October 2025 arXiv papers — page 31

Jiawei Zhou, Lei Chen

As retrieval-augmented generation (RAG) becomes more widespread, the role of retrieval is shifting from retrieving information for human browsing to retrieving context for AI reasoning. This shift creates more complex search environments, where relevance is difficult to pre-define. Existing retrievers rely on supervised fine-tuning (SFT) with human labels or

Tammi Chowdhury, Leah Jenks, Edward W. Kolb, Evan McDonough

We study cosmological gravitational particle production (CGPP) in Higgs inflation, wherein the inflaton is a scalar field with quartic self-coupling $\lambda$ and a nonminimal coupling to gravity $\xi$, and which may, but need not be, the Higgs boson of the Standard Model (SM). We find an explosive particle production peaked on a characteristic comoving wave

Nitin Rai, Daeun, Choi, Nathan S. Boyd

Site-specific disease management (SSDM) in crops has advanced rapidly through machine and deep learning (ML and DL) for real-time computer vision. Research evolved from handcrafted feature extraction to large-scale automated feature learning. With foundation models (FMs), crop disease datasets are now processed in fundamentally new ways. Unlike traditional n

James N. Cobley

Hydrogen peroxide oxidises cysteine residues to control protein function, yet bulk rate constants predict hours for changes that occur in cells in seconds. Here, this work shows that local electromagnetic fields (EMFs), ubiquitous in proteins, membranes and nanodomains, can lawfully modulate the Eyring barrier and orientate reactants, accelerating cysteine o

Tamir Shpiro, Ron Ruimy, Qinghui Yan, Tomer Bucher

New techniques for imaging electromagnetic near-fields in nanostructures drive advancements in nanotechnology, optoelectronics, materials science, and biochemistry. Most existing techniques probe near-fields along surfaces, lacking the ability to extract near-fields confined within the structure. Notable exceptions use free electrons to traverse through nano

Hugo Rydel-Johnston, Alex Kafkas

We ask where, and under what conditions, dyslexic reading costs arise in a large-scale naturalistic reading dataset. Using eye-tracking aligned to word-level features (word length, frequency, and predictability), we model how each feature influences dyslexic time costs. We find that all three features robustly change reading times in both typical and dyslexi

Takeru Omiya, Enrico Pedretti, Pooja Sharma, Albano Cavaleiro

This study examines the tribological behavior of diamond-like carbon (DLC) coatings doped with silicon (Si), cobalt (Co), or silver (Ag) in the presence of an amine-functionalized block copolymer lubricant. Under boundary lubrication, Si-doped DLC (Si-DLC) exhibited the lowest coefficient of friction ($\approx$0.045) and nearly 45% lower wear than undoped DL

Zengzhuang Xu, Bingguang Hao, Zechuan Wang, Yuntao Wen

Function calling (FC) empowers large language models (LLMs) and autonomous agents to interface with external tools, a critical capability for solving complex, real-world problems. As this ability becomes increasingly central to advanced AI systems, the need for high-quality, multi-turn training data to develop and refine it cannot be overstated. Existing dat

Isabella E. Ward, Matija Ćuk

The presence of rings and moons around exoplanets is likely to be one of the next great discoveries in exoplanet research. Using theories developed for the Solar System, we explore the possibility of coupled ring-moon cycles around exoplanets and what these processes mean for the observability of these features. Around Neptune- and Earth-like planets, we fin

Yujun Kim, Chaewon Moon, Chulhee Yun

We study the parameter complexity of robust memorization for $\mathrm{ReLU}$ networks: the number of parameters required to interpolate any given dataset with $\epsilon$-separation between differently labeled points, while ensuring predictions remain consistent within a $\mu$-ball around each training sample. We establish upper and lower bounds on the parame

Charley Cummings, Sira Gratz, Ellen Kirkman, Janina C. Letz

We show that, for a specific grading, the stable categories of graded maximal Cohen-Macaulay modules over hypersurfaces of type $A_\infty$ and $D_\infty$ are equivalent.

