April 2026 arXiv papers — page 156

Mingyu Kim

In this article, we provide an explicit constant $C$ such that there is no regular ternary sum of generalized $m$-gonal numbers for any integer $m$ greater than $C$.

Buseong Kim, Heejun Gwon

In large language models performing long-form reasoning, the KV cache grows rapidly with decode length, creating bottlenecks in memory and inference stability. Existing reasoning-oriented KV compression has mostly followed an eviction-centered view: estimate token importance more accurately, then discard lower-ranked entries. Our analysis suggests that score

Hien Duy Nguyen

We study the Kullback--Leibler (KL) divergence approximation theory of Gaussian mixture models (GMMs) by isolating an abstract mechanism behind several necessary-and-sufficient statements. The necessity direction is universal: if a density is approximable in KL divergence by finite GMMs, then it must have finite second moment. The sufficient direction is red

David H. Yang, Yuxuan Zhu, Mohammad Mohammadi Amiri, Keerthiram Murugesan

Large language models (LLMs) have shown great performance on complex reasoning tasks but often require generating long intermediate thoughts before reaching a final answer. During generation, LLMs rely on a key-value (KV) cache for autoregressive decoding. However, the memory footprint of the KV cache grows with output length. Prior work on KV cache optimiza

Yifan Guo, Junshan Luo, Shilian Wang, Zhenhai Xu

This paper investigates the joint optimization of beamforming and antenna positions in fluid antenna system (FAS)-aided anti-jamming communications. We consider a multi-user multiple-input multiple-output downlink scenario where multiple malicious jammers exist and the jammer channel state information is imperfect. The goal is to maximize the worst-case sum-

Prasad Nimantha Madusanka Ukwatta Hewage, Midhun Chakkravarthy, Ruvan Kumara Abeysekara

Uncertainty quantification (UQ) is essential for deploying machine learning models in safety-critical physical systems, yet classical Bayesian approaches incur substantial computational overhead. We establish a formal connection between Born-rule measurement statistics from variational quantum circuits (VQCs) and Bayesian posterior uncertainty, proving that

Clarice D. Aiello, Brian L. Ross, Alessandro Lodesani, Morgan L. Sosa

Electron spin-dependent chemical reactions in proteins, often discussed under the 'radical-pair mechanism', remain the leading microscopic proposal for magnetic field sensing in biology. Yet the essential physics is often obscured by the complexity of realistic models. In this work, we present a physicist-friendly primer on the simplest radical-pair Hamilton

Hao Jin, Yuko S. Yamamoto

In this study, surface-enhanced Raman scattering (SERS) and density functional theory (DFT) calculations were combined to investigate the SERS spectra of Ln-citrate complexes (Ln: Tb, Dy, Ho, Er, Tm, Yb, and Lu) under 488 and 532 nm excitation. Peak assignment was supported by simulated SERS spectra calculated with an optimized DFT method using large-core ef

Ye Wang, Kai Huang, Sumin Shen, Chenyang Ma

Referring Camouflaged Object Detection (Ref-COD) focuses on segmenting specific camouflaged targets in a query image using category-aligned references. Despite recent advances, existing methods struggle with reference-target semantic alignment, explicit uncertainty modeling, and robust boundary preservation. To address these issues, we propose EviRCOD, an in

Shuhao Zhang, Yuli Chen, Jiale Han, Bo Cheng

Watermarking provides a critical safeguard for large language model (LLM) services by facilitating the detection of LLM-generated text. Correspondingly, stealing watermark algorithms (SWAs) derive watermark information from watermarked texts generated by victim LLMs to craft highly targeted adversarial attacks, which compromise the reliability of watermarks.

Tetsuya Takine

In this paper, we consider the M/GI/1 queue with single vacations under the gated service discipline. We obtain the probability generating function of the stationary queue length, the Laplace-Stieltjes transform of the system delay distribution in steady state, and the joint transform of the busy cycle length and the number of customers served in the busy cy

Haotian Xie, Haoxian Liu, Jingfang Fan, Ying Tang

Forecasting extreme precipitation is essential yet challenging due to its rarity and complexity. We develop a large deviation framework to estimate the return times of extreme precipitation events. We first find that the Landau distribution, originally introduced in plasma physics, accurately captures extreme precipitation at approximately 93% of global loca

Wu Wang, Yuqi Luo, Fei Sun, Sa Wang

We present a self-consistent framework for heavy-quark transport in the quark-gluon plasma across the QCD crossover region. By synthesizing perturbative and nonperturbative interactions into a unified interaction kernel, we circumvent the traditional reliance on arbitrary soft-hard momentum separation scales. The interaction is governed by an in-medium effec

