March 2025 arXiv papers — page 223

Eline de Jong, Sebastian De Haro

Although several accounts of scientific understanding exist, the concept of understanding in relation to technology remains underexplored. This paper addresses this gap by proposing a philosophical account of technological understanding: the type of understanding that is required for and reflected by successfully designing and using technological artefacts.

Haichao Liu, Sikai Guo, Pengfei Mai, Jiahang Cao

This paper introduces RoboDexVLM, an innovative framework for robot task planning and grasp detection tailored for a collaborative manipulator equipped with a dexterous hand. Previous methods focus on simplified and limited manipulation tasks, which often neglect the complexities associated with grasping a diverse array of objects in a long-horizon manner. I

Nicholas Rungi, Andrea Tamburelli

We introduce para-complex and pseudo-Riemannian geometric methods for the study of representations of surface groups in $\mathrm{SL}(2m+1,\mathbb{R})$. For $m=1$ our techniques allow to recover several known results for Hitchin representations without any reference to convex projective geometry or hyperbolic affine spheres. In particular, we describe analyti

Shunsuke Tada

Recently, bipath persistent homology has been proposed as an extension of standard persistent homology, along with its visualization (bipath persistence diagram) and computational methods. In the setting of standard persistent homology, the stability theorem with respect to real-valued functions on a topological space is one of the fundamental results, which

Tom Reichert, Jan Steinheimer, Marcus Bleicher

We present estimates for the production cross sections of exotic states ($\Lambda_c, \Sigma_c, \Xi_c, D\, \mathrm{and}\, D_s$) from secondary $\overline B + B$ annihilations in p+A reactions from $E_\mathrm{lab}=10-30A$~GeV. We focus specifically on the newly planned hadron physics program of CBM at FAIR. These estimates for the production of exotic states a

David Ponce, Thierry Etchegoyhen

Instruction following is a critical ability for Large Language Models to be used directly by humans. This often requires supervised fine-tuning on curated instruction datasets, sometimes complemented with an alignment step. However, in multilingual scenarios, obtaining high-quality data for these stages remains challenging, motivating the exploration of In-C

Chen Guo, Junxuan Li, Yash Kant, Yaser Sheikh

We present Vid2Avatar-Pro, a method to create photorealistic and animatable 3D human avatars from monocular in-the-wild videos. Building a high-quality avatar that supports animation with diverse poses from a monocular video is challenging because the observation of pose diversity and view points is inherently limited. The lack of pose variations typically l

Viola Cavallini, Nicolò Vladi Biesuz, Riccardo Bolzonella, Enrico Calore

DataPix4 (Data Acquisition for Timepix4 Applications) is a new C++ framework for the management of Timepix4 ASIC, a multi-purpose hybrid pixel detector designed at CERN. Timepix4 consists of a matrix of 448x512 pixels that can be connected to several types of sensors, to obtain a pixelated detector suitable for different applications. DataPix4 can be used bo

Yu Zhang, Kexue Fu, Zhicong Lu

As a popular form of science communication, science stories attract readers because they combine engaging narratives with comprehensible scientific knowledge. However, crafting such stories requires substantial skill and effort, as writers must navigate complex scientific concepts and transform them into coherent and accessible narratives tailored to audienc

Siew Yan Hoh, Yuzhi Shang, Ritwika Chakraborty, Tianqi Hu

The muEDM experiment at the Paul Scherrer Institute aims to measure the electric dipole moment with an unprecedented sensitivity of $6 \times 10^{-23}\,\mathrm{e}\cdot\mathrm{cm}$. A key aspect of this experiment is the injection and storage of the muon beam, which traverses a long, narrow superconducting channel before entering a solenoid magnet. The muon i

Zhanghao Hu, Hanqi Yan, Qinglin Zhu, Zhenyi Shen

Large language models have recently pushed open domain question answering (ODQA) to new frontiers. However, prevailing retriever-reader pipelines often depend on multiple rounds of prompt level instructions, leading to high computational overhead, instability, and suboptimal retrieval coverage. In this paper, we propose EmbQA, an embedding-level framework th

Thiago H. Segreto, Juliano Negri, Paulo H. Polegato, João Manoel Herrera Pinheiro

Soybean and cotton are major drivers of many countries' agricultural sectors, offering substantial economic returns but also facing persistent challenges from volunteer plants and weeds that hamper sustainable management. Effectively controlling volunteer plants and weeds demands advanced recognition strategies that can identify these amidst complex crop can

