October 2025 arXiv papers — page 69

Kunlong Zhang, Guiying Li, Ning Lu, Peng Yang

Deploying deep neural networks (DNNs) across homogeneous edge devices (the devices with the same SKU labeled by the manufacturer) often assumes identical performance among them. However, once a device model is widely deployed, the performance of each device becomes different after a period of running. This is caused by the differences in user configurations,

E Horsdal

Mechanical energy is lost to friction during a shot with a trebuchet. The losses are mainly due to sliding friction at the bearings for the throwing arm and at the hinge for the swinging counterweight, but the aerodynamic force on the sling also contributes. Generalized forces for these sliding and aerodynamic frictions are derived and included in the equati

Paul Lerner, François Yvon

The political biases of Large Language Models (LLMs) are usually assessed by simulating their answers to English surveys. In this work, we propose an alternative framing of political biases, relying on principles of fairness in multilingual translation. We systematically compare the translation quality of speeches in the European Parliament (EP), observing s

Panagiotis Giannakopoulos, Bart van Knippenberg, Kishor Chandra Joshi, Nicola Calabretta

Distributed applications increasingly demand low end-to-end latency, especially in edge and cloud environments where co-located workloads contend for limited resources. Traditional load-balancing strategies are typically reactive and rely on outdated or coarse-grained metrics, often leading to suboptimal routing decisions and increased tail latencies. This p

Ruiyi Yang, Hao Xue, Imran Razzak, Hakim Hacid

Retrieval-augmented generation (RAG) remains brittle on multi-step questions and heterogeneous evidence sources, trading accuracy against latency and token/tool budgets. This paper introduces RELOOP, a structure aware framework using Hierarchical Sequence (HSEQ) that (i) linearize documents, tables, and knowledge graphs into a reversible hierarchical sequenc

Xin Zhang, Lin Li, Xiangni Lu, Jianquan Liu

Speech codecs serve as bridges between continuous speech signals and large language models, yet face an inherent conflict between acoustic fidelity and semantic preservation. To mitigate this conflict, prevailing methods augment acoustic codecs with complex semantic supervision. We explore the opposite direction: a semantic-first approach that starts from a

Bo-Rui Li, Jia-Zhou Liu, Wen-Di Guo, Yu-Xiao Liu

Recently, exact charged spherically symmetric black hole solutions within the framework of bumblebee gravity have been obtained, where the Lorentz symmetry is spontaneously broken due to the nonvanishing vacuum expectation value of the bumblebee field. In this work, we investigate the quasinormal modes of this black hole. We compute the quasinormal frequenci

Léon F. Schröder, Sabrina Weber, Lukas Fuchs, Volker Schmidt

The 3D microstructure of solid oxide fuel cell anodes significantly influences their electrochemical performance, but conventional methods for acquiring high-resolution microstructural 3D data such as focused ion beam scanning electron microscopy (FIB-SEM) are costly in both time and resources. In contrast, obtaining 2D images, such as from scanning electron

Jèrôme Dedecker, Florence Merlevède

In this note, we study a condition introduced by Gordin and Lif{\v s}ic in 1981 to establish the Central Limit Theorem for additive functionals of stationary Markov chains with normal transition operator. In the more general setting of strictly stationary sequences satisfying the Gordin-Lif{\v s}ic condition, we give sufficient (and sometimes also necessary)

Akif Çördük, Piotr Sielski, Alice Boucher, Kumar Aatish

We introduce a fusion of GPU accelerated primal heuristics for Mixed Integer Programming. Leveraging GPU acceleration enables exploration of larger search regions and faster iterations. A GPU-accelerated PDLP serves as an approximate LP solver, while a new probing cache facilitates rapid roundings and early infeasibility detection. Several state-of-the-art h

Ruochen Mao, Yuling Shi, Xiaodong Gu, Jiaheng Wei

Aligning large language models with human preferences is critical for creating reliable and controllable AI systems. A human preference can be visualized as a high-dimensional vector where different directions represent trade-offs between desired attributes (e.g., helpfulness vs. verbosity). Yet, because the training data often reflects dominant, average pre

Daniel Barlet

In this paper we introduce and study the ''convergent'' algebra (containing ''a'' and ''b'' and acting on holomorphic germs in ''a'') which naturally acts on the ''generalized Brieskorn modules'' associated to the Gauss-Manin connections of the germs at each point of the singular set of a holomorphic function on a complex manifold. We generalize to this conv

