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https://www.readbyqxmd.com/read/28646763/deep-neural-mapping-support-vector-machines
#1
Yujian Li, Ting Zhang
The choice of kernel has an important effect on the performance of a support vector machine (SVM). The effect could be reduced by NEUROSVM, an architecture using multilayer perceptron for feature extraction and SVM for classification. In binary classification, a general linear kernel NEUROSVM can be theoretically simplified as an input layer, many hidden layers, and an SVM output layer. As a feature extractor, the sub-network composed of the input and hidden layers is first trained together with a virtual ordinary output layer by backpropagation, then with the output of its last hidden layer taken as input of the SVM classifier for further training separately...
June 21, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28646563/cohesive-network-reconfiguration-accompanies-extended-training
#2
Qawi K Telesford, Arian Ashourvan, Nicholas F Wymbs, Scott T Grafton, Jean M Vettel, Danielle S Bassett
Human behavior is supported by flexible neurophysiological processes that enable the fine-scale manipulation of information across distributed neural circuits. Yet, approaches for understanding the dynamics of these circuit interactions have been limited. One promising avenue for quantifying and describing these dynamics lies in multilayer network models. Here, networks are composed of nodes (which represent brain regions) and time-dependent edges (which represent statistical similarities in activity time series)...
June 24, 2017: Human Brain Mapping
https://www.readbyqxmd.com/read/28646241/never-forget-a-name-white-matter-connectivity-predicts-person-memory
#3
Athanasia Metoki, Kylie H Alm, Yin Wang, Chi T Ngo, Ingrid R Olson
Through learning and practice, we can acquire numerous skills, ranging from the simple (whistling) to the complex (memorizing operettas in a foreign language). It has been proposed that complex learning requires a network of brain regions that interact with one another via white matter pathways. One candidate white matter pathway, the uncinate fasciculus (UF), has exhibited mixed results for this hypothesis: some studies have shown UF involvement across a range of memory tasks, while other studies report null results...
June 23, 2017: Brain Structure & Function
https://www.readbyqxmd.com/read/28646177/hyperspectral-imaging-for-presymptomatic-detection-of-tobacco-disease-with-successive-projections-algorithm-and-machine-learning-classifiers
#4
Hongyan Zhu, Bingquan Chu, Chu Zhang, Fei Liu, Linjun Jiang, Yong He
We investigated the feasibility and potentiality of presymptomatic detection of tobacco disease using hyperspectral imaging, combined with the variable selection method and machine-learning classifiers. Images from healthy and TMV-infected leaves with 2, 4, and 6 days post infection were acquired by a pushbroom hyperspectral reflectance imaging system covering the spectral range of 380-1023 nm. Successive projections algorithm was evaluated for effective wavelengths (EWs) selection. Four texture features, including contrast, correlation, entropy, and homogeneity were extracted according to grey-level co-occurrence matrix (GLCM)...
June 23, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28644814/learning-multimodal-parameters-a-bare-bones-niching-differential-evolution-approach
#5
Yue-Jiao Gong, Jun Zhang, Yicong Zhou
Most learning methods contain optimization as a substep, where the nondifferentiability and multimodality of objectives push forward the interplay of evolutionary optimization algorithms and machine learning models. The recently emerged evolutionary multimodal optimization (MMOP) technique enables the learning of diverse sets of effective parameters for the models simultaneously, providing new opportunities to the applications requiring both accuracy and diversity, such as ensemble, interactive, and interpretive learning...
June 20, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28644806/discriminative-deep-metric-learning-for-face-and-kinship-verification
#6
Jiwen Lu, Junlin Hu, Yap-Peng Tan
This paper presents a new discriminative deep metric learning (DDML) method for face and kinship verification in wild conditions. While metric learning has achieved reasonably good performance in face and kinship verification, most existing metric learning methods aim to learn a single Mahalanobis distance metric to maximize the inter-class variations and minimize the intra-class variations, which cannot capture the nonlinear manifold where face images usually lie on. To address this, we propose a DDML method to train a deep neural network to learn a set of hierarchical nonlinear transformations to project face pairs into the same latent feature space, under which the distance of each positive pair is reduced and that of each negative pair is enlarged, respectively...
