March 2025 arXiv papers — page 148

James Brooke, Emyr Clement, Maciej Glowacki, Sudarshan Paramesvaran

The implementation of convolutional neural networks in programmable logic, for applications in fast online event selection at hadron colliders, is studied. In particular, an approach based on full event images for classification is studied, including hardware-aware optimisation of the network architecture, and evaluation of physics performance using simulate

Yaorui Shi, Jiaqi Yang, Changhao Nai, Sihang Li

Single-cell RNA sequencing (scRNA-seq) offers detailed insights into cellular heterogeneity. Recent advancements leverage single-cell large language models (scLLMs) for effective representation learning. These models focus exclusively on transcriptomic data, neglecting complementary biological knowledge from textual descriptions. To overcome this limitation,

Rémi Guénet

In arXiv:1303.3724, the authors provide an axiomatic way of constructing new polynomially bounded o-minimal structures. However, all of the structures satisfying these axioms must also have smooth cell-decomposition. In this paper, we generalize their approach by allowing weakly smooth germs into the construction. In particular, we showed in arXiv:2501.17583

Stefano Ardizzoni, Luca Consolini, Mattia Laurini, Marco Locatelli

In this paper we address the speed planning problem for a vehicle along a predefined path. A weighted average of two (conflicting) terms, energy consumption and travel time, is minimized. After deriving a non-convex mathematical model of the problem, we introduce a convex relaxation of the model and show that, after the application of a suitable feasibility-

Kechun Xu, Xunlong Xia, Kaixuan Wang, Yifei Yang

We study the task of language-conditioned pick and place in clutter, where a robot should grasp a target object in open clutter and move it to a specified place. Some approaches learn end-to-end policies with features from vision foundation models, requiring large datasets. Others combine foundation models in a zero-shot setting, suffering from cascading err

Alvaro Lanza, Xiang Qu, Stefano Bo

Diffusive dynamics abound in nature and have been especially studied in physical, biological, and financial systems. These dynamics are characterised by a linear growth of the mean squared displacement (MSD) with time. Often, the conditions that give rise to simple diffusion are violated, and many systems, such as biomolecules inside cells, microswimmers, or

Andreas Schaefer

We study coined Random Quantum Walks on the hexagonal lattice, where the strength of disorder is monitored by the coin matrix. Each lattice site is equipped with an i.i.d. random variable that is uniformly distributed on the torus and acts as a random phase in every step of the QW. We show exponential decay of the fractional moments of the Green function in

Jialiang Zhang, Futao Huang, Shuo Li, Guojun Yu

Diamond's exceptional properties make it highly suited for applications in challenging radiation environments. Understanding radiation-induced damage in diamond is crucial for enabling its practical applications and advancing materials science. However, direct imaging of radiation-induced crystal defects at the atomic scale remains rare due to diamond's comp

Yifan Zhou, Zeqi Xiao, Shuai Yang, Xingang Pan

Latent Diffusion Models (LDMs) are known to have an unstable generation process, where even small perturbations or shifts in the input noise can lead to significantly different outputs. This hinders their applicability in applications requiring consistent results. In this work, we redesign LDMs to enhance consistency by making them shift-equivariant. While i

Gledson Rodrigo Tondo, Igor Kavrakov, Guido Morgenthal

Knowledge of the force time history of a structure is essential to assess its behaviour, ensure safety and maintain reliability. However, direct measurement of external forces is often challenging due to sensor limitations, unknown force characteristics, or inaccessible load points. This paper presents an efficient dynamic load reconstruction method using ph

Dima Taji, Daniel Zeman

Training models that can perform well on various NLP tasks require large amounts of data, and this becomes more apparent with nuanced tasks such as anaphora and conference resolution. To combat the prohibitive costs of creating manual gold annotated data, this paper explores two methods to automatically create datasets with coreferential annotations; direct

Qi Liu, Weiying Xue, Yuxiao Wang, Zhenao Wei

The video visual relation detection (VidVRD) task is to identify objects and their relationships in videos, which is challenging due to the dynamic content, high annotation costs, and long-tailed distribution of relations. Visual language models (VLMs) help explore open-vocabulary visual relation detection tasks, yet often overlook the connections between va

Nils Sass, Hendrik Roch, Niklas Götz, Renata Krupczak

SPARKX is an open-source Python package developed to analyze simulation data from heavy-ion collision experiments. By offering a comprehensive suite of tools, SPARKX simplifies data analysis workflows, supports multiple formats such as OSCAR2013, and integrates seamlessly with SMASH and JETSCAPE/X-SCAPE. This paper describes SPARKX's architecture, features,

