March 2025 arXiv papers — page 187

John Haslegrave

A fixed number of passengers independently board one of several buses uniformly at random. The lonely passenger problem is to prove that the probability of at least one passenger being the only one in their bus is increasing in the number of buses. It was solved in a strong form by Imre P\'eter T\'oth, who proved stochastic dominance of the number of such pa

Weixiang Zhao, Xingyu Sui, Xinyang Han, Yang Deng

The growing emotional stress in modern society has increased the demand for Emotional Support Conversations (ESC). While Large Language Models (LLMs) show promise for ESC, they face two key challenges: (1) low strategy selection accuracy, and (2) preference bias, limiting their adaptability to emotional needs of users. Existing supervised fine-tuning (SFT) s

Joonsung Jung, Hyunjoong Kim, Hyunghwan Shin, Jip Kim

The increase in renewable energy sources (RES) has reduced power system inertia, making frequency stabilization more challenging and highlighting the need for fast frequency response (FFR) resources. While building energy management systems (BEMS) equipped with distributed energy resources (DERs) can provide FFR, individual BEMS alone cannot fully meet deman

Alexander V. Gheorghiu

Sandqvist's base-extension semantics (B-eS) for intuitionistic sentential logic grounds meaning relative to bases (rather than, say, models), which are arbitrary sets of permitted inferences over sentences. While his soundness proof is standard, his completeness proof, is quite unusual. It closely parallels a method introduced much earlier by Mints, who deve

Sijme-Jan Paardekooper, Hossam Aly

Dust grains embedded in gas flow give rise to a class of hydrodynamic instabilities that can occur whenever there exists a relative velocity between gas and dust. These instabilities have predominantly been studied for single grain sizes, for which a strong interaction can be found between drifting dust and a travelling gas wave, leading to fast-growing pert

Roberto Flores, Alessandro Beolchi, Elena Fantino, Chiara Pozzi

Comets are the most pristine planetesimals left from the formation of the Solar System. They carry unique information on the materials and the physical processes which led to the presence of planets and moons. Many important questions about cometary physics, such as origin, constituents and mechanism of cometary activity, remain unanswered. The next periheli

Jan Fillies, Adrian Paschke

Algorithmic hate speech detection faces significant challenges due to the diverse definitions and datasets used in research and practice. Social media platforms, legal frameworks, and institutions each apply distinct yet overlapping definitions, complicating classification efforts. This study addresses these challenges by demonstrating that existing datasets

Néstor Eduardo Sánchez-Arriaga, Ethan Canzini, Nathan John Espley-Plumb, Michael Farnsworth

Thin-film inspection on large-area substrates in coating manufacture remains a critical parameter to ensure product quality; however, extending the inspection process precisely over a large area presents major challenges, due to the limitations of the available inspection equipment. An additional manipulation problem arises when automating the inspection pro

Rong Li, Qirui Ding, Weicheng Cui

Dynamical universality plays a fundamental role in understanding the scaling properties of critical dynamics, including absorbing phase transitions and physical aging. Although individual universality classes have been extensively studied, the theoretical framework for scaling crossovers between distinct dynamical regimes remains underdeveloped. In this work

Nicholas Christakis, Dimitris Drikakis

This study introduces a new methodology for an Inference Index (InI), called INFerence INdex In Testing model Effectiveness methodology (INFINITE), aiming to evaluate the performance of Large Language Models (LLMs) in code generation tasks. The InI index provides a comprehensive assessment focusing on three key components: efficiency, consistency, and accura

Alexader V. Gheorghiu, Tao Gu

Standard epistemic logics introduce a modal operator K to represent knowledge, but in doing so they presuppose the logical apparatus they aim to explain. By contrast, this paper explores how logic may be derived from the structure of knowledge itself. We begin from a pre-logical notion of a knowledge base understood as a network of inferential connections be

M. Caselle, A. Mariani

The three-dimensional U(1) gauge theory displays a peculiar form of confinement which does not fit in the standard paradigm of effective string theory. In this work, we report results of numerical computations of the ground state energy of the confining string in the lattice theory at finite temperature, which we compared with various theoretical predictions

Cecilie Glittum, Olav F. Syljuåsen

We construct exact ground states of the $J_1$-$J_{3b}$ classical Heisenberg model on the pyrochlore lattice in the presence of a magnetic field. They are non-coplanar multi-$\vec{Q}$ spin configurations with a large magnetic unit cell that generalize the previously found coplanar sublattice pairing states. Using linear spin wave theory, we show that entropy

