May 2024 arXiv papers — page 45

Xinyu Tian, Xiaotong Shen

This paper investigates the accuracy of generative models and the impact of knowledge transfer on their generation precision. Specifically, we examine a generative model for a target task, fine-tuned using a pre-trained model from a source task. Building on the "Shared Embedding" concept, which bridges the source and target tasks, we introduce a novel framew

Andreas Charalampopoulos, Nikolas Chatzis, Foivos Ntoulas-Panagiotopoulos, Charilaos Papaioannou

Fast feedforward networks (FFFs) are a class of neural networks that exploit the observation that different regions of the input space activate distinct subsets of neurons in wide networks. FFFs partition the input space into separate sections using a differentiable binary tree of neurons and during inference descend the binary tree in order to improve compu

Jianbin Zhou, Shen Wang, Chaoshan Wu, Ji Qi

Unlike Li-ion transport in the bulk of carbonaceous materials, little is known about Li-ion diffusion on their surface. In this study, we have discovered an ultra-fast Li-ion transport phenomenon on the surface of carbonaceous materials, particularly when they have limited Li insertion capacity along with a high surface area. This is exemplified by a carbon

Monisankha Pal, Arvind Ramanathan, Ted Wada, Ashutosh Pandey

Deep learning has become a de facto method of choice for speech enhancement tasks with significant improvements in speech quality. However, real-time processing with reduced size and computations for low-power edge devices drastically degrades speech quality. Recently, transformer-based architectures have greatly reduced the memory requirements and provided

Chia-Yi Hsu, Yu-Lin Tsai, Chih-Hsun Lin, Pin-Yu Chen

While large language models (LLMs) such as Llama-2 or GPT-4 have shown impressive zero-shot performance, fine-tuning is still necessary to enhance their performance for customized datasets, domain-specific tasks, or other private needs. However, fine-tuning all parameters of LLMs requires significant hardware resources, which can be impractical for typical u

Arundhati Goldar, Nirmalya Kajuri

The bulk reconstruction program involves expressing local bulk fields as non-local operators on the boundary. It was initiated in the context of AdS/CFT correspondence. Attempts to extend it to de Sitter have been successful for heavy(principal series) scalar fields. For other fields, the construction ran into issues. In particular, divergences were found to

Erjian Cheng, Kaipu Wang, Yiqing Hao, Wenqing Chen

The discovery of a giant anomalous Hall effect (AHE) and its novel mechanism holds significant promise for advancing both fundamental research and practical applications. Magnetic kagome lattice materials are uniquely suited for studying the AHE due to their interplay between electronic structure, topology, and magnetism. However, the geometric frustration i

Shuijing Liu, Kaiwen Hong, Neeloy Chakraborty, Katherine Driggs-Campbell

We investigate the feasibility of deploying reinforcement learning (RL) policies for constrained crowd navigation using a low-fidelity simulator. We introduce a representation of the dynamic environment, separating human and obstacle representations. Humans are represented through detected states, while obstacles are represented as computed point clouds base

Zipeng Wang, Dan Xu

Neural Radiance Fields (NeRFs) have demonstrated remarkable proficiency in synthesizing photorealistic images of large-scale scenes. However, they are often plagued by a loss of fine details and long rendering durations. 3D Gaussian Splatting has recently been introduced as a potent alternative, achieving both high-fidelity visual results and accelerated ren

Jonghyeok Lee, Chen Xu, Yao Xie

In this work, we present a novel conformal prediction method for time-series, which we call Kernel-based Optimally Weighted Conformal Prediction Intervals (KOWCPI). Specifically, KOWCPI adapts the classic Reweighted Nadaraya-Watson (RNW) estimator for quantile regression on dependent data and learns optimal data-adaptive weights. Theoretically, we tackle the

Meng Li, Junjun Wang, Zhen Guan, Zhijie Du

This work is concerned with the construction and analysis of structure-preserving Galerkin methods for computing the dynamics of rotating Bose-Einstein condensate (BEC) based on the Gross-Pitaevskii equation with angular momentum rotation. Due to the presence of the rotation term, constructing finite element methods (FEMs) that preserve both mass and energy

Ashish Kumar Meena, Jasjeet Singh Bagla

Hyperbolic umbilic (HU) is a point singularity of the gravitational lens equation, giving rise to a ring-shaped image formation made of four highly magnified images, off-centred from the lens centre. Recent observations have revealed new strongly lensed image formations near HU singularities, and many more are expected in ongoing and future observations. Lik

