Protein painting reveals solvent-excluded drug targets hidden within native protein–protein interfaces

PubMed Central

Luchini, Alessandra; Espina, Virginia; Liotta, Lance A.

2014-01-01

Identifying the contact regions between a protein and its binding partners is essential for creating therapies that block the interaction. Unfortunately, such contact regions are extremely difficult to characterize because they are hidden inside the binding interface. Here we introduce protein painting as a new tool that employs small molecules as molecular paints to tightly coat the surface of protein–protein complexes. The molecular paints, which block trypsin cleavage sites, are excluded from the binding interface. Following mass spectrometry, only peptides hidden in the interface emerge as positive hits, revealing the functional contact regions that are drug targets. We use protein painting to discover contact regions between the three-way interaction of IL1β ligand, the receptor IL1RI and the accessory protein IL1RAcP. We then use this information to create peptides and monoclonal antibodies that block the interaction and abolish IL1β cell signalling. The technology is broadly applicable to discover protein interaction drug targets. PMID:25048602

Detecting hidden particles with MATHUSLA

NASA Astrophysics Data System (ADS)

Evans, Jared A.

2018-03-01

A hidden sector containing light long-lived particles provides a well-motivated place to find new physics. The recently proposed MATHUSLA experiment has the potential to be extremely sensitive to light particles originating from rare meson decays in the very long lifetime region. In this work, we illustrate this strength with the specific example of a light scalar mixed with the standard model-like Higgs boson, a model where MATHUSLA can further probe unexplored parameter space from exotic Higgs decays. Design augmentations should be considered in order to maximize the ability of MATHUSLA to discover very light hidden sector particles.

Infinite hidden conditional random fields for human behavior analysis.

PubMed

Bousmalis, Konstantinos; Zafeiriou, Stefanos; Morency, Louis-Philippe; Pantic, Maja

2013-01-01

Hidden conditional random fields (HCRFs) are discriminative latent variable models that have been shown to successfully learn the hidden structure of a given classification problem (provided an appropriate validation of the number of hidden states). In this brief, we present the infinite HCRF (iHCRF), which is a nonparametric model based on hierarchical Dirichlet processes and is capable of automatically learning the optimal number of hidden states for a classification task. We show how we learn the model hyperparameters with an effective Markov-chain Monte Carlo sampling technique, and we explain the process that underlines our iHCRF model with the Restaurant Franchise Rating Agencies analogy. We show that the iHCRF is able to converge to a correct number of represented hidden states, and outperforms the best finite HCRFs--chosen via cross-validation--for the difficult tasks of recognizing instances of agreement, disagreement, and pain. Moreover, the iHCRF manages to achieve this performance in significantly less total training, validation, and testing time.

Analyzing Hidden Semantics in Social Bookmarking of Open Educational Resources

NASA Astrophysics Data System (ADS)

Minguillón, Julià

Web 2.0 services such as social bookmarking allow users to manage and share the links they find interesting, adding their own tags for describing them. This is especially interesting in the field of open educational resources, as delicious is a simple way to bridge the institutional point of view (i.e. learning object repositories) with the individual one (i.e. personal collections), thus promoting the discovering and sharing of such resources by other users. In this paper we propose a methodology for analyzing such tags in order to discover hidden semantics (i.e. taxonomies and vocabularies) that can be used to improve descriptions of learning objects and make learning object repositories more visible and discoverable. We propose the use of a simple statistical analysis tool such as principal component analysis to discover which tags create clusters that can be semantically interpreted. We will compare the obtained results with a collection of resources related to open educational resources, in order to better understand the real needs of people searching for open educational resources.

Detecting cell division of Pseudomonas aeruginosa bacteria from bright-field microscopy images with hidden conditional random fields.

PubMed

Ong, Lee-Ling S; Xinghua Zhang; Kundukad, Binu; Dauwels, Justin; Doyle, Patrick; Asada, H Harry

2016-08-01

An approach to automatically detect bacteria division with temporal models is presented. To understand how bacteria migrate and proliferate to form complex multicellular behaviours such as biofilms, it is desirable to track individual bacteria and detect cell division events. Unlike eukaryotic cells, prokaryotic cells such as bacteria lack distinctive features, causing bacteria division difficult to detect in a single image frame. Furthermore, bacteria may detach, migrate close to other bacteria and may orientate themselves at an angle to the horizontal plane. Our system trains a hidden conditional random field (HCRF) model from tracked and aligned bacteria division sequences. The HCRF model classifies a set of image frames as division or otherwise. The performance of our HCRF model is compared with a Hidden Markov Model (HMM). The results show that a HCRF classifier outperforms a HMM classifier. From 2D bright field microscopy data, it is a challenge to separate individual bacteria and associate observations to tracks. Automatic detection of sequences with bacteria division will improve tracking accuracy.

Modular Neural Networks for Speech Recognition.

DTIC Science & Technology

1996-08-01

automatic speech rccogni- tion, understanding and translation since the early 1950’ s . Although researchers have demonstrated impressive results with...nodes. It serves only as a data source for the following hidden layer( s ). Finally, the networks output is computed by neurons in the output layer. The...following update rule for weights in the hidden layer: w (,,•+I) ("’) E/V S (W W k- = wj, -- 7 - / v It is easy to generalize the backpropagation

Discovering Hidden Connections among Diseases, Genes and Drugs Based on Microarray Expression Profiles with Negative-Term Filtering

PubMed Central

2014-01-01

Microarrays based on gene expression profiles (GEPs) can be tailored specifically for a variety of topics to provide a precise and efficient means with which to discover hidden information. This study proposes a novel means of employing existing GEPs to reveal hidden relationships among diseases, genes, and drugs within a rich biomedical database, PubMed. Unlike the co-occurrence method, which considers only the appearance of keywords, the proposed method also takes into account negative relationships and non-relationships among keywords, the importance of which has been demonstrated in previous studies. Three scenarios were conducted to verify the efficacy of the proposed method. In Scenario 1, disease and drug GEPs (disease: lymphoma cancer, lymph node cancer, and drug: cyclophosphamide) were used to obtain lists of disease- and drug-related genes. Fifteen hidden connections were identified between the diseases and the drug. In Scenario 2, we adopted different diseases and drug GEPs (disease: AML-ALL dataset and drug: Gefitinib) to obtain lists of important diseases and drug-related genes. In this case, ten hidden connections were identified. In Scenario 3, we obtained a list of disease-related genes from the disease-related GEP (liver cancer) and the drug (Capecitabine) on the PharmGKB website, resulting in twenty-two hidden connections. Experimental results demonstrate the efficacy of the proposed method in uncovering hidden connections among diseases, genes, and drugs. Following implementation of the weight function in the proposed method, a large number of the documents obtained in each of the scenarios were judged to be related: 834 of 4028 documents, 789 of 1216 documents, and 1928 of 3791 documents in Scenarios 1, 2, and 3, respectively. The negative-term filtering scheme also uncovered a large number of negative relationships as well as non-relationships among these connections: 97 of 834, 38 of 789, and 202 of 1928 in Scenarios 1, 2, and 3, respectively. PMID:24915461

Using ADOPT Algorithm and Operational Data to Discover Precursors to Aviation Adverse Events

NASA Technical Reports Server (NTRS)

Janakiraman, Vijay; Matthews, Bryan; Oza, Nikunj

2018-01-01

The US National Airspace System (NAS) is making its transition to the NextGen system and assuring safety is one of the top priorities in NextGen. At present, safety is managed reactively (correct after occurrence of an unsafe event). While this strategy works for current operations, it may soon become ineffective for future airspace designs and high density operations. There is a need for proactive management of safety risks by identifying hidden and "unknown" risks and evaluating the impacts on future operations. To this end, NASA Ames has developed data mining algorithms that finds anomalies and precursors (high-risk states) to safety issues in the NAS. In this paper, we describe a recently developed algorithm called ADOPT that analyzes large volumes of data and automatically identifies precursors from real world data. Precursors help in detecting safety risks early so that the operator can mitigate the risk in time. In addition, precursors also help identify causal factors and help predict the safety incident. The ADOPT algorithm scales well to large data sets and to multidimensional time series, reduce analyst time significantly, quantify multiple safety risks giving a holistic view of safety among other benefits. This paper details the algorithm and includes several case studies to demonstrate its application to discover the "known" and "unknown" safety precursors in aviation operation.

Look at my Arms!

NASA Image and Video Library

2005-07-25

This image shows the hidden spiral arms that were discovered around the galaxy called NGC 4625 top by the ultraviolet eyes of NASA Galaxy Evolution Explorer. An armless companion galaxy called NGC 4618 is pictured below.

What Is Going on Inside the Arrows? Discovering the Hidden Springs in Causal Models

PubMed Central

Murray-Watters, Alexander; Glymour, Clark

2016-01-01

Using Gebharter's (2014) representation, we consider aspects of the problem of discovering the structure of unmeasured sub-mechanisms when the variables in those sub-mechanisms have not been measured. Exploiting an early insight of Sober's (1998), we provide a correct algorithm for identifying latent, endogenous structure—sub-mechanisms—for a restricted class of structures. The algorithm can be merged with other methods for discovering causal relations among unmeasured variables, and feedback relations between measured variables and unobserved causes can sometimes be learned. PMID:27313331

Solving the "Hidden Line" Problem

NASA Technical Reports Server (NTRS)

1984-01-01

David Hedgley Jr., a mathematician at Dryden Flight Research Center, has developed an accurate computer program that considers whether a line in a graphic model of a three dimensional object should or should not be visible. The Hidden Line Computer Code, program automatically removes superfluous lines and permits the computer to display an object from specific viewpoints, just as the human eye would see it. Users include Rowland Institute for Science in Cambridge, MA, several departments of Lockheed Georgia Co., and Nebraska Public Power District (NPPD).

Finding your next core business.

PubMed

Zook, Chris

2007-04-01

How do you know when your core needs to change? And how do you determine what should replace it? From an in-depth study of 25 companies, the author, a strategy consultant, has discovered that it's possible to measure the vitality of a business's core. If it needs reinvention, he says, the best course is to mine hidden assets. Some of the 25 companies were in deep crisis when they began the process of redefining themselves. But, says Zook, management teams can learn to recognize early signs of erosion. He offers five diagnostic questions with which to evaluate the customers, key sources of differentiation, profit pools, capabilities, and organizational culture of your core business. The next step is strategic regeneration. In four-fifths of the companies Zook examined, a hidden asset was the centerpiece of the new strategy. He provides a map for identifying the hidden assets in your midst, which tend to fall into three categories: undervalued business platforms, untapped insights into customers, and underexploited capabilities. The Swedish company Dometic, for example, was manufacturing small absorption refrigerators for boats and RVs when it discovered a hidden asset: its understanding of, and access to, customers in the RV market. The company took advantage of a boom in that market to refocus on complete systems for live-in vehicles. The Danish company Novozymes, which produced relatively low-tech commodity enzymes such as those used in detergents, realized that its underutilized biochemical capability in genetic and protein engineering was a hidden asset and successfully refocused on creating bioengineered specialty enzymes. Your next core business is not likely to announce itself with fanfare. Use the author's tools to conduct an internal audit of possibilities and pinpoint your new focus.

Discovering Hidden Controlling Parameters using Data Analytics and Dimensional Analysis

NASA Astrophysics Data System (ADS)

Del Rosario, Zachary; Lee, Minyong; Iaccarino, Gianluca

2017-11-01

Dimensional Analysis is a powerful tool, one which takes a priori information and produces important simplifications. However, if this a priori information - the list of relevant parameters - is missing a relevant quantity, then the conclusions from Dimensional Analysis will be incorrect. In this work, we present novel conclusions in Dimensional Analysis, which provide a means to detect this failure mode of missing or hidden parameters. These results are based on a restated form of the Buckingham Pi theorem that reveals a ridge function structure underlying all dimensionless physical laws. We leverage this structure by constructing a hypothesis test based on sufficient dimension reduction, allowing for an experimental data-driven detection of hidden parameters. Both theory and examples will be presented, using classical turbulent pipe flow as the working example. Keywords: experimental techniques, dimensional analysis, lurking variables, hidden parameters, buckingham pi, data analysis. First author supported by the NSF GRFP under Grant Number DGE-114747.

Discovering Knowledge from AIS Database for Application in VTS

NASA Astrophysics Data System (ADS)

Tsou, Ming-Cheng

The widespread use of the Automatic Identification System (AIS) has had a significant impact on maritime technology. AIS enables the Vessel Traffic Service (VTS) not only to offer commonly known functions such as identification, tracking and monitoring of vessels, but also to provide rich real-time information that is useful for marine traffic investigation, statistical analysis and theoretical research. However, due to the rapid accumulation of AIS observation data, the VTS platform is often unable quickly and effectively to absorb and analyze it. Traditional observation and analysis methods are becoming less suitable for the modern AIS generation of VTS. In view of this, we applied the same data mining technique used for business intelligence discovery (in Customer Relation Management (CRM) business marketing) to the analysis of AIS observation data. This recasts the marine traffic problem as a business-marketing problem and integrates technologies such as Geographic Information Systems (GIS), database management systems, data warehousing and data mining to facilitate the discovery of hidden and valuable information in a huge amount of observation data. Consequently, this provides the marine traffic managers with a useful strategic planning resource.

Optimized hardware framework of MLP with random hidden layers for classification applications

NASA Astrophysics Data System (ADS)

Zyarah, Abdullah M.; Ramesh, Abhishek; Merkel, Cory; Kudithipudi, Dhireesha

2016-05-01

Multilayer Perceptron Networks with random hidden layers are very efficient at automatic feature extraction and offer significant performance improvements in the training process. They essentially employ large collection of fixed, random features, and are expedient for form-factor constrained embedded platforms. In this work, a reconfigurable and scalable architecture is proposed for the MLPs with random hidden layers with a customized building block based on CORDIC algorithm. The proposed architecture also exploits fixed point operations for area efficiency. The design is validated for classification on two different datasets. An accuracy of ~ 90% for MNIST dataset and 75% for gender classification on LFW dataset was observed. The hardware has 299 speed-up over the corresponding software realization.

DNA barcodes, species delimitation, and bioassessment: issues of diversity, analysis, and standardization

EPA Science Inventory

DNA barcoding has the capability to uncover cryptic diversity otherwise undetectable using morphology alone. For aquatic bioassessment, this opportunity to discover hidden biodiversity presents new data for incorporation into environmental monitoring programs. Unfortunately, the ...

Discovering and visualizing indirect associations between biomedical concepts

PubMed Central

Tsuruoka, Yoshimasa; Miwa, Makoto; Hamamoto, Kaisei; Tsujii, Jun'ichi; Ananiadou, Sophia

2011-01-01

Motivation: Discovering useful associations between biomedical concepts has been one of the main goals in biomedical text-mining, and understanding their biomedical contexts is crucial in the discovery process. Hence, we need a text-mining system that helps users explore various types of (possibly hidden) associations in an easy and comprehensible manner. Results: This article describes FACTA+, a real-time text-mining system for finding and visualizing indirect associations between biomedical concepts from MEDLINE abstracts. The system can be used as a text search engine like PubMed with additional features to help users discover and visualize indirect associations between important biomedical concepts such as genes, diseases and chemical compounds. FACTA+ inherits all functionality from its predecessor, FACTA, and extends it by incorporating three new features: (i) detecting biomolecular events in text using a machine learning model, (ii) discovering hidden associations using co-occurrence statistics between concepts, and (iii) visualizing associations to improve the interpretability of the output. To the best of our knowledge, FACTA+ is the first real-time web application that offers the functionality of finding concepts involving biomolecular events and visualizing indirect associations of concepts with both their categories and importance. Availability: FACTA+ is available as a web application at http://refine1-nactem.mc.man.ac.uk/facta/, and its visualizer is available at http://refine1-nactem.mc.man.ac.uk/facta-visualizer/. Contact: tsuruoka@jaist.ac.jp PMID:21685059

Asymmetric author-topic model for knowledge discovering of big data in toxicogenomics.

PubMed

Chung, Ming-Hua; Wang, Yuping; Tang, Hailin; Zou, Wen; Basinger, John; Xu, Xiaowei; Tong, Weida

2015-01-01

The advancement of high-throughput screening technologies facilitates the generation of massive amount of biological data, a big data phenomena in biomedical science. Yet, researchers still heavily rely on keyword search and/or literature review to navigate the databases and analyses are often done in rather small-scale. As a result, the rich information of a database has not been fully utilized, particularly for the information embedded in the interactive nature between data points that are largely ignored and buried. For the past 10 years, probabilistic topic modeling has been recognized as an effective machine learning algorithm to annotate the hidden thematic structure of massive collection of documents. The analogy between text corpus and large-scale genomic data enables the application of text mining tools, like probabilistic topic models, to explore hidden patterns of genomic data and to the extension of altered biological functions. In this paper, we developed a generalized probabilistic topic model to analyze a toxicogenomics dataset that consists of a large number of gene expression data from the rat livers treated with drugs in multiple dose and time-points. We discovered the hidden patterns in gene expression associated with the effect of doses and time-points of treatment. Finally, we illustrated the ability of our model to identify the evidence of potential reduction of animal use.

High pressure air compressor valve fault diagnosis using feedforward neural networks

NASA Astrophysics Data System (ADS)

James Li, C.; Yu, Xueli

1995-09-01

Feedforward neural networks (FNNs) are developed and implemented to classify a four-stage high pressure air compressor into one of the following conditions: baseline, suction or exhaust valve faults. These FNNs are used for the compressor's automatic condition monitoring and fault diagnosis. Measurements of 39 variables are obtained under different baseline conditions and third-stage suction and exhaust valve faults. These variables include pressures and temperatures at all stages, voltage between phase aand phase b, voltage between phase band phase c, total three-phase real power, cooling water flow rate, etc. To reduce the number of variables, the amount of their discriminatory information is quantified by scattering matrices to identify statistical significant ones. Measurements of the selected variables are then used by a fully automatic structural and weight learning algorithm to construct three-layer FNNs to classify the compressor's condition. This learning algorithm requires neither guesses of initial weight values nor number of neurons in the hidden layer of an FNN. It takes an incremental approach in which a hidden neuron is trained by exemplars and then augmented to the existing network. These exemplars are then made orthogonal to the newly identified hidden neuron. They are subsequently used for the training of the next hidden neuron. The betterment continues until a desired accuracy is reached. After the neural networks are established, novel measurements from various conditions that haven't been previously seen by the FNNs are then used to evaluate their ability in fault diagnosis. The trained neural networks provide very accurate diagnosis for suction and discharge valve defects.

Exhibitions in Sight.

ERIC Educational Resources Information Center

Wasserman, Burton

1978-01-01

Early in the eighteenth century, Pompeii was discovered, a city that had been hidden for sixteen centuries by volcanic lava. There is a traveling exhibition of the sculptures, friezes, mosaics, and paintings being shown around the United States. Described is the history and contents of "Pompeii--A.D. 79." (RK)

Knowledge Discovery for Smart Grid Operation, Control, and Situation Awareness -- A Big Data Visualization Platform

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

Gu, Yi; Jiang, Huaiguang; Zhang, Yingchen

In this paper, a big data visualization platform is designed to discover the hidden useful knowledge for smart grid (SG) operation, control and situation awareness. The spawn of smart sensors at both grid side and customer side can provide large volume of heterogeneous data that collect information in all time spectrums. Extracting useful knowledge from this big-data poll is still challenging. In this paper, the Apache Spark, an open source cluster computing framework, is used to process the big-data to effectively discover the hidden knowledge. A high-speed communication architecture utilizing the Open System Interconnection (OSI) model is designed to transmitmore » the data to a visualization platform. This visualization platform uses Google Earth, a global geographic information system (GIS) to link the geological information with the SG knowledge and visualize the information in user defined fashion. The University of Denver's campus grid is used as a SG test bench and several demonstrations are presented for the proposed platform.« less

Cross-modal learning to rank via latent joint representation.

PubMed

Wu, Fei; Jiang, Xinyang; Li, Xi; Tang, Siliang; Lu, Weiming; Zhang, Zhongfei; Zhuang, Yueting

2015-05-01

Cross-modal ranking is a research topic that is imperative to many applications involving multimodal data. Discovering a joint representation for multimodal data and learning a ranking function are essential in order to boost the cross-media retrieval (i.e., image-query-text or text-query-image). In this paper, we propose an approach to discover the latent joint representation of pairs of multimodal data (e.g., pairs of an image query and a text document) via a conditional random field and structural learning in a listwise ranking manner. We call this approach cross-modal learning to rank via latent joint representation (CML²R). In CML²R, the correlations between multimodal data are captured in terms of their sharing hidden variables (e.g., topics), and a hidden-topic-driven discriminative ranking function is learned in a listwise ranking manner. The experiments show that the proposed approach achieves a good performance in cross-media retrieval and meanwhile has the capability to learn the discriminative representation of multimodal data.

Object Permanence After a 24-Hr Delay and Leaving the Locale of Disappearance: The Role of Memory, Space, and Identity

PubMed Central

Moore, M. Keith; Meltzoff, Andrew N.

2005-01-01

Fourteen-month-old infants saw an object hidden inside a container and were removed from the disappearance locale for 24 hr. Upon their return, they searched correctly for the hidden object, demonstrating object permanence and long-term memory. Control infants who saw no disappearance did not search. In Experiment 2, infants returned to see the container either in the same or a different room. Performance by room-change infants dropped to baseline levels, suggesting that infant search for hidden objects is guided by numerical identity. Infants seek the individual object that disappeared, which exists in its original location, not in a different room. A new behavior, identity-verifying search, was discovered and quantified. Implications are drawn for memory, spatial understanding, object permanence, and object identity. PMID:15238047

Object permanence after a 24-hr delay and leaving the locale of disappearance: the role of memory, space, and identity.

PubMed

Moore, M Keith; Meltzoff, Andrew N

2004-07-01

Fourteen-month-old infants saw an object hidden inside a container and were removed from the disappearance locale for 24 hr. Upon their return, they searched correctly for the hidden object, demonstrating object permanence and long-term memory. Control infants who saw no disappearance did not search. In Experiment 2, infants returned to see the container either in the same or a different room. Performance by room-change infants dropped to baseline levels, suggesting that infant search for hidden objects is guided by numerical identity. Infants seek the individual object that disappeared, which exists in its original location, not in a different room. A new behavior, identity-verifying search, was discovered and quantified. Implications are drawn for memory, spatial understanding, object permanence, and object identity. Copyright 2004 APA, all rights reserved

Probabilistic Reasoning Over Seismic Time Series: Volcano Monitoring by Hidden Markov Models at Mt. Etna

NASA Astrophysics Data System (ADS)

Cassisi, Carmelo; Prestifilippo, Michele; Cannata, Andrea; Montalto, Placido; Patanè, Domenico; Privitera, Eugenio

2016-07-01

From January 2011 to December 2015, Mt. Etna was mainly characterized by a cyclic eruptive behavior with more than 40 lava fountains from New South-East Crater. Using the RMS (Root Mean Square) of the seismic signal recorded by stations close to the summit area, an automatic recognition of the different states of volcanic activity (QUIET, PRE-FOUNTAIN, FOUNTAIN, POST-FOUNTAIN) has been applied for monitoring purposes. Since values of the RMS time series calculated on the seismic signal are generated from a stochastic process, we can try to model the system generating its sampled values, assumed to be a Markov process, using Hidden Markov Models (HMMs). HMMs analysis seeks to recover the sequence of hidden states from the observations. In our framework, observations are characters generated by the Symbolic Aggregate approXimation (SAX) technique, which maps RMS time series values with symbols of a pre-defined alphabet. The main advantages of the proposed framework, based on HMMs and SAX, with respect to other automatic systems applied on seismic signals at Mt. Etna, are the use of multiple stations and static thresholds to well characterize the volcano states. Its application on a wide seismic dataset of Etna volcano shows the possibility to guess the volcano states. The experimental results show that, in most of the cases, we detected lava fountains in advance.

Overcoming Stereotypes, Discovering Hidden Capitals

ERIC Educational Resources Information Center

Beckett, Lori; Wrigley, Terry

2014-01-01

This article presents a model of teacher research supported by academic partners to develop a better understanding of the barriers to education faced by young people growing up in poverty. It critiques politicians' demands for teachers to "close the gap" for ignoring the cumulative intergenerational effects of deprivation. The authors…

Automatic speech recognition using a predictive echo state network classifier.

PubMed

Skowronski, Mark D; Harris, John G

2007-04-01

We have combined an echo state network (ESN) with a competitive state machine framework to create a classification engine called the predictive ESN classifier. We derive the expressions for training the predictive ESN classifier and show that the model was significantly more noise robust compared to a hidden Markov model in noisy speech classification experiments by 8+/-1 dB signal-to-noise ratio. The simple training algorithm and noise robustness of the predictive ESN classifier make it an attractive classification engine for automatic speech recognition.

Detecting targets hidden in random forests

NASA Astrophysics Data System (ADS)

Kouritzin, Michael A.; Luo, Dandan; Newton, Fraser; Wu, Biao

2009-05-01

Military tanks, cargo or troop carriers, missile carriers or rocket launchers often hide themselves from detection in the forests. This plagues the detection problem of locating these hidden targets. An electro-optic camera mounted on a surveillance aircraft or unmanned aerial vehicle is used to capture the images of the forests with possible hidden targets, e.g., rocket launchers. We consider random forests of longitudinal and latitudinal correlations. Specifically, foliage coverage is encoded with a binary representation (i.e., foliage or no foliage), and is correlated in adjacent regions. We address the detection problem of camouflaged targets hidden in random forests by building memory into the observations. In particular, we propose an efficient algorithm to generate random forests, ground, and camouflage of hidden targets with two dimensional correlations. The observations are a sequence of snapshots consisting of foliage-obscured ground or target. Theoretically, detection is possible because there are subtle differences in the correlations of the ground and camouflage of the rocket launcher. However, these differences are well beyond human perception. To detect the presence of hidden targets automatically, we develop a Markov representation for these sequences and modify the classical filtering equations to allow the Markov chain observation. Particle filters are used to estimate the position of the targets in combination with a novel random weighting technique. Furthermore, we give positive proof-of-concept simulations.

Discovering Hidden Resources: Assistive Technology Recycling, Refurbishing, and Redistribution. RESNA Technical Assistance Project.

ERIC Educational Resources Information Center

RESNA: Association for the Advancement of Rehabilitation Technology, Arlington, VA.

This monograph discusses the benefits of recycling and reusing assistive technology for students with disabilities. It begins by discussing the benefits of recycled assistive technology for suppliers, students, and consumers, and then profiles programmatic models for assistive technology recycling programs. The advantages and disadvantages for…

Geocaching: 21st-Century Hide-and-Seek

ERIC Educational Resources Information Center

Schlatter, Barbara Elwood; Hurd, Amy R.

2005-01-01

Looking for a new adventure that combines technology and physical activity with nature? Try geocaching (pronounced geocashing)! Geocachers use Global Positioning System (GPS) receivers and satellite data to search and find hidden treasures (or caches) around the world. Enthusiasts visit web sites (e.g., www.geocaching.com) to discover the…

Mining the Values in the Curriculum.

ERIC Educational Resources Information Center

Ryan, Kevin

1993-01-01

Schools must provide opportunities for students to discover what is most worth knowing, as they prepare to be citizens, good workers, good private individuals. Formal curriculum is one vehicle for teaching Tao (universal path to becoming a good person). Hidden curriculum can also convey profound teachings, if a spirit of fairness predominates,…

Hidden sketches by Leonardo da Vinci revealed

NASA Astrophysics Data System (ADS)

Dumé, Belle

2009-02-01

Three drawings on the back of Leonardo da Vinci's The Virgin and Child with St Anne (circa 1508) have been discovered by researchers led by Michel Menu from the Centre de Recherche et de Restauration des Musées de France (C2RMF) and the Louvre Museum in Paris.

When Stones Teach.

ERIC Educational Resources Information Center

Lucier, Todd

2001-01-01

Creating towers of balanced stones is a versatile outdoor learning activity that can be experienced in the classroom, school yard, forest, or parking lot. Students discover hidden talents, learn to work and communicate clearly with others, and reconnect with the natural world. Several variations on the exercise are given, along with principles of…

Memetic Approaches for Optimizing Hidden Markov Models: A Case Study in Time Series Prediction

NASA Astrophysics Data System (ADS)

Bui, Lam Thu; Barlow, Michael

We propose a methodology for employing memetics (local search) within the framework of evolutionary algorithms to optimize parameters of hidden markov models. With this proposal, the rate and frequency of using local search are automatically changed over time either at a population or individual level. At the population level, we allow the rate of using local search to decay over time to zero (at the final generation). At the individual level, each individual is equipped with information of when it will do local search and for how long. This information evolves over time alongside the main elements of the chromosome representing the individual.

Self-Organizing Hidden Markov Model Map (SOHMMM): Biological Sequence Clustering and Cluster Visualization.

PubMed

Ferles, Christos; Beaufort, William-Scott; Ferle, Vanessa

2017-01-01

The present study devises mapping methodologies and projection techniques that visualize and demonstrate biological sequence data clustering results. The Sequence Data Density Display (SDDD) and Sequence Likelihood Projection (SLP) visualizations represent the input symbolical sequences in a lower-dimensional space in such a way that the clusters and relations of data elements are depicted graphically. Both operate in combination/synergy with the Self-Organizing Hidden Markov Model Map (SOHMMM). The resulting unified framework is in position to analyze automatically and directly raw sequence data. This analysis is carried out with little, or even complete absence of, prior information/domain knowledge.

Mining Bug Databases for Unidentified Software Vulnerabilities

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

Dumidu Wijayasekara; Milos Manic; Jason Wright

2012-06-01

Identifying software vulnerabilities is becoming more important as critical and sensitive systems increasingly rely on complex software systems. It has been suggested in previous work that some bugs are only identified as vulnerabilities long after the bug has been made public. These vulnerabilities are known as hidden impact vulnerabilities. This paper discusses the feasibility and necessity to mine common publicly available bug databases for vulnerabilities that are yet to be identified. We present bug database analysis of two well known and frequently used software packages, namely Linux kernel and MySQL. It is shown that for both Linux and MySQL, amore » significant portion of vulnerabilities that were discovered for the time period from January 2006 to April 2011 were hidden impact vulnerabilities. It is also shown that the percentage of hidden impact vulnerabilities has increased in the last two years, for both software packages. We then propose an improved hidden impact vulnerability identification methodology based on text mining bug databases, and conclude by discussing a few potential problems faced by such a classifier.« less

On some dynamical chameleon systems

NASA Astrophysics Data System (ADS)

Burkin, I. M.; Kuznetsova, O. I.

2018-03-01

It is now well known that dynamical systems can be categorized into systems with self-excited attractors and systems with hidden attractors. A self-excited attractor has a basin of attraction that is associated with an unstable equilibrium, while a hidden attractor has a basin of attraction that does not intersect with small neighborhoods of any equilibrium points. Hidden attractors play the important role in engineering applications because they allow unexpected and potentially disastrous responses to perturbations in a structure like a bridge or an airplane wing. In addition, complex behaviors of chaotic systems have been applied in various areas from image watermarking, audio encryption scheme, asymmetric color pathological image encryption, chaotic masking communication to random number generator. Recently, researchers have discovered the so-called “chameleon systems”. These systems were so named because they demonstrate self-excited or hidden oscillations depending on the value of parameters. The present paper offers a simple algorithm of synthesizing one-parameter chameleon systems. The authors trace the evolution of Lyapunov exponents and the Kaplan-Yorke dimension of such systems which occur when parameters change.

Hidden correlations entailed by q-non additivity render the q-monoatomic gas highly non trivial

NASA Astrophysics Data System (ADS)

Plastino, A.; Rocca, M. C.

2018-01-01

It ts known that Tsallis' q-non-additivity entails hidden correlations. It has also been shown that even for a monoatomic gas, both the q-partition function Z and the mean energy 〈 U 〉 diverge and, in particular, exhibit poles for certain values of the Tsallis non additivity parameter q. This happens because Z and 〈 U 〉 both depend on a Γ-function. This Γ, in turn, depends upon the spatial dimension ν. We encounter three different regimes according to the argument A of the Γ-function. (1) A > 0, (2) A < 0 and Γ > 0 outside the poles. (3) A displays poles and the physics is obtained via dimensional regularization. In cases (2) and (3) one discovers gravitational effects and quartets of particles. Moreover, bound states and gravitational effects emerge as a consequence of the hidden q-correlations.

An adaptive Hidden Markov Model for activity recognition based on a wearable multi-sensor device

USDA-ARS?s Scientific Manuscript database

Human activity recognition is important in the study of personal health, wellness and lifestyle. In order to acquire human activity information from the personal space, many wearable multi-sensor devices have been developed. In this paper, a novel technique for automatic activity recognition based o...

FIRE ALARM SYSTEM OUTDATED.

ERIC Educational Resources Information Center

CHANDLER, L.T.

AN EFFICIENT FIRE ALARM SYSTEM SHOULD--(1) PROVIDE WARNING OF FIRES THAT START IN HIDDEN OR UNOCCUPIED LOCATIONS, (2) INDICATE WHERE THE FIRE IS, (3) GIVE ADVANCE WARNING TO FACULTY AND ADMINISTRATION SO THAT PANIC AND CONFUSION CAN BE AVOIDED AND ORDERLY EVACUATION OCCUR, (4) AUTOMATICALLY NOTIFY CITY FIRE HEADQUARTERS OF THE FIRE, (5) OPERATE BY…

Hidden treasures - 50 km points of interests

NASA Astrophysics Data System (ADS)

Lommi, Matias; Kortelainen, Jaana

2015-04-01

Tampere is third largest city in Finland and a regional centre. During 70's there occurred several communal mergers. Nowadays this local area has both strong and diversed identity - from wilderness and agricultural fields to high density city living. Outside the city center there are interesting geological points unknown for modern city settlers. There is even a local proverb, "Go abroad to Teisko!". That is the area the Hidden Treasures -student project is focused on. Our school Tammerkoski Upper Secondary School (or Gymnasium) has emphasis on visual arts. We are going to offer our art students scientific and artistic experiences and knowledge about the hidden treasures of Teisko area and involve the Teisko inhabitants into this project. Hidden treasures - Precambrian subduction zone and a volcanism belt with dense bed of gold (Au) and arsenic (As), operating goldmines and quarries of minerals and metamorphic slates. - North of subduction zone a homogenic precambrian magmastone area with quarries, products known as Kuru Grey. - Former ashores of post-glasial Lake Näsijärvi and it's sediments enabled the developing agriculture and sustained settlement. Nowadays these ashores have both scenery and biodiversity values. - Old cattle sheds and dairy buildings made of local granite stones related to cultural stonebuilding inheritance. - Local active community of Kapee, about 100 inhabitants. Students will discover information of these "hidden" phenomena, and rendering this information trough Enviromental Art Method. Final form of this project will be published in several artistic and informative geocaches. These caches are achieved by a GPS-based special Hidden Treasures Cycling Route and by a website guiding people to find these hidden points of interests.

Efficiently Exploring Multilevel Data with Recursive Partitioning

ERIC Educational Resources Information Center

Martin, Daniel P.; von Oertzen, Timo; Rimm-Kaufman, Sara E.

2015-01-01

There is an increasing number of datasets with many participants, variables, or both, in education and other fields that often deal with large, multilevel data structures. Once initial confirmatory hypotheses are exhausted, it can be difficult to determine how best to explore the dataset to discover hidden relationships that could help to inform…

Fibonacci Numbers Revisited: Technology-Motivated Inquiry into a Two-Parametric Difference Equation

ERIC Educational Resources Information Center

Abramovich, Sergei; Leonov, Gennady A.

2008-01-01

This article demonstrates how within an educational context, supported by the notion of hidden mathematics curriculum and enhanced by the use of technology, new mathematical knowledge can be discovered. More specifically, proceeding from the well-known representation of Fibonacci numbers through a second-order difference equation, this article…

The Jossey-Bass Reader on Gender in Education. The Jossey-Bass Education Series.

ERIC Educational Resources Information Center

2002

These papers examine various perspectives on the gender debate in education: (1) "'Too Strong for a Woman': The Five Words That Created Title IX" (Bernice R. Sandler); (2) "Feminists Discover the Hidden Injuries of Coeducation" (David Tyack and Elisabeth Hansot); (3) "Images of Relationship" (Carol Gilligan); (4)…

The Implicit Association Test as a Class Assignment: Student Affective and Attitudinal Reactions

ERIC Educational Resources Information Center

Morris, Kathryn A.; Ashburn-Nardo, Leslie

2010-01-01

The Implicit Association Test (IAT) is a popular means of examining "hidden" biases. However, some express concerns about classroom use of the IAT, citing students' potentially negative affective reactions to taking the IAT and discovering their implicit biases. To investigate the validity of this criticism, 35 social psychology students completed…

Exploring the Integration of Data Mining and Data Visualization

ERIC Educational Resources Information Center

Zhang, Yi

2011-01-01

Due to the rapid advances in computing and sensing technologies, enormous amounts of data are being generated everyday in various applications. The integration of data mining and data visualization has been widely used to analyze these massive and complex data sets to discover hidden patterns. For both data mining and visualization to be…

Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data

PubMed Central

2017-01-01

Mapping gene expression as a quantitative trait using whole genome-sequencing and transcriptome analysis allows to discover the functional consequences of genetic variation. We developed a novel method and ultra-fast software Findr for higly accurate causal inference between gene expression traits using cis-regulatory DNA variations as causal anchors, which improves current methods by taking into consideration hidden confounders and weak regulations. Findr outperformed existing methods on the DREAM5 Systems Genetics challenge and on the prediction of microRNA and transcription factor targets in human lymphoblastoid cells, while being nearly a million times faster. Findr is publicly available at https://github.com/lingfeiwang/findr. PMID:28821014

Accelerating the discovery of hidden two-dimensional magnets using machine learning and first principle calculations

NASA Astrophysics Data System (ADS)

Miyazato, Itsuki; Tanaka, Yuzuru; Takahashi, Keisuke

2018-02-01

Two-dimensional (2D) magnets are explored in terms of data science and first principle calculations. Machine learning determines four descriptors for predicting the magnetic moments of 2D materials within reported 216 2D materials data. With the trained machine, 254 2D materials are predicted to have high magnetic moments. First principle calculations are performed to evaluate the predicted 254 2D materials where eight undiscovered stable 2D materials with high magnetic moments are revealed. The approach taken in this work indicates that undiscovered materials can be surfaced by utilizing data science and materials data, leading to an innovative way of discovering hidden materials.

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…

A deep learning framework for modeling structural features of RNA-binding protein targets

PubMed Central

Zhang, Sai; Zhou, Jingtian; Hu, Hailin; Gong, Haipeng; Chen, Ligong; Cheng, Chao; Zeng, Jianyang

2016-01-01

RNA-binding proteins (RBPs) play important roles in the post-transcriptional control of RNAs. Identifying RBP binding sites and characterizing RBP binding preferences are key steps toward understanding the basic mechanisms of the post-transcriptional gene regulation. Though numerous computational methods have been developed for modeling RBP binding preferences, discovering a complete structural representation of the RBP targets by integrating their available structural features in all three dimensions is still a challenging task. In this paper, we develop a general and flexible deep learning framework for modeling structural binding preferences and predicting binding sites of RBPs, which takes (predicted) RNA tertiary structural information into account for the first time. Our framework constructs a unified representation that characterizes the structural specificities of RBP targets in all three dimensions, which can be further used to predict novel candidate binding sites and discover potential binding motifs. Through testing on the real CLIP-seq datasets, we have demonstrated that our deep learning framework can automatically extract effective hidden structural features from the encoded raw sequence and structural profiles, and predict accurate RBP binding sites. In addition, we have conducted the first study to show that integrating the additional RNA tertiary structural features can improve the model performance in predicting RBP binding sites, especially for the polypyrimidine tract-binding protein (PTB), which also provides a new evidence to support the view that RBPs may own specific tertiary structural binding preferences. In particular, the tests on the internal ribosome entry site (IRES) segments yield satisfiable results with experimental support from the literature and further demonstrate the necessity of incorporating RNA tertiary structural information into the prediction model. The source code of our approach can be found in https://github.com/thucombio/deepnet-rbp. PMID:26467480

Using "1 = 2" to Inspire and Learn

ERIC Educational Resources Information Center

Premadasa, Kirthi; Samaranayake, Geetha

2012-01-01

Mathematical fallacies have an embedded sense of awe and mystery that can be used effectively in a classroom to inspire students to tackle a fallacy and find the "hidden" violation. In doing so, the student may discover the consequence of a rule violation in a stimulating manner, thus making a lasting impact of the rule as well as providing the…

Writing to Survive: How Teachers and Teens Negotiate the Effects of Abuse, Violence, and Disaster

ERIC Educational Resources Information Center

Alvarez, Deborah M.

2010-01-01

This ethnographic research investigates how adolescents use writing. Deborah M. Alvarez uncovers the hidden abuses and violence that adolescents bore with each school day. In two different research sites, the author follows adolescents through their academic and personal lives to discover how they use writing only to uncover the impact the public…

The Use of Tetrads in the Analysis of Arts-Based Media

ERIC Educational Resources Information Center

Gouzouasis, Peter; LaMonde, Anne-Marie

2005-01-01

In this article, we chose the musical form of a sonata to examine tetrads, a simple four-fold structure that Marshall McLuhan coined and employed to describe various technologies. Tetrads, as cognitive models, are used to refine, focus, or discover entities in cultures and technologies, which are hidden from view in the psyche. Tetradic logic…

Training, Sharing or Cheating? Gamer Strategies to Get a Digital Upper Hand

ERIC Educational Resources Information Center

Mortensen, Torill Elvira

2010-01-01

Digital game-players devote a large amount of their time to discovering rules hidden in the code and discoverable through empirical study, experiments, and developing or rediscovering the mathematical formulae governing the code. They do this through their own independent play as they test areas, gear and abilities, through data mining using…

Analysis and Design of Complex Networks

DTIC Science & Technology

2014-12-02

systems. 08-NOV-10, . : , Barlas Oguz, Venkat Anantharam. Long range dependent Markov chains with applications , Information Theory and Applications ...JUL-12, . : , Michael Krishnan, Ehsan Haghani, Avideh Zakhor. Packet Length Adaptation in WLANs with Hidden Nodes and Time-Varying Channels, IEEE... WLAN networks with multi-antenna beam-forming nodes. VII. Use of busy/idle signals for discovering optimum AP association VIII

STDP Installs in Winner-Take-All Circuits an Online Approximation to Hidden Markov Model Learning

PubMed Central

Kappel, David; Nessler, Bernhard; Maass, Wolfgang

2014-01-01

In order to cross a street without being run over, we need to be able to extract very fast hidden causes of dynamically changing multi-modal sensory stimuli, and to predict their future evolution. We show here that a generic cortical microcircuit motif, pyramidal cells with lateral excitation and inhibition, provides the basis for this difficult but all-important information processing capability. This capability emerges in the presence of noise automatically through effects of STDP on connections between pyramidal cells in Winner-Take-All circuits with lateral excitation. In fact, one can show that these motifs endow cortical microcircuits with functional properties of a hidden Markov model, a generic model for solving such tasks through probabilistic inference. Whereas in engineering applications this model is adapted to specific tasks through offline learning, we show here that a major portion of the functionality of hidden Markov models arises already from online applications of STDP, without any supervision or rewards. We demonstrate the emergent computing capabilities of the model through several computer simulations. The full power of hidden Markov model learning can be attained through reward-gated STDP. This is due to the fact that these mechanisms enable a rejection sampling approximation to theoretically optimal learning. We investigate the possible performance gain that can be achieved with this more accurate learning method for an artificial grammar task. PMID:24675787

A semi-supervised learning framework for biomedical event extraction based on hidden topics.

PubMed

Zhou, Deyu; Zhong, Dayou

2015-05-01

Scientists have devoted decades of efforts to understanding the interaction between proteins or RNA production. The information might empower the current knowledge on drug reactions or the development of certain diseases. Nevertheless, due to the lack of explicit structure, literature in life science, one of the most important sources of this information, prevents computer-based systems from accessing. Therefore, biomedical event extraction, automatically acquiring knowledge of molecular events in research articles, has attracted community-wide efforts recently. Most approaches are based on statistical models, requiring large-scale annotated corpora to precisely estimate models' parameters. However, it is usually difficult to obtain in practice. Therefore, employing un-annotated data based on semi-supervised learning for biomedical event extraction is a feasible solution and attracts more interests. In this paper, a semi-supervised learning framework based on hidden topics for biomedical event extraction is presented. In this framework, sentences in the un-annotated corpus are elaborately and automatically assigned with event annotations based on their distances to these sentences in the annotated corpus. More specifically, not only the structures of the sentences, but also the hidden topics embedded in the sentences are used for describing the distance. The sentences and newly assigned event annotations, together with the annotated corpus, are employed for training. Experiments were conducted on the multi-level event extraction corpus, a golden standard corpus. Experimental results show that more than 2.2% improvement on F-score on biomedical event extraction is achieved by the proposed framework when compared to the state-of-the-art approach. The results suggest that by incorporating un-annotated data, the proposed framework indeed improves the performance of the state-of-the-art event extraction system and the similarity between sentences might be precisely described by hidden topics and structures of the sentences. Copyright © 2015 Elsevier B.V. All rights reserved.

Variable complexity online sequential extreme learning machine, with applications to streamflow prediction

NASA Astrophysics Data System (ADS)

Lima, Aranildo R.; Hsieh, William W.; Cannon, Alex J.

2017-12-01

In situations where new data arrive continually, online learning algorithms are computationally much less costly than batch learning ones in maintaining the model up-to-date. The extreme learning machine (ELM), a single hidden layer artificial neural network with random weights in the hidden layer, is solved by linear least squares, and has an online learning version, the online sequential ELM (OSELM). As more data become available during online learning, information on the longer time scale becomes available, so ideally the model complexity should be allowed to change, but the number of hidden nodes (HN) remains fixed in OSELM. A variable complexity VC-OSELM algorithm is proposed to dynamically add or remove HN in the OSELM, allowing the model complexity to vary automatically as online learning proceeds. The performance of VC-OSELM was compared with OSELM in daily streamflow predictions at two hydrological stations in British Columbia, Canada, with VC-OSELM significantly outperforming OSELM in mean absolute error, root mean squared error and Nash-Sutcliffe efficiency at both stations.

Monitoring volcano activity through Hidden Markov Model

NASA Astrophysics Data System (ADS)

Cassisi, C.; Montalto, P.; Prestifilippo, M.; Aliotta, M.; Cannata, A.; Patanè, D.

2013-12-01

During 2011-2013, Mt. Etna was mainly characterized by cyclic occurrences of lava fountains, totaling to 38 episodes. During this time interval Etna volcano's states (QUIET, PRE-FOUNTAIN, FOUNTAIN, POST-FOUNTAIN), whose automatic recognition is very useful for monitoring purposes, turned out to be strongly related to the trend of RMS (Root Mean Square) of the seismic signal recorded by stations close to the summit area. Since RMS time series behavior is considered to be stochastic, we can try to model the system generating its values, assuming to be a Markov process, by using Hidden Markov models (HMMs). HMMs are a powerful tool in modeling any time-varying series. HMMs analysis seeks to recover the sequence of hidden states from the observed emissions. In our framework, observed emissions are characters generated by the SAX (Symbolic Aggregate approXimation) technique, which maps RMS time series values with discrete literal emissions. The experiments show how it is possible to guess volcano states by means of HMMs and SAX.

Design Graphics

NASA Technical Reports Server (NTRS)

1990-01-01

A mathematician, David R. Hedgley, Jr. developed a computer program that considers whether a line in a graphic model of a three-dimensional object should or should not be visible. Known as the Hidden Line Computer Code, the program automatically removes superfluous lines and displays an object from a specific viewpoint, just as the human eye would see it. An example of how one company uses the program is the experience of Birdair which specializes in production of fabric skylights and stadium covers. The fabric called SHEERFILL is a Teflon coated fiberglass material developed in cooperation with DuPont Company. SHEERFILL glazed structures are either tension structures or air-supported tension structures. Both are formed by patterned fabric sheets supported by a steel or aluminum frame or cable network. Birdair uses the Hidden Line Computer Code, to illustrate a prospective structure to an architect or owner. The program generates a three- dimensional perspective with the hidden lines removed. This program is still used by Birdair and continues to be commercially available to the public.

Towards automating the discovery of certain innovative design principles through a clustering-based optimization technique

NASA Astrophysics Data System (ADS)

Bandaru, Sunith; Deb, Kalyanmoy

2011-09-01

In this article, a methodology is proposed for automatically extracting innovative design principles which make a system or process (subject to conflicting objectives) optimal using its Pareto-optimal dataset. Such 'higher knowledge' would not only help designers to execute the system better, but also enable them to predict how changes in one variable would affect other variables if the system has to retain its optimal behaviour. This in turn would help solve other similar systems with different parameter settings easily without the need to perform a fresh optimization task. The proposed methodology uses a clustering-based optimization technique and is capable of discovering hidden functional relationships between the variables, objective and constraint functions and any other function that the designer wishes to include as a 'basis function'. A number of engineering design problems are considered for which the mathematical structure of these explicit relationships exists and has been revealed by a previous study. A comparison with the multivariate adaptive regression splines (MARS) approach reveals the practicality of the proposed approach due to its ability to find meaningful design principles. The success of this procedure for automated innovization is highly encouraging and indicates its suitability for further development in tackling more complex design scenarios.

Design and implementation of monitoring and evaluation of healthcare organization management

NASA Astrophysics Data System (ADS)

Charalampos, Platis; Emmanouil, Zoulias; Dimitrios, Iracleous; Lappa, Evaggelia

2017-09-01

The management of a healthcare organization is monitored using a suitably designed questionnaire to 271 nurses operating in Greek hospital. The data are fed to an automatic data mining system to obtain a suitable series of models to analyse, visualise and study the obtained information. Hidden patterns, correlations and interdependencies are investigated and the results are analytically presented.

Micro-XRF complemented by x-radiography and digital microscopy imaging for the study of hidden paintings

NASA Astrophysics Data System (ADS)

Gasanova, Svetlana; Hermon, Sorin

2017-07-01

The present study describes a novel approach to the study of hidden by integrating the non-invasive micro-X-Ray Fluorescence spectroscopy, X-radiography and digital microscopy. The case study analysed is a portrait of a male figure discovered under the painting of Ecce Homo, attributed to Titian's studio with an estimated date in the 1550s. The X-radiography images exposed the details of the underpainting, which appeared to be a nearly finished portrait of a standing man, overpainted by the current composition of Ecce Homo at a 180° angle. The microscopy observations of the upper painting's cracks and flaked areas enabled the study of the exposed underlayers in terms of their colour appearance and pigment particles. The subsequent pigment analysis was performed by micro-XRF. Since the described XRF analysis was performed not in scanner mode, the correct selection of the measurement spots for the micro analysis and separation between pigments of the lower and the upper painting was of paramount importance. The described approach for spot selection was based on the results of the preceding X-radiography and digital microscopy tests. The presence of lead white, vermilion, copper green and iron earth in the underlying portrait was confirmed by the multiple point XRF analysis of Pb, Hg, Cu, Fe and Mn lines. The described investigation method proved to be useful in the identification of the pigments of the underlying painting and consequently assisted in the tentative reconstruction of its colour palette. Moreover, the undertaken approach allowed discovering the potential of micro-XRF technique in the study of hidden compositions.

Stable heavy pentaquarks in constituent models

NASA Astrophysics Data System (ADS)

Richard, J.-M.; Valcarce, A.; Vijande, J.

2017-11-01

It is shown that standard constituent quark models produce (c bar cqqq) hidden-charm pentaquarks, where c denotes the charmed quark and q a light quark, which lie below the lowest threshold for spontaneous dissociation and thus are stable in the limit where the internal c bar c annihilation is neglected. The binding is a cooperative effect of the chromoelectric and chromomagnetic components of the interaction, and it disappears in the static limit with a pure chromoelectric potential. Their wave function contains color sextet and color octet configurations for the subsystems and can hardly be reduced to a molecular state made of two interacting hadrons. These pentaquark states could be searched for in the experiments having discovered or confirmed the hidden-charm meson and baryon resonances.

Discovering the Science Hidden behind Real Objects

ERIC Educational Resources Information Center

Desforges, Ruth

2018-01-01

The Zoological Society of London (ZSL) has a huge collection of unique and curious objects from the natural world that have been loaned to us by HM Revenue and Customs after being seized at the UK border. Among the turtle shells and snake skins, the strangest of these is perhaps the freestanding rhino-foot ash tray. This single object can open up…

Alvarez, Luis Walter (1911-88)

NASA Astrophysics Data System (ADS)

Murdin, P.

2000-11-01

Physicist and astronomer, born in San Francisco, CA, professor at the University of California, Nobel prizewinner (1968) for his discoveries in particle physics. Used cosmic rays to `x-ray' the pyramids of Egypt, finding in particular that the tombs in the Great Pyramid at Giza had no hidden rooms. Alvarez (and his son) discovered globally distributed iridium at the Cretaceous/Tertiary boundary i...

Openness--A Way Forward: Development Education Research Centre

ERIC Educational Resources Information Center

Hare-Heremia, Mahora

2014-01-01

Education is a vital aspect in the lives of humankind. It contributes and shapes our future as citizens of the world. To understand it is to discover the many hidden talents the world has in store for all. The Development Education Research Centre (DERC) holds many resources that aid in the development of education at a global level. With the…

BIRI: a new approach for automatically discovering and indexing available public bioinformatics resources from the literature.

PubMed

de la Calle, Guillermo; García-Remesal, Miguel; Chiesa, Stefano; de la Iglesia, Diana; Maojo, Victor

2009-10-07

The rapid evolution of Internet technologies and the collaborative approaches that dominate the field have stimulated the development of numerous bioinformatics resources. To address this new framework, several initiatives have tried to organize these services and resources. In this paper, we present the BioInformatics Resource Inventory (BIRI), a new approach for automatically discovering and indexing available public bioinformatics resources using information extracted from the scientific literature. The index generated can be automatically updated by adding additional manuscripts describing new resources. We have developed web services and applications to test and validate our approach. It has not been designed to replace current indexes but to extend their capabilities with richer functionalities. We developed a web service to provide a set of high-level query primitives to access the index. The web service can be used by third-party web services or web-based applications. To test the web service, we created a pilot web application to access a preliminary knowledge base of resources. We tested our tool using an initial set of 400 abstracts. Almost 90% of the resources described in the abstracts were correctly classified. More than 500 descriptions of functionalities were extracted. These experiments suggest the feasibility of our approach for automatically discovering and indexing current and future bioinformatics resources. Given the domain-independent characteristics of this tool, it is currently being applied by the authors in other areas, such as medical nanoinformatics. BIRI is available at http://edelman.dia.fi.upm.es/biri/.

Leveraging Automatic Speech Recognition Errors to Detect Challenging Speech Segments in TED Talks

ERIC Educational Resources Information Center

Mirzaei, Maryam Sadat; Meshgi, Kourosh; Kawahara, Tatsuya

2016-01-01

This study investigates the use of Automatic Speech Recognition (ASR) systems to epitomize second language (L2) listeners' problems in perception of TED talks. ASR-generated transcripts of videos often involve recognition errors, which may indicate difficult segments for L2 listeners. This paper aims to discover the root-causes of the ASR errors…

Numerical Nonlinear Robust Control with Applications to Humanoid Robots

DTIC Science & Technology

2015-07-01

automatically. While optimization and optimal control theory have been widely applied in humanoid robot control, it is not without drawbacks . A blind... drawback of Galerkin-based approaches is the need to successively produce discrete forms, which is difficult to implement in practice. Related...universal function approx- imation ability, these approaches are not without drawbacks . In practice, while a single hidden layer neural network can

Did You Remember to DID

DTIC Science & Technology

2010-04-01

threats (also known as a SWOT analysis) is a very useful method in identifying potential issues, hidden agendas, and competing egos. • Defining a...comprehensive communications plan uses what’s been defined and informs (the second key component to DID) government and con - tractor teams of the essential...program execution strategies. Inform Inform means communicating to internal and external stake- holders what was defined, expected, discovered, con

"Hidden" O(2) and SO(2) symmetry in lepton mixing

NASA Astrophysics Data System (ADS)

Heeck, Julian; Rodejohann, Werner

2012-02-01

To generate the minimal neutrino Majorana mass matrix that has a free solar mixing angle and Δ m_{{^{text{sol}}}}^2 = 0 it suffices to implement an O(2) symmetry, or one of its subgroups SO(2), ZN ≥3, or DN ≥3. This O(2) generalizes the hidden {text{Z}}_{{^{{2}}}}^s of lepton mixing and leads in addition automatically to μ-τ symmetry. Flavor-democratic perturbations, as expected e.g. from the Planck scale, then result in tri-bimaximal mixing. We present a minimal model with three Higgs doublets implementing a type-I seesaw mechanism with a spontaneous breakdown of the symmetry, leading to large θ 13 and small Δ m_{{^{text{sol}}}}^2 = 0 due to the particular decomposition of the perturbations under μ-τ symmetry.

Directed Hidden-Code Extractor for Environment-Sensitive Malwares

NASA Astrophysics Data System (ADS)

Jia, Chunfu; Wang, Zhi; Lu, Kai; Liu, Xinhai; Liu, Xin

Malware writers often use packing technique to hide malicious payload. A number of dynamic unpacking tools are.designed in order to identify and extract the hidden code in the packed malware. However, such unpacking methods.are all based on a highly controlled environment that is vulnerable to various anti-unpacking techniques. If execution.environment is suspicious, malwares may stay inactive for a long time or stop execution immediately to evade.detection. In this paper, we proposed a novel approach that automatically reasons about the environment requirements.imposed by malware, then directs a unpacking tool to change the controlled environment to extract the hide code at.the new environment. The experimental results show that our approach significantly increases the resilience of the.traditional unpacking tools to environment-sensitive malware.

Suspicious activity recognition in infrared imagery using Hidden Conditional Random Fields for outdoor perimeter surveillance

NASA Astrophysics Data System (ADS)

Rogotis, Savvas; Ioannidis, Dimosthenis; Tzovaras, Dimitrios; Likothanassis, Spiros

2015-04-01

The aim of this work is to present a novel approach for automatic recognition of suspicious activities in outdoor perimeter surveillance systems based on infrared video processing. Through the combination of size, speed and appearance based features, like the Center-Symmetric Local Binary Patterns, short-term actions are identified and serve as input, along with user location, for modeling target activities using the theory of Hidden Conditional Random Fields. HCRFs are used to directly link a set of observations to the most appropriate activity label and as such to discriminate high risk activities (e.g. trespassing) from zero risk activities (e.g loitering outside the perimeter). Experimental results demonstrate the effectiveness of our approach in identifying suspicious activities for video surveillance systems.

Nuclear scissors modes and hidden angular momenta

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

Balbutsev, E. B., E-mail: balbuts@theor.jinr.ru; Molodtsova, I. V.; Schuck, P.

The coupled dynamics of low-lying modes and various giant resonances are studied with the help of the Wigner Function Moments method generalized to take into account spin degrees of freedom and pair correlations simultaneously. The method is based on Time-Dependent Hartree–Fock–Bogoliubov equations. The model of the harmonic oscillator including spin–orbit potential plus quadrupole–quadrupole and spin–spin interactions is considered. New low-lying spin-dependent modes are analyzed. Special attention is paid to the scissors modes. A new source of nuclear magnetism, connected with counter-rotation of spins up and down around the symmetry axis (hidden angular momenta), is discovered. Its inclusion into the theorymore » allows one to improve substantially the agreement with experimental data in the description of energies and transition probabilities of scissors modes.« less

Automatic detection of snow avalanches in continuous seismic data using hidden Markov models

NASA Astrophysics Data System (ADS)

Heck, Matthias; Hammer, Conny; van Herwijnen, Alec; Schweizer, Jürg; Fäh, Donat

2018-01-01

Snow avalanches generate seismic signals as many other mass movements. Detection of avalanches by seismic monitoring is highly relevant to assess avalanche danger. In contrast to other seismic events, signals generated by avalanches do not have a characteristic first arrival nor is it possible to detect different wave phases. In addition, the moving source character of avalanches increases the intricacy of the signals. Although it is possible to visually detect seismic signals produced by avalanches, reliable automatic detection methods for all types of avalanches do not exist yet. We therefore evaluate whether hidden Markov models (HMMs) are suitable for the automatic detection of avalanches in continuous seismic data. We analyzed data recorded during the winter season 2010 by a seismic array deployed in an avalanche starting zone above Davos, Switzerland. We re-evaluated a reference catalogue containing 385 events by grouping the events in seven probability classes. Since most of the data consist of noise, we first applied a simple amplitude threshold to reduce the amount of data. As first classification results were unsatisfying, we analyzed the temporal behavior of the seismic signals for the whole data set and found that there is a high variability in the seismic signals. We therefore applied further post-processing steps to reduce the number of false alarms by defining a minimal duration for the detected event, implementing a voting-based approach and analyzing the coherence of the detected events. We obtained the best classification results for events detected by at least five sensors and with a minimal duration of 12 s. These processing steps allowed identifying two periods of high avalanche activity, suggesting that HMMs are suitable for the automatic detection of avalanches in seismic data. However, our results also showed that more sensitive sensors and more appropriate sensor locations are needed to improve the signal-to-noise ratio of the signals and therefore the classification.

Extending Wi-Fi Direct for Automated Operations

DTIC Science & Technology

2015-03-01

functionalities. These added functionalities include: automatic device discovery, a mutual awareness of capabilities between devices (inter-device capability ...functionalities include: automatic device discove1y, a mutual awareness of capabilities between devices (inter-device capability awareness...Figure 7. P2P Device GO Negotiation Request (The P2P IE includes P2P Capability , P2P Device Info, Group Owner Intent, Configuration Timeout, Listen

A Regularized Linear Dynamical System Framework for Multivariate Time Series Analysis.

PubMed

Liu, Zitao; Hauskrecht, Milos

2015-01-01

Linear Dynamical System (LDS) is an elegant mathematical framework for modeling and learning Multivariate Time Series (MTS). However, in general, it is difficult to set the dimension of an LDS's hidden state space. A small number of hidden states may not be able to model the complexities of a MTS, while a large number of hidden states can lead to overfitting. In this paper, we study learning methods that impose various regularization penalties on the transition matrix of the LDS model and propose a regularized LDS learning framework (rLDS) which aims to (1) automatically shut down LDSs' spurious and unnecessary dimensions, and consequently, address the problem of choosing the optimal number of hidden states; (2) prevent the overfitting problem given a small amount of MTS data; and (3) support accurate MTS forecasting. To learn the regularized LDS from data we incorporate a second order cone program and a generalized gradient descent method into the Maximum a Posteriori framework and use Expectation Maximization to obtain a low-rank transition matrix of the LDS model. We propose two priors for modeling the matrix which lead to two instances of our rLDS. We show that our rLDS is able to recover well the intrinsic dimensionality of the time series dynamics and it improves the predictive performance when compared to baselines on both synthetic and real-world MTS datasets.

Stylistic gait synthesis based on hidden Markov models

NASA Astrophysics Data System (ADS)

Tilmanne, Joëlle; Moinet, Alexis; Dutoit, Thierry

2012-12-01

In this work we present an expressive gait synthesis system based on hidden Markov models (HMMs), following and modifying a procedure originally developed for speaking style adaptation, in speech synthesis. A large database of neutral motion capture walk sequences was used to train an HMM of average walk. The model was then used for automatic adaptation to a particular style of walk using only a small amount of training data from the target style. The open source toolkit that we adapted for motion modeling also enabled us to take into account the dynamics of the data and to model accurately the duration of each HMM state. We also address the assessment issue and propose a procedure for qualitative user evaluation of the synthesized sequences. Our tests show that the style of these sequences can easily be recognized and look natural to the evaluators.

