October 2024 arXiv papers — page 27

Patrik Reizinger, Alice Bizeul, Attila Juhos, Julia E. Vogt

Supervised learning has become a cornerstone of modern machine learning, yet a comprehensive theory explaining its effectiveness remains elusive. Empirical phenomena, such as neural analogy-making and the linear representation hypothesis, suggest that supervised models can learn interpretable factors of variation in a linear fashion. Recent advances in self-

M. Mehdi Mojarradi, Lingyi Yang, Robert McCraith, Adam Mahdi

Large language models (LLMs) have shown impressive capabilities across various tasks, but their performance on domain-specific tasks remains limited. While methods like retrieval augmented generation and fine-tuning can help to address this, they require significant resources. In-context learning (ICL) is a cheap and efficient alternative but cannot match th

Bartolomeo Trefoloni, Xihan Ji, Roberto Maiolino, Francesco D'Eugenio

Recent JWST observations have revealed a large population of intermediate/low-luminosity AGN at early times with peculiar properties, different from local AGN or luminous quasars. To better understand the physical conditions in the BLRs of these early AGN, we used the optical FeII (4434--4684 \AA) and the broad $\rm H \beta$ emission, and the ratio between t

A. L. Bassant, M. F. van Willigen, R. A. Duine

Materials exhibit various wave-like excitations, among which phonons (lattice vibrations) and magnons (oscillations in ferromagnetic ordering) hold significant promise for future nanoscale technologies. Exploring the interaction between these excitations may pave the way for innovative devices that leverage their complementary strengths. This article present

Ivana Kovacevic, Tamara Rankovic, Milan Stojkov, Milos Simic

The immense shift to cloud computing has brought changes in security and privacy requirements, impacting critical Identity Management services. Currently, many IdM systems and solutions are accessible as cloud services, delivering identity services for applications in closed domains and the public cloud. This research paper centres on identity management in

Andreas Reinhart

Let $p$ be a prime number with $p\equiv 1\mod 4$, let $\omega=\frac{1+\sqrt{p}}{2}$, let $\varepsilon>1$ be the fundamental unit of $\mathbb{Z}[\omega]$ and let $x$ and $y$ be the unique nonnegative integers with $\varepsilon=x+y\omega$. The Ankeny-Artin-Chowla-Conjecture states that $p$ is not a divisor of $y$. In this note, we provide and discuss a counter

Bao-Zhu Guo, Huaiqiang Yu, Meixuan Zhang

In this paper, we establish the invariance of observability for the observed backward stochastic differential equations (BSDEs) with constant coefficients, relative to the filtered probability space. This signifies that the observability of these observed BSDEs with constant coefficients remains unaffected by the selection of the filtered probability space.

Massimo Bilancia, Samuele Magro

This paper presents a variant of the Multinomial mixture model tailored to the unsupervised classification of short text data. While the Multinomial probability vector is traditionally assigned a Dirichlet prior distribution, this work explores an alternative formulation based on the Beta-Liouville distribution, which offers a more flexible correlation struc

Xudong Wang, Jiaran Zhou, Huiyu Zhou, Junyu Dong

Image manipulation detection is to identify the authenticity of each pixel in images. One typical approach to uncover manipulation traces is to model image correlations. The previous methods commonly adopt the grids, which are fixed-size squares, as graph nodes to model correlations. However, these grids, being independent of image content, struggle to retai

Nahomi Kan, Takuya Maki, Kiyoshi Shiraishi

We analytically study isothermal spheres in the light of nonextensive statistical mechanics. The equations for the isothermal spheres are derived from the grand partition function of the gravitating particle system in the Tsallis statistical mechanics. The effect of nonextensive statistics appears in relatively dense state, which appears at the center of the

N. A. Koliada, Y. G. Isaeva, D. V. Brazhnikov, A. A. Filonov

We propose and study a method for measuring the chromatic dispersion parameter (D) of optical fibers and bulk optical elements through the use of a single-arm three-wave interferometer and a fiber femtosecond frequency comb (FFFC). The FFFC is frequency locked to a single-ytterbium-ion optical standard and employed as a source of highly stable and broadband

Sang Yang, Jun Cai, Yanxia Chen, Ming Gong

Nowadays the vibrational Stark effect (VSE) of adsorbates at the electrochemical interfaces is generally investigated using the Lambert theory, in which the strong electric field across the interfaces can be treated as some kind of perturbation. Lambert found that the VSE arises mainly from the classical effect, and the quantum effect is negligible. This ide