Francisco Arana-Herrera, Giovanni Forni

Motivated by work of Dolgopyat and N\'andori, we establish a general method for upgrading limit theorems for Birkhoff sums and cocycles over dynamical systems to mixing limit theorems under mild ergodicity and hyperbolicity assumptions. Building on previous work of Al-Saqban and Forni, we apply this method to obtain mixing limit theorems for particular subbu

Mikiya Doi, Masayuki Ohzeki

Compressed sensing is a signal processing scheme that reconstructs high-dimensional sparse signals from a limited number of observations. In recent years, various problems involving signals with a finite number of discrete values have been attracting attention in the field of compressed sensing. In particular, binary compressed sensing, which restricts signa

Gihyun Kwon, Jangho Park, Jong Chul Ye

While text-to-image models have achieved impressive capabilities in image generation and editing, their application across various modalities often necessitates training separate models. Inspired by existing method of single image editing with self attention injection and video editing with shared attention, we propose a novel unified editing framework that

Yikai Wang, Xinzhou Wang, Zilong Chen, Zhengyi Wang

Video generative models are receiving particular attention given their ability to generate realistic and imaginative frames. Besides, these models are also observed to exhibit strong 3D consistency, significantly enhancing their potential to act as world simulators. In this work, we present Vidu4D, a novel reconstruction model that excels in accurately recon

Jun-Yu Ma, Hong Wang, Hao-Xiang Xu, Zhen-Hua Ling

Model editing is an emerging field that focuses on updating the knowledge embedded within large language models (LLMs) without extensive retraining. However, current model editing methods significantly compromise the general abilities of LLMs as the number of edits increases, and this trade-off poses a substantial challenge to the continual learning of LLMs.

Robert Wolfe, Isaac Slaughter, Bin Han, Bingbing Wen

The rapid proliferation of generative AI has raised questions about the competitiveness of lower-parameter, locally tunable, open-weight models relative to high-parameter, API-guarded, closed-weight models in terms of performance, domain adaptation, cost, and generalization. Centering under-resourced yet risk-intolerant settings in government, research, and

Xuhan Zuo, Minghao Wang, Tianqing Zhu, Lefeng Zhang

With the growing need to comply with privacy regulations and respond to user data deletion requests, integrating machine unlearning into IoT-based federated learning has become imperative. Traditional unlearning methods, however, often lack verifiable mechanisms, leading to challenges in establishing trust. This paper delves into the innovative integration o

Ryuichiro Hataya, Kota Matsui, Masaaki Imaizumi

Selecting or designing an appropriate domain adaptation algorithm for a given problem remains challenging. This paper presents a Transformer model that can provably approximate and opt for domain adaptation methods for a given dataset in the in-context learning framework, where a foundation model performs new tasks without updating its parameters at test tim

Victor A. Kich, Jair A. Bottega, Raul Steinmetz, Ricardo B. Grando

This paper introduces YamaS, a simulator integrating Unity3D Engine with Robotic Operating System for robot navigation research and aims to facilitate the development of both Deep Reinforcement Learning (Deep-RL) and Natural Language Processing (NLP). It supports single and multi-agent configurations with features like procedural environment generation, RGB

Shoma Iwai, Tomo Miyazaki, Shinichiro Omachi

In recent years, neural network-driven image compression (NIC) has gained significant attention. Some works adopt deep generative models such as GANs and diffusion models to enhance perceptual quality (realism). A critical obstacle of these generative NIC methods is that each model is optimized for a single bit rate. Consequently, multiple models are require

M. A. Arroyave, B. Behera, F. Cavanna, A. Feld

Power-over-Fiber (PoF) technology has been used extensively in settings where high voltages require isolation from ground. In a novel application of PoF, power is provided to photon detector modules located on a surface at $\sim$ 300 kV with respect to ground in the planned DUNE experiment. In cryogenic environments, PoF offers a reliable means of power tran

Trung Dang, Huy Hoang Nguyen, Aleksei Tiulpin

Accurate retinal vessel (RV) segmentation is a crucial step in the quantitative assessment of retinal vasculature, which is needed for the early detection of retinal diseases and other conditions. Numerous studies have been conducted to tackle the problem of segmenting vessels automatically using a pixel-wise classification approach. The common practice of c

Akerele Olofin Segun

We find various series that involves the central binomial coefficients $\binom{2n}{n}$, harmonic numbers and Fibonacci Numbers.\\ Contrary to the traditional hypergeometric function $_pF_q$ approach, our method utilizes a straightforward transformation to obtain new evaluations linked to Fibonacci numbers and the golden ratio. Before the end of this paper, w