Sri Maryani, Miho Murata

In this paper, we consider a resolvent problem arising from the free boundary problem for the compressible fluid model of the Korteweg type, which is called the Navier-Stokes-Korteweg system, with surface tension in general domains. The Navier-Stokes-Korteweg system describes the liquid-vapor two-phase flow with non-zero thickness phase boundaries, which is

Julian Skolaut, Štěpán Marek, Nico Balzer, María Camarasa-Gómez

The generation of unidirectional motion has been a long-standing challenge in engineering of molecular motors and, more generally, machines. A molecular motor is characterized by a set of low energy states that differ in their configuration, i.e. position or rotation. In biology and Feringa-type motors, unidirectional motion is driven by excitation of the mo

V. P. Stefanov, D. S. Mogilevtsev, I. Y. Rybak, A. Stefanov

Here we discuss an influence of an external weak gravitational field on the gravitational self-decoherence effect with help of the stochastic extension of regularized Shr\"odinger-Newton equation in a curved background. We derive the master equation and demonstrate that it leads to the experimentally verifiable conclusions about applicability of the classica

Jorge Navarro, José M. Zapata

We provide conditions for the stochastic dominance comparisons of a risk $X$ and an associated risk $X+Z$, where $Z$ represents the uncertainty due to the environment and where $X$ and $Z$ can be dependent. The comparisons depend on both the copula $C$ between the distributions of $X$ and $Z$ and on the distribution of $Z$. We provide two different condition

A. Capolupo, S. Monda, G. Pisacane, A. Quaranta

In the framework of quantum field theory, we analyze the neutrino oscillations in the presence of a torsion background. We consider the Einstein Cartan theory and we study the cases of constant torsion and of linearly time-dependent torsion. We derive new neutrino oscillation formulae which depend on the spin orientation and the CP asymmetry formula. Experim

Zhenxuan Zhang, Kinhei Lee, Peiyuan Jing, Weihang Deng

Automatic medical report generation has the potential to support clinical diagnosis, reduce the workload of radiologists, and demonstrate potential for enhancing diagnostic consistency. However, current evaluation metrics often fail to reflect the clinical reliability of generated reports. Early overlap-based methods focus on textual matches between predicte

Xiaosong Jia, Junqi You, Zhiyuan Zhang, Junchi Yan

End-to-end autonomous driving (E2E-AD) has emerged as a trend in the field of autonomous driving, promising a data-driven, scalable approach to system design. However, existing E2E-AD methods usually adopt the sequential paradigm of perception-prediction-planning, which leads to cumulative errors and training instability. The manual ordering of tasks also li

Leming Shen, Qiang Yang, Yuanqing Zheng, Mo Li

The advent of Large Language Models (LLMs) has profoundly transformed our lives, revolutionizing interactions with AI and lowering the barrier to AI usage. While LLMs are primarily designed for natural language interaction, the extensive embedded knowledge empowers them to comprehend digital sensor data. This capability enables LLMs to engage with the physic

Mairi W. O'Brien, Pier-Emmanuel Tremblay, Beth L. Klein, Carl Melis

We present Keck High Resolution Echelle Spectrometer (HIRES) observations and model atmosphere analysis for two nearby, cool, helium-dominated atmosphere white dwarfs that have been polluted by accretion: WD J1927-0355 and WD J2141-3300. Detected elements common to both white dwarfs are Mg, Ca, Ti, Cr, Fe, and Ni, with additional detections of Na, Al, Si and

Masatoshi Ishii, Hammam Qassim, Tomochika Kurita, Joseph Emerson

In near-term quantum computations that do not employ fault tolerant error correction, noise can proliferate rapidly, corrupting the quantum state and making results unreliable. These errors originate from both decoherence and control imprecision and the latter can manifest as coherent error that is especially detrimental. In the pre-fault tolerant setting, p

Baiying Liu, Freydoon Shahidi

This is a report on the progress made on a conjecture of Jiang on the upper bound nilpotent orbits in the wave front sets of representations in local Arthur packets of classical groups, which is a natural generalization of the Shahidi conjecture. We partially prove this conjecture, confirming the relation between the structure of wave front sets and the loca

Anastasios Kokkinakis

We introduce framed versions of the $L$-moves and prove a one move theorem for the extension of the Markov theorem for framed braids. We further introduce framed versions of the Hilden and Pure Hilden groups, we give presentations and we use them to state and prove a framed version of the Birman theorem for framed links in plat representation.