Recognizing suspicious activities in infrared imagery using appearance-based features and the theory of hidden conditional random fields for outdoor perimeter surveillance

NASA Astrophysics Data System (ADS)

Rogotis, Savvas; Palaskas, Christos; Ioannidis, Dimosthenis; Tzovaras, Dimitrios; Likothanassis, Spiros

2015-11-01

This work aims to present an extended framework for automatically recognizing suspicious activities in outdoor perimeter surveilling systems based on infrared video processing. By combining size-, speed-, and appearance-based features, like the local phase quantization and the histograms of oriented gradients, actions of small duration are recognized and used as input, along with spatial information, for modeling target activities using the theory of hidden conditional random fields (HCRFs). HCRFs are used to classify an observation sequence into the most appropriate activity label class, thus discriminating high-risk activities like trespassing from zero risk activities, such as loitering outside the perimeter. The effectiveness of this approach is demonstrated with experimental results in various scenarios that represent suspicious activities in perimeter surveillance systems.

Galactic optical cloaking of visible baryonic matter

NASA Astrophysics Data System (ADS)

Smolyaninov, Igor I.

2018-05-01

Three-dimensional gravitational cloaking is known to require exotic matter and energy sources, which makes it arguably physically unrealizable. On the other hand, typical astronomical observations are performed using one-dimensional paraxial line of sight geometries. We demonstrate that unidirectional line of sight gravitational cloaking does not require exotic matter, and it may occur in multiple natural astronomical scenarios that involve gravitational lensing. In particular, recently discovered double gravitational lens SDSSJ 0 9 4 6 +1 0 0 6 together with the Milky Way appear to form a natural paraxial cloak. A natural question to ask, then, is how much matter in the Universe may be hidden from view by such natural gravitational cloaks? It is estimated that the total volume hidden from an observer by gravitational cloaking may reach about 1% of the total volume of the visible Universe.

Effectiveness Testing of Embedded User Support for U.S. Army Installation-Level Software

DTIC Science & Technology

1991-06-01

under what conditions Dynamic Help could influence performance and satisfaction. The ACIFS program was modified to provide automatic collection of all...under what conditions Dynamic Help can influence user performance and satisfaction. This chapter reports the design, implementation, and analysis of...ambiguous or is hidden in the body of the message. The ACIFS program has many user interface deficiencies, but it does allow the user to use trial and

Study on real-time elevator brake failure predictive system

NASA Astrophysics Data System (ADS)

Guo, Jun; Fan, Jinwei

2013-10-01

This paper presented a real-time failure predictive system of the elevator brake. Through inspecting the running state of the coil by a high precision long range laser triangulation non-contact measurement sensor, the displacement curve of the coil is gathered without interfering the original system. By analyzing the displacement data using the diagnostic algorithm, the hidden danger of the brake system can be discovered in time and thus avoid the according accident.

Instinctive analytics for coalition operations (Conference Presentation)

NASA Astrophysics Data System (ADS)

de Mel, Geeth R.; La Porta, Thomas; Pham, Tien; Pearson, Gavin

2017-05-01

The success of future military coalition operations—be they combat or humanitarian—will increasingly depend on a system's ability to share data and processing services (e.g. aggregation, summarization, fusion), and automatically compose services in support of complex tasks at the network edge. We call such an infrastructure instinctive—i.e., an infrastructure that reacts instinctively to address the analytics task at hand. However, developing such an infrastructure is made complex for the coalition environment due to its dynamism both in terms of user requirements and service availability. In order to address the above challenge, in this paper, we highlight our research vision and sketch some initial solutions into the problem domain. Specifically, we propose means to (1) automatically infer formal task requirements from mission specifications; (2) discover data, services, and their features automatically to satisfy the identified requirements; (3) create and augment shared domain models automatically; (4) efficiently offload services to the network edge and across coalition boundaries adhering to their computational properties and costs; and (5) optimally allocate and adjust services while respecting the constraints of operating environment and service fit. We envision that the research will result in a framework which enables self-description, discover, and assemble capabilities to both data and services in support of coalition mission goals.

Generalising better: Applying deep learning to integrate deleteriousness prediction scores for whole-exome SNV studies.

PubMed

Korvigo, Ilia; Afanasyev, Andrey; Romashchenko, Nikolay; Skoblov, Mikhail

2018-01-01

Many automatic classifiers were introduced to aid inference of phenotypical effects of uncategorised nsSNVs (nonsynonymous Single Nucleotide Variations) in theoretical and medical applications. Lately, several meta-estimators have been proposed that combine different predictors, such as PolyPhen and SIFT, to integrate more information in a single score. Although many advances have been made in feature design and machine learning algorithms used, the shortage of high-quality reference data along with the bias towards intensively studied in vitro models call for improved generalisation ability in order to further increase classification accuracy and handle records with insufficient data. Since a meta-estimator basically combines different scoring systems with highly complicated nonlinear relationships, we investigated how deep learning (supervised and unsupervised), which is particularly efficient at discovering hierarchies of features, can improve classification performance. While it is believed that one should only use deep learning for high-dimensional input spaces and other models (logistic regression, support vector machines, Bayesian classifiers, etc) for simpler inputs, we still believe that the ability of neural networks to discover intricate structure in highly heterogenous datasets can aid a meta-estimator. We compare the performance with various popular predictors, many of which are recommended by the American College of Medical Genetics and Genomics (ACMG), as well as available deep learning-based predictors. Thanks to hardware acceleration we were able to use a computationally expensive genetic algorithm to stochastically optimise hyper-parameters over many generations. Overfitting was hindered by noise injection and dropout, limiting coadaptation of hidden units. Although we stress that this work was not conceived as a tool comparison, but rather an exploration of the possibilities of deep learning application in ensemble scores, our results show that even relatively simple modern neural networks can significantly improve both prediction accuracy and coverage. We provide open-access to our finest model via the web-site: http://score.generesearch.ru/services/badmut/.

Bayesian structural inference for hidden processes.

PubMed

Strelioff, Christopher C; Crutchfield, James P

2014-04-01

We introduce a Bayesian approach to discovering patterns in structurally complex processes. The proposed method of Bayesian structural inference (BSI) relies on a set of candidate unifilar hidden Markov model (uHMM) topologies for inference of process structure from a data series. We employ a recently developed exact enumeration of topological ε-machines. (A sequel then removes the topological restriction.) This subset of the uHMM topologies has the added benefit that inferred models are guaranteed to be ε-machines, irrespective of estimated transition probabilities. Properties of ε-machines and uHMMs allow for the derivation of analytic expressions for estimating transition probabilities, inferring start states, and comparing the posterior probability of candidate model topologies, despite process internal structure being only indirectly present in data. We demonstrate BSI's effectiveness in estimating a process's randomness, as reflected by the Shannon entropy rate, and its structure, as quantified by the statistical complexity. We also compare using the posterior distribution over candidate models and the single, maximum a posteriori model for point estimation and show that the former more accurately reflects uncertainty in estimated values. We apply BSI to in-class examples of finite- and infinite-order Markov processes, as well to an out-of-class, infinite-state hidden process.

Bayesian structural inference for hidden processes

NASA Astrophysics Data System (ADS)

Strelioff, Christopher C.; Crutchfield, James P.

2014-04-01

We introduce a Bayesian approach to discovering patterns in structurally complex processes. The proposed method of Bayesian structural inference (BSI) relies on a set of candidate unifilar hidden Markov model (uHMM) topologies for inference of process structure from a data series. We employ a recently developed exact enumeration of topological ɛ-machines. (A sequel then removes the topological restriction.) This subset of the uHMM topologies has the added benefit that inferred models are guaranteed to be ɛ-machines, irrespective of estimated transition probabilities. Properties of ɛ-machines and uHMMs allow for the derivation of analytic expressions for estimating transition probabilities, inferring start states, and comparing the posterior probability of candidate model topologies, despite process internal structure being only indirectly present in data. We demonstrate BSI's effectiveness in estimating a process's randomness, as reflected by the Shannon entropy rate, and its structure, as quantified by the statistical complexity. We also compare using the posterior distribution over candidate models and the single, maximum a posteriori model for point estimation and show that the former more accurately reflects uncertainty in estimated values. We apply BSI to in-class examples of finite- and infinite-order Markov processes, as well to an out-of-class, infinite-state hidden process.

Automatic annotation of histopathological images using a latent topic model based on non-negative matrix factorization

PubMed Central

Cruz-Roa, Angel; Díaz, Gloria; Romero, Eduardo; González, Fabio A.

2011-01-01

Histopathological images are an important resource for clinical diagnosis and biomedical research. From an image understanding point of view, the automatic annotation of these images is a challenging problem. This paper presents a new method for automatic histopathological image annotation based on three complementary strategies, first, a part-based image representation, called the bag of features, which takes advantage of the natural redundancy of histopathological images for capturing the fundamental patterns of biological structures, second, a latent topic model, based on non-negative matrix factorization, which captures the high-level visual patterns hidden in the image, and, third, a probabilistic annotation model that links visual appearance of morphological and architectural features associated to 10 histopathological image annotations. The method was evaluated using 1,604 annotated images of skin tissues, which included normal and pathological architectural and morphological features, obtaining a recall of 74% and a precision of 50%, which improved a baseline annotation method based on support vector machines in a 64% and 24%, respectively. PMID:22811960

Passive Acoustic Leak Detection for Sodium Cooled Fast Reactors Using Hidden Markov Models

NASA Astrophysics Data System (ADS)

Marklund, A. Riber; Kishore, S.; Prakash, V.; Rajan, K. K.; Michel, F.

2016-06-01

Acoustic leak detection for steam generators of sodium fast reactors have been an active research topic since the early 1970s and several methods have been tested over the years. Inspired by its success in the field of automatic speech recognition, we here apply hidden Markov models (HMM) in combination with Gaussian mixture models (GMM) to the problem. To achieve this, we propose a new feature calculation scheme, based on the temporal evolution of the power spectral density (PSD) of the signal. Using acoustic signals recorded during steam/water injection experiments done at the Indira Gandhi Centre for Atomic Research (IGCAR), the proposed method is tested. We perform parametric studies on the HMM+GMM model size and demonstrate that the proposed method a) performs well without a priori knowledge of injection noise, b) can incorporate several noise models and c) has an output distribution that simplifies false alarm rate control.

Offline Signature Verification Using the Discrete Radon Transform and a Hidden Markov Model

NASA Astrophysics Data System (ADS)

Coetzer, J.; Herbst, B. M.; du Preez, J. A.

2004-12-01

We developed a system that automatically authenticates offline handwritten signatures using the discrete Radon transform (DRT) and a hidden Markov model (HMM). Given the robustness of our algorithm and the fact that only global features are considered, satisfactory results are obtained. Using a database of 924 signatures from 22 writers, our system achieves an equal error rate (EER) of 18% when only high-quality forgeries (skilled forgeries) are considered and an EER of 4.5% in the case of only casual forgeries. These signatures were originally captured offline. Using another database of 4800 signatures from 51 writers, our system achieves an EER of 12.2% when only skilled forgeries are considered. These signatures were originally captured online and then digitally converted into static signature images. These results compare well with the results of other algorithms that consider only global features.

Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression

PubMed Central

Wiedenhoeft, John; Brugel, Eric; Schliep, Alexander

2016-01-01

By integrating Haar wavelets with Hidden Markov Models, we achieve drastically reduced running times for Bayesian inference using Forward-Backward Gibbs sampling. We show that this improves detection of genomic copy number variants (CNV) in array CGH experiments compared to the state-of-the-art, including standard Gibbs sampling. The method concentrates computational effort on chromosomal segments which are difficult to call, by dynamically and adaptively recomputing consecutive blocks of observations likely to share a copy number. This makes routine diagnostic use and re-analysis of legacy data collections feasible; to this end, we also propose an effective automatic prior. An open source software implementation of our method is available at http://schlieplab.org/Software/HaMMLET/ (DOI: 10.5281/zenodo.46262). This paper was selected for oral presentation at RECOMB 2016, and an abstract is published in the conference proceedings. PMID:27177143

Global-constrained hidden Markov model applied on wireless capsule endoscopy video segmentation

NASA Astrophysics Data System (ADS)

Wan, Yiwen; Duraisamy, Prakash; Alam, Mohammad S.; Buckles, Bill

2012-06-01

Accurate analysis of wireless capsule endoscopy (WCE) videos is vital but tedious. Automatic image analysis can expedite this task. Video segmentation of WCE into the four parts of the gastrointestinal tract is one way to assist a physician. The segmentation approach described in this paper integrates pattern recognition with statiscal analysis. Iniatially, a support vector machine is applied to classify video frames into four classes using a combination of multiple color and texture features as the feature vector. A Poisson cumulative distribution, for which the parameter depends on the length of segments, models a prior knowledge. A priori knowledge together with inter-frame difference serves as the global constraints driven by the underlying observation of each WCE video, which is fitted by Gaussian distribution to constrain the transition probability of hidden Markov model.Experimental results demonstrated effectiveness of the approach.

An Implementation of Privacy Protection for a Surveillance Camera Using ROI Coding of JPEG2000 with Face Detection

NASA Astrophysics Data System (ADS)

Muneyasu, Mitsuji; Odani, Shuhei; Kitaura, Yoshihiro; Namba, Hitoshi

On the use of a surveillance camera, there is a case where privacy protection should be considered. This paper proposes a new privacy protection method by automatically degrading the face region in surveillance images. The proposed method consists of ROI coding of JPEG2000 and a face detection method based on template matching. The experimental result shows that the face region can be detected and hidden correctly.

The Development of the Speaker Independent ARM Continuous Speech Recognition System

DTIC Science & Technology

1992-01-01

spokeTi airborne reconnaissance reports u-ing a speech recognition system based on phoneme-level hidden Markov models (HMMs). Previous versions of the ARM...will involve automatic selection from multiple model sets, corresponding to different speaker types, and that the most rudimen- tary partition of a...The vocabulary size for the ARM task is 497 words. These words are related to the phoneme-level symbols corresponding to the models in the model set

SOA approach to battle command: simulation interoperability

NASA Astrophysics Data System (ADS)

Mayott, Gregory; Self, Mid; Miller, Gordon J.; McDonnell, Joseph S.

2010-04-01

NVESD is developing a Sensor Data and Management Services (SDMS) Service Oriented Architecture (SOA) that provides an innovative approach to achieve seamless application functionality across simulation and battle command systems. In 2010, CERDEC will conduct a SDMS Battle Command demonstration that will highlight the SDMS SOA capability to couple simulation applications to existing Battle Command systems. The demonstration will leverage RDECOM MATREX simulation tools and TRADOC Maneuver Support Battle Laboratory Virtual Base Defense Operations Center facilities. The battle command systems are those specific to the operation of a base defense operations center in support of force protection missions. The SDMS SOA consists of four components that will be discussed. An Asset Management Service (AMS) will automatically discover the existence, state, and interface definition required to interact with a named asset (sensor or a sensor platform, a process such as level-1 fusion, or an interface to a sensor or other network endpoint). A Streaming Video Service (SVS) will automatically discover the existence, state, and interfaces required to interact with a named video stream, and abstract the consumers of the video stream from the originating device. A Task Manager Service (TMS) will be used to automatically discover the existence of a named mission task, and will interpret, translate and transmit a mission command for the blue force unit(s) described in a mission order. JC3IEDM data objects, and software development kit (SDK), will be utilized as the basic data object definition for implemented web services.

Efficient discovery of overlapping communities in massive networks

PubMed Central

Gopalan, Prem K.; Blei, David M.

2013-01-01

Detecting overlapping communities is essential to analyzing and exploring natural networks such as social networks, biological networks, and citation networks. However, most existing approaches do not scale to the size of networks that we regularly observe in the real world. In this paper, we develop a scalable approach to community detection that discovers overlapping communities in massive real-world networks. Our approach is based on a Bayesian model of networks that allows nodes to participate in multiple communities, and a corresponding algorithm that naturally interleaves subsampling from the network and updating an estimate of its communities. We demonstrate how we can discover the hidden community structure of several real-world networks, including 3.7 million US patents, 575,000 physics articles from the arXiv preprint server, and 875,000 connected Web pages from the Internet. Furthermore, we demonstrate on large simulated networks that our algorithm accurately discovers the true community structure. This paper opens the door to using sophisticated statistical models to analyze massive networks. PMID:23950224

A Hybrid of Deep Network and Hidden Markov Model for MCI Identification with Resting-State fMRI.

PubMed

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2015-10-01

In this paper, we propose a novel method for modelling functional dynamics in resting-state fMRI (rs-fMRI) for Mild Cognitive Impairment (MCI) identification. Specifically, we devise a hybrid architecture by combining Deep Auto-Encoder (DAE) and Hidden Markov Model (HMM). The roles of DAE and HMM are, respectively, to discover hierarchical non-linear relations among features, by which we transform the original features into a lower dimension space, and to model dynamic characteristics inherent in rs-fMRI, i.e. , internal state changes. By building a generative model with HMMs for each class individually, we estimate the data likelihood of a test subject as MCI or normal healthy control, based on which we identify the clinical label. In our experiments, we achieved the maximal accuracy of 81.08% with the proposed method, outperforming state-of-the-art methods in the literature.

A Hybrid of Deep Network and Hidden Markov Model for MCI Identification with Resting-State fMRI

PubMed Central

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2015-01-01

In this paper, we propose a novel method for modelling functional dynamics in resting-state fMRI (rs-fMRI) for Mild Cognitive Impairment (MCI) identification. Specifically, we devise a hybrid architecture by combining Deep Auto-Encoder (DAE) and Hidden Markov Model (HMM). The roles of DAE and HMM are, respectively, to discover hierarchical non-linear relations among features, by which we transform the original features into a lower dimension space, and to model dynamic characteristics inherent in rs-fMRI, i.e., internal state changes. By building a generative model with HMMs for each class individually, we estimate the data likelihood of a test subject as MCI or normal healthy control, based on which we identify the clinical label. In our experiments, we achieved the maximal accuracy of 81.08% with the proposed method, outperforming state-of-the-art methods in the literature. PMID:27054199

Understanding overlay signatures using machine learning on non-lithography context information

NASA Astrophysics Data System (ADS)

Overcast, Marshall; Mellegaard, Corey; Daniel, David; Habets, Boris; Erley, Georg; Guhlemann, Steffen; Thrun, Xaver; Buhl, Stefan; Tottewitz, Steven

2018-03-01

Overlay errors between two layers can be caused by non-lithography processes. While these errors can be compensated by the run-to-run system, such process and tool signatures are not always stable. In order to monitor the impact of non-lithography context on overlay at regular intervals, a systematic approach is needed. Using various machine learning techniques, significant context parameters that relate to deviating overlay signatures are automatically identified. Once the most influential context parameters are found, a run-to-run simulation is performed to see how much improvement can be obtained. The resulting analysis shows good potential for reducing the influence of hidden context parameters on overlay performance. Non-lithographic contexts are significant contributors, and their automatic detection and classification will enable the overlay roadmap, given the corresponding control capabilities.

Fuzzy hidden Markov chains segmentation for volume determination and quantitation in PET.

PubMed

Hatt, M; Lamare, F; Boussion, N; Turzo, A; Collet, C; Salzenstein, F; Roux, C; Jarritt, P; Carson, K; Cheze-Le Rest, C; Visvikis, D

2007-06-21

Accurate volume of interest (VOI) estimation in PET is crucial in different oncology applications such as response to therapy evaluation and radiotherapy treatment planning. The objective of our study was to evaluate the performance of the proposed algorithm for automatic lesion volume delineation; namely the fuzzy hidden Markov chains (FHMC), with that of current state of the art in clinical practice threshold based techniques. As the classical hidden Markov chain (HMC) algorithm, FHMC takes into account noise, voxel intensity and spatial correlation, in order to classify a voxel as background or functional VOI. However the novelty of the fuzzy model consists of the inclusion of an estimation of imprecision, which should subsequently lead to a better modelling of the 'fuzzy' nature of the object of interest boundaries in emission tomography data. The performance of the algorithms has been assessed on both simulated and acquired datasets of the IEC phantom, covering a large range of spherical lesion sizes (from 10 to 37 mm), contrast ratios (4:1 and 8:1) and image noise levels. Both lesion activity recovery and VOI determination tasks were assessed in reconstructed images using two different voxel sizes (8 mm3 and 64 mm3). In order to account for both the functional volume location and its size, the concept of % classification errors was introduced in the evaluation of volume segmentation using the simulated datasets. Results reveal that FHMC performs substantially better than the threshold based methodology for functional volume determination or activity concentration recovery considering a contrast ratio of 4:1 and lesion sizes of <28 mm. Furthermore differences between classification and volume estimation errors evaluated were smaller for the segmented volumes provided by the FHMC algorithm. Finally, the performance of the automatic algorithms was less susceptible to image noise levels in comparison to the threshold based techniques. The analysis of both simulated and acquired datasets led to similar results and conclusions as far as the performance of segmentation algorithms under evaluation is concerned.

Marine Ship Automatic Identification System (AIS) for Enhanced Coastal Security Capabilities: An Oil Spill Tracking Application

DTIC Science & Technology

2007-09-01

in port, harbor or waterway incidents; and, oil or oily wastes illegally dumped at sea, including illegal discharge of oily bilge or ballast waters ...quantities of oily waste and oily bilge water and sludge at sea using specially installed pipes, which they were careful to have removed and hidden...detailing specifics for oil and bilge water handling equipment, oil hold washing protocols, and a 15 part per million discharge limit of oil content in

The Antiaircraft Journal. Volume 93, Number 6, November-December 1950

DTIC Science & Technology

1950-12-01

some reference to "in ground de - tating effects of both the "air and automatic artillery." fense roles command rests with so-and-so." In Korea no one...Battalion, were contacted with their units in air de - fense roles in critical areas. 111eir main difficulty was in displacing forward at the rate...is a most effective means of de - stroying emplaced or masked weapons. However, emplace- ments hidden from the air prove devastating to advancing

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

Lapidus, Alla L.

From the date its role in heredity was discovered, DNA has been generating interest among scientists from different fields of knowledge: physicists have studied the three dimensional structure of the DNA molecule, biologists tried to decode the secrets of life hidden within these long molecules, and technologists invent and improve methods of DNA analysis. The analysis of the nucleotide sequence of DNA occupies a special place among the methods developed. Thanks to the variety of sequencing technologies available, the process of decoding the sequence of genomic DNA (or whole genome sequencing) has become robust and inexpensive. Meanwhile the assembly ofmore » whole genome sequences remains a challenging task. In addition to the need to assemble millions of DNA fragments of different length (from 35 bp (Solexa) to 800 bp (Sanger)), great interest in analysis of microbial communities (metagenomes) of different complexities raises new problems and pushes some new requirements for sequence assembly tools to the forefront. The genome assembly process can be divided into two steps: draft assembly and assembly improvement (finishing). Despite the fact that automatically performed assembly (or draft assembly) is capable of covering up to 98% of the genome, in most cases, it still contains incorrectly assembled reads. The error rate of the consensus sequence produced at this stage is about 1/2000 bp. A finished genome represents the genome assembly of much higher accuracy (with no gaps or incorrectly assembled areas) and quality ({approx}1 error/10,000 bp), validated through a number of computer and laboratory experiments.« less

Discover the Hidden Jewels in Your Library and Sharing the Wealth through Collaboration. Selected Papers from PIALA 2011, Pacific Islands Association of Libraries, Archives, and Museums Annual Conference (21st, Kosrae, Federated States of Micronesia, November 14-17, 2011)

ERIC Educational Resources Information Center

Drake, Paul B., Ed.

2012-01-01

This publication follows the tradition of publishing selected papers from Pacific Islands Association of Libraries, Archives and Museums (PIALA) annual conferences. This 21st annual conference was held in Kosrae, Federated States of Micronesia, November 14-17, 2011. The volume begins with a listing of the members of the PIALA 2011 Planning…

Hidden MHC genetic diversity in the Iberian ibex (Capra pyrenaica).

PubMed

Angelone, Samer; Jowers, Michael J; Molinar Min, Anna Rita; Fandos, Paulino; Prieto, Paloma; Pasquetti, Mario; Cano-Manuel, Francisco Javier; Mentaberre, Gregorio; Olvera, Jorge Ramón López; Ráez-Bravo, Arián; Espinosa, José; Pérez, Jesús M; Soriguer, Ramón C; Rossi, Luca; Granados, José Enrique

2018-05-08

Defining hidden genetic diversity within species is of great significance when attempting to maintain the evolutionary potential of natural populations and conduct appropriate management. Our hypothesis is that isolated (and eventually small) wild animal populations hide unexpected genetic diversity due to their maintenance of ancient polymorphisms or introgressions. We tested this hypothesis using the Iberian ibex (Capra pyrenaica) as an example. Previous studies based on large sample sizes taken from its principal populations have revealed that the Iberian ibex has a remarkably small MHC DRB1 diversity (only six remnant alleles) as a result of recent population bottlenecks and a marked demographic decline that has led to the extinction of two recognized subspecies. Extending on the geographic range to include non-studied isolated Iberian ibex populations, we sequenced a new MHC DRB1 in what seemed three small isolated populations in Southern Spain (n = 132). The findings indicate a higher genetic diversity than previously reported in this important gene. The newly discovered allele, MHC DRB1*7, is identical to one reported in the domestic goat C. aegagrus hircus. Whether or not this is the result of ancient polymorphisms maintained by balancing selection or, alternatively, introgressions from domestic goats through hybridization needs to be clarified in future studies. However, hybridization between Iberian ibex and domestic goats has been reported in Spain and the fact that the newly discovered allele is only present in one of the small isolated populations and not in the others suggests introgression. The new discovered allele is not expected to increase fitness in C. pyrenaica since it generates the same protein as the existing MHC DRB1*6. Analysis of a microsatellite locus (OLADRB1) near the new MHC DRB1*7 gene reveals a linkage disequilibrium between these two loci. The allele OLADRB1, 187 bp in length, was unambiguously linked to the MHC DRB1*7 allele. This enabled us to perform a DRB-STR matching method for the recently discovered MHC allele. This finding is critical for the conservation of the Iberian ibex since it directly affects the identification of the units of this species that should be managed and conserved separately (Evolutionarily Significant Units).

Automatic discovery of cell types and microcircuitry from neural connectomics

PubMed Central

Jonas, Eric; Kording, Konrad

2015-01-01

Neural connectomics has begun producing massive amounts of data, necessitating new analysis methods to discover the biological and computational structure. It has long been assumed that discovering neuron types and their relation to microcircuitry is crucial to understanding neural function. Here we developed a non-parametric Bayesian technique that identifies neuron types and microcircuitry patterns in connectomics data. It combines the information traditionally used by biologists in a principled and probabilistically coherent manner, including connectivity, cell body location, and the spatial distribution of synapses. We show that the approach recovers known neuron types in the retina and enables predictions of connectivity, better than simpler algorithms. It also can reveal interesting structure in the nervous system of Caenorhabditis elegans and an old man-made microprocessor. Our approach extracts structural meaning from connectomics, enabling new approaches of automatically deriving anatomical insights from these emerging datasets. DOI: http://dx.doi.org/10.7554/eLife.04250.001 PMID:25928186

Automatic discovery of cell types and microcircuitry from neural connectomics

DOE PAGES

Jonas, Eric; Kording, Konrad

2015-04-30

Neural connectomics has begun producing massive amounts of data, necessitating new analysis methods to discover the biological and computational structure. It has long been assumed that discovering neuron types and their relation to microcircuitry is crucial to understanding neural function. Here we developed a non-parametric Bayesian technique that identifies neuron types and microcircuitry patterns in connectomics data. It combines the information traditionally used by biologists in a principled and probabilistically coherent manner, including connectivity, cell body location, and the spatial distribution of synapses. We show that the approach recovers known neuron types in the retina and enables predictions of connectivity,more » better than simpler algorithms. It also can reveal interesting structure in the nervous system of Caenorhabditis elegans and an old man-made microprocessor. Our approach extracts structural meaning from connectomics, enabling new approaches of automatically deriving anatomical insights from these emerging datasets.« less

Automatic discovery of cell types and microcircuitry from neural connectomics

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

Jonas, Eric; Kording, Konrad

Neural connectomics has begun producing massive amounts of data, necessitating new analysis methods to discover the biological and computational structure. It has long been assumed that discovering neuron types and their relation to microcircuitry is crucial to understanding neural function. Here we developed a non-parametric Bayesian technique that identifies neuron types and microcircuitry patterns in connectomics data. It combines the information traditionally used by biologists in a principled and probabilistically coherent manner, including connectivity, cell body location, and the spatial distribution of synapses. We show that the approach recovers known neuron types in the retina and enables predictions of connectivity,more » better than simpler algorithms. It also can reveal interesting structure in the nervous system of Caenorhabditis elegans and an old man-made microprocessor. Our approach extracts structural meaning from connectomics, enabling new approaches of automatically deriving anatomical insights from these emerging datasets.« less

Flagstaff Robotic Survey Telescope (FRoST): Rapid Response for NEOs

NASA Astrophysics Data System (ADS)

Avner, Louis Daniel; Trilling, David E.; Dunham, Edward W.

2016-10-01

The Flagstaff Robotic Survey Telescope (FRoST) is a robotic 0.6m Schmidt telescope that will be used for instant follow-up observations of newly discovered Near Earth Objects (NEOs). Here, we present the progress being made on FRoST as well as the remaining tasks until the telescope is fully operational. With more than one thousand NEOs being found yearly, more telescopes are needed to carry out follow-up observations. Most NEOs are found at their peak brightness, meaning that these observations need to happen quickly before they fade. By using the Catalina Sky Survey Queue Manager, FRoST will be able to accept interruptions during the night and prioritize observations automatically, allowing instant follow-up observations. FRoST will help refine the orbit of these newly discovered objects while providing optical colors. We will ingest information from the NEOCP and JPL's Scout program at five minute intervals and observe newly discovered targets robotically, process the data automatically, and autonomously generate astrometry and colors. We estimate that will we provide essentially 100% recovery of objects brighter than V~20. This work was supported by the NSF MRI program as well as by NAU and Lowell Observatory.

Holographic radar imaging privacy techniques utilizing dual-frequency implementation

NASA Astrophysics Data System (ADS)

McMakin, Douglas L.; Hall, Thomas E.; Sheen, David M.

2008-04-01

Over the last 15 years, the Pacific Northwest National Laboratory has performed significant research and development activities to enhance the state of the art of holographic radar imaging systems to be used at security checkpoints for screening people for concealed threats hidden under their garments. These enhancement activities included improvements to privacy techniques to remove human features and providing automatic detection of body-worn concealed threats. The enhanced privacy and detection methods used both physical and software imaging techniques. The physical imaging techniques included polarization-diversity illumination and reception, dual-frequency implementation, and high-frequency imaging at 60 GHz. Software imaging techniques to enhance the privacy of the person under surveillance included extracting concealed threat artifacts from the imagery to automatically detect the threat. This paper will focus on physical privacy techniques using dual-frequency implementation.

Holographic Radar Imaging Privacy Techniques Utilizing Dual-Frequency Implementation

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

McMakin, Douglas L.; Hall, Thomas E.; Sheen, David M.

2008-04-18

Over the last 15 years, the Pacific Northwest National Laboratory has performed significant research and development activities to enhance the state of the art of holographic radar imaging systems to be used at security checkpoints for screening people for concealed threats hidden under their garments. These enhancement activities included improvements to privacy techniques to remove human features and providing automatic detection of body-worn concealed threats. The enhanced privacy and detection methods used both physical and software imaging techniques. The physical imaging techniques included polarization-diversity illumination and reception, dual-frequency implementation, and high-frequency imaging at 60 GHz. Software imaging techniques to enhancemore » the privacy of the person under surveillance included extracting concealed threat artifacts from the imagery to automatically detect the threat. This paper will focus on physical privacy techniques using dual-frequency implementation.« less

MASGOMAS project: building a bona-fide catalog of massive star cluster candidates

NASA Astrophysics Data System (ADS)

Herrero, Artemio; Rübke, Klaus; Ramírez Alegría, Sebastián; Garcia, Miriam; Marín-Franch, Antonio

2017-11-01

MASGOMAS (MAssive Stars in Galactic Obscured MAssive clusterS) is a project aiming at discovering OB stars in Galactic, dust enshrouded, star-forming massive clusters (Marín-Franch et al. 2009, A&A 502, 559). The project has gone through different phases of increasing automatization, that have allowed us to discover massive clusters like MASGOMAS-1 (Ramírez Alegría et al. 2012, A&A 541, A75) (with M~20,000 M⊙).

The Hidden Diversity of Flagellated Protists in Soil.

PubMed

Venter, Paul Christiaan; Nitsche, Frank; Arndt, Hartmut

2018-07-01

Protists are among the most diverse and abundant eukaryotes in soil. However, gaps between described and sequenced protist morphospecies still present a pending problem when surveying environmental samples for known species using molecular methods. The number of sequences in the molecular PR 2 database (∼130,000) is limited compared to the species richness expected (>1 million protist species) - limiting the recovery rate. This is important, since high throughput sequencing (HTS) methods are used to find associative patterns between functional traits, taxa and environmental parameters. We performed HTS to survey soil flagellates in 150 grasslands of central Europe, and tested the recovery rate of ten previously isolated and cultivated cercomonad species, among locally found diversity. We recovered sequences for reference soil flagellate species, but also a great number of their phylogenetically evaluated genetic variants, among rare and dominant taxa with presumably own biogeography. This was recorded among dominant (cercozoans, Sandona), rare (apusozoans) and a large hidden diversity of predominantly aquatic protists in soil (choanoflagellates, bicosoecids) often forming novel clades associated with uncultured environmental sequences. Evaluating the reads, instead of the OTUs that individual reads are usually clustered into, we discovered that much of this hidden diversity may be lost due to clustering. Copyright © 2018 Elsevier GmbH. All rights reserved.

The Nash Equilibrium Revisited: Chaos and Complexity Hidden in Simplicity

NASA Astrophysics Data System (ADS)

Fellman, Philip V.

The Nash Equilibrium is a much discussed, deceptively complex, method for the analysis of non-cooperative games (McLennan and Berg, 2005). If one reads many of the commonly available definitions the description of the Nash Equilibrium is deceptively simple in appearance. Modern research has discovered a number of new and important complex properties of the Nash Equilibrium, some of which remain as contemporary conundrums of extraordinary difficulty and complexity (Quint and Shubik, 1997). Among the recently discovered features which the Nash Equilibrium exhibits under various conditions are heteroclinic Hamiltonian dynamics, a very complex asymptotic structure in the context of two-player bi-matrix games and a number of computationally complex or computationally intractable features in other settings (Sato, Akiyama and Farmer, 2002). This paper reviews those findings and then suggests how they may inform various market prediction strategies.

Geodetic imaging: A new tool for Mesoamerican archaeology

NASA Astrophysics Data System (ADS)

Carter, William E.; Shrestha, Ramesh L.; Fisher, Christopher; Leisz, Stephen

2012-10-01

On 15 May 2012, Honduran President Porfirio Lobo convened a press conference to announce that researchers mapping areas of the Mosquitia region of Honduras, using airborne light detection and ranging (lidar), had discovered what appeared to be an extensive complex of archaeological ruins hidden beneath the dense canopy of rain forest that shrouds the terrain [UTL Scientific, LLC, 2012]. President Lobo released preliminary images of the ruins derived from the airborne lidar observations (Figure 1a) but withheld information about their precise location so that measures could be taken to protect and preserve this newly discovered cultural heritage. The coordinates of the ruins, determined from the lidar observations with an accuracy of a few decimeters, will enable archaeological teams to use the Global Positioning System to navigate through the dense forest directly to features of interest.

Discovering Sentinel Rules for Business Intelligence

NASA Astrophysics Data System (ADS)

Middelfart, Morten; Pedersen, Torben Bach

This paper proposes the concept of sentinel rules for multi-dimensional data that warns users when measure data concerning the external environment changes. For instance, a surge in negative blogging about a company could trigger a sentinel rule warning that revenue will decrease within two months, so a new course of action can be taken. Hereby, we expand the window of opportunity for organizations and facilitate successful navigation even though the world behaves chaotically. Since sentinel rules are at the schema level as opposed to the data level, and operate on data changes as opposed to absolute data values, we are able to discover strong and useful sentinel rules that would otherwise be hidden when using sequential pattern mining or correlation techniques. We present a method for sentinel rule discovery and an implementation of this method that scales linearly on large data volumes.

A hidden pygmy devil from the Philippines: Arulenus miae sp. nov.-a new species serendipitously discovered in an amateur Facebook post
(Tetrigidae: Discotettiginae).

PubMed

Skejo, Josip; Caballero, Joy Honezza S

2016-01-21

Arulenus miae Skejo & Caballero sp. nov. is described from Buknidon and Davao, Mindanao, the Philippines. The species was serendipitously found in an amateur photo posted in Orthoptera Facebook group by Leif Gabrielsen. Holotype and paratype are deposited in Nederlands Centrum voor Biodiversiteit in Leiden, the Netherlands. Detailed comparison with Arulenus validispinus Stål, 1877 is given. A new diagnosis of the genus and A. validispinus is given. The paper is part of the revision of the subfamily Discotettiginae. This study provides a good example of how social networks can be used as a modern tool of discovering biodiversity if the regulations of the International Code of the Zoological Nomenclature are followed. A brief insight into habitat and ecology of this rainforest and mountainous species is presented.

Hidden Markov models in automatic speech recognition

NASA Astrophysics Data System (ADS)

Wrzoskowicz, Adam

1993-11-01

This article describes a method for constructing an automatic speech recognition system based on hidden Markov models (HMMs). The author discusses the basic concepts of HMM theory and the application of these models to the analysis and recognition of speech signals. The author provides algorithms which make it possible to train the ASR system and recognize signals on the basis of distinct stochastic models of selected speech sound classes. The author describes the specific components of the system and the procedures used to model and recognize speech. The author discusses problems associated with the choice of optimal signal detection and parameterization characteristics and their effect on the performance of the system. The author presents different options for the choice of speech signal segments and their consequences for the ASR process. The author gives special attention to the use of lexical, syntactic, and semantic information for the purpose of improving the quality and efficiency of the system. The author also describes an ASR system developed by the Speech Acoustics Laboratory of the IBPT PAS. The author discusses the results of experiments on the effect of noise on the performance of the ASR system and describes methods of constructing HMM's designed to operate in a noisy environment. The author also describes a language for human-robot communications which was defined as a complex multilevel network from an HMM model of speech sounds geared towards Polish inflections. The author also added mandatory lexical and syntactic rules to the system for its communications vocabulary.

Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks

PubMed Central

Jang, Hojin; Plis, Sergey M.; Calhoun, Vince D.; Lee, Jong-Hwan

2016-01-01

Feedforward deep neural networks (DNN), artificial neural networks with multiple hidden layers, have recently demonstrated a record-breaking performance in multiple areas of applications in computer vision and speech processing. Following the success, DNNs have been applied to neuroimaging modalities including functional/structural magnetic resonance imaging (MRI) and positron-emission tomography data. However, no study has explicitly applied DNNs to 3D whole-brain fMRI volumes and thereby extracted hidden volumetric representations of fMRI that are discriminative for a task performed as the fMRI volume was acquired. Our study applied fully connected feedforward DNN to fMRI volumes collected in four sensorimotor tasks (i.e., left-hand clenching, right-hand clenching, auditory attention, and visual stimulus) undertaken by 12 healthy participants. Using a leave-one-subject-out cross-validation scheme, a restricted Boltzmann machine-based deep belief network was pretrained and used to initialize weights of the DNN. The pretrained DNN was fine-tuned while systematically controlling weight-sparsity levels across hidden layers. Optimal weight-sparsity levels were determined from a minimum validation error rate of fMRI volume classification. Minimum error rates (mean ± standard deviation; %) of 6.9 (± 3.8) were obtained from the three-layer DNN with the sparsest condition of weights across the three hidden layers. These error rates were even lower than the error rates from the single-layer network (9.4 ± 4.6) and the two-layer network (7.4 ± 4.1). The estimated DNN weights showed spatial patterns that are remarkably task-specific, particularly in the higher layers. The output values of the third hidden layer represented distinct patterns/codes of the 3D whole-brain fMRI volume and encoded the information of the tasks as evaluated from representational similarity analysis. Our reported findings show the ability of the DNN to classify a single fMRI volume based on the extraction of hidden representations of fMRI volumes associated with tasks across multiple hidden layers. Our study may be beneficial to the automatic classification/diagnosis of neuropsychiatric and neurological diseases and prediction of disease severity and recovery in (pre-) clinical settings using fMRI volumes without requiring an estimation of activation patterns or ad hoc statistical evaluation. PMID:27079534

Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks.

PubMed

Jang, Hojin; Plis, Sergey M; Calhoun, Vince D; Lee, Jong-Hwan

2017-01-15

Feedforward deep neural networks (DNNs), artificial neural networks with multiple hidden layers, have recently demonstrated a record-breaking performance in multiple areas of applications in computer vision and speech processing. Following the success, DNNs have been applied to neuroimaging modalities including functional/structural magnetic resonance imaging (MRI) and positron-emission tomography data. However, no study has explicitly applied DNNs to 3D whole-brain fMRI volumes and thereby extracted hidden volumetric representations of fMRI that are discriminative for a task performed as the fMRI volume was acquired. Our study applied fully connected feedforward DNN to fMRI volumes collected in four sensorimotor tasks (i.e., left-hand clenching, right-hand clenching, auditory attention, and visual stimulus) undertaken by 12 healthy participants. Using a leave-one-subject-out cross-validation scheme, a restricted Boltzmann machine-based deep belief network was pretrained and used to initialize weights of the DNN. The pretrained DNN was fine-tuned while systematically controlling weight-sparsity levels across hidden layers. Optimal weight-sparsity levels were determined from a minimum validation error rate of fMRI volume classification. Minimum error rates (mean±standard deviation; %) of 6.9 (±3.8) were obtained from the three-layer DNN with the sparsest condition of weights across the three hidden layers. These error rates were even lower than the error rates from the single-layer network (9.4±4.6) and the two-layer network (7.4±4.1). The estimated DNN weights showed spatial patterns that are remarkably task-specific, particularly in the higher layers. The output values of the third hidden layer represented distinct patterns/codes of the 3D whole-brain fMRI volume and encoded the information of the tasks as evaluated from representational similarity analysis. Our reported findings show the ability of the DNN to classify a single fMRI volume based on the extraction of hidden representations of fMRI volumes associated with tasks across multiple hidden layers. Our study may be beneficial to the automatic classification/diagnosis of neuropsychiatric and neurological diseases and prediction of disease severity and recovery in (pre-) clinical settings using fMRI volumes without requiring an estimation of activation patterns or ad hoc statistical evaluation. Copyright © 2016 Elsevier Inc. All rights reserved.

Utilization of Automatic Tagging Using Web Information to Datamining

NASA Astrophysics Data System (ADS)

Sugimura, Hiroshi; Matsumoto, Kazunori

This paper proposes a data annotation system using the automatic tagging approach. Although annotations of data are useful for deep analysis and mining of it, the cost of providing them becomes huge in most of the cases. In order to solve this problem, we develop a semi-automatic method that consists of two stages. In the first stage, it searches the Web space for relating information, and discovers candidates of effective annotations. The second stage uses knowledge of a human user. The candidates are investigated and refined by the user, and then they become annotations. We in this paper focus on time-series data, and show effectiveness of a GUI tool that supports the above process.

Discovering hidden biodiversity: the use of complementary monitoring of fish diet based on DNA barcoding in freshwater ecosystems.

PubMed

Jo, Hyunbin; Ventura, Marc; Vidal, Nicolas; Gim, Jeong-Soo; Buchaca, Teresa; Barmuta, Leon A; Jeppesen, Erik; Joo, Gea-Jae

2016-01-01

Ecological monitoring contributes to the understanding of complex ecosystem functions. The diets of fish reflect the surrounding environment and habitats and may, therefore, act as useful integrating indicators of environmental status. It is, however, often difficult to visually identify items in gut contents to species level due to digestion of soft-bodied prey beyond visual recognition, but new tools rendering this possible are now becoming available. We used a molecular approach to determine the species identities of consumed diet items of an introduced generalist feeder, brown trout (Salmo trutta), in 10 Tasmanian lakes and compared the results with those obtained from visual quantification of stomach contents. We obtained 44 unique taxa (OTUs) belonging to five phyla, including seven classes, using the barcode of life approach from cytochrome oxidase I (COI). Compared with visual quantification, DNA analysis showed greater accuracy, yielding a 1.4-fold higher number of OTUs. Rarefaction curve analysis showed saturation of visually inspected taxa, while the curves from the DNA barcode did not saturate. The OTUs with the highest proportions of haplotypes were the families of terrestrial insects Formicidae, Chrysomelidae, and Torbidae and the freshwater Chironomidae. Haplotype occurrence per lake was negatively correlated with lake depth and transparency. Nearly all haplotypes were only found in one fish gut from a single lake. Our results indicate that DNA barcoding of fish diets is a useful and complementary method for discovering hidden biodiversity.

ODISEES Availability and Feedback Request

Atmospheric Science Data Center

2014-09-06

... As a follow-up Action from the Atmospheric Science Data Center (ASDC) User Working Group (UWG) held on 24-25 June, we are ... for a common language to describe scientific terms so that a computer can scour the internet, automatically discover relevant information ...

Automatic discovery of the communication network topology for building a supercomputer model

NASA Astrophysics Data System (ADS)

Sobolev, Sergey; Stefanov, Konstantin; Voevodin, Vadim

2016-10-01

The Research Computing Center of Lomonosov Moscow State University is developing the Octotron software suite for automatic monitoring and mitigation of emergency situations in supercomputers so as to maximize hardware reliability. The suite is based on a software model of the supercomputer. The model uses a graph to describe the computing system components and their interconnections. One of the most complex components of a supercomputer that needs to be included in the model is its communication network. This work describes the proposed approach for automatically discovering the Ethernet communication network topology in a supercomputer and its description in terms of the Octotron model. This suite automatically detects computing nodes and switches, collects information about them and identifies their interconnections. The application of this approach is demonstrated on the "Lomonosov" and "Lomonosov-2" supercomputers.

EMG-based speech recognition using hidden markov models with global control variables.

PubMed

Lee, Ki-Seung

2008-03-01

It is well known that a strong relationship exists between human voices and the movement of articulatory facial muscles. In this paper, we utilize this knowledge to implement an automatic speech recognition scheme which uses solely surface electromyogram (EMG) signals. The sequence of EMG signals for each word is modelled by a hidden Markov model (HMM) framework. The main objective of the work involves building a model for state observation density when multichannel observation sequences are given. The proposed model reflects the dependencies between each of the EMG signals, which are described by introducing a global control variable. We also develop an efficient model training method, based on a maximum likelihood criterion. In a preliminary study, 60 isolated words were used as recognition variables. EMG signals were acquired from three articulatory facial muscles. The findings indicate that such a system may have the capacity to recognize speech signals with an accuracy of up to 87.07%, which is superior to the independent probabilistic model.

Quantitative evaluation of hidden defects in cast iron components using ultrasound activated lock-in vibrothermography.

PubMed

Montanini, R; Freni, F; Rossi, G L

2012-09-01

This paper reports one of the first experimental results on the application of ultrasound activated lock-in vibrothermography for quantitative assessment of buried flaws in complex cast parts. The use of amplitude modulated ultrasonic heat generation allowed selective response of defective areas within the part, as the defect itself is turned into a local thermal wave emitter. Quantitative evaluation of hidden damages was accomplished by estimating independently both the area and the depth extension of the buried flaws, while x-ray 3D computed tomography was used as reference for sizing accuracy assessment. To retrieve flaw's area, a simple yet effective histogram-based phase image segmentation algorithm with automatic pixels classification has been developed. A clear correlation was found between the thermal (phase) signature measured by the infrared camera on the target surface and the actual mean cross-section area of the flaw. Due to the very fast cycle time (<30 s/part), the method could potentially be applied for 100% quality control of casting components.

Maximum mutual information estimation of a simplified hidden MRF for offline handwritten Chinese character recognition

NASA Astrophysics Data System (ADS)

Xiong, Yan; Reichenbach, Stephen E.

1999-01-01

Understanding of hand-written Chinese characters is at such a primitive stage that models include some assumptions about hand-written Chinese characters that are simply false. So Maximum Likelihood Estimation (MLE) may not be an optimal method for hand-written Chinese characters recognition. This concern motivates the research effort to consider alternative criteria. Maximum Mutual Information Estimation (MMIE) is an alternative method for parameter estimation that does not derive its rationale from presumed model correctness, but instead examines the pattern-modeling problem in automatic recognition system from an information- theoretic point of view. The objective of MMIE is to find a set of parameters in such that the resultant model allows the system to derive from the observed data as much information as possible about the class. We consider MMIE for recognition of hand-written Chinese characters using on a simplified hidden Markov Random Field. MMIE provides improved performance improvement over MLE in this application.

Passive acoustic leak detection for sodium cooled fast reactors using hidden Markov models

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

Riber Marklund, A.; Kishore, S.; Prakash, V.

2015-07-01

Acoustic leak detection for steam generators of sodium fast reactors have been an active research topic since the early 1970's and several methods have been tested over the years. Inspired by its success in the field of automatic speech recognition, we here apply hidden Markov models (HMM) in combination with Gaussian mixture models (GMM) to the problem. To achieve this, we propose a new feature calculation scheme, based on the temporal evolution of the power spectral density (PSD) of the signal. Using acoustic signals recorded during steam/water injection experiments done at the Indira Gandhi Centre for Atomic Research (IGCAR), themore » proposed method is tested. We perform parametric studies on the HMM+GMM model size and demonstrate that the proposed method a) performs well without a priori knowledge of injection noise, b) can incorporate several noise models and c) has an output distribution that simplifies false alarm rate control. (authors)« less

Phenomenology of pure-gauge hidden valleys at hadron colliders

NASA Astrophysics Data System (ADS)

Juknevich, Jose E.

Expectations for new physics at the LHC have been greatly influenced by the Hierarchy problem of electroweak symmetry breaking. However, there are reasons to believe that the LHC may still discover new physics, but not directly related to the resolution of the Hierarchy problem. To ensure that such a physics does not go undiscovered requires precise understanding of how new phenomena will reveal themselves in the current and future generation of particle-physics experiments. Given this fact it seems sensible to explore other approaches to this problem; we study three alternatives here. In this thesis I argue for the plausibility that the standard model is coupled, through new massive charged or colored particles, to a hidden sector whose low energy dynamics is controlled by a pure Yang-Mills theory, with no light matter. Such a sector would have numerous metastable "hidden glueballs" built from the hidden gluons. These states would decay to particles of the standard model. I consider the phenomenology of this scenario, and find formulas for the lifetimes and branching ratios of the most important of these states. The dominant decays are to two standard model gauge bosons or to fermion-antifermion pairs, or by radiative decays with photon or Higgs emission, leading to jet- and photon-rich signals, and some occasional leptons. The presence of effective operators of different mass dimensions, often competing with each other, together with a great diversity of states, leads to a great variability in the lifetimes and decay modes of the hidden glueballs. I find that most of the operators considered in this work are not heavily constrained by precision electroweak physics, therefore leaving plenty of room in the parameter space to be explored by the future experiments at the LHC. Finally, I discuss several issues on the phenomenology of the new massive particles as well as an outlook for experimental searches.

Hamiltonian dynamics of a quantum of space: hidden symmetries and spectrum of the volume operator, and discrete orthogonal polynomials

NASA Astrophysics Data System (ADS)

Aquilanti, Vincenzo; Marinelli, Dimitri; Marzuoli, Annalisa

2013-05-01

The action of the quantum mechanical volume operator, introduced in connection with a symmetric representation of the three-body problem and recently recognized to play a fundamental role in discretized quantum gravity models, can be given as a second-order difference equation which, by a complex phase change, we turn into a discrete Schrödinger-like equation. The introduction of discrete potential-like functions reveals the surprising crucial role here of hidden symmetries, first discovered by Regge for the quantum mechanical 6j symbols; insight is provided into the underlying geometric features. The spectrum and wavefunctions of the volume operator are discussed from the viewpoint of the Hamiltonian evolution of an elementary ‘quantum of space’, and a transparent asymptotic picture of the semiclassical and classical regimes emerges. The definition of coordinates adapted to the Regge symmetry is exploited for the construction of a novel set of discrete orthogonal polynomials, characterizing the oscillatory components of torsion-like modes.

The importance of situation-specific encodings: analysis of a simple connectionist model of letter transposition effects

NASA Astrophysics Data System (ADS)

Fang, Shin-Yi; Smith, Garrett; Tabor, Whitney

2018-04-01

This paper analyses a three-layer connectionist network that solves a translation-invariance problem, offering a novel explanation for transposed letter effects in word reading. Analysis of the hidden unit encodings provides insight into two central issues in cognitive science: (1) What is the novelty of claims of "modality-specific" encodings? and (2) How can a learning system establish a complex internal structure needed to solve a problem? Although these topics (embodied cognition and learnability) are often treated separately, we find a close relationship between them: modality-specific features help the network discover an abstract encoding by causing it to break the initial symmetries of the hidden units in an effective way. While this neural model is extremely simple compared to the human brain, our results suggest that neural networks need not be black boxes and that carefully examining their encoding behaviours may reveal how they differ from classical ideas about the mind-world relationship.

Conserved patterns hidden within group A Streptococcus M protein hypervariability recognize human C4b-binding protein

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

Buffalo, Cosmo Z.; Bahn-Suh, Adrian J.; Hirakis, Sophia P.

No vaccine exists against group A Streptococcus (GAS), a leading cause of worldwide morbidity and mortality. A severe hurdle is the hypervariability of its major antigen, the M protein, with >200 different M types known. Neutralizing antibodies typically recognize M protein hypervariable regions (HVRs) and confer narrow protection. In stark contrast, human C4b-binding protein (C4BP), which is recruited to the GAS surface to block phagocytic killing, interacts with a remarkably large number of M protein HVRs (apparently ~90%). Such broad recognition is rare, and we discovered a unique mechanism for this through the structure determination of four sequence-diverse M proteinsmore » in complexes with C4BP. The structures revealed a uniform and tolerant ‘reading head’ in C4BP, which detected conserved sequence patterns hidden within hypervariability. Our results open up possibilities for rational therapies that target the M–C4BP interaction, and also inform a path towards vaccine design.« less

Identifying influential user communities on the social network

NASA Astrophysics Data System (ADS)

Hu, Weishu; Gong, Zhiguo; Hou U, Leong; Guo, Jingzhi

2015-10-01

Nowadays social network services have been popularly used in electronic commerce systems. Users on the social network can develop different relationships based on their common interests and activities. In order to promote the business, it is interesting to explore hidden relationships among users developed on the social network. Such knowledge can be used to locate target users for different advertisements and to provide effective product recommendations. In this paper, we define and study a novel community detection problem that is to discover the hidden community structure in large social networks based on their common interests. We observe that the users typically pay more attention to those users who share similar interests, which enable a way to partition the users into different communities according to their common interests. We propose two algorithms to detect influential communities using common interests in large social networks efficiently and effectively. We conduct our experimental evaluation using a data set from Epinions, which demonstrates that our method achieves 4-11.8% accuracy improvement over the state-of-the-art method.

Hierarchically Structured Non-Intrusive Sign Language Recognition. Chapter 2

NASA Technical Reports Server (NTRS)

Zieren, Jorg; Zieren, Jorg; Kraiss, Karl-Friedrich

2007-01-01

This work presents a hierarchically structured approach at the nonintrusive recognition of sign language from a monocular frontal view. Robustness is achieved through sophisticated localization and tracking methods, including a combined EM/CAMSHIFT overlap resolution procedure and the parallel pursuit of multiple hypotheses about hands position and movement. This allows handling of ambiguities and automatically corrects tracking errors. A biomechanical skeleton model and dynamic motion prediction using Kalman filters represents high level knowledge. Classification is performed by Hidden Markov Models. 152 signs from German sign language were recognized with an accuracy of 97.6%.

Protecting the axion with local baryon number

NASA Astrophysics Data System (ADS)

Duerr, Michael; Schmidt-Hoberg, Kai; Unwin, James

2018-05-01

The Peccei-Quinn (PQ) solution to the Strong CP Problem is expected to fail unless the global symmetry U(1)PQ is protected from Planck-scale operators up to high mass dimension. Suitable protection can be achieved if the PQ symmetry is an automatic consequence of some gauge symmetry. We highlight that if baryon number is promoted to a gauge symmetry, the exotic fermions needed for anomaly cancellation can elegantly provide an implementation of the Kim-Shifman-Vainshtein-Zakharov 'hidden axion' mechanism with a PQ symmetry protected from Planck-scale physics.

Accelerometry-based classification of human activities using Markov modeling.

PubMed

Mannini, Andrea; Sabatini, Angelo Maria

2011-01-01

Accelerometers are a popular choice as body-motion sensors: the reason is partly in their capability of extracting information that is useful for automatically inferring the physical activity in which the human subject is involved, beside their role in feeding biomechanical parameters estimators. Automatic classification of human physical activities is highly attractive for pervasive computing systems, whereas contextual awareness may ease the human-machine interaction, and in biomedicine, whereas wearable sensor systems are proposed for long-term monitoring. This paper is concerned with the machine learning algorithms needed to perform the classification task. Hidden Markov Model (HMM) classifiers are studied by contrasting them with Gaussian Mixture Model (GMM) classifiers. HMMs incorporate the statistical information available on movement dynamics into the classification process, without discarding the time history of previous outcomes as GMMs do. An example of the benefits of the obtained statistical leverage is illustrated and discussed by analyzing two datasets of accelerometer time series.

An investigation of prior knowledge in Automatic Music Transcription systems.

PubMed

Cazau, Dorian; Revillon, Guillaume; Krywyk, Julien; Adam, Olivier

2015-10-01

Automatic transcription of music is a long-studied research field with many operational systems available commercially. In this paper, a generic transcription system able to host various prior knowledge parameters has been developed, followed by an in-depth investigation of their impact on music transcription. Explicit links between musical knowledge and algorithmic formalism have been made. Musical knowledge covers classes of timbre, musicology, and playing style of an instrument repertoire. An evaluation sound corpus gathering musical pieces played by human performers from three different instrument repertoires, namely, classical piano, steel-string acoustic guitar, and the marovany zither from Madagascar, has been developed. The different components of musical knowledge have been successively incorporated in a complete transcription system, consisting mainly of a Probabilistic Latent Component Analysis algorithm post-processed with a Hidden Markov Model, and their impact on transcription results have been comparatively evaluated.

Hidden messenger revealed in Hawking radiation: A resolution to the paradox of black hole information loss

NASA Astrophysics Data System (ADS)

Zhang, Baocheng; Cai, Qing-yu; You, Li; Zhan, Ming-sheng

2009-05-01

Using standard statistical method, we discover the existence of correlations among Hawking radiations (of tunneled particles) from a black hole. The information carried by such correlations is quantified by mutual information between sequential emissions. Through a careful counting of the entropy taken out by the emitted particles, we show that the black hole radiation as tunneling is an entropy conservation process. While information is leaked out through the radiation, the total entropy is conserved. Thus, we conclude the black hole evaporation process is unitary.

Results of a Formal Methods Demonstration Project

NASA Technical Reports Server (NTRS)

Kelly, J.; Covington, R.; Hamilton, D.

1994-01-01

This paper describes the results of a cooperative study conducted by a team of researchers in formal methods at three NASA Centers to demonstrate FM techniques and to tailor them to critical NASA software systems. This pilot project applied FM to an existing critical software subsystem, the Shuttle's Jet Select subsystem (Phase I of an ongoing study). The present study shows that FM can be used successfully to uncover hidden issues in a highly critical and mature Functional Subsystem Software Requirements (FSSR) specification which are very difficult to discover by traditional means.

An Exploration of Latent Structure in Observational Huntington’s Disease Studies

PubMed Central

Ghosh, Soumya; Sun, Zhaonan; Li, Ying; Cheng, Yu; Mohan, Amrita; Sampaio, Cristina; Hu, Jianying

2017-01-01

Huntington’s disease (HD) is a monogenic neurodegenerative disorder characterized by the progressive decay of motor and cognitive abilities accompanied by psychiatric episodes. Tracking and modeling the progression of the multi-faceted clinical symptoms of HD is a challenging problem that has important implications for staging of HD patients and the development of improved enrollment criteria for future HD studies and trials. In this paper, we describe the first steps towards this goal. We begin by curating data from four recent observational HD studies, each containing a diverse collection of clinical assessments. The resulting dataset is unprecedented in size and contains data from 19,269 study participants. By analyzing this large dataset, we are able to discover hidden low dimensional structure in the data that correlates well with surrogate measures of HD progression. The discovered structures are promising candidates for future consumption by downstream statistical HD progression models. PMID:28815114

A Hybrid Computational Method for the Discovery of Novel Reproduction-Related Genes

PubMed Central

Chen, Lei; Chu, Chen; Kong, Xiangyin; Huang, Guohua; Huang, Tao; Cai, Yu-Dong

2015-01-01

Uncovering the molecular mechanisms underlying reproduction is of great importance to infertility treatment and to the generation of healthy offspring. In this study, we discovered novel reproduction-related genes with a hybrid computational method, integrating three different types of method, which offered new clues for further reproduction research. This method was first executed on a weighted graph, constructed based on known protein-protein interactions, to search the shortest paths connecting any two known reproduction-related genes. Genes occurring in these paths were deemed to have a special relationship with reproduction. These newly discovered genes were filtered with a randomization test. Then, the remaining genes were further selected according to their associations with known reproduction-related genes measured by protein-protein interaction score and alignment score obtained by BLAST. The in-depth analysis of the high confidence novel reproduction genes revealed hidden mechanisms of reproduction and provided guidelines for further experimental validations. PMID:25768094

A hybrid computational method for the discovery of novel reproduction-related genes.

PubMed

Chen, Lei; Chu, Chen; Kong, Xiangyin; Huang, Guohua; Huang, Tao; Cai, Yu-Dong

2015-01-01

Uncovering the molecular mechanisms underlying reproduction is of great importance to infertility treatment and to the generation of healthy offspring. In this study, we discovered novel reproduction-related genes with a hybrid computational method, integrating three different types of method, which offered new clues for further reproduction research. This method was first executed on a weighted graph, constructed based on known protein-protein interactions, to search the shortest paths connecting any two known reproduction-related genes. Genes occurring in these paths were deemed to have a special relationship with reproduction. These newly discovered genes were filtered with a randomization test. Then, the remaining genes were further selected according to their associations with known reproduction-related genes measured by protein-protein interaction score and alignment score obtained by BLAST. The in-depth analysis of the high confidence novel reproduction genes revealed hidden mechanisms of reproduction and provided guidelines for further experimental validations.

Hidden lesions of the posterior horn of the medial meniscus: a systematic arthroscopic exploration of the concealed portion of the knee.

PubMed

Sonnery-Cottet, Bertrand; Conteduca, Jacopo; Thaunat, Mathieu; Gunepin, François Xavier; Seil, Romain

2014-04-01

Anterior cruciate ligament (ACL) tears are frequently associated with meniscal lesions. Despite improvements in meniscal repair techniques, failure rates remain significant, especially for the posterior horn of the medial meniscus. To determine whether a systematic arthroscopic exploration of the posterior horn of the medial meniscus with an additional posteromedial portal is useful to identify otherwise unrecognized lesions. Case series; Level of evidence, 4. In a consecutive series of 302 ACL reconstructions, a systematic arthroscopic exploration of the posterior horn of the medial meniscus was performed. The first stage of the exploration was achieved through anterior visualization via a standard anterolateral portal. In the second stage, the posterior horn of the medial meniscus was visualized posteriorly via the anterolateral portal with the scope positioned deep in the notch. In the third stage, the posterior horn was probed through an additional posteromedial portal. A χ2 test and logistic regression analysis were performed to determine if the time from injury to surgery was associated with the meniscal tear pattern. A medial meniscal tear was diagnosed in 125 of the 302 patients (41.4%). Seventy-five lesions (60%) located in the meniscal body were diagnosed at the first stage of the arthroscopic exploration. Fifty lesions located in the ramp area were diagnosed: 29 (23.2%) at the second stage and 21 lesions (16.8%) at the third stage after minimal debridement of the superficial soft tissue layer. The latter type of lesion is called a "hidden lesion." Altogether, the prevalence of ramp lesions in this population was 40%. Meniscal body lesions (odds ratio, 2.6; 95% confidence interval, 1.18-5.18; P < .02) were found to be significantly correlated with a longer delay between injury and surgery. Posterior visualization and posteromedial probing of the posterior horn of the medial meniscus can help in discovering a higher rate of lesions that could be easily missed through a standard anterior exploration. In numerous cases, these lesions were "hidden" under a membrane-like tissue and were discovered after minimal debridement through a posteromedial portal.

