March 2025 arXiv papers — page 24

Francesco Sylos Labini, Giordano De Marzo, Matteo Straccamore

Observations of the line-of-sight component of emitter velocities in galaxies are valuable for reconstructing their 2D velocity fields, albeit requiring certain assumptions. A common one is that radial flows can be neglected in the outer regions of galaxies, while their geometry can be deformed by a warp. A specular approach assumes that galactic discs are f

Atsushi Ikeda

The Andreotti-Mayer locus is a subset of the moduli space of principally polarized abelian varieties, defined by a condition on the dimension of the singular locus of the theta divisor. It is known that the Jacobian locus in the moduli space is an irreducible component of the Andreotti-Mayer locus. In this paper, we generalize the Andreotti-Mayer locus to th

Taras Demchuk, Tymofii Nikolaienko, Aniruddha Panda, Subodh Madhav Joshi

This study introduces a physics-informed machine learning framework to accelerate the computation of the microkinetic model of Fischer-Tropsch synthesis. A neural network, trained within the NVIDIA Modulus framework, approximates the fraction of vacant catalytic sites with high accuracy. The combination of implicit differentiation and the Newton-Raphson meth

Magdalena Kaiser, Gerhard Weikum

Conversational Question Answering (ConvQA) involves multiple subtasks, i) to understand incomplete questions in their context, ii) to retrieve relevant information, and iii) to generate answers. This work presents PRAISE, a pipeline-based approach for ConvQA that trains LLM adapters for each of the three subtasks. As labeled training data for individual subt

Mikhail Cherkasskii, Fabian Engelhardt, Manuel Müller, Johannes Weber

We develop a theoretical model for polarization-selective phonon pumping induced by magnon-phonon coupling in a ferromagnetic/non-magnetic acoustic bilayer structure, focusing on the effects arising from a misalignment between the magnetic and crystallographic symmetry axes. Our model considers the coupled equations of motion describing uniform magnetization

Asiye Arif, Tuğba Yurdakadim

In the present paper, we introduce three neural network operators of convolution type activated by symmetrized, deformed and parametrized B-generalized logistic function. We deal with the approximation properties of these operators to the identity by using modulus of continuity. Furthermore, we show that our operators preserve global smoothness and consider

A. Devillez, J. Robert, E. Lhotel, R. Ballou

The ground state and Hamiltonian of the honeycomb lattice material BaCo$_{2}$(AsO$_{4}$)$_{2}$ hosting magnetic Co$^{2+}$, have been debated for decades. The recent proposal for anisotropic bond-dependent interactions in such honeycomb cobaltates has raised the prospect of revisiting its Hamiltonian in the context of Kitaev physics. To test this hypothesis,

Ramniwas Meena, Chandan Kumar, Subhashish Banerjee

The Husimi phase distribution, an experimentally measurable quantity, is investigated for single-mode and two-mode squeezed vacuum states. The analysis highlights that non-Gaussian operations, i.e., photon subtraction (PS), photon addition (PA) and photon catalysis (PC), are effective tools for localizing phase distribution and enhancing phase robustness in

T. Chaumont-Frelet

We consider second-order PDE problems set in unbounded domains and discretized by Lagrange finite elements on a finite mesh, thus introducing an artificial boundary in the discretization. Specifically, we consider the reaction diffusion equation as well as Helmholtz problems in waveguides with perfectly matched layers. The usual procedure to deal with such p

Holger Drees

The extremal dependence structure of a regularly varying $d$-dimensional random vector can be described by its angular measure. The standard nonparametric estimator of this measure is the empirical measure of the observed angles of the $k$ random vectors with largest norm, for a suitably chosen number $k$. Due to the curse of dimensionality, for moderate or

Karin Erdmann, Alicja Jaworska-Pastuszak, Grzegorz Pastuszak

We determine the Krull-Gabriel dimension of weighted surface algebras, a class of algebras which recently appeared in the context of classification of tame symmetric periodic algebras of non-polynomial growth. Moreover, we consider Krull-Gabriel dimension of idempotent algebras of weighted surface algebras and generalize the result in some cases.

