December 2024 arXiv papers — page 61

Ravi Prakash, Tony Thomas

The rise of the industrial metaverse has brought digital twins (DTs) to the forefront. Blockchain-powered non-fungible tokens (NFTs) offer a decentralized approach to creating and owning these cloneable DTs. However, the potential for unauthorized duplication, or counterfeiting, poses a significant threat to the security of NFT-DTs. Existing NFT clone detect

Léonard Guetta, Lyne Moser

Building on work by Fiore-Pronk-Paoli, we construct four model structures on the category of double categories, each modeling one of the following: simplicial spaces, Segal spaces, $(\infty,1)$-categories, and $\infty$-groupoids. Additionally, we provide an explicit formula for computing homotopy colimits in these models using the Grothendieck construction.

Huatao Xu, Panrong Tong, Mo Li, Mani Srivastava

This paper introduces a novel mobile sensing application - life journaling - designed to generate semantic descriptions of users' daily lives. We present AutoLife, an automatic life journaling system based on commercial smartphones. AutoLife only inputs low-cost sensor data (without photos or audio) from smartphones and can automatically generate comprehensi

Martin Koehncke, Yogi Jaelani, Alexander Mendler, Lizzie Neumann

Static and dynamic load tests were conducted on an anonymized in-service prestressed concrete box girder bridge constructed in 1972 and designed for Bridge Class~30 according to DIN~1072. The bridge is instrumented as part of the DTEC-SHM research initiative with a permanent structural health monitoring system comprising 128 sensors, including accelerometers

Maike Züfle, Jan Niehues

Large language models (LLMs) excel in natural language processing but adapting these LLMs to speech processing tasks efficiently is not straightforward. Direct task-specific fine-tuning is limited by overfitting risks, data requirements, and computational costs. To address these challenges, we propose a scalable, two-stage training approach: (1) A task-indep

D. Papo, J. M. Buldú

Graph theory is now becoming a standard tool in system-level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network structure does not entail that the brain actually works as a network. Asking whether the brain behaves as a network means asking whether network properties count. From the viewpoint of neurophysiology and, po

Matteo Canciani, Stefano Vichi, Oksana Koplak, Sergio Bietti

A novel approach for the measurement of the Nitrogen active species generated by a plasma source in the molecular beam epitaxy environment is here presented. The method is based on the analysis of the variations in the optical signal measured by a pyrometer during a two step, Gallium rich and Nitrogen controlled, growth modes. The method permits a precise, q

S. Arati, P. Devaraj, Shankhadeep Mondal

The study involves characterizations of dual pairs of frames which are optimal to handle erasures among all dual pairs for a finite dimensional Hilbert space. A new optimality measure using the Frobenius norm of the error operator has been introduced and the corresponding optimal dual pairs have been analyzed for any number of erasures. Also, other measures

Fan Xu, Bin Liu

This paper establishes the global well-posedness of the Landau-Lifshitz-Baryakhtar (LLBar) equation in the whole space $\mathbb{R}^3$. The study first demonstrates the existence and uniqueness of global strong solutions using the weak compactness approach. Furthermore, the existence and uniqueness of classical solutions, as well as arbitrary smooth solutions

Xing-Long Zhu, Min Chen, Zheng-Ming Sheng

The continuous development of bright x/gamma-ray sources has opened up new frontiers of science and advanced applications. Currently, there is still a lack of efficient approaches to produce gamma-rays with photon energies up to GeV and with high peak brilliance comparable to modern free-electron lasers. Here we report a novel mechanism called beam fast pinc

Andrzej A. Zdziarski, Srimanta Banerjee, Michal Szanecki, Ranjeev Misra

We have studied the accreting black hole binary GX 339--4 using two highly accurate broad-band X-ray data sets in very soft spectral states from simultaneous NICER and NuSTAR observations. Joint fitting of both data sets with relativistic models of the disk, its Comptonization and reflection allows us to relatively accurately determine the black-hole mass an

Lisha Shuai, Shaofeng Tan, Nan Zhang, Jiamin Zhang

Local Differential Privacy (LDP), a robust privacy-protection model, is widely adopted in the Industrial Internet of Things (IIoT) due to its lightweight, decentralized, and scalable. However, its perturbation-based privacy-protection mechanism hinders distinguishing between any two data, thereby facilitating LDP poisoning attacks. The exposed physical-layer

