March 2025 arXiv papers — page 171

Yanis Basso-Bert, Anca Molnos, Romain Lemaire, William Guicquero

Binary Neural Networks (BNNs) are a promising approach to enable Artificial Neural Network (ANN) implementation on ultra-low power edge devices. Such devices may compute data in highly dynamic environments, in which the classes targeted for inference can evolve or even novel classes may arise, requiring continual learning. Class Incremental Learning (CIL) is

Mordehai Milgrom

The iconic, deep-MOND-limit (DML) relation between acceleration and mass, $a\sim (M\mathcal{A}_0)^{1/2}/r$, implies that, in MOND, accelerations cannot be linear in the mass distribution ($\mathcal{A}_0\equiv Ga_0$ is the DML constant, and $a_0$ the MOND acceleration). This leads to important idiosyncracies of MOND, such as a breakdown of the strong equivale

Nicolas Borchard, Gerd Wachsmuth

We address optimal control problems on the space of measures for an objective containing a smooth functional and an optimal transport regularization. That is, the quadratic Monge-Kantorovich distance between a given prior measure and the control is penalized in the objective. We consider optimality conditions and reparametrize the problem using the celebrate

Valentin von Bornhaupt, Johannes Grün, and Justus Bisten, Tobias Bauer

Whole-brain tractography in diffusion MRI is often followed by a parcellation in which each streamline is classified as belonging to a specific white matter bundle, or discarded as a false positive. Efficient parcellation is important both in large-scale studies, which have to process huge amounts of data, and in the clinic, where computational resources are

Alessandro Giagnorio, Antonio Mastropaolo, Saima Afrin, Massimiliano Di Penta

Large Language Models (LLMs) have shown an impressive capability in code generation. The LLM effectiveness generally increases with its size: The higher the number of LLM's trainable parameters the better its ability to implement code. However, when it comes to deploying LLM-based code generators, larger LLMs pose significant challenges related to their memo

Mengbing Liu, Xin Li, Jiancheng An, Chau Yuen

This paper introduces a novel approach for real-time onboard terrain classification from Sentinel-1 (S1) level-0 raw In-phase/Quadrature (IQ) data, leveraging a Stacked Intelligent Metasurface (SIM) to perform inference directly in the analog wave domain. Unlike conventional digital deep neural networks, the proposed multi-layer Diffractive Deep Neural Netwo

Zhongqi Deng, Yuan Wang, Jian Huang, Hui Zhang

The paper proposes a novel Economic Model Predictive Control (EMPC) scheme for Autonomous Surface Vehicles (ASVs) to simultaneously address path following accuracy and energy constraints under environmental disturbances. By formulating lateral deviations as energy-equivalent penalties in the cost function, our method enables explicit trade-offs between track

Haiyang Xie, Xi Shen, Shihua Huang, Qirui Wang

Most visual models are designed for sRGB images, yet RAW data offers significant advantages for object detection by preserving sensor information before ISP processing. This enables improved detection accuracy and more efficient hardware designs by bypassing the ISP. However, RAW object detection is challenging due to limited training data, unbalanced pixel

Sungwoo Park, Rajan Gupta, Tanmoy Bhattacharya, Fangcheng He

We present lattice results for the flavor diagonal charges of the proton from the analysis of eight ensembles generated using 2+1+1-flavors of highly improved staggered quarks (HISQ) by the MILC collaboration. The calculation includes all the needed connected and disconnected contributions to nucleon three-point function. For extracting matrix elements using

Vik. S. Kulikov

The article examines a set of irreducible germs $f_P:U_P\to V_p$ of %finite generic morphisms $f:S\to\mathbb P^2$ to the projective plane whose branch curve germs $B_P\subset V_p$ have singularities equisingular deformation equivalent to singularities given by equations $x^{k_1}-y^{k_2}=0$ with coprime $k_1,k_2\in\mathbb N$.

Ding Zhong, Xu Zheng, Chenfei Liao, Yuanhuiyi Lyu

Segment Anything Model 2 (SAM2) has emerged as a strong base model in various pinhole imaging segmentation tasks. However, when applying it to $360^\circ$ domain, the significant field-of-view (FoV) gap between pinhole ($70^\circ \times 70^\circ$) and panoramic images ($180^\circ \times 360^\circ$) poses unique challenges. Two major concerns for this applica

Xiaoyan Kui, Zijie Fan, Zexin Ji, Qinsong Li

Magnetic resonance imaging (MRI) reconstruction is a fundamental task aimed at recovering high-quality images from undersampled or low-quality MRI data. This process enhances diagnostic accuracy and optimizes clinical applications. In recent years, deep learning-based MRI reconstruction has made significant progress. Advancements include single-modality feat

