SU-E-J-110: A Novel Level Set Active Contour Algorithm for Multimodality Joint Segmentation/Registration Using the Jensen-Rényi Divergence.

PubMed

Markel, D; Naqa, I El; Freeman, C; Vallières, M

2012-06-01

To present a novel joint segmentation/registration for multimodality image-guided and adaptive radiotherapy. A major challenge to this framework is the sensitivity of many segmentation or registration algorithms to noise. Presented is a level set active contour based on the Jensen-Renyi (JR) divergence to achieve improved noise robustness in a multi-modality imaging space. To present a novel joint segmentation/registration for multimodality image-guided and adaptive radiotherapy. A major challenge to this framework is the sensitivity of many segmentation or registration algorithms to noise. Presented is a level set active contour based on the Jensen-Renyi (JR) divergence to achieve improved noise robustness in a multi-modality imaging space. It was found that JR divergence when used for segmentation has an improved robustness to noise compared to using mutual information, or other entropy-based metrics. The MI metric failed at around 2/3 the noise power than the JR divergence. The JR divergence metric is useful for the task of joint segmentation/registration of multimodality images and shows improved results compared entropy based metric. The algorithm can be easily modified to incorporate non-intensity based images, which would allow applications into multi-modality and texture analysis. © 2012 American Association of Physicists in Medicine.

Mutual regulatory interactions of the trunk gap genes during blastoderm patterning in the hemipteran Oncopeltus fasciatus.

PubMed

Ben-David, Jonathan; Chipman, Ariel D

2010-10-01

The early embryo of the milkweed bug, Oncopeltus fasciatus, appears as a single cell layer - the embryonic blastoderm - covering the entire egg. It is at this blastoderm stage that morphological domains are first determined, long before the appearance of overt segmentation. Central to the process of patterning the blastoderm into distinct domains are a group of transcription factors known as gap genes. In Drosophila melanogaster these genes form a network of interactions, and maintain sharp expression boundaries through strong mutual repression. Their restricted expression domains define specific areas along the entire body. We have studied the expression domains of the four trunk gap gene homologues in O. fasciatus and have determined their interactions through dsRNA gene knockdown experiments, followed by expression analyses. While the blastoderm in O. fasciatus includes only the first six segments of the embryo, the expression domains of the gap genes within these segments are broadly similar to those in Drosophila where the blastoderm includes all 15 segments. However, the interactions between the gap genes are surprisingly different from those in Drosophila, and mutual repression between the genes seems to play a much less significant role. This suggests that the well-studied interaction pattern in Drosophila is evolutionarily derived, and has evolved from a less strongly interacting network. Copyright © 2010 Elsevier Inc. All rights reserved.

Multimodality image integration for radiotherapy treatment: an easy approach

NASA Astrophysics Data System (ADS)

Santos, Andres; Pascau, Javier; Desco, Manuel; Santos, Juan A.; Calvo, Felipe A.; Benito, Carlos; Garcia-Barreno, Rafael

2001-05-01

The interest of using combined MR and CT information for radiotherapy planning is well documented. However, many planning workstations do not allow to use MR images, nor import predefined contours. This paper presents a new simple approach for transferring segmentation results from MRI to a CT image that will be used for radiotherapy planning, using the same original CT format. CT and MRI images of the same anatomical area are registered using mutual information (MI) algorithm. Targets and organs at risk are segmented by the physician on the MR image, where their contours are easy to track. A locally developed software running on PC is used for this step, with several facilities for the segmentation process. The result is transferred onto the CT by slightly modifying up and down the original Hounsfield values of some points of the contour. This is enough to visualize the contour on the CT, but does not affect dose calculations. The CT is then stored using the original file format of the radiotherapy planning workstation, where the technician uses the segmented contour to design the correct beam positioning. The described method has been tested in five patients. Simulations and patient results show that the dose distribution is not affected by the small modification of pixels of the CT image, while the segmented structures can be tracked in the radiotherapy planning workstation-using adequate window/level settings. The presence of the physician is not requires at the planning workstation, and he/she can perform the segmentation process using his/her own PC. This new approach makes it possible to take advantage from the anatomical information present on the MRI and to transfer the segmentation to the CT used for planning, even when the planning workstation does not allow to import external contours. The physician can draw the limits of the target and areas at risk off-line, thus separating in time the segmentation and planning tasks and increasing the efficiency.

α-Information Based Registration of Dynamic Scans for Magnetic Resonance Cystography

PubMed Central

Han, Hao; Lin, Qin; Li, Lihong; Duan, Chaijie; Lu, Hongbing; Li, Haifang; Yan, Zengmin; Fitzgerald, John

2015-01-01

To continue our effort on developing magnetic resonance (MR) cystography, we introduce a novel non–rigid 3D registration method to compensate for bladder wall motion and deformation in dynamic MR scans, which are impaired by relatively low signal–to–noise ratio in each time frame. The registration method is developed on the similarity measure of α–information, which has the potential of achieving higher registration accuracy than the commonly-used mutual information (MI) measure for either mono-modality or multi-modality image registration. The α–information metric was also demonstrated to be superior to both the mean squares and the cross-correlation metrics in multi-modality scenarios. The proposed α–registration method was applied for bladder motion compensation via real patient studies, and its effect to the automatic and accurate segmentation of bladder wall was also evaluated. Compared with the prevailing MI-based image registration approach, the presented α–information based registration was more effective to capture the bladder wall motion and deformation, which ensured the success of the following bladder wall segmentation to achieve the goal of evaluating the entire bladder wall for detection and diagnosis of abnormality. PMID:26087506

An adaptive Fuzzy C-means method utilizing neighboring information for breast tumor segmentation in ultrasound images.

PubMed

Feng, Yuan; Dong, Fenglin; Xia, Xiaolong; Hu, Chun-Hong; Fan, Qianmin; Hu, Yanle; Gao, Mingyuan; Mutic, Sasa

2017-07-01

Ultrasound (US) imaging has been widely used in breast tumor diagnosis and treatment intervention. Automatic delineation of the tumor is a crucial first step, especially for the computer-aided diagnosis (CAD) and US-guided breast procedure. However, the intrinsic properties of US images such as low contrast and blurry boundaries pose challenges to the automatic segmentation of the breast tumor. Therefore, the purpose of this study is to propose a segmentation algorithm that can contour the breast tumor in US images. To utilize the neighbor information of each pixel, a Hausdorff distance based fuzzy c-means (FCM) method was adopted. The size of the neighbor region was adaptively updated by comparing the mutual information between them. The objective function of the clustering process was updated by a combination of Euclid distance and the adaptively calculated Hausdorff distance. Segmentation results were evaluated by comparing with three experts' manual segmentations. The results were also compared with a kernel-induced distance based FCM with spatial constraints, the method without adaptive region selection, and conventional FCM. Results from segmenting 30 patient images showed the adaptive method had a value of sensitivity, specificity, Jaccard similarity, and Dice coefficient of 93.60 ± 5.33%, 97.83 ± 2.17%, 86.38 ± 5.80%, and 92.58 ± 3.68%, respectively. The region-based metrics of average symmetric surface distance (ASSD), root mean square symmetric distance (RMSD), and maximum symmetric surface distance (MSSD) were 0.03 ± 0.04 mm, 0.04 ± 0.03 mm, and 1.18 ± 1.01 mm, respectively. All the metrics except sensitivity were better than that of the non-adaptive algorithm and the conventional FCM. Only three region-based metrics were better than that of the kernel-induced distance based FCM with spatial constraints. Inclusion of the pixel neighbor information adaptively in segmenting US images improved the segmentation performance. The results demonstrate the potential application of the method in breast tumor CAD and other US-guided procedures. © 2017 American Association of Physicists in Medicine.

A noninvasive brain computer interface using visually-induced near-infrared spectroscopy responses.

PubMed

Chen, Cheng-Hsuan; Ho, Ming-Shan; Shyu, Kuo-Kai; Hsu, Kou-Cheng; Wang, Kuo-Wei; Lee, Po-Lei

2014-09-19

Visually-induced near-infrared spectroscopy (NIRS) response was utilized to design a brain computer interface (BCI) system. Four circular checkerboards driven by distinct flickering sequences were displayed on a LCD screen as visual stimuli to induce subjects' NIRS responses. Each flickering sequence was a concatenated sequence of alternative flickering segments and resting segments. The flickering segment was designed with fixed duration of 3s whereas the resting segment was chosen randomly within 15-20s to create the mutual independencies among different flickering sequences. Six subjects were recruited in this study and subjects were requested to gaze at the four visual stimuli one-after-one in a random order. Since visual responses in human brain are time-locked to the onsets of visual stimuli and the flicker sequences of distinct visual stimuli were designed mutually independent, the NIRS responses induced by user's gazed targets can be discerned from non-gazed targets by applying a simple averaging process. The accuracies for the six subjects were higher than 90% after 10 or more epochs being averaged. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

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.

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

Optimal Multiple Surface Segmentation With Shape and Context Priors

PubMed Central

Bai, Junjie; Garvin, Mona K.; Sonka, Milan; Buatti, John M.; Wu, Xiaodong

2014-01-01

Segmentation of multiple surfaces in medical images is a challenging problem, further complicated by the frequent presence of weak boundary evidence, large object deformations, and mutual influence between adjacent objects. This paper reports a novel approach to multi-object segmentation that incorporates both shape and context prior knowledge in a 3-D graph-theoretic framework to help overcome the stated challenges. We employ an arc-based graph representation to incorporate a wide spectrum of prior information through pair-wise energy terms. In particular, a shape-prior term is used to penalize local shape changes and a context-prior term is used to penalize local surface-distance changes from a model of the expected shape and surface distances, respectively. The globally optimal solution for multiple surfaces is obtained by computing a maximum flow in a low-order polynomial time. The proposed method was validated on intraretinal layer segmentation of optical coherence tomography images and demonstrated statistically significant improvement of segmentation accuracy compared to our earlier graph-search method that was not utilizing shape and context priors. The mean unsigned surface positioning errors obtained by the conventional graph-search approach (6.30 ± 1.58 μm) was improved to 5.14 ± 0.99 μm when employing our new method with shape and context priors. PMID:23193309

An efficient global energy optimization approach for robust 3D plane segmentation of point clouds

NASA Astrophysics Data System (ADS)

Dong, Zhen; Yang, Bisheng; Hu, Pingbo; Scherer, Sebastian

2018-03-01

Automatic 3D plane segmentation is necessary for many applications including point cloud registration, building information model (BIM) reconstruction, simultaneous localization and mapping (SLAM), and point cloud compression. However, most of the existing 3D plane segmentation methods still suffer from low precision and recall, and inaccurate and incomplete boundaries, especially for low-quality point clouds collected by RGB-D sensors. To overcome these challenges, this paper formulates the plane segmentation problem as a global energy optimization because it is robust to high levels of noise and clutter. First, the proposed method divides the raw point cloud into multiscale supervoxels, and considers planar supervoxels and individual points corresponding to nonplanar supervoxels as basic units. Then, an efficient hybrid region growing algorithm is utilized to generate initial plane set by incrementally merging adjacent basic units with similar features. Next, the initial plane set is further enriched and refined in a mutually reinforcing manner under the framework of global energy optimization. Finally, the performances of the proposed method are evaluated with respect to six metrics (i.e., plane precision, plane recall, under-segmentation rate, over-segmentation rate, boundary precision, and boundary recall) on two benchmark datasets. Comprehensive experiments demonstrate that the proposed method obtained good performances both in high-quality TLS point clouds (i.e., http://SEMANTIC3D.NET)

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.

Visual enhancement of unmixed multispectral imagery using adaptive smoothing

USGS Publications Warehouse

Lemeshewsky, G.P.; Rahman, Z.-U.; Schowengerdt, R.A.; Reichenbach, S.E.

2004-01-01

Adaptive smoothing (AS) has been previously proposed as a method to smooth uniform regions of an image, retain contrast edges, and enhance edge boundaries. The method is an implementation of the anisotropic diffusion process which results in a gray scale image. This paper discusses modifications to the AS method for application to multi-band data which results in a color segmented image. The process was used to visually enhance the three most distinct abundance fraction images produced by the Lagrange constraint neural network learning-based unmixing of Landsat 7 Enhanced Thematic Mapper Plus multispectral sensor data. A mutual information-based method was applied to select the three most distinct fraction images for subsequent visualization as a red, green, and blue composite. A reported image restoration technique (partial restoration) was applied to the multispectral data to reduce unmixing error, although evaluation of the performance of this technique was beyond the scope of this paper. The modified smoothing process resulted in a color segmented image with homogeneous regions separated by sharpened, coregistered multiband edges. There was improved class separation with the segmented image, which has importance to subsequent operations involving data classification.

3D Registration of mpMRI for Assessment of Prostate Cancer Focal Therapy.

PubMed

Orczyk, Clément; Rosenkrantz, Andrew B; Mikheev, Artem; Villers, Arnauld; Bernaudin, Myriam; Taneja, Samir S; Valable, Samuel; Rusinek, Henry

2017-12-01

This study aimed to assess a novel method of three-dimensional (3D) co-registration of prostate magnetic resonance imaging (MRI) examinations performed before and after prostate cancer focal therapy. We developed a software platform for automatic 3D deformable co-registration of prostate MRI at different time points and applied this method to 10 patients who underwent focal ablative therapy. MRI examinations were performed preoperatively, as well as 1 week and 6 months post treatment. Rigid registration served as reference for assessing co-registration accuracy and precision. Segmentation of preoperative and postoperative prostate revealed a significant postoperative volume decrease of the gland that averaged 6.49 cc (P = .017). Applying deformable transformation based on mutual information from 120 pairs of MRI slices, we refined by 2.9 mm (max. 6.25 mm) the alignment of the ablation zone, segmented from contrast-enhanced images on the 1-week postoperative examination, to the 6-month postoperative T2-weighted images. This represented a 500% improvement over the rigid approach (P = .001), corrected by volume. The dissimilarity by Dice index of the mapped ablation zone using deformable transformation vs rigid control was significantly (P = .04) higher at the ablation site than in the whole gland. Our findings illustrate our method's ability to correct for deformation at the ablation site. The preliminary analysis suggests that deformable transformation computed from mutual information of preoperative and follow-up MRI is accurate in co-registration of MRI examinations performed before and after focal therapy. The ability to localize the previously ablated tissue in 3D space may improve targeting for image-guided follow-up biopsy within focal therapy protocols. Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Color Improves Speed of Processing But Not Perception in a Motion Illusion

PubMed Central

Perry, Carolyn J.; Fallah, Mazyar

2012-01-01

When two superimposed surfaces of dots move in different directions, the perceived directions are shifted away from each other. This perceptual illusion has been termed direction repulsion and is thought to be due to mutual inhibition between the representations of the two directions. It has further been shown that a speed difference between the two surfaces attenuates direction repulsion. As speed and direction are both necessary components of representing motion, the reduction in direction repulsion can be attributed to the additional motion information strengthening the representations of the two directions and thus reducing the mutual inhibition. We tested whether bottom-up attention and top-down task demands, in the form of color differences between the two surfaces, would also enhance motion processing, reducing direction repulsion. We found that the addition of color differences did not improve direction discrimination and reduce direction repulsion. However, we did find that adding a color difference improved performance on the task. We hypothesized that the performance differences were due to the limited presentation time of the stimuli. We tested this in a follow-up experiment where we varied the time of presentation to determine the duration needed to successfully perform the task with and without the color difference. As we expected, color segmentation reduced the amount of time needed to process and encode both directions of motion. Thus we find a dissociation between the effects of attention on the speed of processing and conscious perception of direction. We propose four potential mechanisms wherein color speeds figure-ground segmentation of an object, attentional switching between objects, direction discrimination and/or the accumulation of motion information for decision-making, without affecting conscious perception of the direction. Potential neural bases are also explored. PMID:22479255

Color improves speed of processing but not perception in a motion illusion.

PubMed

Perry, Carolyn J; Fallah, Mazyar

2012-01-01

When two superimposed surfaces of dots move in different directions, the perceived directions are shifted away from each other. This perceptual illusion has been termed direction repulsion and is thought to be due to mutual inhibition between the representations of the two directions. It has further been shown that a speed difference between the two surfaces attenuates direction repulsion. As speed and direction are both necessary components of representing motion, the reduction in direction repulsion can be attributed to the additional motion information strengthening the representations of the two directions and thus reducing the mutual inhibition. We tested whether bottom-up attention and top-down task demands, in the form of color differences between the two surfaces, would also enhance motion processing, reducing direction repulsion. We found that the addition of color differences did not improve direction discrimination and reduce direction repulsion. However, we did find that adding a color difference improved performance on the task. We hypothesized that the performance differences were due to the limited presentation time of the stimuli. We tested this in a follow-up experiment where we varied the time of presentation to determine the duration needed to successfully perform the task with and without the color difference. As we expected, color segmentation reduced the amount of time needed to process and encode both directions of motion. Thus we find a dissociation between the effects of attention on the speed of processing and conscious perception of direction. We propose four potential mechanisms wherein color speeds figure-ground segmentation of an object, attentional switching between objects, direction discrimination and/or the accumulation of motion information for decision-making, without affecting conscious perception of the direction. Potential neural bases are also explored.

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.

Implementation and evaluation of a new workflow for registration and segmentation of pulmonary MRI data for regional lung perfusion assessment.

PubMed

Böttger, T; Grunewald, K; Schöbinger, M; Fink, C; Risse, F; Kauczor, H U; Meinzer, H P; Wolf, Ivo

2007-03-07

Recently it has been shown that regional lung perfusion can be assessed using time-resolved contrast-enhanced magnetic resonance (MR) imaging. Quantification of the perfusion images has been attempted, based on definition of small regions of interest (ROIs). Use of complete lung segmentations instead of ROIs could possibly increase quantification accuracy. Due to the low signal-to-noise ratio, automatic segmentation algorithms cannot be applied. On the other hand, manual segmentation of the lung tissue is very time consuming and can become inaccurate, as the borders of the lung to adjacent tissues are not always clearly visible. We propose a new workflow for semi-automatic segmentation of the lung from additionally acquired morphological HASTE MR images. First the lung is delineated semi-automatically in the HASTE image. Next the HASTE image is automatically registered with the perfusion images. Finally, the transformation resulting from the registration is used to align the lung segmentation from the morphological dataset with the perfusion images. We evaluated rigid, affine and locally elastic transformations, suitable optimizers and different implementations of mutual information (MI) metrics to determine the best possible registration algorithm. We located the shortcomings of the registration procedure and under which conditions automatic registration will succeed or fail. Segmentation results were evaluated using overlap and distance measures. Integration of the new workflow reduces the time needed for post-processing of the data, simplifies the perfusion quantification and reduces interobserver variability in the segmentation process. In addition, the matched morphological data set can be used to identify morphologic changes as the source for the perfusion abnormalities.

The segmentation of bones in pelvic CT images based on extraction of key frames.

PubMed

Yu, Hui; Wang, Haijun; Shi, Yao; Xu, Ke; Yu, Xuyao; Cao, Yuzhen

2018-05-22

Bone segmentation is important in computed tomography (CT) imaging of the pelvis, which assists physicians in the early diagnosis of pelvic injury, in planning operations, and in evaluating the effects of surgical treatment. This study developed a new algorithm for the accurate, fast, and efficient segmentation of the pelvis. The proposed method consists of two main parts: the extraction of key frames and the segmentation of pelvic CT images. Key frames were extracted based on pixel difference, mutual information and normalized correlation coefficient. In the pelvis segmentation phase, skeleton extraction from CT images and a marker-based watershed algorithm were combined to segment the pelvis. To meet the requirements of clinical application, physician's judgment is needed. Therefore the proposed methodology is semi-automated. In this paper, 5 sets of CT data were used to test the overlapping area, and 15 CT images were used to determine the average deviation distance. The average overlapping area of the 5 sets was greater than 94%, and the minimum average deviation distance was approximately 0.58 pixels. In addition, the key frame extraction efficiency and the running time of the proposed method were evaluated on 20 sets of CT data. For each set, approximately 13% of the images were selected as key frames, and the average processing time was approximately 2 min (the time for manual marking was not included). The proposed method is able to achieve accurate, fast, and efficient segmentation of pelvic CT image sequences. Segmentation results not only provide an important reference for early diagnosis and decisions regarding surgical procedures, they also offer more accurate data for medical image registration, recognition and 3D reconstruction.

Automatic liver segmentation in computed tomography using general-purpose shape modeling methods.

PubMed

Spinczyk, Dominik; Krasoń, Agata

2018-05-29

Liver segmentation in computed tomography is required in many clinical applications. The segmentation methods used can be classified according to a number of criteria. One important criterion for method selection is the shape representation of the segmented organ. The aim of the work is automatic liver segmentation using general purpose shape modeling methods. As part of the research, methods based on shape information at various levels of advancement were used. The single atlas based segmentation method was used as the simplest shape-based method. This method is derived from a single atlas using the deformable free-form deformation of the control point curves. Subsequently, the classic and modified Active Shape Model (ASM) was used, using medium body shape models. As the most advanced and main method generalized statistical shape models, Gaussian Process Morphable Models was used, which are based on multi-dimensional Gaussian distributions of the shape deformation field. Mutual information and sum os square distance were used as similarity measures. The poorest results were obtained for the single atlas method. For the ASM method in 10 analyzed cases for seven test images, the Dice coefficient was above 55[Formula: see text], of which for three of them the coefficient was over 70[Formula: see text], which placed the method in second place. The best results were obtained for the method of generalized statistical distribution of the deformation field. The DICE coefficient for this method was 88.5[Formula: see text] CONCLUSIONS: This value of 88.5 [Formula: see text] Dice coefficient can be explained by the use of general-purpose shape modeling methods with a large variance of the shape of the modeled object-the liver and limitations on the size of our training data set, which was limited to 10 cases. The obtained results in presented fully automatic method are comparable with dedicated methods for liver segmentation. In addition, the deforamtion features of the model can be modeled mathematically by using various kernel functions, which allows to segment the liver on a comparable level using a smaller learning set.

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

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.

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.

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.

Doctors' opinion on the contribution of coordination mechanisms to improving clinical coordination between primary and outpatient secondary care in the Catalan national health system.

PubMed

Aller, Marta-Beatriz; Vargas, Ingrid; Coderch, Jordi; Vázquez, Maria-Luisa

2017-12-22

Clinical coordination is considered a health policy priority as its absence can lead to poor quality of care and inefficiency. A key challenge is to identify which strategies should be implemented to improve coordination. The aim is to analyse doctors' opinions on the contribution of mechanisms to improving clinical coordination between primary and outpatient secondary care and the factors influencing their use. A qualitative descriptive study in three healthcare networks of the Catalan national health system. A two-stage theoretical sample was designed: in the first stage, networks with different management models were selected; in the second, primary care (n = 26) and secondary care (n = 24) doctors. Data were collected using semi-structured interviews. Final sample size was reached by saturation. A thematic content analysis was conducted, segmented by network and care level. With few differences across networks, doctors identified similar mechanisms contributing to clinical coordination: 1) shared EMR facilitating clinical information transfer and uptake; 2) mechanisms enabling problem-solving communication and agreement on clinical approaches, which varied across networks (joint clinical case conferences, which also promote mutual knowledge and training of primary care doctors; virtual consultations through EMR and email); and 3) referral protocols and use of the telephone facilitating access to secondary care after referrals. Doctors identified organizational (insufficient time, incompatible timetables, design of mechanisms) and professional factors (knowing each other, attitude towards collaboration, concerns over misdiagnosis) that influence the use of mechanisms. Mechanisms that most contribute to clinical coordination are feedback mechanisms, that is those based on mutual adjustment, that allow doctors to exchange information and communicate. Their use might be enhanced by focusing on adequate working conditions, mechanism design and creating conditions that promote mutual knowledge and positive attitudes towards collaboration.

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.

A unified perspective on ankle push-off in human walking

PubMed Central

Adamczyk, Peter G.

2016-01-01

ABSTRACT Muscle–tendon units about the ankle joint generate a burst of positive power during the step-to-step transition in human walking, termed ankle push-off, but there is no scientific consensus on its functional role. A central question embodied in the biomechanics literature is: does ankle push-off primarily contribute to leg swing, or to center of mass (COM) acceleration? This question has been debated in various forms for decades. However, it actually presents a false dichotomy, as these two possibilities are not mutually exclusive. If we ask either question independently, the answer is the same: yes! (1) Does ankle push-off primarily contribute to leg swing acceleration? Yes. (2) Does ankle push-off primarily contribute to COM acceleration? Yes. Here, we summarize the historical debate, then synthesize the seemingly polarized perspectives and demonstrate that both descriptions are valid. The principal means by which ankle push-off affects COM mechanics is by a localized action that increases the speed and kinetic energy of the trailing push-off limb. Because the limb is included in body COM computations, this localized segmental acceleration also accelerates the COM, and most of the segmental energy change also appears as COM energy change. Interpretation of ankle mechanics should abandon an either/or contrast of leg swing versus COM acceleration. Instead, ankle push-off should be interpreted in light of both mutually consistent effects. This unified perspective informs our fundamental understanding of the role of ankle push-off, and has important implications for the design of clinical interventions (e.g. prostheses, orthoses) intended to restore locomotor function to individuals with disabilities. PMID:27903626

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

Mutual information-based feature selection for radiomics

NASA Astrophysics Data System (ADS)

Oubel, Estanislao; Beaumont, Hubert; Iannessi, Antoine

2016-03-01

Background The extraction and analysis of image features (radiomics) is a promising field in the precision medicine era, with applications to prognosis, prediction, and response to treatment quantification. In this work, we present a mutual information - based method for quantifying reproducibility of features, a necessary step for qualification before their inclusion in big data systems. Materials and Methods Ten patients with Non-Small Cell Lung Cancer (NSCLC) lesions were followed over time (7 time points in average) with Computed Tomography (CT). Five observers segmented lesions by using a semi-automatic method and 27 features describing shape and intensity distribution were extracted. Inter-observer reproducibility was assessed by computing the multi-information (MI) of feature changes over time, and the variability of global extrema. Results The highest MI values were obtained for volume-based features (VBF). The lesion mass (M), surface to volume ratio (SVR) and volume (V) presented statistically significant higher values of MI than the rest of features. Within the same VBF group, SVR showed also the lowest variability of extrema. The correlation coefficient (CC) of feature values was unable to make a difference between features. Conclusions MI allowed to discriminate three features (M, SVR, and V) from the rest in a statistically significant manner. This result is consistent with the order obtained when sorting features by increasing values of extrema variability. MI is a promising alternative for selecting features to be considered as surrogate biomarkers in a precision medicine context.

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.

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.

Split spline screw

NASA Technical Reports Server (NTRS)

Vranish, John M. (Inventor)

1993-01-01

A split spline screw type payload fastener assembly, including three identical male and female type split spline sections, is discussed. The male spline sections are formed on the head of a male type spline driver. Each of the split male type spline sections has an outwardly projecting load baring segment including a convex upper surface which is adapted to engage a complementary concave surface of a female spline receptor in the form of a hollow bolt head. Additionally, the male spline section also includes a horizontal spline releasing segment and a spline tightening segment below each load bearing segment. The spline tightening segment consists of a vertical web of constant thickness. The web has at least one flat vertical wall surface which is designed to contact a generally flat vertically extending wall surface tab of the bolt head. Mutual interlocking and unlocking of the male and female splines results upon clockwise and counter clockwise turning of the driver element.

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.

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...

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...

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

A Minimum Spanning Forest Based Method for Noninvasive Cancer Detection with Hyperspectral Imaging

PubMed Central

Pike, Robert; Lu, Guolan; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei

2016-01-01

Goal The purpose of this paper is to develop a classification method that combines both spectral and spatial information for distinguishing cancer from healthy tissue on hyperspectral images in an animal model. Methods An automated algorithm based on a minimum spanning forest (MSF) and optimal band selection has been proposed to classify healthy and cancerous tissue on hyperspectral images. A support vector machine (SVM) classifier is trained to create a pixel-wise classification probability map of cancerous and healthy tissue. This map is then used to identify markers that are used to compute mutual information for a range of bands in the hyperspectral image and thus select the optimal bands. An MSF is finally grown to segment the image using spatial and spectral information. Conclusion The MSF based method with automatically selected bands proved to be accurate in determining the tumor boundary on hyperspectral images. Significance Hyperspectral imaging combined with the proposed classification technique has the potential to provide a noninvasive tool for cancer detection. PMID:26285052

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.

Method of artificial DNA splicing by directed ligation (SDL).

PubMed Central

Lebedenko, E N; Birikh, K R; Plutalov, O V; Berlin YuA

1991-01-01

An approach to directed genetic recombination in vitro has been devised, which allows for joining together, in a predetermined way, a series of DNA segments to give a precisely spliced polynucleotide sequence (DNA splicing by directed ligation, SDL). The approach makes use of amplification, by means of several polymerase chain reactions (PCR), of a chosen set of DNA segments. Primers for the amplifications contain recognition sites of the class IIS restriction endonucleases, which transform blunt ends of the amplification products into protruding ends of unique primary structures, the ends to be used for joining segments together being mutually complementary. Ligation of the mixture of the segments so synthesized gives the desired sequence in an unambiguous way. The suggested approach has been exemplified by the synthesis of a totally processed (intronless) gene encoding human mature interleukin-1 alpha. Images PMID:1662363

Basic test framework for the evaluation of text line segmentation and text parameter extraction.

PubMed

Brodić, Darko; Milivojević, Dragan R; Milivojević, Zoran

2010-01-01

Text line segmentation is an essential stage in off-line optical character recognition (OCR) systems. It is a key because inaccurately segmented text lines will lead to OCR failure. Text line segmentation of handwritten documents is a complex and diverse problem, complicated by the nature of handwriting. Hence, text line segmentation is a leading challenge in handwritten document image processing. Due to inconsistencies in measurement and evaluation of text segmentation algorithm quality, some basic set of measurement methods is required. Currently, there is no commonly accepted one and all algorithm evaluation is custom oriented. In this paper, a basic test framework for the evaluation of text feature extraction algorithms is proposed. This test framework consists of a few experiments primarily linked to text line segmentation, skew rate and reference text line evaluation. Although they are mutually independent, the results obtained are strongly cross linked. In the end, its suitability for different types of letters and languages as well as its adaptability are its main advantages. Thus, the paper presents an efficient evaluation method for text analysis algorithms.

Basic Test Framework for the Evaluation of Text Line Segmentation and Text Parameter Extraction

PubMed Central

Brodić, Darko; Milivojević, Dragan R.; Milivojević, Zoran

2010-01-01

Text line segmentation is an essential stage in off-line optical character recognition (OCR) systems. It is a key because inaccurately segmented text lines will lead to OCR failure. Text line segmentation of handwritten documents is a complex and diverse problem, complicated by the nature of handwriting. Hence, text line segmentation is a leading challenge in handwritten document image processing. Due to inconsistencies in measurement and evaluation of text segmentation algorithm quality, some basic set of measurement methods is required. Currently, there is no commonly accepted one and all algorithm evaluation is custom oriented. In this paper, a basic test framework for the evaluation of text feature extraction algorithms is proposed. This test framework consists of a few experiments primarily linked to text line segmentation, skew rate and reference text line evaluation. Although they are mutually independent, the results obtained are strongly cross linked. In the end, its suitability for different types of letters and languages as well as its adaptability are its main advantages. Thus, the paper presents an efficient evaluation method for text analysis algorithms. PMID:22399932

A unified perspective on ankle push-off in human walking.

PubMed

Zelik, Karl E; Adamczyk, Peter G

2016-12-01

Muscle-tendon units about the ankle joint generate a burst of positive power during the step-to-step transition in human walking, termed ankle push-off, but there is no scientific consensus on its functional role. A central question embodied in the biomechanics literature is: does ankle push-off primarily contribute to leg swing, or to center of mass (COM) acceleration? This question has been debated in various forms for decades. However, it actually presents a false dichotomy, as these two possibilities are not mutually exclusive. If we ask either question independently, the answer is the same: yes! (1) Does ankle push-off primarily contribute to leg swing acceleration? Yes. (2) Does ankle push-off primarily contribute to COM acceleration? Yes. Here, we summarize the historical debate, then synthesize the seemingly polarized perspectives and demonstrate that both descriptions are valid. The principal means by which ankle push-off affects COM mechanics is by a localized action that increases the speed and kinetic energy of the trailing push-off limb. Because the limb is included in body COM computations, this localized segmental acceleration also accelerates the COM, and most of the segmental energy change also appears as COM energy change. Interpretation of ankle mechanics should abandon an either/or contrast of leg swing versus COM acceleration. Instead, ankle push-off should be interpreted in light of both mutually consistent effects. This unified perspective informs our fundamental understanding of the role of ankle push-off, and has important implications for the design of clinical interventions (e.g. prostheses, orthoses) intended to restore locomotor function to individuals with disabilities. © 2016. Published by The Company of Biologists Ltd.

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.

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

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...

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

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.

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

Benefits of urban landscape eco-volunteerism: mixed methods segmentation analysis and implications for volunteer retention

Treesearch

Stanley T. Asah; Miku M. Lenetine; Dale J. Blahna

2014-01-01

Urban landscape restoration and conservation initiatives are challenged by financial and other constraints. Consequently, these initiatives are increasingly reliant on volunteer stewards. Knowledge of why people volunteer to restore and conserve urban ecosystems can help practitioners enhance volunteering as a social-ecological process that is mutually beneficial to...

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

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...

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

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

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.

Evaluation of non-rigid registration parameters for atlas-based segmentation of CT images of human cochlea

NASA Astrophysics Data System (ADS)

Elfarnawany, Mai; Alam, S. Riyahi; Agrawal, Sumit K.; Ladak, Hanif M.

2017-02-01

Cochlear implant surgery is a hearing restoration procedure for patients with profound hearing loss. In this surgery, an electrode is inserted into the cochlea to stimulate the auditory nerve and restore the patient's hearing. Clinical computed tomography (CT) images are used for planning and evaluation of electrode placement, but their low resolution limits the visualization of internal cochlear structures. Therefore, high resolution micro-CT images are used to develop atlas-based segmentation methods to extract these nonvisible anatomical features in clinical CT images. Accurate registration of the high and low resolution CT images is a prerequisite for reliable atlas-based segmentation. In this study, we evaluate and compare different non-rigid B-spline registration parameters using micro-CT and clinical CT images of five cadaveric human cochleae. The varying registration parameters are cost function (normalized correlation (NC), mutual information and mean square error), interpolation method (linear, windowed-sinc and B-spline) and sampling percentage (1%, 10% and 100%). We compare the registration results visually and quantitatively using the Dice similarity coefficient (DSC), Hausdorff distance (HD) and absolute percentage error in cochlear volume. Using MI or MSE cost functions and linear or windowed-sinc interpolation resulted in visually undesirable deformation of internal cochlear structures. Quantitatively, the transforms using 100% sampling percentage yielded the highest DSC and smallest HD (0.828+/-0.021 and 0.25+/-0.09mm respectively). Therefore, B-spline registration with cost function: NC, interpolation: B-spline and sampling percentage: moments 100% can be the foundation of developing an optimized atlas-based segmentation algorithm of intracochlear structures in clinical CT images.

An Approach to a Comprehensive Test Framework for Analysis and Evaluation of Text Line Segmentation Algorithms

PubMed Central

Brodic, Darko; Milivojevic, Dragan R.; Milivojevic, Zoran N.

2011-01-01

The paper introduces a testing framework for the evaluation and validation of text line segmentation algorithms. Text line segmentation represents the key action for correct optical character recognition. Many of the tests for the evaluation of text line segmentation algorithms deal with text databases as reference templates. Because of the mismatch, the reliable testing framework is required. Hence, a new approach to a comprehensive experimental framework for the evaluation of text line segmentation algorithms is proposed. It consists of synthetic multi-like text samples and real handwritten text as well. Although the tests are mutually independent, the results are cross-linked. The proposed method can be used for different types of scripts and languages. Furthermore, two different procedures for the evaluation of algorithm efficiency based on the obtained error type classification are proposed. The first is based on the segmentation line error description, while the second one incorporates well-known signal detection theory. Each of them has different capabilities and convenience, but they can be used as supplements to make the evaluation process efficient. Overall the proposed procedure based on the segmentation line error description has some advantages, characterized by five measures that describe measurement procedures. PMID:22164106

An approach to a comprehensive test framework for analysis and evaluation of text line segmentation algorithms.

PubMed

Brodic, Darko; Milivojevic, Dragan R; Milivojevic, Zoran N

2011-01-01

The paper introduces a testing framework for the evaluation and validation of text line segmentation algorithms. Text line segmentation represents the key action for correct optical character recognition. Many of the tests for the evaluation of text line segmentation algorithms deal with text databases as reference templates. Because of the mismatch, the reliable testing framework is required. Hence, a new approach to a comprehensive experimental framework for the evaluation of text line segmentation algorithms is proposed. It consists of synthetic multi-like text samples and real handwritten text as well. Although the tests are mutually independent, the results are cross-linked. The proposed method can be used for different types of scripts and languages. Furthermore, two different procedures for the evaluation of algorithm efficiency based on the obtained error type classification are proposed. The first is based on the segmentation line error description, while the second one incorporates well-known signal detection theory. Each of them has different capabilities and convenience, but they can be used as supplements to make the evaluation process efficient. Overall the proposed procedure based on the segmentation line error description has some advantages, characterized by five measures that describe measurement procedures.

SU-C-BRA-06: Automatic Brain Tumor Segmentation for Stereotactic Radiosurgery Applications

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

Liu, Y; Stojadinovic, S; Jiang, S

Purpose: Stereotactic radiosurgery (SRS), which delivers a potent dose of highly conformal radiation to the target in a single fraction, requires accurate tumor delineation for treatment planning. We present an automatic segmentation strategy, that synergizes intensity histogram thresholding, super-voxel clustering, and level-set based contour evolving methods to efficiently and accurately delineate SRS brain tumors on contrast-enhance T1-weighted (T1c) Magnetic Resonance Images (MRI). Methods: The developed auto-segmentation strategy consists of three major steps. Firstly, tumor sites are localized through 2D slice intensity histogram scanning. Then, super voxels are obtained through clustering the corresponding voxels in 3D with reference to the similaritymore » metrics composited from spatial distance and intensity difference. The combination of the above two could generate the initial contour surface. Finally, a localized region active contour model is utilized to evolve the surface to achieve the accurate delineation of the tumors. The developed method was evaluated on numerical phantom data, synthetic BRATS (Multimodal Brain Tumor Image Segmentation challenge) data, and clinical patients’ data. The auto-segmentation results were quantitatively evaluated by comparing to ground truths with both volume and surface similarity metrics. Results: DICE coefficient (DC) was performed as a quantitative metric to evaluate the auto-segmentation in the numerical phantom with 8 tumors. DCs are 0.999±0.001 without noise, 0.969±0.065 with Rician noise and 0.976±0.038 with Gaussian noise. DC, NMI (Normalized Mutual Information), SSIM (Structural Similarity) and Hausdorff distance (HD) were calculated as the metrics for the BRATS and patients’ data. Assessment of BRATS data across 25 tumor segmentation yield DC 0.886±0.078, NMI 0.817±0.108, SSIM 0.997±0.002, and HD 6.483±4.079mm. Evaluation on 8 patients with total 14 tumor sites yield DC 0.872±0.070, NMI 0.824±0.078, SSIM 0.999±0.001, and HD 5.926±6.141mm. Conclusion: The developed automatic segmentation strategy, which yields accurate brain tumor delineation in evaluation cases, is promising for its application in SRS treatment planning.« less

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.

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.

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.

An EMA/EPG Study of Vowel-to-Vowel Articulation across Velars in Southern British English

ERIC Educational Resources Information Center

Fletcher, Janet

2004-01-01

Recent studies have attested that the extent of transconsonantal vowel-to-vowel coarticulation is at least partly dependent on degree of prosodic accentuation, in languages like English. A further important factor is the mutual compatibility of consonant and vowel gestures associated with the segments in question. In this study two speakers of…

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...

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.

Probabilistic peak detection for first-order chromatographic data.

PubMed

Lopatka, M; Vivó-Truyols, G; Sjerps, M J

2014-03-19

We present a novel algorithm for probabilistic peak detection in first-order chromatographic data. Unlike conventional methods that deliver a binary answer pertaining to the expected presence or absence of a chromatographic peak, our method calculates the probability of a point being affected by such a peak. The algorithm makes use of chromatographic information (i.e. the expected width of a single peak and the standard deviation of baseline noise). As prior information of the existence of a peak in a chromatographic run, we make use of the statistical overlap theory. We formulate an exhaustive set of mutually exclusive hypotheses concerning presence or absence of different peak configurations. These models are evaluated by fitting a segment of chromatographic data by least-squares. The evaluation of these competing hypotheses can be performed as a Bayesian inferential task. We outline the potential advantages of adopting this approach for peak detection and provide several examples of both improved performance and increased flexibility afforded by our approach. Copyright © 2014 Elsevier B.V. All rights reserved.

