November 2025 arXiv papers — page 192

Qiling Liu

We prove that for arbitrary g, there is a surface K of genus g embedded in S4, which has finitely many extendable self-homeomorphisms' action on H1(K,Z), by defining a norm on H1(K,Z) and proving its additivity.

Patrick Valageas

We investigate the statistical properties of one-dimensional Burgers dynamics evolving from stochastic initial conditions defined by a Poisson point process for the velocity potential, with a power-law intensity. Thanks to the geometrical interpretation of the solution in the inviscid limit, in terms of first-contact parabolas, we obtain explicit results for

Aimee Norton, Alex Mendez, Ruizhu Chen, Mausumi Dikpati

Solar Cycle 24 data are used to determine how often the Sun emerges sunspots in `activity nests', i.e., regions where sunspots and active regions (ARs) repeatedly emerge. We use the Solar Photospheric Ephemeral Active Region (SPEAR) catalog created from Helioseismic and Magnetic Imager (HMI) data as well as the HMI Carrington Rotation maps of radial magn

Vittal L. Rao

Localisation tasks in biomedical data often require models to learn meaningful spatial or temporal relationships from signals with complex intensity distributions. A common strategy, exemplified by CoordConv layers, is to append coordinate channels to convolutional inputs, enabling networks to learn absolute positions. In this work, we propose a signal inten

Jeremy Coulson, Alberto Padoan, Cyrus Mostajeran

This paper presents a methodology for solving a geometrically robust least squares problem, which arises in various applications where the model is subject to geometric constraints. The problem is formulated as a minimax optimization problem on a product manifold, where one variable is constrained to a ball describing uncertainty. To handle the constraint, a

Thomas Souverain

To foster trustworthy Artificial Intelligence (AI) within the European Union, the AI Act requires providers to mark and detect the outputs of their general-purpose models. The Article 50 and Recital 133 call for marking methods that are ''sufficiently reliable, interoperable, effective and robust''. Yet, the rapidly evolving and heterogeneous landscape of wa

Zoltán M. Balogh, Eric Ströher, Tamás Titkos, Dániel Virosztek

We study $p-$Wasserstein spaces $ \mathcal{W}_p(\mathbb{R}^n, d_N)$ over $\mathbb{R}^n$ equipped with a norm metric $d_N$. We show that, if the norm is smooth enough, then the Wasserstein space is isometrically rigid whenever $p \neq 2$. We also show that, even when $p=2$, we can recover the isometric rigidity of the Wasserstein space $\mathcal{W}_2(\mathbb{

Yili Wang, Wenbin Zhao, Che Ming Ko, Fengkun Guo

The $f_{0}(980)$ is a light scalar meson whose internal structure remains under debate and investigation. Assuming that the $f_0(980)$ is a $K\bar K$ molecule that can only survive at the kinetic freeze-out of the evolving bulk matter, we implement the coalescence model to study its transverse momentum ($p_T$) spectra and elliptic flow ($v_2$) in high-multip

Yili Wang, Wenbin Zhao, Che Ming Ko, Feng-Kun Guo

We investigate the production and elliptic flow of the $f_0(980)$ in high-multiplicity p-Pb collisions at $\sqrt{s_{NN}}=5.02$ TeV using a hadronic coalescence model with the $K$ and $\bar K$ phase-space distributions provided by the Hydro-Coal-Frag hybrid model. Our results, which agree with the ALICE and CMS measurements, support the $K\bar K$ molecular in

M. H. Jalali Kanafi, S. M. S. Movahed

We perform a simulation-based forecasting analysis to compare the cosmological constraining power of higher-order summary statistics of the large-scale structure, the Minkowski Functionals (MFs) and a class weighted morphological measure known as the Conditional Moments of Derivatives (CMD), with that of the redshift-space halo power spectrum multipoles (PS)

Apoorva Upadhyaya, Wolfgang Nejdl, Marco Fisichella

Zero-Shot Stance Detection (ZSSD) identifies the attitude of the post toward unseen targets. Existing research using contrastive, meta-learning, or data augmentation suffers from generalizability issues or lack of coherence between text and target. Recent works leveraging large language models (LLMs) for ZSSD focus either on improving unseen target-specific

Alexander Pfefferle, Johannes Hog, Lennart Purucker, Frank Hutter

Tabular foundation models such as TabPFN have revolutionized predictive machine learning for tabular data. At the same time, the driving factors of this revolution are hard to understand. Existing open-source tabular foundation models are implemented in complicated pipelines boasting over 10,000 lines of code, lack architecture documentation or code quality.