Zhao Jin, Lu Jin, Yizhe Luo, Shuo Feng

Despite significant progress in AI and decision-making technologies in safety-critical fields, challenges remain in verifying the correctness of decision output schemes and verification-result driven design. We propose correctness learning (CL) to enhance human-AI collaboration integrating deductive verification methods and insights from historical high-qual

Chunxu Zhang, Guodong Long, Zijian Zhang, Zhiwei Li

Federated recommender systems (FedRecSys) have emerged as a pivotal solution for privacy-aware recommendations, balancing growing demands for data security and personalized experiences. Current research efforts predominantly concentrate on adapting traditional recommendation architectures to federated environments, optimizing communication efficiency, and mi

Mike N. Pionteck, Myrta Grüning, Simone Sanna, Claudio Attaccalite

We propose a computational framework to extract non-linear response functions from real-time simulations in the presence of more than one external field. We apply this approach to the calculation of sum frequency generation (SFG) and difference frequency generation (DFG). SFG and DFG are second-order nonlinear processes where two lasers with frequencies $\om

Xiaoyi Liang, Mouxiao Bian, Moxin Chen, Lihao Liu

In recent years, large language models (LLMs) have demonstrated remarkable potential across various medical applications. Building on this foundation, multimodal large language models (MLLMs) integrate LLMs with visual models to process diverse inputs, including clinical data and medical images. In ophthalmology, LLMs have been explored for analyzing optical

Xiaofan Cai, Yaqing Han, Jiawei Jiang, Renjun Du

Introducing topologically protected skyrmions in graphene holds significant importance for developing high-speed, low-energy spintronic devices. Here, we present a centrosymmetric ferromagnetic graphene/trilayer Cr2Ge2Te6/graphene heterostructure, demonstrating the anomalous and topological Hall effect due to the magnetic proximity effect. Through gate volta

Huayuan Huang, M. Kanat Camlibel, Raffaella Carloni, Henk J. van Waarde

This paper studies data-driven stabilization of a class of unknown polynomial systems using data corrupted by bounded noise. Existing work addressing this problem has focused on designing a controller and a Lyapunov function so that a certain state-dependent matrix is negative definite, which ensures asymptotic stability of all closed-loop systems compatible

Shuhe Wang, Xiaoya Li, Jiwei Li, Guoyin Wang

Due to the data-driven nature of current face identity (FaceID) customization methods, all state-of-the-art models rely on large-scale datasets containing millions of high-quality text-image pairs for training. However, none of these datasets are publicly available, which restricts transparency and hinders further advancements in the field. To address this i

Yuxuan Zhang, An-An Lu, Xiqi Gao

In this paper, we propose a cross subcarrier precoder design (CSPD) for massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. The aim is to maximize the weighted sum-rate (WSR) performance while considering the smoothness of the frequency domain effective channel. To quantify the smoothness of the effective

Kei Tohme, Hideo Suganuma

Inspired by the one-dimensional color-electric flux-tube in a hadron, we propose a possible way of low-dimensionalization of 4D QCD. As a strategy, we use gauge degrees of freedom and propose a new gauge fixing of ``dimensional reduction (DR) gauge". The DR gauge is defined so as to minimize $R_{\rm DR} \equiv \int d^4s~{\rm Tr}~[A^2_x(s)+A^2_y(s)]$, which p

Emmanuel De Dieu Nkou, Fridolin Melong

We construct a family of estimators for a regression function based on a sample following a qdistribution. Our approach is nonparametric, using kernel methods built from operations that leverage the properties of q-calculus. Furthermore, under appropriate assumptions, we establish the weak convergence and strong consistency of this family of estimators.

Zexin Zheng, Jia-Feng Cai, Xiao-Ming Wu, Yi-Lin Wei

The development of a generalist agent with adaptive multiple manipulation skills has been a long-standing goal in the robotics community. In this paper, we explore a crucial task, skill-incremental learning, in robotic manipulation, which is to endow the robots with the ability to learn new manipulation skills based on the previous learned knowledge without

Shangxuan Wu, Wendi Luan, Yong Wang, Dan Zeng

Automated data insight mining and visualization have been widely used in various business intelligence applications (e.g., market analysis and product promotion). However, automated insight mining techniques often output the same mining results to different analysts without considering their personal preferences, while interactive insight discovery requires

Ruidan Xing, Runyi Huang, Qing Xu, Lei He

End-to-end autonomous driving solutions, which process multi-modal sensory data to directly generate refined control commands, have become a dominant paradigm in autonomous driving research. However, these approaches predominantly depend on single-vehicle data collection for model training and optimization, resulting in significant challenges such as high da

