March 2024 arXiv papers — page 174

Zexuan Qiu, Jingjing Li, Shijue Huang, Xiaoqi Jiao

Developing Large Language Models (LLMs) with robust long-context capabilities has been the recent research focus, resulting in the emergence of long-context LLMs proficient in Chinese. However, the evaluation of these models remains underdeveloped due to a lack of benchmarks. To address this gap, we present CLongEval, a comprehensive Chinese benchmark for ev

Indrajit Jana, Sunita Rani

We analyze the asymptotic fluctuations of linear eigenvalue statistics of random centrosymmetric matrices with i.i.d. entries. We prove that for a complex analytic test function, the centered and normalized linear eigenvalue statistics of random centrosymmetric matrices converge to a normal distribution. We find the exact expression of the variance of the li

Lu Wen, Zhenghao Feng, Yun Hou, Peng Wang

Semi-supervised learning is a sound measure to relieve the strict demand of abundant annotated datasets, especially for challenging multi-organ segmentation . However, most existing SSL methods predict pixels in a single image independently, ignoring the relations among images and categories. In this paper, we propose a two-stage Dual Contrastive Learning Ne

Marta Wolinska, Aron Walsh, Antoine Cully

The identification of materials with exceptional properties is an essential objective to enable technological progress. We propose the application of \textit{Quality-Diversity} algorithms to the field of crystal structure prediction. The objective of these algorithms is to identify a diverse set of high-performing solutions, which has been successful in a ra

Stefan Schoder, Paul Maurerlehner

The development of acoustic simulation workflows in the time-domain description is essential for predicting the sound of aeroacoustic or other transient acoustic effects. A common practice for noise mitigation is using absorbers. The modeling of these acoustic absorbers is typically provided in the frequency domain. Several, methods established bridging this

A. Delattre, I. Golokolenov, R. Pedurand, X. Zhou

Cavity optomechanics implements a unique platform where moving objects can be probed by quantum fields, either laser light or microwave signals. With a pump tone driving at a frequency above the cavity resonance, self-sustained oscillations can be triggered at large injected powers. These limit cycle dynamics are particularly rich, presenting hysteretic beha

Håkon Hanisch Kjærnli, Lluis Mas-Ribas, Hans Jakob Håland, Vegard Sjåvik

When deploying time series forecasting models based on machine learning to real world settings, one often encounter situations where the data distribution drifts. Such drifts expose the forecasting models to out-of-distribution (OOD) data, and machine learning models lack robustness in these settings. Robustness can be improved by using deep generative model

Jiawei Zhao, Zhenyu Zhang, Beidi Chen, Zhangyang Wang

Training Large Language Models (LLMs) presents significant memory challenges, predominantly due to the growing size of weights and optimizer states. Common memory-reduction approaches, such as low-rank adaptation (LoRA), add a trainable low-rank matrix to the frozen pre-trained weight in each layer, reducing trainable parameters and optimizer states. However

Zijie Zeng, Shiqi Liu, Lele Sha, Zhuang Li

This study explores the challenge of sentence-level AI-generated text detection within human-AI collaborative hybrid texts. Existing studies of AI-generated text detection for hybrid texts often rely on synthetic datasets. These typically involve hybrid texts with a limited number of boundaries. We contend that studies of detecting AI-generated content withi

Hassen Nigatu, Li Jihao, Gaokun Shi, Guodong Lu

This study presents a systematic enumeration of spherical ($SO(3)$) type parallel robots' variants using an analytical velocity-level approach. These robots are known for their ability to perform arbitrary rotations around a fixed point, making them suitable for numerous applications. Despite their architectural diversity, existing research has predominantly

Marcell Dietl, Andre Gemünd, Daniel Oeltz, Felix M. Thiele

In this report, we introduce a novel approach to visualize extremely large graphs efficiently. Our method combines two force-directed algorithms, Kamada-Kawai and ForceAtlas2, to handle different graph components based on their node count. Additionally, we suggest utilizing the Fast Multipole method to enhance the speed of ForceAtlas2. Although initially des

Yang-yang Tan, Yong-rui Chen, Wei-jie Fu, Wei-Jia Li

Real-time dynamics of strongly correlated systems, in particular its critical dynamics near phase transitions, have been always on the cutting edge of studies in diverse fields of physics, e.g., high energy physics, condensed matter, holography, etc. In this work, we investigate the critical damping of collective modes associated with spontaneous breaking of