Automated generation of massive image knowledge collections using Microsoft Live Labs Pivot to promote neuroimaging and translational research.

PubMed

Viangteeravat, Teeradache; Anyanwu, Matthew N; Ra Nagisetty, Venkateswara; Kuscu, Emin

2011-07-15

Massive datasets comprising high-resolution images, generated in neuro-imaging studies and in clinical imaging research, are increasingly challenging our ability to analyze, share, and filter such images in clinical and basic translational research. Pivot collection exploratory analysis provides each user the ability to fully interact with the massive amounts of visual data to fully facilitate sufficient sorting, flexibility and speed to fluidly access, explore or analyze the massive image data sets of high-resolution images and their associated meta information, such as neuro-imaging databases from the Allen Brain Atlas. It is used in clustering, filtering, data sharing and classifying of the visual data into various deep zoom levels and meta information categories to detect the underlying hidden pattern within the data set that has been used. We deployed prototype Pivot collections using the Linux CentOS running on the Apache web server. We also tested the prototype Pivot collections on other operating systems like Windows (the most common variants) and UNIX, etc. It is demonstrated that the approach yields very good results when compared with other approaches used by some researchers for generation, creation, and clustering of massive image collections such as the coronal and horizontal sections of the mouse brain from the Allen Brain Atlas. Pivot visual analytics was used to analyze a prototype of dataset Dab2 co-expressed genes from the Allen Brain Atlas. The metadata along with high-resolution images were automatically extracted using the Allen Brain Atlas API. It is then used to identify the hidden information based on the various categories and conditions applied by using options generated from automated collection. A metadata category like chromosome, as well as data for individual cases like sex, age, and plan attributes of a particular gene, is used to filter, sort and to determine if there exist other genes with a similar characteristics to Dab2. And online access to the mouse brain pivot collection can be viewed using the link http://edtech-dev.uthsc.edu/CTSI/teeDev1/unittest/PaPa/collection.html (user name: tviangte and password: demome) Our proposed algorithm has automated the creation of large image Pivot collections; this will enable investigators of clinical research projects to easily and quickly analyse the image collections through a perspective that is useful for making critical decisions about the image patterns discovered.

Memory Lane Is a Two-Way Street.

ERIC Educational Resources Information Center

Sprenger, Marilee

1998-01-01

Our memories are not necessarily "bad," but stored in different areas. By understanding the five memory lanes (semantic, episodic, procedural, automatic, and emotional), a high school English teacher discovered why her students could not do fractions (to calculate grades) in English class. Paper-and-pencil tests can be redesigned to assess memory…

Contextual Text Mining

ERIC Educational Resources Information Center

Mei, Qiaozhu

2009-01-01

With the dramatic growth of text information, there is an increasing need for powerful text mining systems that can automatically discover useful knowledge from text. Text is generally associated with all kinds of contextual information. Those contexts can be explicit, such as the time and the location where a blog article is written, and the…

Mentorship in Practice Program: An Effective School-Based Strategy

ERIC Educational Resources Information Center

Bradford, Brent; Kell, Shannon; Forsberg, Nick

2016-01-01

The development of fundamental movement skills is essential in quality physical education. It has become widely accepted that school-age children who fail to reach the automatic phase in fundamental movement-skill development may choose physically inactive and unhealthy lifestyles. Therefore, physical educators must continue to discover ways to…

AFLOW: An Automatic Framework for High-throughput Materials Discovery

DTIC Science & Technology

2011-11-14

computational ma- terials HT applications include combinatorial discov- ery of superconductors [1], Pareto-optimal search for alloys and catalysts [14, 15...Ducastelle, D. Gratias, Physica A 128 (1984) 334–350. [37] D. de Fontaine, Cluster Approach to Order- disorder Transfor- mations in Alloys, volume 47 of

HOW TO LEARN AN UNWRITTEN LANGUAGE.

ERIC Educational Resources Information Center

GUDSCHINSKY, SARAH C.

A PRACTICAL GUIDE FOR THE ANTHROPOLOGY STUDENT CONFRONTED WITH LEARNING A LANGUAGE IN THE FIELD, THIS BOOK FOCUSES ON ACQUIRING EVERYDAY CONVERSATION RATHER THAN DIFFICULT LINGUISTIC PROBLEMS. THE FORM AND CONTENT ARE BASED ON THE FOLLOWING BASIC PREMISES--(1) LEARNING A LANGUAGE CONSISTS OF DISCOVERING AND CONTROLLING AS AUTOMATIC HABITS THE…

Mining Rare Associations between Biological Ontologies

PubMed Central

Benites, Fernando; Simon, Svenja; Sapozhnikova, Elena

2014-01-01

The constantly increasing volume and complexity of available biological data requires new methods for their management and analysis. An important challenge is the integration of information from different sources in order to discover possible hidden relations between already known data. In this paper we introduce a data mining approach which relates biological ontologies by mining cross and intra-ontology pairwise generalized association rules. Its advantage is sensitivity to rare associations, for these are important for biologists. We propose a new class of interestingness measures designed for hierarchically organized rules. These measures allow one to select the most important rules and to take into account rare cases. They favor rules with an actual interestingness value that exceeds the expected value. The latter is calculated taking into account the parent rule. We demonstrate this approach by applying it to the analysis of data from Gene Ontology and GPCR databases. Our objective is to discover interesting relations between two different ontologies or parts of a single ontology. The association rules that are thus discovered can provide the user with new knowledge about underlying biological processes or help improve annotation consistency. The obtained results show that produced rules represent meaningful and quite reliable associations. PMID:24404165

Mining rare associations between biological ontologies.

PubMed

Benites, Fernando; Simon, Svenja; Sapozhnikova, Elena

2014-01-01

The constantly increasing volume and complexity of available biological data requires new methods for their management and analysis. An important challenge is the integration of information from different sources in order to discover possible hidden relations between already known data. In this paper we introduce a data mining approach which relates biological ontologies by mining cross and intra-ontology pairwise generalized association rules. Its advantage is sensitivity to rare associations, for these are important for biologists. We propose a new class of interestingness measures designed for hierarchically organized rules. These measures allow one to select the most important rules and to take into account rare cases. They favor rules with an actual interestingness value that exceeds the expected value. The latter is calculated taking into account the parent rule. We demonstrate this approach by applying it to the analysis of data from Gene Ontology and GPCR databases. Our objective is to discover interesting relations between two different ontologies or parts of a single ontology. The association rules that are thus discovered can provide the user with new knowledge about underlying biological processes or help improve annotation consistency. The obtained results show that produced rules represent meaningful and quite reliable associations.

BioEve Search: A Novel Framework to Facilitate Interactive Literature Search

PubMed Central

Ahmed, Syed Toufeeq; Davulcu, Hasan; Tikves, Sukru; Nair, Radhika; Zhao, Zhongming

2012-01-01

Background. Recent advances in computational and biological methods in last two decades have remarkably changed the scale of biomedical research and with it began the unprecedented growth in both the production of biomedical data and amount of published literature discussing it. An automated extraction system coupled with a cognitive search and navigation service over these document collections would not only save time and effort, but also pave the way to discover hitherto unknown information implicitly conveyed in the texts. Results. We developed a novel framework (named “BioEve”) that seamlessly integrates Faceted Search (Information Retrieval) with Information Extraction module to provide an interactive search experience for the researchers in life sciences. It enables guided step-by-step search query refinement, by suggesting concepts and entities (like genes, drugs, and diseases) to quickly filter and modify search direction, and thereby facilitating an enriched paradigm where user can discover related concepts and keywords to search while information seeking. Conclusions. The BioEve Search framework makes it easier to enable scalable interactive search over large collection of textual articles and to discover knowledge hidden in thousands of biomedical literature articles with ease. PMID:22693501

Discovering System Health Anomalies Using Data Mining Techniques

NASA Technical Reports Server (NTRS)

Sriastava, Ashok, N.

2005-01-01

We present a data mining framework for the analysis and discovery of anomalies in high-dimensional time series of sensor measurements that would be found in an Integrated System Health Monitoring system. We specifically treat the problem of discovering anomalous features in the time series that may be indicative of a system anomaly, or in the case of a manned system, an anomaly due to the human. Identification of these anomalies is crucial to building stable, reusable, and cost-efficient systems. The framework consists of an analysis platform and new algorithms that can scale to thousands of sensor streams to discovers temporal anomalies. We discuss the mathematical framework that underlies the system and also describe in detail how this framework is general enough to encompass both discrete and continuous sensor measurements. We also describe a new set of data mining algorithms based on kernel methods and hidden Markov models that allow for the rapid assimilation, analysis, and discovery of system anomalies. We then describe the performance of the system on a real-world problem in the aircraft domain where we analyze the cockpit data from aircraft as well as data from the aircraft propulsion, control, and guidance systems. These data are discrete and continuous sensor measurements and are dealt with seamlessly in order to discover anomalous flights. We conclude with recommendations that describe the tradeoffs in building an integrated scalable platform for robust anomaly detection in ISHM applications.

Japan's 2014 General Election: Political Bots, Right-Wing Internet Activism, and Prime Minister Shinzō Abe's Hidden Nationalist Agenda.

PubMed

Schäfer, Fabian; Evert, Stefan; Heinrich, Philipp

2017-12-01

In this article, we present results on the identification and behavioral analysis of social bots in a sample of 542,584 Tweets, collected before and after Japan's 2014 general election. Typical forms of bot activity include massive Retweeting and repeated posting of (nearly) the same message, sometimes used in combination. We focus on the second method and present (1) a case study on several patterns of bot activity, (2) methodological considerations on the automatic identification of such patterns and the prerequisite near-duplicate detection, and (3) we give qualitative insights into the purposes behind the usage of social/political bots. We argue that it was in the latency of the semi-public sphere of social media-and not in the visible or manifest public sphere (official campaign platform, mass media)-where Shinzō Abe's hidden nationalist agenda interlocked and overlapped with the one propagated by organizations such as Nippon Kaigi and Internet right-wingers (netto uyo) during the election campaign, the latter potentially forming an enormous online support army of Abe's agenda.

Japan's 2014 General Election: Political Bots, Right-Wing Internet Activism, and Prime Minister Shinzō Abe's Hidden Nationalist Agenda

PubMed Central

Schäfer, Fabian; Evert, Stefan; Heinrich, Philipp

2017-01-01

Abstract In this article, we present results on the identification and behavioral analysis of social bots in a sample of 542,584 Tweets, collected before and after Japan's 2014 general election. Typical forms of bot activity include massive Retweeting and repeated posting of (nearly) the same message, sometimes used in combination. We focus on the second method and present (1) a case study on several patterns of bot activity, (2) methodological considerations on the automatic identification of such patterns and the prerequisite near-duplicate detection, and (3) we give qualitative insights into the purposes behind the usage of social/political bots. We argue that it was in the latency of the semi-public sphere of social media—and not in the visible or manifest public sphere (official campaign platform, mass media)—where Shinzō Abe's hidden nationalist agenda interlocked and overlapped with the one propagated by organizations such as Nippon Kaigi and Internet right-wingers (netto uyo) during the election campaign, the latter potentially forming an enormous online support army of Abe's agenda. PMID:29182493

Forecasting daily streamflow using online sequential extreme learning machines

NASA Astrophysics Data System (ADS)

Lima, Aranildo R.; Cannon, Alex J.; Hsieh, William W.

2016-06-01

While nonlinear machine methods have been widely used in environmental forecasting, in situations where new data arrive continually, the need to make frequent model updates can become cumbersome and computationally costly. To alleviate this problem, an online sequential learning algorithm for single hidden layer feedforward neural networks - the online sequential extreme learning machine (OSELM) - is automatically updated inexpensively as new data arrive (and the new data can then be discarded). OSELM was applied to forecast daily streamflow at two small watersheds in British Columbia, Canada, at lead times of 1-3 days. Predictors used were weather forecast data generated by the NOAA Global Ensemble Forecasting System (GEFS), and local hydro-meteorological observations. OSELM forecasts were tested with daily, monthly or yearly model updates. More frequent updating gave smaller forecast errors, including errors for data above the 90th percentile. Larger datasets used in the initial training of OSELM helped to find better parameters (number of hidden nodes) for the model, yielding better predictions. With the online sequential multiple linear regression (OSMLR) as benchmark, we concluded that OSELM is an attractive approach as it easily outperformed OSMLR in forecast accuracy.

Ultrafast photo-induced hidden phases in strained manganite thin films

NASA Astrophysics Data System (ADS)

Zhang, Jingdi; McLeod, A. S.; Zhang, Gu-Feng; Stoica, Vladimir; Jin, Feng; Gu, Mingqiang; Gopalan, Venkatraman; Freeland, John W.; Wu, Wenbin; Rondinelli, James; Wen, Haidan; Basov, D. N.; Averitt, R. D.

Correlated transition metal oxides (TMOs) are particularly sensitive to external control because of energy degeneracy in a complex energy landscape that promote a plethora of metastable states. However, it remains a grand challenge to actively control and fully explore the rich landscape of TMOs. Dynamic control with pulsed photons can overcome energetic barriers, enabling access to transient or metastable states that are not thermally accessible. In the past, we have demonstrated that mode-selective single-laser-pulse excitation of a strained manganite thin film La2/3Ca1/3MnO3 initiates a persistent phase transition from an emergent antiferromagnetic insulating ground state to a ferromagnetic metallic metastable state. Beyond the photo-induced insulator to metal transition, we recently discovered a new peculiar photo-induced hidden phase, identified by an experimental approach that combines ultrafast pump-probe spectroscopy, THz spectroscopy, X-ray diffraction, cryogenic near-field spectroscopy and SHG probe. This work is funded by the DOE, Office of Science, Office of Basic Energy Science under Award Numbers DE-SC0012375 and DE-SC0012592.

Reverse engineering a social agent-based hidden markov model--visage.

PubMed

Chen, Hung-Ching Justin; Goldberg, Mark; Magdon-Ismail, Malik; Wallace, William A

2008-12-01

We present a machine learning approach to discover the agent dynamics that drives the evolution of the social groups in a community. We set up the problem by introducing an agent-based hidden Markov model for the agent dynamics: an agent's actions are determined by micro-laws. Nonetheless, We learn the agent dynamics from the observed communications without knowing state transitions. Our approach is to identify the appropriate micro-laws corresponding to an identification of the appropriate parameters in the model. The model identification problem is then formulated as a mixed optimization problem. To solve the problem, we develop a multistage learning process for determining the group structure, the group evolution, and the micro-laws of a community based on the observed set of communications among actors, without knowing the semantic contents. Finally, to test the quality of our approximations and the feasibility of the approach, we present the results of extensive experiments on synthetic data as well as the results on real communities, such as Enron email and Movie newsgroups. Insight into agent dynamics helps us understand the driving forces behind social evolution.

An Incremental Type-2 Meta-Cognitive Extreme Learning Machine.

PubMed

Pratama, Mahardhika; Zhang, Guangquan; Er, Meng Joo; Anavatti, Sreenatha

2017-02-01

Existing extreme learning algorithm have not taken into account four issues: 1) complexity; 2) uncertainty; 3) concept drift; and 4) high dimensionality. A novel incremental type-2 meta-cognitive extreme learning machine (ELM) called evolving type-2 ELM (eT2ELM) is proposed to cope with the four issues in this paper. The eT2ELM presents three main pillars of human meta-cognition: 1) what-to-learn; 2) how-to-learn; and 3) when-to-learn. The what-to-learn component selects important training samples for model updates by virtue of the online certainty-based active learning method, which renders eT2ELM as a semi-supervised classifier. The how-to-learn element develops a synergy between extreme learning theory and the evolving concept, whereby the hidden nodes can be generated and pruned automatically from data streams with no tuning of hidden nodes. The when-to-learn constituent makes use of the standard sample reserved strategy. A generalized interval type-2 fuzzy neural network is also put forward as a cognitive component, in which a hidden node is built upon the interval type-2 multivariate Gaussian function while exploiting a subset of Chebyshev series in the output node. The efficacy of the proposed eT2ELM is numerically validated in 12 data streams containing various concept drifts. The numerical results are confirmed by thorough statistical tests, where the eT2ELM demonstrates the most encouraging numerical results in delivering reliable prediction, while sustaining low complexity.

Evaluating topic model interpretability from a primary care physician perspective.

PubMed

Arnold, Corey W; Oh, Andrea; Chen, Shawn; Speier, William

2016-02-01

Probabilistic topic models provide an unsupervised method for analyzing unstructured text. These models discover semantically coherent combinations of words (topics) that could be integrated in a clinical automatic summarization system for primary care physicians performing chart review. However, the human interpretability of topics discovered from clinical reports is unknown. Our objective is to assess the coherence of topics and their ability to represent the contents of clinical reports from a primary care physician's point of view. Three latent Dirichlet allocation models (50 topics, 100 topics, and 150 topics) were fit to a large collection of clinical reports. Topics were manually evaluated by primary care physicians and graduate students. Wilcoxon Signed-Rank Tests for Paired Samples were used to evaluate differences between different topic models, while differences in performance between students and primary care physicians (PCPs) were tested using Mann-Whitney U tests for each of the tasks. While the 150-topic model produced the best log likelihood, participants were most accurate at identifying words that did not belong in topics learned by the 100-topic model, suggesting that 100 topics provides better relative granularity of discovered semantic themes for the data set used in this study. Models were comparable in their ability to represent the contents of documents. Primary care physicians significantly outperformed students in both tasks. This work establishes a baseline of interpretability for topic models trained with clinical reports, and provides insights on the appropriateness of using topic models for informatics applications. Our results indicate that PCPs find discovered topics more coherent and representative of clinical reports relative to students, warranting further research into their use for automatic summarization. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Evaluating Topic Model Interpretability from a Primary Care Physician Perspective

PubMed Central

Arnold, Corey W.; Oh, Andrea; Chen, Shawn; Speier, William

2015-01-01

Background and Objective Probabilistic topic models provide an unsupervised method for analyzing unstructured text. These models discover semantically coherent combinations of words (topics) that could be integrated in a clinical automatic summarization system for primary care physicians performing chart review. However, the human interpretability of topics discovered from clinical reports is unknown. Our objective is to assess the coherence of topics and their ability to represent the contents of clinical reports from a primary care physician’s point of view. Methods Three latent Dirichlet allocation models (50 topics, 100 topics, and 150 topics) were fit to a large collection of clinical reports. Topics were manually evaluated by primary care physicians and graduate students. Wilcoxon Signed-Rank Tests for Paired Samples were used to evaluate differences between different topic models, while differences in performance between students and primary care physicians (PCPs) were tested using Mann-Whitney U tests for each of the tasks. Results While the 150-topic model produced the best log likelihood, participants were most accurate at identifying words that did not belong in topics learned by the 100-topic model, suggesting that 100 topics provides better relative granularity of discovered semantic themes for the data set used in this study. Models were comparable in their ability to represent the contents of documents. Primary care physicians significantly outperformed students in both tasks. Conclusion This work establishes a baseline of interpretability for topic models trained with clinical reports, and provides insights on the appropriateness of using topic models for informatics applications. Our results indicate that PCPs find discovered topics more coherent and representative of clinical reports relative to students, warranting further research into their use for automatic summarization. PMID:26614020

The use of LIDAR Technology for Measuring Mixing Heights under the Photochemical Assessment Monitoring Program; leveraging research under the joint DISCOVER-AQ/FRAPPÉ Missions

EPA Science Inventory

The operational use of ceilometers across the United States has been limited to detection of cloud-base heights across the Automatic Surface Observing Systems (ASOS) primarily operated by the National Weather Service and the Federal Aviation Administration. Continued improvements...

Automatic classification of spectra from the Infrared Astronomical Satellite (IRAS)

NASA Technical Reports Server (NTRS)

Cheeseman, Peter; Stutz, John; Self, Matthew; Taylor, William; Goebel, John; Volk, Kevin; Walker, Helen

1989-01-01

A new classification of Infrared spectra collected by the Infrared Astronomical Satellite (IRAS) is presented. The spectral classes were discovered automatically by a program called Auto Class 2. This program is a method for discovering (inducing) classes from a data base, utilizing a Bayesian probability approach. These classes can be used to give insight into the patterns that occur in the particular domain, in this case, infrared astronomical spectroscopy. The classified spectra are the entire Low Resolution Spectra (LRS) Atlas of 5,425 sources. There are seventy-seven classes in this classification and these in turn were meta-classified to produce nine meta-classes. The classification is presented as spectral plots, IRAS color-color plots, galactic distribution plots and class commentaries. Cross-reference tables, listing the sources by IRAS name and by Auto Class class, are also given. These classes show some of the well known classes, such as the black-body class, and silicate emission classes, but many other classes were unsuspected, while others show important subtle differences within the well known classes.

Reproductive isolation and patterns of genetic differentiation in a cryptic butterfly species complex

PubMed Central

Dincâ, V; Wiklund, C; Lukhtanov, V A; Kodandaramaiah, U; Norén, K; Dapporto, L; Wahlberg, N; Vila, R; Friberg, M

2013-01-01

Molecular studies of natural populations are often designed to detect and categorize hidden layers of cryptic diversity, and an emerging pattern suggests that cryptic species are more common and more widely distributed than previously thought. However, these studies are often decoupled from ecological and behavioural studies of species divergence. Thus, the mechanisms by which the cryptic diversity is distributed and maintained across large spatial scales are often unknown. In 1988, it was discovered that the common Eurasian Wood White butterfly consisted of two species (Leptidea sinapis and Leptidea reali), and the pair became an emerging model for the study of speciation and chromosomal evolution. In 2011, the existence of a third cryptic species (Leptidea juvernica) was proposed. This unexpected discovery raises questions about the mechanisms preventing gene flow and about the potential existence of additional species hidden in the complex. Here, we compare patterns of genetic divergence across western Eurasia in an extensive data set of mitochondrial and nuclear DNA sequences with behavioural data on inter- and intraspecific reproductive isolation in courtship experiments. We show that three species exist in accordance with both the phylogenetic and biological species concepts and that additional hidden diversity is unlikely to occur in Europe. The Leptidea species are now the best studied cryptic complex of butterflies in Europe and a promising model system for understanding the formation of cryptic species and the roles of local processes, colonization patterns and heterospecific interactions for ecological and evolutionary divergence. PMID:23909947

Stop! border ahead: Automatic detection of subthalamic exit during deep brain stimulation surgery.

PubMed

Valsky, Dan; Marmor-Levin, Odeya; Deffains, Marc; Eitan, Renana; Blackwell, Kim T; Bergman, Hagai; Israel, Zvi

2017-01-01

Microelectrode recordings along preplanned trajectories are often used for accurate definition of the subthalamic nucleus (STN) borders during deep brain stimulation (DBS) surgery for Parkinson's disease. Usually, the demarcation of the STN borders is performed manually by a neurophysiologist. The exact detection of the borders is difficult, especially detecting the transition between the STN and the substantia nigra pars reticulata. Consequently, demarcation may be inaccurate, leading to suboptimal location of the DBS lead and inadequate clinical outcomes. We present machine-learning classification procedures that use microelectrode recording power spectra and allow for real-time, high-accuracy discrimination between the STN and substantia nigra pars reticulata. A support vector machine procedure was tested on microelectrode recordings from 58 trajectories that included both STN and substantia nigra pars reticulata that achieved a 97.6% consistency with human expert classification (evaluated by 10-fold cross-validation). We used the same data set as a training set to find the optimal parameters for a hidden Markov model using both microelectrode recording features and trajectory history to enable real-time classification of the ventral STN border (STN exit). Seventy-three additional trajectories were used to test the reliability of the learned statistical model in identifying the exit from the STN. The hidden Markov model procedure identified the STN exit with an error of 0.04 ± 0.18 mm and detection reliability (error < 1 mm) of 94%. The results indicate that robust, accurate, and automatic real-time electrophysiological detection of the ventral STN border is feasible. © 2016 International Parkinson and Movement Disorder Society. © 2016 International Parkinson and Movement Disorder Society.

Speech recognition for embedded automatic positioner for laparoscope

NASA Astrophysics Data System (ADS)

Chen, Xiaodong; Yin, Qingyun; Wang, Yi; Yu, Daoyin

2014-07-01

In this paper a novel speech recognition methodology based on Hidden Markov Model (HMM) is proposed for embedded Automatic Positioner for Laparoscope (APL), which includes a fixed point ARM processor as the core. The APL system is designed to assist the doctor in laparoscopic surgery, by implementing the specific doctor's vocal control to the laparoscope. Real-time respond to the voice commands asks for more efficient speech recognition algorithm for the APL. In order to reduce computation cost without significant loss in recognition accuracy, both arithmetic and algorithmic optimizations are applied in the method presented. First, depending on arithmetic optimizations most, a fixed point frontend for speech feature analysis is built according to the ARM processor's character. Then the fast likelihood computation algorithm is used to reduce computational complexity of the HMM-based recognition algorithm. The experimental results show that, the method shortens the recognition time within 0.5s, while the accuracy higher than 99%, demonstrating its ability to achieve real-time vocal control to the APL.

Automatic variance analysis of multistage care pathways.

PubMed

Li, Xiang; Liu, Haifeng; Zhang, Shilei; Mei, Jing; Xie, Guotong; Yu, Yiqin; Li, Jing; Lakshmanan, Geetika T

2014-01-01

A care pathway (CP) is a standardized process that consists of multiple care stages, clinical activities and their relations, aimed at ensuring and enhancing the quality of care. However, actual care may deviate from the planned CP, and analysis of these deviations can help clinicians refine the CP and reduce medical errors. In this paper, we propose a CP variance analysis method to automatically identify the deviations between actual patient traces in electronic medical records (EMR) and a multistage CP. As the care stage information is usually unavailable in EMR, we first align every trace with the CP using a hidden Markov model. From the aligned traces, we report three types of deviations for every care stage: additional activities, absent activities and violated constraints, which are identified by using the techniques of temporal logic and binomial tests. The method has been applied to a CP for the management of congestive heart failure and real world EMR, providing meaningful evidence for the further improvement of care quality.

Network structure exploration in networks with node attributes

NASA Astrophysics Data System (ADS)

Chen, Yi; Wang, Xiaolong; Bu, Junzhao; Tang, Buzhou; Xiang, Xin

2016-05-01

Complex networks provide a powerful way to represent complex systems and have been widely studied during the past several years. One of the most important tasks of network analysis is to detect structures (also called structural regularities) embedded in networks by determining group number and group partition. Most of network structure exploration models only consider network links. However, in real world networks, nodes may have attributes that are useful for network structure exploration. In this paper, we propose a novel Bayesian nonparametric (BNP) model to explore structural regularities in networks with node attributes, called Bayesian nonparametric attribute (BNPA) model. This model does not only take full advantage of both links between nodes and node attributes for group partition via shared hidden variables, but also determine group number automatically via the Bayesian nonparametric theory. Experiments conducted on a number of real and synthetic networks show that our BNPA model is able to automatically explore structural regularities in networks with node attributes and is competitive with other state-of-the-art models.

Analysis and automatic identification of sleep stages using higher order spectra.

PubMed

Acharya, U Rajendra; Chua, Eric Chern-Pin; Chua, Kuang Chua; Min, Lim Choo; Tamura, Toshiyo

2010-12-01

Electroencephalogram (EEG) signals are widely used to study the activity of the brain, such as to determine sleep stages. These EEG signals are nonlinear and non-stationary in nature. It is difficult to perform sleep staging by visual interpretation and linear techniques. Thus, we use a nonlinear technique, higher order spectra (HOS), to extract hidden information in the sleep EEG signal. In this study, unique bispectrum and bicoherence plots for various sleep stages were proposed. These can be used as visual aid for various diagnostics application. A number of HOS based features were extracted from these plots during the various sleep stages (Wakefulness, Rapid Eye Movement (REM), Stage 1-4 Non-REM) and they were found to be statistically significant with p-value lower than 0.001 using ANOVA test. These features were fed to a Gaussian mixture model (GMM) classifier for automatic identification. Our results indicate that the proposed system is able to identify sleep stages with an accuracy of 88.7%.

Unsupervised daily routine and activity discovery in smart homes.

PubMed

Jie Yin; Qing Zhang; Karunanithi, Mohan

2015-08-01

The ability to accurately recognize daily activities of residents is a core premise of smart homes to assist with remote health monitoring. Most of the existing methods rely on a supervised model trained from a preselected and manually labeled set of activities, which are often time-consuming and costly to obtain in practice. In contrast, this paper presents an unsupervised method for discovering daily routines and activities for smart home residents. Our proposed method first uses a Markov chain to model a resident's locomotion patterns at different times of day and discover clusters of daily routines at the macro level. For each routine cluster, it then drills down to further discover room-level activities at the micro level. The automatic identification of daily routines and activities is useful for understanding indicators of functional decline of elderly people and suggesting timely interventions.

Selections from 2016: Hidden Galaxies Found Behind the Milky Way

NASA Astrophysics Data System (ADS)

Kohler, Susanna

2016-01-01

Editors note:In these last two weeks of 2016, well be looking at a few selections that we havent yet discussed on AAS Nova from among the most-downloaded paperspublished in AAS journals this year. The usual posting schedule will resume after the AAS winter meeting.The Parkes H I Zone of Avoidance SurveyPublished February2016Main takeaway:883 galaxies have been discoveredwithin a few hundredmillion light-years of us, hiding behind the Milky Way. The galaxies were found by a team led by Lister Staveley-Smith (International Center for Radio Astronomy Research, University of Western Australia) using the 64-m Parkes radio telescope in Australia.Distribution of the galaxies discovered in the Zone of Avoidance. Radial distance is measured by the recessional velocities of the galaxies. [Staveley-Smith et al. 2016]Why its interesting:These new galaxies were discovered in whats known as the Zone of Avoidance, a gap that extends roughly 5 above and 5 below the galactic plane. The Zone of Avoidance has been excluded from many past surveys because the stars and dust of the Milky Way prevent us from being able to identify background galaxies in this region. But the Parkes radio telescope equipped with an innovative new receiver was able to peer through the foreground of the Milky Way to detect the hidden galaxies behind it.What this could teach us:The discovery of hundreds of new galaxies may help explain the gravitational anomaly known as the Great Attractor region, a diffuse concentration of mass roughly 250 million light-years away that is pulling the Milky Way and hundreds of thousands of other galaxies toward it.CitationL. Staveley-Smith et al 2016 AJ 151 52. doi:10.3847/0004-6256/151/3/52

Towards a scientific concept of free will as a biological trait: spontaneous actions and decision-making in invertebrates

PubMed Central

Brembs, Björn

2011-01-01

Until the advent of modern neuroscience, free will used to be a theological and a metaphysical concept, debated with little reference to brain function. Today, with ever increasing understanding of neurons, circuits and cognition, this concept has become outdated and any metaphysical account of free will is rightfully rejected. The consequence is not, however, that we become mindless automata responding predictably to external stimuli. On the contrary, accumulating evidence also from brains much smaller than ours points towards a general organization of brain function that incorporates flexible decision-making on the basis of complex computations negotiating internal and external processing. The adaptive value of such an organization consists of being unpredictable for competitors, prey or predators, as well as being able to explore the hidden resource deterministic automats would never find. At the same time, this organization allows all animals to respond efficiently with tried-and-tested behaviours to predictable and reliable stimuli. As has been the case so many times in the history of neuroscience, invertebrate model systems are spearheading these research efforts. This comparatively recent evidence indicates that one common ability of most if not all brains is to choose among different behavioural options even in the absence of differences in the environment and perform genuinely novel acts. Therefore, it seems a reasonable effort for any neurobiologist to join and support a rather illustrious list of scholars who are trying to wrestle the term ‘free will’ from its metaphysical ancestry. The goal is to arrive at a scientific concept of free will, starting from these recently discovered processes with a strong emphasis on the neurobiological mechanisms underlying them. PMID:21159679

Towards a scientific concept of free will as a biological trait: spontaneous actions and decision-making in invertebrates.

PubMed

Brembs, Björn

2011-03-22

Until the advent of modern neuroscience, free will used to be a theological and a metaphysical concept, debated with little reference to brain function. Today, with ever increasing understanding of neurons, circuits and cognition, this concept has become outdated and any metaphysical account of free will is rightfully rejected. The consequence is not, however, that we become mindless automata responding predictably to external stimuli. On the contrary, accumulating evidence also from brains much smaller than ours points towards a general organization of brain function that incorporates flexible decision-making on the basis of complex computations negotiating internal and external processing. The adaptive value of such an organization consists of being unpredictable for competitors, prey or predators, as well as being able to explore the hidden resource deterministic automats would never find. At the same time, this organization allows all animals to respond efficiently with tried-and-tested behaviours to predictable and reliable stimuli. As has been the case so many times in the history of neuroscience, invertebrate model systems are spearheading these research efforts. This comparatively recent evidence indicates that one common ability of most if not all brains is to choose among different behavioural options even in the absence of differences in the environment and perform genuinely novel acts. Therefore, it seems a reasonable effort for any neurobiologist to join and support a rather illustrious list of scholars who are trying to wrestle the term 'free will' from its metaphysical ancestry. The goal is to arrive at a scientific concept of free will, starting from these recently discovered processes with a strong emphasis on the neurobiological mechanisms underlying them.

Visa: AN Automatic Aware and Visual Aids Mechanism for Improving the Correct Use of Geospatial Data

NASA Astrophysics Data System (ADS)

Hong, J. H.; Su, Y. T.

2016-06-01

With the fast growth of internet-based sharing mechanism and OpenGIS technology, users nowadays enjoy the luxury to quickly locate and access a variety of geospatial data for the tasks at hands. While this sharing innovation tremendously expand the possibility of application and reduce the development cost, users nevertheless have to deal with all kinds of "differences" implicitly hidden behind the acquired georesources. We argue the next generation of GIS-based environment, regardless internet-based or not, must have built-in knowledge to automatically and correctly assess the fitness of data use and present the analyzed results to users in an intuitive and meaningful way. The VISA approach proposed in this paper refer to four different types of visual aids that can be respectively used for addressing analyzed results, namely, virtual layer, informative window, symbol transformation and augmented TOC. The VISA-enabled interface works in an automatic-aware fashion, where the standardized metadata serve as the known facts about the selected geospatial resources, algorithms for analyzing the differences of temporality and quality of the geospatial resources were designed and the transformation of analyzed results into visual aids were automatically executed. It successfully presents a new way for bridging the communication gaps between systems and users. GIS has been long seen as a powerful integration tool, but its achievements would be highly restricted if it fails to provide a friendly and correct working platform.

Data mining: sophisticated forms of managed care modeling through artificial intelligence.

PubMed

Borok, L S

1997-01-01

Data mining is a recent development in computer science that combines artificial intelligence algorithms and relational databases to discover patterns automatically, without the use of traditional statistical methods. Work with data mining tools in health care is in a developmental stage that holds great promise, given the combination of demographic and diagnostic information.

Mission impossible? The boss wants to double our inventory turns.

PubMed

Gips, J

1998-11-01

Despite the prolific implementation of manufacturing systems, JIT principles, Kaizen events, and cycle time reduction programs over the past few years, high inventories still plague many companies. The assumption that implementing these principles and techniques will automatically result in inventory levels that satisfy management frequently proves to be false. Events like mergers, introduction of new competition, and a dropoff in business often trigger edicts to cut inventories. The cost of inventories also extends beyond the traditional accounting measurements to include hidden operating costs that everyone should want to eliminate. This article looks at the reasons for inventories and explores strategies for reducing them.

Subliminal speech priming.

PubMed

Kouider, Sid; Dupoux, Emmanuel

2005-08-01

We present a novel subliminal priming technique that operates in the auditory modality. Masking is achieved by hiding a spoken word within a stream of time-compressed speechlike sounds with similar spectral characteristics. Participants were unable to consciously identify the hidden words, yet reliable repetition priming was found. This effect was unaffected by a change in the speaker's voice and remained restricted to lexical processing. The results show that the speech modality, like the written modality, involves the automatic extraction of abstract word-form representations that do not include nonlinguistic details. In both cases, priming operates at the level of discrete and abstract lexical entries and is little influenced by overlap in form or semantics.

State Identification for Planetary Rovers: Learning and Recognition

NASA Technical Reports Server (NTRS)

Aycard, Olivier; Washington, Richard

1999-01-01

A planetary rover must be able to identify states where it should stop or change its plan. With limited and infrequent communication from ground, the rover must recognize states accurately. However, the sensor data is inherently noisy, so identifying the temporal patterns of data that correspond to interesting or important states becomes a complex problem. In this paper, we present an approach to state identification using second-order Hidden Markov Models. Models are trained automatically on a set of labeled training data; the rover uses those models to identify its state from the observed data. The approach is demonstrated on data from a planetary rover platform.

Machine learning methods for classifying human physical activity from on-body accelerometers.

PubMed

Mannini, Andrea; Sabatini, Angelo Maria

2010-01-01

The use of on-body wearable sensors is widespread in several academic and industrial domains. Of great interest are their applications in ambulatory monitoring and pervasive computing systems; here, some quantitative analysis of human motion and its automatic classification are the main computational tasks to be pursued. In this paper, we discuss how human physical activity can be classified using on-body accelerometers, with a major emphasis devoted to the computational algorithms employed for this purpose. In particular, we motivate our current interest for classifiers based on Hidden Markov Models (HMMs). An example is illustrated and discussed by analysing a dataset of accelerometer time series.

How transfer flights shape the structure of the airline network.

PubMed

Ryczkowski, Tomasz; Fronczak, Agata; Fronczak, Piotr

2017-07-17

In this paper, we analyse the gravity model in the global passenger air-transport network. We show that in the standard form, the model is inadequate for correctly describing the relationship between passenger flows and typical geo-economic variables that characterize connected countries. We propose a model for transfer flights that allows exploitation of these discrepancies in order to discover hidden subflows in the network. We illustrate its usefulness by retrieving the distance coefficient in the gravity model, which is one of the determinants of the globalization process. Finally, we discuss the correctness of the presented approach by comparing the distance coefficient to several well-known economic events.

Checklists, safety, my culture and me.

PubMed

Raghunathan, Karthik

2012-07-01

The world is not flat. Hierarchy is a fact of life in society and in healthcare institutions. National, specialty-specific and institutional cultures may play an important role in shaping today's patient-safety climate. The influence of power distance on safety interventions is under-studied. Checklists may make power distance-hampered negotiations easier by providing a standardised aviation-like framework for communications and by democratising the environment. By using surveys and simulation, we might discover patterns of potentially hidden yet problematic interactions that might foster maintenance of the error swamp. We need to understand how people interact as members of a group as this is crucial for the development of generalisable safety interventions.

Exploring relation types for literature-based discovery.

PubMed

Preiss, Judita; Stevenson, Mark; Gaizauskas, Robert

2015-09-01

Literature-based discovery (LBD) aims to identify "hidden knowledge" in the medical literature by: (1) analyzing documents to identify pairs of explicitly related concepts (terms), then (2) hypothesizing novel relations between pairs of unrelated concepts that are implicitly related via a shared concept to which both are explicitly related. Many LBD approaches use simple techniques to identify semantically weak relations between concepts, for example, document co-occurrence. These generate huge numbers of hypotheses, difficult for humans to assess. More complex techniques rely on linguistic analysis, for example, shallow parsing, to identify semantically stronger relations. Such approaches generate fewer hypotheses, but may miss hidden knowledge. The authors investigate this trade-off in detail, comparing techniques for identifying related concepts to discover which are most suitable for LBD. A generic LBD system that can utilize a range of relation types was developed. Experiments were carried out comparing a number of techniques for identifying relations. Two approaches were used for evaluation: replication of existing discoveries and the "time slicing" approach.(1) RESULTS: Previous LBD discoveries could be replicated using relations based either on document co-occurrence or linguistic analysis. Using relations based on linguistic analysis generated many fewer hypotheses, but a significantly greater proportion of them were candidates for hidden knowledge. The use of linguistic analysis-based relations improves accuracy of LBD without overly damaging coverage. LBD systems often generate huge numbers of hypotheses, which are infeasible to manually review. Improving their accuracy has the potential to make these systems significantly more usable. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

An ethics of body scanners: requirements and future challenges from an ethical point of view

NASA Astrophysics Data System (ADS)

Rampp, Benjamin; Wolkenstein, Andreas F. X.; Ammicht Quinn, Regina

2011-05-01

At the moment, body scanners based on terahertz and millimeter-wave technologies are implemented at airports around the world. Thus, challenges of acceptance and acceptability become pressing. In this context, we present the results of an ethical research project on the development and implementation of body scanners. We will show which requirements concerning the system, its developers, and its users should be met in order that the scanners can be acceptable from an ethical point of view. These requirements involve, among others, questions of privacy, health, data protection, and security processes. A special ethical challenge for body scanners, however, still remains: Automatic anonymization processes are based on the assumption of "normal" bodies. Certain groups of persons with "deviant bodies" (e.g. persons with hidden disabilities, persons with aberrant sex characteristics, etc.) are affected in a special way: Their deviation from the standard (for instance their disability) is socially hidden, but eventually exposed by body scanners, even (and even more) if the produced pictures are anonymized. Here, we address the question how the possible discrimination against and exclusion of people with "deviant bodies" could be mitigated or prevented.

Detection of cough signals in continuous audio recordings using hidden Markov models.

PubMed

Matos, Sergio; Birring, Surinder S; Pavord, Ian D; Evans, David H

2006-06-01

Cough is a common symptom of many respiratory diseases. The evaluation of its intensity and frequency of occurrence could provide valuable clinical information in the assessment of patients with chronic cough. In this paper we propose the use of hidden Markov models (HMMs) to automatically detect cough sounds from continuous ambulatory recordings. The recording system consists of a digital sound recorder and a microphone attached to the patient's chest. The recognition algorithm follows a keyword-spotting approach, with cough sounds representing the keywords. It was trained on 821 min selected from 10 ambulatory recordings, including 2473 manually labeled cough events, and tested on a database of nine recordings from separate patients with a total recording time of 3060 min and comprising 2155 cough events. The average detection rate was 82% at a false alarm rate of seven events/h, when considering only events above an energy threshold relative to each recording's average energy. These results suggest that HMMs can be applied to the detection of cough sounds from ambulatory patients. A postprocessing stage to perform a more detailed analysis on the detected events is under development, and could allow the rejection of some of the incorrectly detected events.

Dual Sticky Hierarchical Dirichlet Process Hidden Markov Model and Its Application to Natural Language Description of Motions.

PubMed

Hu, Weiming; Tian, Guodong; Kang, Yongxin; Yuan, Chunfeng; Maybank, Stephen

2017-09-25

In this paper, a new nonparametric Bayesian model called the dual sticky hierarchical Dirichlet process hidden Markov model (HDP-HMM) is proposed for mining activities from a collection of time series data such as trajectories. All the time series data are clustered. Each cluster of time series data, corresponding to a motion pattern, is modeled by an HMM. Our model postulates a set of HMMs that share a common set of states (topics in an analogy with topic models for document processing), but have unique transition distributions. For the application to motion trajectory modeling, topics correspond to motion activities. The learnt topics are clustered into atomic activities which are assigned predicates. We propose a Bayesian inference method to decompose a given trajectory into a sequence of atomic activities. On combining the learnt sources and sinks, semantic motion regions, and the learnt sequence of atomic activities, the action represented by the trajectory can be described in natural language in as automatic a way as possible. The effectiveness of our dual sticky HDP-HMM is validated on several trajectory datasets. The effectiveness of the natural language descriptions for motions is demonstrated on the vehicle trajectories extracted from a traffic scene.

Hidden Markov random field model and Broyden-Fletcher-Goldfarb-Shanno algorithm for brain image segmentation

NASA Astrophysics Data System (ADS)

Guerrout, EL-Hachemi; Ait-Aoudia, Samy; Michelucci, Dominique; Mahiou, Ramdane

2018-05-01

Many routine medical examinations produce images of patients suffering from various pathologies. With the huge number of medical images, the manual analysis and interpretation became a tedious task. Thus, automatic image segmentation became essential for diagnosis assistance. Segmentation consists in dividing the image into homogeneous and significant regions. We focus on hidden Markov random fields referred to as HMRF to model the problem of segmentation. This modelisation leads to a classical function minimisation problem. Broyden-Fletcher-Goldfarb-Shanno algorithm referred to as BFGS is one of the most powerful methods to solve unconstrained optimisation problem. In this paper, we investigate the combination of HMRF and BFGS algorithm to perform the segmentation operation. The proposed method shows very good segmentation results comparing with well-known approaches. The tests are conducted on brain magnetic resonance image databases (BrainWeb and IBSR) largely used to objectively confront the results obtained. The well-known Dice coefficient (DC) was used as similarity metric. The experimental results show that, in many cases, our proposed method approaches the perfect segmentation with a Dice Coefficient above .9. Moreover, it generally outperforms other methods in the tests conducted.

Automatic Generation of Mashups for Personalized Commerce in Digital TV by Semantic Reasoning

NASA Astrophysics Data System (ADS)

Blanco-Fernández, Yolanda; López-Nores, Martín; Pazos-Arias, José J.; Martín-Vicente, Manuela I.

The evolution of information technologies is consolidating recommender systems as essential tools in e-commerce. To date, these systems have focused on discovering the items that best match the preferences, interests and needs of individual users, to end up listing those items by decreasing relevance in some menus. In this paper, we propose extending the current scope of recommender systems to better support trading activities, by automatically generating interactive applications that provide the users with personalized commercial functionalities related to the selected items. We explore this idea in the context of Digital TV advertising, with a system that brings together semantic reasoning techniques and new architectural solutions for web services and mashups.

Topology-Optimized Multilayered Metaoptics

NASA Astrophysics Data System (ADS)

Lin, Zin; Groever, Benedikt; Capasso, Federico; Rodriguez, Alejandro W.; Lončar, Marko

2018-04-01

We propose a general topology-optimization framework for metasurface inverse design that can automatically discover highly complex multilayered metastructures with increased functionalities. In particular, we present topology-optimized multilayered geometries exhibiting angular phase control, including a single-piece nanophotonic metalens with angular aberration correction, as well as an angle-convergent metalens that focuses light onto the same focal spot regardless of the angle of incidence.

DBSCAN-based ROI extracted from SAR images and the discrimination of multi-feature ROI

NASA Astrophysics Data System (ADS)

He, Xin Yi; Zhao, Bo; Tan, Shu Run; Zhou, Xiao Yang; Jiang, Zhong Jin; Cui, Tie Jun

2009-10-01

The purpose of the paper is to extract the region of interest (ROI) from the coarse detected synthetic aperture radar (SAR) images and discriminate if the ROI contains a target or not, so as to eliminate the false alarm, and prepare for the target recognition. The automatic target clustering is one of the most difficult tasks in the SAR-image automatic target recognition system. The density-based spatial clustering of applications with noise (DBSCAN) relies on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN was first used in the SAR image processing, which has many excellent features: only two insensitivity parameters (radius of neighborhood and minimum number of points) are needed; clusters of arbitrary shapes which fit in with the coarse detected SAR images can be discovered; and the calculation time and memory can be reduced. In the multi-feature ROI discrimination scheme, we extract several target features which contain the geometry features such as the area discriminator and Radon-transform based target profile discriminator, the distribution characteristics such as the EFF discriminator, and the EM scattering property such as the PPR discriminator. The synthesized judgment effectively eliminates the false alarms.

Geophysical techniques in detection to river embankments - A case study: To locate sites of potential leaks using surface-wave and electrical methods

USGS Publications Warehouse

Chen, C.; Liu, J.; Xu, S.; Xia, J.; ,

2004-01-01

Geophysical technologies are very effective in environmental, engineering and groundwater applications. Parameters of delineating nature of near-surface materials such as compressional-wave velocity, shear-wave velocity can be obtained using shallow seismic methods. Electric methods are primary approaches for investigating groundwater and detecting leakage. Both of methods are applied to detect embankment in hope of obtaining evidences of the strength and moisture inside the body. A technological experiment has done for detecting and discovering the hidden troubles in the embankment of Yangtze River, Songzi, Hubei, China in 2003. Surface-wave and DC multi-channel array resistivity sounding techniques were used to detect hidden trouble inside and under dike like pipe-seeps. This paper discusses the exploration strategy and the effect of geological characteristics. A practical approach of combining seismic and electric resistivity measurements was applied to locate potential pipe-seeps in embankment in the experiment. The method presents a potential leak factor based on the shear-wave velocity and the resistivity of the medium to evaluate anomalies. An anomaly found in a segment of embankment detected was verified, where occurred a pipe-seep during the 98' flooding.

Hidden phase in parent Fe-pnictide superconductors

NASA Astrophysics Data System (ADS)

Ali, Khadiza; Adhikary, Ganesh; Thakur, Sangeeta; Patil, Swapnil; Mahatha, Sanjoy K.; Thamizhavel, A.; De Ninno, Giovanni; Moras, Paolo; Sheverdyaeva, Polina M.; Carbone, Carlo; Petaccia, Luca; Maiti, Kalobaran

2018-02-01

We investigate the origin of exoticity in Fe-based systems via studying the fermiology of CaFe2As2 employing angle-resolved photoemission spectroscopy. While the Fermi surfaces (FSs) at 200 K and 31 K are observed to exhibit two-dimensional and three-dimensional (3D) topology, respectively, the FSs at intermediate temperatures reveal the emergence of the 3D topology at a temperature much lower than the structural and magnetic phase transition temperature (170 K, for the sample under scrutiny). This leads to the conclusion that the evolution of FS topology is not directly driven by the structural transition. In addition, we discover the existence in ambient conditions of energy bands related to the cT phase. These bands are distinctly resolved in the high-photon energy spectra exhibiting strong Fe 3 d character. They gradually move to higher binding energies due to thermal compression with cooling, leading to the emergence of 3D topology in the Fermi surface. These results reveal the so-far hidden existence of a cT phase under ambient conditions, which is argued to lead to quantum fluctuations responsible for the exotic electronic properties in Fe-pnictide superconductors.

Student profiling on university co-curriculum activities using data visualization tools

NASA Astrophysics Data System (ADS)

Jamil, Jastini Mohd.; Shaharanee, Izwan Nizal Mohd

2017-11-01

Co-curricular activities are playing a vital role in the development of a holistic student. Co-curriculum can be described as an extension of the formal learning experiences in a course or academic program. There are many co-curriculum activities such as students' participation in sports, volunteerism, leadership, entrepreneurship, uniform body, student council, and other social events. The number of student involves in co-curriculum activities are large, thus creating an enormous volume of data including their demographic facts, academic performance and co-curriculum types. The task for discovering and analyzing these information becomes increasingly difficult and hard to comprehend. Data visualization offer a better ways in handling with large volume of information. The need for an understanding of these various co-curriculum activities and their effect towards student performance are essential. Visualizing these information can help related stakeholders to become aware of hidden and interesting information from large amount of data drowning in their student data. The main objective of this study is to provide a clearer understanding of the different trends hidden in the student co-curriculum activities data with related to their activities and academic performances. The data visualization software was used to help visualize the data extracted from the database.

Bacterial symbionts and natural products

PubMed Central

Crawford, Jason M.; Clardy, Jon

2011-01-01

The study of bacterial symbionts of eukaryotic hosts has become a powerful discovery engine for chemistry. This highlight looks at four case studies that exemplify the range of chemistry and biology involved in these symbioses: a bacterial symbiont of a fungus and a marine invertebrate that produce compounds with significant anticancer activity, and bacterial symbionts of insects and nematodes that produce compounds that regulate multilateral symbioses. In the last ten years, a series of shocking revelations – the molecular equivalents of a reality TV show’s uncovering the true parents of a well known individual or a deeply hidden family secret – altered the study of genetically encoded small molecules, natural products for short. These revelations all involved natural products produced by bacterial symbionts, and while details differed, two main plot lines emerged: parentage, in which the real producers of well known natural products with medical potential were not the organisms from which they were originally discovered, and hidden relationships, in which bacterially produced small molecules turned out to be the unsuspected regulators of complex interactions. For chemists, these studies led to new molecules, new biosynthetic pathways, and an understanding of the biological functions these molecules fulfill. PMID:21594283

Functional requirements regarding medical registries--preliminary results.

PubMed

Oberbichler, Stefan; Hörbst, Alexander

2013-01-01

The term medical registry is used to reference tools and processes to support clinical or epidemiologic research or provide a data basis for decisions regarding health care policies. In spite of this wide range of applications the term registry and the functional requirements which a registry should support are not clearly defined. This work presents preliminary results of a literature review to discover functional requirements which form a registry. To extract these requirements a set of peer reviewed articles was collected. These set of articles was screened by using methods from qualitative research. Up to now most discovered functional requirements focus on data quality (e. g. prevent transcription error by conducting automatic domain checks).

Automated generation of massive image knowledge collections using Microsoft Live Labs Pivot to promote neuroimaging and translational research

PubMed Central

2011-01-01

Background Massive datasets comprising high-resolution images, generated in neuro-imaging studies and in clinical imaging research, are increasingly challenging our ability to analyze, share, and filter such images in clinical and basic translational research. Pivot collection exploratory analysis provides each user the ability to fully interact with the massive amounts of visual data to fully facilitate sufficient sorting, flexibility and speed to fluidly access, explore or analyze the massive image data sets of high-resolution images and their associated meta information, such as neuro-imaging databases from the Allen Brain Atlas. It is used in clustering, filtering, data sharing and classifying of the visual data into various deep zoom levels and meta information categories to detect the underlying hidden pattern within the data set that has been used. Method We deployed prototype Pivot collections using the Linux CentOS running on the Apache web server. We also tested the prototype Pivot collections on other operating systems like Windows (the most common variants) and UNIX, etc. It is demonstrated that the approach yields very good results when compared with other approaches used by some researchers for generation, creation, and clustering of massive image collections such as the coronal and horizontal sections of the mouse brain from the Allen Brain Atlas. Results Pivot visual analytics was used to analyze a prototype of dataset Dab2 co-expressed genes from the Allen Brain Atlas. The metadata along with high-resolution images were automatically extracted using the Allen Brain Atlas API. It is then used to identify the hidden information based on the various categories and conditions applied by using options generated from automated collection. A metadata category like chromosome, as well as data for individual cases like sex, age, and plan attributes of a particular gene, is used to filter, sort and to determine if there exist other genes with a similar characteristics to Dab2. And online access to the mouse brain pivot collection can be viewed using the link http://edtech-dev.uthsc.edu/CTSI/teeDev1/unittest/PaPa/collection.html (user name: tviangte and password: demome) Conclusions Our proposed algorithm has automated the creation of large image Pivot collections; this will enable investigators of clinical research projects to easily and quickly analyse the image collections through a perspective that is useful for making critical decisions about the image patterns discovered. PMID:21884637

Call recognition and individual identification of fish vocalizations based on automatic speech recognition: An example with the Lusitanian toadfish.

PubMed

Vieira, Manuel; Fonseca, Paulo J; Amorim, M Clara P; Teixeira, Carlos J C

2015-12-01

The study of acoustic communication in animals often requires not only the recognition of species specific acoustic signals but also the identification of individual subjects, all in a complex acoustic background. Moreover, when very long recordings are to be analyzed, automatic recognition and identification processes are invaluable tools to extract the relevant biological information. A pattern recognition methodology based on hidden Markov models is presented inspired by successful results obtained in the most widely known and complex acoustical communication signal: human speech. This methodology was applied here for the first time to the detection and recognition of fish acoustic signals, specifically in a stream of round-the-clock recordings of Lusitanian toadfish (Halobatrachus didactylus) in their natural estuarine habitat. The results show that this methodology is able not only to detect the mating sounds (boatwhistles) but also to identify individual male toadfish, reaching an identification rate of ca. 95%. Moreover this method also proved to be a powerful tool to assess signal durations in large data sets. However, the system failed in recognizing other sound types.

bcgTree: automatized phylogenetic tree building from bacterial core genomes.

PubMed

Ankenbrand, Markus J; Keller, Alexander

2016-10-01

The need for multi-gene analyses in scientific fields such as phylogenetics and DNA barcoding has increased in recent years. In particular, these approaches are increasingly important for differentiating bacterial species, where reliance on the standard 16S rDNA marker can result in poor resolution. Additionally, the assembly of bacterial genomes has become a standard task due to advances in next-generation sequencing technologies. We created a bioinformatic pipeline, bcgTree, which uses assembled bacterial genomes either from databases or own sequencing results from the user to reconstruct their phylogenetic history. The pipeline automatically extracts 107 essential single-copy core genes, found in a majority of bacteria, using hidden Markov models and performs a partitioned maximum-likelihood analysis. Here, we describe the workflow of bcgTree and, as a proof-of-concept, its usefulness in resolving the phylogeny of 293 publically available bacterial strains of the genus Lactobacillus. We also evaluate its performance in both low- and high-level taxonomy test sets. The tool is freely available at github ( https://github.com/iimog/bcgTree ) and our institutional homepage ( http://www.dna-analytics.biozentrum.uni-wuerzburg.de ).

Research on gait-based human identification

NASA Astrophysics Data System (ADS)

Li, Youguo

Gait recognition refers to automatic identification of individual based on his/her style of walking. This paper proposes a gait recognition method based on Continuous Hidden Markov Model with Mixture of Gaussians(G-CHMM). First, we initialize a Gaussian mix model for training image sequence with K-means algorithm, then train the HMM parameters using a Baum-Welch algorithm. These gait feature sequences can be trained and obtain a Continuous HMM for every person, therefore, the 7 key frames and the obtained HMM can represent each person's gait sequence. Finally, the recognition is achieved by Front algorithm. The experiments made on CASIA gait databases obtain comparatively high correction identification ratio and comparatively strong robustness for variety of bodily angle.

Temporal BYY encoding, Markovian state spaces, and space dimension determination.

PubMed

Xu, Lei

2004-09-01

As a complementary to those temporal coding approaches of the current major stream, this paper aims at the Markovian state space temporal models from the perspective of the temporal Bayesian Ying-Yang (BYY) learning with both new insights and new results on not only the discrete state featured Hidden Markov model and extensions but also the continuous state featured linear state spaces and extensions, especially with a new learning mechanism that makes selection of the state number or the dimension of state space either automatically during adaptive learning or subsequently after learning via model selection criteria obtained from this mechanism. Experiments are demonstrated to show how the proposed approach works.

Topic Model for Graph Mining.

PubMed

Xuan, Junyu; Lu, Jie; Zhang, Guangquan; Luo, Xiangfeng

2015-12-01

Graph mining has been a popular research area because of its numerous application scenarios. Many unstructured and structured data can be represented as graphs, such as, documents, chemical molecular structures, and images. However, an issue in relation to current research on graphs is that they cannot adequately discover the topics hidden in graph-structured data which can be beneficial for both the unsupervised learning and supervised learning of the graphs. Although topic models have proved to be very successful in discovering latent topics, the standard topic models cannot be directly applied to graph-structured data due to the "bag-of-word" assumption. In this paper, an innovative graph topic model (GTM) is proposed to address this issue, which uses Bernoulli distributions to model the edges between nodes in a graph. It can, therefore, make the edges in a graph contribute to latent topic discovery and further improve the accuracy of the supervised and unsupervised learning of graphs. The experimental results on two different types of graph datasets show that the proposed GTM outperforms the latent Dirichlet allocation on classification by using the unveiled topics of these two models to represent graphs.

Better Incident Response with SCOT

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

Bruner, Todd

2015-04-01

SCOT is an incident response management system and knowledge base designed for incident responders by incident responders. SCOT increases the effectiveness of the team without adding undue burdens. Focused on reducing the friction between analysts and their tools, SCOT enables analysts to document and share their research and response efforts in near real time. Automatically identifying indicators and correlating those indicators, SCOT helps analysts discover and respond to advanced threats.

Exploring the epididymis: a personal perspective on careers in science.

PubMed

Turner, Terry T

2015-01-01

Science is a profession of inquiry. We ask ourselves what is it we see and why our observations happen the way they do. Answering those two question puts us in the company of those early explorers, who from Europe found the New World, and from Asia reached west to encounter Europe. Vasco Núñez de Balboa of Spain was such an explorer. He was the first European to see or "discover" the Pacific Ocean. One can imagine his amazement, his excitement when he first saw from a mountain top that vast ocean previously unknown to his culture. A career in science sends each of us seeking our own "Balboa Moments," those observations or results that surprise or even amaze us, those discoveries that open our eyes to new views of nature and medicine. Scientists aim to do what those early explorers did: discover what has previously been unknown, see what has previously been unseen, and reveal what has previously been hidden. Science requires the scientist to discover the facts from among many fictions and to separate the important facts from the trivial so that knowledge can be properly developed. It is only with knowledge that old dogmas can be challenged and corrected. Careers in science produce specific sets of knowledge. When pooled with other knowledge sets they eventually contribute to wisdom and it is wisdom, we hope, that will improve the human condition.

Autoclass: An automatic classification system

NASA Technical Reports Server (NTRS)

Stutz, John; Cheeseman, Peter; Hanson, Robin

1991-01-01

The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework, and using various mathematical and algorithmic approximations, the AutoClass System searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. A simpler version of AutoClass has been applied to many large real data sets, has discovered new independently-verified phenomena, and has been released as a robust software package. Recent extensions allow attributes to be selectively correlated within particular classes, and allow classes to inherit, or share, model parameters through a class hierarchy. The mathematical foundations of AutoClass are summarized.

Data Mining Citizen Science Results

NASA Astrophysics Data System (ADS)

Borne, K. D.

2012-12-01

Scientific discovery from big data is enabled through multiple channels, including data mining (through the application of machine learning algorithms) and human computation (commonly implemented through citizen science tasks). We will describe the results of new data mining experiments on the results from citizen science activities. Discovering patterns, trends, and anomalies in data are among the powerful contributions of citizen science. Establishing scientific algorithms that can subsequently re-discover the same types of patterns, trends, and anomalies in automatic data processing pipelines will ultimately result from the transformation of those human algorithms into computer algorithms, which can then be applied to much larger data collections. Scientific discovery from big data is thus greatly amplified through the marriage of data mining with citizen science.

Automatic Network Fingerprinting through Single-Node Motifs

PubMed Central

Echtermeyer, Christoph; da Fontoura Costa, Luciano; Rodrigues, Francisco A.; Kaiser, Marcus

2011-01-01

Complex networks have been characterised by their specific connectivity patterns (network motifs), but their building blocks can also be identified and described by node-motifs—a combination of local network features. One technique to identify single node-motifs has been presented by Costa et al. (L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser, Europhys. Lett., 87, 1, 2009). Here, we first suggest improvements to the method including how its parameters can be determined automatically. Such automatic routines make high-throughput studies of many networks feasible. Second, the new routines are validated in different network-series. Third, we provide an example of how the method can be used to analyse network time-series. In conclusion, we provide a robust method for systematically discovering and classifying characteristic nodes of a network. In contrast to classical motif analysis, our approach can identify individual components (here: nodes) that are specific to a network. Such special nodes, as hubs before, might be found to play critical roles in real-world networks. PMID:21297963

Rules of engagement: incomplete and complete pronoun resolution.

PubMed

Love, Jessica; McKoon, Gail

2011-07-01

Research on shallow processing suggests that readers sometimes encode only a superficial representation of a text and fail to make use of all available information. Greene, McKoon, and Ratcliff (1992) extended this work to pronouns, finding evidence that readers sometimes fail to automatically identify referents even when these are unambiguous. In this paper we revisit those findings. In 11 recognition probe, priming, and self-report experiments, we manipulated Greene et al.'s stories to discover under what circumstances a pronoun's referent is automatically understood. We lengthened the stories from 4 to 8 lines. This simple manipulation led to automatic and correct resolution, which we attribute to readers' increased engagement with the stories. We found evidence of resolution even when the additional text did not mention the pronoun's referent. In addition, our results suggest that the pronoun temporarily boosts the referent's accessibility, an advantage that disappears by the end of the next sentence. Finally, we present evidence from memory experiments that supports complete pronoun resolution for the longer but not the shorter stories.

