February 2024 arXiv papers — page 79

Rochana Chaturvedi, Sugat Chaturvedi, Elena Zheleva

While exposure to diverse viewpoints may reduce polarization, it can also have a backfire effect and exacerbate polarization when the discussion is adversarial. Here, we examine the question whether intergroup interactions around important events affect polarization between majority and minority groups in social networks. We compile data on the religious ide

Jiahao Ying, Yixin Cao, Yushi Bai, Qianru Sun

Large language models (LLMs) have achieved impressive performance across various natural language benchmarks, prompting a continual need to curate more difficult datasets for larger LLMs, which is costly and time-consuming. In this paper, we propose to automate dataset updating and provide systematic analysis regarding its effectiveness in dealing with bench

Xiaowei Yuan, Zhao Yang, Yequan Wang, Shengping Liu

Large language models internalize enormous parametric knowledge during pre-training. Concurrently, realistic applications necessitate external contextual knowledge to aid models on the underlying tasks. This raises a crucial dilemma known as knowledge conflicts, where the contextual knowledge clashes with the However, existing decoding works are specialized

Thanh Le-Cong, Dat Nguyen, Bach Le, Toby Murray

In this paper, we propose shifting the focus of robustness evaluation for Neural Program Repair (NPR) techniques toward naturally-occurring data transformations. To accomplish this, we first examine the naturalness of semantic-preserving transformations through a two-stage human study. This study includes (1) interviews with senior software developers to est

Shuai Wang, Ekaterina Khramtsova, Shengyao Zhuang, Guido Zuccon

Federated search systems aggregate results from multiple search engines, selecting appropriate sources to enhance result quality and align with user intent. With the increasing uptake of Retrieval-Augmented Generation (RAG) pipelines, federated search can play a pivotal role in sourcing relevant information across heterogeneous data sources to generate infor

Qihuang Zhong, Liang Ding, Li Shen, Juhua Liu

Knowledge distillation (KD) is a common approach to compress a teacher model to reduce its inference cost and memory footprint, by training a smaller student model. However, in the context of autoregressive language models (LMs), we empirically find that larger teacher LMs might dramatically result in a poorer student. In response to this problem, we conduct

Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du

With the development of instruction-tuned large language models (LLMs), improving the safety of LLMs has become more critical. However, the current approaches for aligning the LLMs output with expected safety usually require substantial training efforts, e.g., high-quality safety data and expensive computational resources, which are costly and inefficient. T

Congyun Jin, Ming Zhang, Xiaowei Ma, Li Yujiao

Recent advancements in Large Language Models (LLMs) and Large Multi-modal Models (LMMs) have shown potential in various medical applications, such as Intelligent Medical Diagnosis. Although impressive results have been achieved, we find that existing benchmarks do not reflect the complexity of real medical reports and specialized in-depth reasoning capabilit

Atsushi Nobe

The Volterra lattice, when imposing non-zero constant boundary values, admits the structure of a completely integrable Hamiltonian system if the system size is sufficiently small. Such a Volterra lattice can be regarded as an epidemic model known as the SIR model with vaccination, which extends the celebrated SIR model to account for vaccination. Upon the in

Hezhe Qiao, Qingsong Wen, Xiaoli Li, Ee-Peng Lim

This work considers a practical semi-supervised graph anomaly detection (GAD) scenario, where part of the nodes in a graph are known to be normal, contrasting to the extensively explored unsupervised setting with a fully unlabeled graph. We reveal that having access to the normal nodes, even just a small percentage of normal nodes, helps enhance the detectio

Shir Lissak, Nitay Calderon, Geva Shenkman, Yaakov Ophir

Queer youth face increased mental health risks, such as depression, anxiety, and suicidal ideation. Hindered by negative stigma, they often avoid seeking help and rely on online resources, which may provide incompatible information. Although access to a supportive environment and reliable information is invaluable, many queer youth worldwide have no access t

Takahito Kashiwabara, Haruki Takemura

Error estimates of cubic interpolated pseudo-particle scheme (CIP scheme) for the one-dimensional advection equation with periodic boundary conditions are presented. The CIP scheme is a semi-Lagrangian method involving the piecewise cubic Hermite interpolation. Although it is numerically known that the space-time accuracy of the scheme is third order, its ri

Abhishek Bharadwaj, Brad Rodgers

We study the distribution of large prime factors of a random element $u$ of arithmetic sequences satisfying simple regularity and equidistribution properties. We show that if such an arithmetic sequence has level of distribution $1$ the large prime factors of $u$ tend to a Poisson-Dirichlet process, while if the sequence has any positive level of distributio

