October 2024 arXiv papers — page 38

D. K. He, Y. B. Shi, Z. Song

The Chern number, as a topological invariant, characterizes the topological features of a 2D system and can be experimentally detected through Hall conductivity. In this work, we investigate the connection between the Chern number and the features of two independent chains. It is shown that there exists a class of 2D systems that can be mapped into two indep

Shin'ichi Nojiri, S. D. Odintsov

We construct 2d Jackiw-Teitelboim (JT) gravity in the framework of symmetric teleparallel gravity based on a non-metricity tensor. In symmetric teleparallel gravity, we often use the scalar quantity $Q$ composed of the bilinear terms of the non-metricity tensor but $Q$ is not a unique scalar quantity from the viewpoint of covariance. However, other scalar qu

Xinnong Zhang, Jiayu Lin, Libo Sun, Weihong Qi

The massive population election simulation aims to model the preferences of specific groups in particular election scenarios. It has garnered significant attention for its potential to forecast real-world social trends. Traditional agent-based modeling (ABM) methods are constrained by their ability to incorporate complex individual background information and

Yilun Jin, Zheng Li, Chenwei Zhang, Tianyu Cao

Online shopping is a complex multi-task, few-shot learning problem with a wide and evolving range of entities, relations, and tasks. However, existing models and benchmarks are commonly tailored to specific tasks, falling short of capturing the full complexity of online shopping. Large Language Models (LLMs), with their multi-task and few-shot learning abili

Jakkapat Seeyangnok, Udomsilp Pinsook, Graeme J Ackland

Janus transition metal-dichalcogenide (JTMD) materials have attracted a great deal of attention due to their remarkable physical properties arising from the two-dimensional geometry and the breakdown of the out-of-plane symmetry. Using first-principles density functional theory, we investigated the phase stability, strain-enhanced phase stability, and superc

Keisuke Ozawa, Hiroshi Sugimoto, Daisuke Shima, Tatsuki Hinamoto

A dielectric Mie-resonant nanoantenna is capable of controlling the directionality of the emission from nearby quantum emitters through the excitation of multiple degenerate Mie resonances. A crystalline silicon nanosphere (Si NS) is a promising candidate for a dielectric nanoantenna because crystalline Si has a large refractive index (3.8 at 650 nm) and the

Zhisheng Zhang, Qianyi Yang, Derui Wang, Pengyang Huang

With just a few speech samples, it is possible to perfectly replicate a speaker's voice in recent years, while malicious voice exploitation (e.g., telecom fraud for illegal financial gain) has brought huge hazards in our daily lives. Therefore, it is crucial to protect publicly accessible speech data that contains sensitive information, such as personal voic

Nazife Erkurşun-Özcan, Farrukh Mukhamedov

The first goal of the present paper is to study residualities of the set of uniform $P$-ergodic Markov semigroups defined on abstract state spaces by means of a generalized Dobrushin ergodicity coefficient. In the last part of the paper, we explore uniform mean ergodicities of Markov semigroups.

Jingyun Zhu, Kaixuan Li, Sen Chen, Lingling Fan

To identify security vulnerabilities in Android applications, numerous static application security testing (SAST) tools have been proposed. However, it poses significant challenges to assess their overall performance on diverse vulnerability types. The task is non-trivial and poses considerable challenges. {Firstly, the absence of a unified evaluation platfo

Haein Kong, Yongsu Ahn, Sangyub Lee, Yunho Maeng

LLMs have emerged as a promising tool for assisting individuals in diverse text-generation tasks, including job-related texts. However, LLM-generated answers have been increasingly found to exhibit gender bias. This study evaluates three LLMs (GPT-3.5, GPT-4, Claude) to conduct a multifaceted audit of LLM-generated interview responses across models, question

Yufei Zhao

Expository article on the problem of determining the maximum number of equiangular lines with a fixed angle, and the associated problem of second eigenvalue multiplicity in graphs.