Aleksandra Wierzbicka, Agata Kaminska, Kamil Sobczak, Dawid Jankowski

Series of GaN/AlN superlattices (SLs) with various periods and the same thicknesses of GaN quantum wells and AlN barriers have been investigated. X-ray diffraction, photoluminescence (PL) and transmission electron microscopy (TEM) techniques were used to study the influence of thickness of AlN and GaN sublayers on strain distribution in GaN/AlN SL structures

Kohei Fujikura, Shota Nakagawa, Yuichiro Nakai, Peng Sun

We construct a four-dimensional supersymmetric QCD in conformal window with a marginally relevant deformation which triggers the spontaneous breaking of (approximate) scale invariance and the subsequent confinement, generating a mass gap, at an energy scale hierarchically smaller than the Planck scale without fine-tuning. We analyze the finite temperature sy

Guo Xian Yau, Thirupathaiah Vasantam, Gayane Vardoyan

An Entanglement Generation Switch (EGS) is a quantum network hub that provides entangled states to a set of connected nodes by enabling them to share a limited number of hub resources. As entanglement requests arrive, they join dedicated queues corresponding to the nodes from which they originate. We propose a load-balancing policy wherein the EGS queries no

Bin Zhang, Xiaoyang Qu, Guokuan Li, Jiguang Wan

As object detectors are increasingly deployed as black-box cloud services or pre-trained models with restricted access to the original training data, the challenge of zero-shot object-level out-of-distribution (OOD) detection arises. This task becomes crucial in ensuring the reliability of detectors in open-world settings. While existing methods have demonst

Leonardo Colombo, María Emma Eyrea Irazú, María Eugenia García, Asier López-Gordón

We develop a reduction scheme \`a la Marsden-Weinstein-Meyer for hybrid Hamiltonian systems. Our method does not require the momentum map to be equivariant, neither to be preserved by the impact map. We illustrate the applicability of our theory with an example.

Jerry Jun-Yan Zhang, Nicolas Lodieu, Eduardo L. Martín, Pascal Tremblin

WISEA J181006.18-101000.5 (WISE1810) is the nearest metal-poor ultracool dwarf to the Sun. It has a low effective temperature and has been classified as extreme early-T subdwarf. However, methane, the characteristic molecule of the spectral class T, was not seen in the previous low-resolution spectrum. Using the 10.4-m Gran Telescopio Canarias, we collected

Ruiguang Pei, Junjie Wu, Dan Peng, Min Fang

The advent of edge intelligence and escalating concerns for data privacy protection have sparked a surge of interest in device-cloud collaborative computing. Large-scale device deployments to validate prototype solutions are often prohibitively expensive and practically challenging, resulting in a pronounced demand for simulation tools that can emulate realw

Georgios Marangelis

In first order logic, it is known that you can define a topology so that the countable models of some theory $T$ form a Polish Space (i.e. completely metrizable second countable space). In this paper we use the Baldwin- Boney Relational Presentation Theorem (from [3]; cf. 2.3) to generalize this result to the models of an Abstract Elementary Class (AEC). Mor

Andreas A. Bock, Martin S. Andersen

This paper presents some theoretical results relating the Bregman log determinant matrix divergence to Kaporin's condition number. These can be viewed as nearness measures between a preconditioner and a given matrix, and we show under which conditions these two functions coincide. We also give examples of constraint sets over which it is equivalent to minimi

Bin Zhang, Jinggang Chen, Xiaoyang Qu, Guokuan Li

Enabling object detectors to recognize out-of-distribution (OOD) objects is vital for building reliable systems. A primary obstacle stems from the fact that models frequently do not receive supervisory signals from unfamiliar data, leading to overly confident predictions regarding OOD objects. Despite previous progress that estimates OOD uncertainty based on

Emilie Højbjerre-Frandsen, Mark J. van der Laan, Alejandro Schuler

In randomized clinical trials (RCTs), the accurate estimation of marginal treatment effects is crucial for determining the efficacy of interventions. Enhancing the statistical power of these analyses is a key objective for statisticians. The increasing availability of historical data from registries, prior trials, and health records presents an opportunity t

Francesco Kruk, Savindu Herath, Prithwiraj Choudhury

In recent years, large language models (LLMs) have demonstrated exponential improvements that promise transformative opportunities across various industries. Their ability to generate human-like text and ensure continuous availability facilitates the creation of interactive service chatbots aimed at enhancing customer experience and streamlining enterprise o

Elisa Alòs, Òscar Burés, Josep Vives

In this paper we use Malliavin Calculus techniques in order to obtain expressions for the short-time behavior of the at-the-money implied volatility (ATM-IV) level and skew for a jump-diffusion stock price. The diffusion part is assumed to be the stochastic volatility Bachelier model and the jumps are modeled by a pure-jump L\'evy process with drift so that

Xuan Loc Pham, Mathias Prokop, Bram van Ginneken, Alessa Hering

Image registration is an essential technique for the analysis of Computed Tomography (CT) images in clinical practice. However, existing methodologies are predominantly tailored to a specific organ of interest and often exhibit lower performance on other organs, thus limiting their generalizability and applicability. Multi-organ registration addresses these

