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https://www.readbyqxmd.com/read/28222363/very-short-term-reactive-forecasting-of-the-solar-ultraviolet-index-using-an-extreme-learning-machine-integrated-with-the-solar-zenith-angle
#1
Ravinesh C Deo, Nathan Downs, Alfio V Parisi, Jan F Adamowski, John M Quilty
Exposure to erythemally-effective solar ultraviolet radiation (UVR) that contributes to malignant keratinocyte cancers and associated health-risk is best mitigated through innovative decision-support systems, with global solar UV index (UVI) forecast necessary to inform real-time sun-protection behaviour recommendations. It follows that the UVI forecasting models are useful tools for such decision-making. In this study, a model for computationally-efficient data-driven forecasting of diffuse and global very short-term reactive (VSTR) (10-min lead-time) UVI, enhanced by drawing on the solar zenith angle (θs) data, was developed using an extreme learning machine (ELM) algorithm...
February 18, 2017: Environmental Research
https://www.readbyqxmd.com/read/28222299/adaptive-low-rank-subspace-learning-with-online-optimization-for-robust-visual-tracking
#2
Risheng Liu, Di Wang, Yuzhuo Han, Xin Fan, Zhongxuan Luo
In recent years, sparse and low-rank models have been widely used to formulate appearance subspace for visual tracking. However, most existing methods only consider the sparsity or low-rankness of the coefficients, which is not sufficient enough for appearance subspace learning on complex video sequences. Moreover, as both the low-rank and the column sparse measures are tightly related to all the samples in the sequences, it is challenging to incrementally solve optimization problems with both nuclear norm and column sparse norm on sequentially obtained video data...
February 10, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28221995/deep-cascade-cascading-3d-deep-neural-networks-for-fast-anomaly-detection-and-localization-in-crowded-scenes
#3
Mohammad Sabokrou, Mohsen Fayyaz, Mahmood Fathy, Reinhard Klette
This paper proposes a fast and reliable method for anomaly detection and localization in video data showing crowded scenes. Time-efficient anomaly localization is an ongoing challenge and subject of this paper. We propose a cubicpatch- based method, characterised by a cascade of classifiers, which makes use of an advanced feature-learning approach. Our cascade of classifiers has two main stages. First, a light but deep 3D auto-encoder is used for early identification of "many" normal cubic patches. This deep network operates on small cubic patches as being the first stage, before carefully resizing remaining candidates of interest, and evaluating those at the second stage using a more complex and deeper 3D convolutional neural network (CNN)...
February 17, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28221836/mimicry-among-unequally-defended-prey-should-be-mutualistic-when-predators-sample-optimally
#4
Thomas G Aubier, Mathieu Joron, Thomas N Sherratt
Understanding the conditions under which moderately defended prey evolve to resemble better-defended prey and whether this mimicry is parasitic (quasi-Batesian) or mutualistic (Müllerian) is central to our understanding of warning signals. Models of predator learning generally predict quasi-Batesian relationships. However, predators' attack decisions are based not only on learning alone but also on the potential future rewards. We identify the optimal sampling strategy of predators capable of classifying prey into different profitability categories and contrast the implications of these rules for mimicry evolution with a classical Pavlovian model based on conditioning...
March 2017: American Naturalist
https://www.readbyqxmd.com/read/28218920/a-non-volatile-organic-electrochemical-device-as-a-low-voltage-artificial-synapse-for-neuromorphic-computing
#5
Yoeri van de Burgt, Ewout Lubberman, Elliot J Fuller, Scott T Keene, Grégorio C Faria, Sapan Agarwal, Matthew J Marinella, A Alec Talin, Alberto Salleo
The brain is capable of massively parallel information processing while consuming only ∼1-100 fJ per synaptic event. Inspired by the efficiency of the brain, CMOS-based neural architectures and memristors are being developed for pattern recognition and machine learning. However, the volatility, design complexity and high supply voltages for CMOS architectures, and the stochastic and energy-costly switching of memristors complicate the path to achieve the interconnectivity, information density, and energy efficiency of the brain using either approach...
February 20, 2017: Nature Materials
https://www.readbyqxmd.com/read/28218646/impact-of-consuming-toxic-monarch-caterpillars-on-adult-chinese-mantid-mass-gain-and-fecundity
#6
Jamie L Rafter, Liahna Gonda-King, Daniel Niesen, Navindra P Seeram, Chad M Rigsby, Evan L Preisser
Predators that feed on chemically-defended prey often experience non-lethal effects that result in learned avoidance of the prey species. Some predators are able to consume toxic prey without ill-effect. The Chinese mantid is able to consume cardenolide-containing monarch caterpillars without immediate adverse effects. Although they discard the caterpillars' gut contents, mantids consume sequestered cardenolides. Although consumption of these cardenolides does not elicit an acute response, there may be long-term costs associated with cardenolide consumption...