Juan Hernandez, Andrew Rolph

Scrambling is a diagnostic of quantum chaos in strongly coupled systems, and plays a central role in the holographic description of black hole dynamics. We study scrambling in high-temperature holographic CFTs, with an emphasis on perturbations dual to particles on infalling and bound trajectories in the bulk description. For BTZ and AdS-Schwarzschild geomet

Cemil Vahapoglu, Timothy J. O'Shea, Wan Liu, Sennur Ulukus

Beamforming has significance for enhancing spectral efficiency and mitigating interference in multi-antenna wireless systems, facilitating spatial multiplexing and diversity in dense and high mobility scenarios. Traditional beamforming techniques such as zero-forcing beamforming (ZFBF) and minimum mean square error (MMSE) beamforming experience performance d

Xiangjun Tan, Zhanning Wang

We propose a phase-coherent, narrowband magnetometer for searching couplings between axions or axion-like particles (ALPs) and electron spins, using gate-defined silicon quantum-dot spin qubits. With repeated Ramsey echo sequences and dispersive readout, the qubit precession response can be tracked with sub-Hz spectral resolution. The accessible axion mass w

Hadi Hosseini, Shraddha Pathak, Yu Zhou

We consider the problem of fairly allocating the vertices of a graph among $n$ agents, where the value of a bundle is determined by its cut value -- the number of edges with exactly one endpoint in the bundle. This model naturally captures applications such as team formation and network partitioning, where valuations are inherently non-monotonic: the margina

Haofei Yu, Fenghai Li, Jiaxuan You

Large language models (LLMs) achieve strong performance across benchmarks--from knowledge quizzes and math reasoning to web-agent tasks--but these tests occur in static settings, lacking real dynamics and uncertainty. Consequently, they evaluate isolated reasoning or problem-solving rather than decision-making under uncertainty. To address this, we introduce

Ben Johns, Hannes Kempf, Lakshmi Das, Aitor De Andrés

Near-zero-refractive index materials display unique optical properties such as perfect transmission through distorted waveguides, cloaking, and inhibited diffraction. Compared to conventional media, they can fundamentally behave differently to light impinging from air, owing to the phenomenon of total external reflection. This makes them attractive for evane

Shaan D. Patel, Billy Quarles, Nevin N. Weinberg, Manfred Cuntz

Earth-like planets in the habitable zone (HZ) of M-dwarfs have recently been targeted in the search for exomoons. We study the stability and lifetime of large (Luna-like) moons, accounting for the effects of 3-body interactions and tidal forces using the N-body simulator rebound and its extension library reboundx. We find that those moons have a notably diff

Qiang Fei, Aleks Jevnikar, Sang-Hyuck Moon

The aim of this article is twofold: one one side we introduce and study the properties of a critical sinh-Gordon type flow \begin{equation*} {\frac{\partial}{\partial t}}e^u=\Delta_gu+8\pi\left({\frac{h_1e^u}{\int_{\Sigma}h_1e^udV_g}}-1\right)-\rho_2\left({\frac{h_2e^{-u}}{\int_{\Sigma}h_2e^{-u}dV_g}}-1\right), \end{equation*} where $\rho_2<8\pi$, $h_1,h_2$

Jinlu Li

Mordukhovich derivatives (Mordukhovich coderivatives) of set-valued mappings in Banach spaces have firmly laid the foundation of the theory of generalized differentiation in set-valued analysis, which has been widely applied to optimization theory, equilibrium theory, variational analysis, and so forth, with respect to set-valued mappings. One of the most im

Swadhin Agrawal, Sujoy Bhore, Joseph S. B. Mitchell, P. B. Sujit

We consider the problem of searching for an intruder in a geometric domain by utilizing multiple search robots. The domain is a simply connected orthogonal polygon with edges parallel to the cartesian coordinate axes. Each robot has a limited sensing capability. We study the problem for both static and mobile intruders. It turns out that the problem of findi

P. A. Belevskii, M. N. Vinoslavskii, O. S. Pylypchuk

There has been experimentally implemented a technique of neutralization of an acoustoelectric domain under the conditions of the lateral transport of charge carriers in strong electric fields in the multilayer InGaAs/GaAs heterostructures with quantum wells. The technique is implemented by means of deposition of a shunting semi opaque silver film on the hete

Philipp Hager, Onno Zoeter, Maarten de Rijke

CLAX is a JAX-based library that implements classic click models using modern gradient-based optimization. While neural click models have emerged over the past decade, complex click models based on probabilistic graphical models (PGMs) have not systematically adopted gradient-based optimization, preventing practitioners from leveraging modern deep learning f

