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https://www.readbyqxmd.com/read/28922654/student-s-perception-about-innovative-teaching-learning-practices-in-forensic-medicine
#1
Sanjay Gupta, Utsav N Parekh, Jaishree D Ganjiwale
BACKGROUND: Since decades, Forensic Medicine is mainly taught by didactic methods but in last couple of years some other teachinglearning and assessment methods are also introduced at some places which also lacks uniformity. Feedback from learners is most fundamental aspect to assess effectiveness of applied methods, but is not implemented in practice at most medical schools in India. Unfortunately, medical students are deprived of this practical empowerment and thus may not be efficient enough to contribute potentially to the justice system during their professional life...
September 11, 2017: Journal of Forensic and Legal Medicine
https://www.readbyqxmd.com/read/28922135/learning-from-short-text-streams-with-topic-drifts
#2
Peipei Li, Lu He, Haiyan Wang, Xuegang Hu, Yuhong Zhang, Lei Li, Xindong Wu
Short text streams such as search snippets and micro blogs have been popular on the Web with the emergence of social media. Unlike traditional normal text streams, these data present the characteristics of short length, weak signal, high volume, high velocity, topic drift, etc. Short text stream classification is hence a very challenging and significant task. However, this challenge has received little attention from the research community. Therefore, a new feature extension approach is proposed for short text stream classification with the help of a large-scale semantic network obtained from a Web corpus...
September 18, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28922128/deep-manifold-learning-combined-with-convolutional-neural-networks-for-action-recognition
#3
Xin Chen, Jian Weng, Wei Lu, Jiaming Xu, Jiasi Weng
Learning deep representations have been applied in action recognition widely. However, there have been a few investigations on how to utilize the structural manifold information among different action videos to enhance the recognition accuracy and efficiency. In this paper, we propose to incorporate the manifold of training samples into deep learning, which is defined as deep manifold learning (DML). The proposed DML framework can be adapted to most existing deep networks to learn more discriminative features for action recognition...
September 15, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28922127/jointly-learning-structured-analysis-discriminative-dictionary-and-analysis-multiclass-classifier
#4
Zhao Zhang, Weiming Jiang, Jie Qin, Li Zhang, Fanzhang Li, Min Zhang, Shuicheng Yan
In this paper, we propose an analysis mechanism-based structured analysis discriminative dictionary learning (ADDL) framework. The ADDL seamlessly integrates ADDL, analysis representation, and analysis classifier training into a unified model. The applied analysis mechanism can make sure that the learned dictionaries, representations, and linear classifiers over different classes are independent and discriminating as much as possible. The dictionary is obtained by minimizing a reconstruction error and an analytical incoherence promoting term that encourages the subdictionaries associated with different classes to be independent...
September 14, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28922126/functional-contour-following-via-haptic-perception-and-reinforcement-learning
#5
Randall B Hellman, Cem Tekin, Mihaela van der Schaar, Veronica J Santos
Many tasks involve the fine manipulation of objects despite limited visual feedback. In such scenarios, tactile and proprioceptive feedback can be leveraged for task completion. We present an approach for real-time haptic perception and decision-making for a haptics-driven, functional contour-following task: the closure of a ziplock bag. This task is challenging for robots because the bag is deformable, transparent, and visually occluded by artificial fingertip sensors that are also compliant. A deep neural net classifier was trained to estimate the state of a zipper within a robot's pinch grasp...
September 18, 2017: IEEE Transactions on Haptics
https://www.readbyqxmd.com/read/28921541/automatic-labeling-of-mr-brain-images-through-extensible-learning-and-atlas-forests
#6
Lijun Xu, Hong Liu, Enmin Song, Meng Yan, Renchao Jin, Chih-Cheng Hung
PURPOSE: Multi-atlas-based method is extensively used in MR brain images segmentation because of its simplicity and robustness. This method provides excellent accuracy while time-consuming and limited in terms of obtaining information about new atlases. In this study, an automatic labeling of MR brain images through extensible learning and atlas forest is presented to address these limitations. METHODS: We propose an extensible learning model which allows the multi-atlas-based framework capable of managing the datasets with numerous atlases or dynamic atlas datasets and simultaneously ensure the accuracy of automatic labeling...
September 18, 2017: Medical Physics
https://www.readbyqxmd.com/read/28920911/efficient-online-learning-algorithms-based-on-lstm-neural-networks
#7
Tolga Ergen, Suleyman Serdar Kozat
We investigate online nonlinear regression and introduce novel regression structures based on the long short term memory (LSTM) networks. For the introduced structures, we also provide highly efficient and effective online training methods. To train these novel LSTM-based structures, we put the underlying architecture in a state space form and introduce highly efficient and effective particle filtering (PF)-based updates. We also provide stochastic gradient descent and extended Kalman filter-based updates. Our PF-based training method guarantees convergence to the optimal parameter estimation in the mean square error sense provided that we have a sufficient number of particles and satisfy certain technical conditions...