Christopher Herbig, Nguyen N. Hung

Let $\chi$ be a complex irreducible character of a finite group $G$. The conductor of $\chi$, denoted $c(\chi)$, is the smallest positive integer $n$ such that $\chi(x)\in \mathbb{Q}(\exp({2\pi i/n}))$ for all $x\in G$. We show that for certain rank $1$ finite groups of Lie type, the conductor $c(\chi)$ is realized at a single group element; that is, there e

Sangmin Oh, Jinmu You, Jaesun Kim, Jiho Lee

We introduce a lightweight universal machine-learning interatomic potential (uMLIP), SevenNet-Nano, based on the graph neural network architecture SevenNet and enabled by a knowledge-distillation framework. The model inherits the broad generalization capability of a large multi-task foundation model, SevenNet-Omni, trained on diverse materials datasets acros

Yuki Kodama, Jonte R. Hance, Holger F. Hofmann

Two-photon interference effects arise because photons are indistinguishable particles. In the wellknown Hong-Ou-Mandel (HOM) effect, the transmission of two photons at a beam splitter interferes destructively with the reflection of both photons, requiring both photons to "bunch up" by leaving the beam splitter on the same side. Here, we show that the interfe

Vikrant Chaugule, Abhishek D, Aadheeshwar Vijayakumar, Pravin Bhaskar Ramteke

This paper proposes a method to review public acceptance of products based on their brand by analyzing the facial expression of the customer intending to buy the product from a supermarket or hypermarket. In such cases, facial expression recognition plays a significant role in product review. Here, facial expression detection is performed by extracting featu

Ion Matei, Praveen Kumar Menaka Sekar, Maksym Zhenirovskyy, Hon Yung Wong

Automated generation of executable Business Process Model and Notation (BPMN) models from natural-language specifications is increasingly enabled by large language models. However, ambiguous or underspecified text can yield structurally valid models with different simulated behavior. Our goal is not to prove that one generated BPMN model is semantically corr

Quentin Romero Lauro, Aakash Gautam, Yasmine Kotturi

Entrepreneurs in resource-constrained communities often lack time and support to translate ideas into actionable business plans. While generative AI promises assistance, most systems assume high digital literacy and overlook community infrastructures that shape adoption. We report on the community-centered design and deployment of BizChat, an AI-powered busi

Arghya Mukherjee, Hassan Jameel Asghar, Gavin K. Brennen

Differential privacy is a mathematical notion of data privacy that has fast become the de facto standard in privacy-preserving data analysis. Recently a lot of work has focused on differential privacy in the quantum setting. Continuing on this line of study, we investigate how to answer counting queries on a quantum encoded dataset with differential privacy.

Wen-Xiu Zhang, Wen-Qiang Liu, Hai-Rui Wei

Hyperentangled states are fascinating resources in quantum information processing as they can significantly increase the channel capacity and enhance noise resistance. We explore a hyperfusion mechanism to fuse one n photon hyper-W state and one m-photon hyper-W state into a large-scale (n+m-2)-photon hyper-W state. Another mechanism to fuse one n-photon hyp

Patrizio Cintioli

Richter, Stephan, and Zhang asked whether every nonrecursive many-one degree contains a least finite-one degree. We solve this question in the negative, already within the class of computably enumerable many-one degrees. Positive answers are known in two disjoint natural settings: for a measure-one and comeager class of $m$-rigid sets, and, in a companion pa

Piotr T. Chruściel, Raphaela Wutte

We show that a relative holographic energy coincides with the relative Hamiltonian energy.

Liubin Wang, Xin Wu

In 2021, Lei et al. claimed the equivalence between the two Lagrangians $\mathcal{L}_1 =-mc\sqrt{-g_{\mu\nu}{\dot{x}}^\mu{\dot{x}}^\nu}-V$ and $\mathcal{L}_2 = \frac{1}{2}mg_{\mu\nu} {\dot{x}}^\mu{\dot{x}}^\nu-V$ for describing particle dynamics in combined gravitational and matter fields. In the present work, we rigorously demonstrate that their equivalence

Angela Jin, Alexander Asemota, Dan E. Krane, Nathaniel D. Adams

AI governance efforts increasingly rely on audit standards: agreed-upon practices for conducting audits. However, poorly designed standards can hide and lend credibility to inadequate systems. We explore how an audit standard's design influences its effectiveness through a case study of ASB 018, a standard for auditing probabilistic genotyping software -- so