Suraj Kumar, Aditya Rallapalli, Bharat Kumar GVP

Typical lunar landing missions involve multiple phases of braking to achieve soft-landing. The propulsion system configuration for these missions consists of throttleable engines. This configuration involves complex interconnected hydraulic, mechanical, and pneumatic components each exhibiting non-linear dynamic characteristics. Accurate modelling of the pro

Riccardo Rossetto, Marcel Ernst, David Zwicker

Biological membranes often exhibit heterogeneous protein patterns, which cells control. Strong patterns, like the polarity spot in budding yeast, can be described as surface condensates, formed by physical interactions between constituents. However, it is unclear how these interactions affect the material exchange with the bulk. To study this, we analyze a t

Shangtong Zhang

In this paper, we formalize the almost sure convergence of $Q$-learning and linear temporal difference (TD) learning with Markovian samples using the Lean 4 theorem prover based on the Mathlib library. $Q$-learning and linear TD are among the earliest and most influential reinforcement learning (RL) algorithms. The investigation of their convergence properti

Timo Brand, Henry Förster, Stephen G. Kobourov, Jacob Miller

We evaluate how visualizations can influence the judgment of MLLMs about the presence or absence of bridges in a network. We show that the inclusion of visualization improves confidence over a structured text-based input that could theoretically be helpful for answering the question. On the other hand, we observe that standard visualization techniques create

Iason Chrysomallis, Georgios Chalkiadakis

Imitation learning traditionally requires complete state-action demonstrations from optimal or near-optimal experts. These requirements severely limit practical applicability, as many real-world scenarios provide only state observations without corresponding actions and expert performance is often suboptimal. In this paper we introduce a deep implicit imitat

H. T. Silva, L. C. S. Faria, T. A. Aversi-Ferreira, I. Camps

This computational study investigates glyphosate adsorption mechanisms on hydroxyl-functionalized carbon nanotubes (CNTs) as an alternative approach for environmental remediation. Single-walled CNTs with (10,0) zigzag chirality were functionalized with hydroxyl groups at concentrations of 5-25% and evaluated for interactions with glyphosate in five different

Han Wang, Jeffrey Uncu, Kaushik Srinivasan, Nicolas Grisouard

A fundamental challenge in ocean dynamics is disentangling balanced motions and internal waves. Extracting internal tidal (IT) imprints from surface data is a central part of this challenge. Traditional harmonic analysis can fail under strong incoherence and poor temporal sampling, as in global satellite observations. New wide-swath satellites provide two-di

John M. Myers, Hadi Madjid

Digital network failures stemming from instabilities in measurements of temporal order motivate attention to concurrent events. A century of attempts to resolve the instabilities have never eliminated them. Do concurrent events occur at indeterminate times, or are they better seen as events to which the very concept of temporal order cannot apply? Logical de

Sk Anisur, Kartik Singh, Sayan Choudhury

We theoretically investigate the interplay of interactions and non-Hermiticity in the dynamics of two bosons on the one-dimensional Hatano-Nelson lattice with non-reciprocal tunneling. We find that the non-reciprocity in the tunneling leads to the formation of an asymmetric density cone during the time-evolution of the system; the degree of asymmetry can be

Yingjie Xu, Xuesong Cai, Michiel Sandra, Sara Willhammar

As integrated sensing and communication (ISAC) becomes an integral part of 6G networks, distributed ISAC (DISAC) is expected to enhance both sensing and communication performance through its decentralized architecture. This paper presents a complete framework to address the challenge of cooperative user tracking in DISAC systems. By incorporating a global pr

Mohammed H. Talafha

Understanding and predicting solar-cycle variability requires accounting for nonlinear feedbacks that regulate the buildup of the Sun's polar magnetic field. We present a simplified but physically grounded algebraic approach that models the dipole contribution of active regions (ARs) while incorporating two key nonlinearities: tilt quenching (TQ) and latitud

Ringwald Celian, Gandon, Fabien, Faron Catherine

This article presents a systematic review of relation extraction (RE) research since the advent of Transformer-based models. Using an automated framework to collect and annotate publications, we analyze 34 surveys, 64 datasets, and 104 models published between 2019 and 2024. The review highlights methodological advances, benchmark resources, and the integrat

Cameron Calk, Emmanuel Godard

We introduce a new topological encoding of executions of round-based, full-information distributed protocols via spectral spaces. Such protocols constitute a model of distributed computations which are functorially presented and englobe message adversaries. We give a characterization of the solvability of colorless tasks against compact adversaries. Colorles

Ruaridh A. Clark, Francesca Arrigo, Agathe Bouis, Malcolm Macdonald

Eigenvector centrality is an established measure of global connectivity, from which the importance and influence of nodes can be inferred. We introduce a local eigenvector centrality that incorporates both local and global connectivity. This new measure references prominent eigengaps and combines their associated eigenspectrum, via the Euclidean norm, to det

