March 2025 arXiv papers — page 55

Murong Yue

This paper surveys the development of large language model (LLM)-based agents for question answering (QA). Traditional agents face significant limitations, including substantial data requirements and difficulty in generalizing to new environments. LLM-based agents address these challenges by leveraging LLMs as their core reasoning engine. These agents achiev

Gautham Udayakumar Bekal, Ahmed Ghareeb, Ashish Pujari

Buildings with Heating, Ventilation, and Air Conditioning (HVAC) systems play a crucial role in ensuring indoor comfort and efficiency. While traditionally governed by physics-based models, the emergence of big data has enabled data-driven methods like Deep Reinforcement Learning (DRL). However, Reinforcement Learning (RL)-based techniques often suffer from

Mahdi Nasser, Laura Sayyah, Fadi A. Zaraket

This paper presents MASRAD, a terminology dataset for Arabic terminology management, and a method with supporting tools for its semi-automatic construction. The entries in MASRAD are $(f,a)$ pairs of foreign (non-Arabic) terms $f$, appearing in specialized, academic and field-specific books next to their Arabic $a$ counterparts. MASRAD-Ex systematically extr

Frederico Toulson

In this paper we present a proof of sharp boundedness of the discrete 1-dimensional Hardy-Littlewood nontangential maximal operator, when the parameter is in the range $[\frac{1}{3},+\infty)$. This generalizes a theorem by Bober, Carneiro, Hughes and Pierce, where they prove the same result for the uncentered version of the maximal operator. We also use anal

Tuan Le, Shana Moothedath

In this paper, we propose BR-MTRL, a Byzantine-resilient multi-task representation learning framework that handles faulty or malicious agents. Our approach leverages representation learning through a shared neural network model, where all clients share fixed layers, except for a client-specific final layer. This structure captures shared features among clien

Valeri P. Frolov, Alex Koek

We study propagation of high-frequency electromagnetic and gravitational waves in the gravitational field of a rotating black hole. Due to the interaction of the spin of the field with the spacetime curvature, the standard geometric optics approximation that is used for obtaining the approximate high-frequency solutions of the wave equation should be modifie

Rong Wang, Fabian Prada, Ziyan Wang, Zhongshi Jiang

We present a novel method for reconstructing personalized 3D human avatars with realistic animation from only a few images. Due to the large variations in body shapes, poses, and cloth types, existing methods mostly require hours of per-subject optimization during inference, which limits their practical applications. In contrast, we learn a universal prior f

Jacob Mitchell Springer, Sachin Goyal, Kaiyue Wen, Tanishq Kumar

Large language models are pre-trained on ever-growing token budgets under the assumption that better pre-training performance translates to improved downstream models. In this work, we challenge this assumption and show that extended pre-training can make models harder to fine-tune, leading to degraded final performance. We term this phenomenon catastrophic

Shuo Liu, Calin A. Belta

This paper addresses the challenge of ensuring safety in stochastic control systems with high-relative-degree constraints, while maintaining feasibility and mitigating conservatism in risk evaluation. Control Barrier Functions (CBFs) provide an effective framework for enforcing safety constraints in nonlinear systems. However, existing methods struggle with

W. Andrew MacFarlane, Ryan M. L. McFadden, Signy Spencer, Aris Chatzichristos

We report $\beta$-detected nuclear magnetic resonance ($\beta$-NMR) measurements of implanted $^{8}$Li$^{+}$ in a synthetic single crystal of $\alpha$-SiO$_2$ (quartz). At 6.55 Tesla, the spectrum is comprised of a large amplitude broad resonance and a quadrupolar multiplet that is only revealed by an RF comb excitation. The quadrupole splitting is surprisin

Sara Al-Emadi, Yin Yang, Ferda Ofli

Object detectors have achieved remarkable performance in many applications; however, these deep learning models are typically designed under the i.i.d. assumption, meaning they are trained and evaluated on data sampled from the same (source) distribution. In real-world deployment, however, target distributions often differ from source data, leading to substa

Renpu Liu, Peng Wang, Donghao Li, Cong Shen

Reinforcement Learning from Human Feedback (RLHF) has emerged as a pivotal technique for aligning artificial intelligence systems with human values, achieving remarkable success in fine-tuning large language models. However, existing RLHF frameworks often assume that human preferences are relatively homogeneous and can be captured by a single, unified reward

Mahdis Rabbani, Navid Mojahed, Shima Nazari

Game-theoretic approaches and Nash equilibrium have been widely applied across various engineering domains. However, practical challenges such as disturbances, delays, and actuator limitations can hinder the precise execution of Nash equilibrium strategies. This work investigates the impact of such implementation imperfections on game trajectories and player

