Results 31 to 40 of about 58,822 (196)
Structural Changes in Data Communication in Wireless Sensor Networks [PDF]
, 2013 Wireless sensor networks are an important technology for making distributed autonomous measures in hostile or inaccessible environments. Among the challenges they pose, the way data travel among them is a relevant issue since their structure is quite ...
Aquino, Andre L. L. +4 more
core +2 more sources
Divergence Measure of Belief Function and Its Application in Data Fusion
IEEE Access, 2019 Divergence measure is widely used in many applications. To efficiently deal with uncertainty in real applications, basic probability assignment (BPA) in Dempster-Shafer evidence theory, instead of probability distribution, is adopted.
Yutong Song, Yong Deng
doaj +1 more source
Kullback–Leibler Divergence Measure for Multivariate Skew-Normal Distributions
Entropy, 2012 The aim of this work is to provide the tools to compute the well-known Kullback–Leibler divergence measure for the flexible family of multivariate skew-normal distributions.
Reinaldo B. Arellano-Valle +1 more
doaj +1 more source
Variational Characterizations of Local Entropy and Heat Regularization in Deep Learning
, 2019 The aim of this paper is to provide new theoretical and computational understanding on two loss regularizations employed in deep learning, known as local entropy and heat regularization.
Kaplan, Zach +2 more
core +1 more source
Hard‐Magnetic Soft Millirobots in Underactuated Systems
Advanced Robotics Research, EarlyView.This review provides a comprehensive overview of hard‐magnetic soft millirobots in underactuated systems. It examines key advances in structural design, physics‐informed modeling, and control strategies, while highlighting the interplay among these domains.
Qiong Wang +4 more
wiley +1 more source
Association of Jensen’s inequality for s-convex function with Csiszár divergence
Journal of Inequalities and Applications, 2019 In the article, we establish an inequality for Csiszár divergence associated with s-convex functions, present several inequalities for Kullback–Leibler, Renyi, Hellinger, Chi-square, Jeffery’s, and variational distance divergences by using particular s ...
Muhammad Adil Khan +4 more
doaj +1 more source
Learnable Diffusion Framework for Mouse V1 Neural Decoding
Advanced Science, EarlyView.We introduce Sensorium‐Viz, a diffusion‐based framework for reconstructing high‐fidelity visual stimuli from mouse primary visual cortex activity. By integrating a novel spatial embedding module with a Diffusion Transformer (DiT) and a synthetic‐response augmentation strategy, our model outperforms state‐of‐the‐art fMRI‐based baselines, enabling robust
Kaiwen Deng +2 more
wiley +1 more source
An Introduction to Predictive Processing Models of Perception and Decision‐Making
Topics in Cognitive Science, EarlyView., 2023 Abstract The predictive processing framework includes a broad set of ideas, which might be articulated and developed in a variety of ways, concerning how the brain may leverage predictive models when implementing perception, cognition, decision‐making, and motor control.
Mark Sprevak, Ryan Smith
wiley +1 more source
Entropy, 2023 Bayesian models allow us to investigate children’s belief revision alongside physiological states, such as “surprise”. Recent work finds that pupil dilation (or the “pupillary surprise response”) following expectancy violations is predictive of belief ...
Joseph Colantonio +4 more
doaj +1 more source
Spintronic Bayesian Hardware Driven by Stochastic Magnetic Domain Wall Dynamics
Advanced Science, EarlyView.Magnetic Probabilistic Computing (MPC) utilizes intrinsic stochastic dynamics in domain walls to establish a hardware foundation for uncertainty‐aware artificial intelligence. Thermally driven domain‐wall fluctuations, voltage‐controlled magnetic anisotropy, and TMR readout enable fully electrical, tunable probabilistic inference.
Tianyi Wang +11 more
wiley +1 more source