M. Davies, E. Yüksel, J. -P. Ebran, E. Khan

We investigate the effect of temperature on $\alpha$-particle clustering in the diluted nucleus $^{16}\text{O}$ using the multi-constrained finite-temperature relativistic Hartree-Bogoliubov model with the DD-ME2 interaction. At a critical density the nucleus undergoes a Mott-like transition from a homogeneous to a localised configuration characterised by $\

Suleyman Burcin Suyun, Mustafa Yurdakul, Sakir Tasdemir, Serkan Bilic

Hypertensive retinopathy (HR) is a severe eye disease that may cause permanent vision loss if not diagnosed early. Traditional diagnostic methods are time-consuming and subjective, highlighting the need for an automated, reliable system. Existing studies often use a single Deep Learning (DL) model, struggling to distinguish HR stages. This study introduces a

Jonah Reuß

We prove a necessary criterion for the (non-)existence of nontrivial solutions to the Dirac equation $D\psi=i A \cdot_{Cl} \psi$ on Riemannian manifolds that are either closed or of bounded geometry. This generalizes a result of Rupert Frank and Michael Loss on $\mathbb{R}^n$ where the criterion relates the $L^n$-norm of $A$ to the Sobolev constant on $\math

Muhammad Musab Ansari

Detecting stenosis in coronary angiography is vital for diagnosing and managing cardiovascular diseases. This study evaluates the performance of state-of-the-art object detection models on the ARCADE dataset using the MMDetection framework. The models are assessed using COCO evaluation metrics, including Intersection over Union (IoU), Average Precision (AP),

Karl P. Olson, Laurence D. Marks

In this report, we look at the fundamental physics of triboelectricity, charge transfer due to contact and sliding. While much of the report focuses upon recent advances such as the incorporation of flexoelectric contributions, we also include older work, some from centuries ago, which only now can be understood in a general sense. Basic concepts and theorie

E. C. Matthews, A. L. Carter, P. Pathak, C. V. Morley

Of the ~25 directly imaged planets to date, all are younger than 500Myr and all but 6 are younger than 100Myr. Eps Ind A (HD209100, HIP108870) is a K5V star of roughly solar age (recently derived as 3.7-5.7Gyr and 3.5$^{+0.8}_{-1.3}$Gyr). A long-term radial velocity trend as well as an astrometric acceleration led to claims of a giant planet orbiting the nea

Masoud Kavian, Romain Chor, Milad Sefidgaran, Abdellatif Zaidi

In this paper, we investigate the effect of data heterogeneity across clients on the performance of distributed learning systems, i.e., one-round Federated Learning, as measured by the associated generalization error. Specifically, $K$ clients have each $n$ training samples generated independently according to a possibly different data distribution, and thei

Fangchao Liu, Yusuke Takeuchi, Si Chen, Siyuan Chen

A high-repetition-rate pulsed muon source operating at approximately 50\,kHz holds the potential to improve the sensitivity of various particle physics and material science experiments involving muons. In this article, we propose utilizing the high-repetition-rate pulsed electron beam at the SHINE facility to generate a surface muon beam. Our simulation stud

Ansh Desai, Jonathan Ma, Timo Lahivaara, Peter Monk

Time-harmonic acoustic inverse scattering concerns the ill-posed and nonlinear problem of determining the refractive index of an inaccessible, penetrable scatterer based on far field wave scattering data. When the scattering is weak, the regularized inverse Born approximation provides a linearized model for recovering the shape and material properties of a s

Masih Eskandar, Tooba Imtiaz, Davin Hill, Zifeng Wang

Humans can naturally learn new and varying tasks in a sequential manner. Continual learning is a class of learning algorithms that updates its learned model as it sees new data (on potentially new tasks) in a sequence. A key challenge in continual learning is that as the model is updated to learn new tasks, it becomes susceptible to catastrophic forgetting,

Antonio Capolupo, Aniello Quaranta, Raoul Serao

We report on recent results, according to which the hyphothetical X17 boson could affect the muon g-2 anomaly and the Lamb Shift. Moreover by considering the kinetic mixing between this new boson and the U(1)Y we establish possible contribution of the X17 to the W mass.