Dario Rocca, Cristian L. Cortes, Jerome Gonthier, Pauline J. Ollitrault

Quantum phase estimation based on qubitization is the state-of-the-art fault-tolerant quantum algorithm for computing ground-state energies in chemical applications. In this context, the 1-norm of the Hamiltonian plays a fundamental role in determining the total number of required iterations and also the overall computational cost. In this work, we introduce

Prafullkumar Tale

Consider the Telephone Broadcast problem in which an input is a connected graph $G$ on $n$ vertices, a source vertex $s \in V(G)$, and a positive integer $t$. The objective is to decide whether there is a broadcast protocol from $s$ that ensures that all the vertices of $G$ get the message in at most $t$ rounds. We consider the broadcast protocol where, in a

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Based on $(2712.4\pm14.1)\times10^{6}$ $\psi(3686)$ events collected with the BESIII detector, we study the decays $h_{c}\to3(\pi^{+}\pi^{-})\pi^{0}$, $h_{c}\to2(\pi^{+}\pi^{-})\omega$, $h_{c}\to2(\pi^{+}\pi^{-})\pi^{0}\eta$, $h_{c}\to2(\pi^{+}\pi^{-})\eta$, and $h_{c}\to p\bar{p}$ via $\psi(3686)\to\pi^{0}h_{c}$. The decay channel $h_{c}\to3(\pi^{+}\pi^{-})

Myeongseok Ryu, Kyunghwan Choi

This letter proposes a convolutional neural network (CNN)-based adaptive controller wtih three notable features: 1) it determines control input directly from historical sensor data (in an end-to-end process); 2) it learns the desired control policy during real-time implementation without using a pretrained network (in an online adaptive manner); and 3) the a

Shuji Yamamoto

Recently, Maesaka, Seki and Watanabe discovered a surprising equality between multiple harmonic sums and certain Riemann sums which approximate the iterated integral expression of the multiple zeta values. In this paper, we describe the formula corresponding to the multiple zeta-star values and, more generally, to the Schur multiple zeta values of diagonally

Feipeng Zhang, Te Wu, Long Wang

Direct reciprocity based on the repeated prisoner's dilemma has been intensively studied. Most theoretical investigations have concentrated on memory-$1$ strategies, a class of elementary strategies just reacting to the previous-round outcomes. Though the properties of "All-or-None" strategies ($AoN_K$) have been discovered, simulations just confirmed the go

Xiangci Li, Linfeng Song, Lifeng Jin, Haitao Mi

Knowledge-based, open-domain dialogue generation aims to build chit-chat systems that talk to humans using mined support knowledge. Many types and sources of knowledge have previously been shown to be useful as support knowledge. Even in the era of large language models, response generation grounded in knowledge retrieved from additional up-to-date sources r

Kicheon Kang

Virtual photons play an essential role in the locally realistic description of the Aharonov-Bohm interference. We show that the effect of virtual photons in the interferometer is manifested by a change in their spectrum. In particular, when a vacuum is confined between two ideal conducting plates, the photons obey the two-dimensional Proca equation, the wave

Marco Donadoni, Matthew Feickert, Lukas Heinrich, Yang Liu

In this paper we describe the development of a streamlined framework for large-scale ATLAS pMSSM reinterpretations of LHC Run-2 analyses using containerised computational workflows. The project is looking to assess the global coverage of BSM physics and requires running O(5k) computational workflows representing pMSSM model points. Following ATLAS Analysis P

Liang Peng, Junyuan Gao, Xinran Liu, Weihong Li

In this paper, we introduce a novel benchmark, dubbed VastTrack, towards facilitating the development of more general visual tracking via encompassing abundant classes and videos. VastTrack possesses several attractive properties: (1) Vast Object Category. In particular, it covers target objects from 2,115 classes, largely surpassing object categories of exi

Yongqin Fu, Xianbin Wang

The proliferation of wireless-enabled applications with divergent quality of service (QoS) requirements necessitates tailored QoS provisioning. With the growing complexity of wireless infrastructures, application-specific QoS perceived by a user equipment (UE) is jointly determined by its association with the supporting base station in heterogeneous networks

Bran Purvis, Randy Lafler, R. Nicholas Lanning

Interferometric telescopes are instrumental for the imaging of distant astronomical bodies, but optical loss heavily restricts how far telescopes in an array can be placed from one another, leading to a bottleneck in the resolution that can be achieved. An entanglement-assisted approach to this problem has been proposed by Gottesman, Jennewein, and Croke (GJ

Divij Sharma, Biwei Dai, Uros Seljak

Weak Lensing (WL) surveys are reaching unprecedented depths, enabling the investigation of very small angular scales. At these scales, nonlinear gravitational effects lead to higher-order correlations making the matter distribution highly non-Gaussian. Extracting this information using traditional statistics has proven difficult, and Machine Learning based s

