July 2025 arXiv papers — page 3

Harshit Kumar Sandhu, Saurav Dutta, Rajesh Chaunsali

Nonreciprocal wave propagation allows for directional energy transport. In this work, we systematically investigate wave dynamics in an elastic lattice that combines nonreciprocal stiffness with viscous damping. After establishing how conventional damping counteracts the system's gain, we introduce a non-dissipative form of nonreciprocal damping in the form

Hiroyasu Tajima, Koji Yamaguchi, Ryuji Takagi, Yui Kuramochi

Quantum technologies offer exceptional -- sometimes almost magical -- speed and performance, yet every quantum process costs physical resources. Designing next-generation quantum devices, therefore, depends on solving the following question: which resources, and in what amount, are required to implement a desired quantum process? Casting the problem in the l

Bora Yalkinoglu

In this note, using Borger's theory of periodic Witt vectors, we construct integral refinements of the arithmetic subalgebras associated with Bost-Connes systems for general number fields.

Bernadette Lessel

This paper aims to highlight Pascual Jordan's axiomatic definition of the covariant derivative, as set out in his 1952 textbook "Schwerkraft und Weltall". Developed in light of his \emph{Erweiterte Gravitationstheorie} - a projective reformulation of relativity theory that incorporates a variable gravitational constant - Jordan's definition resembles those i

Aditya Banerjee

Given that any subsystem of a closed out-of-equilibrium quantum system is an open quantum system, its dynamics (reduced from the full system's unitary evolution) can be either Markovian (memory-less) or non-Markovian, with the latter necessarily impeding the process of relaxation and thermalization. Seemingly independently, such non-ergodic dynamics occurs w

Diana Mortagua

This study centers on overcoming the challenge of selecting the best annotators for each query in Active Learning (AL), with the objective of minimizing misclassifications. AL recognizes the challenges related to cost and time when acquiring labeled data, and decreases the number of labeled data needed. Nevertheless, there is still the necessity to reduce an

Rongzhen Zhao, Yi Zhao, Juho Kannala, Joni Pajarinen

Unlike popular solutions based on dense feature maps, Object-Centric Learning (OCL) represents visual scenes as sub-symbolic object-level feature vectors, termed slots, which are highly versatile for tasks involving visual modalities. OCL typically aggregates object superpixels into slots by iteratively applying competitive cross attention, known as Slot Att

W. Joe Meese, Rafael M. Fernandes

The defining property of electronic nematicity -- the spontaneous breaking of rotational symmetry -- implies an unavoidable coupling between the nematic order parameter and elastic strain fields, known as nemato-elasticity. While both quantities are rank-2 tensors, the strain tensor is constrained through the Saint Venant compatibility relations. These three

W. Joe Meese, Rafael M. Fernandes

Electronic nematicity is widely observed in quantum materials with varying degrees of electronic correlation, manifesting through charge, spin, orbital, or superconducting degrees of freedom. A phenomenological model capable of describing this broad set of systems must also account for nemato-elasticity, by which nematic and elastic degrees of freedom become

Kaan Alp Yay, W. Joe Meese, Elliot Kisiel, Matthew J. Krogstad

Electronic nematic order is a correlated phase of matter in which low-energy electronic states spontaneously break a discrete rotational symmetry of a crystal lattice. Bilinear coupling between the electronic nematic and strains of the same symmetry yields a single pseudoproper ferroelastic phase transition at which both the nematic and lattice strain onset

Ping Yu, Jack Lanchantin, Tianlu Wang, Weizhe Yuan

We propose CoT-Self-Instruct, a synthetic data generation method that instructs LLMs to first reason and plan via Chain-of-Thought (CoT) based on given seed tasks, and then generate a new synthetic example of similar quality and complexity. This is followed by a filtering step to select high-quality data using automatic metrics, which are then used for LLM t

Alberto Enciso, Javier Peñafiel-Tomás, Daniel Peralta-Salas

We prove the existence of weak solutions to the 3D ideal MHD equations, of class $C^\alpha$ with $\alpha=1/200$, for which the total energy and the cross helicity (i.e., the so-called Els\"asser energies) are not conserved. The solutions do not possess any symmetry properties and the magnetic helicity, which is necessarily conserved for H\"older continuous s

Pravesh Chndra Awasthi, Jai More, Akhila Kumar Pradhan, Kumar Rao

Neutrino flavour oscillations imply that there must be charged lepton flavour violation (CLFV) also. Different neutrino mass models predict different patterns of CLFV decays. Neutrino mass generation through standard see-saw mechanisms leads to the prediction that the branching ratios of meson CLFV decays will always be smaller than the corresponding radiati