Automatic script identification from images using cluster-based templates

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

Hochberg, J.; Kerns, L.; Kelly, P.

We have developed a technique for automatically identifying the script used to generate a document that is stored electronically in bit image form. Our approach differs from previous work in that the distinctions among scripts are discovered by an automatic learning procedure, without any handson analysis. We first develop a set of representative symbols (templates) for each script in our database (Cyrillic, Roman, etc.). We do this by identifying all textual symbols in a set of training documents, scaling each symbol to a fixed size, clustering similar symbols, pruning minor clusters, and finding each cluster`s centroid. To identify a newmore » document`s script, we identify and scale a subset of symbols from the document and compare them to the templates for each script. We choose the script whose templates provide the best match. Our current system distinguishes among the Armenian, Burmese, Chinese, Cyrillic, Ethiopic, Greek, Hebrew, Japanese, Korean, Roman, and Thai scripts with over 90% accuracy.« less

Entrepreneurial Regions: Do Macro-Psychological Cultural Characteristics of Regions Help Solve the "Knowledge Paradox" of Economics?

PubMed

Obschonka, Martin; Stuetzer, Michael; Gosling, Samuel D; Rentfrow, Peter J; Lamb, Michael E; Potter, Jeff; Audretsch, David B

2015-01-01

In recent years, modern economies have shifted away from being based on physical capital and towards being based on new knowledge (e.g., new ideas and inventions). Consequently, contemporary economic theorizing and key public policies have been based on the assumption that resources for generating knowledge (e.g., education, diversity of industries) are essential for regional economic vitality. However, policy makers and scholars have discovered that, contrary to expectations, the mere presence of, and investments in, new knowledge does not guarantee a high level of regional economic performance (e.g., high entrepreneurship rates). To date, this "knowledge paradox" has resisted resolution. We take an interdisciplinary perspective to offer a new explanation, hypothesizing that "hidden" regional culture differences serve as a crucial factor that is missing from conventional economic analyses and public policy strategies. Focusing on entrepreneurial activity, we hypothesize that the statistical relation between knowledge resources and entrepreneurial vitality (i.e., high entrepreneurship rates) in a region will depend on "hidden" regional differences in entrepreneurial culture. To capture such "hidden" regional differences, we derive measures of entrepreneurship-prone culture from two large personality datasets from the United States (N = 935,858) and Great Britain (N = 417,217). In both countries, the findings were consistent with the knowledge-culture-interaction hypothesis. A series of nine additional robustness checks underscored the robustness of these results. Naturally, these purely correlational findings cannot provide direct evidence for causal processes, but the results nonetheless yield a remarkably consistent and robust picture in the two countries. In doing so, the findings raise the idea of regional culture serving as a new causal candidate, potentially driving the knowledge paradox; such an explanation would be consistent with research on the psychological characteristics of entrepreneurs.

Automated discovery of local search heuristics for satisfiability testing.

PubMed

Fukunaga, Alex S

2008-01-01

The development of successful metaheuristic algorithms such as local search for a difficult problem such as satisfiability testing (SAT) is a challenging task. We investigate an evolutionary approach to automating the discovery of new local search heuristics for SAT. We show that several well-known SAT local search algorithms such as Walksat and Novelty are composite heuristics that are derived from novel combinations of a set of building blocks. Based on this observation, we developed CLASS, a genetic programming system that uses a simple composition operator to automatically discover SAT local search heuristics. New heuristics discovered by CLASS are shown to be competitive with the best Walksat variants, including Novelty+. Evolutionary algorithms have previously been applied to directly evolve a solution for a particular SAT instance. We show that the heuristics discovered by CLASS are also competitive with these previous, direct evolutionary approaches for SAT. We also analyze the local search behavior of the learned heuristics using the depth, mobility, and coverage metrics proposed by Schuurmans and Southey.

Automated image processing and analysis of cartilage MRI: enabling technology for data mining applied to osteoarthritis

PubMed Central

Tameem, Hussain Z.; Sinha, Usha S.

2011-01-01

Osteoarthritis (OA) is a heterogeneous and multi-factorial disease characterized by the progressive loss of articular cartilage. Magnetic Resonance Imaging has been established as an accurate technique to assess cartilage damage through both cartilage morphology (volume and thickness) and cartilage water mobility (Spin-lattice relaxation, T2). The Osteoarthritis Initiative, OAI, is a large scale serial assessment of subjects at different stages of OA including those with pre-clinical symptoms. The electronic availability of the comprehensive data collected as part of the initiative provides an unprecedented opportunity to discover new relationships in complex diseases such as OA. However, imaging data, which provides the most accurate non-invasive assessment of OA, is not directly amenable for data mining. Changes in morphometry and relaxivity with OA disease are both complex and subtle, making manual methods extremely difficult. This chapter focuses on the image analysis techniques to automatically localize the differences in morphometry and relaxivity changes in different population sub-groups (normal and OA subjects segregated by age, gender, and race). The image analysis infrastructure will enable automatic extraction of cartilage features at the voxel level; the ultimate goal is to integrate this infrastructure to discover relationships between the image findings and other clinical features. PMID:21785520

Automated image processing and analysis of cartilage MRI: enabling technology for data mining applied to osteoarthritis

NASA Astrophysics Data System (ADS)

Tameem, Hussain Z.; Sinha, Usha S.

2007-11-01

Osteoarthritis (OA) is a heterogeneous and multi-factorial disease characterized by the progressive loss of articular cartilage. Magnetic Resonance Imaging has been established as an accurate technique to assess cartilage damage through both cartilage morphology (volume and thickness) and cartilage water mobility (Spin-lattice relaxation, T2). The Osteoarthritis Initiative, OAI, is a large scale serial assessment of subjects at different stages of OA including those with pre-clinical symptoms. The electronic availability of the comprehensive data collected as part of the initiative provides an unprecedented opportunity to discover new relationships in complex diseases such as OA. However, imaging data, which provides the most accurate non-invasive assessment of OA, is not directly amenable for data mining. Changes in morphometry and relaxivity with OA disease are both complex and subtle, making manual methods extremely difficult. This chapter focuses on the image analysis techniques to automatically localize the differences in morphometry and relaxivity changes in different population sub-groups (normal and OA subjects segregated by age, gender, and race). The image analysis infrastructure will enable automatic extraction of cartilage features at the voxel level; the ultimate goal is to integrate this infrastructure to discover relationships between the image findings and other clinical features.

Histogram equalization with Bayesian estimation for noise robust speech recognition.

PubMed

Suh, Youngjoo; Kim, Hoirin

2018-02-01

The histogram equalization approach is an efficient feature normalization technique for noise robust automatic speech recognition. However, it suffers from performance degradation when some fundamental conditions are not satisfied in the test environment. To remedy these limitations of the original histogram equalization methods, class-based histogram equalization approach has been proposed. Although this approach showed substantial performance improvement under noise environments, it still suffers from performance degradation due to the overfitting problem when test data are insufficient. To address this issue, the proposed histogram equalization technique employs the Bayesian estimation method in the test cumulative distribution function estimation. It was reported in a previous study conducted on the Aurora-4 task that the proposed approach provided substantial performance gains in speech recognition systems based on the acoustic modeling of the Gaussian mixture model-hidden Markov model. In this work, the proposed approach was examined in speech recognition systems with deep neural network-hidden Markov model (DNN-HMM), the current mainstream speech recognition approach where it also showed meaningful performance improvement over the conventional maximum likelihood estimation-based method. The fusion of the proposed features with the mel-frequency cepstral coefficients provided additional performance gains in DNN-HMM systems, which otherwise suffer from performance degradation in the clean test condition.

QRS complex detection based on continuous density hidden Markov models using univariate observations

NASA Astrophysics Data System (ADS)

Sotelo, S.; Arenas, W.; Altuve, M.

2018-04-01

In the electrocardiogram (ECG), the detection of QRS complexes is a fundamental step in the ECG signal processing chain since it allows the determination of other characteristics waves of the ECG and provides information about heart rate variability. In this work, an automatic QRS complex detector based on continuous density hidden Markov models (HMM) is proposed. HMM were trained using univariate observation sequences taken either from QRS complexes or their derivatives. The detection approach is based on the log-likelihood comparison of the observation sequence with a fixed threshold. A sliding window was used to obtain the observation sequence to be evaluated by the model. The threshold was optimized by receiver operating characteristic curves. Sensitivity (Sen), specificity (Spc) and F1 score were used to evaluate the detection performance. The approach was validated using ECG recordings from the MIT-BIH Arrhythmia database. A 6-fold cross-validation shows that the best detection performance was achieved with 2 states HMM trained with QRS complexes sequences (Sen = 0.668, Spc = 0.360 and F1 = 0.309). We concluded that these univariate sequences provide enough information to characterize the QRS complex dynamics from HMM. Future works are directed to the use of multivariate observations to increase the detection performance.

Detecting hidden volcanic explosions from Mt. Cleveland Volcano, Alaska with infrasound and ground-couples airwaves

USGS Publications Warehouse

De Angelis, Slivio; Fee, David; Haney, Matthew; Schneider, David

2012-01-01

In Alaska, where many active volcanoes exist without ground-based instrumentation, the use of techniques suitable for distant monitoring is pivotal. In this study we report regional-scale seismic and infrasound observations of volcanic activity at Mt. Cleveland between December 2011 and August 2012. During this period, twenty explosions were detected by infrasound sensors as far away as 1827 km from the active vent, and ground-coupled acoustic waves were recorded at seismic stations across the Aleutian Arc. Several events resulting from the explosive disruption of small lava domes within the summit crater were confirmed by analysis of satellite remote sensing data. However, many explosions eluded initial, automated, analyses of satellite data due to poor weather conditions. Infrasound and seismic monitoring provided effective means for detecting these hidden events. We present results from the implementation of automatic infrasound and seismo-acoustic eruption detection algorithms, and review the challenges of real-time volcano monitoring operations in remote regions. We also model acoustic propagation in the Northern Pacific, showing how tropospheric ducting effects allow infrasound to travel long distances across the Aleutian Arc. The successful results of our investigation provide motivation for expanded efforts in infrasound monitoring across the Aleutians and contributes to our knowledge of the number and style of vulcanian eruptions at Mt. Cleveland.

Tumor propagation model using generalized hidden Markov model

NASA Astrophysics Data System (ADS)

Park, Sun Young; Sargent, Dustin

2017-02-01

Tumor tracking and progression analysis using medical images is a crucial task for physicians to provide accurate and efficient treatment plans, and monitor treatment response. Tumor progression is tracked by manual measurement of tumor growth performed by radiologists. Several methods have been proposed to automate these measurements with segmentation, but many current algorithms are confounded by attached organs and vessels. To address this problem, we present a new generalized tumor propagation model considering time-series prior images and local anatomical features using a Hierarchical Hidden Markov model (HMM) for tumor tracking. First, we apply the multi-atlas segmentation technique to identify organs/sub-organs using pre-labeled atlases. Second, we apply a semi-automatic direct 3D segmentation method to label the initial boundary between the lesion and neighboring structures. Third, we detect vessels in the ROI surrounding the lesion. Finally, we apply the propagation model with the labeled organs and vessels to accurately segment and measure the target lesion. The algorithm has been designed in a general way to be applicable to various body parts and modalities. In this paper, we evaluate the proposed algorithm on lung and lung nodule segmentation and tracking. We report the algorithm's performance by comparing the longest diameter and nodule volumes using the FDA lung Phantom data and a clinical dataset.

Enabling the Discovery of Recurring Anomalies in Aerospace System Problem Reports using High-Dimensional Clustering Techniques

NASA Technical Reports Server (NTRS)

Srivastava, Ashok, N.; Akella, Ram; Diev, Vesselin; Kumaresan, Sakthi Preethi; McIntosh, Dawn M.; Pontikakis, Emmanuel D.; Xu, Zuobing; Zhang, Yi

2006-01-01

This paper describes the results of a significant research and development effort conducted at NASA Ames Research Center to develop new text mining techniques to discover anomalies in free-text reports regarding system health and safety of two aerospace systems. We discuss two problems of significant importance in the aviation industry. The first problem is that of automatic anomaly discovery about an aerospace system through the analysis of tens of thousands of free-text problem reports that are written about the system. The second problem that we address is that of automatic discovery of recurring anomalies, i.e., anomalies that may be described m different ways by different authors, at varying times and under varying conditions, but that are truly about the same part of the system. The intent of recurring anomaly identification is to determine project or system weakness or high-risk issues. The discovery of recurring anomalies is a key goal in building safe, reliable, and cost-effective aerospace systems. We address the anomaly discovery problem on thousands of free-text reports using two strategies: (1) as an unsupervised learning problem where an algorithm takes free-text reports as input and automatically groups them into different bins, where each bin corresponds to a different unknown anomaly category; and (2) as a supervised learning problem where the algorithm classifies the free-text reports into one of a number of known anomaly categories. We then discuss the application of these methods to the problem of discovering recurring anomalies. In fact the special nature of recurring anomalies (very small cluster sizes) requires incorporating new methods and measures to enhance the original approach for anomaly detection. ?& pant 0-

Automatic extraction of relations between medical concepts in clinical texts

PubMed Central

Harabagiu, Sanda; Roberts, Kirk

2011-01-01

Objective A supervised machine learning approach to discover relations between medical problems, treatments, and tests mentioned in electronic medical records. Materials and methods A single support vector machine classifier was used to identify relations between concepts and to assign their semantic type. Several resources such as Wikipedia, WordNet, General Inquirer, and a relation similarity metric inform the classifier. Results The techniques reported in this paper were evaluated in the 2010 i2b2 Challenge and obtained the highest F1 score for the relation extraction task. When gold standard data for concepts and assertions were available, F1 was 73.7, precision was 72.0, and recall was 75.3. F1 is defined as 2*Precision*Recall/(Precision+Recall). Alternatively, when concepts and assertions were discovered automatically, F1 was 48.4, precision was 57.6, and recall was 41.7. Discussion Although a rich set of features was developed for the classifiers presented in this paper, little knowledge mining was performed from medical ontologies such as those found in UMLS. Future studies should incorporate features extracted from such knowledge sources, which we expect to further improve the results. Moreover, each relation discovery was treated independently. Joint classification of relations may further improve the quality of results. Also, joint learning of the discovery of concepts, assertions, and relations may also improve the results of automatic relation extraction. Conclusion Lexical and contextual features proved to be very important in relation extraction from medical texts. When they are not available to the classifier, the F1 score decreases by 3.7%. In addition, features based on similarity contribute to a decrease of 1.1% when they are not available. PMID:21846787

Sub-surface defects detection of by using active thermography and advanced image edge detection

NASA Astrophysics Data System (ADS)

Tse, Peter W.; Wang, Gaochao

2017-05-01

Active or pulsed thermography is a popular non-destructive testing (NDT) tool for inspecting the integrity and anomaly of industrial equipment. One of the recent research trends in using active thermography is to automate the process in detecting hidden defects. As of today, human effort has still been using to adjust the temperature intensity of the thermo camera in order to visually observe the difference in cooling rates caused by a normal target as compared to that by a sub-surface crack exists inside the target. To avoid the tedious human-visual inspection and minimize human induced error, this paper reports the design of an automatic method that is capable of detecting subsurface defects. The method used the technique of active thermography, edge detection in machine vision and smart algorithm. An infrared thermo-camera was used to capture a series of temporal pictures after slightly heating up the inspected target by flash lamps. Then the Canny edge detector was employed to automatically extract the defect related images from the captured pictures. The captured temporal pictures were preprocessed by a packet of Canny edge detector and then a smart algorithm was used to reconstruct the whole sequences of image signals. During the processes, noise and irrelevant backgrounds exist in the pictures were removed. Consequently, the contrast of the edges of defective areas had been highlighted. The designed automatic method was verified by real pipe specimens that contains sub-surface cracks. After applying such smart method, the edges of cracks can be revealed visually without the need of using manual adjustment on the setting of thermo-camera. With the help of this automatic method, the tedious process in manually adjusting the colour contract and the pixel intensity in order to reveal defects can be avoided.

Spatio-Temporal Pattern Mining on Trajectory Data Using Arm

NASA Astrophysics Data System (ADS)

Khoshahval, S.; Farnaghi, M.; Taleai, M.

2017-09-01

Preliminary mobile was considered to be a device to make human connections easier. But today the consumption of this device has been evolved to a platform for gaming, web surfing and GPS-enabled application capabilities. Embedding GPS in handheld devices, altered them to significant trajectory data gathering facilities. Raw GPS trajectory data is a series of points which contains hidden information. For revealing hidden information in traces, trajectory data analysis is needed. One of the most beneficial concealed information in trajectory data is user activity patterns. In each pattern, there are multiple stops and moves which identifies users visited places and tasks. This paper proposes an approach to discover user daily activity patterns from GPS trajectories using association rules. Finding user patterns needs extraction of user's visited places from stops and moves of GPS trajectories. In order to locate stops and moves, we have implemented a place recognition algorithm. After extraction of visited points an advanced association rule mining algorithm, called Apriori was used to extract user activity patterns. This study outlined that there are useful patterns in each trajectory that can be emerged from raw GPS data using association rule mining techniques in order to find out about multiple users' behaviour in a system and can be utilized in various location-based applications.

A facility to search for hidden particles at the CERN SPS: the SHiP physics case.

PubMed

Alekhin, Sergey; Altmannshofer, Wolfgang; Asaka, Takehiko; Batell, Brian; Bezrukov, Fedor; Bondarenko, Kyrylo; Boyarsky, Alexey; Choi, Ki-Young; Corral, Cristóbal; Craig, Nathaniel; Curtin, David; Davidson, Sacha; de Gouvêa, André; Dell'Oro, Stefano; deNiverville, Patrick; Bhupal Dev, P S; Dreiner, Herbi; Drewes, Marco; Eijima, Shintaro; Essig, Rouven; Fradette, Anthony; Garbrecht, Björn; Gavela, Belen; Giudice, Gian F; Goodsell, Mark D; Gorbunov, Dmitry; Gori, Stefania; Grojean, Christophe; Guffanti, Alberto; Hambye, Thomas; Hansen, Steen H; Helo, Juan Carlos; Hernandez, Pilar; Ibarra, Alejandro; Ivashko, Artem; Izaguirre, Eder; Jaeckel, Joerg; Jeong, Yu Seon; Kahlhoefer, Felix; Kahn, Yonatan; Katz, Andrey; Kim, Choong Sun; Kovalenko, Sergey; Krnjaic, Gordan; Lyubovitskij, Valery E; Marcocci, Simone; Mccullough, Matthew; McKeen, David; Mitselmakher, Guenakh; Moch, Sven-Olaf; Mohapatra, Rabindra N; Morrissey, David E; Ovchynnikov, Maksym; Paschos, Emmanuel; Pilaftsis, Apostolos; Pospelov, Maxim; Reno, Mary Hall; Ringwald, Andreas; Ritz, Adam; Roszkowski, Leszek; Rubakov, Valery; Ruchayskiy, Oleg; Schienbein, Ingo; Schmeier, Daniel; Schmidt-Hoberg, Kai; Schwaller, Pedro; Senjanovic, Goran; Seto, Osamu; Shaposhnikov, Mikhail; Shchutska, Lesya; Shelton, Jessie; Shrock, Robert; Shuve, Brian; Spannowsky, Michael; Spray, Andy; Staub, Florian; Stolarski, Daniel; Strassler, Matt; Tello, Vladimir; Tramontano, Francesco; Tripathi, Anurag; Tulin, Sean; Vissani, Francesco; Winkler, Martin W; Zurek, Kathryn M

2016-12-01

This paper describes the physics case for a new fixed target facility at CERN SPS. The SHiP (search for hidden particles) experiment is intended to hunt for new physics in the largely unexplored domain of very weakly interacting particles with masses below the Fermi scale, inaccessible to the LHC experiments, and to study tau neutrino physics. The same proton beam setup can be used later to look for decays of tau-leptons with lepton flavour number non-conservation, [Formula: see text] and to search for weakly-interacting sub-GeV dark matter candidates. We discuss the evidence for physics beyond the standard model and describe interactions between new particles and four different portals-scalars, vectors, fermions or axion-like particles. We discuss motivations for different models, manifesting themselves via these interactions, and how they can be probed with the SHiP experiment and present several case studies. The prospects to search for relatively light SUSY and composite particles at SHiP are also discussed. We demonstrate that the SHiP experiment has a unique potential to discover new physics and can directly probe a number of solutions of beyond the standard model puzzles, such as neutrino masses, baryon asymmetry of the Universe, dark matter, and inflation.

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

Karen, Romero Sánchez, E-mail: alphacentauri-hp@hotmail.com, E-mail: marcos-vaquezr@hotmail.com, E-mail: isabeldgg@hotmail.com; Vásquez Reyes Marcos, A., E-mail: alphacentauri-hp@hotmail.com, E-mail: marcos-vaquezr@hotmail.com, E-mail: isabeldgg@hotmail.com; González Gómez Dulce, I., E-mail: alphacentauri-hp@hotmail.com, E-mail: marcos-vaquezr@hotmail.com, E-mail: isabeldgg@hotmail.com

The main goal of this project was to create a computer algorithm based on wavelet analysis of BOLD signals, which automatically diagnosed ADHD using information from resting state MR experiments. Male right handed volunteers (infants with ages between 7 and 11 years old) were studied and compared with age matched controls. Wavelet analysis, which is a mathematical tool used to decompose time series into elementary constituents and detect hidden information, was applied here to the BOLD signal obtained from the cerebellum 8 region of all our volunteers. Statistical differences between the values of the a parameters of wavelet analysis wasmore » found and showed significant differences (p<0.02) between groups. This difference might help in the future to distinguish healthy from ADHD patients and therefore diagnose ADHD.« less

Developing JSequitur to Study the Hierarchical Structure of Biological Sequences in a Grammatical Inference Framework of String Compression Algorithms.

PubMed

Galbadrakh, Bulgan; Lee, Kyung-Eun; Park, Hyun-Seok

2012-12-01

Grammatical inference methods are expected to find grammatical structures hidden in biological sequences. One hopes that studies of grammar serve as an appropriate tool for theory formation. Thus, we have developed JSequitur for automatically generating the grammatical structure of biological sequences in an inference framework of string compression algorithms. Our original motivation was to find any grammatical traits of several cancer genes that can be detected by string compression algorithms. Through this research, we could not find any meaningful unique traits of the cancer genes yet, but we could observe some interesting traits in regards to the relationship among gene length, similarity of sequences, the patterns of the generated grammar, and compression rate.

Intelligent fault diagnosis of rolling bearings using an improved deep recurrent neural network

NASA Astrophysics Data System (ADS)

Jiang, Hongkai; Li, Xingqiu; Shao, Haidong; Zhao, Ke

2018-06-01

Traditional intelligent fault diagnosis methods for rolling bearings heavily depend on manual feature extraction and feature selection. For this purpose, an intelligent deep learning method, named the improved deep recurrent neural network (DRNN), is proposed in this paper. Firstly, frequency spectrum sequences are used as inputs to reduce the input size and ensure good robustness. Secondly, DRNN is constructed by the stacks of the recurrent hidden layer to automatically extract the features from the input spectrum sequences. Thirdly, an adaptive learning rate is adopted to improve the training performance of the constructed DRNN. The proposed method is verified with experimental rolling bearing data, and the results confirm that the proposed method is more effective than traditional intelligent fault diagnosis methods.

A neural network model for credit risk evaluation.

PubMed

Khashman, Adnan

2009-08-01

Credit scoring is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. This paper presents a credit risk evaluation system that uses a neural network model based on the back propagation learning algorithm. We train and implement the neural network to decide whether to approve or reject a credit application, using seven learning schemes and real world credit applications from the Australian credit approval datasets. A comparison of the system performance under the different learning schemes is provided, furthermore, we compare the performance of two neural networks; with one and two hidden layers following the ideal learning scheme. Experimental results suggest that neural networks can be effectively used in automatic processing of credit applications.

Cultural Differences in Perceptual Reorganization in US and Pirahã Adults

PubMed Central

Yoon, Jennifer M. D.; Witthoft, Nathan; Winawer, Jonathan; Frank, Michael C.; Everett, Daniel L.; Gibson, Edward

2014-01-01

Visual illusions and other perceptual phenomena can be used as tools to uncover the otherwise hidden constructive processes that give rise to perception. Although many perceptual processes are assumed to be universal, variable susceptibility to certain illusions and perceptual effects across populations suggests a role for factors that vary culturally. One striking phenomenon is seen with two-tone images—photos reduced to two tones: black and white. Deficient recognition is observed in young children under conditions that trigger automatic recognition in adults. Here we show a similar lack of cue-triggered perceptual reorganization in the Pirahã, a hunter-gatherer tribe with limited exposure to modern visual media, suggesting such recognition is experience- and culture-specific. PMID:25411970

Discovering functional modules by topic modeling RNA-Seq based toxicogenomic data.

PubMed

Yu, Ke; Gong, Binsheng; Lee, Mikyung; Liu, Zhichao; Xu, Joshua; Perkins, Roger; Tong, Weida

2014-09-15

Toxicogenomics (TGx) endeavors to elucidate the underlying molecular mechanisms through exploring gene expression profiles in response to toxic substances. Recently, RNA-Seq is increasingly regarded as a more powerful alternative to microarrays in TGx studies. However, realizing RNA-Seq's full potential requires novel approaches to extracting information from the complex TGx data. Considering read counts as the number of times a word occurs in a document, gene expression profiles from RNA-Seq are analogous to a word by document matrix used in text mining. Topic modeling aiming at to discover the latent structures in text corpora would be helpful to explore RNA-Seq based TGx data. In this study, topic modeling was applied on a typical RNA-Seq based TGx data set to discover hidden functional modules. The RNA-Seq based gene expression profiles were transformed into "documents", on which latent Dirichlet allocation (LDA) was used to build a topic model. We found samples treated by the compounds with the same modes of actions (MoAs) could be clustered based on topic similarities. The topic most relevant to each cluster was identified as a "marker" topic, which was interpreted by gene enrichment analysis with MoAs then confirmed by compound and pathways associations mined from literature. To further validate the "marker" topics, we tested topic transferability from RNA-Seq to microarrays. The RNA-Seq based gene expression profile of a topic specifically associated with peroxisome proliferator-activated receptors (PPAR) signaling pathway was used to query samples with similar expression profiles in two different microarray data sets, yielding accuracy of about 85%. This proof-of-concept study demonstrates the applicability of topic modeling to discover functional modules in RNA-Seq data and suggests a valuable computational tool for leveraging information within TGx data in RNA-Seq era.

One Crystal, Two Temperatures: Cryocooling Penalties Alter Ligand Binding to Transient Protein Sites

DOE PAGES

Fischer, Marcus; Shoichet, Brian K.; Fraser, James S.

2015-05-28

Interrogating fragment libraries by X-ray crystallography is a powerful strategy for discovering allosteric ligands for protein targets. Cryocooling of crystals should theoretically increase the fraction of occupied binding sites and decrease radiation damage. However, it might also perturb protein conformations that can be accessed at room temperature. Using data from crystals measured consecutively at room temperature and at cryogenic temperature, we found that transient binding sites could be abolished at the cryogenic temperatures employed by standard approaches. Finally, changing the temperature at which the crystallographic data was collected could provide a deliberate perturbation to the equilibrium of protein conformations andmore » help to visualize hidden sites with great potential to allosterically modulate protein function.« less

Blood libel rebooted: traditional scapegoats, online media, and the H1N1 epidemic.

PubMed

Atlani-Duault, L; Mercier, A; Rousseau, C; Guyot, P; Moatti, J P

2015-03-01

This study of comments posted on major French print and TV media websites during the H1N1 epidemic illustrates the relationship between the traditional media and social media in responding to an emerging disease. A disturbing "geography of blame" was observed suggesting the metamorphosis of the folk-devil phenomenon to the Internet. We discovered a subterranean discourse about the putative origins and "objectives" of the H1N1 virus, which was absent from the discussions in mainstream television channels and large-circulation print media. These online rumours attributed hidden motives to governments, pharmaceutical companies, and figures of Otherness that were scapegoated in the social history of previous European epidemics, notably Freemasons and Jews.

Beyond the Fermi liquid paradigm: Hidden Fermi liquids

PubMed Central

Jain, J. K.; Anderson, P. W.

2009-01-01

An intense investigation of possible non-Fermi liquid states of matter has been inspired by two of the most intriguing phenomena discovered in the past quarter century, namely, high-temperature superconductivity and the fractional quantum Hall effect. Despite enormous conceptual strides, these two fields have developed largely along separate paths. Two widely employed theories are the resonating valence bond theory for high-temperature superconductivity and the composite fermion theory for the fractional quantum Hall effect. The goal of this perspective article is to note that they subscribe to a common underlying paradigm: They both connect these exotic quantum liquids to certain ordinary Fermi liquids residing in unphysical Hilbert spaces. Such a relation yields numerous nontrivial experimental consequences, exposing these theories to rigorous and definitive tests. PMID:19506260

Medical data mining: knowledge discovery in a clinical data warehouse.

PubMed Central

Prather, J. C.; Lobach, D. F.; Goodwin, L. K.; Hales, J. W.; Hage, M. L.; Hammond, W. E.

1997-01-01

Clinical databases have accumulated large quantities of information about patients and their medical conditions. Relationships and patterns within this data could provide new medical knowledge. Unfortunately, few methodologies have been developed and applied to discover this hidden knowledge. In this study, the techniques of data mining (also known as Knowledge Discovery in Databases) were used to search for relationships in a large clinical database. Specifically, data accumulated on 3,902 obstetrical patients were evaluated for factors potentially contributing to preterm birth using exploratory factor analysis. Three factors were identified by the investigators for further exploration. This paper describes the processes involved in mining a clinical database including data warehousing, data query and cleaning, and data analysis. PMID:9357597

Association algorithm to mine the rules that govern enzyme definition and to classify protein sequences.

PubMed

Chiu, Shih-Hau; Chen, Chien-Chi; Yuan, Gwo-Fang; Lin, Thy-Hou

2006-06-15

The number of sequences compiled in many genome projects is growing exponentially, but most of them have not been characterized experimentally. An automatic annotation scheme must be in an urgent need to reduce the gap between the amount of new sequences produced and reliable functional annotation. This work proposes rules for automatically classifying the fungus genes. The approach involves elucidating the enzyme classifying rule that is hidden in UniProt protein knowledgebase and then applying it for classification. The association algorithm, Apriori, is utilized to mine the relationship between the enzyme class and significant InterPro entries. The candidate rules are evaluated for their classificatory capacity. There were five datasets collected from the Swiss-Prot for establishing the annotation rules. These were treated as the training sets. The TrEMBL entries were treated as the testing set. A correct enzyme classification rate of 70% was obtained for the prokaryote datasets and a similar rate of about 80% was obtained for the eukaryote datasets. The fungus training dataset which lacks an enzyme class description was also used to evaluate the fungus candidate rules. A total of 88 out of 5085 test entries were matched with the fungus rule set. These were otherwise poorly annotated using their functional descriptions. The feasibility of using the method presented here to classify enzyme classes based on the enzyme domain rules is evident. The rules may be also employed by the protein annotators in manual annotation or implemented in an automatic annotation flowchart.

Sma3s: a three-step modular annotator for large sequence datasets.

PubMed

Muñoz-Mérida, Antonio; Viguera, Enrique; Claros, M Gonzalo; Trelles, Oswaldo; Pérez-Pulido, Antonio J

2014-08-01

Automatic sequence annotation is an essential component of modern 'omics' studies, which aim to extract information from large collections of sequence data. Most existing tools use sequence homology to establish evolutionary relationships and assign putative functions to sequences. However, it can be difficult to define a similarity threshold that achieves sufficient coverage without sacrificing annotation quality. Defining the correct configuration is critical and can be challenging for non-specialist users. Thus, the development of robust automatic annotation techniques that generate high-quality annotations without needing expert knowledge would be very valuable for the research community. We present Sma3s, a tool for automatically annotating very large collections of biological sequences from any kind of gene library or genome. Sma3s is composed of three modules that progressively annotate query sequences using either: (i) very similar homologues, (ii) orthologous sequences or (iii) terms enriched in groups of homologous sequences. We trained the system using several random sets of known sequences, demonstrating average sensitivity and specificity values of ~85%. In conclusion, Sma3s is a versatile tool for high-throughput annotation of a wide variety of sequence datasets that outperforms the accuracy of other well-established annotation algorithms, and it can enrich existing database annotations and uncover previously hidden features. Importantly, Sma3s has already been used in the functional annotation of two published transcriptomes. © The Author 2014. Published by Oxford University Press on behalf of Kazusa DNA Research Institute.

ISEScan: automated identification of insertion sequence elements in prokaryotic genomes.

PubMed

Xie, Zhiqun; Tang, Haixu

2017-11-01

The insertion sequence (IS) elements are the smallest but most abundant autonomous transposable elements in prokaryotic genomes, which play a key role in prokaryotic genome organization and evolution. With the fast growing genomic data, it is becoming increasingly critical for biology researchers to be able to accurately and automatically annotate ISs in prokaryotic genome sequences. The available automatic IS annotation systems are either providing only incomplete IS annotation or relying on the availability of existing genome annotations. Here, we present a new IS elements annotation pipeline to address these issues. ISEScan is a highly sensitive software pipeline based on profile hidden Markov models constructed from manually curated IS elements. ISEScan performs better than existing IS annotation systems when tested on prokaryotic genomes with curated annotations of IS elements. Applying it to 2784 prokaryotic genomes, we report the global distribution of IS families across taxonomic clades in Archaea and Bacteria. ISEScan is implemented in Python and released as an open source software at https://github.com/xiezhq/ISEScan. hatang@indiana.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Automatic detection of ischemic stroke based on scaling exponent electroencephalogram using extreme learning machine

NASA Astrophysics Data System (ADS)

Adhi, H. A.; Wijaya, S. K.; Prawito; Badri, C.; Rezal, M.

2017-03-01

Stroke is one of cerebrovascular diseases caused by the obstruction of blood flow to the brain. Stroke becomes the leading cause of death in Indonesia and the second in the world. Stroke also causes of the disability. Ischemic stroke accounts for most of all stroke cases. Obstruction of blood flow can cause tissue damage which results the electrical changes in the brain that can be observed through the electroencephalogram (EEG). In this study, we presented the results of automatic detection of ischemic stroke and normal subjects based on the scaling exponent EEG obtained through detrended fluctuation analysis (DFA) using extreme learning machine (ELM) as the classifier. The signal processing was performed with 18 channels of EEG in the range of 0-30 Hz. Scaling exponents of the subjects were used as the input for ELM to classify the ischemic stroke. The performance of detection was observed by the value of accuracy, sensitivity and specificity. The result showed, performance of the proposed method to classify the ischemic stroke was 84 % for accuracy, 82 % for sensitivity and 87 % for specificity with 120 hidden neurons and sine as the activation function of ELM.

Annotating Atomic Components of Papers in Digital Libraries: The Semantic and Social Web Heading towards a Living Document Supporting eSciences

NASA Astrophysics Data System (ADS)

García Castro, Alexander; García-Castro, Leyla Jael; Labarga, Alberto; Giraldo, Olga; Montaña, César; O'Neil, Kieran; Bateman, John A.

Rather than a document that is being constantly re-written as in the wiki approach, the Living Document (LD) is one that acts as a document router, operating by means of structured and organized social tagging and existing ontologies. It offers an environment where users can manage papers and related information, share their knowledge with their peers and discover hidden associations among the shared knowledge. The LD builds upon both the Semantic Web, which values the integration of well-structured data, and the Social Web, which aims to facilitate interaction amongst people by means of user-generated content. In this vein, the LD is similar to a social networking system, with users as central nodes in the network, with the difference that interaction is focused on papers rather than people. Papers, with their ability to represent research interests, expertise, affiliations, and links to web based tools and databanks, represent a central axis for interaction amongst users. To begin to show the potential of this vision, we have implemented a novel web prototype that enables researchers to accomplish three activities central to the Semantic Web vision: organizing, sharing and discovering. Availability: http://www.scientifik.info/

Nanotechnology drives a paradigm shift on protein misfolding diseases and amyloidosis

NASA Astrophysics Data System (ADS)

Bellotti, Vittorio; Stoppini, Monica

2012-06-01

In almost a century of scientific work on the mechanism of amyloid diseases much of the attention has been focused on the amyloid fibrils, which still represent the diagnostic hallmark of the disease and are easily identified in affected organs for their peculiar tinctorial properties and the fibrillar shape. However, it has been lately discovered that the seeds of the pathogenesis are deeply hidden in the structure and folding dynamics of proteins at the monomeric state which almost indistinguishable from the normal counterpart through classical biochemical approaches. In the recent years soluble oligomeric/prefibrillar species, putatively cytotoxic, were discovered and even more recently polymorphisms of shape and structure of fibrils was emerging as a property that could dictate the bioactivity of amyloid as well as the specificity of its tissue localization. Nanotechnology through the biophysical analysis of the single molecules (monomers or oligomers or fibrils) is the propulsive disciplines in the transformation of our knowledge on the molecular mechanism of this disease. It will provide, in the forthcoming years, precious analytical devices mimicking the biological microenvironment where the molecular events causing the amyloid formation will be monitored and possibly modulated in a real time frame.

Discovering Multimodal Behavior in Ms. Pac-Man through Evolution of Modular Neural Networks.

PubMed

Schrum, Jacob; Miikkulainen, Risto

2016-03-12

Ms. Pac-Man is a challenging video game in which multiple modes of behavior are required: Ms. Pac-Man must escape ghosts when they are threats and catch them when they are edible, in addition to eating all pills in each level. Past approaches to learning behavior in Ms. Pac-Man have treated the game as a single task to be learned using monolithic policy representations. In contrast, this paper uses a framework called Modular Multi-objective NEAT (MM-NEAT) to evolve modular neural networks. Each module defines a separate behavior. The modules are used at different times according to a policy that can be human-designed (i.e. Multitask) or discovered automatically by evolution. The appropriate number of modules can be fixed or discovered using a genetic operator called Module Mutation. Several versions of Module Mutation are evaluated in this paper. Both fixed modular networks and Module Mutation networks outperform monolithic networks and Multitask networks. Interestingly, the best networks dedicate modules to critical behaviors (such as escaping when surrounded after luring ghosts near a power pill) that do not follow the customary division of the game into chasing edible and escaping threat ghosts. The results demonstrate that MM-NEAT can discover interesting and effective behavior for agents in challenging games.

Discovering Multimodal Behavior in Ms. Pac-Man through Evolution of Modular Neural Networks

PubMed Central

Schrum, Jacob; Miikkulainen, Risto

2015-01-01

Ms. Pac-Man is a challenging video game in which multiple modes of behavior are required: Ms. Pac-Man must escape ghosts when they are threats and catch them when they are edible, in addition to eating all pills in each level. Past approaches to learning behavior in Ms. Pac-Man have treated the game as a single task to be learned using monolithic policy representations. In contrast, this paper uses a framework called Modular Multi-objective NEAT (MM-NEAT) to evolve modular neural networks. Each module defines a separate behavior. The modules are used at different times according to a policy that can be human-designed (i.e. Multitask) or discovered automatically by evolution. The appropriate number of modules can be fixed or discovered using a genetic operator called Module Mutation. Several versions of Module Mutation are evaluated in this paper. Both fixed modular networks and Module Mutation networks outperform monolithic networks and Multitask networks. Interestingly, the best networks dedicate modules to critical behaviors (such as escaping when surrounded after luring ghosts near a power pill) that do not follow the customary division of the game into chasing edible and escaping threat ghosts. The results demonstrate that MM-NEAT can discover interesting and effective behavior for agents in challenging games. PMID:27030803

Rules of Engagement: Incomplete and Complete Pronoun Resolution

PubMed Central

Love, Jessica; McKoon, Gail

2011-01-01

Research on shallow processing suggests that readers sometimes encode only a superficial representation of a text, failing to make use of all available information. Greene, McKoon and Ratcliff (1992) extended this work to pronouns, finding evidence that readers sometimes fail to automatically identify referents even when they are unambiguous. In this paper we revisit those findings. In 11 recognition probe, priming, and self-report experiments, we manipulated Greene et al.’s stories to discover under what circumstances a pronoun’s referent is automatically understood. We lengthened the stories from four to eight lines, a simple manipulation that led to automatic and correct resolution, which we attribute to readers’ increased engagement with the stories. We found evidence of resolution even when the additional text did not mention the pronoun’s referent. In addition, our results suggest that the pronoun temporarily boosts the referent’s accessibility, an advantage that disappears by the end of the next sentence. Finally, we present evidence from memory experiments that support complete pronoun resolution for the longer, but not the shorter, stories. PMID:21480757

New approaches to optimization in aerospace conceptual design

NASA Technical Reports Server (NTRS)

Gage, Peter J.

1995-01-01

Aerospace design can be viewed as an optimization process, but conceptual studies are rarely performed using formal search algorithms. Three issues that restrict the success of automatic search are identified in this work. New approaches are introduced to address the integration of analyses and optimizers, to avoid the need for accurate gradient information and a smooth search space (required for calculus-based optimization), and to remove the restrictions imposed by fixed complexity problem formulations. (1) Optimization should be performed in a flexible environment. A quasi-procedural architecture is used to conveniently link analysis modules and automatically coordinate their execution. It efficiently controls a large-scale design tasks. (2) Genetic algorithms provide a search method for discontinuous or noisy domains. The utility of genetic optimization is demonstrated here, but parameter encodings and constraint-handling schemes must be carefully chosen to avoid premature convergence to suboptimal designs. The relationship between genetic and calculus-based methods is explored. (3) A variable-complexity genetic algorithm is created to permit flexible parameterization, so that the level of description can change during optimization. This new optimizer automatically discovers novel designs in structural and aerodynamic tasks.

Combining knowledge discovery from databases (KDD) and case-based reasoning (CBR) to support diagnosis of medical images

NASA Astrophysics Data System (ADS)

Stranieri, Andrew; Yearwood, John; Pham, Binh

1999-07-01

The development of data warehouses for the storage and analysis of very large corpora of medical image data represents a significant trend in health care and research. Amongst other benefits, the trend toward warehousing enables the use of techniques for automatically discovering knowledge from large and distributed databases. In this paper, we present an application design for knowledge discovery from databases (KDD) techniques that enhance the performance of the problem solving strategy known as case- based reasoning (CBR) for the diagnosis of radiological images. The problem of diagnosing the abnormality of the cervical spine is used to illustrate the method. The design of a case-based medical image diagnostic support system has three essential characteristics. The first is a case representation that comprises textual descriptions of the image, visual features that are known to be useful for indexing images, and additional visual features to be discovered by data mining many existing images. The second characteristic of the approach presented here involves the development of a case base that comprises an optimal number and distribution of cases. The third characteristic involves the automatic discovery, using KDD techniques, of adaptation knowledge to enhance the performance of the case based reasoner. Together, the three characteristics of our approach can overcome real time efficiency obstacles that otherwise mitigate against the use of CBR to the domain of medical image analysis.

Distilling free-form natural laws from experimental data.

PubMed

Schmidt, Michael; Lipson, Hod

2009-04-03

For centuries, scientists have attempted to identify and document analytical laws that underlie physical phenomena in nature. Despite the prevalence of computing power, the process of finding natural laws and their corresponding equations has resisted automation. A key challenge to finding analytic relations automatically is defining algorithmically what makes a correlation in observed data important and insightful. We propose a principle for the identification of nontriviality. We demonstrated this approach by automatically searching motion-tracking data captured from various physical systems, ranging from simple harmonic oscillators to chaotic double-pendula. Without any prior knowledge about physics, kinematics, or geometry, the algorithm discovered Hamiltonians, Lagrangians, and other laws of geometric and momentum conservation. The discovery rate accelerated as laws found for simpler systems were used to bootstrap explanations for more complex systems, gradually uncovering the "alphabet" used to describe those systems.

Knowledge discovery with classification rules in a cardiovascular dataset.

PubMed

Podgorelec, Vili; Kokol, Peter; Stiglic, Milojka Molan; Hericko, Marjan; Rozman, Ivan

2005-12-01

In this paper we study an evolutionary machine learning approach to data mining and knowledge discovery based on the induction of classification rules. A method for automatic rules induction called AREX using evolutionary induction of decision trees and automatic programming is introduced. The proposed algorithm is applied to a cardiovascular dataset consisting of different groups of attributes which should possibly reveal the presence of some specific cardiovascular problems in young patients. A case study is presented that shows the use of AREX for the classification of patients and for discovering possible new medical knowledge from the dataset. The defined knowledge discovery loop comprises a medical expert's assessment of induced rules to drive the evolution of rule sets towards more appropriate solutions. The final result is the discovery of a possible new medical knowledge in the field of pediatric cardiology.

Applications of independent component analysis in SAR images

NASA Astrophysics Data System (ADS)

Huang, Shiqi; Cai, Xinhua; Hui, Weihua; Xu, Ping

2009-07-01

The detection of faint, small and hidden targets in synthetic aperture radar (SAR) image is still an issue for automatic target recognition (ATR) system. How to effectively separate these targets from the complex background is the aim of this paper. Independent component analysis (ICA) theory can enhance SAR image targets and improve signal clutter ratio (SCR), which benefits to detect and recognize faint targets. Therefore, this paper proposes a new SAR image target detection algorithm based on ICA. In experimental process, the fast ICA (FICA) algorithm is utilized. Finally, some real SAR image data is used to test the method. The experimental results verify that the algorithm is feasible, and it can improve the SCR of SAR image and increase the detection rate for the faint small targets.

Automatic speech recognition and training for severely dysarthric users of assistive technology: the STARDUST project.

PubMed

Parker, Mark; Cunningham, Stuart; Enderby, Pam; Hawley, Mark; Green, Phil

2006-01-01

The STARDUST project developed robust computer speech recognizers for use by eight people with severe dysarthria and concomitant physical disability to access assistive technologies. Independent computer speech recognizers trained with normal speech are of limited functional use by those with severe dysarthria due to limited and inconsistent proximity to "normal" articulatory patterns. Severe dysarthric output may also be characterized by a small mass of distinguishable phonetic tokens making the acoustic differentiation of target words difficult. Speaker dependent computer speech recognition using Hidden Markov Models was achieved by the identification of robust phonetic elements within the individual speaker output patterns. A new system of speech training using computer generated visual and auditory feedback reduced the inconsistent production of key phonetic tokens over time.

Approximated mutual information training for speech recognition using myoelectric signals.

PubMed

Guo, Hua J; Chan, A D C

2006-01-01

A new training algorithm called the approximated maximum mutual information (AMMI) is proposed to improve the accuracy of myoelectric speech recognition using hidden Markov models (HMMs). Previous studies have demonstrated that automatic speech recognition can be performed using myoelectric signals from articulatory muscles of the face. Classification of facial myoelectric signals can be performed using HMMs that are trained using the maximum likelihood (ML) algorithm; however, this algorithm maximizes the likelihood of the observations in the training sequence, which is not directly associated with optimal classification accuracy. The AMMI training algorithm attempts to maximize the mutual information, thereby training the HMMs to optimize their parameters for discrimination. Our results show that AMMI training consistently reduces the error rates compared to these by the ML training, increasing the accuracy by approximately 3% on average.

Wavelet analysis of MR functional data from the cerebellum

NASA Astrophysics Data System (ADS)

Romero Sánchez, Karen; Vásquez Reyes, Marcos A.; González Gómez, Dulce I.; Hidalgo Tobón, Silvia; Hernández López, Javier M.; Dies Suarez, Pilar; Barragán Pérez, Eduardo; De Celis Alonso, Benito

2014-11-01

The main goal of this project was to create a computer algorithm based on wavelet analysis of BOLD signals, which automatically diagnosed ADHD using information from resting state MR experiments. Male right handed volunteers (infants with ages between 7 and 11 years old) were studied and compared with age matched controls. Wavelet analysis, which is a mathematical tool used to decompose time series into elementary constituents and detect hidden information, was applied here to the BOLD signal obtained from the cerebellum 8 region of all our volunteers. Statistical differences between the values of the a parameters of wavelet analysis was found and showed significant differences (p<0.02) between groups. This difference might help in the future to distinguish healthy from ADHD patients and therefore diagnose ADHD.

Method of recognizing the high-speed railway noise barriers based on the distance image

NASA Astrophysics Data System (ADS)

Ma, Le; Shao, Shuangyun; Feng, Qibo; Liu, Bingqian; Kim, Chol Ryong

2016-10-01

The damage or lack of the noise barriers is one of the important hidden troubles endangering the safety of high-speed railway. In order to obtain the vibration information of the noise barriers, the online detection systems based on laser vision were proposed. The systems capture images of the laser stripe on the noise barriers and export data files containing distance information between the detection systems on the train and the noise barriers. The vibration status or damage of the noise barriers can be estimated depending on the distance information. In this paper, we focused on the method of separating the area of noise barrier from the background automatically. The test results showed that the proposed method is in good efficiency and accuracy.

Prediction Study on Anti-Slide Control of Railway Vehicle Based on RBF Neural Networks

NASA Astrophysics Data System (ADS)

Yang, Lijun; Zhang, Jimin

While railway vehicle braking, Anti-slide control system will detect operating status of each wheel-sets e.g. speed difference and deceleration etc. Once the detected value on some wheel-set is over pre-defined threshold, brake effort on such wheel-set will be adjusted automatically to avoid blocking. Such method takes effect on guarantee safety operation of vehicle and avoid wheel-set flatness, however it cannot adapt itself to the rail adhesion variation. While wheel-sets slide, the operating status is chaotic time series with certain law, and can be predicted with the law and experiment data in certain time. The predicted values can be used as the input reference signals of vehicle anti-slide control system, to judge and control the slide status of wheel-sets. In this article, the RBF neural networks is taken to predict wheel-set slide status in multi-step with weight vector adjusted based on online self-adaptive algorithm, and the center & normalizing parameters of active function of the hidden unit of RBF neural networks' hidden layer computed with K-means clustering algorithm. With multi-step prediction simulation, the predicted signal with appropriate precision can be used by anti-slide system to trace actively and adjust wheel-set slide tendency, so as to adapt to wheel-rail adhesion variation and reduce the risk of wheel-set blocking.

A new look at the Y tetraquarks and Ω _c baryons in the diquark model

NASA Astrophysics Data System (ADS)

Ali, Ahmed; Maiani, Luciano; Borisov, Anatoly V.; Ahmed, Ishtiaq; Aslam, M. Jamil; Parkhomenko, Alexander Ya.; Polosa, Antonio D.; Rehman, Abdur

2018-01-01

We analyze the hidden charm P-wave tetraquarks in the diquark model, using an effective Hamiltonian incorporating the dominant spin-spin, spin-orbit and tensor interactions. We compare with other P-wave systems such as P-wave charmonia and the newly discovered Ω _c baryons, analysed recently in this framework. Given the uncertain experimental situation on the Y states, we allow for different spectra and discuss the related parameters in the diquark model. In addition to the presently observed ones, we expect many more states in the supermultiplet of L=1 diquarkonia, whose J^{PC} quantum numbers and masses are worked out, using the parameters from the currently preferred Y-states pattern. The existence of these new resonances would be a decisive footprint of the underlying diquark dynamics.

[Results of a structurized discussion within the framework of abortion with particular reference to problems of pregnancy, conflict and related topics (author's transl)].

PubMed

Woynar, W; Schuster, E; Oberheuser, F

1980-02-01

Structured discussions within the framework of social counseling were held with 112 patients in connection with abortion. They were structured according to sociopsychologoical criteria in order to discover any hidden conflicts prevailing in those patients seeking abortion. It became clear that there was a discrepancy between the individual expectation and its translation into reality. Also there was a situation in which too much was demanded of the patient, resulting in an inability to cope with the factors governing her life with subsequent fear of mental and social isolation. Sociologically speaking, the group was divided between elderly socially secured patients who already had children and young patients still undergoing educational or vocational training. (Authors' modified)

Earthquake-induced burial of archaeological sites along the southern Washington coast about A.D. 1700

USGS Publications Warehouse

Cole, S.C.; Atwater, B.F.; McCutcheon, P.T.; Stein, J.K.; Hemphill-Haley, E.

1996-01-01

Although inhabited by thousands of people when first reached by Europeans, the Pacific coast of southern Washington has little recognized evidence of prehistoric human occupation. This apparent contradiction may be explained partly by geologic evidence for coastal submergence during prehistoric earthquakes on the Cascadia subduction zone. Recently discovered archaeological sites, exposed in the banks of two tidal streams, show evidence for earthquake-induced submergence and consequent burial by intertidal mud about A.D. 1700. We surmise that, because of prehistoric earthquakes, other archaeological sites may now lie hidden beneath the surfaces of modern tidelands. Such burial of archaeological sites raises questions about the estimation of prehistoric human population densities along coasts subject to earthquake-induced submergence. ?? 1996 John Wiley & Sons, Inc.

An analysis of pilot error-related aircraft accidents

NASA Technical Reports Server (NTRS)

Kowalsky, N. B.; Masters, R. L.; Stone, R. B.; Babcock, G. L.; Rypka, E. W.

1974-01-01

A multidisciplinary team approach to pilot error-related U.S. air carrier jet aircraft accident investigation records successfully reclaimed hidden human error information not shown in statistical studies. New analytic techniques were developed and applied to the data to discover and identify multiple elements of commonality and shared characteristics within this group of accidents. Three techniques of analysis were used: Critical element analysis, which demonstrated the importance of a subjective qualitative approach to raw accident data and surfaced information heretofore unavailable. Cluster analysis, which was an exploratory research tool that will lead to increased understanding and improved organization of facts, the discovery of new meaning in large data sets, and the generation of explanatory hypotheses. Pattern recognition, by which accidents can be categorized by pattern conformity after critical element identification by cluster analysis.

Literature Mining for the Discovery of Hidden Connections between Drugs, Genes and Diseases

PubMed Central

Frijters, Raoul; van Vugt, Marianne; Smeets, Ruben; van Schaik, René; de Vlieg, Jacob; Alkema, Wynand

2010-01-01

The scientific literature represents a rich source for retrieval of knowledge on associations between biomedical concepts such as genes, diseases and cellular processes. A commonly used method to establish relationships between biomedical concepts from literature is co-occurrence. Apart from its use in knowledge retrieval, the co-occurrence method is also well-suited to discover new, hidden relationships between biomedical concepts following a simple ABC-principle, in which A and C have no direct relationship, but are connected via shared B-intermediates. In this paper we describe CoPub Discovery, a tool that mines the literature for new relationships between biomedical concepts. Statistical analysis using ROC curves showed that CoPub Discovery performed well over a wide range of settings and keyword thesauri. We subsequently used CoPub Discovery to search for new relationships between genes, drugs, pathways and diseases. Several of the newly found relationships were validated using independent literature sources. In addition, new predicted relationships between compounds and cell proliferation were validated and confirmed experimentally in an in vitro cell proliferation assay. The results show that CoPub Discovery is able to identify novel associations between genes, drugs, pathways and diseases that have a high probability of being biologically valid. This makes CoPub Discovery a useful tool to unravel the mechanisms behind disease, to find novel drug targets, or to find novel applications for existing drugs. PMID:20885778

The hidden life of integrative and conjugative elements

PubMed Central

Delavat, François; Miyazaki, Ryo; Carraro, Nicolas; Pradervand, Nicolas

2017-01-01

Abstract Integrative and conjugative elements (ICEs) are widespread mobile DNA that transmit both vertically, in a host-integrated state, and horizontally, through excision and transfer to new recipients. Different families of ICEs have been discovered with more or less restricted host ranges, which operate by similar mechanisms but differ in regulatory networks, evolutionary origin and the types of variable genes they contribute to the host. Based on reviewing recent experimental data, we propose a general model of ICE life style that explains the transition between vertical and horizontal transmission as a result of a bistable decision in the ICE–host partnership. In the large majority of cells, the ICE remains silent and integrated, but hidden at low to very low frequencies in the population specialized host cells appear in which the ICE starts its process of horizontal transmission. This bistable process leads to host cell differentiation, ICE excision and transfer, when suitable recipients are present. The ratio of ICE bistability (i.e. ratio of horizontal to vertical transmission) is the outcome of a balance between fitness costs imposed by the ICE horizontal transmission process on the host cell, and selection for ICE distribution (i.e. ICE ‘fitness’). From this emerges a picture of ICEs as elements that have adapted to a mostly confined life style within their host, but with a very effective and dynamic transfer from a subpopulation of dedicated cells. PMID:28369623

A Neighboring Dwarf Irregular Galaxy Hidden by the Milky Way

NASA Astrophysics Data System (ADS)

Massey, Philip; Henning, P. A.; Kraan-Korteweg, R. C.

2003-11-01

We have obtained VLA and optical follow-up observations of the low-velocity H I source HIZSS 3 discovered by Henning et al. and Rivers in a survey for nearby galaxies hidden by the disk of the Milky Way. Its radio characteristics are consistent with this being a nearby (~1.8 Mpc) low-mass dwarf irregular galaxy (dIm). Our optical imaging failed to reveal a resolved stellar population but did detect an extended Hα emission region. The location of the Hα source is coincident with a partially resolved H I cloud in the 21 cm map. Spectroscopy confirms that the Hα source has a similar radial velocity to that of the H I emission at this location, and thus we have identified an optical counterpart. The Hα emission (100 pc in diameter and with a luminosity of 1.4×1038 ergs s-1) is characteristic of a single H II region containing a modest population of OB stars. The galaxy's radial velocity and distance from the solar apex suggests that it is not a Local Group member, although a more accurate distance is needed to be certain. The properties of HIZSS 3 are comparable to those of GR 8, a nearby dIm with a modest amount of current star formation. Further observations are needed to characterize its stellar population, determine the chemical abundances, and obtain a more reliable distance estimate.

Knowledge discovery from patients' behavior via clustering-classification algorithms based on weighted eRFM and CLV model: An empirical study in public health care services.

PubMed

Zare Hosseini, Zeinab; Mohammadzadeh, Mahdi

2016-01-01

The rapid growing of information technology (IT) motivates and makes competitive advantages in health care industry. Nowadays, many hospitals try to build a successful customer relationship management (CRM) to recognize target and potential patients, increase patient loyalty and satisfaction and finally maximize their profitability. Many hospitals have large data warehouses containing customer demographic and transactions information. Data mining techniques can be used to analyze this data and discover hidden knowledge of customers. This research develops an extended RFM model, namely RFML (added parameter: Length) based on health care services for a public sector hospital in Iran with the idea that there is contrast between patient and customer loyalty, to estimate customer life time value (CLV) for each patient. We used Two-step and K-means algorithms as clustering methods and Decision tree (CHAID) as classification technique to segment the patients to find out target, potential and loyal customers in order to implement strengthen CRM. Two approaches are used for classification: first, the result of clustering is considered as Decision attribute in classification process and second, the result of segmentation based on CLV value of patients (estimated by RFML) is considered as Decision attribute. Finally the results of CHAID algorithm show the significant hidden rules and identify existing patterns of hospital consumers.

Workplace ageism: discovering hidden bias.

PubMed

Malinen, Sanna; Johnston, Lucy

2013-01-01

BACKGROUND/STUDY CONTEXT: Research largely shows no performance differences between older and younger employees, or that older workers even outperform younger employees, yet negative attitudes towards older workers can underpin discrimination. Unfortunately, traditional "explicit" techniques for assessing attitudes (i.e., self-report measures) have serious drawbacks. Therefore, using an approach that is novel to organizational contexts, the authors supplemented explicit with implicit (indirect) measures of attitudes towards older workers, and examined the malleability of both. This research consists of two studies. The authors measured self-report (explicit) attitudes towards older and younger workers with a survey, and implicit attitudes with a reaction-time-based measure of implicit associations. In addition, to test whether attitudes were malleable, the authors measured attitudes before and after a mental imagery intervention, where the authors asked participants in the experimental group to imagine respected and valued older workers from their surroundings. Negative, stable implicit attitudes towards older workers emerged in two studies. Conversely, explicit attitudes showed no age bias and were more susceptible to change intervention, such that attitudes became more positive towards older workers following the experimental manipulation. This research demonstrates the unconscious nature of bias against older workers, and highlights the utility of implicit attitude measures in the context of the workplace. In the current era of aging workforce and skill shortages, implicit measures may be necessary to illuminate hidden workplace ageism.

Knowledge discovery from patients’ behavior via clustering-classification algorithms based on weighted eRFM and CLV model: An empirical study in public health care services

PubMed Central

Zare Hosseini, Zeinab; Mohammadzadeh, Mahdi

2016-01-01

The rapid growing of information technology (IT) motivates and makes competitive advantages in health care industry. Nowadays, many hospitals try to build a successful customer relationship management (CRM) to recognize target and potential patients, increase patient loyalty and satisfaction and finally maximize their profitability. Many hospitals have large data warehouses containing customer demographic and transactions information. Data mining techniques can be used to analyze this data and discover hidden knowledge of customers. This research develops an extended RFM model, namely RFML (added parameter: Length) based on health care services for a public sector hospital in Iran with the idea that there is contrast between patient and customer loyalty, to estimate customer life time value (CLV) for each patient. We used Two-step and K-means algorithms as clustering methods and Decision tree (CHAID) as classification technique to segment the patients to find out target, potential and loyal customers in order to implement strengthen CRM. Two approaches are used for classification: first, the result of clustering is considered as Decision attribute in classification process and second, the result of segmentation based on CLV value of patients (estimated by RFML) is considered as Decision attribute. Finally the results of CHAID algorithm show the significant hidden rules and identify existing patterns of hospital consumers. PMID:27610177

Hum-mPLoc 3.0: prediction enhancement of human protein subcellular localization through modeling the hidden correlations of gene ontology and functional domain features.

PubMed

Zhou, Hang; Yang, Yang; Shen, Hong-Bin

2017-03-15

Protein subcellular localization prediction has been an important research topic in computational biology over the last decade. Various automatic methods have been proposed to predict locations for large scale protein datasets, where statistical machine learning algorithms are widely used for model construction. A key step in these predictors is encoding the amino acid sequences into feature vectors. Many studies have shown that features extracted from biological domains, such as gene ontology and functional domains, can be very useful for improving the prediction accuracy. However, domain knowledge usually results in redundant features and high-dimensional feature spaces, which may degenerate the performance of machine learning models. In this paper, we propose a new amino acid sequence-based human protein subcellular location prediction approach Hum-mPLoc 3.0, which covers 12 human subcellular localizations. The sequences are represented by multi-view complementary features, i.e. context vocabulary annotation-based gene ontology (GO) terms, peptide-based functional domains, and residue-based statistical features. To systematically reflect the structural hierarchy of the domain knowledge bases, we propose a novel feature representation protocol denoted as HCM (Hidden Correlation Modeling), which will create more compact and discriminative feature vectors by modeling the hidden correlations between annotation terms. Experimental results on four benchmark datasets show that HCM improves prediction accuracy by 5-11% and F 1 by 8-19% compared with conventional GO-based methods. A large-scale application of Hum-mPLoc 3.0 on the whole human proteome reveals proteins co-localization preferences in the cell. www.csbio.sjtu.edu.cn/bioinf/Hum-mPLoc3/. hbshen@sjtu.edu.cn. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Syntactic structures in languages and biology.

PubMed

Horn, David

2008-08-01

Both natural languages and cell biology make use of one-dimensional encryption. Their investigation calls for syntactic deciphering of the text and semantic understanding of the resulting structures. Here we discuss recently published algorithms that allow for such searches: automatic distillation of structure (ADIOS) that is successful in discovering syntactic structures in linguistic texts and its motif extraction (MEX) component that can be used for uncovering motifs in DNA and protein sequences. The underlying principles of these syntactic algorithms and some of their results will be described.

