdoost, Ali-akbar; Rastegari, Azam; Baneshi, Mohammad Reza

2013-01-01

Background: Policy makers need models to be able to detect groups at high risk of HIV infection. Incomplete records and dirty data are frequently seen in national data sets. Presence of missing data challenges the practice of model development. Several studies suggested that performance of imputation methods is acceptable when missing rate is moderate. One of the issues which was of less concern, to be addressed here, is the role of the pattern of missing data. Methods: We used information of 2720 prisoners. Results derived from fitting regression model to whole data were served as gold standard. Missing data were then generated so that 10%, 20% and 50% of data were lost. In scenario 1, we generated missing values, at above rates, in one variable which was significant in gold model (age). In scenario 2, a small proportion of each of independent variable was dropped out. Four imputation methods, under different Event Per Variable (EPV) values, were compared in terms of selection of important variables and parameter estimation. Results: In scenario 2, bias in estimates was low and performances of all methods for handing missing data were similar. All methods at all missing rates were able to detect significance of age. In scenario 1, biases in estimations were increased, in particular at 50% missing rate. Here at EPVs of 10 and 5, imputation methods failed to capture effect of age. Conclusion: In scenario 2, all imputation methods at all missing rates, were able to detect age as being significant. This was not the case in scenario 1. Our results showed that performance of imputation methods depends on the pattern of missing data. PMID:24596839

Molecular Detection of Hematozoa Infections in Tundra Swans Relative to Migration Patterns and Ecological Conditions at Breeding Grounds

PubMed Central

Ramey, Andrew M.; Ely, Craig R.; Schmutz, Joel A.; Pearce, John M.; Heard, Darryl J.

2012-01-01

Tundra swans (Cygnus columbianus) are broadly distributed in North America, use a wide variety of habitats, and exhibit diverse migration strategies. We investigated patterns of hematozoa infection in three populations of tundra swans that breed in Alaska using satellite tracking to infer host movement and molecular techniques to assess the prevalence and genetic diversity of parasites. We evaluated whether migratory patterns and environmental conditions at breeding areas explain the prevalence of blood parasites in migratory birds by contrasting the fit of competing models formulated in an occupancy modeling framework and calculating the detection probability of the top model using Akaike Information Criterion (AIC). We described genetic diversity of blood parasites in each population of swans by calculating the number of unique parasite haplotypes observed. Blood parasite infection was significantly different between populations of Alaska tundra swans, with the highest estimated prevalence occurring among birds occupying breeding areas with lower mean daily wind speeds and higher daily summer temperatures. Models including covariates of wind speed and temperature during summer months at breeding grounds better predicted hematozoa prevalence than those that included annual migration distance or duration. Genetic diversity of blood parasites in populations of tundra swans appeared to be relative to hematozoa prevalence. Our results suggest ecological conditions at breeding grounds may explain differences of hematozoa infection among populations of tundra swans that breed in Alaska. PMID:23049862

Molecular detection of hematozoa infections in tundra swans relative to migration patterns and ecological conditions at breeding grounds

USGS Publications Warehouse

Ramey, Andrew M.; Ely, Craig R.; Schmutz, Joel A.; Pearce, John M.; Heard, Darryl J.

2012-01-01

Tundra swans (Cygnus columbianus) are broadly distributed in North America, use a wide variety of habitats, and exhibit diverse migration strategies. We investigated patterns of hematozoa infection in three populations of tundra swans that breed in Alaska using satellite tracking to infer host movement and molecular techniques to assess the prevalence and genetic diversity of parasites. We evaluated whether migratory patterns and environmental conditions at breeding areas explain the prevalence of blood parasites in migratory birds by contrasting the fit of competing models formulated in an occupancy modeling framework and calculating the detection probability of the top model using Akaike Information Criterion (AIC). We described genetic diversity of blood parasites in each population of swans by calculating the number of unique parasite haplotypes observed. Blood parasite infection was significantly different between populations of Alaska tundra swans, with the highest estimated prevalence occurring among birds occupying breeding areas with lower mean daily wind speeds and higher daily summer temperatures. Models including covariates of wind speed and temperature during summer months at breeding grounds better predicted hematozoa prevalence than those that included annual migration distance or duration. Genetic diversity of blood parasites in populations of tundra swans appeared to be relative to hematozoa prevalence. Our results suggest ecological conditions at breeding grounds may explain differences of hematozoa infection among populations of tundra swans that breed in Alaska.

Molecular detection of hematozoa infections in tundra swans relative to migration patterns and ecological conditions at breeding grounds.

PubMed

Ramey, Andrew M; Ely, Craig R; Schmutz, Joel A; Pearce, John M; Heard, Darryl J

2012-01-01

Tundra swans (Cygnus columbianus) are broadly distributed in North America, use a wide variety of habitats, and exhibit diverse migration strategies. We investigated patterns of hematozoa infection in three populations of tundra swans that breed in Alaska using satellite tracking to infer host movement and molecular techniques to assess the prevalence and genetic diversity of parasites. We evaluated whether migratory patterns and environmental conditions at breeding areas explain the prevalence of blood parasites in migratory birds by contrasting the fit of competing models formulated in an occupancy modeling framework and calculating the detection probability of the top model using Akaike Information Criterion (AIC). We described genetic diversity of blood parasites in each population of swans by calculating the number of unique parasite haplotypes observed. Blood parasite infection was significantly different between populations of Alaska tundra swans, with the highest estimated prevalence occurring among birds occupying breeding areas with lower mean daily wind speeds and higher daily summer temperatures. Models including covariates of wind speed and temperature during summer months at breeding grounds better predicted hematozoa prevalence than those that included annual migration distance or duration. Genetic diversity of blood parasites in populations of tundra swans appeared to be relative to hematozoa prevalence. Our results suggest ecological conditions at breeding grounds may explain differences of hematozoa infection among populations of tundra swans that breed in Alaska.

Detecting Adaptation in Protein-Coding Genes Using a Bayesian Site-Heterogeneous Mutation-Selection Codon Substitution Model.

PubMed

Rodrigue, Nicolas; Lartillot, Nicolas

2017-01-01

Codon substitution models have traditionally attempted to uncover signatures of adaptation within protein-coding genes by contrasting the rates of synonymous and non-synonymous substitutions. Another modeling approach, known as the mutation-selection framework, attempts to explicitly account for selective patterns at the amino acid level, with some approaches allowing for heterogeneity in these patterns across codon sites. Under such a model, substitutions at a given position occur at the neutral or nearly neutral rate when they are synonymous, or when they correspond to replacements between amino acids of similar fitness; substitutions from high to low (low to high) fitness amino acids have comparatively low (high) rates. Here, we study the use of such a mutation-selection framework as a null model for the detection of adaptation. Following previous works in this direction, we include a deviation parameter that has the effect of capturing the surplus, or deficit, in non-synonymous rates, relative to what would be expected under a mutation-selection modeling framework that includes a Dirichlet process approach to account for across-codon-site variation in amino acid fitness profiles. We use simulations, along with a few real data sets, to study the behavior of the approach, and find it to have good power with a low false-positive rate. Altogether, we emphasize the potential of recent mutation-selection models in the detection of adaptation, calling for further model refinements as well as large-scale applications. © The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Gap junctions mediate large-scale Turing structures in a mean-field cortex driven by subcortical noise

NASA Astrophysics Data System (ADS)

Steyn-Ross, Moira L.; Steyn-Ross, D. A.; Wilson, M. T.; Sleigh, J. W.

2007-07-01

One of the grand puzzles in neuroscience is establishing the link between cognition and the disparate patterns of spontaneous and task-induced brain activity that can be measured clinically using a wide range of detection modalities such as scalp electrodes and imaging tomography. High-level brain function is not a single-neuron property, yet emerges as a cooperative phenomenon of multiply-interacting populations of neurons. Therefore a fruitful modeling approach is to picture the cerebral cortex as a continuum characterized by parameters that have been averaged over a small volume of cortical tissue. Such mean-field cortical models have been used to investigate gross patterns of brain behavior such as anesthesia, the cycles of natural sleep, memory and erasure in slow-wave sleep, and epilepsy. There is persuasive and accumulating evidence that direct gap-junction connections between inhibitory neurons promote synchronous oscillatory behavior both locally and across distances of some centimeters, but, to date, continuum models have ignored gap-junction connectivity. In this paper we employ simple mean-field arguments to derive an expression for D2 , the diffusive coupling strength arising from gap-junction connections between inhibitory neurons. Using recent neurophysiological measurements reported by Fukuda [J. Neurosci. 26, 3434 (2006)], we estimate an upper limit of D2≈0.6cm2 . We apply a linear stability analysis to a standard mean-field cortical model, augmented with gap-junction diffusion, and find this value for the diffusive coupling strength to be close to the critical value required to destabilize the homogeneous steady state. Computer simulations demonstrate that larger values of D2 cause the noise-driven model cortex to spontaneously crystalize into random mazelike Turing structures: centimeter-scale spatial patterns in which regions of high-firing activity are intermixed with regions of low-firing activity. These structures are consistent with the spatial variations in brain activity patterns detected with the BOLD (blood oxygen-level-dependent) signal detected with magnetic resonance imaging, and may provide a natural substrate for synchronous gamma-band rhythms observed across separated EEG (electroencephalogram) electrodes.

In vivo burn diagnosis by camera-phone diffuse reflectance laser speckle detection.

PubMed

Ragol, S; Remer, I; Shoham, Y; Hazan, S; Willenz, U; Sinelnikov, I; Dronov, V; Rosenberg, L; Bilenca, A

2016-01-01

Burn diagnosis using laser speckle light typically employs widefield illumination of the burn region to produce two-dimensional speckle patterns from light backscattered from the entire irradiated tissue volume. Analysis of speckle contrast in these time-integrated patterns can then provide information on burn severity. Here, by contrast, we use point illumination to generate diffuse reflectance laser speckle patterns of the burn. By examining spatiotemporal fluctuations in these time-integrated patterns along the radial direction from the incident point beam, we show the ability to distinguish partial-thickness burns in a porcine model in vivo within the first 24 hours post-burn. Furthermore, our findings suggest that time-integrated diffuse reflectance laser speckle can be useful for monitoring burn healing over time post-burn. Unlike conventional diffuse reflectance laser speckle detection systems that utilize scientific or industrial-grade cameras, our system is designed with a camera-phone, demonstrating the potential for burn diagnosis with a simple imager.

In vivo burn diagnosis by camera-phone diffuse reflectance laser speckle detection

PubMed Central

Ragol, S.; Remer, I.; Shoham, Y.; Hazan, S.; Willenz, U.; Sinelnikov, I.; Dronov, V.; Rosenberg, L.; Bilenca, A.

2015-01-01

Burn diagnosis using laser speckle light typically employs widefield illumination of the burn region to produce two-dimensional speckle patterns from light backscattered from the entire irradiated tissue volume. Analysis of speckle contrast in these time-integrated patterns can then provide information on burn severity. Here, by contrast, we use point illumination to generate diffuse reflectance laser speckle patterns of the burn. By examining spatiotemporal fluctuations in these time-integrated patterns along the radial direction from the incident point beam, we show the ability to distinguish partial-thickness burns in a porcine model in vivo within the first 24 hours post-burn. Furthermore, our findings suggest that time-integrated diffuse reflectance laser speckle can be useful for monitoring burn healing over time post-burn. Unlike conventional diffuse reflectance laser speckle detection systems that utilize scientific or industrial-grade cameras, our system is designed with a camera-phone, demonstrating the potential for burn diagnosis with a simple imager. PMID:26819831

Oceanic eddy detection and lifetime forecast using machine learning methods

NASA Astrophysics Data System (ADS)

Ashkezari, Mohammad D.; Hill, Christopher N.; Follett, Christopher N.; Forget, Gaël.; Follows, Michael J.

2016-12-01

We report a novel altimetry-based machine learning approach for eddy identification and characterization. The machine learning models use daily maps of geostrophic velocity anomalies and are trained according to the phase angle between the zonal and meridional components at each grid point. The trained models are then used to identify the corresponding eddy phase patterns and to predict the lifetime of a detected eddy structure. The performance of the proposed method is examined at two dynamically different regions to demonstrate its robust behavior and region independency.

Detection of Propagating Fast Sausage Waves through Detailed Analysis of a Zebra-pattern Fine Structure in a Solar Radio Burst

NASA Astrophysics Data System (ADS)

Kaneda, K.; Misawa, H.; Iwai, K.; Masuda, S.; Tsuchiya, F.; Katoh, Y.; Obara, T.

2018-03-01

Various magnetohydrodynamic (MHD) waves have recently been detected in the solar corona and investigated intensively in the context of coronal heating and coronal seismology. In this Letter, we report the first detection of short-period propagating fast sausage mode waves in a metric radio spectral fine structure observed with the Assembly of Metric-band Aperture Telescope and Real-time Analysis System. Analysis of Zebra patterns (ZPs) in a type-IV burst revealed a quasi-periodic modulation in the frequency separation between the adjacent stripes of the ZPs (Δf ). The observed quasi-periodic modulation had a period of 1–2 s and exhibited a characteristic negative frequency drift with a rate of 3–8 MHz s‑1. Based on the double plasma resonance model, the most accepted generation model of ZPs, the observed quasi-periodic modulation of the ZP can be interpreted in terms of fast sausage mode waves propagating upward at phase speeds of 3000–8000 km s‑1. These results provide us with new insights for probing the fine structure of coronal loops.

A coupled classification - evolutionary optimization model for contamination event detection in water distribution systems.

PubMed

Oliker, Nurit; Ostfeld, Avi

2014-03-15

This study describes a decision support system, alerts for contamination events in water distribution systems. The developed model comprises a weighted support vector machine (SVM) for the detection of outliers, and a following sequence analysis for the classification of contamination events. The contribution of this study is an improvement of contamination events detection ability and a multi-dimensional analysis of the data, differing from the parallel one-dimensional analysis conducted so far. The multivariate analysis examines the relationships between water quality parameters and detects changes in their mutual patterns. The weights of the SVM model accomplish two goals: blurring the difference between sizes of the two classes' data sets (as there are much more normal/regular than event time measurements), and adhering the time factor attribute by a time decay coefficient, ascribing higher importance to recent observations when classifying a time step measurement. All model parameters were determined by data driven optimization so the calibration of the model was completely autonomic. The model was trained and tested on a real water distribution system (WDS) data set with randomly simulated events superimposed on the original measurements. The model is prominent in its ability to detect events that were only partly expressed in the data (i.e., affecting only some of the measured parameters). The model showed high accuracy and better detection ability as compared to previous modeling attempts of contamination event detection. Copyright © 2013 Elsevier Ltd. All rights reserved.

A hybrid double-observer sightability model for aerial surveys

USGS Publications Warehouse

Griffin, Paul C.; Lubow, Bruce C.; Jenkins, Kurt J.; Vales, David J.; Moeller, Barbara J.; Reid, Mason; Happe, Patricia J.; Mccorquodale, Scott M.; Tirhi, Michelle J.; Schaberi, Jim P.; Beirne, Katherine

2013-01-01

Raw counts from aerial surveys make no correction for undetected animals and provide no estimate of precision with which to judge the utility of the counts. Sightability modeling and double-observer (DO) modeling are 2 commonly used approaches to account for detection bias and to estimate precision in aerial surveys. We developed a hybrid DO sightability model (model MH) that uses the strength of each approach to overcome the weakness in the other, for aerial surveys of elk (Cervus elaphus). The hybrid approach uses detection patterns of 2 independent observer pairs in a helicopter and telemetry-based detections of collared elk groups. Candidate MH models reflected hypotheses about effects of recorded covariates and unmodeled heterogeneity on the separate front-seat observer pair and back-seat observer pair detection probabilities. Group size and concealing vegetation cover strongly influenced detection probabilities. The pilot's previous experience participating in aerial surveys influenced detection by the front pair of observers if the elk group was on the pilot's side of the helicopter flight path. In 9 surveys in Mount Rainier National Park, the raw number of elk counted was approximately 80–93% of the abundance estimated by model MH. Uncorrected ratios of bulls per 100 cows generally were low compared to estimates adjusted for detection bias, but ratios of calves per 100 cows were comparable whether based on raw survey counts or adjusted estimates. The hybrid method was an improvement over commonly used alternatives, with improved precision compared to sightability modeling and reduced bias compared to DO modeling.

Biogeography of Anurans from the Poorly Known and Threatened Coastal Sandplains of Eastern Brazil.

PubMed

Xavier, Ariane Lima; Guedes, Thaís Barreto; Napoli, Marcelo Felgueiras

2015-01-01

The east coast of Brazil comprises an extensive area inserted in the Tropical Atlantic Domain and is represented by sandy plains of beach ridges commonly known as Restingas. The coastal environments are unique and house a rich amphibian fauna, the geographical distribution patterns of which are incipient. Biogeographical studies can explain the current distributional patterns and provide the identification of natural biogeographical units. These areas are important in elucidating the evolutionary history of the taxa and the areas where they occur. The aim of this study was to seek natural biogeographical units in the Brazilian sandy plains of beach ridges by means of distribution data of amphibians and to test the main predictions of the vicariance model to explain the patterns found. We revised and georeferenced data on the geographical distribution of 63 anuran species. We performed a search for latitudinal distribution patterns along the sandy coastal plains of Brazil using the non-metric multidimensional scaling method (NMDS) and the biotic element analysis to identify natural biogeographical units. The results showed a monotonic variation in anuran species composition along the latitudinal gradient with a break in the clinal pattern from 23°S to 25°S latitude (states of Rio de Janeiro to São Paulo). The major predictions of the vicariance model were corroborated by the detection of four biotic elements with significantly clustered distribution and by the presence of congeneric species distributed in distinct biotic elements. The results support the hypothesis that vicariance could be one of the factors responsible for the distribution patterns of the anuran communities along the sandy coastal plains of eastern Brazil. The results of the clusters are also congruent with the predictions of paleoclimatic models made for the Last Glacial Maximum of the Pleistocene, such as the presence of historical forest refugia and biogeographical patterns already detected for amphibians in the Atlantic Rainforest.

Biogeography of Anurans from the Poorly Known and Threatened Coastal Sandplains of Eastern Brazil

PubMed Central

Xavier, Ariane Lima; Guedes, Thaís Barreto; Napoli, Marcelo Felgueiras

2015-01-01

The east coast of Brazil comprises an extensive area inserted in the Tropical Atlantic Domain and is represented by sandy plains of beach ridges commonly known as Restingas. The coastal environments are unique and house a rich amphibian fauna, the geographical distribution patterns of which are incipient. Biogeographical studies can explain the current distributional patterns and provide the identification of natural biogeographical units. These areas are important in elucidating the evolutionary history of the taxa and the areas where they occur. The aim of this study was to seek natural biogeographical units in the Brazilian sandy plains of beach ridges by means of distribution data of amphibians and to test the main predictions of the vicariance model to explain the patterns found. We revised and georeferenced data on the geographical distribution of 63 anuran species. We performed a search for latitudinal distribution patterns along the sandy coastal plains of Brazil using the non-metric multidimensional scaling method (NMDS) and the biotic element analysis to identify natural biogeographical units. The results showed a monotonic variation in anuran species composition along the latitudinal gradient with a break in the clinal pattern from 23°S to 25°S latitude (states of Rio de Janeiro to São Paulo). The major predictions of the vicariance model were corroborated by the detection of four biotic elements with significantly clustered distribution and by the presence of congeneric species distributed in distinct biotic elements. The results support the hypothesis that vicariance could be one of the factors responsible for the distribution patterns of the anuran communities along the sandy coastal plains of eastern Brazil. The results of the clusters are also congruent with the predictions of paleoclimatic models made for the Last Glacial Maximum of the Pleistocene, such as the presence of historical forest refugia and biogeographical patterns already detected for amphibians in the Atlantic Rainforest. PMID:26047484

Distance-dependent pattern blending can camouflage salient aposematic signals.

PubMed

Barnett, James B; Cuthill, Innes C; Scott-Samuel, Nicholas E

2017-07-12

The effect of viewing distance on the perception of visual texture is well known: spatial frequencies higher than the resolution limit of an observer's visual system will be summed and perceived as a single combined colour. In animal defensive colour patterns, distance-dependent pattern blending may allow aposematic patterns, salient at close range, to match the background to distant observers. Indeed, recent research has indicated that reducing the distance from which a salient signal can be detected can increase survival over camouflage or conspicuous aposematism alone. We investigated whether the spatial frequency of conspicuous and cryptically coloured stripes affects the rate of avian predation. Our results are consistent with pattern blending acting to camouflage salient aposematic signals effectively at a distance. Experiments into the relative rate of avian predation on edible model caterpillars found that increasing spatial frequency (thinner stripes) increased survival. Similarly, visual modelling of avian predators showed that pattern blending increased the similarity between caterpillar and background. These results show how a colour pattern can be tuned to reveal or conceal different information at different distances, and produce tangible survival benefits. © 2017 The Author(s).

Serum microRNA expression patterns that predict early treatment failure in prostate cancer patients.

PubMed

Singh, Prashant K; Preus, Leah; Hu, Qiang; Yan, Li; Long, Mark D; Morrison, Carl D; Nesline, Mary; Johnson, Candace S; Koochekpour, Shahriar; Kohli, Manish; Liu, Song; Trump, Donald L; Sucheston-Campbell, Lara E; Campbell, Moray J

2014-02-15

We aimed to identify microRNA (miRNA) expression patterns in the serum of prostate cancer (CaP) patients that predict the risk of early treatment failure following radical prostatectomy (RP). Microarray and Q-RT-PCR analyses identified 43 miRNAs as differentiating disease stages within 14 prostate cell lines and reflectedpublically available patient data. 34 of these miRNA were detectable in the serum of CaP patients. Association with time to biochemical progression was examined in a cohort of CaP patients following RP. A greater than two-fold increase in hazard of biochemical progression associated with altered expression of miR-103, miR-125b and miR-222 (p<.0008) in the serum of CaP patients. Prediction models based on penalized regression analyses showed that the levels of the miRNAs and PSA together were better at detecting false positives than models without miRNAs, for similar level of sensitivity. Analyses of publically available data revealed significant and reciprocal relationships between changes in CpG methylation and miRNA expression patterns suggesting a role for CpG methylation to regulate miRNA. Exploratory validation supported roles for miR-222 and miR-125b to predict progression risk in CaP. The current study established that expression patterns of serum-detectable miRNAs taken at the time of RP are prognostic for men who are at risk of experiencing subsequent early biochemical progression. These non-invasive approaches could be used to augment treatment decisions.

Detecting Beer Intake by Unique Metabolite Patterns.

PubMed

Gürdeniz, Gözde; Jensen, Morten Georg; Meier, Sebastian; Bech, Lene; Lund, Erik; Dragsted, Lars Ove

2016-12-02

Evaluation of the health related effects of beer intake is hampered by the lack of accurate tools for assessing intakes (biomarkers). Therefore, we identified plasma and urine metabolites associated with recent beer intake by untargeted metabolomics and established a characteristic metabolite pattern representing raw materials and beer production as a qualitative biomarker of beer intake. In a randomized, crossover, single-blinded meal study (MSt1), 18 participants were given, one at a time, four different test beverages: strong, regular, and nonalcoholic beers and a soft drink. Four participants were assigned to have two additional beers (MSt2). In addition to plasma and urine samples, test beverages, wort, and hops extract were analyzed by UPLC-QTOF. A unique metabolite pattern reflecting beer metabolome, including metabolites derived from beer raw material (i.e., N-methyl tyramine sulfate and the sum of iso-α-acids and tricyclohumols) and the production process (i.e., pyro-glutamyl proline and 2-ethyl malate), was selected to establish a compliance biomarker model for detection of beer intake based on MSt1. The model predicted the MSt2 samples collected before and up to 12 h after beer intake correctly (AUC = 1). A biomarker model including four metabolites representing both beer raw materials and production steps provided a specific and accurate tool for measurement of beer consumption.

Modelling ground deformation patterns associated with volcanic processes at the Okataina Volcanic Centre

NASA Astrophysics Data System (ADS)

Holden, L.; Cas, R.; Fournier, N.; Ailleres, L.

2017-09-01

The Okataina Volcanic Centre (OVC) is one of two large active rhyolite centres in the modern Taupo Volcanic Zone (TVZ) in the North Island of New Zealand. It is located in a complex section of the Taupo rift, a tectonically active section of the TVZ. The most recent volcanic unrest at the OVC includes the 1315 CE Kaharoa and 1886 Tarawera eruptions. Current monitoring activity at the OVC includes the use of continuous GPS receivers (cGPS), lake levelling and seismographs. The ground deformation patterns preceding volcanic activity the OVC are poorly constrained and restricted to predictions from basic modelling and comparison to other volcanoes worldwide. A better understanding of the deformation patterns preceding renewed volcanic activity is essential to determine if observed deformation is related to volcanic, tectonic or hydrothermal processes. Such an understanding also means that the ability of the present day cGPS network to detect these deformation patterns can also be assessed. The research presented here uses the finite element (FE) modelling technique to investigate ground deformation patterns associated with magma accumulation and diking processes at the OVC in greater detail. A number of FE models are produced and tested using Pylith software and incorporate characteristics of the 1315 CE Kaharoa and 1886 Tarawera eruptions, summarised from the existing body of research literature. The influence of a simple ring fault structure at the OVC on the modelled deformation is evaluated. The ability of the present-day continuous GPS (cGPS) GeoNet monitoring network to detect or observe the modelled deformation is also considered. The results show the modelled horizontal and vertical displacement fields have a number of key features, which include prominent lobe based regions extending northwest and southeast of the OVC. The results also show that the ring fault structure increases the magnitude of the displacements inside the caldera, in particular in the vicinity of the southern margin. As a result, some of the cGPS stations in the vicinity of the OVC are more important for measuring deformation related to volcanic processes than others. The results have important implications for how any future observed deformation at the OVC is observed and interpreted.

Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data.

PubMed

Tan, Qihua; Thomassen, Mads; Burton, Mark; Mose, Kristian Fredløv; Andersen, Klaus Ejner; Hjelmborg, Jacob; Kruse, Torben

2017-06-06

Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.

Automatic multiresolution age-related macular degeneration detection from fundus images

NASA Astrophysics Data System (ADS)

Garnier, Mickaël.; Hurtut, Thomas; Ben Tahar, Houssem; Cheriet, Farida

2014-03-01

Age-related Macular Degeneration (AMD) is a leading cause of legal blindness. As the disease progress, visual loss occurs rapidly, therefore early diagnosis is required for timely treatment. Automatic, fast and robust screening of this widespread disease should allow an early detection. Most of the automatic diagnosis methods in the literature are based on a complex segmentation of the drusen, targeting a specific symptom of the disease. In this paper, we present a preliminary study for AMD detection from color fundus photographs using a multiresolution texture analysis. We analyze the texture at several scales by using a wavelet decomposition in order to identify all the relevant texture patterns. Textural information is captured using both the sign and magnitude components of the completed model of Local Binary Patterns. An image is finally described with the textural pattern distributions of the wavelet coefficient images obtained at each level of decomposition. We use a Linear Discriminant Analysis for feature dimension reduction, to avoid the curse of dimensionality problem, and image classification. Experiments were conducted on a dataset containing 45 images (23 healthy and 22 diseased) of variable quality and captured by different cameras. Our method achieved a recognition rate of 93:3%, with a specificity of 95:5% and a sensitivity of 91:3%. This approach shows promising results at low costs that in agreement with medical experts as well as robustness to both image quality and fundus camera model.

Predicting species distributions from checklist data using site-occupancy models

USGS Publications Warehouse

Kery, M.; Gardner, B.; Monnerat, C.

2010-01-01

Aim: (1) To increase awareness of the challenges induced by imperfect detection, which is a fundamental issue in species distribution modelling; (2) to emphasize the value of replicate observations for species distribution modelling; and (3) to show how 'cheap' checklist data in faunal/floral databases may be used for the rigorous modelling of distributions by site-occupancy models. Location: Switzerland. Methods: We used checklist data collected by volunteers during 1999 and 2000 to analyse the distribution of the blue hawker, Aeshna cyanea (Odonata, Aeshnidae), a common dragonfly in Switzerland. We used data from repeated visits to 1-ha pixels to derive 'detection histories' and apply site-occupancy models to estimate the 'true' species distribution, i.e. corrected for imperfect detection. We modelled blue hawker distribution as a function of elevation and year and its detection probability of elevation, year and season. Results: The best model contained cubic polynomial elevation effects for distribution and quadratic effects of elevation and season for detectability. We compared the site-occupancy model with a conventional distribution model based on a generalized linear model, which assumes perfect detectability (p = 1). The conventional distribution map looked very different from the distribution map obtained using site-occupancy models that accounted for the imperfect detection. The conventional model underestimated the species distribution by 60%, and the slope parameters of the occurrence-elevation relationship were also underestimated when assuming p = 1. Elevation was not only an important predictor of blue hawker occurrence, but also of the detection probability, with a bell-shaped relationship. Furthermore, detectability increased over the season. The average detection probability was estimated at only 0.19 per survey. Main conclusions: Conventional species distribution models do not model species distributions per se but rather the apparent distribution, i.e. an unknown proportion of species distributions. That unknown proportion is equivalent to detectability. Imperfect detection in conventional species distribution models yields underestimates of the extent of distributions and covariate effects that are biased towards zero. In addition, patterns in detectability will erroneously be ascribed to species distributions. In contrast, site-occupancy models applied to replicated detection/non-detection data offer a powerful framework for making inferences about species distributions corrected for imperfect detection. The use of 'cheap' checklist data greatly enhances the scope of applications of this useful class of models. ?? 2010 Blackwell Publishing Ltd.

Spatio-temporal scan statistics for the detection of outbreaks involving common molecular subtypes: using human cases of Escherichia coli O157:H7 provincial PFGE pattern 8 (National Designation ECXAI.0001) in Alberta as an example.

PubMed

So, H C; Pearl, D L; von Königslöw, T; Louie, M; Chui, L; Svenson, L W

2013-08-01

Molecular typing methods have become a common part of the surveillance of foodborne pathogens. In particular, pulsed-field gel electrophoresis (PFGE) has been used successfully to identify outbreaks of Escherichia coli O157:H7 in humans from a variety of food and environmental sources. However, some PFGE patterns appear commonly in surveillance systems, making it more difficult to distinguish between outbreak and sporadic cases based on molecular data alone. In addition, it is unknown whether these common patterns might have unique epidemiological characteristics reflected in their spatial and temporal distributions. Using E. coli O157:H7 surveillance data from Alberta, collected from 2000 to 2002, we investigated whether E. coli O157:H7 with provincial PFGE pattern 8 (national designation ECXAI.0001) clustered in space, time and space-time relative to other PFGE patterns using the spatial scan statistic. Based on our purely spatial and temporal scans using a Bernoulli model, there did not appear to be strong evidence that isolates of E. coli O157:H7 with provincial PFGE pattern 8 are distributed differently from other PFGE patterns. However, we did identify space-time clusters of isolates with PFGE pattern 8, using a Bernoulli model and a space-time permutation model, which included known outbreaks and potentially unrecognized outbreaks or additional outbreak cases. There were differences between the two models in the space-time clusters identified, which suggests that the use of both models could increase the sensitivity of a quantitative surveillance system for identifying outbreaks involving isolates sharing a common PFGE pattern. © 2012 Blackwell Verlag GmbH.

Remote Video Monitor of Vehicles in Cooperative Information Platform

NASA Astrophysics Data System (ADS)

Qin, Guofeng; Wang, Xiaoguo; Wang, Li; Li, Yang; Li, Qiyan

Detection of vehicles plays an important role in the area of the modern intelligent traffic management. And the pattern recognition is a hot issue in the area of computer vision. An auto- recognition system in cooperative information platform is studied. In the cooperative platform, 3G wireless network, including GPS, GPRS (CDMA), Internet (Intranet), remote video monitor and M-DMB networks are integrated. The remote video information can be taken from the terminals and sent to the cooperative platform, then detected by the auto-recognition system. The images are pretreated and segmented, including feature extraction, template matching and pattern recognition. The system identifies different models and gets vehicular traffic statistics. Finally, the implementation of the system is introduced.

Intelligent classifier for dynamic fault patterns based on hidden Markov model

NASA Astrophysics Data System (ADS)

Xu, Bo; Feng, Yuguang; Yu, Jinsong

2006-11-01

It's difficult to build precise mathematical models for complex engineering systems because of the complexity of the structure and dynamics characteristics. Intelligent fault diagnosis introduces artificial intelligence and works in a different way without building the analytical mathematical model of a diagnostic object, so it's a practical approach to solve diagnostic problems of complex systems. This paper presents an intelligent fault diagnosis method, an integrated fault-pattern classifier based on Hidden Markov Model (HMM). This classifier consists of dynamic time warping (DTW) algorithm, self-organizing feature mapping (SOFM) network and Hidden Markov Model. First, after dynamic observation vector in measuring space is processed by DTW, the error vector including the fault feature of being tested system is obtained. Then a SOFM network is used as a feature extractor and vector quantization processor. Finally, fault diagnosis is realized by fault patterns classifying with the Hidden Markov Model classifier. The importing of dynamic time warping solves the problem of feature extracting from dynamic process vectors of complex system such as aeroengine, and makes it come true to diagnose complex system by utilizing dynamic process information. Simulating experiments show that the diagnosis model is easy to extend, and the fault pattern classifier is efficient and is convenient to the detecting and diagnosing of new faults.

Combinatorics of the Breakage-Fusion-Bridge Mechanism

PubMed Central

Bafna, Vineet

2012-01-01

Abstract The breakage-fusion-bridge (BFB) mechanism was proposed over seven decades ago and is a source of genomic variability and gene amplification in cancer. Here we formally model and analyze the BFB mechanism, to our knowledge the first time this has been undertaken. We show that BFB can be modeled as successive inverted prefix duplications of a string. Using this model, we show that BFB can achieve a surprisingly broad range of amplification patterns. We find that a sequence of BFB operations can be found that nearly fits most patterns of copy number increases along a chromosome. We conclude that this limits the usefulness of methods like array CGH for detecting BFB. PMID:22506505

Boosting-Based Optimization as a Generic Framework for Novelty and Fraud Detection in Complex Strategies

NASA Astrophysics Data System (ADS)

Gavrishchaka, Valeriy V.; Kovbasinskaya, Maria; Monina, Maria

2008-11-01

Novelty detection is a very desirable additional feature of any practical classification or forecasting system. Novelty and rare patterns detection is the main objective in such applications as fault/abnormality discovery in complex technical and biological systems, fraud detection and risk management in financial and insurance industry. Although many interdisciplinary approaches for rare event modeling and novelty detection have been proposed, significant data incompleteness due to the nature of the problem makes it difficult to find a universal solution. Even more challenging and much less formalized problem is novelty detection in complex strategies and models where practical performance criteria are usually multi-objective and the best state-of-the-art solution is often not known due to the complexity of the task and/or proprietary nature of the application area. For example, it is much more difficult to detect a series of small insider trading or other illegal transactions mixed with valid operations and distributed over long time period according to a well-designed strategy than a single, large fraudulent transaction. Recently proposed boosting-based optimization was shown to be an effective generic tool for the discovery of stable multi-component strategies/models from the existing parsimonious base strategies/models in financial and other applications. Here we outline how the same framework can be used for novelty and fraud detection in complex strategies and models.

Transitioning from Software Requirements Models to Design Models

NASA Technical Reports Server (NTRS)

Lowry, Michael (Technical Monitor); Whittle, Jon

2003-01-01

Summary: 1. Proof-of-concept of state machine synthesis from scenarios - CTAS case study. 2. CTAS team wants to use the syntheses algorithm to validate trajectory generation. 3. Extending synthesis algorithm towards requirements validation: (a) scenario relationships' (b) methodology for generalizing/refining scenarios, and (c) interaction patterns to control synthesis. 4. Initial ideas tested on conflict detection scenarios.

Mixture IRT Model with a Higher-Order Structure for Latent Traits

ERIC Educational Resources Information Center

Huang, Hung-Yu

2017-01-01

Mixture item response theory (IRT) models have been suggested as an efficient method of detecting the different response patterns derived from latent classes when developing a test. In testing situations, multiple latent traits measured by a battery of tests can exhibit a higher-order structure, and mixtures of latent classes may occur on…

The EZ diffusion model provides a powerful test of simple empirical effects.

PubMed

van Ravenzwaaij, Don; Donkin, Chris; Vandekerckhove, Joachim

2017-04-01

Over the last four decades, sequential accumulation models for choice response times have spread through cognitive psychology like wildfire. The most popular style of accumulator model is the diffusion model (Ratcliff Psychological Review, 85, 59-108, 1978), which has been shown to account for data from a wide range of paradigms, including perceptual discrimination, letter identification, lexical decision, recognition memory, and signal detection. Since its original inception, the model has become increasingly complex in order to account for subtle, but reliable, data patterns. The additional complexity of the diffusion model renders it a tool that is only for experts. In response, Wagenmakers et al. (Psychonomic Bulletin & Review, 14, 3-22, 2007) proposed that researchers could use a more basic version of the diffusion model, the EZ diffusion. Here, we simulate experimental effects on data generated from the full diffusion model and compare the power of the full diffusion model and EZ diffusion to detect those effects. We show that the EZ diffusion model, by virtue of its relative simplicity, will be sometimes better able to detect experimental effects than the data-generating full diffusion model.

Clonal Selection Based Artificial Immune System for Generalized Pattern Recognition

NASA Technical Reports Server (NTRS)

Huntsberger, Terry

2011-01-01

The last two decades has seen a rapid increase in the application of AIS (Artificial Immune Systems) modeled after the human immune system to a wide range of areas including network intrusion detection, job shop scheduling, classification, pattern recognition, and robot control. JPL (Jet Propulsion Laboratory) has developed an integrated pattern recognition/classification system called AISLE (Artificial Immune System for Learning and Exploration) based on biologically inspired models of B-cell dynamics in the immune system. When used for unsupervised or supervised classification, the method scales linearly with the number of dimensions, has performance that is relatively independent of the total size of the dataset, and has been shown to perform as well as traditional clustering methods. When used for pattern recognition, the method efficiently isolates the appropriate matches in the data set. The paper presents the underlying structure of AISLE and the results from a number of experimental studies.

Reducing uncertainty in dust monitoring to detect aeolian sediment transport responses to land cover change

NASA Astrophysics Data System (ADS)

Webb, N.; Chappell, A.; Van Zee, J.; Toledo, D.; Duniway, M.; Billings, B.; Tedela, N.

2017-12-01

Anthropogenic land use and land cover change (LULCC) influence global rates of wind erosion and dust emission, yet our understanding of the magnitude of the responses remains poor. Field measurements and monitoring provide essential data to resolve aeolian sediment transport patterns and assess the impacts of human land use and management intensity. Data collected in the field are also required for dust model calibration and testing, as models have become the primary tool for assessing LULCC-dust cycle interactions. However, there is considerable uncertainty in estimates of dust emission due to the spatial variability of sediment transport. Field sampling designs are currently rudimentary and considerable opportunities are available to reduce the uncertainty. Establishing the minimum detectable change is critical for measuring spatial and temporal patterns of sediment transport, detecting potential impacts of LULCC and land management, and for quantifying the uncertainty of dust model estimates. Here, we evaluate the effectiveness of common sampling designs (e.g., simple random sampling, systematic sampling) used to measure and monitor aeolian sediment transport rates. Using data from the US National Wind Erosion Research Network across diverse rangeland and cropland cover types, we demonstrate how only large changes in sediment mass flux (of the order 200% to 800%) can be detected when small sample sizes are used, crude sampling designs are implemented, or when the spatial variation is large. We then show how statistical rigour and the straightforward application of a sampling design can reduce the uncertainty and detect change in sediment transport over time and between land use and land cover types.

Emergence of Alpha and Gamma Like Rhythms in a Large Scale Simulation of Interacting Neurons

NASA Astrophysics Data System (ADS)

Gaebler, Philipp; Miller, Bruce

2007-10-01

In the normal brain, at first glance the electrical activity appears very random. However, certain frequencies emerge during specific stages of sleep or between quiet wake states. This raises the question of whether current mathematical and computational models of interacting neurons can display similar behavior. A recent model developed by Eugene Izhikevich appears to succeed. However, early dynamical simulations used to detect these patterns were possibly compromised by an over-simplified initial condition and evolution algorithm. Utilizing the same model, but a more robust algorithm, here we present our initial results, showing that these patterns persist under a wide range of initial conditions. We employ spectral analysis of the firing patterns of a system of interacting excitatory and inhibitory neurons to demonstrate a bimodal spectrum centered on two frequencies in the range characteristic of alpha and gamma rhythms in the human brain.

A CMB polarization primer

NASA Astrophysics Data System (ADS)

Hu, Wayne; White, Martin

1997-10-01

We present a pedagogical and phenomenological introduction to the study of cosmic microwave background (CMB) polarization to build intuition about the prospects and challenges facing its detection. Thomson scattering of temperature anisotropies on the last scattering surface generates a linear polarization pattern on the sky that can be simply read off from their quadrupole moments. These in turn correspond directly to the fundamental scalar (compressional), vector (vortical), and tensor (gravitational wave) modes of cosmological perturbations. We explain the origin and phenomenology of the geometric distinction between these patterns in terms of the so-called electric and magnetic parity modes, as well as their correlation with the temperature pattern. By its isolation of the last scattering surface and the various perturbation modes, the polarization provides unique information for the phenomenological reconstruction of the cosmological model. Finally we comment on the comparison of theory with experimental data and prospects for the future detection of CMB polarization.

A new statistical approach to climate change detection and attribution

NASA Astrophysics Data System (ADS)

Ribes, Aurélien; Zwiers, Francis W.; Azaïs, Jean-Marc; Naveau, Philippe

2017-01-01

We propose here a new statistical approach to climate change detection and attribution that is based on additive decomposition and simple hypothesis testing. Most current statistical methods for detection and attribution rely on linear regression models where the observations are regressed onto expected response patterns to different external forcings. These methods do not use physical information provided by climate models regarding the expected response magnitudes to constrain the estimated responses to the forcings. Climate modelling uncertainty is difficult to take into account with regression based methods and is almost never treated explicitly. As an alternative to this approach, our statistical model is only based on the additivity assumption; the proposed method does not regress observations onto expected response patterns. We introduce estimation and testing procedures based on likelihood maximization, and show that climate modelling uncertainty can easily be accounted for. Some discussion is provided on how to practically estimate the climate modelling uncertainty based on an ensemble of opportunity. Our approach is based on the " models are statistically indistinguishable from the truth" paradigm, where the difference between any given model and the truth has the same distribution as the difference between any pair of models, but other choices might also be considered. The properties of this approach are illustrated and discussed based on synthetic data. Lastly, the method is applied to the linear trend in global mean temperature over the period 1951-2010. Consistent with the last IPCC assessment report, we find that most of the observed warming over this period (+0.65 K) is attributable to anthropogenic forcings (+0.67 ± 0.12 K, 90 % confidence range), with a very limited contribution from natural forcings (-0.01± 0.02 K).

Detecting Rumors Through Modeling Information Propagation Networks in a Social Media Environment.

PubMed

Liu, Yang; Xu, Songhua; Tourassi, Georgia

2015-01-01

In the midst of today's pervasive influence of social media content and activities, information credibility has increasingly become a major issue. Accordingly, identifying false information, e.g. rumors circulated in social media environments, attracts expanding research attention and growing interests. Many previous studies have exploited user-independent features for rumor detection. These prior investigations uniformly treat all users relevant to the propagation of a social media message as instances of a generic entity. Such a modeling approach usually adopts a homogeneous network to represent all users, the practice of which ignores the variety across an entire user population in a social media environment. Recognizing this limitation of modeling methodologies, this study explores user-specific features in a social media environment for rumor detection. The new approach hypothesizes that whether a user tends to spread a rumor is dependent upon specific attributes of the user in addition to content characteristics of the message itself. Under this hypothesis, information propagation patterns of rumors versus those of credible messages in a social media environment are systematically differentiable. To explore and exploit this hypothesis, we develop a new information propagation model based on a heterogeneous user representation for rumor recognition. The new approach is capable of differentiating rumors from credible messages through observing distinctions in their respective propagation patterns in social media. Experimental results show that the new information propagation model based on heterogeneous user representation can effectively distinguish rumors from credible social media content.

Alerts Visualization and Clustering in Network-based Intrusion Detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Dr. Li; Gasior, Wade C; Dasireddy, Swetha

2010-04-01

Today's Intrusion detection systems when deployed on a busy network overload the network with huge number of alerts. This behavior of producing too much raw information makes it less effective. We propose a system which takes both raw data and Snort alerts to visualize and analyze possible intrusions in a network. Then we present with two models for the visualization of clustered alerts. Our first model gives the network administrator with the logical topology of the network and detailed information of each node that involves its associated alerts and connections. In the second model, flocking model, presents the network administratormore » with the visual representation of IDS data in which each alert is represented in different color and the alerts with maximum similarity move together. This gives network administrator with the idea of detecting various of intrusions through visualizing the alert patterns.« less

A biological hierarchical model based underwater moving object detection.

PubMed

Shen, Jie; Fan, Tanghuai; Tang, Min; Zhang, Qian; Sun, Zhen; Huang, Fengchen

2014-01-01

Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable background, the mechanism of the visual information sensing and processing pattern from the eye of frogs are imitated to produce a hierarchical background model for detecting underwater objects. Firstly, the image is segmented into several subblocks. The intensity information is extracted for establishing background model which could roughly identify the object and the background regions. The texture feature of each pixel in the rough object region is further analyzed to generate the object contour precisely. Experimental results demonstrate that the proposed method gives a better performance. Compared to the traditional Gaussian background model, the completeness of the object detection is 97.92% with only 0.94% of the background region that is included in the detection results.

A Biological Hierarchical Model Based Underwater Moving Object Detection

PubMed Central

Shen, Jie; Fan, Tanghuai; Tang, Min; Zhang, Qian; Sun, Zhen; Huang, Fengchen

2014-01-01

Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable background, the mechanism of the visual information sensing and processing pattern from the eye of frogs are imitated to produce a hierarchical background model for detecting underwater objects. Firstly, the image is segmented into several subblocks. The intensity information is extracted for establishing background model which could roughly identify the object and the background regions. The texture feature of each pixel in the rough object region is further analyzed to generate the object contour precisely. Experimental results demonstrate that the proposed method gives a better performance. Compared to the traditional Gaussian background model, the completeness of the object detection is 97.92% with only 0.94% of the background region that is included in the detection results. PMID:25140194

Toxicoproteomics: serum proteomic pattern diagnostics for early detection of drug induced cardiac toxicities and cardioprotection.

PubMed

Petricoin, Emanuel F; Rajapaske, Vinodh; Herman, Eugene H; Arekani, Ali M; Ross, Sally; Johann, Donald; Knapton, Alan; Zhang, J; Hitt, Ben A; Conrads, Thomas P; Veenstra, Timothy D; Liotta, Lance A; Sistare, Frank D

2004-01-01

Proteomics is more than just generating lists of proteins that increase or decrease in expression as a cause or consequence of pathology. The goal should be to characterize the information flow through the intercellular protein circuitry which communicates with the extracellular microenvironment and then ultimately to the serum/plasma macroenvironment. The nature of this information can be a cause, or a consequence, of disease and toxicity based processes as cascades of reinforcing information percolate through the system and become reflected in changing proteomic information content of the circulation. Serum Proteomic Pattern Diagnostics is a new type of proteomic platform in which patterns of proteomic signatures from high dimensional mass spectrometry data are used as a diagnostic classifier. While this approach has shown tremendous promise in early detection of cancers, detection of drug-induced toxicity may also be possible with this same technology. Analysis of serum from rat models of anthracycline and anthracenedione induced cardiotoxicity indicate the potential clinical utility of diagnostic proteomic patterns where low molecular weight peptides and protein fragments may have higher accuracy than traditional biomarkers of cardiotoxicity such as troponins. These fragments may one day be harvested by circulating nanoparticles designed to absorb, enrich and amplify the diagnostic biomarker repertoire generated even at the critical initial stages of toxicity.

A novel approach to describing and detecting performance anti-patterns

NASA Astrophysics Data System (ADS)

Sheng, Jinfang; Wang, Yihan; Hu, Peipei; Wang, Bin

2017-08-01

Anti-pattern, as an extension to pattern, describes a widely used poor solution which can bring negative influence to application systems. Aiming at the shortcomings of the existing anti-pattern descriptions, an anti-pattern description method based on first order predicate is proposed. This method synthesizes anti-pattern forms and symptoms, which makes the description more accurate and has good scalability and versatility as well. In order to improve the accuracy of anti-pattern detection, a Bayesian classification method is applied in validation for detection results, which can reduce false negatives and false positives of anti-pattern detection. Finally, the proposed approach in this paper is applied to a small e-commerce system, the feasibility and effectiveness of the approach is demonstrated further through experiments.

Detecting Brain State Changes via Fiber-Centered Functional Connectivity Analysis

PubMed Central

Li, Xiang; Lim, Chulwoo; Li, Kaiming; Guo, Lei; Liu, Tianming

2013-01-01

Diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) have been widely used to study structural and functional brain connectivity in recent years. A common assumption used in many previous functional brain connectivity studies is the temporal stationarity. However, accumulating literature evidence has suggested that functional brain connectivity is under temporal dynamic changes in different time scales. In this paper, a novel and intuitive approach is proposed to model and detect dynamic changes of functional brain states based on multimodal fMRI/DTI data. The basic idea is that functional connectivity patterns of all fiber-connected cortical voxels are concatenated into a descriptive functional feature vector to represent the brain’s state, and the temporal change points of brain states are decided by detecting the abrupt changes of the functional vector patterns via the sliding window approach. Our extensive experimental results have shown that meaningful brain state change points can be detected in task-based fMRI/DTI, resting state fMRI/DTI, and natural stimulus fMRI/DTI data sets. Particularly, the detected change points of functional brain states in task-based fMRI corresponded well to the external stimulus paradigm administered to the participating subjects, thus partially validating the proposed brain state change detection approach. The work in this paper provides novel perspective on the dynamic behaviors of functional brain connectivity and offers a starting point for future elucidation of the complex patterns of functional brain interactions and dynamics. PMID:22941508

[Analysis of dietary pattern and diabetes mellitus influencing factors identified by classification tree model in adults of Fujian].

PubMed

Yu, F L; Ye, Y; Yan, Y S

2017-05-10

Objective: To find out the dietary patterns and explore the relationship between environmental factors (especially dietary patterns) and diabetes mellitus in the adults of Fujian. Methods: Multi-stage sampling method were used to survey residents aged ≥18 years by questionnaire, physical examination and laboratory detection in 10 disease surveillance points in Fujian. Factor analysis was used to identify the dietary patterns, while logistic regression model was applied to analyze relationship between dietary patterns and diabetes mellitus, and classification tree model was adopted to identify the influencing factors for diabetes mellitus. Results: There were four dietary patterns in the population, including meat, plant, high-quality protein, and fried food and beverages patterns. The result of logistic analysis showed that plant pattern, which has higher factor loading of fresh fruit-vegetables and cereal-tubers, was a protective factor for non-diabetes mellitus. The risk of diabetes mellitus in the population at T2 and T3 levels of factor score were 0.727 (95 %CI: 0.561-0.943) times and 0.736 (95 %CI : 0.573-0.944) times higher, respectively, than those whose factor score was in lowest quartile. Thirteen influencing factors and eleven group at high-risk for diabetes mellitus were identified by classification tree model. The influencing factors were dyslipidemia, age, family history of diabetes, hypertension, physical activity, career, sex, sedentary time, abdominal adiposity, BMI, marital status, sleep time and high-quality protein pattern. Conclusion: There is a close association between dietary patterns and diabetes mellitus. It is necessary to promote healthy and reasonable diet, strengthen the monitoring and control of blood lipids, blood pressure and body weight, and have good lifestyle for the prevention and control of diabetes mellitus.

A fingertip force prediction model for grasp patterns characterised from the chaotic behaviour of EEG.

