October 2024 arXiv papers — page 111

Shintaro Ozaki, Kazuki Hayashi, Miyu Oba, Yusuke Sakai

A large part of human communication relies on nonverbal cues such as facial expressions, eye contact, and body language. Unlike language or sign language, such nonverbal communication lacks formal rules, requiring complex reasoning based on commonsense understanding. Enabling current Video Large Language Models (VideoLLMs) to accurately interpret body langua

Ling-Bing He, Jie Ji, Wei-Xi Li

We consider the spatially inhomogeneous Boltzmann equation without angular cutoff for soft potentials. For any given initial datum such that the mass, energy and entropy densities are bounded and the mass is away from vacuum, we establish the local-in-time existence and uniqueness of mild solutions, and further provide the first result on sharp smoothing eff

Aryan Shrivastava, Jessica Hullman, Max Lamparth

There is an increasing interest in using language models (LMs) for automated decision-making, with multiple countries actively testing LMs to aid in military crisis decision-making. To scrutinize relying on LM decision-making in high-stakes settings, we examine the inconsistency of responses in a crisis simulation ("wargame"), similar to reported tests condu

Al Zadid Sultan Bin Habib, Kesheng Wang, Mary-Anne Hartley, Gianfranco Doretto

Effective analysis of tabular data still poses a significant problem in deep learning, mainly because features in tabular datasets are often heterogeneous and have different levels of relevance. This work introduces TabSeq, a novel framework for the sequential ordering of features, addressing the vital necessity to optimize the learning process. Features are

Wenlong Cai, Zanhong Chen, Yuzhang Shi, Daoqian Zhu

Current-induced antiferromagnetic (AFM) switching remains critical in spintronics, yet the interplay between thermal effects and spin torques still lacks clear clarification. Here we experimentally investigate the thermally interplayed spin-orbit torque induced AFM switching in magnetic tunnel junctions via pulse-width dependent reversal and time-resolved me

Yun-Yen Chuang, Hung-Min Hsu, Kevin Lin, Chen-Sheng Gu

The diffusion model, a new generative modeling paradigm, has achieved significant success in generating images, audio, video, and text. It has been adapted for sequence-to-sequence text generation (Seq2Seq) through DiffuSeq, termed S2S Diffusion. Existing S2S-Diffusion models predominantly rely on fixed or hand-crafted rules to schedule noise during the diff

Yu Funami, Kazushi Aoyama

A one-dimensional charge density wave (CDW) is driven to slide by a dc electric field, carrying an electric current. In an additional ac field with frequency ${\omega}_{\mathrm{ex}}$, it is known that the sliding CDW can be synchronized to $\omega_{\mathrm{ex}}$, leading to the occurrence of Shapiro steps in the $I$-$V$ characteristics. Motivated by a recent

Imteaz Rahaman, Botong Li, Hunter D. Ellis, Brian Roy Van Devener

Ultrawide bandgap (UWBG) semiconductors are promising for next-generation power electronics, largely attributed to their substantial bandgap and exceptional breakdown electric field. Rutile GeO2 (r-GeO2) emerges as a promising alternative, particularly because of its ambipolar dopability. However, research on r-GeO2 is still in its infancy, and further inves

Sreyan Ghosh, Mohammad Sadegh Rasooli, Michael Levit, Peidong Wang

Generative Error Correction (GEC) has emerged as a powerful post-processing method to enhance the performance of Automatic Speech Recognition (ASR) systems. However, we show that GEC models struggle to generalize beyond the specific types of errors encountered during training, limiting their ability to correct new, unseen errors at test time, particularly in

O. V. Kaptsov

The article explores the acoustic equations in inhomogeneous media and the linearized shallow water equations. Two methods for integrating these equations are proposed. The first method is based on the of the Laplace cascade method, while the second involves reducing two-dimensional and three-dimensional models to the wave equation. In the case of plane wave

Tangwen Qian, Junhe Li, Yile Chen, Gao Cong

Modeling trajectory data with generic-purpose dense representations has become a prevalent paradigm for various downstream applications, such as trajectory classification, travel time estimation and similarity computation. However, existing methods typically rely on trajectories from a single spatial view, limiting their ability to capture the rich contextua

Jiamin Wu, Kenkun Liu, Yukai Shi, Xiaoke Jiang

In this work, we introduce UniGS, a novel 3D Gaussian reconstruction and novel view synthesis model that predicts a high-fidelity representation of 3D Gaussians from arbitrary number of posed sparse-view images. Previous methods often regress 3D Gaussians locally on a per-pixel basis for each view and then transfer them to world space and merge them through

