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33,704 | Weightless neural network parameters and architecture selection in a
quantum computer | cs.NE | Training artificial neural networks requires a tedious empirical evaluation
to determine a suitable neural network architecture. To avoid this empirical
process several techniques have been proposed to automatise the architecture
selection process. In this paper, we propose a method to perform parameter and
architectur... | computer science |
33,705 | Supervised Learning in Spiking Neural Networks for Precise Temporal
Encoding | cs.NE | Precise spike timing as a means to encode information in neural networks is
biologically supported, and is advantageous over frequency-based codes by
processing input features on a much shorter time-scale. For these reasons, much
recent attention has been focused on the development of supervised learning
rules for spik... | computer science |
33,706 | Artificial neural network approach for condition-based maintenance | cs.NE | In this research, computerized maintenance management will be investigated.
The rise of maintenance cost forced the research community to look for more
effective ways to schedule maintenance operations. Using computerized models to
come up with optimal maintenance policy has led to better equipment utilization
and lowe... | computer science |
33,707 | TrueHappiness: Neuromorphic Emotion Recognition on TrueNorth | cs.NE | We present an approach to constructing a neuromorphic device that responds to
language input by producing neuron spikes in proportion to the strength of the
appropriate positive or negative emotional response. Specifically, we perform a
fine-grained sentiment analysis task with implementations on two different
systems:... | computer science |
33,708 | Building a Learning Database for the Neural Network Retrieval of Sea
Surface Salinity from SMOS Brightness Temperatures | cs.NE | This article deals with an important aspect of the neural network retrieval
of sea surface salinity (SSS) from SMOS brightness temperatures (TBs). The
neural network retrieval method is an empirical approach that offers the
possibility of being independent from any theoretical emissivity model, during
the in-flight pha... | computer science |
33,709 | Model-Coupled Autoencoder for Time Series Visualisation | cs.NE | We present an approach for the visualisation of a set of time series that
combines an echo state network with an autoencoder. For each time series in the
dataset we train an echo state network, using a common and fixed reservoir of
hidden neurons, and use the optimised readout weights as the new
representation. Dimensi... | computer science |
33,710 | A Robust Frame-based Nonlinear Prediction System for Automatic Speech
Coding | cs.SD | In this paper, we propose a neural-based coding scheme in which an artificial
neural network is exploited to automatically compress and decompress speech
signals by a trainable approach. Having a two-stage training phase, the system
can be fully specified to each speech frame and have robust performance across
differen... | computer science |
33,711 | A network that learns Strassen multiplication | math.NA | We study neural networks whose only non-linear components are multipliers, to
test a new training rule in a context where the precise representation of data
is paramount. These networks are challenged to discover the rules of matrix
multiplication, given many examples. By limiting the number of multipliers, the
network... | computer science |
33,712 | Quantum perceptron over a field and neural network architecture
selection in a quantum computer | cs.NE | In this work, we propose a quantum neural network named quantum perceptron
over a field (QPF). Quantum computers are not yet a reality and the models and
algorithms proposed in this work cannot be simulated in actual (or classical)
computers. QPF is a direct generalization of a classical perceptron and solves
some draw... | computer science |
33,713 | FPGA Based Implementation of Deep Neural Networks Using On-chip Memory
Only | cs.AR | Deep neural networks (DNNs) demand a very large amount of computation and
weight storage, and thus efficient implementation using special purpose
hardware is highly desired. In this work, we have developed an FPGA based
fixed-point DNN system using only on-chip memory not to access external DRAM.
The execution time and... | computer science |
33,714 | LSTM Deep Neural Networks Postfiltering for Improving the Quality of
Synthetic Voices | cs.SD | Recent developments in speech synthesis have produced systems capable of
outcome intelligible speech, but now researchers strive to create models that
more accurately mimic human voices. One such development is the incorporation
of multiple linguistic styles in various languages and accents.