Lucky Donald Lyngdoh Kynshi, Umang Soni, Chithra H Sharma, Yu Cheng

Undoped Si-SiGe two-dimensional electron gas (2DEG) provide an ideal platform for hosting quantum-dot spin-qubits owing enhanced spin dephasing times and compatibility with standard CMOS technology. The strained Si quantum well reduces the valley degeneracy into two closely spaced ones. The existence of a near-degenerate valley state act as a leakage channel

Xin Zhang, Yuqi Song, Fei Zuo

The rapid advancement of generative AI has enabled the creation of highly realistic forged facial images, posing significant threats to AI security, digital media integrity, and public trust. Face forgery techniques, ranging from face swapping and attribute editing to powerful diffusion-based image synthesis, are increasingly being used for malicious purpose

Yuxi Liu, Renjia Deng, Yutong He, Xue Wang

The substantial memory demands of pre-training and fine-tuning large language models (LLMs) require memory-efficient optimization algorithms. One promising approach is layer-wise optimization, which treats each transformer block as a single layer and optimizes it sequentially, while freezing the other layers to save optimizer states and activations. Although

Pedro P. Sanchez, Damian Machlanski, Steven McDonagh, Sotirios A. Tsaftaris

Predicting causal structure from time series data is crucial for understanding complex phenomena in physiology, brain connectivity, climate dynamics, and socio-economic behaviour. Causal discovery in time series is hindered by the combinatorial complexity of identifying true causal relationships, especially as the number of variables and time points grow. A

Sabrina Haque, Kyle Henry, Troyee Saha, Kimberly Vanhoose

Maintaining physical activity is essential for older adults' health and well-being, yet participation remains low. Traditional paper-based and in-person interventions have been effective but face scalability issues. Smartphone apps offer a potential solution, but their effectiveness in real-world use remains underexplored. Most prior studies take place in co

Andrea Castagnetti, Alain Pegatoquet, Benoît Miramond

Spiking Neural Networks (SNNs) are one of the most promising bio-inspired neural networks models and have drawn increasing attention in recent years. The event-driven communication mechanism of SNNs allows for sparse and theoretically low-power operations on dedicated neuromorphic hardware. However, the binary nature of instantaneous spikes also leads to con

Mengke Zhang, Yanxia Chen, Marko M. Melander, Jun Huang

Electron transfer (ET) at electrochemical interfaces is central to energy conversion and storage, yet its theoretical and computational modeling remain active research areas. This review elucidates key concepts and theories of ET kinetics, focusing on coupling between classical solvent fluctuations and quantum electronic states of metallic electrodes and red

Ben De Bondt, Boban Velickovic

We present a direct construction of stationary set preserving forcings that make $\omega$-cofinal all the members of some arbitrary set $\mathcal{K}$ of regular cardinals $\kappa > \omega_1$. In addition, it is made possible to ensure that no other uncountable regular cardinals from the ground model acquire countable cofinality in the forcing extension. Our

Mingyue Liu, Andrew Cropper

Inductive logic programming (ILP) is a form of logical machine learning. Most ILP algorithms learn a single hypothesis from a single training run. Ensemble methods train an ILP algorithm multiple times to learn multiple hypotheses. In this paper, we train an ILP algorithm only once and save intermediate hypotheses. We then combine the hypotheses using a mini

Sebastian Matera, Christian Merdon, Daniel Runge

This paper aims at an efficient strategy to solve drift-diffusion problems with non-linear boundary conditions as they appear, e.g., in heterogeneous catalysis. Since the non-linearity only involves the degrees of freedom along (a part of) the boundary, a reduced basis ansatz is suggested that computes discrete Green's-like functions for the present drift-di

Justine Zeghal, Benjamin Remy, Yashar Hezaveh, Francois Lanusse

We propose a new field-level emulator that bridges two simulators using unpaired simulation datasets. Our method leverages a flow-based approach to learn the likelihood transport from one simulator to the other. Since multiple transport maps exist, we employ Conditional Optimal Transport Flow Matching (COT-FM) to ensure that the transformation minimally dist

Pitambar Bagui, Arijit Chatterjee, Bijay Kumar Agarwalla

Quantum Mpemba effect occurs when a quantum system, residing far away from the steady state, relaxes faster than a relatively nearer state. We look for the presence of this highly counterintuitive effect in the relaxation dynamics of the operators within the open quantum system setting. Since the operators evolve under a non-trace preserving map, the trace d

Anh Ngo, Nicolas Rollet, Catherine Pelachaud, Chloe Clavel

Maintaining mutual understanding is a key component in human-human conversation to avoid conversation breakdowns, in which repair, particularly Other-Initiated Repair (OIR, when one speaker signals trouble and prompts the other to resolve), plays a vital role. However, Conversational Agents (CAs) still fail to recognize user repair initiation, leading to bre

Jose M. Pereira, Daniel Tezze, Beatriz Martín-García, Fèlix Casanova

The intercalation of guest species into the gap of van der Waals materials often leads to the emergence of intriguing phenomena, such as superconductivity. While intercalation-induced superconductivity has been reported in several bulk crystals, reaching a zero-resistance state in flakes remains challenging. Here, we show a simple method for enhancing the su

William Held, David Hall, Percy Liang, Diyi Yang

Scaling laws describe how language models improve with additional data, parameters, and compute. While widely used, they are typically measured on aggregate test sets. Aggregate evaluations yield clean trends but average over heterogeneous subpopulations, obscuring performance disparities. We introduce relative scaling laws, which track how performance gaps

