October 2023 arXiv papers — page 46

Yan Yang

In 2015, Brown and Erey conjectured that every $2$-connected graph $G$ on $n$ vertices with chromatic number $k\geq 4$ has at most $(x-1)_{k-1}\big((x-1)^{n-k+1}+(-1)^{n-k}\big)$ proper $x$-colorings for all $x\geq k$. Engbers, Erey, Fox, and He proved this conjecture for $x=k$. In this paper, we prove Brown and Erey's conjecture under the condition that eit

Volodymyr Mazorchuk

We construct large families of simple modules for untwisted affine Lie algebras using induction from one-dimensional modules over nilpotent loop subalgebras. We also show that the vector space of the first self-extensions for these module has uncountable dimension and that generic tensor products of these modules are simple.

Arun Kumar Silivery, Ram Mohan Rao Kovvur

This proposed model introduces novel deep learning methodologies. The objective here is to create a reliable intrusion detection mechanism to help identify malicious attacks. Deep learning based solution framework is developed consisting of three approaches. The first approach is Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) with seven optimizer

Xiting Wang, Liming Jiang, Jose Hernandez-Orallo, David Stillwell

Comprehensive and accurate evaluation of general-purpose AI systems such as large language models allows for effective mitigation of their risks and deepened understanding of their capabilities. Current evaluation methodology, mostly based on benchmarks of specific tasks, falls short of adequately assessing these versatile AI systems, as present techniques l

Rui-Bo Jin, Zi-Qi Zeng, Chenglong You, Chenzhi Yuan

Interference, which refers to the phenomenon associated with the superposition of waves, has played a crucial role in the advancement of physics and finds a wide range of applications in physical and engineering measurements. Interferometers are experimental setups designed to observe and manipulate interference. With the development of technology, many quan

Rin Suyama, Satoshi Satoh, Atsuo Maki

A ship steering control is designed for a nonlinear maneuvering model whose rudder manipulation is constrained in both magnitude and rate. In our method, the tracking problem of the target heading angle with input constraints is converted into the tracking problem for a strict-feedback system without any input constraints. To derive this system, hyperbolic t

Shiqi Lou, Qingyue Zhang, Shujie Yang, Yuyang Tian

Anomaly detection on dynamic graphs refers to detecting entities whose behaviors obviously deviate from the norms observed within graphs and their temporal information. This field has drawn increasing attention due to its application in finance, network security, social networks, and more. However, existing methods face two challenges: dynamic structure cons

Tianchun Wang, Dongsheng Luo, Wei Cheng, Haifeng Chen

Graph Neural Networks (GNNs) resurge as a trending research subject owing to their impressive ability to capture representations from graph-structured data. However, the black-box nature of GNNs presents a significant challenge in terms of comprehending and trusting these models, thereby limiting their practical applications in mission-critical scenarios. Al

Seunghwan An, Jong-June Jeon

The assumption of conditional independence among observed variables, primarily used in the Variational Autoencoder (VAE) decoder modeling, has limitations when dealing with high-dimensional datasets or complex correlation structures among observed variables. To address this issue, we introduced the Cramer-Wold distance regularization, which can be computed i

N. Kalzhigitov, V. S. Vasilevsky

Systematic analysis of parameters and properties of the Pauli resonance states are performed for light nuclei $^{6}$Li, $^{7}$Li, $^{8}$Be, $^{9}$Be and $^{10}$B, which are treated as two-cluster systems. The Pauli resonance states are redundant solutions of the resonating group method appearing when one try use more advanced description of the internal stru

Omkar B. Shende, Liam Storan, Ali Mani

Low Stokes number particles at dilute concentrations in turbulent flows can reasonably be approximated as passive scalars. The added presence of a drift velocity due to buoyancy or gravity when considering the transport of such passive scalars can reduce the turbulent dispersion of the scalar via a diminution of the eddy diffusivity. In this work, we propose

Muhammad Ali Jamshed, Aryan Kaushik, Mesut Toka, Wonjae Shin

On the one hand, Reconfigurable Intelligent Surfaces (RISs) emerge as a promising solution to meet the demand for higher data rates, improved coverage, and efficient spectrum utilization. On the other hand, Non-Terrestrial Networks (NTNs) offer unprecedented possibilities for global connectivity. Moreover, the NTN can also support the upsurge in the number o

Ryo Kashima, Taishi Kurahashi, Sohei Iwata, So Morioka

We say that a Kripke model is a GL-model if the accessibility relation $\prec$ is transitive and converse well-founded. We say that a Kripke model is a D-model if it is obtained by attaching infinitely many worlds $t_1, t_2, \ldots$, and $t_\omega$ to a world $t_0$ of a GL-model so that $t_0 \succ t_1 \succ t_2 \succ \cdots \succ t_\omega$. A non-normal moda

