March 2025 arXiv papers — page 7

Andrin Doll, Chennan Wang, Thomas Prokscha, Jan Dreiser

Coherent control by means of tailored excitation is a key to versatile experimental schemes for spectroscopic investigation and technological utilization of quantum systems. Here we study a quantum system which consists of a coupled electron-moun spin state, i.e., muonium, a light isotope of hydrogen. We demonstrate the most fundamental coherent control tech

Mingyang Gu, Jiamin Zhu, Qipeng Wang, Fengjie Wang

Genomics data is essential in biological and medical domains, and bioinformatics analysts often manually create circos plots to analyze the data and extract valuable insights. However, creating circos plots is complex, as it requires careful design for multiple track attributes and positional relationships between them. Typically, analysts often seek inspira

Giuseppe Carere, Han Cheng Lie

For linear inverse problems with Gaussian priors and Gaussian observation noise, the posterior is Gaussian, with mean and covariance determined by the conditioning formula. Using the Feldman-Hajek theorem, we analyse the prior-to-posterior update and its low-rank approximation for infinite-dimensional Hilbert parameter spaces and finite-dimensional observati

Keshav Das, Julie Keisler, Margaux Brégère, Amaury Durand

Electricity demand forecasting is key to ensuring that supply meets demand lest the grid would blackout. Reliable short-term forecasts may be obtained by combining a Generalized Additive Models (GAM) with a State-Space model (Obst et al., 2021), leading to an adaptive (or online) model. A GAM is an over-parameterized linear model defined by a formula and a s

Aaron Ngai, Sebastian Hartweg, Jakob D. Asmussen, Björn Bastian

Roaming reactions involving a neutral fragment of a molecule that transiently wanders around another fragment before forming a new bond are intriguing and peculiar pathways for molecular rearrangement. Such reactions can occur for example upon double ionization of small organic molecules, and have recently sparked much scientific interest. We have studied th

Chenqi Guo, Mengshuo Rong, Qianli Feng, Rongfan Feng

Crossmodal knowledge distillation (KD) aims to enhance a unimodal student using a multimodal teacher model. In particular, when the teacher's modalities include the student's, additional complementary information can be exploited to improve knowledge transfer. In supervised image classification, image datasets typically include class labels that represent hi

Alexander Tschantz, Magnus Koudahl, Hampus Linander, Lancelot Da Costa

Predictive coding (PC) is an influential theory of information processing in the brain, providing a biologically plausible alternative to backpropagation. It is motivated in terms of Bayesian inference, as hidden states and parameters are optimised via gradient descent on variational free energy. However, implementations of PC rely on maximum \textit{a poste

Fuad Kittaneh, Satyajit Sahoo, Hranislav Stanković

This paper explores refinements of some operator norm inequalities through the generalized spherical Aluthge transform and the spherical Heinz transform. We introduce the spherical Schatten $p$-norm for operator tuples and establish several related inequalities. Additionally, equality conditions for some of these inequalities are also presented. Furthermore,

Minh David Thao Chan, Ruoyu Zhao, Yukuan Jia, Ruiqing Mao

The energy consumption of Convolutional Neural Networks (CNNs) is a critical factor in deploying deep learning models on resource-limited equipment such as mobile devices and autonomous vehicles. We propose an approach involving Proportional Layer Skipping (PLS) and Frequency Scaling (FS). Layer skipping reduces computational complexity by selectively bypass

Gergely Flamich, David Vilar, Jan-Thorsten Peter, Markus Freitag

The goal of translation, be it by human or by machine, is, given some text in a source language, to produce text in a target language that simultaneously 1) preserves the meaning of the source text and 2) achieves natural expression in the target language. However, researchers in the machine translation community usually assess translations using a single sc

Bingyuan Zhang, Yoshikazu Terada

Convex clustering is a modern clustering framework that guarantees globally optimal solutions and performs comparably to other advanced clustering methods. However, obtaining a complete dendrogram (clusterpath) for large-scale datasets remains computationally challenging due to the extensive costs associated with iterative optimization approaches. To address

