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https://www.readbyqxmd.com/read/28212106/view-based-3-d-model-retrieval-a-benchmark
#1
An-An Liu, Wei-Zhi Nie, Yue Gao, Yu-Ting Su
View-based 3-D model retrieval is one of the most important techniques in numerous applications of computer vision. While many methods have been proposed in recent years, to the best of our knowledge, there is no benchmark to evaluate the state-of-the-art methods. To tackle this problem, we systematically investigate and evaluate the related methods by: 1) proposing a clique graph-based method and 2) reimplementing six representative methods. Moreover, we concurrently evaluate both hand-crafted visual features and deep features on four popular datasets (NTU60, NTU216, PSB, and ETH) and one challenging real-world multiview model dataset (MV-RED) prepared by our group with various evaluation criteria to understand how these algorithms perform...
February 15, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28209978/a-distributed-hierarchical-and-recurrent-framework-for-reward-based-choice
#2
REVIEW
Laurence T Hunt, Benjamin Y Hayden
Many accounts of reward-based choice argue for distinct component processes that are serial and functionally localized. In this Opinion article, we argue for an alternative viewpoint, in which choices emerge from repeated computations that are distributed across many brain regions. We emphasize how several features of neuroanatomy may support the implementation of choice, including mutual inhibition in recurrent neural networks and the hierarchical organization of timescales for information processing across the cortex...
February 17, 2017: Nature Reviews. Neuroscience
https://www.readbyqxmd.com/read/28208587/vehicle-detection-in-aerial-images-based-on-region-convolutional-neural-networks-and-hard-negative-example-mining
#3
Tianyu Tang, Shilin Zhou, Zhipeng Deng, Huanxin Zou, Lin Lei
Detecting vehicles in aerial imagery plays an important role in a wide range of applications. The current vehicle detection methods are mostly based on sliding-window search and handcrafted or shallow-learning-based features, having limited description capability and heavy computational costs. Recently, due to the powerful feature representations, region convolutional neural networks (CNN) based detection methods have achieved state-of-the-art performance in computer vision, especially Faster R-CNN. However, directly using it for vehicle detection in aerial images has many limitations: (1) region proposal network (RPN) in Faster R-CNN has poor performance for accurately locating small-sized vehicles, due to the relatively coarse feature maps; and (2) the classifier after RPN cannot distinguish vehicles and complex backgrounds well...
February 10, 2017: Sensors
https://www.readbyqxmd.com/read/28208448/conducting-insulating-transition-in-adiabatic-memristive-networks
#4
Forrest C Sheldon, Massimiliano Di Ventra
The development of neuromorphic systems based on memristive elements-resistors with memory-requires a fundamental understanding of their collective dynamics when organized in networks. Here, we study an experimentally inspired model of two-dimensional disordered memristive networks subject to a slowly ramped voltage and show that they undergo a discontinuous transition in the conductivity for sufficiently high values of memory, as quantified by the memristive ON-OFF ratio. We investigate the consequences of this transition for the memristive current-voltage characteristics both through simulation and theory, and demonstrate the role of current-voltage duality in relating forward and reverse switching processes...
January 2017: Physical Review. E
https://www.readbyqxmd.com/read/28207398/acoustic-classification-and-optimization-for-multi-modal-rendering-of-real-world-scenes
#5
Carl Schissler, Christian Loftin, Dinesh Manocha
We present a novel algorithm to generate virtual acoustic effects in captured 3D models of real-world scenes for multimodal augmented reality. We leverage recent advances in 3D scene reconstruction in order to automatically compute acoustic material properties. Our technique consists of a two-step procedure that first applies a convolutional neural network (CNN) to estimate the acoustic material properties, including frequency-dependent absorption coefficients, that are used for interactive sound propagation...
February 9, 2017: IEEE Transactions on Visualization and Computer Graphics
https://www.readbyqxmd.com/read/28205298/automatic-segmentation-of-the-right-ventricle-from-cardiac-mri-using-a-learning-based-approach
#6
Michael R Avendi, Arash Kheradvar, Hamid Jafarkhani
PURPOSE: This study aims to accurately segment the right ventricle (RV) from cardiac MRI using a fully automatic learning-based method. METHODS: The proposed method uses deep learning algorithms, i.e., convolutional neural networks and stacked autoencoders, for automatic detection and initial segmentation of the RV chamber. The initial segmentation is then combined with the deformable models to improve the accuracy and robustness of the process. We trained our algorithm using 16 cardiac MRI datasets of the MICCAI 2012 RV Segmentation Challenge database and validated our technique using the rest of the dataset (32 subjects)...