HMM-based Speech Synthesi... | computer science |
33,715 | Training of spiking neural networks based on information theoretic costs | cs.NE | Spiking neural network is a type of artificial neural network in which
neurons communicate between each other with spikes. Spikes are identical
Boolean events characterized by the time of their arrival. A spiking neuron has
internal dynamics and responds to the history of inputs as opposed to the
current inputs only. B... | computer science |
33,716 | Encoding Data for HTM Systems | cs.NE | Hierarchical Temporal Memory (HTM) is a biologically inspired machine
intelligence technology that mimics the architecture and processes of the
neocortex. In this white paper we describe how to encode data as Sparse
Distributed Representations (SDRs) for use in HTM systems. We explain several
existing encoders, which a... | computer science |
33,717 | Uniresolution representations of white-matter data from CoCoMac | cs.NE | Tracing data as collated by CoCoMac, a seminal neuroinformatics database, is
at multiple resolutions -- white matter tracts were studied for areas and their
subdivisions by different reports. Network theoretic analysis of this
multi-resolution data often assumes that the data at various resolutions is
equivalent, which... | computer science |
33,718 | Automatically Proving Mathematical Theorems with Evolutionary Algorithms
and Proof Assistants | cs.NE | Mathematical theorems are human knowledge able to be accumulated in the form
of symbolic representation, and proving theorems has been considered
intelligent behavior. Based on the BHK interpretation and the Curry-Howard
isomorphism, proof assistants, software capable of interacting with human for
constructing formal p... | computer science |
33,719 | Cortical Computation via Iterative Constructions | cs.NE | We study Boolean functions of an arbitrary number of input variables that can
be realized by simple iterative constructions based on constant-size
primitives. This restricted type of construction needs little global
coordination or control and thus is a candidate for neurally feasible
computation. Valiant's constructio... | computer science |
33,720 | Exploring the coevolution of predator and prey morphology and behavior | cs.NE | A common idiom in biology education states, "Eyes in the front, the animal
hunts. Eyes on the side, the animal hides." In this paper, we explore one
possible explanation for why predators tend to have forward-facing, high-acuity
visual systems. We do so using an agent-based computational model of evolution,
where preda... | computer science |
33,721 | On higher order computations and synaptic meta-plasticity in the human
brain: IT point of view (June, 2016) | cs.NE | Glia modify neuronal connectivity by creating structural changes in the
neuronal connectome. Glia also influence the functional connectome by modifying
the flow of information through neural networks (Fields et al. 2015). There are
strong experimental evidences that glia are responsible for synaptic
meta-plasticity. Sy... | computer science |
33,722 | Applying Artifical Neural Networks To Predict Nominal Vehicle
Performance | cs.NE | This paper investigates the use of artificial neural networks (ANNs) to
replace traditional algorithms and manual review for identifying anomalies in
vehicle run data. The specific data used for this study is from undersea
vehicle qualification tests. Such data is highly non-linear, therefore
traditional algorithms are... | computer science |
33,723 | Information Processing by Nonlinear Phase Dynamics in Locally Connected
Arrays | cs.NE | Research toward powerful information processing systems that circumvent the
interconnect bottleneck by exploiting the nonlinear evolution of multiple phase
dynamics in locally connected arrays is discussed. We focus on a scheme in
which logic states are defined by the electrical phase of a dynamic process and
informati... | computer science |
33,724 | Genetic cellular neural networks for generating three-dimensional
geometry | cs.NE | There are a number of ways to procedurally generate interesting
three-dimensional shapes, and a method where a cellular neural network is
combined with a mesh growth algorithm is presented here. The aim is to create a
shape from a genetic code in such a way that a crude search can find
interesting shapes. Identical neu... | computer science |
33,725 | The geometry of learning | cs.NE | We establish a correspondence between classical conditioning processes and
fractals. The association strength at a given training trial corresponds to a
point in a disconnected set at a given iteration level. In this way, one can
represent a training process as a hopping on a fractal set, instead of the
traditional lea... | computer science |
33,726 | On the Emergence of Shortest Paths by Reinforced Random Walks | cs.NE | The co-evolution between network structure and functional performance is a
fundamental and challenging problem whose complexity emerges from the intrinsic
interdependent nature of structure and function. Within this context, we
investigate the interplay between the efficiency of network navigation (i.e.,
path lengths) ... | computer science |
33,727 | Dataflow matrix machines as programmable, dynamically expandable,
self-referential generalized recurrent neural networks | cs.NE | Dataflow matrix machines are a powerful generalization of recurrent neural
networks. They work with multiple types of linear streams and multiple types of
neurons, including higher-order neurons which dynamically update the matrix
describing weights and topology of the network in question while the network is
running. ... | computer science |
33,728 | A comparison of semi-deterministic and stochastic search techniques | cs.NE | This paper presents an investigation of two search techniques, tabu search
(TS) and simulated annealing (SA), to assess their relative merits when applied
to engineering design optimisation. Design optimisation problems are generally
characterised as having multi-modal search spaces and discontinuities making
global op... | computer science |
33,729 | Hybrid evolutionary algorithm with extreme machine learning fitness
function evaluation for two-stage capacitated facility location problem | math.OC | This paper considers the two-stage capacitated facility location problem
(TSCFLP) in which products manufactured in plants are delivered to customers
via storage depots. Customer demands are satisfied subject to limited plant
production and limited depot storage capacity. The objective is to determine