Chenyangguang Zhang, Alexandros Delitzas, Fangjinhua Wang, Ruida Zhang

We introduce the task of predicting functional 3D scene graphs for real-world indoor environments from posed RGB-D images. Unlike traditional 3D scene graphs that focus on spatial relationships of objects, functional 3D scene graphs capture objects, interactive elements, and their functional relationships. Due to the lack of training data, we leverage founda

Zu-Jian Ying, Hang-Hang Han, Bo-Jian Li, Simone Felicetti

We investigate a generalized quantum Rabi model (QRM) with two- and four-photon terms with respect to applications for non-linear critical quantum metrology. In the introduced model, the spectral collapse occurring in the standard two-photon QRM is stabilized by the presence of the quartic potential. The collapse is then transformed into a quantum phase tran

Pierre Lazag

We consider an abstract determinantal point process on a general non--elementary Gromov hyperbolic metric space governed by an orthogonal projection in the case when the space is homogeneous and the point process is invariant under isometries. We give a lower bound of the variance of the number of points inside a ball that is proportional to the volume of th

Euclid Collaboration, S. Bhargava, C. Benoist, A. H. Gonzalez

The first survey data release by the Euclid mission covers approximately $63\,\mathrm{deg^2}$ in the Euclid Deep Fields to the same depth as the Euclid Wide Survey. This paper showcases, for the first time, the performance of cluster finders on Euclid data and presents examples of validated clusters in the Quick Release 1 (Q1) imaging data. We identify clust

Chayan Banerjee, Kien Nguyen, Clinton Fookes

Optimizing the mining process -- particularly truck dispatch scheduling -- is a key driver of efficiency in open-pit operations. However, the dynamic and stochastic nature of these environments, with uncertainties such as equipment failures, truck maintenance, and variable haul cycle times, challenges traditional optimization. While Reinforcement Learning (R

Alex Levchenko

The magnetotransport properties of a two-dimensional electron liquid in graphene are analyzed in the hydrodynamic limit under isothermal conditions. It is shown that the Wiedemann-Franz law does not hold in this regime and that the Lorenz ratios constructed from the diagonal and Hall conductivities generally differ from each other. The breakdown of the Wiede

Sky CH-Wang, Darshan Deshpande, Smaranda Muresan, Anand Kannappan

We introduce Browsing Lost Unformed Recollections, a tip-of-the-tongue known-item search and reasoning benchmark for general AI assistants. BLUR introduces a set of 573 real-world validated questions that demand searching and reasoning across multi-modal and multilingual inputs, as well as proficient tool use, in order to excel on. Humans easily ace these qu

Kosuke Asano, Kenichi Noba, Tomio Petrosky

We perform analytical and quantitative analysis of the motion of a non-integrable pendulum with two degrees of freedom, in which an integrable nonlinear pendulum and a harmonic oscillator are weakly coupled through a non-integrable perturbative interaction, based on the eigenvalue problem of the Liouvillian that is the generator of time evolution in classica

Yufan Ren, Zicong Jiang, Tong Zhang, Søren Forchhammer

Text-guided image editing using Text-to-Image (T2I) models often fails to yield satisfactory results, frequently introducing unintended modifications, such as the loss of local detail and color changes. In this paper, we analyze these failure cases and attribute them to the indiscriminate optimization across all frequency bands, even though only specific fre

Michael Unser, Stanislas Ducotterd

This paper addresses the task of learning convex regularizers to guide the reconstruction of images from limited data. By imposing that the reconstruction be amplitude-equivariant, we narrow down the class of admissible functionals to those that can be expressed as a power of a seminorm. We then show that such functionals can be approximated to arbitrary pre

Mingqiang Li, Kun Luo, Xiumei Ma, Boran Kumral

Photo-plasticity in semiconductors, wherein their mechanical properties such as strength, hardness and ductility are influenced by light exposure, has been reported for several decades. Although such phenomena have drawn significant attention for the manufacturability and usage of deformable semiconductor devices, their underlying mechanisms are not well und

Edward A. Hirsch, Ilya Volkovich

Korten and Pitassi (FOCS, 2024) defined a new complexity class $L_2^P$ as the polynomial-time Turing closure of the Linear Ordering Principle. They put it between $MA$ (Merlin--Arthur protocols) and $S_2^P$ (the second symmetric level of the polynomial hierarchy). In this paper we sandwich $L_2^P$ between $P^{prMA}$ and $P^{prSBP}$. (The oracles here are pro

Ingemar Bengtsson, José M M Senovilla

We show that ultra-massive spacetimes exist also in 2 + 1 dimensions with a positive cosmological constant {\Lambda} > 0. They can be created through the collapse of a spherical null dust shell. The exterior of the shell is then a Mess spacetime, that is to say a locally de Sitter spacetime that cannot be obtained as a quotient of de Sitter space.