Sunbowen Lee, Hongqin Lyu, Yicheng Gong, Yingying Sun

Reinforcement learning methods have proposed promising traffic signal control policy that can be trained on large road networks. Current SOTA methods model road networks as topological graph structures, incorporate graph attention into deep Q-learning, and merge local and global embeddings to improve policy. However, graph-based methods are difficult to para

Niklas Ippisch, Anna-Carolina Haensch, Jan Simson, Jacob Beck

Despite the growing use of large language models (LLMs) for providing feedback, limited research has explored how to achieve high-quality feedback. This case study introduces an evaluation framework to assess different zero-shot prompt engineering methods. We varied the prompts systematically and analyzed the provided feedback on programming errors in R. The

Xinchen Zhang, Running Zhao, Zhihan Jiang, Handi Chen

Intrusion Detection Systems (IDS) are crucial for safeguarding digital infrastructure. In dynamic network environments, both threat landscapes and normal operational behaviors are constantly changing, resulting in concept drift. While continuous learning mitigates the adverse effects of concept drift, insufficient attention to drift patterns and excessive pr

Yijia Shao, Vinay Samuel, Yucheng Jiang, John Yang

While the advancement of large language models has spurred the development of AI agents to automate tasks, numerous use cases inherently require agents to collaborate with humans due to humans' latent preferences, domain expertise, or the need for control. To facilitate the study of human-agent collaboration, we introduce Collaborative Gym (Co-Gym), an o

P. S. Vinayagam, R. Radha

In this paper, based on an integrable model governed by four system parameters, namely, spin-orbit coupling and Rabi coupling which are constants while the other two parameters, namely the harmonic trap and scattering lengths which are time dependent, we investigate the spin orbit-Rabi coupled condensates governed by the two coupled Gross-Pitaevskii(GP) equa

Guangchong Zhou, Zeren Zhang, Guoliang Fan

Exploration in cooperative multi-agent reinforcement learning (MARL) remains challenging for value-based agents due to the absence of an explicit policy. Existing approaches include individual exploration based on uncertainty towards the system and collective exploration through behavioral diversity among agents. However, the introduction of additional struc

Marco Gortan, Lorenzo Testa, Giorgio Fagiolo, Francesco Lamperti

High-resolution gridded climate data are readily available from multiple sources, yet climate research and decision-making increasingly require country and region-specific climate information weighted by socio-economic factors. Moreover, the current landscape of disparate data sources and inconsistent weighting methodologies exacerbates the reproducibility c

Thomas Walker

Machine learning models are trained with relatively simple objectives, such as next token prediction. However, on deployment, they appear to capture a more fundamental representation of their input data. It is of interest to understand the nature of these representations to help interpret the model's outputs and to identify ways to improve the salience of th

Tomohiro Shigemura, Keito Shimizu, Sotaro Sugishita, Daichi Takeda

We propose a formulation of black hole thermodynamics that incorporates the notions of heat and work, based on the thermodynamics in quantum theory and the AdS/CFT correspondence. First, for coupled holographic CFTs, we define a coarse-graining procedure adopting the principle of maximum entropy. Employing this approach, when the system is divided into a tar

Mengying Wu, Geng Hong, Jinsong Chen, Qi Liu

In the digital age, device search engines such as Censys and Shodan play crucial roles by scanning the internet to catalog online devices, aiding in the understanding and mitigation of network security risks. While previous research has used these tools to detect devices and assess vulnerabilities, there remains uncertainty regarding the assets they scan, th

Olympio Hacquard

In this paper, we introduce a novel method for extending Ricci flow to hypergraphs by defining probability measures on the edges and transporting them on the line expansion. This approach yields a new weighting on the edges, which proves particularly effective for community detection. We extensively compare this method with a similar notion of Ricci flow def

Nikita Markarian

This is the first of two parts of a project devoted to a geometric interpretation of the Deligne-Terasoma approach to regularized double shuffle relations. The central fact of this approach is the isomorphism between vanishing cycles of multiplicative convolution of certain perverse sheaves and the tensor product of vanishing cycles, which may be written in

Xin Li, Dongya Wu

We consider the nonconvex regularized method for low-rank matrix recovery. Under the assumption on the singular values of the parameter matrix, we provide the recovery bound for any stationary point of the nonconvex method by virtue of regularity conditions on the nonconvex loss function and the regularizer. This recovery bound can be much tighter than that

Vincenzo Calabrò, Carlotta Giannelli, Lorenzo Sacco, Alessandra Sestini

The distinctive feature of a polynomial parametric speed let polynomial Pythagorean-hodograph (PH) curves be attractive for the design of accurate and efficient application algorithms. We propose a robust path following scheme for the construction of smooth spatial motions by exploiting PH spline curves. In order to cover a general configuration setting, we