June 20, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28643948/investigating-the-separate-and-interactive-associations-of-trauma-and-depression-on-brain-structure-implications-for-cognition-and-aging
#7
Aimee J Karstens, Olusola Ajilore, Leah H Rubin, Shaolin Yang, Aifeng Zhang, Alex Leow, Anand Kumar, Melissa Lamar
OBJECTIVE: Trauma and depression are associated with brain structural alterations; their combined effects on these outcomes are unclear. We previously reported a negative effect of trauma, independent of depression, on verbal learning and memory; less is known about underlying structural associates. We investigated separate and interactive associations of trauma and depression on brain structure. METHODS: Adults aged 30-89 (N = 203) evaluated for depression (D+) and trauma history (T+) using structured clinical interviews were divided into 53 D+T+, 42 D+T-, 50 D-T+, and 58 D-T-...
June 23, 2017: International Journal of Geriatric Psychiatry
https://www.readbyqxmd.com/read/28643394/incorporating-deep-learning-with-convolutional-neural-networks-and-position-specific-scoring-matrices-for-identifying-electron-transport-proteins
#8
Nguyen-Quoc-Khanh Le, Quang-Thai Ho, Yu-Yen Ou
In several years, deep learning is a modern machine learning technique using in a variety of fields with state-of-the-art performance. Therefore, utilization of deep learning to enhance performance is also an important solution for current bioinformatics field. In this study, we try to use deep learning via convolutional neural networks and position specific scoring matrices to identify electron transport proteins, which is an important molecular function in transmembrane proteins. Our deep learning method can approach a precise model for identifying of electron transport proteins with achieved sensitivity of 80...
June 22, 2017: Journal of Computational Chemistry
https://www.readbyqxmd.com/read/28643328/drug-repurposing-by-simulating-flow-through-protein-protein-interaction-networks
#9
M Manczinger, V Bodnár, B T Papp, B Sz Bolla, K Szabó, B Balázs, E Csányi, E Szél, G Erős, L Kemény
As drug development is extremely expensive, the identification of novel indications for in-market drugs is financially attractive. Multiple algorithms are used to support such drug repurposing, but highly reliable methods combining simulation of intracellular networks and machine learning are currently not available. We developed an algorithm that simulates drug effects on the flow of information through protein-protein interaction networks, and uses Support Vector Machine to identify potentially effective drugs in our model disease, psoriasis...
June 23, 2017: Clinical Pharmacology and Therapeutics
https://www.readbyqxmd.com/read/28642938/correlation-weighted-sparse-group-representation-for-brain-network-construction-in-mci-classification
#10
Renping Yu, Han Zhang, Le An, Xiaobo Chen, Zhihui Wei, Dinggang Shen
Analysis of brain functional connectivity network (BFCN) has shown great potential in understanding brain functions and identifying biomarkers for neurological and psychiatric disorders, such as Alzheimer's disease and its early stage, mild cognitive impairment (MCI). In all these applications, the accurate construction of biologically meaningful brain network is critical. Due to the sparse nature of the brain network, sparse learning has been widely used for complex BFCN construction. However, the conventional l1-norm penalty in the sparse learning equally penalizes each edge (or link) of the brain network, which ignores the link strength and could remove strong links in the brain network...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28641250/deep-convolutional-neural-network-for-inverse-problems-in-imaging
#11
Kyong Hwan Jin, Michael T McCann, Emmanuel Froustey, Michael Unser
In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. These methods produce excellent results, but can be challenging to deploy in practice due to factors including the high computational cost of the forward and adjoint operators and the difficulty of hyper parameter selection. The starting point of our work is the observation that unrolled iterative methods have the form of a CNN (filtering followed by point-wise nonlinearity) when the normal operator ( H*H where H* is the adjoint of the forward imaging operator, H ) of the forward model is a convolution...
