February 2025 arXiv papers — page 26

Kuang Wang, Xianfei Li, Shenghao Yang, Li Zhou

User simulators are crucial for replicating human interactions with dialogue systems, supporting both collaborative training and automatic evaluation, especially for large language models (LLMs). However, current role-playing methods face challenges such as a lack of utterance-level authenticity and user-level diversity, often hindered by role confusion and

Jost-Hinrich Eschenburg, Ernst Heintze, Peter Quast

We prove that a compact, intrinsically symmetric submanifold of a Euclidean space is extrinsically symmetric if and only if its maximal tori are Clifford tori in the ambient space. Moreover, we show that this result can be used to give a geometric proof of a result of Harish-Chandra on strongly orthogonal roots in semisimple Lie algebras.

Jiaxin Deng, Shiyao Wang, Kuo Cai, Lejian Ren

Recently, generative retrieval-based recommendation systems have emerged as a promising paradigm. However, most modern recommender systems adopt a retrieve-and-rank strategy, where the generative model functions only as a selector during the retrieval stage. In this paper, we propose OneRec, which replaces the cascaded learning framework with a unified gener

Julien Poisat

Random walks with a disordered self-interaction potential may be used to model charged polymers. In this paper we consider a one-dimensional and directed version of the charged polymer model that was introduced by Derrida, Griffiths and Higgs. We prove new results for the associated quenched free energy, including a variational formula based on a quenched la

Stephen Jun Villejo, Sara Martino, Janine Illian, William Ryan

This work tackles the problem of uncertainty propagation in two-stage Bayesian models, with a focus on spatial applications. A two-stage modeling framework has the advantage of being more computationally efficient than a fully Bayesian approach when the first-stage model is already complex in itself, and avoids the potential problem of unwanted feedback effe

Stefano Marchesin, Gianmaria Silvello

Knowledge Graphs (KGs) are widely used in data-driven applications and downstream tasks, such as virtual assistants, recommendation systems, and semantic search. The accuracy of KGs directly impacts the reliability of the inferred knowledge and outcomes. Therefore, assessing the accuracy of a KG is essential for ensuring the quality of facts used in these ta

Weilin Chen, Ruichu Cai, Junjie Wan, Zeqin Yang

Long-term causal inference has drawn increasing attention in many scientific domains. Existing methods mainly focus on estimating average long-term causal effects by combining long-term observational data and short-term experimental data. However, it is still understudied how to robustly and effectively estimate heterogeneous long-term causal effects, signif

Chao Zu, Yufeng Lu

Let $\theta(z),\varphi(w)$ be two nonconstant inner functions and $M$ be a submodule in $H^2(\mathbb{D}^2)$. Let $C_{\theta,\varphi}$ denote the composition operator on $H^2(\mathbb{D}^2)$ defined by $C_{\theta,\varphi}f(z,w)=f(\theta(z),\varphi(w))$, and $M_{\theta,\varphi}$ denote the submodule $[C_{\theta,\varphi}M]$, that is, the smallest submodule conta

Ruiqing Han, Tianxian Zhang, Han Zhong, Yuanhang Wang

The low detectability and low cost of unmanned aerial vehicles (UAVs) allow them to swarm near the radar network for effective jamming. The key to jamming is the reasonable task assignment and resource allocation of UAVs. However, the existing allocation model is somewhat ideal, weakly adaptive to the dynamic environment, and rarely considers frequency match

Tapasvi Panchagnula

Dream narratives provide a unique window into human cognition and emotion, yet their systematic analysis using artificial intelligence has been underexplored. We introduce DreamNet, a novel deep learning framework that decodes semantic themes and emotional states from textual dream reports, optionally enhanced with REM-stage EEG data. Leveraging a transforme

Zhihua Xiong, Jiaqi Li, Xulin Mou, Tiankuo Wang

With the widespread application of composite structures in the fields of building and bridge constructions, thin-covered composite dowels are increasingly adopted in various engineering scenarios. This paper presents a design methodology for thin-covered composite dowels, supported by both experimental and theoretical investigations. In the experiment, a nov

Yiqin Yang, Quanwei Wang, Chenghao Li, Hao Hu

Offline reinforcement learning (RL) represents a significant shift in RL research, allowing agents to learn from pre-collected datasets without further interaction with the environment. A key, yet underexplored, challenge in offline RL is selecting an optimal subset of the offline dataset that enhances both algorithm performance and training efficiency. Redu

Juraj Dončević, Mario Brčić, Danijel Mlinarić

This paper presents an innovative approach to applying bidirectional transformations (BX) in practice. To introduce BX to a wider audience of technologists, engineers, and researchers, we have chosen to use C# to develop Bifrons - a library of BX lenses that replaces domain-specific programming languages (DSL) in practical use. The proposed approach simplifi