Hansle Gwon, Imjin Ahn, Hyoje Jung, Byeolhee Kim

In this paper, we introduce InMD-X, a collection of multiple large language models specifically designed to cater to the unique characteristics and demands of Internal Medicine Doctors (IMD). InMD-X represents a groundbreaking development in natural language processing, offering a suite of language models fine-tuned for various aspects of the internal medici

Imjin Ahn, Hansle Gwon, Young-Hak Kim, Tae Joon Jun

The discharge summary is a one of critical documents in the patient journey, encompassing all events experienced during hospitalization, including multiple visits, medications, tests, surgery/procedures, and admissions/discharge. Providing a summary of the patient's progress is crucial, as it significantly influences future care and planning. Consequently, c

Filip Tokarski

I propose a new approach to solving standard screening problems when the monotonicity constraint binds. A simple geometric argument shows that when virtual values are quasi-concave, the optimal allocation can be found by appropriately truncating the solution to the relaxed problem. I provide an algorithm for finding this optimal truncation when virtual value

Xiaolei Guo, Yuliang Xin

HESS J1813-178 is one of the brightest and most compact TeV $\gamma$-ray sources, and whether its $\gamma$-ray emission is associated with supernova remnant (SNR), pulsar wind nebula (PWN) or young stellar cluster (YSC) is still under debate. By analysing the GeV $\gamma$-ray data in the field of HESS J1813-178 using 14 years of PASS 8 data recorded by the F

Naoto Komeno, Takamitsu Matsubara

In robotic manipulation, preventing objects from slipping and establishing a secure grip on them is critical. Successful manipulation requires tactile sensors that detect the microscopic incipient slip phenomenon at the contact surface. Unfortunately, the tiny signals generated by incipient slip are quickly buried by environmental noise, and precise stress-d

Mikio Morita, Yoshinori Tomita, Junpei Koyama, Koichi Kimura

Advances in quantum simulator technology is increasingly required because research on quantum algorithms is becoming more sophisticated and complex. State vector simulation utilizes CPU and memory resources in computing nodes exponentially with respect to the number of qubits; furthermore, in a variational quantum algorithm, the large number of repeated runs

Han-Dong Lim, HyeAnn Lee, Donghwan Lee

Reinforcement learning has witnessed significant advancements, particularly with the emergence of model-based approaches. Among these, $Q$-learning has proven to be a powerful algorithm in model-free settings. However, the extension of $Q$-learning to a model-based framework remains relatively unexplored. In this paper, we investigate the sample complexity o

Wenjie Hu, TomásCaraballo, Yueliang Duan

The main purpose of this paper is to give an upper bound of Hausdorff dimension of random attractors for a stochastic delayed parabolic equation in Banach spaces. The estimation of dimensions of random attractors are obtained by combining the squeezing property and a covering lemma of finite subspace of Banach spaces, which generalizes the method established

Hongcheng Liu, Pingjie Wang, Yu Wang, Yanfeng Wang

Video-grounded dialogue generation (VDG) requires the system to generate a fluent and accurate answer based on multimodal knowledge. However, the difficulty in multimodal knowledge utilization brings serious hallucinations to VDG models in practice. Although previous works mitigate the hallucination in a variety of ways, they hardly take notice of the import

Haofeng Zhong, Yuchen Hong, Shuchen Weng, Jinxiu Liang

This paper studies the problem of language-guided reflection separation, which aims at addressing the ill-posed reflection separation problem by introducing language descriptions to provide layer content. We propose a unified framework to solve this problem, which leverages the cross-attention mechanism with contrastive learning strategies to construct the c

P. Z. Zhao, Jiangbin Gong

Nonadiabatic holonomic operations are based on nonadiabatic non-Abelian geometric phases, hence possessing the inherent geometric features for robustness against control errors. However, nonadiabatic holonomic operations are still sensitive to the systematic amplitude error induced by imperfect control of pulse timing or laser intensity. In this work, we pre

Eric Mamajek, Karl Stapelfeldt

The Astro 2020 Decadal Survey "Pathways to Discovery in Astronomy and Astrophysics for the 2020s" has recommended that "after a successful mission and technology maturation program, NASA should embark on a program to realize a mission to search for biosignatures from a robust number of about ~25 habitable zone planets and to be a transformative facility for

K. Nguyen, T. Dang, M. Huber

Perceiving a three-dimensional (3D) scene with multiple objects while moving indoors is essential for vision-based mobile cobots, especially for enhancing their manipulation tasks. In this work, we present an end-to-end pipeline with instance segmentation, feature matching, and point-set registration for egocentric robots with binocular vision, and demonstra