Paul-Christian Bürkner, Marvin Schmitt, Stefan T. Radev

Simulations play important and diverse roles in statistical workflows, for example, in model specification, checking, validation, and even directly in model inference. Over the past decades, the application areas and overall potential of simulations in statistical workflows have expanded significantly, driven by the development of new simulation-based algori

Yang Li, Shitu Zhang, Yuanzheng Li

In the era of Industry 4.0, ensuring the resilience of cyber-physical systems against sophisticated cyber threats is increasingly critical. This study proposes a pioneering AI-based control framework that enhances short-term voltage stability assessments (STVSA) in power systems under complex composite cyber-attacks. First, by incorporating white-box and bla

Sophie Bade

Roommate problems with convex preferences always have stable matchings. Efficiency and individual rationality are, moreover, compatible with strategyproofness in such convex roommate problems. Both of these results fail without the assumption of convexity. In the environment under study, preferences are convex if and only if they are single peaked. Any indiv

Jun Cui

This study investigates the relationship between corporate digital innovation and Environmental, Social, and Governance (ESG) performance, with a specific focus on the mediating role of Generative artificial intelligence technology adoption. Using a comprehensive panel dataset of 8,000 observations from the CMARS and WIND database spanning from 2015 to 2023,

Qi Wu, Quanlong Zheng, Yanhao Zhang, Junlin Xie

With the rapid development of multimodal models, the demand for assessing video understanding capabilities has been steadily increasing. However, existing benchmarks for evaluating video understanding exhibit significant limitations in coverage, task diversity, and scene adaptability. These shortcomings hinder the accurate assessment of models' comprehensive

Safa Alsaidi, Marc Vincent, Olivia Boyer, Nicolas Garcelon

In this paper, we address the challenge of patient-note identification, which involves accurately matching an anonymized clinical note to its corresponding patient, represented by a set of related notes. This task has broad applications, including duplicate records detection and patient similarity analysis, which require robust patient-level representations.

Sini Peltonen, Laura Vasko, Inka Tenhunen, Inka Hiltunen

Jerk plays a pivotal role in the thrilling experience of many amusemement park rides. In addition to exploring the physical aspect of jerks, we tackle the empirical observation of an attractive force between passengers in the popular attraction, the spinning teacups. By modeling the complex system of rotating platforms, we show that pseudotorques induced by

Beatrice Franzolini, Antonio Lijoi, Igor Prünster, Giovanni Rebaudo

Species sampling processes have long served as the fundamental framework for modeling random discrete distributions and exchangeable sequences. However, data arising from distinct but related sources require a broader notion of probabilistic invariance, making partial exchangeability a natural choice. Countless models for partially exchangeable data, collect

Houren Hong, Janice L. Scealy, Andrew T. A. Wood, Yanrong Yang

Regression with a spherical response is challenging due to the absence of linear structure, making standard regression models inadequate. Existing methods, mainly parametric, lack the flexibility to capture the complex relationship induced by spherical curvature, while methods based on techniques from Riemannian geometry often suffer from computational diffi

C. Álvarez Roa, Y. C. Gültekin, K. Wu, C. W. Korevaar

We develop a simple approximation for the average BER for an FSO system impacted by weak turbulence and pointing errors. Numerical results show that the proposed expression accurately predicts the true BER.

Maria Bruna, Markus Schmidtchen, Oscar de Wit

We develop and analyse a finite volume scheme for a nonlocal active matter system known to exhibit a rich array of complex behaviours. The model under investigation was derived from a stochastic system of interacting particles describing a foraging ant colony coupled to pheromone dynamics. In this work, we prove that the unique numerical solution converges t

Wei Gao, Xinyu Zhou, Peng Sun, Tianwei Zhang

Key-Value cache (\texttt{KV} \texttt{cache}) compression has emerged as a promising technique to optimize Large Language Model (LLM) serving. It primarily decreases the memory consumption of \texttt{KV} \texttt{cache} to reduce the computation cost. Despite the development of many compression algorithms, their applications in production environments are stil

Yulu Pi, Ella Bettison, Anna Becker

This study investigates malicious AI Assistants' manipulative traits and whether the behaviours of malicious AI Assistants can be detected when interacting with human-like simulated users in various decision-making contexts. We also examine how interaction depth and ability of planning influence malicious AI Assistants' manipulative strategies and effectiven