Kangsoo Jung, Sayan Biswas, Catuscia Palamidessi

Federated Learning (FL) has emerged as a promising paradigm for collaborative model training without the need to share clients' personal data, thereby preserving privacy. However, the non-IID nature of the clients' data introduces major challenges for FL, highlighting the importance of personalized federated learning (PFL) methods. In PFL, models are trained

Xuzhou Yang

The green functions for the heat and Laplace equations with dynamical boundary conditions in a ball are studied. First, the green functions of the Laplace equation with a dynamical boundary condition are given, and the properties of related heat kernels are discussed. Then using these ingredients, two complementary approximations to the heat equation with a

Wageesha N. Manamperi, Thushara D. Abhayapala

Separation of simultaneously active multiple speakers is a difficult task in environments with strong reverberation and many background noise sources. This paper uses the relative transfer matrix (ReTM), a generalization of the relative transfer function of a room, to propose a simple yet novel approach for separating concurrent speakers using noisy multicha

Amit Attia, Tomer Koren

The learning rate in stochastic gradient methods is a critical hyperparameter that is notoriously costly to tune via standard grid search, especially for training modern large-scale models with billions of parameters. We identify a theoretical advantage of learning rate annealing schemes that decay the learning rate to zero at a polynomial rate, such as the

Jiale Wang, Chen Zhao, Wei Ke, Tong Zhang

Random Sample Consensus (RANSAC) is a fundamental approach for robustly estimating parametric models from noisy data. Existing learning-based RANSAC methods utilize deep learning to enhance the robustness of RANSAC against outliers. However, these approaches are trained and tested on the data generated by the same algorithms, leading to limited generalizatio

Yi Chen, Jie Lou, Malte Wabnitz, Johnson Loh

Depthwise separable convolution (DSC) has emerged as a crucial technique, especially for resource-constrained devices. In this paper, we propose a dual-engine for the DSC hardware accelerator, which enables the full utilization of depthwise convolution (DWC) and pointwise convolution (PWC) processing elements (PEs) in all DSC layers. To determine the optimal

Claudius Kienle, Benjamin Alt, Finn Schneider, Tobias Pertlwieser

Despite the widespread adoption of industrial robots in automotive assembly, wire harness installation remains a largely manual process, as it requires precise and flexible manipulation. To address this challenge, we design a novel AI-based framework that automates cable connector mating by integrating force control with deep visuotactile learning. Our syste

Xiuzhen Guo, Lianyuan Yu, Ji Shi, Na Lei

Semi-supervised learning utilizes insights from unlabeled data to improve model generalization, thereby reducing reliance on large labeled datasets. Most existing studies focus on limited samples and fail to capture the overall data distribution. We contend that combining distributional information with detailed information is crucial for achieving more robu

Elaine Zosa, Ville Komulainen, Sampo Pyysalo

As LLMs gain more popularity as chatbots and general assistants, methods have been developed to enable LLMs to follow instructions and align with human preferences. These methods have found success in the field, but their effectiveness has not been demonstrated outside of high-resource languages. In this work, we discuss our experiences in post-training an L

Sulakhana Chowdhury, Geetha Thangavelu

In this article, we study the permutation modules and Young modules of the group algebras of the direct product of symmetric groups $K\mathfrak{S}_{a,b}$, and the walled Brauer algebras $\B_{r,t}(\delta)$. In the category of dual Specht-filtered modules, if the characteristic of the field is neither $2$ nor $3$, then the permutation modules are dual Specht f

Rolf Zeißler, Florian Pabst, Thomas Blochowicz

Molecular dynamics in pure water and aqueous salt solutions remain incompletely understood, partly due to the apparent contradictions between results from different spectroscopic techniques. In this work, we demonstrate, by detailed comparison of light scattering and dielectric spectroscopy data for pure water and aqueous lithium chloride solutions, that the

Yiran Xie, Xu Wang, Yatong Qian, Teng Liu

The Tibetan Plateau (TP) and surrounding regions, vital to global energy and water cycles, are profoundly influenced by climate change and anthropogenic activities. Despite widespread attention to vegetation greening across the region since the 1980s, its underlying mechanisms remain poorly understood. This study employs the eigen microstates method to quant

Xu Jiang, Gehui Li, Bin Chen, Jian Zhang

Image restoration (IR) is challenging due to the complexity of real-world degradations. While many specialized and all-in-one IR models have been developed, they fail to effectively handle complex, mixed degradations. Recent agentic methods RestoreAgent and AgenticIR leverage intelligent, autonomous workflows to alleviate this issue, yet they suffer from sub