Abu Bakkar Miah, Dhananjaya Mahapatra, Soumik Aon, Harekrishna Bhunia

Harmonic Hall measurements were performed on a series of ferromagnetic metal/heavy metal (FM/HM) bilayers consisting of Permalloy (Py) as the FM and beta-Tungsten (W) as the HM, and the efficiencies of the two orthogonal components of the spin-orbit torque (SOT) were extracted. Two sets of Hall bar-shaped devices, differing in the aspect ratio of the voltage

Zhiyun Fan, Xiaoyu Zhang, Di Wang

Matrix-valued time series are ubiquitous in modern economics and finance, yet modeling them requires navigating a trade-off between flexibility and parsimony. We propose the Matrix Autoregressive model with Common Factors (MARCF), a unified framework for high-dimensional matrix time series that bridges the structural gap between the Matrix Autoregression (MA

Zhenxuan Zhang, Peiyuan Jing, Coraline Beitone, Jiahao Huang

Given the scarcity and cost of high-field MRI, the synthesis of high-field MRI from low-field MRI holds significant potential when there is limited data for training downstream tasks (e.g. segmentation). Low-field MRI often suffers from a reduced signal-to-noise ratio (SNR) and spatial resolution compared to high-field MRI. However, synthesizing high-field M

Alex Watts, Davide Sinesi, Jacob Greene

In the context of decentralized blockchains, accurately simulating the outcome of order flow auctions (OFAs) off-chain is challenging due to adversarial sequencing, encrypted bids, and frequent state changes. Existing approaches, such as deterministic sorting via consensus layer modifications (e.g., MEV taxes) (Robinson and White 2024) and BRAID (Resnick 202

María Alejandra Alvarez, Artem Lopatin

We classify all two-dimensional simple algebras (which may be non-associative) over an algebraically closed field. For each two-dimensional algebra $\mathcal{A}$, we describe a minimal (with respect to inclusion) generating set for the algebra of invariants of the $m$-tuples of $\mathcal{A}$ in the case of characteristic zero. In particular, we establish tha

Laura Weidinger, Inioluwa Deborah Raji, Hanna Wallach, Margaret Mitchell

There is an increasing imperative to anticipate and understand the performance and safety of generative AI systems in real-world deployment contexts. However, the current evaluation ecosystem is insufficient: Commonly used static benchmarks face validity challenges, and ad hoc case-by-case audits rarely scale. In this piece, we advocate for maturing an evalu

Joel Honkamaa, Pekka Marttinen

The deformable registration of images of different modalities, essential in many medical imaging applications, remains challenging. The main challenge is developing a robust measure for image overlap despite the compared images capturing different aspects of the underlying tissue. Here, we explore similarity metrics based on functional dependence between int

Martin Spitznagel, Jan Vaillant, Janis Keuper

The image-to-image translation abilities of generative learning models have recently made significant progress in the estimation of complex (steered) mappings between image distributions. While appearance based tasks like image in-painting or style transfer have been studied at length, we propose to investigate the potential of generative models in the conte

Jungho Lee, Donghyeong Kim, Dogyoon Lee, Suhwan Cho

3D Gaussian Splatting (3DGS) has gained significant attention due to its high-quality novel view rendering, motivating research to address real-world challenges. A critical issue is the camera motion blur caused by movement during exposure, which hinders accurate 3D scene reconstruction. In this study, we propose CoMoGaussian, a Continuous Motion-Aware Gauss

Yiwei Li, Jiayi Shi, Shaoxiong Feng, Peiwen Yuan

We propose a novel speculative decoding method tailored for multi-sample reasoning scenarios, such as self-consistency and Best-of-N sampling. Our method exploits the intrinsic consensus of parallel generation paths to synthesize high-quality draft tokens without requiring auxiliary models or external databases. By dynamically analyzing structural patterns a

M. Ali Asadi-Vasfi, Ilan Hirshberg, Apurva Seth

Given 0 \leq r' \leq r \leq \infty, and d \in N, we construct a simple unital AH algebra A with stable rank one, and a pointwise outer action \alpha : Z^d \to Aut(A), such that rc(A)=r and rc (A \rtimes_{\alpha} Z^d)=r'.