Parashmani Thakuria, Madhurjya Lalung, Jayanta Kumar Sarma

We develop a light-front model of the proton to investigate the twist-3 chiral-even generalized parton distributions (GPDs) of light sea quarks. In this framework, sea quarks are treated as spin-\(\frac{1}{2}\) active partons, while the remaining proton constituents are modeled as spin-1 spectators. The light-front momentum wave function, derived from the so

Priyanka Saha, Dipanjan Dey, Kaushik Bhattacharya

In this article, we propose a new way to handle interactions between two scalar fields in the cosmological backdrop where one scalar field oscillates rapidly in the cosmological time scale while the other does not show any periodic behavior in the same time scale. We have interpreted the rapidly oscillating scalar field as the dark matter candidate while the

Rino Saiga, Kaede Shiga, Yo Maruta, Chie Inomoto

Mouse and human brains have different functions that depend on their neuronal networks. In this study, we analyzed nanometer-scale three-dimensional structures of brain tissues of the mouse medial prefrontal cortex and compared them with structures of the human anterior cingulate cortex. The obtained results indicated that mouse neuronal somata are smaller a

Siyuan Chen, Jiahai Wang

Dynamical complex systems composed of interactive heterogeneous agents are prevalent in the world, including urban traffic systems and social networks. Modeling the interactions among agents is the key to understanding and predicting the dynamics of the complex system, e.g., predicting the trajectories of traffic participants in the city. Compared with inter

Jingyi Li, Laurel Ohm, Saverio E. Spagnolie

Active particles in anisotropic, viscoelastic fluids experience competing stresses which guide their trajectories. An aligned suspension of particles can trigger a hydrodynamic bend instability, but the elasticity of the fluid can drive particle orientations back towards alignment. To study these competing effects, we examine a dilute suspension of active pa

Wei Ai, Yinghui Gao, Jianbin Li, Jiayi Du

Entity alignment is crucial for merging knowledge across knowledge graphs, as it matches entities with identical semantics. The standard method matches these entities based on their embedding similarities using semi-supervised learning. However, diverse data sources lead to non-isomorphic neighborhood structures for aligned entities, complicating alignment,

K. R. Arun, Rahuldev Ghorai

We design and analyse an energy stable, structure preserving and well-balanced scheme for the Ripa system of shallow water equations. The energy stability of the numerical solutions is achieved by introducing appropriate stabilisation terms in the discretisation of the convective fluxes of mass and momenta, the pressure gradient and the topography source ter

Chih-Hsiang Hsu, Jyh-Shing Roger Jang

Current approaches in 3D human pose estimation primarily focus on regressing 3D joint locations, often neglecting critical physical constraints such as bone length consistency and body symmetry. This work introduces a recurrent neural network architecture designed to capture holistic information across entire video sequences, enabling accurate prediction of

Yejing Wang, Dong Xu, Xiangyu Zhao, Zhiren Mao

GPRec explicitly categorizes users into groups in a learnable manner and aligns them with corresponding group embeddings. We design the dual group embedding space to offer a diverse perspective on group preferences by contrasting positive and negative patterns. On the individual level, GPRec identifies personal preferences from ID-like features and refines t

Mikhail A. Mikheenko

Every abelian (and even every nilpotent) group contains a solution of any finite unimodular system of equations over itself. However, this is not true for infinite systems. We deduced a criterion for a periodic abelian group to contain a solution of any infinite unimodular system of equations over itself. Using this criterion, we show that nilpotent groups o

Tong Yang, Jincheng Mei, Hanjun Dai, Zixin Wen

Recent advances in aligning large language models with human preferences have corroborated the growing importance of best-of-N distillation (BOND). However, the iterative BOND algorithm is prohibitively expensive in practice due to the sample and computation inefficiency. This paper addresses the problem by revealing a unified game-theoretic connection betwe

Chalachew Muluken Liyew. Rosa Meo, Stefano Ferraris, Elvira Di Nardo

In this paper, an analysis of hourly air temperatures in four groups of 32 stations of the UK highland (five stations), UK lowland (four stations), Italian highland (eleven stations), and Italian lowland (twelve stations) at various altitudes was conducted over the period from 2002 to 2021. The study aimed to examine the trends of each hour of the day in tha

Luis A. Cedeño-Pérez, Hernando Quevedo

The notion of projection families generalizes the classical notions of vector- and operator-valued measures. We show that projection families are general enough to extend the Spectral Theorem to Banach algebras and operators between Banach spaces. To this end, we first develop a Smooth Functional Calculus in Banach algebras using the Cauchy-Pompeiu formula,

Mufei Li, Siqi Miao, Pan Li

Large Language Models (LLMs) demonstrate strong reasoning abilities but face limitations such as hallucinations and outdated knowledge. Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) addresses these issues by grounding LLM outputs in structured external knowledge from KGs. However, current KG-based RAG frameworks still struggle to optimize t

Heewoong Noh, Namkyeong Lee, Gyoung S. Na, Chanyoung Park

While inorganic retrosynthesis planning is essential in the field of chemical science, the application of machine learning in this area has been notably less explored compared to organic retrosynthesis planning. In this paper, we propose Retrieval-Retro for inorganic retrosynthesis planning, which implicitly extracts the precursor information of reference ma