Kevin Qinghong Lin, Mike Zheng Shou

Human daily activities can be concisely narrated as sequences of routine events (e.g., turning off an alarm) in video streams, forming an event vocabulary. Motivated by this, we introduce VLog, a novel video understanding framework that define video narrations as vocabulary, going beyond the typical subword vocabularies in existing generative video-language

Merna Abumusabh, Jacopo Cerasoli, Giulio Dujany, Corentin Santos

In this work we present the Graph-based Full Event Interpretation (GraFEI), a machine learning model based on graph neural networks to inclusively reconstruct events in the Belle~II experiment. Belle~II is well suited to perform measurements of $B$ meson decays involving invisible particles (e.g. neutrinos) in the final state. The kinematical properties of s

Patrick Benjamin, Alessandro Abate

The mean-field framework has been used to find approximate solutions to problems involving very large populations of symmetric, anonymous agents, which may be intractable by other methods. The cooperative mean-field control (MFC) problem has received less attention than the non-cooperative mean-field game (MFG), despite the former potentially being more usef

Yilun Ge, Shuyao Liao, Shengqian Han, Chenyang Yang

Incorporating mathematical properties of a wireless policy to be learned into the design of deep neural networks (DNNs) is effective for enhancing learning efficiency. Multi-user precoding policy in multi-antenna system, which is the mapping from channel matrix to precoding matrix, possesses a permutation equivariance property, which has been harnessed to de

Viktor Dorfler, Dylan Dryden, Viet Lee

In this paper we introduce a Platform created in order to support SMEs' endeavor to extract value from their intangible assets effectively. To implement the Platform, we developed five knowledge bases using a knowledge-based ex-pert system shell that contain knowledge from intangible as-set consultants, patent attorneys and due diligence lawyers. In order to

Sergey Simonov

We study solutions to the system $u_{tt}-u_{xx}+q(x)u=0, x>0,t>0$; $u|_{t=0}=u_t|_{t=0}=0, x>0$; $u|_{x=0}=g(t), t>0$, with a locally summable Hermitian matrix-valued potential $q$ and a $C^{\infty}$-smooth $\mathbb C^n$-valued boundary control $g$ vanishing near the origin. We prove that the solution $u^{g}(\cdot,T)$ is a function from $W^2_1([0,T];\mathbb

Jiatong Xia, Lingqiao Liu

3D Gaussian Splatting (3DGS) has demonstrated impressive performance in synthesizing novel views after training on a given set of viewpoints. However, its rendering quality deteriorates when the synthesized view deviates significantly from the training views. This decline occurs due to (1) the model's difficulty in generalizing to out-of-distribution scenari

Clemens Damke, Eyke Hüllermeier

Quantification learning is the task of predicting the label distribution of a set of instances. We study this problem in the context of graph-structured data, where the instances are vertices. Previously, this problem has only been addressed via node clustering methods. In this paper, we extend the popular Adjusted Classify & Count (ACC) method to graphs. We

Xiaozhen Qiao, Peng Huang, Jiakang Yuan, Xianda Guo

Test-time adaptation (TTA) is crucial in maintaining performance of Vision Language Models (VLMs) when facing distribution shifts, particularly when the source data or target labels are inaccessible. Existing TTA methods predominantly leverage the output probability distribution of CLIP for feature evaluation, resulting in biases under domain shifts, which c

Qi Wu, Zeping Sui, Hien Quoc Ngo, Qun Wan

In this paper, we investigate the joint generalized channel estimation and device identification problem in Internet of Things (IoT) networks {under multipath propagation}. To fully utilize the received signal, we decompose the generalized channel into three components: transmitter hardware characteristics, path gains, and angles of arrival. By modelling the

Matteo Sorbara

The muon anomalous magnetic moment, $a_\mu=\frac{g-2}{2}$, is a low-energy observable which can be both measured and computed to high precision, making it a sensitive test of the Standard Model and a probe for new physics. This anomaly was measured with a precision of $0.20$~parts per million (ppm) by the Fermilab's Muon g-2 (E989) experiment. The final goal

Kexuan Wang, An Liu

In XR downlink transmission, energy-efficient power scheduling (EEPS) is essential for conserving power resource while delivering large data packets within hard-latency constraints. Traditional constrained reinforcement learning (CRL) algorithms show promise in EEPS but still struggle with non-convex stochastic constraints, non-stationary data traffic, and s

