Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
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33,604 | Meteorological time series forecasting with pruned multi-layer
perceptron and 2-stage Levenberg-Marquardt method | cs.NE | A Multi-Layer Perceptron (MLP) defines a family of artificial neural networks
often used in TS modeling and forecasting. Because of its "black box" aspect,
many researchers refuse to use it. Moreover, the optimization (often based on
the exhaustive approach where "all" configurations are tested) and learning
phases of ... | computer science |
33,605 | A Framework for Exploring Non-Linear Functional Connectivity and
Causality in the Human Brain: Mutual Connectivity Analysis (MCA) of
Resting-State Functional MRI with Convergent Cross-Mapping and Non-Metric
Clustering | cs.NE | We present a computational framework for analysis and visualization of
non-linear functional connectivity in the human brain from resting state
functional MRI (fMRI) data for purposes of recovering the underlying network
community structure and exploring causality between network components. Our
proposed methodology of... | computer science |
33,606 | Optimization Under Uncertainty Using the Generalized Inverse
Distribution Function | math.OC | A framework for robust optimization under uncertainty based on the use of the
generalized inverse distribution function (GIDF), also called quantile
function, is here proposed. Compared to more classical approaches that rely on
the usage of statistical moments as deterministic attributes that define the
objectives of t... | computer science |
33,607 | A Comparative Analysis for Determining the Optimal Path using PSO and GA | cs.NI | Significant research has been carried out recently to find the optimal path
in network routing. Among them, the evolutionary algorithm approach is an area
where work is carried out extensively. We in this paper have used particle
swarm optimization (PSO) and genetic algorithm (GA) for finding the optimal
path and the c... | computer science |
33,608 | Artificial Life and the Web: WebAL Comes of Age | cs.NE | A brief survey is presented of the first 18 years of web-based Artificial
Life ("WebAL") research and applications, covering the period 1995-2013. The
survey is followed by a short discussion of common methodologies employed and
current technologies relevant to WebAL research. The paper concludes with a
quick look at w... | computer science |
33,609 | A Genetic Algorithm for Software Design Migration from Structured to
Object Oriented Paradigm | cs.SE | The potential benefit of migrating software design from Structured to Object
Oriented Paradigm is manifolded including modularity, manageability and
extendability. This design migration should be automated as it will reduce the
time required in manual process. Our previous work has addressed this issue in
terms of opti... | computer science |
33,610 | Level-based Analysis of Genetic Algorithms and other Search Processes | cs.NE | Understanding how the time-complexity of evolutionary algorithms (EAs) depend
on their parameter settings and characteristics of fitness landscapes is a
fundamental problem in evolutionary computation. Most rigorous results were
derived using a handful of key analytic techniques, including drift analysis.
However, sinc... | computer science |
33,611 | Performance Analysis on Evolutionary Algorithms for the Minimum Label
Spanning Tree Problem | cs.NE | Some experimental investigations have shown that evolutionary algorithms
(EAs) are efficient for the minimum label spanning tree (MLST) problem.
However, we know little about that in theory. As one step towards this issue,
we theoretically analyze the performances of the (1+1) EA, a simple version of
EAs, and a multi-o... | computer science |
33,612 | Improving files availability for BitTorrent using a diffusion model | cs.NI | The BitTorrent mechanism effectively spreads file fragments by copying the
rarest fragments first. We propose to apply a mathematical model for the
diffusion of fragments on a P2P in order to take into account both the effects
of peer distances and the changing availability of peers while time goes on.
Moreover, we man... | computer science |
33,613 | Identification of Dynamic functional brain network states Through Tensor
Decomposition | cs.NE | With the advances in high resolution neuroimaging, there has been a growing
interest in the detection of functional brain connectivity. Complex network
theory has been proposed as an attractive mathematical representation of
functional brain networks. However, most of the current studies of functional
brain networks ha... | computer science |
33,614 | A probabilistic evolutionary optimization approach to compute
quasiparticle braids | cs.NE | Topological quantum computing is an alternative framework for avoiding the
quantum decoherence problem in quantum computation. The problem of executing a
gate in this framework can be posed as the problem of braiding quasiparticles.
Because these are not Abelian, the problem can be reduced to finding an optimal
product... | computer science |
33,615 | Contributions of natural ventilation on thermal performance of
alternative floor plan designs | cs.NE | During the earliest phase of architectural design process, practitioners
after analyzing the client's design program, legal requirements, topographic
constraints, and preferences synthesize these requirements into architectural
floor plan drawings. Design decisions taken in this phase may significantly
contribute to th... | computer science |
33,616 | Training Algorithm for Neuro-Fuzzy Network Based on Singular Spectrum
Analysis | cs.NE | In this article, we propose a combination of an noise-reduction algorithm
based on Singular Spectrum Analysis (SSA) and a standard feedforward neural
prediction model. Basically, the proposed algorithm consists of two different
steps: data preprocessing based on the SSA filtering method and step-by-step
training proced... | computer science |
33,617 | Multi-Agent Shape Formation and Tracking Inspired from a Social Foraging
Dynamics | cs.NE | Principle of Swarm Intelligence has recently found widespread application in
formation control and automated tracking by the automated multi-agent system.