Qinjiang Niu, Lu Yan

Adverse Outcome Pathways (AOPs) are an important knowledge framework in toxicological research and risk assessment. In recent years, large language models (LLMs) have gradually been applied to AOP-related question answering and mechanistic reasoning tasks. However, due to the existence of the hallucination problem, that is, the model may generate content tha

Kang-Sin Choi

We propose an operational, quantitative definition of intelligence for arbitrary physical systems. The intelligence density of a system is the ratio of the logarithm of its independent outputs to its total description length. A system memorizes if its description length grows with its output count; it knows if its description length remains fixed while its o

Elliot J. Addy, Aretha L. Teckentrup

Sparse grids are popular tools for high-dimensional function approximation. In this work, we study the use of sparse grids for interpolation using separable Mat\'ern kernels $\Phi_{\boldsymbol{\nu},\boldsymbol{\lambda}}(\mathbf{x},\mathbf{x}')=\prod_{j=1}^d\phi_{\nu_j,\lambda_j}(x_j,x_j')$, with a particular focus on the anisotropic setting where the regular

Yang Liu, Renzheng Zhang, Amartya Mandal, Eungkyu Lee

Nanoplastics pose serious environmental and health risks due to their widespread presence in aquatic systems. Detecting trace amounts of nanoplastics is a challenging task, which currently requires sophisticated equipment and tedious sample preparation (e.g., ultrafiltration). In this work, we demonstrate an ultra-sensitive Shrinking Surface Bubble Depositio

Chengyang Liang, Dong Li

Semantic communication (SemCom) presents a transformative paradigm for alleviating bandwidth limitations in mobile networks by transmitting task-relevant semantic features instead of raw data bits. While SemCom systems utilizing diffusion models achieve superior generation quality, existing research treats semantic generation and wireless transmission as tem

Zhitong Ni, Yansha Deng, Jinhong Yuan

Wireless goal-oriented semantic communication (GSC) has emerged as a promising paradigm by directly optimizing task performance. However, existing GSC frameworks typically operate on entire images and rely on labeled data for classification tasks, which can limit their compression efficiency and increase the risk of overfitting. This paper proposes a novel s

Sean Sanford

We establish a 6-term left exact sequence, involving Galois cohomology of the base field $\mathbb K$, and the Brauer-Picard groupoid of a fusion category. This generalizes a result of Etingof, Nikshych, and Ostrik to the setting where $\mathbb K$ is not algebraically closed. Following their example, we use this exact sequence to compute examples of graded ex

Shaul Barkan, Gijs Heuts, Yuqing Shi

There are at least two ways to approach the homotopy theory of spaces `at chromatic height $n$': one may localize with respect to $T(n)$-homology or with respect to $v_n$-periodic homotopy groups. It was already observed by Bousfield that these two options yield rather different results. We build on his work to prove precise comparison results between the tw

Xiaomeng Hu, Yinger Zhang, Fei Huang, Jianhong Tu

AI agents are expected to perform professional work across hundreds of occupational domains (from emergency department triage to nuclear reactor safety monitoring to customs import processing), yet existing benchmarks can only evaluate agents in the few domains where public environments exist. We introduce OccuBench, a benchmark covering 100 real-world profe

Mingjie Zhao, Yunfan Zhang, Yiqun Zhang, Yiu-ming Cheung

Deep Clustering (DC) has emerged as a powerful tool for tabular data analysis in real-world domains like finance and healthcare. However, most existing methods rely on data-level statistical co-occurrence to infer the latent metric space, often overlooking the intrinsic semantic knowledge encapsulated in feature names and values. As a result, semantically re

Yanru Chen, Runyang Tian, Zheyu Li, Mahbod Afarin

While graph-based dynamic programming (DP) is a cornerstone of genomics and network analytics, its efficiency is hampered by fundamentally conflicting computational patterns. Matrix-centric DP drives regular, compute-bound network analytics, while topology-centric DP handles irregular, memory-bound genomic traversals. These two categories of DP have substant

Andrey Shapiro

Let $p$ be a prime number and let $G$ be a graph on $n$ vertices and $m$ edges. The zero-sum Ramsey number of $G$ over $\mathbb{Z}_p$, denoted by $R(G, \mathbb{Z}_p)$, is the minimum $\ell\in \mathbb{N}$ such that for any edge-coloring $c:E(K_\ell)\to\mathbb{Z}_p$, there is a subgraph $G'\subset K_\ell$ isomorphic to $G$ and satisfying $\sum_{e\in E(G')}c(e)

Morris Greenberg, Kieran R Campbell, Radu Craiu

Inferring directed acyclic graphs (DAGs) from data via Markov chain Monte Carlo (MCMC) is computationally challenging in moderate-to-high dimensional settings because their discrete sampling space grows super-exponentially with the number of nodes. To address scalability, several recent MCMC-based graph inference methods restrict the search space to a subset

