February 2024 arXiv papers — page 83

Peijie Sun, Le Wu, Kun Zhang, Xiangzhi Chen

While effective in recommendation tasks, collaborative filtering (CF) techniques face the challenge of data sparsity. Researchers have begun leveraging contrastive learning to introduce additional self-supervised signals to address this. However, this approach often unintentionally distances the target user/item from their collaborative neighbors, limiting i

Shuai Zhang, Yu Lu, Junwen Liu, Jia Yu

With the growing humanlike nature of dialog agents, people are now engaging in extended conversations that can stretch from brief moments to substantial periods of time. Understanding the factors that contribute to sustaining these interactions is crucial, yet existing studies primarily focusing on short-term simulations that rarely explore such prolonged an

Piermarco Cannarsa, Cristian Mendico

This work focuses on the rate of convergence for singular perturbation problems for first-order Hamilton-Jacobi equations. As an application we derive the rate of convergence for singularly perturbed two-players zero-sum deterministic differential games (i.e., leading to Hamilton-Jacobi-Isaacs equations) and, subsequently, in case of singularly perturbed mea

Samar Daou, Achraf Ben-Hamadou, Ahmed Rekik, Abdelaziz Kallel

Lipreading involves using visual data to recognize spoken words by analyzing the movements of the lips and surrounding area. It is a hot research topic with many potential applications, such as human-machine interaction and enhancing audio speech recognition. Recent deep-learning based works aim to integrate visual features extracted from the mouth region wi

Zihao Huang, Xianghe Han, Zhen Zhao, Haitao Yang

Artificial molecular rotors and motors hold great promise for functional nanomachines, but constructing diatomic rotors, crucial for these machines, is challenging due to surface constraints and limited chemical design. Here we report the construction of diatomic Cr-Cs and Fe-Cs rotors where a Cr or Fe atom revolves around a Cs atom at the Sb surface of the

Lin Chen, Fengli Xu, Nian Li, Zhenyu Han

Heterogeneous information networks (HIN) have gained increasing popularity in recent years for capturing complex relations between diverse types of nodes. Meta-structures are proposed as a useful tool to identify the important patterns in HINs, but hand-crafted meta-structures pose significant challenges for scaling up, drawing wide research attention toward

Zijin Hong, Zheng Yuan, Hao Chen, Qinggang Zhang

Generating accurate SQL queries for user questions (text-to-SQL) has been a long-standing challenge since it requires a deep understanding of both the user's question and the corresponding database schema in order to retrieve the desired content accurately. Existing methods rely on the comprehensive capability of large language models (LLMs) to generate the

Lv Cai, Ning-An Lai, Wen-Ze Su

This paper concerns the long time existence to the smooth solutions of the compressible Euler system with critical time dependent damping in $\R^2$. We establish the sharp lifespan estimate from below, with respect to the small parameter of the initial perturbation. For this end, the vector fields $\widehat{Z}$ (defined below) are used instead of the usual o

Wang Jia, Hang Xu

Deep Reinforcement Learning (DRL) has emerged as a promising approach for handling highly dynamic and nonlinear Active Flow Control (AFC) problems. However, the computational cost associated with training DRL models presents a significant performance bottleneck. To address this challenge and enable efficient scaling on high-performance computing architecture

Dawei Guan, Junchen Pei

To synthesize new superheavy elements, the accurate prediction of nuclear masses of superheavy nuclei is essential for calculations of reaction $Q$ values, neutron separation energies and $\alpha$-decay energies, which are important for estimating beam energies, survival probabilities and also for identifications. In this work, we include existing $\alpha$-d

Peng Gao, Liangyi Zhao

We establish upper bounds for moments of smoothed quadratic Dirichlet character sums under the generalized Riemann hypothesis, confirming a conjecture of M. Jutila.

A. B. Shvartsburg, S. N. Artekha, N. S. Artekha

The problem of the emergence of wave dispersion due to the heterogeneity of a transmission line (TL) is considered. An exactly solvable model helps to better understand the physical process of a signal passing through a non-uniform section of the line and to compare the exact solution and solutions obtained using various approximate methods. Based on the tra

Aishik Rakshit, Smriti Singh, Shuvam Keshari, Arijit Ghosh Chowdhury

Embeddings play a pivotal role in the efficacy of Large Language Models. They are the bedrock on which these models grasp contextual relationships and foster a more nuanced understanding of language and consequently perform remarkably on a plethora of complex tasks that require a fundamental understanding of human language. Given that these embeddings themse

