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https://www.readbyqxmd.com/read/28449114/neuro-symbolic-representation-learning-on-biological-knowledge-graphs
#1
Mona Alshahrani, Mohammed Asif Khan, Omar Maddouri, Akira R Kinjo, Núria Queralt-Rosinach, Robert Hoehndorf
Motivation: Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. Results: We develop a novel method for feature learning on biological knowledge graphs...
April 25, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28448800/assessment-of-bilingual-children-what-if-testing-both-languages-is-not-possible
#2
Tessel Boerma, Elma Blom
Language delays of bilingual children can arise from language impairment (LI) but also from insufficient exposure to the target language. A reliable diagnosis of LI in bilingual children is therefore ideally based on the evaluation of both languages, as LI affects each language that is learned. However, due to the multitude of language combinations that are encountered in clinical practice, this is often not feasible. Bilingual norm-referencing may offer a solution, but the heterogeneity within the bilingual population makes it difficult to determine appropriate standards for every child...
April 10, 2017: Journal of Communication Disorders
https://www.readbyqxmd.com/read/28446185/evidence-based-policymaking-is-not-like-evidence-based-medicine-so-how-far-should-you-go-to-bridge-the-divide-between-evidence-and-policy
#3
EDITORIAL
Paul Cairney, Kathryn Oliver
There is extensive health and public health literature on the 'evidence-policy gap', exploring the frustrating experiences of scientists trying to secure a response to the problems and solutions they raise and identifying the need for better evidence to reduce policymaker uncertainty. We offer a new perspective by using policy theory to propose research with greater impact, identifying the need to use persuasion to reduce ambiguity, and to adapt to multi-level policymaking systems.We identify insights from secondary data, namely systematic reviews, critical analysis and policy theories relevant to evidence-based policymaking...
April 26, 2017: Health Research Policy and Systems
https://www.readbyqxmd.com/read/28444139/multi-label-classifier-based-on-histogram-of-gradients-for-predicting-the-anatomical-therapeutic-chemical-class-classes-of-a-given-compound
#4
Loris Nanni, Sheryl Brahnam
Motivation: Given an unknown compound, is it possible to predict its ATC (Anatomical Therapeutic Chemical) class/classes? This is a challenging yet important problem since such a prediction could be used to deduce not only a compound's possible active ingredients but also its therapeutic, pharmacological, and chemical properties, thereby substantially expediting the pace of drug development. The problem is challenging because some drugs and compounds belong to two or more ATC classes, making machine learning extremely difficult...
April 24, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28442456/the-malaria-system-microapp-a-new-mobile-device-based-tool-for-malaria-diagnosis
#5
Allisson Dantas Oliveira, Clara Prats, Mateu Espasa, Francesc Zarzuela Serrat, Cristina Montañola Sales, Aroa Silgado, Daniel Lopez Codina, Mercia Eliane Arruda, Jordi Gomez I Prat, Jones Albuquerque
BACKGROUND: Malaria is a public health problem that affects remote areas worldwide. Climate change has contributed to the problem by allowing for the survival of Anopheles in previously uninhabited areas. As such, several groups have made developing news systems for the automated diagnosis of malaria a priority. OBJECTIVE: The objective of this study was to develop a new, automated, mobile device-based diagnostic system for malaria. The system uses Giemsa-stained peripheral blood samples combined with light microscopy to identify the Plasmodium falciparum species in the ring stage of development...
April 25, 2017: JMIR Research Protocols
https://www.readbyqxmd.com/read/28440094/small-group-activities-within-academic-communities-improve-the-connectedness-of-students-and-faculty
#6
Katharina Brandl, Stephen D Schneid, Sunny Smith, Babbi Winegarden, Jess Mandel, Carolyn J Kelly
BACKGROUND: The University of California, San Diego, School of Medicine implemented a curriculum change that included reduction of lectures, incorporation of problem-based learning and other small group activities. Six academic communities were introduced for teaching longitudinal curricular content and organizing extracurricular activities. METHODS: Surveys were collected from 904 first- and second-year medical students over 6 years. Student satisfaction data with their sense of connectedness and community support were collected before and after the implementation of the new curriculum...
April 25, 2017: Medical Teacher
https://www.readbyqxmd.com/read/28439014/brain-networks-for-confidence-weighting-and-hierarchical-inference-during-probabilistic-learning
#7
Florent Meyniel, Stanislas Dehaene
Learning is difficult when the world fluctuates randomly and ceaselessly. Classical learning algorithms, such as the delta rule with constant learning rate, are not optimal. Mathematically, the optimal learning rule requires weighting prior knowledge and incoming evidence according to their respective reliabilities. This "confidence weighting" implies the maintenance of an accurate estimate of the reliability of what has been learned. Here, using fMRI and an ideal-observer analysis, we demonstrate that the brain's learning algorithm relies on confidence weighting...