Angelo Garofalo, Alessandro Ottaviano, Matteo Perotti, Thomas Benz

Next-generation mixed-criticality Systems-on-chip (SoCs) for robotics, automotive, and space must execute mixed-criticality AI-enhanced sensor processing and control workloads, ensuring reliable and time-predictable execution of critical tasks sharing resources with non-critical tasks, while also fitting within a sub-2W power envelope. To tackle these multi-

Lei Zhao, Sizhou Chen, Linfeng Feng, Jichao Zhang

Text-to-audio (TTA), which generates audio signals from textual descriptions, has received huge attention in recent years. However, recent works focused on text to monaural audio only. As we know, spatial audio provides more immersive auditory experience than monaural audio, e.g. in virtual reality. To address this issue, we propose a text-to-spatial-audio (

Neha Gupta, Aditya Maheshwari, Dheeraj Goyal

In this paper, we study a generalized version of the Poisson-type process by time-changing it with the geometric counting process. Our work generalizes the work done by Meoli (2023) \cite{meoli2023some}. We defined the geometric subordinated Poisson process (GSPP), the geometric subordinated compound Poisson process (GSCPP) and the geometric subordinated mul

Zhiyun Cheng

Recently, Chmutov introduced the partial duality of ribbon graphs, which can be regarded as a generalization of the classical Euler-Poincar\'e duality. The partial-dual genus polynomial $^\partial\varepsilon_G(z)$ is an enumeration of the partial duals of $G$ by Euler genus. For an intersection graph derived from a given chord diagram, the partial-dual genus

Moritz Winterott, Samir Lounis

Due to their particle-like properties, three-dimensional (3D) spin textures have garnered significant interest, particularly for their potential applications in next-generation information storage devices. However, efficiently identifying these textures remains a major challenge. Here, we approach this problem from a new perspective. Rather than relying sole

Alexander J. Gallo, Sribalaji C. Anand, André M. H. Teixeira, Riccardo M. G. Ferrari

Active techniques have been introduced to give better detectability performance for cyber-attack diagnosis in cyber-physical systems (CPS). In this paper, switching multiplicative watermarking is considered, whereby we propose an optimal design strategy to define switching filter parameters. Optimality is evaluated exploiting the so-called output-to-output g

Viktoria Freingruber, Tommaso Lorenzi, Kevin J. Painter, Mariya Ptashnyk

A novel trait-structured Keller-Segel model that explores the dynamics of a migrating cell population guided by chemotaxis in response to an external ligand concentration is derived and analysed. Unlike traditional Keller-Segel models, this framework introduces an explicit representation of ligand-receptor bindings on the cell membrane, where the percentage

Muhammad Salman Ali, Chaoning Zhang, Marco Cagnazzo, Giuseppe Valenzise

3D Gaussian Splatting (3DGS) has recently emerged as a pioneering approach in explicit scene rendering and computer graphics. Unlike traditional neural radiance field (NeRF) methods, which typically rely on implicit, coordinate-based models to map spatial coordinates to pixel values, 3DGS utilizes millions of learnable 3D Gaussians. Its differentiable render

Jingxin Chen, Xiang Huang, Zhihan Qiao, Jiao Li

Electrical transport in oxide thin films under high pressure remains largely unexplored due to the lack of a universal experimental strategy. Here we develop an approach that enables high-pressure transport measurements in freestanding oxide films by enhancing their mechanical robustness and integrating them with nanoscale high-pressure devices. As a demonst

Tao Wang, Xiaojing Yang

We consider a family of toroidal graphs, denoted by $\mathcal{T}_{i, j}$, which contain neither $i$-cycles nor $j$-cycles. A graph $G$ is $(d, h)$-decomposable if it contains a subgraph $H$ with $\Delta(H) \leq h$ such that $G - E(H)$ is a $d$-degenerate graph. For each pair $(i, j) \in \{(3, 4), (3, 6), (4, 6), (4, 7)\}$, Lu and Li proved that every graph i

Annachiara Korchmaros

2-colored quasi best match graphs (2-qBMGs) are directed graphs that arose in phylogenetics. Investigations of 2-qBMGs have mostly focused on computational issues. However, 2-qBMGs also have relevant properties for structural graph theory; in particular, their undirected underlying graph is free from induced paths and cycles of size at least 6. In this paper

Harry Dobbs, Casey Peat, Oliver Batchelor, James Atlas

Accurate 3D modelling of grapevines is crucial for precision viticulture, particularly for informed pruning decisions and automated management techniques. However, the intricate structure of grapevines poses significant challenges for traditional skeletonization algorithms. This paper presents an adaptation of the Smart-Tree algorithm for 3D grapevine modell