This article proposes an elegant and effective method inspired by foraging
dynamics to produce geometric-patterns by the search agents. Starting from a
random initia... | computer science |
33,618 | Logarithmic distributions prove that intrinsic learning is Hebbian | cs.NE | In this paper, we present data for the lognormal distributions of spike
rates, synaptic weights and intrinsic excitability (gain) for neurons in
various brain areas, such as auditory or visual cortex, hippocampus,
cerebellum, striatum, midbrain nuclei. We find a remarkable consistency of
heavy-tailed, specifically logn... | computer science |
33,619 | Initialization of multilayer forecasting artifical neural networks | cs.NE | In this paper, a new method was developed for initialising artificial neural
networks predicting dynamics of time series. Initial weighting coefficients
were determined for neurons analogously to the case of a linear prediction
filter. Moreover, to improve the accuracy of the initialization method for a
multilayer neur... | computer science |
33,620 | A neural circuit for navigation inspired by C. elegans Chemotaxis | cs.NE | We develop an artificial neural circuit for contour tracking and navigation
inspired by the chemotaxis of the nematode Caenorhabditis elegans. In order to
harness the computational advantages spiking neural networks promise over their
non-spiking counterparts, we develop a network comprising 7-spiking neurons
with non-... | computer science |
33,621 | Sub-threshold CMOS Spiking Neuron Circuit Design for Navigation Inspired
by C. elegans Chemotaxis | cs.NE | We demonstrate a spiking neural network for navigation motivated by the
chemotaxis network of Caenorhabditis elegans. Our network uses information
regarding temporal gradients in the tracking variable's concentration to make
navigational decisions. The gradient information is determined by mimicking the
underlying mech... | computer science |
33,622 | Random feedback weights support learning in deep neural networks | cs.NE | The brain processes information through many layers of neurons. This deep
architecture is representationally powerful, but it complicates learning by
making it hard to identify the responsible neurons when a mistake is made. In
machine learning, the backpropagation algorithm assigns blame to a neuron by
computing exact... | computer science |
33,623 | Evolving intraday foreign exchange trading strategies utilizing multiple
instruments price series | cs.NE | We propose a Genetic Programming architecture for the generation of foreign
exchange trading strategies. The system's principal features are the evolution
of free-form strategies which do not rely on any prior models and the
utilization of price series from multiple instruments as input data. This
latter feature consti... | computer science |
33,624 | Turn Down that Noise: Synaptic Encoding of Afferent SNR in a Single
Spiking Neuron | cs.NE | We have added a simplified neuromorphic model of Spike Time Dependent
Plasticity (STDP) to the Synapto-dendritic Kernel Adapting Neuron (SKAN). The
resulting neuron model is the first to show synaptic encoding of afferent
signal to noise ratio in addition to the unsupervised learning of spatio
temporal spike patterns. ... | computer science |
33,625 | Identification of Helicopter Dynamics based on Flight Data using Nature
Inspired Techniques | cs.CE | The complexity of helicopter flight dynamics makes modeling and helicopter
system identification a very difficult task. Most of the traditional techniques
require a model structure to be defined apriori and in case of helicopter
dynamics, this is difficult due to its complexity and the interplay between
various subsyst... | computer science |
33,626 | GreMuTRRR: A Novel Genetic Algorithm to Solve Distance Geometry Problem
for Protein Structures | cs.NE | Nuclear Magnetic Resonance (NMR) Spectroscopy is a widely used technique to
predict the native structure of proteins. However, NMR machines are only able
to report approximate and partial distances between pair of atoms. To build the
protein structure one has to solve the Euclidean distance geometry problem
given the i... | computer science |
33,627 | A Parallel Genetic Algorithm for Three Dimensional Bin Packing with
Heterogeneous Bins | cs.DC | This paper presents a parallel genetic algorithm for three dimensional bin
packing with heterogeneous bins using Hadoop Map-Reduce framework. The most
common three dimensional bin packing problem which packs given set of boxes
into minimum number of equal sized bins is proven to be NP Hard. The variation
of three dimen... | computer science |
33,628 | Pseudo Dynamic Transitional Modeling of Building Heating Energy Demand
Using Artificial Neural Network | cs.CE | This paper presents the building heating demand prediction model with
occupancy profile and operational heating power level characteristics in short
time horizon (a couple of days) using artificial neural network. In addition,
novel pseudo dynamic transitional model is introduced, which consider time
dependent attribut... | computer science |
33,629 | Memcomputing NP-complete problems in polynomial time using polynomial
resources and collective states | cs.ET | Memcomputing is a novel non-Turing paradigm of computation that uses