Alessandro Vignati, Deniz Yilmaz

We introduce and study the noncommutative weak Extension Principle, a lifting principle aiming to characterise $^*$-homomorphisms between coronas of nonunital separable $\mathrm{C}^*$-algebras. While this principle fails if the Continuum Hypothesis is assumed, we show that this principle holds under mild forcing axioms such as the Open Colouring Axiom and Ma

Xukun Zhu, Michael W Lutz, Tananun Songdechakraiwut

Subtle alterations in brain network topology often evade detection by traditional statistical methods. To address this limitation, we introduce a Bayesian inference framework for topological comparison of brain networks that probabilistically models within- and between-group dissimilarities. The framework employs Markov chain Monte Carlo sampling to estimate

Riccardo Buscicchio, Federico Pozzoli, Daniele Chirico, Alberto Sesana

Galactic white-dwarf binaries play a central role in the inference model for the Laser Interferometer Space Antenna. In this manuscript, we employ the $\texttt{bahamas}$ codebase to characterize, in a global-fit fashion, the reconstruction of the Galactic foreground during the first year of observation. To account for its statistical properties, we represent

Yong-Kun Wang, Shifeng Qian, An-Dong Fan, Si Li

Altermagnets, recently identified as a third class of collinear magnetic materials, have attracted significant attention in condensed matter physics. Despite this growing interest, the realization of real Chern insulators in intrinsic altermagnetic systems has rarely been reported. In this work, based on first-principles calculations and theoretical analysis

Chao Yan, Boyong Wu, Peng Yang, Pengfei Tan

We present Step-Audio-EditX, the first open-source LLM-based audio model excelling at expressive and iterative audio editing encompassing emotion, speaking style, and paralinguistics alongside robust zero-shot text-to-speech (TTS) capabilities. Our core innovation lies in leveraging only large-margin synthetic data, which circumvents the need for embedding-b

Wenkai Fu, Finn Carter, Yue Wang, Emily Davis

Robust invisible watermarking aims to embed hidden messages into images such that they survive various manipulations while remaining imperceptible. However, powerful diffusion-based image generation and editing models now enable realistic content-preserving transformations that can inadvertently remove or distort embedded watermarks. In this paper, we presen

Hao Chen, Zhong-Ke Ding, Yuan Yao, Chang-Hao Ding

Negative refraction provides a route to steer and focus wave energy flow, but it remains difficult to realize for coherent terahertz (THz) phonons. The difficulty stems from conventional dispersion-based mechanisms, which require strongly anisotropic or negative-curvature dispersions, while the long-wavelength acoustic phonons most favorable for coherent tra

Hussam Al Daas, Grey Ballard, Laura Grigori, Mariana Martinez Aguilar

Approximating a tensor in the tensor train (TT) format has many important applications in scientific computing. Rounding a TT tensor involves further compressing a tensor that is already in the TT format. This paper proposes new randomized algorithms for TT-rounding that uses sketches based on Khatri-Rao products (KRP). When the TT-ranks are known in advance

Ilia Zykov, Guido Stam, Hanieh Jafarian, Amin Moradi

Microscopy has been key to tremendous advances in science, technology, and medicine, revealing structure and dynamics across time and length scales. However, combining high spatial and temporal resolution in a non-invasive, label-free imaging technique remains a central challenge in microscopy. Here, we introduce Optical Near-field Electron Microscopy (ONEM)

Kyle F. Grosser, Abigail G. Foes, Stellen Li, Vraj Parikh

Huntington disease (HD) is a neurodegenerative disease with progressively worsening symptoms. Accurately modeling time to HD diagnosis is essential for clinical trial design. Langbehn's model, the CAG-Age Product (CAP) model, the Prognostic Index Normed (PIN) model, and the Multivariate Risk Score (MRS) model have all been proposed for this task. However, th

Junyi Wu, Dan Li

High-dimensional reinforcement learning(RL) faces challenges with complex calculations and low sample efficiency in large state-action spaces. Q-learning algorithms struggle particularly with the curse of dimensionality, where the number of state-action pairs grows exponentially with problem size. While neural network-based approaches like Deep Q-Networks ha

Suraj Kumar, Aditya Rallapalli, Ashok Kumar Kakula, Bharat Kumar GVP

India achieved a significant milestone on August $23^{\text{rd}}$ 2023, becoming the fourth country to accomplish a soft landing on the Moon. This paper presents the powered descent trajectory design for the Chandrayaan-3 mission. The optimization framework is based on pseudospectral Radau collocation, and controllability-based waypoint refinement is employe