Damir Filipovic, Paul Schneider

We develop a nonparametric, kernel-based joint estimator for conditional mean and covariance matrices in large and unbalanced panels. The estimator is supported by rigorous consistency results and finite-sample guarantees, ensuring its reliability for empirical applications. We apply it to an extensive panel of monthly US stock excess returns from 1962 to 20

Rongling Zhang, Li Yan, Pengcheng Wei, Hong Xie

Point Cloud Registration (PCR) is a fundamental and significant issue in photogrammetry and remote sensing, aiming to seek the optimal rigid transformation between sets of points. Achieving efficient and precise PCR poses a considerable challenge. We propose a novel micro-structures graph-based global point cloud registration method. The overall method is co

Niklas J. T. Edberg, David J. Andrews, J. Jordi Boldú, Andrew P. Dimmock

We analyze data from multiple flybys by the Solar Orbiter, BepiColombo, and Parker Solar Probe (PSP) missions to study the interaction between Venus' plasma environment and the solar wind forming the induced magnetosphere. Through examination of magnetic field and plasma density signatures we characterize the spatial extent and dynamics of Venus' magnetotail

Dejun Luo, Bin Xie, Guohuan Zhao

For the stochastic linear transport equation with $L^p$-initial data ($1<p<2$) on the full space $\mathbb{R}^d$, we provide quantitative estimates, in negative Sobolev norms, between its solutions and that of the deterministic heat equation. Similar results are proved for the stochastic 2D Euler equations with transport noise.

Motohiko Ezawa

A $p$-wave magnet has a momentum-dependent spin splitting and zero-net magnetization just as in the case of an altermagnet. It will be useful for high-density and ultra-fast memory, where the direction of the spin-splitting vector may be used as a bit. The spin-splitting vector corresponds to the N\'{e}el vector in altermagnets. However, it is a nontrivial p

Gyeonghoon Ko, Hyunsu Kim, Juho Lee

Exploiting symmetry inherent in data can significantly improve the sample efficiency of a learning procedure and the generalization of learned models. When data clearly reveals underlying symmetry, leveraging this symmetry can naturally inform the design of model architectures or learning strategies. Yet, in numerous real-world scenarios, identifying the spe

Francesco Giglio, Giulio Landolfi, Luigi Martina, Andrea Zingarofalo

We analyse a generalised Fokker-Planck equation by making essential use of its linearisability through a Cole-Hopf transformation. We determine solutions of travelling wave and multi-kink type by resorting to a geometric construction in the regime of small viscosity. The resulting asymptotic solutions are time-dependent Heaviside step functions representing

Yang Zheng

For a Polish group $G$, let $E(G)$ be the right coset equivalence relation $G^\omega/c(G)$, where $c(G)$ is the group of all convergent sequences in $G$. We prove a Rigid theorem on locally compact TSI Polish groups admitting open identity component, as follows: Let $G$ be a locally compact TSI Polish group such that $G_0$ is open in $G$, and let $H$ be a no

Shi-Biao Zheng

If a neutrino or antineutrino produced in the decay of an unstable particle is not entangled to its accompanying particles, its mass is necessarily correlated with its momentum. In this manuscript, I illustrate that this entanglement would destroy the quantum coherence between the neutrino's mass eigenstates in both the momentum and position representations,

Finn Bartels, Lu Xing, Cise Midoglu, Matthias Boeker

We present SoccerGuard, a novel framework for predicting injuries in women's soccer using Machine Learning (ML). This framework can ingest data from multiple sources, including subjective wellness and training load reports from players, objective GPS sensor measurements, third-party player statistics, and injury reports verified by medical personnel. We expe

Can Cui, Imran Ahamad Sheikh, Mostafa Sadeghi, Emmanuel Vincent

Distant-microphone meeting transcription is a challenging task. State-of-the-art end-to-end speaker-attributed automatic speech recognition (SA-ASR) architectures lack a multichannel noise and reverberation reduction front-end, which limits their performance. In this paper, we introduce a joint beamforming and SA-ASR approach for real meeting transcription.