interacting memory cells (memprocessors for short) to store and process
information on the same physical platform. It was recently proved
mathematically that memcomputing machines have the same computational power of
non-deterministic Turing machines.... | computer science |
33,630 | Liquid State Machine with Dendritically Enhanced Readout for Low-power,
Neuromorphic VLSI Implementations | cs.ET | In this paper, we describe a new neuro-inspired, hardware-friendly readout
stage for the liquid state machine (LSM), a popular model for reservoir
computing. Compared to the parallel perceptron architecture trained by the
p-delta algorithm, which is the state of the art in terms of performance of
readout stages, our re... | computer science |
33,631 | Hardware-Amenable Structural Learning for Spike-based Pattern
Classification using a Simple Model of Active Dendrites | cs.NE | This paper presents a spike-based model which employs neurons with
functionally distinct dendritic compartments for classifying high dimensional
binary patterns. The synaptic inputs arriving on each dendritic subunit are
nonlinearly processed before being linearly integrated at the soma, giving the
neuron a capacity to... | computer science |
33,632 | Short-Term Memory Through Persistent Activity: Evolution of
Self-Stopping and Self-Sustaining Activity in Spiking Neural Networks | cs.NE | Memories in the brain are separated in two categories: short-term and
long-term memories. Long-term memories remain for a lifetime, while short-term
ones exist from a few milliseconds to a few minutes. Within short-term memory
studies, there is debate about what neural structure could implement it.
Indeed, mechanisms r... | computer science |
33,633 | An Evolutionary Optimization Approach to Risk Parity Portfolio Selection | cs.NE | In this paper we present an evolutionary optimization approach to solve the
risk parity portfolio selection problem. While there exist convex optimization
approaches to solve this problem when long-only portfolios are considered, the
optimization problem becomes non-trivial in the long-short case. To solve this
problem... | computer science |
33,634 | A Parallel Genetic Algorithm for Generalized Vertex Cover Problem | cs.DC | This paper presents a parallel genetic algorithm for generalised vertex cover
problem (GVCP) using Hadoop Map-Reduce framework. The proposed Map-Reduce
implementation helps to run the genetic algorithm for generalized vertex cover
problem (GVCP) on multiple machines parallely and computes the solution in
relatively sho... | computer science |
33,635 | A review of "Mem-computing NP-complete problems in polynomial time using
polynomial resources" (arXiv:1411.4798) | cs.ET | The reviewed paper describes an analog device that empirically solves small
instances of the NP-complete Subset Sum Problem (SSP). The authors claim that
this device can solve the SSP in polynomial time using polynomial space, in
principle, and observe no exponential scaling in resource requirements. We
point out that ... | computer science |
33,636 | GPTIPS 2: an open-source software platform for symbolic data mining | cs.MS | GPTIPS is a free, open source MATLAB based software platform for symbolic
data mining (SDM). It uses a multigene variant of the biologically inspired
machine learning method of genetic programming (MGGP) as the engine that drives
the automatic model discovery process. Symbolic data mining is the process of
extracting h... | computer science |
33,637 | Gene Similarity-based Approaches for Determining Core-Genes of
Chloroplasts | cs.NE | In computational biology and bioinformatics, the manner to understand
evolution processes within various related organisms paid a lot of attention
these last decades. However, accurate methodologies are still needed to
discover genes content evolution. In a previous work, two novel approaches
based on sequence similari... | computer science |
33,638 | Representation of Evolutionary Algorithms in FPGA Cluster for Project of
Large-Scale Networks | cs.DC | Many problems are related to network projects, such as electric distribution,
telecommunication and others. Most of them can be represented by graphs, which
manipulate thousands or millions of nodes, becoming almost an impossible task
to obtain real-time solutions. Many efficient solutions use Evolutionary
Algorithms (... | computer science |
33,639 | A theoretical basis for efficient computations with noisy spiking
neurons | cs.NE | Network of neurons in the brain apply - unlike processors in our current
generation of computer hardware - an event-based processing strategy, where
short pulses (spikes) are emitted sparsely by neurons to signal the occurrence
of an event at a particular point in time. Such spike-based computations
promise to be subst... | computer science |
33,640 | Study of the Influence of the Number Normalization Scheme Used in Two
Chaotic Pseudo Random Number Generators Used as the Source of Randomness in
Differential Evolution | cs.NE | In many publications, authors showed that chaotic pseudo random number
generators (PRNGs) may improve performance of the evolutionary algorithms. In
this paper, we use two chaotic maps Gingerbread man and Tinkerbell as the
chaotic PRNGs instead of the classical PRNG in the differential evolution.
