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https://www.readbyqxmd.com/read/28727737/improving-deep-convolutional-neural-networks-with-mixed-maxout-units
#1
Hui-Zhen Zhao, Fu-Xian Liu, Long-Yue Li
Motivated by insights from the maxout-units-based deep Convolutional Neural Network (CNN) that "non-maximal features are unable to deliver" and "feature mapping subspace pooling is insufficient," we present a novel mixed variant of the recently introduced maxout unit called a mixout unit. Specifically, we do so by calculating the exponential probabilities of feature mappings gained by applying different convolutional transformations over the same input and then calculating the expected values according to their exponential probabilities...
2017: PloS One
https://www.readbyqxmd.com/read/28727640/no-seasonal-changes-in-cognitive-functioning-among-high-school-football-athletes-implementation-of-a-novel-electrophysiological-measure-and-standard-clinical-measures
#2
Steven P Broglio, Richelle Williams, Ashley Rettmann, Brandon Moore, James T Eckner, Sean Meehan
OBJECTIVE: To evaluate neuroelectric and cognitive function relative to a season of football participation. Cognitive and neuroelectric function declines are hypothesized to be present in football athletes. DESIGN: Observational. SETTING: Athletic fields and research laboratory. PATIENTS (OR PARTICIPANTS): Seventy-seven high school athletes (15.9 + 0.9 years, 178.6 + 7.2 cm, 74.4 + 14.7 kg, and 0.8 + 0.8 self-reported concussions) participating in football (n = 46) and noncontact sports (n = 31)...
July 14, 2017: Clinical Journal of Sport Medicine: Official Journal of the Canadian Academy of Sport Medicine
https://www.readbyqxmd.com/read/28727566/model-free-adaptive-control-for-unknown-nonlinear-zero-sum-differential-game
#3
Xiangnan Zhong, Haibo He, Ding Wang, Zhen Ni
In this paper, we present a new model-free globalized dual heuristic dynamic programming (GDHP) approach for the discrete-time nonlinear zero-sum game problems. First, the online learning algorithm is proposed based on the GDHP method to solve the Hamilton-Jacobi-Isaacs equation associated with H∞ optimal regulation control problem. By setting backward one step of the definition of performance index, the requirement of system dynamics, or an identifier is relaxed in the proposed method. Then, three neural networks are established to approximate the optimal saddle point feedback control law, the disturbance law, and the performance index, respectively...
July 17, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28727565/danoc-an-efficient-algorithm-and-hardware-codesign-of-deep-neural-networks-on-chip
#4
Xichuan Zhou, Shengli Li, Fang Tang, Shengdong Hu, Zhi Lin, Lei Zhang
Deep neural networks (NNs) are the state-of-the-art models for understanding the content of images and videos. However, implementing deep NNs in embedded systems is a challenging task, e.g., a typical deep belief network could exhaust gigabytes of memory and result in bandwidth and computational bottlenecks. To address this challenge, this paper presents an algorithm and hardware codesign for efficient deep neural computation. A hardware-oriented deep learning algorithm, named the deep adaptive network, is proposed to explore the sparsity of neural connections...
July 18, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28727547/designing-and-implementation-of-stable-sinusoidal-rough-neural-identifier
#5
Ghasem Ahmadi, Mohammad Teshnehlab
A rough neuron is defined as a pair of conventional neurons that are called the upper and lower bound neurons. In this paper, the sinusoidal rough-neural networks (SR-NNs) are used to identify the discrete dynamic nonlinear systems (DDNSs) with or without noise in series-parallel configuration. In the identification of periodic nonlinear systems, sinusoidal activation functions provide more efficient neural networks than the sigmoidal activation functions. Based on the Lyapunov stability theory, an online learning algorithm is developed to train the SR-NNs...
August 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28727421/shallow-representation-learning-via-kernel-pca-improves-qsar-modelability
#6
Stefano E Rensi, Russ B Altman
Linear models offer a robust, flexible, and computationally efficient set of tools for modeling quantitative structure activity relationships (QSAR), but have been eclipsed in performance by non-linear methods. Support vector machines (SVMs) and neural networks are currently among the most popular and accurate QSAR methods because they learn new representations of the data that greatly improve modelability. In this work we use shallow representation learning to improve the accuracy of L1 regularized logistic regression (LASSO) and meet the performance of Tanimoto SVM...