Rrubaa Panchendrarajan, Rubén Míguez, Arkaitz Zubiaga

In the context of fact-checking, claims are often repeated across various platforms and in different languages, which can benefit from a process that reduces this redundancy. While retrieving previously fact-checked claims has been investigated as a solution, the growing number of unverified claims and expanding size of fact-checked databases calls for alter

Kentaro Mita, Toshiyuki Kodama, Toshihiro Nakanishi, Tetsuya Ueda

We observe microwave nonreciprocal one-way transparency via ultrastrongly-coupled magnetochiral polaritons (MChPs) in a metamolecule at room temperature. The experimental results using MCh metamolecules with simultaneous breaking of time-reversal and space-inversion symmetries are reproduced by numerical simulations. Based on effective polarizability tensor

D. Yavorskiy, F. Le Mardelé, I. Mohelsky, M. Orlita

Low energy excitations of a two-dimensional electron gas (2DEG) in modulation-doped multiple (ten) quantum wells (QWs) was studied using far-infrared magneto-transmission technique at liquid helium temperatures. A large distance between neighbouring QWs of 54 nm excluded a direct interaction of electron wave functions confined in the wells. In four samples w

Fabian Schmidt, Karin Hammerfald, Henrik Haaland Jahren, Vladimir Vlassov

Common factors and microcounseling skills are critical to the effectiveness of psychotherapy. Understanding and measuring these elements provides valuable insights into therapeutic processes and outcomes. However, automatic identification of these change principles from textual data remains challenging due to the nuanced and context-dependent nature of thera

Calvin Kammerlander, Viola Kolb, Marinus Luegmair, Lou Scheermann

Efficient nutrient management and precise fertilization are essential for advancing modern agriculture, particularly in regions striving to optimize crop yields sustainably. The AgroLens project endeavors to address this challenge by develop ing Machine Learning (ML)-based methodologies to predict soil nutrient levels without reliance on laboratory tests. By

Shivam Mehta, Nebojsa Jojic, Hannes Gamper

Integrating audio comprehension and generation into large language models (LLMs) remains challenging due to the continuous nature of audio and the resulting high sampling rates. Here, we introduce a novel approach that combines Variational Quantization with Conditional Flow Matching to convert audio into ultra-low bitrate discrete tokens of 0.23kpbs, allowin

Malo Jézéquel, Jian Wang

For shear flows in a 2D channel, we define resonances near regular values of the shear profile for the Rayleigh equation under an analyticity assumption. This is done via complex deformation of the interval on which Rayleigh equation is considered. We show such resonances are inviscid limits of the eigenvalues of the corresponding Orr--Sommerfeld equation.

Luis Saucedo-Mora, Luis Irastorza-Valera

The Gauss-Seidel method has been used for more than 100 years as the standard method for the solution of linear systems of equations under certain restrictions. This method, as well as Cramer and Jacobi, is widely used in education and engineering, but there is a theoretical gap when we want to solve less restricted systems, or even non-square or non-exact s

Xinghua Liu, Ming Cao

In this work, we propose a factor graph optimization (FGO) framework to simultaneously solve the calibration problem for Ultra-WideBand (UWB) anchors and the robot localization problem. Calibrating UWB anchors manually can be time-consuming and even impossible in emergencies or those situations without special calibration tools. Therefore, automatic estimati

Omini Rathore, Richard Paul, Abigail Morrison, Hanno Scharr

Brain vessel segmentation of MR scans is a critical step in the diagnosis of cerebrovascular diseases. Due to the fine vessel structure, manual vessel segmentation is time consuming. Therefore, automatic deep learning (DL) based segmentation techniques are intensively investigated. As conventional DL models yield a high complexity and lack an indication of d

Jiajun Li, Hui Qi, Feifei Zhou, Yumeng Song

Color center PL5 in 4H silicon carbide (4H-SiC) has drawn significant attention due to its room-temperature quantum coherence properties and promising potential of quantum sensing applications. The preparation of PL5 ensemble is a critical prerequisite for practical applications. In this work, we investigated the formation of PL5 ensembles in types of 4H-SiC

Nan Huang, Wenzhao Zheng, Chenfeng Xu, Kurt Keutzer

Moving object segmentation is a crucial task for achieving a high-level understanding of visual scenes and has numerous downstream applications. Humans can effortlessly segment moving objects in videos. Previous work has largely relied on optical flow to provide motion cues; however, this approach often results in imperfect predictions due to challenges such

