November 2025 arXiv papers — page 71

Marco Carmosino, Ngu Dang, Tim Jackman

The Minimum Circuit Size Problem for Partial Functions ($MCSP^*$) is hard assuming the Exponential Time Hypothesis (ETH) (Ilango, 2020). This breakthrough hardness result leveraged a characterization of the optimal $\{\land, \lor, \neg\}$ circuits for $n$-bit $OR$ ($OR_n$) and a reduction from the partial $f$-Simple Extension Problem where $f = OR_n$. It rem

Michael Luby

Applications such as cloud gaming, video streaming, telemetry, ML inference, and data transfer provide a better experience when data is released at the receiver with timing reflecting how the data enters the sender. In practice, network delay variation and recovery dynamics at the receiver distort this timing even when transports deliver all packets correctl

Lu Zhu, Tiantian Geng, Yangye Chen, Teng Wang

Recently, rapid advancements have been made in multimodal large language models (MLLMs), especially in video understanding tasks. However, current research focuses on simple video scenarios, failing to reflect the complex and diverse nature of real-world audio-visual events in videos. To bridge this gap, we firstly introduce R-AVST, a dataset for audio-visua

Jingzehua Xu, Weiyi Liu, Weihang Zhang, Zhuofan Xi

Autonomous Underwater Vehicles (AUVs) are indispensable for marine exploration; yet, their control is hindered by nonlinear hydrodynamics, time-varying disturbances, and localization uncertainty. Traditional controllers provide only limited adaptability, while Reinforcement Learning (RL), though promising, suffers from sample inefficiency, weak long-term pla

Ariel Slepyan, Laura Xing, Rudy Zhang, Nitish Thakor

Development of large-area, high-speed electronic skins is a grand challenge for robotics, prosthetics, and human-machine interfaces, but is fundamentally limited by wiring complexity and data bottlenecks. Here, we introduce Single-Pixel Tactile Skin (SPTS), a paradigm that uses compressive sampling to reconstruct rich tactile information from an entire senso

Maya Pal Gambhir, Bailey Flanigan, Aaron Roth

Citizens' assemblies - small panels of citizens that convene to deliberate on policy issues - often face the issue of panelists dropping out at the last-minute. Without intervention, these dropouts compromise the size and representativeness of the panel, prompting the question: Without seeing the dropouts ahead of time, can we choose panelists such that afte

MD. Ashikul Islam, Prato Dewan, Md Fuadul Islam, Md. Ataullha

Sign language is a vital communication medium for the hearing-impaired community, enabling effective interaction and self-expression. To help bridge the communication gap between hearing and hearing-impaired individuals, a text-to-sign translation system is essential. Such systems can also support learners interested in acquiring sign language skills. This w

Paul-Édouard Blanchard, Alexander McDonald, Philippe St-Jean

Non-reciprocity is a key resource for pushing the performance of photonic devices beyond the fundamental limits imposed by Lorentz reciprocity. Here, we report on the realization of an optical sensor where non-reciprocal light propagation allows detecting small perturbations with a signal-to-noise ratio (SNR) that scales exponentially with system size. Our a

Shichen Zhang, Li Liu, Kai Guo, Xingli Mu

The development of quantum materials for single-photon emission is crucial for the advancement of quantum information technology. Although significant advancement has been witnessed in recent years for single photon sources in near infrared band ({\lambda}~700-1000 nm), several challenges have yet to be addressed for ideal single photon emission at the telec

Ayumi Kajikawa, Kazuo Sorai, Kana Morokuma-Matsui, Tsutomu T. Takeuchi

This study investigated the relation between the surface density of star formation rate (SFR) ($\Sigma_{\mathrm{SFR}}$), stellar mass ($\Sigma_{M_{\ast}}$), and molecular gas mass ($\Sigma_{M_\mathrm{mol}}$) on nearly 1 kpc scales averaged over concentric tilted rings using the $^{12}$CO $J=1-0$ mapping data of 92 nearby galaxies obtained in the CO Multi-lin

Jonhatan U. Guerrero-González, Vahram Chavushyan, Víctor M. Patiño-Álvarez

Blazars, a highly variable Active Galactic Nuclei (AGNs) subclass, provide a unique opportunity to explore the physical processes within their relativistic jets and emission regions. In this study, we investigate the multiwavelength variability of the blazar TON 599, a Flat Spectrum Radio Quasar (FSRQ), with a particular emphasis on its emission line behavio

Viktoriia Omelchenko

In this paper, the approach for considering fast charged particles scattering on targets of complex structure, which contains some isolated substructures, was expanded to account quadratic potential terms. Based on this approach, the differential cross section for scattering on the set of parallel planes with uniformly distributed atoms in each plane was obt

