Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm.

PubMed

Khushaba, Rami N; Kodagoda, Sarath; Lal, Sara; Dissanayake, Gamini

2011-01-01

Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring physiological signals while driving provides the possibility of detecting and warning of drowsiness and fatigue. The aim of this paper is to maximize the amount of drowsiness-related information extracted from a set of electroencephalogram (EEG), electrooculogram (EOG), and electrocardiogram (ECG) signals during a simulation driving test. Specifically, we develop an efficient fuzzy mutual-information (MI)- based wavelet packet transform (FMIWPT) feature-extraction method for classifying the driver drowsiness state into one of predefined drowsiness levels. The proposed method estimates the required MI using a novel approach based on fuzzy memberships providing an accurate-information content-estimation measure. The quality of the extracted features was assessed on datasets collected from 31 drivers on a simulation test. The experimental results proved the significance of FMIWPT in extracting features that highly correlate with the different drowsiness levels achieving a classification accuracy of 95%-- 97% on an average across all subjects.

Quantum cryptography with 3-state systems.

PubMed

Bechmann-Pasquinucci, H; Peres, A

2000-10-09

We consider quantum cryptographic schemes where the carriers of information are 3-state particles. One protocol uses four mutually unbiased bases and appears to provide better security than obtainable with 2-state carriers. Another possible method allows quantum states to belong to more than one basis. Security is not better, but many curious features arise.

Establishing Criteria for Effective Professional Development and Use in Evaluating an Action Research Based Programme

ERIC Educational Resources Information Center

Piggot-Irvine, Eileen

2006-01-01

A dialectical, or mutually informing and influencing, relationship exists between research, programmes for development and improved teaching and learning. Among a raft of other attributes, current perceptions of effective professional development (summarised in the paper) point to deep, collaborative, active and ongoing features as important. Such…

Part mutual information for quantifying direct associations in networks.

PubMed

Zhao, Juan; Zhou, Yiwei; Zhang, Xiujun; Chen, Luonan

2016-05-03

Quantitatively identifying direct dependencies between variables is an important task in data analysis, in particular for reconstructing various types of networks and causal relations in science and engineering. One of the most widely used criteria is partial correlation, but it can only measure linearly direct association and miss nonlinear associations. However, based on conditional independence, conditional mutual information (CMI) is able to quantify nonlinearly direct relationships among variables from the observed data, superior to linear measures, but suffers from a serious problem of underestimation, in particular for those variables with tight associations in a network, which severely limits its applications. In this work, we propose a new concept, "partial independence," with a new measure, "part mutual information" (PMI), which not only can overcome the problem of CMI but also retains the quantification properties of both mutual information (MI) and CMI. Specifically, we first defined PMI to measure nonlinearly direct dependencies between variables and then derived its relations with MI and CMI. Finally, we used a number of simulated data as benchmark examples to numerically demonstrate PMI features and further real gene expression data from Escherichia coli and yeast to reconstruct gene regulatory networks, which all validated the advantages of PMI for accurately quantifying nonlinearly direct associations in networks.

Using time-delayed mutual information to discover and interpret temporal correlation structure in complex populations

NASA Astrophysics Data System (ADS)

Albers, D. J.; Hripcsak, George

2012-03-01

This paper addresses how to calculate and interpret the time-delayed mutual information (TDMI) for a complex, diversely and sparsely measured, possibly non-stationary population of time-series of unknown composition and origin. The primary vehicle used for this analysis is a comparison between the time-delayed mutual information averaged over the population and the time-delayed mutual information of an aggregated population (here, aggregation implies the population is conjoined before any statistical estimates are implemented). Through the use of information theoretic tools, a sequence of practically implementable calculations are detailed that allow for the average and aggregate time-delayed mutual information to be interpreted. Moreover, these calculations can also be used to understand the degree of homo or heterogeneity present in the population. To demonstrate that the proposed methods can be used in nearly any situation, the methods are applied and demonstrated on the time series of glucose measurements from two different subpopulations of individuals from the Columbia University Medical Center electronic health record repository, revealing a picture of the composition of the population as well as physiological features.

Spatial Mutual Information Based Hyperspectral Band Selection for Classification

PubMed Central

2015-01-01

The amount of information involved in hyperspectral imaging is large. Hyperspectral band selection is a popular method for reducing dimensionality. Several information based measures such as mutual information have been proposed to reduce information redundancy among spectral bands. Unfortunately, mutual information does not take into account the spatial dependency between adjacent pixels in images thus reducing its robustness as a similarity measure. In this paper, we propose a new band selection method based on spatial mutual information. As validation criteria, a supervised classification method using support vector machine (SVM) is used. Experimental results of the classification of hyperspectral datasets show that the proposed method can achieve more accurate results. PMID:25918742

Analysis of respiratory events in obstructive sleep apnea syndrome: Inter-relations and association to simple nocturnal features.

PubMed

Ghandeharioun, H; Rezaeitalab, F; Lotfi, R

2016-01-01

This study carefully evaluates the association of different respiration-related events to each other and to simple nocturnal features in obstructive sleep apnea-hypopnea syndrome (OSAS). The events include apneas, hypopneas, respiratory event-related arousals and snores. We conducted a statistical study on 158 adults who underwent polysomnography between July 2012 and May 2014. To monitor relevance, along with linear statistical strategies like analysis of variance and bootstrapping a correlation coefficient standard error, the non-linear method of mutual information is also applied to illuminate vague results of linear techniques. Based on normalized mutual information weights (NMIW), indices of apnea are 1.3 times more relevant to AHI values than those of hypopnea. NMIW for the number of blood oxygen desaturation below 95% is considerable (0.531). The next relevant feature is "respiratory arousals index" with NMIW of 0.501. Snore indices (0.314), and BMI (0.203) take the next place. Based on NMIW values, snoring events are nearly one-third (29.9%) more dependent to hypopneas than RERAs. 1. The more sever the OSAS is, the more frequently the apneic events happen. 2. The association of snore with hypopnea/RERA revealed which is routinely ignored in regression-based OSAS modeling. 3. The statistical dependencies of oximetry features potentially can lead to home-based screening of OSAS. 4. Poor ESS-AHI relevance in the database under study indicates its disability for the OSA diagnosis compared to oximetry. 5. Based on poor RERA-snore/ESS relevance, detailed history of the symptoms plus polysomnography is suggested for accurate diagnosis of RERAs. Copyright © 2015 Sociedade Portuguesa de Pneumologia. Published by Elsevier España, S.L.U. All rights reserved.

Groupwise registration of cardiac perfusion MRI sequences using normalized mutual information in high dimension

NASA Astrophysics Data System (ADS)

Hamrouni, Sameh; Rougon, Nicolas; Pr"teux, Françoise

2011-03-01

In perfusion MRI (p-MRI) exams, short-axis (SA) image sequences are captured at multiple slice levels along the long-axis of the heart during the transit of a vascular contrast agent (Gd-DTPA) through the cardiac chambers and muscle. Compensating cardio-thoracic motions is a requirement for enabling computer-aided quantitative assessment of myocardial ischaemia from contrast-enhanced p-MRI sequences. The classical paradigm consists of registering each sequence frame on a reference image using some intensity-based matching criterion. In this paper, we introduce a novel unsupervised method for the spatio-temporal groupwise registration of cardiac p-MRI exams based on normalized mutual information (NMI) between high-dimensional feature distributions. Here, local contrast enhancement curves are used as a dense set of spatio-temporal features, and statistically matched through variational optimization to a target feature distribution derived from a registered reference template. The hard issue of probability density estimation in high-dimensional state spaces is bypassed by using consistent geometric entropy estimators, allowing NMI to be computed directly from feature samples. Specifically, a computationally efficient kth-nearest neighbor (kNN) estimation framework is retained, leading to closed-form expressions for the gradient flow of NMI over finite- and infinite-dimensional motion spaces. This approach is applied to the groupwise alignment of cardiac p-MRI exams using a free-form Deformation (FFD) model for cardio-thoracic motions. Experiments on simulated and natural datasets suggest its accuracy and robustness for registering p-MRI exams comprising more than 30 frames.

Prediction of microsleeps using pairwise joint entropy and mutual information between EEG channels.

PubMed

Baseer, Abdul; Weddell, Stephen J; Jones, Richard D

2017-07-01

Microsleeps are involuntary and brief instances of complete loss of responsiveness, typically of 0.5-15 s duration. They adversely affect performance in extended attention-driven jobs and can be fatal. Our aim was to predict microsleeps from 16 channel EEG signals. Two information theoretic concepts - pairwise joint entropy and mutual information - were independently used to continuously extract features from EEG signals. k-nearest neighbor (kNN) with k = 3 was used to calculate both joint entropy and mutual information. Highly correlated features were discarded and the rest were ranked using Fisher score followed by an average of 3-fold cross-validation area under the curve of the receiver operating characteristic (AUC ROC ). Leave-one-out method (LOOM) was performed to test the performance of microsleep prediction system on independent data. The best prediction for 0.25 s ahead was AUCROC, sensitivity, precision, geometric mean (GM), and φ of 0.93, 0.68, 0.33, 0.75, and 0.38 respectively with joint entropy using single linear discriminant analysis (LDA) classifier.

Multi-Sensor Registration of Earth Remotely Sensed Imagery

NASA Technical Reports Server (NTRS)

LeMoigne, Jacqueline; Cole-Rhodes, Arlene; Eastman, Roger; Johnson, Kisha; Morisette, Jeffrey; Netanyahu, Nathan S.; Stone, Harold S.; Zavorin, Ilya; Zukor, Dorothy (Technical Monitor)

2001-01-01

Assuming that approximate registration is given within a few pixels by a systematic correction system, we develop automatic image registration methods for multi-sensor data with the goal of achieving sub-pixel accuracy. Automatic image registration is usually defined by three steps; feature extraction, feature matching, and data resampling or fusion. Our previous work focused on image correlation methods based on the use of different features. In this paper, we study different feature matching techniques and present five algorithms where the features are either original gray levels or wavelet-like features, and the feature matching is based on gradient descent optimization, statistical robust matching, and mutual information. These algorithms are tested and compared on several multi-sensor datasets covering one of the EOS Core Sites, the Konza Prairie in Kansas, from four different sensors: IKONOS (4m), Landsat-7/ETM+ (30m), MODIS (500m), and SeaWIFS (1000m).

Qualities in friendship - Within an outside perspective - Definitions expressed by adolescents with mild intellectual disabilities.

PubMed

Sigstad, Hanne Marie Høybråten

2017-03-01

This study examined how adolescents with mild intellectual disabilities define qualities of friendship and discussed the extent to which these definitions adhere to established definitions of close friendship. The study was based on qualitative interviews with 11 adolescents in secondary school. The interviews were supplemented with information from six parents. A thematic structural analysis was used to identify themes. Qualities of friendship were categorized as mutual preference, mutual enjoyment, shared interactions, care, mutual trust and bonding. The criteria for close friendship seem to be fulfilled, albeit to a moderate degree. Closeness and reciprocity appear to be significant in this study, although these features have been considered less relevant within this target group in previous research. Differences in definitions may explain divergent results compared with other studies, and the need to achieve equivalence in friendship may be another.

Research on electricity consumption forecast based on mutual information and random forests algorithm

NASA Astrophysics Data System (ADS)

Shi, Jing; Shi, Yunli; Tan, Jian; Zhu, Lei; Li, Hu

2018-02-01

Traditional power forecasting models cannot efficiently take various factors into account, neither to identify the relation factors. In this paper, the mutual information in information theory and the artificial intelligence random forests algorithm are introduced into the medium and long-term electricity demand prediction. Mutual information can identify the high relation factors based on the value of average mutual information between a variety of variables and electricity demand, different industries may be highly associated with different variables. The random forests algorithm was used for building the different industries forecasting models according to the different correlation factors. The data of electricity consumption in Jiangsu Province is taken as a practical example, and the above methods are compared with the methods without regard to mutual information and the industries. The simulation results show that the above method is scientific, effective, and can provide higher prediction accuracy.

Information Theory for Gabor Feature Selection for Face Recognition

NASA Astrophysics Data System (ADS)

Shen, Linlin; Bai, Li

2006-12-01

A discriminative and robust feature—kernel enhanced informative Gabor feature—is proposed in this paper for face recognition. Mutual information is applied to select a set of informative and nonredundant Gabor features, which are then further enhanced by kernel methods for recognition. Compared with one of the top performing methods in the 2004 Face Verification Competition (FVC2004), our methods demonstrate a clear advantage over existing methods in accuracy, computation efficiency, and memory cost. The proposed method has been fully tested on the FERET database using the FERET evaluation protocol. Significant improvements on three of the test data sets are observed. Compared with the classical Gabor wavelet-based approaches using a huge number of features, our method requires less than 4 milliseconds to retrieve a few hundreds of features. Due to the substantially reduced feature dimension, only 4 seconds are required to recognize 200 face images. The paper also unified different Gabor filter definitions and proposed a training sample generation algorithm to reduce the effects caused by unbalanced number of samples available in different classes.

Segmentation of the Speaker's Face Region with Audiovisual Correlation

NASA Astrophysics Data System (ADS)

Liu, Yuyu; Sato, Yoichi

The ability to find the speaker's face region in a video is useful for various applications. In this work, we develop a novel technique to find this region within different time windows, which is robust against the changes of view, scale, and background. The main thrust of our technique is to integrate audiovisual correlation analysis into a video segmentation framework. We analyze the audiovisual correlation locally by computing quadratic mutual information between our audiovisual features. The computation of quadratic mutual information is based on the probability density functions estimated by kernel density estimation with adaptive kernel bandwidth. The results of this audiovisual correlation analysis are incorporated into graph cut-based video segmentation to resolve a globally optimum extraction of the speaker's face region. The setting of any heuristic threshold in this segmentation is avoided by learning the correlation distributions of speaker and background by expectation maximization. Experimental results demonstrate that our method can detect the speaker's face region accurately and robustly for different views, scales, and backgrounds.

Drug discovery using very large numbers of patents. General strategy with extensive use of match and edit operations

NASA Astrophysics Data System (ADS)

Robson, Barry; Li, Jin; Dettinger, Richard; Peters, Amanda; Boyer, Stephen K.

2011-05-01

A patent data base of 6.7 million compounds generated by a very high performance computer (Blue Gene) requires new techniques for exploitation when extensive use of chemical similarity is involved. Such exploitation includes the taxonomic classification of chemical themes, and data mining to assess mutual information between themes and companies. Importantly, we also launch candidates that evolve by "natural selection" as failure of partial match against the patent data base and their ability to bind to the protein target appropriately, by simulation on Blue Gene. An unusual feature of our method is that algorithms and workflows rely on dynamic interaction between match-and-edit instructions, which in practice are regular expressions. Similarity testing by these uses SMILES strings and, less frequently, graph or connectivity representations. Examining how this performs in high throughput, we note that chemical similarity and novelty are human concepts that largely have meaning by utility in specific contexts. For some purposes, mutual information involving chemical themes might be a better concept.

Feature Selection Methods for Robust Decoding of Finger Movements in a Non-human Primate

PubMed Central

Padmanaban, Subash; Baker, Justin; Greger, Bradley

2018-01-01

Objective: The performance of machine learning algorithms used for neural decoding of dexterous tasks may be impeded due to problems arising when dealing with high-dimensional data. The objective of feature selection algorithms is to choose a near-optimal subset of features from the original feature space to improve the performance of the decoding algorithm. The aim of our study was to compare the effects of four feature selection techniques, Wilcoxon signed-rank test, Relative Importance, Principal Component Analysis (PCA), and Mutual Information Maximization on SVM classification performance for a dexterous decoding task. Approach: A nonhuman primate (NHP) was trained to perform small coordinated movements—similar to typing. An array of microelectrodes was implanted in the hand area of the motor cortex of the NHP and used to record action potentials (AP) during finger movements. A Support Vector Machine (SVM) was used to classify which finger movement the NHP was making based upon AP firing rates. We used the SVM classification to examine the functional parameters of (i) robustness to simulated failure and (ii) longevity of classification. We also compared the effect of using isolated-neuron and multi-unit firing rates as the feature vector supplied to the SVM. Main results: The average decoding accuracy for multi-unit features and single-unit features using Mutual Information Maximization (MIM) across 47 sessions was 96.74 ± 3.5% and 97.65 ± 3.36% respectively. The reduction in decoding accuracy between using 100% of the features and 10% of features based on MIM was 45.56% (from 93.7 to 51.09%) and 4.75% (from 95.32 to 90.79%) for multi-unit and single-unit features respectively. MIM had best performance compared to other feature selection methods. Significance: These results suggest improved decoding performance can be achieved by using optimally selected features. The results based on clinically relevant performance metrics also suggest that the decoding algorithm can be made robust by using optimal features and feature selection algorithms. We believe that even a few percent increase in performance is important and improves the decoding accuracy of the machine learning algorithm potentially increasing the ease of use of a brain machine interface. PMID:29467602

Weber-aware weighted mutual information evaluation for infrared-visible image fusion

NASA Astrophysics Data System (ADS)

Luo, Xiaoyan; Wang, Shining; Yuan, Ding

2016-10-01

A performance metric for infrared and visible image fusion is proposed based on Weber's law. To indicate the stimulus of source images, two Weber components are provided. One is differential excitation to reflect the spectral signal of visible and infrared images, and the other is orientation to capture the scene structure feature. By comparing the corresponding Weber component in infrared and visible images, the source pixels can be marked with different dominant properties in intensity or structure. If the pixels have the same dominant property label, the pixels are grouped to calculate the mutual information (MI) on the corresponding Weber components between dominant source and fused images. Then, the final fusion metric is obtained via weighting the group-wise MI values according to the number of pixels in different groups. Experimental results demonstrate that the proposed metric performs well on popular image fusion cases and outperforms other image fusion metrics.

[Non-rigid medical image registration based on mutual information and thin-plate spline].

PubMed

Cao, Guo-gang; Luo, Li-min

2009-01-01

To get precise and complete details, the contrast in different images is needed in medical diagnosis and computer assisted treatment. The image registration is the basis of contrast, but the regular rigid registration does not satisfy the clinic requirements. A non-rigid medical image registration method based on mutual information and thin-plate spline was present. Firstly, registering two images globally based on mutual information; secondly, dividing reference image and global-registered image into blocks and registering them; then getting the thin-plate spline transformation according to the shift of blocks' center; finally, applying the transformation to the global-registered image. The results show that the method is more precise than the global rigid registration based on mutual information and it reduces the complexity of getting control points and satisfy the clinic requirements better by getting control points of the thin-plate transformation automatically.

Classifying syndromes in Chinese medicine using multi-label learning algorithm with relevant features for each label.

PubMed

Xu, Jin; Xu, Zhao-Xia; Lu, Ping; Guo, Rui; Yan, Hai-Xia; Xu, Wen-Jie; Wang, Yi-Qin; Xia, Chun-Ming

2016-11-01

To develop an effective Chinese Medicine (CM) diagnostic model of coronary heart disease (CHD) and to confifirm the scientifific validity of CM theoretical basis from an algorithmic viewpoint. Four types of objective diagnostic data were collected from 835 CHD patients by using a self-developed CM inquiry scale for the diagnosis of heart problems, a tongue diagnosis instrument, a ZBOX-I pulse digital collection instrument, and the sound of an attending acquisition system. These diagnostic data was analyzed and a CM diagnostic model was established using a multi-label learning algorithm (REAL). REAL was employed to establish a Xin (Heart) qi defificiency, Xin yang defificiency, Xin yin defificiency, blood stasis, and phlegm fifive-card CM diagnostic model, which had recognition rates of 80.32%, 89.77%, 84.93%, 85.37%, and 69.90%, respectively. The multi-label learning method established using four diagnostic models based on mutual information feature selection yielded good recognition results. The characteristic model parameters were selected by maximizing the mutual information for each card type. The four diagnostic methods used to obtain information in CM, i.e., observation, auscultation and olfaction, inquiry, and pulse diagnosis, can be characterized by these parameters, which is consistent with CM theory.

Holographic control of information and dynamical topology change for composite open quantum systems

NASA Astrophysics Data System (ADS)

Aref'eva, I. Ya.; Volovich, I. V.; Inozemcev, O. V.

2017-12-01

We analyze how the compositeness of a system affects the characteristic time of equilibration. We study the dynamics of open composite quantum systems strongly coupled to the environment after a quantum perturbation accompanied by nonequilibrium heating. We use a holographic description of the evolution of entanglement entropy. The nonsmooth character of the evolution with holographic entanglement is a general feature of composite systems, which demonstrate a dynamical change of topology in the bulk space and a jumplike velocity change of entanglement entropy propagation. Moreover, the number of jumps depends on the system configuration and especially on the number of composite parts. The evolution of the mutual information of two composite systems inherits these jumps. We present a detailed study of the mutual information for two subsystems with one of them being bipartite. We find five qualitatively different types of behavior of the mutual information dynamics and indicate the corresponding regions of the system parameters.

Conjunction of wavelet transform and SOM-mutual information data pre-processing approach for AI-based Multi-Station nitrate modeling of watersheds

NASA Astrophysics Data System (ADS)

Nourani, Vahid; Andalib, Gholamreza; Dąbrowska, Dominika

2017-05-01

Accurate nitrate load predictions can elevate decision management of water quality of watersheds which affects to environment and drinking water. In this paper, two scenarios were considered for Multi-Station (MS) nitrate load modeling of the Little River watershed. In the first scenario, Markovian characteristics of streamflow-nitrate time series were proposed for the MS modeling. For this purpose, feature extraction criterion of Mutual Information (MI) was employed for input selection of artificial intelligence models (Feed Forward Neural Network, FFNN and least square support vector machine). In the second scenario for considering seasonality-based characteristics of the time series, wavelet transform was used to extract multi-scale features of streamflow-nitrate time series of the watershed's sub-basins to model MS nitrate loads. Self-Organizing Map (SOM) clustering technique which finds homogeneous sub-series clusters was also linked to MI for proper cluster agent choice to be imposed into the models for predicting the nitrate loads of the watershed's sub-basins. The proposed MS method not only considers the prediction of the outlet nitrate but also covers predictions of interior sub-basins nitrate load values. The results indicated that the proposed FFNN model coupled with the SOM-MI improved the performance of MS nitrate predictions compared to the Markovian-based models up to 39%. Overall, accurate selection of dominant inputs which consider seasonality-based characteristics of streamflow-nitrate process could enhance the efficiency of nitrate load predictions.

SU-F-J-96: Comparison of Frame-Based and Mutual Information Registration Techniques for CT and MR Image Sets

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

Popple, R; Bredel, M; Brezovich, I

Purpose: To compare the accuracy of CT-MR registration using a mutual information method with registration using a frame-based localizer box. Methods: Ten patients having the Leksell head frame and scanned with a modality specific localizer box were imported into the treatment planning system. The fiducial rods of the localizer box were contoured on both the MR and CT scans. The skull was contoured on the CT images. The MR and CT images were registered by two methods. The frame-based method used the transformation that minimized the mean square distance of the centroids of the contours of the fiducial rods frommore » a mathematical model of the localizer. The mutual information method used automated image registration tools in the TPS and was restricted to a volume-of-interest defined by the skull contours with a 5 mm margin. For each case, the two registrations were adjusted by two evaluation teams, each comprised of an experienced radiation oncologist and neurosurgeon, to optimize alignment in the region of the brainstem. The teams were blinded to the registration method. Results: The mean adjustment was 0.4 mm (range 0 to 2 mm) and 0.2 mm (range 0 to 1 mm) for the frame and mutual information methods, respectively. The median difference between the frame and mutual information registrations was 0.3 mm, but was not statistically significant using the Wilcoxon signed rank test (p=0.37). Conclusion: The difference between frame and mutual information registration techniques was neither statistically significant nor, for most applications, clinically important. These results suggest that mutual information is equivalent to frame-based image registration for radiosurgery. Work is ongoing to add additional evaluators and to assess the differences between evaluators.« less

Feature and Intensity Based Medical Image Registration Using Particle Swarm Optimization.

PubMed

Abdel-Basset, Mohamed; Fakhry, Ahmed E; El-Henawy, Ibrahim; Qiu, Tie; Sangaiah, Arun Kumar

2017-11-03

Image registration is an important aspect in medical image analysis, and kinds use in a variety of medical applications. Examples include diagnosis, pre/post surgery guidance, comparing/merging/integrating images from multi-modal like Magnetic Resonance Imaging (MRI), and Computed Tomography (CT). Whether registering images across modalities for a single patient or registering across patients for a single modality, registration is an effective way to combine information from different images into a normalized frame for reference. Registered datasets can be used for providing information relating to the structure, function, and pathology of the organ or individual being imaged. In this paper a hybrid approach for medical images registration has been developed. It employs a modified Mutual Information (MI) as a similarity metric and Particle Swarm Optimization (PSO) method. Computation of mutual information is modified using a weighted linear combination of image intensity and image gradient vector flow (GVF) intensity. In this manner, statistical as well as spatial image information is included into the image registration process. Maximization of the modified mutual information is effected using the versatile Particle Swarm Optimization which is developed easily with adjusted less parameter. The developed approach has been tested and verified successfully on a number of medical image data sets that include images with missing parts, noise contamination, and/or of different modalities (CT, MRI). The registration results indicate the proposed model as accurate and effective, and show the posture contribution in inclusion of both statistical and spatial image data to the developed approach.

Photoanthropometric face iridial proportions for age estimation: An investigation using features selected via a joint mutual information criterion.

PubMed

Borges, Díbio L; Vidal, Flávio B; Flores, Marta R P; Melani, Rodolfo F H; Guimarães, Marco A; Machado, Carlos E P

2018-03-01

Age assessment from images is of high interest in the forensic community because of the necessity to establish formal protocols to identify child pornography, child missing and abuses where visual evidences are the mostly admissible. Recently, photoanthropometric methods have been found useful for age estimation correlating facial proportions in image databases with samples of some age groups. Notwithstanding the advances, newer facial features and further analysis are needed to improve accuracy and establish larger applicability. In this investigation, frontal images of 1000 individuals (500 females, 500 males), equally distributed in five age groups (6, 10, 14, 18, 22 years old) were used in a 10 fold cross-validated experiment for three age thresholds classifications (<10, <14, <18 years old). A set of novel 40 features, based on a relation between landmark distances and the iris diameter, is proposed and joint mutual information is used to select the most relevant and complementary features for the classification task. In a civil image identification database with diverse ancestry, receiver operating characteristic (ROC) curves were plotted to verify accuracy, and the resultant AUCs achieved 0.971, 0.969, and 0.903 for the age classifications (<10, <14, <18 years old), respectively. These results add support to continuing research in age assessment from images using the metric approach. Still, larger samples are necessary to evaluate reliability in extensive conditions. Copyright © 2017 Elsevier B.V. All rights reserved.

Automatic annotation of protein motif function with Gene Ontology terms.

PubMed

Lu, Xinghua; Zhai, Chengxiang; Gopalakrishnan, Vanathi; Buchanan, Bruce G

2004-09-02

Conserved protein sequence motifs are short stretches of amino acid sequence patterns that potentially encode the function of proteins. Several sequence pattern searching algorithms and programs exist foridentifying candidate protein motifs at the whole genome level. However, a much needed and important task is to determine the functions of the newly identified protein motifs. The Gene Ontology (GO) project is an endeavor to annotate the function of genes or protein sequences with terms from a dynamic, controlled vocabulary and these annotations serve well as a knowledge base. This paper presents methods to mine the GO knowledge base and use the association between the GO terms assigned to a sequence and the motifs matched by the same sequence as evidence for predicting the functions of novel protein motifs automatically. The task of assigning GO terms to protein motifs is viewed as both a binary classification and information retrieval problem, where PROSITE motifs are used as samples for mode training and functional prediction. The mutual information of a motif and aGO term association is found to be a very useful feature. We take advantage of the known motifs to train a logistic regression classifier, which allows us to combine mutual information with other frequency-based features and obtain a probability of correct association. The trained logistic regression model has intuitively meaningful and logically plausible parameter values, and performs very well empirically according to our evaluation criteria. In this research, different methods for automatic annotation of protein motifs have been investigated. Empirical result demonstrated that the methods have a great potential for detecting and augmenting information about the functions of newly discovered candidate protein motifs.

A mutual information-Dempster-Shafer based decision ensemble system for land cover classification of hyperspectral data

NASA Astrophysics Data System (ADS)

Pahlavani, Parham; Bigdeli, Behnaz

2017-12-01

Hyperspectral images contain extremely rich spectral information that offer great potential to discriminate between various land cover classes. However, these images are usually composed of tens or hundreds of spectrally close bands, which result in high redundancy and great amount of computation time in hyperspectral classification. Furthermore, in the presence of mixed coverage pixels, crisp classifiers produced errors, omission and commission. This paper presents a mutual information-Dempster-Shafer system through an ensemble classification approach for classification of hyperspectral data. First, mutual information is applied to split data into a few independent partitions to overcome high dimensionality. Then, a fuzzy maximum likelihood classifies each band subset. Finally, Dempster-Shafer is applied to fuse the results of the fuzzy classifiers. In order to assess the proposed method, a crisp ensemble system based on a support vector machine as the crisp classifier and weighted majority voting as the crisp fusion method are applied on hyperspectral data. Furthermore, a dimension reduction system is utilized to assess the effectiveness of mutual information band splitting of the proposed method. The proposed methodology provides interesting conclusions on the effectiveness and potentiality of mutual information-Dempster-Shafer based classification of hyperspectral data.

A distributed, hierarchical and recurrent framework for reward-based choice

PubMed Central

Hunt, Laurence T.; Hayden, Benjamin Y.

2017-01-01

Many accounts of reward-based choice argue for distinct component processes that are serial and functionally localized. In this article, we argue for an alternative viewpoint, in which choices emerge from repeated computations that are distributed across many brain regions. We emphasize how several features of neuroanatomy may support the implementation of choice, including mutual inhibition in recurrent neural networks and the hierarchical organisation of timescales for information processing across the cortex. This account also suggests that certain correlates of value may be emergent rather than represented explicitly in the brain. PMID:28209978

Registration of PET and CT images based on multiresolution gradient of mutual information demons algorithm for positioning esophageal cancer patients.

PubMed

Jin, Shuo; Li, Dengwang; Wang, Hongjun; Yin, Yong

2013-01-07

Accurate registration of 18F-FDG PET (positron emission tomography) and CT (computed tomography) images has important clinical significance in radiation oncology. PET and CT images are acquired from (18)F-FDG PET/CT scanner, but the two acquisition processes are separate and take a long time. As a result, there are position errors in global and deformable errors in local caused by respiratory movement or organ peristalsis. The purpose of this work was to implement and validate a deformable CT to PET image registration method in esophageal cancer to eventually facilitate accurate positioning the tumor target on CT, and improve the accuracy of radiation therapy. Global registration was firstly utilized to preprocess position errors between PET and CT images, achieving the purpose of aligning these two images on the whole. Demons algorithm, based on optical flow field, has the features of fast process speed and high accuracy, and the gradient of mutual information-based demons (GMI demons) algorithm adds an additional external force based on the gradient of mutual information (GMI) between two images, which is suitable for multimodality images registration. In this paper, GMI demons algorithm was used to achieve local deformable registration of PET and CT images, which can effectively reduce errors between internal organs. In addition, to speed up the registration process, maintain its robustness, and avoid the local extremum, multiresolution image pyramid structure was used before deformable registration. By quantitatively and qualitatively analyzing cases with esophageal cancer, the registration scheme proposed in this paper can improve registration accuracy and speed, which is helpful for precisely positioning tumor target and developing the radiation treatment planning in clinical radiation therapy application.

Prediction of acoustic feature parameters using myoelectric signals.

PubMed

Lee, Ki-Seung

2010-07-01

It is well-known that a clear relationship exists between human voices and myoelectric signals (MESs) from the area of the speaker's mouth. In this study, we utilized this information to implement a speech synthesis scheme in which MES alone was used to predict the parameters characterizing the vocal-tract transfer function of specific speech signals. Several feature parameters derived from MES were investigated to find the optimal feature for maximization of the mutual information between the acoustic and the MES features. After the optimal feature was determined, an estimation rule for the acoustic parameters was proposed, based on a minimum mean square error (MMSE) criterion. In a preliminary study, 60 isolated words were used for both objective and subjective evaluations. The results showed that the average Euclidean distance between the original and predicted acoustic parameters was reduced by about 30% compared with the average Euclidean distance of the original parameters. The intelligibility of the synthesized speech signals using the predicted features was also evaluated. A word-level identification ratio of 65.5% and a syllable-level identification ratio of 73% were obtained through a listening test.

Estimating the mutual information of an EEG-based Brain-Computer Interface.

PubMed

Schlögl, A; Neuper, C; Pfurtscheller, G

2002-01-01

An EEG-based Brain-Computer Interface (BCI) could be used as an additional communication channel between human thoughts and the environment. The efficacy of such a BCI depends mainly on the transmitted information rate. Shannon's communication theory was used to quantify the information rate of BCI data. For this purpose, experimental EEG data from four BCI experiments was analyzed off-line. Subjects imaginated left and right hand movements during EEG recording from the sensorimotor area. Adaptive autoregressive (AAR) parameters were used as features of single trial EEG and classified with linear discriminant analysis. The intra-trial variation as well as the inter-trial variability, the signal-to-noise ratio, the entropy of information, and the information rate were estimated. The entropy difference was used as a measure of the separability of two classes of EEG patterns.

Mutual information estimation reveals global associations between stimuli and biological processes

PubMed Central

Suzuki, Taiji; Sugiyama, Masashi; Kanamori, Takafumi; Sese, Jun

2009-01-01

Background Although microarray gene expression analysis has become popular, it remains difficult to interpret the biological changes caused by stimuli or variation of conditions. Clustering of genes and associating each group with biological functions are often used methods. However, such methods only detect partial changes within cell processes. Herein, we propose a method for discovering global changes within a cell by associating observed conditions of gene expression with gene functions. Results To elucidate the association, we introduce a novel feature selection method called Least-Squares Mutual Information (LSMI), which computes mutual information without density estimaion, and therefore LSMI can detect nonlinear associations within a cell. We demonstrate the effectiveness of LSMI through comparison with existing methods. The results of the application to yeast microarray datasets reveal that non-natural stimuli affect various biological processes, whereas others are no significant relation to specific cell processes. Furthermore, we discover that biological processes can be categorized into four types according to the responses of various stimuli: DNA/RNA metabolism, gene expression, protein metabolism, and protein localization. Conclusion We proposed a novel feature selection method called LSMI, and applied LSMI to mining the association between conditions of yeast and biological processes through microarray datasets. In fact, LSMI allows us to elucidate the global organization of cellular process control. PMID:19208155

Stress assessment based on EEG univariate features and functional connectivity measures.

PubMed

Alonso, J F; Romero, S; Ballester, M R; Antonijoan, R M; Mañanas, M A

2015-07-01

The biological response to stress originates in the brain but involves different biochemical and physiological effects. Many common clinical methods to assess stress are based on the presence of specific hormones and on features extracted from different signals, including electrocardiogram, blood pressure, skin temperature, or galvanic skin response. The aim of this paper was to assess stress using EEG-based variables obtained from univariate analysis and functional connectivity evaluation. Two different stressors, the Stroop test and sleep deprivation, were applied to 30 volunteers to find common EEG patterns related to stress effects. Results showed a decrease of the high alpha power (11 to 12 Hz), an increase in the high beta band (23 to 36 Hz, considered a busy brain indicator), and a decrease in the approximate entropy. Moreover, connectivity showed that the high beta coherence and the interhemispheric nonlinear couplings, measured by the cross mutual information function, increased significantly for both stressors, suggesting that useful stress indexes may be obtained from EEG-based features.

Entropic uncertainty relations and locking: Tight bounds for mutually unbiased bases

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

Ballester, Manuel A.; Wehner, Stephanie

We prove tight entropic uncertainty relations for a large number of mutually unbiased measurements. In particular, we show that a bound derived from the result by Maassen and Uffink [Phys. Rev. Lett. 60, 1103 (1988)] for two such measurements can in fact be tight for up to {radical}(d) measurements in mutually unbiased bases. We then show that using more mutually unbiased bases does not always lead to a better locking effect. We prove that the optimal bound for the accessible information using up to {radical}(d) specific mutually unbiased bases is log d/2, which is the same as can be achievedmore » by using only two bases. Our result indicates that merely using mutually unbiased bases is not sufficient to achieve a strong locking effect and we need to look for additional properties.« less

Parallel mutual information estimation for inferring gene regulatory networks on GPUs

PubMed Central

2011-01-01

Background Mutual information is a measure of similarity between two variables. It has been widely used in various application domains including computational biology, machine learning, statistics, image processing, and financial computing. Previously used simple histogram based mutual information estimators lack the precision in quality compared to kernel based methods. The recently introduced B-spline function based mutual information estimation method is competitive to the kernel based methods in terms of quality but at a lower computational complexity. Results We present a new approach to accelerate the B-spline function based mutual information estimation algorithm with commodity graphics hardware. To derive an efficient mapping onto this type of architecture, we have used the Compute Unified Device Architecture (CUDA) programming model to design and implement a new parallel algorithm. Our implementation, called CUDA-MI, can achieve speedups of up to 82 using double precision on a single GPU compared to a multi-threaded implementation on a quad-core CPU for large microarray datasets. We have used the results obtained by CUDA-MI to infer gene regulatory networks (GRNs) from microarray data. The comparisons to existing methods including ARACNE and TINGe show that CUDA-MI produces GRNs of higher quality in less time. Conclusions CUDA-MI is publicly available open-source software, written in CUDA and C++ programming languages. It obtains significant speedup over sequential multi-threaded implementation by fully exploiting the compute capability of commonly used CUDA-enabled low-cost GPUs. PMID:21672264

On the use of information theory for the analysis of synchronous nociceptive withdrawal reflexes and somatosensory evoked potentials elicited by graded electrical stimulation.

PubMed

Arguissain, Federico G; Biurrun Manresa, José A; Mørch, Carsten D; Andersen, Ole K

2015-01-30

To date, few studies have combined the simultaneous acquisition of nociceptive withdrawal reflexes (NWR) and somatosensory evoked potentials (SEPs). In fact, it is unknown whether the combination of these two signals acquired simultaneously could provide additional information on somatosensory processing at spinal and supraspinal level compared to individual NWR and SEP signals. By using the concept of mutual information (MI), it is possible to quantify the relation between electrical stimuli and simultaneous elicited electrophysiological responses in humans based on the estimated stimulus-response signal probability distributions. All selected features from NWR and SEPs were informative in regard to the stimulus when considered individually. Specifically, the information carried by NWR features was significantly higher than the information contained in the SEP features (p<0.05). Moreover, the joint information carried by the combination of features showed an overall redundancy compared to the sum of the individual contributions. Comparison with existing methods MI can be used to quantify the information that single-trial NWR and SEP features convey, as well as the information carried jointly by NWR and SEPs. This is a model-free approach that considers linear and non-linear correlations at any order and is not constrained by parametric assumptions. The current study introduces a novel approach that allows the quantification of the individual and joint information content of single-trial NWR and SEP features. This methodology could be used to decode and interpret spinal and supraspinal interaction in studies modulating the responsiveness of the nociceptive system. Copyright © 2014 Elsevier B.V. All rights reserved.

Entropy perspective on the thermal crossover in a fermionic Hubbard chain

NASA Astrophysics Data System (ADS)

Bonnes, Lars; Pichler, Hannes; Läuchli, Andreas M.

2013-10-01

We study the Renyi entropy in the finite-temperature crossover regime of a Hubbard chain using quantum Monte Carlo. The ground-state entropy has characteristic features such as a logarithmic divergence with block size and 2kF oscillations that are a hallmark of its Luttinger liquid nature. The interplay between the (extensive) thermal entropy and the ground-state features is studied and we analyze the temperature-induced decay of the amplitude of the oscillations as well as the scaling of the purity. Furthermore, we show how the spin and charge velocities can be extracted from the temperature dependence of the Renyi entropy, bridging our findings to recent experimental proposals on how to implement the measurement of Renyi entropies in the cold atom system. Studying the Renyi mutual information, we also demonstrate how constraints such as particle number conservation can induce persistent correlations visible in the mutual information even at high temperature.

Offshore platform sourced pollution monitoring using space-borne fully polarimetric C and X band synthetic aperture radar.

PubMed

Singha, Suman; Ressel, Rudolf

2016-11-15

Use of polarimetric SAR data for offshore pollution monitoring is relatively new and shows great potential for operational offshore platform monitoring. This paper describes the development of an automated oil spill detection chain for operational purposes based on C-band (RADARSAT-2) and X-band (TerraSAR-X) fully polarimetric images, wherein we use polarimetric features to characterize oil spills and look-alikes. Numbers of near coincident TerraSAR-X and RADARSAT-2 images have been acquired over offshore platforms. Ten polarimetric feature parameters were extracted from different types of oil and 'look-alike' spots and divided into training and validation dataset. Extracted features were then used to develop a pixel based Artificial Neural Network classifier. Mutual information contents among extracted features were assessed and feature parameters were ranked according to their ability to discriminate between oil spill and look-alike spots. Polarimetric features such as Scattering Diversity, Surface Scattering Fraction and Span proved to be most suitable for operational services. Copyright © 2016 Elsevier Ltd. All rights reserved.

Development of stock correlation networks using mutual information and financial big data.

PubMed

Guo, Xue; Zhang, Hu; Tian, Tianhai

2018-01-01

Stock correlation networks use stock price data to explore the relationship between different stocks listed in the stock market. Currently this relationship is dominantly measured by the Pearson correlation coefficient. However, financial data suggest that nonlinear relationships may exist in the stock prices of different shares. To address this issue, this work uses mutual information to characterize the nonlinear relationship between stocks. Using 280 stocks traded at the Shanghai Stocks Exchange in China during the period of 2014-2016, we first compare the effectiveness of the correlation coefficient and mutual information for measuring stock relationships. Based on these two measures, we then develop two stock networks using the Minimum Spanning Tree method and study the topological properties of these networks, including degree, path length and the power-law distribution. The relationship network based on mutual information has a better distribution of the degree and larger value of the power-law distribution than those using the correlation coefficient. Numerical results show that mutual information is a more effective approach than the correlation coefficient to measure the stock relationship in a stock market that may undergo large fluctuations of stock prices.

Development of stock correlation networks using mutual information and financial big data

PubMed Central

Guo, Xue; Zhang, Hu

2018-01-01

Stock correlation networks use stock price data to explore the relationship between different stocks listed in the stock market. Currently this relationship is dominantly measured by the Pearson correlation coefficient. However, financial data suggest that nonlinear relationships may exist in the stock prices of different shares. To address this issue, this work uses mutual information to characterize the nonlinear relationship between stocks. Using 280 stocks traded at the Shanghai Stocks Exchange in China during the period of 2014-2016, we first compare the effectiveness of the correlation coefficient and mutual information for measuring stock relationships. Based on these two measures, we then develop two stock networks using the Minimum Spanning Tree method and study the topological properties of these networks, including degree, path length and the power-law distribution. The relationship network based on mutual information has a better distribution of the degree and larger value of the power-law distribution than those using the correlation coefficient. Numerical results show that mutual information is a more effective approach than the correlation coefficient to measure the stock relationship in a stock market that may undergo large fluctuations of stock prices. PMID:29668715

Registration of PET and CT images based on multiresolution gradient of mutual information demons algorithm for positioning esophageal cancer patients

PubMed Central

Jin, Shuo; Li, Dengwang; Yin, Yong

2013-01-01

Accurate registration of  18F−FDG PET (positron emission tomography) and CT (computed tomography) images has important clinical significance in radiation oncology. PET and CT images are acquired from  18F−FDG PET/CT scanner, but the two acquisition processes are separate and take a long time. As a result, there are position errors in global and deformable errors in local caused by respiratory movement or organ peristalsis. The purpose of this work was to implement and validate a deformable CT to PET image registration method in esophageal cancer to eventually facilitate accurate positioning the tumor target on CT, and improve the accuracy of radiation therapy. Global registration was firstly utilized to preprocess position errors between PET and CT images, achieving the purpose of aligning these two images on the whole. Demons algorithm, based on optical flow field, has the features of fast process speed and high accuracy, and the gradient of mutual information‐based demons (GMI demons) algorithm adds an additional external force based on the gradient of mutual information (GMI) between two images, which is suitable for multimodality images registration. In this paper, GMI demons algorithm was used to achieve local deformable registration of PET and CT images, which can effectively reduce errors between internal organs. In addition, to speed up the registration process, maintain its robustness, and avoid the local extremum, multiresolution image pyramid structure was used before deformable registration. By quantitatively and qualitatively analyzing cases with esophageal cancer, the registration scheme proposed in this paper can improve registration accuracy and speed, which is helpful for precisely positioning tumor target and developing the radiation treatment planning in clinical radiation therapy application. PACS numbers: 87.57.nj, 87.57.Q‐, 87.57.uk PMID:23318381

Effect of various features on the life cycle cost of the timing/synchronization subsystem of the DCS digital communications network

NASA Technical Reports Server (NTRS)

Kimsey, D. B.

1978-01-01

The effect on the life cycle cost of the timing subsystem was examined, when these optional features were included in various combinations. The features included mutual control, directed control, double-ended reference links, independence of clock error measurement and correction, phase reference combining, self-organization, smoothing for link and nodal dropouts, unequal reference weightings, and a master in a mutual control network. An overall design of a microprocessor-based timing subsystem was formulated. The microprocessor (8080) implements the digital filter portion of a digital phase locked loop, as well as other control functions such as organization of the network through communication with processors at neighboring nodes.

Content-based Music Search and Recommendation System

NASA Astrophysics Data System (ADS)

Takegawa, Kazuki; Hijikata, Yoshinori; Nishida, Shogo

Recently, the turn volume of music data on the Internet has increased rapidly. This has increased the user's cost to find music data suiting their preference from such a large data set. We propose a content-based music search and recommendation system. This system has an interface for searching and finding music data and an interface for editing a user profile which is necessary for music recommendation. By exploiting the visualization of the feature space of music and the visualization of the user profile, the user can search music data and edit the user profile. Furthermore, by exploiting the infomation which can be acquired from each visualized object in a mutually complementary manner, we make it easier for the user to search music data and edit the user profile. Concretely, the system gives to the user an information obtained from the user profile when searching music data and an information obtained from the feature space of music when editing the user profile.

Unsupervised Deep Hashing With Pseudo Labels for Scalable Image Retrieval.

PubMed

Zhang, Haofeng; Liu, Li; Long, Yang; Shao, Ling

2018-04-01

In order to achieve efficient similarity searching, hash functions are designed to encode images into low-dimensional binary codes with the constraint that similar features will have a short distance in the projected Hamming space. Recently, deep learning-based methods have become more popular, and outperform traditional non-deep methods. However, without label information, most state-of-the-art unsupervised deep hashing (DH) algorithms suffer from severe performance degradation for unsupervised scenarios. One of the main reasons is that the ad-hoc encoding process cannot properly capture the visual feature distribution. In this paper, we propose a novel unsupervised framework that has two main contributions: 1) we convert the unsupervised DH model into supervised by discovering pseudo labels; 2) the framework unifies likelihood maximization, mutual information maximization, and quantization error minimization so that the pseudo labels can maximumly preserve the distribution of visual features. Extensive experiments on three popular data sets demonstrate the advantages of the proposed method, which leads to significant performance improvement over the state-of-the-art unsupervised hashing algorithms.

An information theory framework for dynamic functional domain connectivity.

PubMed

Vergara, Victor M; Miller, Robyn; Calhoun, Vince

2017-06-01

Dynamic functional network connectivity (dFNC) analyzes time evolution of coherent activity in the brain. In this technique dynamic changes are considered for the whole brain. This paper proposes an information theory framework to measure information flowing among subsets of functional networks call functional domains. Our method aims at estimating bits of information contained and shared among domains. The succession of dynamic functional states is estimated at the domain level. Information quantity is based on the probabilities of observing each dynamic state. Mutual information measurement is then obtained from probabilities across domains. Thus, we named this value the cross domain mutual information (CDMI). Strong CDMIs were observed in relation to the subcortical domain. Domains related to sensorial input, motor control and cerebellum form another CDMI cluster. Information flow among other domains was seldom found. Other methods of dynamic connectivity focus on whole brain dFNC matrices. In the current framework, information theory is applied to states estimated from pairs of multi-network functional domains. In this context, we apply information theory to measure information flow across functional domains. Identified CDMI clusters point to known information pathways in the basal ganglia and also among areas of sensorial input, patterns found in static functional connectivity. In contrast, CDMI across brain areas of higher level cognitive processing follow a different pattern that indicates scarce information sharing. These findings show that employing information theory to formally measured information flow through brain domains reveals additional features of functional connectivity. Copyright © 2017 Elsevier B.V. All rights reserved.

Elman RNN based classification of proteins sequences on account of their mutual information.

PubMed

Mishra, Pooja; Nath Pandey, Paras

2012-10-21

In the present work we have employed the method of estimating residue correlation within the protein sequences, by using the mutual information (MI) of adjacent residues, based on structural and solvent accessibility properties of amino acids. The long range correlation between nonadjacent residues is improved by constructing a mutual information vector (MIV) for a single protein sequence, like this each protein sequence is associated with its corresponding MIVs. These MIVs are given to Elman RNN to obtain the classification of protein sequences. The modeling power of MIV was shown to be significantly better, giving a new approach towards alignment free classification of protein sequences. We also conclude that sequence structural and solvent accessible property based MIVs are better predictor. Copyright © 2012 Elsevier Ltd. All rights reserved.

A Registration Method Based on Contour Point Cloud for 3D Whole-Body PET and CT Images

PubMed Central

Yang, Qiyao; Wang, Zhiguo; Zhang, Guoxu

2017-01-01

The PET and CT fusion image, combining the anatomical and functional information, has important clinical meaning. An effective registration of PET and CT images is the basis of image fusion. This paper presents a multithread registration method based on contour point cloud for 3D whole-body PET and CT images. Firstly, a geometric feature-based segmentation (GFS) method and a dynamic threshold denoising (DTD) method are creatively proposed to preprocess CT and PET images, respectively. Next, a new automated trunk slices extraction method is presented for extracting feature point clouds. Finally, the multithread Iterative Closet Point is adopted to drive an affine transform. We compare our method with a multiresolution registration method based on Mattes Mutual Information on 13 pairs (246~286 slices per pair) of 3D whole-body PET and CT data. Experimental results demonstrate the registration effectiveness of our method with lower negative normalization correlation (NC = −0.933) on feature images and less Euclidean distance error (ED = 2.826) on landmark points, outperforming the source data (NC = −0.496, ED = 25.847) and the compared method (NC = −0.614, ED = 16.085). Moreover, our method is about ten times faster than the compared one. PMID:28316979

Mutual Information Rate and Bounds for It

PubMed Central

Baptista, Murilo S.; Rubinger, Rero M.; Viana, Emilson R.; Sartorelli, José C.; Parlitz, Ulrich; Grebogi, Celso

2012-01-01

The amount of information exchanged per unit of time between two nodes in a dynamical network or between two data sets is a powerful concept for analysing complex systems. This quantity, known as the mutual information rate (MIR), is calculated from the mutual information, which is rigorously defined only for random systems. Moreover, the definition of mutual information is based on probabilities of significant events. This work offers a simple alternative way to calculate the MIR in dynamical (deterministic) networks or between two time series (not fully deterministic), and to calculate its upper and lower bounds without having to calculate probabilities, but rather in terms of well known and well defined quantities in dynamical systems. As possible applications of our bounds, we study the relationship between synchronisation and the exchange of information in a system of two coupled maps and in experimental networks of coupled oscillators. PMID:23112809

TOF-SIMS imaging technique with information entropy

NASA Astrophysics Data System (ADS)

Aoyagi, Satoka; Kawashima, Y.; Kudo, Masahiro

2005-05-01

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is capable of chemical imaging of proteins on insulated samples in principal. However, selection of specific peaks related to a particular protein, which are necessary for chemical imaging, out of numerous candidates had been difficult without an appropriate spectrum analysis technique. Therefore multivariate analysis techniques, such as principal component analysis (PCA), and analysis with mutual information defined by information theory, have been applied to interpret SIMS spectra of protein samples. In this study mutual information was applied to select specific peaks related to proteins in order to obtain chemical images. Proteins on insulated materials were measured with TOF-SIMS and then SIMS spectra were analyzed by means of the analysis method based on the comparison using mutual information. Chemical mapping of each protein was obtained using specific peaks related to each protein selected based on values of mutual information. The results of TOF-SIMS images of proteins on the materials provide some useful information on properties of protein adsorption, optimality of immobilization processes and reaction between proteins. Thus chemical images of proteins by TOF-SIMS contribute to understand interactions between material surfaces and proteins and to develop sophisticated biomaterials.

A new EEG synchronization strength analysis method: S-estimator based normalized weighted-permutation mutual information.

PubMed

Cui, Dong; Pu, Weiting; Liu, Jing; Bian, Zhijie; Li, Qiuli; Wang, Lei; Gu, Guanghua

2016-10-01

Synchronization is an important mechanism for understanding information processing in normal or abnormal brains. In this paper, we propose a new method called normalized weighted-permutation mutual information (NWPMI) for double variable signal synchronization analysis and combine NWPMI with S-estimator measure to generate a new method named S-estimator based normalized weighted-permutation mutual information (SNWPMI) for analyzing multi-channel electroencephalographic (EEG) synchronization strength. The performances including the effects of time delay, embedding dimension, coupling coefficients, signal to noise ratios (SNRs) and data length of the NWPMI are evaluated by using Coupled Henon mapping model. The results show that the NWPMI is superior in describing the synchronization compared with the normalized permutation mutual information (NPMI). Furthermore, the proposed SNWPMI method is applied to analyze scalp EEG data from 26 amnestic mild cognitive impairment (aMCI) subjects and 20 age-matched controls with normal cognitive function, who both suffer from type 2 diabetes mellitus (T2DM). The proposed methods NWPMI and SNWPMI are suggested to be an effective index to estimate the synchronization strength. Copyright © 2016 Elsevier Ltd. All rights reserved.

An Evaluation of optional timing/synchronization features to support selection of an optimum design for the DCS digital communication network

NASA Technical Reports Server (NTRS)

Bradley, D. B.; Cain, J. B., III; Williard, M. W.

1978-01-01

The task was to evaluate the ability of a set of timing/synchronization subsystem features to provide a set of desirable characteristics for the evolving Defense Communications System digital communications network. The set of features related to the approaches by which timing/synchronization information could be disseminated throughout the network and the manner in which this information could be utilized to provide a synchronized network. These features, which could be utilized in a large number of different combinations, included mutual control, directed control, double ended reference links, independence of clock error measurement and correction, phase reference combining, and self organizing.

Reducing Interpolation Artifacts for Mutual Information Based Image Registration

PubMed Central

Soleimani, H.; Khosravifard, M.A.

2011-01-01

Medical image registration methods which use mutual information as similarity measure have been improved in recent decades. Mutual Information is a basic concept of Information theory which indicates the dependency of two random variables (or two images). In order to evaluate the mutual information of two images their joint probability distribution is required. Several interpolation methods, such as Partial Volume (PV) and bilinear, are used to estimate joint probability distribution. Both of these two methods yield some artifacts on mutual information function. Partial Volume-Hanning window (PVH) and Generalized Partial Volume (GPV) methods are introduced to remove such artifacts. In this paper we show that the acceptable performance of these methods is not due to their kernel function. It's because of the number of pixels which incorporate in interpolation. Since using more pixels requires more complex and time consuming interpolation process, we propose a new interpolation method which uses only four pixels (the same as PV and bilinear interpolations) and removes most of the artifacts. Experimental results of the registration of Computed Tomography (CT) images show superiority of the proposed scheme. PMID:22606673

On Whether People Have the Capacity to Make Observations of Mutually Excl usive Physical Phenomena Simultaneously

NASA Astrophysics Data System (ADS)

Snyder

1998-04-01

It has been shown by Einstein, Podolsky, and Rosen that in quantum mechanics two different wave functions can simultaneously characterize the same physical existent. This result means that one can make predictions regarding simultaneous, mutually exclusive features of a physical existent. It is important to ask whether people have the capacity to make observations of mutually exclusive phenomena simultaneously? Our everyday experience informs us that a human observer is capable of observing only one set of physical circumstances at a time. Evidence from psychology, though, indicates that people indeed have the capacity to make observations of mutually exclusive phenomena simultaneously, even though this capacity is not generally recognized. Working independently, Sigmund Freud and William James provided some of this evidence. How the nature of the quantum mechanical wave function is associated with the problem posed by Einstein, Podolsky, and Rosen, is addressed at the end of the paper.

Earthquake Damage Assessment over Port-au-Prince (Haiti) by Fusing Optical and SAR Data

NASA Astrophysics Data System (ADS)

Romaniello, V.; Piscini, A.; Bignami, C.; Anniballe, R.; Pierdicca, N.; Stramondo, S.

2016-08-01

This work proposes methodologies aiming at evaluating the sensitivity of optical and SAR change features obtained from satellite images with respect to the damage grade. The proposed methods are derived from the literature ([1], [2], [3], [4]) and the main novelty concerns the estimation of these change features at object scale.The test case is the Mw 7.0 earthquake that hit Haiti on January 12, 2010.The analysis of change detection indicators is based on ground truth information collected during a post- earthquake survey. We have generated the damage map of Port-au-Prince by considering a set of polygons extracted from the open source Open Street Map geo- database. The resulting damage map was calculated in terms of collapse ratio [5].We selected some features having a good sensitivity with damage at object scale [6]: the Normalised Difference Index, the Kullback-Libler Divergence, the Mutual Information and the Intensity Correlation Difference.The Naive Bayes and the Support Vector Machine classifiers were used to evaluate the goodness of these features. The classification results demonstrate that the simultaneous use of several change features from EO observations can improve the damage estimation at object scale.

Evaluating suitability of Pol-SAR (TerraSAR-X, Radarsat-2) for automated sea ice classification

NASA Astrophysics Data System (ADS)

Ressel, Rudolf; Singha, Suman; Lehner, Susanne

2016-05-01

Satellite borne SAR imagery has become an invaluable tool in the field of sea ice monitoring. Previously, single polarimetric imagery were employed in supervised and unsupervised classification schemes for sea ice investigation, which was preceded by image processing techniques such as segmentation and textural features. Recently, through the advent of polarimetric SAR sensors, investigation of polarimetric features in sea ice has attracted increased attention. While dual-polarimetric data has already been investigated in a number of works, full-polarimetric data has so far not been a major scientific focus. To explore the possibilities of full-polarimetric data and compare the differences in C- and X-bands, we endeavor to analyze in detail an array of datasets, simultaneously acquired, in C-band (RADARSAT-2) and X-band (TerraSAR-X) over ice infested areas. First, we propose an array of polarimetric features (Pauli and lexicographic based). Ancillary data from national ice services, SMOS data and expert judgement were utilized to identify the governing ice regimes. Based on these observations, we then extracted mentioned features. The subsequent supervised classification approach was based on an Artificial Neural Network (ANN). To gain quantitative insight into the quality of the features themselves (and reduce a possible impact of the Hughes phenomenon), we employed mutual information to unearth the relevance and redundancy of features. The results of this information theoretic analysis guided a pruning process regarding the optimal subset of features. In the last step we compared the classified results of all sensors and images, stated respective accuracies and discussed output discrepancies in the cases of simultaneous acquisitions.

Acoustic and Lexical Representations for Affect Prediction in Spontaneous Conversations.

PubMed

Cao, Houwei; Savran, Arman; Verma, Ragini; Nenkova, Ani

2015-01-01

In this article we investigate what representations of acoustics and word usage are most suitable for predicting dimensions of affect|AROUSAL, VALANCE, POWER and EXPECTANCY|in spontaneous interactions. Our experiments are based on the AVEC 2012 challenge dataset. For lexical representations, we compare corpus-independent features based on psychological word norms of emotional dimensions, as well as corpus-dependent representations. We find that corpus-dependent bag of words approach with mutual information between word and emotion dimensions is by far the best representation. For the analysis of acoustics, we zero in on the question of granularity. We confirm on our corpus that utterance-level features are more predictive than word-level features. Further, we study more detailed representations in which the utterance is divided into regions of interest (ROI), each with separate representation. We introduce two ROI representations, which significantly outperform less informed approaches. In addition we show that acoustic models of emotion can be improved considerably by taking into account annotator agreement and training the model on smaller but reliable dataset. Finally we discuss the potential for improving prediction by combining the lexical and acoustic modalities. Simple fusion methods do not lead to consistent improvements over lexical classifiers alone but improve over acoustic models.

Detection of motion artifact patterns in photoplethysmographic signals based on time and period domain analysis.

PubMed

Couceiro, R; Carvalho, P; Paiva, R P; Henriques, J; Muehlsteff, J

2014-12-01

The presence of motion artifacts in photoplethysmographic (PPG) signals is one of the major obstacles in the extraction of reliable cardiovascular parameters in continuous monitoring applications. In the current paper we present an algorithm for motion artifact detection based on the analysis of the variations in the time and the period domain characteristics of the PPG signal. The extracted features are ranked using a normalized mutual information feature selection algorithm and the best features are used in a support vector machine classification model to distinguish between clean and corrupted sections of the PPG signal. The proposed method has been tested in healthy and cardiovascular diseased volunteers, considering 11 different motion artifact sources. The results achieved by the current algorithm (sensitivity--SE: 84.3%, specificity--SP: 91.5% and accuracy--ACC: 88.5%) show that the current methodology is able to identify both corrupted and clean PPG sections with high accuracy in both healthy (ACC: 87.5%) and cardiovascular diseases (ACC: 89.5%) context.

Mutual Information in Frequency and Its Application to Measure Cross-Frequency Coupling in Epilepsy

NASA Astrophysics Data System (ADS)

Malladi, Rakesh; Johnson, Don H.; Kalamangalam, Giridhar P.; Tandon, Nitin; Aazhang, Behnaam

2018-06-01

We define a metric, mutual information in frequency (MI-in-frequency), to detect and quantify the statistical dependence between different frequency components in the data, referred to as cross-frequency coupling and apply it to electrophysiological recordings from the brain to infer cross-frequency coupling. The current metrics used to quantify the cross-frequency coupling in neuroscience cannot detect if two frequency components in non-Gaussian brain recordings are statistically independent or not. Our MI-in-frequency metric, based on Shannon's mutual information between the Cramer's representation of stochastic processes, overcomes this shortcoming and can detect statistical dependence in frequency between non-Gaussian signals. We then describe two data-driven estimators of MI-in-frequency: one based on kernel density estimation and the other based on the nearest neighbor algorithm and validate their performance on simulated data. We then use MI-in-frequency to estimate mutual information between two data streams that are dependent across time, without making any parametric model assumptions. Finally, we use the MI-in- frequency metric to investigate the cross-frequency coupling in seizure onset zone from electrocorticographic recordings during seizures. The inferred cross-frequency coupling characteristics are essential to optimize the spatial and spectral parameters of electrical stimulation based treatments of epilepsy.

Comparing the Behavior of Polarimetric SAR Imagery (TerraSAR-X and Radarsat-2) for Automated Sea Ice Classification

NASA Astrophysics Data System (ADS)

Ressel, Rudolf; Singha, Suman; Lehner, Susanne

2016-08-01

Arctic Sea ice monitoring has attracted increasing attention over the last few decades. Besides the scientific interest in sea ice, the operational aspect of ice charting is becoming more important due to growing navigational possibilities in an increasingly ice free Arctic. For this purpose, satellite borne SAR imagery has become an invaluable tool. In past, mostly single polarimetric datasets were investigated with supervised or unsupervised classification schemes for sea ice investigation. Despite proven sea ice classification achievements on single polarimetric data, a fully automatic, general purpose classifier for single-pol data has not been established due to large variation of sea ice manifestations and incidence angle impact. Recently, through the advent of polarimetric SAR sensors, polarimetric features have moved into the focus of ice classification research. The higher information content four polarimetric channels promises to offer greater insight into sea ice scattering mechanism and overcome some of the shortcomings of single- polarimetric classifiers. Two spatially and temporally coincident pairs of fully polarimetric acquisitions from the TerraSAR-X/TanDEM-X and RADARSAT-2 satellites are investigated. Proposed supervised classification algorithm consists of two steps: The first step comprises a feature extraction, the results of which are ingested into a neural network classifier in the second step. Based on the common coherency and covariance matrix, we extract a number of features and analyze the relevance and redundancy by means of mutual information for the purpose of sea ice classification. Coherency matrix based features which require an eigendecomposition are found to be either of low relevance or redundant to other covariance matrix based features. Among the most useful features for classification are matrix invariant based features (Geometric Intensity, Scattering Diversity, Surface Scattering Fraction).

Quantum correlation exists in any non-product state

PubMed Central

Guo, Yu; Wu, Shengjun

2014-01-01

Simultaneous existence of correlation in complementary bases is a fundamental feature of quantum correlation, and we show that this characteristic is present in any non-product bipartite state. We propose a measure via mutually unbiased bases to study this feature of quantum correlation, and compare it with other measures of quantum correlation for several families of bipartite states. PMID:25434458

[Prediction of regional soil quality based on mutual information theory integrated with decision tree algorithm].

PubMed

Lin, Fen-Fang; Wang, Ke; Yang, Ning; Yan, Shi-Guang; Zheng, Xin-Yu

2012-02-01

In this paper, some main factors such as soil type, land use pattern, lithology type, topography, road, and industry type that affect soil quality were used to precisely obtain the spatial distribution characteristics of regional soil quality, mutual information theory was adopted to select the main environmental factors, and decision tree algorithm See 5.0 was applied to predict the grade of regional soil quality. The main factors affecting regional soil quality were soil type, land use, lithology type, distance to town, distance to water area, altitude, distance to road, and distance to industrial land. The prediction accuracy of the decision tree model with the variables selected by mutual information was obviously higher than that of the model with all variables, and, for the former model, whether of decision tree or of decision rule, its prediction accuracy was all higher than 80%. Based on the continuous and categorical data, the method of mutual information theory integrated with decision tree could not only reduce the number of input parameters for decision tree algorithm, but also predict and assess regional soil quality effectively.