Hossein Kazemi, Isaac N. O. Osahon, Tiankuo Jiao, David Butler

Real-time transmission of high-resolution video signals in an uncompressed and unencrypted format requires an ultra-reliable and low-latency communications (URLLC) medium with high bandwidth to maintain the quality of experience (QoE) for users. We put forward the design and experimental demonstration of a high-performance laser-based optical wireless commun

Apoorva Khare, Prateek Kumar Vishwakarma

For each $n \geq 1$ and sign pattern $\epsilon \in \{ \pm 1 \}^n$, we introduce a cone of real symmetric matrices $LPM_n(\epsilon)$: those with leading principal $k \times k$ minors of signs $\epsilon_k$. These cones are pairwise disjoint and their union $LPM_n$ is an open dense cone in all symmetric matrices; they subsume positive and negative definite matr

Radha Balakrishnan, Rossen Dandoloff, Victor Atanasov, Avadh Saxena

We derive the Schr\"odinger equation for a particle confined to the surface of a normal and a binormal helical nanoribbon, obtain the quantum potentials induced by their respective curved surface geometries, and study the localized states of the particle for each ribbon. When the particle momentum satisfies a certain geometric condition, the particle localiz

Koustav Roy, Dipendu Halder, Koustabh Gogoi, B. Tanatar

Periodically driven systems intertwined with non-Hermiticity opens a rich arena for topological phases that transcend conventional Hermitian limits. The physical significance of these phases hinges on obtaining the topological invariants that restore the bulk-boundary correspondence, a task well explored for static non-Hermitian (NH) systems, while it remain

Melody Huang, Cory McCartan

To conduct causal inference in observational settings, researchers must rely on certain identifying assumptions. In practice, these assumptions are unlikely to hold exactly. This paper considers the bias of selection-on-observables, instrumental variables, and proximal inference estimates under violations of their identifying assumptions. We develop bias exp

Saeed Fakhry, Reyhaneh Vojoudi Salmani, Javad T. Firouzjaee

The James Webb Space Telescope (JWST) has unveiled unexpectedly massive galaxy candidates at high redshifts, challenging standard $\Lambda$CDM cosmological predictions. In this work, we study the predictions of more realistic dark matter halo models combined with modified matter power spectra for interpreting JWST observations of high-redshift galaxies. We e

Marc Túnica, Francesca Chiodi, Michele Amato

Pushing dopant concentrations beyond the solubility limit in semiconductors -- a process known as hyperdoping -- has been demonstrated as an effective strategy for inducing superconductivity in cubic-diamond Si and SiGe materials. Additionally, previous studies have reported that several polytypes of Si may exhibit a type-I superconducting state under high p

Nasim Shirvani-Mahdavi, Devin Wingfield, Amin Ghasemi, Chengkai Li

Knowledge graphs (KGs) often contain sufficient information to support the inference of new facts. Identifying logical rules not only improves the completeness of a knowledge graph but also enables the detection of potential errors, reveals subtle data patterns, and enhances the overall capacity for reasoning and interpretation. However, the complexity of su

Amin Faraji Astaneh, Niloofar Vardian

We extend the concept of Krylov complexity to include general unitary evolutions involving multiple generators. This generalization enables us to formulate a framework for generalized Krylov complexity, which serves as a measure of the complexity of states associated with continuous symmetries within a model. Furthermore, we investigate scenarios where diffe

Eric Cady, Nicholas Bowman, Alexandra Z. Greenbaum, James G. Ingalls

The Nancy Grace Roman Space Telescope (``Roman'') is a 2.4m space telescope scheduled for a 2026 launch. The Coronagraph Instrument (CGI) on Roman is a technology-demonstration instrument with a coronagraph and, for the first time in space, deformable mirrors and active wavefront control. This paper walks through the algorithmic and system-level architecture

Nicolas Clozeau, Harprit Singh

We show local well-posedness of the g-PAM and the $\phi^{K+1}_2$-equation for $K\geq 1$ on the two-dimensional torus when the coefficient field is random and correlated to the driving noise. In the setting considered here, even when the model in the sense of Hairer (2014) is stationary, naive use of renormalisation constants in general leads to variance blow

Kyle Naddeo, Nikolas Koutsoubis, Rahul Krish, Ghulam Rasool

Access to medical imaging and associated text data has the potential to drive major advances in healthcare research and patient outcomes. However, the presence of Protected Health Information (PHI) and Personally Identifiable Information (PII) in Digital Imaging and Communications in Medicine (DICOM) files presents a significant barrier to the ethical and se

