December 2024 arXiv papers — page 152

Danny P. Sawyer, Nan Rosemary Ke, Hubert Soyer, Martin Engelcke

Foundation models excel at single-turn reasoning but struggle with multi-turn exploration in dynamic environments, a requirement for many real-world challenges. We evaluated these models on their ability to learn from experience, adapt, and gather information. First, in "Feature World," a simple setting for testing information gathering, models performed nea

Antoine Henrot, Antoine Lemenant, Yannick Privat

In this article, we address the problem of determining a domain in $\mathbb{R}^N$ that minimizes the first eigenvalue of the Lam\'e system under a volume constraint. We begin by establishing the existence of such an optimal domain within the class of quasi-open sets, showing that in the physically relevant dimensions $N = 2$ and $3$, the optimal domain is in

Lea Bogensperger, Matthias J. Ehrhardt, Thomas Pock, Mohammad Sadegh Salehi

We consider a bilevel learning framework for learning linear operators. In this framework, the learnable parameters are optimized via a loss function that also depends on the minimizer of a convex optimization problem (denoted lower-level problem). We utilize an iterative algorithm called `piggyback' to compute the gradient of the loss and minimizer of the l

Yiding Wang, Yuxuan Chen, Fangwei Zhong, Long Ma

Desires motivate humans to interact autonomously with the complex world. In contrast, current AI agents require explicit task specifications, such as instructions or reward functions, which constrain their autonomy and behavioral diversity. In this paper, we introduce a Desire-driven Autonomous Agent (D2A) that can enable a large language model (LLM) to auto

Théo Brugeat, Christopher Smith

If dark matter carries a baryon number of two, neutron-antineutron oscillations could require its presence to manifest themselves. If it is in addition very light, in the micro-eV range or up to a few orders of magnitude below, these oscillations could even exhibit a Rabi resonance. Though the magnetic tuning required to convert a macroscopic number of neutr

Khun Sang Phukon, Patricia Schmidt, Geraint Pratten

Compact binaries on eccentric orbits are another class of gravitational-wave (GW) sources that can provide a wealth of information on binary formation pathways and astrophysical environments. However, historically, eccentricity is often neglected in modelled GW searches for compact binaries. We show that currently used modelled searches that employ quasi-cir

Marco Wrzalik, Adrian Ulges, Anne Uersfeld, Florian Faust

We address the detection of emission reduction goals in corporate reports, an important task for monitoring companies' progress in addressing climate change. Specifically, we focus on the issue of integrating expert feedback in the form of labeled example passages into LLM-based pipelines, and compare the two strategies of (1) a dynamic selection of few-shot

Jesper Amilon, Zafer Esen, Dilian Gurov, Christian Lidström

In deductive verification and software model checking, dealing with certain specification language constructs can be problematic when the back-end solver is not sufficiently powerful or lacks the required theories. One way to deal with this is to transform, for verification purposes, the program to an equivalent one not using the problematic constructs, and

Xiaoyuan Xie, Yan Song, Songqiang Chen, Jinfu Chen

Deep Learning (DL) libraries, such as PyTorch, are widely used for building and deploying DL models on various hardware platforms. Meanwhile, they are found to contain bugs that lead to incorrect calculation results and cause issues like non-convergence training and inaccurate prediction of DL models. Thus, many efforts have been made to test DL libraries an

Shai M. Chester, De-liang Zhong

We compute the AdS Virasoro-Shapiro amplitude for scattering of dilatons in type IIB string theory with pure RR flux on $AdS_3\times S^3\times M_4$ for $M_4=T^4$ or $K3$, to all orders in $\alpha'$ in a small AdS curvature expansion. This is achieved by comparing the flat space limit of the dual D1D5 CFT correlator to an ansatz for the amplitude as a worldsh

Hiroyoshi Mitake, Panrui Ni

Here, we study the periodic homogenization problem of nonlinear weakly coupled systems of Hamilton-Jacobi equations in the convex setting. We establish a rate of convergence $O(\sqrt{\varepsilon})$ which is sharp.