PubMed

Roy, Rinku; Sikdar, Debdeep; Mahadevappa, Manjunatha; Kumar, C S

2018-05-19

A stable grasp is attained through appropriate hand preshaping and precise fingertip forces. Here, we have proposed a method to decode grasp patterns from motor imagery and subsequent fingertip force estimation model with a slippage avoidance strategy. We have developed a feature-based classification of electroencephalography (EEG) associated with imagination of the grasping postures. Chaotic behaviour of EEG for different grasping patterns has been utilised to capture the dynamics of associated motor activities. We have computed correlation dimension (CD) as the feature and classified with "one against one" multiclass support vector machine (SVM) to discriminate between different grasping patterns. The result of the analysis showed varying classification accuracies at different subband levels. Broad categories of grasping patterns, namely, power grasp and precision grasp, were classified at a 96.0% accuracy rate in the alpha subband. Furthermore, power grasp subtypes were classified with an accuracy of 97.2% in the upper beta subband, whereas precision grasp subtypes showed relatively lower 75.0% accuracy in the alpha subband. Following assessment of fingertip force distributions while grasping, a nonlinear autoregressive (NAR) model with proper prediction of fingertip forces was proposed for each grasp pattern. A slippage detection strategy has been incorporated with automatic recalibration of the regripping force. Intention of each grasp pattern associated with corresponding fingertip force model was virtualised in this work. This integrated system can be utilised as the control strategy for prosthetic hand in the future. The model to virtualise motor imagery based fingertip force prediction with inherent slippage correction for different grasp types ᅟ.

Statistically significant relational data mining :

DOE Office of Scientific and Technical Information (OSTI.GOV)

Berry, Jonathan W.; Leung, Vitus Joseph; Phillips, Cynthia Ann

This report summarizes the work performed under the project (3z(BStatitically significant relational data mining.(3y (BThe goal of the project was to add more statistical rigor to the fairly ad hoc area of data mining on graphs. Our goal was to develop better algorithms and better ways to evaluate algorithm quality. We concetrated on algorithms for community detection, approximate pattern matching, and graph similarity measures. Approximate pattern matching involves finding an instance of a relatively small pattern, expressed with tolerance, in a large graph of data observed with uncertainty. This report gathers the abstracts and references for the eight refereed publicationsmore » that have appeared as part of this work. We then archive three pieces of research that have not yet been published. The first is theoretical and experimental evidence that a popular statistical measure for comparison of community assignments favors over-resolved communities over approximations to a ground truth. The second are statistically motivated methods for measuring the quality of an approximate match of a small pattern in a large graph. The third is a new probabilistic random graph model. Statisticians favor these models for graph analysis. The new local structure graph model overcomes some of the issues with popular models such as exponential random graph models and latent variable models.« less

A new texture descriptor based on local micro-pattern for detection of architectural distortion in mammographic images

NASA Astrophysics Data System (ADS)

de Oliveira, Helder C. R.; Moraes, Diego R.; Reche, Gustavo A.; Borges, Lucas R.; Catani, Juliana H.; de Barros, Nestor; Melo, Carlos F. E.; Gonzaga, Adilson; Vieira, Marcelo A. C.

2017-03-01

This paper presents a new local micro-pattern texture descriptor for the detection of Architectural Distortion (AD) in digital mammography images. AD is a subtle contraction of breast parenchyma that may represent an early sign of breast cancer. Due to its subtlety and variability, AD is more difficult to detect compared to microcalcifications and masses, and is commonly found in retrospective evaluations of false-negative mammograms. Several computer-based systems have been proposed for automatic detection of AD, but their performance are still unsatisfactory. The proposed descriptor, Local Mapped Pattern (LMP), is a generalization of the Local Binary Pattern (LBP), which is considered one of the most powerful feature descriptor for texture classification in digital images. Compared to LBP, the LMP descriptor captures more effectively the minor differences between the local image pixels. Moreover, LMP is a parametric model which can be optimized for the desired application. In our work, the LMP performance was compared to the LBP and four Haralick's texture descriptors for the classification of 400 regions of interest (ROIs) extracted from clinical mammograms. ROIs were selected and divided into four classes: AD, normal tissue, microcalcifications and masses. Feature vectors were used as input to a multilayer perceptron neural network, with a single hidden layer. Results showed that LMP is a good descriptor to distinguish AD from other anomalies in digital mammography. LMP performance was slightly better than the LBP and comparable to Haralick's descriptors (mean classification accuracy = 83%).

Crustal deformation, the earthquake cycle, and models of viscoelastic flow in the asthenosphere

NASA Technical Reports Server (NTRS)

Cohen, S. C.; Kramer, M. J.

1983-01-01

The crustal deformation patterns associated with the earthquake cycle can depend strongly on the rheological properties of subcrustal material. Substantial deviations from the simple patterns for a uniformly elastic earth are expected when viscoelastic flow of subcrustal material is considered. The detailed description of the deformation pattern and in particular the surface displacements, displacement rates, strains, and strain rates depend on the structure and geometry of the material near the seismogenic zone. The origin of some of these differences are resolved by analyzing several different linear viscoelastic models with a common finite element computational technique. The models involve strike-slip faulting and include a thin channel asthenosphere model, a model with a varying thickness lithosphere, and a model with a viscoelastic inclusion below the brittle slip plane. The calculations reveal that the surface deformation pattern is most sensitive to the rheology of the material that lies below the slip plane in a volume whose extent is a few times the fault depth. If this material is viscoelastic, the surface deformation pattern resembles that of an elastic layer lying over a viscoelastic half-space. When the thickness or breath of the viscoelastic material is less than a few times the fault depth, then the surface deformation pattern is altered and geodetic measurements are potentially useful for studying the details of subsurface geometry and structure. Distinguishing among the various models is best accomplished by making geodetic measurements not only near the fault but out to distances equal to several times the fault depth. This is where the model differences are greatest; these differences will be most readily detected shortly after an earthquake when viscoelastic effects are most pronounced.

Quantifying camouflage: how to predict detectability from appearance.

PubMed

Troscianko, Jolyon; Skelhorn, John; Stevens, Martin

2017-01-06

Quantifying the conspicuousness of objects against particular backgrounds is key to understanding the evolution and adaptive value of animal coloration, and in designing effective camouflage. Quantifying detectability can reveal how colour patterns affect survival, how animals' appearances influence habitat preferences, and how receiver visual systems work. Advances in calibrated digital imaging are enabling the capture of objective visual information, but it remains unclear which methods are best for measuring detectability. Numerous descriptions and models of appearance have been used to infer the detectability of animals, but these models are rarely empirically validated or directly compared to one another. We compared the performance of human 'predators' to a bank of contemporary methods for quantifying the appearance of camouflaged prey. Background matching was assessed using several established methods, including sophisticated feature-based pattern analysis, granularity approaches and a range of luminance and contrast difference measures. Disruptive coloration is a further camouflage strategy where high contrast patterns disrupt they prey's tell-tale outline, making it more difficult to detect. Disruptive camouflage has been studied intensely over the past decade, yet defining and measuring it have proven far more problematic. We assessed how well existing disruptive coloration measures predicted capture times. Additionally, we developed a new method for measuring edge disruption based on an understanding of sensory processing and the way in which false edges are thought to interfere with animal outlines. Our novel measure of disruptive coloration was the best predictor of capture times overall, highlighting the importance of false edges in concealment over and above pattern or luminance matching. The efficacy of our new method for measuring disruptive camouflage together with its biological plausibility and computational efficiency represents a substantial advance in our understanding of the measurement, mechanism and definition of disruptive camouflage. Our study also provides the first test of the efficacy of many established methods for quantifying how conspicuous animals are against particular backgrounds. The validation of these methods opens up new lines of investigation surrounding the form and function of different types of camouflage, and may apply more broadly to the evolution of any visual signal.

Unsupervised Ensemble Anomaly Detection Using Time-Periodic Packet Sampling

NASA Astrophysics Data System (ADS)

Uchida, Masato; Nawata, Shuichi; Gu, Yu; Tsuru, Masato; Oie, Yuji

We propose an anomaly detection method for finding patterns in network traffic that do not conform to legitimate (i.e., normal) behavior. The proposed method trains a baseline model describing the normal behavior of network traffic without using manually labeled traffic data. The trained baseline model is used as the basis for comparison with the audit network traffic. This anomaly detection works in an unsupervised manner through the use of time-periodic packet sampling, which is used in a manner that differs from its intended purpose — the lossy nature of packet sampling is used to extract normal packets from the unlabeled original traffic data. Evaluation using actual traffic traces showed that the proposed method has false positive and false negative rates in the detection of anomalies regarding TCP SYN packets comparable to those of a conventional method that uses manually labeled traffic data to train the baseline model. Performance variation due to the probabilistic nature of sampled traffic data is mitigated by using ensemble anomaly detection that collectively exploits multiple baseline models in parallel. Alarm sensitivity is adjusted for the intended use by using maximum- and minimum-based anomaly detection that effectively take advantage of the performance variations among the multiple baseline models. Testing using actual traffic traces showed that the proposed anomaly detection method performs as well as one using manually labeled traffic data and better than one using randomly sampled (unlabeled) traffic data.

Can preferred atmospheric circulation patterns over the North-Atlantic-Eurasian region be associated with arctic sea ice loss?

NASA Astrophysics Data System (ADS)

Crasemann, Berit; Handorf, Dörthe; Jaiser, Ralf; Dethloff, Klaus; Nakamura, Tetsu; Ukita, Jinro; Yamazaki, Koji

2017-12-01

In the framework of atmospheric circulation regimes, we study whether the recent Arctic sea ice loss and Arctic Amplification are associated with changes in the frequency of occurrence of preferred atmospheric circulation patterns during the extended winter season from December to March. To determine regimes we applied a cluster analysis to sea-level pressure fields from reanalysis data and output from an atmospheric general circulation model. The specific set up of the two analyzed model simulations for low and high ice conditions allows for attributing differences between the simulations to the prescribed sea ice changes only. The reanalysis data revealed two circulation patterns that occur more frequently for low Arctic sea ice conditions: a Scandinavian blocking in December and January and a negative North Atlantic Oscillation pattern in February and March. An analysis of related patterns of synoptic-scale activity and 2 m temperatures provides a synoptic interpretation of the corresponding large-scale regimes. The regimes that occur more frequently for low sea ice conditions are resembled reasonably well by the model simulations. Based on those results we conclude that the detected changes in the frequency of occurrence of large-scale circulation patterns can be associated with changes in Arctic sea ice conditions.

Simulation-based MDP verification for leading-edge masks

NASA Astrophysics Data System (ADS)

Su, Bo; Syrel, Oleg; Pomerantsev, Michael; Hagiwara, Kazuyuki; Pearman, Ryan; Pang, Leo; Fujimara, Aki

2017-07-01

For IC design starts below the 20nm technology node, the assist features on photomasks shrink well below 60nm and the printed patterns of those features on masks written by VSB eBeam writers start to show a large deviation from the mask designs. Traditional geometry-based fracturing starts to show large errors for those small features. As a result, other mask data preparation (MDP) methods have become available and adopted, such as rule-based Mask Process Correction (MPC), model-based MPC and eventually model-based MDP. The new MDP methods may place shot edges slightly differently from target to compensate for mask process effects, so that the final patterns on a mask are much closer to the design (which can be viewed as the ideal mask), especially for those assist features. Such an alteration generally produces better masks that are closer to the intended mask design. Traditional XOR-based MDP verification cannot detect problems caused by eBeam effects. Much like model-based OPC verification which became a necessity for OPC a decade ago, we see the same trend in MDP today. Simulation-based MDP verification solution requires a GPU-accelerated computational geometry engine with simulation capabilities. To have a meaningful simulation-based mask check, a good mask process model is needed. The TrueModel® system is a field tested physical mask model developed by D2S. The GPU-accelerated D2S Computational Design Platform (CDP) is used to run simulation-based mask check, as well as model-based MDP. In addition to simulation-based checks such as mask EPE or dose margin, geometry-based rules are also available to detect quality issues such as slivers or CD splits. Dose margin related hotspots can also be detected by setting a correct detection threshold. In this paper, we will demonstrate GPU-acceleration for geometry processing, and give examples of mask check results and performance data. GPU-acceleration is necessary to make simulation-based mask MDP verification acceptable.

Programmed cell death 6 interacting protein (PDCD6IP) and Rabenosyn-5 (ZFYVE20) are potential urinary biomarkers for upper gastrointestinal cancer.

PubMed

Husi, Holger; Skipworth, Richard J E; Cronshaw, Andrew; Stephens, Nathan A; Wackerhage, Henning; Greig, Carolyn; Fearon, Kenneth C H; Ross, James A

2015-06-01

Cancer of the upper digestive tract (uGI) is a major contributor to cancer-related death worldwide. Due to a rise in occurrence, together with poor survival rates and a lack of diagnostic or prognostic clinical assays, there is a clear need to establish molecular biomarkers. Initial assessment was performed on urine samples from 60 control and 60 uGI cancer patients using MS to establish a peak pattern or fingerprint model, which was validated by a further set of 59 samples. We detected 86 cluster peaks by MS above frequency and detection thresholds. Statistical testing and model building resulted in a peak profiling model of five relevant peaks with 88% overall sensitivity and 91% specificity, and overall correctness of 90%. High-resolution MS of 40 samples in the 2-10 kDa range resulted in 646 identified proteins, and pattern matching identified four of the five model peaks within significant parameters, namely programmed cell death 6 interacting protein (PDCD6IP/Alix/AIP1), Rabenosyn-5 (ZFYVE20), protein S100A8, and protein S100A9, of which the first two were validated by Western blotting. We demonstrate that MS analysis of human urine can identify lead biomarker candidates in uGI cancers, which makes this technique potentially useful in defining and consolidating biomarker patterns for uGI cancer screening. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Robust curb detection with fusion of 3D-Lidar and camera data.

PubMed

Tan, Jun; Li, Jian; An, Xiangjing; He, Hangen

2014-05-21

Curb detection is an essential component of Autonomous Land Vehicles (ALV), especially important for safe driving in urban environments. In this paper, we propose a fusion-based curb detection method through exploiting 3D-Lidar and camera data. More specifically, we first fuse the sparse 3D-Lidar points and high-resolution camera images together to recover a dense depth image of the captured scene. Based on the recovered dense depth image, we propose a filter-based method to estimate the normal direction within the image. Then, by using the multi-scale normal patterns based on the curb's geometric property, curb point features fitting the patterns are detected in the normal image row by row. After that, we construct a Markov Chain to model the consistency of curb points which utilizes the continuous property of the curb, and thus the optimal curb path which links the curb points together can be efficiently estimated by dynamic programming. Finally, we perform post-processing operations to filter the outliers, parameterize the curbs and give the confidence scores on the detected curbs. Extensive evaluations clearly show that our proposed method can detect curbs with strong robustness at real-time speed for both static and dynamic scenes.

Pattern uniformity control in integrated structures

NASA Astrophysics Data System (ADS)

Kobayashi, Shinji; Okada, Soichiro; Shimura, Satoru; Nafus, Kathleen; Fonseca, Carlos; Biesemans, Serge; Enomoto, Masashi

2017-03-01

In our previous paper dealing with multi-patterning, we proposed a new indicator to quantify the quality of final wafer pattern transfer, called interactive pattern fidelity error (IPFE). It detects patterning failures resulting from any source of variation in creating integrated patterns. IPFE is a function of overlay and edge placement error (EPE) of all layers comprising the final pattern (i.e. lower and upper layers). In this paper, we extend the use cases with Via in additional to the bridge case (Block on Spacer). We propose an IPFE budget and CD budget using simple geometric and statistical models with analysis of a variance (ANOVA). In addition, we validate the model with experimental data. From the experimental results, improvements in overlay, local-CDU (LCDU) of contact hole (CH) or pillar patterns (especially, stochastic pattern noise (SPN)) and pitch walking are all critical to meet budget requirements. We also provide a special note about the importance of the line length used in analyzing LWR. We find that IPFE and CD budget requirements are consistent to the table of the ITRS's technical requirement. Therefore the IPFE concept can be adopted for a variety of integrated structures comprising digital logic circuits. Finally, we suggest how to use IPFE for yield management and optimization requirements for each process.

Organ-Level Analysis of Idioblast Patterning in Egeria densa Planch. Leaves

PubMed Central

Hara, Takuya; Kobayashi, Emi; Ohtsubo, Kohei; Kumada, Shogo; Kanazawa, Mikako; Abe, Tomoko; Itoh, Ryuuichi D.; Fujiwara, Makoto T.

2015-01-01

Leaf tissues of plants usually contain several types of idioblasts, defined as specialized cells whose shape and contents differ from the surrounding homogeneous cells. The spatial patterning of idioblasts, particularly of trichomes and guard cells, across the leaf epidermis has received considerable attention as it offers a useful biological model for studying the intercellular regulation of cell fate and patterning. Excretory idioblasts in the leaves of the aquatic monocotyledonous plant Egeria densa produced light blue autofluorescence when irradiated with ultraviolet light. The use of epifluorescence microscopy to detect this autofluorescence provided a simple and convenient method for detecting excretory idioblasts and allowed tracking of those cells across the leaf surfaces, enabling quantitative measurement of the clustering and spacing patterns of idioblasts at the whole leaf level. Occurrence of idioblasts was coordinated along the proximal–distal, medial–lateral, and adaxial–abaxial axes, producing a recognizable consensus spatial pattern of idioblast formation among fully expanded leaves. Idioblast clusters, which comprised up to nine cells aligned along the proximal–distal axis, showed no positional bias or regularity in idioblast-forming areas when compared with singlet idioblasts. Up to 75% of idioblasts existed as clusters on every leaf side examined. The idioblast-forming areas varied between leaves, implying phenotypic plasticity. Furthermore, in young expanding leaves, autofluorescence was occasionally detected in a single giant vesicle or else in one or more small vesicles, which eventually grew to occupy a large portion of the idioblast volume as a central vacuole. Differentiation of vacuoles by accumulating the fluorescence substance might be an integral part of idioblast differentiation. Red autofluorescence from chloroplasts was not detected in idioblasts of young expanding leaves, suggesting idioblast differentiation involves an arrest in chloroplast development at a very early stage, rather than transdifferentiation of chloroplast-containing epidermal cells. PMID:25742311

Object detection in natural backgrounds predicted by discrimination performance and models

NASA Technical Reports Server (NTRS)

Rohaly, A. M.; Ahumada, A. J. Jr; Watson, A. B.

1997-01-01

Many models of visual performance predict image discriminability, the visibility of the difference between a pair of images. We compared the ability of three image discrimination models to predict the detectability of objects embedded in natural backgrounds. The three models were: a multiple channel Cortex transform model with within-channel masking; a single channel contrast sensitivity filter model; and a digital image difference metric. Each model used a Minkowski distance metric (generalized vector magnitude) to summate absolute differences between the background and object plus background images. For each model, this summation was implemented with three different exponents: 2, 4 and infinity. In addition, each combination of model and summation exponent was implemented with and without a simple contrast gain factor. The model outputs were compared to measures of object detectability obtained from 19 observers. Among the models without the contrast gain factor, the multiple channel model with a summation exponent of 4 performed best, predicting the pattern of observer d's with an RMS error of 2.3 dB. The contrast gain factor improved the predictions of all three models for all three exponents. With the factor, the best exponent was 4 for all three models, and their prediction errors were near 1 dB. These results demonstrate that image discrimination models can predict the relative detectability of objects in natural scenes.

Temporal motifs reveal homophily, gender-specific patterns, and group talk in call sequences.

PubMed

Kovanen, Lauri; Kaski, Kimmo; Kertész, János; Saramäki, Jari

2013-11-05

Recent studies on electronic communication records have shown that human communication has complex temporal structure. We study how communication patterns that involve multiple individuals are affected by attributes such as sex and age. To this end, we represent the communication records as a colored temporal network where node color is used to represent individuals' attributes, and identify patterns known as temporal motifs. We then construct a null model for the occurrence of temporal motifs that takes into account the interaction frequencies and connectivity between nodes of different colors. This null model allows us to detect significant patterns in call sequences that cannot be observed in a static network that uses interaction frequencies as link weights. We find sex-related differences in communication patterns in a large dataset of mobile phone records and show the existence of temporal homophily, the tendency of similar individuals to participate in communication patterns beyond what would be expected on the basis of their average interaction frequencies. We also show that temporal patterns differ between dense and sparse neighborhoods in the network. Because also this result is independent of interaction frequencies, it can be seen as an extension of Granovetter's hypothesis to temporal networks.

Temporal motifs reveal homophily, gender-specific patterns, and group talk in call sequences

PubMed Central

Kovanen, Lauri; Kaski, Kimmo; Kertész, János; Saramäki, Jari

2013-01-01

Recent studies on electronic communication records have shown that human communication has complex temporal structure. We study how communication patterns that involve multiple individuals are affected by attributes such as sex and age. To this end, we represent the communication records as a colored temporal network where node color is used to represent individuals’ attributes, and identify patterns known as temporal motifs. We then construct a null model for the occurrence of temporal motifs that takes into account the interaction frequencies and connectivity between nodes of different colors. This null model allows us to detect significant patterns in call sequences that cannot be observed in a static network that uses interaction frequencies as link weights. We find sex-related differences in communication patterns in a large dataset of mobile phone records and show the existence of temporal homophily, the tendency of similar individuals to participate in communication patterns beyond what would be expected on the basis of their average interaction frequencies. We also show that temporal patterns differ between dense and sparse neighborhoods in the network. Because also this result is independent of interaction frequencies, it can be seen as an extension of Granovetter’s hypothesis to temporal networks. PMID:24145424

Production and perception rules underlying visual patterns: effects of symmetry and hierarchy.

PubMed

Westphal-Fitch, Gesche; Huber, Ludwig; Gómez, Juan Carlos; Fitch, W Tecumseh

2012-07-19

Formal language theory has been extended to two-dimensional patterns, but little is known about two-dimensional pattern perception. We first examined spontaneous two-dimensional visual pattern production by humans, gathered using a novel touch screen approach. Both spontaneous creative production and subsequent aesthetic ratings show that humans prefer ordered, symmetrical patterns over random patterns. We then further explored pattern-parsing abilities in different human groups, and compared them with pigeons. We generated visual plane patterns based on rules varying in complexity. All human groups tested, including children and individuals diagnosed with autism spectrum disorder (ASD), were able to detect violations of all production rules tested. Our ASD participants detected pattern violations with the same speed and accuracy as matched controls. Children's ability to detect violations of a relatively complex rotational rule correlated with age, whereas their ability to detect violations of a simple translational rule did not. By contrast, even with extensive training, pigeons were unable to detect orientation-based structural violations, suggesting that, unlike humans, they did not learn the underlying structural rules. Visual two-dimensional patterns offer a promising new formally-grounded way to investigate pattern production and perception in general, widely applicable across species and age groups.

Production and perception rules underlying visual patterns: effects of symmetry and hierarchy

PubMed Central

Westphal-Fitch, Gesche; Huber, Ludwig; Gómez, Juan Carlos; Fitch, W. Tecumseh

2012-01-01

Formal language theory has been extended to two-dimensional patterns, but little is known about two-dimensional pattern perception. We first examined spontaneous two-dimensional visual pattern production by humans, gathered using a novel touch screen approach. Both spontaneous creative production and subsequent aesthetic ratings show that humans prefer ordered, symmetrical patterns over random patterns. We then further explored pattern-parsing abilities in different human groups, and compared them with pigeons. We generated visual plane patterns based on rules varying in complexity. All human groups tested, including children and individuals diagnosed with autism spectrum disorder (ASD), were able to detect violations of all production rules tested. Our ASD participants detected pattern violations with the same speed and accuracy as matched controls. Children's ability to detect violations of a relatively complex rotational rule correlated with age, whereas their ability to detect violations of a simple translational rule did not. By contrast, even with extensive training, pigeons were unable to detect orientation-based structural violations, suggesting that, unlike humans, they did not learn the underlying structural rules. Visual two-dimensional patterns offer a promising new formally-grounded way to investigate pattern production and perception in general, widely applicable across species and age groups. PMID:22688636

A neural network model of harmonic detection

NASA Astrophysics Data System (ADS)

Lewis, Clifford F.

2003-04-01

Harmonic detection theories postulate that a virtual pitch is perceived when a sufficient number of harmonics is present. The harmonics need not be consecutive, but higher harmonics contribute less than lower harmonics [J. Raatgever and F. A. Bilsen, in Auditory Physiology and Perception, edited by Y. Cazals, K. Horner, and L. Demany (Pergamon, Oxford, 1992), pp. 215-222 M. K. McBeath and J. F. Wayand, Abstracts of the Psychonom. Soc. 3, 55 (1998)]. A neural network model is presented that has the potential to simulate this operation. Harmonics are first passed through a bank of rounded exponential filters with lateral inhibition. The results are used as inputs for an autoassociator neural network. The model is trained using harmonic data for symphonic musical instruments, in order to test whether it can self-organize by learning associations between co-occurring harmonics. It is shown that the trained model can complete the pattern for missing-fundamental sounds. The Performance of the model in harmonic detection will be compared with experimental results for humans.

Supraglacial channel inception: Modeling and processes

NASA Astrophysics Data System (ADS)

Mantelli, E.; Camporeale, C.; Ridolfi, L.

2015-09-01

Supraglacial drainage systems play a key role in glacial hydrology. Nevertheless, physical processes leading to spatial organization in supraglacial networks are still an open issue. In the present work we thus address from a quantitative point of view the question of what is the physics leading to widely observed patterns made up of evenly spaced channels. To this aim, we set up a novel mathematical model describing a condition antecedent channel formation, i.e., the down-glacier flow of a distributed meltwater film. We then perform a linear stability analysis to assess whether the ice-water interface undergoes a morphological instability compatible with observed patterns. The instability is detected, its features depending on glacier surface slope, ice friction factor, and water as well as ice thermal conditions. By contrast, in our model channel spacing is solely hydrodynamically driven and relies on the interplay between pressure perturbations, flow depth response, and Reynolds stresses. Geometrical features of the predicted pattern are quantitatively consistent with available field data. The hydrodynamic origin of supraglacial channel morphogenesis suggests that alluvial patterns might share the same physical controls.

Pattern Recognition Algorithm for High-Sensitivity Odorant Detection in Unknown Environments

NASA Technical Reports Server (NTRS)

Duong, Tuan A.

2012-01-01

In a realistic odorant detection application environment, the collected sensory data is a mix of unknown chemicals with unknown concentrations and noise. The identification of the odorants among these mixtures is a challenge in data recognition. In addition, deriving their individual concentrations in the mix is also a challenge. A deterministic analytical model was developed to accurately identify odorants and calculate their concentrations in a mixture with noisy data.

Distribution patterns in the native vascular flora of Iceland.

PubMed

Wasowicz, Pawel; Pasierbiński, Andrzej; Przedpelska-Wasowicz, Ewa Maria; Kristinsson, Hörður

2014-01-01

The aim of our study was to reveal biogeographical patterns in the native vascular flora of Iceland and to define ecological factors responsible for these patterns. We analysed dataset of more than 500,000 records containing information on the occurrence of vascular plants. Analysis of ecological factors included climatic (derived from WORLDCLIM data), topographic (calculated from digital elevation model) and geological (bedrock characteristics) variables. Spherical k-means clustering and principal component analysis were used to detect biogeographical patterns and to study the factors responsible for them. We defined 10 biotic elements exhibiting different biogeographical patterns. We showed that climatic (temperature-related) and topographic variables were the most important factors contributing to the spatial patterns within the Icelandic vascular flora and that these patterns are almost completely independent of edaphic factors (bedrock type). Our study is the first one to analyse the biogeographical differentiation of the native vascular flora of Iceland.

Supervised target detection in hyperspectral images using one-class Fukunaga-Koontz Transform

NASA Astrophysics Data System (ADS)

Binol, Hamidullah; Bal, Abdullah

2016-05-01

A novel hyperspectral target detection technique based on Fukunaga-Koontz transform (FKT) is presented. FKT offers significant properties for feature selection and ordering. However, it can only be used to solve multi-pattern classification problems. Target detection may be considered as a two-class classification problem, i.e., target versus background clutter. Nevertheless, background clutter typically contains different types of materials. That's why; target detection techniques are different than classification methods by way of modeling clutter. To avoid the modeling of the background clutter, we have improved one-class FKT (OC-FKT) for target detection. The statistical properties of target training samples are used to define tunnel-like boundary of the target class. Non-target samples are then created synthetically as to be outside of the boundary. Thus, only limited target samples become adequate for training of FKT. The hyperspectral image experiments confirm that the proposed OC-FKT technique provides an effective means for target detection.

Method for detecting damage in carbon-fibre reinforced plastic-steel structures based on eddy current pulsed thermography

NASA Astrophysics Data System (ADS)

Li, Xuan; Liu, Zhiping; Jiang, Xiaoli; Lodewijks, Gabrol

2018-01-01

Eddy current pulsed thermography (ECPT) is well established for non-destructive testing of electrical conductive materials, featuring the advantages of contactless, intuitive detecting and efficient heating. The concept of divergence characterization of the damage rate of carbon fibre-reinforced plastic (CFRP)-steel structures can be extended to ECPT thermal pattern characterization. It was found in this study that the use of ECPT technology on CFRP-steel structures generated a sizeable amount of valuable information for comprehensive material diagnostics. The relationship between divergence and transient thermal patterns can be identified and analysed by deploying mathematical models to analyse the information about fibre texture-like orientations, gaps and undulations in these multi-layered materials. The developed algorithm enabled the removal of information about fibre texture and the extraction of damage features. The model of the CFRP-glue-steel structures with damage was established using COMSOL Multiphysics® software, and quantitative non-destructive damage evaluation from the ECPT image areas was derived. The results of this proposed method illustrate that damaged areas are highly affected by available information about fibre texture. This proposed work can be applied for detection of impact induced damage and quantitative evaluation of CFRP structures.

Change detection in Arctic satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

NASA Astrophysics Data System (ADS)

Moody, Daniela I.; Wilson, Cathy J.; Rowland, Joel C.; Altmann, Garrett L.

2015-06-01

Advanced pattern recognition and computer vision algorithms are of great interest for landscape characterization, change detection, and change monitoring in satellite imagery, in support of global climate change science and modeling. We present results from an ongoing effort to extend neuroscience-inspired models for feature extraction to the environmental sciences, and we demonstrate our work using Worldview-2 multispectral satellite imagery. We use a Hebbian learning rule to derive multispectral, multiresolution dictionaries directly from regional satellite normalized band difference index data. These feature dictionaries are used to build sparse scene representations, from which we automatically generate land cover labels via our CoSA algorithm: Clustering of Sparse Approximations. These data adaptive feature dictionaries use joint spectral and spatial textural characteristics to help separate geologic, vegetative, and hydrologic features. Land cover labels are estimated in example Worldview-2 satellite images of Barrow, Alaska, taken at two different times, and are used to detect and discuss seasonal surface changes. Our results suggest that an approach that learns from both spectral and spatial features is promising for practical pattern recognition problems in high resolution satellite imagery.

New serum biomarkers for detection of tuberculosis using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry.

PubMed

Liu, Ji-Yan; Jin, Lei; Zhao, Meng-Yuan; Zhang, Xin; Liu, Chi-Bo; Zhang, Yu-Xiang; Li, Fu-Jian; Zhou, Jian-Min; Wang, Hua-Jun; Li, Ji-Cheng

2011-10-01

New technologies for the early detection of tuberculosis (TB) are urgently needed. Pathological changes within an organ might be reflected in proteomic patterns in serum. The aim of the present study was to screen for the potential protein biomarkers in serum for the diagnosis of TB using proteomic fingerprint technology. Proteomic fingerprint technology combining protein chips with surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) was used to profile the serum proteins from 50 patients with TB, 25 patients with lung disease other than TB, and 25 healthy volunteers. The protein fingerprint expression of all the serum samples and the resulting profiles between TB and control groups were analyzed with the Biomarker Wizard system. A total of 30 discriminating m/z peaks were detected that were related to TB (p<0.01). The model of biomarkers constructed by the Biomarker Patterns Software based on the three biomarkers (2024, 8007, and 8598 Da) generated excellent separation between the TB and control groups. The sensitivity was 84.0% and the specificity was 86.0%. Blind test data indicated a sensitivity of 80.0% and a specificity of 84.2%. The data suggested a potential application of SELDI-TOF MS as an effective technology to profile serum proteome, and with pattern analysis, a diagnostic model comprising three potential biomarkers was indicated to differentiate people with TB and healthy controls rapidly and precisely.

Tracking signal test to monitor an intelligent time series forecasting model

NASA Astrophysics Data System (ADS)

Deng, Yan; Jaraiedi, Majid; Iskander, Wafik H.

2004-03-01

Extensive research has been conducted on the subject of Intelligent Time Series forecasting, including many variations on the use of neural networks. However, investigation of model adequacy over time, after the training processes is completed, remains to be fully explored. In this paper we demonstrate a how a smoothed error tracking signals test can be incorporated into a neuro-fuzzy model to monitor the forecasting process and as a statistical measure for keeping the forecasting model up-to-date. The proposed monitoring procedure is effective in the detection of nonrandom changes, due to model inadequacy or lack of unbiasedness in the estimation of model parameters and deviations from the existing patterns. This powerful detection device will result in improved forecast accuracy in the long run. An example data set has been used to demonstrate the application of the proposed method.

A novel application of multi-event modeling to estimate class segregation in a highly migratory oceanic vertebrate.

PubMed

Chapple, T K; Chambert, T; Kanive, P E; Jorgensen, S J; Rotella, J J; Anderson, S D; Carlisle, A B; Block, B A

2016-12-01

Spatial segregation of animals by class (i.e., maturity or sex) within a population due to differential rates of temporary emigration (TE) from study sites can be an important life history feature to consider in population assessment and management. However, such rates are poorly known; new quantitative approaches to address these knowledge gaps are needed. We present a novel application of multi-event models that takes advantage of two sources of detections to differentiate temporary emigration from apparent absence to quantify class segregation within a study population of double-marked (photo-identified and tagged with coded acoustic transmitters) white sharks (Carcharodon carcharias) in central California. We use this model to test if sex-specific patterns in TE result in disparate apparent capture probabilities (p o ) between male and female white sharks, which can affect the observed sex ratio. The best-supported model showed a contrasting pattern of Pr(TE) from coastal aggregation sites between sexes (for males Pr[TE] = 0.015 [95% CI = 0.00, 0.31] and Pr[TE]= 0.57 [0.40, 0.72] for females), but not maturity classes. Additionally, by accounting for Pr(TE) and imperfect detection, we were able to estimate class-specific values of true capture probability (p * ) for tagged and untagged sharks. The best-supported model identified differences between maturity classes but no difference between sexes or tagging impacts (tagged mature sharks p *  = 0.55 (0.46-0.63) and sub-adult sharks p*   = 0.36 (0.25, 0.50); and untagged mature sharks p *  = 0.50 (0.39-0.61) and sub-adults p *  = 0.18 (0.10, 0.31). Estimated sex-based differences in p o were linked to sex-specific differences in Pr(TE) but not in p * ; once the Pr(TE) is accounted for, the p * between sexes was not different. These results indicate that the observed sex ratio is not a consequence of unequal detectability and sex-specific values of Pr(TE) are important drivers of the observed male-dominated sex ratio. Our modeling approach reveals complex class-specific patterns in Pr(TE) and p * in a mark-recapture data set, and highlights challenges for the population modeling and conservation of white sharks in central California. The model we develop here can be used to estimate rates of temporary emigration and class segregation when two detection methods are used. © 2016 by the Ecological Society of America.

[Photoelastic stress analysis of human femurs before and after implantation of different models of femur neck prostheses].

PubMed

Wieners, G; Pech, M; Streitparth, F; Jansson, V; Plitz, W

2007-01-01

The aim of this study was the detection of areas at risk at the proximal femur after implantation of different femur neck prostheses using the photoelastic stress analysis. Twelve pairs of human femurs were used as examination material. The analysis of the stress pattern was done with a stepwise increasing load up to the quadruple of body weight before and after implantation of three models of femur neck prostheses which were implanted cementless. The "CUT" and "Cigar" models are coated with a tripod structure. The "Cigar" model has a lateral thrust plate. The lateral end of the "CUT" model is curved and this end is attached to the lateral corticalis. The third model, the "rip prosthesis" has two layers for rotational stability. Subsequently, the micromotions of the implanted prosthesis in the femural neck were examined with alternating weight loads (1000 +/- 700 N). The Cigar prosthesis showed the most changes of stress distribution because of the lateral thrust plate with concentration of isochromatic lines to the lateral boring. In the region of the oseotomy an increase of strain up 1440 microm/m could be detected for the Cigar and up to 1000 microm/m for the rib prosthesis. The stress pattern after implantation of the CUT prosthesis remained very similar apart from a slight increase of stress values (720 microm/m). Only for the Cigar prostheses were the measured micromotions below the critical value for a possible osteointegration with a mean value of 134 microm/m. The stress pattern after implantation of the CUT prosthesis remained most similar to the preinterventional stress distribution. Because of this, it is to be expected that the osseous modification would stay at a low level. The question of osteointegration can only be answered in long-term in-vivo studies.

Simulation of vehicle acoustics in support of netted sensor research and development

NASA Astrophysics Data System (ADS)

Christou, Carol T.; Jacyna, Garry M.

2005-05-01

The MITRE Corporation has initiated a three-year internally-funded research program in netted sensors, the first-year effort focusing on vehicle detection for border monitoring. An important component is developing an understanding of the complex acoustic structure of vehicle noise to aid in netted sensor-based detection and classification. This presentation will discuss the design of a high-fidelity vehicle acoustic simulator to model the generation and transmission of acoustic energy from a moving vehicle to a collection of sensor nodes. Realistic spatially-dependent automobile sounds are generated from models of the engine cylinder firing rates, muffler and manifold resonances, and speed-dependent tire whine noise. Tire noise is the dominant noise source for vehicle speeds in excess of 30 miles per hour (MPH). As a result, we have developed detailed models that successfully predict the tire noise spectrum as a function of speed, road surface wave-number spectrum, tire geometry, and tire tread pattern. We have also included realistic descriptions of the spatial directivity patterns for the engine harmonics, muffler, and tire whine noise components. The acoustic waveforms are propagated to each sensor node using a simple phase-dispersive multi-path model. A brief description of the models and their corresponding outputs is provided.

Detecting and modelling delayed density-dependence in abundance time series of a small mammal (Didelphis aurita)

NASA Astrophysics Data System (ADS)

Brigatti, E.; Vieira, M. V.; Kajin, M.; Almeida, P. J. A. L.; de Menezes, M. A.; Cerqueira, R.

2016-02-01

We study the population size time series of a Neotropical small mammal with the intent of detecting and modelling population regulation processes generated by density-dependent factors and their possible delayed effects. The application of analysis tools based on principles of statistical generality are nowadays a common practice for describing these phenomena, but, in general, they are more capable of generating clear diagnosis rather than granting valuable modelling. For this reason, in our approach, we detect the principal temporal structures on the bases of different correlation measures, and from these results we build an ad-hoc minimalist autoregressive model that incorporates the main drivers of the dynamics. Surprisingly our model is capable of reproducing very well the time patterns of the empirical series and, for the first time, clearly outlines the importance of the time of attaining sexual maturity as a central temporal scale for the dynamics of this species. In fact, an important advantage of this analysis scheme is that all the model parameters are directly biologically interpretable and potentially measurable, allowing a consistency check between model outputs and independent measurements.

Community Detection in Complex Networks via Clique Conductance.

PubMed

Lu, Zhenqi; Wahlström, Johan; Nehorai, Arye

2018-04-13

Network science plays a central role in understanding and modeling complex systems in many areas including physics, sociology, biology, computer science, economics, politics, and neuroscience. One of the most important features of networks is community structure, i.e., clustering of nodes that are locally densely interconnected. Communities reveal the hierarchical organization of nodes, and detecting communities is of great importance in the study of complex systems. Most existing community-detection methods consider low-order connection patterns at the level of individual links. But high-order connection patterns, at the level of small subnetworks, are generally not considered. In this paper, we develop a novel community-detection method based on cliques, i.e., local complete subnetworks. The proposed method overcomes the deficiencies of previous similar community-detection methods by considering the mathematical properties of cliques. We apply the proposed method to computer-generated graphs and real-world network datasets. When applied to networks with known community structure, the proposed method detects the structure with high fidelity and sensitivity. When applied to networks with no a priori information regarding community structure, the proposed method yields insightful results revealing the organization of these complex networks. We also show that the proposed method is guaranteed to detect near-optimal clusters in the bipartition case.

Support Vector Machine Model for Automatic Detection and Classification of Seismic Events

NASA Astrophysics Data System (ADS)

Barros, Vesna; Barros, Lucas

2016-04-01

The automated processing of multiple seismic signals to detect, localize and classify seismic events is a central tool in both natural hazards monitoring and nuclear treaty verification. However, false detections and missed detections caused by station noise and incorrect classification of arrivals are still an issue and the events are often unclassified or poorly classified. Thus, machine learning techniques can be used in automatic processing for classifying the huge database of seismic recordings and provide more confidence in the final output. Applied in the context of the International Monitoring System (IMS) - a global sensor network developed for the Comprehensive Nuclear-Test-Ban Treaty (CTBT) - we propose a fully automatic method for seismic event detection and classification based on a supervised pattern recognition technique called the Support Vector Machine (SVM). According to Kortström et al., 2015, the advantages of using SVM are handleability of large number of features and effectiveness in high dimensional spaces. Our objective is to detect seismic events from one IMS seismic station located in an area of high seismicity and mining activity and classify them as earthquakes or quarry blasts. It is expected to create a flexible and easily adjustable SVM method that can be applied in different regions and datasets. Taken a step further, accurate results for seismic stations could lead to a modification of the model and its parameters to make it applicable to other waveform technologies used to monitor nuclear explosions such as infrasound and hydroacoustic waveforms. As an authorized user, we have direct access to all IMS data and bulletins through a secure signatory account. A set of significant seismic waveforms containing different types of events (e.g. earthquake, quarry blasts) and noise is being analysed to train the model and learn the typical pattern of the signal from these events. Moreover, comparing the performance of the support-vector network to various classical learning algorithms used before in seismic detection and classification is an essential final step to analyze the advantages and disadvantages of the model.

Pipeline Processing with an Iterative, Context-based Detection Model

DTIC Science & Technology

2014-04-19

stripping the incoming data stream of repeating and irrelevant signals prior to running primary detectors , adaptive beamforming and matched field processing...framework, pattern detectors , correlation detectors , subspace detectors , matched field detectors , nuclear explosion monitoring 16. SECURITY CLASSIFICATION...10 5. Teleseismic paths from earthquakes in

Continuous statistical modelling for rapid detection of adulteration of extra virgin olive oil using mid infrared and Raman spectroscopic data.

PubMed

Georgouli, Konstantia; Martinez Del Rincon, Jesus; Koidis, Anastasios

2017-02-15

The main objective of this work was to develop a novel dimensionality reduction technique as a part of an integrated pattern recognition solution capable of identifying adulterants such as hazelnut oil in extra virgin olive oil at low percentages based on spectroscopic chemical fingerprints. A novel Continuous Locality Preserving Projections (CLPP) technique is proposed which allows the modelling of the continuous nature of the produced in-house admixtures as data series instead of discrete points. The maintenance of the continuous structure of the data manifold enables the better visualisation of this examined classification problem and facilitates the more accurate utilisation of the manifold for detecting the adulterants. The performance of the proposed technique is validated with two different spectroscopic techniques (Raman and Fourier transform infrared, FT-IR). In all cases studied, CLPP accompanied by k-Nearest Neighbors (kNN) algorithm was found to outperform any other state-of-the-art pattern recognition techniques. Copyright © 2016 Elsevier Ltd. All rights reserved.

Geomfinder: a multi-feature identifier of similar three-dimensional protein patterns: a ligand-independent approach.

PubMed

Núñez-Vivanco, Gabriel; Valdés-Jiménez, Alejandro; Besoaín, Felipe; Reyes-Parada, Miguel

2016-01-01

Since the structure of proteins is more conserved than the sequence, the identification of conserved three-dimensional (3D) patterns among a set of proteins, can be important for protein function prediction, protein clustering, drug discovery and the establishment of evolutionary relationships. Thus, several computational applications to identify, describe and compare 3D patterns (or motifs) have been developed. Often, these tools consider a 3D pattern as that described by the residues surrounding co-crystallized/docked ligands available from X-ray crystal structures or homology models. Nevertheless, many of the protein structures stored in public databases do not provide information about the location and characteristics of ligand binding sites and/or other important 3D patterns such as allosteric sites, enzyme-cofactor interaction motifs, etc. This makes necessary the development of new ligand-independent methods to search and compare 3D patterns in all available protein structures. Here we introduce Geomfinder, an intuitive, flexible, alignment-free and ligand-independent web server for detailed estimation of similarities between all pairs of 3D patterns detected in any two given protein structures. We used around 1100 protein structures to form pairs of proteins which were assessed with Geomfinder. In these analyses each protein was considered in only one pair (e.g. in a subset of 100 different proteins, 50 pairs of proteins can be defined). Thus: (a) Geomfinder detected identical pairs of 3D patterns in a series of monoamine oxidase-B structures, which corresponded to the effectively similar ligand binding sites at these proteins; (b) we identified structural similarities among pairs of protein structures which are targets of compounds such as acarbose, benzamidine, adenosine triphosphate and pyridoxal phosphate; these similar 3D patterns are not detected using sequence-based methods; (c) the detailed evaluation of three specific cases showed the versatility of Geomfinder, which was able to discriminate between similar and different 3D patterns related to binding sites of common substrates in a range of diverse proteins. Geomfinder allows detecting similar 3D patterns between any two pair of protein structures, regardless of the divergency among their amino acids sequences. Although the software is not intended for simultaneous multiple comparisons in a large number of proteins, it can be particularly useful in cases such as the structure-based design of multitarget drugs, where a detailed analysis of 3D patterns similarities between a few selected protein targets is essential.

Toward Automating HIV Identification: Machine Learning for Rapid Identification of HIV-Related Social Media Data.

PubMed

Young, Sean D; Yu, Wenchao; Wang, Wei

2017-02-01

"Social big data" from technologies such as social media, wearable devices, and online searches continue to grow and can be used as tools for HIV research. Although researchers can uncover patterns and insights associated with HIV trends and transmission, the review process is time consuming and resource intensive. Machine learning methods derived from computer science might be used to assist HIV domain experts by learning how to rapidly and accurately identify patterns associated with HIV from a large set of social data. Using an existing social media data set that was associated with HIV and coded by an HIV domain expert, we tested whether 4 commonly used machine learning methods could learn the patterns associated with HIV risk behavior. We used the 10-fold cross-validation method to examine the speed and accuracy of these models in applying that knowledge to detect HIV content in social media data. Logistic regression and random forest resulted in the highest accuracy in detecting HIV-related social data (85.3%), whereas the Ridge Regression Classifier resulted in the lowest accuracy. Logistic regression yielded the fastest processing time (16.98 seconds). Machine learning can enable social big data to become a new and important tool in HIV research, helping to create a new field of "digital HIV epidemiology." If a domain expert can identify patterns in social data associated with HIV risk or HIV transmission, machine learning models could quickly and accurately learn those associations and identify potential HIV patterns in large social data sets.

Two different immunostaining patterns of beta-amyloid precursor protein (APP) may distinguish traumatic from nontraumatic axonal injury.

PubMed

Hayashi, Takahito; Ago, Kazutoshi; Nakamae, Takuma; Higo, Eri; Ogata, Mamoru

2015-09-01

Immunostaining for beta-amyloid precursor protein (APP) is recognized as an effective tool for detecting traumatic axonal injury, but it also detects axonal injury due to ischemic or other metabolic causes. Previously, we reported two different patterns of APP staining: labeled axons oriented along with white matter bundles (pattern 1) and labeled axons scattered irregularly (pattern 2) (Hayashi et al. (Leg Med (Tokyo) 11:S171-173, 2009). In this study, we investigated whether these two patterns are consistent with patterns of trauma and hypoxic brain damage, respectively. Sections of the corpus callosum from 44 cases of blunt head injury and equivalent control tissue were immunostained for APP. APP was detected in injured axons such as axonal bulbs and varicose axons in 24 of the 44 cases of head injuries that also survived for three or more hours after injury. In 21 of the 24 APP-positive cases, pattern 1 alone was observed in 14 cases, pattern 2 alone was not observed in any cases, and both patterns 1 and 2 were detected in 7 cases. APP-labeled injured axons were detected in 3 of the 44 control cases, all of which were pattern 2. These results suggest that pattern 1 indicates traumatic axonal injury, while pattern 2 results from hypoxic insult. These patterns may be useful to differentiate between traumatic and nontraumatic axonal injuries.

Improving the analytical performance and versatility of paper spray mass spectrometry via paper microfluidics.

PubMed

Murray, Ian; Walker, Glenn; Bereman, Michael S

2016-06-20

Two paper-based microfluidic techniques, photolithography and wax patterning, were investigated for their potential to improve upon the sensitivity, reproducibility, and versatility of paper spray mass spectrometry. The main limitation of photolithography was the significant signal (approximately three orders of magnitude) above background which was attributed to the chemicals used in the photoresist process. Hydrophobic barriers created via wax patterning were discovered to have approximately 2 orders of magnitude less background signal compared to analogous barriers created using photolithography. A minimum printed wax barrier thickness of approximately 0.3 mm was necessary to consistently retain commonly used paper spray solvents (1 : 1 water : acetonitrile/methanol) and avoid leakage. Constricting capillary flow via wax-printed channels yielded both a significant increase in signal and detection time for detection of model analytes. This signal increase, which was attributed to restricting the radial flow of analyte/solvent on paper (i.e., a concentrating effect), afforded a significant increase in sensitivity (p ≪ 0.05) for the detection of pesticides spiked into residential tap water using a five-point calibration curve. Finally, unique mixing designs using wax patterning can be envisioned to perform on-paper analyte derivatization.

Design pattern mining using distributed learning automata and DNA sequence alignment.

PubMed

Esmaeilpour, Mansour; Naderifar, Vahideh; Shukur, Zarina

2014-01-01

Over the last decade, design patterns have been used extensively to generate reusable solutions to frequently encountered problems in software engineering and object oriented programming. A design pattern is a repeatable software design solution that provides a template for solving various instances of a general problem. This paper describes a new method for pattern mining, isolating design patterns and relationship between them; and a related tool, DLA-DNA for all implemented pattern and all projects used for evaluation. DLA-DNA achieves acceptable precision and recall instead of other evaluated tools based on distributed learning automata (DLA) and deoxyribonucleic acid (DNA) sequences alignment. The proposed method mines structural design patterns in the object oriented source code and extracts the strong and weak relationships between them, enabling analyzers and programmers to determine the dependency rate of each object, component, and other section of the code for parameter passing and modular programming. The proposed model can detect design patterns better that available other tools those are Pinot, PTIDEJ and DPJF; and the strengths of their relationships. The result demonstrate that whenever the source code is build standard and non-standard, based on the design patterns, then the result of the proposed method is near to DPJF and better that Pinot and PTIDEJ. The proposed model is tested on the several source codes and is compared with other related models and available tools those the results show the precision and recall of the proposed method, averagely 20% and 9.6% are more than Pinot, 27% and 31% are more than PTIDEJ and 3.3% and 2% are more than DPJF respectively. The primary idea of the proposed method is organized in two following steps: the first step, elemental design patterns are identified, while at the second step, is composed to recognize actual design patterns.

Mechanistic understanding of human-wildlife conflict through a novel application of dynamic occupancy models.

