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Using Shannon information theory to analyse the contributions from two source variables to a target, for example, we can measure the information held by one source about the target, the information held by the other source about the target, and the information held by those sources together about the target. Intuitively, however, there is strong desire to measure further notions of how this directed information interaction may be decomposed, e.g., how much information the two source variables hold redundantly about the target, how much each source variable holds uniquely, and how much information can only be discerned by synergistically examining the two sources together.The absence of measures for such decompositions into redundant, unique and synergistic information is arguably the most fundamental missing piece in classical information theory. Triggered by the formulation of the Partial Information Decomposition framework by Williams and Beer in 2010, the past few years have witnessed a concentration of work by the community in proposing, contrasting, and investigating new measures to capture these notions of information decomposition.This Special Issue seeks to bring together these efforts, to capture a snapshot of the current research, as well as to provide impetus for and focused scrutiny on newer work, present progress to the wider community and attract further research. Our contributions present: several new approaches for measures of such decompotions; commentary on properties, interpretations and limitations of such approaches; and applications to empirical data (in particular to neural data).
Shannon information  information theory  information decomposition  mutual information  synergy  redundancy  shared information transfer entropy
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The aim of this Research Topic is to discuss the state of the art on the use of Informationbased methods in the analysis of neuroimaging data. Informationbased methods, typically built as extensions of the Shannon Entropy, are at the basis of modelfree approaches which, being based on probability distributions rather than on specific expectations, can account for all possible nonlinearities present in the data in a modelindependent fashion.Mutual Informationlike methods can also be applied on interacting dynamical variables described by timeseries, thus addressing the uncertainty reduction (or information) in one variable by conditioning on another set of variables.In the last years, different Informationbased methods have been shown to be flexible and powerful tools to analyze neuroimaging data, with a wide range of different methodologies, including formulationsbased on bivariate vs multivariate representations, frequency vs time domains, etc. Apart from methodological issues, the information bit as a common unit represents a convenient way to open the road for comparison and integration between different measurements of neuroimaging data in three complementary contexts: Structural Connectivity, Dynamical (Functional and Effective) Connectivity, and Modelling of brain activity. Applications are ubiquitous, starting from resting state in healthy subjects to modulations of consciousness and other aspects of pathophysiology.Mutual Informationbased methods have provided new insights about commonprinciples in brain organization, showing the existence of an active default network when the brain is at rest. It is not clear, however, how this default network is generated, the different modules are intrainteracting, or disappearing in the presence of stimulation. Some of these openquestions at the functional level might find their mechanisms on their structural correlates. A key question is the link between structure and function and the use of structural priors for the understanding of the functional connectivity measures. As effective connectivity is concerned, recently a common framework has been proposed for Transfer Entropy and Granger Causality, a wellestablished methodology originally based on autoregressive models. This framework can open the way to new theories and applications.This Research Topic brings together contributions from researchers from different backgrounds which are either developing new approaches, or applying existing methodologies to new data, and we hope it will set the basis for discussing the development and validation of new Informationbased methodologies for the understanding of brain structure, function, and dynamics.
brain connectivity  Information Theory  transfer entropy  Granger causality  mutual information  structural connectome  functional connectome  network theory  computational neuroscience  neuroinformatics
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Statistical relationships among the variables of a complex system reveal a lot about its physical behavior. Therefore, identification of the relevant variables and characterization of their interactions are crucial for a better understanding of a complex system. Linear methods, such as correlation, are widely used to identify these relationships. However, informationtheoretic quantities, such as mutual information and transfer entropy, have been proven to be superior in the case of nonlinear dependencies. Mutual information quantifies the amount of information obtained about one random variable through the other random variable, and it is symmetric. As an asymmetrical measure, transfer entropy quantifies the amount of directed (timeasymmetric) transfer of information between random processes and, thus, it is related to concepts, such as the Granger causality. This Special Issue includes 16 papers elucidating the state of the art of databased transfer entropy estimation techniques and applications, in areas such as finance, biomedicine, fluid dynamics and cellular automata. Analytical derivations in special cases, improvements on the estimation methods and comparisons between certain techniques are some of the other contributions of this Special Issue. The diversity of approaches and applications makes this book unique as a single source of invaluable contributions from experts in the field.