Antonin Schrab

This paper provides a unifying view of optimal kernel hypothesis testing across the MMD two-sample, HSIC independence, and KSD goodness-of-fit frameworks. Minimax optimal separation rates in the kernel and $L^2$ metrics are presented, with two adaptive kernel selection methods (kernel pooling and aggregation), and under various testing constraints: computati

Baruch Meerson

Motivated by the paradigm of a super-Maltusian population catastrophe, we study a simple stochastic population model which exhibits a finite-time blowup of the population size and is strongly affected by intrinsic noise. We focus on the fluctuations of the blowup time $T$ in the asexual binary reproduction model $2A \to 3A$, where two identical individuals g

Spyros Kondylatos, Nikolaos Ioannis Bountos, Dimitrios Michail, Xiao Xiang Zhu

Recent advances in Computer Vision have introduced the concept of pretrained representation uncertainty, enabling zero-shot uncertainty estimation. This holds significant potential for Earth Observation (EO), where trustworthiness is critical, yet the complexity of EO data poses challenges to uncertainty-aware methods. In this work, we investigate the genera

Edward Ashton, Brett Gladman, Mike Alexandersen, Jean-Marc Petit

We report the discovery and careful orbital determination of 64 new irregular moons of Saturn found in images taken using the Canada-France-Hawaii Telescope from 2019-2021, bringing the total number of saturnian irregulars to 122. By more than doubling the sample of saturnian irregular moon orbits, including pushing to smaller sizes, we can now see finer det

Lázaro Costa, Susana Barbosa, Jácome Cunha

In recent years, the research community, but also the general public, has raised serious questions about the reproducibility and replicability of scientific work. Since many studies include some kind of computational work, these issues are also a technological challenge, not only in computer science, but also in most research domains. Computational replicabi

Takao Nakagawa, Susumu Tokumoto, Shogo Tokui, Fuyuki Ishikawa

Systems based on Deep Neural Networks (DNNs) are increasingly being used in industry. In the process of system operation, DNNs need to be updated in order to improve their performance. When updating DNNs, systems used in companies that require high reliability must have as few regressions as possible. Since the update of DNNs has a data-driven nature, it is

Kuo-Hsuan Hung, Xugang Lu, Szu-Wei Fu, Huan-Hsin Tseng

Linguistic knowledge plays a crucial role in spoken language comprehension. It provides essential semantic and syntactic context for speech perception in noisy environments. However, most speech enhancement (SE) methods predominantly rely on acoustic features to learn the mapping relationship between noisy and clean speech, with limited exploration of lingui

Zhaoqi Dong, Zhinan Wang, Quanqi Zheng, Bin Xu

Traditional rule-based decision-making methods with interpretable advantage, such as finite state machine, suffer from the jitter or deadlock(JoD) problems in extremely dynamic scenarios. To realize agent swarm confrontation, decision conflicts causing many JoD problems are a key issue to be solved. Here, we propose a novel decision-making framework that int

Zhihao Huang, Xi Qiu, Yukuo Ma, Yifu Zhou

Autoregressive models have achieved significant success in image generation. However, unlike the inherent hierarchical structure of image information in the spectral domain, standard autoregressive methods typically generate pixels sequentially in a fixed spatial order. To better leverage this spectral hierarchy, we introduce NextFrequency Image Generation (

Hongyu Su, Yifeng Gao, Yifan Ding, Xingjun Ma

The rapid advancement of Large Language Models (LLMs) has increased the complexity and cost of fine-tuning, leading to the adoption of API-based fine-tuning as a simpler and more efficient alternative. While this method is popular among resource-limited organizations, it introduces significant security risks, particularly the potential leakage of model API k

Chuanming Wang, Henming Mao, Huanhuan Zhang, Huiyuan Fu

Vision-Language Models (VLMs) have demonstrated impressive performance on various visual tasks, yet they still require adaptation on downstream tasks to achieve optimal performance. Recently, various adaptation technologies have been proposed, but we observe they often underperform in fine-grained visual recognition, which requires models to capture subtle y

Koki Kakiichi, Xiangyu Jin, Feige Wang, Romain A. Meyer

The spatial correlation between galaxies and the Ly$\alpha$ forest provides insights into how galaxies reionized the Universe. Here, we present initial results on the spatial cross-correlation between [OIII] emitters and Ly$\alpha$ forest at 5.4<z<6.5 from the JWST ASPIRE NIRCam/F356W Grism Spectroscopic Survey in z>6.5 QSO fields. Using data from five QSO f

Zhipeng Gao, Ping Li, Changhong Lu, Rui Sun

The problem of determining the maximum number of copies of $T$ in an $H$-free graph, for any graphs $T$ and $H$, was considered by Alon and Shikhelman. This is a variant of Tur\'{a}n's classical extremal problem. We show lower and upper bounds for the maximum number of $s$-cliques in a graph with no disjoint copies of arbitrary graph. We also determine the m