Parisa Mollaei, Amir Barati Farimani

In this study, we propose a Kernel-PCA model designed to capture structure-function relationships in a protein. This model also enables ranking of reaction coordinates according to their impact on protein properties. By leveraging machine learning techniques, including Kernel and principal component analysis (PCA), our model uncovers meaningful patterns in h

Davide Elia De Falco, Enrico Schiassi, Francesco Calabrò

In this paper, we investigate the use of single hidden-layer neural networks as a family of ansatz functions for the resolution of partial differential equations (PDEs). In particular, we train the network via Extreme Learning Machines (ELMs) on the residual of the equation collocated on -- eventually randomly chosen -- points. Because the approximation is d

Daniel Barbosa, Gabriela Planas, Francisco Guillén-González

The present work deals with a Keller-Segel-Navier-Stokes system with potential consumption, under homogeneous Neumann boundary conditions for cell density and chemical signal, and of Dirichlet type for the velocity field, over a bounded three-dimensional domain. The paper aims to develop a time discretization scheme converging to weak solutions of the system

Carlos Fulgado-Claudio, Alejandro Bermudez

Although particle production in curved quantum field theories (cQFTs) is key to our understanding of the early universe and black hole physics, its direct observation requires extreme conditions or unrealistic sensitivities. Recent progress in quantum simulators indicates that analogues of cosmological particle production can be observed in table-top experim

Reza Rahimi Azghan, Nicholas C. Glodosky, Ramesh Kumar Sah, Carrie Cuttler

Coping with stress is one of the most frequently cited reasons for chronic cannabis use. Therefore, it is hypothesized that cannabis users exhibit distinct physiological stress responses compared to non-users, and these differences would be more pronounced during moments of consumption. However, there is a scarcity of publicly available datasets that allow s

Hayate Iso, Pouya Pezeshkpour, Nikita Bhutani, Estevam Hruschka

Large Language Models (LLMs) offer the potential to automate hiring by matching job descriptions with candidate resumes, streamlining recruitment processes, and reducing operational costs. However, biases inherent in these models may lead to unfair hiring practices, reinforcing societal prejudices and undermining workplace diversity. This study examines the

Cheolwon Heo, Mark Siggers

We consider a reconfiguration version of the homomorphism problem ${\rm Hom}_\mathbb{M}(N)$ for binary matroids $N$. This reconfiguration problem, ${\rm Recol}_\mathbb{M}(N)$, asks, for two homomorphisms $\phi$ and $\psi$ of a matroid $M$ to $N$, if there is a path of homomorphism from $\phi$ to $\psi$ such that consecutive homomorphism in the path differ on

J. L. Rizos, J. L. Ortiz, F. L. Rommel, B. Sicardy

(119951) 2002 KX14 is a large classical TNO with limited previous observations and unresolved questions regarding its physical properties. Five stellar occultations by 2002 KX14 were observed from 2020 to 2023, involving multiple telescopes across different locations in Europe and the Americas. The five occultations resulted in 15 positive chords, accurately

Tian Xie

A large empirical literature regresses outcomes on empirical Bayes shrinkage estimates of value-added, yet little is known about whether this approach leads to unbiased estimates and valid inference for the downstream regression coefficients. We study a general class of empirical Bayes estimators and the properties of the resulting regression coefficients. W

Erveton P. Pinto, Marcelo A. Pires, Rone N. da Silva, Sílvio M. Duarte Queirós

We report the first application of a tailored Complexity-Entropy Plane designed for binary sequences and structures. We do so by considering the daily up/down price fluctuations of the largest cryptocurrencies in terms of capitalization (stable-coins excluded) that are worth $circa \,\, 90 \%$ of the total crypto market capitalization. With that, we focus on

Thomas Fischbacher

This work introduces a generic quantitative framework for studying dynamical processes that involve interactions of polymer sequences. Possible applications range from quantitative studies of the reaction kinetics of polymerization processes to explorations of the behavior of chemical implementations of computational -- including basic life-like -- processes

Yizhu Wen, Ashwin Innuganti, Aaron Bien Ramos, Hanqing Guo

Audio watermarking is increasingly used to verify the provenance of AI-generated content, enabling applications such as detecting AI-generated speech, protecting music IP, and defending against voice cloning. To be effective, audio watermarks must resist removal attacks that distort signals to evade detection. While many schemes claim robustness, these claim