Anar Yeginbergen, Maite Oronoz, Rodrigo Agerri

This paper investigates the role of dynamic external knowledge integration in improving counter-argument generation using Large Language Models (LLMs). While LLMs have shown promise in argumentative tasks, their tendency to generate lengthy, potentially unfactual responses highlights the need for more controlled and evidence-based approaches. We introduce a

Diana Estévez Schwarz, René Lamour, Roswitha März

The relationship between solvability of linear diffential-algebraic equations (DAEs) and their transformability into canonical forms has been investigated for more than forty years. After a comparative analysis of numerous DAE frameworks the notions regularity and almost regularity were established only recently. Regular DAEs resulted to be equivalently tran

Yu-Jia Zhao, Jing-fu Hu

Radio galaxies can be classified into two types, FR I and FR II, depending on their morphology. So far, the reasons for the different behaviour of FR I and FR II in observations have not been clarified. While the Unified Model suggests that the viewing angle and the obscuring effect of the dust ring are the main reasons for the difference in the classificati

Xinyi Cai

Implicit discourse relation recognition is a challenging task in discourse analysis due to the absence of explicit discourse connectives between spans of text. Recent pre-trained language models have achieved great success on this task. However, there is no fine-grained analysis of the performance of these pre-trained language models for this task. Therefore

Niusha Bagheri, Chenyi Wang, Du Guo, Anbharasi Lakshmanan

Lanthanide-based upconversion nanoparticles (UCNPs) have attracted considerable attention in biomedical applications, largely due to their anti-Stokes shifted emission enabling autofluorescence-free signal detection. However, residual excitation light can still interfere with their relatively low brightness. While commonly used lock-in detection can distingu

Ofir Cohen, Jose Yallouz Michael Schapira, Shahar Belkar, Tal Mizrahi

Training large language models (LLMs), and other large machine learning models, involves repeated communication of large volumes of data across a data center network. The communication patterns induced by these training process exhibit high regularity and persistence, giving rise to significant opportunities for optimizing the manner in which flows are route

Sushil Mahavir Varma, Irène Waldspurger, Laurent Massoulié

We consider the graph alignment problem, wherein the objective is to find a vertex correspondence between two graphs that maximizes the edge overlap. The graph alignment problem is an instance of the quadratic assignment problem (QAP), known to be NP-hard in the worst case even to approximately solve. In this paper, we analyze Birkhoff relaxation, a tight co

Pierandrea Cancian, Simone Saitta, Xiaojin Gu, Rudolf L. M. van Herten

In intracoronary optical coherence tomography (OCT), blood residues and gas bubbles cause attenuation artifacts that can obscure critical vessel structures. The presence and severity of these artifacts may warrant re-acquisition, prolonging procedure time and increasing use of contrast agent. Accurate detection of these artifacts can guide targeted re-acquis

Samuel Gruffaz, Josua Sassen

Riemannian metric learning is an emerging field in machine learning, unlocking new ways to encode complex data structures beyond traditional distance metric learning. While classical approaches rely on global distances in Euclidean space, they often fall short in capturing intrinsic data geometry. Enter Riemannian metric learning: a powerful generalization t

Zitao Fang, Guodong DU, Shuyang Yu, Yifei Guo

Fine-tuning pre-trained models on targeted datasets enhances task-specific performance but often comes at the expense of generalization. Model merging techniques, which integrate multiple fine-tuned models into a single multi-task model through task arithmetic, offer a promising solution. However, task interference remains a fundamental challenge, leading to

Soon-Tae Hong

In order to describe properly the gravity interactions including the mass currents, in the gravitomagnetism we construct four Maxwell type gravitational equations which are shown to be analogs of the Maxwell equations in the electromagnetism. Next, exploiting the Maxwell type gravitational equations, we explicitly predict the mass magnetic fields for both th

Xinkun Wang, Yifang Wang, Senwei Liang, Feilong Tang

This paper discusses how ophthalmologists often rely on multimodal data to improve diagnostic accuracy. However, complete multimodal data is rare in real-world applications due to a lack of medical equipment and concerns about data privacy. Traditional deep learning methods typically address these issues by learning representations in latent space. However,

Nico Daheim, Clara Meister, Thomas Möllenhoff, Iryna Gurevych

Despite their outstanding performance in the majority of scenarios, contemporary language models still occasionally generate undesirable outputs, for example, hallucinated text. While such behaviors have previously been linked to uncertainty, there is a notable lack of methods that actively consider uncertainty during text generation. In this work, we show h

J. P. Pridham

We show that Hinich's simplicial nerve of the differential graded Lie algebra (DGLA) of derived derivations of a dg algebra $A$ over a dg properad $\mathcal{P}$ is equivalent to the space of deformations of $A$ as a $\mathcal{P}_{\infty}$-algebra in Positselski's contraderived dg category. This resolves Hinich's counterexamples to the general existence of de