Ahmed Oumar El-Shangiti, Tatsuya Hiraoka, Hilal AlQuabeh, Benjamin Heinzerling

This paper investigates whether large language models (LLMs) utilize numerical attributes encoded in a low-dimensional subspace of the embedding space when answering questions involving numeric comparisons, e.g., Was Cristiano born before Messi? We first identified, using partial least squares regression, these subspaces, which effectively encode the numeric

George I. Kamberov

Many machine learning (ML) classifiers are claimed to outperform humans, but they still make mistakes that humans do not. The most notorious examples of such mistakes are adversarial visual metamers. This paper aims to define and investigate the phenomenon of adversarial Doppelgangers (AD), which includes adversarial visual metamers, and to compare the perfo

Jiatao Li, Xinyu Hu, Xunjian Yin, Xiaojun Wan

The integration of documents generated by LLMs themselves (Self-Docs) alongside retrieved documents has emerged as a promising strategy for retrieval-augmented generation systems. However, previous research primarily focuses on optimizing the use of Self-Docs, with their inherent properties remaining underexplored. To bridge this gap, we first investigate th

Zonghai Yao, Aditya Parashar, Huixue Zhou, Won Seok Jang

Automatic question generation (QG) is essential for AI and NLP, particularly in intelligent tutoring, dialogue systems, and fact verification. Generating multiple-choice questions (MCQG) for professional exams, like the United States Medical Licensing Examination (USMLE), is particularly challenging, requiring domain expertise and complex multi-hop reasoning

Fanyu Meng, Xin Liu, Zhaodan Kong, Xin Chen

eXplainable Artificial Intelligence (XAI) has garnered significant attention for enhancing transparency and trust in machine learning models. However, the scopes of most existing explanation techniques focus either on offering a holistic view of the explainee model (global explanation) or on individual instances (local explanation), while the middle ground,

Dong An, Akwum Onwunta, Gengzhi Yang

We establish improved complexity estimates of quantum algorithms for linear dissipative ordinary differential equations (ODEs) and show that the time dependence can be fast-forwarded to be sub-linear. Specifically, we show that a quantum algorithm based on truncated Dyson series can prepare history states of dissipative ODEs up to time $T$ with cost $\wideti

Steven Gindi

We use Lott's functional and construct a new functional to derive rigidity results for invariant Ricci flow blowdown limits on nilpotent principal bundles with zero associated curvature. Consequently, we prove that the blowdown limit is locally an expanding Ricci soliton when the structure group is the three dimensional Heisenberg group. In addition, we clas

Siyuan Jiang, Jia Li, He Zong, Huanyu Liu

Large Language Models (LLMs) have been widely used in code completion, and researchers are focusing on scaling up LLMs to improve their accuracy. However, larger LLMs have lower inference efficiency, affecting developers' experience and productivity. In this paper, we propose a lightweight and effective LLM for code completion named aiXcoder-7B. Compared to

Yubao Wang, Jingnan Guo

Solar energetic particles (SEPs) are an important space radiation source, especially for the space weather environment in the inner heliosphere. The energy spectrum of SEP events is crucial both for evaluating their radiation effects and for understanding their acceleration process at the source region and their propagation mechanism. In this work, we invest

Long Li, Weiwen Xu, Jiayan Guo, Ruochen Zhao

Effective research ideation is a critical step for scientific research. However, the exponential increase in scientific literature makes it challenging for researchers to stay current with recent advances and identify meaningful research directions. Recent developments in large language models~(LLMs) suggest a promising avenue for automating the generation o

Shwai He, Tao Ge, Guoheng Sun, Bowei Tian

Traditional transformer models often allocate a fixed amount of computational resources to every input token, leading to inefficient and unnecessary computation. To address this, the Mixture of Depths (MoD) was introduced to dynamically adjust the computational depth by skipping less important layers. Despite its promise, current MoD approaches remain under-

Antonio de França

Let $\mathbb{F}$ be a field and $\mathsf{G}$ a group. This work is inspired in the following problem: "{\it given a division (simple) $\mathsf{G}$-graded $\mathbb{F}$-algebra, is there any other division (simple) $\mathsf{G}$-graded $\mathbb{F}$-algebra such that the former can be $\mathsf{G}$-imbedded in the latter?}". In this work, we answer this question

