December 2024 arXiv papers — page 137

Benjamin D. Killeen, Anushri Suresh, Catalina Gomez, Blanca Inigo

Natural language offers a convenient, flexible interface for controlling robotic C-arm X-ray systems, making advanced functionality and controls accessible. However, enabling language interfaces requires specialized AI models that interpret X-ray images to create a semantic representation for reasoning. The fixed outputs of such AI models limit the functiona

Yang Zhang, Yuxing Lu, Guiyang Xin, Yufei Xue

In this work, we present the design, development, and experimental validation of a custom-built quadruped robot, Ask1. The Ask1 robot shares similar morphology with the Unitree Go1, but features custom hardware components and a different control architecture. We transfer and extend previous reinforcement learning (RL)-based control methods to the Ask1 robot,

Samuel L. Krushkal

The Grunsky operator arises from univalence and plays a crucial role in geometric function theory. This operator also implies quasiconformal extendibility and has an intrinsic connection with Teichmuller space theory and its interactions with complex analysis and pluripotential theory. This paper surveys recent results in this field and simultaneously provid

Christian Duffee, Jordan Athas, Yixin Shao, Noraica Davila Melendez

Probabilistic Ising machines (PIMs) provide a path to solving many computationally hard problems more efficiently than deterministic algorithms on von Neumann computers. Stochastic magnetic tunnel junctions (S-MTJs), which are engineered to be thermally unstable, show promise as entropy sources in PIMs. However, scaling up S-MTJ-PIMs is challenging, as it re

Jason Brown, Bohan Chen, Harris Hardiman-Mostow, Jeff Calder

Standard deep learning architectures used for classification generate label predictions with a projection head and softmax activation function. Although successful, these methods fail to leverage the relational information between samples for generating label predictions. In recent works, graph-based learning techniques, namely Laplace learning, have been he

Takehiko Asaka, Hiroyuki Ishida, Kazuki Tanaka

We consider the minimal model of the seesaw mechanism by introducing two right-handed neutrinos, whose masses are comparable to the electroweak scale. This framework is attractive, since it is testable at terrestrial experiments. A critical consequence of this mechanism is the violation of lepton number conservation due to the Majorana masses of both active

Qi Wen, Xiang Wen, Hao Jiang, Siqi Yang

With the rise of the ``metaverse'' and the rapid development of games, it has become more and more critical to reconstruct characters in the virtual world faithfully. The immersive experience is one of the most central themes of the ``metaverse'', while the reducibility of the avatar is the crucial point. Meanwhile, the game is the carrier of the metaverse,

Yun Xing, Nhat Chung, Jie Zhang, Yue Cao

Physical adversarial attacks in driving scenarios can expose critical vulnerabilities in visual perception models. However, developing such attacks remains challenging due to diverse real-world environments and the requirement for maintaining visual naturality. Building upon this challenge, we reformulate physical adversarial attacks as a one-shot patch gene

Himangshu Sekhar Sarma, Subhradip Ghosh

Two-dimensional van der Waals heterostructures are potential game changers both in understanding the fundamental physics and in the realization of various devices that exploit magnetism at the nanoscale. Multiferroic heterostructures comprising a two-dimensional ferroelectric and a two-dimensional ferromagnet are ideal candidates for electrical control of pr

Marco Bressan, Nataly Brukhim, Nicolò Cesa-Bianchi, Emmanuel Esposito

Cost-sensitive loss functions are crucial in many real-world prediction problems, where different types of errors are penalized differently; for example, in medical diagnosis, a false negative prediction can lead to worse consequences than a false positive prediction. However, traditional PAC learning theory has mostly focused on the symmetric 0-1 loss, leav

D. Fargion

The understanding of microquasars in our galaxy is one of the frontiers of high energy astrophysics. Their models are based on a capturing mass Black Hole, with a nearby spiraling binary companion star. The companion star mass feeds the accretion disk around the Black Hole. This energy also fuels an orthogonal precessing X gamma jets. The spiral precessing t

Milan Maksimovic, Ivan S. Maksymov

Modern machine learning (ML) systems excel in recognising and classifying images with remarkable accuracy. However, like many computer software systems, they can fail by generating confusing or erroneous outputs or by deferring to human operators to interpret the results and make final decisions. In this paper, we employ the recently proposed quantum-tunnell

Hiep Vo Dang, Phong C. H. Nguyen

Reconstructing high-fidelity fluid flow fields from sparse sensor measurements is vital for many science and engineering applications but remains challenging because of dimensional disparities between state and observational spaces. Due to such dimensional differences, the measurement operator becomes ill-conditioned and non-invertible, making the reconstruc