Hideki Murakawa, Yoshitaro Tanaka

Numerous evolution equations with nonlocal convolution-type interactions have been proposed. In some cases, a convolution was imposed as the velocity in the advection term. Motivated by analyzing these equations, we approximate advective nonlocal interactions as local ones, thereby converting the effect of nonlocality. In this study, we investigate whether t

Przemysław Bombiński, Patryk Szatkowski, Bartłomiej Sobieski, Tymoteusz Kwieciński

Lung mask creation lacks well-defined criteria and standardized guidelines, leading to a high degree of subjectivity between annotators. In this study, we assess the underestimation of lung regions on chest X-ray segmentation masks created according to the current state-of-the-art method, by comparison with total lung volume evaluated on computed tomography

Alexandr B. Plachenov, Aleksei. P. Kiselev

A theoretical description of a class of unidirectional axisymmetric localized pulses, is given. The equivalence of their representations in the form of relatively undistorted quasi-spherical waves, in the form of Fourier-Bessel integrals and in the form of a superposition of plane waves with wave vectors having positive projections on a given direction is es

Aleksandr Shvartsburg, Sergey Artekha

The theoretical description and experimental verification of resonant phenomena in electromagnetic fields generated in the near zone of all-dielectric rectangular thin sub wavelength frames, subjected to an incident microwave, is considered. The geometry of considered problems is presented by means of arrangements of these frames in three orthogonal planes,

Tzu-Hsuan Hsu, Joshua Campbell, Jack Kramer, Sinwoo Cho

In this work, we investigate the frequency scaling of shear-horizontal (S.H.) surface acoustic wave (SAW) resonators based on a lithium niobate on insulator (LNOI) substrate into the centimeter bands for 6G wireless systems. Prototyped resonators with wavelengths ranging between 240 nm and 400 nm were fabricated, and the experimental results exhibit a succes

Yue-Jiang Dong, Fang-Lue Zhang, Song-Hai Zhang

Depth perception is crucial for a wide range of robotic applications. Multi-frame self-supervised depth estimation methods have gained research interest due to their ability to leverage large-scale, unlabeled real-world data. However, the self-supervised methods often rely on the assumption of a static scene and their performance tends to degrade in dynamic

Dazhuang He, Yu Zhang, Fawzi Boudjema, Hao Sun

$pp \to W^{\pm} h, Zh$ processes at the LHC are studied in the framework of the inert doublet model (IDM). To quantify the effects of the IDM and their observability in these processes we revisit the NLO (QCD and EW) predictions in the Standard Model (SM) and their uncertainty. Taking all available current constraints on the parameter space of the IDM, we co

Jiamu Bai, Daoyuan Chen, Bingchen Qian, Liuyi Yao

Federated Learning (FL) has recently been applied to the parameter-efficient fine-tuning of Large Language Models (LLMs). While promising, it raises significant challenges due to the heterogeneous resources and data distributions of clients. This study introduces FlexLoRA, a simple yet effective aggregation scheme for LLM fine-tuning, which mitigates the ``b

Marco Helbich, Matthew Danish, SM Labib, Britta Ricker

Computer vision-based analysis of street view imagery has transformative impacts on environmental assessments. Interactive web services, particularly Google Street View, play an ever-important role in making imagery data ubiquitous. Despite the technical ease of harnessing millions of Google Street View images, this article questions the current practices in

Naresh Dadhich, K Rajesh Nayak

With his seminal and pioneering work on the stability of the Schwarzschild black hole and its interaction with gravitational radiation, Vishu had opened a new window on black hole astrophysics. One of the interesting results that soon followed was that "a black hole has no hair", it is entirely specified by the three parameters, mass, spin and charge, and no

Wenzhao Zheng, Ruiqi Song, Xianda Guo, Chenming Zhang

Directly producing planning results from raw sensors has been a long-desired solution for autonomous driving and has attracted increasing attention recently. Most existing end-to-end autonomous driving methods factorize this problem into perception, motion prediction, and planning. However, we argue that the conventional progressive pipeline still cannot com

Tomohiro Fukaya

We show that under appropriate assumptions, a blown-up corona of a relatively hyperbolic group is equivariant and the compactification of the universal space for proper action by the blown-up corona is contractible. As a corollary, we establish the formula to determine the covering dimension of the blown-up corona by the cohomological dimension of the group.