Xinguo Yu, Weina Cheng, Chuanzhi Yang, Ting Zhang

Solving algebra problems (APs) continues to attract significant research interest as evidenced by the large number of algorithms and theories proposed over the past decade. Despite these important research contributions, however, the body of work remains incomplete in terms of theoretical justification and scope. The current contribution intends to fill the

Renhao Tian, Jianfeng Huang, Yulin Zhao

In this paper, we study a class of population models with time-varying factors, represented by one-dimensional piecewise smooth autonomous differential equations. We provide several derivative formulas in "discrete" form for the Poincar\'{e} map of such equations, and establish a criterion for the existence of limit cycles. These two tools, together with the

Pierre Calka, Yann Demichel

In this paper, we consider a regular tessellation of the Euclidean plane and the sequence of its geometric scalings by negative powers of a fixed integer. We generate iteratively random sets as the union of adjacent tiles from these rescaled tessellations. We encode this geometric construction into a combinatorial object, namely a multitype Galton-Watson tre

S M Rafi-Ul-Islam, Zhuo Bin Siu, Md. Saddam Hossain Razo, Mansoor B. A. Jalil

In this work, we demonstrate the presence of an anomalous non-Hermitian skin effects which decay from both ends of a system consisting of two coupled Hermitian chains induced by non-reciprocal inter-chain cross-coupling. Another intriguing feature of the system is that its eigenenergy spectrum in thermodynamic limit deviates from the generalized Brillouin zo

Jianlan Luo, Charles Xu, Jeffrey Wu, Sergey Levine

Reinforcement learning (RL) holds great promise for enabling autonomous acquisition of complex robotic manipulation skills, but realizing this potential in real-world settings has been challenging. We present a human-in-the-loop vision-based RL system that demonstrates impressive performance on a diverse set of dexterous manipulation tasks, including dynamic

Reza Shahabi-Shahmiri, Reza Tavakkoli-Moghaddam, Zdenek Hanzalek, Mohammad Ghasemi

Identifying appropriate manufacturing systems for products can be considered a pivotal manufacturing task contributing to the optimization of operational and planning activities. It has gained importance in the food industry due to the distinct constraints and considerations posed by perishable and non-perishable items in this problem. Hence, this study prop

Takaaki Nomura, Hiroshi Okada

We propose a radiatively induced linear seesaw model that is perfectly realized at leading order by a modular $A_4$ symmetry. Because this group also has a flavor symmetry, we obtain some predictions concentrating on two specific regions at nearby fixed points $\tau=\omega$ and $i$. Through our numerical chi square analysis, we show predictions for each the

Yiming Ji, Kaijie Yun, Yang Liu, Zhengpu Wang

The Object Navigation (ObjectNav) task aims to guide an agent to locate target objects in unseen environments using partial observations. Prior approaches have employed location prediction paradigms to achieve long-term goal reasoning, yet these methods often struggle to effectively integrate contextual relation reasoning. Alternatively, map completion-based

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

Using $(10087\pm44)\times 10^{6}$ $J/\psi$ decays collected by the BESIII detector at the BEPCII collider, we search for baryon number violation via $\Lambda-\bar{\Lambda}$ oscillation in the decay $J/\psi \to \Lambda \bar{\Lambda}$. No evidence for $\Lambda-\bar\Lambda$ oscillation is observed. The upper limit on the time-integrated probability of $\Lambda-

Xirong Ma, Junling Fang, Chunpeng Ge, Dung Hoang Duong

Homomorphic permutation is fundamental to privacy-preserving computations based on batch-encoding homomorphic encryption. It underpins nearly all homomorphic matrix operations and predominantly influences their complexity. Permutation decomposition as a potential approach to optimize this critical component remains underexplored. In this paper, we propose no

Thuy Nguyen, Daniel Bonamy

Fracture toughness is the material property characterizing resistance to failure. Predicting its value from the solid structure at the atomistic scale remains elusive, even in the simplest situations of brittle fracture. We report here numerical simulations of crack propagation in two-dimensional fuse networks of different periodic geometries, which are elec

Hoa Thi Le, Wolfram G. Nöhring, Lars Pastewka

High-entropy alloys (HEAs) are complex alloys containing multiple elements in high concentrations. Plasticity in HEAs is carried by dislocations, but the random nature of their composition pins dislocations, effectively hindering their motion. We investigate the resulting complex structure of the dislocation in terms of spatial correlation functions, which a

Flavio Corradini, Flavio Gerosa, Marco Gori, Carlo Lucheroni

In recent years, spatio-temporal graph neural networks (GNNs) have attracted considerable interest in the field of time series analysis, due to their ability to capture, at once, dependencies among variables and across time points. The objective of this systematic literature review is hence to provide a comprehensive overview of the various modeling approach

Sandra Liz Simon, Nitin Kaistha, Vishal Agarwal

In the last few decades, several novel algorithms have been designed for finding critical points on PES and the minimum energy paths connecting them. This has led to considerably improve our understanding of reaction mechanisms and kinetics of the underlying processes. These methods implicitly rely on computation of energy and forces on the PES, which are us