Pasquinel Weckx, Víctor González Alonso, Ewoud Vaneeckhaute, Karel Duerinkcx

Arabinoxylans are constituents of wheat flour that contribute to the dietary fiber properties of wheat. They exist in water-extractable and water-unextractable forms and contribute to human health. In bakery technology, especially the water-extractable arabinoxylans (WE-AX) are important due to their impact on viscosity and dough rheology. This study provide

Apivich Hemachandra, Gregory Kang Ruey Lau, See-Kiong Ng, Bryan Kian Hsiang Low

In many science and engineering settings, system dynamics are characterized by governing PDEs, and a major challenge is to solve inverse problems (IPs) where unknown PDE parameters are inferred based on observational data gathered under limited budget. Due to the high costs of setting up and running experiments, experimental design (ED) is often done with th

Fabio Nicola, Federico Riccardi, Paolo Tilli

We prove that, among all subsets $\Omega\subset \mathbb{C}$ having circular symmetry and prescribed measure, the ball is the only maximizer of the sum of the first $K$ eigenvalues ($K\geq 1$) of the corresponding Toeplitz operator $T_\Omega$ on the Fock space $\mathcal{F}$. As a byproduct, we prove that balls maximize any Schatten $p$-norm of $T_\Omega$ for

Shiori Hironaka, Mitsuo Yoshida, Kazuyuki Shudo

The "Fediverse", a federation of decentralized social media servers, has emerged after a decade in which centralized platforms like X (formerly Twitter) have dominated the landscape. The structure of a federation should affect user activity, as a user selects a server to access the Fediverse and posts are distributed along the structure. This paper reports o

Jongwoo Ko, Tianyi Chen, Sungnyun Kim, Tianyu Ding

Despite the success of distillation in large language models (LLMs), most prior work applies identical loss functions to both teacher- and student-generated data. These strategies overlook the synergy between loss formulations and data types, leading to a suboptimal performance boost in student models. To address this, we propose DistiLLM-2, a contrastive ap

Xiaotian Han, Tianlong Chen, Kaixiong Zhou, Zhimeng Jiang

Deep neural networks are prone to various bias issues, jeopardizing their applications for high-stake decision-making. Existing fairness methods typically offer a fixed accuracy-fairness trade-off, since the weight of the well-trained model is a fixed point (fairness-optimum) in the weight space. Nevertheless, more flexible accuracy-fairness trade-offs at in

Huilin Deng, Ding Zou, Rui Ma, Hongchen Luo

While state-of-the-art vision-language models (VLMs) have demonstrated remarkable capabilities in complex visual-text tasks, their success heavily relies on massive model scaling, limiting their practical deployment. Small-scale VLMs offer a more practical alternative but face significant challenges when trained with traditional supervised fine-tuning (SFT),

Rolf Larsson

We suggest how to construct joint confidence distributions for several parameters and apply these ideas to an autoregressive process of general order. The implied non informative prior for the parameters, i.e. the ratio between the confidence density and the likelihood function, is proved to be asymptotically flat in the stationary case. However, in the pres

Richard Schier, Caterina Cocchi

Recent advances in experimental techniques and computational methods have significantly expanded the family of alkali antimonides, a class of semiconducting materials used as photocathodes in particle accelerators, unveiling new crystal structures and stoichiometries with improved stability and quantum efficiency. This work investigates the electronic and op

Hui Tang, Zhan Yang, Yu Rong, Li Chai

Heart rate (HR) monitoring is crucial for assessing physical fitness, cardiovascular health, and stress management. Millimeter-wave radar offers a promising noncontact solution for long-term monitoring. However, accurate HR estimation remains challenging in low signal-tonoise ratio (SNR) conditions. To deal with both respiration harmonics and intermodulation

Ruben Becker, Nicola Cotumaccio, Sung-Hwan Kim, Nicola Prezza

The Burrows-Wheeler transform (BWT) is a string transformation that enhances string indexing and compressibility. Cotumaccio and Prezza [SODA '21] extended this transformation to nondeterministic finite automata (NFAs) through co-lexicographic partial orders, i.e., by sorting the states of an NFA according to the co-lexicographic order of the strings reachin

E. Yu. Beliayev, I. G. Mirzoiev, V. V. Andrievskii, A. V. Terekhov

Using the example of a pressed sample consisting of chromium dioxide nanoparticles coated with insulating shells, we study the relationship between the electronic transport system and magnetic subsystem in granular spin-polarized metals. It is shown that the spin-polarized tunneling transport current can affect the coercivity fields of the percolation cluste