Trung Dang, Huy Hoang Nguyen, Aleksei Tiulpin

One of the primary challenges in brain tumor segmentation arises from the uncertainty of voxels close to tumor boundaries. However, the conventional process of generating ground truth segmentation masks fails to treat such uncertainties properly. Those "hard labels" with 0s and 1s conceptually influenced the majority of prior studies on brain image segmentat

Lijun Hua, Xiaoyi Hu, Junfeng Zhen, Xuejuan Yang

To investigate the gas-phase hydrogenation processes of large, astronomically relevant cationic polycyclic aromatic hydrocarbon (PAH) molecules under the interstellar environments, the ion-molecule collision reaction between six PAH cations and H-atoms is studied. The experimental results show that the hydrogenated PAH cations are efficiently formed, and no

Xiaoxia Zhang, Xiuyuan Qi, Zixin Teng

Sentiment analysis, an increasingly vital field in both academia and industry, plays a pivotal role in machine learning applications, particularly on social media platforms like Reddit. However, the efficacy of sentiment analysis models is hindered by the lack of expansive and fine-grained emotion datasets. To address this gap, our study leverages the GoEmot

Volodymyr Tkachuk, Gellért Weisz, Csaba Szepesvári

We consider offline reinforcement learning (RL) in $H$-horizon Markov decision processes (MDPs) under the linear $q^\pi$-realizability assumption, where the action-value function of every policy is linear with respect to a given $d$-dimensional feature function. The hope in this setting is that learning a good policy will be possible without requiring a samp

Zheng-Chuan Wang

Quantum spin liquid has massive many spin entanglement in the ground state, we can evaluate it by the entanglement entropy, but the latter can not be observed directly by experiment. In this manuscript, we try to characterize its topological properties by the geometric phase. However the usual adiabatic or non-adiabatic geometric phase can not appear in the

Leo Hoshikawa, Marcos V. Conde, Takeshi Ohashi, Atsushi Irie

Implicit Neural Representations (INRs) and Neural Fields are a novel paradigm for signal representation, from images and audio to 3D scenes and videos. The fundamental idea is to represent a signal as a continuous and differentiable neural network. This new approach poses new theoretical questions and challenges. Considering a neural image as a 2D image repr

Shengyuan Chen, Qinggang Zhang, Junnan Dong, Wen Hua

Entity alignment (EA) aims to merge two knowledge graphs (KGs) by identifying equivalent entity pairs. While existing methods heavily rely on human-generated labels, it is prohibitively expensive to incorporate cross-domain experts for annotation in real-world scenarios. The advent of Large Language Models (LLMs) presents new avenues for automating EA with a

Ruizhong Qiu, Hanghang Tong

We study nonconvex zeroth-order optimization (ZOO) in a high-dimensional space $\mathbb R^d$ for functions with approximately $s$-sparse gradients. To reduce the dependence on the dimensionality $d$ in the query complexity, high-dimensional ZOO methods seek to leverage gradient sparsity to design gradient estimators. The previous best method needs $O\big(s\l

Yin Wu, Xiaoyi Hu, Junfeng Zhen, Xuejuan Yang

In interstellar environment, fullerene species readily react with large molecules (e.g., PAHs and their derivatives) in the gas phase, which may be the formation route of carbon dust grains in space. In this work, the gas-phase ion-molecule collision reaction between fullerene cations (Cn+, n=32, 34, ..., 60) and functionalized PAH molecules (9-hydroxyfluore

Xinyu Zhang, Mengxue Kang, Fei Wei, Shuang Xu

As the field of image generation rapidly advances, traditional diffusion models and those integrated with multimodal large language models (LLMs) still encounter limitations in interpreting complex prompts and preserving image consistency pre and post-editing. To tackle these challenges, we present an innovative image editing framework that employs the robus

Jianqiao Lu, Zhiyang Dou, Hongru Wang, Zeyu Cao

In this work, we propose a novel method named \textbf{Auto}mated \textbf{P}rocess-\textbf{S}upervised \textbf{V}erifier (\textbf{\textsc{AutoPSV}}) to enhance the reasoning capabilities of large language models (LLMs) by automatically annotating the reasoning steps. \textsc{AutoPSV} begins by training a verification model on the correctness of final answers,

Mitrajyoti Ghosh, Yuval Grossman, Walter Tangarife, Xun-Jie Xu

The neutrino force results from the exchange of a pair of neutrinos. A neutrino background can significantly influence this force. In this work, we present a comprehensive calculation of the neutrino force in various neutrino backgrounds with spin dependence taken into account. In particular, we calculate the spin-independent and spin-dependent parity-conser