Andrei Arusoaie, Claudiu-Nicu Bărbieru, Oana-Otilia Captarencu, Paul-Flavian Diac

Ethereum is currently the main blockchain ecosystem providing decentralised trust guarantees for applications ranging from finance to e-government. A common criticism of blockchain networks has been their energy consumption and operational costs. The switch from Proof-of-Work (PoW) protocol to Proof-of-Stake (PoS) protocol has significantly reduced this issu

Haiquan Zhao, Xiong Yin, Jinhui Hu

The cubature Kalman filter based on minimum error entropy (MEE-CKF) offers accurate and robust performance in state of charge (SOC) estimation. However, due to the inflexibility of the minimum error entropy (MEE), this algorithm demonstrates limited robustness when confronted with more complex noise environments. To address these limitations, this paper prop

Shriram Srinivasan, Kaarthik Sundar

The solution of potential-driven steady-state flow in large networks is required in various engineering applications, such as transport of natural gas or water through pipeline networks. The resultant system of nonlinear equations depends on the network topology, and its solution grows more challenging as the network size increases. We present an algorithm t

Kang Wang, Xiangyu Duan, Tianyi Du

Unlike human reasoning in abstract conceptual spaces, large language models (LLMs) typically reason by generating discrete tokens, which potentially limit their expressive power. The recent work Soft Thinking has shown that LLMs' latent reasoning via soft concepts is a promising direction, but LLMs are trained on discrete tokens. To reduce this gap between t

AJ Alvero, Dustin S. Stoltz, Oscar Stuhler, Marshall Taylor

Generative artificial intelligence (GenAI) has garnered considerable attention for its potential utility in research and scholarship. A growing body of work in sociology and related fields demonstrates both the potential advantages and risks of GenAI, but these studies are largely proof-of-concept or specific audits of models and products. We know comparativ

Tom Perel

The recent boom and rapid integration of Large Language Models (LLMs) into a wide range of applications warrants a deeper understanding of their security and safety vulnerabilities. This paper presents a comparative analysis of the susceptibility to jailbreak attacks for two leading publicly available LLMs, Google's Gemini 2.5 Flash and OpenAI's GPT-4 (speci

Zhongjie Dai, Tao Feng, Jiaxuan You

The growing number of Large Language Models (LLMs) with diverse capabilities and response styles provides users with a wider range of choices, which presents challenges in selecting appropriate LLMs, as user preferences vary in terms of performance, cost, and response style. Current LLM selection methods typically optimize for a single fixed objective, such

Tim Menzies, Tao Chen, Yulong Ye, Kishan Kumar Ganguly

Software engineers must make decisions that trade off competing goals (faster vs. cheaper, secure vs. usable, accurate vs. interpretable, etc.). Despite MSR's proven techniques for exploring such goals, researchers still struggle with these trade-offs. Similarly, industrial practitioners deliver sub-optimal products since they lack the tools needed to explor

Ying-Zhao Jiang, Zhan Sun

We systematically investigate the production of $J/\psi + \gamma$ in $\gamma\gamma$ collisions within nonrelativistic QCD (NRQCD) factorization, with the direct-photon channel calculated specifically up to the next-to-leading order in $\alpha_s$. Calculations for CEPC energy region show the resolved photon contribution is negligible, while the direct photon

Xinxing Chen, Thomas Duquesne, Zhan Shi

We study a class of random homogeneous systems. Our main result says that under suitable general assumptions, these systems converge weakly, upon a suitable normalization, to the probability distribution with density $\frac34 \, (1-x^2) \, {\bf 1}_{\{ x\in (-1, \, 1)\} }$. Two special cases are of particular interest: for the effective resistance of the crit

Dhruv Kumar, Ish Gupta, Bangalore Sathyaprakash

Tests of general relativity (GR) with gravitational waves (GWs) introduce additional deviation parameters in the waveform model. The enlarged parameter space makes inference computationally costly, which has so far limited systematic, large-scale studies that are essential to quantify parameter degeneracies, check the effect of waveform systematics, and asse

Keita Yokoyama, Murad Hossain, Sabiq Muhtadi, Caterina Gallippi

Current acoustic radiation force (ARF) based methods for quantifying tissue elasticity primarily rely on shear wave propagation. However, their spatial resolution is limited by the need for spatial averaging, and their accuracy is affected by shear wave guidance, out of plane reflections, and geometric dispersion, which reduce their applicability in mechanic