Maximilian Puelma Touzel, Sneheel Sarangi, Austin Welch, Gayatri Krishnakumar

The rise of AI-driven manipulation poses significant risks to societal trust and democratic processes. Yet, studying these effects in real-world settings at scale is ethically and logistically impractical, highlighting a need for simulation tools that can model these dynamics in controlled settings to enable experimentation with possible defenses. We present

Anurag Kumar, Andrew Perrault, Donald S. Williamson

Objective speech quality measures are typically used to assess speech enhancement algorithms, but it has been shown that they are sub-optimal as learning objectives because they do not always align well with human subjective ratings. This misalignment often results in noticeable distortions and artifacts that cause speech enhancement to be ineffective. To ad

Yikang Chen, Dehui Du, Lili Tian

We propose an importance sampling method for tractable and efficient estimation of counterfactual expressions in general settings, named Exogenous Matching. By minimizing a common upper bound of counterfactual estimators, we transform the variance minimization problem into a conditional distribution learning problem, enabling its integration with existing co

Xingxiang Peng, Peiran Wu, Junhui Zhao, Minghua Xia

The propagation loss of RF signals is a significant issue in simultaneous wireless information and power transfer (SWIPT) systems. Additionally, ensuring information security is crucial due to the broadcasting nature of wireless channels. To address these challenges, we exploit the potential of active intelligent reflecting surface (IRS) in a multiple-input

Ashish Seth, Ramaneswaran Selvakumar, S Sakshi, Sonal Kumar

In this paper, we present EH-MAM (Easy-to-Hard adaptive Masked Acoustic Modeling), a novel self-supervised learning approach for speech representation learning. In contrast to the prior methods that use random masking schemes for Masked Acoustic Modeling (MAM), we introduce a novel selective and adaptive masking strategy. Specifically, during SSL training, w

Ziwei Yang, Zheng Chen, Xin Liu, Rikuto Kotoge

Retrieving gene functional networks from knowledge databases presents a challenge due to the mismatch between disease networks and subtype-specific variations. Current solutions, including statistical and deep learning methods, often fail to effectively integrate gene interaction knowledge from databases or explicitly learn subtype-specific interactions. To

Guochao Yang, Jingkun Zhao, Yanchun Liang, Monique Spite

Based on the high resolution and high signal-to-noise spectra, we derived the chemical abundances of 20 elements for 20 barium (Ba-) stars. For the first time, the detailed abundances of four sample stars, namely HD 92482, HD 150430, HD 151101 and HD 177304 have been analyzed. Additionally, Ba element abundance has been measured using high resolution spectra

Xin Yan, Hongzheng Wu, Changwei Fan, Baiyuan Yang

We investigate the classical-quantum correspondence of non-Hermitian Spin-orbit (SO)-coupled bosonic junctions, where an effective decay term is introduced in one of the two wells. Starting from the normalized two-point functions, we analytically demonstrate that the mean-field system has a classical Hamiltonian structure, and we successfully derive a non-He

Cheng Huang, Pan Mu, Cong Bai, Peter AG Watson

Precipitation from tropical cyclones (TCs) can cause disasters such as flooding, mudslides, and landslides. Predicting such precipitation in advance is crucial, giving people time to prepare and defend against these precipitation-induced disasters. Developing deep learning (DL) rainfall prediction methods offers a new way to predict potential disasters. Howe

The LHAASO Collaboration, Zhen Cao, F. Aharonian, Y. X. Bai

The attenuation length of the muon content in extensive air showers provides important information regarding the generation and development of air showers. This information can be used not only to improve the description of such showers but also to test fundamental models of hadronic interactions. Using data from the LHAASO-KM2A experiment, the development o

Rielly Castle, Narayan Appathurai, Nicholas Simonson, Yasaman Sigari

Young's double slit experiment has been the most explored technique to gauge the coherence properties of a given system. The limits of this technique in characterizing spatial coherence properties of high emittance, hard x-ray synchrotron sources have been performed at the BXDS-IVU beamline, Canadian Light Source (CLS). High emittance synchrotron sources hav

Yusuke Endo, Koujin Takeda

We propose a new method of independent component analysis (ICA) in order to extract appropriate features from high-dimensional data. In general, matrix factorization methods including ICA have a problem regarding the interpretability of extracted features. For the improvement of interpretability, it is considered that sparse constraint on a factorized matrix