Issues concerning the informality and outdoor sex work performed by travestis in São Paulo, Brazil.

PubMed

Garcia, Marcos R V; Lehman, Yvette Piha

2011-12-01

The objective of this article was to discuss a series of issues pertaining to outdoor sex work practiced by low-income travestis in São Paulo, Brazil. Qualitative methods conducted among this segment of the population revealed sex work as almost inseparable from the existence of travestis as a social group. Among them, the outdoor modality was the most prominent and social stigma was a predisposing factor for their entrance (and permanence) in sex work. The results showed that some of the difficulties they faced regarding work must be understood as a consequence of them being self-employed, informal workers occupying public spaces, much like street vendors. The points they shared in common with Brazilian street vendors included the struggle for street space, their rejection by local residents and shopkeepers, police persecution, and higher credit and rental expenses. All this led to mutual exploitation within the group, difficulties in changing occupations, predatory competition for customers, and difficulties in forming professional associations.

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

Molecular modeling of biomembranes and their complexes with protein transmembrane α-helices

NASA Astrophysics Data System (ADS)

Kuznetsov, Andrey S.; Smirnov, Kirill V.; Antonov, Mikhail Yu.; Nikolaev, Ivan N.; Efremov, Roman G.

2017-11-01

Helical segments are common structural elements of membrane proteins. Dimerization and oligomerization of transmembrane (TM) α-helices provides the framework for spatial structure formation and protein-protein interactions. The membrane itself also takes part in the protein functioning. There are some examples of the mutual influence of the lipid bilayer properties and embedded membrane proteins. This work aims at the detail investigation of protein-lipid interactions using model systems: TM peptides corresponding to native protein segments. Three peptides were considered corresponding to TM domains of human glycophorin A (GpA), epidermal growth factor receptor (EGFR) and proposed TM-segment of human neuraminidase-1 (Neu1). A computational analysis of structural and dynamical properties was performed using molecular dynamics method. Monomers of peptides were considered incorporated into hydrated lipid bilayers. It was confirmed, that all these TM peptides have stable helical conformation in lipid environment, and the mutual adaptation of peptides and membrane was observed. It was shown that incorporation of the peptide into membrane results in the modulation of local and mean structural properties of the bilayer. Each peptide interacts with lipid acyl chains having special binding sites on the surface of central part of α-helix that exist for at least 200 ns. However, lipid acyl chains substitute each other faster occupying the same site. The formation of a special pattern of protein-lipid interactions may modulate the association of TM domains of membrane proteins, so membrane environment should be considered when proposing new substances targeting cell receptors.

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.

Technical developments in international satellite business services

NASA Astrophysics Data System (ADS)

Tan, P. P.

At the conception of International Satellite Business Services (ISBS), it was a primary objective to provide flexibility for accommodating a variety of service requirements which might be established by mutual agreement between users. The design guidelines are to ensure that the space segment is efficiently utilized, while other satellite services are protected from interference. Other considerations are related to an acceptable earth segment cost, maximum connectivity in worldwide services, the capability of growth and a reasonably smooth transition into future systems, and the maintenance of high performance objectives. Attention is given to a system overview, the characteristics of satellites for ISBS, and technological developments with some application possibilities for ISBS.

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...

Mutual information and spontaneous symmetry breaking

NASA Astrophysics Data System (ADS)

Hamma, A.; Giampaolo, S. M.; Illuminati, F.

2016-01-01

We show that the metastable, symmetry-breaking ground states of quantum many-body Hamiltonians have vanishing quantum mutual information between macroscopically separated regions and are thus the most classical ones among all possible quantum ground states. This statement is obvious only when the symmetry-breaking ground states are simple product states, e.g., at the factorization point. On the other hand, symmetry-breaking states are in general entangled along the entire ordered phase, and to show that they actually feature the least macroscopic correlations compared to their symmetric superpositions is highly nontrivial. We prove this result in general, by considering the quantum mutual information based on the two-Rényi entanglement entropy and using a locality result stemming from quasiadiabatic continuation. Moreover, in the paradigmatic case of the exactly solvable one-dimensional quantum X Y model, we further verify the general result by considering also the quantum mutual information based on the von Neumann entanglement entropy.

[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.

Hierarchical Higher Order Crf for the Classification of Airborne LIDAR Point Clouds in Urban Areas

NASA Astrophysics Data System (ADS)

Niemeyer, J.; Rottensteiner, F.; Soergel, U.; Heipke, C.

2016-06-01

We propose a novel hierarchical approach for the classification of airborne 3D lidar points. Spatial and semantic context is incorporated via a two-layer Conditional Random Field (CRF). The first layer operates on a point level and utilises higher order cliques. Segments are generated from the labelling obtained in this way. They are the entities of the second layer, which incorporates larger scale context. The classification result of the segments is introduced as an energy term for the next iteration of the point-based layer. This framework iterates and mutually propagates context to improve the classification results. Potentially wrong decisions can be revised at later stages. The output is a labelled point cloud as well as segments roughly corresponding to object instances. Moreover, we present two new contextual features for the segment classification: the distance and the orientation of a segment with respect to the closest road. It is shown that the classification benefits from these features. In our experiments the hierarchical framework improve the overall accuracies by 2.3% on a point-based level and by 3.0% on a segment-based level, respectively, compared to a purely point-based classification.

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.

Accurate tracking of tumor volume change during radiotherapy by CT-CBCT registration with intensity correction

NASA Astrophysics Data System (ADS)

Park, Seyoun; Robinson, Adam; Quon, Harry; Kiess, Ana P.; Shen, Colette; Wong, John; Plishker, William; Shekhar, Raj; Lee, Junghoon

2016-03-01

In this paper, we propose a CT-CBCT registration method to accurately predict the tumor volume change based on daily cone-beam CTs (CBCTs) during radiotherapy. CBCT is commonly used to reduce patient setup error during radiotherapy, but its poor image quality impedes accurate monitoring of anatomical changes. Although physician's contours drawn on the planning CT can be automatically propagated to daily CBCTs by deformable image registration (DIR), artifacts in CBCT often cause undesirable errors. To improve the accuracy of the registration-based segmentation, we developed a DIR method that iteratively corrects CBCT intensities by local histogram matching. Three popular DIR algorithms (B-spline, demons, and optical flow) with the intensity correction were implemented on a graphics processing unit for efficient computation. We evaluated their performances on six head and neck (HN) cancer cases. For each case, four trained scientists manually contoured the nodal gross tumor volume (GTV) on the planning CT and every other fraction CBCTs to which the propagated GTV contours by DIR were compared. The performance was also compared with commercial image registration software based on conventional mutual information (MI), VelocityAI (Varian Medical Systems Inc.). The volume differences (mean±std in cc) between the average of the manual segmentations and automatic segmentations are 3.70+/-2.30 (B-spline), 1.25+/-1.78 (demons), 0.93+/-1.14 (optical flow), and 4.39+/-3.86 (VelocityAI). The proposed method significantly reduced the estimation error by 9% (B-spline), 38% (demons), and 51% (optical flow) over the results using VelocityAI. Although demonstrated only on HN nodal GTVs, the results imply that the proposed method can produce improved segmentation of other critical structures over conventional methods.

Object Segmentation from Motion Discontinuities and Temporal Occlusions–A Biologically Inspired Model

PubMed Central

Beck, Cornelia; Ognibeni, Thilo; Neumann, Heiko

2008-01-01

Background Optic flow is an important cue for object detection. Humans are able to perceive objects in a scene using only kinetic boundaries, and can perform the task even when other shape cues are not provided. These kinetic boundaries are characterized by the presence of motion discontinuities in a local neighbourhood. In addition, temporal occlusions appear along the boundaries as the object in front covers the background and the objects that are spatially behind it. Methodology/Principal Findings From a technical point of view, the detection of motion boundaries for segmentation based on optic flow is a difficult task. This is due to the problem that flow detected along such boundaries is generally not reliable. We propose a model derived from mechanisms found in visual areas V1, MT, and MSTl of human and primate cortex that achieves robust detection along motion boundaries. It includes two separate mechanisms for both the detection of motion discontinuities and of occlusion regions based on how neurons respond to spatial and temporal contrast, respectively. The mechanisms are embedded in a biologically inspired architecture that integrates information of different model components of the visual processing due to feedback connections. In particular, mutual interactions between the detection of motion discontinuities and temporal occlusions allow a considerable improvement of the kinetic boundary detection. Conclusions/Significance A new model is proposed that uses optic flow cues to detect motion discontinuities and object occlusion. We suggest that by combining these results for motion discontinuities and object occlusion, object segmentation within the model can be improved. This idea could also be applied in other models for object segmentation. In addition, we discuss how this model is related to neurophysiological findings. The model was successfully tested both with artificial and real sequences including self and object motion. PMID:19043613

Three-Dimensional Assessment of Temporomandibular Joint Using MRI-CBCT Image Registration

PubMed Central

Lagravere, Manuel; Boulanger, Pierre; Jaremko, Jacob L.; Major, Paul W.

2017-01-01

Purpose To introduce a new approach to reconstruct a 3D model of the TMJ using magnetic resonance imaging (MRI) and cone-beam computed tomography (CBCT) registered images, and to evaluate the intra-examiner reproducibility values of reconstructing the 3D models of the TMJ. Methods MRI and CBCT images of five patients (10 TMJs) were obtained. Multiple MRIs and CBCT images were registered using a mutual information based algorithm. The articular disc, condylar head and glenoid fossa were segmented at two different occasions, at least one-week apart, by one investigator, and 3D models were reconstructed. Differences between the segmentation at two occasions were automatically measured using the surface contours (Average Perpendicular Distance) and the volume overlap (Dice Similarity Index) of the 3D models. Descriptive analysis of the changes at 2 occasions, including means and standard deviation (SD) were reported to describe the intra-examiner reproducibility. Results The automatic segmentation of the condyle revealed maximum distance change of 1.9±0.93 mm, similarity index of 98% and root mean squared distance of 0.1±0.08 mm, and the glenoid fossa revealed maximum distance change of 2±0.52 mm, similarity index of 96% and root mean squared distance of 0.2±0.04 mm. The manual segmentation of the articular disc revealed maximum distance change of 3.6±0.32 mm, similarity index of 80% and root mean squared distance of 0.3±0.1 mm. Conclusion The MRI-CBCT registration provides a reliable tool to reconstruct 3D models of the TMJ’s soft and hard tissues, allows quantification of the articular disc morphology and position changes with associated differences of the condylar head and glenoid fossa, and facilitates measuring tissue changes over time. PMID:28095486

Three-Dimensional Assessment of Temporomandibular Joint Using MRI-CBCT Image Registration.

PubMed

Al-Saleh, Mohammed A Q; Punithakumar, Kumaradevan; Lagravere, Manuel; Boulanger, Pierre; Jaremko, Jacob L; Major, Paul W

2017-01-01

To introduce a new approach to reconstruct a 3D model of the TMJ using magnetic resonance imaging (MRI) and cone-beam computed tomography (CBCT) registered images, and to evaluate the intra-examiner reproducibility values of reconstructing the 3D models of the TMJ. MRI and CBCT images of five patients (10 TMJs) were obtained. Multiple MRIs and CBCT images were registered using a mutual information based algorithm. The articular disc, condylar head and glenoid fossa were segmented at two different occasions, at least one-week apart, by one investigator, and 3D models were reconstructed. Differences between the segmentation at two occasions were automatically measured using the surface contours (Average Perpendicular Distance) and the volume overlap (Dice Similarity Index) of the 3D models. Descriptive analysis of the changes at 2 occasions, including means and standard deviation (SD) were reported to describe the intra-examiner reproducibility. The automatic segmentation of the condyle revealed maximum distance change of 1.9±0.93 mm, similarity index of 98% and root mean squared distance of 0.1±0.08 mm, and the glenoid fossa revealed maximum distance change of 2±0.52 mm, similarity index of 96% and root mean squared distance of 0.2±0.04 mm. The manual segmentation of the articular disc revealed maximum distance change of 3.6±0.32 mm, similarity index of 80% and root mean squared distance of 0.3±0.1 mm. The MRI-CBCT registration provides a reliable tool to reconstruct 3D models of the TMJ's soft and hard tissues, allows quantification of the articular disc morphology and position changes with associated differences of the condylar head and glenoid fossa, and facilitates measuring tissue changes over time.

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.

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

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.

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.

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.

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.

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

Medical image registration based on normalized multidimensional mutual information

NASA Astrophysics Data System (ADS)

Li, Qi; Ji, Hongbing; Tong, Ming

2009-10-01

Registration of medical images is an essential research topic in medical image processing and applications, and especially a preliminary and key step for multimodality image fusion. This paper offers a solution to medical image registration based on normalized multi-dimensional mutual information. Firstly, affine transformation with translational and rotational parameters is applied to the floating image. Then ordinal features are extracted by ordinal filters with different orientations to represent spatial information in medical images. Integrating ordinal features with pixel intensities, the normalized multi-dimensional mutual information is defined as similarity criterion to register multimodality images. Finally the immune algorithm is used to search registration parameters. The experimental results demonstrate the effectiveness of the proposed registration scheme.

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.

SU-E-J-224: Using UTE and T1 Weighted Spin Echo Pulse Sequences for MR-Only Treatment Planning; Phantom Study

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

Yu, H; Fatemi, A; Sahgal, A

Purpose: Investigating a new approach in MRI based treatment planning using the combination of (Ultrashort Echo Time) UTE and T1 weighted spin echo pulse sequences to delineate air, bone and water (soft tissues) in generating pseudo CT images comparable with CT. Methods: A gel phantom containing chicken bones, ping pang balls filled with distilled water and air bubbles, was made. It scanned with MRI using UTE and 2D T1W SE pulse sequences with (in plane resolution= 0.53mm, slice thickness= 2 mm) and CT with (in plane resolution= 0.5 mm and slice thickness= 0.75mm) as a ground truth for geometrical accuracy.more » The UTE and T1W SE images were registered with CT using mutual information registration algorithm provided by Philips Pinnacle treatment planning system. The phantom boundaries were detected using Canny edge detection algorithm for CT, and MR images. The bone, air bubbles and water in ping pong balls were segmented from CT images using threshold 300HU, - 950HU and 0HU, respectively. These tissue inserts were automatically segmented from combined UTE and T1W SE images using edge detection and relative intensity histograms of the phantom. The obtained segmentations of air, bone and water inserts were evaluated with those obtained from CT. Results: Bone and air can be clearly differentiated in UTE images comparable to CT. Combining UTE and T1W SE images successfully segmented the air, bone and water. The maximum segmentation differences from combine MRI images (UTE and T1W SE) and CT are within 1.3 mm, 1.1mm for bone, air, respectively. The geometric distortion of UTE sequence is small less than 1 pixel (0.53 mm) of MR image resolution. Conclusion: Our approach indicates that MRI can be used solely for treatment planning and its quality is comparable with CT.« less

ACIR: automatic cochlea image registration

NASA Astrophysics Data System (ADS)

Al-Dhamari, Ibraheem; Bauer, Sabine; Paulus, Dietrich; Lissek, Friedrich; Jacob, Roland

2017-02-01

Efficient Cochlear Implant (CI) surgery requires prior knowledge of the cochlea's size and its characteristics. This information helps to select suitable implants for different patients. To get these measurements, a segmentation method of cochlea medical images is needed. An important pre-processing step for good cochlea segmentation involves efficient image registration. The cochlea's small size and complex structure, in addition to the different resolutions and head positions during imaging, reveals a big challenge for the automated registration of the different image modalities. In this paper, an Automatic Cochlea Image Registration (ACIR) method for multi- modal human cochlea images is proposed. This method is based on using small areas that have clear structures from both input images instead of registering the complete image. It uses the Adaptive Stochastic Gradient Descent Optimizer (ASGD) and Mattes's Mutual Information metric (MMI) to estimate 3D rigid transform parameters. The use of state of the art medical image registration optimizers published over the last two years are studied and compared quantitatively using the standard Dice Similarity Coefficient (DSC). ACIR requires only 4.86 seconds on average to align cochlea images automatically and to put all the modalities in the same spatial locations without human interference. The source code is based on the tool elastix and is provided for free as a 3D Slicer plugin. Another contribution of this work is a proposed public cochlea standard dataset which can be downloaded for free from a public XNAT server.

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.

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...

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.

Research on polarization imaging information parsing method

NASA Astrophysics Data System (ADS)

Yuan, Hongwu; Zhou, Pucheng; Wang, Xiaolong

2016-11-01

Polarization information parsing plays an important role in polarization imaging detection. This paper focus on the polarization information parsing method: Firstly, the general process of polarization information parsing is given, mainly including polarization image preprocessing, multiple polarization parameters calculation, polarization image fusion and polarization image tracking, etc.; And then the research achievements of the polarization information parsing method are presented, in terms of polarization image preprocessing, the polarization image registration method based on the maximum mutual information is designed. The experiment shows that this method can improve the precision of registration and be satisfied the need of polarization information parsing; In terms of multiple polarization parameters calculation, based on the omnidirectional polarization inversion model is built, a variety of polarization parameter images are obtained and the precision of inversion is to be improve obviously; In terms of polarization image fusion , using fuzzy integral and sparse representation, the multiple polarization parameters adaptive optimal fusion method is given, and the targets detection in complex scene is completed by using the clustering image segmentation algorithm based on fractal characters; In polarization image tracking, the average displacement polarization image characteristics of auxiliary particle filtering fusion tracking algorithm is put forward to achieve the smooth tracking of moving targets. Finally, the polarization information parsing method is applied to the polarization imaging detection of typical targets such as the camouflage target, the fog and latent fingerprints.

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

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…

Peano-like paths for subaperture polishing of optical aspherical surfaces.

PubMed

Tam, Hon-Yuen; Cheng, Haobo; Dong, Zhichao

2013-05-20

Polishing can be more uniform if the polishing path provides uniform coverage of the surface. It is known that Peano paths can provide uniform coverage of planar surfaces. Peano paths also contain short path segments and turns: (1) all path segments have the same length, (2) path segments are mutually orthogonal at the turns, and (3) path segments and turns are uniformity distributed over the domain surface. These make Peano paths an attractive candidate among polishing tool paths because they enhance multidirectional approaches of the tool to each surface location. A method for constructing Peano paths for uniform coverage of aspherical surfaces is proposed in this paper. When mapped to the aspherical surface, the path also contains short path segments and turns, and the above attributes are approximately preserved. Attention is paid so that the path segments are still well distributed near the vertex of the surface. The proposed tool path was used in the polishing of a number of parabolic BK7 specimens using magnetorheological finishing (MRF) and pitch with cerium oxide. The results were rather good for optical lenses and confirm that a Peano-like path was useful for polishing, for MRF, and for pitch polishing. In the latter case, the surface roughness achieved was 0.91 nm according to WYKO measurement.

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.

Temporal subtraction contrast-enhanced dedicated breast CT

NASA Astrophysics Data System (ADS)

Gazi, Peymon M.; Aminololama-Shakeri, Shadi; Yang, Kai; Boone, John M.

2016-09-01

The development of a framework of deformable image registration and segmentation for the purpose of temporal subtraction contrast-enhanced breast CT is described. An iterative histogram-based two-means clustering method was used for the segmentation. Dedicated breast CT images were segmented into background (air), adipose, fibroglandular and skin components. Fibroglandular tissue was classified as either normal or contrast-enhanced then divided into tiers for the purpose of categorizing degrees of contrast enhancement. A variant of the Demons deformable registration algorithm, intensity difference adaptive Demons (IDAD), was developed to correct for the large deformation forces that stemmed from contrast enhancement. In this application, the accuracy of the proposed method was evaluated in both mathematically-simulated and physically-acquired phantom images. Clinical usage and accuracy of the temporal subtraction framework was demonstrated using contrast-enhanced breast CT datasets from five patients. Registration performance was quantified using normalized cross correlation (NCC), symmetric uncertainty coefficient, normalized mutual information (NMI), mean square error (MSE) and target registration error (TRE). The proposed method outperformed conventional affine and other Demons variations in contrast enhanced breast CT image registration. In simulation studies, IDAD exhibited improvement in MSE (0-16%), NCC (0-6%), NMI (0-13%) and TRE (0-34%) compared to the conventional Demons approaches, depending on the size and intensity of the enhancing lesion. As lesion size and contrast enhancement levels increased, so did the improvement. The drop in the correlation between the pre- and post-contrast images for the largest enhancement levels in phantom studies is less than 1.2% (150 Hounsfield units). Registration error, measured by TRE, shows only submillimeter mismatches between the concordant anatomical target points in all patient studies. The algorithm was implemented using a parallel processing architecture resulting in rapid execution time for the iterative segmentation and intensity-adaptive registration techniques. Characterization of contrast-enhanced lesions is improved using temporal subtraction contrast-enhanced dedicated breast CT. Adaptation of Demons registration forces as a function of contrast-enhancement levels provided a means to accurately align breast tissue in pre- and post-contrast image acquisitions, improving subtraction results. Spatial subtraction of the aligned images yields useful diagnostic information with respect to enhanced lesion morphology and uptake.

Optimal graph search segmentation using arc-weighted graph for simultaneous surface detection of bladder and prostate.

PubMed

Song, Qi; Wu, Xiaodong; Liu, Yunlong; Smith, Mark; Buatti, John; Sonka, Milan

2009-01-01

We present a novel method for globally optimal surface segmentation of multiple mutually interacting objects, incorporating both edge and shape knowledge in a 3-D graph-theoretic approach. Hard surface interacting constraints are enforced in the interacting regions, preserving the geometric relationship of those partially interacting surfaces. The soft smoothness a priori shape compliance is introduced into the energy functional to provide shape guidance. The globally optimal surfaces can be simultaneously achieved by solving a maximum flow problem based on an arc-weighted graph representation. Representing the segmentation problem in an arc-weighted graph, one can incorporate a wider spectrum of constraints into the formulation, thus increasing segmentation accuracy and robustness in volumetric image data. To the best of our knowledge, our method is the first attempt to introduce the arc-weighted graph representation into the graph-searching approach for simultaneous segmentation of multiple partially interacting objects, which admits a globally optimal solution in a low-order polynomial time. Our new approach was applied to the simultaneous surface detection of bladder and prostate. The result was quite encouraging in spite of the low saliency of the bladder and prostate in CT images.

Hybrid region merging method for segmentation of high-resolution remote sensing images

NASA Astrophysics Data System (ADS)

Zhang, Xueliang; Xiao, Pengfeng; Feng, Xuezhi; Wang, Jiangeng; Wang, Zuo

2014-12-01

Image segmentation remains a challenging problem for object-based image analysis. In this paper, a hybrid region merging (HRM) method is proposed to segment high-resolution remote sensing images. HRM integrates the advantages of global-oriented and local-oriented region merging strategies into a unified framework. The globally most-similar pair of regions is used to determine the starting point of a growing region, which provides an elegant way to avoid the problem of starting point assignment and to enhance the optimization ability for local-oriented region merging. During the region growing procedure, the merging iterations are constrained within the local vicinity, so that the segmentation is accelerated and can reflect the local context, as compared with the global-oriented method. A set of high-resolution remote sensing images is used to test the effectiveness of the HRM method, and three region-based remote sensing image segmentation methods are adopted for comparison, including the hierarchical stepwise optimization (HSWO) method, the local-mutual best region merging (LMM) method, and the multiresolution segmentation (MRS) method embedded in eCognition Developer software. Both the supervised evaluation and visual assessment show that HRM performs better than HSWO and LMM by combining both their advantages. The segmentation results of HRM and MRS are visually comparable, but HRM can describe objects as single regions better than MRS, and the supervised and unsupervised evaluation results further prove the superiority of HRM.

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-…

Emphasis of spatial cues in the temporal fine structure during the rising segments of amplitude-modulated sounds

PubMed Central

Dietz, Mathias; Marquardt, Torsten; Salminen, Nelli H.; McAlpine, David

2013-01-01

The ability to locate the direction of a target sound in a background of competing sources is critical to the survival of many species and important for human communication. Nevertheless, brain mechanisms that provide for such accurate localization abilities remain poorly understood. In particular, it remains unclear how the auditory brain is able to extract reliable spatial information directly from the source when competing sounds and reflections dominate all but the earliest moments of the sound wave reaching each ear. We developed a stimulus mimicking the mutual relationship of sound amplitude and binaural cues, characteristic to reverberant speech. This stimulus, named amplitude modulated binaural beat, allows for a parametric and isolated change of modulation frequency and phase relations. Employing magnetoencephalography and psychoacoustics it is demonstrated that the auditory brain uses binaural information in the stimulus fine structure only during the rising portion of each modulation cycle, rendering spatial information recoverable in an otherwise unlocalizable sound. The data suggest that amplitude modulation provides a means of “glimpsing” low-frequency spatial cues in a manner that benefits listening in noisy or reverberant environments. PMID:23980161

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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.

Information Theoretic Approaches to Rapid Discovery of Relationships in Large Climate Data Sets

NASA Technical Reports Server (NTRS)

Knuth, Kevin H.; Rossow, William B.; Clancy, Daniel (Technical Monitor)

2002-01-01

Mutual information as the asymptotic Bayesian measure of independence is an excellent starting point for investigating the existence of possible relationships among climate-relevant variables in large data sets, As mutual information is a nonlinear function of of its arguments, it is not beholden to the assumption of a linear relationship between the variables in question and can reveal features missed in linear correlation analyses. However, as mutual information is symmetric in its arguments, it only has the ability to reveal the probability that two variables are related. it provides no information as to how they are related; specifically, causal interactions or a relation based on a common cause cannot be detected. For this reason we also investigate the utility of a related quantity called the transfer entropy. The transfer entropy can be written as a difference between mutual informations and has the capability to reveal whether and how the variables are causally related. The application of these information theoretic measures is rested on some familiar examples using data from the International Satellite Cloud Climatology Project (ISCCP) to identify relation between global cloud cover and other variables, including equatorial pacific sea surface temperature (SST), over seasonal and El Nino Southern Oscillation (ENSO) cycles.

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.

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.

[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.

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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 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.

Comparison of atlas-based techniques for whole-body bone segmentation.

PubMed

Arabi, Hossein; Zaidi, Habib

2017-02-01

We evaluate the accuracy of whole-body bone extraction from whole-body MR images using a number of atlas-based segmentation methods. The motivation behind this work is to find the most promising approach for the purpose of MRI-guided derivation of PET attenuation maps in whole-body PET/MRI. To this end, a variety of atlas-based segmentation strategies commonly used in medical image segmentation and pseudo-CT generation were implemented and evaluated in terms of whole-body bone segmentation accuracy. Bone segmentation was performed on 23 whole-body CT/MR image pairs via leave-one-out cross validation procedure. The evaluated segmentation techniques include: (i) intensity averaging (IA), (ii) majority voting (MV), (iii) global and (iv) local (voxel-wise) weighting atlas fusion frameworks implemented utilizing normalized mutual information (NMI), normalized cross-correlation (NCC) and mean square distance (MSD) as image similarity measures for calculating the weighting factors, along with other atlas-dependent algorithms, such as (v) shape-based averaging (SBA) and (vi) Hofmann's pseudo-CT generation method. The performance evaluation of the different segmentation techniques was carried out in terms of estimating bone extraction accuracy from whole-body MRI using standard metrics, such as Dice similarity (DSC) and relative volume difference (RVD) considering bony structures obtained from intensity thresholding of the reference CT images as the ground truth. Considering the Dice criterion, global weighting atlas fusion methods provided moderate improvement of whole-body bone segmentation (DSC= 0.65 ± 0.05) compared to non-weighted IA (DSC= 0.60 ± 0.02). The local weighed atlas fusion approach using the MSD similarity measure outperformed the other strategies by achieving a DSC of 0.81 ± 0.03 while using the NCC and NMI measures resulted in a DSC of 0.78 ± 0.05 and 0.75 ± 0.04, respectively. Despite very long computation time, the extracted bone obtained from both SBA (DSC= 0.56 ± 0.05) and Hofmann's methods (DSC= 0.60 ± 0.02) exhibited no improvement compared to non-weighted IA. Finding the optimum parameters for implementation of the atlas fusion approach, such as weighting factors and image similarity patch size, have great impact on the performance of atlas-based segmentation approaches. The voxel-wise atlas fusion approach exhibited excellent performance in terms of cancelling out the non-systematic registration errors leading to accurate and reliable segmentation results. Denoising and normalization of MR images together with optimization of the involved parameters play a key role in improving bone extraction accuracy. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

Classification VIA Information-Theoretic Fusion of Vector-Magnetic and Acoustic Sensor Data

DTIC Science & Technology

2007-04-01

10) where tBsBtBsBtBsBtsB zzyyxx, . (11) The operation in (10) may be viewed as a vector matched- filter on to estimate )(tB CPARv . In summary...choosing to maximize the classification information in Y are described in Section 3.2. A 3.2. Maximum mutual information ( MMI ) features We begin with a...review of several desirable properties of features that maximize a mutual information ( MMI ) criterion. Then we review a particular algorithm [2

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.

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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.

A 3D global-to-local deformable mesh model based registration and anatomy-constrained segmentation method for image guided prostate radiotherapy

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

Zhou Jinghao; Kim, Sung; Jabbour, Salma

2010-03-15

Purpose: In the external beam radiation treatment of prostate cancers, successful implementation of adaptive radiotherapy and conformal radiation dose delivery is highly dependent on precise and expeditious segmentation and registration of the prostate volume between the simulation and the treatment images. The purpose of this study is to develop a novel, fast, and accurate segmentation and registration method to increase the computational efficiency to meet the restricted clinical treatment time requirement in image guided radiotherapy. Methods: The method developed in this study used soft tissues to capture the transformation between the 3D planning CT (pCT) images and 3D cone-beam CTmore » (CBCT) treatment images. The method incorporated a global-to-local deformable mesh model based registration framework as well as an automatic anatomy-constrained robust active shape model (ACRASM) based segmentation algorithm in the 3D CBCT images. The global registration was based on the mutual information method, and the local registration was to minimize the Euclidian distance of the corresponding nodal points from the global transformation of deformable mesh models, which implicitly used the information of the segmented target volume. The method was applied on six data sets of prostate cancer patients. Target volumes delineated by the same radiation oncologist on the pCT and CBCT were chosen as the benchmarks and were compared to the segmented and registered results. The distance-based and the volume-based estimators were used to quantitatively evaluate the results of segmentation and registration. Results: The ACRASM segmentation algorithm was compared to the original active shape model (ASM) algorithm by evaluating the values of the distance-based estimators. With respect to the corresponding benchmarks, the mean distance ranged from -0.85 to 0.84 mm for ACRASM and from -1.44 to 1.17 mm for ASM. The mean absolute distance ranged from 1.77 to 3.07 mm for ACRASM and from 2.45 to 6.54 mm for ASM. The volume overlap ratio ranged from 79% to 91% for ACRASM and from 44% to 80% for ASM. These data demonstrated that the segmentation results of ACRASM were in better agreement with the corresponding benchmarks than those of ASM. The developed registration algorithm was quantitatively evaluated by comparing the registered target volumes from the pCT to the benchmarks on the CBCT. The mean distance and the root mean square error ranged from 0.38 to 2.2 mm and from 0.45 to 2.36 mm, respectively, between the CBCT images and the registered pCT. The mean overlap ratio of the prostate volumes ranged from 85.2% to 95% after registration. The average time of the ACRASM-based segmentation was under 1 min. The average time of the global transformation was from 2 to 4 min on two 3D volumes and the average time of the local transformation was from 20 to 34 s on two deformable superquadrics mesh models. Conclusions: A novel and fast segmentation and deformable registration method was developed to capture the transformation between the planning and treatment images for external beam radiotherapy of prostate cancers. This method increases the computational efficiency and may provide foundation to achieve real time adaptive radiotherapy.« less

Diffusion Tensor Imaging Based Thalamic Segmentation in Deep Brain Stimulation for Chronic Pain Conditions

PubMed Central

Kim, Won; Chivukula, Srinivas; Hauptman, Jason; Pouratian, Nader

2016-01-01

Background/Aims Thalamic deep brain stimulation (DBS) for the treatment of medically refractory pain has largely been abandoned on account of its inconsistent and oftentimes poor efficacy. Our aim here was to use diffusion tensor imaging (DTI)-based segmentation to assess the internal thalamic nuclei of patients who have undergone thalamic DBS for intractable pain and retrospectively correlate lead position with clinical outcome. Methods DTI-based segmentation was performed on 5 patients who underwent sensory thalamus DBS for chronic pain. Postoperative computed tomography (CT) images obtained for electrode placement were fused with preoperative MRIs that had undergone DTI-based thalamic segmentation. Sensory thalamus maps of 4 patients were analyzed for lead positioning and interpatient variability. Results Four patients who experienced significant pain relief following DBS demonstrated contact positions within the DTI-determined sensory thalamus or in its vicinity, whereas one who did not respond to stimulation did not. Only four voxels (2%) within the sensory thalamus were mutually shared among patients; 108 voxels (58%) were uniquely represented. Conclusions DTI-based segmentation of the thalamus can be used to confirm thalamic lead placement relative to the sensory thalamus, and may serve as a useful tool to guide thalamic DBS electrode implantation in the future. PMID:27537848

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.

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.

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.

Nonrigid mammogram registration using mutual information

NASA Astrophysics Data System (ADS)

Wirth, Michael A.; Narhan, Jay; Gray, Derek W. S.

2002-05-01

Of the papers dealing with the task of mammogram registration, the majority deal with the task by matching corresponding control-points derived from anatomical landmark points. One of the caveats encountered when using pure point-matching techniques is their reliance on accurately extracted anatomical features-points. This paper proposes an innovative approach to matching mammograms which combines the use of a similarity-measure and a point-based spatial transformation. Mutual information is a cost-function used to determine the degree of similarity between the two mammograms. An initial rigid registration is performed to remove global differences and bring the mammograms into approximate alignment. The mammograms are then subdivided into smaller regions and each of the corresponding subimages is matched independently using mutual information. The centroids of each of the matched subimages are then used as corresponding control-point pairs in association with the Thin-Plate Spline radial basis function. The resulting spatial transformation generates a nonrigid match of the mammograms. The technique is illustrated by matching mammograms from the MIAS mammogram database. An experimental comparison is made between mutual information incorporating purely rigid behavior, and that incorporating a more nonrigid behavior. The effectiveness of the registration process is evaluated using image differences.

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.

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.

Simultaneous segmentation of the bone and cartilage surfaces of a knee joint in 3D

NASA Astrophysics Data System (ADS)

Yin, Y.; Zhang, X.; Anderson, D. D.; Brown, T. D.; Hofwegen, C. Van; Sonka, M.

2009-02-01

We present a novel framework for the simultaneous segmentation of multiple interacting surfaces belonging to multiple mutually interacting objects. The method is a non-trivial extension of our previously reported optimal multi-surface segmentation. Considering an example application of knee-cartilage segmentation, the framework consists of the following main steps: 1) Shape model construction: Building a mean shape for each bone of the joint (femur, tibia, patella) from interactively segmented volumetric datasets. Using the resulting mean-shape model - identification of cartilage, non-cartilage, and transition areas on the mean-shape bone model surfaces. 2) Presegmentation: Employment of iterative optimal surface detection method to achieve approximate segmentation of individual bone surfaces. 3) Cross-object surface mapping: Detection of inter-bone equidistant separating sheets to help identify corresponding vertex pairs for all interacting surfaces. 4) Multi-object, multi-surface graph construction and final segmentation: Construction of a single multi-bone, multi-surface graph so that two surfaces (bone and cartilage) with zero and non-zero intervening distances can be detected for each bone of the joint, according to whether or not cartilage can be locally absent or present on the bone. To define inter-object relationships, corresponding vertex pairs identified using the separating sheets were interlinked in the graph. The graph optimization algorithm acted on the entire multiobject, multi-surface graph to yield a globally optimal solution. The segmentation framework was tested on 16 MR-DESS knee-joint datasets from the Osteoarthritis Initiative database. The average signed surface positioning error for the 6 detected surfaces ranged from 0.00 to 0.12 mm. When independently initialized, the signed reproducibility error of bone and cartilage segmentation ranged from 0.00 to 0.26 mm. The results showed that this framework provides robust, accurate, and reproducible segmentation of the knee joint bone and cartilage surfaces of the femur, tibia, and patella. As a general segmentation tool, the developed framework can be applied to a broad range of multi-object segmentation problems.

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.

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.

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.

SU-C-BRA-04: Automated Segmentation of Head-And-Neck CT Images for Radiotherapy Treatment Planning Via Multi-Atlas Machine Learning (MAML)

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

Ren, X; Gao, H; Sharp, G

Purpose: Accurate image segmentation is a crucial step during image guided radiation therapy. This work proposes multi-atlas machine learning (MAML) algorithm for automated segmentation of head-and-neck CT images. Methods: As the first step, the algorithm utilizes normalized mutual information as similarity metric, affine registration combined with multiresolution B-Spline registration, and then fuses together using the label fusion strategy via Plastimatch. As the second step, the following feature selection strategy is proposed to extract five feature components from reference or atlas images: intensity (I), distance map (D), box (B), center of gravity (C) and stable point (S). The box feature Bmore » is novel. It describes a relative position from each point to minimum inscribed rectangle of ROI. The center-of-gravity feature C is the 3D Euclidean distance from a sample point to the ROI center of gravity, and then S is the distance of the sample point to the landmarks. Then, we adopt random forest (RF) in Scikit-learn, a Python module integrating a wide range of state-of-the-art machine learning algorithms as classifier. Different feature and atlas strategies are used for different ROIs for improved performance, such as multi-atlas strategy with reference box for brainstem, and single-atlas strategy with reference landmark for optic chiasm. Results: The algorithm was validated on a set of 33 CT images with manual contours using a leave-one-out cross-validation strategy. Dice similarity coefficients between manual contours and automated contours were calculated: the proposed MAML method had an improvement from 0.79 to 0.83 for brainstem and 0.11 to 0.52 for optic chiasm with respect to multi-atlas segmentation method (MA). Conclusion: A MAML method has been proposed for automated segmentation of head-and-neck CT images with improved performance. It provides the comparable result in brainstem and the improved result in optic chiasm compared with MA. Xuhua Ren and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000), and the Shanghai Pujiang Talent Program (#14PJ1404500).« less

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

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...

Tensor-product kernel-based representation encoding joint MRI view similarity.

PubMed

Alvarez-Meza, A; Cardenas-Pena, D; Castro-Ospina, A E; Alvarez, M; Castellanos-Dominguez, G

2014-01-01

To support 3D magnetic resonance image (MRI) analysis, a marginal image similarity (MIS) matrix holding MR inter-slice relationship along every axis view (Axial, Coronal, and Sagittal) can be estimated. However, mutual inference from MIS view information poses a difficult task since relationships between axes are nonlinear. To overcome this issue, we introduce a Tensor-Product Kernel-based Representation (TKR) that allows encoding brain structure patterns due to patient differences, gathering all MIS matrices into a single joint image similarity framework. The TKR training strategy is carried out into a low dimensional projected space to get less influence of voxel-derived noise. Obtained results for classifying the considered patient categories (gender and age) on real MRI database shows that the proposed TKR training approach outperforms the conventional voxel-wise sum of squared differences. The proposed approach may be useful to support MRI clustering and similarity inference tasks, which are required on template-based image segmentation and atlas construction.

Image Information Mining Utilizing Hierarchical Segmentation

NASA Technical Reports Server (NTRS)

Tilton, James C.; Marchisio, Giovanni; Koperski, Krzysztof; Datcu, Mihai

2002-01-01

The Hierarchical Segmentation (HSEG) algorithm is an approach for producing high quality, hierarchically related image segmentations. The VisiMine image information mining system utilizes clustering and segmentation algorithms for reducing visual information in multispectral images to a manageable size. The project discussed herein seeks to enhance the VisiMine system through incorporating hierarchical segmentations from HSEG into the VisiMine system.

Temporal subtraction contrast-enhanced dedicated breast CT

PubMed Central

Gazi, Peymon M.; Aminololama-Shakeri, Shadi; Yang, Kai; Boone, John M.