Chongjian Ge, Chenfeng Xu, Yuanfeng Ji, Chensheng Peng

Recent breakthroughs in text-guided image generation have significantly advanced the field of 3D generation. While generating a single high-quality 3D object is now feasible, generating multiple objects with reasonable interactions within a 3D space, a.k.a. compositional 3D generation, presents substantial challenges. This paper introduces CompGS, a novel ge

Chiyu Ma, Jon Donnelly, Wenjun Liu, Soroush Vosoughi

We present ProtoViT, a method for interpretable image classification combining deep learning and case-based reasoning. This method classifies an image by comparing it to a set of learned prototypes, providing explanations of the form ``this looks like that.'' In our model, a prototype consists of \textit{parts}, which can deform over irregular geometries to

Taku Yamanaka

After $CP$ violation was discovered in the $K_L \to \pi^+\pi^-$ decay, many theories were proposed to explain it, and the Kobayashi-Maskawa model and the Superweak model lasted for many years as strong candidates. High-precision kaon experiments with many new techniques and improvements rejected the Superweak model and supported the Kobayashi-Maskawa model i

Shiping Cao, Zhen-Qing Chen

We obtain a uniform boundary Harnack principle (BHP) on any open sets for a large class of non-local operators on metric measure spaces under a jump measure comparability and tail estimate condition, and an upper bound condition on the distribution function for the exit times from balls. These conditions are satisfied by any non-local operator $\mathcal{L}$

Yuzhe Yang, Yipeng Du, Ahmad Farhan, Claudio Angione

The deployment of large-scale models, such as large language models (LLMs) and sophisticated image generation systems, incurs substantial costs due to their computational demands. To mitigate these costs and address challenges related to scalability and data security, there is a growing shift towards decentralized systems for deploying such models. In these

Haomiao Sun, Mingjie He, Tianheng Lian, Hu Han

Although multimodal large language models (MLLMs) have achieved promising results on a wide range of vision-language tasks, their ability to perceive and understand human faces is rarely explored. In this work, we comprehensively evaluate existing MLLMs on face perception tasks. The quantitative results reveal that existing MLLMs struggle to handle these tas

Satoshi Ikehata, Yuta Asano

In this paper, we present a groundbreaking spectrally multiplexed photometric stereo approach for recovering surface normals of dynamic surfaces without the need for calibrated lighting or sensors, a notable advancement in the field traditionally hindered by stringent prerequisites and spectral ambiguity. By embracing spectral ambiguity as an advantage, our

Promit Ghosal, Sumit Mukherjee

We investigate the probability that a random polynomial with independent, mean-zero and finite variance coefficients has no real zeros. Specifically, we consider a random polynomial of degree $2n$ with coefficients given by an i.i.d. sequence of mean-zero, variance-1 random variables, multiplied by an $\frac{\alpha}{2}$-regularly varying sequence for $\alpha

Wenkai Li, Zhijie Liu, Xiaoqi Li, Sen Nie

The web3 applications have recently been growing, especially on the Ethereum platform, starting to become the target of scammers. The web3 scams, imitating the services provided by legitimate platforms, mimic regular activity to deceive users. The current phishing account detection tools utilize graph learning or sampling algorithms to obtain graph features.

Wenkai Li, Xiaoqi Li, Yingjie Mao, Yuqing Zhang

The detection of vulnerabilities in smart contracts remains a significant challenge. While numerous tools are available for analyzing smart contracts in source code, only about 1.79% of smart contracts on Ethereum are open-source. For existing tools that target bytecodes, most of them only consider the semantic logic context and disregard function interface

Ruifeng Li, Wei Liu, Xiangxin Zhou, Mingqian Li

In the drug discovery process, the low success rate of drug candidate screening often leads to insufficient labeled data, causing the few-shot learning problem in molecular property prediction. Existing methods for few-shot molecular property prediction overlook the sample selection bias, which arises from non-random sample selection in chemical experiments.

