Entropy, Volume 28, Issue 10

Forecasting extreme-tail risk in energy equities involves temporal-memory mismatch, quantile crossing, and scarce extreme observations. This study proposes a multi-scale entropy-gated deep quantile learning model, EntropyFusion-VaR. Weighted permutat...

This study examines an apparent discrepancy that can arise between the canonical construction and the Taylor expansion constructed from a high-temperature thermodynamic potential restricted to the trivial Polyakov-loop sector in the presence of the R...

Information dissemination and intercity commuting jointly shape epidemic dynamics, but their combined effects in heterogeneous cities require further investigation. We develop a two-layer model coupling information diffusion through pairwise interact...

Information flow between components of a system takes many forms and is key to understanding the organization and functioning of large-scale, complex systems. We demonstrate three modalities of information flow from time series X to time series Y. In...

Many problems in signal processing, machine learning, and neuroimaging provide several jointly observed measurements of one process, each mixing a component common to all of them with a component private to itself, and require the common component to...

We present a correspondence principle framework that maps a classical field-amplitude decay law $\mathrm{\Gamma }\left(t\right)$ onto a vacuum-loss quantum master equation for a single bosonic mode. Under the strict assumption that an initially coherent state remains pure...

Noncommuting common causes posited as explanations of quantum correlations violate the law of total probability. On the operationalist interpretation of quantum mechanics, this flaw might be interpreted as a result of the noncommuting common causes b...

We present a study on the behavior of Rényi entropy for earthquake magnitudes, focusing on two large-magnitude events in the Pacific coast of Mexico: the M8.2 Tehuantepec earthquake in 2017 and the M7.7 Michoacán earthquake in 2022. Bas...

This paper is concerned with robust performance analysis for continuous time invariant systems governed by linear stochastic differential equations driven by statistically uncertain Ito processes. The uncertainty is understood as the deviation of imp...

High-frequency sampling in mechanical vibration wireless sensor networks (WSNs) generates large volumes of raw data, imposing substantial demands on transmission bandwidth and node energy consumption. To address this problem, this paper proposes a tw...

Density peak clustering (DPC) is popular in unsupervised clustering due to its intuitive theory and the absence of pre-defined cluster numbers. However, traditional DPC relies on a static single computation strategy. It uses all raw samples to calcul...

The extended local-equilibrium approach to stochastic thermodynamics assigns thermodynamic state functions to the instantaneous fluctuating state of a system. In this work, we derive the evolution equations used in this approach from a Markov jump pr...

This paper studies secure multi-user near-field transmission assisted by movable antennas (MAs) and a stacked intelligent metasurface (SIM) in the presence of multiple eavesdroppers. Each transmit antenna moves continuously along a common rail subjec...

We discuss Einstein equations with a non-conserved energy-momentum $T^{\mu \nu }$. The energy-momentum tensor ${\tilde{T}}^{\mu \nu }$ on the right hand side of Einstein equations is expressed in the form $8\pi G{T}^{\mu \nu }-g^{\mu \nu }\mathsf{\Lambda }+{\Sigma }^{\mu \nu }$ wh...

Restoring surveillance images containing both structurally reliable and information-sparse fuzzy regions remains challenging because conventional restoration and diffusion-based methods may either suppress authentic textures or introduce visually pla...

Entropy-based fault diagnosis depends on signal representation and the properties captured by each entropy measure. We evaluated dual-view complementary multiscale entropy fusion (DCMEF), which combines RMS-normalized raw vibration with a normalized...

The complexity of systems in the physical, social, biological, or technological world arises from the need for the fundamental system units (network nodes) to interact with one another (represented by network links), enabling the system to perform a...

Banks and firms are connected through multiple credit relationships that can transmit financial shocks across sectors, amplify default contagion, and ultimately lead to system-wide collapse. Understanding systemic risk therefore requires not only exa...

In quantum physics, the order in which different operations occur can be placed in superposition. The resulting processes have an indefinite causal order which is of fundamental interest and can be viewed as a novel quantum resource enabling new prot...

Let ${\left(X_n\right)}_{n\ge 0}$ be a supercritical Galton–Watson process with offspring mean m > 1, positive variance, and ${\mathbb{E}|X_1-m|}^{2+\rho }<\infty$ for some 0 < $\rho$ ≤ 1. We establish self-normalized Cramértype moderate deviations for th...

We investigate the equilibrium properties of the von Neumann entropy in a dense, dipole–dipole coupled ensemble of two-level emitters confined in a fully symmetric Dicke subspace, which additionally interact through a common thermal bath. The s...

Relativistic motion introduces decoherence that limits quantum parameter estimation. Earlier treatments of bipartite Unruh–DeWitt detectors have often restricted the reduced state to the X-state form. We consider single-qubit and isolated-ancil...

This paper develops a stochastic framework for epidemic spreading on regular networks based on the Fokker–Planck equation and spectral graph theory. Starting from an Itô stochastic differential equation, we derive the corresponding Fokker...

I derive a Lorentzian correlation kernel from a maximum-entropy principle on an abstract possibility space, assuming no spacetime or field theory. Maximizing Shannon entropy over positive phase rates at fixed mean gives $p\left(\omega \right)={\Omega }^{-1}{e}^{\&mi}$...

