November 2025 arXiv papers — page 92

Gennady Komandin, Elena Zhukova, Boris Gorshunov, Alexander Melentyev

A thorough study of the wide-range (40-35000 cm-1) dynamic conductivity spectra of the rare-earth (RE) dodecaborides RB12 (R- Ho, Er and Tm) and Tm1-xYbxB12 substitutional solid solutions was carried out at room temperature. Both the Drude-type components and overdamped excitations have been separated and analyzed. An additional absorption band observed abov

Irin Babu Vathachira, Yael Hillman, Amit Kashi

We investigate the impact of the Bondi--Hoyle--Lyttleton (BHL) accretion mechanism on the evolution of nova eruptions in symbiotic systems by systematically varying three key input parameters: the initial donor (asymptotic giant branch; AGB) mass, the initial white dwarf (WD) mass, and the initial binary separation ($a$). We explore models with AGB masses in

Yuan An, Ruhma Hashmi, Michelle Rogers, Jane Greenberg

Task-oriented knowledge graphs (KGs) enable AI-powered learning assistant systems to automatically generate high-quality multiple-choice questions (MCQs). Yet converting unstructured educational materials, such as lecture notes and slides, into KGs that capture key pedagogical content remains difficult. We propose a framework for knowledge graph construction

Hongshu Lin, Wenston J. T. Zang

Let $A_k(n)$ denote the set of $k$-distinct partitions of $n$, and let $B_k(n)$ be the set of $k$-regular partitions of $n$. Glaisher showed that $\# A_k(n) = \# B_k(n)$. For $k=2$, this equality yields the celebrated Euler's partition theorem. In this paper, we present a new partition set $E_k(n)$, which is equinumerous to $B_k(n)$.

Muhammad AbuGhanem

Quantum computers promise to redefine the boundaries of computational science, offering the potential for exponential speedups in solving complex problems across chemistry, optimization, and materials science. Yet, their practical utility remains constrained by unpredictable performance degradation under real-world noise conditions. A key question is how pro

Angel Abusleme, Thomas Adam, Kai Adamowicz, David Adey

Neutrino oscillations, a quantum effect manifesting at macroscopic scales, are governed by lepton flavor mixing angles and neutrino mass-squared differences that are fundamental parameters of particle physics, representing phenomena beyond the Standard Model. Precision measurements of these parameters are essential for testing the completeness of the three-f

Nick von Felten, Johannes Schöning, Klaus Opwis, Nicolas Scharowski

Humans increasingly interact with artificial intelligence (AI) in decision-making. However, both AI and humans are prone to biases. While AI and human biases have been studied extensively in isolation, this paper examines their complex interaction. Specifically, we examined how class imbalance as an AI bias affects people's ability to appropriately rely on a

Angel Abusleme, Thomas Adam, Kai Adamowicz, David Adey

The Jiangmen Underground Neutrino Observatory (JUNO) started physics data taking on 26 August 2025. JUNO consists of a 20-kton liquid scintillator central detector, surrounded by a 35 kton water pool serving as a Cherenkov veto, and almost 1000 m$^2$ of plastic scintillator veto on top. The detector is located in a shallow underground laboratory with an over

Nikolas P. Breuckmann, Shin Ho Choe, Jens Niklas Eberhardt, Francisco Revson Fernandes Pereira

The recently introduced tile codes are a promising alternative to surface codes, combining two-dimensional locality with higher encoding efficiency. While surface codes are well understood in terms of their logical operators and boundary behavior, much less is known about tile codes. In this work, we establish a natural and precise description of their logic

Julia Machnio, Mads Nielsen, Mostafa Mehdipour Ghazi

White matter hyperintensities (WMH) are key imaging markers in cognitive aging, Alzheimer's disease (AD), and related dementias. Although automated methods for WMH segmentation have advanced, most provide only global lesion load and overlook their spatial distribution across distinct white matter regions. We propose a deep learning framework for robust WMH s

Andrew Knightly, Kimball Martin

We give a formula for the number of newforms in $S_k^{\mathrm{new}}(N)$ that have prescribed ramified supercuspidal components $\pi_p$ at a set $T$ of primes dividing $N$. This dimension is given in terms of the trace of the Atkin--Lehner operator at $T$ on $S_k^{\mathrm{new}}(N)$. It depends only upon the weight, the level, the ramified quadratic extensions

Simão Correia, Gonçalo Pereira, Thyago S. R. Santos

Given a nonlinear dispersive equation which admits a scaling invariance, there may exist self-similar solutions. In this work, we present a systematic approach for the construction of small-amplitude self-similar solutions, together with precise asymptotic descriptions at both small and large frequency scales. These ideas are then applied to three classic di