Jilin Wu, Ruike Wu, Zhijie Xiao

This paper explores testing unit roots based on least absolute deviations (LAD) regression under unconditional heteroskedasticity. We first derive the asymptotic properties of the LAD estimator for a first-order autoregressive process with the coefficient (local to) unity under unconditional heteroskedasticity and weak dependence, revealing that the limiting

Muchuan Hua, Wei-Ying Chen, Hanyu Hou, Venkata Surya Chaitanya Kolluru

Deterministic creation of quantum emitters with high single-photon-purity and excellent indistinguishability is essential for practical applications in quantum information science. Many successful attempts have been carried out in hexagonal boron nitride showing its capability of hosting room temperature quantum emitters. However, most of the existing method

Leo Yoshioka

Configuration space integrals are powerful tools for studying the homotopy type of the space of long embeddings in terms of a combinatorial object called a graph complex. It is unknown whether these integrals give a cochain map due to potential obstructions called hidden faces. The purpose of this paper is to address these hidden faces by modifying configura

Thomas Stemler, Shannon Dee Algar, Jesse Zhou

One of the most striking phenomena in biological systems is the tendency for biological agents to spatially aggregate, and subsequently display further collective behaviours such as rotational motion. One prominent explanation for why agents tend to aggregate is known as the selfish herd hypothesis (SHH). The SHH proposes that each agent has a "domain of dan

Edoardo Cetin, Qi Sun, Tianyu Zhao, Yujin Tang

Prior methods propose to offset the escalating costs of modern foundation models by dropping specific parts of their contexts with hand-designed rules, while attempting to preserve their original performance. We overcome this trade-off with Neural Attention Memory Models (NAMMs), introducing a learned network for memory management that improves both the perf

Ying Chen, Zhenhua Chai, Baochang Shi

In this work, we first develop a unified Bhatnagar-Gross-Krook lattice Boltzmann (BGK-LB) model for the $d$($d\geq 1$)-dimensional linear hyperbolic equation (L-HE), where the natural moments and the D$d$Q$(2d^2+1)$ [($2d^2+1$) discrete velocities in $d$-dimensional space] lattice structure are considered. Subsequently, at the acoustic scaling, we conduct an

Sudipto Saha, Jonathan R. Bradley

The conditional autoregressive (CAR) model, simultaneous autoregressive (SAR) model, and its variants have become the predominant strategies for modeling regional or areal-referenced spatial data. The overwhelming wide-use of the CAR/SAR model motivates the need for new classes of models for areal-referenced data. Thus, we develop a novel class of Markov ran

Shuqi Hu, Changyu Ren, Ziyi Wang

In this paper, we establish Newton-Maclaurin type inequalities for functions arising from linear combinations of primitively symmetric polynomials. This generalization extends the classical Newton-Maclaurin inequality to a broader class of functions.

Prabhanjan Ananth, Saachi Mutreja, Alexander Poremba

Fundamental principles of quantum mechanics have inspired many new research directions, particularly in quantum cryptography. One such principle is quantum no-cloning which has led to the emerging field of revocable cryptography. Roughly speaking, in a revocable cryptographic primitive, a cryptographic object (such as a ciphertext or program) is represented

Shalika R. Bhandari, Mohd Zeeshan, Vivek Gusain, Keshav Shrestha

This work presents a detailed study of the electronic structure, phonon dispersion, Z2 invariant calculation, and Fermi surface of the newly discovered kagome superconductor CsV3Sb5, using density functional theory (DFT). The phonon dispersion in the pristine state reveals two negative modes at the M and L points of the Brillouin zone, indicating lattice ins

Jian Li, Tian Gan, Weifeng Li, Yuhang Liu

In recent years, mobile phone data has been widely used for human mobility analytics. Identifying individual activity locations is the fundamental step for mobile phone data processing. Current methods typically aggregate spatially adjacent location records over multiple days to identify activity locations. However, only considering spatial relationships whi

Mathis Gerdes, Pim de Haan, Roberto Bondesan, Miranda C. N. Cheng

Continuous normalizing flows are known to be highly expressive and flexible, which allows for easier incorporation of large symmetries and makes them a powerful computational tool for lattice field theories. Building on previous work, we present a general continuous normalizing flow architecture for matrix Lie groups that is equivariant under group transform

P. Graham Pritchard, James M. Rondinelli

Reduced transmon qubit $T_1$ coherence times have been linked to the amorphous oxide layers formed by thin film capacitors during processing. Because Ta or Ta capped Nb capacitors exhibit overall superior qubit performance to those fabricated with Nb capacitors, it has been hypothesized that the amorphous, non-stoichiometric Ta$_2$O$_{5-x}$ oxide is less los