Marjan Petreski, Magdalena Olczyk

This study examines the impact of foreign direct investment (FDI) on job creation across 109 regions in the old EU member states from 2012 to 2023. Using dynamic and spatial econometric models combined with a unique dataset of FDI projects, we find that increased FDI inflows significantly enhance regional job creation, but the relationship is nonlinear. Sect

Gabriel Santos-Díaz, Álvaro Rodríguez-Rivas, Alejandro Cuetos

This study explores the application of elongated particle interaction models, traditionally used in liquid crystal phase research, in the context of early bacterial biofilm development. Through computer simulations using an agent-based model, we have investigated the possibilities and limitations of modeling biofilm formation and growth using different model

Hongjie He, Xu Pan, Yudong Yao

As deep learning continues to advance, the transparency of neural network decision-making remains a critical challenge, limiting trust and applicability in high-stakes domains. Class Activation Mapping (CAM) techniques have emerged as a key approach toward visualizing model decisions, yet existing methods face inherent trade-offs. Gradient-based CAM variants

Thomas Meier, Christian Reinhardt, Sho Shibata, Simon Müller

It has been suggested that Jupiter's fuzzy core could be a result of a giant impact. Here, we investigate the expected impact conditions from N-body simulations. We then use state-of-the-art SPH simulations to investigate the results of impacts with different conditions including various impactor masses and composition, different formation stages in Jupiter'

Bin Gui, Hao Zhang

Let $\mathbb V=\bigoplus_{n\in\mathbb N}\mathbb V(n)$ be a $C_2$-cofinite VOA, not necessarily rational or self-dual. In this paper, we establish various versions of the sewing-factorization (SF) theorems for conformal blocks associated to grading-restricted generalized modules of $\mathbb V^{\otimes N}$ (where $N\in\mathbb N$). In addition to the versions a

José M. Arrieta, Raúl Ferreira, Sergio Junquera

In this paper we study the quenching phenomena occurring in a non-local diffusion system of two equations with intertwined singular absorption terms of the type $u^{-p}$. We prove that there exists a range of multiplicative parameters for which every solution presents quenching, while outside this range there are both global and quenching solutions. We also

Ming Yuan, Chuang Zhang, Lei He, Qing Xu

The depth completion task is a critical problem in autonomous driving, involving the generation of dense depth maps from sparse depth maps and RGB images. Most existing methods employ a spatial propagation network to iteratively refine the depth map after obtaining an initial dense depth. In this paper, we propose DenseFormer, a novel method that integrates

Janette Larney, Arno Botha, Gerrit Lodewicus Grobler, Helgard Raubenheimer

Loss Given Default (LGD) is a key risk parameter in determining a bank's regulatory capital. During LGD-estimation, realised recovery cash flows are to be discounted at an appropriate rate. Regulatory guidance mandates that this rate should allow for the time value of money, as well as include a risk premium that reflects the "undiversifiable risk" within th

Gehui Xu, Ting Bai, Andreas A. Malikopoulos, Thomas Parisini

We investigate the relationship between the team-optimal solution and the Nash equilibrium (NE) to assess the impact of strategy deviation on team performance. As a working use case, we focus on a class of flow assignment problems in which each source node acts as a cooperating decision maker (DM) within a team that minimizes the team cost based on the team-

Yumeng Fu, Junjie Wu, Zhongjie Wang, Meishan Zhang

Multimodal emotion recognition in conversation (MERC), the task of identifying the emotion label for each utterance in a conversation, is vital for developing empathetic machines. Current MLLM-based MERC studies focus mainly on capturing the speaker's textual or vocal characteristics, but ignore the significance of video-derived behavior information. Differe

Aditya Pathak, Rachit Gandhi, Vaibhav Uttam, Arnav Ramamoorthy

Since the emergence of Large Language Models (LLMs) popularized by the release of GPT-3 and ChatGPT, LLMs have shown remarkable promise in programming-related tasks. While code generation using LLMs has become a popular field of research, code evaluation using LLMs remains under-explored. In this paper, we focus on LLM-based code evaluation and attempt to fi