transfer entropy  information flow  causality  causal relationships  entropy  mutual information  correlation  entropy estimation  statistical dependency  nonlinear interactions  interacting subsystems  informationtheory  informationtheoretic quantities  Granger causality  machine learning  data mining  statistical signal processing
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As the ultimate information processing device, the brain naturally lends itself to being studied with information theory. The application of information theory to neuroscience has spurred the development of principled theories of brain function, and has led to advances in the study of consciousness, as well as to the development of analytical techniques to crack the neural code—that is, to unveil the language used by neurons to encode and process information. In particular, advances in experimental techniques enabling the precise recording and manipulation of neural activity on a large scale now enable for the first time the precise formulation and the quantitative testing of hypotheses about how the brain encodes and transmits the information used for specific functions across areas. This Special Issue presents twelve original contributions on novel approaches in neuroscience using information theory, and on the development of new information theoretic results inspired by problems in neuroscience.
neural network  Potts model  latching  recursion  functional connectome  graph theoretical analysis  eigenvector centrality  orderness  network eigenentropy  information entropy production  discrete Markov chains  spike train statistics  Gibbs measures  maximum entropy principle  pulsegating  channel capacity  neural coding  feedforward networks  neural information propagation  information theory  mutual information decomposition  synergy  redundancy  integrated information theory  integrated information  minimum information partition  submodularity  Queyranne’s algorithm  consciousness  maximum entropy  higherorder correlations  neural population coding  Ising model  brain network  complex networks  connectome  information theory  graph theory  freeenergy principle  internal model hypothesis  unconscious inference  infomax principle  independent component analysis  principal component analysis  goodness  categorical perception  perceptual magnet  information theory  perceived similarity  mutual information  synergy  redundancy  neural code  hippocampus  entorhinal cortex  navigation  neural code  representation  decoding  spiketime precision  discrimination  noise correlations  information theory  mismatched decoding  information theory  neuroscience
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In the past few decades, the optical communication industry has explored multiple degrees of freedom of the photon, such as time, wavelength, amplitude, phase, polarization, and space, to significantly reduce the cost/bit of data transmission by increasing the capacity per fiber through multiplexing technology and by reducing the size and power through electronic and photonic integration. This book aims to explore the latest advancements in this industry, including the technologies in devices, systems, and network levels with applications from shortreach chiptochip interconnections to longhaul backbone communications at the transoceanic distance.
optical communication  fiber optics  clientside optics  400G Ethernet  CFP8LR8 transceiver  VLC  block code  dimming control  encoding/decoding algorithm  hybrid optical networkonchip (HONoC)  insertion loss  crosstalk noise  signaltonoise ratio (SNR)  semiconductor optical amplifier (SOA)  constellation shaping  probabilistic shaping  geometric shaping  fiberoptic communications  quadrature amplitude modulation  mutual information  generalized mutual information  coherent communications  optical communications  fiber optics  digital signal processing  visible light communications (VLC)  optical orthogonal frequency division multiplexing (OOFDM)  dimming control  pulse width modulation (PWM)  satellitetosea laser communication  acquisition, tracking and pointing  shipborne ATP  pointing error model  coherent optical fiber communication  laser phase noise (LPN)  carrier phase recovery (CPR)  phase noise cancellation (PNC)  equalization enhanced phase noise (EEPN)  radio frequency (RF) pilot tone  pulse amplitude modulation  nyquist pulse shaping  DWDM system  LDPC coding  n/a
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This book presents interesting samples of theoretical and practical advances of symmetry in multidisciplinary engineering applications. It covers several applications, such as accessibility and traffic congestion management, path planning for mobile robots, analysis of shipment service networks, fault diagnosis methods in electrical circuits and electrical machines, geometrical issues in architecture, geometric modeling and virtual reconstruction, design of noise detectors, filters, and segmentation methods for image processing, and cyclic symmetric structures in turbomachinery applications, to name but a few. The contributions included in this book depict the state of the art in this field and lay the foundation for the possibilities that the study of symmetry has in multidisciplinary applications in the field of engineering.
