November 2024 arXiv papers — page 19

Tulio Meneghelli de Oliveira, Vinicius Wiggers, Eduardo Scafi, Silvio Zanin

This study examines the Lyapunov stability under coordinate $q$-contraction and $q$-dilatation in three dynamical systems: the discrete-time dissipative H\'enon map, and the conservative, non-integrable, continuous-time H\'enon-Heiles and diamagnetic Kepler problems. The stability analysis uses the $q$-deformed Jacobian and $q$-derivative, with trajectory st

Nejc Čeplak, Roberto Emparan, Andrea Puhm, Marija Tomašević

In light of the correspondence between black holes and fundamental strings with non-zero spin, we compute the sizes of rotating strings for small, moderate, and large values of the angular momentum and compare them to the sizes of rotating black holes. We argue that the ratio of the size perpendicular to the rotation plane to the size along the rotation plan

Keshav Dasgupta, Fang-Yi Guo, Bohdan Kulinich

Quantum gravity is fundamentally different from the non-gravitational quantum field theories in the sense that most of the techniques derived for the latter cannot be easily extended to the former. For example, correlation functions in quantum gravity become hard to define properly if the bulk Hamiltonian -- in the absence of a boundary Hamiltonian, and as a

Soumya Suvra Ghosal, Souradip Chakraborty, Vaibhav Singh, Tianrui Guan

With the widespread deployment of Multimodal Large Language Models (MLLMs) for visual-reasoning tasks, improving their safety has become crucial. Recent research indicates that despite training-time safety alignment, these models remain vulnerable to jailbreak attacks. In this work, we first highlight an important safety gap to describe that alignment achiev

Srishty Aggarwal, Banibrata Mukhopadhyay, Subhashish Banerjee, Arindam Ghosh

Quantum speed limit (QSL) defines the theoretical upper bound on how fast a quantum system can evolve between states. It imposes a fundamental constraint on the rate of quantum information processing. For a relativistic spin-up electron in a uniform magnetic field, QSL increased with the magnetic field strength till around $10^{15}$ Gauss, before saturating

Jesse Polman, Christoph Mordasini

Recent structure models of Jupiter suggest the existence of an extended region in the deep interior with a high heavy element abundance, referred to as a dilute core. This finding has led to increased interest in modelling the formation and evolution processes with the goal of understanding how and under what circumstances such a structure is formed and reta

Dumitru Călugăru, Yi Jiang, Haoyu Hu, Hanqi Pi

We introduce a new class of moir\'e systems and materials based on monolayers with triangular lattices and low-energy states at the M points of the Brillouin zone. These M-point moir\'e materials are fundamentally distinct from those derived from $\Gamma$- or K-point monolayers, featuring three time-reversal-preserving valleys related by three-fold rotationa

Madyson G. Barber, Andrew W. Mann, Andrew Vanderburg, Daniel Krolikowski

Astronomers have found more than a dozen planets transiting 10-40 million year old stars, but even younger transiting planets have remained elusive. A possible reason for the lack of such discoveries is that newly formed planets are not yet in a configuration that would be recognized as a transiting planet or cannot exhibit transits because our view is block

Yannick Stade, Lukas Burgholzer, Robert Wille

Intermediate representations (IRs) play a crucial role in the software stack of a quantum computer to facilitate efficient optimizations for executing an application on hardware. One of those IRs is the Quantum Intermediate Representation (QIR), which builds on the classical LLVM compiler infrastructure. In this article, we outline different approaches to ho

Anindya Guria, Banibrata Mukhopadhyay

The differentiation between chaotic and stochastic systems has long been scrutinized, particularly in observations where data is often noise-contaminated and finite. Our research examines the dual nature of the black hole X-ray binary IGR J17091-3624, an object whose behavior has been closely studied in parallel to GRS 1915+105. Remarkable similarities in th

Yutaka Hirai, Takayuki R. Saitoh, Michiko S. Fujii, Katsuhiro Kaneko

Metal-poor stars enriched by a single supernova (mono-enriched stars) are direct proof (and provide valuable probes) of supernova nucleosynthesis. Photometric and spectroscopic observations have shown that metal-poor stars have a wide variety of chemical compositions; the star's chemical composition reflects the nucleosynthesis process(es) that occurred befo

Rebecca Diesing, Siddhartha Gupta

Near the ends of their lives, supernova remnants (SNRs) enter a "radiative phase," when efficient cooling of the postshock gas slows expansion. Understanding SNR evolution at this stage is crucial for estimating feedback in galaxies, as SNRs are expected to release energy and momentum into the interstellar medium near the ends of their lives. A standard pred