Anatolii Iu. Egorov, Victor T. Kim, Viktor A. Murzin, Vadim A. Oreshkin

Search for the gravity with large extra dimensions at collider energies is considered in the trans-Planckian eikonal regime, i.e., when $\sqrt{\hat{s}} \gg M_D \gg \sqrt{-\hat{t}}$. Here $\hat{s}$ and $\hat{t}$ are the Mandelstam variables of colliding parton-parton system and $M_D$ is the Planck mass scale in the space-time with compactified $n_D$ extra dim

Pius M. Theiler, Matthew C. Beard

Chirality-induced spin selectivity (CISS) describes how chiral molecules and materials generate spin polarization even at thermal equilibrium. This observation has challenged established principles of microscopic reversibility and Onsager reciprocity. We resolve this paradox by formulating a pseudo-Hermitian quantum framework in which structural chirality an

Nicolai Steinke, Daniel Goehring

In this letter, we introduce GroundLoc, a LiDAR-only localization pipeline designed to localize a mobile robot in large-scale outdoor environments using prior maps. GroundLoc employs a Bird's-Eye View (BEV) image projection focusing on the perceived ground area and utilizes the place recognition network R2D2, or alternatively, the non-learning approach Scale

Roark Habegger, Mateusz Ruszkowski, Ellen G. Zweibel

Observations of $\gamma$-rays from diffuse gas provide the opportunity to study the distribution of high energy particles in different astrophysical environments. In the circumgalactic medium (CGM) and the intracluster medium (ICM), it is expected that relativistic cosmic rays collide with thermal particles and produce $\gamma$-rays through pion decay. The $

Ziyi Fang, Lingxiao Huang, Runkai Yang

We study the robust geometric median problem in Euclidean space $\mathbb{R}^d$, with a focus on coreset construction.A coreset is a compact summary of a dataset $P$ of size $n$ that approximates the robust cost for all centers $c$ within a multiplicative error $\varepsilon$. Given an outlier count $m$, we construct a coreset of size $\tilde{O}(\varepsilon^{-

Xander J. Hall, Malte Busmann, Hauke Koehn, Keerthi Kunnumkai

Over the past LIGO--Virgo--KAGRA (LVK) observing runs, it has become increasingly clear that identifying the next electromagnetic counterparts to gravitational-wave (GW) neutron star mergers will likely be more challenging compared to the case of GW170817. The rarity of these GW events, and their electromagnetic counterparts, motivates rapid searches of any

Snegha A, Sayambhu Sen, Piyush Singh Pasi, Abhishek Singhania

With the release of new large language models (LLMs) like Llama and Mistral, zero-shot cross-lingual transfer has become increasingly feasible due to their multilingual pretraining and strong generalization capabilities. However, adapting these decoder-only LLMs to new tasks across languages remains challenging. While parameter-efficient fine-tuning (PeFT) t

Leonhard Duda, Khadijeh Alibabaei, Elena Vollmer, Leon Klug

Federated Learning (FL) is an approach for training a shared Machine Learning (ML) model with distributed training data and multiple participants. FL allows bypassing limitations of the traditional Centralized Machine Learning CL if data cannot be shared or stored centrally due to privacy or technical restrictions -- the participants train the model locally

Julien Dular, Alexander Glock, Arjan Verweij, Mariusz Wozniak

In this paper, we present the Distributed Inter-Strand Coupling Current (DISCC) model. It is a finite element (FE) model based on a homogenization approach enabling efficient and accurate simulation of the transient magnetic response of superconducting Rutherford cables without explicitly representing individual strands. The DISCC model reproduces the inter-

Florio M. Ciaglia, Fabio Di Cosmo, Laura González-Bravo

We introduce the notion of a field of covariances, a contravariant functor from non-commutative probability spaces to Hilbert spaces, as the natural categorical analogue of statistical covariance. In the case of finite-dimensional non-commutative probability spaces, we obtain a complete classification of such fields. Our results unify classical and quantum i

Yutaka Hirano, Riki Toshio, Tomohiro Itogawa, Keisuke Fujii

Magic state distillation plays a crucial role in fault-tolerant quantum computation and represents a major bottleneck. In contrast to traditional logical-level distillation, physical-level distillation offers significant overhead reduction by enabling direct implementation with physical gates. Magic state cultivation is a state-of-the-art physical-level dist

Stefano Natangelo

Artificial intelligence systems based on large language models (LLMs) can now generate coherent text, music, and images, yet they operate without a persistent state: each inference reconstructs context from scratch. This paper introduces the Narrative Continuity Test (NCT) -- a conceptual framework for evaluating identity persistence and diachronic coherence