PubMed

Goswami, Varun R; Medhi, Kamal; Nichols, James D; Oli, Madan K

2015-08-01

Crop and livestock depredation by wildlife is a primary driver of human-wildlife conflict, a problem that threatens the coexistence of people and wildlife globally. Understanding mechanisms that underlie depredation patterns holds the key to mitigating conflicts across time and space. However, most studies do not consider imperfect detection and reporting of conflicts, which may lead to incorrect inference regarding its spatiotemporal drivers. We applied dynamic occupancy models to elephant crop depredation data from India between 2005 and 2011 to estimate crop depredation occurrence and model its underlying dynamics as a function of spatiotemporal covariates while accounting for imperfect detection of conflicts. The probability of detecting conflicts was consistently <1.0 and was negatively influenced by distance to roads and elevation gradient, averaging 0.08-0.56 across primary periods (distinct agricultural seasons within each year). The probability of crop depredation occurrence ranged from 0.29 (SE 0.09) to 0.96 (SE 0.04). The probability that sites raided by elephants in primary period t would not be raided in primary period t + 1 varied with elevation gradient in different seasons and was influenced negatively by mean rainfall and village density and positively by distance to forests. Negative effects of rainfall variation and distance to forests best explained variation in the probability that sites not raided by elephants in primary period t would be raided in primary period t + 1. With our novel application of occupancy models, we teased apart the spatiotemporal drivers of conflicts from factors that influence how they are observed, thereby allowing more reliable inference on mechanisms underlying observed conflict patterns. We found that factors associated with increased crop accessibility and availability (e.g., distance to forests and rainfall patterns) were key drivers of elephant crop depredation dynamics. Such an understanding is essential for rigorous prediction of future conflicts, a critical requirement for effective conflict management in the context of increasing human-wildlife interactions. © 2015 Society for Conservation Biology.

Mark-recapture and mark-resight methods for estimating abundance with remote cameras: a carnivore case study

USGS Publications Warehouse

Alanso, Robert S.; McClintock, Brett T.; Lyren, Lisa M.; Boydston, Erin E.; Crooks, Kevin R.

2015-01-01

Abundance estimation of carnivore populations is difficult and has prompted the use of non-invasive detection methods, such as remotely-triggered cameras, to collect data. To analyze photo data, studies focusing on carnivores with unique pelage patterns have utilized a mark-recapture framework and studies of carnivores without unique pelage patterns have used a mark-resight framework. We compared mark-resight and mark-recapture estimation methods to estimate bobcat (Lynx rufus) population sizes, which motivated the development of a new "hybrid" mark-resight model as an alternative to traditional methods. We deployed a sampling grid of 30 cameras throughout the urban southern California study area. Additionally, we physically captured and marked a subset of the bobcat population with GPS telemetry collars. Since we could identify individual bobcats with photos of unique pelage patterns and a subset of the population was physically marked, we were able to use traditional mark-recapture and mark-resight methods, as well as the new “hybrid” mark-resight model we developed to estimate bobcat abundance. We recorded 109 bobcat photos during 4,669 camera nights and physically marked 27 bobcats with GPS telemetry collars. Abundance estimates produced by the traditional mark-recapture, traditional mark-resight, and “hybrid” mark-resight methods were similar, however precision differed depending on the models used. Traditional mark-recapture and mark-resight estimates were relatively imprecise with percent confidence interval lengths exceeding 100% of point estimates. Hybrid mark-resight models produced better precision with percent confidence intervals not exceeding 57%. The increased precision of the hybrid mark-resight method stems from utilizing the complete encounter histories of physically marked individuals (including those never detected by a camera trap) and the encounter histories of naturally marked individuals detected at camera traps. This new estimator may be particularly useful for estimating abundance of uniquely identifiable species that are difficult to sample using camera traps alone.

Urinary exosomal transcription factors, a new class of biomarkers for renal disease

PubMed Central

Zhou, Hua; Cheruvanky, Anita; Hu, Xuzhen; Matsumoto, Takayuki; Hiramatsu, Noriyuki; Cho, Monique E.; Berger, Alexandra; Leelahavanichkul, Asada; Doi, Kent; Chawla, Lakhmir S.; Illei, Gabor G.; Kopp, Jeffrey B.; Balow, James E.; Austin, Howard A.; Yuen, Peter S.T.; Star, Robert A.

2008-01-01

Urinary exosomes are excreted from all nephron segments and are a rich source of kidney injury biomarkers. Because exosomes contain intracellular proteins, we asked if transcription factors (TF) can be measured in urinary exosomes. We collected urine from two acute kidney injury (AKI) models (cisplatin or ischemia/reperfusion) and two podocyte injury models (puromycin-treated rats and podocin/Vpr transgenic mice). Human urine was obtained from patients with AKI, focal segmental glomerulosclerosis (FSGS), and matched controls. After isolating urine exosomes by differential centrifugation, activating transcription factor 3 (ATF3) and Wilms Tumor 1 (WT-1) were detected by western blot. ATF3 was continuously detected in urine exosomes 2–24 hr after ischemia/reperfusion and in a biphasic pattern after cisplatin. In both models, urinary ATF3 was detected earlier than serum creatinine. Urinary ATF3 was detected in AKI patients but not in normal subjects or patients with chronic kidney disease (CKD). Urinary WT-1 was detected in animal models before significant glomerular sclerosis. Urinary WT-1 was detected in 9/10 FSGS patients, but not in 8 controls. Transcription factors can be detected in urine exosomes, but not in whole urine. Urinary ATF3 may be a novel renal tubular cell injury biomarker for detecting early AKI, whereas urinary WT-1 may detect early podocyte injury. Urinary exosomal TFs represent a new class of biomarkers for acute and chronic renal diseases and may offer insight into cellular regulatory pathways. PMID:18509321

Passive Acoustic Monitoring the Diel, Lunar, Seasonal and Tidal Patterns in the Biosonar Activity of the Indo-Pacific Humpback Dolphins (Sousa chinensis) in the Pearl River Estuary, China.

PubMed

Wang, Zhi-Tao; Nachtigall, Paul E; Akamatsu, Tomonari; Wang, Ke-Xiong; Wu, Yu-Ping; Liu, Jian-Chang; Duan, Guo-Qin; Cao, Han-Jiang; Wang, Ding

2015-01-01

A growing demand for sustainable energy has led to an increase in construction of offshore windfarms. Guishan windmill farm will be constructed in the Pearl River Estuary, China, which sustains the world's largest known population of Indo-Pacific humpback dolphins (Sousa chinensis). Dolphin conservation is an urgent issue in this region. By using passive acoustic monitoring, a baseline distribution of data on this species in the Pearl River Estuary during pre-construction period had been collected. Dolphin biosonar detection and its diel, lunar, seasonal and tidal patterns were examined using a Generalized Linear Model. Significant higher echolocation detections at night than during the day, in winter-spring than in summer-autumn, at high tide than at flood tide were recognized. Significant higher echolocation detections during the new moon were recognized at night time. The diel, lunar and seasonal patterns for the echolocation encounter duration also significantly varied. These patterns could be due to the spatial-temporal variability of dolphin prey and illumination conditions. The baseline information will be useful for driving further effective action on the conservation of this species and in facilitating later assessments of the effects of the offshore windfarm on the dolphins by comparing the baseline to post construction and post mitigation efforts.

Passive Acoustic Monitoring the Diel, Lunar, Seasonal and Tidal Patterns in the Biosonar Activity of the Indo-Pacific Humpback Dolphins (Sousa chinensis) in the Pearl River Estuary, China

PubMed Central

Wang, Zhi-Tao; Nachtigall, Paul E.; Akamatsu, Tomonari; Wang, Ke-Xiong; Wu, Yu-Ping; Liu, Jian-Chang; Duan, Guo-Qin; Cao, Han-Jiang; Wang, Ding

2015-01-01

A growing demand for sustainable energy has led to an increase in construction of offshore windfarms. Guishan windmill farm will be constructed in the Pearl River Estuary, China, which sustains the world’s largest known population of Indo-Pacific humpback dolphins (Sousa chinensis). Dolphin conservation is an urgent issue in this region. By using passive acoustic monitoring, a baseline distribution of data on this species in the Pearl River Estuary during pre-construction period had been collected. Dolphin biosonar detection and its diel, lunar, seasonal and tidal patterns were examined using a Generalized Linear Model. Significant higher echolocation detections at night than during the day, in winter-spring than in summer-autumn, at high tide than at flood tide were recognized. Significant higher echolocation detections during the new moon were recognized at night time. The diel, lunar and seasonal patterns for the echolocation encounter duration also significantly varied. These patterns could be due to the spatial-temporal variability of dolphin prey and illumination conditions. The baseline information will be useful for driving further effective action on the conservation of this species and in facilitating later assessments of the effects of the offshore windfarm on the dolphins by comparing the baseline to post construction and post mitigation efforts. PMID:26580966

Patterns of glaucomatous visual field loss in sita fields automatically identified using independent component analysis.

PubMed

Goldbaum, Michael H; Jang, Gil-Jin; Bowd, Chris; Hao, Jiucang; Zangwill, Linda M; Liebmann, Jeffrey; Girkin, Christopher; Jung, Tzyy-Ping; Weinreb, Robert N; Sample, Pamela A

2009-12-01

To determine if the patterns uncovered with variational Bayesian-independent component analysis-mixture model (VIM) applied to a large set of normal and glaucomatous fields obtained with the Swedish Interactive Thresholding Algorithm (SITA) are distinct, recognizable, and useful for modeling the severity of the field loss. SITA fields were obtained with the Humphrey Visual Field Analyzer (Carl Zeiss Meditec, Inc, Dublin, California) on 1,146 normal eyes and 939 glaucoma eyes from subjects followed by the Diagnostic Innovations in Glaucoma Study and the African Descent and Glaucoma Evaluation Study. VIM modifies independent component analysis (ICA) to develop separate sets of ICA axes in the cluster of normal fields and the 2 clusters of abnormal fields. Of 360 models, the model with the best separation of normal and glaucomatous fields was chosen for creating the maximally independent axes. Grayscale displays of fields generated by VIM on each axis were compared. SITA fields most closely associated with each axis and displayed in grayscale were evaluated for consistency of pattern at all severities. The best VIM model had 3 clusters. Cluster 1 (1,193) was mostly normal (1,089, 95% specificity) and had 2 axes. Cluster 2 (596) contained mildly abnormal fields (513) and 2 axes; cluster 3 (323) held mostly moderately to severely abnormal fields (322) and 5 axes. Sensitivity for clusters 2 and 3 combined was 88.9%. The VIM-generated field patterns differed from each other and resembled glaucomatous defects (eg, nasal step, arcuate, temporal wedge). SITA fields assigned to an axis resembled each other and the VIM-generated patterns for that axis. Pattern severity increased in the positive direction of each axis by expansion or deepening of the axis pattern. VIM worked well on SITA fields, separating them into distinctly different yet recognizable patterns of glaucomatous field defects. The axis and pattern properties make VIM a good candidate as a preliminary process for detecting progression.

Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data

PubMed Central

Liu, Yu; Sui, Zhengwei; Kang, Chaogui; Gao, Yong

2014-01-01

The article revisits spatial interaction and distance decay from the perspective of human mobility patterns and spatially-embedded networks based on an empirical data set. We extract nationwide inter-urban movements in China from a check-in data set that covers half a million individuals within 370 cities to analyze the underlying patterns of trips and spatial interactions. By fitting the gravity model, we find that the observed spatial interactions are governed by a power law distance decay effect. The obtained gravity model also closely reproduces the exponential trip displacement distribution. The movement of an individual, however, may not obey the same distance decay effect, leading to an ecological fallacy. We also construct a spatial network where the edge weights denote the interaction strengths. The communities detected from the network are spatially cohesive and roughly consistent with province boundaries. We attribute this pattern to different distance decay parameters between intra-province and inter-province trips. PMID:24465849

Uncovering patterns of inter-urban trip and spatial interaction from social media check-in data.

PubMed

Liu, Yu; Sui, Zhengwei; Kang, Chaogui; Gao, Yong

2014-01-01

The article revisits spatial interaction and distance decay from the perspective of human mobility patterns and spatially-embedded networks based on an empirical data set. We extract nationwide inter-urban movements in China from a check-in data set that covers half a million individuals within 370 cities to analyze the underlying patterns of trips and spatial interactions. By fitting the gravity model, we find that the observed spatial interactions are governed by a power law distance decay effect. The obtained gravity model also closely reproduces the exponential trip displacement distribution. The movement of an individual, however, may not obey the same distance decay effect, leading to an ecological fallacy. We also construct a spatial network where the edge weights denote the interaction strengths. The communities detected from the network are spatially cohesive and roughly consistent with province boundaries. We attribute this pattern to different distance decay parameters between intra-province and inter-province trips.

Results from a model-independent method of monitoring a geodetic network for patterns of transient deformation

NASA Technical Reports Server (NTRS)

Hurst, Kenneth; Granat, Robert

2005-01-01

We have implmented two multi-station detectors for transient crustal deformation within the Southern California Integrated GPS (SCGIN). One the the primary goals of SCIGN is to detect transient deformation associated with the earthquake cycle in Southern California.

Use of space-time models to investigate the stability of patterns of disease.

PubMed

Abellan, Juan Jose; Richardson, Sylvia; Best, Nicky

2008-08-01

The use of Bayesian hierarchical spatial models has become widespread in disease mapping and ecologic studies of health-environment associations. In this type of study, the data are typically aggregated over an extensive time period, thus neglecting the time dimension. The output of purely spatial disease mapping studies is therefore the average spatial pattern of risk over the period analyzed, but the results do not inform about, for example, whether a high average risk was sustained over time or changed over time. We investigated how including the time dimension in disease-mapping models strengthens the epidemiologic interpretation of the overall pattern of risk. We discuss a class of Bayesian hierarchical models that simultaneously characterize and estimate the stable spatial and temporal patterns as well as departures from these stable components. We show how useful rules for classifying areas as stable can be constructed based on the posterior distribution of the space-time interactions. We carry out a simulation study to investigate the sensitivity and specificity of the decision rules we propose, and we illustrate our approach in a case study of congenital anomalies in England. Our results confirm that extending hierarchical disease-mapping models to models that simultaneously consider space and time leads to a number of benefits in terms of interpretation and potential for detection of localized excesses.

Summary of and factors affecting pesticide concentrations in streams and shallow wells of the lower Susquehanna River basin, Pennsylvania and Maryland, 1993-95

USGS Publications Warehouse

Hainly, Robert A.; Zimmerman, Tammy M.; Loper, Connie A.; Lindsey, Bruce D.

2001-01-01

This report presents the detection frequency of 83 analyzed pesticides, describes the concentrations of those pesticides measured in water from streams and shallow wells, and presents conceptual models of the major factors affecting seasonal and areal patterns of pesticide concentrations in water from streams and shallow wells in the Lower Susquehanna River Basin. Seasonal and areal patterns of pesticide concentrations were observed in 577 samples and nearly 40,000 pesticide analyses collected from 155 stream sites and 169 shallow wells from 1993 to 1995. For this study, shallow wells were defined as those generally less than 200 feet deep.The most commonly detected pesticides were agricultural herbicides?atrazine, metolachlor, simazine, prometon, alachlor, and cyanazine. Atrazine and metolachlor are the two most-used agricultural pesticides in the Lower Susquehanna River Basin. Atrazine was detected in 92 percent of all the samples and in 98 percent of the stream samples. Metolachlor was detected in 83 percent of all the samples and in 95 percent of the stream samples. Nearly half of all the analyzed pesticides were not detected in any sample. Of the 45 pesticides that were detected at least once, the median concentrations of 39 of the pesticides were less than the detection limit for the individual compounds, indicating that for at least 50 percent of the samples collected, those pesticides were not detected. Only 10 (less than 0.025 percent) of the measured concentrations exceeded any established drinking-water standards; 25 concentrations exceeded 2 mg/L (micrograms per liter) and 55 concentrations exceeded 1 mg/L. None of the elevated concentrations were measured in samples collected from streams that are used for public drinking-water supplies, and 8 of the 10 were measured in storm-affected samples.The timing and rate of agricultural pesticide applications affect the seasonal and areal concentration patterns of atrazine, simazine, chlorpyrifos, and diazinon observed in water from wells and streams in the Lower Susquehanna River Basin. Average annual pesticide use for agricultural purposes and nonagricultural pesticide use indicators were used to explain seasonal and areal patterns. Elevated concentrations of some pesticides in streams during base-flow and storm-affected conditions were related to the seasonality of agricultural-use applications and local climate conditions. Agricultural-use patterns affected areal concentration patterns for the high-use pesticides, but indicators of nonagricultural use were needed to explain concentration patterns of pesticides with smaller amounts used for agricultural purposes.Bedrock type influences the movement and discharge of ground water, which in turn affects concentration patterns of pesticides. The ratio of atrazine concentrations in stream base flow to concentrations in shallow wells varied among the different general rock types found in the Lower Susquehanna River Basin. Median concentrations of atrazine in well water and stream base flow tended to be similar in individual areas underlain by carbonate bedrock, indicating the connectivity of water in streams and shallow wells in these areas. In areas underlain by noncarbonate bedrock, median concentrations of atrazine tended to be significantly higher in stream base flow than in well water. This suggests a deep ground-water system that delivers water to shallow wells and a near-surficial system that supplies base-flow water to streams. In addition to the presence or absence of carbonate bedrock, pesticide leaching potential and persistence, soil infiltration capacity, and agricultural land use affected areal patterns in detection frequency and concentration differences between samples collected from streams during base-flow conditions and shallow wells.

The effect of narrow-band noise maskers on increment detection1

PubMed Central

Messersmith, Jessica J.; Patra, Harisadhan; Jesteadt, Walt

2010-01-01

It is often assumed that listeners detect an increment in the intensity of a pure tone by detecting an increase in the energy falling within the critical band centered on the signal frequency. A noise masker can be used to limit the use of signal energy falling outside of the critical band, but facets of the noise may impact increment detection beyond this intended purpose. The current study evaluated the impact of envelope fluctuation in a noise masker on thresholds for detection of an increment. Thresholds were obtained for detection of an increment in the intensity of a 0.25- or 4-kHz pedestal in quiet and in the presence of noise of varying bandwidth. Results indicate that thresholds for detection of an increment in the intensity of a pure tone increase with increasing bandwidth for an on-frequency noise masker, but are unchanged by an off-frequency noise masker. Neither a model that includes a modulation-filter-bank analysis of envelope modulation nor a model based on discrimination of spectral patterns can account for all aspects of the observed data. PMID:21110593

Discrete analysis of spatial-sensitivity models

NASA Technical Reports Server (NTRS)

Nielsen, Kenneth R. K.; Wandell, Brian A.

1988-01-01

Procedures for reducing the computational burden of current models of spatial vision are described, the simplifications being consistent with the prediction of the complete model. A method for using pattern-sensitivity measurements to estimate the initial linear transformation is also proposed which is based on the assumption that detection performance is monotonic with the vector length of the sensor responses. It is shown how contrast-threshold data can be used to estimate the linear transformation needed to characterize threshold performance.

In Vivo Measurement of Glenohumeral Joint Contact Patterns

NASA Astrophysics Data System (ADS)

Bey, Michael J.; Kline, Stephanie K.; Zauel, Roger; Kolowich, Patricia A.; Lock, Terrence R.

2009-12-01

The objectives of this study were to describe a technique for measuring in-vivo glenohumeral joint contact patterns during dynamic activities and to demonstrate application of this technique. The experimental technique calculated joint contact patterns by combining CT-based 3D bone models with joint motion data that were accurately measured from biplane x-ray images. Joint contact patterns were calculated for the repaired and contralateral shoulders of 20 patients who had undergone rotator cuff repair. Significant differences in joint contact patterns were detected due to abduction angle and shoulder condition (i.e., repaired versus contralateral). Abduction angle had a significant effect on the superior/inferior contact center position, with the average joint contact center of the repaired shoulder 12.1% higher on the glenoid than the contralateral shoulder. This technique provides clinically relevant information by calculating in-vivo joint contact patterns during dynamic conditions and overcomes many limitations associated with conventional techniques for quantifying joint mechanics.

An Adaptive Sensor Mining Framework for Pervasive Computing Applications

NASA Astrophysics Data System (ADS)

Rashidi, Parisa; Cook, Diane J.

Analyzing sensor data in pervasive computing applications brings unique challenges to the KDD community. The challenge is heightened when the underlying data source is dynamic and the patterns change. We introduce a new adaptive mining framework that detects patterns in sensor data, and more importantly, adapts to the changes in the underlying model. In our framework, the frequent and periodic patterns of data are first discovered by the Frequent and Periodic Pattern Miner (FPPM) algorithm; and then any changes in the discovered patterns over the lifetime of the system are discovered by the Pattern Adaptation Miner (PAM) algorithm, in order to adapt to the changing environment. This framework also captures vital context information present in pervasive computing applications, such as the startup triggers and temporal information. In this paper, we present a description of our mining framework and validate the approach using data collected in the CASAS smart home testbed.

Automated phenotype pattern recognition of zebrafish for high-throughput screening.

PubMed

Schutera, Mark; Dickmeis, Thomas; Mione, Marina; Peravali, Ravindra; Marcato, Daniel; Reischl, Markus; Mikut, Ralf; Pylatiuk, Christian

2016-07-03

Over the last years, the zebrafish (Danio rerio) has become a key model organism in genetic and chemical screenings. A growing number of experiments and an expanding interest in zebrafish research makes it increasingly essential to automatize the distribution of embryos and larvae into standard microtiter plates or other sample holders for screening, often according to phenotypical features. Until now, such sorting processes have been carried out by manually handling the larvae and manual feature detection. Here, a prototype platform for image acquisition together with a classification software is presented. Zebrafish embryos and larvae and their features such as pigmentation are detected automatically from the image. Zebrafish of 4 different phenotypes can be classified through pattern recognition at 72 h post fertilization (hpf), allowing the software to classify an embryo into 2 distinct phenotypic classes: wild-type versus variant. The zebrafish phenotypes are classified with an accuracy of 79-99% without any user interaction. A description of the prototype platform and of the algorithms for image processing and pattern recognition is presented.

Automated Detection of Selective Logging in Amazon Forests Using Airborne Lidar Data and Pattern Recognition Algorithms

NASA Astrophysics Data System (ADS)

Keller, M. M.; d'Oliveira, M. N.; Takemura, C. M.; Vitoria, D.; Araujo, L. S.; Morton, D. C.

2012-12-01

Selective logging, the removal of several valuable timber trees per hectare, is an important land use in the Brazilian Amazon and may degrade forests through long term changes in structure, loss of forest carbon and species diversity. Similar to deforestation, the annual area affected by selected logging has declined significantly in the past decade. Nonetheless, this land use affects several thousand km2 per year in Brazil. We studied a 1000 ha area of the Antimary State Forest (FEA) in the State of Acre, Brazil (9.304 ○S, 68.281 ○W) that has a basal area of 22.5 m2 ha-1 and an above-ground biomass of 231 Mg ha-1. Logging intensity was low, approximately 10 to 15 m3 ha-1. We collected small-footprint airborne lidar data using an Optech ALTM 3100EA over the study area once each in 2010 and 2011. The study area contained both recent and older logging that used both conventional and technologically advanced logging techniques. Lidar return density averaged over 20 m-2 for both collection periods with estimated horizontal and vertical precision of 0.30 and 0.15 m. A relative density model comparing returns from 0 to 1 m elevation to returns in 1-5 m elevation range revealed the pattern of roads and skid trails. These patterns were confirmed by ground-based GPS survey. A GIS model of the road and skid network was built using lidar and ground data. We tested and compared two pattern recognition approaches used to automate logging detection. Both segmentation using commercial eCognition segmentation and a Frangi filter algorithm identified the road and skid trail network compared to the GIS model. We report on the effectiveness of these two techniques.

Modulation cues influence binaural masking-level difference in masking-pattern experiments.

PubMed

Nitschmann, Marc; Verhey, Jesko L

2012-03-01

Binaural masking patterns show a steep decrease in the binaural masking-level difference (BMLD) when masker and signal have no frequency component in common. Experimental threshold data are presented together with model simulations for a diotic masker centered at 250 or 500 Hz and a bandwidth of 10 or 100 Hz masking a sinusoid interaurally in phase (S(0)) or in antiphase (S(π)). Simulations with a binaural model, including a modulation filterbank for the monaural analysis, indicate that a large portion of the decrease in the BMLD in remote-masking conditions may be due to an additional modulation cue available for monaural detection. © 2012 Acoustical Society of America

Monkeys and Humans Share a Common Computation for Face/Voice Integration

PubMed Central

Chandrasekaran, Chandramouli; Lemus, Luis; Trubanova, Andrea; Gondan, Matthias; Ghazanfar, Asif A.

2011-01-01

Speech production involves the movement of the mouth and other regions of the face resulting in visual motion cues. These visual cues enhance intelligibility and detection of auditory speech. As such, face-to-face speech is fundamentally a multisensory phenomenon. If speech is fundamentally multisensory, it should be reflected in the evolution of vocal communication: similar behavioral effects should be observed in other primates. Old World monkeys share with humans vocal production biomechanics and communicate face-to-face with vocalizations. It is unknown, however, if they, too, combine faces and voices to enhance their perception of vocalizations. We show that they do: monkeys combine faces and voices in noisy environments to enhance their detection of vocalizations. Their behavior parallels that of humans performing an identical task. We explored what common computational mechanism(s) could explain the pattern of results we observed across species. Standard explanations or models such as the principle of inverse effectiveness and a “race” model failed to account for their behavior patterns. Conversely, a “superposition model”, positing the linear summation of activity patterns in response to visual and auditory components of vocalizations, served as a straightforward but powerful explanatory mechanism for the observed behaviors in both species. As such, it represents a putative homologous mechanism for integrating faces and voices across primates. PMID:21998576

Feature-space-based FMRI analysis using the optimal linear transformation.

PubMed

Sun, Fengrong; Morris, Drew; Lee, Wayne; Taylor, Margot J; Mills, Travis; Babyn, Paul S

2010-09-01

The optimal linear transformation (OLT), an image analysis technique of feature space, was first presented in the field of MRI. This paper proposes a method of extending OLT from MRI to functional MRI (fMRI) to improve the activation-detection performance over conventional approaches of fMRI analysis. In this method, first, ideal hemodynamic response time series for different stimuli were generated by convolving the theoretical hemodynamic response model with the stimulus timing. Second, constructing hypothetical signature vectors for different activity patterns of interest by virtue of the ideal hemodynamic responses, OLT was used to extract features of fMRI data. The resultant feature space had particular geometric clustering properties. It was then classified into different groups, each pertaining to an activity pattern of interest; the applied signature vector for each group was obtained by averaging. Third, using the applied signature vectors, OLT was applied again to generate fMRI composite images with high SNRs for the desired activity patterns. Simulations and a blocked fMRI experiment were employed for the method to be verified and compared with the general linear model (GLM)-based analysis. The simulation studies and the experimental results indicated the superiority of the proposed method over the GLM-based analysis in detecting brain activities.

Deformation patterns in the southwestern part of the Mediterranean Ridge (South Matapan Trench, Western Greece)

NASA Astrophysics Data System (ADS)

Andronikidis, Nikolaos; Kokinou, Eleni; Vafidis, Antonios; Kamberis, Evangelos; Manoutsoglou, Emmanouil

2017-12-01

Seismic reflection data and bathymetry analyses, together with geological information, are combined in the present work to identify seabed structural deformation and crustal structure in the Western Mediterranean Ridge (the backstop and the South Matapan Trench). As a first step, we apply bathymetric data and state of art methods of pattern recognition to automatically detect seabed lineaments, which are possibly related to the presence of tectonic structures (faults). The resulting pattern is tied to seismic reflection data, further assisting in the construction of a stratigraphic and structural model for this part of the Mediterranean Ridge. Structural elements and stratigraphic units in the final model are estimated based on: (a) the detected lineaments on the seabed, (b) the distribution of the interval velocities and the presence of velocity inversions, (c) the continuity and the amplitudes of the seismic reflections, the seismic structure of the units and (d) well and stratigraphic data as well as the main tectonic structures from the nearest onshore areas. Seabed morphology in the study area is probably related with the past and recent tectonics movements that result from African and European plates' convergence. Backthrusts and reverse faults, flower structures and deep normal faults are among the most important extensional/compressional structures interpreted in the study area.

Performance on perceptual word identification is mediated by discrete states.

PubMed

Swagman, April R; Province, Jordan M; Rouder, Jeffrey N

2015-02-01

We contrast predictions from discrete-state models of all-or-none information loss with signal-detection models of graded strength for the identification of briefly flashed English words. Previous assessments have focused on whether ROC curves are straight or not, which is a test of a discrete-state model where detection leads to the highest confidence response with certainty. We along with many others argue this certainty assumption is too constraining, and, consequently, the straight-line ROC test is too stringent. Instead, we assess a core property of discrete-state models, conditional independence, where the pattern of responses depends only on which state is entered. The conditional independence property implies that confidence ratings are a mixture of detect and guess state responses, and that stimulus strength factors, the duration of the flashed word in this report, affect only the probability of entering a state and not responses conditional on a state. To assess this mixture property, 50 participants saw words presented briefly on a computer screen at three variable flash durations followed by either a two-alternative confidence ratings task or a yes-no confidence ratings task. Comparable discrete-state and signal-detection models were fit to the data for each participant and task. The discrete-state models outperformed the signal detection models for 90 % of participants in the two-alternative task and for 68 % of participants in the yes-no task. We conclude discrete-state models are viable for predicting performance across stimulus conditions in a perceptual word identification task.

Trend estimation in populations with imperfect detection

USGS Publications Warehouse

Kery, Marc; Dorazio, Robert M.; Soldaat, Leo; Van Strien, Arco; Zuiderwijk, Annie; Royle, J. Andrew

2009-01-01

1. Trends of animal populations are of great interest in ecology but cannot be directly observed owing to imperfect detection. Binomial mixture models use replicated counts to estimate abundance, corrected for detection, in demographically closed populations. Here, we extend these models to open populations and illustrate them using sand lizard Lacerta agilis counts from the national Dutch reptile monitoring scheme. 2. Our model requires replicated counts from multiple sites in each of several periods, within which population closure is assumed. Counts are described by a hierarchical generalized linear model, where the state model deals with spatio-temporal patterns in true abundance and the observation model with imperfect counts, given that true state. We used WinBUGS to fit the model to lizard counts from 208 transects with 1–10 (mean 3) replicate surveys during each spring 1994–2005. 3. Our state model for abundance contained two independent log-linear Poisson regressions on year for coastal and inland sites, and random site effects to account for unexplained heterogeneity. The observation model for detection of an individual lizard contained effects of region, survey date, temperature, observer experience and random survey effects. 4. Lizard populations increased in both regions but more steeply on the coast. Detectability increased over the first few years of the study, was greater on the coast and for the most experienced observers, and highest around 1 June. Interestingly, the population increase inland was not detectable when the observed counts were analysed without account of detectability. The proportional increase between 1994 and 2005 in total lizard abundance across all sites was estimated at 86% (95% CRI 35–151). 5. Synthesis and applications. Open-population binomial mixture models are attractive for studying true population dynamics while explicitly accounting for the observation process, i.e. imperfect detection. We emphasize the important conceptual benefit provided by temporal replicate observations in terms of the interpretability of animal counts.

Seeing the Errors You Feel Enhances Locomotor Performance but Not Learning.

PubMed

Roemmich, Ryan T; Long, Andrew W; Bastian, Amy J

2016-10-24

In human motor learning, it is thought that the more information we have about our errors, the faster we learn. Here, we show that additional error information can lead to improved motor performance without any concomitant improvement in learning. We studied split-belt treadmill walking that drives people to learn a new gait pattern using sensory prediction errors detected by proprioceptive feedback. When we also provided visual error feedback, participants acquired the new walking pattern far more rapidly and showed accelerated restoration of the normal walking pattern during washout. However, when the visual error feedback was removed during either learning or washout, errors reappeared with performance immediately returning to the level expected based on proprioceptive learning alone. These findings support a model with two mechanisms: a dual-rate adaptation process that learns invariantly from sensory prediction error detected by proprioception and a visual-feedback-dependent process that monitors learning and corrects residual errors but shows no learning itself. We show that our voluntary correction model accurately predicted behavior in multiple situations where visual feedback was used to change acquisition of new walking patterns while the underlying learning was unaffected. The computational and behavioral framework proposed here suggests that parallel learning and error correction systems allow us to rapidly satisfy task demands without necessarily committing to learning, as the relative permanence of learning may be inappropriate or inefficient when facing environments that are liable to change. Copyright © 2016 Elsevier Ltd. All rights reserved.

Monitoring survival rates of landbirds at varying spatial scales: An application of the MAPS Program

USGS Publications Warehouse

Rosenberg, D.K.; DeSante, D.F.; Hines, J.E.; Bonney, Rick; Pashley, David N.; Cooper, Robert; Niles, Larry

2000-01-01

Survivorship is a primary demographic parameter affecting population dynamics, and thus trends in species abundance. The Monitoring Avian Productivity and Survivorship (MAPS) program is a cooperative effort designed to monitor landbird demographic parameters. A principle goal of MAPS is to estimate annual survivorship and identify spatial patterns and temporal trends in these rates. We evaluated hypotheses of spatial patterns in survival rates among a collection of neighboring sampling sites, such as within national forests, among biogeographic provinces, and between breeding populations that winter in either Central or South America, and compared these geographic-specific models to a model of a common survival rate among all sampling sites. We used data collected during 1992-1995 from Swainson's Thrush (Cathorus ustulatus) populations in the western region of the United States. We evaluated the ability to detect spatial and temporal patterns of survivorship with simulated data. We found weak evidence of spatial differences in survival rates at the local scale of 'location,' which typically contained 3 mist-netting stations. There was little evidence of differences in survival rates among biogeographic provinces or between populations that winter in either Central or South America. When data were pooled for a regional estimate of survivorship, the percent relative bias due to pooling 'locations' was 12 years of monitoring. Detection of spatial patterns and temporal trends in survival rates from local to regional scales will provide important information for management and future research directed toward conservation of landbirds.

Persistence rates and detection probabilities of oiled king eider carcasses on St Paul Island, Alaska

USGS Publications Warehouse

Fowler, A.C.; Flint, Paul L.

1997-01-01

Following an oil spill off St Paul Island, Alaska in February 1996, persistence rates and detection probabilities of oiled king eider (Somateria spectabilis) carcasses were estimated using the Cormack-Jolly-Seber model. Carcass persistence rates varied by day, beach type and sex, while detection probabilities varied by day and beach type. Scavenging, wave action and weather influenced carcass persistence. The patterns of persistence differed on rock and sand beaches and female carcasses had a different persistence function than males. Weather, primarily snow storms, and degree of carcass scavenging, diminished carcass detectability. Detection probabilities on rock beaches were lower and more variable than on sand beaches. The combination of persistence rates and detection probabilities can be used to improve techniques of estimating total mortality.

Local orientation and the evolution of foraging: changes in decision making can eliminate evolutionary trade-offs.

PubMed

van der Post, Daniel J; Semmann, Dirk

2011-10-01

Information processing is a major aspect of the evolution of animal behavior. In foraging, responsiveness to local feeding opportunities can generate patterns of behavior which reflect or "recognize patterns" in the environment beyond the perception of individuals. Theory on the evolution of behavior generally neglects such opportunity-based adaptation. Using a spatial individual-based model we study the role of opportunity-based adaptation in the evolution of foraging, and how it depends on local decision making. We compare two model variants which differ in the individual decision making that can evolve (restricted and extended model), and study the evolution of simple foraging behavior in environments where food is distributed either uniformly or in patches. We find that opportunity-based adaptation and the pattern recognition it generates, plays an important role in foraging success, particularly in patchy environments where one of the main challenges is "staying in patches". In the restricted model this is achieved by genetic adaptation of move and search behavior, in light of a trade-off on within- and between-patch behavior. In the extended model this trade-off does not arise because decision making capabilities allow for differentiated behavioral patterns. As a consequence, it becomes possible for properties of movement to be specialized for detection of patches with more food, a larger scale information processing not present in the restricted model. Our results show that changes in decision making abilities can alter what kinds of pattern recognition are possible, eliminate an evolutionary trade-off and change the adaptive landscape.

Robust Curb Detection with Fusion of 3D-Lidar and Camera Data

PubMed Central

Tan, Jun; Li, Jian; An, Xiangjing; He, Hangen

2014-01-01

Curb detection is an essential component of Autonomous Land Vehicles (ALV), especially important for safe driving in urban environments. In this paper, we propose a fusion-based curb detection method through exploiting 3D-Lidar and camera data. More specifically, we first fuse the sparse 3D-Lidar points and high-resolution camera images together to recover a dense depth image of the captured scene. Based on the recovered dense depth image, we propose a filter-based method to estimate the normal direction within the image. Then, by using the multi-scale normal patterns based on the curb's geometric property, curb point features fitting the patterns are detected in the normal image row by row. After that, we construct a Markov Chain to model the consistency of curb points which utilizes the continuous property of the curb, and thus the optimal curb path which links the curb points together can be efficiently estimated by dynamic programming. Finally, we perform post-processing operations to filter the outliers, parameterize the curbs and give the confidence scores on the detected curbs. Extensive evaluations clearly show that our proposed method can detect curbs with strong robustness at real-time speed for both static and dynamic scenes. PMID:24854364

Exploring the Distance Education Students' Cyberbullying, Cybervictimization and Cyberbullying Sensibility Levels

ERIC Educational Resources Information Center

Ozgur, Hasan

2015-01-01

The purpose of present research is to detect cyberbullying, cybervictimization and cyberbullying sensibility levels of distance education students and analyze these levels with respect to several variables. The research has been patterned on relational screening model. Study group consisted of 297 distance education students studying at university…

Automated image based prominent nucleoli detection

PubMed Central

Yap, Choon K.; Kalaw, Emarene M.; Singh, Malay; Chong, Kian T.; Giron, Danilo M.; Huang, Chao-Hui; Cheng, Li; Law, Yan N.; Lee, Hwee Kuan

2015-01-01

Introduction: Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection. Materials and Methods: Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues. Prostate cancer images were used for the development of a computer-based automated prominent nucleoli pattern detector built on a cascade farm. An ensemble of approximately 1000 cascades was constructed by permuting different combinations of classifiers such as support vector machines, eXclusive component analysis, boosting, and logistic regression. The output of cascades was then combined using the RankBoost algorithm. The output of our prominent nucleoli pattern detector is a ranked set of detected image patches of patterns of prominent nucleoli. Results: The mean number of detected prominent nucleoli patterns in the top 100 ranked detected objects was 58 in the prostate cancer dataset, 68 in the breast cancer dataset, 86 in the renal clear cell cancer dataset, and 76 in the renal papillary cell cancer dataset. The proposed cascade farm performs twice as good as the use of a single cascade proposed in the seminal paper by Viola and Jones. For comparison, a naive algorithm that randomly chooses a pixel as a nucleoli pattern would detect five correct patterns in the first 100 ranked objects. Conclusions: Detection of sparse nucleoli patterns in a large background of highly variable tissue patterns is a difficult challenge our method has overcome. This study developed an accurate prominent nucleoli pattern detector with the potential to be used in the clinical settings. PMID:26167383

Automated image based prominent nucleoli detection.

PubMed

Yap, Choon K; Kalaw, Emarene M; Singh, Malay; Chong, Kian T; Giron, Danilo M; Huang, Chao-Hui; Cheng, Li; Law, Yan N; Lee, Hwee Kuan

2015-01-01

Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection. Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues. Prostate cancer images were used for the development of a computer-based automated prominent nucleoli pattern detector built on a cascade farm. An ensemble of approximately 1000 cascades was constructed by permuting different combinations of classifiers such as support vector machines, eXclusive component analysis, boosting, and logistic regression. The output of cascades was then combined using the RankBoost algorithm. The output of our prominent nucleoli pattern detector is a ranked set of detected image patches of patterns of prominent nucleoli. The mean number of detected prominent nucleoli patterns in the top 100 ranked detected objects was 58 in the prostate cancer dataset, 68 in the breast cancer dataset, 86 in the renal clear cell cancer dataset, and 76 in the renal papillary cell cancer dataset. The proposed cascade farm performs twice as good as the use of a single cascade proposed in the seminal paper by Viola and Jones. For comparison, a naive algorithm that randomly chooses a pixel as a nucleoli pattern would detect five correct patterns in the first 100 ranked objects. Detection of sparse nucleoli patterns in a large background of highly variable tissue patterns is a difficult challenge our method has overcome. This study developed an accurate prominent nucleoli pattern detector with the potential to be used in the clinical settings.

Caenorhabditis elegans vulval cell fate patterning

NASA Astrophysics Data System (ADS)

Félix, Marie-Anne

2012-08-01

The spatial patterning of three cell fates in a row of competent cells is exemplified by vulva development in the nematode Caenorhabditis elegans. The intercellular signaling network that underlies fate specification is well understood, yet quantitative aspects remain to be elucidated. Quantitative models of the network allow us to test the effect of parameter variation on the cell fate pattern output. Among the parameter sets that allow us to reach the wild-type pattern, two general developmental patterning mechanisms of the three fates can be found: sequential inductions and morphogen-based induction, the former being more robust to parameter variation. Experimentally, the vulval cell fate pattern is robust to stochastic and environmental challenges, and minor variants can be detected. The exception is the fate of the anterior cell, P3.p, which is sensitive to stochastic variation and spontaneous mutation, and is also evolving the fastest. Other vulval precursor cell fates can be affected by mutation, yet little natural variation can be found, suggesting stabilizing selection. Despite this fate pattern conservation, different Caenorhabditis species respond differently to perturbations of the system. In the quantitative models, different parameter sets can reconstitute their response to perturbation, suggesting that network variation among Caenorhabditis species may be quantitative. Network rewiring likely occurred at longer evolutionary scales.

The ecology of parasites of freshwater fishes: the search for patterns.

PubMed

Kennedy, C R

2009-10-01

Developments in the study of the ecology of helminth parasites of freshwater fishes over the last half century are reviewed. Most research has of necessity been field based and has involved the search for patterns in population and community dynamics that are repeatable in space and time. Mathematical models predict that under certain conditions host and parasite populations can attain equilibrial levels through operation of regulatory factors. Such factors have been identified in several host-parasite systems and some parasite populations have been shown to persist over long time-periods. However, there is no convincing evidence that fish parasite populations are stable and regulated since in all cases alternative explanations are equally acceptable and it appears that they are non-equilibrial systems. It has proved particularly difficult to detect replicable patterns in parasite communities. Inter-specific competition, evidenced by functional and numerical responses, has been detected in several communities but its occurrence is erratic and its significance unclear. Some studies have failed to find any nested patterns in parasite community structure and richness, whereas others have identified such patterns although they are seldom constant over space and time. Departures from randomness appear to be the exception and then only temporary. It appears that parasite communities are non-equilibrial, stochastic assemblages rather than structured and organized.

Blood oxygen level dependent magnetic resonance imaging for detecting pathological patterns in lupus nephritis patients: a preliminary study using a decision tree model.

PubMed

Shi, Huilan; Jia, Junya; Li, Dong; Wei, Li; Shang, Wenya; Zheng, Zhenfeng

2018-02-09

Precise renal histopathological diagnosis will guide therapy strategy in patients with lupus nephritis. Blood oxygen level dependent (BOLD) magnetic resonance imaging (MRI) has been applicable noninvasive technique in renal disease. This current study was performed to explore whether BOLD MRI could contribute to diagnose renal pathological pattern. Adult patients with lupus nephritis renal pathological diagnosis were recruited for this study. Renal biopsy tissues were assessed based on the lupus nephritis ISN/RPS 2003 classification. The Blood oxygen level dependent magnetic resonance imaging (BOLD-MRI) was used to obtain functional magnetic resonance parameter, R2* values. Several functions of R2* values were calculated and used to construct algorithmic models for renal pathological patterns. In addition, the algorithmic models were compared as to their diagnostic capability. Both Histopathology and BOLD MRI were used to examine a total of twelve patients. Renal pathological patterns included five classes III (including 3 as class III + V) and seven classes IV (including 4 as class IV + V). Three algorithmic models, including decision tree, line discriminant, and logistic regression, were constructed to distinguish the renal pathological pattern of class III and class IV. The sensitivity of the decision tree model was better than that of the line discriminant model (71.87% vs 59.48%, P < 0.001) and inferior to that of the Logistic regression model (71.87% vs 78.71%, P < 0.001). The specificity of decision tree model was equivalent to that of the line discriminant model (63.87% vs 63.73%, P = 0.939) and higher than that of the logistic regression model (63.87% vs 38.0%, P < 0.001). The Area under the ROC curve (AUROCC) of the decision tree model was greater than that of the line discriminant model (0.765 vs 0.629, P < 0.001) and logistic regression model (0.765 vs 0.662, P < 0.001). BOLD MRI is a useful non-invasive imaging technique for the evaluation of lupus nephritis. Decision tree models constructed using functions of R2* values may facilitate the prediction of renal pathological patterns.

Estimating site occupancy and abundance using indirect detection indices

USGS Publications Warehouse

Stanley, T.R.; Royle, J. Andrew

2005-01-01

Knowledge of factors influencing animal distribution and abundance is essential in many areas of ecological research, management, and policy-making. Because common methods for modeling and estimating abundance (e.g., capture-recapture, distance sampling) are sometimes not practical for large areas or elusive species, indices are sometimes used as surrogate measures of abundance. We present an extension of the Royle and Nichols (2003) generalization of the MacKenzie et al. (2002) site-occupancy model that incorporates length of the sampling interval into the, model for detection probability. As a result, we obtain a modeling framework that shows how useful information can be extracted from a class of index methods we call indirect detection indices (IDIs). Examples of IDIs include scent station, tracking tube, snow track, tracking plate, and hair snare surveys. Our model is maximum likelihood, and it can be used to estimate site occupancy and model factors influencing patterns of occupancy and abundance in space. Under certain circumstances, it can also be used to estimate abundance. We evaluated model properties using Monte Carlo simulations and illustrate the method with tracking tube and scent station data. We believe this model will be a useful tool for determining factors that influence animal distribution and abundance.

Embedded security system for multi-modal surveillance in a railway carriage

NASA Astrophysics Data System (ADS)

Zouaoui, Rhalem; Audigier, Romaric; Ambellouis, Sébastien; Capman, François; Benhadda, Hamid; Joudrier, Stéphanie; Sodoyer, David; Lamarque, Thierry

2015-10-01

Public transport security is one of the main priorities of the public authorities when fighting against crime and terrorism. In this context, there is a great demand for autonomous systems able to detect abnormal events such as violent acts aboard passenger cars and intrusions when the train is parked at the depot. To this end, we present an innovative approach which aims at providing efficient automatic event detection by fusing video and audio analytics and reducing the false alarm rate compared to classical stand-alone video detection. The multi-modal system is composed of two microphones and one camera and integrates onboard video and audio analytics and fusion capabilities. On the one hand, for detecting intrusion, the system relies on the fusion of "unusual" audio events detection with intrusion detections from video processing. The audio analysis consists in modeling the normal ambience and detecting deviation from the trained models during testing. This unsupervised approach is based on clustering of automatically extracted segments of acoustic features and statistical Gaussian Mixture Model (GMM) modeling of each cluster. The intrusion detection is based on the three-dimensional (3D) detection and tracking of individuals in the videos. On the other hand, for violent events detection, the system fuses unsupervised and supervised audio algorithms with video event detection. The supervised audio technique detects specific events such as shouts. A GMM is used to catch the formant structure of a shout signal. Video analytics use an original approach for detecting aggressive motion by focusing on erratic motion patterns specific to violent events. As data with violent events is not easily available, a normality model with structured motions from non-violent videos is learned for one-class classification. A fusion algorithm based on Dempster-Shafer's theory analyses the asynchronous detection outputs and computes the degree of belief of each probable event.

Detecting Hidden Diversification Shifts in Models of Trait-Dependent Speciation and Extinction.

PubMed

Beaulieu, Jeremy M; O'Meara, Brian C

2016-07-01

The distribution of diversity can vary considerably from clade to clade. Attempts to understand these patterns often employ state-dependent speciation and extinction models to determine whether the evolution of a particular novel trait has increased speciation rates and/or decreased extinction rates. It is still unclear, however, whether these models are uncovering important drivers of diversification, or whether they are simply pointing to more complex patterns involving many unmeasured and co-distributed factors. Here we describe an extension to the popular state-dependent speciation and extinction models that specifically accounts for the presence of unmeasured factors that could impact diversification rates estimated for the states of any observed trait, addressing at least one major criticism of BiSSE (Binary State Speciation and Extinction) methods. Specifically, our model, which we refer to as HiSSE (Hidden State Speciation and Extinction), assumes that related to each observed state in the model are "hidden" states that exhibit potentially distinct diversification dynamics and transition rates than the observed states in isolation. We also demonstrate how our model can be used as character-independent diversification models that allow for a complex diversification process that is independent of the evolution of a character. Under rigorous simulation tests and when applied to empirical data, we find that HiSSE performs reasonably well, and can at least detect net diversification rate differences between observed and hidden states and detect when diversification rate differences do not correlate with the observed states. We discuss the remaining issues with state-dependent speciation and extinction models in general, and the important ways in which HiSSE provides a more nuanced understanding of trait-dependent diversification. © The Author(s) 2016. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Investigation of Skylab imagery for regional planning. [New York, New Jersey, and Connecticut

NASA Technical Reports Server (NTRS)

Harting, W. (Principal Investigator)

1975-01-01

The author has identified the following significant results. It is feasible to use earth terrain camera imagery to detect four land uses (vacant land, developed land, streets, and water) for general regional planning purposes. Multispectral imagery is suitable for detecting, mapping, and measuring water bodies as small as two acres. Sufficient information can be extracted to prepare graphic and pictorial representations of the general growth and development patterns, but cannot be incorporated into an inventory file for predictive models.

An Abstract Model of Rogue Code Insertion into Radio Frequency Wireless Networks. The Effects of Computer Viruses on the Program Management Office

DTIC Science & Technology

1994-04-01

numerous articles on wireless LANs, only one by Lathrop discusses their vulnerabilities’. Lathrop’s paper provides an overview of wireless LANs and...to detect any action which deviates from the user’s observed recorded past behavior. These profiles list the operator’s commonly used commands, typing...current system activity audit records to rules describing past behavior patterns. W&S is especially effective in detecting rogue program penetrations. It

Airplane wing deformation and flight flutter detection method by using three-dimensional speckle image correlation technology.

PubMed

Wu, Jun; Yu, Zhijing; Wang, Tao; Zhuge, Jingchang; Ji, Yue; Xue, Bin

2017-06-01

Airplane wing deformation is an important element of aerodynamic characteristics, structure design, and fatigue analysis for aircraft manufacturing, as well as a main test content of certification regarding flutter for airplanes. This paper presents a novel real-time detection method for wing deformation and flight flutter detection by using three-dimensional speckle image correlation technology. Speckle patterns whose positions are determined through the vibration characteristic of the aircraft are coated on the wing; then the speckle patterns are imaged by CCD cameras which are mounted inside the aircraft cabin. In order to reduce the computation, a matching technique based on Geodetic Systems Incorporated coded points combined with the classical epipolar constraint is proposed, and a displacement vector map for the aircraft wing can be obtained through comparing the coordinates of speckle points before and after deformation. Finally, verification experiments containing static and dynamic tests by using an aircraft wing model demonstrate the accuracy and effectiveness of the proposed method.

Occupancy models for monitoring marine fish: a bayesian hierarchical approach to model imperfect detection with a novel gear combination.

PubMed

Coggins, Lewis G; Bacheler, Nathan M; Gwinn, Daniel C

2014-01-01

Occupancy models using incidence data collected repeatedly at sites across the range of a population are increasingly employed to infer patterns and processes influencing population distribution and dynamics. While such work is common in terrestrial systems, fewer examples exist in marine applications. This disparity likely exists because the replicate samples required by these models to account for imperfect detection are often impractical to obtain when surveying aquatic organisms, particularly fishes. We employ simultaneous sampling using fish traps and novel underwater camera observations to generate the requisite replicate samples for occupancy models of red snapper, a reef fish species. Since the replicate samples are collected simultaneously by multiple sampling devices, many typical problems encountered when obtaining replicate observations are avoided. Our results suggest that augmenting traditional fish trap sampling with camera observations not only doubled the probability of detecting red snapper in reef habitats off the Southeast coast of the United States, but supplied the necessary observations to infer factors influencing population distribution and abundance while accounting for imperfect detection. We found that detection probabilities tended to be higher for camera traps than traditional fish traps. Furthermore, camera trap detections were influenced by the current direction and turbidity of the water, indicating that collecting data on these variables is important for future monitoring. These models indicate that the distribution and abundance of this species is more heavily influenced by latitude and depth than by micro-scale reef characteristics lending credence to previous characterizations of red snapper as a reef habitat generalist. This study demonstrates the utility of simultaneous sampling devices, including camera traps, in aquatic environments to inform occupancy models and account for imperfect detection when describing factors influencing fish population distribution and dynamics.