Exploiting the functional and taxonomic structure of genomic data by probabilistic topic modeling.

PubMed

Chen, Xin; Hu, Xiaohua; Lim, Tze Y; Shen, Xiajiong; Park, E K; Rosen, Gail L

2012-01-01

In this paper, we present a method that enable both homology-based approach and composition-based approach to further study the functional core (i.e., microbial core and gene core, correspondingly). In the proposed method, the identification of major functionality groups is achieved by generative topic modeling, which is able to extract useful information from unlabeled data. We first show that generative topic model can be used to model the taxon abundance information obtained by homology-based approach and study the microbial core. The model considers each sample as a “document,” which has a mixture of functional groups, while each functional group (also known as a “latent topic”) is a weight mixture of species. Therefore, estimating the generative topic model for taxon abundance data will uncover the distribution over latent functions (latent topic) in each sample. Second, we show that, generative topic model can also be used to study the genome-level composition of “N-mer” features (DNA subreads obtained by composition-based approaches). The model consider each genome as a mixture of latten genetic patterns (latent topics), while each functional pattern is a weighted mixture of the “N-mer” features, thus the existence of core genomes can be indicated by a set of common N-mer features. After studying the mutual information between latent topics and gene regions, we provide an explanation of the functional roles of uncovered latten genetic patterns. The experimental results demonstrate the effectiveness of proposed method.

An online sleep apnea detection method based on recurrence quantification analysis.

PubMed

Nguyen, Hoa Dinh; Wilkins, Brek A; Cheng, Qi; Benjamin, Bruce Allen

2014-07-01

This paper introduces an online sleep apnea detection method based on heart rate complexity as measured by recurrence quantification analysis (RQA) statistics of heart rate variability (HRV) data. RQA statistics can capture nonlinear dynamics of a complex cardiorespiratory system during obstructive sleep apnea. In order to obtain a more robust measurement of the nonstationarity of the cardiorespiratory system, we use different fixed amount of neighbor thresholdings for recurrence plot calculation. We integrate a feature selection algorithm based on conditional mutual information to select the most informative RQA features for classification, and hence, to speed up the real-time classification process without degrading the performance of the system. Two types of binary classifiers, i.e., support vector machine and neural network, are used to differentiate apnea from normal sleep. A soft decision fusion rule is developed to combine the results of these classifiers in order to improve the classification performance of the whole system. Experimental results show that our proposed method achieves better classification results compared with the previous recurrence analysis-based approach. We also show that our method is flexible and a strong candidate for a real efficient sleep apnea detection system.

MEG connectivity analysis in patients with Alzheimer's disease using cross mutual information and spectral coherence.

PubMed

Alonso, Joan Francesc; Poza, Jesús; Mañanas, Miguel Angel; Romero, Sergio; Fernández, Alberto; Hornero, Roberto

2011-01-01

Alzheimer's disease (AD) is an irreversible brain disorder which represents the most common form of dementia in western countries. An early and accurate diagnosis of AD would enable to develop new strategies for managing the disease; however, nowadays there is no single test that can accurately predict the development of AD. In this sense, only a few studies have focused on the magnetoencephalographic (MEG) AD connectivity patterns. This study compares brain connectivity in terms of linear and nonlinear couplings by means of spectral coherence and cross mutual information function (CMIF), respectively. The variables defined from these functions provide statistically significant differences (p < 0.05) between AD patients and control subjects, especially the variables obtained from CMIF. The results suggest that AD is characterized by both decreases and increases of functional couplings in different frequency bands as well as by an increase in regularity, that is, more evident statistical deterministic relationships in AD patients' MEG connectivity. The significant differences obtained indicate that AD could disturb brain interactions causing abnormal brain connectivity and operation. Furthermore, the combination of coherence and CMIF features to perform a diagnostic test based on logistic regression improved the tests based on individual variables for its robustness.

Classification of brain signals associated with imagination of hand grasping, opening and reaching by means of wavelet-based common spatial pattern and mutual information.

PubMed

Amanpour, Behzad; Erfanian, Abbas

2013-01-01

An important issue in designing a practical brain-computer interface (BCI) is the selection of mental tasks to be imagined. Different types of mental tasks have been used in BCI including left, right, foot, and tongue motor imageries. However, the mental tasks are different from the actions to be controlled by the BCI. It is desirable to select a mental task to be consistent with the desired action to be performed by BCI. In this paper, we investigated the detecting the imagination of the hand grasping, hand opening, and hand reaching in one hand using electroencephalographic (EEG) signals. The results show that the ERD/ERS patterns, associated with the imagination of hand grasping, opening, and reaching are different. For classification of brain signals associated with these mental tasks and feature extraction, a method based on wavelet packet, regularized common spatial pattern (CSP), and mutual information is proposed. The results of an offline analysis on five subjects show that the two-class mental tasks can be classified with an average accuracy of 77.6% using proposed method. In addition, we examine the proposed method on datasets IVa from BCI Competition III and IIa from BCI Competition IV.

Business model for sensor-based fall recognition systems.

PubMed

Fachinger, Uwe; Schöpke, Birte

2014-01-01

AAL systems require, in addition to sophisticated and reliable technology, adequate business models for their launch and sustainable establishment. This paper presents the basic features of alternative business models for a sensor-based fall recognition system which was developed within the context of the "Lower Saxony Research Network Design of Environments for Ageing" (GAL). The models were developed parallel to the R&D process with successive adaptation and concretization. An overview of the basic features (i.e. nine partial models) of the business model is given and the mutual exclusive alternatives for each partial model are presented. The partial models are interconnected and the combinations of compatible alternatives lead to consistent alternative business models. However, in the current state, only initial concepts of alternative business models can be deduced. The next step will be to gather additional information to work out more detailed models.

Comparison of co-expression measures: mutual information, correlation, and model based indices.

PubMed

Song, Lin; Langfelder, Peter; Horvath, Steve

2012-12-09

Co-expression measures are often used to define networks among genes. Mutual information (MI) is often used as a generalized correlation measure. It is not clear how much MI adds beyond standard (robust) correlation measures or regression model based association measures. Further, it is important to assess what transformations of these and other co-expression measures lead to biologically meaningful modules (clusters of genes). We provide a comprehensive comparison between mutual information and several correlation measures in 8 empirical data sets and in simulations. We also study different approaches for transforming an adjacency matrix, e.g. using the topological overlap measure. Overall, we confirm close relationships between MI and correlation in all data sets which reflects the fact that most gene pairs satisfy linear or monotonic relationships. We discuss rare situations when the two measures disagree. We also compare correlation and MI based approaches when it comes to defining co-expression network modules. We show that a robust measure of correlation (the biweight midcorrelation transformed via the topological overlap transformation) leads to modules that are superior to MI based modules and maximal information coefficient (MIC) based modules in terms of gene ontology enrichment. We present a function that relates correlation to mutual information which can be used to approximate the mutual information from the corresponding correlation coefficient. We propose the use of polynomial or spline regression models as an alternative to MI for capturing non-linear relationships between quantitative variables. The biweight midcorrelation outperforms MI in terms of elucidating gene pairwise relationships. Coupled with the topological overlap matrix transformation, it often leads to more significantly enriched co-expression modules. Spline and polynomial networks form attractive alternatives to MI in case of non-linear relationships. Our results indicate that MI networks can safely be replaced by correlation networks when it comes to measuring co-expression relationships in stationary data.

Exploring Proxy Measures of Mutuality for Strategic Partnership Development: A Case Study.

PubMed

Mayo-Gamble, Tilicia L; Barnes, Priscilla A; Sherwood-Laughlin, Catherine M; Reece, Michael; DeWeese, Sandy; Kennedy, Carol Weiss; Valenta, Mary Ann

2017-07-01

Partnerships between academic and clinical-based health organizations are becoming increasingly important in improving health outcomes. Mutuality is recognized as a vital component of these partnerships. If partnerships are to achieve mutuality, there is a need to define what it means to partnering organizations. Few studies have described the elements contributing to mutuality, particularly in new relationships between academic and clinical partners. This study seeks to identify how mutuality is expressed and to explore potential proxy measures of mutuality for an alliance consisting of a hospital system and a School of Public Health. Key informant interviews were conducted with faculty and hospital representatives serving on the partnership steering committee. Key informants were asked about perceived events that led to the development of the Alliance; perceived goals, expectations, and outcomes; and current/future roles with the Alliance. Four proxy measures of mutuality for an academic-clinical partnership were identified: policy directives, community beneficence, procurement of human capital, and partnership longevity. Findings can inform the development of tools for assisting in strengthening relationships and ensuring stakeholders' interests align with the mission and goal of the partnership by operationalizing elements necessary to evaluate the progress of the partnership.

2D-3D registration using gradient-based MI for image guided surgery systems

NASA Astrophysics Data System (ADS)

Yim, Yeny; Chen, Xuanyi; Wakid, Mike; Bielamowicz, Steve; Hahn, James

2011-03-01

Registration of preoperative CT data to intra-operative video images is necessary not only to compare the outcome of the vocal fold after surgery with the preplanned shape but also to provide the image guidance for fusion of all imaging modalities. We propose a 2D-3D registration method using gradient-based mutual information. The 3D CT scan is aligned to 2D endoscopic images by finding the corresponding viewpoint between the real camera for endoscopic images and the virtual camera for CT scans. Even though mutual information has been successfully used to register different imaging modalities, it is difficult to robustly register the CT rendered image to the endoscopic image due to varying light patterns and shape of the vocal fold. The proposed method calculates the mutual information in the gradient images as well as original images, assigning more weight to the high gradient regions. The proposed method can emphasize the effect of vocal fold and allow a robust matching regardless of the surface illumination. To find the viewpoint with maximum mutual information, a downhill simplex method is applied in a conditional multi-resolution scheme which leads to a less-sensitive result to local maxima. To validate the registration accuracy, we evaluated the sensitivity to initial viewpoint of preoperative CT. Experimental results showed that gradient-based mutual information provided robust matching not only for two identical images with different viewpoints but also for different images acquired before and after surgery. The results also showed that conditional multi-resolution scheme led to a more accurate registration than single-resolution.

Automatic detection of atrial fibrillation in cardiac vibration signals.

PubMed

Brueser, C; Diesel, J; Zink, M D H; Winter, S; Schauerte, P; Leonhardt, S

2013-01-01

We present a study on the feasibility of the automatic detection of atrial fibrillation (AF) from cardiac vibration signals (ballistocardiograms/BCGs) recorded by unobtrusive bedmounted sensors. The proposed system is intended as a screening and monitoring tool in home-healthcare applications and not as a replacement for ECG-based methods used in clinical environments. Based on BCG data recorded in a study with 10 AF patients, we evaluate and rank seven popular machine learning algorithms (naive Bayes, linear and quadratic discriminant analysis, support vector machines, random forests as well as bagged and boosted trees) for their performance in separating 30 s long BCG epochs into one of three classes: sinus rhythm, atrial fibrillation, and artifact. For each algorithm, feature subsets of a set of statistical time-frequency-domain and time-domain features were selected based on the mutual information between features and class labels as well as first- and second-order interactions among features. The classifiers were evaluated on a set of 856 epochs by means of 10-fold cross-validation. The best algorithm (random forests) achieved a Matthews correlation coefficient, mean sensitivity, and mean specificity of 0.921, 0.938, and 0.982, respectively.

Registration of segmented histological images using thin plate splines and belief propagation

NASA Astrophysics Data System (ADS)

Kybic, Jan

2014-03-01

We register images based on their multiclass segmentations, for cases when correspondence of local features cannot be established. A discrete mutual information is used as a similarity criterion. It is evaluated at a sparse set of location on the interfaces between classes. A thin-plate spline regularization is approximated by pairwise interactions. The problem is cast into a discrete setting and solved efficiently by belief propagation. Further speedup and robustness is provided by a multiresolution framework. Preliminary experiments suggest that our method can provide similar registration quality to standard methods at a fraction of the computational cost.

Multimodal data and machine learning for surgery outcome prediction in complicated cases of mesial temporal lobe epilepsy.

PubMed

Memarian, Negar; Kim, Sally; Dewar, Sandra; Engel, Jerome; Staba, Richard J

2015-09-01

This study sought to predict postsurgical seizure freedom from pre-operative diagnostic test results and clinical information using a rapid automated approach, based on supervised learning methods in patients with drug-resistant focal seizures suspected to begin in temporal lobe. We applied machine learning, specifically a combination of mutual information-based feature selection and supervised learning classifiers on multimodal data, to predict surgery outcome retrospectively in 20 presurgical patients (13 female; mean age±SD, in years 33±9.7 for females, and 35.3±9.4 for males) who were diagnosed with mesial temporal lobe epilepsy (MTLE) and subsequently underwent standard anteromesial temporal lobectomy. The main advantage of the present work over previous studies is the inclusion of the extent of ipsilateral neocortical gray matter atrophy and spatiotemporal properties of depth electrode-recorded seizures as training features for individual patient surgery planning. A maximum relevance minimum redundancy (mRMR) feature selector identified the following features as the most informative predictors of postsurgical seizure freedom in this study's sample of patients: family history of epilepsy, ictal EEG onset pattern (positive correlation with seizure freedom), MRI-based gray matter thickness reduction in the hemisphere ipsilateral to seizure onset, proportion of seizures that first appeared in ipsilateral amygdala to total seizures, age, epilepsy duration, delay in the spread of ipsilateral ictal discharges from site of onset, gender, and number of electrode contacts at seizure onset (negative correlation with seizure freedom). Using these features in combination with a least square support vector machine (LS-SVM) classifier compared to other commonly used classifiers resulted in very high surgical outcome prediction accuracy (95%). Supervised machine learning using multimodal compared to unimodal data accurately predicted postsurgical outcome in patients with atypical MTLE. Published by Elsevier Ltd.

78 FR 8488 - Notice of Intent To Request New Information Collection

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-06

... rate. In addition to offering mixed survey modes, the design will integrate multiple and mutually... examples of these design elements: The survey request will be distinguishable from other surveys and will... nonmonetary rewards. A key component of tailored survey design is considering and balancing how features of...

76 FR 53398 - Notice of Intent To Request New Information Collection

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-26

... rate. In addition to offering mixed survey modes, the design will integrate multiple and mutually... examples of these design elements: The survey request will be distinguishable from other surveys and will... nonmonetary rewards. A key component of tailored survey design is considering and balancing how features of...

Permutation auto-mutual information of electroencephalogram in anesthesia

NASA Astrophysics Data System (ADS)

Liang, Zhenhu; Wang, Yinghua; Ouyang, Gaoxiang; Voss, Logan J.; Sleigh, Jamie W.; Li, Xiaoli

2013-04-01

Objective. The dynamic change of brain activity in anesthesia is an interesting topic for clinical doctors and drug designers. To explore the dynamical features of brain activity in anesthesia, a permutation auto-mutual information (PAMI) method is proposed to measure the information coupling of electroencephalogram (EEG) time series obtained in anesthesia. Approach. The PAMI is developed and applied on EEG data collected from 19 patients under sevoflurane anesthesia. The results are compared with the traditional auto-mutual information (AMI), SynchFastSlow (SFS, derived from the BIS index), permutation entropy (PE), composite PE (CPE), response entropy (RE) and state entropy (SE). Performance of all indices is assessed by pharmacokinetic/pharmacodynamic (PK/PD) modeling and prediction probability. Main results. The PK/PD modeling and prediction probability analysis show that the PAMI index correlates closely with the anesthetic effect. The coefficient of determination R2 between PAMI values and the sevoflurane effect site concentrations, and the prediction probability Pk are higher in comparison with other indices. The information coupling in EEG series can be applied to indicate the effect of the anesthetic drug sevoflurane on the brain activity as well as other indices. The PAMI of the EEG signals is suggested as a new index to track drug concentration change. Significance. The PAMI is a useful index for analyzing the EEG dynamics during general anesthesia.

Applications of statistical physics and information theory to the analysis of DNA sequences

NASA Astrophysics Data System (ADS)

Grosse, Ivo

2000-10-01

DNA carries the genetic information of most living organisms, and the of genome projects is to uncover that genetic information. One basic task in the analysis of DNA sequences is the recognition of protein coding genes. Powerful computer programs for gene recognition have been developed, but most of them are based on statistical patterns that vary from species to species. In this thesis I address the question if there exist universal statistical patterns that are different in coding and noncoding DNA of all living species, regardless of their phylogenetic origin. In search for such species-independent patterns I study the mutual information function of genomic DNA sequences, and find that it shows persistent period-three oscillations. To understand the biological origin of the observed period-three oscillations, I compare the mutual information function of genomic DNA sequences to the mutual information function of stochastic model sequences. I find that the pseudo-exon model is able to reproduce the mutual information function of genomic DNA sequences. Moreover, I find that a generalization of the pseudo-exon model can connect the existence and the functional form of long-range correlations to the presence and the length distributions of coding and noncoding regions. Based on these theoretical studies I am able to find an information-theoretical quantity, the average mutual information (AMI), whose probability distributions are significantly different in coding and noncoding DNA, while they are almost identical in all studied species. These findings show that there exist universal statistical patterns that are different in coding and noncoding DNA of all studied species, and they suggest that the AMI may be used to identify genes in different living species, irrespective of their taxonomic origin.

Computing symmetrical strength of N-grams: a two pass filtering approach in automatic classification of text documents.

PubMed

Agnihotri, Deepak; Verma, Kesari; Tripathi, Priyanka

2016-01-01

The contiguous sequences of the terms (N-grams) in the documents are symmetrically distributed among different classes. The symmetrical distribution of the N-Grams raises uncertainty in the belongings of the N-Grams towards the class. In this paper, we focused on the selection of most discriminating N-Grams by reducing the effects of symmetrical distribution. In this context, a new text feature selection method named as the symmetrical strength of the N-Grams (SSNG) is proposed using a two pass filtering based feature selection (TPF) approach. Initially, in the first pass of the TPF, the SSNG method chooses various informative N-Grams from the entire extracted N-Grams of the corpus. Subsequently, in the second pass the well-known Chi Square (χ(2)) method is being used to select few most informative N-Grams. Further, to classify the documents the two standard classifiers Multinomial Naive Bayes and Linear Support Vector Machine have been applied on the ten standard text data sets. In most of the datasets, the experimental results state the performance and success rate of SSNG method using TPF approach is superior to the state-of-the-art methods viz. Mutual Information, Information Gain, Odds Ratio, Discriminating Feature Selection and χ(2).

Resolution improvement in positron emission tomography using anatomical Magnetic Resonance Imaging.

PubMed

Chu, Yong; Su, Min-Ying; Mandelkern, Mark; Nalcioglu, Orhan

2006-08-01

An ideal imaging system should provide information with high-sensitivity, high spatial, and temporal resolution. Unfortunately, it is not possible to satisfy all of these desired features in a single modality. In this paper, we discuss methods to improve the spatial resolution in positron emission imaging (PET) using a priori information from Magnetic Resonance Imaging (MRI). Our approach uses an image restoration algorithm based on the maximization of mutual information (MMI), which has found significant success for optimizing multimodal image registration. The MMI criterion is used to estimate the parameters in the Sharpness-Constrained Wiener filter. The generated filter is then applied to restore PET images of a realistic digital brain phantom. The resulting restored images show improved resolution and better signal-to-noise ratio compared to the interpolated PET images. We conclude that a Sharpness-Constrained Wiener filter having parameters optimized from a MMI criterion may be useful for restoring spatial resolution in PET based on a priori information from correlated MRI.

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.

Cloud-assisted mutual authentication and privacy preservation protocol for telecare medical information systems.

PubMed

Li, Chun-Ta; Shih, Dong-Her; Wang, Chun-Cheng

2018-04-01

 With the rapid development of wireless communication technologies and the growing prevalence of smart devices, telecare medical information system (TMIS) allows patients to receive medical treatments from the doctors via Internet technology without visiting hospitals in person. By adopting mobile device, cloud-assisted platform and wireless body area network, the patients can collect their physiological conditions and upload them to medical cloud via their mobile devices, enabling caregivers or doctors to provide patients with appropriate treatments at anytime and anywhere. In order to protect the medical privacy of the patient and guarantee reliability of the system, before accessing the TMIS, all system participants must be authenticated.  Mohit et al. recently suggested a lightweight authentication protocol for cloud-based health care system. They claimed their protocol ensures resilience of all well-known security attacks and has several important features such as mutual authentication and patient anonymity. In this paper, we demonstrate that Mohit et al.'s authentication protocol has various security flaws and we further introduce an enhanced version of their protocol for cloud-assisted TMIS, which can ensure patient anonymity and patient unlinkability and prevent the security threats of report revelation and report forgery attacks.  The security analysis proves that our enhanced protocol is secure against various known attacks as well as found in Mohit et al.'s protocol. Compared with existing related protocols, our enhanced protocol keeps the merits of all desirable security requirements and also maintains the efficiency in terms of computation costs for cloud-assisted TMIS.  We propose a more secure mutual authentication and privacy preservation protocol for cloud-assisted TMIS, which fixes the mentioned security weaknesses found in Mohit et al.'s protocol. According to our analysis, our authentication protocol satisfies most functionality features for privacy preservation and effectively cope with cloud-assisted TMIS with better efficiency. Copyright © 2018 Elsevier B.V. All rights reserved.

LDA boost classification: boosting by topics

NASA Astrophysics Data System (ADS)

Lei, La; Qiao, Guo; Qimin, Cao; Qitao, Li

2012-12-01

AdaBoost is an efficacious classification algorithm especially in text categorization (TC) tasks. The methodology of setting up a classifier committee and voting on the documents for classification can achieve high categorization precision. However, traditional Vector Space Model can easily lead to the curse of dimensionality and feature sparsity problems; so it affects classification performance seriously. This article proposed a novel classification algorithm called LDABoost based on boosting ideology which uses Latent Dirichlet Allocation (LDA) to modeling the feature space. Instead of using words or phrase, LDABoost use latent topics as the features. In this way, the feature dimension is significantly reduced. Improved Naïve Bayes (NB) is designed as the weaker classifier which keeps the efficiency advantage of classic NB algorithm and has higher precision. Moreover, a two-stage iterative weighted method called Cute Integration in this article is proposed for improving the accuracy by integrating weak classifiers into strong classifier in a more rational way. Mutual Information is used as metrics of weights allocation. The voting information and the categorization decision made by basis classifiers are fully utilized for generating the strong classifier. Experimental results reveals LDABoost making categorization in a low-dimensional space, it has higher accuracy than traditional AdaBoost algorithms and many other classic classification algorithms. Moreover, its runtime consumption is lower than different versions of AdaBoost, TC algorithms based on support vector machine and Neural Networks.

Structural MRI-based detection of Alzheimer's disease using feature ranking and classification error.

PubMed

Beheshti, Iman; Demirel, Hasan; Farokhian, Farnaz; Yang, Chunlan; Matsuda, Hiroshi

2016-12-01

This paper presents an automatic computer-aided diagnosis (CAD) system based on feature ranking for detection of Alzheimer's disease (AD) using structural magnetic resonance imaging (sMRI) data. The proposed CAD system is composed of four systematic stages. First, global and local differences in the gray matter (GM) of AD patients compared to the GM of healthy controls (HCs) are analyzed using a voxel-based morphometry technique. The aim is to identify significant local differences in the volume of GM as volumes of interests (VOIs). Second, the voxel intensity values of the VOIs are extracted as raw features. Third, the raw features are ranked using a seven-feature ranking method, namely, statistical dependency (SD), mutual information (MI), information gain (IG), Pearson's correlation coefficient (PCC), t-test score (TS), Fisher's criterion (FC), and the Gini index (GI). The features with higher scores are more discriminative. To determine the number of top features, the estimated classification error based on training set made up of the AD and HC groups is calculated, with the vector size that minimized this error selected as the top discriminative feature. Fourth, the classification is performed using a support vector machine (SVM). In addition, a data fusion approach among feature ranking methods is introduced to improve the classification performance. The proposed method is evaluated using a data-set from ADNI (130 AD and 130 HC) with 10-fold cross-validation. The classification accuracy of the proposed automatic system for the diagnosis of AD is up to 92.48% using the sMRI data. An automatic CAD system for the classification of AD based on feature-ranking method and classification errors is proposed. In this regard, seven-feature ranking methods (i.e., SD, MI, IG, PCC, TS, FC, and GI) are evaluated. The optimal size of top discriminative features is determined by the classification error estimation in the training phase. The experimental results indicate that the performance of the proposed system is comparative to that of state-of-the-art classification models. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Mutuality and solidarity: assessing risks and sharing losses.

PubMed Central

Wilkie, D

1997-01-01

Mutuality is the principle of private, commercial insurance; individuals enter the pool for sharing losses, and pay according to the best estimate of the risk they bring with them. Solidarity is the sharing of losses with payment according to some other scheme; this is the principle of state social insurance; essential features of solidarity are comprehensiveness and compulsion. Private insurance is subject to the uberrima fides principle, or utmost good faith; each side declares all it knows about the risk. The Disability Discrimination Act requires insurers to justify disability discrimination on the basis of relevant information, acturial, statistical or medical, on which it is reasonable to rely. It could be very damaging to private insurance to abandon uberrima fides. However, although some genetic information is clearly useful to underwriters, other information may be so general as to be of little use. The way in which mortality rates are assessed is also explained. PMID:9304668

Information theory applications for biological sequence analysis.

PubMed

Vinga, Susana

2014-05-01

Information theory (IT) addresses the analysis of communication systems and has been widely applied in molecular biology. In particular, alignment-free sequence analysis and comparison greatly benefited from concepts derived from IT, such as entropy and mutual information. This review covers several aspects of IT applications, ranging from genome global analysis and comparison, including block-entropy estimation and resolution-free metrics based on iterative maps, to local analysis, comprising the classification of motifs, prediction of transcription factor binding sites and sequence characterization based on linguistic complexity and entropic profiles. IT has also been applied to high-level correlations that combine DNA, RNA or protein features with sequence-independent properties, such as gene mapping and phenotype analysis, and has also provided models based on communication systems theory to describe information transmission channels at the cell level and also during evolutionary processes. While not exhaustive, this review attempts to categorize existing methods and to indicate their relation with broader transversal topics such as genomic signatures, data compression and complexity, time series analysis and phylogenetic classification, providing a resource for future developments in this promising area.

Automatic indexing of compound words based on mutual information for Korean text retrieval

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

Pan Koo Kim; Yoo Kun Cho

In this paper, we present an automatic indexing technique for compound words suitable to an aggulutinative language, specifically Korean. Firstly, we present the construction conditions to compose compound words as indexing terms. Also we present the decomposition rules applicable to consecutive nouns to extract all contents of text. Finally we propose a measure to estimate the usefulness of a term, mutual information, to calculate the degree of word association of compound words, based on the information theoretic notion. By applying this method, our system has raised the precision rate of compound words from 72% to 87%.

The coarticulation/invariance scale: Mutual information as a measure of coarticulation resistance, motor synergy, and articulatory invariance

PubMed Central

Iskarous, Khalil; Mooshammer, Christine; Hoole, Phil; Recasens, Daniel; Shadle, Christine H.; Saltzman, Elliot; Whalen, D. H.

2013-01-01

Coarticulation and invariance are two topics at the center of theorizing about speech production and speech perception. In this paper, a quantitative scale is proposed that places coarticulation and invariance at the two ends of the scale. This scale is based on physical information flow in the articulatory signal, and uses Information Theory, especially the concept of mutual information, to quantify these central concepts of speech research. Mutual Information measures the amount of physical information shared across phonological units. In the proposed quantitative scale, coarticulation corresponds to greater and invariance to lesser information sharing. The measurement scale is tested by data from three languages: German, Catalan, and English. The relation between the proposed scale and several existing theories of coarticulation is discussed, and implications for existing theories of speech production and perception are presented. PMID:23927125

SU-C-207B-06: Comparison of Registration Methods for Modeling Pathologic Response of Esophageal Cancer to Chemoradiation Therapy

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

Riyahi, S; Choi, W; Bhooshan, N

2016-06-15

Purpose: To compare linear and deformable registration methods for evaluation of tumor response to Chemoradiation therapy (CRT) in patients with esophageal cancer. Methods: Linear and multi-resolution BSpline deformable registration were performed on Pre-Post-CRT CT/PET images of 20 patients with esophageal cancer. For both registration methods, we registered CT using Mean Square Error (MSE) metric, however to register PET we used transformation obtained using Mutual Information (MI) from the same CT due to being multi-modality. Similarity of Warped-CT/PET was quantitatively evaluated using Normalized Mutual Information and plausibility of DF was assessed using inverse consistency Error. To evaluate tumor response four groupsmore » of tumor features were examined: (1) Conventional PET/CT e.g. SUV, diameter (2) Clinical parameters e.g. TNM stage, histology (3)spatial-temporal PET features that describe intensity, texture and geometry of tumor (4)all features combined. Dominant features were identified using 10-fold cross-validation and Support Vector Machine (SVM) was deployed for tumor response prediction while the accuracy was evaluated by ROC Area Under Curve (AUC). Results: Average and standard deviation of Normalized mutual information for deformable registration using MSE was 0.2±0.054 and for linear registration was 0.1±0.026, showing higher NMI for deformable registration. Likewise for MI metric, deformable registration had 0.13±0.035 comparing to linear counterpart with 0.12±0.037. Inverse consistency error for deformable registration for MSE metric was 4.65±2.49 and for linear was 1.32±2.3 showing smaller value for linear registration. The same conclusion was obtained for MI in terms of inverse consistency error. AUC for both linear and deformable registration was 1 showing no absolute difference in terms of response evaluation. Conclusion: Deformable registration showed better NMI comparing to linear registration, however inverse consistency of transformation was lower in linear registration. We do not expect to see significant difference when warping PET images using deformable or linear registration. This work was supported in part by the National Cancer Institute Grants R01CA172638.« less

Estimating nonrigid motion from inconsistent intensity with robust shape features

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

Liu, Wenyang; Ruan, Dan, E-mail: druan@mednet.ucla.edu; Department of Radiation Oncology, University of California, Los Angeles, California 90095

2013-12-15

Purpose: To develop a nonrigid motion estimation method that is robust to heterogeneous intensity inconsistencies amongst the image pairs or image sequence. Methods: Intensity and contrast variations, as in dynamic contrast enhanced magnetic resonance imaging, present a considerable challenge to registration methods based on general discrepancy metrics. In this study, the authors propose and validate a novel method that is robust to such variations by utilizing shape features. The geometry of interest (GOI) is represented with a flexible zero level set, segmented via well-behaved regularized optimization. The optimization energy drives the zero level set to high image gradient regions, andmore » regularizes it with area and curvature priors. The resulting shape exhibits high consistency even in the presence of intensity or contrast variations. Subsequently, a multiscale nonrigid registration is performed to seek a regular deformation field that minimizes shape discrepancy in the vicinity of GOIs. Results: To establish the working principle, realistic 2D and 3D images were subject to simulated nonrigid motion and synthetic intensity variations, so as to enable quantitative evaluation of registration performance. The proposed method was benchmarked against three alternative registration approaches, specifically, optical flow, B-spline based mutual information, and multimodality demons. When intensity consistency was satisfied, all methods had comparable registration accuracy for the GOIs. When intensities among registration pairs were inconsistent, however, the proposed method yielded pronounced improvement in registration accuracy, with an approximate fivefold reduction in mean absolute error (MAE = 2.25 mm, SD = 0.98 mm), compared to optical flow (MAE = 9.23 mm, SD = 5.36 mm), B-spline based mutual information (MAE = 9.57 mm, SD = 8.74 mm) and mutimodality demons (MAE = 10.07 mm, SD = 4.03 mm). Applying the proposed method on a real MR image sequence also provided qualitatively appealing results, demonstrating good feasibility and applicability of the proposed method. Conclusions: The authors have developed a novel method to estimate the nonrigid motion of GOIs in the presence of spatial intensity and contrast variations, taking advantage of robust shape features. Quantitative analysis and qualitative evaluation demonstrated good promise of the proposed method. Further clinical assessment and validation is being performed.« less

Estimating nonrigid motion from inconsistent intensity with robust shape features.

PubMed

Liu, Wenyang; Ruan, Dan

2013-12-01

To develop a nonrigid motion estimation method that is robust to heterogeneous intensity inconsistencies amongst the image pairs or image sequence. Intensity and contrast variations, as in dynamic contrast enhanced magnetic resonance imaging, present a considerable challenge to registration methods based on general discrepancy metrics. In this study, the authors propose and validate a novel method that is robust to such variations by utilizing shape features. The geometry of interest (GOI) is represented with a flexible zero level set, segmented via well-behaved regularized optimization. The optimization energy drives the zero level set to high image gradient regions, and regularizes it with area and curvature priors. The resulting shape exhibits high consistency even in the presence of intensity or contrast variations. Subsequently, a multiscale nonrigid registration is performed to seek a regular deformation field that minimizes shape discrepancy in the vicinity of GOIs. To establish the working principle, realistic 2D and 3D images were subject to simulated nonrigid motion and synthetic intensity variations, so as to enable quantitative evaluation of registration performance. The proposed method was benchmarked against three alternative registration approaches, specifically, optical flow, B-spline based mutual information, and multimodality demons. When intensity consistency was satisfied, all methods had comparable registration accuracy for the GOIs. When intensities among registration pairs were inconsistent, however, the proposed method yielded pronounced improvement in registration accuracy, with an approximate fivefold reduction in mean absolute error (MAE = 2.25 mm, SD = 0.98 mm), compared to optical flow (MAE = 9.23 mm, SD = 5.36 mm), B-spline based mutual information (MAE = 9.57 mm, SD = 8.74 mm) and mutimodality demons (MAE = 10.07 mm, SD = 4.03 mm). Applying the proposed method on a real MR image sequence also provided qualitatively appealing results, demonstrating good feasibility and applicability of the proposed method. The authors have developed a novel method to estimate the nonrigid motion of GOIs in the presence of spatial intensity and contrast variations, taking advantage of robust shape features. Quantitative analysis and qualitative evaluation demonstrated good promise of the proposed method. Further clinical assessment and validation is being performed.

Improving the Accuracy and Training Speed of Motor Imagery Brain-Computer Interfaces Using Wavelet-Based Combined Feature Vectors and Gaussian Mixture Model-Supervectors.

PubMed

Lee, David; Park, Sang-Hoon; Lee, Sang-Goog

2017-10-07

In this paper, we propose a set of wavelet-based combined feature vectors and a Gaussian mixture model (GMM)-supervector to enhance training speed and classification accuracy in motor imagery brain-computer interfaces. The proposed method is configured as follows: first, wavelet transforms are applied to extract the feature vectors for identification of motor imagery electroencephalography (EEG) and principal component analyses are used to reduce the dimensionality of the feature vectors and linearly combine them. Subsequently, the GMM universal background model is trained by the expectation-maximization (EM) algorithm to purify the training data and reduce its size. Finally, a purified and reduced GMM-supervector is used to train the support vector machine classifier. The performance of the proposed method was evaluated for three different motor imagery datasets in terms of accuracy, kappa, mutual information, and computation time, and compared with the state-of-the-art algorithms. The results from the study indicate that the proposed method achieves high accuracy with a small amount of training data compared with the state-of-the-art algorithms in motor imagery EEG classification.

Hearing Device Manufacturers Call for Interoperability and Standardization of Internet and Audiology.

PubMed

Laplante-Lévesque, Ariane; Abrams, Harvey; Bülow, Maja; Lunner, Thomas; Nelson, John; Riis, Søren Kamaric; Vanpoucke, Filiep

2016-10-01

This article describes the perspectives of hearing device manufacturers regarding the exciting developments that the Internet makes possible. Specifically, it proposes to join forces toward interoperability and standardization of Internet and audiology. A summary of why such a collaborative effort is required is provided from historical and scientific perspectives. A roadmap toward interoperability and standardization is proposed. Information and communication technologies improve the flow of health care data and pave the way to better health care. However, hearing-related products, features, and services are notoriously heterogeneous and incompatible with other health care systems (no interoperability). Standardization is the process of developing and implementing technical standards (e.g., Noah hearing database). All parties involved in interoperability and standardization realize mutual gains by making mutually consistent decisions. De jure (officially endorsed) standards can be developed in collaboration with large national health care systems as well as spokespeople for hearing care professionals and hearing device users. The roadmap covers mutual collaboration; data privacy, security, and ownership; compliance with current regulations; scalability and modularity; and the scope of interoperability and standards. We propose to join forces to pave the way to the interoperable Internet and audiology products, features, and services that the world needs.

Information networks in the stock market based on the distance of the multi-attribute dimensions between listed companies

NASA Astrophysics Data System (ADS)

Liu, Qian; Li, Huajiao; Liu, Xueyong; Jiang, Meihui

2018-04-01

In the stock market, there are widespread information connections between economic agents. Listed companies can obtain mutual information about investment decisions from common shareholders, and the extent of sharing information often determines the relationships between listed companies. Because different shareholder compositions and investment shares lead to different formations of the company's governance mechanisms, we map the investment relationships between shareholders to the multi-attribute dimensional spaces of the listed companies (each shareholder investment in a company is a company dimension). Then, we construct the listed company's information network based on co-shareholder relationships. The weights for the edges in the information network are measured with the Euclidean distance between the listed companies in the multi-attribute dimension space. We define two indices to analyze the information network's features. We conduct an empirical study that analyzes Chinese listed companies' information networks. The results from the analysis show that with the diversification and decentralization of shareholder investments, almost all Chinese listed companies exchanged information through common shareholder relationships, and there is a gradual reduction in information sharing capacity between listed companies that have common shareholders. This network analysis has benefits for risk management and portfolio investments.

Mutual information and phase dependencies: measures of reduced nonlinear cardiorespiratory interactions after myocardial infarction.

PubMed

Hoyer, Dirk; Leder, Uwe; Hoyer, Heike; Pompe, Bernd; Sommer, Michael; Zwiener, Ulrich

2002-01-01

The heart rate variability (HRV) is related to several mechanisms of the complex autonomic functioning such as respiratory heart rate modulation and phase dependencies between heart beat cycles and breathing cycles. The underlying processes are basically nonlinear. In order to understand and quantitatively assess those physiological interactions an adequate coupling analysis is necessary. We hypothesized that nonlinear measures of HRV and cardiorespiratory interdependencies are superior to the standard HRV measures in classifying patients after acute myocardial infarction. We introduced mutual information measures which provide access to nonlinear interdependencies as counterpart to the classically linear correlation analysis. The nonlinear statistical autodependencies of HRV were quantified by auto mutual information, the respiratory heart rate modulation by cardiorespiratory cross mutual information, respectively. The phase interdependencies between heart beat cycles and breathing cycles were assessed basing on the histograms of the frequency ratios of the instantaneous heart beat and respiratory cycles. Furthermore, the relative duration of phase synchronized intervals was acquired. We investigated 39 patients after acute myocardial infarction versus 24 controls. The discrimination of these groups was improved by cardiorespiratory cross mutual information measures and phase interdependencies measures in comparison to the linear standard HRV measures. This result was statistically confirmed by means of logistic regression models of particular variable subsets and their receiver operating characteristics.

Least-dependent-component analysis based on mutual information

NASA Astrophysics Data System (ADS)

Stögbauer, Harald; Kraskov, Alexander; Astakhov, Sergey A.; Grassberger, Peter

2004-12-01

We propose to use precise estimators of mutual information (MI) to find the least dependent components in a linearly mixed signal. On the one hand, this seems to lead to better blind source separation than with any other presently available algorithm. On the other hand, it has the advantage, compared to other implementations of “independent” component analysis (ICA), some of which are based on crude approximations for MI, that the numerical values of the MI can be used for (i) estimating residual dependencies between the output components; (ii) estimating the reliability of the output by comparing the pairwise MIs with those of remixed components; and (iii) clustering the output according to the residual interdependencies. For the MI estimator, we use a recently proposed k -nearest-neighbor-based algorithm. For time sequences, we combine this with delay embedding, in order to take into account nontrivial time correlations. After several tests with artificial data, we apply the resulting MILCA (mutual-information-based least dependent component analysis) algorithm to a real-world dataset, the ECG of a pregnant woman.

Maximum likelihood clustering with dependent feature trees

NASA Technical Reports Server (NTRS)

Chittineni, C. B. (Principal Investigator)

1981-01-01

The decomposition of mixture density of the data into its normal component densities is considered. The densities are approximated with first order dependent feature trees using criteria of mutual information and distance measures. Expressions are presented for the criteria when the densities are Gaussian. By defining different typs of nodes in a general dependent feature tree, maximum likelihood equations are developed for the estimation of parameters using fixed point iterations. The field structure of the data is also taken into account in developing maximum likelihood equations. Experimental results from the processing of remotely sensed multispectral scanner imagery data are included.

New Features for Neuron Classification.

PubMed

Hernández-Pérez, Leonardo A; Delgado-Castillo, Duniel; Martín-Pérez, Rainer; Orozco-Morales, Rubén; Lorenzo-Ginori, Juan V

2018-04-28

This paper addresses the problem of obtaining new neuron features capable of improving results of neuron classification. Most studies on neuron classification using morphological features have been based on Euclidean geometry. Here three one-dimensional (1D) time series are derived from the three-dimensional (3D) structure of neuron instead, and afterwards a spatial time series is finally constructed from which the features are calculated. Digitally reconstructed neurons were separated into control and pathological sets, which are related to three categories of alterations caused by epilepsy, Alzheimer's disease (long and local projections), and ischemia. These neuron sets were then subjected to supervised classification and the results were compared considering three sets of features: morphological, features obtained from the time series and a combination of both. The best results were obtained using features from the time series, which outperformed the classification using only morphological features, showing higher correct classification rates with differences of 5.15, 3.75, 5.33% for epilepsy and Alzheimer's disease (long and local projections) respectively. The morphological features were better for the ischemia set with a difference of 3.05%. Features like variance, Spearman auto-correlation, partial auto-correlation, mutual information, local minima and maxima, all related to the time series, exhibited the best performance. Also we compared different evaluators, among which ReliefF was the best ranked.

The evolution of plant-insect mutualisms.

PubMed

Bronstein, Judith L; Alarcón, Ruben; Geber, Monica

2006-01-01

Mutualisms (cooperative interactions between species) have had a central role in the generation and maintenance of life on earth. Insects and plants are involved in diverse forms of mutualism. Here we review evolutionary features of three prominent insect-plant mutualisms: pollination, protection and seed dispersal. We focus on addressing five central phenomena: evolutionary origins and maintenance of mutualism; the evolution of mutualistic traits; the evolution of specialization and generalization; coevolutionary processes; and the existence of cheating. Several features uniting very diverse insect-plant mutualisms are identified and their evolutionary implications are discussed: the involvement of one mobile and one sedentary partner; natural selection on plant rewards; the existence of a continuum from specialization to generalization; and the ubiquity of cheating, particularly on the part of insects. Plant-insect mutualisms have apparently both arisen and been lost repeatedly. Many adaptive hypotheses have been proposed to explain these transitions, and it is unlikely that any one of them dominates across interactions differing so widely in natural history. Evolutionary theory has a potentially important, but as yet largely unfilled, role to play in explaining the origins, maintenance, breakdown and evolution of insect-plant mutualisms.

Mutual information-based analysis of JPEG2000 contexts.

PubMed

Liu, Zhen; Karam, Lina J

2005-04-01

Context-based arithmetic coding has been widely adopted in image and video compression and is a key component of the new JPEG2000 image compression standard. In this paper, the contexts used in JPEG2000 are analyzed using the mutual information, which is closely related to the compression performance. We first show that, when combining the contexts, the mutual information between the contexts and the encoded data will decrease unless the conditional probability distributions of the combined contexts are the same. Given I, the initial number of contexts, and F, the final desired number of contexts, there are S(I, F) possible context classification schemes where S(I, F) is called the Stirling number of the second kind. The optimal classification scheme is the one that gives the maximum mutual information. Instead of using an exhaustive search, the optimal classification scheme can be obtained through a modified generalized Lloyd algorithm with the relative entropy as the distortion metric. For binary arithmetic coding, the search complexity can be reduced by using dynamic programming. Our experimental results show that the JPEG2000 contexts capture the correlations among the wavelet coefficients very well. At the same time, the number of contexts used as part of the standard can be reduced without loss in the coding performance.

Optimal reconstruction of the states in qutrit systems

NASA Astrophysics Data System (ADS)

Yan, Fei; Yang, Ming; Cao, Zhuo-Liang

2010-10-01

Based on mutually unbiased measurements, an optimal tomographic scheme for the multiqutrit states is presented explicitly. Because the reconstruction process of states based on mutually unbiased states is free of information waste, we refer to our scheme as the optimal scheme. By optimal we mean that the number of the required conditional operations reaches the minimum in this tomographic scheme for the states of qutrit systems. Special attention will be paid to how those different mutually unbiased measurements are realized; that is, how to decompose each transformation that connects each mutually unbiased basis with the standard computational basis. It is found that all those transformations can be decomposed into several basic implementable single- and two-qutrit unitary operations. For the three-qutrit system, there exist five different mutually unbiased-bases structures with different entanglement properties, so we introduce the concept of physical complexity to minimize the number of nonlocal operations needed over the five different structures. This scheme is helpful for experimental scientists to realize the most economical reconstruction of quantum states in qutrit systems.

Bases for qudits from a nonstandard approach to SU(2)

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

Kibler, M. R., E-mail: kibler@ipnl.in2p3.fr

2011-06-15

Bases of finite-dimensional Hilbert spaces (in dimension d) of relevance for quantum information and quantum computation are constructed from angular momentum theory and su(2) Lie algebraic methods. We report on a formula for deriving in one step the (1 + p)p qupits (i.e., qudits with d = p a prime integer) of a complete set of 1 + p mutually unbiased bases in C{sup p}. Repeated application of the formula can be used for generating mutually unbiased bases in C{sup d} with d = p{sup e} (e {>=} 2) a power of a prime integer. A connection between mutually unbiasedmore » bases and the unitary group SU(d) is briefly discussed in the case d = p{sup e}.« less

Vanishing Point Extraction and Refinement for Robust Camera Calibration

PubMed Central

Tsai, Fuan

2017-01-01

This paper describes a flexible camera calibration method using refined vanishing points without prior information. Vanishing points are estimated from human-made features like parallel lines and repeated patterns. With the vanishing points extracted from the three mutually orthogonal directions, the interior and exterior orientation parameters can be further calculated using collinearity condition equations. A vanishing point refinement process is proposed to reduce the uncertainty caused by vanishing point localization errors. The fine-tuning algorithm is based on the divergence of grouped feature points projected onto the reference plane, minimizing the standard deviation of each of the grouped collinear points with an O(1) computational complexity. This paper also presents an automated vanishing point estimation approach based on the cascade Hough transform. The experiment results indicate that the vanishing point refinement process can significantly improve camera calibration parameters and the root mean square error (RMSE) of the constructed 3D model can be reduced by about 30%. PMID:29280966

Mutual information in the evolution of trajectories in discrete aiming movements.

PubMed

Lai, Shih-Chiung; Mayer-Kress, Gottfried; Newell, Karl M

2008-07-01

This study investigated the mutual information in the trajectories of discrete aiming movements on a computer controlled graphics tablet where movement time ( 300 - 2050 ms) was manipulated in a given distance (100 mm) and movement distance (15-240 mm) in 2 given movement times (300 ms and 800 ms ). For the distance-fixed conditions, there was higher mutual information in the slower movements in the 0 vs. 80-100% trajectory point comparisons, whereas the mutual information was higher for the faster movements when comparing within the 80 and 100% points of the movement trajectory. For the time-fixed conditions, the spatial constraints led to a decreasing pattern of the mutual information throughout the points of the trajectory, with the highest mutual information found in the 80 vs. 100% comparison. Overall, the pattern of mutual information reveals systematic modulation of the trajectories between the attractive fixed point of the target as a function of movement condition. These mutual information patterns are postulated to be the consequence of the different relative contributions of feedforward and feedback control processes in trajectory formation as a function of task constraints.

Personalized features for attention detection in children with Attention Deficit Hyperactivity Disorder.

PubMed

Fahimi, Fatemeh; Guan, Cuntai; Wooi Boon Goh; Kai Keng Ang; Choon Guan Lim; Tih Shih Lee

2017-07-01

Measuring attention from electroencephalogram (EEG) has found applications in the treatment of Attention Deficit Hyperactivity Disorder (ADHD). It is of great interest to understand what features in EEG are most representative of attention. Intensive research has been done in the past and it has been proven that frequency band powers and their ratios are effective features in detecting attention. However, there are still unanswered questions, like, what features in EEG are most discriminative between attentive and non-attentive states? Are these features common among all subjects or are they subject-specific and must be optimized for each subject? Using Mutual Information (MI) to perform subject-specific feature selection on a large data set including 120 ADHD children, we found that besides theta beta ratio (TBR) which is commonly used in attention detection and neurofeedback, the relative beta power and theta/(alpha+beta) (TBAR) are also equally significant and informative for attention detection. Interestingly, we found that the relative theta power (which is also commonly used) may not have sufficient discriminative information itself (it is informative only for 3.26% of ADHD children). We have also demonstrated that although these features (relative beta power, TBR and TBAR) are the most important measures to detect attention on average, different subjects have different set of most discriminative features.

Recurrence plot statistics and the effect of embedding

NASA Astrophysics Data System (ADS)

March, T. K.; Chapman, S. C.; Dendy, R. O.

2005-01-01

Recurrence plots provide a graphical representation of the recurrent patterns in a timeseries, the quantification of which is a relatively new field. Here we derive analytical expressions which relate the values of key statistics, notably determinism and entropy of line length distribution, to the correlation sum as a function of embedding dimension. These expressions are obtained by deriving the transformation which generates an embedded recurrence plot from an unembedded plot. A single unembedded recurrence plot thus provides the statistics of all possible embedded recurrence plots. If the correlation sum scales exponentially with embedding dimension, we show that these statistics are determined entirely by the exponent of the exponential. This explains the results of Iwanski and Bradley [J.S. Iwanski, E. Bradley, Recurrence plots of experimental data: to embed or not to embed? Chaos 8 (1998) 861-871] who found that certain recurrence plot statistics are apparently invariant to embedding dimension for certain low-dimensional systems. We also examine the relationship between the mutual information content of two timeseries and the common recurrent structure seen in their recurrence plots. This allows time-localized contributions to mutual information to be visualized. This technique is demonstrated using geomagnetic index data; we show that the AU and AL geomagnetic indices share half their information, and find the timescale on which mutual features appear.

Parallel Mutual Information Based Construction of Genome-Scale Networks on the Intel® Xeon Phi™ Coprocessor.

PubMed

Misra, Sanchit; Pamnany, Kiran; Aluru, Srinivas

2015-01-01

Construction of whole-genome networks from large-scale gene expression data is an important problem in systems biology. While several techniques have been developed, most cannot handle network reconstruction at the whole-genome scale, and the few that can, require large clusters. In this paper, we present a solution on the Intel Xeon Phi coprocessor, taking advantage of its multi-level parallelism including many x86-based cores, multiple threads per core, and vector processing units. We also present a solution on the Intel® Xeon® processor. Our solution is based on TINGe, a fast parallel network reconstruction technique that uses mutual information and permutation testing for assessing statistical significance. We demonstrate the first ever inference of a plant whole genome regulatory network on a single chip by constructing a 15,575 gene network of the plant Arabidopsis thaliana from 3,137 microarray experiments in only 22 minutes. In addition, our optimization for parallelizing mutual information computation on the Intel Xeon Phi coprocessor holds out lessons that are applicable to other domains.

Mutual Information and Information Gating in Synfire Chains

DOE PAGES

Xiao, Zhuocheng; Wang, Binxu; Sornborger, Andrew Tyler; ...

2018-02-01

Here, coherent neuronal activity is believed to underlie the transfer and processing of information in the brain. Coherent activity in the form of synchronous firing and oscillations has been measured in many brain regions and has been correlated with enhanced feature processing and other sensory and cognitive functions. In the theoretical context, synfire chains and the transfer of transient activity packets in feedforward networks have been appealed to in order to describe coherent spiking and information transfer. Recently, it has been demonstrated that the classical synfire chain architecture, with the addition of suitably timed gating currents, can support the gradedmore » transfer of mean firing rates in feedforward networks (called synfire-gated synfire chains—SGSCs). Here we study information propagation in SGSCs by examining mutual information as a function of layer number in a feedforward network. We explore the effects of gating and noise on information transfer in synfire chains and demonstrate that asymptotically, two main regions exist in parameter space where information may be propagated and its propagation is controlled by pulse-gating: a large region where binary codes may be propagated, and a smaller region near a cusp in parameter space that supports graded propagation across many layers.« less

Mutual Information and Information Gating in Synfire Chains

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

Xiao, Zhuocheng; Wang, Binxu; Sornborger, Andrew Tyler

Here, coherent neuronal activity is believed to underlie the transfer and processing of information in the brain. Coherent activity in the form of synchronous firing and oscillations has been measured in many brain regions and has been correlated with enhanced feature processing and other sensory and cognitive functions. In the theoretical context, synfire chains and the transfer of transient activity packets in feedforward networks have been appealed to in order to describe coherent spiking and information transfer. Recently, it has been demonstrated that the classical synfire chain architecture, with the addition of suitably timed gating currents, can support the gradedmore » transfer of mean firing rates in feedforward networks (called synfire-gated synfire chains—SGSCs). Here we study information propagation in SGSCs by examining mutual information as a function of layer number in a feedforward network. We explore the effects of gating and noise on information transfer in synfire chains and demonstrate that asymptotically, two main regions exist in parameter space where information may be propagated and its propagation is controlled by pulse-gating: a large region where binary codes may be propagated, and a smaller region near a cusp in parameter space that supports graded propagation across many layers.« less

On Information Metrics for Spatial Coding.

PubMed

Souza, Bryan C; Pavão, Rodrigo; Belchior, Hindiael; Tort, Adriano B L

2018-04-01

The hippocampal formation is involved in navigation, and its neuronal activity exhibits a variety of spatial correlates (e.g., place cells, grid cells). The quantification of the information encoded by spikes has been standard procedure to identify which cells have spatial correlates. For place cells, most of the established metrics derive from Shannon's mutual information (Shannon, 1948), and convey information rate in bits/s or bits/spike (Skaggs et al., 1993, 1996). Despite their widespread use, the performance of these metrics in relation to the original mutual information metric has never been investigated. In this work, using simulated and real data, we find that the current information metrics correlate less with the accuracy of spatial decoding than the original mutual information metric. We also find that the top informative cells may differ among metrics, and show a surrogate-based normalization that yields comparable spatial information estimates. Since different information metrics may identify different neuronal populations, we discuss current and alternative definitions of spatially informative cells, which affect the metric choice. Copyright © 2018 IBRO. Published by Elsevier Ltd. All rights reserved.

Adaptive DSPI phase denoising using mutual information and 2D variational mode decomposition

NASA Astrophysics Data System (ADS)

Xiao, Qiyang; Li, Jian; Wu, Sijin; Li, Weixian; Yang, Lianxiang; Dong, Mingli; Zeng, Zhoumo

2018-04-01

In digital speckle pattern interferometry (DSPI), noise interference leads to a low peak signal-to-noise ratio (PSNR) and measurement errors in the phase map. This paper proposes an adaptive DSPI phase denoising method based on two-dimensional variational mode decomposition (2D-VMD) and mutual information. Firstly, the DSPI phase map is subjected to 2D-VMD in order to obtain a series of band-limited intrinsic mode functions (BLIMFs). Then, on the basis of characteristics of the BLIMFs and in combination with mutual information, a self-adaptive denoising method is proposed to obtain noise-free components containing the primary phase information. The noise-free components are reconstructed to obtain the denoising DSPI phase map. Simulation and experimental results show that the proposed method can effectively reduce noise interference, giving a PSNR that is higher than that of two-dimensional empirical mode decomposition methods.

A consumer-resource approach to the density-dependent population dynamics of mutualism.

PubMed

Holland, J Nathaniel; DeAngelis, Donald L

2010-05-01

Like predation and competition, mutualism is now recognized as a consumer-resource (C-R) interaction, including, in particular, bi-directional (e.g., coral, plant-mycorrhizae) and uni-directional (e.g., ant-plant defense, plant-pollinator) C-R mutualisms. Here, we develop general theory for the density-dependent population dynamics of mutualism based on the C-R mechanism of interspecific interaction. To test the influence of C-R interactions on the dynamics and stability of bi- and uni-directional C-R mutualisms, we developed simple models that link consumer functional response of one mutualistic species with the resources supplied by another. Phase-plane analyses show that the ecological dynamics of C-R mutualisms are stable in general. Most transient behavior leads to an equilibrium of mutualistic coexistence, at which both species densities are greater than in the absence of interactions. However, due to the basic nature of C-R interactions, certain density-dependent conditions can lead to C-R dynamics characteristic of predator-prey interactions, in which one species overexploits and causes the other to go extinct. Consistent with empirical phenomena, these results suggest that the C-R interaction can provide a broad mechanism for understanding density-dependent population dynamics of mutualism. By unifying predation, competition, and mutualism under the common ecological framework of consumer-resource theory, we may also gain a better understanding of the universal features of interspecific interactions in general.

A consumer-resource approach to the density-dependent population dynamics of mutualism

USGS Publications Warehouse

Holland, J. Nathaniel; DeAngelis, Donald L.

2010-01-01

Like predation and competition, mutualism is now recognized as a consumer resource (C-R) interaction, including, in particular, bi-directional (e.g., coral, plant- mycorrhizae) and uni-directional (e.g., ant-plant defense, plant-pollinator) C-R mutualisms. Here, we develop general theory for the density-dependent population dynamics of mutualism based on the C-R mechanism of interspecific interaction. To test the influence of C-R interactions on the dynamics and stability of bi- and uni-directional C-R mutualisms, we developed simple models that link consumer functional response of one mutualistic species with the resources supplied by another. Phase-plane analyses show that the ecological dynamics of C-R mutualisms are stable in general. Most transient behavior leads to an equilibrium of mutualistic coexistence, at which both species densities are greater than in the absence of interactions. However, due to the basic nature of C-R interactions, certain density-dependent conditions can lead to C-R dynamics characteristic of predator-prey interactions, in which one species overexploits and causes the other to go extinct. Consistent with empirical phenomena, these results suggest that the C-R interaction can provide a broad mechanism for understanding density-dependent population dynamics of mutualism. By unifying predation, competition, and mutualism under the common ecological framework of consumer-resource theory, we may also gain a better understanding of the universal features of interspecific interactions in general.

A Method for the Alignment of Heterogeneous Macromolecules from Electron Microscopy

PubMed Central

Shatsky, Maxim; Hall, Richard J.; Brenner, Steven E.; Glaeser, Robert M.

2009-01-01

We propose a feature-based image alignment method for single-particle electron microscopy that is able to accommodate various similarity scoring functions while efficiently sampling the two-dimensional transformational space. We use this image alignment method to evaluate the performance of a scoring function that is based on the Mutual Information (MI) of two images rather than one that is based on the cross-correlation function. We show that alignment using MI for the scoring function has far less model-dependent bias than is found with cross-correlation based alignment. We also demonstrate that MI improves the alignment of some types of heterogeneous data, provided that the signal to noise ratio is relatively high. These results indicate, therefore, that use of MI as the scoring function is well suited for the alignment of class-averages computed from single particle images. Our method is tested on data from three model structures and one real dataset. PMID:19166941

A parameter for the assessment of the segmentation of TEM tomography reconstructed volumes based on mutual information.

PubMed

Okariz, Ana; Guraya, Teresa; Iturrondobeitia, Maider; Ibarretxe, Julen

2017-12-01

A method is proposed and verified for selecting the optimum segmentation of a TEM reconstruction among the results of several segmentation algorithms. The selection criterion is the accuracy of the segmentation. To do this selection, a parameter for the comparison of the accuracies of the different segmentations has been defined. It consists of the mutual information value between the acquired TEM images of the sample and the Radon projections of the segmented volumes. In this work, it has been proved that this new mutual information parameter and the Jaccard coefficient between the segmented volume and the ideal one are correlated. In addition, the results of the new parameter are compared to the results obtained from another validated method to select the optimum segmentation. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Synchronous Digital Duplexing Technique for OFDMA-Based Indoor Communications

NASA Astrophysics Data System (ADS)

Park, Chang-Hwan; Ko, Yo-Han; Kim, Yeong-Jun; Park, Kyung-Won; Jeon, Won-Gi; Paik, Jong-Ho; Lee, Seok-Pil; Cho, Yong-Soo

In this paper, we propose a new digital duplexing scheme, called synchronous digital duplexing (SDD), which can increase data efficiency and flexibility of resource by transmitting uplink signal and downlink signal simultaneously in wireless communication. In order to transmit uplink and downlink signals simultaneously, the proposed SDD obtains mutual information among subscriber stations (SSs) with a mutual ranging symbol. This information is used for selection of transmission time, decision on cyclic suffix (CS) insertion, determination of CS length, and re-establishment of FFT starting point.

Identification of Chinese medicine syndromes in persistent insomnia associated with major depressive disorder: a latent tree analysis.

PubMed

Yeung, Wing-Fai; Chung, Ka-Fai; Zhang, Nevin Lian-Wen; Zhang, Shi Ping; Yung, Kam-Ping; Chen, Pei-Xian; Ho, Yan-Yee

2016-01-01

Chinese medicine (CM) syndrome (zheng) differentiation is based on the co-occurrence of CM manifestation profiles, such as signs and symptoms, and pulse and tongue features. Insomnia is a symptom that frequently occurs in major depressive disorder despite adequate antidepressant treatment. This study aims to identify co-occurrence patterns in participants with persistent insomnia and major depressive disorder from clinical feature data using latent tree analysis, and to compare the latent variables with relevant CM syndromes. One hundred and forty-two participants with persistent insomnia and a history of major depressive disorder completed a standardized checklist (the Chinese Medicine Insomnia Symptom Checklist) specially developed for CM syndrome classification of insomnia. The checklist covers symptoms and signs, including tongue and pulse features. The clinical features assessed by the checklist were analyzed using Lantern software. CM practitioners with relevant experience compared the clinical feature variables under each latent variable with reference to relevant CM syndromes, based on a previous review of CM syndromes. The symptom data were analyzed to build the latent tree model and the model with the highest Bayes information criterion score was regarded as the best model. This model contained 18 latent variables, each of which divided participants into two clusters. Six clusters represented more than 50 % of the sample. The clinical feature co-occurrence patterns of these six clusters were interpreted as the CM syndromes Liver qi stagnation transforming into fire, Liver fire flaming upward, Stomach disharmony, Hyperactivity of fire due to yin deficiency, Heart-kidney noninteraction, and Qi deficiency of the heart and gallbladder. The clinical feature variables that contributed significant cumulative information coverage (at least 95 %) were identified. Latent tree model analysis on a sample of depressed participants with insomnia revealed 13 clinical feature co-occurrence patterns, four mutual-exclusion patterns, and one pattern with a single clinical feature variable.

Learning and Transition of Symbols: Towards a Dynamical Model of a Symbolic Individual

NASA Astrophysics Data System (ADS)

Hashimoto, Takashi; Masumi, Akira

The remarkable feature of linguistic communications is the use of symbols for transmitting information and mutual understanding. Deacon (1997) pointed out that humans are symbolic species, namely, we show symbolic cognitive activities such as learning, formation, and manipulation of symbols. In research into the origin and the evolution of language, we should elucidate the emerging process of such symbolic cognitive activities.

A Hierarchical Feature and Sample Selection Framework and Its Application for Alzheimer’s Disease Diagnosis

NASA Astrophysics Data System (ADS)

An, Le; Adeli, Ehsan; Liu, Mingxia; Zhang, Jun; Lee, Seong-Whan; Shen, Dinggang

2017-03-01

Classification is one of the most important tasks in machine learning. Due to feature redundancy or outliers in samples, using all available data for training a classifier may be suboptimal. For example, the Alzheimer’s disease (AD) is correlated with certain brain regions or single nucleotide polymorphisms (SNPs), and identification of relevant features is critical for computer-aided diagnosis. Many existing methods first select features from structural magnetic resonance imaging (MRI) or SNPs and then use those features to build the classifier. However, with the presence of many redundant features, the most discriminative features are difficult to be identified in a single step. Thus, we formulate a hierarchical feature and sample selection framework to gradually select informative features and discard ambiguous samples in multiple steps for improved classifier learning. To positively guide the data manifold preservation process, we utilize both labeled and unlabeled data during training, making our method semi-supervised. For validation, we conduct experiments on AD diagnosis by selecting mutually informative features from both MRI and SNP, and using the most discriminative samples for training. The superior classification results demonstrate the effectiveness of our approach, as compared with the rivals.

Towards a Theory of Mutual Dependency between School Administrators and Teachers: Bargaining Theory as Research Heuristic

ERIC Educational Resources Information Center

Elstad, Eyvind

2008-01-01

The aim of this article is to contribute to an improved comprehension of the relations between administrators and teachers. The main argument is that mutual dependency is an operational feature of a school organization. I analyse a school case that shows essential features of the interconnections between the parties. An extensive commitment to ICT…

Estimation of time-delayed mutual information and bias for irregularly and sparsely sampled time-series

PubMed Central

Albers, D. J.; Hripcsak, George

2012-01-01

A method to estimate the time-dependent correlation via an empirical bias estimate of the time-delayed mutual information for a time-series is proposed. In particular, the bias of the time-delayed mutual information is shown to often be equivalent to the mutual information between two distributions of points from the same system separated by infinite time. Thus intuitively, estimation of the bias is reduced to estimation of the mutual information between distributions of data points separated by large time intervals. The proposed bias estimation techniques are shown to work for Lorenz equations data and glucose time series data of three patients from the Columbia University Medical Center database. PMID:22536009

False match elimination for face recognition based on SIFT algorithm

NASA Astrophysics Data System (ADS)

Gu, Xuyuan; Shi, Ping; Shao, Meide

2011-06-01

The SIFT (Scale Invariant Feature Transform) is a well known algorithm used to detect and describe local features in images. It is invariant to image scale, rotation and robust to the noise and illumination. In this paper, a novel method used for face recognition based on SIFT is proposed, which combines the optimization of SIFT, mutual matching and Progressive Sample Consensus (PROSAC) together and can eliminate the false matches of face recognition effectively. Experiments on ORL face database show that many false matches can be eliminated and better recognition rate is achieved.