Yu He, Boheng Li, Liu Liu, Zhongjie Ba

Membership Inference Attacks (MIAs) aim to predict whether a data sample belongs to the model's training set or not. Although prior research has extensively explored MIAs in Large Language Models (LLMs), they typically require accessing to complete output logits (\ie, \textit{logits-based attacks}), which are usually not available in practice. In this paper,

Felicia Lucke, Joseph Marchand, Jannik Olbrich

A matching cut is a matching that is also an edge cut. In the problem Minimum Matching Cut, we ask for a matching cut with the minimum number of edges in the matching. We investigate the differences in complexity between Minimum Matching Cut, its counterpart Maximum Matching Cut, and the decision problem Matching Cut. Our polynomial-time algorithms for $P_8$

Chengrui Wang, Haohao Qiu, Sibo Yao, James Lam

Set representations are the foundation of various set-based approaches in state estimation, reachability analysis and fault diagnosis. In this paper, we investigate spectrahedral shadows, a class of nonlinear geometric objects previously studied in semidefinite programming and real algebraic geometry. We demonstrate spectrahedral shadows generalize tradition

Jakub Macina, Nico Daheim, Ido Hakimi, Manu Kapur

Evaluating the pedagogical capabilities of AI-based tutoring models is critical for making guided progress in the field. Yet, we lack a reliable, easy-to-use, and simple-to-run evaluation that reflects the pedagogical abilities of models. To fill this gap, we present MathTutorBench, an open-source benchmark for holistic tutoring model evaluation. MathTutorBe

Sina Mohammadi, Van-Hai Bui, Wencong Su

Low voltage distribution networks (LVDNs) suffer from limited visibility due to sparse or nonexistent measurement systems, leaving distribution network service providers with incomplete data. Maintenance activities, such as transformer upgrades and power line replacements, sometimes go undocumented, leading to unmonitored topology changes. This lack of overs

Sarah Marie Lößlein, Rolf Merz, Yerila Rodríguez-Martínez, Florian Schäfer

To understand the complex interplay of topography and surface chemistry in wetting, fundamental studies investigating both parameters are needed. Due to the sensitivity of wetting to miniscule changes in one of the parameters it is imperative to precisely control the experimental approach. A profound understanding of their influence on wetting facilitates a

M. Behnami, D. V. Efremov, S. Aswartham, G. Shipunov

TaRhTe$_4$ is a type-IIWeyl semimetal, exhibiting fourWeyl points in proximity to the Fermi level. In this article, we report our results of a systematic study of longitudinal magnetoresistance in TaRhTe$_4$. Our findings indicate that magnetoresistance becomes negative only when the magnetic field is applied parallel to the electric field. By rotating E (as

Kaveen Perera, Fouad Khelifi, Ammar Belatreche

This article presents an extended author's version based on our previous work, where we introduced the Multiple Overlapping Tiles (MOT) method for palm vein image enhancement. To better reflect the specific operations involved, we rename MOT to ILACS-LGOT (Intensity-Limited Adaptive Contrast Stretching with Layered Gaussian-weighted Overlapping Tiles). This

Go Ichikawa, Kenji Mishima

A neutron whispering gallery state is a quantum state localized on a material surface bound by the centrifugal force and the material potential. Precise measurements of such quantum states enable tests of quantum mechanics in non-inertial frames, characterization of the surface potential, and searches for hypothetical short-range interactions at the nanomete

Shuyi Liu, Simiao Cui, Haoran Bu, Yuming Shang

Large language models (LLMs) have demonstrated remarkable capabilities across various applications, highlighting the urgent need for comprehensive safety evaluations. In particular, the enhanced Chinese language proficiency of LLMs, combined with the unique characteristics and complexity of Chinese expressions, has driven the emergence of Chinese-specific be

Kanana LLM Team, Yunju Bak, Hojin Lee, Minho Ryu

We introduce Kanana, a series of bilingual language models that demonstrate exceeding performance in Korean and competitive performance in English. The computational cost of Kanana is significantly lower than that of state-of-the-art models of similar size. The report details the techniques employed during pre-training to achieve compute-efficient yet compet

J. Dzian, P. Kubaščík, S. Tázlarů, M. Białek

Antiferromagnetic resonance in a bulk $\alpha$-MnTe crystal is investigated using both frequency-domain and time-domain THz spectroscopy techniques. At low temperatures, an excitation at the photon energy of $(3.5\pm 0.1)$~meV is observed and identified as a magnon mode through its distinctive dependence on temperature and magnetic field. This behavior is re