Hyunjune Shin, Dong-Wan Choi

Data-free knowledge distillation (DFKD) aims to distill pretrained knowledge to a student model with the help of a generator without using original data. In such data-free scenarios, achieving stable performance of DFKD is essential due to the unavailability of validation data. Unfortunately, this paper has discovered that existing DFKD methods are quite sen

Haipeng Zhou, Ruoyang Chen, Changyan Yi, Juan Li

In this paper, a repeated coalition formation game (RCFG) with dynamic decision-making for physical layer security (PLS) in wireless communications with intelligent reflecting surfaces (IRSs) has been investigated. In the considered system, one central legitimate transmitter (LT) aims to transmit secret signals to a group of legitimate receivers (LRs) under

Kai Zhu, Jijun Liu, Min Zhong

We study the numerical reconstruction problem in acousto-electric tomography (AET) of recovering the conductivity distribution in a bounded domain from multiple interior power density data. The Two-Point-Gradient-$\Theta$ (TPG-$\Theta$) in Kaczmarz type is proposed, with a general convex penalty term $\Theta$, the algorithm can be utilized in AET problem for

Benedict Quartey, Eric Rosen, Stefanie Tellex, George Konidaris

When instructing robots, users want to flexibly express constraints, refer to arbitrary landmarks, and verify robot behavior, while robots must disambiguate instructions into specifications and ground instruction referents in the real world. To address this problem, we propose Language Instruction grounding for Motion Planning (LIMP), an approach that enable

Jian Wang, Xin Yang, Xiaohong Jia, Wufeng Xue

Thyroid nodule classification and segmentation in ultrasound images are crucial for computer-aided diagnosis; however, they face limitations owing to insufficient labeled data. In this study, we proposed a multi-view contrastive self-supervised method to improve thyroid nodule classification and segmentation performance with limited manual labels. Our method

Xiaosa Li, Runze Zhao, Chengyue Lu, Xiao Xiao

Surface vibration tactile feedback is capable of conveying various semantic information to humans via the handheld electronic devices, like smartphone, touch panel,and game controller. However, covering the whole device contacting surface with dense actuator arrangement can affect its normal use, how to produce desired vibration patterns at any contact point

Yujie Li, Yiwei Liu, Peiyue Li, Yifan Jia

Malicious URL detection and webpage classification are critical tasks in cybersecurity and information management. In recent years, extensive research has explored using BERT or similar language models to replace traditional machine learning methods for detecting malicious URLs and classifying webpages. While previous studies show promising results, they oft

Qitian Wu, Fan Nie, Chenxiao Yang, Tianyi Bao

Out-of-distribution (OOD) generalization has gained increasing attentions for learning on graphs, as graph neural networks (GNNs) often exhibit performance degradation with distribution shifts. The challenge is that distribution shifts on graphs involve intricate interconnections between nodes, and the environment labels are often absent in data. In this pap

Xunjian Yin, Xu Zhang, Jie Ruan, Xiaojun Wan

In recent years, substantial advancements have been made in the development of large language models, achieving remarkable performance across diverse tasks. To evaluate the knowledge ability of language models, previous studies have proposed lots of benchmarks based on question-answering pairs. We argue that it is not reliable and comprehensive to evaluate l

Ku Du, Yu Kang

This research investigates the intricate domain of synchronization problem among multiple agents operating within a dynamic fast switching network topology. We concentrate on cluster synchronization within coupled linear system under pinning control, providing both necessary and sufficient conditions. As a pivotal aspect, this paper aim to president the weak

R. Arkhipov

In a fixed spectral range, single- and half-cycle electromagnetic pulses have the shortest duration. Half-cycle pulses are promising tools for ultrafast control of quantum systems. Previously, the possibility of using a sequence of single- and half-cycle attosecond pulses to generate and ultrafast control light-induced population difference gratings has been

Kamna Sharma, Deepak Kumar Das, Saibal Ray

In this paper, we present a bibliometric analysis of the Mendeleev Periodic Table. We have conducted a comprehensive analysis of the Scopus-based database using the keyword "Mendeleev Periodic Table". Our findings suggest that the Mendeleev Periodic Table is an influential topic in the field of Inorganic as well as Organic Chemistry. Future researchers may f

Eran Hirsch, Guy Uziel, Ateret Anaby-Tavor

Planning is a fundamental task in artificial intelligence that involves finding a sequence of actions that achieve a specified goal in a given environment. Large language models (LLMs) are increasingly used for applications that require planning capabilities, such as web or embodied agents. In line with recent studies, we demonstrate through experimentation