2016-01-01

Purpose To develop a framework of deformable image registration and segmentation for the purpose of temporal subtraction contrast-enhanced breast CT is described. Methods An iterative histogram-based two-means clustering method was used for the segmentation. Dedicated breast CT images were segmented into background (air), adipose, fibroglandular and skin components. Fibroglandular tissue was classified as either normal or contrast-enhanced then divided into tiers for the purpose of categorizing degrees of contrast enhancement. A variant of the Demons deformable registration algorithm, Intensity Difference Adaptive Demons (IDAD), was developed to correct for the large deformation forces that stemmed from contrast enhancement. In this application, the accuracy of the proposed method was evaluated in both mathematically-simulated and physically-acquired phantom images. Clinical usage and accuracy of the temporal subtraction framework was demonstrated using contrast-enhanced breast CT datasets from five patients. Registration performance was quantified using Normalized Cross Correlation (NCC), Symmetric Uncertainty Coefficient (SUC), Normalized Mutual Information (NMI), Mean Square Error (MSE) and Target Registration Error (TRE). Results The proposed method outperformed conventional affine and other Demons variations in contrast enhanced breast CT image registration. In simulation studies, IDAD exhibited improvement in MSE(0–16%), NCC (0–6%), NMI (0–13%) and TRE (0–34%) compared to the conventional Demons approaches, depending on the size and intensity of the enhancing lesion. As lesion size and contrast enhancement levels increased, so did the improvement. The drop in the correlation between the pre- and post-contrast images for the largest enhancement levels in phantom studies is less than 1.2% (150 Hounsfield units). Registration error, measured by TRE, shows only submillimeter mismatches between the concordant anatomical target points in all patient studies. The algorithm was implemented using a parallel processing architecture resulting in rapid execution time for the iterative segmentation and intensity-adaptive registration techniques. Conclusion Characterization of contrast-enhanced lesions is improved using temporal subtraction contrast-enhanced dedicated breast CT. Adaptation of Demons registration forces as a function of contrast-enhancement levels provided a means to accurately align breast tissue in pre- and post-contrast image acquisitions, improving subtraction results. Spatial subtraction of the aligned images yields useful diagnostic information with respect to enhanced lesion morphology and uptake. PMID:27494376

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.

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

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.

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.

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.

Market Segmentation for Information Services.

ERIC Educational Resources Information Center

Halperin, Michael

1981-01-01

Discusses the advantages and limitations of market segmentation as strategy for the marketing of information services made available by nonprofit organizations, particularly libraries. Market segmentation is defined, a market grid for libraries is described, and the segmentation of information services is outlined. A 16-item reference list is…

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.

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

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.

Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters

PubMed Central

Zhu, Hongchun; Cai, Lijie; Liu, Haiying; Huang, Wei

2016-01-01

Multi-scale image segmentation and the selection of optimal segmentation parameters are the key processes in the object-oriented information extraction of high-resolution remote sensing images. The accuracy of remote sensing special subject information depends on this extraction. On the basis of WorldView-2 high-resolution data, the optimal segmentation parameters methodof object-oriented image segmentation and high-resolution image information extraction, the following processes were conducted in this study. Firstly, the best combination of the bands and weights was determined for the information extraction of high-resolution remote sensing image. An improved weighted mean-variance method was proposed andused to calculatethe optimal segmentation scale. Thereafter, the best shape factor parameter and compact factor parameters were computed with the use of the control variables and the combination of the heterogeneity and homogeneity indexes. Different types of image segmentation parameters were obtained according to the surface features. The high-resolution remote sensing images were multi-scale segmented with the optimal segmentation parameters. Ahierarchical network structure was established by setting the information extraction rules to achieve object-oriented information extraction. This study presents an effective and practical method that can explain expert input judgment by reproducible quantitative measurements. Furthermore the results of this procedure may be incorporated into a classification scheme. PMID:27362762

Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters.

PubMed

Zhu, Hongchun; Cai, Lijie; Liu, Haiying; Huang, Wei

2016-01-01

Multi-scale image segmentation and the selection of optimal segmentation parameters are the key processes in the object-oriented information extraction of high-resolution remote sensing images. The accuracy of remote sensing special subject information depends on this extraction. On the basis of WorldView-2 high-resolution data, the optimal segmentation parameters methodof object-oriented image segmentation and high-resolution image information extraction, the following processes were conducted in this study. Firstly, the best combination of the bands and weights was determined for the information extraction of high-resolution remote sensing image. An improved weighted mean-variance method was proposed andused to calculatethe optimal segmentation scale. Thereafter, the best shape factor parameter and compact factor parameters were computed with the use of the control variables and the combination of the heterogeneity and homogeneity indexes. Different types of image segmentation parameters were obtained according to the surface features. The high-resolution remote sensing images were multi-scale segmented with the optimal segmentation parameters. Ahierarchical network structure was established by setting the information extraction rules to achieve object-oriented information extraction. This study presents an effective and practical method that can explain expert input judgment by reproducible quantitative measurements. Furthermore the results of this procedure may be incorporated into a classification scheme.

Hippocampus segmentation using locally weighted prior based level set

NASA Astrophysics Data System (ADS)

Achuthan, Anusha; Rajeswari, Mandava

2015-12-01

Segmentation of hippocampus in the brain is one of a major challenge in medical image segmentation due to its' imaging characteristics, with almost similar intensity between another adjacent gray matter structure, such as amygdala. The intensity similarity has causes the hippocampus to have weak or fuzzy boundaries. With this main challenge being demonstrated by hippocampus, a segmentation method that relies on image information alone may not produce accurate segmentation results. Therefore, it is needed an assimilation of prior information such as shape and spatial information into existing segmentation method to produce the expected segmentation. Previous studies has widely integrated prior information into segmentation methods. However, the prior information has been utilized through a global manner integration, and this does not reflect the real scenario during clinical delineation. Therefore, in this paper, a locally integrated prior information into a level set model is presented. This work utilizes a mean shape model to provide automatic initialization for level set evolution, and has been integrated as prior information into the level set model. The local integration of edge based information and prior information has been implemented through an edge weighting map that decides at voxel level which information need to be observed during a level set evolution. The edge weighting map shows which corresponding voxels having sufficient edge information. Experiments shows that the proposed integration of prior information locally into a conventional edge-based level set model, known as geodesic active contour has shown improvement of 9% in averaged Dice coefficient.

An improved discriminative filter bank selection approach for motor imagery EEG signal classification using mutual information.

PubMed

Kumar, Shiu; Sharma, Alok; Tsunoda, Tatsuhiko

2017-12-28

Common spatial pattern (CSP) has been an effective technique for feature extraction in electroencephalography (EEG) based brain computer interfaces (BCIs). However, motor imagery EEG signal feature extraction using CSP generally depends on the selection of the frequency bands to a great extent. In this study, we propose a mutual information based frequency band selection approach. The idea of the proposed method is to utilize the information from all the available channels for effectively selecting the most discriminative filter banks. CSP features are extracted from multiple overlapping sub-bands. An additional sub-band has been introduced that cover the wide frequency band (7-30 Hz) and two different types of features are extracted using CSP and common spatio-spectral pattern techniques, respectively. Mutual information is then computed from the extracted features of each of these bands and the top filter banks are selected for further processing. Linear discriminant analysis is applied to the features extracted from each of the filter banks. The scores are fused together, and classification is done using support vector machine. The proposed method is evaluated using BCI Competition III dataset IVa, BCI Competition IV dataset I and BCI Competition IV dataset IIb, and it outperformed all other competing methods achieving the lowest misclassification rate and the highest kappa coefficient on all three datasets. Introducing a wide sub-band and using mutual information for selecting the most discriminative sub-bands, the proposed method shows improvement in motor imagery EEG signal classification.

Stochastic Modeling based on Dictionary Approach for the Generation of Daily Precipitation Occurrences

NASA Astrophysics Data System (ADS)

Panu, U. S.; Ng, W.; Rasmussen, P. F.

2009-12-01

The modeling of weather states (i.e., precipitation occurrences) is critical when the historical data are not long enough for the desired analysis. Stochastic models (e.g., Markov Chain and Alternating Renewal Process (ARP)) of the precipitation occurrence processes generally assume the existence of short-term temporal-dependency between the neighboring states while implying the existence of long-term independency (randomness) of states in precipitation records. Existing temporal-dependent models for the generation of precipitation occurrences are restricted either by the fixed-length memory (e.g., the order of a Markov chain model), or by the reining states in segments (e.g., persistency of homogenous states within dry/wet-spell lengths of an ARP). The modeling of variable segment lengths and states could be an arduous task and a flexible modeling approach is required for the preservation of various segmented patterns of precipitation data series. An innovative Dictionary approach has been developed in the field of genome pattern recognition for the identification of frequently occurring genome segments in DNA sequences. The genome segments delineate the biologically meaningful ``words" (i.e., segments with a specific patterns in a series of discrete states) that can be jointly modeled with variable lengths and states. A meaningful “word”, in hydrology, can be referred to a segment of precipitation occurrence comprising of wet or dry states. Such flexibility would provide a unique advantage over the traditional stochastic models for the generation of precipitation occurrences. Three stochastic models, namely, the alternating renewal process using Geometric distribution, the second-order Markov chain model, and the Dictionary approach have been assessed to evaluate their efficacy for the generation of daily precipitation sequences. Comparisons involved three guiding principles namely (i) the ability of models to preserve the short-term temporal-dependency in data through the concepts of autocorrelation, average mutual information, and Hurst exponent, (ii) the ability of models to preserve the persistency within the homogenous dry/wet weather states through analysis of dry/wet-spell lengths between the observed and generated data, and (iii) the ability to assesses the goodness-of-fit of models through the likelihood estimates (i.e., AIC and BIC). Past 30 years of observed daily precipitation records from 10 Canadian meteorological stations were utilized for comparative analyses of the three models. In general, the Markov chain model performed well. The remainders of the models were found to be competitive from one another depending upon the scope and purpose of the comparison. Although the Markov chain model has a certain advantage in the generation of daily precipitation occurrences, the structural flexibility offered by the Dictionary approach in modeling the varied segment lengths of heterogeneous weather states provides a distinct and powerful advantage in the generation of precipitation sequences.

Speaker Segmentation and Clustering Using Gender Information

DTIC Science & Technology

2006-02-01

used in the first stages of segmentation forder information in the clustering of the opposite-gender speaker diarization of news broadcasts. files, the...AFRL-HE-WP-TP-2006-0026 AIR FORCE RESEARCH LABORATORY Speaker Segmentation and Clustering Using Gender Information Brian M. Ore General Dynamics...COVERED (From - To) February 2006 ProceedinLgs 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Speaker Segmentation and Clustering Using Gender Information 5b

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.

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.

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.

Feature Selection for Chemical Sensor Arrays Using Mutual Information

PubMed Central

Wang, X. Rosalind; Lizier, Joseph T.; Nowotny, Thomas; Berna, Amalia Z.; Prokopenko, Mikhail; Trowell, Stephen C.

2014-01-01

We address the problem of feature selection for classifying a diverse set of chemicals using an array of metal oxide sensors. Our aim is to evaluate a filter approach to feature selection with reference to previous work, which used a wrapper approach on the same data set, and established best features and upper bounds on classification performance. We selected feature sets that exhibit the maximal mutual information with the identity of the chemicals. The selected features closely match those found to perform well in the previous study using a wrapper approach to conduct an exhaustive search of all permitted feature combinations. By comparing the classification performance of support vector machines (using features selected by mutual information) with the performance observed in the previous study, we found that while our approach does not always give the maximum possible classification performance, it always selects features that achieve classification performance approaching the optimum obtained by exhaustive search. We performed further classification using the selected feature set with some common classifiers and found that, for the selected features, Bayesian Networks gave the best performance. Finally, we compared the observed classification performances with the performance of classifiers using randomly selected features. We found that the selected features consistently outperformed randomly selected features for all tested classifiers. The mutual information filter approach is therefore a computationally efficient method for selecting near optimal features for chemical sensor arrays. PMID:24595058

Universal partitioning of the hierarchical fold network of 50-residue segments in proteins

PubMed Central

Ito, Jun-ichi; Sonobe, Yuki; Ikeda, Kazuyoshi; Tomii, Kentaro; Higo, Junichi

2009-01-01

Background Several studies have demonstrated that protein fold space is structured hierarchically and that power-law statistics are satisfied in relation between the numbers of protein families and protein folds (or superfamilies). We examined the internal structure and statistics in the fold space of 50 amino-acid residue segments taken from various protein folds. We used inter-residue contact patterns to measure the tertiary structural similarity among segments. Using this similarity measure, the segments were classified into a number (Kc) of clusters. We examined various Kc values for the clustering. The special resolution to differentiate the segment tertiary structures increases with increasing Kc. Furthermore, we constructed networks by linking structurally similar clusters. Results The network was partitioned persistently into four regions for Kc ≥ 1000. This main partitioning is consistent with results of earlier studies, where similar partitioning was reported in classifying protein domain structures. Furthermore, the network was partitioned naturally into several dozens of sub-networks (i.e., communities). Therefore, intra-sub-network clusters were mutually connected with numerous links, although inter-sub-network ones were rarely done with few links. For Kc ≥ 1000, the major sub-networks were about 40; the contents of the major sub-networks were conserved. This sub-partitioning is a novel finding, suggesting that the network is structured hierarchically: Segments construct a cluster, clusters form a sub-network, and sub-networks constitute a region. Additionally, the network was characterized by non-power-law statistics, which is also a novel finding. Conclusion Main findings are: (1) The universe of 50 residue segments found here was characterized by non-power-law statistics. Therefore, the universe differs from those ever reported for the protein domains. (2) The 50-residue segments were partitioned persistently and universally into some dozens (ca. 40) of major sub-networks, irrespective of the number of clusters. (3) These major sub-networks encompassed 90% of all segments. Consequently, the protein tertiary structure is constructed using the dozens of elements (sub-networks). PMID:19454039

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...

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%.

Corpus callosum segmentation using deep neural networks with prior information from multi-atlas images

NASA Astrophysics Data System (ADS)

Park, Gilsoon; Hong, Jinwoo; Lee, Jong-Min

2018-03-01

In human brain, Corpus Callosum (CC) is the largest white matter structure, connecting between right and left hemispheres. Structural features such as shape and size of CC in midsagittal plane are of great significance for analyzing various neurological diseases, for example Alzheimer's disease, autism and epilepsy. For quantitative and qualitative studies of CC in brain MR images, robust segmentation of CC is important. In this paper, we present a novel method for CC segmentation. Our approach is based on deep neural networks and the prior information generated from multi-atlas images. Deep neural networks have recently shown good performance in various image processing field. Convolutional neural networks (CNN) have shown outstanding performance for classification and segmentation in medical image fields. We used convolutional neural networks for CC segmentation. Multi-atlas based segmentation model have been widely used in medical image segmentation because atlas has powerful information about the target structure we want to segment, consisting of MR images and corresponding manual segmentation of the target structure. We combined the prior information, such as location and intensity distribution of target structure (i.e. CC), made from multi-atlas images in CNN training process for more improving training. The CNN with prior information showed better segmentation performance than without.

Atlas-based system for functional neurosurgery

NASA Astrophysics Data System (ADS)

Nowinski, Wieslaw L.; Yeo, Tseng T.; Yang, Guo L.; Dow, Douglas E.

1997-05-01

This paper addresses the development of an atlas-based system for preoperative functional neurosurgery planning and training, intraoperative support and postoperative analysis. The system is based on Atlas of Stereotaxy of the Human Brain by Schaltenbrand and Wahren used for interactive segmentation and labeling of clinical data in 2D/3D, and for assisting stereotactic targeting. The atlas microseries are digitized, enhanced, segmented, labeled, aligned and organized into mutually preregistered atlas volumes 3D models of the structures are also constructed. The atlas may be interactively registered with the actual patient's data. Several other features are also provided including data reformatting, visualization, navigation, mensuration, and stereotactic path display and editing in 2D/3D. The system increases the accuracy of target definition, reduces the time of planning and time of the procedure itself. It also constitutes a research platform for the construction of more advanced neurosurgery supporting tools and brain atlases.

Partially segmented deformable mirror

DOEpatents

Bliss, E.S.; Smith, J.R.; Salmon, J.T.; Monjes, J.A.

1991-05-21

A partially segmented deformable mirror is formed with a mirror plate having a smooth and continuous front surface and a plurality of actuators to its back surface. The back surface is divided into triangular areas which are mutually separated by grooves. The grooves are deep enough to make the plate deformable and the actuators for displacing the mirror plate in the direction normal to its surface are inserted in the grooves at the vertices of the triangular areas. Each actuator includes a transducer supported by a receptacle with outer shells having outer surfaces. The vertices have inner walls which are approximately perpendicular to the mirror surface and make planar contacts with the outer surfaces of the outer shells. The adhesive which is used on these contact surfaces tends to contract when it dries but the outer shells can bend and serve to minimize the tendency of the mirror to warp. 5 figures.

Partially segmented deformable mirror

DOEpatents

Bliss, Erlan S.; Smith, James R.; Salmon, J. Thaddeus; Monjes, Julio A.

1991-01-01

A partially segmented deformable mirror is formed with a mirror plate having a smooth and continuous front surface and a plurality of actuators to its back surface. The back surface is divided into triangular areas which are mutually separated by grooves. The grooves are deep enough to make the plate deformable and the actuators for displacing the mirror plate in the direction normal to its surface are inserted in the grooves at the vertices of the triangular areas. Each actuator includes a transducer supported by a receptacle with outer shells having outer surfaces. The vertices have inner walls which are approximately perpendicular to the mirror surface and make planar contacts with the outer surfaces of the outer shells. The adhesive which is used on these contact surfaces tends to contract when it dries but the outer shells can bend and serve to minimize the tendency of the mirror to warp.

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.

Mutual information based feature selection for medical image retrieval

NASA Astrophysics Data System (ADS)

Zhi, Lijia; Zhang, Shaomin; Li, Yan

2018-04-01

In this paper, authors propose a mutual information based method for lung CT image retrieval. This method is designed to adapt to different datasets and different retrieval task. For practical applying consideration, this method avoids using a large amount of training data. Instead, with a well-designed training process and robust fundamental features and measurements, the method in this paper can get promising performance and maintain economic training computation. Experimental results show that the method has potential practical values for clinical routine application.

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.

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.

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.

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.

Surfing the implicit wave.

PubMed

Shea, C H; Wulf, G; Whitacre, C A; Park, J H

2001-08-01

Implicit learning was investigated in two experiments involving a complex motor task. Participants were required to balance on a stabilometer and to move the platform on which they were standing to match a constantly changing target position. Experiment 1 examined whether a segment (middle third) that was repeated on each trial would be learned without participants becoming aware of the repetitions (i.e., implicitly). The purpose of Experiment 2 was to determine the relative effectiveness of explicit versus implicit learning. Here, two identical segments were presented on each trial (first and last thirds), with participants only being informed that one segment (either first or last) was repeated. The acquisition results from both experiments indicated large improvements in performance across 4 days of practice, with performance on the repeated segments being generally superior to that on the non-repeated segment. On the retention tests on Day 5, errors on the repeated segment(s) were smaller than those on the random segment(s). Furthermore, in Experiment 2, the errors on the repeated-known segment, although smaller than those on the random segment, were larger than those on the repeated-unknown segment. Interview results indicated that participants were not consciously aware that a segment was repeated unless they were informed. These results suggest that implicit learning can occur for relatively complex motor tasks and that withholding information concerning the regularities is more beneficial than providing this information.

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.

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.

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…

TU-G-BRA-05: Predicting Volume Change of the Tumor and Critical Structures Throughout Radiation Therapy by CT-CBCT Registration with Local Intensity Correction

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

Park, S; Robinson, A; Kiess, A

2015-06-15

Purpose: The purpose of this study is to develop an accurate and effective technique to predict and monitor volume changes of the tumor and organs at risk (OARs) from daily cone-beam CTs (CBCTs). Methods: While CBCT is typically used to minimize the patient setup error, its poor image quality impedes accurate monitoring of daily anatomical changes in radiotherapy. Reconstruction artifacts in CBCT often cause undesirable errors in registration-based contour propagation from the planning CT, a conventional way to estimate anatomical changes. To improve the registration and segmentation accuracy, we developed a new deformable image registration (DIR) that iteratively corrects CBCTmore » intensities using slice-based histogram matching during the registration process. Three popular DIR algorithms (hierarchical B-spline, demons, optical flow) augmented by the intensity correction were implemented on a graphics processing unit for efficient computation, and their performances were evaluated on six head and neck (HN) cancer cases. Four trained scientists manually contoured nodal gross tumor volume (GTV) on the planning CT and every other fraction CBCTs for each case, to which the propagated GTV contours by DIR were compared. The performance was also compared with commercial software, VelocityAI (Varian Medical Systems Inc.). Results: Manual contouring showed significant variations, [-76, +141]% from the mean of all four sets of contours. The volume differences (mean±std in cc) between the average manual segmentation and four automatic segmentations are 3.70±2.30(B-spline), 1.25±1.78(demons), 0.93±1.14(optical flow), and 4.39±3.86 (VelocityAI). In comparison to the average volume of the manual segmentations, the proposed approach significantly reduced the estimation error by 9%(B-spline), 38%(demons), and 51%(optical flow) over the conventional mutual information based method (VelocityAI). Conclusion: The proposed CT-CBCT registration with local CBCT intensity correction can accurately predict the tumor volume change with reduced errors. Although demonstrated only on HN nodal GTVs, the results imply improved accuracy for other critical structures. This work was supported by NIH/NCI under grant R42CA137886.« less

TU-AB-303-08: GPU-Based Software Platform for Efficient Image-Guided Adaptive Radiation Therapy

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

Park, S; Robinson, A; McNutt, T

2015-06-15

Purpose: In this study, we develop an integrated software platform for adaptive radiation therapy (ART) that combines fast and accurate image registration, segmentation, and dose computation/accumulation methods. Methods: The proposed system consists of three key components; 1) deformable image registration (DIR), 2) automatic segmentation, and 3) dose computation/accumulation. The computationally intensive modules including DIR and dose computation have been implemented on a graphics processing unit (GPU). All required patient-specific data including the planning CT (pCT) with contours, daily cone-beam CTs, and treatment plan are automatically queried and retrieved from their own databases. To improve the accuracy of DIR between pCTmore » and CBCTs, we use the double force demons DIR algorithm in combination with iterative CBCT intensity correction by local intensity histogram matching. Segmentation of daily CBCT is then obtained by propagating contours from the pCT. Daily dose delivered to the patient is computed on the registered pCT by a GPU-accelerated superposition/convolution algorithm. Finally, computed daily doses are accumulated to show the total delivered dose to date. Results: Since the accuracy of DIR critically affects the quality of the other processes, we first evaluated our DIR method on eight head-and-neck cancer cases and compared its performance. Normalized mutual-information (NMI) and normalized cross-correlation (NCC) computed as similarity measures, and our method produced overall NMI of 0.663 and NCC of 0.987, outperforming conventional methods by 3.8% and 1.9%, respectively. Experimental results show that our registration method is more consistent and roust than existing algorithms, and also computationally efficient. Computation time at each fraction took around one minute (30–50 seconds for registration and 15–25 seconds for dose computation). Conclusion: We developed an integrated GPU-accelerated software platform that enables accurate and efficient DIR, auto-segmentation, and dose computation, thus supporting an efficient ART workflow. This work was supported by NIH/NCI under grant R42CA137886.« less

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.

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.

The use of phrase-level prosodic information in lexical segmentation: evidence from word-spotting experiments in Korean.

PubMed

Kim, Sahyang; Cho, Taehong

2009-05-01

This study investigated the role of phrase-level prosodic boundary information in word segmentation in Korean with two word-spotting experiments. In experiment 1, it was found that intonational cues alone helped listeners with lexical segmentation. Listeners paid more attention to local intonational cues (...H#L...) across the prosodic boundary than the intonational information within a prosodic phrase. The results imply that intonation patterns with high frequency are used, though not exclusively, in lexical segmentation. In experiment 2, final lengthening was added to see how multiple prosodic cues influence lexical segmentation. The results showed that listeners did not necessarily benefit from the presence of both intonational and final lengthening cues: Their performance was improved only when intonational information contained infrequent tonal patterns for boundary marking, showing only partially cumulative effects of prosodic cues. When the intonational information was optimal (frequent) for boundary marking, however, poorer performance was observed with final lengthening. This is arguably because the phrase-initial segmental allophonic cues for the accentual phrase were not matched with the prosodic cues for the intonational phrase. It is proposed that the asymmetrical use of multiple cues was due to interaction between prosodic and segmental information that are computed in parallel in lexical segmentation.

3D deeply supervised network for automated segmentation of volumetric medical images.

PubMed

Dou, Qi; Yu, Lequan; Chen, Hao; Jin, Yueming; Yang, Xin; Qin, Jing; Heng, Pheng-Ann

2017-10-01

While deep convolutional neural networks (CNNs) have achieved remarkable success in 2D medical image segmentation, it is still a difficult task for CNNs to segment important organs or structures from 3D medical images owing to several mutually affected challenges, including the complicated anatomical environments in volumetric images, optimization difficulties of 3D networks and inadequacy of training samples. In this paper, we present a novel and efficient 3D fully convolutional network equipped with a 3D deep supervision mechanism to comprehensively address these challenges; we call it 3D DSN. Our proposed 3D DSN is capable of conducting volume-to-volume learning and inference, which can eliminate redundant computations and alleviate the risk of over-fitting on limited training data. More importantly, the 3D deep supervision mechanism can effectively cope with the optimization problem of gradients vanishing or exploding when training a 3D deep model, accelerating the convergence speed and simultaneously improving the discrimination capability. Such a mechanism is developed by deriving an objective function that directly guides the training of both lower and upper layers in the network, so that the adverse effects of unstable gradient changes can be counteracted during the training procedure. We also employ a fully connected conditional random field model as a post-processing step to refine the segmentation results. We have extensively validated the proposed 3D DSN on two typical yet challenging volumetric medical image segmentation tasks: (i) liver segmentation from 3D CT scans and (ii) whole heart and great vessels segmentation from 3D MR images, by participating two grand challenges held in conjunction with MICCAI. We have achieved competitive segmentation results to state-of-the-art approaches in both challenges with a much faster speed, corroborating the effectiveness of our proposed 3D DSN. Copyright © 2017 Elsevier B.V. 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

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.

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.

Entanglement measures based on observable correlations

NASA Astrophysics Data System (ADS)

Luo, Shunlong

2008-06-01

By regarding quantum states as communication channels and using observable correlations quantitatively expressed by mutual information, we introduce a hierarchy of entanglement measures that includes the entanglement of formation as a particular instance. We compare the maximal and minimal measures and indicate the conceptual advantages of the minimal measure over the entanglement of formation. We reveal a curious feature of the entanglement of formation by showing that it can exceed the quantum mutual information, which is usually regarded as a theoretical measure of total correlations. This places the entanglement of formation in a broader scenario, highlights its peculiarity in relation to pure-state ensembles, and introduces a competing definition with intrinsic informational significance.

Cross-Level Effects Between Neurophysiology and Communication During Team Training.

PubMed

Gorman, Jamie C; Martin, Melanie J; Dunbar, Terri A; Stevens, Ronald H; Galloway, Trysha L; Amazeen, Polemnia G; Likens, Aaron D

2016-02-01

We investigated cross-level effects, which are concurrent changes across neural and cognitive-behavioral levels of analysis as teams interact, between neurophysiology and team communication variables under variations in team training. When people work together as a team, they develop neural, cognitive, and behavioral patterns that they would not develop individually. It is currently unknown whether these patterns are associated with each other in the form of cross-level effects. Team-level neurophysiology and latent semantic analysis communication data were collected from submarine teams in a training simulation. We analyzed whether (a) both neural and communication variables change together in response to changes in training segments (briefing, scenario, or debriefing), (b) neural and communication variables mutually discriminate teams of different experience levels, and (c) peak cross-correlations between neural and communication variables identify how the levels are linked. Changes in training segment led to changes in both neural and communication variables, neural and communication variables mutually discriminated between teams of different experience levels, and peak cross-correlations indicated that changes in communication precede changes in neural patterns in more experienced teams. Cross-level effects suggest that teamwork is not reducible to a fundamental level of analysis and that training effects are spread out across neural and cognitive-behavioral levels of analysis. Cross-level effects are important to consider for theories of team performance and practical aspects of team training. Cross-level effects suggest that measurements could be taken at one level (e.g., neural) to assess team experience (or skill) on another level (e.g., cognitive-behavioral). © 2015, Human Factors and Ergonomics Society.

Burnout and demographic characteristics of workers experiencing different types of work-home interaction.

PubMed

Merecz, Dorota; Andysz, Aleksandra

2014-12-01

The purpose of this study was to explore configurations of positive versus negative interactions between work and home (WHI) and their relation to burnout and demographic characteristics. Sample of 533 Polish workers were interviewed by means of self-administered questionnaires (SWING and MBI-GS). Demographic and work characteristics were also controlled. Cluster analysis distinguished 5 types of WHIs: positive WHI (18%), negative WHI (15.9%), no interaction (29.3%), mutual positive interactions (15.4%) and positive HWI (21.4%). The quality of WHI was associated with number of work hours and tenure at main place of employment. The effect of gender on the quality of work-home interaction was not significant. Configuration of WHIs affected the level of burnout. Again, there was no significant difference between men and women in terms of burnout and its sub-dimensions. The least burned-out were people from positive WHI, positive HWI and mutual positive interaction groups. The most burned-out were people who experienced negative WHI the most often. In this group, predominance of men working more than 10 h per day was observed. The majority of study group (71%) experienced rather integration than segmentation of both spheres. Our results suggest that segmentation is not an universal and effective strategy of coping with work and home demands - it may prevent the positive home-work spillover, which can be buffer or remedy against stress or burnout. We consider cluster analysis the appropriate method in research on relation to work-family balance issue, which may be useful in unraveling relationships between this phenomenon and attitudes and behaviors.

Automatic metastatic brain tumor segmentation for stereotactic radiosurgery applications.

PubMed

Liu, Yan; Stojadinovic, Strahinja; Hrycushko, Brian; Wardak, Zabi; Lu, Weiguo; Yan, Yulong; Jiang, Steve B; Timmerman, Robert; Abdulrahman, Ramzi; Nedzi, Lucien; Gu, Xuejun

2016-12-21

The objective of this study is to develop an automatic segmentation strategy for efficient and accurate metastatic brain tumor delineation on contrast-enhanced T1-weighted (T1c) magnetic resonance images (MRI) for stereotactic radiosurgery (SRS) applications. The proposed four-step automatic brain metastases segmentation strategy is comprised of pre-processing, initial contouring, contour evolution, and contour triage. First, T1c brain images are preprocessed to remove the skull. Second, an initial tumor contour is created using a multi-scaled adaptive threshold-based bounding box and a super-voxel clustering technique. Third, the initial contours are evolved to the tumor boundary using a regional active contour technique. Fourth, all detected false-positive contours are removed with geometric characterization. The segmentation process was validated on a realistic virtual phantom containing Gaussian or Rician noise. For each type of noise distribution, five different noise levels were tested. Twenty-one cases from the multimodal brain tumor image segmentation (BRATS) challenge dataset and fifteen clinical metastases cases were also included in validation. Segmentation performance was quantified by the Dice coefficient (DC), normalized mutual information (NMI), structural similarity (SSIM), Hausdorff distance (HD), mean value of surface-to-surface distance (MSSD) and standard deviation of surface-to-surface distance (SDSSD). In the numerical phantom study, the evaluation yielded a DC of 0.98  ±  0.01, an NMI of 0.97  ±  0.01, an SSIM of 0.999  ±  0.001, an HD of 2.2  ±  0.8 mm, an MSSD of 0.1  ±  0.1 mm, and an SDSSD of 0.3  ±  0.1 mm. The validation on the BRATS data resulted in a DC of 0.89  ±  0.08, which outperform the BRATS challenge algorithms. Evaluation on clinical datasets gave a DC of 0.86  ±  0.09, an NMI of 0.80  ±  0.11, an SSIM of 0.999  ±  0.001, an HD of 8.8  ±  12.6 mm, an MSSD of 1.5  ±  3.2 mm, and an SDSSD of 1.8  ±  3.4 mm when comparing to the physician drawn ground truth. The result indicated that the developed automatic segmentation strategy yielded accurate brain tumor delineation and presented as a useful clinical tool for SRS applications.

Automatic metastatic brain tumor segmentation for stereotactic radiosurgery applications

NASA Astrophysics Data System (ADS)

Liu, Yan; Stojadinovic, Strahinja; Hrycushko, Brian; Wardak, Zabi; Lu, Weiguo; Yan, Yulong; Jiang, Steve B.; Timmerman, Robert; Abdulrahman, Ramzi; Nedzi, Lucien; Gu, Xuejun

2016-12-01

The objective of this study is to develop an automatic segmentation strategy for efficient and accurate metastatic brain tumor delineation on contrast-enhanced T1-weighted (T1c) magnetic resonance images (MRI) for stereotactic radiosurgery (SRS) applications. The proposed four-step automatic brain metastases segmentation strategy is comprised of pre-processing, initial contouring, contour evolution, and contour triage. First, T1c brain images are preprocessed to remove the skull. Second, an initial tumor contour is created using a multi-scaled adaptive threshold-based bounding box and a super-voxel clustering technique. Third, the initial contours are evolved to the tumor boundary using a regional active contour technique. Fourth, all detected false-positive contours are removed with geometric characterization. The segmentation process was validated on a realistic virtual phantom containing Gaussian or Rician noise. For each type of noise distribution, five different noise levels were tested. Twenty-one cases from the multimodal brain tumor image segmentation (BRATS) challenge dataset and fifteen clinical metastases cases were also included in validation. Segmentation performance was quantified by the Dice coefficient (DC), normalized mutual information (NMI), structural similarity (SSIM), Hausdorff distance (HD), mean value of surface-to-surface distance (MSSD) and standard deviation of surface-to-surface distance (SDSSD). In the numerical phantom study, the evaluation yielded a DC of 0.98  ±  0.01, an NMI of 0.97  ±  0.01, an SSIM of 0.999  ±  0.001, an HD of 2.2  ±  0.8 mm, an MSSD of 0.1  ±  0.1 mm, and an SDSSD of 0.3  ±  0.1 mm. The validation on the BRATS data resulted in a DC of 0.89  ±  0.08, which outperform the BRATS challenge algorithms. Evaluation on clinical datasets gave a DC of 0.86  ±  0.09, an NMI of 0.80  ±  0.11, an SSIM of 0.999  ±  0.001, an HD of 8.8  ±  12.6 mm, an MSSD of 1.5  ±  3.2 mm, and an SDSSD of 1.8  ±  3.4 mm when comparing to the physician drawn ground truth. The result indicated that the developed automatic segmentation strategy yielded accurate brain tumor delineation and presented as a useful clinical tool for SRS applications.

Chinese and American Women: Issues of Mutual Concern. Wingspread Brief.

ERIC Educational Resources Information Center

Johnson Foundation, Inc., Racine, WI.

This article briefly describes a conference of Chinese and American women held to discuss womens' issues and promote mutual understanding between the two groups. The cultural exchange of information at the conference focused on discussion of the All China Womens' Federation (ACWF); the roles of women in China and the United States in the areas of…

Children's Use of Mutual Exclusivity to Learn Labels for Parts of Objects

ERIC Educational Resources Information Center

Hansen, Mikkel B.; Markman, Ellen M.

2009-01-01

When teaching children part terms, adults frequently outline the relevant part rather than simply point. This pragmatic information very likely helps children interpret the label correctly. But the importance of gestures may not negate the need for default lexical biases such as the whole object assumption and mutual exclusivity. On this view,…

Study on the application of MRF and the D-S theory to image segmentation of the human brain and quantitative analysis of the brain tissue

NASA Astrophysics Data System (ADS)

Guan, Yihong; Luo, Yatao; Yang, Tao; Qiu, Lei; Li, Junchang

2012-01-01

The features of the spatial information of Markov random field image was used in image segmentation. It can effectively remove the noise, and get a more accurate segmentation results. Based on the fuzziness and clustering of pixel grayscale information, we find clustering center of the medical image different organizations and background through Fuzzy cmeans clustering method. Then we find each threshold point of multi-threshold segmentation through two dimensional histogram method, and segment it. The features of fusing multivariate information based on the Dempster-Shafer evidence theory, getting image fusion and segmentation. This paper will adopt the above three theories to propose a new human brain image segmentation method. Experimental result shows that the segmentation result is more in line with human vision, and is of vital significance to accurate analysis and application of tissues.

Reply to ``Comment on `Performance of different synchronization measures in real data: A case study on electroencephalographic signals' ''

NASA Astrophysics Data System (ADS)

Quian Quiroga, R.; Kraskov, A.; Kreuz, T.; Grassberger, P.

2003-06-01

We agree with the Comment by Duckrow and Albano [Phys. Rev. E 67, 063901 (2003)] that mutual information, estimated with an optimized algorithm, can be a useful tool for studying synchronization in real data. However, we point out that the improvement they found is mainly due to an interesting but nonstandard embedding technique used, and not so much due to the algorithm used for the estimation of mutual information itself. We also address the issue of stationarity of electroencephalographic (EEG) data.

The use of atlas registration and graph cuts for prostate segmentation in magnetic resonance images

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

Korsager, Anne Sofie, E-mail: asko@hst.aau.dk; Østergaard, Lasse Riis; Fortunati, Valerio

2015-04-15

Purpose: An automatic method for 3D prostate segmentation in magnetic resonance (MR) images is presented for planning image-guided radiotherapy treatment of prostate cancer. Methods: A spatial prior based on intersubject atlas registration is combined with organ-specific intensity information in a graph cut segmentation framework. The segmentation is tested on 67 axial T{sub 2}-weighted MR images in a leave-one-out cross validation experiment and compared with both manual reference segmentations and with multiatlas-based segmentations using majority voting atlas fusion. The impact of atlas selection is investigated in both the traditional atlas-based segmentation and the new graph cut method that combines atlas andmore » intensity information in order to improve the segmentation accuracy. Best results were achieved using the method that combines intensity information, shape information, and atlas selection in the graph cut framework. Results: A mean Dice similarity coefficient (DSC) of 0.88 and a mean surface distance (MSD) of 1.45 mm with respect to the manual delineation were achieved. Conclusions: This approaches the interobserver DSC of 0.90 and interobserver MSD 0f 1.15 mm and is comparable to other studies performing prostate segmentation in MR.« less

Heat engine driven by purely quantum information.

PubMed

Park, Jung Jun; Kim, Kang-Hwan; Sagawa, Takahiro; Kim, Sang Wook

2013-12-06

The key question of this Letter is whether work can be extracted from a heat engine by using purely quantum mechanical information. If the answer is yes, what is its mathematical formula? First, by using a bipartite memory we show that the work extractable from a heat engine is bounded not only by the free energy change and the sum of the entropy change of an individual memory but also by the change of quantum mutual information contained inside the memory. We then find that the engine can be driven by purely quantum information, expressed as the so-called quantum discord, forming a part of the quantum mutual information. To confirm it, as a physical example we present the Szilard engine containing a diatomic molecule with a semipermeable wall.

Universal recovery map for approximate Markov chains.

PubMed

Sutter, David; Fawzi, Omar; Renner, Renato

2016-02-01

A central question in quantum information theory is to determine how well lost information can be reconstructed. Crucially, the corresponding recovery operation should perform well without knowing the information to be reconstructed. In this work, we show that the quantum conditional mutual information measures the performance of such recovery operations. More precisely, we prove that the conditional mutual information I ( A : C | B ) of a tripartite quantum state ρ ABC can be bounded from below by its distance to the closest recovered state [Formula: see text], where the C -part is reconstructed from the B -part only and the recovery map [Formula: see text] merely depends on ρ BC . One particular application of this result implies the equivalence between two different approaches to define topological order in quantum systems.

Universal recovery map for approximate Markov chains

PubMed Central

Sutter, David; Fawzi, Omar; Renner, Renato

2016-01-01

A central question in quantum information theory is to determine how well lost information can be reconstructed. Crucially, the corresponding recovery operation should perform well without knowing the information to be reconstructed. In this work, we show that the quantum conditional mutual information measures the performance of such recovery operations. More precisely, we prove that the conditional mutual information I(A:C|B) of a tripartite quantum state ρABC can be bounded from below by its distance to the closest recovered state RB→BC(ρAB), where the C-part is reconstructed from the B-part only and the recovery map RB→BC merely depends on ρBC. One particular application of this result implies the equivalence between two different approaches to define topological order in quantum systems. PMID:27118889

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

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.

Contextually guided very-high-resolution imagery classification with semantic segments

NASA Astrophysics Data System (ADS)

Zhao, Wenzhi; Du, Shihong; Wang, Qiao; Emery, William J.

2017-10-01

Contextual information, revealing relationships and dependencies between image objects, is one of the most important information for the successful interpretation of very-high-resolution (VHR) remote sensing imagery. Over the last decade, geographic object-based image analysis (GEOBIA) technique has been widely used to first divide images into homogeneous parts, and then to assign semantic labels according to the properties of image segments. However, due to the complexity and heterogeneity of VHR images, segments without semantic labels (i.e., semantic-free segments) generated with low-level features often fail to represent geographic entities (such as building roofs usually be partitioned into chimney/antenna/shadow parts). As a result, it is hard to capture contextual information across geographic entities when using semantic-free segments. In contrast to low-level features, "deep" features can be used to build robust segments with accurate labels (i.e., semantic segments) in order to represent geographic entities at higher levels. Based on these semantic segments, semantic graphs can be constructed to capture contextual information in VHR images. In this paper, semantic segments were first explored with convolutional neural networks (CNN) and a conditional random field (CRF) model was then applied to model the contextual information between semantic segments. Experimental results on two challenging VHR datasets (i.e., the Vaihingen and Beijing scenes) indicate that the proposed method is an improvement over existing image classification techniques in classification performance (overall accuracy ranges from 82% to 96%).