K. S. Vikrant, Prosanto Biswas, S. O. Reza Moheimani

Near-field acoustic levitation (NFAL) enables nanometer-scale positioning resolution and bandwidth exceeding several hundred hertz specifically along the vertical (Z) direction, owing to its high acoustic stiffness and squeeze film damping. However, its application to horizontal (XY) positioning is limited by significantly lower acoustic stiffness and insuff

Yunsheng Bai, Ghaith Bany Hamad, Syed Suhaib, Haoxing Ren

Generating SystemVerilog Assertions (SVAs) from natural language specifications remains a major challenge in formal verification (FV) due to the inherent ambiguity and incompleteness of specifications. Existing LLM-based approaches, such as AssertLLM, focus on extracting information solely from specification documents, often failing to capture essential inte

Robert R. Nerem, Samantha Chen, Sanjoy Dasgupta, Yusu Wang

Neural networks (NNs), despite their success and wide adoption, still struggle to extrapolate out-of-distribution (OOD), i.e., to inputs that are not well-represented by their training dataset. Addressing the OOD generalization gap is crucial when models are deployed in environments significantly different from the training set, such as applying Graph Neural

Robinson Umeike

This paper presents an implementation and analysis of a five-fingered robotic grasping system that combines contact-based control with inverse kinematics solutions. Using the PyBullet simulation environment and the DexHand v2 model, we demonstrate a comprehensive approach to achieving stable grasps through contact point optimization with force closure valida

Molly Griston, Bethany R. Wilcox

Despite the recognition that reflection is an essential part of problem solving, it is often not emphasized in upper-division physics courses. In this paper, we discuss homework corrections (HWCs) as a pedagogical tool to motivate reflection on homework assignments. We focus on gaining a qualitative understanding of how students may engage with the process o

Fabián E. Peña Arellano, Nelson L. Leon, Leonardo González López, Riccardo DeSalvo

This paper presents an analysis of the conceptual design of a novel silicon suspension for the cryogenic test-mass mirrors of the low-frequency detector of the Einstein Telescope gravitational-wave observatory. In traditional suspensions, tensional stress is a severe limitation for achieving low thermal noise, safer mechanical margins and high thermal conduc

Yinghao Li, Rushi Qiang, Lama Moukheiber, Chao Zhang

Accurately quantifying a large language model's (LLM) predictive uncertainty is crucial for judging the reliability of its answers. While most existing research focuses on short, directly answerable questions with closed-form outputs (e.g., multiple-choice), involving intermediate reasoning steps in LLM responses is increasingly important. This added complex

Sheikh Z. Ahmed, Shafat Shahnewaz, Samiran Ganguly, Joe C Campbell

Matrix based quantum kinetic simulations have been widely used for the predictive modeling of electronic devices. Inelastic scattering from phonons and electrons are typically treated as higher order processes in these treatments, captured using mean-field approximations. Carrier multiplication in Avalanche Photodiodes (APDs), however, relies entirely on str

Zimin Liang, Miqing Li

Recently, there has been growing interest within the theoretical community in analytically studying multi-objective evolutionary algorithms. This runtime analysis-focused research can help formally understand algorithm behaviour, explain empirical observations, and provide theoretical insights to support algorithm development and exploration. However, the te

Laurits Tani, Joosep Pata, Joschka Birk

The limited availability and accuracy of simulated data has motivated the use of foundation models in high energy physics, with the idea to first train a task-agnostic model on large and potentially unlabeled datasets. This enables the subsequent fine-tuning of the learned representation for specific downstream tasks, potentially requiring much smaller datas

Benjamín García, Alberto G. Raggi-Cárdenas

In this paper, we describe the indecomposable factors of a fibered Burnside ring in terms of bases coming from the standard basis. We provide a further characterization of each factor as the solvable component of the fibered Burnside ring of a corresponding Weyl group.