Potential and limitations of webcam images for snow cover monitoring in the Swiss Alps

NASA Astrophysics Data System (ADS)

Dizerens, Céline; Hüsler, Fabia; Wunderle, Stefan

2017-04-01

In Switzerland, several thousands of outdoor webcams are currently connected to the Internet. They deliver freely available images that can be used to analyze snow cover variability on a high spatio-temporal resolution. To make use of this big data source, we have implemented a webcam-based snow cover mapping procedure, which allows to almost automatically derive snow cover maps from such webcam images. As there is mostly no information about the webcams and its parameters available, our registration approach automatically resolves these parameters (camera orientation, principal point, field of view) by using an estimate of the webcams position, the mountain silhouette, and a high-resolution digital elevation model (DEM). Combined with an automatic snow classification and an image alignment using SIFT features, our procedure can be applied to arbitrary images to generate snow cover maps with a minimum of effort. Resulting snow cover maps have the same resolution as the digital elevation model and indicate whether each grid cell is snow-covered, snow-free, or hidden from webcams' positions. Up to now, we processed images of about 290 webcams from our archive, and evaluated images of 20 webcams using manually selected ground control points (GCPs) to evaluate the mapping accuracy of our procedure. We present methodological limitations and ongoing improvements, show some applications of our snow cover maps, and demonstrate that webcams not only offer a great opportunity to complement satellite-derived snow retrieval under cloudy conditions, but also serve as a reference for improved validation of satellite-based approaches.

Automatic computational labeling of glomerular textural boundaries

NASA Astrophysics Data System (ADS)

Ginley, Brandon; Tomaszewski, John E.; Sarder, Pinaki

2017-03-01

The glomerulus, a specialized bundle of capillaries, is the blood filtering unit of the kidney. Each human kidney contains about 1 million glomeruli. Structural damages in the glomerular micro-compartments give rise to several renal conditions; most severe of which is proteinuria, where excessive blood proteins flow freely to the urine. The sole way to confirm glomerular structural damage in renal pathology is by examining histopathological or immunofluorescence stained needle biopsies under a light microscope. However, this method is extremely tedious and time consuming, and requires manual scoring on the number and volume of structures. Computational quantification of equivalent features promises to greatly ease this manual burden. The largest obstacle to computational quantification of renal tissue is the ability to recognize complex glomerular textural boundaries automatically. Here we present a computational pipeline to accurately identify glomerular boundaries with high precision and accuracy. The computational pipeline employs an integrated approach composed of Gabor filtering, Gaussian blurring, statistical F-testing, and distance transform, and performs significantly better than standard Gabor based textural segmentation method. Our integrated approach provides mean accuracy/precision of 0.89/0.97 on n = 200Hematoxylin and Eosin (HE) glomerulus images, and mean 0.88/0.94 accuracy/precision on n = 200 Periodic Acid Schiff (PAS) glomerulus images. Respective accuracy/precision of the Gabor filter bank based method is 0.83/0.84 for HE and 0.78/0.8 for PAS. Our method will simplify computational partitioning of glomerular micro-compartments hidden within dense textural boundaries. Automatic quantification of glomeruli will streamline structural analysis in clinic, and can help realize real time diagnoses and interventions.

Association algorithm to mine the rules that govern enzyme definition and to classify protein sequences

PubMed Central

Chiu, Shih-Hau; Chen, Chien-Chi; Yuan, Gwo-Fang; Lin, Thy-Hou

2006-01-01

Background The number of sequences compiled in many genome projects is growing exponentially, but most of them have not been characterized experimentally. An automatic annotation scheme must be in an urgent need to reduce the gap between the amount of new sequences produced and reliable functional annotation. This work proposes rules for automatically classifying the fungus genes. The approach involves elucidating the enzyme classifying rule that is hidden in UniProt protein knowledgebase and then applying it for classification. The association algorithm, Apriori, is utilized to mine the relationship between the enzyme class and significant InterPro entries. The candidate rules are evaluated for their classificatory capacity. Results There were five datasets collected from the Swiss-Prot for establishing the annotation rules. These were treated as the training sets. The TrEMBL entries were treated as the testing set. A correct enzyme classification rate of 70% was obtained for the prokaryote datasets and a similar rate of about 80% was obtained for the eukaryote datasets. The fungus training dataset which lacks an enzyme class description was also used to evaluate the fungus candidate rules. A total of 88 out of 5085 test entries were matched with the fungus rule set. These were otherwise poorly annotated using their functional descriptions. Conclusion The feasibility of using the method presented here to classify enzyme classes based on the enzyme domain rules is evident. The rules may be also employed by the protein annotators in manual annotation or implemented in an automatic annotation flowchart. PMID:16776838

The Hidden Diversity of Zanclea Associated with Scleractinians Revealed by Molecular Data

PubMed Central

Montano, Simone; Maggioni, Davide; Arrigoni, Roberto; Seveso, Davide; Puce, Stefania; Galli, Paolo

2015-01-01

Scleractinian reef corals have recently been acknowledged as the most numerous host group found in association with hydroids belonging to the Zanclea genus. However, knowledge of the molecular phylogenetic relationships among Zanclea species associated with scleractinians is just beginning. This study, using the nuclear 28S rDNA region and the fast-evolving mitochondrial 16S rRNA and COI genes, provides the most comprehensive phylogenetic reconstruction of the genus Zanclea with a particular focus on the genetic diversity among Zanclea specimens associated with 13 scleractinian genera. The monophyly of Zanclea associated with scleractinians was strongly supported in all nuclear and mitochondrial phylogenetic reconstructions. Furthermore, a combined mitochondrial 16S and COI phylogenetic tree revealed a multitude of hidden molecular lineages within this group (Clades I, II, III, V, VI, VII, and VIII), suggesting the existence of both host-generalist and genus-specific lineages of Zanclea associated with scleractinians. In addition to Z. gallii living in association with the genus Acropora, we discovered four well-supported lineages (Clades I, II, III, and VII), each one forming a strict association with a single scleractinian genus, including sequences of Zanclea associated with Montipora from two geographically separated areas (Maldives and Taiwan). Two host-generalist Zanclea lineages were also observed, and one of them was formed by Zanclea specimens symbiotic with seven scleractinian genera (Clade VIII). We also found that the COI gene allows the recognition of separated hidden lineages in agreement with the commonly recommended mitochondrial 16S as a DNA barcoding gene for Hydrozoa and shows reasonable potential for phylogenetic and evolutionary analyses in the genus Zanclea. Finally, as no DNA sequences are available for the majority of the nominal Zanclea species known, we note that they will be necessary to elucidate the diversity of the Zanclea-scleractinian association. PMID:26207903

Ethical violations in the clinical setting: the hidden curriculum learning experience of Pakistani nurses.

PubMed

Jafree, Sara Rizvi; Zakar, Rubeena; Fischer, Florian; Zakar, Muhammad Zakria

2015-03-19

The importance of the hidden curriculum is recognised as a practical training ground for the absorption of medical ethics by healthcare professionals. Pakistan's healthcare sector is hampered by the exclusion of ethics from medical and nursing education curricula and the absence of monitoring of ethical violations in the clinical setting. Nurses have significant knowledge of the hidden curriculum taught during clinical practice, due to long working hours in the clinic and front-line interaction with patients and other practitioners. The means of inquiry for this study was qualitative, with 20 interviews and four focus group discussions used to identify nurses' clinical experiences of ethical violations. Content analysis was used to discover sub-categories of ethical violations, as perceived by nurses, within four pre-defined categories of nursing codes of ethics: 1) professional guidelines and integrity, 2) patient informed consent, 3) patient rights, and 4) co-worker coordination for competency, learning and patient safety. Ten sub-categories of ethical violations were found: nursing students being used as adjunct staff, nurses having to face frequent violence in the hospital setting, patient reluctance to receive treatment from nurses, the near-absence of consent taken from patients for most non-surgical medical procedures, the absence of patient consent taking for receiving treatment from student nurses, the practice of patient discrimination on the basis of a patient's socio-demographic status, nurses withdrawing treatment out of fear for their safety, a non-learning culture and, finally, blame-shifting and non-reportage of errors. Immediate and urgent attention is required to reduce ethical violations in the healthcare sector in Pakistan through collaborative efforts by the government, the healthcare sector, and ethics regulatory bodies. Also, changes in socio-cultural values in hospital organisation, public awareness of how to conveniently report ethical violations by practitioners and public perceptions of nurse identity are needed.

[Dawning of inhalational anesthesia: a historical perspective.].

PubMed

Maia, R Icardo Jakson de Freitas; Fernandes, Cláudia Regina

2002-11-01

History, unlike one may imagine, is not something unchangeable and limited to the past. It is adapted according to conveniences of one or other ruling social class. Deliberately or accidentally hidden information, when unveiled may change current concepts, so far taken for granted. So, history, as any other science, is not totally impartial; it suffers influences and interferences of political, religious, economic and cultural thinking. The same is true for anesthesia. Some questions remain unanswered: Why did it take so long for the civilization to control pain? Who did in fact discover Anesthesia? How was the world when Anesthesia was officially discovered? To discuss such questions it is necessary to go back to the History of Anesthesia. This paper addresses the surgical act, pain and anesthesia from the Hellenic culture to the first officially recognized anesthesia, often emphasizing forgotten names and historical peculiarities which have benefited or harmed one or other discoverer. It also focuses on values, culture and scientific developments of the 19th century, correlating them to events that marked the dawning of anesthesia. It would be unfair to attribute the merit of discovering anesthesia to a single person. Historical peculiarities that benefited or harmed one or other researcher cannot be forgotten. Morton was undoubtedly the most favored by the circumstances. He lived in a privileged time and place and has met the most adequate people to his intent. However there is still a question. After all, who is the most important: the father of the idea or who disclosed it? The answer will certainly remain in the field of subjectivity.

Automatic reconstruction of a bacterial regulatory network using Natural Language Processing

PubMed Central

Rodríguez-Penagos, Carlos; Salgado, Heladia; Martínez-Flores, Irma; Collado-Vides, Julio

2007-01-01

Background Manual curation of biological databases, an expensive and labor-intensive process, is essential for high quality integrated data. In this paper we report the implementation of a state-of-the-art Natural Language Processing system that creates computer-readable networks of regulatory interactions directly from different collections of abstracts and full-text papers. Our major aim is to understand how automatic annotation using Text-Mining techniques can complement manual curation of biological databases. We implemented a rule-based system to generate networks from different sets of documents dealing with regulation in Escherichia coli K-12. Results Performance evaluation is based on the most comprehensive transcriptional regulation database for any organism, the manually-curated RegulonDB, 45% of which we were able to recreate automatically. From our automated analysis we were also able to find some new interactions from papers not already curated, or that were missed in the manual filtering and review of the literature. We also put forward a novel Regulatory Interaction Markup Language better suited than SBML for simultaneously representing data of interest for biologists and text miners. Conclusion Manual curation of the output of automatic processing of text is a good way to complement a more detailed review of the literature, either for validating the results of what has been already annotated, or for discovering facts and information that might have been overlooked at the triage or curation stages. PMID:17683642

Role of Automatic Wireless Remote Monitoring Immediately Following ICD Implant: The Lumos-T Reduces Routine Office Device Follow-Up Study (TRUST) Trial.

PubMed

Varma, Niraj; Epstein, Andrew E; Schweikert, Robert; Michalski, Justin; Love, Charles J

2016-03-01

The incidence of unscheduled encounters and problem occurrence between ICD implant and first in-person evaluation (IPE) recommended at 12 weeks is unknown. Automatic remote home monitoring (HM) may be useful in this potentially unstable period. ICD patients were randomized 2:1 to HM enabled post-implant (n = 908) or to conventional monitoring (CM; n = 431). Groups were compared between implant and prior to first scheduled IPE for IPE incidence, causes, and actionability (reprogramming, system revision, medication changes) and event detection time. HM and CM patients were similar (mean age 63 years, 72% male, LVEF 29%, primary prevention 73%, DDD 57%). In the post-implant interval assessed (HM 100 ± 21.3 days vs. CM 101 ± 20.8 days, P = 0.54), 85.4% (776/908) HM patients and 87.7% CM (378/431) patients had no cause for IPE (P = 0.31). When IPE occurred, actionability in HM (64/177 [36.2%]) was greater versus CM (15/62 [24.2%], P = 0.12). Actionable items were discovered sooner with HM (P = 0.025). Device reprogramming or lead revision was triggered following 53/177 (29.9%) IPEs in HM versus 9/62 (14.5%) in CM (P = 0.018). Arrhythmia detection was enhanced by HM: 276 atrial and ventricular episodes were detected in 135 follow-ups in contrast to CM (65 episodes at 17 IPEs). More silent arrhythmic episodes were discovered by HM (7.2% vs. 1.5% [P = 0.15]). Since 27/42 (64.3%) IPEs driven by HM alerts were actionable, event notification was a valuable method for problem detection. Importantly, HM did not increase incidence of non-actionable IPEs (P = 0.72). Activation of automatic remote monitoring should be encouraged soon post-ICD implant. © 2015 Wiley Periodicals, Inc.

Structure-based manual screening and automatic networking for systematically exploring sansanmycin analogues using high performance liquid chromatography tandem mass spectroscopy.

PubMed

Jiang, Zhi-Bo; Ren, Wei-Cong; Shi, Yuan-Yuan; Li, Xing-Xing; Lei, Xuan; Fan, Jia-Hui; Zhang, Cong; Gu, Ren-Jie; Wang, Li-Fei; Xie, Yun-Ying; Hong, Bin

2018-05-18

Sansanmycins (SS), one of several known uridyl peptide antibiotics (UPAs) possessing a unique chemical scaffold, showed a good inhibitory effect on the highly refractory pathogens Pseudomonas aeruginosa and Mycobacterium tuberculosis, especially on the multi-drug resistant M. tuberculosis. This study employed high performance liquid chromatography-mass spectrometry detector (HPLC-MSD) ion trap and LTQ orbitrap tandem mass spectrometry (MS/MS) to explore sansanmycin analogues manually and automatically by re-analysis of the Streptomyces sp. SS fermentation broth. The structure-based manual screening method, based on analysis of the fragmentation pathway of known UPAs and on comparisons of the MS/MS spectra with that of sansanmycin A (SS-A), resulted in identifying twenty sansanmycin analogues, including twelve new structures (1-12). Furthermore, to deeply explore sansanmycin analogues, we utilized a GNPS based molecular networking workflow to re-analyze the HPLC-MS/MS data automatically. As a result, eight more new sansanmycins (13-20) were discovered. Compound 1 was discovered to lose two amino acids of residue 1 (AA 1 ) and (2S, 3S)-N 3 -methyl-2,3-diamino butyric acid (DABA) from the N-terminus, and compounds 6, 11 and 12 were found to contain a 2',3'-dehydrated 4',5'-enamine-3'-deoxyuridyl moiety, which have not been reported before. Interestingly, three trace components with novel 5,6-dihydro-5'-aminouridyl group (16-18) were detected for the first time in the sansanmycin-producing strain. Their structures were primarily determined by detail analysis of the data from MS/MS. Compounds 8 and 10 were further confirmed by nuclear magnetic resonance (NMR) data, which proved the efficiency and accuracy of the method of HPLC-MS/MS for exploration of novel UPAs. Comparing to manual screening, the networking method can provide systematic visualization results. Manual screening and networking method may complement with each other to facilitate the mining of novel UPAs. Copyright © 2018 Elsevier B.V. All rights reserved.

The Semantic Automated Discovery and Integration (SADI) Web service Design-Pattern, API and Reference Implementation

PubMed Central

2011-01-01

Background The complexity and inter-related nature of biological data poses a difficult challenge for data and tool integration. There has been a proliferation of interoperability standards and projects over the past decade, none of which has been widely adopted by the bioinformatics community. Recent attempts have focused on the use of semantics to assist integration, and Semantic Web technologies are being welcomed by this community. Description SADI - Semantic Automated Discovery and Integration - is a lightweight set of fully standards-compliant Semantic Web service design patterns that simplify the publication of services of the type commonly found in bioinformatics and other scientific domains. Using Semantic Web technologies at every level of the Web services "stack", SADI services consume and produce instances of OWL Classes following a small number of very straightforward best-practices. In addition, we provide codebases that support these best-practices, and plug-in tools to popular developer and client software that dramatically simplify deployment of services by providers, and the discovery and utilization of those services by their consumers. Conclusions SADI Services are fully compliant with, and utilize only foundational Web standards; are simple to create and maintain for service providers; and can be discovered and utilized in a very intuitive way by biologist end-users. In addition, the SADI design patterns significantly improve the ability of software to automatically discover appropriate services based on user-needs, and automatically chain these into complex analytical workflows. We show that, when resources are exposed through SADI, data compliant with a given ontological model can be automatically gathered, or generated, from these distributed, non-coordinating resources - a behaviour we have not observed in any other Semantic system. Finally, we show that, using SADI, data dynamically generated from Web services can be explored in a manner very similar to data housed in static triple-stores, thus facilitating the intersection of Web services and Semantic Web technologies. PMID:22024447

The Semantic Automated Discovery and Integration (SADI) Web service Design-Pattern, API and Reference Implementation.

PubMed

Wilkinson, Mark D; Vandervalk, Benjamin; McCarthy, Luke

2011-10-24

The complexity and inter-related nature of biological data poses a difficult challenge for data and tool integration. There has been a proliferation of interoperability standards and projects over the past decade, none of which has been widely adopted by the bioinformatics community. Recent attempts have focused on the use of semantics to assist integration, and Semantic Web technologies are being welcomed by this community. SADI - Semantic Automated Discovery and Integration - is a lightweight set of fully standards-compliant Semantic Web service design patterns that simplify the publication of services of the type commonly found in bioinformatics and other scientific domains. Using Semantic Web technologies at every level of the Web services "stack", SADI services consume and produce instances of OWL Classes following a small number of very straightforward best-practices. In addition, we provide codebases that support these best-practices, and plug-in tools to popular developer and client software that dramatically simplify deployment of services by providers, and the discovery and utilization of those services by their consumers. SADI Services are fully compliant with, and utilize only foundational Web standards; are simple to create and maintain for service providers; and can be discovered and utilized in a very intuitive way by biologist end-users. In addition, the SADI design patterns significantly improve the ability of software to automatically discover appropriate services based on user-needs, and automatically chain these into complex analytical workflows. We show that, when resources are exposed through SADI, data compliant with a given ontological model can be automatically gathered, or generated, from these distributed, non-coordinating resources - a behaviour we have not observed in any other Semantic system. Finally, we show that, using SADI, data dynamically generated from Web services can be explored in a manner very similar to data housed in static triple-stores, thus facilitating the intersection of Web services and Semantic Web technologies.

GAFFE: a gaze-attentive fixation finding engine.

PubMed

Rajashekar, U; van der Linde, I; Bovik, A C; Cormack, L K

2008-04-01

The ability to automatically detect visually interesting regions in images has many practical applications, especially in the design of active machine vision and automatic visual surveillance systems. Analysis of the statistics of image features at observers' gaze can provide insights into the mechanisms of fixation selection in humans. Using a foveated analysis framework, we studied the statistics of four low-level local image features: luminance, contrast, and bandpass outputs of both luminance and contrast, and discovered that image patches around human fixations had, on average, higher values of each of these features than image patches selected at random. Contrast-bandpass showed the greatest difference between human and random fixations, followed by luminance-bandpass, RMS contrast, and luminance. Using these measurements, we present a new algorithm that selects image regions as likely candidates for fixation. These regions are shown to correlate well with fixations recorded from human observers.

Auditing hierarchical cycles to locate other inconsistencies in the UMLS.

PubMed

Halper, Michael; Morrey, C Paul; Chen, Yan; Elhanan, Gai; Hripcsak, George; Perl, Yehoshua

2011-01-01

A cycle in the parent relationship hierarchy of the UMLS is a configuration that effectively makes some concept(s) an ancestor of itself. Such a structural inconsistency can easily be found automatically. A previous strategy for disconnecting cycles is to break them with the deletion of one or more parent relationships-irrespective of the correctness of the deleted relationships. A methodology is introduced for auditing of cycles that seeks to discover and delete erroneous relationships only. Cycles involving three concepts are the primary consideration. Hypotheses about the high probability of locating an erroneous parent relationship in a cycle are proposed and confirmed with statistical confidence and lend credence to the auditing approach. A cycle may serve as an indicator of other non-structural inconsistencies that are otherwise difficult to detect automatically. An extensive auditing example shows how a cycle can indicate further inconsistencies.

Auditing Hierarchical Cycles to Locate Other Inconsistencies in the UMLS

PubMed Central

Halper, Michael; Morrey, C. Paul; Chen, Yan; Elhanan, Gai; Hripcsak, George; Perl, Yehoshua

2011-01-01

A cycle in the parent relationship hierarchy of the UMLS is a configuration that effectively makes some concept(s) an ancestor of itself. Such a structural inconsistency can easily be found automatically. A previous strategy for disconnecting cycles is to break them with the deletion of one or more parent relationships—irrespective of the correctness of the deleted relationships. A methodology is introduced for auditing of cycles that seeks to discover and delete erroneous relationships only. Cycles involving three concepts are the primary consideration. Hypotheses about the high probability of locating an erroneous parent relationship in a cycle are proposed and confirmed with statistical confidence and lend credence to the auditing approach. A cycle may serve as an indicator of other non-structural inconsistencies that are otherwise difficult to detect automatically. An extensive auditing example shows how a cycle can indicate further inconsistencies. PMID:22195107

A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules.

PubMed

Nguyen, Su; Mei, Yi; Xue, Bing; Zhang, Mengjie

2018-06-04

Designing effective dispatching rules for production systems is a difficult and timeconsuming task if it is done manually. In the last decade, the growth of computing power, advanced machine learning, and optimisation techniques has made the automated design of dispatching rules possible and automatically discovered rules are competitive or outperform existing rules developed by researchers. Genetic programming is one of the most popular approaches to discovering dispatching rules in the literature, especially for complex production systems. However, the large heuristic search space may restrict genetic programming from finding near optimal dispatching rules. This paper develops a new hybrid genetic programming algorithm for dynamic job shop scheduling based on a new representation, a new local search heuristic, and efficient fitness evaluators. Experiments show that the new method is effective regarding the quality of evolved rules. Moreover, evolved rules are also significantly smaller and contain more relevant attributes.

Automatic evidence retrieval for systematic reviews.

PubMed

Choong, Miew Keen; Galgani, Filippo; Dunn, Adam G; Tsafnat, Guy

2014-10-01

Snowballing involves recursively pursuing relevant references cited in the retrieved literature and adding them to the search results. Snowballing is an alternative approach to discover additional evidence that was not retrieved through conventional search. Snowballing's effectiveness makes it best practice in systematic reviews despite being time-consuming and tedious. Our goal was to evaluate an automatic method for citation snowballing's capacity to identify and retrieve the full text and/or abstracts of cited articles. Using 20 review articles that contained 949 citations to journal or conference articles, we manually searched Microsoft Academic Search (MAS) and identified 78.0% (740/949) of the cited articles that were present in the database. We compared the performance of the automatic citation snowballing method against the results of this manual search, measuring precision, recall, and F1 score. The automatic method was able to correctly identify 633 (as proportion of included citations: recall=66.7%, F1 score=79.3%; as proportion of citations in MAS: recall=85.5%, F1 score=91.2%) of citations with high precision (97.7%), and retrieved the full text or abstract for 490 (recall=82.9%, precision=92.1%, F1 score=87.3%) of the 633 correctly retrieved citations. The proposed method for automatic citation snowballing is accurate and is capable of obtaining the full texts or abstracts for a substantial proportion of the scholarly citations in review articles. By automating the process of citation snowballing, it may be possible to reduce the time and effort of common evidence surveillance tasks such as keeping trial registries up to date and conducting systematic reviews.

Supporting the annotation of chronic obstructive pulmonary disease (COPD) phenotypes with text mining workflows.

PubMed

Fu, Xiao; Batista-Navarro, Riza; Rak, Rafal; Ananiadou, Sophia

2015-01-01

Chronic obstructive pulmonary disease (COPD) is a life-threatening lung disorder whose recent prevalence has led to an increasing burden on public healthcare. Phenotypic information in electronic clinical records is essential in providing suitable personalised treatment to patients with COPD. However, as phenotypes are often "hidden" within free text in clinical records, clinicians could benefit from text mining systems that facilitate their prompt recognition. This paper reports on a semi-automatic methodology for producing a corpus that can ultimately support the development of text mining tools that, in turn, will expedite the process of identifying groups of COPD patients. A corpus of 30 full-text papers was formed based on selection criteria informed by the expertise of COPD specialists. We developed an annotation scheme that is aimed at producing fine-grained, expressive and computable COPD annotations without burdening our curators with a highly complicated task. This was implemented in the Argo platform by means of a semi-automatic annotation workflow that integrates several text mining tools, including a graphical user interface for marking up documents. When evaluated using gold standard (i.e., manually validated) annotations, the semi-automatic workflow was shown to obtain a micro-averaged F-score of 45.70% (with relaxed matching). Utilising the gold standard data to train new concept recognisers, we demonstrated that our corpus, although still a work in progress, can foster the development of significantly better performing COPD phenotype extractors. We describe in this work the means by which we aim to eventually support the process of COPD phenotype curation, i.e., by the application of various text mining tools integrated into an annotation workflow. Although the corpus being described is still under development, our results thus far are encouraging and show great potential in stimulating the development of further automatic COPD phenotype extractors.

Occupational self-coding and automatic recording (OSCAR): a novel web-based tool to collect and code lifetime job histories in large population-based studies.

PubMed

De Matteis, Sara; Jarvis, Deborah; Young, Heather; Young, Alan; Allen, Naomi; Potts, James; Darnton, Andrew; Rushton, Lesley; Cullinan, Paul

2017-03-01

Objectives The standard approach to the assessment of occupational exposures is through the manual collection and coding of job histories. This method is time-consuming and costly and makes it potentially unfeasible to perform high quality analyses on occupational exposures in large population-based studies. Our aim was to develop a novel, efficient web-based tool to collect and code lifetime job histories in the UK Biobank, a population-based cohort of over 500 000 participants. Methods We developed OSCAR (occupations self-coding automatic recording) based on the hierarchical structure of the UK Standard Occupational Classification (SOC) 2000, which allows individuals to collect and automatically code their lifetime job histories via a simple decision-tree model. Participants were asked to find each of their jobs by selecting appropriate job categories until they identified their job title, which was linked to a hidden 4-digit SOC code. For each occupation a job title in free text was also collected to estimate Cohen's kappa (κ) inter-rater agreement between SOC codes assigned by OSCAR and an expert manual coder. Results OSCAR was administered to 324 653 UK Biobank participants with an existing email address between June and September 2015. Complete 4-digit SOC-coded lifetime job histories were collected for 108 784 participants (response rate: 34%). Agreement between the 4-digit SOC codes assigned by OSCAR and the manual coder for a random sample of 400 job titles was moderately good [κ=0.45, 95% confidence interval (95% CI) 0.42-0.49], and improved when broader job categories were considered (κ=0.64, 95% CI 0.61-0.69 at a 1-digit SOC-code level). Conclusions OSCAR is a novel, efficient, and reasonably reliable web-based tool for collecting and automatically coding lifetime job histories in large population-based studies. Further application in other research projects for external validation purposes is warranted.

Paying attention to reading: the neurobiology of reading and dyslexia.

PubMed

Shaywitz, Sally E; Shaywitz, Bennett A

2008-01-01

Extraordinary progress in functional brain imaging, primarily advances in functional magnetic resonance imaging, now allows scientists to understand the neural systems serving reading and how these systems differ in dyslexic readers. Scientists now speak of the neural signature of dyslexia, a singular achievement that for the first time has made what was previously a hidden disability, now visible. Paralleling this achievement in understanding the neurobiology of dyslexia, progress in the identification and treatment of dyslexia now offers the hope of identifying children at risk for dyslexia at a very young age and providing evidence-based, effective interventions. Despite these advances, for many dyslexic readers, becoming a skilled, automatic reader remains elusive, in great part because though children with dyslexia can be taught to decode words, teaching children to read fluently and automatically represents the next frontier in research on dyslexia. We suggest that to break through this "fluency" barrier, investigators will need to reexamine the more than 20-year-old central dogma in reading research: the generation of the phonological code from print is modular, that is, automatic and not attention demanding, and not requiring any other cognitive process. Recent findings now present a competing view: other cognitive processes are involved in reading, particularly attentional mechanisms, and that disruption of these attentional mechanisms play a causal role in reading difficulties. Recognition of the role of attentional mechanisms in reading now offer potentially new strategies for interventions in dyslexia. In particular, the use of pharmacotherapeutic agents affecting attentional mechanisms not only may provide a window into the neurochemical mechanisms underlying dyslexia but also may offer a potential adjunct treatment for teaching dyslexic readers to read fluently and automatically. Preliminary studies suggest that agents traditionally used to treat disorders of attention, particularly attention-deficit/hyperactivity disorder, may prove to be an effective adjunct to improving reading in dyslexic students.

Automated circumferential construction of first-order aqueous humor outflow pathways using spectral-domain optical coherence tomography

NASA Astrophysics Data System (ADS)

Huang, Alex S.; Belghith, Akram; Dastiridou, Anna; Chopra, Vikas; Zangwill, Linda M.; Weinreb, Robert N.

2017-06-01

The purpose was to create a three-dimensional (3-D) model of circumferential aqueous humor outflow (AHO) in a living human eye with an automated detection algorithm for Schlemm's canal (SC) and first-order collector channels (CC) applied to spectral-domain optical coherence tomography (SD-OCT). Anterior segment SD-OCT scans from a subject were acquired circumferentially around the limbus. A Bayesian Ridge method was used to approximate the location of the SC on infrared confocal laser scanning ophthalmoscopic images with a cross multiplication tool developed to initiate SC/CC detection automated through a fuzzy hidden Markov Chain approach. Automatic segmentation of SC and initial CC's was manually confirmed by two masked graders. Outflow pathways detected by the segmentation algorithm were reconstructed into a 3-D representation of AHO. Overall, only <1% of images (5114 total B-scans) were ungradable. Automatic segmentation algorithm performed well with SC detection 98.3% of the time and <0.1% false positive detection compared to expert grader consensus. CC was detected 84.2% of the time with 1.4% false positive detection. 3-D representation of AHO pathways demonstrated variably thicker and thinner SC with some clear CC roots. Circumferential (360 deg), automated, and validated AHO detection of angle structures in the living human eye with reconstruction was possible.

Efficient self-organizing multilayer neural network for nonlinear system modeling.

PubMed

Han, Hong-Gui; Wang, Li-Dan; Qiao, Jun-Fei

2013-07-01

It has been shown extensively that the dynamic behaviors of a neural system are strongly influenced by the network architecture and learning process. To establish an artificial neural network (ANN) with self-organizing architecture and suitable learning algorithm for nonlinear system modeling, an automatic axon-neural network (AANN) is investigated in the following respects. First, the network architecture is constructed automatically to change both the number of hidden neurons and topologies of the neural network during the training process. The approach introduced in adaptive connecting-and-pruning algorithm (ACP) is a type of mixed mode operation, which is equivalent to pruning or adding the connecting of the neurons, as well as inserting some required neurons directly. Secondly, the weights are adjusted, using a feedforward computation (FC) to obtain the information for the gradient during learning computation. Unlike most of the previous studies, AANN is able to self-organize the architecture and weights, and to improve the network performances. Also, the proposed AANN has been tested on a number of benchmark problems, ranging from nonlinear function approximating to nonlinear systems modeling. The experimental results show that AANN can have better performances than that of some existing neural networks. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Protein remote homology detection based on bidirectional long short-term memory.

PubMed

Li, Shumin; Chen, Junjie; Liu, Bin

2017-10-10

Protein remote homology detection plays a vital role in studies of protein structures and functions. Almost all of the traditional machine leaning methods require fixed length features to represent the protein sequences. However, it is never an easy task to extract the discriminative features with limited knowledge of proteins. On the other hand, deep learning technique has demonstrated its advantage in automatically learning representations. It is worthwhile to explore the applications of deep learning techniques to the protein remote homology detection. In this study, we employ the Bidirectional Long Short-Term Memory (BLSTM) to learn effective features from pseudo proteins, also propose a predictor called ProDec-BLSTM: it includes input layer, bidirectional LSTM, time distributed dense layer and output layer. This neural network can automatically extract the discriminative features by using bidirectional LSTM and the time distributed dense layer. Experimental results on a widely-used benchmark dataset show that ProDec-BLSTM outperforms other related methods in terms of both the mean ROC and mean ROC50 scores. This promising result shows that ProDec-BLSTM is a useful tool for protein remote homology detection. Furthermore, the hidden patterns learnt by ProDec-BLSTM can be interpreted and visualized, and therefore, additional useful information can be obtained.

The Iqmulus Urban Showcase: Automatic Tree Classification and Identification in Huge Mobile Mapping Point Clouds

NASA Astrophysics Data System (ADS)

Böhm, J.; Bredif, M.; Gierlinger, T.; Krämer, M.; Lindenberg, R.; Liu, K.; Michel, F.; Sirmacek, B.

2016-06-01

Current 3D data capturing as implemented on for example airborne or mobile laser scanning systems is able to efficiently sample the surface of a city by billions of unselective points during one working day. What is still difficult is to extract and visualize meaningful information hidden in these point clouds with the same efficiency. This is where the FP7 IQmulus project enters the scene. IQmulus is an interactive facility for processing and visualizing big spatial data. In this study the potential of IQmulus is demonstrated on a laser mobile mapping point cloud of 1 billion points sampling ~ 10 km of street environment in Toulouse, France. After the data is uploaded to the IQmulus Hadoop Distributed File System, a workflow is defined by the user consisting of retiling the data followed by a PCA driven local dimensionality analysis, which runs efficiently on the IQmulus cloud facility using a Spark implementation. Points scattering in 3 directions are clustered in the tree class, and are separated next into individual trees. Five hours of processing at the 12 node computing cluster results in the automatic identification of 4000+ urban trees. Visualization of the results in the IQmulus fat client helps users to appreciate the results, and developers to identify remaining flaws in the processing workflow.

An overview of animal science research 1945-2011 through science mapping analysis.

PubMed

Rodriguez-Ledesma, A; Cobo, M J; Lopez-Pujalte, C; Herrera-Viedma, E

2015-12-01

The conceptual structure of the field of Animal Science (AS) research is examined by means of a longitudinal science mapping analysis. The whole of the AS research field is analysed, revealing its conceptual evolution. To this end, an automatic approach to detecting and visualizing hidden themes or topics and their evolution across a consecutive span of years was applied to AS publications of the JCR category 'Agriculture, Dairy & Animal Science' during the period 1945-2011. This automatic approach was based on a coword analysis and combines performance analysis and science mapping. To observe the conceptual evolution of AS, six consecutive periods were defined: 1945-1969, 1970-1979, 1980-1989, 1990-1999, 2000-2005 and 2006-2011. Research in AS was identified as having focused on ten main thematic areas: ANIMAL-FEEDING, SMALL-RUMINANTS, ANIMAL-REPRODUCTION, DAIRY-PRODUCTION, MEAT-QUALITY, SWINE-PRODUCTION, GENETICS-AND-ANIMAL-BREEDING, POULTRY, ANIMAL-WELFARE and GROWTH-FACTORS-AND-FATTY-ACIDS. The results show how genomic studies gain in weight and integrate with other thematic areas. The whole of AS research has become oriented towards an overall framework in which animal welfare, sustainable management and human health play a major role. All this would affect the future structure and management of livestock farming. © 2014 Blackwell Verlag GmbH.

Data mining for multiagent rules, strategies, and fuzzy decision tree structure

NASA Astrophysics Data System (ADS)

Smith, James F., III; Rhyne, Robert D., II; Fisher, Kristin

2002-03-01

A fuzzy logic based resource manager (RM) has been developed that automatically allocates electronic attack resources in real-time over many dissimilar platforms. Two different data mining algorithms have been developed to determine rules, strategies, and fuzzy decision tree structure. The first data mining algorithm uses a genetic algorithm as a data mining function and is called from an electronic game. The game allows a human expert to play against the resource manager in a simulated battlespace with each of the defending platforms being exclusively directed by the fuzzy resource manager and the attacking platforms being controlled by the human expert or operating autonomously under their own logic. This approach automates the data mining problem. The game automatically creates a database reflecting the domain expert's knowledge. It calls a data mining function, a genetic algorithm, for data mining of the database as required and allows easy evaluation of the information mined in the second step. The criterion for re- optimization is discussed as well as experimental results. Then a second data mining algorithm that uses a genetic program as a data mining function is introduced to automatically discover fuzzy decision tree structures. Finally, a fuzzy decision tree generated through this process is discussed.

Co-evolutionary data mining for fuzzy rules: automatic fitness function creation phase space, and experiments

NASA Astrophysics Data System (ADS)

Smith, James F., III; Blank, Joseph A.

2003-03-01

An approach is being explored that involves embedding a fuzzy logic based resource manager in an electronic game environment. Game agents can function under their own autonomous logic or human control. This approach automates the data mining problem. The game automatically creates a cleansed database reflecting the domain expert's knowledge, it calls a data mining function, a genetic algorithm, for data mining of the data base as required and allows easy evaluation of the information extracted. The co-evolutionary fitness functions, chromosomes and stopping criteria for ending the game are discussed. Genetic algorithm and genetic program based data mining procedures are discussed that automatically discover new fuzzy rules and strategies. The strategy tree concept and its relationship to co-evolutionary data mining are examined as well as the associated phase space representation of fuzzy concepts. The overlap of fuzzy concepts in phase space reduces the effective strategies available to adversaries. Co-evolutionary data mining alters the geometric properties of the overlap region known as the admissible region of phase space significantly enhancing the performance of the resource manager. Procedures for validation of the information data mined are discussed and significant experimental results provided.

Automatic dirt trail analysis in dermoscopy images.

PubMed

Cheng, Beibei; Joe Stanley, R; Stoecker, William V; Osterwise, Christopher T P; Stricklin, Sherea M; Hinton, Kristen A; Moss, Randy H; Oliviero, Margaret; Rabinovitz, Harold S

2013-02-01

Basal cell carcinoma (BCC) is the most common cancer in the US. Dermatoscopes are devices used by physicians to facilitate the early detection of these cancers based on the identification of skin lesion structures often specific to BCCs. One new lesion structure, referred to as dirt trails, has the appearance of dark gray, brown or black dots and clods of varying sizes distributed in elongated clusters with indistinct borders, often appearing as curvilinear trails. In this research, we explore a dirt trail detection and analysis algorithm for extracting, measuring, and characterizing dirt trails based on size, distribution, and color in dermoscopic skin lesion images. These dirt trails are then used to automatically discriminate BCC from benign skin lesions. For an experimental data set of 35 BCC images with dirt trails and 79 benign lesion images, a neural network-based classifier achieved a 0.902 are under a receiver operating characteristic curve using a leave-one-out approach. Results obtained from this study show that automatic detection of dirt trails in dermoscopic images of BCC is feasible. This is important because of the large number of these skin cancers seen every year and the challenge of discovering these earlier with instrumentation. © 2011 John Wiley & Sons A/S.

A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.

PubMed

Gong, Maoguo; Liu, Jia; Li, Hao; Cai, Qing; Su, Linzhi

2015-12-01

Hierarchical deep neural networks are currently popular learning models for imitating the hierarchical architecture of human brain. Single-layer feature extractors are the bricks to build deep networks. Sparse feature learning models are popular models that can learn useful representations. But most of those models need a user-defined constant to control the sparsity of representations. In this paper, we propose a multiobjective sparse feature learning model based on the autoencoder. The parameters of the model are learnt by optimizing two objectives, reconstruction error and the sparsity of hidden units simultaneously to find a reasonable compromise between them automatically. We design a multiobjective induced learning procedure for this model based on a multiobjective evolutionary algorithm. In the experiments, we demonstrate that the learning procedure is effective, and the proposed multiobjective model can learn useful sparse features.

The GPRIME approach to finite element modeling

NASA Technical Reports Server (NTRS)

Wallace, D. R.; Mckee, J. H.; Hurwitz, M. M.

1983-01-01

GPRIME, an interactive modeling system, runs on the CDC 6000 computers and the DEC VAX 11/780 minicomputer. This system includes three components: (1) GPRIME, a user friendly geometric language and a processor to translate that language into geometric entities, (2) GGEN, an interactive data generator for 2-D models; and (3) SOLIDGEN, a 3-D solid modeling program. Each component has a computer user interface of an extensive command set. All of these programs make use of a comprehensive B-spline mathematics subroutine library, which can be used for a wide variety of interpolation problems and other geometric calculations. Many other user aids, such as automatic saving of the geometric and finite element data bases and hidden line removal, are available. This interactive finite element modeling capability can produce a complete finite element model, producing an output file of grid and element data.

Modeling and Classifying Six-Dimensional Trajectories for Teleoperation Under a Time Delay

NASA Technical Reports Server (NTRS)

SunSpiral, Vytas; Wheeler, Kevin R.; Allan, Mark B.; Martin, Rodney

2006-01-01

Within the context of teleoperating the JSC Robonaut humanoid robot under 2-10 second time delays, this paper explores the technical problem of modeling and classifying human motions represented as six-dimensional (position and orientation) trajectories. A dual path research agenda is reviewed which explored both deterministic approaches and stochastic approaches using Hidden Markov Models. Finally, recent results are shown from a new model which represents the fusion of these two research paths. Questions are also raised about the possibility of automatically generating autonomous actions by reusing the same predictive models of human behavior to be the source of autonomous control. This approach changes the role of teleoperation from being a stand-in for autonomy into the first data collection step for developing generative models capable of autonomous control of the robot.

Recognition of Emotions in Mexican Spanish Speech: An Approach Based on Acoustic Modelling of Emotion-Specific Vowels

PubMed Central

Caballero-Morales, Santiago-Omar

2013-01-01

An approach for the recognition of emotions in speech is presented. The target language is Mexican Spanish, and for this purpose a speech database was created. The approach consists in the phoneme acoustic modelling of emotion-specific vowels. For this, a standard phoneme-based Automatic Speech Recognition (ASR) system was built with Hidden Markov Models (HMMs), where different phoneme HMMs were built for the consonants and emotion-specific vowels associated with four emotional states (anger, happiness, neutral, sadness). Then, estimation of the emotional state from a spoken sentence is performed by counting the number of emotion-specific vowels found in the ASR's output for the sentence. With this approach, accuracy of 87–100% was achieved for the recognition of emotional state of Mexican Spanish speech. PMID:23935410

The sunstone and polarised skylight: ancient Viking navigational tools?

NASA Astrophysics Data System (ADS)

Ropars, Guy; Lakshminarayanan, Vasudevan; Le Floch, Albert

2014-10-01

Although the polarisation of the light was discovered at the beginning of the nineteenth century, the Vikings could have used the polarised light around the tenth century in their navigation to America, using a 'sunstone' evoked in the Icelandic Sagas. Indeed, the birefringence of the Iceland spar (calcite), a common crystal in Scandinavia, permits a simple observation of the axis of polarisation of the skylight at the zenith. From this, it is possible to guess the azimuth of a hidden Sun below the horizon, for instance. The high sensitivity of the differential method provided by the ordinary and extraordinary beams of calcite at its so-called isotropy point is about two orders higher than that of the best dichroic polariser and permits to reach an accuracy of ±1° for the Sun azimuth (at sunrise and sunset). Unfortunately, due to the relative fragility of calcite, only the so-called Alderney crystal was discovered on board a 16th ancient ship. Curiously, beyond its use as a sunstone by the Vikings, during these last millennia calcite has led to the discovery of the polarisation of the light itself by Malus and is currently being used to detect the atmospheres of exoplanets. Moreover, the differential method for the light polarisation detection is widely used in the animal world.

Multi-agents and learning: Implications for Webusage mining.

PubMed

Lotfy, Hewayda M S; Khamis, Soheir M S; Aboghazalah, Maie M

2016-03-01

Characterization of user activities is an important issue in the design and maintenance of websites. Server weblog files have abundant information about the user's current interests. This information can be mined and analyzed therefore the administrators may be able to guide the users in their browsing activity so they may obtain relevant information in a shorter span of time to obtain user satisfaction. Web-based technology facilitates the creation of personally meaningful and socially useful knowledge through supportive interactions, communication and collaboration among educators, learners and information. This paper suggests a new methodology based on learning techniques for a Web-based Multiagent-based application to discover the hidden patterns in the user's visited links. It presents a new approach that involves unsupervised, reinforcement learning, and cooperation between agents. It is utilized to discover patterns that represent the user's profiles in a sample website into specific categories of materials using significance percentages. These profiles are used to make recommendations of interesting links and categories to the user. The experimental results of the approach showed successful user pattern recognition, and cooperative learning among agents to obtain user profiles. It indicates that combining different learning algorithms is capable of improving user satisfaction indicated by the percentage of precision, recall, the progressive category weight and F 1-measure.

Modular representation of layered neural networks.

PubMed

Watanabe, Chihiro; Hiramatsu, Kaoru; Kashino, Kunio

2018-01-01

Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatics. However, it is still difficult to discover or interpret knowledge from the inference provided by a layered neural network, since its internal representation has many nonlinear and complex parameters embedded in hierarchical layers. Therefore, it becomes important to establish a new methodology by which layered neural networks can be understood. In this paper, we propose a new method for extracting a global and simplified structure from a layered neural network. Based on network analysis, the proposed method detects communities or clusters of units with similar connection patterns. We show its effectiveness by applying it to three use cases. (1) Network decomposition: it can decompose a trained neural network into multiple small independent networks thus dividing the problem and reducing the computation time. (2) Training assessment: the appropriateness of a trained result with a given hyperparameter or randomly chosen initial parameters can be evaluated by using a modularity index. And (3) data analysis: in practical data it reveals the community structure in the input, hidden, and output layers, which serves as a clue for discovering knowledge from a trained neural network. Copyright © 2017 Elsevier Ltd. All rights reserved.

Novel Insights into the Transcriptome of Dirofilaria immitis

PubMed Central

Zhang, Zhihe; Hou, Rong; Wu, Xuhang; Yang, Deying; Zhang, Runhui; Zheng, Wanpeng; Nie, Huaming; Xie, Yue; Yan, Ning; Yang, Zhi; Wang, Chengdong; Luo, Li; Liu, Li; Gu, Xiaobin; Wang, Shuxian; Peng, Xuerong; Yang, Guangyou

2012-01-01

Background The heartworm Dirofilaria immitis is the causal agent of cardiopulmonary dirofilariosis in dogs and cats, and also infects a wide range of wild mammals as well as humans. One bottleneck for the design of fundamentally new intervention and management strategies against D. immitis may be the currently limited knowledge of fundamental molecular aspects of D. immitis. Methodology/Principal Findings A next-generation sequencing platform combining computational approaches was employed to assess a global view of the heartworm transcriptome. A total of 20,810 unigenes (mean length  = 1,270 bp) were assembled from 22.3 million clean reads. From these, 15,698 coding sequences (CDS) were inferred, and about 85% of the unigenes had orthologs/homologs in public databases. Comparative transcriptomic study uncovered 4,157 filarial-specific genes as well as 3,795 genes potentially involved in filarial-Wolbachia symbiosis. In addition, the potential intestine transcriptome of D. immitis (1,101 genes) was mined for the first time, which might help to discover ‘hidden antigens’. Conclusions/Significance This study provides novel insights into the transcriptome of D. immitis and sheds light on its molecular processes and survival mechanisms. Furthermore, it provides a platform to discover new vaccine candidates and potential targets for new drugs against dirofilariosis. PMID:22911833

Multi-agents and learning: Implications for Webusage mining

PubMed Central

Lotfy, Hewayda M.S.; Khamis, Soheir M.S.; Aboghazalah, Maie M.

2015-01-01

Characterization of user activities is an important issue in the design and maintenance of websites. Server weblog files have abundant information about the user’s current interests. This information can be mined and analyzed therefore the administrators may be able to guide the users in their browsing activity so they may obtain relevant information in a shorter span of time to obtain user satisfaction. Web-based technology facilitates the creation of personally meaningful and socially useful knowledge through supportive interactions, communication and collaboration among educators, learners and information. This paper suggests a new methodology based on learning techniques for a Web-based Multiagent-based application to discover the hidden patterns in the user’s visited links. It presents a new approach that involves unsupervised, reinforcement learning, and cooperation between agents. It is utilized to discover patterns that represent the user’s profiles in a sample website into specific categories of materials using significance percentages. These profiles are used to make recommendations of interesting links and categories to the user. The experimental results of the approach showed successful user pattern recognition, and cooperative learning among agents to obtain user profiles. It indicates that combining different learning algorithms is capable of improving user satisfaction indicated by the percentage of precision, recall, the progressive category weight and F1-measure. PMID:26966569

Data Mining of NASA Boeing 737 Flight Data: Frequency Analysis of In-Flight Recorded Data

NASA Technical Reports Server (NTRS)

Butterfield, Ansel J.

2001-01-01

Data recorded during flights of the NASA Trailblazer Boeing 737 have been analyzed to ascertain the presence of aircraft structural responses from various excitations such as the engine, aerodynamic effects, wind gusts, and control system operations. The NASA Trailblazer Boeing 737 was chosen as a focus of the study because of a large quantity of its flight data records. The goal of this study was to determine if any aircraft structural characteristics could be identified from flight data collected for measuring non-structural phenomena. A number of such data were examined for spatial and frequency correlation as a means of discovering hidden knowledge of the dynamic behavior of the aircraft. Data recorded from on-board dynamic sensors over a range of flight conditions showed consistently appearing frequencies. Those frequencies were attributed to aircraft structural vibrations.

Temporal competition between differentiation programs determines cell fate choice

NASA Astrophysics Data System (ADS)

Kuchina, Anna; Espinar, Lorena; Cagatay, Tolga; Balbin, Alejandro; Alvarado, Alma; Garcia-Ojalvo, Jordi; Suel, Gurol

2011-03-01

During pluripotent differentiation, cells adopt one of several distinct fates. The dynamics of this decision-making process are poorly understood, since cell fate choice may be governed by interactions between differentiation programs that are active at the same time. We studied the dynamics of decision-making in the model organism Bacillus subtilis by simultaneously measuring the activities of competing differentiation programs (sporulation and competence) in single cells. We discovered a precise switch-like point of cell fate choice previously hidden by cell-cell variability. Engineered artificial crosslinks between competence and sporulation circuits revealed that the precision of this choice is generated by temporal competition between the key players of two differentiation programs. Modeling suggests that variable progression towards a switch-like decision might represent a general strategy to maximize adaptability and robustness of cellular decision-making.

Lorentzian symmetry predicts universality beyond scaling laws

NASA Astrophysics Data System (ADS)

Watson, Stephen J.

2017-06-01

We present a covariant theory for the ageing characteristics of phase-ordering systems that possess dynamical symmetries beyond mere scalings. A chiral spin dynamics which conserves the spin-up (+) and spin-down (-) fractions, μ+ and μ- , serves as the emblematic paradigm of our theory. Beyond a parabolic spatio-temporal scaling, we discover a hidden Lorentzian dynamical symmetry therein, and thereby prove that the characteristic length L of spin domains grows in time t according to L = \\fracβ{\\sqrt{1 - σ^2}}t\\frac{1{2}} , where σ:= μ+ - μ- (the invariant spin-excess) and β is a universal constant. Furthermore, the normalised length distributions of the spin-up and the spin-down domains each provably adopt a coincident universal (σ-independent) time-invariant form, and this supra-universal probability distribution is empirically verified to assume a form reminiscent of the Wigner surmise.

Leveraging health social networking communities in translational research.

PubMed

Webster, Yue W; Dow, Ernst R; Koehler, Jacob; Gudivada, Ranga C; Palakal, Mathew J

2011-08-01

Health social networking communities are emerging resources for translational research. We have designed and implemented a framework called HyGen, which combines Semantic Web technologies, graph algorithms and user profiling to discover and prioritize novel associations across disciplines. This manuscript focuses on the key strategies developed to overcome the challenges in handling patient-generated content in Health social networking communities. Heuristic and quantitative evaluations were carried out in colorectal cancer. The results demonstrate the potential of our approach to bridge silos and to identify hidden links among clinical observations, drugs, genes and diseases. In Amyotrophic Lateral Sclerosis case studies, HyGen has identified 15 of the 20 published disease genes. Additionally, HyGen has highlighted new candidates for future investigations, as well as a scientifically meaningful connection between riluzole and alcohol abuse. Copyright © 2011 Elsevier Inc. All rights reserved.

Multiband full-bandwidth anisotropic Eliashberg theory of interfacial electron-phonon coupling and high - Tc superconductivity in FeSe /SrTiO3

NASA Astrophysics Data System (ADS)

Aperis, Alex; Oppeneer, Peter M.

2018-02-01

We examine the impact of interfacial phonons on the superconducting state of FeSe /SrTiO3 developing a material's specific multiband, full bandwidth, and anisotropic Eliashberg theory for this system. Our self-consistent calculations highlight the importance of the interfacial electron-phonon interaction, which is hidden behind the seemingly weak-coupling constant λm=0.4 , in mediating the high Tc, and explain other puzzling experimental observations, such as the s -wave symmetry and replica bands. We discover that the formation of replica bands has a Tc decreasing effect that is nevertheless compensated by deep Fermi-sea Cooper pairing which has a Tc enhancing effect. We predict a strong-coupling dip-hump signature in the tunneling spectra due to the interfacial coupling.

Primary sacral hydatid cyst. A case report.

PubMed

Joshi, Nayana; Hernandez-Martinez, Alejandro; Seijas-Vazquez, Roberto

2007-10-01

This case report highlights an unusual osseous spinal presentation of a well described disease, hydatidosis. A 59-year-old woman presented with increasing back pain and bilateral radiculopathy. Examination disclosed symptoms of spinal stenosis and urinary incontinence. Radiographs showed an expansive lytic lesion affecting the pelvic bones with destruction of the bone cortex. Laboratory analyses were performed and the patient underwent CT and MRI studies. Serology for Echinococcus was positive. When assessing sciatica, low back pain or lower limb weakness the pelvic cavity should be examined for hidden disease that might explain the neurological symptoms. Hydatid disease of bone should be considered in the differential diagnosis of any bone mass discovered in the human body. Diagnosis was delayed in this case because the pelvic cavity was not studied when radiculopathy symptoms started and there was no coexisting visceral involvement.

A harmonic linear dynamical system for prominent ECG feature extraction.

PubMed

Thi, Ngoc Anh Nguyen; Yang, Hyung-Jeong; Kim, SunHee; Do, Luu Ngoc

2014-01-01

Unsupervised mining of electrocardiography (ECG) time series is a crucial task in biomedical applications. To have efficiency of the clustering results, the prominent features extracted from preprocessing analysis on multiple ECG time series need to be investigated. In this paper, a Harmonic Linear Dynamical System is applied to discover vital prominent features via mining the evolving hidden dynamics and correlations in ECG time series. The discovery of the comprehensible and interpretable features of the proposed feature extraction methodology effectively represents the accuracy and the reliability of clustering results. Particularly, the empirical evaluation results of the proposed method demonstrate the improved performance of clustering compared to the previous main stream feature extraction approaches for ECG time series clustering tasks. Furthermore, the experimental results on real-world datasets show scalability with linear computation time to the duration of the time series.

Problem decomposition by mutual information and force-based clustering

NASA Astrophysics Data System (ADS)

Otero, Richard Edward

The scale of engineering problems has sharply increased over the last twenty years. Larger coupled systems, increasing complexity, and limited resources create a need for methods that automatically decompose problems into manageable sub-problems by discovering and leveraging problem structure. The ability to learn the coupling (inter-dependence) structure and reorganize the original problem could lead to large reductions in the time to analyze complex problems. Such decomposition methods could also provide engineering insight on the fundamental physics driving problem solution. This work forwards the current state of the art in engineering decomposition through the application of techniques originally developed within computer science and information theory. The work describes the current state of automatic problem decomposition in engineering and utilizes several promising ideas to advance the state of the practice. Mutual information is a novel metric for data dependence and works on both continuous and discrete data. Mutual information can measure both the linear and non-linear dependence between variables without the limitations of linear dependence measured through covariance. Mutual information is also able to handle data that does not have derivative information, unlike other metrics that require it. The value of mutual information to engineering design work is demonstrated on a planetary entry problem. This study utilizes a novel tool developed in this work for planetary entry system synthesis. A graphical method, force-based clustering, is used to discover related sub-graph structure as a function of problem structure and links ranked by their mutual information. This method does not require the stochastic use of neural networks and could be used with any link ranking method currently utilized in the field. Application of this method is demonstrated on a large, coupled low-thrust trajectory problem. Mutual information also serves as the basis for an alternative global optimizer, called MIMIC, which is unrelated to Genetic Algorithms. Advancement to the current practice demonstrates the use of MIMIC as a global method that explicitly models problem structure with mutual information, providing an alternate method for globally searching multi-modal domains. By leveraging discovered problem inter- dependencies, MIMIC may be appropriate for highly coupled problems or those with large function evaluation cost. This work introduces a useful addition to the MIMIC algorithm that enables its use on continuous input variables. By leveraging automatic decision tree generation methods from Machine Learning and a set of randomly generated test problems, decision trees for which method to apply are also created, quantifying decomposition performance over a large region of the design space.

Hidden momentum of electrons, nuclei, atoms, and molecules

NASA Astrophysics Data System (ADS)

Cameron, Robert P.; Cotter, J. P.

2018-04-01

We consider the positions and velocities of electrons and spinning nuclei and demonstrate that these particles harbour hidden momentum when located in an electromagnetic field. This hidden momentum is present in all atoms and molecules, however it is ultimately canceled by the momentum of the electromagnetic field. We point out that an electron vortex in an electric field might harbour a comparatively large hidden momentum and recognize the phenomenon of hidden hidden momentum.

On the use of hidden Markov models for gaze pattern modeling

NASA Astrophysics Data System (ADS)

Mannaru, Pujitha; Balasingam, Balakumar; Pattipati, Krishna; Sibley, Ciara; Coyne, Joseph

2016-05-01

Some of the conventional metrics derived from gaze patterns (on computer screens) to study visual attention, engagement and fatigue are saccade counts, nearest neighbor index (NNI) and duration of dwells/fixations. Each of these metrics has drawbacks in modeling the behavior of gaze patterns; one such drawback comes from the fact that some portions on the screen are not as important as some other portions on the screen. This is addressed by computing the eye gaze metrics corresponding to important areas of interest (AOI) on the screen. There are some challenges in developing accurate AOI based metrics: firstly, the definition of AOI is always fuzzy; secondly, it is possible that the AOI may change adaptively over time. Hence, there is a need to introduce eye-gaze metrics that are aware of the AOI in the field of view; at the same time, the new metrics should be able to automatically select the AOI based on the nature of the gazes. In this paper, we propose a novel way of computing NNI based on continuous hidden Markov models (HMM) that model the gazes as 2D Gaussian observations (x-y coordinates of the gaze) with the mean at the center of the AOI and covariance that is related to the concentration of gazes. The proposed modeling allows us to accurately compute the NNI metric in the presence of multiple, undefined AOI on the screen in the presence of intermittent casual gazing that is modeled as random gazes on the screen.

Identification and classification of conopeptides using profile Hidden Markov Models.

PubMed

Laht, Silja; Koua, Dominique; Kaplinski, Lauris; Lisacek, Frédérique; Stöcklin, Reto; Remm, Maido

2012-03-01

Conopeptides are small toxins produced by predatory marine snails of the genus Conus. They are studied with increasing intensity due to their potential in neurosciences and pharmacology. The number of existing conopeptides is estimated to be 1 million, but only about 1000 have been described to date. Thanks to new high-throughput sequencing technologies the number of known conopeptides is likely to increase exponentially in the near future. There is therefore a need for a fast and accurate computational method for identification and classification of the novel conopeptides in large data sets. 62 profile Hidden Markov Models (pHMMs) were built for prediction and classification of all described conopeptide superfamilies and families, based on the different parts of the corresponding protein sequences. These models showed very high specificity in detection of new peptides. 56 out of 62 models do not give a single false positive in a test with the entire UniProtKB/Swiss-Prot protein sequence database. Our study demonstrates the usefulness of mature peptide models for automatic classification with accuracy of 96% for the mature peptide models and 100% for the pro- and signal peptide models. Our conopeptide profile HMMs can be used for finding and annotation of new conopeptides from large datasets generated by transcriptome or genome sequencing. To our knowledge this is the first time this kind of computational method has been applied to predict all known conopeptide superfamilies and some conopeptide families. Copyright © 2012 Elsevier B.V. All rights reserved.

A simulation study of gene-by-environment interactions in GWAS implies ample hidden effects

PubMed Central

Marigorta, Urko M.; Gibson, Greg

2014-01-01

The switch to a modern lifestyle in recent decades has coincided with a rapid increase in prevalence of obesity and other diseases. These shifts in prevalence could be explained by the release of genetic susceptibility for disease in the form of gene-by-environment (GxE) interactions. Yet, the detection of interaction effects requires large sample sizes, little replication has been reported, and a few studies have demonstrated environmental effects only after summing the risk of GWAS alleles into genetic risk scores (GRSxE). We performed extensive simulations of a quantitative trait controlled by 2500 causal variants to inspect the feasibility to detect gene-by-environment interactions in the context of GWAS. The simulated individuals were assigned either to an ancestral or a modern setting that alters the phenotype by increasing the effect size by 1.05–2-fold at a varying fraction of perturbed SNPs (from 1 to 20%). We report two main results. First, for a wide range of realistic scenarios, highly significant GRSxE is detected despite the absence of individual genotype GxE evidence at the contributing loci. Second, an increase in phenotypic variance after environmental perturbation reduces the power to discover susceptibility variants by GWAS in mixed cohorts with individuals from both ancestral and modern environments. We conclude that a pervasive presence of gene-by-environment effects can remain hidden even though it contributes to the genetic architecture of complex traits. PMID:25101110

Application of a hybrid association rules/decision tree model for drought monitoring

NASA Astrophysics Data System (ADS)

Nourani, Vahid; Molajou, Amir

2017-12-01

The previous researches have shown that the incorporation of the oceanic-atmospheric climate phenomena such as Sea Surface Temperature (SST) into hydro-climatic models could provide important predictive information about hydro-climatic variability. In this paper, the hybrid application of two data mining techniques (decision tree and association rules) was offered to discover affiliation between drought of Tabriz and Kermanshah synoptic stations (located in Iran) and de-trend SSTs of the Black, Mediterranean and Red Seas. Two major steps of the proposed model were the classification of de-trend SST data and selecting the most effective groups and extracting hidden information involved in the data. The techniques of decision tree which can identify the good traits from a data set for the classification purpose were used for classification and selecting the most effective groups and association rules were employed to extract the hidden predictive information from the large observed data. To examine the accuracy of the rules, confidence and Heidke Skill Score (HSS) measures were calculated and compared for different considering lag times. The computed measures confirm reliable performance of the proposed hybrid data mining method to forecast drought and the results show a relative correlation between the Mediterranean, Black and Red Sea de-trend SSTs and drought of Tabriz and Kermanshah synoptic stations so that the confidence between the monthly Standardized Precipitation Index (SPI) values and the de-trend SST of seas is higher than 70 and 80% respectively for Tabriz and Kermanshah synoptic stations.

Critical object recognition in millimeter-wave images with robustness to rotation and scale.

PubMed

Mohammadzade, Hoda; Ghojogh, Benyamin; Faezi, Sina; Shabany, Mahdi

2017-06-01

Locating critical objects is crucial in various security applications and industries. For example, in security applications, such as in airports, these objects might be hidden or covered under shields or secret sheaths. Millimeter-wave images can be utilized to discover and recognize the critical objects out of the hidden cases without any health risk due to their non-ionizing features. However, millimeter-wave images usually have waves in and around the detected objects, making object recognition difficult. Thus, regular image processing and classification methods cannot be used for these images and additional pre-processings and classification methods should be introduced. This paper proposes a novel pre-processing method for canceling rotation and scale using principal component analysis. In addition, a two-layer classification method is introduced and utilized for recognition. Moreover, a large dataset of millimeter-wave images is collected and created for experiments. Experimental results show that a typical classification method such as support vector machines can recognize 45.5% of a type of critical objects at 34.2% false alarm rate (FAR), which is a drastically poor recognition. The same method within the proposed recognition framework achieves 92.9% recognition rate at 0.43% FAR, which indicates a highly significant improvement. The significant contribution of this work is to introduce a new method for analyzing millimeter-wave images based on machine vision and learning approaches, which is not yet widely noted in the field of millimeter-wave image analysis.