Zheng Zhang, Yuntong Hu, Bo Pan, Chen Ling

Text-Attributed Graphs (TAGs) enhance graph structures with natural language descriptions, enabling detailed representation of data and their relationships across a broad spectrum of real-world scenarios. Despite the potential for deeper insights, existing TAG representation learning primarily relies on supervised methods, necessitating extensive labeled dat

Shanshan Wang, Fangzheng Yuan, Keyang Wang, Xun Yang

Knowledge tracing has been widely used in online learning systems to guide the students' future learning. However, most existing KT models primarily focus on extracting abundant information from the question sets and explore the relationships between them, but ignore the personalized student behavioral information in the learning process. This will limit the

Wei Qian, Aobo Chen, Chenxu Zhao, Yangyi Li

In education data mining (EDM) communities, machine learning has achieved remarkable success in discovering patterns and structures to tackle educational challenges. Notably, fairness and algorithmic bias have gained attention in learning analytics of EDM. With the increasing demand for the right to be forgotten, there is a growing need for machine learning

Jidong Jia, Pei Zhao, Di Wang

Voice activity detection (VAD) is the task of detecting speech in an audio stream, which is challenging due to numerous unseen noises and low signal-to-noise ratios in real environments. Recently, neural network-based VADs have alleviated the degradation of performance to some extent. However, the majority of existing studies have employed excessively large

Mostofa Rafid Uddin, Min Xu

Unsupervised disentanglement of content and transformation is significantly important for analyzing shape-focused scientific image datasets, given their efficacy in solving downstream image-based shape-analyses tasks. The existing relevant works address the problem by explicitly parameterizing the transformation latent codes in a generative model, significan

Yucheng Xu, Jia-Qi Yang, Kebin Fan, Sheng Wang

Emerging reconfigurable metasurfaces offer various possibilities in programmatically manipulating electromagnetic waves across spatial, spectral, and temporal domains, showcasing great potential for enhancing terahertz applications. However, they are hindered by limited tunability, particularly evident in relatively small phase tuning over 270o, due to the d

Sachiko Takeuchi, Yasuhiro Yamaguchi, Atsushi Hosaka, Makoto Takizawa

The $X(3872)$ is investigated by employing the quark-hadron hybrid model, that consists of the $c\bar c$ core, $D^{(*)}\bar D{}^*$, $J/\psi\omega$, and $J/\psi\rho$ two-meson states. Due to the attraction from the $c\bar c$-$D\bar D{}^*$ coupling and from the OPEP tensor coupling, a very thin peak can appear at the $D^{0}\bar D{}^{*0}$ threshold. The energy

Fatemeh Mostafavi

While the fundamental principles of light-matter interaction are well-understood and drive countless technologies, the world of multiphoton processes remains a fascinating puzzle, holding the potential to drastically alter our understanding of how light interacts with matter at its most basic level. This rich interplay of light and matter unveils novel pheno

Eric Mugnier, Emmanuel Anaya Gonzalez, Ranjit Jhala, Nadia Polikarpova

Program verifiers such as Dafny automate proofs by outsourcing them to an SMT solver. This automation is not perfect, however, and the solver often requires hints in the form of assertions, creating a burden for the proof engineer. In this paper, we propose Laurel, a tool that alleviates this burden by automatically generating assertions using large language

Mingxin Chen, Ming-Min Zhao, An Liu, Min Li

In this paper, we consider a cooperative sensing framework in the context of future multi-functional network with both communication and sensing ability, where one base station (BS) serves as a sensing transmitter and several nearby BSs serve as sensing receivers. Each receiver receives the sensing signal reflected by the target and communicates with the fus

Liwen Hu, Lei Ma, Yijia Guo, Tiejun Huang

Spike cameras, with their exceptional temporal resolution, are revolutionizing high-speed visual applications. Large-scale synthetic datasets have significantly accelerated the development of these cameras, particularly in reconstruction and optical flow. However, current synthetic datasets for spike cameras lack sophistication. Addressing this gap, we intro

Chao Zhang, Haoxin Zhang, Shiwei Wu, Di Wu

Large Language Models (LLMs) have demonstrated exceptional proficiency in text understanding and embedding tasks. However, their potential in multimodal representation, particularly for item-to-item (I2I) recommendations, remains underexplored. While leveraging existing Multimodal Large Language Models (MLLMs) for such tasks is promising, challenges arise du