Numbers generated by t... | computer science |
33,641 | Reverse Engineering Chemical Reaction Networks from Time Series Data | cs.NE | The automated inference of physically interpretable (bio)chemical reaction
network models from measured experimental data is a challenging problem whose
solution has significant commercial and academic ramifications. It is
demonstrated, using simulations, how sets of elementary reactions comprising
chemical reaction ne... | computer science |
33,642 | Parameter Selection In Particle Swarm Optimization For Transportation
Network Design Problem | math.OC | In transportation planning and development, transport network design problem
seeks to optimize specific objectives (e.g. total travel time) through choosing
among a given set of projects while keeping consumption of resources (e.g.
budget) within their limits. Due to the numerous cases of choosing projects,
solving suc... | computer science |
33,643 | Neural Implementation of Probabilistic Models of Cognition | cs.NE | Bayesian models of cognition hypothesize that human brains make sense of data
by representing probability distributions and applying Bayes' rule to find the
best explanation for available data. Understanding the neural mechanisms
underlying probabilistic models remains important because Bayesian models
provide a comput... | computer science |
33,644 | Nonlinear Model Predictive Control of A Gasoline HCCI Engine Using
Extreme Learning Machines | cs.SY | Homogeneous charge compression ignition (HCCI) is a futuristic combustion
technology that operates with a high fuel efficiency and reduced emissions.
HCCI combustion is characterized by complex nonlinear dynamics which
necessitates a model based control approach for automotive application. HCCI
engine control is a nonl... | computer science |
33,645 | Coevolutionary intransitivity in games: A landscape analysis | cs.NE | Intransitivity is supposed to be a main reason for deficits in coevolutionary
progress and inheritable superiority. Besides, coevolutionary dynamics is
characterized by interactions yielding subjective fitness, but aiming at
solutions that are superior with respect to an objective measurement. Such an
approximation of ... | computer science |
33,646 | On the impact of topological properties of smart grids in power losses
optimization problems | cs.CE | Power losses reduction is one of the main targets for any electrical energy
distribution company. In this paper, we face the problem of joint optimization
of both topology and network parameters in a real smart grid. We consider a
portion of the Italian electric distribution network managed by the ACEA
Distribuzione S.... | computer science |
33,647 | Massively-concurrent Agent-based Evolutionary Computing | cs.MA | The fusion of the multi-agent paradigm with evolutionary computation yielded
promising results in many optimization problems. Evolutionary multi-agent
system (EMAS) are more similar to biological evolution than classical
evolutionary algorithms. However, technological limitations prevented the use
of fully asynchronous... | computer science |
33,648 | Heuristic algorithms for obtaining Polynomial Threshold Functions with
low densities | cs.CC | In this paper we present several heuristic algorithms, including a Genetic
Algorithm (GA), for obtaining polynomial threshold function (PTF)
representations of Boolean functions (BFs) with small number of monomials. We
compare these among each other and against the algorithm of Oztop via
computational experiments. The ... | computer science |
33,649 | OneMax in Black-Box Models with Several Restrictions | cs.NE | Black-box complexity studies lower bounds for the efficiency of
general-purpose black-box optimization algorithms such as evolutionary
algorithms and other search heuristics. Different models exist, each one being
designed to analyze a different aspect of typical heuristics such as the memory
size or the variation oper... | computer science |
33,650 | Computing trading strategies based on financial sentiment data using
evolutionary optimization | cs.NE | In this paper we apply evolutionary optimization techniques to compute
optimal rule-based trading strategies based on financial sentiment data. The
sentiment data was extracted from the social media service StockTwits to
accommodate the level of bullishness or bearishness of the online trading
community towards certain... | computer science |
33,651 | Genetic algorithm implementation for effective document subject search | cs.IR | This paper describes the software implementation of genetic algorithm for
identifying and selecting most relevant results received during sequentially
executed subject search operations. Simulated evolutionary process generates
sustainable and effective population of search queries, forms search pattern of
documents or... | computer science |
33,652 | Network Plasticity as Bayesian Inference | cs.NE | General results from statistical learning theory suggest to understand not
only brain computations, but also brain plasticity as probabilistic inference.