A Parallel Processing and Diversified-Hidden-Gene-Based Genetic Algorithm Framework for Fuel-Optimal Trajectory Design for Interplanetary Spacecraft Missions

NASA Astrophysics Data System (ADS)

Somavarapu, Dhathri H.

This thesis proposes a new parallel computing genetic algorithm framework for designing fuel-optimal trajectories for interplanetary spacecraft missions. The framework can capture the deep search space of the problem with the use of a fixed chromosome structure and hidden-genes concept, can explore the diverse set of candidate solutions with the use of the adaptive and twin-space crowding techniques and, can execute on any high-performance computing (HPC) platform with the adoption of the portable message passing interface (MPI) standard. The algorithm is implemented in C++ with the use of the MPICH implementation of the MPI standard. The algorithm uses a patched-conic approach with two-body dynamics assumptions. New procedures are developed for determining trajectories in the Vinfinity-leveraging legs of the flight from the launch and non-launch planets and, deep-space maneuver legs of the flight from the launch and non-launch planets. The chromosome structure maintains the time of flight as a free parameter within certain boundaries. The fitness or the cost function of the algorithm uses only the mission Delta V, and does not include time of flight. The optimization is conducted with two variations for the minimum mission gravity-assist sequence, the 4-gravity-assist, and the 3-gravity-assist, with a maximum of 5 gravity-assists allowed in both the cases. The optimal trajectories discovered using the framework in both of the cases demonstrate the success of this framework.

Francis Bacon's behavioral psychology.

PubMed

MacDonald, Paul S

2007-01-01

Francis Bacon offers two accounts of the nature and function of the human mind: one is a medical-physical account of the composition and operation of spirits specific to human beings, the other is a behavioral account of the character and activities of individual persons. The medical-physical account is a run-of-the-mill version of the late Renaissance model of elemental constituents and humoral temperaments. The other, less well-known, behavioral account represents an unusual position in early modern philosophy. This theory espouses a form of behavioral psychology according to which (a) supposed mental properties are "hidden forms" best described in dispositional terms, (b) the true character of an individual can be discovered in his observable behavior, and (c) an "informed" understanding of these properties permits the prediction and control of human behavior. Both of Bacon's theories of human nature fall under his general notion of systematic science: his medical-physical theory of vital spirits is theoretical natural philosophy and his behavioral theory of disposition and expression is operative natural philosophy. Because natural philosophy as a whole is "the inquiry of causes and the production of effects," knowledge of human nature falls under the same two-part definition. It is an inquisition of forms that pertains to the patterns of minute motions in the vital spirits and the production of effects that pertains both to the way these hidden motions produce behavioral effects and to the way in which a skillful agent is able to produce desired effects in other persons' behavior. (c) 2007 Wiley Periodicals, Inc.

Sneaking a peek: pigeons use peripheral vision (not mirrors) to find hidden food.

PubMed

Ünver, Emre; Garland, Alexis; Tabrik, Sepideh; Güntürkün, Onur

2017-07-01

A small number of species are capable of recognizing themselves in the mirror when tested with the mark-and-mirror test. This ability is often seen as evidence of self-recognition and possibly even self-awareness. Strangely, a number of species, for example monkeys, pigs and dogs, are unable to pass the mark test but can locate rewarding objects by using the reflective properties of a mirror. Thus, these species seem to understand how a visual reflection functions but cannot apply it to their own image. We tested this discrepancy in pigeons-a species that does not spontaneously pass the mark test. Indeed, we discovered that pigeons can successfully find a hidden food reward using only the reflection, suggesting that pigeons can also use and potentially understand the reflective properties of mirrors, even in the absence of self-recognition. However, tested under monocular conditions, the pigeons approached and attempted to walk through the mirror rather than approach the physical food, displaying similar behavior to patients with mirror agnosia. These findings clearly show that pigeons do not use the reflection of mirrors to locate reward, but actually see the food peripherally with their near-panoramic vision. A re-evaluation of our current understanding of mirror-mediated behavior might be necessary-especially taking more fully into account species differences in visual field. This study suggests that use of reflections in a mirrored surface as a tool may be less widespread than currently thought.

Improving the Quality of Alerts and Predicting Intruder's Next Goal with Hidden Colored Petri-Net

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

Yu, Dong; Frincke, Deb A.

2006-06-22

Intrusion detection systems (IDS) often provide poor quality alerts, which are insufficient to support rapid identification of ongoing attacks or predict an intruder’s next likely goal. In this paper, we propose a novel approach to alert post-processing and correlation, the Hidden Colored Petri-Net (HCPN). Different from most other alert correlation methods, our approach treats the alert correlation problem as an inference problem rather than a filter problem. Our approach assumes that the intruder’s actions are unknown to the IDS and can be inferred only from the alerts generated by the IDS sensors. HCPN can describe the relationship between different stepsmore » carried out by intruders, model observations (alerts) and transitions (actions) separately, and associate each token element (system state) with a probability (or confidence). The model is an extension to Colored Petri-Net (CPN) .It is so called “hidden” because the transitions (actions) are not directly observable but can be inferred by looking through the observations (alerts). These features make HCPN especially suitable for discovering intruders’ actions from their partial observations (alerts,) and predicting intruders’ next goal. Our experiments on DARPA evaluation datasets and the attack scenarios from the Grand Challenge Problem (GCP) show that HCPN has promise as a way to reducing false positives and negatives, predicting intruder’s next possible action, uncovering intruders’ intrusion strategies after the attack scenario has happened, and providing confidence scores.« less

Out of the white hole: a holographic origin for the Big Bang

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

Pourhasan, Razieh; Afshordi, Niayesh; Mann, Robert B., E-mail: rpourhasan@perimeterinstitute.ca, E-mail: nafshordi@pitp.ca, E-mail: rbmann@uwaterloo.ca

While most of the singularities of General Relativity are expected to be safely hidden behind event horizons by the cosmic censorship conjecture, we happen to live in the causal future of the classical Big Bang singularity, whose resolution constitutes the active field of early universe cosmology. Could the Big Bang be also hidden behind a causal horizon, making us immune to the decadent impacts of a naked singularity? We describe a braneworld description of cosmology with both 4d induced and 5D bulk gravity (otherwise known as Dvali-Gabadadze-Porati, or DGP model), which exhibits this feature: the universe emerges as a sphericalmore » 3-brane out of the formation of a 5D Schwarzschild black hole. In particular, we show that a pressure singularity of the holographic fluid, discovered earlier, happens inside the white hole horizon, and thus need not be real or imply any pathology. Furthermore, we outline a novel mechanism through which any thermal atmosphere for the brane, with comoving temperature of ∼20% of the 5D Planck mass can induce scale-invariant primordial curvature perturbations on the brane, circumventing the need for a separate process (such as cosmic inflation) to explain current cosmological observations. Finally, we note that 5D space-time is asymptotically flat, and thus potentially allows an S-matrix or (after minor modifications) an AdS/CFT description of the cosmological Big Bang.« less

Correlation Effects and Hidden Spin-Orbit Entangled Electronic Order in Parent and Electron-Doped Iridates Sr2 IrO4

NASA Astrophysics Data System (ADS)

Zhou, Sen; Jiang, Kun; Chen, Hua; Wang, Ziqiang

2017-10-01

Analogs of the high-Tc cuprates have been long sought after in transition metal oxides. Because of the strong spin-orbit coupling, the 5 d perovskite iridates Sr2 IrO4 exhibit a low-energy electronic structure remarkably similar to the cuprates. Whether a superconducting state exists as in the cuprates requires understanding the correlated spin-orbit entangled electronic states. Recent experiments discovered hidden order in the parent and electron-doped iridates, some with striking analogies to the cuprates, including Fermi surface pockets, Fermi arcs, and pseudogap. Here, we study the correlation and disorder effects in a five-orbital model derived from the band theory. We find that the experimental observations are consistent with a d -wave spin-orbit density wave order that breaks the symmetry of a joint twofold spin-orbital rotation followed by a lattice translation. There is a Berry phase and a plaquette spin flux due to spin procession as electrons hop between Ir atoms, akin to the intersite spin-orbit coupling in quantum spin Hall insulators. The associated staggered circulating Jeff=1 /2 spin current can be probed by advanced techniques of spin-current detection in spintronics. This electronic order can emerge spontaneously from the intersite Coulomb interactions between the spatially extended iridium 5 d orbitals, turning the metallic state into an electron-doped quasi-2D Dirac semimetal with important implications on the possible superconducting state suggested by recent experiments.

Project Integration Architecture as a Foundation for Autonomous Solution Systems: The Postulation of a Meaningful "SolveYourself" Method

NASA Technical Reports Server (NTRS)

Jones, William Henry

2005-01-01

The Project Integration Architecture (PIA) uses object-oriented technology to implement self-revelation and semantic infusion through class derivation. That is, the kind of an object can be discovered through program inquiry and the well-known, well-defined meaning of that object can be utilized as a result of that discovery. This technology has already been demonstrated by the PIA effort in its parameter object classes. It is proposed that, by building on this technology, an autonomous, automatic, goal-seeking, solution system may be devised.

Automatic Evidence Retrieval for Systematic Reviews

PubMed Central

Choong, Miew Keen; Galgani, Filippo; Dunn, Adam G

2014-01-01

Background Snowballing involves recursively pursuing relevant references cited in the retrieved literature and adding them to the search results. Snowballing is an alternative approach to discover additional evidence that was not retrieved through conventional search. Snowballing’s effectiveness makes it best practice in systematic reviews despite being time-consuming and tedious. Objective Our goal was to evaluate an automatic method for citation snowballing’s capacity to identify and retrieve the full text and/or abstracts of cited articles. Methods Using 20 review articles that contained 949 citations to journal or conference articles, we manually searched Microsoft Academic Search (MAS) and identified 78.0% (740/949) of the cited articles that were present in the database. We compared the performance of the automatic citation snowballing method against the results of this manual search, measuring precision, recall, and F1 score. Results The automatic method was able to correctly identify 633 (as proportion of included citations: recall=66.7%, F1 score=79.3%; as proportion of citations in MAS: recall=85.5%, F1 score=91.2%) of citations with high precision (97.7%), and retrieved the full text or abstract for 490 (recall=82.9%, precision=92.1%, F1 score=87.3%) of the 633 correctly retrieved citations. Conclusions The proposed method for automatic citation snowballing is accurate and is capable of obtaining the full texts or abstracts for a substantial proportion of the scholarly citations in review articles. By automating the process of citation snowballing, it may be possible to reduce the time and effort of common evidence surveillance tasks such as keeping trial registries up to date and conducting systematic reviews. PMID:25274020

"mysterium Cosmographicum", for Orchestra, Narrator/actor, and Computer Music on Tape. (with Original Composition)

NASA Astrophysics Data System (ADS)

Keefe, Robert Michael

Mysterium Cosmographicum is a musical chronicle of an astronomy treatise by the German astronomer Johannes Kepler (1571-1630). Kepler's Mysterium Cosmographicum (Tubingen, 1596), or "Secret of the Universe," was a means by which he justified the existence of the six planets discovered during his lifetime. Kepler, through flawless a priori reasoning, goes to great lengths to explain that the reason there are six and only six planets (Mercury, Venus, Earth, Mars, Jupiter, and Saturn) is because God had placed one of the five regular solids (tetrahedron, cube, octa-, dodeca-, and icosahedron) around each orbiting body. Needless to say, the publication was not very successful, nor did it gain much comment from Kepler's peers, Galileo Galilei (1564-1642) and Tycho Brahe (1546-1601). But hidden within the Mysterium Cosmographicum, almost like a new planet waiting to be discovered, is one of Kepler's three laws of planetary motion, a law that held true for planets discovered long after Kepler's lifetime. Mysterium Cosmographicum is a monologue with music in three parts for orchestra, narrator/actor, and computer music on tape. All musical data structures are generated via an interactive Pascal computer program that computes latitudinal and longitudinal coordinates for each of the nine planets as seen from a fixed point on Earth for any given time frame. These coordinates are then mapped onto selected musical parameters as determined by the composer. Whenever Kepler reads from his treatise or from a lecture or correspondence, the monologue is supported by orchestral planetary data generated from the exact place, date, and time of the treatise, lecture, or correspondence. To the best of my knowledge, Mysterium Cosmographicum is the first composition ever written that employs planetary data as a supporting chronology to action and monologue.

A Graph-Based Recovery and Decomposition of Swanson’s Hypothesis using Semantic Predications

PubMed Central

Cameron, Delroy; Bodenreider, Olivier; Yalamanchili, Hima; Danh, Tu; Vallabhaneni, Sreeram; Thirunarayan, Krishnaprasad; Sheth, Amit P.; Rindflesch, Thomas C.

2014-01-01

Objectives This paper presents a methodology for recovering and decomposing Swanson’s Raynaud Syndrome–Fish Oil Hypothesis semi-automatically. The methodology leverages the semantics of assertions extracted from biomedical literature (called semantic predications) along with structured background knowledge and graph-based algorithms to semi-automatically capture the informative associations originally discovered manually by Swanson. Demonstrating that Swanson’s manually intensive techniques can be undertaken semi-automatically, paves the way for fully automatic semantics-based hypothesis generation from scientific literature. Methods Semantic predications obtained from biomedical literature allow the construction of labeled directed graphs which contain various associations among concepts from the literature. By aggregating such associations into informative subgraphs, some of the relevant details originally articulated by Swanson has been uncovered. However, by leveraging background knowledge to bridge important knowledge gaps in the literature, a methodology for semi-automatically capturing the detailed associations originally explicated in natural language by Swanson has been developed. Results Our methodology not only recovered the 3 associations commonly recognized as Swanson’s Hypothesis, but also decomposed them into an additional 16 detailed associations, formulated as chains of semantic predications. Altogether, 14 out of the 19 associations that can be attributed to Swanson were retrieved using our approach. To the best of our knowledge, such an in-depth recovery and decomposition of Swanson’s Hypothesis has never been attempted. Conclusion In this work therefore, we presented a methodology for semi- automatically recovering and decomposing Swanson’s RS-DFO Hypothesis using semantic representations and graph algorithms. Our methodology provides new insights into potential prerequisites for semantics-driven Literature-Based Discovery (LBD). These suggest that three critical aspects of LBD include: 1) the need for more expressive representations beyond Swanson’s ABC model; 2) an ability to accurately extract semantic information from text; and 3) the semantic integration of scientific literature with structured background knowledge. PMID:23026233

Sound Classification in Hearing Aids Inspired by Auditory Scene Analysis

NASA Astrophysics Data System (ADS)

Büchler, Michael; Allegro, Silvia; Launer, Stefan; Dillier, Norbert

2005-12-01

A sound classification system for the automatic recognition of the acoustic environment in a hearing aid is discussed. The system distinguishes the four sound classes "clean speech," "speech in noise," "noise," and "music." A number of features that are inspired by auditory scene analysis are extracted from the sound signal. These features describe amplitude modulations, spectral profile, harmonicity, amplitude onsets, and rhythm. They are evaluated together with different pattern classifiers. Simple classifiers, such as rule-based and minimum-distance classifiers, are compared with more complex approaches, such as Bayes classifier, neural network, and hidden Markov model. Sounds from a large database are employed for both training and testing of the system. The achieved recognition rates are very high except for the class "speech in noise." Problems arise in the classification of compressed pop music, strongly reverberated speech, and tonal or fluctuating noises.

A novelty detection diagnostic methodology for gearboxes operating under fluctuating operating conditions using probabilistic techniques

NASA Astrophysics Data System (ADS)

Schmidt, S.; Heyns, P. S.; de Villiers, J. P.

2018-02-01

In this paper, a fault diagnostic methodology is developed which is able to detect, locate and trend gear faults under fluctuating operating conditions when only vibration data from a single transducer, measured on a healthy gearbox are available. A two-phase feature extraction and modelling process is proposed to infer the operating condition and based on the operating condition, to detect changes in the machine condition. Information from optimised machine and operating condition hidden Markov models are statistically combined to generate a discrepancy signal which is post-processed to infer the condition of the gearbox. The discrepancy signal is processed and combined with statistical methods for automatic fault detection and localisation and to perform fault trending over time. The proposed methodology is validated on experimental data and a tacholess order tracking methodology is used to enhance the cost-effectiveness of the diagnostic methodology.

Sakurai Prize: Why the Higgs Boson data implies an M-theory world

NASA Astrophysics Data System (ADS)

Kane, Gordon

2017-01-01

Compactifying 11D M-theory on a 7D G2 manifold automatically gives a supersymmetric 4D relativistic quantum field theory. The supersymmetry is softly broken by gluino condensation of the largest gauge group hidden sector, which runs fastest. The resulting gravitino mass is about 40 TeV, and the scalar masses and trilinears of the soft breaking Lagrangian have similar values. All solutions having electroweak symmetry breaking are in the two doublet decoupling region. The coefficient λ of the effective Higgs potential is calculable and determines Mh/MZ. Using the most recent match and run methods, and running down to the TeV scale gives Mh = 126 GeV, and decay BR within a few per cent of the SM Higgs. This was reported in summer 2011, before LHC data, though the result does not depend on any adjustable parameters so it would be unchanged whenever it was reported.

A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders

NASA Astrophysics Data System (ADS)

Shao, Haidong; Jiang, Hongkai; Lin, Ying; Li, Xingqiu

2018-03-01

Automatic and accurate identification of rolling bearings fault categories, especially for the fault severities and fault orientations, is still a major challenge in rotating machinery fault diagnosis. In this paper, a novel method called ensemble deep auto-encoders (EDAEs) is proposed for intelligent fault diagnosis of rolling bearings. Firstly, different activation functions are employed as the hidden functions to design a series of auto-encoders (AEs) with different characteristics. Secondly, EDAEs are constructed with various auto-encoders for unsupervised feature learning from the measured vibration signals. Finally, a combination strategy is designed to ensure accurate and stable diagnosis results. The proposed method is applied to analyze the experimental bearing vibration signals. The results confirm that the proposed method can get rid of the dependence on manual feature extraction and overcome the limitations of individual deep learning models, which is more effective than the existing intelligent diagnosis methods.

Proteopedia: Exciting Advances in the 3D Encyclopedia of Biomolecular Structure

NASA Astrophysics Data System (ADS)

Prilusky, Jaime; Hodis, Eran; Sussman, Joel L.

Proteopedia is a collaborative, 3D web-encyclopedia of protein, nucleic acid and other structures. Proteopedia ( http://www.proteopedia.org ) presents 3D biomolecule structures in a broadly accessible manner to a diverse scientific audience through easy-to-use molecular visualization tools integrated into a wiki environment that anyone with a user account can edit. We describe recent advances in the web resource in the areas of content and software. In terms of content, we describe a large growth in user-added content as well as improvements in automatically-generated content for all PDB entry pages in the resource. In terms of software, we describe new features ranging from the capability to create pages hidden from public view to the capability to export pages for offline viewing. New software features also include an improved file-handling system and availability of biological assemblies of protein structures alongside their asymmetric units.

Study on automatic ECT data evaluation by using neural network

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

Komatsu, H.; Matsumoto, Y.; Badics, Z.

1994-12-31

At the in--service inspection of the steam generator (SG) tubings in Pressurized Water Reactor (PWR) plant, eddy current testing (ECT) has been widely used at each outage. At present, ECT data evaluation is mainly performed by ECT data analyst, therefore it has the following problems. Only ECT signal configuration on the impedance trajectory is used in the evaluation. It is an enormous time consuming process. The evaluation result is influenced by the ability and experience of the analyst. Especially, it is difficult to identify the true defect signal hidden in background signals such as lift--off noise and deposit signals. Inmore » this work, the authors performed the study on the possibility of the application of neural network to ECT data evaluation. It was demonstrated that the neural network proved to be effective to identify the nature of defect, by selecting several optimum input parameters to categorize the raw ECT signals.« less

Automatic stage identification of Drosophila egg chamber based on DAPI images

PubMed Central

Jia, Dongyu; Xu, Qiuping; Xie, Qian; Mio, Washington; Deng, Wu-Min

2016-01-01

The Drosophila egg chamber, whose development is divided into 14 stages, is a well-established model for developmental biology. However, visual stage determination can be a tedious, subjective and time-consuming task prone to errors. Our study presents an objective, reliable and repeatable automated method for quantifying cell features and classifying egg chamber stages based on DAPI images. The proposed approach is composed of two steps: 1) a feature extraction step and 2) a statistical modeling step. The egg chamber features used are egg chamber size, oocyte size, egg chamber ratio and distribution of follicle cells. Methods for determining the on-site of the polytene stage and centripetal migration are also discussed. The statistical model uses linear and ordinal regression to explore the stage-feature relationships and classify egg chamber stages. Combined with machine learning, our method has great potential to enable discovery of hidden developmental mechanisms. PMID:26732176

Online gesture spotting from visual hull data.

PubMed

Peng, Bo; Qian, Gang

2011-06-01

This paper presents a robust framework for online full-body gesture spotting from visual hull data. Using view-invariant pose features as observations, hidden Markov models (HMMs) are trained for gesture spotting from continuous movement data streams. Two major contributions of this paper are 1) view-invariant pose feature extraction from visual hulls, and 2) a systematic approach to automatically detecting and modeling specific nongesture movement patterns and using their HMMs for outlier rejection in gesture spotting. The experimental results have shown the view-invariance property of the proposed pose features for both training poses and new poses unseen in training, as well as the efficacy of using specific nongesture models for outlier rejection. Using the IXMAS gesture data set, the proposed framework has been extensively tested and the gesture spotting results are superior to those reported on the same data set obtained using existing state-of-the-art gesture spotting methods.

Support-vector-machine tree-based domain knowledge learning toward automated sports video classification

NASA Astrophysics Data System (ADS)

Xiao, Guoqiang; Jiang, Yang; Song, Gang; Jiang, Jianmin

2010-12-01

We propose a support-vector-machine (SVM) tree to hierarchically learn from domain knowledge represented by low-level features toward automatic classification of sports videos. The proposed SVM tree adopts a binary tree structure to exploit the nature of SVM's binary classification, where each internal node is a single SVM learning unit, and each external node represents the classified output type. Such a SVM tree presents a number of advantages, which include: 1. low computing cost; 2. integrated learning and classification while preserving individual SVM's learning strength; and 3. flexibility in both structure and learning modules, where different numbers of nodes and features can be added to address specific learning requirements, and various learning models can be added as individual nodes, such as neural networks, AdaBoost, hidden Markov models, dynamic Bayesian networks, etc. Experiments support that the proposed SVM tree achieves good performances in sports video classifications.

Baseline estimation in flame's spectra by using neural networks and robust statistics

NASA Astrophysics Data System (ADS)

Garces, Hugo; Arias, Luis; Rojas, Alejandro

2014-09-01

This work presents a baseline estimation method in flame spectra based on artificial intelligence structure as a neural network, combining robust statistics with multivariate analysis to automatically discriminate measured wavelengths belonging to continuous feature for model adaptation, surpassing restriction of measuring target baseline for training. The main contributions of this paper are: to analyze a flame spectra database computing Jolliffe statistics from Principal Components Analysis detecting wavelengths not correlated with most of the measured data corresponding to baseline; to systematically determine the optimal number of neurons in hidden layers based on Akaike's Final Prediction Error; to estimate baseline in full wavelength range sampling measured spectra; and to train an artificial intelligence structure as a Neural Network which allows to generalize the relation between measured and baseline spectra. The main application of our research is to compute total radiation with baseline information, allowing to diagnose combustion process state for optimization in early stages.

Visibility enhancement of color images using Type-II fuzzy membership function

NASA Astrophysics Data System (ADS)

Singh, Harmandeep; Khehra, Baljit Singh

2018-04-01

Images taken in poor environmental conditions decrease the visibility and hidden information of digital images. Therefore, image enhancement techniques are necessary for improving the significant details of these images. An extensive review has shown that histogram-based enhancement techniques greatly suffer from over/under enhancement issues. Fuzzy-based enhancement techniques suffer from over/under saturated pixels problems. In this paper, a novel Type-II fuzzy-based image enhancement technique has been proposed for improving the visibility of images. The Type-II fuzzy logic can automatically extract the local atmospheric light and roughly eliminate the atmospheric veil in local detail enhancement. The proposed technique has been evaluated on 10 well-known weather degraded color images and is also compared with four well-known existing image enhancement techniques. The experimental results reveal that the proposed technique outperforms others regarding visible edge ratio, color gradients and number of saturated pixels.

Analysis of mesenchymal stem cell differentiation in vitro using classification association rule mining.

PubMed

Wang, Weiqi; Wang, Yanbo Justin; Bañares-Alcántara, René; Coenen, Frans; Cui, Zhanfeng

2009-12-01

In this paper, data mining is used to analyze the data on the differentiation of mammalian Mesenchymal Stem Cells (MSCs), aiming at discovering known and hidden rules governing MSC differentiation, following the establishment of a web-based public database containing experimental data on the MSC proliferation and differentiation. To this effect, a web-based public interactive database comprising the key parameters which influence the fate and destiny of mammalian MSCs has been constructed and analyzed using Classification Association Rule Mining (CARM) as a data-mining technique. The results show that the proposed approach is technically feasible and performs well with respect to the accuracy of (classification) prediction. Key rules mined from the constructed MSC database are consistent with experimental observations, indicating the validity of the method developed and the first step in the application of data mining to the study of MSCs.

Modeling-Enabled Systems Nutritional Immunology

PubMed Central

Verma, Meghna; Hontecillas, Raquel; Abedi, Vida; Leber, Andrew; Tubau-Juni, Nuria; Philipson, Casandra; Carbo, Adria; Bassaganya-Riera, Josep

2016-01-01

This review highlights the fundamental role of nutrition in the maintenance of health, the immune response, and disease prevention. Emerging global mechanistic insights in the field of nutritional immunology cannot be gained through reductionist methods alone or by analyzing a single nutrient at a time. We propose to investigate nutritional immunology as a massively interacting system of interconnected multistage and multiscale networks that encompass hidden mechanisms by which nutrition, microbiome, metabolism, genetic predisposition, and the immune system interact to delineate health and disease. The review sets an unconventional path to apply complex science methodologies to nutritional immunology research, discovery, and development through “use cases” centered around the impact of nutrition on the gut microbiome and immune responses. Our systems nutritional immunology analyses, which include modeling and informatics methodologies in combination with pre-clinical and clinical studies, have the potential to discover emerging systems-wide properties at the interface of the immune system, nutrition, microbiome, and metabolism. PMID:26909350

Hidden patterns of reciprocity.

PubMed

Syi

2014-03-21

Reciprocity can help the evolution of cooperation. To model both types of reciprocity, we need the concept of strategy. In the case of direct reciprocity there are four second-order action rules (Simple Tit-for-tat, Contrite Tit-for-tat, Pavlov, and Grim Trigger), which are able to promote cooperation. In the case of indirect reciprocity the key component of cooperation is the assessment rule. There are, again, four elementary second-order assessment rules (Image Scoring, Simple Standing, Stern Judging, and Shunning). The eight concepts can be formalized in an ontologically thin way we need only an action predicate and a value function, two agent concepts, and the constant of goodness. The formalism helps us to discover that the action and assessment rules can be paired, and that they show the same patterns. The logic of these patterns can be interpreted with the concept of punishment that has an inherent paradoxical nature. Copyright © 2013 Elsevier Ltd. All rights reserved.

Life and Death on Mars and Earth

NASA Technical Reports Server (NTRS)

Zahnle, K. J.; Sleep, N. H.

1999-01-01

Failure to discover life on Mars has led a great many experts to conclude that it must be hiding. Where? The likeliest hiding places are deep beneath the surface, where geothermal heat could permit liquid water. In this the search for life on Mars parallels the search for water on Mars. Liquid water has been, at least on occasion, a geologically significant presence on the surface. Channels were cut and plains dissected. This water is now hidden, in all likelihood having drained to the base of the porous regolith, where it fills possibly frozen aquifers. Presumably any surviving biota has followed the water from the surface to its hiding places in the deep. Accordingly, we have extended our environmental impact assessment of the environmental hazards posed by large asteroid and comet impacts to Mars, and compare its case to Earth's. In particular, we address the continuous habitability of surface and subsurface environments.

Cardiac data mining (CDM); organization and predictive analytics on biomedical (cardiac) data

NASA Astrophysics Data System (ADS)

Bilal, M. Musa; Hussain, Masood; Basharat, Iqra; Fatima, Mamuna

2013-10-01

Data mining and data analytics has been of immense importance to many different fields as we witness the evolution of data sciences over recent years. Biostatistics and Medical Informatics has proved to be the foundation of many modern biological theories and analysis techniques. These are the fields which applies data mining practices along with statistical models to discover hidden trends from data that comprises of biological experiments or procedures on different entities. The objective of this research study is to develop a system for the efficient extraction, transformation and loading of such data from cardiologic procedure reports given by Armed Forces Institute of Cardiology. It also aims to devise a model for the predictive analysis and classification of this data to some important classes as required by cardiologists all around the world. This includes predicting patient impressions and other important features.

Geophysical, geochemical, and geological investigations of the Dunes geothermal system, Imperial Valley, California

NASA Technical Reports Server (NTRS)

Elders, W. A.; Combs, J.; Coplen, T. B.; Kolesar, P.; Bird, D. K.

1974-01-01

The Dunes anomaly is a water-dominated geothermal system in the alluvium of the Salton Trough, lacking any surface expression. It was discovered by shallow-temperature gradient measurements. A 612-meter-deep test well encountered several temperature-gradient reversals, with a maximum of 105 C at 114 meters. The program involves surface geophysics, including electrical, gravity, and seismic methods, down-hole geophysics and petrophysics of core samples, isotopic and chemical studies of water samples, and petrological and geochemical studies of the cores and cuttings. The aim is (1) to determine the source and temperature history of the brines, (2) to understand the interaction between the brines and rocks, and (3) to determine the areal extent, nature, origin, and history of the geothermal system. These studies are designed to provide better definition of exploration targets for hidden geothermal anomalies and to contribute to improved techniques of exploration and resource assessment.

Revealing the hidden language of complex networks.

PubMed

Yaveroğlu, Ömer Nebil; Malod-Dognin, Noël; Davis, Darren; Levnajic, Zoran; Janjic, Vuk; Karapandza, Rasa; Stojmirovic, Aleksandar; Pržulj, Nataša

2014-04-01

Sophisticated methods for analysing complex networks promise to be of great benefit to almost all scientific disciplines, yet they elude us. In this work, we make fundamental methodological advances to rectify this. We discover that the interaction between a small number of roles, played by nodes in a network, can characterize a network's structure and also provide a clear real-world interpretation. Given this insight, we develop a framework for analysing and comparing networks, which outperforms all existing ones. We demonstrate its strength by uncovering novel relationships between seemingly unrelated networks, such as Facebook, metabolic, and protein structure networks. We also use it to track the dynamics of the world trade network, showing that a country's role of a broker between non-trading countries indicates economic prosperity, whereas peripheral roles are associated with poverty. This result, though intuitive, has escaped all existing frameworks. Finally, our approach translates network topology into everyday language, bringing network analysis closer to domain scientists.

Rapid experimental SAD phasing and hot-spot identification with halogenated fragments

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

Bauman, Joseph D.; Harrison, Jerry Joe E. K.; Arnold, Eddy

2016-01-01

Through X-ray crystallographic fragment screening, 4-bromopyrazole was discovered to be a `magic bullet' that is capable of binding at many of the ligand `hot spots' found in HIV-1 reverse transcriptase (RT). The binding locations can be in pockets that are `hidden' in the unliganded crystal form, allowing rapid identification of these sites forin silicoscreening. In addition to hot-spot identification, this ubiquitous yet specific binding provides an avenue for X-ray crystallographic phase determination, which can be a significant bottleneck in the determination of the structures of novel proteins. The anomalous signal from 4-bromopyrazole or 4-iodopyrazole was sufficient to determine the structuresmore » of three proteins (HIV-1 RT, influenza A endonuclease and proteinase K) by single-wavelength anomalous dispersion (SAD) from single crystals. Both compounds are inexpensive, readily available, safe and very soluble in DMSO or water, allowing efficient soaking into crystals.« less

Statistical significance of combinatorial regulations

PubMed Central

Terada, Aika; Okada-Hatakeyama, Mariko; Tsuda, Koji; Sese, Jun

2013-01-01

More than three transcription factors often work together to enable cells to respond to various signals. The detection of combinatorial regulation by multiple transcription factors, however, is not only computationally nontrivial but also extremely unlikely because of multiple testing correction. The exponential growth in the number of tests forces us to set a strict limit on the maximum arity. Here, we propose an efficient branch-and-bound algorithm called the “limitless arity multiple-testing procedure” (LAMP) to count the exact number of testable combinations and calibrate the Bonferroni factor to the smallest possible value. LAMP lists significant combinations without any limit, whereas the family-wise error rate is rigorously controlled under the threshold. In the human breast cancer transcriptome, LAMP discovered statistically significant combinations of as many as eight binding motifs. This method may contribute to uncover pathways regulated in a coordinated fashion and find hidden associations in heterogeneous data. PMID:23882073

Experimental evolution reveals hidden diversity in evolutionary pathways.

PubMed

Lind, Peter A; Farr, Andrew D; Rainey, Paul B

2015-03-25

Replicate populations of natural and experimental organisms often show evidence of parallel genetic evolution, but the causes are unclear. The wrinkly spreader morph of Pseudomonas fluorescens arises repeatedly during experimental evolution. The mutational causes reside exclusively within three pathways. By eliminating these, 13 new mutational pathways were discovered with the newly arising WS types having fitnesses similar to those arising from the commonly passaged routes. Our findings show that parallel genetic evolution is strongly biased by constraints and we reveal the genetic bases. From such knowledge, and in instances where new phenotypes arise via gene activation, we suggest a set of principles: evolution proceeds firstly via pathways subject to negative regulation, then via promoter mutations and gene fusions, and finally via activation by intragenic gain-of-function mutations. These principles inform evolutionary forecasting and have relevance to interpreting the diverse array of mutations associated with clinically identical instances of disease in humans.

Integrated Taxonomy Reveals Hidden Diversity in Northern Australian Fishes: A New Species of Seamoth (Genus Pegasus)

PubMed Central

Osterhage, Deborah; Pogonoski, John J.; Appleyard, Sharon A.; White, William T.

2016-01-01

Fishes are one of the most intensively studied marine taxonomic groups yet cryptic species are still being discovered. An integrated taxonomic approach is used herein to delineate and describe a new cryptic seamoth (genus Pegasus) from what was previously a wide-ranging species. Preliminary mitochondrial DNA barcoding indicated possible speciation in Pegasus volitans specimens collected in surveys of the Torres Strait and Great Barrier Reef off Queensland in Australia. Morphological and meristic investigations found key differences in a number of characters between P. volitans and the new species, P. tetrabelos. Further mt DNA barcoding of both the COI and the slower mutating 16S genes of additional specimens provided strong support for two separate species. Pegasus tetrabelos and P. volitans are sympatric in northern Australia and were frequently caught together in trawls at the same depths. PMID:26934529

Pulsar-irradiated stars in dense globular clusters

NASA Technical Reports Server (NTRS)

Tavani, Marco

1992-01-01

We discuss the properties of stars irradiated by millisecond pulsars in 'hard' binaries of dense globular clusters. Irradiation by a relativistic pulsar wind as in the case of the eclipsing millisecond pulsar PSR 1957+20 alter both the magnitude and color of the companion star. Some of the blue stragglers (BSs) recently discovered in dense globular clusters can be irradiated stars in binaries containing powerful millisecond pulsars. The discovery of pulsar-driven orbital modulations of BS brightness and color with periods of a few hours together with evidence for radio and/or gamma-ray emission from BS binaries would valuably contribute to the understanding of the evolution of collapsed stars in globular clusters. Pulsar-driven optical modulation of cluster stars might be the only observable effect of a new class of binary pulsars, i.e., hidden millisecond pulsars enshrouded in the evaporated material lifted off from the irradiated companion star.

A dedicated network for social interaction processing in the primate brain.

PubMed

Sliwa, J; Freiwald, W A

2017-05-19

Primate cognition requires interaction processing. Interactions can reveal otherwise hidden properties of intentional agents, such as thoughts and feelings, and of inanimate objects, such as mass and material. Where and how interaction analyses are implemented in the brain is unknown. Using whole-brain functional magnetic resonance imaging in macaque monkeys, we discovered a network centered in the medial and ventrolateral prefrontal cortex that is exclusively engaged in social interaction analysis. Exclusivity of specialization was found for no other function anywhere in the brain. Two additional networks, a parieto-premotor and a temporal one, exhibited both social and physical interaction preference, which, in the temporal lobe, mapped onto a fine-grain pattern of object, body, and face selectivity. Extent and location of a dedicated system for social interaction analysis suggest that this function is an evolutionary forerunner of human mind-reading capabilities. Copyright © 2017, American Association for the Advancement of Science.

A synopsis of centipedes in Brazilian caves: hidden species diversity that needs conservation (Myriapoda, Chilopoda)

PubMed Central

Chagas-Jr, Amazonas; Bichuette, Maria Elina

2018-01-01

Abstract This study revises centipede fauna found in Brazilian caves, focusing on troglomorphic taxa and emphasizing conservation status. We present 563 centipede specimens from 274 caves across eleven Brazilian states. Of these, 22 records were derived from existing literature and 252 are newly collected. Specimens represent four orders, ten families, 18 genera, and 47 morphospecies. Together, the cave records represent 21 % of Brazil’s centipede fauna. Scolopendromorpha was the most representative order (41 %), followed by Geophilomorpha (26 %), Scutigeromorpha (23 %), and Lithobiomorpha (10 %). Six species were found only in caves, with four considered troglobitic. The distribution of Cryptops iporangensis, the first Brazilian troglobitic centipede species to be discovered, was expanded to other three caves. Cryptops spelaeoraptor and Cryptops iporangensis are two troglobitic species considered Vulnerable and Endangered, respectively, according to the IUCN Red List. Main threats to Brazilian caves are mining, hydroelectric projects, water pollution, and unregulated tourism. PMID:29674871

Taare Zameen Par and dyslexic savants

PubMed Central

Chakravarty, Ambar

2009-01-01

The film Taare Zameen Par (Stars upon the Ground) portrays the tormented life at school and at home of a child with dyslexia and his eventual success after his artistic talents are discovered by his art teacher at the boarding school. The film hints at a curious neurocognitive phenomenon of creativity in the midst of language disability, as exemplified in the lives of people like Leonardo da Vinci and Albert Einstein, both of whom demonstrated extraordinary creativity even though they were probably affected with developmental learning disorders. It has been hypothesized that a developmental delay in the dominant hemisphere most likely ‘disinhibits’ the nondominant parietal lobe, unmasking talents—artistic or otherwise—in some such individuals. It has been suggested that, in remedial training, children with learning disorders be encouraged to develop such hidden talents to full capacity, rather than be subjected to the usual overemphasis on the correction of the disturbed coded symbol operations. PMID:20142854

Taare Zameen Par and dyslexic savants.

PubMed

Chakravarty, Ambar

2009-04-01

The film Taare Zameen Par (Stars upon the Ground) portrays the tormented life at school and at home of a child with dyslexia and his eventual success after his artistic talents are discovered by his art teacher at the boarding school. The film hints at a curious neurocognitive phenomenon of creativity in the midst of language disability, as exemplified in the lives of people like Leonardo da Vinci and Albert Einstein, both of whom demonstrated extraordinary creativity even though they were probably affected with developmental learning disorders. It has been hypothesized that a developmental delay in the dominant hemisphere most likely 'disinhibits' the nondominant parietal lobe, unmasking talents-artistic or otherwise-in some such individuals. It has been suggested that, in remedial training, children with learning disorders be encouraged to develop such hidden talents to full capacity, rather than be subjected to the usual overemphasis on the correction of the disturbed coded symbol operations.

Feature-Based Morphometry: Discovering Group-related Anatomical Patterns

PubMed Central

Toews, Matthew; Wells, William; Collins, D. Louis; Arbel, Tal

2015-01-01

This paper presents feature-based morphometry (FBM), a new, fully data-driven technique for discovering patterns of group-related anatomical structure in volumetric imagery. In contrast to most morphometry methods which assume one-to-one correspondence between subjects, FBM explicitly aims to identify distinctive anatomical patterns that may only be present in subsets of subjects, due to disease or anatomical variability. The image is modeled as a collage of generic, localized image features that need not be present in all subjects. Scale-space theory is applied to analyze image features at the characteristic scale of underlying anatomical structures, instead of at arbitrary scales such as global or voxel-level. A probabilistic model describes features in terms of their appearance, geometry, and relationship to subject groups, and is automatically learned from a set of subject images and group labels. Features resulting from learning correspond to group-related anatomical structures that can potentially be used as image biomarkers of disease or as a basis for computer-aided diagnosis. The relationship between features and groups is quantified by the likelihood of feature occurrence within a specific group vs. the rest of the population, and feature significance is quantified in terms of the false discovery rate. Experiments validate FBM clinically in the analysis of normal (NC) and Alzheimer's (AD) brain images using the freely available OASIS database. FBM automatically identifies known structural differences between NC and AD subjects in a fully data-driven fashion, and an equal error classification rate of 0.80 is achieved for subjects aged 60-80 years exhibiting mild AD (CDR=1). PMID:19853047

Microvascular fractal dimension predicts prognosis and response to chemotherapy in glioblastoma: an automatic image analysis study.

PubMed

Chen, Cong; He, Zhi-Cheng; Shi, Yu; Zhou, Wenchao; Zhang, Xia; Xiao, Hua-Liang; Wu, Hai-Bo; Yao, Xiao-Hong; Luo, Wan-Chun; Cui, You-Hong; Bao, Shideng; Kung, Hsiang-Fu; Bian, Xiu-Wu; Ping, Yi-Fang

2018-05-15

The microvascular profile has been included in the WHO glioma grading criteria. Nevertheless, microvessels in gliomas of the same WHO grade, e.g., WHO IV glioblastoma (GBM), exhibit heterogeneous and polymorphic morphology, whose possible clinical significance remains to be determined. In this study, we employed a fractal geometry-derived parameter, microvascular fractal dimension (mvFD), to quantify microvessel complexity and developed a home-made macro in Image J software to automatically determine mvFD from the microvessel-stained immunohistochemical images of GBM. We found that mvFD effectively quantified the morphological complexity of GBM microvasculature. Furthermore, high mvFD favored the survival of GBM patients as an independent prognostic indicator and predicted a better response to chemotherapy of GBM patients. When investigating the underlying relations between mvFD and tumor growth by deploying Ki67/mvFD as an index for microvasculature-normalized tumor proliferation, we discovered an inverse correlation between mvFD and Ki67/mvFD. Furthermore, mvFD inversely correlated with the expressions of a glycolytic marker, LDHA, which indicated poor prognosis of GBM patients. Conclusively, we developed an automatic approach for mvFD measurement, and demonstrated that mvFD could predict the prognosis and response to chemotherapy of GBM patients.

Extending gene ontology with gene association networks.

PubMed

Peng, Jiajie; Wang, Tao; Wang, Jixuan; Wang, Yadong; Chen, Jin

2016-04-15

Gene ontology (GO) is a widely used resource to describe the attributes for gene products. However, automatic GO maintenance remains to be difficult because of the complex logical reasoning and the need of biological knowledge that are not explicitly represented in the GO. The existing studies either construct whole GO based on network data or only infer the relations between existing GO terms. None is purposed to add new terms automatically to the existing GO. We proposed a new algorithm 'GOExtender' to efficiently identify all the connected gene pairs labeled by the same parent GO terms. GOExtender is used to predict new GO terms with biological network data, and connect them to the existing GO. Evaluation tests on biological process and cellular component categories of different GO releases showed that GOExtender can extend new GO terms automatically based on the biological network. Furthermore, we applied GOExtender to the recent release of GO and discovered new GO terms with strong support from literature. Software and supplementary document are available at www.msu.edu/%7Ejinchen/GOExtender jinchen@msu.edu or ydwang@hit.edu.cn Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Principal visual word discovery for automatic license plate detection.

PubMed

Zhou, Wengang; Li, Houqiang; Lu, Yijuan; Tian, Qi

2012-09-01

License plates detection is widely considered a solved problem, with many systems already in operation. However, the existing algorithms or systems work well only under some controlled conditions. There are still many challenges for license plate detection in an open environment, such as various observation angles, background clutter, scale changes, multiple plates, uneven illumination, and so on. In this paper, we propose a novel scheme to automatically locate license plates by principal visual word (PVW), discovery and local feature matching. Observing that characters in different license plates are duplicates of each other, we bring in the idea of using the bag-of-words (BoW) model popularly applied in partial-duplicate image search. Unlike the classic BoW model, for each plate character, we automatically discover the PVW characterized with geometric context. Given a new image, the license plates are extracted by matching local features with PVW. Besides license plate detection, our approach can also be extended to the detection of logos and trademarks. Due to the invariance virtue of scale-invariant feature transform feature, our method can adaptively deal with various changes in the license plates, such as rotation, scaling, illumination, etc. Promising results of the proposed approach are demonstrated with an experimental study in license plate detection.

A probabilistic union model with automatic order selection for noisy speech recognition.

PubMed

Jancovic, P; Ming, J

2001-09-01

A critical issue in exploiting the potential of the sub-band-based approach to robust speech recognition is the method of combining the sub-band observations, for selecting the bands unaffected by noise. A new method for this purpose, i.e., the probabilistic union model, was recently introduced. This model has been shown to be capable of dealing with band-limited corruption, requiring no knowledge about the band position and statistical distribution of the noise. A parameter within the model, which we call its order, gives the best results when it equals the number of noisy bands. Since this information may not be available in practice, in this paper we introduce an automatic algorithm for selecting the order, based on the state duration pattern generated by the hidden Markov model (HMM). The algorithm has been tested on the TIDIGITS database corrupted by various types of additive band-limited noise with unknown noisy bands. The results have shown that the union model equipped with the new algorithm can achieve a recognition performance similar to that achieved when the number of noisy bands is known. The results show a very significant improvement over the traditional full-band model, without requiring prior information on either the position or the number of noisy bands. The principle of the algorithm for selecting the order based on state duration may also be applied to other sub-band combination methods.

Automated circumferential construction of first-order aqueous humor outflow pathways using spectral-domain optical coherence tomography.

PubMed

Huang, Alex S; Belghith, Akram; Dastiridou, Anna; Chopra, Vikas; Zangwill, Linda M; Weinreb, Robert N

2017-06-01

The purpose was to create a three-dimensional (3-D) model of circumferential aqueous humor outflow (AHO) in a living human eye with an automated detection algorithm for Schlemm’s canal (SC) and first-order collector channels (CC) applied to spectral-domain optical coherence tomography (SD-OCT). Anterior segment SD-OCT scans from a subject were acquired circumferentially around the limbus. A Bayesian Ridge method was used to approximate the location of the SC on infrared confocal laser scanning ophthalmoscopic images with a cross multiplication tool developed to initiate SC/CC detection automated through a fuzzy hidden Markov Chain approach. Automatic segmentation of SC and initial CC’s was manually confirmed by two masked graders. Outflow pathways detected by the segmentation algorithm were reconstructed into a 3-D representation of AHO. Overall, only <1% of images (5114 total B-scans) were ungradable. Automatic segmentation algorithm performed well with SC detection 98.3% of the time and <0.1% false positive detection compared to expert grader consensus. CC was detected 84.2% of the time with 1.4% false positive detection. 3-D representation of AHO pathways demonstrated variably thicker and thinner SC with some clear CC roots. Circumferential (360 deg), automated, and validated AHO detection of angle structures in the living human eye with reconstruction was possible.

Multivariate Approach for Alzheimer's Disease Detection Using Stationary Wavelet Entropy and Predator-Prey Particle Swarm Optimization.

PubMed

Zhang, Yudong; Wang, Shuihua; Sui, Yuxiu; Yang, Ming; Liu, Bin; Cheng, Hong; Sun, Junding; Jia, Wenjuan; Phillips, Preetha; Gorriz, Juan Manuel

2017-07-17

The number of patients with Alzheimer's disease is increasing rapidly every year. Scholars often use computer vision and machine learning methods to develop an automatic diagnosis system. In this study, we developed a novel machine learning system that can make diagnoses automatically from brain magnetic resonance images. First, the brain imaging was processed, including skull stripping and spatial normalization. Second, one axial slice was selected from the volumetric image, and stationary wavelet entropy (SWE) was done to extract the texture features. Third, a single-hidden-layer neural network was used as the classifier. Finally, a predator-prey particle swarm optimization was proposed to train the weights and biases of the classifier. Our method used 4-level decomposition and yielded 13 SWE features. The classification yielded an overall accuracy of 92.73±1.03%, a sensitivity of 92.69±1.29%, and a specificity of 92.78±1.51%. The area under the curve is 0.95±0.02. Additionally, this method only cost 0.88 s to identify a subject in online stage, after its volumetric image is preprocessed. In terms of classification performance, our method performs better than 10 state-of-the-art approaches and the performance of human observers. Therefore, this proposed method is effective in the detection of Alzheimer's disease.

Automated Discovery and Modeling of Sequential Patterns Preceding Events of Interest

NASA Technical Reports Server (NTRS)

Rohloff, Kurt

2010-01-01

The integration of emerging data manipulation technologies has enabled a paradigm shift in practitioners' abilities to understand and anticipate events of interest in complex systems. Example events of interest include outbreaks of socio-political violence in nation-states. Rather than relying on human-centric modeling efforts that are limited by the availability of SMEs, automated data processing technologies has enabled the development of innovative automated complex system modeling and predictive analysis technologies. We introduce one such emerging modeling technology - the sequential pattern methodology. We have applied the sequential pattern methodology to automatically identify patterns of observed behavior that precede outbreaks of socio-political violence such as riots, rebellions and coups in nation-states. The sequential pattern methodology is a groundbreaking approach to automated complex system model discovery because it generates easily interpretable patterns based on direct observations of sampled factor data for a deeper understanding of societal behaviors that is tolerant of observation noise and missing data. The discovered patterns are simple to interpret and mimic human's identifications of observed trends in temporal data. Discovered patterns also provide an automated forecasting ability: we discuss an example of using discovered patterns coupled with a rich data environment to forecast various types of socio-political violence in nation-states.

Enhancing the usability and performance of structured association mapping algorithms using automation, parallelization, and visualization in the GenAMap software system

PubMed Central

2012-01-01

Background Structured association mapping is proving to be a powerful strategy to find genetic polymorphisms associated with disease. However, these algorithms are often distributed as command line implementations that require expertise and effort to customize and put into practice. Because of the difficulty required to use these cutting-edge techniques, geneticists often revert to simpler, less powerful methods. Results To make structured association mapping more accessible to geneticists, we have developed an automatic processing system called Auto-SAM. Auto-SAM enables geneticists to run structured association mapping algorithms automatically, using parallelization. Auto-SAM includes algorithms to discover gene-networks and find population structure. Auto-SAM can also run popular association mapping algorithms, in addition to five structured association mapping algorithms. Conclusions Auto-SAM is available through GenAMap, a front-end desktop visualization tool. GenAMap and Auto-SAM are implemented in JAVA; binaries for GenAMap can be downloaded from http://sailing.cs.cmu.edu/genamap. PMID:22471660

Benefit Analyses of Technologies for Automatic Identification to Be Implemented in the Healthcare Sector

NASA Astrophysics Data System (ADS)

Krey, Mike; Schlatter, Ueli

The tasks and objectives of automatic identification (Auto-ID) are to provide information on goods and products. It has already been established for years in the areas of logistics and trading and can no longer be ignored by the German healthcare sector. Some German hospitals have already discovered the capabilities of Auto-ID. Improvements in quality, safety and reductions in risk, cost and time are aspects and areas where improvements are achievable. Privacy protection, legal restraints, and the personal rights of patients and staff members are just a few aspects which make the heath care sector a sensible field for the implementation of Auto-ID. Auto-ID in this context contains the different technologies, methods and products for the registration, provision and storage of relevant data. With the help of a quantifiable and science-based evaluation, an answer is sought as to which Auto-ID has the highest capability to be implemented in healthcare business.

Graph-Based Semantic Web Service Composition for Healthcare Data Integration.

PubMed

Arch-Int, Ngamnij; Arch-Int, Somjit; Sonsilphong, Suphachoke; Wanchai, Paweena

2017-01-01

Within the numerous and heterogeneous web services offered through different sources, automatic web services composition is the most convenient method for building complex business processes that permit invocation of multiple existing atomic services. The current solutions in functional web services composition lack autonomous queries of semantic matches within the parameters of web services, which are necessary in the composition of large-scale related services. In this paper, we propose a graph-based Semantic Web Services composition system consisting of two subsystems: management time and run time. The management-time subsystem is responsible for dependency graph preparation in which a dependency graph of related services is generated automatically according to the proposed semantic matchmaking rules. The run-time subsystem is responsible for discovering the potential web services and nonredundant web services composition of a user's query using a graph-based searching algorithm. The proposed approach was applied to healthcare data integration in different health organizations and was evaluated according to two aspects: execution time measurement and correctness measurement.

Graph-Based Semantic Web Service Composition for Healthcare Data Integration

PubMed Central

2017-01-01

Within the numerous and heterogeneous web services offered through different sources, automatic web services composition is the most convenient method for building complex business processes that permit invocation of multiple existing atomic services. The current solutions in functional web services composition lack autonomous queries of semantic matches within the parameters of web services, which are necessary in the composition of large-scale related services. In this paper, we propose a graph-based Semantic Web Services composition system consisting of two subsystems: management time and run time. The management-time subsystem is responsible for dependency graph preparation in which a dependency graph of related services is generated automatically according to the proposed semantic matchmaking rules. The run-time subsystem is responsible for discovering the potential web services and nonredundant web services composition of a user's query using a graph-based searching algorithm. The proposed approach was applied to healthcare data integration in different health organizations and was evaluated according to two aspects: execution time measurement and correctness measurement. PMID:29065602

An application of data mining in district heating substations for improving energy performance

NASA Astrophysics Data System (ADS)

Xue, Puning; Zhou, Zhigang; Chen, Xin; Liu, Jing

2017-11-01

Automatic meter reading system is capable of collecting and storing a huge number of district heating (DH) data. However, the data obtained are rarely fully utilized. Data mining is a promising technology to discover potential interesting knowledge from vast data. This paper applies data mining methods to analyse the massive data for improving energy performance of DH substation. The technical approach contains three steps: data selection, cluster analysis and association rule mining (ARM). Two-heating-season data of a substation are used for case study. Cluster analysis identifies six distinct heating patterns based on the primary heat of the substation. ARM reveals that secondary pressure difference and secondary flow rate have a strong correlation. Using the discovered rules, a fault occurring in remote flow meter installed at secondary network is detected accurately. The application demonstrates that data mining techniques can effectively extrapolate potential useful knowledge to better understand substation operation strategies and improve substation energy performance.

Finding Street Gang Members on Twitter

PubMed Central

Balasuriya, Lakshika; Wijeratne, Sanjaya; Doran, Derek; Sheth, Amit

2017-01-01

Most street gang members use Twitter to intimidate others, to present outrageous images and statements to the world, and to share recent illegal activities. Their tweets may thus be useful to law enforcement agencies to discover clues about recent crimes or to anticipate ones that may occur. Finding these posts, however, requires a method to discover gang member Twitter profiles. This is a challenging task since gang members represent a very small population of the 320 million Twitter users. This paper studies the problem of automatically finding gang members on Twitter. It outlines a process to curate one of the largest sets of verifiable gang member profiles that have ever been studied. A review of these profiles establishes differences in the language, images, YouTube links, and emojis gang members use compared to the rest of the Twitter population. Features from this review are used to train a series of supervised classifiers. Our classifier achieves a promising F1 score with a low false positive rate. PMID:28713880

Finding Street Gang Members on Twitter.

PubMed

Balasuriya, Lakshika; Wijeratne, Sanjaya; Doran, Derek; Sheth, Amit

2016-08-01

Most street gang members use Twitter to intimidate others, to present outrageous images and statements to the world, and to share recent illegal activities. Their tweets may thus be useful to law enforcement agencies to discover clues about recent crimes or to anticipate ones that may occur. Finding these posts, however, requires a method to discover gang member Twitter profiles. This is a challenging task since gang members represent a very small population of the 320 million Twitter users. This paper studies the problem of automatically finding gang members on Twitter. It outlines a process to curate one of the largest sets of verifiable gang member profiles that have ever been studied. A review of these profiles establishes differences in the language, images, YouTube links, and emojis gang members use compared to the rest of the Twitter population. Features from this review are used to train a series of supervised classifiers. Our classifier achieves a promising F 1 score with a low false positive rate.

The secret art of managing healthcare expenses: investigating implicit rationing and autonomy in public healthcare systems.

PubMed

Lauridsen, S M R; Norup, M S; Rossel, P J H

2007-12-01

Rationing healthcare is a difficult task, which includes preventing patients from accessing potentially beneficial treatments. Proponents of implicit rationing argue that politicians cannot resist pressure from strong patient groups for treatments and conclude that physicians should ration without informing patients or the public. The authors subdivide this specific programme of implicit rationing, or "hidden rationing", into local hidden rationing, unsophisticated global hidden rationing and sophisticated global hidden rationing. They evaluate the appropriateness of these methods of rationing from the perspectives of individual and political autonomy and conclude that local hidden rationing and unsophisticated global hidden rationing clearly violate patients' individual autonomy, that is, their right to participate in medical decision-making. While sophisticated global hidden rationing avoids this charge, the authors point out that it nonetheless violates the political autonomy of patients, that is, their right to engage in public affairs as citizens. A defence of any of the forms of hidden rationing is therefore considered to be incompatible with a defence of autonomy.

What Makes You Tick? An Empirical Study of Space Science Related Social Media Communications Using Machine Learning

NASA Astrophysics Data System (ADS)

Hwong, Y. L.; Oliver, C.; Van Kranendonk, M. J.

2016-12-01

The rise of social media has transformed the way the public engages with scientists and science organisations. `Retweet', `Like', `Share' and `Comment' are a few ways users engage with messages on Twitter and Facebook, two of the most popular social media platforms. Despite the availability of big data from these digital footprints, research into social media science communication is scant. This paper presents the results of an empirical study into the processes and outcomes of space science related social media communications using machine learning. The study is divided into two main parts. The first part is dedicated to the use of supervised learning methods to investigate the features of highly engaging messages., e.g. highly retweeted tweets and shared Facebook posts. It is hypothesised that these messages contain certain psycholinguistic features that are unique to the field of space science. We built a predictive model to forecast the engagement levels of social media posts. By using four feature sets (n-grams, psycholinguistics, grammar and social media), we were able to achieve prediction accuracies in the vicinity of 90% using three supervised learning algorithms (Naive Bayes, linear classifier and decision tree). We conducted the same experiments on social media messages from three other fields (politics, business and non-profit) and discovered several features that are exclusive to space science communications: anger, authenticity, hashtags, visual descriptions and a tentative tone. The second part of the study focuses on the extraction of topics from a corpus of texts using topic modelling. This part of the study is exploratory in nature and uses an unsupervised method called Latent Dirichlet Allocation (LDA) to uncover previously unknown topics within a large body of documents. Preliminary results indicate a strong potential of topic model algorithms to automatically uncover themes hidden within social media chatters on space related issues, with keywords such as `exoplanet', `water' and `life' being clustered together forming a topic (i.e. 'Astrobiology'). Results also demonstrate the freewheeling nature of social media conversations, while providing evidence for the role of these platforms in facilitating meaningful exchanges among science audience.

Biogeography and genetic diversity of the atlantid heteropods

NASA Astrophysics Data System (ADS)

Wall-Palmer, Deborah; Burridge, Alice K.; Goetze, Erica; Stokvis, Frank R.; Janssen, Arie W.; Mekkes, Lisette; Moreno-Alcántara, María; Bednaršek, Nina; Schiøtte, Tom; Sørensen, Martin Vinther; Smart, Christopher W.; T. C. A. Peijnenburg, Katja

2018-01-01

The atlantid heteropods are regularly encountered, but rarely studied marine planktonic gastropods. Relying on a small (<14 mm), delicate aragonite shell and living in the upper ocean means that, in common with pteropods, atlantids are likely to be affected by imminent ocean changes. Variable shell morphology and widespread distributions indicate that the family is more diverse than the 23 currently known species. Uncovering this diversity is fundamental to determining the distribution of atlantids and to understanding their environmental tolerances. Here we present phylogenetic analyses of all described species of the family Atlantidae using 437 new and 52 previously published cytochrome c oxidase subunit 1 mitochondrial DNA (mtCO1) sequences. Specimens and published sequences were gathered from 32 Atlantic Ocean stations, 14 Indian Ocean stations and 21 Pacific Ocean stations between 35°N and 43°S. DNA barcoding and Automatic Barcode Gap Discovery (ABGD) proved to be valuable tools for the identification of described atlantid species, and also revealed ten additional distinct clades, suggesting that the diversity within this family has been underestimated. Only two of these clades displayed obvious morphological characteristics, demonstrating that much of the newly discovered diversity is hidden from morphology-based identification techniques. Investigation of six large atlantid collections demonstrated that 61% of previously described (morpho) species have a circumglobal distribution. Of the remaining 39%, two species were restricted to the Atlantic Ocean, five occurred in the Indian and Pacific oceans, one species was only found in the northeast Pacific Ocean, and one occurred only in the Southern Subtropical Convergence Zone. Molecular analysis showed that seven of the species with wide distributions were comprised of two or more clades that occupied distinct oceanographic regions. These distributions may suggest narrower environmental tolerances than the described morphospecies. Results provide an updated biogeography and mtCO1 reference dataset of the Atlantidae that may be used to identify atlantid species and provide a first step in understanding their evolutionary history and accurate distribution, encouraging the inclusion of this family in future plankton research.

Biogeography and genetic diversity of the atlantid heteropods.

PubMed

Wall-Palmer, Deborah; Burridge, Alice K; Goetze, Erica; Stokvis, Frank R; Janssen, Arie W; Mekkes, Lisette; Moreno-Alcántara, María; Bednaršek, Nina; Schiøtte, Tom; Sørensen, Martin Vinther; Smart, Christopher W; T C A Peijnenburg, Katja

2018-01-01

The atlantid heteropods are regularly encountered, but rarely studied marine planktonic gastropods. Relying on a small (<14 mm), delicate aragonite shell and living in the upper ocean means that, in common with pteropods, atlantids are likely to be affected by imminent ocean changes. Variable shell morphology and widespread distributions indicate that the family is more diverse than the 23 currently known species. Uncovering this diversity is fundamental to determining the distribution of atlantids and to understanding their environmental tolerances. Here we present phylogenetic analyses of all described species of the family Atlantidae using 437 new and 52 previously published cytochrome c oxidase subunit 1 mitochondrial DNA (mtCO1) sequences. Specimens and published sequences were gathered from 32 Atlantic Ocean stations, 14 Indian Ocean stations and 21 Pacific Ocean stations between 35°N and 43°S. DNA barcoding and Automatic Barcode Gap Discovery (ABGD) proved to be valuable tools for the identification of described atlantid species, and also revealed ten additional distinct clades, suggesting that the diversity within this family has been underestimated. Only two of these clades displayed obvious morphological characteristics, demonstrating that much of the newly discovered diversity is hidden from morphology-based identification techniques. Investigation of six large atlantid collections demonstrated that 61% of previously described (morpho) species have a circumglobal distribution. Of the remaining 39%, two species were restricted to the Atlantic Ocean, five occurred in the Indian and Pacific oceans, one species was only found in the northeast Pacific Ocean, and one occurred only in the Southern Subtropical Convergence Zone. Molecular analysis showed that seven of the species with wide distributions were comprised of two or more clades that occupied distinct oceanographic regions. These distributions may suggest narrower environmental tolerances than the described morphospecies. Results provide an updated biogeography and mtCO1 reference dataset of the Atlantidae that may be used to identify atlantid species and provide a first step in understanding their evolutionary history and accurate distribution, encouraging the inclusion of this family in future plankton research.