express shipment  service network design  linearization technique  railway network  path planning  mobile robot  environmental modeling  optimization criteria  path search  aged  high order urban hospitals (HOUHs)  accessibility  evaluation model  trip impedance based on public transportation  urban traffic planning  3D slicer  classification  extension  random forest  segmentation  sensitivity analysis  support vector machine  tumor  thinwalled gear  ring damper  vibration  energy dissipation  friction damping  fault diagnosis  lifting wavelet  local preserving projection  Fisher linear discriminant analysis  semisupervised random forest  adaptive threshold  clustering  edge preserving  noise detector  random value impulse noise  weighted mean filter  anomaly detection  local data features  BP neural network  local monotonicity  convexity/concavity  local inflection  peaks distribution  inclined plane  Coalbrookdale (Shropshire)  Agustín de Betancourt  geometric modeling  virtual reconstruction  industrial heritage  industrial archaeology  symmetry  rampant arch  geometry  optimum  flying buttresses  cathedral  rolling bearings  fault diagnosis  broad learning model  variational mode decomposition  Hilbert transform  railway transportation  timespace network  A* algorithm  traffic congestion  traffic forecasting  traffic control  railcar flow distribution  asymmetry  synchronization  topology  electrical circuits  electronic devices  mechanical structures  robots  graphic modelling  complex networks  optimization  computing applications  feature selection  conditional mutual information  feature interaction  classification  computer engineering
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The aim of this special issue is to publish original research papers that cover recent advances in the theory and application of stochastic processes. There is especial focus on applications of stochastic processes as models of dynamic phenomena in various research areas, such as queuing theory, physics, biology, economics, medicine, reliability theory, and financial mathematics. Potential topics include, but are not limited to: Markov chains and processes; large deviations and limit theorems; random motions; stochastic biological model; reliability, availability, maintenance, inspection; queueing models; queueing network models; computational methods for stochastic models; applications to risk theory, insurance and mathematical finance.
measure of information  cumulative inaccuracy  mutual information  lower record values  parabolic equation  Cauchy problem  Monte Carlo method  unbiased estimator  vonNeumann–Ulam scheme  compound poisson insurance risk model  expected discounted penalty function  estimation  Fourier transform  Fouriercosine series  multidimensional birthdeath process  inhomogeneous continuoustime Markov chain  rate of convergence  one dimensional projection  Wiener–Poisson risk model  survival probability  Nonparametric threshold estimation  wet periods  total precipitation volume  asymptotic approximation  extreme order statistics  random sample size  testing statistical hypotheses  queueing systems  rate of convergence  nonstationary  Markovian queueing models  limiting characteristics  queuing network  retrials  statedependent marked Markovian arrival process  wireless telecommunication networks  timedependent queuelength probability  discretetime Geo/D/1 queue  closedform solution  Monte Carlo method  quasiMonte Carlo method  KoksmaHlawka inequality  quasirandom sequences  stochastic processes  processor heating and cooling  markovian arrival process  phasetype service time distribution  impatience  QuasiBirthandDeath process  matrixgeometric solution  truncated distribution  Markovian arrival process  multiclass arrival processes  product form  equitylinked death benefits  Fourier cosine series expansion  guaranteed minimum death benefit  option  valuation  Lévy process  compound Poisson risk model  generalized Gerber–Shiu discounted penalty function  Laplace transform  Dickson–Hipp operator  recursive formula
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This Special Issue presents research papers on various topics within many different branches of mathematics, applied mathematics, and mathematical physics. Each paper presents mathematical theories, methods, and their application based on current and recently developed symmetric polynomials. Also, each one aims to provide the full understanding of current research problems, theories, and applications on the chosen topics and includes the most recent advances made in the area of symmetric functions and polynomials.