Yu-Fei Ling, Min-Huan Chu, Jian Liang, Jun Hua

We investigate several approaches to address the inverse problem that arises in the limited inverse Fourier transform (L-IDFT) of quasi-distributions. The methods explored include Tikhonov regularization, the Backus-Gilbert method, the Bayesian approach with Gaussian Random Walk (GRW) prior, and the feedforward artificial neural networks (ANNs). We evaluate

Annachiara Korchmaros, Guillaume E. Scholz, Peter F. Stadler

Bipartite best match graphs (BMG) and their generalizations arise in mathematical phylogenetics as combinatorial models describing evolutionary relationships among related genes in a pair of species. In this work, we characterize the class of \emph{undirected 2-quasi-BMGs} (un2qBMGs), which form a proper subclass of the $P_6$-free chordal bipartite graphs. W

Mahek Desai, Rowena Quinn, Marjan Asadinia

As transistor-based memory technologies like dynamic random access memory (DRAM) approach their scalability limits, the need to explore alternative storage solutions becomes increasingly urgent. Phase-change memory (PCM) has gained attention as a promising option due to its scalability, fast access speeds, and zero leakage power compared to conventional memo

Raymond Puzio, Sam McCrosson

The Kadanoff-Wilson-Fisher approach to renormalization is based upon studying the renormalization transform, which may be described as an action of the monoid $\mathbb{R}^{\times}_{\geq 1}$ on a suitable space of interactions. It is typically computed by manipulating the path integral or the perturbation series. Here we will present an alternative algebraic

Heikki Mäntysaari, Björn Schenke, Chun Shen, Wenbin Zhao

We study how the inclusion of energy dependence as dictated by quantum chromodynamic (QCD) small-$x$ evolution equations affects key observables in ultra-relativistic heavy-ion collisions. Specifically, we incorporate JIMWLK evolution into the IP-Glasma framework, which serves as the initial condition for a simulation pipeline that includes viscous relativis

Florian Neukart

The interstellar comet 3I/ATLAS displayed a small but statistically significant non-gravitational acceleration during its passage through the inner Solar System. Using a thermophysical model coupled with stochastic sampling of jet configurations, we investigate whether standard volatile-driven activity can account for the observed acceleration. The model inc

Howard E. Bond, Calvin Carter, Eric Coles, Peter Goodhew

We present deep images of the faint nebulosity StDr 90, which we have discovered surrounds the cataclysmic variable (CV) star FY Vulpeculae. Archival photometric and spectroscopic observations, and a new optical spectrum, confirm that FY Vul belongs to the Z Camelopardalis subclass of CVs. Our imagery, obtained by accumulating long exposures with amateur tel

Andrei Ivanov, Siyuan Shen, Gioele Gottardo, Marcin Chrapek

The increasing complexity of machine learning models and the proliferation of diverse hardware architectures (CPUs, GPUs, accelerators) make achieving optimal performance a significant challenge. Heterogeneity in instruction sets, specialized kernel requirements for different data types and model features (e.g., sparsity, quantization), and architecture-spec

Jia Kaixin, Zhu Kewen, Deng Huanghuang, Qiu Yiwu

Our study aims to establish a unified, systematic, and referable knowledge framework for the annotation of art image datasets, addressing issues of ambiguous definitions and inconsistent results caused by the lack of common standards during the annotation process. To achieve this goal, a hierarchical and systematic art image knowledge graph was constructed.

Alessandro Pietro Contini

The purpose of this paper is to review the asymptotic distribution of eigenvalues of the Dirichlet Laplacian. We introduce and recall all the relevant spectral quantities and provide a proof based on the Fourier Tauberian Theorem.

Cosmin-Constantin Popescu, Maarten Robbert Anton Peters, Oleg Maksimov, Harish Bhandari

Active metasurfaces enable dynamic control of light for applications in beam steering, pixelated holography, and adaptive optics, but demonstrations of two-dimensional (2D) electrically addressable arrays have so far been limited. Here we introduce a scalable 2D architecture based on phase-change materials (PCMs) integrated metasurfaces and apply it to reali

A. Yu. Levakhova, A. L. Vasiliev, N. S. Pavlov, A. V. Ovcharov

We report a study of nano-scale structural peculiarities of the antiferromagnetic layered semimetal EuSn2As2, and show that they are responsible for its puzzling magnetic properties. The high resolution transmission electron microscopy revealed the presence of planar defects in the lattice of the studied single crystals. Using a combination of microstructura

