Read by QxMD icon Read

representational learning

Gerben E Breimer, Faizal A Haji, Vivek Bodani, Melissa S Cunningham, Adriana-Lucia Lopez-Rios, Allan Okrainec, James M Drake
BACKGROUND: The relative educational benefits of virtual reality (VR) and physical simulation models for endoscopic third ventriculostomy (ETV) have not been evaluated "head to head." OBJECTIVE: To compare and identify the relative utility of a physical and VR ETV simulation model for use in neurosurgical training. METHODS: Twenty-three neurosurgical residents and 3 fellows performed an ETV on both a physical and VR simulation model. Trainees rated the models using 5-point Likert scales evaluating the domains of anatomy, instrument handling, procedural content, and the overall fidelity of the simulation...
February 1, 2017: Operative Neurosurgery (Hagerstown, Md.)
Panagiotis Sapountzis, Georgia G Gregoriou
Understanding brain function and the computations that individual neurons and neuronal ensembles carry out during cognitive functions is one of the biggest challenges in neuroscientific research. To this end, invasive electrophysiological studies have provided important insights by recording the activity of single neurons in behaving animals. To average out noise, responses are typically averaged across repetitions and across neurons that are usually recorded on different days. However, the brain makes decisions on short time scales based on limited exposure to sensory stimulation by interpreting responses of populations of neurons on a moment to moment basis...
January 1, 2018: Frontiers in Bioscience (Landmark Edition)
Clio Janssens, Esther De Loof, C Nico Boehler, Gilles Pourtois, Tom Verguts
Recent associative models of cognitive control hypothesize that cognitive control can be learned (optimized) for task-specific settings via associations between perceptual, motor, and control representations, and, once learned, control can be implemented rapidly. Midfrontal brain areas signal the need for control, and control is subsequently implemented by biasing sensory representations, boosting or suppressing activity in brain areas processing task-relevant or task-irrelevant information. To assess the timescale of this process, we employed EEG...
September 20, 2017: Psychophysiology
Felix A Faber, Luke Hutchison, Bing Huang, Justin Gilmer, Samuel S Schoenholz, George E Dahl, Oriol Vinyals, Steven Kearnes, Patrick F Riley, O Anatole von Lilienfeld
We investigate the impact of choosing regres- sors and molecular representations for the construction of fast machine learning (ML) models of thirteen electronic ground-state properties of organic molecules. The performance of each regressor/representation/property combination is assessed using learning curves which report out- of-sample errors as a function of training set size with up to ∼118k distinct molecules. Molecular structures and properties at hybrid density functional theory (DFT) level of theory come from the QM9 database [Ramakrishnan et al, Scientific Data 1 140022 (2014)] and include enthalpies and free energies of atomization , HOMO/LUMO energies and gap, dipole moment, polarizability, zero point vibrational energy, heat capacity and the highest fundamental vibrational frequency...
September 19, 2017: Journal of Chemical Theory and Computation
Soojin Cho-Reyes, Jennifer E Mack, Cynthia K Thompson
The present study addressed open questions about the nature of sentence production deficits in agrammatic aphasia. In two structural priming experiments, 13 aphasic and 13 age-matched control speakers repeated visually- and auditorily-presented prime sentences, and then used visually-presented word arrays to produce dative sentences. Experiment 1 examined whether agrammatic speakers form structural and thematic representations during sentence production, whereas Experiment 2 tested the lasting effects of structural priming in lags of two and four sentences...
December 2016: Journal of Memory and Language
Yunan Luo, Xinbin Zhao, Jingtian Zhou, Jinglin Yang, Yanqing Zhang, Wenhua Kuang, Jian Peng, Ligong Chen, Jianyang Zeng
The emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. In this work, we develop a computational pipeline, called DTINet, to predict novel drug-target interactions from a constructed heterogeneous network, which integrates diverse drug-related information. DTINet focuses on learning a low-dimensional vector representation of features, which accurately explains the topological properties of individual nodes in the heterogeneous network, and then makes prediction based on these representations via a vector space projection scheme...