Markus Buchholz, Ignacio Carlucho, Michele Grimaldi, Yvan R. Petillot

Achieving robust cognitive autonomy in robots navigating complex, unpredictable environments remains a fundamental challenge in robotics. This paper presents Underwater Robot Self-Organizing Autonomy (UROSA), a groundbreaking architecture leveraging distributed Large Language Model AI agents integrated within the Robot Operating System 2 (ROS 2) framework to

Ammar Daskin

Schmidt decomposition of a vector can be understood as writing the singular value decomposition (SVD) in vector form. A vector can be written as a linear combination of tensor product of two dimensional vectors by recursively applying Schmidt decompositions via SVD to all subsystems. Given a vector expressed as a linear combination of tensor products, using

Dongming Wu, Yanping Fu, Saike Huang, Yingfei Liu

General robotic grasping systems require accurate object affordance perception in diverse open-world scenarios following human instructions. However, current studies suffer from the problem of lacking reasoning-based large-scale affordance prediction data, leading to considerable concern about open-world effectiveness. To address this limitation, we build a

Theodoros Depastas, Aldo Bonasera, Joseph B. Natowitz

Solitons are special shape-conserving hydrodynamical solutions that appear in many areas of physics. Here, we explore the existence of such solutions in microscopic descriptions of the heavy ion reaction $^{12}$C + $^{28}$Si $\rightarrow$ $^{12}$C$^*$ + 7$\alpha$ in the range E/A=10-65 MeV/u. After recognizing the centrality of the collision and time-reversi

Yifan Wang, Hongfeng Ai, Quangao Liu, Maowei Jiang

Vision Language Models (VLMs) face challenges in effectively coordinating diverse attention mechanisms for cross-modal embedding learning, leading to mismatched attention and suboptimal performance. We propose Consistent Cross-layer Regional Alignment (CCRA), which introduces Layer-Patch-wise Cross Attention (LPWCA) to capture fine-grained regional-semantic

Bidhan Modok, Amarjit Kundu, Shovan Chowdhury

This paper proposes a control chart for monitoring percentiles of a process that follows a truncated beta distribution, utilizing a studentized parametric bootstrap method to account for the case when in-control parameters are unknown. To evaluate the in-control performance, extensive Monte Carlo simulations are conducted across various combinations of perce

Jason P. Bell, Ken A. Brown, J. Toby Stafford

This paper addresses the interactions between three properties that a group algebra or more generally a pointed Hopf algebra may possess: being noetherian, having finite Gelfand-Kirillov dimension, and satisfying the Dixmier-Moeglin equivalence. First it is shown that the second and third of these properties are equivalent for group algebras $kG$ of polycycl

Kazuki Doi, Tadashi Takayanagi

We consider the evolution of entanglement entropy in a two-dimensional conformal field theory with a holographic dual. Specifically, we are interested in a class of excited states produced by a combination of pure-state (local operator) and mixed-state local quenches. We employ a method that allows us to determine the full time evolution analytically. While

Cordian Riener, Thi Xuan Vu

Symmetry plays a central role in accelerating symbolic computation involving polynomials. This chapter surveys recent developments and foundational methods that leverage the inherent symmetries of polynomial systems to reduce complexity, improve algorithmic efficiency, and reveal deeper structural insights. The main focus is on symmetry by the permutation of

Brindhu Malani S, Eugen Klein, Ronja Maria Piehler, Rostyslav Lesyuk

Quasi-2D metal halide perovskites have emerged as a promising material for photodetection due to excellent optoelectronic properties, simple synthesis, and robust stability. Albeit, developing high-performance photodetectors based on low-dimensional quasi-2D metal halide perovskite nanoparticles remains challenging due to quantum and dielectric confinement e

Luoxin Chen, Jinming Gu, Liankai Huang, Wenhao Huang

LLMs have demonstrated strong mathematical reasoning abilities by leveraging reinforcement learning with long chain-of-thought, yet they continue to struggle with theorem proving due to the lack of clear supervision signals when solely using natural language. Dedicated domain-specific languages like Lean provide clear supervision via formal verification of p

Ilya Kuruzov, Xiaokai Chen, Gesualdo Scutari, Alexander Gasnikov

We study decentralized optimization where multiple agents minimize the average of their (strongly) convex, smooth losses over a communication graph. Convergence of the existing decentralized methods generally hinges on an apriori, proper selection of the stepsize. Choosing this value is notoriously delicate: (i) it demands global knowledge from all the agent

Alexis Anagnostakis

We introduce the Space-Time Markov Chain Approximation (STMCA) for a general diffusion process on a finite metric graph $\Gamma$. The STMCA is a doubly asymmetric (in both time and space) random walk defined on a subdivisions of $\Gamma$, with transition probabilities and conditional transition times that match, in expectation, those of the target diffusion.