Nicholas Kunz, H. Oliver Gao

Urban transit bus idling is a contributor to ecological stress, economic inefficiency, and medically hazardous health outcomes due to emissions. The global accumulation of this frequent pattern of undesirable driving behavior is enormous. In order to measure its scale, we propose GRD-TRT-BUF-4I (Ground Truth Buffer for Idling) an extensible, realtime detecti

Yu Guo, Axel Davy, Gabriele Facciolo, Jean-Michel Morel

With the widespread application of convolutional neural networks (CNNs), the traditional model based denoising algorithms are now outperformed. However, CNNs face two problems. First, they are computationally demanding, which makes their deployment especially difficult for mobile terminals. Second, experimental evidence shows that CNNs often over-smooth regu

Tousif Islam

We study the phenomenology of non-spinning eccentric binary black hole (BBH) mergers using numerical relativity (NR) waveforms and \texttt{EccentricIMR} waveform model, as presented in Ref. \cite{Hinder:2017sxy} (Hinder, Kidder, and Pfeiffer, arXiv:1709.02007). This model is formulated by combining an eccentric inspiral, derived from a post-Newtonian (PN) ap

Hongming Fei, Owen Millwood, Gope Prosanta, Jack Miskelly

Physical Unclonable Functions (PUFs) have been shown to be a highly promising solution for enabling high security systems tailored for low-power devices. Commonly, PUFs are utilised to generate cryptographic keys on-the-fly, replacing the need to store keys in vulnerable, non-volatile memories. Due to the physical nature of PUFs, environmental variations cau

Takahiro Shirakawa, Seiichi Uchida

Layout-aware text-to-image generation is a task to generate multi-object images that reflect layout conditions in addition to text conditions. The current layout-aware text-to-image diffusion models still have several issues, including mismatches between the text and layout conditions and quality degradation of generated images. This paper proposes a novel l

Noriaki Kitazawa

The values of Hubble constant H0 by direct measurements with standard distance ladder are typically larger than those obtained from the observation of cosmic microwave background and the galaxy survey with inverse distance ladder. On the other hand, although the errors are still large, various determinations of the value of matter density parameter Omega_m a

Lirong Wu, Haitao Lin, Zhangyang Gao, Guojiang Zhao

Recent years have witnessed great success in handling graph-related tasks with Graph Neural Networks (GNNs). Despite their great academic success, Multi-Layer Perceptrons (MLPs) remain the primary workhorse for practical industrial applications. One reason for such an academic-industry gap is the neighborhood-fetching latency incurred by data dependency in G

Zhepeng Zhang, Lauren Hoang, Marisa Hocking, Jenny Hu

Two-dimensional (2D) semiconducting transition-metal dichalcogenides (TMDCs) are an exciting platform for new excitonic physics and next-generation electronics, creating a strong demand to understand their growth, doping, and heterostructures. Despite significant progress in solid-source (SS-) and metal-organic chemical vapor deposition (MOCVD), further opti

Edward Misback, Zachary Tatlock, Steven L. Tanimoto

Text documents, including programs, typically have human-readable semantic structure. Historically, programmatic access to these semantics has required explicit in-document tagging. Especially in systems where the text has an execution semantics, this means it is an opt-in feature that is hard to support properly. Today, language models offer a new method: m

Baptiste Gault, Leonardo Shoji Aota, Mathias Krämer, Se-Ho Kim

The numerous stories around LK-99 as a possible room-temperature superconductor over the summer of 2023 epitomise that materials are more than a bulk crystallographic structure or an expected composition. Like all materials, those at the core of technologies for the energy generation transition, including batteries, catalysts or quantum materials draw their

Yong-Guang Zheng, An Luo, Ying-Chao Shen, Ming-Gen He

The counterflow superfluidity (CSF) was predicted two decades ago. Counterintuitively, while both components in the CSF have fluidity, their correlated counterflow currents cancel out leading the overall system to an incompressible Mott insulator. However, realizing and identifying the CSF remain challenging due to the request on extreme experimental capabil

Zhong-Xia Shang

We propose a new variational quantum algorithm named Variational Open Quantum Eigensolver (VOQE) for solving steady states of open quantum systems described by either Lindblad master equations or non-Hermitian Hamiltonians. In VOQE, density matrices of mixed states are represented by pure states in doubled Hilbert space. We give a framework for building circ