Danrun Cao, Nicolas Béchet, Pierre-François Marteau

We address in this article the the quality of the WikiNER corpus, a multilingual Named Entity Recognition corpus, and provide a consolidated version of it. The annotation of WikiNER was produced in a semi-supervised manner i.e. no manual verification has been carried out a posteriori. Such corpus is called silver-standard. In this paper we propose WikiNER-fr

Chaebin Lee, Seungyeon Seo, Heejin Do, Gary Geunbae Lee

With the advancement of chatbots and the growing demand for automatic depression detection, identifying depression in patient conversations has gained more attention. However, prior methods often assess depression in a binary way or only a single score without diverse feedback and lack focus on enhancing dialogue responses. In this paper, we present a novel

Niklas Knobel

This article considers the ideal 2D magnetohydrodynamic equations on an infinite periodic channel close to a combination of an affine shear flow, called Couette flow, and a constant magnetic field. This setting combines important physical effects of mixing and coupling of velocity and magnetic field. We establish the existence and stability of the velocity a

Sa Wang, Shuang Li, Yao Li, Ben-Wei Zhang

The production of heavy quark (HQ) jets provides a new arena to address the mass effect of jet quenching in heavy-ion physics. This paper presents a theoretical study of HQ jet yield suppression in Pb+Pb collisions at the LHC and focuses on the energy loss of HQ jets produced by different mechanisms. The p+p baseline is carried out by the SHERPA generator, a

François Le Gall

We construct classical algorithms computing an approximation of the ground state energy of an arbitrary $k$-local Hamiltonian acting on $n$ qubits. We first consider the setting where a good ``guiding state'' is available, which is the main setting where quantum algorithms are expected to achieve an exponential speedup over classical methods. We show that a

Shunichiro Orihara, Shinpei Imori, Kosuke Morikawa, Atsushi Goto

Controlled Direct Effect (CDE) is one of the causal estimands used to evaluate both exposure and mediation effects on an outcome. When there are unmeasured confounders existing between the mediator and the outcome, the ordinary identification assumption does not work. In this manuscript, we consider an identification condition to identify CDE in the presence

Aiman Farooq, Utkarsh Sharma, Deepak Mishra

Accurate survival prediction in head and neck cancer (HNC) is essential for guiding clinical decision-making and optimizing treatment strategies. Traditional models, such as Cox proportional hazards, have been widely used but are limited in their ability to handle complex multi-modal data. This paper proposes a deep learning-based approach leveraging CT and

Donghoon Kim, Gusang Lee, Kyuhong Shim, Byonghyo Shim

Recently, we have observed that Large Multi-modal Models (LMMs) are revolutionizing the way machines interact with the world, unlocking new possibilities across various multi-modal applications. To adapt LMMs for downstream tasks, parameter-efficient fine-tuning (PEFT) which only trains additional prefix tokens or modules, has gained popularity. Nevertheless

Agnès Lagnoux, T. M. Ngoc Nguyen, Bruno Demory, Manuel Henner

In automotive industry, client needs evolve quickly in a competitiveness context, particularly, regarding the fan involved in the engine cooling module. This study has been done in cooperation with the automotive supplier Valeo. Here, we propose to use the Kriging interpolation and the Expected Improvement algorithm to provide new fan designs with high perfo

Yatian Wang, Hua Xiang, Chi Zhang, Songling Zhang

This paper is concerned with the low-rank approximation for large-scale nonsymmetric matrices. Inspired by the classical Nystrom method, which is a popular method to find the low-rank approximation for symmetric positive semidefinite matrices, we explore an extension of the Nystrom method to approximate nonsymmetric matrices. The proposed method is a general

Michela Rigoselli, Sandro Mereghetti, Jules P. Halpern, Eric V. Gotthelf

Calvera (1RXS J141256.0+792204) is a pulsar of characteristic age 285 kyr at a high Galactic latitude of b=+37{\deg}, detected only in soft thermal X-rays. We measure a new and precise proper motion for Calvera using Chandra HRC-I observations obtained 10 years apart. We also derive a new phase-connected ephemeris using 6 years of NICER data, including the a

Zhaohe Lv, Guoliang Zhao, Zhanbo Xu, Jiang Wu

A reliable comfort model is essential to improve occupant satisfaction and reduce building energy consumption. As two types of the most common and intuitive thermal adaptive behaviors, precise recognition of dressing and undressing can effectively support thermal comfort prediction. However, traditional activity recognition suffers from shortcomings in priva

Suhyun Ahn, Wonjung Park, Jihoon Cho, Seunghyuck Park

Spatial control methods using additional modules on pretrained diffusion models have gained attention for enabling conditional generation in natural images. These methods guide the generation process with new conditions while leveraging the capabilities of large models. They could be beneficial as training strategies in the context of 3D medical imaging, whe