But a model for that has been missing. We propose that inherently stochastic
features of synaptic plasticity and spine motility enable cortical networks of
neurons to... | computer science |
33,653 | On the Runtime of Randomized Local Search and Simple Evolutionary
Algorithms for Dynamic Makespan Scheduling | cs.DS | Evolutionary algorithms have been frequently used for dynamic optimization
problems. With this paper, we contribute to the theoretical understanding of
this research area. We present the first computational complexity analysis of
evolutionary algorithms for a dynamic variant of a classical combinatorial
optimization pr... | computer science |
33,654 | Recognition of convolutional neural network based on CUDA Technology | cs.DC | For the problem whether Graphic Processing Unit(GPU),the stream processor
with high performance of floating-point computing is applicable to neural
networks, this paper proposes the parallel recognition algorithm of
Convolutional Neural Networks(CNNs).It adopts Compute Unified Device
Architecture(CUDA)technology, defin... | computer science |
33,655 | IDSA: Intelligent Distributed Sensor Activation Algorithm For Target
Tracking With Wireless Sensor Network | cs.NI | One important application of the Wireless Sensor Network(WSN) is target
tracking, the aim of this application is converging to an event or object in an
area. In this paper, we propose an energy-efficient distributed sensor
activation protocol based on predicted location technique, called Intelligent
Distributed Sensor ... | computer science |
33,656 | A CMOS Spiking Neuron for Dense Memristor-Synapse Connectivity for
Brain-Inspired Computing | cs.NE | Neuromorphic systems that densely integrate CMOS spiking neurons and
nano-scale memristor synapses open a new avenue of brain-inspired computing.
Existing silicon neurons have molded neural biophysical dynamics but are
incompatible with memristor synapses, or used extra training circuitry thus
eliminating much of the d... | computer science |
33,657 | Real time unsupervised learning of visual stimuli in neuromorphic VLSI
systems | cs.NE | Neuromorphic chips embody computational principles operating in the nervous
system, into microelectronic devices. In this domain it is important to
identify computational primitives that theory and experiments suggest as
generic and reusable cognitive elements. One such element is provided by
attractor dynamics in recu... | computer science |
33,658 | A Java Implementation of the SGA, UMDA, ECGA, and HBOA | cs.NE | The Simple Genetic Algorithm, the Univariate Marginal Distribution Algorithm,
the Extended Compact Genetic Algorithm, and the Hierarchical Bayesian
Optimization Algorithm are all well known Evolutionary Algorithms.
In this report we present a Java implementation of these four algorithms with
detailed instructions on ... | computer science |
33,659 | Simultaneously Solving Computational Problems Using an Artificial
Chemical Reactor | cs.ET | This paper is centered on using chemical reaction as a computational metaphor
for simultaneously solving problems. An artificial chemical reactor that can
simultaneously solve instances of three unrelated problems was created. The
reactor is a distributed stochastic algorithm that simulates a chemical
universe wherein ... | computer science |
33,660 | A Java Implementation of Parameter-less Evolutionary Algorithms | cs.MS | The Parameter-less Genetic Algorithm was first presented by Harik and Lobo in
1999 as an alternative to the usual trial-and-error method of finding, for each
given problem, an acceptable set-up of the parameter values of the genetic
algorithm. Since then, the same strategy has been successfully applied to
create parame... | computer science |
33,661 | Java Implementation of a Parameter-less Evolutionary Portfolio | cs.MS | The Java implementation of a portfolio of parameter-less evolutionary
algorithms is presented. The Parameter-less Evolutionary Portfolio implements a
heuristic that performs adaptive selection of parameter-less evolutionary
algorithms in accordance with performance criteria that are measured during
running time. At pre... | computer science |
33,662 | Artificial Catalytic Reactions in 2D for Combinatorial Optimization | cs.ET | Presented in this paper is a derivation of a 2D catalytic reaction-based
model to solve combinatorial optimization problems (COPs). The simulated
catalytic reactions, a computational metaphor, occurs in an artificial chemical
reactor that finds near-optimal solutions to COPs. The artificial environment
is governed by c... | computer science |
33,663 | The quasispecies regime for the simple genetic algorithm with
roulette-wheel selection | cs.NE | We introduce a new parameter to discuss the behavior of a genetic algorithm.