Naman Shah, Jayesh Nagpal, Siddharth Srivastava

Robots still lag behind humans in their ability to generalize from limited experience, particularly when transferring learned behaviors to long-horizon tasks in unseen environments. We present the first method that enables robots to autonomously invent symbolic, relational concepts directly from a small number of raw, unsegmented, and unannotated demonstrati

Qiang Li, Yehuai Feng, Miaowen Wen, Jinming Wen

In this paper, a novel amplitude phase shift keying (APSK) modulation scheme for cooperative backscatter communications aided by a reconfigurable intelligent surface (RIS-CBC) is presented, according to which the RIS is configured to modulate backscatter information onto unmodulated or PSK-modulated signals impinging on its surface via APSK. We consider both

Ryota Kojima, Masahiko Kamoshita, Keita Kanno

Quantum chemistry is a key target for quantum computing, but benchmarking quantum algorithms for large molecular systems remains challenging due to the lack of exactly solvable yet structurally realistic models. In particular, molecular Hamiltonians typically contain $O(N^4)$ Pauli terms, significantly increasing the cost of quantum simulations, while many e

Akira SaiToh

A C++ library ZKCM and its extension library ZKCM_QC have been developed since 2011 for multiple-precision matrix computation and accurate matrix-product-state (MPS) quantum circuit simulation, respectively. In this report, a recent progress in the extensions of these libraries is described, which are mainly for parallel processing with the OpenMP and CUDA f

Uijeong Jang, Jason D. Lee, Ernest K. Ryu

Low-rank adaptation (LoRA) has become the standard approach for parameter-efficient fine-tuning of large language models (LLM), but our theoretical understanding of LoRA has been limited. In this work, we theoretically analyze LoRA fine-tuning in the neural tangent kernel (NTK) regime with $N$ data points, showing: (i) full fine-tuning (without LoRA) admits

Jeremy J. Lin, Tomoro Mochida, Riley C. W. O'Neill, Atsuro Yoshida

Our aim of this paper is to develop new map matching algorithms and to improve upon previous work. We address two key approaches: Analytic Hierarchy Process (AHP) map matching and fuzzy logic map matching. AHP is a decision-making method that combines mathematical analysis with human judgment, and fuzzy logic is an approach to computing based on the degree o

Nan Yang, Jiaji Wu, Xianyun Dong, Longyu Xiao

We introduce a new entanglement measure based on optimal entanglement witness. First of all, we show that the entanglement measure satisfies some necessary properties, including zero entanglements for all separable states, convexity, continuity, invariance under local unitary operations and non-increase under local operations and classical communication(LOCC

Sebastian Jaimungal, Xiaofei Shi

When an investor is faced with the option to purchase additional information regarding an asset price, how much should she pay? To address this question, we solve for the indifference price of information in a setting where a trader maximizes her expected utility of terminal wealth over a finite time horizon. If she does not purchase the information, then sh

Renee Hložek, Chenzhou Cui, Mark Allen, Patricia Whitelock

Policy Brief on "Global Data in Astronomy: Challenges and Opportunities", distilled from the corresponding panel that was part of the discussions during S20 Policy Webinar on Astroinformatics for Sustainable Development held on 6-7 July 2023. Astronomy is increasingly becoming a data-driven science. Advances in our understanding of the physical mechanisms at

Wei Jie Yeo, Ranjan Satapathy, Rick Siow Mong Goh, Erik Cambria

Prompt Engineering has garnered significant attention for enhancing the performance of large language models across a multitude of tasks. Techniques such as the Chain-of-Thought not only bolster task performance but also delineate a clear trajectory of reasoning steps, offering a tangible form of explanation for the audience. Prior works on interpretability

Xiaowei Chen, Margault Sacré, Gabriele Lenzini, Samuel Greiff

Organizations rely on phishing interventions to enhance employees' vigilance and safe responses to phishing emails that bypass technical solutions. While various resources are available to counteract phishing, studies emphasize the need for interactive and practical training approaches. To investigate the effectiveness of such an approach, we developed and d

Dong-Jie Liu, Yuan-Chuan Zou

Gamma-ray bursts (GRBs), especially short GRBs, are often considered potential candidates for exhibiting kilohertz quasi-periodic oscillations (QPOs) due to their origin from binary mergers. It has already been discovered that two bursts exhibit QPOs. While systematic searches for QPOs in GRBs typically concentrate on the kilohertz range, there has been no c