Norimi Yokozaki, Junhao Zhu

We propose a grand unified model in five dimensions that addresses the strong CP problem. In this framework, the boundary conditions that break the unified gauge group into the Standard Model gauge group simultaneously explain the mass splitting of the new matter content required for gauge coupling unification and the emergence of the QCD axion as the $U(1)_

Xiantao Hu, Ying Tai, Xu Zhao, Chen Zhao

Multimodal tracking has garnered widespread attention as a result of its ability to effectively address the inherent limitations of traditional RGB tracking. However, existing multimodal trackers mainly focus on the fusion and enhancement of spatial features or merely leverage the sparse temporal relationships between video frames. These approaches do not fu

Hongbo Li, Lingjie Duan

In mobile edge computing (MEC) networks, mobile users generate diverse machine learning tasks dynamically over time. These tasks are typically offloaded to the nearest available edge server, by considering communication and computational efficiency. However, its operation does not ensure that each server specializes in a specific type of tasks and leads to s

Zihan Ding, Chi Jin, Difan Liu, Haitian Zheng

Diffusion probabilistic models have shown significant progress in video generation; however, their computational efficiency is limited by the large number of sampling steps required. Reducing sampling steps often compromises video quality or generation diversity. In this work, we introduce a distillation method that combines variational score distillation an

Mahsa Zare, Saeid Alikhani, Mohammad Reza Oboudi

Let $ G=(V,E) $ be a simple graph of order $ n $ and size $ m $. A connected edge cover set of a graph is a subset $S$ of edges such that every vertex of the graph is incident to at least one edge of $S$ and the subgraph induced by $S$ is connected. We initiate the study of the number of the connected edge cover sets of a graph $G$ with cardinality $i$, $ e_

Mihai Alexe, Eulalie Boucher, Peter Lean, Ewan Pinnington

We introduce GraphDOP, a new data-driven, end-to-end forecast system developed at the European Centre for Medium-Range Weather Forecasts (ECMWF) that is trained and initialised exclusively from Earth System observations, with no physics-based (re)analysis inputs or feedbacks. GraphDOP learns the correlations between observed quantities - such as brightness t

Valerio Buttinelli

We study the projective normality of the projective bundle of an Ulrich vector bundle embedded through the complete linear system of its tautological line bundle. The focus will be on Ulrich bundles defined over curves, surfaces with $q=p_g=0$ and hypersurfaces of dimension $2$ and $3.$

Marco Buratti, Anita Pasotti

The shiftable Heffter arrays are naturally generalized to the shiftable Heffter spaces. We present a recursive construction which starting from a single shiftable Heffter space leads to infinitely many other shiftable Heffter spaces of the same degree. We also present a direct construction making use of pandiagonal magic squares leading to a shiftable $(16\e

Manoj Kumar, Nikhila Awasthi, Monika Randhawa, Manmohan Gupta

Taking clue from the minimal structure of texture 4-zero hermitian mass matrices, which are very successful in accommodating quark mixing data, we propose a form of texture 2-zero complex symmetric neutrino mass matrix with only one phase parameter. This minimal mass matrix not only accommodates the available neutrino oscillation data, but also makes interes

Evgenia Ilia, Wilker Aziz

With the broader use of language models (LMs) comes the need to estimate their ability to respond reliably to prompts (e.g., are generated responses likely to be correct?). Uncertainty quantification tools (notions of confidence and entropy, i.a.) can be used to that end (e.g., to reject a response when the model is `uncertain'). For example, Kuhn et al. (se

J. Muñoz-Cámara, D. Crespí-Llorens, J. P. Solano, P. G. Vicente

Three-orifice baffles equally spaced along a circular tube are investigated as a means for heat transfer enhancement under net, oscillatory and compound flows. An unprecedented, systematic analysis of the relative orientation of consecutive baffles -- aligned or opposed -- is accomplished to assess the changes induced on the flow structure and their impact o

P Raghavendra Rao, Pooja Vyavahare

We study the distributed consensus of state vectors in a discrete-time multi-agent network with matrix edge weights using stochastic matrix convergence theory. We present a distributed asynchronous time update model wherein one randomly selected agent updates its state vector at a time by interacting with its neighbors. We prove that all agents converge to s