Henry Daniel Vera Ramirez

The text points out that one of the main contradictions of quantum realism, which is particularly relevant to the social sciences, is the tension between the existence of an observer-independent reality and the idea that this reality is mediated by the cognitive and interpretative processes of the subject. This contradiction arises from the central role of t

Hyo-Jeong Jang

Multimodal learning systems often face substantial uncertainty due to noisy data, low-quality labels, and heterogeneous modality characteristics. These issues become especially critical in human-computer interaction settings, where data quality, semantic reliability, and annotation consistency vary across users and recording conditions. This thesis tackles t

CMS Collaboration

A search for heavy resonances decaying into a Higgs boson (H) or a Z boson and a photon ($\gamma$), with the H or Z bosons decaying to a bottom quark-antiquark pair ($\mathrm{b\bar{b}}$) is presented. The analysis is performed using proton-proton collision data at $\sqrt{s}$ = 13 TeV collected by the CMS experiment at the CERN Large Hadron Collider, correspo

Keda Tao, Kele Shao, Bohan Yu, Weiqiang Wang

Omnimodal large language models (OmniLLMs) have attracted increasing research attention of late towards unified audio-video understanding. However, the high computational cost of processing longer joint audio-video token sequences has become a key bottleneck. Existing token compression methods have not addressed the emerging need to jointly compress multimod

Yiming Zhu

In this note, we study quasi-Albanese morphisms for log canonical Calabi-Yau pairs and obtain several structural results. As an application, we prove a characterization of toric pairs.

Amanuel Anteneh, Olivier Pfister

We present a technique for performing quantum detector tomography (QDT) of phase insensitive quantum detectors, a category under which many detectors of interest fall under, using gradient descent-based optimization to learn the positive operator-valued measure (POVM) that best describes the data collected using the detector under study. We numerically bench

Noam Soker

I identify a point-symmetric morphology in the core-collapse supernova (CCSN) remnant SNR G11.2-0.3 composed of three pairs of opposite morphological features, and attribute their shaping to three energetic pairs of jets during the explosion process in the frame of the jittering jets explosion mechanism (JJEM). The pairs of morphological features are two opp

P. Janin-Potiron, M. Gray, B. Neichel, M. Dumont

As the Extremely Large Telescope (ELT) approaches operational status, optimising its imaging performance is critical. A differential piston, arising from either the adaptive optics (AO) control loop, thermomechanical effects, or other sources, significantly degrades the image quality and is detrimental to the telescope's overall performance. In a numerical s

Victor Ahlquist

We study the low-lying zeros of certain Artin $L$-functions associated with $D_4$-quartic function fields. Specifically, we prove that when ordered by conductor, at least $77\%$ of these $L$-functions are non-vanishing at the central point. This generalises and extends results over $\mathbb{Q}$ due to Durlanik, proving that an infinite number of these $L$-fu

Michael R. Blanton, Arjun Suresh, Kyle B. Westfall, Dou Liu

We measure the Eddington ratio distribution of local optical narrow-line active galactic nuclei (AGN) as a function of host galaxy properties, as a potential test of galaxy formation theories of AGN feedback. We extract central emission-line fluxes using data from the Mapping Nearby Galaxies at APO (MaNGA) sample of the Sloan Digital Sky Survey IV Data Relea

Chen-Te Ma

We derive the Seiberg-Witten map to first order in the non-commutativity parameter for D-branes in the presence of a large R-R background field. This result enables a systematic investigation of the commutative formulation of the corresponding Lagrangian. In the SU($N$) sector, the map introduces a non-local operator. In contrast, in the U(1) sector, this no

H. R Paz

This study extends the CAPIRE framework with a macro-shock module to analyse the impact of teacher strikes and inflation on student trajectories in engineering education. Using data from 1,343 students across 15 cohorts (2004-2019) in a public engineering faculty in Argentina, we construct a leak-aware, multilevel feature set that incorporates national infla

Adam Hazimeh, Alessandro Favero, Pascal Frossard

Task arithmetic has recently emerged as a promising method for editing pre-trained \textit{open-vocabulary} models, offering a cost-effective alternative to standard multi-task fine-tuning. However, despite the abundance of \textit{closed-vocabulary} models that are not pre-trained with language supervision, applying task arithmetic to these models remains u

Dae San Kim, Taekyun Kim

Let Y be a random variable whose moment generating function exists in a neighborhood of the origin. Recently, probabilistic Stirling numbers of the first kind and of the second kind associated with Y have been introduced. However, probabilistic stirling number of the first kind based on the cumulant generating function of Y, and probabilistic stirling number