Elayne Lemos, Rodrigo Oliveira, Jairson Rodrigues, Rosalvo F. Oliveira Neto

The deployment of Machine Learning models in the cloud has grown among tech companies. Hardware requirements are higher when these models involve Deep Learning techniques, and the cloud providers' costs may be a barrier. We explore deploying Deep Learning models, using for experiments the GECToR model, a Deep Learning solution for Grammatical Error Correctio

Linus Bergqvist, Adem Limani, Bartosz Malman

We revisit the problem of characterizing cyclic elements for the shift operator in a broad class of radial growth spaces of holomorphic functions on the unit disk, focusing on functions of finite Nevanlinna characteristic. We provide results in the range of Dini regular weights, and in the regime of logarithmic integral divergence. Our proofs are largely con

Francisco Gomes Figueira, Martin Derka, Ching Lun Chiu, Jan Gorzny

A rollup network is a type of popular "Layer 2" scaling solution for general purpose "Layer 1" blockchains like Ethereum. Rollups networks separate execution of transactions from other aspects like consensus, processing transactions off of the Layer 1, and posting the data onto the underlying layer for security. While rollups offer significant scalability ad

Zixi Liu, Yang Feng, Yunbo Ni, Shaohua Li

Rust is gaining popularity for its well-known memory safety guarantees and high performance, distinguishing it from C/C++ and JVM-based languages. Its compiler, rustc, enforces these guarantees through specialized mechanisms such as trait solving, borrow checking, and specific optimizations. However, Rust's unique language mechanisms introduce complexity to

Adelmo Niccolai, Maurizio Clemente, Theo Hofman, Niccolò Baldanzini

In this paper, we propose an optimization framework for the powertrain design of a two-wheel-driven electric superbike, minimizing energy consumption. Specifically, we jointly optimize the force distribution between the wheels with the gear ratio, and rear motor and battery sizing while explicitly considering vehicle dynamics and performance constraints. Fir

Joachim Knapik, Bruno Senjean, Benjamin Lasorne, Yohann Scribano

Quantum computing has recently been emerging in theoretical chemistry as a realistic avenue meant to offer computational speedup to challenging eigenproblems in the context of strongly-correlated molecular systems or extended materials. Most studies so far have been devoted to the quantum treatment of electronic structure and only a few were directed to the

Mike Winer, Boris Hanin

Neural networks are complex functions of both their inputs and parameters. Much prior work in deep learning theory analyzes the distribution of network outputs at a fixed a set of inputs (e.g. a training dataset) over random initializations of the network parameters. The purpose of this article is to consider the opposite situation: we view a randomly initia

Vitor Cerqueira, Luis Roque, Carlos Soares

Accurate evaluation of forecasting models is essential for ensuring reliable predictions. Current practices for evaluating and comparing forecasting models focus on summarising performance into a single score, using metrics such as SMAPE. While convenient, averaging performance over all samples dilutes relevant information about model behavior under varying

Chenyi Huang, Xianchao Xiu

Although federated learning has gained prominence as a privacy-preserving framework tailored for distributed Internet of Things (IoT) environments, current federated principal component analysis (PCA) methods lack integration of sparsity, a critical feature for robust anomaly detection. To address this limitation, we propose a novel federated structured spar

Yanbo Wang, Yongtao Chen, Chuan Cao, Tianchen Deng

We propose a flexible Semi-Automatic Labeling Tool (SALT) for general LiDAR point clouds with cross-scene adaptability and 4D consistency. Unlike recent approaches that rely on camera distillation, SALT operates directly on raw LiDAR data, automatically generating pre-segmentation results. To achieve this, we propose a novel zero-shot learning paradigm, term

Arturo Pérez-Peralta, Sandra Benítez-Peña, Rosa E. Lillo

Machine Learning algorithms are ubiquitous in key decision-making contexts such as organizational justice or healthcare, which has spawned a great demand for fairness in these procedures. In this paper we focus on the application of fair ML in finance, more concretely on the use of fairness techniques on credit scoring. This paper makes two contributions. On