Yu Zhao, Huxian Liu, Xiang Chen, Jiankai Sun

Physical intelligence holds immense promise for advancing embodied intelligence, enabling robots to acquire complex behaviors from demonstrations. However, achieving generalization and transfer across diverse robotic platforms and environments requires careful design of model architectures, training strategies, and data diversity. Meanwhile existing systems

Saumya Chaturvedi, Aman Chadha, Laurent Bindschaedler

Code embeddings are essential for semantic code search; however, current approaches often struggle to capture the precise syntactic and contextual nuances inherent in code. Open-source models such as CodeBERT and UniXcoder exhibit limitations in scalability and efficiency, while high-performing proprietary systems impose substantial computational costs. We i

Indrajith. V. S

This article presents a comparative analysis of quantum Otto and Stirling engines using atom-photon interactions as the working substance. Two models are considered: a two-level Jaynes-Cummings system and a four-level atomic system confined in an optical cavity. The thermodynamic cycles are analyzed, highlighting the role of critical points, quantum correlat

Navid Keshtiarast, Pradyumna Kumar Bishoyi, Marina Petrova

In this paper, we address the problem of scheduling sensing and communication functionality in an integrated sensing and communication (ISAC) enabled base station (BS) operating in an indoor factory (InF) environment. The BS is performing the task of detecting an AGV while managing downlink transmission of ultra-reliable low-latency communication (URLLC) dat

Sriram Bhyravarapu, Swati Kumari, I. Vinod Reddy

A proper vertex coloring of a connected graph $G$ is called an odd coloring if, for every vertex $v$ in $G$, there exists a color that appears odd number of times in the open neighborhood of $v$. The minimum number of colors required to obtain an odd coloring of $G$ is called the \emph{odd chromatic number} of $G$, denoted by $\chi_{o}(G)$. Determining $\chi

Anirban Basak, Shaibal Karmakar

We consider the upper tail large deviations of subgraph counts for irregular graphs $\mathrm{H}$ in $\mathbb{G}(n,p)$, the sparse Erd\H{o}s-R\'enyi graph on $n$ vertices with edge connectivity probability $p \in (0,1)$. For $n^{-1/\Delta} \ll p \ll 1$, where $\Delta$ is the maximum degree of $\mathrm{H}$, we derive the upper tail large deviations for any irr

Anna K. Berryman, Joris Bücker, Fernanda Senra de Moura, Pete Barbrook-Johnson

Structural change is necessary for all countries transitioning to a more environmentally sustainable economy, but what are the likely impacts on workers? Studies often find that green transition scenarios result in net positive job creation numbers overall but rarely provide insights into the more granular dynamics of the labour market. This paper combines a

Rajini Makam, Nadav Cohen, Sumukh Shadakshari, Srinivasa Puranika Bhatta

Navigation is a critical aspect of autonomous underwater vehicles (AUVs) operating in complex underwater environments. Since global navigation satellite system (GNSS) signals are unavailable underwater, navigation relies on inertial sensing, which tends to accumulate errors over time. To mitigate this, the Doppler velocity log (DVL) plays a crucial role in d

Hancheng Bi, Clément Sarrazin, Bernhard Schmitzer, Thilo D. Stier

Dynamical systems can be analyzed via their Frobenius-Perron transfer operator and its estimation from data is an active field of research. Recently entropic transfer operators have been introduced to estimate the operator of deterministic systems. The approach is based on the regularizing properties of entropic optimal transport plans. In this article we ge

J. P. Pridham

We summarise the chain of comparisons showing Hinich's derived Maurer-Cartan functor gives an equivalence between differential graded Lie algebras and derived Schlessinger functors on Artinian differential graded-commutative algebras. We include some motivating deformation problems and analogues for more general Koszul dual pairs of operads.