Naoki Shioji, Satoshi Tanaka, Kohtaro Watanabe

The uniqueness and multiple existence of positive radial solutions to the Brezis-Nirenberg problem on a domain in the 3-dimensional unit sphere ${\mathbb S}^3$ \begin{equation*} \left\{ \begin{aligned} \Delta_{{\mathbb S}^3}U -\lambda U + U^p&=0,\, U>0 && \text{in $\Omega_{\theta_1,\theta_2}$,}\\ U &= 0&&\text{on $\partial \Omega_{\theta_1,\theta_2}$,} \end{

Hauke F. Maathuis, Roeland De Breuker, Saullo G. P. Castro

Design optimisation offers the potential to develop lightweight aircraft structures with reduced environmental impact. Due to the high number of design variables and constraints, these challenges are typically addressed using gradient-based optimisation methods to maintain efficiency. However, this approach often results in a local solution, overlooking the

Yu Tian, Yixuan Li, Baoliang Chen, Hanwei Zhu

Assessing the quality of artificial intelligence-generated images (AIGIs) plays a crucial role in their application in real-world scenarios. However, traditional image quality assessment (IQA) algorithms primarily focus on low-level visual perception, while existing IQA works on AIGIs overemphasize the generated content itself, neglecting its effectiveness i

Jahnavi Kumar, Sridhar Chimalakonda

Code review is a crucial process before deploying code to production, as it validates the code, provides suggestions for improvements, and identifies errors such as missed edge cases. In projects with regular production releases, the effort required for peer code-reviews remains high. Consequently, there has been significant interest from software engineerin

Vishwajeet Marathe, Deewan Bajracharya, Changhui Yan

During a virus's evolution,various regions of the genome are subjected to distinct levels of functional constraints.Combined with factors like codon bias and DNA repair efficiency,these constraints contribute to unique mutation patterns within the genome or a specific gene. In this project, we harnessed the power of Large Language Models(LLMs) to predict the

Lea Multerer, Pierluigi Francesco De Paola, Marta Lenatti, Alessia Paglialonga

Type 2 diabetes progresses slowly and may be reversed through lifestyle changes, but quantifying the long-term impact of regular physical activity remains challenging due to sparse longitudinal data. Mechanistic models offer a powerful tool by simulating metabolic processes over extended timescales. However, multi-scale formulations that capture both the sho

Marwan AbdElhameed, Pavly Halim

Recent advances in large language models (LLMs) have predominantly focused on maximizing accuracy and reasoning capabilities, often overlooking crucial computational efficiency considerations. While this approach has yielded impressive accuracy improvements, it has led to methods that may be impractical for real-world deployment due to computational overhead

Sandra Albrechtsen, Matthias Hamann

We prove that every locally finite, quasi-transitive graph with a thick end whose cycle space is generated by cycles of bounded length contains the full-grid as an asymptotic minor and as a diverging minor. This in particular includes all locally finite Cayley graphs of finitely presented groups that are not virtually free, and partially solves problems of G

Xinzhe Li, Jiahui Zhan, Shengfeng He, Yangyang Xu

Personalized image generation has made significant strides in adapting content to novel concepts. However, a persistent challenge remains: balancing the accurate reconstruction of unseen concepts with the need for editability according to the prompt, especially when dealing with the complex nuances of facial features. In this study, we delve into the tempora

Mengshi Qi, Yuxin Yang, Huadong Ma

Effective modeling of group interactions and dynamic semantic intentions is crucial for forecasting behaviors like trajectories or movements. In complex scenarios like sports, agents' trajectories are influenced by group interactions and intentions, including team strategies and opponent actions. To this end, we propose a novel diffusion-based trajectory pre

Ziye Wang, Yakefu Reyimuaji, Nijiati Yalikun

A theoretical framework based on a spontaneously broken $Z_4$ symmetry is proposed to simultaneously explain neutrino mass generation via the inverse seesaw mechanism and dark matter (DM) production through a freeze-in scenario. This work extends the standard model with right-handed neutrinos $N_i$, additional fermions $\chi_i$, and a complex scalar $S$. An

Gennaro Cordasco, Luisa Gargano, Adele A. Rescigno

With the rise of social media, misinformation has significant negative effects on the decision-making of individuals, organizations and communities within society. Identifying and mitigating the spread of fake information is a challenging issue. We consider a generalization of the Domination problem that can be used to detect a set of individuals who, throug

Yifei Sun, Zhanghao Chen, Hao Zheng, Wenming Deng

Lung diseases represent a significant global health challenge, with Chest X-Ray (CXR) being a key diagnostic tool due to its accessibility and affordability. Nonetheless, the detection of pulmonary lesions is often hindered by overlapping bone structures in CXR images, leading to potential misdiagnoses. To address this issue, we develop an end-to-end framewo