Shota Komatsu, Adrien Martina, Joao Penedones, Antoine Vuignier

Using supersymmetric localization, we compute the partition function and some protected correlators of the polarized IKKT matrix model. Surprisingly, we find that the original IKKT model is different from polarized IKKT in the limit of vanishing mass deformation. We study different regimes of the localization results and recover the electrostatic problem whi

Alejandro Pardo, Fabio Pizzati, Tong Zhang, Alexander Pondaven

Match-cuts are powerful cinematic tools that create seamless transitions between scenes, delivering strong visual and metaphorical connections. However, crafting match-cuts is a challenging, resource-intensive process requiring deliberate artistic planning. In MatchDiffusion, we present the first training-free method for match-cut generation using text-to-vi

Brian Chao, Hung-Yu Tseng, Lorenzo Porzi, Chen Gao

3D Gaussian Splatting (3DGS) has recently emerged as a state-of-the-art 3D reconstruction and rendering technique due to its high-quality results and fast training and rendering time. However, pixels covered by the same Gaussian are always shaded in the same color up to a Gaussian falloff scaling factor. Furthermore, the finest geometric detail any individua

Wentao Wang, Hang Ye, Fangzhou Hong, Xue Yang

Given a single in-the-wild human photo, it remains a challenging task to reconstruct a high-fidelity 3D human model. Existing methods face difficulties including a) the varying body proportions captured by in-the-wild human images; b) diverse personal belongings within the shot; and c) ambiguities in human postures and inconsistency in human textures. In add

Yueru Jia, Jiaming Liu, Sixiang Chen, Chenyang Gu

3D geometric information is essential for manipulation tasks, as robots need to perceive the 3D environment, reason about spatial relationships, and interact with intricate spatial configurations. Recent research has increasingly focused on the explicit extraction of 3D features, while still facing challenges such as the lack of large-scale robotic 3D data a

Aoran Shen, Minghao Dai, Jiacheng Hu, Yingbin Liang

In the 21st-century information age, with the development of big data technology, effectively extracting valuable information from massive data has become a key issue. Traditional data mining methods are inadequate when faced with large-scale, high-dimensional and complex data. Especially when labeled data is scarce, their performance is greatly limited. Thi

Andrii Skliar, Ties van Rozendaal, Romain Lepert, Todor Boinovski

Mixture of Experts (MoE) LLMs have recently gained attention for their ability to enhance performance by selectively engaging specialized subnetworks or "experts" for each input. However, deploying MoEs on memory-constrained devices remains challenging, particularly when generating tokens sequentially with a batch size of one, as opposed to typical high-thro

Kiarash Firouzi, Mohammad Jelodari Mamaghani

We suggest employing log-ergodic processes to simulate the velocity of money in an ergodic manner. Our approach sheds light on economic behavior, policy implications, and financial dynamics by maintaining long-term stability. By bridging theory and practice, the partially ergodic model helps analysts and policymakers comprehend and forecast velocity of money

Sergio Pino-Alarcón, Vladimir Juričić

Dirac materials, hosting linearly dispersing quasiparticles at low energies, exhibit an emergent Lorentz symmetry close to a quantum critical point (QCP) separating semimetallic state from a strongly-coupled gapped insulator or superconductor. This feature appears to be quite robust even in the open Dirac systems coupled to an environment, featuring non-Herm

Zhi Zhang, Srishti Yadav, Fengze Han, Ekaterina Shutova

The recent advancements in auto-regressive multimodal large language models (MLLMs) have demonstrated promising progress for vision-language tasks. While there exists a variety of studies investigating the processing of linguistic information within large language models, little is currently known about the inner working mechanism of MLLMs and how linguistic

A. Kudlis, I. A. Aleksandrov, Y. S. Krivosenko, I. A. Shelykh

We derive semiclassical Boltzmann equations describing thermalization of an ensemble of excitons due to exciton-phonon interactions taking into account the fact that excitons are not ideal bosons but composite particles consisting of electrons and holes. We demonstrate that with a standard definition of excitonic creation and annihilation operators, one face

Saiyad Ashanujjaman, Sumit Banik, Guglielmo Coloretti, Andreas Crivellin

In this article, we examine the Standard Model extended by a $Y=0$ real Higgs triplet, the $\Delta$SM. It contains a $CP$-even neutral Higgs ($\Delta^0$) and two charged Higgs bosons ($\Delta^\pm$), which are quasi-degenerate in mass. We first study the theoretical constraints from vacuum stability and perturbative unitarity and then calculate the Higgs deca

Esty Kelman, Ephraim Linder, Sofya Raskhodnikova

We study property testing with incomplete or noisy inputs. The models we consider allow for adversarial manipulation of the input, but differ in whether the manipulation can be done only offline, i.e., before the execution of the algorithm, or online, i.e., as the algorithm runs. The manipulations by an adversary can come in the form of erasures or corruptio