Occupancy Models for Monitoring Marine Fish: A Bayesian Hierarchical Approach to Model Imperfect Detection with a Novel Gear Combination

PubMed Central

Coggins, Lewis G.; Bacheler, Nathan M.; Gwinn, Daniel C.

2014-01-01

Occupancy models using incidence data collected repeatedly at sites across the range of a population are increasingly employed to infer patterns and processes influencing population distribution and dynamics. While such work is common in terrestrial systems, fewer examples exist in marine applications. This disparity likely exists because the replicate samples required by these models to account for imperfect detection are often impractical to obtain when surveying aquatic organisms, particularly fishes. We employ simultaneous sampling using fish traps and novel underwater camera observations to generate the requisite replicate samples for occupancy models of red snapper, a reef fish species. Since the replicate samples are collected simultaneously by multiple sampling devices, many typical problems encountered when obtaining replicate observations are avoided. Our results suggest that augmenting traditional fish trap sampling with camera observations not only doubled the probability of detecting red snapper in reef habitats off the Southeast coast of the United States, but supplied the necessary observations to infer factors influencing population distribution and abundance while accounting for imperfect detection. We found that detection probabilities tended to be higher for camera traps than traditional fish traps. Furthermore, camera trap detections were influenced by the current direction and turbidity of the water, indicating that collecting data on these variables is important for future monitoring. These models indicate that the distribution and abundance of this species is more heavily influenced by latitude and depth than by micro-scale reef characteristics lending credence to previous characterizations of red snapper as a reef habitat generalist. This study demonstrates the utility of simultaneous sampling devices, including camera traps, in aquatic environments to inform occupancy models and account for imperfect detection when describing factors influencing fish population distribution and dynamics. PMID:25255325

Collaborative mining and transfer learning for relational data

NASA Astrophysics Data System (ADS)

Levchuk, Georgiy; Eslami, Mohammed

2015-06-01

Many of the real-world problems, - including human knowledge, communication, biological, and cyber network analysis, - deal with data entities for which the essential information is contained in the relations among those entities. Such data must be modeled and analyzed as graphs, with attributes on both objects and relations encode and differentiate their semantics. Traditional data mining algorithms were originally designed for analyzing discrete objects for which a set of features can be defined, and thus cannot be easily adapted to deal with graph data. This gave rise to the relational data mining field of research, of which graph pattern learning is a key sub-domain [11]. In this paper, we describe a model for learning graph patterns in collaborative distributed manner. Distributed pattern learning is challenging due to dependencies between the nodes and relations in the graph, and variability across graph instances. We present three algorithms that trade-off benefits of parallelization and data aggregation, compare their performance to centralized graph learning, and discuss individual benefits and weaknesses of each model. Presented algorithms are designed for linear speedup in distributed computing environments, and learn graph patterns that are both closer to ground truth and provide higher detection rates than centralized mining algorithm.

The Ability of Atmospheric Data to Reduce Disagreements in Wetland Methane Flux Estimates over North America

NASA Astrophysics Data System (ADS)

Miller, S. M.; Andrews, A. E.; Benmergui, J. S.; Commane, R.; Dlugokencky, E. J.; Janssens-Maenhout, G.; Melton, J. R.; Michalak, A. M.; Sweeney, C.; Worthy, D. E. J.

2015-12-01

Existing estimates of methane fluxes from wetlands differ in both magnitude and distribution across North America. We discuss seven different bottom-up methane estimates in the context of atmospheric methane data collected across the US and Canada. In the first component of this study, we explore whether the observation network can even detect a methane pattern from wetlands. We find that the observation network can identify a methane pattern from Canadian wetlands but not reliably from US wetlands. Over Canada, the network can even identify spatial patterns at multi-provence scales. Over the US, by contrast, anthropogenic emissions and modeling errors obscure atmospheric patterns from wetland fluxes. In the second component of the study, we then use these observations to reconcile disagreements in the magnitude, seasonal cycle, and spatial distribution of existing estimates. Most existing estimates predict fluxes that are too large with a seasonal cycle that is too narrow. A model known as LPJ-Bern has a spatial distribution most consistent with atmospheric observations. By contrast, a spatially-constant model outperforms the distribution of most existing flux estimates across Canada. The results presented here provide several pathways to reduce disagreements among existing wetland flux estimates across North America.

Spatial Analysis of Land Subsidence and Flood Pattern Based on DInSAR Method in Sentinel Sar Imagery and Weighting Method in Geo-Hazard Parameters Combination in North Jakarta Region

NASA Astrophysics Data System (ADS)

Prasetyo, Y.; Yuwono, B. D.; Ramadhanis, Z.

2018-02-01

The reclamation program carried out in most cities in North Jakarta is directly adjacent to the Jakarta Bay. Beside this program, the density of population and development center in North Jakarta office has increased the need for underground water excessively. As a result of these things, land subsidence in North Jakarta area is relatively high and so intense. The research methodology was developed based on the method of remote sensing and geographic information systems, expected to describe the spatial correlation between the land subsidence and flood phenomenon in North Jakarta. The DInSAR (Differential Interferometric Synthetic Aperture Radar) method with satellite image data Radar (SAR Sentinel 1A) for the years 2015 to 2016 acquisitions was used in this research. It is intended to obtain a pattern of land subsidence in North Jakarta and then combined with flood patterns. For the preparation of flood threat zoning pattern, this research has been modeling in spatial technique based on a weighted parameter of rainfall, elevation, flood zones and land use. In the final result, we have obtained a flood hazard zonation models then do the overlap against DInSAR processing results. As a result of the research, Geo-hazard modelling has a variety results as: 81% of flood threat zones consist of rural area, 12% consists of un-built areas and 7% consists of water areas. Furthermore, the correlation of land subsidence to flood risk zone is divided into three levels of suitability with 74% in high class, 22% in medium class and 4% in low class. For the result of spatial correlation area between land subsidence and flood risk zone are 77% detected in rural area, 17% detected in un-built area and 6% detected in a water area. Whereas the research product is the geo-hazard maps in North Jakarta as the basis of the spatial correlation analysis between the land subsidence and flooding phenomena.double point.

Computational modeling and experimental characterization of bacterial microcolonies for rapid detection using light scattering

NASA Astrophysics Data System (ADS)

Bai, Nan

A label-free and nondestructive optical elastic forward light scattering method has been extended for the analysis of microcolonies for food-borne bacteria detection and identification. To understand the forward light scattering phenomenon, a model based on the scalar diffraction theory has been employed: a bacterial colony is considered as a biological spatial light modulator with amplitude and phase modulation to the incoming light, which continues to propagate to the far-field to form a distinct scattering 'fingerprint'. Numerical implementation via angular spectrum method (ASM) and Fresnel approximation have been carried out through Fast Fourier Transform (FFT) to simulate this optical model. Sampling criteria to achieve unbiased and un-aliased simulation results have been derived and the effects of violating these conditions have been studied. Diffraction patterns predicted by these two methods (ASM and Fresnel) have been compared to show their applicability to different simulation settings. Through the simulation work, the correlation between the colony morphology and its forward scattering pattern has been established to link the number of diffraction rings and the half cone angle with the diameter and the central height of the Gaussian-shaped colonies. In order to experimentally prove the correlation, a colony morphology analyzer has been built and used to characterize the morphology of different bacteria genera and investigate their growth dynamics. The experimental measurements have demonstrated the possibility of differentiating bacteria Salmonella, Listeria, Escherichia in their early growth stage (100˜500 µm) based on their phenotypic characteristics. This conclusion has important implications in microcolony detection, as most bacteria of our interest need much less incubation time (8˜12 hours) to grow into this size range. The original forward light scatterometer has been updated to capture scattering patterns from microcolonies. Experiments have been performed to reveal the time dependent nature of scattering patterns. The experimental work has been compared with simulation results and demonstrated the feasibility of extending this technique for microcolony identification. Lastly, a quantitative phase imaging technique based on the phase gradient driven intensity variation has been studied and implemented to render the 2D phase map of the colony sample.

Prediction of the light scattering patterns from bacteria colonies by a time-resolved reaction-diffusion model and the scalar diffraction theory

NASA Astrophysics Data System (ADS)

Bae, Euiwon; Bai, Nan; Aroonnual, Amornrat; Bhunia, Arun K.; Robinson, J. Paul; Hirleman, E. Daniel

2009-05-01

In order to maximize the utility of the optical scattering technology in the area of bacterial colony identification, it is necessary to have a thorough understanding of how bacteria species grow into different morphological aggregation and subsequently function as distinctive optical amplitude and phase modulators to alter the incoming Gaussian laser beam. In this paper, a 2-dimentional reaction-diffusion (RD) model with nutrient concentration, diffusion coefficient, and agar hardness as variables is investigated to explain the correlation between the various environmental parameters and the distinctive morphological aggregations formed by different bacteria species. More importantly, the morphological change of the bacterial colony against time is demonstrated by this model, which is able to characterize the spatio-temporal patterns formed by the bacteria colonies over their entire growth curve. The bacteria population density information obtained from the RD model is mathematically converted to the amplitude/phase modulation factor used in the scalar diffraction theory which predicts the light scattering patterns for bacterial colonies. The conclusions drawn from the RD model combined with the scalar diffraction theory are useful in guiding the design of the optical scattering instrument aiming at bacteria colony detection and classification.

Detecting Submicron Pattern Defects On Optical Photomasks Using An Enhanced El-3 Electron-Beam Lithography Tool

NASA Astrophysics Data System (ADS)

Simpson, R. A.; Davis, D. E.

1982-09-01

This paper describes techniques to detect submicron pattern defects on optical photomasks with an enhanced direct-write, electron-beam lithographic tool. EL-3 is a third generation, shaped spot, electron-beam lithography tool developed by IBM to fabricate semiconductor devices and masks. This tool is being upgraded to provide 100% inspection of optical photomasks for submicron pattern defects, which are subsequently repaired. Fixed-size overlapped spots are stepped over the mask patterns while a signal derived from the back-scattered electrons is monitored to detect pattern defects. Inspection does not require pattern recognition because the inspection scan patterns are derived from the original design data. The inspection spot is square and larger than the minimum defect to be detected, to improve throughput. A new registration technique provides the beam-to-pattern overlay required to locate submicron defects. The 'guard banding" of inspection shapes prevents mask and system tolerances from producing false alarms that would occur should the spots be mispositioned such that they only partially covered a shape being inspected. A rescanning technique eliminates noise-related false alarms and significantly improves throughput. Data is accumulated during inspection and processed offline, as required for defect repair. EL-3 will detect 0.5 um pattern defects at throughputs compatible with mask manufacturing.

Neuronal periodicity detection as a basis for the perception of consonance: a mathematical model of tonal fusion.

PubMed

Ebeling, Martin

2008-10-01

A mathematical model is presented here to explain the sensation of consonance and dissonance on the basis of neuronal coding and the properties of a neuronal periodicity detection mechanism. This mathematical model makes use of physiological data from a neuronal model of periodicity analysis in the midbrain, whose operation can be described mathematically by autocorrelation functions with regard to time windows. Musical intervals produce regular firing patterns in the auditory nerve that depend on the vibration ratio of the two tones. The mathematical model makes it possible to define a measure for the degree of these regularities for each vibration ratio. It turns out that this measure value is in line with the degree of tonal fusion as described by Stumpf [Tonpsychologie (Psychology of Tones) (Knuf, Hilversum), reprinted 1965]. This finding makes it probable that tonal fusion is a consequence of certain properties of the neuronal periodicity detection mechanism. Together with strong roughness resulting from interval tones with fundamentals close together or close to the octave, this neuronal mechanism may be regarded as the basis of consonance and dissonance.

Mining Recent Temporal Patterns for Event Detection in Multivariate Time Series Data

PubMed Central

Batal, Iyad; Fradkin, Dmitriy; Harrison, James; Moerchen, Fabian; Hauskrecht, Milos

2015-01-01

Improving the performance of classifiers using pattern mining techniques has been an active topic of data mining research. In this work we introduce the recent temporal pattern mining framework for finding predictive patterns for monitoring and event detection problems in complex multivariate time series data. This framework first converts time series into time-interval sequences of temporal abstractions. It then constructs more complex temporal patterns backwards in time using temporal operators. We apply our framework to health care data of 13,558 diabetic patients and show its benefits by efficiently finding useful patterns for detecting and diagnosing adverse medical conditions that are associated with diabetes. PMID:25937993

Keeping the band together: evidence for false boundary disruptive coloration in a butterfly.

PubMed

Seymoure, B M; Aiello, A

2015-09-01

There is a recent surge of evidence supporting disruptive coloration, in which patterns break up the animal's outline through false edges or boundaries, increasing survival in animals by reducing predator detection and/or preventing recognition. Although research has demonstrated that false edges are successful for reducing predation of prey, research into the role of internal false boundaries (i.e. stripes and bands) in reducing predation remains warranted. Many animals have stripes and bands that may function disruptively. Here, we test the possible disruptive function of wing band patterning in a butterfly, Anartia fatima, using artificial paper and plasticine models in Panama. We manipulated the band so that one model type had the band shifted to the wing margin (nondisruptive treatment) and another model had a discontinuous band located on the wing margin (discontinuous edge treatment). We kept the natural wing pattern to represent the false boundary treatment. Across all treatment groups, we standardized the area of colour and used avian visual models to confirm a match between manipulated and natural wing colours. False boundary models had higher survival than either the discontinuous edge model or the nondisruptive model. There was no survival difference between the discontinuous edge model and the nondisruptive model. Our results demonstrate the importance of wing bands in reducing predation on butterflies and show that markings set in from the wing margin can reduce predation more effectively than marginal bands and discontinuous marginal patterns. This study demonstrates an adaptive benefit of having stripes and bands. © 2015 European Society For Evolutionary Biology.

The unusual suspect: Land use is a key predictor of biodiversity patterns in the Iberian Peninsula

NASA Astrophysics Data System (ADS)

Martins, Inês Santos; Proença, Vânia; Pereira, Henrique Miguel

2014-11-01

Although land use change is a key driver of biodiversity change, related variables such as habitat area and habitat heterogeneity are seldom considered in modeling approaches at larger extents. To address this knowledge gap we tested the contribution of land use related variables to models describing richness patterns of amphibians, reptiles and passerines in the Iberian Peninsula. We analyzed the relationship between species richness and habitat heterogeneity at two spatial resolutions (i.e., 10 km × 10 km and 50 km × 50 km). Using both ordinary least square and simultaneous autoregressive models, we assessed the relative importance of land use variables, climate variables and topographic variables. We also compare the species-area relationship with a multi-habitat model, the countryside species-area relationship, to assess the role of the area of different types of habitats on species diversity across scales. The association between habitat heterogeneity and species richness varied with the taxa and spatial resolution. A positive relationship was detected for all taxa at a grain size of 10 km × 10 km, but only passerines responded at a grain size of 50 km × 50 km. Species richness patterns were well described by abiotic predictors, but habitat predictors also explained a considerable portion of the variation. Moreover, species richness patterns were better described by a multi-habitat species-area model, incorporating land use variables, than by the classic power model, which only includes area as the single explanatory variable. Our results suggest that the role of land use in shaping species richness patterns goes beyond the local scale and persists at larger spatial scales. These findings call for the need of integrating land use variables in models designed to assess species richness response to large scale environmental changes.

Tea polyphenols dominate the short-term tea (Camellia sinensis) leaf litter decomposition*

PubMed Central

Fan, Dong-mei; Fan, Kai; Yu, Cui-ping; Lu, Ya-ting; Wang, Xiao-chang

2017-01-01

Polyphenols are one of the most important secondary metabolites, and affect the decomposition of litter and soil organic matter. This study aims to monitor the mass loss rate of tea leaf litter and nutrient release pattern, and investigate the role of tea polyphenols played in this process. High-performance liquid chromatography (HPLC) and classical litter bag method were used to simulate the decomposition process of tea leaf litter and track the changes occurring in major polyphenols over eight months. The release patterns of nitrogen, potassium, calcium, and magnesium were also determined. The decomposition pattern of tea leaf litter could be described by a two-phase decomposition model, and the polyphenol/N ratio effectively regulated the degradation process. Most of the catechins decreased dramatically within two months; gallic acid (GA), catechin gallate (CG), and gallocatechin (GC) were faintly detected, while others were outside the detection limits by the end of the experiment. These results demonstrated that tea polyphenols transformed quickly and catechins had an effect on the individual conversion rate. The nutrient release pattern was different from other plants which might be due to the existence of tea polyphenols. PMID:28124839

Tea polyphenols dominate the short-term tea (Camellia sinensis) leaf litter decomposition.

PubMed

Fan, Dong-Mei; Fan, Kai; Yu, Cui-Ping; Lu, Ya-Ting; Wang, Xiao-Chang

Polyphenols are one of the most important secondary metabolites, and affect the decomposition of litter and soil organic matter. This study aims to monitor the mass loss rate of tea leaf litter and nutrient release pattern, and investigate the role of tea polyphenols played in this process. High-performance liquid chromatography (HPLC) and classical litter bag method were used to simulate the decomposition process of tea leaf litter and track the changes occurring in major polyphenols over eight months. The release patterns of nitrogen, potassium, calcium, and magnesium were also determined. The decomposition pattern of tea leaf litter could be described by a two-phase decomposition model, and the polyphenol/N ratio effectively regulated the degradation process. Most of the catechins decreased dramatically within two months; gallic acid (GA), catechin gallate (CG), and gallocatechin (GC) were faintly detected, while others were outside the detection limits by the end of the experiment. These results demonstrated that tea polyphenols transformed quickly and catechins had an effect on the individual conversion rate. The nutrient release pattern was different from other plants which might be due to the existence of tea polyphenols.

A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility.

PubMed

Moore, Jason H; Gilbert, Joshua C; Tsai, Chia-Ti; Chiang, Fu-Tien; Holden, Todd; Barney, Nate; White, Bill C

2006-07-21

Detecting, characterizing, and interpreting gene-gene interactions or epistasis in studies of human disease susceptibility is both a mathematical and a computational challenge. To address this problem, we have previously developed a multifactor dimensionality reduction (MDR) method for collapsing high-dimensional genetic data into a single dimension (i.e. constructive induction) thus permitting interactions to be detected in relatively small sample sizes. In this paper, we describe a comprehensive and flexible framework for detecting and interpreting gene-gene interactions that utilizes advances in information theory for selecting interesting single-nucleotide polymorphisms (SNPs), MDR for constructive induction, machine learning methods for classification, and finally graphical models for interpretation. We illustrate the usefulness of this strategy using artificial datasets simulated from several different two-locus and three-locus epistasis models. We show that the accuracy, sensitivity, specificity, and precision of a naïve Bayes classifier are significantly improved when SNPs are selected based on their information gain (i.e. class entropy removed) and reduced to a single attribute using MDR. We then apply this strategy to detecting, characterizing, and interpreting epistatic models in a genetic study (n = 500) of atrial fibrillation and show that both classification and model interpretation are significantly improved.

Identification of nuclear weapons

DOEpatents

Mihalczo, J.T.; King, W.T.

1987-04-10

A method and apparatus for non-invasively indentifying different types of nuclear weapons is disclosed. A neutron generator is placed against the weapon to generate a stream of neutrons causing fissioning within the weapon. A first detects the generation of the neutrons and produces a signal indicative thereof. A second particle detector located on the opposite side of the weapon detects the fission particles and produces signals indicative thereof. The signals are converted into a detected pattern and a computer compares the detected pattern with known patterns of weapons and indicates which known weapon has a substantially similar pattern. Either a time distribution pattern or noise analysis pattern, or both, is used. Gamma-neutron discrimination and a third particle detector for fission particles adjacent the second particle detector are preferably used. The neutrons are generated by either a decay neutron source or a pulled neutron particle accelerator.

Using Gaussian mixture models to detect and classify dolphin whistles and pulses.

PubMed

Peso Parada, Pablo; Cardenal-López, Antonio

2014-06-01

In recent years, a number of automatic detection systems for free-ranging cetaceans have been proposed that aim to detect not just surfaced, but also submerged, individuals. These systems are typically based on pattern-recognition techniques applied to underwater acoustic recordings. Using a Gaussian mixture model, a classification system was developed that detects sounds in recordings and classifies them as one of four types: background noise, whistles, pulses, and combined whistles and pulses. The classifier was tested using a database of underwater recordings made off the Spanish coast during 2011. Using cepstral-coefficient-based parameterization, a sound detection rate of 87.5% was achieved for a 23.6% classification error rate. To improve these results, two parameters computed using the multiple signal classification algorithm and an unpredictability measure were included in the classifier. These parameters, which helped to classify the segments containing whistles, increased the detection rate to 90.3% and reduced the classification error rate to 18.1%. Finally, the potential of the multiple signal classification algorithm and unpredictability measure for estimating whistle contours and classifying cetacean species was also explored, with promising results.

Model-free inference of direct network interactions from nonlinear collective dynamics.

PubMed

Casadiego, Jose; Nitzan, Mor; Hallerberg, Sarah; Timme, Marc

2017-12-19

The topology of interactions in network dynamical systems fundamentally underlies their function. Accelerating technological progress creates massively available data about collective nonlinear dynamics in physical, biological, and technological systems. Detecting direct interaction patterns from those dynamics still constitutes a major open problem. In particular, current nonlinear dynamics approaches mostly require to know a priori a model of the (often high dimensional) system dynamics. Here we develop a model-independent framework for inferring direct interactions solely from recording the nonlinear collective dynamics generated. Introducing an explicit dependency matrix in combination with a block-orthogonal regression algorithm, the approach works reliably across many dynamical regimes, including transient dynamics toward steady states, periodic and non-periodic dynamics, and chaos. Together with its capabilities to reveal network (two point) as well as hypernetwork (e.g., three point) interactions, this framework may thus open up nonlinear dynamics options of inferring direct interaction patterns across systems where no model is known.

Turtle groups or turtle soup: dispersal patterns of hawksbill turtles in the Caribbean.

PubMed

Blumenthal, J M; Abreu-Grobois, F A; Austin, T J; Broderick, A C; Bruford, M W; Coyne, M S; Ebanks-Petrie, G; Formia, A; Meylan, P A; Meylan, A B; Godley, B J

2009-12-01

Despite intense interest in conservation of marine turtles, spatial ecology during the oceanic juvenile phase remains relatively unknown. Here, we used mixed stock analysis and examination of oceanic drift to elucidate movements of hawksbill turtles (Eretmochelys imbricata) and address management implications within the Caribbean. Among samples collected from 92 neritic juvenile hawksbills in the Cayman Islands we detected 11 mtDNA control region haplotypes. To estimate contributions to the aggregation, we performed 'many-to-many' mixed stock analysis, incorporating published hawksbill genetic and population data. The Cayman Islands aggregation represents a diverse mixed stock: potentially contributing source rookeries spanned the Caribbean basin, delineating a scale of recruitment of 200-2500 km. As hawksbills undergo an extended phase of oceanic dispersal, ocean currents may drive patterns of genetic diversity observed on foraging aggregations. Therefore, using high-resolution Aviso ocean current data, we modelled movement of particles representing passively drifting oceanic juvenile hawksbills. Putative distribution patterns varied markedly by origin: particles from many rookeries were broadly distributed across the region, while others would appear to become entrained in local gyres. Overall, we detected a significant correlation between genetic profiles of foraging aggregations and patterns of particle distribution produced by a hatchling drift model (Mantel test, r = 0.77, P < 0.001; linear regression, r = 0.83, P < 0.001). Our results indicate that although there is a high degree of mixing across the Caribbean (a 'turtle soup'), current patterns play a substantial role in determining genetic structure of foraging aggregations (forming turtle groups). Thus, for marine turtles and other widely distributed marine species, integration of genetic and oceanographic data may enhance understanding of population connectivity and management requirements.

Geostationary microwave imagers detection criteria

NASA Technical Reports Server (NTRS)

Stacey, J. M.

1986-01-01

Geostationary orbit is investigated as a vantage point from which to sense remotely the surface features of the planet and its atmosphere, with microwave sensors. The geometrical relationships associated with geostationary altitude are developed to produce an efficient search pattern for the detection of emitting media and metal objects. Power transfer equations are derived from the roots of first principles and explain the expected values of the signal-to-clutter ratios for the detection of aircraft, ships, and buoys and for the detection of natural features where they are manifested as cold and warm eddies. The transport of microwave power is described for modeled detection where the direction of power flow is explained by the Zeroth and Second Laws of Thermodynamics. Mathematical expressions are derived that elucidate the detectability of natural emitting media and metal objects. Signal-to-clutter ratio comparisons are drawn among detectable objects that show relative detectability with a thermodynamic sensor and with a short-pulse radar.

Estimates of Social Contact in a Middle School Based on Self-Report and Wireless Sensor Data.

PubMed

Leecaster, Molly; Toth, Damon J A; Pettey, Warren B P; Rainey, Jeanette J; Gao, Hongjiang; Uzicanin, Amra; Samore, Matthew

2016-01-01

Estimates of contact among children, used for infectious disease transmission models and understanding social patterns, historically rely on self-report logs. Recently, wireless sensor technology has enabled objective measurement of proximal contact and comparison of data from the two methods. These are mostly small-scale studies, and knowledge gaps remain in understanding contact and mixing patterns and also in the advantages and disadvantages of data collection methods. We collected contact data from a middle school, with 7th and 8th grades, for one day using self-report contact logs and wireless sensors. The data were linked for students with unique initials, gender, and grade within the school. This paper presents the results of a comparison of two approaches to characterize school contact networks, wireless proximity sensors and self-report logs. Accounting for incomplete capture and lack of participation, we estimate that "sensor-detectable", proximal contacts longer than 20 seconds during lunch and class-time occurred at 2 fold higher frequency than "self-reportable" talk/touch contacts. Overall, 55% of estimated talk-touch contacts were also sensor-detectable whereas only 15% of estimated sensor-detectable contacts were also talk-touch. Contacts detected by sensors and also in self-report logs had longer mean duration than contacts detected only by sensors (6.3 vs 2.4 minutes). During both lunch and class-time, sensor-detectable contacts demonstrated substantially less gender and grade assortativity than talk-touch contacts. Hallway contacts, which were ascertainable only by proximity sensors, were characterized by extremely high degree and short duration. We conclude that the use of wireless sensors and self-report logs provide complementary insight on in-school mixing patterns and contact frequency.

Authentication of monofloral Yemeni Sidr honey using ultraviolet spectroscopy and chemometric analysis.

PubMed

Roshan, Abdul-Rahman A; Gad, Haidy A; El-Ahmady, Sherweit H; Khanbash, Mohamed S; Abou-Shoer, Mohamed I; Al-Azizi, Mohamed M

2013-08-14

This work describes a simple model developed for the authentication of monofloral Yemeni Sidr honey using UV spectroscopy together with chemometric techniques of hierarchical cluster analysis (HCA), principal component analysis (PCA), and soft independent modeling of class analogy (SIMCA). The model was constructed using 13 genuine Sidr honey samples and challenged with 25 honey samples of different botanical origins. HCA and PCA were successfully able to present a preliminary clustering pattern to segregate the genuine Sidr samples from the lower priced local polyfloral and non-Sidr samples. The SIMCA model presented a clear demarcation of the samples and was used to identify genuine Sidr honey samples as well as detect admixture with lower priced polyfloral honey by detection limits >10%. The constructed model presents a simple and efficient method of analysis and may serve as a basis for the authentication of other honey types worldwide.

Anomalous Cases of Astronaut Helmet Detection

NASA Technical Reports Server (NTRS)

Dolph, Chester; Moore, Andrew J.; Schubert, Matthew; Woodell, Glenn

2015-01-01

An astronaut's helmet is an invariant, rigid image element that is well suited for identification and tracking using current machine vision technology. Future space exploration will benefit from the development of astronaut detection software for search and rescue missions based on EVA helmet identification. However, helmets are solid white, except for metal brackets to attach accessories such as supplementary lights. We compared the performance of a widely used machine vision pipeline on a standard-issue NASA helmet with and without affixed experimental feature-rich patterns. Performance on the patterned helmet was far more robust. We found that four different feature-rich patterns are sufficient to identify a helmet and determine orientation as it is rotated about the yaw, pitch, and roll axes. During helmet rotation the field of view changes to frames containing parts of two or more feature-rich patterns. We took reference images in these locations to fill in detection gaps. These multiple feature-rich patterns references added substantial benefit to detection, however, they generated the majority of the anomalous cases. In these few instances, our algorithm keys in on one feature-rich pattern of the multiple feature-rich pattern reference and makes an incorrect prediction of the location of the other feature-rich patterns. We describe and make recommendations on ways to mitigate anomalous cases in which detection of one or more feature-rich patterns fails. While the number of cases is only a small percentage of the tested helmet orientations, they illustrate important design considerations for future spacesuits. In addition to our four successful feature-rich patterns, we present unsuccessful patterns and discuss the cause of their poor performance from a machine vision perspective. Future helmets designed with these considerations will enable automated astronaut detection and thereby enhance mission operations and extraterrestrial search and rescue.

Semi-supervised anomaly detection - towards model-independent searches of new physics

NASA Astrophysics Data System (ADS)

Kuusela, Mikael; Vatanen, Tommi; Malmi, Eric; Raiko, Tapani; Aaltonen, Timo; Nagai, Yoshikazu

2012-06-01

Most classification algorithms used in high energy physics fall under the category of supervised machine learning. Such methods require a training set containing both signal and background events and are prone to classification errors should this training data be systematically inaccurate for example due to the assumed MC model. To complement such model-dependent searches, we propose an algorithm based on semi-supervised anomaly detection techniques, which does not require a MC training sample for the signal data. We first model the background using a multivariate Gaussian mixture model. We then search for deviations from this model by fitting to the observations a mixture of the background model and a number of additional Gaussians. This allows us to perform pattern recognition of any anomalous excess over the background. We show by a comparison to neural network classifiers that such an approach is a lot more robust against misspecification of the signal MC than supervised classification. In cases where there is an unexpected signal, a neural network might fail to correctly identify it, while anomaly detection does not suffer from such a limitation. On the other hand, when there are no systematic errors in the training data, both methods perform comparably.

Using missing ordinal patterns to detect nonlinearity in time series data.

PubMed

Kulp, Christopher W; Zunino, Luciano; Osborne, Thomas; Zawadzki, Brianna

2017-08-01

The number of missing ordinal patterns (NMP) is the number of ordinal patterns that do not appear in a series after it has been symbolized using the Bandt and Pompe methodology. In this paper, the NMP is demonstrated as a test for nonlinearity using a surrogate framework in order to see if the NMP for a series is statistically different from the NMP of iterative amplitude adjusted Fourier transform (IAAFT) surrogates. It is found that the NMP works well as a test statistic for nonlinearity, even in the cases of very short time series. Both model and experimental time series are used to demonstrate the efficacy of the NMP as a test for nonlinearity.

Detection of changes in SEMG signals with myofascial pain using the pattern-classifier

NASA Astrophysics Data System (ADS)

Jiang, Ching-Fen; Huang, Pao-Tieh

2013-10-01

Myofascial pain on the upper back (MFPUB) has been a common occupational hazard associated with consistent computer use. Investigations into any sort neuromuscular functional changes due to myofascial pain are rare. This study aims to differentiate the wavelet energy patterns of the surface electromyography signals measured from 30 normal and 26 patient subjects using the K-means clustering process. The results show that the wavelet energy pattern of patient subjects was different to that of normal subject and reveals a sensitivity of 57.69% at a specificity of 76.67% in the identification of myofascial pain. Therefore, this model could provide a reliable feature for clinical diagnosis of myofascial pain.

Accounting for non-independent detection when estimating abundance of organisms with a Bayesian approach

USGS Publications Warehouse

Martin, Julien; Royle, J. Andrew; MacKenzie, Darryl I.; Edwards, Holly H.; Kery, Marc; Gardner, Beth

2011-01-01

Summary 1. Binomial mixture models use repeated count data to estimate abundance. They are becoming increasingly popular because they provide a simple and cost-effective way to account for imperfect detection. However, these models assume that individuals are detected independently of each other. This assumption may often be violated in the field. For instance, manatees (Trichechus manatus latirostris) may surface in turbid water (i.e. become available for detection during aerial surveys) in a correlated manner (i.e. in groups). However, correlated behaviour, affecting the non-independence of individual detections, may also be relevant in other systems (e.g. correlated patterns of singing in birds and amphibians). 2. We extend binomial mixture models to account for correlated behaviour and therefore to account for non-independent detection of individuals. We simulated correlated behaviour using beta-binomial random variables. Our approach can be used to simultaneously estimate abundance, detection probability and a correlation parameter. 3. Fitting binomial mixture models to data that followed a beta-binomial distribution resulted in an overestimation of abundance even for moderate levels of correlation. In contrast, the beta-binomial mixture model performed considerably better in our simulation scenarios. We also present a goodness-of-fit procedure to evaluate the fit of beta-binomial mixture models. 4. We illustrate our approach by fitting both binomial and beta-binomial mixture models to aerial survey data of manatees in Florida. We found that the binomial mixture model did not fit the data, whereas there was no evidence of lack of fit for the beta-binomial mixture model. This example helps illustrate the importance of using simulations and assessing goodness-of-fit when analysing ecological data with N-mixture models. Indeed, both the simulations and the goodness-of-fit procedure highlighted the limitations of the standard binomial mixture model for aerial manatee surveys. 5. Overestimation of abundance by binomial mixture models owing to non-independent detections is problematic for ecological studies, but also for conservation. For example, in the case of endangered species, it could lead to inappropriate management decisions, such as downlisting. These issues will be increasingly relevant as more ecologists apply flexible N-mixture models to ecological data.

Improvement of Alternative Crop Phenology Detection Algorithms using MODIS NDVI Time Series Data in US Corn Belt Region

NASA Astrophysics Data System (ADS)

Lee, J.; Kang, S.; Seo, B.; Lee, K.

2017-12-01

Predicting crop phenology is important for understanding of crop development and growth processes and improving the accuracy of crop model. Remote sensing offers a feasible tool for monitoring spatio-temporal patterns of crop phenology in region and continental scales. Various methods have been developed to determine the timing of crop phenological stages using spectral vegetation indices (i.e. NDVI and EVI) derived from satellite data. In our study, it was compared four alternative detection methods to identify crop phenological stages (i.e. the emergence and harvesting date) using high quality NDVI time series data derived from MODIS. Also we investigated factors associated with crop development rate. Temperature and photoperiod are the two main factors which would influence the crop's growth pattern expressed in the VI data. Only the effect of temperature on crop development rate was considered. The temperature response function in the Wang-Engel (WE) model was used, which simulates crop development using nonlinear models with response functions that range from zero to one. It has attempted at the state level over 14 years (2003-2016) in Iowa and Illinois state of USA, where the estimated phenology date by using four methods for both corn and soybean. Weekly crop progress reports produced by the USDA NASS were used to validate phenology detection algorithms effected by temperature. All methods showed substantial uncertainty but the threshold method showed relatively better agreement with the State-level data for soybean phenology.

A Healthcare Utilization Analysis Framework for Hot Spotting and Contextual Anomaly Detection

PubMed Central

Hu, Jianying; Wang, Fei; Sun, Jimeng; Sorrentino, Robert; Ebadollahi, Shahram

2012-01-01

Patient medical records today contain vast amount of information regarding patient conditions along with treatment and procedure records. Systematic healthcare resource utilization analysis leveraging such observational data can provide critical insights to guide resource planning and improve the quality of care delivery while reducing cost. Of particular interest to providers are hot spotting: the ability to identify in a timely manner heavy users of the systems and their patterns of utilization so that targeted intervention programs can be instituted, and anomaly detection: the ability to identify anomalous utilization cases where the patients incurred levels of utilization that are unexpected given their clinical characteristics which may require corrective actions. Past work on medical utilization pattern analysis has focused on disease specific studies. We present a framework for utilization analysis that can be easily applied to any patient population. The framework includes two main components: utilization profiling and hot spotting, where we use a vector space model to represent patient utilization profiles, and apply clustering techniques to identify utilization groups within a given population and isolate high utilizers of different types; and contextual anomaly detection for utilization, where models that map patient’s clinical characteristics to the utilization level are built in order to quantify the deviation between the expected and actual utilization levels and identify anomalies. We demonstrate the effectiveness of the framework using claims data collected from a population of 7667 diabetes patients. Our analysis demonstrates the usefulness of the proposed approaches in identifying clinically meaningful instances for both hot spotting and anomaly detection. In future work we plan to incorporate additional sources of observational data including EMRs and disease registries, and develop analytics models to leverage temporal relationships among medical encounters to provide more in-depth insights. PMID:23304306

A healthcare utilization analysis framework for hot spotting and contextual anomaly detection.

PubMed

Hu, Jianying; Wang, Fei; Sun, Jimeng; Sorrentino, Robert; Ebadollahi, Shahram

2012-01-01

Patient medical records today contain vast amount of information regarding patient conditions along with treatment and procedure records. Systematic healthcare resource utilization analysis leveraging such observational data can provide critical insights to guide resource planning and improve the quality of care delivery while reducing cost. Of particular interest to providers are hot spotting: the ability to identify in a timely manner heavy users of the systems and their patterns of utilization so that targeted intervention programs can be instituted, and anomaly detection: the ability to identify anomalous utilization cases where the patients incurred levels of utilization that are unexpected given their clinical characteristics which may require corrective actions. Past work on medical utilization pattern analysis has focused on disease specific studies. We present a framework for utilization analysis that can be easily applied to any patient population. The framework includes two main components: utilization profiling and hot spotting, where we use a vector space model to represent patient utilization profiles, and apply clustering techniques to identify utilization groups within a given population and isolate high utilizers of different types; and contextual anomaly detection for utilization, where models that map patient's clinical characteristics to the utilization level are built in order to quantify the deviation between the expected and actual utilization levels and identify anomalies. We demonstrate the effectiveness of the framework using claims data collected from a population of 7667 diabetes patients. Our analysis demonstrates the usefulness of the proposed approaches in identifying clinically meaningful instances for both hot spotting and anomaly detection. In future work we plan to incorporate additional sources of observational data including EMRs and disease registries, and develop analytics models to leverage temporal relationships among medical encounters to provide more in-depth insights.

Lensless Photoluminescence Hyperspectral Camera Employing Random Speckle Patterns.

PubMed

Žídek, Karel; Denk, Ondřej; Hlubuček, Jiří

2017-11-10

We propose and demonstrate a spectrally-resolved photoluminescence imaging setup based on the so-called single pixel camera - a technique of compressive sensing, which enables imaging by using a single-pixel photodetector. The method relies on encoding an image by a series of random patterns. In our approach, the image encoding was maintained via laser speckle patterns generated by an excitation laser beam scattered on a diffusor. By using a spectrometer as the single-pixel detector we attained a realization of a spectrally-resolved photoluminescence camera with unmatched simplicity. We present reconstructed hyperspectral images of several model scenes. We also discuss parameters affecting the imaging quality, such as the correlation degree of speckle patterns, pattern fineness, and number of datapoints. Finally, we compare the presented technique to hyperspectral imaging using sample scanning. The presented method enables photoluminescence imaging for a broad range of coherent excitation sources and detection spectral areas.

A connectionist model of category learning by individuals with high-functioning autism spectrum disorder.

PubMed

Dovgopoly, Alexander; Mercado, Eduardo

2013-06-01

Individuals with autism spectrum disorder (ASD) show atypical patterns of learning and generalization. We explored the possible impacts of autism-related neural abnormalities on perceptual category learning using a neural network model of visual cortical processing. When applied to experiments in which children or adults were trained to classify complex two-dimensional images, the model can account for atypical patterns of perceptual generalization. This is only possible, however, when individual differences in learning are taken into account. In particular, analyses performed with a self-organizing map suggested that individuals with high-functioning ASD show two distinct generalization patterns: one that is comparable to typical patterns, and a second in which there is almost no generalization. The model leads to novel predictions about how individuals will generalize when trained with simplified input sets and can explain why some researchers have failed to detect learning or generalization deficits in prior studies of category learning by individuals with autism. On the basis of these simulations, we propose that deficits in basic neural plasticity mechanisms may be sufficient to account for the atypical patterns of perceptual category learning and generalization associated with autism, but they do not account for why only a subset of individuals with autism would show such deficits. If variations in performance across subgroups reflect heterogeneous neural abnormalities, then future behavioral and neuroimaging studies of individuals with ASD will need to account for such disparities.

Density estimation of Yangtze finless porpoises using passive acoustic sensors and automated click train detection.

PubMed

Kimura, Satoko; Akamatsu, Tomonari; Li, Songhai; Dong, Shouyue; Dong, Lijun; Wang, Kexiong; Wang, Ding; Arai, Nobuaki

2010-09-01

A method is presented to estimate the density of finless porpoises using stationed passive acoustic monitoring. The number of click trains detected by stereo acoustic data loggers (A-tag) was converted to an estimate of the density of porpoises. First, an automated off-line filter was developed to detect a click train among noise, and the detection and false-alarm rates were calculated. Second, a density estimation model was proposed. The cue-production rate was measured by biologging experiments. The probability of detecting a cue and the area size were calculated from the source level, beam patterns, and a sound-propagation model. The effect of group size on the cue-detection rate was examined. Third, the proposed model was applied to estimate the density of finless porpoises at four locations from the Yangtze River to the inside of Poyang Lake. The estimated mean density of porpoises in a day decreased from the main stream to the lake. Long-term monitoring during 466 days from June 2007 to May 2009 showed variation in the density 0-4.79. However, the density was fewer than 1 porpoise/km(2) during 94% of the period. These results suggest a potential gap and seasonal migration of the population in the bottleneck of Poyang Lake.

When can ocean acidification impacts be detected from decadal alkalinity measurements?

NASA Astrophysics Data System (ADS)

Carter, B. R.; Frölicher, T. L.; Dunne, J. P.; Rodgers, K. B.; Slater, R. D.; Sarmiento, J. L.

2016-04-01

We use a large initial condition suite of simulations (30 runs) with an Earth system model to assess the detectability of biogeochemical impacts of ocean acidification (OA) on the marine alkalinity distribution from decadally repeated hydrographic measurements such as those produced by the Global Ship-Based Hydrographic Investigations Program (GO-SHIP). Detection of these impacts is complicated by alkalinity changes from variability and long-term trends in freshwater and organic matter cycling and ocean circulation. In our ensemble simulation, variability in freshwater cycling generates large changes in alkalinity that obscure the changes of interest and prevent the attribution of observed alkalinity redistribution to OA. These complications from freshwater cycling can be mostly avoided through salinity normalization of alkalinity. With the salinity-normalized alkalinity, modeled OA impacts are broadly detectable in the surface of the subtropical gyres by 2030. Discrepancies between this finding and the finding of an earlier analysis suggest that these estimates are strongly sensitive to the patterns of calcium carbonate export simulated by the model. OA impacts are detectable later in the subpolar and equatorial regions due to slower responses of alkalinity to OA in these regions and greater seasonal equatorial alkalinity variability. OA impacts are detectable later at depth despite lower variability due to smaller rates of change and consistent measurement uncertainty.

Method for detecting the signature of noise-induced structures in spatiotemporal data sets: an application to excitable media

NASA Astrophysics Data System (ADS)

Huett, Marc-Thorsten

2003-05-01

We formulate mathematical tools for analyzing spatiotemporal data sets. The tools are based on nearest-neighbor considerations similar to cellular automata. One of the analysis tools allows for reconstructing the noise intensity in a data set and is an appropriate method for detecting a variety of noise-induced phenomena in spatiotemporal data. The functioning of these methods is illustrated on sample data generated with the forest fire model and with networks of nonlinear oscillators. It is seen that these methods allow the characterization of spatiotemporal stochastic resonance (STSR) in experimental data. Application of these tools to biological spatiotemporal patterns is discussed. For one specific example, the slime mold Dictyostelium discoideum, it is seen, how transitions between different patterns are clearly marked by changes in the spatiotemporal observables.

Squids in the Study of Cerebral Magnetic Field

NASA Astrophysics Data System (ADS)

Romani, G. L.; Narici, L.

The following sections are included: * INTRODUCTION * HISTORICAL OVERVIEW * NEUROMAGNETIC FIELDS AND AMBIENT NOISE * DETECTORS * Room temperature sensors * SQUIDs * DETECTION COILS * Magnetometers * Gradiometers * Balancing * Planar gradiometers * Choice of the gradiometer parameters * MODELING * Current pattern due to neural excitations * Action potentials and postsynaptic currents * The current dipole model * Neural population and detected fields * Spherically bounded medium * SPATIAL CONFIGURATION OF THE SENSORS * SOURCE LOCALIZATION * Localization procedure * Experimental accuracy and reproducibility * SIGNAL PROCESSING * Analog Filtering * Bandpass filters * Line rejection filters * DATA ANALYSIS * Analysis of evoked/event-related responses * Simple average * Selected average * Recursive techniques * Similarity analysis * Analysis of spontaneous activity * Mapping and localization * EXAMPLES OF NEUROMAGNETIC STUDIES * Neuromagnetic measurements * Studies on the normal brain * Clinical applications * Epilepsy * Tinnitus * CONCLUSIONS * ACKNOWLEDGEMENTS * REFERENCES

Influence Of Momentum Excess On The Pattern And Dynamics Of Intermediate-Range Stratified Wakes

DTIC Science & Technology

2016-06-01

excess in order to model the fundamental differences between signatures generated by towed and self- propelled bodies in various ocean states. In cases...which can be used on the operational level for developing and improving algorithms for non- acoustic signature prediction and detection. 14. SUBJECT...order to model the fundamental differences between signatures generated by towed and self- propelled bodies in various ocean states. In cases where

Hidden Markov models for fault detection in dynamic systems

NASA Technical Reports Server (NTRS)

Smyth, Padhraic J. (Inventor)

1995-01-01

The invention is a system failure monitoring method and apparatus which learns the symptom-fault mapping directly from training data. The invention first estimates the state of the system at discrete intervals in time. A feature vector x of dimension k is estimated from sets of successive windows of sensor data. A pattern recognition component then models the instantaneous estimate of the posterior class probability given the features, p(w(sub i) (vertical bar)/x), 1 less than or equal to i isless than or equal to m. Finally, a hidden Markov model is used to take advantage of temporal context and estimate class probabilities conditioned on recent past history. In this hierarchical pattern of information flow, the time series data is transformed and mapped into a categorical representation (the fault classes) and integrated over time to enable robust decision-making.

Hidden Markov models for fault detection in dynamic systems

NASA Technical Reports Server (NTRS)

Smyth, Padhraic J. (Inventor)

1993-01-01

The invention is a system failure monitoring method and apparatus which learns the symptom-fault mapping directly from training data. The invention first estimates the state of the system at discrete intervals in time. A feature vector x of dimension k is estimated from sets of successive windows of sensor data. A pattern recognition component then models the instantaneous estimate of the posterior class probability given the features, p(w(sub i) perpendicular to x), 1 less than or equal to i is less than or equal to m. Finally, a hidden Markov model is used to take advantage of temporal context and estimate class probabilities conditioned on recent past history. In this hierarchical pattern of information flow, the time series data is transformed and mapped into a categorical representation (the fault classes) and integrated over time to enable robust decision-making.

SU-F-J-72: A Clinical Usable Integrated Contouring Quality Evaluation Software for Radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, S; Dolly, S; Cai, B

Purpose: To introduce the Auto Contour Evaluation (ACE) software, which is the clinical usable, user friendly, efficient and all-in-one toolbox for automatically identify common contouring errors in radiotherapy treatment planning using supervised machine learning techniques. Methods: ACE is developed with C# using Microsoft .Net framework and Windows Presentation Foundation (WPF) for elegant GUI design and smooth GUI transition animations through the integration of graphics engines and high dots per inch (DPI) settings on modern high resolution monitors. The industrial standard software design pattern, Model-View-ViewModel (MVVM) pattern, is chosen to be the major architecture of ACE for neat coding structure, deepmore » modularization, easy maintainability and seamless communication with other clinical software. ACE consists of 1) a patient data importing module integrated with clinical patient database server, 2) a 2D DICOM image and RT structure simultaneously displaying module, 3) a 3D RT structure visualization module using Visualization Toolkit or VTK library and 4) a contour evaluation module using supervised pattern recognition algorithms to detect contouring errors and display detection results. ACE relies on supervised learning algorithms to handle all image processing and data processing jobs. Implementations of related algorithms are powered by Accord.Net scientific computing library for better efficiency and effectiveness. Results: ACE can take patient’s CT images and RT structures from commercial treatment planning software via direct user input or from patients’ database. All functionalities including 2D and 3D image visualization and RT contours error detection have been demonstrated with real clinical patient cases. Conclusion: ACE implements supervised learning algorithms and combines image processing and graphical visualization modules for RT contours verification. ACE has great potential for automated radiotherapy contouring quality verification. Structured with MVVM pattern, it is highly maintainable and extensible, and support smooth connections with other clinical software tools.« less

Structural damage detection based on stochastic subspace identification and statistical pattern recognition: I. Theory

NASA Astrophysics Data System (ADS)

Ren, W. X.; Lin, Y. Q.; Fang, S. E.

2011-11-01

One of the key issues in vibration-based structural health monitoring is to extract the damage-sensitive but environment-insensitive features from sampled dynamic response measurements and to carry out the statistical analysis of these features for structural damage detection. A new damage feature is proposed in this paper by using the system matrices of the forward innovation model based on the covariance-driven stochastic subspace identification of a vibrating system. To overcome the variations of the system matrices, a non-singularity transposition matrix is introduced so that the system matrices are normalized to their standard forms. For reducing the effects of modeling errors, noise and environmental variations on measured structural responses, a statistical pattern recognition paradigm is incorporated into the proposed method. The Mahalanobis and Euclidean distance decision functions of the damage feature vector are adopted by defining a statistics-based damage index. The proposed structural damage detection method is verified against one numerical signal and two numerical beams. It is demonstrated that the proposed statistics-based damage index is sensitive to damage and shows some robustness to the noise and false estimation of the system ranks. The method is capable of locating damage of the beam structures under different types of excitations. The robustness of the proposed damage detection method to the variations in environmental temperature is further validated in a companion paper by a reinforced concrete beam tested in the laboratory and a full-scale arch bridge tested in the field.

Automated Detection of Essay Revising Patterns: Applications for Intelligent Feedback in a Writing Tutor

ERIC Educational Resources Information Center

Roscoe, Rod D.; Snow, Erica L.; Allen, Laura K.; McNamara, Danielle S.

2015-01-01

The Writing Pal is an intelligent tutoring system designed to support writing proficiency and strategy acquisition for adolescent writers. A fundamental aspect of the instructional model is automated formative feedback that provides concrete information and strategies oriented toward student improvement. In this paper, the authors explore…

Outlier Detection in High-Stakes Certification Testing. Research Report.

ERIC Educational Resources Information Center

Meijer, Rob R.

Recent developments of person-fit analysis in computerized adaptive testing (CAT) are discussed. Methods from statistical process control are presented that have been proposed to classify an item score pattern as fitting or misfitting the underlying item response theory (IRT) model in a CAT. Most person-fit research in CAT is restricted to…

Outlier Detection in High-Stakes Certification Testing.

ERIC Educational Resources Information Center

Meijer, Rob R.

2002-01-01

Used empirical data from a certification test to study methods from statistical process control that have been proposed to classify an item score pattern as fitting or misfitting the underlying item response theory model in computerized adaptive testing. Results for 1,392 examinees show that different types of misfit can be distinguished. (SLD)

Diagnosis of Cognitive Errors by Statistical Pattern Recognition Methods.

ERIC Educational Resources Information Center

Tatsuoka, Kikumi K.; Tatsuoka, Maurice M.