Péter Madarasi

A multigraph $G = (V, E)$ is $(k, \ell)$-sparse if every subset $X \subseteq V$ induces at most $\max\{k|X| - \ell, 0\}$ edges. Finding a maximum-size $(k, \ell)$-sparse subgraph is a classical problem in rigidity theory and combinatorial optimization, with known polynomial-time algorithms. This paper presents a highly efficient and flexible implementation o

Xin Xiong, Samuel Fernández-Menduiña, Eduardo Pavez, Antonio Ortega

Video-sharing platforms must re-encode large volumes of noisy user-generated content (UGC) to meet streaming demands. However, conventional codecs, which aim to minimize the mean squared error (MSE) between the compressed and input videos, can cause quality saturation (QS) when applied to UGC, i.e., increasing the bitrate preserves input artifacts without im

Yuan Lu

O'Grady's generalized Franchetta conjecture asks whether any codimension two cycle on the universal polarized K3 surface restricts to a multiple of the Beauville--Voisin class on a given K3 surface. We apply Mukai's program for genus 11 curves and K3 surfaces, together with a result on the tautological generation of the second Chow group of the moduli space

G. Teruya, G. Lugones, A. G. Grunfeld

We investigate self-bound quark stars in a flavor-dependent quark-mass density-dependent model with an excluded-volume correction. We chart the parameter space at zero pressure to identify self-bound regimes, including parametrizations in which self-bound two-flavor matter undergoes a genuine first-order $ud \to uds$ transition at finite pressure. We constru

Siddharth Mahendraker

Let $E/F$ be a quadratic extension of number fields. We introduce truncated geometric and spectral RTF distributions associated to a Galois symmetric pair $G \subset \mathrm{Res}_{E/F} G_E$, subject to the constraint that $G$ and $\mathrm{Res}_{E/F} G_E$ have the same split rank, and formulate a precise coarse RTF identity. Specializing to $SL_{2, F} \subset

Johannes Krotz

We develop and validate a simulation framework for colloidal gelation. We first reproduce the benchmark results of Santos, Campanella, and Carignano for spherical, gel-forming particles, then extend the methodology to more complex systems of ``sticky'' spherocylindrical rods interacting via a Kihara-like potential. Using comprehensive parameter sweeps docume

Marshall Rosenhoover, Huaming Zhang

Graph neural networks (GNN) typically rely on localized message passing, requiring increasing depth to capture long range dependencies. In this work, we introduce Graph Linear Transformations, a linear transformation that realizes direct and indirect feature mixing on graphs through a single, well-defined linear operator derived from the graph structure. By

Shuying Zhou, Mouyuan Sun, Guobin Mou, Da-bin Lin

The IceCube Neutrino Observatory has identified several individual neutrino emitters associated with supermassive black hole accretion phenomena, including blazars, tidal disruption events, and, unexpectedly, Seyfert galaxies. A key open question is which types of active galactic nuclei (AGNs) are most likely to be neutrino emitters. Here we show that high-c

Hadi Khodaei Jooshin, Inna Partin-Vaisband

Global routing is a critical stage in electronic design automation (EDA) that enables early estimation and optimization of the routability of modern integrated circuits with respect to congestion, power dissipation, and design complexity. Batching is a primary concern in top-performing global routers, grouping nets into manageable sets to enable parallel pro

Francisco Ricardo Torres Arvizu, Adrián Ortega, Hernán Larralde

We study the phenomenon of quantum backflow in tight-binding systems with complex couplings, considering different boundary conditions and lattice sizes. Backflow is an intrinsically non-classical effect where the density flux associated with a particle described by the superposition of wave functions with, say, positive momentum, acquires negative values. W

Naoya Enomoto, Takao Satoh

In this paper, we show that there are infinitely many linearly independent elements in the abelianization of the Lie algebra of special derivations of a free Lie algebra by using the Morita traces. Furthermore, we show that the abelianization contains non-trivial elements which are killed by the Morita traces.

Tai Hyun Yoon

We present a quantum-optical platform for simulating relativistic detector-field interactions using entangled nonlinear biphoton sources (ENBSs), realized through phase-controlled single-photon frequency-comb (SPFC) sources. By mapping the dynamical evolution of this system onto the Unruh-DeWitt (UDW) detector model, we show that signal-mode excitations emul

The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, A. G. Abac

We present the results of a blind all-sky search for continuous gravitational-wave signals from neutron stars in binary systems using data from the first part of the fourth observing run (O4a) using LIGO detectors data. Rapidly rotating, non-axisymmetric neutron stars are expected to emit continuous gravitational waves, whose detection would significantly im

Azlaan Mustafa Samad, Hoang H. Nguyen, Lukas Berg, Henrik Müller

Modern cities produce vast streams of heterogeneous data, from infrastructure maps to mobility logs and satellite imagery. However, integrating these sources into coherent spatial models for planning and prediction remains a major challenge. Existing agent-centric methods often rely on direct environmental sensing, limiting scalability and raising privacy co