Kensuke Mitsuzawa, Margherita Grossi, Stefano Bortoli, Motonobu Kanagawa

Given a pair of multivariate time-series data of the same length and dimensions, an approach is proposed to select variables and time intervals where the two series are significantly different. In applications where one time series is an output from a computationally expensive simulator, the approach may be used for validating the simulator against real data

Vivek Kumar, Vittorio Cecconi, Antonio Cutrona, Luke Peters

Manipulating broadband fields in scattering media is a modern challenge across photonics and other wave domains. Recent studies have shown that complex propagation in scattering media can be harnessed to manipulate broadband light wave packets in space-time for focusing, imaging, and computing applications. Interestingly, while many proposed methodologies op

Simone Pesatori

Using lattice theory, Hulek and Sch\"utt proved that for every $m\in\mathbb{Z}_+$ there exists a nine-dimensional family $\mathcal{F}_m$ of K3 surfaces covering Enriques surfaces having an elliptic pencil with a rational bisection of arithmetic genus $m$. We present a purely geometrical lattice free construction of these surfaces, that allows us to prove tha

B. Cao, Z. Liu, X. Han, S. Zhou

In warehousing systems, to enhance logistical efficiency amid surging demand volumes, much focus is placed on how to reasonably allocate tasks to robots. However, the robots labor is still inevitably wasted to some extent. In response to this, we propose a pre-scheduling enhanced warehousing framework that predicts task flow and acts in advance. It consists

Renlong Wu, Zhilu Zhang, Mingyang Chen, Zifei Yan

Recent 4D reconstruction methods have yielded impressive results but rely on sharp videos as supervision. However, motion blur often occurs in videos due to camera shake and object movement, while existing methods render blurry results when using such videos for reconstructing 4D models. Although a few approaches attempted to address the problem, they strugg

Nicolás Arévalo-Hurtado

We prove the existence of equilibrium states for geometric potentials in a class of piecewise weakly convex interval maps. This class includes systems with indifferent fixed points and non-Markov partitions. Under additional hypotheses we also obtain uniqueness.

Marcel de Jeu, Alexey Kuzmin, Paulo R. Pinto

We give an explicit injective representation of the universal $\mathrm{C}^\ast$-algebra that is generated by doubly non-commuting isometries. This injectivity allows us to prove that such universal algebras embed naturally into each other and also, when combined with Rieffel's theory of deformation, to show that they are nuclear and to compute their K-theory

Basab Jha, Ujjwal Puri

While large language models, such as GPT and BERT, have already demonstrated unprecedented skills in everything from natural language processing to domain-specific applications, there came an unexplored phenomenon we term the Rosetta Paradox. The Rosetta Paradox characterizes the counterintuitive performance inversions across domains of knowledge. This parad

Xinyue Pei, Xingwei Wang, Min Huang, Yingyang Chen

In this work, we investigate the physical layer security (PLS) of ambient backscatter communication non-orthogonal multiple access (AmBC-NOMA) networks where non-colluding eavesdroppers (Eves) are randomly distributed. In the proposed system, a base station (BS) transmits a superimposed signal to a typical NOMA user pair, while a backscatter device~(BD) simu

Catrin Campbell-Moore

Measures of accuracy usually score how accurate a specified credence depending on whether the proposition is true or false. A key requirement for such measures is strict propriety; that probabilities expect themselves to be most accurate. We discuss characterisation results for strictly proper measures of accuracy. By making some restrictive assumptions, we

Haihang Wu

Large language models (LLMs) have demonstrated remarkable performance across various language tasks, but their widespread deployment is impeded by their large size and high computational costs. Structural pruning is a prevailing technique used to introduce sparsity into pre-trained models and facilitate direct hardware acceleration during inference by removi

Jinglong Yang, Yichen Wu, Jun Cen, Wenjian Huang

Although the current different types of SAM adaptation methods have achieved promising performance for various downstream tasks, such as prompt-based ones and adapter-based ones, most of them belong to the one-step adaptation paradigm. In real-world scenarios, we are generally confronted with the dynamic scenario where the data comes in a streaming manner. D

Howard Caulfield, James P. Gleeson

Financial time series (FTS) generation models are a core pillar to applications in finance. Risk management and portfolio optimization rely on realistic multivariate price generation models. Accordingly, there is a strong modelling literature dating back to Bachelier's Theory of Speculation in 1901. Generating FTS using deep generative models (DGMs) is still

Tanmoy Kumar Paul, Atanu Kumar Saha, Sumeet Kumar Gupta

In this work, we analyze dead layer comprising non-polar monoclinic (m) phase in $Hf_xZr_{1-x}O_2$ (HZO)-based ferroelectric (FE) material using first principles analysis. We show that with widely used tungsten (W) metal electrode, the spatial distribution of the oxygen vacancy across the cross-section plays a key role in dictating the favorability of m- pha

