March 2025 arXiv papers — page 195

Owen Cook, Yida Mu, Xinye Yang, Xingyi Song

Framing is an essential device in news reporting, allowing the writer to influence public perceptions of current affairs. While there are existing automatic news framing detection datasets in various languages, none of them focus on news framing in the Chinese language which has complex character meanings and unique linguistic features. This study introduces

Maxime Ramzi

We compute endomorphisms of topological Hochschild homology ($\mathrm{THH}$) as a functor on stable $\infty$-categories, as well as variants thereof: we also compute endomorphisms of the $k$-linear Hochschild homology functor $\mathrm{HH}_k$ over some base $k$; and endomorphisms of $\mathrm{THH}$ as a functor on stably symmetric monoidal $\infty$-categories.

Filippo Baruffaldi, Anna Bergamaschi, Maurizio Boscardin, Martin Brueckner

Soft X-ray experiments at synchrotron light sources are essential for a wide range of research fields. However, commercially available detectors for this energy range often cannot deliver the necessary combination of quantum efficiency, signal-to-noise ratio, dynamic range, speed, and radiation hardness within a single system. While hybrid detectors have add

Stephane Ngnepiepaye Wembe, Vincent Rousseau, Johann Laconte, Roland Lenain

In this paper, we present a novel method to control a rigidly connected location on the vehicle, such as a point on the implement in case of agricultural tasks. Agricultural robots are transforming modern farming by enabling precise and efficient operations, replacing humans in arduous tasks while reducing the use of chemicals. Traditionnaly, path_following

Andrey Makarov, Evgenia Gonchrova, Jichao Qiao, Roman Konchakov

We performed high-frequency shear modulus and calorimetry measurements on seven high-entropy metallic glasses (HEMGs) in the initial, relaxed and crystalline states. It is shown that the shear modulus of HEMGs is intrinsically related with the concentration of defects responsible for structural relaxation. In the absence of structural relaxation, temperature

Ulrich Kaiser, Jose Mata

We examine whether gender norms - proxied by the outcome of Switzerland's 1981 public referendum on constitutional gender equality - continue to shape local female startup activity today, despite substantial population changes over the past four decades. Using startup data for all Swiss municipalities from 2016 to 2023, we find that municipalities that histo

Paolo Giommi

This paper introduces Firmamento, a new online platform designed for astronomical research, particularly for studying blazars and other multi-messenger emitters. Firmamento provides access to a wealth of astronomical data, including imaging, spectral, and timing information, along with tools for data analysis and machine learning. A key feature is the Error

Gabriele Dessena, Alessandro Pontillo, Marco Civera, Dmitry I. Ignatyev

Predicting flutter remains a key challenge in aeroelastic research, with certain models relying on modal parameters, such as natural frequencies and damping ratios. These models are particularly useful in early design stages or for the development of small Unmanned Aerial Vehicles (maximum take-off mass below 7 kg). This study evaluates two frequency-domain

Jogesh Rout, Suchismita Sahoo

We investigate the potential of light calorimetry in liquid argon time projection chambers and its intrinsic self compensation properties, emphasizing its advantages alongside conventional charge calorimetry. Previous studies have demonstrated that light calorimetry can achieve energy resolution comparable to advanced charge based techniques, particularly fo

Yun Yen, Marcel Reutzel, Andi Li, Zehua Wang

Electromagnetic fields not only induce electronic transitions but also fundamentally modify the quantum states of matter through strong light-matter interactions. As one established route, Floquet engineering provides a powerful framework to dress electronic states with time-periodic fields, giving rise to quasi-stationary Floquet states. With increasing fie

Arturo De Faveri

We present a unified framework for representing commutative rings through affine algebraic theories and Boolean rings through hyperaffine algebraic theories. This yields categorical equivalences between these theories and, respectively, commutative rings and Boolean rings. We then analyse models of affine theories over a Boolean ring $B$, comparing them with

Narmeen Oozeer, Dhruv Nathawani, Nirmalendu Prakash, Michael Lan

The study of representation universality in AI models reveals growing convergence across domains, modalities, and architectures. However, the practical applications of representation universality remain largely unexplored. We bridge this gap by demonstrating that safety interventions can be transferred between models through learned mappings of their shared

Lou Roussel-Hard, Edouard Audit, Luc Dessart, Thomas Padioleau

Numerical simulations of multidimensional astrophysical fluids present considerable challenges. However, the development of exascale computing has significantly enhanced computational capabilities, motivating the development of new codes that can take full advantage of these resources. In this article, we introduce HERACLES++, a new hydrodynamics code with h