Eli Bagno, Thierry Dana-Picard, Shulamit Reches

As soon as a new technology emerges, the education community explores its affordances and the possibilities to apply it in education. In this paper, we analyze sessions with ChatGPT around topics in basic Linear Algebra. We reflect the process undertaken by the ChatGPT along the recent year in our area of interest, emphasising the vast improvement that has b

P. Bilha Githinji, Keming Zhao, Jiantao Wang, Peiwu Qin

Ocular conditions are a global concern and computational tools utilizing retinal fundus color photographs can aid in routine screening and management. Obtaining comprehensive and sufficiently sized datasets, however, is non-trivial for the intricate retinal fundus, which exhibits heterogeneities within pathologies, in addition to variations from demographics

Tanzila Rahman, Shweta Mahajan, Hsin-Ying Lee, Jian Ren

Text-to-image (TTI) diffusion models have demonstrated impressive results in generating high-resolution images of complex and imaginative scenes. Recent approaches have further extended these methods with personalization techniques that allow them to integrate user-illustrated concepts (e.g., the user him/herself) using a few sample image illustrations. Howe

Haorong Wu, Xilong Fan, Lixiang Chen

The particle definition varies across different theories. The quantum field theory in curved spacetime shows that from the perspective of a linearly accelerated observer, an inertial empty space may be full of thermal particles. This effect is known as the Unruh effect. When the degrees of freedom of orbital angular momentum (OAM) are considered, all OAM mod

Ikuya Yamada, Ryokan Ri

Adapting English-based large language models (LLMs) to other languages has become increasingly popular due to the efficiency and potential of cross-lingual transfer. However, existing language adaptation methods often overlook the benefits of cross-lingual supervision. In this study, we introduce LEIA, a language adaptation tuning method that utilizes Wikipe

Xuanmin Zhu, Dezheng Zhang, Runping Gao, Qun wei

To improve the efficiency of the state tomography strategy via weak value, we have searched the optimal coupling strength between the system and measuring device. For an arbitrary d-dimensional quantum system, the optimal strengths being used in measuring the real and imaginary parts of the density matrix are obtained. The optimal efficiency of the state tom

Yancheng Zhu, Sean B. Andersson

This paper considers the problem of localizing a set of nodes in a wireless sensor network when both their positions and the parameters of the communication model are unknown. We assume that a single agent moves through the environment, taking measurements of the Received Signal Strength (RSS), and seek a controller that optimizes a performance metric based

Jiaqi Li, Miaozeng Du, Chuanyi Zhang, Yongrui Chen

Multimodal knowledge editing represents a critical advancement in enhancing the capabilities of Multimodal Large Language Models (MLLMs). Despite its potential, current benchmarks predominantly focus on coarse-grained knowledge, leaving the intricacies of fine-grained (FG) multimodal entity knowledge largely unexplored. This gap presents a notable challenge,

Weiquan Chen, Zhao Dong

In this paper, we construct martingale suitable weak solutions for $3$-dimensional incompressible stochastic Navier-Stokes equations with generally non-linear noise. In deterministic setting, as widely known, ``suitable weak solutions'' are Leray-Hopf weak solutions enjoying two different types of local energy inequalities (LEIs). In stochastic setting, we a

YiQiu Guo, Yuchen Yang, Ya Zhang, Yu Wang

Structured data offers a sophisticated mechanism for the organization of information. Existing methodologies for the text-serialization of structured data in the context of large language models fail to adequately address the heterogeneity inherent in key-value structured data. These methods are not ideal and frequently result in larger input sizes and poor

Kun Ma, Cong Xu, Zeyuan Chen, Wei Zhang

A transparent decision-making process is essential for developing reliable and trustworthy recommender systems. For sequential recommendation, it means that the model can identify key items that account for its recommendation results. However, achieving both interpretability and recommendation performance simultaneously is challenging, especially for models

Bakhrom A. Omirov, Isamiddin S. Rakhimov, Gulkhayo O. Solijanova

In this paper we establish some basic properties of superderivations of Lie superalgebras. Under certain conditions, for solvable Lie superalgebras with given nilradicals, we give estimates for upper bounds to dimensions of complementary subspaces to the nilradicals. Moreover, under these conditions we describe the solvable Lie superalgebras of maximal rank.