the locations of ... | computer science |
33,730 | BMOBench: Black-Box Multi-Objective Optimization Benchmarking Platform | math.OC | This document briefly describes the Black-Box Multi-Objective Optimization
Benchmarking (BMOBench) platform. It presents the test problems, evaluation
procedure, and experimental setup. To this end, the BMOBench is demonstrated by
comparing recent multi-objective solvers from the literature, namely SMS-EMOA,
DMS, and M... | computer science |
33,731 | Robust Downbeat Tracking Using an Ensemble of Convolutional Networks | cs.SD | In this paper, we present a novel state of the art system for automatic
downbeat tracking from music signals. The audio signal is first segmented in
frames which are synchronized at the tatum level of the music. We then extract
different kind of features based on harmony, melody, rhythm and bass content to
feed convolu... | computer science |
33,732 | CaMKII activation supports reward-based neural network optimization
through Hamiltonian sampling | cs.NE | Synaptic plasticity is implemented and controlled through over thousand
different types of molecules in the postsynaptic density and presynaptic
boutons that assume a staggering array of different states through
phosporylation and other mechanisms. One of the most prominent molecule in the
postsynaptic density is CaMKI... | computer science |
33,733 | Grid-like structure is optimal for path integration | cs.NE | Grid cells in medial entorhinal cortex are believed to play a key role in
path integration. However, the relation between path integration and the
grid-like arrangement of their firing field remains unclear. We provide
theoretical evidence that grid-like structure and path integration are closely
related. In one dimens... | computer science |
33,734 | View-tolerant face recognition and Hebbian learning imply
mirror-symmetric neural tuning to head orientation | cs.NE | The primate brain contains a hierarchy of visual areas, dubbed the ventral
stream, which rapidly computes object representations that are both specific
for object identity and relatively robust against identity-preserving
transformations like depth-rotations. Current computational models of object
recognition, includin... | computer science |
33,735 | A Lower Bound Analysis of Population-based Evolutionary Algorithms for
Pseudo-Boolean Functions | cs.NE | Evolutionary algorithms (EAs) are population-based general-purpose
optimization algorithms, and have been successfully applied in various
real-world optimization tasks. However, previous theoretical studies often
employ EAs with only a parent or offspring population and focus on specific
problems. Furthermore, they oft... | computer science |
33,736 | A Systematic Approach to Blocking Convolutional Neural Networks | cs.DC | Convolutional Neural Networks (CNNs) are the state of the art solution for
many computer vision problems, and many researchers have explored optimized
implementations. Most implementations heuristically block the computation to
deal with the large data sizes and high data reuse of CNNs. This paper explores
how to block... | computer science |
33,737 | Self-adaptation of Mutation Rates in Non-elitist Populations | cs.NE | The runtime of evolutionary algorithms (EAs) depends critically on their
parameter settings, which are often problem-specific. Automated schemes for
parameter tuning have been developed to alleviate the high costs of manual
parameter tuning. Experimental results indicate that self-adaptation, where
parameter settings a... | computer science |
33,738 | Contravening Esotery: Cryptanalysis of Knapsack Cipher using Genetic
Algorithms | cs.CR | Cryptanalysis of knapsack cipher is a fascinating problem which has eluded
the computing fraternity for decades. However, in most of the cases either the
time complexity of the proposed algorithm is colossal or an insufficient number
of samples have been taken for verification. The present work proposes a
Genetic Algor... | computer science |
33,739 | An active efficient coding model of the optokinetic nystagmus | cs.NE | Optokinetic nystagmus (OKN) is an involuntary eye movement responsible for
stabilizing retinal images in the presence of relative motion between an
observer and the environment. Fully understanding the development of
optokinetic nystagmus requires a neurally plausible computational model that
accounts for the neural de... | computer science |
33,740 | An Approach for Parallel Genetic Algorithms in the Cloud using Software
Containers | cs.NE | Genetic Algorithms (GAs) are a powerful technique to address hard
optimisation problems. However, scalability issues might prevent them from
being applied to real-world problems. Exploiting parallel GAs in the cloud
might be an affordable approach to get time efficient solutions that benefit of
the appealing features o... | computer science |
33,741 | Exploiting the Short-term to Long-term Plasticity Transition in
Memristive Nanodevice Learning Architectures | cs.NE | Memristive nanodevices offer new frontiers for computing systems that unite
arithmetic and memory operations on-chip. Here, we explore the integration of
electrochemical metallization cell (ECM) nanodevices with tunable filamentary
switching in nanoscale learning systems. Such devices offer a natural
transition between... | computer science |
33,742 | Programming Patterns in Dataflow Matrix Machines and Generalized
Recurrent Neural Nets | cs.PL | Dataflow matrix machines arise naturally in the context of synchronous
dataflow programming with linear streams. They can be viewed as a rather
powerful generalization of recurrent neural networks. Similarly to recurrent
neural networks, large classes of dataflow matrix machines are described by
matrices of numbers, an... | computer science |
33,743 | The Evolution of Sex through the Baldwin Effect | cs.NE | This paper suggests that the fundamental haploid-diploid cycle of eukaryotic
sex exploits a rudimentary form of the Baldwin effect. With this explanation
for the basic cycle, the other associated phenomena can be explained as
evolution tuning the amount and frequency of learning experienced by an
organism. Using the we... | computer science |
33,744 | Formal Definitions of Unbounded Evolution and Innovation Reveal
Universal Mechanisms for Open-Ended Evolution in Dynamical Systems | cs.NE | Open-ended evolution (OEE) is relevant to a variety of biological, artificial
and technological systems, but has been challenging to reproduce in silico.