Yizheng Gong, Siyue Yu, Xiaoyang Wang, Jimin Xiao

Most continual segmentation methods tackle the problem as a per-pixel classification task. However, such a paradigm is very challenging, and we find query-based segmenters with built-in objectness have inherent advantages compared with per-pixel ones, as objectness has strong transfer ability and forgetting resistance. Based on these findings, we propose CoM

V. B. Kiran Kumar, M. N. N. Namboodiri, P. C. Vinaya

The classical Korovkin theorem traditionally relies on the positivity of the underlying sequence of operators. In 1968, D. E. Wulbert obtained a non-positive version by exploiting geometric properties of function spaces, namely the Choquet boundary and the unique extension property of extreme points of the dual unit ball for weakly separating subspaces. In t

Dheeraj Raghunathan, Y. Sudhakar

A major limitation of the weakly compressible approaches to simulate incompressible flows is the appearance of artificial acoustic waves that introduce a large mass conservation error and lead to spurious oscillations in the force coefficients. In this work, we propose a non-homogeneous anisotropic bulk viscosity term to effectively damp the acoustic waves.

Kaushal Agarwal, Sumit Kumar Mehta, Pranab Kumar Mondal

The initial emergence of the primary root from a germinating seed is a pivotal phase that influences a plant's survival. Abiotic factors such as pH, nutrient availability, and soil composition significantly affect root morphology and architecture. Of particular interest is the impact of nutrient flow on thigmomorphogenesis, a response to mechanical stimulati

Paul Tricot

We study perfect $2$-coloring of the Johnson graphs $J(n,3)$ associated with the third largest eigenvalue and symmetric quotient matrix, which exists only when $n \in \{6, 10\}$. We survey the known constructions in the case $n=6$, give a new construction for the two known perfect $2$-colorings in the case $n=10$, and prove that these are the only possible o

Xinwei Ou, Ce Zhu, Xiaolin Huang, Yipeng Liu

Second-order optimization techniques have the potential to achieve faster convergence rates compared to first-order methods through the incorporation of second-order derivatives or statistics. However, their utilization in deep learning is limited due to their computational inefficiency. Various approaches have been proposed to address this issue, primarily

Weihao Jiang, Guodong Liu, Di He, Kun He

Few-shot learning, a challenging task in machine learning, aims to learn a classifier adaptable to recognize new, unseen classes with limited labeled examples. Meta-learning has emerged as a prominent framework for few-shot learning. Its training framework is originally a task-level learning method, such as Model-Agnostic Meta-Learning (MAML) and Prototypica

Peter Bouwknegt, Bolin Han

We study a specific class of CFTs that involve coupled free fermions, arising from parafermion CFTs and lattice constructions. We analyse their representation spaces and the underlying exclusion statistics of coupled free fermions using specific bases. In one particular case, we reveal an unexpected connection between the coset construction of parafermions,

Cécile Huneau, Jonathan Luk

We prove Burnett's conjecture in general relativity when the metrics satisfy a generalized wave coordinate condition, i.e., suppose $\{g_n\}_{n=1}^\infty$ is a sequence of Lorentzian metrics (in arbitrary dimensions $d \geq 3$) satisfying a generalized wave coordinate condition and such that $g_n\to g$ in a suitably weak and "high-frequency" manner, then the

Robbie King, Kianna Wan, Jarrod McClean

The ability of quantum computers to directly manipulate and analyze quantum states stored in quantum memory allows them to learn about aspects of our physical world that would otherwise be invisible given a modest number of measurements. Here we investigate a new learning resource which could be available to quantum computers in the future -- measurements on

Wonhyeok Choi, Mingyu Shin, Hyukzae Lee, Jaehoon Cho

Real-time processing is crucial in autonomous driving systems due to the imperative of instantaneous decision-making and rapid response. In real-world scenarios, autonomous vehicles are continuously tasked with interpreting their surroundings, analyzing intricate sensor data, and making decisions within split seconds to ensure safety through numerous compute

Aruto Hosaka, Shintaro Niimura, Masaya Tomita, Akihito Omi

Suprecontinuum (SC) light contains complex spectral noise structure and its accurate characterization is important for fundamental understanding of its physics as well as for its applications. Several experimental and theoretical noise characterizations have been performed so far. However, none of them takes into account the quantum mechanical properties. He

Hyeondeok Shin, Kevin Gasperich, Tomas Rojas, Anh T. Ngo

Accurate determination of electronic properties of correlated oxides remains a significant challenge for computational theory. Traditional Hubbard-corrected density functional theory (DFT+U) frequently encounters limitations in precisely capturing electron correlation, particularly when predicting band gaps. We introduce a systematic methodology to enhance t