This parameter is the mean number of exact copies of the best fit chromosomes
from one generation to the next. We argue that the genetic algorithm should
operate efficiently when this parameter is slightly larger than $1$. We
consider the case... | computer science |
33,664 | Energy-efficient neuromorphic classifiers | cs.NE | Neuromorphic engineering combines the architectural and computational
principles of systems neuroscience with semiconductor electronics, with the aim
of building efficient and compact devices that mimic the synaptic and neural
machinery of the brain. Neuromorphic engineering promises extremely low energy
consumptions, ... | computer science |
33,665 | A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware
Query Suggestion | cs.NE | Users may strive to formulate an adequate textual query for their information
need. Search engines assist the users by presenting query suggestions. To
preserve the original search intent, suggestions should be context-aware and
account for the previous queries issued by the user. Achieving context
awareness is challen... | computer science |
33,666 | Modeling Website Workload Using Neural Networks | cs.DC | In this article, artificial neural networks (ANN) are used for modeling the
number of requests received by 1998 FIFA World Cup website. Modeling is done by
means of time-series forecasting. The log traces of the website, available
through the Internet Traffic Archive (ITA), are processed to obtain two
time-series data ... | computer science |
33,667 | Requirements for Open-Ended Evolution in Natural and Artificial Systems | cs.NE | Open-ended evolutionary dynamics remains an elusive goal for artificial
evolutionary systems. Many ideas exist in the biological literature beyond the
basic Darwinian requirements of variation, differential reproduction and
inheritance. I argue that these ideas can be seen as aspects of five
fundamental requirements fo... | computer science |
33,668 | Efficient and robust calibration of the Heston option pricing model for
American options using an improved Cuckoo Search Algorithm | cs.NE | In this paper an improved Cuckoo Search Algorithm is developed to allow for
an efficient and robust calibration of the Heston option pricing model for
American options. Calibration of stochastic volatility models like the Heston
is significantly harder than classical option pricing models as more parameters
have to be ... | computer science |
33,669 | The study of cuckoo optimization algorithm for production planning
problem | math.OC | Constrained Nonlinear programming problems are hard problems, and one of the
most widely used and common problems for production planning problem to
optimize. In this study, one of the mathematical models of production planning
is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method t... | computer science |
33,670 | Simulating Brain Reaction to Methamphetamine Regarding Consumer
Personality | cs.NE | Addiction, as a nervous disease, can be analysed using mathematical modelling
and computer simulations. In this paper, we use an existing mathematical model
to predict and simulate human brain response to the consumption of a single
dose of methamphetamine. The model is implemented and coded in Matlab. Three
types of p... | computer science |
33,671 | Topology Control of wireless sensor network using Quantum Inspired
Genetic algorithm | cs.NE | In this work, an evolving Linked Quantum register has been introduced, which
are group vector of binary pair of genes, which in its local proximity
represent those nodes that will have high connectivity and keep the energy
consumption at low, and which are taken into account for topology control. The
register works in ... | computer science |
33,672 | Possible Mechanisms for Neural Reconfigurability and their Implications | cs.NE | The paper introduces a biologically and evolutionarily plausible neural
architecture that allows a single group of neurons, or an entire cortical
pathway, to be dynamically reconfigured to perform multiple, potentially very
different computations. The paper shows that reconfigurability can account for
the observed stoc... | computer science |
33,673 | Logical N-AND Gate on a Molecular Turing Machine | cs.ET | In Boolean algebra, it is known that the logical function that corresponds to
the negation of the conjunction --NAND-- is universal in the sense that any
other logical function can be built based on it. This property makes it
essential to modern digital electronics and computer processor design. Here, we
design a molec... | computer science |
33,674 | An evolutionary approach to the identification of Cellular Automata
based on partial observations | cs.NE | In this paper we consider the identification problem of Cellular Automata
(CAs). The problem is defined and solved in the context of partial observations
with time gaps of unknown length, i.e. pre-recorded, partial configurations of
the system at certain, unknown time steps. A solution method based on a
modified varian... | computer science |
33,675 | Introducing Elitist Black-Box Models: When Does Elitist Selection Weaken
the Performance of Evolutionary Algorithms? | cs.NE | Black-box complexity theory provides lower bounds for the runtime of
black-box optimizers like evolutionary algorithms and serves as an inspiration
for the design of new genetic algorithms. Several black-box models covering
different classes of algorithms exist, each highlighting a different aspect of
the algorithms un... | computer science |
33,676 | A hybrid COA-DEA method for solving multi-objective problems | math.OC | The Cuckoo optimization algorithm (COA) is developed for solving
single-objective problems and it cannot be used for solving multi-objective
problems. So the multi-objective cuckoo optimization algorithm based on data
envelopment analysis (DEA) is developed in this paper and it can gain the