February 17, 2017: Insects
https://www.readbyqxmd.com/read/28217305/redesigning-care-delivery-with-patient-support-personnel-learning-from-accountable-care-organizations
#7
Ksenia O Gorbenko, Taressa Fraze, Valerie A Lewis
INTRODUCTION: Accountable care organizations (ACOs) are a value-based payment model in the United States rooted in holding groups of healthcare providers financially accountable for the quality and total cost of care of their attributed population. To succeed in reaching their quality and efficiency goals, ACOs implement a variety of care delivery changes, including workforce redesign. Patient support personnel (PSP)-non-physician staff such as care coordinators, community health workers, and others-are critical to restructuring care delivery...
September 2016: Int J Care Coord
https://www.readbyqxmd.com/read/28216068/stages-of-dysfunctional-decision-making-in-addiction
#8
REVIEW
Antonio Verdejo-Garcia, Trevor T-J Chong, Julie C Stout, Murat Yücel, Edythe D London
Drug use is a choice with immediate positive outcomes, but long-term negative consequences. Thus, the repeated use of drugs in the face of negative consequences suggests dysfunction in the cognitive mechanisms underpinning decision-making. This cognitive dysfunction can be mapped into three stages: the formation of preferences involving valuation of decision options; choice implementation including motivation, self-regulation and inhibitory processes; and feedback processing implicating reinforcement learning...
February 16, 2017: Pharmacology, Biochemistry, and Behavior
https://www.readbyqxmd.com/read/28214787/sparse-and-dense-hybrid-representation-via-subspace-modeling-for-dynamic-mri
#9
Qiegen Liu, Shanshan Wang, Dong Liang
Recent theoretical results on compressed sensing and low-rank matrix recovery have inspired significant interest in joint sparse and low rank modeling of dynamic magnetic resonance imaging (dMRI). Existing approaches usually describe these two respective prior information with different formulations. In this paper, we present a novel sparse and dense hybrid representation (SDR) model which describes the sparse plus low rank properties by a unified way. More specifically, under the learned dictionary consisting of temporal basis functions, SDR models the spatial coefficients in two subspaces with Laplacian and Gaussian prior distributions, respectively...
February 5, 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/28214535/multilevel-ensemble-model-for-prediction-of-iga-and-igg-antibodies
#10
Divya Khanna, Prashant Singh Rana
Identification of antigen for inducing specific class of antibody is prime objective in peptide based vaccine designs, immunodiagnosis, and antibody productions. It's urge to introduce a reliable system with high accuracy and efficiency for prediction. In the present study, a novel multilevel ensemble model is developed for prediction of antibodies IgG and IgA. Epitope length is important in training the model and it is efficient to use variable length of epitopes. In this ensemble approach, seven different machine learning models are combined to predict variable length of epitopes (4 to 50)...
February 15, 2017: Immunology Letters
https://www.readbyqxmd.com/read/28214378/calcium-signaling-through-l-type-calcium-channels-role-in-pathophysiology-of-spinal-nociceptive-transmission
#11
REVIEW
Olivier Roca-Lapirot, Houda Radwani, Franck Aby, Frédéric Nagy, Marc Landry Pascal Fossat
L-type voltage-gated calcium channels (VGCCs) are ubiquitous channels in the central nervous system. L-type calcium channels (LTCs) are mostly post-synaptic channels regulating neuronal firing and gene expression. They play a role in important physio-pathological processes such as learning and memory, Parkinson's disease, autism and, as recognized more recently, in the pathophysiology of pain processes. Classically, the fundamental role of these channels in cardiovascular functions has limited the use of classical molecules to treat LTC-dependent disorders...
February 18, 2017: British Journal of Pharmacology
https://www.readbyqxmd.com/read/28212101/fast-solving-quasi-optimal-ls-s-%C3%A2-vm-based-on-an-extended-candidate-set
#12
Yuefeng Ma, Xun Liang, James T Kwok, Jianping Li, Xiaoping Zhou, Haiyan Zhang
The semisupervised least squares support vector machine (LS-S³VM) is an important enhancement of least squares support vector machines in semisupervised learning. Given that most data collected from the real world are without labels, semisupervised approaches are more applicable than standard supervised approaches. Although a few training methods for LS-S³VM exist, the problem of deriving the optimal decision hyperplane efficiently and effectually has not been solved. In this paper, a fully weighted model of LS-S³VM is proposed, and a simple integer programming (IP) model is introduced through an equivalent transformation to solve the model...
February 14, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28211943/a-general-statistical-framework-for-subgroup-identification-and-comparative-treatment-scoring
#13
Shuai Chen, Lu Tian, Tianxi Cai, Menggang Yu
Many statistical methods have recently been developed for identifying subgroups of patients who may benefit from different available treatments. Compared with the traditional outcome-modeling approaches, these methods focus on modeling interactions between the treatments and covariates while by-pass or minimize modeling the main effects of covariates because the subgroup identification only depends on the sign of the interaction. However, these methods are scattered and often narrow in scope. In this article, we propose a general framework, by weighting and A-learning, for subgroup identification in both randomized clinical trials and observational studies...