Fubini polynomials  wtorsion Fubini polynomials  fermionic padic integrals  symmetric identities  Chebyshev polynomials  sums of finite products  hypergeometric function  Fubini polynomials  Euler numbers  symmetric identities  elementary method  computational formula  two variable qBerstein polynomial  two variable qBerstein operator  qEuler number  qEuler polynomial  Fubini polynomials  Euler numbers  congruence  elementary method  qBernoulli numbers  qBernoulli polynomials  two variable qBernstein polynomials  two variable qBernstein operators  padic integral on ?p  the degenerate gamma function  the modified degenerate gamma function  the degenerate Laplace transform  the modified degenerate Laplace transform  Fibonacci  Lucas  linear form in logarithms  continued fraction  reduction method  sums of finite products of Chebyshev polynomials of the third and fourth kinds  Hermite  generalized Laguerre  Legendre  Gegenbauer  Jacobi  thirdorder character  classical Gauss sums  rational polynomials  analytic method  recursive formula  fermionic padic qintegral on ?p  qEuler polynomials  qChanghee polynomials  symmetry group  Apostoltype Frobenius–Euler polynomials  threevariable Hermite polynomials  symmetric identities  explicit relations  operational connection  qVolkenborn integral on ?p  Bernoulli numbers and polynomials  generalized Bernoulli polynomials and numbers of arbitrary complex order  generalized Bernoulli polynomials and numbers attached to a Dirichlet character ?  Changhee polynomials  Changhee polynomials of type two  fermionic padic integral on ?p  Chebyshev polynomials of the first, second, third, and fourth kinds  sums of finite products  representation  catalan numbers  elementary and combinatorial methods  recursive sequence  convolution sums  wellposedness  stability  acoustic wave equation  perfectly matched layer  Fibonacci polynomials  Lucas polynomials  trivariate Fibonacci polynomials  trivariate Lucas polynomials  generating functions  central incomplete Bell polynomials  central complete Bell polynomials  central complete Bell numbers  Legendre polynomials  Laguerre polynomials  generalized Laguerre polynomials  Gegenbauer polynomials  hypergeometric functions 1F1 and 2F1  Euler polynomials  Bernoulli polynomials  elementary method  identity  congruence  new sequence  Catalan numbers  elementary and combinatorial methods  congruence  conjecture  fluctuation theorem  thermodynamics of information  stochastic thermodynamics  mutual information  nonequilibrium free energy  entropy production
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Entropy theory has wide applications to a range of problems in the fields of environmental and water engineering, including river hydraulic geometry, fluvial hydraulics, water monitoring network design, river flow forecasting, floods and droughts, river network analysis, infiltration, soil moisture, sediment transport, surface water and groundwater quality modeling, ecosystems modeling, water distribution networks, environmental and water resources management, and parameter estimation. Such applications have used several different entropy formulations, such as Shannon, Tsallis, Reacutenyi Burg, Kolmogorov, Kapur, configurational, and relative entropies, which can be derived in time, space, or frequency domains. More recently, entropybased concepts have been coupled with other theories, including copula and wavelets, to study various issues associated with environmental and water resources systems. Recent studies indicate the enormous scope and potential of entropy theory in advancing research in the fields of environmental and water engineering, including establishing and explaining physical connections between theory and reality. The objective of this Special Issue is to provide a platform for compiling important recent and current research on the applications of entropy theory in environmental and water engineering. The contributions to this Special Issue have addressed many aspects associated with entropy theory applications and have shown the enormous scope and potential of entropy theory in advancing research in the fields of environmental and water engineering.
complexity  streamflow  water level  composite multiscale sample entropy  trend  Poyang Lake basin  fourparameter exponential gamma distribution  principle of maximum entropy  precipitation frequency analysis  methods of moments  maximum likelihood estimation  flood frequency analysis  generalized gamma (GG) distribution  principle of maximum entropy (POME)  entropy theory  principle of maximum entropy (POME)  GB2 distribution  flood frequency analysis  nonpoint source pollution  ANN  entropy weighting method  datascarce  multievents  spatiotemporal variability  soil water content  entropy  arid region  joint entropy  NDVI  temperature  precipitation  groundwater depth  Hei River basin  turbulent flow  canopy flow  randomness  coherent structures  Shannon entropy  Kolmogorov complexity  entropy  information transfer  optimization  radar  rainfall network  water resource carrying capacity  forewarning model  entropy of information  fuzzy analytic hierarchy process  projection pursuit  accelerating genetic algorithm  entropy production  conditional entropy production  stochastic processes  scaling  climacogram  turbulence  water resources vulnerability  connection entropy  changing environment  set pair analysis  Anhui Province  crossentropy minimization  land suitability evaluation  spatial optimization  monthly streamflow forecasting  Burg entropy  configurational entropy  entropy spectral analysis time series analysis  entropy  water monitoring  network design  hydrometric network  information theory  entropy applications  hydrological risk analysis  maximum entropycopula method  uncertainty  Loess Plateau  entropy  water engineering  Tsallis entropy  principle of maximum entropy  Lagrangian function  probability distribution function  flux concentration relation  uncertainty  information  informational entropy  variation of information  continuous probability distribution functions  confidence intervals  precipitation  variability  marginal entropy  crop yield  Hexi corridor  flow duration curve  Shannon entropy  entropy parameter  modeling  spatial and dynamics characteristic  hydrology  tropical rainfall  statistical scaling  Tsallis entropy  multiplicative cascades  BetaLognormal model  rainfall forecast  cross entropy  ant colony fuzzy clustering  combined forecast  information entropy  mutual information  kernel density estimation  ENSO  nonlinear relation  scaling laws  power laws  water distribution networks  robustness  flow entropy  entropy theory  frequency analysis  hydrometeorological extremes  Bayesian technique  rainfall  entropy ensemble filter  ensemble model simulation criterion  EEF method  bootstrap aggregating  bagging  bootstrap neural networks  El Niño  ENSO  neural network forecast  sea surface temperature  tropical Pacific  entropy  cross elasticity  mean annual runoff  water resources  resilience  quaternary catchment  complement  substitute  entropy theory  complex systems  hydraulics  hydrology  water engineering  environmental engineering
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This book presents an interesting sample of the latest advances in optimization techniques applied to electrical power engineering. It covers a variety of topics from various fields, ranging from classical optimization such as Linear and Nonlinear Programming and Integer and MixedInteger Programming to the most modern methods based on bioinspired metaheuristics. The featured papers invite readers to delve further into emerging optimization techniques and their real application to case studies such as conventional and renewable energy generation, distributed generation, transport and distribution of electrical energy, electrical machines and power electronics, network optimization, intelligent systems, advances in electric mobility, etc.