The rule space model permits measurement of cognitive skill acquisition, diagnosis of cognitive errors, and detection of the strengths and weaknesses of knowledge possessed by individuals. Two ways to classify an individual into his or her most plausible latent state of knowledge include: (1) hypothesis testing--Bayes' decision rules for minimum…

Framework for Detection and Localization of Extreme Climate Event with Pixel Recursive Super Resolution

NASA Astrophysics Data System (ADS)

Kim, S. K.; Lee, J.; Zhang, C.; Ames, S.; Williams, D. N.

2017-12-01

Deep learning techniques have been successfully applied to solve many problems in climate and geoscience using massive-scaled observed and modeled data. For extreme climate event detections, several models based on deep neural networks have been recently proposed and attend superior performance that overshadows all previous handcrafted expert based method. The issue arising, though, is that accurate localization of events requires high quality of climate data. In this work, we propose framework capable of detecting and localizing extreme climate events in very coarse climate data. Our framework is based on two models using deep neural networks, (1) Convolutional Neural Networks (CNNs) to detect and localize extreme climate events, and (2) Pixel recursive recursive super resolution model to reconstruct high resolution climate data from low resolution climate data. Based on our preliminary work, we have presented two CNNs in our framework for different purposes, detection and localization. Our results using CNNs for extreme climate events detection shows that simple neural nets can capture the pattern of extreme climate events with high accuracy from very coarse reanalysis data. However, localization accuracy is relatively low due to the coarse resolution. To resolve this issue, the pixel recursive super resolution model reconstructs the resolution of input of localization CNNs. We present a best networks using pixel recursive super resolution model that synthesizes details of tropical cyclone in ground truth data while enhancing their resolution. Therefore, this approach not only dramat- ically reduces the human effort, but also suggests possibility to reduce computing cost required for downscaling process to increase resolution of data.

iASeq: integrative analysis of allele-specificity of protein-DNA interactions in multiple ChIP-seq datasets

PubMed Central

2012-01-01

Background ChIP-seq provides new opportunities to study allele-specific protein-DNA binding (ASB). However, detecting allelic imbalance from a single ChIP-seq dataset often has low statistical power since only sequence reads mapped to heterozygote SNPs are informative for discriminating two alleles. Results We develop a new method iASeq to address this issue by jointly analyzing multiple ChIP-seq datasets. iASeq uses a Bayesian hierarchical mixture model to learn correlation patterns of allele-specificity among multiple proteins. Using the discovered correlation patterns, the model allows one to borrow information across datasets to improve detection of allelic imbalance. Application of iASeq to 77 ChIP-seq samples from 40 ENCODE datasets and 1 genomic DNA sample in GM12878 cells reveals that allele-specificity of multiple proteins are highly correlated, and demonstrates the ability of iASeq to improve allelic inference compared to analyzing each individual dataset separately. Conclusions iASeq illustrates the value of integrating multiple datasets in the allele-specificity inference and offers a new tool to better analyze ASB. PMID:23194258

Oblique reconstructions in tomosynthesis. II. Super-resolution

PubMed Central

Acciavatti, Raymond J.; Maidment, Andrew D. A.

2013-01-01

Purpose: In tomosynthesis, super-resolution has been demonstrated using reconstruction planes parallel to the detector. Super-resolution allows for subpixel resolution relative to the detector. The purpose of this work is to develop an analytical model that generalizes super-resolution to oblique reconstruction planes. Methods: In a digital tomosynthesis system, a sinusoidal test object is modeled along oblique angles (i.e., “pitches”) relative to the plane of the detector in a 3D divergent-beam acquisition geometry. To investigate the potential for super-resolution, the input frequency is specified to be greater than the alias frequency of the detector. Reconstructions are evaluated in an oblique plane along the extent of the object using simple backprojection (SBP) and filtered backprojection (FBP). By comparing the amplitude of the reconstruction against the attenuation coefficient of the object at various frequencies, the modulation transfer function (MTF) is calculated to determine whether modulation is within detectable limits for super-resolution. For experimental validation of super-resolution, a goniometry stand was used to orient a bar pattern phantom along various pitches relative to the breast support in a commercial digital breast tomosynthesis system. Results: Using theoretical modeling, it is shown that a single projection image cannot resolve a sine input whose frequency exceeds the detector alias frequency. The high frequency input is correctly visualized in SBP or FBP reconstruction using a slice along the pitch of the object. The Fourier transform of this reconstructed slice is maximized at the input frequency as proof that the object is resolved. Consistent with the theoretical results, experimental images of a bar pattern phantom showed super-resolution in oblique reconstructions. At various pitches, the highest frequency with detectable modulation was determined by visual inspection of the bar patterns. The dependency of the highest detectable frequency on pitch followed the same trend as the analytical model. It was demonstrated that super-resolution is not achievable if the pitch of the object approaches 90°, corresponding to the case in which the test frequency is perpendicular to the breast support. Only low frequency objects are detectable at pitches close to 90°. Conclusions: This work provides a platform for investigating super-resolution in oblique reconstructions for tomosynthesis. In breast imaging, this study should have applications in visualizing microcalcifications and other subtle signs of cancer. PMID:24320445

A Bioinformatics Approach for Detecting Repetitive Nested Motifs using Pattern Matching.

PubMed

Romero, José R; Carballido, Jessica A; Garbus, Ingrid; Echenique, Viviana C; Ponzoni, Ignacio

2016-01-01

The identification of nested motifs in genomic sequences is a complex computational problem. The detection of these patterns is important to allow the discovery of transposable element (TE) insertions, incomplete reverse transcripts, deletions, and/or mutations. In this study, a de novo strategy for detecting patterns that represent nested motifs was designed based on exhaustive searches for pairs of motifs and combinatorial pattern analysis. These patterns can be grouped into three categories, motifs within other motifs, motifs flanked by other motifs, and motifs of large size. The methodology used in this study, applied to genomic sequences from the plant species Aegilops tauschii and Oryza sativa , revealed that it is possible to identify putative nested TEs by detecting these three types of patterns. The results were validated through BLAST alignments, which revealed the efficacy and usefulness of the new method, which is called Mamushka.

Effects of musical training on sound pattern processing in high-school students.

PubMed

Wang, Wenjung; Staffaroni, Laura; Reid, Errold; Steinschneider, Mitchell; Sussman, Elyse

2009-05-01

Recognizing melody in music involves detection of both the pitch intervals and the silence between sequentially presented sounds. This study tested the hypothesis that active musical training in adolescents facilitates the ability to passively detect sequential sound patterns compared to musically non-trained age-matched peers. Twenty adolescents, aged 15-18 years, were divided into groups according to their musical training and current experience. A fixed order tone pattern was presented at various stimulus rates while electroencephalogram was recorded. The influence of musical training on passive auditory processing of the sound patterns was assessed using components of event-related brain potentials (ERPs). The mismatch negativity (MMN) ERP component was elicited in different stimulus onset asynchrony (SOA) conditions in non-musicians than musicians, indicating that musically active adolescents were able to detect sound patterns across longer time intervals than age-matched peers. Musical training facilitates detection of auditory patterns, allowing the ability to automatically recognize sequential sound patterns over longer time periods than non-musical counterparts.

Digital modeling of radioactive and chemical waste transport in the aquifer underlying the Snake River Plain at the National Reactor Testing Station, Idaho

USGS Publications Warehouse

Robertson, J.B.

1974-01-01

Industrial and low-level radioactive liquid wastes at the National Reactor Testing Station (NRTS) in Idaho have been disposed to the Snake River Plain aquifer since 1952. Monitoring studies have indicated that tritium and chloride have dispersed over a 15-square mile (39-square kilometer) area of the aquifer in low but detectable concentrations and have only migrated as far as 5 miles (8 kilometers) downgradient from discharge points. The movement of cationic waste solutes, particularly 90Sr and 137Cs, has been significantly retarded due to sorption phenomena, principally ion exchange. 137Cs has shown no detectable migration in the aquifer and 90Sr has migrated only about 1.5 miles (2 kilometers) from the Idaho Chemical Processing Plant (ICPP) discharge well, and is detectable over an area of only 1.5 square miles ( 4 square kilometers) of the aquifer. Digital modeling techniques have been applied successfully to the analysis of the complex waste-transport system by utilizing numerical solution of the coupled equations of groundwater motion and mass transport. The model includes the effects of convective transport, flow divergence, two-dimensional hydraulic dispersion, radioactive decay, and reversible linear sorption. The hydraulic phase of the model uses the iterative, alternating direction, implicit finite-difference scheme to solve the groundwater flow equations, while the waste-transport phase uses a modified method of characteristics to solve the solute transport equations simulated by the model. The modeling results indicate that hydraulic dispersion (especially transverse) is a much more significant influence than previously suggested by earlier studies. The model has been used to estimate future waste migration patterns for varied assumed hydrological and waste conditions up through the year 2000. The hydraulic effects of recharge from the Big Lost River have an important (but not predominant) influence on the simulated future migration patterns. For the assumed conditions, the model indicates that detectable concentrations of waste chloride and tritium could move as much as 15 miles (24 kilometers) downgradient from the original discharge points by the year 2000. However, the model shows 90Sr moving only 2 to 3 miles (3 to 5 kilometers) downgradient in the same time. The model may also be used to estimate the effects of the various future waste disposal practices and hydrologic conditions on subsequent migration of waste products.

A comment on priors for Bayesian occupancy models.

PubMed

Northrup, Joseph M; Gerber, Brian D

2018-01-01

Understanding patterns of species occurrence and the processes underlying these patterns is fundamental to the study of ecology. One of the more commonly used approaches to investigate species occurrence patterns is occupancy modeling, which can account for imperfect detection of a species during surveys. In recent years, there has been a proliferation of Bayesian modeling in ecology, which includes fitting Bayesian occupancy models. The Bayesian framework is appealing to ecologists for many reasons, including the ability to incorporate prior information through the specification of prior distributions on parameters. While ecologists almost exclusively intend to choose priors so that they are "uninformative" or "vague", such priors can easily be unintentionally highly informative. Here we report on how the specification of a "vague" normally distributed (i.e., Gaussian) prior on coefficients in Bayesian occupancy models can unintentionally influence parameter estimation. Using both simulated data and empirical examples, we illustrate how this issue likely compromises inference about species-habitat relationships. While the extent to which these informative priors influence inference depends on the data set, researchers fitting Bayesian occupancy models should conduct sensitivity analyses to ensure intended inference, or employ less commonly used priors that are less informative (e.g., logistic or t prior distributions). We provide suggestions for addressing this issue in occupancy studies, and an online tool for exploring this issue under different contexts.

Application of a clustering-remote sensing method in analyzing security patterns

NASA Astrophysics Data System (ADS)

López-Caloca, Alejandra; Martínez-Viveros, Elvia; Chapela-Castañares, José Ignacio

2009-04-01

In Mexican academic and government circles, research on criminal spatial behavior has been neglected. Only recently has there been an interest in criminal data geo-reference. However, more sophisticated spatial analyses models are needed to disclose spatial patterns of crime and pinpoint their changes overtime. The main use of these models lies in supporting policy making and strategic intelligence. In this paper we present a model for finding patterns associated with crime. It is based on a fuzzy logic algorithm which finds the best fit within cluster numbers and shapes of groupings. We describe the methodology for building the model and its validation. The model was applied to annual data for types of felonies from 2005 to 2006 in the Mexican city of Hermosillo. The results are visualized as a standard deviational ellipse computed for the points identified to be a "cluster". These areas indicate a high to low demand for public security, and they were cross-related to urban structure analyzed by SPOT images and statistical data such as population, poverty levels, urbanization, and available services. The fusion of the model results with other geospatial data allows detecting obstacles and opportunities for crime commission in specific high risk zones and guide police activities and criminal investigations.

Looking for age-related growth decline in natural forests: unexpected biomass patterns from tree rings and simulated mortality

USGS Publications Warehouse

Foster, Jane R.; D'Amato, Anthony W.; Bradford, John B.

2014-01-01

Forest biomass growth is almost universally assumed to peak early in stand development, near canopy closure, after which it will plateau or decline. The chronosequence and plot remeasurement approaches used to establish the decline pattern suffer from limitations and coarse temporal detail. We combined annual tree ring measurements and mortality models to address two questions: first, how do assumptions about tree growth and mortality influence reconstructions of biomass growth? Second, under what circumstances does biomass production follow the model that peaks early, then declines? We integrated three stochastic mortality models with a census tree-ring data set from eight temperate forest types to reconstruct stand-level biomass increments (in Minnesota, USA). We compared growth patterns among mortality models, forest types and stands. Timing of peak biomass growth varied significantly among mortality models, peaking 20–30 years earlier when mortality was random with respect to tree growth and size, than when mortality favored slow-growing individuals. Random or u-shaped mortality (highest in small or large trees) produced peak growth 25–30 % higher than the surviving tree sample alone. Growth trends for even-aged, monospecific Pinus banksiana or Acer saccharum forests were similar to the early peak and decline expectation. However, we observed continually increasing biomass growth in older, low-productivity forests of Quercus rubra, Fraxinus nigra, and Thuja occidentalis. Tree-ring reconstructions estimated annual changes in live biomass growth and identified more diverse development patterns than previous methods. These detailed, long-term patterns of biomass development are crucial for detecting recent growth responses to global change and modeling future forest dynamics.

Application of DNA Machineries for the Barcode Patterned Detection of Genes or Proteins.

PubMed

Zhou, Zhixin; Luo, Guofeng; Wulf, Verena; Willner, Itamar

2018-06-05

The study introduces an analytical platform for the detection of genes or aptamer-ligand complexes by nucleic acid barcode patterns generated by DNA machineries. The DNA machineries consist of nucleic acid scaffolds that include specific recognition sites for the different genes or aptamer-ligand analytes. The binding of the analytes to the scaffolds initiate, in the presence of the nucleotide mixture, a cyclic polymerization/nicking machinery that yields displaced strands of variable lengths. The electrophoretic separation of the resulting strands provides barcode patterns for the specific detection of the different analytes. Mixtures of DNA machineries that yield, upon sensing of different genes (or aptamer ligands), one-, two-, or three-band barcode patterns are described. The combination of nucleic acid scaffolds acting, in the presence of polymerase/nicking enzyme and nucleotide mixture, as DNA machineries, that generate multiband barcode patterns provide an analytical platform for the detection of an individual gene out of many possible genes. The diversity of genes (or other analytes) that can be analyzed by the DNA machineries and the barcode patterned imaging is given by the Pascal's triangle. As a proof-of-concept, the detection of one of six genes, that is, TP53, Werner syndrome, Tay-Sachs normal gene, BRCA1, Tay-Sachs mutant gene, and cystic fibrosis disorder gene by six two-band barcode patterns is demonstrated. The advantages and limitations of the detection of analytes by polymerase/nicking DNA machineries that yield barcode patterns as imaging readout signals are discussed.

Utilizing Electronic Medical Records to Discover Changing Trends of Medical Behaviors Over Time.

PubMed

Yin, Liangying; Huang, Zhengxing; Dong, Wei; He, Chunhua; Duan, Huilong

2017-05-05

Medical behaviors are playing significant roles in the delivery of high quality and cost-effective health services. Timely discovery of changing frequencies of medical behaviors is beneficial for the improvement of health services. The main objective of this work is to discover the changing trends of medical behaviors over time. This study proposes a two-steps approach to detect essential changing patterns of medical behaviors from Electronic Medical Records (EMRs). In detail, a probabilistic topic model, i.e., Latent Dirichlet allocation (LDA), is firstly applied to disclose yearly treatment patterns in regard to the risk stratification of patients from a large volume of EMRs. After that, the changing trends by comparing essential/critical medical behaviors in a specific time period are detected and analyzed, including changes of significant patient features with their values, and changes of critical treatment interventions with their occurring time stamps. We verify the effectiveness of the proposed approach on a clinical dataset containing 12,152 patient cases with a time range of 10 years. Totally, 135 patients features and 234 treatment interventions in three treatment patterns were selected to detect their changing trends. In particular, evolving trends of yearly occurring probabilities of the selected medical behaviors were categorized into six content changing patterns (i.e, 112 growing, 123 declining, 43 up-down, 16 down-up, 35 steady, and 40 jumping), using the proposed approach. Besides, changing trends of execution time of treatment interventions were classified into three occurring time changing patterns (i.e., 175 early-implemented, 50 steady-implemented and 9 delay-implemented). Experimental results show that our approach has an ability to utilize EMRs to discover essential evolving trends of medical behaviors, and thus provide significant potential to be further explored for health services redesign and improvement.

Remote Sensing-Based Detection and Spatial Pattern Analysis for Geo-Ecological Niche Modeling of Tillandsia SPP. In the Atacama, Chile

NASA Astrophysics Data System (ADS)

Wolf, N.; Siegmund, A.; del Río, C.; Osses, P.; García, J. L.

2016-06-01

In the coastal Atacama Desert in Northern Chile plant growth is constrained to so-called `fog oases' dominated by monospecific stands of the genus Tillandsia. Adapted to the hyperarid environmental conditions, these plants specialize on the foliar uptake of fog as main water and nutrient source. It is this characteristic that leads to distinctive macro- and micro-scale distribution patterns, reflecting complex geo-ecological gradients, mainly affected by the spatiotemporal occurrence of coastal fog respectively the South Pacific Stratocumulus clouds reaching inlands. The current work employs remote sensing, machine learning and spatial pattern/GIS analysis techniques to acquire detailed information on the presence and state of Tillandsia spp. in the Tarapacá region as a base to better understand the bioclimatic and topographic constraints determining the distribution patterns of Tillandsia spp. Spatial and spectral predictors extracted from WorldView-3 satellite data are used to map present Tillandsia vegetation in the Tarapaca region. Regression models on Vegetation Cover Fraction (VCF) are generated combining satellite-based as well as topographic variables and using aggregated high spatial resolution information on vegetation cover derived from UAV flight campaigns as a reference. The results are a first step towards mapping and modelling the topographic as well as bioclimatic factors explaining the spatial distribution patterns of Tillandsia fog oases in the Atacama, Chile.

Factors influencing detection of the federally endangered Diamond Darter Crystallaria cincotta: Implications for long-term monitoring strategies

USGS Publications Warehouse

Rizzo, Austin A.; Brown, Donald J.; Welsh, Stuart A.; Thompson, Patricia A.

2017-01-01

Population monitoring is an essential component of endangered species recovery programs. The federally endangered Diamond Darter Crystallaria cincotta is in need of an effective monitoring design to improve our understanding of its distribution and track population trends. Because of their small size, cryptic coloration, and nocturnal behavior, along with limitations associated with current sampling methods, individuals are difficult to detect at known occupied sites. Therefore, research is needed to determine if survey efforts can be improved by increasing probability of individual detection. The primary objective of this study was to determine if there are seasonal and diel patterns in Diamond Darter detectability during population surveys. In addition to temporal factors, we also assessed five habitat variables that might influence individual detection. We used N-mixture models to estimate site abundances and relationships between covariates and individual detectability and ranked models using Akaike's information criteria. During 2015 three known occupied sites were sampled 15 times each between May and Oct. The best supported model included water temperature as a quadratic function influencing individual detectability, with temperatures around 22 C resulting in the highest detection probability. Detection probability when surveying at the optimal temperature was approximately 6% and 7.5% greater than when surveying at 16 C and 29 C, respectively. Time of Night and day of year were not strong predictors of Diamond Darter detectability. The results of this study will allow researchers and agencies to maximize detection probability when surveying populations, resulting in greater monitoring efficiency and likely more precise abundance estimates.

Multiscale Region-Level VHR Image Change Detection via Sparse Change Descriptor and Robust Discriminative Dictionary Learning

PubMed Central

Xu, Yuan; Ding, Kun; Huo, Chunlei; Zhong, Zisha; Li, Haichang; Pan, Chunhong

2015-01-01

Very high resolution (VHR) image change detection is challenging due to the low discriminative ability of change feature and the difficulty of change decision in utilizing the multilevel contextual information. Most change feature extraction techniques put emphasis on the change degree description (i.e., in what degree the changes have happened), while they ignore the change pattern description (i.e., how the changes changed), which is of equal importance in characterizing the change signatures. Moreover, the simultaneous consideration of the classification robust to the registration noise and the multiscale region-consistent fusion is often neglected in change decision. To overcome such drawbacks, in this paper, a novel VHR image change detection method is proposed based on sparse change descriptor and robust discriminative dictionary learning. Sparse change descriptor combines the change degree component and the change pattern component, which are encoded by the sparse representation error and the morphological profile feature, respectively. Robust change decision is conducted by multiscale region-consistent fusion, which is implemented by the superpixel-level cosparse representation with robust discriminative dictionary and the conditional random field model. Experimental results confirm the effectiveness of the proposed change detection technique. PMID:25918748

Design Pattern Mining Using Distributed Learning Automata and DNA Sequence Alignment

PubMed Central

Esmaeilpour, Mansour; Naderifar, Vahideh; Shukur, Zarina

2014-01-01

Context Over the last decade, design patterns have been used extensively to generate reusable solutions to frequently encountered problems in software engineering and object oriented programming. A design pattern is a repeatable software design solution that provides a template for solving various instances of a general problem. Objective This paper describes a new method for pattern mining, isolating design patterns and relationship between them; and a related tool, DLA-DNA for all implemented pattern and all projects used for evaluation. DLA-DNA achieves acceptable precision and recall instead of other evaluated tools based on distributed learning automata (DLA) and deoxyribonucleic acid (DNA) sequences alignment. Method The proposed method mines structural design patterns in the object oriented source code and extracts the strong and weak relationships between them, enabling analyzers and programmers to determine the dependency rate of each object, component, and other section of the code for parameter passing and modular programming. The proposed model can detect design patterns better that available other tools those are Pinot, PTIDEJ and DPJF; and the strengths of their relationships. Results The result demonstrate that whenever the source code is build standard and non-standard, based on the design patterns, then the result of the proposed method is near to DPJF and better that Pinot and PTIDEJ. The proposed model is tested on the several source codes and is compared with other related models and available tools those the results show the precision and recall of the proposed method, averagely 20% and 9.6% are more than Pinot, 27% and 31% are more than PTIDEJ and 3.3% and 2% are more than DPJF respectively. Conclusion The primary idea of the proposed method is organized in two following steps: the first step, elemental design patterns are identified, while at the second step, is composed to recognize actual design patterns. PMID:25243670

A Hidden Markov Model for Analysis of Frontline Veterinary Data for Emerging Zoonotic Disease Surveillance

PubMed Central

Robertson, Colin; Sawford, Kate; Gunawardana, Walimunige S. N.; Nelson, Trisalyn A.; Nathoo, Farouk; Stephen, Craig

2011-01-01

Surveillance systems tracking health patterns in animals have potential for early warning of infectious disease in humans, yet there are many challenges that remain before this can be realized. Specifically, there remains the challenge of detecting early warning signals for diseases that are not known or are not part of routine surveillance for named diseases. This paper reports on the development of a hidden Markov model for analysis of frontline veterinary sentinel surveillance data from Sri Lanka. Field veterinarians collected data on syndromes and diagnoses using mobile phones. A model for submission patterns accounts for both sentinel-related and disease-related variability. Models for commonly reported cattle diagnoses were estimated separately. Region-specific weekly average prevalence was estimated for each diagnoses and partitioned into normal and abnormal periods. Visualization of state probabilities was used to indicate areas and times of unusual disease prevalence. The analysis suggests that hidden Markov modelling is a useful approach for surveillance datasets from novel populations and/or having little historical baselines. PMID:21949763

Time-Elastic Generative Model for Acceleration Time Series in Human Activity Recognition

PubMed Central

Munoz-Organero, Mario; Ruiz-Blazquez, Ramona

2017-01-01

Body-worn sensors in general and accelerometers in particular have been widely used in order to detect human movements and activities. The execution of each type of movement by each particular individual generates sequences of time series of sensed data from which specific movement related patterns can be assessed. Several machine learning algorithms have been used over windowed segments of sensed data in order to detect such patterns in activity recognition based on intermediate features (either hand-crafted or automatically learned from data). The underlying assumption is that the computed features will capture statistical differences that can properly classify different movements and activities after a training phase based on sensed data. In order to achieve high accuracy and recall rates (and guarantee the generalization of the system to new users), the training data have to contain enough information to characterize all possible ways of executing the activity or movement to be detected. This could imply large amounts of data and a complex and time-consuming training phase, which has been shown to be even more relevant when automatically learning the optimal features to be used. In this paper, we present a novel generative model that is able to generate sequences of time series for characterizing a particular movement based on the time elasticity properties of the sensed data. The model is used to train a stack of auto-encoders in order to learn the particular features able to detect human movements. The results of movement detection using a newly generated database with information on five users performing six different movements are presented. The generalization of results using an existing database is also presented in the paper. The results show that the proposed mechanism is able to obtain acceptable recognition rates (F = 0.77) even in the case of using different people executing a different sequence of movements and using different hardware. PMID:28208736

Time-Elastic Generative Model for Acceleration Time Series in Human Activity Recognition.

PubMed

Munoz-Organero, Mario; Ruiz-Blazquez, Ramona

2017-02-08

Body-worn sensors in general and accelerometers in particular have been widely used in order to detect human movements and activities. The execution of each type of movement by each particular individual generates sequences of time series of sensed data from which specific movement related patterns can be assessed. Several machine learning algorithms have been used over windowed segments of sensed data in order to detect such patterns in activity recognition based on intermediate features (either hand-crafted or automatically learned from data). The underlying assumption is that the computed features will capture statistical differences that can properly classify different movements and activities after a training phase based on sensed data. In order to achieve high accuracy and recall rates (and guarantee the generalization of the system to new users), the training data have to contain enough information to characterize all possible ways of executing the activity or movement to be detected. This could imply large amounts of data and a complex and time-consuming training phase, which has been shown to be even more relevant when automatically learning the optimal features to be used. In this paper, we present a novel generative model that is able to generate sequences of time series for characterizing a particular movement based on the time elasticity properties of the sensed data. The model is used to train a stack of auto-encoders in order to learn the particular features able to detect human movements. The results of movement detection using a newly generated database with information on five users performing six different movements are presented. The generalization of results using an existing database is also presented in the paper. The results show that the proposed mechanism is able to obtain acceptable recognition rates ( F = 0.77) even in the case of using different people executing a different sequence of movements and using different hardware.

A pseudo-equilibrium thermodynamic model of information processing in nonlinear brain dynamics.

PubMed

Freeman, Walter J

2008-01-01

Computational models of brain dynamics fall short of performance in speed and robustness of pattern recognition in detecting minute but highly significant pattern fragments. A novel model employs the properties of thermodynamic systems operating far from equilibrium, which is analyzed by linearization near adaptive operating points using root locus techniques. Such systems construct order by dissipating energy. Reinforcement learning of conditioned stimuli creates a landscape of attractors and their basins in each sensory cortex by forming nerve cell assemblies in cortical connectivity. Retrieval of a selected category of stored knowledge is by a phase transition that is induced by a conditioned stimulus, and that leads to pattern self-organization. Near self-regulated criticality the cortical background activity displays aperiodic null spikes at which analytic amplitude nears zero, and which constitute a form of Rayleigh noise. Phase transitions in recognition and recall are initiated at null spikes in the presence of an input signal, owing to the high signal-to-noise ratio that facilitates capture of cortex by an attractor, even by very weak activity that is typically evoked by a conditioned stimulus.

A New Biomarkers Feature Pattern Consisting of TNF-α, IL-10, and IL-8 for Blood Stasis Syndrome with Myocardial Ischemia

PubMed Central

Chen, Jianxin; Chuo, Wenjing; Liu, Lei; Lian, Hongjian; Zheng, Lei; Wang, Yong; Xie, Hua; Luo, Liangtao; Zheng, Chenglong; Fu, Bangze; Wang, Wei

2013-01-01

Objective. To explore new diagnostic patterns for syndromes to overcome the insufficiency of obtainable macrocharacteristics and specific biomarkers. Methods. Chinese miniswines were subjected to Ameroid constrictor, placed around the proximal left anterior descending branch. On the 4th week, macrocharacteristics, coronary angiography, echocardiography, and hemorheology indices were detected for diagnosis. IL-1, IL-6, IL-8, IL-10, TNF-α, and hsCRP in serum were detected, and Decision Tree was built. Results. According to current official-issued standard, model animals matched the diagnosis of blood stasis syndrome with myocardial ischemia based on findings, including >90% occlusion, attenuated left ventricular segmental motion, dark red or purple tongues, and higher blood viscosity. Significant decrease of IL-10 and increase of TNF-α were found in model animals. However, in the Decision Tree, besides IL-10 and TNF-α, IL-8 helped to increase the accuracy of classification to 86%. Conclusions. The Decision Tree building with TNF-α, IL-10, and IL-8 is helpful for the diagnosis of blood stasis syndrome in myocardial ischemia animals. What is more is that our data set up a new path to the differentiation of syndrome by feature patterns consisting of multiple biomarkers not only for animals but also for patients. We believe that it will contribute to the standardization and international application of syndromes. PMID:24371451

Study on Dynamic Development of Three-dimensional Weld Pool Surface in Stationary GTAW

NASA Astrophysics Data System (ADS)

Huang, Jiankang; He, Jing; He, Xiaoying; Shi, Yu; Fan, Ding

2018-04-01

The weld pool contains abundant information about the welding process. In particular, the type of the weld pool surface shape, i. e., convex or concave, is determined by the weld penetration. To detect it, an innovative laser-vision-based sensing method is employed to observe the weld pool surface of the gas tungsten arc welding (GTAW). A low-power laser dots pattern is projected onto the entire weld pool surface. Its reflection is intercepted by a screen and captured by a camera. Then the dynamic development process of the weld pool surface can be detected. By observing and analyzing, the change of the reflected laser dots reflection pattern, for shape of the weld pool surface shape, was found to closely correlate to the penetration of weld pool in the welding process. A mathematical model was proposed to correlate the incident ray, reflected ray, screen and surface of weld pool based on structured laser specular reflection. The dynamic variation of the weld pool surface and its corresponding dots laser pattern were simulated and analyzed. By combining the experimental data and the mathematical analysis, the results show that the pattern of the reflected laser dots pattern is closely correlated to the development of weld pool, such as the weld penetration. The concavity of the pool surface was found to increase rapidly after the surface shape was changed from convex to concave during the stationary GTAW process.

Evaluation of the capabilities of satellite imagery for monitoring regional air pollution episodes

NASA Technical Reports Server (NTRS)

Barnes, J. C.; Bowley, C. J.; Burke, H. H. K.

1979-01-01

A comparative analysis of satellite visible channel imagery and ground based aerosol measurements is carried out for three cases representing a significant pollution episodes based on low surface visibility and high sulfate levels. The feasibility of detecting pollution episodes from space is also investigated using a simulation model. The model results are compared to quantitative information derived from digitized satellite data. The results show that when levels are or = 30 micrograms/cu, a haze pattern that correlates closely with the area of reported low surface visibilities and high micrograms sulfate levels can be detected in satellite visible channel imagery. The model simulation demonstrates the potential of the satellite to monitor the magnitude and areal extent of pollution episodes. Quantitative information on total aerosol amount derived from the satellite digitized data using the atmospheric radiative transfer model agrees well with the results obtained from the ground based measurements.

Automatic detection of rhythmic and periodic patterns in critical care EEG based on American Clinical Neurophysiology Society (ACNS) standardized terminology.

PubMed

Fürbass, F; Hartmann, M M; Halford, J J; Koren, J; Herta, J; Gruber, A; Baumgartner, C; Kluge, T

2015-09-01

Continuous EEG from critical care patients needs to be evaluated time efficiently to maximize the treatment effect. A computational method will be presented that detects rhythmic and periodic patterns according to the critical care EEG terminology (CCET) of the American Clinical Neurophysiology Society (ACNS). The aim is to show that these detected patterns support EEG experts in writing neurophysiological reports. First of all, three case reports exemplify the evaluation procedure using graphically presented detections. Second, 187 hours of EEG from 10 critical care patients were used in a comparative trial study. For each patient the result of a review session using the EEG and the visualized pattern detections was compared to the original neurophysiology report. In three out of five patients with reported seizures, all seizures were reported correctly. In two patients, several subtle clinical seizures with unclear EEG correlation were missed. Lateralized periodic patterns (LPD) were correctly found in 2/2 patients and EEG slowing was correctly found in 7/9 patients. In 8/10 patients, additional EEG features were found including LPDs, EEG slowing, and seizures. The use of automatic pattern detection will assist in review of EEG and increase efficiency. The implementation of bedside surveillance devices using our detection algorithm appears to be feasible and remains to be confirmed in further multicenter studies. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Superparticle phenomenology from the natural mini-landscape

NASA Astrophysics Data System (ADS)

Baer, Howard; Barger, Vernon; Savoy, Michael; Serce, Hasan; Tata, Xerxes

2017-06-01

The methodology of the heterotic mini-landscape attempts to zero in on phenomenologically viable corners of the string landscape where the effective low energy theory is the Minimal Supersymmetric Standard Model with localized grand unification. The gaugino mass pattern is that of mirage-mediation. The magnitudes of various SM Yukawa couplings point to a picture where scalar soft SUSY breaking terms are related to the geography of fields in the compactified dimensions. Higgs fields and third generation scalars extend to the bulk and occur in split multiplets with TeV scale soft masses. First and second generation scalars, localized at orbifold fixed points or tori with enhanced symmetry, occur in complete GUT multiplets and have much larger masses. This picture can be matched onto the parameter space of generalized mirage mediation. Naturalness considerations, the requirement of the observed electroweak symmetry breaking pattern, and LHC bounds on m g together limit the gravitino mass to the m 3/2 ˜ 5-60 TeV range. The mirage unification scale is bounded from below with the limit depending on the ratio of squark to gravitino masses. We show that while natural SUSY in this realization may escape detection even at the high luminosity LHC, the high energy LHC with √{s}=33 TeV could unequivocally confirm or exclude this scenario. It should be possible to detect the expected light higgsinos at the ILC if these are kinematically accessible, and possibly also discriminate the expected compression of gaugino masses in the natural mini-landscape picture from the mass pattern expected in models with gaugino mass unification. The thermal WIMP signal should be accessible via direct detection searches at the multi-ton noble liquid detectors such as XENONnT or LZ.

Trajectories of premorbid childhood and adolescent functioning in schizophrenia-spectrum psychoses: A first-episode study.

PubMed

Horton, Leslie E; Tarbox, Sarah I; Olino, Thomas M; Haas, Gretchen L

2015-06-30

Evidence of social and behavioral problems preceding the onset of schizophrenia-spectrum psychoses is consistent with a neurodevelopmental model of these disorders. Here we predict that individuals with a first episode of schizophrenia-spectrum psychoses will evidence one of three patterns of premorbid adjustment: an early deficit, a deteriorating pattern, or adequate or good social adjustment. Participants were 164 (38% female; 31% black) individuals ages 15-50 with a first episode of schizophrenia-spectrum psychoses. Premorbid adjustment was assessed using the Cannon-Spoor Premorbid Adjustment Scale. We compared the fit of a series of growth mixture models to examine premorbid adjustment trajectories, and found the following 3-class model provided the best fit with: a "stable-poor" adjustment class (54%), a "stable-good" adjustment class (39%), and a "deteriorating" adjustment class (7%). Relative to the "stable-good" class, the "stable-poor" class experienced worse negative symptoms at 1-year follow-up, particularly in the social amotivation domain. This represents the first known growth mixture modeling study to examine premorbid functioning patterns in first-episode schizophrenia-spectrum psychoses. Given that the stable-poor adjustment pattern was most prevalent, detection of social and academic maladjustment as early as childhood may help identify people at increased risk for schizophrenia-spectrum psychoses, potentially increasing feasibility of early interventions. Published by Elsevier Ireland Ltd.

A data fusion approach to indications and warnings of terrorist attacks

NASA Astrophysics Data System (ADS)

McDaniel, David; Schaefer, Gregory

2014-05-01

Indications and Warning (I&W) of terrorist attacks, particularly IED attacks, require detection of networks of agents and patterns of behavior. Social Network Analysis tries to detect a network; activity analysis tries to detect anomalous activities. This work builds on both to detect elements of an activity model of terrorist attack activity - the agents, resources, networks, and behaviors. The activity model is expressed as RDF triples statements where the tuple positions are elements or subsets of a formal ontology for activity models. The advantage of a model is that elements are interdependent and evidence for or against one will influence others so that there is a multiplier effect. The advantage of the formality is that detection could occur hierarchically, that is, at different levels of abstraction. The model matching is expressed as a likelihood ratio between input text and the model triples. The likelihood ratio is designed to be analogous to track correlation likelihood ratios common in JDL fusion level 1. This required development of a semantic distance metric for positive and null hypotheses as well as for complex objects. The metric uses the Web 1Terabype database of one to five gram frequencies for priors. This size requires the use of big data technologies so a Hadoop cluster is used in conjunction with OpenNLP natural language and Mahout clustering software. Distributed data fusion Map Reduce jobs distribute parts of the data fusion problem to the Hadoop nodes. For the purposes of this initial testing, open source models and text inputs of similar complexity to terrorist events were used as surrogates for the intended counter-terrorist application.

Threat-detection in child development: an evolutionary perspective.

PubMed

Boyer, Pascal; Bergstrom, Brian

2011-03-01

Evidence for developmental aspects of fear-targets and anxiety suggests a complex but stable pattern whereby specific kinds of fears emerge at different periods of development. This developmental schedule seems appropriate to dangers encountered repeatedly during human evolution. Also consistent with evolutionary perspective, the threat-detection systems are domain-specific, comprising different kinds of cues to do with predation, intraspecific violence, contamination-contagion and status loss. Proper evolutionary models may also be relevant to outstanding issues in the domain, notably the connections between typical development and pathology. Copyright © 2010 Elsevier Ltd. All rights reserved.

Population density estimated from locations of individuals on a passive detector array

USGS Publications Warehouse

Efford, Murray G.; Dawson, Deanna K.; Borchers, David L.

2009-01-01

The density of a closed population of animals occupying stable home ranges may be estimated from detections of individuals on an array of detectors, using newly developed methods for spatially explicit capture–recapture. Likelihood-based methods provide estimates for data from multi-catch traps or from devices that record presence without restricting animal movement ("proximity" detectors such as camera traps and hair snags). As originally proposed, these methods require multiple sampling intervals. We show that equally precise and unbiased estimates may be obtained from a single sampling interval, using only the spatial pattern of detections. This considerably extends the range of possible applications, and we illustrate the potential by estimating density from simulated detections of bird vocalizations on a microphone array. Acoustic detection can be defined as occurring when received signal strength exceeds a threshold. We suggest detection models for binary acoustic data, and for continuous data comprising measurements of all signals above the threshold. While binary data are often sufficient for density estimation, modeling signal strength improves precision when the microphone array is small.

Application of graph-based semi-supervised learning for development of cyber COP and network intrusion detection

NASA Astrophysics Data System (ADS)

Levchuk, Georgiy; Colonna-Romano, John; Eslami, Mohammed

2017-05-01

The United States increasingly relies on cyber-physical systems to conduct military and commercial operations. Attacks on these systems have increased dramatically around the globe. The attackers constantly change their methods, making state-of-the-art commercial and military intrusion detection systems ineffective. In this paper, we present a model to identify functional behavior of network devices from netflow traces. Our model includes two innovations. First, we define novel features for a host IP using detection of application graph patterns in IP's host graph constructed from 5-min aggregated packet flows. Second, we present the first application, to the best of our knowledge, of Graph Semi-Supervised Learning (GSSL) to the space of IP behavior classification. Using a cyber-attack dataset collected from NetFlow packet traces, we show that GSSL trained with only 20% of the data achieves higher attack detection rates than Support Vector Machines (SVM) and Naïve Bayes (NB) classifiers trained with 80% of data points. We also show how to improve detection quality by filtering out web browsing data, and conclude with discussion of future research directions.

A goodness-of-fit test for occupancy models with correlated within-season revisits

USGS Publications Warehouse

Wright, Wilson; Irvine, Kathryn M.; Rodhouse, Thomas J.

2016-01-01

Occupancy modeling is important for exploring species distribution patterns and for conservation monitoring. Within this framework, explicit attention is given to species detection probabilities estimated from replicate surveys to sample units. A central assumption is that replicate surveys are independent Bernoulli trials, but this assumption becomes untenable when ecologists serially deploy remote cameras and acoustic recording devices over days and weeks to survey rare and elusive animals. Proposed solutions involve modifying the detection-level component of the model (e.g., first-order Markov covariate). Evaluating whether a model sufficiently accounts for correlation is imperative, but clear guidance for practitioners is lacking. Currently, an omnibus goodnessof- fit test using a chi-square discrepancy measure on unique detection histories is available for occupancy models (MacKenzie and Bailey, Journal of Agricultural, Biological, and Environmental Statistics, 9, 2004, 300; hereafter, MacKenzie– Bailey test). We propose a join count summary measure adapted from spatial statistics to directly assess correlation after fitting a model. We motivate our work with a dataset of multinight bat call recordings from a pilot study for the North American Bat Monitoring Program. We found in simulations that our join count test was more reliable than the MacKenzie–Bailey test for detecting inadequacy of a model that assumed independence, particularly when serial correlation was low to moderate. A model that included a Markov-structured detection-level covariate produced unbiased occupancy estimates except in the presence of strong serial correlation and a revisit design consisting only of temporal replicates. When applied to two common bat species, our approach illustrates that sophisticated models do not guarantee adequate fit to real data, underscoring the importance of model assessment. Our join count test provides a widely applicable goodness-of-fit test and specifically evaluates occupancy model lack of fit related to correlation among detections within a sample unit. Our diagnostic tool is available for practitioners that serially deploy survey equipment as a way to achieve cost savings.

Patterns of Detection and Capture Are Associated with Cohabiting Predators and Prey

PubMed Central

Lazenby, Billie T.; Dickman, Christopher R.

2013-01-01

Avoidance behaviour can play an important role in structuring ecosystems but can be difficult to uncover and quantify. Remote cameras have great but as yet unrealized potential to uncover patterns arising from predatory, competitive or other interactions that structure animal communities by detecting species that are active at the same sites and recording their behaviours and times of activity. Here, we use multi-season, two-species occupancy models to test for evidence of interactions between introduced (feral cat Felis catus) and native predator (Tasmanian devil Sarcophilus harrisii) and predator and small mammal (swamp rat Rattus lutreolus velutinus) combinations at baited camera sites in the cool temperate forests of southern Tasmania. In addition, we investigate the capture rates of swamp rats in traps scented with feral cat and devil faecal odours. We observed that one species could reduce the probability of detecting another at a camera site. In particular, feral cats were detected less frequently at camera sites occupied by devils, whereas patterns of swamp rat detection associated with devils or feral cats varied with study site. Captures of swamp rats were not associated with odours on traps, although fewer captures tended to occur in traps scented with the faecal odour of feral cats. The observation that a native carnivorous marsupial, the Tasmanian devil, can suppress the detectability of an introduced eutherian predator, the feral cat, is consistent with a dominant predator – mesopredator relationship. Such a relationship has important implications for the interaction between feral cats and the lower trophic guilds that form their prey, especially if cat activity increases in places where devil populations are declining. More generally, population estimates derived from devices such as remote cameras need to acknowledge the potential for one species to change the detectability of another, and incorporate this in assessments of numbers and survival. PMID:23565172

Sign language spotting with a threshold model based on conditional random fields.

PubMed

Yang, Hee-Deok; Sclaroff, Stan; Lee, Seong-Whan

2009-07-01

Sign language spotting is the task of detecting and recognizing signs in a signed utterance, in a set vocabulary. The difficulty of sign language spotting is that instances of signs vary in both motion and appearance. Moreover, signs appear within a continuous gesture stream, interspersed with transitional movements between signs in a vocabulary and nonsign patterns (which include out-of-vocabulary signs, epentheses, and other movements that do not correspond to signs). In this paper, a novel method for designing threshold models in a conditional random field (CRF) model is proposed which performs an adaptive threshold for distinguishing between signs in a vocabulary and nonsign patterns. A short-sign detector, a hand appearance-based sign verification method, and a subsign reasoning method are included to further improve sign language spotting accuracy. Experiments demonstrate that our system can spot signs from continuous data with an 87.0 percent spotting rate and can recognize signs from isolated data with a 93.5 percent recognition rate versus 73.5 percent and 85.4 percent, respectively, for CRFs without a threshold model, short-sign detection, subsign reasoning, and hand appearance-based sign verification. Our system can also achieve a 15.0 percent sign error rate (SER) from continuous data and a 6.4 percent SER from isolated data versus 76.2 percent and 14.5 percent, respectively, for conventional CRFs.

Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques

NASA Astrophysics Data System (ADS)

Fernández Pozo, Rubén; Blanco Murillo, Jose Luis; Hernández Gómez, Luis; López Gonzalo, Eduardo; Alcázar Ramírez, José; Toledano, Doroteo T.

2009-12-01

This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.

Surface defect detection in tiling Industries using digital image processing methods: analysis and evaluation.

PubMed

Karimi, Mohammad H; Asemani, Davud

2014-05-01

Ceramic and tile industries should indispensably include a grading stage to quantify the quality of products. Actually, human control systems are often used for grading purposes. An automatic grading system is essential to enhance the quality control and marketing of the products. Since there generally exist six different types of defects originating from various stages of tile manufacturing lines with distinct textures and morphologies, many image processing techniques have been proposed for defect detection. In this paper, a survey has been made on the pattern recognition and image processing algorithms which have been used to detect surface defects. Each method appears to be limited for detecting some subgroup of defects. The detection techniques may be divided into three main groups: statistical pattern recognition, feature vector extraction and texture/image classification. The methods such as wavelet transform, filtering, morphology and contourlet transform are more effective for pre-processing tasks. Others including statistical methods, neural networks and model-based algorithms can be applied to extract the surface defects. Although, statistical methods are often appropriate for identification of large defects such as Spots, but techniques such as wavelet processing provide an acceptable response for detection of small defects such as Pinhole. A thorough survey is made in this paper on the existing algorithms in each subgroup. Also, the evaluation parameters are discussed including supervised and unsupervised parameters. Using various performance parameters, different defect detection algorithms are compared and evaluated. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Magnetic particle-scanning for ultrasensitive immunodetection on-chip.

PubMed

Cornaglia, Matteo; Trouillon, Raphaël; Tekin, H Cumhur; Lehnert, Thomas; Gijs, Martin A M

2014-08-19

We describe the concept of magnetic particle-scanning for on-chip detection of biomolecules: a magnetic particle, carrying a low number of antigens (Ag's) (down to a single molecule), is transported by hydrodynamic forces and is subjected to successive stochastic reorientations in an engineered magnetic energy landscape. The latter consists of a pattern of substrate-bound small magnetic particles that are functionalized with antibodies (Ab's). Subsequationuent counting of the captured Ag-carrying particles provides the detection signal. The magnetic particle-scanning principle is investigated in a custom-built magneto-microfluidic chip and theoretically described by a random walk-based model, in which the trajectory of the contact point between an Ag-carrying particle and the small magnetic particle pattern is described by stochastic moves over the surface of the mobile particle, until this point coincides with the position of an Ag, resulting in the binding of the particle. This model explains the particular behavior of previously reported experimental dose-response curves obtained for two different ligand-receptor systems (biotin/streptavidin and TNF-α) over a wide range of concentrations. Our model shows that magnetic particle-scanning results in a very high probability of immunocomplex formation for very low Ag concentrations, leading to an extremely low limit of detection, down to the single molecule-per-particle level. When compared to other types of magnetic particle-based surface coverage assays, our strategy was found to offer a wider dynamic range (>8 orders of magnitude), as the system does not saturate for concentrations as high as 10(11) Ag molecules in a 5 μL drop. Furthermore, by emphasizing the importance of maximizing the encounter probability between the Ag and the Ab to improve sensitivity, our model also contributes to explaining the behavior of other particle-based heterogeneous immunoassays.

Population structure and covariate analysis based on pairwise microsatellite allele matching frequencies.

PubMed

Givens, Geof H; Ozaksoy, Isin

2007-01-01

We describe a general model for pairwise microsatellite allele matching probabilities. The model can be used for analysis of population substructure, and is particularly focused on relating genetic correlation to measurable covariates. The approach is intended for cases when the existence of subpopulations is uncertain and a priori assignment of samples to hypothesized subpopulations is difficult. Such a situation arises, for example, with western Arctic bowhead whales, where genetic samples are available only from a possibly mixed migratory assemblage. We estimate genetic structure associated with spatial, temporal, or other variables that may confound the detection of population structure. In the bowhead case, the model permits detection of genetic patterns associated with a temporally pulsed multi-population assemblage in the annual migration. Hypothesis tests for population substructure and for covariate effects can be carried out using permutation methods. Simulated and real examples illustrate the effectiveness and reliability of the approach and enable comparisons with other familiar approaches. Analysis of the bowhead data finds no evidence for two temporally pulsed subpopulations using the best available data, although a significant pattern found by other researchers using preliminary data is also confirmed here. Code in the R language is available from www.stat.colostate.edu/~geof/gammmp.html.

ERTS-1 imagery use in reconnaissance prospecting: Evaluation of commercial utility of ERTS-1 imagery in structural reconnaissance for minerals and petroleum

NASA Technical Reports Server (NTRS)

Saunders, D. F.; Thomas, G. E. (Principal Investigator); Kinsman, F. E.; Beatty, D. F.

1973-01-01

The author has identified the following significant results. This study was performed to investigate applications of ERTS-1 imagery in commercial reconnaissance for mineral and hydrocarbon resources. ERTS-1 imagery collected over five areas in North America (Montana; Colorado; New Mexico-West Texas; Superior Province, Canada; and North Slope, Alaska) has been analyzed for data content including linears, lineaments, and curvilinear anomalies. Locations of these features were mapped and compared with known locations of mineral and hydrocarbon accumulations. Results were analyzed in the context of a simple-shear, block-coupling model. Data analyses have resulted in detection of new lineaments, some of which may be continental in extent, detection of many curvilinear patterns not generally seen on aerial photos, strong evidence of continental regmatic fracture patterns, and realization that geological features can be explained in terms of a simple-shear, block-coupling model. The conculsions are that ERTS-1 imagery is of great value in photogeologic/geomorphic interpretations of regional features, and the simple-shear, block-coupling model provides a means of relating data from ERTS imagery to structures that have controlled emplacement of ore deposits and hydrocarbon accumulations, thus providing a basis for a new approach for reconnaissance for mineral, uranium, gas, and oil deposits and structures.

Estimating secular changes in longitudinal growth patterns underlying adult height with the QEPS model: the Grow Up Gothenburg cohorts.

PubMed

Holmgren, Anton; Niklasson, Aimon; Nierop, Andreas F M; Gelander, Lars; Aronson, A Stefan; Sjöberg, Agneta; Lissner, Lauren; Albertsson-Wikland, Kerstin

2018-05-23

Over the past 150 years, humans have become taller, and puberty has begun earlier. It is unclear if these changes are continuing in Sweden, and how longitudinal growth patterns are involved. We aimed to evaluate the underlying changes in growth patterns from birth to adulthood by QEPS estimates in two Swedish cohorts born in 1974 and 1990. Growth characteristics of the longitudinal 1974 and 1990-birth cohorts (n = 4181) were compared using the QEPS model together with adult heights. There was more rapid fetal/infancy growth in girls/boys born in 1990 compared to 1974, as shown by a faster Etimescale and they were heavier at birth. The laterborn were taller also in childhood as shown by a higher Q-function. Girls born in 1990 had earlier and more pronounced growth during puberty than girls born in 1974. Individuals in the 1990 cohort attained greater adult heights than those in the 1974 cohort; 6 mm taller for females and 10 mm for males. A positive change in adult height was attributed to more growth during childhood in both sexes and during puberty for girls. The QEPS model proved to be effective detecting small changes of growth patterns, between two longitudinal growth cohorts born only 16 years apart.