Most theoretical efforts focus on key aspects of open-ended evolution as it
appears in biology. We recast the problem as a more general one in dynamical
systems theor... | computer science |
33,745 | The encoding of proprioceptive inputs in the brain: knowns and unknowns
from a robotic perspective | cs.NE | Somatosensory inputs can be grossly divided into tactile (or cutaneous) and
proprioceptive -- the former conveying information about skin stimulation, the
latter about limb position and movement. The principal proprioceptors are
constituted by muscle spindles, which deliver information about muscle length
and speed. In... | computer science |
33,746 | Guided macro-mutation in a graded energy based genetic algorithm for
protein structure prediction | cs.NE | Protein structure prediction is considered as one of the most challenging and
computationally intractable combinatorial problem. Thus, the efficient modeling
of convoluted search space, the clever use of energy functions, and more
importantly, the use of effective sampling algorithms become crucial to address
this prob... | computer science |
33,747 | Machine Learning with Memristors via Thermodynamic RAM | cs.ET | Thermodynamic RAM (kT-RAM) is a neuromemristive co-processor design based on
the theory of AHaH Computing and implemented via CMOS and memristors. The
co-processor is a 2-D array of differential memristor pairs (synapses) that can
be selectively coupled together (neurons) via the digital bit addressing of the
underlyin... | computer science |
33,748 | Optimal Management of Naturally Regenerating Uneven-aged Forests | math.OC | A shift from even-aged forest management to uneven-aged management practices
leads to a problem rather different from the existing straightforward practice
that follows a rotation cycle of artificial regeneration, thinning of inferior
trees and a clearcut. A lack of realistic models and methods suggesting how to
manage... | computer science |
33,749 | Haploid-Diploid Evolutionary Algorithms | cs.NE | This paper uses the recent idea that the fundamental haploid-diploid
lifecycle of eukaryotic organisms implements a rudimentary form of learning
within evolution. A general approach for evolutionary computation is here
derived that differs from all previous known work using diploid
representations. The primary role of ... | computer science |
33,750 | Reconstructing Neural Parameters and Synapses of arbitrary
interconnected Neurons from their Simulated Spiking Activity | cs.NE | To understand the behavior of a neural circuit it is a presupposition that we
have a model of the dynamical system describing this circuit. This model is
determined by several parameters, including not only the synaptic weights, but
also the parameters of each neuron. Existing works mainly concentrate on either
the syn... | computer science |
33,751 | Learning and Inferring Relations in Cortical Networks | cs.NE | A pressing scientific challenge is to understand how brains work. Of
particular interest is the neocortex,the part of the brain that is especially
large in humans, capable of handling a wide variety of tasks including visual,
auditory, language, motor, and abstract processing. These functionalities are
processed in dif... | computer science |
33,752 | Multiplex visibility graphs to investigate recurrent neural networks
dynamics | cs.NE | A recurrent neural network (RNN) is a universal approximator of dynamical
systems, whose performance often depends on sensitive hyperparameters. Tuning
of such hyperparameters may be difficult and, typically, based on a
trial-and-error approach. In this work, we adopt a graph-based framework to
interpret and characteri... | computer science |
33,753 | Visualisation of Survey Responses using Self-Organising Maps: A Case
Study on Diabetes Self-care Factors | cs.NE | Due to the chronic nature of diabetes, patient self-care factors play an
important role in any treatment plan. In order to understand the behaviour of
patients in response to medical advice on self-care, clinicians often conduct
cross-sectional surveys. When analysing the survey data, statistical machine
learning metho... | computer science |
33,754 | Online Training of an Opto-Electronic Reservoir Computer Applied to
Real-Time Channel Equalisation | cs.ET | Reservoir Computing is a bio-inspired computing paradigm for processing time
dependent signals. The performance of its analogue implementation are
comparable to other state of the art algorithms for tasks such as speech
recognition or chaotic time series prediction, but these are often constrained
by the offline traini... | computer science |
33,755 | Embodiment of Learning in Electro-Optical Signal Processors | cs.ET | Delay-coupled electro-optical systems have received much attention for their
dynamical properties and their potential use in signal processing. In
particular it has recently been demonstrated, using the artificial intelligence
algorithm known as reservoir computing, that photonic implementations of such
systems solve c... | computer science |
33,756 | STDP allows close-to-optimal spatiotemporal spike pattern detection by
single coincidence detector neurons | cs.NE | By recording multiple cells simultaneously, electrophysiologists have found
evidence for repeating spatiotemporal spike patterns, which can carry