Hui Zhou, Yansha Deng

To provide Uplink Centric Broadband Communication (UCBC), New Radio Unlicensed (NR-U) network has been standardized to exploit the unlicensed spectrum using Listen Before Talk (LBT) scheme to fairly coexist with the incumbent Wireless Fidelity (WiFi) network. Existing access schemes over unlicensed spectrum are required to perform Clear Channel Assessment (C

Sunny Rai, Khushi Shelat, Devansh R Jain, Kishen Sivabalan

Culture moderates the way individuals perceive and express mental distress. Current understandings of mental health expressions on social media, however, are predominantly derived from WEIRD (Western, Educated, Industrialized, Rich, and Democratic) contexts. To address this gap, we examine mental health posts on Reddit made by individuals geolocated in India

Qiaozhi Tan, Long Bai, Guankun Wang, Mobarakol Islam

Wireless capsule endoscopy (WCE) is a non-invasive diagnostic procedure that enables visualization of the gastrointestinal (GI) tract. Deep learning-based methods have shown effectiveness in disease screening using WCE data, alleviating the burden on healthcare professionals. However, existing capsule endoscopy classification methods mostly rely on pre-defin

Salvatore Tringali

Let $\mathcal P(S)$ be the semigroup obtained by equipping the family of all non-empty subsets of a (multiplicatively written) semigroup $S$ with the operation of setwise multiplication induced by $S$ itself. We call a subsemigroup $P$ of $\mathcal P(S)$ downward complete if any element of $S$ lies in at least one set $X \in P$ and any non-empty subset of a

Ankan Sur, Yajie Yuan, Alexander Philippov

NICER has observed a few millisecond pulsars where the geometry of the X-ray emitting hotspots on the neutron star is analyzed in order to constrain the mass and radius from X-ray light curve modeling. One example, PSR J0030+0451, is shown to possibly have significant multipolar magnetic fields at the stellar surface. Using force-free simulations of the magn

Jiawei Liang, Siyuan Liang, Aishan Liu, Xiaojun Jia

The proliferation of face forgery techniques has raised significant concerns within society, thereby motivating the development of face forgery detection methods. These methods aim to distinguish forged faces from genuine ones and have proven effective in practical applications. However, this paper introduces a novel and previously unrecognized threat in fac

Yingying Wang, Yun Xiong, Xixi Wu, Xiangguo Sun

Drug combinations can cause adverse drug-drug interactions(DDIs). Identifying specific effects is crucial for developing safer therapies. Previous works on DDI event prediction have typically been limited to using labels of specific events as supervision, which renders them insufficient to address two significant challenges: (1) the bias caused by \textbf{hi

Zhongqin Gao, Yan Lv, Xiaowen Zhou

In this paper we propose a refracted skew Brownian motion as a risk model with endogenous regime switching, which generalizes the refracted diffusion risk process introduced by Gerber and Shiu. We consider an optimal dividend problem for the refracted skew Brownian risk model and identify sufficient conditions, respectively, for barrier strategy, band strate

Zhongqi Yang, Elahe Khatibi, Nitish Nagesh, Mahyar Abbasian

The profound impact of food on health necessitates advanced nutrition-oriented food recommendation services. Conventional methods often lack the crucial elements of personalization, explainability, and interactivity. While Large Language Models (LLMs) bring interpretability and explainability, their standalone use falls short of achieving true personalizatio

Siheng Li, Keyang Liu, Jiří Klimeš, Ji Chen

Transition metal dichalcogenides (TMDs) are a class of two-dimensional (2D) materials been widely studied for emerging electronic properties. In this work, we use computational simulations to examine the water adsorption on TMDs systematically and the wetting property of WSe2 specifically. We start with density functional theory (DFT) based random phase appr

Cuong Dang, Dung D. Le, Thai Le

Existing works have shown that fine-tuned textual transformer models achieve state-of-the-art prediction performances but are also vulnerable to adversarial text perturbations. Traditional adversarial evaluation is often done \textit{only after} fine-tuning the models and ignoring the training data. In this paper, we want to prove that there is also a strong

Hang Zhou, Heye Huang, Peng Zhang, Haotian Shi

This paper introduces an online physical enhanced residual learning (PERL) framework for Connected Autonomous Vehicles (CAVs) platoon, aimed at addressing the challenges posed by the dynamic and unpredictable nature of traffic environments. The proposed framework synergistically combines a physical model, represented by Model Predictive Control (MPC), with d

Heye Huang, Jinxin Liu, Guanya Shi, Shiyue Zhao

This paper proposes an adaptive behavioral decision-making method for autonomous vehicles (AVs) focusing on complex merging scenarios. Leveraging principles from non-cooperative game theory, we develop a vehicle interaction behavior model that defines key traffic elements and integrates a multifactorial reward function. Maximum entropy inverse reinforcement