Izabela Babiarz, Piotr Lebiedowicz, Wolfgang Schäfer, Antoni Szczurek

We discuss the production of $D \bar{D}$ pairs in $e^+ e^-$ collisions, where $D$ refers to either $D^0$ or $D^+$. The continuum mechanism with the $t/u$-channel vector-meson $D^*$ exchanges are considered. The results of the calculation depend on the parameter of the off-shell form-factor for the virtual $D^*$ mesons. The $D^* D \gamma$ coupling constants a

Man Leong Chan, Jess McIver, Yannick Lecoeuche, Dhatri Raghunathan

Data from ground-based gravitational wave detectors are often contaminated by non-Gaussian instrumental artifacts or detector noise transients. Unbiased source property estimation relies on the ability to correctly identify and characterize these artifacts and remove them if necessary. To this end, the LIGO-Virgo-KAGRA Collaboration has implemented candidate

Tianyi Shen, Huijuan Xu, Nilesh Ahuja, Omesh Tickoo

Skeleton action recognition involves recognizing human action from human skeletons. The use of graph convolutional networks (GCNs) has driven major advances in this recognition task. In real-world scenarios, the captured skeletons are not always perfect or complete because of occlusions of parts of the human body or poor communication quality, leading to mis

Hiroto Odaka, Luke Smith, Kunihiko Taira

Extreme gust encounters by finite wings with disturbance velocity exceeding their cruise speed remain largely unexplored, while particularly relevant to miniature-scale aircraft. This study considers extreme aerodynamic flows around a square wing and the large, unsteady forces that result from gust encounters. We analyse the evolution of three-dimensional, l

Haytham Younus, Sohag Kabir, Felician Campean, Pascal Bonnaud

This article presents a state-of-the-art review of recent advances aimed at transforming traditional Failure Mode and Effects Analysis (FMEA) into a more intelligent, data-driven, and semantically enriched process. As engineered systems grow in complexity, conventional FMEA methods, largely manual, document-centric, and expert-dependent, have become increasi

Lasse Sternemann, David Maximilian Janas, Eshan Banerjee, Richard Leven

Chromium thiophosphate (CrPS$_4$) is a layered two-dimensional antiferromagnetic semiconductor exhibiting intriguing spintronic and magneto-optical properties, yet its electronic band structure has remained experimentally uncharacterized. Here, we employ momentum-resolved photoemission spectroscopy above and below the Néel temperature, complemented by densit

Xiaoyun Jin, Mirjam Ernestus, R. Harald Baayen

We present a corpus-based investigation of how the pitch contours of monosyllabic words are realized in spontaneous conversational Mandarin, focusing on the effects of words' meanings. We used the generalized additive model to decompose a given observed pitch contour into a set of component pitch contours that are tied to different control variables and

Julien Merand, Boris Meden, Mathieu Grossard

This paper presents an efficient approach for determining the joint configuration of a multifingered gripper solely from the point cloud data of its poly-articulated chain, as generated by visual sensors, simulations or even generative neural networks. Well-known inverse kinematics (IK) techniques can provide mathematically exact solutions (when they exist)

Prabhat K. Mishra, Mateus V. Gasparino, Girish Chowdhary

Deep Model Predictive Control (Deep MPC) is an evolving field that integrates model predictive control and deep learning. This manuscript is focused on a particular approach, which employs deep neural network in the loop with MPC. This class of approaches distributes control authority between a neural network and an MPC controller, in such a way that the neu

Sonia Dupuis, Nando Metzger, Konrad Schindler, Frank Göttsche

Land surface temperature (LST) is an essential climate variable (ECV) crucial for understanding land-atmosphere energy exchange and monitoring climate change, especially in the rapidly warming Arctic. Long-term satellite-based LST records, such as those derived from the Advanced Very High Resolution Radiometer (AVHRR), are essential for detecting climate tre

Risako Kikuchi, Junya Endo, Ai Yamakage

The energy-dependent electrical conductivity in spin-1 chiral fermion systems with disorder is studied using the self-consistent Born approximation. A distinct cusp-like feature appears at an energy different from the band-crossing point, arising from the multifold band-crossing structure formed by the Dirac and trivial bands. The energy position of the cusp

Mihai Nadas, Laura Diosan

Automatic diacritic restoration is crucial for text processing in languages with rich diacritical marks, such as Romanian. This study evaluates the performance of several large language models (LLMs) in restoring diacritics in Romanian texts. Using a comprehensive corpus, we tested models including OpenAI's GPT-3.5, GPT-4, GPT-4o, Google's Gemini 1.0