Heerin Yang, Sseung-won Hwang, Jungmin So

Although pre-trained language models show good performance on various natural language processing tasks, they often rely on non-causal features and patterns to determine the outcome. For natural language inference tasks, previous results have shown that even a model trained on a large number of data fails to perform well on counterfactually revised data, ind

Mrityunjay Kumar

India produces about nine hundred thousand (900K) engineers annually, and many seek computer science and related technology jobs. Given that the IT workforce in India is still young, new graduates get jobs only when the industry grows. A liberal estimate based on the data from MeitY (Ministry of Electronics and Information Technology) and NASSCOM puts the an

Kaijing Lv, Junmin Wang, Yihuai Zhang, Huan Yu

Uncertainty and delayed reactions in human driving behavior lead to stop-and-go traffic congestion on freeways. The freeway traffic dynamics are governed by the Aw-Rascle-Zhang (ARZ) traffic Partial Differential Equation (PDE) models with unknown relaxation time. Motivated by the adaptive traffic control problem, this paper presents a neural operator (NO) ba

Kien Trung Nguyen, Lan Anh Dong, Hien Thi Dinh, Thi Huyen Trang Bui

Thermoelectricity in telluride materials is often improved by replacing telluride with selenium in its crystal. Most work, however, focuses on bulk crystal and leaves the 2D thin films intact. In this paper, we optimize the fabrication of selenium-doped bismuth telluride (Bi$_2$Te$_{3-\rm{x}}$Se$_{\rm{x}}$) thin films using a 3-source thermal co-evaporation.

Hao-Lei Chen, Wei-jie Fu, Xu-Guang Huang, Guo-Liang Ma

In this work, we examine the impact of QCD phase transitions on the quark spin fluctuations and correlations. We propose the quark-antiquark correlation, which relates to the vector meson spin alignment and the $\Lambda-\bar\Lambda$ correlation, can be used as a novel probe of the critical end point (CEP) in the QCD phase diagram. Using the Nambu-Jona-Lanisi

Majid Behravan, Elham Mohammadrezaei, Mohamed Azab, Denis Gracanin

In disaster scenarios, effective communication is crucial, yet language barriers often hinder timely and accurate information dissemination, exacerbating vulnerabilities and complicating response efforts. This paper presents a novel, multilingual, voice-based social network specifically designed to address these challenges. The proposed system integrates adv

Chengjie Zhang, Xinyang Han

The accuracy of time difference of arrival (TDOA)-based source localization is influenced by sensor location deployment. Many studies focus on optimal sensor placement (OSP) for TDOA-based localization without sensor location noises (OSP-WSLN). In practice, there are sensor location errors due to installation deviations, etc, which implies the necessity of s

Duxing Hao, Wen-Hao Chang, Yu-Chen Chang, Wei-Tung Liu

In semiconducting monolayer transition metal dichalcogenides (ML-TMDs), broken inversion symmetry and strong spin-orbit coupling result in spin-valley lock-in effects so that the valley degeneracy may be lifted by external magnetic fields, potentially leading to real-space structural transformation. Here, we report magnetic field (B)-induced giant electric h

Zhiwei Chen, Yushan Xie, Youjun Lu, Huanyuan Shan

Gravitational waves (GWs) from stellar binary black hole (sBBH) mergers can be strongly gravitational lensed by intervening galaxies/galaxy clusters. Only a few works investigated the cluster-lensed sBBH mergers by adopting oversimplified models, while galaxy-lensed ones were intensively studied. In this paper, we estimate the detection rate of cluuster-lens

Zhaoran Liu, Tadayuki Kodama, Takahiro Morishita, Kianhong Lee

We present a joint analysis of high-resolution CO(2-1) and Paschen-$\alpha$ emission lines to trace gas dynamics and spatially resolved star formation in ASPECS-LP.3mm.06, a $z=1.1$ main sequence galaxy. Utilizing data from the ALMA and JWST NIRCam Wide Field Slitless Spectroscopy (WFSS), we explore both ionized gas and molecular gas within this galaxy. With

Xiaoyi Liu, Ruina Du, Lianghao Tan, Junran Xu

Ensuring safety on construction sites is critical, with helmets playing a key role in reducing injuries. Traditional safety checks are labor-intensive and often insufficient. This study presents a computer vision-based solution using YOLO for real-time helmet detection, leveraging the SHEL5K dataset. Our proposed CIB-SE-YOLOv8 model incorporates SE attention

Jiani Guo, Shanshan Song, Hao Chen, Bingwen Huangfu

Simulators are essential to troubleshoot and optimize Underwater Acoustic Network (UAN) schemes (network protocols and communication technologies) before real field experiments. However, due to programming differences between the above two contents, most existing simulators concentrate on one while weakening the other, leading to non-generic simulations and

Minji Lee, Jeongmin Lee, Dongjun Lee

Narrow passage path planning is a prevalent problem from industrial to household sites, often facing difficulties in finding feasible paths or requiring excessive computational resources. Given that deep penetration into the environment can cause optimization failure, we propose a framework to ensure feasibility throughout the process using a series of subpr

Gal Beeri, Benoit Chamot, Elena Latchem, Shruthi Venkatesh

This exploratory pilot study investigated the potential of combining a domain-specific model, BERN2, with large language models (LLMs) to enhance automated disease phenotyping from research survey data. Motivated by the need for efficient and accurate methods to harmonize the growing volume of survey data with standardized disease ontologies, we employed BER

Chenzi Jin, Yanir A. Rubinstein, Gang Tian

This article initiates the study of discrete Okounkov bodies and higher-dimensional Weierstrass gap phenomena, with applications to asymptotic analysis of stability and global log canonical thresholds.