efficient Pareto frontiers. ... | computer science |
33,677 | Sampling-based Causal Inference in Cue Combination and its Neural
Implementation | cs.NE | Causal inference in cue combination is to decide whether the cues have a
single cause or multiple causes. Although the Bayesian causal inference model
explains the problem of causal inference in cue combination successfully, how
causal inference in cue combination could be implemented by neural circuits, is
unclear. Th... | computer science |
33,678 | Central Pattern Generators for the control of robotic systems | cs.RO | Bio-inspired control of motion is an active field of research with many
applications in real world tasks. In the case of robotic systems that need to
exhibit oscillatory behaviour (i.e. locomotion of snake-type or legged robots),
Central Pattern Generators (CPGs) are among the most versatile solutions. These
controller... | computer science |
33,679 | Computational evolution of decision-making strategies | cs.NE | Most research on adaptive decision-making takes a strategy-first approach,
proposing a method of solving a problem and then examining whether it can be
implemented in the brain and in what environments it succeeds. We present a
method for studying strategy development based on computational evolution that
takes the opp... | computer science |
33,680 | Impact of noise on a dynamical system: prediction and uncertainties from
a swarm-optimized neural network | cs.NE | In this study, an artificial neural network (ANN) based on particle swarm
optimization (PSO) was developed for the time series prediction. The hybrid
ANN+PSO algorithm was applied on Mackey--Glass chaotic time series in the
short-term $x(t+6)$. The performance prediction was evaluated and compared with
another studies ... | computer science |
33,681 | A Multi-Agent System Approach to Load-Balancing and Resource Allocation
for Distributed Computing | cs.NE | In this research we use a decentralized computing approach to allocate and
schedule tasks on a massively distributed grid. Using emergent properties of
multi-agent systems, the algorithm dynamically creates and dissociates clusters
to serve the changing resource demands of a global task queue. The algorithm is
compared... | computer science |
33,682 | Estimating Random Delays in Modbus Network Using Experiments and General
Linear Regression Neural Networks with Genetic Algorithm Smoothing | cs.SY | Time-varying delays adversely affect the performance of networked control
sys-tems (NCS) and in the worst-case can destabilize the entire system.
Therefore, modelling network delays is important for designing NCS. However,
modelling time-varying delays is challenging because of their dependence on
multiple pa-rameters ... | computer science |
33,683 | Well Tops Guided Prediction of Reservoir Properties using Modular Neural
Network Concept A Case Study from Western Onshore, India | cs.NE | This paper proposes a complete framework consisting pre-processing, modeling,
and post-processing stages to carry out well tops guided prediction of a
reservoir property (sand fraction) from three seismic attributes (seismic
impedance, instantaneous amplitude, and instantaneous frequency) using the
concept of modular a... | computer science |
33,684 | Mapping Generative Models onto a Network of Digital Spiking Neurons | cs.NE | Stochastic neural networks such as Restricted Boltzmann Machines (RBMs) have
been successfully used in applications ranging from speech recognition to image
classification. Inference and learning in these algorithms use a Markov Chain
Monte Carlo procedure called Gibbs sampling, where a logistic function forms
the kern... | computer science |
33,685 | A Revisit of Infinite Population Models for Evolutionary Algorithms on
Continuous Optimization Problems | cs.NE | Infinite population models are important tools for studying population
dynamics of evolutionary algorithms. They describe how the distributions of
populations change between consecutive generations. In general, infinite
population models are derived from Markov chains by exploiting symmetries
between individuals in the... | computer science |
33,686 | VLSI Implementation of Deep Neural Network Using Integral Stochastic
Computing | cs.NE | The hardware implementation of deep neural networks (DNNs) has recently
received tremendous attention: many applications in fact require high-speed
operations that suit a hardware implementation. However, numerous elements and
complex interconnections are usually required, leading to a large area
occupation and copious... | computer science |
33,687 | Fault Tolerance in Distributed Neural Computing | cs.NE | With the increasing complexity of computing systems, complete hardware
reliability can no longer be guaranteed. We need, however, to ensure overall
system reliability. One of the most important features of artificial neural
networks is their intrinsic fault-tolerance. The aim of this work is to
investigate whether such... | computer science |
33,688 | Autonomous Perceptron Neural Network Inspired from Quantum computing | cs.NE | This abstract will be modified after correcting the minor error in Eq.(2) | computer science |
33,689 | Nonlinear functional mapping of the human brain | cs.NE | The field of neuroimaging has truly become data rich, and novel analytical
methods capable of gleaning meaningful information from large stores of imaging
data are in high demand. Those methods that might also be applicable on the
level of individual subjects, and thus potentially useful clinically, are of
special inte... | computer science |
33,690 | Towards Trainable Media: Using Waves for Neural Network-Style Training | cs.NE | In this paper we study the concept of using the interaction between waves and
a trainable medium in order to construct a matrix-vector multiplier. In
particular we study such a device in the context of the backpropagation