James Josep Perry, Pablo Garcia-Conde Ortiz, George Konstantinou, Cornelie Vergouwen

Health indicators (HIs) are central to diagnosing and prognosing the condition of aerospace composite structures, enabling efficient maintenance and operational safety. However, extracting reliable HIs remains challenging due to variability in material properties, stochastic damage evolution, and diverse damage modes. Manufacturing defects (e.g., disbonds) a

Anne Gagneux, Ségolène Martin, Rémi Gribonval, Mathurin Massias

Flow matching has achieved remarkable success, yet the factors influencing the quality of its generation process remain poorly understood. In this work, we adopt a denoising perspective and design a framework to empirically probe the generation process. Laying down the formal connections between flow matching models and denoisers, we provide a common ground

Daniel Tezze, Jose M. Pereira, Dogukan Tutar, Maria Ramos

The magnetic properties of van der Waals materials are profoundly influenced by structural defects. The layered antiferromagnet MnPS3 offers a unique opportunity to explore defect-related magnetism, as Mn2+ vacancies can be generated by the intercalation of specific guest molecules. However, the effectiveness of this process in atomically thin flakes and the

PandaX Collaboration, Zhe Yuan, Zihao Bo, Wei Chen

Nuclear $\beta$ decay, a sensitive probe of nuclear structure and weak interactions, has become a precision test bed for physics beyond the Standard Model, driven by recent advances in spectrometric techniques. Here we introduce tomographic $\beta$-$\gamma$ spectroscopy (TBGS) of nuclear $\beta$ decay, a method that detects the energies of $\beta$, $\gamma$,

Azadeh Pourkabirian, Amir Masoud Rahmani, Kai Li, Wei Ni

Delay-sensitive Internet of Things (IoT) applications have drawn significant attention. Running many of these applications on IoT devices is challenging due to the limited processing resources of these devices and the need for real-time responses. Task offloading can minimize latency by transferring computationally intensive tasks from IoT devices to resourc

Daniele De Gennaro, Antonio De Rosa

We construct a positive measure on the space of positively oriented $2$-vectors in $\mathbb{R}^4$, whose barycenter is a simple $2$-vector, yet which cannot be approximated by weighted Gaussian images of Lipschitz $Q$-graphs for any fixed $Q \in \mathbb{N}$. The construction extends to positively oriented $m$-vectors in $\mathbb{R}^n$ whenever $n-2 \ge m\geq

Françoise Dal'bo, James Farre, Or Landesberg, Yair Minsky

We construct geometrically infinite hyperbolic surfaces supporting horocycles with tailored recurrence properties. In particular, we obtain the first examples of non-trivial minimal horocyclic orbit closures and of infinite locally-finite conservative horocyclic invariant measures which are singular with respect to the geodesic flow. Other examples include s

Peter Cowal, Nicholas F. Marshall, Sara Pollock

In this paper, we construct families of polynomials defined by recurrence relations related to mean-zero random walks. We show these families of polynomials can be used to approximate $z^n$ by a polynomial of degree $\sim \sqrt{n}$ in associated radially convex domains in the complex plane. Moreover, we show that the constructed families of polynomials have

Yimeng Qiu

We introduce Entropy-Guided Multiplicative Updates (EGMU), a convex optimization framework for constructing multi-factor target-exposure portfolios by minimizing Kullback-Leibler divergence from a benchmark under linear factor constraints. We establish feasibility and uniqueness of strictly positive solutions when the benchmark and targets satisfy convex-hul

Unzela Talpur, Zafi Sherhan Syed, Muhammad Shehram Shah Syed, Abbas Shah Syed

Speech Emotion Recognition (SER) is a key affective computing technology that enables emotionally intelligent artificial intelligence. While SER is challenging in general, it is particularly difficult for low-resource languages such as Urdu. This study investigates Urdu SER in a cross-corpus setting, an area that has remained largely unexplored. We employ a

Gio Huh, Dhruv Sheth, Rayhan Zirvi, Frank Xiao

While Vision-Language Models (VLMs) excel in many areas, they struggle with complex spatial reasoning, which requires problem decomposition and strategic tool use. Fine-tuning smaller, more deployable models offers an efficient path to strong performance, but this is hampered by a major bottleneck: the absence of high-quality, step-by-step reasoning data. To

Yicun Yang, Cong Wang, Shaobo Wang, Zichen Wen

Diffusion-based large language models (dLLMs) have exhibited substantial potential for parallel text generation, which may enable more efficient generation compared to autoregressive models. However, current dLLMs suffer from fixed generation lengths, which indicates the generation lengths of dLLMs have to be determined before decoding as a hyper-parameter,