Dongyu Bai, Junxian Liu, Yihan Nie, Yuantong Gu

Polar domains and their manipulation-particularly the creation and dynamic control-have garnered significant attention, owing to their rich physics and promising applications in digital memory devices. In this work, using density functional theory (DFT) and deep learning molecular dynamics (DLMD) simulations, we demonstrate that polar domains can be created

Amira Guesmi, Bassem Ouni, Muhammad Shafique

Quantized Neural Networks (QNNs) have emerged as a promising solution for reducing model size and computational costs, making them well-suited for deployment in edge and resource-constrained environments. While quantization is known to disrupt gradient propagation and enhance robustness against pixel-level adversarial attacks, its effectiveness against patch

Joshua Edzards, Julia Santana Andreo, Holger-Dietrich Saßnick, Caterina Cocchi

Accurate computational predictions of metal-organic frameworks (MOFs) and their properties is crucial for discovering optimal compositions and applying them in relevant technological areas. This work benchmarks density functional theory (DFT) approaches, including semi-local, meta-GGA, and hybrid functionals with various dispersion corrections, on MOF-5 and

Shisong Deng, Qiang Zhang, Zhengyang Cai

Inverse design approach, which directly generates optimal aerodynamic shape with neural network models to meet designated performance targets, has drawn enormous attention. However, the current state-of-the-art inverse design approach for airfoils, which is based on generative adversarial network, demonstrates insufficient precision in its generating and tra

Qian Wu, Zhu-Fang Cui, Jorge Segovia

The valence quark parton distribution functions (PDFs) of all ground state heavy mesons that composed of $b$ or $c$ quarks, are discussed; namely, the pseudoscalar $\eta_c(1S)$, $\eta_b(1S)$ and $B_c$, together with the corresponding vector ones, $J/\psi$, $\Upsilon(1S)$ and $B_c^\ast$. We use a QCD-inspired constituent quark model, which has been applied wi

Reza Mirzaie

We consider a compact submanifold $M$ of a Riemannian manifold $N$ and we use the second variation formula as a tool to drive some geometric results on reach$(M, N)$ the reach of $M$ in $N$, including some useful relations between the extrinsic geometry of $M$ in $N$ and reach$(M, N)$. Our results generalize some theorems previously proved for the special ca

Kanchan Meena, P. Singha Deo

We revealed that with the measurement of the scattering phase shift of electron in low-dimensional or mesoscopic systems local objects of hierarchy of density of states can also determine experimentally. In recent times, it has been exhibited that in mesoscopic systems certain objects of density of states (DOS) hierarchy like local partial DOS, partial DOS,

Chuanshou Li, Sheng-Li Qin, Tie Liu, Xunchuan Liu

Context. Acetone (CH3COCH3) is a carbonyl-bearing complex organic molecule, yet interstellar observations of acetone remain limited. Studying the formation and distribution of CH3COCH3 in the interstellar medium can provide valuable insights into prebiotic chemistry and the evolution of interstellar molecules. Aims. We explore the spatial distribution of CH3

Zheng Qin, Ruobing Zheng, Yabing Wang, Tianqi Li

In filmmaking, directors typically allow actors to perform freely based on the script before providing specific guidance on how to present key actions. AI-generated content faces similar requirements, where users not only need automatic generation of lip synchronization and basic gestures from audio input but also desire semantically accurate and expressive

Yixin Guo, Tomoya Naito, Hiroyuki Tajima, Haozhao Liang

We theoretically investigate Cooper quartet correlations in $ N = Z $ doubly-magic nuclei ($ {}^{40} \mathrm{Ca} $, $ {}^{100} \mathrm{Sn} $, and $ {}^{164} \mathrm{Pb} $). We first examine the quartet condensation fraction in infinite symmetric nuclear matter by using the quartet Bardeen-Cooper-Schrieffer theory. Together with the total nucleon density prof

Victor Shea-Jay Huang, Le Zhuo, Yi Xin, Zhaokai Wang

Diffusion Transformers (DiTs) are a powerful yet underexplored class of generative models compared to U-Net-based diffusion architectures. We propose TIDE-Temporal-aware sparse autoencoders for Interpretable Diffusion transformErs-a framework designed to extract sparse, interpretable activation features across timesteps in DiTs. TIDE effectively captures tem

Fu Chen, Rui Wan, Peidong Liu, Nanxing Zheng

Bipedal robots, due to their anthropomorphic design, offer substantial potential across various applications, yet their control is hindered by the complexity of their structure. Currently, most research focuses on proprioception-based methods, which lack the capability to overcome complex terrain. While visual perception is vital for operation in human-centr

Ivan Tjuawinata, Jiabo Wang, Mengmeng Yang, Shanxiang Lyu

In an MPC-protected distributed computation, although the use of MPC assures data privacy during computation, sensitive information may still be inferred by curious MPC participants from the computation output. This can be observed, for instance, in the inference attacks on either federated learning or a more standard statistical computation with distributed