Chen Chen, Cuong Nguyen, Alexa Siu, Dingzeyu Li

Accessing 3D models remains challenging for Screen Reader (SR) users. While some existing 3D viewers allow creators to provide alternative text, they often lack sufficient detail about the 3D models. Grounded on a formative study, this paper introduces SweeperBot, a system that enables SR users to leverage visual question answering to explore and compare 3D

Lucia Makaiova, Martin Fajcik, Antonin Jarolim

Document-level claim extraction remains an open challenge in the field of fact-checking, and subsequently, methods for evaluating extracted claims have received limited attention. In this work, we explore approaches to aligning two sets of claims pertaining to the same source document and computing their similarity through an alignment score. We investigate

Barbara Steffen, Edward A. Lee, Moshe Y. Vardi, Bernhard Steffen

Artificial intelligence (AI) is no longer futuristic; it is a daily companion shaping our private and work lives. While AI simplifies our lives, its rise also invites us to rethink who we are - and who we wish to remain - as humans. Even if AI does not think, feel, or desire, it learns from our behavior, mirroring our collective values, biases, and aspiratio

Minyoung Hwang, Alexandra Forsey-Smerek, Nathaniel Dennler, Andreea Bobu

Robots can adapt to user preferences by learning reward functions from demonstrations, but with limited data, reward models often overfit to spurious correlations and fail to generalize. This happens because demonstrations show robots how to do a task but not what matters for that task, causing the model to focus on irrelevant state details. Natural language

Fatma Çiçek, Pranendu Darbar, Allysa Lumley

In the function field setting with a fixed characteristic, it was proven by the second and third authors that the values $\log \big|L\big(\frac12, \chi_D\big)\big|$ as $D$ varies over monic and square-free polynomials are asymptotically Gaussian distributed on the assumption of a low lying zeros hypothesis as the degree of $D$ tends to $\infty$. For real dis

Wei-Juan Zhang, Dimitri Leemans

In this paper we give group-theoretical conditions on the maximal parabolic subgroups of a coset geometry for it to be a chiral hypertope, bypassing the need to construct the incidence graph of the coset geometry to determine whether or not it is a chiral hypertope. This result permits to study much larger coset geometries with computers and gives hope on pr

Aled Horner, Fani E. Taifakou, Choudhry Z. Amjad, Filip Aniés

Thermal neutron detectors are crucial to a wide range of applications, including nuclear safety and security, cancer treatment, space research, non-destructive testing, and more. However, neutrons are notoriously difficult to capture due to their absence of charge, and only a handful of isotopes have a sufficient neutron cross-section. Meanwhile, commerciall

Xinzhe Zheng, Shiyu Jiang, Gustavo Seabra, Chenglong Li

Deep generative models are rapidly advancing structure-based drug design, offering substantial promise for generating small molecule ligands that bind to specific protein targets. However, most current approaches assume a rigid protein binding pocket, neglecting the intrinsic flexibility of proteins and the conformational rearrangements induced by ligand bin

Birka Zimmermann, Stefanie Walch, Seamus D. Clarke, Richard Wünsch

To advance our understanding of massive star formation, it is essential to perform a comprehensive suite of simulations that explore the relevant parameter space and include enough physics to enable a comparison with observational data. We simulate the gravitational collapse of isolated, parsec-scale turbulent cores using the FLASH code, modelling stars as s

Mihajlo Cekić, Thibault Lefeuvre, Andrei Moroianu, Uwe Semmelmann

In this short note, we prove a global Pestov identity on the (orthonormal) frame bundle of a Riemannian manifold and deduce similar identities on associated homogeneous fibrations. As a particular example, this provides a concise proof of the Pestov identity on the unit tangent bundle of the manifold.

Mohammad Romani

Modern deepfakes evade detection by leaving subtle, domain-speci c artifacts that single branch networks miss. ForensicFlow addresses this by fusing evidence across three forensic dimensions: global visual inconsistencies (via ConvNeXt-tiny), ne-grained texture anomalies (via Swin Transformer-tiny), and spectral noise patterns (via CNN with channel attention

Zhehao Yi, Rahul Bhadani

Variational quantum algorithms (VQAs) combine the advantages of classical optimization and quantum computation, making them one of the most promising approaches in the Noisy Intermediate-Scale Quantum (NISQ) era. However, when optimized using gradient descent, VQAs often suffer from the vanishing gradient problem, commonly known as the barren plateau. Variou