Yazhuang Miao, Yiming Zhao, Yong Wang, Jie Qiao

We investigate a non-Hermitian extension of the Su--Schrieffer--Heeger model that incorporates spin-dependent SU(2) gauge fields, represented by non-Abelian couplings between lattice sites, as well as independent nonreciprocal hopping amplitudes. This framework gives rise to a rich phase structure characterized by complex-energy braiding and tunable non-Herm

Gunwoo Kim, Meike Hatzel, Stephan Kreutzer

Butterfly minors are a generalisation of the minor containment relation for undirected graphs to directed graphs. Many results in directed structural graph theory use this notion as a central tool next to directed treewidth, a generalisation of the width measure treewidth to directed graphs. Adler [JCTB'07] showed that the directed treewidth is not closed un

Yongqiang Liu, Masahiko Yoshinaga

Let $\mathcal{L}$ be a rank one local system with field coefficient on the complement $M(\mathcal{A})$ of an essential complex hyperplane arrangement $\mathcal{A}$ in $\mathbb{C}^\ell$. Dimca-Papadima and Randell independently showed that $M(\mathcal{A})$ is homotopy equivalent to a minimal CW-complex. It implies that $\dim H^k(M(\mathcal{A}),\mathcal{L}) \l

Chenyu Zhang, Shiying Sun, Kuan Liu, Chuanbao Zhou

As an important branch of embodied artificial intelligence, mobile manipulators are increasingly applied in intelligent services, but their redundant degrees of freedom also limit efficient motion planning in cluttered environments. To address this issue, this paper proposes a hybrid learning and optimization framework for reactive whole-body motion planning

Sangita Dutta, Erik Fransson, Tobias Hainer, Benjamin M. Gallant

FAPbI$_3$ is a material of interest for its potential in solar cell applications, driven by its remarkable optoelectronic properties. However, the low-temperature phase of FAPbI$_3$ remains poorly understood, with open questions surrounding its crystal structure, octahedral tilting, and the arrangement of formamidinium (FA) cations. Using our trained machine

Nanao Kita

This paper is the second in a series of papers characterizing the maximum packing of \( T \)-cuts in bipartite grafts, following the first paper (N.~Kita, ``Tight cuts in bipartite grafts~I: Capital distance components,'' {arXiv:2202.00192v2}, 2022). Given a graft $(G, T)$, a minimum join $F$, and a specified vertex $r$ called the root, the distance componen

Jesús García Fernández, Nasir Ahmad, Marcel van Gerven

The pursuit of energy-efficient and adaptive artificial intelligence (AI) has positioned neuromorphic computing as a promising alternative to conventional computing. However, achieving learning on these platforms requires techniques that prioritize local information while enabling effective credit assignment. Here, we propose noise-based reward-modulated lea

Yurii Kulinich, Bohdan Novosyadlyj

The formation of first stars and galaxies at the Cosmic Dawn had been preceded by the chain of primordial chemistry reactions during Dark Ages which generated first molecules, mostly H$_2$, HD, and HeH$^+$, so crucial for first stars to emerge. These molecules absorbed and scattered CMB quanta this way distorting CMB spectrum. Estimate how much bound-bound t

Jianhang Xie, Changrong Zhu

This paper investigates the dynamical behaviors of a Holling type I Leslie-Gower predator-prey model where the predator exhibits an Allee effect and is subjected to constant harvesting. The model demonstrates three types of equilibrium points under different parameter conditions, which could be either stable or unstable nodes (foci), saddle nodes, weak cente

M. Samsel-Czekała, M. Werwiński, A. Szajek, G. Chełkowska

Based on experimental crystallographic data, electronic structure of UGe$_2$ have been calculated and compared with our results of X-ray photoelectron spectroscopy (XPS) measurements. We employed two different advanced full potential (FP) methods: FP-local-orbital (FPLO) and FP-linear augmented plane waves (Wien2k) codes for non-magnetic and ferromagnetic st

Jingjun Han, Chen Jiang

We prove that the total Cartier index of a bounded family of projective varieties of klt type is bounded.