Taichi Yamashima, Takahiro Kashiwazaki, Takumi Suzuki, Rajveer Nehra

Optical circuit systems, unlike other systems, have the potential to perform quantum information processing (QIP) at higher clock rate than conventional processing. The approach utilizing the electromagnetic field of light allows deterministic QIP by feedforward process, which counteracts the quantum randomness by performing adaptive quantum operation accord

Fuliang Lu, Jinxin Xue

An edge e in a matching covered graph G is removable if G-e is matching covered; a pair {e; f} of edges of G is a removable doubleton if G-e-f is matching covered, but neither G-e nor G-f is. Removable edges and removable doubletons are called removable classes, which was introduced by Lovasz and Plummer in connection with ear decompositions of matching cove

Harris Borman, Anna Leontjeva, Luiz Pizzato, Max Kun Jiang

Large Language Models (LLMs) have demonstrated the ability to adopt a personality and behave in a human-like manner. There is a large body of research that investigates the behavioural impacts of personality in less obvious areas such as investment attitudes or creative decision making. In this study, we investigated whether an LLM persona with a specific Bi

Xiachong Lin, Arian Prabowo, Imran Razzak, Hao Xue

The growing integration of digitized infrastructure with Internet of Things (IoT) networks has transformed the management and optimization of building energy consumption. By leveraging IoT-based monitoring systems, stakeholders such as building managers, energy suppliers, and policymakers can make data-driven decisions to improve energy efficiency. However,

Xinke Xie, Yang Lu, Chong-Yung Chi, Wei Chen

This paper proposes an unsupervised deep-learning (DL) approach by integrating transformer and Kolmogorov-Arnold networks (KAN) termed KANsformer to realize scalable beamforming for mobile communication systems. Specifically, we consider a classic multi-input-single-output energy efficiency maximization problem subject to the total power budget. The proposed

Xiangxin Zhou, Jiaqi Guan, Yijia Zhang, Xingang Peng

Dual-target therapeutic strategies have become a compelling approach and attracted significant attention due to various benefits, such as their potential in overcoming drug resistance in cancer therapy. Considering the tremendous success that deep generative models have achieved in structure-based drug design in recent years, we formulate dual-target drug de

Suyoung Lee, Jaeyoung Chung, Jaeyoo Huh, Kyoung Mu Lee

Omnidirectional (or 360-degree) images are increasingly being used for 3D applications since they allow the rendering of an entire scene with a single image. Existing works based on neural radiance fields demonstrate successful 3D reconstruction quality on egocentric videos, yet they suffer from long training and rendering times. Recently, 3D Gaussian splatt

B. Debski, K. Wysocka, W. Skrobacz

Here we report our study on a low-mass ratio contact binary system NSVS 3198272. We subjected our multi-filter ground-based photometry to the light curve numerical modeling using a modified Wilson-Devinney code. We present three scenarios fitting to the data best: a simple, non-spotted model, a model with a circumpolar spot and a model with a marginal third

Yinghe Zhao, Jiamin Liu, Zhi-Yu Zhang, Thomas G. Bisbas

In this paper we present a statistical study of the [C II] 158 $\mu$m line and the CO(1-0) emission for a sample of $\sim$200 local and high-$z$ (32 sources with $z>1$) galaxies with much different physical conditions. We explore the correlation between the luminosities of [C II] and CO(1-0) lines, and obtain a strong linear relationship, confirming that [C

Eunwon Kim, Chanho Park, Buru Chang

Shared memories between two individuals strengthen their bond and are crucial for facilitating their ongoing conversations. This study aims to make long-term dialogue more engaging by leveraging these shared memories. To this end, we introduce a new long-term dialogue dataset named SHARE, constructed from movie scripts, which are a rich source of shared memo

Patrick Chi-Kit Cheong, Francois Foucart, Harry Ho-Yin Ng, Arthur Offermans

We investigate the influence of inelastic neutrino microphysics in general-relativistic magnetohydrodynamics simulations of a hypermassive neutron star. In particular, we include species/energy groups coupled neutrino-matter interactions, such as inelastic neutrino-electron scattering and electron-positron annihilation kernels, into simulations up to 50 ms.