Alexander D. Kurilov, Anastasia V. Gubareva, Sergei A. Zubkov, Denis N. Chausov

A model of sound propagation in a magnetized magnetic fluid containing ellipsoidal aggregates is proposed. The model quantitatively describes the geometry of the aggregates formed from nanoparticles. Expressions for the attenuation coefficient and the sound velocity have been derived, taking into account dipole-dipole interaction between the aggregates. The

Martin Guillemaud, Alice Longhena, Louis Cousyn, Valerio Frazzini

Epilepsy surgery, particularly for temporal lobe epilepsy (TLE), remains a vital treatment option for patients with drug-resistant seizures. However, accurately predicting surgical outcomes remains a significant challenge. This study introduces a novel biomarker derived from brain connectivity, analyzed using non-Euclidean network geometry, to predict the su

Junhe Zhang, Wanli Ni, Dongyu Wang

As a paradigm of distributed machine learning, federated learning typically requires all edge devices to train a complete model locally. However, with the increasing scale of artificial intelligence models, the limited resources on edge devices often become a bottleneck for efficient fine-tuning. To address this challenge, federated split learning (FedSL) im

Yu Zhong, Rui Zhang, Zihao Zhang, Shuo Wang

Vision-and-Language Navigation (VLN) is a challenging task that requires an agent to navigate through photorealistic environments following natural-language instructions. One main obstacle existing in VLN is data scarcity, leading to poor generalization performance over unseen environments. Though data argumentation is a promising way for scaling up the data

Cunshi Wang, Yu Zhang, Yuyang Li, Xinjie Hu

The exponential growth of large-scale telescope arrays has boosted time-domain astronomy development but introduced operational bottlenecks, including labor-intensive observation planning, data processing, and real-time decision-making. Here we present the StarWhisper Telescope system, an AI agent framework automating end-to-end astronomical observations for

Steven G. Krantz, Kaushal Verma

The Wong--Rosay theorem provides a characterization of the unit ball among all strongly pseudoconvex domains in terms of holomorphic automorphism group actions. We explore variants of this theorem in the quasiconformal setting.

Bart Bussmann, Patrick Leask, Neel Nanda

Sparse autoencoders (SAEs) have emerged as a powerful tool for interpreting language model activations by decomposing them into sparse, interpretable features. A popular approach is the TopK SAE, that uses a fixed number of the most active latents per sample to reconstruct the model activations. We introduce BatchTopK SAEs, a training method that improves up

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Using $(27.12\pm 0.14)\times 10^{8}$ $\psi(3686)$ events collected with the BESIII detector operating at the BEPCII collider, the decay $\psi(3686)\to\Sigma^{0}\bar{\Sigma}^{0}\phi$ is observed for the first time with a statistical significance of 7.6$\sigma$. Its branching fraction is measured to be $(2.64 \pm 0.32_{\textrm{stat}} \pm 0.12_{\textrm{sys}}) \

A. Tasnim Aynul, L. Cruz Rodriguez, C. Figueira de Morisson Faria

We investigate the phase-space dynamics of the Kramers Henneberger (KH) atom solving the time-dependent Schr\"odinger equation for reduced-dimensionality models and using Wigner quasiprobability distributions. We find that, for the time-averaged KH potential, coherent superpositions of eigenstates perform a cyclic motion confined in momentum space, whose fre

Gonzalo E. Imaz

Nested answer set programming (NASP; Lifschitz et al., 1999) generalizes answer set programming (ASP) by admitting nested expressions in rule bodies and heads, and thus, NASP aims at exploiting program succinctness. Yet, although NASP expressiveness is undoubtedly superior to ASP one, the former's reasoning capabilities remain unexplored. This reality seems

Oleksandr V. Maslyuchenko, Janusz Morawiec, Thomas Zürcher

It is well-known that the Lebesgue measure is the unique absolutely continuous invariant probability measure under the $p$-adic transformation. The purpose of this paper is to characterize the family of all invariant probability measures under the $p$-adic transformation and to provide some description of them. In particular, we describe the subfamily of all

Adam Kollarčík adn Zdeněk Hanzálek

This paper investigates the problem of trajectory planning for autonomous vehicles at unsignalized intersections, specifically focusing on scenarios where the vehicle lacks the right of way and yet must cross safely. To address this issue, we have employed a method based on the Partially Observable Markov Decision Processes (POMDPs) framework designed for pl

D. Barbato, D. Mesa, V. D'Orazi, S. Desidera

The synergy between different detection methods is a key asset in exoplanetology, allowing for both precise characterization of detected exoplanets and robust constraints even in the case of non-detection. Recently, the interplay between imaging, radial velocities and astrometry has produced significant advancements in exoplanetary science. We report a first

