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https://www.readbyqxmd.com/read/28335558/modeling-the-development-of-audiovisual-cue-integration-in-speech-perception
#1
Laura M Getz, Elke R Nordeen, Sarah C Vrabic, Joseph C Toscano
Adult speech perception is generally enhanced when information is provided from multiple modalities. In contrast, infants do not appear to benefit from combining auditory and visual speech information early in development. This is true despite the fact that both modalities are important to speech comprehension even at early stages of language acquisition. How then do listeners learn how to process auditory and visual information as part of a unified signal? In the auditory domain, statistical learning processes provide an excellent mechanism for acquiring phonological categories...
March 21, 2017: Brain Sciences
https://www.readbyqxmd.com/read/28333955/producing-or-reproducing-reasoning-socratic-dialog-is-very-effective-but-only-for-a-few
#2
Andrea Paula Goldin, Olivia Pedroncini, Mariano Sigman
Successful communication between a teacher and a student is at the core of pedagogy. A well known example of a pedagogical dialog is 'Meno', a socratic lesson of geometry in which a student learns (or 'discovers') how to double the area of a given square 'in essence, a demonstration of Pythagoras' theorem. In previous studies we found that after engaging in the dialog participants can be divided in two kinds: those who can only apply a rule to solve the problem presented in the dialog and those who can go beyond and generalize that knowledge to solve any square problems...
2017: PloS One
https://www.readbyqxmd.com/read/28333644/random-forest-classifier-for-zero-shot-learning-based-on-relative-attribute
#3
Yuhu Cheng, Xue Qiao, Xuesong Wang, Qiang Yu
For the zero-shot image classification with relative attributes (RAs), the traditional method requires that not only all seen and unseen images obey Gaussian distribution, but also the classifications on testing samples are made by maximum likelihood estimation. We therefore propose a novel zero-shot image classifier called random forest based on relative attribute. First, based on the ordered and unordered pairs of images from the seen classes, the idea of ranking support vector machine is used to learn ranking functions for attributes...
March 21, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28333274/making-room-for-interactivity-using-the-cloud-based-audience-response-system-nearpod-to-enhance-engagement-in-lectures
#4
Stephen McClean, William Crowe
Active and collaborative learning provide distinct advantages for students in higher education, yet can often be hampered by the barrier of large class sizes. Solutions that combine a "bring your own device culture" (BOYD) with cloud-based technologies may facilitate a more interactive learning experience. In this pilot study we describe the use of one such technology, Nearpod, to enhance interactivity in lectures delivered to pharmacy and bioscience students at Ulster University. Existing material in PowerPoint or Keynote format is uploaded to the instructor area of Nearpod, interactive elements are added, and the lecture is then broadcast via the internet to student devices...
March 8, 2017: FEMS Microbiology Letters
https://www.readbyqxmd.com/read/28333088/multi-view-structural-local-subspace-tracking
#5
Jie Guo, Tingfa Xu, Guokai Shi, Zhitao Rao, Xiangmin Li
In this paper, we propose a multi-view structural local subspace tracking algorithm based on sparse representation. We approximate the optimal state from three views: (1) the template view; (2) the PCA (principal component analysis) basis view; and (3) the target candidate view. Then we propose a unified objective function to integrate these three view problems together. The proposed model not only exploits the intrinsic relationship among target candidates and their local patches, but also takes advantages of both sparse representation and incremental subspace learning...
March 23, 2017: Sensors
https://www.readbyqxmd.com/read/28330163/probing-an-optimal-class-distribution-for-enhancing-prediction-and-feature-characterization-of-plant-virus-encoded-rna-silencing-suppressors
#6
Abhigyan Nath, Karthikeyan Subbiah
To counter the host RNA silencing defense mechanism, many plant viruses encode RNA silencing suppressor proteins. These groups of proteins share very low sequence and structural similarities among them, which consequently hamper their annotation using sequence similarity-based search methods. Alternatively the machine learning-based methods can become a suitable choice, but the optimal performance through machine learning-based methods is being affected by various factors such as class imbalance, incomplete learning, selection of inappropriate features, etc...
June 2016: 3 Biotech
https://www.readbyqxmd.com/read/28328516/anrad-a-neuromorphic-anomaly-detection-framework-for-massive-concurrent-data-streams
#7
Qiuwen Chen, Ryan Luley, Qing Wu, Morgan Bishop, Richard W Linderman, Qinru Qiu
The evolution of high performance computing technologies has enabled the large-scale implementation of neuromorphic models and pushed the research in computational intelligence into a new era. Among the machine learning applications, unsupervised detection of anomalous streams is especially challenging due to the requirements of detection accuracy and real-time performance. Designing a computing framework that harnesses the growing computing power of the multicore systems while maintaining high sensitivity and specificity to the anomalies is an urgent research topic...