Zhyar Rzgar K Rostam, Gábor Kertész

The exponential growth of online textual content across diverse domains has necessitated advanced methods for automated text classification. Large Language Models (LLMs) based on transformer architectures have shown significant success in this area, particularly in natural language processing (NLP) tasks. However, general-purpose LLMs often struggle with dom

Shengqu Cai, Eric Chan, Yunzhi Zhang, Leonidas Guibas

Text-to-image diffusion models produce impressive results but are frustrating tools for artists who desire fine-grained control. For example, a common use case is to create images of a specific instance in novel contexts, i.e., "identity-preserving generation". This setting, along with many other tasks (e.g., relighting), is a natural fit for image+text-cond

Zhi Zhang, Jiayi Shen, Congfeng Cao, Gaole Dai

Advancing towards generalist agents necessitates the concurrent processing of multiple tasks using a unified model, thereby underscoring the growing significance of simultaneous model training on multiple downstream tasks. A common issue in multi-task learning is the occurrence of gradient conflict, which leads to potential competition among different tasks

Sathwik Karnik, Zhang-Wei Hong, Nishant Abhangi, Yen-Chen Lin

Language-conditioned robot models have the potential to enable robots to perform a wide range of tasks based on natural language instructions. However, assessing their safety and effectiveness remains challenging because it is difficult to test all the different ways a single task can be phrased. Current benchmarks have two key limitations: they rely on a li

Rundi Wu, Ruiqi Gao, Ben Poole, Alex Trevithick

We present CAT4D, a method for creating 4D (dynamic 3D) scenes from monocular video. CAT4D leverages a multi-view video diffusion model trained on a diverse combination of datasets to enable novel view synthesis at any specified camera poses and timestamps. Combined with a novel sampling approach, this model can transform a single monocular video into a mult

Cheng Tang, Zhishuai Liu, Pan Xu

The Robust Regularized Markov Decision Process (RRMDP) is proposed to learn policies robust to dynamics shifts by adding regularization to the transition dynamics in the value function. Existing methods mostly use unstructured regularization, potentially leading to conservative policies under unrealistic transitions. To address this limitation, we propose a

Parampreet Singh, Adwik Gupta, Aakarsh Mishra, Vipul Arora

Raga classification in Indian Art Music is an open-set problem where unseen classes may appear during testing. However, traditional approaches often treat it as a closed set problem, rejecting the possibility of encountering unseen classes. In this work, we try to tackle this problem by first employing an Uncertainty-based Out-Of-Distribution (OOD) detection

Matthew Hernandez, Neel Patel, Elena Salguero

The relativistic Vlasov-Maxwell system is a kinetic model for collisionless plasmas. For the two-dimensional model, global well-posedness of this model is known and was proven by deriving global bounds on the momentum support of the particle density function. In this paper, we prove bounds on the magnitude of the momentum support in one direction depending o

P. Ranaivomanana, M. Uzundag, C. Johnston, P. J. Groot

Hot sub-luminous stars represent a population of stripped and evolved red giants that is located on the extreme horizontal branch. Since they exhibit a wide range of variability due to pulsations or binary interactions, it is crucial to unveil their intrinsic and extrinsic variability to understand the physical processes of their formation. In the Hertzsprun

Shivangi Aneja, Artem Sevastopolsky, Tobias Kirschstein, Justus Thies

We introduce GaussianSpeech, a novel approach that synthesizes high-fidelity animation sequences of photo-realistic, personalized 3D human head avatars from spoken audio. To capture the expressive, detailed nature of human heads, including skin furrowing and finer-scale facial movements, we propose to couple speech signal with 3D Gaussian splatting to create

Joshua Quinton, Mayada Fadel, Junqing Xu, Adela Habib

The electron spin decay lifetime in materials can be characterized by relaxation (T1) and irreversible (T2) and reversible (T2*) decoherence processes. Their interplay leads to a complex dependence of spin relaxation times on the direction and magnitude of magnetic fields, relevant for spintronics and quantum information applications. Here, we use real-time

Zhixu Silvia Tao, Ian Mason, Sanjeev Kulkarni, Xavier Boix

Task Arithmetic is a model merging technique that enables the combination of multiple models' capabilities into a single model through simple arithmetic in the weight space, without the need for additional fine-tuning or access to the original training data. However, the factors that determine the success of Task Arithmetic remain unclear. In this paper, we