Optimal protocol for maximum work extraction in a feedback process with a time-varying potential

NASA Astrophysics Data System (ADS)

Kwon, Chulan

2017-12-01

The nonequilibrium nature of information thermodynamics is characterized by the inequality or non-negativity of the total entropy change of the system, memory, and reservoir. Mutual information change plays a crucial role in the inequality, in particular if work is extracted and the paradox of Maxwell's demon is raised. We consider the Brownian information engine where the protocol set of the harmonic potential is initially chosen by the measurement and varies in time. We confirm the inequality of the total entropy change by calculating, in detail, the entropic terms including the mutual information change. We rigorously find the optimal values of the time-dependent protocol for maximum extraction of work both for the finite-time and the quasi-static process.

Novel active contour model based on multi-variate local Gaussian distribution for local segmentation of MR brain images

NASA Astrophysics Data System (ADS)

Zheng, Qiang; Li, Honglun; Fan, Baode; Wu, Shuanhu; Xu, Jindong

2017-12-01

Active contour model (ACM) has been one of the most widely utilized methods in magnetic resonance (MR) brain image segmentation because of its ability of capturing topology changes. However, most of the existing ACMs only consider single-slice information in MR brain image data, i.e., the information used in ACMs based segmentation method is extracted only from one slice of MR brain image, which cannot take full advantage of the adjacent slice images' information, and cannot satisfy the local segmentation of MR brain images. In this paper, a novel ACM is proposed to solve the problem discussed above, which is based on multi-variate local Gaussian distribution and combines the adjacent slice images' information in MR brain image data to satisfy segmentation. The segmentation is finally achieved through maximizing the likelihood estimation. Experiments demonstrate the advantages of the proposed ACM over the single-slice ACM in local segmentation of MR brain image series.

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.

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.

Computing algebraic transfer entropy and coupling directions via transcripts

NASA Astrophysics Data System (ADS)

Amigó, José M.; Monetti, Roberto; Graff, Beata; Graff, Grzegorz

2016-11-01

Most random processes studied in nonlinear time series analysis take values on sets endowed with a group structure, e.g., the real and rational numbers, and the integers. This fact allows to associate with each pair of group elements a third element, called their transcript, which is defined as the product of the second element in the pair times the first one. The transfer entropy of two such processes is called algebraic transfer entropy. It measures the information transferred between two coupled processes whose values belong to a group. In this paper, we show that, subject to one constraint, the algebraic transfer entropy matches the (in general, conditional) mutual information of certain transcripts with one variable less. This property has interesting practical applications, especially to the analysis of short time series. We also derive weak conditions for the 3-dimensional algebraic transfer entropy to yield the same coupling direction as the corresponding mutual information of transcripts. A related issue concerns the use of mutual information of transcripts to determine coupling directions in cases where the conditions just mentioned are not fulfilled. We checked the latter possibility in the lowest dimensional case with numerical simulations and cardiovascular data, and obtained positive results.

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.

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.

Dendritic excitability modulates dendritic information processing in a purkinje cell model.

PubMed

Coop, Allan D; Cornelis, Hugo; Santamaria, Fidel

2010-01-01

Using an electrophysiological compartmental model of a Purkinje cell we quantified the contribution of individual active dendritic currents to processing of synaptic activity from granule cells. We used mutual information as a measure to quantify the information from the total excitatory input current (I(Glu)) encoded in each dendritic current. In this context, each active current was considered an information channel. Our analyses showed that most of the information was encoded by the calcium (I(CaP)) and calcium activated potassium (I(Kc)) currents. Mutual information between I(Glu) and I(CaP) and I(Kc) was sensitive to different levels of excitatory and inhibitory synaptic activity that, at the same time, resulted in the same firing rate at the soma. Since dendritic excitability could be a mechanism to regulate information processing in neurons we quantified the changes in mutual information between I(Glu) and all Purkinje cell currents as a function of the density of dendritic Ca (g(CaP)) and Kca (g(Kc)) conductances. We extended our analysis to determine the window of temporal integration of I(Glu) by I(CaP) and I(Kc) as a function of channel density and synaptic activity. The window of information integration has a stronger dependence on increasing values of g(Kc) than on g(CaP), but at high levels of synaptic stimulation information integration is reduced to a few milliseconds. Overall, our results show that different dendritic conductances differentially encode synaptic activity and that dendritic excitability and the level of synaptic activity regulate the flow of information in dendrites.

Chemical protein synthesis: Inventing synthetic methods to decipher how proteins work.

PubMed

Kent, Stephen

2017-09-15

Total chemical synthesis of proteins has been rendered practical by the chemical ligation principle: chemoselective condensation of unprotected peptide segments equipped with unique, mutually reactive functional groups, enabled by formation of a non-native replacement for the peptide bond. Ligation chemistries are briefly described, including native chemical ligation - thioester-mediated, amide-forming reaction at Xaa-Cys sites - and its extensions. Case studies from the author's own works are used to illustrate the utility and applications of chemical protein synthesis. Selected recent developments in the field are briefly discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

It Takes Two to Tango: How Parents' and Adolescents' Personalities Link to the Quality of Their Mutual Relationship

ERIC Educational Resources Information Center

Denissen, Jaap J. A.; van Aken, Marcel A. G.; Dubas, Judith S.

2009-01-01

According to J. Belsky's (1984) process model of parenting, both adolescents' and parents' personality should exert a significant impact on the quality of their mutual relationship. Using multi-informant, symmetric data on the Big Five personality traits and the relationship quality of mothers, fathers, and two adolescent children, the current…

Shape based segmentation of MRIs of the bones in the knee using phase and intensity information

NASA Astrophysics Data System (ADS)

Fripp, Jurgen; Bourgeat, Pierrick; Crozier, Stuart; Ourselin, Sébastien

2007-03-01

The segmentation of the bones from MR images is useful for performing subsequent segmentation and quantitative measurements of cartilage tissue. In this paper, we present a shape based segmentation scheme for the bones that uses texture features derived from the phase and intensity information in the complex MR image. The phase can provide additional information about the tissue interfaces, but due to the phase unwrapping problem, this information is usually discarded. By using a Gabor filter bank on the complex MR image, texture features (including phase) can be extracted without requiring phase unwrapping. These texture features are then analyzed using a support vector machine classifier to obtain probability tissue matches. The segmentation of the bone is fully automatic and performed using a 3D active shape model based approach driven using gradient and texture information. The 3D active shape model is automatically initialized using a robust affine registration. The approach is validated using a database of 18 FLASH MR images that are manually segmented, with an average segmentation overlap (Dice similarity coefficient) of 0.92 compared to 0.9 obtained using the classifier only.

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.

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

Critical scaling of the mutual information in two-dimensional disordered Ising models

NASA Astrophysics Data System (ADS)

Sriluckshmy, P. V.; Mandal, Ipsita

2018-04-01

Rényi mutual information, computed from second Rényi entropies, can identify classical phase transitions from their finite-size scaling at critical points. We apply this technique to examine the presence or absence of finite temperature phase transitions in various two-dimensional models on a square lattice, which are extensions of the conventional Ising model by adding a quenched disorder. When the quenched disorder causes the nearest neighbor bonds to be both ferromagnetic and antiferromagnetic, (a) a spin glass phase exists only at zero temperature, and (b) a ferromagnetic phase exists at a finite temperature when the antiferromagnetic bond distributions are sufficiently dilute. Furthermore, finite temperature paramagnetic-ferromagnetic transitions can also occur when the disordered bonds involve only ferromagnetic couplings of random strengths. In our numerical simulations, the ‘zero temperature only’ phase transitions are identified when there is no consistent finite-size scaling of the Rényi mutual information curves, while for finite temperature critical points, the curves can identify the critical temperature T c by their crossings at T c and 2 Tc .

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.

Effect of age on changes in motor units functional connectivity.

PubMed

Arjunan, Sridhar P; Kumar, Dinesh

2015-08-01

With age, there is a change in functional connectivity of motor units in muscle. This leads to reduced muscle strength. This study has investigated the effect of age on the changes in the motor unit recruitment by measuring the mutual information between multiple channels of surface electromyogram (sEMG) of biceps brachii muscle. It is hypothesised that with ageing, there is a reduction in number of motor units, which can lead to an increase in the dependency of remaining motor units. This increase can be observed in the mutual information between the multiple channels of the muscle activity. Two channels of sEMG were recorded during the maximum level of isometric contraction. 28 healthy subjects (Young: age range 20-35years and Old: age range - 60-70years) participated in the experiments. The normalized mutual information (NMI), a measure of dependency factor, was computed for the sEMG recordings. Statistical analysis was performed to test the effect of age on NMI. The results show that the NMI among the older cohort was significantly higher when compared with the young adults.

Atlas selection for hippocampus segmentation: Relevance evaluation of three meta-information parameters.

PubMed

Dill, Vanderson; Klein, Pedro Costa; Franco, Alexandre Rosa; Pinho, Márcio Sarroglia

2018-04-01

Current state-of-the-art methods for whole and subfield hippocampus segmentation use pre-segmented templates, also known as atlases, in the pre-processing stages. Typically, the input image is registered to the template, which provides prior information for the segmentation process. Using a single standard atlas increases the difficulty in dealing with individuals who have a brain anatomy that is morphologically different from the atlas, especially in older brains. To increase the segmentation precision in these cases, without any manual intervention, multiple atlases can be used. However, registration to many templates leads to a high computational cost. Researchers have proposed to use an atlas pre-selection technique based on meta-information followed by the selection of an atlas based on image similarity. Unfortunately, this method also presents a high computational cost due to the image-similarity process. Thus, it is desirable to pre-select a smaller number of atlases as long as this does not impact on the segmentation quality. To pick out an atlas that provides the best registration, we evaluate the use of three meta-information parameters (medical condition, age range, and gender) to choose the atlas. In this work, 24 atlases were defined and each is based on the combination of the three meta-information parameters. These atlases were used to segment 352 vol from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Hippocampus segmentation with each of these atlases was evaluated and compared to reference segmentations of the hippocampus, which are available from ADNI. The use of atlas selection by meta-information led to a significant gain in the Dice similarity coefficient, which reached 0.68 ± 0.11, compared to 0.62 ± 0.12 when using only the standard MNI152 atlas. Statistical analysis showed that the three meta-information parameters provided a significant improvement in the segmentation accuracy. Copyright © 2018 Elsevier Ltd. All rights reserved.

Effect of acculturation and mutuality on family loyalty among Mexican American caregivers of elders.

PubMed

Kao, Hsueh-Fen S; An, Kyungeh

2012-06-01

Informal family care for elders is conventional in Mexican American communities despite increasing intergenerational gaps in filial values. In our study, we explored whether acculturation and dyadic mutuality, as perceived by Mexican American family caregivers, explain the caregivers' expectations of family loyalty toward elderly relatives. A nonexperimental, correlational design with convenience sampling was used in El Paso, Texas, from October 2007 to January 2008. Three bilingual promotoras collected data from 193 Mexican American adult caregivers of community-dwelling elders using three scales designed for Mexican Americans: the Acculturation Rating Scale for Mexican Americans II-Short Form, the Mutuality Scale, and the Expectations of Family Loyalty of Children Toward Elderly Relatives Scale. Confirmatory factor analysis was used to analyze the data. Acculturation had a marginal effect (r = .21, p < .05), but mutuality presented a strong correlation (r = .45, p < .001) with the expectations of family loyalty toward elderly relatives. There was no significant correlation between acculturation and mutuality (r = .05). Although Mexican American caregivers with strong Mexican orientation may have high expectations of family loyalty toward elderly relatives, mutuality exhibits more significant effects on expectations. Among Mexican Americans, mutuality between the caregiving dyad, as perceived by caregivers, may be a better predictor of filial values than caregivers' acculturation alone. It may be useful to incorporate the dual paradigm of acculturation and mutuality into immigrant family care for elderly relatives. © 2012 Sigma Theta Tau International.

Caregiving and mutuality among long-term colorectal cancer survivors with ostomies: qualitative study.

PubMed

Altschuler, Andrea; Liljestrand, Petra; Grant, Marcia; Hornbrook, Mark C; Krouse, Robert S; McMullen, Carmit K

2018-02-01

The cancer caregiving literature focuses on the early phases of survivorship, but caregiving can continue for decades when cancer creates disability. Survivors with an ostomy following colorectal cancer (CRC) have caregiving needs that may last decades. Mutuality has been identified as a relationship component that can affect caregiving. This paper discusses how mutuality may affect long-term ostomy caregiving. We conducted semi-structured, in-depth interviews with 31 long-term CRC survivors with ostomies and their primary informal caregivers. Interviewees were members of an integrated health care delivery system in the USA. We used inductive theme analysis techniques to analyze the interviews. Most survivors were 71 years of age or older (67%), female (55%), and with some college education (54%). Two thirds lived with and received care from spouses. Caregiving ranged from minimal support to intimate assistance with daily ostomy care. While some survivors received caregiving far beyond what was needed, others did not receive adequate caregiving for their health care needs. Low mutuality created challenges for ostomy caregiving. Mutuality impacts the quality of caregiving, and this quality may change over time, depending on various factors. Emotional feedback and amplification is the proposed mechanism by which mutuality may shift over time. Survivorship care should include assessment and support of mutuality as a resource to enhance health outcomes and quality of life for survivors with long-term caregiving needs and their caregivers. Appropriate questionnaires can be identified or developed to assess mutuality over the survivorship trajectory.

Resolution of Probabilistic Weather Forecasts with Application in Disease Management.

PubMed

Hughes, G; McRoberts, N; Burnett, F J

2017-02-01

Predictive systems in disease management often incorporate weather data among the disease risk factors, and sometimes this comes in the form of forecast weather data rather than observed weather data. In such cases, it is useful to have an evaluation of the operational weather forecast, in addition to the evaluation of the disease forecasts provided by the predictive system. Typically, weather forecasts and disease forecasts are evaluated using different methodologies. However, the information theoretic quantity expected mutual information provides a basis for evaluating both kinds of forecast. Expected mutual information is an appropriate metric for the average performance of a predictive system over a set of forecasts. Both relative entropy (a divergence, measuring information gain) and specific information (an entropy difference, measuring change in uncertainty) provide a basis for the assessment of individual forecasts.

Infrared Ship Target Segmentation Based on Spatial Information Improved FCM.

PubMed

Bai, Xiangzhi; Chen, Zhiguo; Zhang, Yu; Liu, Zhaoying; Lu, Yi

2016-12-01

Segmentation of infrared (IR) ship images is always a challenging task, because of the intensity inhomogeneity and noise. The fuzzy C-means (FCM) clustering is a classical method widely used in image segmentation. However, it has some shortcomings, like not considering the spatial information or being sensitive to noise. In this paper, an improved FCM method based on the spatial information is proposed for IR ship target segmentation. The improvements include two parts: 1) adding the nonlocal spatial information based on the ship target and 2) using the spatial shape information of the contour of the ship target to refine the local spatial constraint by Markov random field. In addition, the results of K -means are used to initialize the improved FCM method. Experimental results show that the improved method is effective and performs better than the existing methods, including the existing FCM methods, for segmentation of the IR ship images.

Information flow to assess cardiorespiratory interactions in patients on weaning trials.

PubMed

Vallverdú, M; Tibaduisa, O; Clariá, F; Hoyer, D; Giraldo, B; Benito, S; Caminal, P

2006-01-01

Nonlinear processes of the autonomic nervous system (ANS) can produce breath-to-breath variability in the pattern of breathing. In order to provide assess to these nonlinear processes, nonlinear statistical dependencies between heart rate variability and respiratory pattern variability are analyzed. In this way, auto-mutual information and cross-mutual information concepts are applied. This information flow analysis is presented as a short-term non linear analysis method to investigate the information flow interactions in patients on weaning trials. 78 patients from mechanical ventilation were studied: Group A of 28 patients that failed to maintain spontaneous breathing and were reconnected; Group B of 50 patients with successful trials. The results show lower complexity with an increase of information flow in group A than in group B. Furthermore, a more (weakly) coupled nonlinear oscillator behavior is observed in the series of group A than in B.

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.

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.

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.

Stochastic information transfer from cochlear implant electrodes to auditory nerve fibers

NASA Astrophysics Data System (ADS)

Gao, Xiao; Grayden, David B.; McDonnell, Mark D.

2014-08-01

Cochlear implants, also called bionic ears, are implanted neural prostheses that can restore lost human hearing function by direct electrical stimulation of auditory nerve fibers. Previously, an information-theoretic framework for numerically estimating the optimal number of electrodes in cochlear implants has been devised. This approach relies on a model of stochastic action potential generation and a discrete memoryless channel model of the interface between the array of electrodes and the auditory nerve fibers. Using these models, the stochastic information transfer from cochlear implant electrodes to auditory nerve fibers is estimated from the mutual information between channel inputs (the locations of electrodes) and channel outputs (the set of electrode-activated nerve fibers). Here we describe a revised model of the channel output in the framework that avoids the side effects caused by an "ambiguity state" in the original model and also makes fewer assumptions about perceptual processing in the brain. A detailed comparison of how different assumptions on fibers and current spread modes impact on the information transfer in the original model and in the revised model is presented. We also mathematically derive an upper bound on the mutual information in the revised model, which becomes tighter as the number of electrodes increases. We found that the revised model leads to a significantly larger maximum mutual information and corresponding number of electrodes compared with the original model and conclude that the assumptions made in this part of the modeling framework are crucial to the model's overall utility.

Determination of rational parameters for process of grinding materials pre-crushed by pressure in ball mill

NASA Astrophysics Data System (ADS)

Romanovich, A. A.; Romanovich, L. G.; Chekhovskoy, E. I.

2018-03-01

The article presents the results of experimental studies on the grinding process of a clinker preliminarily ground in press roller mills in a ball mill equipped with energy exchange devices. The authors studied the influence of the coefficients of loading for grinding bodies of the first and second mill chambers, their lengths, angles of inclination, and the mutual location of energy exchange devices (the ellipse segment and the double-acting blade) on the output parameters of the grinding process (productivity, drive power consumption and specific energy consumption). It is clarified that the best results of the disaggregation and grinding process, judging by the minimum specific energy consumption in the grinding of clinker with an anisotropic texture after force deformation between the rolls of a press roller shredder, are achieved at a certain angle of ellipse segment inclination; the length of the first chamber and the coefficients of loading the chambers with grinding bodies.

Benefit and cost curves for typical pollination mutualisms.

PubMed

Morris, William F; Vázquez, Diego P; Chacoff, Natacha P

2010-05-01

Mutualisms provide benefits to interacting species, but they also involve costs. If costs come to exceed benefits as population density or the frequency of encounters between species increases, the interaction will no longer be mutualistic. Thus curves that represent benefits and costs as functions of interaction frequency are important tools for predicting when a mutualism will tip over into antagonism. Currently, most of what we know about benefit and cost curves in pollination mutualisms comes from highly specialized pollinating seed-consumer mutualisms, such as the yucca moth-yucca interaction. There, benefits to female reproduction saturate as the number of visits to a flower increases (because the amount of pollen needed to fertilize all the flower's ovules is finite), but costs continue to increase (because pollinator offspring consume developing seeds), leading to a peak in seed production at an intermediate number of visits. But for most plant-pollinator mutualisms, costs to the plant are more subtle than consumption of seeds, and how such costs scale with interaction frequency remains largely unknown. Here, we present reasonable benefit and cost curves that are appropriate for typical pollinator-plant interactions, and we show how they can result in a wide diversity of relationships between net benefit (benefit minus cost) and interaction frequency. We then use maximum-likelihood methods to fit net-benefit curves to measures of female reproductive success for three typical pollination mutualisms from two continents, and for each system we chose the most parsimonious model using information-criterion statistics. We discuss the implications of the shape of the net-benefit curve for the ecology and evolution of plant-pollinator mutualisms, as well as the challenges that lie ahead for disentangling the underlying benefit and cost curves for typical pollination mutualisms.

The Philosophy of Information as an Underlying and Unifying Theory of Information Science

ERIC Educational Resources Information Center

Tomic, Taeda

2010-01-01

Introduction: Philosophical analyses of theoretical principles underlying these sub-domains reveal philosophy of information as underlying meta-theory of information science. Method: Conceptual research on the knowledge sub-domains in information science and philosophy and analysis of their mutual connection. Analysis: Similarities between…

78 FR 45550 - Agency Information Collection Activities: Extension, Without Change, of an Existing Information...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-29

... Request. ACTION: 30-Day Notice of Information Collection; 73-028; ICE Mutual Agreement between Government... technological collection techniques or other forms of information technology, e.g., permitting electronic... program. The information provided by the company plays a vital role in determining that company's...

Algorithm and program for information processing with the filin apparatus

NASA Technical Reports Server (NTRS)

Gurin, L. S.; Morkrov, V. S.; Moskalenko, Y. I.; Tsoy, K. A.

1979-01-01

The reduction of spectral radiation data from space sources is described. The algorithm and program for identifying segments of information obtained from the Film telescope-spectrometer on the Salyut-4 are presented. The information segments represent suspected X-ray sources. The proposed algorithm is an algorithm of the lowest level. Following evaluation, information free of uninformative segments is subject to further processing with algorithms of a higher level. The language used is FORTRAN 4.

Classification of building infrastructure and automatic building footprint delineation using airborne laser swath mapping data

NASA Astrophysics Data System (ADS)

Caceres, Jhon

Three-dimensional (3D) models of urban infrastructure comprise critical data for planners working on problems in wireless communications, environmental monitoring, civil engineering, and urban planning, among other tasks. Photogrammetric methods have been the most common approach to date to extract building models. However, Airborne Laser Swath Mapping (ALSM) observations offer a competitive alternative because they overcome some of the ambiguities that arise when trying to extract 3D information from 2D images. Regardless of the source data, the building extraction process requires segmentation and classification of the data and building identification. In this work, approaches for classifying ALSM data, separating building and tree points, and delineating ALSM footprints from the classified data are described. Digital aerial photographs are used in some cases to verify results, but the objective of this work is to develop methods that can work on ALSM data alone. A robust approach for separating tree and building points in ALSM data is presented. The method is based on supervised learning of the classes (tree vs. building) in a high dimensional feature space that yields good class separability. Features used for classification are based on the generation of local mappings, from three-dimensional space to two-dimensional space, known as "spin images" for each ALSM point to be classified. The method discriminates ALSM returns in compact spaces and even where the classes are very close together or overlapping spatially. A modified algorithm of the Hough Transform is used to orient the spin images, and the spin image parameters are specified such that the mutual information between the spin image pixel values and class labels is maximized. This new approach to ALSM classification allows us to fully exploit the 3D point information in the ALSM data while still achieving good class separability, which has been a difficult trade-off in the past. Supported by the spin image analysis for obtaining an initial classification, an automatic approach for delineating accurate building footprints is presented. The physical fact that laser pulses that happen to strike building edges can produce very different 1st and last return elevations has been long recognized. However, in older generation ALSM systems (<50 kHz pulse rates) such points were too few and far between to delineate building footprints precisely. Furthermore, without the robust separation of nearby trees and vegetation from the buildings, simply extracting ALSM shots where the elevation of the first return was much higher than the elevation of the last return, was not a reliable means of identifying building footprints. However, with the advent of ALSM systems with pulse rates in excess of 100 kHz, and by using spin-imaged based segmentation, it is now possible to extract building edges from the point cloud. A refined classification resulting from incorporating "on-edge" information is developed for obtaining quadrangular footprints. The footprint fitting process involves line generalization, least squares-based clustering and dominant points finding for segmenting individual building edges. In addition, an algorithm for fitting complex footprints using the segmented edges and data inside footprints is also proposed.

Variability and reliability of the vastus lateralis muscle anatomy.

PubMed

D'Arpa, Salvatore; Toia, Francesca; Brenner, Erich; Melloni, Carlo; Moschella, Francesco; Cordova, Adriana

2016-08-01

The aims of this study are to investigate the variability of the morphological and neurovascular anatomy of the vastus lateralis (VL) muscle and to describe the relationships among its intramuscular partitions and with the other muscles of the quadriceps femoris. Clinical implications in its reliability as a flap donor are also discussed. In 2012, the extra- and intramuscular neurovascular anatomy of the VL was investigated in 10 cadaveric lower limbs. In three specimens, the segmental arterial pedicles were injected with latex of different colors to point out their anastomotic connections. The morphological anatomy was investigated with regard to the mutual relationship of the three muscular partitions and the relation of the VL with the other muscles of the quadriceps femoris. The VL has a segmental morphological anatomy. However, the fibers of its three partitions interconnect individually and with the other bellies of the quadriceps femoris, particularly, in several variable portions with the vastus intermedius and mainly in the posterior part of the VL. The lateral circumflex femoral artery and its branches have variable origin, but demonstrate constant segmental distribution. Intramuscular dissection and colored latex injections show a rich anastomotic vascular network among the three partitions. Moderate variability exists in both the myological and the neurovascular anatomy of the VL. Despite this variability, the anatomy of the VL always has a constant segmental pattern, which makes the VL a reliable flap donor. Detailed knowledge of the VL anatomy could have useful applications in a broad clinical field.

Statistical shape modeling of human cochlea: alignment and principal component analysis

NASA Astrophysics Data System (ADS)

Poznyakovskiy, Anton A.; Zahnert, Thomas; Fischer, Björn; Lasurashvili, Nikoloz; Kalaidzidis, Yannis; Mürbe, Dirk

2013-02-01

The modeling of the cochlear labyrinth in living subjects is hampered by insufficient resolution of available clinical imaging methods. These methods usually provide resolutions higher than 125 μm. This is too crude to record the position of basilar membrane and, as a result, keep apart even the scala tympani from other scalae. This problem could be avoided by the means of atlas-based segmentation. The specimens can endure higher radiation loads and, conversely, provide better-resolved images. The resulting surface can be used as the seed for atlas-based segmentation. To serve this purpose, we have developed a statistical shape model (SSM) of human scala tympani based on segmentations obtained from 10 μCT image stacks. After segmentation, we aligned the resulting surfaces using Procrustes alignment. This algorithm was slightly modified to accommodate single models with nodes which do not necessarily correspond to salient features and vary in number between models. We have established correspondence by mutual proximity between nodes. Rather than using the standard Euclidean norm, we have applied an alternative logarithmic norm to improve outlier treatment. The minimization was done using BFGS method. We have also split the surface nodes along an octree to reduce computation cost. Subsequently, we have performed the principal component analysis of the training set with Jacobi eigenvalue algorithm. We expect the resulting method to help acquiring not only better understanding in interindividual variations of cochlear anatomy, but also a step towards individual models for pre-operative diagnostics prior to cochlear implant insertions.

Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data

NASA Technical Reports Server (NTRS)

Tilton, James C.; Lawrence, William T.

2005-01-01

NASA's Goddard Space Flight Center has developed a fast and effective method for generating image segmentation hierarchies. These segmentation hierarchies organize image data in a manner that makes their information content more accessible for analysis. Image segmentation enables analysis through the examination of image regions rather than individual image pixels. In addition, the segmentation hierarchy provides additional analysis clues through the tracing of the behavior of image region characteristics at several levels of segmentation detail. The potential for extracting the information content from imagery data based on segmentation hierarchies has not been fully explored for the benefit of the Earth and space science communities. This paper explores the potential of exploiting these segmentation hierarchies for the analysis of multi-date data sets, and for the particular application of change monitoring.

>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.

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.

Send-side matching of data communications messages

DOEpatents

Archer, Charles J.; Blocksome, Michael A.; Ratterman, Joseph D.; Smith, Brian E.

2014-07-01

Send-side matching of data communications messages includes a plurality of compute nodes organized for collective operations, including: issuing by a receiving node to source nodes a receive message that specifies receipt of a single message to be sent from any source node, the receive message including message matching information, a specification of a hardware-level mutual exclusion device, and an identification of a receive buffer; matching by two or more of the source nodes the receive message with pending send messages in the two or more source nodes; operating by one of the source nodes having a matching send message the mutual exclusion device, excluding messages from other source nodes with matching send messages and identifying to the receiving node the source node operating the mutual exclusion device; and sending to the receiving node from the source node operating the mutual exclusion device a matched pending message.

Send-side matching of data communications messages

DOEpatents

Archer, Charles J.; Blocksome, Michael A.; Ratterman, Joseph D.; Smith, Brian E.

2014-06-17

Send-side matching of data communications messages in a distributed computing system comprising a plurality of compute nodes, including: issuing by a receiving node to source nodes a receive message that specifies receipt of a single message to be sent from any source node, the receive message including message matching information, a specification of a hardware-level mutual exclusion device, and an identification of a receive buffer; matching by two or more of the source nodes the receive message with pending send messages in the two or more source nodes; operating by one of the source nodes having a matching send message the mutual exclusion device, excluding messages from other source nodes with matching send messages and identifying to the receiving node the source node operating the mutual exclusion device; and sending to the receiving node from the source node operating the mutual exclusion device a matched pending message.

The activation of segmental and tonal information in visual word recognition.

PubMed

Li, Chuchu; Lin, Candise Y; Wang, Min; Jiang, Nan

2013-08-01

Mandarin Chinese has a logographic script in which graphemes map onto syllables and morphemes. It is not clear whether Chinese readers activate phonological information during lexical access, although phonological information is not explicitly represented in Chinese orthography. In the present study, we examined the activation of phonological information, including segmental and tonal information in Chinese visual word recognition, using the Stroop paradigm. Native Mandarin speakers named the presentation color of Chinese characters in Mandarin. The visual stimuli were divided into five types: color characters (e.g., , hong2, "red"), homophones of the color characters (S+T+; e.g., , hong2, "flood"), different-tone homophones (S+T-; e.g., , hong1, "boom"), characters that shared the same tone but differed in segments with the color characters (S-T+; e.g., , ping2, "bottle"), and neutral characters (S-T-; e.g., , qian1, "leading through"). Classic Stroop facilitation was shown in all color-congruent trials, and interference was shown in the incongruent trials. Furthermore, the Stroop effect was stronger for S+T- than for S-T+ trials, and was similar between S+T+ and S+T- trials. These findings suggested that both tonal and segmental forms of information play roles in lexical constraints; however, segmental information has more weight than tonal information. We proposed a revised visual word recognition model in which the functions of both segmental and suprasegmental types of information and their relative weights are taken into account.

Semi-automatic segmentation of brain tumors using population and individual information.

PubMed

Wu, Yao; Yang, Wei; Jiang, Jun; Li, Shuanqian; Feng, Qianjin; Chen, Wufan

2013-08-01

Efficient segmentation of tumors in medical images is of great practical importance in early diagnosis and radiation plan. This paper proposes a novel semi-automatic segmentation method based on population and individual statistical information to segment brain tumors in magnetic resonance (MR) images. First, high-dimensional image features are extracted. Neighborhood components analysis is proposed to learn two optimal distance metrics, which contain population and patient-specific information, respectively. The probability of each pixel belonging to the foreground (tumor) and the background is estimated by the k-nearest neighborhood classifier under the learned optimal distance metrics. A cost function for segmentation is constructed through these probabilities and is optimized using graph cuts. Finally, some morphological operations are performed to improve the achieved segmentation results. Our dataset consists of 137 brain MR images, including 68 for training and 69 for testing. The proposed method overcomes segmentation difficulties caused by the uneven gray level distribution of the tumors and even can get satisfactory results if the tumors have fuzzy edges. Experimental results demonstrate that the proposed method is robust to brain tumor segmentation.

48 CFR 952.204-72 - Disclosure of information.

Code of Federal Regulations, 2013 CFR

2013-10-01

... classified information or restricted data: Disclosure of Information (APR 1994) (a) It is mutually expected... 48 Federal Acquisition Regulations System 5 2013-10-01 2013-10-01 false Disclosure of information... FORMS SOLICITATION PROVISIONS AND CONTRACT CLAUSES Text of Provisions and Clauses 952.204-72 Disclosure...

48 CFR 952.204-72 - Disclosure of information.

Code of Federal Regulations, 2014 CFR

2014-10-01

... classified information or restricted data: Disclosure of Information (APR 1994) (a) It is mutually expected... 48 Federal Acquisition Regulations System 5 2014-10-01 2014-10-01 false Disclosure of information... FORMS SOLICITATION PROVISIONS AND CONTRACT CLAUSES Text of Provisions and Clauses 952.204-72 Disclosure...

48 CFR 952.204-72 - Disclosure of information.

Code of Federal Regulations, 2012 CFR

2012-10-01

... classified information or restricted data: Disclosure of Information (APR 1994) (a) It is mutually expected... 48 Federal Acquisition Regulations System 5 2012-10-01 2012-10-01 false Disclosure of information... FORMS SOLICITATION PROVISIONS AND CONTRACT CLAUSES Text of Provisions and Clauses 952.204-72 Disclosure...

Building the Vertebrate Spine

NASA Astrophysics Data System (ADS)

Pourquié, Olivier

2008-03-01

The vertebrate body can be subdivided along the antero-posterior (AP) axis into repeated structures called segments. This periodic pattern is established during embryogenesis by the somitogenesis process. Somites are generated in a rhythmic fashion from the paraxial mesoderm and subsequently differentiate to give rise to the vertebrae and skeletal muscles of the body. Somite formation involves an oscillator-the segmentation clock-whose periodic signal is converted into the periodic array of somite boundaries. This clock drives the dynamic expression of cyclic genes in the presomitic mesoderm and requires Notch and Wnt signaling. Microarray studies of the mouse presomitic mesoderm transcriptome reveal that the segmentation clock drives the periodic expression of a large network of cyclic genes involved in cell signaling. Mutually exclusive activation of the Notch/FGF and Wnt pathways during each cycle suggests that coordinated regulation of these three pathways underlies the clock oscillator. In humans, mutations in the genes associated to the function of this oscillator such as Dll3 or Lunatic Fringe result in abnormal segmentation of the vertebral column such as those seen in congenital scoliosis. Whereas the segmentation clock is thought to set the pace of vertebrate segmentation, the translation of this pulsation into the reiterated arrangement of segment boundaries along the AP axis involves dynamic gradients of FGF and Wnt signaling. The FGF signaling gradient is established based on an unusual mechanism involving mRNA decay which provides an efficient means to couple the spatio-temporal activation of segmentation to the posterior elongation of the embryo. Another striking aspect of somite production is the strict bilateral symmetry of the process. Retinoic acid was shown to control aspects of this coordination by buffering destabilizing effects from the embryonic left-right machinery. Defects in this embryonic program controlling vertebral symmetry might lead to scoliosis in humans. Finally, the subsequent regional differentiation of the precursors of the vertebrae is controlled by Hox genes, whose collinear expression controls both gastrulation of somite precursors and their subsequent patterning into region-specific types of structures. Therefore somite development provides an outstanding paradigm to study patterning and differentiation in vertebrate embryos.

Redundant imprinting of information in nonideal environments: Objective reality via a noisy channel

NASA Astrophysics Data System (ADS)

Zwolak, Michael; Quan, H. T.; Zurek, Wojciech H.

2010-06-01

Quantum Darwinism provides an information-theoretic framework for the emergence of the objective, classical world from the quantum substrate. The key to this emergence is the proliferation of redundant information throughout the environment where observers can then intercept it. We study this process for a purely decohering interaction when the environment, E, is in a nonideal (e.g., mixed) initial state. In the case of good decoherence, that is, after the pointer states have been unambiguously selected, the mutual information between the system, S, and an environment fragment, F, is given solely by F’s entropy increase. This demonstrates that the environment’s capacity for recording the state of S is directly related to its ability to increase its entropy. Environments that remain nearly invariant under the interaction with S, either because they have a large initial entropy or a misaligned initial state, therefore have a diminished ability to acquire information. To elucidate the concept of good decoherence, we show that, when decoherence is not complete, the deviation of the mutual information from F’s entropy change is quantified by the quantum discord, i.e., the excess mutual information between S and F is information regarding the initial coherence between pointer states of S. In addition to illustrating these results with a single-qubit system interacting with a multiqubit environment, we find scaling relations for the redundancy of information acquired by the environment that display a universal behavior independent of the initial state of S. Our results demonstrate that Quantum Darwinism is robust with respect to nonideal initial states of the environment: the environment almost always acquires redundant information about the system but its rate of acquisition can be reduced.

Fluctuation sensitivity of a transcriptional signaling cascade

NASA Astrophysics Data System (ADS)

Pilkiewicz, Kevin R.; Mayo, Michael L.

2016-09-01

The internal biochemical state of a cell is regulated by a vast transcriptional network that kinetically correlates the concentrations of numerous proteins. Fluctuations in protein concentration that encode crucial information about this changing state must compete with fluctuations caused by the noisy cellular environment in order to successfully transmit information across the network. Oftentimes, one protein must regulate another through a sequence of intermediaries, and conventional wisdom, derived from the data processing inequality of information theory, leads us to expect that longer sequences should lose more information to noise. Using the metric of mutual information to characterize the fluctuation sensitivity of transcriptional signaling cascades, we find, counter to this expectation, that longer chains of regulatory interactions can instead lead to enhanced informational efficiency. We derive an analytic expression for the mutual information from a generalized chemical kinetics model that we reduce to simple, mass-action kinetics by linearizing for small fluctuations about the basal biological steady state, and we find that at long times this expression depends only on a simple ratio of protein production to destruction rates and the length of the cascade. We place bounds on the values of these parameters by requiring that the mutual information be at least one bit—otherwise, any received signal would be indistinguishable from noise—and we find not only that nature has devised a way to circumvent the data processing inequality, but that it must be circumvented to attain this one-bit threshold. We demonstrate how this result places informational and biochemical efficiency at odds with one another by correlating high transcription factor binding affinities with low informational output, and we conclude with an analysis of the validity of our assumptions and propose how they might be tested experimentally.

Information-Theoretical Analysis of EEG Microstate Sequences in Python.

PubMed

von Wegner, Frederic; Laufs, Helmut

2018-01-01

We present an open-source Python package to compute information-theoretical quantities for electroencephalographic data. Electroencephalography (EEG) measures the electrical potential generated by the cerebral cortex and the set of spatial patterns projected by the brain's electrical potential on the scalp surface can be clustered into a set of representative maps called EEG microstates. Microstate time series are obtained by competitively fitting the microstate maps back into the EEG data set, i.e., by substituting the EEG data at a given time with the label of the microstate that has the highest similarity with the actual EEG topography. As microstate sequences consist of non-metric random variables, e.g., the letters A-D, we recently introduced information-theoretical measures to quantify these time series. In wakeful resting state EEG recordings, we found new characteristics of microstate sequences such as periodicities related to EEG frequency bands. The algorithms used are here provided as an open-source package and their use is explained in a tutorial style. The package is self-contained and the programming style is procedural, focusing on code intelligibility and easy portability. Using a sample EEG file, we demonstrate how to perform EEG microstate segmentation using the modified K-means approach, and how to compute and visualize the recently introduced information-theoretical tests and quantities. The time-lagged mutual information function is derived as a discrete symbolic alternative to the autocorrelation function for metric time series and confidence intervals are computed from Markov chain surrogate data. The software package provides an open-source extension to the existing implementations of the microstate transform and is specifically designed to analyze resting state EEG recordings.

Fracture and Stress Evolution on Europa: New Insights Into Fracture Interpretation and Ice Thickness Estimates Using Fracture Mechanics Analyses

NASA Technical Reports Server (NTRS)

Kattenhorn, Simon

2004-01-01

The work completed during the funding period has provided many important insights into fracturing behavior in Europa's ice shell. It has been determined that fracturing through time is likely to have been controlled by the effects of nonsynchronous rotation stresses and that as much as 720 deg of said rotation may have occurred during the visible geologic history. It has been determined that there are at least two distinct styles of strike-slip faulting and that their mutual evolutionary styles are likely to have been different, with one involving a significant dilational component during shear motion. It has been determined that secondary fracturing in perturbed stress fields adjacent to older structures such as faults is a prevalent process on Europa. It has been determined that cycloidal ridges are likely to experience shear stresses along the existing segment portions as they propagate, which affects propagation direction and ultimately induces tailcracking at the segment tip than then initiates a new cycle of cycloid segment growth. Finally, it has been established that mechanical methods (e.g., flexure analysis) can be used to determine the elastic thickness of the ice shell, which, although probably only several km thick, is likely to be spatially variable, being thinner under bands but thicker under ridged plains terrain.