Balasubramanian Sundarapandian, Mohit Raghuwanshi, Patrik Straňák, Yuan Yu

Molybdenum (Mo) due to its optimal structural, physical, and acoustic properties find application as electrode material in aluminum scandium nitride (AlScN) and aluminum nitride (AlN) based bulk acoustic wave (BAW) resonators. Epitaxial Mo thin films exhibiting low resistivity can improve the performance of the BAW resonator by enhancing both the electromech

Arseniy Kholod, Yuriy Polyakov, Michael Schlottke-Lakemper

Data privacy is a significant concern when using numerical simulations for sensitive information such as medical, financial, or engineering data -- especially in untrusted environments like public cloud infrastructures. Fully homomorphic encryption (FHE) offers a promising solution for achieving data privacy by enabling secure computations directly on encryp

Nicolas Arbor, Laurent Bartolucci, Botao Dai, Halima El Azhar

Skin dose in radiotherapy is a key issue for reducing patient side effects, but dose calculations in this high-gradient region remains a challenge. To support radiation therapists and medical physicist in their decisions, a computational tool has been developed to systematically recalculate the skin dose for each treatment session, taking into account change

Ming Kang, Fung Fung Ting, Raphaël C. -W. Phan, Chee-Ming Ting

Brain tumor detection in multiplane Magnetic Resonance Imaging (MRI) slices is a challenging task due to the various appearances and relationships in the structure of the multiplane images. In this paper, we propose a new You Only Look Once (YOLO)-based detection model that incorporates Pretrained Knowledge (PK), called PK-YOLO, to improve the performance fo

Dongmin Park, Sebin Kim, Taehong Moon, Minkyu Kim

State-of-the-art text-to-image (T2I) diffusion models often struggle to generate rare compositions of concepts, e.g., objects with unusual attributes. In this paper, we show that the compositional generation power of diffusion models on such rare concepts can be significantly enhanced by the Large Language Model (LLM) guidance. We start with empirical and th

Guang-Da Hu

We investigate stability of linear delay differential systems. Stability criteria of the systems are derived based on integrals of the fundamental matrix. They are necessary and sufficient conditions for delay-dependent stability of the systems. Numerical examples are given to illustrate the main results.

Pavel Exner, Jonathan Rohleder

We discuss Laplacian spectrum on a finite metric graph with vertex couplings violating the time-reversal invariance. For the class of star graphs we determine, under the condition of a fixed total edge length, the configurations for which the ground state eigenvalue is maximized. Furthermore, for general finite metric graphs we provide upper bounds for all e

Koki Wataoka, Tsubasa Takahashi, Ryokan Ri

Automated evaluation leveraging large language models (LLMs), commonly referred to as LLM evaluators or LLM-as-a-judge, has been widely used in measuring the performance of dialogue systems. However, the self-preference bias in LLMs has posed significant risks, including promoting specific styles or policies intrinsic to the LLMs. Despite the importance of t

Rajko Nenadov

A subset of $\mathbb{F}_q^2$ is called an arc if it does not contain three collinear points. We show that there are at most $\binom{(1 + o(1))q}{m}$ arcs of size $m \gg q^{1/2} (\log q)^{3/2}$, nearly matching a trivial lower bound $\binom{q}{m}$. This was previously known to hold for $m \gg q^{2/3} (\log q)^3$, due to Bhowmick and Roche-Newton. The lower bo

Yun-Zhi Du, Hui-Hua Zhao, Yang Zhang, Qiang Gu

As we know that due to the quantum gravitational effects black hole horizons are ``fractalized'' into a sphereflake by Barrow. Based on this issue, in this work we investigate the phase structure and stability of the Einstein-Power-Yang-Mills AdS black holes with the fractal structure on the black hole horizon in the restricted phase space. Through the therm

Shaobo Wang, Hongxuan Tang, Mingyang Wang, Hongrui Zhang

The debate between self-interpretable models and post-hoc explanations for black-box models is central to Explainable AI (XAI). Self-interpretable models, such as concept-based networks, offer insights by connecting decisions to human-understandable concepts but often struggle with performance and scalability. Conversely, post-hoc methods like Shapley values

Aakash Shandilya, Sundeep Kapila, Radha Krishnan, Bent Weber

The spin-valley or Kramers qubit promises significantly enhanced spin-valley lifetimes due to strong coupling of the electrons' spin to their momentum (valley) degrees of freedom. In transition metal dichalcogenides (TMDCs) such spin-valley locking is expected to be particularly strong owing to the significant intrinsic spin-orbit coupling strength. Very rec