Hao Lin

The analysis of riverbed evolution is an important basis for decision-making on river improvement, flood control project construction, and water conservancy hub dispatching and application. From the perspective of river nonlinear dynamic system, this paper deeply understands the disorder and order in the evolution and development of river bed. Using the chao

Saeed Shakuri, Alireza Rezvanian

Lung cancer has the highest rate of cancer-caused deaths, and early-stage diagnosis could increase the survival rate. Lung nodules are common indicators of lung cancer, making their detection crucial. Various lung nodule detection models exist, but many lack efficiency. Hence, we propose a more efficient approach by leveraging 2D CT slices, reducing computat

Pedro Reis, Ana Paula Serra, João Gama

This review systematically examines deep learning applications in financial asset management. Unlike prior reviews, this study focuses on identifying emerging trends, such as the integration of explainable artificial intelligence (XAI) and deep reinforcement learning (DRL), and their transformative potential. It highlights new developments, including hybrid

Lorenzo Bastonero, Cristiano Malica, Eric Macke, Marnik Bercx

We introduce an automated, flexible framework (aiida-hubbard) to self-consistently calculate Hubbard $U$ and $V$ parameters from first-principles. By leveraging density-functional perturbation theory, the computation of the Hubbard parameters is efficiently parallelized using multiple concurrent and inexpensive primitive cell calculations. Furthermore, the i

Chungpa Lee, Jongho Im, Joseph H. T. Kim

Mixup is a widely adopted data augmentation technique known for enhancing the generalization of machine learning models by interpolating between data points. Despite its success and popularity, limited attention has been given to understanding the statistical properties of the synthetic data it generates. In this paper, we delve into the theoretical underpin

Hans Z. Munthe-Kaas, Olivier Verdier, Gilles Vilmart

We show that Isserlis' theorem follows as a corollary to the invariant tensor theorem for isotropic tensors.

Luca Saluzzi

The State-Dependent Riccati Equation (SDRE) approach is extensively utilized in nonlinear optimal control as a reliable framework for designing robust feedback control strategies. This work provides an analysis of the SDRE approach, examining its theoretical foundations, error bounds, and numerical approximation techniques. We explore the relationship betwee

Yuhao Zhou, Sirui Song, Boyang Liu, Zhiheng Xi

Rotary Position Embedding (RoPE) enables each attention head to capture multi-frequency information along the sequence dimension and is widely applied in foundation models. However, the nonlinearity introduced by RoPE complicates optimization of the key state in the Key-Value (KV) cache for RoPE-based attention. Existing KV cache compression methods typicall

Gayoung Park, Katalin Schäffer, Margaret M. Coad

In this paper, we explore the use of a soft gripper, specifically a soft inverting-everting toroidal hydrostat, as a prosthetic hand. We present a design of the gripper integrated into a body-powered elbow-driven system and evaluate its performance compared to similar body-powered terminal devices: the Kwawu 3D-printed hand and the Hosmer hook. Our experimen

Cansu Sancaktar, Christian Gumbsch, Andrii Zadaianchuk, Pavel Kolev

Exploration is a cornerstone of reinforcement learning (RL). Intrinsic motivation attempts to decouple exploration from external, task-based rewards. However, established approaches to intrinsic motivation that follow general principles such as information gain, often only uncover low-level interactions. In contrast, children's play suggests that they engage

Athar A. Dar, Zahir shah, Sunder Sahayanathan, Naseer Iqbal

The Flat Spectrum Radio Quasar (FSRQ) B2\,1308+326 was in its highest $\gamma$-ray flaring state during 60260-60310\,MJD. During this period, the source was detected in very high energy (VHE) by the large-sized telescope (LST-1). We conducted a detailed broadband spectral study of this source using the simultaneous data available in optical/UV, X-ray, and $\

Saad Ejaz, Hriday Bavle, Laura Ribeiro, Holger Voos

In 3D object mapping, category-level priors enable efficient object reconstruction and canonical pose estimation, requiring only a single prior per semantic category (e.g., chair, book, laptop, etc.). DeepSDF has been used predominantly as a category-level shape prior, but it struggles to reconstruct sharp geometry and is computationally expensive. In contra

Hanmo Liu, Shimin Di, Haoyang Li, Xun Jian

Node classification is a key task in temporal graph learning (TGL). Real-life temporal graphs often introduce new node classes over time, but existing TGL methods assume a fixed set of classes. This assumption brings limitations, as updating models with full data is costly, while focusing only on new classes results in forgetting old ones. Graph continual le

Nicholas Martis, Sunna Withers, Giordano Felicioni, Adam Muzzin

We present a sample of 146 high-redshift ($z>7.5$) galaxies from the CANUCS/Technicolor surveys, showcasing photometry in every wide- and medium-band NIRCam filter in addition to ancillary HST data sampling $0.4-5 \mu m$ (22 JWST bands out of 29 bands total). Additionally, 48 ($33\%$) galaxies in our sample meet criteria to be classified as extreme emission

A. Liashyk, S. Pakuliak, E. Ragoucy

We compute scalar products of off-shell Bethe vectors in models with $o_{2n+1}$ symmetry. The scalar products are expressed as a sum over partitions of the Bethe parameter sets, the building blocks being the so-called highest coefficients. We prove some recurrence relations and a residue theorem for these highest coefficients, and prove that they are consist

K. Ertini, T. A. Regna, L. Ferrari, M. C. Bersten

SN 2024ggi is a Type II supernova that exploded in the nearby galaxy NGC 3621 at a distance of approximately 7 Mpc, making it one of the closest supernovae of the decade. This SN shows clear signs of interaction with a dense circumstellar material, and several studies have investigated the properties of its possible progenitor star using pre-explosion data.