Aleksei G. Sorokin, Pieterjan Robbe, Gianluca Geraci, Michael S. Eldred

Existing multilevel quasi-Monte Carlo (MLQMC) methods often rely on multiple independent randomizations of a low-discrepancy (LD) sequence to estimate statistical errors on each level. While this approach is standard, it can be less efficient than simply increasing the number of points from a single LD sequence. However, a single LD sequence does not permit

Kaniba Mady Keita

Association rule machine learning is applied to the dataset of complete intersection Calabi--Yau 5-folds and 6-folds in order to uncover hidden patterns among their Hodge numbers. These Hodge numbers -- six for the 5-folds and nine for the 6-folds -- serve as the items in our analysis. For the 5-folds, we discover 60 significant association rules. For exampl

Federica Ferretti, Mehran Kardar, Arvind Murugan

Evolutionary systems must learn to generalize, often extrapolating from a limited set of selective conditions to anticipate future environmental changes. The mechanisms enabling such generalization remain poorly understood, despite their importance to predict ecological robustness, drug resistance, or design future-proof vaccination strategies. Here, we demo

Kateryna Akbash, Ivan Matsak, Oleg Zakusylo

The problem of estimating the probability of a random process reaching a certain level is well known. In this article, two-sided estimates are established for the probability that a regenerative process reaches a high level. Two auxiliary results for geometric sums with delay will play an important role. Examples of application to random processes describing

Harsha Kokel, Aamod Khatiwada, Tejaswini Pedapati, Haritha Ananthakrishnan

Joinable Column Discovery is a critical challenge in automating enterprise data analysis. While existing approaches focus on syntactic overlap and semantic similarity, there remains limited understanding of which methods perform best for different data characteristics and how multiple criteria influence discovery effectiveness. We present a comprehensive exp

Longpan Wang, Zhuoran Zhang, Zhenyuan Li, Xuetao Gan

Digital metasurfaces have attracted significant attention in recent years due to their ability to manipulate electromagnetic (EM) waves for secure sensing and communication. However, most reported metasurfaces operate at relatively high frequencies, primarily due to the constraints imposed by the physical scale of the dielectric substrate, thus limiting thei

Colin Morningstar

Calculating the properties of baryon resonances from quantum chromodynamics requires evaluating the temporal correlations between hadronic operators using integrations over field configurations weighted by a phase associated with the action. By formulating quantum chromodynamics on a space-time lattice in imaginary time, such integrations can be carried out

Azadeh Pourkabirian, Kai Li, Photios A. Stavrou, Wei Ni

Hybrid precoding is an indispensable technique to harness the full potential of a multi-user massive multiple-input, multiple-output (MU-MMIMO) system. In this paper, we propose a new hybrid precoding approach that combines digital and analog precoding to optimize data transmission over multiple antennas. This approach steers signals in specific directions,

Dapeng Zhang, Marina Katoh, Weiping Pei

The widespread adoption of generative AI (GenAI) has introduced new challenges in crowdsourced data collection, particularly in survey-based research. While GenAI offers powerful capabilities, its unintended use in crowdsourcing, such as generating automated survey responses, threatens the integrity of empirical research and complicates efforts to understand

Karen Habermann, Emmanuel Hartman

We introduce and study Brownian motion on spaces of discrete regular curves in Euclidean space equipped with discrete Sobolev-type metrics. It has been established that these spaces of discrete regular curves are geodesically complete if and only if the Sobolev-type metric is of order two or higher. By relying on a general result by Grigor'yan and controllin

Guoxin Chen, Jing Wu, Xinjie Chen, Wayne Xin Zhao

Autoformalization, which translates natural language mathematics into machine-verifiable formal statements, is critical for using formal mathematical reasoning to solve math problems stated in natural language. While Large Language Models can generate syntactically correct formal statements, they often fail to preserve the original problem's semantic intent.

Christine Ye, Sihan Yuan, Suchetha Cooray, Steven Dillmann

Frontier AI agents show increasing promise as scientific research assistants, and may eventually be useful for extended, open-ended research workflows. However, in order to use agents for novel research, we must first assess the underlying faithfulness and correctness of their work. To evaluate agents as research assistants, we introduce ReplicationBench, an

Espen Sande, Timo Koch, Miroslav Kuchta, Kent-Andre Mardal

We identify a norm on the pressure variable in the Stokes equation that allows us to prove a continuous inf-sup condition with a constant independent of the domain's aspect ratio. This is in contrast to the standard inf-sup constant, which breaks down as the aspect ratio increases. We further apply our result to construct robust operator preconditioners for

Andrey Kudryashov, Ivan Lukichev, Nikita Bityurin

A short review on two-step laser processing of material is presented. The main focus is on the processes which can be called recording-development ones. Here, the first step of laser processing provides an initial pattern on the material surface, which is enhanced or developed at the second step. A new type of the recordingd-development process is considered

Jake Huryn

Let $K$ be a number field, and let $A$ be an Abelian variety over $K$ which has no CM isogeny-factors over $\overline{K}$. We prove that $A$ has only finitely many torsion points over the maximal $n$-step-solvable extension of $K$ for any $n$ and only finitely many torsion points of prime order over the maximal prosolvable extension of $K$.