Aleksandra Deptuch, Natalia Osiecka-Drewniak, Anna Paliga, Natalia Górska

The liquid crystalline 11OS5 compound, forming the nematic phase and a few smectic phases, is investigated by broadband dielectric spectroscopy and infra-red spectroscopy. The dielectric relaxation times, ionic conductivity, and positions of infra-red absorption bands corresponding to selected intra-molecular vibrations are determined as a function of temper

Tobias Marauli, Hubert Gattringer, Andreas Mueller

In this paper the computational challenges of time-optimal path following are addressed. The standard approach is to minimize the travel time, which inevitably leads to singularities at zero path speed, when reformulating the optimization problem in terms of a path parameter. Thus, smooth trajectory generation while maintaining a low computational effort is

Panagiotis Giannakopoulos, Bart van Knippenberg, Kishor Chandra Joshi, Nicola Calabretta

Accurate prediction of application performance is critical for enabling effective scheduling and resource management in resource-constrained dynamic edge environments. However, achieving predictable performance in such environments remains challenging due to the co-location of multiple applications and the node heterogeneity. To address this, we propose a me

Florian Hofer, Barbara Russo

Cyber-physical systems and the Internet of Things (IoT) are key technologies in the Industry 4.0 vision. They incorporate sensors and actuators to interact with the physical environment. However, when creating and interconnecting components to form a heterogeneous smart systems architecture, these face challenges in cybersecurity. This paper presents an expe

Jacky J. Chong, Hao Liang, Phan Thành Nam

We propose a simplified localization method for Bose gases, based on a Poincare-type inequality, which leads to a new derivation of Bose--Einstein condensation for dilute Bose gases beyond the Gross--Pitaevskii scaling regime.

Guanghao Zheng, Bowen Shi, Mingxing Xu, Ruoyu Sun

Vision encoders are indispensable for allowing impressive performance of Multi-modal Large Language Models (MLLMs) in vision language tasks such as visual question answering and reasoning. However, existing vision encoders focus on global image representations but overlook fine-grained regional analysis. They are limited in fine grained perception due to the

Zhenhao Cai, Jian Ding

For the Gaussian free field on the metric graph of $\mathbb{Z}^d$ ($d\ge 3$), we consider the heterochromatic two-arm probability, i.e., the probability that two points $v$ and $v'$ are contained in distinct clusters of opposite signs with diameters at least $N$. For all $d\ge 3$ except the critical dimension $d_c=6$, we prove that this probability is asympt

Thomas Kordik, Hubert Gattringer, Andreas Mueller

A major field of industrial robot applications deals with repetitive tasks that alternate between operating points. For these so-called pick-and-place operations, parallel kinematic manipulators (PKM) are frequently employed. These tasks tend to automatically run for a long period of time and therefore minimizing energy consumption is always of interest. Rec

Dmitri Melikhov

In quantum field theory, characteristics of resonances are related to self-energy diagrams, which are ultra-violet divergent and require renormalization. We demonstrate the proper way to define the resonance coupling $g_M$ such that the resonance properties calculated in quantum field theory are finite and scheme-independent quantities.

Tim Tian Hua, Andrew Qin, Samuel Marks, Neel Nanda

Large language models (LLMs) can sometimes detect when they are being evaluated and adjust their behavior to appear more aligned, compromising the reliability of safety evaluations. In this paper, we show that adding a steering vector to an LLM's activations can suppress evaluation-awareness and make the model act like it is deployed during evaluation. To st

Danying Ge, Jianhua Gao, Yixue Yang, Weixing Ji

Retrieval-Augmented Generation (RAG) improves model output accuracy by leveraging external knowledge bases, serving as an effective solution to address hallucination issues and knowledge-update delays in Large Language Models (LLMs). However, the introduction of external knowledge bases presents RAG with challenges in long-context processing, significantly i

Fangjian Zhang, Xiaoyong Zhuge, Wenlan Wang, Haixia Xiao

Artificial intelligence has advanced quantitative remote sensing, yet its effectiveness is constrained by imbalanced label distribution. This imbalance leads conventionally trained models to favor common samples, which in turn degrades retrieval performance for rare ones. Rainfall retrieval exemplifies this issue, with performance particularly compromised fo

Simone Caletti, Aude Gehrmann-De Ridder, Matteo Marcoli

We present precise predictions for a variety of jet observables in $ZH$ production at electron-positron colliders, with the $Z$ boson decaying leptonically and the Higgs boson decaying into two hadronic jets, up to NNLO in perturbative QCD. We consider a Higgs boson decaying into bottom (charm) quark pairs via Yukawa interaction and into gluons via an effect

Simone D'Onofrio, Sergei Odintsov, Tanmoy Paul

Recent evidences of stochastic gravitational wave background (SGWB) through Pulsar Time Array (PTA) observations hint towards an alternative inflationary scenario, compared to the usual inflation, for describing the early stage of the universe in order to be compatible with the PTA data. Moreover, currently the Atacama Cosmology Telescope (combined with the