Marcel Schweitzer

Polynomial Krylov subspace methods are among the most widely used methods for approximating $f(A)b$, the action of a matrix function on a vector, in particular when $A$ is large and sparse. When $A$ is Hermitian positive definite, the Lanczos method is the standard choice of Krylov method, and despite being very simplistic in nature, it often outperforms oth

Natalia Cherezova, Artur Jutman, Maksim Jenihhin

The emergence of Deep Neural Networks (DNNs) in mission- and safety-critical applications brings their reliability to the front. High performance demands of DNNs require the use of specialized hardware accelerators. Systolic array architecture is widely used in DNN accelerators due to its parallelism and regular structure. This work presents a run-time recon

Roland Donninger, Birgit Schörkhuber, Alexander Wittenstein

We consider wave maps from $(1+d)$-dimensional Minkowski space, $d\geq3$, into rotationally symmetric manifolds which arise from small perturbations of the sphere $\mathbb S^d$. We prove the existence of co-rotational self-similar finite time blowup solutions with smooth blowup profiles. Furthermore, we show the nonlinear asymptotic stability of these soluti

Siavash Ameli, Chris van der Heide, Liam Hodgkinson, Fred Roosta

Calculating or accurately estimating log-determinants of large positive definite matrices is of fundamental importance in many machine learning tasks. While its cubic computational complexity can already be prohibitive, in modern applications, even storing the matrices themselves can pose a memory bottleneck. To address this, we derive a novel hierarchical a

Mario Collura, Guglielmo Lami, Nishan Ranabhat, Alessandro Santini

This book serves as an introductory yet thorough guide to tensor networks and their applications in quantum computation and quantum information, designed for advanced undergraduate and graduate-level readers. In Part I, foundational topics are covered, including tensor structures and network representations like Matrix Product States (MPS) and Tree Tensor Ne

Leonardo Kuffo, Elena Krippner, Peter Boncz

We propose Partition Dimensions Across (PDX), a data layout for vectors (e.g., embeddings) that, similar to PAX [6], stores multiple vectors in one block, using a vertical layout for the dimensions (Figure 1). PDX accelerates exact and approximate similarity search thanks to its dimension-by-dimension search strategy that operates on multiple-vectors-at-a-ti

Yifei Yuan, Anders Søgaard

Li et al. (2023) used the Othello board game as a test case for the ability of GPT-2 to induce world models, and were followed up by Nanda et al. (2023b). We briefly discuss the original experiments, expanding them to include more language models with more comprehensive probing. Specifically, we analyze sequences of Othello board states and train the model t

Harry J. F. Owen, Matthew J. A. Allen, Stuart W. D. Grieve, Phill Wilkes

Point clouds from Terrestrial Laser Scanning (TLS) are an increasingly popular source of data for studying plant structure and function but typically require extensive manual processing to extract ecologically important information. One key task is the accurate semantic segmentation of different plant material within point clouds, particularly wood and leave

Stephan Held, Edgar Perner

The cost-distance Steiner tree problem seeks a Steiner tree that minimizes the total congestion cost plus the weighted sum of source-sink delays. This problem arises as a subroutine in timing-constrained global routing with a linear delay model, used before buffer insertion. Here, the congestion cost and the delay of an edge are essentially uncorrelated, unl

Xiaoqi Wang, Hongyang Du, Yuehong Gao, Dong In Kim

Recent advancements in large language models (LLMs) have led to their widespread adoption and large-scale deployment across various domains. However, their environmental impact, particularly during inference, has become a growing concern due to their substantial energy consumption and carbon footprint. Existing research has focused on inference computation a

Felix Ocker, Stefan Menzel, Ahmed Sadik, Thiago Rios

Creating digital models using Computer Aided Design (CAD) is a process that requires in-depth expertise. In industrial product development, this process typically involves entire teams of engineers, spanning requirements engineering, CAD itself, and quality assurance. We present an approach that mirrors this team structure with a Vision Language Model (VLM)-

Maxime Burchi, Radu Timofte

The DreamerV3 algorithm recently obtained remarkable performance across diverse environment domains by learning an accurate world model based on Recurrent Neural Networks (RNNs). Following the success of model-based reinforcement learning algorithms and the rapid adoption of the Transformer architecture for its superior training efficiency and favorable scal

Alexandra Blessing, Mazyar Ghani Varzaneh

This work develops moment bounds for the controlled rough path norm of the solution of semilinear rough partial differential equations.~The novel aspects are two-fold: first we consider rough paths of low time regularity $\gamma\in(1/4,1/2)$ and second treat unbounded diffusion coefficients. To this aim we introduce a suitable notion of a controlled rough pa