Benjamin Karic, Nina Herrmann, Jan Stenkamp, Paula Scharf

The integration of the Internet of Things (IoT) and Artificial Intelligence offers significant opportunities to enhance our ability to monitor and address ecological changes. As environmental challenges become increasingly pressing, the need for effective remote monitoring solutions is more critical than ever. A major challenge in designing IoT applications

Tianshi Xu, Difeng Cai, Hua Huang, Edmond Chow

Many large-scale stochastic optimization algorithms involve repeated solutions of linear systems or evaluations of log-determinants. In these regimes, computing exact solutions is often unnecessary; it is more computationally efficient to construct unbiased stochastic estimators with controlled variance. However, classical iterative solvers incur truncation

Michal Botur, Ivan Chajda, Helmut Länger

Given a complemented poset P, we can assign to every element x of P the set x^+ of all its complements. We study properties of the operator ^+ on P, in particular, we are interested in the case when x^+ forms an antichain or when ^+ is involutive or antitone. We apply ^+ to the set Min U(x,y) of all minimal elements of the upper cone U(x,y) of x,y and to the

Raffaele Reda, Valentina Penza, Serena Criscuoli, Luca Bertello

Reconstructions of solar spectral irradiance - especially in the ultraviolet (UV) range - are crucial for understanding Earth's climate system. Although total solar irradiance (TSI) has been thoroughly investigated, the spectral composition of solar radiation offers a deeper insight into its interactions with the atmosphere, biosphere, and climate. UV radiat

Jørgen Anker Olsen, Lars Rønhaug Pettersen, Kostas Alexis

This paper presents a curriculum-based reinforcement learning framework for training precise and high-performance jumping policies for the robot `Olympus'. Separate policies are developed for vertical and horizontal jumps, leveraging a simple yet effective strategy. First, we densify the inherently sparse jumping reward using the laws of projectile motion. N

Ismaël Zighed, Nicolas Thome, Patrick Gallinari, Taraneh Sayadi

Simulating turbulent fluid flows is a computationally prohibitive task, as it requires the resolution of fine-scale structures and the capture of complex nonlinear interactions across multiple scales. This is particularly the case in direct numerical simulation (DNS) applied to real-world turbulent applications. Consequently, extensive research has focused o

Xuenan Cao, Wai Kei Chung, Ye Zhao, Lidia Mengyuan Zhou

Ask your chatbot to impersonate an expert from Russia and an expert from US and query it on Chinese politics. How might the outputs differ? Or, to prepare ourselves for the worse, how might they converge? Scholars have raised concerns LLM based applications can homogenize cultures and flatten perspectives. But exactly how much does LLM generated outputs conv

Adrien Le Boudec

A locally compact group $G$ is a cocompact envelope of a group $\Gamma$ if $G$ contains a copy of $\Gamma$ as a discrete and cocompact subgroup. We study the problem that takes two finitely generated groups $\Gamma,\Lambda$ having a common cocompact envelope, and asks what properties must be shared between $\Gamma$ and $\Lambda$. We first consider the settin

Ganesh K Rajahmundry, Tarak K Patra

Solid polymer electrolytes (SPEs) are ion-containing solid materials composed of a polymer matrix that enables ionic transport while maintaining the mechanical stability. The conventional wisdom is that for a high ion concentration, ions microphase separate from the polymer matrix, resulting in poor conductivity. Instead, we show that a high ion size ratio p

Xu Jiang, Huiying Pan, Ligen Shi, Jianing Sun

Cone-beam CT (CBCT) employs a flat-panel detector to achieve three-dimensional imaging with high spatial resolution. However, CBCT is susceptible to scatter during data acquisition, which introduces CT value bias and reduced tissue contrast in the reconstructed images, ultimately degrading diagnostic accuracy. To address this issue, we propose a deep learnin

Przemysław Ohrysko, Tom Sanders, Michał Wojciechowski

Suppose that $G$ is a compact Hausdorff Abelian group. We say $μ\in M(G)$ is strongly continuous if $|μ|(x+H)=0$ for any $x \in G$ and any $H \leq G$ that is closed and of infinite index. We prove that for any sufficiently rapidly decreasing sequence $(a_{n})_{n=1}^{\infty}\in c_{0}(\mathbb{N})$, for every strongly continuous $μ\in M(G)$ with $\|μ\| \leq 1$