Gianluca Giacomelli, Simone Formentin, Victor G. Lopez, Matthias A. Müller

Data-enabled Predictive Control (DeePC) has recently gained the spotlight as an easy-to-use control technique that allows for constraint handling while relying on raw data only. Initially proposed for linear time-invariant systems, several DeePC extensions are now available to cope with nonlinear systems. Nonetheless, these solutions mainly focus on ensuring

Jakob Abeßer, Simon Schwär, Meinard Müller

This study examines pitch contours as a unifying semantic construct prevalent across various audio domains including music, speech, bioacoustics, and everyday sounds. Analyzing pitch contours offers insights into the universal role of pitch in the perceptual processing of audio signals and contributes to a deeper understanding of auditory mechanisms in both

Vadim Limousin, Nelly Pustelnik, Bruno Deremble, Antoine Venaille

The reconstruction of deep ocean currents is a major challenge in data assimilation due to the scarcity of interior data. In this work, we present a proof of concept for deep ocean flow reconstruction using a Physics-Informed Neural Network (PINN), a machine learning approach that offers an alternative to traditional data assimilation methods. We introduce a

David Marguerit

Artificial intelligence (AI) is reshaping the labor market by changing the task content of occupations. This study investigates the impact of AI development on the emergence of new work, employment, and wages in the United States from 2015 to 2022. I develop innovative methods to measure occupational and industry exposure to AI technologies that substitute l

Mingzhen Huang, Fu-Jen Chu, Bugra Tekin, Kevin J Liang

We introduce HOIGPT, a token-based generative method that unifies 3D hand-object interactions (HOI) perception and generation, offering the first comprehensive solution for captioning and generating high-quality 3D HOI sequences from a diverse range of conditional signals (\eg text, objects, partial sequences). At its core, HOIGPT utilizes a large language m

Luis A. Guardiola, Ana Meca

Big Boss Games represent a specific class of cooperative games where a single veto player, known as the Big Boss, plays a central role in determining resource allocation and maintaining coalition stability. In this paper, we introduce a novel allocation scheme for Big Boss games, based on two classical solution concepts: the Shapley value and the $\tau$-valu

Alejandra Castillo, Jamie Haddock, Iryna Hartsock, Paulina Hoyos

Randomized iterative algorithms, such as the randomized Kaczmarz method and the randomized Gauss-Seidel method, have gained considerable popularity due to their efficacy in solving matrix-vector and matrix-matrix regression problems. Our present work leverages the insights gained from studying such algorithms to develop regression methods for tensors, which

José A. Carrillo, Razvan C. Fetecau, Hansol Park

We investigate the existence of ground states for a free energy functional on Cartan-Hadamard manifolds. The energy, which consists of an entropy and an interaction term, is associated to a macroscopic aggregation model that includes nonlinear diffusion and nonlocal interactions. We consider specifically the regime of fast diffusion, and establish necessary

Kate Roberts, Luke Moore, James O'Donoghue, Henrik Melin

Global temperatures in Jupiter's upper atmosphere are poorly constrained. Other than an in situ measurement by the Galileo Probe, all temperature data come from remote sensing methods which primarily rely on emissions from H$_3^+$, the dominant molecular ion in giant planet ionospheres. While H$_3^+$ temperature serves as a proxy for thermospheric temperatur

Shoffan Saifullah, Rafał Dreżewski

Medical image segmentation, particularly for brain tumor analysis, demands precise and computationally efficient models due to the complexity of multimodal MRI datasets and diverse tumor morphologies. This study introduces PSO-UNet, which integrates Particle Swarm Optimization (PSO) with the U-Net architecture for dynamic hyperparameter optimization. Unlike

Eoin O'Connor, Hailan Ma, Marco G. Genoni

Achieving unit fidelity in quantum state preparation is often impossible in the presence of environmental decoherence. While continuous monitoring and feedback control can improve fidelity, perfect state preparation remains elusive in many scenarios. Inspired by quantum speed limits, we derive a fundamental bound on the steady-state average fidelity achievab

Lucas Martin, Martin Parlanti, Martin Schvellinger

An explicit derivation of the four-dilatino scattering amplitude from type II superstring theory is presented. This is obtained by using the Kawai-Lewellen-Tye relations for closed string scattering amplitudes in terms of the product of two open string scattering amplitudes. We also investigate its Regge limit.

Christian John Hurry, Jinjie Zhang, Olubukola Ishola, Emma Slade

Developing computer vision for high-content screening is challenging due to various sources of distribution-shift caused by changes in experimental conditions, perturbagens, and fluorescent markers. The impact of different sources of distribution-shift are confounded in typical evaluations of models based on transfer learning, which limits interpretations of

Chu-Liang Fu, Mouyang Cheng, Nguyen Tuan Hung, Eunbi Rha

Thermoelectric materials offer a promising pathway to directly convert waste heat to electricity. However, achieving high performance remains challenging due to intrinsic trade-offs between electrical conductivity, the Seebeck coefficient, and thermal conductivity, which are further complicated by the presence of defects. This review explores how artificial