Fech Scen Khoo

Quasinormal modes are characteristic signatures of compact objects. Here we consider rotating regular black holes, representing rotating generalizations of the Simpson and Visser metric. We present the spectrum of scalar quasinormal modes and compare it with the spectrum of Kerr black holes. The calculations are done using a spectral decomposition method. Th

Karamatou Yacoubou Djima, Ka Man Yim

Point signatures based on the Laplacian operators on graphs, point clouds, and manifolds have become popular tools in machine learning for graphs, clustering, and shape analysis. In this work, we propose a novel point signature, the power spectrum signature, a measure on $\mathbb{R}$ defined as the squared graph Fourier transform of a graph signal. Unlike ei

Soumya Kanti Pal, Shamik Gupta

We study canonical-equilibrium properties of Random Field $O(n)$ Models involving classical continuous vector spins of $n$ components with mean-field interactions and subject to disordered fields acting on individual spins. To this end, we employ two complementary approaches: the mean-field approximation, valid for any disorder distribution, and the replica

Katherine Dearstyne, Pedro, Alarcon Granadeno, Theodore Chambers

Cyber-Physical Systems (CPS) often leverage Reinforcement Learning (RL) techniques to adapt dynamically to changing environments and optimize performance. However, it is challenging to construct safety cases for RL components. We therefore propose the SAFE-RL (Safety and Accountability Framework for Evaluating Reinforcement Learning) for supporting the devel

Zongwu Wang, Peng Xu, Fangxin Liu, Yiwei Hu

Large language models (LLMs) are increasingly utilized for complex tasks requiring longer context lengths, with some models supporting up to 128K or 1M tokens. This trend, however, presents significant challenges in inference speed and memory management. Quantization emerges as a promising approach to address the widening gap between LLM size and memory capa

Ruanjun Li, Yuedong Tan, Yuanming Shi, Jiawei Shao

This paper introduces VideoScan, an efficient vision-language model (VLM) inference framework designed for real-time video interaction that effectively comprehends and retains streamed video inputs while delivering rapid and accurate responses. A longstanding challenge in video understanding--particularly for long-term or real-time applications--stems from t

Ram Manohar, Kedarnath Buda, B. V. Rathish Kumar

In this article, the limiting behavior of the solution $\bar u_s$ of the optimal control problem subjected to the fractional Poisson equation $$(-\Delta)^s u_s(x)=f_s(x), \quad x\in \Omega$$ defined on domain $\Omega$ bounded by smooth boundary with zero exterior boundary conditions $u_s(x)\equiv 0, \quad x \in \Omega^c $ is established. We will prove that $

Masoud Jamshidiyan Tehrani, Jinhan Kim, Paolo Tonella

Recent research on testing autonomous driving agents has grown significantly, especially in simulation environments. The CARLA simulator is often the preferred choice, and the autonomous agents from the CARLA Leaderboard challenge are regarded as the best-performing agents within this environment. However, researchers who test these agents, rather than train

Arthur da Cunha, Mikael Møller Høgsgaard, Andrea Paudice, Yuxin Sun

Boosting is a key method in statistical learning, allowing for converting weak learners into strong ones. While well studied in the realizable case, the statistical properties of weak-to-strong learning remain less understood in the agnostic setting, where there are no assumptions on the distribution of the labels. In this work, we propose a new agnostic boo

James Macpherson, Vanessa Miemietz, Mateusz Stroiński

In this article, we define and investigate Hochschild cohomology for finitary 2-representations of quasi-fiat 2-categories.

Jiani Huang, Shijie Wang, Liang-bo Ning, Wenqi Fan

Recommender systems (RecSys) are widely used across various modern digital platforms and have garnered significant attention. Traditional recommender systems usually focus only on fixed and simple recommendation scenarios, making it difficult to generalize to new and unseen recommendation tasks in an interactive paradigm. Recently, the advancement of large l

Kaoru Teranishi, Kiminao Kogiso, Takashi Tanaka

We propose a protocol based on mechanism design theory and encrypted control to solve average consensus problems among rational and strategic agents while preserving their privacy. The proposed protocol provides a mechanism that incentivizes the agents to faithfully implement the intended behavior specified in the protocol. Furthermore, the protocol runs ove

Aliyah Maxwell-Abrams, Robert Schneider

We prove identities for six infinite series whose values involve linear combinations of $\pi$ and $\operatorname{ln} 2$, that do not appear in standard infinite series references.