Ekaterina Shemyakova, Theodore Voronov

As a by-product of our work on super Pl\"{u}cker embedding, we came to the notion of a weighted projective superspace $P_{+1,-1}(V\oplus W)$ with weights $+1,-1$. The construction is not in itself super and makes sense in ordinary (purely even) framework. Unlike the familiar weighted projective spaces with positive weights, the (super)space $P_{+1,-1}(V\oplu

L. Weissitsch, S. Wurster, H. Krenn, A. Bachmaier

The ferromagnetic {\alpha}-MnBi phase as non-rare earth magnetic material has gained increasing interest, but fabrication of large volumes containing a significant amount of {\alpha}-MnBi is still challenging. Targeting successful processing strategies, we apply multistep severe plastic deformation with intermediate magnetic field assisted annealing. The sub

Xi-Lin Li

This report investigates the fitting of the Hessian or its inverse for stochastic optimizations using a Hessian fitting criterion derived from the preconditioned stochastic gradient descent (PSGD) method. This criterion is closely related to many widely used second-order and adaptive gradient optimization methods, including BFGS, the Gauss-Newton algorithm,

Yifei Cheng, Li Shen, Linli Xu, Xun Qian

Gradient compression with error compensation has attracted significant attention with the target of reducing the heavy communication overhead in distributed learning. However, existing compression methods either perform only unidirectional compression in one iteration with higher communication cost, or bidirectional compression with slower convergence rate.

Wenjie Hu, Tomás Caraballo

The main objective of this paper is to investigate exponential attractors for a nonlocal delayed reaction-diffusion equation on an unbounded domain. We first obtain the existence of a globally attractive absorbing set for the dynamical system generated by the equation under the assumption that the nonlinear term is bounded. Then, we construct exponential att

Zhen Yang, Zhou Shao, Yuxiao Dong, Jie Tang

Negative sampling stands as a pivotal technique in dense retrieval, essential for training effective retrieval models and significantly impacting retrieval performance. While existing negative sampling methods have made commendable progress by leveraging hard negatives, a comprehensive guiding principle for constructing negative candidates and designing nega

Benoit Dherin, Alan Weinstein

We introduce a special family of distributional alpha-densities and give a transversality criterion stating when their product is defined, closely related to Hormander's criterion for general distributions. Moreover, we show that for the subspace of distributional half-densities in this family the distribution product naturally yields a pairing that extends

Yuanchao Li, Lachlan Urquhart, Nihan Karatas, Shun Shao

In-car Voice Assistants (VAs) play an increasingly critical role in automotive user interface design. However, existing VAs primarily perform simple 'query-answer' tasks, limiting their ability to sustain drivers' long-term attention. In this study, we investigate the effectiveness of an in-car Robot Assistant (RA) that offers functionalities beyond voice in

Diego A. Mejía

Cicho\'n's diagram describes the connections between combinatorial notions related to measure, category, and compactness of sets of irrational numbers. In the second part of the 2010's, Goldstern, Kellner and Shelah constructed a forcing model of Cicho\'n's Maximum (meaning that all non-dependent cardinal characteristics are pairwise different) by using larg

Yao Liu, Jian Wang, Meng-ge Zhang

By the probabilistic coupling approach which combines a new refined basic coupling with the synchronous coupling for L\'evy processes, we obtain explicit exponential contraction rates in terms of the standard $L^1$-Wasserstein distance for the following Langevin dynamic $(X_t,Y_t)_{t\ge0}$ of McKean-Vlasov type on $\mathbb{R}^{2d}$: \begin{equation*}\left\{\

Joel H. Kastner, David Wilner, Paula Moraga Baez, Jesse Bublitz

We present Submillimeter Array (SMA) mapping of $^{12}$CO $J=2\rightarrow 1$, $^{13}$CO $J=2\rightarrow 1$, and CN $N=2\rightarrow 1$ emission from the Ring-like planetary nebula (PN) NGC 3132, one of the subjects of JWST Early Release Observation (ERO) near-infrared imaging. The $\sim$5$''$ resolution SMA data demonstrate that the Southern Ring's main, brig

Yan Hong, Jianfu Zhang

Recent advancements in personalizing text-to-image (T2I) diffusion models have shown the capability to generate images based on personalized visual concepts using a limited number of user-provided examples. However, these models often struggle with maintaining high visual fidelity, particularly in manipulating scenes as defined by textual inputs. Addressing

Masumi Shimojo, Kosuke Namekata, Kazumasa Iwai, Ayumi Asai

Thermal microwave emissions detected from stellar atmospheres contain information on stellar activity. However, even for the Sun, the relationship between multifrequency microwave data and other activity indices remains unclear. We investigated the relationships among the thermal microwave fluxes with 1, 2, 3.75 and 9.4 GHz, their circular polarizations, and

Paige Bright, Ryan Bushling, Caleb Marshall, Alex Ortiz

This expository piece expounds on major themes and clarifies technical details of the paper "Kaufman and Falconer estimates for radial projections and a continuum version of Beck's theorem" of Orponen, Shmerkin, and Wang.

