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https://www.readbyqxmd.com/read/28641262/scalable-multi-view-semi-supervised-classification-via-adaptive-regression
#1
Hong Tao, Chenping Hou, Feiping Nie, Jubo Zhu, Dongyun Yi
With the advent of multi-view data, multi-view learning has become an important research direction in machine learning and image processing. Considering the difficulty of obtaining labeled data in many machine learning applications, we focus on the multi-view semi-supervised classification problem. In this paper, we propose an algorithm named Multi-View Semi-Supervised Classification via Adaptive Regression (MVAR) to address this problem. Specifically, regression based loss functions with ℓ2,1 matrix norm are adopted for each view and the final objective function is formulated as the linear weighted combination of all the loss functions...
June 19, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28641250/deep-convolutional-neural-network-for-inverse-problems-in-imaging
#2
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
#3
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
#4
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/28641208/an-associative-account-of-the-development-of-word-learning
#5
Vladimir M Sloutsky, Hyungwook Yim, Xin Yao, Simon Dennis
Word learning is a notoriously difficult induction problem because meaning is underdetermined by positive examples. How do children solve this problem? Some have argued that word learning is achieved by means of inference: young word learners rely on a number of assumptions that reduce the overall hypothesis space by favoring some meanings over others. However, these approaches have difficulty explaining how words are learned from conversations or text, without pointing or explicit instruction. In this research, we propose an associative mechanism that can account for such learning...
June 19, 2017: Cognitive Psychology
https://www.readbyqxmd.com/read/28635623/compressed-sensing-reconstruction-based-on-block-sparse-bayesian-learning-in-bearing-condition-monitoring
#6
Jiedi Sun, Yang Yu, Jiangtao Wen
Remote monitoring of bearing conditions, using wireless sensor network (WSN), is a developing trend in the industrial field. In complicated industrial environments, WSN face three main constraints: low energy, less memory, and low operational capability. Conventional data-compression methods, which concentrate on data compression only, cannot overcome these limitations. Aiming at these problems, this paper proposed a compressed data acquisition and reconstruction scheme based on Compressed Sensing (CS) which is a novel signal-processing technique and applied it for bearing conditions monitoring via WSN...
June 21, 2017: Sensors
https://www.readbyqxmd.com/read/28635122/sketching-the-invisible-to-predict-the-visible-from-drawing-to-modeling-in-chemistry
#7
Melanie M Cooper, Mike Stieff, Dane DeSutter
Sketching as a scientific practice goes beyond the simple act of inscribing diagrams onto paper. Scientists produce a wide range of representations through sketching, as it is tightly coupled to model-based reasoning. Chemists in particular make extensive use of sketches to reason about chemical phenomena and to communicate their ideas. However, the chemical sciences have a unique problem in that chemists deal with the unseen world of the atomic-molecular level. Using sketches, chemists strive to develop causal mechanisms that emerge from the structure and behavior of molecular-level entities, to explain observations of the macroscopic visible world...
June 21, 2017: Topics in Cognitive Science
https://www.readbyqxmd.com/read/28634789/detection-and-grading-of-prostate-cancer-using-temporal-enhanced-ultrasound-combining-deep-neural-networks-and-tissue-mimicking-simulations
#8
Shekoofeh Azizi, Sharareh Bayat, Pingkun Yan, Amir Tahmasebi, Guy Nir, Jin Tae Kwak, Sheng Xu, Storey Wilson, Kenneth A Iczkowski, M Scott Lucia, Larry Goldenberg, Septimiu E Salcudean, Peter A Pinto, Bradford Wood, Purang Abolmaesumi, Parvin Mousavi
PURPOSE  : Temporal Enhanced Ultrasound (TeUS) has been proposed as a new paradigm for tissue characterization based on a sequence of ultrasound radio frequency (RF) data. We previously used TeUS to successfully address the problem of prostate cancer detection in the fusion biopsies. METHODS  : In this paper, we use TeUS to address the problem of grading prostate cancer in a clinical study of 197 biopsy cores from 132 patients. Our method involves capturing high-level latent features of TeUS with a deep learning approach followed by distribution learning to cluster aggressive cancer in a biopsy core...