He Yang, Fei Ren, Francesco Calabro, Hai-Sui Yu

We are delighted to see the recent development of physics-informed extreme learning machine (PIELM) for its higher computational efficiency and accuracy compared to other physics-informed machine learning (PIML) paradigms. Since a comprehensive summary or review of PIELM is currently unavailable, we would like to take this opportunity to share our perspectiv

Erick Gordillo Herrerías, Nolwenn Le Quellec

We study flute surfaces and extend results of Pandazis and \v{S}ari\'c giving necessary and sufficient conditions on the Fenchel-Nielsen coordinates of the surface to be of the first kind. As a consequence of the first result, we characterize parabolic flute surfaces (i.e. flute surfaces with ergodic geodesic flow) with twist parameters in {0,1/2}, extending

Mikayla J. Wilson, Mary Anne Limbach, Andrew J. Skemer, Johanna M. Vos

JWST is collecting time-series observations of many free-floating planets (FFPs) to study their weather, but these light curves are the ideal datasets to search for exomoons that transit the FFP during observations. In this paper, we present observations of the planetary-mass Y dwarf ($T=250-285K$, $M = 6.5\pm3.5 M_{Jup}$, d = 2.3$\,$pc) WISE J085510.83-0714

Hao Wang, Licheng Pan, Yuan Lu, Zhixuan Chu

Training time-series forecasting models requires aligning the conditional distribution of model forecasts with that of the label sequence. The standard direct forecast (DF) approach resorts to minimizing the conditional negative log-likelihood, typically estimated by the mean squared error. However, this estimation proves biased when the label sequence exhib

David Izabel

Recent work by Ringermacher and Mead has revealed discrete oscillations in the cosmological scale factor, suggesting that spacetime may exhibit vibrational properties akin to those of a crystal. Building on this idea, we propose a novel geometric interpretation of the Cosmic Microwave Background CMB angular power spectrum by treating it as an inverted X-ray

M. Elyajouri, A. Abergel, N. Ysard, E. Habart

Carbonaceous nano-grains are a significant component of interstellar dust and dominate the mid-infrared emission of photodissociation regions (PDRs). We study the evolution of nano-grains across the illuminated edge of the Horsehead PDR, especially their abundance and size properties. This work is part of the Physics and Chemistry of PDR Fronts program study

Samuel Alperin

The ability to engineer non-Gaussian quantum resources underlies quantum technologies from communication and metrology to universal computation. However, while a number of canonical works have set no-go limits for attaining such resources from Gaussian operations, it is widely assumed that such resources can be tuned freely by non-Gaussian Hamiltonian dynami

Hongxu Zhao, Guangyang Zeng, Yunling Shao, Tengfei Zhang

The calibration of extrinsic parameters and clock offsets between sensors for high-accuracy performance in underwater SLAM systems remains insufficiently explored. Existing methods for Doppler Velocity Log (DVL) calibration are either constrained to specific sensor configurations or rely on oversimplified assumptions, and none jointly estimate translational

Haoyang Zhang, Zhou Yang, Ke Sun, Yucai Pang

Multimodal emotion recognition is crucial for future human-computer interaction. However, accurate emotion recognition still faces significant challenges due to differences between different modalities and the difficulty of characterizing unimodal emotional information. To solve these problems, a hybrid network model based on multipath cross-modal interactio

Raphaël Bagat, Irina Illina, Emmanuel Vincent

Automatic Speech Recognition (ASR) systems, despite large multilingual training, struggle in low-resource scenarios where labeled data is scarce. We propose BEARD (BEST-RQ Encoder Adaptation with Re-training and Distillation), a novel framework designed to adapt Whisper's encoder with unlabeled data. Unlike traditional self-supervised learning methods, BEARD

Ashkan Jafari Fesharaki, Yasser Mestrah, Yi Ma, Rahim Tafazolli

6G wireless networks are poised to seamlessly integrate communication, computing, localization, and sensing functionalities, ensuring high reliability and trustworthiness. This paper introduces Smart Sensing Feedback (SSF), a limited-feedback framework designed to enhance sensing capabilities while maintaining communication performance. SSF adapts the concep

Satyaki Bhattacharya, Edward Crane, Tom Johnston

The Rademacher random walk associated with a deterministic sequence $(a_n)_{n \geq 1}$ is the walk which starts at zero and, at step $i$, independently steps either up or down by $a_i$ with equal probability. We continue the study begun by Bhattacharya and Volkov in 2023 of the transience or recurrence of one-dimensional Rademacher random walks. In particula