Davide Tebaldi, Dario Onfiani, Luigi Biagiotti

The interest in Physical Human-Robot Interaction (pHRI) has significantly increased over the last two decades thanks to the availability of collaborative robots that guarantee user safety during force exchanges. For this reason, stability concerns have been addressed extensively in the literature while proposing new control schemes for pHRI applications. Bec

Hyunwoo Yoo

This study demonstrates that generative large language models can be utilized in a more flexible manner for DNA sequence analysis and classification tasks compared to traditional transformer encoder-based models. While recent encoder-based models such as DNABERT and Nucleotide Transformer have shown significant performance in DNA sequence classification, tra

Yuichi Inoue, Kou Misaki, Yuki Imajuku, So Kuroki

Recent advances demonstrate that increasing inference-time computation can significantly boost the reasoning capabilities of large language models (LLMs). Although repeated sampling (i.e., generating multiple candidate outputs) is a highly effective strategy, it does not leverage external feedback signals for refinement, which are often available in tasks li

Jingzong Zhang, Huanjing Gong, Hua Zheng, Wei Dai

Using the recombination model (RM) extended to the heavy flavor scenario, we investigate the production of charmed mesons, i.e., $D^0$, $D_s^+$ and $J/\psi$, in Pb+Pb collisions at $\sqrt{s_{NN}}=2.76$ and 5.02 TeV at the CERN Large Hadron Collider (LHC) including the heavy flavor energy loss inside the hot and dense medium. Soft, semihard, and hard partons

H. Kühne, Y. Ihara

This article provides an introduction to nuclear magnetic resonance spectroscopy in pulsed magnetic fields (PFNMR), focusing on its capabilities, applications, and future developments in research involving high magnetic fields. It highlights the significance of PFNMR in enhancing the understanding of solid-state materials, with particular emphasis on those e

Cankut Bora Tuncer, Dilruba Sultan Haliloglu, Ozgur S. Oguz

In this paper, we present SeGMan, a hybrid motion planning framework that integrates sampling-based and optimization-based techniques with a guided forward search to address complex, constrained sequential manipulation challenges, such as pick-and-place puzzles. SeGMan incorporates an adaptive subgoal selection method that adjusts the granularity of subgoals

Ugo Dal Lago, Federico Olimpieri

We introduce the structural resource lambda-calculus, a new formalism in which strongly normalizing terms of the lambda-calculus can naturally be represented, and at the same time any type derivation can be internally rewritten to its linearization. The calculus is shown to be normalizing and confluent. Noticeably, every strongly normalizable lambda-term can

Xiang Chen, Ming-Min Zhao, Min Li, Liyan Li

In this paper, we propose a movable antenna (MA)-enabled frequency-hopping (FH) multiple-input multiple-output (MIMO) radar system and investigate its sensing resolution. Specifically, we derive the expression of the ambiguity function and analyze the relationship between its main lobe width and the transmit antenna positions. In particular, the optimal ante

Yu-Seung Roh, Joo-Young Kim, Jin-Duk Park, Won-Yong Shin

Multimodal recommender systems improve the performance of canonical recommender systems with no item features by utilizing diverse content types such as text, images, and videos, while alleviating inherent sparsity of user-item interactions and accelerating user engagement. However, current neural network-based models often incur significant computational ov

Felipe Olivares, Massimiliano Zanin

We propose a novel approach for quantifying deviations from Gaussianity by leveraging the permutation Jensen-Shannon distance. Using stable distributions as a flexible framework, we analyze the effects of skewness and heavy tails in synthetic sequences. We employ phase-randomized surrogates as Gaussian references to systematically evaluate the statistical di

Sanjib Narzary, Bihung Brahma, Haradip Mahilary, Mahananda Brahma

Named Entity Recognition (NER) and Part-of-Speech (POS) tagging are critical tasks for Natural Language Processing (NLP), yet their availability for low-resource languages (LRLs) like Bodo remains limited. This article presents a comparative empirical study investigating the effectiveness of Google's Gemini 2.0 Flash Thinking Experiment model for zero-shot c

Majed Luay, Siamak Layeghy, Seyedehfaezeh Hosseininoorbin, Mohanad Sarhan

This paper investigates the temporal analysis of NetFlow datasets for machine learning (ML)-based network intrusion detection systems (NIDS). Although many previous studies have highlighted the critical role of temporal features, such as inter-packet arrival time and flow length/duration, in NIDS, the currently available NetFlow datasets for NIDS lack these

