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https://www.readbyqxmd.com/read/28213145/how-are-you-feeling-a-personalized-methodology-for-predicting-mental-states-from-temporally-observable-physical-and-behavioral-information
#1
Suppawong Tuarob, Conrad S Tucker, Soundar Kumara, C Lee Giles, Aaron L Pincus, David E Conroy, Nilam Ram
It is believed that anomalous mental states such as stress and anxiety not only cause suffering for the individuals, but also lead to tragedies in some extreme cases. The ability to predict the mental state of an individual at both current and future time periods could prove critical to healthcare practitioners. Currently, the practical way to predict an individual's mental state is through mental examinations that involve psychological experts performing the evaluations. However, such methods can be time and resource consuming, mitigating their broad applicability to a wide population...
February 14, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28213134/speech-in-noise-perception-in-musicians-a-review
#2
REVIEW
Emily B J Coffey, Nicolette Mogilever, Robert J Zatorre
The ability to understand speech in the presence of competing sound sources is an important neuroscience question in terms of how the nervous system solves this computational problem. It is also a critical clinical problem that disproportionally affects the elderly, children with language-related learning disorders, and those with hearing loss. Recent evidence that musicians have an advantage on this multifaceted skill has led to the suggestion that musical training might be used to improve or delay the decline of speech-in-noise (SIN) function...
February 14, 2017: Hearing Research
https://www.readbyqxmd.com/read/28212422/probability-matching-in-perceptrons-effects-of-conditional-dependence-and-linear-nonseparability
#3
Michael R W Dawson, Maya Gupta
Probability matching occurs when the behavior of an agent matches the likelihood of occurrence of events in the agent's environment. For instance, when artificial neural networks match probability, the activity in their output unit equals the past probability of reward in the presence of a stimulus. Our previous research demonstrated that simple artificial neural networks (perceptrons, which consist of a set of input units directly connected to a single output unit) learn to match probability when presented different cues in isolation...
2017: PloS One
https://www.readbyqxmd.com/read/28212101/fast-solving-quasi-optimal-ls-s-%C3%A2-vm-based-on-an-extended-candidate-set
#4
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/28212086/salient-object-detection-via-multiple-instance-learning
#5
Fang Huang, Qi Jinqing, Huchuan Lu, Lihe Zhang, Xiang Ruan
Object proposals are a series of candidate segments containing objects of interest, which are taken as preprocessing and widely applied in various vision tasks. However, most of existing saliency approaches only utilize the proposals to compute a location prior. In this paper, we naturally take the proposals as the bags of instances of multiple instance learning (MIL), where the instances are the superpixels contained in the proposals, and formulate saliency detection problem as a MIL task (i.e., predict the labels of instances using the classifier in the MIL framework)...
February 15, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28212079/multi-view-multi-instance-learning-based-on-joint-sparse-representation-and-multi-view-dictionary-learning
#6
Bing Li, Chunfeng Yuan, Weihua Xiong, Weiming Hu, Houwen Peng, Xinmiao Ding, Stephen Maybank
In multi-instance learning (MIL), the relations among instances in a bag convey important contextual information in many applications. Previous studies on MIL either ignore such relations or simply model them with a fixed graph structure so that the overall performance inevitably degrades in complex environments. To address this problem, this paper proposes a novel multi-view multi-instance learning algorithm (M2IL) that combines multiple context structures in a bag into a unified framework. The novel aspects are: (i) we propose a sparse "-graph model that can generate different graphs with different parameters to represent various context relations in a bag, (ii) we propose a multi-view joint sparse representation that integrates these graphs into a unified framework for bag classification, and (iii) we propose a multi-view dictionary learning algorithm to obtain a multi-view graph dictionary that considers cues from all views simultaneously to improve the discrimination of the M2IL...
February 14, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28209159/do-coursework-summative-assessments-predict-clinical-performance-a-systematic-review
#7
Rebecca Terry, Wayne Hing, Robin Orr, Nikki Milne
BACKGROUND: Two goals of summative assessment in health profession education programs are to ensure the robustness of high stakes decisions such as progression and licensing, and predict future performance. This systematic and critical review aims to investigate the ability of specific modes of summative assessment to predict the clinical performance of health profession education students. METHODS: PubMed, CINAHL, SPORTDiscus, ERIC and EMBASE databases were searched using key terms with articles collected subjected to dedicated inclusion criteria...