Frank J. Tackmann

We develop a new approach to estimate the uncertainty due to missing higher orders in perturbative predictions (the perturbative "theory uncertainty"), which overcomes many inherent limitations of the currently prevalent methods based on varying unphysical renormalization scales. In our approach, the true underlying sources of the theory uncertainty, namely

Marguerite Bin

We study parameters of the convexity spaces associated with families of sets in $\mathbb{R}^d$ where every intersection between $t$ sets of the family has its Betti numbers bounded from above by a function of $t$. Although the Radon number of such families may not be bounded, we show that these families satisfy a fractional Helly theorem. To achieve this, we

Vishaal Udandarao, Nikhil Parthasarathy, Muhammad Ferjad Naeem, Talfan Evans

Knowledge distillation (KD) is the de facto standard for compressing large-scale models into smaller ones. Prior works have explored ever more complex KD strategies involving different objective functions, teacher-ensembles, and weight inheritance. In this work we explore an alternative, yet simple approach -- active data curation as effective distillation f

Sherwin Bahmani, Ivan Skorokhodov, Guocheng Qian, Aliaksandr Siarohin

Numerous works have recently integrated 3D camera control into foundational text-to-video models, but the resulting camera control is often imprecise, and video generation quality suffers. In this work, we analyze camera motion from a first principles perspective, uncovering insights that enable precise 3D camera manipulation without compromising synthesis q

Abhishek Kumar Jha, Banibrata Mukhopadhyay, Mriganka Dutta, Mayank Pathak

We investigate the quantum speed limit (QSL) during the time evolution of neutrino-antineutrino system under the influence of the gravitational field of a spinning primordial black hole (PBH). We derive an analytical expression for the four-vector gravitational potential in the underlying Hermitian Dirac Hamiltonian using the Boyer-Lindquist (BL) coordinates

Gui-Jun Ding, Si-Yi Jiang, Yong Xu, Wenbin Zhao

We use modular symmetry as an organizing principle that attempts to simultaneously address the lepton flavor puzzle, inflation, and post-inflationary reheating. We demonstrate this approach using the finite modular group $A_4$ in the lepton sector. In our model, neutrino masses are generated via the Type-I see-saw mechanism, with modular symmetry dictating t

Eva Prakash, Jeya Maria Jose Valanarasu, Zhihong Chen, Eduardo Pontes Reis

Purpose: To explore best-practice approaches for generating synthetic chest X-ray images and augmenting medical imaging datasets to optimize the performance of deep learning models in downstream tasks like classification and segmentation. Materials and Methods: We utilized a latent diffusion model to condition the generation of synthetic chest X-rays on text

Miguel Alcubierre, Juan Barranco, Argelia Bernal, Juan Carlos Degollado

We construct gravitational atoms including self-gravity, obtaining solutions of the Einstein-Klein-Gordon equations for a scalar field surrounding a non-rotating black hole in a quasi-stationary approximation. We resolve the region near the horizon as well as the far field region. Our results are relevant in a wide range of masses, from ultralight to MeV sca

T. Bountis, J. Cantisán, J. Cuevas-Maraver, J. E. Macías-Díaz

In the present work we explore the dynamics of single kinks, kink-anti-kink pairs and bound states in the prototypical fractional Klein-Gordon example of the sine-Gordon equation. In particular, we modify the order $\beta$ of the temporal derivative to that of a Caputo fractional type and find that, for $1<\beta<2$, this imposes a dissipative dynamical behav

Rohan Raha, Banibrata Mukhopadhyay, Koushik Chatterjee

An optically thin advective accretion disk is crucial for explaining the hard state of black hole sources. Using general relativistic magnetohydrodynamic (GRMHD) simulations, we investigate how a large-scale, strong magnetic field influences accretion and outflows/jets, depending on the field geometry, magnetic field strength, and the spin parameter of the b

Pengyi Jia, Xianbin Wang, Yongxu Zhu, Shi Jin

The rapid evolution and convergence of wireless technologies and vertical applications have fundamentally reshaped our lifestyles and industries. Future wireless networks, especially 6G, are poised to support a wide range of applications enabled by heterogeneous services, leveraging both traditional connectivity-centric functions and emerging beyond-communic

Dhawal Sirikonda, Praneeth Chakravarthula, Ioannis Gkioulekas, Adithya Pediredla

We introduce a structured light system that enables full-frame 3D scanning at speeds of $1000\text{ fps}$, four times faster than the previous fastest systems. Our key innovation is the use of a custom acousto-optic light scanning device capable of projecting two million light planes per second. Coupling this device with an event camera allows our system to