David Vokrouhlický, David Nesvorný, Miroslav Brož, William F. Bottke

The Hilda population of asteroids is located in a large orbital zone of long-term stability associated with the Jupiter J3/2 mean motion resonance. They are a sister population of the Jupiter Trojans, since both of them are likely made up of objects captured from the primordial Kuiper belt early in the solar system history. Comparisons between the orbital an

Kai-Li Lin, Peng-Lei Wang, Yi-Bo Peng, Shiyu Hu

Chaos lidars detect targets through the cross-correlation between the back-scattered chaos signal from the target and the local reference one. Chaos lidars have excellent anti-jamming and anti-interference capabilities, owing to the random nature of chaotic oscillations. However, most chaos lidars operate in the near-infrared spectral regime, where the atmos

Nishant Rathee, Ayush Udeep

In this paper, we study the irreducible representations of skew braces of order \( pq \), which is equivalent to studying the representation theory of groups of order \( p^2q^2 \) arising from skew left braces, where \( p > q \) are primes. To achieve this, we classify all semidirect product groups \( \Lambda_A \) associated with skew left braces $A$ of orde

Rustem Khasanov, Vahid Sazgari, Thomas J. Hicken, Igor Plokhikh

Understanding the interplay between magnetism and superconductivity in nickelate systems is a key objective in condensed matter physics. Here, we present a systematic muon-spin rotation/relaxation ($\mu$SR) and resistivity study of the trilayer Ruddlesden-Popper nickelate La$_4$Ni$_3$O$_{10}$ under ambient and applied pressure, combined with oxygen-isotope s

Yuehan Xu, Qijun Zhang, Junpeng Zhang, Xiaojuan Liao

In classical communication, the introduction of polychromatic resources has rapidly boosted classical networks' rate and scale. Quantum communication is now at a similar critical stage in its development, and therefore, it is essential to investigate polychromatic quantum communication networks. In this letter, we report a polychromatic continuous-variable q

Yan Li, Zhenyu Zhang, Zhengang Wang, Pengfei Chen

Prevailing LLM serving engines employ expert parallelism (EP) to implement multi-device inference of massive MoE models. However, the efficiency of expert parallel inference is largely bounded by inter-device communication, as EP embraces expensive all-to-all collectives to route tokens to the remote experts if not collocating on the same GPU/NPU device. Nev

Tobias Jülg, Wolfram Burgard, Florian Walter

Vision-Language-Action Models (VLAs) have demonstrated remarkable generalization capabilities in real-world experiments. However, their success rates are often not on par with expert policies, and they require fine-tuning when the setup changes. In this work, we introduce Refined Policy Distillation (RPD), a novel Reinforcement Learning (RL)-based policy ref

Dongdong Yang, Bin Li, Dusit Niyato

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) can expand the coverage of mobile edge computing (MEC) services by reflecting and transmitting signals simultaneously, enabling full-space coverage. The orientation of the STAR-RIS plays a crucial role in optimizing the gain of received and transmitted signals, and a rot

Xinyi He, Yihao Liu, Mengyu Zhou, Yeye He

Tabular data are crucial in many fields and their understanding by large language models (LLMs) under high parameter efficiency paradigm is important. However, directly applying parameter-efficient fine-tuning (PEFT) techniques to tabular tasks presents significant challenges, particularly in terms of better table serialization and the representation of two-

Tom Kouwenhoven, Max Peeperkorn, Roy de Kleijn, Tessa Verhoef

Languages are shaped by the inductive biases of their users. Using a classical referential game, we investigate how artificial languages evolve when optimised for inductive biases in humans and large language models (LLMs) via Human-Human, LLM-LLM and Human-LLM experiments. We show that referentially grounded vocabularies emerge that enable reliable communic

Sabine Körbel

Evidence from first-principles calculations indicates that excess electrons in BiFeO$_3$ form small polarons with energy levels deep inside the electronic band gap. Hence, $n$-type electronic transport could occur by hopping of small electron polarons rather than by band-like transport. Here, by means of first-principles calculations, small electron polaron

Francis Tong, Stanisław Lasocki, Beata Orlecka-Sikora

Frequent significant deviations of the observed magnitude distribution of anthropogenic seismicity from the Gutenberg-Richter relation require alternative estimation methods for probabilistic seismic hazard assessments. We evaluate five nonparametric kernel density estimation (KDE) methods on simulated samples drawn from four magnitude distribution models: t