Identifying Driver Genomic Alterations in Cancers by Searching Minimum-Weight, Mutually Exclusive Sets

PubMed Central

Lu, Songjian; Lu, Kevin N.; Cheng, Shi-Yuan; Hu, Bo; Ma, Xiaojun; Nystrom, Nicholas; Lu, Xinghua

2015-01-01

An important goal of cancer genomic research is to identify the driving pathways underlying disease mechanisms and the heterogeneity of cancers. It is well known that somatic genome alterations (SGAs) affecting the genes that encode the proteins within a common signaling pathway exhibit mutual exclusivity, in which these SGAs usually do not co-occur in a tumor. With some success, this characteristic has been utilized as an objective function to guide the search for driver mutations within a pathway. However, mutual exclusivity alone is not sufficient to indicate that genes affected by such SGAs are in common pathways. Here, we propose a novel, signal-oriented framework for identifying driver SGAs. First, we identify the perturbed cellular signals by mining the gene expression data. Next, we search for a set of SGA events that carries strong information with respect to such perturbed signals while exhibiting mutual exclusivity. Finally, we design and implement an efficient exact algorithm to solve an NP-hard problem encountered in our approach. We apply this framework to the ovarian and glioblastoma tumor data available at the TCGA database, and perform systematic evaluations. Our results indicate that the signal-oriented approach enhances the ability to find informative sets of driver SGAs that likely constitute signaling pathways. PMID:26317392

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.

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.

Feed-forward segmentation of figure-ground and assignment of border-ownership.

PubMed

Supèr, Hans; Romeo, August; Keil, Matthias

2010-05-19

Figure-ground is the segmentation of visual information into objects and their surrounding backgrounds. Two main processes herein are boundary assignment and surface segregation, which rely on the integration of global scene information. Recurrent processing either by intrinsic horizontal connections that connect surrounding neurons or by feedback projections from higher visual areas provide such information, and are considered to be the neural substrate for figure-ground segmentation. On the contrary, a role of feedforward projections in figure-ground segmentation is unknown. To have a better understanding of a role of feedforward connections in figure-ground organization, we constructed a feedforward spiking model using a biologically plausible neuron model. By means of surround inhibition our simple 3-layered model performs figure-ground segmentation and one-sided border-ownership coding. We propose that the visual system uses feed forward suppression for figure-ground segmentation and border-ownership assignment.

Feed-Forward Segmentation of Figure-Ground and Assignment of Border-Ownership

PubMed Central

Supèr, Hans; Romeo, August; Keil, Matthias

2010-01-01

Figure-ground is the segmentation of visual information into objects and their surrounding backgrounds. Two main processes herein are boundary assignment and surface segregation, which rely on the integration of global scene information. Recurrent processing either by intrinsic horizontal connections that connect surrounding neurons or by feedback projections from higher visual areas provide such information, and are considered to be the neural substrate for figure-ground segmentation. On the contrary, a role of feedforward projections in figure-ground segmentation is unknown. To have a better understanding of a role of feedforward connections in figure-ground organization, we constructed a feedforward spiking model using a biologically plausible neuron model. By means of surround inhibition our simple 3-layered model performs figure-ground segmentation and one-sided border-ownership coding. We propose that the visual system uses feed forward suppression for figure-ground segmentation and border-ownership assignment. PMID:20502718

Modeling Mental Health Information Preferences During the Early Adult Years: A Discrete Choice Conjoint Experiment

PubMed Central

Cunningham, Charles E.; Walker, John R.; Eastwood, John D.; Westra, Henny; Rimas, Heather; Chen, Yvonne; Marcus, Madalyn; Swinson, Richard P.; Bracken, Keyna

2013-01-01

Although most young adults with mood and anxiety disorders do not seek treatment, those who are better informed about mental health problems are more likely to use services. The authors used conjoint analysis to model strategies for providing information about anxiety and depression to young adults. Participants (N = 1,035) completed 17 choice tasks presenting combinations of 15 four-level attributes of a mental health information strategy. Latent class analysis yielded 3 segments. The virtual segment (28.7%) preferred working independently on the Internet to obtain information recommended by young adults who had experienced anxiety or depression. Self-assessment options and links to service providers were more important to this segment. Conventional participants (30.1%) preferred books or pamphlets recommended by a doctor, endorsed by mental health professionals, and used with a doctor's support. They would devote more time to information acquisition but were less likely to use Internet social networking options. Brief sources of information were more important to the low interest segment (41.2%). All segments preferred information about alternative ways to reduce anxiety or depression rather than psychological approaches or medication. Maximizing the use of information requires active and passive approaches delivered through old-media (e.g. books) and new-media (e.g., Internet) channels. PMID:24266450

Modeling mental health information preferences during the early adult years: a discrete choice conjoint experiment.

PubMed

Cunningham, Charles E; Walker, John R; Eastwood, John D; Westra, Henny; Rimas, Heather; Chen, Yvonne; Marcus, Madalyn; Swinson, Richard P; Bracken, Keyna; The Mobilizing Minds Research Group

2014-04-01

Although most young adults with mood and anxiety disorders do not seek treatment, those who are better informed about mental health problems are more likely to use services. The authors used conjoint analysis to model strategies for providing information about anxiety and depression to young adults. Participants (N = 1,035) completed 17 choice tasks presenting combinations of 15 four-level attributes of a mental health information strategy. Latent class analysis yielded 3 segments. The virtual segment (28.7%) preferred working independently on the Internet to obtain information recommended by young adults who had experienced anxiety or depression. Self-assessment options and links to service providers were more important to this segment. Conventional participants (30.1%) preferred books or pamphlets recommended by a doctor, endorsed by mental health professionals, and used with a doctor's support. They would devote more time to information acquisition but were less likely to use Internet social networking options. Brief sources of information were more important to the low interest segment (41.2%). All segments preferred information about alternative ways to reduce anxiety or depression rather than psychological approaches or medication. Maximizing the use of information requires active and passive approaches delivered through old-media (e.g., books) and new-media (e.g., Internet) channels.

78 FR 28894 - Agency Information Collection Activities: Extension, Without Change, of an Existing Information...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-16

... entity in the private sector to participate in the program and the information obtained from the company... Request ACTION: 60-Day Notice of Information Collection; 73-028; ICE Mutual Agreement between Government... other forms of information technology, e.g., permitting electronic submission of responses. Overview of...

Mutual information criterion for feature selection with application to classification of breast microcalcifications

NASA Astrophysics Data System (ADS)

Diamant, Idit; Shalhon, Moran; Goldberger, Jacob; Greenspan, Hayit

2016-03-01

Classification of clustered breast microcalcifications into benign and malignant categories is an extremely challenging task for computerized algorithms and expert radiologists alike. In this paper we present a novel method for feature selection based on mutual information (MI) criterion for automatic classification of microcalcifications. We explored the MI based feature selection for various texture features. The proposed method was evaluated on a standardized digital database for screening mammography (DDSM). Experimental results demonstrate the effectiveness and the advantage of using the MI-based feature selection to obtain the most relevant features for the task and thus to provide for improved performance as compared to using all features.

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.

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

Interpretation of the auto-mutual information rate of decrease in the context of biomedical signal analysis. Application to electroencephalogram recordings.

PubMed

Escudero, Javier; Hornero, Roberto; Abásolo, Daniel

2009-02-01

The mutual information (MI) is a measure of both linear and nonlinear dependences. It can be applied to a time series and a time-delayed version of the same sequence to compute the auto-mutual information function (AMIF). Moreover, the AMIF rate of decrease (AMIFRD) with increasing time delay in a signal is correlated with its entropy and has been used to characterize biomedical data. In this paper, we aimed at gaining insight into the dependence of the AMIFRD on several signal processing concepts and at illustrating its application to biomedical time series analysis. Thus, we have analysed a set of synthetic sequences with the AMIFRD. The results show that the AMIF decreases more quickly as bandwidth increases and that the AMIFRD becomes more negative as there is more white noise contaminating the time series. Additionally, this metric detected changes in the nonlinear dynamics of a signal. Finally, in order to illustrate the analysis of real biomedical signals with the AMIFRD, this metric was applied to electroencephalogram (EEG) signals acquired with eyes open and closed and to ictal and non-ictal intracranial EEG recordings.

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.

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.

Segmentation of brain volume based on 3D region growing by integrating intensity and edge for image-guided surgery

NASA Astrophysics Data System (ADS)

Tsagaan, Baigalmaa; Abe, Keiichi; Goto, Masahiro; Yamamoto, Seiji; Terakawa, Susumu

2006-03-01

This paper presents a segmentation method of brain tissues from MR images, invented for our image-guided neurosurgery system under development. Our goal is to segment brain tissues for creating biomechanical model. The proposed segmentation method is based on 3-D region growing and outperforms conventional approaches by stepwise usage of intensity similarities between voxels in conjunction with edge information. Since the intensity and the edge information are complementary to each other in the region-based segmentation, we use them twice by performing a coarse-to-fine extraction. First, the edge information in an appropriate neighborhood of the voxel being considered is examined to constrain the region growing. The expanded region of the first extraction result is then used as the domain for the next processing. The intensity and the edge information of the current voxel only are utilized in the final extraction. Before segmentation, the intensity parameters of the brain tissues as well as partial volume effect are estimated by using expectation-maximization (EM) algorithm in order to provide an accurate data interpretation into the extraction. We tested the proposed method on T1-weighted MR images of brain and evaluated the segmentation effectiveness comparing the results with ground truths. Also, the generated meshes from the segmented brain volume by using mesh generating software are shown in this paper.

Study of acoustic correlates associate with emotional speech

NASA Astrophysics Data System (ADS)

Yildirim, Serdar; Lee, Sungbok; Lee, Chul Min; Bulut, Murtaza; Busso, Carlos; Kazemzadeh, Ebrahim; Narayanan, Shrikanth

2004-10-01

This study investigates the acoustic characteristics of four different emotions expressed in speech. The aim is to obtain detailed acoustic knowledge on how a speech signal is modulated by changes from neutral to a certain emotional state. Such knowledge is necessary for automatic emotion recognition and classification and emotional speech synthesis. Speech data obtained from two semi-professional actresses are analyzed and compared. Each subject produces 211 sentences with four different emotions; neutral, sad, angry, happy. We analyze changes in temporal and acoustic parameters such as magnitude and variability of segmental duration, fundamental frequency and the first three formant frequencies as a function of emotion. Acoustic differences among the emotions are also explored with mutual information computation, multidimensional scaling and acoustic likelihood comparison with normal speech. Results indicate that speech associated with anger and happiness is characterized by longer duration, shorter interword silence, higher pitch and rms energy with wider ranges. Sadness is distinguished from other emotions by lower rms energy and longer interword silence. Interestingly, the difference in formant pattern between [happiness/anger] and [neutral/sadness] are better reflected in back vowels such as /a/(/father/) than in front vowels. Detailed results on intra- and interspeaker variability will be reported.

77 FR 18849 - Notice of Proposed Information Collection: Comment Request: Insurance Termination Request for...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-03-28

... Information Collection: Comment Request: Insurance Termination Request for Multifamily Mortgage AGENCY: Office... also lists the following information: Title of Proposal: Insurance Termination Request for Multifamily... mortgagee mutually agree to terminate the HUD multifamily mortgage insurance. Agency form numbers, if...

Finding Useful Questions: On Bayesian Diagnosticity, Probability, Impact, and Information Gain

ERIC Educational Resources Information Center

Nelson, Jonathan D.

2005-01-01

Several norms for how people should assess a question's usefulness have been proposed, notably Bayesian diagnosticity, information gain (mutual information), Kullback-Liebler distance, probability gain (error minimization), and impact (absolute change). Several probabilistic models of previous experiments on categorization, covariation assessment,…

Optimal averaging of soil moisture predictions from ensemble land surface model simulations

USDA-ARS?s Scientific Manuscript database

The correct interpretation of ensemble information obtained from the parallel implementation of multiple land surface models (LSMs) requires information concerning the LSM ensemble’s mutual error covariance. Here we propose a new technique for obtaining such information using an instrumental variabl...

Morphology and innervation pattern of the feline urogenital junction.

PubMed

Wrobel, K H; Gürtler, A

2004-12-01

The feline urogenital junction is situated between the extratesticular rete and the spacious initial segments of the efferent ductules. The rete epithelium is cuboidal to low columnar. The rete cells forming the junction rest on a wavy basal lamina, display deep mutual invaginations, possess central nuclei with several infoldings and form a distinct border with the columnar epithelial cells of the initial segments of the ductuli efferentes. The epithelium of the initial segments is composed of ciliated cells and non-ciliated principal cells. The latter are the dominating type and characterized by an apical brush-border and a supranuclear endocytotic apparatus. The stroma of the extratesticular rete contains an abundance of collagen whereas contractile cells are here generally absent. In contrast, the initial segments of the efferent ductules are surrounded by elastic fibres and a layer of contractile cells. All nerves for the feline urogenital junction come from the nervus spermaticus superior. In the epididymal head, small nerve bundles deviate into the septa between the ductules. Single fibres establish a dense network within the muscular coat of the ductuli. At the transition to the extratesticular rete, this network ends abruptly. Nerve fibres in the confines of the rete are associated with blood vessels or proceed to the testicular interior, but establish no relationships with the rete epithelium or the myofibroblasts of the mediastinum. The nervous network in the walls of the efferent ductules and their initial segments is not only composed of sympathetic but also parasympathetic, non-myelinated fibres. Particularly noteworthy is the abundance of calcitonin gene-related peptide (CGRP)- and substance P (SP)-containing axons around the initial segments. Both neuroproteins are consistent markers for sensory neurones. Taken together, it can be assumed that the entry of seminal fluid and spermatozoa into the efferent ductules is controlled by a regulatory nervous chain provided with afferent and efferent components.

Ethics of care in medical tourism: Informal caregivers' narratives of responsibility, vulnerability and mutuality.

PubMed

Whitmore, Rebecca; Crooks, Valorie A; Snyder, Jeremy

2015-09-01

This study examines the experiences of informal caregivers in medical tourism through an ethics of care lens. We conducted semi-structured interviews with 20 Canadians who had accompanied their friends or family members abroad for surgery, asking questions that dealt with their experiences prior to, during and after travel. Thematic analysis revealed three themes central to an ethics of care: responsibility, vulnerability and mutuality. Ethics of care theorists have highlighted how care has been historically devalued. We posit that medical tourism reproduces dominant narratives about care in a novel care landscape. Informal care goes unaccounted for by the industry, as it occurs in largely private spaces at a geographic distance from the home countries of medical tourists. Copyright © 2015 Elsevier Ltd. All rights reserved.

A new fractional order derivative based active contour model for colon wall segmentation

NASA Astrophysics Data System (ADS)

Chen, Bo; Li, Lihong C.; Wang, Huafeng; Wei, Xinzhou; Huang, Shan; Chen, Wensheng; Liang, Zhengrong

2018-02-01

Segmentation of colon wall plays an important role in advancing computed tomographic colonography (CTC) toward a screening modality. Due to the low contrast of CT attenuation around colon wall, accurate segmentation of the boundary of both inner and outer wall is very challenging. In this paper, based on the geodesic active contour model, we develop a new model for colon wall segmentation. First, tagged materials in CTC images were automatically removed via a partial volume (PV) based electronic colon cleansing (ECC) strategy. We then present a new fractional order derivative based active contour model to segment the volumetric colon wall from the cleansed CTC images. In this model, the regionbased Chan-Vese model is incorporated as an energy term to the whole model so that not only edge/gradient information but also region/volume information is taken into account in the segmentation process. Furthermore, a fractional order differentiation derivative energy term is also developed in the new model to preserve the low frequency information and improve the noise immunity of the new segmentation model. The proposed colon wall segmentation approach was validated on 16 patient CTC scans. Experimental results indicate that the present scheme is very promising towards automatically segmenting colon wall, thus facilitating computer aided detection of initial colonic polyp candidates via CTC.

Maximum-likelihood techniques for joint segmentation-classification of multispectral chromosome images.

PubMed

Schwartzkopf, Wade C; Bovik, Alan C; Evans, Brian L

2005-12-01

Traditional chromosome imaging has been limited to grayscale images, but recently a 5-fluorophore combinatorial labeling technique (M-FISH) was developed wherein each class of chromosomes binds with a different combination of fluorophores. This results in a multispectral image, where each class of chromosomes has distinct spectral components. In this paper, we develop new methods for automatic chromosome identification by exploiting the multispectral information in M-FISH chromosome images and by jointly performing chromosome segmentation and classification. We (1) develop a maximum-likelihood hypothesis test that uses multispectral information, together with conventional criteria, to select the best segmentation possibility; (2) use this likelihood function to combine chromosome segmentation and classification into a robust chromosome identification system; and (3) show that the proposed likelihood function can also be used as a reliable indicator of errors in segmentation, errors in classification, and chromosome anomalies, which can be indicators of radiation damage, cancer, and a wide variety of inherited diseases. We show that the proposed multispectral joint segmentation-classification method outperforms past grayscale segmentation methods when decomposing touching chromosomes. We also show that it outperforms past M-FISH classification techniques that do not use segmentation information.

Semantic Image Segmentation with Contextual Hierarchical Models.

PubMed

Seyedhosseini, Mojtaba; Tasdizen, Tolga

2016-05-01

Semantic segmentation is the problem of assigning an object label to each pixel. It unifies the image segmentation and object recognition problems. The importance of using contextual information in semantic segmentation frameworks has been widely realized in the field. We propose a contextual framework, called contextual hierarchical model (CHM), which learns contextual information in a hierarchical framework for semantic segmentation. At each level of the hierarchy, a classifier is trained based on downsampled input images and outputs of previous levels. Our model then incorporates the resulting multi-resolution contextual information into a classifier to segment the input image at original resolution. This training strategy allows for optimization of a joint posterior probability at multiple resolutions through the hierarchy. Contextual hierarchical model is purely based on the input image patches and does not make use of any fragments or shape examples. Hence, it is applicable to a variety of problems such as object segmentation and edge detection. We demonstrate that CHM performs at par with state-of-the-art on Stanford background and Weizmann horse datasets. It also outperforms state-of-the-art edge detection methods on NYU depth dataset and achieves state-of-the-art on Berkeley segmentation dataset (BSDS 500).

Efficient graph-cut tattoo segmentation

NASA Astrophysics Data System (ADS)

Kim, Joonsoo; Parra, Albert; Li, He; Delp, Edward J.

2015-03-01

Law enforcement is interested in exploiting tattoos as an information source to identify, track and prevent gang-related crimes. Many tattoo image retrieval systems have been described. In a retrieval system tattoo segmentation is an important step for retrieval accuracy since segmentation removes background information in a tattoo image. Existing segmentation methods do not extract the tattoo very well when the background includes textures and color similar to skin tones. In this paper we describe a tattoo segmentation approach by determining skin pixels in regions near the tattoo. In these regions graph-cut segmentation using a skin color model and a visual saliency map is used to find skin pixels. After segmentation we determine which set of skin pixels are connected with each other that form a closed contour including a tattoo. The regions surrounded by the closed contours are considered tattoo regions. Our method segments tattoos well when the background includes textures and color similar to skin.

[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.

Prostate Cancer Rates by Race and Ethnicity

MedlinePlus

... P25–1130). For more information, see the USCS technical notes. † Race categories are not mutually exclusive from ... with caution. For more information, see the USCS technical notes. ¶ Data are compiled from cancer registries that ...

The Roles of Tonal and Segmental Information in Mandarin Spoken Word Recognition: An Eyetracking Study

ERIC Educational Resources Information Center

Malins, Jeffrey G.; Joanisse, Marc F.

2010-01-01

We used eyetracking to examine how tonal versus segmental information influence spoken word recognition in Mandarin Chinese. Participants heard an auditory word and were required to identify its corresponding picture from an array that included the target item ("chuang2" "bed"), a phonological competitor (segmental: chuang1 "window"; cohort:…

Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks.

PubMed

Bi, Lei; Kim, Jinman; Ahn, Euijoon; Kumar, Ashnil; Fulham, Michael; Feng, Dagan

2017-09-01

Segmentation of skin lesions is an important step in the automated computer aided diagnosis of melanoma. However, existing segmentation methods have a tendency to over- or under-segment the lesions and perform poorly when the lesions have fuzzy boundaries, low contrast with the background, inhomogeneous textures, or contain artifacts. Furthermore, the performance of these methods are heavily reliant on the appropriate tuning of a large number of parameters as well as the use of effective preprocessing techniques, such as illumination correction and hair removal. We propose to leverage fully convolutional networks (FCNs) to automatically segment the skin lesions. FCNs are a neural network architecture that achieves object detection by hierarchically combining low-level appearance information with high-level semantic information. We address the issue of FCN producing coarse segmentation boundaries for challenging skin lesions (e.g., those with fuzzy boundaries and/or low difference in the textures between the foreground and the background) through a multistage segmentation approach in which multiple FCNs learn complementary visual characteristics of different skin lesions; early stage FCNs learn coarse appearance and localization information while late-stage FCNs learn the subtle characteristics of the lesion boundaries. We also introduce a new parallel integration method to combine the complementary information derived from individual segmentation stages to achieve a final segmentation result that has accurate localization and well-defined lesion boundaries, even for the most challenging skin lesions. We achieved an average Dice coefficient of 91.18% on the ISBI 2016 Skin Lesion Challenge dataset and 90.66% on the PH2 dataset. Our extensive experimental results on two well-established public benchmark datasets demonstrate that our method is more effective than other state-of-the-art methods for skin lesion segmentation.

Knowledge guided information fusion for segmentation of multiple sclerosis lesions in MRI images

NASA Astrophysics Data System (ADS)

Zhu, Chaozhe; Jiang, Tianzi

2003-05-01

In this work, T1-, T2- and PD-weighted MR images of multiple sclerosis (MS) patients, providing information on the properties of tissues from different aspects, are treated as three independent information sources for the detection and segmentation of MS lesions. Based on information fusion theory, a knowledge guided information fusion framework is proposed to accomplish 3-D segmentation of MS lesions. This framework consists of three parts: (1) information extraction, (2) information fusion, and (3) decision. Information provided by different spectral images is extracted and modeled separately in each spectrum using fuzzy sets, aiming at managing the uncertainty and ambiguity in the images due to noise and partial volume effect. In the second part, the possible fuzzy map of MS lesions in each spectral image is constructed from the extracted information under the guidance of experts' knowledge, and then the final fuzzy map of MS lesions is constructed through the fusion of the fuzzy maps obtained from different spectrum. Finally, 3-D segmentation of MS lesions is derived from the final fuzzy map. Experimental results show that this method is fast and accurate.

21 CFR 26.19 - Information relating to quality aspects.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 1 2013-04-01 2013-04-01 false Information relating to quality aspects. 26.19... relating to quality aspects. The authorities will establish an appropriate means of exchanging information... MUTUAL RECOGNITION OF PHARMACEUTICAL GOOD MANUFACTURING PRACTICE REPORTS, MEDICAL DEVICE QUALITY SYSTEM...

21 CFR 26.19 - Information relating to quality aspects.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 1 2012-04-01 2012-04-01 false Information relating to quality aspects. 26.19... relating to quality aspects. The authorities will establish an appropriate means of exchanging information... MUTUAL RECOGNITION OF PHARMACEUTICAL GOOD MANUFACTURING PRACTICE REPORTS, MEDICAL DEVICE QUALITY SYSTEM...

21 CFR 26.19 - Information relating to quality aspects.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 1 2011-04-01 2011-04-01 false Information relating to quality aspects. 26.19... relating to quality aspects. The authorities will establish an appropriate means of exchanging information... MUTUAL RECOGNITION OF PHARMACEUTICAL GOOD MANUFACTURING PRACTICE REPORTS, MEDICAL DEVICE QUALITY SYSTEM...

21 CFR 26.19 - Information relating to quality aspects.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 1 2014-04-01 2014-04-01 false Information relating to quality aspects. 26.19... relating to quality aspects. The authorities will establish an appropriate means of exchanging information... MUTUAL RECOGNITION OF PHARMACEUTICAL GOOD MANUFACTURING PRACTICE REPORTS, MEDICAL DEVICE QUALITY SYSTEM...

Joint Attention Enhances Visual Working Memory

ERIC Educational Resources Information Center

Gregory, Samantha E. A.; Jackson, Margaret C.

2017-01-01

Joint attention--the mutual focus of 2 individuals on an item--speeds detection and discrimination of target information. However, what happens to that information beyond the initial perceptual episode? To fully comprehend and engage with our immediate environment also requires working memory (WM), which integrates information from second to…

Optimal averaging of soil moisture predictions from ensemble land surface model simulations

USDA-ARS?s Scientific Manuscript database

The correct interpretation of ensemble 3 soil moisture information obtained from the parallel implementation of multiple land surface models (LSMs) requires information concerning the LSM ensemble’s mutual error covariance. Here we propose a new technique for obtaining such information using an inst...

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

Based on the CSI regional segmentation indoor localization algorithm

NASA Astrophysics Data System (ADS)

Zeng, Xi; Lin, Wei; Lan, Jingwei

2017-08-01

To solve the problem of high cost and low accuracy, the method of Channel State Information (CSI) regional segmentation are proposed in the indoor positioning. Because Channel State Information (CSI) stability, and effective against multipath effect, we used the Channel State Information (CSI) to segment location area. The method Acquisition CSI the influence of different link to pinpoint the location of the area. Then the method can improve the accuracy of positioning, and reduce the cost of the fingerprint localization algorithm.

Quantum corrections to holographic mutual information

DOE PAGES

Agon, Cesar A.; Faulkner, Thomas

2016-08-22

We compute the leading contribution to the mutual information (MI) of two disjoint spheres in the large distance regime for arbitrary conformal field theories (CFT) in any dimension. This is achieved by refining the operator product expansion method introduced by Cardy [1]. For CFTs with holographic duals the leading contribution to the MI at long distances comes from bulk quantum corrections to the Ryu-Takayanagi area formula. According to the FLM proposal [2] this equals the bulk MI between the two disjoint regions spanned by the boundary spheres and their corresponding minimal area surfaces. We compute this quantum correction and providemore » in this way a non-trivial check of the FLM proposal.« less

Delocalizing entanglement of anisotropic black branes

NASA Astrophysics Data System (ADS)

Jahnke, Viktor

2018-01-01

We study the mutual information between pairs of regions on the two asymptotic boundaries of maximally extended anisotropic black branes. This quantity characterizes the local pattern of entanglement of the thermofield double states which are dual to these geometries. We analyze the disruption of the mutual information in anisotropic shock wave geometries and show that the entanglement velocity plays an important role in this phenomenon. Moreover, we compute several chaos-related properties of this system, such as the entanglement velocity, the butterfly velocity, and the scrambling time. We find that the butterfly velocity and the entanglement velocity violate the upper bounds proposed in [1-3], but remain bounded by their corresponding values in the infrared effective theory.

Variation in motor output and motor performance in a centrally generated motor pattern

PubMed Central

Norris, Brian J.; Doloc-Mihu, Anca; Calabrese, Ronald L.

2014-01-01

Central pattern generators (CPGs) produce motor patterns that ultimately drive motor outputs. We studied how functional motor performance is achieved, specifically, whether the variation seen in motor patterns is reflected in motor performance and whether fictive motor patterns differ from those in vivo. We used the leech heartbeat system in which a bilaterally symmetrical CPG coordinates segmental heart motor neurons and two segmented heart tubes into two mutually exclusive coordination modes: rear-to-front peristaltic on one side and nearly synchronous on the other, with regular side-to-side switches. We assessed individual variability of the motor pattern and the beat pattern in vivo. To quantify the beat pattern we imaged intact adults. To quantify the phase relations between motor neurons and heart constrictions we recorded extracellularly from two heart motor neurons and movement from the corresponding heart segments in minimally dissected leeches. Variation in the motor pattern was reflected in motor performance only in the peristaltic mode, where larger intersegmental phase differences in the motor neurons resulted in larger phase differences between heart constrictions. Fictive motor patterns differed from those in vivo only in the synchronous mode, where intersegmental phase differences in vivo had a larger front-to-rear bias and were more constrained. Additionally, load-influenced constriction timing might explain the amplification of the phase differences between heart segments in the peristaltic mode and the higher variability in motor output due to body shape assumed in this soft-bodied animal. The motor pattern determines the beat pattern, peristaltic or synchronous, but heart mechanics influence the phase relations achieved. PMID:24717348

Defect detection in slab surface: a novel dual Charge-coupled Device imaging-based fuzzy connectedness strategy.

PubMed

Zhao, Liming; Ouyang, Qi; Chen, Dengfu; Udupa, Jayaram K; Wang, Huiqian; Zeng, Yuebin

2014-11-01

To provide an accurate surface defects inspection system and make the automation of robust image segmentation method a reality in routine production line, a general approach is presented for continuous casting slab (CC-slab) surface defects extraction and delineation. The applicability of the system is not tied to CC-slab exclusively. We combined the line array CCD (Charge-coupled Device) traditional scanning imaging (LS-imaging) and area array CCD laser three-dimensional (3D) scanning imaging (AL-imaging) strategies in designing the system. Its aim is to suppress the respective imaging system's limitations. In the system, the images acquired from the two CCD sensors are carefully aligned in space and in time by maximum mutual information-based full-fledged registration schema. Subsequently, the image information is fused from these two subsystems such as the unbroken 2D information in LS-imaging and 3D depressed information in AL-imaging. Finally, on the basis of the established dual scanning imaging system the region of interest (ROI) localization by seed specification was designed, and the delineation for ROI by iterative relative fuzzy connectedness (IRFC) algorithm was utilized to get a precise inspection result. Our method takes into account the complementary advantages in the two common machine vision (MV) systems and it performs competitively with the state-of-the-art as seen from the comparison of experimental results. For the first time, a joint imaging scanning strategy is proposed for CC-slab surface defect inspection that allows a feasible way of powerful ROI delineation strategies to be applied to the MV inspection field. Multi-ROI delineation by using IRFC in this research field may further improve the results.

Curvature and the Visual Perception of Shape: Theory on Information along Object Boundaries and the Minima Rule Revisited

ERIC Educational Resources Information Center

Lim, Ik Soo; Leek, E. Charles

2012-01-01

Previous empirical studies have shown that information along visual contours is known to be concentrated in regions of high magnitude of curvature, and, for closed contours, segments of negative curvature (i.e., concave segments) carry greater perceptual relevance than corresponding regions of positive curvature (i.e., convex segments). Lately,…

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

Item Selection Criteria with Practical Constraints for Computerized Classification Testing

ERIC Educational Resources Information Center

Lin, Chuan-Ju

2011-01-01

This study compares four item selection criteria for a two-category computerized classification testing: (1) Fisher information (FI), (2) Kullback-Leibler information (KLI), (3) weighted log-odds ratio (WLOR), and (4) mutual information (MI), with respect to the efficiency and accuracy of classification decision using the sequential probability…

77 FR 35705 - Notice of Submission of Proposed Information Collection to OMB Request for Termination of...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-14

... Proposed Information Collection to OMB Request for Termination of Multifamily Mortgage Insurance AGENCY... mutually agree to terminate the HUD multifamily mortgage insurance. DATES: Comments Due Date: July 16, 2012... following information: Title of Proposal: Request for Termination of Multifamily Mortgage Insurance. OMB...

32 CFR 700.334 - The Chief of Information.

Code of Federal Regulations, 2010 CFR

2010-07-01

...) The Chief of Information is the direct representative of the Secretary of the Navy in all public affairs and internal relations matters. The Chief of Information is authorized to implement Navy public affairs and internal relations policies and to coordinate those Navy and Marine Corps activities of mutual...

Multi-scale image segmentation method with visual saliency constraints and its application

NASA Astrophysics Data System (ADS)

Chen, Yan; Yu, Jie; Sun, Kaimin

2018-03-01

Object-based image analysis method has many advantages over pixel-based methods, so it is one of the current research hotspots. It is very important to get the image objects by multi-scale image segmentation in order to carry out object-based image analysis. The current popular image segmentation methods mainly share the bottom-up segmentation principle, which is simple to realize and the object boundaries obtained are accurate. However, the macro statistical characteristics of the image areas are difficult to be taken into account, and fragmented segmentation (or over-segmentation) results are difficult to avoid. In addition, when it comes to information extraction, target recognition and other applications, image targets are not equally important, i.e., some specific targets or target groups with particular features worth more attention than the others. To avoid the problem of over-segmentation and highlight the targets of interest, this paper proposes a multi-scale image segmentation method with visually saliency graph constraints. Visual saliency theory and the typical feature extraction method are adopted to obtain the visual saliency information, especially the macroscopic information to be analyzed. The visual saliency information is used as a distribution map of homogeneity weight, where each pixel is given a weight. This weight acts as one of the merging constraints in the multi- scale image segmentation. As a result, pixels that macroscopically belong to the same object but are locally different can be more likely assigned to one same object. In addition, due to the constraint of visual saliency model, the constraint ability over local-macroscopic characteristics can be well controlled during the segmentation process based on different objects. These controls will improve the completeness of visually saliency areas in the segmentation results while diluting the controlling effect for non- saliency background areas. Experiments show that this method works better for texture image segmentation than traditional multi-scale image segmentation methods, and can enable us to give priority control to the saliency objects of interest. This method has been used in image quality evaluation, scattered residential area extraction, sparse forest extraction and other applications to verify its validation. All applications showed good results.

A Dynamic Graph Cuts Method with Integrated Multiple Feature Maps for Segmenting Kidneys in 2D Ultrasound Images.

PubMed

Zheng, Qiang; Warner, Steven; Tasian, Gregory; Fan, Yong

2018-02-12

Automatic segmentation of kidneys in ultrasound (US) images remains a challenging task because of high speckle noise, low contrast, and large appearance variations of kidneys in US images. Because texture features may improve the US image segmentation performance, we propose a novel graph cuts method to segment kidney in US images by integrating image intensity information and texture feature maps. We develop a new graph cuts-based method to segment kidney US images by integrating original image intensity information and texture feature maps extracted using Gabor filters. To handle large appearance variation within kidney images and improve computational efficiency, we build a graph of image pixels close to kidney boundary instead of building a graph of the whole image. To make the kidney segmentation robust to weak boundaries, we adopt localized regional information to measure similarity between image pixels for computing edge weights to build the graph of image pixels. The localized graph is dynamically updated and the graph cuts-based segmentation iteratively progresses until convergence. Our method has been evaluated based on kidney US images of 85 subjects. The imaging data of 20 randomly selected subjects were used as training data to tune parameters of the image segmentation method, and the remaining data were used as testing data for validation. Experiment results demonstrated that the proposed method obtained promising segmentation results for bilateral kidneys (average Dice index = 0.9446, average mean distance = 2.2551, average specificity = 0.9971, average accuracy = 0.9919), better than other methods under comparison (P < .05, paired Wilcoxon rank sum tests). The proposed method achieved promising performance for segmenting kidneys in two-dimensional US images, better than segmentation methods built on any single channel of image information. This method will facilitate extraction of kidney characteristics that may predict important clinical outcomes such as progression of chronic kidney disease. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

- and Graph-Based Point Cloud Segmentation of 3d Scenes Using Perceptual Grouping Laws

NASA Astrophysics Data System (ADS)

Xu, Y.; Hoegner, L.; Tuttas, S.; Stilla, U.

2017-05-01

Segmentation is the fundamental step for recognizing and extracting objects from point clouds of 3D scene. In this paper, we present a strategy for point cloud segmentation using voxel structure and graph-based clustering with perceptual grouping laws, which allows a learning-free and completely automatic but parametric solution for segmenting 3D point cloud. To speak precisely, two segmentation methods utilizing voxel and supervoxel structures are reported and tested. The voxel-based data structure can increase efficiency and robustness of the segmentation process, suppressing the negative effect of noise, outliers, and uneven points densities. The clustering of voxels and supervoxel is carried out using graph theory on the basis of the local contextual information, which commonly conducted utilizing merely pairwise information in conventional clustering algorithms. By the use of perceptual laws, our method conducts the segmentation in a pure geometric way avoiding the use of RGB color and intensity information, so that it can be applied to more general applications. Experiments using different datasets have demonstrated that our proposed methods can achieve good results, especially for complex scenes and nonplanar surfaces of objects. Quantitative comparisons between our methods and other representative segmentation methods also confirms the effectiveness and efficiency of our proposals.

Multi atlas based segmentation: Should we prefer the best atlas group over the group of best atlases?

PubMed

Zaffino, Paolo; Ciardo, Delia; Raudaschl, Patrik; Fritscher, Karl; Ricotti, Rosalinda; Alterio, Daniela; Marvaso, Giulia; Fodor, Cristiana; Baroni, Guido; Amato, Francesco; Orecchia, Roberto; Jereczek-Fossa, Barbara Alicja; Sharp, Gregory C; Spadea, Maria Francesca

2018-05-22

Multi Atlas Based Segmentation (MABS) uses a database of atlas images, and an atlas selection process is used to choose an atlas subset for registration and voting. In the current state of the art, atlases are chosen according to a similarity criterion between the target subject and each atlas in the database. In this paper, we propose a new concept for atlas selection that relies on selecting the best performing group of atlases rather than the group of highest scoring individual atlases. Experiments were performed using CT images of 50 patients, with contours of brainstem and parotid glands. The dataset was randomly split in 2 groups: 20 volumes were used as an atlas database and 30 served as target subjects for testing. Classic oracle group selection, where atlases are chosen by the highest Dice Similarity Coefficient (DSC) with the target, was performed. This was compared to oracle Group selection, where all the combinations of atlas subgroups were considered and scored by computing DSC with the target subject. Subsequently, Convolutional Neural Networks (CNNs) were designed to predict the best group of atlases. The results were compared also with the selection strategy based on Normalized Mutual Information (NMI). Oracle group was proved to be significantly better that classic oracle selection (p<10-5). Atlas group selection led to a median±interquartile DSC of 0.740±0.084, 0.718±0.086 and 0.670±0.097 for brainstem and left/right parotid glands respectively, outperforming NMI selection 0.676±0.113, 0.632±0.104 and 0.606±0.118 (p<0.001) as well as classic oracle selection. The implemented methodology is a proof of principle that selecting the atlases by considering the performance of the entire group of atlases instead of each single atlas leads to higher segmentation accuracy, being even better then current oracle strategy. This finding opens a new discussion about the most appropriate atlas selection criterion for MABS. © 2018 Institute of Physics and Engineering in Medicine.

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.

Measuring the usefulness of hidden units in Boltzmann machines with mutual information.

PubMed

Berglund, Mathias; Raiko, Tapani; Cho, Kyunghyun

2015-04-01

Restricted Boltzmann machines (RBMs) and deep Boltzmann machines (DBMs) are important models in deep learning, but it is often difficult to measure their performance in general, or measure the importance of individual hidden units in specific. We propose to use mutual information to measure the usefulness of individual hidden units in Boltzmann machines. The measure is fast to compute, and serves as an upper bound for the information the neuron can pass on, enabling detection of a particular kind of poor training results. We confirm experimentally that the proposed measure indicates how much the performance of the model drops when some of the units of an RBM are pruned away. We demonstrate the usefulness of the measure for early detection of poor training in DBMs. Copyright © 2014 Elsevier Ltd. All rights reserved.

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.

Cancer cell redirection biomarker discovery using a mutual information approach.

PubMed

Roche, Kimberly; Feltus, F Alex; Park, Jang Pyo; Coissieux, Marie-May; Chang, Chenyan; Chan, Vera B S; Bentires-Alj, Mohamed; Booth, Brian W

2017-01-01

Introducing tumor-derived cells into normal mammary stem cell niches at a sufficiently high ratio of normal to tumorous cells causes those tumor cells to undergo a change to normal mammary phenotype and yield normal mammary progeny. This phenomenon has been termed cancer cell redirection. We have developed an in vitro model that mimics in vivo redirection of cancer cells by the normal mammary microenvironment. Using the RNA profiling data from this cellular model, we examined high-level characteristics of the normal, redirected, and tumor transcriptomes and found the global expression profiles clearly distinguish the three expression states. To identify potential redirection biomarkers that cause the redirected state to shift toward the normal expression pattern, we used mutual information relationships between normal, redirected, and tumor cell groups. Mutual information relationship analysis reduced a dataset of over 35,000 gene expression measurements spread over 13,000 curated gene sets to a set of 20 significant molecular signatures totaling 906 unique loci. Several of these molecular signatures are hallmark drivers of the tumor state. Using differential expression as a guide, we further refined the gene set to 120 core redirection biomarker genes. The expression levels of these core biomarkers are sufficient to make the normal and redirected gene expression states indistinguishable from each other but radically different from the tumor state.