Mutual information estimation for irregularly sampled time series

NASA Astrophysics Data System (ADS)

Rehfeld, K.; Marwan, N.; Heitzig, J.; Kurths, J.

2012-04-01

For the automated, objective and joint analysis of time series, similarity measures are crucial. Used in the analysis of climate records, they allow for a complimentary, unbiased view onto sparse datasets. The irregular sampling of many of these time series, however, makes it necessary to either perform signal reconstruction (e.g. interpolation) or to develop and use adapted measures. Standard linear interpolation comes with an inevitable loss of information and bias effects. We have recently developed a Gaussian kernel-based correlation algorithm with which the interpolation error can be substantially lowered, but this would not work should the functional relationship in a bivariate setting be non-linear. We therefore propose an algorithm to estimate lagged auto and cross mutual information from irregularly sampled time series. We have extended the standard and adaptive binning histogram estimators and use Gaussian distributed weights in the estimation of the (joint) probabilities. To test our method we have simulated linear and nonlinear auto-regressive processes with Gamma-distributed inter-sampling intervals. We have then performed a sensitivity analysis for the estimation of actual coupling length, the lag of coupling and the decorrelation time in the synthetic time series and contrast our results to the performance of a signal reconstruction scheme. Finally we applied our estimator to speleothem records. We compare the estimated memory (or decorrelation time) to that from a least-squares estimator based on fitting an auto-regressive process of order 1. The calculated (cross) mutual information results are compared for the different estimators (standard or adaptive binning) and contrasted with results from signal reconstruction. We find that the kernel-based estimator has a significantly lower root mean square error and less systematic sampling bias than the interpolation-based method. It is possible that these encouraging results could be further improved by using non-histogram mutual information estimators, like k-Nearest Neighbor or Kernel-Density estimators, but for short (<1000 points) and irregularly sampled datasets the proposed algorithm is already a great improvement.

Analytical Calculation of Mutual Information between Weakly Coupled Poisson-Spiking Neurons in Models of Dynamically Gated Communication.

PubMed

Cannon, Jonathan

2017-01-01

Mutual information is a commonly used measure of communication between neurons, but little theory exists describing the relationship between mutual information and the parameters of the underlying neuronal interaction. Such a theory could help us understand how specific physiological changes affect the capacity of neurons to synaptically communicate, and, in particular, they could help us characterize the mechanisms by which neuronal dynamics gate the flow of information in the brain. Here we study a pair of linear-nonlinear-Poisson neurons coupled by a weak synapse. We derive an analytical expression describing the mutual information between their spike trains in terms of synapse strength, neuronal activation function, the time course of postsynaptic currents, and the time course of the background input received by the two neurons. This expression allows mutual information calculations that would otherwise be computationally intractable. We use this expression to analytically explore the interaction of excitation, information transmission, and the convexity of the activation function. Then, using this expression to quantify mutual information in simulations, we illustrate the information-gating effects of neural oscillations and oscillatory coherence, which may either increase or decrease the mutual information across the synapse depending on parameters. Finally, we show analytically that our results can quantitatively describe the selection of one information pathway over another when multiple sending neurons project weakly to a single receiving neuron.

Hierarchical mutual information for the comparison of hierarchical community structures in complex networks

NASA Astrophysics Data System (ADS)

Perotti, Juan Ignacio; Tessone, Claudio Juan; Caldarelli, Guido

2015-12-01

The quest for a quantitative characterization of community and modular structure of complex networks produced a variety of methods and algorithms to classify different networks. However, it is not clear if such methods provide consistent, robust, and meaningful results when considering hierarchies as a whole. Part of the problem is the lack of a similarity measure for the comparison of hierarchical community structures. In this work we give a contribution by introducing the hierarchical mutual information, which is a generalization of the traditional mutual information and makes it possible to compare hierarchical partitions and hierarchical community structures. The normalized version of the hierarchical mutual information should behave analogously to the traditional normalized mutual information. Here the correct behavior of the hierarchical mutual information is corroborated on an extensive battery of numerical experiments. The experiments are performed on artificial hierarchies and on the hierarchical community structure of artificial and empirical networks. Furthermore, the experiments illustrate some of the practical applications of the hierarchical mutual information, namely the comparison of different community detection methods and the study of the consistency, robustness, and temporal evolution of the hierarchical modular structure of networks.

Spatially weighted mutual information image registration for image guided radiation therapy.

PubMed

Park, Samuel B; Rhee, Frank C; Monroe, James I; Sohn, Jason W

2010-09-01

To develop a new metric for image registration that incorporates the (sub)pixelwise differential importance along spatial location and to demonstrate its application for image guided radiation therapy (IGRT). It is well known that rigid-body image registration with mutual information is dependent on the size and location of the image subset on which the alignment analysis is based [the designated region of interest (ROI)]. Therefore, careful review and manual adjustments of the resulting registration are frequently necessary. Although there were some investigations of weighted mutual information (WMI), these efforts could not apply the differential importance to a particular spatial location since WMI only applies the weight to the joint histogram space. The authors developed the spatially weighted mutual information (SWMI) metric by incorporating an adaptable weight function with spatial localization into mutual information. SWMI enables the user to apply the selected transform to medically "important" areas such as tumors and critical structures, so SWMI is neither dominated by, nor neglects the neighboring structures. Since SWMI can be utilized with any weight function form, the authors presented two examples of weight functions for IGRT application: A Gaussian-shaped weight function (GW) applied to a user-defined location and a structures-of-interest (SOI) based weight function. An image registration example using a synthesized 2D image is presented to illustrate the efficacy of SWMI. The convergence and feasibility of the registration method as applied to clinical imaging is illustrated by fusing a prostate treatment planning CT with a clinical cone beam CT (CBCT) image set acquired for patient alignment. Forty-one trials are run to test the speed of convergence. The authors also applied SWMI registration using two types of weight functions to two head and neck cases and a prostate case with clinically acquired CBCT/ MVCT image sets. The SWMI registration with a Gaussian weight function (SWMI-GW) was tested between two different imaging modalities: CT and MRI image sets. SWMI-GW converges 10% faster than registration using mutual information with an ROI. SWMI-GW as well as SWMI with SOI-based weight function (SWMI-SOI) shows better compensation of the target organ's deformation and neighboring critical organs' deformation. SWMI-GW was also used to successfully fuse MRI and CT images. Rigid-body image registration using our SWMI-GW and SWMI-SOI as cost functions can achieve better registration results in (a) designated image region(s) as well as faster convergence. With the theoretical foundation established, we believe SWMI could be extended to larger clinical testing.

A Spiking Neural Network in sEMG Feature Extraction.

PubMed

Lobov, Sergey; Mironov, Vasiliy; Kastalskiy, Innokentiy; Kazantsev, Victor

2015-11-03

We have developed a novel algorithm for sEMG feature extraction and classification. It is based on a hybrid network composed of spiking and artificial neurons. The spiking neuron layer with mutual inhibition was assigned as feature extractor. We demonstrate that the classification accuracy of the proposed model could reach high values comparable with existing sEMG interface systems. Moreover, the algorithm sensibility for different sEMG collecting systems characteristics was estimated. Results showed rather equal accuracy, despite a significant sampling rate difference. The proposed algorithm was successfully tested for mobile robot control.

Contrast-Enhanced Ultrasound Angiogenesis Imaging by Mutual Information Analysis for Prostate Cancer Localization.

PubMed

Schalk, Stefan G; Demi, Libertario; Bouhouch, Nabil; Kuenen, Maarten P J; Postema, Arnoud W; de la Rosette, Jean J M C H; Wijkstra, Hessel; Tjalkens, Tjalling J; Mischi, Massimo

2017-03-01

The role of angiogenesis in cancer growth has stimulated research aimed at noninvasive cancer detection by blood perfusion imaging. Recently, contrast ultrasound dispersion imaging was proposed as an alternative method for angiogenesis imaging. After the intravenous injection of an ultrasound-contrast-agent bolus, dispersion can be indirectly estimated from the local similarity between neighboring time-intensity curves (TICs) measured by ultrasound imaging. Up until now, only linear similarity measures have been investigated. Motivated by the promising results of this approach in prostate cancer (PCa), we developed a novel dispersion estimation method based on mutual information, thus including nonlinear similarity, to further improve its ability to localize PCa. First, a simulation study was performed to establish the theoretical link between dispersion and mutual information. Next, the method's ability to localize PCa was validated in vivo in 23 patients (58 datasets) referred for radical prostatectomy by comparison with histology. A monotonic relationship between dispersion and mutual information was demonstrated. The in vivo study resulted in a receiver operating characteristic (ROC) curve area equal to 0.77, which was superior (p = 0.21-0.24) to that obtained by linear similarity measures (0.74-0.75) and (p <; 0.05) to that by conventional perfusion parameters (≤0.70). Mutual information between neighboring time-intensity curves can be used to indirectly estimate contrast dispersion and can lead to more accurate PCa localization. An improved PCa localization method can possibly lead to better grading and staging of tumors, and support focal-treatment guidance. Moreover, future employment of the method in other types of angiogenic cancer can be considered.

Community structure detection based on the neighbor node degree information

NASA Astrophysics Data System (ADS)

Tang, Li-Ying; Li, Sheng-Nan; Lin, Jian-Hong; Guo, Qiang; Liu, Jian-Guo

2016-11-01

Community structure detection is of great significance for better understanding the network topology property. By taking into account the neighbor degree information of the topological network as the link weight, we present an improved Nonnegative Matrix Factorization (NMF) method for detecting community structure. The results for empirical networks show that the largest improved ratio of the Normalized Mutual Information value could reach 63.21%. Meanwhile, for synthetic networks, the highest Normalized Mutual Information value could closely reach 1, which suggests that the improved method with the optimal λ can detect the community structure more accurately. This work is helpful for understanding the interplay between the link weight and the community structure detection.

Generalized mutual information and Tsirelson's bound

NASA Astrophysics Data System (ADS)

Wakakuwa, Eyuri; Murao, Mio

2014-12-01

We introduce a generalization of the quantum mutual information between a classical system and a quantum system into the mutual information between a classical system and a system described by general probabilistic theories. We apply this generalized mutual information (GMI) to a derivation of Tsirelson's bound from information causality, and prove that Tsirelson's bound can be derived from the chain rule of the GMI. By using the GMI, we formulate the "no-supersignalling condition" (NSS), that the assistance of correlations does not enhance the capability of classical communication. We prove that NSS is never violated in any no-signalling theory.

Generalized mutual information and Tsirelson's bound

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

Wakakuwa, Eyuri; Murao, Mio

2014-12-04

We introduce a generalization of the quantum mutual information between a classical system and a quantum system into the mutual information between a classical system and a system described by general probabilistic theories. We apply this generalized mutual information (GMI) to a derivation of Tsirelson's bound from information causality, and prove that Tsirelson's bound can be derived from the chain rule of the GMI. By using the GMI, we formulate the 'no-supersignalling condition' (NSS), that the assistance of correlations does not enhance the capability of classical communication. We prove that NSS is never violated in any no-signalling theory.

Predicting couple therapy outcomes based on speech acoustic features

PubMed Central

Nasir, Md; Baucom, Brian Robert; Narayanan, Shrikanth

2017-01-01

Automated assessment and prediction of marital outcome in couples therapy is a challenging task but promises to be a potentially useful tool for clinical psychologists. Computational approaches for inferring therapy outcomes using observable behavioral information obtained from conversations between spouses offer objective means for understanding relationship dynamics. In this work, we explore whether the acoustics of the spoken interactions of clinically distressed spouses provide information towards assessment of therapy outcomes. The therapy outcome prediction task in this work includes detecting whether there was a relationship improvement or not (posed as a binary classification) as well as discerning varying levels of improvement or decline in the relationship status (posed as a multiclass recognition task). We use each interlocutor’s acoustic speech signal characteristics such as vocal intonation and intensity, both independently and in relation to one another, as cues for predicting the therapy outcome. We also compare prediction performance with one obtained via standardized behavioral codes characterizing the relationship dynamics provided by human experts as features for automated classification. Our experiments, using data from a longitudinal clinical study of couples in distressed relations, showed that predictions of relationship outcomes obtained directly from vocal acoustics are comparable or superior to those obtained using human-rated behavioral codes as prediction features. In addition, combining direct signal-derived features with manually coded behavioral features improved the prediction performance in most cases, indicating the complementarity of relevant information captured by humans and machine algorithms. Additionally, considering the vocal properties of the interlocutors in relation to one another, rather than in isolation, showed to be important for improving the automatic prediction. This finding supports the notion that behavioral outcome, like many other behavioral aspects, is closely related to the dynamics and mutual influence of the interlocutors during their interaction and their resulting behavioral patterns. PMID:28934302

Mutual information and the fidelity of response of gene regulatory models

NASA Astrophysics Data System (ADS)

Tabbaa, Omar P.; Jayaprakash, C.

2014-08-01

We investigate cellular response to extracellular signals by using information theory techniques motivated by recent experiments. We present results for the steady state of the following gene regulatory models found in both prokaryotic and eukaryotic cells: a linear transcription-translation model and a positive or negative auto-regulatory model. We calculate both the information capacity and the mutual information exactly for simple models and approximately for the full model. We find that (1) small changes in mutual information can lead to potentially important changes in cellular response and (2) there are diminishing returns in the fidelity of response as the mutual information increases. We calculate the information capacity using Gillespie simulations of a model for the TNF-α-NF-κ B network and find good agreement with the measured value for an experimental realization of this network. Our results provide a quantitative understanding of the differences in cellular response when comparing experimentally measured mutual information values of different gene regulatory models. Our calculations demonstrate that Gillespie simulations can be used to compute the mutual information of more complex gene regulatory models, providing a potentially useful tool in synthetic biology.

An unsupervised technique for optimal feature selection in attribute profiles for spectral-spatial classification of hyperspectral images

NASA Astrophysics Data System (ADS)

Bhardwaj, Kaushal; Patra, Swarnajyoti

2018-04-01

Inclusion of spatial information along with spectral features play a significant role in classification of remote sensing images. Attribute profiles have already proved their ability to represent spatial information. In order to incorporate proper spatial information, multiple attributes are required and for each attribute large profiles need to be constructed by varying the filter parameter values within a wide range. Thus, the constructed profiles that represent spectral-spatial information of an hyperspectral image have huge dimension which leads to Hughes phenomenon and increases computational burden. To mitigate these problems, this work presents an unsupervised feature selection technique that selects a subset of filtered image from the constructed high dimensional multi-attribute profile which are sufficiently informative to discriminate well among classes. In this regard the proposed technique exploits genetic algorithms (GAs). The fitness function of GAs are defined in an unsupervised way with the help of mutual information. The effectiveness of the proposed technique is assessed using one-against-all support vector machine classifier. The experiments conducted on three hyperspectral data sets show the robustness of the proposed method in terms of computation time and classification accuracy.

Reservoir computing with a slowly modulated mask signal for preprocessing using a mutually coupled optoelectronic system

NASA Astrophysics Data System (ADS)

Tezuka, Miwa; Kanno, Kazutaka; Bunsen, Masatoshi

2016-08-01

Reservoir computing is a machine-learning paradigm based on information processing in the human brain. We numerically demonstrate reservoir computing with a slowly modulated mask signal for preprocessing by using a mutually coupled optoelectronic system. The performance of our system is quantitatively evaluated by a chaotic time series prediction task. Our system can produce comparable performance with reservoir computing with a single feedback system and a fast modulated mask signal. We showed that it is possible to slow down the modulation speed of the mask signal by using the mutually coupled system in reservoir computing.

Maximally Informative Stimuli and Tuning Curves for Sigmoidal Rate-Coding Neurons and Populations

NASA Astrophysics Data System (ADS)

McDonnell, Mark D.; Stocks, Nigel G.

2008-08-01

A general method for deriving maximally informative sigmoidal tuning curves for neural systems with small normalized variability is presented. The optimal tuning curve is a nonlinear function of the cumulative distribution function of the stimulus and depends on the mean-variance relationship of the neural system. The derivation is based on a known relationship between Shannon’s mutual information and Fisher information, and the optimality of Jeffrey’s prior. It relies on the existence of closed-form solutions to the converse problem of optimizing the stimulus distribution for a given tuning curve. It is shown that maximum mutual information corresponds to constant Fisher information only if the stimulus is uniformly distributed. As an example, the case of sub-Poisson binomial firing statistics is analyzed in detail.

Mapping interictal epileptic discharges using mutual information between concurrent EEG and fMRI.

PubMed

Caballero-Gaudes, César; Van de Ville, Dimitri; Grouiller, Frédéric; Thornton, Rachel; Lemieux, Louis; Seeck, Margitta; Lazeyras, François; Vulliemoz, Serge

2013-03-01

The mapping of haemodynamic changes related to interictal epileptic discharges (IED) in simultaneous electroencephalography (EEG) and functional MRI (fMRI) studies is usually carried out by means of EEG-correlated fMRI analyses where the EEG information specifies the model to test on the fMRI signal. The sensitivity and specificity critically depend on the accuracy of EEG detection and the validity of the haemodynamic model. In this study we investigated whether an information theoretic analysis based on the mutual information (MI) between the presence of epileptic activity on EEG and the fMRI data can provide further insights into the haemodynamic changes related to interictal epileptic activity. The important features of MI are that: 1) both recording modalities are treated symmetrically; 2) no requirement for a-priori models for the haemodynamic response function, or assumption of a linear relationship between the spiking activity and BOLD responses, and 3) no parametric model for the type of noise or its probability distribution is necessary for the computation of MI. Fourteen patients with pharmaco-resistant focal epilepsy underwent EEG-fMRI and intracranial EEG and/or surgical resection with positive postoperative outcome (seizure freedom or considerable reduction in seizure frequency) was available in 7/14 patients. We used nonparametric statistical assessment of the MI maps based on a four-dimensional wavelet packet resampling method. The results of MI were compared to the statistical parametric maps obtained with two conventional General Linear Model (GLM) analyses based on the informed basis set (canonical HRF and its temporal and dispersion derivatives) and the Finite Impulse Response (FIR) models. The MI results were concordant with the electro-clinically or surgically defined epileptogenic area in 8/14 patients and showed the same degree of concordance as the results obtained with the GLM-based methods in 12 patients (7 concordant and 5 discordant). In one patient, the information theoretic analysis improved the delineation of the irritative zone compared with the GLM-based methods. Our findings suggest that an information theoretic analysis can provide clinically relevant information about the BOLD signal changes associated with the generation and propagation of interictal epileptic discharges. The concordance between the MI, GLM and FIR maps support the validity of the assumptions adopted in GLM-based analyses of interictal epileptic activity with EEG-fMRI in such a manner that they do not significantly constrain the localization of the epileptogenic zone. Copyright © 2012 Elsevier Inc. All rights reserved.

Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric MRI

NASA Astrophysics Data System (ADS)

Niaf, Emilie; Rouvière, Olivier; Mège-Lechevallier, Florence; Bratan, Flavie; Lartizien, Carole

2012-06-01

This study evaluated a computer-assisted diagnosis (CADx) system for determining a likelihood measure of prostate cancer presence in the peripheral zone (PZ) based on multiparametric magnetic resonance (MR) imaging, including T2-weighted, diffusion-weighted and dynamic contrast-enhanced MRI at 1.5 T. Based on a feature set derived from grey-level images, including first-order statistics, Haralick features, gradient features, semi-quantitative and quantitative (pharmacokinetic modelling) dynamic parameters, four kinds of classifiers were trained and compared : nonlinear support vector machine (SVM), linear discriminant analysis, k-nearest neighbours and naïve Bayes classifiers. A set of feature selection methods based on t-test, mutual information and minimum-redundancy-maximum-relevancy criteria were also compared. The aim was to discriminate between the relevant features as well as to create an efficient classifier using these features. The diagnostic performances of these different CADx schemes were evaluated based on a receiver operating characteristic (ROC) curve analysis. The evaluation database consisted of 30 sets of multiparametric MR images acquired from radical prostatectomy patients. Using histologic sections as the gold standard, both cancer and nonmalignant (but suspicious) tissues were annotated in consensus on all MR images by two radiologists, a histopathologist and a researcher. Benign tissue regions of interest (ROIs) were also delineated in the remaining prostate PZ. This resulted in a series of 42 cancer ROIs, 49 benign but suspicious ROIs and 124 nonsuspicious benign ROIs. From the outputs of all evaluated feature selection methods on the test bench, a restrictive set of about 15 highly informative features coming from all MR sequences was discriminated, thus confirming the validity of the multiparametric approach. Quantitative evaluation of the diagnostic performance yielded a maximal area under the ROC curve (AUC) of 0.89 (0.81-0.94) for the discrimination of the malignant versus nonmalignant tissues and 0.82 (0.73-0.90) for the discrimination of the malignant versus suspicious tissues when combining the t-test feature selection approach with a SVM classifier. A preliminary comparison showed that the optimal CADx scheme mimicked, in terms of AUC, the human experts in differentiating malignant from suspicious tissues, thus demonstrating its potential for assisting cancer identification in the PZ.

Long-range dismount activity classification: LODAC

NASA Astrophysics Data System (ADS)

Garagic, Denis; Peskoe, Jacob; Liu, Fang; Cuevas, Manuel; Freeman, Andrew M.; Rhodes, Bradley J.

2014-06-01

Continuous classification of dismount types (including gender, age, ethnicity) and their activities (such as walking, running) evolving over space and time is challenging. Limited sensor resolution (often exacerbated as a function of platform standoff distance) and clutter from shadows in dense target environments, unfavorable environmental conditions, and the normal properties of real data all contribute to the challenge. The unique and innovative aspect of our approach is a synthesis of multimodal signal processing with incremental non-parametric, hierarchical Bayesian machine learning methods to create a new kind of target classification architecture. This architecture is designed from the ground up to optimally exploit correlations among the multiple sensing modalities (multimodal data fusion) and rapidly and continuously learns (online self-tuning) patterns of distinct classes of dismounts given little a priori information. This increases classification performance in the presence of challenges posed by anti-access/area denial (A2/AD) sensing. To fuse multimodal features, Long-range Dismount Activity Classification (LODAC) develops a novel statistical information theoretic approach for multimodal data fusion that jointly models multimodal data (i.e., a probabilistic model for cross-modal signal generation) and discovers the critical cross-modal correlations by identifying components (features) with maximal mutual information (MI) which is efficiently estimated using non-parametric entropy models. LODAC develops a generic probabilistic pattern learning and classification framework based on a new class of hierarchical Bayesian learning algorithms for efficiently discovering recurring patterns (classes of dismounts) in multiple simultaneous time series (sensor modalities) at multiple levels of feature granularity.

Rényi squashed entanglement, discord, and relative entropy differences

NASA Astrophysics Data System (ADS)

Seshadreesan, Kaushik P.; Berta, Mario; Wilde, Mark M.

2015-10-01

The squashed entanglement quantifies the amount of entanglement in a bipartite quantum state, and it satisfies all of the axioms desired for an entanglement measure. The quantum discord is a measure of quantum correlations that are different from those due to entanglement. What these two measures have in common is that they are both based upon the conditional quantum mutual information. In Berta et al (2015 J. Math. Phys. 56 022205), we recently proposed Rényi generalizations of the conditional quantum mutual information of a tripartite state on ABC (with C being the conditioning system), which were shown to satisfy some properties that hold for the original quantity, such as non-negativity, duality, and monotonicity with respect to local operations on the system B (with it being left open to show that the Rényi quantity is monotone with respect to local operations on system A). Here we define a Rényi squashed entanglement and a Rényi quantum discord based on a Rényi conditional quantum mutual information and investigate these quantities in detail. Taking as a conjecture that the Rényi conditional quantum mutual information is monotone with respect to local operations on both systems A and B, we prove that the Rényi squashed entanglement and the Rényi quantum discord satisfy many of the properties of the respective original von Neumann entropy based quantities. In our prior work (Berta et al 2015 Phys. Rev. A 91 022333), we also detailed a procedure to obtain Rényi generalizations of any quantum information measure that is equal to a linear combination of von Neumann entropies with coefficients chosen from the set \\{-1,0,1\\}. Here, we extend this procedure to include differences of relative entropies. Using the extended procedure and a conjectured monotonicity of the Rényi generalizations in the Rényi parameter, we discuss potential remainder terms for well known inequalities such as monotonicity of the relative entropy, joint convexity of the relative entropy, and the Holevo bound.

Common Grounds for Modelling Mathematics in Educational Software

ERIC Educational Resources Information Center

Neuper, Walther

2010-01-01

Two kinds of software, CAS and DGS, are starting to work towards mutual integration. This paper envisages common grounds for such integration based on principles of computer theorem proving (CTP). Presently, the CTP community seems to lack awareness as to which of their products' features might serve mathematics education from high-school to…

ICT Support for Collaborative Learning--A Tale of Two Cities

ERIC Educational Resources Information Center

Consiglio, Teresa; van der Veer, Gerrit C.

2013-01-01

Based on experiences in teaching service design in a blended learning context, we developed an electronic learning environment (ELE) including features that turned out to be suitable for learners from different cultures. We used this ELE in Italy and in China. Students were guided through collaborative learning and mutual teaching. Students were…

Nonlinear pattern analysis of ventricular premature beats by mutual information

NASA Technical Reports Server (NTRS)

Osaka, M.; Saitoh, H.; Yokoshima, T.; Kishida, H.; Hayakawa, H.; Cohen, R. J.

1997-01-01

The frequency of ventricular premature beats (VPBs) has been related to the risk of mortality. However, little is known about the temporal pattern of occurrence of VPBs and its relationship to autonomic activity. Hence, we applied a general correlation measure, mutual information, to quantify how VPBs are generated over time. We also used mutual information to determine the correlation between VPB production and heart rate in order to evaluate effects of autonomic activity on VPB production. We examined twenty subjects with more than 3000 VPBs/day and simulated random time series of VPB occurrence. We found that mutual information values could be used to characterize quantitatively the temporal patterns of VPB generation. Our data suggest that VPB production is not random and VPBs generated with a higher value of mutual information may be more greatly affected by autonomic activity.

Equitability, mutual information, and the maximal information coefficient.

PubMed

Kinney, Justin B; Atwal, Gurinder S

2014-03-04

How should one quantify the strength of association between two random variables without bias for relationships of a specific form? Despite its conceptual simplicity, this notion of statistical "equitability" has yet to receive a definitive mathematical formalization. Here we argue that equitability is properly formalized by a self-consistency condition closely related to Data Processing Inequality. Mutual information, a fundamental quantity in information theory, is shown to satisfy this equitability criterion. These findings are at odds with the recent work of Reshef et al. [Reshef DN, et al. (2011) Science 334(6062):1518-1524], which proposed an alternative definition of equitability and introduced a new statistic, the "maximal information coefficient" (MIC), said to satisfy equitability in contradistinction to mutual information. These conclusions, however, were supported only with limited simulation evidence, not with mathematical arguments. Upon revisiting these claims, we prove that the mathematical definition of equitability proposed by Reshef et al. cannot be satisfied by any (nontrivial) dependence measure. We also identify artifacts in the reported simulation evidence. When these artifacts are removed, estimates of mutual information are found to be more equitable than estimates of MIC. Mutual information is also observed to have consistently higher statistical power than MIC. We conclude that estimating mutual information provides a natural (and often practical) way to equitably quantify statistical associations in large datasets.

Mutual information analysis and detection of interictal morphological differences in interictal epileptiform discharges of patients with partial epilepsies.

PubMed

Varma, N K; Kushwaha, R; Beydoun, A; Williams, W J; Drury, I

1997-10-01

The purpose of this paper is to compare the morphological features of interictal epileptiform discharges (IED) in patients with benign epilepsy of childhood with centrotemporal spikes to IED of those with symptomatic localization related epilepsies using information theory. Three patients from each clinical group were selected. Two-second epochs centered at the peak negativity of the sharp waves were analyzed from a referential montage during stage I sleep. The epochs from the two groups were compared using parametric and information theory analysis. Information analysis determined the likelihood of correctly identifying the clinical group based on the IED. Standard parametric, morphological and spectral analyses were also performed. We found no significant difference in the morphology of the sharp wave between the two groups. The after-going slow wave contained the greatest information that separated the two groups. This result was supported by morphological and spectral differences in the after-going slow wave. Greater distinguishing information is held in the after-going slow wave than the sharp wave for the identification of clinical groups. Information analysis may assist in differentiating clinical syndromes from EEG signals.

Buses of Cuernavaca—an agent-based model for universal random matrix behavior minimizing mutual information

NASA Astrophysics Data System (ADS)

Warchoł, Piotr

2018-06-01

The public transportation system of Cuernavaca, Mexico, exhibits random matrix theory statistics. In particular, the fluctuation of times between the arrival of buses on a given bus stop, follows the Wigner surmise for the Gaussian unitary ensemble. To model this, we propose an agent-based approach in which each bus driver tries to optimize his arrival time to the next stop with respect to an estimated arrival time of his predecessor. We choose a particular form of the associated utility function and recover the appropriate distribution in numerical experiments for a certain value of the only parameter of the model. We then investigate whether this value of the parameter is otherwise distinguished within an information theoretic approach and give numerical evidence that indeed it is associated with a minimum of averaged pairwise mutual information.

Mutual information against correlations in binary communication channels.

PubMed

Pregowska, Agnieszka; Szczepanski, Janusz; Wajnryb, Eligiusz

2015-05-19

Explaining how the brain processing is so fast remains an open problem (van Hemmen JL, Sejnowski T., 2004). Thus, the analysis of neural transmission (Shannon CE, Weaver W., 1963) processes basically focuses on searching for effective encoding and decoding schemes. According to the Shannon fundamental theorem, mutual information plays a crucial role in characterizing the efficiency of communication channels. It is well known that this efficiency is determined by the channel capacity that is already the maximal mutual information between input and output signals. On the other hand, intuitively speaking, when input and output signals are more correlated, the transmission should be more efficient. A natural question arises about the relation between mutual information and correlation. We analyze the relation between these quantities using the binary representation of signals, which is the most common approach taken in studying neuronal processes of the brain. We present binary communication channels for which mutual information and correlation coefficients behave differently both quantitatively and qualitatively. Despite this difference in behavior, we show that the noncorrelation of binary signals implies their independence, in contrast to the case for general types of signals. Our research shows that the mutual information cannot be replaced by sheer correlations. Our results indicate that neuronal encoding has more complicated nature which cannot be captured by straightforward correlations between input and output signals once the mutual information takes into account the structure and patterns of the signals.

Estimation and classification by sigmoids based on mutual information

NASA Technical Reports Server (NTRS)

Baram, Yoram

1994-01-01

An estimate of the probability density function of a random vector is obtained by maximizing the mutual information between the input and the output of a feedforward network of sigmoidal units with respect to the input weights. Classification problems can be solved by selecting the class associated with the maximal estimated density. Newton's s method, applied to an estimated density, yields a recursive maximum likelihood estimator, consisting of a single internal layer of sigmoids, for a random variable or a random sequence. Applications to the diamond classification and to the prediction of a sun-spot process are demonstrated.

A Method for Evaluating Tuning Functions of Single Neurons based on Mutual Information Maximization

NASA Astrophysics Data System (ADS)

Brostek, Lukas; Eggert, Thomas; Ono, Seiji; Mustari, Michael J.; Büttner, Ulrich; Glasauer, Stefan

2011-03-01

We introduce a novel approach for evaluation of neuronal tuning functions, which can be expressed by the conditional probability of observing a spike given any combination of independent variables. This probability can be estimated out of experimentally available data. By maximizing the mutual information between the probability distribution of the spike occurrence and that of the variables, the dependence of the spike on the input variables is maximized as well. We used this method to analyze the dependence of neuronal activity in cortical area MSTd on signals related to movement of the eye and retinal image movement.

LCD denoise and the vector mutual information method in the application of the gear fault diagnosis under different working conditions

NASA Astrophysics Data System (ADS)

Xiangfeng, Zhang; Hong, Jiang

2018-03-01

In this paper, the full vector LCD method is proposed to solve the misjudgment problem caused by the change of the working condition. First, the signal from different working condition is decomposed by LCD, to obtain the Intrinsic Scale Component (ISC)whose instantaneous frequency with physical significance. Then, calculate of the cross correlation coefficient between ISC and the original signal, signal denoising based on the principle of mutual information minimum. At last, calculate the sum of absolute Vector mutual information of the sample under different working condition and the denoised ISC as the characteristics to classify by use of Support vector machine (SVM). The wind turbines vibration platform gear box experiment proves that this method can identify fault characteristics under different working conditions. The advantage of this method is that it reduce dependence of man’s subjective experience, identify fault directly from the original data of vibration signal. It will has high engineering value.

76 FR 35084 - Mutual to Stock Conversion Application

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-15

... DEPARTMENT OF THE TREASURY Office of Thrift Supervision Mutual to Stock Conversion Application... invite comments on the following information collection. Title of Proposal: Mutual to Stock Conversion... and soundness of the proposed stock conversion. The purpose of the information collection is to...

Analysis of underlying causes of inter-expert disagreement in retinopathy of prematurity diagnosis. Application of machine learning principles.

PubMed

Ataer-Cansizoglu, E; Kalpathy-Cramer, J; You, S; Keck, K; Erdogmus, D; Chiang, M F

2015-01-01

Inter-expert variability in image-based clinical diagnosis has been demonstrated in many diseases including retinopathy of prematurity (ROP), which is a disease affecting low birth weight infants and is a major cause of childhood blindness. In order to better understand the underlying causes of variability among experts, we propose a method to quantify the variability of expert decisions and analyze the relationship between expert diagnoses and features computed from the images. Identification of these features is relevant for development of computer-based decision support systems and educational systems in ROP, and these methods may be applicable to other diseases where inter-expert variability is observed. The experiments were carried out on a dataset of 34 retinal images, each with diagnoses provided independently by 22 experts. Analysis was performed using concepts of Mutual Information (MI) and Kernel Density Estimation. A large set of structural features (a total of 66) were extracted from retinal images. Feature selection was utilized to identify the most important features that correlated to actual clinical decisions by the 22 study experts. The best three features for each observer were selected by an exhaustive search on all possible feature subsets and considering joint MI as a relevance criterion. We also compared our results with the results of Cohen's Kappa [36] as an inter-rater reliability measure. The results demonstrate that a group of observers (17 among 22) decide consistently with each other. Mean and second central moment of arteriolar tortuosity is among the reasons of disagreement between this group and the rest of the observers, meaning that the group of experts consider amount of tortuosity as well as the variation of tortuosity in the image. Given a set of image-based features, the proposed analysis method can identify critical image-based features that lead to expert agreement and disagreement in diagnosis of ROP. Although tree-based features and various statistics such as central moment are not popular in the literature, our results suggest that they are important for diagnosis.

Parallel interactive retrieval of item and associative information from event memory.

PubMed

Cox, Gregory E; Criss, Amy H

2017-09-01

Memory contains information about individual events (items) and combinations of events (associations). Despite the fundamental importance of this distinction, it remains unclear exactly how these two kinds of information are stored and whether different processes are used to retrieve them. We use both model-independent qualitative properties of response dynamics and quantitative modeling of individuals to address these issues. Item and associative information are not independent and they are retrieved concurrently via interacting processes. During retrieval, matching item and associative information mutually facilitate one another to yield an amplified holistic signal. Modeling of individuals suggests that this kind of facilitation between item and associative retrieval is a ubiquitous feature of human memory. Copyright © 2017 Elsevier Inc. All rights reserved.

Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer.

PubMed

Whitney, Jon; Corredor, German; Janowczyk, Andrew; Ganesan, Shridar; Doyle, Scott; Tomaszewski, John; Feldman, Michael; Gilmore, Hannah; Madabhushi, Anant

2018-05-30

Gene-expression companion diagnostic tests, such as the Oncotype DX test, assess the risk of early stage Estrogen receptor (ER) positive (+) breast cancers, and guide clinicians in the decision of whether or not to use chemotherapy. However, these tests are typically expensive, time consuming, and tissue-destructive. In this paper, we evaluate the ability of computer-extracted nuclear morphology features from routine hematoxylin and eosin (H&E) stained images of 178 early stage ER+ breast cancer patients to predict corresponding risk categories derived using the Oncotype DX test. A total of 216 features corresponding to the nuclear shape and architecture categories from each of the pathologic images were extracted and four feature selection schemes: Ranksum, Principal Component Analysis with Variable Importance on Projection (PCA-VIP), Maximum-Relevance, Minimum Redundancy Mutual Information Difference (MRMR MID), and Maximum-Relevance, Minimum Redundancy - Mutual Information Quotient (MRMR MIQ), were employed to identify the most discriminating features. These features were employed to train 4 machine learning classifiers: Random Forest, Neural Network, Support Vector Machine, and Linear Discriminant Analysis, via 3-fold cross validation. The four sets of risk categories, and the top Area Under the receiver operating characteristic Curve (AUC) machine classifier performances were: 1) Low ODx and Low mBR grade vs. High ODx and High mBR grade (Low-Low vs. High-High) (AUC = 0.83), 2) Low ODx vs. High ODx (AUC = 0.72), 3) Low ODx vs. Intermediate and High ODx (AUC = 0.58), and 4) Low and Intermediate ODx vs. High ODx (AUC = 0.65). Trained models were tested independent validation set of 53 cases which comprised of Low and High ODx risk, and demonstrated per-patient accuracies ranging from 75 to 86%. Our results suggest that computerized image analysis of digitized H&E pathology images of early stage ER+ breast cancer might be able predict the corresponding Oncotype DX risk categories.

77 FR 11601 - Proposed Collection; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2012-02-27

..., Washington, DC 20549-0213. Extension: Mutual Fund Interactive Data; SEC File No. 270-580; OMB Control No... information for submitting risk/ return summary information in interactive data format is ``Mutual Fund.... The purpose of the Mutual Fund Interactive Data requirements is to make risk/return summary...

Integrating dimension reduction and out-of-sample extension in automated classification of ex vivo human patellar cartilage on phase contrast X-ray computed tomography.

PubMed

Nagarajan, Mahesh B; Coan, Paola; Huber, Markus B; Diemoz, Paul C; Wismüller, Axel

2015-01-01

Phase contrast X-ray computed tomography (PCI-CT) has been demonstrated as a novel imaging technique that can visualize human cartilage with high spatial resolution and soft tissue contrast. Different textural approaches have been previously investigated for characterizing chondrocyte organization on PCI-CT to enable classification of healthy and osteoarthritic cartilage. However, the large size of feature sets extracted in such studies motivates an investigation into algorithmic feature reduction for computing efficient feature representations without compromising their discriminatory power. For this purpose, geometrical feature sets derived from the scaling index method (SIM) were extracted from 1392 volumes of interest (VOI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. The extracted feature sets were subject to linear and non-linear dimension reduction techniques as well as feature selection based on evaluation of mutual information criteria. The reduced feature set was subsequently used in a machine learning task with support vector regression to classify VOIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver-operating characteristic (ROC) curve (AUC). Our results show that the classification performance achieved by 9-D SIM-derived geometric feature sets (AUC: 0.96 ± 0.02) can be maintained with 2-D representations computed from both dimension reduction and feature selection (AUC values as high as 0.97 ± 0.02). Thus, such feature reduction techniques can offer a high degree of compaction to large feature sets extracted from PCI-CT images while maintaining their ability to characterize the underlying chondrocyte patterns.

The Impact of Different Sources of Fluctuations on Mutual Information in Biochemical Networks

PubMed Central

Chevalier, Michael; Venturelli, Ophelia; El-Samad, Hana

2015-01-01

Stochastic fluctuations in signaling and gene expression limit the ability of cells to sense the state of their environment, transfer this information along cellular pathways, and respond to it with high precision. Mutual information is now often used to quantify the fidelity with which information is transmitted along a cellular pathway. Mutual information calculations from experimental data have mostly generated low values, suggesting that cells might have relatively low signal transmission fidelity. In this work, we demonstrate that mutual information calculations might be artificially lowered by cell-to-cell variability in both initial conditions and slowly fluctuating global factors across the population. We carry out our analysis computationally using a simple signaling pathway and demonstrate that in the presence of slow global fluctuations, every cell might have its own high information transmission capacity but that population averaging underestimates this value. We also construct a simple synthetic transcriptional network and demonstrate using experimental measurements coupled to computational modeling that its operation is dominated by slow global variability, and hence that its mutual information is underestimated by a population averaged calculation. PMID:26484538

An incompressible fluid flow model with mutual information for MR image registration

NASA Astrophysics Data System (ADS)

Tsai, Leo; Chang, Herng-Hua

2013-03-01

Image registration is one of the fundamental and essential tasks within image processing. It is a process of determining the correspondence between structures in two images, which are called the template image and the reference image, respectively. The challenge of registration is to find an optimal geometric transformation between corresponding image data. This paper develops a new MR image registration algorithm that uses a closed incompressible viscous fluid model associated with mutual information. In our approach, we treat the image pixels as the fluid elements of a viscous fluid flow governed by the nonlinear Navier-Stokes partial differential equation (PDE). We replace the pressure term with the body force mainly used to guide the transformation with a weighting coefficient, which is expressed by the mutual information between the template and reference images. To solve this modified Navier-Stokes PDE, we adopted the fast numerical techniques proposed by Seibold1. The registration process of updating the body force, the velocity and deformation fields is repeated until the mutual information weight reaches a prescribed threshold. We applied our approach to the BrainWeb and real MR images. As consistent with the theory of the proposed fluid model, we found that our method accurately transformed the template images into the reference images based on the intensity flow. Experimental results indicate that our method is of potential in a wide variety of medical image registration applications.

Multimodality Inferring of Human Cognitive States Based on Integration of Neuro-Fuzzy Network and Information Fusion Techniques

NASA Astrophysics Data System (ADS)

Yang, G.; Lin, Y.; Bhattacharya, P.

2007-12-01

To achieve an effective and safe operation on the machine system where the human interacts with the machine mutually, there is a need for the machine to understand the human state, especially cognitive state, when the human's operation task demands an intensive cognitive activity. Due to a well-known fact with the human being, a highly uncertain cognitive state and behavior as well as expressions or cues, the recent trend to infer the human state is to consider multimodality features of the human operator. In this paper, we present a method for multimodality inferring of human cognitive states by integrating neuro-fuzzy network and information fusion techniques. To demonstrate the effectiveness of this method, we take the driver fatigue detection as an example. The proposed method has, in particular, the following new features. First, human expressions are classified into four categories: (i) casual or contextual feature, (ii) contact feature, (iii) contactless feature, and (iv) performance feature. Second, the fuzzy neural network technique, in particular Takagi-Sugeno-Kang (TSK) model, is employed to cope with uncertain behaviors. Third, the sensor fusion technique, in particular ordered weighted aggregation (OWA), is integrated with the TSK model in such a way that cues are taken as inputs to the TSK model, and then the outputs of the TSK are fused by the OWA which gives outputs corresponding to particular cognitive states under interest (e.g., fatigue). We call this method TSK-OWA. Validation of the TSK-OWA, performed in the Northeastern University vehicle drive simulator, has shown that the proposed method is promising to be a general tool for human cognitive state inferring and a special tool for the driver fatigue detection.

Evaluation of Early and Prolonged Effects of Acute Neurotoxicity and Neuroprotection Using Novel Functional Imaging Techniques

DTIC Science & Technology

2004-08-01

Mutual Information (NMI) voxel match algorithm of the ANALYZE software package and cubic spline interpolation (Brownell et al. 2003, Appendix). 2...nuclear inclusion and cell survival. Materials and Methods Animals: Male transgenic R6/2 mice, which depict many clinical features of juvenile HD were...purchased from the Jackson Laboratories (Bar Harbor, ME). The mice were housed 3-4 per cage under standard conditions with free access to food and water

77 FR 26051 - Submission for OMB Review; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-02

..., Washington, DC 20549-0213. Extension: Mutual Fund Interactive Data; SEC File No. 270-580; OMB Control No... information in interactive data format is ``Mutual Fund Interactive Data.'' This collection of information... disclosure requirements for funds and other issuers. The purpose of the Mutual Fund Interactive Data...

Information trimming: Sufficient statistics, mutual information, and predictability from effective channel states

NASA Astrophysics Data System (ADS)

James, Ryan G.; Mahoney, John R.; Crutchfield, James P.

2017-06-01

One of the most basic characterizations of the relationship between two random variables, X and Y , is the value of their mutual information. Unfortunately, calculating it analytically and estimating it empirically are often stymied by the extremely large dimension of the variables. One might hope to replace such a high-dimensional variable by a smaller one that preserves its relationship with the other. It is well known that either X (or Y ) can be replaced by its minimal sufficient statistic about Y (or X ) while preserving the mutual information. While intuitively reasonable, it is not obvious or straightforward that both variables can be replaced simultaneously. We demonstrate that this is in fact possible: the information X 's minimal sufficient statistic preserves about Y is exactly the information that Y 's minimal sufficient statistic preserves about X . We call this procedure information trimming. As an important corollary, we consider the case where one variable is a stochastic process' past and the other its future. In this case, the mutual information is the channel transmission rate between the channel's effective states. That is, the past-future mutual information (the excess entropy) is the amount of information about the future that can be predicted using the past. Translating our result about minimal sufficient statistics, this is equivalent to the mutual information between the forward- and reverse-time causal states of computational mechanics. We close by discussing multivariate extensions to this use of minimal sufficient statistics.

Filter Bank Regularized Common Spatial Pattern Ensemble for Small Sample Motor Imagery Classification.

PubMed

Park, Sang-Hoon; Lee, David; Lee, Sang-Goog

2018-02-01

For the last few years, many feature extraction methods have been proposed based on biological signals. Among these, the brain signals have the advantage that they can be obtained, even by people with peripheral nervous system damage. Motor imagery electroencephalograms (EEG) are inexpensive to measure, offer a high temporal resolution, and are intuitive. Therefore, these have received a significant amount of attention in various fields, including signal processing, cognitive science, and medicine. The common spatial pattern (CSP) algorithm is a useful method for feature extraction from motor imagery EEG. However, performance degradation occurs in a small-sample setting (SSS), because the CSP depends on sample-based covariance. Since the active frequency range is different for each subject, it is also inconvenient to set the frequency range to be different every time. In this paper, we propose the feature extraction method based on a filter bank to solve these problems. The proposed method consists of five steps. First, motor imagery EEG is divided by a using filter bank. Second, the regularized CSP (R-CSP) is applied to the divided EEG. Third, we select the features according to mutual information based on the individual feature algorithm. Fourth, parameter sets are selected for the ensemble. Finally, we classify using ensemble based on features. The brain-computer interface competition III data set IVa is used to evaluate the performance of the proposed method. The proposed method improves the mean classification accuracy by 12.34%, 11.57%, 9%, 4.95%, and 4.47% compared with CSP, SR-CSP, R-CSP, filter bank CSP (FBCSP), and SR-FBCSP. Compared with the filter bank R-CSP ( , ), which is a parameter selection version of the proposed method, the classification accuracy is improved by 3.49%. In particular, the proposed method shows a large improvement in performance in the SSS.

Strain features and condition assessment of orthotropic steel deck cable-supported bridges subjected to vehicle loads by using dense FBG strain sensors

NASA Astrophysics Data System (ADS)

Wei, Shiyin; Zhang, Zhaohui; Li, Shunlong; Li, Hui

2017-10-01

Strain is a direct indicator of structural safety. Therefore, strain sensors have been used in most structural health monitoring systems for bridges. However, until now, the investigation of strain response has been insufficient. This paper conducts a comprehensive study of the strain features of the U ribs and transverse diaphragm on an orthotropic steel deck and proposes a statistical paradigm for crack detection based on the features of vehicle-induced strain response by using the densely distributed optic fibre Bragg grating (FBG) strain sensors. The local feature of strain under vehicle load is highlighted, which enables the use of measurement data to determine the vehicle loading event and to make a decision regarding the health status of a girder near the strain sensors via technical elimination of the load information. Time-frequency analysis shows that the strain contains three features: the long-term trend item, the short-term trend item, and the instantaneous vehicle-induced item (IVII). The IVII is the wheel-induced strain with a remarkable local feature, and the measured wheel-induced strain is only influenced by the vehicle near the FBG sensor, while other vehicles slightly farther away have no effect on the wheel-induced strain. This causes the local strain series, among the FBG strain sensors in the same transverse locations of different cross-sections, to present similarities in shape to some extent and presents a time delay in successive order along the driving direction. Therefore, the strain series induced by an identical vehicle can be easily tracked and compared by extracting the amplitude and calculating the mutual ratio to eliminate vehicle loading information, leaving the girder information alone. The statistical paradigm for crack detection is finally proposed, and the detection accuracy is then validated by using dense FBG strain sensors on a long-span suspension bridge in China.

Signal processing in local neuronal circuits based on activity-dependent noise and competition

NASA Astrophysics Data System (ADS)

Volman, Vladislav; Levine, Herbert

2009-09-01

We study the characteristics of weak signal detection by a recurrent neuronal network with plastic synaptic coupling. It is shown that in the presence of an asynchronous component in synaptic transmission, the network acquires selectivity with respect to the frequency of weak periodic stimuli. For nonperiodic frequency-modulated stimuli, the response is quantified by the mutual information between input (signal) and output (network's activity) and is optimized by synaptic depression. Introducing correlations in signal structure resulted in the decrease in input-output mutual information. Our results suggest that in neural systems with plastic connectivity, information is not merely carried passively by the signal; rather, the information content of the signal itself might determine the mode of its processing by a local neuronal circuit.

MIDER: network inference with mutual information distance and entropy reduction.

PubMed

Villaverde, Alejandro F; Ross, John; Morán, Federico; Banga, Julio R

2014-01-01

The prediction of links among variables from a given dataset is a task referred to as network inference or reverse engineering. It is an open problem in bioinformatics and systems biology, as well as in other areas of science. Information theory, which uses concepts such as mutual information, provides a rigorous framework for addressing it. While a number of information-theoretic methods are already available, most of them focus on a particular type of problem, introducing assumptions that limit their generality. Furthermore, many of these methods lack a publicly available implementation. Here we present MIDER, a method for inferring network structures with information theoretic concepts. It consists of two steps: first, it provides a representation of the network in which the distance among nodes indicates their statistical closeness. Second, it refines the prediction of the existing links to distinguish between direct and indirect interactions and to assign directionality. The method accepts as input time-series data related to some quantitative features of the network nodes (such as e.g. concentrations, if the nodes are chemical species). It takes into account time delays between variables, and allows choosing among several definitions and normalizations of mutual information. It is general purpose: it may be applied to any type of network, cellular or otherwise. A Matlab implementation including source code and data is freely available (http://www.iim.csic.es/~gingproc/mider.html). The performance of MIDER has been evaluated on seven different benchmark problems that cover the main types of cellular networks, including metabolic, gene regulatory, and signaling. Comparisons with state of the art information-theoretic methods have demonstrated the competitive performance of MIDER, as well as its versatility. Its use does not demand any a priori knowledge from the user; the default settings and the adaptive nature of the method provide good results for a wide range of problems without requiring tuning.

Mutual information-based LPI optimisation for radar network

NASA Astrophysics Data System (ADS)

Shi, Chenguang; Zhou, Jianjiang; Wang, Fei; Chen, Jun

2015-07-01

Radar network can offer significant performance improvement for target detection and information extraction employing spatial diversity. For a fixed number of radars, the achievable mutual information (MI) for estimating the target parameters may extend beyond a predefined threshold with full power transmission. In this paper, an effective low probability of intercept (LPI) optimisation algorithm is presented to improve LPI performance for radar network. Based on radar network system model, we first provide Schleher intercept factor for radar network as an optimisation metric for LPI performance. Then, a novel LPI optimisation algorithm is presented, where for a predefined MI threshold, Schleher intercept factor for radar network is minimised by optimising the transmission power allocation among radars in the network such that the enhanced LPI performance for radar network can be achieved. The genetic algorithm based on nonlinear programming (GA-NP) is employed to solve the resulting nonconvex and nonlinear optimisation problem. Some simulations demonstrate that the proposed algorithm is valuable and effective to improve the LPI performance for radar network.

Cross contrast multi-channel image registration using image synthesis for MR brain images.

PubMed

Chen, Min; Carass, Aaron; Jog, Amod; Lee, Junghoon; Roy, Snehashis; Prince, Jerry L

2017-02-01

Multi-modal deformable registration is important for many medical image analysis tasks such as atlas alignment, image fusion, and distortion correction. Whereas a conventional method would register images with different modalities using modality independent features or information theoretic metrics such as mutual information, this paper presents a new framework that addresses the problem using a two-channel registration algorithm capable of using mono-modal similarity measures such as sum of squared differences or cross-correlation. To make it possible to use these same-modality measures, image synthesis is used to create proxy images for the opposite modality as well as intensity-normalized images from each of the two available images. The new deformable registration framework was evaluated by performing intra-subject deformation recovery, intra-subject boundary alignment, and inter-subject label transfer experiments using multi-contrast magnetic resonance brain imaging data. Three different multi-channel registration algorithms were evaluated, revealing that the framework is robust to the multi-channel deformable registration algorithm that is used. With a single exception, all results demonstrated improvements when compared against single channel registrations using the same algorithm with mutual information. Copyright © 2016 Elsevier B.V. All rights reserved.

Multiparty quantum mutual information: An alternative definition

NASA Astrophysics Data System (ADS)

Kumar, Asutosh

2017-07-01

Mutual information is the reciprocal information that is common to or shared by two or more parties. Quantum mutual information for bipartite quantum systems is non-negative, and bears the interpretation of total correlation between the two subsystems. This may, however, no longer be true for three or more party quantum systems. In this paper, we propose an alternative definition of multipartite information, taking into account the shared information between two and more parties. It is non-negative, observes monotonicity under partial trace as well as completely positive maps, and equals the multipartite information measure in literature for pure states. We then define multiparty quantum discord, and give some examples. Interestingly, we observe that quantum discord increases when a measurement is performed on a large number of subsystems. Consequently, the symmetric quantum discord, which involves a measurement on all parties, reveals the maximal quantumness. This raises a question on the interpretation of measured mutual information as a classical correlation.

Energy prediction using spatiotemporal pattern networks

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

Jiang, Zhanhong; Liu, Chao; Akintayo, Adedotun

This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dynamical filtering, the STPN framework is used to capture not only the individual system characteristics but also the pair-wise causal dependencies among different sub-systems. To quantify causal dependencies, a mutual information based metric is presented and an energy prediction approach is subsequently proposed based on the STPN framework. To validate the proposed scheme, two case studies are presented, one involving wind turbine power prediction (supply side energy) using the Western Wind Integration data set generated bymore » the National Renewable Energy Laboratory (NREL) for identifying spatiotemporal characteristics, and the other, residential electric energy disaggregation (demand side energy) using the Building America 2010 data set from NREL for exploring temporal features. In the energy disaggregation context, convex programming techniques beyond the STPN framework are developed and applied to achieve improved disaggregation performance.« less

What Makes Informal Mentorship in the Medical Realm Effective?

ERIC Educational Resources Information Center

Mohtady, Heba A.; Könings, Karen D.; van Merriënboer, Jeroen J. G.

2016-01-01

Informal mentoring is based on a natural match between a junior individual and a senior one who share mutual interests. It usually aids in the professional and personal development of both parties involved. We reviewed the literature regarding factors that make informal mentoring effective within the medical realm, by searching a major academic…

Multimodal registration via spatial-context mutual information.

PubMed

Yi, Zhao; Soatto, Stefano

2011-01-01

We propose a method to efficiently compute mutual information between high-dimensional distributions of image patches. This in turn is used to perform accurate registration of images captured under different modalities, while exploiting their local structure otherwise missed in traditional mutual information definition. We achieve this by organizing the space of image patches into orbits under the action of Euclidean transformations of the image plane, and estimating the modes of a distribution in such an orbit space using affinity propagation. This way, large collections of patches that are equivalent up to translations and rotations are mapped to the same representative, or "dictionary element". We then show analytically that computing mutual information for a joint distribution in this space reduces to computing mutual information between the (scalar) label maps, and between the transformations mapping each patch into its closest dictionary element. We show that our approach improves registration performance compared with the state of the art in multimodal registration, using both synthetic and real images with quantitative ground truth.

Mutual Information between Discrete Variables with Many Categories using Recursive Adaptive Partitioning

PubMed Central

Seok, Junhee; Seon Kang, Yeong

2015-01-01

Mutual information, a general measure of the relatedness between two random variables, has been actively used in the analysis of biomedical data. The mutual information between two discrete variables is conventionally calculated by their joint probabilities estimated from the frequency of observed samples in each combination of variable categories. However, this conventional approach is no longer efficient for discrete variables with many categories, which can be easily found in large-scale biomedical data such as diagnosis codes, drug compounds, and genotypes. Here, we propose a method to provide stable estimations for the mutual information between discrete variables with many categories. Simulation studies showed that the proposed method reduced the estimation errors by 45 folds and improved the correlation coefficients with true values by 99 folds, compared with the conventional calculation of mutual information. The proposed method was also demonstrated through a case study for diagnostic data in electronic health records. This method is expected to be useful in the analysis of various biomedical data with discrete variables. PMID:26046461

>From naive to sophisticated behavior in multiagents-based financial market models

NASA Astrophysics Data System (ADS)

Mansilla, R.

2000-09-01

The behavior of physical complexity and mutual information function of the outcome of a model of heterogeneous, inductive rational agents inspired by the El Farol Bar problem and the Minority Game is studied. The first magnitude is a measure rooted in the Kolmogorov-Chaitin theory and the second a measure related to Shannon's information entropy. Extensive computer simulations were done, as a result of which, is proposed an ansatz for physical complexity of the type C(l)=lα and the dependence of the exponent α from the parameters of the model is established. The accuracy of our results and the relationship with the behavior of mutual information function as a measure of time correlation of agents choice are discussed.

Quantifying Complexity in Quantum Phase Transitions via Mutual Information Complex Networks

NASA Astrophysics Data System (ADS)

Valdez, Marc Andrew; Jaschke, Daniel; Vargas, David L.; Carr, Lincoln D.

2017-12-01

We quantify the emergent complexity of quantum states near quantum critical points on regular 1D lattices, via complex network measures based on quantum mutual information as the adjacency matrix, in direct analogy to quantifying the complexity of electroencephalogram or functional magnetic resonance imaging measurements of the brain. Using matrix product state methods, we show that network density, clustering, disparity, and Pearson's correlation obtain the critical point for both quantum Ising and Bose-Hubbard models to a high degree of accuracy in finite-size scaling for three classes of quantum phase transitions, Z2, mean field superfluid to Mott insulator, and a Berzinskii-Kosterlitz-Thouless crossover.

Information Theory to Probe Intrapartum Fetal Heart Rate Dynamics

NASA Astrophysics Data System (ADS)

Granero-Belinchon, Carlos; Roux, Stéphane; Abry, Patrice; Doret, Muriel; Garnier, Nicolas

2017-11-01

Intrapartum fetal heart rate (FHR) monitoring constitutes a reference tool in clinical practice to assess the baby health status and to detect fetal acidosis. It is usually analyzed by visual inspection grounded on FIGO criteria. Characterization of Intrapartum fetal heart rate temporal dynamics remains a challenging task and continuously receives academic research efforts. Complexity measures, often implemented with tools referred to as \\emph{Approximate Entropy} (ApEn) or \\emph{Sample Entropy} (SampEn), have regularly been reported as significant features for intrapartum FHR analysis. We explore how Information Theory, and especially {\\em auto mutual information} (AMI), is connected to ApEn and SampEn and can be used to probe FHR dynamics. Applied to a large (1404 subjects) and documented database of FHR data, collected in a French academic hospital, it is shown that i) auto mutual information outperforms ApEn and SampEn for acidosis detection in the first stage of labor and continues to yield the best performance in the second stage; ii) Shannon entropy increases as labor progresses, and is always much larger in the second stage;iii) babies suffering from fetal acidosis additionally show more structured temporal dynamics than healthy ones and that this progressive structuration can be used for early acidosis detection.

Automated Registration of Multimodal Optic Disc Images: Clinical Assessment of Alignment Accuracy.

PubMed

Ng, Wai Siene; Legg, Phil; Avadhanam, Venkat; Aye, Kyaw; Evans, Steffan H P; North, Rachel V; Marshall, Andrew D; Rosin, Paul; Morgan, James E

2016-04-01

To determine the accuracy of automated alignment algorithms for the registration of optic disc images obtained by 2 different modalities: fundus photography and scanning laser tomography. Images obtained with the Heidelberg Retina Tomograph II and paired photographic optic disc images of 135 eyes were analyzed. Three state-of-the-art automated registration techniques Regional Mutual Information, rigid Feature Neighbourhood Mutual Information (FNMI), and nonrigid FNMI (NRFNMI) were used to align these image pairs. Alignment of each composite picture was assessed on a 5-point grading scale: "Fail" (no alignment of vessels with no vessel contact), "Weak" (vessels have slight contact), "Good" (vessels with <50% contact), "Very Good" (vessels with >50% contact), and "Excellent" (complete alignment). Custom software generated an image mosaic in which the modalities were interleaved as a series of alternate 5×5-pixel blocks. These were graded independently by 3 clinically experienced observers. A total of 810 image pairs were assessed. All 3 registration techniques achieved a score of "Good" or better in >95% of the image sets. NRFNMI had the highest percentage of "Excellent" (mean: 99.6%; range, 95.2% to 99.6%), followed by Regional Mutual Information (mean: 81.6%; range, 86.3% to 78.5%) and FNMI (mean: 73.1%; range, 85.2% to 54.4%). Automated registration of optic disc images by different modalities is a feasible option for clinical application. All 3 methods provided useful levels of alignment, but the NRFNMI technique consistently outperformed the others and is recommended as a practical approach to the automated registration of multimodal disc images.

Statistical Feature Extraction for Artifact Removal from Concurrent fMRI-EEG Recordings

PubMed Central

Liu, Zhongming; de Zwart, Jacco A.; van Gelderen, Peter; Kuo, Li-Wei; Duyn, Jeff H.

2011-01-01

We propose a set of algorithms for sequentially removing artifacts related to MRI gradient switching and cardiac pulsations from electroencephalography (EEG) data recorded during functional magnetic resonance imaging (fMRI). Special emphases are directed upon the use of statistical metrics and methods for the extraction and selection of features that characterize gradient and pulse artifacts. To remove gradient artifacts, we use a channel-wise filtering based on singular value decomposition (SVD). To remove pulse artifacts, we first decompose data into temporally independent components and then select a compact cluster of components that possess sustained high mutual information with the electrocardiogram (ECG). After the removal of these components, the time courses of remaining components are filtered by SVD to remove the temporal patterns phase-locked to the cardiac markers derived from the ECG. The filtered component time courses are then inversely transformed into multi-channel EEG time series free of pulse artifacts. Evaluation based on a large set of simultaneous EEG-fMRI data obtained during a variety of behavioral tasks, sensory stimulations and resting conditions showed excellent data quality and robust performance attainable by the proposed methods. These algorithms have been implemented as a Matlab-based toolbox made freely available for public access and research use. PMID:22036675

Statistical feature extraction for artifact removal from concurrent fMRI-EEG recordings.

PubMed

Liu, Zhongming; de Zwart, Jacco A; van Gelderen, Peter; Kuo, Li-Wei; Duyn, Jeff H

2012-02-01

We propose a set of algorithms for sequentially removing artifacts related to MRI gradient switching and cardiac pulsations from electroencephalography (EEG) data recorded during functional magnetic resonance imaging (fMRI). Special emphasis is directed upon the use of statistical metrics and methods for the extraction and selection of features that characterize gradient and pulse artifacts. To remove gradient artifacts, we use channel-wise filtering based on singular value decomposition (SVD). To remove pulse artifacts, we first decompose data into temporally independent components and then select a compact cluster of components that possess sustained high mutual information with the electrocardiogram (ECG). After the removal of these components, the time courses of remaining components are filtered by SVD to remove the temporal patterns phase-locked to the cardiac timing markers derived from the ECG. The filtered component time courses are then inversely transformed into multi-channel EEG time series free of pulse artifacts. Evaluation based on a large set of simultaneous EEG-fMRI data obtained during a variety of behavioral tasks, sensory stimulations and resting conditions showed excellent data quality and robust performance attainable with the proposed methods. These algorithms have been implemented as a Matlab-based toolbox made freely available for public access and research use. Published by Elsevier Inc.

75 FR 33319 - Agency Information Collection Activities: New Information Collection; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-11

... Information Collection; ICE Mutual Agreement Between Government and Employers (IMAGE). The Department of... technological collection techniques or other forms of information technology, e.g., permitting electronic... information collection. (2) Title of the Form/Collection: ICE Mutual Agreement between Government and...