Hanyu Chen, Bailey Miller, Ioannis Gkioulekas

We introduce a method for high-quality 3D reconstruction from multi-view images. Our method uses a new point-based representation, the regularized dipole sum, which generalizes the winding number to allow for interpolation of per-point attributes in point clouds with noisy or outlier points. Using regularized dipole sums, we represent implicit geometry and r

Shoki Koyanagi, Yoshitaka Tanimura

We formulate a thermodynamic theory applicable to both classical and quantum systems. These systems are depicted as thermodynamic system-bath models capable of handling isothermal, isentropic, thermostatic, and entropic processes. Our approach is based on the use of a dimensionless thermodynamic potential expressed as a function of the intensive and extensiv

Kevin Coulembier, Pavel Etingof, Victor Ostrik, Daniel Tubbenhauer

We study the number of indecomposable summands in tensor powers of the vector representation of SL2. Our main focus is on positive characteristic where this sequence of numbers and its generating function show fractal behavior akin to Mahler functions.

Yongsheng Yu, Ziyun Zeng, Hang Hua, Jianlong Fu

Diffusion models equipped with language models demonstrate excellent controllability in image generation tasks, allowing image processing to adhere to human instructions. However, the lack of diverse instruction-following data hampers the development of models that effectively recognize and execute user-customized instructions, particularly in low-level task

Jaeseong Jeong, Namhun Koo, Soonhak Kwon

The Feistel Boomerang Connectivity Table (FBCT) was proposed as the feistel counterpart of the Boomerang Connectivity Table. The entries of the FBCT are actually related to the second-order zero differential spectrum. Recently, several results on the second-order zero differential uniformity of some functions were introduced. However, almost all of them were

Yuzhou. Nie, Yanting. Wang, Jinyuan. Jia, Michael J. De Lucia

One key challenge in backdoor attacks against large foundation models is the resource limits. Backdoor attacks usually require retraining the target model, which is impractical for very large foundation models. Existing backdoor attacks are mainly designed for supervised classifiers or small foundation models (e.g., BERT). None of these attacks has successfu

I. L. Buchbinder, S. M. Kuzenko

The Freedman-Townsend model is quantized using the Batalin-Vilkovisky approach to Lagrangian quantization of gauge theories with linearly dependent generators. Path integral arguments are then applied to demonstrate the quantum equivalence of the Freedman-Townsend model to the principal chiral $\sigma$-model.

R. Okuma, K. Yamagami, Y. Fujisawa, C. H. Hsu

Analogous to the charged electron-electron pair condensation in superconductors, an excitonic insulator (EI) represents Fermi surface instability due to spontaneous formation and condensation of charge-neutral electron-hole pair (exciton). Unlike in superconductors, however, the charge-neutral nature of exciton makes probing emergent EI phase via macroscopic

Qinqing Liu, Xiang Peng, Tao Zhang, Yuhao Deng

Although randomized controlled trials have long been regarded as the ``gold standard'' for evaluating treatment effects, there is no natural prevention from post-treatment events. For example, non-compliance makes the actual treatment different from the assigned treatment, truncation-by-death renders the outcome undefined or ill-defined, and missingness prev

F. Qu

Let $X$ be a Deligne-Mumford stack locally of finite type over an algebraically closed field $k$ of characteristic zero. We show that the intrinsic normal cone $C_X$ of $X$ is supported in the subcone $\mathbb{V}(\Omega_X[-1])$ ($h^1/h^0((\Omega^1_X)^\vee)$) of its intrinsic normal sheaf $N_X$. This leads to an alternative proof of cone reduction by cosectio

Xingyu Ma, Minghuan Zeng, Huaiming Guo, Shiping Feng

The transport experiments demonstrate a dramatic switch from the low-temperature linear in temperature (T-linear) resistivity in the overdoped strange-metal phase of cuprate superconductors to the low-temperature quadratic in temperature (T-quadratic) resistivity in the underdoped pseudogap phase, however, a consensus on the origin of this unusual switch is

Luofang Jiao, Tianqi Zhang, Jiwei Zhao, Yunting Xu

With the increasing of connected vehicles in the fifth-generation mobile communication networks (5G) and beyond 5G (B5G), ensuring the reliable and high-speed cellular vehicle-to-everything (C-V2X) communication has posed significant challenges due to the high mobility of vehicles. For improving the network performance and reliability, multi-connectivity tec

Daigo Ito, Hiroki Matsui

In 2005, Balmer defined the ringed space $\operatorname{Spec}_\otimes \mathcal{T}$ for a given tensor triangulated category, while in 2023, the second author introduced the ringed space $\operatorname{Spec}_\vartriangle \mathcal{T}$ for a given triangulated category. In the algebro-geometric context, these spectra provided several reconstruction theorems usi