June 15, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28641247/modeling-task-fmri-data-via-deep-convolutional-autoencoder
#12
Heng Huang, Xintao Hu, Yu Zhao, Milad Makkie, Qinglin Dong, Shijie Zhao, Lei Guo, Tianming Liu
Task-based fMRI (tfMRI) has been widely used to study functional brain networks under task performance. Modeling tfMRI data is challenging due to at least two problems: the lack of the ground truth of underlying neural activity and the highly complex intrinsic structure of tfMRI data. To better understand brain networks based on fMRI data, data-driven approaches have been proposed, for instance, Independent Component Analysis (ICA) and Sparse Dictionary Learning (SDL). However, both ICA and SDL only build shallow models, and they are under the strong assumption that original fMRI signal could be linearly decomposed into time series components with their corresponding spatial maps...
June 15, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28641239/automatic-recognition-of-fmri-derived-functional-networks-using-3d-convolutional-neural-networks
#13
Yu Zhao, Qinglin Dong, Shu Zhang, Wei Zhang, Hanbo Chen, Xi Jiang, Lei Guo, Xintao Hu, Junwei Han, Tianming Liu
Current fMRI data modeling techniques such as Independent Component Analysis (ICA) and Sparse Coding methods can effectively reconstruct dozens or hundreds of concurrent interacting functional brain networks simultaneously from the whole brain fMRI signals. However, such reconstructed networks have no correspondences across different subjects. Thus, automatic, effective and accurate classification and recognition of these large numbers of fMRI-derived functional brain networks are very important for subsequent steps of functional brain analysis in cognitive and clinical neuroscience applications...
June 15, 2017: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/28640883/an-artificial-emg-generation-model-based-on-signal-dependent-noise-and-related-application-to-motion-classification
#14
Akira Furui, Hideaki Hayashi, Go Nakamura, Takaaki Chin, Toshio Tsuji
This paper proposes an artificial electromyogram (EMG) signal generation model based on signal-dependent noise, which has been ignored in existing methods, by introducing the stochastic construction of the EMG signals. In the proposed model, an EMG signal variance value is first generated from a probability distribution with a shape determined by a commanded muscle force and signal-dependent noise. Artificial EMG signals are then generated from the associated Gaussian distribution with a zero mean and the generated variance...
2017: PloS One
https://www.readbyqxmd.com/read/28640840/applying-artificial-intelligence-to-disease-staging-deep-learning-for-improved-staging-of-diabetic-retinopathy
#15
Hidenori Takahashi, Hironobu Tampo, Yusuke Arai, Yuji Inoue, Hidetoshi Kawashima
PURPOSE: Disease staging involves the assessment of disease severity or progression and is used for treatment selection. In diabetic retinopathy, disease staging using a wide area is more desirable than that using a limited area. We investigated if deep learning artificial intelligence (AI) could be used to grade diabetic retinopathy and determine treatment and prognosis. METHODS: The retrospective study analyzed 9,939 posterior pole photographs of 2,740 patients with diabetes...
2017: PloS One
https://www.readbyqxmd.com/read/28640825/olfactory-learning-without-the-mushroom-bodies-spiking-neural-network-models-of-the-honeybee-lateral-antennal-lobe-tract-reveal-its-capacities-in-odour-memory-tasks-of-varied-complexities
#16
HaDi MaBouDi, Hideaki Shimazaki, Martin Giurfa, Lars Chittka
The honeybee olfactory system is a well-established model for understanding functional mechanisms of learning and memory. Olfactory stimuli are first processed in the antennal lobe, and then transferred to the mushroom body and lateral horn through dual pathways termed medial and lateral antennal lobe tracts (m-ALT and l-ALT). Recent studies reported that honeybees can perform elemental learning by associating an odour with a reward signal even after lesions in m-ALT or blocking the mushroom bodies. To test the hypothesis that the lateral pathway (l-ALT) is sufficient for elemental learning, we modelled local computation within glomeruli in antennal lobes with axons of projection neurons connecting to a decision neuron (LHN) in the lateral horn...