Yu Chen, Jie Han, Haoran Luo

An $n$-vertex $k$-uniform hypergraph $G$ is $(d,\alpha)$-degenerate if $m_1(G)\le{d}$ and there exists a constant $\varepsilon >0$ such that for every subset $U\subseteq{V(G)}$ with size $2\le|U|\le{\varepsilon n}$, we have $e\left(G[U]\right)\le{d\left(|U|-1\right)-\alpha}$. These hypergraphs include many natural graph classes, such as the degenerate hyperg

David C. Catling, Joshua Krissansen-Totton, Tyler D. Robinson

Deuterium-deuterium (DD) fusion is viewed as an ideal energy source for humanity in the far future, given a vast seawater supply of D. Here, we consider long-lived, extraterrestrial, technological societies that develop DD fusion. If such a society persists over geologic timescales, oceanic deuterium would diminish. For an ocean mass and initial D/H that are

Shah Md Ahasan Siddique, Ragib Tahshin Rinath, Shakil Mosharrof, Syed Tanjib Mahmud

The search for evidence of past life on Mars presents a tremendous challenge that requires the usage of very advanced robotic technologies to overcome it. Current digital microscopic imagers and spectrometers used for astrobiological examination suffer from limitations such as insufficient resolution, narrow detection range, and lack of portability. To overc

Mathew Jacob, Maya Taylor, Laxmikant Kale

Parallel input performance issues are often neglected in large scale parallel applications in Computational Science and Engineering. Traditionally, there has been less focus on input performance because either input sizes are small (as in biomolecular simulations) or the time doing input is insignificant compared with the simulation with many timesteps. But

Carlos Hoyos, Niko Jokela, José Manuel Penin

We reinterpret the Nieh-Yan (NY) anomaly using holography, finding that the U(1) axial symmetry remains unbroken and the axial current coupling to an external gauge field is conserved. Instead, the anomaly arises from a breakdown of Hodge duality relations between fermion bilinears due to symmetry constraints on one-form currents. We show that the axial resp

Matthew J. Filipovich, A. I. Lvovsky

TorchOptics is an open-source Python library for differentiable Fourier optics simulations, developed using PyTorch to enable GPU-accelerated tensor computations and automatic differentiation. It provides a comprehensive framework for modeling, analyzing, and designing optical systems using Fourier optics, with applications in imaging, diffraction, holograph

Christoph Grüne, Lasse Wulf

Recoverable robust optimization is a popular multi-stage approach, in which it is possible to adjust a first-stage solution after the uncertain cost scenario is revealed. We consider recoverable robust optimization in combination with discrete budgeted uncertainty. In this setting, it seems plausible that many problems become $\Sigma^p_3$-complete and theref

Gerd Niestegge

It is quite common to use the generalized probabilistic theories (GPTs) as generic models to reconstruct quantum theory from a few basic principles and to gain a better understanding of the probabilistic or information theoretic foundations of quantum physics and quantum computing. A variety of symmetry postulates was introduced and studied in this framework

Yawei Li, Bin Ren, Jingyun Liang, Rakesh Ranjan

While vision transformers show promise in numerous image restoration (IR) tasks, the challenge remains in efficiently generalizing and scaling up a model for multiple IR tasks. To strike a balance between efficiency and model capacity for a generalized transformer-based IR method, we propose a hierarchical information flow mechanism for image restoration, du

Alice Heiman, Xiaoman Zhang, Emma Chen, Sung Eun Kim

Medical vision-language models often struggle with generating accurate quantitative measurements in radiology reports, leading to hallucinations that undermine clinical reliability. We introduce FactCheXcker, a modular framework that de-hallucinates radiology report measurements by leveraging an improved query-code-update paradigm. Specifically, FactCheXcker

Suzanne Oliver, Tomoko Kitago, Adam Buchwald, S. Farokh Atashzar

User engagement, cognitive participation, and motivation during task execution in physical human-robot interaction are crucial for motor learning. These factors are especially important in contexts like robotic rehabilitation, where neuroplasticity is targeted. However, traditional robotic rehabilitation systems often face challenges in maintaining user enga

Weixuan Li, Sharun Kuhar, Jung-Hee Seo, Rajat Mittal

The geometry and motility of the stomach play a critical role in the digestion of ingested liquid meals. Sleeve gastrectomy, a common type of bariatric surgery used to reduce the size of the stomach, significantly alters the stomach's anatomy and motility, which impacts gastric emptying and digestion. In this study, we use an imaging data-based computational

Kareem A. Wahid, Cem Dede, Dina M. El-Habashy, Serageldin Kamel

Magnetic resonance (MR)-guided radiation therapy (RT) is enhancing head and neck cancer (HNC) treatment through superior soft tissue contrast and longitudinal imaging capabilities. However, manual tumor segmentation remains a significant challenge, spurring interest in artificial intelligence (AI)-driven automation. To accelerate innovation in this field, we