Marius Kurz, Rohan Kaushik, Marcel Blind, Patrick Kopper

Reinforcement learning has gained traction for active flow control tasks, with initial applications exploring drag mitigation via flow field augmentation around a two-dimensional cylinder. RL has since been extended to more complex turbulent flows and has shown significant potential in learning complex control strategies. However, such applications remain co

Charalampos D. Passalidis

In this paper we introduce and study the class of multivariate strong and strongly subexponential distributions. Some first properties are verified, as for example a type of multivariate analogue of Kesten's inequality, the closure property with respect to convolution, and the conditional closure property with respect to convolution roots. Next, we establish

K. S. Krishnamurthy, S. Krishna Prasad, D. S. Shankar Rao, R. J. Mandle

We report the effect of AC electric fields in the range of 0.1-300 kHz on planar antiferroelectric SmZA layers of DIO. Significant results are (a) primary bifurcation into a quasistationary periodic instability with its voltage threshold Uc and wave vector qc along the initial director being, respectively, quadratic and linear functions of f over 10-150 kHz,

Haomin Zhang, Chang Liu, Junjie Zheng, Zihao Chen

Currently, high-quality, synchronized audio is synthesized using various multi-modal joint learning frameworks, leveraging video and optional text inputs. In the video-to-audio benchmarks, video-to-audio quality, semantic alignment, and audio-visual synchronization are effectively achieved. However, in real-world scenarios, speech and audio often coexist in

Damian Serwata, Mateusz Nurek, Radoslaw Michalski

Typically, for analysing and modelling social phenomena, networks are a convenient framework that allows for the representation of the interconnectivity of individuals. These networks are often considered transmission structures for processes that happen in society, e.g. diffusion of information, epidemics, and spread of influence. However, constructing a ne

Dongping Liao, Xitong Gao, Yabo Xu, Chengzhong Xu

The increasing emphasis on privacy and data security has driven the adoption of federated learning, a decentralized approach to train machine learning models without sharing raw data. Prompt learning, which fine-tunes prompt embeddings of pretrained models, offers significant advantages in federated settings by reducing computational costs and communication

Songsong Yu, Yuxin Chen, Zhongang Qi, Zeke Xie

With the rapid proliferation of 3D devices and the shortage of 3D content, stereo conversion is attracting increasing attention. Recent works introduce pretrained Diffusion Models (DMs) into this task. However, due to the scarcity of large-scale training data and comprehensive benchmarks, the optimal methodologies for employing DMs in stereo conversion and t

Satoru Isogawa

In this paper, we introduce the notion of $\gamma$-regular sequences to characterize the property that graded modules have componentwise linear syzygies. This extends Harima and Watanabe's characterization of componentwise linear ideals in terms of $\mathfrak{m}$-full property.

Jeet Amrit Pattnaik, Santosh Kumar, S. K. Singh, R. N. Panda

Using a relativistic mean field formalism, we analyzed the magic number sequence for finite nuclei in the superheavy valley. The result for the IOPB-I parameter set is compared with the well-known NL3 force. The magic numbers obtained from IOPB-I and NL3 interactions are found to be similar. Analysing the single-particle levels and the number of nucleons occ

N. La Palombara, L. Sidoli, S. Mereghetti, P. Esposito

We report the results obtained with a XMM-Newton observation, performed in April 2023, of the poorly known Galactic Be X-ray binary pulsar 4U 0728-25. It was revealed at a flux level (not corrected for the absorption) $f_{\rm X}$(0.2-12 keV) = 1.7$\times 10^{-11}$ erg cm$^{-2}$ s$^{-1}$, which implies an unabsorbed source luminosity $L_{\rm X} \simeq 1.3 \ti

Andreas Habring, Alexander Falk, Martin Zach, Thomas Pock

We propose a method for sampling from Gibbs distributions of the form $\pi(x)\propto\exp(-U(x))$ by considering a family $(\pi^{t})_t$ of approximations of the target density which is such that $\pi^{t}$ exhibits favorable properties for sampling when $t$ is large, and $\pi^{t} \to \pi$ as $t \to 0$. This sequence is obtained by replacing (parts of) the pote

Munib Mesinovic, Soheila Molaei, Peter Watkinson, Tingting Zhu

Learning from longitudinal electronic health records is limited if it does not capture the temporal trajectories of the patient's state in a clinical setting. Graph models allow us to capture the hidden dependencies of the multivariate time-series when the graphs are constructed in a similar dynamic manner. Previous dynamic graph models require a pre-defined

Jason Aebischer, Atakan Tugberk Akmete, Riccardo Aliberti, Wolfgang Altmannshofer