Mojtaba Safari, Shansong Wang, Zach Eidex, Qiang Li

Objective:This study introduces a residual error-shifting mechanism that drastically reduces sampling steps while preserving critical anatomical details, thus accelerating MRI reconstruction. Approach:We propose a novel diffusion-based SR framework called Res-SRDiff, which integrates residual error shifting into the forward diffusion process. This enables ef

Mathias L. Laursen

Using an application of Schmidt's Subspace Theorem, this paper gives new transcendence criteria for rapidly converging infinite products of algebraic numbers. The paper also improves existing criteria for irrationality of products and criteria for irrationality and transcendence of infinite series. These results generalize a classical theorem on the irration

Francesco Coghi, Juan P. Garrahan

We connect self-interacting processes, that is, stochastic processes where transitions depend on the time spent by a trajectory in each configuration, to Doob conditioning. In this way we demonstrate that Markov processes with constrained occupation measures are realised optimally by self-interacting dynamics. We use a tensor network framework to guide our d

Caleb B. Goates, Kendrick M. Shepherd

Harmonic maps are important in generating parameterizations for various domains, particularly in two and three dimensions. General extensions of two-dimensional harmonic parameterizations for volumetric parameterizations are known to fail in a variety of contexts, though more specialized volumetric parameterizations have been proposed. This work provides and

Marc Speckmann, Theresa Eimer

Reinforcement learning (RL) agents can forget tasks they have previously been trained on. There is a rich body of work on such forgetting effects in humans. Therefore we look for commonalities in the forgetting behavior of humans and RL agents across tasks and test the viability of forgetting prevention measures from learning theory in RL. We find that in ma

Johannes Stangl, András Borsos, Stefan Thurner

While many national and international climate policies clearly outline decarbonization targets and the timelines for achieving them, there is a notable lack of effort to objectively monitor progress. A significant share of the transition from fossil fuels to low-carbon energy will be borne by industry and the economy, requiring both the decarbonization of th

Chao Ye, Haoyuan Li, Weiyang Lin, Xianqiang Yang

In this paper we introduce MLINE-VINS, a novel monocular visual-inertial odometry (VIO) system that leverages line features and Manhattan Word assumption. Specifically, for line matching process, we propose a novel geometric line optical flow algorithm that efficiently tracks line features with varying lengths, whitch is do not require detections and descrip

Kangrou Guo, Masahiro Ogihara, Shigeru Ida, Yasunori Hori

The occurrence rate of giant planets increases with orbital period and turns over at a location that roughly corresponds to the snow line of solar-type stars. Further, the density distribution of cold Jupiters (CJs) on the semi-major axis - mass diagram shows a relatively steep inner boundary, shaping the desert of warm Jupiters. The eccentricities of CJs sh

Muhammad Aqeel, Shakiba Sharifi, Marco Cristani, Francesco Setti

This study investigates the performance of robust anomaly detection models in industrial inspection, focusing particularly on their ability to handle noisy data. We propose to leverage the adaptation ability of meta learning approaches to identify and reject noisy training data to improve the learning process. In our model, we employ Model Agnostic Meta Lear

Emma Franchino, Luciana Ciringione, Luisa Canal, Ottavia Marina Epifania

Dealing with mathematics can induce significant anxiety, strongly affecting psychology students' academic performance and career prospects. This phenomenon is known as maths anxiety and several scales can measure it. Most scales were created in English and abbreviated versions were translated and validated among Italian populations (e.g. Abbreviated Maths An

Michael Björklund, Mattias Byléhn

We introduce the notion of Bartlett spectral measure for isometrically invariant random measures on proper metric commutative spaces. When the underlying Gelfand pair corresponds to a higher-rank, connected, simple matrix Lie group with finite center and a maximal compact subgroup, we prove that the number variance is asymptotically bounded above, uniformly