Junyuan Mao, Fanci Meng, Yifan Duan, Miao Yu

Large Language Model based multi-agent systems are revolutionizing autonomous communication and collaboration, yet they remain vulnerable to security threats like unauthorized access and data breaches. To address this, we introduce AgentSafe, a novel framework that enhances MAS security through hierarchical information management and memory protection. Agent

Fynn Foerger, Marija Boberg, Niklas Hackelberg, Philip Heinisch

Accurate and efficient volumetric magnetic field measurements are essential for a wide range of applications. Conventional methods are often limited in terms of measurement speed and applicability, or suffer from scaling problems at larger volumes. This work presents the development of a magnetometer array designed to measure magnetic fields within a spheric

Majid Soleimani, Maedeh H. Toosi, Siamak Mohammadi, Babak Hossein Khalaj

Atrial fibrillation (AF) detection from electrocardiogram (ECG) signals is crucial for early diagnosis and management of cardiovascular diseases. However, deploying robust AF detection models across different datasets with significant domain variations remains a challenge. In this paper, we use test-time data augmentation (TTA) to address the cross-domain pr

Franz Luggin

We show that the Lipschitz-Free Space over a connected orientable $n$-di\-men\-sio\-nal Riemannian manifold $M$ is isometrically isomorphic to a quotient of $L^1(M,TM)$, the integrable sections of the tangent bundle $TM$, if $M$ is either complete or lies isometrically inside a complete manifold $N$. Two functions are deemed equivalent in this quotient space

Carl Friedrich Bolz-Tereick, Luke Panayi, Ferdia McKeogh, Tom Spink

We present Pydrofoil, a multi-stage compiler that generates instruction set simulators (ISSs) from processor instruction set architectures (ISAs) expressed in the high-level, verification-oriented ISA specification language Sail. Pydrofoil shows a > 230x speedup over the C-based ISS generated by Sail on our benchmarks, and is based on the following insights.

Shahar Levy, Nir Mazor, Lihi Shalmon, Michael Hassid

Retrieval-Augmented Generation (RAG) enhances the accuracy of Large Language Model (LLM) responses by leveraging relevant external documents during generation. Although previous studies noted that retrieving many documents can degrade performance, they did not isolate how the quantity of documents affects performance while controlling for context length. We

Bin Li, Haichen Cai, Lei Liu, Zesong Fei

The synchronization of digital twins (DT) serves as the cornerstone for effective operation of the DT framework. However, the limitations of channel capacity can greatly affect the data transmission efficiency of wireless communication. Unlike traditional communication methods, semantic communication transmits the intended meanings of physical objects instea

Yiyong Luo, Brooks Paige, Jim Griffin

Recent economic events, including the global financial crisis and COVID-19 pandemic, have exposed limitations in linear Factor Augmented Vector Autoregressive (FAVAR) models for forecasting and structural analysis. Nonlinear dimension techniques, particularly autoencoders, have emerged as promising alternatives in a FAVAR framework, but challenges remain in

Yihao Huang, Xin Luo, Qing Guo, Felix Juefei-Xu

The advent of local continuous image function (LIIF) has garnered significant attention for arbitrary-scale super-resolution (SR) techniques. However, while the vulnerabilities of fixed-scale SR have been assessed, the robustness of continuous representation-based arbitrary-scale SR against adversarial attacks remains an area warranting further exploration.

Se-Chan Lee, Yuanyuan Lian, Hyungsung Yun, Kai Zhang

We establish the boundedness of time derivatives of solutions to parabolic $p$-Laplace equations. Our approach relies on the Bernstein technique combined with a suitable approximation method. As a consequence, we obtain an optimal regularity result with a connection to the well-known $C^{p'}$-conjecture in the elliptic setting. Finally, we extend our method

Daniel Barlet

Our main result is to show that the existence of a root in. --$\alpha$--Nfor the p-th Bernstein polynomial of the (a,b)-module generated by a holomorphicform in the (convergent) Brieskorn (a,b)-module associated to f, under the hypothesis that f has an isolated singularity at the origin relative to the eigenvalue exp(2i$\pi$$\alpha$) of the monodromy, produc

Shoshauna Gauvin

We bound finite quantum information loss and black-hole entropy increments using a positive scalar response and its variation. On Hessian branches, the integrated cubic norm equals the total variation of logarithmic response. We derive an explicit optimal asymmetry bound under simultaneous response-range and affine logarithmic-slope constraints, including no

Cheng-Han Chiang, Hung-yi Lee, Michal Lukasik

The LLM-as-a-judge paradigm uses large language models (LLMs) for automated text evaluation, where a numerical assessment is assigned by an LLM to the input text following scoring rubrics. Existing methods for LLM-as-a-judge use cross-entropy (CE) loss for fine-tuning, which neglects the numeric nature of score prediction. Recent work addresses numerical pre