information. How this information is extracted by downstream neurons is
unclear. In this theoretical paper, we investigate to what extent a single cell
could detect a given s... | computer science |
33,757 | Recurrent Neural Networks for Spatiotemporal Dynamics of Intrinsic
Networks from fMRI Data | cs.NE | Functional magnetic resonance imaging (fMRI) of temporally-coherent blood
oxygenization level-dependent (BOLD) signal provides an effective means of
analyzing functionally coherent patterns in the brain. Intrinsic networks and
functional connectivity are important outcomes of fMRI studies and are central
to understandi... | computer science |
33,758 | A Self-Driving Robot Using Deep Convolutional Neural Networks on
Neuromorphic Hardware | cs.NE | Neuromorphic computing is a promising solution for reducing the size, weight
and power of mobile embedded systems. In this paper, we introduce a realization
of such a system by creating the first closed-loop battery-powered
communication system between an IBM TrueNorth NS1e and an autonomous
Android-Based Robotics plat... | computer science |
33,759 | Low-effort place recognition with WiFi fingerprints using deep learning | cs.RO | Using WiFi signals for indoor localization is the main localization modality
of the existing personal indoor localization systems operating on mobile
devices. WiFi fingerprinting is also used for mobile robots, as WiFi signals
are usually available indoors and can provide rough initial position estimate
or can be used ... | computer science |
33,760 | Proceedings of the Workshop on Brain Analysis using COnnectivity
Networks - BACON 2016 | cs.NE | Understanding brain connectivity in a network-theoretic context has shown
much promise in recent years. This type of analysis identifies brain
organisational principles, bringing a new perspective to neuroscience. At the
same time, large public databases of connectomic data are now available.
However, connectome analys... | computer science |
33,761 | Advancing Memristive Analog Neuromorphic Networks: Increasing
Complexity, and Coping with Imperfect Hardware Components | cs.ET | We experimentally demonstrate classification of 4x4 binary images into 4
classes, using a 3-layer mixed-signal neuromorphic network ("MLP perceptron"),
based on two passive 20x20 memristive crossbar arrays, board-integrated with
discrete CMOS components. The network features 10 hidden-layer and 4
output-layer analog CM... | computer science |
33,762 | Prediction of Seasonal Temperature Using Soft Computing Techniques:
Application in Benevento (Southern Italy) Area | cs.NE | In this work two soft computing methods, Artificial Neural Networks and
Genetic Programming, are proposed in order to forecast the mean temperature
that will occur in future seasons. The area in which the soft computing
techniques were applied is that of the surroundings of the town of Benevento,
in the south of Italy,... | computer science |
33,763 | Comparison of Brain Networks with Unknown Correspondences | cs.NE | Graph theory has drawn a lot of attention in the field of Neuroscience during
the last decade, mainly due to the abundance of tools that it provides to
explore the interactions of elements in a complex network like the brain. The
local and global organization of a brain network can shed light on mechanisms
of complex c... | computer science |
33,764 | An Empirical Study of Continuous Connectivity Degree Sequence
Equivalents | cs.NE | In the present work we demonstrate the use of a parcellation free
connectivity model based on Poisson point processes. This model produces for
each subject a continuous bivariate intensity function that represents for
every possible pair of points the relative rate at which we observe tracts
terminating at those points... | computer science |
33,765 | Using inspiration from synaptic plasticity rules to optimize traffic
flow in distributed engineered networks | cs.NE | Controlling the flow and routing of data is a fundamental problem in many
distributed networks, including transportation systems, integrated circuits,
and the Internet. In the brain, synaptic plasticity rules have been discovered
that regulate network activity in response to environmental inputs, which
enable circuits ... | computer science |
33,766 | A Review of Neural Network Based Machine Learning Approaches for Rotor
Angle Stability Control | cs.SY | This paper reviews the current status and challenges of Neural Networks (NNs)
based machine learning approaches for modern power grid stability control
including their design and implementation methodologies. NNs are widely
accepted as Artificial Intelligence (AI) approaches offering an alternative way
to control compl... | computer science |
33,767 | Automatic Knot Adjustment Using Dolphin Echolocation Algorithm for
B-Spline Curve Approximation | cs.GR | In this paper, a new approach to solve the cubic B-spline curve fitting
problem is presented based on a meta-heuristic algorithm called " dolphin
echolocation ". The method minimizes the proximity error value of the selected
nodes that measured using the least squares method and the Euclidean distance
method of the new... | computer science |
33,768 | NMODE --- Neuro-MODule Evolution | cs.NE | Modularisation, repetition, and symmetry are structural features shared by