Mengying Jiang, Guizhong Liu, Yuanchao Su, Xinliang Wu

In representation learning on graph-structured data, many popular graph neural networks (GNNs) fail to capture long-range dependencies, leading to performance degradation. Furthermore, this weakness is magnified when the concerned graph is characterized by heterophily (low homophily). To solve this issue, this paper proposes a novel graph learning framework

Peter Bouwknegt, Shane Chern, Bolin Han

In this paper we will prove a series of $q$-identities suggested by the realisation of certain conformal field theories by so-called `coupled free fermions'. We will consider $q$-series arising from coupled free fermions constructed by the parafermion coset construction as well as from scaled root lattices, and some interesting relations between the two.

Hao Wang, Sayed Pedram Haeri Boroujeni, Xiwen Chen, Ashish Bastola

Wildfires are a significant threat to ecosystems and human infrastructure, leading to widespread destruction and environmental degradation. Recent advancements in deep learning and generative models have enabled new methods for wildfire detection and monitoring. However, the scarcity of annotated wildfire images limits the development of robust models for th

Ali Ayub, Chrystopher Nehaniv, Kerstin Dautenhahn

For robots to perform assistive tasks in unstructured home environments, they must learn and reason on the semantic knowledge of the environments. Despite a resurgence in the development of semantic reasoning architectures, these methods assume that all the training data is available a priori. However, each user's environment is unique and can continue to ch

Cheng-Yen Yang, Hsiang-Wei Huang, Zhongyu Jiang, Hao Wang

Dense object counting or crowd counting has come a long way thanks to the recent development in the vision community. However, indiscernible object counting, which aims to count the number of targets that are blended with respect to their surroundings, has been a challenge. Image-based object counting datasets have been the mainstream of the current publicly

Xunjie Chen, Aditya Anikode, Jingang Yi, Tao Liu

Legged robots have demonstrated high efficiency and effectiveness in unstructured and dynamic environments. However, it is still challenging for legged robots to achieve rapid and efficient locomotion on deformable, yielding substrates, such as granular terrains. We present an enhanced resistive force model for bipedal walkers on soft granular terrains by in

Yanlai Chen, Yajie Ji, Akil Narayan, Zhenli Xu

We introduce the Transformed Generative Pre-Trained Physics-Informed Neural Networks (TGPT-PINN) for accomplishing nonlinear model order reduction (MOR) of transport-dominated partial differential equations in an MOR-integrating PINNs framework. Building on the recent development of the GPT-PINN that is a network-of-networks design achieving snapshot-based m

Shanka Subhra Mondal, Jonathan D. Cohen, Taylor W. Webb

Abstract visual reasoning is a characteristically human ability, allowing the identification of relational patterns that are abstracted away from object features, and the systematic generalization of those patterns to unseen problems. Recent work has demonstrated strong systematic generalization in visual reasoning tasks involving multi-object inputs, throug

Qucheng Gao, Tianci Zhou, Pengfei Zhang, Xiao Chen

In quantum many-body systems, interactions play a crucial role in the emergence of information scrambling. When particles interact throughout the system, the entanglement between them can lead to a rapid and chaotic spreading of quantum information, typically probed by the growth in operator size in the Heisenberg picture. In this study, we explore whether t

Xiangquan Gui, Binxuan Zhang, Li Li, Yi Yang

Chinese landscape painting has a unique and artistic style, and its drawing technique is highly abstract in both the use of color and the realistic representation of objects. Previous methods focus on transferring from modern photos to ancient ink paintings. However, little attention has been paid to translating landscape paintings into modern photos. To sol

Zheng Gong, Sylvia Herbert

Control Lyapunov Functions (CLFs) have been extensively used in the control community. A well-known drawback is the absence of a systematic way to construct CLFs for general nonlinear systems, and the problem can become more complex with input or state constraints. Our preliminary work on constructing Control Lyapunov Value Functions (CLVFs) using Hamilton-J

James Kotary, Ferdinando Fioretto

Learning to Optimize (LtO) is a problem setting in which a machine learning (ML) model is trained to emulate a constrained optimization solver. Learning to produce optimal and feasible solutions subject to complex constraints is a difficult task, but is often made possible by restricting the input space to a limited distribution of related problems. Most LtO