Waving goodbye

NASA Image and Video Library

2015-10-05

This planetary nebula is called PK 329-02.2 and is located in the constellation of Norma in the southern sky. It is also sometimes referred to as Menzel 2, or Mz 2, named after the astronomer Donald Menzel who discovered the nebula in 1922. When stars that are around the mass of the Sun reach their final stages of life, they shed their outer layers into space, which appear as glowing clouds of gas called planetary nebulae. The ejection of mass in stellar burnout is irregular and not symmetrical, so that planetary nebulae can have very complex shapes. In the case of Menzel 2 the nebula forms a winding blue cloud that perfectly aligns with two stars at its centre. In 1999 astronomers discovered that the star at the upper right is in fact the central star of the nebula, and the star to the lower left is probably a true physical companion of the central star. For tens of thousands of years the stellar core will be cocooned in spectacular clouds of gas and then, over a period of a few thousand years, the gas will fade away into the depths of the Universe. The curving structure of Menzel 2 resembles a last goodbye before the star reaches its final stage of retirement as a white dwarf. A version of this image was entered into the Hubble's Hidden Treasures image processing competition by contestant Serge Meunier.

External Dependencies-Driven Architecture Discovery and Analysis of Implemented Systems

NASA Technical Reports Server (NTRS)

Ganesan, Dharmalingam; Lindvall, Mikael; Ron, Monica

2014-01-01

A method for architecture discovery and analysis of implemented systems (AIS) is disclosed. The premise of the method is that architecture decisions are inspired and influenced by the external entities that the software system makes use of. Examples of such external entities are COTS components, frameworks, and ultimately even the programming language itself and its libraries. Traces of these architecture decisions can thus be found in the implemented software and is manifested in the way software systems use such external entities. While this fact is often ignored in contemporary reverse engineering methods, the AIS method actively leverages and makes use of the dependencies to external entities as a starting point for the architecture discovery. The AIS method is demonstrated using the NASA's Space Network Access System (SNAS). The results show that, with abundant evidence, the method offers reusable and repeatable guidelines for discovering the architecture and locating potential risks (e.g. low testability, decreased performance) that are hidden deep in the implementation. The analysis is conducted by using external dependencies to identify, classify and review a minimal set of key source code files. Given the benefits of analyzing external dependencies as a way to discover architectures, it is argued that external dependencies deserve to be treated as first-class citizens during reverse engineering. The current structure of a knowledge base of external entities and analysis questions with strategies for getting answers is also discussed.

Quantum Nash Equilibria and Quantum Computing

NASA Astrophysics Data System (ADS)

Fellman, Philip Vos; Post, Jonathan Vos

In 2004, At the Fifth International Conference on Complex Systems, we drew attention to some remarkable findings by researchers at the Santa Fe Institute (Sato, Farmer and Akiyama, 2001) about hitherto unsuspected complexity in the Nash Equilibrium. As we progressed from these findings about heteroclinic Hamiltonians and chaotic transients hidden within the learning patterns of the simple rock-paper-scissors game to some related findings on the theory of quantum computing, one of the arguments we put forward was just as in the late 1990's a number of new Nash equilibria were discovered in simple bi-matrix games (Shubik and Quint, 1996; Von Stengel, 1997, 2000; and McLennan and Park, 1999) we would begin to see new Nash equilibria discovered as the result of quantum computation. While actual quantum computers remain rather primitive (Toibman, 2004), and the theory of quantum computation seems to be advancing perhaps a bit more slowly than originally expected, there have, nonetheless, been a number of advances in computation and some more radical advances in an allied field, quantum game theory (Huberman and Hogg, 2004) which are quite significant. In the course of this paper we will review a few of these discoveries and illustrate some of the characteristics of these new "Quantum Nash Equilibria". The full text of this research can be found at http://necsi.org/events/iccs6/viewpaper.php?id-234

Hello to Arms

NASA Technical Reports Server (NTRS)

2005-01-01

This image highlights the hidden spiral arms (blue) that were discovered around the nearby galaxy NGC 4625 by the ultraviolet eyes of NASA's Galaxy Evolution Explorer.

The image is composed of ultraviolet and visible-light data, from the Galaxy Evolution Explorer and the California Institute of Technology's Digitized Sky Survey, respectively. Near-ultraviolet light is colored green; far-ultraviolet light is colored blue; and optical light is colored red.

As the image demonstrates, the lengthy spiral arms are nearly invisible when viewed in optical light while bright in ultraviolet. This is because they are bustling with hot, newborn stars that radiate primarily ultraviolet light.

The youthful arms are also very long, stretching out to a distance four times the size of the galaxy's core. They are part of the largest ultraviolet galactic disk discovered so far.

Located 31 million light-years away in the constellation Canes Venatici, NGC 4625 is the closest galaxy ever seen with such a young halo of arms. It is slightly smaller than our Milky Way, both in size and mass. However, the fact that this galaxy's disk is forming stars very actively suggests that it might evolve into a more massive and mature galaxy resembling our own.

The armless companion galaxy seen below NGC 4625 is called NGC 4618. Astronomers do not know why it lacks arms but speculate that it may have triggered the development of arms in NGC 4625.

3XMM J185246.6+003317: Another Low Magnetic Field Magnetar

NASA Astrophysics Data System (ADS)

Rea, N.; Viganò, D.; Israel, G. L.; Pons, J. A.; Torres, D. F.

2014-01-01

We study the outburst of the newly discovered X-ray transient 3XMM J185246.6+003317, re-analyzing all available XMM-Newton observations of the source to perform a phase-coherent timing analysis, and derive updated values of the period and period derivative. We find the source rotating at P = 11.55871346(6) s (90% confidence level; at epoch MJD 54728.7) but no evidence for a period derivative in the seven months of outburst decay spanned by the observations. This translates to a 3σ upper limit for the period derivative of \\dot{P}< 1.4\\times 10^{-13} s s-1, which, assuming the classical magneto-dipolar braking model, gives a limit on the dipolar magnetic field of B dip < 4.1 × 1013 G. The X-ray outburst and spectral characteristics of 3XMM J185246.6+003317 confirm its identification as a magnetar, but the magnetic field upper limit we derive defines it as the third "low-B" magnetar discovered in the past 3 yr, after SGR 0418+5729 and Swift J1822.3-1606. We have also obtained an upper limit to the quiescent luminosity (<4 × 1033 erg s-1), in line with the expectations for an old magnetar. The discovery of this new low field magnetar reaffirms the prediction of about one outburst per year from the hidden population of aged magnetars.

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.

The Discovery of a Second Luminous Low Mass X-Ray Binary System in the Globular Cluster M15

NASA Technical Reports Server (NTRS)

White, Nicholas E.; Angelini, Lorella

2001-01-01

Using the Chandra X-ray Observatory we have discovered a second bright X-ray source in the globular cluster M15 that is 2.7" to the west of AC211, the previously known low mass X-ray binary (LMXB) in this system. Prior to the 0.5" imaging capability of Chandra this second source could not have been resolved from AC211. The luminosity and spectrum of this new source, which we call M15-X2, are consistent with it also being a LMXB system. This is the first time that two LMXBs have been seen to be simultaneously active in a globular cluster. The new source, M15-X2, is coincident with a 18th U magnitude very blue star. The discovery of a second LMXB in M15 clears up a long standing puzzle where the X-ray and optical properties of AC211 appear consistent with the central source being hidden behind an accretion disk corona, and yet also showed a luminous X-ray burst suggesting the neutron star is directly visible. This discovery suggests instead that the X-ray burst did not come from AC211, but rather from the newly discovered X-ray source. We discuss the implications of this discovery for X-ray observations of globular clusters in nearby galaxies.

The Hidden Curriculum as Emancipatory and Non-Emancipatory Tools.

ERIC Educational Resources Information Center

Kanpol, Barry

Moral values implied in school practices and policies constitute the "hidden curriculum." Because the hidden curriculum may promote certain moral values to students, teachers are partially responsible for the moral education of students. A component of the hidden curriculum, institutional political resistance, concerns teacher opposition to…

Photoacoustic imaging of hidden dental caries by using a fiber-based probing system

NASA Astrophysics Data System (ADS)

Koyama, Takuya; Kakino, Satoko; Matsuura, Yuji

2017-04-01

Photoacoustic method to detect hidden dental caries is proposed. It was found that high frequency ultrasonic waves are generated from hidden carious part when radiating laser light to occlusal surface of model tooth. By making a map of intensity of these high frequency components, photoacoustic images of hidden caries were successfully obtained. A photoacoustic imaging system using a bundle of hollow optical fiber was fabricated for using clinical application, and clear photoacoustic image of hidden caries was also obtained by this system.

Feedback enhanced plasma spray tool

DOEpatents

Gevelber, Michael Alan; Wroblewski, Donald Edward; Fincke, James Russell; Swank, William David; Haggard, Delon C.; Bewley, Randy Lee

2005-11-22

An improved automatic feedback control scheme enhances plasma spraying of powdered material through reduction of process variability and providing better ability to engineer coating structure. The present inventors discovered that controlling centroid position of the spatial distribution along with other output parameters, such as particle temperature, particle velocity, and molten mass flux rate, vastly increases control over the sprayed coating structure, including vertical and horizontal cracks, voids, and porosity. It also allows improved control over graded layers or compositionally varying layers of material, reduces variations, including variation in coating thickness, and allows increasing deposition rate. Various measurement and system control schemes are provided.

The Optical Gravitational Lensing Experiment. Eclipsing Binary Stars in the Small Magellanic Cloud

NASA Astrophysics Data System (ADS)

Wyrzykowski, L.; Udalski, A.; Kubiak, M.; Szymanski, M. K.; Zebrun, K.; Soszynski, I.; Wozniak, P. R.; Pietrzynski, G.; Szewczyk, O.

2004-03-01

We present new version of the OGLE-II catalog of eclipsing binary stars detected in the Small Magellanic Cloud, based on Difference Image Analysis catalog of variable stars in the Magellanic Clouds containing data collected from 1997 to 2000. We found 1351 eclipsing binary stars in the central 2.4 square degree area of the SMC. 455 stars are newly discovered objects, not found in the previous release of the catalog. The eclipsing objects were selected with the automatic search algorithm based on the artificial neural network. The full catalog is accessible from the OGLE Internet archive.

A constraint-based evolutionary learning approach to the expectation maximization for optimal estimation of the hidden Markov model for speech signal modeling.

PubMed

Huda, Shamsul; Yearwood, John; Togneri, Roberto

2009-02-01

This paper attempts to overcome the tendency of the expectation-maximization (EM) algorithm to locate a local rather than global maximum when applied to estimate the hidden Markov model (HMM) parameters in speech signal modeling. We propose a hybrid algorithm for estimation of the HMM in automatic speech recognition (ASR) using a constraint-based evolutionary algorithm (EA) and EM, the CEL-EM. The novelty of our hybrid algorithm (CEL-EM) is that it is applicable for estimation of the constraint-based models with many constraints and large numbers of parameters (which use EM) like HMM. Two constraint-based versions of the CEL-EM with different fusion strategies have been proposed using a constraint-based EA and the EM for better estimation of HMM in ASR. The first one uses a traditional constraint-handling mechanism of EA. The other version transforms a constrained optimization problem into an unconstrained problem using Lagrange multipliers. Fusion strategies for the CEL-EM use a staged-fusion approach where EM has been plugged with the EA periodically after the execution of EA for a specific period of time to maintain the global sampling capabilities of EA in the hybrid algorithm. A variable initialization approach (VIA) has been proposed using a variable segmentation to provide a better initialization for EA in the CEL-EM. Experimental results on the TIMIT speech corpus show that CEL-EM obtains higher recognition accuracies than the traditional EM algorithm as well as a top-standard EM (VIA-EM, constructed by applying the VIA to EM).

Revealing the hidden networks of interaction in mobile animal groups allows prediction of complex behavioral contagion.

PubMed

Rosenthal, Sara Brin; Twomey, Colin R; Hartnett, Andrew T; Wu, Hai Shan; Couzin, Iain D

2015-04-14

Coordination among social animals requires rapid and efficient transfer of information among individuals, which may depend crucially on the underlying structure of the communication network. Establishing the decision-making circuits and networks that give rise to individual behavior has been a central goal of neuroscience. However, the analogous problem of determining the structure of the communication network among organisms that gives rise to coordinated collective behavior, such as is exhibited by schooling fish and flocking birds, has remained almost entirely neglected. Here, we study collective evasion maneuvers, manifested through rapid waves, or cascades, of behavioral change (a ubiquitous behavior among taxa) in schooling fish (Notemigonus crysoleucas). We automatically track the positions and body postures, calculate visual fields of all individuals in schools of ∼150 fish, and determine the functional mapping between socially generated sensory input and motor response during collective evasion. We find that individuals use simple, robust measures to assess behavioral changes in neighbors, and that the resulting networks by which behavior propagates throughout groups are complex, being weighted, directed, and heterogeneous. By studying these interaction networks, we reveal the (complex, fractional) nature of social contagion and establish that individuals with relatively few, but strongly connected, neighbors are both most socially influential and most susceptible to social influence. Furthermore, we demonstrate that we can predict complex cascades of behavioral change at their moment of initiation, before they actually occur. Consequently, despite the intrinsic stochasticity of individual behavior, establishing the hidden communication networks in large self-organized groups facilitates a quantitative understanding of behavioral contagion.

Revealing the hidden networks of interaction in mobile animal groups allows prediction of complex behavioral contagion

PubMed Central

Rosenthal, Sara Brin; Twomey, Colin R.; Hartnett, Andrew T.; Wu, Hai Shan; Couzin, Iain D.

2015-01-01

Coordination among social animals requires rapid and efficient transfer of information among individuals, which may depend crucially on the underlying structure of the communication network. Establishing the decision-making circuits and networks that give rise to individual behavior has been a central goal of neuroscience. However, the analogous problem of determining the structure of the communication network among organisms that gives rise to coordinated collective behavior, such as is exhibited by schooling fish and flocking birds, has remained almost entirely neglected. Here, we study collective evasion maneuvers, manifested through rapid waves, or cascades, of behavioral change (a ubiquitous behavior among taxa) in schooling fish (Notemigonus crysoleucas). We automatically track the positions and body postures, calculate visual fields of all individuals in schools of ∼150 fish, and determine the functional mapping between socially generated sensory input and motor response during collective evasion. We find that individuals use simple, robust measures to assess behavioral changes in neighbors, and that the resulting networks by which behavior propagates throughout groups are complex, being weighted, directed, and heterogeneous. By studying these interaction networks, we reveal the (complex, fractional) nature of social contagion and establish that individuals with relatively few, but strongly connected, neighbors are both most socially influential and most susceptible to social influence. Furthermore, we demonstrate that we can predict complex cascades of behavioral change at their moment of initiation, before they actually occur. Consequently, despite the intrinsic stochasticity of individual behavior, establishing the hidden communication networks in large self-organized groups facilitates a quantitative understanding of behavioral contagion. PMID:25825752

A recurrent self-organizing neural fuzzy inference network.

PubMed

Juang, C F; Lin, C T

1999-01-01

A recurrent self-organizing neural fuzzy inference network (RSONFIN) is proposed in this paper. The RSONFIN is inherently a recurrent multilayered connectionist network for realizing the basic elements and functions of dynamic fuzzy inference, and may be considered to be constructed from a series of dynamic fuzzy rules. The temporal relations embedded in the network are built by adding some feedback connections representing the memory elements to a feedforward neural fuzzy network. Each weight as well as node in the RSONFIN has its own meaning and represents a special element in a fuzzy rule. There are no hidden nodes (i.e., no membership functions and fuzzy rules) initially in the RSONFIN. They are created on-line via concurrent structure identification (the construction of dynamic fuzzy if-then rules) and parameter identification (the tuning of the free parameters of membership functions). The structure learning together with the parameter learning forms a fast learning algorithm for building a small, yet powerful, dynamic neural fuzzy network. Two major characteristics of the RSONFIN can thus be seen: 1) the recurrent property of the RSONFIN makes it suitable for dealing with temporal problems and 2) no predetermination, like the number of hidden nodes, must be given, since the RSONFIN can find its optimal structure and parameters automatically and quickly. Moreover, to reduce the number of fuzzy rules generated, a flexible input partition method, the aligned clustering-based algorithm, is proposed. Various simulations on temporal problems are done and performance comparisons with some existing recurrent networks are also made. Efficiency of the RSONFIN is verified from these results.

Automatic building of a web-like structure based on thermoplastic adhesive.

PubMed

Leach, Derek; Wang, Liyu; Reusser, Dorothea; Iida, Fumiya

2014-09-01

Animals build structures to extend their control over certain aspects of the environment; e.g., orb-weaver spiders build webs to capture prey, etc. Inspired by this behaviour of animals, we attempt to develop robotics technology that allows a robot to automatically builds structures to help it accomplish certain tasks. In this paper we show automatic building of a web-like structure with a robot arm based on thermoplastic adhesive (TPA) material. The material properties of TPA, such as elasticity, adhesiveness, and low melting temperature, make it possible for a robot to form threads across an open space by an extrusion-drawing process and then combine several of these threads into a web-like structure. The problems addressed here are discovering which parameters determine the thickness of a thread and determining how web-like structures may be used for certain tasks. We first present a model for the extrusion and the drawing of TPA threads which also includes the temperature-dependent material properties. The model verification result shows that the increasing relative surface area of the TPA thread as it is drawn thinner increases the heat loss of the thread, and that by controlling how quickly the thread is drawn, a range of diameters can be achieved from 0.2-0.75 mm. We then present a method based on a generalized nonlinear finite element truss model. The model was validated and could predict the deformation of various web-like structures when payloads are added. At the end, we demonstrate automatic building of a web-like structure for payload bearing.

--No Title--

Science.gov Websites

;height:auto;overflow:hidden}.poc_table .top_row{background-color:#eee;height:auto;overflow:hidden}.poc_table ;background-color:#FFF;height:auto;overflow:hidden;border-top:1px solid #ccc}.poc_table .main_row .name :200px;padding:5px;height:auto;overflow:hidden}.tli_grey_box{background-color:#eaeaea;text-align:center

Natural hidden antibodies reacting with DNA or cardiolipin bind to thymocytes and evoke their death.

PubMed

Zamulaeva, I A; Lekakh, I V; Kiseleva, V I; Gabai, V L; Saenko, A S; Shevchenko, A S; Poverenny, A M

1997-08-18

Both free and hidden natural antibodies to DNA or cardiolipin were obtained from immunoglobulins of a normal donor. The free antibodies reacting with DNA or cardiolipin were isolated by means of affinity chromatography. Antibodies occurring in an hidden state were disengaged from the depleted immunoglobulins by ion-exchange chromatography and were then affinity-isolated on DNA or cardiolipin sorbents. We used flow cytometry to study the ability of free and hidden antibodies to bind to rat thymocytes. Simultaneously, plasma membrane integrity was tested by propidium iodide (PI) exclusion. The hidden antibodies reacted with 65.2 +/- 10.9% of the thymocytes and caused a fast plasma membrane disruption. Cells (28.7 +/- 7.1%) were stained with PI after incubation with the hidden antibodies for 1 h. The free antibodies bound to a very small fraction of the thymocytes and did not evoke death as compared to control without antibodies. The possible reason for the observed effects is difference in reactivity of the free and hidden antibodies to phospholipids. While free antibodies reacted preferentially with phosphotidylcholine, hidden antibodies reacted with cardiolipin and phosphotidylserine.

Raising awareness of the hidden curriculum in veterinary medical education: a review and call for research.

PubMed

Whitcomb, Tiffany L

2014-01-01

The hidden curriculum is characterized by information that is tacitly conveyed to and among students about the cultural and moral environment in which they find themselves. Although the hidden curriculum is often defined as a distinct entity, tacit information is conveyed to students throughout all aspects of formal and informal curricula. This unconsciously communicated knowledge has been identified across a wide spectrum of educational environments and is known to have lasting and powerful impacts, both positive and negative. Recently, medical education research on the hidden curriculum of becoming a doctor has come to the forefront as institutions struggle with inconsistencies between formal and hidden curricula that hinder the practice of patient-centered medicine. Similarly, the complex ethical questions that arise during the practice and teaching of veterinary medicine have the potential to cause disagreement between what the institution sets out to teach and what is actually learned. However, the hidden curriculum remains largely unexplored for this field. Because the hidden curriculum is retained effectively by students, elucidating its underlying messages can be a key component of program refinement. A review of recent literature about the hidden curriculum in a variety of fields, including medical education, will be used to explore potential hidden curricula in veterinary medicine and draw attention to the need for further investigation.

Topological data analysis (TDA) applied to reveal pedogenetic principles of European topsoil system.

PubMed

Savic, Aleksandar; Toth, Gergely; Duponchel, Ludovic

2017-05-15

Recent developments in applied mathematics are bringing new tools that are capable to synthesize knowledge in various disciplines, and help in finding hidden relationships between variables. One such technique is topological data analysis (TDA), a fusion of classical exploration techniques such as principal component analysis (PCA), and a topological point of view applied to clustering of results. Various phenomena have already received new interpretations thanks to TDA, from the proper choice of sport teams to cancer treatments. For the first time, this technique has been applied in soil science, to show the interaction between physical and chemical soil attributes and main soil-forming factors, such as climate and land use. The topsoil data set of the Land Use/Land Cover Area Frame survey (LUCAS) was used as a comprehensive database that consists of approximately 20,000 samples, each described by 12 physical and chemical parameters. After the application of TDA, results obtained were cross-checked against known grouping parameters including five types of land cover, nine types of climate and the organic carbon content of soil. Some of the grouping characteristics observed using standard approaches were confirmed by TDA (e.g., organic carbon content) but novel subtle relationships (e.g., magnitude of anthropogenic effect in soil formation), were discovered as well. The importance of this finding is that TDA is a unique mathematical technique capable of extracting complex relations hidden in soil science data sets, giving the opportunity to see the influence of physicochemical, biotic and abiotic factors on topsoil formation through fresh eyes. Copyright © 2017 Elsevier B.V. All rights reserved.

A review on computational systems biology of pathogen–host interactions

PubMed Central

Durmuş, Saliha; Çakır, Tunahan; Özgür, Arzucan; Guthke, Reinhard

2015-01-01

Pathogens manipulate the cellular mechanisms of host organisms via pathogen–host interactions (PHIs) in order to take advantage of the capabilities of host cells, leading to infections. The crucial role of these interspecies molecular interactions in initiating and sustaining infections necessitates a thorough understanding of the corresponding mechanisms. Unlike the traditional approach of considering the host or pathogen separately, a systems-level approach, considering the PHI system as a whole is indispensable to elucidate the mechanisms of infection. Following the technological advances in the post-genomic era, PHI data have been produced in large-scale within the last decade. Systems biology-based methods for the inference and analysis of PHI regulatory, metabolic, and protein–protein networks to shed light on infection mechanisms are gaining increasing demand thanks to the availability of omics data. The knowledge derived from the PHIs may largely contribute to the identification of new and more efficient therapeutics to prevent or cure infections. There are recent efforts for the detailed documentation of these experimentally verified PHI data through Web-based databases. Despite these advances in data archiving, there are still large amounts of PHI data in the biomedical literature yet to be discovered, and novel text mining methods are in development to unearth such hidden data. Here, we review a collection of recent studies on computational systems biology of PHIs with a special focus on the methods for the inference and analysis of PHI networks, covering also the Web-based databases and text-mining efforts to unravel the data hidden in the literature. PMID:25914674

A hidden oncogenic positive feedback loop caused by crosstalk between Wnt and ERK pathways.

PubMed

Kim, D; Rath, O; Kolch, W; Cho, K-H

2007-07-05

The Wnt and the extracellular signal regulated-kinase (ERK) pathways are both involved in the pathogenesis of various kinds of cancers. Recently, the existence of crosstalk between Wnt and ERK pathways was reported. Gathering all reported results, we have discovered a positive feedback loop embedded in the crosstalk between the Wnt and ERK pathways. We have developed a plausible model that represents the role of this hidden positive feedback loop in the Wnt/ERK pathway crosstalk based on the integration of experimental reports and employing established basic mathematical models of each pathway. Our analysis shows that the positive feedback loop can generate bistability in both the Wnt and ERK signaling pathways, and this prediction was further validated by experiments. In particular, using the commonly accepted assumption that mutations in signaling proteins contribute to cancerogenesis, we have found two conditions through which mutations could evoke an irreversible response leading to a sustained activation of both pathways. One condition is enhanced production of beta-catenin, the other is a reduction of the velocity of MAP kinase phosphatase(s). This enables that high activities of Wnt and ERK pathways are maintained even without a persistent extracellular signal. Thus, our study adds a novel aspect to the molecular mechanisms of carcinogenesis by showing that mutational changes in individual proteins can cause fundamental functional changes well beyond the pathway they function in by a positive feedback loop embedded in crosstalk. Thus, crosstalk between signaling pathways provides a vehicle through which mutations of individual components can affect properties of the system at a larger scale.

Heating up the Galaxy with hidden photons

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

Dubovsky, Sergei; Hernández-Chifflet, Guzmán, E-mail: dubovsky@nyu.edu, E-mail: ghc236@nyu.edu

2015-12-01

We elaborate on the dynamics of ionized interstellar medium in the presence of hidden photon dark matter. Our main focus is the ultra-light regime, where the hidden photon mass is smaller than the plasma frequency in the Milky Way. We point out that as a result of the Galactic plasma shielding direct detection of ultra-light photons in this mass range is especially challenging. However, we demonstrate that ultra-light hidden photon dark matter provides a powerful heating source for the ionized interstellar medium. This results in a strong bound on the kinetic mixing between hidden and regular photons all the waymore » down to the hidden photon masses of order 10{sup −20} eV.« less

Heating up the Galaxy with hidden photons

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

Dubovsky, Sergei; Hernández-Chifflet, Guzmán; Instituto de Física, Facultad de Ingeniería, Universidad de la República,Montevideo, 11300

2015-12-29

We elaborate on the dynamics of ionized interstellar medium in the presence of hidden photon dark matter. Our main focus is the ultra-light regime, where the hidden photon mass is smaller than the plasma frequency in the Milky Way. We point out that as a result of the Galactic plasma shielding direct detection of ultra-light photons in this mass range is especially challenging. However, we demonstrate that ultra-light hidden photon dark matter provides a powerful heating source for the ionized interstellar medium. This results in a strong bound on the kinetic mixing between hidden and regular photons all the waymore » down to the hidden photon masses of order 10{sup −20} eV.« less

"It's Not Always What It Seems": Exploring the Hidden Curriculum within a Doctoral Program

ERIC Educational Resources Information Center

Foot, Rachel Elizabeth

2017-01-01

The purpose of this qualitative, naturalistic study was to explore the ways in which hidden curriculum might influence doctoral student success. Two questions guided the study: (a) How do doctoral students experience the hidden curriculum? (b) What forms of hidden curricula can be identified in a PhD program? Data were collected from twelve…

The hidden and informal curriculum across the continuum of training: A cross-sectional qualitative study.

PubMed

Doja, Asif; Bould, M Dylan; Clarkin, Chantalle; Eady, Kaylee; Sutherland, Stephanie; Writer, Hilary

2016-04-01

The hidden and informal curricula refer to learning in response to unarticulated processes and constraints, falling outside the formal medical curriculum. The hidden curriculum has been identified as requiring attention across all levels of learning. We sought to assess the knowledge and perceptions of the hidden and informal curricula across the continuum of learning at a single institution. Focus groups were held with undergraduate and postgraduate learners and faculty to explore knowledge and perceptions relating to the hidden and informal curricula. Thematic analysis was conducted both inductively by research team members and deductively using questions structured by the existing literature. Participants highlighted several themes related to the presence of the hidden and informal curricula in medical training and practice, including: the privileging of some specialties over others; the reinforcement of hierarchies within medicine; and a culture of tolerance towards unprofessional behaviors. Participants acknowledged the importance of role modeling in the development of professional identities and discussed the deterioration in idealism that occurs. Common issues pertaining to the hidden curriculum exist across all levels of learners, including faculty. Increased awareness of these issues could allow for the further development of methods to address learning within the hidden curriculum.

The hidden and informal curriculum across the continuum of training: A cross-sectional qualitative study.

PubMed

Doja, Asif; Bould, M Dylan; Clarkin, Chantalle; Eady, Kaylee; Sutherland, Stephanie; Writer, Hilary

2016-01-01

The hidden and informal curricula refer to learning in response to unarticulated processes and constraints, falling outside the formal medical curriculum. The hidden curriculum has been identified as requiring attention across all levels of learning. We sought to assess the knowledge and perceptions of the hidden and informal curricula across the continuum of learning at a single institution. Focus groups were held with undergraduate and postgraduate learners and faculty to explore knowledge and perceptions relating to the hidden and informal curricula. Thematic analysis was conducted both inductively by research team members and deductively using questions structured by the existing literature. Participants highlighted several themes related to the presence of the hidden and informal curricula in medical training and practice, including: the privileging of some specialties over others; the reinforcement of hierarchies within medicine; and a culture of tolerance towards unprofessional behaviors. Participants acknowledged the importance of role modeling in the development of professional identities and discussed the deterioration in idealism that occurs. Common issues pertaining to the hidden curriculum exist across all levels of learners, including faculty. Increased awareness of these issues could allow for the further development of methods to address learning within the hidden curriculum.

Hidden Farmworker Labor Camps in North Carolina: An Indicator of Structural Vulnerability

PubMed Central

Summers, Phillip; Quandt, Sara A.; Talton, Jennifer W.; Galván, Leonardo

2015-01-01

Objectives. We used geographic information systems (GIS) to delineate whether farmworker labor camps were hidden and to determine whether hidden camps differed from visible camps in terms of physical and resident characteristics. Methods. We collected data using observation, interview, and public domain GIS data for 180 farmworker labor camps in east central North Carolina. A hidden camp was defined as one that was at least 0.15 miles from an all-weather road or located behind natural or manufactured objects. Hidden camps were compared with visible camps in terms of physical and resident characteristics. Results. More than one third (37.8%) of the farmworker labor camps were hidden. Hidden camps were significantly larger (42.7% vs 17.0% with 21 or more residents; P ≤ .001; and 29.4% vs 13.5% with 3 or more dwellings; P = .002) and were more likely to include barracks (50% vs 19.6%; P ≤ .001) than were visible camps. Conclusions. Poor housing conditions in farmworker labor camps often go unnoticed because they are hidden in the rural landscape, increasing farmworker vulnerability. Policies that promote greater community engagement with farmworker labor camp residents to reduce structural vulnerability should be considered. PMID:26469658

Graph-based biomedical text summarization: An itemset mining and sentence clustering approach.

PubMed

Nasr Azadani, Mozhgan; Ghadiri, Nasser; Davoodijam, Ensieh

2018-06-12

Automatic text summarization offers an efficient solution to access the ever-growing amounts of both scientific and clinical literature in the biomedical domain by summarizing the source documents while maintaining their most informative contents. In this paper, we propose a novel graph-based summarization method that takes advantage of the domain-specific knowledge and a well-established data mining technique called frequent itemset mining. Our summarizer exploits the Unified Medical Language System (UMLS) to construct a concept-based model of the source document and mapping the document to the concepts. Then, it discovers frequent itemsets to take the correlations among multiple concepts into account. The method uses these correlations to propose a similarity function based on which a represented graph is constructed. The summarizer then employs a minimum spanning tree based clustering algorithm to discover various subthemes of the document. Eventually, it generates the final summary by selecting the most informative and relative sentences from all subthemes within the text. We perform an automatic evaluation over a large number of summaries using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. The results demonstrate that the proposed summarization system outperforms various baselines and benchmark approaches. The carried out research suggests that the incorporation of domain-specific knowledge and frequent itemset mining equips the summarization system in a better way to address the informativeness measurement of the sentences. Moreover, clustering the graph nodes (sentences) can enable the summarizer to target different main subthemes of a source document efficiently. The evaluation results show that the proposed approach can significantly improve the performance of the summarization systems in the biomedical domain. Copyright © 2018. Published by Elsevier Inc.

OGLE II Eclipsing Binaries In The LMC: Analysis With Class

NASA Astrophysics Data System (ADS)

Devinney, Edward J.; Prsa, A.; Guinan, E. F.; DeGeorge, M.

2011-01-01

The Eclipsing Binaries (EBs) via Artificial Intelligence (EBAI) Project is applying machine learning techniques to elucidate the nature of EBs. Previously, Prsa, et al. applied artificial neural networks (ANNs) trained on physically-realistic Wilson-Devinney models to solve the light curves of the 1882 detached EBs in the LMC discovered by the OGLE II Project (Wyrzykowski, et al.) fully automatically, bypassing the need for manually-derived starting solutions. A curious result is the non-monotonic distribution of the temperature ratio parameter T2/T1, featuring a subsidiary peak noted previously by Mazeh, et al. in an independent analysis using the EBOP EB solution code (Tamuz, et al.). To explore this and to gain a fuller understanding of the multivariate EBAI LMC observational plus solutions data, we have employed automatic clustering and advanced visualization (CAV) techniques. Clustering the OGLE II data aggregates objects that are similar with respect to many parameter dimensions. Measures of similarity for example, could include the multidimensional Euclidean Distance between data objects, although other measures may be appropriate. Applying clustering, we find good evidence that the T2/T1 subsidiary peak is due to evolved binaries, in support of Mazeh et al.'s speculation. Further, clustering suggests that the LMC detached EBs occupying the main sequence region belong to two distinct classes. Also identified as a separate cluster in the multivariate data are stars having a Period-I band relation. Derekas et al. had previously found a Period-K band relation for LMC EBs discovered by the MACHO Project (Alcock, et al.). We suggest such CAV techniques will prove increasingly useful for understanding the large, multivariate datasets increasingly being produced in astronomy. We are grateful for the support of this research from NSF/RUI Grant AST-05-75042 f.

A Web 2.0 and OGC Standards Enabled Sensor Web Architecture for Global Earth Observing System of Systems

NASA Technical Reports Server (NTRS)

Mandl, Daniel; Unger, Stephen; Ames, Troy; Frye, Stuart; Chien, Steve; Cappelaere, Pat; Tran, Danny; Derezinski, Linda; Paules, Granville

2007-01-01

This paper will describe the progress of a 3 year research award from the NASA Earth Science Technology Office (ESTO) that began October 1, 2006, in response to a NASA Announcement of Research Opportunity on the topic of sensor webs. The key goal of this research is to prototype an interoperable sensor architecture that will enable interoperability between a heterogeneous set of space-based, Unmanned Aerial System (UAS)-based and ground based sensors. Among the key capabilities being pursued is the ability to automatically discover and task the sensors via the Internet and to automatically discover and assemble the necessary science processing algorithms into workflows in order to transform the sensor data into valuable science products. Our first set of sensor web demonstrations will prototype science products useful in managing wildfires and will use such assets as the Earth Observing 1 spacecraft, managed out of NASA/GSFC, a UASbased instrument, managed out of Ames and some automated ground weather stations, managed by the Forest Service. Also, we are collaborating with some of the other ESTO awardees to expand this demonstration and create synergy between our research efforts. Finally, we are making use of Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) suite of standards and some Web 2.0 capabilities to Beverage emerging technologies and standards. This research will demonstrate and validate a path for rapid, low cost sensor integration, which is not tied to a particular system, and thus be able to absorb new assets in an easily evolvable, coordinated manner. This in turn will help to facilitate the United States contribution to the Global Earth Observation System of Systems (GEOSS), as agreed by the U.S. and 60 other countries at the third Earth Observation Summit held in February of 2005.

Zipf exponent of trajectory distribution in the hidden Markov model

NASA Astrophysics Data System (ADS)

Bochkarev, V. V.; Lerner, E. Yu

2014-03-01

This paper is the first step of generalization of the previously obtained full classification of the asymptotic behavior of the probability for Markov chain trajectories for the case of hidden Markov models. The main goal is to study the power (Zipf) and nonpower asymptotics of the frequency list of trajectories of hidden Markov frequencys and to obtain explicit formulae for the exponent of the power asymptotics. We consider several simple classes of hidden Markov models. We prove that the asymptotics for a hidden Markov model and for the corresponding Markov chain can be essentially different.

APIS—a novel approach for conditioning honey bees

PubMed Central

Kirkerud, Nicholas H.; Wehmann, Henja-Niniane; Galizia, C. Giovanni; Gustav, David

2013-01-01

Honey bees perform robustly in different conditioning paradigms. This makes them excellent candidates for studying mechanisms of learning and memory at both an individual and a population level. Here we introduce a novel method of honey bee conditioning: APIS, the Automatic Performance Index System. In an enclosed walking arena where the interior is covered with an electric grid, presentation of odors from either end can be combined with weak electric shocks to form aversive associations. To quantify behavioral responses, we continuously monitor the movement of the bee by an automatic tracking system. We found that escapes from one side to the other, changes in velocity as well as distance and time spent away from the punished odor are suitable parameters to describe the bee's learning capabilities. Our data show that in a short-term memory test the response rate for the conditioned stimulus (CS) in APIS correlates well with response rate obtained from conventional Proboscis Extension Response (PER)-conditioning. Additionally, we discovered that bees modulate their behavior to aversively learned odors by reducing their rate, speed and magnitude of escapes and that both generalization and extinction seem to be different between appetitive and aversive stimuli. The advantages of this automatic system make it ideal for assessing learning rates in a standardized and convenient way, and its flexibility adds to the toolbox for studying honey bee behavior. PMID:23616753

APIS-a novel approach for conditioning honey bees.

PubMed

Kirkerud, Nicholas H; Wehmann, Henja-Niniane; Galizia, C Giovanni; Gustav, David

2013-01-01

Honey bees perform robustly in different conditioning paradigms. This makes them excellent candidates for studying mechanisms of learning and memory at both an individual and a population level. Here we introduce a novel method of honey bee conditioning: APIS, the Automatic Performance Index System. In an enclosed walking arena where the interior is covered with an electric grid, presentation of odors from either end can be combined with weak electric shocks to form aversive associations. To quantify behavioral responses, we continuously monitor the movement of the bee by an automatic tracking system. We found that escapes from one side to the other, changes in velocity as well as distance and time spent away from the punished odor are suitable parameters to describe the bee's learning capabilities. Our data show that in a short-term memory test the response rate for the conditioned stimulus (CS) in APIS correlates well with response rate obtained from conventional Proboscis Extension Response (PER)-conditioning. Additionally, we discovered that bees modulate their behavior to aversively learned odors by reducing their rate, speed and magnitude of escapes and that both generalization and extinction seem to be different between appetitive and aversive stimuli. The advantages of this automatic system make it ideal for assessing learning rates in a standardized and convenient way, and its flexibility adds to the toolbox for studying honey bee behavior.

Automated Deployment of Advanced Controls and Analytics in Buildings

NASA Astrophysics Data System (ADS)

Pritoni, Marco

Buildings use 40% of primary energy in the US. Recent studies show that developing energy analytics and enhancing control strategies can significantly improve their energy performance. However, the deployment of advanced control software applications has been mostly limited to academic studies. Larger-scale implementations are prevented by the significant engineering time and customization required, due to significant differences among buildings. This study demonstrates how physics-inspired data-driven models can be used to develop portable analytics and control applications for buildings. Specifically, I demonstrate application of these models in all phases of the deployment of advanced controls and analytics in buildings: in the first phase, "Site Preparation and Interface with Legacy Systems" I used models to discover or map relationships among building components, automatically gathering metadata (information about data points) necessary to run the applications. During the second phase: "Application Deployment and Commissioning", models automatically learn system parameters, used for advanced controls and analytics. In the third phase: "Continuous Monitoring and Verification" I utilized models to automatically measure the energy performance of a building that has implemented advanced control strategies. In the conclusions, I discuss future challenges and suggest potential strategies for these innovative control systems to be widely deployed in the market. This dissertation provides useful new tools in terms of procedures, algorithms, and models to facilitate the automation of deployment of advanced controls and analytics and accelerate their wide adoption in buildings.

Discovering Free Energy Basins for Macromolecular Systems via Guided Multiscale Simulation

PubMed Central

Sereda, Yuriy V.; Singharoy, Abhishek B.; Jarrold, Martin F.; Ortoleva, Peter J.

2012-01-01

An approach for the automated discovery of low free energy states of macromolecular systems is presented. The method does not involve delineating the entire free energy landscape but proceeds in a sequential free energy minimizing state discovery, i.e., it first discovers one low free energy state and then automatically seeks a distinct neighboring one. These states and the associated ensembles of atomistic configurations are characterized by coarse-grained variables capturing the large-scale structure of the system. A key facet of our approach is the identification of such coarse-grained variables. Evolution of these variables is governed by Langevin dynamics driven by thermal-average forces and mediated by diffusivities, both of which are constructed by an ensemble of short molecular dynamics runs. In the present approach, the thermal-average forces are modified to account for the entropy changes following from our knowledge of the free energy basins already discovered. Such forces guide the system away from the known free energy minima, over free energy barriers, and to a new one. The theory is demonstrated for lactoferrin, known to have multiple energy-minimizing structures. The approach is validated using experimental structures and traditional molecular dynamics. The method can be generalized to enable the interpretation of nanocharacterization data (e.g., ion mobility – mass spectrometry, atomic force microscopy, chemical labeling, and nanopore measurements). PMID:22423635

A composite model for the 750 GeV diphoton excess

DOE PAGES

Harigaya, Keisuke; Nomura, Yasunori

2016-03-14

We study a simple model in which the recently reported 750 GeV diphoton excess arises from a composite pseudo Nambu-Goldstone boson — hidden pion — produced by gluon fusion and decaying into two photons. The model only introduces an extra hidden gauge group at the TeV scale with a vectorlike quark in the bifundamental representation of the hidden and standard model gauge groups. We calculate the masses of all the hidden pions and analyze their experimental signatures and constraints. We find that two colored hidden pions must be near the current experimental limits, and hence are probed in the nearmore » future. We study physics of would-be stable particles — the composite states that do not decay purely by the hidden and standard model gauge dynamics — in detail, including constraints from cosmology. We discuss possible theoretical structures above the TeV scale, e.g. conformal dynamics and supersymmetry, and their phenomenological implications. We also discuss an extension of the minimal model in which there is an extra hidden quark that is singlet under the standard model and has a mass smaller than the hidden dynamical scale. This provides two standard model singlet hidden pions that can both be viewed as diphoton/diboson resonances produced by gluon fusion. We discuss several scenarios in which these (and other) resonances can be used to explain various excesses seen in the LHC data.« less

Radio for hidden-photon dark matter detection

DOE PAGES

Chaudhuri, Saptarshi; Graham, Peter W.; Irwin, Kent; ...

2015-10-08

We propose a resonant electromagnetic detector to search for hidden-photon dark matter over an extensive range of masses. Hidden-photon dark matter can be described as a weakly coupled “hidden electric field,” oscillating at a frequency fixed by the mass, and able to penetrate any shielding. At low frequencies (compared to the inverse size of the shielding), we find that the observable effect of the hidden photon inside any shielding is a real, oscillating magnetic field. We outline experimental setups designed to search for hidden-photon dark matter, using a tunable, resonant LC circuit designed to couple to this magnetic field. Ourmore » “straw man” setups take into consideration resonator design, readout architecture and noise estimates. At high frequencies, there is an upper limit to the useful size of a single resonator set by 1/ν. However, many resonators may be multiplexed within a hidden-photon coherence length to increase the sensitivity in this regime. Hidden-photon dark matter has an enormous range of possible frequencies, but current experiments search only over a few narrow pieces of that range. As a result, we find the potential sensitivity of our proposal is many orders of magnitude beyond current limits over an extensive range of frequencies, from 100 Hz up to 700 GHz and potentially higher.« less

Automated tracking, segmentation and trajectory classification of pelvic organs on dynamic MRI.

PubMed

Nekooeimehr, Iman; Lai-Yuen, Susana; Bao, Paul; Weitzenfeld, Alfredo; Hart, Stuart

2016-08-01

Pelvic organ prolapse is a major health problem in women where pelvic floor organs (bladder, uterus, small bowel, and rectum) fall from their normal position and bulge into the vagina. Dynamic Magnetic Resonance Imaging (DMRI) is presently used to analyze the organs' movements from rest to maximum strain providing complementary support for diagnosis. However, there is currently no automated or quantitative approach to measure the movement of the pelvic organs and their correlation with the severity of prolapse. In this paper, a two-stage method is presented to automatically track and segment pelvic organs on DMRI followed by a multiple-object trajectory classification method to improve the diagnosis of pelvic organ prolapse. Organs are first tracked using particle filters and K-means clustering with prior information. Then, they are segmented using the convex hull of the cluster of particles. Finally, the trajectories of the pelvic organs are modeled using a new Coupled Switched Hidden Markov Model (CSHMM) to classify the severity of pelvic organ prolapse. The tracking and segmentation results are validated using Dice Similarity Index (DSI) whereas the classification results are compared with two manual clinical measurements. Results demonstrate that the presented method is able to automatically track and segment pelvic organs with a DSI above 82% for 26 out of 46 cases and DSI above 75% for all 46 tested cases. The accuracy of the trajectory classification model is also better than current manual measurements.

Automatic thermographic scanning with the creation of 3D panoramic views of buildings

NASA Astrophysics Data System (ADS)

Ferrarini, G.; Cadelano, G.; Bortolin, A.

2016-05-01

Infrared thermography is widely applied to the inspection of building, enabling the identification of thermal anomalies due to the presence of hidden structures, air leakages, and moisture. One of the main advantages of this technique is the possibility to acquire rapidly a temperature map of a surface. However, due to the actual low-resolution of thermal camera and the necessity of scanning surfaces with different orientation, during a building survey it is necessary to take multiple images. In this work a device based on quantitative infrared thermography, called aIRview, has been applied during building surveys to automatically acquire thermograms with a camera mounted on a robotized pan tilt unit. The goal is to perform a first rapid survey of the building that could give useful information for the successive quantitative thermal investigations. For each data acquisition, the instrument covers a rotational field of view of 360° around the vertical axis and up to 180° around the horizontal one. The obtained images have been processed in order to create a full equirectangular projection of the ambient. For this reason the images have been integrated into a web visualization tool, working with web panorama viewers such as Google Street View, creating a webpage where it is possible to have a three dimensional virtual visit of the building. The thermographic data are embedded with the visual imaging and with other sensor data, facilitating the understanding of the physical phenomena underlying the temperature distribution.

A hybrid model based on neural networks for biomedical relation extraction.

PubMed

Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian; Zhang, Shaowu; Sun, Yuanyuan; Yang, Liang

2018-05-01

Biomedical relation extraction can automatically extract high-quality biomedical relations from biomedical texts, which is a vital step for the mining of biomedical knowledge hidden in the literature. Recurrent neural networks (RNNs) and convolutional neural networks (CNNs) are two major neural network models for biomedical relation extraction. Neural network-based methods for biomedical relation extraction typically focus on the sentence sequence and employ RNNs or CNNs to learn the latent features from sentence sequences separately. However, RNNs and CNNs have their own advantages for biomedical relation extraction. Combining RNNs and CNNs may improve biomedical relation extraction. In this paper, we present a hybrid model for the extraction of biomedical relations that combines RNNs and CNNs. First, the shortest dependency path (SDP) is generated based on the dependency graph of the candidate sentence. To make full use of the SDP, we divide the SDP into a dependency word sequence and a relation sequence. Then, RNNs and CNNs are employed to automatically learn the features from the sentence sequence and the dependency sequences, respectively. Finally, the output features of the RNNs and CNNs are combined to detect and extract biomedical relations. We evaluate our hybrid model using five public (protein-protein interaction) PPI corpora and a (drug-drug interaction) DDI corpus. The experimental results suggest that the advantages of RNNs and CNNs in biomedical relation extraction are complementary. Combining RNNs and CNNs can effectively boost biomedical relation extraction performance. Copyright © 2018 Elsevier Inc. All rights reserved.

PCSYS: The optimal design integration system picture drawing system with hidden line algorithm capability for aerospace vehicle configurations

NASA Technical Reports Server (NTRS)

Hague, D. S.; Vanderburg, J. D.

1977-01-01

A vehicle geometric definition based upon quadrilateral surface elements to produce realistic pictures of an aerospace vehicle. The PCSYS programs can be used to visually check geometric data input, monitor geometric perturbations, and to visualize the complex spatial inter-relationships between the internal and external vehicle components. PCSYS has two major component programs. The between program, IMAGE, draws a complex aerospace vehicle pictorial representation based on either an approximate but rapid hidden line algorithm or without any hidden line algorithm. The second program, HIDDEN, draws a vehicle representation using an accurate but time consuming hidden line algorithm.

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

Farina, Marco; Pappadopulo, Duccio; Ruderman, Joshua T.

A hidden sector with a mass gap undergoes an epoch of cannibalism if number changing interactions are active when the temperature drops below the mass of the lightest hidden particle. During cannibalism, the hidden sector temperature decreases only logarithmically with the scale factor. We consider the possibility that dark matter resides in a hidden sector that underwent cannibalism, and has relic density set by the freeze-out of two-to-two annihilations. We identify three novel phases, depending on the behavior of the hidden sector when dark matter freezes out. During the cannibal phase, dark matter annihilations decouple while the hidden sector ismore » cannibalizing. During the chemical phase, only two-to-two interactions are active and the total number of hidden particles is conserved. During the one way phase, the dark matter annihilation products decay out of equilibrium, suppressing the production of dark matter from inverse annihilations. We map out the distinct phenomenology of each phase, which includes a boosted dark matter annihilation rate, new relativistic degrees of freedom, warm dark matter, and observable distortions to the spectrum of the cosmic microwave background.« less

Phases of cannibal dark matter

NASA Astrophysics Data System (ADS)

Farina, Marco; Pappadopulo, Duccio; Ruderman, Joshua T.; Trevisan, Gabriele

2016-12-01

A hidden sector with a mass gap undergoes an epoch of cannibalism if number changing interactions are active when the temperature drops below the mass of the lightest hidden particle. During cannibalism, the hidden sector temperature decreases only logarithmically with the scale factor. We consider the possibility that dark matter resides in a hidden sector that underwent cannibalism, and has relic density set by the freeze-out of two-to-two annihilations. We identify three novel phases, depending on the behavior of the hidden sector when dark matter freezes out. During the cannibal phase, dark matter annihilations decouple while the hidden sector is cannibalizing. During the chemical phase, only two-to-two interactions are active and the total number of hidden particles is conserved. During the one way phase, the dark matter annihilation products decay out of equilibrium, suppressing the production of dark matter from inverse annihilations. We map out the distinct phenomenology of each phase, which includes a boosted dark matter annihilation rate, new relativistic degrees of freedom, warm dark matter, and observable distortions to the spectrum of the cosmic microwave background.

Phases of cannibal dark matter

DOE PAGES

Farina, Marco; Pappadopulo, Duccio; Ruderman, Joshua T.; ...

2016-12-13

A hidden sector with a mass gap undergoes an epoch of cannibalism if number changing interactions are active when the temperature drops below the mass of the lightest hidden particle. During cannibalism, the hidden sector temperature decreases only logarithmically with the scale factor. We consider the possibility that dark matter resides in a hidden sector that underwent cannibalism, and has relic density set by the freeze-out of two-to-two annihilations. We identify three novel phases, depending on the behavior of the hidden sector when dark matter freezes out. During the cannibal phase, dark matter annihilations decouple while the hidden sector ismore » cannibalizing. During the chemical phase, only two-to-two interactions are active and the total number of hidden particles is conserved. During the one way phase, the dark matter annihilation products decay out of equilibrium, suppressing the production of dark matter from inverse annihilations. We map out the distinct phenomenology of each phase, which includes a boosted dark matter annihilation rate, new relativistic degrees of freedom, warm dark matter, and observable distortions to the spectrum of the cosmic microwave background.« less

Generalization and capacity of extensively large two-layered perceptrons.

PubMed

Rosen-Zvi, Michal; Engel, Andreas; Kanter, Ido

2002-09-01

The generalization ability and storage capacity of a treelike two-layered neural network with a number of hidden units scaling as the input dimension is examined. The mapping from the input to the hidden layer is via Boolean functions; the mapping from the hidden layer to the output is done by a perceptron. The analysis is within the replica framework where an order parameter characterizing the overlap between two networks in the combined space of Boolean functions and hidden-to-output couplings is introduced. The maximal capacity of such networks is found to scale linearly with the logarithm of the number of Boolean functions per hidden unit. The generalization process exhibits a first-order phase transition from poor to perfect learning for the case of discrete hidden-to-output couplings. The critical number of examples per input dimension, alpha(c), at which the transition occurs, again scales linearly with the logarithm of the number of Boolean functions. In the case of continuous hidden-to-output couplings, the generalization error decreases according to the same power law as for the perceptron, with the prefactor being different.

Can a CNN recognize Catalan diet?

NASA Astrophysics Data System (ADS)

Herruzo, P.; Bolaños, M.; Radeva, P.

2016-10-01

Nowadays, we can find several diseases related to the unhealthy diet habits of the population, such as diabetes, obesity, anemia, bulimia and anorexia. In many cases, these diseases are related to the food consumption of people. Mediterranean diet is scientifically known as a healthy diet that helps to prevent many metabolic diseases. In particular, our work focuses on the recognition of Mediterranean food and dishes. The development of this methodology would allow to analise the daily habits of users with wearable cameras, within the topic of lifelogging. By using automatic mechanisms we could build an objective tool for the analysis of the patient's behavior, allowing specialists to discover unhealthy food patterns and understand the user's lifestyle. With the aim to automatically recognize a complete diet, we introduce a challenging multi-labeled dataset related to Mediter-ranean diet called FoodCAT. The first type of label provided consists of 115 food classes with an average of 400 images per dish, and the second one consists of 12 food categories with an average of 3800 pictures per class. This dataset will serve as a basis for the development of automatic diet recognition. In this context, deep learning and more specifically, Convolutional Neural Networks (CNNs), currently are state-of-the-art methods for automatic food recognition. In our work, we compare several architectures for image classification, with the purpose of diet recognition. Applying the best model for recognising food categories, we achieve a top-1 accuracy of 72.29%, and top-5 of 97.07%. In a complete diet recognition of dishes from Mediterranean diet, enlarged with the Food-101 dataset for international dishes recognition, we achieve a top-1 accuracy of 68.07%, and top-5 of 89.53%, for a total of 115+101 food classes.

Lepton flavor universality violation without new sources of quark flavor violation

NASA Astrophysics Data System (ADS)

Kamenik, Jernej F.; Soreq, Yotam; Zupan, Jure

2018-02-01

We show that new physics models without new flavor violating interactions can explain the recent anomalies in the b →s ℓ+ℓ- transitions. The b →s ℓ+ℓ- arises from a Z' penguin which automatically predicts the V -A structure for the quark currents in the effective operators. This framework can either be realized in a renormalizable U (1 )' setup or be due to new strongly interacting dynamics. The dimuon resonance searches at the LHC are becoming sensitive to this scenario since the Z' is relatively light, and could well be discovered in future searches by ATLAS and CMS.

Deductive Evaluation: Formal Code Analysis With Low User Burden

NASA Technical Reports Server (NTRS)

Di Vito, Ben. L

2016-01-01

We describe a framework for symbolically evaluating iterative C code using a deductive approach that automatically discovers and proves program properties. Although verification is not performed, the method can infer detailed program behavior. Software engineering work flows could be enhanced by this type of analysis. Floyd-Hoare verification principles are applied to synthesize loop invariants, using a library of iteration-specific deductive knowledge. When needed, theorem proving is interleaved with evaluation and performed on the fly. Evaluation results take the form of inferred expressions and type constraints for values of program variables. An implementation using PVS (Prototype Verification System) is presented along with results for sample C functions.

A Self-Organizing Incremental Neural Network based on local distribution learning.

PubMed

Xing, Youlu; Shi, Xiaofeng; Shen, Furao; Zhou, Ke; Zhao, Jinxi

2016-12-01

In this paper, we propose an unsupervised incremental learning neural network based on local distribution learning, which is called Local Distribution Self-Organizing Incremental Neural Network (LD-SOINN). The LD-SOINN combines the advantages of incremental learning and matrix learning. It can automatically discover suitable nodes to fit the learning data in an incremental way without a priori knowledge such as the structure of the network. The nodes of the network store rich local information regarding the learning data. The adaptive vigilance parameter guarantees that LD-SOINN is able to add new nodes for new knowledge automatically and the number of nodes will not grow unlimitedly. While the learning process continues, nodes that are close to each other and have similar principal components are merged to obtain a concise local representation, which we call a relaxation data representation. A denoising process based on density is designed to reduce the influence of noise. Experiments show that the LD-SOINN performs well on both artificial and real-word data. Copyright © 2016 Elsevier Ltd. All rights reserved.

Controlling state explosion during automatic verification of delay-insensitive and delay-constrained VLSI systems using the POM verifier

NASA Technical Reports Server (NTRS)

Probst, D.; Jensen, L.

1991-01-01

Delay-insensitive VLSI systems have a certain appeal on the ground due to difficulties with clocks; they are even more attractive in space. We answer the question, is it possible to control state explosion arising from various sources during automatic verification (model checking) of delay-insensitive systems? State explosion due to concurrency is handled by introducing a partial-order representation for systems, and defining system correctness as a simple relation between two partial orders on the same set of system events (a graph problem). State explosion due to nondeterminism (chiefly arbitration) is handled when the system to be verified has a clean, finite recurrence structure. Backwards branching is a further optimization. The heart of this approach is the ability, during model checking, to discover a compact finite presentation of the verified system without prior composition of system components. The fully-implemented POM verification system has polynomial space and time performance on traditional asynchronous-circuit benchmarks that are exponential in space and time for other verification systems. We also sketch the generalization of this approach to handle delay-constrained VLSI systems.

Modeling perceived stress via HRV and accelerometer sensor streams.

PubMed

Wu, Min; Cao, Hong; Nguyen, Hai-Long; Surmacz, Karl; Hargrove, Caroline

2015-08-01

Discovering and modeling of stress patterns of human beings is a key step towards achieving automatic stress monitoring, stress management and healthy lifestyle. As various wearable sensors become popular, it becomes possible for individuals to acquire their own relevant sensory data and to automatically assess their stress level on the go. Previous studies for stress analysis were conducted in the controlled laboratory and clinic settings. These studies are not suitable for stress monitoring in one's daily life as various physical activities may affect the physiological signals. In this paper, we address such issue by integrating two modalities of sensors, i.e., HRV sensors and accelerometers, to monitor the perceived stress levels in daily life. We gathered both the heart and the motion data from 8 participants continuously for about 2 weeks. We then extracted features from both sensory data and compared the existing machine learning methods for learning personalized models to interpret the perceived stress levels. Experimental results showed that Bagging classifier with feature selection is able to achieve a prediction accuracy 85.7%, indicating our stress monitoring on daily basis is fairly practical.

A mess of stars

NASA Image and Video Library

2015-08-10

Bursts of pink and red, dark lanes of mottled cosmic dust, and a bright scattering of stars — this NASA/ESA Hubble Space Telescope image shows part of a messy barred spiral galaxy known as NGC 428. It lies approximately 48 million light-years away from Earth in the constellation of Cetus (The Sea Monster). Although a spiral shape is still just about visible in this close-up shot, overall NGC 428’s spiral structure appears to be quite distorted and warped, thought to be a result of a collision between two galaxies. There also appears to be a substantial amount of star formation occurring within NGC 428 — another telltale sign of a merger. When galaxies collide their clouds of gas can merge, creating intense shocks and hot pockets of gas and often triggering new waves of star formation. NGC 428 was discovered by William Herschel in December 1786. More recently a type Ia supernova designated SN2013ct was discovered within the galaxy by Stuart Parker of the BOSS (Backyard Observatory Supernova Search) project in Australia and New Zealand, although it is unfortunately not visible in this image. This image was captured by Hubble’s Advanced Camera for Surveys (ACS) and Wide Field and Planetary Camera 2 (WFPC2). A version of this image was entered into the Hubble’s Hidden Treasures Image Processing competition by contestants Nick Rose and the Flickr user penninecloud. Links: Nick Rose’s image on Flickr Penninecloud’s image on Flickr

What if Finding Data was as Easy as Subscribing to the News?

NASA Astrophysics Data System (ADS)

Duerr, R. E.

2011-12-01

Data are the "common wealth of humanity," the fuel that drives the sciences; but much of the data that exist are inaccessible, buried in one of numerous stove-piped data systems, or entirely hidden unless you have direct knowledge of and contact with the investigator that acquired them. Much of the "wealth" is squandered and overall scientific progress inhibited, a situation that is becoming increasingly untenable with the openness required by data-driven science. What are needed are simple interoperability protocols and advertising mechanisms that allow data from disparate data systems to be easily discovered, explored, and accessed. The tools must be simple enough that individual investigators can use them without IT support. The tools cannot rely on centralized repositories or registries but must enable the development of ad-hoc or special purpose aggregations of data and services tailored to individual community needs. In addition, the protocols must scale to support the discovery of and access to the holdings of the global, interdisciplinary community, be they individual investigators or major data centers. NSIDC, in conjunction with other members of the Federation of Earth Science Information Partners and the Polar Information Commons, are working on just such a suite of tools and protocols. In this talk, I discuss data and service casting, aggregation, data badging, and OpenSearch - a suite of tools and protocols which, when used in conjunction with each other, have the potential of completely changing the way that data and services worldwide are discovered and used.

Out of Reach, Out of Mind? Infants' Comprehension of References to Hidden Inaccessible Objects.

PubMed

Osina, Maria A; Saylor, Megan M; Ganea, Patricia A

2017-09-01

This study investigated the nature of infants' difficulty understanding references to hidden inaccessible objects. Twelve-month-old infants (N = 32) responded to the mention of objects by looking at, pointing at, or approaching them when the referents were visible or accessible, but not when they were hidden and inaccessible (Experiment I). Twelve-month-olds (N = 16) responded robustly when a container with the hidden referent was moved from a previously inaccessible position to an accessible position before the request, but failed to respond when the reverse occurred (Experiment II). This suggests that infants might be able to track the hidden object's dislocations and update its accessibility as it changes. Knowing the hidden object is currently inaccessible inhibits their responding. Older, 16-month-old (N = 17) infants' performance was not affected by object accessibility. © 2016 The Authors. Child Development © 2016 Society for Research in Child Development, Inc.

Digital dental surface registration with laser scanner for orthodontics set-up planning

NASA Astrophysics Data System (ADS)

Alcaniz-Raya, Mariano L.; Albalat, Salvador E.; Grau Colomer, Vincente; Monserrat, Carlos A.

1997-05-01

We present an optical measuring system based on laser structured light suitable for its diary use in orthodontics clinics that fit four main requirements: (1) to avoid use of stone models, (2) to automatically discriminate geometric points belonging to teeth and gum, (3) to automatically calculate diagnostic parameters used by orthodontists, (4) to make use of low cost and easy to use technology for future commercial use. Proposed technique is based in the use of hydrocolloids mould used by orthodontists for stone model obtention. These mould of the inside of patient's mouth are composed of very fluent materials like alginate or hydrocolloids that reveal fine details of dental anatomy. Alginate mould are both very easy to obtain and very low costly. Once captured, alginate moulds are digitized by mean of a newly developed and patented 3D dental scanner. Developed scanner is based in the optical triangulation method based in the projection of a laser line on the alginate mould surface. Line deformation gives uncalibrated shape information. Relative linear movements of the mould with respect to the sensor head gives more sections thus obtaining a full 3D uncalibrated dentition model. Developed device makes use of redundant CCD in the sensor head and servocontrolled linear axis for mould movement. Last step is calibration to get a real and precise X, Y, Z image. All the process is done automatically. The scanner has been specially adapted for 3D dental anatomy capturing in order to fulfill specific requirements such as: scanning time, accuracy, security and correct acquisition of 'hidden points' in alginate mould. Measurement realized on phantoms with known geometry quite similar to dental anatomy present errors less than 0,1 mm. Scanning of global dental anatomy is 2 minutes, and generation of 3D graphics of dental cast takes approximately 30 seconds in a Pentium-based PC.