Victor Vantilborgh, Hrishikesh Sathyanarayan, Guillaume Crevecoeur, Ian Abraham

This work addresses the problem of robot manipulation tasks under unknown dynamics, such as pick-and-place tasks under payload uncertainty, where active exploration and(/for) online parameter adaptation during task execution are essential to enable accurate model-based control. The problem is framed as dual control seeking a closed-loop optimal control probl

Alexandre Fournier-Montgieux, Hervé Le Borgne, Adrian Popescu, Bertrand Luvison

Fairness evaluation in face analysis systems (FAS) typically depends on automatic demographic attribute inference (DAI), which itself relies on predefined demographic segmentation. However, the validity of fairness auditing hinges on the reliability of the DAI process. We begin by providing a theoretical motivation for this dependency, showing that improved

Théotime Le Goff, Émilie Devijver

Out-of-distribution generalization is key to building models that remain reliable across diverse environments. Recent causality-based methods address this challenge by learning invariant causal relationships in the underlying data-generating process. Yet, measuring how causal structures differ across environments, and the resulting generalization difficulty,

Bowen Wang, Haiyuan Wan, Liwen Shi, Chen Yang

We unveil that internal representations in large language models (LLMs) serve as reliable proxies of learned knowledge, and propose RECALL, a novel representation-aware model merging framework for continual learning without access to historical data. RECALL computes inter-model similarity from layer-wise hidden representations over clustered typical samples,

Ondřej Kreml, Šárka Nečasová, Tong Tang

Inspired by Abbatiello, Feireisl and Novotn\'y, we prove the global existence of dissipative turbulent solution for the compressible Navier-Stokes equations with anisotropic viscous stress tensor on unbounded domain. Our work complements the result of Bresch and Jabin, where the authors used the new compactness method to prove the existence of a weak solutio

Lukas Edman, Alexander Fraser

We describe our strategy for the 2025 edition of the BabyLM Challenge. Our main contribution is that of an improved form of Masked Language Modeling (MLM), which adapts the probabilities of the tokens masked according to the model's ability to predict them. The results show a substantial increase in performance on (Super)GLUE tasks over the standard MLM. We

Frank Gounelas, Daniel Huybrechts

We construct universal Brauer-Severi varieties of fixed period and index and study their geometry. We determine their cohomology and their Brauer and Picard groups and show that they are almost always simply connected. As an application, we reinterpret the discriminant avoidance result of de Jong and Starr in terms of universal Brauer-Severi varieties.

Bernhard Rameder, Hubert Gattringer, Andreas Mueller, Ronald Naderer

This paper presents a method for planning a trajectory in workspace coordinates using a spatially fixed tool center point (TCP), while taking into account the processing path on a part. This approach is beneficial if it is easier to move the part rather than moving the tool. Whether a mathematical description that defines the shape to be processed or single

Touqeer Ahmad, Mohammadreza M. Kalan, François Portier, Gilles Stupfler

Synthetic oversampling of minority examples using SMOTE and its variants is a leading strategy for addressing imbalanced classification problems. Despite the success of this approach in practice, its theoretical foundations remain underexplored. We develop a theoretical framework to analyze the behavior of SMOTE and related methods when classifiers are train

Ioana Ciotir, Dan Goreac, Jonas M. Tölle

This short survey article stems from recent progress on critical cases of stochastic evolution equations in variational formulation with additive, multiplicative or gradient noises. Typical examples appear as the limit cases of the stochastic porous medium equation, stochastic fast- and super fast-diffusion equations, self-organized criticality, stochastic s

Kun Ouyang, Yuanxin Liu, Linli Yao, Yishuo Cai

Video reasoning, which requires multi-step deduction across frames, remains a major challenge for multimodal large language models (MLLMs). While reinforcement learning (RL)-based methods enhance reasoning capabilities, they often rely on text-only chains that yield ungrounded or hallucinated conclusions. Conversely, frame-retrieval approaches introduce visu

Horacio Paggi, Juan A. Lara, Javier Soriano

There has recently been a major advance with respect to how information fusion is performed. Information fusion has gone from being conceived as a purely hierarchical procedure, as is the case of traditional military applications, to now being regarded collaboratively, as holonic fusion, which is better suited for civil applications and edge organizations. T

Somasekhara Goud Sontti, Arnab Atta

In this article, we have developed a computational model to determine the droplet formation regime and its transition in a square microfluidic flow-focusing device that eventually dictate the droplet shape, size, and its formation frequency. We have methodically explored the influences of various physicochemical parameters on the droplet dynamics and flow re