The kaon physics programme, long heralded as a cutting-edge frontier by the European Strategy for Particle Physics, continues to stand at the intersection of discovery and innovation in high-energy physics (HEP). With its unparalleled capacity to explore new physics at the multi-TeV scale, kaon research is poised to unveil phenomena that could reshape our un

Maurício Collares, Joshua Erde, Anna Geisler, Mihyun Kang

In this paper we provide an asymptotic expansion for the number of independent sets in a general class of regular, bipartite graphs satisfying some vertex-expansion properties, extending results of Jenssen and Perkins on the hypercube and strengthening results of Jenssen, Perkins and Potukuchi. More precisely, we give an expansion of the independence polynom

Marek Rogatko, Paritosh Verma

We elaborate the problem of magnetized particle motion in the spacetime of a static, spherically symmetric black hole influenced by weak magnetic fields stemming from visible and dark matter sectors. The Wald's procedure for obtaining the weakly magnetized solution, generalized to the case of dark photon - Einstein-Maxwell gravity was implemented. The collis

Bargava Subramanian, Shajeev Jaikumar, Praveen Shastry, Naveen Kumarasami

Study Design: The study outlines the development of an autonomous AI system for chest X-ray (CXR) interpretation, trained on a vast dataset of over 5 million X rays sourced from healthcare systems across India. This AI system integrates advanced architectures including Vision Transformers, Faster R-CNN, and various U Net models (such as Attention U-Net, U-Ne

Anirban Misra, Urbashi Satpathi, Supurna Sinha, Sanjukta Roy

In the context of non-equilibrium statistical physics, the position response of a particle, coupled to a bath, subjected to an external force is a topic of broad interest. A topic of further interest is two distinguishable sets of interacting particles in contact with two different baths. Here, we report the experimental evidence of the modification of the p

Rui Gao, Zhi Cheng, Rajeev K. Jaiman

Graph neural networks, recently introduced into the field of fluid flow surrogate modeling, have been successfully applied to model the temporal evolution of various fluid flow systems. Existing applications, however, are mostly restricted to cases where the domain is time-invariant. The present work extends the application of graph neural network-based mode

Guneet Mutreja, Ksenia Bittner

Accurate classification of building roof types from aerial imagery is crucial for various remote sensing applications, including urban planning, disaster management, and infrastructure monitoring. However, this task is often hindered by the limited availability of labeled data for supervised learning approaches. To address this challenge, this paper investig

Anna Bodonhelyi, Christian Stegemann-Philipps, Alessandra Sonanini, Lea Herschbach

Effective patient communication is pivotal in healthcare, yet traditional medical training often lacks exposure to diverse, challenging interpersonal dynamics. To bridge this gap, this study proposes the use of Large Language Models (LLMs) to simulate authentic patient communication styles, specifically the "accuser" and "rationalizer" personas derived from

Xianqi Zhang, Hongliang Wei, Wenrui Wang, Xingtao Wang

Humanoid robots have attracted significant attention in recent years. Reinforcement Learning (RL) is one of the main ways to control the whole body of humanoid robots. RL enables agents to complete tasks by learning from environment interactions, guided by task rewards. However, existing RL methods rarely explicitly consider the impact of body stability on h

Xinwei Gao, Arambam James Singh, Gangadhar Royyuru, Michael Yuhas

Lane keeping in autonomous driving systems requires scenario-specific weight tuning for different objectives. We formulate lane-keeping as a constrained reinforcement learning problem, where weight coefficients are automatically learned along with the policy, eliminating the need for scenario-specific tuning. Empirically, our approach outperforms traditional

Jyothi, T. Satyanarayana Murthy

The rise of deep learning in natural language processing has fostered the creation of text to structured query language models composed of an encoder and a decoder. Researchers have experimented with various intermediate processing like schema linking, table type aware, value extract. To generate accurate SQL results for the user question. However error anal

Mohammad Shadman Hashem, Ahsan Raza, Sama E Shan, Seokhee Jeon

A wide range of haptic feedback is crucial for achieving high realism and immersion in virtual environments. Therefore, a multi-modal haptic interface that provides various haptic signals simultaneously is highly beneficial. This paper introduces a novel silicone fingertip actuator that is pneumatically actuated, delivering a realistic and effective haptic e

Masakiyo Kitazawa, Tatsuya Wada, Kazuyuki Kanaya

We explore the distribution of Lee-Yang zeros around the critical point that appears in the heavy-quark region of QCD at nonzero temperature in lattice numerical simulations. With the aid of the hopping-parameter expansion that is well justified around the critical point in our setting, our numerical analysis is capable of analyzing the partition function fo