Syed Haider Ali, Ashfaq Ahmad, Muhammad Saiel, Nadeem Shaukat

The simultaneous production of four top quarks in association with a ($W$) boson at $(\sqrt{s} = 13)$ TeV is an rare SM process with a next-to-leading-order (NLO) cross-section of $(6.6^{+2.4}_{-2.6} {ab})$\cite{saiel}. Identifying this process in the fully hadronic decay channel is particularly challenging due to overwhelming backgrounds from $t\bar{t}, t\b

Wolfgang Altmannshofer, P. S. Bhupal Dev, Amarjit Soni, Fang Xu

We revisit the electric dipole moments (EDMs) of quarks and leptons in the Minimal Supersymmetric Standard Model (MSSM) with trilinear $R$-parity violation (RPV). In this framework, EDMs are induced at the two-loop level via RPV interactions. We perform a comprehensive recalculation of several classes of Barr-Zee type diagrams in a general $R_\xi$ gauge. Whi

Sajawal Zafar, Qazi Maaz Us Salam, Rana Khan, Ishtiaq Ahmed

We investigate the cascade decay $B^{*0}_{s} \rightarrow D_s^-(\rightarrow \tau^-\,\bar\nu_{\tau})\,\ell^{+}\,{\nu}_\ell$ induced by flavor changing charged currents in the context of the Standard Model and in vector-like couplings beyond the Standard Model. We employ the helicity amplitude formalism for analysis and highlight the role of new vector-like cou

Jingzhou Liu

In this work, we investigate the nonlinear dynamics of molecules with an octahedral configuration, with particular focus on sulfur hexafluoride SF6. Under the assumption of isotypic nonresonance, we apply the method of equivariant gradient degree to prove the existence of branches of periodic solutions emerging from the critical orbit of equilibrium, corresp

Parker McDonnell, Lingsheng Meng, Hari Krishna Hariprasad, Alexander Hedrick

The American cockroach (Periplaneta americana) uses its soft antennae to guide decision making by extracting rich tactile information from tens of thousands of distributed mechanosensors. Although tactile sensors enable robust, autonomous perception and navigation in natural systems, replicating these capabilities in insect-scale robots remains challenging d

Mowafak Allaham, Kimon Kieslich, Nicholas Diakopoulos

Risk-based approaches to AI governance often center the technological artifact as the primary focus of risk assessments, overlooking systemic risks that emerge from the complex interaction between AI systems and society. One potential source to incorporate more societal context into these approaches is the news media, as it embeds and reflects complex intera

Alfonso de Miguel-Arribas, Chengbin Sun, Carlos Gracia-Lázaro, Yamir Moreno

Cooperation and competition are fundamental forces shaping both natural and human systems, yet their interplay remains poorly understood. The Prisoner's Dilemma Game (PDG) has long served as a foundational framework in Game Theory for studying cooperation and defection, yet it overlooks explicit competitive interactions. Contest Theory, in turn, provides too

Avatar Tulsi

Quantum Phase Estimation (QPE) has potential for a scientific revolution through numerous practical applications like finding better medicines, batteries, materials, catalysts etc. Many QPE algorithms use the Hadamard test to estimate $\langle \psi|U^{k}|\psi\rangle$ for a large integer $k$ for an efficiently preparable initial state $|\psi\rangle$ and an ef

Emery Pierson, Lei Li, Angela Dai, Maks Ovsjanikov

Deep functional maps have recently emerged as a powerful tool for solving non-rigid shape correspondence tasks. Methods that use this approach combine the power and flexibility of the functional map framework, with data-driven learning for improved accuracy and generality. However, most existing methods in this area restrict the learning aspect only to the f

Admitos Passadakis, Yingjin Song, Albert Gatt

Visual Storytelling is a challenging multimodal task between Vision & Language, where the purpose is to generate a story for a stream of images. Its difficulty lies on the fact that the story should be both grounded to the image sequence but also narrative and coherent. The aim of this work is to balance between these aspects, by treating Visual Storytelling

Aymane Abdali, Bartosz Boguslawski, Lucas Drumetz, Vincent Gripon

Several anomaly detection and classification methods rely on large amounts of non-anomalous or "normal" samples under the assump- tion that anomalous data is typically harder to acquire. This hypothesis becomes questionable in Few-Shot settings, where as little as one anno- tated sample can make a significant difference. In this paper, we tackle the question

Pierre Lechifflart, Raisa-Ioana Biega, Linn Leppert

Three-dimensional metal halide double perovskites such as Cs$_2$AgBiBr$_6$ exhibit pronounced excitonic effects due to their anisotropic electronic structure and chemical localization effects. Their two-dimensional derivatives, formed by inserting organic spacer molecules between perovskite layers, were expected to follow well-established trends seen in Pb-b