Tomáš Souček, Sylvestre-Alvise Rebuffi, Pierre Fernandez, Nikola Jovanović

Recent years have seen a surge in interest in digital content watermarking techniques, driven by the proliferation of generative models and increased legal pressure. With an ever-growing percentage of AI-generated content available online, watermarking plays an increasingly important role in ensuring content authenticity and attribution at scale. There have

Yiwen Peng, Thomas Bonald, Fabian M. Suchanek

Knowledge graph alignment is the task of matching equivalent entities (that is, instances and classes) and relations across two knowledge graphs. Most existing methods focus on pure entity-level alignment, computing the similarity of entities in some embedding space. They lack interpretable reasoning and need training data to work. In this paper, we propose

Navonil Saha, Peter Berczik, Andreas Just, Margarita Sobolenko

The hierarchical nature of galaxy formation in the $\Lambda$CDM framework often leads to multiple supermassive black holes (SMBHs) in the galactic nuclei. The timescale over which galaxies merge, plays a crucial role in shaping the dynamical evolution and the merger dynamics of their central SMBHs. While binary SMBH evolution is well studied, the long-term d

Linus Lind, Hilda Sandström, Patrick Rinke

The study of aerosol formation and chemistry using machine learning is limited by the lack of molecular descriptors suited to atmospheric compounds. Interpretable models are particularly affected because they often rely on dictionary-based descriptors tied to specific molecular substructures, which currently fail to capture the full range of organic atmosphe

Amadou Sy

The aim of this article is to show that if there exists $u \in \Omega_{h} \subset T^{1}S$ an infinite quasi-minimizing ray which do not intersect any closed geodesic on the surface $S$ (untwisted flute), then $T_{u}=\{ t \in \mathbb{R} \; ; \; g_{t}u \in \overline{h_{\mathbb{R}}u} \}=\{0\}$.

Yuan-Hao Yang, Jia-Qi Wang, Zheng-Xu Zhu, Xin-Biao Xu

Cavity optomechanical systems enable coherent photon-phonon interactions essential for quantum technologies, yet high-performance devices have been limited to suspended structures. Here, we overcome this limitation by demonstrating cavity Brillouin optomechanics in a suspension-free racetrack microring resonator on a lithium-niobate-on-sapphire chip, a platf

Fabio Martinelli, Assaf Shapira, Cristina Toninelli

Kinetically constrained models (KCMs) are interacting particle systems introduced in the '80s by physicists to have accessible stochastic models with glassy-type dynamics. The key mechanism behind the complex evolution of these otherwise simple models is the so-called dynamical facilitation, a feature embedded into the models via appropriate kinetic constrai

Christian Hobelsberger, Theresa Winner, Andreas Nawroth, Oliver Mitevski

Large language models (LLMs) produce outputs with varying levels of uncertainty, and, just as often, varying levels of correctness; making their practical reliability far from guaranteed. To quantify this uncertainty, we systematically evaluate four approaches for confidence estimation in LLM outputs: VCE, MSP, Sample Consistency, and CoCoA (Vashurin et al.,

Rajasmita Sahoo, Arunkarthiheyan Thiyagarajan, Asutosh Panda, Somnath Mukhopadhyay

In this work, we investigate the structure and gravitational wave (GW) signatures of strongly magnetized, oblate spheroidal quark stars by employing an anisotropic equation of state (EoS) derived from the MIT Bag model, extended to include the effects of density-dependent strong magnetic fields and the resulting pressure anisotropy arising from the breaking

Wei Liu, Benjamin Theisel, Yulia Klunnikova, Konstantin Skokov

Cost-effective materials are essential for large-scale deployment. The emerging magnetocaloric hydrogen liquefaction technology could transform the liquid hydrogen industry due to its potential in achieving higher efficiency. Most studies of the cryogenic magnetocaloric effect (MCE) have focused on resource-critical rare-earth-based compounds. Here we report

Louis Mozart Kamdem Teyou, Luke Friedrichs, N'Dah Jean Kouagou, Caglar Demir

Concept learning exploits background knowledge in the form of description logic axioms to learn explainable classification models from knowledge bases. Despite recent breakthroughs in neuro-symbolic concept learning, most approaches still cannot be deployed on real-world knowledge bases. This is due to their use of description logic reasoners, which are not

Baoquan Gong, Xiyuan Gao, Pengfei Zhu, Qinghua Hu

Multimodal learning systems often encounter challenges related to modality imbalance, where a dominant modality may overshadow others, thereby hindering the learning of weak modalities. Conventional approaches often force weak modalities to align with dominant ones in "Learning to be (the same)" (Positive Learning), which risks suppressing the unique informa