Hossein Nasiri, Seda Dogan-Tusha, Muhammad Iqbal Rochman, Monisha Ghosh

Robust classification of the operational environment of wireless devices is becoming increasingly important for wireless network optimization, particularly in a shared spectrum environment. Distinguishing between indoor and outdoor devices can enhance reliability and improve coexistence with existing, outdoor, incumbents. For instance, the unlicensed but sha

Jun Hu, Shixuan Wang

The cyclotomic Hecke algebra $H_{r,p,n}$ of type $G(r,p,n)$ (where $r=pd$) can be realized as the $\sigma$-fixed point subalgebra of certain cyclotomic Hecke algebra $H_{r,n}$ of type $G(r,1,n)$ with some special cyclotomic parameters, where $\sigma$ is an automorphism of $H_{r,n}$ of order $p$. In this paper we prove a number of rational properties on the $

J. Keski-Rahkonen, C. Zou, A. M. Graf, Q. Yao

A quantum eigenstate of a classically chaotic system is referred as scarred by an unstable periodic orbit if its probability density is concentrated in the vicinity of that orbit. Recently, a new class of scarring - variational scarring - was discovered in numerical studies of disordered quantum dots, arising from near-degeneracies in the quantum spectrum as

Juncong Xu, Yang Yang, Han Fang, Honggu Liu

The explosive growth of generative AI has saturated the internet with AI-generated images, raising security concerns and increasing the need for reliable detection methods. The primary requirement for such detection is generalizability, typically achieved by training on numerous fake images from various models. However, practical limitations, such as closed-

Xianyang Zhan, Agam Goyal, Yilun Chen, Eshwar Chandrasekharan

Large language models (LLMs) have shown promise in many natural language understanding tasks, including content moderation. However, these models can be expensive to query in real-time and do not allow for a community-specific approach to content moderation. To address these challenges, we explore the use of open-source small language models (SLMs) for commu

Rômulo Damasclin Chaves dos Santos, Jorge Henrique de Oliveira Sales, Erickson F. M. S. Silva

This paper presents an innovative framework for analyzing the regularity of solutions to the stochastic Navier-Stokes equations by integrating Sobolev-Besov hybrid spaces with fractional operators and quantum-inspired dynamics. We propose new regularity theorems that address the multiscale and chaotic nature of fluid flows, offering novel insights into energ

Krishno Dey, Prerona Tarannum, Md. Arid Hasan, Imran Razzak

Large Language Models (LLMs) are trained on massive amounts of data, enabling their application across diverse domains and tasks. Despite their remarkable performance, most LLMs are developed and evaluated primarily in English. Recently, a few multi-lingual LLMs have emerged, but their performance in low-resource languages, especially the most spoken languag

Louigi Addario-Berry, Christina Goldschmidt

This work will appear as a chapter in a forthcoming volume titled "Topics in Probabilistic Graph Theory". A theory of scaling limits for random graphs has been developed in recent years. This theory gives access to the large-scale geometric structure of these random objects in the limit as their size goes to infinity, with distances appropriately rescaled. W

Wangseok Shin

We consider the periodic dispersion generalized Benjamin-Ono equations with polynomial nonlinearity. We establish the nonlinear smoothing properties of these equations, according to which the difference between the solution and the linear evolution is smoother than the initial data. In addition, we establish new local well-posedness results for these equatio

Raphaël Carroy, Yann Pequignot

We prove that continuous reducibility is a well-quasi-order on the class of continuous functions between separable metrizable spaces with analytic zero-dimensional domain. To achieve this, we define scattered functions, which generalize scattered spaces, and describe exhaustively scattered functions between zero-dimensional separable metrizable spaces up to

Yusuke Tsunoda, Shoken Otsuka, Kazuki Ito, Runze Xiao

Recently, the navigation of mobile robots in unknown environments has become a particularly significant research topic. Previous studies have primarily employed real-time environmental mapping using cameras and LiDAR, along with self-localization and path generation based on those maps. Additionally, there is research on Sim-to-Real transfer, where robots ac

Felix J. Yu, Nicholas Kamp, Carlos A. Argüelles

Neutrino telescopes detect rare interactions of particles produced in some of the most extreme environments in the Universe. This is accomplished by instrumenting a cubic-kilometer scale volume of naturally occurring transparent medium with light sensors. Given their substantial size and the high frequency of background interactions, these telescopes amass a