Jia Wei, Yun Li, Xiaomao Fan, Wenjun Ma

Laryngo-pharyngeal cancer (LPC) is a highly lethal malignancy in the head and neck region. Recent advancements in tumor detection, particularly through dual-branch network architectures, have significantly improved diagnostic accuracy by integrating global and local feature extraction. However, challenges remain in accurately localizing lesions and fully cap

Zhiyuan Yao, Pengfei Zhang

Quantum many-body scars (QMBS) -- rare eigenstates that evade thermalization -- are typically characterized by their low entanglement entropies compared to surrounding thermal eigenstates. However, due to finite-size effects in systems accessible via exact diagonalization, this measure can be ambiguous. To address this limitation, we propose using the correl

Zongbo Bao, Philippe van Dordrecht, Jonas Helsen

We prove a conjecture of Arunachalam & Dutt ([AD24]) on the existence of a tolerant stabilizer testing algorithm, and achieve an exponential improvement in the parameters of the tester. Key to our argument is a generalized uncertainty relation for sets of Pauli operators, based on the Lovasz theta function.

Bin He, Yuzhe Ying, Yejiong Shi, Zhe Meng

Current surgical procedures for spinal cord tumors lack in vivo high-resolution, high-speed multifunctional imaging systems, posing challenges for precise tumor resection and intraoperative decision-making. This study introduces the Fast Adaptive Focus Tracking Robotic Optical Coherence Tomography (FACT-ROCT) system,designed to overcome these obstacles by pr

P. Francis, Abraham M. Illickan, Lijo M. Jose, Deepak Rajendraprasad

We show that every planar triangulation on $n$ vertices has a maximal independent set of size at most $n/3$. This affirms a conjecture by Botler, Fernandes and Guti\'errez [Electron.\ J.\ Comb., 2024], which in turn would follow if an open question of Goddard and Henning [Appl.\ Math.\ Comput., 2020] which asks if every planar triangulation has three disjoin

Sumit Purohit, George Chin, Patrick S Mackey, Joseph A Cottam

Curating knowledge from multiple siloed sources that contain both structured and unstructured data is a major challenge in many real-world applications. Pattern matching and querying represent fundamental tasks in modern data analytics that leverage this curated knowledge. The development of such applications necessitates overcoming several research challeng

Zhong Ji, Shuo Yang, Jingren Liu, Yanwei Pang

Generalized Category Discovery (GCD) aims to classify both base and novel images using labeled base data. However, current approaches inadequately address the intrinsic optimization of the co-occurrence matrix $\bar{A}$ based on cosine similarity, failing to achieve zero base-novel regions and adequate sparsity in base and novel domains. To address these def

Zhiyuan Zhong, Zhezhen Cao, Zhanwei Zhang

Fuzzing has been incredibly successful in uncovering bugs and vulnerabilities across diverse software systems. JSON parsers play a vital role in modern software development, and ensuring their reliability is of great importance. This research project focuses on leveraging Large Language Models (LLMs) to enhance JSON parser testing. The primary objectives are

M. K. Volkov, A. A. Pivovarov, K. Nurlan

The decay widths of $V \to K(\pi) l \bar{\nu}_l $ are calculated within the Nambu--Jona-Lasinio model, where $V=K^*, \rho, \omega, \phi$ and $l=\mu, e$. The results are obtained using the previously fixed model parameters without introducing any arbitrary parameters. The obtained results are considered as predictions due to the absence of experimental data.

Li Shen, Anke Tang, Enneng Yang, Guibing Guo

Multi-task learning (MTL) leverages a shared model to accomplish multiple tasks and facilitate knowledge transfer. Recent research on task arithmetic-based MTL demonstrates that merging the parameters of independently fine-tuned models can effectively achieve MTL. However, existing merging methods primarily seek a static optimal solution within the original

Ruofan Wang, Juncheng Li, Yixu Wang, Bo Wang

As large Vision-Language Models (VLMs) gain prominence, ensuring their safe deployment has become critical. Recent studies have explored VLM robustness against jailbreak attacks-techniques that exploit model vulnerabilities to elicit harmful outputs. However, the limited availability of diverse multimodal data has constrained current approaches to rely heavi

Lu Yu, Haiyang Zhang, Changsheng Xu

Due to the impressive zero-shot capabilities, pre-trained vision-language models (e.g. CLIP), have attracted widespread attention and adoption across various domains. Nonetheless, CLIP has been observed to be susceptible to adversarial examples. Through experimental analysis, we have observed a phenomenon wherein adversarial perturbations induce shifts in te