Jonathan Weitsman

We study Chern-Simons Gauge Theory in axial gauge on ${\mathbb R}^3.$ This theory has a quadratic Lagrangian and therefore expectations can be computed nonperturbatively by explicit formulas, giving an (unbounded) linear functional on a space of polynomial functions in the gauge fields, as a mathematically well-defined avatar of the formal functional integra

Vedant Bhandari, Jasmin James, Tyson Phillips, P. Ross McAree

This paper explores the question of creating and maintaining terrain maps in environments where the terrain changes. The specific example explored is the construction of terrain maps from 3D LiDAR measurements on an electric rope shovel. The approach extends the height grid representation of terrain to include a Hidden Markov Model in each cell, enabling con

Aditya Dhariwal, Thomas H. Speak, Linshan Zeng, Amirhossein Rashidi

Infrared emission features toward interstellar gas of the IC 348 star cluster in Perseus have been recently proposed to originate from the amino acid tryptophan. The assignment was based on laboratory infrared spectra of tryptophan pressed into pellets, a method which is known to cause large frequency shifts compared to the gas phase. We assess the validity

Xin He, Wenqi Fan, Ruobing Wang, Yili Wang

Social recommendation models weave social interactions into their design to provide uniquely personalized recommendation results for users. However, social networks not only amplify the popularity bias in recommendation models, resulting in more frequent recommendation of hot items and fewer long-tail items, but also include a substantial amount of redundant

Harry Zhang, Luca Carlone

We introduce CHAMP, a novel method for learning sequence-to-sequence, multi-hypothesis 3D human poses from 2D keypoints by leveraging a conditional distribution with a diffusion model. To predict a single output 3D pose sequence, we generate and aggregate multiple 3D pose hypotheses. For better aggregation results, we develop a method to score these hypothes

Yixin Liu, Shiyuan Li, Yu Zheng, Qingfeng Chen

Graph anomaly detection (GAD), which aims to identify abnormal nodes that differ from the majority within a graph, has garnered significant attention. However, current GAD methods necessitate training specific to each dataset, resulting in high training costs, substantial data requirements, and limited generalizability when being applied to new datasets and

Yuxiang Gao, Soheil Kolouri, Ravindra Duddu

With the rapid advancement of graphical processing units, Physics-Informed Neural Networks (PINNs) are emerging as a promising tool for solving partial differential equations (PDEs). However, PINNs are not well suited for solving PDEs with multiscale features, particularly suffering from slow convergence and poor accuracy. To address this limitation of PINNs

Jianmin Shen, Shiyang Chen, Feiyi Liu, Wei Li

The wide application of machine learning (ML) techniques in statistics physics has presented new avenues for research in this field. In this paper, we introduce a semi-supervised learning method based on Siamese Neural Networks (SNN), trying to explore the potential of neural network (NN) in the study of critical behaviors beyond the approaches of supervised

Luo-bin Lin, Fu-quan Chen, Chang-jie Zheng, Yi-qun Huang

This paper identifies the nonzero resultant and consequent unique displacement singularity of time-dependent complex variable method on quasi-three dimensional shallow tunnelling in visco-elastic and gravitational geomaterial. The quasi-three dimensional problem is equivalently simplified into a plane-strain one using a time-dependent coefficient of converge

Masaki Waga, Kotaro Matsuoka, Takashi Suwa, Naoki Matsumoto

When monitoring a cyber-physical system (CPS) from a remote server, keeping the monitored data secret is crucial, particularly when they contain sensitive information, e.g., biological or location data. Recently, Banno et al. (CAV'22) proposed a protocol for online LTL monitoring that keeps data concealed from the server using Fully Homomorphic Encryption (F

Yuxiao Lee, Xiaofeng Cao, Jingcai Guo, Wei Ye

The remarkable achievements of Large Language Models (LLMs) have captivated the attention of both academia and industry, transcending their initial role in dialogue generation. To expand the usage scenarios of LLM, some works enhance the effectiveness and capabilities of the model by introducing more external information, which is called the agent paradigm.