Camilla Scolini, Bin Zhuang, Noé Lugaz, Réka M. Winslow

We investigate whether Alfv\'enic fluctuations (AFs) can affect the structure of magnetic ejecta (MEs) within interplanetary coronal mass ejections (ICMEs). We study an ICME observed on 2001 December 29 at 1 au by ACE and Wind, at a total angular separation of $\sim$0.8$^\circ$ ($\sim0.014$~au). We focus on the correlation and complexity of its magnetic stru

Ivan G. Avramidi, Roberto Niardi

We develop a novel approach to Quantum Mechanics that we call Curved Quantum Mechanics. We introduce an infinite-dimensional K\"ahler manifold ${\cal M}$, that we call the state manifold, such that the cotangent space $T_z^*{\cal M}$ is a Hilbert space. In this approach, a state of a quantum system is described by a point in the cotangent bundle $T^*{\cal M}

Alkım B. Bozkurt, Omid Golami, Yue Yu, Hao Tian

Long-lived mechanical oscillators are actively pursued as critical resources for quantum storage, sensing, and transduction. However, achieving deterministic quantum control while limiting mechanical dissipation remains a persistent challenge. Here, we demonstrate strong coupling between a transmon superconducting qubit and an ultra-long-lived nanomechanical

Abu Jafar Md Muzahid, Xiaopeng Zhao, Zhenbo Wang

The capabilities of automated vehicles are advancing rapidly, yet achieving full autonomy remains a significant challenge, requiring ongoing human cognition in decision-making processes. Incorporating human cognition into control algorithms has become increasingly important, as researchers work to develop strategies that minimize conflicts between human driv

Douglas M. Gingrich, Saeed Rastgoo

We examine the geometry of a generalized uncertainty-inspired quantum black hole. The diagonal line element is not $t$-$r$ symmetric, i.e. $g_{00} \ne -1/g_{11}$, which leads to an interesting approach to resolving the classical curvature singularity. In this paper, we show, in Schwarzschild coordinates, the $r = 0$ coordinate location is a null surface whic

Chi-Shiang Gau, Xingyu Chen, Tara Javidi, Xinyu Zhang

Radio-frequency (RF) Radiance Field reconstruction is a challenging problem. The difficulty lies in the interactions between the propagating signal and objects, such as reflections and diffraction, which are hard to model precisely, especially when the shapes and materials of the objects are unknown. Previously, a neural network-based method was proposed to

Emily F. Kerrison, Elaine M. Sadler, Vanessa A. Moss, Elizabeth K. Mahony

We present here a study of the broadband spectral properties of 33 sources detected in HI absorption as part of the ASKAP-FLASH Pilot Surveys. We outline our approach to spectral classification and discuss the correlation seen between spectral shape and the detection of HI absorption. We further consider the implications of the observed correlation on the sp

Shanghua Zheng, Shiyu Huang, Li Guo

Under the common theme of splitting of operations, the notions of (tri)dendriform algebras, pre-Lie algebras and post-Lie algebras have attracted sustained attention with broad applications. An important aspect of their studies is as the derived structures of Rota-Baxter operators on associative or Lie algebras. This paper introduces extended versions of (tr

Gabriel L. Moraes, Renato M. Angelo, Ana C. S. Costa

Correlations play a pivotal role in various fields of science, particularly in quantum mechanics, yet their proper quantification remains a subject of debate. In this work, we aim to discuss the challenge of defining a reliable measure of total correlations. We first outline essential properties that an effective correlation measure should satisfy and review

Holden Lee, Kexin Zhang

We propose using a modified conductance-based method to study the mixing time of an important class of two-block Gibbs samplers, the data augmentation (DA) algorithm. %, which is of prominent interest in both theoretical and empirical research. Using this method, we prove the first non-asymptotic polynomial upper bounds on mixing times of three important DA

Yuchen Hui, Fengran Mo, Milan Mao, Jian-Yun Nie

The Recherche Appliquee en Linguistique Informatique (RALI) team participated in the 2024 TREC Interactive Knowledge Assistance (iKAT) Track. In personalized conversational search, effectively capturing a user's complex search intent requires incorporating both contextual information and key elements from the user profile into query reformulation. The user p

Kun Li, Xinwei Chen, Tianyou Song, Hansong Zhang

In the burgeoning field of AI-driven image generation, the quest for precision and relevance in response to textual prompts remains paramount. This paper introduces GPTDrawer, an innovative pipeline that leverages the generative prowess of GPT-based models to enhance the visual synthesis process. Our methodology employs a novel algorithm that iteratively ref