Xuecen Zhang, Vipin Chaudhary

The rapid advancement of diffusion-based generative models has made face forgery detection a critical challenge in digital forensics. Current detection methods face two fundamental limitations: poor cross-domain generalization when encountering unseen forgery types, and substantial computational overhead that hinders deployment on resource-constrained device

Aliaksei Korshuk, Alexander Buyantuev, Ilya Makarov

Training language model agents for multi-agent strategic interaction presents a core difficulty: the quality of any action may depend on future events that never materialize, on moves that violate game rules, or on decisions made by other players. Standard reinforcement learning assumes that rewards can be assigned at each step, but this assumption fails in

Aria Nourbakhsh, Adelaide Danilov, Christoph Schommer, Salima Lamsiyah

Detecting AI-generated text is becoming increasingly challenging as modern language models approach human-level fluency and can evade detectors that rely on surface statistics or likelihood-based signals. We propose \textsc{AEyeDE}, an attribution-driven approach to human-AI authorship detection that leverages model attention as a discriminative signal. Spec

Yifan Zhang, Xinkui Zhao, Zuxin Wang, Zhengyi Zhou

Operating Systems (OS) courses are among the most challenging in computer science education due to the complexity of internal structures and the diversity of running environments. Traditional teaching methods often fail to address the diverse backgrounds, learning speeds, and practical needs of students. To tackle these challenges, we present SortingHat, a p

Hao Xu, Rite Bo, Fausto Giunchiglia, Yingji Li

Although studies have demonstrated that Large Language Models (LLMs) can perform well on Out-of-Distribution (OOD) tasks, their advantage tends to diminish as the distribution shift becomes more severe. Consequently, researchers aim to retrieve distributionally similar and informative demonstrations from the available source domain to boost the inference cap

Yana Venerina, Dmitry Koch, Nare Meloyan, Gerda Prutko

Social conformity is a well-documented phenomenon in which individuals shift their opinions towards those of a social majority. As artificial intelligence (AI) becomes increasingly integrated into everyday life it may also create a novel source of influence giving rise to algorithmic conformity, mechanisms of which are poorly understood. The present study ex

Shannan Liu, Peifeng Li, Yaxin Fan, Qiaoming Zhu

Multi-party dialogue discourse parsing aims to identify dependency structures and relation types between utterances in conversations. Previous studies are mostly limited to textual modality or two-party dialogue, failing to meet the multimodal and multi-party settings. In this paper, we construct the first publicly available English multimodal dataset DraDDP

Mahounan Pericles Adjovi, Victor Olufemi, Roald Eiselen, Prasenjit Mitra

This survey provides a comprehensive catalog of publicly available text and speech resources for two West African languages: Hausa, an Afroasiatic language with approximately 80-100 million speakers, and Fongbe, a Niger-Congo language spoken by approximately 2 million people in Benin. These languages represent contrasting cases on the resource availability s

Joseph S. Boyle, Anthony Dranfield, Mike O'Neil, Maria Liakata

In this paper we introduce ClinQueryAgent, a system for translating natural language population health questions into executable database queries using agents with access to both local and external knowledge bases. Our novel architecture enables the use of powerful cloud-based language models whilst ensuring that no patient data leaves the secure environment

Litong Zhang, Jiaxin Li, Kuo Zhao

Multi-hop question answering requires aggregating information from multiple documents, a critical capability for knowledge-intensive applications. A fundamental challenge lies in efficiently identifying the minimal relevant document set from retrieved candidates while maintaining high recall. We present an efficient dual-view cascaded reranking framework for

Dhriti Krishnan, Jaromir Savelka

Predicting the difficulty of multiple-choice questions (MCQs) is important for effective assessment, yet current methods typically assume a unimodal student ability distribution, overlooking the heterogeneous nature of student misconceptions. We propose a persona-driven framework that replaces theoretical ability sampling with data-driven cognitive profiling

Mohamed Soufan

Linguistic uncertainty is common in social media, but its relationship with engagement remains unclear across languages and topics. Using 2,258 English-language posts on Federal Reserve policy, inflation, and electoral politics collected over three days in April 2026, we test whether the Uncertainty-Reply Asymmetry observed in prior Arabic-language research

Christian Kolb

Large language models (LLMs) are increasingly used in clinical and care settings. This exploratory study investigates whether LLMs exhibit sycophantic behavior - adapting their responses to social expectation signals rather than maintaining professional quality - in the context of dementia care. Five prompts with systematically increasing confirmatory and au