Cancer cell redirection biomarker discovery using a mutual information approach

PubMed Central

Roche, Kimberly; Feltus, F. Alex; Park, Jang Pyo; Coissieux, Marie-May; Chang, Chenyan; Chan, Vera B. S.; Bentires-Alj, Mohamed

2017-01-01

Introducing tumor-derived cells into normal mammary stem cell niches at a sufficiently high ratio of normal to tumorous cells causes those tumor cells to undergo a change to normal mammary phenotype and yield normal mammary progeny. This phenomenon has been termed cancer cell redirection. We have developed an in vitro model that mimics in vivo redirection of cancer cells by the normal mammary microenvironment. Using the RNA profiling data from this cellular model, we examined high-level characteristics of the normal, redirected, and tumor transcriptomes and found the global expression profiles clearly distinguish the three expression states. To identify potential redirection biomarkers that cause the redirected state to shift toward the normal expression pattern, we used mutual information relationships between normal, redirected, and tumor cell groups. Mutual information relationship analysis reduced a dataset of over 35,000 gene expression measurements spread over 13,000 curated gene sets to a set of 20 significant molecular signatures totaling 906 unique loci. Several of these molecular signatures are hallmark drivers of the tumor state. Using differential expression as a guide, we further refined the gene set to 120 core redirection biomarker genes. The expression levels of these core biomarkers are sufficient to make the normal and redirected gene expression states indistinguishable from each other but radically different from the tumor state. PMID:28594912

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

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

Bayesian Fusion of Color and Texture Segmentations

NASA Technical Reports Server (NTRS)

Manduchi, Roberto

2000-01-01

In many applications one would like to use information from both color and texture features in order to segment an image. We propose a novel technique to combine "soft" segmentations computed for two or more features independently. Our algorithm merges models according to a mean entropy criterion, and allows to choose the appropriate number of classes for the final grouping. This technique also allows to improve the quality of supervised classification based on one feature (e.g. color) by merging information from unsupervised segmentation based on another feature (e.g., texture.)

Visual speech segmentation: using facial cues to locate word boundaries in continuous speech

PubMed Central

Mitchel, Aaron D.; Weiss, Daniel J.

2014-01-01

Speech is typically a multimodal phenomenon, yet few studies have focused on the exclusive contributions of visual cues to language acquisition. To address this gap, we investigated whether visual prosodic information can facilitate speech segmentation. Previous research has demonstrated that language learners can use lexical stress and pitch cues to segment speech and that learners can extract this information from talking faces. Thus, we created an artificial speech stream that contained minimal segmentation cues and paired it with two synchronous facial displays in which visual prosody was either informative or uninformative for identifying word boundaries. Across three familiarisation conditions (audio stream alone, facial streams alone, and paired audiovisual), learning occurred only when the facial displays were informative to word boundaries, suggesting that facial cues can help learners solve the early challenges of language acquisition. PMID:25018577

3D characterization of trans- and inter-lamellar fatigue crack in (α + β) Ti alloy

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

Babout, Laurent, E-mail: Laurent.babout@p.lodz.pl; Jopek, Łukasz; Preuss, Michael

2014-12-15

This paper presents a three dimensional image processing strategy that has been developed to quantitatively analyze and correlate the path of a fatigue crack with the lamellar microstructure found in Ti-6246. The analysis is carried out on X-ray microtomography images acquired in situ during uniaxial fatigue testing. The crack, the primary β-grain boundaries and the α lamellae have been segmented separately and merged for the first time to allow a better characterization and understanding of their mutual interaction. This has particularly emphasized the role of translamellar crack growth at a very high propagation angle with regard to the lamellar orientation,more » supporting the central role of colonies favorably oriented for basal 〈a〉 slip to guide the crack in the fully lamellar microstructure of Ti alloy. - Highlights: • 3D tomography images reveal strong short fatigue crack interaction with α lamellae. • Proposed 3D image processing methodology makes their segmentation possible. • Crack-lamellae orientation maps show prevalence of translamellar cracking. • Angle study comforts the influence of basal/prismatic slip on crack path.« less

Temporally coherent 4D video segmentation for teleconferencing

NASA Astrophysics Data System (ADS)

Ehmann, Jana; Guleryuz, Onur G.

2013-09-01

We develop an algorithm for 4-D (RGB+Depth) video segmentation targeting immersive teleconferencing ap- plications on emerging mobile devices. Our algorithm extracts users from their environments and places them onto virtual backgrounds similar to green-screening. The virtual backgrounds increase immersion and interac- tivity, relieving the users of the system from distractions caused by disparate environments. Commodity depth sensors, while providing useful information for segmentation, result in noisy depth maps with a large number of missing depth values. By combining depth and RGB information, our work signi¯cantly improves the other- wise very coarse segmentation. Further imposing temporal coherence yields compositions where the foregrounds seamlessly blend with the virtual backgrounds with minimal °icker and other artifacts. We achieve said improve- ments by correcting the missing information in depth maps before fast RGB-based segmentation, which operates in conjunction with temporal coherence. Simulation results indicate the e±cacy of the proposed system in video conferencing scenarios.

International Librarianship: Developing Professional, Intercultural, and Educational Leadership

ERIC Educational Resources Information Center

Constantinou, Constantia, Ed.; Miller, Michael J., Ed.; Schlesinger, Kenneth, Ed.

2017-01-01

International librarianship stems from a desire to bring about political change, transcultural understanding, collaboration, and mutual respect. Historically, librarians have been deeply involved with challenging issues of information sharing, equity in information access, and bridging the digital divide between different socioeconomic…

A combined learning algorithm for prostate segmentation on 3D CT images.

PubMed

Ma, Ling; Guo, Rongrong; Zhang, Guoyi; Schuster, David M; Fei, Baowei

2017-11-01

Segmentation of the prostate on CT images has many applications in the diagnosis and treatment of prostate cancer. Because of the low soft-tissue contrast on CT images, prostate segmentation is a challenging task. A learning-based segmentation method is proposed for the prostate on three-dimensional (3D) CT images. We combine population-based and patient-based learning methods for segmenting the prostate on CT images. Population data can provide useful information to guide the segmentation processing. Because of inter-patient variations, patient-specific information is particularly useful to improve the segmentation accuracy for an individual patient. In this study, we combine a population learning method and a patient-specific learning method to improve the robustness of prostate segmentation on CT images. We train a population model based on the data from a group of prostate patients. We also train a patient-specific model based on the data of the individual patient and incorporate the information as marked by the user interaction into the segmentation processing. We calculate the similarity between the two models to obtain applicable population and patient-specific knowledge to compute the likelihood of a pixel belonging to the prostate tissue. A new adaptive threshold method is developed to convert the likelihood image into a binary image of the prostate, and thus complete the segmentation of the gland on CT images. The proposed learning-based segmentation algorithm was validated using 3D CT volumes of 92 patients. All of the CT image volumes were manually segmented independently three times by two, clinically experienced radiologists and the manual segmentation results served as the gold standard for evaluation. The experimental results show that the segmentation method achieved a Dice similarity coefficient of 87.18 ± 2.99%, compared to the manual segmentation. By combining the population learning and patient-specific learning methods, the proposed method is effective for segmenting the prostate on 3D CT images. The prostate CT segmentation method can be used in various applications including volume measurement and treatment planning of the prostate. © 2017 American Association of Physicists in Medicine.

Using multimodal information for the segmentation of fluorescent micrographs with application to virology and microbiology.

PubMed

Held, Christian; Wenzel, Jens; Webel, Rike; Marschall, Manfred; Lang, Roland; Palmisano, Ralf; Wittenberg, Thomas

2011-01-01

In order to improve reproducibility and objectivity of fluorescence microscopy based experiments and to enable the evaluation of large datasets, flexible segmentation methods are required which are able to adapt to different stainings and cell types. This adaption is usually achieved by the manual adjustment of the segmentation methods parameters, which is time consuming and challenging for biologists with no knowledge on image processing. To avoid this, parameters of the presented methods automatically adapt to user generated ground truth to determine the best method and the optimal parameter setup. These settings can then be used for segmentation of the remaining images. As robust segmentation methods form the core of such a system, the currently used watershed transform based segmentation routine is replaced by a fast marching level set based segmentation routine which incorporates knowledge on the cell nuclei. Our evaluations reveal that incorporation of multimodal information improves segmentation quality for the presented fluorescent datasets.

Dual-modality brain PET-CT image segmentation based on adaptive use of functional and anatomical information.

PubMed

Xia, Yong; Eberl, Stefan; Wen, Lingfeng; Fulham, Michael; Feng, David Dagan

2012-01-01

Dual medical imaging modalities, such as PET-CT, are now a routine component of clinical practice. Medical image segmentation methods, however, have generally only been applied to single modality images. In this paper, we propose the dual-modality image segmentation model to segment brain PET-CT images into gray matter, white matter and cerebrospinal fluid. This model converts PET-CT image segmentation into an optimization process controlled simultaneously by PET and CT voxel values and spatial constraints. It is innovative in the creation and application of the modality discriminatory power (MDP) coefficient as a weighting scheme to adaptively combine the functional (PET) and anatomical (CT) information on a voxel-by-voxel basis. Our approach relies upon allowing the modality with higher discriminatory power to play a more important role in the segmentation process. We compared the proposed approach to three other image segmentation strategies, including PET-only based segmentation, combination of the results of independent PET image segmentation and CT image segmentation, and simultaneous segmentation of joint PET and CT images without an adaptive weighting scheme. Our results in 21 clinical studies showed that our approach provides the most accurate and reliable segmentation for brain PET-CT images. Copyright © 2011 Elsevier Ltd. All rights reserved.

SU-E-J-179: Prediction of Pelvic Nodal Coverage Using Mutual Information Between Cone-Beam and Planning CTs

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

Jani, S; Kishan, A; O'Connell, D

2014-06-01

Purpose: To investigate if pelvic nodal coverage for prostate patients undergoing intensity modulated radiotherapy (IMRT) can be predicted using mutual image information computed between planning and cone-beam CTs (CBCTs). Methods: Four patients with high-risk prostate adenocarcinoma were treated with IMRT on a Varian TrueBeam. Plans were designed such that 95% of the nodal planning target volume (PTV) received the prescription dose of 45 Gy (N=1) or 50.4 Gy (N=3). Weekly CBCTs (N=25) were acquired and the nodal clinical target volumes and organs at risk were contoured by a physician. The percent nodal volume receiving prescription dose was recorded as amore » ground truth. Using the recorded shifts performed by the radiation therapists at the time of image acquisition, CBCTs were aligned with the planning kVCT. Mutual image information (MI) was calculated between the CBCT and the aligned planning CT within the contour of the nodal PTV. Due to variable CBCT fields-of-view, CBCT images covering less than 90% of the nodal volume were excluded from the analysis, resulting in the removal of eight CBCTs. Results: A correlation coefficient of 0.40 was observed between the MI metric and the percent of the nodal target volume receiving the prescription dose. One patient's CBCTs had clear outliers from the rest of the patients. Upon further investigation, we discovered image artifacts that were present only in that patient's images. When those four images were excluded, the correlation improved to 0.81. Conclusion: This pilot study shows the potential of predicting pelvic nodal dosimetry by computing the mutual image information between planning CTs and patient setup CBCTs. Importantly, this technique does not involve manual or automatic contouring of the CBCT images. Additional patients and more robust exclusion criteria will help validate our findings.« less

Color segmentation in the HSI color space using the K-means algorithm

NASA Astrophysics Data System (ADS)

Weeks, Arthur R.; Hague, G. Eric

1997-04-01

Segmentation of images is an important aspect of image recognition. While grayscale image segmentation has become quite a mature field, much less work has been done with regard to color image segmentation. Until recently, this was predominantly due to the lack of available computing power and color display hardware that is required to manipulate true color images (24-bit). TOday, it is not uncommon to find a standard desktop computer system with a true-color 24-bit display, at least 8 million bytes of memory, and 2 gigabytes of hard disk storage. Segmentation of color images is not as simple as segmenting each of the three RGB color components separately. The difficulty of using the RGB color space is that it doesn't closely model the psychological understanding of color. A better color model, which closely follows that of human visual perception is the hue, saturation, intensity model. This color model separates the color components in terms of chromatic and achromatic information. Strickland et al. was able to show the importance of color in the extraction of edge features form an image. His method enhances the edges that are detectable in the luminance image with information from the saturation image. Segmentation of both the saturation and intensity components is easily accomplished with any gray scale segmentation algorithm, since these spaces are linear. The modulus 2(pi) nature of the hue color component makes its segmentation difficult. For example, a hue of 0 and 2(pi) yields the same color tint. Instead of applying separate image segmentation to each of the hue, saturation, and intensity components, a better method is to segment the chromatic component separately from the intensity component because of the importance that the chromatic information plays in the segmentation of color images. This paper presents a method of using the gray scale K-means algorithm to segment 24-bit color images. Additionally, this paper will show the importance the hue component plays in the segmentation of color images.

Airplane takeoff and landing performance monitoring system

NASA Technical Reports Server (NTRS)

Middleton, David B. (Inventor); Srivatsan, Raghavachari (Inventor); Person, Lee H., Jr. (Inventor)

1994-01-01

The invention is a real-time takeoff and landing performance monitoring system for an aircraft which provides a pilot with graphic and metric information to assist in decisions related to achieving rotation speed (VR) within the safe zone of a runway, or stopping the aircraft on the runway after landing or take-off abort. The system processes information in two segments: a pretakeoff segment and a real-time segment. One-time inputs of ambient conditions and airplane configuration information are used in the pretakeoff segment to generate scheduled performance data. The real-time segment uses the scheduled performance data, runway length data and transducer measured parameters to monitor the performance of the airplane throughout the takeoff roll. Airplane acceleration and engine-performance anomalies are detected and annunciated. A novel and important feature of this segment is that it updates the estimated runway rolling friction coefficient. Airplane performance predictions also reflect changes in head wind occurring as the takeoff roll progresses. The system provides a head-down display and a head-up display. The head-up display is projected onto a partially reflective transparent surface through which the pilot views the runway. By comparing the present performance of the airplane with a continually predicted nominal performance based upon given conditions, performance deficiencies are detected by the system and conveyed to pilot in form of both elemental information and integrated information.

Airplane takeoff and landing performance monitoring system

NASA Technical Reports Server (NTRS)

Middleton, David B. (Inventor); Srivatsan, Raghavachari (Inventor); Person, Jr., Lee H. (Inventor)

1996-01-01

The invention is a real-time takeoff and landing performance monitoring system for an aircraft which provides a pilot with graphic and metric information to assist in decisions related to achieving rotation speed (V.sub.R) within the safe zone of a runway, or stopping the aircraft on the runway after landing or take-off abort. The system processes information in two segments: a pretakeoff segment and a real-time segment. One-time inputs of ambient conditions and airplane configuration information are used in the pretakeoff segment to generate scheduled performance data. The real-time segment uses the scheduled performance data, runway length data and transducer measured parameters to monitor the performance of the airplane throughout the takeoff roll. Airplane acceleration and engine-performance anomalies are detected and annunciated. A novel and important feature of this segment is that it updates the estimated runway rolling friction coefficient. Airplane performance predictions also reflect changes in head wind occurring as the takeoff roll progresses. The system provides a head-down display and a head-up display. The head-up display is projected onto a partially reflective transparent surface through which the pilot views the runway. By comparing the present performance of the airplane with a continually predicted nominal performance based upon given conditions, performance deficiencies are detected by the system and conveyed to pilot in form of both elemental information and integrated information.

Multineuronal vectorization is more efficient than time-segmental vectorization for information extraction from neuronal activities in the inferior temporal cortex.

PubMed

Kaneko, Hidekazu; Tamura, Hiroshi; Tate, Shunta; Kawashima, Takahiro; Suzuki, Shinya S; Fujita, Ichiro

2010-08-01

In order for patients with disabilities to control assistive devices with their own neural activity, multineuronal spike trains must be efficiently decoded because only limited computational resources can be used to generate prosthetic control signals in portable real-time applications. In this study, we compare the abilities of two vectorizing procedures (multineuronal and time-segmental) to extract information from spike trains during the same total neuron-seconds. In the multineuronal vectorizing procedure, we defined a response vector whose components represented the spike counts of one to five neurons. In the time-segmental vectorizing procedure, a response vector consisted of components representing a neuron's spike counts for one to five time-segment(s) of a response period of 1 s. Spike trains were recorded from neurons in the inferior temporal cortex of monkeys presented with visual stimuli. We examined whether the amount of information of the visual stimuli carried by these neurons differed between the two vectorizing procedures. The amount of information calculated with the multineuronal vectorizing procedure, but not the time-segmental vectorizing procedure, significantly increased with the dimensions of the response vector. We conclude that the multineuronal vectorizing procedure is superior to the time-segmental vectorizing procedure in efficiently extracting information from neuronal signals. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Do Chinese Readers Follow the National Standard Rules for Word Segmentation during Reading?

PubMed Central

Liu, Ping-Ping; Li, Wei-Jun; Lin, Nan; Li, Xing-Shan

2013-01-01

We conducted a preliminary study to examine whether Chinese readers’ spontaneous word segmentation processing is consistent with the national standard rules of word segmentation based on the Contemporary Chinese language word segmentation specification for information processing (CCLWSSIP). Participants were asked to segment Chinese sentences into individual words according to their prior knowledge of words. The results showed that Chinese readers did not follow the segmentation rules of the CCLWSSIP, and their word segmentation processing was influenced by the syntactic categories of consecutive words. In many cases, the participants did not consider the auxiliary words, adverbs, adjectives, nouns, verbs, numerals and quantifiers as single word units. Generally, Chinese readers tended to combine function words with content words to form single word units, indicating they were inclined to chunk single words into large information units during word segmentation. Additionally, the “overextension of monosyllable words” hypothesis was tested and it might need to be corrected to some degree, implying that word length have an implicit influence on Chinese readers’ segmentation processing. Implications of these results for models of word recognition and eye movement control are discussed. PMID:23408981

An Active Learning Framework for Hyperspectral Image Classification Using Hierarchical Segmentation

NASA Technical Reports Server (NTRS)

Zhang, Zhou; Pasolli, Edoardo; Crawford, Melba M.; Tilton, James C.

2015-01-01

Augmenting spectral data with spatial information for image classification has recently gained significant attention, as classification accuracy can often be improved by extracting spatial information from neighboring pixels. In this paper, we propose a new framework in which active learning (AL) and hierarchical segmentation (HSeg) are combined for spectral-spatial classification of hyperspectral images. The spatial information is extracted from a best segmentation obtained by pruning the HSeg tree using a new supervised strategy. The best segmentation is updated at each iteration of the AL process, thus taking advantage of informative labeled samples provided by the user. The proposed strategy incorporates spatial information in two ways: 1) concatenating the extracted spatial features and the original spectral features into a stacked vector and 2) extending the training set using a self-learning-based semi-supervised learning (SSL) approach. Finally, the two strategies are combined within an AL framework. The proposed framework is validated with two benchmark hyperspectral datasets. Higher classification accuracies are obtained by the proposed framework with respect to five other state-of-the-art spectral-spatial classification approaches. Moreover, the effectiveness of the proposed pruning strategy is also demonstrated relative to the approaches based on a fixed segmentation.

Market Segmentation from a Behavioral Perspective

ERIC Educational Resources Information Center

Wells, Victoria K.; Chang, Shing Wan; Oliveira-Castro, Jorge; Pallister, John

2010-01-01

A segmentation approach is presented using both traditional demographic segmentation bases (age, social class/occupation, and working status) and a segmentation by benefits sought. The benefits sought in this case are utilitarian and informational reinforcement, variables developed from the Behavioral Perspective Model (BPM). Using data from 1,847…

Independently Controlled Wing Stroke Patterns in the Fruit Fly Drosophila melanogaster

PubMed Central

Chakraborty, Soma; Bartussek, Jan; Fry, Steven N.; Zapotocky, Martin

2015-01-01

Flies achieve supreme flight maneuverability through a small set of miniscule steering muscles attached to the wing base. The fast flight maneuvers arise from precisely timed activation of the steering muscles and the resulting subtle modulation of the wing stroke. In addition, slower modulation of wing kinematics arises from changes in the activity of indirect flight muscles in the thorax. We investigated if these modulations can be described as a superposition of a limited number of elementary deformations of the wing stroke that are under independent physiological control. Using a high-speed computer vision system, we recorded the wing motion of tethered flying fruit flies for up to 12 000 consecutive wing strokes at a sampling rate of 6250 Hz. We then decomposed the joint motion pattern of both wings into components that had the minimal mutual information (a measure of statistical dependence). In 100 flight segments measured from 10 individual flies, we identified 7 distinct types of frequently occurring least-dependent components, each defining a kinematic pattern (a specific deformation of the wing stroke and the sequence of its activation from cycle to cycle). Two of these stroke deformations can be associated with the control of yaw torque and total flight force, respectively. A third deformation involves a change in the downstroke-to-upstroke duration ratio, which is expected to alter the pitch torque. A fourth kinematic pattern consists in the alteration of stroke amplitude with a period of 2 wingbeat cycles, extending for dozens of cycles. Our analysis indicates that these four elementary kinematic patterns can be activated mutually independently, and occur both in isolation and in linear superposition. The results strengthen the available evidence for independent control of yaw torque, pitch torque, and total flight force. Our computational method facilitates systematic identification of novel patterns in large kinematic datasets. PMID:25710715

Independently controlled wing stroke patterns in the fruit fly Drosophila melanogaster.

PubMed

Chakraborty, Soma; Bartussek, Jan; Fry, Steven N; Zapotocky, Martin

2015-01-01

Flies achieve supreme flight maneuverability through a small set of miniscule steering muscles attached to the wing base. The fast flight maneuvers arise from precisely timed activation of the steering muscles and the resulting subtle modulation of the wing stroke. In addition, slower modulation of wing kinematics arises from changes in the activity of indirect flight muscles in the thorax. We investigated if these modulations can be described as a superposition of a limited number of elementary deformations of the wing stroke that are under independent physiological control. Using a high-speed computer vision system, we recorded the wing motion of tethered flying fruit flies for up to 12,000 consecutive wing strokes at a sampling rate of 6250 Hz. We then decomposed the joint motion pattern of both wings into components that had the minimal mutual information (a measure of statistical dependence). In 100 flight segments measured from 10 individual flies, we identified 7 distinct types of frequently occurring least-dependent components, each defining a kinematic pattern (a specific deformation of the wing stroke and the sequence of its activation from cycle to cycle). Two of these stroke deformations can be associated with the control of yaw torque and total flight force, respectively. A third deformation involves a change in the downstroke-to-upstroke duration ratio, which is expected to alter the pitch torque. A fourth kinematic pattern consists in the alteration of stroke amplitude with a period of 2 wingbeat cycles, extending for dozens of cycles. Our analysis indicates that these four elementary kinematic patterns can be activated mutually independently, and occur both in isolation and in linear superposition. The results strengthen the available evidence for independent control of yaw torque, pitch torque, and total flight force. Our computational method facilitates systematic identification of novel patterns in large kinematic datasets.

The PHESAT95 catalogue of observations of the mutual events of the Saturnian satellites

NASA Astrophysics Data System (ADS)

Thuillot, W.; Arlot, J.-E.; Ruatti, C.; Berthier, J.; Blanco, C.; Colas, F.; Czech, W.; Damani, M.; D'Ambrosio, V.; Descamps, P.; Dourneau, G.; Emelianov, N.; Foglia, S.; Helmer, G.; Irsmambetova, T. R.; James, N.; Laques, P.; Lecacheux, J.; Le Campion, J.-F.; Ledoux, C.; Le Floch, J.-C.; Oprescu, G.; Rapaport, M.; Riccioli, R.; Starosta, B.; Tejfel, V. G.; Trunkovsky, E. M.; Viateau, B.; Veiga, C. H.; Vu, D. T.

2001-05-01

In 1994-1996 the Sun and the Earth passed through the equatorial plane of Saturn and therefore through the orbital planes of its main satellites. During this period, phenomena involving seven of these satellites were observed. Light curves of eclipses by Saturn and of mutual eclipses and occultations were recorded by the observers of the international campaign PHESAT95 organized by the Institut de mécanique céleste, Paris, France. Herein, we report 66 observations of 43 mutual events from 16 sites. For each observation, information is given about the telescope, the receptor, the site and the observational conditions. This paper gathers together all these data and gives a first estimate of the precision providing accurate astrometric data useful for the development of dynamical models.

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…

Pushing Library Information to First-Year Students: An Exploratory Study of Faculty/Library Collaboration

ERIC Educational Resources Information Center

Dobozy, Eva; Gross, Julia

2010-01-01

The authors contend that better information literacy and library skills development practice is needed for students entering university. This paper presents a case study of how a teacher education (TE) lecturer and a faculty librarian collaborated in an Australian university to provide information literacy practice. A mutual interest in…

Inference of topology and the nature of synapses, and the flow of information in neuronal networks

NASA Astrophysics Data System (ADS)

Borges, F. S.; Lameu, E. L.; Iarosz, K. C.; Protachevicz, P. R.; Caldas, I. L.; Viana, R. L.; Macau, E. E. N.; Batista, A. M.; Baptista, M. S.

2018-02-01

The characterization of neuronal connectivity is one of the most important matters in neuroscience. In this work, we show that a recently proposed informational quantity, the causal mutual information, employed with an appropriate methodology, can be used not only to correctly infer the direction of the underlying physical synapses, but also to identify their excitatory or inhibitory nature, considering easy to handle and measure bivariate time series. The success of our approach relies on a surprising property found in neuronal networks by which nonadjacent neurons do "understand" each other (positive mutual information), however, this exchange of information is not capable of causing effect (zero transfer entropy). Remarkably, inhibitory connections, responsible for enhancing synchronization, transfer more information than excitatory connections, known to enhance entropy in the network. We also demonstrate that our methodology can be used to correctly infer directionality of synapses even in the presence of dynamic and observational Gaussian noise, and is also successful in providing the effective directionality of intermodular connectivity, when only mean fields can be measured.

A graph-based watershed merging using fuzzy C-means and simulated annealing for image segmentation

NASA Astrophysics Data System (ADS)

Vadiveloo, Mogana; Abdullah, Rosni; Rajeswari, Mandava

2015-12-01

In this paper, we have addressed the issue of over-segmented regions produced in watershed by merging the regions using global feature. The global feature information is obtained from clustering the image in its feature space using Fuzzy C-Means (FCM) clustering. The over-segmented regions produced by performing watershed on the gradient of the image are then mapped to this global information in the feature space. Further to this, the global feature information is optimized using Simulated Annealing (SA). The optimal global feature information is used to derive the similarity criterion to merge the over-segmented watershed regions which are represented by the region adjacency graph (RAG). The proposed method has been tested on digital brain phantom simulated dataset to segment white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) soft tissues regions. The experiments showed that the proposed method performs statistically better, with average of 95.242% regions are merged, than the immersion watershed and average accuracy improvement of 8.850% in comparison with RAG-based immersion watershed merging using global and local features.

Three-dimensional choroidal segmentation in spectral OCT volumes using optic disc prior information

NASA Astrophysics Data System (ADS)

Hu, Zhihong; Girkin, Christopher A.; Hariri, Amirhossein; Sadda, SriniVas R.

2016-03-01

Recently, much attention has been focused on determining the role of the peripapillary choroid - the layer between the outer retinal pigment epithelium (RPE)/Bruchs membrane (BM) and choroid-sclera (C-S) junction, whether primary or secondary in the pathogenesis of glaucoma. However, the automated choroidal segmentation in spectral-domain optical coherence tomography (SD-OCT) images of optic nerve head (ONH) has not been reported probably due to the fact that the presence of the BM opening (BMO, corresponding to the optic disc) can deflect the choroidal segmentation from its correct position. The purpose of this study is to develop a 3D graph-based approach to identify the 3D choroidal layer in ONH-centered SD-OCT images using the BMO prior information. More specifically, an initial 3D choroidal segmentation was first performed using the 3D graph search algorithm. Note that varying surface interaction constraints based on the choroidal morphological model were applied. To assist the choroidal segmentation, two other surfaces of internal limiting membrane and innerouter segment junction were also segmented. Based on the segmented layer between the RPE/BM and C-S junction, a 2D projection map was created. The BMO in the projection map was detected by a 2D graph search. The pre-defined BMO information was then incorporated into the surface interaction constraints of the 3D graph search to obtain more accurate choroidal segmentation. Twenty SD-OCT images from 20 healthy subjects were used. The mean differences of the choroidal borders between the algorithm and manual segmentation were at a sub-voxel level, indicating a high level segmentation accuracy.

Image registration method for medical image sequences

DOEpatents

Gee, Timothy F.; Goddard, James S.

2013-03-26

Image registration of low contrast image sequences is provided. In one aspect, a desired region of an image is automatically segmented and only the desired region is registered. Active contours and adaptive thresholding of intensity or edge information may be used to segment the desired regions. A transform function is defined to register the segmented region, and sub-pixel information may be determined using one or more interpolation methods.

Hierarchical combinatorial deep learning architecture for pancreas segmentation of medical computed tomography cancer images.

PubMed

Fu, Min; Wu, Wenming; Hong, Xiafei; Liu, Qiuhua; Jiang, Jialin; Ou, Yaobin; Zhao, Yupei; Gong, Xinqi

2018-04-24

Efficient computational recognition and segmentation of target organ from medical images are foundational in diagnosis and treatment, especially about pancreas cancer. In practice, the diversity in appearance of pancreas and organs in abdomen, makes detailed texture information of objects important in segmentation algorithm. According to our observations, however, the structures of previous networks, such as the Richer Feature Convolutional Network (RCF), are too coarse to segment the object (pancreas) accurately, especially the edge. In this paper, we extend the RCF, proposed to the field of edge detection, for the challenging pancreas segmentation, and put forward a novel pancreas segmentation network. By employing multi-layer up-sampling structure replacing the simple up-sampling operation in all stages, the proposed network fully considers the multi-scale detailed contexture information of object (pancreas) to perform per-pixel segmentation. Additionally, using the CT scans, we supply and train our network, thus get an effective pipeline. Working with our pipeline with multi-layer up-sampling model, we achieve better performance than RCF in the task of single object (pancreas) segmentation. Besides, combining with multi scale input, we achieve the 76.36% DSC (Dice Similarity Coefficient) value in testing data. The results of our experiments show that our advanced model works better than previous networks in our dataset. On the other words, it has better ability in catching detailed contexture information. Therefore, our new single object segmentation model has practical meaning in computational automatic diagnosis.

Curvature and the visual perception of shape: theory on information along object boundaries and the minima rule revisited.

PubMed

Lim, Ik Soo; Leek, E Charles

2012-07-01

Previous empirical studies have shown that information along visual contours is known to be concentrated in regions of high magnitude of curvature, and, for closed contours, segments of negative curvature (i.e., concave segments) carry greater perceptual relevance than corresponding regions of positive curvature (i.e., convex segments). Lately, Feldman and Singh (2005, Psychological Review, 112, 243-252) proposed a mathematical derivation to yield information content as a function of curvature along a contour. Here, we highlight several fundamental errors in their derivation and in its associated implementation, which are problematic in both mathematical and psychological senses. Instead, we propose an alternative mathematical formulation for information measure of contour curvature that addresses these issues. Additionally, unlike in previous work, we extend this approach to 3-dimensional (3D) shape by providing a formal measure of information content for surface curvature and outline a modified version of the minima rule relating to part segmentation using curvature in 3D shape. Copyright 2012 APA, 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.

Compressed Secret Key Agreement:Maximizing Multivariate Mutual Information per Bit

NASA Astrophysics Data System (ADS)

Chan, Chung

2017-10-01

The multiterminal secret key agreement problem by public discussion is formulated with an additional source compression step where, prior to the public discussion phase, users independently compress their private sources to filter out strongly correlated components for generating a common secret key. The objective is to maximize the achievable key rate as a function of the joint entropy of the compressed sources. Since the maximum achievable key rate captures the total amount of information mutual to the compressed sources, an optimal compression scheme essentially maximizes the multivariate mutual information per bit of randomness of the private sources, and can therefore be viewed more generally as a dimension reduction technique. Single-letter lower and upper bounds on the maximum achievable key rate are derived for the general source model, and an explicit polynomial-time computable formula is obtained for the pairwise independent network model. In particular, the converse results and the upper bounds are obtained from those of the related secret key agreement problem with rate-limited discussion. A precise duality is shown for the two-user case with one-way discussion, and such duality is extended to obtain the desired converse results in the multi-user case. In addition to posing new challenges in information processing and dimension reduction, the compressed secret key agreement problem helps shed new light on resolving the difficult problem of secret key agreement with rate-limited discussion, by offering a more structured achieving scheme and some simpler conjectures to prove.

Prior Knowledge Facilitates Mutual Gaze Convergence and Head Nodding Synchrony in Face-to-face Communication

PubMed Central

Thepsoonthorn, C.; Yokozuka, T.; Miura, S.; Ogawa, K.; Miyake, Y.

2016-01-01

As prior knowledge is claimed to be an essential key to achieve effective education, we are interested in exploring whether prior knowledge enhances communication effectiveness. To demonstrate the effects of prior knowledge, mutual gaze convergence and head nodding synchrony are observed as indicators of communication effectiveness. We conducted an experiment on lecture task between lecturer and student under 2 conditions: prior knowledge and non-prior knowledge. The students in prior knowledge condition were provided the basic information about the lecture content and were assessed their understanding by the experimenter before starting the lecture while the students in non-prior knowledge had none. The result shows that the interaction in prior knowledge condition establishes significantly higher mutual gaze convergence (t(15.03) = 6.72, p < 0.0001; α = 0.05, n = 20) and head nodding synchrony (t(16.67) = 1.83, p = 0.04; α = 0.05, n = 19) compared to non-prior knowledge condition. This study reveals that prior knowledge facilitates mutual gaze convergence and head nodding synchrony. Furthermore, the interaction with and without prior knowledge can be evaluated by measuring or observing mutual gaze convergence and head nodding synchrony. PMID:27910902

Prior Knowledge Facilitates Mutual Gaze Convergence and Head Nodding Synchrony in Face-to-face Communication.

PubMed

Thepsoonthorn, C; Yokozuka, T; Miura, S; Ogawa, K; Miyake, Y

2016-12-02

As prior knowledge is claimed to be an essential key to achieve effective education, we are interested in exploring whether prior knowledge enhances communication effectiveness. To demonstrate the effects of prior knowledge, mutual gaze convergence and head nodding synchrony are observed as indicators of communication effectiveness. We conducted an experiment on lecture task between lecturer and student under 2 conditions: prior knowledge and non-prior knowledge. The students in prior knowledge condition were provided the basic information about the lecture content and were assessed their understanding by the experimenter before starting the lecture while the students in non-prior knowledge had none. The result shows that the interaction in prior knowledge condition establishes significantly higher mutual gaze convergence (t(15.03) = 6.72, p < 0.0001; α = 0.05, n = 20) and head nodding synchrony (t(16.67) = 1.83, p = 0.04; α = 0.05, n = 19) compared to non-prior knowledge condition. This study reveals that prior knowledge facilitates mutual gaze convergence and head nodding synchrony. Furthermore, the interaction with and without prior knowledge can be evaluated by measuring or observing mutual gaze convergence and head nodding synchrony.

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

Communicating with parents of obese children: which channels are most effective?

PubMed

Randle, Melanie; Okely, Anthony D; Dolnicar, Sara

2017-04-01

One of the strategies proven most successful in curbing rising rates of childhood obesity involves targeting parents as agents of change. Prior studies have focused on what messages to communicate, but few have investigated how they should be communicated. To identify the channels most effective for communicating with parents of overweight and obese children and understand whether their use of parenting information sources differs from others in the community. This study utilizes data from the Longitudinal Study of Australian Children (LSAC). Families were included if weight and height information was available for parents and children at three data collection points: Waves 1, 2 and 4 (collected 2004, 2006 and 2010, respectively, n = 5107). A priori and a posteriori segmentation methods identified groups of parents that were similar in the sources used to obtain information about parenting, and examined whether some segments were more likely to have obese children. Four segments were identified that differed in their information source use: the 'personal networks', 'books', 'official sources' and 'mixed approach' segments. The 'official sources' and 'mixed approach' segments were most likely to have obese children, and they used doctors, government/community organizations and friends to obtain information on parenting. These segments were also less educated and had lower employment. Messages are most likely to reach families with obese children if communicated through doctors, government publications and community organizations. Further, messages targeting social groupings of parents will leverage the power of advice from friends, which is another valuable information source for this group. © 2016 The Authors. Health Expectations Published by John Wiley & Sons Ltd.

Airplane takeoff and landing performance monitoring system

NASA Technical Reports Server (NTRS)

Middleton, David B. (Inventor); Srivatsan, Raghavachari (Inventor); Person, Lee H. (Inventor)

1989-01-01

The invention is a real-time takeoff and landing performance monitoring system which provides the pilot with graphic and metric information to assist in decisions related to achieving rotation speed (V sub R) within the safe zone of the runway or stopping the aircraft on the runway after landing or take off abort. The system processes information in two segments: a pretakeoff segment and a real-time segment. One-time inputs of ambient conditions and airplane configuration information are used in the pretakeoff segment to generate scheduled performance data. The real-time segment uses the scheduled performance data, runway length data and transducer measured parameters to monitor the performance of the airplane throughout the takeoff roll. An important feature of this segment is that it updates the estimated runway rolling friction coefficient. Airplane performance predictions also reflect changes in headwind occurring as the takeoff roll progresses. The system displays the position of the airplane on the runway, indicating runway used and runway available, summarizes the critical information into a situation advisory flag, flags engine failures and off-nominal acceleration performance, and indicates where on the runway particular events such as decision speed (V sub 1), rotation speed (V sub R) and expected stop points will occur based on actual or predicted performance. The display also indicates airspeed, wind vector, engine pressure ratios, second segment climb speed, and balanced field length (BFL). The system detects performance deficiencies by comparing the airplane's present performance with a predicted nominal performance based upon the given conditions.

Statistics of optimal information flow in ensembles of regulatory motifs

NASA Astrophysics Data System (ADS)

Crisanti, Andrea; De Martino, Andrea; Fiorentino, Jonathan

2018-02-01

Genetic regulatory circuits universally cope with different sources of noise that limit their ability to coordinate input and output signals. In many cases, optimal regulatory performance can be thought to correspond to configurations of variables and parameters that maximize the mutual information between inputs and outputs. Since the mid-2000s, such optima have been well characterized in several biologically relevant cases. Here we use methods of statistical field theory to calculate the statistics of the maximal mutual information (the "capacity") achievable by tuning the input variable only in an ensemble of regulatory motifs, such that a single controller regulates N targets. Assuming (i) sufficiently large N , (ii) quenched random kinetic parameters, and (iii) small noise affecting the input-output channels, we can accurately reproduce numerical simulations both for the mean capacity and for the whole distribution. Our results provide insight into the inherent variability in effectiveness occurring in regulatory systems with heterogeneous kinetic parameters.

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

Learning from each other: cross-cultural insights on palliative care in Indian and Australian regions.

PubMed

McGrath, Pam; Holewa, Hamish; Koilparampil, Thomas; Koshy, Cherian; George, Shobha

2009-10-01

This article presents the findings of a cross-cultural research project that explored similarities and differences between palliative care service provision in Kerala, India and South-East Queensland, Australia, to inform a process of mutual learning for service development. Three major points of difference that can inform this process of mutual learning were identified: 1) an understanding of the significance of honesty in information-giving to the patient, 2) recognition of the importance of palliative care specialists providing education to mainstream health professionals, and 3) appreciation of the need for palliative care to be cognizant of the socio-economic impact of dying-especially for families experiencing poverty-by embracing strategies for financial and material support. The findings highlight the effectiveness of a cross-cultural collaboration between health professionals and researchers in South-East Queensland, Australia and Kerala, India.

MRI brain tumor segmentation based on improved fuzzy c-means method

NASA Astrophysics Data System (ADS)

Deng, Wankai; Xiao, Wei; Pan, Chao; Liu, Jianguo

2009-10-01

This paper focuses on the image segmentation, which is one of the key problems in medical image processing. A new medical image segmentation method is proposed based on fuzzy c- means algorithm and spatial information. Firstly, we classify the image into the region of interest and background using fuzzy c means algorithm. Then we use the information of the tissues' gradient and the intensity inhomogeneities of regions to improve the quality of segmentation. The sum of the mean variance in the region and the reciprocal of the mean gradient along the edge of the region are chosen as an objective function. The minimum of the sum is optimum result. The result shows that the clustering segmentation algorithm is effective.

Estimating Mutual Information for High-to-Low Calibration

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

Michaud, Isaac James; Williams, Brian J.; Weaver, Brian Phillip

Presentation shows that KSG 2 is superior to KSG 1 because it scales locally automatically; KSG estimators are limited to a maximum MI due to sample size; LNC extends the capability of KSG without onerous assumptions; iLNC allows LNC to estimate information gain.

43 CFR 17.333 - Investigation.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., Conciliation, and Enforcement Procedures § 17.333 Investigation. (a) Informal investigation. (1) DOI will... mediation agreement. (2) As part of the initial investigation, DOI will use informal fact finding methods... possible, settle the complaint on terms that are mutually agreeable to the parties. DOI may seek the...

A Menagerie of Tracks at Maryland: HARD, Enterprise, QA, and Genomics, Oh My!

DTIC Science & Technology

2006-01-01

mutually agreeable search strategy for acquiring the desired information. Like information need negotiation in a reference interview, clarification...answer key to identify relevant nuggets in system responses. The obvious downside of this approach is that the process requires human intervention

43 CFR 17.333 - Investigation.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., Conciliation, and Enforcement Procedures § 17.333 Investigation. (a) Informal investigation. (1) DOI will... mediation agreement. (2) As part of the initial investigation, DOI will use informal fact finding methods... possible, settle the complaint on terms that are mutually agreeable to the parties. DOI may seek the...