Cuong Pham, Benjamin R. Baer, Ashkan Ertefaie

Marginal structural models have been widely used in causal inference to estimate mean outcomes under either a static or a prespecified set of treatment decision rules. This approach requires imposing a working model for the mean outcome given a sequence of treatments and possibly baseline covariates. In this paper, we introduce a dynamic marginal structural

Akanksha Agrawal, Paloma T. Lima, Daniel Lokshtanov, Pawel Rzążewski

An independent set in a graph G is a set of pairwise non-adjacent vertices. A graph $G$ is bipartite if its vertex set can be partitioned into two independent sets. In the Odd Cycle Transversal problem, the input is a graph $G$ along with a weight function $w$ associating a rational weight with each vertex, and the task is to find a smallest weight vertex su

Liang Xiao, Qi Zhang, Chongyang Shi, Shoujin Wang

The proliferation of social media platforms has fueled the rapid dissemination of fake news, posing threats to our real-life society. Existing methods use multimodal data or contextual information to enhance the detection of fake news by analyzing news content and/or its social context. However, these methods often overlook essential textual news content (ar

Cong Li, Meng Xiao, Pengfei Wang, Guihai Feng

Despite the inherent limitations of existing Large Language Models in directly reading and interpreting single-cell omics data, they demonstrate significant potential and flexibility as the Foundation Model. This research focuses on how to train and adapt the Large Language Model with the capability to interpret and distinguish cell types in single-cell RNA

Niharika Kakoty, Surajit Borkotokey, Rajnish Kumar, Abhijit Bora

We study the weighted Myerson value for Network games extending a similar concept for communication situations. Network games, unlike communication situations, treat direct and indirect links among players differently and distinguish their effects in both worth generation and allocation processes. The weighted Myerson value is an allocation rule for Network

Jiaxi Hu, Yuehong Hu, Wei Chen, Ming Jin

In long-term time series forecasting (LTSF) tasks, an increasing number of models have acknowledged that discrete time series originate from continuous dynamic systems and have attempted to model their dynamical structures. Recognizing the chaotic nature of real-world data, our model, \textbf{\textit{Attraos}}, incorporates chaos theory into LTSF, perceiving

Erkan Bayram, Melih Bastopcu, Mohamed-Ali Belabbas, Tamer Başar

We consider information update systems on a gossip network, which consists of a single source and $n$ receiver nodes. The source encrypts the information into $n$ distinct keys with version stamps, sending a unique key to each node. For decoding the information in a $k$-out-of-$n$ system, each receiver node requires at least $k+1$ different keys with the sam

Xiaokai Zhang, Yang Li, Na Zhu, Cheng Qin

Geometric problem solving has always been a long-standing challenge in the fields of mathematical reasoning and artificial intelligence. We built a neural-symbolic system, called FGeo-HyperGNet, to automatically perform human-like geometric problem solving. The symbolic component is a formal system built on FormalGeo, which can automatically perform geometri

Rounak Biswas, Falguni Roy

For two given idempotents $p\text{ and }q$ from an associative algebra $\mathcal{A},$ in this paper, we offer a comprehensive classification of algebras spanned by the idempotents $p\text{ and }q$. This classification is based on the condition that $p\text{ and }q$ are not tightly coupled and satisfies $(pq)^{m-1}=(pq)^{m}$ but $(pq)^{m-2}p\neq (pq)^{m-1}p$

Yufei Huang, Odin Zhang, Lirong Wu, Cheng Tan

Accurate prediction of protein-ligand binding structures, a task known as molecular docking is crucial for drug design but remains challenging. While deep learning has shown promise, existing methods often depend on holo-protein structures (docked, and not accessible in realistic tasks) or neglect pocket sidechain conformations, leading to limited practical

Jing Xu, Beiwen Tian, Hao Zhao

In this paper, we introduce a novel algorithm named Key Patch Proposer (KPP) designed to select key patches in an image without additional training. Our experiments showcase KPP's robust capacity to capture semantic information by both reconstruction and classification tasks. The efficacy of KPP suggests its potential application in active learning for seman

Shiyu Ni, Keping Bi, Jiafeng Guo, Xueqi Cheng

Large Language Models (LLMs) have been found to have difficulty knowing they do not possess certain knowledge and tend to provide specious answers in such cases. Retrieval Augmentation (RA) has been extensively studied to mitigate LLMs' hallucinations. However, due to the extra overhead and unassured quality of retrieval, it may not be optimal to conduct RA