Yongle Zhang, Yimin Liu, Yan Huang, Qiang Wu

Text-guided diffusion models have achieved remarkable success in object inpainting by providing high-level semantic guidance through text prompts. However, they often lack precise pixel-level spatial control, especially in scenarios involving partially corrupted objects where critical uncorrupted cues remain. To overcome this limitation, sketch-guided method

Juntian Du, Zhihu Zhou, Runzhe Zhang, Yuan Sun

Recently, the Mamba architecture has demonstrated significant successes in various computer vision tasks, such as classification and segmentation. However, its application to optical flow estimation remains unexplored. In this paper, we introduce MambaFlow, a novel framework designed to leverage the high accuracy and efficiency of the Mamba architecture for

Daniele Dominici

The history of the Arcetri Institute of Physics at the University of Florence is analyzed from the beginning of the 20th century to the 1960s. Thanks to the arrival of Garbasso in 1913, not only did the Institute gain new premises on Arcetri hill, but also hosted brilliant young physicists such as Rita Brunetti, Enrico Fermi, Franco Rasetti in the '20s and E

Ziming You, Yumiao Zhang, Dexuan Xu, Yiwei Lou

Existing large language model (LLM) agents for automating data science show promise, but they remain constrained by narrow task scopes, limited generalization across tasks and models, and over-reliance on state-of-the-art (SOTA) LLMs. We introduce DatawiseAgent, a notebook-centric LLM agent framework for adaptive and robust data science automation. Inspired

Bin Li, Yuxiang Fan, Chuanhui Yin, Junjie Zhai

Inspired by recent predictions of superconductivity in B-C framework clathrates, we employ density functional theory to explore potential superconductors among hexagonal hydride-substituted compounds with compositions XB$_8$C, XB$_7$C$_2$, XB$_6$C$_3$, XB$_3$C$_6$, XB$_2$C$_7$, and XBC$_8$. Our high-throughput calculations on 96 compounds reveal several dyna

Tianai Huang, Lu Lu, Jiayuan Chen, Lihao Liu

Large language models (LLMs) excel in various NLP tasks and modern medicine, but their evaluation in traditional Chinese medicine (TCM) is underexplored. To address this, we introduce TCM3CEval, a benchmark assessing LLMs in TCM across three dimensions: core knowledge mastery, classical text understanding, and clinical decision-making. We evaluate diverse mo

Ali Eghbali, Simin Ghasemi-Sorkhabi, Adel Rezaei-Aghdam

We show that the Yang-Baxter (YB) deformed backgrounds of the Wess-Zumino-Witten (WZW) model based on the $H_{_{4}}$ Lie group can be considered as solutions of the generalized supergravity equations (GSEs). Then, by applying the Poisson-Lie T-duality in the presence of spectator fields, we obtain the non-Abelian target space duals of those models. It is sho

Dawid Śnieżek, Cezary Śliwa, Krzysztof Dybko, Jarosław Wróbel

In this work, we used 20 nm thick CdTe/SnTe/CdTe [001] quantum wells to make 6- and 8-terminal nano-structures with the etched cross-junctions of sub-micron width with walls directed along the [10], [01], and [11] surface crystallographic directions. We studied the low-temperature quantum magneto-transport to investigate the impact of lateral confinement on

Michael Green, Matan Levy, Issar Tzachor, Dvir Samuel

We address the challenge of Small Object Image Retrieval (SoIR), where the goal is to retrieve images containing a specific small object, in a cluttered scene. The key challenge in this setting is constructing a single image descriptor, for scalable and efficient search, that effectively represents all objects in the image. In this paper, we first analyze th

Yan Jiang, Zhongmiao Qi, Jianhao Li, Jiangbo Qian

Hashing algorithms have been widely used in large-scale image retrieval tasks, especially for seen class data. Zero-shot hashing algorithms have been proposed to handle unseen class data. The key technique in these algorithms involves learning features from seen classes and transferring them to unseen classes, that is, aligning the feature embeddings between

Nardine Basta, Conor Atkins, Dali Kaafar

We present "Bot Wars," a framework using Large Language Models (LLMs) scam-baiters to counter phone scams through simulated adversarial dialogues. Our key contribution is a formal foundation for strategy emergence through chain-of-thought reasoning without explicit optimization. Through a novel two-layer prompt architecture, our framework enables LLMs to cra

Sheng Luo, Yi Zhou, Tao Zhou

Incremental learning (IL) aims to overcome catastrophic forgetting of previous tasks while learning new ones. Existing IL methods make strong assumptions that the incoming task type will either only increases new classes or domains (i.e. Class IL, Domain IL), or increase by a static scale in a class- and domain-agnostic manner (i.e. Versatile IL (VIL)), whic