Bernhard Haeupler, Yonggang Jiang, Yaowei Long, Thatchaphol Saranurak

We present a parallel algorithm for computing $(1+\epsilon)$-approximate mincost flow on an undirected graph with $m$ edges, where capacities and costs are assigned to both edges and vertices. Our algorithm achieves $\hat{O}(m)$ work and $\hat{O}(1)$ depth when $\epsilon > 1/\mathrm{polylog}(m)$, making both the work and depth almost optimal, up to a subpoly

Xiaokai Wei, Jiajun Wu, Daiyao Yi, Reza Shirkavand

Generative recommenders, typically transformer-based autoregressive models, predict the next item or action from a user's interaction history. Their effectiveness depends on how the model represents where an interaction event occurs in the sequence (discrete index) and when it occurred in wall-clock time. Prevailing approaches inject time via learned embeddi

Mohammad Ali S Afshar, Saeed Noori Gashti, Mohammad Reza Alipour, Behnam Pourhassan

We investigate the consistency of radiatively corrected inflationary models with both the latest observational data from the Atacama Cosmology Telescope (ACT) combined with Planck 2018 and Baryon Acoustic Oscillation (BAO) measurements, and the theoretical constraints imposed by the swampland program. We systematically test two distinct models against three

Haicheng Liao, Bonan Wang, Junxian Yang, Chengyue Wang

Accurate and reliable motion forecasting is essential for the safe deployment of autonomous vehicles (AVs), particularly in rare but safety-critical scenarios known as corner cases. Existing models often underperform in these situations due to an over-representation of common scenes in training data and limited generalization capabilities. To address this li

Shehu AbdusSalam, Steven Abel, Deaglan Bartlett, Miguel Crispim Romão

We demonstrate the efficacy of symbolic regression (SR) to probe models of particle physics Beyond the Standard Model (BSM), by considering the so-called Constrained Minimal Supersymmetric Standard Model (CMSSM). Like many incarnations of BSM physics this model has a number (four) of arbitrary parameters, which determine the experimental signals, and cosmolo

Kostia Chardonnet, Emmanuel Hainry, Romain Péchoux, Thomas Vinet

This paper introduces the hybrid quantum language with general recursion $\mathtt{Hyrql}$, driven towards resource-analysis. By design, $\mathtt{Hyrql}$ does not require the specification of an initial set of quantum gates. Hence, it is well amenable towards a generic cost analysis, unlike languages that use different sets of quantum gates, which yield quant

Yuanshan Gao, Yang Bai, Yifan Cui

Dynamic treatment regimes are sequential decision rules that adapt treatment according to individual time-varying characteristics and outcomes to achieve optimal effects, with applications in precision medicine, personalized recommendations, and dynamic marketing. Estimating optimal dynamic treatment regimes via sequential randomized trials might face costly

Andrea Bussone, Alessandro Conigli, Julien Frison, Gregorio Herdoíza

We introduce a lattice QCD mixed action approach that employs Wilson-type quarks in the sea and valence sectors. The sea sector is based on gauge ensembles with $N_{\rm f}=2+1$ flavours of non-perturbatively O($a$)-improved Wilson fermions generated by the Coordinated Lattice Simulations (CLS) initiative. The parameter space of the considered ensembles encom

Xuan Lin, Aocheng Ding, Tengfei Ma, Hua Liang

Drug combinations offer therapeutic benefits but also carry the risk of adverse drug-drug interactions (DDIs), especially under complex molecular structures. Accurate DDI event prediction requires capturing fine-grained inter-drug relationships, which are critical for modeling metabolic mechanisms such as enzyme-mediated competition. However, existing approa

Giovanni Di Bartolomeo, Giulio Crognaletti, Angelo Bassi, Michele Vischi

Stabilizer states are a central resource in quantum information processing, underpinning a wide range of applications. While they can be efficiently generated via Clifford circuits, the presence of coherent errors, such as small-angle miscalibrations in native gate implementations, can significantly impact their quality. In this work, we introduce Variationa

Kamran Rehan, Hengchao Tu, Tadeu Tassis, Menglin Zou

We experimentally investigate trapped ion dynamics in the strong-driving regime, where the Rabi frequency (Omega) is comparable to the vibrational mode frequency (nu). In the conventional weak-driving regime (Omega << nu), the dynamics is well described by effective Hamiltonians for the carrier and motional sidebands, associated with detunings (delta = n nu

Shuang Qi, Bin Lin, Yiqin Deng, Hongyang Pan

Devices within the marine Internet of Things (MIoT) can connect to low Earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs) to facilitate low-latency data transmission and execution, as well as enhanced-capacity data storage. However, without proper traffic handling strategy, it is still difficult to effectively meet the low-latency requirements.

