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Neural networks computing

Keaton Larson Lesnik, Hong Liu
The complex interactions that occur in mixed-species bioelectrochemical reactors, like microbial fuel cells (MFCs), make accurate predictions of performance outcomes under untested conditions difficult. While direct correlations between any individual waste stream characteristic or microbial community structure and reactor performance have not been able to be directly established, the increase in sequencing data and readily available computational power enables the development of alternate approaches. In the current study, 33 MFCs were evaluated under a range of conditions including 8 separate substrates and 3 different wastewaters...
August 16, 2017: Environmental Science & Technology
Qazi Zia Ullah, Shahzad Hassan, Gul Muhammad Khan
Infrastructure as a Service (IaaS) cloud provides resources as a service from a pool of compute, network, and storage resources. Cloud providers can manage their resource usage by knowing future usage demand from the current and past usage patterns of resources. Resource usage prediction is of great importance for dynamic scaling of cloud resources to achieve efficiency in terms of cost and energy consumption while keeping quality of service. The purpose of this paper is to present a real-time resource usage prediction system...
2017: Computational Intelligence and Neuroscience
Kai Zhao, Yongduan Song, Tiedong Ma, Liu He
This paper studies the zero-error tracking control problem of Euler-Lagrange systems subject to full-state constraints and nonparametric uncertainties. By blending an error transformation with barrier Lyapunov function, a neural adaptive tracking control scheme is developed, resulting in a solution with several salient features: 1) the control action is continuous and C¹ smooth; 2) the full-state tracking error converges to a prescribed compact set around origin within a given finite time at a controllable rate of convergence that can be uniformly prespecified; 3) with Nussbaum gain in the loop, the tracking error further shrinks to zero as t→∞; and 4) the neural network (NN) unit can be safely included in the loop during the entire system operational envelope without the danger of violating the compact set precondition imposed on the NN training inputs...
August 11, 2017: IEEE Transactions on Neural Networks and Learning Systems
Junming Zhang, Yan Wu
Traditionally, automatic sleep stage classification is quite a challenging task because of the difficulty in translating open-textured standards to mathematical models and the limitations of handcrafted features. In this paper, a new system for automatic sleep stage classification is presented. Compared with existing sleep stage methods, our method can capture the sleep information hidden inside electroencephalography (EEG) signals and automatically extract features from raw data. To translate open sleep stage standards into machine rules recognized by computers, a new model named fast discriminative complex-valued convolutional neural network (FDCCNN) is proposed to extract features from raw EEG data and classify sleep stages...
August 14, 2017: IEEE Transactions on Biomedical Circuits and Systems
Shuo Chen, Jian Yang, Lei Luo, Yang Wei, Kaihua Zhang, Ying Tai
This paper develops a novel method to address the structural noise in samples for image classification. Recently, regression related classification methods have shown promising results when facing the pixel-wise noise. However, they become weak in coping with the structural noise due to ignoring of relationships between pixels of noise image. Meanwhile, most of them need to implement the iterative process for computing representation coefficients, which leads to the high time consumption. To overcome these problems, we exploit a latent pattern model called Low-Rank Latent Pattern Approximation (LLPA) to reconstruct the test image having structural noise...
August 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Jorie Koster-Hale, Hilary Richardson, Natalia Velez, Mika Asaba, Liane Young, Rebecca Saxe
The human capacity to reason about others' minds includes making causal inferences about intentions, beliefs, values, and goals. Previous fMRI research has suggested that a network of brain regions, including bilateral temporo-parietal junction (TPJ), superior temporal sulcus (STS), and medial prefrontal-cortex (MPFC), are reliably recruited for mental state reasoning. Here, in two fMRI experiments, we investigate the representational content of these regions. Building on existing computational and neural evidence, we hypothesized that social brain regions contain at least two functionally and spatially distinct components: one that represents information related to others' motivations and values, and another that represents information about others' beliefs and knowledge...