almost all biological neural networks. These features are very unlikely to be
found by the means of structural evolution of artificial neural networks. This
paper introduces NMODE, which is specifically designed to operate on
neuro-modules. NMOD... | computer science |
33,769 | Temporal Overdrive Recurrent Neural Network | cs.NE | In this work we present a novel recurrent neural network architecture
designed to model systems characterized by multiple characteristic timescales
in their dynamics. The proposed network is composed by several recurrent groups
of neurons that are trained to separately adapt to each timescale, in order to
improve the s... | computer science |
33,770 | Optimized Spatial Partitioning via Minimal Swarm Intelligence | stat.ME | Optimized spatial partitioning algorithms are the corner stone of many
successful experimental designs and statistical methods. Of these algorithms,
the Centroidal Voronoi Tessellation (CVT) is the most widely utilized. CVT
based methods require global knowledge of spatial boundaries, do not readily
allow for weighted ... | computer science |
33,771 | Beyond Evolutionary Algorithms for Search-based Software Engineering | cs.SE | Context: Evolutionary algorithms typically require a large number of
evaluations (of solutions) to converge - which can be very slow and expensive
to evaluate.Objective: To solve search-based software engineering (SE)
problems, using fewer evaluations than evolutionary methods.Method: Instead of
mutating a small popula... | computer science |
33,772 | Design of PI Controller for Automatic Generation Control of Multi Area
Interconnected Power System using Bacterial Foraging Optimization | cs.SY | The system comprises of three interconnected power system networks based on
thermal, wind and hydro power generation. The load variation in any one of the
network results in frequency deviation in all the connected systems.The PI
controllers have been connected separately with each system for the frequency
control and ... | computer science |
33,773 | A Hybrid Approach for Secured Optimal Power Flow and Voltage Stability
with TCSC Placement | cs.NE | This paper proposes a hybrid technique for secured optimal power flow coupled
with enhancing voltage stability with FACTS device installation. The hybrid
approach of Improved Gravitational Search algorithm (IGSA) and Firefly
algorithm (FA) performance is analyzed by optimally placing TCSC controller.
The algorithm is i... | computer science |
33,774 | An Extremal Optimization approach to parallel resonance constrained
capacitor placement problem | math.OC | Installation of capacitors in distribution networks is one of the most used
procedure to compensate reactive power generated by loads and, consequently, to
reduce technical losses. So, the problem consists in identifying the optimal
placement and sizing of capacitors. This problem is known in the literature as
optimal ... | computer science |
33,775 | Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network
(RED-CNN) | cs.NE | Given the potential X-ray radiation risk to the patient, low-dose CT has
attracted a considerable interest in the medical imaging field. The current
main stream low-dose CT methods include vendor-specific sinogram domain
filtration and iterative reconstruction, but they need to access original raw
data whose formats ar... | computer science |
33,776 | Dominance Move: A Measure of Comparing Solution Sets in Multiobjective
Optimization | cs.NE | One of the most common approaches for multiobjective optimization is to
generate a solution set that well approximates the whole Pareto-optimal
frontier to facilitate the later decision-making process. However, how to
evaluate and compare the quality of different solution sets remains
challenging. Existing measures typ... | computer science |
33,777 | Optimal Experimental Design of Field Trials using Differential Evolution | cs.NE | When setting up field experiments, to test and compare a range of genotypes
(e.g. maize hybrids), it is important to account for any possible field effect
that may otherwise bias performance estimates of genotypes. To do so, we
propose a model-based method aimed at optimizing the allocation of the tested
genotypes and ... | computer science |
33,778 | Eye-Movement behavior identification for AD diagnosis | cs.NE | In the present work, we develop a deep-learning approach for differentiating
the eye-movement behavior of people with neurodegenerative diseases over
healthy control subjects during reading well-defined sentences. We define an
information compaction of the eye-tracking data of subjects without and with
probable Alzheim... | computer science |
33,779 | An Efficient Method for online Detection of Polychronous Patterns in
Spiking Neural Network | cs.NE | Polychronous neural groups are effective structures for the recognition of
precise spike-timing patterns but the detection method is an inefficient
multi-stage brute force process that works off-line on pre-recorded simulation
data. This work presents a new model of polychronous patterns that can capture
precise sequen... | computer science |
33,780 | RESPARC: A Reconfigurable and Energy-Efficient Architecture with
Memristive Crossbars for Deep Spiking Neural Networks | cs.ET | Neuromorphic computing using post-CMOS technologies is gaining immense