Oleg Bulashenko, Nino Villanueva, Roberto Bada Nerin, José A. Font

We present a framework for detecting gravitational-wave signals lensed by cosmic strings (CSs), addressing a key gap in current searches. CSs, whose detection would provide a unique probe of high-energy physics and the early Universe, possess distinct topological and geometric features that require a dedicated search strategy. Our approach employs a full-wav

Heiko Geppert, Frank Dürr, Simon Naß, Kurt Rothermel

Multicast allows sending a message to multiple recipients without having to create and send a separate message for each recipient. This preserves network bandwidth, which is particularly important in time-sensitive networks. These networks are commonly used to provide latency-bounded communication for real-time systems in domains like automotive, avionics, i

Quannian Zhang, Michael Röder, Nikit Srivastava, N'Dah Jean Kouagou

Evaluating competing systems in a comparable way, i.e., benchmarking them, is an undeniable pillar of the scientific method. However, system performance is often summarized via a small number of metrics. The analysis of the evaluation details and the derivation of insights for further development or use remains a tedious manual task with often biased results

Saif Ur Rehman Khan, Muhammad Nabeel Asim, Sebastian Vollmer, Andreas Dengel

This paper presents the FuzzyDistillViT-MobileNet model, a novel approach for lung cancer (LC) classification, leveraging dynamic fuzzy logic-driven knowledge distillation (KD) to address uncertainty and complexity in disease diagnosis. Unlike traditional models that rely on static KD with fixed weights, our method dynamically adjusts the distillation weight

Alvaro Carrizosa-Rendon, Jian Zhou, Erik Frisk, Vicenc Puig

Predicting the motion of surrounding vehicles is key to safe autonomous driving, especially in unstructured environments without prior information. This paper proposes a novel online method to accurately predict the occupancy sets of surrounding vehicles based solely on motion observations. The approach is divided into two stages: first, an Extended Kalman F

Federico Lozano-Cuadra, Beatriz Soret, Marc Sanchez Net, Abhishek Cauligi

We present a fully decentralized routing framework for multi-robot exploration missions operating under the constraints of a Lunar Delay-Tolerant Network (LDTN). In this setting, autonomous rovers must relay collected data to a lander under intermittent connectivity and unknown mobility patterns. We formulate the problem as a Partially Observable Markov Deci

Jitendra Bajpai, Srijan Das, Kiran S. Kedlaya, Nam H. Le

In a 1965 paper, R. Robinson made five conjectures about the classification of cyclotomic algebraic integers for which the maximum absolute value in any complex embedding (the house) is small, modulo the equivalence relation generated by Galois conjugation and multiplication by roots of unity. In response to one of these conjectures, Cassels showed in 1969 t

Rosella Giacometti, Gabriele Torri, Marco Bonomelli, Davide Lauria

In this work we propose a framework to construct Market-Implied Sustainability (MIS) scores for individual firms by exploiting fund-level sustainability classifications and granular portfolio holdings. The central idea is that the relative over/under-representation of a stock in sustainability-oriented funds reveals a market-based assessment of its sustainab

Ming Ng

This paper provides a complete presentation of $K_1(Var)$, the $K_1$ group of varieties, resolving and simplifying a problem left open in \cite{ZakhK1}. Our approach adapts Gillet-Grayson's $G$-Construction to define an un-delooped $K$-theory spectrum of varieties. There are two levels on which one can read the present paper. On a technical level, we streaml

Javier Marín

We document empirical capability ceilings in decoder-only autoregressive language models across knowledge-intensive tasks. Systematic evaluation of OPT and Pythia model families (70M-30B parameters, spanning 240 times scaling) reveals that knowledge retrieval tasks show negligible accuracy improvement despite smooth loss reduction. On MMLU mathematics benchm

Filippo Maria Cassanello, Eurica Henriques

We prove local H\"older continuity for non negative, locally bounded, local weak solutions to the class of doubly nonlinear parabolic equations $\partial_t (u_q) - \text{div} (|Du|^{p-2} Du) = 0$ for $p > 2$, $ 0 < q < p-1$. The proof relies on expansion of positivity results combined with the study of an alternative (related to DeGiorgi-type lemmas) and an

David Stein, Bjoern Andres, Silvia Di Gregorio

The higher-order correlation clustering problem for a graph $G$ and costs associated with cliques of $G$ consists in finding a clustering of $G$ so as to minimize the sum of the costs of those cliques whose nodes all belong to the same cluster. To tackle this NP-hard problem in practice, local search heuristics have been proposed and studied in the context o

Mario Riccio, Marco De Corato

We study the rising dynamics of a bubble driven into periodic volumetric oscillations by an external pressure driving within a highly viscous shear-thinning fluid. We perform axisymmetric direct numerical simulations employing the Carreau-Yasuda model to describe the rheological behavior of the fluid and the finite element method to discretize the equations.