July 20, 2017: Journal of Chemical Information and Modeling
https://www.readbyqxmd.com/read/28726544/evaluating-wais-iv-structure-through-a-different-psychometric-lens-structural-causal-model-discovery-as-an-alternative-to-confirmatory-factor-analysis
#7
Marjolein J A M van Dijk, Tom Claassen, Christiany Suwartono, William M van der Veld, Paul T van der Heijden, Marc P H Hendriks
OBJECTIVE: Since the publication of the WAIS-IV in the U.S. in 2008, efforts have been made to explore the structural validity by applying factor analysis to various samples. This study aims to achieve a more fine-grained understanding of the structure of the Dutch language version of the WAIS-IV (WAIS-IV-NL) by applying an alternative analysis based on causal modeling in addition to confirmatory factor analysis (CFA). The Bayesian Constraint-based Causal Discovery (BCCD) algorithm learns underlying network structures directly from data and assesses more complex structures than is possible with factor analysis...
July 20, 2017: Clinical Neuropsychologist
https://www.readbyqxmd.com/read/28725482/systems-biology-driving-drug-development-from-design-to-the-clinical-testing-of-the-anti-erbb3-antibody-seribantumab-mm-121
#8
REVIEW
Birgit Schoeberl, Art Kudla, Kristina Masson, Ashish Kalra, Michael Curley, Gregory Finn, Emily Pace, Brian Harms, Jaeyeon Kim, Jeff Kearns, Aaron Fulgham, Olga Burenkova, Viara Grantcharova, Defne Yarar, Violette Paragas, Jonathan Fitzgerald, Marisa Wainszelbaum, Kip West, Sara Mathews, Rachel Nering, Bambang Adiwijaya, Gabriela Garcia, Bill Kubasek, Victor Moyo, Akos Czibere, Ulrik B Nielsen, Gavin MacBeath
The ErbB family of receptor tyrosine kinases comprises four members: epidermal growth factor receptor (EGFR/ErbB1), human EGFR 2 (HER2/ErbB2), ErbB3/HER3, and ErbB4/HER4. The first two members of this family, EGFR and HER2, have been implicated in tumorigenesis and cancer progression for several decades, and numerous drugs have now been approved that target these two proteins. Less attention, however, has been paid to the role of this family in mediating cancer cell survival and drug tolerance. To better understand the complex signal transduction network triggered by the ErbB receptor family, we built a computational model that quantitatively captures the dynamics of ErbB signaling...
2017: NPJ Systems Biology and Applications
https://www.readbyqxmd.com/read/28725174/altered-functional-connectivity-following-an-inflammatory-white-matter-injury-in-the-newborn-rat-a-high-spatial-and-temporal-resolution-intrinsic-optical-imaging-study
#9
Edgar Guevara, Wyston C Pierre, Camille Tessier, Luis Akakpo, Irène Londono, Frédéric Lesage, Gregory A Lodygensky
Very preterm newborns have an increased risk of developing an inflammatory cerebral white matter injury that may lead to severe neuro-cognitive impairment. In this study we performed functional connectivity (fc) analysis using resting-state optical imaging of intrinsic signals (rs-OIS) to assess the impact of inflammation on resting-state networks (RSN) in a pre-clinical model of perinatal inflammatory brain injury. Lipopolysaccharide (LPS) or saline injections were administered in postnatal day (P3) rat pups and optical imaging of intrinsic signals were obtained 3 weeks later...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28724682/from-the-memphis-model-to-the-north-carolina-way-lessons-learned-from-emerging-health-system-and-faith-community-partnerships
#10
Teresa Cutts, Gary Gunderson, Dean Carter, Melanie Childers, Phillip Long, Lisa Marisiddaiah, Helen Milleson, Dennis Stamper, Annika Archie, Jeremy Moseley, Emily Viverette, Bobby Baker
National health care policy has encouraged health systems to develop community partnerships designed to decrease costs and readmissions, particularly for underserved populations. This commentary describes and compares the Congregational Health Network's Memphis Model to early local efforts at clinical-faith community partnerships in North Carolina, which we call "The North Carolina Way." Necessary components for building robust health system and congregational partnerships to address social determinants of health and impact health care utilization include partnership growth, allocation of health system resources, community trust, and time...