Masashi Oshika, Kosuke Yamada, Ryohei Sasano, Koichi Takeda

It has been known to be difficult to generate adequate sports updates from a sequence of vast amounts of diverse live tweets, although the live sports viewing experience with tweets is gaining the popularity. In this paper, we focus on soccer matches and work on building a system to generate live updates for soccer matches from tweets so that users can insta

Zili Huang, Yiwen Shao, Shi-Xiong Zhang, Dong Yu

The speech field is evolving to solve more challenging scenarios, such as multi-channel recordings with multiple simultaneous talkers. Given the many types of microphone setups out there, we present the UniX-Encoder. It's a universal encoder designed for multiple tasks, and worked with any microphone array, in both solo and multi-talker environments. Our res

Tomasz M. Rusin

The Coulomb Green's function (GF) for non-relativistic charged particle in field of attractive Coulomb force is extended to describe the interaction of two non-relativistic electrons through repulsive Coulomb forces. Closed-form expressions for the GF, in the absence of electron spins, are derived as one-dimensional integrals. The results are then generalize

Radu Balan, Efstratios Tsoukanis

Consider a finite-dimensional real vector space equipped with a finite group acting unitarily on it. We address the general problem of constructing Euclidean stable embeddings of the quotient space of orbits. Our approach is based on subsets of sorted coorbits with respect to selected window vectors. We derive conditions under which such embeddings are injec

Manyuan Zhang, Bingqi Ma, Guanglu Song, Yunxiao Wang

During the COVID-19 coronavirus epidemic, almost everyone is wearing masks, which poses a huge challenge for deep learning-based face recognition algorithms. In this paper, we will present our \textbf{championship} solutions in ICCV MFR WebFace260M and InsightFace unconstrained tracks. We will focus on four challenges in large-scale masked face recognition,

Prashansa Panda, Shalabh Bhatnagar

Actor Critic methods have found immense applications on a wide range of Reinforcement Learning tasks especially when the state-action space is large. In this paper, we consider actor critic and natural actor critic algorithms with function approximation for constrained Markov decision processes (C-MDP) involving inequality constraints and carry out a non-asy

Muhammad Alhaddad, Konstantin Mironov, Aleksey Staroverov, Aleksandr Panov

Model predictive control (MPC) may provide local motion planning for mobile robotic platforms. The challenging aspect is the analytic representation of collision cost for the case when both the obstacle map and robot footprint are arbitrary. We propose a Neural Potential Field: a neural network model that returns a differentiable collision cost based on robo

Besnik Fetahu, Zhiyu Chen, Oleg Rokhlenko, Shervin Malmasi

E-commerce product catalogs contain billions of items. Most products have lengthy titles, as sellers pack them with product attributes to improve retrieval, and highlight key product aspects. This results in a gap between such unnatural products titles, and how customers refer to them. It also limits how e-commerce stores can use these seller-provided titles

Osim Kumar Pal, Md Sakib Hossain Shovon, M. F. Mridha, Jungpil Shin

In recent years, the combination of artificial intelligence (AI) and unmanned aerial vehicles (UAVs) has brought about advancements in various areas. This comprehensive analysis explores the changing landscape of AI-powered UAVs and friendly computing in their applications. It covers emerging trends, futuristic visions, and the inherent challenges that come

Xin Qiu, Zeng-Qi Ou, Ying Lv

In this paper, we study the existence of normalized solutions to the following Kirchhoff equation with a perturbation: $$ \left\{ \begin{aligned} &-\left(a+b\int _{\mathbb{R}^{N}}\left | \nabla u \right|^{2} dx\right)\Delta u+\lambda u=|u|^{p-2} u+h(x)\left |u\right |^{q-2}u, \quad \text{ in } \mathbb{R}^{N}, \\ &\int_{\mathbb{R}^{N}}\left|u\right|^{2}dx=c,

Quzhe Huang, Yanxi Zhang, Dongyan Zhao

Document-level Event Argument Extraction (EAE) requires the model to extract arguments of multiple events from a single document. Considering the underlying dependencies between these events, recent efforts leverage the idea of "memory", where the results of already predicted events are cached and can be retrieved to help the prediction of upcoming events. T

Andronikos Paliathanasis

We consider the Brans-Dicke theory in non-metricity gravity, which belongs to the family of symmetric teleparallel scalar-tensor theories. Our focus lies in exploring the implications of the conformal transformation, as we derive the conformal equivalent theory in the Einstein frame, distinct from the minimally coupled scalar field theory. The fundamental pr