March 17, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28325604/early-prediction-of-radiotherapy-induced-parotid-shrinkage-and-toxicity-based-on-ct-radiomics-and-fuzzy-classification
#8
Marco Pota, Elisa Scalco, Giuseppe Sanguineti, Alessia Farneti, Giovanni Mauro Cattaneo, Giovanna Rizzo, Massimo Esposito
MOTIVATION: Patients under radiotherapy for head-and-neck cancer often suffer of long-term xerostomia, and/or consistent shrinkage of parotid glands. In order to avoid these drawbacks, adaptive therapy can be planned for patients at risk, if the prediction is obtained timely, before or during the early phase of treatment. Artificial intelligence can address the problem, by learning from examples and building classification models. In particular, fuzzy logic has shown its suitability for medical applications, in order to manage uncertain data, and to build transparent rule-based classifiers...
March 18, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28323747/supporting-a-youth-with-cerebellar-ataxia-into-adolescence
#9
Veronica Meneses, Zurisadai Gonzalez-Castillo, Veronica B Edgar, Marilyn Augustyn
Zoe, a 13-year-old white girl, presents as a new patient to your pediatric clinic with complaints of frequent emesis, anxiety, and learning problems, and previous diagnosis of cerebellar ataxia. Parents accompany Zoe and state, "it is really hard for her to go out, she gets sick and falls easily." She was born full term by vaginal delivery without complications. Given globally delayed milestones, she received early intervention services. Feeding problems began at infancy, including gastroesophageal reflux and aspiration pneumonia...
March 17, 2017: Journal of Developmental and Behavioral Pediatrics: JDBP
https://www.readbyqxmd.com/read/28322513/site-of-metabolism-prediction-based-on-ab-initio-derived-atom-representations
#10
Arndt R Finkelmann, Andreas H Göller, Gisbert Schneider
Machine learning models for site of metabolism (SoM) prediction offer the ability to identify metabolic soft spots in low molecular weight drug molecules at low computational cost and enable data-based reactivity prediction. SoM prediction is an atom classification problem. Successful construction of machine learning models requires atom representations that capture the reactivity-determining features of a potential reaction site. We have developed a descriptor scheme that characterizes an atom's steric and electronic environment and its relative location in the molecular structure...
March 21, 2017: ChemMedChem
https://www.readbyqxmd.com/read/28321248/patch-based-multiple-instance-learning-algorithm-for-object-tracking
#11
Zhenjie Wang, Lijia Wang, Hua Zhang
To deal with the problems of illumination changes or pose variations and serious partial occlusion, patch based multiple instance learning (P-MIL) algorithm is proposed. The algorithm divides an object into many blocks. Then, the online MIL algorithm is applied on each block for obtaining strong classifier. The algorithm takes account of both the average classification score and classification scores of all the blocks for detecting the object. In particular, compared with the whole object based MIL algorithm, the P-MIL algorithm detects the object according to the unoccluded patches when partial occlusion occurs...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28320846/working-memory-load-strengthens-reward-prediction-errors
#12
Anne G E Collins, Brittany Ciullo, Michael J Frank, David Badre
Reinforcement learning in simple instrumental tasks is usually modeled as a monolithic process in which reward prediction errors are used to update expected values of choice options. This modeling ignores the different contributions of different memory and decision-making systems thought to contribute even to simple learning. In an fMRI experiment, we asked how working memory and incremental reinforcement learning processes interact to guide human learning. Working memory load was manipulated by varying the number of stimuli to be learned across blocks...
March 20, 2017: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/28320667/learning-correspondence-structures-for-person-re-identification
#13
Weiyao Lin, Yang Shen, Junchi Yan, Mingliang Xu, Jianxin Wu, Jingdong Wang, Ke Lu
This paper addresses the problem of handling spatial misalignments due to camera-view changes or humanpose variations in person re-identification. We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair. The learned correspondence structure can not only capture the spatial correspondence pattern between cameras but also handle the viewpoint or humanpose variation in individual images. We further introduce a global constraint-based matching process...