Han Wang, Jacek Pawlak, Aruna Sivakumar

Stated preference (SP) surveys are a key method to research how individuals make trade-offs in hypothetical, also futuristic, scenarios. In energy context this includes key decarbonisation enablement contexts, such as low-carbon technologies, distributed renewable energy generation, and demand-side response [1,2]. However, they tend to be costly, time-consum

Hyungkyu Kang, Min-hwan Oh

In this paper, we study offline preference-based reinforcement learning (PbRL), where learning is based on pre-collected preference feedback over pairs of trajectories. While offline PbRL has demonstrated remarkable empirical success, existing theoretical approaches face challenges in ensuring conservatism under uncertainty, requiring computationally intract

Hu Yu, Hao Luo, Hangjie Yuan, Yu Rong

Autoregressive (AR) models for image generation typically adopt a two-stage paradigm of vector quantization and raster-scan ``next-token prediction", inspired by its great success in language modeling. However, due to the huge modality gap, image autoregressive models may require a systematic reevaluation from two perspectives: tokenizer format and regressio

João P. Rodrigues

We use a loop truncated Jevicki-Sakita effective collective field Hamiltonian to obtain, over a very large range of values of 't Hooft's coupling, and directly in the large N limit, the large N (planar) ground state energy, the planar ground state expectation values of invariant correlators, and the 1/N spectrum of the quantum mechanical system of three mass

Betül Güvenç Paltun, Ramin Fuladi, Rim El Malki

Machine learning (ML) models serve as powerful tools for threat detection and mitigation; however, they also introduce potential new risks. Adversarial input can exploit these models through standard interfaces, thus creating new attack pathways that threaten critical network operations. As ML advancements progress, adversarial strategies become more advance

Harsh Mehta, Suvodip Mukherjee

The conversion of CMB photons to axions (or axion-like particles (ALPs)) can lead to a unique spectral distortion in the temperature and polarization sky which can be explored in upcoming CMB experiments. In this work we have developed a numerical simulation-based technique of photons to ALPs conversion in the galaxy clusters and show for the first time that

Adrian Pfisterer, Xing Li, Vito Mengers, Oliver Brock

Visual uncertainties such as occlusions, lack of texture, and noise present significant challenges in obtaining accurate kinematic models for safe robotic manipulation. We introduce a probabilistic real-time approach that leverages the human hand as a prior to mitigate these uncertainties. By tracking the constrained motion of the human hand during manipulat

Xian Li, Xuan Liang, Tao Zou

This article introduces a subbagging (subsample aggregating) approach for variable selection in regression within the context of big data. The proposed subbagging approach not only ensures that variable selection is scalable given the constraints of available computational resources, but also preserves the statistical efficiency of the resulting estimator. I

Zongli Chen, Chenzhe Li, Ying Zhao

Estimating the tortuosity of porous electrodes is important for understanding the performance of lithium-ion batteries and optimizing the design of electrode microstructures. In this work, a new method for estimating the tortuosity of porous electrodes is proposed based on radical tessellation, and the results agree well with those calculated by empirical fo

Nikolai Ilinykh, Shalom Lappin, Asad Sayeed, Sharid Loáiciga

We demonstrate that large multimodal language models differ substantially from humans in the distribution of coreferential expressions in a visual storytelling task. We introduce a number of metrics to quantify the characteristics of coreferential patterns in both human- and machine-written texts. Humans distribute coreferential expressions in a way that mai

Pierre Alquier, Mathieu Gerber

The Maximum Mean Discrepancy (MMD) is a kernel-based metric widely used for nonparametric tests and estimation. Recently, it has also been studied as an objective function for parametric estimation, as it has been shown to yield robust estimators. We have implemented MMD minimization for parameter inference in a wide range of statistical models, including va

Rupert Hölzl, Sören Kleine, Frank Stephan

We study diophantine equations of the form ${a_1 + \ldots + a_n = 0}$ where the $a_i$'s are assumed to be coprime and to satisfy certain subsum conditions. We are interested in the limit superior of the qualities of the admissible solutions of these equations, a question that in the case ${n = 3}$ is closely related to the famous $abc$-conjecture. In a previ

D. V. Fedorov, A. M. Pedersen

We introduce a recipe to estimate the low-energy scattering parameters of a quantum few-body system - scattering length, effective range, and shape parameter - by using only discrete state calculations. We place the system in an artificial oscillator trap of appropriate size and calculate the energies of the resulting discrete states close to the threshold o

B. Pellacci, G. Pisante, D. Schiera

The goal of this note is to fill a gap in the proof of the first two items of Theorem 5.1 in [4], which relies on Polya type inequalities and the characterization of the equality cases for monotone rearrangements given in Propositions 4.1 and 4.2 of [4], whose statements and proofs require some adjustments.