Martin Monperrus, Benoit Baudry, Clément Vidal

This paper documents Project Rachel, an action research study that created and tracked a complete AI academic identity named Rachel So. Through careful publication of AI-generated research papers, we investigate how the scholarly ecosystem responds to AI authorship. Rachel So published 10+ papers between March and October 2025, was cited, and received a peer

Konrad Friedrichs, Franz Dammaß, Karl A. Kalina, Markus Kästner

We propose a precise and efficient physics-augmented neural network (PANN) to model strain-induced crystallization in rubbery polymers. We demonstrate that the model can be flexibly employed for both unfilled and filled natural rubber (NR). The approach is based on a two potential framework, similar to the concept of generalized standard materials (GSMs). To

Bruno P. Schnepper, Jefferson L. D. de Oliveira, Yan A. C. Avó, Carlos H. S. Vieira

Coherence is an inherently quantum property that deeply affects microscopic processes, including thermalization phenomena. A striking example is the quantum Mpemba effect (QME), in which a system can exhibit anomalous relaxation, thermalizing faster from a state initially farther from equilibrium than from one closer. Here, we experimentally investigate the

Gabriel Mastrilli

We investigate the problem of estimating the structure factor, or spectra, of stationary spatial point processes. In the first part, we establish a minimax lower bound for this estimation problem, using an approach tailored to second-order properties of spatial point processes. Although not the main focus, this methodology also extends naturally to a minimax

Dimitrios Dimopoulos, Apostolis K. Salkintzis, Dimitris Tsolkas, Nikos Passas

Modern mobile and stationary devices are equipped with multiple network interfaces aiming to provide wireless and wireline connectivity either in a local LAN or the Internet. Multipath TCP (MPTCP) protocol has been developed on top of legacy TCP to allow the simultaneous use of multiple network paths in the communication route between two end-systems. Althou

Floyd M. Creevey, Lloyd C. L. Hollenberg

Quantum computing holds immense potential for transforming financial analysis and decision-making. Realising this potential necessitates the efficient encoding and processing of financial data on quantum computers. In this study, we propose using the GASP (Genetic Algorithm for State Preparation) framework to optimise the encoding of stock price data into qu

Su Yang, Sathyanarayanan Chandramouli, Panayotis G. Kevrekidis

We introduce and systematically investigate the generation of dispersive shock waves, which arise naturally in physical settings such as optical waveguide arrays and superfluids confined within optical lattices. The underlying physically relevant model is a nonlinear Schr\"odinger (NLS) equation with a periodic potential. We consider the evolution of piecewi

Elisabeth Vogel, Peter Langendörfer

Cyber-physical Systems of Systems (CPSoS) are becoming increasingly prevalent across sectors such as Industry 4.0 and smart homes, where they play a critical role in enabling intelligent, interconnected functionality. Addressing the challenges and resilience requirements of these complex environments, we propose a modified Cyber-Resilience Life-Cycle as a pr

Mikołaj Myszkowski, Mattia Damia Paciarini, Francesco Sannino

We propose quantum-mechanical systems in which the number of spatial dimensions is promoted to a dynamical quantum variable, making the effective dimension state-dependent. Interestingly, systems of this form can exhibit enhanced symmetries compared to their fixed-dimensional counterparts. As an explicit example, we analyze a two-state system for which the n

Alessandro Ansani, Elena Rinallo

Despite its evanescent nature, statistical power is crucial for planning Partial Least Squares Structural Equation Modelling (PLS-SEM) studies. This brief paper introduces PLS-SEM-power, a Shiny Application and R package that implements the inverse square root method by Kock and Hadaya (2018) to calculate both the minimum required sample size (a priori analy

Haorui Ma, Dennis Frauen, Stefan Feuerriegel

Structural nested mean models (SNMMs) are a principled approach to estimate the treatment effects over time. A particular strength of SNMMs is to break the joint effect of treatment sequences over time into localized, time-specific ``blip effects''. This decomposition promotes interpretability through the incremental effects and enables the efficient offline

Jaume Ros, Alessio Arleo, Fernando Paulovich

Dimensionality Reduction (DR) techniques are commonly used for the visual exploration and analysis of high-dimensional data due to their ability to project datasets of high-dimensional points onto the 2D plane. However, projecting datasets in lower dimensions often entails some distortion, which is not necessarily easy to recognize but can lead users to misl

Youran Zhou, Mohamed Reda Bouadjenek, Sunil Aryal

Incomplete data are common in real-world tabular applications, where numerical, categorical, and discrete attributes coexist within a single dataset. This heterogeneous structure presents significant challenges for existing diffusion-based imputation models, which typically assume a homogeneous feature space and rely on stochastic denoising trajectories. Suc