June 20, 2017: International Journal of Computer Assisted Radiology and Surgery
https://www.readbyqxmd.com/read/28634344/omniga-optimized-omnivariate-decision-trees-for-generalizable-classification-models
#9
Arturo Magana-Mora, Vladimir B Bajic
Classification problems from different domains vary in complexity, size, and imbalance of the number of samples from different classes. Although several classification models have been proposed, selecting the right model and parameters for a given classification task to achieve good performance is not trivial. Therefore, there is a constant interest in developing novel robust and efficient models suitable for a great variety of data. Here, we propose OmniGA, a framework for the optimization of omnivariate decision trees based on a parallel genetic algorithm, coupled with deep learning structure and ensemble learning methods...
June 20, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28633551/a-pareto-based-ensemble-with-feature-and-instance-selection-for-learning-from-multi-class-imbalanced-datasets
#10
Alberto Fernández, Cristobal José Carmona, María José Del Jesus, Francisco Herrera
Imbalanced classification is related to those problems that have an uneven distribution among classes. In addition to the former, when instances are located into the overlapped areas, the correct modeling of the problem becomes harder. Current solutions for both issues are often focused on the binary case study, as multi-class datasets require an additional effort to be addressed. In this research, we overcome these problems by carrying out a combination between feature and instance selections. Feature selection will allow simplifying the overlapping areas easing the generation of rules to distinguish among the classes...
April 11, 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/28632009/self-observation-and-peer-feedback-as-a-faculty-development-approach-for-problem-based-learning-tutors-a-program-evaluation
#11
Irène Garcia, Richard W James, Paul Bischof, Anne Baroffio
PROBLEM: Good teaching requires spontaneous, immediate, and appropriate action in response to various situations. It is even more crucial in problem-based learning (PBL) tutorials, as the tutors, while directing students toward the identification and attainment of learning objectives, must stimulate them to contribute to the process and provide them with constructive feedback. PBL tutors in medicine lack opportunities to receive feedback from their peers on their teaching strategies. Moreover, as tutorials provide little or no time to stop and think, more could be learned by reflecting on the experience than from the experience itself...
March 2, 2017: Teaching and Learning in Medicine
https://www.readbyqxmd.com/read/28631234/quality-matters-implementation-moderates-student-outcomes-in-the-paths-curriculum
#12
Neil Humphrey, Alexandra Barlow, Ann Lendrum
Analyses of the relationship between levels of implementation and outcomes of school-based social and emotional learning (SEL) interventions are relatively infrequent and are typically narrowly focused. Thus, our objective was to assess the relationship between variability in a range of implementation dimensions and intervention outcomes in the Promoting Alternative Thinking Strategies (PATHS) curriculum. Implementation of PATHS was examined in 69 classrooms across 23 schools in the first year of a major randomized controlled trial...
June 19, 2017: Prevention Science: the Official Journal of the Society for Prevention Research
https://www.readbyqxmd.com/read/28628367/learning-to-spell-phonology-and-beyond
#13
Rebecca Treiman
An understanding of the nature of writing systems and of the typical course of spelling development is an essential foundation for understanding the problems of children who have serious difficulties in learning to spell. The present article seeks to provide that foundation. It argues that the dual-route models of spelling that underlie much existing research and practice are based on overly simple assumptions about how writing systems work and about how spelling skills develop. Many writing systems include not only context-free links from phonemes to letters but also context-sensitive phonological patterns, morphological influences, and graphotactic patterns...
June 19, 2017: Cognitive Neuropsychology
https://www.readbyqxmd.com/read/28627920/reliability-and-validity-of-a-spanish-language-assessment-of-children-s-social-emotional-learning-skills
#14
Jaclyn M Russo, Clark McKown, Nicole M Russo-Ponsaran, Adelaide Allen
Few Spanish language tools are available for assessing important social-emotional learning (SEL) skills. The present study presents evidence of the psychometric properties of a Spanish-language version of SELweb (SELweb-S), a web-based system for assessing children's ability to recognize others' emotions and perspectives, solve social problems, and engage in self-control. With a sample of 524 students in Grades K to 3, we examined the reliability and validity of SELweb-S. This study provided evidence that (a) individual assessment modules exhibited moderate to high internal consistency and moderate 6-month temporal stability, (b) composite assessment scores exhibited high reliability, (c) assessment module scores fit a theoretically coherent factor structure, and (d) performance on SELweb-S assessment modules was positively related to teacher-reported SEL skills...