S. P. Järvinen, S. Hubrig, M. Küker, U. Ziegler

The Herbig Ae/Be star HD190073 is one of the very few magnetic Herbig Ae/Be stars for which close low-mass companions have been reported. Previously published magnetic field measurements indicated an annual change in the field configuration. We aim to study in detail the spectral and magnetic variability of this star and characterise its magnetosphere for th

Weizhen Wang, Jianping He, Xiaoming Duan

Policy gradient methods are one of the most successful approaches for solving challenging reinforcement learning problems. Despite their empirical successes, many state-of-the-art policy gradient algorithms for discounted problems deviate from the theoretical policy gradient theorem due to the existence of a distribution mismatch. In this work, we analyze th

Z. Fodor, A. Yu. Kotov, T. G. Kovacs, K. K. Szabo

We discuss properties of thermal Quantum Chromodynamics obtained by means of lattice simulations with overlap fermions. This fermion discretisation preserves chiral symmetry at finite lattice spacing. We present details of the formulation and results for the chiral observables. We determine the topological susceptibility from simulations at fixed global topo

Max Auer, Tanja I. Schindler

Studying Birkhoff sums of non-integrable functions involves the challenge of large observations depending on the sampled orbit, which prevents pointwise limit theorems. To address this issue, the largest observations are removed, this process is commonly known as trimming. While this method is well studied for independent identically distributed sequences an

Ziye Chen, Yiqun Duan, Riheng Zhu, Zhenbang Sun

Personalized multiple clustering aims to generate diverse partitions of a dataset based on different user-specific aspects, rather than a single clustering. It has recently drawn research interest for accommodating varying user preferences. Recent approaches primarily use CLIP embeddings with proxy learning to extract representations biased toward user clust

Ryuta Nagahama, Weiwei Wan, Zhengtao Hu, Kensuke Harada

Precisely grasping an object is a challenging task due to pose uncertainties. Conventional methods have used cameras and fixtures to reduce object uncertainty. They are effective but require intensive preparation, such as designing jigs based on the object geometry and calibrating cameras with high-precision tools fabricated using lasers. In this study, we p

Kausutua Tjikundi, Jackie Kleynhans, Stefano Tempia, Cheryl Cohen

Households play a crucial role in the propagation of infectious diseases due to the frequent and prolonged interactions that typically occur between their members. Recent studies have emphasized the need to include socioeconomic variables in epidemic models to account for the heterogeneity induced by human behavior. While sub-Saharan Africa suffers the highe

Maryam Khan, Muhammad Azeem Akbar, Jussi Kasurinen

The increasing adoption of Large Language Models (LLMs) in software engineering education presents both opportunities and challenges. While LLMs offer benefits such as enhanced learning experiences, automated assessments, and personalized tutoring, their integration also raises concerns about academic integrity, student over-reliance, and ethical considerati

Kunliang Liu, Jianming Wang, Rize Jin, Wonjun Hwang

Vision Foundation Model (VFM) such as the Segment Anything Model (SAM) and Contrastive Language-Image Pre-training Model (CLIP) has shown promising performance for segmentation and detection tasks. However, although SAM excels in fine-grained segmentation, it faces major challenges when applying it to semantic-aware segmentation. While CLIP exhibits a strong

Chongjie Ye, Yushuang Wu, Ziteng Lu, Jiahao Chang

With the growing demand for high-fidelity 3D models from 2D images, existing methods still face significant challenges in accurately reproducing fine-grained geometric details due to limitations in domain gaps and inherent ambiguities in RGB images. To address these issues, we propose Hi3DGen, a novel framework for generating high-fidelity 3D geometry from i

Haoxing Du, Lyna Kim, Joan Creus-Costa, Jack Michaels

We present WeatherMesh-3 (WM-3), an operational transformer-based global weather forecasting system that improves the state of the art in both accuracy and computational efficiency. We introduce the following advances: 1) a latent rollout that enables arbitrary-length predictions in latent space without intermediate encoding or decoding; and 2) a modular arc

Xinyi Yuan, Weiwei Wan, Kensuke Harada

This paper revisits the numerical inverse kinematics (IK) problem, leveraging modern computational resources and refining the seed selection process to develop a solver that is competitive with analytical-based methods. The proposed seed selection strategy consists of three key stages: (1) utilizing a K-Dimensional Tree (KDTree) to identify seed candidates b