Yujun Zhu, Danqing Shi, Hee-Seung Moon, Antti Oulasvirta

We present a model for inferring where users look during interaction based on keypress data only. Given a key log, it outputs a scanpath that tells, moment-by-moment, how the user had moved eyes while entering those keys. The model can be used as a proxy for human data in cases where collecting real eye tracking data is expensive or impossible. Our technical

Liping Yang, Hao Zhang

Adolphson and Sperber characterized the unique unit root of $L$-function associated with toric exponential sums in terms of the $\mathcal{A}$-hypergeometric functions. For the unit root $L$-function associated with a family of toric exponential sums, Haessig and Sperber conjectured its unit root behaves similarly to the classical case studied by Adolphson an

Feng Yan, Andreas Koch, Oliver Sinnen

This paper thoroughly surveys machine learning (ML) algorithms acceleration in hardware accelerators, focusing on Field-Programmable Gate Arrays (FPGAs). It reviews 287 out of 1138 papers from the past six years, sourced from four top FPGA conferences. Such selection underscores the increasing integration of ML and FPGA technologies and their mutual importan

Friedrich Wagner, Frauke Liers

Motivated by recent progress in quantum hardware and algorithms researchers have developed quantum heuristics for optimization problems, aiming for advantages over classical methods. To date, quantum hardware is still error-prone and limited in size such that quantum heuristics cannot be scaled to relevant problem sizes and are often outperformed by their cl

Xiaohan Zhang, Sebastian Starke, Vladimir Guzov, Zhensong Zhang

Synthesizing natural human motion that adapts to complex environments while allowing creative control remains a fundamental challenge in motion synthesis. Existing models often fall short, either by assuming flat terrain or lacking the ability to control motion semantics through text. To address these limitations, we introduce SCENIC, a diffusion model desig

Gennaro Cordasco, Luisa Garagano, Adele A. Rescigno

Identifying and mitigating the spread of fake information is a challenging issue that has become dominant with the rise of social media. We consider a generalization of the Domination problem that can be used to detect a set of individuals who, once immunized, can prevent the spreading of fake narratives. The considered problem, named {\em Distance Vector Do

Sara Oliver-Bonafoux, Raul Toral, Amitabha Chakrabarti

We study the Ising model under a time-varying, but spatially homogeneous, Gaussian random magnetic field. In the Monte Carlo simulations, we go beyond the standard analysis of the order parameter by measuring the magnetization probability distribution as a function of temperature and field strength, and by computing the time required for the system to escape

Shamse Tasnim Cynthia, Saikat Mondal, Joy Krishan Das, Banani Roy

Gender diversity enhances research by bringing diverse perspectives and innovative approaches. It ensures equitable solutions that address the needs of diverse populations. However, gender disparity persists in research where women remain underrepresented, which might limit diversity and innovation. Many even leave scientific careers as their contributions o

Guancheng Zeng, Wentao Ding, Beining Xu, Chi Zhang

Enterprises possess a vast array of API assets scattered across various functions, forming the backbone of existing business processes. By leveraging these APIs as functional tools, enterprises can design diverse, scenario-specific agent applications, driven by on-premise function-calling models as the core engine. However, generic models often fail to meet

Yuebei Xiong, Zhirui Gong, Hao Jin

Due to the time reversal symmetry, the linear anomalous Hall effect (AHE) usually vanishes in MoS2 monolayer. In contrast, the nonlinear AHE plays an essential role in such system when the uniaxial strain breaks the C3v symmetry and eventually results in the nonzero Berry curvature dipole (BCD). We find that not only the magnitude of the AHE but also the non

ATLAS Collaboration

A measurement is presented of elliptic ($v_2$) and triangular ($v_3$) azimuthal anisotropy coefficients for charged particles produced in Pb+Pb collisions at $\sqrt{s_{_\text{NN}}} = 5.02$ TeV using a dataset corresponding to an integrated luminosity of $0.44$ nb$^{-1}$ collected with the ATLAS detector at the LHC in 2018. The values of $v_2$ and $v_3$ are m

Ian M. Musson

Let ${\mathtt{k}}$ be an algebraically closed field of characteristic zero and $n, m$ coprime positive integers. Let ${\stackrel{{\rm o}}{{\mathfrak{g}}}}$ be the Lie superalgebra ${\mathfrak{gl}}(n|m)$ with root system $\Delta$. Using $\Delta$, Sergeev and Veselov, \cite{SV2} introduced an action of the Weyl groupoid ${\mathcal{W}}$, in connection with thei