popularity due to its promising abilities to address the memory and power
bottlenecks in von-Neumann computing systems. In this paper, we propose RESPARC
- a reconfigurable and energy efficient architecture built-on Memristive
Crossbar Arrays (MCA) ... | computer science |
33,781 | End-to-End Prediction of Buffer Overruns from Raw Source Code via Neural
Memory Networks | cs.SE | Detecting buffer overruns from a source code is one of the most common and
yet challenging tasks in program analysis. Current approaches have mainly
relied on rigid rules and handcrafted features devised by a few experts,
limiting themselves in terms of flexible applicability and robustness due to
diverse bug patterns ... | computer science |
33,782 | Deep Reservoir Computing Using Cellular Automata | cs.NE | Recurrent Neural Networks (RNNs) have been a prominent concept within
artificial intelligence. They are inspired by Biological Neural Networks (BNNs)
and provide an intuitive and abstract representation of how BNNs work. Derived
from the more generic Artificial Neural Networks (ANNs), the recurrent ones are
meant to be... | computer science |
33,783 | Integer Factorization with a Neuromorphic Sieve | cs.NE | The bound to factor large integers is dominated by the computational effort
to discover numbers that are smooth, typically performed by sieving a
polynomial sequence. On a von Neumann architecture, sieving has log-log
amortized time complexity to check each value for smoothness. This work
presents a neuromorphic sieve ... | computer science |
33,784 | Drone Squadron Optimization: a Self-adaptive Algorithm for Global
Numerical Optimization | math.OC | This paper proposes Drone Squadron Optimization, a new self-adaptive
metaheuristic for global numerical optimization which is updated online by a
hyper-heuristic. DSO is an artifact-inspired technique, as opposed to many
algorithms used nowadays, which are nature-inspired. DSO is very flexible
because it is not related... | computer science |
33,785 | Implicit Gradient Neural Networks with a Positive-Definite Mass Matrix
for Online Linear Equations Solving | cs.NE | Motivated by the advantages achieved by implicit analogue net for solving
online linear equations, a novel implicit neural model is designed based on
conventional explicit gradient neural networks in this letter by introducing a
positive-definite mass matrix. In addition to taking the advantages of the
implicit neural ... | computer science |
33,786 | A wake-sleep algorithm for recurrent, spiking neural networks | cs.NE | We investigate a recently proposed model for cortical computation which
performs relational inference. It consists of several interconnected,
structurally equivalent populations of leaky integrate-and-fire (LIF) neurons,
which are trained in a self-organized fashion with spike-timing dependent
plasticity (STDP). Despit... | computer science |
33,787 | An Accelerated Analog Neuromorphic Hardware System Emulating NMDA- and
Calcium-Based Non-Linear Dendrites | cs.NE | This paper presents an extension of the BrainScaleS accelerated analog
neuromorphic hardware model. The scalable neuromorphic architecture is extended
by the support for multi-compartment models and non-linear dendrites. These
features are part of a \SI{65}{\nano\meter} prototype ASIC. It allows to
emulate different sp... | computer science |
33,788 | Why do similarity matching objectives lead to Hebbian/anti-Hebbian
networks? | cs.NE | Modeling self-organization of neural networks for unsupervised learning using
Hebbian and anti-Hebbian plasticity has a long history in neuroscience. Yet,
derivations of single-layer networks with such local learning rules from
principled optimization objectives became possible only recently, with the
introduction of s... | computer science |
33,789 | Exploring Heritability of Functional Brain Networks with Inexact Graph
Matching | cs.NE | Data-driven brain parcellations aim to provide a more accurate representation
of an individual's functional connectivity, since they are able to capture
individual variability that arises due to development or disease. This renders
comparisons between the emerging brain connectivity networks more challenging,
since cor... | computer science |
33,790 | Deep Neural Network Optimized to Resistive Memory with Nonlinear
Current-Voltage Characteristics | cs.ET | Artificial Neural Network computation relies on intensive vector-matrix
multiplications. Recently, the emerging nonvolatile memory (NVM) crossbar array
showed a feasibility of implementing such operations with high energy
efficiency, thus there are many works on efficiently utilizing emerging NVM
crossbar array as anal... | computer science |
33,791 | On Self-Adaptive Mutation Restarts for Evolutionary Robotics with Real
Rotorcraft | cs.NE | Self-adaptive parameters are increasingly used in the field of Evolutionary
Robotics, as they allow key evolutionary rates to vary autonomously in a
context-sensitive manner throughout the optimisation process. A significant
limitation to self-adaptive mutation is that rates can be set unfavourably,
which hinders conve... | computer science |
33,792 | A correlation game for unsupervised learning yields computational