Yoshinari Fujinuma, Siddharth Varia, Nishant Sankaran, Srikar Appalaraju

Document image classification is different from plain-text document classification and consists of classifying a document by understanding the content and structure of documents such as forms, emails, and other such documents. We show that the only existing dataset for this task (Lewis et al., 2006) has several limitations and we introduce two newly curated

Bowen Tan, Yun Zhu, Lijuan Liu, Hongyi Wang

The recent progress of AI can be largely attributed to large language models (LLMs). However, their escalating memory requirements introduce challenges for machine learning (ML) researchers and engineers. Addressing this requires developers to partition a large model to distribute it across multiple GPUs or TPUs. This necessitates considerable coding and int

Steven C. Woo, Wendy Elsasser, Mike Hamburg, Eric Linstadt

RowHammer attacks are a growing security and reliability concern for DRAMs and computer systems as they can induce many bit errors that overwhelm error detection and correction capabilities. System-level solutions are needed as process technology and circuit improvements alone are unlikely to provide complete protection against RowHammer attacks in the futur

Gernot Eichmann, Andrés Gómez, Jan Horak, Jan M. Pawlowski

We compute the bound state properties of three-dimensional scalar $\phi^4$ theory in the broken phase. To this end, we extend the recently developed technique of spectral Dyson-Schwinger equations to solve the Bethe-Salpeter equation and determine the bound state spectrum. We employ consistent truncations for the two-, three- and four-point functions of the

Ethan Abraham, Abraham Nitzan

Chiral phonons have been proposed to be involved in various physical phenomena, yet the chirality of molecular normal modes has not been well defined mathematically. Here we examine two approaches for assigning and quantifying the chirality of molecular normal modes in double-helical molecular wires with various levels of twist. First, associating with each

Sally Dong, Gramoz Goranci, Lawrence Li, Sushant Sachdeva

In numerical linear algebra, considerable effort has been devoted to obtaining faster algorithms for linear systems whose underlying matrices exhibit structural properties. A prominent success story is the method of generalized nested dissection~[Lipton-Rose-Tarjan'79] for separable matrices. On the other hand, the majority of recent developments in the desi

Xiaobing Sun, Jiaxi Li, Wei Lu

The underlying mechanism of neural networks in capturing precise knowledge has been the subject of consistent research efforts. In this work, we propose a theoretical approach based on Neural Tangent Kernels (NTKs) to investigate such mechanisms. Specifically, considering the infinite network width, we hypothesize the learning dynamics of target models may i

Se-Ho Kim, Inyong Koo, Inyoung Lee, Byeongjun Park

Denoising diffusion models show remarkable performances in generative tasks, and their potential applications in perception tasks are gaining interest. In this paper, we introduce a novel framework named DiffRef3D which adopts the diffusion process on 3D object detection with point clouds for the first time. Specifically, we formulate the proposal refinement

Peter Cox, Matthew J. Dolan, Maaz Hayat, Andrea Thamm

We provide an exhaustive classification of three-family DFSZ axion models that have no cosmological domain wall problem. This result is obtained by making the Peccei-Quinn symmetry flavour dependent in certain specific ways, thus reinforcing a possible connection between the strong CP problem and the flavour puzzle. Known DFSZ flavour variants such as the to

Wai-Yiu Keung, Hei Victor Cheng, Wing-Kin Ma

Transmitting data using the phases on reconfigurable intelligent surfaces (RIS) is a promising solution for future energy-efficient communication systems. Recent work showed that a virtual phased massive multiuser multiple-input-multiple-out (MIMO) transmitter can be formed using only one active antenna and a large passive RIS. In this paper, we are interest

Xun-Jie Xu, Siyu Zhou, Junyu Zhu

Right-handed neutrinos ($\nu_{R}$) offer an intriguing portal to new physics in hidden sectors where dark matter (DM) may reside. In this work, we delve into the simplest hidden sector involving only a real scalar exclusively coupled to $\nu_{R}$, referred to as the $\nu_{R}$-philic scalar. We investigate the viability of the $\nu_{R}$-philic scalar to serve

Yi Lu, Fei Gao, Bao-Chi Fu, Hui-Chao Song

We construct the equation of state (EoS) of QCD based on the finite chemical potential information from the functional QCD approaches, with the assistance of the lattice QCD EoS. The obtained EoS is consistent with the up-to-date estimations of the QCD phase diagram, including a phase transition temperature at zero chemical potential of $T=155$ MeV, the curv