Khalil Sabour, Yaroslav V. Kartashov

We introduce higher order polariton topological insulator (HOTI) realized with fractal array of microcavity pillars arranged into Sierpinski gasket-like geometry. This system exhibiting self similarity in different generations, can support localized modes either in the external or multiple internal corners depending on the controllable distortion introduced

Eusebio Gardella, Mathias Palmstrøm, Hannes Thiel

We show that the topological full group of a Hausdorff ample groupoid with compact unit space coincides with the group of homotopy classes of invertible isometries in pseudofunction algebras associated with the groupoid. Moreover, if the groupoid $\mathcal{G}$ is also effective, then we show that the group of (inner) automorphisms in pseudofunction algebras

Qiang Bai, Bojian Wu, Xi Yang, Zhizhong Han

Neural signed distance functions (SDFs) have been a vital representation to represent 3D shapes or scenes with neural networks. An SDF is an implicit function that can query signed distances at specific coordinates for recovering a 3D surface. Although implicit functions work well on a single shape or scene, they pose obstacles when analyzing multiple SDFs w

N. Rathee, F. P. Keenan, R. J. R. Williams, G. J. Ferland

We have generated strongly photoionized Ar plasmas in experiments designed to use primarily X-ray L-shell line emission generated from Ag foils irradiated by the VULCAN high-power laser at the UK Central Laser Facility. The principle of the experiment is that use of line emission rather than the usual sub-keV quasi-blackbody source allows keV radiation to pl

Ayham Makhamra, Yelyzaveta Satynska, Michael Weselcouch

In this paper we examine the effectiveness of five mathematical models used to predict the outcomes of amateur darts games. These models not only predict the outcomes at the start of the game, but also update their estimations as the game score changes. The models were trained and tested on a dataset consisting of games played by amateur players involving st

Florian Meier, Jan Boeckmann, Clemens Thielen

Physician rostering in hospitals is complex due to varying shift structures, qualifications, and department- or hospital-specific regulations. Most existing optimization models are highly tailored to a single hospital or department and rarely see practical use. We present a general framework and a corresponding mixed-integer programming (MIP) model for physi

Weiyue Zheng, Andrew Elliott, Claire Miller, Marian Scott

We propose a spatio-temporal data-fusion framework for point data and gridded data with variables observed on different spatial supports. A latent Gaussian field with a Mat\'ern-SPDE prior provides a continuous space representation, while source-specific observation operators map observations to both point measurements and gridded averages, addressing change

Jiahao Wu, Shengwen Yu

Bridging continuous perceptual signals and discrete symbolic reasoning is a fundamental challenge in AI systems that must operate under uncertainty. We present a neuro-symbolic framework that explicitly models and propagates uncertainty from perception to planning, providing a principled connection between these two abstraction levels. Our approach couples a

Subhajit Ghosh, Kimberly K. Boddy, Tien-Tien Yu

We study the cosmological impact of ultralight dark matter (ULDM) with a quadratic coupling to Standard Model particles. In addition to the suppression of small-scale power from ULDM itself, the coupling induces a variation of fundamental constants that is modulated by the ULDM oscillatory field value. In this work, we consider the ULDM-induced, time-depende

David Carmel, Simone Filice, Guy Horowitz, Yoelle Maarek

With Retrieval Augmented Generation (RAG) becoming more and more prominent in generative AI solutions, there is an emerging need for systematically evaluating their effectiveness. We introduce the LiveRAG benchmark, a publicly available dataset of 895 synthetic questions and answers designed to support systematic evaluation of RAG-based Q&A systems. This syn

Xiangchen Yin, Jiahui Yuan, Zhangchi Hu, Wenzhang Sun

Existing video Variational Autoencoders (VAEs) generally overlook the similarity between frame contents, leading to redundant latent modeling. In this paper, we propose decoupled VAE (DeCo-VAE) to achieve compact latent representation. Instead of encoding RGB pixels directly, we decompose video content into distinct components via explicit decoupling: keyfra

Haisu Wu, Hong Ren, Cunhua Pan, Boshi Wang

The evolution of next-generation wireless networks has spurred the vigorous development of the low-altitude economy (LAE). To support this emerging field while remaining compatible with existing network architectures, integrated sensing and communication (ISAC) based on 5G New Radio (NR) signals is regarded as a promising solution. However, merely leveraging

Tatiane Ornelas, Allysson Allex Araújo, Júlia Araújo, Marina Araújo

[Context] Large Language Models (LLMs) are increasingly used to assist qualitative research in Software Engineering (SE), yet the methodological implications of this usage remain underexplored. Their integration into interpretive processes such as thematic analysis raises fundamental questions about rigor, transparency, and researcher agency. [Objective] Thi