September 18, 2017: Nature Communications
Therma W C Cheung, Lindy Clemson, Kate O' Loughlin, Russell Shuttleworth
BACKGROUND: Ergonomic education in housework that aims to facilitate behavior change is important for women with upper limb repetitive strain injury. Therapists usually conduct such programs based on implicit reasoning. Making this reasoning explicit is important in contributing to the profession's knowledge. AIM: To construct a conceptual representation of how occupational therapists make clinical decisions for such program. METHOD: Based on a constructivist-grounded theory methodology, data were collected through in-depth interviewing with 14 occupational therapists from a major hospital in Singapore...
September 18, 2017: Disability and Rehabilitation
Xin Chen, Jian Weng, Wei Lu, Jiaming Xu, Jiasi Weng
Learning deep representations have been applied in action recognition widely. However, there have been a few investigations on how to utilize the structural manifold information among different action videos to enhance the recognition accuracy and efficiency. In this paper, we propose to incorporate the manifold of training samples into deep learning, which is defined as deep manifold learning (DML). The proposed DML framework can be adapted to most existing deep networks to learn more discriminative features for action recognition...
September 15, 2017: IEEE Transactions on Neural Networks and Learning Systems
Zhao Zhang, Weiming Jiang, Jie Qin, Li Zhang, Fanzhang Li, Min Zhang, Shuicheng Yan
In this paper, we propose an analysis mechanism-based structured analysis discriminative dictionary learning (ADDL) framework. The ADDL seamlessly integrates ADDL, analysis representation, and analysis classifier training into a unified model. The applied analysis mechanism can make sure that the learned dictionaries, representations, and linear classifiers over different classes are independent and discriminating as much as possible. The dictionary is obtained by minimizing a reconstruction error and an analytical incoherence promoting term that encourages the subdictionaries associated with different classes to be independent...
September 14, 2017: IEEE Transactions on Neural Networks and Learning Systems
Nora Ouzir, Adrian Basarab, Herve Liebgott, Brahim Harbaoui, Jean-Yves Tourneret
This paper introduces a new method for cardiac motion estimation in 2D ultrasound images. The motion estimation problem is formulated as an energy minimization, whose data fidelity term is built using the assumption that the images are corrupted by multiplicative Rayleigh noise. In addition to a classical spatial smoothness constraint, the proposed method exploits the sparse properties of the cardiac motion to regularize the solution via an appropriate dictionary learning step. The proposed method is evaluated on one dataset with available ground-truth, including four sequences of highly realistic simulations...
September 18, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Yusuf Aytar, Lluis Castrejon, Carl Vondrick, Hamed Pirsiavash, Antonio Torralba
People can recognize scenes across many different modalities beyond natural images. In this paper, we investigate how to learn cross-modal scene representations that transfer across modalities. To study this problem, we introduce a new cross-modal scene dataset. While convolutional neural networks can categorize scenes well, they also learn an intermediate representation not aligned across modalities, which is undesirable for cross-modal transfer applications. We present methods to regularize cross-modal convolutional neural networks so that they have a shared representation that is agnostic of the modality...
September 18, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Antonio Agudo, Francesc Moreno-Noguer
This paper addresses the problem of simultaneously recovering 3D shape, pose and the elastic model of a deformable object from only 2D point tracks in a monocular video. This is a severely under-constrained problem that has been typically addressed by enforcing the shape or the point trajectories to lie on low-rank dimensional spaces. We show that formulating the problem in terms of a low-rank force space that induces the deformation and introducing the elastic model as an additional unknown, allows for a better physical interpretation of the resulting priors and a more accurate representation of the actual object's behavior...
September 15, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Sam McKenzie
Hippocampal neurons become tuned to stimuli that predict behaviorally salient outcomes. This plasticity suggests that memory formation depends upon shifts in how different anatomical inputs can drive hippocampal activity. Here, I present evidence that inhibitory neurons can provide such a mechanism for learning-related changes in the tuning of pyramidal cells. Inhibitory currents arriving on the dendrites of pyramidal cells determine whether an excitatory input can drive action potential output. Specificity and plasticity of this dendritic modulation allows for precise, modifiable changes in how afferent inputs are integrated, a process that defines a neuron's receptive field...