Tatsuru Kikuchi

Emergency medical services (EMS) response times are critical determinants of patient survival, yet existing approaches to spatial coverage analysis rely on discrete distance buffers or ad-hoc geographic information system (GIS) isochrones without theoretical foundation. This paper derives continuous spatial boundaries for emergency response from first princi

Xuran Li, Jingyi Wang

Large language models (LLMs) are increasingly deployed in real-world systems, yet they can produce toxic or biased outputs that undermine safety and trust. Post-hoc model repair provides a practical remedy, but the high cost of parameter updates motivates selective use of repair data. Despite extensive prior work on data selection for model training, it rema

Thomas Jaffard

Let $f, g^1, \dots, g^d : \mathbb{R}^d \longrightarrow \mathbb{R}$ be H\"older continuous functions. If the H\"older exponents of these functions are less than $1$ but sufficiently large, we use the integral introduced by Z\"ust to construct a distribution, denoted by $f \, \mathrm{d}g^1 \wedge \dots \wedge \, \mathrm{d}g^d$ which depends continuously on the

T. V. Pavlova, V. M. Shevlyuga

Silicon dangling bonds (DBs) on an adsorbate-covered Si(100) surface can be created in a scanning tunneling microscope (STM) with high precision required for a number of applications. However, vacancies containing DBs can diffuse, disrupting precisely created structures. In this work, we study the diffusion of Br vacancies on a Si(100)-2$\times$1-Br surface

Ziyang Li, Chunfeng Cui, Jiaxin Xie

Dual quaternions have gained significant attention due to their wide applications in areas such as multi-agent formation control, 3D motion modeling, and robotics. A fundamental aspect in dual quaternion research involves the projection onto the unit dual quaternion set. In this paper, we systematically study such projections under the $2^R$-norm, which is c

Lokesh Manickavasaham, Manjunath Bestha, Sivarani Thirupathi, Arun Surya

Ground-based transmission spectroscopy is often dominated by systematics, which obstructs our ability to leverage the advantages of larger aperture sizes compared to space-based observations. These systematics could be time-correlated, uniform across all spectroscopic light curves, or wavelength-correlated, which could significantly affect the characterizati

Alberto Ibort, Arnau Mas

In order to provide a good categorical setting to the many different spaces of fields arising in the description of physical theories, a pedagogical introduction to the categorical notion of smooth sets is provided and some simple properties of the topos of smooth sets are discussed. The introduction of geometrical structures into such spaces is illustrated

Vladimir M. Shevlyuga, Yulia A. Vorontsova, Tatiana V. Pavlova

The adsorption of PBr3 on the Si(100)-2$\times$1 surface was studied by scanning tunneling microscopy (STM) and density functional theory (DFT). The PBr3 molecule completely dissociates on the Si(100) surface at room temperature into P and Br atoms. In most cases, the dissociated molecule was observed in STM on three neighboring Si dimers. DFT calculations c

Jochen Bröcker, Eviatar Bach

A generalised concept of the signal-to-noise ratio (or equivalently the ratio of predictable components, or RPC) is provided, based on proper scoring rules. This definition is the natural generalisation of the classical RPC, yet it allows one to define and analyse the signal-to-noise properties of any type of forecast that is amenable to scoring, thus drasti

Eric Wagner, David Heye, Jan Bauer, Klaus Wehrle

Aggregating Message Authentication Codes (MACs) promises to save valuable bandwidth in resource-constrained environments. The idea is simple: Instead of appending an authentication tag to each message in a communication stream, the integrity protection of multiple messages is aggregated into a single tag. Recent studies postulate, e.g., based on simulations,

Yassine Guerboussa, Maria Guedri

We show that every finitely generated cohomologically trivial module over $RG$, where $G$ is a finite $p$-group and $R$ is a $p$-adic ring, splits as the direct sum of a finite cohomologically trivial $RG$-module and a free $RG$-module. Along the way, we also establish other results concerning generators and relators of such modules.