September 13, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28920910/online-density-estimation-of-nonstationary-sources-using-exponential-family-of-distributions
#8
Kaan Gokcesu, Suleyman S Kozat
We investigate online probability density estimation (or learning) of nonstationary (and memoryless) sources using exponential family of distributions. To this end, we introduce a truly sequential algorithm that achieves Hannan-consistent log-loss regret performance against true probability distribution without requiring any information about the observation sequence (e.g., the time horizon T and the drift of the underlying distribution C) to optimize its parameters. Our results are guaranteed to hold in an individual sequence manner...
September 13, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28920896/analysis-and-optimization-of-loss-functions-for-multiclass-top-k-and-multilabel-classification
#9
Maksim Lapin, Matthias Hein, Bernt Schiele
Top-k error is currently a popular performance measure on large scale image classification benchmarks such as ImageNet and Places. Despite its wide acceptance, our understanding of this metric is limited as most of the previous research is focused on its special case, the top-1 error. In this work, we explore two directions that shed light on the top-k error. First, we provide an in-depth analysis of established and recently proposed single-label multiclass methods along with a detailed account of efficient optimization algorithms for them...
September 13, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28918412/prediction-of-early-unplanned-intensive-care-unit-readmission-in-a-uk-tertiary-care-hospital-a-cross-sectional-machine-learning-approach
#10
Thomas Desautels, Ritankar Das, Jacob Calvert, Monica Trivedi, Charlotte Summers, David J Wales, Ari Ercole
OBJECTIVES: Unplanned readmissions to the intensive care unit (ICU) are highly undesirable, increasing variance in care, making resource planning difficult and potentially increasing length of stay and mortality in some settings. Identifying patients who are likely to suffer unplanned ICU readmission could reduce the frequency of this adverse event. SETTING: A single academic, tertiary care hospital in the UK. PARTICIPANTS: A set of 3326 ICU episodes collected between October 2014 and August 2016...
September 15, 2017: BMJ Open
https://www.readbyqxmd.com/read/28917180/learning-from-the-operation-pathology-and-maintenance-of-a-bioretention-system-to-optimize-urban-drainage-practices
#11
Marina Batalini de Macedo, Altair Rosa, César Ambrogi Ferreira do Lago, Eduardo Mario Mendiondo, Vladimir Caramori Borges de Souza
LID practices for runoff control are increasingly being used as an integrated solution in urban drainage, helping to achieve hydrological balance close to the pre-urbanized period and decrease the diffuse pollution transported to urban rivers. Regarding bioretention, there is already broad knowledge about the detention of peak flows and their treatment capacity for many pollutants. However, there are still few field studies in microdrainage scale, which analyze the actual operation of these devices and raise common problems found, especially in subtropical climate...
September 13, 2017: Journal of Environmental Management
https://www.readbyqxmd.com/read/28917135/failing-to-learn-from-negative-prediction-errors-obesity-is-associated-with-alterations-in-a-fundamental-neural-learning-mechanism
#12
David Mathar, Jane Neumann, Arno Villringer, Annette Horstmann
Prediction errors (PEs) encode the difference between expected and actual action outcomes in the brain via dopaminergic modulation. Integration of these learning signals ensures efficient behavioral adaptation. Obesity has recently been linked to altered dopaminergic fronto-striatal circuits, thus implying impairments in cognitive domains that rely on its integrity. 28 obese and 30 lean human participants performed an implicit stimulus-response learning paradigm inside an fMRI scanner. Computational modeling and psycho-physiological interaction (PPI) analysis was utilized for assessing PE-related learning and associated functional connectivity...
August 24, 2017: Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
https://www.readbyqxmd.com/read/28915948/the-economics-of-surgical-simulation
#13
REVIEW
Noel Jabbour, Carl H Snyderman
There are massive hidden costs in the current paradigm of surgical training related to increased operative times for procedures with resident involvement and costs of medical errors. Shifting procedural training outside of the operating room through use of simulation has the potential to improve patient safety, minimize learning time to achieve competency, and increase operative efficiency. Investment in surgical simulation has the potential to reduce costs to health care systems through improved operating room efficiency and reduction of medical errors...
October 2017: Otolaryngologic Clinics of North America
https://www.readbyqxmd.com/read/28915930/predicting-activities-of-daily-living-for-cancer-patients-using-an-ontology-guided-machine-learning-methodology
#14
Hua Min, Hedyeh Mobahi, Katherine Irvin, Sanja Avramovic, Janusz Wojtusiak
BACKGROUND: Bio-ontologies are becoming increasingly important in knowledge representation and in the machine learning (ML) fields. This paper presents a ML approach that incorporates bio-ontologies and its application to the SEER-MHOS dataset to discover patterns of patient characteristics that impact the ability to perform activities of daily living (ADLs). Bio-ontologies are used to provide computable knowledge for ML methods to "understand" biomedical data. RESULTS: This retrospective study included 723 cancer patients from the SEER-MHOS dataset...