Evaluation of an Automatic Registration-Based Algorithm for Direct Measurement of Volume Change in Tumors

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

Sarkar, Saradwata; Johnson, Timothy D.; Ma, Bing

2012-07-01

Purpose: Assuming that early tumor volume change is a biomarker for response to therapy, accurate quantification of early volume changes could aid in adapting an individual patient's therapy and lead to shorter clinical trials. We investigated an image registration-based approach for tumor volume change quantification that may more reliably detect smaller changes that occur in shorter intervals than can be detected by existing algorithms. Methods and Materials: Variance and bias of the registration-based approach were evaluated using retrospective, in vivo, very-short-interval diffusion magnetic resonance imaging scans where true zero tumor volume change is unequivocally known and synthetic data, respectively. Themore » interval scans were nonlinearly registered using two similarity measures: mutual information (MI) and normalized cross-correlation (NCC). Results: The 95% confidence interval of the percentage volume change error was (-8.93% to 10.49%) for MI-based and (-7.69%, 8.83%) for NCC-based registrations. Linear mixed-effects models demonstrated that error in measuring volume change increased with increase in tumor volume and decreased with the increase in the tumor's normalized mutual information, even when NCC was the similarity measure being optimized during registration. The 95% confidence interval of the relative volume change error for the synthetic examinations with known changes over {+-}80% of reference tumor volume was (-3.02% to 3.86%). Statistically significant bias was not demonstrated. Conclusion: A low-noise, low-bias tumor volume change measurement algorithm using nonlinear registration is described. Errors in change measurement were a function of tumor volume and the normalized mutual information content of the tumor.« less

[Study of the Consumers' preference on the universal health coverage development strategy through health mutual in Ziguinchor Region, Southwest of Senegal].

PubMed

Sagna, O; Seck, I; Dia, A T; Sall, F L; Diouf, S; Mendy, J; Ka, O; Kassoka, B

2016-08-01

In Senegal, the informal and rural sector that accounts for over 80% of the population is covered only up to 7% by a health insurance system. That is why, for the implementation of development strategy of the universal health coverage (UHC) through mutual health insurance providers, the Government of Senegal has focused on this sector. The objective of this study was to assess the consumer's preference on the UHC development strategies through mutual health insurance providers. This was a qualitative and exploratory study based on a literature review, and indepth interview with the heads of households. It was also based on focus groups of people with and without health mutual membership, and the Expert Committee meetings. The results showed that the most critical attributes in the decision-making of consumers to join the health mutual in Ziguinchor were the membership units; the content of the benefit package, the payment modalities of the premium, the premium amount, the availability of transportation, the co-payment level, convention arrangement with health facilities, and health mutual governance. For a successful implementation of the UHC development strategy through health mutual organizations, policymakers should explore the possibility of introducing the modality of payment in kind, the revision of the co-payment amount, and the promotion of equity through the introduction of a differentiated premium contribution by income. They should also establish a crossborder strategy with The Gambia and Guinea-Bissau to improve health care access to people living in the borders. The promotion of innovative funding and risk equalization between health insurance schemes is also recommended. In areas where the microfinance institutions are well organized and structured their substitution to health mutuals should be an option the decision-makers have to explore.

A new mutually reinforcing network node and link ranking algorithm

PubMed Central

Wang, Zhenghua; Dueñas-Osorio, Leonardo; Padgett, Jamie E.

2015-01-01

This study proposes a novel Normalized Wide network Ranking algorithm (NWRank) that has the advantage of ranking nodes and links of a network simultaneously. This algorithm combines the mutual reinforcement feature of Hypertext Induced Topic Selection (HITS) and the weight normalization feature of PageRank. Relative weights are assigned to links based on the degree of the adjacent neighbors and the Betweenness Centrality instead of assigning the same weight to every link as assumed in PageRank. Numerical experiment results show that NWRank performs consistently better than HITS, PageRank, eigenvector centrality, and edge betweenness from the perspective of network connectivity and approximate network flow, which is also supported by comparisons with the expensive N-1 benchmark removal criteria based on network efficiency. Furthermore, it can avoid some problems, such as the Tightly Knit Community effect, which exists in HITS. NWRank provides a new inexpensive way to rank nodes and links of a network, which has practical applications, particularly to prioritize resource allocation for upgrade of hierarchical and distributed networks, as well as to support decision making in the design of networks, where node and link importance depend on a balance of local and global integrity. PMID:26492958

Empirical and Theoretical Aspects of Generation and Transfer of Information in a Neuromagnetic Source Network

PubMed Central

Vakorin, Vasily A.; Mišić, Bratislav; Krakovska, Olga; McIntosh, Anthony Randal

2011-01-01

Variability in source dynamics across the sources in an activated network may be indicative of how the information is processed within a network. Information-theoretic tools allow one not only to characterize local brain dynamics but also to describe interactions between distributed brain activity. This study follows such a framework and explores the relations between signal variability and asymmetry in mutual interdependencies in a data-driven pipeline of non-linear analysis of neuromagnetic sources reconstructed from human magnetoencephalographic (MEG) data collected as a reaction to a face recognition task. Asymmetry in non-linear interdependencies in the network was analyzed using transfer entropy, which quantifies predictive information transfer between the sources. Variability of the source activity was estimated using multi-scale entropy, quantifying the rate of which information is generated. The empirical results are supported by an analysis of synthetic data based on the dynamics of coupled systems with time delay in coupling. We found that the amount of information transferred from one source to another was correlated with the difference in variability between the dynamics of these two sources, with the directionality of net information transfer depending on the time scale at which the sample entropy was computed. The results based on synthetic data suggest that both time delay and strength of coupling can contribute to the relations between variability of brain signals and information transfer between them. Our findings support the previous attempts to characterize functional organization of the activated brain, based on a combination of non-linear dynamics and temporal features of brain connectivity, such as time delay. PMID:22131968

Rényi generalizations of the conditional quantum mutual information

NASA Astrophysics Data System (ADS)

Berta, Mario; Seshadreesan, Kaushik P.; Wilde, Mark M.

2015-02-01

The conditional quantum mutual information I(A; B|C) of a tripartite state ρABC is an information quantity which lies at the center of many problems in quantum information theory. Three of its main properties are that it is non-negative for any tripartite state, that it decreases under local operations applied to systems A and B, and that it obeys the duality relation I(A; B|C) = I(A; B|D) for a four-party pure state on systems ABCD. The conditional mutual information also underlies the squashed entanglement, an entanglement measure that satisfies all of the axioms desired for an entanglement measure. As such, it has been an open question to find Rényi generalizations of the conditional mutual information, that would allow for a deeper understanding of the original quantity and find applications beyond the traditional memoryless setting of quantum information theory. The present paper addresses this question, by defining different α-Rényi generalizations Iα(A; B|C) of the conditional mutual information, some of which we can prove converge to the conditional mutual information in the limit α → 1. Furthermore, we prove that many of these generalizations satisfy non-negativity, duality, and monotonicity with respect to local operations on one of the systems A or B (with it being left as an open question to prove that monotonicity holds with respect to local operations on both systems). The quantities defined here should find applications in quantum information theory and perhaps even in other areas of physics, but we leave this for future work. We also state a conjecture regarding the monotonicity of the Rényi conditional mutual informations defined here with respect to the Rényi parameter α. We prove that this conjecture is true in some special cases and when α is in a neighborhood of one.

Rényi generalizations of the conditional quantum mutual information

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

Berta, Mario; Seshadreesan, Kaushik P.; Wilde, Mark M.

2015-02-15

The conditional quantum mutual information I(A; B|C) of a tripartite state ρ{sub ABC} is an information quantity which lies at the center of many problems in quantum information theory. Three of its main properties are that it is non-negative for any tripartite state, that it decreases under local operations applied to systems A and B, and that it obeys the duality relation I(A; B|C) = I(A; B|D) for a four-party pure state on systems ABCD. The conditional mutual information also underlies the squashed entanglement, an entanglement measure that satisfies all of the axioms desired for an entanglement measure. As such,more » it has been an open question to find Rényi generalizations of the conditional mutual information, that would allow for a deeper understanding of the original quantity and find applications beyond the traditional memoryless setting of quantum information theory. The present paper addresses this question, by defining different α-Rényi generalizations I{sub α}(A; B|C) of the conditional mutual information, some of which we can prove converge to the conditional mutual information in the limit α → 1. Furthermore, we prove that many of these generalizations satisfy non-negativity, duality, and monotonicity with respect to local operations on one of the systems A or B (with it being left as an open question to prove that monotonicity holds with respect to local operations on both systems). The quantities defined here should find applications in quantum information theory and perhaps even in other areas of physics, but we leave this for future work. We also state a conjecture regarding the monotonicity of the Rényi conditional mutual informations defined here with respect to the Rényi parameter α. We prove that this conjecture is true in some special cases and when α is in a neighborhood of one.« less

MIDER: Network Inference with Mutual Information Distance and Entropy Reduction

PubMed Central

Villaverde, Alejandro F.; Ross, John; Morán, Federico; Banga, Julio R.

2014-01-01

The prediction of links among variables from a given dataset is a task referred to as network inference or reverse engineering. It is an open problem in bioinformatics and systems biology, as well as in other areas of science. Information theory, which uses concepts such as mutual information, provides a rigorous framework for addressing it. While a number of information-theoretic methods are already available, most of them focus on a particular type of problem, introducing assumptions that limit their generality. Furthermore, many of these methods lack a publicly available implementation. Here we present MIDER, a method for inferring network structures with information theoretic concepts. It consists of two steps: first, it provides a representation of the network in which the distance among nodes indicates their statistical closeness. Second, it refines the prediction of the existing links to distinguish between direct and indirect interactions and to assign directionality. The method accepts as input time-series data related to some quantitative features of the network nodes (such as e.g. concentrations, if the nodes are chemical species). It takes into account time delays between variables, and allows choosing among several definitions and normalizations of mutual information. It is general purpose: it may be applied to any type of network, cellular or otherwise. A Matlab implementation including source code and data is freely available (http://www.iim.csic.es/~gingproc/mider.html). The performance of MIDER has been evaluated on seven different benchmark problems that cover the main types of cellular networks, including metabolic, gene regulatory, and signaling. Comparisons with state of the art information–theoretic methods have demonstrated the competitive performance of MIDER, as well as its versatility. Its use does not demand any a priori knowledge from the user; the default settings and the adaptive nature of the method provide good results for a wide range of problems without requiring tuning. PMID:24806471

Mobile Healthcare for Automatic Driving Sleep-Onset Detection Using Wavelet-Based EEG and Respiration Signals

PubMed Central

Lee, Boon-Giin; Lee, Boon-Leng; Chung, Wan-Young

2014-01-01

Driving drowsiness is a major cause of traffic accidents worldwide and has drawn the attention of researchers in recent decades. This paper presents an application for in-vehicle non-intrusive mobile-device-based automatic detection of driver sleep-onset in real time. The proposed application classifies the driving mental fatigue condition by analyzing the electroencephalogram (EEG) and respiration signals of a driver in the time and frequency domains. Our concept is heavily reliant on mobile technology, particularly remote physiological monitoring using Bluetooth. Respiratory events are gathered, and eight-channel EEG readings are captured from the frontal, central, and parietal (Fpz-Cz, Pz-Oz) regions. EEGs are preprocessed with a Butterworth bandpass filter, and features are subsequently extracted from the filtered EEG signals by employing the wavelet-packet-transform (WPT) method to categorize the signals into four frequency bands: α, β, θ, and δ. A mutual information (MI) technique selects the most descriptive features for further classification. The reduction in the number of prominent features improves the sleep-onset classification speed in the support vector machine (SVM) and results in a high sleep-onset recognition rate. Test results reveal that the combined use of the EEG and respiration signals results in 98.6% recognition accuracy. Our proposed application explores the possibility of processing long-term multi-channel signals. PMID:25264954

Principal Components of Recurrence Quantification Analysis of EMG

DTIC Science & Technology

2001-10-25

Springer, 1981, pp. 366-381. 4. M. Fraser and H. L. Swinney, “ Independent coordinates for strange attractors from mutual information ,” Phys. Rev. A...autocorrelation function of s(n), although it has also been argued that the first local minimum of the auto mutual information function is more appropriate [4...recordings from a given subject. T was taken as the lag corresponding to the first minimum of the auto mutual information function, calculated as

Cyber Mutual Assistance Workshop Report

DTIC Science & Technology

2018-02-01

Information Technology, Nuclear Reactors, Materials/Waste, Defense Industrial Base, Critical Manufacturing, Food/ Agriculture Government Facilities and...Manufacturing, Food/ Agriculture Government Facilities and Chemical, Commercial Facilities [DHS 2017c]. Distributed Energy Resources (DER) are

An automated landmark-based elastic registration technique for large deformation recovery from 4-D CT lung images

NASA Astrophysics Data System (ADS)

Negahdar, Mohammadreza; Zacarias, Albert; Milam, Rebecca A.; Dunlap, Neal; Woo, Shiao Y.; Amini, Amir A.

2012-03-01

The treatment plan evaluation for lung cancer patients involves pre-treatment and post-treatment volume CT imaging of the lung. However, treatment of the tumor volume lung results in structural changes to the lung during the course of treatment. In order to register the pre-treatment volume to post-treatment volume, there is a need to find robust and homologous features which are not affected by the radiation treatment along with a smooth deformation field. Since airways are well-distributed in the entire lung, in this paper, we propose use of airway tree bifurcations for registration of the pre-treatment volume to the post-treatment volume. A dedicated and automated algorithm has been developed that finds corresponding airway bifurcations in both images. To derive the 3-D deformation field, a B-spline transformation model guided by mutual information similarity metric was used to guarantee the smoothness of the transformation while combining global information from bifurcation points. Therefore, the approach combines both global statistical intensity information with local image feature information. Since during normal breathing, the lung undergoes large nonlinear deformations, it is expected that the proposed method would also be applicable to large deformation registration between maximum inhale and maximum exhale images in the same subject. The method has been evaluated by registering 3-D CT volumes at maximum exhale data to all the other temporal volumes in the POPI-model data.

Study of the correlation parameters of the surface structure of disordered semiconductors by the two-dimensional DFA and average mutual information methods

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

Alpatov, A. V.; Vikhrov, S. P.; Rybina, N. V., E-mail: pgnv@mail.ru

The processes of self-organization of the surface structure of hydrogenated amorphous silicon are studied by the methods of fluctuation analysis and average mutual information on the basis of atomic-force-microscopy images of the surface. It is found that all of the structures can be characterized by a correlation vector and represented as a superposition of harmonic components and noise. It is shown that, under variations in the technological parameters of the production of a-Si:H films, the correlation properties of their structure vary as well. As the substrate temperature is increased, the formation of structural irregularities becomes less efficient; in this case,more » the length of the correlation vector and the degree of structural ordering increase. It is shown that the procedure based on the method of fluctuation analysis in combination with the method of average mutual information provides a means for studying the self-organization processes in any structures on different length scales.« less

A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD).

PubMed

Mumtaz, Wajid; Ali, Syed Saad Azhar; Yasin, Mohd Azhar Mohd; Malik, Aamir Saeed

2018-02-01

Major depressive disorder (MDD), a debilitating mental illness, could cause functional disabilities and could become a social problem. An accurate and early diagnosis for depression could become challenging. This paper proposed a machine learning framework involving EEG-derived synchronization likelihood (SL) features as input data for automatic diagnosis of MDD. It was hypothesized that EEG-based SL features could discriminate MDD patients and healthy controls with an acceptable accuracy better than measures such as interhemispheric coherence and mutual information. In this work, classification models such as support vector machine (SVM), logistic regression (LR) and Naïve Bayesian (NB) were employed to model relationship between the EEG features and the study groups (MDD patient and healthy controls) and ultimately achieved discrimination of study participants. The results indicated that the classification rates were better than chance. More specifically, the study resulted into SVM classification accuracy = 98%, sensitivity = 99.9%, specificity = 95% and f-measure = 0.97; LR classification accuracy = 91.7%, sensitivity = 86.66%, specificity = 96.6% and f-measure = 0.90; NB classification accuracy = 93.6%, sensitivity = 100%, specificity = 87.9% and f-measure = 0.95. In conclusion, SL could be a promising method for diagnosing depression. The findings could be generalized to develop a robust CAD-based tool that may help for clinical purposes.

Event-based criteria in GT-STAF information indices: theory, exploratory diversity analysis and QSPR applications.

PubMed

Barigye, S J; Marrero-Ponce, Y; Martínez López, Y; Martínez Santiago, O; Torrens, F; García Domenech, R; Galvez, J

2013-01-01

Versatile event-based approaches for the definition of novel information theory-based indices (IFIs) are presented. An event in this context is the criterion followed in the "discovery" of molecular substructures, which in turn serve as basis for the construction of the generalized incidence and relations frequency matrices, Q and F, respectively. From the resultant F, Shannon's, mutual, conditional and joint entropy-based IFIs are computed. In previous reports, an event named connected subgraphs was presented. The present study is an extension of this notion, in which we introduce other events, namely: terminal paths, vertex path incidence, quantum subgraphs, walks of length k, Sach's subgraphs, MACCs, E-state and substructure fingerprints and, finally, Ghose and Crippen atom-types for hydrophobicity and refractivity. Moreover, we define magnitude-based IFIs, introducing the use of the magnitude criterion in the definition of mutual, conditional and joint entropy-based IFIs. We also discuss the use of information-theoretic parameters as a measure of the dissimilarity of codified structural information of molecules. Finally, a comparison of the statistics for QSPR models obtained with the proposed IFIs and DRAGON's molecular descriptors for two physicochemical properties log P and log K of 34 derivatives of 2-furylethylenes demonstrates similar to better predictive ability than the latter.

Mutually orthogonal Latin squares from the inner products of vectors in mutually unbiased bases

NASA Astrophysics Data System (ADS)

Hall, Joanne L.; Rao, Asha

2010-04-01

Mutually unbiased bases (MUBs) are important in quantum information theory. While constructions of complete sets of d + 1 MUBs in {\\bb C}^d are known when d is a prime power, it is unknown if such complete sets exist in non-prime power dimensions. It has been conjectured that complete sets of MUBs only exist in {\\bb C}^d if a maximal set of mutually orthogonal Latin squares (MOLS) of side length d also exists. There are several constructions (Roy and Scott 2007 J. Math. Phys. 48 072110; Paterek, Dakić and Brukner 2009 Phys. Rev. A 79 012109) of complete sets of MUBs from specific types of MOLS, which use Galois fields to construct the vectors of the MUBs. In this paper, two known constructions of MUBs (Alltop 1980 IEEE Trans. Inf. Theory 26 350-354 Wootters and Fields 1989 Ann. Phys. 191 363-381), both of which use polynomials over a Galois field, are used to construct complete sets of MOLS in the odd prime case. The MOLS come from the inner products of pairs of vectors in the MUBs.

An Efficient and Adaptive Mutual Authentication Framework for Heterogeneous Wireless Sensor Network-Based Applications

PubMed Central

Kumar, Pardeep; Ylianttila, Mika; Gurtov, Andrei; Lee, Sang-Gon; Lee, Hoon-Jae

2014-01-01

Robust security is highly coveted in real wireless sensor network (WSN) applications since wireless sensors' sense critical data from the application environment. This article presents an efficient and adaptive mutual authentication framework that suits real heterogeneous WSN-based applications (such as smart homes, industrial environments, smart grids, and healthcare monitoring). The proposed framework offers: (i) key initialization; (ii) secure network (cluster) formation (i.e., mutual authentication and dynamic key establishment); (iii) key revocation; and (iv) new node addition into the network. The correctness of the proposed scheme is formally verified. An extensive analysis shows the proposed scheme coupled with message confidentiality, mutual authentication and dynamic session key establishment, node privacy, and message freshness. Moreover, the preliminary study also reveals the proposed framework is secure against popular types of attacks, such as impersonation attacks, man-in-the-middle attacks, replay attacks, and information-leakage attacks. As a result, we believe the proposed framework achieves efficiency at reasonable computation and communication costs and it can be a safeguard to real heterogeneous WSN applications. PMID:24521942

An efficient and adaptive mutual authentication framework for heterogeneous wireless sensor network-based applications.

PubMed

Kumar, Pardeep; Ylianttila, Mika; Gurtov, Andrei; Lee, Sang-Gon; Lee, Hoon-Jae

2014-02-11

Robust security is highly coveted in real wireless sensor network (WSN) applications since wireless sensors' sense critical data from the application environment. This article presents an efficient and adaptive mutual authentication framework that suits real heterogeneous WSN-based applications (such as smart homes, industrial environments, smart grids, and healthcare monitoring). The proposed framework offers: (i) key initialization; (ii) secure network (cluster) formation (i.e., mutual authentication and dynamic key establishment); (iii) key revocation; and (iv) new node addition into the network. The correctness of the proposed scheme is formally verified. An extensive analysis shows the proposed scheme coupled with message confidentiality, mutual authentication and dynamic session key establishment, node privacy, and message freshness. Moreover, the preliminary study also reveals the proposed framework is secure against popular types of attacks, such as impersonation attacks, man-in-the-middle attacks, replay attacks, and information-leakage attacks. As a result, we believe the proposed framework achieves efficiency at reasonable computation and communication costs and it can be a safeguard to real heterogeneous WSN applications.

Robots Learn to Recognize Individuals from Imitative Encounters with People and Avatars

NASA Astrophysics Data System (ADS)

Boucenna, Sofiane; Cohen, David; Meltzoff, Andrew N.; Gaussier, Philippe; Chetouani, Mohamed

2016-02-01

Prior to language, human infants are prolific imitators. Developmental science grounds infant imitation in the neural coding of actions, and highlights the use of imitation for learning from and about people. Here, we used computational modeling and a robot implementation to explore the functional value of action imitation. We report 3 experiments using a mutual imitation task between robots, adults, typically developing children, and children with Autism Spectrum Disorder. We show that a particular learning architecture - specifically one combining artificial neural nets for (i) extraction of visual features, (ii) the robot’s motor internal state, (iii) posture recognition, and (iv) novelty detection - is able to learn from an interactive experience involving mutual imitation. This mutual imitation experience allowed the robot to recognize the interactive agent in a subsequent encounter. These experiments using robots as tools for modeling human cognitive development, based on developmental theory, confirm the promise of developmental robotics. Additionally, findings illustrate how person recognition may emerge through imitative experience, intercorporeal mapping, and statistical learning.

Robots Learn to Recognize Individuals from Imitative Encounters with People and Avatars

PubMed Central

Boucenna, Sofiane; Cohen, David; Meltzoff, Andrew N.; Gaussier, Philippe; Chetouani, Mohamed

2016-01-01

Prior to language, human infants are prolific imitators. Developmental science grounds infant imitation in the neural coding of actions, and highlights the use of imitation for learning from and about people. Here, we used computational modeling and a robot implementation to explore the functional value of action imitation. We report 3 experiments using a mutual imitation task between robots, adults, typically developing children, and children with Autism Spectrum Disorder. We show that a particular learning architecture - specifically one combining artificial neural nets for (i) extraction of visual features, (ii) the robot’s motor internal state, (iii) posture recognition, and (iv) novelty detection - is able to learn from an interactive experience involving mutual imitation. This mutual imitation experience allowed the robot to recognize the interactive agent in a subsequent encounter. These experiments using robots as tools for modeling human cognitive development, based on developmental theory, confirm the promise of developmental robotics. Additionally, findings illustrate how person recognition may emerge through imitative experience, intercorporeal mapping, and statistical learning. PMID:26844862

31 CFR 1024.520 - Special information sharing procedures to deter money laundering and terrorist activity for...

Code of Federal Regulations, 2011 CFR

2011-07-01

... to deter money laundering and terrorist activity for mutual funds. 1024.520 Section 1024.520 Money... ENFORCEMENT NETWORK, DEPARTMENT OF THE TREASURY RULES FOR MUTUAL FUNDS Special Information Sharing Procedures... deter money laundering and terrorist activity for mutual funds. (a) Refer to § 1010.520 of this chapter...

Assessment of resting-state blood flow through anterior cerebral arteries using trans-cranial doppler recordings.

PubMed

Huang, Hanrui; Sejdić, Ervin

2013-12-01

Trans-cranial Doppler (TCD) recordings are used to monitor cerebral blood flow in the main cerebral arteries. The resting state is usually characterized by the mean velocity or the maximum Doppler shift frequency (an envelope signal) by insonating the middle cerebral arteries. In this study, we characterized cerebral blood flow in the anterior cerebral arteries. We analyzed both envelope signals and raw signals obtained from bilateral insonation. We recruited 20 healthy patients and conducted the data acquisition for 15 min. Features were extracted from the time domain, the frequency domain and the time-frequency domain. The results indicate that a gender-based statistical difference exists in the frequency and time-frequency domains. However, no handedness effect was found. In the time domain, information-theoretic features indicated that mutual dependence is higher in raw signals than in envelope signals. Finally, we concluded that insonation of the anterior cerebral arteries serves as a complement to middle cerebral artery studies. Additionally, investigation of the raw signals provided us with additional information that is not otherwise available from envelope signals. Use of direct trans-cranial Doppler raw data is therefore validated as a valuable method for characterizing the resting state. Copyright © 2013 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Detection of Delamination in Concrete Bridge Decks Using Mfcc of Acoustic Impact Signals

NASA Astrophysics Data System (ADS)

Zhang, G.; Harichandran, R. S.; Ramuhalli, P.

2010-02-01

Delamination of the concrete cover is a commonly observed damage in concrete bridge decks. The delamination is typically initiated by corrosion of the upper reinforcing bars and promoted by freeze-thaw cycling and traffic loading. The detection of delamination is important for bridge maintenance and acoustic non-destructive evaluation (NDE) is widely used due to its low cost, speed, and easy implementation. In traditional acoustic approaches, the inspector sounds the surface of the deck by impacting it with a hammer or bar, or by dragging a chain, and assesses delamination by the "hollowness" of the sound. The detection of the delamination is subjective and requires extensive training. To improve performance, this paper proposes an objective method for delamination detection. In this method, mel-frequency cepstral coefficients (MFCC) of the signal are extracted. Some MFCC are then selected as features for detection purposes using a mutual information criterion. Finally, the selected features are used to train a classifier which is subsequently used for detection. In this work, a simple quadratic Bayesian classifier is used. Different numbers of features are used to compare the performance of the detection method. The results show that the performance first increases with the number of features, but then decreases after an optimal value. The optimal number of features based on the recorded signals is four, and the mean error rate is only 3.3% when four features are used. Therefore, the proposed algorithm has sufficient accuracy to be used in field detection.

Discovering the intelligence in molecular biology.

PubMed

Uberbacher, E

1995-12-01

The Third International Conference on Intelligent Systems in Molecular Biology was truly an outstanding event. Computational methods in molecular biology have reached a new level of maturity and utility, resulting in many high-impact applications. The success of this meeting bodes well for the rapid and continuing development of computational methods, intelligent systems and information-based approaches for the biosciences. The basic technology, originally most often applied to 'feasibility' problems, is now dealing effectively with the most difficult real-world problems. Significant progress has been made in understanding protein-structure information, structural classification, and how functional information and the relevant features of active-site geometry can be gleaned from structures by automated computational approaches. The value and limits of homology-based methods, and the ability to classify proteins by structure in the absence of homology, have reached a new level of sophistication. New methods for covariation analysis in the folding of large structures such as RNAs have shown remarkably good results, indicating the long-term potential to understand very complicated molecules and multimolecular complexes using computational means. Novel methods, such as HMMs, context-free grammars and the uses of mutual information theory, have taken center stage as highly valuable tools in our quest to represent and characterize biological information. A focus on creative uses of intelligent systems technologies and the trend toward biological application will undoubtedly continue and grow at the 1996 ISMB meeting in St Louis.

Integrating Dimension Reduction and Out-of-Sample Extension in Automated Classification of Ex Vivo Human Patellar Cartilage on Phase Contrast X-Ray Computed Tomography

PubMed Central

Nagarajan, Mahesh B.; Coan, Paola; Huber, Markus B.; Diemoz, Paul C.; Wismüller, Axel

2015-01-01

Phase contrast X-ray computed tomography (PCI-CT) has been demonstrated as a novel imaging technique that can visualize human cartilage with high spatial resolution and soft tissue contrast. Different textural approaches have been previously investigated for characterizing chondrocyte organization on PCI-CT to enable classification of healthy and osteoarthritic cartilage. However, the large size of feature sets extracted in such studies motivates an investigation into algorithmic feature reduction for computing efficient feature representations without compromising their discriminatory power. For this purpose, geometrical feature sets derived from the scaling index method (SIM) were extracted from 1392 volumes of interest (VOI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. The extracted feature sets were subject to linear and non-linear dimension reduction techniques as well as feature selection based on evaluation of mutual information criteria. The reduced feature set was subsequently used in a machine learning task with support vector regression to classify VOIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver-operating characteristic (ROC) curve (AUC). Our results show that the classification performance achieved by 9-D SIM-derived geometric feature sets (AUC: 0.96 ± 0.02) can be maintained with 2-D representations computed from both dimension reduction and feature selection (AUC values as high as 0.97 ± 0.02). Thus, such feature reduction techniques can offer a high degree of compaction to large feature sets extracted from PCI-CT images while maintaining their ability to characterize the underlying chondrocyte patterns. PMID:25710875

Determine Neuronal Tuning Curves by Exploring Optimum Firing Rate Distribution for Information Efficiency

PubMed Central

Han, Fang; Wang, Zhijie; Fan, Hong

2017-01-01

This paper proposed a new method to determine the neuronal tuning curves for maximum information efficiency by computing the optimum firing rate distribution. Firstly, we proposed a general definition for the information efficiency, which is relevant to mutual information and neuronal energy consumption. The energy consumption is composed of two parts: neuronal basic energy consumption and neuronal spike emission energy consumption. A parameter to model the relative importance of energy consumption is introduced in the definition of the information efficiency. Then, we designed a combination of exponential functions to describe the optimum firing rate distribution based on the analysis of the dependency of the mutual information and the energy consumption on the shape of the functions of the firing rate distributions. Furthermore, we developed a rapid algorithm to search the parameter values of the optimum firing rate distribution function. Finally, we found with the rapid algorithm that a combination of two different exponential functions with two free parameters can describe the optimum firing rate distribution accurately. We also found that if the energy consumption is relatively unimportant (important) compared to the mutual information or the neuronal basic energy consumption is relatively large (small), the curve of the optimum firing rate distribution will be relatively flat (steep), and the corresponding optimum tuning curve exhibits a form of sigmoid if the stimuli distribution is normal. PMID:28270760

Advanced image fusion algorithms for Gamma Knife treatment planning. Evaluation and proposal for clinical use.

PubMed

Apostolou, N; Papazoglou, Th; Koutsouris, D

2006-01-01

Image fusion is a process of combining information from multiple sensors. It is a useful tool implemented in the treatment planning programme of Gamma Knife Radiosurgery. In this paper we evaluate advanced image fusion algorithms for Matlab platform and head images. We develop nine level grayscale image fusion methods: average, principal component analysis (PCA), discrete wavelet transform (DWT) and Laplacian, filter - subtract - decimate (FSD), contrast, gradient, morphological pyramid and a shift invariant discrete wavelet transform (SIDWT) method in Matlab platform. We test these methods qualitatively and quantitatively. The quantitative criteria we use are the Root Mean Square Error (RMSE), the Mutual Information (MI), the Standard Deviation (STD), the Entropy (H), the Difference Entropy (DH) and the Cross Entropy (CEN). The qualitative are: natural appearance, brilliance contrast, presence of complementary features and enhancement of common features. Finally we make clinically useful suggestions.

Information Seeking and Avoidance Behavior in School Library Distance Learning

ERIC Educational Resources Information Center

Du, Yunfei

2010-01-01

Library science students in school librarianship were surveyed to determine their information seeking and avoidance behaviors in Web-based online environments. Two coping styles were identified among students. Barriers to student online collaboration, such as individual preferences, concerns on efficiency, and lack of mutual trust, were observed.…

Term Dependence: A Basis for Luhn and Zipf Models.

ERIC Educational Resources Information Center

Losee, Robert M.

2001-01-01

Discusses relationships between the frequency-based characteristics of neighboring terms in natural language and the rank or frequency of the terms. Topics include information theory measures, including expected mutual information measure (EMIM); entropy and rank; Luhn's model of term aboutness; Zipf's law; and implications for indexing and…

Coastal Geographic Structures in Coastal-Marine Environmental Management

NASA Astrophysics Data System (ADS)

Baklanov, P. Ya.; Ganzei, K. S.; Ermoshin, V. V.

2018-01-01

It has been proposed to distinguish the coastal geographic structures consisting of a spatial combination of three interconnected and mutually conditioned parts (coastal-territorial, coastal, coastal-marine), which are interlinked with each other by the cumulative effect of real-energy flows. Distinguishing specific resource features of the coastal structures, by which they play a connecting role in the complex coastalmarine management, has been considered. The main integral resource feature of the coastal structures is their connecting functions, which form transitional parts mutually connecting the coastal-territorial and coastalmarine environmental management.

Quantum Darwinism for mixed-state environment

NASA Astrophysics Data System (ADS)

Quan, Haitao; Zwolak, Michael; Zurek, Wojciech

2009-03-01

We exam quantum darwinism when a system is in the presence of a mixed environment, and we find a general relation between the mutual information for the mixed-state environment and the change of the entropy of the fraction of the environment. We then look at a particular solvable model, and we numerically exam the time evolution of the ``mutual information" for large environment. Finally we discuss about the exact expressions for all entropies and the mutual information at special time.

Entanglement entropy and mutual information production rates in acoustic black holes.

PubMed

Giovanazzi, Stefano

2011-01-07

A method to investigate acoustic Hawking radiation is proposed, where entanglement entropy and mutual information are measured from the fluctuations of the number of particles. The rate of entropy radiated per one-dimensional (1D) channel is given by S=κ/12, where κ is the sound acceleration on the sonic horizon. This entropy production is accompanied by a corresponding formation of mutual information to ensure the overall conservation of information. The predictions are confirmed using an ab initio analytical approach in transonic flows of 1D degenerate ideal Fermi fluids.

Visual words for lip-reading

NASA Astrophysics Data System (ADS)

Hassanat, Ahmad B. A.; Jassim, Sabah

2010-04-01

In this paper, the automatic lip reading problem is investigated, and an innovative approach to providing solutions to this problem has been proposed. This new VSR approach is dependent on the signature of the word itself, which is obtained from a hybrid feature extraction method dependent on geometric, appearance, and image transform features. The proposed VSR approach is termed "visual words". The visual words approach consists of two main parts, 1) Feature extraction/selection, and 2) Visual speech feature recognition. After localizing face and lips, several visual features for the lips where extracted. Such as the height and width of the mouth, mutual information and the quality measurement between the DWT of the current ROI and the DWT of the previous ROI, the ratio of vertical to horizontal features taken from DWT of ROI, The ratio of vertical edges to horizontal edges of ROI, the appearance of the tongue and the appearance of teeth. Each spoken word is represented by 8 signals, one of each feature. Those signals maintain the dynamic of the spoken word, which contains a good portion of information. The system is then trained on these features using the KNN and DTW. This approach has been evaluated using a large database for different people, and large experiment sets. The evaluation has proved the visual words efficiency, and shown that the VSR is a speaker dependent problem.

Stratiform/convective rain delineation for TRMM microwave imager

NASA Astrophysics Data System (ADS)

Islam, Tanvir; Srivastava, Prashant K.; Dai, Qiang; Gupta, Manika; Wan Jaafar, Wan Zurina

2015-10-01

This article investigates the potential for using machine learning algorithms to delineate stratiform/convective (S/C) rain regimes for passive microwave imager taking calibrated brightness temperatures as only spectral parameters. The algorithms have been implemented for the Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI), and calibrated as well as validated taking the Precipitation Radar (PR) S/C information as the target class variables. Two different algorithms are particularly explored for the delineation. The first one is metaheuristic adaptive boosting algorithm that includes the real, gentle, and modest versions of the AdaBoost. The second one is the classical linear discriminant analysis that includes the Fisher's and penalized versions of the linear discriminant analysis. Furthermore, prior to the development of the delineation algorithms, a feature selection analysis has been conducted for a total of 85 features, which contains the combinations of brightness temperatures from 10 GHz to 85 GHz and some derived indexes, such as scattering index, polarization corrected temperature, and polarization difference with the help of mutual information aided minimal redundancy maximal relevance criterion (mRMR). It has been found that the polarization corrected temperature at 85 GHz and the features derived from the "addition" operator associated with the 85 GHz channels have good statistical dependency to the S/C target class variables. Further, it has been shown how the mRMR feature selection technique helps to reduce the number of features without deteriorating the results when applying through the machine learning algorithms. The proposed scheme is able to delineate the S/C rain regimes with reasonable accuracy. Based on the statistical validation experience from the validation period, the Matthews correlation coefficients are in the range of 0.60-0.70. Since, the proposed method does not rely on any a priori information, this makes it very suitable for other microwave sensors having similar channels to the TMI. The method could possibly benefit the constellation sensors in the Global Precipitation Measurement (GPM) mission era.

Mutually unbiased product bases for multiple qudits

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

McNulty, Daniel; Pammer, Bogdan; Weigert, Stefan

We investigate the interplay between mutual unbiasedness and product bases for multiple qudits of possibly different dimensions. A product state of such a system is shown to be mutually unbiased to a product basis only if each of its factors is mutually unbiased to all the states which occur in the corresponding factors of the product basis. This result implies both a tight limit on the number of mutually unbiased product bases which the system can support and a complete classification of mutually unbiased product bases for multiple qubits or qutrits. In addition, only maximally entangled states can be mutuallymore » unbiased to a maximal set of mutually unbiased product bases.« less

PREMER: a Tool to Infer Biological Networks.

PubMed

Villaverde, Alejandro F; Becker, Kolja; Banga, Julio R

2017-10-04

Inferring the structure of unknown cellular networks is a main challenge in computational biology. Data-driven approaches based on information theory can determine the existence of interactions among network nodes automatically. However, the elucidation of certain features - such as distinguishing between direct and indirect interactions or determining the direction of a causal link - requires estimating information-theoretic quantities in a multidimensional space. This can be a computationally demanding task, which acts as a bottleneck for the application of elaborate algorithms to large-scale network inference problems. The computational cost of such calculations can be alleviated by the use of compiled programs and parallelization. To this end we have developed PREMER (Parallel Reverse Engineering with Mutual information & Entropy Reduction), a software toolbox that can run in parallel and sequential environments. It uses information theoretic criteria to recover network topology and determine the strength and causality of interactions, and allows incorporating prior knowledge, imputing missing data, and correcting outliers. PREMER is a free, open source software tool that does not require any commercial software. Its core algorithms are programmed in FORTRAN 90 and implement OpenMP directives. It has user interfaces in Python and MATLAB/Octave, and runs on Windows, Linux and OSX (https://sites.google.com/site/premertoolbox/).

EEG-based driver fatigue detection using hybrid deep generic model.

PubMed

Phyo Phyo San; Sai Ho Ling; Rifai Chai; Tran, Yvonne; Craig, Ashley; Hung Nguyen

2016-08-01

Classification of electroencephalography (EEG)-based application is one of the important process for biomedical engineering. Driver fatigue is a major case of traffic accidents worldwide and considered as a significant problem in recent decades. In this paper, a hybrid deep generic model (DGM)-based support vector machine is proposed for accurate detection of driver fatigue. Traditionally, a probabilistic DGM with deep architecture is quite good at learning invariant features, but it is not always optimal for classification due to its trainable parameters are in the middle layer. Alternatively, Support Vector Machine (SVM) itself is unable to learn complicated invariance, but produces good decision surface when applied to well-behaved features. Consolidating unsupervised high-level feature extraction techniques, DGM and SVM classification makes the integrated framework stronger and enhance mutually in feature extraction and classification. The experimental results showed that the proposed DBN-based driver fatigue monitoring system achieves better testing accuracy of 73.29 % with 91.10 % sensitivity and 55.48 % specificity. In short, the proposed hybrid DGM-based SVM is an effective method for the detection of driver fatigue in EEG.

The use of information theory for the evaluation of biomarkers of aging and physiological age.

PubMed

Blokh, David; Stambler, Ilia

2017-04-01

The present work explores the application of information theoretical measures, such as entropy and normalized mutual information, for research of biomarkers of aging. The use of information theory affords unique methodological advantages for the study of aging processes, as it allows evaluating non-linear relations between biological parameters, providing the precise quantitative strength of those relations, both for individual and multiple parameters, showing cumulative or synergistic effect. Here we illustrate those capabilities utilizing a dataset on heart disease, including diagnostic parameters routinely available to physicians. The use of information-theoretical methods, utilizing normalized mutual information, revealed the exact amount of information that various diagnostic parameters or their combinations contained about the persons' age. Based on those exact informative values for the correlation of measured parameters with age, we constructed a diagnostic rule (a decision tree) to evaluate physiological age, as compared to chronological age. The present data illustrated that younger subjects suffering from heart disease showed characteristics of people of higher age (higher physiological age). Utilizing information-theoretical measures, with additional data, it may be possible to create further clinically applicable information-theory-based markers and models for the evaluation of physiological age, its relation to age-related diseases and its potential modifications by therapeutic interventions. Copyright © 2017 Elsevier B.V. All rights reserved.

75 FR 53322 - Agency Information Collection Activities: New Information Collection; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-31

... Information Collection for Review; ICE Mutual Agreement between Government and Employers (IMAGE), OMB No. 1653...) Title of the Form/Collection: IMAGE Information Request and Membership Application/ICE Mutual Agreement between Government and Employers (IMAGE) (3) Agency form number, if any, and the applicable component of...

Learning dependence from samples.

PubMed

Seth, Sohan; Príncipe, José C

2014-01-01

Mutual information, conditional mutual information and interaction information have been widely used in scientific literature as measures of dependence, conditional dependence and mutual dependence. However, these concepts suffer from several computational issues; they are difficult to estimate in continuous domain, the existing regularised estimators are almost always defined only for real or vector-valued random variables, and these measures address what dependence, conditional dependence and mutual dependence imply in terms of the random variables but not finite realisations. In this paper, we address the issue that given a set of realisations in an arbitrary metric space, what characteristic makes them dependent, conditionally dependent or mutually dependent. With this novel understanding, we develop new estimators of association, conditional association and interaction association. Some attractive properties of these estimators are that they do not require choosing free parameter(s), they are computationally simpler, and they can be applied to arbitrary metric spaces.

Construction of mutually unbiased bases with cyclic symmetry for qubit systems

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

Seyfarth, Ulrich; Ranade, Kedar S.

2011-10-15

For the complete estimation of arbitrary unknown quantum states by measurements, the use of mutually unbiased bases has been well established in theory and experiment for the past 20 years. However, most constructions of these bases make heavy use of abstract algebra and the mathematical theory of finite rings and fields, and no simple and generally accessible construction is available. This is particularly true in the case of a system composed of several qubits, which is arguably the most important case in quantum information science and quantum computation. In this paper, we close this gap by providing a simple andmore » straightforward method for the construction of mutually unbiased bases in the case of a qubit register. We show that our construction is also accessible to experiments, since only Hadamard and controlled-phase gates are needed, which are available in most practical realizations of a quantum computer. Moreover, our scheme possesses the optimal scaling possible, i.e., the number of gates scales only linearly in the number of qubits.« less

Discovering latent commercial networks from online financial news articles

NASA Astrophysics Data System (ADS)

Xia, Yunqing; Su, Weifeng; Lau, Raymond Y. K.; Liu, Yi

2013-08-01

Unlike most online social networks where explicit links among individual users are defined, the relations among commercial entities (e.g. firms) may not be explicitly declared in commercial Web sites. One main contribution of this article is the development of a novel computational model for the discovery of the latent relations among commercial entities from online financial news. More specifically, a CRF model which can exploit both structural and contextual features is applied to commercial entity recognition. In addition, a point-wise mutual information (PMI)-based unsupervised learning method is developed for commercial relation identification. To evaluate the effectiveness of the proposed computational methods, a prototype system called CoNet has been developed. Based on the financial news articles crawled from Google finance, the CoNet system achieves average F-scores of 0.681 and 0.754 in commercial entity recognition and commercial relation identification, respectively. Our experimental results confirm that the proposed shallow natural language processing methods are effective for the discovery of latent commercial networks from online financial news.

Decision Making for Healthcare Resource Allocation: Joint v. Separate Decisions on Interacting Interventions.

PubMed

Dakin, Helen; Gray, Alastair

2018-05-01

Standard guidance for allocating healthcare resources based on cost-effectiveness recommends using different decision rules for independent and mutually exclusive alternatives, although there is some confusion around the definition of "mutually exclusive." This paper reviews the definitions used in the literature and shows that interactions (i.e., non-additive effects, whereby the effect of giving 2 interventions simultaneously does not equal the sum of their individual effects) are the defining feature of mutually exclusive alternatives: treatments cannot be considered independent if the costs and/or benefits of one treatment are affected by the other treatment. The paper then identifies and categorizes the situations in which interventions are likely to have non-additive effects, including interventions targeting the same goal or clinical event, or life-saving interventions given to overlapping populations. We demonstrate that making separate decisions on interventions that have non-additive effects can prevent us from maximizing health gained from the healthcare budget. In contrast, treating combinations of independent options as though they were "mutually exclusive" makes the analysis more complicated but does not affect the conclusions. Although interactions are considered by the World Health Organization, other decision makers, such as the National Institute for Health and Care Excellence (NICE), currently make independent decisions on treatments likely to have non-additive effects. We propose a framework by which interactions could be considered when selecting, prioritizing, and appraising healthcare technologies to ensure efficient, evidence-based decision making.

Decision Making for Healthcare Resource Allocation: Joint v. Separate Decisions on Interacting Interventions

PubMed Central

Dakin, Helen; Gray, Alastair

2018-01-01

Standard guidance for allocating healthcare resources based on cost-effectiveness recommends using different decision rules for independent and mutually exclusive alternatives, although there is some confusion around the definition of “mutually exclusive.” This paper reviews the definitions used in the literature and shows that interactions (i.e., non-additive effects, whereby the effect of giving 2 interventions simultaneously does not equal the sum of their individual effects) are the defining feature of mutually exclusive alternatives: treatments cannot be considered independent if the costs and/or benefits of one treatment are affected by the other treatment. The paper then identifies and categorizes the situations in which interventions are likely to have non-additive effects, including interventions targeting the same goal or clinical event, or life-saving interventions given to overlapping populations. We demonstrate that making separate decisions on interventions that have non-additive effects can prevent us from maximizing health gained from the healthcare budget. In contrast, treating combinations of independent options as though they were “mutually exclusive” makes the analysis more complicated but does not affect the conclusions. Although interactions are considered by the World Health Organization, other decision makers, such as the National Institute for Health and Care Excellence (NICE), currently make independent decisions on treatments likely to have non-additive effects. We propose a framework by which interactions could be considered when selecting, prioritizing, and appraising healthcare technologies to ensure efficient, evidence-based decision making. PMID:29683792

Seismic wavefield propagation in 2D anisotropic media: Ray theory versus wave-equation simulation

NASA Astrophysics Data System (ADS)

Bai, Chao-ying; Hu, Guang-yi; Zhang, Yan-teng; Li, Zhong-sheng

2014-05-01

Despite the ray theory that is based on the high frequency assumption of the elastic wave-equation, the ray theory and the wave-equation simulation methods should be mutually proof of each other and hence jointly developed, but in fact parallel independent progressively. For this reason, in this paper we try an alternative way to mutually verify and test the computational accuracy and the solution correctness of both the ray theory (the multistage irregular shortest-path method) and the wave-equation simulation method (both the staggered finite difference method and the pseudo-spectral method) in anisotropic VTI and TTI media. Through the analysis and comparison of wavefield snapshot, common source gather profile and synthetic seismogram, it is able not only to verify the accuracy and correctness of each of the methods at least for kinematic features, but also to thoroughly understand the kinematic and dynamic features of the wave propagation in anisotropic media. The results show that both the staggered finite difference method and the pseudo-spectral method are able to yield the same results even for complex anisotropic media (such as a fault model); the multistage irregular shortest-path method is capable of predicting similar kinematic features as the wave-equation simulation method does, which can be used to mutually test each other for methodology accuracy and solution correctness. In addition, with the aid of the ray tracing results, it is easy to identify the multi-phases (or multiples) in the wavefield snapshot, common source point gather seismic section and synthetic seismogram predicted by the wave-equation simulation method, which is a key issue for later seismic application.

Generating intrinsically disordered protein conformational ensembles from a Markov chain

NASA Astrophysics Data System (ADS)

Cukier, Robert I.

2018-03-01

Intrinsically disordered proteins (IDPs) sample a diverse conformational space. They are important to signaling and regulatory pathways in cells. An entropy penalty must be payed when an IDP becomes ordered upon interaction with another protein or a ligand. Thus, the degree of conformational disorder of an IDP is of interest. We create a dichotomic Markov model that can explore entropic features of an IDP. The Markov condition introduces local (neighbor residues in a protein sequence) rotamer dependences that arise from van der Waals and other chemical constraints. A protein sequence of length N is characterized by its (information) entropy and mutual information, MIMC, the latter providing a measure of the dependence among the random variables describing the rotamer probabilities of the residues that comprise the sequence. For a Markov chain, the MIMC is proportional to the pair mutual information MI which depends on the singlet and pair probabilities of neighbor residue rotamer sampling. All 2N sequence states are generated, along with their probabilities, and contrasted with the probabilities under the assumption of independent residues. An efficient method to generate realizations of the chain is also provided. The chain entropy, MIMC, and state probabilities provide the ingredients to distinguish different scenarios using the terminologies: MoRF (molecular recognition feature), not-MoRF, and not-IDP. A MoRF corresponds to large entropy and large MIMC (strong dependence among the residues' rotamer sampling), a not-MoRF corresponds to large entropy but small MIMC, and not-IDP corresponds to low entropy irrespective of the MIMC. We show that MorFs are most appropriate as descriptors of IDPs. They provide a reasonable number of high-population states that reflect the dependences between neighbor residues, thus classifying them as IDPs, yet without very large entropy that might lead to a too high entropy penalty.

A Bayesian framework for early risk prediction in traumatic brain injury

NASA Astrophysics Data System (ADS)

Chaganti, Shikha; Plassard, Andrew J.; Wilson, Laura; Smith, Miya A.; Patel, Mayur B.; Landman, Bennett A.

2016-03-01

Early detection of risk is critical in determining the course of treatment in traumatic brain injury (TBI). Computed tomography (CT) acquired at admission has shown latent prognostic value in prior studies; however, no robust clinical risk predictions have been achieved based on the imaging data in large-scale TBI analysis. The major challenge lies in the lack of consistent and complete medical records for patients, and an inherent bias associated with the limited number of patients samples with high-risk outcomes in available TBI datasets. Herein, we propose a Bayesian framework with mutual information-based forward feature selection to handle this type of data. Using multi-atlas segmentation, 154 image-based features (capturing intensity, volume and texture) were computed over 22 ROIs in 1791 CT scans. These features were combined with 14 clinical parameters and converted into risk likelihood scores using Bayes modeling. We explore the prediction power of the image features versus the clinical measures for various risk outcomes. The imaging data alone were more predictive of outcomes than the clinical data (including Marshall CT classification) for discharge disposition with an area under the curve of 0.81 vs. 0.67, but less predictive than clinical data for discharge Glasgow Coma Scale (GCS) score with an area under the curve of 0.65 vs. 0.85. However, in both cases, combining imaging and clinical data increased the combined area under the curve with 0.86 for discharge disposition and 0.88 for discharge GCS score. In conclusion, CT data have meaningful prognostic value for TBI patients beyond what is captured in clinical measures and the Marshall CT classification.

12 CFR 12.101 - National bank disclosure of remuneration for mutual fund transactions.

Code of Federal Regulations, 2011 CFR

2011-01-01

... mutual fund transactions. 12.101 Section 12.101 Banks and Banking COMPTROLLER OF THE CURRENCY, DEPARTMENT... Interpretations § 12.101 National bank disclosure of remuneration for mutual fund transactions. A national bank... by § 12.4, for mutual fund transactions by providing this information to the customer in a current...

12 CFR 12.101 - National bank disclosure of remuneration for mutual fund transactions.

Code of Federal Regulations, 2010 CFR

2010-01-01

... mutual fund transactions. 12.101 Section 12.101 Banks and Banking COMPTROLLER OF THE CURRENCY, DEPARTMENT... Interpretations § 12.101 National bank disclosure of remuneration for mutual fund transactions. A national bank... by § 12.4, for mutual fund transactions by providing this information to the customer in a current...

Unravelling Some of the Key Transformations in the Hydrothermal Liquefaction of Lignin.

PubMed

Lui, Matthew Y; Chan, Bun; Yuen, Alexander K L; Masters, Anthony F; Montoya, Alejandro; Maschmeyer, Thomas

2017-05-22

Using both experimental and computational methods, focusing on intermediates and model compounds, some of the main features of the reaction mechanisms that operate during the hydrothermal processing of lignin were elucidated. Key reaction pathways and their connection to different structural features of lignin were proposed. Under neutral conditions, subcritical water was demonstrated to act as a bifunctional acid/base catalyst for the dissection of lignin structures. In a complex web of mutually dependent interactions, guaiacyl units within lignin were shown to significantly affect overall lignin reactivity. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Holographic mutual information of two disjoint spheres

NASA Astrophysics Data System (ADS)

Chen, Bin; Fan, Zhong-Ying; Li, Wen-Ming; Zhang, Cheng-Yong

2018-04-01

We study quantum corrections to holographic mutual information for two disjoint spheres at a large separation by using the operator product expansion of the twist field. In the large separation limit, the holographic mutual information is vanishing at the semiclassical order, but receive quantum corrections from the fluctuations. We show that the leading contributions from the quantum fluctuations take universal forms as suggested from the boundary CFT. We find the universal behavior for the scalar, the vector, the tensor and the fermionic fields by treating these fields as free fields propagating in the fixed background and by using the 1 /n prescription. In particular, for the fields with gauge symmetries, including the massless vector boson and massless graviton, we find that the gauge parts in the propagators play an indispensable role in reading the leading order corrections to the bulk mutual information.

A two-phase copula entropy-based multiobjective optimization approach to hydrometeorological gauge network design

NASA Astrophysics Data System (ADS)

Xu, Pengcheng; Wang, Dong; Singh, Vijay P.; Wang, Yuankun; Wu, Jichun; Wang, Lachun; Zou, Xinqing; Chen, Yuanfang; Chen, Xi; Liu, Jiufu; Zou, Ying; He, Ruimin

2017-12-01

Hydrometeorological data are needed for obtaining point and areal mean, quantifying the spatial variability of hydrometeorological variables, and calibration and verification of hydrometeorological models. Hydrometeorological networks are utilized to collect such data. Since data collection is expensive, it is essential to design an optimal network based on the minimal number of hydrometeorological stations in order to reduce costs. This study proposes a two-phase copula entropy- based multiobjective optimization approach that includes: (1) copula entropy-based directional information transfer (CDIT) for clustering the potential hydrometeorological gauges into several groups, and (2) multiobjective method for selecting the optimal combination of gauges for regionalized groups. Although entropy theory has been employed for network design before, the joint histogram method used for mutual information estimation has several limitations. The copula entropy-based mutual information (MI) estimation method is shown to be more effective for quantifying the uncertainty of redundant information than the joint histogram (JH) method. The effectiveness of this approach is verified by applying to one type of hydrometeorological gauge network, with the use of three model evaluation measures, including Nash-Sutcliffe Coefficient (NSC), arithmetic mean of the negative copula entropy (MNCE), and MNCE/NSC. Results indicate that the two-phase copula entropy-based multiobjective technique is capable of evaluating the performance of regional hydrometeorological networks and can enable decision makers to develop strategies for water resources management.

Combining features from ERP components in single-trial EEG for discriminating four-category visual objects.

PubMed

Wang, Changming; Xiong, Shi; Hu, Xiaoping; Yao, Li; Zhang, Jiacai

2012-10-01

Categorization of images containing visual objects can be successfully recognized using single-trial electroencephalograph (EEG) measured when subjects view images. Previous studies have shown that task-related information contained in event-related potential (ERP) components could discriminate two or three categories of object images. In this study, we investigated whether four categories of objects (human faces, buildings, cats and cars) could be mutually discriminated using single-trial EEG data. Here, the EEG waveforms acquired while subjects were viewing four categories of object images were segmented into several ERP components (P1, N1, P2a and P2b), and then Fisher linear discriminant analysis (Fisher-LDA) was used to classify EEG features extracted from ERP components. Firstly, we compared the classification results using features from single ERP components, and identified that the N1 component achieved the highest classification accuracies. Secondly, we discriminated four categories of objects using combining features from multiple ERP components, and showed that combination of ERP components improved four-category classification accuracies by utilizing the complementarity of discriminative information in ERP components. These findings confirmed that four categories of object images could be discriminated with single-trial EEG and could direct us to select effective EEG features for classifying visual objects.

The comparison and analysis of extracting video key frame

NASA Astrophysics Data System (ADS)

Ouyang, S. Z.; Zhong, L.; Luo, R. Q.

2018-05-01

Video key frame extraction is an important part of the large data processing. Based on the previous work in key frame extraction, we summarized four important key frame extraction algorithms, and these methods are largely developed by comparing the differences between each of two frames. If the difference exceeds a threshold value, take the corresponding frame as two different keyframes. After the research, the key frame extraction based on the amount of mutual trust is proposed, the introduction of information entropy, by selecting the appropriate threshold values into the initial class, and finally take a similar mean mutual information as a candidate key frame. On this paper, several algorithms is used to extract the key frame of tunnel traffic videos. Then, with the analysis to the experimental results and comparisons between the pros and cons of these algorithms, the basis of practical applications is well provided.

A Lightweight RFID Mutual Authentication Protocol Based on Physical Unclonable Function.

PubMed

Xu, He; Ding, Jie; Li, Peng; Zhu, Feng; Wang, Ruchuan

2018-03-02

With the fast development of the Internet of Things, Radio Frequency Identification (RFID) has been widely applied into many areas. Nevertheless, security problems of the RFID technology are also gradually exposed, when it provides life convenience. In particular, the appearance of a large number of fake and counterfeit goods has caused massive loss for both producers and customers, for which the clone tag is a serious security threat. If attackers acquire the complete information of a tag, they can then obtain the unique identifier of the tag by some technological means. In general, because there is no extra identifier of a tag, it is difficult to distinguish an original tag and its clone one. Once the legal tag data is obtained, attackers can be able to clone this tag. Therefore, this paper shows an efficient RFID mutual verification protocol. This protocol is based on the Physical Unclonable Function (PUF) and the lightweight cryptography to achieve efficient verification of a single tag. The protocol includes three process: tag recognition, mutual verification and update. The tag recognition is that the reader recognizes the tag; mutual verification is that the reader and tag mutually verify the authenticity of each other; update is supposed to maintain the latest secret key for the following verification. Analysis results show that this protocol has a good balance between performance and security.

A Lightweight RFID Mutual Authentication Protocol Based on Physical Unclonable Function

PubMed Central

Ding, Jie; Zhu, Feng; Wang, Ruchuan

2018-01-01

With the fast development of the Internet of Things, Radio Frequency Identification (RFID) has been widely applied into many areas. Nevertheless, security problems of the RFID technology are also gradually exposed, when it provides life convenience. In particular, the appearance of a large number of fake and counterfeit goods has caused massive loss for both producers and customers, for which the clone tag is a serious security threat. If attackers acquire the complete information of a tag, they can then obtain the unique identifier of the tag by some technological means. In general, because there is no extra identifier of a tag, it is difficult to distinguish an original tag and its clone one. Once the legal tag data is obtained, attackers can be able to clone this tag. Therefore, this paper shows an efficient RFID mutual verification protocol. This protocol is based on the Physical Unclonable Function (PUF) and the lightweight cryptography to achieve efficient verification of a single tag. The protocol includes three process: tag recognition, mutual verification and update. The tag recognition is that the reader recognizes the tag; mutual verification is that the reader and tag mutually verify the authenticity of each other; update is supposed to maintain the latest secret key for the following verification. Analysis results show that this protocol has a good balance between performance and security. PMID:29498684

Timing the state of light with anomalous dispersion and a gradient echo memory

NASA Astrophysics Data System (ADS)

Clark, Jeremy B.

We study the effects of anomalous dispersion on the continuous-variable entanglement of EPR states (generated using four-wave mixing in 85 Rb) by sending one part of the state through a fast-light medium and measuring the state's quantum mutual information. We observe an advance in the maximum of the quantum mutual information between modes. In contrast, due to uncorrelated noise added by a small phase-insensitive gain, we do not observe any statistically significant advance in the leading edge of the mutual information. We also study the storage and retrieval of multiplexed optical signals in a Gradient Echo Memory (GEM) at relevant four-wave mixing frequencies in 85Rb. Temporal multiplexing capabilities are demonstrated by storing multiple classical images in the memory simultaneously and observing the expected first-in last-out order of recall without obvious cross-talk. We also develop a technique wherein selected portions of an image written into the memory can be spatially targeted for readout and erasure on demand. The effect of diffusion on the quality of the recalled images is characterized. Our results indicate that Raman-based atomic memories may serve as a flexible platform for the storage and retrieval of multiplexed optical signals.

Reconstructing regulatory networks from the dynamic plasticity of gene expression by mutual information

PubMed Central

Wang, Jianxin; Chen, Bo; Wang, Yaqun; Wang, Ningtao; Garbey, Marc; Tran-Son-Tay, Roger; Berceli, Scott A.; Wu, Rongling

2013-01-01

The capacity of an organism to respond to its environment is facilitated by the environmentally induced alteration of gene and protein expression, i.e. expression plasticity. The reconstruction of gene regulatory networks based on expression plasticity can gain not only new insights into the causality of transcriptional and cellular processes but also the complex regulatory mechanisms that underlie biological function and adaptation. We describe an approach for network inference by integrating expression plasticity into Shannon’s mutual information. Beyond Pearson correlation, mutual information can capture non-linear dependencies and topology sparseness. The approach measures the network of dependencies of genes expressed in different environments, allowing the environment-induced plasticity of gene dependencies to be tested in unprecedented details. The approach is also able to characterize the extent to which the same genes trigger different amounts of expression in response to environmental changes. We demonstrated the usefulness of this approach through analysing gene expression data from a rabbit vein graft study that includes two distinct blood flow environments. The proposed approach provides a powerful tool for the modelling and analysis of dynamic regulatory networks using gene expression data from distinct environments. PMID:23470995

Cross Correlation versus Normalized Mutual Information on Image Registration

NASA Technical Reports Server (NTRS)

Tan, Bin; Tilton, James C.; Lin, Guoqing

2016-01-01

This is the first study to quantitatively assess and compare cross correlation and normalized mutual information methods used to register images in subpixel scale. The study shows that the normalized mutual information method is less sensitive to unaligned edges due to the spectral response differences than is cross correlation. This characteristic makes the normalized image resolution a better candidate for band to band registration. Improved band-to-band registration in the data from satellite-borne instruments will result in improved retrievals of key science measurements such as cloud properties, vegetation, snow and fire.

On approximately symmetric informationally complete positive operator-valued measures and related systems of quantum states

NASA Astrophysics Data System (ADS)

Klappenecker, Andreas; Rötteler, Martin; Shparlinski, Igor E.; Winterhof, Arne

2005-08-01

We address the problem of constructing positive operator-valued measures (POVMs) in finite dimension n consisting of n2 operators of rank one which have an inner product close to uniform. This is motivated by the related question of constructing symmetric informationally complete POVMs (SIC-POVMs) for which the inner products are perfectly uniform. However, SIC-POVMs are notoriously hard to construct and, despite some success of constructing them numerically, there is no analytic construction known. We present two constructions of approximate versions of SIC-POVMs, where a small deviation from uniformity of the inner products is allowed. The first construction is based on selecting vectors from a maximal collection of mutually unbiased bases and works whenever the dimension of the system is a prime power. The second construction is based on perturbing the matrix elements of a subset of mutually unbiased bases. Moreover, we construct vector systems in Cn which are almost orthogonal and which might turn out to be useful for quantum computation. Our constructions are based on results of analytic number theory.

An information theoretic model of information processing in the Drosophila olfactory system: the role of inhibitory neurons for system efficiency.

PubMed

Faghihi, Faramarz; Kolodziejski, Christoph; Fiala, André; Wörgötter, Florentin; Tetzlaff, Christian

2013-12-20

Fruit flies (Drosophila melanogaster) rely on their olfactory system to process environmental information. This information has to be transmitted without system-relevant loss by the olfactory system to deeper brain areas for learning. Here we study the role of several parameters of the fly's olfactory system and the environment and how they influence olfactory information transmission. We have designed an abstract model of the antennal lobe, the mushroom body and the inhibitory circuitry. Mutual information between the olfactory environment, simulated in terms of different odor concentrations, and a sub-population of intrinsic mushroom body neurons (Kenyon cells) was calculated to quantify the efficiency of information transmission. With this method we study, on the one hand, the effect of different connectivity rates between olfactory projection neurons and firing thresholds of Kenyon cells. On the other hand, we analyze the influence of inhibition on mutual information between environment and mushroom body. Our simulations show an expected linear relation between the connectivity rate between the antennal lobe and the mushroom body and firing threshold of the Kenyon cells to obtain maximum mutual information for both low and high odor concentrations. However, contradicting all-day experiences, high odor concentrations cause a drastic, and unrealistic, decrease in mutual information for all connectivity rates compared to low concentration. But when inhibition on the mushroom body is included, mutual information remains at high levels independent of other system parameters. This finding points to a pivotal role of inhibition in fly information processing without which the system efficiency will be substantially reduced.

78 FR 35325 - Agency Information Collection Activities; Submission for OMB Review; Comment Request; Prohibited...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-12

...-end investment company (mutual fund) when a fiduciary with respect to the plan is also the investment advisor for the mutual fund. There are three basic disclosure requirements incorporated within PTE 77-4... mutual fund. The second requirement is that, at the time of the purchase or sale of such mutual fund...