Darren Han, Michelle Huang, Benjamin Keller, Suho Oh

The Demazure product, also called the 0-Hecke product, is an associative operation on Coxeter groups with interesting properties and applications. In (Li et al 2024) it was shown that the Demazure product of two permutations can be described purely combinatorially: using only their one-line notation and not relying on reduced words. In this paper, we extend

Nurshat Fateh Ali, Md. Mahdi Mohtasim, Shakil Mosharrof, T. Gopi Krishna

This research presents and compares multiple approaches to automate the generation of literature reviews using several Natural Language Processing (NLP) techniques and retrieval-augmented generation (RAG) with a Large Language Model (LLM). The ever-increasing number of research articles provides a huge challenge for manual literature review. It has resulted

Abhisek Mishra, Pritam Das, Rupalipriyadarsini Chhatoi, Soubhagya Dash

Transition metal dichalcogenides (TMDs) are a novel class of quantum materials with significant potential in spintronics, optoelectronics, valleytronics, and opto-valleytronics. TMDs exhibit strong spin-orbit coupling, enabling efficient spin-charge interconversion, which makes them ideal candidates for spin-orbit torque-driven spintronic devices. In this st

Ishan Bansal, Oktay Günlük, Richard Shapley

In this paper we study a combinatorial reconfiguration problem that involves finding an optimal sequence of swaps to move an initial configuration of tokens that are placed on the vertices of a graph to a final desired one. This problem arises as a crucial step in reducing the depth of a quantum circuit when compiling a quantum algorithm. We provide the firs

Kieran A. Murphy, Yujing Zhang, Dani S. Bassett

Multivariate information theory provides a general and principled framework for understanding how the components of a complex system are connected. Existing analyses are coarse in nature -- built up from characterizations of discrete subsystems -- and can be computationally prohibitive. In this work, we propose to study the continuous space of possible descr

Tien Vu-Van, Dat Du Thanh, Nguyen Ho, Mai Vu

Convolutional Neural Networks (CNNs) achieve high performance in image classification tasks but are challenging to deploy on resource-limited hardware due to their large model sizes. To address this issue, we leverage Mutual Information, a metric that provides valuable insights into how deep learning models retain and process information through measuring th

Shruti Jagdale, Omkar Khade, Gauri Takalikar, Mihir Inamdar

Code-mixing is the practice of using two or more languages in a single sentence, which often occurs in multilingual communities such as India where people commonly speak multiple languages. Classic NLP tools, trained on monolingual data, face challenges when dealing with code-mixed data. Extracting meaningful information from sentences containing multiple la

Mordechai Gruda, Ofer Biham, Eytan Katzav, Reimer Kühn

We present analytical results for the joint probability distribution $P(T_{FR}=t,S=s)$ of first return (FR) times t and of the number of distinct sites s visited by a random walk (RW) on a one dimensional lattice before returning to the origin. The RW on a one dimensional lattice is recurrent, namely the probability to return to the origin is $P_{R}=1$. Howe

Pedro Delicado, Cristian Pachón-García

The presence of artificial intelligence (AI) in our society is increasing, which brings with it the need to understand the behavior of AI mechanisms, including machine learning predictive algorithms fed with tabular data, text or images, among others. This work focuses on interpretability of predictive models based on functional data. Designing interpretabil

Radu I. Boţ, Enis Chenchene, Jalal M. Fadili

We study accelerated Krasnoselskii-Mann-type methods with preconditioners in both continuous and discrete time. From a continuous-time model, we derive a generalized fast Krasnoselskii-Mann method, providing a new yet simple proof of convergence that leads to unprecedented flexibility in parameter tuning, allowing up to twice larger relaxation parameters. Ou

Érico Goulart, Eduardo Bittencourt

We exhibit the first analogue model of a rotating black hole constructed in the framework of nonlinear electrodynamics. The background electromagnetic field is assumed to be algebraically special and adapted to a geodesic shear-free congruence of null rays in Minkowski spacetime, the Kerr congruence. The corresponding optical metric has a Kerr-Schild form an

Haoyue Wang, Sheng Li, Ji He, Zhenxing Qian

Face manipulation detection has been receiving a lot of attention for the reliability and security of the face images/videos. Recent studies focus on using auxiliary information or prior knowledge to capture robust manipulation traces, which are shown to be promising. As one of the important face features, the face depth map, which has shown to be effective

Omkar Khade, Shruti Jagdale, Abhishek Phaltankar, Gauri Takalikar

Large Language Models (LLMs) have demonstrated remarkable multilingual capabilities, yet challenges persist in adapting these models for low-resource languages. In this study, we investigate the effects of Low-Rank Adaptation (LoRA) Parameter-Efficient Fine-Tuning (PEFT) on multilingual Gemma models for Marathi, a language with limited resources. Using a tra