This study introduces the Visibility-Graph-Based Relative Strength Index (VGRSI), a causal indicator that selects and implicitly weights price increments using backward natural visibility. Independent parameters specify the aggregation window and the...

Accelerated life testing is an efficient methodology that enables the acquisition of failure time data in a shorter period by subjecting test units to different stress levels, compared to tests conducted under normal operating conditions. In this stu...

Path-dependent entropic Lagrangians formulate irreversible evolution through terminal variations of accumulated process power. This paper establishes a mathematical basis for calibrated terminal spending and weak diffusion power-port decompositions....

The spread of epidemics in real social systems is often coupled with competitive information diffusion, higher-order group interactions, and dynamic contact behaviors. To capture these mechanisms, we develop a bilayer competitive information–ep...

Wireless Sensor Networks (WSNs) remain vulnerable to clone-node attacks, in which malicious replicas use the identity or credentials of legitimate sensor nodes. This study presents an intelligent learning-based approach for clone-node detection using...

Lexical-disambiguation tasks with supplied context do not reveal whether readers would choose to seek evidence before deciding. Therefore, we adapted the spatial T-maze in the active-inference package pymdp to show how agents seek and use contextual...

Network growth and node-removal robustness are often compared while node arrivals and link budgets also vary. We hold these two quantities fixed using the email-Eu-core and CollegeMsg temporal communication networks and vary only the selection of old...

Autoregressive probability models improve the rate–distortion performance of learned image compression, but their spatial conditioning introduces serial neural inference and high decoding latency. This paper presents a latency-oriented learned...

Due to the complex practical working conditions, fault diagnosis is very challenging in rotary machinery. Rotary machinery is often used in places with a high temperature, strong corrosion, or where acceleration sensors are hard to install. Thus, aco...

The free-energy machine (FEM) relaxes discrete combinatorial optimization by minimizing expected energy plus a temperature-dependent negative-entropy penalty over product distributions. This contribution investigates the computational and mathematica...

Hyperspectral unmixing aims to recover endmembers and their corresponding abundances from mixed pixels, which is a key task in quantitative hyperspectral remote sensing. Although convolutional neural networks (CNNs) are effective in modeling local sp...

Spiking Neural Networks (SNNs) promise ultra-low-power, event-driven computation; however, current efforts to integrate self-attention into spiking transformers predominantly rely on global attention mechanisms, introducing growing token complexity w...

Renewable power generation plays a central role in the transition toward sustainable, low-carbon energy systems. However, selecting an appropriate technology remains challenging because technical, economic, environmental, and operational criteria may...

Low-rank adaptation (LoRA) fine-tunes a large model with a low-rank update over frozen weights. The update travels as a separate adapter file that is easy to copy, alter, and re-upload; so, a hub may have to decide whether a disputed file derives fro...

This study applies Transfer Entropy (TE) to floating car data from the urban road network of Catania (Italy) to investigate directional dependencies in traffic flows and identify relevant patterns of information transfer. Unlike traditional correlati...

Nanothermodynamics is the theory describing how large systems subdivide into equilibrium distributions of small subsystems. A key ingredient is Hill’s subdivision potential ($\mathcal{E}$) that ensures adherence to the 1st and 2nd laws of thermodynamics in...

Entropy-based metrics can provide a quantitative representation of the information content of UML models, but their practical usefulness depends on a systematic and reproducible calibration of the parameters used to construct the underlying probabili...

The effective tuning of extensive, high-dimensional parameter sets within nuclear power simulation systems presents considerable challenges for conventional, experience-based methodologies. Such approaches often exhibit limitations including slow con...

This paper studies a family of $(3,29)$-regular bipartite graphs introduced by Tanner for low-density parity-check (LDPC) codes. For a prime $p\equiv 1(\mathrm{mod}87)$, the graph has $3p$ left vertices and $29p$ right vertices. In the associated LDPC code, the right...

The paper derives the fundamental physical units in a universe defined over a finite holographic substrate: a finite totality whose capacity bounds the state count of every embedded observer. The units are anchored at the substrate’s four horiz...

Aiming at the difficulties in extracting gear fault feature information and the unsatisfactory performance of commonly used machine learning classification models in terms of feature separability and stable within-distribution recognition, this paper...

Conservation laws reshape quantum information dynamics by restricting how scrambling can occur. In monitored circuits, this raises a natural dynamical question: when a symmetry constraint is gradually relaxed, how quickly do the entanglement dynamics...

A two-parameter weighted beta distribution is introduced for modeling bounded data, which has many similarities to the beta distribution. We propose a class of regression models where the response is weighted beta distributed and the two shape parame...

Encrypted recurrent inference must preserve both ciphertext depth and the numerical behavior of a feedback model. We develop OneBoot GRU, which couples the validation-locked selection of degree-three Sigmoid and degree-seven Tanh domains with a CKKS...

Identifying essential proteins lays a foundation for dissecting disease mechanisms and mining therapeutic targets. Existing algorithms mostly adopt unimodal inputs and struggle to capture multi-level biological interactions from genomics, subcellular...

Stacking task adapters on a frozen domain adapter is a common approach for modular specialization of large language models, usually justified by knowledge injection; whether the surface-form graders standard in this setting can measure such specializ...