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https://www.readbyqxmd.com/read/28437797/central-and-peripheral-vision-for-scene-recognition-a-neurocomputational-modeling-exploration
#1
Panqu Wang, Garrison W Cottrell
What are the roles of central and peripheral vision in human scene recognition? Larson and Loschky (2009) showed that peripheral vision contributes more than central vision in obtaining maximum scene recognition accuracy. However, central vision is more efficient for scene recognition than peripheral, based on the amount of visual area needed for accurate recognition. In this study, we model and explain the results of Larson and Loschky (2009) using a neurocomputational modeling approach. We show that the advantage of peripheral vision in scene recognition, as well as the efficiency advantage for central vision, can be replicated using state-of-the-art deep neural network models...
April 1, 2017: Journal of Vision
https://www.readbyqxmd.com/read/28437528/automated-staging-of-age-related-macular-degeneration-using-optical-coherence-tomography
#2
Freerk G Venhuizen, Bram van Ginneken, Freekje van Asten, Mark J J P van Grinsven, Sascha Fauser, Carel B Hoyng, Thomas Theelen, Clara I Sánchez
Purpose: To evaluate a machine learning algorithm that automatically grades age-related macular degeneration (AMD) severity stages from optical coherence tomography (OCT) scans. Methods: A total of 3265 OCT scans from 1016 patients with either no signs of AMD or with signs of early, intermediate, or advanced AMD were randomly selected from a large European multicenter database. A machine learning system was developed to automatically grade unseen OCT scans into different AMD severity stages without requiring retinal layer segmentation...
April 1, 2017: Investigative Ophthalmology & Visual Science
https://www.readbyqxmd.com/read/28437486/prediction-of-crime-occurrence-from-multi-modal-data-using-deep-learning
#3
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/28437028/esut-educational-video-on-fluoroscopic-guided-puncture-in-pcnl-all-techniques-step-by-step
#4
Iason Kyriazis, Evangelos Liatsikos, Odysseas Sopilidis, Panagiotis Kallidonis, Andreas Skolarikos
OBJECTIVE: Kidney puncture during percutaneous nephrolithotomy (PCNL) is regarded as one of the most demanding aspects of the procedure and only a minority of urologists perform this step without assistance by a radiologist. Currently a wide variation of fluoroscopic guided techniques is available in clinical practice. In this work we describe the most common fluoroscopic guided access techniques in a step-by-step manner aiming to assist on the standardization of their technique and terminology...
April 24, 2017: BJU International
https://www.readbyqxmd.com/read/28436902/learning-to-predict-consequences-as-a-method-of-knowledge-transfer-in-reinforcement-learning
#5
Eric Chalmers, Edgar Bermudez Contreras, Brandon Robertson, Artur Luczak, Aaron Gruber
The reinforcement learning (RL) paradigm allows agents to solve tasks through trial-and-error learning. To be capable of efficient, long-term learning, RL agents should be able to apply knowledge gained in the past to new tasks they may encounter in the future. The ability to predict actions' consequences may facilitate such knowledge transfer. We consider here domains where an RL agent has access to two kinds of information: agent-centric information with constant semantics across tasks, and environment-centric information, which is necessary to solve the task, but with semantics that differ between tasks...
April 17, 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
#6
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/28436896/online-hashing
#7
Long-Kai Huang, Qiang Yang, Wei-Shi Zheng
Although hash function learning algorithms have achieved great success in recent years, most existing hash models are off-line, which are not suitable for processing sequential or online data. To address this problem, this paper proposes an online hash model to accommodate data coming in stream for online learning. Specifically, a new loss function is proposed to measure the similarity loss between a pair of data samples in hamming space. Then, a structured hash model is derived and optimized in a passive-aggressive way...
April 24, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28436893/a-parallel-multiclassification-algorithm-for-big-data-using-an-extreme-learning-machine
#8
Mingxing Duan, Kenli Li, Xiangke Liao, Keqin Li
As data sets become larger and more complicated, an extreme learning machine (ELM) that runs in a traditional serial environment cannot realize its ability to be fast and effective. Although a parallel ELM (PELM) based on MapReduce to process large-scale data shows more efficient learning speed than identical ELM algorithms in a serial environment, some operations, such as intermediate results stored on disks and multiple copies for each task, are indispensable, and these operations create a large amount of extra overhead and degrade the learning speed and efficiency of the PELMs...
April 24, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28436884/novelty-indicator-for-enhanced-prioritization-of-predicted-gene-ontology-annotations
#9
Davide Chicco, Fernando Palluzzi, Marco Masseroli
Biomolecular controlled annotations have become pivotal in computational biology, because they allow scientists to analyze large amounts of biological data to better understand test results, and to infer new knowledge. Yet, biomolecular annotation databases are incomplete by definition, like our knowledge of biology, and might contain errors and inconsistent information. In this context, machine-learning algorithms able to predict and prioritize new annotations are both effective and efficient, especially if compared with time-consuming trials of biological validation...