February 16, 2017: BMC Medical Education
https://www.readbyqxmd.com/read/28208882/pbl-trigger-design-by-medical-students-an-effective-active-learning-strategy-outside-the-classroom
#8
Maya Roche, Indira Kakkunje Adiga, Akshatha G Nayak
INTRODUCTION: Problem Based Learning (PBL) is known world over as an effective, active learning strategy with many benefits for the student. Usually, in medical schools, PBL triggers are designed by a well-trained group of faculty from basic and clinical sciences. The challenge was whether this task could be given to students in the first year of their curriculum and be executed by them effectively. AIM: To enhance active learning, comprehension and critical thinking with a view to promote horizontal and vertical integration between subjects...
December 2016: Journal of Clinical and Diagnostic Research: JCDR
https://www.readbyqxmd.com/read/28208697/hybrid-analytical-and-data-driven-modeling-for-feed-forward-robot-control-%C3%A2
#9
René Felix Reinhart, Zeeshan Shareef, Jochen Jakob Steil
Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accurate knowledge of manipulator kinematics or dynamics. However, mechanical and analytical models do not capture all aspects of a plant's intrinsic properties and there remain unmodeled dynamics due to varying parameters, unmodeled friction or soft materials. In this context, machine learning is an alternative suitable technique to extract non-linear plant models from data. However, fully data-based models suffer from inaccuracies as well and are inefficient if they include learning of well known analytical models...
February 8, 2017: Sensors
https://www.readbyqxmd.com/read/28207407/deep-pain-exploiting-long-short-term-memory-networks-for-facial-expression-classification
#10
Pau Rodriguez, Guillem Cucurull, Jordi Gonalez, Josep M Gonfaus, Kamal Nasrollahi, Thomas B Moeslund, F Xavier Roca
Pain is an unpleasant feeling that has been shown to be an important factor for the recovery of patients. Since this is costly in human resources and difficult to do objectively, there is the need for automatic systems to measure it. In this paper, contrary to current state-of-the-art techniques in pain assessment, which are based on facial features only, we suggest that the performance can be enhanced by feeding the raw frames to deep learning models, outperforming the latest state-of-the-art results while also directly facing the problem of imbalanced data...
February 9, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28207397/stacked-learning-to-search-for-scene-labeling
#11
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
#12
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/28201916/artificial-neural-network-for-the-configuration-problem-in-solids
#13
Hyunjun Ji, Yousung Jung
A machine learning approach based on the artificial neural network (ANN) is applied for the configuration problem in solids. The proposed method provides a direct mapping from configuration vectors to energies. The benchmark conducted for the M1 phase of Mo-V-Te-Nb oxide showed that only a fraction of configurations needs to be calculated, thus the computational burden significantly decreased, by a factor of 20-50, with R(2) = 0.96 and MAD = 0.12 eV. It is shown that ANN can also handle the effects of geometry relaxation when properly trained, resulting in R(2) = 0...
February 14, 2017: Journal of Chemical Physics
https://www.readbyqxmd.com/read/28198674/sequence-specific-bias-correction-for-rna-seq-data-using-recurrent-neural-networks
#14
Yao-Zhong Zhang, Rui Yamaguchi, Seiya Imoto, Satoru Miyano
BACKGROUND: The recent success of deep learning techniques in machine learning and artificial intelligence has stimulated a great deal of interest among bioinformaticians, who now wish to bring the power of deep learning to bare on a host of bioinformatical problems. Deep learning is ideally suited for biological problems that require automatic or hierarchical feature representation for biological data when prior knowledge is limited. In this work, we address the sequence-specific bias correction problem for RNA-seq data redusing Recurrent Neural Networks (RNNs) to model nucleotide sequences without pre-determining sequence structures...