Philippe Vong, Laurent Dolla, Alexandros Koukras, Jacques Gustin

The spatial extension of active regions (ARs) of the Sun can vary from one case to the next. This is a problem when studying solar flares with Convolutional Neural Networks (CNNs) as they generally use input images of a fixed size. Different processes can be performed to retrieve a database with homogeneous-sized data, such as resizing. Unfortunately, key fe

Aditi Singh, Eduardo Fabiano, Szymon Śmiga

Density Functional Theory has long struggled to obtain the exact exchange-correlational (XC) functional. Numerous approximations have been designed with the hope of achieving chemical accuracy. However, designing a functional involves numerous methodologies, which has a greater possibility for error accumulation if the functionals are poorly formulated. This

Fernando Moreno

Context. We present the COMetary dust TAIL Simulator (COMTAILS), a numerical Monte Carlo code to generate images of dust tail brightness from comets and active asteroids in the Solar System. Aims. We describe a numerical code, available to interested users, capable of generating simulated images of dust tail brightness for comparison with observations, to re

Qunyou Liu, Marina Zapater, David Atienza

Transformers are central to advances in artificial intelligence (AI), excelling in fields ranging from computer vision to natural language processing. Despite their success, their large parameter count and computational demands challenge efficient acceleration. To address these limitations, this paper proposes MatrixFlow, a novel co-designed system-accelerat

Eliav Mor, Yair Carmon

We study class-imbalanced linear classification in a high-dimensional Gaussian mixture model. We develop a tight, closed form approximation for the test error of several practical learning methods, including logit adjustment and class dependent temperature. Our approximation allows us to analytically tune and compare these methods, highlighting how and when

Keiichi Ochiai, Yutaka Matsuo

Deep learning has had a great impact on various fields of computer science by enabling data-driven representation learning in a decade. Because science and technology policy decisions for a nation can be made on the impact of each technology, quantifying research impact is an important task. The number of citations and impact factor can be used to measure th

Igor Plokhikh, Thomas J. Hicken, Lukas Keller, Vladimir Pomjakushin

The discovery of pressure-induced superconductivity in two- and three-layer Ruddlesden-Popper nickelates has generated significant interest in these materials as a platform for unconventional superconductivity. While their ground state exhibits magnetism, a direct determination of their magnetic structure remains elusive. Understanding this aspect is crucial

Sönke Beier, Veronika Pfeifer, Agniva Datta, Robert Großmann

Chemotaxis of bacterial swimmers that move in a run-and-turn pattern is well studied in uniform bulk fluid. It is primarily based on modulating the run time in dependence on the swimming direction with respect to the source of chemoattractant (run time bias). Here, we provide evidence that the lophotrichously flagellated soil bacterium Pseudomonas putida may

Martina Vinetti, Martin Fabian

Modern assembly processes require flexibility and adaptability to handle increasing product variety and customization. Traditional assembly planning methods often prioritize finding an optimal assembly sequence, overlooking the requirements of contemporary manufacturing. This work uses Supervisory Control Theory to systematically generate all feasible assemb

G. Giavaras, Ruben Seoane Souto, Maria Jose Calderon, Ramon Aguado

Leveraging the higher harmonics content of the Josephson potential in a superconducting circuit offers a promising route in the search for new qubits with increased protection against decoherence. In this work, we demonstrate how the flux tunability of a hybrid semiconductor-superconductor Josephson junction based on a single full-shell nanowire enables this

Sefik Serengil, Alper Ozpinar

This paper explores the use of partially homomorphic encryption (PHE) for encrypted vector similarity search, with a focus on facial recognition and broader applications like reverse image search, recommendation engines, and large language models (LLMs). While fully homomorphic encryption (FHE) exists, we demonstrate that encrypted cosine similarity can be c

Souhail Hadgi, Luca Moschella, Andrea Santilli, Diego Gomez

Recent works have shown that, when trained at scale, uni-modal 2D vision and text encoders converge to learned features that share remarkable structural properties, despite arising from different representations. However, the role of 3D encoders with respect to other modalities remains unexplored. Furthermore, existing 3D foundation models that leverage larg

Marlis Hochbruck, Malik Scheifinger

In this paper, we address the full discretization of Friedrichs' systems with a two-field structure, such as Maxwell's equations or the acoustic wave equation in div-grad form, cf. [14]. We focus on a discontinuous Galerkin space discretization applied to a locally refined mesh or a small region with high wave speed. This results in a stiff system of ordinar

Jie He, Wendi Zhou, Xiang Lorraine Li, Jeff Z. Pan

Unsupervised domain adaptation leverages abundant labeled data from various source domains to generalize onto unlabeled target data. Prior research has primarily focused on learning domain-invariant features across the source and target domains. However, these methods often require training a model using source domain data, which is time-consuming and can li