Alaa Mazouz, Carl De Sousa Tria, Sumanta Chaudhuri, Attilio Fiandrotti

Learnable Image Compression (LIC) has proven capable of outperforming standardized video codecs in compression efficiency. However, achieving both real-time and secure LIC operations on hardware presents significant conceptual and methodological challenges. The present work addresses these challenges by providing an integrated workflow and platform for train

Xu Xie, Yuchen Xu, Huilin Mou, Xi Li

Background: Spinal cord injury (SCI) rehabilitation remains a major clinical challenge, with limited treatment options for functional recovery. Temporal interference (TI) electrical stimulation has emerged as a promising non-invasive neuromodulation technique capable of delivering deep and targeted stimulation. However, the application of TI stimulation in S

Zhilin Wang, Jiaqi Zeng, Olivier Delalleau, Daniel Egert

Inference-Time Scaling has been critical to the success of recent models such as OpenAI o1 and DeepSeek R1. However, many techniques used to train models for inference-time scaling require tasks to have answers that can be verified, limiting their application to domains such as math, coding and logical reasoning. We take inspiration from how humans make firs

Mingxue Xu, Lisa Alazraki, Danilo P. Mandic

Pruning assumes a subnetwork exists in the original deep neural network, which can achieve comparative model performance with less computation than the original. However, it is unclear how the model performance varies with the different subnetwork extractions. In this paper, we choose the representation dimension (or embedding dimension, model dimension, the

Peng Xu, Zhiyu Xiang, Jingyun Fu, Tianyu Pu

Despite the significant advances in domain generalized stereo matching, existing methods still exhibit domain-specific preferences when transferring from synthetic to real domains, hindering their practical applications in complex and diverse scenarios. The probability distributions predicted by the stereo network naturally encode rich similarity and uncerta

Donglai Ma, Xiaoyu Cao, Bo Zeng, Qing-Shan Jia

To address the power system hardening problem, traditional approaches often adopt robust optimization (RO) that considers a fixed set of concerned contingencies, regardless of the fact that hardening some components actually renders relevant contingencies impractical. In this paper, we directly adopt a dynamic uncertainty set that explicitly incorporates the

Marcello M. Bersani, Matteo Rossi, Pierluigi San Pietro

In this paper, we introduce a restriction of Timed Automata (TA), called non-resetting test Timed Automata (nrtTA). An nrtTA does not allow to test and reset the same clock on the same transition. The model has the same expressive power of TA, but it may require one more clock than an TA to recognize the same language. We consider the parametric version of n

Yitao Sun, Marcel Niedermeier, Tiago V. C. Antão, Adolfo O. Fumega

Moir\'e and super-moir\'e materials provide exceptional platforms to engineer exotic correlated quantum matter. The vast number of sites required to model moir\'e systems in real space remains a formidable challenge due to the immense computational resources required. Super-moir\'e materials push this requirement to the limit, where millions or even billions

Orfeas Menis Mastromichalakis, Giorgos Filandrianos, Maria Symeonaki, Giorgos Stamou

Machine Translation (MT) systems frequently encounter gender-ambiguous occupational terms, where they must assign gender without explicit contextual cues. While individual translations in such cases may not be inherently biased, systematic patterns-such as consistently translating certain professions with specific genders-can emerge, reflecting and perpetuat

The Belle, Belle II Collaborations, :, I. Adachi

We report a precise measurement of the ratio of branching fractions $\mathcal{B}(\Lambda_c^+\to p K_S^0 \pi^0)/\mathcal{B}(\Lambda_c^+\to p K^- \pi^+)$ using 980 fb$^{-1}$ of $e^+e^-$ data from the Belle experiment. We obtain a value of $\mathcal{B}(\Lambda_c^+\to p K_S^0 \pi^0)/\mathcal{B}(\Lambda_c^+\to p K^- \pi^+)=0.339\pm 0.002\pm 0.009$, where the firs

Antonio Guillén-Teruel, Marcos Caracena, Jose A. Pardo, Fernando de-la-Gándara

This research addresses the challenges of handling unbalanced datasets for binary classification tasks. In such scenarios, standard evaluation metrics are often biased by the disproportionate representation of the minority class. Conducting experiments across seven datasets, we uncovered inconsistencies in evaluation metrics when determining the model that o