Cable joint  internal defect  thermal probability density  power system optimization  optimal power flow  developed grew wolf optimizer  energy internet  prosumer  energy management  consensus  demand response  dayahead load forecasting  modular predictor  feature selection  microphasor measurement unit  mutual information theory  stochastic state estimation  twopoint estimation method  JAYA algorithm  multipopulation method (MP)  chaos optimization algorithm (COA)  economic load dispatch problem (ELD)  optimization methods  constrained parameter estimation  extended Kalman filter  power systems  C&I particle swarm optimization  ringdown detection  optimal reactive power dispatch  loss minimization  voltage deviation  hybrid method  tabu search  particle swarm optimization  artificial lighting  simulation  calibration  radiance  GenOpt  street light points  DC optimal power flow  power transfer distribution factors  generalized generation distribution factors  unit commitment  adaptive consensus algorithm  distributed heatelectricity energy management  eight searching subregions  islanded microgrid  dragonfly algorithm  metaheuristic  optimal power flow  particle swarm optimization  CCHP system  energy storage  offdesign performance  dynamic solving framework  battery energy storage system  micro grid  MILP  PCS efficiency  piecewise linear techniques  renewable energy sources  optimal operation  UC  demand bidding  demand response  genetic algorithm  load curtailment  optimization  hybrid renewable energy system  pumpedhydro energy storage  offgrid  optimization  HOMER software  rural electrification  subSaharan Africa  Cameroon  building energy management system  HVAC system  energy storage system  energy flow model  dependability  sustainability  data center  power architectures  optimization  AC/DC hybrid active distribution  hierarchical scheduling  multistakeholders  discrete wind driven optimization  multiobjective optimization  optimal power flow  metaheuristic  wind energy  photovoltaic  smart grid  transformerfault diagnosis  principal component analysis  particle swarm optimization  support vector machine  wind power  integration assessment  interactive load  considerable decomposition  controllable response  SOCP relaxations  optimal power flow  current margins  affine arithmetic  interval variables  optimizingscenarios method  power flow  wind power  active distribution system  virtual power plant  stochastic optimization  decentralized and collaborative optimization  genetic algorithm  multiobjective particle swarm optimization algorithm  artificial bee colony  IEEE Std. 802000  Schwarz’s equation  fuzzy algorithm  radial basis function  neural network  ETAP  distributed generations (DGs)  distribution network reconfiguration  runnerroot algorithm (RRA)  interturn shortedcircuit fault (ISCF)  strong track filter (STF)  linear discriminant analysis (LDA)  switched reluctance machine (SRM)  charging/discharging  electric vehicle  energy management  genetic algorithm  intelligent scatter search  electric vehicles  heterogeneous networks  demand uncertainty  power optimization  Stackelberg game  power system unit commitment  hybrid membrane computing  crossentropy  the genetic algorithm based P system  the biomimetic membrane computing  transient stability  twostage feature selection  particle encoding method  fitness function  power factor compensation  nonsinusoidal circuits  geometric algebra  evolutionary algorithms  electric power contracts  electric energy costs  cost minimization  evolutionary computation  bioinspired algorithms  congestion management  lowvoltage networks  multiobjective particle swarm optimization  affinity propagation clustering  optimal congestion threshold  optimization  magnetic field mitigation  overhead  underground  passive shielding  active shielding  MV/LV substation  n/a
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