Annie D'souza, Swetha M, Sunita Sarawagi

Handling imbalance in class distribution when building a classifier over tabular data has been a problem of long-standing interest. One popular approach is augmenting the training dataset with synthetically generated data. While classical augmentation techniques were limited to linear interpolation of existing minority class examples, recently higher capacit

Takahiro Kanazawa, Kenta Ishimoto

We studied locomotion of a crawler on a thin Newtonian fluid film whose viscosity varied spatially. We first derived a general locomotion velocity formula with fluid viscosity variations via the lubrication theory. For further analysis, the surface of the crawler was described by a combination of transverse and longitudinal travelling waves and we analysed t

Sieun Hyeon, Kyudan Jung, Jaehee Won, Nam-Joon Kim

In various academic and professional settings, such as mathematics lectures or research presentations, it is often necessary to convey mathematical expressions orally. However, reading mathematical expressions aloud without accompanying visuals can significantly hinder comprehension, especially for those who are hearing-impaired or rely on subtitles due to l

Alina Mierna, Sabino Matarrese, Nicola Bartolo, Angelo Ricciardone

The Cosmological Gravitational Wave Background (CGWB) anisotropies contain valuable information about the physics of the early universe. Given that General Relativity is intrinsically nonlinear, it is important to look beyond first-order contributions in cosmological perturbations. In this work, we present a non-perturbative approach for the computation of C

Rui-Heng Liu, Xin Liu

Flat band (FB) systems provide ideal playgrounds for studying correlation physics, whereas multi-orbital characteristics in real materials are distinguished from most simple FB models. Here, we propose a systematic and versatile framework for FB constructions in tight-binding (TB) models based on symmetric compact localized states (CLSs), integrating lattice

Barry Menglong Yao, Qifan Wang, Lifu Huang

Large Multimodal Models (LMMs) have demonstrated impressive performance across numerous academic benchmarks. However, fine-tuning still remains essential to achieve satisfactory performance on downstream tasks, while the task-specific tuning samples are usually not readily available or expensive and time-consuming to obtain. To address this, we propose an er

Alessandro Goffi

We establish a linear $L^p$ rate of convergence, $1<p<\infty$, with respect to the viscosity $\varepsilon$ for the vanishing viscosity process of semiconcave solutions of Hamilton-Jacobi equations by regularizing the PDE with the half-Laplacian $-\varepsilon(-\Delta)^{1/2}$. Our result reveals a nonlocal phenomenon, since it improves the known estimates obta

Wentao Tan, Qiong Cao, Yibing Zhan, Chao Xue

Human preference alignment can greatly enhance Multimodal Large Language Models (MLLMs), but collecting high-quality preference data is costly. A promising solution is the self-evolution strategy, where models are iteratively trained on data they generate. However, current techniques still rely on human- or GPT-annotated data and sometimes require additional

Wenxi Chen, Ziyang Ma, Ruiqi Yan, Yuzhe Liang

Recent advancements highlight the potential of end-to-end real-time spoken dialogue systems, showcasing their low latency and high quality. In this paper, we introduce SLAM-Omni, a timbre-controllable, end-to-end voice interaction system with single-stage training. SLAM-Omni achieves zero-shot timbre control by modeling spoken language with semantic tokens a

Kaoru Yoneda, Tsuyoshi Yoneda

We give a sufficient condition for the local limit theorem. To construct it, we employ infinite times of convolutions of probability density functions.

Tobias Glasmachers

We design a class of variable metric evolution strategies well suited for high-dimensional problems. We target problems with many variables, not (necessarily) with many objectives. The construction combines two independent developments: efficient algorithms for scaling covariance matrix adaptation to high dimensions, and evolution strategies for multi-object

Xiuli Bi, Jian Lu, Bo Liu, Xiaodong Cun

Benefiting from large-scale pre-training of text-video pairs, current text-to-video (T2V) diffusion models can generate high-quality videos from the text description. Besides, given some reference images or videos, the parameter-efficient fine-tuning method, i.e. LoRA, can generate high-quality customized concepts, e.g., the specific subject or the motions f

Chuanrui Hu, Shichong Xie, Baoxin Wang, Bin Chen

Large language models (LLMs), adopted to understand human language, drive the development of artificial intelligence (AI) web search agents. Compared to traditional search engines, LLM-powered AI search agents are capable of understanding and responding to complex queries with greater depth, enabling more accurate operations and better context recognition. H