Yangfan Xu, Qu Hao, Lilian Zhang, Jun Mao

Visual SLAM is essential for mobile robots, drone navigation, and VR/AR, but traditional RGB camera systems struggle in low-light conditions, driving interest in thermal SLAM, which excels in such environments. However, thermal imaging faces challenges like low contrast, high noise, and limited large-scale annotated datasets, restricting the use of deep lear

Koichi Hamaguchi, Natsumi Nagata, Jiaming Zheng

We investigate axion emission from singlet proton Cooper pairs in neutron stars, a process that dominates axion emission in young neutron stars in the KSVZ model. By re-deriving its emissivity, we confirm consistency with most existing literature, except for a recent study that exhibits a different dependence on the effective mass. This discrepancy results i

Sucai Niu, Junyi Zhu, Xueru Wang

The massive Thirring model in the non-laboratory coordinates is considered by the Riemann-Hilbert approach. Existence of global solutions is shown for the cases of the associated Riemann-Hilbert problem without eigenvalues or resonances. The Lipschitz continuity of the map from the potential $v_0(x)\in H^2(\mathbb{R})\cap H^{1,1}(\mathbb{R})$ to the scatteri

Achmad Anggawirya Alimin, Dominik P. Goldstein, Lukas Schulze Balhorn, Artur M. Schweidtmann

We propose a methodology that allows communication with Piping and Instrumentation Diagrams (P&IDs) using natural language. In particular, we represent P&IDs through the DEXPI data model as labeled property graphs and integrate them with Large Language Models (LLMs). The approach consists of three main parts: 1) P&IDs are cast into a graph representation fro

Qiao Liang, Xinwei Deng

Multi-brand analysis based on review comments and ratings is a commonly used strategy to compare different brands in marketing. It can help consumers make more informed decisions and help marketers understand their brand's position in the market. In this work, we propose a multifacet hierarchical sentiment-topic model (MH-STM) to detect brand-associated sent

Lukas J. Spieß, Shuying Chen, Alexander Wilzewski, Malte Wehrheim

We measured the $g$-factor of the excited state $^3\text{P}_1$ in $\text{Ca}^{14+}$ ion to be $g = 1.499032(6)$ with a relative uncertainty of $4\times10^{-6}$. The magnetic field magnitude is derived from the Zeeman splitting of a $\text{Be}^+$ ion, co-trapped in the same linear Paul trap as the highly charged $\text{Ca}^{14+}$ ion. Furthermore, we experime

Weiyan Wang, Xingjian Shi, Ruiqi Shu, Yuan Gao

In practice, physical spatiotemporal forecasting can suffer from data scarcity, because collecting large-scale data is non-trivial, especially for extreme events. Hence, we propose \method{}, a novel probabilistic framework to realize iterative self-training with new self-ensemble strategies, achieving better physical consistency and generalization on extrem

Ziyue Jiang, Yi Ren, Ruiqi Li, Shengpeng Ji

While recent zero-shot text-to-speech (TTS) models have significantly improved speech quality and expressiveness, mainstream systems still suffer from issues related to speech-text alignment modeling: 1) models without explicit speech-text alignment modeling exhibit less robustness, especially for hard sentences in practical applications; 2) predefined align

Wanyi Li, Wei Wei, Yongkang Luo, Peng Wang

Few-shot class-incremental learning (FSCIL) poses significant challenges for artificial neural networks due to the need to efficiently learn from limited data while retaining knowledge of previously learned tasks. Inspired by the brain's mechanisms for categorization and analogical learning, we propose a novel approach called Brain-inspired Analogical Mixtur

Daiki Motai, Issei Suzuki, Taichi Nogami, Takahisa Omata

This study presents a novel method for fabricating stoichiometric SnS thin films with large grain sizes via reactive sputtering using a metallic Sn target and sulfur plasma (S-plasma). Unlike conventional approaches that rely on toxic H2S gas, this method employs a S-plasma to enhance sulfur reactivity and mitigate sulfur deficiencies during film deposition.

Fan Liu, Rui Wang, Jie Yang, Wei-Zhong Zhao

We reinvestigate the Calogero-Sutherland-type (CS-type) models and generalized hypergeometric functions. We construct the generalized CS operators for circular, Hermite, Laguerre, Jacobi and Bessel cases and establish the generalized Lassalle-Nekrasov correspondence. A family of operators are constructed based on the spherical degenerate double affine Hecke

Hanna Anop, Laura Dal Compare, Frederic Nallet, Achille Giacometti

Charged colloids can form ordered structures like Wigner crystals or glasses at very low concentrations due to long-range electrostatic repulsions. Here, we combine small-angle x-ray scattering (SAXS) and optical experiments with simulations to investigate the phase behavior of charged rodlike colloids across a wide range of salt concentrations and packing f