March 15, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28320666/rgbd-salient-object-detection-via-deep-fusion
#14
Liangqiong Qu, Shengfeng He, Jiawei Zhang, Jiandong Tian, Yandong Tang, Qingxiong Yang
Numerous efforts have been made to design various low-level saliency cues for RGBD saliency detection, such as color and depth contrast features as well as background and color compactness priors. However, how these low-level saliency cues interact with each other and how they can be effectively incorporated to generate a master saliency map remain challenging problems. In this paper, we design a new convolutional neural network (CNN) to automatically learn the interaction mechanism for RGBD salient object detection...
March 15, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28320663/iterative-re-constrained-group-sparse-face-recognition-with-adaptive-weights-learning
#15
Jianwei Zheng, Ping Yang, Shengyong Chen, Guojiang Shen, Wanliang Wang
In this paper, we consider the robust face recognition problem via iterative re-constrained group sparse classifier with adaptive weights learning (IRGSC). Specifically, we propose a group sparse representation classification (GSRC) approach in which weighted features and groups are collaboratively adopted to encode more structure information and discriminative information than other regression based methods. In addition, we derive an efficient algorithm to optimize the proposed objective function, and theoretically prove the convergence...
March 13, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28320267/modulating-effect-of-cytokines-on-mechanisms-of-synaptic-plasticity-in-the-brain
#16
REVIEW
S G Levin, O V Godukhin
After accumulation of data showing that resident brain cells (neurons, astrocytes, and microglia) produce mediators of the immune system, such as cytokines and their receptors under normal physiological conditions, a critical need emerged for investigating the role of these mediators in cognitive processes. The major problem for understanding the functional role of cytokines in the mechanisms of synaptic plasticity, de novo neurogenesis, and learning and memory is the small number of investigated cytokines...
March 2017: Biochemistry. Biokhimii︠a︡
https://www.readbyqxmd.com/read/28319275/mrf-ann-a-machine-learning-approach-for-automated-er-scoring-of-breast-cancer-immunohistochemical-images
#17
T Mungle, S Tewary, D K DAS, I Arun, B Basak, S Agarwal, R Ahmed, S Chatterjee, C Chakraborty
Molecular pathology, especially immunohistochemistry, plays an important role in evaluating hormone receptor status along with diagnosis of breast cancer. Time-consumption and inter-/intraobserver variability are major hindrances for evaluating the receptor score. In view of this, the paper proposes an automated Allred Scoring methodology for estrogen receptor (ER). White balancing is used to normalize the colour image taking into consideration colour variation during staining in different labs. Markov random field model with expectation-maximization optimization is employed to segment the ER cells...
March 20, 2017: Journal of Microscopy
https://www.readbyqxmd.com/read/28318904/dual-memory-neural-networks-for-modeling-cognitive-activities-of-humans-via-wearable-sensors
#18
Sang-Woo Lee, Chung-Yeon Lee, Dong-Hyun Kwak, Jung-Woo Ha, Jeonghee Kim, Byoung-Tak Zhang
Wearable devices, such as smart glasses and watches, allow for continuous recording of everyday life in a real world over an extended period of time or lifelong. This possibility helps better understand the cognitive behavior of humans in real life as well as build human-aware intelligent agents for practical purposes. However, modeling the human cognitive activity from wearable-sensor data stream is challenging because learning new information often results in loss of previously acquired information, causing a problem known as catastrophic forgetting...
February 20, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28318505/machine-learning-in-the-prediction-of-cardiac-epicardial-and-mediastinal-fat-volumes
#19
É O Rodrigues, V H A Pinheiro, P Liatsis, A Conci
We propose a methodology to predict the cardiac epicardial and mediastinal fat volumes in computed tomography images using regression algorithms. The obtained results indicate that it is feasible to predict these fats with a high degree of correlation, thus alleviating the requirement for manual or automatic segmentation of both fat volumes. Instead, segmenting just one of them suffices, while the volume of the other may be predicted fairly precisely. The correlation coefficient obtained by the Rotation Forest algorithm using MLP Regressor for predicting the mediastinal fat based on the epicardial fat was 0...
February 24, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28316639/feature-extraction-and-classification-of-ehg-between-pregnancy-and-labour-group-using-hilbert-huang-transform-and-extreme-learning-machine
#20
Lili Chen, Yaru Hao
Preterm birth (PTB) is the leading cause of perinatal mortality and long-term morbidity, which results in significant health and economic problems. The early detection of PTB has great significance for its prevention. The electrohysterogram (EHG) related to uterine contraction is a noninvasive, real-time, and automatic novel technology which can be used to detect, diagnose, or predict PTB. This paper presents a method for feature extraction and classification of EHG between pregnancy and labour group, based on Hilbert-Huang transform (HHT) and extreme learning machine (ELM)...
2017: Computational and Mathematical Methods in Medicine
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