Ori Matityahu, Raanan Fattal

In this paper we identify the source of a singularity in the training loss of key denoising models, that causes the denoiser's predictions to collapse towards the mean of the source or target distributions. This degeneracy creates false basins of attraction, distorting the denoising trajectories and ultimately increasing the number of steps required to sampl

Boyan Duan, Minghui Ouyang, Zheng Wang

We say that two partial orders on $[n]$ are compatible if there exists a partial order that refines both of them. This compatibility relation induces a natural set system structure between the collection $\mathcal{F}$ of all partial orders and the collection $\mathcal{G}$ of all total orders on $[n]$, where each order is associated with the set of orders com

Sen Kong, Meng Wang

To address the challenges in networked environments and control problems associated with complex nonlinear uncertain systems, this paper investigates the design of a membership-function-dependent (MFD) memory output-feedback (MOF) controller for interval type-2 (IT2) fuzzy systems under fading channels, leveraging a memory dynamic event-triggering mechanism

Tian-Le Gao, Ri-Qing Qian, Xiang Liu

In this work, we investigate the production of charmonium $2P$ states via the $e^+e^-\to \gamma D\bar{D}$ process at $\sqrt{s} = 4.23$ GeV. Using the measured cross-section data for $e^+e^-\to \gamma X(3872)$ as a reference, we calculate the cross sections for $e^+e^-\to \gamma \chi_{c0}(2P)$ and $e^+e^-\to \gamma \chi_{c2}(2P)$. Since the $\chi_{c0}(2P)$ an

Xingyu Lei, Shuchao Li

An important theorem about the spectral Tur\'an problem of $K_{a,b}$ was largely developed in separate papers. Recently it was completely resolved by Zhai and Lin [J. Comb. Theory, Ser. B 157 (2022) 184-215], which also confirms a conjecture proposed by Tait [J. Comb. Theory, Ser. A 166 (2019) 42-58]. Here, the prior work is fully stated, and then generalize

Yang Hua, Sheng-Qin Feng

We study the two-flavor NJL model under the rotation and chiral chemical potential $\mu_{5}$. Firstly, the influence of chiral imbalance on the chiral phase transition in the $T_{pc}-\omega$ plane is investigated. Research manifests that as $\mu_{5}$ increases, the critical point (CEP) of the $T_{pc}-\omega$ plane chiral phase transition will move closer to

Yang Lu, Donghui Quan, Qiang Chang, Long-Fei Chen

In recent years, a significant number of oxygen-bearing complex organic molecules (COMs) have been detected in the gas phase of cold dark clouds such as TMC-1. The formation of these COMs cannot be explained by diffusive mechanisms on grains and gas phase reactions. This study investigates the formation of oxygen-bearing COMs in cold dark clouds using multip

Huan-Qiang Zhou, Qian-Qian Shi, Ian P. McCulloch, Murray T. Batchelor

A generic scheme is proposed to investigate the entanglement entropy for a type of scale-invariant states, valid for orthonormal basis states in the ground state subspace of quantum many-body systems undergoing spontaneous symmetry breaking with type-B Goldstone modes in two spatial dimensions and beyond. It is argued that a contribution from the area law to

Nikhil S. Mande, Karteek Sreenivasaiah

We initiate the study of a new model of query complexity of Boolean functions where, in addition to 0 and 1, the oracle can answer queries with ``unknown''. The query algorithm is expected to output the function value if it can be conclusively determined by the partial information gathered, and it must output ``unknown'' if not. We formalize this model by us

Lanxiang Hu, Qiyu Li, Anze Xie, Nan Jiang

Evaluating the reasoning abilities of large language models (LLMs) is challenging. Existing benchmarks often depend on static datasets, which are vulnerable to data contamination and may get saturated over time, or on binary live human feedback that conflates reasoning with other abilities. As the most prominent dynamic benchmark, Chatbot Arena evaluates ope

James R. Maddison

Ocean turbulence parameterization has principally been based on processed-based approaches, seeking to embed physical principles so that coarser resolution calculations can capture the net influence of smaller scale unresolved processes. More recently there has been an increasing focus on the application of data-driven approaches to this problem. Here we con

Gabriel Meletti, Stéphane Abide, Uwe Harlander, Isabelle Raspo

Experiments on rotating annuli that are differentially heated in the radial direction have been largely contributing to a better understanding of baroclinic instabilities. This configuration creates waves at a laboratory scale that are related to atmospheric circulations. Pioneer studies in baroclinic tanks have shown that experiments with low aspect ratios