Venkatesan Guruswami, Xuandi Ren, Sai Sandeep

The Parameterized Inapproximability Hypothesis (PIH) is the analog of the PCP theorem in the world of parameterized complexity. It asserts that no FPT algorithm can distinguish a satisfiable 2CSP instance from one which is only $(1-\varepsilon)$-satisfiable (where the parameter is the number of variables) for some constant $0<\varepsilon<1$. We consider a mi

Xiang Chen, Xiaojun Wan

Advancements in natural language generation (NLG) and large language models (LLMs) have led to proficient text generation in various tasks. However, integrating intricate constraints into neural text generation, due to LLMs' opacity, remains challenging. This study investigates constrained text generation for LLMs, where predefined constraints are applied du

Dan Lei, Disheng Guo, Jun Xin, Xiao-Ming Lu

Attenuation and amplification are the most common processes for optical communications. Amplification can be used to compensate the attenuation of the complex amplitude of an optical field, but is unable to recover the coherence lost, provided that the attenuation channel and the amplification channel are independent. In this work, we show that the quantum c

Ali Bai, Morteza Vahedian, Rashin Ghahreman, Hasan Piri

In today's rapidly evolving workplace, the dynamics of job satisfaction and its determinants have become a focal point of organizational studies. This research offers a comprehensive examination of the nexus between spiritual intelligence and job satisfaction among female employees, with particular emphasis on the moderating role of ethical work environments

Zefan Wang, Zichuan Liu, Yingying Zhang, Aoxiao Zhong

Large language model (LLM) applications in cloud root cause analysis (RCA) have been actively explored recently. However, current methods are still reliant on manual workflow settings and do not unleash LLMs' decision-making and environment interaction capabilities. We present RCAgent, a tool-augmented LLM autonomous agent framework for practical and privacy

Vinh Nguyen

In this note, we consider a kinetic Fokker-Planck-Alignment equation with Rayleigh-type friction and self-propulsion force which is derived from general environmental averaging models. We show the exponential relaxation in time toward equilibrium of the solutions provided certain spectral gap conditions are satisfied. The result is proved by using Desvillett

Alexander H. Liu, Matt Le, Apoorv Vyas, Bowen Shi

Generative models have gained more and more attention in recent years for their remarkable success in tasks that required estimating and sampling data distribution to generate high-fidelity synthetic data. In speech, text-to-speech synthesis and neural vocoder are good examples where generative models have shined. While generative models have been applied to

Rahul Agrawal, Sanjeeb T. Bose, Kevin P. Griffin, Parviz Moin

Thwaites (1949) developed an approximate method for determining the evolution of laminar boundary layers. The approximation follows from an assumption that the growth of a laminar boundary layer in the presence of pressure gradients could be parameterized solely as a function of a flow parameter, $m = \theta^2/\nu \frac{dU_e}{ds}$, thus reducing the von Karm

Zichong Li, Yanbo Xu, Simiao Zuo, Haoming Jiang

Transformer Hawkes process models have shown to be successful in modeling event sequence data. However, most of the existing training methods rely on maximizing the likelihood of event sequences, which involves calculating some intractable integral. Moreover, the existing methods fail to provide uncertainty quantification for model predictions, e.g., confide

Sixiao Zhang, Hongzhi Yin, Hongxu Chen, Cheng Long

The robustness of recommender systems has become a prominent topic within the research community. Numerous adversarial attacks have been proposed, but most of them rely on extensive prior knowledge, such as all the white-box attacks or most of the black-box attacks which assume that certain external knowledge is available. Among these attacks, the model extr

Jingwei Zhao, Gus Xia, Ziyu Wang, Ye Wang

In the realm of music AI, arranging rich and structured multi-track accompaniments from a simple lead sheet presents significant challenges. Such challenges include maintaining track cohesion, ensuring long-term coherence, and optimizing computational efficiency. In this paper, we introduce a novel system that leverages prior modelling over disentangled styl

Amir Farakhor, Di Wu, Yebin Wang, Huazhen Fang

Large-scale battery energy storage systems (BESS) are helping transition the world towards sustainability with their broad use, among others, in electrified transportation, power grid, and renewables. However, optimal power management for them is often computationally formidable. To overcome this challenge, we develop a scalable approach in the paper. The pr

Divyansh Srivastava, Tuomas Oikarinen, Tsui-Wei Weng

The inability of DNNs to explain their black-box behavior has led to a recent surge of explainability methods. However, there are growing concerns that these explainability methods are not robust and trustworthy. In this work, we perform the first robustness analysis of Neuron Explanation Methods under a unified pipeline and show that these explanations can