February 17, 2017: Biometrics
https://www.readbyqxmd.com/read/28211489/sleep-supports-the-slow-abstraction-of-gist-from-visual-perceptual-memories
#14
Nicolas D Lutz, Susanne Diekelmann, Patricia Hinse-Stern, Jan Born, Karsten Rauss
Sleep benefits the consolidation of individual episodic memories. In the long run, however, it may be more efficient to retain the abstract gist of single, related memories, which can be generalized to similar instances in the future. While episodic memory is enhanced after one night of sleep, effective gist abstraction is thought to require multiple nights. We tested this hypothesis using a visual Deese-Roediger-McDermott paradigm, examining gist abstraction and episodic-like memory consolidation after 20 min, after 10 hours, as well as after one year of retention...
February 17, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28210819/evidence-for-instructions-based-updating-of-task-set-representations-the-informed-fadeout-effect
#15
Maayan Pereg, Nachshon Meiran
The cognitive system can be updated rapidly and efficiently to maximize performance in cognitive tasks. This paper used a task-switching task to explore updating at the level of the plausible task-sets held for future performance. Previous research suggested a "fadeout effect", performance improvement when moving from task-switching context to single-task context, yet this effect could reflect passive learning rather than intentional control. In a novel "informed fadeout paradigm", one of two tasks was canceled for a certain number of trials and participants were informed or uninformed regarding task cancelation...
February 16, 2017: Psychological Research
https://www.readbyqxmd.com/read/28208233/neonatal-exposure-to-endocrine-disrupting-chemicals-impair-learning-behaviour-by-disrupting-hippocampal-organization-in-male-swiss-albino-mice
#16
Rakesh Bhaskar, Ashish K Mishra, Banalata Mohanty
Hippocampus is highly susceptible to endocrine disrupting chemicals exposure particularly during the critical phase of brain development. In the present study, mice offspring were exposed to endocrine disruptors mancozeb (MCZ) and imidacloprid (IMI) individually (40 mg MCZ and 0.65 mg IMI/kg/day) as well as to their equimixture (40 mg MCZ + 0.65 mg IMI/kg/day) through the diet of lactating mothers from post-natal day (PND) 1 to PND 28. Half of the randomly selected male offspring were killed at PND 29 and the rest half were left unexposed and killed at PND 63...
February 16, 2017: Basic & Clinical Pharmacology & Toxicology
https://www.readbyqxmd.com/read/28207397/stacked-learning-to-search-for-scene-labeling
#17
Feiyang Cheng, Xuming He, Hong Zhang
Search-based structured prediction methods have shown promising successes in both computer vision and natural language processing recently. However, most existing search-based approaches lead to a complex multi-stage learning process, which is ill-suited for scene labeling problems with a high-dimensional output space. In this paper, a stacked learning to search method is proposed to address scene labeling tasks. We design a simplified search process consisting of a sequence of ranking functions, which are learned based on a stacked learning strategy to prevent over-fitting...
February 13, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28207296/gambit-a-parameterless-model-based-evolutionary-algorithm-for-mixed-integer-problems
#18
Krzysztof L Sadowski, Dirk Thierens, Peter A N Bosman
Learning and exploiting problem structure is one of the key challenges in optimization. This is especially important for black-box optimization (BBO) where prior structural knowledge of a problem is not available. Existing model-based Evolutionary Algorithms (EAs) are very efficient at learning structure in both the discrete, and in the continuous domain. In this paper, discrete and continuous model-building mechanisms are integrated for the Mixed-Integer (MI) domain, comprising discrete and continuous variables...
February 16, 2017: Evolutionary Computation
https://www.readbyqxmd.com/read/28206744/alkane-oxidation-methane-monooxygenases-related-enzymes-and-their-biomimetics
#19
Vincent C-C Wang, Suman Maji, Peter P-Y Chen, Hung Kay Lee, Steve S-F Yu, Sunney I Chan
Methane monooxygenases (MMOs) mediate the facile conversion of methane into methanol in methanotrophic bacteria with high efficiency under ambient conditions. Because the selective oxidation of methane is extremely challenging, there is considerable interest in understanding how these enzymes carry out this difficult chemistry. The impetus of these efforts is to learn from the microbes to develop a biomimetic catalyst to accomplish the same chemical transformation. Here, we review the progress made over the past two to three decades toward delineating the structures and functions of the catalytic sites in two MMOs: soluble methane monooxygenase (sMMO) and particulate methane monooxygenase (pMMO)...
February 16, 2017: Chemical Reviews
https://www.readbyqxmd.com/read/28205307/segmentation-of-organs-at-risks-in-head-and-neck-ct-images-using-convolutional-neural-networks
#20
Bulat Ibragimov, Lei Xing
PURPOSE: Accurate segmentation of organs-at-risks (OARs) is the key step for efficient planning of radiation therapy for head and neck (HaN) cancer treatment. In the work, we proposed the first deep learning-based algorithm, for segmentation of OARs in HaN CT images, and compared its performance against state-of-the-art automated segmentation algorithms, commercial software, and interobserver variability. METHODS: Convolutional neural networks (CNNs)-a concept from the field of deep learning-were used to study consistent intensity patterns of OARs from training CT images and to segment the OAR in a previously unseen test CT image...
February 2017: Medical Physics
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