New statistical potential for quality assessment of protein models and a survey of energy functions

PubMed Central

2010-01-01

Background Scoring functions, such as molecular mechanic forcefields and statistical potentials are fundamentally important tools in protein structure modeling and quality assessment. Results The performances of a number of publicly available scoring functions are compared with a statistical rigor, with an emphasis on knowledge-based potentials. We explored the effect on accuracy of alternative choices for representing interaction center types and other features of scoring functions, such as using information on solvent accessibility, on torsion angles, accounting for secondary structure preferences and side chain orientation. Partially based on the observations made, we present a novel residue based statistical potential, which employs a shuffled reference state definition and takes into account the mutual orientation of residue side chains. Atom- and residue-level statistical potentials and Linux executables to calculate the energy of a given protein proposed in this work can be downloaded from http://www.fiserlab.org/potentials. Conclusions Among the most influential terms we observed a critical role of a proper reference state definition and the benefits of including information about the microenvironment of interaction centers. Molecular mechanical potentials were also tested and found to be over-sensitive to small local imperfections in a structure, requiring unfeasible long energy relaxation before energy scores started to correlate with model quality. PMID:20226048

Determination of Relevant Neuron–Neuron Connections for Neural Prosthetics Using Time-Delayed Mutual Information: Tutorial and Preliminary Results

PubMed Central

Taghva, Alexander; Song, Dong; Hampson, Robert E.; Deadwyler, Sam A.; Berger, Theodore W.

2013-01-01

BACKGROUND Identification of functional dependence among neurons is a necessary component in both the rational design of neural prostheses as well as in the characterization of network physiology. The objective of this article is to provide a tutorial for neurosurgeons regarding information theory, specifically time-delayed mutual information, and to compare time-delayed mutual information, an information theoretic quantity based on statistical dependence, with cross-correlation, a commonly used metric for this task in a preliminary analysis of rat hippocampal neurons. METHODS Spike trains were recorded from rats performing delayed nonmatch-to-sample task using an array of electrodes surgically implanted into the hippocampus of each hemisphere of the brain. In addition, spike train simulations of positively correlated neurons, negatively correlated neurons, and neurons correlated by nonlinear functions were generated. These were evaluated by time-delayed mutual information (MI) and cross-correlation. RESULTS Application of time-delayed MI to experimental data indicated the optimal bin size for information capture in the CA3-CA1 system was 40 ms, which may provide some insight into the spatiotemporal nature of encoding in the rat hippocampus. On simulated data, time-delayed MI showed peak values at appropriate time lags in positively correlated, negatively correlated, and complexly correlated data. Cross-correlation showed peak and troughs with positively correlated and negatively correlated data, but failed to capture some higher order correlations. CONCLUSIONS Comparison of time-delayed MI to cross-correlation in identification of functionally dependent neurons indicates that the methods are not equivalent. Time-delayed MI appeared to capture some interactions between CA3-CA1 neurons at physiologically plausible time delays missed by cross-correlation. It should be considered as a method for identification of functional dependence between neurons and may be useful in the development of neural prosthetics. PMID:22120279

Determination of relevant neuron-neuron connections for neural prosthetics using time-delayed mutual information: tutorial and preliminary results.

PubMed

Taghva, Alexander; Song, Dong; Hampson, Robert E; Deadwyler, Sam A; Berger, Theodore W

2012-12-01

Identification of functional dependence among neurons is a necessary component in both the rational design of neural prostheses as well as in the characterization of network physiology. The objective of this article is to provide a tutorial for neurosurgeons regarding information theory, specifically time-delayed mutual information, and to compare time-delayed mutual information, an information theoretic quantity based on statistical dependence, with cross-correlation, a commonly used metric for this task in a preliminary analysis of rat hippocampal neurons. Spike trains were recorded from rats performing delayed nonmatch-to-sample task using an array of electrodes surgically implanted into the hippocampus of each hemisphere of the brain. In addition, spike train simulations of positively correlated neurons, negatively correlated neurons, and neurons correlated by nonlinear functions were generated. These were evaluated by time-delayed mutual information (MI) and cross-correlation. Application of time-delayed MI to experimental data indicated the optimal bin size for information capture in the CA3-CA1 system was 40 ms, which may provide some insight into the spatiotemporal nature of encoding in the rat hippocampus. On simulated data, time-delayed MI showed peak values at appropriate time lags in positively correlated, negatively correlated, and complexly correlated data. Cross-correlation showed peak and troughs with positively correlated and negatively correlated data, but failed to capture some higher order correlations. Comparison of time-delayed MI to cross-correlation in identification of functionally dependent neurons indicates that the methods are not equivalent. Time-delayed MI appeared to capture some interactions between CA3-CA1 neurons at physiologically plausible time delays missed by cross-correlation. It should be considered as a method for identification of functional dependence between neurons and may be useful in the development of neural prosthetics. Copyright © 2012 Elsevier Inc. All rights reserved.

Empirical mode decomposition-based facial pose estimation inside video sequences

NASA Astrophysics Data System (ADS)

Qing, Chunmei; Jiang, Jianmin; Yang, Zhijing

2010-03-01

We describe a new pose-estimation algorithm via integration of the strength in both empirical mode decomposition (EMD) and mutual information. While mutual information is exploited to measure the similarity between facial images to estimate poses, EMD is exploited to decompose input facial images into a number of intrinsic mode function (IMF) components, which redistribute the effect of noise, expression changes, and illumination variations as such that, when the input facial image is described by the selected IMF components, all the negative effects can be minimized. Extensive experiments were carried out in comparisons to existing representative techniques, and the results show that the proposed algorithm achieves better pose-estimation performances with robustness to noise corruption, illumination variation, and facial expressions.

Optimisation of reconstruction--reprojection-based motion correction for cardiac SPECT.

PubMed

Kangasmaa, Tuija S; Sohlberg, Antti O

2014-07-01

Cardiac motion is a challenging cause of image artefacts in myocardial perfusion SPECT. A wide range of motion correction methods have been developed over the years, and so far automatic algorithms based on the reconstruction--reprojection principle have proved to be the most effective. However, these methods have not been fully optimised in terms of their free parameters and implementational details. Two slightly different implementations of reconstruction--reprojection-based motion correction techniques were optimised for effective, good-quality motion correction and then compared with each other. The first of these methods (Method 1) was the traditional reconstruction-reprojection motion correction algorithm, where the motion correction is done in projection space, whereas the second algorithm (Method 2) performed motion correction in reconstruction space. The parameters that were optimised include the type of cost function (squared difference, normalised cross-correlation and mutual information) that was used to compare measured and reprojected projections, and the number of iterations needed. The methods were tested with motion-corrupt projection datasets, which were generated by adding three different types of motion (lateral shift, vertical shift and vertical creep) to motion-free cardiac perfusion SPECT studies. Method 2 performed slightly better overall than Method 1, but the difference between the two implementations was small. The execution time for Method 2 was much longer than for Method 1, which limits its clinical usefulness. The mutual information cost function gave clearly the best results for all three motion sets for both correction methods. Three iterations were sufficient for a good quality correction using Method 1. The traditional reconstruction--reprojection-based method with three update iterations and mutual information cost function is a good option for motion correction in clinical myocardial perfusion SPECT.

Hierarchical clustering using mutual information

NASA Astrophysics Data System (ADS)

Kraskov, A.; Stögbauer, H.; Andrzejak, R. G.; Grassberger, P.

2005-04-01

We present a conceptually simple method for hierarchical clustering of data called mutual information clustering (MIC) algorithm. It uses mutual information (MI) as a similarity measure and exploits its grouping property: The MI between three objects X, Y, and Z is equal to the sum of the MI between X and Y, plus the MI between Z and the combined object (XY). We use this both in the Shannon (probabilistic) version of information theory and in the Kolmogorov (algorithmic) version. We apply our method to the construction of phylogenetic trees from mitochondrial DNA sequences and to the output of independent components analysis (ICA) as illustrated with the ECG of a pregnant woman.

The distinguishing characteristics of narrative identity in adults with features of borderline personality disorder: an empirical investigation.

PubMed

Adler, Jonathan M; Chin, Erica D; Kolisetty, Aiswarya P; Oltmanns, Thomas F

2012-08-01

While identity disturbance has long been considered one of the defining features of Borderline Personality Disorder (BPD), the present study marks only the third empirical investigation to assess it and the first to do so from the perspective of research on narrative identity. Drawing on the rich tradition of studying narrative identity, the present study examined identity disturbance in a group of 40 mid-life adults, 20 with features of BPD and a matched sample of 20 without BPD. Extensive life story interviews were analyzed for a variety of narrative elements and the themes of agency, communion fulfillment (but not communion), and narrative coherence significantly distinguished the stories of those people with features of BPD from those without the disorder. In addition, associations between the theme of agency and psychopathology were evident six and twelve months following the life story interview. This study seeks to bridge the mutually-informative fields of research on personality disorders and normal identity processes.

The distinguishing characteristics of narrative identity in adults with features of Borderline Personality Disorder: An empirical investigation

PubMed Central

Adler, Jonathan M.; Chin, Erica D.; Kolisetty, Aiswarya P.; Oltmanns, Thomas F.

2011-01-01

While identity disturbance has long been considered one of the defining features of Borderline Personality Disorder (BPD), the present study marks only the third empirical investigation to assess it and the first to do so from the perspective of research on narrative identity. Drawing on the rich tradition of studying narrative identity, the present study examined identity disturbance in a group of 40 mid-life adults, 20 with features of BPD and a matched sample of 20 without BPD. Extensive life story interviews were analyzed for a variety of narrative elements and the themes of agency, communion fulfillment (but not communion), and narrative coherence significantly distinguished the stories of those people with features of BPD from those without the disorder. In addition, associations between the theme of agency and psychopathology were evident six and twelve months following the life story interview. This study seeks to bridge the mutually-informative fields of research on personality disorders and normal identity processes. PMID:22867502

Mutually unbiased projectors and duality between lines and bases in finite quantum systems

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

Shalaby, M.; Vourdas, A., E-mail: a.vourdas@bradford.ac.uk

2013-10-15

Quantum systems with variables in the ring Z(d) are considered, and the concepts of weak mutually unbiased bases and mutually unbiased projectors are discussed. The lines through the origin in the Z(d)×Z(d) phase space, are classified into maximal lines (sets of d points), and sublines (sets of d{sub i} points where d{sub i}|d). The sublines are intersections of maximal lines. It is shown that there exists a duality between the properties of lines (resp., sublines), and the properties of weak mutually unbiased bases (resp., mutually unbiased projectors). -- Highlights: •Lines in discrete phase space. •Bases in finite quantum systems. •Dualitymore » between bases and lines. •Weak mutually unbiased bases.« less

Deep and Structured Robust Information Theoretic Learning for Image Analysis.

PubMed

Deng, Yue; Bao, Feng; Deng, Xuesong; Wang, Ruiping; Kong, Youyong; Dai, Qionghai

2016-07-07

This paper presents a robust information theoretic (RIT) model to reduce the uncertainties, i.e. missing and noisy labels, in general discriminative data representation tasks. The fundamental pursuit of our model is to simultaneously learn a transformation function and a discriminative classifier that maximize the mutual information of data and their labels in the latent space. In this general paradigm, we respectively discuss three types of the RIT implementations with linear subspace embedding, deep transformation and structured sparse learning. In practice, the RIT and deep RIT are exploited to solve the image categorization task whose performances will be verified on various benchmark datasets. The structured sparse RIT is further applied to a medical image analysis task for brain MRI segmentation that allows group-level feature selections on the brain tissues.

Model-based decoding, information estimation, and change-point detection techniques for multineuron spike trains.

PubMed

Pillow, Jonathan W; Ahmadian, Yashar; Paninski, Liam

2011-01-01

One of the central problems in systems neuroscience is to understand how neural spike trains convey sensory information. Decoding methods, which provide an explicit means for reading out the information contained in neural spike responses, offer a powerful set of tools for studying the neural coding problem. Here we develop several decoding methods based on point-process neural encoding models, or forward models that predict spike responses to stimuli. These models have concave log-likelihood functions, which allow efficient maximum-likelihood model fitting and stimulus decoding. We present several applications of the encoding model framework to the problem of decoding stimulus information from population spike responses: (1) a tractable algorithm for computing the maximum a posteriori (MAP) estimate of the stimulus, the most probable stimulus to have generated an observed single- or multiple-neuron spike train response, given some prior distribution over the stimulus; (2) a gaussian approximation to the posterior stimulus distribution that can be used to quantify the fidelity with which various stimulus features are encoded; (3) an efficient method for estimating the mutual information between the stimulus and the spike trains emitted by a neural population; and (4) a framework for the detection of change-point times (the time at which the stimulus undergoes a change in mean or variance) by marginalizing over the posterior stimulus distribution. We provide several examples illustrating the performance of these estimators with simulated and real neural data.

Developmental Experience Alters Information Coding in Auditory Midbrain and Forebrain Neurons

PubMed Central

Woolley, Sarah M. N.; Hauber, Mark E.; Theunissen, Frederic E.

2010-01-01

In songbirds, species identity and developmental experience shape vocal behavior and behavioral responses to vocalizations. The interaction of species identity and developmental experience may also shape the coding properties of sensory neurons. We tested whether responses of auditory midbrain and forebrain neurons to songs differed between species and between groups of conspecific birds with different developmental exposure to song. We also compared responses of individual neurons to conspecific and heterospecific songs. Zebra and Bengalese finches that were raised and tutored by conspecific birds, and zebra finches that were cross-tutored by Bengalese finches were studied. Single-unit responses to zebra and Bengalese finch songs were recorded and analyzed by calculating mutual information, response reliability, mean spike rate, fluctuations in time-varying spike rate, distributions of time-varying spike rates, and neural discrimination of individual songs. Mutual information quantifies a response’s capacity to encode information about a stimulus. In midbrain and forebrain neurons, mutual information was significantly higher in normal zebra finch neurons than in Bengalese finch and cross-tutored zebra finch neurons, but not between Bengalese finch and cross-tutored zebra finch neurons. Information rate differences were largely due to spike rate differences. Mutual information did not differ between responses to conspecific and heterospecific songs. Therefore, neurons from normal zebra finches encoded more information about songs than did neurons from other birds, but conspecific and heterospecific songs were encoded equally. Neural discrimination of songs and mutual information were highly correlated. Results demonstrate that developmental exposure to vocalizations shapes the information coding properties of songbird auditory neurons. PMID:20039264

A SWOT Analysis of the Various Backup Scenarios Used in Electronic Medical Record Systems.

PubMed

Seo, Hwa Jeong; Kim, Hye Hyeon; Kim, Ju Han

2011-09-01

Electronic medical records (EMRs) are increasingly being used by health care services. Currently, if an EMR shutdown occurs, even for a moment, patient safety and care can be seriously impacted. Our goal was to determine the methodology needed to develop an effective and reliable EMR backup system. Our "independent backup system by medical organizations" paradigm implies that individual medical organizations develop their own EMR backup systems within their organizations. A "personal independent backup system" is defined as an individual privately managing his/her own medical records, whereas in a "central backup system by the government" the government controls all the data. A "central backup system by private enterprises" implies that individual companies retain control over their own data. A "cooperative backup system among medical organizations" refers to a networked system established through mutual agreement. The "backup system based on mutual trust between an individual and an organization" means that the medical information backup system at the organizational level is established through mutual trust. Through the use of SWOT analysis it can be shown that cooperative backup among medical organizations is possible to be established through a network composed of various medical agencies and that it can be managed systematically. An owner of medical information only grants data access to the specific person who gave the authorization for backup based on the mutual trust between an individual and an organization. By employing SWOT analysis, we concluded that a linkage among medical organizations or between an individual and an organization can provide an efficient backup system.

A SWOT Analysis of the Various Backup Scenarios Used in Electronic Medical Record Systems

PubMed Central

Seo, Hwa Jeong; Kim, Hye Hyeon

2011-01-01

Objectives Electronic medical records (EMRs) are increasingly being used by health care services. Currently, if an EMR shutdown occurs, even for a moment, patient safety and care can be seriously impacted. Our goal was to determine the methodology needed to develop an effective and reliable EMR backup system. Methods Our "independent backup system by medical organizations" paradigm implies that individual medical organizations develop their own EMR backup systems within their organizations. A "personal independent backup system" is defined as an individual privately managing his/her own medical records, whereas in a "central backup system by the government" the government controls all the data. A "central backup system by private enterprises" implies that individual companies retain control over their own data. A "cooperative backup system among medical organizations" refers to a networked system established through mutual agreement. The "backup system based on mutual trust between an individual and an organization" means that the medical information backup system at the organizational level is established through mutual trust. Results Through the use of SWOT analysis it can be shown that cooperative backup among medical organizations is possible to be established through a network composed of various medical agencies and that it can be managed systematically. An owner of medical information only grants data access to the specific person who gave the authorization for backup based on the mutual trust between an individual and an organization. Conclusions By employing SWOT analysis, we concluded that a linkage among medical organizations or between an individual and an organization can provide an efficient backup system. PMID:22084811

Quantization of Gaussian samples at very low SNR regime in continuous variable QKD applications

NASA Astrophysics Data System (ADS)

Daneshgaran, Fred; Mondin, Marina

2016-09-01

The main problem for information reconciliation in continuous variable Quantum Key Distribution (QKD) at low Signal to Noise Ratio (SNR) is quantization and assignment of labels to the samples of the Gaussian Random Variables (RVs) observed at Alice and Bob. Trouble is that most of the samples, assuming that the Gaussian variable is zero mean which is de-facto the case, tend to have small magnitudes and are easily disturbed by noise. Transmission over longer and longer distances increases the losses corresponding to a lower effective SNR exasperating the problem. This paper looks at the quantization problem of the Gaussian samples at very low SNR regime from an information theoretic point of view. We look at the problem of two bit per sample quantization of the Gaussian RVs at Alice and Bob and derive expressions for the mutual information between the bit strings as a result of this quantization. The quantization threshold for the Most Significant Bit (MSB) should be chosen based on the maximization of the mutual information between the quantized bit strings. Furthermore, while the LSB string at Alice and Bob are balanced in a sense that their entropy is close to maximum, this is not the case for the second most significant bit even under optimal threshold. We show that with two bit quantization at SNR of -3 dB we achieve 75.8% of maximal achievable mutual information between Alice and Bob, hence, as the number of quantization bits increases beyond 2-bits, the number of additional useful bits that can be extracted for secret key generation decreases rapidly. Furthermore, the error rates between the bit strings at Alice and Bob at the same significant bit level are rather high demanding very powerful error correcting codes. While our calculations and simulation shows that the mutual information between the LSB at Alice and Bob is 0.1044 bits, that at the MSB level is only 0.035 bits. Hence, it is only by looking at the bits jointly that we are able to achieve a mutual information of 0.2217 bits which is 75.8% of maximum achievable. The implication is that only by coding both MSB and LSB jointly can we hope to get close to this 75.8% limit. Hence, non-binary codes are essential to achieve acceptable performance.

Waking and scrambling in holographic heating up

NASA Astrophysics Data System (ADS)

Ageev, D. S.; Aref'eva, I. Ya.

2017-10-01

Using holographic methods, we study the heating up process in quantum field theory. As a holographic dual of this process, we use absorption of a thin shell on a black brane. We find the explicit form of the time evolution of the quantum mutual information during heating up from the temperature Ti to the temperature T f in a system of two intervals in two-dimensional space-time. We determine the geometric characteristics of the system under which the time dependence of the mutual information has a bell shape: it is equal to zero at the initial instant, becomes positive at some subsequent instant, further attains its maximum, and again decreases to zero. Such a behavior of the mutual information occurs in the process of photosynthesis. We show that if the distance x between the intervals is less than log 2/2π T i, then the evolution of the holographic mutual information has a bell shape only for intervals whose lengths are bounded from above and below. For sufficiently large x, i.e., for x < log 2/2π T i, the bell-like shape of the time dependence of the quantum mutual information is present only for sufficiently large intervals. Moreover, the zone narrows as T i increases and widens as T f increases.

The two-brain approach: how can mutually interacting brains teach us something about social interaction?

PubMed Central

Konvalinka, Ivana; Roepstorff, Andreas

2012-01-01

Measuring brain activity simultaneously from two people interacting is intuitively appealing if one is interested in putative neural markers of social interaction. However, given the complex nature of interactions, it has proven difficult to carry out two-person brain imaging experiments in a methodologically feasible and conceptually relevant way. Only a small number of recent studies have put this into practice, using fMRI, EEG, or NIRS. Here, we review two main two-brain methodological approaches, each with two conceptual strategies. The first group has employed two-brain fMRI recordings, studying (1) turn-based interactions on the order of seconds, or (2) pseudo-interactive scenarios, where only one person is scanned at a time, investigating the flow of information between brains. The second group of studies has recorded dual EEG/NIRS from two people interacting, in (1) face-to-face turn-based interactions, investigating functional connectivity between theory-of-mind regions of interacting partners, or in (2) continuous mutual interactions on millisecond timescales, to measure coupling between the activity in one person's brain and the activity in the other's brain. We discuss the questions these approaches have addressed, and consider scenarios when simultaneous two-brain recordings are needed. Furthermore, we suggest that (1) quantification of inter-personal neural effects via measures of emergence, and (2) multivariate decoding models that generalize source-specific features of interaction, may provide novel tools to study brains in interaction. This may allow for a better understanding of social cognition as both representation and participation. PMID:22837744

Mutual information identifies spurious Hurst phenomena in resting state EEG and fMRI data

NASA Astrophysics Data System (ADS)

von Wegner, Frederic; Laufs, Helmut; Tagliazucchi, Enzo

2018-02-01

Long-range memory in time series is often quantified by the Hurst exponent H , a measure of the signal's variance across several time scales. We analyze neurophysiological time series from electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) resting state experiments with two standard Hurst exponent estimators and with the time-lagged mutual information function applied to discretized versions of the signals. A confidence interval for the mutual information function is obtained from surrogate Markov processes with equilibrium distribution and transition matrix identical to the underlying signal. For EEG signals, we construct an additional mutual information confidence interval from a short-range correlated, tenth-order autoregressive model. We reproduce the previously described Hurst phenomenon (H >0.5 ) in the analytical amplitude of alpha frequency band oscillations, in EEG microstate sequences, and in fMRI signals, but we show that the Hurst phenomenon occurs without long-range memory in the information-theoretical sense. We find that the mutual information function of neurophysiological data behaves differently from fractional Gaussian noise (fGn), for which the Hurst phenomenon is a sufficient condition to prove long-range memory. Two other well-characterized, short-range correlated stochastic processes (Ornstein-Uhlenbeck, Cox-Ingersoll-Ross) also yield H >0.5 , whereas their mutual information functions lie within the Markovian confidence intervals, similar to neural signals. In these processes, which do not have long-range memory by construction, a spurious Hurst phenomenon occurs due to slow relaxation times and heteroscedasticity (time-varying conditional variance). In summary, we find that mutual information correctly distinguishes long-range from short-range dependence in the theoretical and experimental cases discussed. Our results also suggest that the stationary fGn process is not sufficient to describe neural data, which seem to belong to a more general class of stochastic processes, in which multiscale variance effects produce Hurst phenomena without long-range dependence. In our experimental data, the Hurst phenomenon and long-range memory appear as different system properties that should be estimated and interpreted independently.

Using Large-Scale Precipitation to Validate AMSR-E Satellite Soil Moisture Estimates by Means of Mutual Information

NASA Astrophysics Data System (ADS)

Tuttle, S. E.; Salvucci, G.

2013-12-01

Validation of remotely sensed soil moisture is complicated by the difference in scale between remote sensing footprints and traditional ground-based soil moisture measurements. To address this issue, a new method was developed to evaluate the useful information content of remotely sensed soil moisture data using only large-scale precipitation (i.e. without modeling). Under statistically stationary conditions [Salvucci, 2001], precipitation conditionally averaged according to soil moisture (denoted E[P|S]) results in a sigmoidal shape in a manner that reflects the dependence of drainage, runoff, and evapotranspiration on soil moisture. However, errors in satellite measurement and algorithmic conversion of satellite data to soil moisture can degrade this relationship. Thus, remotely sensed soil moisture products can be assessed by the degree to which the natural sigmoidal relationship is preserved. The metric of mutual information was used as an error-dependent measure of the strength of the sigmoidal relationship, calculated from a two-dimensional histogram of soil moisture versus precipitation estimated using Gaussian mixture models. Three AMSR-E algorithms (VUA-NASA [Owe et al., 2001], NASA [Njoku et al., 2003], and U. Montana [Jones & Kimball, 2010]) were evaluated with the method for a nine-year period (2002-2011) over the contiguous United States at ¼° latitude-longitude resolution, using precipitation from the North American Land Data Assimilation System (NLDAS). The U. Montana product resulted in the highest mutual information for 57% of the region, followed by VUA-NASA and NASA at 40% and 3%, respectively. Areas where the U. Montana product yielded the maximum mutual information generally coincided with low vegetation biomass and flatter terrain, while the VUA-NASA product contained more useful information in more rugged and highly vegetated areas. Additionally, E[P|S] curves resulting from the Gaussian mixture method can potentially be decomposed into their conditional evapotranspiration and drainage plus runoff components using matrix factorization methods, allowing for time-averaged mapping of these fluxes over the study area.

Estimating mutual information using B-spline functions – an improved similarity measure for analysing gene expression data

PubMed Central

Daub, Carsten O; Steuer, Ralf; Selbig, Joachim; Kloska, Sebastian

2004-01-01

Background The information theoretic concept of mutual information provides a general framework to evaluate dependencies between variables. In the context of the clustering of genes with similar patterns of expression it has been suggested as a general quantity of similarity to extend commonly used linear measures. Since mutual information is defined in terms of discrete variables, its application to continuous data requires the use of binning procedures, which can lead to significant numerical errors for datasets of small or moderate size. Results In this work, we propose a method for the numerical estimation of mutual information from continuous data. We investigate the characteristic properties arising from the application of our algorithm and show that our approach outperforms commonly used algorithms: The significance, as a measure of the power of distinction from random correlation, is significantly increased. This concept is subsequently illustrated on two large-scale gene expression datasets and the results are compared to those obtained using other similarity measures. A C++ source code of our algorithm is available for non-commercial use from kloska@scienion.de upon request. Conclusion The utilisation of mutual information as similarity measure enables the detection of non-linear correlations in gene expression datasets. Frequently applied linear correlation measures, which are often used on an ad-hoc basis without further justification, are thereby extended. PMID:15339346

The Value of Information for Populations in Varying Environments

NASA Astrophysics Data System (ADS)

Rivoire, Olivier; Leibler, Stanislas

2011-04-01

The notion of information pervades informal descriptions of biological systems, but formal treatments face the problem of defining a quantitative measure of information rooted in a concept of fitness, which is itself an elusive notion. Here, we present a model of population dynamics where this problem is amenable to a mathematical analysis. In the limit where any information about future environmental variations is common to the members of the population, our model is equivalent to known models of financial investment. In this case, the population can be interpreted as a portfolio of financial assets and previous analyses have shown that a key quantity of Shannon's communication theory, the mutual information, sets a fundamental limit on the value of information. We show that this bound can be violated when accounting for features that are irrelevant in finance but inherent to biological systems, such as the stochasticity present at the individual level. This leads us to generalize the measures of uncertainty and information usually encountered in information theory.

Equity trees and graphs via information theory

NASA Astrophysics Data System (ADS)

Harré, M.; Bossomaier, T.

2010-01-01

We investigate the similarities and differences between two measures of the relationship between equities traded in financial markets. Our measures are the correlation coefficients and the mutual information. In the context of financial markets correlation coefficients are well established whereas mutual information has not previously been as well studied despite its theoretically appealing properties. We show that asset trees which are derived from either the correlation coefficients or the mutual information have a mixture of both similarities and differences at the individual equity level and at the macroscopic level. We then extend our consideration from trees to graphs using the "genus 0" condition recently introduced in order to study the networks of equities.

Registration of 2D to 3D joint images using phase-based mutual information

NASA Astrophysics Data System (ADS)

Dalvi, Rupin; Abugharbieh, Rafeef; Pickering, Mark; Scarvell, Jennie; Smith, Paul

2007-03-01

Registration of two dimensional to three dimensional orthopaedic medical image data has important applications particularly in the area of image guided surgery and sports medicine. Fluoroscopy to computer tomography (CT) registration is an important case, wherein digitally reconstructed radiographs derived from the CT data are registered to the fluoroscopy data. Traditional registration metrics such as intensity-based mutual information (MI) typically work well but often suffer from gross misregistration errors when the image to be registered contains a partial view of the anatomy visible in the target image. Phase-based MI provides a robust alternative similarity measure which, in addition to possessing the general robustness and noise immunity that MI provides, also employs local phase information in the registration process which makes it less susceptible to the aforementioned errors. In this paper, we propose using the complex wavelet transform for computing image phase information and incorporating that into a phase-based MI measure for image registration. Tests on a CT volume and 6 fluoroscopy images of the knee are presented. The femur and the tibia in the CT volume were individually registered to the fluoroscopy images using intensity-based MI, gradient-based MI and phase-based MI. Errors in the coordinates of fiducials present in the bone structures were used to assess the accuracy of the different registration schemes. Quantitative results demonstrate that the performance of intensity-based MI was the worst. Gradient-based MI performed slightly better, while phase-based MI results were the best consistently producing the lowest errors.

FPGA-based LDPC-coded APSK for optical communication systems.

PubMed

Zou, Ding; Lin, Changyu; Djordjevic, Ivan B

2017-02-20

In this paper, with the aid of mutual information and generalized mutual information (GMI) capacity analyses, it is shown that the geometrically shaped APSK that mimics an optimal Gaussian distribution with equiprobable signaling together with the corresponding gray-mapping rules can approach the Shannon limit closer than conventional quadrature amplitude modulation (QAM) at certain range of FEC overhead for both 16-APSK and 64-APSK. The field programmable gate array (FPGA) based LDPC-coded APSK emulation is conducted on block interleaver-based and bit interleaver-based systems; the results verify a significant improvement in hardware efficient bit interleaver-based systems. In bit interleaver-based emulation, the LDPC-coded 64-APSK outperforms 64-QAM, in terms of symbol signal-to-noise ratio (SNR), by 0.1 dB, 0.2 dB, and 0.3 dB at spectral efficiencies of 4.8, 4.5, and 4.2 b/s/Hz, respectively. It is found by emulation that LDPC-coded 64-APSK for spectral efficiencies of 4.8, 4.5, and 4.2 b/s/Hz is 1.6 dB, 1.7 dB, and 2.2 dB away from the GMI capacity.

Spatial attention improves the quality of population codes in human visual cortex.

PubMed

Saproo, Sameer; Serences, John T

2010-08-01

Selective attention enables sensory input from behaviorally relevant stimuli to be processed in greater detail, so that these stimuli can more accurately influence thoughts, actions, and future goals. Attention has been shown to modulate the spiking activity of single feature-selective neurons that encode basic stimulus properties (color, orientation, etc.). However, the combined output from many such neurons is required to form stable representations of relevant objects and little empirical work has formally investigated the relationship between attentional modulations on population responses and improvements in encoding precision. Here, we used functional MRI and voxel-based feature tuning functions to show that spatial attention induces a multiplicative scaling in orientation-selective population response profiles in early visual cortex. In turn, this multiplicative scaling correlates with an improvement in encoding precision, as evidenced by a concurrent increase in the mutual information between population responses and the orientation of attended stimuli. These data therefore demonstrate how multiplicative scaling of neural responses provides at least one mechanism by which spatial attention may improve the encoding precision of population codes. Increased encoding precision in early visual areas may then enhance the speed and accuracy of perceptual decisions computed by higher-order neural mechanisms.

Adaptive design optimization: a mutual information-based approach to model discrimination in cognitive science.

PubMed

Cavagnaro, Daniel R; Myung, Jay I; Pitt, Mark A; Kujala, Janne V

2010-04-01

Discriminating among competing statistical models is a pressing issue for many experimentalists in the field of cognitive science. Resolving this issue begins with designing maximally informative experiments. To this end, the problem to be solved in adaptive design optimization is identifying experimental designs under which one can infer the underlying model in the fewest possible steps. When the models under consideration are nonlinear, as is often the case in cognitive science, this problem can be impossible to solve analytically without simplifying assumptions. However, as we show in this letter, a full solution can be found numerically with the help of a Bayesian computational trick derived from the statistics literature, which recasts the problem as a probability density simulation in which the optimal design is the mode of the density. We use a utility function based on mutual information and give three intuitive interpretations of the utility function in terms of Bayesian posterior estimates. As a proof of concept, we offer a simple example application to an experiment on memory retention.

Dynamic Substrate for the Physical Encoding of Sensory Information in Bat Biosonar

NASA Astrophysics Data System (ADS)

Müller, Rolf; Gupta, Anupam K.; Zhu, Hongxiao; Pannala, Mittu; Gillani, Uzair S.; Fu, Yanqing; Caspers, Philip; Buck, John R.

2017-04-01

Horseshoe bats have dynamic biosonar systems with interfaces for ultrasonic emission (reception) that change shape while diffracting the outgoing (incoming) sound waves. An information-theoretic analysis based on numerical and physical prototypes shows that these shape changes add sensory information (mutual information between distant shape conformations <20 %), increase the number of resolvable directions of sound incidence, and improve the accuracy of direction finding. These results demonstrate that horseshoe bats have a highly effective substrate for dynamic encoding of sensory information.

Dynamic Substrate for the Physical Encoding of Sensory Information in Bat Biosonar.

PubMed

Müller, Rolf; Gupta, Anupam K; Zhu, Hongxiao; Pannala, Mittu; Gillani, Uzair S; Fu, Yanqing; Caspers, Philip; Buck, John R

2017-04-14

Horseshoe bats have dynamic biosonar systems with interfaces for ultrasonic emission (reception) that change shape while diffracting the outgoing (incoming) sound waves. An information-theoretic analysis based on numerical and physical prototypes shows that these shape changes add sensory information (mutual information between distant shape conformations <20%), increase the number of resolvable directions of sound incidence, and improve the accuracy of direction finding. These results demonstrate that horseshoe bats have a highly effective substrate for dynamic encoding of sensory information.

The Chaos of Katrina

DTIC Science & Technology

2007-03-01

partners for their mutual benefit. Unfortunately, based on government reports, FEMA did not have adequate control of its supply chain information ...is one attractor . “Edge of chaos” systems have two to eight attractors and in chaotic systems many attractors . Some are called strange attractors ...investigates whether chaos theory, part of complexity science, can extract information from Katrina contracting data to help managers make better logistics

Direction of coupling from phases of interacting oscillators: An information-theoretic approach

NASA Astrophysics Data System (ADS)

Paluš, Milan; Stefanovska, Aneta

2003-05-01

A directionality index based on conditional mutual information is proposed for application to the instantaneous phases of weakly coupled oscillators. Its abilities to distinguish unidirectional from bidirectional coupling, as well as to reveal and quantify asymmetry in bidirectional coupling, are demonstrated using numerical examples of quasiperiodic, chaotic, and noisy oscillators, as well as real human cardiorespiratory data.

31 CFR 1024.500 - General.

Code of Federal Regulations, 2011 CFR

2011-07-01

... ENFORCEMENT NETWORK, DEPARTMENT OF THE TREASURY RULES FOR MUTUAL FUNDS Special Information Sharing Procedures To Deter Money Laundering and Terrorist Activity § 1024.500 General. Mutual funds are subject to the... forth and cross referenced in this subpart. Mutual funds should also refer to subpart E of part 1010 of...

Traditional risk-sharing arrangements and informal social insurance in Eritrea.

PubMed

Habtom, GebreMichael Kibreab; Ruys, Pieter

2007-01-01

In Eritrea neither the state nor the market is effective in providing health insurance to low-income people (in rural and informal job sector). Schemes intended for the informal sector are confronted with low and irregular incomes of target populations and consequently negligible potential for profit making. Because of this there, are no formal health insurance systems in Eritrea that cover people in the traditional (or informal) sector of the economy. In the absence of formal safety nets traditional Eritrean societies use their local social capital to alleviate unexpected social costs. In Eritrea traditional risk-sharing arrangements are made within extended families and mutual aid community associations. This study reveals that in a situation where the state no longer provides free public health services any more and access to private insurance is denied, the extension of the voluntary mutual aid community associations to Mahber-based health insurance schemes at the local level is a viable way for providing modern health services.

The evaluation of multi-structure, multi-atlas pelvic anatomy features in a prostate MR lymphography CAD system

NASA Astrophysics Data System (ADS)

Meijs, M.; Debats, O.; Huisman, H.

2015-03-01

In prostate cancer, the detection of metastatic lymph nodes indicates progression from localized disease to metastasized cancer. The detection of positive lymph nodes is, however, a complex and time consuming task for experienced radiologists. Assistance of a two-stage Computer-Aided Detection (CAD) system in MR Lymphography (MRL) is not yet feasible due to the large number of false positives in the first stage of the system. By introducing a multi-structure, multi-atlas segmentation, using an affine transformation followed by a B-spline transformation for registration, the organ location is given by a mean density probability map. The atlas segmentation is semi-automatically drawn with ITK-SNAP, using Active Contour Segmentation. Each anatomic structure is identified by a label number. Registration is performed using Elastix, using Mutual Information and an Adaptive Stochastic Gradient optimization. The dataset consists of the MRL scans of ten patients, with lymph nodes manually annotated in consensus by two expert readers. The feature map of the CAD system consists of the Multi-Atlas and various other features (e.g. Normalized Intensity and multi-scale Blobness). The voxel-based Gentleboost classifier is evaluated using ROC analysis with cross validation. We show in a set of 10 studies that adding multi-structure, multi-atlas anatomical structure likelihood features improves the quality of the lymph node voxel likelihood map. Multiple structure anatomy maps may thus make MRL CAD more feasible.

Independent EEG Sources Are Dipolar

PubMed Central

Delorme, Arnaud; Palmer, Jason; Onton, Julie; Oostenveld, Robert; Makeig, Scott

2012-01-01

Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI) in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR) effected by each decomposition, and decomposition ‘dipolarity’ defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least well-performing algorithm was principal component analysis (PCA); best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonly-used decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partially-synchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons, the data and software used to prepare the results have been made available (http://sccn.ucsd.edu/wiki/BSSComparison). PMID:22355308

EURONET: Guidelines for Cooperation between Data Base Suppliers and Host Organizations.

ERIC Educational Resources Information Center

Commission des Communautes Europeennes (Luxembourg). Bureau de Terminologie.

The purpose of the guidelines is to focus on the mutual benefits which arise from close cooperation between Data Base Suppliers (DBS) and Hosts, and to identify some of the individual responsibilities. The guidelines have been assembled from accepted policies and procedures in information supply activities to assist EURONET Hosts and DBS to work…

Effect of interacting second- and third-order stimulus-dependent correlations on population-coding asymmetries.

PubMed

Montangie, Lisandro; Montani, Fernando

2016-10-01

Spike correlations among neurons are widely encountered in the brain. Although models accounting for pairwise interactions have proved able to capture some of the most important features of population activity at the level of the retina, the evidence shows that pairwise neuronal correlation analysis does not resolve cooperative population dynamics by itself. By means of a series expansion for short time scales of the mutual information conveyed by a population of neurons, the information transmission can be broken down into firing rate and correlational components. In a proposed extension of this framework, we investigate the information components considering both second- and higher-order correlations. We show that the existence of a mixed stimulus-dependent correlation term defines a new scenario for the interplay between pairwise and higher-than-pairwise interactions in noise and signal correlations that would lead either to redundancy or synergy in the information-theoretic sense.

Using Mutual Information for Adaptive Item Comparison and Student Assessment

ERIC Educational Resources Information Center

Liu, Chao-Lin

2005-01-01

The author analyzes properties of mutual information between dichotomous concepts and test items. The properties generalize some common intuitions about item comparison, and provide principled foundations for designing item-selection heuristics for student assessment in computer-assisted educational systems. The proposed item-selection strategies…

Exploratory study of the impacts of Mutual Health Organizations on social dynamics in Benin.

PubMed

Ridde, Valery; Haddad, Slim; Yacoubou, Moussa; Yacoubou, Ismaelou

2010-08-01

The primary aim of Mutual Health Organizations (MHOs) is the financial protection of their members. However, given their community-based, participative and voluntary nature, it is conceivable that MHOs, as social organizations, would affect social dynamics. In an exploratory study in Benin, we studied social dynamics related to mutual aid, relationships of trust, and empowerment. Four MHOs, as contrasted cases, were selected from among the 11 in the region. Focus groups (n = 20) and individual interviews (n = 29) were conducted with members, non-members, and elected leaders of the four MHOs, and with professionals from the health facilities concerned. We carried out a qualitative thematic analysis of the content. Mutual aid practices, which pre-date MHOs, can be mobilized to promote MHO membership. Mutual aid practices are based on relationships of trust. The primary reason for joining an MHO is to improve financial accessibility to health services. Non-members see that members have a strong sense of empowerment in this regard, based on a high level of trust in MHOs and their elected leaders, even if their trust in health professionals is not as strong. Non-members share these feelings of confidence in MHOs and their leadership, although they trust health professionals somewhat less than do the members. The MHOs' low penetration rate therefore cannot be explained by lack of trust, as this study shows that, even with some distrust of the professionals, the overall level of trust in MHOs is high and MHOs and their leaders function as intermediaries with health professionals. Other explanatory factors are the lack of information available to villagers and, most especially, the problems they face in being able to pay the MHO premiums. Copyright 2010 Elsevier Ltd. All rights reserved.

Increasing the information rates of optical communications via coded modulation: a study of transceiver performance

NASA Astrophysics Data System (ADS)

Maher, Robert; Alvarado, Alex; Lavery, Domaniç; Bayvel, Polina

2016-02-01

Optical fibre underpins the global communications infrastructure and has experienced an astonishing evolution over the past four decades, with current commercial systems transmitting data rates in excess of 10 Tb/s over a single fibre core. The continuation of this dramatic growth in throughput has become constrained due to a power dependent nonlinear distortion arising from a phenomenon known as the Kerr effect. The mitigation of fibre nonlinearities is an area of intense research. However, even in the absence of nonlinear distortion, the practical limit on the transmission throughput of a single fibre core is dominated by the finite signal-to-noise ratio (SNR) afforded by current state-of-the-art coherent optical transceivers. Therefore, the key to maximising the number of information bits that can be reliably transmitted over a fibre channel hinges on the simultaneous optimisation of the modulation format and code rate, based on the SNR achieved at the receiver. In this work, we use an information theoretic approach based on the mutual information and the generalised mutual information to characterise a state-of-the-art dual polarisation m-ary quadrature amplitude modulation transceiver and subsequently apply this methodology to a 15-carrier super-channel to achieve the highest throughput (1.125 Tb/s) ever recorded using a single coherent receiver.

Increasing the information rates of optical communications via coded modulation: a study of transceiver performance

PubMed Central

Maher, Robert; Alvarado, Alex; Lavery, Domaniç; Bayvel, Polina

2016-01-01

Optical fibre underpins the global communications infrastructure and has experienced an astonishing evolution over the past four decades, with current commercial systems transmitting data rates in excess of 10 Tb/s over a single fibre core. The continuation of this dramatic growth in throughput has become constrained due to a power dependent nonlinear distortion arising from a phenomenon known as the Kerr effect. The mitigation of fibre nonlinearities is an area of intense research. However, even in the absence of nonlinear distortion, the practical limit on the transmission throughput of a single fibre core is dominated by the finite signal-to-noise ratio (SNR) afforded by current state-of-the-art coherent optical transceivers. Therefore, the key to maximising the number of information bits that can be reliably transmitted over a fibre channel hinges on the simultaneous optimisation of the modulation format and code rate, based on the SNR achieved at the receiver. In this work, we use an information theoretic approach based on the mutual information and the generalised mutual information to characterise a state-of-the-art dual polarisation m-ary quadrature amplitude modulation transceiver and subsequently apply this methodology to a 15-carrier super-channel to achieve the highest throughput (1.125 Tb/s) ever recorded using a single coherent receiver. PMID:26864633

Characteristics analysis of acupuncture electroencephalograph based on mutual information Lempel—Ziv complexity

NASA Astrophysics Data System (ADS)

Luo, Xi-Liu; Wang, Jiang; Han, Chun-Xiao; Deng, Bin; Wei, Xi-Le; Bian, Hong-Rui

2012-02-01

As a convenient approach to the characterization of cerebral cortex electrical information, electroencephalograph (EEG) has potential clinical application in monitoring the acupuncture effects. In this paper, a method composed of the mutual information method and Lempel—Ziv complexity method (MILZC) is proposed to investigate the effects of acupuncture on the complexity of information exchanges between different brain regions based on EEGs. In the experiments, eight subjects are manually acupunctured at ‘Zusanli’ acupuncture point (ST-36) with different frequencies (i.e., 50, 100, 150, and 200 times/min) and the EEGs are recorded simultaneously. First, MILZC values are compared in general. Then average brain connections are used to quantify the effectiveness of acupuncture under the above four frequencies. Finally, significance index P values are used to study the spatiality of the acupuncture effect on the brain. Three main findings are obtained: (i) MILZC values increase during the acupuncture; (ii) manual acupunctures (MAs) with 100 times/min and 150 times/min are more effective than with 50 times/min and 200 times/min; (iii) contralateral hemisphere activation is more prominent than ipsilateral hemisphere's. All these findings suggest that acupuncture contributes to the increase of brain information exchange complexity and the MILZC method can successfully describe these changes.

Feature Biases in Early Word Learning: Network Distinctiveness Predicts Age of Acquisition

ERIC Educational Resources Information Center

Engelthaler, Tomas; Hills, Thomas T.

2017-01-01

Do properties of a word's features influence the order of its acquisition in early word learning? Combining the principles of mutual exclusivity and shape bias, the present work takes a network analysis approach to understanding how feature distinctiveness predicts the order of early word learning. Distance networks were built from nouns with edge…

An Efficient Method to Detect Mutual Overlap of a Large Set of Unordered Images for Structure-From

NASA Astrophysics Data System (ADS)

Wang, X.; Zhan, Z. Q.; Heipke, C.

2017-05-01

Recently, low-cost 3D reconstruction based on images has become a popular focus of photogrammetry and computer vision research. Methods which can handle an arbitrary geometric setup of a large number of unordered and convergent images are of particular interest. However, determining the mutual overlap poses a considerable challenge. We propose a new method which was inspired by and improves upon methods employing random k-d forests for this task. Specifically, we first derive features from the images and then a random k-d forest is used to find the nearest neighbours in feature space. Subsequently, the degree of similarity between individual images, the image overlaps and thus images belonging to a common block are calculated as input to a structure-from-motion (sfm) pipeline. In our experiments we show the general applicability of the new method and compare it with other methods by analyzing the time efficiency. Orientations and 3D reconstructions were successfully conducted with our overlap graphs by sfm. The results show a speed-up of a factor of 80 compared to conventional pairwise matching, and of 8 and 2 compared to the VocMatch approach using 1 and 4 CPU, respectively.

Mutual-information-based image to patient re-registration using intraoperative ultrasound in image-guided neurosurgery

PubMed Central

Ji, Songbai; Wu, Ziji; Hartov, Alex; Roberts, David W.; Paulsen, Keith D.

2008-01-01

An image-based re-registration scheme has been developed and evaluated that uses fiducial registration as a starting point to maximize the normalized mutual information (nMI) between intraoperative ultrasound (iUS) and preoperative magnetic resonance images (pMR). We show that this scheme significantly (p⪡0.001) reduces tumor boundary misalignment between iUS pre-durotomy and pMR from an average of 2.5 mm to 1.0 mm in six resection surgeries. The corrected tumor alignment before dural opening provides a more accurate reference for assessing subsequent intraoperative tumor displacement, which is important for brain shift compensation as surgery progresses. In addition, we report the translational and rotational capture ranges necessary for successful convergence of the nMI registration technique (5.9 mm and 5.2 deg, respectively). The proposed scheme is automatic, sufficiently robust, and computationally efficient (<2 min), and holds promise for routine clinical use in the operating room during image-guided neurosurgical procedures. PMID:18975707

Practical image registration concerns overcome by the weighted and filtered mutual information metric

NASA Astrophysics Data System (ADS)

Keane, Tommy P.; Saber, Eli; Rhody, Harvey; Savakis, Andreas; Raj, Jeffrey

2012-04-01

Contemporary research in automated panorama creation utilizes camera calibration or extensive knowledge of camera locations and relations to each other to achieve successful results. Research in image registration attempts to restrict these same camera parameters or apply complex point-matching schemes to overcome the complications found in real-world scenarios. This paper presents a novel automated panorama creation algorithm by developing an affine transformation search based on maximized mutual information (MMI) for region-based registration. Standard MMI techniques have been limited to applications with airborne/satellite imagery or medical images. We show that a novel MMI algorithm can approximate an accurate registration between views of realistic scenes of varying depth distortion. The proposed algorithm has been developed using stationary, color, surveillance video data for a scenario with no a priori camera-to-camera parameters. This algorithm is robust for strict- and nearly-affine-related scenes, while providing a useful approximation for the overlap regions in scenes related by a projective homography or a more complex transformation, allowing for a set of efficient and accurate initial conditions for pixel-based registration.

Measurement Matrix Design for Phase Retrieval Based on Mutual Information

NASA Astrophysics Data System (ADS)

Shlezinger, Nir; Dabora, Ron; Eldar, Yonina C.

2018-01-01

In phase retrieval problems, a signal of interest (SOI) is reconstructed based on the magnitude of a linear transformation of the SOI observed with additive noise. The linear transform is typically referred to as a measurement matrix. Many works on phase retrieval assume that the measurement matrix is a random Gaussian matrix, which, in the noiseless scenario with sufficiently many measurements, guarantees invertability of the transformation between the SOI and the observations, up to an inherent phase ambiguity. However, in many practical applications, the measurement matrix corresponds to an underlying physical setup, and is therefore deterministic, possibly with structural constraints. In this work we study the design of deterministic measurement matrices, based on maximizing the mutual information between the SOI and the observations. We characterize necessary conditions for the optimality of a measurement matrix, and analytically obtain the optimal matrix in the low signal-to-noise ratio regime. Practical methods for designing general measurement matrices and masked Fourier measurements are proposed. Simulation tests demonstrate the performance gain achieved by the proposed techniques compared to random Gaussian measurements for various phase recovery algorithms.

Frog sound identification using extended k-nearest neighbor classifier

NASA Astrophysics Data System (ADS)

Mukahar, Nordiana; Affendi Rosdi, Bakhtiar; Athiar Ramli, Dzati; Jaafar, Haryati

2017-09-01

Frog sound identification based on the vocalization becomes important for biological research and environmental monitoring. As a result, different types of feature extractions and classifiers have been employed to evaluate the accuracy of frog sound identification. This paper presents a frog sound identification with Extended k-Nearest Neighbor (EKNN) classifier. The EKNN classifier integrates the nearest neighbors and mutual sharing of neighborhood concepts, with the aims of improving the classification performance. It makes a prediction based on who are the nearest neighbors of the testing sample and who consider the testing sample as their nearest neighbors. In order to evaluate the classification performance in frog sound identification, the EKNN classifier is compared with competing classifier, k -Nearest Neighbor (KNN), Fuzzy k -Nearest Neighbor (FKNN) k - General Nearest Neighbor (KGNN)and Mutual k -Nearest Neighbor (MKNN) on the recorded sounds of 15 frog species obtained in Malaysia forest. The recorded sounds have been segmented using Short Time Energy and Short Time Average Zero Crossing Rate (STE+STAZCR), sinusoidal modeling (SM), manual and the combination of Energy (E) and Zero Crossing Rate (ZCR) (E+ZCR) while the features are extracted by Mel Frequency Cepstrum Coefficient (MFCC). The experimental results have shown that the EKNCN classifier exhibits the best performance in terms of accuracy compared to the competing classifiers, KNN, FKNN, GKNN and MKNN for all cases.

Integrative approach for inference of gene regulatory networks using lasso-based random featuring and application to psychiatric disorders.

PubMed

Kim, Dongchul; Kang, Mingon; Biswas, Ashis; Liu, Chunyu; Gao, Jean

2016-08-10

Inferring gene regulatory networks is one of the most interesting research areas in the systems biology. Many inference methods have been developed by using a variety of computational models and approaches. However, there are two issues to solve. First, depending on the structural or computational model of inference method, the results tend to be inconsistent due to innately different advantages and limitations of the methods. Therefore the combination of dissimilar approaches is demanded as an alternative way in order to overcome the limitations of standalone methods through complementary integration. Second, sparse linear regression that is penalized by the regularization parameter (lasso) and bootstrapping-based sparse linear regression methods were suggested in state of the art methods for network inference but they are not effective for a small sample size data and also a true regulator could be missed if the target gene is strongly affected by an indirect regulator with high correlation or another true regulator. We present two novel network inference methods based on the integration of three different criteria, (i) z-score to measure the variation of gene expression from knockout data, (ii) mutual information for the dependency between two genes, and (iii) linear regression-based feature selection. Based on these criterion, we propose a lasso-based random feature selection algorithm (LARF) to achieve better performance overcoming the limitations of bootstrapping as mentioned above. In this work, there are three main contributions. First, our z score-based method to measure gene expression variations from knockout data is more effective than similar criteria of related works. Second, we confirmed that the true regulator selection can be effectively improved by LARF. Lastly, we verified that an integrative approach can clearly outperform a single method when two different methods are effectively jointed. In the experiments, our methods were validated by outperforming the state of the art methods on DREAM challenge data, and then LARF was applied to inferences of gene regulatory network associated with psychiatric disorders.

Has Group Work Education Lost Its Social Group Work Essence? A Content Analysis of MSW Course Syllabi in Search of Mutual Aid and Group Conflict Content

ERIC Educational Resources Information Center

Sweifach, Jay Stephen

2015-01-01

This article presents the results of a content analysis of MSW group work course syllabi in an effort to better understand the extent to which mutual aid and group conflict, two important dimensions of social group work, are included and featured as prominent elements in MSW-level group work instruction.

Field Day: A Case Study examining scientists’ oral performance skills

USDA-ARS?s Scientific Manuscript database

Communication is a complex cyclic process wherein senders and receivers encode and decode information in an effort to reach a state of mutuality or mutual understanding. When the communication of scientific or technical information occurs in a public space, effective speakers follow a formula for co...

Mutual Information Item Selection in Adaptive Classification Testing

ERIC Educational Resources Information Center

Weissman, Alexander

2007-01-01

A general approach for item selection in adaptive multiple-category classification tests is provided. The approach uses mutual information (MI), a special case of the Kullback-Leibler distance, or relative entropy. MI works efficiently with the sequential probability ratio test and alleviates the difficulties encountered with using other local-…

75 FR 4894 - Self-Regulatory Organizations; National Securities Clearing Corporation; Notice of Filing and...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-01-29

... activity.\\5\\ There will also be an increase in the monthly fee for the mutual fund Profile Phase II Service.... Profile Phase I transmits mutual fund price and rate information. Profile Phase II stores data elements such as accumulation, breakpoints, and commission eligibility that relate to mutual fund processing...

Granular Flow Graph, Adaptive Rule Generation and Tracking.

PubMed

Pal, Sankar Kumar; Chakraborty, Debarati Bhunia

2017-12-01

A new method of adaptive rule generation in granular computing framework is described based on rough rule base and granular flow graph, and applied for video tracking. In the process, several new concepts and operations are introduced, and methodologies formulated with superior performance. The flow graph enables in defining an intelligent technique for rule base adaptation where its characteristics in mapping the relevance of attributes and rules in decision-making system are exploited. Two new features, namely, expected flow graph and mutual dependency between flow graphs are defined to make the flow graph applicable in the tasks of both training and validation. All these techniques are performed in neighborhood granular level. A way of forming spatio-temporal 3-D granules of arbitrary shape and size is introduced. The rough flow graph-based adaptive granular rule-based system, thus produced for unsupervised video tracking, is capable of handling the uncertainties and incompleteness in frames, able to overcome the incompleteness in information that arises without initial manual interactions and in providing superior performance and gaining in computation time. The cases of partial overlapping and detecting the unpredictable changes are handled efficiently. It is shown that the neighborhood granulation provides a balanced tradeoff between speed and accuracy as compared to pixel level computation. The quantitative indices used for evaluating the performance of tracking do not require any information on ground truth as in the other methods. Superiority of the algorithm to nonadaptive and other recent ones is demonstrated extensively.

Mutually unbiased bases and semi-definite programming

NASA Astrophysics Data System (ADS)

Brierley, Stephen; Weigert, Stefan

2010-11-01

A complex Hilbert space of dimension six supports at least three but not more than seven mutually unbiased bases. Two computer-aided analytical methods to tighten these bounds are reviewed, based on a discretization of parameter space and on Gröbner bases. A third algorithmic approach is presented: the non-existence of more than three mutually unbiased bases in composite dimensions can be decided by a global optimization method known as semidefinite programming. The method is used to confirm that the spectral matrix cannot be part of a complete set of seven mutually unbiased bases in dimension six.

Information-theoretical noninvasive damage detection in bridge structures

NASA Astrophysics Data System (ADS)

Sudu Ambegedara, Amila; Sun, Jie; Janoyan, Kerop; Bollt, Erik

2016-11-01

Damage detection of mechanical structures such as bridges is an important research problem in civil engineering. Using spatially distributed sensor time series data collected from a recent experiment on a local bridge in Upper State New York, we study noninvasive damage detection using information-theoretical methods. Several findings are in order. First, the time series data, which represent accelerations measured at the sensors, more closely follow Laplace distribution than normal distribution, allowing us to develop parameter estimators for various information-theoretic measures such as entropy and mutual information. Second, as damage is introduced by the removal of bolts of the first diaphragm connection, the interaction between spatially nearby sensors as measured by mutual information becomes weaker, suggesting that the bridge is "loosened." Finally, using a proposed optimal mutual information interaction procedure to prune away indirect interactions, we found that the primary direction of interaction or influence aligns with the traffic direction on the bridge even after damaging the bridge.

12-Step Interventions and Mutual Support Programs for Substance Use Disorders: An Overview

PubMed Central

Donovan, Dennis M.; Ingalsbe, Michelle H.; Benbow, James; Daley, Dennis C.

2013-01-01

Social workers and other behavioral health professionals are likely to encounter individuals with substance use disorders in a variety of practice settings outside of specialty treatment. 12-Step mutual support programs represent readily available, no cost community-based resources for such individuals; however, practitioners are often unfamiliar with such programs. The present article provides a brief overview of 12-Step programs, the positive substance use and psychosocial outcomes associated with active 12-Step involvement, and approaches ranging from ones that can be utilized by social workers in any practice setting to those developed for specialty treatment programs to facilitate engagement in 12-Step meetings and recovery activities. The goal is to familiarize social workers with 12-Step approaches so that they are better able to make informed referrals that match clients to mutual support groups that best meet the individual’s needs and maximize the likelihood of engagement and positive outcomes. PMID:23731422

A piecewise probabilistic regression model to decode hand movement trajectories from epidural and subdural ECoG signals

NASA Astrophysics Data System (ADS)

Farrokhi, Behraz; Erfanian, Abbas

2018-06-01

Objective. The primary concern of this study is to develop a probabilistic regression method that would improve the decoding of the hand movement trajectories from epidural ECoG as well as from subdural ECoG signals. Approach. The model is characterized by the conditional expectation of the hand position given the ECoG signals. The conditional expectation of the hand position is then modeled by a linear combination of the conditional probability density functions defined for each segment of the movement. Moreover, a spatial linear filter is proposed for reducing the dimension of the feature space. The spatial linear filter is applied to each frequency band of the ECoG signals and extract the features with highest decoding performance. Main results. For evaluating the proposed method, a dataset including 28 ECoG recordings from four adult Japanese macaques is used. The results show that the proposed decoding method outperforms the results with respect to the state of the art methods using this dataset. The relative kinematic information of each frequency band is also investigated using mutual information and decoding performance. The decoding performance shows that the best performance was obtained for high gamma bands from 50 to 200 Hz as well as high frequency ECoG band from 200 to 400 Hz for subdural recordings. However, the decoding performance was decreased for these frequency bands using epidural recordings. The mutual information shows that, on average, the high gamma band from 50 to 200 Hz and high frequency ECoG band from 200 to 400 Hz contain significantly more information than the average of the rest of the frequency bands ≤ft( p<0.001 \\right) for both subdural and epidural recordings. The results of high resolution time-frequency analysis show that ERD/ERS patterns in all frequency bands could reveal the dynamics of the ECoG responses during the movement. The onset and offset of the movement can be clearly identified by the ERD/ERS patterns. Significance. Reliable decoding the kinematic information from the brain signals paves the way for robust control of external devices.

Receptive Field Vectors of Genetically-Identified Retinal Ganglion Cells Reveal Cell-Type-Dependent Visual Functions

PubMed Central

Katz, Matthew L.; Viney, Tim J.; Nikolic, Konstantin

2016-01-01

Sensory stimuli are encoded by diverse kinds of neurons but the identities of the recorded neurons that are studied are often unknown. We explored in detail the firing patterns of eight previously defined genetically-identified retinal ganglion cell (RGC) types from a single transgenic mouse line. We first introduce a new technique of deriving receptive field vectors (RFVs) which utilises a modified form of mutual information (“Quadratic Mutual Information”). We analysed the firing patterns of RGCs during presentation of short duration (~10 second) complex visual scenes (natural movies). We probed the high dimensional space formed by the visual input for a much smaller dimensional subspace of RFVs that give the most information about the response of each cell. The new technique is very efficient and fast and the derivation of novel types of RFVs formed by the natural scene visual input was possible even with limited numbers of spikes per cell. This approach enabled us to estimate the 'visual memory' of each cell type and the corresponding receptive field area by calculating Mutual Information as a function of the number of frames and radius. Finally, we made predictions of biologically relevant functions based on the RFVs of each cell type. RGC class analysis was complemented with results for the cells’ response to simple visual input in the form of black and white spot stimulation, and their classification on several key physiological metrics. Thus RFVs lead to predictions of biological roles based on limited data and facilitate analysis of sensory-evoked spiking data from defined cell types. PMID:26845435

Teambuilding, Innovation and the Engineering Communication Interface

ERIC Educational Resources Information Center

Prescott, David; El-Sakran, Tharwat; Albasha, Lutfi; Aloul, Fadi; Al-Assaf, Yousef

2012-01-01

Recent engineering industry-based research has identified a number of skill deficiencies in graduating engineers. Emphasis on communication and teamwork informed by attributes of self management, problem solving and mutual accountability have been recognized as important needs by The Engineering Accreditation Commission of ABET of the United…

Improving Archives-Library Relations: User-Centered Solutions to a Sibling Rivalry.

ERIC Educational Resources Information Center

Maher, William J.

1990-01-01

Examines the sources of tension between academic archives and academic libraries, prospects for improved relations based on common ground, and factors that might limit improved relations. Areas where cooperation might occur are discussed, including mutual fund raising efforts, documentation strategies, administrative information services, and…

Entanglement entropy of dispersive media from thermodynamic entropy in one higher dimension.

PubMed

Maghrebi, M F; Reid, M T H

2015-04-17

A dispersive medium becomes entangled with zero-point fluctuations in the vacuum. We consider an arbitrary array of material bodies weakly interacting with a quantum field and compute the quantum mutual information between them. It is shown that the mutual information in D dimensions can be mapped to classical thermodynamic entropy in D+1 dimensions. As a specific example, we compute the mutual information both analytically and numerically for a range of separation distances between two bodies in D=2 dimensions and find a logarithmic correction to the area law at short separations. A key advantage of our method is that it allows the strong subadditivity property to be easily verified.

Osteoporosis and Periodontitis.

PubMed

Wang, Chin-Wei Jeff; McCauley, Laurie K

2016-12-01

Osteoporosis and periodontitis are both diseases characterized by bone resorption. Osteoporosis features systemic degenerative bone loss that leads to loss of skeletal cancellous microstructure and subsequent fracture, whereas periodontitis involves local inflammatory bone loss, following an infectious breach of the alveolar cortical bone, and it may result in tooth loss. Most cross-sectional studies have confirmed the association of osteoporosis and periodontitis primarily on radiographic measurements and to a lesser degree on clinical parameters. Multiple shared risk factors include age, genetics, hormonal change, smoking, as well as calcium and vitamin D deficiency. Both diseases could also be risk factors for each other and have a mutual impact that requires concomitant management. Suggested mechanisms underlying the linkage are disruption of the homeostasis concerning bone remodeling, hormonal balance, and inflammation resolution. A mutual interventional approach is emerging with complex treatment interactions. Prevention and management of both diseases require interdisciplinary approaches and warrants future well-controlled longitudinal and interventional studies for evidence-based clinical guidelines.

Temporal Limitations in the Effective Binding of Attended Target Attributes in the Mutual Masking of Visual Objects

ERIC Educational Resources Information Center

Hommuk, Karita; Bachmann, Talis

2009-01-01

The problem of feature binding has been examined under conditions of distributed attention or with spatially dispersed stimuli. We studied binding by asking whether selective attention to a feature of a masked object enables perceptual access to the other features of that object using conditions in which spatial attention was directed at a single…

Higher-Order Statistical Correlations and Mutual Information Among Particles in a Quantum Well

NASA Astrophysics Data System (ADS)

Yépez, V. S.; Sagar, R. P.; Laguna, H. G.

2017-12-01

The influence of wave function symmetry on statistical correlation is studied for the case of three non-interacting spin-free quantum particles in a unidimensional box, in position and in momentum space. Higher-order statistical correlations occurring among the three particles in this quantum system is quantified via higher-order mutual information and compared to the correlation between pairs of variables in this model, and to the correlation in the two-particle system. The results for the higher-order mutual information show that there are states where the symmetric wave functions are more correlated than the antisymmetric ones with same quantum numbers. This holds in position as well as in momentum space. This behavior is opposite to that observed for the correlation between pairs of variables in this model, and the two-particle system, where the antisymmetric wave functions are in general more correlated. These results are also consistent with those observed in a system of three uncoupled oscillators. The use of higher-order mutual information as a correlation measure, is monitored and examined by considering a superposition of states or systems with two Slater determinants.