Nicholas X. Armendarez, Ahmed S. Mohamed, Anurag Dhungel, Md Razuan Hossain

Recent advancements in reservoir computing research have created a demand for analog devices with dynamics that can facilitate the physical implementation of reservoirs, promising faster information processing while consuming less energy and occupying a smaller area footprint. Studies have demonstrated that dynamic memristors, with nonlinear and short-term m

Indranil Biswas, Sorin Dumitrescu, Lynn Heller, Sebastian Heller

First we survey and explain the strategy of some recent results that construct holomorphic $\text{sl}(2, \mathbb C)$-differential systems over some Riemann surfaces $\Sigma_g$ of genus $g\geq 2$, satisfying the condition that the image of the associated monodromy homomorphism is (real) Fuchsian \cite{BDHH} or some cocompact Kleinian subgroup $$\Gamma \subset

Aviya Maimon, Reut Tsarfaty

Coherence is a linguistic term that refers to the relations between small textual units (sentences, propositions), which make the text logically consistent and meaningful to the reader. With the advances of generative foundational models in NLP, there is a pressing need to automatically assess the human-perceived coherence of automatically generated texts. U

Seong Hyun Park, Sung-Chul Yoon, Sergei Blinnikov

Previous numerical simulations of double-peaked SNe IIb light curves have demonstrated that the radius and mass of the hydrogen-rich envelope of the progenitor star can significantly influence the brightness and timescale of the early-time light curve around the first peak. In this study, we investigate how Thomson scattering and chemical mixing in the SN ej

Henri Gode, Simon Doclo

This paper addresses the challenge of estimating the relative transfer function (RTF) vectors of multiple speakers in a noisy and reverberant environment. More specifically, we consider a scenario where two speakers activate successively. In this scenario, the RTF vector of the first speaker can be estimated in a straightforward way and the main challenge li

Peihan Huo, Oscar Peralta, Junyu Guo, Qiaomin Xie

The Mean-Field approximation is a tractable approach for studying large population dynamics. However, its assumption on homogeneity and universal connections among all agents limits its applicability in many real-world scenarios. Multi-Population Mean-Field Game (MP-MFG) models have been introduced in the literature to address these limitations. When the und

Morimitsu Tanimoto, Kei Yamamoto

We study the interplay of New Physics (NP) among the lepton magnetic moment, the lepton flavor violation (LFV) and the electron electric dipole moment (EDM) in light of recent data of the muon $(g-2)_\mu$. The NP is discussed in the leptonic dipole operator with the $U(2)$ flavor symmetry of the charged leptons, where possible CP violating phases of the thre

Saeid Bayat, Nastaran Shahmansouri, Satya RT Peddada, Alex Tessier

As mechanical systems become more complex and technological advances accelerate, the traditional reliance on heritage designs for engineering endeavors is being diminished in its effectiveness. Considering the dynamic nature of the design industry where new challenges are continually emerging, alternative sources of knowledge need to be sought to guide futur

Wenjie Li, Qifan Song, Jean Honorio

In this work, we study the personalized federated $\mathcal{X}$-armed bandit problem, where the heterogeneous local objectives of the clients are optimized simultaneously in the federated learning paradigm. We propose the \texttt{PF-PNE} algorithm with a unique double elimination strategy, which safely eliminates the non-optimal regions while encouraging fed

Jan Christian Blaise Cruz

In this paper, we describe the constrained MT systems submitted by Samsung R&D Institute Philippines to the WMT 2023 General Translation Task for two directions: en$\rightarrow$he and he$\rightarrow$en. Our systems comprise of Transformer-based sequence-to-sequence models that are trained with a mix of best practices: comprehensive data preprocessing pipelin

John Hoang

The astronomy community has witnessed an explosive growth in the use of deep-learning techniques based on neural networks since the mid-2010s. The widespread adoption of these nature-inspired technologies has helped astronomers tackle previously insurmountable problems and provided an unprecedented opportunity for new discoveries. However, one of the primary

Ziyi Wang, Yujie Chen, Qifan Song, Ruqi Zhang

Low-precision training has emerged as a promising low-cost technique to enhance the training efficiency of deep neural networks without sacrificing much accuracy. Its Bayesian counterpart can further provide uncertainty quantification and improved generalization accuracy. This paper investigates low-precision sampling via Stochastic Gradient Hamiltonian Mont

Yukun Zhao, Lingyong Yan, Weiwei Sun, Chong Meng

Dialogue assessment plays a critical role in the development of open-domain dialogue systems. Existing work are uncapable of providing an end-to-end and human-epistemic assessment dataset, while they only provide sub-metrics like coherence or the dialogues are conversed between annotators far from real user settings. In this paper, we release a large-scale d