Anjali Agarwal, Wageesh Mishra

Studies for inferring the global characteristics of coronal mass ejections (CMEs) from its multipoint local in situ observations have been undertaken earlier, but there are limited studies utilizing measurements from multiple spacecraft with sufficiently small radial and angular separations. In the present study, we investigate a magnetic cloud (MC) region o

Suguru Shimomura, Jun Tanida, Yusuke Ogura

A spatial photonic Ising machine (SPIM) handles large-scale combinatorial optimization problems owing to optical processing with spatial parallelism. However, iterative feedback in the search for optimal solutions limits processing speed even though the Ising Hamiltonian is computed optically. We propose a parallel spatial photonic Ising machine (pSPIM) util

Borin Keo, Bin Li, Waqas Younis

High trade costs pose substantial barriers to the process of trade liberalization. This study aims to measure trade costs and explore the driving forces behind the growth of bilateral trade between Cambodia and its top 30 trading partners from 1993 to 2019. Using a micro-founded measure of trade costs derived from the gravity model, we find that Cambodia's a

S. Ananya, Channabasavayya, D. Ranganatha, R. G. Veeresha

In the recent past, the work in the area of vanishing coefficients of infinite $q$-products has been taken to the forefront. Weaving the same thread as Ramanujan, Richmond, Szekeres, Andrews, Alladi, Gordon, Mc Laughlin, Baruah, Kaur, Tang, we further prove vanishing coefficients in arithmetic progressions moduli 5, 7, 11, 13, 19, 21, 23 and 29 of the follow

Takumi Kobayashi, Akiko Nishiyama, Kazumoto Hosaka, Daisuke Akamatsu

We report improved absolute frequency measurement of the $^{1}$S$_{0}-^{3}$P$_{0}$ transition of $^{171}$Yb at National Metrology Institute of Japan (NMIJ) by comparing the $^{171}$Yb optical lattice clock NMIJ-Yb1 with 13 Cs primary frequency standards via International Atomic Time from August 2021 to May 2023. The measured absolute frequency is 518 295 836

Jiaming Zhou, Yujie Guo, Shiwan Zhao, Haoqin Sun

Code-switching (CS), the alternation between two or more languages within a single conversation, presents significant challenges for automatic speech recognition (ASR) systems. Existing Mandarin-English code-switching datasets often suffer from limitations in size, spontaneity, and the lack of full-length dialogue recordings with transcriptions, hindering th

So Chigusa, Sudhakantha Girmohanta, Yuichiro Nakai, Yufei Zhang

Axion-like particles can couple to Standard Model gluons, electroweak gauge bosons, and massive fermions. A future multi-TeV muon collider provides a favorable environment to probe axion-like particles through multiple production channels, including vector boson fusion via electroweak gauge boson couplings and the top-associated production mediated by direct

Jin Liu, Wei-Wu Jin, Zhao-Fan Cai, Xin Wang

Topological phase transitions can be remarkably induced purely by manipulating gain and loss mechanisms, offering a novel approach to engineering topological properties. Recent theoretical studies have revealed gain-loss-induced topological disclination states, along with the associated fractional charge trapped at the disclination sites. Here, we present th

Ping Zhang, Zhaorui Zhang, Sheng Di, Yao Xin

Large language model fine-tuning has been identified as an efficient approach to applying the pre-trained Large language models to other domains. To guarantee data privacy for different data owners, models are often fine-tuned in federated learning environments across different data owners, which often involve data heterogeneity issues and affect the fine-tu

Matin Shokri, Ramin Hasibi

Network Traffic Classification (NTC) is one of the most important tasks in network management. The imbalanced nature of classes on the internet presents a critical challenge in classification tasks. For example, some classes of applications are much more prevalent than others, such as HTTP. As a result, machine learning classification models do not perform w

Nizar El Idrissi, Hicham Zoubeir

This note examines the implications of randomly selecting vectors from an infinite-dimensional Hilbert space on linear independence, assuming that for all $k$, the first $k$ vectors follow an absolutely continuous law with respect to a probability measure. It demonstrates that no constraints on the random dimension of their span are necessary, provided that

Ioana Cristina Gerber, Ilarion Mihaila, Valentin Pohoata, Andrei Sandu

"Fluffy" hydrogenated amorphouscarbon(a-C:H)wassynthesizedusingadielectric barrier discharge plasma, driven by nanosec ond high voltage pulses at 1 kHz frequency in a helium-butane mixture. The a-C:H samples were characterized by scanning and transmission electron microscopy, laser-assisted and secondary ion mass spectrometry, and Raman and Fourier-transform