Peng Wang, Eros Kuikel, Jia Ye, Mohamed-Slim Alouini

Conventional high-altitude platforms (HAPs) face challenges in achieving continuous all-weather operation due to intermittent photovoltaic power generation, limited energy storage capacity, and high mission loads resulting from functional integration. To address this fundamental issue, we propose a stratospheric energy grid in which wireless power transfer (

Kristóf Bérczi, Benedek Nádor

An open problem in convex geometry asks whether two simplices $A,B\subseteq\mathbb{R}^d$, both containing the origin in their convex hulls, admit a polynomial-length sequence of vertex exchanges transforming $A$ into $B$ while maintaining the origin in the convex hull throughout. We propose a matroidal generalization of the problem to oriented matroids, conc

Khiem Hong Phan, Quang Hoang-Minh Pham

The first results for charged Higgs pair production associated with neutral gauge bosons at future multi--TeV muon colliders are presented within the framework of the Two-Higgs-Doublet Model. In the phenomenological studies for the production processes, we first update the parameter space of the Type-X Two-Higgs-Doublet Model. From the viable regions of the

Sunday A. Adetunji

Background: Linear mixed-effects models are central for analyzing longitudinal continuous data, yet many learners meet them as scattered formulas or software output rather than as a coherent workflow. There is a need for a single, reproducible case study that links questions, model building, diagnostics, and interpretation. Methods: We reanalyze a published

Yufeng Tian, Yifan Chen, Zhe Sun, Libang Chen

Underwater image restoration and enhancement are crucial for correcting color distortion and restoring image details, thereby establishing a fundamental basis for subsequent underwater visual tasks. However, current deep learning methodologies in this area are frequently constrained by the scarcity of high-quality paired datasets. Since it is difficult to ob

Haoyu Liang, Zhentian Zhang, Jian Dang, Hao Jiang

Fluid antenna systems (FASs) offer substantial spatial diversity by exploiting the electromagnetic port correlation within compact array spaces, thereby generating favorable small-scale fading conditions with beneficial channel gain envelope fluctuations. This unique capability opens new opportunities for a wide range of communication applications and emergi

Taifour Yousra Nabila, Azeddine Beghdadi, Marie Luong, Zuheng Ming

Low Dose Computed Tomography (LDCT) is widely used as an imaging solution to aid diagnosis and other clinical tasks. However, this comes at the price of a deterioration in image quality due to the low dose of radiation used to reduce the risk of secondary cancer development. While some efficient methods have been proposed to enhance LDCT quality, many overes

Cheng-Jie Zhao, Zhaolin Wang, Hyundong Shin, Yuanwei Liu

A novel fully-connected (FC) tri-hybrid beamforming (THB) architecture is proposed for pinching antenna systems (PASS). In contrast to conventional sub-connected (SC) PASS, the proposed FC architecture employs a tunable phase-shifter network to interconnect all radio frequency (RF) chains with all waveguides. This facilitates a THB framework that integrates

Hao Qian, Shikui Tu, Lei Xu

Diffusion and flow matching models have recently emerged as promising approaches for peptide binder design. Despite their progress, these models still face two major challenges. First, categorical sampling of discrete residue types collapses their continuous parameters into onehot assignments, while continuous variables (e.g., atom positions) evolve smoothly

Xinxin Tang, Bin Qin, Yufang Li

Achieving a balance between lightweight design and high performance remains a significant challenge for speech enhancement (SE) tasks on resource-constrained devices. Existing state-of-the-art methods, such as MUSE, have established a strong baseline with only 0.51M parameters by introducing a Multi-path Enhanced Taylor (MET) transformer and Deformable Embed

Julien Bensmail, Noémie Catherinot, Foivos Fioravantes, Clara Marcille

We pursue the study of edge-irregulators of graphs, which were recently introduced in [Fioravantes et al. Parametrised Distance to Local Irregularity. IPEC, 2024]. That is, we are interested in the parameter Ie(G), which, for a given graph G, denotes the smallest k >= 0 such that G can be made locally irregular (i.e., with no two adjacent vertices having the

Gaia Forghieri, Viacheslav Dubovitskii, Matteo A. C. Rossi, Matteo G. A. Paris

Reconstructing protein-protein interaction networks is a central challenge in network medicine, often addressed using link prediction algorithms. Recent studies suggest that quantum walk-based approaches hold promise for this task. In this paper, we build on these algorithms by introducing chirality through the addition of random phases in the Hamiltonian ge