Yoshihiro Sugimoto

In this paper, we study homological monodromy of a Lagrangian submanifold. We prove that homological Lagrangian monodromy is trivial if Hofer energy of a Hamiltonian isotopy is smaller than the minimum energy of J-holomorphic spheres and discs.

Shiyu Wang, Xiaoli Xu, Yong Zeng

Channel knowledge map (CKM) is a novel technique for achieving environment awareness, and thereby improving the communication and sensing performance for wireless systems. A fundamental problem associated with CKM is how to construct a complete CKM that provides channel knowledge for a large number of locations based solely on sparse data measurements. This

Yukun Zhang, Guanzhong Chen, Zenglin Xu, Jianyong Wang

Cardiovascular diseases (CVDs) are currently the leading cause of death worldwide, highlighting the critical need for early diagnosis and treatment. Machine learning (ML) methods can help diagnose CVDs early, but their performance relies on access to substantial data with high quality. However, the sensitive nature of healthcare data often restricts individu

Armin Mostafavi, Zhiwen Qiu, Tong Bill Xu, Saleh Kalantari

Virtual locomotion technique (VLT) is a fundamental component of virtual reality (VR) systems that translates physical and controller inputs into virtual translational movements and viewpoint transitions. Poorly designed VLTs can result in discomfort, nausea, and reductions in task performance. Understanding the effectiveness of VLTs across various levels of

Srishti Dixit, Swayangsiddha Ghosh, Sanskar Mishra, Nisha Shahi

Spin gapless semiconductors based Heusler alloys are the special class of materials due to their unique band structure, high spin polarization and high Curie temperature. These materials exhibit a distinct electronic structure: a nonzero band gap in one spin channel while the other spin channel remains gapless, making them highly suitable for tunable spintro

Sangmin Bae, Adam Fisch, Hrayr Harutyunyan, Ziwei Ji

Large language models (LLMs) are expensive to deploy. Parameter sharing offers a possible path towards reducing their size and cost, but its effectiveness in modern LLMs remains fairly limited. In this work, we revisit "layer tying" as form of parameter sharing in Transformers, and introduce novel methods for converting existing LLMs into smaller "Recursive

Prabrisha Rakshit, Zijian Guo

Large-scale datasets with count outcome variables are widely present in various applications, and the Poisson regression model is among the most popular models for handling count outcomes. This paper considers the high-dimensional sparse Poisson regression model and proposes bias-corrected estimators for both linear and quadratic transformations of high-dime

Xingchi Li, Guanxun Li, Xianyang Zhang

Watermarking is a technique that involves embedding nearly unnoticeable statistical signals within generated content to help trace its source. This work focuses on a scenario where an untrusted third-party user sends prompts to a trusted language model (LLM) provider, who then generates a text from their LLM with a watermark. This setup makes it possible for

Guangyang Fu, Jiang Zhou

In this paper, we discuss hyponormal block Toeplitz operators $T_{\Phi}$ over the vector-valued weighted Bergman space $A_\alpha^2\left(\mathbb{C}^n\right)$. And two conditions about hyponormal block Toeplitz operators $T_{\Phi}$ on $A_\alpha^2\left(\mathbb{C}^n\right)$ were discussed separately, where $ \Phi(z)=A z^p \bar{z}^q + B z^s \bar{z}^t $, $A,B$ are

Hao Sun, Alexander Kazantsev, Alessandro Principi, Giovanni Vignale

We present a linear response theory for stationary density accumulations in anomalous transport phenomena, such as the orbital Hall effect, where the transported density is odd under time reversal and the underlying charge is not conserved. Our framework applies to both metals and insulators, topologically trivial or nontrivial, and distinguishes between con

Leyao Wang, Rishab Pulugurta, Pranay Vure, Yinuo Zhang

Peptide therapeutics, including macrocycles, peptide inhibitors, and bioactive linear peptides, play a crucial role in therapeutic development due to their unique physicochemical properties. However, predicting these properties remains challenging. While structure-based models primarily focus on local interactions, language models are capable of capturing gl

Sangmim Song, Sarath Kodagoda, Amal Gunatilake, Marc G. Carmichael

Navigation presents a significant challenge for persons with visual impairments (PVI). While traditional aids such as white canes and guide dogs are invaluable, they fall short in delivering detailed spatial information and precise guidance to desired locations. Recent developments in large language models (LLMs) and vision-language models (VLMs) offer new a