Yuan-Cheng Yu, Yen-Chieh Ouyang, Chun-An Lin

Time-series anomaly detection plays a central role across a wide range of application domains. With the increasing proliferation of the Internet of Things (IoT) and smart manufacturing, time-series data has dramatically increased in both scale and dimensionality. This growth has exposed the limitations of traditional statistical methods in handling the high

Saeid Ghasemshirazi, Ghazaleh Shirvani, Marziye Ranjbar Tavakoli, Bahar Ghaedi

The pharmaceutical supply chain faces escalating cybersecurity challenges threatening patient safety and operational continuity. This paper examines the transformative potential of zero trust architecture for enhancing security and resilience within this critical ecosystem. We explore the challenges posed by data breaches, counterfeiting, and disruptions and

Alfie Roddan, Chi Xu, Serine Ajlouni, Irini Kakaletri

The deployment of Machine Learning models intraoperatively for tissue characterisation can assist decision making and guide safe tumour resections. For image classification models, pixel attribution methods are popular to infer explainability. However, overconfidence in deep learning model's predictions translates to overconfidence in pixel attribution. In t

Renato Luis Garrido Cavalcante, Tomasz Piotrowski, Slawomir Stanczak

We introduce a unified framework for analyzing utility regions of wireless networks, with a focus on signal-to-interference-plus-noise-ratio (SINR) and achievable rate regions. The framework provides valuable insights into interference patterns of modern network architectures, including extremely large MIMO and cell-less networks. A central contribution is a

Sayan Baig, R. L. Smart, Hugh R. A. Jones, E. Pinna

We report the discovery of the ultracool dwarf binary system J1250+0455AB, a low-mass (M$_\odot$$_\mathrm{tot} <$ 0.2 M$_\odot$) system in which the components straddle the M/L dwarf boundary. The binary was resolved through near-infrared adaptive optics imaging with LUCI1-SOUL on the Large Binocular Telescope, revealing a projected angular separation of 0.1

Zhenyang Li, Xiaoyang Bai, Tongchen Zhang, Pengfei Shen

High-fidelity 3D video reconstruction is essential for enabling real-time rendering of dynamic scenes with realistic motion in virtual and augmented reality (VR/AR). The deformation field paradigm of 3D Gaussian splatting has achieved near-photorealistic results in video reconstruction due to the great representation capability of deep deformation networks.

Murray R. Bremner

We consider algebraic identities for linear operators on associative algebras in which each term has degree 2 (the number of variables) and multiplicity 3 (the number of occurrences of the operator). We apply the methods of earlier work by the author and Elgendy which classified operator identities of degree 2, multiplicities 1 and 2. We begin with the gener

Duc Thien Hua, Mohammadali Mohammadi, Hien Quoc Ngo, Michail Matthaiou

We investigate the integration of beyond diagonal reconfigurable intelligent surfaces (BDRISs) into cell free massive multiple input multiple output (CFmMIMO) systems to enhance simultaneous wireless information and power transfer (SWIPT). To simultaneously support two groups of users energy receivers (ERs) and information receivers (IRs) without sacrificing

Long Phan, Mantas Mazeika, Andy Zou, Dan Hendrycks

Evaluating AI agents within complex, interactive environments that mirror real-world challenges is critical for understanding their practical capabilities. While existing agent benchmarks effectively assess skills like tool use or performance on structured tasks, they often do not fully capture an agent's ability to operate autonomously in exploratory enviro

Andrey Prokopenko, Daniel Arndt, Damien Lebrun-Grandié, Bruno Turcksin

This paper provides an overview of the 2.0 release of the ArborX library, a performance portable geometric search library based on Kokkos. We describe the major changes in ArborX 2.0 including a new interface for the library to support a wider range of user problems, new search data structures (brute force, distributed), support for user functions to be exec

Utkucan Balci, Michael Sirivianos, Jeremy Blackburn

This study investigates the presence of left-wing extremism on the Lemmygrad.ml instance of the decentralized social media platform Lemmy, from its launch in 2019 up to a month after the bans of the subreddits r/GenZedong and r/GenZhou. We conduct a temporal analysis on Lemmygrad.ml's user activity, with also measuring the degree of highly abusive or hateful

Shaofei Cai, Zhancun Mu, Haiwen Xia, Bowei Zhang

While Reinforcement Learning (RL) has achieved remarkable success in language modeling, its triumph hasn't yet fully translated to visuomotor agents. A primary challenge in RL models is their tendency to overfit specific tasks or environments, thereby hindering the acquisition of generalizable behaviors across diverse settings. This paper provides a prelimin