Isaac C. Nunes, Michelle O. Araújo, Jesús P. Lopez, Thierry Passerat de Silans

The propagation of light that undergoes multiple-scattering by resonant atomic vapor can be described as a L\'evy flight. L\'evy flight is a random walk with heavy tailed step-size (r) distribution, decaying asymptotically as $P(r)\sim r^{-1-\alpha}$, with $\alpha<2$. The large steps, typical of L\'evy flights, have its origins in frequency redistribution of

Tijana Zrnic, William Fithian

Across science and policy, decision-makers often need to draw conclusions about the best candidate among competing alternatives. For instance, researchers may seek to infer the effectiveness of the most successful treatment or determine which demographic group benefits most from a specific treatment. Similarly, in machine learning, practitioners are often in

Tianyuan Zheng, Alessandro Rondina, Gos Micklem, Pietro Liò

Deep generative models show promise for $\textit{de novo}$ protein design, yet reliably producing designs that are geometrically plausible, evolutionarily consistent, functionally relevant, and dynamically stable remains challenging. We present a deep generative modeling pipeline for early $\textit{de novo}$ design of monomeric proteins, based on Score Match

Lauren Doyle, Caleb I. Cañas, Jessica E. Libby-Roberts, Heather M. Cegla

We report the first instance of an M dwarf/brown dwarf obliquity measurement for the TOI-2119 system using the Rossiter-McLaughlin effect. TOI-2119 b is a transiting brown dwarf orbiting a young, active early M dwarf ($T_{\rm{eff}}$ = 3553 K). It has a mass of 64.4 M$_{\rm{J}}$ and radius of 1.08 R$_{\rm{J}}$, with an eccentric orbit ($e$ = 0.3) at a period

Oliver Maximilian Zobel, Johannes Maierhofer, Andreas Köstler, Daniel J. Rixen

OASIS-UROS continues the previously published Open Acquisition System for IEPE Sensors (OASIS). While still building on the ESP32 microcontroller, this version improves the overall performance by switching to an SD card caching system and upgrading the analog-digital converter to an AD7606C-18, which has a higher resolution, provides eight channels, oversamp

Rong Wang, Kun Sun, Jonas Kuhn

Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks, yet they often struggle with spatial reasoning. This paper presents a novel neural-symbolic framework that enhances LLMs' spatial reasoning abilities through iterative feedback between LLMs and Answer Set Programming (ASP). We evaluate our approach on two benchmark d

Matthew Jenssen, Will Perkins, Aditya Potukuchi, Michael Simkin

We study the probability that the random graph $G(n,p)$ is triangle-free. When $p =o(n^{-1/2})$ or $p = \omega(n^{-1/2})$ the asymptotics of the logarithm of this probability are known via Janson's inequality in the former case and via regularity or hypergraph container methods in the latter case. We prove for the first time an asymptotic formula for the log

Zhixuan Liang, Yao Mu, Yixiao Wang, Tianxing Chen

Dexterous manipulation with contact-rich interactions is crucial for advanced robotics. While recent diffusion-based planning approaches show promise for simple manipulation tasks, they often produce unrealistic ghost states (e.g., the object automatically moves without hand contact) or lack adaptability when handling complex sequential interactions. In this

Julia E. Bergner

The notion of a simplicial set originated in algebraic topology, and has also been utilized extensively in category theory, but until relatively recently was not used outside of those fields. However, with the increasing prominence of higher categorical methods in a wide range of applications, it is important for researchers in a range of fields to have a go

Yingze Hou, Hoda Bidkhori, Taposh Banerjee

The problem of robust quickest change detection (QCD) in non-stationary processes under a multi-stream setting is studied. In classical QCD theory, optimal solutions are developed to detect a sudden change in the distribution of stationary data. Most studies have focused on single-stream data. In non-stationary processes, the data distribution both before an

Yu Jia, Jichen Pan

The rare annihilation decays of $P$-wave spin-triplet quarkonia into lepton pair have to proceed via two-photon intermediate state, which are plagued with the infrared divergence symptom. We recognize that the physical root of the IR divergence and its remedy is the same as the Lamb shift in QED. In this work we provide a complete solution to this IR problem

Xincheng Zhu, Rui Xu, Dandan Xu

In modern cosmology, the discovery of the universe's accelerated expansion has significantly transformed our understanding of cosmic evolution and expansion history. The unknown properties of dark energy, the driver of this acceleration, have not only prompted extensive studies on its nature but also spurred interest in modified gravity theories that might s