H. H. Jen

Photon-mediated dipole-dipole interactions arise from atom-light interactions, which are universal and prevalent in a wide range of open quantum systems. This pairwise and long-range spin-exchange interaction results from multiple light scattering among the atoms. A recent surge of interests and progresses in both experiments and theories promises this core

Md Abdur Rahman, Md Abdul Barek, ABM Kamrul Islam Riad, Md Mostafizur Rahman

Although software developers of mHealth apps are responsible for protecting patient data and adhering to strict privacy and security requirements, many of them lack awareness of HIPAA regulations and struggle to distinguish between HIPAA rules categories. Therefore, providing guidance of HIPAA rules patterns classification is essential for developing secured

Hai-Long Huang, Jun-Qian Jiang, Jibin He, Yu-Tong Wang

The James Webb Space Telescope (JWST) has uncovered an abundant population of compact, extremely red, and X-ray weak objects at $z\gtrsim4$, knows as ``Little Red Dots" (LRDs). These objects exhibit spectral energy distributions that resemble both active galactic nuclei (AGN) and stellar population templates. However, whether dominated by AGN activity or com

Masamichi Miyaji

We study the spectrum of the interior length and the horizon timeshift of a two-sided black hole by constructing non-perturbative length and timeshift operators in Jackiew-Teitelboim gravity. We first construct projection operators onto the fixed length or fixed horizon timeshift subspaces using the replica trick. We calculate the densities of state for the

Takuto Fujieda, Takeshi Katsura, Tomoki Uchimura

We introduce a category of inverse semigroup actions and a category of \'etale groupoids. We show that there are three functors which send inverse semigroups to their spectral actions, inverse semigroup actions to their transformation groupoids, and \'etale groupoids to their groupoid C*-algebras, respectively. The composition of these functors is naturally

Kiwoong Yoo, Owen Oertell, Junhyun Lee, Sanghoon Lee

Navigating the vast chemical space of druggable compounds is a formidable challenge in drug discovery, where generative models are increasingly employed to identify viable candidates. Conditional 3D structure-based drug design (3D-SBDD) models, which take into account complex three-dimensional interactions and molecular geometries, are particularly promising

Saptarshi Chakraborty, Peter L. Bartlett

Despite significant research on the optimization aspects of federated learning, the exploration of generalization error, especially in the realm of heterogeneous federated learning, remains an area that has been insufficiently investigated, primarily limited to developments in the parametric regime. This paper delves into the generalization properties of dee

Jing-Jing Jiang, Kai-Hong Zhuang, Jia-Ding Chen, Jian-Xing Li

High-energy vortex $\gamma$ photons have significant applications in many fields, however, their generation and angular momentum manipulation are still great challenges. Here, we first investigated the generation of vortex $\gamma$ photons with controllable spin and orbital angular momenta via nonlinear Compton scattering of two-color counter-rotating circul

Yintai Ma, Diego Klabjan, Jean Utke

The development of sophisticated models for video-to-video synthesis has been facilitated by recent advances in deep reinforcement learning and generative adversarial networks (GANs). In this paper, we propose RL-V2V-GAN, a new deep neural network approach based on reinforcement learning for unsupervised conditional video-to-video synthesis. While preserving

Xiang Zhang, Yongzhuang Zhou, Chengjie Gao, Kangrui Chang

The output pulse spectra of the figure-8 and figure-9 lasers typically exhibit more pronounced distortion than those from mode-locked lasers based on other saturable absorbers, as well as the spectra of their own intracavity pulses. Here, we demonstrate two figure-9 lasers with repetition rates of 190.6 MHz and 92.4 MHz and introduce a self-designed beam spl

Xiaozhi Liu, Ang Gao, Qinghua Zhang, Yaxian Wang

One-dimensional van der Waals (1D vdW) materials, characterized by atomic chains bonded ionically or covalently in one direction and held together by van der Waals interactions in the perpendicular directions, have recently gained intensive attention due to their exceptional functions. In this work, we report the discovery of 1D ionic-bonded structures in Ni

Kai-Xuan Xu, Xin-bao Liu, Simin Pang, Zhe Zhang

Self-trapped excitons (STEs), renowned for their unique radiative properties, have been harnessed in diverse photonic devices. Yet, a full comprehension and manipulation of STEs remain elusive. In this study, we present novel experimental and theoretical evidence of the hybrid nature and optical tuning of the STEs state in Cs2Ag0.4Na0.6InCl6. The detection o