April 18, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28436862/part-based-deep-hashing-for-large-scale-person-re-identification
#10
Fuqing Zhu, Xiangwei Kong, Liang Zheng, Haiyan Fu, Qi Tian
Large scale is a trend in person re-identification (re-id). It is important that real-time search be performed in a large gallery. While previous methods mostly focus on discriminative learning, this paper makes the attempt in integrating deep learning and hashing into one framework to evaluate the efficiency and accuracy for large-scale person reidentification. We integrate spatial information for discriminative visual representation by partitioning the pedestrian image into horizontal parts. Specifically, Part-based Deep Hashing (PDH) is proposed, in which batches of triplet samples are employed as the input of the deep hashing architecture...
April 18, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28436853/automatic-skin-lesion-segmentation-using-deep-fully-convolutional-networks-with-jaccard-distance
#11
Yading Yuan, Ming Chao, Yeh-Chi Lo
Automatic skin lesion segmentation in dermoscopic images is a challenging task due to the low contrast between lesion and the surrounding skin, the irregular and fuzzy lesion borders, the existence of various artifacts, and various imaging acquisition conditions. In this article, we present a fully automatic method for skin lesion segmentation by leveraging a 19-layer deep convolutional neural networks (CNNs) that is trained end-to- end and does not rely on prior knowledge of the data. We propose a set of strategies to ensure effective and efficient learning with limited training data...
April 18, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28436847/learning-trans-dimensional-random-fields-with-applications-to-language-modeling
#12
Bin Wang, Zhijian Ou, Zhiqiang Tan
To describe trans-dimensional observations in sample spaces of different dimensions, we propose a probabilistic model, called the trans-dimensional random field (TRF) by explicitly mixing a collection of random fields. In the framework of stochastic approximation (SA), we develop an effective training algorithm, called augmented SA, which jointly estimates the model parameters and normalizing constants while using trans-dimensional mixture sampling to generate observations of different dimensions. Furthermore, we introduce several statistical and computational techniques to improve the convergence of the training algorithm and reduce computational cost, which together enable us to successfully train TRF models on large datasets...
April 24, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28436846/efficient-2d-and-3d-facade-segmentation-using-auto-context
#13
Raghudeep Gadde, Varun Jampani, Renaud Marlet, Peter Gehler
This paper introduces a fast and efficient segmentation technique for 2D images and 3D point clouds of building facades. Facades of buildings are highly structured and consequently most methods that have been proposed for this problem aim to make use of this strong prior information. Contrary to most prior work, we are describing a system that is almost domain independent and consists of standard segmentation methods. We train a sequence of boosted decision trees using auto-context features. This is learned using stacked generalization...
April 24, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28436664/cppred-rf-a-sequence-based-predictor-for-identifying-cell-penetrating-peptides-and-their-uptake-efficiency
#14
Leyi Wei, Pengwei Xing, Ran Su, Gaotao Shi, Zhanshan Sam Ma, Quan Zou
Cell-penetrating peptides (CPPs), have been proven as important drug delivery vehicles, demonstrating the potential as therapeutic candidates. The last decade has witnessed a rapid growth in CPP-based research. Recently, many computational efforts have been made to develop machine learning based methods for identifying CPPs. Although much progress has been made, existing methods still suffer low feature representation capability that limits further performance improvement. In this study, we propose a novel predictor called CPPred-RF, in which we integrate multiple sequence-based feature descriptors to sufficiently explore distinct information embedded in CPPs, employ a well-established feature selection technique to improve the feature representation, and at the first time, construct a 2-layer prediction framework based on the random forest algorithm...
April 24, 2017: Journal of Proteome Research
https://www.readbyqxmd.com/read/28436072/real-time-individualization-of-the-unified-model-of-performance
#15
Jianbo Liu, Sridhar Ramakrishnan, Srinivas Laxminarayan, Thomas J Balkin, Jaques Reifman
Existing mathematical models for predicting neurobehavioural performance are not suited for mobile computing platforms because they cannot adapt model parameters automatically in real time to reflect individual differences in the effects of sleep loss. We used an extended Kalman filter to develop a computationally efficient algorithm that continually adapts the parameters of the recently developed Unified Model of Performance (UMP) to an individual. The algorithm accomplishes this in real time as new performance data for the individual become available...