April 24, 2017: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/28438725/analyzing-and-predicting-user-participations-in-online-health-communities-a-social-support-perspective
#8
Xi Wang, Kang Zhao, Nick Street
BACKGROUND: Online health communities (OHCs) have become a major source of social support for people with health problems. Members of OHCs interact online with similar peers to seek, receive, and provide different types of social support, such as informational support, emotional support, and companionship. As active participations in an OHC are beneficial to both the OHC and its users, it is important to understand factors related to users' participations and predict user churn for user retention efforts...
April 24, 2017: Journal of Medical Internet Research
https://www.readbyqxmd.com/read/28437486/prediction-of-crime-occurrence-from-multi-modal-data-using-deep-learning
#9
Hyeon-Woo Kang, Hang-Bong Kang
In recent years, various studies have been conducted on the prediction of crime occurrences. This predictive capability is intended to assist in crime prevention by facilitating effective implementation of police patrols. Previous studies have used data from multiple domains such as demographics, economics, and education. Their prediction models treat data from different domains equally. These methods have problems in crime occurrence prediction, such as difficulty in discovering highly nonlinear relationships, redundancies, and dependencies between multiple datasets...
2017: PloS One
https://www.readbyqxmd.com/read/28437451/predicting-explorative-motor-learning-using-decision-making-and-motor-noise
#10
Xiuli Chen, Kieran Mohr, Joseph M Galea
A fundamental problem faced by humans is learning to select motor actions based on noisy sensory information and incomplete knowledge of the world. Recently, a number of authors have asked whether this type of motor learning problem might be very similar to a range of higher-level decision-making problems. If so, participant behaviour on a high-level decision-making task could be predictive of their performance during a motor learning task. To investigate this question, we studied performance during an explorative motor learning task and a decision-making task which had a similar underlying structure with the exception that it was not subject to motor (execution) noise...
April 24, 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28436910/discovering-the-relationship-between-generalization-and-uncertainty-by-incorporating-complexity-of-classification
#11
Xi-Zhao Wang, Ran Wang, Chen Xu
The generalization ability of a classifier learned from a training set is usually dependent on the classifier's uncertainty, which is often described by the fuzziness of the classifier's outputs on the training set. Since the exact dependency relation between generalization and uncertainty of a classifier is quite complicated, it is difficult to clearly or explicitly express this relation in general. This paper shows a specific study on this relation from the viewpoint of complexity of classification by choosing extreme learning machines as the classification algorithms...
April 24, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28436905/robust-structured-nonnegative-matrix-factorization-for-image-representation
#12
Zechao Li, Jinhui Tang, Xiaofei He
Dimensionality reduction has attracted increasing attention, because high-dimensional data have arisen naturally in numerous domains in recent years. As one popular dimensionality reduction method, nonnegative matrix factorization (NMF), whose goal is to learn parts-based representations, has been widely studied and applied to various applications. In contrast to the previous approaches, this paper proposes a novel semisupervised NMF learning framework, called robust structured NMF, that learns a robust discriminative representation by leveraging the block-diagonal structure and the ℓ2,p-norm (especially when 0 < p ≤ q 1) loss function...
April 17, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28436904/user-preference-based-dual-memory-neural-model-with-memory-consolidation-approach
#13
Jauwairia Nasir, Yong-Ho Yoo, Deok-Hwa Kim, Jong-Hwan Kim
Memory modeling has been a popular topic of research for improving the performance of autonomous agents in cognition related problems. Apart from learning distinct experiences correctly, significant or recurring experiences are expected to be learned better and be retrieved easier. In order to achieve this objective, this paper proposes a user preference-based dual-memory adaptive resonance theory network model, which makes use of a user preference to encode memories with various strengths and to learn and forget at various rates...
April 24, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28436898/extended-polynomial-growth-transforms-for-design-and-training-of-generalized-support-vector-machines
#14
Ahana Gangopadhyay, Oindrila Chatterjee, Shantanu Chakrabartty
Growth transformations constitute a class of fixed-point multiplicative update algorithms that were originally proposed for optimizing polynomial and rational functions over a domain of probability measures. In this paper, we extend this framework to the domain of bounded real variables which can be applied towards optimizing the dual cost function of a generic support vector machine (SVM). The approach can, therefore, not only be used to train traditional soft-margin binary SVMs, one-class SVMs, and probabilistic SVMs but can also be used to design novel variants of SVMs with different types of convex and quasi-convex loss functions...