S. Boula, A. Nathanail

OJ 287, a nearby blazar, has exhibited remarkable variability in its optical light curve since 1888, characterized by ~12-year quasi-periodic outbursts. These events are attributed to the orbital dynamics of a supermassive binary black hole system at the heart of the blazar. This study explores the role of magnetic reconnection and the formation of plasmoid

Fangzheng Qi, Zhenjie Hou, En Lin, Xing Li

The recognition of pig behavior plays a crucial role in smart farming and welfare assurance for pigs. Currently, in the field of pig behavior recognition, the lack of publicly available behavioral datasets not only limits the development of innovative algorithms but also hampers model robustness and algorithm optimization.This paper proposes a dataset contai

Mei Choi Chiu, Ling Wang, Hoi Ying Wong

Empirical studies with publicly available life tables identify long-range dependence (LRD) in national mortality data. Although the longevity market is supposed to benchmark against the national force of mortality, insurers are more concerned about the forces of mortality associated with their own portfolios than the national ones. Recent advances on mortali

Rui Huang, Siyu Tang, Zhiqian Cai, Lin Zhao

Modular Aerial Robotic Systems (MARS) consist of multiple drone units assembled into a single, integrated rigid flying platform. With inherent redundancy, MARS can self-reconfigure into different configurations to mitigate rotor or unit failures and maintain stable flight. However, existing works on MARS self-reconfiguration often overlook the practical cont

Suryansh Upadhyay, Koustubh Phalak, Jungeun Lee, Kathleen Mitchell Hill

Quantum computing is an emerging paradigm with the potential to transform numerous application areas by addressing problems considered intractable in the classical domain. However, its integration into cyberspace introduces significant security and privacy challenges. The exponential rise in cyber attacks, further complicated by quantum capabilities, poses s

Li-Li Wang, Guang-Hui Zheng

The preconditioned Metropolis adjusted Langevin algorithm (MALA) is a widely used method in statistical applications, where the choice of the preconditioning matrix plays a critical role. Recently, Titsias \cite{Titsias2024} demonstrated that the inverse Fisher information matrix is the optimal preconditioner by minimizing the expected squared jump distance

Sebastian Staacks, Dominik Dorsel, Alexander Krampe, Marcel Hagedorn

In order to extend the available sensors of smartphone experiments with cheap microcontroller-based external sensors, the smartphone experimentation app "phyphox" has been extended with a generic Bluetooth Low Energy interface. Since its application requires an in-depth understanding of the underlying technologies, the direct use of that interface for educat

A. Parvizi, M. M. Sheikh-Jabbari, V. Taghiloo

We continue developing the freelance holography program, formulating gauge/gravity correspondence where the gravity side is formulated on a space bounded by a generic timelike codimension-one surface inside AdS and arbitrary boundary conditions are imposed on the gravity fields on the surface. Our analysis is performed within the Covariant Phase Space Formal

A. Parvizi, M. M. Sheikh-Jabbari, V. Taghiloo

We explore AdS/CFT duality in the large $N$ limit, where the duality reduces to gauge/gravity correspondence, from the viewpoint of covariant phase space formalism (CPSF). In particular, we elucidate the role of the $W, Y$, and $Z$ freedoms (also known as ambiguities) in the CPSF and their meaning in the gauge/gravity correspondence. We show that $W$-freedom

Yang Nan, Huichi Zhou, Xiaodan Xing, Giorgos Papanastasiou

As artificial intelligence and digital medicine increasingly permeate healthcare systems, robust governance frameworks are essential to ensure ethical, secure, and effective implementation. In this context, medical image retrieval becomes a critical component of clinical data management, playing a vital role in decision-making and safeguarding patient inform

Anqi Dong, Karl H. Johansson, Johan Karlsson

We consider a probabilistic model for large-scale task allocation problems for multi-agent systems, aiming to determine an optimal deployment strategy that minimizes the overall transport cost. Specifically, we assign transportation agents to delivery tasks with given pick-up and drop-off locations, pairing the spatial distribution of transport resources wit

Nikolai Körber, Eduard Kromer, Andreas Siebert, Sascha Hauke

We introduce PerCoV2, a novel and open ultra-low bit-rate perceptual image compression system designed for bandwidth- and storage-constrained applications. Building upon prior work by Careil et al., PerCoV2 extends the original formulation to the Stable Diffusion 3 ecosystem and enhances entropy coding efficiency by explicitly modeling the discrete hyper-lat