Yafu Li, Ronghao Zhang, Zhilin Wang, Huajian Zhang

Large language models (LLMs) have achieved remarkable success in machine translation, demonstrating impressive performance across diverse languages. However, translationese, characterized by overly literal and unnatural translations, remains a persistent challenge in LLM-based translation systems. Despite their pre-training on vast corpora of natural utteran

Felix Karbstein, Fabian Schütze

Studies of nonlinear quantum vacuum signals often model the driving laser fields as paraxial beams. This in particular holds for analytic approaches. While this allows for reliable predictions in most situations, there are also notable exceptions. A prominent example is the overestimation of the polarization-flipped signal photon yield in the collision of tw

Subramanian Mathimalar, Ambikesh Gupta, Yotam Roet, Stanislaw Galeski

Topological states of matter exhibit unique protection against scattering by disorder. Different topological classes exhibit distinct forms and degrees of protection. Here, we investigate the response of the ferromagnetic nodal line semimetal Fe$_3$GeTe$_2$ to disorder and electronic interactions. By combining global magneto-transport with atomic-scale scann

Qing Wang, Yuhang Li, Shaofeng Wang, Shuo Cao

Nanophotonic devices with moir\'e superlattice is currently attracting broad interest due to the unique periodicity and high efficiency control of photons. Till now, experimental investigations mainly focus on the single layer device, i.e., two or more layers of photonic crystal patterns are merged and etched in a single layer of material. By comparison, twi

Jiangling Liu, Ya Liu, Banyun Zheng, Longjian Liu

Background: Multiple medical and non-medical stressors, along with the complicity of their exposure pathways, have posted significant challenges to the epidemiological interpretation of the non-communicable diseases, including cardiovascular disease (CVD). Objective: To develop a protocol for deconstructing the complex exposure pathways linking various stres

R. V. Ibañez Bustos, A. P. Buccino, N. Nardetto, D. Mourard

Context. M dwarfs are an ideal laboratory for hunting Earth-like planets, and the study of chromospheric activity is an important part of this task. On the one hand, its short-term activity show high levels of magnetic activity that can affect habitability and make it difficult to detect exoplanets orbiting around them. But on the other hand, long-term activ

Giovanni De Felice, Arianna Casanova Flores, Francesco De Santis, Silvia Santini

Concept-based models are an emerging paradigm in deep learning that constrains the inference process to operate through human-interpretable variables, facilitating explainability and human interaction. However, these architectures, on par with popular opaque neural models, fail to account for the true causal mechanisms underlying the target phenomena represe

Yiheng Zhu, Mingyang Li, Junlong Liu, Kun Fu

Structure-based drug discovery (SBDD) is a systematic scientific process that develops new drugs by leveraging the detailed physical structure of the target protein. Recent advancements in pre-trained models for biomolecules have demonstrated remarkable success across various biochemical applications, including drug discovery and protein engineering. However

Souvik Bhowmick, Sekhar Ghosh

In this paper, we use the method of invariant sets of descending flows to demonstrate the existence of multiple sign-changing solutions for a class of elliptic problems with zero Dirichlet boundary conditions. By combining Nehari manifold techniques with a constrained variational approach and Brouwer degree theory, we establish the existence of a least-energ

Jiayi Chang, Mingqi Gao, Xinyu Hu, Xiaojun Wan

Previous research has shown that LLMs have potential in multilingual NLG evaluation tasks. However, existing research has not fully explored the differences in the evaluation capabilities of LLMs across different languages. To this end, this study provides a comprehensive analysis of the multilingual evaluation performance of 10 recent LLMs, spanning high-re

Anna Elivanova

As the use of facial recognition technology is expanding in different domains, ensuring its responsible use is gaining more importance. This paper conducts a comprehensive literature review of existing studies on facial recognition technology from the perspective of privacy, which is one of the key Responsible AI principles. Cloud providers, such as Microsof

Jia Li, Xuyuan Guo, Lei Li, Kechi Zhang

Current advanced long-context language models offer great potential for real-world software engineering applications. However, progress in this critical domain remains hampered by a fundamental limitation: the absence of a rigorous evaluation framework for long code understanding. To gap this obstacle, we propose a long code understanding benchmark LONGCODEU

Homer Durand, Gherardo Varando, Gustau Camps-Valls

We propose a novel approach for learning causal response representations. Our method aims to extract directions in which a multidimensional outcome is most directly caused by a treatment variable. By bridging conditional independence testing with causal representation learning, we formulate an optimisation problem that maximises the evidence against conditio