Neemesh Yadav, Jiarui Liu, Francesco Ortu, Roya Ensafi

The ability of Natural Language Processing (NLP) methods to categorize text into multiple classes has motivated their use in online content moderation tasks, such as hate speech and fake news detection. However, there is limited understanding of how or why these methods make such decisions, or why certain content is moderated in the first place. To investiga

Achiel Colpaert, Zhuangzhuang Cui, Sofie Pollin

Connecting aerial and terrestrial users with a single base station (BS) is increasingly challenging due to the rising number of aerial users like unmanned aerial vehicles (UAVs). Traditional BSs, designed with down-tilted beams, focus mainly on ground users, but massive MIMO (mMIMO) systems can significantly enhance coverage in low-altitude airspace. This pa

Byeoksong Lee, Joongoo Kang

Symmetry plays a crucial role in shaping the theories of fundamental forces. For example, general covariance -- the equivalence of all possible coordinate systems of spacetime -- dictates the law of gravity. Here, we extend this concept to nonequilibrium thermodynamics by developing a theory of ionic thermoelectricity (thermoelectricity without electrons) in

Anton Alekseev, Arkady Berenstein, Anfisa Gurenkova, Yanpeng Li

The multiplicative multiple Horn problem is asking to determine possible singular values of the combinations $AB, BC$ and $ABC$ for a triple of invertible matrices $A,B,C$ with given singular values. There are similar problems for eigenvalues of sums of Hermitian matrices (the additive problem), and for maximal weights of multi-paths in concatenations of pla

Umur Hasturk, Albert H. Schrotenboer, Kees Jan Roodbergen, Evrim Ursavas

Green hydrogen has multiple use cases and is produced from renewable energy, such as solar or wind energy. It can be stored in large quantities, decoupling renewable energy generation from its use, and is therefore considered essential for achieving a climate-neutral economy. The intermittency of renewable energy generation and the stochastic nature of deman

Jie Han, Lin Sun, Guanghui Wang

We show that for $ \eta>0 $ and sufficiently large $ n $, every 5-graph on $ n $ vertices with $\delta_{2}(H)\ge (91/216+\eta)\binom{n}{3}$ contains a Hamilton 2-cycle. This minimum 2-degree condition is asymptotically best possible. Moreover, we give some related results on Hamilton $ \ell $-cycles with $ d $-degree for $\ell\le d \le k-1$ and $1\le \ell <

Sajad Marvi, Christoph Rist, Julian Schmidt, Julian Jordan

Accurate trajectory prediction is crucial for autonomous driving, yet uncertainty in agent behavior and perception noise makes it inherently challenging. While multi-modal trajectory prediction models generate multiple plausible future paths with associated probabilities, effectively quantifying uncertainty remains an open problem. In this work, we propose a

Paula Alvarez Cartelle, Alessia Anelli, Anna Balboni, Anja Beck

The LHCb Ring-Imaging Cherenkov detectors are built to provide charged hadron identification over a large range of momentum. The upgraded detectors are also capable of providing an independent measurement of the luminosity for the LHCb experiment during LHC Run 3. The modelling of the opto-electronics chain, the application of the powering strategy during op

Michelangelo Domina, Stefano Sanvito

The formulation of descriptors of the local chemical environment, enabling the construction of machine-learning models, is usually obtained by studying the properties of the expansion coefficients of a neighborhood density. In this work, we show that all the transformation properties of the descriptors and their behaviour under rotation, inversion and comple

Joel Fine, Weiyong He, Chengjian Yao

A hypersymplectic structure on a 4-manifold is a triple of symplectic forms for which any non-zero linear combination is again symplectic. In 2006, Donaldson conjectured that on a compact 4-manifold any hypersymplectic structure can be deformed through cohomologous hypersymplectic structures to a hyperk\"ahler triple. We prove this under the assumption that

Brian Cunningham

Building on a beyond-GW many-body framework that incorporates higher-order vertex effects in the self-energy -- giving rise to T-matrix and second-order exchange contributions -- this approach is extended to now include the vertex derived in that work to the kernel in the Bethe-Salpeter Equation (BSE) for the reducible polarization function. This results in

Annika Hüselitz, Housen Li, Axel Munk

We consider the problem of sequential (online) estimation of a single change point in a piecewise linear regression model under a Gaussian setup. We demonstrate that certain CUSUM-type statistics attain the minimax optimal rates for localizing the change point. Our minimax analysis unveils an interesting phase transition from a jump (discontinuity in functio