Beatrice M. Kulterer, Asunción Fuente, Maria N. Drozdovskaya, Silvia Spezzano

The isotopic ratios of deuterated methanol derived around protostars are commonly used to infer the physical conditions under which they formed in the earlier prestellar stage. However, there is a discrepancy in the ratio of the singly deuterated methanol isotopologues, CH2DOH/CH3OD, between low- and high-mass protostars, which puts into question whether pre

Jaden Thomas-Markarian, Rodrigo Arrieta, Shu-Ching Yang, Arthur J. Parzygnat

This paper presents a rigorous proof that arbitrarily weak perturbations produce localized vibrational (phonon) modes in one- and two-dimensional discrete lattices, inspired by analogous results for the Schr{\"o}dinger and Maxwell equations, and complementing previous explicit solutions for specific perturbations (e.g., decreasing a single mass). In particul

Weishi Wang, Casey Dowdle, James D. Whitfield

We propose a system-oriented basis-set design based on even-tempered basis functions to variationally encode electronic ground-state information into molecular orbitals. First, we introduce a reduced formalism of concentric even-tempered orbitals that achieves hydrogen energy accuracy on par with the conventional formalism, with lower optimization cost and i

Mainak Singha

Neural networks can approximate solutions to partial differential equations, but they often break the very laws they are meant to model-creating mass from nowhere, drifting shocks, or violating conservation and entropy. We address this by training within the laws of physics rather than beside them. Our framework, called Constraint-Projected Learning (CPL), k

Antoine Aspeel, Antoine Girard, Thiago Alves Lima

We study the control of nonlinear constrained systems via over-approximations. Our key observation is that the over-approximation error, rather than being an unknown disturbance, can be exploited as input-dependent preview information. This leads to the notion of informed policies, which depend on both the state and the error. We formulate the concretization

Aniol Civit, Antonio Andriella, Carles Sierra, Guillem Alenyà

While personalisation in Human-Robot Interaction (HRI) has advanced significantly, most existing approaches focus on single-user adaptation, overlooking scenarios involving multiple stakeholders with potentially conflicting preferences. To address this, we propose the Multi-User Preferences Quantitative Bipolar Argumentation Framework (MUP-QBAF), a novel mul

Mahek Desai, Rowena Quinn, Marjan Asadinia

As dynamic random access memory (DRAM) and other current transistor-based memories approach their scalability limits, the search for alternative storage methods becomes increasingly urgent. Phase-change memory (PCM) emerges as a promising candidate due to its scalability, fast access time, and zero leakage power compared to many existing memory technologies.

P. P. Biswas, A. Różańska, F. H. Vincent, D. Lančová

We present that the broad feature usually observed in X-ray spectra can be explained by a ray-traced emission from a two-slab system containing a dissipative, warm corona on top of an accretion disk in an AGN. Such an accretion flow is externally illuminated by X-ray radiation from a lamp located above a central SMBH. Thermal lines from highly ionized iron i

Yimeng Fan, Changsong Liu, Mingyang Li, Yuzhou Dai

Edge detection with Artificial Neural Networks (ANNs) has achieved remarkable prog\-ress but faces two major challenges. First, it requires pre-training on large-scale extra data and complex designs for prior knowledge, leading to high energy consumption. Second, the predicted edges perform poorly in crispness and heavily rely on post-processing. Spiking Neu

Hao Shi, Ze Wang, Shangwei Guo, Mengfei Duan

Robust 3D semantic occupancy is crucial for legged/humanoid robots, yet most semantic scene completion (SSC) systems target wheeled platforms with forward-facing sensors. We present OneOcc, a vision-only panoramic SSC framework designed for gait-introduced body jitter and 360{\deg} continuity. OneOcc combines: (i) Dual-Projection fusion (DP-ER) to exploit th

Günther Schindler, Maximilian Schambach, Michael Medek, Sam Thelin

We study LLMs for tabular prediction with mixed text, numeric, and categorical fields. We introduce TabGemma, a schema-agnostic in-context learner that treats rows as sequences and tackles two practical hurdles when adapting pretrained LLMs for tabular predictions: unstable numeric tokenization and limited context size. We propose to canonicalize numbers via

René Henke, Jesper Levinsen, Meera M. Parish, Jordi Boronat

Mobile impurities interacting with a quantum medium form quasiparticles known as polarons, a central concept in many-body physics. While the quantum impurity problem has been extensively studied with ultracold atomic gases, repulsive polarons in the strongly correlated regime have remained elusive. Typically, the impurity atoms bind into molecules or rapidly