August 11, 2017: NeuroImage
Carlos Lopez-Franco, Javier Gomez-Avila, Alma Y Alanis, Nancy Arana-Daniel, Carlos Villaseñor
In recent years, unmanned aerial vehicles (UAVs) have gained significant attention. However, we face two major drawbacks when working with UAVs: high nonlinearities and unknown position in 3D space since it is not provided with on-board sensors that can measure its position with respect to a global coordinate system. In this paper, we present a real-time implementation of a servo control, integrating vision sensors, with a neural proportional integral derivative (PID), in order to develop an hexarotor image based visual servo control (IBVS) that knows the position of the robot by using a velocity vector as a reference to control the hexarotor position...
August 12, 2017: Sensors
Ekadashi Pradhan, Alex Brown
The minimum energy structures, i.e., trans-HONO, cis-HONO, HNO2, and OH + NO, as well as the corresponding transition states, i.e., TStrans↔cis, TS1,2H-shift, and TS1,3H-shift, on the ground state potential energy surface (PES) of HONO have been characterized at the CCSD(T)-F12/cc-pVTZ-F12 level of theory. Using the same level of theory, a six-dimensional (6D) PES, encompassing the trans- and cis-isomers as well as the associated transition state, is fit in a sum-of-products form using neural network exponential fitting functions...
August 14, 2017: Physical Chemistry Chemical Physics: PCCP
Vasyl Kovalishyn, Natalia Abramenko, Iryna Kopernyk, Larysa Charochkina, Larysa Metelytsia, Igor V Tetko, Willie Peijnenburg, Leonid Kustov
Inorganic nanomaterials have become one of the new areas of modern knowledge and technology and have already found an increasing number of applications. However, some nanoparticles show toxicity to living organisms, and can potentially have a negative influence on environmental ecosystems. While toxicity can be determined experimentally, such studies are time consuming and costly. Computational toxicology can provide an alternative approach and there is a need to develop methods to reliably assess Quantitative Structure-Property Relationships for nanomaterials (nano-QSPRs)...
August 9, 2017: Food and Chemical Toxicology
U K Lopes, J F Valiati
It is estimated that in 2015, approximately 1.8 million people infected by tuberculosis died, most of them in developing countries. Many of those deaths could have been prevented if the disease had been detected at an earlier stage, but the most advanced diagnosis methods are still cost prohibitive for mass adoption. One of the most popular tuberculosis diagnosis methods is the analysis of frontal thoracic radiographs; however, the impact of this method is diminished by the need for individual analysis of each radiography by properly trained radiologists...
August 4, 2017: Computers in Biology and Medicine
Alireza Sharifi, Yagob Dinpashoh, Rasoul Mirabbasi
Runoff prediction, as a nonlinear and complex process, is essential for designing canals, water management and planning, flood control and predicting soil erosion. There are a number of techniques for runoff prediction based on the hydro-meteorological and geomorphological variables. In recent years, several soft computing techniques have been developed to predict runoff. There are some challenging issues in runoff modeling including the selection of appropriate inputs and determination of the optimum length of training and testing data sets...
August 2017: Water Science and Technology: a Journal of the International Association on Water Pollution Research
Yuanpu Xie, Fuyong Xing, Xiaoshuang Shi, Xiangfei Kong, Hai Su, Lin Yang
Efficient and robust cell detection serves as a critical prerequisite for many subsequent biomedical image analysis methods and computer-aided diagnosis (CAD). It remains a challenging task due to touching cells, inhomogeneous background noise, and large variations in cell sizes and shapes. In addition, the ever-increasing amount of available datasets and the high resolution of whole-slice scanned images pose a further demand for efficient processing algorithms. In this paper, we present a novel structured regression model based on a proposed fully residual convolutional neural network for efficient cell detection...
July 26, 2017: Medical Image Analysis
Muhammad Aizaz Akmal, Nouman Rasool, Yaser Daanial Khan
Glycosylation is one of the most complex post translation modification in eukaryotic cells. Almost 50% of the human proteome is glycosylated as glycosylation plays a vital role in various biological functions such as antigen's recognition, cell-cell communication, expression of genes and protein folding. It is a significant challenge to identify glycosylation sites in protein sequences as experimental methods are time taking and expensive. A reliable computational method is desirable for the identification of glycosylation sites...