September 16, 2017: Journal of Biomedical Semantics
https://www.readbyqxmd.com/read/28915298/the-effect-of-stimulus-variability-on-learning-and-generalization-of-reading-in-a-novel-script
#15
Jasmeen Adwan-Mansour, Tali Bitan
Purpose: The benefit of stimulus variability for generalization of acquired skills and knowledge has been shown in motor, perceptual, and language learning but has rarely been studied in reading. We studied the effect of variable training in a novel language on reading trained and untrained words. Method: Sixty typical adults received 2 sessions of training in reading an artificial script. Participants were assigned to 1 of 3 groups: a variable training group practicing a large set of 24 words, and 2 nonvariable training groups practicing a smaller set of 12 words, with twice the number of repetitions per word...
September 15, 2017: Journal of Speech, Language, and Hearing Research: JSLHR
https://www.readbyqxmd.com/read/28915266/evaluation-of-a-conceptual-framework-for-predicting-navigation-performance-in-virtual-reality
#16
Jascha Grübel, Tyler Thrash, Christoph Hölscher, Victor R Schinazi
Previous research in spatial cognition has often relied on simple spatial tasks in static environments in order to draw inferences regarding navigation performance. These tasks are typically divided into categories (e.g., egocentric or allocentric) that reflect different two-systems theories. Unfortunately, this two-systems approach has been insufficient for reliably predicting navigation performance in virtual reality (VR). In the present experiment, participants were asked to learn and navigate towards goal locations in a virtual city and then perform eight simple spatial tasks in a separate environment...
2017: PloS One
https://www.readbyqxmd.com/read/28914185/power-potential-and-pitfalls-in-global-health-academic-partnerships-review-and-reflections-on-an-approach-in-nepal
#17
David Citrin, Stephen Mehanni, Bibhav Acharya, Lena Wong, Isha Nirola, Rekha Sherchan, Bikash Gauchan, Khem Bahadur Karki, Dipendra Raman Singh, Sriram Shamasunder, Phuoc Le, Dan Schwarz, Ryan Schwarz, Binod Dangal, Santosh Kumar Dhungana, Sheela Maru, Ramesh Mahar, Poshan Thapa, Anant Raut, Mukesh Adhikari, Indira Basnett, Shankar Prasad Kaluanee, Grace Deukmedjian, Scott Halliday, Duncan Maru
BACKGROUND: Global health academic partnerships are centered around a core tension: they often mirror or reproduce the very cross-national inequities they seek to alleviate. On the one hand, they risk worsening power dynamics that perpetuate health disparities; on the other, they form an essential response to the need for healthcare resources to reach marginalized populations across the globe. OBJECTIVES: This study characterizes the broader landscape of global health academic partnerships, including challenges to developing ethical, equitable, and sustainable models...
2017: Global Health Action
https://www.readbyqxmd.com/read/28912801/efficient-multiple-kernel-learning-algorithms-using-low-rank-representation
#18
Wenjia Niu, Kewen Xia, Baokai Zu, Jianchuan Bai
Unlike Support Vector Machine (SVM), Multiple Kernel Learning (MKL) allows datasets to be free to choose the useful kernels based on their distribution characteristics rather than a precise one. It has been shown in the literature that MKL holds superior recognition accuracy compared with SVM, however, at the expense of time consuming computations. This creates analytical and computational difficulties in solving MKL algorithms. To overcome this issue, we first develop a novel kernel approximation approach for MKL and then propose an efficient Low-Rank MKL (LR-MKL) algorithm by using the Low-Rank Representation (LRR)...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28912739/contextual-cueing-effect-in-spatial-layout-defined-by-binocular-disparity
#19
Guang Zhao, Qian Zhuang, Jie Ma, Shen Tu, Qiang Liu, Hong-Jin Sun
Repeated visual context induces higher search efficiency, revealing a contextual cueing effect, which depends on the association between the target and its visual context. In this study, participants performed a visual search task where search items were presented with depth information defined by binocular disparity. When the 3-dimensional (3D) configurations were repeated over blocks, the contextual cueing effect was obtained (Experiment 1). When depth information was in chaos over repeated configurations, visual search was not facilitated and the contextual cueing effect largely crippled (Experiment 2)...
2017: Frontiers in Psychology
https://www.readbyqxmd.com/read/28911966/fluoxetine-administration-during-adolescence-attenuates-cognitive-and-synaptic-deficits-in-adult-3%C3%A3-tgad-mice
#20
Dong-Sheng Sun, Li-Feng Gao, Li Jin, Hao Wu, Qun Wang, You Zhou, Shuhao Fan, Xia Jiang, Dan Ke, Hao Lei, Jian-Zhi Wang, Gong-Ping Liu
Fluoxetine (FLX) has broad neurobiological functions and neuroprotective effects; however, the preventive effects of FLX on cognitive impairments in Alzheimer's disease (AD) have not been reported. Here, we studied whether adolescent administration of fluoxetine can prevent memory deficits in AD transgenic mice that harbour PS1m146v, APPswe and TauP301L mutations (3 × TgAD). FLX was applied through peritoneal injection to the mice at postnatal day 35 (p35) for 15 consecutive days, and the effects of FLX were observed at 6-month...
September 11, 2017: Neuropharmacology
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