J. P. Hidalgo, D. R. G Schleicher, D. P. González

We present an assessment of the effects of stellar wind magnetic and mechanical components on the habitability of Earth-like exoplanets orbiting the inner and outer radii of the habitable zone (HZ) of M dwarfs. We consider stars with masses in the range of $0.09 - 0.75 M_\odot$ and planets with a surface dipolar magnetic field of 0.5 G. We estimate the size

Eulalie Boucher, Mihai Alexe, Peter Lean, Ewan Pinnington

Interactions between different components of the Earth System (e.g. ocean, atmosphere, land and cryosphere) are a crucial driver of global weather patterns. Modern Numerical Weather Prediction (NWP) systems typically run separate models of the different components, explicitly coupled across their interfaces to additionally model exchanges between the differe

F. I. Qowy

Caching and prefetching techniques are fundamental to modern computing, serving to bridge the growing performance gap between processors and memory. Traditional prefetching strategies are often limited by their reliance on predefined heuristics or simplified statistical models, which fail to capture the complex, non-linear dependencies in modern data access

Parviz Zolfaghari, Beril Yagmur Koca, Taher Abbasiasl, Hakan Urey

We present a multifunctional, antenna-integrated capacitive sensing (MAiCaS) platform for passive, wireless, and real-time cardiovascular monitoring. Unlike conventional systems that require separate sensors and wireless modules, our device unifies sensing, telemetry, and mechanical functionality into a compact and scalable design by exploiting the parasitic

Sishun Liu, Ke Deng, Yongli Ren, Yan Wang

Marked Temporal Point Process (MTPP) has been well studied to model the event distribution in marked event streams, which can be used to predict the mark and arrival time of the next event. However, existing studies overlook that the distribution of event marks is highly imbalanced in many real-world applications, with some marks being frequent but others ra

Mihir Panchal, Ying-Jung Chen, Surya Parkash

Landslides are a growing climate induced hazard with severe environmental and human consequences, particularly in high mountain Asia. Despite increasing access to satellite and temporal datasets, timely detection and disaster response remain underdeveloped and fragmented. This work introduces CC-GRMAS, a framework leveraging a series of satellite observation

Aditya Gopalan, Sayak Ray Chowdhury, Debangshu Banerjee

Direct alignment algorithms such as Direct Preference Optimization (DPO) fine-tune models based on preference data, using only supervised learning instead of two-stage reinforcement learning with human feedback (RLHF). We show that DPO encodes a statistical estimation problem over reward functions induced by a parametric policy class. When the true reward fu

Benjamin Girard, Alain Plagne

Given an additively written abelian group $G$ and a set $X\subseteq G$, we let $\mathsf{D}(X)$ denote the Davenport constant of $X$, namely the largest non-negative integer $n$ for which there exists a sequence $x_1, \dots, x_n$ of elements of $X$ such that $\sum_{i=1}^n x_i =0$ and $\sum_{i \in I} x_i \ne 0$ for each non-empty proper subset $I$ of $\{1, \ld

Suchir Salhan, Hongyi Gu, Donya Rooein, Diana Galvan-Sosa

Multi-turn dialogues between a child and a caregiver are characterized by a property called contingency - that is, prompt, direct, and meaningful exchanges between interlocutors. We introduce ContingentChat, a teacher-student framework that benchmarks and improves multi-turn contingency in a BabyLM trained on 100M words. Using a novel alignment dataset for p

Jiandong Liu, Ehouarn Millour, François Forget, François Lott

This paper presents a formalism of mixing induced by non-orographic gravity waves (GWs) to integrate with the stochastic GWs scheme in the Mars Planetary Climate Model. We derive the formalism of GWs and their mixing under the same assumptions, integrating the two schemes within a unified framework. Specifically, a surface-to-exosphere parameterization of GW

Yi Li, Francesco Chiossi, Helena Anna Frijns, Jan Leusmann

As autonomous agents, from self-driving cars to virtual assistants, become increasingly present in everyday life, safe and effective collaboration depends on human understanding of agents' intentions. Current intent communication approaches are often rigid, agent-specific, and narrowly scoped, limiting their adaptability across tasks, environments, and user

Tom Maus, Asma Atamna, Tobias Glasmachers

Autonomous control of multi-stage industrial processes requires both local specialization and global coordination. Reinforcement learning (RL) offers a promising approach, but its industrial adoption remains limited due to challenges such as reward design, modularity, and action space management. Many academic benchmarks differ markedly from industrial contr

Kohei Nishi, Masato Kobayashi, Yuki Uranishi

We present a mixed reality-based underwater robot arm teleoperation system with a reaction torque indicator via bilateral control (MR-UBi). The reaction torque indicator (RTI) overlays a color and length-coded torque bar in the MR-HMD, enabling seamless integration of visual and haptic feedback during underwater robot arm teleoperation. User studies with six