Implementation of mutual information and bayes theorem for classification microarray data

NASA Astrophysics Data System (ADS)

Dwifebri Purbolaksono, Mahendra; Widiastuti, Kurnia C.; Syahrul Mubarok, Mohamad; Adiwijaya; Aminy Ma’ruf, Firda

2018-03-01

Microarray Technology is one of technology which able to read the structure of gen. The analysis is important for this technology. It is for deciding which attribute is more important than the others. Microarray technology is able to get cancer information to diagnose a person’s gen. Preparation of microarray data is a huge problem and takes a long time. That is because microarray data contains high number of insignificant and irrelevant attributes. So, it needs a method to reduce the dimension of microarray data without eliminating important information in every attribute. This research uses Mutual Information to reduce dimension. System is built with Machine Learning approach specifically Bayes Theorem. This theorem uses a statistical and probability approach. By combining both methods, it will be powerful for Microarray Data Classification. The experiment results show that system is good to classify Microarray data with highest F1-score using Bayesian Network by 91.06%, and Naïve Bayes by 88.85%.

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.

Information-theoretic approach to lead-lag effect on financial markets

NASA Astrophysics Data System (ADS)

Fiedor, Paweł

2014-08-01

Recently the interest of researchers has shifted from the analysis of synchronous relationships of financial instruments to the analysis of more meaningful asynchronous relationships. Both types of analysis are concentrated mostly on Pearson's correlation coefficient and consequently intraday lead-lag relationships (where one of the variables in a pair is time-lagged) are also associated with them. Under the Efficient-Market Hypothesis such relationships are not possible as all information is embedded in the prices, but in real markets we find such dependencies. In this paper we analyse lead-lag relationships of financial instruments and extend known methodology by using mutual information instead of Pearson's correlation coefficient. Mutual information is not only a more general measure, sensitive to non-linear dependencies, but also can lead to a simpler procedure of statistical validation of links between financial instruments. We analyse lagged relationships using New York Stock Exchange 100 data not only on an intraday level, but also for daily stock returns, which have usually been ignored.

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

NASA Astrophysics Data System (ADS)

Xiong, Yan; Reichenbach, Stephen E.

1999-01-01

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

Segmenting Continuous Motions with Hidden Semi-markov Models and Gaussian Processes

PubMed Central

Nakamura, Tomoaki; Nagai, Takayuki; Mochihashi, Daichi; Kobayashi, Ichiro; Asoh, Hideki; Kaneko, Masahide

2017-01-01

Humans divide perceived continuous information into segments to facilitate recognition. For example, humans can segment speech waves into recognizable morphemes. Analogously, continuous motions are segmented into recognizable unit actions. People can divide continuous information into segments without using explicit segment points. This capacity for unsupervised segmentation is also useful for robots, because it enables them to flexibly learn languages, gestures, and actions. In this paper, we propose a Gaussian process-hidden semi-Markov model (GP-HSMM) that can divide continuous time series data into segments in an unsupervised manner. Our proposed method consists of a generative model based on the hidden semi-Markov model (HSMM), the emission distributions of which are Gaussian processes (GPs). Continuous time series data is generated by connecting segments generated by the GP. Segmentation can be achieved by using forward filtering-backward sampling to estimate the model's parameters, including the lengths and classes of the segments. In an experiment using the CMU motion capture dataset, we tested GP-HSMM with motion capture data containing simple exercise motions; the results of this experiment showed that the proposed GP-HSMM was comparable with other methods. We also conducted an experiment using karate motion capture data, which is more complex than exercise motion capture data; in this experiment, the segmentation accuracy of GP-HSMM was 0.92, which outperformed other methods. PMID:29311889

Words in Puddles of Sound: Modelling Psycholinguistic Effects in Speech Segmentation

ERIC Educational Resources Information Center

Monaghan, Padraic; Christiansen, Morten H.

2010-01-01

There are numerous models of how speech segmentation may proceed in infants acquiring their first language. We present a framework for considering the relative merits and limitations of these various approaches. We then present a model of speech segmentation that aims to reveal important sources of information for speech segmentation, and to…

The Analysis of Image Segmentation Hierarchies with a Graph-based Knowledge Discovery System

NASA Technical Reports Server (NTRS)

Tilton, James C.; Cooke, diane J.; Ketkar, Nikhil; Aksoy, Selim

2008-01-01

Currently available pixel-based analysis techniques do not effectively extract the information content from the increasingly available high spatial resolution remotely sensed imagery data. A general consensus is that object-based image analysis (OBIA) is required to effectively analyze this type of data. OBIA is usually a two-stage process; image segmentation followed by an analysis of the segmented objects. We are exploring an approach to OBIA in which hierarchical image segmentations provided by the Recursive Hierarchical Segmentation (RHSEG) software developed at NASA GSFC are analyzed by the Subdue graph-based knowledge discovery system developed by a team at Washington State University. In this paper we discuss out initial approach to representing the RHSEG-produced hierarchical image segmentations in a graphical form understandable by Subdue, and provide results on real and simulated data. We also discuss planned improvements designed to more effectively and completely convey the hierarchical segmentation information to Subdue and to improve processing efficiency.

Geometric Methods for Controlled Active Vision

DTIC Science & Technology

2012-02-07

information -based criteria, such as the Kullback - Leibler divergence, have been employed. Returning to the problem of segmentation, one can think of a data...Transactions on Information Technology in Biomedicine, 2012. 32. “3D automatic segmentation of the hippocampus using wavelets with applications to... used to induce shape information to the estimated curve without the need for explicit incorporation of shape information into the motion prior. In

1 CFR 301.2 - Purposes.

Code of Federal Regulations, 2011 CFR

2011-01-01

... agencies, assisted by outside experts, may cooperatively study mutual problems, exchange information, and develop recommendations for action by proper authorities to the end that private rights may be fully...

Graph-based surface reconstruction from stereo pairs using image segmentation

NASA Astrophysics Data System (ADS)

Bleyer, Michael; Gelautz, Margrit

2005-01-01

This paper describes a novel stereo matching algorithm for epipolar rectified images. The method applies colour segmentation on the reference image. The use of segmentation makes the algorithm capable of handling large untextured regions, estimating precise depth boundaries and propagating disparity information to occluded regions, which are challenging tasks for conventional stereo methods. We model disparity inside a segment by a planar equation. Initial disparity segments are clustered to form a set of disparity layers, which are planar surfaces that are likely to occur in the scene. Assignments of segments to disparity layers are then derived by minimization of a global cost function via a robust optimization technique that employs graph cuts. The cost function is defined on the pixel level, as well as on the segment level. While the pixel level measures the data similarity based on the current disparity map and detects occlusions symmetrically in both views, the segment level propagates the segmentation information and incorporates a smoothness term. New planar models are then generated based on the disparity layers' spatial extents. Results obtained for benchmark and self-recorded image pairs indicate that the proposed method is able to compete with the best-performing state-of-the-art algorithms.

Probabilistic brain tissue segmentation in neonatal magnetic resonance imaging.

PubMed

Anbeek, Petronella; Vincken, Koen L; Groenendaal, Floris; Koeman, Annemieke; van Osch, Matthias J P; van der Grond, Jeroen

2008-02-01

A fully automated method has been developed for segmentation of four different structures in the neonatal brain: white matter (WM), central gray matter (CEGM), cortical gray matter (COGM), and cerebrospinal fluid (CSF). The segmentation algorithm is based on information from T2-weighted (T2-w) and inversion recovery (IR) scans. The method uses a K nearest neighbor (KNN) classification technique with features derived from spatial information and voxel intensities. Probabilistic segmentations of each tissue type were generated. By applying thresholds on these probability maps, binary segmentations were obtained. These final segmentations were evaluated by comparison with a gold standard. The sensitivity, specificity, and Dice similarity index (SI) were calculated for quantitative validation of the results. High sensitivity and specificity with respect to the gold standard were reached: sensitivity >0.82 and specificity >0.9 for all tissue types. Tissue volumes were calculated from the binary and probabilistic segmentations. The probabilistic segmentation volumes of all tissue types accurately estimated the gold standard volumes. The KNN approach offers valuable ways for neonatal brain segmentation. The probabilistic outcomes provide a useful tool for accurate volume measurements. The described method is based on routine diagnostic magnetic resonance imaging (MRI) and is suitable for large population studies.

African American women describe support processes during high-risk pregnancy and postpartum.

PubMed

Coffman, Sherrilyn; Ray, Marilyn A

2002-01-01

To explore social support processes in low-income African American women during high-risk pregnancy and postpartum. A qualitative grounded theory approach. Interview was the primary data collection technique and was combined with observation, medical chart review, and literature review. A high-risk pregnancy clinic and participants' homes. Ten pregnant women, 3 social network members, and 11 health care providers. Four of the women at high risk tell their in-depth stories in this article: Yolanda, coping with gestational diabetes; Frances, participating in drug rehabilitation; Trista, waiting to deliver a fetus with severe congenital anomalies; and Beatrice, HIV positive and carrying her seventh child. The substantive theory of support developed in the study was termed mutual intentionality. Narratives illustrate the mutual roles that women at high risk and support givers played in the helping process. Support themes included being there, caring, respecting, sharing information, knowing, believing in, and doing for the other. The theorsy of mutual intentionality suggests that social support is a process or transaction involving intentionality. For support to happen, the therapeutic relationship must be valued as a mutual resource.

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.

H-Ransac a Hybrid Point Cloud Segmentation Combining 2d and 3d Data

NASA Astrophysics Data System (ADS)

Adam, A.; Chatzilari, E.; Nikolopoulos, S.; Kompatsiaris, I.

2018-05-01

In this paper, we present a novel 3D segmentation approach operating on point clouds generated from overlapping images. The aim of the proposed hybrid approach is to effectively segment co-planar objects, by leveraging the structural information originating from the 3D point cloud and the visual information from the 2D images, without resorting to learning based procedures. More specifically, the proposed hybrid approach, H-RANSAC, is an extension of the well-known RANSAC plane-fitting algorithm, incorporating an additional consistency criterion based on the results of 2D segmentation. Our expectation that the integration of 2D data into 3D segmentation will achieve more accurate results, is validated experimentally in the domain of 3D city models. Results show that HRANSAC can successfully delineate building components like main facades and windows, and provide more accurate segmentation results compared to the typical RANSAC plane-fitting algorithm.

Abdominal multi-organ segmentation from CT images using conditional shape–location and unsupervised intensity priors

PubMed Central

Linguraru, Marius George; Hori, Masatoshi; Summers, Ronald M; Tomiyama, Noriyuki

2015-01-01

This paper addresses the automated segmentation of multiple organs in upper abdominal computed tomography (CT) data. The aim of our study is to develop methods to effectively construct the conditional priors and use their prediction power for more accurate segmentation as well as easy adaptation to various imaging conditions in CT images, as observed in clinical practice. We propose a general framework of multi-organ segmentation which effectively incorporates interrelations among multiple organs and easily adapts to various imaging conditions without the need for supervised intensity information. The features of the framework are as follows: (1) A method for modeling conditional shape and location (shape–location) priors, which we call prediction-based priors, is developed to derive accurate priors specific to each subject, which enables the estimation of intensity priors without the need for supervised intensity information. (2) Organ correlation graph is introduced, which defines how the conditional priors are constructed and segmentation processes of multiple organs are executed. In our framework, predictor organs, whose segmentation is sufficiently accurate by using conventional single-organ segmentation methods, are pre-segmented, and the remaining organs are hierarchically segmented using conditional shape–location priors. The proposed framework was evaluated through the segmentation of eight abdominal organs (liver, spleen, left and right kidneys, pancreas, gallbladder, aorta, and inferior vena cava) from 134 CT data from 86 patients obtained under six imaging conditions at two hospitals. The experimental results show the effectiveness of the proposed prediction-based priors and the applicability to various imaging conditions without the need for supervised intensity information. Average Dice coefficients for the liver, spleen, and kidneys were more than 92%, and were around 73% and 67% for the pancreas and gallbladder, respectively. PMID:26277022

Abdominal multi-organ segmentation from CT images using conditional shape-location and unsupervised intensity priors.

PubMed

Okada, Toshiyuki; Linguraru, Marius George; Hori, Masatoshi; Summers, Ronald M; Tomiyama, Noriyuki; Sato, Yoshinobu

2015-12-01

This paper addresses the automated segmentation of multiple organs in upper abdominal computed tomography (CT) data. The aim of our study is to develop methods to effectively construct the conditional priors and use their prediction power for more accurate segmentation as well as easy adaptation to various imaging conditions in CT images, as observed in clinical practice. We propose a general framework of multi-organ segmentation which effectively incorporates interrelations among multiple organs and easily adapts to various imaging conditions without the need for supervised intensity information. The features of the framework are as follows: (1) A method for modeling conditional shape and location (shape-location) priors, which we call prediction-based priors, is developed to derive accurate priors specific to each subject, which enables the estimation of intensity priors without the need for supervised intensity information. (2) Organ correlation graph is introduced, which defines how the conditional priors are constructed and segmentation processes of multiple organs are executed. In our framework, predictor organs, whose segmentation is sufficiently accurate by using conventional single-organ segmentation methods, are pre-segmented, and the remaining organs are hierarchically segmented using conditional shape-location priors. The proposed framework was evaluated through the segmentation of eight abdominal organs (liver, spleen, left and right kidneys, pancreas, gallbladder, aorta, and inferior vena cava) from 134 CT data from 86 patients obtained under six imaging conditions at two hospitals. The experimental results show the effectiveness of the proposed prediction-based priors and the applicability to various imaging conditions without the need for supervised intensity information. Average Dice coefficients for the liver, spleen, and kidneys were more than 92%, and were around 73% and 67% for the pancreas and gallbladder, respectively. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

Strategic Information Systems Planning in Malaysian Public Universities

ERIC Educational Resources Information Center

Ismail, Noor Azizi; Raja Mohd Ali, Raja Haslinda; Mat Saat, Rafeah; Hsbollah, Hafizah Mohamad

2007-01-01

Purpose: The paper's purpose is to investigate the current status, problems and benefits of strategic information systems planning implementation in Malaysian public universities. Design/methodology/approach: The study uses dual but mutually supportive strands of investigation, i.e. a questionnaire survey and interviews. Findings: Malaysian public…

21 CFR 26.71 - Exchange of information.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 1 2010-04-01 2010-04-01 false Exchange of information. 26.71 Section 26.71 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES GENERAL MUTUAL RECOGNITION OF PHARMACEUTICAL GOOD MANUFACTURING PRACTICE REPORTS, MEDICAL DEVICE QUALITY SYSTEM AUDIT REPORTS...

A Robust Apnea Period Detection Method in Changing Sleep Posture by Average Mutual Information of Heartbeat and Respiration

NASA Astrophysics Data System (ADS)

Kurihara, Yosuke; Watanabe, Kajiro; Kobayashi, Kazuyuki; Tanaka, Tanaka

Sleep disorders disturb the recovery from mental and physical fatigues, one of the functions of the sleep. The majority of those who with the disorders are suffering from Sleep Apnea Syndrome (SAS). Continuous Hypoxia during sleep due to SAS cause Circulatory Disturbances, such as hypertension and ischemic heart disease, and Malfunction of Autonomic Nervous System, and other severe complications, often times bringing the suffers to death. In order to prevent these from happening, it is important to detect the SAS in its early stage by monitoring the daily respirations during sleep, and to provide appropriate treatments at medical institutions. In this paper, the Pneumatic Method to detect the Apnea period during sleep is proposed. Pneumatic method can measure heartbeat and respiration signal. Respiration signal can be considered as noise against heartbeat signal, and the decrease in the respiration signal due to Apnea increases the Average Mutual Information of heartbeat. The result of scaling analysis of the average mutual information is defined as threshold to detect the apnea period. The root mean square error between the lengths of Apnea measured by Strain Gauge using for reference and those measured by using the proposed method was 3.1 seconds. And, error of the number of apnea times judged by doctor and proposal method in OSAS patients was 3.3 times.

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.

Classical mutual information in mean-field spin glass models

NASA Astrophysics Data System (ADS)

Alba, Vincenzo; Inglis, Stephen; Pollet, Lode

2016-03-01

We investigate the classical Rényi entropy Sn and the associated mutual information In in the Sherrington-Kirkpatrick (S-K) model, which is the paradigm model of mean-field spin glasses. Using classical Monte Carlo simulations and analytical tools we investigate the S-K model in the n -sheet booklet. This is achieved by gluing together n independent copies of the model, and it is the main ingredient for constructing the Rényi entanglement-related quantities. We find a glassy phase at low temperatures, whereas at high temperatures the model exhibits paramagnetic behavior, consistent with the regular S-K model. The temperature of the paramagnetic-glassy transition depends nontrivially on the geometry of the booklet. At high temperatures we provide the exact solution of the model by exploiting the replica symmetry. This is the permutation symmetry among the fictitious replicas that are used to perform disorder averages (via the replica trick). In the glassy phase the replica symmetry has to be broken. Using a generalization of the Parisi solution, we provide analytical results for Sn and In and for standard thermodynamic quantities. Both Sn and In exhibit a volume law in the whole phase diagram. We characterize the behavior of the corresponding densities, Sn/N and In/N , in the thermodynamic limit. Interestingly, at the critical point the mutual information does not exhibit any crossing for different system sizes, in contrast with local spin models.

Semi-automated brain tumor and edema segmentation using MRI.

PubMed

Xie, Kai; Yang, Jie; Zhang, Z G; Zhu, Y M

2005-10-01

Manual segmentation of brain tumors from magnetic resonance images is a challenging and time-consuming task. A semi-automated method has been developed for brain tumor and edema segmentation that will provide objective, reproducible segmentations that are close to the manual results. Additionally, the method segments non-enhancing brain tumor and edema from healthy tissues in magnetic resonance images. In this study, a semi-automated method was developed for brain tumor and edema segmentation and volume measurement using magnetic resonance imaging (MRI). Some novel algorithms for tumor segmentation from MRI were integrated in this medical diagnosis system. We exploit a hybrid level set (HLS) segmentation method driven by region and boundary information simultaneously, region information serves as a propagation force which is robust and boundary information serves as a stopping functional which is accurate. Ten different patients with brain tumors of different size, shape and location were selected, a total of 246 axial tumor-containing slices obtained from 10 patients were used to evaluate the effectiveness of segmentation methods. This method was applied to 10 non-enhancing brain tumors and satisfactory results were achieved. Two quantitative measures for tumor segmentation quality estimation, namely, correspondence ratio (CR) and percent matching (PM), were performed. For the segmentation of brain tumor, the volume total PM varies from 79.12 to 93.25% with the mean of 85.67+/-4.38% while the volume total CR varies from 0.74 to 0.91 with the mean of 0.84+/-0.07. For the segmentation of edema, the volume total PM varies from 72.86 to 87.29% with the mean of 79.54+/-4.18% while the volume total CR varies from 0.69 to 0.85 with the mean of 0.79+/-0.08. The HLS segmentation method perform better than the classical level sets (LS) segmentation method in PM and CR. The results of this research may have potential applications, both as a staging procedure and a method of evaluating tumor response during treatment, this method can be used as a clinical image analysis tool for doctors or radiologists.

Modifications to the accuracy assessment analysis routine SPATL to produce an output file

NASA Technical Reports Server (NTRS)

Carnes, J. G.

1978-01-01

The SPATL is an analysis program in the Accuracy Assessment Software System which makes comparisons between ground truth information and dot labeling for an individual segment. In order to facilitate the aggregation cf this information, SPATL was modified to produce a disk output file containing the necessary information about each segment.

21 CFR 26.19 - Information relating to quality aspects.

Code of Federal Regulations, 2010 CFR

2010-04-01

... MUTUAL RECOGNITION OF PHARMACEUTICAL GOOD MANUFACTURING PRACTICE REPORTS, MEDICAL DEVICE QUALITY SYSTEM AUDIT REPORTS, AND CERTAIN MEDICAL DEVICE PRODUCT EVALUATION REPORTS: UNITED STATES AND THE EUROPEAN...

Note: A simple picture of subdiffusive polymer motion from stochastic simulations

NASA Astrophysics Data System (ADS)

Gniewek, Pawel; Kolinski, Andrzej

2011-02-01

Entangled polymer solutions and melts exhibit unusual frictional properties. In the entanglement limit self-diffusion coefficient of long flexible polymers decays with the second power of chain length and viscosity increases with 3-3.5 power of chain length.1 It is very difficult to provide detailed molecular-level explanation of the entanglement effect.2 Perhaps, the problem of many entangled polymer chains is the most complex multibody issue of classical physics. There are different approaches to polymer melt dynamics. Some of these recognize hydrodynamic interactions as a dominant term, while topological constraints for polymer chains are assumed as a secondary factor. Other theories consider the topological constraints as the most important factors controlling polymer dynamics. Herman and co-workers describe polymer dynamics in melts, as a lateral sliding of a chain along other chains until complete mutual disentanglement. Despite the success in explaining the power-laws for viscosity, the model has some limitations. First of all, memory effects are ignored, that is, polymer segments are treated independently. Also, each entanglement/obstacle is treated as a separate entity, which is certainly a simplification of the memory effect problem. In addition to that, correlated motions of segments are addressed within the framework of renormalized Rouse-chain theory,7 without calling any topological entanglements in advance. This approach leads to the generalized Langevin equation characterized by distinct memory kernels describing local and nonlocal segment correlations or to the Smoluchowski equation in which the segments' mobility is treated as a stochastic variable.11 Both models describe the polymer segments motion at a microscopic level. An interesting alternative is to solve the integrodifferential equation for the chain relaxation with a sophisticated kernel function.12 The design of the kernel function is based on a mesoscopic description of the polymer melt. These theories explain some experimental data, although the description of the crossover between the Rouse and non-Rouse behavior is not satisfactory. Obviously, within the scope of a short note we cannot review all theoretical concepts of the polymer melt dynamics. Here we focus just on the interpretation of the observed single segment autocorrelation function.

Multi-pulse frequency shifted (MPFS) multiple access modulation for ultra wideband

DOEpatents

Nekoogar, Faranak [San Ramon, CA; Dowla, Farid U [Castro Valley, CA

2012-01-24

The multi-pulse frequency shifted technique uses mutually orthogonal short duration pulses o transmit and receive information in a UWB multiuser communication system. The multiuser system uses the same pulse shape with different frequencies for the reference and data for each user. Different users have a different pulse shape (mutually orthogonal to each other) and different transmit and reference frequencies. At the receiver, the reference pulse is frequency shifted to match the data pulse and a correlation scheme followed by a hard decision block detects the data.

Optimal Co-segmentation of Tumor in PET-CT Images with Context Information

PubMed Central

Song, Qi; Bai, Junjie; Han, Dongfeng; Bhatia, Sudershan; Sun, Wenqing; Rockey, William; Bayouth, John E.; Buatti, John M.

2014-01-01

PET-CT images have been widely used in clinical practice for radiotherapy treatment planning of the radiotherapy. Many existing segmentation approaches only work for a single imaging modality, which suffer from the low spatial resolution in PET or low contrast in CT. In this work we propose a novel method for the co-segmentation of the tumor in both PET and CT images, which makes use of advantages from each modality: the functionality information from PET and the anatomical structure information from CT. The approach formulates the segmentation problem as a minimization problem of a Markov Random Field (MRF) model, which encodes the information from both modalities. The optimization is solved using a graph-cut based method. Two sub-graphs are constructed for the segmentation of the PET and the CT images, respectively. To achieve consistent results in two modalities, an adaptive context cost is enforced by adding context arcs between the two subgraphs. An optimal solution can be obtained by solving a single maximum flow problem, which leads to simultaneous segmentation of the tumor volumes in both modalities. The proposed algorithm was validated in robust delineation of lung tumors on 23 PET-CT datasets and two head-and-neck cancer subjects. Both qualitative and quantitative results show significant improvement compared to the graph cut methods solely using PET or CT. PMID:23693127

Let the right one in: a microeconomic approach to partner choice in mutualisms.

PubMed

Archetti, Marco; Ubeda, Francisco; Fudenberg, Drew; Green, Jerry; Pierce, Naomi E; Yu, Douglas W

2011-01-01

One of the main problems impeding the evolution of cooperation is partner choice. When information is asymmetric (the quality of a potential partner is known only to himself), it may seem that partner choice is not possible without signaling. Many mutualisms, however, exist without signaling, and the mechanisms by which hosts might select the right partners are unclear. Here we propose a general mechanism of partner choice, "screening," that is similar to the economic theory of mechanism design. Imposing the appropriate costs and rewards may induce the informed individuals to screen themselves according to their types and therefore allow a noninformed individual to establish associations with the correct partners in the absence of signaling. Several types of biological symbioses are good candidates for screening, including bobtail squid, ant-plants, gut microbiomes, and many animal and plant species that produce reactive oxygen species. We describe a series of diagnostic tests for screening. Screening games can apply to the cases where by-products, partner fidelity feedback, or host sanctions do not apply, therefore explaining the evolution of mutualism in systems where it is impossible for potential symbionts to signal their cooperativeness beforehand and where the host does not punish symbiont misbehavior.

An Investigation of the Effects of Different Types of Activities during Pauses in a Segmented Instructional Animation

ERIC Educational Resources Information Center

Cheon, Jongpil; Chung, Sungwon; Crooks, Steven M.; Song, Jaeki; Kim, Jeakyeong

2014-01-01

Since the complex and transient information in instructional animations requires more cognitive resources, the segmenting principle has been proposed to reduce cognitive overload by providing smaller chunks with pauses between segments. This study examined the effects of different types of activities during pauses in a segmented animation. Four…

A fully automatic approach for multimodal PET and MR image segmentation in gamma knife treatment planning.

PubMed

Rundo, Leonardo; Stefano, Alessandro; Militello, Carmelo; Russo, Giorgio; Sabini, Maria Gabriella; D'Arrigo, Corrado; Marletta, Francesco; Ippolito, Massimo; Mauri, Giancarlo; Vitabile, Salvatore; Gilardi, Maria Carla

2017-06-01

Nowadays, clinical practice in Gamma Knife treatments is generally based on MRI anatomical information alone. However, the joint use of MRI and PET images can be useful for considering both anatomical and metabolic information about the lesion to be treated. In this paper we present a co-segmentation method to integrate the segmented Biological Target Volume (BTV), using [ 11 C]-Methionine-PET (MET-PET) images, and the segmented Gross Target Volume (GTV), on the respective co-registered MR images. The resulting volume gives enhanced brain tumor information to be used in stereotactic neuro-radiosurgery treatment planning. GTV often does not match entirely with BTV, which provides metabolic information about brain lesions. For this reason, PET imaging is valuable and it could be used to provide complementary information useful for treatment planning. In this way, BTV can be used to modify GTV, enhancing Clinical Target Volume (CTV) delineation. A novel fully automatic multimodal PET/MRI segmentation method for Leksell Gamma Knife ® treatments is proposed. This approach improves and combines two computer-assisted and operator-independent single modality methods, previously developed and validated, to segment BTV and GTV from PET and MR images, respectively. In addition, the GTV is utilized to combine the superior contrast of PET images with the higher spatial resolution of MRI, obtaining a new BTV, called BTV MRI . A total of 19 brain metastatic tumors, undergone stereotactic neuro-radiosurgery, were retrospectively analyzed. A framework for the evaluation of multimodal PET/MRI segmentation is also presented. Overlap-based and spatial distance-based metrics were considered to quantify similarity concerning PET and MRI segmentation approaches. Statistics was also included to measure correlation among the different segmentation processes. Since it is not possible to define a gold-standard CTV according to both MRI and PET images without treatment response assessment, the feasibility and the clinical value of BTV integration in Gamma Knife treatment planning were considered. Therefore, a qualitative evaluation was carried out by three experienced clinicians. The achieved experimental results showed that GTV and BTV segmentations are statistically correlated (Spearman's rank correlation coefficient: 0.898) but they have low similarity degree (average Dice Similarity Coefficient: 61.87 ± 14.64). Therefore, volume measurements as well as evaluation metrics values demonstrated that MRI and PET convey different but complementary imaging information. GTV and BTV could be combined to enhance treatment planning. In more than 50% of cases the CTV was strongly or moderately conditioned by metabolic imaging. Especially, BTV MRI enhanced the CTV more accurately than BTV in 25% of cases. The proposed fully automatic multimodal PET/MRI segmentation method is a valid operator-independent methodology helping the clinicians to define a CTV that includes both metabolic and morphologic information. BTV MRI and GTV should be considered for a comprehensive treatment planning. Copyright © 2017 Elsevier B.V. All rights reserved.

Efficient terrestrial laser scan segmentation exploiting data structure

NASA Astrophysics Data System (ADS)

Mahmoudabadi, Hamid; Olsen, Michael J.; Todorovic, Sinisa

2016-09-01

New technologies such as lidar enable the rapid collection of massive datasets to model a 3D scene as a point cloud. However, while hardware technology continues to advance, processing 3D point clouds into informative models remains complex and time consuming. A common approach to increase processing efficiently is to segment the point cloud into smaller sections. This paper proposes a novel approach for point cloud segmentation using computer vision algorithms to analyze panoramic representations of individual laser scans. These panoramas can be quickly created using an inherent neighborhood structure that is established during the scanning process, which scans at fixed angular increments in a cylindrical or spherical coordinate system. In the proposed approach, a selected image segmentation algorithm is applied on several input layers exploiting this angular structure including laser intensity, range, normal vectors, and color information. These segments are then mapped back to the 3D point cloud so that modeling can be completed more efficiently. This approach does not depend on pre-defined mathematical models and consequently setting parameters for them. Unlike common geometrical point cloud segmentation methods, the proposed method employs the colorimetric and intensity data as another source of information. The proposed algorithm is demonstrated on several datasets encompassing variety of scenes and objects. Results show a very high perceptual (visual) level of segmentation and thereby the feasibility of the proposed algorithm. The proposed method is also more efficient compared to Random Sample Consensus (RANSAC), which is a common approach for point cloud segmentation.

Mutual information and redundancy in spontaneous communication between cortical neurons.

PubMed

Szczepanski, J; Arnold, M; Wajnryb, E; Amigó, J M; Sanchez-Vives, M V

2011-03-01

An important question in neural information processing is how neurons cooperate to transmit information. To study this question, we resort to the concept of redundancy in the information transmitted by a group of neurons and, at the same time, we introduce a novel concept for measuring cooperation between pairs of neurons called relative mutual information (RMI). Specifically, we studied these two parameters for spike trains generated by neighboring neurons from the primary visual cortex in the awake, freely moving rat. The spike trains studied here were spontaneously generated in the cortical network, in the absence of visual stimulation. Under these conditions, our analysis revealed that while the value of RMI oscillated slightly around an average value, the redundancy exhibited a behavior characterized by a higher variability. We conjecture that this combination of approximately constant RMI and greater variable redundancy makes information transmission more resistant to noise disturbances. Furthermore, the redundancy values suggest that neurons can cooperate in a flexible way during information transmission. This mostly occurs via a leading neuron with higher transmission rate or, less frequently, through the information rate of the whole group being higher than the sum of the individual information rates-in other words in a synergetic manner. The proposed method applies not only to the stationary, but also to locally stationary neural signals.

Mutual information-based facial expression recognition

NASA Astrophysics Data System (ADS)

Hazar, Mliki; Hammami, Mohamed; Hanêne, Ben-Abdallah

2013-12-01

This paper introduces a novel low-computation discriminative regions representation for expression analysis task. The proposed approach relies on interesting studies in psychology which show that most of the descriptive and responsible regions for facial expression are located around some face parts. The contributions of this work lie in the proposition of new approach which supports automatic facial expression recognition based on automatic regions selection. The regions selection step aims to select the descriptive regions responsible or facial expression and was performed using Mutual Information (MI) technique. For facial feature extraction, we have applied Local Binary Patterns Pattern (LBP) on Gradient image to encode salient micro-patterns of facial expressions. Experimental studies have shown that using discriminative regions provide better results than using the whole face regions whilst reducing features vector dimension.

Low-dimensional approximation searching strategy for transfer entropy from non-uniform embedding

PubMed Central

2018-01-01

Transfer entropy from non-uniform embedding is a popular tool for the inference of causal relationships among dynamical subsystems. In this study we present an approach that makes use of low-dimensional conditional mutual information quantities to decompose the original high-dimensional conditional mutual information in the searching procedure of non-uniform embedding for significant variables at different lags. We perform a series of simulation experiments to assess the sensitivity and specificity of our proposed method to demonstrate its advantage compared to previous algorithms. The results provide concrete evidence that low-dimensional approximations can help to improve the statistical accuracy of transfer entropy in multivariate causality analysis and yield a better performance over other methods. The proposed method is especially efficient as the data length grows. PMID:29547669

Performance of different synchronization measures in real data: A case study on electroencephalographic signals

NASA Astrophysics Data System (ADS)

Quian Quiroga, R.; Kraskov, A.; Kreuz, T.; Grassberger, P.

2002-04-01

We study the synchronization between left and right hemisphere rat electroencephalographic (EEG) channels by using various synchronization measures, namely nonlinear interdependences, phase synchronizations, mutual information, cross correlation, and the coherence function. In passing we show a close relation between two recently proposed phase synchronization measures and we extend the definition of one of them. In three typical examples we observe that except mutual information, all these measures give a useful quantification that is hard to be guessed beforehand from the raw data. Despite their differences, results are qualitatively the same. Therefore, we claim that the applied measures are valuable for the study of synchronization in real data. Moreover, in the particular case of EEG signals their use as complementary variables could be of clinical relevance.

Entropy, energy, and entanglement of localized states in bent triatomic molecules

NASA Astrophysics Data System (ADS)

Yuan, Qiang; Hou, Xi-Wen

2017-05-01

The dynamics of quantum entropy, energy, and entanglement is studied for various initial states in an important spectroscopic Hamiltonian of bent triatomic molecules H2O, D2O, and H2S. The total quantum correlation is quantified in terms of the mutual information and the entanglement by the concurrence borrowed from the theory of quantum information. The Pauli entropy and the intramolecular energy usually used in the theory of molecules are calculated to establish a possible relationship between both theories. Sections of two quantities among these four quantities are introduced to visualize such relationship. Analytic and numerical simulations demonstrate that if an initial state is taken to be the stretch- or the bend-vibrationally localized state, the mutual information, the Pauli entropy, and the concurrence are dominant-positively correlated while they are dominantly anti-correlated with the interacting energy among three anharmonic vibrational modes. In particular, such correlation is more distinct for the localized state with high excitations in the bending mode. The nice quasi-periodicity of those quantities in D2O molecule reveals that this molecule prepared in the localized state in the stretching or the bending mode can be more appreciated for molecular quantum computation. However, the dynamical correlations of those quantities behave irregularly for the dislocalized states. Moreover, the hierarchy of the mutual information and the Pauli entropy is explicitly proved. Quantum entropy and energy in every vibrational mode are investigated. Thereby, the relation between bipartite and tripartite entanglements is discussed as well. Those are useful for the understanding of quantum correlations in high-dimensional states in polyatomic molecules from quantum information and intramolecular dynamics.

Network model of human aging: Frailty limits and information measures

NASA Astrophysics Data System (ADS)

Farrell, Spencer G.; Mitnitski, Arnold B.; Rockwood, Kenneth; Rutenberg, Andrew D.

2016-11-01

Aging is associated with the accumulation of damage throughout a persons life. Individual health can be assessed by the Frailty Index (FI). The FI is calculated simply as the proportion f of accumulated age-related deficits relative to the total, leading to a theoretical maximum of f ≤1 . Observational studies have generally reported a much more stringent bound, with f ≤fmax<1 . The value of fmax in observational studies appears to be nonuniversal, but fmax≈0.7 is often reported. A previously developed network model of individual aging was unable to recover fmax<1 while retaining the other observed phenomenology of increasing f and mortality rates with age. We have developed a computationally accelerated network model that also allows us to tune the scale-free network exponent α . The network exponent α significantly affects the growth of mortality rates with age. However, we are only able to recover fmax by also introducing a deficit sensitivity parameter 1 -q , which is equivalent to a false-negative rate q . Our value of q =0.3 is comparable to finite sensitivities of age-related deficits with respect to mortality that are often reported in the literature. In light of nonzero q , we use mutual information I to provide a nonparametric measure of the predictive value of the FI with respect to individual mortality. We find that I is only modestly degraded by q <1 , and this degradation is mitigated when increasing number of deficits are included in the FI. We also find that the information spectrum, i.e., the mutual information of individual deficits versus connectivity, has an approximately power-law dependence that depends on the network exponent α . Mutual information I is therefore a useful tool for characterizing the network topology of aging populations.

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

Network inference from functional experimental data (Conference Presentation)

NASA Astrophysics Data System (ADS)

Desrosiers, Patrick; Labrecque, Simon; Tremblay, Maxime; Bélanger, Mathieu; De Dorlodot, Bertrand; Côté, Daniel C.

2016-03-01

Functional connectivity maps of neuronal networks are critical tools to understand how neurons form circuits, how information is encoded and processed by neurons, how memory is shaped, and how these basic processes are altered under pathological conditions. Current light microscopy allows to observe calcium or electrical activity of thousands of neurons simultaneously, yet assessing comprehensive connectivity maps directly from such data remains a non-trivial analytical task. There exist simple statistical methods, such as cross-correlation and Granger causality, but they only detect linear interactions between neurons. Other more involved inference methods inspired by information theory, such as mutual information and transfer entropy, identify more accurately connections between neurons but also require more computational resources. We carried out a comparative study of common connectivity inference methods. The relative accuracy and computational cost of each method was determined via simulated fluorescence traces generated with realistic computational models of interacting neurons in networks of different topologies (clustered or non-clustered) and sizes (10-1000 neurons). To bridge the computational and experimental works, we observed the intracellular calcium activity of live hippocampal neuronal cultures infected with the fluorescent calcium marker GCaMP6f. The spontaneous activity of the networks, consisting of 50-100 neurons per field of view, was recorded from 20 to 50 Hz on a microscope controlled by a homemade software. We implemented all connectivity inference methods in the software, which rapidly loads calcium fluorescence movies, segments the images, extracts the fluorescence traces, and assesses the functional connections (with strengths and directions) between each pair of neurons. We used this software to assess, in real time, the functional connectivity from real calcium imaging data in basal conditions, under plasticity protocols, and epileptic conditions.

Aggression and Moral Development: Integrating Social Information Processing and Moral Domain Models

ERIC Educational Resources Information Center

Arsenio, William F.; Lemerise, Elizabeth A.

2004-01-01

Social information processing and moral domain theories have developed in relative isolation from each other despite their common focus on intentional harm and victimization, and mutual emphasis on social cognitive processes in explaining aggressive, morally relevant behaviors. This article presents a selective summary of these literatures with…

77 FR 50757 - 60-Day Notice of Proposed Information Collection: Exchange Visitor Program Participant Survey...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-22

.... Type of Request: New Collection. Originating Office: Bureau of Educational and Cultural Affairs, ECA/EC... of technology. Abstract of proposed collection: This collection of information is under the provisions of the Mutual Educational and Cultural Exchange Act, as amended, and its implementing regulations...

78 FR 15800 - 30-Day Notice of Proposed Information Collection: Exchange Student Survey

Federal Register 2010, 2011, 2012, 2013, 2014

2013-03-12

... Collection. Originating Office: Educational and Cultural Affairs (ECA/ PE/C/PY). Form Number: SV2012-0007... automated collection techniques or other forms of information technology. Please note that comments... provisions of the Mutual Educational and Cultural Exchange Act, as amended, and the Exchange Visitor Program...

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.…

The Information Ambassadors: The 1990-91 Library/Book Fellows.

ERIC Educational Resources Information Center

American Library Association, Chicago, IL.

The American Library Association (ALA) Library/Book Fellows program began in 1986 with a grant from the U.S. Information Agency. The program's purpose is threefold: (1) to increase international understanding through the establishment of professional and personal relationships and the accomplishment of mutual goals; (2) to promote international…

Digitally Included: Business-Community Partnerships To Promote the Use of Information and Communication Technologies.

ERIC Educational Resources Information Center

2003

This publication describes efforts in the United Kingdom (UK) to develop mutually beneficial, collaborative partnerships between businesses and communities that promote digital inclusion (access to information and communication technologies). Case studies of different kinds of relationships are listed, including UK online centers, schools, events…

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…

Integrated Battlefield Effects Research for the National Training Center. Appendix E. Requirements Design Specification and Demonstration Report for the Exercise Coordination and Control (ECC) Demonstration System

DTIC Science & Technology

1984-12-31

mode 3. Edit AAR mode 4. Run AR mode Each of the modes shall be mutually eclusive. In the real-time mode, the station tactical display shall reflect...periods of the exercise segment. The edit AAR mode shall provide the operator with the capability of creating/ editing a command file for use during an...II .,II IH 0 - hUi I -24 .6d, -I" @ 0 M% A A a a dJ a) 6 I I - -- a’ 1.* % 46- 8 ’ 0 "S 8 -- -- "- - I .-36 aISa "S-a:z:-a I I b 0 Il -- I -- I1 -I

Ambient occlusion effects for combined volumes and tubular geometry.