Léo Andrès, Filipe Marques, Arthur Carcano, Pierre Chambart

In this paper, we present the design of Owi, a symbolic interpreter for WebAssembly written in OCaml, and how we used it to create a state-of-the-art tool to find bugs in programs combining C and Rust code. WebAssembly (Wasm) is a binary format for executable programs. Originally intended for web applications, Wasm is also considered a serious alternative fo

Alberto Sinigaglia, Niccolò Turcato, Ruggero Carli, Gian Antonio Susto

Deep Reinforcement Learning is gaining increasing attention thanks to its capability to learn complex policies in high-dimensional settings. Recent advancements utilize a dual-network architecture to learn optimal policies through the Q-learning algorithm. However, this approach has notable drawbacks, such as an overestimation bias that can disrupt the learn

George Kontogiannis, Pantelis Tzamalis, Sotiris Nikoletseas

In the evolving domain of Human Activity Recognition (HAR) using Internet of Things (IoT) devices, there is an emerging interest in employing Deep Generative Models (DGMs) to address data scarcity, enhance data quality, and improve classification metrics scores. Among these types of models, Generative Adversarial Networks (GANs) have arisen as a powerful too

Jayden Dongwoo Lee, Youngjae Kim, Yoonseong Kim, Hyochoong Bang

This paper proposes a data-driven model predictive control for multirotor collision avoidance considering uncertainty and an unknown model from a payload. To address this challenge, sparse identification of nonlinear dynamics (SINDy) is used to obtain the governing equation of the multirotor system. The SINDy can discover the equations of target systems with

Lajos G. Balázs, Gábor B. Kovács

Recent space-borne and ground-based observations provide photometric measurements as time series. The effect of interstellar dust extinction in the near-infrared range is only 10% of that measured in the V band. However, the sensitivity of the light curve shape to the physical parameters in the near-infrared is much lower. So, interpreting these types of dat

Akiyoshi Shioura

We consider a class of nonlinear integer programming problems arising from re-allocation of dock-capacity in a bike sharing system. The main aim of this note is to derive an improved proximity bound for the problem and its scaled variant. This makes it possible to refine the time bound for the polynomial-time proximity-scaling algorithm by Freund et al. (202

Christophe Eyral, Mutsuo Oka

Let $f$ be a (possibly Newton degenerate) weighted homogeneous polynomial defining an isolated surface singularity at the origin of $\mathbb{C}^3$, and let $\{f_s\}$ be a generic deformation of its coefficients such that $f_s$ is Newton non-degenerate for $s\not=0$. We show that there exists an ''admissible'' simultaneous good resolution of the family of fun

Maarten Stroeks, Barbara M. Terhal

We introduce the fermionic satisfiability problem, Fermionic $k$-SAT: this is the problem of deciding whether there is a fermionic state in the null-space of a collection of fermionic, parity-conserving, projectors on $n$ fermionic modes, where each fermionic projector involves at most $k$ fermionic modes. We prove that this problem can be solved efficiently

Kevin Gao, Maxwell A. Xu, James M. Rehg, Alexander Moreno

We introduce PyPulse, a Python package for imputation of biosignals in both clinical and wearable sensor settings. Missingness is commonplace in these settings and can arise from multiple causes, such as insecure sensor attachment or data transmission loss. PyPulse's framework provides a modular and extendable framework with high ease-of-use for a broad user

Khoat Than, Dat Phan, Giang Vu

Robustness and generalization ability of machine learning models are of utmost importance in various application domains. There is a wide interest in efficient ways to analyze those properties. One important direction is to analyze connection between those two properties. Prior theories suggest that a robust learning algorithm can produce trained models with

Raul Sena Ferreira, Joris Guérin, Kevin Delmas, Jérémie Guiochet

Machine Learning (ML) models, such as deep neural networks, are widely applied in autonomous systems to perform complex perception tasks. New dependability challenges arise when ML predictions are used in safety-critical applications, like autonomous cars and surgical robots. Thus, the use of fault tolerance mechanisms, such as safety monitors, is essential

Olivier Vu Thanh

Low-rank matrix factorizations are a class of linear models widely used in various fields such as machine learning, signal processing, and data analysis. These models approximate a matrix as the product of two smaller matrices, where the left matrix captures latent features while the right matrix linearly decomposes the data based on these features. There ar