April 17, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28436895/online-learning-algorithm-based-on-adaptive-control-theory
#15
Jian-Wei Liu, Jia-Jia Zhou, Mohamed S Kamel, Xiong-Lin Luo
This paper proposes a new online learning algorithm which is based on adaptive control (AC) theory, thus, we call this proposed algorithm as AC algorithm. Comparing to the gradient descent (GD) and exponential gradient (EG) algorithm which have been applied to online prediction problems, we find a new form of AC theory for online prediction problems and investigate two key questions: how to get a new update law which has a tighter upper bound on the error than the square loss? How to compare the upper bound for accumulated losses for the three algorithms? We obtain a new update law which fully utilizes model reference AC theory...
April 18, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28436874/track-everything-limiting-prior-knowledge-in-online-multi-object-recognition
#16
Sebastien C Wong, Victor Stamatescu, Adam Gatt, David Kearney, Ivan Lee, Mark D McDonnell
This paper addresses the problem of online tracking and classification of multiple objects in an image sequence. Our proposed solution is to first track all objects in the scene without relying on object-specific prior knowledge, which in other systems can take the form of hand-crafted features or user-based track initialization. We then classify the tracked objects with a fastlearning image classifier that is based on a shallow convolutional neural network architecture and demonstrate that object recognition improves when this is combined with object state information from the tracking algorithm...
April 24, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28436868/high-order-local-pooling-and-encoding-gaussians-over-a-dictionary-of-gaussians
#17
Peihua Li, Hui Zeng, Qilong Wang, Simon Shiu, Lei Zhang
Local pooling (LP) in configuration (feature) space proposed by Boureau et al. explicitly restricts similar features to be aggregated, which can preserve as much discriminative information as possible. At the time it appeared, this method combined with sparse coding achieved competitive classification results with only a small dictionary. However, its performance lags far behind state-of-the-art results as only zero-order information is exploited. Inspired by the success of high-order statistical information in existing advanced feature coding or pooling methods, we make an attempt to address the limitation of LP...
April 19, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28432039/simulation-training-evaluating-the-instructor-s-contribution-to-a-wizard-of-oz-simulator-in-obstetrics-and-gynecology-ultrasound-training
#18
Aric Katz, Ronnie Tepper, Avraham Shtub
BACKGROUND: Workplaces today demand graduates who are prepared with field-specific knowledge, advanced social skills, problem-solving skills, and integration capabilities. Meeting these goals with didactic learning (DL) is becoming increasingly difficult. Enhanced training methods that would better prepare tomorrow's graduates must be more engaging and game-like, such as feedback based e-learning or simulation-based training, while saving time. Empirical evidence regarding the effectiveness of advanced learning methods is lacking...
April 21, 2017: JMIR Medical Education
https://www.readbyqxmd.com/read/28428048/multi-center-machine-learning-in-imaging-psychiatry-a-meta-model-approach
#19
Petr Dluhoš, Daniel Schwarz, Wiepke Cahn, Neeltje van Haren, René Kahn, Filip Španiel, Jiří Horáček, Tomáš Kašpárek, Hugo Schnack
One of the biggest problems in automated diagnosis of psychiatric disorders from medical images is the lack of sufficiently large samples for training. Sample size is especially important in the case of highly heterogeneous disorders such as schizophrenia, where machine learning models built on relatively low numbers of subjects may suffer from poor generalizability. Via multicenter studies and consortium initiatives researchers have tried to solve this problem by combining data sets from multiple sites. The necessary sharing of (raw) data is, however, often hindered by legal and ethical issues...
April 17, 2017: NeuroImage
https://www.readbyqxmd.com/read/28427388/movement-cognition-and-narration-of-the-emotions-treatment-versus-standard-speech-therapy-in-the-treatment-of-children-with-borderline-intellectual-functioning-a-randomized-controlled-trial
#20
V Blasi, G Baglio, F Baglio, M P Canevini, M Zanette
BACKGROUND: Borderline intellectual functioning (BIF) is defined as a "health meta-condition… characterized by various cognitive dysfunctions associated with an intellectual quotient (IQ) between 71 and 85 which determines a deficit in the individual's functioning both in the restriction of activities and in the limitation of social participation". It can be caused by many factors, including a disadvantaged background and prematurity. BIF affects 7-12% of primary school children that show academic difficulties due to poor executive functioning...
April 20, 2017: BMC Psychiatry
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