January 25, 2017: BMC Genomics
https://www.readbyqxmd.com/read/28196473/2-7-million-samples-genotyped-for-hla-by-next-generation-sequencing-lessons-learned
#15
Gerhard Schöfl, Kathrin Lang, Philipp Quenzel, Irina Böhme, Jürgen Sauter, Jan A Hofmann, Julia Pingel, Alexander H Schmidt, Vinzenz Lange
BACKGROUND: At the DKMS Life Science Lab, Next Generation Sequencing (NGS) has been used for ultra-high-volume high-resolution genotyping of HLA loci for the last three and a half years. Here, we report on our experiences in genotyping the HLA, CCR5, ABO, RHD and KIR genes using a direct amplicon sequencing approach on Illumina MiSeq and HiSeq 2500 instruments. RESULTS: Between January 2013 and June 2016, 2,714,110 samples largely from German, Polish and UK-based potential stem cell donors have been processed...
February 14, 2017: BMC Genomics
https://www.readbyqxmd.com/read/28195967/procedural-learning-perspectives-of-pulmonary-fellows-and-practitioners
#16
Hans J Lee, Briana Coleman, Andrew D Lerner, David Feller-Kopman, Roy Semaan, Bernice Frimpong, Lonny Yarmus
BACKGROUND: Procedural learning requires both didactic knowledge and motor skills. Optimal teaching styles and techniques remain to be defined for pulmonary procedural learning. We investigated the preferences of learners at 2 different points in a pulmonary career; as pulmonary fellows and as clinical practitioners. METHODS: A perception survey was conducted among pulmonary fellows and practitioners from multiple institutions throughout the United States. Fellows and practitioners were immediately surveyed on procedural learning factors after completing a procedural learning course using low/high-fidelity and/or cadaver simulators...
February 10, 2017: Journal of Bronchology & Interventional Pulmonology
https://www.readbyqxmd.com/read/28192508/multiswarm-comprehensive-learning-particle-swarm-optimization-for-solving-multiobjective-optimization-problems
#17
Xiang Yu, Xueqing Zhang
Comprehensive learning particle swarm optimization (CLPSO) is a powerful state-of-the-art single-objective metaheuristic. Extending from CLPSO, this paper proposes multiswarm CLPSO (MSCLPSO) for multiobjective optimization. MSCLPSO involves multiple swarms, with each swarm associated with a separate original objective. Each particle's personal best position is determined just according to the corresponding single objective. Elitists are stored externally. MSCLPSO differs from existing multiobjective particle swarm optimizers in three aspects...
2017: PloS One
https://www.readbyqxmd.com/read/28188200/stop-think-a-simple-approach-to-encourage-the-self-assessment-of-learning
#18
Richard Guy, Bruce Byrne, Marian Dobos
A simple "stop think" approach was developed to encourage the self-assessment of learning. A key element was the requirement for students to rate their feeling of difficulty before [FOD(pre)] and after [FOD(post)] completing each of three authentic anatomy and physiology concept map exercises. The cohort was divided into low- (group L) and high-performing (group H) groups (based on final subject marks). Both FOD(pre) (group L) and FOD(post) (groups L and H) were significantly negatively correlated with score for some maps...
March 1, 2017: Advances in Physiology Education
https://www.readbyqxmd.com/read/28187898/vessel-segmentation-and-microaneurysm-detection-using-discriminative-dictionary-learning-and-sparse-representation
#19
Malihe Javidi, Hamid-Reza Pourreza, Ahad Harati
Diabetic retinopathy (DR) is a major cause of visual impairment, and the analysis of retinal image can assist patients to take action earlier when it is more likely to be effective. The accurate segmentation of blood vessels in the retinal image can diagnose DR directly. In this paper, a novel scheme for blood vessel segmentation based on discriminative dictionary learning (DDL) and sparse representation has been proposed. The proposed system yields a strong representation which contains the semantic concept of the image...
February 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28187881/a-clinical-decision-making-mechanism-for-context-aware-and-patient-specific-remote-monitoring-systems-using-the-correlations-of-multiple-vital-signs
#20
Abdur Rahim Mohammad Forkan, Ibrahim Khalil
BACKGROUND AND OBJECTIVES: In home-based context-aware monitoring patient's real-time data of multiple vital signs (e.g. heart rate, blood pressure) are continuously generated from wearable sensors. The changes in such vital parameters are highly correlated. They are also patient-centric and can be either recurrent or can fluctuate. The objective of this study is to develop an intelligent method for personalized monitoring and clinical decision support through early estimation of patient-specific vital sign values, and prediction of anomalies using the interrelation among multiple vital signs...
February 2017: Computer Methods and Programs in Biomedicine
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