Sunil Das

A real matrix is said to be positive if its every entry is positive, and a real square matrix A is algebraically positive if there exists a real polynomial f such that f(A) is a positive matrix. A sign pattern matrix A is said to require a property if all matrices having sign pattern as A have that property. In this paper, we characterize all sign pattern ma

Wan-Qian Zhao, Shu-Jie Zhang, Zhan-Yong Guo, Zeng-Yuan Tian

This article critically examines the methodologies applied in ancient DNA (aDNA) research, particularly those developed by Dr. P\"a\"abo's team, which have significantly influenced the field. The focus is on the challenges of distinguishing original in situ DNA (oriDNA) from environmental DNA (eDNA) contamination in fossil samples. Recent analyses indicate t

Jindrich Dunik, Ladislav Kral, Jakub Matousek, Ondrej Straka

This paper deals with the state prediction of nonlinear stochastic dynamic systems. The emphasis is laid on a solution to the integral Chapman-Kolmogorov equation by a deterministic-integration-rule-based point-mass method. A novel concept of reliable data-augmented, i.e., mathematics- and data-informed, integration rule is developed to enhance the point-mas

Rui Li, Anyao Wang, Mingqing Zhai

We resolve a problem posed by Guiduli (1996) on the spectral radius of graphs satisfying the Hereditarily Bounded Property $P_{t,r}$, which requires that every subgraph $H$ with $|V(H)| \geq t$ satisfies $|E(H)| \leq t|V(H)| + r$. For an $n$-vertex graph $G$ satisfying $P_{t,r}$, where $t > 0$ and $r \geq -\binom{\lfloor t+1 \rfloor}{2}$, we prove that the s

Taher Jalal

Stable distributions are a celebrated class of probability laws used in various fields. The $\alpha$-stable process, and its exponentially tempered counterpart, the Classical Tempered Stable (CTS) process, are also prominent examples of L\'evy processes. Simulating these processes is critical for many applications, yet it remains computationally challenging,

Oliver Kost, Jindrch Dunik, Ondrej Straka

The problem of noise covariance matrix identification of stochastic linear time-varying state-space models is addressed. The measurement difference method (MDM) is generalized to time-varying dimensions of the measurement and control. Three MDM identification techniques that differ in weighting used in the underlying least squares method are proposed. The te

A. Charlet, J. Granot, P. Beniamini

Among the models used to explain the prompt emission of gamma-ray bursts (GRBs), internal shocks is a leading one. Its most basic ingredient is a collision between two cold shells of different Lorentz factors in an ultra-relativistic outflow, which forms a pair of shock fronts that accelerate electrons in their wake. In this model, key features of GRB prompt

Emanuele Frittaion

In "Extensional realizability for intuitionistic set theory", we introduced an extensional variant of generic realizability, where realizers act extensionally on realizers, and showed that this form of realizability provides "inner" models of $\sf CZF$ (constructive Zermelo-Fraenkel set theory) and $\sf IZF$ (intuitionistic Zermelo-Fraenkel set theory), that

Joshua Freeman, Chloe Rippe, Edoardo Debenedetti, Maksym Andriushchenko

Copyright infringement in frontier LLMs has received much attention recently due to the New York Times v. OpenAI lawsuit, filed in December 2023. The New York Times claims that GPT-4 has infringed its copyrights by reproducing articles for use in LLM training and by memorizing the inputs, thereby publicly displaying them in LLM outputs. Our work aims to meas

Wenting Diao, Xi Wang, Ke Di, Yu Liu

We investigate the absorption and transmission properties of a weak probe field in an atom opto-magnomechanics system. The system comprises an assembly of two-level atoms and a magnon mode within a ferrimagnetic crystal, which directly interacts with an optical cavity mode through the crystal's deformation displacement. We observe optomechanically induced tr

Songkang Wen, Vasilii Feofanov, Jianfeng Zhang

Recently, there has been a growing interest in time series foundation models that generalize across different downstream tasks. A key to strong foundation models is a diverse pre-training dataset, which is particularly challenging to collect for time series classification. In this work, we explore the performance of a contrastive-learning-based foundation mo

Francesca Menescardi, Davide Ceresoli, Donato Belmonte

The thermodynamic behavior of calcium oxide (\ce{CaO}) under high temperature and pressure conditions is critical for understanding the physics of planetary interiors. This study employs molecular dynamics (MD) simulations, including both classical and ab-initio approaches, to investigate the melting behavior of CaO. We calculate the melting temperature of \