Mikhail V. Sokolov

The payback period is unambiguously defined for conventional investment projects, projects in which a series of cash outflows is followed by a series of cash inflows. Its definition for nonconventional projects is more challenging, since their balances (cumulative cash flow streams) may have multiple break-even points. Academics and practitioners offer a few

Kalman Szenes, Riya Kayal, Kantharuban Sivalingam, Robin Feldmann

In this paper, an efficient implementation of the renormalized internally-contracted multreference coupled cluster with singles and doubles (RIC-MRCCSD) into the ORCA quantum chemistry program suite is reported. To this end, Evangelista's Wick&d equation generator was combined with ORCA's native AGE code generator in order to implement the many-body residual

Federico Battista, Ted K. Ralphs

We consider the central role of improving directions in solution methods for mixed integer bilevel linear optimization problems (MIBLPs). Current state-of-the-art methods for solving MIBLPs employ the branch-and-cut framework originally developed for solving mixed integer linear optimization problems. This approach relies on oracles for two kinds of subprobl

Iason Chrysomallis, Georgios Chalkiadakis

Imitation learning (IL) enables agents to acquire skills by observing and replicating the behavior of one or multiple experts. In recent years, advances in deep learning have significantly expanded the capabilities and scalability of imitation learning across a range of domains, where expert data can range from full state-action trajectories to partial obser

G. P. A. Nobre, R. Capote, M. T. Pigni, A. Trkov

The ENDF/B-VIII.1 library is the newest recommended evaluated nuclear data file by the Cross Section Evaluation Working Group (CSEWG) for use in nuclear science and technology applications, and incorporates advances made in the six years since the release of ENDF/B-VIII.0. Among key advances made are that the $^{239}$Pu file was reevaluated by a joint intern

One Octadion, Bondan Sapta Prakoso, Nanang Yudi Setiawan, Novanto Yudistira

In this study, we explore the fine-tuning of Large Language Models (LLMs) to better support policymakers in their crucial work of understanding, analyzing, and crafting legal regulations. To equip the model with a deep understanding of legal texts, we curated a supervised dataset tailored to the specific needs of the legal domain. Additionally, we integrated

Ana Mitrašinović

Periodic boundary conditions (PBCs) are a practical necessity in cosmological simulations, but they can also introduce numerical artifacts. We quantified the prevalence of PBC-related artifacts in the IllustrisTNG using dark-matter-deprived galaxies (DMDGs) as tracers. We found that their occurrence scales inversely with simulation volume. We demonstrated th

Hyunwoo Choi, Jisang Seo, Weng C. Chew, Dong-Yeop Na

In this work, we present a numerical framework for modeling single photon emission from a two level system in open and dissipative systems beyond the Markovian approximation. The method can be readily integrated into standard computational electromagnetic (CEM) solvers such as finite difference time domain (FDTD) and finite element method (FEM). We numerical

Joachim Diederich

This paper presents the first empirical demonstration of controllable locality in transformer language models, a novel architectural framework that enables continuous control over the degree of representation localization through a tunable locality dial parameter. Unlike traditional language models that rely exclusively on distributed representations, our ap

Anu Sreedevi, Bidya Binay Karak, Bibhuti Kumar Jha, Rambahadur Gupta

Bipolar sunspots, or more generally, Bipolar Magnetic Regions, BMRs, are the dynamic magnetic regions that appear on the solar surface and are central to solar activity. One striking feature of these regions is that they are often tilted with respect to the equator, and this tilt increases with the latitude of appearance, popularly known as Joys law. Althoug

Ragnar Freij-Hollanti, Teemu Lundström

In this paper, we study the simplex faces of the order polytope $\mathcal{O}(P)$ and the chain polytope $\mathcal{C}(P)$ of a finite poset $P$. We show that, if $P$ can be recursively constructed from $\mathbf{X}$-free posets using disjoint unions and ordinal sums, then $\mathcal{C}(P)$ has at least as many $k$-dimensional simplex faces as $\mathcal{O}(P)$ d

Candace Ross, Florian Bordes, Adina Williams, Polina Kirichenko

Multimodal language models possess a remarkable ability to handle an open-vocabulary's worth of objects. Yet the best models still suffer from hallucinations when reasoning about scenes in the real world, revealing a gap between their seemingly strong performance on existing perception benchmarks that are saturating and their reasoning in the real world. To

Zhongqi Zhao, Erik Rosendahl Kjellgren, Sonia Coriani, Jacob Kongsted

Error mitigation is essential for the practical implementation of quantum algorithms on noisy intermediate-scale quantum (NISQ) devices. This work explores and extends Clifford Data Regression (CDR) to mitigate noise in quantum chemistry simulations using the Variational Quantum Eigensolver (VQE). Using the H$_4$ molecule with the tiled Unitary Product State