Abhishek Kumar Jha, Mriganka Dutta, Subhashish Banerjee, Banibrata Mukhopadhyay

The quantum speed limits (QSLs) determine the minimal amount of time required for a quantum system to evolve from an initial to a final state. We investigate QSLs for the unitary evolution of the neutrino-antineutrino system in the presence of a gravitational field. It is known that the transition probabilities between neutrino and antineutrino in the framew

Sajjad Abdollahramezani, Darrell Omo-Lamai, Gerlof Bosman, Omid Hemmatyar

Empirical investigation of the quintillion-scale, functionally diverse antibody repertoires that can be generated synthetically or naturally is critical for identifying potential biotherapeutic leads, yet remains burdensome. We present high-throughput nanophotonics- and bioprinter-enabled screening (HT-NaBS), a multiplexed assay for large-scale, sample-effic

Simon Yves, Michel Fruchart, Romain Fleury, Gal Shmuel

Phonons are quasiparticles associated with mechanical vibrations in materials. They are at the root of the propagation of sound and elastic waves, as well as of thermal phenomena, which are pervasive in our everyday life and in many technologies. The fundamental understanding and control of phonon responses in natural and artificial media are key in the cont

Matthias Georg Mayer

We give a new characterization for mutual absolute continuity of probability measures on a filtered space. For this, we introduce a martingale limit $M$ that measures the similarity between the tails of the probability measures restricted to the filtration. The measures are mutually absolutely continuous if and only if $M = 1$ holds almost surely for both me

Tristan C. Collins, Jason Lo, Yun Shi, Shing-Tung Yau

In this paper, we study the action of an autoequivalence, the spherical twist associated to a torsion sheaf, on the standard Bridgeland stability conditions and a generalized weak stability condition on the derived category of a K3 surface. As a special case, we construct a Bridgeland stability condition associated to a non-nef divisor, which conjecturally l

Darius Feher, Ivan Vulić, Benjamin Minixhofer

Current language models (LMs) use a fixed, static subword tokenizer. This default choice typically results in degraded efficiency and language capabilities, especially in languages other than English. To address this issue, we challenge the static design and propose retrofitting LMs with dynamic tokenization: a way to dynamically decide on token boundaries b

Haosen Yang, Adrian Bulat, Isma Hadji, Hai X. Pham

Diffusion models are proficient at generating high-quality images. They are however effective only when operating at the resolution used during training. Inference at a scaled resolution leads to repetitive patterns and structural distortions. Retraining at higher resolutions quickly becomes prohibitive. Thus, methods enabling pre-existing diffusion models t

Borna Sayedana, Peter E. Caines, Aditya Mahajan

In this paper, we investigate the concentration properties of cumulative reward in Markov Decision Processes (MDPs), focusing on both asymptotic and non-asymptotic settings. We introduce a unified approach to characterize reward concentration in MDPs, covering both infinite-horizon settings (i.e., average and discounted reward frameworks) and finite-horizon

Thomas Bothner, Toby Shepherd

We study invariant random matrix ensembles \begin{equation*} \mathbb{P}_n(d M)=Z_n^{-1}\exp(-n\,tr(V(M)))\,d M \end{equation*} defined on complex Hermitian matrices $M$ of size $n\times n$, where $V$ is real analytic such that the underlying density of states is one-cut regular. Considering the average \begin{equation*} E_n[\phi;\lambda,\alpha,\beta]:=\mathb

Leo Pasquazzi

In this article we consider Bowley's skewness measure and the Groeneveld-Meeden $b_{3}$ index in the context of finite population sampling. We employ the functional delta method to obtain asymptotic variance formulae for plug-in estimators and propose corresponding variance estimators. We then consider plug-in estimators based on the H\'{a}jek cdf-estimator

Han Yan, Mingrui Zhang, Yang Li, Chao Ma

We present PhyCAGE, the first approach for physically plausible compositional 3D asset generation from a single image. Given an input image, we first generate consistent multi-view images for components of the assets. These images are then fitted with 3D Gaussian Splatting representations. To ensure that the Gaussians representing objects are physically comp

Joseph A. M. Paddison

An important property of neutron scattering data is that they can be normalised in absolute intensity units. In practice, however, such normalisation is often not performed, since it can be time-consuming and subject to systematic uncertainties. Here, a straightforward approach is presented for absolute intensity normalisation of neutron scattering data from

Julia E. Bergner

The definition of the homotopy limit of a diagram of left Quillen functors of model categories has been useful in a number of applications. In this paper we review its definition and summarize some of these applications. We conclude with a discussion of why we could work with right Quillen functors instead, but cannot work with a combination of the two.