Mohamed Abdelkader, Mohamed Rhaima

The foremost aim of this study is to introduce and study several combinatorial properties and highlight specific aspects of a new class of polynomials sequences known as degenerate Krawtchouk Appell polynomials associated with the degenerate Pascal measure. As applications, the connection that exists between brand-new polynomials, Stirling numbers, scaling o

Aidan Alexander, Chitrangada Juneja, Napaluck Tontrasathien, Miro Vanek

Different AI tools such as ChatGPT, Gemini, and Claude are becoming very popular. Although they are helpful for many day-to-day tasks, they can be used in unexpected ways. For example, the learning objectives of a course may not be achieved if students use these tools to solve their homework problems. This paper proposes a simple method to address this issue

Eslam Reda, Sara El-Metwally

Large Language Models have demonstrated remarkable capabilities in generating contextually relevant and grammatically correct text. However, they fundamentally lack the ability to process and respond to emotional context in a manner analogous to human emotional cognition. Current approaches to emotion modeling in NLP systems rely primarily on discrete emotio

Yuting Hu, Lei Zhuang, Chen Wang, Ruiyang Qin

As feature sizes shrink to the nanometer scale, accurately transferring circuit patterns from photomasks to silicon wafers becomes increasingly challenging. Optical proximity correction (OPC) is widely used to ensure pattern fidelity and manufacturability. Recent generative mask optimization models based on encoder-decoder architecture can synthesize near-op

Marcus Binder Nilsen, Julian Quick, Tuhfe Göçmen, Nikolay Dimitrov

Reinforcement learning (RL) offers a promising approach for adaptive wind farm flow control, yet its practical deployment is hindered by slow training convergence and poor initial performance, factors that could translate to years of reduced power output if an untrained agent were deployed directly. This work investigates whether domain knowledge from steady

Margaret E. Verrico, K. Decker French, Vivienne F. Baldassare, Colin J. Burke

Changing-Look AGN (CL-AGN) are AGN which transition between Seyfert types, challenging AGN unification models. Most CL-AGN have been identified via repeat spectroscopy, making it difficult to determine the duration and magnitude of the CL-AGN transition. As such, the physical mechanisms behind this transition are still unknown. We use synthetic photometry in

Annie Z. Xia, Dong Wang, Catherine La Riviere, Rebecca Kramer-Bottiglio

Multiscale periodic metamaterials have been designed for numerous applications, such as impact absorption, acoustic cloaking, photonic band gaps, and mechanical logic gates. This prior work has focused on optimizing mesoscale structure for desired bulk isotropic properties. In contrast, we seek to develop materials with highly anisotropic elastic properties.

Ignacio Negueruela, Sara R. Berlanas, Lee J. Townsend, Javier Lorenzo

Be/X-ray binaries, the most common class of high-mass X-ray binaries, are characterised by OBe companions, but display a rich variety of X-ray behaviours. One of the most atypical systems is X0726-260, which also has the earliest optical counterpart among the whole Milky Way and Magellanic Cloud sample. We intend to improve the characterisation of the optica

Luca Ricci, Luisa Ostorero, Raffaella Morganti, Judith H. Croston

The jets and lobes of nearby radio galaxies are ideal laboratories to explore the spectral and dynamical evolution of the radio-emitting plasma that emanates from active galactic nuclei. Here, we present a high-resolution radio continuum study of the low-redshift (z=0.01713), Fanaroff-Riley I (FRI) radio galaxy 3C 449 performed by combining radio data at 145

Limei Yuan, Chen-Hung Hsiao, Yidun Wan

We study Bardeen-like regular black holes without Cauchy horizons via gravitational lensing. In the weak field, the deflection angle receives a positive $\ell$-dependent correction, producing a slightly larger Einstein ring. For the galaxy ESO 325-G004, the predicted ring radius is consistent with current observations. In the strong field, for Sgr A* and M87

Jiale Liu, Nanzhe Wang

High-fidelity numerical simulation of subsurface flow is computationally intensive, especially for many-query tasks such as uncertainty quantification and data assimilation. Deep learning (DL) surrogates can significantly accelerate forward simulations, yet constructing them requires substantial machine learning (ML) expertise - from architecture design to h

Adrien Houge, Anders Johansen, Andrea Banzatti, Sierra Grant

In protoplanetary discs, the presence of dust traps can significantly alter the transport of solids from the outer to the inner regions, and hence they are often invoked as an explanation for the chemical diversity of inner discs observed with JWST (e.g., varying oxygen abundances and C/O ratios). As a detailed treatment of dust transport around dust traps i