Zheshi Zheng, Yuanyuan Li, Peter X. K. Song, Jiming Jiang

We carry out a post-election analysis of the 2024 U.S. Presidential Election (USPE) using a prediction model derived from the Small Area Estimation (SAE) methodology. With pollster data obtained one week prior to the election day, retrospectively, our SAE-based prediction model can perfectly predict the Electoral College election results in all 44 states whe

Ido Nachum, Rüdiger Urbanke, Thomas Weinberger

Despite ongoing theoretical research on cross-validation (CV), many theoretical questions remain widely open. This motivates our investigation into how properties of algorithm-distribution pairs can affect the choice for the number of folds in $k$-fold CV. Our results consist of a novel decomposition of the mean-squared error of cross-validation for risk est

Jonathan R Dunkley

We pose the converse Madelung question: not whether Fisher information can reproduce quantum mechanics, but whether it is necessary. We work with minimal, physically motivated axioms on density and phase: locality, probability conservation, Euclidean invariance with a global phase symmetry, reversibility, and convex regularity. Within the resulting class of

Christian Laudagé, Jörn Sass

Under Solvency II, the Value-at-Risk (VaR) is applied, although there is broad consensus that the Expected Shortfall (ES) constitutes a more appropriate risk measure. Moving towards ES would necessitate specifying the corresponding ES level. The recently introduced Probability Equivalent Level of VaR and ES (PELVE) determines this by requiring that ES equals

Hong Wang, Ridhima Phatak, James Ocampo, Zhao Han

Research indicates that humans can mistakenly assume that robots and humans have the same field of view, possessing an inaccurate mental model of robots. This misperception may lead to failures during human-robot collaboration tasks where robots might be asked to complete impossible tasks about out-of-view objects. The issue is more severe when robots do not

Ziv Nevo, Orna Raz, Karen Yorav

Understanding the purpose of source code is a critical task in software maintenance, onboarding, and modernization. While large language models (LLMs) have shown promise in generating code explanations, they often lack grounding in the broader software engineering context. We propose a novel approach that leverages natural language artifacts from GitHub -- s

J Borresen, H Burger

This paper critically examines the conceptual and methodological limitations underlying the current understanding and application of VO2max in exercise science and physical activity prescription. Despite the establishment of WHO guidelines on physical activity, population-level adherence remains low. A key contributing factor is the continued reliance on VO2

Longlong Lin, Quanao Li, Miao Qiao, Zeli Wang

Identifying locally dense communities closely connected to the user-initiated query node is crucial for a wide range of applications. Existing approaches either solely depend on rule-based constraints or exclusively utilize deep learning technologies to identify target communities. Therefore, an important question is proposed: can deep learning be integrated

Sebastian Ordyniak, Giacomo Paesani, Mateusz Rychlicki, Stefan Szeider

This paper presents a comprehensive theoretical investigation into the parameterized complexity of explanation problems in various machine learning (ML) models. Contrary to the prevalent black-box perception, our study focuses on models with transparent internal mechanisms. We address two principal types of explanation problems: abductive and contrastive, bo

Robert J. Berman, Bo Berndtsson

This article is an expository introduction to our paper Convexity of the K-energy and Uniqueness of Extremal metrics. We present the main ideas behind the proof that Mabuchi's K-energy functional is convex along weak geodesics in the space of Kahler potentials and explain how this leads to the uniqueness of constant scalar curvature Kahler metrics and extrem

Bratati Bhat, Ankan Das, Prasanta Gorai, Dipen Sahu

NGC 1333 IRAS4A is a well-studied low-mass sun-like proto-binary system. It has two components, A1 and A2, which are diverse according to their physical and chemical properties. We modeled this hot corino using the RATRAN radiative transfer code and explained different spectral signatures observed towards A1 and A2, specifically for CH3OH and H2CO. Our main

Roberta Di Marino, Giovanni Dioguardi, Antonio Romano, Giuseppe Riccio

Medical question answering systems face deployment challenges including hallucinations, bias, computational demands, privacy concerns, and the need for specialized expertise across diverse domains. Here, we present SOLVE-Med, a multi-agent architecture combining domain-specialized small language models for complex medical queries. The system employs a Router

S. S. Agaev, K. Azizi, H. Sundu

The hadronic scalar molecules $\mathcal{M}_{b}$ and $\mathcal{M}_{c}$ with asymmetric quark contents $bb \overline{b}\overline{c}$ and $cc \overline{c} \overline{b}$ are explored by means of the QCD sum rule method. Their masses and current couplings are calculated using the two-point sum rule approach. The obtained results show that they are strong-interact