Thomas Eiter, Ana Leonor Silvestre

We consider a time-periodic incompressible three-dimensional Navier-Stokes flow past a translating rigid body. In the first part of the paper, we establish the existence and uniqueness of strong solutions in the exterior domain $\Omega \subset {\mathbb R}^3$ that satisfy pointwise estimates for both the velocity and pressure. The fundamental solution of the

Felipe I. Flores

We study the problem of continuity of derivations over Banach algebras. More specifically, we consider a class of Banach algebras that contain a dense '$C^*$-like' subalgebra. We discuss applications to $L^p$-crossed products and symmetrized $L^p$-crossed products. As an example, our results imply that every derivation over the $L^p$-crossed product $F^p(G,X

Mouli Chakraborty, Subhash Chandra, Avishek Nag, Anshu Mukherjee

We present a comparative study of the Gaussian mixture model (GMM) and the Deep Autoencoder Gaussian Mixture Model (DAGMM) for estimating satellite quantum channel capacity, considering hybrid quantum noise (HQN) and transmission constraints. While GMM is simple and interpretable, DAGMM better captures non-linear variations and noise distributions. Simulatio

Virginia Padilla, Jacinto Dávila

We provide a comprehensive examination of agent-based approaches that codify the principles and linkages underlying multi-agent systems, simulations, and information systems. Based on two decades of study, this paper confirms a framework intended as a formal specification for geosimulation platforms. Our findings show that large language models (LLMs) can be

Ruo Yu Tao, Kaicheng Guo, Cameron Allen, George Konidaris

Mitigating partial observability is a necessary but challenging task for general reinforcement learning algorithms. To improve an algorithm's ability to mitigate partial observability, researchers need comprehensive benchmarks to gauge progress. Most algorithms tackling partial observability are only evaluated on benchmarks with simple forms of state aliasin

Zhaoyue Xu, Long Wang, Chunyu Wang, Yixin Chen

We introduce CFDagent, a zero-shot, multi-agent system that enables fully autonomous computational fluid dynamics (CFD) simulations from natural language prompts. CFDagent integrates three specialized LLM-driven agents: (i) the Preprocessing Agent that generates 3D geometries from textual or visual inputs using a hybrid text-to-3D diffusion model (Point-E) a

Angela F. Gao, John D. Wilding, Ettore Biondi, Katherine L. Bouman

Images of the Earth's interior can provide us with insight into the underlying properties of the Earth, such as how seismic activity might emerge and the interplay between seismic and volcanic activity. Understanding these systems requires reliable high-resolution images to understand mechanisms and estimate physical quantities. However, reliable images are

Javiera Hernández-Morales, Daniel M. Siegel

Neutrino-cooled accretion disks can form in the aftermath of neutron-star mergers as well as during the collapse of rapidly rotating massive stars (collapsars) and the accretion-induced collapse of rapidly rotating white dwarfs. Due to Pauli blocking as electrons become degenerate at sufficiently high accretion rates $\dot{M}$, the resulting 'self-neutroniza

Lina Chen

In this note, we will give an positive answer to Pan-Rong's conjecture that for an open manifold with nonnegative Ricci curvature, if its universal cover has Euclidean volume growth, then its fundamental group is finitely generated. Moreover the fundamental group is virtually abelian. The same result has been given by H.Huang-X.Huang for dimension 4. In fact

F. Romeo, J. Settino

We show that global properties of an unknown quantum network, such as the average degree, hub density, and the number of closed paths of fixed length, can be inferred from strictly local quantum measurements. In particular, we demonstrate that a malicious agent with access to only a small subset of nodes can initialize quantum states locally and, through rep

Stephen Deterding

Let $U$ be a bounded domain in $\mathbb C^d$ and let $L^p_a(U)$, $1 \leq p < \infty$, denote the space of functions that are analytic on $\overline{U}$ and bounded in the $L^p$ norm on $U$. A point $x \in \overline{U}$ is said to be a bounded point evaluation for $L^p_a(U)$ if the linear functional $f \to f(x)$ is bounded in $L^p_a(U)$. In this paper, we pro

Menghan Li, Yulin Shao, Runxin Zhang, Lu Lu

We suggest a re-examination of the conventional view that hybrid optical-radio frequency (O-RF) systems are primarily diversity-driven networks that switch between RF and optical links for robustness. Instead, we uncover a new architectural opportunity: repurposing the optical downlink to enable real-time feedback channel coding over the RF uplink, where str

Zihan Cheng, Liangtai Zhou, Dian Chen, Ni Tang

All-in-One Image Restoration (AiOIR) has emerged as a promising yet challenging research direction. To address the core challenges of diverse degradation modeling and detail preservation, we propose UniLDiff, a unified framework enhanced with degradation- and detail-aware mechanisms, unlocking the power of diffusion priors for robust image restoration. Speci