Arjen Vaartjes, Rocky Yue Su, Laura A. O'Neill, Paul Steinacker

Quantum error correction (QEC) requires non-invasive measurements for fault tolerant quantum computing. Deviations from ideal quantum non-demolition (QND) measurements can disturb the encoded information. To address this challenge, we develop a readout protocol for a $D-$dimensional system that, after a single positive outcome, switches to probing only the $

Ali Farki, Elaheh Moradi, Deepika Koundal, Jussi Tohka

Predicting future brain state from a baseline magnetic resonance image (MRI) is a central challenge in neuroimaging and has important implications for studying neurodegenerative diseases such as Alzheimer's disease (AD). Most existing approaches predict future cognitive scores or clinical outcomes, such as conversion from mild cognitive impairment to dem

Gurvir Singh, Arvind

Unextendible product bases(UPBs) are central to the study of local distinguishability of orthogonal product states. While their connection to quantum nonlocality via Bell inequalities is well established, their link to quantum contextuality remains largely unexplored. We establish a graph theoretic connection between contextuality and UPBs. First, an equival

Jasper Riebesehl, Lutz Mertenskötter, Wiebke Pohlandt, Wilhelm Stannat

We present a novel application of the Extended Kalman Smoother (EKS) for highly accurate frequency estimation from free spin precession signals of polarized $^3$He. Traditional approaches often rely on nonlinear least-squares fitting, which can suffer from limited robustness to signal decay and time-dependent frequency shifts. By contrast, our EKS-based meth

Giulia Piccitto, Giuliano Chiriacò, Davide Rossini, Angelo Russomanno

We study the asymptotic bipartite entanglement in various integrable and nonintegrable models of monitored fermions. We find that, for the integrable cases, the entanglement versus the system size is well fitted, over more than one order of magnitude, by a function interpolating between a linear and a power-law behavior. Up to the sizes we are able to reach,

Andrew Pham

Given a simple, finite, nonempty graph $G=(V(G),E(G))$, a vertex subset $D\subseteq V(G)$ is said to be a dominating set if every vertex $v\in V(G)-D$ is adjacent to a vertex in $D$. The independent domination number $γ_i(G)$ is the minimum cardinality among all independent dominating sets of $G$. Since determining the domination number for general graphs is

Isar Nejadgholi, Maryam Molamohammadi, Samir Bakhtawar

The non-profit settlement sector in Canada supports newcomers in achieving successful integration. This sector faces increasing operational pressures amidst rising immigration targets, which highlights a need for enhanced efficiency and innovation, potentially through reliable AI solutions. The ad-hoc use of general-purpose generative AI, such as ChatGPT, mi

Igor Dolgachev, Shigeyuki Kondō

We study the congruence of bitangent lines of an irreducible surface in the 3-dimensional projective space in arbitrary characteristic, with special attention to quartic surfaces with rational double points and, in particular, Kummer quartic surfaces.

Alberto Dayan, Adrián Llinares, Karl-Mikael Perfekt

We characterize the restrictions of Békollé--Bonami weights of bounded hyperbolic oscillation, to subsets of the unit disc, thus proving an analogue of Wolff's restriction theorem for Muckenhoupt weights. Sundberg proved a discrete version of Wolff's original theorem, by characterizing the trace of $BMO$-functions onto interpolating sequences. We con

Vincent Savignac, Eve J. Lee

Noble gases are powerful probes of the Earth's early history, as they are chemically inert. Neon isotopic ratios in deep mantle plumes suggest that nebular gases were incorporated into the Earth's interior. This evidence implies the Earth's formation began when there was still gas around, with Earth embryos accreting primordial gas and a fraction of that gas

Toufique Ahmed, Jatin Ganhotra, Avraham Shinnar, Martin Hirzel

Tests can be useful towards resolving issues on code repositories. However, relying too much on tests for issue resolution can lead to code that technically passes observed tests but actually misses important cases or even breaks functionality. This problem, called test overfitting, is exacerbated by the fact that issues usually lack readily executable tests

Vineet Bhat, Sungsu Kim, Valts Blukis, Greg Heinrich

Vision Language Models (VLMs) have achieved impressive performance on spatial reasoning benchmarks, yet these evaluations mask critical weaknesses in understanding object interactions. Current benchmarks test high level relationships ('left of,' 'behind', etc.) but ignore fine-grained spatial understanding needed for real world applications: precise 3D local

Stefano Epifani, Giuliano Castigliego, Laura Kecskemeti, Giuliano Razzicchia

Background: Mentalization integrates cognitive, affective, and intersubjective components. Large Language Models (LLMs) display an increasing ability to generate reflective texts, raising questions regarding the relationship between linguistic form and mental representation. This study assesses the extent to which a single LLM can reproduce the linguistic st