June 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28640450/network-supervision-of-adult-experience-and-learning-dependent-sensory-cortical-plasticity
#17
David T Blake
The brain is capable of remodeling throughout life. The sensory cortices provide a useful preparation for studying neuroplasticity both during development and thereafter. In adulthood, sensory cortices change in the cortical area activated by behaviorally relevant stimuli, by the strength of response within that activated area, and by the temporal profiles of those responses. Evidence supports forms of unsupervised, reinforcement, and fully supervised network learning rules. Studies on experience-dependent plasticity have mostly not controlled for learning, and they find support for unsupervised learning mechanisms...
June 18, 2017: Comprehensive Physiology
https://www.readbyqxmd.com/read/28640031/identifying-gaps-in-the-performance-of-pediatric-trainees-who-receive-marginal-unsatisfactory-ratings
#18
Su-Ting T Li, Daniel J Tancredi, Alan Schwartz, Ann Guillot, Ann Burke, R Franklin Trimm, Susan Guralnick, John D Mahan, Kimberly A Gifford
PURPOSE: To perform a derivation study to determine in which subcompetencies marginal/unsatisfactory pediatric residents had the greatest deficits compared with their satisfactorily performing peers and which subcompetencies best discriminated between marginal/unsatisfactory and satisfactorily performing residents. METHOD: Multi-institutional cohort study of all 21 milestones (rated on four or five levels) reported to the Accreditation Council for Graduate Medical Education, and global marginal/unsatisfactory versus satisfactory performance reported to the American Board of Pediatrics...
June 20, 2017: Academic Medicine: Journal of the Association of American Medical Colleges
https://www.readbyqxmd.com/read/28639489/healthcare-provider-education-to-support-integration-of-pharmacogenomics-in-practice-the-emerge-network-experience
#19
Carolyn R Rohrer Vitek, Noura S Abul-Husn, John J Connolly, Andrea L Hartzler, Terrie Kitchner, Josh F Peterson, Luke V Rasmussen, Maureen E Smith, Sarah Stallings, Marc S Williams, Wendy A Wolf, Cynthia A Prows
Ten organizations within the Electronic Medical Records and Genomics Network developed programs to implement pharmacogenomic sequencing and clinical decision support into clinical settings. Recognizing the importance of informed prescribers, a variety of strategies were used to incorporate provider education to support implementation. Education experiences with pharmacogenomics are described within the context of each organization's prior involvement, including the scope and scale of implementation specific to their Electronic Medical Records and Genomics projects...
June 22, 2017: Pharmacogenomics
https://www.readbyqxmd.com/read/28637818/the-cerebellum-does-more-than-sensory-prediction-error-based-learning-in-sensorimotor-adaptation-tasks
#20
Peter A Butcher, Richard B Ivry, Sheng-Han Kuo, David Rydz, John W Krakauer, Jordan A Taylor
Individuals with damage to the cerebellum perform poorly in sensorimotor adaptation paradigms. This deficit has been attributed to impairment in sensory-prediction-error-based updating of an internal forward model, a form of implicit learning. These individuals can, however, successfully counter a perturbation when instructed with an explicit aiming strategy. This successful use of an instructed aiming strategy presents a paradox: In adaptation tasks, why don't individuals with cerebellar damage come up with an aiming solution on their own to compensate for their implicit learning deficit? To explore this question, we employed a variant of a visuomotor rotation task in which, prior to executing a movement on each trial, the participants verbally reported their intended aiming location...
June 21, 2017: Journal of Neurophysiology
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