Jiani Zheng, Lu Wang, Fangkai Yang, Chaoyun Zhang

Training Vision-Language Models (VLMs) for Graphical User Interfaces (GUI) agents via Reinforcement Learning (RL) faces critical challenges: environment-based RL requires costly interactions, while environment-free methods struggle with distribution shift and reward generalization. We propose an environment-free RL framework that decouples value estimation f

Sebastian Haug, Christoph Böhm, Daniel Mayer

This paper proposes a method for generating software components for embedded systems, integrating seamlessly into existing implementations without developer intervention. We demonstrate this by automatically generating hardware abstraction layer (HAL) code for GPIO operations on the STM32F407 microcontroller. Using Abstract Syntax Trees (AST) for code analys

Yifan Wu, Yunpeng Wang, Ying Li, Wei Tao

Commit messages concisely describe code changes in natural language and are important for software maintenance. Several approaches have been proposed to automatically generate commit messages, but they still suffer from critical limitations, such as time-consuming training and poor generalization ability. To tackle these limitations, we propose to borrow the

Oksana Bezushchak, Iryna Kashuba, Efim Zelmanov

An associative ring $A$ gives rise to the Lie ring $A^{(-)}=(A,[a,b ]=ab-ba)$. The subject of isomorphisms of Lie rings $A^{(-)}$ and $[A,A]$ has attracted considerable attention in the literature. We prove that if the identity element of $A$ decomposes into a sum of at least three full orthogonal idempotents, then any isomorphism from the Lie ring $[A,A]$ t

Tian Guo, Hui Yuan, Qi Liu, Honglei Su

Point cloud compression significantly reduces data volume but sacrifices reconstruction quality, highlighting the need for advanced quality enhancement techniques. Most existing approaches focus primarily on point-to-point fidelity, often neglecting the importance of perceptual quality as interpreted by the human visual system. To address this issue, we prop

Huaxing Huang, Wenhao Cui, Tonghe Zhang, Shengtao Li

While it is relatively easier to train humanoid robots to mimic specific locomotion skills, it is more challenging to learn from various motions and adhere to continuously changing commands. These robots must accurately track motion instructions, seamlessly transition between a variety of movements, and master intermediate motions not present in their refere

Jialian Liu, Xiaofeng Wang, Yi Yang, Alexei V. Filippenko

SN 2022pul gains special attention due to its possible origin of a super-Chandarsekhar-mass white dwarf explosion (or called a 03fg-like type Ia supernova), which shows prominent [O I], [Ne II], and [Ca II] lines in its late-time spectra taken at $\sim+$300 days after the peak brightness. In this paper, we present new optical observations for this peculiar o

Yulun Xu, Lanzhou Ge, Wenguang Nan

The spreading behaviour of cohesive sand powder is modelled by Discrete Element Method, and the spreadability and the mechanical jamming are focused. The empty patches and total particle volume of the spread layer are examined, followed by the analysis of the geometry force and jamming structure. The results show that several empty patches with different siz

Yu-Hsueh Chen, Tarun Grover

The outcomes of projective measurements on a quantum many-body system in a chosen basis are inherently probabilistic. The Shannon entropy of this probability distribution (the "diagonal entropy") often reveals universal features, such as the existence of a quantum phase transition. A brute-force tomographic approach to estimating this entropy scales exponent

Mahnaz Rezaei, Jahanfar Abouie, Fariba Nazari

MN4-embedded graphene (MN4-G) layers, incorporating transition metal elements (M), represent a class of experimentally accessible two-dimensional materials with significant potential for stable nanoscale magnetization. In these systems, magnetic exchange interactions are primarily governed by Ruderman-Kittel-Kasuya-Yosida (RKKY) coupling, exhibiting an anoma

Lingzhou Mu, Baiji Liu, Ruonan Zhang, Guiming Mo

Diffusion-based video generation techniques have significantly improved zero-shot talking-head avatar generation, enhancing the naturalness of both head motion and facial expressions. However, existing methods suffer from poor controllability, making them less applicable to real-world scenarios such as filmmaking and live streaming for e-commerce. To address

Yat Tin Chow

In this work, we first recall the definition of the relative distance zeta function in [42, 43, 44, 46, 47] and slightly generalize this notion from sets to probability measures, and then move on to propose a novel definition a relative distance (and tube) zeta functional for a class of states over a C* algebra. With such an extension, we look into the chanc

Shuai Guo, Qingsheng Zhang

This is the first part of a series of papers on {\it Virasoro constraints for Cohomological Field Theory (CohFT)}. For a CohFT with vacuum, we introduce the concepts of $S$-calibration and $\nu$-calibration. Then, we define the (formal) total descendent potential corresponding to a given calibration. Finally, we introduce an additional structure, namely homo

Yuki Mizuno, Tomoki Yoshida

This article discusses the Bridgeland stability of some sheaves on the blow-up of $\mathbb{P}^{2}$ at two general points. We have determined the destabilizing objects of the line bundles and have shown that $\mathscr{O}(E)|_{E}$ is Bridgeland stable for any $(-1)$-curve $E$ and any divisorial Bridgeland stability condition.