September 16, 2017: Hippocampus
Tao Zhou, Fanghui Liu, Harish Bhaskar, Jie Yang
In this paper, we propose a novel and robust tracking framework based on online discriminative and low-rank dictionary learning. The primary aim of this paper is to obtain compact and low-rank dictionaries that can provide good discriminative representations of both target and background. We accomplish this by exploiting the recovery ability of low-rank matrices. That is if we assume that the data from the same class are linearly correlated, then the corresponding basis vectors learned from the training set of each class shall render the dictionary to become approximately low-rank...
September 12, 2017: IEEE Transactions on Cybernetics
Fritjof Helmchen, Ariel Gilad, Jerry L Chen
A fundamental task frequently encountered by brains is to rapidly and reliably discriminate between sensory stimuli of the same modality, be it distinct auditory sounds, odors, visual patterns, or tactile textures. A key mammalian brain structure involved in discrimination behavior is the neocortex. Sensory processing not only involves the respective primary sensory area, which is crucial for perceptual detection, but additionally relies on cortico-cortical communication among several regions including higher-order sensory areas as well as frontal cortical areas...
September 14, 2017: Neuroscience
Jurek Müller, Martin Nawrot, Randolf Menzel, Tim Landgraf
How complex is the memory structure that honeybees use to navigate? Recently, an insect-inspired parsimonious spiking neural network model was proposed that enabled simulated ground-moving agents to follow learned routes. We adapted this model to flying insects and evaluate the route following performance in three different worlds with gradually decreasing object density. In addition, we propose an extension to the model to enable the model to associate sensory input with a behavioral context, such as foraging or homing...
September 15, 2017: Biological Cybernetics
Hua Min, Hedyeh Mobahi, Katherine Irvin, Sanja Avramovic, Janusz Wojtusiak
BACKGROUND: Bio-ontologies are becoming increasingly important in knowledge representation and in the machine learning (ML) fields. This paper presents a ML approach that incorporates bio-ontologies and its application to the SEER-MHOS dataset to discover patterns of patient characteristics that impact the ability to perform activities of daily living (ADLs). Bio-ontologies are used to provide computable knowledge for ML methods to "understand" biomedical data. RESULTS: This retrospective study included 723 cancer patients from the SEER-MHOS dataset...
September 16, 2017: Journal of Biomedical Semantics
Fan Meng, Xiaomei Yang, Chenghu Zhou, Zhi Li
Cloud cover is inevitable in optical remote sensing (RS) imagery on account of the influence of observation conditions, which limits the availability of RS data. Therefore, it is of great significance to be able to reconstruct the cloud-contaminated ground information. This paper presents a sparse dictionary learning-based image inpainting method for adaptively recovering the missing information corrupted by thick clouds patch-by-patch. A feature dictionary was learned from exemplars in the cloud-free regions, which was later utilized to infer the missing patches via sparse representation...
September 15, 2017: Sensors
Lei Chen, Yu-Hang Zhang, Guohua Huang, Xiaoyong Pan, ShaoPeng Wang, Tao Huang, Yu-Dong Cai
As non-coding RNAs, circular RNAs (cirRNAs) and long non-coding RNAs (lncRNAs) have attracted an increasing amount of attention. They have been confirmed to participate in many biological processes, including playing roles in transcriptional regulation, regulating protein-coding genes, and binding to RNA-associated proteins. Until now, the differences between these two types of non-coding RNAs have not been fully uncovered. It is still quite difficult to detect cirRNAs from other lncRNAs using simple techniques...
September 14, 2017: Molecular Genetics and Genomics: MGG
Robert Lowe, Alexander Almér, Erik Billing, Yulia Sandamirskaya, Christian Balkenius
The partial reinforcement extinction effect (PREE) is an experimentally established phenomenon: behavioural response to a given stimulus is more persistent when previously inconsistently rewarded than when consistently rewarded. This phenomenon is, however, controversial in animal/human learning theory. Contradictory findings exist regarding when the PREE occurs. One body of research has found a within-subjects PREE, while another has found a within-subjects reversed PREE (RPREE). These opposing findings constitute what is considered the most important problem of PREE for theoreticians to explain...
September 14, 2017: Biological Cybernetics
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"