Y. Li, W. Xiao, L. Zhao, Z. Huang

Standard Direction of Arrival (DOA) estimation methods are typically derived based on the Gaussian noise assumption, making them highly sensitive to outliers. Therefore, in the presence of impulsive noise, the performance of these methods may significantly deteriorate. In this paper, we model impulsive noise as Gaussian noise mixed with sparse outliers. By e

Masakiyo Miyazawa

A semi-martingale reflecting Brownian motion is a popular process for diffusion approximations of queueing models including their networks. In this paper, we are concerned with the case that it lives on the nonnegative half-line, but the drift and variance of its Brownian component discontinuously change at its finitely many states. This reflecting diffusion

Samuel Pfrommer, Brendon G. Anderson, Somayeh Sojoudi

Machine learning often aims to produce latent embeddings of inputs which lie in a larger, abstract mathematical space. For example, in the field of 3D modeling, subsets of Euclidean space can be embedded as vectors using implicit neural representations. Such subsets also have a natural algebraic structure including operations (e.g., union) and corresponding

Evan Dong, Aaron Schein, Yixin Wang, Nikhil Garg

Racial and other demographic imputation is necessary for many applications, especially in auditing disparities and outreach targeting in political campaigns. The canonical approach is to construct continuous predictions -- e.g., based on name and geography -- and then to $\textit{discretize}$ the predictions by selecting the most likely class (argmax). We st

Shiming Ge, Weijia Guo, Chenyu Li, Junzheng Zhang

Masked face recognition is important for social good but challenged by diverse occlusions that cause insufficient or inaccurate representations. In this work, we propose a unified deep network to learn generative-to-discriminative representations for facilitating masked face recognition. To this end, we split the network into three modules and learn them on

Yan Chen, Tao Li, Xiaofeng Zong

We study a class of graphon particle systems with time-varying random coefficients. In a graphon particle system, the interactions among particles are characterized by the coupled mean field terms through an underlying graphon and the randomness of the coefficients comes from exogenous stochastic processes. By constructing two-level approximated sequences co

Cristina N. Vasconcelos, Abdullah Rashwan, Austin Waters, Trevor Walker

We address the long-standing problem of how to learn effective pixel-based image diffusion models at scale, introducing a remarkably simple greedy growing method for stable training of large-scale, high-resolution models. without the needs for cascaded super-resolution components. The key insight stems from careful pre-training of core components, namely, th

Kristian Gjorgjieski, Rogério Capobianco

We investigate thick accretion structures around Kerr black holes in a swirling background. This stationary and axisymmetric spacetime is composed of a rotating black hole, which is immersed in a rotating background. The swirling background is characterized by an odd $\mathcal{Z}_2$ symmetry, where the northern and southern hemispheres are rotating in opposi

Tatsuro Sakaguchi, Yositake Takane

In a three-dimensional strong topological insulator, gapless helical surface states appear everywhere on its surface. In the presence of a screw dislocation, gapless helical modes also appear in the vicinity of the corresponding dislocation line. Let us focus on a case where a pair of screw dislocations connects the top and bottom surfaces of a strong topolo

Jianke Yang, Wang Rao, Nima Dehmamy, Robin Walters

Despite the advancements in learning governing differential equations from observations of dynamical systems, data-driven methods are often unaware of fundamental physical laws, such as frame invariance. As a result, these algorithms may search an unnecessarily large space and discover less accurate or overly complex equations. In this paper, we propose to l

Shayan Talaei, Mohammadreza Pourreza, Yu-Chen Chang, Azalia Mirhoseini

Translating natural language questions into SQL queries, known as text-to-SQL, is a long-standing research problem. Effective text-to-SQL synthesis can become very challenging due to (i) the extensive size of database catalogs (descriptions of tables and their columns) and database values, (ii) reasoning over large database schemas, (iii) ensuring the functi

Youqi Pan, Wugen Zhou, Yingdian Cao, Hongbin Zha

Visual-inertial odometry (VIO) has demonstrated remarkable success due to its low-cost and complementary sensors. However, existing VIO methods lack the generalization ability to adjust to different environments and sensor attributes. In this paper, we propose Adaptive VIO, a new monocular visual-inertial odometry that combines online continual learning with

Zhefan Li, Pingyi Fan

As the rapidly developments of artificial intelligence and machine learning, behavior tree design in multiagent system or AI game become more important. The behavior tree design problem is highly related to the source coding in information theory. "Twenty Questions" problem is a typical example for the behavior tree design, usually used to explain the source

Ira Globus-Harris, Varun Gupta, Michael Kearns, Aaron Roth

There is a long history in machine learning of model ensembling, beginning with boosting and bagging and continuing to the present day. Much of this history has focused on combining models for classification and regression, but recently there is interest in more complex settings such as ensembling policies in reinforcement learning. Strong connections have a