Soumitra Daptari

In this paper, we study several variants of Hahn-Banach smoothness, viz., property-$(SU)$/$(HB)$/$(wU)$, where property-$(SU)$ and property-$(HB)$ are stronger notions and property-$(wU)$ is a weaker notion of Hahn-Banach smoothness. We characterize property-$(wU)$ and property-$(HB)$. It is observed that $L_1(\mu)$ has property-$(wU)$ in $L_1(\mu,(\mathbb{R

Ruizhuo Song, Beiming Yuan

This paper is dedicated to addressing the challenge of enhancing the abstract reasoning capabilities of AI, particularly for tasks involving complex human concepts. We introduce Lico-Net, a novel reasoning engine grounded in deep learning theory, which encodes the logical structure of Raven's Progressive Matrices (RPM) problems into probabilistic representat

Trevor McCourt

Significant progress is required in the engineering of large, interacting quantum systems in order to realize the promises of gate-model quantum computing. Designing such systems is challenging, as the dynamics of continuous variable quantum systems are generally unintuitive, and brute-force numerical solutions are difficult to impossible in more than a few

Ke Xiao, Chen-Xu Wu

Capillary-driven flow of fluids occurs frequently in nature and has a wide range of technological applications in the fields of industry, agriculture, medicine, biotechnology, and microfluidics. By using the Onsager variational principle, we propose a model to systematically study the capillary imbibition in titled tubes, and find different laws of time-depe

Juntong Chen, Haiwen Huang, Huayuan Ye, Zhong Peng

The voluminous nature of geospatial temporal data from physical monitors and simulation models poses challenges to efficient data access, often resulting in cumbersome temporal selection experiences in web-based data portals. Thus, selecting a subset of time steps for prioritized visualization and pre-loading is highly desirable. Addressing this issue, this

Rina Su, Yu Guo, Caiying Wu, Qiyu Jin

The main objective of the Multiple Kernel k-Means (MKKM) algorithm is to extract non-linear information and achieve optimal clustering by optimizing base kernel matrices. Current methods enhance information diversity and reduce redundancy by exploiting interdependencies among multiple kernels based on correlations or dissimilarities. Nevertheless, relying so

Gangwei Xu, Yujin Wang, Jinwei Gu, Tianfan Xue

Reconstructing High Dynamic Range (HDR) video from image sequences captured with alternating exposures is challenging, especially in the presence of large camera or object motion. Existing methods typically align low dynamic range sequences using optical flow or attention mechanism for deghosting. However, they often struggle to handle large complex motions

Koya Endo, Yumiharu Nakano

We consider the weak convergence of the Euler-Maruyama approximation for Schr\"odinger-F\"ollmer diffusions, which are solutions of Schr\"odinger bridge problems and can be used for sampling from given distributions. We show that the distribution of the terminal random variable of the time-discretized process weakly converges to the target one under mild reg

Bruce C. Berndt, Sun Kim, Alexandru Zaharescu

Several methods are used to evaluate finite trigonometric sums. In each case, either the sum had not previously been evaluated, or it had been evaluated, but only by analytic means, e.g., by complex analysis or modular transformation formulas. We establish both reciprocity and three sum relations for trigonometric sums. Motivated by certain sums that we have

Andrew Pensoneault, Xueyu Zhu

In recent years, operator learning, particularly the DeepONet, has received much attention for efficiently learning complex mappings between input and output functions across diverse fields. However, in practical scenarios with limited and noisy data, accessing the uncertainty in DeepONet predictions becomes essential, especially in mission-critical or safet

Sridhar P. Narayanan

The permutation matrices form a subgroup of $\text{GL}_n(\mathbb{C})$ that is isomorphic to the symmetric group $S_n$. Let $r_{\mu\lambda}$ denote the multiplicity of the irreducible representation $V_\mu$ of $S_n$, corresponding to a partition $\mu$ of $n$, in the restriction of an irreducible polynomial representation $W_\lambda(\mathbb{C})$ of $\text{GL}_

Mengyuan Li, Shiyi Liu, Mohammad Mehdi Sharifi, X. Sharon Hu

Content addressable memory (CAM) stands out as an efficient hardware solution for memory-intensive search operations by supporting parallel computation in memory. However, developing a CAM-based accelerator architecture that achieves acceptable accuracy, while minimizing hardware cost and catering to both exact and approximate search, still presents a signif

David J. Moss

All-optical signal processing based on nonlinear optical devices is promising for ultrafast information processing in optical communication systems. Recent advances in two-dimensional (2D) layered materials with unique structures and distinctive properties have opened up new ave-nues for nonlinear optics and the fabrication of related devices with high perfo