Wala Draidi Areed, Thi Thanh Thao Nguyen, Kien Quoc Do, Thinh Nguyen

The Mekong Delta Region of Vietnam faces increasing dengue risks driven by urbanization, globalization, and climate change. This study introduces a probabilistic forecasting model for predicting dengue incidence and outbreaks with one to three month lead times, integrating meteorological, sociodemographic, preventive, and epidemiological data. Seventy-two mo

Xuefei Cao, Shijia Wang, Yongdao Zhou

In this paper, we address the challenge of Markov Chain Monte Carlo (MCMC) algorithms within the approximate Bayesian Computation (ABC) framework, which often get trapped in local optima due to their inherent local exploration mechanism. We propose a novel Global-Local ABC-MCMC algorithm that combines the ``exploration" capabilities of global proposals with

Yong Luo, Xianjing Zheng

In this paper we study eigenvalues of the Dirichlet Laplacian on conformally flat Riemannian manifolds. In particular we establish some universal inequality for eigenvalues of the Dirichlet Laplacian on the hyperbolic space $\mathbb{H}^n(-1)$.

Malena T. Bauer, Udo Seifert, Jann van der Meer

A major goal of stochastic thermodynamics is to estimate the inevitable dissipation that accompanies particular observable phenomena in an otherwise not fully accessible system. Quantitative results are often formulated as lower bounds on the total entropy production, which capture the part of the total dissipation that can be determined based on the availab

Saikat Das, Ayuki Kamada, Takumi Kuwahara, Kohta Murase

Composite asymmetric dark matter (ADM) is the framework that naturally explains the coincidence of the baryon density and the dark matter density of the Universe. Through a portal interaction sharing particle-antiparticle asymmetries in the Standard Model and dark sectors, dark matter particles, which are dark-sector counterparts of baryons, can decay into a

Mihail Hamamdjiev, Milen Ivanov, Nadia Zlateva

We present a version of the Clarke-Ledyaev inequality that does not involve elements of the dual space. The proof relies mainly on geometry and on the classical lemma of Bishop and Phelps. In addition, this approach allows us to provide a simplified proof of the Clakre-Ledyaev inequality. The approach is primal in the sense that no dual arguments are used.

Lunjun Liu, Weilai Jiang, Yaonan Wang

In multi-agent reinforcement learning (MARL), the centralized training with decentralized execution (CTDE) framework has gained widespread adoption due to its strong performance. However, the further development of CTDE faces two key challenges. First, agents struggle to autonomously assess the relevance of input information for cooperative tasks, impairing

Anh-Tuan Tran, Nguyen Quang Bau, Nguyen Dinh Nam, Cao Thi Vi Ba

Based on the quantum kinetic equation for electrons, we theoretically study the quantum multi-photon non-linear absorption of a strong electromagnetic wave (EMW) in two-dimensional graphene. Two cases of the electron scattering mechanism are considered: Electron-optical phonon scattering and electron-acoustic phonon scattering. The general multi-photon absor

Kushagra Srivastava, Damodar Datta Kancharla, Rizvi Tahereen, Pradeep Kumar Ramancharla

Crack segmentation plays a crucial role in ensuring the structural integrity and seismic safety of civil structures. However, existing crack segmentation algorithms encounter challenges in maintaining accuracy with domain shifts across datasets. To address this issue, we propose a novel deep network that employs incremental training with unsupervised domain

Yong Luo, Xianjing Zheng

In this paper we study eigenvalues of Laplacian and biharmonic operators on compact domains in complete manifolds. We establish several new inequalities for eigenvalues of Laplacian and biharmonic operators respectively by using Sobolev type inequalities.

S. G. Pyatkov, O. A. Soldatov

Under consideration are mathematical models of heat and mass transfer. We study inverse problems of recovering lower-order coefficients in a second order parabolic equation. The coefficients are representable in the form of a finite segments of the series whose coefficients depending on time are to be determined. The linear case is also considered. The overd

Xin Du, Shifan Ye, Qian Zheng, Yangfan Hu

Large language models (LLMs) have been widely applied in various practical applications, typically comprising billions of parameters, with inference processes requiring substantial energy and computational resources. In contrast, the human brain, employing bio-plausible spiking mechanisms, can accomplish the same tasks while significantly reducing energy con

Alberto Quaini

These lecture notes cover advanced topics in linear regression, with an in-depth exploration of the existence, uniqueness, relations, computation, and non-asymptotic properties of the most prominent estimators in this setting. The covered estimators include least squares, ridgeless, ridge, and lasso. The content follows a proposition-proof structure, making