A configuration space of homologous proteins conserving mutual information and allowing a phylogeny inference based on pair-wise Z-score probabilities.

PubMed

Bastien, Olivier; Ortet, Philippe; Roy, Sylvaine; Maréchal, Eric

2005-03-10

Popular methods to reconstruct molecular phylogenies are based on multiple sequence alignments, in which addition or removal of data may change the resulting tree topology. We have sought a representation of homologous proteins that would conserve the information of pair-wise sequence alignments, respect probabilistic properties of Z-scores (Monte Carlo methods applied to pair-wise comparisons) and be the basis for a novel method of consistent and stable phylogenetic reconstruction. We have built up a spatial representation of protein sequences using concepts from particle physics (configuration space) and respecting a frame of constraints deduced from pair-wise alignment score properties in information theory. The obtained configuration space of homologous proteins (CSHP) allows the representation of real and shuffled sequences, and thereupon an expression of the TULIP theorem for Z-score probabilities. Based on the CSHP, we propose a phylogeny reconstruction using Z-scores. Deduced trees, called TULIP trees, are consistent with multiple-alignment based trees. Furthermore, the TULIP tree reconstruction method provides a solution for some previously reported incongruent results, such as the apicomplexan enolase phylogeny. The CSHP is a unified model that conserves mutual information between proteins in the way physical models conserve energy. Applications include the reconstruction of evolutionary consistent and robust trees, the topology of which is based on a spatial representation that is not reordered after addition or removal of sequences. The CSHP and its assigned phylogenetic topology, provide a powerful and easily updated representation for massive pair-wise genome comparisons based on Z-score computations.

An online community of practice to support evidence-based physiotherapy practice in manual therapy.

PubMed

Evans, Cathy; Yeung, Euson; Markoulakis, Roula; Guilcher, Sara

2014-01-01

The purpose of this study was to explore how a community of practice promoted the creation and sharing of new knowledge in evidence-based manual therapy using Wenger's constructs of mutual engagement, joint enterprise, and shared repertoire as a theoretical framework. We used a qualitative approach to analyze the discussion board contributions of the 19 physiotherapists who participated in the 10-week online continuing education course in evidence-based practice (EBP) in manual therapy. The course was founded on community of practice, constructivism, social, and situated learning principles. The 1436 postings on 9 active discussion boards revealed that the community of practice was a social learning environment that supported strong participation and mutual engagement. Design features such as consistent facilitation, weekly guiding questions, and collaborative assignments promoted the creation and sharing of knowledge. Participants applied research evidence to the contexts in which they worked through reflective comparison of what they were reading to its applicability in their everyday practice. Participants' shared goals contributed to the common ground established in developing collective knowledge about different study designs, how to answer research questions, and the difficulties of conducting sound research. An online longitudinal community of practice utilized as a continuing education approach to deliver an online course based on constructivist and social learning principles allowed geographically dispersed physiotherapists to be mutually engaged in a joint enterprise in evidence-based manual therapy. Advantages included opportunity for reflection, modeling, and collaboration. Future studies should examine the impact of participation on clinical practice. © 2014 The Alliance for Continuing Education in the Health Professions, the Society for Academic Continuing Medical Education, and the Council on Continuing Medical Education, Association for Hospital Medical Education.

Bipartite entangled stabilizer mutually unbiased bases as maximum cliques of Cayley graphs

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

Dam, Wim van; Howard, Mark; Department of Physics, University of California, Santa Barbara, California 93106

2011-07-15

We examine the existence and structure of particular sets of mutually unbiased bases (MUBs) in bipartite qudit systems. In contrast to well-known power-of-prime MUB constructions, we restrict ourselves to using maximally entangled stabilizer states as MUB vectors. Consequently, these bipartite entangled stabilizer MUBs (BES MUBs) provide no local information, but are sufficient and minimal for decomposing a wide variety of interesting operators including (mixtures of) Jamiolkowski states, entanglement witnesses, and more. The problem of finding such BES MUBs can be mapped, in a natural way, to that of finding maximum cliques in a family of Cayley graphs. Some relationships withmore » known power-of-prime MUB constructions are discussed, and observables for BES MUBs are given explicitly in terms of Pauli operators.« less

Bipartite entangled stabilizer mutually unbiased bases as maximum cliques of Cayley graphs

NASA Astrophysics Data System (ADS)

van Dam, Wim; Howard, Mark

2011-07-01

We examine the existence and structure of particular sets of mutually unbiased bases (MUBs) in bipartite qudit systems. In contrast to well-known power-of-prime MUB constructions, we restrict ourselves to using maximally entangled stabilizer states as MUB vectors. Consequently, these bipartite entangled stabilizer MUBs (BES MUBs) provide no local information, but are sufficient and minimal for decomposing a wide variety of interesting operators including (mixtures of) Jamiołkowski states, entanglement witnesses, and more. The problem of finding such BES MUBs can be mapped, in a natural way, to that of finding maximum cliques in a family of Cayley graphs. Some relationships with known power-of-prime MUB constructions are discussed, and observables for BES MUBs are given explicitly in terms of Pauli operators.

75 FR 69130 - Proposed Extension of Information Collection Request Submitted for Public Comment; Prohibited...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-10

.... Description: Without the relief provided by this exemption, an open-end mutual fund would be unable to sell... investment advisor for the mutual fund. As a result, plans would be compelled to liquidate their existing... disclosure requirements. The first requires at the time of the purchase or sale of such mutual fund shares...

Non-Markovianity Measure Based on Brukner-Zeilinger Invariant Information for Unital Quantum Dynamical Maps

NASA Astrophysics Data System (ADS)

He, Zhi; Zhu, Lie-Qiang; Li, Li

2017-03-01

A non-Markovianity measure based on Brukner-Zeilinger invariant information to characterize non-Markovian effect of open systems undergoing unital dynamical maps is proposed. The method takes advantage of non-increasing property of the Brukner-Zeilinger invariant information under completely positive and trace-preserving unital maps. The simplicity of computing the Brukner-Zeilinger invariant information is the advantage of the proposed measure because of mainly depending on the purity of quantum state. The measure effectively captures the characteristics of non-Markovianity of unital dynamical maps. As some concrete application, we consider two typical non-Markovian noise channels, i.e., the phase damping channel and the random unitary channel to show the sensitivity of the proposed measure. By investigation, we find that the conditions of detecting the non-Markovianity for the phase damping channel are consistent with the results of existing measures for non-Markovianity, i.e., information flow, divisibility and quantum mutual information. However, for the random unitary channel non-Markovian conditions are same to that of the information flow, but is different from that of the divisibility and quantum mutual information. Supported by the National Natural Science Foundation of China under Grant No. 61505053, the Natural Science Foundation of Hunan Province under Grant No. 2015JJ3092, the Research Foundation of Education Bureau of Hunan Province, China under Grant No. 16B177, the School Foundation from the Hunan University of Arts and Science under Grant No. 14ZD01

Categorization-based stranger avoidance does not explain the uncanny valley effect.

PubMed

MacDorman, Karl F; Chattopadhyay, Debaleena

2017-04-01

The uncanny valley hypothesis predicts that an entity appearing almost human risks eliciting cold, eerie feelings in viewers. Categorization-based stranger avoidance theory identifies the cause of this feeling as categorizing the entity into a novel category. This explanation is doubtful because stranger is not a novel category in adults; infants do not avoid strangers while the category stranger remains novel; infants old enough to fear strangers prefer photographs of strangers to those more closely resembling a familiar person; and the uncanny valley's characteristic eeriness is seldom felt when meeting strangers. We repeated our original experiment with a more realistic 3D computer model and found no support for categorization-based stranger avoidance theory. By contrast, realism inconsistency theory explains cold, eerie feelings elicited by transitions between instances of two different, mutually exclusive categories, given that at least one category is anthropomorphic: Cold, eerie feelings are caused by prediction error from perceiving some features as features of the first category and other features as features of the second category. In principle, realism inconsistency theory can explain not only negative evaluations of transitions between real and computer modeled humans but also between different vertebrate species. Copyright © 2017 Elsevier B.V. All rights reserved.

The Various Forms of Neuroplasticity: Biological Bases of Learning and Teaching

ERIC Educational Resources Information Center

Tovar-Moll, Fernanda; Lent, Roberto

2016-01-01

Education is a socially structured form of learning. It involves the brains of different players--students, teachers, family members, and others--in permanent interaction. The biological set of mechanisms by which these brains receive, encode, store, and retrieve mutually exchanged information is called "neuroplasticity". This is the…

SU-E-J-237: Image Feature Based DRR and Portal Image Registration

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

Wang, X; Chang, J

Purpose: Two-dimensional (2D) matching of the kV X-ray and digitally reconstructed radiography (DRR) images is an important setup technique for image-guided radiotherapy (IGRT). In our clinics, mutual information based methods are used for this purpose on commercial linear accelerators, but with often needs for manual corrections. This work proved the feasibility that feature based image transform can be used to register kV and DRR images. Methods: The scale invariant feature transform (SIFT) method was implemented to detect the matching image details (or key points) between the kV and DRR images. These key points represent high image intensity gradients, and thusmore » the scale invariant features. Due to the poor image contrast from our kV image, direct application of the SIFT method yielded many detection errors. To assist the finding of key points, the center coordinates of the kV and DRR images were read from the DICOM header, and the two groups of key points with similar relative positions to their corresponding centers were paired up. Using these points, a rigid transform (with scaling, horizontal and vertical shifts) was estimated. We also artificially introduced vertical and horizontal shifts to test the accuracy of our registration method on anterior-posterior (AP) and lateral pelvic images. Results: The results provided a satisfactory overlay of the transformed kV onto the DRR image. The introduced vs. detected shifts were fit into a linear regression. In the AP image experiments, linear regression analysis showed a slope of 1.15 and 0.98 with an R2 of 0.89 and 0.99 for the horizontal and vertical shifts, respectively. The results are 1.2 and 1.3 with R2 of 0.72 and 0.82 for the lateral image shifts. Conclusion: This work provided an alternative technique for kV to DRR alignment. Further improvements in the estimation accuracy and image contrast tolerance are underway.« less

Knee osteoarthritis image registration: data from the Osteoarthritis Initiative

NASA Astrophysics Data System (ADS)

Galván-Tejada, Jorge I.; Celaya-Padilla, José M.; Treviño, Victor; Tamez-Peña, José G.

2015-03-01

Knee osteoarthritis is a very common disease, in early stages, changes in joint structures are shown, some of the most common symptoms are; formation of osteophytes, cartilage degradation and joint space reduction, among others. Based on a joint space reduction measurement, Kellgren-Lawrence grading scale, is a very extensive used tool to asses radiological OA knee x-ray images, based on information obtained from these assessments, the objective of this work is to correlate the Kellgren-Lawrence score to the bilateral asymmetry between knees. Using public data from the Osteoarthritis initiative (OAI), a set of images with different Kellgren-Lawrencescores were used to determine a relationship of Kellgren-Lawrence score and the bilateral asymmetry, in order to measure the asymmetry between the knees, the right knee was registered to match the left knee, then a series of similarity metrics, mutual information, correlation, and mean squared error where computed to correlate the deformation (mismatch) of the knees to the Kellgren-Lawrence score. Radiological information was evaluated and scored by OAI radiologist groups. The results of the study suggest an association between Radiological Kellgren-Lawrence score and image registration metrics, mutual information and correlation is higher in the early stages, and mean squared error is higher in advanced stages. This association can be helpful to develop a computer aided grading tool.

Information fusion performance evaluation for motion imagery data using mutual information: initial study

NASA Astrophysics Data System (ADS)

Grieggs, Samuel M.; McLaughlin, Michael J.; Ezekiel, Soundararajan; Blasch, Erik

2015-06-01

As technology and internet use grows at an exponential rate, video and imagery data is becoming increasingly important. Various techniques such as Wide Area Motion imagery (WAMI), Full Motion Video (FMV), and Hyperspectral Imaging (HSI) are used to collect motion data and extract relevant information. Detecting and identifying a particular object in imagery data is an important step in understanding visual imagery, such as content-based image retrieval (CBIR). Imagery data is segmented and automatically analyzed and stored in dynamic and robust database. In our system, we seek utilize image fusion methods which require quality metrics. Many Image Fusion (IF) algorithms have been proposed based on different, but only a few metrics, used to evaluate the performance of these algorithms. In this paper, we seek a robust, objective metric to evaluate the performance of IF algorithms which compares the outcome of a given algorithm to ground truth and reports several types of errors. Given the ground truth of a motion imagery data, it will compute detection failure, false alarm, precision and recall metrics, background and foreground regions statistics, as well as split and merge of foreground regions. Using the Structural Similarity Index (SSIM), Mutual Information (MI), and entropy metrics; experimental results demonstrate the effectiveness of the proposed methodology for object detection, activity exploitation, and CBIR.

Color model comparative analysis for breast cancer diagnosis using H and E stained images

NASA Astrophysics Data System (ADS)

Li, Xingyu; Plataniotis, Konstantinos N.

2015-03-01

Digital cancer diagnosis is a research realm where signal processing techniques are used to analyze and to classify color histopathology images. Different from grayscale image analysis of magnetic resonance imaging or X-ray, colors in histopathology images convey large amount of histological information and thus play significant role in cancer diagnosis. Though color information is widely used in histopathology works, as today, there is few study on color model selections for feature extraction in cancer diagnosis schemes. This paper addresses the problem of color space selection for digital cancer classification using H and E stained images, and investigates the effectiveness of various color models (RGB, HSV, CIE L*a*b*, and stain-dependent H and E decomposition model) in breast cancer diagnosis. Particularly, we build a diagnosis framework as a comparison benchmark and take specific concerns of medical decision systems into account in evaluation. The evaluation methodologies include feature discriminate power evaluation and final diagnosis performance comparison. Experimentation on a publicly accessible histopathology image set suggests that the H and E decomposition model outperforms other assessed color spaces. For reasons behind various performance of color spaces, our analysis via mutual information estimation demonstrates that color components in the H and E model are less dependent, and thus most feature discriminate power is collected in one channel instead of spreading out among channels in other color spaces.

Neural population encoding and decoding of sound source location across sound level in the rabbit inferior colliculus

PubMed Central

Delgutte, Bertrand

2015-01-01

At lower levels of sensory processing, the representation of a stimulus feature in the response of a neural population can vary in complex ways across different stimulus intensities, potentially changing the amount of feature-relevant information in the response. How higher-level neural circuits could implement feature decoding computations that compensate for these intensity-dependent variations remains unclear. Here we focused on neurons in the inferior colliculus (IC) of unanesthetized rabbits, whose firing rates are sensitive to both the azimuthal position of a sound source and its sound level. We found that the azimuth tuning curves of an IC neuron at different sound levels tend to be linear transformations of each other. These transformations could either increase or decrease the mutual information between source azimuth and spike count with increasing level for individual neurons, yet population azimuthal information remained constant across the absolute sound levels tested (35, 50, and 65 dB SPL), as inferred from the performance of a maximum-likelihood neural population decoder. We harnessed evidence of level-dependent linear transformations to reduce the number of free parameters in the creation of an accurate cross-level population decoder of azimuth. Interestingly, this decoder predicts monotonic azimuth tuning curves, broadly sensitive to contralateral azimuths, in neurons at higher levels in the auditory pathway. PMID:26490292

Test of mutually unbiased bases for six-dimensional photonic quantum systems

PubMed Central

D'Ambrosio, Vincenzo; Cardano, Filippo; Karimi, Ebrahim; Nagali, Eleonora; Santamato, Enrico; Marrucci, Lorenzo; Sciarrino, Fabio

2013-01-01

In quantum information, complementarity of quantum mechanical observables plays a key role. The eigenstates of two complementary observables form a pair of mutually unbiased bases (MUBs). More generally, a set of MUBs consists of bases that are all pairwise unbiased. Except for specific dimensions of the Hilbert space, the maximal sets of MUBs are unknown in general. Even for a dimension as low as six, the identification of a maximal set of MUBs remains an open problem, although there is strong numerical evidence that no more than three simultaneous MUBs do exist. Here, by exploiting a newly developed holographic technique, we implement and test different sets of three MUBs for a single photon six-dimensional quantum state (a “qusix”), encoded exploiting polarization and orbital angular momentum of photons. A close agreement is observed between theory and experiments. Our results can find applications in state tomography, quantitative wave-particle duality, quantum key distribution. PMID:24067548

Test of mutually unbiased bases for six-dimensional photonic quantum systems.

PubMed

D'Ambrosio, Vincenzo; Cardano, Filippo; Karimi, Ebrahim; Nagali, Eleonora; Santamato, Enrico; Marrucci, Lorenzo; Sciarrino, Fabio

2013-09-25

In quantum information, complementarity of quantum mechanical observables plays a key role. The eigenstates of two complementary observables form a pair of mutually unbiased bases (MUBs). More generally, a set of MUBs consists of bases that are all pairwise unbiased. Except for specific dimensions of the Hilbert space, the maximal sets of MUBs are unknown in general. Even for a dimension as low as six, the identification of a maximal set of MUBs remains an open problem, although there is strong numerical evidence that no more than three simultaneous MUBs do exist. Here, by exploiting a newly developed holographic technique, we implement and test different sets of three MUBs for a single photon six-dimensional quantum state (a "qusix"), encoded exploiting polarization and orbital angular momentum of photons. A close agreement is observed between theory and experiments. Our results can find applications in state tomography, quantitative wave-particle duality, quantum key distribution.

Security analysis and enhancements of an effective biometric-based remote user authentication scheme using smart cards.

PubMed

An, Younghwa

2012-01-01

Recently, many biometrics-based user authentication schemes using smart cards have been proposed to improve the security weaknesses in user authentication system. In 2011, Das proposed an efficient biometric-based remote user authentication scheme using smart cards that can provide strong authentication and mutual authentication. In this paper, we analyze the security of Das's authentication scheme, and we have shown that Das's authentication scheme is still insecure against the various attacks. Also, we proposed the enhanced scheme to remove these security problems of Das's authentication scheme, even if the secret information stored in the smart card is revealed to an attacker. As a result of security analysis, we can see that the enhanced scheme is secure against the user impersonation attack, the server masquerading attack, the password guessing attack, and the insider attack and provides mutual authentication between the user and the server.

Security Analysis and Enhancements of an Effective Biometric-Based Remote User Authentication Scheme Using Smart Cards

PubMed Central

An, Younghwa

2012-01-01

Recently, many biometrics-based user authentication schemes using smart cards have been proposed to improve the security weaknesses in user authentication system. In 2011, Das proposed an efficient biometric-based remote user authentication scheme using smart cards that can provide strong authentication and mutual authentication. In this paper, we analyze the security of Das's authentication scheme, and we have shown that Das's authentication scheme is still insecure against the various attacks. Also, we proposed the enhanced scheme to remove these security problems of Das's authentication scheme, even if the secret information stored in the smart card is revealed to an attacker. As a result of security analysis, we can see that the enhanced scheme is secure against the user impersonation attack, the server masquerading attack, the password guessing attack, and the insider attack and provides mutual authentication between the user and the server. PMID:22899887

Automatic registration of ICG images using mutual information and perfusion analysis

NASA Astrophysics Data System (ADS)

Kim, Namkug; Seo, Jong-Mo; Lee, June-goo; Kim, Jong Hyo; Park, Kwangsuk; Yu, Hyeong-Gon; Yu, Young Suk; Chung, Hum

2005-04-01

Introduction: Indocyanin green fundus angiographic images (ICGA) of the eyes is useful method in detecting and characterizing the choroidal neovascularization (CNV), which is the major cause of the blindness over 65 years of age. To investigate the quantitative analysis of the blood flow on ICGA, systematic approach for automatic registration of using mutual information and a quantitative analysis was developed. Methods: Intermittent sequential images of indocyanin green angiography were acquired by Heidelberg retinal angiography that uses the laser scanning system for the image acquisition. Misalignment of the each image generated by the minute eye movement of the patients was corrected by the mutual information method because the distribution of the contrast media on image is changing throughout the time sequences. Several region of interest (ROI) were selected by a physician and the intensities of the selected region were plotted according to the time sequences. Results: The registration of ICGA time sequential images is required not only translate transform but also rotational transform. Signal intensities showed variation based on gamma-variate function depending on ROIs and capillary vessels show more variance of signal intensity than major vessels. CNV showed intermediate variance of signal intensity and prolonged transit time. Conclusion: The resulting registered images can be used not only for quantitative analysis, but also for perfusion analysis. Various investigative approached on CNV using this method will be helpful in the characterization of the lesion and follow-up.

Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method

PubMed Central

Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui

2017-01-01

Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli, and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs. PMID:29113310

Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.

PubMed

Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui

2017-10-06

Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli , and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.

Volunteering and mutual aid in health and social care in the Czech Republic as an example of active citizenship.

PubMed

Krízová, Eva

2012-06-01

This article informs about recent research findings on voluntary and mutual aid in the Czech Republic with a special attention paid to formal volunteering in health and social care. The data suggest that public involvement is comparable to middle-frequency experienced in European countries. In this respect, volunteering is higher in the Czech Republic than in other former Eastern European countries and is an evidence of a successful and rapid restoration of the civic sector. New patterns of volunteering featured by planning, coordination, and contracting have spread out being strongly supported by national and EU policy measures. Managerial patterns of volunteering are dominating in health and social care institutions. Volunteering in health and social care is firmly motivated by emotional altruism; however, reciprocal (instrumental) and normative motivations are also present, though to a lesser extent compared to other sectors of volunteer activities. In the managerial pattern of volunteering altruism is balanced with personal gains and benefits for those who volunteer. Volunteering is deeply embedded in a civic, humanitarian paradigm instead of a religious faith and duty.

Wang-Landau method for calculating Rényi entropies in finite-temperature quantum Monte Carlo simulations.

PubMed

Inglis, Stephen; Melko, Roger G

2013-01-01

We implement a Wang-Landau sampling technique in quantum Monte Carlo (QMC) simulations for the purpose of calculating the Rényi entanglement entropies and associated mutual information. The algorithm converges an estimate for an analog to the density of states for stochastic series expansion QMC, allowing a direct calculation of Rényi entropies without explicit thermodynamic integration. We benchmark results for the mutual information on two-dimensional (2D) isotropic and anisotropic Heisenberg models, a 2D transverse field Ising model, and a three-dimensional Heisenberg model, confirming a critical scaling of the mutual information in cases with a finite-temperature transition. We discuss the benefits and limitations of broad sampling techniques compared to standard importance sampling methods.

Entanglement of purification in free scalar field theories

NASA Astrophysics Data System (ADS)

Bhattacharyya, Arpan; Takayanagi, Tadashi; Umemoto, Koji

2018-04-01

We compute the entanglement of purification (EoP) in a 2d free scalar field theory with various masses. This quantity measures correlations between two subsystems and is reduced to the entanglement entropy when the total system is pure. We obtain explicit numerical values by assuming minimal gaussian wave functionals for the purified states. We find that when the distance between the subsystems is large, the EoP behaves like the mutual information. However, when the distance is small, the EoP shows a characteristic behavior which qualitatively agrees with the conjectured holographic computation and which is different from that of the mutual information. We also study behaviors of mutual information in purified spaces and violations of monogamy/strong superadditivity.

A Novel Information-Theoretic Approach for Variable Clustering and Predictive Modeling Using Dirichlet Process Mixtures

PubMed Central

Chen, Yun; Yang, Hui

2016-01-01

In the era of big data, there are increasing interests on clustering variables for the minimization of data redundancy and the maximization of variable relevancy. Existing clustering methods, however, depend on nontrivial assumptions about the data structure. Note that nonlinear interdependence among variables poses significant challenges on the traditional framework of predictive modeling. In the present work, we reformulate the problem of variable clustering from an information theoretic perspective that does not require the assumption of data structure for the identification of nonlinear interdependence among variables. Specifically, we propose the use of mutual information to characterize and measure nonlinear correlation structures among variables. Further, we develop Dirichlet process (DP) models to cluster variables based on the mutual-information measures among variables. Finally, orthonormalized variables in each cluster are integrated with group elastic-net model to improve the performance of predictive modeling. Both simulation and real-world case studies showed that the proposed methodology not only effectively reveals the nonlinear interdependence structures among variables but also outperforms traditional variable clustering algorithms such as hierarchical clustering. PMID:27966581

A Novel Information-Theoretic Approach for Variable Clustering and Predictive Modeling Using Dirichlet Process Mixtures.

PubMed

Chen, Yun; Yang, Hui

2016-12-14

In the era of big data, there are increasing interests on clustering variables for the minimization of data redundancy and the maximization of variable relevancy. Existing clustering methods, however, depend on nontrivial assumptions about the data structure. Note that nonlinear interdependence among variables poses significant challenges on the traditional framework of predictive modeling. In the present work, we reformulate the problem of variable clustering from an information theoretic perspective that does not require the assumption of data structure for the identification of nonlinear interdependence among variables. Specifically, we propose the use of mutual information to characterize and measure nonlinear correlation structures among variables. Further, we develop Dirichlet process (DP) models to cluster variables based on the mutual-information measures among variables. Finally, orthonormalized variables in each cluster are integrated with group elastic-net model to improve the performance of predictive modeling. Both simulation and real-world case studies showed that the proposed methodology not only effectively reveals the nonlinear interdependence structures among variables but also outperforms traditional variable clustering algorithms such as hierarchical clustering.

Identifying functionally informative evolutionary sequence profiles.

PubMed

Gil, Nelson; Fiser, Andras

2018-04-15

Multiple sequence alignments (MSAs) can provide essential input to many bioinformatics applications, including protein structure prediction and functional annotation. However, the optimal selection of sequences to obtain biologically informative MSAs for such purposes is poorly explored, and has traditionally been performed manually. We present Selection of Alignment by Maximal Mutual Information (SAMMI), an automated, sequence-based approach to objectively select an optimal MSA from a large set of alternatives sampled from a general sequence database search. The hypothesis of this approach is that the mutual information among MSA columns will be maximal for those MSAs that contain the most diverse set possible of the most structurally and functionally homogeneous protein sequences. SAMMI was tested to select MSAs for functional site residue prediction by analysis of conservation patterns on a set of 435 proteins obtained from protein-ligand (peptides, nucleic acids and small substrates) and protein-protein interaction databases. Availability and implementation: A freely accessible program, including source code, implementing SAMMI is available at https://github.com/nelsongil92/SAMMI.git. andras.fiser@einstein.yu.edu. Supplementary data are available at Bioinformatics online.

76 FR 19790 - Agency Information Collection Activities; Submission for OMB Review; Comment Request; Extension...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-04-08

... shares of an open-end investment company (mutual fund) when a fiduciary with respect to the plan is also the investment advisor for the mutual fund. In order to ensure that the exemption is not abused and... mutual fund shares that the independent fiduciary of the plan receive a copy of the current prospectus...

The Generalization of Mutual Information as the Information between a Set of Variables: The Information Correlation Function Hierarchy and the Information Structure of Multi-Agent Systems

NASA Technical Reports Server (NTRS)

Wolf, David R.

2004-01-01

The topic of this paper is a hierarchy of information-like functions, here named the information correlation functions, where each function of the hierarchy may be thought of as the information between the variables it depends upon. The information correlation functions are particularly suited to the description of the emergence of complex behaviors due to many- body or many-agent processes. They are particularly well suited to the quantification of the decomposition of the information carried among a set of variables or agents, and its subsets. In more graphical language, they provide the information theoretic basis for understanding the synergistic and non-synergistic components of a system, and as such should serve as a forceful toolkit for the analysis of the complexity structure of complex many agent systems. The information correlation functions are the natural generalization to an arbitrary number of sets of variables of the sequence starting with the entropy function (one set of variables) and the mutual information function (two sets). We start by describing the traditional measures of information (entropy) and mutual information.

Analogy between gambling and measurement-based work extraction

NASA Astrophysics Data System (ADS)

Vinkler, Dror A.; Permuter, Haim H.; Merhav, Neri

2016-04-01

In information theory, one area of interest is gambling, where mutual information characterizes the maximal gain in wealth growth rate due to knowledge of side information; the betting strategy that achieves this maximum is named the Kelly strategy. In the field of physics, it was recently shown that mutual information can characterize the maximal amount of work that can be extracted from a single heat bath using measurement-based control protocols, i.e. using ‘information engines’. However, to the best of our knowledge, no relation between gambling and information engines has been presented before. In this paper, we briefly review the two concepts and then demonstrate an analogy between gambling, where bits are converted into wealth, and information engines, where bits representing measurements are converted into energy. From this analogy follows an extension of gambling to the continuous-valued case, which is shown to be useful for investments in currency exchange rates or in the stock market using options. Moreover, the analogy enables us to use well-known methods and results from one field to solve problems in the other. We present three such cases: maximum work extraction when the probability distributions governing the system and measurements are unknown, work extraction when some energy is lost in each cycle, e.g. due to friction, and an analysis of systems with memory. In all three cases, the analogy enables us to use known results in order to obtain new ones.

Mutual information optimization for mass spectra data alignment.

PubMed

Zoppis, Italo; Gianazza, Erica; Borsani, Massimiliano; Chinello, Clizia; Mainini, Veronica; Galbusera, Carmen; Ferrarese, Carlo; Galimberti, Gloria; Sorbi, Sandro; Borroni, Barbara; Magni, Fulvio; Antoniotti, Marco; Mauri, Giancarlo

2012-01-01

"Signal" alignments play critical roles in many clinical setting. This is the case of mass spectrometry data, an important component of many types of proteomic analysis. A central problem occurs when one needs to integrate (mass spectrometry) data produced by different sources, e.g., different equipment and/or laboratories. In these cases some form of "data integration'" or "data fusion'" may be necessary in order to discard some source specific aspects and improve the ability to perform a classification task such as inferring the "disease classes'" of patients. The need for new high performance data alignments methods is therefore particularly important in these contexts. In this paper we propose an approach based both on an information theory perspective, generally used in a feature construction problem, and on the application of a mathematical programming task (i.e. the weighted bipartite matching problem). We present the results of a competitive analysis of our method against other approaches. The analysis was conducted on data from plasma/ethylenediaminetetraacetic acid (EDTA) of "control" and Alzheimer patients collected from three different hospitals. The results point to a significant performance advantage of our method with respect to the competing ones tested.

Phase space interrogation of the empirical response modes for seismically excited structures

NASA Astrophysics Data System (ADS)

Paul, Bibhas; George, Riya C.; Mishra, Sudib K.

2017-07-01

Conventional Phase Space Interrogation (PSI) for structural damage assessment relies on exciting the structure with low dimensional chaotic waveform, thereby, significantly limiting their applicability to large structures. The PSI technique is presently extended for structure subjected to seismic excitations. The high dimensionality of the phase space for seismic response(s) are overcome by the Empirical Mode Decomposition (EMD), decomposing the responses to a number of intrinsic low dimensional oscillatory modes, referred as Intrinsic Mode Functions (IMFs). Along with their low dimensionality, a few IMFs, retain sufficient information of the system dynamics to reflect the damage induced changes. The mutually conflicting nature of low-dimensionality and the sufficiency of dynamic information are taken care by the optimal choice of the IMF(s), which is shown to be the third/fourth IMFs. The optimal IMF(s) are employed for the reconstruction of the Phase space attractor following Taken's embedding theorem. The widely referred Changes in Phase Space Topology (CPST) feature is then employed on these Phase portrait(s) to derive the damage sensitive feature, referred as the CPST of the IMFs (CPST-IMF). The legitimacy of the CPST-IMF is established as a damage sensitive feature by assessing its variation with a number of damage scenarios benchmarked in the IASC-ASCE building. The damage localization capability, remarkable tolerance to noise contamination and the robustness under different seismic excitations of the feature are demonstrated.

Evaluating true BCI communication rate through mutual information and language models.

PubMed

Speier, William; Arnold, Corey; Pouratian, Nader

2013-01-01

Brain-computer interface (BCI) systems are a promising means for restoring communication to patients suffering from "locked-in" syndrome. Research to improve system performance primarily focuses on means to overcome the low signal to noise ratio of electroencephalogric (EEG) recordings. However, the literature and methods are difficult to compare due to the array of evaluation metrics and assumptions underlying them, including that: 1) all characters are equally probable, 2) character selection is memoryless, and 3) errors occur completely at random. The standardization of evaluation metrics that more accurately reflect the amount of information contained in BCI language output is critical to make progress. We present a mutual information-based metric that incorporates prior information and a model of systematic errors. The parameters of a system used in one study were re-optimized, showing that the metric used in optimization significantly affects the parameter values chosen and the resulting system performance. The results of 11 BCI communication studies were then evaluated using different metrics, including those previously used in BCI literature and the newly advocated metric. Six studies' results varied based on the metric used for evaluation and the proposed metric produced results that differed from those originally published in two of the studies. Standardizing metrics to accurately reflect the rate of information transmission is critical to properly evaluate and compare BCI communication systems and advance the field in an unbiased manner.

Mutual coupling, channel model, and BER for curvilinear antenna arrays

NASA Astrophysics Data System (ADS)

Huang, Zhiyong

This dissertation introduces a wireless communications system with an adaptive beam-former and investigates its performance with different antenna arrays. Mutual coupling, real antenna elements and channel models are included to examine the system performance. In a beamforming system, mutual coupling (MC) among the elements can significantly degrade the system performance. However, MC effects can be compensated if an accurate model of mutual coupling is available. A mutual coupling matrix model is utilized to compensate mutual coupling in the beamforming of a uniform circular array (UCA). Its performance is compared with other models in uplink and downlink beamforming scenarios. In addition, the predictions are compared with measurements and verified with results from full-wave simulations. In order to accurately investigate the minimum mean-square-error (MSE) of an adaptive array in MC, two different noise models, the environmental and the receiver noise, are modeled. The minimum MSEs with and without data domain MC compensation are analytically compared. The influence of mutual coupling on the convergence is also examined. In addition, the weight compensation method is proposed to attain the desired array pattern. Adaptive arrays with different geometries are implemented with the minimum MSE algorithm in the wireless communications system to combat interference at the same frequency. The bit-error-rate (BER) of systems with UCA, uniform rectangular array (URA) and UCA with center element are investigated in additive white Gaussian noise plus well-separated signals or random direction signals scenarios. The output SINR of an adaptive array with multiple interferers is analytically examined. The influence of the adaptive algorithm convergence on the BER is investigated. The UCA is then investigated in a narrowband Rician fading channel. The channel model is built and the space correlations are examined. The influence of the number of signal paths, number of the interferers, Doppler spread and convergence are investigated. The tracking mode is introduced to the adaptive array system, and it further improves the BER. The benefit of using faster data rate (wider bandwidth) is discussed. In order to have better performance in a 3D space, the geometries of uniform spherical array (USAs) are presented and different configurations of USAs are discussed. The LMS algorithm based on temporal a priori information is applied to UCAs and USAs to beamform the patterns. Their performances are compared based on simulation results. Based on the analytical and simulation results, it can be concluded that mutual coupling slightly influences the performance of the adaptive array in communication systems. In addition, arrays with curvilinear geometries perform well in AWGN and fading channels.

Mutually unbiased coarse-grained measurements of two or more phase-space variables

NASA Astrophysics Data System (ADS)

Paul, E. C.; Walborn, S. P.; Tasca, D. S.; Rudnicki, Łukasz

2018-05-01

Mutual unbiasedness of the eigenstates of phase-space operators—such as position and momentum, or their standard coarse-grained versions—exists only in the limiting case of infinite squeezing. In Phys. Rev. Lett. 120, 040403 (2018), 10.1103/PhysRevLett.120.040403, it was shown that mutual unbiasedness can be recovered for periodic coarse graining of these two operators. Here we investigate mutual unbiasedness of coarse-grained measurements for more than two phase-space variables. We show that mutual unbiasedness can be recovered between periodic coarse graining of any two nonparallel phase-space operators. We illustrate these results through optics experiments, using the fractional Fourier transform to prepare and measure mutually unbiased phase-space variables. The differences between two and three mutually unbiased measurements is discussed. Our results contribute to bridging the gap between continuous and discrete quantum mechanics, and they could be useful in quantum-information protocols.

Mutually opposing forces during locomotion can eliminate the tradeoff between maneuverability and stability

NASA Astrophysics Data System (ADS)

Cowan, Noah; Sefati, Shahin; Neveln, Izaak; Roth, Eatai; Mitchell, Terence; Snyder, James; Maciver, Malcolm; Fortune, Eric

A surprising feature of animal locomotion is that organisms typically produce substantial forces in directions other than what is necessary to move the animal through its environment, such as perpendicular to, or counter to, the direction of travel. The effect of these forces has been difficult to observe because they are often mutually opposing and therefore cancel out. Using a combination of robotic physical modeling, computational modeling, and biological experiments, we discovered that these forces serve an important role: to simplify and enhance the control of locomotion. Specifically, we examined a well-suited model system, the glass knifefish Eigenmannia virescens, which produces mutually opposing forces during a hovering behavior. By systematically varying the locomotor parameters of our biomimetic robot, and measuring the resulting forces and kinematics, we demonstrated that the production and differential control of mutually opposing forces is a strategy that generates passive stabilization while simultaneously enhancing maneuverability. Mutually opposing forces during locomotion are widespread across animal taxa, and these results indicate that such forces can eliminate the tradeoff between stability and maneuverability, thereby simplifying robotic and neural control.

Assessing spatial coupling in complex population dynamics using mutual prediction and continuity statistics

USGS Publications Warehouse

Nichols, J.M.; Moniz, L.; Nichols, J.D.; Pecora, L.M.; Cooch, E.

2005-01-01

A number of important questions in ecology involve the possibility of interactions or ?coupling? among potential components of ecological systems. The basic question of whether two components are coupled (exhibit dynamical interdependence) is relevant to investigations of movement of animals over space, population regulation, food webs and trophic interactions, and is also useful in the design of monitoring programs. For example, in spatially extended systems, coupling among populations in different locations implies the existence of redundant information in the system and the possibility of exploiting this redundancy in the development of spatial sampling designs. One approach to the identification of coupling involves study of the purported mechanisms linking system components. Another approach is based on time series of two potential components of the same system and, in previous ecological work, has relied on linear cross-correlation analysis. Here we present two different attractor-based approaches, continuity and mutual prediction, for determining the degree to which two population time series (e.g., at different spatial locations) are coupled. Both approaches are demonstrated on a one-dimensional predator?prey model system exhibiting complex dynamics. Of particular interest is the spatial asymmetry introduced into the model as linearly declining resource for the prey over the domain of the spatial coordinate. Results from these approaches are then compared to the more standard cross-correlation analysis. In contrast to cross-correlation, both continuity and mutual prediction are clearly able to discern the asymmetry in the flow of information through this system.

Evolutionary stability of mutualism: interspecific population regulation as an evolutionarily stable strategy.

PubMed

Holland, J Nathaniel; DeAngelis, Donald L; Schultz, Stewart T

2004-09-07

Interspecific mutualisms are often vulnerable to instability because low benefit : cost ratios can rapidly lead to extinction or to the conversion of mutualism to parasite-host or predator-prey interactions. We hypothesize that the evolutionary stability of mutualism can depend on how benefits and costs to one mutualist vary with the population density of its partner, and that stability can be maintained if a mutualist can influence demographic rates and regulate the population density of its partner. We test this hypothesis in a model of mutualism with key features of senita cactus (Pachycereus schottii)-senita moth (Upiga virescens) interactions, in which benefits of pollination and costs of larval seed consumption to plant fitness depend on pollinator density. We show that plants can maximize their fitness by allocating resources to the production of excess flowers at the expense of fruit. Fruit abortion resulting from excess flower production reduces pre-adult survival of the pollinating seed-consumer, and maintains its density beneath a threshold that would destabilize the mutualism. Such a strategy of excess flower production and fruit abortion is convergent and evolutionarily stable against invasion by cheater plants that produce few flowers and abort few to no fruit. This novel mechanism of achieving evolutionarily stable mutualism, namely interspecific population regulation, is qualitatively different from other mechanisms invoking partner choice or selective rewards, and may be a general process that helps to preserve mutualistic interactions in nature.

Mutual-Information-Based Incremental Relaying Communications for Wireless Biomedical Implant Systems

PubMed Central

Liao, Yangzhe; Cai, Qing; Ai, Qingsong; Liu, Quan

2018-01-01

Network lifetime maximization of wireless biomedical implant systems is one of the major research challenges of wireless body area networks (WBANs). In this paper, a mutual information (MI)-based incremental relaying communication protocol is presented where several on-body relay nodes and one coordinator are attached to the clothes of a patient. Firstly, a comprehensive analysis of a system model is investigated in terms of channel path loss, energy consumption, and the outage probability from the network perspective. Secondly, only when the MI value becomes smaller than the predetermined threshold is data transmission allowed. The communication path selection can be either from the implanted sensor to the on-body relay then forwards to the coordinator or from the implanted sensor to the coordinator directly, depending on the communication distance. Moreover, mathematical models of quality of service (QoS) metrics are derived along with the related subjective functions. The results show that the MI-based incremental relaying technique achieves better performance in comparison to our previous proposed protocol techniques regarding several selected performance metrics. The outcome of this paper can be applied to intra-body continuous physiological signal monitoring, artificial biofeedback-oriented WBANs, and telemedicine system design. PMID:29419784

Mutual-Information-Based Incremental Relaying Communications for Wireless Biomedical Implant Systems.

PubMed

Liao, Yangzhe; Leeson, Mark S; Cai, Qing; Ai, Qingsong; Liu, Quan

2018-02-08

Network lifetime maximization of wireless biomedical implant systems is one of the major research challenges of wireless body area networks (WBANs). In this paper, a mutual information (MI)-based incremental relaying communication protocol is presented where several on-body relay nodes and one coordinator are attached to the clothes of a patient. Firstly, a comprehensive analysis of a system model is investigated in terms of channel path loss, energy consumption, and the outage probability from the network perspective. Secondly, only when the MI value becomes smaller than the predetermined threshold is data transmission allowed. The communication path selection can be either from the implanted sensor to the on-body relay then forwards to the coordinator or from the implanted sensor to the coordinator directly, depending on the communication distance. Moreover, mathematical models of quality of service (QoS) metrics are derived along with the related subjective functions. The results show that the MI-based incremental relaying technique achieves better performance in comparison to our previous proposed protocol techniques regarding several selected performance metrics. The outcome of this paper can be applied to intra-body continuous physiological signal monitoring, artificial biofeedback-oriented WBANs, and telemedicine system design.

Netchain coordinator services in pork supply chains including inspections according to the AMOR principle.

PubMed

O'Hagan, Juliane; Ellebrecht, Susanne; Petersen, Brigitte

2013-11-01

This paper presents a mutual inspection concept between livestock production and processing, coordinated by a netchain coordinator acting as the service provider. The concept is based on an analysis of demand and offer with respect to the coordination of livestock health status certificates. Surveys have been conducted amongst piglet producers and pig fatteners (nfarmers=206) to reflect the demand for coordination services. Another survey amongst service providers (nproviders=21) determines the availability of these services. The results show a clear demand amongst farmers for the netchain coordinator services of livestock health status certificates. It has also been found that some service providers offer audit and monitoring procedures to enable certification with highly varying degrees of service quality. Based on the surveys, the joint organisation of livestock health status certificates between producer, fattener and service provider will be presented. It will be shown that for greater efficiency, coordination should be based on the principles of Alliances for the Mutual Organisation of Risk oriented inspection strategies (AMOR). To enable the mutual organisation of inspections, four key steps have been identified to develop and test AMOR as an innovative form of cooperation: (i) inspection design, (ii) responsibilities and tasks, (iii) information and communication structures, and (iv) shared cost and benefit model. Copyright © 2013 Elsevier Ltd. All rights reserved.

Earthquake damage mapping by using remotely sensed data: the Haiti case study

NASA Astrophysics Data System (ADS)

Romaniello, Vito; Piscini, Alessandro; Bignami, Christian; Anniballe, Roberta; Stramondo, Salvatore

2017-01-01

This work proposes methodologies aimed at evaluating the sensitivity of optical and synthetic aperture radar (SAR) change features obtained from satellite images with respect to the damage grade due to an earthquake. The test case is the Mw 7.0 earthquake that hit Haiti on January 12, 2010, located 25 km west-south-west of the city of Port-au-Prince. The disastrous shock caused the collapse of a huge number of buildings and widespread damage. The objective is to investigate possible parameters that can affect the robustness and sensitivity of the proposed methods derived from the literature. It is worth noting how the proposed analysis concerns the estimation of derived features at object scale. For this purpose, a segmentation of the study area into several regions has been done by considering a set of polygons, over the city of Port-au-Prince, extracted from the open source open street map geo-database. The analysis of change detection indicators is based on ground truth information collected during a postearthquake survey and is available from a Joint Research Centre database. The resulting damage map is expressed in terms of collapse ratio, thus indicating the areas with a greater number of collapsed buildings. The available satellite dataset is composed of optical and SAR images, collected before and after the seismic event. In particular, we used two GeoEye-1 optical images (one preseismic and one postseismic) and three TerraSAR-X SAR images (two preseismic and one postseismic). Previous studies allowed us to identify some features having a good sensitivity with damage at the object scale. Regarding the optical data, we selected the normalized difference index and two quantities coming from the information theory, namely the Kullback-Libler divergence (KLD) and the mutual information (MI). In addition, for the SAR data, we picked out the intensity correlation difference and the KLD parameter. In order to analyze the capability of these parameters to correctly detect damaged areas, two different classifiers were used: the Naive Bayes and the support vector machine classifiers. The classification results demonstrate that the simultaneous use of several change features from Earth observations can improve the damage estimation at object scale.

Design and simulation of a new bidirectional actuator for haptic systems featuring MR fluid

NASA Astrophysics Data System (ADS)

Hung, Nguyen Quoc; Tri, Diep Bao; Cuong, Vo Van; Choi, Seung-Bok

2017-04-01

In this research, a new configuration of bidirectional actuator featuring MR fluid (BMRA) is proposed for haptic application. The proposed BMRA consists of a driving disc, a driving housing and a driven disc. The driving disc is placed inside the driving housing and rotates counter to each other by a servo DC motor and a bevel gear system. The driven shaft is also placed inside the housing and next to the driving disc. The gap between the two disc and the gap between the discs and the housing are filled with MR fluid. On the driven disc, two mutual magnetic coils are placed. By applying currents to the two coils mutually, the torque at the output shaft, which is fixed to the driven disc, can be controlled with positive, zero or negative value. This make the actuator be suitable for haptic application. After a review of MR fluid and its application, configuration of the proposed BMRA is presented. The modeling of the actuator is then derived based on Bingham rheological model of MRF and magnetic finite element analysis (FEA). The optimal design of the actuator is then performed to minimize the mass of the BMRA. From the optimal design result, performance characteristics of the actuator is simulated and detailed design of a prototype actuator is conducted.

Building strong relationships between conservation genetics and primary industry leads to mutually beneficial genomic advances.

PubMed

Galla, Stephanie J; Buckley, Thomas R; Elshire, Rob; Hale, Marie L; Knapp, Michael; McCallum, John; Moraga, Roger; Santure, Anna W; Wilcox, Phillip; Steeves, Tammy E

2016-11-01

Several reviews in the past decade have heralded the benefits of embracing high-throughput sequencing technologies to inform conservation policy and the management of threatened species, but few have offered practical advice on how to expedite the transition from conservation genetics to conservation genomics. Here, we argue that an effective and efficient way to navigate this transition is to capitalize on emerging synergies between conservation genetics and primary industry (e.g., agriculture, fisheries, forestry and horticulture). Here, we demonstrate how building strong relationships between conservation geneticists and primary industry scientists is leading to mutually-beneficial outcomes for both disciplines. Based on our collective experience as collaborative New Zealand-based scientists, we also provide insight for forging these cross-sector relationships. © 2016 The Authors. Molecular Ecology Published by John Wiley & Sons Ltd.

Assessing Peer Support and Usability of Blogging in Hybrid Learning Environments

ERIC Educational Resources Information Center

Chang, Y. J.; Chang, Y. S.

2014-01-01

Blogs provide contextualization of the information which is vital to the process of peer support. Through dialogues initiated by blog authors and followed by readers, blog platforms build a viable base of shared experiences and mutual relationships. We employ blogs as interactive learning tools for communities of practice in higher education.…

Improved 2D/3D registration robustness using local spatial information

NASA Astrophysics Data System (ADS)

De Momi, Elena; Eckman, Kort; Jaramaz, Branislav; DiGioia, Anthony, III

2006-03-01

Xalign is a tool designed to measure implant orientation after joint arthroplasty by co-registering a projection of an implant model and a digitally reconstructed radiograph of the patient's anatomy with a post operative x-ray. A mutual information based registration method is used to automate alignment. When using basic mutual information, the presence of local maxima can result in misregistration. To increase robustness of registration, our research is aimed at improving the similarity function by modifying the information measure and incorporating local spatial information. A test dataset with known groundtruth parameters was created to evaluate the performance of this measure. A synthetic radiograph was generated first from a preoperative pelvic CT scan to act as the gold standard. The voxel weights used to generate the image were then modified and new images were generated with the CT rigidly transformed. The roll, pitch and yaw angles span a range of -10/+10 degrees, while x, y and z translations range from -10mm to +10mm. These images were compared with the reference image. The proposed cost function correctly identified the correct pose in all tests and did not exhibit any local maxima which would slow or prevent locating the global maximum.

Quantum formalism as an optimisation procedure of information flows for physical and biological systems.

PubMed

Baladrón, Carlos; Khrennikov, Andrei

2016-12-01

The similarities between biological and physical systems as respectively defined in quantum information biology (QIB) and in a Darwinian approach to quantum mechanics (DAQM) have been analysed. In both theories the processing of information is a central feature characterising the systems. The analysis highlights a mutual support on the thesis contended by each theory. On the one hand, DAQM provides a physical basis that might explain the key role played by quantum information at the macroscopic level for bio-systems in QIB. On the other hand, QIB offers the possibility, acting as a macroscopic testing ground, to analyse the emergence of quantumness from classicality in the terms held by DAQM. As an added result of the comparison, a tentative definition of quantum information in terms of classical information flows has been proposed. The quantum formalism would appear from this comparative analysis between QIB and DAQM as an optimal information scheme that would maximise the stability of biological and physical systems at any scale. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Acoustic resonance at the dawn of life: musical fundamentals of the psychoanalytic relationship.

PubMed

Pickering, Judith

2015-11-01

This paper uses a case vignette to show how musical elements of speech are a crucial source of information regarding the patient's emotional states and associated memory systems that are activated at a given moment in the analytic field. There are specific psychoacoustic markers associated with different memory systems which indicate whether a patient is immersed in a state of creative intersubjective relatedness related to autobiographical memory, or has been triggered into a traumatic memory system. When a patient feels immersed in an atmosphere of intersubjective mutuality, dialogue features a rhythmical and tuneful form of speech featuring improvized reciprocal imitation, theme and variation. When the patient is catapulted into a traumatic memory system, speech becomes monotone and disjointed. Awareness of such acoustic features of the traumatic memory system helps to alert the analyst that such a shift has taken place informing appropriate responses and interventions. Communicative musicality (Malloch & Trevarthen 2009) originates in the earliest non-verbal vocal communication between infant and care-giver, states of primary intersubjectivity. Such musicality continues to be the primary vehicle for transmitting emotional meaning and for integrating right and left hemispheres. This enables communication that expresses emotional significance, personal value as well as conceptual reasoning. © 2015, The Society of Analytical Psychology.

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.

Mutual information as an order parameter for quantum synchronization

NASA Astrophysics Data System (ADS)

Ameri, V.; Eghbali-Arani, M.; Mari, A.; Farace, A.; Kheirandish, F.; Giovannetti, V.; Fazio, R.

2015-01-01

Spontaneous synchronization is a fundamental phenomenon, important in many theoretical studies and applications. Recently, this effect has been analyzed and observed in a number of physical systems close to the quantum-mechanical regime. In this work we propose mutual information as a useful order parameter which can capture the emergence of synchronization in very different contexts, ranging from semiclassical to intrinsically quantum-mechanical systems. Specifically, we first study the synchronization of two coupled Van der Pol oscillators in both classical and quantum regimes and later we consider the synchronization of two qubits inside two coupled optical cavities. In all these contexts, we find that mutual information can be used as an appropriate figure of merit for determining the synchronization phases independently of the specific details of the system.

Building an information model (with the help of PSL/PSA). [Problem Statement Language/Problem Statement Analyzer

NASA Technical Reports Server (NTRS)

Callender, E. D.; Farny, A. M.

1983-01-01

Problem Statement Language/Problem Statement Analyzer (PSL/PSA) applications, which were once a one-step process in which product system information was immediately translated into PSL statements, have in light of experience been shown to result in inconsistent representations. These shortcomings have prompted the development of an intermediate step, designated the Product System Information Model (PSIM), which provides a basis for the mutual understanding of customer terminology and the formal, conceptual representation of that product system in a PSA data base. The PSIM is initially captured as a paper diagram, followed by formal capture in the PSL/PSA data base.

A study on the integrity and authentication of weather observation data using Identity Based Encryption.

PubMed

Seo, Jung Woo; Lee, Sang Jin

2016-01-01

Weather information provides a safe working environment by contributing to the economic activity of the nation, and plays role of the prevention of natural disasters, which can cause large scaled casualties and damage of property. Especially during times of war, weather information plays a more important role than strategy, tactics and information about trends of the enemy. Also, it plays an essential role for the taking off and landing of fighter jet and the sailing of warships. If weather information, which plays a major role in national security and economy, gets misused for cyber terrorism resulting false weather information, it could be a huge threat for national security and the economy. We propose a plan to safely transmit the measured value from meteorological sensors through a meteorological telecommunication network in order to guarantee the confidentiality and integrity of the data despite cyber-attacks. Also, such a plan allows one to produce reliable weather forecasts by performing mutual authentication through authentication devices. To make sure of this, one can apply an Identity Based Signature to ensure the integrity of measured data, and transmit the encrypted weather information with mutual authentication about the authentication devices. There are merits of this research: It is not necessary to manage authentication certificates unlike the Public Key Infrastructure methodology, and it provides a powerful security measure with the capability to be realized in a small scale computing environment, such as the meteorological observation system due to the low burden on managing keys.

Prediction of beta-turns and beta-turn types by a novel bidirectional Elman-type recurrent neural network with multiple output layers (MOLEBRNN).

PubMed

Kirschner, Andreas; Frishman, Dmitrij

2008-10-01

Prediction of beta-turns from amino acid sequences has long been recognized as an important problem in structural bioinformatics due to their frequent occurrence as well as their structural and functional significance. Because various structural features of proteins are intercorrelated, secondary structure information has been often employed as an additional input for machine learning algorithms while predicting beta-turns. Here we present a novel bidirectional Elman-type recurrent neural network with multiple output layers (MOLEBRNN) capable of predicting multiple mutually dependent structural motifs and demonstrate its efficiency in recognizing three aspects of protein structure: beta-turns, beta-turn types, and secondary structure. The advantage of our method compared to other predictors is that it does not require any external input except for sequence profiles because interdependencies between different structural features are taken into account implicitly during the learning process. In a sevenfold cross-validation experiment on a standard test dataset our method exhibits the total prediction accuracy of 77.9% and the Mathew's Correlation Coefficient of 0.45, the highest performance reported so far. It also outperforms other known methods in delineating individual turn types. We demonstrate how simultaneous prediction of multiple targets influences prediction performance on single targets. The MOLEBRNN presented here is a generic method applicable in a variety of research fields where multiple mutually depending target classes need to be predicted. http://webclu.bio.wzw.tum.de/predator-web/.

Mammographic images segmentation based on chaotic map clustering algorithm

PubMed Central

2014-01-01

Background This work investigates the applicability of a novel clustering approach to the segmentation of mammographic digital images. The chaotic map clustering algorithm is used to group together similar subsets of image pixels resulting in a medically meaningful partition of the mammography. Methods The image is divided into pixels subsets characterized by a set of conveniently chosen features and each of the corresponding points in the feature space is associated to a map. A mutual coupling strength between the maps depending on the associated distance between feature space points is subsequently introduced. On the system of maps, the simulated evolution through chaotic dynamics leads to its natural partitioning, which corresponds to a particular segmentation scheme of the initial mammographic image. Results The system provides a high recognition rate for small mass lesions (about 94% correctly segmented inside the breast) and the reproduction of the shape of regions with denser micro-calcifications in about 2/3 of the cases, while being less effective on identification of larger mass lesions. Conclusions We can summarize our analysis by asserting that due to the particularities of the mammographic images, the chaotic map clustering algorithm should not be used as the sole method of segmentation. It is rather the joint use of this method along with other segmentation techniques that could be successfully used for increasing the segmentation performance and for providing extra information for the subsequent analysis stages such as the classification of the segmented ROI. PMID:24666766

Amenable to reason: Aristotle's rhetoric and the moral psychology of practical ethics.

PubMed

London, A J

2000-12-01

An Aristotelian conception of practical ethics can be derived from the account of practical reasoning that Aristotle articulates in is Rhetoric and this has important implications for the way we understand the nature and limits of practical ethics. an important feature of this conception of practical ethics is its responsiveness to the complex ways in which agents form and maintain moral commitments, and this has important implications for the debate concerning methods of ethics in applied ethics. In particular, this feature enables us to understand casuistry, narrative, and principlism as mutually supportive modes of moral inquiry, rather than divergent and mutually exclusive methods of ethics. As a result, an Aristotelian conception of practical ethics clears the conceptual common ground upon which practical ethicists can forge a stable and realistic self-understanding.

Suppression of intestinal tumorigenesis in Apc mutant mice upon Musashi-1 deletion.

PubMed

Wolfe, Andy R; Ernlund, Amanda; McGuinness, William; Lehmann, Carl; Carl, Kaitlyn; Balmaceda, Nicole; Neufeld, Kristi L

2017-02-15

Therapeutic strategies based on a specific oncogenic target are better justified when elimination of that particular oncogene reduces tumorigenesis in a model organism. One such oncogene, Musashi-1 ( Msi-1 ), regulates translation of target mRNAs and is implicated in promoting tumorigenesis in the colon and other tissues. Msi-1 targets include the tumor suppressor adenomatous polyposis coli ( Apc ), a Wnt pathway antagonist lost in ∼80% of all colorectal cancers. Cell culture experiments have established that Msi-1 is a Wnt target, thus positioning Msi-1 and Apc as mutual antagonists in a mutually repressive feedback loop. Here, we report that intestines from mice lacking Msi-1 display aberrant Apc and Msi-1 mutually repressive feedback, reduced Wnt and Notch signaling, decreased proliferation, and changes in stem cell populations, features predicted to suppress tumorigenesis. Indeed, mice with germline Apc mutations ( Apc Min ) or with the Apc 1322T truncation mutation have a dramatic reduction in intestinal polyp number when Msi-1 is deleted. Taken together, these results provide genetic evidence that Msi-1 contributes to intestinal tumorigenesis driven by Apc loss, and validate the pursuit of Msi-1 inhibitors as chemo-prevention agents to reduce tumor burden. © 2017. Published by The Company of Biologists Ltd.

An Efficient Mutual Authentication Framework for Healthcare System in Cloud Computing.

PubMed

Kumar, Vinod; Jangirala, Srinivas; Ahmad, Musheer

2018-06-28

The increasing role of Telecare Medicine Information Systems (TMIS) makes its accessibility for patients to explore medical treatment, accumulate and approach medical data through internet connectivity. Security and privacy preservation is necessary for medical data of the patient in TMIS because of the very perceptive purpose. Recently, Mohit et al.'s proposed a mutual authentication protocol for TMIS in the cloud computing environment. In this work, we reviewed their protocol and found that it is not secure against stolen verifier attack, many logged in patient attack, patient anonymity, impersonation attack, and fails to protect session key. For enhancement of security level, we proposed a new mutual authentication protocol for the similar environment. The presented framework is also more capable in terms of computation cost. In addition, the security evaluation of the protocol protects resilience of all possible security attributes, and we also explored formal security evaluation based on random oracle model. The performance of the proposed protocol is much better in comparison to the existing protocol.

Multipass Target Search in Natural Environments

PubMed Central

Otte, Michael W.; Sofge, Donald; Gupta, Satyandra K.

2017-01-01

Consider a disaster scenario where search and rescue workers must search difficult to access buildings during an earthquake or flood. Often, finding survivors a few hours sooner results in a dramatic increase in saved lives, suggesting the use of drones for expedient rescue operations. Entropy can be used to quantify the generation and resolution of uncertainty. When searching for targets, maximizing mutual information of future sensor observations will minimize expected target location uncertainty by minimizing the entropy of the future estimate. Motion planning for multi-target autonomous search requires planning over an area with an imperfect sensor and may require multiple passes, which is hindered by the submodularity property of mutual information. Further, mission duration constraints must be handled accordingly, requiring consideration of the vehicle’s dynamics to generate feasible trajectories and must plan trajectories spanning the entire mission duration, something which most information gathering algorithms are incapable of doing. If unanticipated changes occur in an uncertain environment, new plans must be generated quickly. In addition, planning multipass trajectories requires evaluating path dependent rewards, requiring planning in the space of all previously selected actions, compounding the problem. We present an anytime algorithm for autonomous multipass target search in natural environments. The algorithm is capable of generating long duration dynamically feasible multipass coverage plans that maximize mutual information using a variety of techniques such as ϵ-admissible heuristics to speed up the search. To the authors’ knowledge this is the first attempt at efficiently solving multipass target search problems of such long duration. The proposed algorithm is based on best first branch and bound and is benchmarked against state of the art algorithms adapted to the problem in natural Simplex environments, gathering the most information in the given search time. PMID:29099087

Multivariate EMD and full spectrum based condition monitoring for rotating machinery

NASA Astrophysics Data System (ADS)

Zhao, Xiaomin; Patel, Tejas H.; Zuo, Ming J.

2012-02-01

Early assessment of machinery health condition is of paramount importance today. A sensor network with sensors in multiple directions and locations is usually employed for monitoring the condition of rotating machinery. Extraction of health condition information from these sensors for effective fault detection and fault tracking is always challenging. Empirical mode decomposition (EMD) is an advanced signal processing technology that has been widely used for this purpose. Standard EMD has the limitation in that it works only for a single real-valued signal. When dealing with data from multiple sensors and multiple health conditions, standard EMD faces two problems. First, because of the local and self-adaptive nature of standard EMD, the decomposition of signals from different sources may not match in either number or frequency content. Second, it may not be possible to express the joint information between different sensors. The present study proposes a method of extracting fault information by employing multivariate EMD and full spectrum. Multivariate EMD can overcome the limitations of standard EMD when dealing with data from multiple sources. It is used to extract the intrinsic mode functions (IMFs) embedded in raw multivariate signals. A criterion based on mutual information is proposed for selecting a sensitive IMF. A full spectral feature is then extracted from the selected fault-sensitive IMF to capture the joint information between signals measured from two orthogonal directions. The proposed method is first explained using simple simulated data, and then is tested for the condition monitoring of rotating machinery applications. The effectiveness of the proposed method is demonstrated through monitoring damage on the vane trailing edge of an impeller and rotor-stator rub in an experimental rotor rig.

Potentials for mutually beneficial collaboration between FIA specialists and IEG-40 pathologists and geneticists working on fusiform rust

Treesearch

Ellis Cowling; KaDonna Randolph

2013-01-01

The purpose of this article is to encourage development of an enduring mutually beneficial collaboration between data and information analysts in the US Forest Service’s "Enhanced Forest Inventory and Analysis (FIA) Program" and forest pathologists and geneticists in the information exchange group (IEG) titled "Genetics and Breeding of Southern Forest...

Independent components analysis to increase efficiency of discriminant analysis methods (FDA and LDA): Application to NMR fingerprinting of wine.

PubMed

Monakhova, Yulia B; Godelmann, Rolf; Kuballa, Thomas; Mushtakova, Svetlana P; Rutledge, Douglas N

2015-08-15

Discriminant analysis (DA) methods, such as linear discriminant analysis (LDA) or factorial discriminant analysis (FDA), are well-known chemometric approaches for solving classification problems in chemistry. In most applications, principle components analysis (PCA) is used as the first step to generate orthogonal eigenvectors and the corresponding sample scores are utilized to generate discriminant features for the discrimination. Independent components analysis (ICA) based on the minimization of mutual information can be used as an alternative to PCA as a preprocessing tool for LDA and FDA classification. To illustrate the performance of this ICA/DA methodology, four representative nuclear magnetic resonance (NMR) data sets of wine samples were used. The classification was performed regarding grape variety, year of vintage and geographical origin. The average increase for ICA/DA in comparison with PCA/DA in the percentage of correct classification varied between 6±1% and 8±2%. The maximum increase in classification efficiency of 11±2% was observed for discrimination of the year of vintage (ICA/FDA) and geographical origin (ICA/LDA). The procedure to determine the number of extracted features (PCs, ICs) for the optimum DA models was discussed. The use of independent components (ICs) instead of principle components (PCs) resulted in improved classification performance of DA methods. The ICA/LDA method is preferable to ICA/FDA for recognition tasks based on NMR spectroscopic measurements. Copyright © 2015 Elsevier B.V. All rights reserved.

Martian Clouds Data Workshop

NASA Technical Reports Server (NTRS)

Lee, Steven (Editor)

1987-01-01

The major topics covered were a discussion of the structure of relational data base systems and features of the Britton Lee Relational Data Base Management System (RDBMS); a discussion of the workshop's objectives, approach, and research scenarios; and an overview of the Atmospheres Node User's Guide, which details the datasets stored on the Britton Lee, the structure of the query and data analysis system, and examples of the exact menu screens encountered. Also discussed were experience with the system, review of the system performance, and a strategy to produce queries and performance data retrievals of mutual interest. The goals were defined as examining correlations between cloud occurrence, water vapor abundance, and surface properties.