77 FR 19231 - Takes of Marine Mammals Incidental to Specified Activities; Navy Training Conducted at the Silver...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-03-30

... break, presence of glassy foam, and/or perhaps scattered whitecaps). Applicable mitigation zones are... California based on passive acoustic detection of two distinct echolocation click patterns. No population... 10 1,020 1,220 (2) ZOI and Swim Speed-Time-Buffer Addition Based on acoustic propagation modeling and...

Markov models of genome segmentation

NASA Astrophysics Data System (ADS)

Thakur, Vivek; Azad, Rajeev K.; Ramaswamy, Ram

2007-01-01

We introduce Markov models for segmentation of symbolic sequences, extending a segmentation procedure based on the Jensen-Shannon divergence that has been introduced earlier. Higher-order Markov models are more sensitive to the details of local patterns and in application to genome analysis, this makes it possible to segment a sequence at positions that are biologically meaningful. We show the advantage of higher-order Markov-model-based segmentation procedures in detecting compositional inhomogeneity in chimeric DNA sequences constructed from genomes of diverse species, and in application to the E. coli K12 genome, boundaries of genomic islands, cryptic prophages, and horizontally acquired regions are accurately identified.

Does the Organized Sexual Murderer Better Delay and Avoid Detection?

PubMed

Beauregard, Eric; Martineau, Melissa

2016-01-01

According to the organized-disorganized model, organized sexual murderers adopt specific behaviors during the commission of their crimes that contribute to avoiding police detection. The current study examines the effect of sexual murderers' organized behaviors on their ability to both delay and/or avoid police detection. Using a combination of negative binomial and logistic regression analyses on a sample of 350 sexual murder cases, findings showed that although both measures of delaying and avoiding detection are positively correlated, different behavioral patterns were observed. For instance, offenders who moved the victim's body were more likely to avoid detection but the victim's body was likely to be recovered faster. Moreover, victim characteristics have an impact on both measures; however, this effect disappears for the measure of delaying detection once the organized behaviors are introduced. Implications of the findings are discussed. © The Author(s) 2014.

Estimates of Social Contact in a Middle School Based on Self-Report and Wireless Sensor Data

PubMed Central

Leecaster, Molly; Toth, Damon J. A.; Pettey, Warren B. P.; Rainey, Jeanette J.; Gao, Hongjiang; Uzicanin, Amra; Samore, Matthew

2016-01-01

Estimates of contact among children, used for infectious disease transmission models and understanding social patterns, historically rely on self-report logs. Recently, wireless sensor technology has enabled objective measurement of proximal contact and comparison of data from the two methods. These are mostly small-scale studies, and knowledge gaps remain in understanding contact and mixing patterns and also in the advantages and disadvantages of data collection methods. We collected contact data from a middle school, with 7th and 8th grades, for one day using self-report contact logs and wireless sensors. The data were linked for students with unique initials, gender, and grade within the school. This paper presents the results of a comparison of two approaches to characterize school contact networks, wireless proximity sensors and self-report logs. Accounting for incomplete capture and lack of participation, we estimate that “sensor-detectable”, proximal contacts longer than 20 seconds during lunch and class-time occurred at 2 fold higher frequency than “self-reportable” talk/touch contacts. Overall, 55% of estimated talk-touch contacts were also sensor-detectable whereas only 15% of estimated sensor-detectable contacts were also talk-touch. Contacts detected by sensors and also in self-report logs had longer mean duration than contacts detected only by sensors (6.3 vs 2.4 minutes). During both lunch and class-time, sensor-detectable contacts demonstrated substantially less gender and grade assortativity than talk-touch contacts. Hallway contacts, which were ascertainable only by proximity sensors, were characterized by extremely high degree and short duration. We conclude that the use of wireless sensors and self-report logs provide complementary insight on in-school mixing patterns and contact frequency. PMID:27100090

Pattern-histogram-based temporal change detection using personal chest radiographs

NASA Astrophysics Data System (ADS)

Ugurlu, Yucel; Obi, Takashi; Hasegawa, Akira; Yamaguchi, Masahiro; Ohyama, Nagaaki

1999-05-01

An accurate and reliable detection of temporal changes from a pair of images has considerable interest in the medical science. Traditional registration and subtraction techniques can be applied to extract temporal differences when,the object is rigid or corresponding points are obvious. However, in radiological imaging, loss of the depth information, the elasticity of object, the absence of clearly defined landmarks and three-dimensional positioning differences constraint the performance of conventional registration techniques. In this paper, we propose a new method in order to detect interval changes accurately without using an image registration technique. The method is based on construction of so-called pattern histogram and comparison procedure. The pattern histogram is a graphic representation of the frequency counts of all allowable patterns in the multi-dimensional pattern vector space. K-means algorithm is employed to partition pattern vector space successively. Any differences in the pattern histograms imply that different patterns are involved in the scenes. In our experiment, a pair of chest radiographs of pneumoconiosis is employed and the changing histogram bins are visualized on both of the images. We found that the method can be used as an alternative way of temporal change detection, particularly when the precise image registration is not available.

A Poisson hierarchical modelling approach to detecting copy number variation in sequence coverage data.

PubMed

Sepúlveda, Nuno; Campino, Susana G; Assefa, Samuel A; Sutherland, Colin J; Pain, Arnab; Clark, Taane G

2013-02-26

The advent of next generation sequencing technology has accelerated efforts to map and catalogue copy number variation (CNV) in genomes of important micro-organisms for public health. A typical analysis of the sequence data involves mapping reads onto a reference genome, calculating the respective coverage, and detecting regions with too-low or too-high coverage (deletions and amplifications, respectively). Current CNV detection methods rely on statistical assumptions (e.g., a Poisson model) that may not hold in general, or require fine-tuning the underlying algorithms to detect known hits. We propose a new CNV detection methodology based on two Poisson hierarchical models, the Poisson-Gamma and Poisson-Lognormal, with the advantage of being sufficiently flexible to describe different data patterns, whilst robust against deviations from the often assumed Poisson model. Using sequence coverage data of 7 Plasmodium falciparum malaria genomes (3D7 reference strain, HB3, DD2, 7G8, GB4, OX005, and OX006), we showed that empirical coverage distributions are intrinsically asymmetric and overdispersed in relation to the Poisson model. We also demonstrated a low baseline false positive rate for the proposed methodology using 3D7 resequencing data and simulation. When applied to the non-reference isolate data, our approach detected known CNV hits, including an amplification of the PfMDR1 locus in DD2 and a large deletion in the CLAG3.2 gene in GB4, and putative novel CNV regions. When compared to the recently available FREEC and cn.MOPS approaches, our findings were more concordant with putative hits from the highest quality array data for the 7G8 and GB4 isolates. In summary, the proposed methodology brings an increase in flexibility, robustness, accuracy and statistical rigour to CNV detection using sequence coverage data.

Detection of motor imagery of swallow EEG signals based on the dual-tree complex wavelet transform and adaptive model selection

NASA Astrophysics Data System (ADS)

Yang, Huijuan; Guan, Cuntai; Sui Geok Chua, Karen; San Chok, See; Wang, Chuan Chu; Kok Soon, Phua; Tang, Christina Ka Yin; Keng Ang, Kai

2014-06-01

Objective. Detection of motor imagery of hand/arm has been extensively studied for stroke rehabilitation. This paper firstly investigates the detection of motor imagery of swallow (MI-SW) and motor imagery of tongue protrusion (MI-Ton) in an attempt to find a novel solution for post-stroke dysphagia rehabilitation. Detection of MI-SW from a simple yet relevant modality such as MI-Ton is then investigated, motivated by the similarity in activation patterns between tongue movements and swallowing and there being fewer movement artifacts in performing tongue movements compared to swallowing. Approach. Novel features were extracted based on the coefficients of the dual-tree complex wavelet transform to build multiple training models for detecting MI-SW. The session-to-session classification accuracy was boosted by adaptively selecting the training model to maximize the ratio of between-classes distances versus within-class distances, using features of training and evaluation data. Main results. Our proposed method yielded averaged cross-validation (CV) classification accuracies of 70.89% and 73.79% for MI-SW and MI-Ton for ten healthy subjects, which are significantly better than the results from existing methods. In addition, averaged CV accuracies of 66.40% and 70.24% for MI-SW and MI-Ton were obtained for one stroke patient, demonstrating the detectability of MI-SW and MI-Ton from the idle state. Furthermore, averaged session-to-session classification accuracies of 72.08% and 70% were achieved for ten healthy subjects and one stroke patient using the MI-Ton model. Significance. These results and the subjectwise strong correlations in classification accuracies between MI-SW and MI-Ton demonstrated the feasibility of detecting MI-SW from MI-Ton models.

Electromagnetic Thermography Nondestructive Evaluation: Physics-based Modeling and Pattern Mining

PubMed Central

Gao, Bin; Woo, Wai Lok; Tian, Gui Yun

2016-01-01

Electromagnetic mechanism of Joule heating and thermal conduction on conductive material characterization broadens their scope for implementation in real thermography based Nondestructive testing and evaluation (NDT&E) systems by imparting sensitivity, conformability and allowing fast and imaging detection, which is necessary for efficiency. The issue of automatic material evaluation has not been fully addressed by researchers and it marks a crucial first step to analyzing the structural health of the material, which in turn sheds light on understanding the production of the defects mechanisms. In this study, we bridge the gap between the physics world and mathematical modeling world. We generate physics-mathematical modeling and mining route in the spatial-, time-, frequency-, and sparse-pattern domains. This is a significant step towards realizing the deeper insight in electromagnetic thermography (EMT) and automatic defect identification. This renders the EMT a promising candidate for the highly efficient and yet flexible NDT&E. PMID:27158061

Temporal abstraction-based clinical phenotyping with Eureka!

PubMed

Post, Andrew R; Kurc, Tahsin; Willard, Richie; Rathod, Himanshu; Mansour, Michel; Pai, Akshatha Kalsanka; Torian, William M; Agravat, Sanjay; Sturm, Suzanne; Saltz, Joel H

2013-01-01

Temporal abstraction, a method for specifying and detecting temporal patterns in clinical databases, is very expressive and performs well, but it is difficult for clinical investigators and data analysts to understand. Such patterns are critical in phenotyping patients using their medical records in research and quality improvement. We have previously developed the Analytic Information Warehouse (AIW), which computes such phenotypes using temporal abstraction but requires software engineers to use. We have extended the AIW's web user interface, Eureka! Clinical Analytics, to support specifying phenotypes using an alternative model that we developed with clinical stakeholders. The software converts phenotypes from this model to that of temporal abstraction prior to data processing. The model can represent all phenotypes in a quality improvement project and a growing set of phenotypes in a multi-site research study. Phenotyping that is accessible to investigators and IT personnel may enable its broader adoption.

Describing litho-constrained layout by a high-resolution model filter

NASA Astrophysics Data System (ADS)

Tsai, Min-Chun

2008-05-01

A novel high-resolution model (HRM) filtering technique was proposed to describe litho-constrained layouts. Litho-constrained layouts are layouts that have difficulties to pattern or are highly sensitive to process-fluctuations under current lithography technologies. HRM applies a short-wavelength (or high NA) model simulation directly on the pre-OPC, original design layout to filter out low spatial-frequency regions, and retain high spatial-frequency components which are litho-constrained. Since no OPC neither mask-synthesis steps are involved, this new technique is highly efficient in run time and can be used in design stage to detect and fix litho-constrained patterns. This method has successfully captured all the hot-spots with less than 15% overshoots on a realistic 80 mm2 full-chip M1 layout in 65nm technology node. A step by step derivation of this HRM technique is presented in this paper.

From The Cover: Induction of antiviral immunity requires Toll-like receptor signaling in both stromal and dendritic cell compartments

NASA Astrophysics Data System (ADS)

Sato, Ayuko; Iwasaki, Akiko

2004-11-01

Pattern recognition by Toll-like receptors (TLRs) is known to be important for the induction of dendritic cell (DC) maturation. DCs, in turn, are critically important in the initiation of T cell responses. However, most viruses do not infect DCs. This recognition system poses a biological problem in ensuring that most viral infections be detected by pattern recognition receptors. Furthermore, it is unknown what, if any, is the contribution of TLRs expressed by cells that are infected by a virus, versus TLRs expressed by DCs, in the initiation of antiviral adaptive immunity. Here we address these issues using a physiologically relevant model of mucosal infection with herpes simplex virus type 2. We demonstrate that innate immune recognition of viral infection occurs in two distinct stages, one at the level of the infected epithelial cells and the other at the level of the noninfected DCs. Importantly, both TLR-mediated recognition events are required for the induction of effector T cells. Our results demonstrate that virally infected tissues instruct DCs to initiate the appropriate class of effector T cell responses and reveal the critical importance of the stromal cells in detecting infectious agents through their own pattern recognition receptors. mucosal immunity | pattern recognition | viral infection

Synergetic use of Sentinel-1 and 2 to improve agro-hydrological modeling. Results of groundwater pumping estimates in south-India and nitrogen excess in south-west of France

NASA Astrophysics Data System (ADS)

Ferrant, S.; Le Page, M.; Kerr, Y. H.; Selles, A.; Mermoz, S.; Al-Bitar, A.; Muddu, S.; Gascoin, S.; Marechal, J. C.; Durand, P.; Salmon-Monviola, J.; Ceschia, E.; Bustillo, V.

2016-12-01

Nitrogen transfers at agricultural catchment level are intricately linked to water transfers. Agro-hydrological modeling approaches aim at integrating spatial heterogeneity of catchment physical properties together with agricultural practices to spatially estimate the water and nitrogen cycles. As in hydrology, the calibration schemes are designed to optimize the performance of the temporal dynamics and biases in model simulations, while ignoring the simulated spatial pattern. Yet, crop uses, i.e. transpiration and nitrogen exported by harvest, are the main fluxes at the catchment scale, highly variable in space and time. Geo-information time-series of vegetation and water index with multi-spectral optical detection S2 together with surface roughness time series with C-band radar detection S1 are used to reset soil water holding capacity parameters (depth, porosity) and agricultural practices (sowing date, irrigated area extent) of a crop model coupled with a hydrological model. This study takes two agro-hydrological contexts as demonstrators: 1-spatial nitrogen excess estimation in south-west of France, and 2-groundwater extraction for rice irrigation in south-India. Spatio-temporal patterns are involved in respectively surface water contamination due to over-fertilization and local groundwater shortages due to over-pumping for above rice inundation. Optimized Leaf Area Index profiles are simulated at the satellite images pixel level using an agro-hydrological model to reproduce spatial and temporal crop growth dynamics in south-west of France, improving the in-stream nitrogen fluxes by 12%. Accurate detection of irrigated area extents are obtained with the thresholding method based on optical indices, with a kappa of 0.81 for the dry season 2016. The actual monsoon season is monitored and will be presented. These extents drive the groundwater pumping and are highly variable in time (from 2 to 8% of the total area).

A feature illustration and application of azimuthal P receiver function patterns

NASA Astrophysics Data System (ADS)

Eckhardt, C.; Rabbel, W.

2009-12-01

Based on a synthetic catalog of thirty azimuthal patterns of P receiver functions for crustal structures down to thirty km depth we have summarized and illustrated the most important azimuthal features. We have constructed five model classes encompassing (an-)isotropic horizontal and dipping layers. The model classes were initialized by in situ observations of three deep reflection seismic profiles (DEKORP) of varying high reflective zones and a spiral shaped foliation scheme of an upper crustal bore hole out of the German Continental Deep Drilling Program (KTB). Up to fourteen azimuthal features were extracted out of the synthetic patterns and could be grouped into an already known fundamental part, a multiple part and into an extension part. Each feature was rated by a specific grade A, B, C to inform about the type of its initialization ((an-) isotropy and/or layer dipping). We have evaluated the fourteen features on the synthetic patterns to apply a hierarchical classification. From the classification of the model objects we found that nearly eighty percent of the models are well explained by the fundamental part. The hierarchical order of the model objects can be used as a template to screen real observed azimuthal patterns to find a starting model for a forward modeling or an inversion procedure. For one station of the German Regional Seismic Network (GRSN) we have evaluated the features and screened them through the template. A forward simulation of the azimuthal pattern, using the modified first found model explanation out of the hierarchical order for station MOX, leads to a good coincidence between the real and the simulated pattern. The final 1D model could be divided into an upper crustal part (8 km deep) with an axis of symmetry tilt of 55° and 20°NW trend (direction of axis tilt) and a lower crustal part (24 km thickness) with an axis of symmetry of increasing tilt from 55° to 85° and a trend orientation of 20°SE. For the simulation we have assumed 8 and 7 percent of negative P+S anisotropy for hexagonal symmetry of the upper and lower crust, respectively. From the synthetic and the real observations it is evident that additional boundaries beside the Moho discontinuity are merely detectable for certain circumstances in an azimuthal resolution and will be blinded out in the traditional radial stack.

Reservoir inflow forecasting with a modified coactive neuro-fuzzy inference system: a case study for a semi-arid region

NASA Astrophysics Data System (ADS)

Allawi, Mohammed Falah; Jaafar, Othman; Mohamad Hamzah, Firdaus; Mohd, Nuruol Syuhadaa; Deo, Ravinesh C.; El-Shafie, Ahmed

2017-10-01

Existing forecast models applied for reservoir inflow forecasting encounter several drawbacks, due to the difficulty of the underlying mathematical procedures being to cope with and to mimic the naturalization and stochasticity of the inflow data patterns. In this study, appropriate adjustments to the conventional coactive neuro-fuzzy inference system (CANFIS) method are proposed to improve the mathematical procedure, thus enabling a better detection of the high nonlinearity patterns found in the reservoir inflow training data. This modification includes the updating of the back propagation algorithm, leading to a consequent update of the membership rules and the induction of the centre-weighted set rather than the global weighted set used in feature extraction. The modification also aids in constructing an integrated model that is able to not only detect the nonlinearity in the training data but also the wide range of features within the training data records used to simulate the forecasting model. To demonstrate the model's efficacy, the proposed CANFIS method has been applied to forecast monthly inflow data at Aswan High Dam (AHD), located in southern Egypt. Comparative analyses of the forecasting skill of the modified CANFIS and the conventional ANFIS model are carried out with statistical score indicators to assess the reliability of the developed method. The statistical metrics support the better performance of the developed CANFIS model, which significantly outperforms the ANFIS model to attain a low relative error value (23%), mean absolute error (1.4 BCM month-1), root mean square error (1.14 BCM month-1), and a relative large coefficient of determination (0.94). The present study ascertains the better utility of the modified CANFIS model in respect to the traditional ANFIS model applied in reservoir inflow forecasting for a semi-arid region.

Investigation of Time Series Representations and Similarity Measures for Structural Damage Pattern Recognition

PubMed Central

Swartz, R. Andrew

2013-01-01

This paper investigates the time series representation methods and similarity measures for sensor data feature extraction and structural damage pattern recognition. Both model-based time series representation and dimensionality reduction methods are studied to compare the effectiveness of feature extraction for damage pattern recognition. The evaluation of feature extraction methods is performed by examining the separation of feature vectors among different damage patterns and the pattern recognition success rate. In addition, the impact of similarity measures on the pattern recognition success rate and the metrics for damage localization are also investigated. The test data used in this study are from the System Identification to Monitor Civil Engineering Structures (SIMCES) Z24 Bridge damage detection tests, a rigorous instrumentation campaign that recorded the dynamic performance of a concrete box-girder bridge under progressively increasing damage scenarios. A number of progressive damage test case datasets and damage test data with different damage modalities are used. The simulation results show that both time series representation methods and similarity measures have significant impact on the pattern recognition success rate. PMID:24191136

Image discrimination models predict detection in fixed but not random noise

NASA Technical Reports Server (NTRS)

Ahumada, A. J. Jr; Beard, B. L.; Watson, A. B. (Principal Investigator)

1997-01-01

By means of a two-interval forced-choice procedure, contrast detection thresholds for an aircraft positioned on a simulated airport runway scene were measured with fixed and random white-noise masks. The term fixed noise refers to a constant, or unchanging, noise pattern for each stimulus presentation. The random noise was either the same or different in the two intervals. Contrary to simple image discrimination model predictions, the same random noise condition produced greater masking than the fixed noise. This suggests that observers seem unable to hold a new noisy image for comparison. Also, performance appeared limited by internal process variability rather than by external noise variability, since similar masking was obtained for both random noise types.

Local Orientation and the Evolution of Foraging: Changes in Decision Making Can Eliminate Evolutionary Trade-offs

PubMed Central

van der Post, Daniel J.; Semmann, Dirk

2011-01-01

Information processing is a major aspect of the evolution of animal behavior. In foraging, responsiveness to local feeding opportunities can generate patterns of behavior which reflect or “recognize patterns” in the environment beyond the perception of individuals. Theory on the evolution of behavior generally neglects such opportunity-based adaptation. Using a spatial individual-based model we study the role of opportunity-based adaptation in the evolution of foraging, and how it depends on local decision making. We compare two model variants which differ in the individual decision making that can evolve (restricted and extended model), and study the evolution of simple foraging behavior in environments where food is distributed either uniformly or in patches. We find that opportunity-based adaptation and the pattern recognition it generates, plays an important role in foraging success, particularly in patchy environments where one of the main challenges is “staying in patches”. In the restricted model this is achieved by genetic adaptation of move and search behavior, in light of a trade-off on within- and between-patch behavior. In the extended model this trade-off does not arise because decision making capabilities allow for differentiated behavioral patterns. As a consequence, it becomes possible for properties of movement to be specialized for detection of patches with more food, a larger scale information processing not present in the restricted model. Our results show that changes in decision making abilities can alter what kinds of pattern recognition are possible, eliminate an evolutionary trade-off and change the adaptive landscape. PMID:21998571

Modeling carbachol-induced hippocampal network synchronization using hidden Markov models

NASA Astrophysics Data System (ADS)

Dragomir, Andrei; Akay, Yasemin M.; Akay, Metin

2010-10-01

In this work we studied the neural state transitions undergone by the hippocampal neural network using a hidden Markov model (HMM) framework. We first employed a measure based on the Lempel-Ziv (LZ) estimator to characterize the changes in the hippocampal oscillation patterns in terms of their complexity. These oscillations correspond to different modes of hippocampal network synchronization induced by the cholinergic agonist carbachol in the CA1 region of mice hippocampus. HMMs are then used to model the dynamics of the LZ-derived complexity signals as first-order Markov chains. Consequently, the signals corresponding to our oscillation recordings can be segmented into a sequence of statistically discriminated hidden states. The segmentation is used for detecting transitions in neural synchronization modes in data recorded from wild-type and triple transgenic mice models (3xTG) of Alzheimer's disease (AD). Our data suggest that transition from low-frequency (delta range) continuous oscillation mode into high-frequency (theta range) oscillation, exhibiting repeated burst-type patterns, occurs always through a mode resembling a mixture of the two patterns, continuous with burst. The relatively random patterns of oscillation during this mode may reflect the fact that the neuronal network undergoes re-organization. Further insight into the time durations of these modes (retrieved via the HMM segmentation of the LZ-derived signals) reveals that the mixed mode lasts significantly longer (p < 10-4) in 3xTG AD mice. These findings, coupled with the documented cholinergic neurotransmission deficits in the 3xTG mice model, may be highly relevant for the case of AD.

Deep Learning for Extreme Weather Detection

NASA Astrophysics Data System (ADS)

Prabhat, M.; Racah, E.; Biard, J.; Liu, Y.; Mudigonda, M.; Kashinath, K.; Beckham, C.; Maharaj, T.; Kahou, S.; Pal, C.; O'Brien, T. A.; Wehner, M. F.; Kunkel, K.; Collins, W. D.

2017-12-01

We will present our latest results from the application of Deep Learning methods for detecting, localizing and segmenting extreme weather patterns in climate data. We have successfully applied supervised convolutional architectures for the binary classification tasks of detecting tropical cyclones and atmospheric rivers in centered, cropped patches. We have subsequently extended our architecture to a semi-supervised formulation, which is capable of learning a unified representation of multiple weather patterns, predicting bounding boxes and object categories, and has the capability to detect novel patterns (w/ few, or no labels). We will briefly present our efforts in scaling the semi-supervised architecture to 9600 nodes of the Cori supercomputer, obtaining 15PF performance. Time permitting, we will highlight our efforts in pixel-level segmentation of weather patterns.

Subthalamic nucleus detects unnatural android movement.

PubMed

Ikeda, Takashi; Hirata, Masayuki; Kasaki, Masashi; Alimardani, Maryam; Matsushita, Kojiro; Yamamoto, Tomoyuki; Nishio, Shuichi; Ishiguro, Hiroshi

2017-12-19

An android, i.e., a realistic humanoid robot with human-like capabilities, may induce an uncanny feeling in human observers. The uncanny feeling about an android has two main causes: its appearance and movement. The uncanny feeling about an android increases when its appearance is almost human-like but its movement is not fully natural or comparable to human movement. Even if an android has human-like flexible joints, its slightly jerky movements cause a human observer to detect subtle unnaturalness in them. However, the neural mechanism underlying the detection of unnatural movements remains unclear. We conducted an fMRI experiment to compare the observation of an android and the observation of a human on which the android is modelled, and we found differences in the activation pattern of the brain regions that are responsible for the production of smooth and natural movement. More specifically, we found that the visual observation of the android, compared with that of the human model, caused greater activation in the subthalamic nucleus (STN). When the android's slightly jerky movements are visually observed, the STN detects their subtle unnaturalness. This finding suggests that the detection of unnatural movements is attributed to an error signal resulting from a mismatch between a visual input and an internal model for smooth movement.

Convolution Comparison Pattern: An Efficient Local Image Descriptor for Fingerprint Liveness Detection

PubMed Central

Gottschlich, Carsten

2016-01-01

We present a new type of local image descriptor which yields binary patterns from small image patches. For the application to fingerprint liveness detection, we achieve rotation invariant image patches by taking the fingerprint segmentation and orientation field into account. We compute the discrete cosine transform (DCT) for these rotation invariant patches and attain binary patterns by comparing pairs of two DCT coefficients. These patterns are summarized into one or more histograms per image. Each histogram comprises the relative frequencies of pattern occurrences. Multiple histograms are concatenated and the resulting feature vector is used for image classification. We name this novel type of descriptor convolution comparison pattern (CCP). Experimental results show the usefulness of the proposed CCP descriptor for fingerprint liveness detection. CCP outperforms other local image descriptors such as LBP, LPQ and WLD on the LivDet 2013 benchmark. The CCP descriptor is a general type of local image descriptor which we expect to prove useful in areas beyond fingerprint liveness detection such as biological and medical image processing, texture recognition, face recognition and iris recognition, liveness detection for face and iris images, and machine vision for surface inspection and material classification. PMID:26844544

Detection of quantum well induced single degenerate-transition-dipoles in ZnO nanorods.

PubMed

Ghosh, Siddharth; Ghosh, Moumita; Seibt, Michael; Rao, G Mohan

2016-02-07

Quantifying and characterising atomic defects in nanocrystals is difficult and low-throughput using the existing methods such as high resolution transmission electron microscopy (HRTEM). In this article, using a defocused wide-field optical imaging technique, we demonstrate that a single ultrahigh-piezoelectric ZnO nanorod contains a single defect site. We model the observed dipole-emission patterns from optical imaging with a multi-dimensional dipole and find that the experimentally observed dipole pattern and model-calculated patterns are in excellent agreement. This agreement suggests the presence of vertically oriented degenerate-transition-dipoles in vertically aligned ZnO nanorods. The HRTEM of the ZnO nanorod shows the presence of a stacking fault, which generates a localised quantum well induced degenerate-transition-dipole. Finally, we elucidate that defocused wide-field imaging can be widely used to characterise defects in nanomaterials to answer many difficult questions concerning the performance of low-dimensional devices, such as in energy harvesting, advanced metal-oxide-semiconductor storage, and nanoelectromechanical and nanophotonic devices.

Sensitivity to image recurrence across eye-movement-like image transitions through local serial inhibition in the retina

PubMed Central

Krishnamoorthy, Vidhyasankar; Weick, Michael; Gollisch, Tim

2017-01-01

Standard models of stimulus encoding in the retina postulate that image presentations activate neurons according to the increase of preferred contrast inside the receptive field. During natural vision, however, images do not arrive in isolation, but follow each other rapidly, separated by sudden gaze shifts. We here report that, contrary to standard models, specific ganglion cells in mouse retina are suppressed after a rapid image transition by changes in visual patterns across the transition, but respond with a distinct spike burst when the same pattern reappears. This sensitivity to image recurrence depends on opposing effects of glycinergic and GABAergic inhibition and can be explained by a circuit of local serial inhibition. Rapid image transitions thus trigger a mode of operation that differs from the processing of simpler stimuli and allows the retina to tag particular image parts or to detect transition types that lead to recurring stimulus patterns. DOI: http://dx.doi.org/10.7554/eLife.22431.001 PMID:28230526

The Effects Of Dental Anxiety And Irregular Attendance On Referral For Dental Treatment Under Sedation Within The National Health Service In London

PubMed Central

Milgrom, Peter; Newton, J. T.; Boyle, Carole; Heaton, Lisa J.; Donaldson, Nora

2010-01-01

Objective To investigate whether the relationship between dental anxiety and referral for treatment under sedation is explained by attendance patterns and oral health. Methods Structural Equation Modeling was used on the covariance matrix of the covariates to test hypothesized inter-relationships. Subsequently, we modeled the probability of referral for treatment under sedation with a multiple logistic regression taking into account inter-relationships between the independent variables. Results A direct significant association of referral with dental anxiety and attendance patterns was detected but not with oral health status. However, oral health and anxiety were highly correlated. Also signaled were correlations between age and education and between gender and bad past experience. Conclusion Referral for treatment under sedation appears to be motivated by both fear and irregular patterns of attendance. Coupled with behavioral treatments to address dental fear and attendance, sedation can part of comprehensive care where curative treatments are long or unpleasant for patients. PMID:20545723

Time-series modeling of long-term weight self-monitoring data.

PubMed

Helander, Elina; Pavel, Misha; Jimison, Holly; Korhonen, Ilkka

2015-08-01

Long-term self-monitoring of weight is beneficial for weight maintenance, especially after weight loss. Connected weight scales accumulate time series information over long term and hence enable time series analysis of the data. The analysis can reveal individual patterns, provide more sensitive detection of significant weight trends, and enable more accurate and timely prediction of weight outcomes. However, long term self-weighing data has several challenges which complicate the analysis. Especially, irregular sampling, missing data, and existence of periodic (e.g. diurnal and weekly) patterns are common. In this study, we apply time series modeling approach on daily weight time series from two individuals and describe information that can be extracted from this kind of data. We study the properties of weight time series data, missing data and its link to individuals behavior, periodic patterns and weight series segmentation. Being able to understand behavior through weight data and give relevant feedback is desired to lead to positive intervention on health behaviors.

Particle Size, Composition, and Ocean Temperature Govern the Global Distribution of Particle Transfer Efficiency to the Mesopelagic

NASA Astrophysics Data System (ADS)

Cram, J. A.; Weber, T. S.; Leung, S.; Deutsch, C. A.

2016-02-01

New analyses of geochemical tracer data detect significant differences between ocean basins in the depth scale of particle remineralization, with deepest in high latitudes, shallowest in the subtropical gyres, and intermediate in the tropics. We evaluate the possible causes of this pattern using a mechanistic model of particle dynamics that includes microbial colonization, detachment, and degradation of sinking particles. The model represents the size structure of particles, the effects of mineral ballast (diagnosed from alkalinity and silicate distributions) and seawater temperature (which influences particle velocity and microbial metabolic rates). We find that diagnosed spatial patterns in particle flux profiles can be best reproduced through a combination of surface particle size distribution and temperature, which both favor low transfer efficiency in subtropical gyres, and high transfer efficiency in higher latitudes and intermediate tropical values. Particle mineral content is shown to significantly modulate these patterns, albeit with a high remaining uncertainty. Implications of these mechanisms for changes in biological carbon storage in a warmer ocean are examined.

Heterogeneous occupancy and density estimates of the pathogenic fungus Batrachochytrium dendrobatidis in waters of North America

USGS Publications Warehouse

Chestnut, Tara E.; Anderson, Chauncey; Popa, Radu; Blaustein, Andrew R.; Voytek, Mary; Olson, Deanna H.; Kirshtein, Julie

2014-01-01

Biodiversity losses are occurring worldwide due to a combination of stressors. For example, by one estimate, 40% of amphibian species are vulnerable to extinction, and disease is one threat to amphibian populations. The emerging infectious disease chytridiomycosis, caused by the aquatic fungus Batrachochytrium dendrobatidis (Bd), is a contributor to amphibian declines worldwide. Bd research has focused on the dynamics of the pathogen in its amphibian hosts, with little emphasis on investigating the dynamics of free-living Bd. Therefore, we investigated patterns of Bd occupancy and density in amphibian habitats using occupancy models, powerful tools for estimating site occupancy and detection probability. Occupancy models have been used to investigate diseases where the focus was on pathogen occurrence in the host. We applied occupancy models to investigate free-living Bd in North American surface waters to determine Bd seasonality, relationships between Bd site occupancy and habitat attributes, and probability of detection from water samples as a function of the number of samples, sample volume, and water quality. We also report on the temporal patterns of Bd density from a 4-year case study of a Bd-positive wetland. We provide evidence that Bd occurs in the environment year-round. Bd exhibited temporal and spatial heterogeneity in density, but did not exhibit seasonality in occupancy. Bd was detected in all months, typically at less than 100 zoospores L−1. The highest density observed was ∼3 million zoospores L−1. We detected Bd in 47% of sites sampled, but estimated that Bd occupied 61% of sites, highlighting the importance of accounting for imperfect detection. When Bd was present, there was a 95% chance of detecting it with four samples of 600 ml of water or five samples of 60 mL. Our findings provide important baseline information to advance the study of Bd disease ecology, and advance our understanding of amphibian exposure to free-living Bd in aquatic habitats over time.

Salient Object Detection via Structured Matrix Decomposition.

PubMed

Peng, Houwen; Li, Bing; Ling, Haibin; Hu, Weiming; Xiong, Weihua; Maybank, Stephen J

2016-05-04

Low-rank recovery models have shown potential for salient object detection, where a matrix is decomposed into a low-rank matrix representing image background and a sparse matrix identifying salient objects. Two deficiencies, however, still exist. First, previous work typically assumes the elements in the sparse matrix are mutually independent, ignoring the spatial and pattern relations of image regions. Second, when the low-rank and sparse matrices are relatively coherent, e.g., when there are similarities between the salient objects and background or when the background is complicated, it is difficult for previous models to disentangle them. To address these problems, we propose a novel structured matrix decomposition model with two structural regularizations: (1) a tree-structured sparsity-inducing regularization that captures the image structure and enforces patches from the same object to have similar saliency values, and (2) a Laplacian regularization that enlarges the gaps between salient objects and the background in feature space. Furthermore, high-level priors are integrated to guide the matrix decomposition and boost the detection. We evaluate our model for salient object detection on five challenging datasets including single object, multiple objects and complex scene images, and show competitive results as compared with 24 state-of-the-art methods in terms of seven performance metrics.

Using occupancy models to understand the distribution of an amphibian pathogen, Batrachochytrium dendrobatidis

USGS Publications Warehouse

Adams, Michael J.; Chelgren, Nathan; Reinitz, David M.; Cole, Rebecca A.; Rachowicz, L.J.; Galvan, Stephanie; Mccreary, Brome; Pearl, Christopher A.; Bailey, Larissa L.; Bettaso, Jamie B.; Bull, Evelyn L.; Leu, Matthias

2010-01-01

Batrachochytrium dendrobatidis is a fungal pathogen that is receiving attention around the world for its role in amphibian declines. Study of its occurrence patterns is hampered by false negatives: the failure to detect the pathogen when it is present. Occupancy models are a useful but currently underutilized tool for analyzing detection data when the probability of detecting a species is <1. We use occupancy models to evaluate hypotheses concerning the occurrence and prevalence of B. dendrobatidis and discuss how this application differs from a conventional occupancy approach. We found that the probability of detecting the pathogen, conditional on presence of the pathogen in the anuran population, was related to amphibian development stage, day of the year, elevation, and human activities. Batrachochytrium dendrobatidis was found throughout our study area but was only estimated to occur in 53.4% of 78 populations of native amphibians and 66.4% of 40 populations of nonnative Rana catesbeiana tested. We found little evidence to support any spatial hypotheses concerning the probability that the pathogen occurs in a population, but did find evidence of some taxonomic variation. We discuss the interpretation of occupancy model parameters, when, unlike a conventional occupancy application, the number of potential samples or observations is finite.

Calibrating passive acoustic monitoring: correcting humpback whale call detections for site-specific and time-dependent environmental characteristics.

PubMed

Helble, Tyler A; D'Spain, Gerald L; Campbell, Greg S; Hildebrand, John A

2013-11-01

This paper demonstrates the importance of accounting for environmental effects on passive underwater acoustic monitoring results. The situation considered is the reduction in shipping off the California coast between 2008-2010 due to the recession and environmental legislation. The resulting variations in ocean noise change the probability of detecting marine mammal vocalizations. An acoustic model was used to calculate the time-varying probability of detecting humpback whale vocalizations under best-guess environmental conditions and varying noise. The uncorrected call counts suggest a diel pattern and an increase in calling over a two-year period; the corrected call counts show minimal evidence of these features.

A hierarchical model combining distance sampling and time removal to estimate detection probability during avian point counts

USGS Publications Warehouse

Amundson, Courtney L.; Royle, J. Andrew; Handel, Colleen M.

2014-01-01

Imperfect detection during animal surveys biases estimates of abundance and can lead to improper conclusions regarding distribution and population trends. Farnsworth et al. (2005) developed a combined distance-sampling and time-removal model for point-transect surveys that addresses both availability (the probability that an animal is available for detection; e.g., that a bird sings) and perceptibility (the probability that an observer detects an animal, given that it is available for detection). We developed a hierarchical extension of the combined model that provides an integrated analysis framework for a collection of survey points at which both distance from the observer and time of initial detection are recorded. Implemented in a Bayesian framework, this extension facilitates evaluating covariates on abundance and detection probability, incorporating excess zero counts (i.e. zero-inflation), accounting for spatial autocorrelation, and estimating population density. Species-specific characteristics, such as behavioral displays and territorial dispersion, may lead to different patterns of availability and perceptibility, which may, in turn, influence the performance of such hierarchical models. Therefore, we first test our proposed model using simulated data under different scenarios of availability and perceptibility. We then illustrate its performance with empirical point-transect data for a songbird that consistently produces loud, frequent, primarily auditory signals, the Golden-crowned Sparrow (Zonotrichia atricapilla); and for 2 ptarmigan species (Lagopus spp.) that produce more intermittent, subtle, and primarily visual cues. Data were collected by multiple observers along point transects across a broad landscape in southwest Alaska, so we evaluated point-level covariates on perceptibility (observer and habitat), availability (date within season and time of day), and abundance (habitat, elevation, and slope), and included a nested point-within-transect and park-level effect. Our results suggest that this model can provide insight into the detection process during avian surveys and reduce bias in estimates of relative abundance but is best applied to surveys of species with greater availability (e.g., breeding songbirds).

SU-E-I-77: X-Ray Coherent Scatter Diffraction Pattern Modeling in GEANT4.

PubMed

Kapadia, A; Samei, E; Harrawood, B; Sahbaee, P; Chawla, A; Tan, Z; Brady, D

2012-06-01

To model X-ray coherent scatter diffraction patterns in GEANT4 for simulating experiments involving material detection through diffraction pattern measurement. Although coherent scatter cross-sections are modeled accurately in GEANT4, diffraction patterns for crystalline materials are not yet included. Here we describe our modeling of crystalline diffraction patterns in GEANT4 for specific materials and the validation of the results against experimentally measured data. Coherent scatter in GEANT4 is currently based on Hubbell's non-relativistic form factor tabulations from EPDL97. We modified the form-factors by introducing an interference function that accounts for the angular dependence between the Rayleigh-scattered photons and the photon wavelength. The modified form factors were used to replace the inherent form-factors in GEANT4. The simulation was tested using monochromatic and polychromatic x-ray beams (separately) incident on objects containing one or more elements with modified form-factors. The simulation results were compared against the experimentally measured diffraction images of corresponding objects using an in-house x-ray diffraction imager for validation. The comparison was made using the following metrics: number of diffraction rings, radial distance, absolute intensity, and relative intensity. Sharp diffraction pattern rings were observed in the monochromatic simulations at locations consistent with the angular dependence of the photon wavelength. In the polychromatic simulations, the diffraction patterns exhibited a radial blur consistent with the energy spread of the polychromatic spectrum. The simulated and experimentally measured patterns showed identical numbers of rings with close agreement in radial distance, absolute and relative intensities (barring statistical fluctuations). No significant change was observed in the execution time of the simulations. This work demonstrates the ability to model coherent scatter diffraction in GEANT4 in an accurate and efficient manner without compromising the accuracy or runtime of the simulation. This work was supported by the Department of Homeland Security under grant DHS (BAA 10-01 F075), and by the Department of Defense under award W81XWH-09-1-0066. © 2012 American Association of Physicists in Medicine.

Embryonic Wnt gene expression in the nitrofen-induced hypoplastic lung using 3-dimensional imaging.

PubMed

Takayasu, Hajime; Murphy, Paula; Sato, Hideaki; Doi, Takashi; Puri, Prem

2010-11-01

Wnts have been reported to play a key role in the lung morphogenesis. We have previously reported that pulmonary gene expression of Wnt2 and Wnt7b is downregulated on day 15 of gestation in the nitrofen-induced congenital diaphragmatic hernia (CDH) model. However, the distribution pattern of gene expression of Wnts in the very early lung development remains unclear. Optical projection tomography (OPT) is a new technique for 3-dimensional imaging of small developing organs and gene distribution combined with whole-mount in situ hybridization. We designed this study to investigate the distribution pattern of Wnts gene expression in lung buds of nitrofen-induced CDH model using OPT. Embryos from normal and nitrofen-treated dams were harvested on embryonic day 10 (E10), and divided into controls and nitrofen group, respectively. Whole-mount in situ hybridization to detect transcripts of Wnt2 and Wnt7b was performed, analyzed, and reconstructed using OPT. The expression of Wnt2 transcripts was detected in the lung bud mesenchyme and markedly diminished in nitrofen group compared to controls, whereas Wnt7b transcripts were expressed in the mesoderm of bronchi and the lung bud with no detectable difference between 2 groups. We provide evidence for the first time that Wnt2 expression is downregulated at lung bud stage in the nitrofen model. Optical projection tomography is potentially a useful approach to visualize both gene expression and morphology during very early stages of lung development. Copyright © 2010 Elsevier Inc. All rights reserved.

In silico mapping of quantitative trait loci in maize.

PubMed

Parisseaux, B; Bernardo, R

2004-08-01

Quantitative trait loci (QTL) are most often detected through designed mapping experiments. An alternative approach is in silico mapping, whereby genes are detected using existing phenotypic and genomic databases. We explored the usefulness of in silico mapping via a mixed-model approach in maize (Zea mays L.). Specifically, our objective was to determine if the procedure gave results that were repeatable across populations. Multilocation data were obtained from the 1995-2002 hybrid testing program of Limagrain Genetics in Europe. Nine heterotic patterns comprised 22,774 single crosses. These single crosses were made from 1,266 inbreds that had data for 96 simple sequence repeat (SSR) markers. By a mixed-model approach, we estimated the general combining ability effects associated with marker alleles in each heterotic pattern. The numbers of marker loci with significant effects--37 for plant height, 24 for smut [Ustilago maydis (DC.) Cda.] resistance, and 44 for grain moisture--were consistent with previous results from designed mapping experiments. Each trait had many loci with small effects and few loci with large effects. For smut resistance, a marker in bin 8.05 on chromosome 8 had a significant effect in seven (out of a maximum of 18) instances. For this major QTL, the maximum effect of an allele substitution ranged from 5.4% to 41.9%, with an average of 22.0%. We conclude that in silico mapping via a mixed-model approach can detect associations that are repeatable across different populations. We speculate that in silico mapping will be more useful for gene discovery than for selection in plant breeding programs. Copyright 2004 Springer-Verlag

Determination of the influence of dispersion pattern of pesticide-resistant individuals on the reliability of resistance estimates using different sampling plans.

PubMed

Shah, R; Worner, S P; Chapman, R B

2012-10-01

Pesticide resistance monitoring includes resistance detection and subsequent documentation/ measurement. Resistance detection would require at least one (≥1) resistant individual(s) to be present in a sample to initiate management strategies. Resistance documentation, on the other hand, would attempt to get an estimate of the entire population (≥90%) of the resistant individuals. A computer simulation model was used to compare the efficiency of simple random and systematic sampling plans to detect resistant individuals and to document their frequencies when the resistant individuals were randomly or patchily distributed. A patchy dispersion pattern of resistant individuals influenced the sampling efficiency of systematic sampling plans while the efficiency of random sampling was independent of such patchiness. When resistant individuals were randomly distributed, sample sizes required to detect at least one resistant individual (resistance detection) with a probability of 0.95 were 300 (1%) and 50 (10% and 20%); whereas, when resistant individuals were patchily distributed, using systematic sampling, sample sizes required for such detection were 6000 (1%), 600 (10%) and 300 (20%). Sample sizes of 900 and 400 would be required to detect ≥90% of resistant individuals (resistance documentation) with a probability of 0.95 when resistant individuals were randomly dispersed and present at a frequency of 10% and 20%, respectively; whereas, when resistant individuals were patchily distributed, using systematic sampling, a sample size of 3000 and 1500, respectively, was necessary. Small sample sizes either underestimated or overestimated the resistance frequency. A simple random sampling plan is, therefore, recommended for insecticide resistance detection and subsequent documentation.

An expert panel-based study on recognition of gastro-esophageal reflux in difficult esophageal pH-impedance tracings.

PubMed

Smits, M J; Loots, C M; van Wijk, M P; Bredenoord, A J; Benninga, M A; Smout, A J P M

2015-05-01

Despite existing criteria for scoring gastro-esophageal reflux (GER) in esophageal multichannel pH-impedance measurement (pH-I) tracings, inter- and intra-rater variability is large and agreement with automated analysis is poor. To identify parameters of difficult to analyze pH-I patterns and combine these into a statistical model that can identify GER episodes with an international consensus as gold standard. Twenty-one experts from 10 countries were asked to mark GER presence for adult and pediatric pH-I patterns in an online pre-assessment. During a consensus meeting, experts voted on patterns not reaching majority consensus (>70% agreement). Agreement was calculated between raters, between consensus and individual raters, and between consensus and software generated automated analysis. With eight selected parameters, multiple logistic regression analysis was performed to describe an algorithm sensitive and specific for detection of GER. Majority consensus was reached for 35/79 episodes in the online pre-assessment (interrater κ = 0.332). Mean agreement between pre-assessment scores and final consensus was moderate (κ = 0.466). Combining eight pH-I parameters did not result in a statistically significant model able to identify presence of GER. Recognizing a pattern as retrograde is the best indicator of GER, with 100% sensitivity and 81% specificity with expert consensus as gold standard. Agreement between experts scoring difficult impedance patterns for presence or absence of GER is poor. Combining several characteristics into a statistical model did not improve diagnostic accuracy. Only the parameter 'retrograde propagation pattern' is an indicator of GER in difficult pH-I patterns. © 2015 John Wiley & Sons Ltd.

Characterizing recent and projecting future potential patterns of mountain pine beetle outbreaks in the Southern Rocky Mountains

USGS Publications Warehouse

Liang, Lu; Hawbaker, Todd J.; Chen, Yanlei; Zhu, Zhi-Liang; Gong, Peng

2014-01-01

The recent widespread mountain pine beetle (MPB) outbreak in the Southern Rocky Mountains presents an opportunity to investigate the relative influence of anthropogenic, biologic, and physical drivers that have shaped the spatiotemporal patterns of the outbreak. The aim of this study was to quantify the landscape-level drivers that explained the dynamic patterns of MPB mortality, and simulate areas with future potential MPB mortality under projected climate-change scenarios in Grand County, Colorado, USA. The outbreak patterns of MPB were characterized by analysis of a decade-long Landsat time-series stack, aided by automatic attribution of change detected by the Landsat-based Detection of Trends in Disturbance and Recovery algorithm (LandTrendr). The annual area of new MPB mortality was then related to a suite of anthropogenic, biologic, and physical predictor variables under a general linear model (GLM) framework. Data from years 2001–2005 were used to train the model and data from years 2006–2011 were retained for validation. After stepwise removal of non-significant predictors, the remaining predictors in the GLM indicated that neighborhood mortality, winter mean temperature anomaly, and residential housing density were positively associated with MPB mortality, whereas summer precipitation was negatively related. The final model had an average area under the curve (AUC) of a receiver operating characteristic plot value of 0.72 in predicting the annual area of new mortality for the independent validation years, and the mean deviation from the base maps in the MPB mortality areal estimates was around 5%. The extent of MPB mortality will likely expand under two climate-change scenarios (RCP 4.5 and 8.5) in Grand County, which implies that the impacts of MPB outbreaks on vegetation composition and structure, and ecosystem functioning are likely to increase in the future.

Developmental stage related patterns of codon usage and genomic GC content: searching for evolutionary fingerprints with models of stem cell differentiation

PubMed Central

2007-01-01

Background The usage of synonymous codons shows considerable variation among mammalian genes. How and why this usage is non-random are fundamental biological questions and remain controversial. It is also important to explore whether mammalian genes that are selectively expressed at different developmental stages bear different molecular features. Results In two models of mouse stem cell differentiation, we established correlations between codon usage and the patterns of gene expression. We found that the optimal codons exhibited variation (AT- or GC-ending codons) in different cell types within the developmental hierarchy. We also found that genes that were enriched (developmental-pivotal genes) or specifically expressed (developmental-specific genes) at different developmental stages had different patterns of codon usage and local genomic GC (GCg) content. Moreover, at the same developmental stage, developmental-specific genes generally used more GC-ending codons and had higher GCg content compared with developmental-pivotal genes. Further analyses suggest that the model of translational selection might be consistent with the developmental stage-related patterns of codon usage, especially for the AT-ending optimal codons. In addition, our data show that after human-mouse divergence, the influence of selective constraints is still detectable. Conclusion Our findings suggest that developmental stage-related patterns of gene expression are correlated with codon usage (GC3) and GCg content in stem cell hierarchies. Moreover, this paper provides evidence for the influence of natural selection at synonymous sites in the mouse genome and novel clues for linking the molecular features of genes to their patterns of expression during mammalian ontogenesis. PMID:17349061

Factor demand in Swedish manufacturing industry with special reference to the demand for energy. Instantaneous adjustment models; some results

NASA Astrophysics Data System (ADS)

Sjoeholm, K. R.

1981-02-01

The dual approach to the theory of production is used to estimate factor demand functions of the Swedish manufacturing industry. Two approximations of the cost function, the translog and the generalized Leontief models, are used. The price elasticities of the factor demand do not seem to depend on the choice of model. This is at least true as to the sign pattern and as to the inputs capital, labor, total energy and other materials. Total energy is separated into solid fuels, gasoline, fuel oil, electricity and a residual. Fuel oil and electricity are found to be substitutes by both models. Capital and energy are shown to be substitutes. This implies that Swedish industry will save more energy if the capital cost can be reduced. Both models are, in the best versions, able to detect an inappropriate variable. The assumption of perfect competition on the product market, is shown to be inadequate by both models. When this assumption is relaxed, the normal substitution pattern among the inputs is resumed.

On the importance of controlling for effort in analysis of count survey data: Modeling population change from Christmas Bird Count data

USGS Publications Warehouse

Link, W.A.; Sauer, J.R.; Helbig, Andreas J.; Flade, Martin

1999-01-01

Count survey data are commonly used for estimating temporal and spatial patterns of population change. Since count surveys are not censuses, counts can be influenced by 'nuisance factors' related to the probability of detecting animals but unrelated to the actual population size. The effects of systematic changes in these factors can be confounded with patterns of population change. Thus, valid analysis of count survey data requires the identification of nuisance factors and flexible models for their effects. We illustrate using data from the Christmas Bird Count (CBC), a midwinter survey of bird populations in North America. CBC survey effort has substantially increased in recent years, suggesting that unadjusted counts may overstate population growth (or understate declines). We describe a flexible family of models for the effect of effort, that includes models in which increasing effort leads to diminishing returns in terms of the number of birds counted.