Tingfei Li

We investigate the $q=2$ SYK model with $R$-para-particles ($R$-PSYK$_2$), analyzing its thermodynamics and spectral form factor (SFF) using random matrix theory. The Hamiltonian is quadratic, with coupling coefficients randomly drawn from the Gaussian Unitary Ensemble (GUE). The model displays self-averaging behavior and exhibits an exponential ramp in its

Zehan Li, Jinzhi Deng, Haibing Ma, Chi Zhang

This paper introduces the Translational Evaluation of Multimodal AI for Inspection (TEMAI) framework, bridging multimodal AI capabilities with industrial inspection implementation. Adapting translational research principles from healthcare to industrial contexts, TEMAI establishes three core dimensions: Capability (technical feasibility), Adoption (organizat

Yi Yao, Miao Fan, Shengtong Xu, Haoyi Xiong

Lane topology reasoning techniques play a crucial role in high-definition (HD) mapping and autonomous driving applications. While recent years have witnessed significant advances in this field, there has been limited effort to consolidate these works into a comprehensive overview. This survey systematically reviews the evolution and current state of lane top

Miao Fan, Xuxu Kong, Shengtong Xu, Haoyi Xiong

Real-time traffic light recognition is fundamental for autonomous driving safety and navigation in urban environments. While existing approaches rely on single-frame analysis from onboard cameras, they struggle with complex scenarios involving occlusions and adverse lighting conditions. We present \textit{ViTLR}, a novel video-based end-to-end neural network

Coen del Valle, Colva M. Roney-Dougal

A base for a permutation group $G$ acting on a set $\Omega$ is a subset $\mathcal{B}$ of $\Omega$ whose pointwise stabiliser $G_{(\mathcal{B})}$ is trivial. There is a natural greedy algorithm for constructing a base of relatively small size. We write $\mathcal{G}(G)$ the maximum size of a base it produces, and $b(G)$ for the size of the smallest base for $G

Miao Fan, Shanshan Yu, Shengtong Xu, Kun Jiang

Autonomous driving faces safety challenges due to a lack of global perspective and the semantic information of vectorized high-definition (HD) maps. Information from roadside cameras can greatly expand the map perception range through vehicle-to-infrastructure (V2I) communications. However, there is still no dataset from the real world available for the stud

Francisco J. Fernández, Ignacio Márquez Albés, F. Adrián F. Tojo, Carlos Villanueva Mariz

We investigate the existence and uniqueness of solutions to first-order Stieltjes differential problems, focusing on the role of the Stieltjes derivative and its kernel. Unlike the classical case, the kernel of the Stieltjes derivative operator is nontrivial, leading to non-uniqueness issues in Cauchy problems. We characterize this kernel by providing necess

Antoni Szczurek, Anna Cisek, Rafal Maciula

We discuss the production of $D$ mesons and $J/\psi$ quarkonia in proton-nucleus collisions in the fixed-target LHCb experiment. We consider gluon-gluon fusion within $k_t$-factorization, processes initiated by intrinsic charm in the nucleon and perturbative recombination mechanism. All the mechanisms seem to be necessary to describe the LHCb experimental da

Won-Ki Seo, Han Lin Shang

We develop a statistical testing procedure to examine whether the curve-valued time series of interest is integrated of order d for an integer d. The proposed procedure can distinguish between integer-integrated time series and fractionally-integrated ones, and it has broad applicability in practice. Monte Carlo simulation experiments show that the proposed

Bizhe Bai, Jianjian Cao, Yadan Luo, Tao Chen

Grounded Conversation Generation (GCG) is an emerging vision-language task that requires models to generate natural language responses seamlessly intertwined with corresponding object segmentation masks. Recent models, such as GLaMM and OMG-LLaVA, achieve pixel-level grounding but incur significant computational costs due to processing a large number of visu

Nima Torbati, Anastasia Meshcheryakova, Ramona Woitek, Sepideh Hatamikia

Melanoma is the most lethal form of skin cancer, with an increasing incidence rate worldwide. Analyzing histological images of melanoma by localizing and classifying tissues and cell nuclei is considered the gold standard method for diagnosis and treatment options for patients. While many computerized approaches have been proposed for automatic analysis, mos

Joanne Tan

We all love the ecstasy that comes with submitting papers to journals or arXiv. Some have described it as yeeting their back-breaking products of labor into the void, wishing they could never deal with them ever again. The very act of yeeting papers onto arXiv contributes to the expansion of the arXiverse; however, we have yet to quantify our contribution to