Khiem Le, Ting Hua, Nitesh V. Chawla

Molecular editing-modifying a given molecule to improve desired properties-is a fundamental task in drug discovery. While LLMs hold the potential to solve this task using natural language to drive the editing, straightforward prompting achieves limited accuracy. In this work, we propose AgentDrug, an agentic workflow that leverages LLMs in a structured refin

Kuleen Sasse, Shan Chen, Jackson Pond, Danielle Bitterman

As Vision Language Models (VLMs) gain widespread use, their fairness remains under-explored. In this paper, we analyze demographic biases across five models and six datasets. We find that portrait datasets like UTKFace and CelebA are the best tools for bias detection, finding gaps in performance and fairness for both LLaVa and CLIP models. Scene-based datase

Yu Liu, Mohan Chen

MXenes are a large family of two-dimensional transition metal carbides and nitrides that possess excellent electrical conductivity, high volumetric capacitance, great mechanical properties, and hydrophilicity. In this work, we generalize the concept of multihyperuniformity (MH), an exotic state that can exist in a disordered multi-component system, to two-di

Kin Yip Wong

Using Linear Response Theory, with appropriate wave functions and energies from perturbation method, the absorption profiles can be calculated for all three classes of mixed-valence systems as defined by Robin and Day : Class III (delocalized), Class I (localized) and Class II (intermediate between III and I). Based on these absorption profiles, one can calc

Manuel Sebastian Torres, Yicheng Feng, Fuqiang Wang

Stimulated by the keen interest of possible collective behavior in high-energy proton-proton and proton-nucleus collisions, we study two-particle angular correlations in pseudorapidity and azimuthal differences in simulated p + p interactions by the Pythia 8 event generator. Multi-parton interactions and color connection are included in these simulations whi

David Choi

We give an approach for characterizing interference by lower bounding the number of units whose outcome depends on selected groups of treated individuals, such as depending on the treatment of others, or others who are at least a certain distance away. The approach is applicable to randomized experiments with binary-valued outcomes. Asymptotically conservati

Handi Zhang, Langchen Liu, Lu Lu

By leveraging neural networks, the emerging field of scientific machine learning (SciML) offers novel approaches to address complex problems governed by partial differential equations (PDEs). In practical applications, challenges arise due to the distributed essence of data, concerns about data privacy, or the impracticality of transferring large volumes of

Sikai Yang, Kang Yang, Yuning Chen, Fan Zhao

This work presents ARD2, a framework that enables real-time through-wall surveillance using two aerial drones and an augmented reality (AR) device. ARD2 consists of two main steps: target direction estimation and contour reconstruction. In the first stage, ARD2 leverages geometric relationships between the drones, the user, and the target to project the targ

William Agnew, Harry H. Jiang, Cella Sum, Maarten Sap

Large language models excel at performing inference over text to extract information, summarize information, or generate additional text. These inference capabilities are implicated in a variety of ethical harms spanning surveillance, labor displacement, and IP/copyright theft. While many policy, legal, and technical mitigations have been proposed to counter

Sajay Sunny Mathew, Christoph Federrath, Amit Seta

Crucial for star formation is the interplay between gravity and turbulence. The observed cloud virial parameter, $\alpha_{\mathrm{vir}}$, which is the ratio of twice the turbulent kinetic energy to the gravitational energy, is found to vary significantly in different environments, where the scatter among individual star-forming clouds can exceed an order of

Jiwan Hur, Dong-Jae Lee, Gyojin Han, Jaehyun Choi

Masked generative models (MGMs) have shown impressive generative ability while providing an order of magnitude efficient sampling steps compared to continuous diffusion models. However, MGMs still underperform in image synthesis compared to recent well-developed continuous diffusion models with similar size in terms of quality and diversity of generated samp

Patrick Kwon, Chen Chen, Hanbyul Joo

Recent generative models can synthesize high-quality images, but they often fail to generate humans interacting with objects using their hands. This arises mostly from the model's misunderstanding of such interactions and the hardships of synthesizing intricate regions of the body. In this paper, we propose \textbf{GraspDiffusion}, a novel generative method

Shun Hashiyada, Yoshito Y. Tanaka

We present a theoretical framework for analyzing the loss of optical angular momentum (AM), including spin (SAM) and orbital (OAM) components, in light-matter interactions. Conventional SAM and OAM conservation laws rely on transverse field components, neglecting longitudinal fields and limiting applicability to vacuum. Our approach defines optical AM using