Pablo J. Bilbao, Thales Silva, Luis O. Silva

Plasma-based accelerators are beginning to employ relativistic beams with unprecedented charge and ultrashort durations. These dense driver beams can drive wakes even in high-density plasmas ($\gtrsim10^{19}$ cm$^{-3}$), where betatron radiation becomes increasingly important and begins to affect the dynamics of the accelerated beam. In this Letter, we show

Xiaobo Jing, Qi Wang

In recent decades, considerable research has been devoted to partial differential equations (PDEs) with dynamic boundary conditions. However, the physical interpretation of the parameters involved often remains unclear, which in turn limits both theoretical analysis and numerical computation. For instance, the Robin boundary condition used in thermodynamical

Wei-Xiang Feng, Hai-Bo Yu, Yi-Ming Zhong

We investigate the dynamical instability of a self-gravitating thermal system in the quantum regime, where Fermi degeneracy pressure becomes significant. Using a truncated Fermi-Dirac distribution and solving the Tolman-Oppenheimer-Volkoff equation, we identify marginally stable configurations following Chandrasekhar's criterion. While Fermi pressure stabili

Prajwal M. Laxmeesha, Rajesh Dutta, Rajeev Kumar Rai, Sharup Sheikh

Kagome metals are an intriguing class of quantum materials as the presence of both flat bands and Dirac points provides access to functional properties present in strongly correlated and topological materials. To fully harness these electronic features, the ability to tune the Fermi level relative to the band positions is needed. Here we explore the structur

Hongrui Jia, Jitong Liao, Xi Zhang, Haiyang Xu

With advances in decision-making and reasoning capabilities, multimodal agents show strong potential in computer application scenarios. Past evaluations have mainly assessed GUI interaction skills, while tool invocation abilities, such as those enabled by the Model Context Protocol (MCP), have been largely overlooked. Comparing agents with integrated tool in

Wojciech Jamroga, Damian Kurpiewski, Łukasz Mikulski

Model checking of strategic abilities is a notoriously hard problem, even more so in the realistic case of agents with imperfect information, acting in a stochastic environment. Assume-guarantee reasoning can be of great help here, providing a way to decompose the complex problem into a small set of easier subproblems. In this paper, we propose several schem

Alberto Chiarini, Emanuele Pasqui

We study the discrete Gaussian free field (harmonic crystal) on $\mathbb{Z}^d$, $d\geq 3$, with uniformly elliptic and bounded random conductances sampled according to a sufficiently mixing environment measure. We consider the hard wall event that the field is non-negative on the discrete blow-up of a bounded regular domain $V\subseteq\mathbb{R}^d$, and esta

XRISM Collaboration, Marc Audard, Hisamitsu Awaki, Ralf Ballhausen

We stack 3.75 Megaseconds of early XRISM Resolve observations of ten galaxy clusters to search for unidentified spectral lines in the $E=$ 2.5-15 keV band (rest frame), including the $E=3.5$ keV line reported in earlier, low spectral resolution studies of cluster samples. Such an emission line may originate from the decay of the sterile neutrino, a warm dark

Victoria Hoskins, Joshua Jackson, Tanguy Vernet

We construct new moduli spaces of quiver representations with multiplicities, i.e. over rings of truncated power series. This includes moduli of framed representations and analogues of Nakajima quiver varieties. Our construction relies on tools from relative affine Geometric Invariant Theory for non-reductive groups and new stability conditions for quiver re

Yu-tin Huang, Henrik Johansson, Michele Santagata, Congkao Wen

In four dimensions, it has long been established that gravity coupled to matter exhibits ultraviolet divergences at one loop, irrespective of supersymmetry. Notably, the four-matter one-loop amplitudes of half-maximal supergravity coupled to Maxwell multiplets were shown in the 1970s to be divergent. Surprisingly, we demonstrate in this work that half-maxima

Niklas Göschel, Sebastian Götschel, Daniel Ruprecht

Machine-learning based methods like physics-informed neural networks and physics-informed neural operators are becoming increasingly adept at solving even complex systems of partial differential equations. Boundary conditions can be enforced either weakly by penalizing deviations in the loss function or strongly by training a solution structure that inherent

Dinesh Kumar Keshari, Shubhankar Mandal, Avijit Pal

A subset of $\mathbb{C}^7$ (respectively, of $\mathbb{C}^5$) associated with the structured singular value $\mu_E$, defined on $3 \times 3$ matrices, is denoted by $G_{E(3;3;1,1,1)}$ (respectively, by $G_{E(3;2;1,2)}$). In control engineering, the structured singular value $\mu_E$ plays a crucial role in analyzing the robustness and performance of linear fee