Sebastian Antony Joseph, Lily Chen, Jan Trienes, Hannah Louisa Göke

Plain language summarization with LLMs can be useful for improving textual accessibility of technical content. But how factual are these summaries in a high-stakes domain like medicine? This paper presents FactPICO, a factuality benchmark for plain language summarization of medical texts describing randomized controlled trials (RCTs), which are the basis of

Hanqing Wang, Bowen Ping, Shuo Wang, Xu Han

LoRA employs lightweight modules to customize large language models (LLMs) for each downstream task or domain, where different learned additional modules represent diverse skills. Combining existing LoRAs to address new tasks can enhance the reusability of learned LoRAs, particularly beneficial for tasks with limited annotated data. Most prior works on LoRA

Subhajit Sahu

Community detection is the problem of identifying densely connected clusters within a network. While the Louvain algorithm is commonly used for this task, it can produce internally-disconnected communities. To address this, the Leiden algorithm was introduced. This technical report introduces GSP-Louvain, a parallel algorithm based on Louvain, which mitigate

Zhiyu Yang, Zihan Zhou, Shuo Wang, Xin Cong

Scientific data visualization plays a crucial role in research by enabling the direct display of complex information and assisting researchers in identifying implicit patterns. Despite its importance, the use of Large Language Models (LLMs) for scientific data visualization remains rather unexplored. In this study, we introduce MatPlotAgent, an efficient mod

Zhaorun Chen, Zhuokai Zhao, Zhihong Zhu, Ruiqi Zhang

Recent advancements in large language models (LLMs) have shown promise in multi-step reasoning tasks, yet their reliance on extensive manual labeling to provide procedural feedback remains a significant impediment. To address this challenge, in this paper, we propose a novel self-supervised framework AutoPRM that efficiently enhances the fine-tuning of LLMs

Yubo Ma, Zhibin Gou, Junheng Hao, Ruochen Xu

Scientific reasoning poses an excessive challenge for even the most advanced Large Language Models (LLMs). To make this task more practical and solvable for LLMs, we introduce a new task setting named tool-augmented scientific reasoning. This setting supplements LLMs with scalable toolsets, and shifts the focus from pursuing an omniscient problem solver to a

Jacky Liang, Fei Xia, Wenhao Yu, Andy Zeng

Large language models (LLMs) have been shown to exhibit a wide range of capabilities, such as writing robot code from language commands -- enabling non-experts to direct robot behaviors, modify them based on feedback, or compose them to perform new tasks. However, these capabilities (driven by in-context learning) are limited to short-term interactions, wher

Y. Q. Liu, M. S. Si, G. P. Zhang

Bilayer antiferromagnets CrX$_{3}$ (X $=$ Cl, Br, and I) are promising materials for spintronics and optoelectronics that are rooted in their peculiar electronic structures. However, their bands are often hybridized from the interlayer antiferromagnetic ordering, which are difficult to disentangle by traditional methods. In this work, we theoretically show t

Gonzalo Arranz, Adrian Lozano-Durán

Not all the information in a turbulent field is relevant for understanding particular regions or variables in the flow. Here, we present a method for decomposing a source field into its informative $\boldsymbol{\Phi}_I(\boldsymbol{x},t)$ and residual $\boldsymbol{\Phi}_R(\boldsymbol{x},t)$ components relative to another target field. The method is referred t

Zhichao Xu, Daniel Cohen, Bei Wang, Vivek Srikumar

By allowing models to predict without task-specific training, in-context learning (ICL) with pretrained LLMs has enormous potential in NLP. However, a number of problems persist in ICL. In particular, its performance is sensitive to the choice and order of in-context examples. Given the same set of in-context examples with different orderings, model performa

Anh Nguyen, Xiaokuan Zhang, Zhisheng Yan

In this paper, we present the first contactless side-channel attack for identifying 360 videos being viewed in a Virtual Reality (VR) Head Mounted Display (HMD). Although the video content is displayed inside the HMD without any external exposure, we observe that user head movements are driven by the video content, which creates a unique side channel that do

Qiu-Yan Song, Umair Zulfiqar, Zhi-Hua Xiao, Mohammad Monir Uddin

Model order reduction involves constructing a reduced-order approximation of a high-order model while retaining its essential characteristics. This reduced-order model serves as a substitute for the original one in various applications such as simulation, analysis, and design. Often, there's a need to maintain high accuracy within a specific time or frequenc

Antonios Saravanos, Eleftheria K. Pissadaki, Wayne S. Singh, Donatella Delfino

Public acceptance of conditionally automated vehicles is a crucial step in the realization of smart cities. Prior research in Europe has shown that the factors of hedonic motivation, social influence, and performance expectancy, in decreasing order of importance, influence acceptance. Moreover, a generally positive acceptance of the technology was reported.