Lang Cao, Renhong Chen, Yingtian Zou, Chao Peng

We introduce the Entropy-Driven Uncertainty Process Reward Model (EDU-PRM), a novel entropy-driven training framework for process reward modeling that enables dynamic, uncertainty-aligned segmentation of complex reasoning steps, eliminating the need for costly manual step annotations. Unlike previous Process Reward Models (PRMs) that rely on static partition

Ahmed Mohamed Hussain, Panos Papadimitratos

Traditional Neighbor Discovery (ND) and Secure Neighbor Discovery (SND) are key elements for network functionality. SND is a hard problem, satisfying not only typical security properties (authentication, integrity) but also verification of direct communication, which involves distance estimation based on time measurements and device coordinates. Defeating re

Yishen Ji, Ziyue Zhu, Zhenxin Zhu, Kaixin Xiong

Recent progress in driving video generation has shown significant potential for enhancing self-driving systems by providing scalable and controllable training data. Although pretrained state-of-the-art generation models, guided by 2D layout conditions (e.g., HD maps and bounding boxes), can produce photorealistic driving videos, achieving controllable multi-

Wei Shen, Guanlin Liu, Zheng Wu, Ruofei Zhu

Reinforcement Learning from Human Feedback (RLHF) is crucial for aligning large language models with human preferences. While recent research has focused on algorithmic improvements, the importance of prompt-data construction has been overlooked. This paper addresses this gap by exploring data-driven bottlenecks in RLHF performance scaling, particularly rewa

S. Liang, W. G. Yang, Y. G. Zheng, S. J. Kang

A sample of 312 low-frequency peaked BL Lacertae objects (LBLs) and 694 flat spectrum radio quasars (FSRQs) with the parameters both redshift and $\gamma$-ray photon spectral index ($\Gamma _\gamma$) is compiled from the active galactic nuclei (AGNs) Catalog Data Release 2 (4LAC-DR2) from Fermi-LAT. The multi-wavelength data of the sample sources are downloa

Jie Su, Liansai Deng, Cheng Wen, Rong Wang

Currently, many verification algorithms are available to improve the reliability of software systems. Selecting the appropriate verification algorithm typically demands domain expertise and non-trivial manpower. An automated algorithm selector is thus desired. However, existing selectors, either depend on machine-learned strategies or manually designed heuri

Qirui Li, Rui Zong

We introduce an open-source GPU-accelerated fully homomorphic encryption (FHE) framework CAT, which surpasses existing solutions in functionality and efficiency. \emph{CAT} features a three-layer architecture: a foundation of core math, a bridge of pre-computed elements and combined operations, and an API-accessible layer of FHE operators. It utilizes techni

Noufel Frikha, Xuanye Song

Building on the well-posedness of the backward Kolmogorov partial differential equation in the Wasserstein space, we analyze the strong and weak convergence rates for approximating the unique solution of a class of McKean-Vlasov stochastic differential equations via the Euler-Maruyama time discretization scheme applied to the associated system of interacting

Haijie Yang, Zhenyu Zhang, Hao Tang, Jianjun Qian

Pre-trained conditional diffusion models have demonstrated remarkable potential in image editing. However, they often face challenges with temporal consistency, particularly in the talking head domain, where continuous changes in facial expressions intensify the level of difficulty. These issues stem from the independent editing of individual images and the

Huixiang Zhen, Xiaotong Li, Wenyin Gong, Xiangyun Hu

In expensive multi-objective optimization, where the evaluation budget is strictly limited, selecting promising candidate solutions for expensive fitness evaluations is critical for accelerating convergence and improving algorithmic performance. However, designing an optimization strategy that effectively balances convergence, diversity, and distribution rem

M. U. Ashraf

The comment raises serious concerns regarding the authors claims about the phase transition from the QGP phase to the hadron gas phase. Additionally, the comment critiques the fundamental distinction between the kinetic freeze-out temperature and the critical temperature, as the authors erroneously treat them as identical in their article. The authors also a

Soichi Shirai, Shih-Yen Tseng, Hokuto Iwakiri, Takahiro Horiba

Quantum-selected configuration interaction (QSCI) is a novel quantum-classical hybrid algorithm for quantum chemistry calculations. This method identifies electron configurations having large weights for the target state using quantum devices and allows CI calculations to be performed with the selected configurations on classical computers. In principle, the

Petar Žugec, Marta Sabate Gilarte, Michael Bacak, Vasilis Vlachoudis

This study addresses a challenge of parametrizing a resolution function of the neutron beam from the neutron time of flight facility n_TOF at CERN. A difficulty stems from a fact that a resolution function exhibits rather strong variations in shape, over approximately 10 orders of magnitude in neutron energy. In order to avoid a need for a manual identificat