Sebastian Winter, Christian Agrell, Juan Camilo Guevara Gómez, Erik Vanem

Extreme response assessment is important in the design and operation of engineering structures, and is a crucial part of structural risk and reliability analyses. Structures should be designed in a way that enables them to withstand the environmental loads they are expected to experience over their lifetime, without designs being unnecessarily conservative a

Yuheng Xu, Shijie Yang, Xin Liu, Jie Liu

In recent years, the increasing popularity of Hi-DPI screens has driven a rising demand for high-resolution images. However, the limited computational power of edge devices poses a challenge in deploying complex super-resolution neural networks, highlighting the need for efficient methods. While prior works have made significant progress, they have not fully

Eli Sason, Darya Frolova, Boris Nazarov, Felix Goldberd

As the context window expands, self-attention increasingly dominates the transformer's inference time. Therefore, accelerating attention computation while minimizing performance degradation is essential for the efficient deployment of Large Language Models (LLMs). In this study we extend a theoretical framework of attention sparsity in LLMs. A customized los

Dorothee Henke, Henri Lefebvre, Martin Schmidt, Johannes Thürauf

The literature on pessimistic linear bilevel optimization with coupling constraints is rather scarce and it has been common sense that these problems are harder to tackle than pessimistic bilevel problems without coupling constraints. In this note, we show that this is not the case. To this end, given a pessimistic problem with coupling constraints, we deriv

Biao Xiong, Longjun Zhang, Ruiqi Huang, Junwei Zhou

Viewpoint planning is critical for efficient 3D data acquisition in applications such as 3D reconstruction, building life-cycle management, navigation, and interior decoration. However, existing methods often neglect key optimization objectives specific to static LiDAR systems, resulting in redundant or disconnected viewpoint networks. The viewpoint planning

Muhammad Ihsan Al Hafiz, Naresh Ravichandran, Anders Lansner, Pawel Herman

Brain-inspired algorithms are attractive and emerging alternatives to classical deep learning methods for use in various machine learning applications. Brain-inspired systems can feature local learning rules, both unsupervised/semi-supervised learning and different types of plasticity (structural/synaptic), allowing them to potentially be faster and more ene

Michał Klincewicz, Mark Alfano, Amir Ebrahimi Fard

At least since Francis Bacon, the slogan 'knowledge is power' has been used to capture the relationship between decision-making at a group level and information. We know that being able to shape the informational environment for a group is a way to shape their decisions; it is essentially a way to make decisions for them. This paper focuses on strategies tha

Yan-Zhi Meng, Shu-Qing Zhong, Jia-Hong Gu, Xin-Fei Li

Context: Despite more than half a century of research, the dominant radiation mechanism of gamma-ray burst (GRB) prompt emission remains unsolved. Some progress has been made through the analyses of the observational spectra of Swift/BAT, Konus/Wind, and Fermi/GBM, as well as the spectra of the photosphere or synchrotron models, but it is still insufficient

Kai Fang, Jiangtao Deng, Chengzu Dong, Usman Naseem

Frequent fluctuations of client nodes in highly dynamic mobile clusters can lead to significant changes in feature space distribution and data drift, posing substantial challenges to the robustness of existing federated learning (FL) strategies. To address these issues, we proposed a mobile cluster federated learning framework (MoCFL). MoCFL enhances feature

Yao Zou, Dawei Cheng

Credit card fraud imposes significant costs on both cardholders and issuing banks. Fraudsters often disguise their crimes, such as using legitimate transactions through several benign users to bypass anti-fraud detection. Existing graph neural network (GNN) models struggle with learning features of camouflaged, indirect multi-hop transactions due to their in

Kang Ma, Xiulan Liu, Xi Chen, Xiaohu Liu

Accurate electric energy metering (EEM) of fast charging stations (FCSs), serving as critical infrastructure in the electric vehicle (EV) industry and as significant carriers of vehicle-to-grid (V2G) technology, is the cornerstone for ensuring fair electric energy transactions. Traditional on-site verification methods, constrained by their high costs and low

Yuang Chen, Haojing Tan, Jiahua Zhuang, Yang Xu

The interaction mechanism between a single microscopic object like a cell, a particle, a molecule, or an atom and its interacting electromagnetic field is fundamental in single-object manipulation such as optical trap and magnetic trap. Function-on-demand, single-object manipulation relies on a high degree of freedom control of electromagnetic field at local

Lisa Lepp, Dimitar Shterionov, Mirella De Sisto, Grzegorz Chrupała

Sign language machine translation (SLMT) -- the task of automatically translating between sign and spoken languages or between sign languages -- is a complex task within the field of NLP. Its multi-modal and non-linear nature require the joint efforts of sign language (SL) linguists, technical experts and SL users. Effective user involvement is a challenge t