algorithm, which is commonly used for training neural networks. Here, the
weights of the connectio... | computer science |
33,691 | LM-CMA: an Alternative to L-BFGS for Large Scale Black-box Optimization | cs.NE | The limited memory BFGS method (L-BFGS) of Liu and Nocedal (1989) is often
considered to be the method of choice for continuous optimization when first-
and/or second- order information is available. However, the use of L-BFGS can
be complicated in a black-box scenario where gradient information is not
available and th... | computer science |
33,692 | There is no fast lunch: an examination of the running speed of
evolutionary algorithms in several languages | cs.NE | It is quite usual when an evolutionary algorithm tool or library uses a
language other than C, C++, Java or Matlab that a reviewer or the audience
questions its usefulness based on the speed of those other languages,
purportedly slower than the aforementioned ones. Despite speed being not
everything needed to design a ... | computer science |
33,693 | Large Scale Artificial Neural Network Training Using Multi-GPUs | cs.DC | This paper describes a method for accelerating large scale Artificial Neural
Networks (ANN) training using multi-GPUs by reducing the forward and backward
passes to matrix multiplication. We propose an out-of-core multi-GPU matrix
multiplication and integrate the algorithm with the ANN training. The
experiments demonst... | computer science |
33,694 | An Approximate Backpropagation Learning Rule for Memristor Based Neural
Networks Using Synaptic Plasticity | cs.NE | We describe an approximation to backpropagation algorithm for training deep
neural networks, which is designed to work with synapses implemented with
memristors. The key idea is to represent the values of both the input signal
and the backpropagated delta value with a series of pulses that trigger
multiple positive or ... | computer science |
33,695 | Super-Linear Gate and Super-Quadratic Wire Lower Bounds for Depth-Two
and Depth-Three Threshold Circuits | cs.CC | In order to formally understand the power of neural computing, we first need
to crack the frontier of threshold circuits with two and three layers, a regime
that has been surprisingly intractable to analyze. We prove the first
super-linear gate lower bounds and the first super-quadratic wire lower bounds
for depth-two ... | computer science |
33,696 | A Normative Theory of Adaptive Dimensionality Reduction in Neural
Networks | cs.NE | To make sense of the world our brains must analyze high-dimensional datasets
streamed by our sensory organs. Because such analysis begins with
dimensionality reduction, modelling early sensory processing requires
biologically plausible online dimensionality reduction algorithms. Recently, we
derived such an algorithm, ... | computer science |
33,697 | Optimization theory of Hebbian/anti-Hebbian networks for PCA and
whitening | cs.NE | In analyzing information streamed by sensory organs, our brains face
challenges similar to those solved in statistical signal processing. This
suggests that biologically plausible implementations of online signal
processing algorithms may model neural computation. Here, we focus on such
workhorses of signal processing ... | computer science |
33,698 | Minimal Perceptrons for Memorizing Complex Patterns | cs.NE | Feedforward neural networks have been investigated to understand learning and
memory, as well as applied to numerous practical problems in pattern
classification. It is a rule of thumb that more complex tasks require larger
networks. However, the design of optimal network architectures for specific
tasks is still an un... | computer science |
33,699 | Linear Models of Computation and Program Learning | cs.LO | We consider two classes of computations which admit taking linear
combinations of execution runs: probabilistic sampling and generalized
animation. We argue that the task of program learning should be more tractable
for these architectures than for conventional deterministic programs. We look
at the recent advances in ... | computer science |
33,700 | Continuous online sequence learning with an unsupervised neural network
model | cs.NE | The ability to recognize and predict temporal sequences of sensory inputs is
vital for survival in natural environments. Based on many known properties of
cortical neurons, hierarchical temporal memory (HTM) sequence memory is
recently proposed as a theoretical framework for sequence learning in the
cortex. In this pap... | computer science |
33,701 | Hardware Architecture for Large Parallel Array of Random Feature
Extractors applied to Image Recognition | cs.ET | We demonstrate a low-power and compact hardware implementation of Random
Feature Extractor (RFE) core. With complex tasks like Image Recognition
requiring a large set of features, we show how weight reuse technique can allow
to virtually expand the random features available from RFE core. Further, we
show how to avoid ... | computer science |
33,702 | Approximate Hubel-Wiesel Modules and the Data Structures of Neural
Computation | cs.NE | This paper describes a framework for modeling the interface between
perception and memory on the algorithmic level of analysis. It is consistent
with phenomena associated with many different brain regions. These include
view-dependence (and invariance) effects in visual psychophysics and
inferotemporal cortex physiolog... | computer science |
33,703 | NodIO, a JavaScript framework for volunteer-based evolutionary
algorithms : first results | cs.DC | JavaScript is an interpreted language mainly known for its inclusion in web
browsers, making them a container for rich Internet based applications. This
has inspired its use, for a long time, as a tool for evolutionary algorithms,
mainly so in browser-based volunteer computing environments. Several libraries
have also ... | computer science |
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