Heng Er Metilda Chee, Jiayin Wang, Zhiqiang Guo, Weizhi Ma

Instant Messaging is a popular means for daily communication, allowing users to send text and stickers. As the saying goes, "a picture is worth a thousand words", so developing an effective sticker retrieval technique is crucial for enhancing user experience. However, existing sticker retrieval methods rely on labeled data to interpret stickers, and general-

Aakash Warke, Janis Nötzel, Kan Takase, Warit Asavanant

We propose an efficient decomposition scheme for a quantum receiver that attains the Helstrom bound in the low-photon regime for discriminating binary coherent states. Our method, which avoids feedback as used in Dolinar's case, breaks down nonlinear operations into basic gates used in continuous-variable quantum computation. We account for realistic conditi

Lina Zhu, Lin Zhou

In multiple classification, one aims to determine whether a testing sequence is generated from the same distribution as one of the M training sequences or not. Unlike most of existing studies that focus on discrete-valued sequences with perfect distribution match, we study multiple classification for continuous sequences with distribution uncertainty, where

Jiale Amber Wang, Kaiyuan Wang, Pengyu Nie

Code coverage analysis has been widely adopted in the continuous integration of open-source and industry software repositories to monitor the adequacy of regression test suites. However, computing code coverage can be costly, introducing significant overhead during test execution. Plus, re-collecting code coverage for the entire test suite is usually unneces

Seunghyeon Shin, Seokjin Lee

The detection of anomalous sounds in machinery operation presents a significant challenge due to the difficulty in generalizing anomalous acoustic patterns. This task is typically approached as an unsupervised learning or novelty detection problem, given the complexities associated with the acquisition of comprehensive anomalous acoustic data. Conventional m

R. Alfaro, C. Alvarez, J. C. Arteaga-Velázquez, D. Avila Rojas

Very-high-energy (0.1-100 TeV) gamma-ray emission was observed in HAWC data from the lobes of the microquasar SS 433, making them the first set of astrophysical jets that were resolved at TeV energies. In this work, we update the analysis of SS 433 using 2,565 days of data from the High Altitude Water Cherenkov (HAWC) observatory. Our analysis reports the de

Yuwei Fu, Haichao Zhang, Di Wu, Wei Xu

Reward specification is one of the most tricky problems in Reinforcement Learning, which usually requires tedious hand engineering in practice. One promising approach to tackle this challenge is to adopt existing expert video demonstrations for policy learning. Some recent work investigates how to learn robot policies from only a single/few expert video demo

Qian Long, Ruoyan Li, Minglu Zhao, Tao Gao

A major challenge for Multi-Agent Systems is enabling agents to adapt dynamically to diverse environments in which opponents and teammates may continually change. Agents trained using conventional methods tend to excel only within the confines of their training cohorts; their performance drops significantly when confronting unfamiliar agents. To address this

Giorgio Natale Buttiglieri, Luca De Martini, Alessandro Margara

In recent years, the serverless paradigm has been widely adopted to develop cloud applications, as it enables building scalable solutions while delegating operational concerns such as infrastructure management and resource provisioning to the serverless provider. Despite bringing undisputed advantages, the serverless model requires a change in programming pa

Letícia Vitorazi, Jean-François Berret

In recent years, there has been a renewed interest in complex coacervation, driven by concerted efforts to offer novel experimental and theoretical insights into electrostatic charge-induced association. While previous studies have primarily focused on polyelectrolytes, proteins or surfactants, our work explores the potential of using cerium (CeO2) and iron

Jingbo Su

Large language models (LLMs) have demonstrated impressive performance across various domains but remain susceptible to safety concerns. Prior research indicates that gradient-based adversarial attacks are particularly effective against aligned LLMs and the chain of thought (CoT) prompting can elicit desired answers through step-by-step reasoning. This paper

Eric Sun, Kuan-hui Elaine Lin, Wan-Ling Tseng, Pao K. Wang

We propose a novel approach for reconstructing annual temperatures in East Asia from 1368 to 1911, leveraging the Reconstructed East Asian Climate Historical Encoded Series (REACHES). The lack of instrumental data during this period poses significant challenges to understanding past climate conditions. REACHES digitizes historical documents from the Ming and

Yu Zeng, Yang Zhang, Jiachen Liu, Linlin Shen

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with mu

James Nianias, Jeremy Lim, Yik Lok Wong, Gordon Wong

Blind spectroscopy of massive lensing galaxy clusters with MUSE has revealed large numbers of gravitationally-lensed Lyman-$ \alpha $ emitters exhibiting asymmetric profiles at $ 2.9 \leq z \leq 6.7 $, suggesting abundant outflows from low-mass star-forming galaxies in the early universe. Are these primaeval galaxies experiencing their first bursts of star f