Van-Giang Trinh, Samuel Pastva, Jordan Rozum, Kyu Hyong Park

Boolean Networks (BNs) describe the time evolution of binary states using logic functions on the nodes of a network. They are fundamental models for complex discrete dynamical systems, with applications in various areas of science and engineering, and especially in systems biology. A key aspect of the dynamical behavior of BNs is the number of attractors, wh

Yorick Estievenart, Sukanya Patra, Souhaib Ben Taieb

Efficient and reliable operation of Concentrated Solar Power (CSP) plants is essential for meeting the growing demand for sustainable energy. However, high-temperature solar receivers face severe operational risks, such as freezing, deformation, and corrosion, resulting in costly downtime and maintenance. To monitor CSP plants, cameras mounted on solar recei

Marco Garosi, Alessandro Conti, Gaowen Liu, Elisa Ricci

Attribute detection is crucial for many computer vision tasks, as it enables systems to describe properties such as color, texture, and material. Current approaches often rely on labor-intensive annotation processes which are inherently limited: objects can be described at an arbitrary level of detail (e.g., color vs. color shades), leading to ambiguities wh

Hester Graves, Benjamin Weiss

Silverman showed that, assuming the $abc$ conjecture, there are $\gg \log x$ non-Wieferich primes base $a$ less than $x$ \cite{silverman}, for all non-zero $a$. This inspired Graves and Murty \cite{Graves}, Chen and Ding \cite{Chen1} \cite{Chen2}, and then Ding \cite{Ding} to find growth results, assuming the $abc$ conjecture, for non-Wieferich primes $p$ ba

Qu Luo, Jing Zhu, Zilong Liu, Yanqun Tang

Affine frequency division multiplexing (AFDM) is a promising chirp-assisted multicarrier waveform for future high-mobility communications. This paper is devoted to enhanced receiver design for multiple input and multiple output AFDM (MIMO-AFDM) systems. Firstly, we introduce a unified variational inference (VI) approach to approximate the target posterior di

Jesse Spielman, David Oswald, Mark Ryan, Jo Van Bulck

With high-stakes machine learning applications increasingly moving to untrusted end-user or cloud environments, safeguarding pre-trained model parameters becomes essential for protecting intellectual property and user privacy. Recent advancements in hardware-isolated enclaves, notably Intel SGX, hold the promise to secure the internal state of machine learni

Kaimin Cheng, Du Sheng

Let $p$ be a prime, and let $N$ be a positive integer such that $p$ is a primitive root modulo $N$. Define $q = p^e$, where $e = \phi(N)$, and let $\mathbb{F}_q$ be the finite field of order $q$ with $\mathbb{F}_p$ as its prime subfield. Denote by $\mathrm{Tr}$ the trace function from $\mathbb{F}_q$ to $\mathbb{F}_p$. For $\alpha \in \mathbb{F}_p$ and $\beta

Anuj Pokhrel, Aniket Datar, Xuesu Xiao

When pushing the speed limit for aggressive off-road navigation on uneven terrain, it is inevitable that vehicles may become airborne from time to time. During time-sensitive tasks, being able to fly over challenging terrain can also save time, instead of cautiously circumventing or slowly negotiating through. However, most off-road autonomy systems operate

Oleg Zenin, Stanislav Alexeyev

Applying the definition of the turnaround radius and the fact that the best agreement with observational data on extragalactic scales is currently provided by general relativity with the cosmological constant we consider the behaviour of spherically symmetric solutions of the Horndeski and Dvali-Gabadadze-Poratti models on these scales. Therefore, the condit

Tobias Kauer, Marian Dörk, Benjamin Bach

This work investigates personal perspectives in visualization annotations as devices for collective data-driven storytelling. Inspired by existing efforts in critical cartography, we show how people share personal memories in a visualization of COVID-19 data and how comments by other visualization readers influence the reading and understanding of visualizat

Bojan B. Guzina, Marc Bonnet

Motivated by the recent experimental and analytical developments enabling high-fidelity material characterization of (heterogeneous) sheet-like solid specimens, we seek to elucidate the constitutive behavior of linear viscoelastic solids under the plane stress condition. More specifically, our goal is to expose the relationship between the plane-stress visco

Sidhanth Holalkere, David S. Bindel, Silvia Sellán, Alexander Terenin

Poisson Surface Reconstruction is a widely-used algorithm for reconstructing a surface from an oriented point cloud. To facilitate applications where only partial surface information is available, or scanning is performed sequentially, a recent line of work proposes to incorporate uncertainty into the reconstructed surface via Gaussian process models. The re