SeungWon Seo, SeongRae Noh, Junhyeok Lee, SooBin Lim

We address the challenge of multi-agent cooperation, where agents achieve a common goal by cooperating with decentralized agents under complex partial observations. Existing cooperative agent systems often struggle with efficiently processing continuously accumulating information, managing globally suboptimal planning due to lack of consideration of collabor

Ya-Xin Zhao, Zi-Yi Han, Ya-Ning Ren, Ruo-Han Zhang

Layered van der Waals transition metal dichalcogenides (TMDCs), generally composed of three atomic X-M-X planes in each layer (M = transition metal, X = chalcogen), provide versatile platforms for exploring diverse quantum phenomena. In each MX2 layer, the M-X bonds are predominantly covalent in nature, as a result, the cleavage of TMDC crystals always occur

Hengkang Wang, Xu Zhang, Taihui Li, Yuxiang Wan

Pretrained diffusion models (DMs) have recently been popularly used in solving inverse problems (IPs). The existing methods mostly interleave iterative steps in the reverse diffusion process and iterative steps to bring the iterates closer to satisfying the measurement constraint. However, such interleaving methods struggle to produce final results that look

Loc Hoang Tran

Face recognition is a very important topic in data science and biometric security research areas. It has multiple applications in military, finance, and retail, to name a few. In this paper, the novel hypergraph Laplacian Eigenmaps will be proposed and combine with the k nearest-neighbor method and/or with the kernel ridge regression method to solve the face

Akiyoshi Tomihari, Issei Sato

The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. This holds true for both in-distribution (ID) and out-of-distribution (OOD) data. One key reason for its success is the preservation of pre-trained features, achieved by obtaining a near-optimal linear head during LP. However, despite

Zhou Yang, Jieke Shi, Premkumar Devanbu, David Lo

The availability of vast amounts of publicly accessible data of source code and the advances in modern language models, coupled with increasing computational resources, have led to a remarkable surge in the development of large language models for code (LLM4Code, for short). The interaction between code datasets and models gives rise to a complex ecosystem c

YoungJu Choie, Winfried Kohnen, Yichao Zhang

We generalize the linear relation formula between the square of normalized Hecke eigenforms of weight $k$ and normalized Hecke eigenforms of weight $2k$, to Rankin-Cohen brackets of general degree. As an ingredient of the proof, we also generalize a formula of Zagier on the Petersson inner product of Rankin-Cohen brackets involving Eisenstein series.

Yuki Okoda, Yoko Oya, Nami Sakai, Yoshimasa Watanabe

Deuterium fractionation in the closest vicinity of a protostar is important in understanding its potential heritage to a planetary system. Here, we have detected the spectral line emission of CH3OH and its three deuterated species, CH2DOH, CHD2OH, and CH3OD, toward the low-mass protostellar source B335 at a resolution of 0.''03 (5 au) with Atacama Large Mill

M. A. Khan, Madeeha Atif, Michael N. Leuenberger

A novel material consisting of a monolayer of C$_{60}$ buckyballs with hexagonal symmetry has recently been observed experimentally, named graphullerene. In this study, we present a comprehensive \textit{ab-initio} theoretical analysis of the electronic and optical properties of both pristine and impurity-engineered monolayer graphullerene using spin-depende

Hiroyuki Miyoshi, Hiroki Miyazako, Takaaki Nara

Active nematics are influenced by alignment angle singularities called topological defects. The localization of these defects is of major interest for biological applications. The total distortion of alignment angles due to defects is evaluated using Frank free energy, which is one of the criteria used to determine the location and stability of these defects

S. Liu, J. T. Su, X. Y. Bai, Y. Y. Deng

Utilizing data from the $Solar$ $Magnetism$ and $Activity$ $Telescope$ (SMAT), analytical solutions of polarized radiative transfer equations, and in-orbit test data from the Full-disk Magnetograph (FMG) onboard the Advanced Space based Solar Observatory (ASO-S), this study reveals the magnetic-sensitivity spectral positions for the Fe {\sc i} $\lambda$5234.

Md Mostafijur Rahman, Mustafa Munir, Debesh Jha, Ulas Bagci

The Segment Anything Model (SAM), originally designed for general-purpose segmentation tasks, has been used recently for polyp segmentation. Nonetheless, fine-tuning SAM with data from new imaging centers or clinics poses significant challenges. This is because this necessitates the creation of an expensive and time-intensive annotated dataset, along with th

Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta

Reinforcement learning (RL) in large or infinite state spaces is notoriously challenging, both theoretically (where worst-case sample and computational complexities must scale with state space cardinality) and experimentally (where function approximation and policy gradient techniques often scale poorly and suffer from instability and high variance). One lin