June 19, 2017: Psychological Assessment
https://www.readbyqxmd.com/read/28626952/multivariate-binary-classification-of-imbalanced-datasets-a-case-study-based-on-high-dimensional-multiplex-autoimmune-assay-data
#15
Laura Schlieker, Anna Telaar, Angelika Lueking, Peter Schulz-Knappe, Carmen Theek, Katja Ickstadt
The classification of a population by a specific trait is a major task in medicine, for example when in a diagnostic setting groups of patients with specific diseases are identified, but also when in predictive medicine a group of patients is classified into specific disease severity classes that might profit from different treatments. When the sizes of those subgroups become small, for example in rare diseases, imbalances between the classes are more the rule than the exception and make statistical classification problematic when the error rate of the minority class is high...
June 19, 2017: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/28626291/machine-learning-in-a-graph-framework-for-subcortical-segmentation
#16
Zhihui Guo, Satyananda Kashyap, Milan Sonka, Ipek Oguz
Automated and reliable segmentation of subcortical structures from human brain magnetic resonance images is of great importance for volumetric and shape analyses in quantitative neuroimaging studies. However, poor boundary contrast and variable shape of these structures make the automated segmentation a tough task. We propose a 3D graph-based machine learning method, called LOGISMOS-RF, to segment the caudate and the putamen from brain MRI scans in a robust and accurate way. An atlas-based tissue classification and bias-field correction method is applied to the images to generate an initial segmentation for each structure...
February 11, 2017: Proceedings of SPIE
https://www.readbyqxmd.com/read/28623886/epsilon-cp-using-deep-learning-to-combine-information-from-multiple-sources-for-protein-contact-prediction
#17
Kolja Stahl, Michael Schneider, Oliver Brock
BACKGROUND: Accurately predicted contacts allow to compute the 3D structure of a protein. Since the solution space of native residue-residue contact pairs is very large, it is necessary to leverage information to identify relevant regions of the solution space, i.e. correct contacts. Every additional source of information can contribute to narrowing down candidate regions. Therefore, recent methods combined evolutionary and sequence-based information as well as evolutionary and physicochemical information...
June 17, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28622266/an-integrative-literature-review-of-evidence-based-teaching-strategies-for-nurse-educators
#18
Cecile Breytenbach, Wilma Ten Ham-Baloyi, Portia J Jordan
AIM: The aim of the study was to explore and describe the best available literature on evidence-based teaching strategies that can be used by nurse educators. BACKGROUND: Evidence-based teaching strategies in nursing education are fundamental to promote an in-depth understanding of information. Although some teaching strategies for nurse educators were identified, no integrative literature review was found summarizing the best teaching strategies for nurse educators...
July 2017: Nursing Education Perspectives
https://www.readbyqxmd.com/read/28620339/the-stress-acceleration-hypothesis-of-nightmares
#19
Tore Nielsen
Adverse childhood experiences can deleteriously affect future physical and mental health, increasing risk for many illnesses, including psychiatric problems, sleep disorders, and, according to the present hypothesis, idiopathic nightmares. Much like post-traumatic nightmares, which are triggered by trauma and lead to recurrent emotional dreaming about the trauma, idiopathic nightmares are hypothesized to originate in early adverse experiences that lead in later life to the expression of early memories and emotions in dream content...
2017: Frontiers in Neurology
https://www.readbyqxmd.com/read/28615761/prediction-of-risk-for-boys-involvement-in-drug-use-based-on-levels-of-self-evaluations-in-russia
#20
Svetlana N Islamova, Rafael Sh Islamov
BACKGROUND: Adolescents often experiment with drug use, which can impact on their health and well-being and increase the probability of problem drug use. Yet, not enough is known about which psychological indicators is related with the initiation of drug use among young adults and have predictive power. MATERIALS AND METHODS: Participants in this study were 311 boys (school and college students) aged 15-17. Data were collected in the towns of Moscow region. A modified version of Dembo-Rubinstein test was used to assess the self-evaluation (SE)...
May 2017: Indian Journal of Psychological Medicine
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