Fabian Galis, Darian Onchis

In the context of unsupervised learning, effective clustering plays a vital role in revealing patterns and insights from unlabeled data. However, the success of clustering algorithms often depends on the relevance and contribution of features, which can differ between various datasets. This paper explores feature weighting for clustering and presents new wei

Ping Li

Shi, Walsh and Yu demonstrated that any dense planar graph with certain property (known as circuit graph) contains a large near-triangulation. We extend the result to $2$-connected plane graphs, thereby addressing a question posed by them. Using the result, we prove that the planar Tu\'{a}n number of $2C_k$ is $\left[3-\Theta(k^{\log_23})^{-1}\right]n$ when

Ziyi Huang, Yang Li, Dushuai Li, Yao Mu

Modeling the interactions among agents for trajectory prediction of autonomous driving has been challenging due to the inherent uncertainty in agents' behavior. The interactions involved in the predicted trajectories of agents, also called post-interactions, have rarely been considered in trajectory prediction models. To this end, we propose a coarse-to-fine

Daniel Jiménez-López, Nuria Rodríguez-Barroso, M. Victoria Luzón, Francisco Herrera

Deep learning models have an intrinsic privacy issue as they memorize parts of their training data, creating a privacy leakage. Membership Inference Attacks (MIA) exploit it to obtain confidential information about the data used for training, aiming to steal information. They can be repurposed as a measurement of data integrity by inferring whether it was us

Nadir Samos Sáenz de Buruaga

Quantum complexity measures the difficulty of obtaining a given state starting from a typically unentangled state. In this work, we show that complexity, when defined through the minimization of a Riemannian cost functional over the manifold of Gaussian states, provides the same information as quantum fidelity and is therefore capable of detecting quantum ph

Yuxiang Wang, Wenqi Fan, Suhang Wang, Yao Ma

In recent years, Graph Foundation Models (GFMs) have gained significant attention for their potential to generalize across diverse graph domains and tasks. Some works focus on Domain-Specific GFMs, which are designed to address a variety of tasks within a specific domain, while others aim to create General-Purpose GFMs that extend the capabilities of domain-

Jaydeep Mandal, Chandan Dasgupta, Prabal K. Maiti

We have studied, using molecular dynamics simulations, the pressure-induced melting in a monolayer of soft repulsive spherocylinders whose centers of mass are constrained to move on the surface of a sphere. We show that the orientational degrees of freedom of the spherocylinders exhibit nematic order, whereas the positions of their centers of mass exhibit me

Katharina Prasse, Marcel Kleinmann, Inken Adam, Kerstin Beckersjuergen

Climate change is one of the most pressing challenges of the 21st century, sparking widespread discourse across social media platforms. Activists, policymakers, and researchers seek to understand public sentiment and narratives while access to social media data has become increasingly restricted in the post-API era. In this study, we analyze a dataset of cli

Matteo Beccaria, Stefan A. Kurlyand, Arkady A. Tseytlin

Planar parts of conformal dimensions of primary operators in $U_k(N) \times U_{-k}(N)$ ABJM theory are controlled by integrability. Strong coupling asymptotics of planar dimensions of operators with large spins can be found from the energy of semiclassical strings in AdS$_{4}\times$CP$^3$ but computing non-planar corrections requires understanding higher gen

Mohammad Reza Daneshvar Garmroodi, Ida Karimfazli

We explore the mechanisms and regimes of mixing in yield-stress fluids by simulating the stirring of an infinite, two-dimensional domain filled with a Bingham fluid. A cylindrical stirrer moves along a circular path at constant speed to stir the fluid, with an initially quiescent domain marked by a passive dye in the lower half, facilitating the analysis of

Jiushen Cai, Weihang Zhang, Hanruo Liu, Ningli Wang

Standardization of clinical reports is crucial for improving the quality of healthcare and facilitating data integration. The lack of unified standards, including format, terminology, and style, is a great challenge in clinical fundus diagnostic reports, which increases the difficulty for large language models (LLMs) to understand the data. To address this,

Nils-Christian Kempke, Thorsten Koch

We investigate the use of low-precision first-order methods (FOMs) within a fix-and-propagate (FP) framework for solving mixed-integer programming problems (MIPs). We employ GPU-accelerated PDLP, a variant of the Primal-Dual Hybrid Gradient (PDHG) method specialized to LP problems, to solve the \LP-relaxation of our MIPs to low accuracy. This solution is use

Ruifeng She, Bowen Pang, Kai Li, Zehua Liu

As the artificial intelligence community advances into the era of large models with billions of parameters, distributed training and inference have become essential. While various parallelism strategies-data, model, sequence, and pipeline-have been successfully implemented for popular neural networks on main-stream hardware, optimizing the distributed deploy