S. V. Anishchenko

For the field amplitude, a nonlinear integro-differential equation is derived that describes the operation of a Compton FEL in the presence of electron velocity spread typical for modern facilities. Numerical solutions of the equation are in good agreement with particle simulations for the bunching factor less than 0.6, reproduce the frequency detuning spect

X. J. Shen, L. G. Hou, H. L. Liu, X. Y. Gao

The spiral structure of the Milky Way is not conclusive, even for the disc regions in the solar neighbourhood. Especially, the arm-like structures uncovered from the over-density maps of evolved stars are inconsistent with the commonly adopted spiral arm models based on young objects. We aim to re-examine the arm segments traced by young objects and better u

Zhi Rui Tam, Cheng-Kuang Wu, Chieh-Yen Lin, Yun-Nung Chen

Multiple-choice exam questions with "None of the above" (NA) options have been extensively studied in educational testing, in which existing research suggests that they better assess true knowledge. However, their impact on Large Language Models (LLMs) evaluation remains underexplored. Through systematic experiments with 28 LLMs on the MMLU benchmark, we exa

Hongteng Wang, Haichuan Li, Yijia Xin, Weizhen Chen

As transparent electrodes, patterned silver nanowire (AgNW) networks suffer from noticeable pattern visibility, which is an unsettled issue for practical applications such as display. Here, we introduce a Gibbs-Thomson effect (GTE)-based patterning method to effectively reduce pattern visibility. Unlike conventional top-down and bottom-up strategies that rel

Narek Harutyunyan, Brady Moon, Seungchan Kim, Cherie Ho

Path planning for robotic exploration is challenging, requiring reasoning over unknown spaces and anticipating future observations. Efficient exploration requires selecting budget-constrained paths that maximize information gain. Despite advances in autonomous exploration, existing algorithms still fall short of human performance, particularly in structured

Arash Nasr Esfahani, Hamed Hosseini, Mehdi Tale Masouleh, Ahmad Kalhor

As smart homes become more prevalent in daily life, the ability to understand dynamic environments is essential which is increasingly dependent on AI systems. This study focuses on developing an intelligent algorithm which can navigate a robot through a kitchen, recognizing objects, and tracking their relocation. The kitchen was chosen as the testing ground

Ruolin Chai, Guoqing Cai, Qiongtao Xie, Huaizhi Wu

We propose a hardware-efficient photon routing scheme based on a dynamically modulated multi-mode ring resonator and a driven cyclic three-level artificial atom, which effectively models a two-level giant atom coupled to a pair of one-dimensional lattices in a synthetic frequency dimension. The routing dynamics of single-photon wave packets in the frequency

Xin Wang, Yabo Wang, Bo Qi, Rebing Wu

It remains unclear whether quantum machine learning (QML) has real advantages when dealing with practical and meaningful tasks. Encoding classical data into quantum states is one of the key steps in QML. Amplitude encoding has been widely used owing to its remarkable efficiency in encoding a number of $2^{n}$ classical data into $n$ qubits simultaneously. Ho

Gilad Yehudai, Noah Amsel, Joan Bruna

It is well understood that different neural network architectures are suited to different tasks, but is there always a single best architecture for a given task? We compare the expressive power of transformers, RNNs, and transformers with chain of thought tokens on a simple and natural class of tasks we term Compositional Reasoning Questions (CRQ). This fami

Xintao Chao, Shilong Mu, Yushan Liu, Shoujie Li

Imitation learning has emerged as a powerful paradigm for robot skills learning. However, traditional data collection systems for dexterous manipulation face challenges, including a lack of balance between acquisition efficiency, consistency, and accuracy. To address these issues, we introduce Exo-ViHa, an innovative 3D-printed exoskeleton system that enable

Yizhuo Ding, Xinwei Sun, Yanwei Fu, Guosheng Hu

Model pruning technique is vital for accelerating large language models by reducing their size and computational requirements. However, the generalizability of existing pruning methods across diverse datasets and tasks remains unclear. Thus, we conduct extensive evaluations on 24 datasets and 4 tasks using popular pruning methods. Based on these evaluations,

Jeroen Hanselman

The advent of computers has allowed mathematicians to do increasingly more difficult computations that used to be practically impossible. Peer reviewers will seldom look at any code attached to a math paper, however. In this article, we advocate for a software peer-reviewing process and will discuss some best practices for reviewers on how to peer review mat