Jiajiang Shen, Weiyan Wu, Qianyu Xu

In recent years, the importance of accurate weather forecasting has become increasingly prominent due to the impacts of global climate change and the rapid development of data science. Traditional forecasting methods often struggle to handle the complexity and nonlinearity inherent in climate data. To address these challenges, we propose a weather prediction

Masora Okano, Koichi Ito, Masakatsu Nishigaki, Tetsushi Ohki

This paper discusses the attack feasibility of Remote Adversarial Patch (RAP) targeting face detectors. The RAP that targets face detectors is similar to the RAP that targets general object detectors, but the former has multiple issues in the attack process the latter does not. (1) It is possible to detect objects of various scales. In particular, the area o

Sara Faridi, Peilin Li

Motivated by the fact that as the number of generators of an ideal grows so does the complexity of calculating relations among the generators, this paper identifies collections of monomial ideals with a growing number of generators which have predictable free resolutions. We use elementary collapses from discrete homotopy theory to construct infinitely many

Indira Chatterji, Benjamin Zarka

We generalize the notion of rapid decay property for a group $G$ to pairs of groups $(G,H)$ where $H$ is a finitely generated subgroup of $G$, where typically the subgroup $H$ does not have rapid decay. We deduce some isomorphisms in $K$-theory, and investigate relatively spectral injections in the reduced group $C^*$-algebra. Rapid decay property for the pa

Yifei Sun, Mariano Cabezas, Jiah Lee, Chenyu Wang

The human brain is a complex and highly dynamic system, and our current knowledge of its functional mechanism is still very limited. Fortunately, with functional magnetic resonance imaging (fMRI), we can observe blood oxygen level-dependent (BOLD) changes, reflecting neural activity, to infer brain states and dynamics. In this paper, we ask the question of w

Ella Nikodem, Jakob Schluck, Henry F. Legg, Max Geier

Many efforts have been made in the past decade to realize topological superconductivity using superconducting proximity effect, but an ideal platform is still lacking. A 3D topological insulator (TI) is promising for this purpose due to the spin-momentum-locked surface state. Here we propose a novel yet simple TI platform which gives rise to a topological ph

Chung-En Sun, Tuomas Oikarinen, Berk Ustun, Tsui-Wei Weng

We introduce Concept Bottleneck Large Language Models (CB-LLMs), a novel framework for building inherently interpretable Large Language Models (LLMs). In contrast to traditional black-box LLMs that rely on limited post-hoc interpretations, CB-LLMs integrate intrinsic interpretability directly into the LLMs -- allowing accurate explanations with scalability a

Han Cao, Augusto Anguita, Charline Warembourg, Xavier Escriba-Montagut

Machine learning has been widely adopted in biomedical research, fueled by the increasing availability of data. However, integrating datasets across institutions is challenging due to legal restrictions and data governance complexities. Federated learning allows the direct, privacy preserving training of machine learning models using geographically distribut

Yashwanthi Anand, Nnamdi Nwagwu, Kevin Sabbe, Naomi T. Fitter

Learning from human feedback is a popular approach to train robots to adapt to user preferences and improve safety. Existing approaches typically consider a single querying (interaction) format when seeking human feedback and do not leverage multiple modes of user interaction with a robot. We examine how to learn a penalty function associated with unsafe beh

Subham Bhakta, Igor Shparlinski

We obtain new bounds on complete rational exponential sums with sparse polynomials modulo a prime, under some mild conditions on the degrees of the monomials of such polynomials. These bounds, when they apply, give explicit versions of a result of J. Bourgain (2005). In turn, as an application, we also obtain an explicit version of a result of J. Bourgain (2

Michael Hinz, Waldemar Schefer

We study first order equations of continuity and transport type on metric spaces of martingale dimension one, including finite metric graphs, p.c.f. self-similar sets and classical Sierpi\'nski carpets. On such spaces solutions of the continuity equation in the weak sense are generally non-unique. We use semigroup theory to prove a well-posedness result for

Rongzheng Wang, Shuang Liang, Qizhi Chen, Jiasheng Zhang

Large language models (LLMs) have been demonstrated to possess the capabilities to understand fundamental graph properties and address various graph reasoning tasks. Existing methods fine-tune LLMs to understand and execute graph reasoning tasks by specially designed task instructions. However, these Text-Instruction methods generally exhibit poor performanc