Yihua Zhang, Chongyu Fan, Yimeng Zhang, Yuguang Yao

The technological advancements in diffusion models (DMs) have demonstrated unprecedented capabilities in text-to-image generation and are widely used in diverse applications. However, they have also raised significant societal concerns, such as the generation of harmful content and copyright disputes. Machine unlearning (MU) has emerged as a promising soluti

Rui Cao, Roy Ka-Wei Lee, Jing Jiang

In this paper, we address the challenge of detecting hateful memes in the low-resource setting where only a few labeled examples are available. Our approach leverages the compositionality of Low-rank adaptation (LoRA), a widely used parameter-efficient tuning technique. We commence by fine-tuning large language models (LLMs) with LoRA on selected tasks perti

Srimanta Banerjee, Jeroen Homan

We perform a comprehensive spectral study of a carefully selected sample (total exposure $\sim 50.5$ ks) of NICER observations of the atoll neutron star low-mass X-ray binary 4U 1702-429. Our sample encompasses nearly all classical spectral states found within the NICER dataset. We require two thermal emission components, originating from the accretion disc

Yan Hong, Jianfu Zhang

The extraordinary ability of generative models enabled the generation of images with such high quality that human beings cannot distinguish Artificial Intelligence (AI) generated images from real-life photographs. The development of generation techniques opened up new opportunities but concurrently introduced potential risks to privacy, authenticity, and sec

Zian Su, Xiangzhe Xu, Ziyang Huang, Zhuo Zhang

Transformer based code models have impressive performance in many software engineering tasks. However, their effectiveness degrades when symbols are missing or not informative. The reason is that the model may not learn to pay attention to the right correlations/contexts without the help of symbols. We propose a new method to pre-train general code models wh

Christian D. Tica, Eric A. Galapon

We applied the method of finite-part integration [Galapon E.A Proc.R.Soc A 473, 20160567(2017)] to evaluate in closed-form the exact one-loop integral representations of the Heisenberg-Euler Lagrangian from QED for a constant electric field and electric-like self-dual background. We also devise a prescription based on the finite-part integration of the Cauch

Haitian Yang, Degang Sun, Wen Liu, Yanshu Li

Logs are widely used in the development and maintenance of software systems. Logs can help engineers understand the runtime behavior of systems and diagnose system failures. For anomaly diagnosis, existing methods generally use log event data extracted from historical logs to build diagnostic models. However, we find that existing methods do not make full us

Jan Emily Mangulabnan, Roger D. Soberanis-Mukul, Timo Teufel, Manish Sahu

Purpose: Preoperative imaging plays a pivotal role in sinus surgery where CTs offer patient-specific insights of complex anatomy, enabling real-time intraoperative navigation to complement endoscopy imaging. However, surgery elicits anatomical changes not represented in the preoperative model, generating an inaccurate basis for navigation during surgery prog

Mahsa Azarshab, Mohammad Fathian, Babak Amiri

Text document clustering can play a vital role in organizing and handling the everincreasing number of text documents. Uninformative and redundant features included in large text documents reduce the effectiveness of the clustering algorithm. Feature selection (FS) is a well-known technique for removing these features. Since FS can be formulated as an optimi

Yuan Yuan, Jingtao Ding, Jie Feng, Depeng Jin

Urban spatio-temporal prediction is crucial for informed decision-making, such as traffic management, resource optimization, and emergence response. Despite remarkable breakthroughs in pretrained natural language models that enable one model to handle diverse tasks, a universal solution for spatio-temporal prediction remains challenging Existing prediction a

Yeonjun In, Kanghoon Yoon, Kibum Kim, Kijung Shin

Recent studies have revealed that GNNs are vulnerable to adversarial attacks. To defend against such attacks, robust graph structure refinement (GSR) methods aim at minimizing the effect of adversarial edges based on node features, graph structure, or external information. However, we have discovered that existing GSR methods are limited by narrowassumptions

Nillan Nimal, Wenbin Li, Ronald Clark, Sajad Saeedi

The creation of accurate virtual models of real-world objects is imperative to robotic simulations and applications such as computer vision, artificial intelligence, and machine learning. This paper documents the different methods employed for generating a database of mesh models of real-world objects. These methods address the tedious and time-intensive pro