Chao Yu, Qixin Tan, Jiaxuan Gao, Shi Yu

Reasoning reinforcement learning (RL) has recently revealed a new scaling effect: test-time scaling. Thinking models such as R1 and o1 improve their reasoning accuracy at test time as the length of the reasoning context increases. However, compared with training-time scaling, test-time scaling is fundamentally limited by the limited context length of base mo

Yupei Huang, Xiaoqian Xu

In this paper, we prove that the $L^2$ norm of spatial mean-free solutions to the advection--diffusion equation on $\mathbb{T}^2$ with shear drifts satisfies an \emph{exponential lower bound} in time. This lower bound shows that diffusion can fundamentally suppress passive-scalar mixing.

Chenyu Shi, Hao Wang

The Variational Quantum Eigensolver (VQE) is one of the most promising algorithms for current quantum devices. It employs a classical optimizer to iteratively update the parameters of a variational quantum circuit in order to search for the ground state of a given Hamiltonian. The efficacy of VQEs largely depends on the optimizer employed. Recent studies sug

Jiawei Yi, Ping Gong, Youhui Bai, Zewen Jin

During LLM inference, KVCache memory usage grows linearly with sequence length and batch size and often exceeds GPU capacity. Recent proposals offload KV states to host memory and reduce transfers using top-k attention. But their CPU-centric management of the on-GPU cache and CPU-GPU data movement incurs high overhead and fragments the bulk GPU execution tha

Alba Bernabeu, Jorge Mateu

Spatio-temporal Hawkes point processes are a particularly interesting class of stochastic point processes for modeling self-exciting behavior, in which the occurrence of one event increases the probability of other events occurring. These processes are able to handle complex interrelationships between stochastic and deterministic components of spatio-tempora

Lizhong Chen, Hongyang Wang

The HVN is a graph formed by removing two edges incident to the same vertex from the complete graph $K_5$. In this paper, we prove that every ($P_2\cup P_4$, HVN)-free graph $G$ satisfies $\chi(G)\leq\lceil\frac{4}{3}\omega(G)\rceil$ when $\omega(G)\ge4$, where $\chi(G)$ and $\omega(G)$ denote the chromatic number and clique number of $G$, respectively. Furt

Victor Ale, Tommaso Rainaldi, Enrique Rico, Felix Ringer

We develop a hybrid qubit-qumode framework for simulating quantum electrodynamics in 2+1 dimensions. In this approach, fermionic matter fields are represented by qubits, while U(1) gauge fields are encoded in continuous-variable bosonic modes whose canonical quadratures capture the electric and vector-potential components of the theory. To reconcile the non-

Qinghao Meng, Junbo Yin, Jianbing Shen, Yunde Jia

Center-aligned regression remains dominant in LiDAR-based 3D object detection, yet it suffers from fundamental instability: object centers often fall in sparse or empty regions of the bird's-eye-view (BEV) due to the front-surface-biased nature of LiDAR point clouds, leading to noisy and inaccurate bounding box predictions. To circumvent this limitation, we

Fenglin Deng, Yi Lu, Xiaodong Cao, Zhicheng Zhong

We introduce a neural network impurity solver for real-frequency DMFT that employs a multihead cross-attention mechanism to map hybridization functions to spectral functions, conditioned on impurity parameters. Trained on high-quality MPS data from complex contour time evolution and incorporating derivative constraints with respect to the complex-time angle,

Jan Quenzel, Valerij Sekin, Daniel Schleich, Alexander Miller

Fires in industrial facilities pose special challenges to firefighters, e.g., due to the sheer size and scale of the buildings. The resulting visual obstructions impair firefighting accuracy, further compounded by inaccurate assessments of the fire's location. Such imprecision simultaneously increases the overall damage and prolongs the fire-brigades operati

Xinzhuo Yu, Yunzhi Zhuge, Sitong Gong, Lu Zhang

Understanding the inter-relations and interactions between tasks is crucial for multi-task dense prediction. Existing methods predominantly utilize convolutional layers and attention mechanisms to explore task-level interactions. In this work, we introduce a novel decoder-based framework, Parameter Aware Mamba Model (PAMM), specifically designed for dense pr

Sviatoslav Stumpf, Vladislav Povyshev

The aim of this paper is to examine and demonstrate how integer-based datetime labels (integer surrogate keys for time) can optimize data-warehouse and time-series performance, proposing practical formats and algorithms and validating their efficiency on real-world workloads. It is shown that replacing standard DATE and TIMESTAMP types with 32- and 64-bit in