Kazuhide Ichikawa, Genta Hayashi, Satoru Ohuchi, Tomoyasu Yokoyama

Optimal elemental configuration search in crystal is a crucial task to discovering industrially important materials such as lithium-ion battery cathodes. In this paper we present application of quantum approximate optimization algorithm, the representative near-term quantum algorithm for combinatorial optimization, to finding the most stable elemental config

Lianyuan Yu, Xiuzhen Guo, Ji Shi, Hongxiao Wang

Semi-supervised learning enhances medical image segmentation by leveraging unlabeled data, reducing reliance on extensive labeled datasets. On the one hand, the distribution discrepancy between limited labeled data and abundant unlabeled data can hinder model generalization. Most existing methods rely on local similarity matching, which may introduce bias. I

Christoph Huber, Dino Knoll, Michael Guthe

Visual Quality Inspection plays a crucial role in modern manufacturing environments as it ensures customer safety and satisfaction. The introduction of Computer Vision (CV) has revolutionized visual quality inspection by improving the accuracy and efficiency of defect detection. However, traditional CV models heavily rely on extensive datasets for training,

Lucky K. Antonopoulos, Dominic G. Lewis, Jack Davis, Nicholas Funai

Wigner functions help visualise quantum states and dynamics while supporting quantitative analysis in quantum information. In the discrete setting, many inequivalent constructions coexist for each Hilbert-space dimension. This fragmentation obscures which features are fundamental and which are artefacts of representation. We introduce a stencil-based framewo

Benchao Su

Let $d\ge 1$ be an integer. We use the methods introduced by Lue Pan to prove that the compactly supported cohomology of Lubin-Tate towers and Drinfeld towers are isomorphic, as $\text{GL}_{d+1}(L)\times D_{L,\frac{1}{d+1}}^\times$-modules.

Yizhen Luo, Jiashuo Wang, Siqi Fan, Zaiqing Nie

Structural biology relies on accurate three-dimensional biomolecular structures to advance our understanding of biological functions, disease mechanisms, and therapeutics. While recent advances in deep learning have enabled the development of all-atom foundation models for molecular modeling and generation, existing approaches face challenges in generalizati

Rui Huang, Zhenyu Zhang, Siyu Tang, Zhiqian Cai

Modular Aerial Robot Systems (MARS) consist of multiple drone units that can self-reconfigure to adapt to various mission requirements and fault conditions. However, existing fault-tolerant control methods exhibit significant oscillations during docking and separation, impacting system stability. To address this issue, we propose a novel fault-tolerant contr

Alex Kehagias, Davide Perrone, Antonio Riotto

The Penrose limit connects a plane wave geometry to the photon ring of a black hole, where the quasi-normal modes are located in the eikonal limit. Utilizing this simplification, we analytically extract the quadratic-level non-linearities in the quasi-normal modes of a Schwarzschild black hole for the $(\ell\times\ell)\to 2\ell$ channel. We demonstrate that

Simon Geirnaert, Jonas Vanthornhout, Tom Francart, Alexander Bertrand

Correlation-based auditory attention decoding (AAD) algorithms exploit neural tracking mechanisms to determine listener attention among competing speech sources via, e.g., electroencephalography signals. The correlation coefficients between the decoded neural responses and encoded speech stimuli of the different speakers then serve as AAD decision variables.

Zhoutong Ye, Mingze Sun, Huan-ang Gao, Xutong Wang

Large multimodal models (LMMs) have demonstrated significant potential as generalists in vision-language (VL) tasks. However, adoption of LMMs in real-world tasks is hindered by their poor performance in tasks that require a combination of VL capabilities, as well as in tasks that involve the grounding of complex text or visual instructions. To thoroughly in

Hongyu Chen, Seraphina Goldfarb-Tarrant

Large Language Models (LLMs) are increasingly employed as automated evaluators to assess the safety of generated content, yet their reliability in this role remains uncertain. This study evaluates a diverse set of 11 LLM judge models across critical safety domains, examining three key aspects: self-consistency in repeated judging tasks, alignment with human

Weitong Fan, Chunzhao Ma, Wenxun Li, Danqing Liu

Nonplanar ring oscillators (NPROs) are building blocks for high-performance single-frequency lasers and ring-laser gyroscopes that have profoundly improved the state-of-the-art laser technologies, fundamental research and precision measurements. However, a comprehensive investigation of fundamental mode profiles in monolithic NPROs has been a missing part ev