Luca D'Amico-Wong, Hugh Zhang, Marc Lanctot, David C. Parkes

We propose ABCs (Adaptive Branching through Child stationarity), a best-of-both-worlds algorithm combining Boltzmann Q-learning (BQL), a classic reinforcement learning algorithm for single-agent domains, and counterfactual regret minimization (CFR), a central algorithm for learning in multi-agent domains. ABCs adaptively chooses what fraction of the environm

Khaled Humadi, Imene Trigui, Wei-Ping Zhu, Wessam Ajib

Beam misalignment is one of the main challenges for the design of reliable wireless systems in terahertz (THz) bands. This paper investigates how to apply user-centric base station (BS) clustering as a valuable add-on in THz networks. In particular, to reduce the impact of beam misalignment, a user-centric BS clustering design that provides multi-connectivit

Kiyoon Eum

Let $Ω$ be a bounded pseudoconvex domain in $\mathbb{C}^N$, and let $u$ be a continuous plurisubharmonic function on $Ω$. We construct a sequence of Gaussian analytic functions $f_n$ on $Ω$, associated with $u$, such that $\frac{1}{n}\log|f_n|$ converges to $u$ in $L^1_{loc}(Ω)$ almost surely, as $n\rightarrow\infty$. Consequently, the normalized zero curren

Junan Lin, Nayeli A. Rodríguez-Briones, Eduardo Martín-Martínez, Raymond Laflamme

Algorithmic cooling (AC) protocols have been predominantly studied for their cooling capabilities, with limited attention paid to their thermodynamic properties. This work explores a novel perspective by analyzing a broad family of AC protocols from a thermodynamic standpoint. First, we give an in-depth review and formal classification of standard AC protoco

Sining Zhoubian, Yuyang Wang, Zhihuan Jiang

Rock Classification is an essential geological problem since it provides important formation information. However, exploration on this problem using convolutional neural networks is not sufficient. To tackle this problem, we propose two approaches using residual neural networks. We first adopt data augmentation methods to enlarge our dataset. By modifying ke

Dror Baron, Hrushikesh Pramod Patil, Huiyang Zhou

Quantum error mitigation is an important technique to reduce the impact of noise in quantum computers. With more and more qubits being supported on quantum computers, there are two emerging fundamental challenges. First, the number of shots required for quantum algorithms with large numbers of qubits needs to increase in order to obtain a meaningful distribu

Iqtiar Md Siddique, Selim Molla, MD Rakib Hasan, Anamika Ahmed Siddique

This study focuses on the implementation of modern and intelligent logistics vehicles equipped with advanced tracking and security features. In response to the evolving landscape of logistics management, the proposed system integrates cutting edge technologies to enhance efficiency and ensure the security of the entire logistics process. The core component o

Xiaoyu Liu, Zhe Wang

We consider a family of one-dimensional self interacting walks whose dynamics characterized by a monotone weight function $w$ on $\mathbb{N}\cup \{0\}$. The weight function takes the form $w(n) = (1 + 2^p Bn^{-p} + O(n^{-1-\kappa}))^{-1}$, for some $B \in \mathbb{R} $, $\kappa>0$ and $p\in (0,1]$. Our main model parameter is $p$, and for $p\in (0,1/2]$ we sh

Chanwoong Yoon, Gangwoo Kim, Byeongguk Jeon, Sungdong Kim

Conversational search, unlike single-turn retrieval tasks, requires understanding the current question within a dialogue context. The common approach of rewrite-then-retrieve aims to decontextualize questions to be self-sufficient for off-the-shelf retrievers, but most existing methods produce sub-optimal query rewrites due to the limited ability to incorpor

Sungjun Ahn, Hyun-Jeong Yim, Youngwan Lee, Sung-Ik Park

This paper introduces a media service model that exploits artificial intelligence (AI) video generators at the receive end. This proposal deviates from the traditional multimedia ecosystem, completely relying on in-house production, by shifting part of the content creation onto the receiver. We bring a semantic process into the framework, allowing the distri

Jialei Xu, Xianming Liu, Junjun Jiang, Kui Jiang

Monocular depth estimation from RGB images plays a pivotal role in 3D vision. However, its accuracy can deteriorate in challenging environments such as nighttime or adverse weather conditions. While long-wave infrared cameras offer stable imaging in such challenging conditions, they are inherently low-resolution, lacking rich texture and semantics as deliver