Mohammadhossein Ghahramani, Yan Qiao, NaiQi Wu, Mengchu Zhou

The integration of advanced technologies, such as Artificial Intelligence (AI), into manufacturing processes is attracting significant attention, paving the way for the development of intelligent systems that enhance efficiency and automation. This paper uses the term "Gentelligent system" to refer to systems that incorporate inherent component information (

Yenni P. Ortiz, Arnau Cortés-Llamas, Jordi Ribas-Arino, Stefan T. Bromley

Metal-free porphyrins can switch between hydrogen-bonded tautomers, potentially enabling reversible control in molecular electronics. However, electric field gating of porphyrin tautomerism, which is critical for device integration, has not been fully realized. We propose metal-free benziporphyrins (MFBPs), in which one pyrrole ring is replaced with a phenol

Katia Pires Nascimento do Sacramento, Elliot Q. C. Garcia, Nicéias Silva Vilela, Vinicius P. Sacramento

Parkinson's disease (PD) is a progressive neurodegenerative disorder that, in addition to directly impairing functional mobility, is frequently associated with vocal impairments such as hypophonia and dysarthria, which typically manifest in the early stages. The use of vocal biomarkers to support the early diagnosis of PD presents a non-invasive, low-cost, a

Linzhuo Li, Yiling Lin, Lingfei Wu

New ideas are often thought to arise from recombining existing knowledge. Yet despite rapid publication growth - and expanding opportunities for recombination - scientific breakthroughs remain rare. This gap between productivity and progress challenges recombinant growth theory as the prevailing account of innovation. We argue that the limitation of this the

Haruki Mitarai, Yukihiro Tadokoro, Hiroya Tanaka

The increasing number of qubits in quantum processors necessitates a corresponding increase in the number of control lines between the processor, which is typically operated at cryogenic temperatures, and external electronics. Scaling poses significant challenges in terms of the thermal loads, forming a major bottleneck in the realization of large-scale quan

Mohammad Khateri, Serge Vasylechko, Morteza Ghahremani, Liam Timms

High-resolution (HR) magnetic resonance imaging (MRI) is crucial for many clinical and research applications. However, achieving it remains costly and constrained by technical trade-offs and experimental limitations. Super-resolution (SR) presents a promising computational approach to overcome these challenges by generating HR images from more affordable low

Xizhe Xue, Xiao Xiang Zhu

Recent progress in vision language models (VLMs) has enabled remarkable perception and reasoning capabilities, yet their potential for scientific regression in Earth Observation (EO) remains largely unexplored. Existing EO datasets mainly emphasize semantic understanding tasks such as captioning or classification, lacking benchmarks that align multimodal per

Philippe Malbos, Tanguy Massacrier, Georg Struth

We study the confluence property of abstract rewriting systems internal to cubical categories. We introduce cubical contractions, a higher-dimensional generalisation of reductions to normal forms, and employ them to construct cubical polygraphic resolutions of convergent rewriting systems. Within this categorical framework, we establish cubical proofs of fun

Shamminuj Aktar, Rishabh Bhardwaj, Andreas Bärtschi, Tanmoy Bhattacharya

Quantum data learning (QDL) provides a framework for extracting physical insights directly from quantum states, bypassing the need for any identification of the classical observable of the theory. A central challenge in many-body physics is that the identity of quantum phases, especially those with topological order, are often inaccessible through local obse

Kritti Sharma, Vikram Ravi, Liam Connor, Elisabeth Krause

Fast Radio Bursts (FRBs) have emerged as powerful probes of baryonic matter in the Universe, offering constraints on cosmological and feedback parameters through their extragalactic dispersion measure-redshift (DM$_\mathrm{exgal}$-$z$) relation. However, the observed FRB population is shaped by complex selection effects arising from instrument sensitivity, D

Leonardo Pepino, Pablo Riera, Juan Kamienkowski, Luciana Ferrer

Artificial neural networks are increasingly powerful models of brain computation, yet it remains unclear whether improving their performance in downstream tasks also makes their internal representations more similar to brain signals. To address this question in the auditory domain, we quantified the alignment between the internal representations of 36 differ

Feliciano Pedro Francisco Domingos, Isibor Kennedy Ihianle, Omprakash Kaiwartya, Ahmad Lotfi

Monitoring aquatic species, especially elusive ones like lobsters, presents challenges. This study focuses on Homarus gammarus (European lobster), a key species for fisheries and aquaculture, and leverages non-invasive Passive Acoustic Monitoring (PAM). Understanding lobster habitats, welfare, reproduction, sex, and age is crucial for management and conserva