Huiwon Jang, Jihoon Tack, Daewon Choi, Jongheon Jeong

Despite its practical importance across a wide range of modalities, recent advances in self-supervised learning (SSL) have been primarily focused on a few well-curated domains, e.g., vision and language, often relying on their domain-specific knowledge. For example, Masked Auto-Encoder (MAE) has become one of the popular architectures in these domains, but l

Ahmed H. Dorrah, Alfonso Palmieri, Lisa Li, Federico Capasso

Vortices are ubiquitous in nature and can be observed in fluids, condensed matter, and even in the formation of galaxies. Light, too, can evolve like a vortex. Optical vortices are exploited in light-matter interaction, free-space communications, and imaging. Here, we introduce optical rotatum; a new degree-of-freedom of light in which an optical vortex expe

Weiqiu You, Helen Qu, Marco Gatti, Bhuvnesh Jain

Self-attributing neural networks (SANNs) present a potential path towards interpretable models for high-dimensional problems, but often face significant trade-offs in performance. In this work, we formally prove a lower bound on errors of per-feature SANNs, whereas group-based SANNs can achieve zero error and thus high performance. Motivated by these insight

Ahsan Ali, Chuanliang Wang, Jinyang Cai, Khadga Jung Karki

Wide bandgap semiconductors are widely used in photonic technologies due to their advantageous features, such as large optical bandgap, low losses, and fast operational speeds. Silicon carbide is a prototypical wide bandgap semiconductor with high optical nonlinearities, large electron transport, and a high breakdown threshold. Integration of silicon carbide

Debanjan Mondal, Abhilasha Lodha, Ankita Sahoo, Beena Kumari

This paper delves into the intricacies of code summarization using advanced transformer-based language models. Through empirical studies, we evaluate the efficacy of code summarization by altering function and variable names to explore whether models truly understand code semantics or merely rely on textual cues. We have also introduced adversaries like dead

Gautham Adamane Pallathadka, Vedant Chandra, Nadia L. Zakamska, Hsiang-Chih Hwang

We report the discovery of SDSS~J022932.28+713002.7, a nascent extremely low-mass (ELM) white dwarf (WD) orbiting a massive ($> 1\,M_\odot$ at 2$\sigma$ confidence) companion with a period of 36 hours. We use a combination of spectroscopy, including data from the ongoing SDSS-V survey, and photometry to measure the stellar parameters for the primary pre-ELM

Juan Atalaya, Alex Opremcak, Ani Nersisyan, Kenny Lee

Off-resonant interaction of fluctuating photons in a resonator with a qubit increases the qubit dephasing rate. We use this effect to measure a small average number of intracavity photons that are coherently or thermally driven. For spectral resolution, we do this by subjecting the qubit to a Carr-Purcell-Meiboom-Gill (CPMG) sequence and record the qubit dep

Lixuan Zheng, Kaifa Luo, Zeliang Sun, Dan Zhao

Revealing the role of Coulomb interaction in topological semimetals with Dirac/Weyl-like band dispersion shapes a new frontier in condensed matter physics. Topological node-line semimetals (TNLSMs), anticipated as a fertile ground for exploring electronic correlation effects due to the anisotropy associated with their node-line structure, have recently attra

Zichong Li, Qunzhi Xu, Zhenghao Xu, Yajun Mei

Spatio-temporal point processes (STPPs) are potent mathematical tools for modeling and predicting events with both temporal and spatial features. Despite their versatility, most existing methods for learning STPPs either assume a restricted form of the spatio-temporal distribution, or suffer from inaccurate approximations of the intractable integral in the l

Shafaq Gulzar Elahi, Soumya Samrat Mandal, Soumitra SenGupta

Braneworld models remain the most promising candidates to address several important questions in low-energy particle phenomenology and cosmology. The role of the moduli field(s) and its stabilization is an integral part of this question. In this work, we show that a 5-dimensional warped braneworld model with higher curvature gravity in bulk admits de-Sitter

Mark Hagmann

Earlier, in the CINT program at Los Alamos National Laboratory, we focused ultrafast mode-locked lasers on the tip-sample junction of a scanning tunneling microscope to generate currents at hundreds of harmonics of the laser pulse repetition frequency. Each harmonic has a signal-to-noise ratio of 20 dB with a 10-dB linewidth of only 3 Hz. Now we model closed

Peter Devlin, Jian-Wei Qiu, Felix Ringer, Nobuo Sato

Generative AI is a fast-growing area of research offering various avenues for exploration in high-energy nuclear physics. In this work, we explore the use of generative models for simulating electron-proton collisions relevant to experiments like CEBAF and the future Electron-Ion Collider (EIC). These experiments play a critical role in advancing our underst