Pei-Lun He, Zhao-Han Zhang, Karen Z. Hatsagortsyan, Christoph H. Keitel

The spin polarization of photoelectrons induced by an intense linearly polarized laser field is investigated using numerical solutions of the time-dependent Schr\"odinger equation in companion with our analytic treatment via the spin-resolved strong-field approximation and classical trajectory Monte Carlo simulations. We demonstrate that, even though the tot

Hao Shi, Wangqian Miao, Xi Dai

Electron-phonon coupling in magic-angle twisted bilayer graphene is an important but difficult topic. We propose a scheme to simplify and understand this problem. Weighted by the coupling strength with the low-energy heavy electrons ($f$ orbitals), several moir\'{e} optical phonons are singled out which strongly couple to the flat bands. These modes have loc

Peter Neigel, Andrew Vargo, Benjamin Tag, Koichi Kise

University students encounter various forms of stress during their academic journey, including cyclical stress associated with final exams. Supporting their well-being means helping them manage their stress levels. In this study, we used a wearable health-tracking ring on a cohort of 103 Japanese university students for up to 28 months in the wild. The study

Vahid Karimi, Viktoriia Babicheva

The chiral response from nanoantennas is useful for enabling advanced applications in areas such as optical communication, sensing, and imaging, due to its ability to selectively interact with circularly polarized light. Lattice resonances in periodic nanoantenna arrays can enhance the optical response of the nanostructure and facilitate stronger light-matte

Yanbang Wang, Hejie Cui, Jon Kleinberg

Graphs data is crucial for many applications, and much of it exists in the relations described in textual format. As a result, being able to accurately recall and encode a graph described in earlier text is a basic yet pivotal ability that LLMs need to demonstrate if they are to perform reasoning tasks that involve graph-structured information. Human perform

Ali Darejeh, Nadine Marcusa, Gelareh Mohammadi, John Sweller

Usability testing is an essential part of product design, particularly for user interfaces. To enhance the reliability of usability evaluations, employing cognitive load measurement methods can be highly effective in assessing the mental effort required to complete tasks during user testing. This review aims to provide an overview of the most suitable cognit

Zouying Cao, Yifei Yang, Hai Zhao

Large Language Models (LLMs) suffer from huge number of parameters, which restricts their deployment on edge devices. Weight sharing is one promising solution that encourages weight reuse, effectively reducing memory usage with less performance drop. However, current weight sharing techniques primarily focus on small-scale models like BERT and employ coarse-

Sameer Jain, Sedrick Scott Keh, Shova Chettri, Karun Dewan

Environmental conservation organizations routinely monitor news content on conservation in protected areas to maintain situational awareness of developments that can have an environmental impact. Existing automated media monitoring systems require large amounts of data labeled by domain experts, which is only feasible at scale for high-resource languages lik

Jahang Prathap, Andrew M. Hopkins, Aaron S. G. Robotham, Sabine Bellstedt

We propose a new method for identifying active galactic nuclei (AGN) in low mass ($\rm M_*\leq10^{10}M_\odot$) galaxies. This method relies on spectral energy distribution (SED) fitting to identify galaxies whose radio flux density has an excess over that expected from star formation alone. Combining data in the Galaxy and Mass Assembly (GAMA) G23 region fro

Jihai Zhang, Xiang Lan, Xiaoye Qu, Yu Cheng

Self-Supervised Contrastive Learning has proven effective in deriving high-quality representations from unlabeled data. However, a major challenge that hinders both unimodal and multimodal contrastive learning is feature suppression, a phenomenon where the trained model captures only a limited portion of the information from the input data while overlooking

Shubhashis Roy Dipta, Sadat Shahriar

This paper describes our system developed for SemEval-2024 Task 8, ``Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection'' Machine-generated texts have been one of the main concerns due to the use of large language models (LLM) in fake text generation, phishing, cheating in exams, or even plagiarizing copyright materials.

Minghao Shao, Boyuan Chen, Sofija Jancheska, Brendan Dolan-Gavitt

Capture The Flag (CTF) challenges are puzzles related to computer security scenarios. With the advent of large language models (LLMs), more and more CTF participants are using LLMs to understand and solve the challenges. However, so far no work has evaluated the effectiveness of LLMs in solving CTF challenges with a fully automated workflow. We develop two C

Linh Trinh, Quang-Hung Luu, Thai M. Nguyen, Hai L. Vu

Stress testing is an approach for evaluating the reliability of systems under extreme conditions which help reveal vulnerable scenarios that standard testing may overlook. Identifying such scenarios is of great importance in autonomous vehicles (AV) and other safety-critical systems. Since failure events are rare, naive random search approaches require a lar