February 16, 2017: Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine
https://www.readbyqxmd.com/read/28201916/artificial-neural-network-for-the-configuration-problem-in-solids
#7
Hyunjun Ji, Yousung Jung
A machine learning approach based on the artificial neural network (ANN) is applied for the configuration problem in solids. The proposed method provides a direct mapping from configuration vectors to energies. The benchmark conducted for the M1 phase of Mo-V-Te-Nb oxide showed that only a fraction of configurations needs to be calculated, thus the computational burden significantly decreased, by a factor of 20-50, with R(2) = 0.96 and MAD = 0.12 eV. It is shown that ANN can also handle the effects of geometry relaxation when properly trained, resulting in R(2) = 0...
February 14, 2017: Journal of Chemical Physics
https://www.readbyqxmd.com/read/28195556/how-neuroscience-can-inform-the-study-of-individual-differences-in-cognitive-abilities
#8
Dennis J McFarland
Theories of human mental abilities should be consistent with what is known in neuroscience. Currently, tests of human mental abilities are modeled by cognitive constructs such as attention, working memory, and speed of information processing. These constructs are in turn related to a single general ability. However, brains are very complex systems and whether most of the variability between the operations of different brains can be ascribed to a single factor is questionable. Research in neuroscience suggests that psychological processes such as perception, attention, decision, and executive control are emergent properties of interacting distributed networks...
February 14, 2017: Reviews in the Neurosciences
https://www.readbyqxmd.com/read/28195521/a-computational-model-of-perceptual-and-mnemonic-deficits-in-medial-temporal-lobe-amnesia
#9
Patrick S Sadil, Rosemary A Cowell
Damage to the medial temporal lobe (MTL) has long been known to impair declarative memory, and recent evidence suggests that it also impairs visual perception. A theory termed the representational-hierarchical account explains such impairments by assuming that MTL stores conjunctive representations of items and events, and that individuals with MTL damage must rely upon representations of simple visual features in posterior visual cortex, which are inadequate to support memory and perception under certain circumstances...
February 14, 2017: Journal of Cognitive Neuroscience
https://www.readbyqxmd.com/read/28193692/goal-directed-and-habit-like-modulations-of-stimulus-processing-during-reinforcement-learning
#10
David Luque, Tom Beesley, Richard Morris, Bradley N Jack, Oren Griffiths, Thomas Whitford, Mike E Le Pelley
Recent research has shown that perceptual processing of stimuli previously associated with high-value rewards is automatically prioritized, even when rewards are no longer available. It has been hypothesized that such reward-related modulation of stimulus salience is conceptually similar to an 'attentional habit'. Recording event-related potentials in humans during a reinforcement learning task, we show strong evidence in favor of this hypothesis. Resistance to outcome devaluation (the defining feature of a habit) was shown by the stimulus-locked P1 component, reflecting activity in the extrastriate visual cortex...
February 13, 2017: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/28193201/an-artificial-neural-networks-approach-for-assessment-treatment-response-in-oncological-patients-using-pet-ct-images
#11
Mariana A Nogueira, Pedro H Abreu, Pedro Martins, Penousal Machado, Hugo Duarte, João Santos
BACKGROUND: Positron Emission Tomography - Computed Tomography (PET/CT) imaging is the basis for the evaluation of response-to-treatment of several oncological diseases. In practice, such evaluation is manually performed by specialists, which is rather complex and time-consuming. Evaluation measures have been proposed, but with questionable reliability. The usage of before and after-treatment image descriptors of the lesions for treatment response evaluation is still a territory to be explored...
February 13, 2017: BMC Medical Imaging
https://www.readbyqxmd.com/read/28192762/neural-mass-models-describing-possible-origin-of-the-excessive-beta-oscillations-correlated-with-parkinsonian-state
#12
Chen Liu, Yulin Zhu, Fei Liu, Jiang Wang, Huiyan Li, Bin Deng, Chris Fietkiewicz, Kenneth A Loparo
In Parkinson's disease, the enhanced beta rhythm is closely associated with akinesia/bradykinesia and rigidity. An increase in beta oscillations (12-35 Hz) within the basal ganglia (BG) nuclei does not proliferate throughout the cortico-basal ganglia loop in uniform fashion; rather it can be subdivided into two distinct frequency bands, i.e. the lower beta (12-20 Hz) and upper beta (21-35 Hz). A computational model of the excitatory and inhibitory neural network that focuses on the population properties is proposed to explore the mechanism underlying the pathological beta oscillations...
January 30, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28192624/mr-based-synthetic-ct-generation-using-a-deep-convolutional-neural-network-method
#13
Xiao Han
PURPOSE: Interests have been rapidly growing in the field of radiotherapy to replace CT with magnetic resonance imaging (MRI), due to superior soft tissue contrast offered by MRI and the desire to reduce unnecessary radiation dose. MR-only radiotherapy also simplifies clinical workflow and avoids uncertainties in aligning MR with CT. Methods, however, are needed to derive CT-equivalent representations, often known as synthetic CT (sCT), from patient MR images for the purpose of dose calculation and DRR-based patient positioning...