Ryoichiro Noda

In this paper, we prove that if a sequence of electrical networks converges in the local Gromov-Hausdorff topology and satisfies a non-explosion condition, then the associated Bouchaud trap models (BTMs) also converge and exhibit aging. Moreover, when local structures of electrical networks converge, we prove sub-aging. Our results are applicable to a wide c

Haijun Wu, Nilo Mata-Cervera, Haiwen Wang, Zhihan Zhu

Creation and control of topological complex excitations play crucial roles in both fundamental physics and modern information science. Torons are a sophisticated class of 3D chiral polar topological structures with both skyrmionic quasiparticle textures and monopole point defects, so far only observed in liquid crystal nonpolar models. Here, we experimentall

Yuya O. Nakagawa, Masahiko Kamoshita, Wataru Mizukami, Shotaro Sudo

We present a quantum-classical hybrid algorithm for calculating the ground state and its energy of the quantum many-body Hamiltonian by proposing an adaptive construction of a quantum state for the quantum-selected configuration interaction (QSCI) method. QSCI allows us to select important electronic configurations in the system to perform CI calculation (su

Christoph Carnehl, Johannes Schneider

Is more novel research always desirable? We develop a model in which knowledge shapes society's policies and guides the search for discoveries. Researchers select a question and how intensely to study it. The novelty of a question determines both the value and difficulty of discovering its answer. We show that the benefits of discoveries are nonmonotone

Shijie Cui, Danning Li, Runze Li, Lingzhou Xue

This paper addresses hypothesis testing for the mean of matrix-valued data in high-dimensional settings. We investigate the minimum discrepancy test, originally proposed by Cragg (1997), which serves as a rank test for lower-dimensional matrices. We evaluate the performance of this test as the matrix dimensions increase proportionally with the sample size, a

Erich Grädel, Val Tannen

A provenance analysis for a query evaluation or a model checking computation extracts information on how its result depends on the atomic facts of the model or database. Traditional work on data provenance was, to a large extent, restricted to positive query languages or the negation-free fragment of first-order logic and showed how provenance abstractions c

Alejandro Saravia, Eduardo Rodas-Quito, Loreto Barcos-Muñoz, Aaron S. Evans

The molecular Kennicutt-Schmidt (mK-S) Law has been key for understanding star formation (SF) in galaxies across all redshifts. However, recent sub-kpc observations of nearby galaxies reveal deviations from the nearly unity slope (N) obtained with disk-averaged measurements. We study SF and molecular gas (MG) distribution in the early-stage luminous infrared

Eyal Gomel, Lior Wolf

We present a novel method for 3D scene editing using diffusion models, designed to ensure view consistency and realism across perspectives. Our approach leverages attention features extracted from a single reference image to define the intended edits. These features are warped across multiple views by aligning them with scene geometry derived from Gaussian s

Randy Bartels, Olivier Pinaud

Simulating scalar wave propagation in strongly heterogeneous media comes at a steep computational cost, and the widely used approach to simplification - split-step operators - sacrifices accuracy. The recently proposed multi-layer Born method has sought to resolve that problem, but because it discards evanescent modes, also produces large errors. In this wor

Kelsey Nelson, Javad Mohammadi

As extreme weather events become more common and threaten power grids, the continuing adoption of electric vehicles (EVs) introduces a growing opportunity for their use as a distributed energy storage resource. This energy storage can be used as backup generation through the use of vehicle-to-grid (V2G) technology, where electricity is sent back from EV batt

Guang Hu, Tim Miller, Nir Lipovetzky

Epistemic planning is the sub-field of AI planning that focuses on changing knowledge and belief. It is important in both multi-agent domains where agents need to have knowledge/belief regarding the environment, but also the beliefs of other agents, including nested beliefs. When modeling knowledge in multi-agent settings, many models face an exponential gro

Mingxi Lei, Chunwei Ma, Meng Ding, Yufan Zhou

Deep learning models often struggle with generalization when deploying on real-world data, due to the common distributional shift to the training data. Test-time adaptation (TTA) is an emerging scheme used at inference time to address this issue. In TTA, models are adapted online at the same time when making predictions to test data. Neighbor-based approache

Ajay Jagannath, Aayush Upadhyay, Anant Mehta

Contrastive learning has emerged as a pivotal framework for representation learning, underpinning advances in both unimodal and bimodal applications like SimCLR and CLIP. To address fundamental limitations like large batch size dependency and bimodality, methods such as SogCLR leverage stochastic optimization for the global contrastive objective. Inspired by