Charles-Edouard Bréhier, David Cohen, Yoshio Komori

We propose and study conformal integrators for linearly damped stochastic Poisson systems. We analyse the qualitative and quantitative properties of these numerical integrators: preservation of dynamics of certain Casimir and Hamiltonian functions, almost sure bounds of the numerical solutions, and strong and weak rates of convergence under appropriate condi

Xi Chen, Shuo Wang

The rapid development of large language models (LLMs) gives rise to ethical concerns about their performance, while opening new avenues for developing toxic language detection techniques. However, LLMs' unethical output and their capability of detecting toxicity have primarily been tested on language data that do not demand complex meaning inference, such as

Christophe Crochet, John Aoga, Axel Legay

The rapid evolution of cyber threats has increased the need for robust methods to discover vulnerabilities in increasingly complex and diverse network protocols. This paper introduces Network Attack-centric Compositional Testing (NACT), a novel methodology designed to discover new vulnerabilities in network protocols and create scenarios to reproduce these v

Roberto Sebastiani

Many procedures for SAT-related problems, in particular for those requiring the complete enumeration of satisfying truth assignments, rely their efficiency and effectiveness on the detection of (possibly small) partial assignments satisfying an input formula. Surprisingly, there seems to be no unique universally-agreed definition of formula satisfaction by a

Jisong Gao, Haijun Cao, Xuegao Hu, Hui Zhou

Flat-band materials have garnered extensive attention due to their captivating properties associated with strong correlation effects. While flat bands have been discovered in several types of 2D materials, their existence in 1D systems remains elusive. Here, we propose a 1D frustrated lattice, specifically the 1D zigzag lattice, as a platform for hosting fla

Srdjan Stavrić, Giuseppe Cuono, Baishun Yang, Álvaro R. Puente-Uriona

Multiferroic materials, characterized by the coexistence of ferroelectricity and ferromagnetism, may unveil band structures suggestive of complex phenomena and new functionalities. In this Letter, we analyze the band structure of EuO in its multiferroic phase. Using density functional theory calculations and detailed symmetry analysis, we reveal a previously

Anthony G. A. Brown

On January 15 2025, the Gaia mission completed the collection of the astrometric, photometric, and spectroscopic data for about 2.5 billion celestial sources, from the solar system to the Milky Way to the distant universe. Work is ongoing to produce Gaia DR4 based on the first 5.5 years of data, with the release expected in 2026. The full 10.5 year survey wi

Bryan Chen Zhengyu Tan, Roy Ka-Wei Lee

Large language models (LLMs) have demonstrated remarkable capabilities in simulating human behaviour and social intelligence. However, they risk perpetuating societal biases, especially when demographic information is involved. We introduce a novel framework using cosine distance to measure semantic shifts in responses and an LLM-judged Preference Win Rate (

Songlin Dong, Zhengdong Zhou, Chenhao Ding, Xinyuan Gao

Prompt tuning can further enhance the performance of visual-language models across various downstream tasks (e.g., few-shot learning), enabling them to better adapt to specific applications and needs. In this paper, we present a Diversity Covariance-Aware framework that learns distributional information from the data to enhance the few-shot ability of the pr

Liang Wu, Ruixi Hu, Yunwen Lei

Pairwise learning includes various machine learning tasks, with ranking and metric learning serving as the primary representatives. While randomized coordinate descent (RCD) is popular in various learning problems, there is much less theoretical analysis on the generalization behavior of models trained by RCD, especially under the pairwise learning framework

Anna Lunghi, Matteo Castiglioni, Alberto Marchesi

We study a repeated trading problem in which a mechanism designer facilitates trade between a single seller and multiple buyers. Our model generalizes the classic bilateral trade setting to a multi-buyer environment. Specifically, the mechanism designer runs a second-price auction among the buyers -- extending the fixed-price mechanism used in bilateral trad

Elena Kim, Nicholas Miller

We examine semiclassical measures for Laplace eigenfunctions on compact hyperbolic $(n+1)$-manifolds. We prove their support must contain the cosphere bundle of a compact immersed totally geodesic submanifold. Our proof adapts the argument of Dyatlov and Jin to higher dimensions and classifies the closures of horocyclic orbits using Ratner theory. An importa

Wenhui Chen, Mengjun Ma, Xulong Qin

This paper studies some $L^p-L^q$ estimates for the dissipative or conservative Moore-Gibson-Thompson (MGT) equations in the whole space $\mathbb{R}^n$. Our contributions are twofold. By applying the Fourier analysis associated with the modified Bessel function in the dissipative case, we derive some $L^p-L^q$ estimates of solutions. Then, introducing a good