Jingchao Zhang, Jinsheng Cai, Shucheng Pan

A component-splitting method is proposed to improve convergence characteristics for implicit time integration of compressible multicomponent reactive flows. The characteristic decomposition of flux jacobian of multicomponent Navier-Stokes equations yields a large sparse eigensystem, presenting challenges of slow convergence and high computational costs for i

Tadashi Okazaki, Douglas J. Smith

We study the Wilson line defect half-indices of 3d $\mathcal{N}=2$ supersymmetric $SU(N)$ Chern-Simons theories of level $k\le -N$ with Neumann boundary conditions for the gauge fields, together with 2d Fermi multiplets and fundamental 3d chiral multiplets to cancel the gauge anomaly. We derive some exact results and also make some conjectures based on expan

David J. Moss

Realizing optical analogues of quantum phenomena in atomic, molecular, or condensed matter physics has underpinned a range of photonic technologies. Rabi splitting is a quantum phenomenon induced by a strong interaction between two quantum states, and its optical analogues are of fundamental importance for the manipulation of light-matter interactions with w

Junhao Liu, Qizhou Zhang, Yuxin Lin, Keping Qiu

We have comprehensively studied the multi-scale physical properties of the infrared dark cloud (IRDC) G28.34 (the Dragon cloud) with dust polarization and molecular line data from Planck, FCRAO-14m, JCMT, and ALMA. We find that the averaged magnetic fields of clumps tend to be either parallel with or perpendicular to the cloud-scale magnetic fields, while th

Jeong-Eun Lee, Chul-Hwan Kim, Seokho Lee, Seonjae Lee

An unbiased spectral survey of V883 Ori, an eruptive young star, was carried out with the Atacama Large Millimeter/submillimeter Array (ALMA) in Band 6. The detected line emission from various molecules reveals morphological/kinematical features in both the Keplerian disk and the infalling envelope. A direct infall signature, red-shifted absorption against c

Vu Tran, Ha-Thanh Nguyen, Trung Vo, Son T. Luu

In this new era of rapid AI development, especially in language processing, the demand for AI in the legal domain is increasingly critical. In the context where research in other languages such as English, Japanese, and Chinese has been well-established, we introduce the first fundamental research for the Vietnamese language in the legal domain: legal textua

Sijin Zhang, Alvaro Orsi, Lei Chen

Agent Based Models (ABMs) have emerged as a powerful tool for investigating complex social interactions, particularly in the context of public health and infectious disease investigation. In an effort to enhance the conventional ABM, enabling automated model calibration and reducing the computational resources needed for scaling up the model, we have develop

Jiangshan Liu, Shuang Liu, Junjie Chen

In this paper, we introduce I3DE (Inconsistency Inspecting IDE) - an IDE plugin to inspect inconsistencies in PL/SQL code. We first observed the potential issues, e.g., misuses or bugs, that are introduced by the inconsistent understanding of PL/SQL semantics by PL/SQL programmers and DBMS developers, and propose a metamorphic testing-based approach for insp

Wenfeng Feng, Chuzhan Hao, Yuewei Zhang, Yu Han

Instruction Tuning has the potential to stimulate or enhance specific capabilities of large language models (LLMs). However, achieving the right balance of data is crucial to prevent catastrophic forgetting and interference between tasks. To address these limitations and enhance training flexibility, we propose the Mixture-of-LoRAs (MoA) architecture which i

Bingyan Liu, Chengyu Wang, Tingfeng Cao, Kui Jia

Deep Text-to-Image Synthesis (TIS) models such as Stable Diffusion have recently gained significant popularity for creative Text-to-image generation. Yet, for domain-specific scenarios, tuning-free Text-guided Image Editing (TIE) is of greater importance for application developers, which modify objects or object properties in images by manipulating feature c

Junhyeok Byeon, Seung-Yeal Ha, Joong-Ho Won

We propose Discrete Consensus-Based Optimization (DCBO), a fully discrete version of the Consensus-Based Optimization (CBO) framework. DCBO is a multi-agent method for the global optimization of possibly non-convex and non-differentiable functions. It aligns with the CBO paradigm, which promotes a consensus among agents towards a global optimum through simpl

Amirmohammad Nazari, Souti Chattopadhyay, Swabha Swayamdipta, Mukund Raghothaman

Despite great advances in program synthesis techniques, they remain algorithmic black boxes. Although they guarantee that when synthesis is successful, the implementation satisfies the specification, they provide no additional information regarding how the implementation works or the manner in which the specification is realized. One possibility to answer th