Dhruba Jyoti Gogoi, Jyatsnasree Bora, Ali Övgün

We investigate equatorial periodic orbits and their gravitational wave radiation in the spacetime of an Euler--Heisenberg (EH) black hole surrounded by perfect fluid dark matter (PFDM). The combined effects of quantum electrodynamic corrections and dark matter are incorporated through an effective metric, and the dynamics of timelike geodesics are analyzed u

Ameya Kolhatkar, P. K. Sahoo

We present a rigorous theoretical and observational analysis of the Hybrid $ f(Q) $ class of models by including the late-time modifying $ 1/Q $ term. After deriving strict viability conditions from the analytical expansion history, we show that preserving early-universe structure formation dictates that the linear coupling be exactly unity. This fixes the b

Nikita Zhidkov, Andrei Zenkevich, Anton Khanas

Rapid progress of artificial neural network applications in recent years has led to the issue of an unprecedented energy consumption. It can be solved by the implementation of energy efficient hardware based on non-von-Neumann architectures, which requires the development of electronic components emulating the behavior of synapses and neurons. While research

A. Frasca, M. Qin, J. ALonso-Santiago, G. Catanzaro

In this work we present the results of our analysis of medium-resolution LAMOST spectra of candidate members of the cluster NGC 1647 with the aim of determining the stellar parameters, activity level, lithium abundance, and to study the cluster properties. We used the code ROTFIT to determine the atmospheric parameters (Teff, logg, and [Fe/H]), radial veloci

Deeksha Prahlad, Daniel Fan, Hokeun Kim

Foundation models, including large language models (LLMs), are increasingly used for human-in-the-loop (HITL) cyber-physical systems (CPS) because foundation model-based AI agents can potentially interact with both the physical environments and human users. However, the unpredictable behavior of human users and AI agents, in addition to the dynamically chang

Xinyu Shi, Zirui He, An-An Sun, Siqing Shen

Copper is a detrimental impurity in silicon with high diffusivity and a high tendency to precipitate. Interaction between Cu and other defects is essential for understanding the nature of Cu precipitation in silicon. Despite extensive experimental investigations of Cu-related defects in silicon, a comprehensive understanding remains elusive due to limitation

Heinke Frerichs, Dieter Boeyaert, Yuhe Feng, Detlev Reiter

The divertor in a magnetic confinement fusion reactor is an essential component for power dissipation and particle removal. The FIREFLY package for rapid evaluation of divertor designs is presented as an extension of the FLARE code for field line reconstruction from a flux tube mesh. First, divertor loads are approximated with a simplified heat transport mod

Omri Shaltiel, Eran Sharon

We report high-resolution measurements of three-dimensional (3D) turbulence in a rapidly rotating fluid. By decomposing the velocity field into a vertically averaged component and a three-dimensional residual, we show that each dominates distinct frequency ranges: the quasi-2D component at low frequencies and the 3D component at higher ones. This separation

Leo Wilhelm Lierse, Mahyar Tourchi Moghaddam, Sebastian Werner

As carbon pricing mechanisms like the EU Emissions Trading System are set to increase prices of energy consumption, software architects face growing pressure to design applications that operate within financially predictable emission constraints. Existing approaches typically enforce rigid per-interval emission rates, which prove unsuitable in electrical gri

E. Traianou, G. Bruni, J. Rodi, G. F. Paraschos

We present 43\,GHz VLBI observations of the radio galaxy 3C~120 during its brightest $γ$-ray outburst (March 2018), recently associated with the IceCube neutrino alert IC-180213A. Despite reaching $L_γ= 3.7 \times 10^{44}$\,erg\,s$^{-1}$, contemporaneous X-ray monitoring from INTEGRAL/ISGRI, MAXI/GSC, and \textit{Swift}/XRT revealed no variability across 0.3

Paulo Luz, Sante Carloni

We introduce a new type of generating theorems in General Relativity for anisotropic, static, spherically symmetric solutions of the Einstein field equations. The results are used to derive a class of solutions that can serve as new models for the interiors of compact stars. Their geometric and thermodynamic properties are studied in detail, and we show that

Giulia Ricciardi, Francesco Zagaria, Anna Miotello, Carlo F. Manara

A large fraction of planet-forming disks observed with ALMA show faint CO emission, often interpreted as strong CO depletion. However, faint emission may also arise from spatially unresolved disks, whose sizes are overestimated, making them appear intrinsically faint. The limited sensitivity of previous observations has prevented testing this scenario, hinde