Shuxiang Cao, Zijian Zhang, Mohammed Alghadeer, Simone D Fasciati

Fully automated self-driving laboratories are promising to enable high-throughput and large-scale scientific discovery by reducing repetitive labour. However, effective automation requires deep integration of laboratory knowledge, which is often unstructured, multimodal, and difficult to incorporate into current AI systems. This paper introduces the k-agents

Stefan Dernbach, Alejandro Michel, Khushbu Agarwal, Christopher Brissette

This paper introduces lateral thinking to implement System-2 reasoning capabilities in AI systems, focusing on anticipatory and causal reasoning under uncertainty. We present a framework for systematic generation and modeling of lateral thinking queries and evaluation datasets. We introduce Streaming Agentic Lateral Thinking (SALT), a multi-agent framework d

Ian Good, Srivatsan Balaji, Jeffrey I. Lipton

Soft grippers have demonstrated a strong ability to successfully pick and manipulate many objects. A key limitation to their wider adoption is their inability to grasp larger payloads due to objects slipping out of grasps. We have overcome this limitation by introducing a torsionally rigid strain limiting layer (TR-SLL). This reduces out-of-plane bending whi

Yuyang Cheng, Xingwei Qu, Tomas Goldsack, Chenghua Lin

Thomas C. Schelling, awarded the 2005 Nobel Memorial Prize in Economic Sciences, pointed out that ``individuals decisions (micromotives), while often personal and localized, can lead to societal outcomes (macrobehavior) that are far more complex and different from what the individuals intended.'' The current research related to large language models' (LLMs')

Davide Nunes, Luis Antunes

One goal of Artificial Intelligence is to learn meaningful representations for natural language expressions, but what this entails is not always clear. A variety of new linguistic behaviours present themselves embodied as computers, enhanced humans, and collectives with various kinds of integration and communication. But to measure and understand the behavio

Andrey Kupavskii

We say that a family of $k$-subsets of an $n$-element set is {\it intersecting}, if any two of its sets intersect. In this paper, we study the structure of large intersecting families. Several years ago, Han and Kohayakawa (Proc. AMS, 2017), and then Kostochka and Mubayi (Proc. AMS, 2017) obtained certain structural results concerning large intersecting fami

G. Bruce Berriman

The Virtual Observatory (VO) is a global ecosystem of interoperating services that connect worldwide data archives. The VO is implemented in all major astronomy archives through common interfaces developed by the 22 members of the International Virtual Observatory Alliance (IVOA). It was founded in 2002, and the newest members, the SKA Observatory and the Ka

Santiago Aranguri, Francesco Insulla

We analyze the training of a two-layer autoencoder used to parameterize a flow-based generative model for sampling from a high-dimensional Gaussian mixture. Previous work shows that the phase where the relative probability between the modes is learned disappears as the dimension goes to infinity without an appropriate time schedule. We introduce a time dilat

Heng Zhu, Harsh Vardhan, Arya Mazumdar

In distributed training of machine learning models, gradient descent with local iterative steps, commonly known as Local (Stochastic) Gradient Descent (Local-(S)GD) or Federated averaging (FedAvg), is a very popular method to mitigate communication burden. In this method, gradient steps based on local datasets are taken independently in distributed compute n

Yuxuan Yuan, Sergio Martin-Alvarez, Martin G. Haehnelt, Thibault Garel

The visibility of the Lyman-$\alpha$ (Ly$\alpha$) emission from reionization-epoch galaxies depends sensitively on the extent of the intrinsic \lya emission redwards of 1215.67~\AA. The prominent red peak resulting from resonant radiative transfer in the interstellar medium is often modelled as a single Gaussian. We use the \textsc{Azahar} simulation suite o

Rumi Allbert, James K. Wiles, Vlad Grankovsky

The field of large language models (LLMs) has grown rapidly in recent years, driven by the desire for better efficiency, interpretability, and safe use. Building on the novel approach of "activation engineering," this study explores personality modification in LLMs, drawing inspiration from research like Refusal in LLMs Is Mediated by a Single Direction (arX

Siyuan Niu, Efekan Kokcu, Anupam Mitra, Aaron Szasz

Dynamic quantum circuits incorporate mid-circuit measurements and feed-forward operations originally intended to realize Quantum Error Correction. This paradigm has recently been utilized to prepare certain states and long-range entangling gates as well as reduce resource overhead in quantum algorithms such as Quantum Fourier Transformation and Quantum Phase