April 24, 2017: Journal of Sleep Research
https://www.readbyqxmd.com/read/28434860/direct-brain-stimulation-modulates-encoding-states-and-memory-performance-in-humans
#16
Youssef Ezzyat, James E Kragel, John F Burke, Deborah F Levy, Anastasia Lyalenko, Paul Wanda, Logan O'Sullivan, Katherine B Hurley, Stanislav Busygin, Isaac Pedisich, Michael R Sperling, Gregory A Worrell, Michal T Kucewicz, Kathryn A Davis, Timothy H Lucas, Cory S Inman, Bradley C Lega, Barbara C Jobst, Sameer A Sheth, Kareem Zaghloul, Michael J Jutras, Joel M Stein, Sandhitsu R Das, Richard Gorniak, Daniel S Rizzuto, Michael J Kahana
People often forget information because they fail to effectively encode it. Here, we test the hypothesis that targeted electrical stimulation can modulate neural encoding states and subsequent memory outcomes. Using recordings from neurosurgical epilepsy patients with intracranially implanted electrodes, we trained multivariate classifiers to discriminate spectral activity during learning that predicted remembering from forgetting, then decoded neural activity in later sessions in which we applied stimulation during learning...
April 13, 2017: Current Biology: CB
https://www.readbyqxmd.com/read/28434153/machine-learning-xgboost-analysis-of-language-networks-to-classify-patients-with-epilepsy
#17
L Torlay, M Perrone-Bertolotti, E Thomas, M Baciu
Our goal was to apply a statistical approach to allow the identification of atypical language patterns and to differentiate patients with epilepsy from healthy subjects, based on their cerebral activity, as assessed by functional MRI (fMRI). Patients with focal epilepsy show reorganization or plasticity of brain networks involved in cognitive functions, inducing 'atypical' (compared to 'typical' in healthy people) brain profiles. Moreover, some of these patients suffer from drug-resistant epilepsy, and they undergo surgery to stop seizures...
April 22, 2017: Brain Informatics
https://www.readbyqxmd.com/read/28432138/inhibition-of-drp1-ameliorates-synaptic-depression-a%C3%AE-deposition-and-cognitive-impairment-in-alzheimer-s-disease-model
#18
Seung-Hyun Baek, So Jung Park, Jae In Jeong, Sung Hyun Kim, Jihoon Han, Jae Won Kyung, Sang-Ha Baik, Yuri Choi, Bo-Youn Choi, Jinsu Park, Gahee Bahn, Ji Hyun Shin, Doo Sin Jo, Joo-Yong Lee, Choon-Gon Jang, Thiruma V Arumugam, Jongpil Kim, Jeung-Whan Han, Jae-Young Koh, Dong-Hyung Cho, Dong-Gyu Jo
Excessive mitochondrial fission is a prominent early event, and contributes to mitochondrial dysfunction, synaptic failure and neuronal cell death in the progression of Alzheimer's disease (AD). However, it remains to be determined whether inhibition of excessive mitochondrial fission is beneficial in mammal models of AD. To determine whether dynamin-related protein 1 (Drp1), a key regulator of mitochondrial fragmentation, can be a disease-modifying therapeutic target for AD, we examine the effects of Drp1 inhibitor on mitochondrial and synaptic dysfunctions induced by oligomeric β-amyloid (Aβ) in neurons, and neuropathology and cognitive functions in APP/PS1 double transgenic AD mice...
April 21, 2017: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/28430147/suitability-of-strain-gage-sensors-for-integration-into-smart-sport-equipment-a-golf-club-example
#19
Anton Umek, Yuan Zhang, Sašo Tomažič, Anton Kos
Wearable devices and smart sport equipment are being increasingly used in amateur and professional sports. Smart sport equipment employs various sensors for detecting its state and actions. The correct choice of the most appropriate sensor(s) is of paramount importance for efficient and successful operation of sport equipment. When integrated into the sport equipment, ideal sensors are unobstructive, and do not change the functionality of the equipment. The article focuses on experiments for identification and selection of sensors that are suitable for the integration into a golf club with the final goal of their use in real time biofeedback applications...
April 21, 2017: Sensors
https://www.readbyqxmd.com/read/28427713/on-the-efficiency-of-instruction-based-rule-encoding
#20
Hannes Ruge, Tatjana Karcz, Tony Mark, Victoria Martin, Katharina Zwosta, Uta Wolfensteller
Instructions have long been considered a highly efficient route to knowledge acquisition especially compared to trial-and-error learning. We aimed at substantiating this claim by identifying boundary conditions for such an efficiency gain, including the influence of active learning intention, repeated instructions, and working memory load and span. Our experimental design allowed us to not only assess how well the instructed stimulus-response (S-R) rules were implemented later on, but also to directly measure prior instruction encoding processes...
April 18, 2017: Acta Psychologica
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