Tommaso Bevilacqua, Andrey Gumenyuk, Niloufar Habibi, Philipp Hartwig

Laser beam welding is a non-contact joining technique that has gained significant importance in the course of the increasing degree of automation in industrial manufacturing. This process has established itself as a suitable joining tool for metallic materials due to its non-contact processing, short cycle times, and small heat-affected zones. One potential

Lehan Yang, Lu Qi, Xiangtai Li, Sheng Li

We present a unified network for simultaneously generating videos and their corresponding entity segmentation and depth maps from text prompts. We utilize colormap to represent entity masks and depth maps, tightly integrating dense prediction with RGB video generation. Introducing dense prediction information improves video generation's consistency and motio

Michihiko Sugawara, Takahiko Satoh

We present a novel approach for implementing pulse-efficient SU(4) gates on cross resonance (CR)-based superconducting quantum devices. Our method introduces a parameterized unitary derived from the CR-Hamiltonian propagator, which accounts for static-$ZZ$ interactions. Leveraging the Weyl chamber's geometric structure, we successfully realize a continuous 2

Aymen Mir, Arthur Moreau, Helisa Dhamo, Zhensong Zhang

We present GASPACHO, a method for generating photorealistic, controllable renderings of human-object interactions from multi-view RGB video. Unlike prior work that reconstructs only the human and treats objects as background, GASPACHO simultaneously recovers animatable templates for both the human and the interacting object as distinct sets of Gaussians, the

Joshua Jacas, Hana Winchester, Alicia Boyd, Brittany Johnson

Detecting harmful and non-inclusive terminology in technical contexts is critical for fostering inclusive environments in computing. This study explores the impact of model architecture on harmful language detection by evaluating a curated database of technical terms, each paired with specific use cases. We tested a range of encoder, decoder, and encoder-dec

Anand J Kulkarni, Isha Purnapatre, Apoorva S Shastri

The nature inspired algorithms are becoming popular due to their simplicity and wider applicability. In the recent past several such algorithms have been developed. They are mainly bio-inspired, swarm based, physics based and socio-inspired; however, the domain based on symbiotic relation between creatures is still to be explored. A novel metaheuristic optim

Chunzhao Ma, Wenxun Li, Weitong Fan, Danqing Liu

We report a single-frequency, narrow-linewidth semiconductor pulsed laser based on pump current modulation and optical injection locking technique. A monolithic non-planar ring oscillator laser is employed as the seed source to guarantee the single-frequency narrow-linewidth performance. Simultaneously, pulse operation is achieved by directly modulating the

Stephen Meisenbacher, Alexandra Klymenko, Alexander Karpp, Florian Matthes

Recent literature has seen a considerable uptick in $\textit{Differentially Private Natural Language Processing}$ (DP NLP). This includes DP text privatization, where potentially sensitive input texts are transformed under DP to achieve privatized output texts that ideally mask sensitive information $\textit{and}$ maintain original semantics. Despite continu

Ferdaous Ait Addi, Abdeslem Hafid Bentbib, Khalide Jbilou

Dictionary learning is a widely used technique in signal processing and machine learning that aims to represent data as a linear combination of a few elements from an overcomplete dictionary. In this work, we propose a generalization of the dictionary learning technique using the t-product framework, enabling efficient handling of multidimensional tensor dat

Nasim Katebi, Mohammad Ahmad, Mohsen Motie-Shirazi, Daniel Phan

Perinatal complications, defined as conditions that arise during pregnancy, childbirth, and the immediate postpartum period, represent a significant burden on maternal and neonatal health worldwide. Factors contributing to these disparities include limited access to quality healthcare, socioeconomic inequalities, and variations in healthcare infrastructure.

Yu Feng, Dingxin Zhang, Runkai Zhao, Yong Xia

Backdoor attacks pose a severe threat to deep neural networks (DNNs) by implanting hidden backdoors that can be activated with predefined triggers to manipulate model behaviors maliciously. Recent studies have extended backdoor attacks to 3D point clouds, but most existing triggers are sample-wise and often cause visible geometric artifacts or high optimizat

Yuzhi Lai, Shenghai Yuan, Youssef Nassar, Mingyu Fan

Effective Human-Robot Interaction (HRI) is crucial for future service robots in aging societies. Existing solutions are biased toward only well-trained objects, creating a gap when dealing with new objects. Currently, HRI systems using predefined gestures or language tokens for pretrained objects pose challenges for all individuals, especially elderly ones.