Unmodeled observation error induces bias when inferring patterns and dynamics of species occurrence via aural detections

USGS Publications Warehouse

McClintock, Brett T.; Bailey, Larissa L.; Pollock, Kenneth H.; Simons, Theodore R.

2010-01-01

The recent surge in the development and application of species occurrence models has been associated with an acknowledgment among ecologists that species are detected imperfectly due to observation error. Standard models now allow unbiased estimation of occupancy probability when false negative detections occur, but this is conditional on no false positive detections and sufficient incorporation of explanatory variables for the false negative detection process. These assumptions are likely reasonable in many circumstances, but there is mounting evidence that false positive errors and detection probability heterogeneity may be much more prevalent in studies relying on auditory cues for species detection (e.g., songbird or calling amphibian surveys). We used field survey data from a simulated calling anuran system of known occupancy state to investigate the biases induced by these errors in dynamic models of species occurrence. Despite the participation of expert observers in simplified field conditions, both false positive errors and site detection probability heterogeneity were extensive for most species in the survey. We found that even low levels of false positive errors, constituting as little as 1% of all detections, can cause severe overestimation of site occupancy, colonization, and local extinction probabilities. Further, unmodeled detection probability heterogeneity induced substantial underestimation of occupancy and overestimation of colonization and local extinction probabilities. Completely spurious relationships between species occurrence and explanatory variables were also found. Such misleading inferences would likely have deleterious implications for conservation and management programs. We contend that all forms of observation error, including false positive errors and heterogeneous detection probabilities, must be incorporated into the estimation framework to facilitate reliable inferences about occupancy and its associated vital rate parameters.

A comment on priors for Bayesian occupancy models

PubMed Central

Gerber, Brian D.

2018-01-01

Understanding patterns of species occurrence and the processes underlying these patterns is fundamental to the study of ecology. One of the more commonly used approaches to investigate species occurrence patterns is occupancy modeling, which can account for imperfect detection of a species during surveys. In recent years, there has been a proliferation of Bayesian modeling in ecology, which includes fitting Bayesian occupancy models. The Bayesian framework is appealing to ecologists for many reasons, including the ability to incorporate prior information through the specification of prior distributions on parameters. While ecologists almost exclusively intend to choose priors so that they are “uninformative” or “vague”, such priors can easily be unintentionally highly informative. Here we report on how the specification of a “vague” normally distributed (i.e., Gaussian) prior on coefficients in Bayesian occupancy models can unintentionally influence parameter estimation. Using both simulated data and empirical examples, we illustrate how this issue likely compromises inference about species-habitat relationships. While the extent to which these informative priors influence inference depends on the data set, researchers fitting Bayesian occupancy models should conduct sensitivity analyses to ensure intended inference, or employ less commonly used priors that are less informative (e.g., logistic or t prior distributions). We provide suggestions for addressing this issue in occupancy studies, and an online tool for exploring this issue under different contexts. PMID:29481554

Using Atmospheric Circulation Patterns to Detect and Attribute Changes in the Risk of Extreme Climate Events

NASA Astrophysics Data System (ADS)

Diffenbaugh, N. S.; Horton, D. E.; Singh, D.; Swain, D. L.; Touma, D. E.; Mankin, J. S.

2015-12-01

Because of the high cost of extreme events and the growing evidence that global warming is likely to alter the statistical distribution of climate variables, detection and attribution of changes in the probability of extreme climate events has become a pressing topic for the scientific community, elected officials, and the public. While most of the emphasis has thus far focused on analyzing the climate variable of interest (most often temperature or precipitation, but also flooding and drought), there is an emerging emphasis on applying detection and attribution analysis techniques to the underlying physical causes of individual extreme events. This approach is promising in part because the underlying physical causes (such as atmospheric circulation patterns) can in some cases be more accurately represented in climate models than the more proximal climate variable (such as precipitation). In addition, and more scientifically critical, is the fact that the most extreme events result from a rare combination of interacting causes, often referred to as "ingredients". Rare events will therefore always have a strong influence of "natural" variability. Analyzing the underlying physical mechanisms can therefore help to test whether there have been changes in the probability of the constituent conditions of an individual event, or whether the co-occurrence of causal conditions cannot be distinguished from random chance. This presentation will review approaches to applying detection/attribution analysis to the underlying physical causes of extreme events (including both "thermodynamic" and "dynamic" causes), and provide a number of case studies, including the role of frequency of atmospheric circulation patterns in the probability of hot, cold, wet and dry events.

Principal sequence pattern analysis of episodes of excess mortality due to heat in the Barcelona metropolitan area.

PubMed

Peña, Juan Carlos; Aran, Montserrat; Raso, José Miguel; Pérez-Zanón, Nuria

2015-04-01

The aim of the study is to classify the synoptic sequences associated with excess mortality during the warm season in the Barcelona metropolitan area. To achieve this purpose, we undertook a principal sequence pattern analysis that incorporates different atmospheric levels, in an attempt at identifying the main features that account for dynamic and thermodynamic atmospheric processes. The sequence length was determined by the short-term displacement between temperature and mortality. To detect this lag, we applied the cross-correlation function to the residuals obtained from the modelling of the daily temperature and mortality series of summer. These residuals were estimated by means of an autoregressive integrated moving average (ARIMA) model. A 7-day sequence emerged as the basic temporal unit for evaluating the synoptic background that triggers the temperature related to excess mortality in the Barcelona metropolitan area. The principal sequence pattern analysis distinguished three main synoptic patterns: two dynamic configurations produced by southern fluxes related to an Atlantic low, which can be associated with heat waves recorded in southern Europe, and a third pattern identified by a stagnation situation associated with the persistence of a blocking anticyclone over Europe, related to heat waves recorded in northern and central western Europe.

Schlieren optics for leak detection

NASA Technical Reports Server (NTRS)

Peale, Robert E.; Ruffin, Alranzo B.

1995-01-01

The purpose of this research was to develop an optical method of leak detection. Various modifications of schlieren optics were explored with initial emphasis on leak detection of the plumbing within the orbital maneuvering system of the space shuttle (OMS pod). The schlieren scheme envisioned for OMS pod leak detection was that of a high contrast pattern on flexible reflecting material imaged onto a negative of the same pattern. We find that the OMS pod geometry constrains the characteristic length scale of the pattern to the order of 0.001 inch. Our experiments suggest that optical modulation transfer efficiency will be very low for such patterns, which will limit the sensitivity of the technique. Optical elements which allow a negative of the scene to be reversibly recorded using light from the scene itself were explored for their potential in adaptive single-ended schlieren systems. Elements studied include photochromic glass, bacteriorhodopsin, and a transmissive liquid crystal display. The dynamics of writing and reading patterns were studied using intensity profiles from recorded images. Schlieren detection of index gradients in air was demonstrated.

Classification of SD-OCT volumes for DME detection: an anomaly detection approach

NASA Astrophysics Data System (ADS)

Sankar, S.; Sidibé, D.; Cheung, Y.; Wong, T. Y.; Lamoureux, E.; Milea, D.; Meriaudeau, F.

2016-03-01

Diabetic Macular Edema (DME) is the leading cause of blindness amongst diabetic patients worldwide. It is characterized by accumulation of water molecules in the macula leading to swelling. Early detection of the disease helps prevent further loss of vision. Naturally, automated detection of DME from Optical Coherence Tomography (OCT) volumes plays a key role. To this end, a pipeline for detecting DME diseases in OCT volumes is proposed in this paper. The method is based on anomaly detection using Gaussian Mixture Model (GMM). It starts with pre-processing the B-scans by resizing, flattening, filtering and extracting features from them. Both intensity and Local Binary Pattern (LBP) features are considered. The dimensionality of the extracted features is reduced using PCA. As the last stage, a GMM is fitted with features from normal volumes. During testing, features extracted from the test volume are evaluated with the fitted model for anomaly and classification is made based on the number of B-scans detected as outliers. The proposed method is tested on two OCT datasets achieving a sensitivity and a specificity of 80% and 93% on the first dataset, and 100% and 80% on the second one. Moreover, experiments show that the proposed method achieves better classification performances than other recently published works.

Interior rotation of a sample of γ Doradus stars from ensemble modelling of their gravity-mode period spacings

NASA Astrophysics Data System (ADS)

Van Reeth, T.; Tkachenko, A.; Aerts, C.

2016-10-01

Context. Gamma Doradus stars (hereafter γ Dor stars) are known to exhibit gravity- and/or gravito-intertial modes that probe the inner stellar region near the convective core boundary. The non-equidistant spacing of the pulsation periods is an observational signature of the stellar evolutions and current internal structure and is heavily influenced by rotation. Aims: We aim to constrain the near-core rotation rates for a sample of γ Dor stars for which we have detected period spacing patterns. Methods: We combined the asymptotic period spacing with the traditional approximation of stellar pulsation to fit the observed period spacing patterns using χ2-optimisation. The method was applied to the observed period spacing patterns of a sample of stars and used for ensemble modelling. Results: For the majority of stars with an observed period spacing pattern we successfully determined the rotation rates and the asymptotic period spacing values, although the uncertainty margins on the latter were typically large. This also resulted directly in the identification of the modes that correspond to the detected pulsation frequencies, which for most stars were prograde dipole gravity and gravito-inertial modes. The majority of the observed retrograde modes were found to be Rossby modes. We also discuss the limitations of the method that are due to the neglect of the centrifugal force and the incomplete treatment of the Coriolis force. Conclusions: Despite its current limitations, the proposed method was successful to derive the rotation rates and to identify the modes from the observed period spacing patterns. It forms the first step towards detailed seismic modelling based on observed period spacing patterns of moderately to rapidly rotating γDor stars. Based on data gathered with the NASA Discovery mission Kepler and the HERMES spectrograph, which is installed at the Mercator Telescope, operated on the island of La Palma by the Flemish Community at the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofísica de Canarias, and supported by the Fund for Scientific Research of Flanders (FWO), Belgium, the Research Council of KU Leuven, Belgium, the Fonds National de la Recherche Scientifique (F.R.S.-FNRS), Belgium, the Royal Observatory of Belgium, the Observatoire de Genève, Switzerland, and the Thüringer Landessternwarte Tautenburg, Germany.

Nonlinear gamma correction via normed bicoherence minimization in optical fringe projection metrology

NASA Astrophysics Data System (ADS)

Kamagara, Abel; Wang, Xiangzhao; Li, Sikun

2018-03-01

We propose a method to compensate for the projector intensity nonlinearity induced by gamma effect in three-dimensional (3-D) fringe projection metrology by extending high-order spectra analysis and bispectral norm minimization to digital sinusoidal fringe pattern analysis. The bispectrum estimate allows extraction of vital signal information features such as spectral component correlation relationships in fringe pattern images. Our approach exploits the fact that gamma introduces high-order harmonic correlations in the affected fringe pattern image. Estimation and compensation of projector nonlinearity is realized by detecting and minimizing the normed bispectral coherence of these correlations. The proposed technique does not require calibration information and technical knowledge or specification of fringe projection unit. This is promising for developing a modular and calibration-invariant model for intensity nonlinear gamma compensation in digital fringe pattern projection profilometry. Experimental and numerical simulation results demonstrate this method to be efficient and effective in improving the phase measuring accuracies with phase-shifting fringe pattern projection profilometry.

Material damage modeling and detection in a thin metallic sheet and sandwich panel using passive acoustic transmission

NASA Astrophysics Data System (ADS)

Jiang, Hao

A method is developed for modeling, detecting, and locating material damage in homogeneous thin metallic sheets and sandwich panels. Analytical and numerical models are used along with non-contact, passive acoustic transmission measurements. It is shown that global and local damage mechanisms characterized by both material and geometrical changes in structural components can be detected using passive acoustic transmission measurements. Theoretical models of a flat sheet and sandwich panel are developed to describe the effects of global material damage due to density, modulus, or thickness changes on backplane radiated sound pressure level distributions. To describe the effects of local material damage, a three-segment stepped beam model and finite element beam, plate, and sandwich panel models are developed and analyzed using the acoustic transmission approach. It is shown that increases or decreases in transmitted sound energy occur behind a damaged material component that exhibits changes in thickness or other geometric or material properties. The damage due to thickness and density changes can be detected from the acoustic transmission, but modulus changes cannot. If the damage is located at an anti-node of a certain forced vibration pattern, the damage can be more readily observed in the data. Higher excitation frequencies within the operating spectrum are preferred to lower frequencies for damage detection. With the finite element beam, plate, and sandwich panel models, local damage detection has been performed in simulations. Experiments on a baffled homogeneous sheet and sandwich panel subjected to broadband acoustic energy show that transmitted intensity measurements with non-contact probes can be used to identify and locate material defects in the sheet and sandwich panel. Material damage is most readily identified where the changes in transmitted sound intensity are largest in the resonant frequency range of the panel. The three main contributions of this research are: (1) the use of non-contact sensing to detect global and localized damage in structural components; (2) the analytical and numerical modeling of material and geometrical damage mechanisms in structural components; and, (3) the experimental verification of acoustic transmission measurements for detecting both material and geometric damage mechanisms.

A Healthy Dietary Pattern Reduces Lung Cancer Risk: A Systematic Review and Meta-Analysis.

PubMed

Sun, Yanlai; Li, Zhenxiang; Li, Jianning; Li, Zengjun; Han, Jianjun

2016-03-04

Diet and nutrients play an important role in cancer development and progress; a healthy dietary pattern has been found to be associated with several types of cancer. However, the association between a healthy eating pattern and lung cancer risk is still unclear. Therefore, we conducted a systematic review with meta-analysis to evaluate whether a healthy eating pattern might reduce lung cancer risk. We identified relevant studies from the PubMed and Embase databases up to October 2015, and the relative risks were extracted and combined by the fixed-effects model when no substantial heterogeneity was observed; otherwise, the random-effects model was employed. Subgroup and publication bias analyses were also performed. Finally, eight observational studies were included in the meta-analysis. The pooled relative risk of lung cancer for the highest vs. lowest category of healthy dietary pattern was 0.81 (95% confidence interval, CI: 0.75-0.86), and no significant heterogeneity was detected. The relative risks (RRs) for non-smokers, former smokers and current smokers were 0.89 (95% CI: 0.63-1.27), 0.74 (95% CI: 0.62-0.89) and 0.86 (95% CI: 0.79-0.93), respectively. The results remained stable in subgroup analyses by other confounders and sensitivity analysis. The results of our meta-analysis suggest that a healthy dietary pattern is associated with a lower lung cancer risk, and they provide more beneficial evidence for changing the diet pattern in the general population.

Ramsey-type spectroscopy in the XUV spectral region

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pirri, A.; European Laboratory for Nonlinear Spectroscopy, Via N. Carrara 1, I-50019 Sesto Fiorentino; Sali, E.

2010-02-02

We report an experimental and theoretical investigation of Ramsey-type spectroscopy with high-order harmonic generation applied to autoionizing states of Krypton. The ionization yield, detected by an ion-mass spectrometer, shows the characteristic quantum interference pattern. The behaviour of the fringe contrast was interpreted on the basis of a simple analytic model, which reproduces the experimental data without any free parameter.

Predicting and Detecting Emerging Cyberattack Patterns Using StreamWorks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chin, George; Choudhury, Sutanay; Feo, John T.

2014-06-30

The number and sophistication of cyberattacks on industries and governments have dramatically grown in recent years. To counter this movement, new advanced tools and techniques are needed to detect cyberattacks in their early stages such that defensive actions may be taken to avert or mitigate potential damage. From a cybersecurity analysis perspective, detecting cyberattacks may be cast as a problem of identifying patterns in computer network traffic. Logically and intuitively, these patterns may take on the form of a directed graph that conveys how an attack or intrusion propagates through the computers of a network. Such cyberattack graphs could providemore » cybersecurity analysts with powerful conceptual representations that are natural to express and analyze. We have been researching and developing graph-centric approaches and algorithms for dynamic cyberattack detection. The advanced dynamic graph algorithms we are developing will be packaged into a streaming network analysis framework known as StreamWorks. With StreamWorks, a scientist or analyst may detect and identify precursor events and patterns as they emerge in complex networks. This analysis framework is intended to be used in a dynamic environment where network data is streamed in and is appended to a large-scale dynamic graph. Specific graphical query patterns are decomposed and collected into a graph query library. The individual decomposed subpatterns in the library are continuously and efficiently matched against the dynamic graph as it evolves to identify and detect early, partial subgraph patterns. The scalable emerging subgraph pattern algorithms will match on both structural and semantic network properties.« less

Genome-wide DNA methylation measurements in prostate tissues uncovers novel prostate cancer diagnostic biomarkers and transcription factor binding patterns.

PubMed

Kirby, Marie K; Ramaker, Ryne C; Roberts, Brian S; Lasseigne, Brittany N; Gunther, David S; Burwell, Todd C; Davis, Nicholas S; Gulzar, Zulfiqar G; Absher, Devin M; Cooper, Sara J; Brooks, James D; Myers, Richard M

2017-04-17

Current diagnostic tools for prostate cancer lack specificity and sensitivity for detecting very early lesions. DNA methylation is a stable genomic modification that is detectable in peripheral patient fluids such as urine and blood plasma that could serve as a non-invasive diagnostic biomarker for prostate cancer. We measured genome-wide DNA methylation patterns in 73 clinically annotated fresh-frozen prostate cancers and 63 benign-adjacent prostate tissues using the Illumina Infinium HumanMethylation450 BeadChip array. We overlaid the most significantly differentially methylated sites in the genome with transcription factor binding sites measured by the Encyclopedia of DNA Elements consortium. We used logistic regression and receiver operating characteristic curves to assess the performance of candidate diagnostic models. We identified methylation patterns that have a high predictive power for distinguishing malignant prostate tissue from benign-adjacent prostate tissue, and these methylation signatures were validated using data from The Cancer Genome Atlas Project. Furthermore, by overlaying ENCODE transcription factor binding data, we observed an enrichment of enhancer of zeste homolog 2 binding in gene regulatory regions with higher DNA methylation in malignant prostate tissues. DNA methylation patterns are greatly altered in prostate cancer tissue in comparison to benign-adjacent tissue. We have discovered patterns of DNA methylation marks that can distinguish prostate cancers with high specificity and sensitivity in multiple patient tissue cohorts, and we have identified transcription factors binding in these differentially methylated regions that may play important roles in prostate cancer development.

Cortical atrophy patterns in early Parkinson's disease patients using hierarchical cluster analysis.

PubMed

Uribe, Carme; Segura, Barbara; Baggio, Hugo Cesar; Abos, Alexandra; Garcia-Diaz, Anna Isabel; Campabadal, Anna; Marti, Maria Jose; Valldeoriola, Francesc; Compta, Yaroslau; Tolosa, Eduard; Junque, Carme

2018-05-01

Cortical brain atrophy detectable with MRI in non-demented advanced Parkinson's disease (PD) is well characterized, but its presence in early disease stages is still under debate. We aimed to investigate cortical atrophy patterns in a large sample of early untreated PD patients using a hypothesis-free data-driven approach. Seventy-seven de novo PD patients and 50 controls from the Parkinson's Progression Marker Initiative database with T1-weighted images in a 3-tesla Siemens scanner were included in this study. Mean cortical thickness was extracted from 360 cortical areas defined by the Human Connectome Project Multi-Modal Parcellation version 1.0, and a hierarchical cluster analysis was performed using Ward's linkage method. A general linear model with cortical thickness data was then used to compare clustering groups using FreeSurfer software. We identified two patterns of cortical atrophy. Compared with controls, patients grouped in pattern 1 (n = 33) were characterized by cortical thinning in bilateral orbitofrontal, anterior cingulate, and lateral and medial anterior temporal gyri. Patients in pattern 2 (n = 44) showed cortical thinning in bilateral occipital gyrus, cuneus, superior parietal gyrus, and left postcentral gyrus, and they showed neuropsychological impairment in memory and other cognitive domains. Even in the early stages of PD, there is evidence of cortical brain atrophy. Neuroimaging clustering analysis is able to detect two subgroups of cortical thinning, one with mainly anterior atrophy, and the other with posterior predominance and worse cognitive performance. Copyright © 2018 Elsevier Ltd. All rights reserved.

Consumption Patterns of Nightlife Attendees in Munich: A Latent-Class Analysis.

PubMed

Hannemann, Tessa-Virginia; Kraus, Ludwig; Piontek, Daniela

2017-09-19

The affinity for substance use among patrons of nightclubs has been well established. With novel psychoactive substances (NPS) quickly emerging on the European drug market, trends, and patterns of use are potentially changing. (1) The detection of subgroups of consumers in the electronic dance music scene of a major German metropolitan city, (2) describing the consumption patterns of these subgroups, (3) exploring the prevalence and type of NPS consumption in this population at nightlife events in Munich. A total of 1571 patrons answered questions regarding their own substance use and the emergence of NPS as well as their experience with these substances. A latent class analysis was employed to detect consumption patterns within the sample. A four class model was determined reflecting different consumption patterns: the conservative class (34.9%) whose substance was limited to cannabis; the traditional class (36.6%) which especially consumed traditional club drugs; the psychedelic class (17.5%) which, in addition to traditional club drugs also consumed psychedelic drugs; and an unselective class (10.9%) which displayed the greatest likelihood of consumption of all assessed drugs. "Smoking mixtures" and methylone were the new substances mentioned most often, the number of substances mentioned differed between latent classes. Specific strategies are needed to reduce harm in those displaying the riskiest substance use. Although NPS use is still a fringe phenomenon its prevalence is greater in this subpopulation than in the general population, especially among users in the high-risk unselective class.

Endogenous electromagnetic fields in plant leaves: a new hypothesis for vascular pattern formation.

PubMed

Pietak, Alexis Mari

2011-06-01

Electromagnetic (EM) phenomena have long been implicated in biological development, but few detailed, practical mechanisms have been put forth to connect electromagnetism with morphogenetic processes. This work describes a new hypothesis for plant leaf veination, whereby an endogenous electric field forming as a result of a coherent Frohlich process, and corresponding to an EM resonant mode of the developing leaf structure, is capable of instigating leaf vascularisation. In order to test the feasibility of this hypothesis, a three-dimensional, EM finite-element model (FEM) of a leaf primordium was constructed to determine if suitable resonant modes were physically possible for geometric and physical parameters similar to those of developing leaf tissue. Using the FEM model, resonant EM modes with patterns of relevance to developing leaf vein modalities were detected. On account of the existence of shared geometric signatures in a leaf's vascular pattern and the electric field component of EM resonant modes supported by a developing leaf structure, further theoretical and experimental investigations are warranted. Significantly, this hypothesis is not limited to leaf vascular patterning, but may be applicable to a variety of morphogenetic phenomena in a number of living systems.

Activation of the innate immune receptor Dectin-1 upon formation of a “phagocytic synapse”

PubMed Central

Goodridge, Helen S.; Reyes, Christopher N.; Becker, Courtney A.; Katsumoto, Tamiko R.; Ma, Jun; Wolf, Andrea J.; Bose, Nandita; Chan, Anissa S. H.; Magee, Andrew S.; Danielson, Michael E.; Weiss, Arthur; Vasilakos, John P.; Underhill, David M.

2011-01-01

Innate immune cells must be able to distinguish between direct binding to microbes and detection of components shed from the surface of microbes located at a distance. Dectin-1 is a pattern recognition receptor expressed by myeloid phagocytes (macrophages, dendritic cells and neutrophils) that detects β-glucans in fungal cell walls and triggers direct cellular anti-microbial activity, including phagocytosis and production of reactive oxygen species1, 2. In contrast to inflammatory responses stimulated upon detection of soluble ligands by other pattern recognition receptors, such as Toll-like receptors (TLRs), these responses are only useful when a cell comes into direct contact with a microbe and must not be spuriously activated by soluble stimuli. In this study we show that despite its ability to bind both soluble and particulate β-glucan polymers, Dectin-1 signalling is only activated by particulate β-glucans, which cluster the receptor in synapse-like structures from which regulatory tyrosine phosphatases CD45 and CD148 are excluded (Supplementary Figure 1). The “phagocytic synapse” now provides a model mechanism by which innate immune receptors can distinguish direct microbial contact from detection of microbes at a distance, thereby initiating direct cellular anti-microbial responses only when they are required. PMID:21525931

Perception of global image contrast involves transparent spatial filtering and the integration and suppression of local contrasts (not RMS contrast)

PubMed Central

2017-01-01

When adjusting the contrast setting on a television set, we experience a perceptual change in the global image contrast. But how is that statistic computed? We addressed this using a contrast-matching task for checkerboard configurations of micro-patterns in which the contrasts and spatial spreads of two interdigitated components were controlled independently. When the patterns differed greatly in contrast, the higher contrast determined the perceived global contrast. Crucially, however, low contrast additions of one pattern to intermediate contrasts of the other caused a paradoxical reduction in the perceived global contrast. None of the following metrics/models predicted this: max, linear sum, average, energy, root mean squared (RMS), Legge and Foley. However, a nonlinear gain control model, derived from contrast detection and discrimination experiments, incorporating wide-field summation and suppression, did predict the results with no free parameters, but only when spatial filtering was removed. We conclude that our model describes fundamental processes in human contrast vision (the pattern of results was the same for expert and naive observers), but that above threshold—when contrast pedestals are clearly visible—vision's spatial filtering characteristics become transparent, tending towards those of a delta function prior to spatial summation. The global contrast statistic from our model is as easily derived as the RMS contrast of an image, and since it more closely relates to human perception, we suggest it be used as an image contrast metric in practical applications. PMID:28989735

Automated Gait Analysis Through Hues and Areas (AGATHA): a method to characterize the spatiotemporal pattern of rat gait

PubMed Central

Kloefkorn, Heidi E.; Pettengill, Travis R.; Turner, Sara M. F.; Streeter, Kristi A.; Gonzalez-Rothi, Elisa J.; Fuller, David D.; Allen, Kyle D.

2016-01-01

While rodent gait analysis can quantify the behavioral consequences of disease, significant methodological differences exist between analysis platforms and little validation has been performed to understand or mitigate these sources of variance. By providing the algorithms used to quantify gait, open-source gait analysis software can be validated and used to explore methodological differences. Our group is introducing, for the first time, a fully-automated, open-source method for the characterization of rodent spatiotemporal gait patterns, termed Automated Gait Analysis Through Hues and Areas (AGATHA). This study describes how AGATHA identifies gait events, validates AGATHA relative to manual digitization methods, and utilizes AGATHA to detect gait compensations in orthopaedic and spinal cord injury models. To validate AGATHA against manual digitization, results from videos of rodent gait, recorded at 1000 frames per second (fps), were compared. To assess one common source of variance (the effects of video frame rate), these 1000 fps videos were re-sampled to mimic several lower fps and compared again. While spatial variables were indistinguishable between AGATHA and manual digitization, low video frame rates resulted in temporal errors for both methods. At frame rates over 125 fps, AGATHA achieved a comparable accuracy and precision to manual digitization for all gait variables. Moreover, AGATHA detected unique gait changes in each injury model. These data demonstrate AGATHA is an accurate and precise platform for the analysis of rodent spatiotemporal gait patterns. PMID:27554674

Automated Gait Analysis Through Hues and Areas (AGATHA): A Method to Characterize the Spatiotemporal Pattern of Rat Gait.

PubMed

Kloefkorn, Heidi E; Pettengill, Travis R; Turner, Sara M F; Streeter, Kristi A; Gonzalez-Rothi, Elisa J; Fuller, David D; Allen, Kyle D

2017-03-01

While rodent gait analysis can quantify the behavioral consequences of disease, significant methodological differences exist between analysis platforms and little validation has been performed to understand or mitigate these sources of variance. By providing the algorithms used to quantify gait, open-source gait analysis software can be validated and used to explore methodological differences. Our group is introducing, for the first time, a fully-automated, open-source method for the characterization of rodent spatiotemporal gait patterns, termed Automated Gait Analysis Through Hues and Areas (AGATHA). This study describes how AGATHA identifies gait events, validates AGATHA relative to manual digitization methods, and utilizes AGATHA to detect gait compensations in orthopaedic and spinal cord injury models. To validate AGATHA against manual digitization, results from videos of rodent gait, recorded at 1000 frames per second (fps), were compared. To assess one common source of variance (the effects of video frame rate), these 1000 fps videos were re-sampled to mimic several lower fps and compared again. While spatial variables were indistinguishable between AGATHA and manual digitization, low video frame rates resulted in temporal errors for both methods. At frame rates over 125 fps, AGATHA achieved a comparable accuracy and precision to manual digitization for all gait variables. Moreover, AGATHA detected unique gait changes in each injury model. These data demonstrate AGATHA is an accurate and precise platform for the analysis of rodent spatiotemporal gait patterns.

Tc1 mouse model of trisomy-21 dissociates properties of short- and long-term recognition memory.

PubMed

Hall, Jessica H; Wiseman, Frances K; Fisher, Elizabeth M C; Tybulewicz, Victor L J; Harwood, John L; Good, Mark A

2016-04-01

The present study examined memory function in Tc1 mice, a transchromosomic model of Down syndrome (DS). Tc1 mice demonstrated an unusual delay-dependent deficit in recognition memory. More specifically, Tc1 mice showed intact immediate (30sec), impaired short-term (10-min) and intact long-term (24-h) memory for objects. A similar pattern was observed for olfactory stimuli, confirming the generality of the pattern across sensory modalities. The specificity of the behavioural deficits in Tc1 mice was confirmed using APP overexpressing mice that showed the opposite pattern of object memory deficits. In contrast to object memory, Tc1 mice showed no deficit in either immediate or long-term memory for object-in-place information. Similarly, Tc1 mice showed no deficit in short-term memory for object-location information. The latter result indicates that Tc1 mice were able to detect and react to spatial novelty at the same delay interval that was sensitive to an object novelty recognition impairment. These results demonstrate (1) that novelty detection per se and (2) the encoding of visuo-spatial information was not disrupted in adult Tc1 mice. The authors conclude that the task specific nature of the short-term recognition memory deficit suggests that the trisomy of genes on human chromosome 21 in Tc1 mice impacts on (perirhinal) cortical systems supporting short-term object and olfactory recognition memory. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Automated biosurveillance data from England and Wales, 1991-2011.

PubMed

Enki, Doyo G; Noufaily, Angela; Garthwaite, Paul H; Andrews, Nick J; Charlett, André; Lane, Chris; Farrington, C Paddy

2013-01-01

Outbreak detection systems for use with very large multiple surveillance databases must be suited both to the data available and to the requirements of full automation. To inform the development of more effective outbreak detection algorithms, we analyzed 20 years of data (1991-2011) from a large laboratory surveillance database used for outbreak detection in England and Wales. The data relate to 3,303 distinct types of infectious pathogens, with a frequency range spanning 6 orders of magnitude. Several hundred organism types were reported each week. We describe the diversity of seasonal patterns, trends, artifacts, and extra-Poisson variability to which an effective multiple laboratory-based outbreak detection system must adjust. We provide empirical information to guide the selection of simple statistical models for automated surveillance of multiple organisms, in the light of the key requirements of such outbreak detection systems, namely, robustness, flexibility, and sensitivity.

The Temporal Dynamics of Visual Search: Evidence for Parallel Processing in Feature and Conjunction Searches

PubMed Central

McElree, Brian; Carrasco, Marisa

2012-01-01

Feature and conjunction searches have been argued to delineate parallel and serial operations in visual processing. The authors evaluated this claim by examining the temporal dynamics of the detection of features and conjunctions. The 1st experiment used a reaction time (RT) task to replicate standard mean RT patterns and to examine the shapes of the RT distributions. The 2nd experiment used the response-signal speed–accuracy trade-off (SAT) procedure to measure discrimination (asymptotic detection accuracy) and detection speed (processing dynamics). Set size affected discrimination in both feature and conjunction searches but affected detection speed only in the latter. Fits of models to the SAT data that included a serial component overpredicted the magnitude of the observed dynamics differences. The authors concluded that both features and conjunctions are detected in parallel. Implications for the role of attention in visual processing are discussed. PMID:10641310

Automated Biosurveillance Data from England and Wales, 1991–2011

PubMed Central

Enki, Doyo G.; Noufaily, Angela; Garthwaite, Paul H.; Andrews, Nick J.; Charlett, André; Lane, Chris

2013-01-01

Outbreak detection systems for use with very large multiple surveillance databases must be suited both to the data available and to the requirements of full automation. To inform the development of more effective outbreak detection algorithms, we analyzed 20 years of data (1991–2011) from a large laboratory surveillance database used for outbreak detection in England and Wales. The data relate to 3,303 distinct types of infectious pathogens, with a frequency range spanning 6 orders of magnitude. Several hundred organism types were reported each week. We describe the diversity of seasonal patterns, trends, artifacts, and extra-Poisson variability to which an effective multiple laboratory-based outbreak detection system must adjust. We provide empirical information to guide the selection of simple statistical models for automated surveillance of multiple organisms, in the light of the key requirements of such outbreak detection systems, namely, robustness, flexibility, and sensitivity. PMID:23260848

Model-based diagnosis of large diesel engines based on angular speed variations of the crankshaft

NASA Astrophysics Data System (ADS)

Desbazeille, M.; Randall, R. B.; Guillet, F.; El Badaoui, M.; Hoisnard, C.

2010-07-01

This work aims at monitoring large diesel engines by analyzing the crankshaft angular speed variations. It focuses on a powerful 20-cylinder diesel engine with crankshaft natural frequencies within the operating speed range. First, the angular speed variations are modeled at the crankshaft free end. This includes modeling both the crankshaft dynamical behavior and the excitation torques. As the engine is very large, the first crankshaft torsional modes are in the low frequency range. A model with the assumption of a flexible crankshaft is required. The excitation torques depend on the in-cylinder pressure curve. The latter is modeled with a phenomenological model. Mechanical and combustion parameters of the model are optimized with the help of actual data. Then, an automated diagnosis based on an artificially intelligent system is proposed. Neural networks are used for pattern recognition of the angular speed waveforms in normal and faulty conditions. Reference patterns required in the training phase are computed with the model, calibrated using a small number of actual measurements. Promising results are obtained. An experimental fuel leakage fault is successfully diagnosed, including detection and localization of the faulty cylinder, as well as the approximation of the fault severity.

A framework for periodic outlier pattern detection in time-series sequences.

PubMed

Rasheed, Faraz; Alhajj, Reda

2014-05-01

Periodic pattern detection in time-ordered sequences is an important data mining task, which discovers in the time series all patterns that exhibit temporal regularities. Periodic pattern mining has a large number of applications in real life; it helps understanding the regular trend of the data along time, and enables the forecast and prediction of future events. An interesting related and vital problem that has not received enough attention is to discover outlier periodic patterns in a time series. Outlier patterns are defined as those which are different from the rest of the patterns; outliers are not noise. While noise does not belong to the data and it is mostly eliminated by preprocessing, outliers are actual instances in the data but have exceptional characteristics compared with the majority of the other instances. Outliers are unusual patterns that rarely occur, and, thus, have lesser support (frequency of appearance) in the data. Outlier patterns may hint toward discrepancy in the data such as fraudulent transactions, network intrusion, change in customer behavior, recession in the economy, epidemic and disease biomarkers, severe weather conditions like tornados, etc. We argue that detecting the periodicity of outlier patterns might be more important in many sequences than the periodicity of regular, more frequent patterns. In this paper, we present a robust and time efficient suffix tree-based algorithm capable of detecting the periodicity of outlier patterns in a time series by giving more significance to less frequent yet periodic patterns. Several experiments have been conducted using both real and synthetic data; all aspects of the proposed approach are compared with the existing algorithm InfoMiner; the reported results demonstrate the effectiveness and applicability of the proposed approach.

On-Chip, Amplification-Free Quantification of Nucleic Acid for Point-of-Care Diagnosis

NASA Astrophysics Data System (ADS)

Yen, Tony Minghung

This dissertation demonstrates three physical device concepts to overcome limitations in point-of-care quantification of nucleic acids. Enabling sensitive, high throughput nucleic acid quantification on a chip, outside of hospital and centralized laboratory setting, is crucial for improving pathogen detection and cancer diagnosis and prognosis. Among existing platforms, microarray have the advantages of being amplification free, low instrument cost, and high throughput, but are generally less sensitive compared to sequencing and PCR assays. To bridge this performance gap, this dissertation presents theoretical and experimental progress to develop a platform nucleic acid quantification technology that is drastically more sensitive than current microarrays while compatible with microarray architecture. The first device concept explores on-chip nucleic acid enrichment by natural evaporation of nucleic acid solution droplet. Using a micro-patterned super-hydrophobic black silicon array device, evaporative enrichment is coupled with nano-liter droplet self-assembly workflow to produce a 50 aM concentration sensitivity, 6 orders of dynamic range, and rapid hybridization time at under 5 minutes. The second device concept focuses on improving target copy number sensitivity, instead of concentration sensitivity. A comprehensive microarray physical model taking into account of molecular transport, electrostatic intermolecular interactions, and reaction kinetics is considered to guide device optimization. Device pattern size and target copy number are optimized based on model prediction to achieve maximal hybridization efficiency. At a 100-mum pattern size, a quantum leap in detection limit of 570 copies is achieved using black silicon array device with self-assembled pico-liter droplet workflow. Despite its merits, evaporative enrichment on black silicon device suffers from coffee-ring effect at 100-mum pattern size, and thus not compatible with clinical patient samples. The third device concept utilizes an integrated optomechanical laser system and a Cytop microarray device to reverse coffee-ring effect during evaporative enrichment at 100-mum pattern size. This method, named "laser-induced differential evaporation" is expected to enable 570 copies detection limit for clinical samples in near future. While the work is ongoing as of the writing of this dissertation, a clear research plan is in place to implement this method on microarray platform toward clinical sample testing for disease applications and future commercialization.

Snow Pattern Delineation, Scaling, Fidelity, and Landscape Factors

NASA Astrophysics Data System (ADS)

Hiemstra, C. A.; Wagner, A. M.; Deeb, E. J.; Morriss, B. F.; Sturm, M.

2014-12-01

In many snow-covered landscapes, snow tends to be shallow or deep in the same locations year after year. As snowmelt progresses in spring, areas of shallow snow become snow-free earlier than areas with deep snow. This pattern (Sturm and Wagner 2010) could likely be used to inform or improve modeled snow depth estimates where ground measurements are not collected; however, we must be certain of their utility before ingesting them into model calculations. Do patterns, as we detect them, have a relationship with earlier measured snow distributions? Second, are certain areas on the landscape likely to yield patterns that are influenced too highly by melting to be useful? Our Imnavait Creek Study Area (11 by 19 km) is on Alaska's North Slope, where we have examined a vast library of spring satellite imagery (ranging from mostly snow-covered to mostly snow-free). Landsat TM Imagery has been collected from the early 1980s-present, and the temporal and spatial resolution is roughly two weeks and 30 m, respectively. High resolution satellite imagery (WorldView 1, WorldView 2, IKONOS) has been obtained from 2010-2013 for the same area with almost daily- to monthly-temporal and at 2.5 m spatial resolutions, respectively. We found that there is a striking similarity among patterns from year to year across the span of decades and resolutions. However, the relationship of pattern with observed snow depths was strong in some areas and less clear in others. Overall, we suspect spatial scaling, spatial mismatch, sampling errors, and melt patterns explain most of the areas of pattern and depth disparity.

QuakeSim 2.0

NASA Technical Reports Server (NTRS)

Donnellan, Andrea; Parker, Jay W.; Lyzenga, Gregory A.; Granat, Robert A.; Norton, Charles D.; Rundle, John B.; Pierce, Marlon E.; Fox, Geoffrey C.; McLeod, Dennis; Ludwig, Lisa Grant

2012-01-01

QuakeSim 2.0 improves understanding of earthquake processes by providing modeling tools and integrating model applications and various heterogeneous data sources within a Web services environment. QuakeSim is a multisource, synergistic, data-intensive environment for modeling the behavior of earthquake faults individually, and as part of complex interacting systems. Remotely sensed geodetic data products may be explored, compared with faults and landscape features, mined by pattern analysis applications, and integrated with models and pattern analysis applications in a rich Web-based and visualization environment. Integration of heterogeneous data products with pattern informatics tools enables efficient development of models. Federated database components and visualization tools allow rapid exploration of large datasets, while pattern informatics enables identification of subtle, but important, features in large data sets. QuakeSim is valuable for earthquake investigations and modeling in its current state, and also serves as a prototype and nucleus for broader systems under development. The framework provides access to physics-based simulation tools that model the earthquake cycle and related crustal deformation. Spaceborne GPS and Inter ferometric Synthetic Aperture (InSAR) data provide information on near-term crustal deformation, while paleoseismic geologic data provide longerterm information on earthquake fault processes. These data sources are integrated into QuakeSim's QuakeTables database system, and are accessible by users or various model applications. UAVSAR repeat pass interferometry data products are added to the QuakeTables database, and are available through a browseable map interface or Representational State Transfer (REST) interfaces. Model applications can retrieve data from Quake Tables, or from third-party GPS velocity data services; alternatively, users can manually input parameters into the models. Pattern analysis of GPS and seismicity data has proved useful for mid-term forecasting of earthquakes, and for detecting subtle changes in crustal deformation. The GPS time series analysis has also proved useful as a data-quality tool, enabling the discovery of station anomalies and data processing and distribution errors. Improved visualization tools enable more efficient data exploration and understanding. Tools provide flexibility to science users for exploring data in new ways through download links, but also facilitate standard, intuitive, and routine uses for science users and end users such as emergency responders.

Specializing network analysis to detect anomalous insider actions

PubMed Central

Chen, You; Nyemba, Steve; Zhang, Wen; Malin, Bradley

2012-01-01

Collaborative information systems (CIS) enable users to coordinate efficiently over shared tasks in complex distributed environments. For flexibility, they provide users with broad access privileges, which, as a side-effect, leave such systems vulnerable to various attacks. Some of the more damaging malicious activities stem from internal misuse, where users are authorized to access system resources. A promising class of insider threat detection models for CIS focuses on mining access patterns from audit logs, however, current models are limited in that they assume organizations have significant resources to generate label cases for training classifiers or assume the user has committed a large number of actions that deviate from “normal” behavior. In lieu of the previous assumptions, we introduce an approach that detects when specific actions of an insider deviate from expectation in the context of collaborative behavior. Specifically, in this paper, we introduce a specialized network anomaly detection model, or SNAD, to detect such events. This approach assesses the extent to which a user influences the similarity of the group of users that access a particular record in the CIS. From a theoretical perspective, we show that the proposed model is appropriate for detecting insider actions in dynamic collaborative systems. From an empirical perspective, we perform an extensive evaluation of SNAD with the access logs of two distinct environments: the patient record access logs a large electronic health record system (6,015 users, 130,457 patients and 1,327,500 accesses) and the editing logs of Wikipedia (2,394,385 revisors, 55,200 articles and 6,482,780 revisions). We compare our model with several competing methods and demonstrate SNAD is significantly more effective: on average it achieves 20–30% greater area under an ROC curve. PMID:23399988

Nonlinear relative-proportion-based route adjustment process for day-to-day traffic dynamics: modeling, equilibrium and stability analysis

NASA Astrophysics Data System (ADS)

Zhu, Wenlong; Ma, Shoufeng; Tian, Junfang; Li, Geng

2016-11-01

Travelers' route adjustment behaviors in a congested road traffic network are acknowledged as a dynamic game process between them. Existing Proportional-Switch Adjustment Process (PSAP) models have been extensively investigated to characterize travelers' route choice behaviors; PSAP has concise structure and intuitive behavior rule. Unfortunately most of which have some limitations, i.e., the flow over adjustment problem for the discrete PSAP model, the absolute cost differences route adjustment problem, etc. This paper proposes a relative-Proportion-based Route Adjustment Process (rePRAP) maintains the advantages of PSAP and overcomes these limitations. The rePRAP describes the situation that travelers on higher cost route switch to those with lower cost at the rate that is unilaterally depended on the relative cost differences between higher cost route and its alternatives. It is verified to be consistent with the principle of the rational behavior adjustment process. The equivalence among user equilibrium, stationary path flow pattern and stationary link flow pattern is established, which can be applied to judge whether a given network traffic flow has reached UE or not by detecting the stationary or non-stationary state of link flow pattern. The stability theorem is proved by the Lyapunov function approach. A simple example is tested to demonstrate the effectiveness of the rePRAP model.

Comparing Distribution of Harbour Porpoises (Phocoena phocoena) Derived from Satellite Telemetry and Passive Acoustic Monitoring

PubMed Central

Rigét, Frank F.; Kyhn, Line A.; Sveegaard, Signe; Dietz, Rune; Tougaard, Jakob; Carlström, Julia A. K.; Carlén, Ida; Koblitz, Jens C.; Teilmann, Jonas

2016-01-01

Cetacean monitoring is essential in determining the status of a population. Different monitoring methods should reflect the real trends in abundance and patterns in distribution, and results should therefore ideally be independent of the selected method. Here, we compare two independent methods of describing harbour porpoise (Phocoena phocoena) relative distribution pattern in the western Baltic Sea. Satellite locations from 13 tagged harbour porpoises were used to build a Maximum Entropy (MaxEnt) model of suitable habitats. The data set was subsampled to one location every second day, which were sufficient to make reliable models over the summer (Jun-Aug) and autumn (Sep-Nov) seasons. The modelled results were compared to harbour porpoise acoustic activity obtained from 36 static acoustic monitoring stations (C-PODs) covering the same area. The C-POD data was expressed as the percentage of porpoise positive days/hours (the number of days/hours per day with porpoise detections) by season. The MaxEnt model and C-POD data showed a significant linear relationship with a strong decline in porpoise occurrence from west to east. This study shows that two very different methods provide comparable information on relative distribution patterns of harbour porpoises even in a low density area. PMID:27463509

Development of Dual-Retrieval Processes in Recall: Learning, Forgetting, and Reminiscence

PubMed Central

Brainerd, C. J.; Aydin, C.; Reyna, V. F.

2012-01-01

We investigated the development of dual-retrieval processes with a low-burden paradigm that is suitable for research with children and neurocognitively impaired populations (e.g., older adults with mild cognitive impairment or dementia). Rich quantitative information can be obtained about recollection, reconstruction, and familiarity judgment by defining a Markov model over simple recall tasks like those that are used in clinical neuropsychology batteries. The model measures these processes separately for learning, forgetting, and reminiscence. We implemented this procedure in some developmental experiments, whose aims were (a) to measure age changes in recollective and nonrecollective retrieval during learning, forgetting, and reminiscence and (b) to measure age changes in content dimensions (e.g., taxonomic relatedness) that affect the two forms of retrieval. The model provided excellent fits in all three domains. Concerning (a), recollection, reconstruction, and familiarity judgment all improved during the child-to-adolescent age range in the learning domain, whereas only recollection improved in the forgetting domain, and the processes were age-invariant in the reminiscence domain. Concerning (b), although some elements of the adult pattern of taxonomic relatedness effects were detected by early adolescence, the adult pattern differs qualitatively from corresponding patterns in children and adolescents. PMID:22778491

Automation based on knowledge modeling theory and its applications in engine diagnostic systems using Space Shuttle Main Engine vibrational data. M.S. Thesis

NASA Technical Reports Server (NTRS)

Kim, Jonnathan H.

1995-01-01

Humans can perform many complicated tasks without explicit rules. This inherent and advantageous capability becomes a hurdle when a task is to be automated. Modern computers and numerical calculations require explicit rules and discrete numerical values. In order to bridge the gap between human knowledge and automating tools, a knowledge model is proposed. Knowledge modeling techniques are discussed and utilized to automate a labor and time intensive task of detecting anomalous bearing wear patterns in the Space Shuttle Main Engine (SSME) High Pressure Oxygen Turbopump (HPOTP).

Modeling off-frequency binaural masking for short- and long-duration signals.

PubMed

Nitschmann, Marc; Yasin, Ifat; Henning, G Bruce; Verhey, Jesko L

2017-08-01

Experimental binaural masking-pattern data are presented together with model simulations for 12- and 600-ms signals. The masker was a diotic 11-Hz wide noise centered on 500 Hz. The tonal signal was presented either diotically or dichotically (180° interaural phase difference) with frequencies ranging from 400 to 600 Hz. The results and the modeling agree with previous data and hypotheses; simulations with a binaural model sensitive to monaural modulation cues show that the effect of duration on off-frequency binaural masking-level differences is mainly a result of modulation cues which are only available in the monaural detection of long signals.

Efficient estimation of abundance for patchily distributed populations via two-phase, adaptive sampling.

USGS Publications Warehouse

Conroy, M.J.; Runge, J.P.; Barker, R.J.; Schofield, M.R.; Fonnesbeck, C.J.

2008-01-01

Many organisms are patchily distributed, with some patches occupied at high density, others at lower densities, and others not occupied. Estimation of overall abundance can be difficult and is inefficient via intensive approaches such as capture-mark-recapture (CMR) or distance sampling. We propose a two-phase sampling scheme and model in a Bayesian framework to estimate abundance for patchily distributed populations. In the first phase, occupancy is estimated by binomial detection samples taken on all selected sites, where selection may be of all sites available, or a random sample of sites. Detection can be by visual surveys, detection of sign, physical captures, or other approach. At the second phase, if a detection threshold is achieved, CMR or other intensive sampling is conducted via standard procedures (grids or webs) to estimate abundance. Detection and CMR data are then used in a joint likelihood to model probability of detection in the occupancy sample via an abundance-detection model. CMR modeling is used to estimate abundance for the abundance-detection relationship, which in turn is used to predict abundance at the remaining sites, where only detection data are collected. We present a full Bayesian modeling treatment of this problem, in which posterior inference on abundance and other parameters (detection, capture probability) is obtained under a variety of assumptions about spatial and individual sources of heterogeneity. We apply the approach to abundance estimation for two species of voles (Microtus spp.) in Montana, USA. We also use a simulation study to evaluate the frequentist properties of our procedure given known patterns in abundance and detection among sites as well as design criteria. For most population characteristics and designs considered, bias and mean-square error (MSE) were low, and coverage of true parameter values by Bayesian credibility intervals was near nominal. Our two-phase, adaptive approach allows efficient estimation of abundance of rare and patchily distributed species and is particularly appropriate when sampling in all patches is impossible, but a global estimate of abundance is required.

A novel growth mode of Physarum polycephalum during starvation

NASA Astrophysics Data System (ADS)

Lee, Jonghyun; Oettmeier, Christina; Döbereiner, Hans-Günther

2018-06-01

Organisms are constantly looking to forage and respond to various environmental queues to maximize their chance of survival. This is reflected in the unicellular organism Physarum polycephalum, which is known to grow as an optimized network. Here, we describe a new growth pattern of Physarum mesoplasmodium, where sheet-like motile bodies termed ‘satellites’ are formed. This non-network pattern formation is induced only when nutrients are scarce, suggesting that it is a type of emergency response. Our goal is to construct a model to describe the behaviour of satellites based on negative chemotaxis. We conjecture a diffusion-based model which implements detection of a signal molecule above a threshold concentration. Then we calculate how far the satellites must travel until the concentration signal falls below the threshold. These calculated distances are in good agreement with the distances where satellites stop. Based on the Akaike weight analysis, our threshold model is at least 2.3 times more likely to be the better model than the others we have considered. Based on the model, we estimate the diffusion coefficient of this molecule, which corresponds to typical signalling molecules.