C. L. Yang, W. Z. Jia

Waveguide quantum electrodynamics (wQED) with giant atoms provides a distinctive opportunity to study one-dimensional (1D) coupled spin systems through its unique decoherence-free interactions. This study presents a theoretical framework for simulating the diagonal Aubry-Andr\'e-Harper (AAH) model in the context of giant-atom wQED. The proposed scheme employ

Yiming Cui, Wei-Nan Zhang, Ting Liu

The attention mechanism plays an important role in the machine reading comprehension (MRC) model. Here, we describe a pipeline for building an MRC model with a pretrained language model and visualizing the effect of each attention zone in different layers, which can indicate the explainability of the model. With the presented protocol and accompanying code,

Huzaifa Pardawala, Siddhant Sukhani, Agam Shah, Veer Kejriwal

Fact-checking is extensively studied in the context of misinformation and disinformation, addressing objective inaccuracies. However, a softer form of misinformation involves responses that are factually correct but lack certain features such as clarity and relevance. This challenge is prevalent in formal Question-Answer (QA) settings such as press conferenc

Yongchang Hao, Yanshuai Cao, Lili Mou

The performance of neural networks improves when more parameters are used. However, the model sizes are constrained by the available on-device memory during training and inference. Although applying techniques like quantization can alleviate the constraint, they suffer from performance degradation. In this work, we introduce NeuZip, a new weight compression

Eric Zhao, Tatjana Chavdarova, Michael Jordan

Variational inequalities (VIs) are a broad class of optimization problems encompassing machine learning problems ranging from standard convex minimization to more complex scenarios like min-max optimization and computing the equilibria of multi-player games. In convex optimization, strong convexity allows for fast statistical learning rates requiring only $\

Sujit S. Datta

The following is an unedited version of two short articles that are forthcoming in Nature Chemical Engineering. Inspired by Purcell's classic lecture "Life at low Reynolds number", I discuss how scaling arguments, dimensional analysis, and fundamental concepts from chemical engineering science can be used to quantitatively describe microbial swimming -- ther

Sanjeev Naguleswaran

The advent of quantum computing has opened new possibilities in data science, offering unique capabilities for addressing complex, data-intensive problems. Traditional machine learning algorithms often face challenges in high-dimensional or limited-quality datasets, which are common in healthcare. Quantum Machine Learning leverages quantum properties, such a

Marco Jiralerspong, Thomas Jiralerspong, Vedant Shah, Dhanya Sridhar

Given two sets of elements (such as cell types and drug compounds), researchers typically only have access to a limited subset of their interactions. The task of causal imputation involves using this subset to predict unobserved interactions. Squires et al. (2022) have proposed two estimators for this task based on the synthetic interventions (SI) estimator:

Mihailo Stojnic

We consider the injectivity property of the ReLU networks layers. Determining the ReLU injectivity capacity (ratio of the number of layer's inputs and outputs) is established as isomorphic to determining the capacity of the so-called $\ell_0$ spherical perceptron. Employing \emph{fully lifted random duality theory} (fl RDT) a powerful program is developed an

Wu-Long Xu, Jin Min Yang, Bin Zhu

Self-interacting dark matter (SIDM) can address the small-scale anomalies and previous researches focused on such a SIDM heavier than GeV, for which the self-scattering cross-section is in the quantum resonance region and has a non-trivial velocity dependence. For a SIDM lighter than GeV, the self-scattering cross-section falls within the Born region. In thi

Yue Yu, Prayag Tiwari

Large Language Models (LLMs), such as ChatGPT, Phi3 and Llama-3, are leading a significant leap in AI, as they can generalize knowledge from their training to new tasks without fine-tuning. However, their application in the financial domain remains relatively limited. The financial field is inherently complex, requiring a deep understanding across various pe

Xueyun Fu, Jing Lin, Qian Wang, Lina Zhang

Fungal keratitis is a severe vision-threatening corneal infection with a prognosis influenced by fungal virulence and the host's immune defense mechanisms. The immune system, through its regulation of the inflammatory response, ensures cells and tissues can effectively activate defense mechanisms in response to infection and injury. However, there is still a

Wilson Wongso, Hao Xue, Flora D. Salim

Traditional Point-of-Interest (POI) recommendation systems often lack transparency, interpretability, and scrutability due to their reliance on dense vector-based user embeddings. Furthermore, the cold-start problem -- where systems have insufficient data for new users -- limits their ability to generate accurate recommendations. Existing methods often addre