M. Pritzkuleit, M. Dorsch, M. M. Miller Bertolami, S. Geier

Hot subdwarf stars with masses above $0.8 M_\odot$ ascend the helium giant branch after the end of core helium burning, before entering the white dwarf cooling track or exploding as type Ib/c supernovae. Such massive helium stars are typically expected to form through the stripping of an intermediate mass star by a binary companion after which some hydrogen

Pranjali Sharma, Lucia Kleint, Jonas Zbinden

Identifying the physical mechanisms of continuum emission in solar flares is important to improve our understanding of the transport of energy in the chromosphere. This study aims to quantify the occurrence statistics and spatial and temporal characteristics of near-ultraviolet (NUV) continuum enhancements across various classes of solar flares. We analyzed

Stefano Savazzi, Fabio Paonessa, Sanaz Kianoush, Alessandro Nordio

RF sensing exploits phase-sensitive measurements of stray electromagnetic (EM) fields from wireless devices across various frequency bands to detect EM blockage and to reconstruct and map the surrounding environment in 2D/3D. Although blockage effects caused by objects or human motion are well-studied in ISM bands and frequencies up to 60~GHz, there is a sig

Laurine Martinien, Gaspard Duchêne, Álvaro Ribas, Marion Villenave

[Abridged] We aim to study the radial and vertical extents of 12CO gas, millimeter dust thermal emission and optical/NIR scattered light by dust in disks. We analyze a sample of 14 highly inclined protoplanetary disks. We present ALMA high angular resolution band 7 (0.9 mm) continuum images and 12CO (3-2) gas moment maps as well as HST and VLT/SPHERE scatter

Vaclav Javorek, Jakub Honzik, Ivan Gruber, Tomas Zelezny

Recent Vision Language Models (VLMs) have demonstrated strong performance across a wide range of multimodal reasoning tasks. This raises the question of whether such general-purpose models can also address specialized visual recognition problems such as isolated sign language recognition (ISLR) without task-specific training. In this work, we investigate the

Lucien Dupuy, Toni Chiti, Jérémy Morere, Emmanuel Fromager

Ground-state electronic structure calculations using Kohn-Sham density functional theory (KS-DFT) offer an unprecedented balance between efficiency and accuracy, now paradigmatic to the fields of quantum chemistry and condensed matter physics. KS-DFT can be extended to model electronic excitations through a density mapping onto a non-interacting ensemble sta

Axel Lazzarotto, Alain Hui-Bon-Hoa, Torsten Boehm, Matthew Gent

Abridged : The fundamental parameters of rapidly rotating stars are key quantities to understand the impact of rotation on stellar evolution. A few nearby early-type stars offer the possibility of precise measurements of these parameters, which will help us constrain newly available two-dimensional models. We propose a method to retrieve the fundamental para

G. Janka, Z. Salman, A. Suter, T. Prokscha

Low-energy $μ$SR (LE-$μ$SR) enables depth-resolved studies of magnetic and electronic properties from the surface into the near-surface region, but the measured transverse-field asymmetry is not an intrinsic constant and depends on implantation energy and beamline conditions. Following an upgrade of the single-muon tagging system at the LEM beamline at PSI i

Abhishek Sawaika, Samuel Yen-Chi Chen, Udaya Parampalli, Rajkumar Buyya

Reinforcement learning (RL) is one of the most practical ways to learn from real-life use-cases. Motivated from the cognitive methods used by humans makes it a widely acceptable strategy in the field of artificial intelligence. Most of the environments used for RL are often high-dimensional, and traditional RL algorithms becomes computationally expensive and

N. K. Patra, Sk Md Adil Imam, Kai Zhou

We investigate how the transition density \(ρ_{tr}\) affects hybrid constructions of the neutron-star equation of state (EoS) in which a nucleonic description at low densities is matched to a model-agnostic high-density extension based on a speed-of-sound parametrization. Using four representative nucleonic models--Taylor expansion, \(\frac{n}{3}\) expansion

Jaewon Lee, Md Eimran Hossain Eimon, Avinash Srinivasan, Hari Kalva

Digital forensic investigations often face significant challenges when recovering fragmented multimedia files that lack file system metadata. While traditional file carving relies on signatures and discriminative deep learning models for fragment classification, these methods cannot reconstruct or predict missing data. We propose a generative approach to mul

Yusuke Terasawa, Yukiyasu Ozeki

We propose a dynamical scaling analysis improved by a deep learning approach. While Gaussian process regression has been widely employed for estimating scaling parameters, its computational cost for parameter optimization becomes a limitation in dynamical scaling analysis, where large datasets are involved. In contrast, the present method employs a neural ne