Henrique Gieg, Maximiliano Ujevic, Armen Sedrakian, Tim Dietrich

We present a new set of numerical-relativity simulations of merging binary neutron stars, aiming to identify possible observable signatures of the slope of the symmetry energy $L_{\rm sym}$. To achieve this goal, we employ a set of equations of state based on a parameterization of the covariant density functional theory of nuclear matter that allows controll

Jialei Chen, Wuhao Xu, Sipeng He, Baoru Huang

Vast and high-quality data are essential for end-to-end autonomous driving systems. However, current driving data is mainly collected by vehicles, which is expensive and inefficient. A potential solution lies in synthesizing data from real-world images. Recent advancements in 3D reconstruction demonstrate photorealistic novel view synthesis, highlighting the

Xiaoyu Chen, Hangxing Wei, Pushi Zhang, Chuheng Zhang

Vision-Language-Action (VLA) models have emerged as a popular paradigm for learning robot manipulation policies that can follow language instructions and generalize to novel scenarios. Recent works have begun to explore the incorporation of latent actions, abstract representations of motion between two frames, into VLA pre-training. In this paper, we introdu

Daniele D'Angeli, Francesco Matucci, Davide Perego, Emanuele Rodaro

Generalizing works of D'Angeli and Donno, we describe, starting from an infinite sequence over $r$ letters with $r \neq 4i$ and $i \in \mathbb{N}$, a sequence of pointed finite graphs. We study the pointed Gromov-Hausdorff limit graphs giving a description of isomorphim classes in terms of dihedral groups and providing insights on the horofunction boundaries

Alexis Béjar-López, Rafael Granero-Belinchón, Carlos Pulido, Juan Soler

In this work, we introduce a cross-diffusion model that couples population density and occupied area to investigate how internal pressure drives growth and motility. By blending nonlinear nonlocal interactions with porous-medium diffusion and an antidiffusive pressure term, the model captures the two-way feedback between local density fluctuations and tissue

Teodor Parella-Dilmé, Jakob S. Kottmann, Antonio Acín

Efficient parametrizations of quantum states are essential for trainable hybrid classical-quantum algorithms. A key challenge in their design consists in adapting to the available qubit connectivity of the quantum processor, which limits the capacity to generate correlations between distant qubits in a resource-efficient and trainable manner. In this work we

David Papo, Javier M. Buldú

If brain anatomy and dynamics have a genuine complex network structure as it has become standard to posit, it is also reasonable to assume that such a structure should play a key role not only in brain function but also in brain dysfunction. However, exactly how network structure is implicated in brain damage and whether at least some pathologies can be thou

Xiaohan Li, Ziren Gong, Fabio Tosi, Matteo Poggi

3D Gaussian Splatting (3DGS) has recently gained popularity in SLAM applications due to its fast rendering and high-fidelity representation. However, existing 3DGS-SLAM systems have predominantly focused on indoor environments and relied on active depth sensors, leaving a gap for large-scale outdoor applications. We present BGS-SLAM, the first binocular 3D G

Rabeya Tus Sadia, Qiang Cheng

Microbiome data analysis is essential for understanding host health and disease, yet its inherent sparsity and noise pose major challenges for accurate imputation, hindering downstream tasks such as biomarker discovery. Existing imputation methods, including recent diffusion-based models, often fail to capture the complex interdependencies between microbial

Tianyi Chen, Haitong Ma, Na Li, Kai Wang

Diffusion policies have achieved great success in online reinforcement learning (RL) due to their strong expressive capacity. However, the inference of diffusion policy models relies on a slow iterative sampling process, which limits their responsiveness. To overcome this limitation, we propose Flow Policy Mirror Descent (FPMD), an online RL algorithm that e

Muhammad Taha Cheema, Abeer Aamir, Khawaja Gul Muhammad, Naveed Anwar Bhatti

Large Language Models (LLMs) process millions of queries daily, making efficient response caching a compelling optimization for reducing cost and latency. However, preserving relevance to user queries using this approach proves difficult due to the personalized nature of chatbot interactions and the limited accuracy of semantic similarity search. To address

Alfie Roddan, Tobias Czempiel, Chi Xu, Daniel S. Elson

Hyperspectral imaging (HSI) provides rich spectral information for medical imaging, yet encounters significant challenges due to data limitations and hardware variations. We introduce SAMSA, a novel interactive segmentation framework that combines an RGB foundation model with spectral analysis. SAMSA efficiently utilizes user clicks to guide both RGB segment