interpretations of Hebbian excitation, anti-Hebbian inhibition, and synapse
elimination | cs.NE | Much has been learned about plasticity of biological synapses from empirical
studies. Hebbian plasticity is driven by correlated activity of presynaptic and
postsynaptic neurons. Synapses that converge onto the same neuron often behave
as if they compete for a fixed resource; some survive the competition while
others a... | computer science |
33,793 | Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO | cs.NE | The design of spacecraft trajectories for missions visiting multiple
celestial bodies is here framed as a multi-objective bilevel optimization
problem. A comparative study is performed to assess the performance of
different Beam Search algorithms at tackling the combinatorial problem of
finding the ideal sequence of bo... | computer science |
33,794 | Using Echo State Networks for Cryptography | cs.CR | Echo state networks are simple recurrent neural networks that are easy to
implement and train. Despite their simplicity, they show a form of memory and
can predict or regenerate sequences of data. We make use of this property to
realize a novel neural cryptography scheme. The key idea is to assume that
Alice and Bob sh... | computer science |
33,795 | Associative content-addressable networks with exponentially many robust
stable states | cs.NE | The brain must robustly store a large number of memories, corresponding to
the many events encountered over a lifetime. However, the number of memory
states in existing neural network models either grows weakly with network size
or recall fails catastrophically with vanishingly little noise. We construct an
associative... | computer science |
33,796 | Threat analysis of IoT networks Using Artificial Neural Network
Intrusion Detection System | cs.NE | The Internet of things (IoT) is still in its infancy and has attracted much
interest in many industrial sectors including medical fields, logistics
tracking, smart cities and automobiles. However as a paradigm, it is
susceptible to a range of significant intrusion threats. This paper presents a
threat analysis of the I... | computer science |
33,797 | Evolution and Analysis of Embodied Spiking Neural Networks Reveals
Task-Specific Clusters of Effective Networks | cs.NE | Elucidating principles that underlie computation in neural networks is
currently a major research topic of interest in neuroscience. Transfer Entropy
(TE) is increasingly used as a tool to bridge the gap between network
structure, function, and behavior in fMRI studies. Computational models allow
us to bridge the gap e... | computer science |
33,798 | On Improving the Capacity of Solving Large-scale Wireless Network Design
Problems by Genetic Algorithms | math.OC | Over the last decade, wireless networks have experienced an impressive growth
and now play a main role in many telecommunications systems. As a consequence,
scarce radio resources, such as frequencies, became congested and the need for
effective and efficient assignment methods arose. In this work, we present a
Genetic... | computer science |
33,799 | The True Destination of EGO is Multi-local Optimization | math.OC | Efficient global optimization is a popular algorithm for the optimization of
expensive multimodal black-box functions. One important reason for its
popularity is its theoretical foundation of global convergence. However, as the
budgets in expensive optimization are very small, the asymptotic properties
only play a mino... | computer science |
33,800 | Solving the Uncapacitated Single Allocation p-Hub Median Problem on GPU | cs.DM | A parallel genetic algorithm (GA) implemented on GPU clusters is proposed to
solve the Uncapacitated Single Allocation p-Hub Median problem. The GA uses
binary and integer encoding and genetic operators adapted to this problem. Our
GA is improved by generated initial solution with hubs located at middle nodes.
The obta... | computer science |
33,801 | Firing Cell: An Artificial Neuron with a Simulation of
Long-Term-Potentiation-Related Memory | cs.NE | We propose a computational model of neuron, called firing cell (FC),
properties of which cover such phenomena as attenuation of receptors for
external stimuli, delay and decay of postsynaptic potentials, modification of
internal weights due to propagation of postsynaptic potentials through the
dendrite, modification of... | computer science |
33,802 | Feed-forward approximations to dynamic recurrent network architectures | cs.NE | Recurrent neural network architectures can have useful computational
properties, with complex temporal dynamics and input-sensitive attractor
states. However, evaluation of recurrent dynamic architectures requires
solution of systems of differential equations, and the number of evaluations
required to determine their r... | computer science |
33,803 | A dynamic resource allocation decision model for IT security | cs.CR | Today, with the continued growth in using information and communication
technologies (ICT) for business purposes, business organizations become
increasingly dependent on their information systems. Thus, they need to protect
them from the different attacks exploiting their vulnerabilities. To do so, the
organization has... | computer science |
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