M. W. AlMasri

In this work, we investigate the geometry of quantum logic gates within the holomorphic representation of quantum mechanics. We begin by embedding the physical qubit subspace into the space of holomorphic functions that are homogeneous of degree one in each Schwinger boson pair $(z_{a_j}, z_{b_j})$. Within this framework, we derive explicit closed-form diffe

Gianluca Peri, Lorenzo Buffoni, Giacomo Chiti, Duccio Fanelli

We study the statistical properties of trainable agents moving in discrete space. After introducing the mathematical framework, we first analyze the dynamics of two completely random walkers, mutually competing in a chaser-target interaction scheme. The statistics of the encounters is analytically obtained and the predictions tested versus numerical simulati

Jan Derezinski, Vojkan Jaksic, Claude-Alain Pillet

We presents a unified and concise exposition of key topics in the mathematical theory of open quantum systems, developed within the framework of operator algebras. The manuscript consolidates and extends a series of invited articles originally prepared for the Modern Encyclopedia of Mathematical Physics, combining foundational material with modern perspectiv

Jing Ming, Maria Alessandra Papa, Heinz-Bernd Eggenstein, Bernd Machenschalk

We carry out the deepest and broadest search for continuous gravitational-wave signals from three neutron stars at the center of the supernova remnants Cassiopeia A, Vela Jr., and G347.3-0.5. This search was made possible by the computing power shared by thousands of Einstein@Home volunteers. After the initial Einstein@Home search, which used O3a data, we pe

Anish Acharya, Luca Giuggioli, Shamik Gupta

Reduced transport and localization in isolated quantum systems are typically attributed to spatially-extended disorder, but may also emerge from the influence of a few controllable defects. We show here how a single defect profoundly reshapes wave-function spreading on a finite and periodic tight-binding lattice. Adapting the defect technique from classical

Alipriyo Hoory, Jerome Martin, Arnab Paul, L. Sriramkumar

[Abridged] The primary gravitational waves (PGWs) are generated in the early universe from the quantum vacuum during inflation. In slow roll inflation, the power spectrum (PS) of PGWs over large scales, which leave the Hubble radius during inflation, is nearly scale-invariant. However, over very small scales, which never leave the Hubble radius, the PS of PG

G. Sordi, E. M. O'Callaghan, C. Walsh, M. Charlebois

The frequency structure of the superconducting correlations in cuprates gives insights on the pairing mechanism. Here we present an exhaustive study of this problem in the two-dimensional Hubbard model with cellular dynamical mean-field theory. To this end, we systematically quantify the dependence on doping $δ$ and interaction strength $U$ of the supercondu

Soham Bakshi, Snigdha Panigrahi

Decision trees are widely used for non-linear modeling, as they capture interactions between predictors while producing inherently interpretable models. Despite their popularity, performing inference on the non-linear fit remains largely unaddressed. This paper focuses on classification trees and makes two key contributions. First, we introduce a novel tree-

Lorenzo Calibbi, Xiyuan Gao, Man Yuan

We revisit the minimal type II seesaw mechanism generating the Majorana neutrino mass matrix $M^ν$, under the assumption that two entries of $M^ν$ vanish. Such flavor structures are known as two-zero textures. Processes with charged lepton flavor violation (CLFV), absent in the Standard Model (SM), can have sizable rates in this framework and are directly li

Viktor A. Lilja, Albin J. Svärdsby, Timo Gahlmann, Philippe Tassin

Neural networks have been demonstrated to be able to accelerate the modeling and inverse design of optical and electromagnetic devices by serving as fast surrogates for electromagnetic solvers. Nevertheless, such neural networks can be unreliable and normally require extreme amounts of data to train. Here it is shown that these limitations can be alleviated

Thomas Burton, Sean Symon, Davide Lasagna

We present a robust optimisation framework for computing invariant solutions of wall-bounded flows by recasting the Navier-Stokes equations as a variational problem as established in Ashtari and Schneider, JFM (2023). The approach minimises the residual of the governing equations over a finite time horizon, seeking periodic or equilibrium solutions. A novel

Fabian Ballar Trigueros, José Antonio Marín Guzmán

We investigate how noise impacts nonstabilizerness - a key resource for quantum advantage - in many-body qubit systems. While noise typically degrades quantum resources, we show that amplitude damping, a nonunital channel, can generate or enhance magic, whereas depolarizing noise provably cannot. In an encoding-decoding protocol, we find that, unlike in the

Brian Barch, Daniel Lidar

Non-Hermitian (NH) quantum systems demonstrate striking differences from their Hermitian counterparts, leading to claims of NH advantage in areas ranging from metrology to entanglement generation. We show that in the context of quantum computation, any such NH advantage is unlikely to be scalable as an efficient computational resource: if coherent normalized