Tohid Kargar Tasooji, Sakineh Khodadadi, Guangjun Liu, Richard Wang

This paper introduces a novel methodology for the cooperative control of multiple quadrotors transporting cablesuspended payloads, emphasizing obstacle-aware planning and event-based Nonlinear Model Predictive Control (NMPC). Our approach integrates trajectory planning with real-time control through a combination of the A* algorithm for global path planning

Pin-Yu Chen, Han Shen, Payel Das, Tianyi Chen

Fine-tuning Large Language Models (LLMs) on some task-specific datasets has been a primary use of LLMs. However, it has been empirically observed that this approach to enhancing capability inevitably compromises safety, a phenomenon also known as the safety-capability trade-off in LLM fine-tuning. This paper presents a theoretical framework for understanding

Wenhao You, Bryan Hooi, Yiwei Wang, Youke Wang

While safety mechanisms have significantly progressed in filtering harmful text inputs, MLLMs remain vulnerable to multimodal jailbreaks that exploit their cross-modal reasoning capabilities. We present MIRAGE, a novel multimodal jailbreak framework that exploits narrative-driven context and role immersion to circumvent safety mechanisms in Multimodal Large

Victor Enrique Vizcarra Ruiz

This paper presents an innovative approach to the study of recurrent sequences by introducing the concept of arithmetic pseudo-operators. Unlike conventional operators, these pseudo-operators are pure complex numbers with specific structural properties, allowing for unprecedented operational reformulations. Represented by the symbols ``$+$'', ``$/$'' (slash)

Fatmagül Katmer, Milena Jovanovic, Jennifer Cano, Lukas Muechler

Materials in which atoms are arranged in a pyrochlore lattice have found renewed interest, as, at least theoretically, orbitals on those lattices can form flat bands. However, real materials often do not behave according to theoretical models, which is why there has been a dearth of pyrochlore materials exhibiting flat band physics. Here we examine the condi

Jichen Zhu, Pedro Sanches, Vasiliki Tsaknaki, Willem van der Maden

This paper critically examines the machine learning (ML) modeling of humans in three case studies of well-being technologies. Through a critical technical approach, it examines how these apps were experienced in daily life (technology in use) to surface breakdowns and to identify the assumptions about the "human" body entrenched in the ML models (technology

Muhammed Irshad P, Pallavi Bhat, Kandaswamy Subramanian, Anvar Shukurov

The amplification of magnetic fields is crucial for understanding the observed magnetization of stars and galaxies. Turbulent dynamo is the primary mechanism responsible for that but the understanding of its action in a collapsing environment is still rudimentary and relies on limited numerical experiments. We develop an analytical framework and perform nume

B. Pritychenko, V. I. Tretyak

The double-beta (2$\beta$)-decay is the rarest nuclear physics process, and its experimental half-lives (T$_{1/2}$) exceed the age of the Universe from nine to fourteen orders of magnitude. Double-beta decay was observed, and its half-life was measured in 14 parent nuclei using direct, radiochemical, and geochemical methods. The decay observables are analyze

Zachary Lee, Nataša Pavlović

In this work, we address an inverse problem for a defocusing cubic nonlinear Schr\"{o}dinger (NLS) equation in dimensions $d\in\{1, 2,3\}$ in a range of Sobolev spaces $H^s(\mathbb{R}^d)$ by employing the method of approximate solutions. We recover a smooth, space-dependent and compactly supported function $\alpha$ that controls the nonlinearity (and thus se

Ahmad Sohrabi, Robert L. West

In typical Stroop experiments, participants perform better when the colors and words are congruent compared to when they are incongruent or neutral. Paradoxically, in some experimental conditions, neutral trials are faster than congruent trials. This phenomenon is known as Reverse Facilitation Effect (RFE). However, RFE has not been consistently replicated,

John Sperger, Kelley M. Kidwell, Matthew C. Mauck, Beibo Zhao

Chronic low back pain (cLBP) is a prevalent condition with profound impacts on functioning and quality of life. While multiple evidence-based treatments exist, they all have modest average treatment effects$\unicode{x2013}$potentially due to individual variation in treatment response and the diverse etiologies of cLBP. This multi-site sequential, multiple-as

Maya V. Marmary, Christian Grussler

The problem of finding the sparsest solution to a linear underdetermined system of equations, often appearing, e.g., in data analysis, optimal control, system identification, or sensor selection problems, is considered. This non-convex problem is commonly solved by convexification via $\ell_1$-norm minimization, known as basis pursuit (BP). In this work, a c