Hao Yan, Xiang Lin, Siyuan Xie

Interferometric techniques are crucial for space-based gravitational wave detection, requiring a picometer-level stable optical bench, precise phasemeter, interstellar transponder low-light phase locking, and laser sideband communication. These technologies must be rigorously tested on the ground before deployment in space. The AEI group has previously devel

Liyue Chen, Jielan Ding, Donghuan Song, Zihao Qu

Scientific contributions are a direct reflection of a research paper's value, illustrating its impact on existing theories or practices. Existing measurement methods assess contributions based on the authors' perceived or self-identified contributions, while the actual contributions made by the papers are rarely investigated. This study measures the actual c

Ruichen Xu

In this article, we compute the Gan-Gross-Prasad period integral of Klingen Eisenstein series over the unitary group $\mathrm{U}(m+1, n+1)$ with a cuspidal automorphic form over $\mathrm{U}(m+1, n)$, and show that it is related to certain special Rankin-Selberg $L$-values. We $p$-adically interpolate these Gan-Gross-Prasad period integrals as the Klingen Eis

H. Touati, R. C. de Lamare

In this work, we investigate the decoding of Low-Density Parity-Check (LDPC) codes using informed dynamic scheduling algorithms that require a reduced number of iterations. In particular, we devise the weighted residual layered belief propagation (WR-LBP) decoding algorithm, which exploits the residual within a structured layer framework to speed the number

Aram Dermenjian

We define MC left regular bands and study their adjacency graphs. We prove that for thin MC left regular bands, the adjacency graph is particularly nice and is represented by edge labeled graphs where every simple cycle has an even number of edges. Conversely, we define a set of graphs which we call thin LRB graphs which encode rank two thin MC left regular

Hojin Chu, Homoon Ryu

In this paper, we investigate properties of a symmetric Toeplitz matrix and a Hankel matrix by studying the components of its graph. To this end, we introduce the notion of ``weighted Toeplitz graph" and ``weighted Hankel graph", which are weighted graphs whose adjacency matrix are a symmetric Toeplitz matrix and a Hankel matrix, respectively. By studying th

Renkuan Cao, Fan Peng, Yunhan Zhang, Hao Sun

Non-classical two-step nucleation including preordering and crystal nucleation has been widely proposed to challenge the one-step nucleation framework in diverse materials, while what drives preordering has not been explicitly resolved yet. With molecular dynamics simulation, we find that two-step nucleation occurs in polyethylene, during which preordering p

Jincheng Yang, Vincent R. Martinez, Anna L. Mazzucato, Alexis F. Vasseur

We consider the incompressible Navier-Stokes and Euler equations in a bounded domain with non-characteristic boundary condition, and study the energy dissipation near the outflow boundary in the zero-viscosity limit. We show that in a general setting, the energy dissipation rate is proportional to $\bar U \bar V ^2$, where $\bar U$ is the strength of the suc

Tony Z. Zhao, Jonathan Tompson, Danny Driess, Pete Florence

Recent work has shown promising results for learning end-to-end robot policies using imitation learning. In this work we address the question of how far can we push imitation learning for challenging dexterous manipulation tasks. We show that a simple recipe of large scale data collection on the ALOHA 2 platform, combined with expressive models such as Diffu

Dairui Liu, Honghui Du, Boming Yang, Neil Hurley

Pre-trained transformer models have shown great promise in various natural language processing tasks, including personalized news recommendations. To harness the power of these models, we introduce Transformers4NewsRec, a new Python framework built on the \textbf{Transformers} library. This framework is designed to unify and compare the performance of variou

William Xie, Stefan Caldararu, Nikolaus Correll

Robot trajectories used for learning end-to-end robot policies typically contain end-effector and gripper position, workspace images, and language. Policies learned from such trajectories are unsuitable for delicate grasping, which require tightly coupled and precise gripper force and gripper position. We collect and make publically available 130 trajectorie

Manuel Lafond

Given a bipartite graph $G$, the \textsc{Bicluster Editing} problem asks for the minimum number of edges to insert or delete in $G$ so that every connected component is a bicluster, i.e. a complete bipartite graph. This has several applications, including in bioinformatics and social network analysis. In this work, we study the parameterized complexity under

Nashrah Haque, Xiang Li, Zhehui Chen, Yanzhao Wu

We propose a novel framework, Stable Diffusion-based Momentum Integrated Adversarial Examples (SD-MIAE), for generating adversarial examples that can effectively mislead neural network classifiers while maintaining visual imperceptibility and preserving the semantic similarity to the original class label. Our method leverages the text-to-image generation cap