PubMed

Schott, Mathias; Martin, Tobias; Grosset, A V Pascal; Smith, Sean T; Hansen, Charles D

2013-06-01

This paper details a method for interactive direct volume rendering that computes ambient occlusion effects for visualizations that combine both volumetric and geometric primitives, specifically tube-shaped geometric objects representing streamlines, magnetic field lines or DTI fiber tracts. The algorithm extends the recently presented the directional occlusion shading model to allow the rendering of those geometric shapes in combination with a context providing 3D volume, considering mutual occlusion between structures represented by a volume or geometry. Stream tube geometries are computed using an effective spline-based interpolation and approximation scheme that avoids self-intersection and maintains coherent orientation of the stream tube segments to avoid surface deforming twists. Furthermore, strategies to reduce the geometric and specular aliasing of the stream tubes are discussed.

Ambient Occlusion Effects for Combined Volumes and Tubular Geometry

PubMed Central

Schott, Mathias; Martin, Tobias; Grosset, A.V. Pascal; Smith, Sean T.; Hansen, Charles D.

2013-01-01

This paper details a method for interactive direct volume rendering that computes ambient occlusion effects for visualizations that combine both volumetric and geometric primitives, specifically tube-shaped geometric objects representing streamlines, magnetic field lines or DTI fiber tracts. The algorithm extends the recently presented the directional occlusion shading model to allow the rendering of those geometric shapes in combination with a context providing 3D volume, considering mutual occlusion between structures represented by a volume or geometry. Stream tube geometries are computed using an effective spline-based interpolation and approximation scheme that avoids self-intersection and maintains coherent orientation of the stream tube segments to avoid surface deforming twists. Furthermore, strategies to reduce the geometric and specular aliasing of the stream tubes are discussed. PMID:23559506

Targeting Information to Market Segments: An Action Oriented Study of Attitudes Toward Vocational Education Among Target Populations. Final Report.

ERIC Educational Resources Information Center

Sikorski, Linda A.; And Others

Research was conducted to show how segments of the population of minority and disadvantaged youth might be positively influenced by selective information campaigns to participate in vocational education programs. The first-year effort (stage 1) undertook to measure student attitudes and to develop recommendations for using this information in…

Facilitated peer support in breast cancer: a pre- and post-program evaluation of women's expectations and experiences of a facilitated peer support program.

PubMed

Power, Sinead; Hegarty, Josephine

2010-01-01

Peer support programs are associated with the provision of emotional, informational, and appraisal support. The benefits of peer support for women with breast cancer include reduced social isolation, enhanced coping, and access to information. The aim of this study was to conduct a pre- and post-program evaluation of a 7-week facilitated breast cancer peer support program in a cancer support house. Women with primary breast cancer (n = 8) participated in pre- and post-program focus groups. The interviews were recorded and were transcribed verbatim by the researcher. The data were analyzed using content analysis. Eight themes were identified. The key themes emerging from the pre and post programme focus groups included: The need for mutual identification; Post-treatment isolation; Help with moving on; The impact of hair loss; Consolidation of information; Enablement/empowerment; The importance of the cancer survivor; Mutual sharing. It is essential that facilitated peer support programs are tailored to meet the support needs of women with breast cancer. There is a particular need to facilitate mutual sharing and support for hair loss within these programs. Implications for practice emerging from this study include the importance of pre- and post-program evaluations in identifying whether peer support programs meet the expectations of women with breast cancer, the need for peer/professional programs to support women with treatment-induced hair loss, the importance of including cancer survivors in support programs, and the need to allow more informal sharing to occur in facilitated peer support programs.

A multivariate extension of mutual information for growing neural networks.

PubMed

Ball, Kenneth R; Grant, Christopher; Mundy, William R; Shafer, Timothy J

2017-11-01

Recordings of neural network activity in vitro are increasingly being used to assess the development of neural network activity and the effects of drugs, chemicals and disease states on neural network function. The high-content nature of the data derived from such recordings can be used to infer effects of compounds or disease states on a variety of important neural functions, including network synchrony. Historically, synchrony of networks in vitro has been assessed either by determination of correlation coefficients (e.g. Pearson's correlation), by statistics estimated from cross-correlation histograms between pairs of active electrodes, and/or by pairwise mutual information and related measures. The present study examines the application of Normalized Multiinformation (NMI) as a scalar measure of shared information content in a multivariate network that is robust with respect to changes in network size. Theoretical simulations are designed to investigate NMI as a measure of complexity and synchrony in a developing network relative to several alternative approaches. The NMI approach is applied to these simulations and also to data collected during exposure of in vitro neural networks to neuroactive compounds during the first 12 days in vitro, and compared to other common measures, including correlation coefficients and mean firing rates of neurons. NMI is shown to be more sensitive to developmental effects than first order synchronous and nonsynchronous measures of network complexity. Finally, NMI is a scalar measure of global (rather than pairwise) mutual information in a multivariate network, and hence relies on less assumptions for cross-network comparisons than historical approaches. Copyright © 2017 Elsevier Ltd. All rights reserved.

Interactive tele-radiological segmentation systems for treatment and diagnosis.

PubMed

Zimeras, S; Gortzis, L G

2012-01-01

Telehealth is the exchange of health information and the provision of health care services through electronic information and communications technology, where participants are separated by geographic, time, social and cultural barriers. The shift of telemedicine from desktop platforms to wireless and mobile technologies is likely to have a significant impact on healthcare in the future. It is therefore crucial to develop a general information exchange e-medical system to enables its users to perform online and offline medical consultations through diagnosis. During the medical diagnosis, image analysis techniques combined with doctor's opinions could be useful for final medical decisions. Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. In medical images, segmentation has traditionally been done by human experts. Even with the aid of image processing software (computer-assisted segmentation tools), manual segmentation of 2D and 3D CT images is tedious, time-consuming, and thus impractical, especially in cases where a large number of objects must be specified. Substantial computational and storage requirements become especially acute when object orientation and scale have to be considered. Therefore automated or semi-automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use. The main purpose of this work is to analyze segmentation techniques for the definition of anatomical structures under telemedical systems.

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

Information and communication technology in cross-industry glossaries

NASA Astrophysics Data System (ADS)

Pronichev, A. N.; Polyakov, E. V.; Nikitaev, V. G.; Vasilyev, N. P.; Dmitrieva, V. V.; Ulina, I. V.

2017-01-01

Interdisciplinary glossary is proposed to ensure mutual understanding of specialists from various fields of science and technology. Glossary is designed with application of information technologies. The field of information technologies is considered. It is necessary for the understanding and cooperation of specialists in various areas. The technological solutions and applications for multi-disciplinary areas, results of testing of the developed techniques are presented.

A Reward-Maximizing Spiking Neuron as a Bounded Rational Decision Maker.

PubMed

Leibfried, Felix; Braun, Daniel A

2015-08-01

Rate distortion theory describes how to communicate relevant information most efficiently over a channel with limited capacity. One of the many applications of rate distortion theory is bounded rational decision making, where decision makers are modeled as information channels that transform sensory input into motor output under the constraint that their channel capacity is limited. Such a bounded rational decision maker can be thought to optimize an objective function that trades off the decision maker's utility or cumulative reward against the information processing cost measured by the mutual information between sensory input and motor output. In this study, we interpret a spiking neuron as a bounded rational decision maker that aims to maximize its expected reward under the computational constraint that the mutual information between the neuron's input and output is upper bounded. This abstract computational constraint translates into a penalization of the deviation between the neuron's instantaneous and average firing behavior. We derive a synaptic weight update rule for such a rate distortion optimizing neuron and show in simulations that the neuron efficiently extracts reward-relevant information from the input by trading off its synaptic strengths against the collected reward.

The locking-decoding frontier for generic dynamics.

PubMed

Dupuis, Frédéric; Florjanczyk, Jan; Hayden, Patrick; Leung, Debbie

2013-11-08

It is known that the maximum classical mutual information, which can be achieved between measurements on pairs of quantum systems, can drastically underestimate the quantum mutual information between them. In this article, we quantify this distinction between classical and quantum information by demonstrating that after removing a logarithmic-sized quantum system from one half of a pair of perfectly correlated bitstrings, even the most sensitive pair of measurements might yield only outcomes essentially independent of each other. This effect is a form of information locking but the definition we use is strictly stronger than those used previously. Moreover, we find that this property is generic, in the sense that it occurs when removing a random subsystem. As such, the effect might be relevant to statistical mechanics or black hole physics. While previous works had always assumed a uniform message, we assume only a min-entropy bound and also explore the effect of entanglement. We find that classical information is strongly locked almost until it can be completely decoded. Finally, we exhibit a quantum key distribution protocol that is 'secure' in the sense of accessible information but in which leakage of even a logarithmic number of bits compromises the secrecy of all others.

The locking-decoding frontier for generic dynamics

PubMed Central

Dupuis, Frédéric; Florjanczyk, Jan; Hayden, Patrick; Leung, Debbie

2013-01-01

It is known that the maximum classical mutual information, which can be achieved between measurements on pairs of quantum systems, can drastically underestimate the quantum mutual information between them. In this article, we quantify this distinction between classical and quantum information by demonstrating that after removing a logarithmic-sized quantum system from one half of a pair of perfectly correlated bitstrings, even the most sensitive pair of measurements might yield only outcomes essentially independent of each other. This effect is a form of information locking but the definition we use is strictly stronger than those used previously. Moreover, we find that this property is generic, in the sense that it occurs when removing a random subsystem. As such, the effect might be relevant to statistical mechanics or black hole physics. While previous works had always assumed a uniform message, we assume only a min-entropy bound and also explore the effect of entanglement. We find that classical information is strongly locked almost until it can be completely decoded. Finally, we exhibit a quantum key distribution protocol that is ‘secure’ in the sense of accessible information but in which leakage of even a logarithmic number of bits compromises the secrecy of all others. PMID:24204183

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.

Incorporating Edge Information into Best Merge Region-Growing Segmentation

NASA Technical Reports Server (NTRS)

Tilton, James C.; Pasolli, Edoardo

2014-01-01

We have previously developed a best merge region-growing approach that integrates nonadjacent region object aggregation with the neighboring region merge process usually employed in region growing segmentation approaches. This approach has been named HSeg, because it provides a hierarchical set of image segmentation results. Up to this point, HSeg considered only global region feature information in the region growing decision process. We present here three new versions of HSeg that include local edge information into the region growing decision process at different levels of rigor. We then compare the effectiveness and processing times of these new versions HSeg with each other and with the original version of HSeg.

A Novel Segmentation Approach Combining Region- and Edge-Based Information for Ultrasound Images

PubMed Central

Luo, Yaozhong; Liu, Longzhong; Li, Xuelong

2017-01-01

Ultrasound imaging has become one of the most popular medical imaging modalities with numerous diagnostic applications. However, ultrasound (US) image segmentation, which is the essential process for further analysis, is a challenging task due to the poor image quality. In this paper, we propose a new segmentation scheme to combine both region- and edge-based information into the robust graph-based (RGB) segmentation method. The only interaction required is to select two diagonal points to determine a region of interest (ROI) on the original image. The ROI image is smoothed by a bilateral filter and then contrast-enhanced by histogram equalization. Then, the enhanced image is filtered by pyramid mean shift to improve homogeneity. With the optimization of particle swarm optimization (PSO) algorithm, the RGB segmentation method is performed to segment the filtered image. The segmentation results of our method have been compared with the corresponding results obtained by three existing approaches, and four metrics have been used to measure the segmentation performance. The experimental results show that the method achieves the best overall performance and gets the lowest ARE (10.77%), the second highest TPVF (85.34%), and the second lowest FPVF (4.48%). PMID:28536703

Segmentation of clustered cells in negative phase contrast images with integrated light intensity and cell shape information.

PubMed

Wang, Y; Wang, C; Zhang, Z

2018-05-01

Automated cell segmentation plays a key role in characterisations of cell behaviours for both biology research and clinical practices. Currently, the segmentation of clustered cells still remains as a challenge and is the main reason for false segmentation. In this study, the emphasis was put on the segmentation of clustered cells in negative phase contrast images. A new method was proposed to combine both light intensity and cell shape information through the construction of grey-weighted distance transform (GWDT) within preliminarily segmented areas. With the constructed GWDT, the clustered cells can be detected and then separated with a modified region skeleton-based method. Moreover, a contour expansion operation was applied to get optimised detection of cell boundaries. In this paper, the working principle and detailed procedure of the proposed method are described, followed by the evaluation of the method on clustered cell segmentation. Results show that the proposed method achieves an improved performance in clustered cell segmentation compared with other methods, with 85.8% and 97.16% accuracy rate for clustered cells and all cells, respectively. © 2017 The Authors Journal of Microscopy © 2017 Royal Microscopical Society.

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

Automatic segmentation of colon glands using object-graphs.

PubMed

Gunduz-Demir, Cigdem; Kandemir, Melih; Tosun, Akif Burak; Sokmensuer, Cenk

2010-02-01

Gland segmentation is an important step to automate the analysis of biopsies that contain glandular structures. However, this remains a challenging problem as the variation in staining, fixation, and sectioning procedures lead to a considerable amount of artifacts and variances in tissue sections, which may result in huge variances in gland appearances. In this work, we report a new approach for gland segmentation. This approach decomposes the tissue image into a set of primitive objects and segments glands making use of the organizational properties of these objects, which are quantified with the definition of object-graphs. As opposed to the previous literature, the proposed approach employs the object-based information for the gland segmentation problem, instead of using the pixel-based information alone. Working with the images of colon tissues, our experiments demonstrate that the proposed object-graph approach yields high segmentation accuracies for the training and test sets and significantly improves the segmentation performance of its pixel-based counterparts. The experiments also show that the object-based structure of the proposed approach provides more tolerance to artifacts and variances in tissues.

Application of State-Space Smoothing to fMRI Data for Calculation of Lagged Transinformation between Human Brain Activations

NASA Astrophysics Data System (ADS)

Watanabe, Jobu

2009-09-01

Mutual information can be given a directional sense by introducing a time lag in one of the variables. In an author's previous study, to investigate the network dynamics of human brain regions, lagged transinformation (LTI) was introduced using time delayed mutual information. The LTI makes it possible to quantify the time course of dynamic information transfer between regions in the temporal domain. The LTI was applied to functional magnetic resonance imaging (fMRI) data involved in neural processing of the transformation and comparison from three-dimensional (3D) visual information to a two-dimensional (2D) location to calculate directed information flows between the activated brain regions. In the present study, for more precise estimation of LTI, Kalman filter smoothing was applied to the same fMRI data. Because the smoothing method exploits the full length of the time series data for the estimation, its application increases the precision. Large information flows were found from the bilateral prefrontal cortices to the parietal cortices. The results suggest that information of the 3D images stored as working memory was retrieved and transferred from the prefrontal cortices to the parietal cortices for comparison with information of the 2D images.

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

Cell nuclei segmentation in fluorescence microscopy images using inter- and intra-region discriminative information.

PubMed

Song, Yang; Cai, Weidong; Feng, David Dagan; Chen, Mei

2013-01-01

Automated segmentation of cell nuclei in microscopic images is critical to high throughput analysis of the ever increasing amount of data. Although cell nuclei are generally visually distinguishable for human, automated segmentation faces challenges when there is significant intensity inhomogeneity among cell nuclei or in the background. In this paper, we propose an effective method for automated cell nucleus segmentation using a three-step approach. It first obtains an initial segmentation by extracting salient regions in the image, then reduces false positives using inter-region feature discrimination, and finally refines the boundary of the cell nuclei using intra-region contrast information. This method has been evaluated on two publicly available datasets of fluorescence microscopic images with 4009 cells, and has achieved superior performance compared to popular state of the art methods using established metrics.

Localized Statistics for DW-MRI Fiber Bundle Segmentation

PubMed Central

Lankton, Shawn; Melonakos, John; Malcolm, James; Dambreville, Samuel; Tannenbaum, Allen

2013-01-01

We describe a method for segmenting neural fiber bundles in diffusion-weighted magnetic resonance images (DWMRI). As these bundles traverse the brain to connect regions, their local orientation of diffusion changes drastically, hence a constant global model is inaccurate. We propose a method to compute localized statistics on orientation information and use it to drive a variational active contour segmentation that accurately models the non-homogeneous orientation information present along the bundle. Initialized from a single fiber path, the proposed method proceeds to capture the entire bundle. We demonstrate results using the technique to segment the cingulum bundle and describe several extensions making the technique applicable to a wide range of tissues. PMID:23652079

Delineation and validation of river network spatial scales for water resources and fisheries management.

PubMed

Wang, Lizhu; Brenden, Travis; Cao, Yong; Seelbach, Paul

2012-11-01

Identifying appropriate spatial scales is critically important for assessing health, attributing data, and guiding management actions for rivers. We describe a process for identifying a three-level hierarchy of spatial scales for Michigan rivers. Additionally, we conduct a variance decomposition of fish occurrence, abundance, and assemblage metric data to evaluate how much observed variability can be explained by the three spatial scales as a gage of their utility for water resources and fisheries management. The process involved the development of geographic information system programs, statistical models, modification by experienced biologists, and simplification to meet the needs of policy makers. Altogether, 28,889 reaches, 6,198 multiple-reach segments, and 11 segment classes were identified from Michigan river networks. The segment scale explained the greatest amount of variation in fish abundance and occurrence, followed by segment class, and reach. Segment scale also explained the greatest amount of variation in 13 of the 19 analyzed fish assemblage metrics, with segment class explaining the greatest amount of variation in the other six fish metrics. Segments appear to be a useful spatial scale/unit for measuring and synthesizing information for managing rivers and streams. Additionally, segment classes provide a useful typology for summarizing the numerous segments into a few categories. Reaches are the foundation for the identification of segments and segment classes and thus are integral elements of the overall spatial scale hierarchy despite reaches not explaining significant variation in fish assemblage data.

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.

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

Method and apparatus for optimizing a train trip using signal information

DOEpatents

Kumar, Ajith Kuttannair; Daum, Wolfgang; Otsubo, Tom; Hershey, John Erik; Hess, Gerald James

2014-06-10

A system is provided for operating a railway network including a first railway vehicle during a trip along track segments. The system includes a first element for determining travel parameters of the first railway vehicle, a second element for determining travel parameters of a second railway vehicle relative to the track segments to be traversed by the first vehicle during the trip, a processor for receiving information from the first and the second elements and for determining a relationship between occupation of a track segment by the second vehicle and later occupation of the same track segment by the first vehicle and an algorithm embodied within the processor having access to the information to create a trip plan that determines a speed trajectory for the first vehicle. The speed trajectory is responsive to the relationship and further in accordance with one or more operational criteria for the first vehicle.

On the Connection Between Flap Side-Edge Noise and Tip Vortex Dynamics

NASA Technical Reports Server (NTRS)

Casalino, D.; Hazir, A.; Fares, E.; Duda, B.; Khorrami, M. R.

2015-01-01

The goal of the present work is to investigate how the dynamics of the vortical flow about the flap side edge of an aircraft determine the acoustic radiation. A validated lattice- Boltzmann CFD solution of the unsteady flow about a detailed business jet configuration in approach conditions is used for the present analysis. Evidence of the connection between the noise generated by several segments of the inboard flap tip and the aerodynamic forces acting on the same segments is given, proving that the noise generation mechanism has a spatially coherent and acoustically compact character on the scale of the flap chord, and that the edge-scattering effects are of secondary importance. Subsequently, evidence of the connection between the kinematics of the tip vortex system and the aerodynamic force is provided. The kinematics of the dual vortex system are investigated via a core detection technique. Emphasis is placed on the mutual induction effects between the two main vortices rolling up from the pressure and suction sides of the flap edge. A simple heuristic formula that relates the far-field noise spectrum and the cross-spectrum of the unsteady vortical positions is developed.

Automated segmentation of neuroanatomical structures in multispectral MR microscopy of the mouse brain.

PubMed

Ali, Anjum A; Dale, Anders M; Badea, Alexandra; Johnson, G Allan

2005-08-15

We present the automated segmentation of magnetic resonance microscopy (MRM) images of the C57BL/6J mouse brain into 21 neuroanatomical structures, including the ventricular system, corpus callosum, hippocampus, caudate putamen, inferior colliculus, internal capsule, globus pallidus, and substantia nigra. The segmentation algorithm operates on multispectral, three-dimensional (3D) MR data acquired at 90-microm isotropic resolution. Probabilistic information used in the segmentation is extracted from training datasets of T2-weighted, proton density-weighted, and diffusion-weighted acquisitions. Spatial information is employed in the form of prior probabilities of occurrence of a structure at a location (location priors) and the pairwise probabilities between structures (contextual priors). Validation using standard morphometry indices shows good consistency between automatically segmented and manually traced data. Results achieved in the mouse brain are comparable with those achieved in human brain studies using similar techniques. The segmentation algorithm shows excellent potential for routine morphological phenotyping of mouse models.

Compound image segmentation of published biomedical figures.

PubMed

Li, Pengyuan; Jiang, Xiangying; Kambhamettu, Chandra; Shatkay, Hagit

2018-04-01

Images convey essential information in biomedical publications. As such, there is a growing interest within the bio-curation and the bio-databases communities, to store images within publications as evidence for biomedical processes and for experimental results. However, many of the images in biomedical publications are compound images consisting of multiple panels, where each individual panel potentially conveys a different type of information. Segmenting such images into constituent panels is an essential first step toward utilizing images. In this article, we develop a new compound image segmentation system, FigSplit, which is based on Connected Component Analysis. To overcome shortcomings typically manifested by existing methods, we develop a quality assessment step for evaluating and modifying segmentations. Two methods are proposed to re-segment the images if the initial segmentation is inaccurate. Experimental results show the effectiveness of our method compared with other methods. The system is publicly available for use at: https://www.eecis.udel.edu/~compbio/FigSplit. The code is available upon request. shatkay@udel.edu. Supplementary data are available online at Bioinformatics.

17 CFR 229.102 - (Item 102) Description of property.

Code of Federal Regulations, 2010 CFR

2010-04-01

... segment(s), as reported in the financial statements, that use the properties described. If any such... held. Instructions to Item 102: 1. What is required is such information as reasonably will inform investors as to the suitability, adequacy, productive capacity and extent of utilization of the facilities...

Individual Rocks Segmentation in Terrestrial Laser Scanning Point Cloud Using Iterative Dbscan Algorithm

NASA Astrophysics Data System (ADS)

Walicka, A.; Jóźków, G.; Borkowski, A.

2018-05-01

The fluvial transport is an important aspect of hydrological and geomorphologic studies. The knowledge about the movement parameters of different-size fractions is essential in many applications, such as the exploration of the watercourse changes, the calculation of the river bed parameters or the investigation of the frequency and the nature of the weather events. Traditional techniques used for the fluvial transport investigations do not provide any information about the long-term horizontal movement of the rocks. This information can be gained by means of terrestrial laser scanning (TLS). However, this is a complex issue consisting of several stages of data processing. In this study the methodology for individual rocks segmentation from TLS point cloud has been proposed, which is the first step for the semi-automatic algorithm for movement detection of individual rocks. The proposed algorithm is executed in two steps. Firstly, the point cloud is classified as rocks or background using only geometrical information. Secondly, the DBSCAN algorithm is executed iteratively on points classified as rocks until only one stone is detected in each segment. The number of rocks in each segment is determined using principal component analysis (PCA) and simple derivative method for peak detection. As a result, several segments that correspond to individual rocks are formed. Numerical tests were executed on two test samples. The results of the semi-automatic segmentation were compared to results acquired by manual segmentation. The proposed methodology enabled to successfully segment 76 % and 72 % of rocks in the test sample 1 and test sample 2, respectively.

Segmentation of white matter hyperintensities using convolutional neural networks with global spatial information in routine clinical brain MRI with none or mild vascular pathology.

PubMed

Rachmadi, Muhammad Febrian; Valdés-Hernández, Maria Del C; Agan, Maria Leonora Fatimah; Di Perri, Carol; Komura, Taku

2018-06-01

We propose an adaptation of a convolutional neural network (CNN) scheme proposed for segmenting brain lesions with considerable mass-effect, to segment white matter hyperintensities (WMH) characteristic of brains with none or mild vascular pathology in routine clinical brain magnetic resonance images (MRI). This is a rather difficult segmentation problem because of the small area (i.e., volume) of the WMH and their similarity to non-pathological brain tissue. We investigate the effectiveness of the 2D CNN scheme by comparing its performance against those obtained from another deep learning approach: Deep Boltzmann Machine (DBM), two conventional machine learning approaches: Support Vector Machine (SVM) and Random Forest (RF), and a public toolbox: Lesion Segmentation Tool (LST), all reported to be useful for segmenting WMH in MRI. We also introduce a way to incorporate spatial information in convolution level of CNN for WMH segmentation named global spatial information (GSI). Analysis of covariance corroborated known associations between WMH progression, as assessed by all methods evaluated, and demographic and clinical data. Deep learning algorithms outperform conventional machine learning algorithms by excluding MRI artefacts and pathologies that appear similar to WMH. Our proposed approach of incorporating GSI also successfully helped CNN to achieve better automatic WMH segmentation regardless of network's settings tested. The mean Dice Similarity Coefficient (DSC) values for LST-LGA, SVM, RF, DBM, CNN and CNN-GSI were 0.2963, 0.1194, 0.1633, 0.3264, 0.5359 and 5389 respectively. Crown Copyright © 2018. Published by Elsevier Ltd. All rights reserved.

Automatic lumen segmentation in IVOCT images using binary morphological reconstruction

PubMed Central

2013-01-01

Background Atherosclerosis causes millions of deaths, annually yielding billions in expenses round the world. Intravascular Optical Coherence Tomography (IVOCT) is a medical imaging modality, which displays high resolution images of coronary cross-section. Nonetheless, quantitative information can only be obtained with segmentation; consequently, more adequate diagnostics, therapies and interventions can be provided. Since it is a relatively new modality, many different segmentation methods, available in the literature for other modalities, could be successfully applied to IVOCT images, improving accuracies and uses. Method An automatic lumen segmentation approach, based on Wavelet Transform and Mathematical Morphology, is presented. The methodology is divided into three main parts. First, the preprocessing stage attenuates and enhances undesirable and important information, respectively. Second, in the feature extraction block, wavelet is associated with an adapted version of Otsu threshold; hence, tissue information is discriminated and binarized. Finally, binary morphological reconstruction improves the binary information and constructs the binary lumen object. Results The evaluation was carried out by segmenting 290 challenging images from human and pig coronaries, and rabbit iliac arteries; the outcomes were compared with the gold standards made by experts. The resultant accuracy was obtained: True Positive (%) = 99.29 ± 2.96, False Positive (%) = 3.69 ± 2.88, False Negative (%) = 0.71 ± 2.96, Max False Positive Distance (mm) = 0.1 ± 0.07, Max False Negative Distance (mm) = 0.06 ± 0.1. Conclusions In conclusion, by segmenting a number of IVOCT images with various features, the proposed technique showed to be robust and more accurate than published studies; in addition, the method is completely automatic, providing a new tool for IVOCT segmentation. PMID:23937790

Airplane takeoff and landing performance monitoring system

NASA Technical Reports Server (NTRS)

Middleton, David B. (Inventor); Srivatsan, Raghavachari (Inventor); Person, Jr., Lee H. (Inventor)

1991-01-01

The invention is a real-time takeoff and landing performance monitoring system for an aircraft which provides a pilot with graphic and metric information to assist in decisions related to achieving rotation speed (V.sub.R) within the safe zone of a runway, or stopping the aircraft on the runway after landing or take-off abort. The system processes information in two segments: a pretakeoff segment and a real-time segment. One-time inputs of ambient conditions and airplane configuration information are used in the pretakeoff segment to generate scheduled performance data. The real-time segment uses the scheduled performance data, runway length data and transducer measured parameters to monitor the performance of the airplane throughout the takeoff roll. Airplane and engine performance deficiencies are detected and annunciated. A novel and important feature of this segment is that it updates the estimated runway rolling friction coefficient. Airplane performance predictions also reflect changes in head wind occurring as the takeoff roll progresses. The system provides a head-down display and a head-up display. The head-up display is projected onto a partially reflective transparent surface through which the pilot views the runway. By comparing the present performance of the airplane with a predicted nominal performance based upon given conditions, performance deficiencies are detected by the system.

U.S. Information Policy and Cultural Diplomacy. Headline Series No. 308.

ERIC Educational Resources Information Center

Ninkovich, Frank

This booklet examines U.S. involvement with cultural diplomacy, emphasizing exchanges of persons and ideas that have lasting effects on relatively small numbers of people and information programs using the mass media to influence large numbers of people. Whereas the cultural exchange programs are internationalist in nature, promoting mutual and…

Informal Social Support Interventions and their Role in Violence Prevention: An Agenda for Future Evaluation

ERIC Educational Resources Information Center

Budde, Stephen; Schene, Patricia

2004-01-01

There is increasing interest among policymakers and practitioners in tapping the potential of family, friends, volunteers, peer support groups, and mutual aid organizations to help prevent violence. The popularity of these informal social support (ISS) interventions stems, in part, from their flexibility, responsiveness to individual needs, and…

Internet Resources for Reference: Finance and Investment.

ERIC Educational Resources Information Center

Mai, Brent Alan

1997-01-01

When called upon to aid in filtering through finance and investment information on the Internet, the business librarian is also faced with knowing what is available and how to find it. Web sites are identified that provide information about stocks and their exchanges, mutual funds, bonds, company annual reports, and taxes. (Author/AEF)

78 FR 64197 - Renewable Energy Policy Business Roundtable in Livermore, CA

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-28

... allowed the private sector to explore areas of mutual concern and share with government officials their... rolling basis in the order they are received. Selected companies will be contacted with information about... send an email to [email protected] with the following information. Name of Applicant Company Name Company...

U.S. Information Ambassadors: The 1991-92 Library Fellows and Debriefing Report.

ERIC Educational Resources Information Center

Doyle, Robert P.

The American Library Association (ALA) Library Fellows program began in 1986 with a grant from the U.S. Information Agency. The program's purpose is threefold: (1) to increase international understanding through the establishment of professional and personal relationships and the accomplishment of mutual goals; (2) to promote international sharing…

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.

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.

Tightening the entropic uncertainty bound in the presence of quantum memory

NASA Astrophysics Data System (ADS)

Adabi, F.; Salimi, S.; Haseli, S.

2016-06-01

The uncertainty principle is a fundamental principle in quantum physics. It implies that the measurement outcomes of two incompatible observables cannot be predicted simultaneously. In quantum information theory, this principle can be expressed in terms of entropic measures. M. Berta et al. [Nat. Phys. 6, 659 (2010), 10.1038/nphys1734] have indicated that uncertainty bound can be altered by considering a particle as a quantum memory correlating with the primary particle. In this article, we obtain a lower bound for entropic uncertainty in the presence of a quantum memory by adding an additional term depending on the Holevo quantity and mutual information. We conclude that our lower bound will be tightened with respect to that of Berta et al. when the accessible information about measurements outcomes is less than the mutual information about the joint state. Some examples have been investigated for which our lower bound is tighter than Berta et al.'s lower bound. Using our lower bound, a lower bound for the entanglement of formation of bipartite quantum states has been obtained, as well as an upper bound for the regularized distillable common randomness.

Alternate entropy measure for assessing volatility in financial markets.

PubMed

Bose, Ranjan; Hamacher, Kay

2012-11-01

We propose two alternate information theoretical approaches to assess non-Gaussian fluctuations in the return dynamics of financial markets. Specifically, we use superinformation, which is a measure of the disorder of the entropy of time series. We argue on theoretical grounds on its usefulness and show that it can be applied effectively for analyzing returns. A study of stock market data for over five years has been carried out using this approach. We show how superinformation helps to identify and classify important signals in the time series. The financial crisis of 2008 comes out very clearly in the superinformation plots. In addition, we introduce the super mutual information. Distinct super mutual information signatures are observed that might be used to mitigate idiosyncratic risk. The universality of our approach has been tested by carrying out the analysis for the 100 stocks listed in S&P100 index. The average superinformation values for the S&P100 stocks correlates very well with the VIX.

Alternate entropy measure for assessing volatility in financial markets

NASA Astrophysics Data System (ADS)

Bose, Ranjan; Hamacher, Kay

2012-11-01

We propose two alternate information theoretical approaches to assess non-Gaussian fluctuations in the return dynamics of financial markets. Specifically, we use superinformation, which is a measure of the disorder of the entropy of time series. We argue on theoretical grounds on its usefulness and show that it can be applied effectively for analyzing returns. A study of stock market data for over five years has been carried out using this approach. We show how superinformation helps to identify and classify important signals in the time series. The financial crisis of 2008 comes out very clearly in the superinformation plots. In addition, we introduce the super mutual information. Distinct super mutual information signatures are observed that might be used to mitigate idiosyncratic risk. The universality of our approach has been tested by carrying out the analysis for the 100 stocks listed in S&P100 index. The average superinformation values for the S&P100 stocks correlates very well with the VIX.

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.

Computing quantum discord is NP-complete

NASA Astrophysics Data System (ADS)

Huang, Yichen

2014-03-01

We study the computational complexity of quantum discord (a measure of quantum correlation beyond entanglement), and prove that computing quantum discord is NP-complete. Therefore, quantum discord is computationally intractable: the running time of any algorithm for computing quantum discord is believed to grow exponentially with the dimension of the Hilbert space so that computing quantum discord in a quantum system of moderate size is not possible in practice. As by-products, some entanglement measures (namely entanglement cost, entanglement of formation, relative entropy of entanglement, squashed entanglement, classical squashed entanglement, conditional entanglement of mutual information, and broadcast regularization of mutual information) and constrained Holevo capacity are NP-hard/NP-complete to compute. These complexity-theoretic results are directly applicable in common randomness distillation, quantum state merging, entanglement distillation, superdense coding, and quantum teleportation; they may offer significant insights into quantum information processing. Moreover, we prove the NP-completeness of two typical problems: linear optimization over classical states and detecting classical states in a convex set, providing evidence that working with classical states is generically computationally intractable.

Women's experiences of social support during the first year following primary breast cancer surgery.

PubMed

Drageset, Sigrunn; Lindstrøm, Torill Christine; Giske, Tove; Underlid, Kjell

2016-06-01

The aim of this qualitative follow-up study was to describe women's individual experiences of social support during their first year after primary breast cancer surgery. Individual semi-structured interviews with 10 women 1 year after surgery analysed by Kvales' meaning condensation method. Sharing experiences, being understood as an individual, continuity, and information and explanations were themes identified. Sharing mutual experiences increased the women's knowledge regarding cancer, increased experience of support and minimised rumination. After 1 year, the women felt that the network around them had 'normalised' and was less supportive. Being seen as a person, not as 'a diagnosis being treated', and continuity of professional support were important, giving feelings of security and trust. The women felt uncertainty after loss of professional support post-treatment. Information and explanations regarding treatment and treatment-related problems were essential. Mutual sharing of experiences is an important part of social support. Continuity, availability, information and respect were essential aspects of experienced professional support. © 2015 Nordic College of Caring Science.

Tripartite counterfactual quantum cryptography

NASA Astrophysics Data System (ADS)

Salih, Hatim

2014-07-01

We show how two distrustful parties, "Bob" and "Charlie," can share a secret key with the help of a mutually trusted "Alice" counterfactually; that is, with no information-carrying particles traveling between any of the three.

Mutual information detects a decreased interdependence between RR and SAP in orthostatic intolerance after microgravity condition.

PubMed

Raimondi, G; Chillemi, S; Michelassi, C; Di Garbo, A; Varanini, M; Legramante, J; Balocchi, R

2002-07-01

Orthostatic intolerance is the most serious symptom of cardiovascular deconditioning induced by microgravity. However, the exact mechanisms underlying these alterations have not been completely clarified. Several methods for studying the time series of systolic arterial pressure and RR interval have been proposed both in the time and in the frequency domain. However, these methods did not produce definitive results. In fact heart rate and arterial pressure show a complex pattern of global variability which is likely due to non linear feedback which involves the autonomic nervous system and to "stochastic" influences. Aim of this study was to evaluate the degree of interdependence between the mechanisms responsible for the variability of SAP and RR signals in subjects exposed to head down (HD). This quantification was achieved by using Mutual Information (MI).

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.

TU-H-CAMPUS-IeP3-01: Simultaneous PET Restoration and PET/CT Co-Segmentation Using a Variational Method

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

Li, L; Tan, S; Lu, W

Purpose: PET images are usually blurred due to the finite spatial resolution, while CT images suffer from low contrast. Segment a tumor from either a single PET or CT image is thus challenging. To make full use of the complementary information between PET and CT, we propose a novel variational method for simultaneous PET image restoration and PET/CT images co-segmentation. Methods: The proposed model was constructed based on the Γ-convergence approximation of Mumford-Shah (MS) segmentation model for PET/CT co-segmentation. Moreover, a PET de-blur process was integrated into the MS model to improve the segmentation accuracy. An interaction edge constraint termmore » over the two modalities were specially designed to share the complementary information. The energy functional was iteratively optimized using an alternate minimization (AM) algorithm. The performance of the proposed method was validated on ten lung cancer cases and five esophageal cancer cases. The ground truth were manually delineated by an experienced radiation oncologist using the complementary visual features of PET and CT. The segmentation accuracy was evaluated by Dice similarity index (DSI) and volume error (VE). Results: The proposed method achieved an expected restoration result for PET image and satisfactory segmentation results for both PET and CT images. For lung cancer dataset, the average DSI (0.72) increased by 0.17 and 0.40 than single PET and CT segmentation. For esophageal cancer dataset, the average DSI (0.85) increased by 0.07 and 0.43 than single PET and CT segmentation. Conclusion: The proposed method took full advantage of the complementary information from PET and CT images. This work was supported in part by the National Cancer Institute Grants R01CA172638. Shan Tan and Laquan Li were supported in part by the National Natural Science Foundation of China, under Grant Nos. 60971112 and 61375018.« less

Automatic generation of pictorial transcripts of video programs

NASA Astrophysics Data System (ADS)

Shahraray, Behzad; Gibbon, David C.

1995-03-01

An automatic authoring system for the generation of pictorial transcripts of video programs which are accompanied by closed caption information is presented. A number of key frames, each of which represents the visual information in a segment of the video (i.e., a scene), are selected automatically by performing a content-based sampling of the video program. The textual information is recovered from the closed caption signal and is initially segmented based on its implied temporal relationship with the video segments. The text segmentation boundaries are then adjusted, based on lexical analysis and/or caption control information, to account for synchronization errors due to possible delays in the detection of scene boundaries or the transmission of the caption information. The closed caption text is further refined through linguistic processing for conversion to lower- case with correct capitalization. The key frames and the related text generate a compact multimedia presentation of the contents of the video program which lends itself to efficient storage and transmission. This compact representation can be viewed on a computer screen, or used to generate the input to a commercial text processing package to generate a printed version of the program.

Mutual Neutralization of Atomic Rare-Gas Cations (Ne+, Ar+, Kr+, Xe+) with Atomic Halide Anions (Cl-, Br-, I-)

DTIC Science & Technology

2015-01-07

DOCUMENTATION PAGE Form Approved OMB No. 0704-0188 Public reporting burden for this collection of information is estimated to average 1 hour per...reviewing this collection of information . Send comments regarding this burden estimate or any other aspect of this collection of information , including...suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports

Handheld Synthetic Array Final Report, Part B

DTIC Science & Technology

2014-12-01

Multiple Model IMU Inertial Measurement Unit 4/154 IEEE Institute of Electrical and Electronics Engineers KF Kalman Filter KL Kullback - Leibler LAMBDA...important metric in information theory is the input–output mutual information (MI) that is used as an indicator of how much coded information can be...tracking using best- fitting Gaussian distributions,” Proc. Int. Conf. Inform . Fusion, pp. 1–8, 2005. [liv] L. Svensson, “On the Bayesian Cramér-Rao

SEGMENTATION OF MITOCHONDRIA IN ELECTRON MICROSCOPY IMAGES USING ALGEBRAIC CURVES.

PubMed

Seyedhosseini, Mojtaba; Ellisman, Mark H; Tasdizen, Tolga

2013-01-01

High-resolution microscopy techniques have been used to generate large volumes of data with enough details for understanding the complex structure of the nervous system. However, automatic techniques are required to segment cells and intracellular structures in these multi-terabyte datasets and make anatomical analysis possible on a large scale. We propose a fully automated method that exploits both shape information and regional statistics to segment irregularly shaped intracellular structures such as mitochondria in electron microscopy (EM) images. The main idea is to use algebraic curves to extract shape features together with texture features from image patches. Then, these powerful features are used to learn a random forest classifier, which can predict mitochondria locations precisely. Finally, the algebraic curves together with regional information are used to segment the mitochondria at the predicted locations. We demonstrate that our method outperforms the state-of-the-art algorithms in segmentation of mitochondria in EM images.