Ziqi Yang, Roberto Tron

In multi-robot system (MRS) applications, efficient task assignment is essential not only for coordinating agents and ensuring mission success but also for maintaining overall system security. In this work, we first propose an optimization-based distributed task assignment algorithm that dynamically assigns mandatory security-critical tasks and optional task

Menghao Li, Zhenghao Zhang, Junchao Liao, Long Qin

Recent developments in Video Diffusion Models (VDMs) have demonstrated remarkable capability to generate high-quality video content. Nonetheless, the potential of VDMs for creating transparent videos remains largely uncharted. In this paper, we introduce TransVDM, the first diffusion-based model specifically designed for transparent video generation. TransVD

Yingkun Li, Tonghai Yang, Dongxi Ye

Yui and Zagier made some fascinating conjectures on the factorization on the norm of the difference of Weber class invariants $ f(\mathfrak a_1) - f(\mathfrak a_2)$ based on their calculation in \cite{YZ}. Here $\mathfrak a_i$ belong two diferent ideal classes of discrimants $D_i$ in imagainary quadratic fields $\mathbb{Q}(\sqrt{D_i})$. In \cite{LY}, we prov

Ziyuan Zhong, Junyang Zhou

In this study, we propose an innovative dynamic classification algorithm aimed at achieving zero missed detections and minimal false positives,acritical in safety-critical domains (e.g., medical diagnostics) where undetected cases risk severe outcomes. The algorithm partitions data in a self-supervised learning-generated way, which allows the model to learn

Tong Wu, Junzhe Shen, Zixia Jia, Yuxuan Wang

Generating ultra-long sequences with large language models (LLMs) has become increasingly crucial but remains a highly time-intensive task, particularly for sequences up to 100K tokens. While traditional speculative decoding methods exist, simply extending their generation limits fails to accelerate the process and can be detrimental. Through an in-depth ana

Tianyun Liu

Traditional text-to-speech (TTS) methods primarily focus on establishing a mapping between phonemes and mel-spectrograms. However, during the phoneme encoding stage, there is often a lack of real mel-spectrogram auxiliary information, which results in the encoding process lacking true semantic understanding. At the same time, traditional TTS systems often st

Meihui Liu, Shu Sun, Ruifeng Gao, Meixia Tao

Integrated sensing and communication (ISAC) represents a pivotal advancement for future wireless networks. This paper introduces a novel ISAC beamforming method for enhancing sensing performance while preserving communication quality by leveraging the ambiguity function (AF). We formulate an optimization problem to minimize the integrated sidelobe level rati

Fei Deng, Lanjing Zhang

Cross-dataset testing is critical for examining machine learning (ML) model's performance. However, most studies on modelling transcriptomic and clinical data only conducted intra-dataset testing. It is also unclear whether normalization and non-differentially expressed genes (NDEG) can improve cross-dataset modeling performance of ML. We thus aim to underst

Ting-Ting Duan, Sahanaa Büriechin, Hai-Ling Lao, Fu-Hu Liu

To present a prediction of the multi-particle production and multi-fragment emission in electron-nucleus ($eA$) collisions at the forthcoming Electron-Ion Collider (EIC), a simple hybrid model which is based on the multi-source thermal model and the ideal gas model is proposed in this article. According to the hybrid model, some statistical laws such as the

Kunato Nishina, Yusuke Matsui

Vector format has been popular for representing icons and sketches. It has also been famous for design purposes. Regarding image editing, research on vector graphics editing rarely exists in contrast with the raster counterpart. We considered the reason to be the lack of datasets and benchmarks. Thus, we propose SVGEditBench V2, a benchmark dataset for instr

Tamer Ghattas, Michael Hassid, Roy Schwartz

Recent work proposed state-space models (SSMs) as an efficient alternative to transformer-based LLMs. Can these models be pruned to further reduce their computation costs? We adapt several pruning methods to the SSM structure, and apply them to four SSM-based LLMs across multiple tasks. We find that such models are quite robust to some pruning methods (e.g.