2017: PloS One
Moi Hoon Yap, Gerard Pons, Joan Marti, Sergi Ganau, Melcior Sentis, Reyer Zwiggelaar, Adrian K Davison, Robert Marti
Breast lesion detection using ultrasound imaging is considered an important step of Computer-Aided Diagnosis systems. Over the past decade, researchers have demonstrated the possibilities to automate the initial lesion detection. However, the lack of a common dataset impedes research when comparing the performance of such algorithms. This paper proposes the use of deep learning approaches for breast ultrasound lesion detection and investigates three different methods: a Patch-based LeNet, a U-Net, and a transfer learning approach with a pretrained FCN-AlexNet...
August 7, 2017: IEEE Journal of Biomedical and Health Informatics
Ziwei Liu, Xiaoxiao Li, Ping Luo, Chen Change Loy, Xiaoou Tang
Semantic segmentation tasks can be well modeled by Markov Random Field (MRF). This paper addresses semantic segmentation by incorporating high-order relations and mixture of label contexts into MRF. Unlike previous works that optimized MRFs using iterative algorithm, we solve MRF by proposing a Convolutional Neural Network (CNN), namely Deep Parsing Network (DPN), which enables deterministic end-to-end computation in a single forward pass. Specifically, DPN extends a contemporary CNN to model unary terms and additional layers are devised to approximate the mean field (MF) algorithm for pairwise terms...
August 9, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Suhas Kumar, John Paul Strachan, R Stanley Williams
At present, machine learning systems use simplified neuron models that lack the rich nonlinear phenomena observed in biological systems, which display spatio-temporal cooperative dynamics. There is evidence that neurons operate in a regime called the edge of chaos that may be central to complexity, learning efficiency, adaptability and analogue (non-Boolean) computation in brains. Neural networks have exhibited enhanced computational complexity when operated at the edge of chaos, and networks of chaotic elements have been proposed for solving combinatorial or global optimization problems...
August 9, 2017: Nature
Ida E J Aasebø, Mikkel E Lepperød, Maria Stavrinou, Sandra Nøkkevangen, Gaute Einevoll, Torkel Hafting, Marianne Fyhn
The activity pattern and temporal dynamics within and between neuron ensembles are essential features of information processing and believed to be profoundly affected by anesthesia. Much of our general understanding of sensory information processing, including computational models aimed at mathematically simulating sensory information processing, rely on parameters derived from recordings conducted on animals under anesthesia. Due to the high variety of neuronal subtypes in the brain, population-based estimates of the impact of anesthesia may conceal unit- or ensemble-specific effects of the transition between states...
July 2017: ENeuro
Ritankar Das, David J Wales
The theory and computational tools developed to interpret and explore energy landscapes in molecular science are applied to the landscapes defined by local minima for neural networks. These machine learning landscapes correspond to fits of training data, where the inputs are vital signs and laboratory measurements for a database of patients, and the objective is to predict a clinical outcome. In this contribution, we test the predictions obtained by fitting to single measurements, and then to combinations of between 2 and 10 different patient medical data items...
July 2017: Royal Society Open Science
Logan T Trujillo, Candice T Stanfield, Ruben D Vela
Converging evidence suggests that human cognition and behavior emerge from functional brain networks interacting on local and global scales. We investigated two information-theoretic measures of functional brain segregation and integration-interaction complexity C I (X), and integration I(X)-as applied to electroencephalographic (EEG) signals and how these measures are affected by choice of EEG reference. CI(X) is a statistical measure of the system entropy accounted for by interactions among its elements, whereas I(X) indexes the overall deviation from statistical independence of the individual elements of a system...
2017: Frontiers in Neuroscience
Michael W Cole, Todd S Braver, Nachshon Meiran
Rapid instructed task learning (RITL) is one of the most remarkable human abilities, when considered from both computational and evolutionary perspectives. A key feature of RITL is that it enables new goals to be immediately pursued (and shared) following formation of task representations. Although RITL is a form of cognitive control that engenders immense flexibility, it also seems to produce inflexible activation of action plans in inappropriate contexts. We argue that this "prepared reflex" effect arises because RITL is implemented in the brain via a "flexible hub" mechanism, in which top-down influences from the frontoparietal control network reroute pathways among procedure-implementing brain areas (e...
August 5, 2017: Neuroscience and Biobehavioral Reviews
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