Patrizio Angelini, Carla Binucci, Giuseppe Di Battista, Emilio Di Giacomo

Unit edge-length drawings, rectilinear drawings (where each edge is either a horizontal or a vertical segment), and rectangular face drawings are among the most studied subjects in Graph Drawing. However, most of the literature on these topics refers to planar graphs and planar drawings. In this paper we study drawings with all the above nice properties but

Yossi Ben-Zion, Jianqiang Liu, Chun-Ming Wang, Atanu Rajak

Trigonometric derivatives are fundamental in both mathematics and physics, yet their proper application, particularly the distinction between radians and degrees, poses a significant challenge for college students globally. This study identifies a widespread "blind spot" in understanding trigonometric derivatives and their implications for physical systems,

Kiyoon Eum

In this paper, we study the full asymptotic expansion of the partition functions of determinantal point processes defined on a polarized K\"ahler manifold. We show that the coefficients of the expansion are given by geometric functionals on K\"ahler metrics satisfying the cocycle identity, whose first variations can be expressed through the TYZ expansion coe

Ryo Nakanishi, Koji Hukushima

This study presents an enhanced method for analyzing cluster dynamics, with a particular focus on tracking clusters' continuity over time using time-series data from molecular dynamics (MD) simulation. The proposed method was applied to spatio-temporal cluster data obtained from a non-equilibrium MD simulation of a chromatin polymer model. In this model, clu

Hari Krishnan P. Anilkumar, Neha Sangwan, Varun Narayanan, Vinod M. Prabhakaran

We study the distributed function computation problem with $k$ users of which at most $s$ may be controlled by an adversary and characterize the set of functions of the sources the decoder can reconstruct robustly in the following sense -- if the users behave honestly, the function is recovered with high probability (w.h.p.); if they behave adversarially, w.

Yuval Ben Dror

Over recent decades, extensive research has aimed to overcome the restrictive underlying assumptions required for a Generalized Linear Model to generate accurate and meaningful predictions. These efforts include regularizing coefficients, selecting features, and clustering ordinal categories, among other approaches. Despite these advances, efficiently cluste

Oliwia Agnieszka Kałuzińska, Leena Diehl, Eva Sicking, Marie Christin Mühlnikel

To face the higher levels of radiation due to the 10-fold increase in integrated luminosity during the High-Luminosity LHC, the CMS detector will replace the current Calorimeter Endcap (CE) using the High-Granularity Calorimeter (HGCAL) concept. The electromagnetic section as well as the high-radiation regions of the hadronic section of the CE will be equipp

Ivan Sviridov, Konstantin Egorov

Cardiovascular diseases (CVDs) are disorders impacting the heart and circulatory system. These disorders are the foremost and continuously escalating cause of mortality worldwide. One of the main tasks when working with CVDs is analyzing and identifying pathologies on a 12-lead electrocardiogram (ECG) with a standard 10-second duration. Using machine learnin

Han Li, Zhaohui Wang, Shengtai Fan, Jiaseng Xu

Superconductivity is achieved by the pairing of electrons and phase coherence between the Cooper pairs. According to the Bardeen-Cooper-Schrieffer theory, the quasiparticles with Bogoliubov dispersion exists and reveal particle-hole symmetric coherence peaks on the single particle tunneling spectrum. Here we report the observation of two kinds of tunneling s

Ali Reza Safi, Elizabeth Mathew, Rupesh Chafle, Benjamin Klusemann

In this study, the role of elastic and interfacial energies in the shape evolution of T1 precipitates in Al-Cu-Li alloys is investigated using phase-field modeling. We employ a formulation considering the stoichiometric nature of the precipitate phase explicitly, including coupled equation systems for various order parameters. Inputs such as elastic properti

Farid Labib, Kazuhiro Nawa, Yusuke Nambu, Hiroyuki Takakura

Magnetic orders of non-Heisenberg Tsai-type 1/1 approximant crystals (ACs) in the Au-Ga-Dy system were studied through bulk magnetization, neutron diffraction, and inelastic neutron scattering techniques. The results uncovered noncoplanar, ferromagnetic (FM) and antiferromagnetic (AFM) spin configurations whirling along [111] crystallographic axis, which is

Romulo Damaselin Chaves dos Santos, Jorge Henrique de Oliveira Sales

This paper introduces significant advancements in fractional neural operators (FNOs) through the integration of adaptive hybrid kernels and stochastic multiscale analysis. We address several open problems in the existing literature by establishing four foundational theorems. First, we achieve exact bias-variance separation for Riemann-Liouville operators usi