Detecting Smoking Events Using Accelerometer Data Collected Via Smartwatch Technology: Validation Study

PubMed Central

Cole, Casey A; Anshari, Dien; Lambert, Victoria; Thrasher, James F

2017-01-01

Background Smoking is the leading cause of preventable death in the world today. Ecological research on smoking in context currently relies on self-reported smoking behavior. Emerging smartwatch technology may more objectively measure smoking behavior by automatically detecting smoking sessions using robust machine learning models. Objective This study aimed to examine the feasibility of detecting smoking behavior using smartwatches. The second aim of this study was to compare the success of observing smoking behavior with smartwatches to that of conventional self-reporting. Methods A convenience sample of smokers was recruited for this study. Participants (N=10) recorded 12 hours of accelerometer data using a mobile phone and smartwatch. During these 12 hours, they engaged in various daily activities, including smoking, for which they logged the beginning and end of each smoking session. Raw data were classified as either smoking or nonsmoking using a machine learning model for pattern recognition. The accuracy of the model was evaluated by comparing the output with a detailed description of a modeled smoking session. Results In total, 120 hours of data were collected from participants and analyzed. The accuracy of self-reported smoking was approximately 78% (96/123). Our model was successful in detecting 100 of 123 (81%) smoking sessions recorded by participants. After eliminating sessions from the participants that did not adhere to study protocols, the true positive detection rate of the smartwatch based-detection increased to more than 90%. During the 120 hours of combined observation time, only 22 false positive smoking sessions were detected resulting in a 2.8% false positive rate. Conclusions Smartwatch technology can provide an accurate, nonintrusive means of monitoring smoking behavior in natural contexts. The use of machine learning algorithms for passively detecting smoking sessions may enrich ecological momentary assessment protocols and cessation intervention studies that often rely on self-reported behaviors and may not allow for targeted data collection and communications around smoking events. PMID:29237580

Measurement agreement between a newly developed sensing insole and traditional laboratory-based method for footstrike pattern detection in runners

PubMed Central

Cheung, Roy T. H.; An, Winko W.; Au, Ivan P. H.; Zhang, Janet H.; Chan, Zoe Y. S.; Man, Alfred; Lau, Fannie O. Y.; Lam, Melody K. Y.; Lau, K. K.; Leung, C. Y.; Tsang, N. W.; Sze, Louis K. Y.; Lam, Gilbert W. K.

2017-01-01

This study introduced a novel but simple method to continuously measure footstrike patterns in runners using inexpensive force sensors. Two force sensing resistors were firmly affixed at the heel and second toe of both insoles to collect the time signal of foot contact. A total of 109 healthy young adults (42 males and 67 females) were recruited in this study. They ran on an instrumented treadmill at 0°, +10°, and -10° inclinations and attempted rearfoot, midfoot, and forefoot landings using real time visual biofeedback. Intra-step strike index and onset time difference between two force sensors were measured and analyzed with univariate linear regression. We analyzed 25,655 footfalls and found that onset time difference between two sensors explained 80–84% of variation in the prediction model of strike index (R-squared = 0.799–0.836, p<0.001). However, the time windows to detect footstrike patterns on different surface inclinations were not consistent. These findings may allow laboratory-based gait retraining to be implemented in natural running environments to aid in both injury prevention and performance enhancement. PMID:28599003

Enhanced perception in savant syndrome: patterns, structure and creativity

PubMed Central

Mottron, Laurent; Dawson, Michelle; Soulières, Isabelle

2009-01-01

According to the enhanced perceptual functioning (EPF) model, autistic perception is characterized by: enhanced low-level operations; locally oriented processing as a default setting; greater activation of perceptual areas during a range of visuospatial, language, working memory or reasoning tasks; autonomy towards higher processes; and superior involvement in intelligence. EPF has been useful in accounting for autistic relative peaks of ability in the visual and auditory modalities. However, the role played by atypical perceptual mechanisms in the emergence and character of savant abilities remains underdeveloped. We now propose that enhanced detection of patterns, including similarity within and among patterns, is one of the mechanisms responsible for operations on human codes, a type of material with which savants show particular facility. This mechanism would favour an orientation towards material possessing the highest level of internal structure, through the implicit detection of within- and between-code isomorphisms. A second mechanism, related to but exceeding the existing concept of redintegration, involves completion, or filling-in, of missing information in memorized or perceived units or structures. In the context of autistics' enhanced perception, the nature and extent of these two mechanisms, and their possible contribution to the creativity evident in savant performance, are explored. PMID:19528021

Enhanced perception in savant syndrome: patterns, structure and creativity.

PubMed

Mottron, Laurent; Dawson, Michelle; Soulières, Isabelle

2009-05-27

According to the enhanced perceptual functioning (EPF) model, autistic perception is characterized by: enhanced low-level operations; locally oriented processing as a default setting; greater activation of perceptual areas during a range of visuospatial, language, working memory or reasoning tasks; autonomy towards higher processes; and superior involvement in intelligence. EPF has been useful in accounting for autistic relative peaks of ability in the visual and auditory modalities. However, the role played by atypical perceptual mechanisms in the emergence and character of savant abilities remains underdeveloped. We now propose that enhanced detection of patterns, including similarity within and among patterns, is one of the mechanisms responsible for operations on human codes, a type of material with which savants show particular facility. This mechanism would favour an orientation towards material possessing the highest level of internal structure, through the implicit detection of within- and between-code isomorphisms. A second mechanism, related to but exceeding the existing concept of redintegration, involves completion, or filling-in, of missing information in memorized or perceived units or structures. In the context of autistics' enhanced perception, the nature and extent of these two mechanisms, and their possible contribution to the creativity evident in savant performance, are explored.

Measuring the visual salience of alignments by their non-accidentalness.

PubMed

Blusseau, S; Carboni, A; Maiche, A; Morel, J M; Grompone von Gioi, R

2016-09-01

Quantitative approaches are part of the understanding of contour integration and the Gestalt law of good continuation. The present study introduces a new quantitative approach based on the a contrario theory, which formalizes the non-accidentalness principle for good continuation. This model yields an ideal observer algorithm, able to detect non-accidental alignments in Gabor patterns. More precisely, this parameterless algorithm associates with each candidate percept a measure, the Number of False Alarms (NFA), quantifying its degree of masking. To evaluate the approach, we compared this ideal observer with the human attentive performance on three experiments of straight contours detection in arrays of Gabor patches. The experiments showed a strong correlation between the detectability of the target stimuli and their degree of non-accidentalness, as measured by our model. What is more, the algorithm's detection curves were very similar to the ones of human subjects. This fact seems to validate our proposed measurement method as a convenient way to predict the visibility of alignments. This framework could be generalized to other Gestalts. Copyright © 2015 Elsevier Ltd. All rights reserved.

Patterns in coupled water and energy cycle: Modeling, synthesis with observations, and assessing the subsurface-landsurface interactions

NASA Astrophysics Data System (ADS)

Rahman, A.; Kollet, S. J.; Sulis, M.

2013-12-01

In the terrestrial hydrological cycle, the atmosphere and the free groundwater table act as the upper and lower boundary condition, respectively, in the non-linear two-way exchange of mass and energy across the land surface. Identifying and quantifying the interactions among various atmospheric-subsurface-landsurface processes is complicated due to the diverse spatiotemporal scales associated with these processes. In this study, the coupled subsurface-landsurface model ParFlow.CLM was applied over a ~28,000 km2 model domain encompassing the Rur catchment, Germany, to simulate the fluxes of the coupled water and energy cycle. The model was forced by hourly atmospheric data from the COSMO-DE model (numerical weather prediction system of the German Weather Service) over one year. Following a spinup period, the model results were synthesized with observed river discharge, soil moisture, groundwater table depth, temperature, and landsurface energy flux data at different sites in the Rur catchment. It was shown that the model is able to reproduce reasonably the dynamics and also absolute values in observed fluxes and state variables without calibration. The spatiotemporal patterns in simulated water and energy fluxes as well as the interactions were studied using statistical, geostatistical and wavelet transform methods. While spatial patterns in the mass and energy fluxes can be predicted from atmospheric forcing and power law scaling in the transition and winter months, it appears that, in the summer months, the spatial patterns are determined by the spatially correlated variability in groundwater table depth. Continuous wavelet transform techniques were applied to study the variability of the catchment average mass and energy fluxes at varying time scales. From this analysis, the time scales associated with significant interactions among different mass and energy balance components were identified. The memory of precipitation variability in subsurface hydrodynamics acts at the 20-30 day time scale, while the groundwater contribution to sustain the long-term variability patterns in evapotranspiration acts at the 40-60 day scale. Diurnal patterns in connection with subsurface hydrodynamics were also detected. Thus, it appears that the subsurface hydrodynamics respond to the temporal patterns in land surface fluxes due to the variability in atmospheric forcing across multiple space and time scales.

Particle detection for patterned wafers of 100nm design rule by evanescent light illumination: analysis of evanescent light scattering using Finite-Difference Time-Domain (FDTD) method

NASA Astrophysics Data System (ADS)

Yoshioka, Toshie; Miyoshi, Takashi; Takaya, Yasuhiro

2005-12-01

To realize high productivity and reliability of the semiconductor, patterned wafers inspection technology to maintain high yield becomes essential in modern semiconductor manufacturing processes. As circuit feature is scaled below 100nm, the conventional imaging and light scattering methods are impossible to apply to the patterned wafers inspection technique, because of diffraction limit and lower S/N ratio. So, we propose a new particle detection method using annular evanescent light illumination. In this method, a converging annular light used as a light source is incident on a micro-hemispherical lens. When the converging angle is larger than critical angle, annular evanescent light is generated under the bottom surface of the hemispherical lens. Evanescent light is localized near by the bottom surface and decays exponentially away from the bottom surface. So, the evanescent light selectively illuminates the particles on the patterned wafer surface, because it can't illuminate the patterned wafer surface. The proposed method evaluates particles on a patterned wafer surface by detecting scattered evanescent light distribution from particles. To analyze the fundamental characteristics of the proposed method, the computer simulation was performed using FDTD method. The simulation results show that the proposed method is effective for detecting 100nm size particle on patterned wafer of 100nm lines and spaces, particularly under the condition that the evanescent light illumination with p-polarization and parallel incident to the line orientation. Finally, the experiment results suggest that 220nm size particle on patterned wafer of about 200nm lines and spaces can be detected.

Mouse epileptic seizure detection with multiple EEG features and simple thresholding technique

NASA Astrophysics Data System (ADS)

Tieng, Quang M.; Anbazhagan, Ashwin; Chen, Min; Reutens, David C.

2017-12-01

Objective. Epilepsy is a common neurological disorder characterized by recurrent, unprovoked seizures. The search for new treatments for seizures and epilepsy relies upon studies in animal models of epilepsy. To capture data on seizures, many applications require prolonged electroencephalography (EEG) with recordings that generate voluminous data. The desire for efficient evaluation of these recordings motivates the development of automated seizure detection algorithms. Approach. A new seizure detection method is proposed, based on multiple features and a simple thresholding technique. The features are derived from chaos theory, information theory and the power spectrum of EEG recordings and optimally exploit both linear and nonlinear characteristics of EEG data. Main result. The proposed method was tested with real EEG data from an experimental mouse model of epilepsy and distinguished seizures from other patterns with high sensitivity and specificity. Significance. The proposed approach introduces two new features: negative logarithm of adaptive correlation integral and power spectral coherence ratio. The combination of these new features with two previously described features, entropy and phase coherence, improved seizure detection accuracy significantly. Negative logarithm of adaptive correlation integral can also be used to compute the duration of automatically detected seizures.

One Shot Detection with Laplacian Object and Fast Matrix Cosine Similarity.

PubMed

Biswas, Sujoy Kumar; Milanfar, Peyman

2016-03-01

One shot, generic object detection involves searching for a single query object in a larger target image. Relevant approaches have benefited from features that typically model the local similarity patterns. In this paper, we combine local similarity (encoded by local descriptors) with a global context (i.e., a graph structure) of pairwise affinities among the local descriptors, embedding the query descriptors into a low dimensional but discriminatory subspace. Unlike principal components that preserve global structure of feature space, we actually seek a linear approximation to the Laplacian eigenmap that permits us a locality preserving embedding of high dimensional region descriptors. Our second contribution is an accelerated but exact computation of matrix cosine similarity as the decision rule for detection, obviating the computationally expensive sliding window search. We leverage the power of Fourier transform combined with integral image to achieve superior runtime efficiency that allows us to test multiple hypotheses (for pose estimation) within a reasonably short time. Our approach to one shot detection is training-free, and experiments on the standard data sets confirm the efficacy of our model. Besides, low computation cost of the proposed (codebook-free) object detector facilitates rather straightforward query detection in large data sets including movie videos.

Development of Speckle Interferometry Algorithm and System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shamsir, A. A. M.; Jafri, M. Z. M.; Lim, H. S.

2011-05-25

Electronic speckle pattern interferometry (ESPI) method is a wholefield, non destructive measurement method widely used in the industries such as detection of defects on metal bodies, detection of defects in intergrated circuits in digital electronics components and in the preservation of priceless artwork. In this research field, this method is widely used to develop algorithms and to develop a new laboratory setup for implementing the speckle pattern interferometry. In speckle interferometry, an optically rough test surface is illuminated with an expanded laser beam creating a laser speckle pattern in the space surrounding the illuminated region. The speckle pattern is opticallymore » mixed with a second coherent light field that is either another speckle pattern or a smooth light field. This produces an interferometric speckle pattern that will be detected by sensor to count the change of the speckle pattern due to force given. In this project, an experimental setup of ESPI is proposed to analyze a stainless steel plate using 632.8 nm (red) wavelength of lights.« less

Nanoscale observation of local bound charges of patterned protein arrays by scanning force microscopy

NASA Astrophysics Data System (ADS)

Oh, Y. J.; Jo, W.; Kim, S.; Park, S.; Kim, Y. S.

2008-09-01

A protein patterned surface using micro-contact printing methods has been investigated by scanning force microscopy. Electrostatic force microscopy (EFM) was utilized for imaging the topography and detecting the electrical properties such as the local bound charge distribution of the patterned proteins. It was found that the patterned IgG proteins are arranged down to 1 µm, and the 90° rotation of patterned anti-IgG proteins was successfully undertaken. Through the estimation of the effective areas, it was possible to determine the local bound charges of patterned proteins which have opposite electrostatic force behaviors. Moreover, we studied the binding probability between IgG and anti-IgG in a 1 µm2 MIMIC system by topographic and electrostatic signals for applicable label-free detections. We showed that the patterned proteins can be used for immunoassay of proteins on the functional substrate, and that they can also be used for bioelectronics device application, indicating distinct advantages with regard to accuracy and a label-free detection.

Image-based spectroscopy for environmental monitoring

NASA Astrophysics Data System (ADS)

Bachmakov, Eduard; Molina, Carolyn; Wynne, Rosalind

2014-03-01

An image-processing algorithm for use with a nano-featured spectrometer chemical agent detection configuration is presented. The spectrometer chip acquired from Nano-Optic DevicesTM can reduce the size of the spectrometer down to a coin. The nanospectrometer chip was aligned with a 635nm laser source, objective lenses, and a CCD camera. The images from a nanospectrometer chip were collected and compared to reference spectra. Random background noise contributions were isolated and removed from the diffraction pattern image analysis via a threshold filter. Results are provided for the image-based detection of the diffraction pattern produced by the nanospectrometer. The featured PCF spectrometer has the potential to measure optical absorption spectra in order to detect trace amounts of contaminants. MATLAB tools allow for implementation of intelligent, automatic detection of the relevant sub-patterns in the diffraction patterns and subsequent extraction of the parameters using region-detection algorithms such as the generalized Hough transform, which detects specific shapes within the image. This transform is a method for detecting curves by exploiting the duality between points on a curve and parameters of that curve. By employing this imageprocessing technique, future sensor systems will benefit from new applications such as unsupervised environmental monitoring of air or water quality.

The evolution of the competition-dispersal trade-off affects α- and β-diversity in a heterogeneous metacommunity.

PubMed

Laroche, Fabien; Jarne, Philippe; Perrot, Thomas; Massol, Francois

2016-04-27

Difference in dispersal ability is a key driver of species coexistence in metacommunities. However, the available frameworks for interpreting species diversity patterns in natura often overlook trade-offs and evolutionary constraints associated with dispersal. Here, we build a metacommunity model accounting for dispersal evolution and a competition-dispersal trade-off. Depending on the distribution of carrying capacities among communities, species dispersal values are distributed either around a single strategy (evolutionarily stable strategy, ESS), or around distinct strategies (evolutionary branching, EB). We show that limited dispersal generates spatial aggregation of dispersal traits in ESS and EB scenarios, and that the competition-dispersal trade-off strengthens the pattern in the EB scenario. Importantly, individuals in larger (respectively (resp.) smaller) communities tend to harbour lower (resp. higher) dispersal, especially under the EB scenario. We explore how dispersal evolution affects species diversity patterns by comparing those from our model to the predictions of a neutral metacommunity model. The most marked difference is detected under EB, with distinctive values of both α- and β-diversity (e.g. the dissimilarity in species composition between small and large communities was significantly larger than neutral predictions). We conclude that, from an empirical perspective, jointly assessing community carrying capacity with species dispersal strategies should improve our understanding of diversity patterns in metacommunities. © 2016 The Author(s).

Self-Organized Patterns in Gas-Discharge: Particle-Like Behaviour and Dissipative Solitons

NASA Astrophysics Data System (ADS)

Purwins, H.-G.

2008-03-01

The understanding of self-organise patterns in spatially extended nonlinear dissipative systems (SOPs) is one of the most challenging subjects in modern natural sciences. In the last 20 years it turned out that research in the field of low temperature gas-discharge can help to obtain insight into important aspect of SOPs. At the same time, due to the practical relevance of plasma systems one might expect interesting applications. In the present paper the focus is on self-organised filamentary patterns in planar dc and ac systems with high ohmic and dielectric barrier, respectively. - In the discharge plane of these systems filaments show up as spots which are also referred to as dissipative solitons (DSs). In many respect experimentally detected DSs exhibit particle-like behaviour. Among other things, isolated stationary or travelling DSs, stationary, travelling or rotating "molecules" and various kinds of many-body systems have been observed. Also scattering, generation and annihilation of DSs are frequent phenomena. - At least some of these patterns can be described quantitatively in terms of a drift diffusion model. It is also demonstrated that a simple reaction diffusion model allows for an intuitive understanding of many of the observed phenomena. At the same time this model is the basis for a theoretical foundation of the particle picture and the experimentally observed universal behaviour of SOPs. - Finally some hypothetical applications are discussed.

Gender-Specificity of Initial and Controlled Visual Attention to Sexual Stimuli in Androphilic Women and Gynephilic Men

PubMed Central

Dawson, Samantha J.; Chivers, Meredith L.

2016-01-01

Research across groups and methods consistently finds a gender difference in patterns of specificity of genital response; however, empirically supported mechanisms to explain this difference are lacking. The information-processing model of sexual arousal posits that automatic and controlled cognitive processes are requisite for the generation of sexual responses. Androphilic women’s gender-nonspecific response patterns may be the result of sexually-relevant cues that are common to both preferred and nonpreferred genders capturing attention and initiating an automatic sexual response, whereas men’s attentional system may be biased towards the detection and response to sexually-preferred cues only. In the present study, we used eye tracking to assess visual attention to sexually-preferred and nonpreferred cues in a sample of androphilic women and gynephilic men. Results support predictions from the information-processing model regarding gendered processing of sexual stimuli in men and women. Men’s initial attention patterns were gender-specific, whereas women’s were nonspecific. In contrast, both men and women exhibited gender-specific patterns of controlled attention, although this effect was stronger among men. Finally, measures of attention and self-reported attraction were positively related in both men and women. These findings are discussed in the context of the information-processing model and evolutionary mechanisms that may have evolved to promote gendered attentional systems. PMID:27088358

Winter Precipitation Forecast in the European and Mediterranean Regions Using Cluster Analysis

NASA Astrophysics Data System (ADS)

Totz, Sonja; Tziperman, Eli; Coumou, Dim; Pfeiffer, Karl; Cohen, Judah

2017-12-01

The European climate is changing under global warming, and especially the Mediterranean region has been identified as a hot spot for climate change with climate models projecting a reduction in winter rainfall and a very pronounced increase in summertime heat waves. These trends are already detectable over the historic period. Hence, it is beneficial to forecast seasonal droughts well in advance so that water managers and stakeholders can prepare to mitigate deleterious impacts. We developed a new cluster-based empirical forecast method to predict precipitation anomalies in winter. This algorithm considers not only the strength but also the pattern of the precursors. We compare our algorithm with dynamic forecast models and a canonical correlation analysis-based prediction method demonstrating that our prediction method performs better in terms of time and pattern correlation in the Mediterranean and European regions.

Pattern detection in forensic case data using graph theory: application to heroin cutting agents.

PubMed

Terrettaz-Zufferey, Anne-Laure; Ratle, Frédéric; Ribaux, Olivier; Esseiva, Pierre; Kanevski, Mikhail

2007-04-11

Pattern recognition techniques can be very useful in forensic sciences to point out to relevant sets of events and potentially encourage an intelligence-led style of policing. In this study, these techniques have been applied to categorical data corresponding to cutting agents found in heroin seizures. An application of graph theoretic methods has been performed, in order to highlight the possible relationships between the location of seizures and co-occurrences of particular heroin cutting agents. An analysis of the co-occurrences to establish several main combinations has been done. Results illustrate the practical potential of mathematical models in forensic data analysis.

Microwave responses of the western North Atlantic

NASA Technical Reports Server (NTRS)

Stacey, J. M.; Girard, M. A.

1985-01-01

Features and objects in the Western North Atlantic Ocean - the Eastern Seaboard of the United States - are observed from Earth orbit by passive microwaves. The intensities of their radiated flux signatures are measured and displayed in color as a microwave flux image. The features of flux emitting objects such as the course of the Gulf Stream and the occurrence of cold eddies near the Gulf Stream are identified by contoured patterns of relative flux intensities. The flux signatures of ships and their wakes are displayed and discussed. Metal data buoys and aircraft are detected. Signal to clutter ratios and probabilities of detection are computed from their measured irradiances. Theoretical models and the range equations that explain passive microwave detection using the irradiances of natural sources are summarized.

Abeta targets of the biosimilar antibodies of Bapineuzumab, Crenezumab, Solanezumab in comparison to an antibody against N‑truncated Abeta in sporadic Alzheimer disease cases and mouse models.

PubMed

Bouter, Yvonne; Lopez Noguerola, Jose Socrates; Tucholla, Petra; Crespi, Gabriela A N; Parker, Michael W; Wiltfang, Jens; Miles, Luke A; Bayer, Thomas A

2015-11-01

Solanezumab and Crenezumab are two humanized antibodies targeting Amyloid-β (Aβ) which are currently tested in multiple clinical trials for the prevention of Alzheimer's disease. However, there is a scientific discussion ongoing about the target engagement of these antibodies. Here, we report the immunohistochemical staining profiles of biosimilar antibodies of Solanezumab, Crenezumab and Bapineuzumab in human formalin-fixed, paraffin-embedded tissue and human fresh frozen tissue. Furthermore, we performed a direct comparative immunohistochemistry analysis of the biosimilar versions of the humanized antibodies in different mouse models including 5XFAD, Tg4-42, TBA42, APP/PS1KI, 3xTg. The staining pattern with these humanized antibodies revealed a surprisingly similar profile. All three antibodies detected plaques, cerebral amyloid angiopathy and intraneuronal Aβ in a similar fashion. Remarkably, Solanezumab showed a strong binding affinity to plaques. We also reaffirmed that Bapineuzumab does not recognize N-truncated or modified Aβ, while Solanezumab and Crenezumab do detect N-terminally modified Aβ peptides Aβ4-42 and pyroglutamate Aβ3-42. In addition, we compared the results with the staining pattern of the mouse NT4X antibody that recognizes specifically Aβ4-42 and pyroglutamate Aβ3-42, but not full-length Aβ1-42. In contrast to the biosimilar antibodies of Solanezumab, Crenezumab and Bapineuzumab, the murine NT4X antibody shows a unique target engagement. NT4X does barely cross-react with amyloid plaques in human tissue. It does, however, detect cerebral amyloid angiopathy in human tissue. In Alzheimer mouse models, NT4X detects intraneuronal Aβ and plaques comparable to the humanized antibodies. In conclusion, the biosimilar antibodies Solanezumab, Crenezumab and Bapineuzumab strongly react with amyloid plaques, which are in contrast to the NT4X antibody that hardly recognizes plaques in human tissue. Therefore, NT4X is the first of a new class of therapeutic antibodies.

Transient dynamics of invasive competition: barred owls, spotted owls, habitat, and the demons of competition present.

PubMed

Dugger, Katie M; Anthony, Robert G; Andrews, Lawrence S

2011-10-01

The recent range expansion of Barred Owls (Strix varia) into the Pacific Northwest, where the species now co-occurs with the endemic Northern Spotted Owl (Strix occidentalis caurina), resulted in a unique opportunity to investigate potential competition between two congeneric, previously allopatric species. The primary criticism of early competition research was the use of current species' distribution patterns to infer past processes; however, the recent expansion of the Barred Owl and the ability to model the processes that result in site occupancy (i.e., colonization and extinction) allowed us to address the competitive process directly rather than inferring past processes through current patterns. The purpose of our study was to determine whether Barred Owls had any negative effects on occupancy dynamics of nesting territories by Northern Spotted Owls and how these effects were influenced by habitat characteristics of Spotted Owl territories. We used single-species, multi-season occupancy models and covariates quantifying Barred Owl detections and habitat characteristics to model extinction and colonization rates of Spotted Owl pairs in southern Oregon, USA. We observed a strong, negative association between Barred Owl detections and colonization rates and a strong positive effect of Barred Owl detections on extinction rates of Spotted Owls. We observed increased extinction rates in response to decreased amounts of old forest at the territory core and higher colonization rates when old-forest habitat was less fragmented. Annual site occupancy for pairs reflected the strong effects of Barred Owls on occupancy dynamics with much lower occupancy rates predicted for territories where Barred Owls were detected. The strong Barred Owl and habitat effects on occupancy dynamics of Spotted Owls provided evidence of interference competition between the species. These effects increase the importance of conserving large amounts of contiguous, old-forest habitat to maintain Northern Spotted Owls in the landscape.

Exploring Spatiotemporal Trends in Commercial Fishing Effort of an Abalone Fishing Zone: A GIS-Based Hotspot Model

PubMed Central

Jalali, M. Ali; Ierodiaconou, Daniel; Gorfine, Harry; Monk, Jacquomo; Rattray, Alex

2015-01-01

Assessing patterns of fisheries activity at a scale related to resource exploitation has received particular attention in recent times. However, acquiring data about the distribution and spatiotemporal allocation of catch and fishing effort in small scale benthic fisheries remains challenging. Here, we used GIS-based spatio-statistical models to investigate the footprint of commercial diving events on blacklip abalone (Haliotis rubra) stocks along the south-west coast of Victoria, Australia from 2008 to 2011. Using abalone catch data matched with GPS location we found catch per unit of fishing effort (CPUE) was not uniformly spatially and temporally distributed across the study area. Spatial autocorrelation and hotspot analysis revealed significant spatiotemporal clusters of CPUE (with distance thresholds of 100’s of meters) among years, indicating the presence of CPUE hotspots focused on specific reefs. Cumulative hotspot maps indicated that certain reef complexes were consistently targeted across years but with varying intensity, however often a relatively small proportion of the full reef extent was targeted. Integrating CPUE with remotely-sensed light detection and ranging (LiDAR) derived bathymetry data using generalized additive mixed model corroborated that fishing pressure primarily coincided with shallow, rugose and complex components of reef structures. This study demonstrates that a geospatial approach is efficient in detecting patterns and trends in commercial fishing effort and its association with seafloor characteristics. PMID:25992800

Statistical methods for detecting and comparing periodic data and their application to the nycthemeral rhythm of bodily harm: A population based study

PubMed Central

2010-01-01

Background Animals, including humans, exhibit a variety of biological rhythms. This article describes a method for the detection and simultaneous comparison of multiple nycthemeral rhythms. Methods A statistical method for detecting periodic patterns in time-related data via harmonic regression is described. The method is particularly capable of detecting nycthemeral rhythms in medical data. Additionally a method for simultaneously comparing two or more periodic patterns is described, which derives from the analysis of variance (ANOVA). This method statistically confirms or rejects equality of periodic patterns. Mathematical descriptions of the detecting method and the comparing method are displayed. Results Nycthemeral rhythms of incidents of bodily harm in Middle Franconia are analyzed in order to demonstrate both methods. Every day of the week showed a significant nycthemeral rhythm of bodily harm. These seven patterns of the week were compared to each other revealing only two different nycthemeral rhythms, one for Friday and Saturday and one for the other weekdays. PMID:21059197

ROKU: a novel method for identification of tissue-specific genes.

PubMed

Kadota, Koji; Ye, Jiazhen; Nakai, Yuji; Terada, Tohru; Shimizu, Kentaro

2006-06-12

One of the important goals of microarray research is the identification of genes whose expression is considerably higher or lower in some tissues than in others. We would like to have ways of identifying such tissue-specific genes. We describe a method, ROKU, which selects tissue-specific patterns from gene expression data for many tissues and thousands of genes. ROKU ranks genes according to their overall tissue specificity using Shannon entropy and detects tissues specific to each gene if any exist using an outlier detection method. We evaluated the capacity for the detection of various specific expression patterns using synthetic and real data. We observed that ROKU was superior to a conventional entropy-based method in its ability to rank genes according to overall tissue specificity and to detect genes whose expression pattern are specific only to objective tissues. ROKU is useful for the detection of various tissue-specific expression patterns. The framework is also directly applicable to the selection of diagnostic markers for molecular classification of multiple classes.

Detecting Abnormal Vehicular Dynamics at Intersections Based on an Unsupervised Learning Approach and a Stochastic Model

PubMed Central

Jiménez-Hernández, Hugo; González-Barbosa, Jose-Joel; Garcia-Ramírez, Teresa

2010-01-01

This investigation demonstrates an unsupervised approach for modeling traffic flow and detecting abnormal vehicle behaviors at intersections. In the first stage, the approach reveals and records the different states of the system. These states are the result of coding and grouping the historical motion of vehicles as long binary strings. In the second stage, using sequences of the recorded states, a stochastic graph model based on a Markovian approach is built. A behavior is labeled abnormal when current motion pattern cannot be recognized as any state of the system or a particular sequence of states cannot be parsed with the stochastic model. The approach is tested with several sequences of images acquired from a vehicular intersection where the traffic flow and duration used in connection with the traffic lights are continuously changed throughout the day. Finally, the low complexity and the flexibility of the approach make it reliable for use in real time systems. PMID:22163616

Detecting abnormal vehicular dynamics at intersections based on an unsupervised learning approach and a stochastic model.

PubMed

Jiménez-Hernández, Hugo; González-Barbosa, Jose-Joel; Garcia-Ramírez, Teresa

2010-01-01

This investigation demonstrates an unsupervised approach for modeling traffic flow and detecting abnormal vehicle behaviors at intersections. In the first stage, the approach reveals and records the different states of the system. These states are the result of coding and grouping the historical motion of vehicles as long binary strings. In the second stage, using sequences of the recorded states, a stochastic graph model based on a Markovian approach is built. A behavior is labeled abnormal when current motion pattern cannot be recognized as any state of the system or a particular sequence of states cannot be parsed with the stochastic model. The approach is tested with several sequences of images acquired from a vehicular intersection where the traffic flow and duration used in connection with the traffic lights are continuously changed throughout the day. Finally, the low complexity and the flexibility of the approach make it reliable for use in real time systems.

Modeling the distribution of illicit oily discharges detected by aerial surveillance in western Canadian marine waters.

PubMed

Serra-Sogas, Norma; O'Hara, Patrick D; Canessa, Rosaline

2014-10-15

Oily discharges from vessel operations have been documented in Canada's Pacific region by the National Aerial Surveillance Program (NASP) since the early 1990s. We explored a number of regression methods to explain the distribution and counts per grid cell of oily discharges detected from 1998 to 2007 using independent predictor variables, while trying to address the large number of zeros present in the data. Best-fit models indicate that discharges are generally concentrated close to shore typically in association with small harbours, and with major commercial and tourist centers. Oily discharges were also concentrated in Barkley Sound and at the entrance of Juan de Fuca Strait. The identification of important factors associated with discharge patterns, and predicting discharge rates in areas with surveillance effort can be used to inform future surveillance. Model output can also be used as inputs for risk models for existing conditions and as baseline for future scenarios. Crown Copyright © 2014. Published by Elsevier Ltd. All rights reserved.

Modelling disease outbreaks in realistic urban social networks

NASA Astrophysics Data System (ADS)

Eubank, Stephen; Guclu, Hasan; Anil Kumar, V. S.; Marathe, Madhav V.; Srinivasan, Aravind; Toroczkai, Zoltán; Wang, Nan

2004-05-01

Most mathematical models for the spread of disease use differential equations based on uniform mixing assumptions or ad hoc models for the contact process. Here we explore the use of dynamic bipartite graphs to model the physical contact patterns that result from movements of individuals between specific locations. The graphs are generated by large-scale individual-based urban traffic simulations built on actual census, land-use and population-mobility data. We find that the contact network among people is a strongly connected small-world-like graph with a well-defined scale for the degree distribution. However, the locations graph is scale-free, which allows highly efficient outbreak detection by placing sensors in the hubs of the locations network. Within this large-scale simulation framework, we then analyse the relative merits of several proposed mitigation strategies for smallpox spread. Our results suggest that outbreaks can be contained by a strategy of targeted vaccination combined with early detection without resorting to mass vaccination of a population.

Modeling species occurrence dynamics with multiple states and imperfect detection

USGS Publications Warehouse

MacKenzie, D.I.; Nichols, J.D.; Seamans, M.E.; Gutierrez, R.J.

2009-01-01

Recent extensions of occupancy modeling have focused not only on the distribution of species over space, but also on additional state variables (e.g., reproducing or not, with or without disease organisms, relative abundance categories) that provide extra information about occupied sites. These biologist-driven extensions are characterized by ambiguity in both species presence and correct state classification, caused by imperfect detection. We first show the relationships between independently published approaches to the modeling of multistate occupancy. We then extend the pattern-based modeling to the case of sampling over multiple seasons or years in order to estimate state transition probabilities associated with system dynamics. The methodology and its potential for addressing relevant ecological questions are demonstrated using both maximum likelihood (occupancy and successful reproduction dynamics of California Spotted Owl) and Markov chain Monte Carlo estimation approaches (changes in relative abundance of green frogs in Maryland). Just as multistate capture-recapture modeling has revolutionized the study of individual marked animals, we believe that multistate occupancy modeling will dramatically increase our ability to address interesting questions about ecological processes underlying population-level dynamics. ?? 2009 by the Ecological Society of America.

External Influences on Modeled and Observed Cloud Trends

NASA Technical Reports Server (NTRS)

Marvel, Kate; Zelinka, Mark; Klein, Stephen A.; Bonfils, Celine; Caldwell, Peter; Doutriaux, Charles; Santer, Benjamin D.; Taylor, Karl E.

2015-01-01

Understanding the cloud response to external forcing is a major challenge for climate science. This crucial goal is complicated by intermodel differences in simulating present and future cloud cover and by observational uncertainty. This is the first formal detection and attribution study of cloud changes over the satellite era. Presented herein are CMIP5 (Coupled Model Intercomparison Project - Phase 5) model-derived fingerprints of externally forced changes to three cloud properties: the latitudes at which the zonally averaged total cloud fraction (CLT) is maximized or minimized, the zonal average CLT at these latitudes, and the height of high clouds at these latitudes. By considering simultaneous changes in all three properties, the authors define a coherent multivariate fingerprint of cloud response to external forcing and use models from phase 5 of CMIP (CMIP5) to calculate the average time to detect these changes. It is found that given perfect satellite cloud observations beginning in 1983, the models indicate that a detectable multivariate signal should have already emerged. A search is then made for signals of external forcing in two observational datasets: ISCCP (International Satellite Cloud Climatology Project) and PATMOS-x (Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres - Extended). The datasets are both found to show a poleward migration of the zonal CLT pattern that is incompatible with forced CMIP5 models. Nevertheless, a detectable multivariate signal is predicted by models over the PATMOS-x time period and is indeed present in the dataset. Despite persistent observational uncertainties, these results present a strong case for continued efforts to improve these existing satellite observations, in addition to planning for new missions.

Mining association patterns of drug-interactions using post marketing FDA's spontaneous reporting data.

PubMed

Ibrahim, Heba; Saad, Amr; Abdo, Amany; Sharaf Eldin, A

2016-04-01

Pharmacovigilance (PhV) is an important clinical activity with strong implications for population health and clinical research. The main goal of PhV is the timely detection of adverse drug events (ADEs) that are novel in their clinical nature, severity and/or frequency. Drug interactions (DI) pose an important problem in the development of new drugs and post marketing PhV that contribute to 6-30% of all unexpected ADEs. Therefore, the early detection of DI is vital. Spontaneous reporting systems (SRS) have served as the core data collection system for post marketing PhV since the 1960s. The main objective of our study was to particularly identify signals of DI from SRS. In addition, we are presenting an optimized tailored mining algorithm called "hybrid Apriori". The proposed algorithm is based on an optimized and modified association rule mining (ARM) approach. A hybrid Apriori algorithm has been applied to the SRS of the United States Food and Drug Administration's (U.S. FDA) adverse events reporting system (FAERS) in order to extract significant association patterns of drug interaction-adverse event (DIAE). We have assessed the resulting DIAEs qualitatively and quantitatively using two different triage features: a three-element taxonomy and three performance metrics. These features were applied on two random samples of 100 interacting and 100 non-interacting DIAE patterns. Additionally, we have employed logistic regression (LR) statistic method to quantify the magnitude and direction of interactions in order to test for confounding by co-medication in unknown interacting DIAE patterns. Hybrid Apriori extracted 2933 interacting DIAE patterns (including 1256 serious ones) and 530 non-interacting DIAE patterns. Referring to the current knowledge using four different reliable resources of DI, the results showed that the proposed method can extract signals of serious interacting DIAEs. Various association patterns could be identified based on the relationships among the elements which composed a pattern. The average performance of the method showed 85% precision, 80% negative predictive value, 81% sensitivity and 84% specificity. The LR modeling could provide the statistical context to guard against spurious DIAEs. The proposed method could efficiently detect DIAE signals from SRS data as well as, identifying rare adverse drug reactions (ADRs). Copyright © 2016 Elsevier Inc. All rights reserved.

Can camera traps monitor Komodo dragons a large ectothermic predator?

PubMed

Ariefiandy, Achmad; Purwandana, Deni; Seno, Aganto; Ciofi, Claudio; Jessop, Tim S

2013-01-01

Camera trapping has greatly enhanced population monitoring of often cryptic and low abundance apex carnivores. Effectiveness of passive infrared camera trapping, and ultimately population monitoring, relies on temperature mediated differences between the animal and its ambient environment to ensure good camera detection. In ectothermic predators such as large varanid lizards, this criterion is presumed less certain. Here we evaluated the effectiveness of camera trapping to potentially monitor the population status of the Komodo dragon (Varanus komodoensis), an apex predator, using site occupancy approaches. We compared site-specific estimates of site occupancy and detection derived using camera traps and cage traps at 181 trapping locations established across six sites on four islands within Komodo National Park, Eastern Indonesia. Detection and site occupancy at each site were estimated using eight competing models that considered site-specific variation in occupancy (ψ)and varied detection probabilities (p) according to detection method, site and survey number using a single season site occupancy modelling approach. The most parsimonious model [ψ (site), p (site survey); ω = 0.74] suggested that site occupancy estimates differed among sites. Detection probability varied as an interaction between site and survey number. Our results indicate that overall camera traps produced similar estimates of detection and site occupancy to cage traps, irrespective of being paired, or unpaired, with cage traps. Whilst one site showed some evidence detection was affected by trapping method detection was too low to produce an accurate occupancy estimate. Overall, as camera trapping is logistically more feasible it may provide, with further validation, an alternative method for evaluating long-term site occupancy patterns in Komodo dragons, and potentially other large reptiles, aiding conservation of this species.

Can Camera Traps Monitor Komodo Dragons a Large Ectothermic Predator?

PubMed Central

Ariefiandy, Achmad; Purwandana, Deni; Seno, Aganto; Ciofi, Claudio; Jessop, Tim S.

2013-01-01

Camera trapping has greatly enhanced population monitoring of often cryptic and low abundance apex carnivores. Effectiveness of passive infrared camera trapping, and ultimately population monitoring, relies on temperature mediated differences between the animal and its ambient environment to ensure good camera detection. In ectothermic predators such as large varanid lizards, this criterion is presumed less certain. Here we evaluated the effectiveness of camera trapping to potentially monitor the population status of the Komodo dragon (Varanus komodoensis), an apex predator, using site occupancy approaches. We compared site-specific estimates of site occupancy and detection derived using camera traps and cage traps at 181 trapping locations established across six sites on four islands within Komodo National Park, Eastern Indonesia. Detection and site occupancy at each site were estimated using eight competing models that considered site-specific variation in occupancy (ψ)and varied detection probabilities (p) according to detection method, site and survey number using a single season site occupancy modelling approach. The most parsimonious model [ψ (site), p (site*survey); ω = 0.74] suggested that site occupancy estimates differed among sites. Detection probability varied as an interaction between site and survey number. Our results indicate that overall camera traps produced similar estimates of detection and site occupancy to cage traps, irrespective of being paired, or unpaired, with cage traps. Whilst one site showed some evidence detection was affected by trapping method detection was too low to produce an accurate occupancy estimate. Overall, as camera trapping is logistically more feasible it may provide, with further validation, an alternative method for evaluating long-term site occupancy patterns in Komodo dragons, and potentially other large reptiles, aiding conservation of this species. PMID:23527027

Exploiting physical constraints for multi-spectral exo-planet detection

NASA Astrophysics Data System (ADS)

Thiébaut, Éric; Devaney, Nicholas; Langlois, Maud; Hanley, Kenneth

2016-07-01

We derive a physical model of the on-axis PSF for a high contrast imaging system such as GPI or SPHERE. This model is based on a multi-spectral Taylor series expansion of the diffraction pattern and predicts that the speckles should be a combination of spatial modes with deterministic chromatic magnification and weighting. We propose to remove most of the residuals by fitting this model on a set of images at multiple wavelengths and times. On simulated data, we demonstrate that our approach achieves very good speckle suppression without additional heuristic parameters. The residual speckles1, 2 set the most serious limitation in the detection of exo-planets in high contrast coronographic images provided by instruments such as SPHERE3 at the VLT, GPI4, 5 at Gemini, or SCExAO6 at Subaru. A number of post-processing methods have been proposed to remove as much as possible of the residual speckles while preserving the signal from the planets. These methods exploit the fact that the speckles and the planetary signal have different temporal and spectral behaviors. Some methods like LOCI7 are based on angular differential imaging8 (ADI), spectral differential imaging9, 10 (SDI), or on a combination of ADI and SDI.11 Instead of working on image differences, we propose to tackle the exo-planet detection as an inverse problem where a model of the residual speckles is fit on the set of multi-spectral images and, possibly, multiple exposures. In order to reduce the number of degrees of freedom, we impose specific constraints on the spatio-spectral distribution of stellar speckles. These constraints are deduced from a multi-spectral Taylor series expansion of the diffraction pattern for an on-axis source which implies that the speckles are a combination of spatial modes with deterministic chromatic magnification and weighting. Using simulated data, the efficiency of speckle removal by fitting the proposed multi-spectral model is compared to the result of using an approximation based on the singular value decomposition of the rescaled images. We show how the difficult problem to fitting a bilinear model on the can be solved in practise. The results are promising for further developments including application to real data and joint planet detection in multi-variate data (multi-spectral and multiple exposures images).

Inhomogeneity Based Characterization of Distribution Patterns on the Plasma Membrane

PubMed Central

Paparelli, Laura; Corthout, Nikky; Wakefield, Devin L.; Sannerud, Ragna; Jovanovic-Talisman, Tijana; Annaert, Wim; Munck, Sebastian

2016-01-01

Cell surface protein and lipid molecules are organized in various patterns: randomly, along gradients, or clustered when segregated into discrete micro- and nano-domains. Their distribution is tightly coupled to events such as polarization, endocytosis, and intracellular signaling, but challenging to quantify using traditional techniques. Here we present a novel approach to quantify the distribution of plasma membrane proteins and lipids. This approach describes spatial patterns in degrees of inhomogeneity and incorporates an intensity-based correction to analyze images with a wide range of resolutions; we have termed it Quantitative Analysis of the Spatial distributions in Images using Mosaic segmentation and Dual parameter Optimization in Histograms (QuASIMoDOH). We tested its applicability using simulated microscopy images and images acquired by widefield microscopy, total internal reflection microscopy, structured illumination microscopy, and photoactivated localization microscopy. We validated QuASIMoDOH, successfully quantifying the distribution of protein and lipid molecules detected with several labeling techniques, in different cell model systems. We also used this method to characterize the reorganization of cell surface lipids in response to disrupted endosomal trafficking and to detect dynamic changes in the global and local organization of epidermal growth factor receptors across the cell surface. Our findings demonstrate that QuASIMoDOH can be used to assess protein and lipid patterns, quantifying distribution changes and spatial reorganization at the cell surface. An ImageJ/Fiji plugin of this analysis tool is provided. PMID:27603951

Visual detection following retinal damage: predictions of an inhomogeneous retino-cortical model

NASA Astrophysics Data System (ADS)

Arnow, Thomas L.; Geisler, Wilson S.

1996-04-01

A model of human visual detection performance has been developed, based on available anatomical and physiological data for the primate visual system. The inhomogeneous retino- cortical (IRC) model computes detection thresholds by comparing simulated neural responses to target patterns with responses to a uniform background of the same luminance. The model incorporates human ganglion cell sampling distributions; macaque monkey ganglion cell receptive field properties; macaque cortical cell contrast nonlinearities; and a optical decision rule based on ideal observer theory. Spatial receptive field properties of cortical neurons were not included. Two parameters were allowed to vary while minimizing the squared error between predicted and observed thresholds. One parameter was decision efficiency, the other was the relative strength of the ganglion-cell center and surround. The latter was only allowed to vary within a small range consistent with known physiology. Contrast sensitivity was measured for sinewave gratings as a function of spatial frequency, target size and eccentricity. Contrast sensitivity was also measured for an airplane target as a function of target size, with and without artificial scotomas. The results of these experiments, as well as contrast sensitivity data from the literature were compared to predictions of the IRC model. Predictions were reasonably good for grating and airplane targets.

Urban street patterns detectable from ERTS-1

NASA Technical Reports Server (NTRS)

Morain, S. A. (Principal Investigator); Williams, D. L.

1973-01-01

The author has identified the following significant results. The major street patterns in Lincoln, Nebraska, are detectable on the January 24, 1973 ERTS MSS-4 image. To further study and identify the street patterns, a 3x Polaroid enlargement was made of the city from the image. An overlay of the enlargement was used to map the street patterns, with reference to the original image for clarity. The technique seems to be adaptable for updating standard road maps.

Saliency detection using mutual consistency-guided spatial cues combination

NASA Astrophysics Data System (ADS)

Wang, Xin; Ning, Chen; Xu, Lizhong

2015-09-01

Saliency detection has received extensive interests due to its remarkable contribution to wide computer vision and pattern recognition applications. However, most existing computational models are designed for detecting saliency in visible images or videos. When applied to infrared images, they may suffer from limitations in saliency detection accuracy and robustness. In this paper, we propose a novel algorithm to detect visual saliency in infrared images by mutual consistency-guided spatial cues combination. First, based on the luminance contrast and contour characteristics of infrared images, two effective saliency maps, i.e., the luminance contrast saliency map and contour saliency map are constructed, respectively. Afterwards, an adaptive combination scheme guided by mutual consistency is exploited to integrate these two maps to generate the spatial saliency map. This idea is motivated by the observation that different maps are actually related to each other and the fusion scheme should present a logically consistent view of these maps. Finally, an enhancement technique is adopted to incorporate spatial saliency maps at various scales into a unified multi-scale framework to improve the reliability of the final saliency map. Comprehensive evaluations on real-life infrared images and comparisons with many state-of-the-art saliency models demonstrate the effectiveness and superiority of the proposed method for saliency detection in infrared images.

Sensor Fault Detection and Diagnosis Simulation of a Helicopter Engine in an Intelligent Control Framework

NASA Technical Reports Server (NTRS)

Litt, Jonathan; Kurtkaya, Mehmet; Duyar, Ahmet

1994-01-01

This paper presents an application of a fault detection and diagnosis scheme for the sensor faults of a helicopter engine. The scheme utilizes a model-based approach with real time identification and hypothesis testing which can provide early detection, isolation, and diagnosis of failures. It is an integral part of a proposed intelligent control system with health monitoring capabilities. The intelligent control system will allow for accommodation of faults, reduce maintenance cost, and increase system availability. The scheme compares the measured outputs of the engine with the expected outputs of an engine whose sensor suite is functioning normally. If the differences between the real and expected outputs exceed threshold values, a fault is detected. The isolation of sensor failures is accomplished through a fault parameter isolation technique where parameters which model the faulty process are calculated on-line with a real-time multivariable parameter estimation algorithm. The fault parameters and their patterns can then be analyzed for diagnostic and accommodation purposes. The scheme is applied to the detection and diagnosis of sensor faults of a T700 turboshaft engine. Sensor failures are induced in a T700 nonlinear performance simulation and data obtained are used with the scheme to detect, isolate, and estimate the magnitude of the faults.

Utilizing Electronic Medical Records to Discover Changing Trends of Medical Behaviors Over Time*

PubMed Central

Yin, Liangying; Dong, Wei; He, Chunhua; Duan, Huilong

2017-01-01

Summary Objectives Medical behaviors are playing significant roles in the delivery of high quality and cost-effective health services. Timely discovery of changing frequencies of medical behaviors is beneficial for the improvement of health services. The main objective of this work is to discover the changing trends of medical behaviors over time. Methods This study proposes a two-steps approach to detect essential changing patterns of medical behaviors from Electronic Medical Records (EMRs). In detail, a probabilistic topic model, i.e., Latent Dirichlet allocation (LDA), is firstly applied to disclose yearly treatment patterns in regard to the risk stratification of patients from a large volume of EMRs. After that, the changing trends by comparing essential/critical medical behaviors in a specific time period are detected and analyzed, including changes of significant patient features with their values, and changes of critical treatment interventions with their occurring time stamps. Results We verify the effectiveness of the proposed approach on a clinical dataset containing 12,152 patient cases with a time range of 10 years. Totally, 135 patients features and 234 treatment interventions in three treatment patterns were selected to detect their changing trends. In particular, evolving trends of yearly occurring probabilities of the selected medical behaviors were categorized into six content changing patterns (i.e, 112 growing, 123 declining, 43 up-down, 16 down-up, 35 steady, and 40 jumping), using the proposed approach. Besides, changing trends of execution time of treatment interventions were classified into three occurring time changing patterns (i.e., 175 early-implemented, 50 steady-implemented and 9 delay-implemented). Conclusions Experimental results show that our approach has an ability to utilize EMRs to discover essential evolving trends of medical behaviors, and thus provide significant potential to be further explored for health services redesign and improvement. PMID:28474729

A Multi-Index Integrated Change detection method for updating the National Land Cover Database

USGS Publications Warehouse

Jin, Suming; Yang, Limin; Xian, George Z.; Danielson, Patrick; Homer, Collin G.

2010-01-01

Land cover change is typically captured by comparing two or more dates of imagery and associating spectral change with true thematic change. A new change detection method, Multi-Index Integrated Change (MIIC), has been developed to capture a full range of land cover disturbance patterns for updating the National Land Cover Database (NLCD). Specific indices typically specialize in identifying only certain types of disturbances; for example, the Normalized Burn Ratio (NBR) has been widely used for monitoring fire disturbance. Recognizing the potential complementary nature of multiple indices, we integrated four indices into one model to more accurately detect true change between two NLCD time periods. The four indices are NBR, Normalized Difference Vegetation Index (NDVI), Change Vector (CV), and a newly developed index called the Relative Change Vector (RCV). The model is designed to provide both change location and change direction (e.g. biomass increase or biomass decrease). The integrated change model has been tested on five image pairs from different regions exhibiting a variety of disturbance types. Compared with a simple change vector method, MIIC can better capture the desired change without introducing additional commission errors. The model is particularly accurate at detecting forest disturbances, such as forest harvest, forest fire, and forest regeneration. Agreement between the initial change map areas derived from MIIC and the retained final land cover type change areas will be showcased from the pilot test sites.