February 13, 2017: Medical Physics
https://www.readbyqxmd.com/read/28188881/abnormal-regional-activity-and-functional-connectivity-in-resting-state-brain-networks-associated-with-etiology-confirmed-unilateral-pulsatile-tinnitus-in-the-early-stage-of-disease
#14
Han Lv, Pengfei Zhao, Zhaohui Liu, Rui Li, Ling Zhang, Peng Wang, Fei Yan, Liheng Liu, Guopeng Wang, Rong Zeng, Ting Li, Cheng Dong, Shusheng Gong, Zhenchang Wang
Abnormal neural activities can be revealed by resting-state functional magnetic resonance imaging (rs-fMRI) using analyses of the regional activity and functional connectivity (FC) of the networks in the brain. This study was designed to demonstrate the functional network alterations in the patients with pulsatile tinnitus (PT). In this study, we recruited 45 patients with unilateral PT in the early stage of disease (less than 48 months of disease duration) and 45 normal controls. We used regional homogeneity (ReHo) and seed-based FC computational methods to reveal resting-state brain activity features associated with pulsatile tinnitus...
February 7, 2017: Hearing Research
https://www.readbyqxmd.com/read/28187892/interactive-radiographic-image-retrieval-system
#15
Malay Kumar Kundu, Manish Chowdhury, Sudeb Das
BACKGROUND AND OBJECTIVE: Content based medical image retrieval (CBMIR) systems enable fast diagnosis through quantitative assessment of the visual information and is an active research topic over the past few decades. Most of the state-of-the-art CBMIR systems suffer from various problems: computationally expensive due to the usage of high dimensional feature vectors and complex classifier/clustering schemes. Inability to properly handle the "semantic gap" and the high intra-class versus inter-class variability problem of the medical image database (like radiographic image database)...
February 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28186913/a-stochastic-spiking-neural-network-for-virtual-screening
#16
A Morro, V Canals, A Oliver, M L Alomar, F Galan-Prado, P J Ballester, J L Rossello
Virtual screening (VS) has become a key computational tool in early drug design and screening performance is of high relevance due to the large volume of data that must be processed to identify molecules with the sought activity-related pattern. At the same time, the hardware implementations of spiking neural networks (SNNs) arise as an emerging computing technique that can be applied to parallelize processes that normally present a high cost in terms of computing time and power. Consequently, SNN represents an attractive alternative to perform time-consuming processing tasks, such as VS...
February 7, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28186883/detection-and-localization-of-robotic-tools-in-robot-assisted-surgery-videos-using-deep-neural-networks-for-region-proposal-and-detection
#17
Duygu Sarikaya, Jason Corso, Khurshid Guru
Video understanding of robot-assisted surgery (RAS) videos is an active research area. Modeling the gestures and skill level of surgeons presents an interesting problem. The insights drawn may be applied in effective skill acquisition, objective skill assessment, real-time feedback, and human-robot collaborative surgeries. We propose a solution to the tool detection and localization open problem in RAS video understanding, using a strictly computer vision approach and the recent advances of deep learning. We propose an architecture using multimodal convolutional neural networks for fast detection and localization of tools in RAS videos...
February 8, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28186182/topological-determinants-of-self-sustained-activity-in-a-simple-model-of-excitable-dynamics-on-graphs
#18
Christoph Fretter, Annick Lesne, Claus C Hilgetag, Marc-Thorsten Hütt
Simple models of excitable dynamics on graphs are an efficient framework for studying the interplay between network topology and dynamics. This topic is of practical relevance to diverse fields, ranging from neuroscience to engineering. Here we analyze how a single excitation propagates through a random network as a function of the excitation threshold, that is, the relative amount of activity in the neighborhood required for the excitation of a node. We observe that two sharp transitions delineate a region of sustained activity...
February 10, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28183973/solving-the-quantum-many-body-problem-with-artificial-neural-networks
#19
Giuseppe Carleo, Matthias Troyer
The challenge posed by the many-body problem in quantum physics originates from the difficulty of describing the nontrivial correlations encoded in the exponential complexity of the many-body wave function. Here we demonstrate that systematic machine learning of the wave function can reduce this complexity to a tractable computational form for some notable cases of physical interest. We introduce a variational representation of quantum states based on artificial neural networks with a variable number of hidden neurons...
February 10, 2017: Science
https://www.readbyqxmd.com/read/28182559/quantum-inspired-multidirectional-associative-memory-with-a-self-convergent-iterative-learning
#20
Naoki Masuyama, Chu Kiong Loo, Manjeevan Seera, Naoyuki Kubota
Quantum-inspired computing is an emerging research area, which has significantly improved the capabilities of conventional algorithms. In general, quantum-inspired hopfield associative memory (QHAM) has demonstrated quantum information processing in neural structures. This has resulted in an exponential increase in storage capacity while explaining the extensive memory, and it has the potential to illustrate the dynamics of neurons in the human brain when viewed from quantum mechanics perspective although the application of QHAM is limited as an autoassociation...
February 6, 2017: IEEE Transactions on Neural Networks and Learning Systems
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