Mohamed Yassine Arkhis, Denis Efimov

We prove that under a small-gain condition, an interconnection of two globally incrementally exponentially stable systems inherits this property on any compact connected forward invariant set. It is also demonstrated that the interconnection inherits a weaker version of incremental exponential stability globally. An example illustrating the theoretical findi

David Brett, Anniek Myatt

Large language models (LLMs) have grown in their usage to provide support for question answering across numerous disciplines. The models on their own have already shown promise for answering basic questions, however fail quickly where expert domain knowledge is required or the question is nuanced. Scientific research often involves searching for relevant lit

Wenjie Liu, Zhongliang Liu, Xiaoyan Yang, Man Sha

3D scene stylization approaches based on Neural Radiance Fields (NeRF) achieve promising results by optimizing with Nearest Neighbor Feature Matching (NNFM) loss. However, NNFM loss does not consider global style information. In addition, the implicit representation of NeRF limits their fine-grained control over the resulting scenes. In this paper, we introd

Mingfa Chen

In this paper we introduce a local-refinement procedure to investigate finite t-stabilities on a triangulated category, and show a direct sufficient condition for a finite t-stability to be finite finest. We classify all finite finest t-stabilities for certain triangulated categories, including those from the projective plane, weighted projective lines, and

Felix M. Schmitt-Koopmann, Elaine M. Huang, Hans-Peter Hutter, Alireza Darvishy

PDF inaccessibility is an ongoing challenge that hinders individuals with visual impairments from reading and navigating PDFs using screen readers. This paper presents a step-by-step process for both novice and experienced users to create accessible PDF documents, including an approach for creating alternative text for mathematical formulas without expert kn

Zhihan Zhou, Feng Hong, Jiaan Luo, Jiangchao Yao

We propose L2T, an advancement of visual instruction tuning (VIT). While VIT equips Multimodal LLMs (MLLMs) with promising multimodal capabilities, the current design choices for VIT often result in overfitting and shortcut learning, potentially degrading performance. This gap arises from an overemphasis on instruction-following abilities, while neglecting t

A. Ya. Maltsev

We consider a special class of quasi-periodic potentials arising in the physics of photonic systems and possessing rotational symmetry of the 8th order. We are interested in the ``scaling'' properties of such potentials, namely, the growth rate of their closed level lines near the percolation threshold. Estimates of the corresponding scaling indices allow, i

Congyu Wang, Mingjing Du, Xiang Jiang, Yongquan Dong

The rapid growth of unlabeled time series data, driven by the Internet of Things (IoT), poses significant challenges in uncovering underlying patterns. Traditional unsupervised clustering methods often fail to capture the complex nature of time series data. Recent deep learning-based clustering approaches, while effective, struggle with insufficient represen

Mohamed Yassine Arkhis, Denis Efimov

In this paper, first, it is shown that if a nonlinear time-varying system is contractive, then it is incrementally exponentially stable. Second, leveraging this result, under mild restrictions, an approach is proposed to design feedforward inputs for affine in control systems providing contraction/incremental exponential stability. Unlike standard stability

Wonhyeok Choi, Kyumin Hwang, Minwoo Choi, Kiljoon Han

Self-supervised monocular depth estimation (SSMDE) has gained attention in the field of deep learning as it estimates depth without requiring ground truth depth maps. This approach typically uses a photometric consistency loss between a synthesized image, generated from the estimated depth, and the original image, thereby reducing the need for extensive data

Yunming Liang, Zihao Chen, Chaofan Ding, Xinhan Di

Currently, high-quality, synchronized audio is synthesized from video and optional text inputs using various multi-modal joint learning frameworks. However, the precise alignment between the visual and generated audio domains remains far from satisfactory. One key factor is the lack of sufficient temporal and semantic alignment annotations in open-source vid

Praveen Kumar Roy

Let $X_r$ denote the blow-up of the hyperelliptic surface $X$ at $r$ very general points. In this paper, we first provide a criterion for the ampleness of a line bundle on $X_r$ and compare it with an existing result. We then study the multi-point Seshadri constants of ample line bundles on hyperelliptic surfaces $X$. Next, we compute single-point Seshadri c

Yuki Sato, Shrinathan Esaki Muthu Pandara Kone, Junpei Oba, Kenichi Yatsugi

Valley photonic crystals (VPCs) offer topological kink states that ensure robust, unidirectional, and backscattering-immune light propagation. The design of VPCs is typically based on analogies with condensed-matter topological insulators that exhibit the quantum valley Hall effect; trial-and-error approaches are often used to tailor the photonic band struct