Andrew Rufail, Daniel Kim, Sean O'Brien, Kevin Zhu

We introduce CLEAR (Contrasting Textual Feedback with Experts and Amateurs for Reasoning), a novel approach to language model reasoning that leverages the strengths of a larger (expert) model and smaller (amateur) model. The expert and amateur models each provide feedback on a model's initial output and are contrasted with each other into refined feedback. T

Ibrahim Yildirim, Tho Le-Ngoc

Beam split is a critical challenge in wideband THz massive MIMO systems, arising from frequency-dependent beam misalignment that degrades communication performance, particularly in scenarios with narrow beamwidths and large arrays. This work proposes an angular-based hybrid beamforming framework that leverages angular spread to mitigate the beam split effect

Haebin Shin, Lei Ji, Xiao Liu, Yeyun Gong

Using large teacher models to guide the training of smaller student models has become the prevailing paradigm for efficient and effective learning. However, vocabulary mismatches between teacher and student language models pose significant challenges in language modeling, resulting in divergent token sequences and output distributions. To overcome these limi

Philippe Belanger, Guido Sterbini

Particle tracking codes are one of the fundamental tools used in the design and the study of complex magnetic lattices in accelerator physics. For most practical applications, non-linear lenses are included and the Courant-Snyder formalism falls short of a complete description of the motion. Likewise, when the longitudinal motion is added, synchro-betatron c

I. Marini, P. Popesso, K. Dolag, V. Biffi

Galaxy groups represent a significant fraction of the halo population, playing a crucial role in galaxy formation and evolution. However, their detection in X-rays remains challenging, raising questions about the physical mechanisms driving their detectability in current surveys. Using the Magneticum simulations, we construct a mock X-ray lightcone of the lo

Armineh Nourbakhsh, Siddharth Parekh, Pranav Shetty, Zhao Jin

Document Visual Question Answering (VQA) models have evolved at an impressive rate over the past few years, coming close to or matching human performance on some benchmarks. We argue that common evaluation metrics used by popular benchmarks do not account for the semantic and multimodal groundedness of a model's outputs. As a result, hallucinations and major

Yiling Wang, Elia Lombardo, Adrian Thummerer, Tom Blöcker

Purpose: Magnetic resonance imaging (MRI) to visualize anatomical motion is becoming increasingly important when treating cancer patients with radiotherapy. Hybrid MRI-linear accelerator (MRI-linac) systems allow real-time motion management during irradiation. This paper presents a multi-institutional real-time MRI time series dataset from different MRI-lina

Shigeru Wakita, Brandon C. Johnson, Jason M. Soderblom, Jordan K. Steckloff

Titan is the only icy satellite in the solar system with a dense atmosphere. This atmosphere is composed primarily of nitrogen with a few percent methane, which supports an active, methane-based hydrological cycle on Titan. The presence of methane, however, is intriguing, as its lifetime is likely much shorter than the age of the solar system due to its irre

Mahsa Nadifar, Andriette Bekker, Mohammad Arashi, Abel Ramoelo

The appropriateness of the Poisson model is frequently challenged when examining spatial count data marked by unbalanced distributions, over-dispersion, or under-dispersion. Moreover, traditional parametric models may inadequately capture the relationships among variables when covariates display ambiguous functional forms or when spatial patterns are intrica

A. Emran, K. M. Stack

Hollows on Mercury are small depressions formed by volatile loss, providing important clues about the volatile inventory of the planet's surface and shallow subsurface. We investigate the composition of hollows in various phases of devolatilization at Dominici crater. By applying a machine learning approach to MESSENGER Mercury Dual Imaging System data, we d

Amey Choudhary, Jiaxin Jin, Abhishek Deshpande

Can machine learning algorithms be implemented using chemistry? We demonstrate that this is possible in the case of support vector machines (SVMs). SVMs are powerful tools for data classification, leveraging Vapnik-Chervonenkis theory to handle high-dimensional data and small datasets effectively. In this work, we propose a chemical reaction network scheme f

Weronika Łajewska, Momchil Hardalov, Laura Aina, Neha Anna John

Recent advancements in large language models (LLMs) have enabled their successful application to a broad range of tasks. However, in information-intensive tasks, the prompt length can grow fast, leading to increased computational requirements, performance degradation, and induced biases from irrelevant or redundant information. Recently, various prompt compr

S. Saracino, S. Kamann, F. Wragg, S. Dreizler

Omega Centauri ($\omega$ Cen) is one of the most complex star clusters in the Milky Way, and likely the stripped nucleus of an accreted dwarf galaxy. Being the subject of debate between it hosting an intermediate-mass black hole (IMBH) or a collection of stellar-mass black holes (BHs) in its center, $\omega$ Cen has been intensively studied over the past dec