July 2017: North Carolina Medical Journal
https://www.readbyqxmd.com/read/28723811/the-afya-bora-fellowship-an-innovative-program-focused-on-creating-an-interprofessional-network-of-leaders-in-global-health
#11
Wendy M Green, Carey Farquhar, Yohana Mashalla
PROBLEM: Most current health professions education programs are focused on the development of clinical skills. As a result, they may not address the complex and interconnected nature of global health. Trainees require relevant clinical, programmatic, and leadership skills to meet the challenges of practicing in an increasingly globalized environment. APPROACH: To develop health care leaders within sub-Saharan Africa, the Afya Bora Consortium developed a one-year fellowship for medical doctors and nurses...
July 18, 2017: Academic Medicine: Journal of the Association of American Medical Colleges
https://www.readbyqxmd.com/read/28723578/convolutional-neural-network-based-encoding-and-decoding-of-visual-object-recognition-in-space-and-time
#12
REVIEW
K Seeliger, M Fritsche, U Güçlü, S Schoenmakers, J-M Schoffelen, S E Bosch, M A J van Gerven
Representations learned by deep convolutional neural networks (CNNs) for object recognition are a widely investigated model of the processing hierarchy in the human visual system. Using functional magnetic resonance imaging, CNN representations of visual stimuli have previously been shown to correspond to processing stages in the ventral and dorsal streams of the visual system. Whether this correspondence between models and brain signals also holds for activity acquired at high temporal resolution has been explored less exhaustively...
July 16, 2017: NeuroImage
https://www.readbyqxmd.com/read/28723311/visual-perceptual-learning-and-models
#13
Barbara Dosher, Zhong-Lin Lu
Visual perceptual learning through practice or training can significantly improve performance on visual tasks. Originally seen as a manifestation of plasticity in the primary visual cortex, perceptual learning is more readily understood as improvements in the function of brain networks that integrate processes, including sensory representations, decision, attention, and reward, and balance plasticity with system stability. This review considers the primary phenomena of perceptual learning, theories of perceptual learning, and perceptual learning's effect on signal and noise in visual processing and decision...
July 19, 2017: Annual Review of Vision Science
https://www.readbyqxmd.com/read/28720701/de-novo-peptide-sequencing-by-deep-learning
#14
Ngoc Hieu Tran, Xianglilan Zhang, Lei Xin, Baozhen Shan, Ming Li
De novo peptide sequencing from tandem MS data is the key technology in proteomics for the characterization of proteins, especially for new sequences, such as mAbs. In this study, we propose a deep neural network model, DeepNovo, for de novo peptide sequencing. DeepNovo architecture combines recent advances in convolutional neural networks and recurrent neural networks to learn features of tandem mass spectra, fragment ions, and sequence patterns of peptides. The networks are further integrated with local dynamic programming to solve the complex optimization task of de novo sequencing...
July 18, 2017: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/28720023/early-motor-development-and-cognitive-abilities-among-mexican-preschoolers
#15
Erika Osorio-Valencia, Luisa Torres-Sánchez, Lizbeth López-Carrillo, Stephen J Rothenberg, Lourdes Schnaas
Psychomotricity plays a very important role in children's development, especially for learning involving reading-writing and mathematical calculations. Evaluate motor development in children 3 years old and its relationship with their cognitive abilities at the age of 5 years. Based on a cohort study, we analyzed the information about motor performance evaluated at 3 years old by Peabody Motor Scale and cognitive abilities at 5 years old. The association was estimated using linear regression models adjusted by mother's intelligence quotient, sex, Bayley mental development index at 18 months, and quality of the environment at home (HOME scale)...
July 18, 2017: Child Neuropsychology: a Journal on Normal and Abnormal Development in Childhood and Adolescence
https://www.readbyqxmd.com/read/28718193/metacommunity-theory-meets-restoration-isolation-may-mediate-how-ecological-communities-respond-to-stream-restoration
#16
Christopher M Swan, Bryan L Brown
An often-cited benefit of river restoration is an increase in biodiversity or shift in composition to more desirable taxa. Yet, hard manipulations of habitat structure often fail to elicit a significant response in terms of biodiversity patterns. In contrast to conventional wisdom, the dispersal of organisms may have as large an influence on biodiversity patterns as environmental conditions. This influence of dispersal may be particularly influential in river networks which are linear branching, or dendritic, and thus constrain most dispersal to the river corridor...