Entanglement structures in qubit systems

NASA Astrophysics Data System (ADS)

Rangamani, Mukund; Rota, Massimiliano

2015-09-01

Using measures of entanglement such as negativity and tangles we provide a detailed analysis of entanglement structures in pure states of non-interacting qubits. The motivation for this exercise primarily comes from holographic considerations, where entanglement is inextricably linked with the emergence of geometry. We use the qubit systems as toy models to probe the internal structure, and introduce some useful measures involving entanglement negativity to quantify general features of entanglement. In particular, our analysis focuses on various constraints on the pattern of entanglement which are known to be satisfied by holographic sates, such as the saturation of Araki-Lieb inequality (in certain circumstances), and the monogamy of mutual information. We argue that even systems as simple as few non-interacting qubits can be useful laboratories to explore how the emergence of the bulk geometry may be related to quantum information principles.

An interdisciplinary approach for earthquake modelling and forecasting

NASA Astrophysics Data System (ADS)

Han, P.; Zhuang, J.; Hattori, K.; Ogata, Y.

2016-12-01

Earthquake is one of the most serious disasters, which may cause heavy casualties and economic losses. Especially in the past two decades, huge/mega earthquakes have hit many countries. Effective earthquake forecasting (including time, location, and magnitude) becomes extremely important and urgent. To date, various heuristically derived algorithms have been developed for forecasting earthquakes. Generally, they can be classified into two types: catalog-based approaches and non-catalog-based approaches. Thanks to the rapid development of statistical seismology in the past 30 years, now we are able to evaluate the performances of these earthquake forecast approaches quantitatively. Although a certain amount of precursory information is available in both earthquake catalogs and non-catalog observations, the earthquake forecast is still far from satisfactory. In most case, the precursory phenomena were studied individually. An earthquake model that combines self-exciting and mutually exciting elements was developed by Ogata and Utsu from the Hawkes process. The core idea of this combined model is that the status of the event at present is controlled by the event itself (self-exciting) and all the external factors (mutually exciting) in the past. In essence, the conditional intensity function is a time-varying Poisson process with rate λ(t), which is composed of the background rate, the self-exciting term (the information from past seismic events), and the external excitation term (the information from past non-seismic observations). This model shows us a way to integrate the catalog-based forecast and non-catalog-based forecast. Against this background, we are trying to develop a new earthquake forecast model which combines catalog-based and non-catalog-based approaches.

Six-State Quantum Key Distribution Using Photons with Orbital Angular Momentum

NASA Astrophysics Data System (ADS)

Li, Jun-Lin; Wang, Chuan

2010-11-01

A new implementation of high-dimensional quantum key distribution (QKD) protocol is discussed. Using three mutual unbiased bases, we present a d-level six-state QKD protocol that exploits the orbital angular momentum with the spatial mode of the light beam. The protocol shows that the feature of a high capacity since keys are encoded using photon modes in d-level Hilbert space. The devices for state preparation and measurement are also discussed. This protocol has high security and the alignment of shared reference frames is not needed between sender and receiver.

Mutual information measures applied to EEG signals for sleepiness characterization.

PubMed

Melia, Umberto; Guaita, Marc; Vallverdú, Montserrat; Embid, Cristina; Vilaseca, Isabel; Salamero, Manel; Santamaria, Joan

2015-03-01

Excessive daytime sleepiness (EDS) is one of the main symptoms of several sleep related disorders with a great impact on the patient lives. While many studies have been carried out in order to assess daytime sleepiness, the automatic EDS detection still remains an open problem. In this work, a novel approach to this issue based on non-linear dynamical analysis of EEG signal was proposed. Multichannel EEG signals were recorded during five maintenance of wakefulness (MWT) and multiple sleep latency (MSLT) tests alternated throughout the day from patients suffering from sleep disordered breathing. A group of 20 patients with excessive daytime sleepiness (EDS) was compared with a group of 20 patients without daytime sleepiness (WDS), by analyzing 60-s EEG windows in waking state. Measures obtained from cross-mutual information function (CMIF) and auto-mutual-information function (AMIF) were calculated in the EEG. These functions permitted a quantification of the complexity properties of the EEG signal and the non-linear couplings between different zones of the scalp. Statistical differences between EDS and WDS groups were found in β band during MSLT events (p-value < 0.0001). WDS group presented more complexity than EDS in the occipital zone, while a stronger nonlinear coupling between occipital and frontal zones was detected in EDS patients than in WDS. The AMIF and CMIF measures yielded sensitivity and specificity above 80% and AUC of ROC above 0.85 in classifying EDS and WDS patients. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

Geometric k-nearest neighbor estimation of entropy and mutual information

NASA Astrophysics Data System (ADS)

Lord, Warren M.; Sun, Jie; Bollt, Erik M.

2018-03-01

Nonparametric estimation of mutual information is used in a wide range of scientific problems to quantify dependence between variables. The k-nearest neighbor (knn) methods are consistent, and therefore expected to work well for a large sample size. These methods use geometrically regular local volume elements. This practice allows maximum localization of the volume elements, but can also induce a bias due to a poor description of the local geometry of the underlying probability measure. We introduce a new class of knn estimators that we call geometric knn estimators (g-knn), which use more complex local volume elements to better model the local geometry of the probability measures. As an example of this class of estimators, we develop a g-knn estimator of entropy and mutual information based on elliptical volume elements, capturing the local stretching and compression common to a wide range of dynamical system attractors. A series of numerical examples in which the thickness of the underlying distribution and the sample sizes are varied suggest that local geometry is a source of problems for knn methods such as the Kraskov-Stögbauer-Grassberger estimator when local geometric effects cannot be removed by global preprocessing of the data. The g-knn method performs well despite the manipulation of the local geometry. In addition, the examples suggest that the g-knn estimators can be of particular relevance to applications in which the system is large, but the data size is limited.

Community-based approaches to strengthen cultural competency in nursing education and practice.

PubMed

Anderson, Nancy Lois Ruth; Calvillo, Evelyn Ruiz; Fongwa, Marie Ngetiko

2007-01-01

This article explores existing informal as well as formal approaches that address health disparities in the communities where they occur, enhancing the opportunity to strengthen the cultural competency of providers, students, and faculty. A particular focus centers on the community-based participatory research approaches that involve community members, providing opportunities to develop mutually respectful, trusting relationships through co-teaching and co-learning experiences. With community-based participatory research approaches to community involvement in place, the stage is set for partnerships between communities and schools of nursing to collaboratively design, implement, and integrate informal and formal cultural competence components in nursing curricula.

How much a galaxy knows about its large-scale environment?: An information theoretic perspective

NASA Astrophysics Data System (ADS)

Pandey, Biswajit; Sarkar, Suman

2017-05-01

The small-scale environment characterized by the local density is known to play a crucial role in deciding the galaxy properties but the role of large-scale environment on galaxy formation and evolution still remain a less clear issue. We propose an information theoretic framework to investigate the influence of large-scale environment on galaxy properties and apply it to the data from the Galaxy Zoo project that provides the visual morphological classifications of ˜1 million galaxies from the Sloan Digital Sky Survey. We find a non-zero mutual information between morphology and environment that decreases with increasing length-scales but persists throughout the entire length-scales probed. We estimate the conditional mutual information and the interaction information between morphology and environment by conditioning the environment on different length-scales and find a synergic interaction between them that operates up to at least a length-scales of ˜30 h-1 Mpc. Our analysis indicates that these interactions largely arise due to the mutual information shared between the environments on different length-scales.

A Collaborative Endeavour between Mathematics and Science Educators: Focus on the Use of Percent in Chemistry

ERIC Educational Resources Information Center

Ramful, Ajay; Bedgood, Danny; Lowrie, Thomas

2016-01-01

This paper is the outcome of a collaborative endeavour between mathematics and science educators where the insight from each field mutually informed one another. Specifically, building on the knowledge base from mathematics education research, this study analyses the ways in which percent is interpreted by first year university students in general…

Evolutionary stability of mutualism: interspecific population regulation as an evolutionarily stable strategy

USGS Publications Warehouse

Holland, J. Nathaniel; DeAngelis, Donald L.; Schultz, Stewart T.

2004-01-01

Interspecific mutualisms are often vulnerable to instability because low benefit : cost ratios can rapidly lead to extinction or to the conversion of mutualism to parasite–host or predator–prey interactions. We hypothesize that the evolutionary stability of mutualism can depend on how benefits and costs to one mutualist vary with the population density of its partner, and that stability can be maintained if a mutualist can influence demographic rates and regulate the population density of its partner. We test this hypothesis in a model of mutualism with key features of senita cactus (Pachycereus schottii) – senita moth (Upiga virescens) interactions, in which benefits of pollination and costs of larval seed consumption to plant fitness depend on pollinator density. We show that plants can maximize their fitness by allocating resources to the production of excess flowers at the expense of fruit. Fruit abortion resulting from excess flower production reduces pre–adult survival of the pollinating seed–consumer, and maintains its density beneath a threshold that would destabilize the mutualism. Such a strategy of excess flower production and fruit abortion is convergent and evolutionarily stable against invasion by cheater plants that produce few flowers and abort few to no fruit. This novel mechanism of achieving evolutionarily stable mutualism, namely interspecific population regulation, is qualitatively different from other mechanisms invoking partner choice or selective rewards, and may be a general process that helps to preserve mutualistic interactions in nature.

Cloud classification from satellite data using a fuzzy sets algorithm: A polar example

NASA Technical Reports Server (NTRS)

Key, J. R.; Maslanik, J. A.; Barry, R. G.

1988-01-01

Where spatial boundaries between phenomena are diffuse, classification methods which construct mutually exclusive clusters seem inappropriate. The Fuzzy c-means (FCM) algorithm assigns each observation to all clusters, with membership values as a function of distance to the cluster center. The FCM algorithm is applied to AVHRR data for the purpose of classifying polar clouds and surfaces. Careful analysis of the fuzzy sets can provide information on which spectral channels are best suited to the classification of particular features, and can help determine likely areas of misclassification. General agreement in the resulting classes and cloud fraction was found between the FCM algorithm, a manual classification, and an unsupervised maximum likelihood classifier.

Concepts of formal concept analysis

NASA Astrophysics Data System (ADS)

Žáček, Martin; Homola, Dan; Miarka, Rostislav

2017-07-01

The aim of this article is apply of Formal Concept Analysis on concept of world. Formal concept analysis (FCA) as a methodology of data analysis, information management and knowledge representation has potential to be applied to a verity of linguistic problems. FCA is mathematical theory for concepts and concept hierarchies that reflects an understanding of concept. Formal concept analysis explicitly formalizes extension and intension of a concept, their mutual relationships. A distinguishing feature of FCA is an inherent integration of three components of conceptual processing of data and knowledge, namely, the discovery and reasoning with concepts in data, discovery and reasoning with dependencies in data, and visualization of data, concepts, and dependencies with folding/unfolding capabilities.

Nonrigid synthetic aperture radar and optical image coregistration by combining local rigid transformations using a Kohonen network.

PubMed

Salehpour, Mehdi; Behrad, Alireza

2017-10-01

This study proposes a new algorithm for nonrigid coregistration of synthetic aperture radar (SAR) and optical images. The proposed algorithm employs point features extracted by the binary robust invariant scalable keypoints algorithm and a new method called weighted bidirectional matching for initial correspondence. To refine false matches, we assume that the transformation between SAR and optical images is locally rigid. This property is used to refine false matches by assigning scores to matched pairs and clustering local rigid transformations using a two-layer Kohonen network. Finally, the thin plate spline algorithm and mutual information are used for nonrigid coregistration of SAR and optical images.

A Semi-Supervised Learning Algorithm for Predicting Four Types MiRNA-Disease Associations by Mutual Information in a Heterogeneous Network.

PubMed

Zhang, Xiaotian; Yin, Jian; Zhang, Xu

2018-03-02

Increasing evidence suggests that dysregulation of microRNAs (miRNAs) may lead to a variety of diseases. Therefore, identifying disease-related miRNAs is a crucial problem. Currently, many computational approaches have been proposed to predict binary miRNA-disease associations. In this study, in order to predict underlying miRNA-disease association types, a semi-supervised model called the network-based label propagation algorithm is proposed to infer multiple types of miRNA-disease associations (NLPMMDA) by mutual information derived from the heterogeneous network. The NLPMMDA method integrates disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity information of miRNAs and diseases to construct a heterogeneous network. NLPMMDA is a semi-supervised model which does not require verified negative samples. Leave-one-out cross validation (LOOCV) was implemented for four known types of miRNA-disease associations and demonstrated the reliable performance of our method. Moreover, case studies of lung cancer and breast cancer confirmed effective performance of NLPMMDA to predict novel miRNA-disease associations and their association types.

75 FR 81606 - Agency Information Collection Activities: Announcement of Board Approval Under Delegated...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-12-28

... of most mutual funds. Current Actions: On October 14, 2010, the Federal Reserve published a notice in... margin stock, and (3) shares of most mutual funds. Lenders other than brokers and dealers and banks must...

Renyi generalizations of the conditional quantum mutual information

DTIC Science & Technology

2015-02-23

D) for a four-party pure state on systems ABCD. The conditional mutual information also underlies the squashed entanglement , an entanglement measure...that satisfies all of the axioms desired for an entanglement measure. As such, it has been an open question to find Rényi generalizations of the...possessing the C systems, and the sender and receiver sharing noiseless entanglement before communication begins, the optimal rate of quantum communication

Automatic registration of optical imagery with 3d lidar data using local combined mutual information

NASA Astrophysics Data System (ADS)

Parmehr, E. G.; Fraser, C. S.; Zhang, C.; Leach, J.

2013-10-01

Automatic registration of multi-sensor data is a basic step in data fusion for photogrammetric and remote sensing applications. The effectiveness of intensity-based methods such as Mutual Information (MI) for automated registration of multi-sensor image has been previously reported for medical and remote sensing applications. In this paper, a new multivariable MI approach that exploits complementary information of inherently registered LiDAR DSM and intensity data to improve the robustness of registering optical imagery and LiDAR point cloud, is presented. LiDAR DSM and intensity information has been utilised in measuring the similarity of LiDAR and optical imagery via the Combined MI. An effective histogramming technique is adopted to facilitate estimation of a 3D probability density function (pdf). In addition, a local similarity measure is introduced to decrease the complexity of optimisation at higher dimensions and computation cost. Therefore, the reliability of registration is improved due to the use of redundant observations of similarity. The performance of the proposed method for registration of satellite and aerial images with LiDAR data in urban and rural areas is experimentally evaluated and the results obtained are discussed.

Utilizing Mutual Information Analysis to Explore the Relationship Between Gray and White Matter Structural Pathologies in Schizophrenia.

PubMed

Lyall, Amanda E; Savadjiev, Peter; Del Re, Elisabetta C; Seitz, Johanna; O'Donnell, Lauren J; Westin, Carl-Fredrik; Mesholam-Gately, Raquelle I; Petryshen, Tracey; Wojcik, Joanne D; Nestor, Paul; Niznikiewicz, Margaret; Goldstein, Jill; Seidman, Larry J; McCarley, Robert W; Shenton, Martha E; Kubicki, Marek

2018-04-03

Schizophrenia has been characterized as a neurodevelopmental disorder, with structural brain abnormalities reported at all stages. However, at present, it remains unclear whether gray and white matter abnormalities represent related or independent pathologies in schizophrenia. In this study, we present findings from an integrative analysis exploring the morphological relationship between gray and white matter in 45 schizophrenia participants and 49 healthy controls. We utilized mutual information (MI), a measure of how much information two variables share, to assess the morphological dependence between gray and white matter in three segments of the corpus callsoum, and the gray matter regions these segments connect: (1) the genu and the left and right rostral middle frontal gyrus (rMFG), (2) the isthmus and the left and right superior temporal gyrus (STG), (3) the splenium and the left and right lateral occipital gyrus (LOG). We report significantly reduced MI between white matter tract dispersion of the right hemispheric callosal connections to the STG and both cortical thickness and area in the right STG in schizophrenia patients, despite a lack of group differences in cortical thickness, surface area, or dispersion. We believe that this reduction in morphological dependence between gray and white matter may reflect a possible decoupling of the developmental processes that shape morphological features of white and gray matter early in life. The present study also demonstrates the importance of studying the relationship between gray and white matter measures, as opposed to restricting analyses to gray and white matter measures independently.

Aspects of mutually unbiased bases in odd-prime-power dimensions

NASA Astrophysics Data System (ADS)

Chaturvedi, S.

2002-04-01

We rephrase the Wootters-Fields construction [W. K. Wootters and B. C. Fields, Ann. Phys. 191, 363 (1989)] of a full set of mutually unbiased bases in a complex vector space of dimensions N=pr, where p is an odd prime, in terms of the character vectors of the cyclic group G of order p. This form may be useful in explicitly writing down mutually unbiased bases for N=pr.

Differential network analysis reveals the genome-wide landscape of estrogen receptor modulation in hormonal cancers

PubMed Central

Hsiao, Tzu-Hung; Chiu, Yu-Chiao; Hsu, Pei-Yin; Lu, Tzu-Pin; Lai, Liang-Chuan; Tsai, Mong-Hsun; Huang, Tim H.-M.; Chuang, Eric Y.; Chen, Yidong

2016-01-01

Several mutual information (MI)-based algorithms have been developed to identify dynamic gene-gene and function-function interactions governed by key modulators (genes, proteins, etc.). Due to intensive computation, however, these methods rely heavily on prior knowledge and are limited in genome-wide analysis. We present the modulated gene/gene set interaction (MAGIC) analysis to systematically identify genome-wide modulation of interaction networks. Based on a novel statistical test employing conjugate Fisher transformations of correlation coefficients, MAGIC features fast computation and adaption to variations of clinical cohorts. In simulated datasets MAGIC achieved greatly improved computation efficiency and overall superior performance than the MI-based method. We applied MAGIC to construct the estrogen receptor (ER) modulated gene and gene set (representing biological function) interaction networks in breast cancer. Several novel interaction hubs and functional interactions were discovered. ER+ dependent interaction between TGFβ and NFκB was further shown to be associated with patient survival. The findings were verified in independent datasets. Using MAGIC, we also assessed the essential roles of ER modulation in another hormonal cancer, ovarian cancer. Overall, MAGIC is a systematic framework for comprehensively identifying and constructing the modulated interaction networks in a whole-genome landscape. MATLAB implementation of MAGIC is available for academic uses at https://github.com/chiuyc/MAGIC. PMID:26972162

Quality assurance, benchmarking, assessment and mutual international recognition of qualifications.

PubMed

Hobson, R; Rolland, S; Rotgans, J; Schoonheim-Klein, M; Best, H; Chomyszyn-Gajewska, M; Dymock, D; Essop, R; Hupp, J; Kundzina, R; Love, R; Memon, R A; Moola, M; Neumann, L; Ozden, N; Roth, K; Samwel, P; Villavicencio, J; Wright, P; Harzer, W

2008-02-01

The aim of this report is to provide guidance to assist in the international convergence of quality assurance, benchmarking and assessment systems to improve dental education. Proposals are developed for mutual recognition of qualifications, to aid international movement and exchange of staff and students including and supporting developing countries. Quality assurance is the responsibility of all staff involved in dental education and involves three levels: internal, institutional and external. Benchmarking information provides a subject framework. Benchmarks are useful for a variety of purposes including design and validation of programmes, examination and review; they can also strengthen the accreditation process undertaken by professional and statutory bodies. Benchmark information can be used by institutions as part of their programme approval process, to set degree standards. The standards should be developed by the dental academic community through formal groups of experts. Assessment outcomes of student learning are a measure of the quality of the learning programme. The goal of an effective assessment strategy should be that it provides the starting point for students to adopt a positive approach to effective and competent practice, reflective and lifelong learning. All assessment methods should be evidence based or based upon research. Mutual recognition of professional qualifications means that qualifications gained in one country (the home country) are recognized in another country (the host country). It empowers movement of skilled workers, which can help resolve skills shortages within participating countries. These proposals are not intended to be either exhaustive or prescriptive; they are purely for guidance and derived from the identification of what is perceived to be 'best practice'.

Relationships between healthcare and research records.

PubMed

Duwe, Helmut

2004-01-01

The ultimate end-point of healthcare and health-related life sciences, more or less as regulatory idea, is the prevention and cure of diseases, considering the fate of individual patients as well as the challenge of providing sufficient care for all. However, all undertakings stand under the "proviso of rightness of action". The movement of evidence-based medicine has triggered a renaissance of systematic self-assurance of best practise. The systematic utilization of healthcare records and research study recordings in an inter-linked manner provides a better enabling environment to improve evidence. Good e-health must contribute to accumulate inter-generation clinical experience. Qualified research should ensure methodological strictness via gold standards like controlled, randomised and masked trials. Building information systems for e-health as well as for e-science bears as a special focus the mutual cross-fertilization of these application domains. Analysing a variety of building blocks shows that both areas can benefit from generic solution pattern, keeping in mind that each domain has distinguished knowledge realms. Generic patterns as well as distinguished special features are illustrated by analysing state of the art solutions plus some experimental approaches, as there are: the generic part of the HL7 V3 RIM, the RCRIM work, laboratory information handling, vital sign standardization efforts, like ECG information models.Finally, the precision of the usage of the ubiquitous term "metadata" is taken as example of an open issue.

Myocardial strain estimation from CT: towards computer-aided diagnosis on infarction identification

NASA Astrophysics Data System (ADS)

Wong, Ken C. L.; Tee, Michael; Chen, Marcus; Bluemke, David A.; Summers, Ronald M.; Yao, Jianhua

2015-03-01

Regional myocardial strains have the potential for early quantification and detection of cardiac dysfunctions. Although image modalities such as tagged and strain-encoded MRI can provide motion information of the myocardium, they are uncommon in clinical routine. In contrary, cardiac CT images are usually available, but they only provide motion information at salient features such as the cardiac boundaries. To estimate myocardial strains from a CT image sequence, we adopted a cardiac biomechanical model with hyperelastic material properties to relate the motion on the cardiac boundaries to the myocardial deformation. The frame-to-frame displacements of the cardiac boundaries are obtained using B-spline deformable image registration based on mutual information, which are enforced as boundary conditions to the biomechanical model. The system equation is solved by the finite element method to provide the dense displacement field of the myocardium, and the regional values of the three principal strains and the six strains in cylindrical coordinates are computed in terms of the American Heart Association nomenclature. To study the potential of the estimated regional strains on identifying myocardial infarction, experiments were performed on cardiac CT image sequences of ten canines with artificially induced myocardial infarctions. The leave-one-subject-out cross validations show that, by using the optimal strain magnitude thresholds computed from ROC curves, the radial strain and the first principal strain have the best performance.

Bet hedging based cooperation can limit kin selection and form a basis for mutualism.

PubMed

Uitdehaag, Joost C M

2011-07-07

Mutualism is a mechanism of cooperation in which partners that differ help each other. As such, mutualism opposes mechanisms of kin selection and tag-based selection (for example the green beard mechanism), which are based on giving exclusive help to partners that are related or carry the same tag. In contrast to kin selection, which is a basis for parochialism and intergroup warfare, mutualism can therefore be regarded as a mechanism that drives peaceful coexistence between different groups and individuals. Here the competition between mutualism and kin (tag) selection is studied. In a model where kin selection and tag-based selection are dominant, mutualism is promoted by introducing environmental fluctuations. These fluctuations cause reduction in reproductive success by the mechanism of variance discount. The best strategy to counter variance discount is to share with agents who experience the most anticorrelated fluctuations, a strategy called bet hedging. In this way, bet hedging stimulates cooperation with the most unrelated partners, which is a basis for mutualism. Analytic results and simulations reveal that, if this effect is large enough, mutualistic strategies can dominate kin selective strategies. In addition, mutants of these mutualistic strategies that experience fluctuations that are more anticorrelated to their partner, can outcompete wild type, which can lead to the evolution of specialization. In this way, the evolutionary success of mutualistic strategies can be explained by bet hedging-based cooperation. Copyright © 2011 Elsevier Ltd. All rights reserved.

Time and Category Information in Pattern-Based Codes

PubMed Central

Eyherabide, Hugo Gabriel; Samengo, Inés

2010-01-01

Sensory stimuli are usually composed of different features (the what) appearing at irregular times (the when). Neural responses often use spike patterns to represent sensory information. The what is hypothesized to be encoded in the identity of the elicited patterns (the pattern categories), and the when, in the time positions of patterns (the pattern timing). However, this standard view is oversimplified. In the real world, the what and the when might not be separable concepts, for instance, if they are correlated in the stimulus. In addition, neuronal dynamics can condition the pattern timing to be correlated with the pattern categories. Hence, timing and categories of patterns may not constitute independent channels of information. In this paper, we assess the role of spike patterns in the neural code, irrespective of the nature of the patterns. We first define information-theoretical quantities that allow us to quantify the information encoded by different aspects of the neural response. We also introduce the notion of synergy/redundancy between time positions and categories of patterns. We subsequently establish the relation between the what and the when in the stimulus with the timing and the categories of patterns. To that aim, we quantify the mutual information between different aspects of the stimulus and different aspects of the response. This formal framework allows us to determine the precise conditions under which the standard view holds, as well as the departures from this simple case. Finally, we study the capability of different response aspects to represent the what and the when in the neural response. PMID:21151371

Advanced approach to information security management system model for industrial control system.

PubMed

Park, Sanghyun; Lee, Kyungho

2014-01-01

Organizations make use of important information in day-to-day business. Protecting sensitive information is imperative and must be managed. Companies in many parts of the world protect sensitive information using the international standard known as the information security management system (ISMS). ISO 27000 series is the international standard ISMS used to protect confidentiality, integrity, and availability of sensitive information. While an ISMS based on ISO 27000 series has no particular flaws for general information systems, it is unfit to manage sensitive information for industrial control systems (ICSs) because the first priority of industrial control is safety of the system. Therefore, a new information security management system based on confidentiality, integrity, and availability as well as safety is required for ICSs. This new ISMS must be mutually exclusive of an ICS. This paper provides a new paradigm of ISMS for ICSs, which will be shown to be more suitable than the existing ISMS.

Advanced Approach to Information Security Management System Model for Industrial Control System

PubMed Central

2014-01-01

Organizations make use of important information in day-to-day business. Protecting sensitive information is imperative and must be managed. Companies in many parts of the world protect sensitive information using the international standard known as the information security management system (ISMS). ISO 27000 series is the international standard ISMS used to protect confidentiality, integrity, and availability of sensitive information. While an ISMS based on ISO 27000 series has no particular flaws for general information systems, it is unfit to manage sensitive information for industrial control systems (ICSs) because the first priority of industrial control is safety of the system. Therefore, a new information security management system based on confidentiality, integrity, and availability as well as safety is required for ICSs. This new ISMS must be mutually exclusive of an ICS. This paper provides a new paradigm of ISMS for ICSs, which will be shown to be more suitable than the existing ISMS. PMID:25136659

Joint eigenvector estimation from mutually anisotropic tensors improves susceptibility tensor imaging of the brain, kidney, and heart.

PubMed

Dibb, Russell; Liu, Chunlei

2017-06-01

To develop a susceptibility-based MRI technique for probing microstructure and fiber architecture of magnetically anisotropic tissues-such as central nervous system white matter, renal tubules, and myocardial fibers-in three dimensions using susceptibility tensor imaging (STI) tools. STI can probe tissue microstructure, but is limited by reconstruction artifacts because of absent phase information outside the tissue and noise. STI accuracy may be improved by estimating a joint eigenvector from mutually anisotropic susceptibility and relaxation tensors. Gradient-recalled echo image data were simulated using a numerical phantom and acquired from the ex vivo mouse brain, kidney, and heart. Susceptibility tensor data were reconstructed using STI, regularized STI, and the proposed algorithm of mutually anisotropic and joint eigenvector STI (MAJESTI). Fiber map and tractography results from each technique were compared with diffusion tensor data. MAJESTI reduced the estimated susceptibility tensor orientation error by 30% in the phantom, 36% in brain white matter, 40% in the inner medulla of the kidney, and 45% in myocardium. This improved the continuity and consistency of susceptibility-based fiber tractography in each tissue. MAJESTI estimation of the susceptibility tensors yields lower orientation errors for susceptibility-based fiber mapping and tractography in the intact brain, kidney, and heart. Magn Reson Med 77:2331-2346, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Pointwise Partial Information Decomposition Using the Specificity and Ambiguity Lattices

NASA Astrophysics Data System (ADS)

Finn, Conor; Lizier, Joseph

2018-04-01

What are the distinct ways in which a set of predictor variables can provide information about a target variable? When does a variable provide unique information, when do variables share redundant information, and when do variables combine synergistically to provide complementary information? The redundancy lattice from the partial information decomposition of Williams and Beer provided a promising glimpse at the answer to these questions. However, this structure was constructed using a much criticised measure of redundant information, and despite sustained research, no completely satisfactory replacement measure has been proposed. In this paper, we take a different approach, applying the axiomatic derivation of the redundancy lattice to a single realisation from a set of discrete variables. To overcome the difficulty associated with signed pointwise mutual information, we apply this decomposition separately to the unsigned entropic components of pointwise mutual information which we refer to as the specificity and ambiguity. This yields a separate redundancy lattice for each component. Then based upon an operational interpretation of redundancy, we define measures of redundant specificity and ambiguity enabling us to evaluate the partial information atoms in each lattice. These atoms can be recombined to yield the sought-after multivariate information decomposition. We apply this framework to canonical examples from the literature and discuss the results and the various properties of the decomposition. In particular, the pointwise decomposition using specificity and ambiguity satisfies a chain rule over target variables, which provides new insights into the so-called two-bit-copy example.

Alert Exchange Process Protocol

NASA Technical Reports Server (NTRS)

Groen, Frank

2015-01-01

The National Aeronautics and Space Administration of the United States of America (NASA), and the European Space Agency (ESA), and the Japanese Aerospace Exploration Agency (JAXA), acknowledging that NASA, ESA and JAXA have a mutual interest in exchanging Alerts and Alert Status Lists to enhance the information base for each system participant while fortifying the general level of cooperation between the policy agreement subscribers, and each Party will exchange Alert listings on regular basis and detailed Alert information on a need to know basis to the extent permitted by law.

On the relevance of Gibson's affordance concept for geographical information science (GISc).

PubMed

Jonietz, David; Timpf, Sabine

2015-09-01

J. J. Gibson's concept of affordances has provided a theoretical basis for various studies in geographical information science (GISc). This paper sets out to explain its popularity from a GISc perspective. Based on a short review of previous work, it will be argued that its main contributions to GISc are twofold, including an action-centered view of spatial entities and the notion of agent-environment mutuality. Using the practical example of pedestrian behavior simulation, new potentials for using and extending affordances are discussed.

Identification of ghost artifact using texture analysis in pediatric spinal cord diffusion tensor images.

PubMed

Alizadeh, Mahdi; Conklin, Chris J; Middleton, Devon M; Shah, Pallav; Saksena, Sona; Krisa, Laura; Finsterbusch, Jürgen; Faro, Scott H; Mulcahey, M J; Mohamed, Feroze B

2018-04-01

Ghost artifacts are a major contributor to degradation of spinal cord diffusion tensor images. A multi-stage post-processing pipeline was designed, implemented and validated to automatically remove ghost artifacts arising from reduced field of view diffusion tensor imaging (DTI) of the pediatric spinal cord. A total of 12 pediatric subjects including 7 healthy subjects (mean age=11.34years) with no evidence of spinal cord injury or pathology and 5 patients (mean age=10.96years) with cervical spinal cord injury were studied. Ghost/true cords, labeled as region of interests (ROIs), in non-diffusion weighted b0 images were segmented automatically using mathematical morphological processing. Initially, 21 texture features were extracted from each segmented ROI including 5 first-order features based on the histogram of the image (mean, variance, skewness, kurtosis and entropy) and 16s-order feature vector elements, incorporating four statistical measures (contrast, correlation, homogeneity and energy) calculated from co-occurrence matrices in directions of 0°, 45°, 90° and 135°. Next, ten features with a high value of mutual information (MI) relative to the pre-defined target class and within the features were selected as final features which were input to a trained classifier (adaptive neuro-fuzzy interface system) to separate the true cord from the ghost cord. The implemented pipeline was successfully able to separate the ghost artifacts from true cord structures. The results obtained from the classifier showed a sensitivity of 91%, specificity of 79%, and accuracy of 84% in separating the true cord from ghost artifacts. The results show that the proposed method is promising for the automatic detection of ghost cords present in DTI images of the spinal cord. This step is crucial towards development of accurate, automatic DTI spinal cord post processing pipelines. Copyright © 2017 Elsevier Inc. All rights reserved.

Tracking and Classification of In-Air Hand Gesture Based on Thermal Guided Joint Filter.

PubMed

Kim, Seongwan; Ban, Yuseok; Lee, Sangyoun

2017-01-17

The research on hand gestures has attracted many image processing-related studies, as it intuitively conveys the intention of a human as it pertains to motional meaning. Various sensors have been used to exploit the advantages of different modalities for the extraction of important information conveyed by the hand gesture of a user. Although many works have focused on learning the benefits of thermal information from thermal cameras, most have focused on face recognition or human body detection, rather than hand gesture recognition. Additionally, the majority of the works that take advantage of multiple modalities (e.g., the combination of a thermal sensor and a visual sensor), usually adopting simple fusion approaches between the two modalities. As both thermal sensors and visual sensors have their own shortcomings and strengths, we propose a novel joint filter-based hand gesture recognition method to simultaneously exploit the strengths and compensate the shortcomings of each. Our study is motivated by the investigation of the mutual supplementation between thermal and visual information in low feature level for the consistent representation of a hand in the presence of varying lighting conditions. Accordingly, our proposed method leverages the thermal sensor's stability against luminance and the visual sensors textural detail, while complementing the low resolution and halo effect of thermal sensors and the weakness against illumination of visual sensors. A conventional region tracking method and a deep convolutional neural network have been leveraged to track the trajectory of a hand gesture and to recognize the hand gesture, respectively. Our experimental results show stability in recognizing a hand gesture against varying lighting conditions based on the contribution of the joint kernels of spatial adjacency and thermal range similarity.

Tracking and Classification of In-Air Hand Gesture Based on Thermal Guided Joint Filter

PubMed Central

Kim, Seongwan; Ban, Yuseok; Lee, Sangyoun

2017-01-01

The research on hand gestures has attracted many image processing-related studies, as it intuitively conveys the intention of a human as it pertains to motional meaning. Various sensors have been used to exploit the advantages of different modalities for the extraction of important information conveyed by the hand gesture of a user. Although many works have focused on learning the benefits of thermal information from thermal cameras, most have focused on face recognition or human body detection, rather than hand gesture recognition. Additionally, the majority of the works that take advantage of multiple modalities (e.g., the combination of a thermal sensor and a visual sensor), usually adopting simple fusion approaches between the two modalities. As both thermal sensors and visual sensors have their own shortcomings and strengths, we propose a novel joint filter-based hand gesture recognition method to simultaneously exploit the strengths and compensate the shortcomings of each. Our study is motivated by the investigation of the mutual supplementation between thermal and visual information in low feature level for the consistent representation of a hand in the presence of varying lighting conditions. Accordingly, our proposed method leverages the thermal sensor’s stability against luminance and the visual sensors textural detail, while complementing the low resolution and halo effect of thermal sensors and the weakness against illumination of visual sensors. A conventional region tracking method and a deep convolutional neural network have been leveraged to track the trajectory of a hand gesture and to recognize the hand gesture, respectively. Our experimental results show stability in recognizing a hand gesture against varying lighting conditions based on the contribution of the joint kernels of spatial adjacency and thermal range similarity. PMID:28106716

Large distance expansion of mutual information for disjoint disks in a free scalar theory

DOE PAGES

Agón, Cesar A.; Cohen-Abbo, Isaac; Schnitzer, Howard J.

2016-11-11

We compute the next-to-leading order term in the long-distance expansion of the mutual information for free scalars in three space-time dimensions. The geometry considered is two disjoint disks separated by a distance r between their centers. No evidence for non-analyticity in the Rényi parameter n for the continuation n → 1 in the next-to-leading order term is found.

A Search for Strange Attractors in the Saturation of Middle Atmosphere Gravity Waves

DTIC Science & Technology

1990-09-01

Fraser, A. M. and H. L. Swinney, 1986: Independent coordinates for strange attractors from mutual information . Phvs. Rev. A, 33, 1134-1140. Fraser...vectors implies that the two are linearly independent . However, data characterized by a strange attractor are usually highly nonlinear, thus making...noise in this data set. The degree of autocorrelation and the lack of general independence as determined from the mutual information also reduces the

Patients’ Data Management System Protected by Identity-Based Authentication and Key Exchange

PubMed Central

Rivero-García, Alexandra; Santos-González, Iván; Hernández-Goya, Candelaria; Caballero-Gil, Pino; Yung, Moti

2017-01-01

A secure and distributed framework for the management of patients’ information in emergency and hospitalization services is proposed here in order to seek improvements in efficiency and security in this important area. In particular, confidentiality protection, mutual authentication, and automatic identification of patients are provided. The proposed system is based on two types of devices: Near Field Communication (NFC) wristbands assigned to patients, and mobile devices assigned to medical staff. Two other main elements of the system are an intermediate server to manage the involved data, and a second server with a private key generator to define the information required to protect communications. An identity-based authentication and key exchange scheme is essential to provide confidential communication and mutual authentication between the medical staff and the private key generator through an intermediate server. The identification of patients is carried out through a keyed-hash message authentication code. Thanks to the combination of the aforementioned tools, a secure alternative mobile health (mHealth) scheme for managing patients’ data is defined for emergency and hospitalization services. Different parts of the proposed system have been implemented, including mobile application, intermediate server, private key generator and communication channels. Apart from that, several simulations have been performed, and, compared with the current system, significant improvements in efficiency have been observed. PMID:28362328

Patients' Data Management System Protected by Identity-Based Authentication and Key Exchange.

PubMed

Rivero-García, Alexandra; Santos-González, Iván; Hernández-Goya, Candelaria; Caballero-Gil, Pino; Yung, Moti

2017-03-31

A secure and distributed framework for the management of patients' information in emergency and hospitalization services is proposed here in order to seek improvements in efficiency and security in this important area. In particular, confidentiality protection, mutual authentication, and automatic identification of patients are provided. The proposed system is based on two types of devices: Near Field Communication (NFC) wristbands assigned to patients, and mobile devices assigned to medical staff. Two other main elements of the system are an intermediate server to manage the involved data, and a second server with a private key generator to define the information required to protect communications. An identity-based authentication and key exchange scheme is essential to provide confidential communication and mutual authentication between the medical staff and the private key generator through an intermediate server. The identification of patients is carried out through a keyed-hash message authentication code. Thanks to the combination of the aforementioned tools, a secure alternative mobile health (mHealth) scheme for managing patients' data is defined for emergency and hospitalization services. Different parts of the proposed system have been implemented, including mobile application, intermediate server, private key generator and communication channels. Apart from that, several simulations have been performed, and, compared with the current system, significant improvements in efficiency have been observed.

Faithful Squashed Entanglement

NASA Astrophysics Data System (ADS)

Brandão, Fernando G. S. L.; Christandl, Matthias; Yard, Jon

2011-09-01

Squashed entanglement is a measure for the entanglement of bipartite quantum states. In this paper we present a lower bound for squashed entanglement in terms of a distance to the set of separable states. This implies that squashed entanglement is faithful, that is, it is strictly positive if and only if the state is entangled. We derive the lower bound on squashed entanglement from a lower bound on the quantum conditional mutual information which is used to define squashed entanglement. The quantum conditional mutual information corresponds to the amount by which strong subadditivity of von Neumann entropy fails to be saturated. Our result therefore sheds light on the structure of states that almost satisfy strong subadditivity with equality. The proof is based on two recent results from quantum information theory: the operational interpretation of the quantum mutual information as the optimal rate for state redistribution and the interpretation of the regularised relative entropy of entanglement as an error exponent in hypothesis testing. The distance to the set of separable states is measured in terms of the LOCC norm, an operationally motivated norm giving the optimal probability of distinguishing two bipartite quantum states, each shared by two parties, using any protocol formed by local quantum operations and classical communication (LOCC) between the parties. A similar result for the Frobenius or Euclidean norm follows as an immediate consequence. The result has two applications in complexity theory. The first application is a quasipolynomial-time algorithm solving the weak membership problem for the set of separable states in LOCC or Euclidean norm. The second application concerns quantum Merlin-Arthur games. Here we show that multiple provers are not more powerful than a single prover when the verifier is restricted to LOCC operations thereby providing a new characterisation of the complexity class QMA.

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

PubMed Central

Giordano, Bruno L.; Kayser, Christoph; Rousselet, Guillaume A.; Gross, Joachim; Schyns, Philippe G.

2016-01-01

Abstract We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open‐source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541–1573, 2017. © 2016 Wiley Periodicals, Inc. PMID:27860095

Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale

PubMed Central

Kobourov, Stephen; Gallant, Mike; Börner, Katy

2016-01-01

Overview Notions of community quality underlie the clustering of networks. While studies surrounding network clustering are increasingly common, a precise understanding of the realtionship between different cluster quality metrics is unknown. In this paper, we examine the relationship between stand-alone cluster quality metrics and information recovery metrics through a rigorous analysis of four widely-used network clustering algorithms—Louvain, Infomap, label propagation, and smart local moving. We consider the stand-alone quality metrics of modularity, conductance, and coverage, and we consider the information recovery metrics of adjusted Rand score, normalized mutual information, and a variant of normalized mutual information used in previous work. Our study includes both synthetic graphs and empirical data sets of sizes varying from 1,000 to 1,000,000 nodes. Cluster Quality Metrics We find significant differences among the results of the different cluster quality metrics. For example, clustering algorithms can return a value of 0.4 out of 1 on modularity but score 0 out of 1 on information recovery. We find conductance, though imperfect, to be the stand-alone quality metric that best indicates performance on the information recovery metrics. Additionally, our study shows that the variant of normalized mutual information used in previous work cannot be assumed to differ only slightly from traditional normalized mutual information. Network Clustering Algorithms Smart local moving is the overall best performing algorithm in our study, but discrepancies between cluster evaluation metrics prevent us from declaring it an absolutely superior algorithm. Interestingly, Louvain performed better than Infomap in nearly all the tests in our study, contradicting the results of previous work in which Infomap was superior to Louvain. We find that although label propagation performs poorly when clusters are less clearly defined, it scales efficiently and accurately to large graphs with well-defined clusters. PMID:27391786

Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale.

PubMed

Emmons, Scott; Kobourov, Stephen; Gallant, Mike; Börner, Katy

2016-01-01

Notions of community quality underlie the clustering of networks. While studies surrounding network clustering are increasingly common, a precise understanding of the realtionship between different cluster quality metrics is unknown. In this paper, we examine the relationship between stand-alone cluster quality metrics and information recovery metrics through a rigorous analysis of four widely-used network clustering algorithms-Louvain, Infomap, label propagation, and smart local moving. We consider the stand-alone quality metrics of modularity, conductance, and coverage, and we consider the information recovery metrics of adjusted Rand score, normalized mutual information, and a variant of normalized mutual information used in previous work. Our study includes both synthetic graphs and empirical data sets of sizes varying from 1,000 to 1,000,000 nodes. We find significant differences among the results of the different cluster quality metrics. For example, clustering algorithms can return a value of 0.4 out of 1 on modularity but score 0 out of 1 on information recovery. We find conductance, though imperfect, to be the stand-alone quality metric that best indicates performance on the information recovery metrics. Additionally, our study shows that the variant of normalized mutual information used in previous work cannot be assumed to differ only slightly from traditional normalized mutual information. Smart local moving is the overall best performing algorithm in our study, but discrepancies between cluster evaluation metrics prevent us from declaring it an absolutely superior algorithm. Interestingly, Louvain performed better than Infomap in nearly all the tests in our study, contradicting the results of previous work in which Infomap was superior to Louvain. We find that although label propagation performs poorly when clusters are less clearly defined, it scales efficiently and accurately to large graphs with well-defined clusters.

Efficiency at rest: magnetoencephalographic resting-state connectivity and individual differences in verbal working memory.

PubMed

del Río, David; Cuesta, Pablo; Bajo, Ricardo; García-Pacios, Javier; López-Higes, Ramón; del-Pozo, Francisco; Maestú, Fernando

2012-11-01

Inter-individual differences in cognitive performance are based on an efficient use of task-related brain resources. However, little is known yet on how these differences might be reflected on resting-state brain networks. Here we used Magnetoencephalography resting-state recordings to assess the relationship between a behavioral measurement of verbal working memory and functional connectivity as measured through Mutual Information. We studied theta (4-8 Hz), low alpha (8-10 Hz), high alpha (10-13 Hz), low beta (13-18 Hz) and high beta (18-30 Hz) frequency bands. A higher verbal working memory capacity was associated with a lower mutual information in the low alpha band, prominently among right-anterior and left-lateral sensors. The results suggest that an efficient brain organization in the domain of verbal working memory might be related to a lower resting-state functional connectivity across large-scale brain networks possibly involving right prefrontal and left perisylvian areas. Copyright © 2012 Elsevier B.V. All rights reserved.

The joint effect of ethnicity and gender on occupational segregation. An approach based on the Mutual Information Index.

PubMed

Guinea-Martin, Daniel; Mora, Ricardo; Ruiz-Castillo, Javier

2015-01-01

In this article, we study the effects of ethnicity and gender on occupational segregation. Traditionally, researchers have examined the two sources of segregation separately. In contrast, we measure their joint effect by applying a multigroup segregation index-the Mutual Information or M index-to the product of the seven ethnic groups and two genders distinguished in our 2001 Census data for England and Wales. We exploit M's additive decomposability property to pose the following two questions: (i) Is there an interaction effect? (ii) How much does each source contribute to occupational segregation, controlling for the effect of the other? Although the role of ethnicity is non-negligible in the areas where minorities are concentrated, our findings confirm the greater importance of gender over ethnicity as a source of segregation. Moreover, we find a small "dwindling" interaction effect between the two sources of segregation: ethnicity slightly weakens the segregating power of gender and vice versa. Copyright © 2014 Elsevier Inc. All rights reserved.

Edge theory approach to topological entanglement entropy, mutual information, and entanglement negativity in Chern-Simons theories

NASA Astrophysics Data System (ADS)

Wen, Xueda; Matsuura, Shunji; Ryu, Shinsei

2016-06-01

We develop an approach based on edge theories to calculate the entanglement entropy and related quantities in (2+1)-dimensional topologically ordered phases. Our approach is complementary to, e.g., the existing methods using replica trick and Witten's method of surgery, and applies to a generic spatial manifold of genus g , which can be bipartitioned in an arbitrary way. The effects of fusion and braiding of Wilson lines can be also straightforwardly studied within our framework. By considering a generic superposition of states with different Wilson line configurations, through an interference effect, we can detect, by the entanglement entropy, the topological data of Chern-Simons theories, e.g., the R symbols, monodromy, and topological spins of quasiparticles. Furthermore, by using our method, we calculate other entanglement/correlation measures such as the mutual information and the entanglement negativity. In particular, it is found that the entanglement negativity of two adjacent noncontractible regions on a torus provides a simple way to distinguish Abelian and non-Abelian topological orders.

Information theoretic partitioning and confidence based weight assignment for multi-classifier decision level fusion in hyperspectral target recognition applications

NASA Astrophysics Data System (ADS)

Prasad, S.; Bruce, L. M.

2007-04-01

There is a growing interest in using multiple sources for automatic target recognition (ATR) applications. One approach is to take multiple, independent observations of a phenomenon and perform a feature level or a decision level fusion for ATR. This paper proposes a method to utilize these types of multi-source fusion techniques to exploit hyperspectral data when only a small number of training pixels are available. Conventional hyperspectral image based ATR techniques project the high dimensional reflectance signature onto a lower dimensional subspace using techniques such as Principal Components Analysis (PCA), Fisher's linear discriminant analysis (LDA), subspace LDA and stepwise LDA. While some of these techniques attempt to solve the curse of dimensionality, or small sample size problem, these are not necessarily optimal projections. In this paper, we present a divide and conquer approach to address the small sample size problem. The hyperspectral space is partitioned into contiguous subspaces such that the discriminative information within each subspace is maximized, and the statistical dependence between subspaces is minimized. We then treat each subspace as a separate source in a multi-source multi-classifier setup and test various decision fusion schemes to determine their efficacy. Unlike previous approaches which use correlation between variables for band grouping, we study the efficacy of higher order statistical information (using average mutual information) for a bottom up band grouping. We also propose a confidence measure based decision fusion technique, where the weights associated with various classifiers are based on their confidence in recognizing the training data. To this end, training accuracies of all classifiers are used for weight assignment in the fusion process of test pixels. The proposed methods are tested using hyperspectral data with known ground truth, such that the efficacy can be quantitatively measured in terms of target recognition accuracies.

A spectral-structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery

NASA Astrophysics Data System (ADS)

Zhao, Bei; Zhong, Yanfei; Zhang, Liangpei

2016-06-01

Land-use classification of very high spatial resolution remote sensing (VHSR) imagery is one of the most challenging tasks in the field of remote sensing image processing. However, the land-use classification is hard to be addressed by the land-cover classification techniques, due to the complexity of the land-use scenes. Scene classification is considered to be one of the expected ways to address the land-use classification issue. The commonly used scene classification methods of VHSR imagery are all derived from the computer vision community that mainly deal with terrestrial image recognition. Differing from terrestrial images, VHSR images are taken by looking down with airborne and spaceborne sensors, which leads to the distinct light conditions and spatial configuration of land cover in VHSR imagery. Considering the distinct characteristics, two questions should be answered: (1) Which type or combination of information is suitable for the VHSR imagery scene classification? (2) Which scene classification algorithm is best for VHSR imagery? In this paper, an efficient spectral-structural bag-of-features scene classifier (SSBFC) is proposed to combine the spectral and structural information of VHSR imagery. SSBFC utilizes the first- and second-order statistics (the mean and standard deviation values, MeanStd) as the statistical spectral descriptor for the spectral information of the VHSR imagery, and uses dense scale-invariant feature transform (SIFT) as the structural feature descriptor. From the experimental results, the spectral information works better than the structural information, while the combination of the spectral and structural information is better than any single type of information. Taking the characteristic of the spatial configuration into consideration, SSBFC uses the whole image scene as the scope of the pooling operator, instead of the scope generated by a spatial pyramid (SP) commonly used in terrestrial image classification. The experimental results show that the whole image as the scope of the pooling operator performs better than the scope generated by SP. In addition, SSBFC codes and pools the spectral and structural features separately to avoid mutual interruption between the spectral and structural features. The coding vectors of spectral and structural features are then concatenated into a final coding vector. Finally, SSBFC classifies the final coding vector by support vector machine (SVM) with a histogram intersection kernel (HIK). Compared with the latest scene classification methods, the experimental results with three VHSR datasets demonstrate that the proposed SSBFC performs better than the other classification methods for VHSR image scenes.

Joint estimation of subject motion and tracer kinetic parameters of dynamic PET data in an EM framework

NASA Astrophysics Data System (ADS)

Jiao, Jieqing; Salinas, Cristian A.; Searle, Graham E.; Gunn, Roger N.; Schnabel, Julia A.

2012-02-01

Dynamic Positron Emission Tomography is a powerful tool for quantitative imaging of in vivo biological processes. The long scan durations necessitate motion correction, to maintain the validity of the dynamic measurements, which can be particularly challenging due to the low signal-to-noise ratio (SNR) and spatial resolution, as well as the complex tracer behaviour in the dynamic PET data. In this paper we develop a novel automated expectation-maximisation image registration framework that incorporates temporal tracer kinetic information to correct for inter-frame subject motion during dynamic PET scans. We employ the Zubal human brain phantom to simulate dynamic PET data using SORTEO (a Monte Carlo-based simulator), in order to validate the proposed method for its ability to recover imposed rigid motion. We have conducted a range of simulations using different noise levels, and corrupted the data with a range of rigid motion artefacts. The performance of our motion correction method is compared with pairwise registration using normalised mutual information as a voxel similarity measure (an approach conventionally used to correct for dynamic PET inter-frame motion based solely on intensity information). To quantify registration accuracy, we calculate the target registration error across the images. The results show that our new dynamic image registration method based on tracer kinetics yields better realignment of the simulated datasets, halving the target registration error when compared to the conventional method at small motion levels, as well as yielding smaller residuals in translation and rotation parameters. We also show that our new method is less affected by the low signal in the first few frames, which the conventional method based on normalised mutual information fails to realign.

Deformable and rigid registration of MRI and microPET images for photodynamic therapy of cancer in mice

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

Fei Baowei; Wang Hesheng; Muzic, Raymond F. Jr.

2006-03-15

We are investigating imaging techniques to study the tumor response to photodynamic therapy (PDT). Positron emission tomography (PET) can provide physiological and functional information. High-resolution magnetic resonance imaging (MRI) can provide anatomical and morphological changes. Image registration can combine MRI and PET images for improved tumor monitoring. In this study, we acquired high-resolution MRI and microPET {sup 18}F-fluorodeoxyglucose (FDG) images from C3H mice with RIF-1 tumors that were treated with Pc 4-based PDT. We developed two registration methods for this application. For registration of the whole mouse body, we used an automatic three-dimensional, normalized mutual information algorithm. For tumor registration,more » we developed a finite element model (FEM)-based deformable registration scheme. To assess the quality of whole body registration, we performed slice-by-slice review of both image volumes; manually segmented feature organs, such as the left and right kidneys and the bladder, in each slice; and computed the distance between corresponding centroids. Over 40 volume registration experiments were performed with MRI and microPET images. The distance between corresponding centroids of organs was 1.5{+-}0.4 mm which is about 2 pixels of microPET images. The mean volume overlap ratios for tumors were 94.7% and 86.3% for the deformable and rigid registration methods, respectively. Registration of high-resolution MRI and microPET images combines anatomical and functional information of the tumors and provides a useful tool for evaluating photodynamic therapy.« less

Multisource image fusion method using support value transform.

PubMed

Zheng, Sheng; Shi, Wen-Zhong; Liu, Jian; Zhu, Guang-Xi; Tian, Jin-Wen

2007-07-01

With the development of numerous imaging sensors, many images can be simultaneously pictured by various sensors. However, there are many scenarios where no one sensor can give the complete picture. Image fusion is an important approach to solve this problem and produces a single image which preserves all relevant information from a set of different sensors. In this paper, we proposed a new image fusion method using the support value transform, which uses the support value to represent the salient features of image. This is based on the fact that, in support vector machines (SVMs), the data with larger support values have a physical meaning in the sense that they reveal relative more importance of the data points for contributing to the SVM model. The mapped least squares SVM (mapped LS-SVM) is used to efficiently compute the support values of image. The support value analysis is developed by using a series of multiscale support value filters, which are obtained by filling zeros in the basic support value filter deduced from the mapped LS-SVM to match the resolution of the desired level. Compared with the widely used image fusion methods, such as the Laplacian pyramid, discrete wavelet transform methods, the proposed method is an undecimated transform-based approach. The fusion experiments are undertaken on multisource images. The results demonstrate that the proposed approach is effective and is superior to the conventional image fusion methods in terms of the pertained quantitative fusion evaluation indexes, such as quality of visual information (Q(AB/F)), the mutual information, etc.

[Analysis of on medication rules for Qi-deficiency and blood-stasis syndrome of chronic heart failure based on data mining technology].

PubMed

Wang, Qian; Yao, Geng-Zhen; Pan, Guang-Ming; Huang, Jing-Yi; An, Yi-Pei; Zou, Xu

2017-01-01

To analyze the medication features and the regularity of prescriptions of traditional Chinese medicine in treating patients with Qi-deficiency and blood-stasis syndrome of chronic heart failure based on modern literature. In this article, CNKI Chinese academic journal database, Wanfang Chinese academic journal database and VIP Chinese periodical database were all searched from January 2000 to December 2015 for the relevant literature on traditional Chinese medicine treatment for Qi-deficiency and blood-stasis syndrome of chronic heart failure. Then a normalized database was established for further data mining and analysis. Subsequently, the medication features and the regularity of prescriptions were mined by using traditional Chinese medicine inheritance support system(V2.5), association rules, improved mutual information algorithm, complex system entropy clustering and other mining methods. Finally, a total of 171 articles were included, involving 171 prescriptions, 140 kinds of herbs, with a total frequency of 1 772 for the herbs. As a result, 19 core prescriptions and 7 new prescriptions were mined. The most frequently used herbs included Huangqi(Astragali Radix), Danshen(Salviae Miltiorrhizae Radix et Rhizoma), Fuling(Poria), Renshen(Ginseng Radix et Rhizoma), Tinglizi(Semen Lepidii), Baizhu(Atractylodis Macrocephalae Rhizoma), and Guizhi(Cinnamomum Ramulus). The core prescriptions were composed of Huangqi(Astragali Radix), Danshen(Salviae Miltiorrhizae Radix et Rhizoma) and Fuling(Poria), etc. The high frequent herbs and core prescriptions not only highlight the medication features of Qi-invigorating and blood-circulating therapy, but also reflect the regularity of prescriptions of blood-circulating, Yang-warming, and urination-promoting therapy based on syndrome differentiation. Moreover, the mining of the new prescriptions provide new reference and inspiration for clinical treatment of various accompanying symptoms of chronic heart failure. In conclusion, this article provides new reference for traditional Chinese medicine in the treatment of chronic heart failure. Copyright© by the Chinese Pharmaceutical Association.

Accurate Diabetes Risk Stratification Using Machine Learning: Role of Missing Value and Outliers.

PubMed

Maniruzzaman, Md; Rahman, Md Jahanur; Al-MehediHasan, Md; Suri, Harman S; Abedin, Md Menhazul; El-Baz, Ayman; Suri, Jasjit S

2018-04-10

Diabetes mellitus is a group of metabolic diseases in which blood sugar levels are too high. About 8.8% of the world was diabetic in 2017. It is projected that this will reach nearly 10% by 2045. The major challenge is that when machine learning-based classifiers are applied to such data sets for risk stratification, leads to lower performance. Thus, our objective is to develop an optimized and robust machine learning (ML) system under the assumption that missing values or outliers if replaced by a median configuration will yield higher risk stratification accuracy. This ML-based risk stratification is designed, optimized and evaluated, where: (i) the features are extracted and optimized from the six feature selection techniques (random forest, logistic regression, mutual information, principal component analysis, analysis of variance, and Fisher discriminant ratio) and combined with ten different types of classifiers (linear discriminant analysis, quadratic discriminant analysis, naïve Bayes, Gaussian process classification, support vector machine, artificial neural network, Adaboost, logistic regression, decision tree, and random forest) under the hypothesis that both missing values and outliers when replaced by computed medians will improve the risk stratification accuracy. Pima Indian diabetic dataset (768 patients: 268 diabetic and 500 controls) was used. Our results demonstrate that on replacing the missing values and outliers by group median and median values, respectively and further using the combination of random forest feature selection and random forest classification technique yields an accuracy, sensitivity, specificity, positive predictive value, negative predictive value and area under the curve as: 92.26%, 95.96%, 79.72%, 91.14%, 91.20%, and 0.93, respectively. This is an improvement of 10% over previously developed techniques published in literature. The system was validated for its stability and reliability. RF-based model showed the best performance when outliers are replaced by median values.

Improved separability criteria via some classes of measurements

NASA Astrophysics Data System (ADS)

Shen, Shu-Qian; Li, Ming; Li-Jost, Xianqing; Fei, Shao-Ming

2018-05-01

The entanglement detection via local measurements can be experimentally implemented. Based on mutually unbiased measurements and general symmetric informationally complete positive-operator-valued measures, we present separability criteria for bipartite quantum states, which, by theoretical analysis, are stronger than the related existing criteria via these measurements. Two detailed examples are supplemented to show the efficiency of the presented separability criteria.

Uncertainty Propagation and the Fano Based Infromation Theoretic Method: A Radar Example

DTIC Science & Technology

2015-02-01

Hogg, “Phase transitions and the search problem by, artificial intellience ”, (an Elsevier journal) volume 81, published in 1996, Pages 1- 15. [39] R...dispersion of the mean mutual information of the estimate is low enough to support the use of the linear approximation. M ut ua l In M uf or m at io n

Estimation of Delta Wave by Mutual Information of Heartbeat During Sleep

NASA Astrophysics Data System (ADS)

Kurihara, Yosuke; Watanabe, Kajiro; Kobayashi, Kazuyuki; Tanaka, Hiroshi

The quality of sleep is evaluated based on the sleep stages judged by R-K method or the manual of American Academy of Sleep Medicine. The brainwaves, eye movements, and chin EMG of sleeping subjects are used for the judgment. These methods above, however, require some electrodes to be attached to the head and the face to obtain the brainwaves, eye movements, and chin EMG, thus making the measurements troublesome to be held on a daily basis. If non-invasive measurements of brainwaves, eye movements, and chin EMG are feasible, or their equivalent data can be estimated through other bio-signals, the monitoring of the quality of daily sleeps, which influences the health condition, will be easy. In this paper, we discuss the appearance rate of delta wave occurrences, which is deeply related with the depth of sleep, can be estimated based on the average amount of mutual information calculated by pulse wave signals and body movements measured non-invasively by the pneumatic method. As a result, the root mean square error between the appearance rate of delta wave occurrences measured with a polysomnography and the estimated delta pulse was 14.93%.

Measuring entanglement entropy in a quantum many-body system.

PubMed

Islam, Rajibul; Ma, Ruichao; Preiss, Philipp M; Tai, M Eric; Lukin, Alexander; Rispoli, Matthew; Greiner, Markus

2015-12-03

Entanglement is one of the most intriguing features of quantum mechanics. It describes non-local correlations between quantum objects, and is at the heart of quantum information sciences. Entanglement is now being studied in diverse fields ranging from condensed matter to quantum gravity. However, measuring entanglement remains a challenge. This is especially so in systems of interacting delocalized particles, for which a direct experimental measurement of spatial entanglement has been elusive. Here, we measure entanglement in such a system of itinerant particles using quantum interference of many-body twins. Making use of our single-site-resolved control of ultracold bosonic atoms in optical lattices, we prepare two identical copies of a many-body state and interfere them. This enables us to directly measure quantum purity, Rényi entanglement entropy, and mutual information. These experiments pave the way for using entanglement to characterize quantum phases and dynamics of strongly correlated many-body systems.

enoLOGOS: a versatile web tool for energy normalized sequence logos

PubMed Central

Workman, Christopher T.; Yin, Yutong; Corcoran, David L.; Ideker, Trey; Stormo, Gary D.; Benos, Panayiotis V.

2005-01-01

enoLOGOS is a web-based tool that generates sequence logos from various input sources. Sequence logos have become a popular way to graphically represent DNA and amino acid sequence patterns from a set of aligned sequences. Each position of the alignment is represented by a column of stacked symbols with its total height reflecting the information content in this position. Currently, the available web servers are able to create logo images from a set of aligned sequences, but none of them generates weighted sequence logos directly from energy measurements or other sources. With the advent of high-throughput technologies for estimating the contact energy of different DNA sequences, tools that can create logos directly from binding affinity data are useful to researchers. enoLOGOS generates sequence logos from a variety of input data, including energy measurements, probability matrices, alignment matrices, count matrices and aligned sequences. Furthermore, enoLOGOS can represent the mutual information of different positions of the consensus sequence, a unique feature of this tool. Another web interface for our software, C2H2-enoLOGOS, generates logos for the DNA-binding preferences of the C2H2 zinc-finger transcription factor family members. enoLOGOS and C2H2-enoLOGOS are accessible over the web at . PMID:15980495

Quantification of Degeneracy in Biological Systems for Characterization of Functional Interactions Between Modules

PubMed Central

Li, Yao; Dwivedi, Gaurav; Huang, Wen; Yi, Yingfei

2012-01-01

There is an evolutionary advantage in having multiple components with overlapping functionality (i.e degeneracy) in organisms. While theoretical considerations of degeneracy have been well established in neural networks using information theory, the same concepts have not been developed for differential systems, which form the basis of many biochemical reaction network descriptions in systems biology. Here we establish mathematical definitions of degeneracy, complexity and robustness that allow for the quantification of these properties in a system. By exciting a dynamical system with noise, the mutual information associated with a selected observable output and the interacting subspaces of input components can be used to define both complexity and degeneracy. The calculation of degeneracy in a biological network is a useful metric for evaluating features such as the sensitivity of a biological network to environmental evolutionary pressure. Using a two-receptor signal transduction network, we find that redundant components will not yield high degeneracy whereas compensatory mechanisms established by pathway crosstalk will. This form of analysis permits interrogation of large-scale differential systems for non-identical, functionally equivalent features that have evolved to maintain homeostasis during disruption of individual components. PMID:22619750

Practical and secure telemedicine systems for user mobility.

PubMed

Rezaeibagha, Fatemeh; Mu, Yi

2018-02-01

The application of wireless devices has led to a significant improvement in the quality delivery of care in telemedicine systems. Patients who live in a remote area are able to communicate with the healthcare provider and benefit from the doctor consultations. However, it has been a challenge to provide a secure telemedicine system, which captures users (patients and doctors) mobility and patient privacy. In this work, we present several secure protocols for telemedicine systems, which ensure the secure communication between patients and doctors who are located in different geographical locations. Our protocols are the first of this kind featured with confidentiality of patient information, mutual authentication, patient anonymity, data integrity, freshness of communication, and mobility. Our protocols are based on symmetric-key schemes and capture all desirable security requirements in order to better serve our objectives of research for secure telemedicine services; therefore, they are very efficient in implementation. A comparison with related works shows that our work contributes first comprehensive solution to capture user mobility and patient privacy for telemedicine systems. Copyright © 2018 Elsevier Inc. All rights reserved.

Lightweight Sensor Authentication Scheme for Energy Efficiency in Ubiquitous Computing Environments.