Chung-Ming Pan, Tat Dat Tô

We study the weighted constant scalar curvature K\"ahler equations on mildly singular K\"ahler varieties. Assuming the existence of a suitable resolution of singularities, we establish the existence of singular weighted cscK metrics when the weighted Mabuchi functional is coercive for an extremal weight. This extends the works of Chen-Cheng and He to the sin

Lucia Oliva, Gabriele Parisi, Vincenzo Greco, Marco Ruggieri

We study the melting of $c\bar c$ and $b\bar b$ pairs in the early stage of high-energy proton-nucleus collisions. We describe the early stage in terms of an evolving $SU(3)$ glasma stage, that is dominated by intense, out-of-equilibrium gluon fields. On top of these fields, we liberate heavy quark-antiquark pairs, whose constituents are let evolve according

Kurt H. W. Stolle

In this work, we present Multiformer, a novel approach to depth-aware video panoptic segmentation (DVPS) based on the mask transformer paradigm. Our method learns object representations that are shared across segmentation, monocular depth estimation, and object tracking subtasks. In contrast to recent unified approaches that progressively refine a common obj

Tianyi Li, Erenay Dayanik, Shubhi Tyagi, Andrea Pierleoni

In this paper, we present HalluCana, a canary lookahead to detect and correct factuality hallucinations of Large Language Models (LLMs) in long-form generation. HalluCana detects and intervenes as soon as traces of hallucination emerge, during and even before generation. To support timely detection, we exploit the internal factuality representation in the LL

Matt Piekenbrock

Delay Tolerant Networking (DTN) aims to address a myriad of significant networking challenges that appear in time-varying settings, such as mobile and satellite networks, wherein changes in network topology are frequent and often subject to environmental constraints. Within this paradigm, routing problems are often solved by extending classical graph-theoret

Nader Haghighipour, Morris Podolak, Esther Podolak

Continuing our initiative on advancing the calculations of planetesimal accretion in the core-accretion model, we present here the results of our recent study of the contributions of planetesimals around and beyond the orbit of Saturn. In our first two papers [ApJ, 899:45; 941:117], where our focus was on the effects of the Sun and Saturn, the initial distri

Christopher Bian, Albert Cheu, Stanislav Chiknavaryan, Zoe Gong

This paper introduces Mayfly, a federated analytics approach enabling aggregate queries over ephemeral on-device data streams without central persistence of sensitive user data. Mayfly minimizes data via on-device windowing and contribution bounding through SQL-programmability, anonymizes user data via streaming differential privacy (DP), and mandates immedi

Eric Bigelow, Ari Holtzman, Hidenori Tanaka, Tomer Ullman

Estimating uncertainty in Large Language Models (LLMs) is important for properly evaluating LLMs, and ensuring safety for users. However, prior approaches to uncertainty estimation focus on the final answer in generated text, ignoring intermediate steps that might dramatically impact the outcome. We hypothesize that there exist key forking tokens, such that

Jonathan Olivares, Tyler Depe, Kanika Sood, Rakeshkumar Mahto

Drones have become indispensable assets during human-made and natural disasters, offering damage assessment, aid delivery, and communication restoration capabilities. However, most drones rely on batteries that require frequent recharging, limiting their effectiveness in continuous missions. Photovoltaic (PV) powered drones are an ideal alternative. However,

Harish Saranathan

Multi-phase trajectories of aerospace vehicle systems involve multiple flight segments whose transitions may be triggered by boolean logic in continuous state variables, control and time. When the boolean logic is represented using only states and/or time, such systems are termed autonomously switched hybrid systems. The relaxed autonomously switched hybrid

Lorenzo Vianello, Clément Lhoste, Emek Barış Küçüktabak, Matthew Short

Partial-assistance exoskeletons hold significant potential for gait rehabilitation by promoting active participation during (re)learning of normative walking patterns. Typically, the control of interaction torques in partial-assistance exoskeletons relies on a hierarchical control structure. These approaches require extensive calibration due to the complexit

Shambhavi Krishna, Zheng Chen, Yuan Ling, Xiaojiang Huang

Modern AI assistants have made significant progress in natural language understanding and tool-use, with emerging efforts to interact with Web interfaces. However, current approaches that heavily rely on repeated LLM-driven HTML parsing are computationally expensive and error-prone, particularly when handling dynamic web interfaces and multi-step tasks. We i

Muyang Shi, Likun Zhang, Mark D. Risser, Benjamin A. Shaby

Extreme events over large spatial domains may exhibit highly heterogeneous tail dependence characteristics, yet most existing spatial extremes models yield only one dependence class over the entire spatial domain. To accurately characterize "data-level dependence'' in analysis of extreme events, we propose a mixture model that achieves flexible dependence pr