Abdurakhmon Sadiev, Yury Demidovich, Igor Sokolov, Grigory Malinovsky

Communication compression is essential for scalable distributed training of modern machine learning models, but it often degrades convergence due to the noise it introduces. Error Feedback (EF) mechanisms are widely adopted to mitigate this issue of distributed compression algorithms. Despite their popularity and training efficiency, existing distributed EF

Xenie Lytvynenko, Marie Urbanová, Ondřej Lalinský, Vilém Vojta

This study investigates the synthesis and luminescence behavior of $\mathrm{Ce_{x}La_{0.95-x}Tb_{0.05}F_{3}}$ nanoparticles with varying $\mathrm{Ce^{3+}}$ content. The materials were prepared via a wet chemical route and thermally annealed to improve crystallinity and reduce defects. Phase composition and structural parameters were examined by X-ray diffrac

Jack Qin, Zhitao Wang, Yinan Zheng, Keyu Chen

The autonomous driving (AD) system has exhibited remarkable performance in complex driving scenarios. However, generalization is still a key limitation for the current system, which refers to the ability to handle unseen scenarios or unfamiliar sensor configurations.Related works have explored the use of Vision-Language Models (VLMs) to address few-shot or z

Ju-Young Oh

Conventional VQA approaches primarily rely on question-answer (Q&A) pairs to learn the spatio-temporal dynamics of video content. However, most existing annotations are event-centric, which restricts the model's ability to capture the comprehensive context of a scene. The lack of fundamental information such as object categories, spatial configurations, and

Mohammad Reza Ahmadi Zand, Hamid Torabi Ardakani

This paper introduces the concept of slender generalized groups, extending the classical notion of slender abelian groups to the setting of generalized groups (completely simple semigroups). We establish fundamental properties of slender generalized groups and prove that, in the abelian case, the classical and generalized definitions of slenderness coincide.

Ritik Pal, Soubhik Mukherjee, Urmi Dutta, Arghya Choudhury

In recent years, Physics-Informed Neural Networks (PINNs) have emerged as a powerful and robust framework for solving nonlinear differential equations across a wide range of scientific and engineering disciplines, including biology, geophysics, astrophysics and fluid dynamics. In the PINN framework, the governing partial differential equations, along with in

Sauvik Poddar, Sucharita Biswas, Angsuman Das

Quasi-strongly regular graphs form a significant generalization of strongly regular graphs. We study the eigenvalues of a family of such graphs, $\Gamma_H(G)$, constructed from a finite group $G$ and a subgroup $H$. Our main results include a sufficient condition for $\Gamma_H(G)$ to be integral and an explicit computation of its entire spectrum when $H$ is

Jiaming Zhang, Jiajun He, Tianyu Lu, Jie Zhang

This letter presents a feature-guided adversarial framework, namely ComGAN, which is designed to reconstruct an incomplete fingerprint database by inferring missing received signal strength (RSS) values at unmeasured reference points (RPs). An auxiliary subnetwork is integrated into a conditional generative adversarial network (cGAN) to enable spatial featur

Guoliang Tang, Jiaqun Wei

Let $T_R(M)$ be a tensor ring, where $R$ is a ring and $M$ is an $N$-nilpotent $R$-bimodule. Under certain conditions, we characterize projectively coresolved Gorenstein flat modules over $T_R(M)$, showing that a $T_R(M)$ module $(X,u)$ is projectively coresolved Gorenstein flat if and only if $u$ is monomorphic and $coker(u)$ is a projectively coresolved Go

Aleksi Pyörälä

Let $\mu$ be a self-conformal measure on $\mathbb{R}^d$. In this note we establish conditions for $\mu$ under which $\dim(\mu*\nu) = \min\lbrace d,\dim\mu+\dim\nu\rbrace$ holds when $\nu$ is any Ahlfors-regular or self-conformal measure on $\mathbb{R}^d$. Our main result states the following sufficient condition: $\mu$ is totally non-linear and not supported

Jin Jer Huang

The displacement current, introduced by Maxwell, has long caused confusion regarding its relationship to the magnetic field. To develop a new understanding of classical magnetism, this work first decomposes the displacement current into a localized internal component and an external field distribution by employing a discal regularization of the dipole distri

Evgenii Vlasov, Wouter Heyvaert, Tom Stoops, Sandra Van Aert

Secondary electron (SE) imaging offers a powerful complementary capabilities to conventional scanning transmission electron microscopy (STEM) by providing surface-sensitive, pseudo-3D topographic information. However, contrast interpretation of such images remains empirical due to complex interactions of emitted SE with the magnetic field in the objective fi