Hao-Tian Lan, Zhen-Yin Zhao, Yu-Jia Wei, F. Y. Wang

Fast radio bursts (FRBs) are transient radio signals with millisecond-duration, large dispersion measure (DM) and extremely high brightness temperature. Among them, FRB 20180916B has been found to have a 16-day periodically modulated activity. However, the physical origin of the periodicity is still a mystery. Here, we utilize the comprehensive observational

Ren-Tong Guo, Mamutjan Ababekri, Qian Zhao, Yousef I. Salamin

Vortex $\gamma$ photons, which carry large intrinsic orbital angular momenta (OAM), have significant applications in nuclear, atomic, hadron, particle and astro-physics, but their production remains unclear. In this work, we investigate the generation of such photons from nonlinear Compton scattering of circularly polarized monochromatic lasers on vortex ele

Yilin Wang, Zeyuan Chen, Liangjun Zhong, Zheng Ding

In this paper, we introduce a novel generative model, Diffusion Layout Transformers without Autoencoder (Dolfin), which significantly improves the modeling capability with reduced complexity compared to existing methods. Dolfin employs a Transformer-based diffusion process to model layout generation. In addition to an efficient bi-directional (non-causal joi

Krishna Chunduri, Mithun Mahesh

Data-driven approaches play a crucial role in space computing, and our paper focuses on analyzing data to learn more about celestial objects. Photometric redshift, a measure of the shift of light towards the red part of the spectrum, helps determine the distance of celestial objects. This study used a dataset from the Sloan Digital Sky Survey (SDSS) with fiv

Danilo Quartullo, Simon Albright, Heiko Damerau, Giulia Papotti

Precisely determining the gap voltage and phase in an RF cavity is essential for the calibration of the LLRF feedbacks. Following the conventional approach, measured RF power is converted into gap voltage, assuming a given shunt impedance. However, power and impedance evaluations can both have large uncertainties. Alternatively, the voltage can be obtained p

Ayesha Qamar, Chetan Verma, Ahmed El-Kishky, Sumit Binnani

Understanding and representing webpages is crucial to online social networks where users may share and engage with URLs. Common language model (LM) encoders such as BERT can be used to understand and represent the textual content of webpages. However, these representations may not model thematic information of web domains and URLs or accurately capture their

Dadong Jiang, Zhihui Ke, Xiaobo Zhou, Xidong Shi

This paper targets interactive object-level editing (e.g., deletion, recoloring, transformation, composition) in dynamic scenes. Recently, some methods aiming for flexible editing static scenes represented by neural radiance field (NeRF) have shown impressive synthesis quality, while similar capabilities in time-variant dynamic scenes remain limited. To solv

Xiucheng Wang, Nan Cheng, Longfei Ma, Zhisheng Yin

Deep Reinforcement Learning (DRL) is widely used to optimize the performance of multi-UAV networks. However, the training of DRL relies on the frequent interactions between the UAVs and the environment, which consumes lots of energy due to the flying and communication of UAVs in practical experiments. Inspired by the growing digital twin (DT) technology, whi

Chao-Hong Tan, Jia-Chen Gu, Zhen-Hua Ling

Large Language Models (LLMs) have emerged as influential instruments within the realm of natural language processing; nevertheless, their capacity to handle multi-party conversations (MPCs) -- a scenario marked by the presence of multiple interlocutors involved in intricate information exchanges -- remains uncharted. In this paper, we delve into the potentia

Xiaohui Zhong, Lei Chen, Jun Liu, Chensen Lin

Significant advancements in the development of machine learning (ML) models for weather forecasting have produced remarkable results. State-of-the-art ML-based weather forecast models, such as FuXi, have demonstrated superior statistical forecast performance in comparison to the high-resolution forecasts (HRES) of the European Centre for Medium-Range Weather

Suyash Mahar, Mingyao Shen, Terence Kelly, Steven Swanson

Crash consistency using persistent memory programming libraries requires programmers to use complex transactions and manual annotations. In contrast, the failure-atomic msync() (FAMS) interface is much simpler as it transparently tracks updates and guarantees that modified data is atomically durable on a call to the failure-atomic variant of msync(). However

Yao He, Ivan Cisneros, Nikhil Keetha, Jay Patrikar

Robust and accurate localization for Unmanned Aerial Vehicles (UAVs) is an essential capability to achieve autonomous, long-range flights. Current methods either rely heavily on GNSS, face limitations in visual-based localization due to appearance variances and stylistic dissimilarities between camera and reference imagery, or operate under the assumption of