Fang Liu, Yun-Zhi Du, Jian-Xin Sun, Huai-Fan Li

This paper investigates the thermodynamic properties of the coexistence region of two horizons in the charged 4-dimensional Einstein-Gauss-Bonnet (4D-EGB) spacetime. Initially, we apply the universal first law of thermodynamics to derive the corresponding thermodynamic quantities for the coexistence region between the black hole event horizon and the cosmolo

Chenhui Hao, Jingtao Shi, Shuaiqi Zhang

This paper is devoted to an optimal control problem of fully coupled forward-backward stochastic differential equations driven by sub-diffusion, whose solutions are not Markov processes. The stochastic maximum principle is obtained, where the control domain may not be convex and the diffusion term is independent of the control variable. Additionally, problem

Haolong Ma, Hui Li, Chunyang Cheng, Zeyang Zhang

All-in-One Degradation-Aware Fusion Models (ADFMs) as one of multi-modal image fusion models, which aims to address complex scenes by mitigating degradations from source images and generating high-quality fused images. Mainstream ADFMs rely on end-to-end learning and heavily synthesized datasets to achieve degradation awareness and fusion. This rough learnin

Zhi Qin, Qianhui Gui, Mouxiao Bian, Rui Wang

Medical imaging quality control (QC) is essential for accurate diagnosis, yet traditional QC methods remain labor-intensive and subjective. To address this challenge, in this study, we establish a standardized dataset and evaluation framework for medical imaging QC, systematically assessing large language models (LLMs) in image quality assessment and report

Kanchan Meena, Souvik Ghosh, P. Singha Deo

Real quantum systems can exhibit a local object called local partial density of states (LPDOS) that cannot be proved within the axiomatic approach of quantum mechanics. We demonstrate that real mesoscopic system that can exhibit Fano resonances will show this object and also very counterintuitively it can become negative, resulting in the enhancement of cohe

Marta Zagorowska, Lars Imsland

Online Feedback Optimization uses optimization algorithms as dynamic systems to design optimal control inputs. The results obtained from Online Feedback Optimization depend on the setup of the chosen optimization algorithm. In this work we analyse the sensitivity of Online Feedback Optimization to the parameters of projected gradient descent as the algorithm

Dong-Hee Paek, Seung-Hyun Kong

Sensor fusion of camera, LiDAR, and 4-dimensional (4D) Radar has brought a significant performance improvement in autonomous driving. However, there still exist fundamental challenges: deeply coupled fusion methods assume continuous sensor availability, making them vulnerable to sensor degradation and failure, whereas sensor-wise cross-attention fusion metho

Yuxuan Zhang, Yirui Yuan, Yiren Song, Haofan Wang

Recent advancements in Unet-based diffusion models, such as ControlNet and IP-Adapter, have introduced effective spatial and subject control mechanisms. However, the DiT (Diffusion Transformer) architecture still struggles with efficient and flexible control. To tackle this issue, we propose EasyControl, a novel framework designed to unify condition-guided d

Yi Liu, Hao Zhou, Wenxiang Shang, Ran Lin

Erase inpainting, or object removal, aims to precisely remove target objects within masked regions while preserving the overall consistency of the surrounding content. Despite diffusion-based methods have made significant strides in the field of image inpainting, challenges remain regarding the emergence of unexpected objects or artifacts. We assert that the

Sriram Vasudevan

Labeled datasets are essential for modern search engines, which increasingly rely on supervised learning methods like Learning to Rank and massive amounts of data to power deep learning models. However, creating these datasets is both time-consuming and costly, leading to the common use of user click and activity logs as proxies for relevance. In this paper,

A. V. Terekhov, A. P. Kazakov, P. M. Fesenko, V. M. Yarovyi

For the first time, a detailed analysis of the behaviour of the temperature dependences of the upper critical fields Hc2(T) has been carried out in the compounds (Dy1-xErx)Rh3.8Ru0.2B4 (x = 0, 0.2, 0.4). It is found that the Hc2(T) in (Dy0.8Er0.2)Rh3.8Ru0.2B4 has an inflection point at 3 kOe, which may be related to the low-temperature magnetic ordering, whi

Armin Rainer

It is well-known that a function on an open set in $\mathbb R^d$ is smooth if and only if it is arc-smooth, i.e., its composites with all smooth curves are smooth. In recent work, we extended this and related results (for instance, a real analytic version) to suitable closed sets, notably, sets with H\"older boundary and fat subanalytic sets satisfying a nec