Viraj Prabhu, Senthil Purushwalkam, An Yan, Caiming Xiong

Vision-Language Models (VLMs) often generate plausible but incorrect responses to visual queries. However, reliably quantifying the effect of such hallucinations in free-form responses to open-ended queries is challenging as it requires visually verifying each claim within the response. We propose Programmatic VLM Evaluation (PROVE), a new benchmarking parad

Kyung Hoon Han, Seung-Hyeok Kye

We look for all linear isomorphisms from the mapping spaces onto the tensor products of matrices which send $k$-superpositive maps onto unnormalized bi-partite states of Schmidt numbers less than or equal to $k$. They also send $k$-positive maps onto $k$-block-positive matrices. We also look for all the bilinear pairings between the mapping spaces and tensor

Shiying Lu, Qiusheng Gu, Yulong Gao, Yong Shi

Lenticular galaxies (S0s) are formed mainly from the gas stripping of spirals in the cluster. But how S0s form and evolve in the field is still untangled. Based on spatially resolved observations from the optical Hispanic Astronomical Center in Andalusia 3.5-m telescope with the PPAK Integral Field Spectroscopy instrument and NOrthern Extended Millimeter Arr

Enzo Shiraishi, Raphael Y. de Camargo, Henrique L. P. Silva, Ronaldo C. Prati

When combined with In-Context Learning, a technique that enables models to adapt to new tasks by incorporating task-specific examples or demonstrations directly within the input prompt, autoregressive language models have achieved good performance in a wide range of tasks and applications. However, this combination has not been properly explored in the conte

Shuo Liu, An Zhang, Guoqing Hu, Hong Qian

Recommender systems predict personalized item rankings based on user preference distributions derived from historical behavior data. Recently, diffusion models (DMs) have gained attention in recommendation for their ability to model complex distributions, yet current DM-based recommenders often rely on traditional objectives like mean squared error (MSE) or

Hwanjun Song, Taewon Yun, Yuho Lee, Jihwan Oh

Developing effective text summarizers remains a challenge due to issues like hallucinations, key information omissions, and verbosity in LLM-generated summaries. This work explores using LLM-generated feedback to improve summary quality by aligning the summaries with human preferences for faithfulness, completeness, and conciseness. We introduce FeedSum, a l

Xiaoqian Wang, Rob J Hyndman

We consider the problem of constructing distribution-free prediction intervals for multi-step time series forecasting, with a focus on the temporal dependencies inherent in multi-step forecast errors. We establish that the optimal $h$-step-ahead forecast errors exhibit serial correlation up to lag $(h-1)$ under a general non-stationary autoregressive data ge

William Agnew, Julia Barnett, Annie Chu, Rachel Hong

Generative audio models are rapidly advancing in both capabilities and public utilization -- several powerful generative audio models have readily available open weights, and some tech companies have released high quality generative audio products. Yet, while prior work has enumerated many ethical issues stemming from the data on which generative visual and

Jiacong Du, Xu Shi, Bhramar Mukherjee

Biobanks with genetics-linked electronic health records (EHR) have opened up opportunities to study associations between genetic, social, or environmental factors and longitudinal lab biomarkers. However, in EHRs, the timing of patient visits and the recording of lab tests often depend on patient health status, referred to as informative presence (IP) and in

Jacob Feitelberg, Kyuseong Choi, Anish Agarwal, Raaz Dwivedi

We study the problem of distributional matrix completion: Given a sparsely observed matrix of empirical distributions, we seek to impute the true distributions associated with both observed and unobserved matrix entries. This is a generalization of traditional matrix completion, where the observations per matrix entry are scalar-valued. To do so, we utilize

Kareem Ahmed, Kai-Wei Chang, Guy Van den Broeck

Autoregressive models have demonstrated an unprecedented ability at modeling the intricacies of natural language. However, they continue to struggle with generating complex outputs that adhere to logical constraints. Sampling from a fully-independent distribution subject to a constraint is hard. Sampling from an autoregressive distribution subject to a const

Xiangping Chen, Xing Hu, Yuan Huang, He Jiang

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks

Lai Wei, Ambuj Tewari, Michael A. Cianfrocco

We introduce a latency-aware contextual bandit framework that generalizes the standard contextual bandit problem, where the learner adaptively selects arms and switches decision sets under action delays. In this setting, the learner observes the context and may select multiple arms from a decision set, with the total time determined by the selected subset. T