Jingxi Xu, Ava Chen, Lauren Winterbottom, Joaquin Palacios

Intent inferral, the process by which a robotic device predicts a user's intent from biosignals, offers an effective and intuitive way to control wearable robots. Classical intent inferral methods treat biosignal inputs as unidirectional ground truths for training machine learning models, where the internal state of the model is not directly observable by th

Massimiliano Puglisi, Thomas Schick, Vito Felice Zenobi

In this note, we study equivariant versions of Stolz' $R$-groups, the positive scalar curvature structure groups $R^{\rm spin}_n(X)^G$, for proper actions of discrete groups $G$. We define the concept of a fundamental groupoid functor for a $G$-space, encapsulating all the fundamental group information of all the fixed point sets and their relations. We cons

Parsa Ghazvinian, Robert Podschwadt, Prajwal Panzade, Mohammad H. Rafiei

Due to the extensive application of machine learning (ML) in a wide range of fields and the necessity of data privacy, privacy-preserving machine learning (PPML) solutions have recently gained significant traction. One group of approaches relies on Homomorphic Encryption (HE), which enables us to perform ML tasks over encrypted data. However, even with state

Jared Diks, Cosmin Ilie

In this work we explore the potential for Neutron Stars (NSs) at the Galactic center and Population~III stars to constrain bosonic Asymmetric Dark Matter (ADM). We demonstrate that for NSs in an environment of sufficiently high DM density ($\rho_\chi\gtrsim10^{9}\text{GeV/cm}^3$), the effects of both multiscatter capture and DM evaporation cannot be neglecte

Dominik Beck

New selected values of odd random simplex volumetric moments (moments of the volume of a random simplex picked from a given body) are derived in an exact form in various bodies in dimensions three, four, five, and six. In three dimensions, the well-known Efron's formula was used by Buchta & Reitzner and Zinani to deduce the mean volume of a random tetrahedro

Mohit Chandra, Suchismita Naik, Denae Ford, Ebele Okoli

Recent gains in popularity of AI conversational agents have led to their increased use for improving productivity and supporting well-being. While previous research has aimed to understand the risks associated with interactions with AI conversational agents, these studies often fall short in capturing the lived experiences of individuals. Additionally, psych

Luke B. Handley, Andrew W. Howard, Ryan A. Rubenzahl, Fei Dai

We present spectral observations of the multiplanet host TOI-1694 during the transit of TOI-1694b, a 26.1 $M_\oplus$ hot Neptune with a 3.77-day orbit. By analyzing radial velocities obtained from the Keck Planet Finder, we modeled the Rossiter-McLaughlin effect and constrained the sky-projected obliquity to ${9\degree}^{+22\degree}_{-18\degree}$, which is s

Frank Wang

We present a transformation between the Schwarzschild coordinates and local inertial coordinates, and demonstrate the effect of gravitational bending of light near a massive body in a small region. When a photon is emitted from a point near the Earth's surface with an initial horizontal direction, its parabolic trajectory would have a vertical deflection tha

Jan Retkowski, Jakub Stępniak, Mateusz Modrzejewski

In this paper we introduce the Frechet Music Distance (FMD), a novel evaluation metric for generative symbolic music models, inspired by the Frechet Inception Distance (FID) in computer vision and Frechet Audio Distance (FAD) in generative audio. FMD calculates the distance between distributions of reference and generated symbolic music embeddings, capturing

Johannes Knittel, Tushaar Gangavarapu, Hendrik Strobelt, Hanspeter Pfister

Understanding the general priniciples behind transformer models remains a complex endeavor. Experiments with probing and disentangling features using sparse autoencoders (SAE) suggest that these models might manage linear features embedded as directions in the residual stream. This paper explores the resemblance between decoder-only transformer architecture

Ravi Jagadeesan, Alexander Teytelboym

This paper develops a theory of competitive equilibrium with indivisible goods based entirely on economic conditions on demand. The key idea is to analyze complementarity and substitutability between bundles of goods, rather than merely between goods themselves. This approach allows us to formulate sufficient, and essentially necessary, conditions for equili

Gaëtan Fichet de Clairfontaine, Manel Perucho, José María Martí, Yuri Kovalev

The interactions between jets from active galactic nuclei (AGN) and their stellar environments significantly influence jet dynamics and emission characteristics. In low-power jets, such as those in Fanaroff-Riley I (FR I) galaxies, the jet-star interactions can notably affect jet deceleration and energy dissipation. Recent numerical studies suggest that mass