Johannes Brutsche, Angelika Rohde

For some discretely observed path of oscillating Brownian motion with level of self-organized criticality $\rho_0$, we prove in the infill asymptotics that the MLE is $n$-consistent, where $n$ denotes the sample size, and derive its limit distribution with respect to stable convergence. As the transition density of this homogeneous Markov process is not even

Hugo Senetaire, Paul Jeha, Pierre-Alexandre Mattei, Jes Frellsen

Training an energy-based model (EBM) with maximum likelihood is challenging due to the intractable normalisation constant. Traditional methods rely on expensive Markov chain Monte Carlo (MCMC) sampling to estimate the gradient of logartihm of the normalisation constant. We propose a novel objective called self-normalised log-likelihood (SNL) that introduces

Yuting Hu, Chenhui Xu, Ruiyang Qin, Dancheng Liu

Partial perception deficits can compromise autonomous vehicle safety by disrupting environmental understanding. Existing protocols typically default to entirely risk-avoidant actions such as immediate stops, which are detrimental to navigation goals and lack flexibility for rare driving scenarios. Yet, in cases of minor risk, halting the vehicle may be unnec

Keyu Du, Hao Xu, Haipeng Li, Hong Qu

Scene-level point cloud registration is very challenging when considering dynamic foregrounds. Existing indoor datasets mostly assume rigid motions, so the trained models cannot robustly handle scenes with non-rigid motions. On the other hand, non-rigid datasets are mainly object-level, so the trained models cannot generalize well to complex scenes. This pap

Xintong Li, Jalend Bantupalli, Ria Dharmani, Yuwei Zhang

There has been a surge in the use of large language models (LLM) conversational agents to generate responses based on long-term history from multiple sessions. However, existing long-term open-domain dialogue datasets lack complex, real-world personalization and fail to capture implicit reasoning-where relevant information is embedded in subtle, syntactic, o

Haozhuo Li, Yuchen Cui, Dorsa Sadigh

Imitation learning is a promising approach for learning robot policies with user-provided data. The way demonstrations are provided, i.e., demonstration modality, influences the quality of the data. While existing research shows that kinesthetic teaching (physically guiding the robot) is preferred by users for the intuitiveness and ease of use, the majority

Nazanin Tour-Savadkoohi, Jafar Fathali

In traditional facility location problems, a set of points is provided, and the objective is to determine the best location for a new facility based on criteria such as minimizing cost, time, and distances between clients and facilities. Conversely, inverse single facility location problems focus on adjusting the problem's parameters at minimal cost to make

Seigo Nakazawa, Rina Tazai, Youichi Yamakawa, Seiichiro Onari

In the kagome superconductors AV3Sb5 (A=Cs,Rb,K), a cascade of correlated electron phases cause exotic symmetry-breaking quantum states. In particular, the dissipationless chiral loop-current phase has been attracting increasing attention. A crucial clue is offered by the chirality of the quasiparticle interference signal observed in scanning tunneling micro

Kailing Zhou, Chengwei Zhang, Furui Zhan, Wanting Liu

Recently, with the development of Multi-agent reinforcement learning (MARL), adaptive traffic signal control (ATSC) has achieved satisfactory results. In traffic scenarios with multiple intersections, MARL treats each intersection as an agent and optimizes traffic signal control strategies through learning and real-time decision-making. Considering that obse

Xinyu Lu, Hao Ma, Hui Li, Jia Li

There will be a paradigm shift in chemical and biological research, to be enabled by autonomous, closed-loop, real-time self-directed decision-making experimentation. Spectrum-to-structure correlation, which is to elucidate molecular structures with spectral information, is the core step in understanding the experimental results and to close the loop. Howeve

Lei Zhang, Siddharth Das, Tanner Merry, Wenlong Zhang

We consider the problem of learning Nash equilibrial policies for two-player risk-sensitive collision-avoiding interactions. Solving the Hamilton-Jacobi-Isaacs equations of such general-sum differential games in real time is an open challenge due to the discontinuity of equilibrium values on the state space. A common solution is to learn a neural network tha

Tsubasa Sugeno, Takahiro Yokokura, Kazuya Yonekura

In confining large $N$ theories with a $\theta$ angle such as four-dimensional $\mathrm{SU}(N)$ pure Yang-Mills theory, there are multiple metastable vacua and it makes sense to consider the parameter region of ``large $\theta$ of order $N$'' despite the fact that $\theta$ is a $2\pi$-periodic parameter. We investigate this parameter region in the two-dimens

M. F. Fauzi, H. S. Ramadhan, A. Sulaksono, Hasanuddin

A regular black hole, unconstrained by the weak cosmic censorship conjecture, can exceed its critical spin limit and transition into a superspinar. In this paper, we investigate the observational appearance of a rotating regular black hole, specifically the Ghosh black hole and its superspinar counterpart, when surrounded by a thin accretion disk. The result