Gefei Cai, Wen-Bo Li, Tim Mesikepp

We initiate a study of the quasisymmetric uniformization of naturally arising random fractals and show that many of them fall outside the realm of quasisymmetric uniformization to simple canonical spaces. We begin with the trace, the graph of Brownian motion, and various variants of the Schramm-Loewner evolution $\mathrm{SLE}_\kappa$ for $\kappa>0$, and show

Jonathan Ganz, Jonas Ammeling, Emely Rosbach, Ludwig Lausser

Foundation models (FMs), i.e., models trained on a vast amount of typically unlabeled data, have become popular and available recently for the domain of histopathology. The key idea is to extract semantically rich vectors from any input patch, allowing for the use of simple subsequent classification networks potentially reducing the required amounts of label

Hiromichi Hoshina

To elucidate the mechanism by which THz radiation non-thermally affects living organisms, the lateral diffusion constants of lipid molecules in the cell membranes of HeLa cells were measured using fluorescence recovery after photobleaching under THz wave irradiation (THz-FRAP) at frequencies of 0.10, 0.29, and 0.48 THz, with power densities ranging from 20 t

Lola Ciapa, Yvette Tran, Christian Frétigny, Antoine Chateauminois

Friction experiments were conducted on hydrogel thin films sliding against a rigid sphere in a low velocity regime where molecular adsorption at the sliding interface sets the friction force, through a dissipative adsorption-stretching-desorption mechanism initially postulated by Schallamach. By carefully imaging the contact from the initial indentation step

V. A. Kiryukhina, A. V. Dodin

We studied rotational modulation of the radial velocities of narrow emission lines in four classical T Tauri stars. We found that the previously declared shift of the mean velocity of neutral and ionized helium lines relative to the mean radial velocity of the star is not associated with the inflow of accreted gas into the hotspot, since the radial velocity

Johannes Rauch

We describe the implementation of the exact solver weberknecht and the heuristic solver weberknecht_h for the One-Sided Crossing Minimization problem.

Ran Yan, Nan Zhou, Minda Ma, Chao Mao

As an emerging emitter poised for significant growth in space cooling demand, India requires comprehensive insights into historical emission trends and decarbonization performance to shape future low-carbon cooling strategies. By integrating a bottom-up demand resource energy analysis model and a top-down decomposition method, this study is the first to cond

Jesse Hagenaars, Yilun Wu, Federico Paredes-Vallés, Stein Stroobants

Event cameras provide low-latency perception for only milliwatts of power. This makes them highly suitable for resource-restricted, agile robots such as small flying drones. Self-supervised learning based on contrast maximization holds great potential for event-based robot vision, as it foregoes the need for high-frequency ground truth and allows for online

A. N. Vulfson, P. V. Nikolaev

The paper discusses a variant of the local similarity theory, employing the second moment of vertical velocity and the "spectral" Prandtl mixing length as basic parameters. This approach allows expressing the turbulent exchange coefficient, dissipations of kinetic energy and buoyancy square, mixed moments of the buoyancy and vertical velocity, as well as str

Mohammad Ali S. Afshar, Jafar Sadeghi

General relativity predicts that a rotating black hole drags the spacetime due to its spin. This effect can influence the motion of nearby objects, causing them to either fall into the black hole or orbit around it. In classical Newtonian mechanics, as the radius of the orbit increases, the angular velocity of an object in a stable circular orbit decreases.

Alex Lence, Ahmad Fall, Samuel David Cohen, Federica Granese

Electrocardiograms (ECGs) are essential for diagnosing cardiac pathologies, yet traditional paper-based ECG storage poses significant challenges for automated analysis. This study introduces ECGtizer, an open-source, fully automated tool designed to digitize paper ECGs and recover signals lost during storage. ECGtizer facilitates automated analyses using mod

Lilian Edwards, Reuben Binns

Generative AI systems often generate outputs about real people, even when not explicitly prompted to do so. This can lead to significant reputational and privacy harms, especially when sensitive, misleading, and outright false. This paper considers what legal tools currently exist to protect such individuals, with a particular focus on defamation and data pr

Yifan Huang, Wei Fang, Zhengyu Ma, Guoqi Li

Dendritic computation endows biological neurons with rich nonlinear integration and high representational capacity, yet it is largely missing in existing deep spiking neural networks (SNNs). Although detailed multi-compartment models can capture dendritic computations, their high computational cost and limited flexibility make them impractical for deep learn

Carlo Lucibello, Aurora Rossi

GraphNeuralNetworks.jl is an open-source framework for deep learning on graphs, written in the Julia programming language. It supports multiple GPU backends, generic sparse or dense graph representations, and offers convenient interfaces for manipulating standard, heterogeneous, and temporal graphs with attributes at the node, edge, and graph levels. The fra