Wenxuan Zhao, Liang Yuan, Baicheng Yan, Penghao Ma

Current architectures are now equipped with matrix computation units designed to enhance AI and high-performance computing applications. Within these architectures, two fundamental instruction types are matrix multiplication and vector outer product, with the latter being lighter due to its vector inputs. This characteristic not only allows for the developme

Rong-Xin Miao

This paper investigates the brane-localized interactions, including DGP gravity and higher derivative (HD) gravity localized on the brane. We derive the effective action on the brane, which suggests the brane-localized HD gravity suffers the ghost problem generally. Besides, we obtain novel algebraic identities of the mass spectrum, which reveal the global n

Whee Yeon Cheong, Sang-Sung Lee, Sang-Hyun Kim, Sincheol Kang

We present the multiwavelength flaring activity of the blazar AO 0235+164 during its recent active period from 2013 to 2019. From a discrete correlation function (DCF) analysis, we find a significant (>95%) correlation between radio and $\gamma$-ray light curves with flares at longer wavelengths following flares at shorter wavelengths. We identify a new jet

Zhimin Li, Shusen Liu, Kailkhura Bhavya, Timo Bremer

Neural network have achieved remarkable successes in many scientific fields. However, the interpretability of the neural network model is still a major bottlenecks to deploy such technique into our daily life. The challenge can dive into the non-linear behavior of the neural network, which rises a critical question that how a model use input feature to make

Yan Wang, Shan Ba

Recommender systems have become an integral part of online platforms, providing personalized recommendations for purchases, content consumption, and interpersonal connections. These systems consist of two sides: the producer side comprises product sellers, content creators, or service providers, etc., and the consumer side includes buyers, viewers, or custom

Mohammad S. Majdi, Jeffrey J. Rodriguez

Crowdsourcing systems have been used to accumulate massive amounts of labeled data for applications such as computer vision and natural language processing. However, because crowdsourced labeling is inherently dynamic and uncertain, developing a technique that can work in most situations is extremely challenging. In this paper, we introduce Crowd-Certain, a

Zhan Bai, Yu-Xin Liu

We take the Dyson-Schwinger equation (DSE) approach of QCD to study the phase transition and the equation of state of cold dense matter. Besides the bare vertex and Gauss gluon model, we take into account an improved truncation scheme, the CLRQ vertex and infrared-constant gluon model. For the dynamical chiral symmetry breaking solution of the DSE, we requir

Sahbani Rachid

Let $T=(V,A)$ be a tournament. For $X\subseteq V$, the subtournament of $T$ induced by $X$ is denoted by $T[X]$. A subset $I$ of $V$ is an interval of $T$ provided that for every $a,b\in I$ and $x\in V\setminus I$, $(a,x)\in A$ if and only if $(b,x)\in A$. For example, $\varnothing $, ${x}$ ($x \in V$) and $V$ are intervals of $T$, called trivial intervals.

Waverly Wei, Xinwei Ma, Jingshen Wang

Randomized experiments have been the gold standard for assessing the effectiveness of a treatment or policy. The classical complete randomization approach assigns treatments based on a prespecified probability and may lead to inefficient use of data. Adaptive experiments improve upon complete randomization by sequentially learning and updating treatment assi

Fan Xing, Xiaoyi Zhou, Xuefeng Fan, Zhuo Tian

Collected and annotated datasets, which are obtained through extensive efforts, are effective for training Deep Neural Network (DNN) models. However, these datasets are susceptible to be misused by unauthorized users, resulting in infringement of Intellectual Property (IP) rights owned by the dataset creators. Reversible Adversarial Exsamples (RAE) can help

Shahnewaz Ahmed, Caroline Lima, Eduardo Martín-Martínez

We show that it is possible to still use semiclassical gravity together with quantum field theory beyond the regimes where the field state is coherent. In particular, we identify families of cat states (superposition of almost-distinguishable coherent states that have very non-classical features) for which the gravitational backreaction can be modeled by sem

Soroush Mehraban, Vida Adeli, Babak Taati

Recent transformer-based approaches have demonstrated excellent performance in 3D human pose estimation. However, they have a holistic view and by encoding global relationships between all the joints, they do not capture the local dependencies precisely. In this paper, we present a novel Attention-GCNFormer (AGFormer) block that divides the number of channel

Tejas S. Prabhune, Peter Wu, Bohan Yu, Gopala K. Anumanchipalli

Streaming speech-to-avatar synthesis creates real-time animations for a virtual character from audio data. Accurate avatar representations of speech are important for the visualization of sound in linguistics, phonetics, and phonology, visual feedback to assist second language acquisition, and virtual embodiment for paralyzed patients. Previous works have hi