PubMed

Lee, Jaeseung; Sung, Yunsick; Park, Jong Hyuk

2016-12-01

The Internet of Things (IoT) is the intelligent technologies and services that mutually communicate information between humans and devices or between Internet-based devices. In IoT environments, various device information is collected from the user for intelligent technologies and services that control the devices. Recently, wireless sensor networks based on IoT environments are being used in sectors as diverse as medicine, the military, and commerce. Specifically, sensor techniques that collect relevant area data via mini-sensors after distributing smart dust in inaccessible areas like forests or military zones have been embraced as the future of information technology. IoT environments that utilize smart dust are composed of the sensor nodes that detect data using wireless sensors and transmit the detected data to middle nodes. Currently, since the sensors used in these environments are composed of mini-hardware, they have limited memory, processing power, and energy, and a variety of research that aims to make the best use of these limited resources is progressing. This paper proposes a method to utilize these resources while considering energy efficiency, and suggests lightweight mutual verification and key exchange methods based on a hash function that has no restrictions on operation quantity, velocity, and storage space. This study verifies the security and energy efficiency of this method through security analysis and function evaluation, comparing with existing approaches. The proposed method has great value in its applicability as a lightweight security technology for IoT environments.

Lightweight Sensor Authentication Scheme for Energy Efficiency in Ubiquitous Computing Environments

PubMed Central

Lee, Jaeseung; Sung, Yunsick; Park, Jong Hyuk

2016-01-01

The Internet of Things (IoT) is the intelligent technologies and services that mutually communicate information between humans and devices or between Internet-based devices. In IoT environments, various device information is collected from the user for intelligent technologies and services that control the devices. Recently, wireless sensor networks based on IoT environments are being used in sectors as diverse as medicine, the military, and commerce. Specifically, sensor techniques that collect relevant area data via mini-sensors after distributing smart dust in inaccessible areas like forests or military zones have been embraced as the future of information technology. IoT environments that utilize smart dust are composed of the sensor nodes that detect data using wireless sensors and transmit the detected data to middle nodes. Currently, since the sensors used in these environments are composed of mini-hardware, they have limited memory, processing power, and energy, and a variety of research that aims to make the best use of these limited resources is progressing. This paper proposes a method to utilize these resources while considering energy efficiency, and suggests lightweight mutual verification and key exchange methods based on a hash function that has no restrictions on operation quantity, velocity, and storage space. This study verifies the security and energy efficiency of this method through security analysis and function evaluation, comparing with existing approaches. The proposed method has great value in its applicability as a lightweight security technology for IoT environments. PMID:27916962

Registration and Fusion of Multiple Source Remotely Sensed Image Data

NASA Technical Reports Server (NTRS)

LeMoigne, Jacqueline

2004-01-01

Earth and Space Science often involve the comparison, fusion, and integration of multiple types of remotely sensed data at various temporal, radiometric, and spatial resolutions. Results of this integration may be utilized for global change analysis, global coverage of an area at multiple resolutions, map updating or validation of new instruments, as well as integration of data provided by multiple instruments carried on multiple platforms, e.g. in spacecraft constellations or fleets of planetary rovers. Our focus is on developing methods to perform fast, accurate and automatic image registration and fusion. General methods for automatic image registration are being reviewed and evaluated. Various choices for feature extraction, feature matching and similarity measurements are being compared, including wavelet-based algorithms, mutual information and statistically robust techniques. Our work also involves studies related to image fusion and investigates dimension reduction and co-kriging for application-dependent fusion. All methods are being tested using several multi-sensor datasets, acquired at EOS Core Sites, and including multiple sensors such as IKONOS, Landsat-7/ETM+, EO1/ALI and Hyperion, MODIS, and SeaWIFS instruments. Issues related to the coregistration of data from the same platform (i.e., AIRS and MODIS from Aqua) or from several platforms of the A-train (i.e., MLS, HIRDLS, OMI from Aura with AIRS and MODIS from Terra and Aqua) will also be considered.

Computing the Energy Cost of the Information Transmitted by Model Biological Neurons

NASA Astrophysics Data System (ADS)

Torrealdea, F. J.; Sarasola, C.; d'Anjou, A.; Moujahid, A.

2009-08-01

We assign an energy function to a Hindmarsh-Rose model of a neuron and use it to compute values of average energy consumption during its signalling activity. We also compute values of information entropy of an isolated neuron and of mutual information between two electrically coupled neurons. We find that for the isolated neuron the chaotic signaling regime is the one with the biggest ratio of information entropy to energy consumption. We also find that in the case of electrically coupled neurons there are values of the coupling strength at which the mutual information to energy consumption ratio is maximum, that is, that transmitting at that coupling conditions is energetically less expensive.

An evaluation of data-driven motion estimation in comparison to the usage of external-surrogates in cardiac SPECT imaging

PubMed Central

Mukherjee, Joyeeta Mitra; Hutton, Brian F; Johnson, Karen L; Pretorius, P Hendrik; King, Michael A

2014-01-01

Motion estimation methods in single photon emission computed tomography (SPECT) can be classified into methods which depend on just the emission data (data-driven), or those that use some other source of information such as an external surrogate. The surrogate-based methods estimate the motion exhibited externally which may not correlate exactly with the movement of organs inside the body. The accuracy of data-driven strategies on the other hand is affected by the type and timing of motion occurrence during acquisition, the source distribution, and various degrading factors such as attenuation, scatter, and system spatial resolution. The goal of this paper is to investigate the performance of two data-driven motion estimation schemes based on the rigid-body registration of projections of motion-transformed source distributions to the acquired projection data for cardiac SPECT studies. Comparison is also made of six intensity based registration metrics to an external surrogate-based method. In the data-driven schemes, a partially reconstructed heart is used as the initial source distribution. The partially-reconstructed heart has inaccuracies due to limited angle artifacts resulting from using only a part of the SPECT projections acquired while the patient maintained the same pose. The performance of different cost functions in quantifying consistency with the SPECT projection data in the data-driven schemes was compared for clinically realistic patient motion occurring as discrete pose changes, one or two times during acquisition. The six intensity-based metrics studied were mean-squared difference (MSD), mutual information (MI), normalized mutual information (NMI), pattern intensity (PI), normalized cross-correlation (NCC) and entropy of the difference (EDI). Quantitative and qualitative analysis of the performance is reported using Monte-Carlo simulations of a realistic heart phantom including degradation factors such as attenuation, scatter and system spatial resolution. Further the visual appearance of motion-corrected images using data-driven motion estimates was compared to that obtained using the external motion-tracking system in patient studies. Pattern intensity and normalized mutual information cost functions were observed to have the best performance in terms of lowest average position error and stability with degradation of image quality of the partial reconstruction in simulations. In all patients, the visual quality of PI-based estimation was either significantly better or comparable to NMI-based estimation. Best visual quality was obtained with PI-based estimation in 1 of the 5 patient studies, and with external-surrogate based correction in 3 out of 5 patients. In the remaining patient study there was little motion and all methods yielded similar visual image quality. PMID:24107647

Optimization of stable quadruped locomotion using mutual information

NASA Astrophysics Data System (ADS)

Silva, Pedro; Santos, Cristina P.; Polani, Daniel

2013-10-01

Central Pattern Generators (CPG)s have been widely used in the field of robotics to address the task of legged locomotion generation. The adequate configuration of these structures for a given platform can be accessed through evolutionary strategies, according to task dependent selection pressures. Information driven evolution, accounts for information theoretical measures as selection pressures, as an alternative to a fully task dependent selection pressure. In this work we exploit this concept and evaluate the use of mean Mutual Information, as a selection pressure towards a CPG configuration capable of faster, yet more coordinated and stabler locomotion than when only a task dependent selection pressure is used.

Friendship relations from the perspective of children with experience of cancer treatment: a focus group study with a salutogenic approach.

PubMed

Einberg, Eva-Lena; Svedberg, Petra; Enskär, Karin; Nygren, Jens M

2015-01-01

Friendships are significant to child development and health but diseases such as cancer can interrupt the contact with friends. The purpose of this study was to describe perceptions of friendship from the perspective of children undergoing cancer treatment, in order to build knowledge that can be used in a health promotion intervention for these children. Fifteen children between 8 and 12 years of age participated in focus groups, where a mixture of informative and creative techniques were used. The focus group discussions were analyzed using qualitative content analysis. The analysis resulted in three generic categories, "Common interests and experiences," "Mutual empathic actions." and "Mutual trust and understanding," incorporating seven subcategories. Based on children's descriptions from a salutogenic perspective, friendship emerged as An equal and mutual commitment that evolves over time and with interactions face-to-face and digitally, a child perspective on friendship should be central to the development of health promotion interventions designed to support friendship relations of children treated for cancer. © 2014 by Association of Pediatric Hematology/Oncology Nurses.

Mutual information-based template matching scheme for detection of breast masses: from mammography to digital breast tomosynthesis

PubMed Central

Mazurowski, Maciej A; Lo, Joseph Y; Harrawood, Brian P; Tourassi, Georgia D

2011-01-01

Development of a computational decision aid for a new medical imaging modality typically is a long and complicated process. It consists of collecting data in the form of images and annotations, development of image processing and pattern recognition algorithms for analysis of the new images and finally testing of the resulting system. Since new imaging modalities are developed more rapidly than ever before, any effort for decreasing the time and cost of this development process could result in maximizing the benefit of the new imaging modality to patients by making the computer aids quickly available to radiologists that interpret the images. In this paper, we make a step in this direction and investigate the possibility of translating the knowledge about the detection problem from one imaging modality to another. Specifically, we present a computer-aided detection (CAD) system for mammographic masses that uses a mutual information-based template matching scheme with intelligently selected templates. We presented principles of template matching with mutual information for mammography before. In this paper, we present an implementation of those principles in a complete computer-aided detection system. The proposed system, through an automatic optimization process, chooses the most useful templates (mammographic regions of interest) using a large database of previously collected and annotated mammograms. Through this process, the knowledge about the task of detecting masses in mammograms is incorporated in the system. Then we evaluate whether our system developed for screen-film mammograms can be successfully applied not only to other mammograms but also to digital breast tomosynthesis (DBT) reconstructed slices without adding any DBT cases for training. Our rationale is that since mutual information is known to be a robust intermodality image similarity measure, it has high potential of transferring knowledge between modalities in the context of the mass detection task. Experimental evaluation of the system on mammograms showed competitive performance compared to other mammography CAD systems recently published in the literature. When the system was applied “as-is” to DBT, its performance was notably worse than that for mammograms. However, with a simple additional preprocessing step, the performance of the system reached levels similar to that obtained for mammograms. In conclusion, the presented CAD system not only performed competitively on screen-film mammograms but it also performed robustly on DBT showing that direct transfer of knowledge across breast imaging modalities for mass detection is in fact possible. PMID:21554985

Public-channel cryptography based on mutual chaos pass filters.

PubMed

Klein, Einat; Gross, Noam; Kopelowitz, Evi; Rosenbluh, Michael; Khaykovich, Lev; Kinzel, Wolfgang; Kanter, Ido

2006-10-01

We study the mutual coupling of chaotic lasers and observe both experimentally and in numeric simulations that there exists a regime of parameters for which two mutually coupled chaotic lasers establish isochronal synchronization, while a third laser coupled unidirectionally to one of the pair does not synchronize. We then propose a cryptographic scheme, based on the advantage of mutual coupling over unidirectional coupling, where all the parameters of the system are public knowledge. We numerically demonstrate that in such a scheme the two communicating lasers can add a message signal (compressed binary message) to the transmitted coupling signal and recover the message in both directions with high fidelity by using a mutual chaos pass filter procedure. An attacker, however, fails to recover an errorless message even if he amplifies the coupling signal.

High quality topic extraction from business news explains abnormal financial market volatility.

PubMed

Hisano, Ryohei; Sornette, Didier; Mizuno, Takayuki; Ohnishi, Takaaki; Watanabe, Tsutomu

2013-01-01

Understanding the mutual relationships between information flows and social activity in society today is one of the cornerstones of the social sciences. In financial economics, the key issue in this regard is understanding and quantifying how news of all possible types (geopolitical, environmental, social, financial, economic, etc.) affects trading and the pricing of firms in organized stock markets. In this article, we seek to address this issue by performing an analysis of more than 24 million news records provided by Thompson Reuters and of their relationship with trading activity for 206 major stocks in the S&P US stock index. We show that the whole landscape of news that affects stock price movements can be automatically summarized via simple regularized regressions between trading activity and news information pieces decomposed, with the help of simple topic modeling techniques, into their "thematic" features. Using these methods, we are able to estimate and quantify the impacts of news on trading. We introduce network-based visualization techniques to represent the whole landscape of news information associated with a basket of stocks. The examination of the words that are representative of the topic distributions confirms that our method is able to extract the significant pieces of information influencing the stock market. Our results show that one of the most puzzling stylized facts in financial economies, namely that at certain times trading volumes appear to be "abnormally large," can be partially explained by the flow of news. In this sense, our results prove that there is no "excess trading," when restricting to times when news is genuinely novel and provides relevant financial information.

47 CFR 25.263 - Information sharing requirements for SDARS terrestrial repeater operators.

Code of Federal Regulations, 2011 CFR

2011-10-01

... SDARS licensee and all potentially affected WCS licensees reach a mutual agreement to provide... SDARS licensee and all potentially affected WCS licensees reach a mutual agreement to provide... notice period. (e) Duty to cooperate. SDARS licensees must cooperate in good faith in the selection and...

47 CFR 27.72 - Information sharing requirements.

Code of Federal Regulations, 2011 CFR

2011-10-01

... WCS licensees in the 2305-2320 MHz and 2345-2360 MHz bands. (a) Sites and frequency selections. WCS..., unless the SDARS licensee and all potentially affected WCS licensees reach a mutual agreement to provide... affected WCS licensees reach a mutual agreement to provide notification by some other means, that agreement...

75 FR 9453 - Submission for OMB Review; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-02

... certain investment advisory programs. These programs, which include ``wrap fee'' and ``mutual fund wrap... size of most mutual funds. Under wrap fee and similar programs, a client's account is typically managed... securities and funds in the account. The requirement that the sponsor (or its designee) obtain information...

32 CFR 264.4 - Policy.

Code of Federal Regulations, 2010 CFR

2010-07-01

... purposes only. 2. This information shall be accorded substantially the same degree of security protection... 413(a) of the Mutual Security Act of 1954, as amended (22 U.S.C. 1933(a)), and pursuant to the... the Mutual Security Program, to relieve the Department of Defense of administrative burdens, and to...

Origin of the Two Bands in the B800 Ring and Their Involvement in the Energy Transfer Network of Allochromatium vinosum.

PubMed

Schröter, Marco; Alcocer, Marcelo J P; Cogdell, Richard J; Kühn, Oliver; Zigmantas, Donatas

2018-03-15

Bacterial photosynthesis features robust and adaptable energy-harvesting processes in which light-harvesting proteins play a crucial role. The peripheral light-harvesting complex of the purple bacterium Allochromatium vinosum is particularly distinct, featuring a double peak structure in its B800 absorption band. Two hypotheses-not necessarily mutually exclusive-concerning the origin of this splitting have been proposed; either two distinct B800 bacteriochlorophyll site energies are involved, or an excitonic dimerization of bacteriochlorophylls within the B800 ring takes place. Through the use of two-dimensional electronic spectroscopy, we present unambiguous evidence that excitonic interaction shapes the split band. We further identify and characterize all of the energy transfer pathways within this complex by using a global kinetic fitting procedure. Our approach demonstrates how the combination of two-dimensional spectral resolution and self-consistent fitting allows for extraction of information on light-harvesting processes, which would otherwise be inaccessible due to signal congestion.

The interactions of ants with their biotic environment.

PubMed

Chomicki, Guillaume; Renner, Susanne S

2017-03-15

This s pecial feature results from the symposium 'Ants 2016: ant interactions with their biotic environments' held in Munich in May 2016 and deals with the interactions between ants and other insects, plants, microbes and fungi, studied at micro- and macroevolutionary levels with a wide range of approaches, from field ecology to next-generation sequencing, chemical ecology and molecular genetics. In this paper, we review key aspects of these biotic interactions to provide background information for the papers of this s pecial feature After listing the major types of biotic interactions that ants engage in, we present a brief overview of ant/ant communication, ant/plant interactions, ant/fungus symbioses, and recent insights about ants and their endosymbionts. Using a large molecular clock-dated Formicidae phylogeny, we map the evolutionary origins of different ant clades' interactions with plants, fungi and hemiptera. Ants' biotic interactions provide ideal systems to address fundamental ecological and evolutionary questions about mutualism, coevolution, adaptation and animal communication. © 2017 The Author(s).

The interactions of ants with their biotic environment

PubMed Central

Renner, Susanne S.

2017-01-01

This special feature results from the symposium ‘Ants 2016: ant interactions with their biotic environments’ held in Munich in May 2016 and deals with the interactions between ants and other insects, plants, microbes and fungi, studied at micro- and macroevolutionary levels with a wide range of approaches, from field ecology to next-generation sequencing, chemical ecology and molecular genetics. In this paper, we review key aspects of these biotic interactions to provide background information for the papers of this special feature. After listing the major types of biotic interactions that ants engage in, we present a brief overview of ant/ant communication, ant/plant interactions, ant/fungus symbioses, and recent insights about ants and their endosymbionts. Using a large molecular clock-dated Formicidae phylogeny, we map the evolutionary origins of different ant clades' interactions with plants, fungi and hemiptera. Ants' biotic interactions provide ideal systems to address fundamental ecological and evolutionary questions about mutualism, coevolution, adaptation and animal communication. PMID:28298352

Evaluation of mathematical algorithms for automatic patient alignment in radiosurgery.

PubMed

Williams, Kenneth M; Schulte, Reinhard W; Schubert, Keith E; Wroe, Andrew J

2015-06-01

Image registration techniques based on anatomical features can serve to automate patient alignment for intracranial radiosurgery procedures in an effort to improve the accuracy and efficiency of the alignment process as well as potentially eliminate the need for implanted fiducial markers. To explore this option, four two-dimensional (2D) image registration algorithms were analyzed: the phase correlation technique, mutual information (MI) maximization, enhanced correlation coefficient (ECC) maximization, and the iterative closest point (ICP) algorithm. Digitally reconstructed radiographs from the treatment planning computed tomography scan of a human skull were used as the reference images, while orthogonal digital x-ray images taken in the treatment room were used as the captured images to be aligned. The accuracy of aligning the skull with each algorithm was compared to the alignment of the currently practiced procedure, which is based on a manual process of selecting common landmarks, including implanted fiducials and anatomical skull features. Of the four algorithms, three (phase correlation, MI maximization, and ECC maximization) demonstrated clinically adequate (ie, comparable to the standard alignment technique) translational accuracy and improvements in speed compared to the interactive, user-guided technique; however, the ICP algorithm failed to give clinically acceptable results. The results of this work suggest that a combination of different algorithms may provide the best registration results. This research serves as the initial groundwork for the translation of automated, anatomy-based 2D algorithms into a real-world system for 2D-to-2D image registration and alignment for intracranial radiosurgery. This may obviate the need for invasive implantation of fiducial markers into the skull and may improve treatment room efficiency and accuracy. © The Author(s) 2014.

One-dimensional barcode reading: an information theoretic approach

NASA Astrophysics Data System (ADS)

Houni, Karim; Sawaya, Wadih; Delignon, Yves

2008-03-01

In the convergence context of identification technology and information-data transmission, the barcode found its place as the simplest and the most pervasive solution for new uses, especially within mobile commerce, bringing youth to this long-lived technology. From a communication theory point of view, a barcode is a singular coding based on a graphical representation of the information to be transmitted. We present an information theoretic approach for 1D image-based barcode reading analysis. With a barcode facing the camera, distortions and acquisition are modeled as a communication channel. The performance of the system is evaluated by means of the average mutual information quantity. On the basis of this theoretical criterion for a reliable transmission, we introduce two new measures: the theoretical depth of field and the theoretical resolution. Simulations illustrate the gain of this approach.

One-dimensional barcode reading: an information theoretic approach.

PubMed

Houni, Karim; Sawaya, Wadih; Delignon, Yves

2008-03-10

In the convergence context of identification technology and information-data transmission, the barcode found its place as the simplest and the most pervasive solution for new uses, especially within mobile commerce, bringing youth to this long-lived technology. From a communication theory point of view, a barcode is a singular coding based on a graphical representation of the information to be transmitted. We present an information theoretic approach for 1D image-based barcode reading analysis. With a barcode facing the camera, distortions and acquisition are modeled as a communication channel. The performance of the system is evaluated by means of the average mutual information quantity. On the basis of this theoretical criterion for a reliable transmission, we introduce two new measures: the theoretical depth of field and the theoretical resolution. Simulations illustrate the gain of this approach.

Classification of Informal Settlements Through the Integration of 2d and 3d Features Extracted from Uav Data

NASA Astrophysics Data System (ADS)

Gevaert, C. M.; Persello, C.; Sliuzas, R.; Vosselman, G.

2016-06-01

Unmanned Aerial Vehicles (UAVs) are capable of providing very high resolution and up-to-date information to support informal settlement upgrading projects. In order to provide accurate basemaps, urban scene understanding through the identification and classification of buildings and terrain is imperative. However, common characteristics of informal settlements such as small, irregular buildings with heterogeneous roof material and large presence of clutter challenge state-of-the-art algorithms. Especially the dense buildings and steeply sloped terrain cause difficulties in identifying elevated objects. This work investigates how 2D radiometric and textural features, 2.5D topographic features, and 3D geometric features obtained from UAV imagery can be integrated to obtain a high classification accuracy in challenging classification problems for the analysis of informal settlements. It compares the utility of pixel-based and segment-based features obtained from an orthomosaic and DSM with point-based and segment-based features extracted from the point cloud to classify an unplanned settlement in Kigali, Rwanda. Findings show that the integration of 2D and 3D features leads to higher classification accuracies.

Clinical and pharmacogenomic data mining: 1. Generalized theory of expected information and application to the development of tools.

PubMed

Robson, Barry

2003-01-01

New scientific problems, arising from the human genome project, are challenging the classical means of using statistics. Yet quantified knowledge in the form of rules and rule strengths based on real relationships in data, as opposed to expert opinion, is urgently required for researcher and physician decision support. The problem is that with many parameters, the space to be analyzed is highly dimensional. That is, the combinations of data to examine are subject to a combinatorial explosion as the number of possible events (entries, items, sub-records) (a),(b),(c),... per record (a,b,c,..) increases, and hence much of the space is sparsely populated. These combinatorial considerations are particularly problematic for identifying those associations called "Unicorn Events" which occur significantly less than expected to the extent that they are never seen to be counted. To cope with the combinatorial explosion, a novel numerical "book keeping" approach is taken to generate information terms relating to the combinatorial subsets of events (a,b,c,..), and, most importantly, the zeta (Zeta) function is employed. The incomplete Zeta function zeta(s,n) with s = 1, in which frequencies of occurrence such as n = n(a,b,c,...) determine the range of summation n, is argued to be the natural choice of information function. It emerges from Bayesian integration, taken over the distribution of possible values of information measures for sparse and ample data alike. Expected mutual information l(a;b;c) in nats (i.e., natural units analogous to bits but based on the natural logarithm), such as is available to the observer, is measured as e.g., the difference zeta(s,o(a,b,c..)) - zeta(s,e(a,b,c..)) where o(a,b,c,..) and e(a,b,c,..) are, or relate to, the observed and expected frequencies of occurrence, respectively. For real values of s > 1 the qualitative impact of strongly (positively or negatively) ranked data is preserved despite several numerical approximations. As real s increases, and the output of the information functions converge into three values +1, 0, and -1 nats representing a trinary logic system. For quantitative data, a useful ad hoc method, to report sigma-normalized covariations in an analogous manner to mutual information for significance comparison purposes, is demonstrated. Finally, the potential ability to make use of mutual information in a complex biomedical study, and to include Bayesian prior information derived from statistical, tabular, anecdotal, and expert opinion is briefly illustrated.

Spatial and Feature-Based Attention in a Layered Cortical Microcircuit Model

PubMed Central

Wagatsuma, Nobuhiko; Potjans, Tobias C.; Diesmann, Markus; Sakai, Ko; Fukai, Tomoki

2013-01-01

Directing attention to the spatial location or the distinguishing feature of a visual object modulates neuronal responses in the visual cortex and the stimulus discriminability of subjects. However, the spatial and feature-based modes of attention differently influence visual processing by changing the tuning properties of neurons. Intriguingly, neurons' tuning curves are modulated similarly across different visual areas under both these modes of attention. Here, we explored the mechanism underlying the effects of these two modes of visual attention on the orientation selectivity of visual cortical neurons. To do this, we developed a layered microcircuit model. This model describes multiple orientation-specific microcircuits sharing their receptive fields and consisting of layers 2/3, 4, 5, and 6. These microcircuits represent a functional grouping of cortical neurons and mutually interact via lateral inhibition and excitatory connections between groups with similar selectivity. The individual microcircuits receive bottom-up visual stimuli and top-down attention in different layers. A crucial assumption of the model is that feature-based attention activates orientation-specific microcircuits for the relevant feature selectively, whereas spatial attention activates all microcircuits homogeneously, irrespective of their orientation selectivity. Consequently, our model simultaneously accounts for the multiplicative scaling of neuronal responses in spatial attention and the additive modulations of orientation tuning curves in feature-based attention, which have been observed widely in various visual cortical areas. Simulations of the model predict contrasting differences between excitatory and inhibitory neurons in the two modes of attentional modulations. Furthermore, the model replicates the modulation of the psychophysical discriminability of visual stimuli in the presence of external noise. Our layered model with a biologically suggested laminar structure describes the basic circuit mechanism underlying the attention-mode specific modulations of neuronal responses and visual perception. PMID:24324628

Single-cell RNA-seq analysis unveils a prevalent epithelial/mesenchymal hybrid state during mouse organogenesis.

PubMed

Dong, Ji; Hu, Yuqiong; Fan, Xiaoying; Wu, Xinglong; Mao, Yunuo; Hu, Boqiang; Guo, Hongshan; Wen, Lu; Tang, Fuchou

2018-03-14

Organogenesis is crucial for proper organ formation during mammalian embryonic development. However, the similarities and shared features between different organs and the cellular heterogeneity during this process at single-cell resolution remain elusive. We perform single-cell RNA sequencing analysis of 1916 individual cells from eight organs and tissues of E9.5 to E11.5 mouse embryos, namely, the forebrain, hindbrain, skin, heart, somite, lung, liver, and intestine. Based on the regulatory activities rather than the expression patterns, all cells analyzed can be well classified into four major groups with epithelial, mesodermal, hematopoietic, and neuronal identities. For different organs within the same group, the similarities and differences of their features and developmental paths are revealed and reconstructed. We identify mutual interactions between epithelial and mesenchymal cells and detect epithelial cells with prevalent mesenchymal features during organogenesis, which are similar to the features of intermediate epithelial/mesenchymal cells during tumorigenesis. The comprehensive transcriptome at single-cell resolution profiled in our study paves the way for future mechanistic studies of the gene-regulatory networks governing mammalian organogenesis.

Information-theoretic CAD system in mammography: Entropy-based indexing for computational efficiency and robust performance

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

Tourassi, Georgia D.; Harrawood, Brian; Singh, Swatee

2007-08-15

We have previously presented a knowledge-based computer-assisted detection (KB-CADe) system for the detection of mammographic masses. The system is designed to compare a query mammographic region with mammographic templates of known ground truth. The templates are stored in an adaptive knowledge database. Image similarity is assessed with information theoretic measures (e.g., mutual information) derived directly from the image histograms. A previous study suggested that the diagnostic performance of the system steadily improves as the knowledge database is initially enriched with more templates. However, as the database increases in size, an exhaustive comparison of the query case with each stored templatemore » becomes computationally burdensome. Furthermore, blind storing of new templates may result in redundancies that do not necessarily improve diagnostic performance. To address these concerns we investigated an entropy-based indexing scheme for improving the speed of analysis and for satisfying database storage restrictions without compromising the overall diagnostic performance of our KB-CADe system. The indexing scheme was evaluated on two different datasets as (i) a search mechanism to sort through the knowledge database, and (ii) a selection mechanism to build a smaller, concise knowledge database that is easier to maintain but still effective. There were two important findings in the study. First, entropy-based indexing is an effective strategy to identify fast a subset of templates that are most relevant to a given query. Only this subset could be analyzed in more detail using mutual information for optimized decision making regarding the query. Second, a selective entropy-based deposit strategy may be preferable where only high entropy cases are maintained in the knowledge database. Overall, the proposed entropy-based indexing scheme was shown to reduce the computational cost of our KB-CADe system by 55% to 80% while maintaining the system's diagnostic performance.« less

Wideband Direction of Arrival Estimation in the Presence of Unknown Mutual Coupling

PubMed Central

Li, Weixing; Zhang, Yue; Lin, Jianzhi; Guo, Rui; Chen, Zengping

2017-01-01

This paper investigates a subarray based algorithm for direction of arrival (DOA) estimation of wideband uniform linear array (ULA), under the presence of frequency-dependent mutual coupling effects. Based on the Toeplitz structure of mutual coupling matrices, the whole array is divided into the middle subarray and the auxiliary subarray. Then two-sided correlation transformation is applied to the correlation matrix of the middle subarray instead of the whole array. In this way, the mutual coupling effects can be eliminated. Finally, the multiple signal classification (MUSIC) method is utilized to derive the DOAs. For the condition when the blind angles exist, we refine DOA estimation by using a simple approach based on the frequency-dependent mutual coupling matrixes (MCMs). The proposed method can achieve high estimation accuracy without any calibration sources. It has a low computational complexity because iterative processing is not required. Simulation results validate the effectiveness and feasibility of the proposed algorithm. PMID:28178177

Quantum dynamics of a two-atom-qubit system

NASA Astrophysics Data System (ADS)

Van Hieu, Nguyen; Bich Ha, Nguyen; Linh, Le Thi Ha

2009-09-01

A physical model of the quantum information exchange between two qubits is studied theoretically. The qubits are two identical two-level atoms, the physical mechanism of the quantum information exchange is the mutual dependence of the reduced density matrices of two qubits generated by their couplings with a multimode radiation field. The Lehmberg-Agarwal master equation is exactly solved. The explicit form of the mutual dependence of two reduced density matrices is established. The application to study the entanglement of two qubits is discussed.

Entanglement and purity of two-mode Gaussian states in noisy channels

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

Serafini, Alessio; Illuminati, Fabrizio; De Siena, Silvio

2004-02-01

We study the evolution of purity, entanglement, and total correlations of general two-mode continuous variable Gaussian states in arbitrary uncorrelated Gaussian environments. The time evolution of purity, von Neumann entropy, logarithmic negativity, and mutual information is analyzed for a wide range of initial conditions. In general, we find that a local squeezing of the bath leads to a faster degradation of purity and entanglement, while it can help to preserve the mutual information between the modes.

Analytical techniques for the study of some parameters of multispectral scanner systems for remote sensing

NASA Technical Reports Server (NTRS)

Wiswell, E. R.; Cooper, G. R. (Principal Investigator)

1978-01-01

The author has identified the following significant results. The concept of average mutual information in the received spectral random process about the spectral scene was developed. Techniques amenable to implementation on a digital computer were also developed to make the required average mutual information calculations. These techniques required identification of models for the spectral response process of scenes. Stochastic modeling techniques were adapted for use. These techniques were demonstrated on empirical data from wheat and vegetation scenes.

Feature Selection Using Information Gain for Improved Structural-Based Alert Correlation

PubMed Central

Siraj, Maheyzah Md; Zainal, Anazida; Elshoush, Huwaida Tagelsir; Elhaj, Fatin

2016-01-01

Grouping and clustering alerts for intrusion detection based on the similarity of features is referred to as structurally base alert correlation and can discover a list of attack steps. Previous researchers selected different features and data sources manually based on their knowledge and experience, which lead to the less accurate identification of attack steps and inconsistent performance of clustering accuracy. Furthermore, the existing alert correlation systems deal with a huge amount of data that contains null values, incomplete information, and irrelevant features causing the analysis of the alerts to be tedious, time-consuming and error-prone. Therefore, this paper focuses on selecting accurate and significant features of alerts that are appropriate to represent the attack steps, thus, enhancing the structural-based alert correlation model. A two-tier feature selection method is proposed to obtain the significant features. The first tier aims at ranking the subset of features based on high information gain entropy in decreasing order. The‏ second tier extends additional features with a better discriminative ability than the initially ranked features. Performance analysis results show the significance of the selected features in terms of the clustering accuracy using 2000 DARPA intrusion detection scenario-specific dataset. PMID:27893821

Secure anonymous mutual authentication for star two-tier wireless body area networks.

PubMed

Ibrahim, Maged Hamada; Kumari, Saru; Das, Ashok Kumar; Wazid, Mohammad; Odelu, Vanga

2016-10-01

Mutual authentication is a very important service that must be established between sensor nodes in wireless body area network (WBAN) to ensure the originality and integrity of the patient's data sent by sensors distributed on different parts of the body. However, mutual authentication service is not enough. An adversary can benefit from monitoring the traffic and knowing which sensor is in transmission of patient's data. Observing the traffic (even without disclosing the context) and knowing its origin, it can reveal to the adversary information about the patient's medical conditions. Therefore, anonymity of the communicating sensors is an important service as well. Few works have been conducted in the area of mutual authentication among sensor nodes in WBAN. However, none of them has considered anonymity among body sensor nodes. Up to our knowledge, our protocol is the first attempt to consider this service in a two-tier WBAN. We propose a new secure protocol to realize anonymous mutual authentication and confidential transmission for star two-tier WBAN topology. The proposed protocol uses simple cryptographic primitives. We prove the security of the proposed protocol using the widely-accepted Burrows-Abadi-Needham (BAN) logic, and also through rigorous informal security analysis. In addition, to demonstrate the practicality of our protocol, we evaluate it using NS-2 simulator. BAN logic and informal security analysis prove that our proposed protocol achieves the necessary security requirements and goals of an authentication service. The simulation results show the impact on the various network parameters, such as end-to-end delay and throughput. The nodes in the network require to store few hundred bits. Nodes require to perform very few hash invocations, which are computationally very efficient. The communication cost of the proposed protocol is few hundred bits in one round of communication. Due to the low computation cost, the energy consumed by the nodes is also low. Our proposed protocol is a lightweight anonymous mutually authentication protocol to mutually authenticate the sensor nodes with the controller node (hub) in a star two-tier WBAN topology. Results show that our protocol proves efficiency over previously proposed protocols and at the same time, achieves the necessary security requirements for a secure anonymous mutual authentication scheme. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Progress on Ultra-Dense Quantum Communication Using Integrated Photonic Architecture

DTIC Science & Technology

2012-05-09

REPORT Progress on Ultra-Dense Quantum Communication Using Integrated Photonic Architecture 14. ABSTRACT 16. SECURITY CLASSIFICATION OF: The goal of...including the development of a large-alphabet quantum key distribution protocol that uses measurements in mutually unbiased bases. 1. REPORT DATE (DD-MM... quantum information, integrated optics, photonic integrated chip Dirk Englund, Karl Berggren, Jeffrey Shapiro, Chee Wei Wong, Franco Wong, and Gregory

Finding the multipath propagation of multivariable crude oil prices using a wavelet-based network approach

NASA Astrophysics Data System (ADS)

Jia, Xiaoliang; An, Haizhong; Sun, Xiaoqi; Huang, Xuan; Gao, Xiangyun

2016-04-01

The globalization and regionalization of crude oil trade inevitably give rise to the difference of crude oil prices. The understanding of the pattern of the crude oil prices' mutual propagation is essential for analyzing the development of global oil trade. Previous research has focused mainly on the fuzzy long- or short-term one-to-one propagation of bivariate oil prices, generally ignoring various patterns of periodical multivariate propagation. This study presents a wavelet-based network approach to help uncover the multipath propagation of multivariable crude oil prices in a joint time-frequency period. The weekly oil spot prices of the OPEC member states from June 1999 to March 2011 are adopted as the sample data. First, we used wavelet analysis to find different subseries based on an optimal decomposing scale to describe the periodical feature of the original oil price time series. Second, a complex network model was constructed based on an optimal threshold selection to describe the structural feature of multivariable oil prices. Third, Bayesian network analysis (BNA) was conducted to find the probability causal relationship based on periodical structural features to describe the various patterns of periodical multivariable propagation. Finally, the significance of the leading and intermediary oil prices is discussed. These findings are beneficial for the implementation of periodical target-oriented pricing policies and investment strategies.

MIrExpress: A Database for Gene Coexpression Correlation in Immune Cells Based on Mutual Information and Pearson Correlation

PubMed Central

Wang, Luman; Mo, Qiaochu; Wang, Jianxin

2015-01-01

Most current gene coexpression databases support the analysis for linear correlation of gene pairs, but not nonlinear correlation of them, which hinders precisely evaluating the gene-gene coexpression strengths. Here, we report a new database, MIrExpress, which takes advantage of the information theory, as well as the Pearson linear correlation method, to measure the linear correlation, nonlinear correlation, and their hybrid of cell-specific gene coexpressions in immune cells. For a given gene pair or probe set pair input by web users, both mutual information (MI) and Pearson correlation coefficient (r) are calculated, and several corresponding values are reported to reflect their coexpression correlation nature, including MI and r values, their respective rank orderings, their rank comparison, and their hybrid correlation value. Furthermore, for a given gene, the top 10 most relevant genes to it are displayed with the MI, r, or their hybrid perspective, respectively. Currently, the database totally includes 16 human cell groups, involving 20,283 human genes. The expression data and the calculated correlation results from the database are interactively accessible on the web page and can be implemented for other related applications and researches. PMID:26881263

MIrExpress: A Database for Gene Coexpression Correlation in Immune Cells Based on Mutual Information and Pearson Correlation.

PubMed

Wang, Luman; Mo, Qiaochu; Wang, Jianxin

2015-01-01

Most current gene coexpression databases support the analysis for linear correlation of gene pairs, but not nonlinear correlation of them, which hinders precisely evaluating the gene-gene coexpression strengths. Here, we report a new database, MIrExpress, which takes advantage of the information theory, as well as the Pearson linear correlation method, to measure the linear correlation, nonlinear correlation, and their hybrid of cell-specific gene coexpressions in immune cells. For a given gene pair or probe set pair input by web users, both mutual information (MI) and Pearson correlation coefficient (r) are calculated, and several corresponding values are reported to reflect their coexpression correlation nature, including MI and r values, their respective rank orderings, their rank comparison, and their hybrid correlation value. Furthermore, for a given gene, the top 10 most relevant genes to it are displayed with the MI, r, or their hybrid perspective, respectively. Currently, the database totally includes 16 human cell groups, involving 20,283 human genes. The expression data and the calculated correlation results from the database are interactively accessible on the web page and can be implemented for other related applications and researches.

Estimating Temporal Causal Interaction between Spike Trains with Permutation and Transfer Entropy

PubMed Central

Li, Zhaohui; Li, Xiaoli

2013-01-01

Estimating the causal interaction between neurons is very important for better understanding the functional connectivity in neuronal networks. We propose a method called normalized permutation transfer entropy (NPTE) to evaluate the temporal causal interaction between spike trains, which quantifies the fraction of ordinal information in a neuron that has presented in another one. The performance of this method is evaluated with the spike trains generated by an Izhikevich’s neuronal model. Results show that the NPTE method can effectively estimate the causal interaction between two neurons without influence of data length. Considering both the precision of time delay estimated and the robustness of information flow estimated against neuronal firing rate, the NPTE method is superior to other information theoretic method including normalized transfer entropy, symbolic transfer entropy and permutation conditional mutual information. To test the performance of NPTE on analyzing simulated biophysically realistic synapses, an Izhikevich’s cortical network that based on the neuronal model is employed. It is found that the NPTE method is able to characterize mutual interactions and identify spurious causality in a network of three neurons exactly. We conclude that the proposed method can obtain more reliable comparison of interactions between different pairs of neurons and is a promising tool to uncover more details on the neural coding. PMID:23940662

Revealing Relationships among Relevant Climate Variables with Information Theory

NASA Technical Reports Server (NTRS)

Knuth, Kevin H.; Golera, Anthony; Curry, Charles T.; Huyser, Karen A.; Kevin R. Wheeler; Rossow, William B.

2005-01-01

The primary objective of the NASA Earth-Sun Exploration Technology Office is to understand the observed Earth climate variability, thus enabling the determination and prediction of the climate's response to both natural and human-induced forcing. We are currently developing a suite of computational tools that will allow researchers to calculate, from data, a variety of information-theoretic quantities such as mutual information, which can be used to identify relationships among climate variables, and transfer entropy, which indicates the possibility of causal interactions. Our tools estimate these quantities along with their associated error bars, the latter of which is critical for describing the degree of uncertainty in the estimates. This work is based upon optimal binning techniques that we have developed for piecewise-constant, histogram-style models of the underlying density functions. Two useful side benefits have already been discovered. The first allows a researcher to determine whether there exist sufficient data to estimate the underlying probability density. The second permits one to determine an acceptable degree of round-off when compressing data for efficient transfer and storage. We also demonstrate how mutual information and transfer entropy can be applied so as to allow researchers not only to identify relations among climate variables, but also to characterize and quantify their possible causal interactions.

Decoding 3-D Reach and Grasp Kinematics from High-Frequency Local Field Potentials in Primate Primary Motor Cortex

PubMed Central

Zhuang, Jun; Vargas-Irwin, Carlos; Donoghue, John P.

2011-01-01

Intracortical microelectrode array recordings generate a variety of neural signals with potential application as control signals in neural interface systems. Previous studies have focused on single and multiunit activity, as well as low frequency local field potentials (LFPs), but have not explored higher frequency (>200 Hz) LFPs. In addition, the potential to decode three dimensional (3-D) reach and grasp kinematics based on LFPs has not been demonstrated. Here, we use mutual information and decoding analyses to probe the information content about 3-D reaching and grasping of 7 different LFP frequency bands in the range of 0.3 Hz – 400 Hz. LFPs were recorded via 96-microelectrode arrays in primary motor cortex (M1) of two monkeys performing free reaching to grasp moving objects. Mutual information analyses revealed that higher frequency bands (e.g. 100 – 200 Hz and 200 – 400 Hz) carried the most information about the examined kinematics. Furthermore, Kalman filter decoding revealed that broadband high frequency LFPs, likely reflecting multiunit activity, provided the best decoding performance as well as substantial accuracy in reconstructing reach kinematics, grasp aperture and aperture velocity. These results indicate that LFPs, especially high frequency bands, could be useful signals for neural interfaces controlling 3-D reach and grasp kinematics. PMID:20403782

Family solidarity and informal care: the case of care for people with dementia.

PubMed

ter Meulen, Ruud; Wright, Katharine

2012-09-01

According to Bayertz the core meaning of solidarity is the perception of mutual obligations between the members of a community. This definition leaves open the various ways solidarity is perceived by individuals in different communities and how it manifests itself in a particular community. This paper explores solidarity as manifested in the context of families in respect of caregiving for a family member who has become dependent because of disease or illness. Though family caregiving is based on the same perception of mutual obligation as the solidarity that supports welfare arrangements in society, the manifestation of solidarity in families is different. Solidarity that underpins welfare arrangements is based on a perception of mutual obligation towards an anonymous dependent other and is enforced by the government. Solidarity in families is directed towards a concrete other and is based on free choice, albeit often accompanied by a strong sense of personal duty. In this paper we try to distinguish between solidarity as a sociological concept and as a moral concept. An important moral element of solidarity, as expressed in families, is the need for recognition of family caregivers, not only of their concrete practical efforts, but also of their own identity as caregivers and as individuals. We illustrate this argument by referring to examples in the Nuffield Council on Bioethics report Dementia: ethical issues, about the experiences of family caregivers in dementia care and the importance for them of recognition of their role. © 2012 Blackwell Publishing Ltd.

Modelling nutritional mutualisms: challenges and opportunities for data integration.

PubMed

Clark, Teresa J; Friel, Colleen A; Grman, Emily; Shachar-Hill, Yair; Friesen, Maren L

2017-09-01

Nutritional mutualisms are ancient, widespread, and profoundly influential in biological communities and ecosystems. Although much is known about these interactions, comprehensive answers to fundamental questions, such as how resource availability and structured interactions influence mutualism persistence, are still lacking. Mathematical modelling of nutritional mutualisms has great potential to facilitate the search for comprehensive answers to these and other fundamental questions by connecting the physiological and genomic underpinnings of mutualisms with ecological and evolutionary processes. In particular, when integrated with empirical data, models enable understanding of underlying mechanisms and generalisation of principles beyond the particulars of a given system. Here, we demonstrate how mathematical models can be integrated with data to address questions of mutualism persistence at four biological scales: cell, individual, population, and community. We highlight select studies where data has been or could be integrated with models to either inform model structure or test model predictions. We also point out opportunities to increase model rigour through tighter integration with data, and describe areas in which data is urgently needed. We focus on plant-microbe systems, for which a wealth of empirical data is available, but the principles and approaches can be generally applied to any nutritional mutualism. © 2017 John Wiley & Sons Ltd/CNRS.

Graph-state formalism for mutually unbiased bases

NASA Astrophysics Data System (ADS)

Spengler, Christoph; Kraus, Barbara

2013-11-01

A pair of orthonormal bases is called mutually unbiased if all mutual overlaps between any element of one basis and an arbitrary element of the other basis coincide. In case the dimension, d, of the considered Hilbert space is a power of a prime number, complete sets of d+1 mutually unbiased bases (MUBs) exist. Here we present a method based on the graph-state formalism to construct such sets of MUBs. We show that for n p-level systems, with p being prime, one particular graph suffices to easily construct a set of pn+1 MUBs. In fact, we show that a single n-dimensional vector, which is associated with this graph, can be used to generate a complete set of MUBs and demonstrate that this vector can be easily determined. Finally, we discuss some advantages of our formalism regarding the analysis of entanglement structures in MUBs, as well as experimental realizations.

The Indochinese Mutual Assistance Associations: Characteristics, Composition, Capacity Building Needs and Future Directions.

ERIC Educational Resources Information Center

Bui, Diana D.; And Others

The results of an informal survey of the characteristics, composition, capacity building needs and future directions of sixty Cambodian, Laotian and Vietnamese Mutual Assistance Associations (MAAs) are documented in this report. Included among the survey findings are the purposes, current achievements, and future goals of the associations,…

School/Business Partnerships: We Expanded the Idea into a Mutual-Benefit Plan.

ERIC Educational Resources Information Center

Cameron, S. L.

1987-01-01

Describes a "mutual benefit" arrangement that expanded the school-business partnership model. Westfall Secondary School and an industrial operation in Owen Sound Ontario, Canada, linked their strengths and needs to offer students actual work and project experiences and to give the company useful information, services, and adult basic…

Spectral embedding-based registration (SERg) for multimodal fusion of prostate histology and MRI

NASA Astrophysics Data System (ADS)

Hwuang, Eileen; Rusu, Mirabela; Karthigeyan, Sudha; Agner, Shannon C.; Sparks, Rachel; Shih, Natalie; Tomaszewski, John E.; Rosen, Mark; Feldman, Michael; Madabhushi, Anant

2014-03-01

Multi-modal image registration is needed to align medical images collected from different protocols or imaging sources, thereby allowing the mapping of complementary information between images. One challenge of multimodal image registration is that typical similarity measures rely on statistical correlations between image intensities to determine anatomical alignment. The use of alternate image representations could allow for mapping of intensities into a space or representation such that the multimodal images appear more similar, thus facilitating their co-registration. In this work, we present a spectral embedding based registration (SERg) method that uses non-linearly embedded representations obtained from independent components of statistical texture maps of the original images to facilitate multimodal image registration. Our methodology comprises the following main steps: 1) image-derived textural representation of the original images, 2) dimensionality reduction using independent component analysis (ICA), 3) spectral embedding to generate the alternate representations, and 4) image registration. The rationale behind our approach is that SERg yields embedded representations that can allow for very different looking images to appear more similar, thereby facilitating improved co-registration. Statistical texture features are derived from the image intensities and then reduced to a smaller set by using independent component analysis to remove redundant information. Spectral embedding generates a new representation by eigendecomposition from which only the most important eigenvectors are selected. This helps to accentuate areas of salience based on modality-invariant structural information and therefore better identifies corresponding regions in both the template and target images. The spirit behind SERg is that image registration driven by these areas of salience and correspondence should improve alignment accuracy. In this work, SERg is implemented using Demons to allow the algorithm to more effectively register multimodal images. SERg is also tested within the free-form deformation framework driven by mutual information. Nine pairs of synthetic T1-weighted to T2-weighted brain MRI were registered under the following conditions: five levels of noise (0%, 1%, 3%, 5%, and 7%) and two levels of bias field (20% and 40%) each with and without noise. We demonstrate that across all of these conditions, SERg yields a mean squared error that is 81.51% lower than that of Demons driven by MRI intensity alone. We also spatially align twenty-six ex vivo histology sections and in vivo prostate MRI in order to map the spatial extent of prostate cancer onto corresponding radiologic imaging. SERg performs better than intensity registration by decreasing the root mean squared distance of annotated landmarks in the prostate gland via both Demons algorithm and mutual information-driven free-form deformation. In both synthetic and clinical experiments, the observed improvement in alignment of the template and target images suggest the utility of parametric eigenvector representations and hence SERg for multimodal image registration.

Isoflurane and Ketamine Anesthesia have Different Effects on Ventilatory Pattern Variability in Rats

PubMed Central

Chung, Augustine; Fishman, Mikkel; Dasenbrook, Elliot C.; Loparo, Kenneth A.; Dick, Thomas E.; Jacono, Frank J.

2013-01-01

We hypothesize that isoflurane and ketamine impact ventilatory pattern variability (VPV) differently. Adult Sprague-Dawley rats were recorded in a whole-body plethysmograph before, during and after deep anesthesia. VPV was quantified from 60-s epochs using a complementary set of analytic techniques that included constructing surrogate data sets that preserved the linear structure but disrupted nonlinear deterministic properties of the original data. Even though isoflurane decreased and ketamine increased respiratory rate, VPV as quantified by the coefficient of variation decreased for both anesthetics. Further, mutual information increased and sample entropy decreased and the nonlinear complexity index (NLCI) increased during anesthesia despite qualitative differences in the shape and period of the waveform. Surprisingly mutual information and sample entropy did not change in the surrogate sets constructed from isoflurane data, but in those constructed from ketamine data, mutual information increased and sample entropy decreased significantly in the surrogate segments constructed from anesthetized relative to unanesthetized epochs. These data suggest that separate mechanisms modulate linear and nonlinear variability of breathing. PMID:23246800

Mutual Intelligibility between Closely Related Languages in Europe

ERIC Educational Resources Information Center

Gooskens, Charlotte; van Heuven, Vincent J.; Golubovic, Jelena; Schüppert, Anja; Swarte, Femke; Voigt, Stefanie

2018-01-01

By means of a large-scale web-based investigation, we established the degree of mutual intelligibility of 16 closely related spoken languages within the Germanic, Slavic and Romance language families in Europe. We first present the results of a selection of 1833 listeners representing the mutual intelligibility between young, educated Europeans…

Information filtering on coupled social networks.

PubMed

Nie, Da-Cheng; Zhang, Zi-Ke; Zhou, Jun-Lin; Fu, Yan; Zhang, Kui

2014-01-01

In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm, based on the coupled social networks, considers the effects of both social similarity and personalized preference. Experimental results based on two real datasets, Epinions and Friendfeed, show that the hybrid pattern can not only provide more accurate recommendations, but also enlarge the recommendation coverage while adopting global metric. Further empirical analyses demonstrate that the mutual reinforcement and rich-club phenomenon can also be found in coupled social networks where the identical individuals occupy the core position of the online system. This work may shed some light on the in-depth understanding of the structure and function of coupled social networks.

Improving Classification of Protein Interaction Articles Using Context Similarity-Based Feature Selection.

PubMed

Chen, Yifei; Sun, Yuxing; Han, Bing-Qing

2015-01-01

Protein interaction article classification is a text classification task in the biological domain to determine which articles describe protein-protein interactions. Since the feature space in text classification is high-dimensional, feature selection is widely used for reducing the dimensionality of features to speed up computation without sacrificing classification performance. Many existing feature selection methods are based on the statistical measure of document frequency and term frequency. One potential drawback of these methods is that they treat features separately. Hence, first we design a similarity measure between the context information to take word cooccurrences and phrase chunks around the features into account. Then we introduce the similarity of context information to the importance measure of the features to substitute the document and term frequency. Hence we propose new context similarity-based feature selection methods. Their performance is evaluated on two protein interaction article collections and compared against the frequency-based methods. The experimental results reveal that the context similarity-based methods perform better in terms of the F1 measure and the dimension reduction rate. Benefiting from the context information surrounding the features, the proposed methods can select distinctive features effectively for protein interaction article classification.

Improved contour detection model with spatial summation properties based on nonclassical receptive field

NASA Astrophysics Data System (ADS)

Lin, Chuan; Xu, Guili; Cao, Yijun; Liang, Chenghua; Li, Ya

2016-07-01

The responses of cortical neurons to a stimulus in a classical receptive field (CRF) can be modulated by stimulating the non-CRF (nCRF) of neurons in the primary visual cortex (V1). In the very early stages (at around 40 ms), a neuron in V1 exhibits strong responses to a small set of stimuli. Later, however (after 100 ms), the neurons in V1 become sensitive to the scene's global organization. As per these visual cortical mechanisms, a contour detection model based on the spatial summation properties is proposed. Unlike in previous studies, the responses of the nCRF to the higher visual cortex that results in the inhibition of the neuronal responses in the primary visual cortex by the feedback pathway are considered. In this model, the individual neurons in V1 receive global information from the higher visual cortex to participate in the inhibition process. Computationally, global Gabor energy features are involved, leading to the more coherent physiological characteristics of the nCRF. We conducted an experiment where we compared our model with those proposed by other researchers. Our model explains the role of the mutual inhibition of neurons in V1, together with an approach for object recognition in machine vision.

Reverse engineering and analysis of large genome-scale gene networks

PubMed Central

Aluru, Maneesha; Zola, Jaroslaw; Nettleton, Dan; Aluru, Srinivas

2013-01-01

Reverse engineering the whole-genome networks of complex multicellular organisms continues to remain a challenge. While simpler models easily scale to large number of genes and gene expression datasets, more accurate models are compute intensive limiting their scale of applicability. To enable fast and accurate reconstruction of large networks, we developed Tool for Inferring Network of Genes (TINGe), a parallel mutual information (MI)-based program. The novel features of our approach include: (i) B-spline-based formulation for linear-time computation of MI, (ii) a novel algorithm for direct permutation testing and (iii) development of parallel algorithms to reduce run-time and facilitate construction of large networks. We assess the quality of our method by comparison with ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks) and GeneNet and demonstrate its unique capability by reverse engineering the whole-genome network of Arabidopsis thaliana from 3137 Affymetrix ATH1 GeneChips in just 9 min on a 1024-core cluster. We further report on the development of a new software Gene Network Analyzer (GeNA) for extracting context-specific subnetworks from a given set of seed genes. Using TINGe and GeNA, we performed analysis of 241 Arabidopsis AraCyc 8.0 pathways, and the results are made available through the web. PMID:23042249

A Robust and Effective Smart-Card-Based Remote User Authentication Mechanism Using Hash Function

PubMed Central

Odelu, Vanga; Goswami, Adrijit

2014-01-01

In a remote user authentication scheme, a remote server verifies whether a login user is genuine and trustworthy, and also for mutual authentication purpose a login user validates whether the remote server is genuine and trustworthy. Several remote user authentication schemes using the password, the biometrics, and the smart card have been proposed in the literature. However, most schemes proposed in the literature are either computationally expensive or insecure against several known attacks. In this paper, we aim to propose a new robust and effective password-based remote user authentication scheme using smart card. Our scheme is efficient, because our scheme uses only efficient one-way hash function and bitwise XOR operations. Through the rigorous informal and formal security analysis, we show that our scheme is secure against possible known attacks. We perform the simulation for the formal security analysis using the widely accepted AVISPA (Automated Validation Internet Security Protocols and Applications) tool to ensure that our scheme is secure against passive and active attacks. Furthermore, our scheme supports efficiently the password change phase always locally without contacting the remote server and correctly. In addition, our scheme performs significantly better than other existing schemes in terms of communication, computational overheads, security, and features provided by our scheme. PMID:24892078

A robust and effective smart-card-based remote user authentication mechanism using hash function.

PubMed

Das, Ashok Kumar; Odelu, Vanga; Goswami, Adrijit

2014-01-01

In a remote user authentication scheme, a remote server verifies whether a login user is genuine and trustworthy, and also for mutual authentication purpose a login user validates whether the remote server is genuine and trustworthy. Several remote user authentication schemes using the password, the biometrics, and the smart card have been proposed in the literature. However, most schemes proposed in the literature are either computationally expensive or insecure against several known attacks. In this paper, we aim to propose a new robust and effective password-based remote user authentication scheme using smart card. Our scheme is efficient, because our scheme uses only efficient one-way hash function and bitwise XOR operations. Through the rigorous informal and formal security analysis, we show that our scheme is secure against possible known attacks. We perform the simulation for the formal security analysis using the widely accepted AVISPA (Automated Validation Internet Security Protocols and Applications) tool to ensure that our scheme is secure against passive and active attacks. Furthermore, our scheme supports efficiently the password change phase always locally without contacting the remote server and correctly. In addition, our scheme performs significantly better than other existing schemes in terms of communication, computational overheads, security, and features provided by our scheme.

Exploring social structure effect on language evolution based on a computational model

NASA Astrophysics Data System (ADS)

Gong, Tao; Minett, James; Wang, William

2008-06-01

A compositionality-regularity coevolution model is adopted to explore the effect of social structure on language emergence and maintenance. Based on this model, we explore language evolution in three experiments, and discuss the role of a popular agent in language evolution, the relationship between mutual understanding and social hierarchy, and the effect of inter-community communications and that of simple linguistic features on convergence of communal languages in two communities. This work embodies several important interactions during social learning, and introduces a new approach that manipulates individuals' probabilities to participate in social interactions to study the effect of social structure. We hope it will stimulate further theoretical and empirical explorations on language evolution in a social environment.

A secure and efficient authentication and key agreement scheme based on ECC for telecare medicine information systems.

PubMed

Xu, Xin; Zhu, Ping; Wen, Qiaoyan; Jin, Zhengping; Zhang, Hua; He, Lian

2014-01-01

In the field of the Telecare Medicine Information System, recent researches have focused on consummating more convenient and secure healthcare delivery services for patients. In order to protect the sensitive information, various attempts such as access control have been proposed to safeguard patients' privacy in this system. However, these schemes suffered from some certain security defects and had costly consumption, which were not suitable for the telecare medicine information system. In this paper, based on the elliptic curve cryptography, we propose a secure and efficient two-factor mutual authentication and key agreement scheme to reduce the computational cost. Such a scheme enables to provide the patient anonymity by employing the dynamic identity. Compared with other related protocols, the security analysis and performance evaluation show that our scheme overcomes some well-known attacks and has a better performance in the telecare medicine information system.

Demonstration of accuracy and clinical versatility of mutual information for automatic multimodality image fusion using affine and thin-plate spline warped geometric deformations.

PubMed

Meyer, C R; Boes, J L; Kim, B; Bland, P H; Zasadny, K R; Kison, P V; Koral, K; Frey, K A; Wahl, R L

1997-04-01

This paper applies and evaluates an automatic mutual information-based registration algorithm across a broad spectrum of multimodal volume data sets. The algorithm requires little or no pre-processing, minimal user input and easily implements either affine, i.e. linear or thin-plate spline (TPS) warped registrations. We have evaluated the algorithm in phantom studies as well as in selected cases where few other algorithms could perform as well, if at all, to demonstrate the value of this new method. Pairs of multimodal gray-scale volume data sets were registered by iteratively changing registration parameters to maximize mutual information. Quantitative registration errors were assessed in registrations of a thorax phantom using PET/CT and in the National Library of Medicine's Visible Male using MRI T2-/T1-weighted acquisitions. Registrations of diverse clinical data sets were demonstrated including rotate-translate mapping of PET/MRI brain scans with significant missing data, full affine mapping of thoracic PET/CT and rotate-translate mapping of abdominal SPECT/CT. A five-point thin-plate spline (TPS) warped registration of thoracic PET/CT is also demonstrated. The registration algorithm converged in times ranging between 3.5 and 31 min for affine clinical registrations and 57 min for TPS warping. Mean error vector lengths for rotate-translate registrations were measured to be subvoxel in phantoms. More importantly the rotate-translate algorithm performs well even with missing data. The demonstrated clinical fusions are qualitatively excellent at all levels. We conclude that such automatic, rapid, robust algorithms significantly increase the likelihood that multimodality registrations will be routinely used to aid clinical diagnoses and post-therapeutic assessment in the near future.

SIC-POVMS and MUBS: Geometrical Relationships in Prime Dimension

NASA Astrophysics Data System (ADS)

Appleby, D. M.

2009-03-01

The paper concerns Weyl-Heisenberg covariant SIC-POVMs (symmetric informationally complete positive operator valued measures) and full sets of MUBs (mutually unbiased bases) in prime dimension. When represented as vectors in generalized Bloch space a SIC-POVM forms a d2-1 dimensional regular simplex (d being the Hilbert space dimension). By contrast, the generalized Bloch vectors representing a full set of MUBs form d+1 mutually orthogonal d-1 dimensional regular simplices. In this paper we show that, in the Weyl-Heisenberg case, there are some simple geometrical relationships between the single SIC-POVM simplex and the d+1 MUB simplices. We go on to give geometrical interpretations of the minimum uncertainty states introduced by Wootters and Sussman, and by Appleby, Dang and Fuchs, and of the fiduciality condition given by Appleby, Dang and Fuchs.

Information-Theoretic Limits on Broadband Multi-Antenna Systems in the Presence of Mutual Coupling

NASA Astrophysics Data System (ADS)

Taluja, Pawandeep Singh

2011-12-01

Multiple-input, multiple-output (MIMO) systems have received considerable attention over the last decade due to their ability to provide high throughputs and mitigate multipath fading effects. While most of these benefits are obtained for ideal arrays with large separation between the antennas, practical devices are often constrained in physical dimensions. With smaller inter-element spacings, signal correlation and mutual coupling between the antennas start to degrade the system performance, thereby limiting the deployment of a large number of antennas. Various studies have proposed transceiver designs based on optimal matching networks to compensate for this loss. However, such networks are considered impractical due to their multiport structure and sensitivity to the RF bandwidth of the system. In this dissertation, we investigate two aspects of compact transceiver design. First, we consider simpler architectures that exploit coupling between the antennas, and second, we establish information-theoretic limits of broadband communication systems with closely-spaced antennas. We begin with a receiver model of a diversity antenna selection system and propose novel strategies that make use of inactive elements by virtue of mutual coupling. We then examine the limits on the matching efficiency of a single antenna system using broadband matching theory. Next, we present an extension to this theory for coupled MIMO systems to elucidate the impact of coupling on the RF bandwidth of the system, and derive optimal transceiver designs. Lastly, we summarize the main findings of this dissertation and suggest open problems for future work.

Photovoltaic panel extraction from very high-resolution aerial imagery using region-line primitive association analysis and template matching

NASA Astrophysics Data System (ADS)

Wang, Min; Cui, Qi; Sun, Yujie; Wang, Qiao

2018-07-01

In object-based image analysis (OBIA), object classification performance is jointly determined by image segmentation, sample or rule setting, and classifiers. Typically, as a crucial step to obtain object primitives, image segmentation quality significantly influences subsequent feature extraction and analyses. By contrast, template matching extracts specific objects from images and prevents shape defects caused by image segmentation. However, creating or editing templates is tedious and sometimes results in incomplete or inaccurate templates. In this study, we combine OBIA and template matching techniques to address these problems and aim for accurate photovoltaic panel (PVP) extraction from very high-resolution (VHR) aerial imagery. The proposed method is based on the previously proposed region-line primitive association framework, in which complementary information between region (segment) and line (straight line) primitives is utilized to achieve a more powerful performance than routine OBIA. Several novel concepts, including the mutual fitting ratio and best-fitting template based on region-line primitive association analyses, are proposed. Automatic template generation and matching method for PVP extraction from VHR imagery are designed for concept and model validation. Results show that the proposed method can successfully extract PVPs without any user-specified matching template or training sample. High user independency and accuracy are the main characteristics of the proposed method in comparison with routine OBIA and template matching techniques.

Estimation of the genome sizes of the chigger mites Leptotrombidium pallidum and Leptotrombidium scutellare based on quantitative PCR and k-mer analysis

PubMed Central

2014-01-01

Background Leptotrombidium pallidum and Leptotrombidium scutellare are the major vector mites for Orientia tsutsugamushi, the causative agent of scrub typhus. Before these organisms can be subjected to whole-genome sequencing, it is necessary to estimate their genome sizes to obtain basic information for establishing the strategies that should be used for genome sequencing and assembly. Method The genome sizes of L. pallidum and L. scutellare were estimated by a method based on quantitative real-time PCR. In addition, a k-mer analysis of the whole-genome sequences obtained through Illumina sequencing was conducted to verify the mutual compatibility and reliability of the results. Results The genome sizes estimated using qPCR were 191 ± 7 Mb for L. pallidum and 262 ± 13 Mb for L. scutellare. The k-mer analysis-based genome lengths were estimated to be 175 Mb for L. pallidum and 286 Mb for L. scutellare. The estimates from these two independent methods were mutually complementary and within a similar range to those of other Acariform mites. Conclusions The estimation method based on qPCR appears to be a useful alternative when the standard methods, such as flow cytometry, are impractical. The relatively small estimated genome sizes should facilitate whole-genome analysis, which could contribute to our understanding of Arachnida genome evolution and provide key information for scrub typhus prevention and mite vector competence. PMID:24947244