Siyuan Wang, Zhuohan Long, Zhihao Fan, Zhongyu Wei

This paper presents a benchmark self-evolving framework to dynamically evaluate rapidly advancing Large Language Models (LLMs), aiming for a more accurate assessment of their capabilities and limitations. We utilize a multi-agent system to manipulate the context or question of original instances, reframing new evolving instances with high confidence that dyn

Siyuan Wang, Zhongyu Wei, Yejin Choi, Xiang Ren

Large language models (LLMs) have achieved impressive human-like performance across various reasoning tasks. However, their mastery of underlying inferential rules still falls short of human capabilities. To investigate this, we propose a logic scaffolding inferential rule generation framework, to construct an inferential rule base, ULogic, comprising both p

Fali Wang, Runxue Bao, Suhang Wang, Wenchao Yu

Large Language Models (LLMs) have achieved exceptional capabilities in open generation across various domains, yet they encounter difficulties with tasks that require intensive knowledge. To address these challenges, methods for integrating knowledge have been developed, which augment LLMs with domain-specific knowledge graphs through external modules. These

Jose Balanza-Martinez, Angel A. Cantu, Robert Schweller, Tim Wylie

In this paper, we seek to provide a simpler proof that the relocation problem in Ricochet Robots (Lunar Lockout with fixed geometry) is PSPACE-complete via a reduction from Finite Function Generation (FFG). Although this result was originally proven in 2003, we give a simpler reduction by utilizing the FFG problem, and put the result in context with recent p

Javier Roulet, Tejaswi Venumadhav

This review provides a conceptual and technical survey of methods for parameter estimation of gravitational wave signals in ground-based interferometers such as LIGO and Virgo. We introduce the framework of Bayesian inference and provide an overview of models for the generation and detection of gravitational waves from compact binary mergers, focusing on the

Wenhao Wang, Linke Song, Benshan Mei, Shuang Liu

Integrity is critical for maintaining system security, as it ensures that only genuine software is loaded onto a machine. Although confidential virtual machines (CVMs) function within isolated environments separate from the host, it is important to recognize that users still encounter challenges in maintaining control over the integrity of the code running w

Vijay V. Vazirani

The assignment game is a classical model for profit sharing and a cornerstone of cooperative game theory. While an imputation in its core guarantees fairness among coalitions, it provides no fairness guarantee at the level of individual agents: single agents or one sided coalitions have zero standalone value and may receive arbitrarily small payoffs. Motivat

Wenda Xu, Guanglei Zhu, Xuandong Zhao, Liangming Pan

Recent studies show that large language models (LLMs) improve their performance through self-feedback on certain tasks while degrade on others. We discovered that such a contrary is due to LLM's bias in evaluating their own output. In this paper, we formally define LLM's self-bias - the tendency to favor its own generation - using two statistics. We analyze

Long Qian, Juncheng Li, Yu Wu, Yaobo Ye

Large Language Models (LLMs) demonstrate remarkable proficiency in comprehending and handling text-based tasks. Many efforts are being made to transfer these attributes to video modality, which are termed Video-LLMs. However, existing Video-LLMs can only capture the coarse-grained semantics and are unable to effectively handle tasks related to comprehension

Woo Seok Lee, Taegeun Song, Kyoung-Min Kim

The application of twist engineering in van der Waals magnets has opened new frontiers in the field of two-dimensional magnetism, yielding distinctive magnetic domain structures. Despite the introduction of numerous theoretical methods, limitations persist in terms of accuracy or efficiency due to the complex nature of the magnetic Hamiltonians pertinent to

M. W. P. Maduranga

The rise of the Internet of Things (IoT) and mobile internet applications has spurred interest in location-based services (LBS) for commercial, military, and social applications. While the global positioning system (GPS) dominates outdoor localization, its efficacy wanes indoors due to signal challenges. Indoor localization systems leverage wireless technolo

Kang Chen, Zheng Lian, Haiyang Sun, Rui Liu

Deception detection has attracted increasing attention due to its importance in real-world scenarios. Its main goal is to detect deceptive behaviors from multimodal clues such as gestures, facial expressions, prosody, etc. However, these bases are usually subjective and related to personal habits. Therefore, we extend deception detection to deception reasoni