Zhen Yu, Jianan Han, Yang Liu, Qingchao Chen

Single-cell RNA sequencing (scRNA-seq) technology has profiled hundreds of millions of human cells across organs, diseases, development and perturbations to date. However, the high-dimensional sparsity, batch effect noise, category imbalance, and ever-increasing data scale of the original sequencing data pose significant challenges for multi-center knowledge

Adele Valpreda, Hendrik W. Lokhorst, Jacobus M. Sturm, Andrey E. Yakshin

In this study, we investigate the W-on-Si interface and the effect of adding a B4C interlayer at such interface, using low-energy ion scattering (LEIS) spectroscopy, X-ray reflectivity, X-ray diffraction, and transmission electron microscopy with energy dispersive X-ray spectroscopy. We extract the effective width of the interface in three different structur

Zhenghua Wang, Yiran Ding, Changze Lv, Zhibo Xu

Although large language models (LLMs) have achieved significant progress in handling long-context inputs, they still suffer from the ``lost-in-the-middle'' problem, where crucial information in the middle of the context is often underrepresented or lost. Our extensive experiments reveal that this issue may arise from the rapid long-term decay in Rotary Posit

Hao Cheng, Cornelis Weemstra, Katrin Löer, Max A. N. Hendriks

This paper investigates the influence of elastic deformation on the velocity of body waves in compressible isotropic materials making use of the framework of acoustoelasticity. Specifically, it examines body waves propagating at an angle to the principal deformation axes, where both shear and normal deformations are present in the coordinate system defined b

Chanda Grover Kamra, Indra Deep Mastan, Debayan Gupta

We propose ObjMST, an object-focused multimodal style transfer framework that provides separate style supervision for salient objects and surrounding elements while addressing alignment issues in multimodal representation learning. Existing image-text multimodal style transfer methods face the following challenges: (1) generating non-aligned and inconsistent

Hidetsugu Ikegami

This article presents a new particle beam cooling scheme, namely cyclotron maser cooling (CMC). Relativistic gyrating particles, forced by a solenoidal magnetic field over some length of their trajectory, move in a helical path and undergo emission or absorption of radiations stimulated by a resonance RF field. Theoretical and experimental investigations on

Kim Kiehn, Albin Ahlbäck, Kathlén Kohn

We completely classify all minimal problems for Structure-from-Motion (SfM) where arrangements of points and lines are fully observed by multiple uncalibrated pinhole cameras. We find 291 minimal problems, 73 of which have unique solutions and can thus be solved linearly. Two of the linear problems allow an arbitrary number of views, while all other minimal

Joana Simões, João Correia

Automated Machine Learning (AutoML) gained popularity due to the increased demand for Machine Learning (ML) specialists, allowing them to apply ML techniques effortlessly and quickly. AutoML implementations use optimisation methods to identify the most effective ML solution for a given dataset, aiming to improve one or more predefined metrics. However, most

Christoph Steinacker, Mads Paulsen, Malte Schröder, Jeppe Rich

Promoting active mobility like cycling relies on the availability of well-connected, high-quality bicycle networks. However, expanding these networks over an extended planning horizon presents one of the most complex challenges in transport science. This complexity arises from the intricate interactions between infrastructure availability and usage, such as

Maciej Borodzik, Mark Powell, Peter Teichner

We show that link concordance implies link homotopy for immersions of codimension at least two. As a consequence, we prove that every link $\sqcup^r S^n \hookrightarrow S^{n+2}$ is link homotopically trivial for $n\geq 2$, that is, there is a {\em link} homotopy that (for each time parameter) maps distinct component $n$-spheres disjointly into $S^{n+2}$. In

Izzy Newsham, Luka Kovačević, Richard Moulange, Nan Rosemary Ke

Genes, proteins and other biological entities influence one another via causal molecular networks. Causal relationships in such networks are mediated by complex and diverse mechanisms, through latent variables, and are often specific to cellular context. It remains challenging to characterise such networks in practice. Here, we present a novel framework to e

Wenhong Zhu, Weinan Zhang, Rui Wang

Post-training alignment has increasingly become a crucial factor in enhancing the usability of language models (LMs). However, the strength of alignment varies depending on individual preferences. This paper proposes a method to incorporate alignment control into a single model, referred to as CLM. This approach adds one identity layer preceding the initial

Théophile Gaichies, Bryan Giraud, Anniina Salonen, Arnaud Antkowiak

A capillary jet plunging into a quasi-2D slab of monodisperse foam of the same solution is studied experimentally. We show that the jet can have a drastic impact on the foam. At small speeds it inflates the channels separating the bubbles. At intermediate speeds, impact with channels creates very small bubbles. At higher speeds, the jet thins the foam films,