Simone Fobi Nsutezo, Amrita Gupta, Duncan Kebut, Seema Iyer

As of 2023, a record 117 million people have been displaced worldwide, more than double the number from a decade ago [22]. Of these, 32 million are refugees under the UNHCR mandate, with 8.7 million residing in refugee camps. A critical issue faced by these populations is the lack of access to electricity, with 80% of the 8.7 million refugees and displaced p

Carina Boyallian, Jose I. Liberati

We classify simple differential Lie and Jordan (super)coalgebras of finite rank. In particular, we provide an explicit description of the Lie supercoalgebras associated with the operator product expansion (OPE) of the n=2,3,4 superconformal Lie algebras and the exceptional Lie conformal superalgebra CK$_6$

Ari Brill

Deep neural networks exhibit empirical neural scaling laws, with error decreasing as a power law with increasing model or data size, across a wide variety of architectures, tasks, and datasets. This universality suggests that scaling laws may result from general properties of natural learning tasks. We develop a mathematical model intended to describe natura

Guang Hu, Weijia Li, Yangmengfei Xu

Epistemic Planning (EP) is an important research area dedicated to reasoning about the knowledge and beliefs of agents in multi-agent cooperative or adversarial settings. The Justified Perspective (JP) model is the state-of-the-art approach to solving EP problems with efficiency and expressiveness. However, all existing EP methods inherit the static environm

Anya Belz, Craig Thomson

This paper presents version 3.0 of the Human Evaluation Datasheet (HEDS). This update is the result of our experience using HEDS in the context of numerous recent human evaluation experiments, including reproduction studies, and of feedback received. Our main overall goal was to improve clarity, and to enable users to complete the datasheet more consistently

Bhaskarjit Sarmah, Mingshu Li, Jingrao Lyu, Sebastian Frank

To ensure and monitor large language models (LLMs) reliably, various evaluation metrics have been proposed in the literature. However, there is little research on prescribing a methodology to identify a robust threshold on these metrics even though there are many serious implications of an incorrect choice of the thresholds during deployment of the LLMs. Tra

Yujie Zhao, Hejia Zhang, Hanxian Huang, Zhongming Yu

The automatic generation of RTL code (e.g., Verilog) through natural language instructions has emerged as a promising direction with the advancement of large language models (LLMs). However, producing RTL code that is both syntactically and functionally correct remains a significant challenge. Existing single-LLM-agent approaches face substantial limitations

Chengshuai Zhao, Garima Agrawal, Fan Zhang, Tharindu Kumarage

Integrating AI into education has the potential to transform the teaching of science and technology courses, particularly in the field of cybersecurity. AI-driven question-answering (QA) systems can actively manage uncertainty in cybersecurity problem-solving, offering interactive, inquiry-based learning experiences. Recently, Large language models (LLMs) ha

Sotirios Henning

We define computational atoms named "actions" equipped primarily with three operations: reduction, collection, and inspection. We show how actions can be used for decision-making algorithms from simple axioms. We describe the encodings of typical data structures as actions, and provide a method of analysis for algorithms on the basis of data mutation.

Brendan Boyd, Alan Calder, Dean Townsley, Michael Zingale

A proposed setting for thermonuclear (Type Ia) supernovae is a white dwarf that has gained mass from a companion to the point of carbon ignition in the core. In the early stages of carbon burning, called the simmering phase, energy released by the reactions in the core drive the formation and growth of a core convection zone. One aspect of this phase is the

Quinten McNamara, Miguel Ángel del Río Fernández, Nishchal Bhandari, Martin Ratajczak

Word error rate (WER) as a metric has a variety of limitations that have plagued the field of speech recognition. Evaluation datasets suffer from varying style, formality, and inherent ambiguity of the transcription task. In this work, we attempt to mitigate some of these differences by performing style-agnostic evaluation of ASR systems using multiple refer

Madhur Tulsiani, June Wu

We present a new method for obtaining norm bounds for random matrices, where each entry is a low-degree polynomial in an underlying set of independent real-valued random variables. Such matrices arise in a variety of settings in the analysis of spectral and optimization algorithms, which require understanding the spectrum of a random matrix depending on data

Henry Li

Diffusion models generate samples by incrementally reversing a process that turns data into noise. We show that when the step size goes to zero, the reversed process is invariant to the distribution of these increments. This reveals a previously unconsidered parameter in the design of diffusion models: the distribution of the diffusion step $\Delta x_k := x_