Ricardo Freire, Claudio Muñoz

We study the $L^2$-supercritical generalized Korteweg-de Vries equation (gKdV) with nonlinearities $p>5$. While local well-posedness in $H^1$ is classical, the long-time dynamics in the supercritical regime remains largely unexplored beyond small data global solutions, the construction of multi-solitons for any power and self-similar blow-up near the critica

Seyed Mohssen Ghafari, Ronny Kol, Juan C. Quiroz, Nella Luan

Large language models (LLMs) frequently generate responses that are lengthy and verbose, filled with redundant or unnecessary details. This diminishes clarity and user satisfaction, and it increases costs for model developers, especially with well-known proprietary models that charge based on the number of output tokens. In this paper, we introduce a novel r

Zijian Zhang, Xinyu Chen, Yuanjie Shi, Liyuan Lillian Ma

Ordinal classification has been widely applied in many high-stakes applications, e.g., medical imaging and diagnosis, where reliable uncertainty quantification (UQ) is essential for decision making. Conformal prediction (CP) is a general UQ framework that provides statistically valid guarantees, which is especially useful in practice. However, prior ordinal

Disha Kamale, Xi Yu, Cristian-Ioan Vasile

In this work, we consider the problem of planning for temporal logic tasks in large robot environments. When full task compliance is unattainable, we aim to achieve the best possible task satisfaction by integrating user preferences for relaxation into the planning process. Utilizing the automata-based representations for temporal logic goals and user prefer

Mark D. Groves, Erik Wahlén

Fully localised three-dimensional solitary waves are steady water waves which are evanescent in every horizontal direction. This paper presents an existence theory for such waves under the assumptions that the relative vorticity and velocity fields are parallel (`Beltrami flows'), that the free surface of the water takes the form $\{z=\eta(x,y)\}$ for some f

Sang Truong, Yuheng Tu, Michael Hardy, Anka Reuel

Benchmarks are pivotal in driving AI progress, and invalid benchmark questions frequently undermine their reliability. Manually identifying and correcting errors among thousands of benchmark questions is not only infeasible but also a critical bottleneck for reliable evaluation. In this work, we introduce a framework for systematic benchmark revision that le

Anshid Aboobacker

This paper investigates the correlation between $k$-type dynamical properties of $\mathbb{Z}^d$-actions on compact metric spaces and their induced actions on the corresponding hyperspaces. We extend the classical results from discrete dynamical systems and general group actions to the specific setting of $k$-type dynamics. Specifically, we define and study $

Yicong Zheng, Kevin L. McKee, Thomas Miconi, Zacharie Bugaud

How to enable human-like long-term memory in large language models (LLMs) has been a central question for unlocking more general capabilities such as few-shot generalization. Existing memory frameworks and benchmarks focus on finding the optimal memory compression algorithm for higher performance in tasks that require recollection and sometimes further reaso

Tulin Altunoz, Mehmetcik Pamuk, Oguz Yildiz

The mapping class group of an orientable surface, which records its symmetries up to isotopy, plays a central role in low-dimensional topology. This chapter explores the foundational problem of determining minimal generating sets for these groups. We chart the development of this area from classical results involving Dehn twist generators to more recent brea

Anup Budhathoki, Leonardo Rydin Gorjão, Pedro G. Lind, Shailendra Bhandari

We present a data-driven framework to model the stochastic evolution of volume-price distribution from the New York Stock Exchange (NYSE) equities. The empirical distributions are sampled every 10 minutes over 976 trading days, and fitted to different models, namely Gamma, Inverse Gamma, Weibull, and Log-Normal distributions. Each of these models is paramete

Oliver Kramer

Cognitive BASIC is a minimal, BASIC-style prompting language and in-model interpreter that structures large language model (LLM) reasoning into explicit, stepwise execution traces. Inspired by the simplicity of retro BASIC, we repurpose numbered lines and simple commands as an interpretable cognitive control layer. Modern LLMs can reliably simulate such shor

Daniel A. Stariolo, Fernando L. Metz

We analytically solve the zero-temperature dynamics of the spherical model with non-reciprocal random interactions drawn from the real elliptic ensemble of random matrices, where a single parameter $\eta$ continuously interpolates between purely symmetric ($\eta=1$) and purely antisymmetric ($\eta=-1$) couplings. We show that the two-time correlation and res

Anshid Aboobacker, Sharan Gopal

We introduce the concept of \(k\)-type entropy for dynamical systems generated by \(\mathbb{Z}^d\)-actions on compact metric spaces. We investigate its fundamental properties and establish connections with classical entropy and other \(k\)-type dynamical notions. The $k$-type entropy of some $\mathbb{Z}^2$-actions on a two dimensional torus is also calculate