Thevasha Sathiyakumar, Shantanu Sur, Sumona Mondal, Marko Budišić

In a system of many similar self-propelled entities such as flocks of birds, fish school, cells and molecules, the interactions with neighbors can lead to a "coherent state", meaning the formation of visually compelling aggregation patterns due to the local adjustment of speed and direction. In this study, we explore one of the open questions that arise in s

Matthew T. Hansen, Jason A. Dittmann

Exoplanet discovery at long orbital periods requires reliably detecting individual transits without additional information about the system. Techniques like phase-folding of light curves and periodogram analysis of radial velocity data are more sensitive to planets with shorter orbital periods, leaving a dearth of planet discoveries at long periods. We prese

Zhiqing Yang, Cheng Zhou, Gangcheng Wang, Lijun Song

Ghost imaging is a non local imaging technology, which can obtain target information by measuring the second-order intensity correlation between the reference light field and the target detection light field. However, the current imaging environment requires a large number of measurement data, and the imaging results also have the problems of low image resol

Kaiwei Zhang, Yange Lin, Guangcheng Wu, Yuxiang Ren

The integration of deep learning, particularly AI-Generated Content, with high-quality data derived from ab initio calculations has emerged as a promising avenue for transforming the landscape of scientific research. However, the challenge of designing molecular drugs or materials that incorporate multi-modality prior knowledge remains a critical and complex

Shen Gao, Jiabao Fang, Quan Tu, Zhitao Yao

Most existing news recommendation methods tackle this task by conducting semantic matching between candidate news and user representation produced by historical clicked news. However, they overlook the high-level connections among different news articles and also ignore the profound relationship between these news articles and users. And the definition of th

Jun-Yi Shan, Nathaniel Morrison, Su-Di Chen, Feng Wang

Microwave impedance microscopy (MIM) is an emerging scanning probe technique for nanoscale complex permittivity mapping and has made significant impacts in diverse fields from semiconductors to quantum materials. To date, the most significant hurdles that limit its widespread use are the requirements of specialized microwave probes and high-precision cancell

Paweł Hitczenko

We consider sequences of polynomials that satisfy differential-difference recurrences. Polynomials satisfying such recurrences frequently appear as generating polynomials of integer valued random variables that are of interest in discrete mathematics. It is, therefore, of interest to understand the properties of such polynomials and their probabilistic conse

Sanqing Qu, Tianpei Zou, Lianghua He, Florian Röhrbein

Universal Domain Adaptation (UniDA) targets knowledge transfer in the presence of both covariate and label shifts. Recently, Source-free Universal Domain Adaptation (SF-UniDA) has emerged to achieve UniDA without access to source data, which tends to be more practical due to data protection policies. The main challenge lies in determining whether covariate-s

Okuto Morikawa, Soma Onoda, Hiroshi Suzuki

Recently, lattice formulations of Abelian chiral gauge theory in two dimensions have been devised on the basis of the Abelian bosonization. A salient feature of these 2D lattice formulations is that the gauge invariance is \emph{exactly\/} preserved for anomaly-free theories and thus is completely free from the question of the gauge mode decoupling. In the p

Shitong Duan, Xiaoyuan Yi, Peng Zhang, Yan Liu

Large language models (LLMs) have revolutionized the role of AI, yet pose potential social risks. To steer LLMs towards human preference, alignment technologies have been introduced and gained increasing attention. Nevertheless, existing methods heavily rely on high-quality positive-negative training pairs, suffering from noisy positive responses that are ba

Zhiming Chen, Yong Liu

We consider the reliable implementation of an adaptive high-order unfitted finite element method on Cartesian meshes for solving elliptic interface problems with geometrically curved singularities. We extend our previous work on the reliable cell merging algorithm for smooth interfaces to automatically generate the induced mesh for piecewise smooth interface

Meng Qi, Wenrong Wang, Yuan-ji Li, Jin-xia Feng

The principle and experimental realization of a novel Raman spectroscopic technique entitled stimulated Raman phase shift (SRPS) spectroscopy was demonstrated. This technique depends on the measurement of the stimulated Raman scattering (SRS) induced phase shift of Stokes light field ($\Delta$ $\phi$) that is related to the real part of the third order nonli

Shaoxuan Cui, Guofeng Zhang, Hildeberto Jardón-Kojakhmetov, Ming Cao

This paper studies the stability of discrete-time polynomial dynamical systems on hypergraphs by utilizing the Perron-Frobenius theorem for nonnegative tensors with respect to the tensors Z-eigenvalues and Z-eigenvectors. Firstly, for a multilinear polynomial system on a uniform hypergraph, we study the stability of the origin of the corresponding systems. N