Xinyao Guo, Teng Zhang, Denis Martynov, Haixing Miao

Detection of gravitational waves in kilohertz frequency range is crucial for understanding the physical processes of binary neutron star mergers. In Ref. [Phys. Rev. X {\bf 13}, 021019 (2023)], a new interferometric configuration has been proposed, employing an L-shaped optical resonant cavity as arm cavity. This alteration enhances the detector's response t

Kamyar Rajabalifardi, Sagnik Bhattacharya, Mehrnaz Afshang, Mohammad Mozaffari

This paper introduces a novel power allocation and subcarrier optimization algorithm tailored for fixed wireless access (FWA) networks operating under low-rank channel conditions, where the number of subscriber antennas far exceeds those at the base station (BS). As FWA networks grow to support more users, traditional approaches like orthogonal multiple acce

Wenting Xu, Yong Xu, Xiaoyu Yang, Bin Pei

This work is concerned with the large deviation principle for a family of slow-fast systems perturbed by infinite-dimensional mixed fractional Brownian motion with Hurst parameter $H\in(\frac12,1)$. We adopt the weak convergence method which is based on the variational representation formula for infinite-dimensional mixed fractional Brownian motion. To obtai

Anubhav Shrimal, Stanley Kanagaraj, Kriti Biswas, Swarnalatha Raghuraman

Large language model advancements have enabled the development of multi-agent frameworks to tackle complex, real-world problems such as to automate tasks that require interactions with diverse tools, reasoning, and human collaboration. We present MARCO, a Multi-Agent Real-time Chat Orchestration framework for automating tasks using LLMs. MARCO addresses key

Yu-Chen Zhuang, Qing-Feng Sun

The wavefront dislocation is an important and ubiquitous phenomenon in wave fields. It is closely related to the phase singularity in a wave function. Some recent studies have verified that the wavefront dislocations in the local density of states (LDOS) map can well manifest the intrinsic topological characteristics in graphene and some topological systems.

Sagnik Bhattacharya, Kamyar Rajabalifardi, Muhammad Ahmed Mohsin, Rohan Pote

The ever-evolving landscape of distributed wireless systems, e.g. multi-user AR/VR systems, demands high data rates (up to 500 Mbps per user) and low power consumption. With increasing number of participating users, uplink data transmission in the situation where the number of transmitter user antennas exceeds the number of access point (AP) antennas present

Olya Mandelshtam, Travis Scrimshaw

We define an algorithm on fermionic and bosonic twisted multiline queues that projects to the multispecies totally asymmetric simple exclusion process (TASEP) and the totally asymmetric zero range process (TAZRP) on a ring, respectively. Our algorithm on fermionic multiline queues generalizes the Ferrari-Martin algorithm for TASEP, and we show it is equivale

S. Kourkinejat, A. Mahdifar, E. Amooghorban

In this paper, we consider a quantum Otto cycle with a quantum harmonic oscillator on a circle as its working substance. Since the eigen-energies of this oscillator depend on the curvature of the circle, this model, as an analog model, enables us to investigate the curvature effects of the physical space on properties of quantum heat engines. We assume that

Qingchuan Li, Jiatong Li, Tongxuan Liu, Yuting Zeng

Large Language Models (LLMs) have exhibited remarkable potential across a wide array of reasoning tasks, including logical reasoning. Although massive efforts have been made to empower the logical reasoning ability of LLMs via external logical symbolic solvers, crucial challenges of the poor generalization ability to questions with different features and ine

Vasile Păiş, Radu Ion, Andrei-Marius Avram, Maria Mitrofan

This paper presents the design and evolution of the RELATE platform. It provides a high-performance environment for natural language processing activities, specially constructed for Romanian language. Initially developed for text processing, it has been recently updated to integrate audio processing tools. Technical details are provided with regard to core c

U-Rae Kim, Jungil Lee, Soo-hyeon Nam

We study two types of models involving a scalar dark matter multiplet of global $O(N)$ symmetry. These models are distinguished by the absence (Type I) or presence (Type II) of a scalar mediator with $Z_{2}$ symmetry. We derive the allowed regions for the dark matter mass and new scalar couplings based on constraints from Higgs invisible decay, the relic abu

Hao Zhang, Yuanyuan Li, Jianping Huang

Full waveform inversion (FWI) is capable of reconstructing subsurface properties with high resolution from seismic data. However, conventional FWI faces challenges such as cycle-skipping and high computational costs. Recently, deep learning method has emerged as a promising solution for efficient velocity estimation. We develop DiffusionVel, a data-driven te