On the unsupervised analysis of domain-specific Chinese texts

PubMed Central

Deng, Ke; Bol, Peter K.; Li, Kate J.; Liu, Jun S.

2016-01-01

With the growing availability of digitized text data both publicly and privately, there is a great need for effective computational tools to automatically extract information from texts. Because the Chinese language differs most significantly from alphabet-based languages in not specifying word boundaries, most existing Chinese text-mining methods require a prespecified vocabulary and/or a large relevant training corpus, which may not be available in some applications. We introduce an unsupervised method, top-down word discovery and segmentation (TopWORDS), for simultaneously discovering and segmenting words and phrases from large volumes of unstructured Chinese texts, and propose ways to order discovered words and conduct higher-level context analyses. TopWORDS is particularly useful for mining online and domain-specific texts where the underlying vocabulary is unknown or the texts of interest differ significantly from available training corpora. When outputs from TopWORDS are fed into context analysis tools such as topic modeling, word embedding, and association pattern finding, the results are as good as or better than that from using outputs of a supervised segmentation method. PMID:27185919

Latent topic discovery of clinical concepts from hospital discharge summaries of a heterogeneous patient cohort.

PubMed

Lehman, Li-Wei; Long, William; Saeed, Mohammed; Mark, Roger

2014-01-01

Patients in critical care often exhibit complex disease patterns. A fundamental challenge in clinical research is to identify clinical features that may be characteristic of adverse patient outcomes. In this work, we propose a data-driven approach for phenotype discovery of patients in critical care. We used Hierarchical Dirichlet Process (HDP) as a non-parametric topic modeling technique to automatically discover the latent "topic" structure of diseases, symptoms, and findings documented in hospital discharge summaries. We show that the latent topic structure can be used to reveal phenotypic patterns of diseases and symptoms shared across subgroups of a patient cohort, and may contain prognostic value in stratifying patients' post hospital discharge mortality risks. Using discharge summaries of a large patient cohort from the MIMIC II database, we evaluate the clinical utility of the discovered topic structure in identifying patients who are at high risk of mortality within one year post hospital discharge. We demonstrate that the learned topic structure has statistically significant associations with mortality post hospital discharge, and may provide valuable insights in defining new feature sets for predicting patient outcomes.

Unsupervised Decoding of Long-Term, Naturalistic Human Neural Recordings with Automated Video and Audio Annotations

PubMed Central

Wang, Nancy X. R.; Olson, Jared D.; Ojemann, Jeffrey G.; Rao, Rajesh P. N.; Brunton, Bingni W.

2016-01-01

Fully automated decoding of human activities and intentions from direct neural recordings is a tantalizing challenge in brain-computer interfacing. Implementing Brain Computer Interfaces (BCIs) outside carefully controlled experiments in laboratory settings requires adaptive and scalable strategies with minimal supervision. Here we describe an unsupervised approach to decoding neural states from naturalistic human brain recordings. We analyzed continuous, long-term electrocorticography (ECoG) data recorded over many days from the brain of subjects in a hospital room, with simultaneous audio and video recordings. We discovered coherent clusters in high-dimensional ECoG recordings using hierarchical clustering and automatically annotated them using speech and movement labels extracted from audio and video. To our knowledge, this represents the first time techniques from computer vision and speech processing have been used for natural ECoG decoding. Interpretable behaviors were decoded from ECoG data, including moving, speaking and resting; the results were assessed by comparison with manual annotation. Discovered clusters were projected back onto the brain revealing features consistent with known functional areas, opening the door to automated functional brain mapping in natural settings. PMID:27148018

An NN-Based SRD Decomposition Algorithm and Its Application in Nonlinear Compensation

PubMed Central

Yan, Honghang; Deng, Fang; Sun, Jian; Chen, Jie

2014-01-01

In this study, a neural network-based square root of descending (SRD) order decomposition algorithm for compensating for nonlinear data generated by sensors is presented. The study aims at exploring the optimized decomposition of data 1.00,0.00,0.00 and minimizing the computational complexity and memory space of the training process. A linear decomposition algorithm, which automatically finds the optimal decomposition of N subparts and reduces the training time to 1N and memory cost to 1N, has been implemented on nonlinear data obtained from an encoder. Particular focus is given to the theoretical access of estimating the numbers of hidden nodes and the precision of varying the decomposition method. Numerical experiments are designed to evaluate the effect of this algorithm. Moreover, a designed device for angular sensor calibration is presented. We conduct an experiment that samples the data of an encoder and compensates for the nonlinearity of the encoder to testify this novel algorithm. PMID:25232912

Phonetic Spelling Filter for Keyword Selection in Drug Mention Mining from Social Media

PubMed Central

Pimpalkhute, Pranoti; Patki, Apurv; Nikfarjam, Azadeh; Gonzalez, Graciela

2014-01-01

Social media postings are rich in information that often remain hidden and inaccessible for automatic extraction due to inherent limitations of the site’s APIs, which mostly limit access via specific keyword-based searches (and limit both the number of keywords and the number of postings that are returned). When mining social media for drug mentions, one of the first problems to solve is how to derive a list of variants of the drug name (common misspellings) that can capture a sufficient number of postings. We present here an approach that filters the potential variants based on the intuition that, faced with the task of writing an unfamiliar, complex word (the drug name), users will tend to revert to phonetic spelling, and we thus give preference to variants that reflect the phonemes of the correct spelling. The algorithm allowed us to capture 50.4 – 56.0 % of the user comments using only about 18% of the variants. PMID:25717407

BELM: Bayesian extreme learning machine.

PubMed

Soria-Olivas, Emilio; Gómez-Sanchis, Juan; Martín, José D; Vila-Francés, Joan; Martínez, Marcelino; Magdalena, José R; Serrano, Antonio J

2011-03-01

The theory of extreme learning machine (ELM) has become very popular on the last few years. ELM is a new approach for learning the parameters of the hidden layers of a multilayer neural network (as the multilayer perceptron or the radial basis function neural network). Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This brief proposes a bayesian approach to ELM, which presents some advantages over other approaches: it allows the introduction of a priori knowledge; obtains the confidence intervals (CIs) without the need of applying methods that are computationally intensive, e.g., bootstrap; and presents high generalization capabilities. Bayesian ELM is benchmarked against classical ELM in several artificial and real datasets that are widely used for the evaluation of machine learning algorithms. Achieved results show that the proposed approach produces a competitive accuracy with some additional advantages, namely, automatic production of CIs, reduction of probability of model overfitting, and use of a priori knowledge.

Identification of related gene/protein names based on an HMM of name variations.

PubMed

Yeganova, L; Smith, L; Wilbur, W J

2004-04-01

Gene and protein names follow few, if any, true naming conventions and are subject to great variation in different occurrences of the same name. This gives rise to two important problems in natural language processing. First, can one locate the names of genes or proteins in free text, and second, can one determine when two names denote the same gene or protein? The first of these problems is a special case of the problem of named entity recognition, while the second is a special case of the problem of automatic term recognition (ATR). We study the second problem, that of gene or protein name variation. Here we describe a system which, given a query gene or protein name, identifies related gene or protein names in a large list. The system is based on a dynamic programming algorithm for sequence alignment in which the mutation matrix is allowed to vary under the control of a fully trainable hidden Markov model.

An efficient robust sound classification algorithm for hearing aids.

PubMed

Nordqvist, Peter; Leijon, Arne

2004-06-01

An efficient robust sound classification algorithm based on hidden Markov models is presented. The system would enable a hearing aid to automatically change its behavior for differing listening environments according to the user's preferences. This work attempts to distinguish between three listening environment categories: speech in traffic noise, speech in babble, and clean speech, regardless of the signal-to-noise ratio. The classifier uses only the modulation characteristics of the signal. The classifier ignores the absolute sound pressure level and the absolute spectrum shape, resulting in an algorithm that is robust against irrelevant acoustic variations. The measured classification hit rate was 96.7%-99.5% when the classifier was tested with sounds representing one of the three environment categories included in the classifier. False-alarm rates were 0.2%-1.7% in these tests. The algorithm is robust and efficient and consumes a small amount of instructions and memory. It is fully possible to implement the classifier in a DSP-based hearing instrument.

Depth data research of GIS based on clustering analysis algorithm

NASA Astrophysics Data System (ADS)

Xiong, Yan; Xu, Wenli

2018-03-01

The data of GIS have spatial distribution. Geographic data has both spatial characteristics and attribute characteristics, and also changes with time. Therefore, the amount of data is very large. Nowadays, many industries and departments in the society are using GIS. However, without proper data analysis and mining scheme, GIS will not exert its maximum effectiveness and will waste a lot of data. In this paper, we use the geographic information demand of a national security department as the experimental object, combining the characteristics of GIS data, taking into account the characteristics of time, space, attributes and so on, and using cluster analysis algorithm. We further study the mining scheme for depth data, and get the algorithm model. This algorithm can automatically classify sample data, and then carry out exploratory analysis. The research shows that the algorithm model and the information mining scheme can quickly find hidden depth information from the surface data of GIS, thus improving the efficiency of the security department. This algorithm can also be extended to other fields.

Pattern Recognition of Momentary Mental Workload Based on Multi-Channel Electrophysiological Data and Ensemble Convolutional Neural Networks.

PubMed

Zhang, Jianhua; Li, Sunan; Wang, Rubin

2017-01-01

In this paper, we deal with the Mental Workload (MWL) classification problem based on the measured physiological data. First we discussed the optimal depth (i.e., the number of hidden layers) and parameter optimization algorithms for the Convolutional Neural Networks (CNN). The base CNNs designed were tested according to five classification performance indices, namely Accuracy, Precision, F-measure, G-mean, and required training time. Then we developed an Ensemble Convolutional Neural Network (ECNN) to enhance the accuracy and robustness of the individual CNN model. For the ECNN design, three model aggregation approaches (weighted averaging, majority voting and stacking) were examined and a resampling strategy was used to enhance the diversity of individual CNN models. The results of MWL classification performance comparison indicated that the proposed ECNN framework can effectively improve MWL classification performance and is featured by entirely automatic feature extraction and MWL classification, when compared with traditional machine learning methods.

Explaining the electroweak scale and stabilizing moduli in M theory

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

Acharya, Bobby S.; Bobkov, Konstantin; Kane, Gordon L.

2007-12-15

In a recent paper [B. Acharya, K. Bobkov, G. Kane, P. Kumar, and D. Vaman, Phys. Rev. Lett. 97, 191601 (2006).] it was shown that in fluxless M theory vacua with at least two hidden sectors undergoing strong gauge dynamics and a particular form of the Kaehler potential, all moduli are stabilized by the effective potential and a stable hierarchy is generated, consistent with standard gauge unification. This paper explains the results of [B. Acharya, K. Bobkov, G. Kane, P. Kumar, and D. Vaman, Phys. Rev. Lett. 97, 191601 (2006).] in more detail and generalizes them, finding an essentially uniquemore » de Sitter vacuum under reasonable conditions. One of the main phenomenological consequences is a prediction which emerges from this entire class of vacua: namely, gaugino masses are significantly suppressed relative to the gravitino mass. We also present evidence that, for those vacua in which the vacuum energy is small, the gravitino mass, which sets all the superpartner masses, is automatically in the TeV-100 TeV range.« less

Phonetic spelling filter for keyword selection in drug mention mining from social media.

PubMed

Pimpalkhute, Pranoti; Patki, Apurv; Nikfarjam, Azadeh; Gonzalez, Graciela

2014-01-01

Social media postings are rich in information that often remain hidden and inaccessible for automatic extraction due to inherent limitations of the site's APIs, which mostly limit access via specific keyword-based searches (and limit both the number of keywords and the number of postings that are returned). When mining social media for drug mentions, one of the first problems to solve is how to derive a list of variants of the drug name (common misspellings) that can capture a sufficient number of postings. We present here an approach that filters the potential variants based on the intuition that, faced with the task of writing an unfamiliar, complex word (the drug name), users will tend to revert to phonetic spelling, and we thus give preference to variants that reflect the phonemes of the correct spelling. The algorithm allowed us to capture 50.4 - 56.0 % of the user comments using only about 18% of the variants.

Motion effects in multistatic millimeter-wave imaging systems

NASA Astrophysics Data System (ADS)

Schiessl, Andreas; Ahmed, Sherif Sayed; Schmidt, Lorenz-Peter

2013-10-01

At airport security checkpoints, authorities are demanding improved personnel screening devices for increased security. Active mm-wave imaging systems deliver the high quality images needed for reliable automatic detection of hidden threats. As mm-wave imaging systems assume static scenarios, motion effects caused by movement of persons during the screening procedure can degrade image quality, so very short measurement time is required. Multistatic imaging array designs and fully electronic scanning in combination with digital beamforming offer short measurement time together with high resolution and high image dynamic range, which are critical parameters for imaging systems used for passenger screening. In this paper, operational principles of such systems are explained, and the performance of the imaging systems with respect to motion within the scenarios is demonstrated using mm-wave images of different test objects and standing as well as moving persons. Electronic microwave imaging systems using multistatic sparse arrays are suitable for next generation screening systems, which will support on the move screening of passengers.

Applying deep neural networks to HEP job classification

NASA Astrophysics Data System (ADS)

Wang, L.; Shi, J.; Yan, X.

2015-12-01

The cluster of IHEP computing center is a middle-sized computing system which provides 10 thousands CPU cores, 5 PB disk storage, and 40 GB/s IO throughput. Its 1000+ users come from a variety of HEP experiments. In such a system, job classification is an indispensable task. Although experienced administrator can classify a HEP job by its IO pattern, it is unpractical to classify millions of jobs manually. We present how to solve this problem with deep neural networks in a supervised learning way. Firstly, we built a training data set of 320K samples by an IO pattern collection agent and a semi-automatic process of sample labelling. Then we implemented and trained DNNs models with Torch. During the process of model training, several meta-parameters was tuned with cross-validations. Test results show that a 5- hidden-layer DNNs model achieves 96% precision on the classification task. By comparison, it outperforms a linear model by 8% precision.

Robust artificial intelligence tool for automatic start-up of the supplementary medium feeding in recombinant E. coli cultivations.

PubMed

Horta, Antônio Carlos Luperni; da Silva, Adilson José; Sargo, Cíntia Regina; Gonçalves, Viviane Maimoni; Zangirolami, Teresa Cristina; Giordano, Roberto de Campos

2011-09-01

One of the most important events in fed-batch fermentations is the definition of the moment to start the feeding. This paper presents a methodology for a rational selection of the architecture of an artificial intelligence (AI) system, based on a neural network committee (NNC), which identifies the end of the batch phase. The AI system was successfully used during high cell density cultivations of recombinant Escherichia coli. The AI algorithm was validated for different systems, expressing three antigens to be used in human and animal vaccines: fragments of surface proteins of Streptococcus pneumoniae (PspA), clades 1 and 3, and of Erysipelothrix rhusiopathiae (SpaA). Standard feed-forward neural networks (NNs), with a single hidden layer, were the basis for the NNC. The NN architecture with best performance had the following inputs: stirrer speed, inlet air, and oxygen flow rates, carbon dioxide evolution rate, and CO2 molar fraction in the exhaust gas.

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

Montanini, R.; Freni, F.; Rossi, G. L.

This paper reports one of the first experimental results on the application of ultrasound activated lock-in vibrothermography for quantitative assessment of buried flaws in complex cast parts. The use of amplitude modulated ultrasonic heat generation allowed selective response of defective areas within the part, as the defect itself is turned into a local thermal wave emitter. Quantitative evaluation of hidden damages was accomplished by estimating independently both the area and the depth extension of the buried flaws, while x-ray 3D computed tomography was used as reference for sizing accuracy assessment. To retrieve flaw's area, a simple yet effective histogram-based phasemore » image segmentation algorithm with automatic pixels classification has been developed. A clear correlation was found between the thermal (phase) signature measured by the infrared camera on the target surface and the actual mean cross-section area of the flaw. Due to the very fast cycle time (<30 s/part), the method could potentially be applied for 100% quality control of casting components.« less

[Creating language model of the forensic medicine domain for developing a autopsy recording system by automatic speech recognition].

PubMed

Niijima, H; Ito, N; Ogino, S; Takatori, T; Iwase, H; Kobayashi, M

2000-11-01

For the purpose of practical use of speech recognition technology for recording of forensic autopsy, a language model of the speech recording system, specialized for the forensic autopsy, was developed. The language model for the forensic autopsy by applying 3-gram model was created, and an acoustic model for Japanese speech recognition by Hidden Markov Model in addition to the above were utilized to customize the speech recognition engine for forensic autopsy. A forensic vocabulary set of over 10,000 words was compiled and some 300,000 sentence patterns were made to create the forensic language model, then properly mixing with a general language model to attain high exactitude. When tried by dictating autopsy findings, this speech recognition system was proved to be about 95% of recognition rate that seems to have reached to the practical usability in view of speech recognition software, though there remains rooms for improving its hardware and application-layer software.

A male-specific QTL for social interaction behavior in mice mapped with automated pattern detection by a hidden Markov model incorporated into newly developed freeware.

PubMed

Arakawa, Toshiya; Tanave, Akira; Ikeuchi, Shiho; Takahashi, Aki; Kakihara, Satoshi; Kimura, Shingo; Sugimoto, Hiroki; Asada, Nobuhiko; Shiroishi, Toshihiko; Tomihara, Kazuya; Tsuchiya, Takashi; Koide, Tsuyoshi

2014-08-30

Owing to their complex nature, social interaction tests normally require the observation of video data by a human researcher, and thus are difficult to use in large-scale studies. We previously established a statistical method, a hidden Markov model (HMM), which enables the differentiation of two social states ("interaction" and "indifference"), and three social states ("sniffing", "following", and "indifference"), automatically in silico. Here, we developed freeware called DuoMouse for the rapid evaluation of social interaction behavior. This software incorporates five steps: (1) settings, (2) video recording, (3) tracking from the video data, (4) HMM analysis, and (5) visualization of the results. Using DuoMouse, we mapped a genetic locus related to social interaction. We previously reported that a consomic strain, B6-Chr6C(MSM), with its chromosome 6 substituted for one from MSM/Ms, showed more social interaction than C57BL/6 (B6). We made four subconsomic strains, C3, C5, C6, and C7, each of which has a shorter segment of chromosome 6 derived from B6-Chr6C, and conducted social interaction tests on these strains. DuoMouse indicated that C6, but not C3, C5, and C7, showed higher interaction, sniffing, and following than B6, specifically in males. The data obtained by human observation showed high concordance to those from DuoMouse. The results indicated that the MSM-derived chromosomal region present in C6-but not in C3, C5, and C7-associated with increased social behavior. This method to analyze social interaction will aid primary screening for difference in social behavior in mice. Copyright © 2014 Elsevier B.V. All rights reserved.

Using chemical organization theory for model checking

PubMed Central

Kaleta, Christoph; Richter, Stephan; Dittrich, Peter

2009-01-01

Motivation: The increasing number and complexity of biomodels makes automatic procedures for checking the models' properties and quality necessary. Approaches like elementary mode analysis, flux balance analysis, deficiency analysis and chemical organization theory (OT) require only the stoichiometric structure of the reaction network for derivation of valuable information. In formalisms like Systems Biology Markup Language (SBML), however, information about the stoichiometric coefficients required for an analysis of chemical organizations can be hidden in kinetic laws. Results: First, we introduce an algorithm that uncovers stoichiometric information that might be hidden in the kinetic laws of a reaction network. This allows us to apply OT to SBML models using modifiers. Second, using the new algorithm, we performed a large-scale analysis of the 185 models contained in the manually curated BioModels Database. We found that for 41 models (22%) the set of organizations changes when modifiers are considered correctly. We discuss one of these models in detail (BIOMD149, a combined model of the ERK- and Wnt-signaling pathways), whose set of organizations drastically changes when modifiers are considered. Third, we found inconsistencies in 5 models (3%) and identified their characteristics. Compared with flux-based methods, OT is able to identify those species and reactions more accurately [in 26 cases (14%)] that can be present in a long-term simulation of the model. We conclude that our approach is a valuable tool that helps to improve the consistency of biomodels and their repositories. Availability: All data and a JAVA applet to check SBML-models is available from http://www.minet.uni-jena.de/csb/prj/ot/tools Contact: dittrich@minet.uni-jena.de Supplementary information: Supplementary data are available at Bioinformatics online. PMID:19468053

Nonlocal Quantum Information Transfer Without Superluminal Signalling and Communication

NASA Astrophysics Data System (ADS)

Walleczek, Jan; Grössing, Gerhard

2016-09-01

It is a frequent assumption that—via superluminal information transfers—superluminal signals capable of enabling communication are necessarily exchanged in any quantum theory that posits hidden superluminal influences. However, does the presence of hidden superluminal influences automatically imply superluminal signalling and communication? The non-signalling theorem mediates the apparent conflict between quantum mechanics and the theory of special relativity. However, as a `no-go' theorem there exist two opposing interpretations of the non-signalling constraint: foundational and operational. Concerning Bell's theorem, we argue that Bell employed both interpretations, and that he finally adopted the operational position which is associated often with ontological quantum theory, e.g., de Broglie-Bohm theory. This position we refer to as "effective non-signalling". By contrast, associated with orthodox quantum mechanics is the foundational position referred to here as "axiomatic non-signalling". In search of a decisive communication-theoretic criterion for differentiating between "axiomatic" and "effective" non-signalling, we employ the operational framework offered by Shannon's mathematical theory of communication, whereby we distinguish between Shannon signals and non-Shannon signals. We find that an effective non-signalling theorem represents two sub-theorems: (1) Non-transfer-control (NTC) theorem, and (2) Non-signification-control (NSC) theorem. Employing NTC and NSC theorems, we report that effective, instead of axiomatic, non-signalling is entirely sufficient for prohibiting nonlocal communication. Effective non-signalling prevents the instantaneous, i.e., superluminal, transfer of message-encoded information through the controlled use—by a sender-receiver pair —of informationally-correlated detection events, e.g., in EPR-type experiments. An effective non-signalling theorem allows for nonlocal quantum information transfer yet—at the same time—effectively denies superluminal signalling and communication.

Simulation of intrafraction motion and overall geometric accuracy of a frameless intracranial radiosurgery process

PubMed Central

Walker, Luke; Chinnaiyan, Prakash; Forster, Kenneth

2008-01-01

We conducted a comprehensive evaluation of the clinical accuracy of an image‐guided frameless intracranial radiosurgery system. All links in the process chain were tested. Using healthy volunteers, we evaluated a novel method to prospectively quantify the range of target motion for optimal determination of the planning target volume (PTV) margin. The overall system isocentric accuracy was tested using a rigid anthropomorphic phantom containing a hidden target. Intrafraction motion was simulated in 5 healthy volunteers. Reinforced head‐and‐shoulders thermoplastic masks were used for immobilization. The subjects were placed in a treatment position for 15 minutes (the maximum expected time between repeated isocenter localizations) and the six‐degrees‐of‐freedom target displacements were recorded with high frequency by tracking infrared markers. The markers were placed on a customized piece of thermoplastic secured to the head independently of the immobilization mask. Additional data were collected with the subjects holding their breath, talking, and deliberately moving. As compared with fiducial matching, the automatic registration algorithm did not introduce clinically significant errors (<0.3 mm difference). The hidden target test confirmed overall system isocentric accuracy of ≤1 mm (total three‐dimensional displacement). The subjects exhibited various patterns and ranges of head motion during the mock treatment. The total displacement vector encompassing 95% of the positional points varied from 0.4 mm to 2.9 mm. Pre‐planning motion simulation with optical tracking was tested on volunteers and appears promising for determination of patient‐specific PTV margins. Further patient study is necessary and is planned. In the meantime, system accuracy is sufficient for confident clinical use with 3 mm PTV margins. PACS number: 87.53.Ly

Learning and inference in a nonequilibrium Ising model with hidden nodes.

PubMed

Dunn, Benjamin; Roudi, Yasser

2013-02-01

We study inference and reconstruction of couplings in a partially observed kinetic Ising model. With hidden spins, calculating the likelihood of a sequence of observed spin configurations requires performing a trace over the configurations of the hidden ones. This, as we show, can be represented as a path integral. Using this representation, we demonstrate that systematic approximate inference and learning rules can be derived using dynamical mean-field theory. Although naive mean-field theory leads to an unstable learning rule, taking into account Gaussian corrections allows learning the couplings involving hidden nodes. It also improves learning of the couplings between the observed nodes compared to when hidden nodes are ignored.

Discovering protein complexes in protein interaction networks via exploring the weak ties effect

PubMed Central

2012-01-01

Background Studying protein complexes is very important in biological processes since it helps reveal the structure-functionality relationships in biological networks and much attention has been paid to accurately predict protein complexes from the increasing amount of protein-protein interaction (PPI) data. Most of the available algorithms are based on the assumption that dense subgraphs correspond to complexes, failing to take into account the inherence organization within protein complex and the roles of edges. Thus, there is a critical need to investigate the possibility of discovering protein complexes using the topological information hidden in edges. Results To provide an investigation of the roles of edges in PPI networks, we show that the edges connecting less similar vertices in topology are more significant in maintaining the global connectivity, indicating the weak ties phenomenon in PPI networks. We further demonstrate that there is a negative relation between the weak tie strength and the topological similarity. By using the bridges, a reliable virtual network is constructed, in which each maximal clique corresponds to the core of a complex. By this notion, the detection of the protein complexes is transformed into a classic all-clique problem. A novel core-attachment based method is developed, which detects the cores and attachments, respectively. A comprehensive comparison among the existing algorithms and our algorithm has been made by comparing the predicted complexes against benchmark complexes. Conclusions We proved that the weak tie effect exists in the PPI network and demonstrated that the density is insufficient to characterize the topological structure of protein complexes. Furthermore, the experimental results on the yeast PPI network show that the proposed method outperforms the state-of-the-art algorithms. The analysis of detected modules by the present algorithm suggests that most of these modules have well biological significance in context of complexes, suggesting that the roles of edges are critical in discovering protein complexes. PMID:23046740

Global metagenomic survey reveals a new bacterial candidate phylum in geothermal springs

PubMed Central

Eloe-Fadrosh, Emiley A.; Paez-Espino, David; Jarett, Jessica; Dunfield, Peter F.; Hedlund, Brian P.; Dekas, Anne E.; Grasby, Stephen E.; Brady, Allyson L.; Dong, Hailiang; Briggs, Brandon R.; Li, Wen-Jun; Goudeau, Danielle; Malmstrom, Rex; Pati, Amrita; Pett-Ridge, Jennifer; Rubin, Edward M.; Woyke, Tanja; Kyrpides, Nikos C.; Ivanova, Natalia N.

2016-01-01

Analysis of the increasing wealth of metagenomic data collected from diverse environments can lead to the discovery of novel branches on the tree of life. Here we analyse 5.2 Tb of metagenomic data collected globally to discover a novel bacterial phylum (‘Candidatus Kryptonia') found exclusively in high-temperature pH-neutral geothermal springs. This lineage had remained hidden as a taxonomic ‘blind spot' because of mismatches in the primers commonly used for ribosomal gene surveys. Genome reconstruction from metagenomic data combined with single-cell genomics results in several high-quality genomes representing four genera from the new phylum. Metabolic reconstruction indicates a heterotrophic lifestyle with conspicuous nutritional deficiencies, suggesting the need for metabolic complementarity with other microbes. Co-occurrence patterns identifies a number of putative partners, including an uncultured Armatimonadetes lineage. The discovery of Kryptonia within previously studied geothermal springs underscores the importance of globally sampled metagenomic data in detection of microbial novelty, and highlights the extraordinary diversity of microbial life still awaiting discovery. PMID:26814032

Perspectives of intellectual processing of large volumes of astronomical data using neural networks

NASA Astrophysics Data System (ADS)

Gorbunov, A. A.; Isaev, E. A.; Samodurov, V. A.

2018-01-01

In the process of astronomical observations vast amounts of data are collected. BSA (Big Scanning Antenna) LPI used in the study of impulse phenomena, daily logs 87.5 GB of data (32 TB per year). This data has important implications for both short-and long-term monitoring of various classes of radio sources (including radio transients of different nature), monitoring the Earth’s ionosphere, the interplanetary and the interstellar plasma, the search and monitoring of different classes of radio sources. In the framework of the studies discovered 83096 individual pulse events (in the interval of the study highlighted July 2012 - October 2013), which may correspond to pulsars, twinkling springs, and a rapid radio transients. Detected impulse events are supposed to be used to filter subsequent observations. The study suggests approach, using the creation of the multilayered artificial neural network, which processes the input raw data and after processing, by the hidden layer, the output layer produces a class of impulsive phenomena.

Analysis and prediction of leucine-rich nuclear export signals.

PubMed

la Cour, Tanja; Kiemer, Lars; Mølgaard, Anne; Gupta, Ramneek; Skriver, Karen; Brunak, Søren

2004-06-01

We present a thorough analysis of nuclear export signals and a prediction server, which we have made publicly available. The machine learning prediction method is a significant improvement over the generally used consensus patterns. Nuclear export signals (NESs) are extremely important regulators of the subcellular location of proteins. This regulation has an impact on transcription and other nuclear processes, which are fundamental to the viability of the cell. NESs are studied in relation to cancer, the cell cycle, cell differentiation and other important aspects of molecular biology. Our conclusion from this analysis is that the most important properties of NESs are accessibility and flexibility allowing relevant proteins to interact with the signal. Furthermore, we show that not only the known hydrophobic residues are important in defining a nuclear export signals. We employ both neural networks and hidden Markov models in the prediction algorithm and verify the method on the most recently discovered NESs. The NES predictor (NetNES) is made available for general use at http://www.cbs.dtu.dk/.

Light Higgs channel of the resonant decay of magnon condensate in superfluid (3)He-B.

PubMed

Zavjalov, V V; Autti, S; Eltsov, V B; Heikkinen, P J; Volovik, G E

2016-01-08

In superfluids the order parameter, which describes spontaneous symmetry breaking, is an analogue of the Higgs field in the Standard Model of particle physics. Oscillations of the field amplitude are massive Higgs bosons, while oscillations of the orientation are massless Nambu-Goldstone bosons. The 125 GeV Higgs boson, discovered at Large Hadron Collider, is light compared with electroweak energy scale. Here, we show that such light Higgs exists in superfluid (3)He-B, where one of three Nambu-Goldstone spin-wave modes acquires small mass due to the spin-orbit interaction. Other modes become optical and acoustic magnons. We observe parametric decay of Bose-Einstein condensate of optical magnons to light Higgs modes and decay of optical to acoustic magnons. Formation of a light Higgs from a Nambu-Goldstone mode observed in (3)He-B opens a possibility that such scenario can be realized in other systems, where violation of some hidden symmetry is possible, including the Standard Model.

Discriminative motif discovery via simulated evolution and random under-sampling.

PubMed

Song, Tao; Gu, Hong

2014-01-01

Conserved motifs in biological sequences are closely related to their structure and functions. Recently, discriminative motif discovery methods have attracted more and more attention. However, little attention has been devoted to the data imbalance problem, which is one of the main reasons affecting the performance of the discriminative models. In this article, a simulated evolution method is applied to solve the multi-class imbalance problem at the stage of data preprocessing, and at the stage of Hidden Markov Models (HMMs) training, a random under-sampling method is introduced for the imbalance between the positive and negative datasets. It is shown that, in the task of discovering targeting motifs of nine subcellular compartments, the motifs found by our method are more conserved than the methods without considering data imbalance problem and recover the most known targeting motifs from Minimotif Miner and InterPro. Meanwhile, we use the found motifs to predict protein subcellular localization and achieve higher prediction precision and recall for the minority classes.

Theoretical description of the decays Λb→Λ(*)(1/2±,3/2±)+J /ψ

NASA Astrophysics Data System (ADS)

Gutsche, Thomas; Ivanov, Mikhail A.; Körner, Jürgen G.; Lyubovitskij, Valery E.; Lyubushkin, Vladimir V.; Santorelli, Pietro

2017-07-01

We calculate the invariant and helicity amplitudes for the transitions Λb→Λ(*)(JP)+J /ψ , where the Λ(*)(JP) are Λ (s u d )-type ground and excited states with JP quantum numbers JP=1/2± , 3/2± . The calculations are performed in the framework of a covariant confined quark model previously developed by us. We find that the values of the helicity amplitudes for the Λ*(1520 ,3/2-) and the Λ*(1890 ,3/2+) are suppressed compared with those for the ground state Λ (1116 ,1/2+) and the excited state Λ*(1405 ,1/2-). This analysis is important for the identification of the hidden charm pentaquark states Pc+(4380 ) and Pc+(4450 ) which were discovered in the decay chain Λb0→Pc+(→p J /ψ )+K- because the cascade decay chain Λb→Λ*(3/2±)(→p K-)+J /ψ involves the same final state.

Discovery of naked charm particles and lifetime differences among charm species using nuclear emulsion techniques innovated in Japan

PubMed Central

NIU, Kiyoshi

2008-01-01

This is a historical review of the discovery of naked charm particles and lifetime differences among charm species. These discoveries in the field of cosmic-ray physics were made by the innovation of nuclear emulsion techniques in Japan. A pair of naked charm particles was discovered in 1971 in a cosmic-ray interaction, three years prior to the discovery of the hidden charm particle, J/Ψ, in western countries. Lifetime differences between charged and neutral charm particles were pointed out in 1975, which were later re-confirmed by the collaborative Experiment E531 at Fermilab. Japanese physicists led by K.Niu made essential contributions to it with improved emulsion techniques, complemented by electronic detectors. This review also discusses the discovery of artificially produced naked charm particles by us in an accelerator experiment at Fermilab in 1975 and of multiple-pair productions of charm particles in a single interaction in 1987 by the collaborative Experiment WA75 at CERN. PMID:18941283

A CAD Approach to Integrating NDE With Finite Element

NASA Technical Reports Server (NTRS)

Abdul-Aziz, Ali; Downey, James; Ghosn, Louis J.; Baaklini, George Y.

2004-01-01

Nondestructive evaluation (NDE) is one of several technologies applied at NASA Glenn Research Center to determine atypical deformities, cracks, and other anomalies experienced by structural components. NDE consists of applying high-quality imaging techniques (such as x-ray imaging and computed tomography (CT)) to discover hidden manufactured flaws in a structure. Efforts are in progress to integrate NDE with the finite element (FE) computational method to perform detailed structural analysis of a given component. This report presents the core outlines for an in-house technical procedure that incorporates this combined NDE-FE interrelation. An example is presented to demonstrate the applicability of this analytical procedure. FE analysis of a test specimen is performed, and the resulting von Mises stresses and the stress concentrations near the anomalies are observed, which indicates the fidelity of the procedure. Additional information elaborating on the steps needed to perform such an analysis is clearly presented in the form of mini step-by-step guidelines.

Profiling Oman education data using data visualization technique

NASA Astrophysics Data System (ADS)

Alalawi, Sultan Juma Sultan; Shaharanee, Izwan Nizal Mohd; Jamil, Jastini Mohd

2016-10-01

This research works presents an innovative data visualization technique to understand and visualize the information of Oman's education data generated from the Ministry of Education Oman "Educational Portal". The Ministry of Education in Sultanate of Oman have huge databases contains massive information. The volume of data in the database increase yearly as many students, teachers and employees enter into the database. The task for discovering and analyzing these vast volumes of data becomes increasingly difficult. Information visualization and data mining offer a better ways in dealing with large volume of information. In this paper, an innovative information visualization technique is developed to visualize the complex multidimensional educational data. Microsoft Excel Dashboard, Visual Basic Application (VBA) and Pivot Table are utilized to visualize the data. Findings from the summarization of the data are presented, and it is argued that information visualization can help related stakeholders to become aware of hidden and interesting information from large amount of data drowning in their educational portal.

Decision-making regarding organ donation in Korean adults: A grounded-theory study.

PubMed

Yeun, Eun Ja; Kwon, Young Mi; Kim, Jung A

2015-06-01

The aim of this study was to identify the hidden patterns of behavior leading toward the decision to donate organs. Thirteen registrants at the Association for Organ Sharing in Korea were recruited. Data were collected using in-depth interview and the interview transcripts were analyzed using Glaserian grounded-theory methodology. The main problem of participants was "body attachment" and the core category (management process) was determined to be "pursuing life." The theme consisted of four phases, which were: "hesitating," "investigating," "releasing," and "re-discovering. " Therefore, to increase organ donations, it is important to find a strategy that will create positive attitudes about organ donation through education and public relations. These results explain and provide a deeper understanding of the main problem that Korean people have about organ donation and their management of decision-making processes. These findings can help care providers to facilitate the decision-making process and respond to public needs while taking into account the sociocultural context within which decisions are made. © 2014 Wiley Publishing Asia Pty Ltd.

Global metagenomic survey reveals a new bacterial candidate phylum in geothermal springs.

PubMed

Eloe-Fadrosh, Emiley A; Paez-Espino, David; Jarett, Jessica; Dunfield, Peter F; Hedlund, Brian P; Dekas, Anne E; Grasby, Stephen E; Brady, Allyson L; Dong, Hailiang; Briggs, Brandon R; Li, Wen-Jun; Goudeau, Danielle; Malmstrom, Rex; Pati, Amrita; Pett-Ridge, Jennifer; Rubin, Edward M; Woyke, Tanja; Kyrpides, Nikos C; Ivanova, Natalia N

2016-01-27

Analysis of the increasing wealth of metagenomic data collected from diverse environments can lead to the discovery of novel branches on the tree of life. Here we analyse 5.2 Tb of metagenomic data collected globally to discover a novel bacterial phylum ('Candidatus Kryptonia') found exclusively in high-temperature pH-neutral geothermal springs. This lineage had remained hidden as a taxonomic 'blind spot' because of mismatches in the primers commonly used for ribosomal gene surveys. Genome reconstruction from metagenomic data combined with single-cell genomics results in several high-quality genomes representing four genera from the new phylum. Metabolic reconstruction indicates a heterotrophic lifestyle with conspicuous nutritional deficiencies, suggesting the need for metabolic complementarity with other microbes. Co-occurrence patterns identifies a number of putative partners, including an uncultured Armatimonadetes lineage. The discovery of Kryptonia within previously studied geothermal springs underscores the importance of globally sampled metagenomic data in detection of microbial novelty, and highlights the extraordinary diversity of microbial life still awaiting discovery.

Global metagenomic survey reveals a new bacterial candidate phylum in geothermal springs

DOE PAGES

Eloe-Fadrosh, Emiley A.; Paez-Espino, David; Jarett, Jessica; ...

2016-01-27

Analysis of the increasing wealth of metagenomic data collected from diverse environments can lead to the discovery of novel branches on the tree of life. Here we analyse 5.2 Tb of metagenomic data collected globally to discover a novel bacterial phylum (' Candidatus Kryptonia') found exclusively in higherature pH-neutral geothermal springs. This lineage had remained hidden as a taxonomic 'blind spot' because of mismatches in the primers commonly used for ribosomal gene surveys. Genome reconstruction from metagenomic data combined with single-cell genomics results in several high-quality genomes representing four genera from the new phylum. Metabolic reconstruction indicates a heterotrophic lifestylemore » with conspicuous nutritional deficiencies, suggesting the need for metabolic complementarity with other microbes. Co-occurrence patterns identifies a number of putative partners, including an uncultured Armatimonadetes lineage. The discovery of Kryptonia within previously studied geothermal springs underscores the importance of globally sampled metagenomic data in detection of microbial novelty, and highlights the extraordinary diversity of microbial life still awaiting discovery.« less

Number theoretical foundations in cryptography

NASA Astrophysics Data System (ADS)

Atan, Kamel Ariffin Mohd

2017-08-01

In recent times the hazards in relationships among entities in different establishments worldwide have generated exciting developments in cryptography. Central to this is the theory of numbers. This area of mathematics provides very rich source of fundamental materials for constructing secret codes. Some number theoretical concepts that have been very actively used in designing crypto systems will be highlighted in this presentation. This paper will begin with introduction to basic number theoretical concepts which for many years have been thought to have no practical applications. This will include several theoretical assertions that were discovered much earlier in the historical development of number theory. This will be followed by discussion on the "hidden" properties of these assertions that were later exploited by designers of cryptosystems in their quest for developing secret codes. This paper also highlights some earlier and existing cryptosystems and the role played by number theoretical concepts in their constructions. The role played by cryptanalysts in detecting weaknesses in the systems developed by cryptographers concludes this presentation.

Virtual unrolling and deciphering of Herculaneum papyri by X-ray phase-contrast tomography

PubMed Central

Bukreeva, I.; Mittone, A.; Bravin, A.; Festa, G.; Alessandrelli, M.; Coan, P.; Formoso, V.; Agostino, R. G.; Giocondo, M.; Ciuchi, F.; Fratini, M.; Massimi, L.; Lamarra, A.; Andreani, C.; Bartolino, R.; Gigli, G.; Ranocchia, G.; Cedola, A.

2016-01-01

A collection of more than 1800 carbonized papyri, discovered in the Roman ‘Villa dei Papiri’ at Herculaneum is the unique classical library survived from antiquity. These papyri were charred during 79 A.D. Vesuvius eruption, a circumstance which providentially preserved them until now. This magnificent collection contains an impressive amount of treatises by Greek philosophers and, especially, Philodemus of Gadara, an Epicurean thinker of 1st century BC. We read many portions of text hidden inside carbonized Herculaneum papyri using enhanced X-ray phase-contrast tomography non-destructive technique and a new set of numerical algorithms for ‘virtual-unrolling’. Our success lies in revealing the largest portion of Greek text ever detected so far inside unopened scrolls, with unprecedented spatial resolution and contrast, all without damaging these precious historical manuscripts. Parts of text have been decoded and the ‘voice’ of the Epicurean philosopher Philodemus is brought back again after 2000 years from Herculaneum papyri. PMID:27265417

Light Higgs channel of the resonant decay of magnon condensate in superfluid 3He-B

PubMed Central

Zavjalov, V. V.; Autti, S.; Eltsov, V. B.; Heikkinen, P. J.; Volovik, G. E.

2016-01-01

In superfluids the order parameter, which describes spontaneous symmetry breaking, is an analogue of the Higgs field in the Standard Model of particle physics. Oscillations of the field amplitude are massive Higgs bosons, while oscillations of the orientation are massless Nambu-Goldstone bosons. The 125 GeV Higgs boson, discovered at Large Hadron Collider, is light compared with electroweak energy scale. Here, we show that such light Higgs exists in superfluid 3He-B, where one of three Nambu-Goldstone spin-wave modes acquires small mass due to the spin–orbit interaction. Other modes become optical and acoustic magnons. We observe parametric decay of Bose-Einstein condensate of optical magnons to light Higgs modes and decay of optical to acoustic magnons. Formation of a light Higgs from a Nambu-Goldstone mode observed in 3He-B opens a possibility that such scenario can be realized in other systems, where violation of some hidden symmetry is possible, including the Standard Model. PMID:26743951

Experimental evolution reveals hidden diversity in evolutionary pathways

PubMed Central

Lind, Peter A; Farr, Andrew D; Rainey, Paul B

2015-01-01

Replicate populations of natural and experimental organisms often show evidence of parallel genetic evolution, but the causes are unclear. The wrinkly spreader morph of Pseudomonas fluorescens arises repeatedly during experimental evolution. The mutational causes reside exclusively within three pathways. By eliminating these, 13 new mutational pathways were discovered with the newly arising WS types having fitnesses similar to those arising from the commonly passaged routes. Our findings show that parallel genetic evolution is strongly biased by constraints and we reveal the genetic bases. From such knowledge, and in instances where new phenotypes arise via gene activation, we suggest a set of principles: evolution proceeds firstly via pathways subject to negative regulation, then via promoter mutations and gene fusions, and finally via activation by intragenic gain-of-function mutations. These principles inform evolutionary forecasting and have relevance to interpreting the diverse array of mutations associated with clinically identical instances of disease in humans. DOI: http://dx.doi.org/10.7554/eLife.07074.001 PMID:25806684

[Shushu (ancient Chinese numerology) in Lingshu: Gudu (Miraculous Pivot: Bone-Length Measurement)].

PubMed

Zhuo, Lian-Shi

2010-10-01

Lingshu: Gudu (Miraculous Pivot: Bone-Length Measurement) is compared with literatures concerning the Shushu (ancient Chinese numerology) of the Qin Dynasty (221 B. C. - 206 B. C. ) and the Han Dynasty (206 B. C.-220 A. D.) in this article. And it is discovered that "the number of heaven and earth" in Yijing (The Book of Change) was implied in the bone-length measurement. The theory of Shushu is hidden in the sized of head, neck, chest, abdomen, back and 4 extremities according to the measurement. The meaning of establishment of bone-length measurement, which is found to have universality, laid in setting down the measurement of meridians. And it is the origin of the proportional measurement of locating acupoints. Checked with the theory of Shushu, errors in the description of bone-length measurement could also be found in Lingshu: Gudu (Miraculous Pivot: Bone-Length Measurement) of the present edition, which is helpful for the modern study on the measurement.

Discovering Higgs boson decays to lepton jets at hadron colliders.

PubMed

Falkowski, Adam; Ruderman, Joshua T; Volansky, Tomer; Zupan, Jure

2010-12-10

The Higgs boson may decay predominantly into a hidden sector, producing lepton jets instead of the standard Higgs signatures. We propose a search strategy for such a signal at hadron colliders. A promising channel is the associated production of the Higgs boson with a Z or W. The dominant background is Z or W plus QCD jets. The lepton jets can be discriminated from QCD jets by cutting on the electromagnetic fraction and charge ratio. The former is the fraction of jet energy deposited in the electromagnetic calorimeter and the latter is the ratio of energy carried by charged particles to the electromagnetic energy. We use a Monte Carlo description of detector response to estimate QCD rejection efficiencies of O(10⁻³) per jet. The expected 5σ (3σ) discovery reach in Higgs boson mass is ∼115 GeV (150 GeV) at the Tevatron with 10 fb⁻¹ of data and ∼110 GeV (130 GeV) at the 7 TeV LHC with 1 fb⁻¹.

Visual exploration of high-dimensional data through subspace analysis and dynamic projections

DOE PAGES

Liu, S.; Wang, B.; Thiagarajan, J. J.; ...

2015-06-01

Here, we introduce a novel interactive framework for visualizing and exploring high-dimensional datasets based on subspace analysis and dynamic projections. We assume the high-dimensional dataset can be represented by a mixture of low-dimensional linear subspaces with mixed dimensions, and provide a method to reliably estimate the intrinsic dimension and linear basis of each subspace extracted from the subspace clustering. Subsequently, we use these bases to define unique 2D linear projections as viewpoints from which to visualize the data. To understand the relationships among the different projections and to discover hidden patterns, we connect these projections through dynamic projections that createmore » smooth animated transitions between pairs of projections. We introduce the view transition graph, which provides flexible navigation among these projections to facilitate an intuitive exploration. Finally, we provide detailed comparisons with related systems, and use real-world examples to demonstrate the novelty and usability of our proposed framework.« less

Visual Exploration of High-Dimensional Data through Subspace Analysis and Dynamic Projections

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

Liu, S.; Wang, B.; Thiagarajan, Jayaraman J.

2015-06-01

We introduce a novel interactive framework for visualizing and exploring high-dimensional datasets based on subspace analysis and dynamic projections. We assume the high-dimensional dataset can be represented by a mixture of low-dimensional linear subspaces with mixed dimensions, and provide a method to reliably estimate the intrinsic dimension and linear basis of each subspace extracted from the subspace clustering. Subsequently, we use these bases to define unique 2D linear projections as viewpoints from which to visualize the data. To understand the relationships among the different projections and to discover hidden patterns, we connect these projections through dynamic projections that create smoothmore » animated transitions between pairs of projections. We introduce the view transition graph, which provides flexible navigation among these projections to facilitate an intuitive exploration. Finally, we provide detailed comparisons with related systems, and use real-world examples to demonstrate the novelty and usability of our proposed framework.« less

A Fast Density-Based Clustering Algorithm for Real-Time Internet of Things Stream

PubMed Central

Ying Wah, Teh

2014-01-01

Data streams are continuously generated over time from Internet of Things (IoT) devices. The faster all of this data is analyzed, its hidden trends and patterns discovered, and new strategies created, the faster action can be taken, creating greater value for organizations. Density-based method is a prominent class in clustering data streams. It has the ability to detect arbitrary shape clusters, to handle outlier, and it does not need the number of clusters in advance. Therefore, density-based clustering algorithm is a proper choice for clustering IoT streams. Recently, several density-based algorithms have been proposed for clustering data streams. However, density-based clustering in limited time is still a challenging issue. In this paper, we propose a density-based clustering algorithm for IoT streams. The method has fast processing time to be applicable in real-time application of IoT devices. Experimental results show that the proposed approach obtains high quality results with low computation time on real and synthetic datasets. PMID:25110753

Revealing the Hidden Language of Complex Networks

PubMed Central

Yaveroğlu, Ömer Nebil; Malod-Dognin, Noël; Davis, Darren; Levnajic, Zoran; Janjic, Vuk; Karapandza, Rasa; Stojmirovic, Aleksandar; Pržulj, Nataša

2014-01-01

Sophisticated methods for analysing complex networks promise to be of great benefit to almost all scientific disciplines, yet they elude us. In this work, we make fundamental methodological advances to rectify this. We discover that the interaction between a small number of roles, played by nodes in a network, can characterize a network's structure and also provide a clear real-world interpretation. Given this insight, we develop a framework for analysing and comparing networks, which outperforms all existing ones. We demonstrate its strength by uncovering novel relationships between seemingly unrelated networks, such as Facebook, metabolic, and protein structure networks. We also use it to track the dynamics of the world trade network, showing that a country's role of a broker between non-trading countries indicates economic prosperity, whereas peripheral roles are associated with poverty. This result, though intuitive, has escaped all existing frameworks. Finally, our approach translates network topology into everyday language, bringing network analysis closer to domain scientists. PMID:24686408

Implications of Emerging Data Mining

NASA Astrophysics Data System (ADS)

Kulathuramaiyer, Narayanan; Maurer, Hermann

Data Mining describes a technology that discovers non-trivial hidden patterns in a large collection of data. Although this technology has a tremendous impact on our lives, the invaluable contributions of this invisible technology often go unnoticed. This paper discusses advances in data mining while focusing on the emerging data mining capability. Such data mining applications perform multidimensional mining on a wide variety of heterogeneous data sources, providing solutions to many unresolved problems. This paper also highlights the advantages and disadvantages arising from the ever-expanding scope of data mining. Data Mining augments human intelligence by equipping us with a wealth of knowledge and by empowering us to perform our daily tasks better. As the mining scope and capacity increases, users and organizations become more willing to compromise privacy. The huge data stores of the ‚master miners` allow them to gain deep insights into individual lifestyles and their social and behavioural patterns. Data integration and analysis capability of combining business and financial trends together with the ability to deterministically track market changes will drastically affect our lives.

Uncovering the inertia of dislocation motion and negative mechanical response in crystals.

PubMed

Tang, Yizhe

2018-01-09

Dislocations are linear defects in crystals and their motion controls crystals' mechanical behavior. The dissipative nature of dislocation propagation is generally accepted although the specific mechanisms are still not fully understood. The inertia, which is undoubtedly the nature of motion for particles with mass, seems much less convincing for configuration propagation. We utilize atomistic simulations in conditions that minimize dissipative effects to enable uncovering of the hidden nature of dislocation motion, in three typical model metals Mg, Cu and Ta. We find that, with less/no dissipation, dislocation motion is under-damped and explicitly inertial at both low and high velocities. The inertia of dislocation motion is intrinsic, and more fundamental than the dissipative nature. The inertia originates from the kinetic energy imparted from strain energy and stored in the moving core. Peculiar negative mechanical response associated with the inertia is also discovered. These findings shed light on the fundamental nature of dislocation motion, reveal the underlying physics, and provide a new physical explanation for phenomena relevant to high-velocity dislocations.

A fast density-based clustering algorithm for real-time Internet of Things stream.

PubMed

Amini, Amineh; Saboohi, Hadi; Wah, Teh Ying; Herawan, Tutut

2014-01-01

Data streams are continuously generated over time from Internet of Things (IoT) devices. The faster all of this data is analyzed, its hidden trends and patterns discovered, and new strategies created, the faster action can be taken, creating greater value for organizations. Density-based method is a prominent class in clustering data streams. It has the ability to detect arbitrary shape clusters, to handle outlier, and it does not need the number of clusters in advance. Therefore, density-based clustering algorithm is a proper choice for clustering IoT streams. Recently, several density-based algorithms have been proposed for clustering data streams. However, density-based clustering in limited time is still a challenging issue. In this paper, we propose a density-based clustering algorithm for IoT streams. The method has fast processing time to be applicable in real-time application of IoT devices. Experimental results show that the proposed approach obtains high quality results with low computation time on real and synthetic datasets.

Stacking order dynamics in the quasi-two-dimensional dichalcogenide 1T-TaS2 probed with MeV ultrafast electron diffraction.

PubMed

Le Guyader, L; Chase, T; Reid, A H; Li, R K; Svetin, D; Shen, X; Vecchione, T; Wang, X J; Mihailovic, D; Dürr, H A

2017-07-01

Transitions between different charge density wave (CDW) states in quasi-two-dimensional materials may be accompanied also by changes in the inter-layer stacking of the CDW. Using MeV ultrafast electron diffraction, the out-of-plane stacking order dynamics in the quasi-two-dimensional dichalcogenide 1 T -TaS 2 is investigated for the first time. From the intensity of the CDW satellites aligned around the commensurate l  = 1/6 characteristic stacking order, it is found out that this phase disappears with a 0.3 ps time constant. Simultaneously, in the same experiment, the emergence of the incommensurate phase, with a slightly slower 2.0 ps time constant, is determined from the intensity of the CDW satellites aligned around the incommensurate l  = 1/3 characteristic stacking order. These results might be of relevance in understanding the metallic character of the laser-induced metastable "hidden" state recently discovered in this compound.

Biggest Radio-Telescope in Northern Europe, the RT-32 in Latvia

NASA Astrophysics Data System (ADS)

Monstein, Christian

2014-08-01

Hidden in the dense coastal forests of Slítere a mysterious ex-Soviet spy center is now used for science. Almost everyone including me who entered the site of the two large radio telescopes called Irbene, are amazed by the surrealistic atmosphere of the abandoned ghost town and two large radio dish antennas in the middle of nowhere. This article will tell more about this site; see also [1]. As the Cold War between the US and USSR entered the space age, the need for Space espionage led to the Soviets designing ways to track and decode signals from US satellites. The project began in 1967 when the remote areas of the Ventspils district were allocated for secret buildup of a site codenamed "Starlet". The location was chosen because of low population and dense forest areas of Slí;tere that also were part of the Soviet border zone - ensuring that no strangers could ever discover it.

A tale of two fractals: The Hofstadter butterfly and the integral Apollonian gaskets

NASA Astrophysics Data System (ADS)

Satija, Indubala I.

2016-11-01

This paper unveils a mapping between a quantum fractal that describes a physical phenomena, and an abstract geometrical fractal. The quantum fractal is the Hofstadter butterfly discovered in 1976 in an iconic condensed matter problem of electrons moving in a two-dimensional lattice in a transverse magnetic field. The geometric fractal is the integer Apollonian gasket characterized in terms of a 300 BC problem of mutually tangent circles. Both of these fractals are made up of integers. In the Hofstadter butterfly, these integers encode the topological quantum numbers of quantum Hall conductivity. In the Apollonian gaskets an infinite number of mutually tangent circles are nested inside each other, where each circle has integer curvature. The mapping between these two fractals reveals a hidden D3 symmetry embedded in the kaleidoscopic images that describe the asymptotic scaling properties of the butterfly. This paper also serves as a mini review of these fractals, emphasizing their hierarchical aspects in terms of Farey fractions.

An enigmatic crocodyliform tooth from the bauxites of western Hungary suggests hidden mesoeucrocodylian diversity in the Early Cretaceous European archipelago

PubMed Central

Rabi, Márton; Makádi, László

2015-01-01

Background. The Cretaceous of southern Europe was characterized by an archipelago setting with faunas of mixed composition of endemic, Laurasian and Gondwanan elements. However, little is known about the relative timing of these faunal influences. The Lower Cretaceous of East-Central Europe holds a great promise for understanding the biogeographic history of Cretaceous European biotas because of the former proximity of the area to Gondwana (as part of the Apulian microcontinent). However, East-Central European vertebrates are typically poorly known from this time period. Here, we report on a ziphodont crocodyliform tooth discovered in the Lower Cretaceous (Albian) Alsópere Bauxite Formation of Olaszfalu, western Hungary. Methods. The morphology of the tooth is described and compared with that of other similar Cretaceous crocodyliforms. Results. Based on the triangular, slightly distally curved, constricted and labiolingually flattened crown, the small, subequal-sized true serrations on the carinae mesially and distally, the longitudinal fluting labially, and the extended shelves along the carinae lingually the tooth is most similar to some peirosaurid, non-baurusuchian sebecosuchian, and uruguaysuchid notosuchians. In addition, the paralligatorid Wannchampsus also possesses similar anterior teeth, thus the Hungarian tooth is referred here to Mesoeucrocodylia indet. Discussion. Supposing a notosuchian affinity, this tooth is the earliest occurrence of the group in Europe and one of the earliest in Laurasia. In case of a paralligatorid relationship the Hungarian tooth would represent their first European record, further expanding their cosmopolitan distribution. In any case, the ziphodont tooth from the Albian bauxite deposit of western Hungary belongs to a group still unknown from the Early Cretaceous European archipelago and therefore implies a hidden diversity of crocodyliforms in the area. PMID:26339542

ArtArctic Science: a polarTREC effort to educate about Antarctica through art

NASA Astrophysics Data System (ADS)

Botella, J.; Racette, B.

2013-12-01

Formal scientific education is as important as ever for raising awarness about Antarctic issues, but some people resistance to learning about scienctific issues demands novel approaches for reaching people who are not in the classroom. ArtArctic Science is an interactive exhibit of photography and paintings presented at the Overture Center for the Arts, in Madison, WI by Monona Grove High School students and a science teacher that attempts to educate the general audience about Antarctic science. The exhibit explores art as a form of perceiving and understanding the world around us, and as a way of igniting the spark of curiosity that can lead to scientific inquiries. Antarctica has inspired explorers and scientists for over 100 years, and we add our work to efforts that share scientific results with common people. Antarctica offers stunning views of amazing geometric ice structures complemented and contrasted by the organisms that inhabit it that fascinate most everyone. We probe these scenes through photography and paintings knowing that there is more in each image than what the eye can 'see'. We invite the viewer to discover these secrets by engaging the observer in a mimicking of the scientific method (observation, questioning, finding an explanation, revising the explanation). Each art piece has a question and a scientific explanation hidden under a wooden lid. The observer is invited to explore the scene, involve itself with the scientific query, come up with an answer, and compare his or her idea with the hidden explanation. The exhibit is inspired by an Antarctic PolarTREC expedition in which our science teacher participated as a member of a scientific research team. In this presentation we share the knowledge acquired through this experience in hopes that it will help others attempting a similar Project.

Application of data mining techniques to explore predictors of HCC in Egyptian patients with HCV-related chronic liver disease.

PubMed

Omran, Dalia Abd El Hamid; Awad, AbuBakr Hussein; Mabrouk, Mahasen Abd El Rahman; Soliman, Ahmad Fouad; Aziz, Ashraf Omar Abdel

2015-01-01

Hepatocellular carcinoma (HCC) is the second most common malignancy in Egypt. Data mining is a method of predictive analysis which can explore tremendous volumes of information to discover hidden patterns and relationships. Our aim here was to develop a non-invasive algorithm for prediction of HCC. Such an algorithm should be economical, reliable, easy to apply and acceptable by domain experts. This cross-sectional study enrolled 315 patients with hepatitis C virus (HCV) related chronic liver disease (CLD); 135 HCC, 116 cirrhotic patients without HCC and 64 patients with chronic hepatitis C. Using data mining analysis, we constructed a decision tree learning algorithm to predict HCC. The decision tree algorithm was able to predict HCC with recall (sensitivity) of 83.5% and precession (specificity) of 83.3% using only routine data. The correctly classified instances were 259 (82.2%), and the incorrectly classified instances were 56 (17.8%). Out of 29 attributes, serum alpha fetoprotein (AFP), with an optimal cutoff value of ≥50.3 ng/ml was selected as the best predictor of HCC. To a lesser extent, male sex, presence of cirrhosis, AST>64U/L, and ascites were variables associated with HCC. Data mining analysis allows discovery of hidden patterns and enables the development of models to predict HCC, utilizing routine data as an alternative to CT and liver biopsy. This study has highlighted a new cutoff for AFP (≥50.3 ng/ml). Presence of a score of >2 risk variables (out of 5) can successfully predict HCC with a sensitivity of 96% and specificity of 82%.

Federal Geothermal Research Program Update, FY 2000

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

Renner, Joel Lawrence

2001-08-01

The Department of Energy's Geothermal Program serves two broad purposes: 1) to assist industry in overcoming near-term barriers by conducting cost-shared research and field verification that allows geothermal energy to compete in today's aggressive energy markets; and 2) to undertake fundamental research with potentially large economic payoffs. The four categories of work used to distinguish the research activities of the Geothermal Program during FY 2000 reflect the main components of real-world geothermal projects. These categories form the main sections of the project descriptions in this Research Update. Exploration Technology research focuses on developing instruments and techniques to discover hidden hydrothermalmore » systems and to explore the deep portions of known systems. Research in geophysical and geochemical methods is expected to yield increased knowledge of hidden geothermal systems. Reservoir Technology research combines laboratory and analytical investigations with equipment development and field testing to establish practical tools for resource development and management for both hydrothermal reservoirs and enhanced geothermal systems. Research in various reservoir analysis techniques is generating a wide range of information that facilitates development of improved reservoir management tools. Drilling Technology focuses on developing improved, economic drilling and completion technology for geothermal wells. Ongoing research to avert lost circulation episodes in geothermal drilling is yielding positive results. Conversion Technology research focuses on reducing costs and improving binary conversion cycle efficiency, to permit greater use of the more abundant moderate-temperature geothermal resource, and on the development of materials that will improve the operating characteristics of many types of geothermal energy equipment. Increased output and improved performance of binary cycles will result from investigations in heat cycle research.« less

Federal Geothermal Research Program Update Fiscal Year 2000

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

Renner, J.L.

2001-08-15

The Department of Energy's Geothermal Program serves two broad purposes: (1) to assist industry in overcoming near-term barriers by conducting cost-shared research and field verification that allows geothermal energy to compete in today's aggressive energy markets; and (2) to undertake fundamental research with potentially large economic payoffs. The four categories of work used to distinguish the research activities of the Geothermal Program during FY 2000 reflect the main components of real-world geothermal projects. These categories form the main sections of the project descriptions in this Research Update. Exploration Technology research focuses on developing instruments and techniques to discover hidden hydrothermalmore » systems and to explore the deep portions of known systems. Research in geophysical and geochemical methods is expected to yield increased knowledge of hidden geothermal systems. Reservoir Technology research combines laboratory and analytical investigations with equipment development and field testing to establish practical tools for resource development and management for both hydrothermal reservoirs and enhanced geothermal systems. Research in various reservoir analysis techniques is generating a wide range of information that facilitates development of improved reservoir management tools. Drilling Technology focuses on developing improved, economic drilling and completion technology for geothermal wells. Ongoing research to avert lost circulation episodes in geothermal drilling is yielding positive results. Conversion Technology research focuses on reducing costs and improving binary conversion cycle efficiency, to permit greater use of the more abundant moderate-temperature geothermal resource, and on the development of materials that will improve the operating characteristics of many types of geothermal energy equipment. Increased output and improved performance of binary cycles will result from investigations in heat cycle research.« less

State-space model with deep learning for functional dynamics estimation in resting-state fMRI.

PubMed

Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang

2016-04-01

Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach. Copyright © 2016 Elsevier Inc. All rights reserved.