Jiaxin Wu, Chong-Wah Ngo

Answering query with semantic concepts has long been the mainstream approach for video search. Until recently, its performance is surpassed by concept-free approach, which embeds queries in a joint space as videos. Nevertheless, the embedded features as well as search results are not interpretable, hindering subsequent steps in video browsing and query refor

Junru Lu, Siyu An, Min Zhang, Yulan He

When the quality of naive prompts is carefully optimized by human experts, the task performance of large language models (LLMs) can be significantly improved. However, expert-based prompt optimizations are expensive. Herein, some works have proposed Automatic Prompt Optimization (APO), to optimize naive prompts according to task outputs of given in-box testi

Robel Efrem, Alex Coutu, Sajad Saeedi

In this paper, the focus is on improving the efficiency and precision of mineral data collection using UAVs by addressing key challenges associated with sensor integration. These challenges include mitigating electromagnetic interference, reducing vibration noise, and ensuring consistent sensor performance during flight. The paper demonstrates how innovative

Hanling Yi, Feng Lin, Hongbin Li, Peiyang Ning

This research aims to accelerate the inference speed of large language models (LLMs) with billions of parameters. We propose \textbf{S}mart \textbf{P}arallel \textbf{A}uto-\textbf{C}orrect d\textbf{E}coding (SPACE), an innovative approach designed for achieving lossless acceleration of LLMs. By integrating semi-autoregressive inference and speculative decodi

Molla Basir Ahamed, Partha Pratim Roy

Let $ \mathcal{H}(\Omega) $ be the class of complex-valued functions harmonic in $ \Omega\subset\mathbb{C} $ and each $f=h+\overline{g}\in \mathcal{H}(\Omega)$, where $ h $ and $ g $ are analytic. In the study of Bohr phenomenon for certain class of harmonic mappings, it is to find a constant $ r_f\in (0, 1) $ such that the inequality \begin{align*} M_f(r):=

Alexander D. Gilbert, Frances Y. Kuo, Abirami Srikumar

In this paper, we apply quasi-Monte Carlo (QMC) methods with an initial preintegration step to estimate cumulative distribution functions and probability density functions in uncertainty quantification (UQ). The distribution and density functions correspond to a quantity of interest involving the solution to an elliptic partial differential equation (PDE) wi

Binjie He, Dong Zhang, Seng W. Loke, Shengrui Lin

The research of architecture has tremendous significance in realizing quantum Internet. Although there is not yet a standard quantum Internet architecture, the distributed architecture is one of the possible solutions, which utilizes quantum repeaters or dedicated entanglement sources in a flat structure for entanglement preparation & distribution. In this p

Merve Eryilmaz, Artem Goncharov, Gyeo-Re Han, Hyou-Arm Joung

The rapid spread of SARS-CoV-2 caused the COVID-19 pandemic and accelerated vaccine development to prevent the spread of the virus and control the disease. Given the sustained high infectivity and evolution of SARS-CoV-2, there is an ongoing interest in developing COVID-19 serology tests to monitor population-level immunity. To address this critical need, we

Hui Hu, Jia Wang, Xia-Ji Liu

We develop a general theory of Fermi polarons at nonzero temperature, including particle-hole excitations of the Fermi sea shake-up to arbitrarily high orders. The exact set of equations of the spectral function is derived by using both Chevy ansatz and diagrammatic approach, and their equivalence is clarified to hold in free space only, with an unregularize

Kai Wang, Yuwei Xu, Zhiyong Wu, Siqiang Luo

Knowledge Graph (KG) inductive reasoning, which aims to infer missing facts from new KGs that are not seen during training, has been widely adopted in various applications. One critical challenge of KG inductive reasoning is handling low-resource scenarios with scarcity in both textual and structural aspects. In this paper, we attempt to address this challen

Hoshu Hiyane, Thomas Busch, Thomás Fogarty

Strongly correlated impurities immersed in a Bose-Einstein condensate (BEC) can form a periodic structure of tightly localized single atoms due to competing inter- and intra-species interactions, leading to a self-organized pinned state. In this work, we show numerically that the impurities in the self-pinned state form a soliton-train, as a consequence of a

Zhou Yang, Zhaochun Ren, Wang Yufeng, Shizhong Peng

Empathetic response generation is increasingly significant in AI, necessitating nuanced emotional and cognitive understanding coupled with articulate response expression. Current large language models (LLMs) excel in response expression; however, they lack the ability to deeply understand emotional and cognitive nuances, particularly in pinpointing fine-grai