Yiqiu Wang, Meixia Tao, Shu Sun, Wei Cao

This paper investigates an integrated sensing and communication (ISAC) system where the sensing target is a three-dimensional (3D) extended target, for which multiple scatterers from the target surface can be resolved. We first introduce a second-order truncated Fourier series surface model for an arbitrarily-shaped 3D ET. Utilizing this model, we derive tra

Zeru Shi, Zengxi Zhang, Kemeng Cui, Ruizhe An

Images captured in harsh environments often exhibit blurred details, reduced contrast, and color distortion, which hinder feature detection and matching, thereby affecting the accuracy and robustness of homography estimation. While visual enhancement can improve contrast and clarity, it may introduce visual-tolerant artifacts that obscure the structural inte

Riccardo Bernardini

In this paper we are interested in the class numbers of a family of real quadratic fields for which the square roots of the discriminants have a known expansion in continued fraction. In particular we prove that $h(D)>1$, with possibly a finite number of exceptions.

Zhe Wang, Catalin-Mihai Halati, Jean-Sébastien Bernier, Alexey Ponomaryov

Stable composite objects, such as hadrons, nuclei, atoms, molecules and superconducting pairs, formed by attractive forces are ubiquitous in nature. By contrast, composite objects stabilized by means of repulsive forces were long thought to be theoretical constructions owing to their fragility in naturally occurring systems. Surprisingly, the formation of bo

Jason Choy, Yavar Kian

In this work, we consider the inverse problem of simultaneously recovering two classes of quasilinear terms appearing in a parabolic equation from boundary measurements. It is motivated by several industrial and scientific applications, including the problems of heat conduction and population dynamics, and we study the issue of stability. More precisely, we

Ankit Bhojak, Surjeet Singh Choudhary, Siddhartha Samanta, Saurabh Shrivastava

In this article, we study discrete maximal function associated with the Birch-Magyar averages over sparse sequences. We establish sparse domination principle for such operators. As a consequence, we obtain $\ell^p$-estimates for such discrete maximal function over sparse sequences for all $p>1$. The proof of sparse bounds is based on scale-free $\ell^p-$impr

Roman Gruber, Tim Harris, Marina Krstic Marinkovic

We develop a generalization of low-mode averaging in which the number of low quark modes of the Dirac operator required for a constant variance reduction can be kept independent of the volume by exploiting their local coherence. Typically in lattice QCD simulations, the benefit of translation averaging quark propagators over the space-time volume is spoiled

Boyang Zhang, Daning Cheng, Yunquan Zhang, Fangming Liu

This work focus on how to stabilize and lossless model compression, aiming to reduce model complexity and enhance efficiency without sacrificing performance due to compression errors. A key challenge is effectively leveraging compression errors and defining the boundaries for lossless compression to minimize model loss. i.e., compression for better. Currentl

Pedro Miguel Campos

We introduce a new family of function spaces, the fractional generalized Sobolev-Orlicz spaces $\Lambda^{s,A}_0(\Omega)$, where $A$ is a generalized $\Phi$-function satisfying the $(\mathrm{Inc})_{p}$ and $(\mathrm{Dec})_{q}$ conditions for $1<p\leq q<\infty$, as an extension of the Lions-Calder\'on spaces (also known as Bessel potential spaces) $\Lambda^{s,

Dan-Dan Hu, Meng-Yan Li, Fen-Zhuo Guo, Yu-Kun Wang

The violation of a Bell inequality implies the existence of nonlocality, making device-independent randomness certification possible. This paper derives a tight upper bound for the maximal quantum violation of Gisin's elegant Bell inequality (EBI) for arbitrary two-qubit states, along with the constraints required to achieve this bound. This method provides

Jaume de Dios Pont

The hot spots conjecture asserts that for any convex bounded domain $\Omega$ in $\mathbb R^d$, the first non-trivial Neumann eigenfunction of the Laplace operator in $\Omega$ attains its maximum at the boundary. We construct counterexamples to the conjecture for all sufficiently large values of $d$. The construction is based on an extension of the conjecture

Sourav Majumdar, Arnab Kumar Laha

We develop diffusion models for time-varying correlation using stochastic processes defined on the unit circle. Specifically, we study Brownian motion on the circle and the von Mises diffusion, and propose their use as continuous-time models for correlation dynamics. The von Mises process, introduced by Kent (1975) as a characterization of the von Mises dist