July 17, 2017: Ecological Applications: a Publication of the Ecological Society of America
https://www.readbyqxmd.com/read/28717716/breast-cancer-and-african-ancestry-lessons-learned-at-the-10-year-anniversary-of-the-ghana-michigan-research-partnership-and-international-breast-registry
#17
Evelyn Jiagge, Joseph Kwaku Oppong, Jessica Bensenhaver, Francis Aitpillah, Kofi Gyan, Ishmael Kyei, Ernest Osei-Bonsu, Ernest Adjei, Michael Ohene-Yeboah, Kathy Toy, Karen Eubanks Jackson, Marian Akpaloo, Dorcas Acheampong, Beatrice Antwi, Faustina Obeng Agyeman, Zainab Alhassan, Linda Ahenkorah Fondjo, Osei Owusu-Afriyie, Robert Newman Brewer, Amma Gyamfuah, Barbara Salem, Timothy Johnson, Max Wicha, Sofia Merajver, Celina Kleer, Judy Pang, Emmanuel Amankwaa-Frempong, Azadeh Stark, Francis Abantanga, Lisa Newman, Baffour Awuah
Women with African ancestry in western, sub-Saharan Africa and in the United States represent a population subset facing an increased risk of being diagnosed with biologically aggressive phenotypes of breast cancer that are negative for the estrogen receptor, the progesterone receptor, and the HER2/neu marker. These tumors are commonly referred to as triple-negative breast cancer. Disparities in breast cancer incidence and outcome related to racial or ethnic identity motivated the establishment of the International Breast Registry, on the basis of partnerships between the Komfo Anokye Teaching Hospital in Kumasi, Ghana, the University of Michigan Comprehensive Cancer Center in Ann Arbor, Michigan, and the Henry Ford Health System in Detroit, Michigan...
October 2016: Journal of Global Oncology
https://www.readbyqxmd.com/read/28717579/deep-learning-based-automated-segmentation-of-macular-edema-in-optical-coherence-tomography
#18
Cecilia S Lee, Ariel J Tyring, Nicolaas P Deruyter, Yue Wu, Ariel Rokem, Aaron Y Lee
Evaluation of clinical images is essential for diagnosis in many specialties. Therefore the development of computer vision algorithms to help analyze biomedical images will be important. In ophthalmology, optical coherence tomography (OCT) is critical for managing retinal conditions. We developed a convolutional neural network (CNN) that detects intraretinal fluid (IRF) on OCT in a manner indistinguishable from clinicians. Using 1,289 OCT images, the CNN segmented images with a 0.911 cross-validated Dice coefficient, compared with segmentations by experts...
July 1, 2017: Biomedical Optics Express
https://www.readbyqxmd.com/read/28716117/stress-prevention-work-a-study-protocol-for-the-evaluation-of-a-multifaceted-integral-stress-prevention-strategy-to-prevent-employee-stress-in-a-healthcare-organization-a-cluster-controlled-trial
#19
Rianne J A Hoek, Bo M Havermans, Irene L D Houtman, Evelien P M Brouwers, Yvonne F Heerkens, Moniek C Zijlstra-Vlasveld, Johannes R Anema, Allard J van der Beek, Cécile R L Boot
BACKGROUND: Adequate implementation of work-related stress management interventions can reduce or prevent work-related stress and sick leave in organizations. We developed a multifaceted integral stress-prevention strategy for organizations from several sectors that includes a digital platform and collaborative learning network. The digital platform contains a stepwise protocol to implement work-related stress-management interventions. It includes stress screeners, interventions and intervention providers to facilitate access to and the selection of matching work-related stress-management interventions...
July 17, 2017: BMC Public Health
https://www.readbyqxmd.com/read/28715343/deep-belief-networks-for-electroencephalography-a-review-of-recent-contributions-and-future-outlooks
#20
Faezeh Movahedi, James L Coyle, Ervin Sejdic
Deep learning, a relatively new branch of machine learning, has been investigated for use in a variety of biomedical applications. Deep learning algorithms have been used to analyze different physiological signals and gain a better understanding of human physiology for automated diagnosis of abnormal conditions. In this manuscript, we provide an overview of deep learning approaches with a focus on deep belief networks in electroencephalography applications. We investigate the state of- the-art algorithms for deep belief networks and then cover the application of these algorithms and their performances in electroencephalographic applications...
July 14, 2017: IEEE Journal of Biomedical and Health Informatics
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