Tran Ky Vi, Nguyen Anh Tuan, Le Nguyen Dinh Khoi, Nguyen Quang Hoc

We present a systematic theoretical study of the multi-photon nonlinear optical absorption properties of a $\text{GaAs/A}{{\text{l}}_{x}}\text{G}{{\text{a}}_{1-x}}\text{As}$ based quantum well (QW) structure with Morse confinement potential under the influence of a magnetic field. Based on the stationary states due to the electron confinement in Morse QWs an

Gustavo Bergantiños, Juan D. Moreno-Ternero

We study the problem of allocating the revenues raised via paid subscriptions to music streaming platforms among participating artists. We show that the main methods to solve streaming problems (pro-rata, user-centric and families generalizing them) can be seen as specific (well-known) rules to solve (multi-issue) claims problems. Our results permit to provi

Nguyen Thu Huong, Nguyen Quang Bau, Cao Thi Vi Ba, Bui Thi Dung

Magnetoresistance oscillations in semiconductor quantum wells, with the semi-parabolic plus semi-inverse squared potential, under the influence of intense electromagnetic waves (IEMW), is studied theoretically. Analytical expression for the longitudinal magnetoresistance (LMR) is derived from the quantum kinetic equation for electrons, using the Fr\"ohlich H

Jeongsoo Kang, Chanpyo Kim, Younghun Kim, Younghun Kwon

The transmon, which has a short gate time and remarkable scalability, is the most commonly utilized superconducting qubit, based on the Cooper pair box as a qubit or coupler in superconducting quantum computers. Lattice and heavy-hexagon structures are well-known large-scale configurations for transmon-based quantum computers that classical computers cannot

Mengyu Ye, Tatsuki Kuribayashi, Goro Kobayashi, Jun Suzuki

Interpreting the internal process of neural models has long been a challenge. This challenge remains relevant in the era of large language models (LLMs) and in-context learning (ICL); for example, ICL poses a new issue of interpreting which example in the few-shot examples contributed to identifying/solving the task. To this end, in this paper, we design syn

Richard E. Spinney, Richard G. Morris

A prototypical model of symmetry-broken active matter -- biased quorum-sensing active particles (bQSAPs) -- is used to extend notions of dynamic critical phenomena to the paradigmatic setting of driven transport, where characteristic behaviours are nonstationary and involve persistent fluxes. To do so, we construct an effective field theory with a single ord

Tomasz Grzywny, Karol Szczypkowski, Zbigniew Palmowski, Bartosz Trojan

We study a $d$-dimensional stochastic process $\mathbf{X}$ which arises from a L\'evy process $\mathbf{Y}$ by partial resetting, that is the position of the process $\mathbf{X}$ at a Poisson moment equals $c$ times its position right before the moment, and it develops as $\mathbf{Y}$ between these two consecutive moments, $c \in (0, 1)$. We focus on $\mathbf

Mihaela Ifrim, Ben Pineau, Daniel Tataru, Mitchell A. Taylor

In this article, we provide a definitive well-posedness theory for the free boundary problem in incompressible magnetohyrodynamics. Despite the clear physical interest in this system and the remarkable progress in the study of the free boundary Euler equations in recent decades, the low regularity well-posedness of the free boundary MHD equations has remaine

Rudrajit Choudhuri, Ambareesh Ramakrishnan, Amreeta Chatterjee, Bianca Trinkenreich

Generative AI (genAI) tools (e.g., ChatGPT, Copilot) have become ubiquitous in software engineering (SE). As SE educators, it behooves us to understand the consequences of genAI usage among SE students and to create a holistic view of where these tools can be successfully used. Through 16 reflective interviews with SE students, we explored their academic exp

Hongyi Li, Jiawei Ye, Jie Wu, Tianjie Yan

Large Language Models (LLMs) aligned with human feedback have recently garnered significant attention. However, it remains vulnerable to jailbreak attacks, where adversaries manipulate prompts to induce harmful outputs. Exploring jailbreak attacks enables us to investigate the vulnerabilities of LLMs and further guides us in enhancing their security. Unfortu

Tommaso Lorenzi, Nadia Loy, Chiara Villa

The capability of cells to form surface extensions to non-locally probe the surrounding environment plays a key role in cell migration. The existing mathematical models for migration of cell populations driven by this non-local form of environmental sensing rely on the simplifying assumption that cells in the population share the same cytoskeletal properties