Hugo FM Milan, Aline Q Alves, Thatiane AT Souza, Juliana M Galo

Performance ratio (PR) is a established measure of efficiency of photovoltaic (PV) systems. While previous research demonstrated the effects of meteorological and technical variables on PR, a gap persists in the literature on which variables strongly influence PR in large-scale, real-world, heterogeneous PV systems. This paper aims to fill this gap, applying

Robert S. Hoy

Using molecular dynamics simulations, we show that a widely-accepted theoretical prediction for glassy-polymeric strain hardening moduli ($G_R \propto \rho_e$, where $\rho_e$ is the entanglement density) fails badly for semiflexible polymers with $N_e \lesssim 4C_\infty$. By postulating that the length, energy and strain scales controlling $G_R$ are the Kuhn

Nikesh Gyawali, Doina Caragea, Cornelia Caragea, Saif M. Mohammad

In this work, we study English-language vaccine discourse in social media posts, specifically posts on X (formerly Twitter), in seven years before the COVID-19 outbreak (2013 to 2019) and three years after the outbreak was first reported (2020 to 2022). Drawing on theories from social cognition and the stereotype content model in Social Psychology, we analyz

Yipeng Wang, Mengtian Yang, Chieh-pu Lo, Jaydeep P. Kulkarni

3D Gaussian Splatting (3DGS) has recently emerged as a foundational technique for real-time neural rendering, 3D scene generation, volumetric video (4D) capture. However, its rendering and training impose massive computation, making real-time rendering on edge devices and real-time 4D reconstruction on workstations currently infeasible. Given its fixed-funct

Oscar Chew, Po-Yi Lu, Jayden Lin, Kuan-Hao Huang

Recent studies show that text-to-image (T2I) diffusion models are vulnerable to backdoor attacks, where a trigger in the input prompt can steer generation toward harmful or unintended content. Beyond the trigger token itself, backdoor effects can spread to neighboring tokens in the text embedding space. To address this, we introduce PEPPER (PErcePtion-Guided

Tulin Altunoz, Mehmetcik Pamuk, Oguz Yildiz

This chapter provides a comprehensive survey of foundational results and recent advances concerning minimal generating sets for the mapping class group of a nonorientable surface, $\mathrm{Mod}(N_{g})$, and its index-two twist subgroup, $\mathcal{T}_{g}$. Although the theory for orientable surfaces is well established, the nonorientable case presents unique

Yihang Fu, Lifang He, Qingyu Chen

Existing EEG foundation models mainly treat neural signals as generic time series in Euclidean space, ignoring the intrinsic geometric structure of neural dynamics that constrains brain activity to low-dimensional manifolds. This fundamental mismatch between model assumptions and neural geometry limits representation quality and cross-subject generalization.

Debmita Roy

This study enhances breast cancer prediction by using large language models to infer the likelihood of confounding diseases, namely diabetes, obesity, and cardiovascular disease, from routine clinical data. These AI-generated features improved Random Forest model performance, particularly for LLMs like Gemma (3.9%) and Llama (6.4%). The approach shows promis

Zhe Li, Haijun Cao, Lijuan Li, Huixia Fu

Valley topology has emerged as a key concept for realizing new classes of quantum states. Here, we investigate Floquet-engineered topological phase transitions in anisotropic 1T'-WSe$_2$ and its Janus derivative WSeTe monolayers, which exhibit valley-degenerate and valley-polarized characteristics, respectively. In 1T'-WSe$_2$, a single topological-phase-tra

Yizhou Xu, Janet Davis

Lightweight language models remain attractive for on-device and privacy-sensitive applications, but their responses are highly sensitive to prompt quality. For open-ended generation, non-expert users often lack the knowledge or time to consistently craft high-quality prompts, leading them to rely on prompt optimization tools. However, a key challenge is ensu

Bassel Abou Ali Modad, Xin Yu, Yao-Yi Chiang, Andreas F. Molisch

Accurate modeling of line-of-sight (LOS) probability is crucial for wireless channel description and coverage planning. The presence of a LOS impacts other channel characteristics such as pathloss, fading depth, delay- and angular spread, etc.. Existing models, although useful, are based on very limited datasets. In this paper, we establish a framework to pr

David Lannes, Martin Oen Paulsen

In this paper, we address the well-posedness theory of F. John's problem for freely floating objects in a two-dimensional framework. This problem is a linear description of the interactions between an incompressible, irrotational free-surface fluid and a partially immersed solid object. It is related to the Cummins equations, which are a set of coupled integ