G. Bruno De Luca, Nicolò De Ponti, Andrea Mondino, Alessandro Tomasiello

Using the localization technique, we prove a sharp upper bound on the first Dirichlet eigenvalue of metric balls in essentially non-branching $\mathsf{CD}^{\star}(K,N)$ spaces. This extends a celebrated result of Cheng to the non-smooth setting of metric measure spaces satisfying Ricci curvature lower bounds in a synthetic sense, via optimal transport. Rigid

Muhammad Akashi, Ealeal Bear, Noam Soker

We conduct three-dimensional hydrodynamical simulations of jets launched into a dense shell, reproducing two rings in a bipolar structure that resemble the two dusty rings of the planetary nebula (PN) NGC 1514. The scenario we simulate assumes that a strong binary interaction enhanced the mass loss rate from the asymptotic giant branch (AGB) stellar progenit

Diego Russo, Gian Marco Orlando, Valerio La Gatta, Vincenzo Moscato

Artificial Intelligence (AI) systems are transforming critical sectors such as healthcare, finance, and transportation, enhancing operational efficiency and decision-making processes. However, their deployment in high-stakes domains has exposed vulnerabilities that can result in significant societal harm. To systematically study and mitigate these risk, init

R. M. Salem, R. E. Abdel-Khalek, N. Abdelnasser

In this article, we proceed on the transfer of the left endo-Noetherian property on certain ring extensions. We transfer of the right (left) endo-Noetherian property to the right (left) quotient rings. For a subring $T$ of $R$ and a finite set of indeterminates $X$, we prove that $T + XR[[X]]$ is left endo-Noetherian if and only if $R[[X]]$ is left endo-Noet

Masamichi Miyaji, Shan-Ming Ruan, Shono Shibuya, Kazuyoshi Yano

Holographic complexity, as the bulk dual of quantum complexity, encodes the geometric structure of black hole interiors. Motivated by the complexity=anything proposal, we introduce the spectral representation for generating functions associated with codimension-one and codimension-zero holographic complexity measures. These generating functions exhibit a uni

Matt Clay, Josh Thompson

We introduce a type of surgery on metric spaces. This surgery, in some sense, seeks to replace a subspace $S$ of a metric space $X$ with another metric space $T$ via a function $f : S \to T$. When $T$ is a discrete space, this amounts to collapsing the subspace according to the function. This surgery results in a new metric space we denote $\widehat{X}_f$ an

Amal Saadallah

Feature attribution methods such as SHapley Additive exPlanations (SHAP) have become instrumental in understanding machine learning models, but their role in guiding model optimization remains underexplored. In this paper, we propose a SHAP-guided regularization framework that incorporates feature importance constraints into model training to enhance both pr

Haipeng Liu, Yuxuan Liu, Ting Long

Personalized question recommendation aims to guide individual students through questions to enhance their mastery of learning targets. Most previous methods model this task as a Markov Decision Process and use reinforcement learning to solve, but they struggle with efficient exploration, failing to identify the best questions for each student during training

Lillie Szemraj, Sylvia Biscoveanu

The individual component spins of binary black holes (BBHs) are difficult to resolve using gravitational-wave observations but carry key signatures of the processes shaping their formation and evolution. Recent analyses have found conflicting evidence for a sub-population of black holes with negligible spin, but the Default spin magnitude population model us

Keith Poletti, Stella S. R. Offner, Rachel A. Ward

Neural Operators (NOs) are a leading method for surrogate modeling of partial differential equations. Unlike traditional neural networks, which approximate individual functions, NOs learn the mappings between function spaces. While NOs have been predominantly tested on simplified 1D and 2D problems, such as those explored in prior works, these studies fail t

Salam Thabet Doghmash, Motaz Saad

Hate speech identification in social media has become an increasingly important issue in recent years. In this research, we address two problems: 1) to detect hate speech in Arabic text, 2) to clean a given text from hate speech. The meaning of cleaning here is replacing each bad word with stars based on the number of letters for each word. Regarding the fir

Haoxuan Jiang, Peicong Qian, Yusen Xie, Xiaocong Li

LiDAR-based localization serves as a critical component in autonomous systems, yet existing approaches face persistent challenges in balancing repeatability, accuracy, and environmental adaptability. Traditional point cloud registration methods relying solely on offline maps often exhibit limited robustness against long-term environmental changes, leading to

Kaisei Kishi, Kazuki Kai, Yuto Nakashima, Shunsuke Inenaga

The Nyldon factorization is a string factorization that is a non-decreasing product of Nyldon words. Nyldon words and Nyldon factorizations are recently defined combinatorial objects inspired by the well-known Lyndon words and Lyndon factorizations. In this paper, we investigate the Nyldon factorization of several words. First, we fully characterize the Nyld