Sai Saraswathi Yarajena, Akshay K. Naik

Piezoelectric materials play a vital role in energy harvesting, piezotronics and various self-powered sensing applications. The piezoelectric strength of 2D materials is limited by the carrier charge screening, leading to reduced open circuit voltages and poor piezotronic performances. Reducing the carrier screening in devices is a key requirement to fully u

Yanfu Yan, Viet Duong, Huajie Shao, Denys Poshyvanyk

Numerous machine learning (ML) models have been developed, including those for software engineering (SE) tasks, under the assumption that training and testing data come from the same distribution. However, training and testing distributions often differ, as training datasets rarely encompass the entire distribution, while testing distribution tends to shift

Prashant Thakur, Yashmitha Kumaran, Lakshana Sudarsan, Krishna Kunnampully

We investigate the properties of neutron stars with antikaon condensation in the framework of the Relativistic Mean-Field (RMF) model with a $\sigma$-cut potential. The well-known RMF models, TM1 and TM1e, are used to analyze the structure and composition of neutron stars. The antikaon condensation part of the equation of state (EoS) is constrained from the

Hayeon Jeon, Suhwoo Yoon, Keyeun Lee, Seo Hyeong Kim

Young adults often encounter challenges in career exploration. Self-guided interventions, such as the letter-exchange exercise, where participants envision and adopt the perspective of their future selves by exchanging letters with their envisioned future selves, can support career development. However, the broader adoption of such interventions may be limit

Haoyun Zhang, Yu-Ting Lei, Xing-bo Pan

Quantum homomorphic encryption integrates quantum computing with homomorphic encryption, which allows calculations to be performed directly on encrypted data without decryption on the server side. In this paper, we explore distributed quantum homomorphic encryption, focusing on the coordination of multiple evaluators to achieve evaluation tasks, which not on

Yaxi Lu, Haolun Li, Xin Cong, Zhong Zhang

This study investigates the structured generation capabilities of large language models (LLMs), focusing on producing valid JSON outputs against a given schema. Despite the widespread use of JSON in integrating language models with programs, there is a lack of comprehensive analysis and benchmarking of these capabilities. We explore various aspects of JSON g

Tongfei Chen, Ankita Sharma, Adam Pauls, Benjamin Van Durme

Generative retrieval employs sequence models for conditional generation of document IDs based on a query (DSI (Tay et al., 2022); NCI (Wang et al., 2022); inter alia). While this has led to improved performance in zero-shot retrieval, it is a challenge to support documents not seen during training. We identify the performance of generative retrieval lies in

Frank Yang, Kai Hao Yang

We characterize the extreme points of multidimensional monotone functions from $[0,1]^n$ to $[0,1]$, as well as the extreme points of the set of one-dimensional marginals of these functions. These characterizations lead to new results for various mechanism design and information design problems, including public good provision with interdependent values; int

Ziming Nie, Qiao Wu, Chenlei Lv, Siwen Quan

Point cloud upsampling aims to generate dense and uniformly distributed point sets from sparse point clouds. Existing point cloud upsampling methods typically approach the task as an interpolation problem. They achieve upsampling by performing local interpolation between point clouds or in the feature space, then regressing the interpolated points to appropr

Fanglei Xue, Meihan Zhang, Shuqi Li, Xinyu Gao

Targeted protein degradation (TPD) induced by small molecules has emerged as a rapidly evolving modality in drug discovery, targeting proteins traditionally considered "undruggable". Proteolysis-targeting chimeras (PROTACs) and molecular glue degraders (MGDs) are the primary small molecules that induce TPD. Both types of molecules form a ternary complex link

Kaishuai Xu, Tiezheng Yu, Wenjun Hou, Yi Cheng

Large Language Models (LLMs) are being used more and more extensively for automated evaluation in various scenarios. Previous studies have attempted to fine-tune open-source LLMs to replicate the evaluation explanations and judgments of powerful proprietary models, such as GPT-4. However, these methods are largely limited to text-based analyses under predefi

Sen Yang, Yafu Li, Wai Lam, Yu Cheng

Large language models (LLMs) often struggle with complex reasoning tasks due to their limitations in addressing the vast reasoning space and inherent ambiguities of natural language. We propose the Mixture-of-Search-Agents (MoSA) paradigm, a novel approach leveraging the collective expertise of multiple LLMs to enhance search-based reasoning. MoSA integrates

Duo-Lun Ge, Zhi-Wei Liu, Jun-Xu Lu, Li-Sheng Geng

The interaction between deuterons ($d$-$d$) is pivotal for understanding the characteristics of certain light nuclei from the perspective of the deuteron cluster and achieving a precise reproduction of $d$-$d$ fusion cross sections. In this work, we construct a new set of elastic $d$-$d$ interactions by fitting the phase shifts using potentials parameterized

Youngtae Kim, Soonju Jeong, Sardar Arslan, Dhananjay Agnihotri

This study proposes a two-phase methodology for detecting and classifying auxiliary insulation in structural components. In the detection phase, a YOLOv8x model is trained on a dataset of complete structural blueprints, each annotated with bounding boxes indicating areas that should contain insulation. In the classification phase, these detected insulation p