Kai Huang, Hao Zou, Bochen Wang, Ye Xi

Recent advancements in Large Visual Language Models (LVLMs) have gained significant attention due to their remarkable reasoning capabilities and proficiency in generalization. However, processing a large number of visual tokens and generating long-context outputs impose substantial computational overhead, leading to excessive demands for key-value (KV) cache

Johanna Kangas, Janne S. Kotiaho, Markku Ollikainen

The European Union's Biodiversity Strategy sets an ambitious goal to increase the area of protected land and sea to 30% with 10% devoted to strict protection by 2030. The large land areas required to fulfil the conservation target and the quick schedule of implementation challenge both the current policy instruments and public funding for conservation. We in

Eric B. Gregory, Feng-Kun Guo, Christoph Hanhart, Stefan Krieg

The nature of low-lying scalar and axial-vector charmed mesons has been debated for decades, with hadronic molecular and compact tetraquark models being prominent candidates. These two models predict quite different features for the accessible SU(3) multiplets in the scalar and axial-vector sectors, which can be tested through lattice calculations at SU(3) s

Chris Kent, Adam A. Scaife, Nick J. Dunstone, Doug Smith

Machine learning weather models trained on observed atmospheric conditions can outperform conventional physics-based models at short- to medium-range (1-14 day) forecast timescales. Here we take the machine learning weather model ACE2, trained to predict 6-hourly steps in atmospheric evolution and which can remain stable over long forecast periods, and asses

Bicheng Yang, Jingkai He, Dong Du, Yubin Xia

Cloud-native systems increasingly rely on infrastructure services (e.g., service meshes, monitoring agents), which compete for resources with user applications, degrading performance and scalability. We propose HeteroPod, a new abstraction that offloads these services to Data Processing Units (DPUs) to enforce strict isolation while reducing host resource co

Fangda Chen, Shanshan Zhao, Chuanfu Xu, Long Lan

Recent advancements in customized video generation have led to significant improvements in the simultaneous adaptation of appearance and motion. Typically, decoupling the appearance and motion training, prior methods often introduce concept interference, resulting in inaccurate rendering of appearance features or motion patterns. In addition, these methods o

Taehun Kim, E. H. Hwang, Hongki Min

In multilayer structures, the coupling between layers gives rise to unique plasmon modes, but analytic solutions are typically available only for bilayers due to the increasing complexity as the number of layers increases. We investigate plasmons in multilayer structures, including the effects of interlayer tunneling. By introducing the Coulomb eigenvector b

Florian Bayer, Christian Rathgeb

Biometric systems strive to balance security and usability. The use of multi-biometric systems combining multiple biometric modalities is usually recommended for high-security applications. However, the presentation of multiple biometric modalities can impair the user-friendliness of the overall system and might not be necessary in all cases. In this work, w

Jiaxiang Chen, Jingwei Shi, Lei Gan, Jiale Zhang

As AI technology advances, it is driving innovation across industries, increasing the demand for scalable AI project deployment. However, deployment remains a critical challenge due to complex environment configurations, dependency conflicts, cross-platform adaptation, and debugging difficulties, which hinder automation and adoption. This paper introduces AI

Guhnoo Yun, Juhan Yoo, Kijung Kim, Jeongho Lee

Recent studies have shown that 2D convolution and self-attention exhibit distinct spectral behaviors, and optimizing their spectral properties can enhance vision model performance. However, theoretical analyses remain limited in explaining why 2D convolution is more effective in high-pass filtering than self-attention and why larger kernels favor shape bias,

M Krithika, P Vanchinathan

In 2018, Legrand and Paran proved a weaker form of the Inverse Galois Problem for all Hilbertian fields and all finite groups: that is, there exist possibly non-Galois extensions over given Hilbertian base field with given finite group as the group of field automorphisms fixing the base field. For $\mathbf Q$ it was proved earlier by M. Fried. In this paper

Yi Ren, Chenhao Xue, Jiaxing Zhang, Chen Zhang

The proliferation of deep learning accelerators calls for efficient and cost-effective hardware design solutions, where parameterized modular hardware generator and electronic design automation (EDA) tools play crucial roles in improving productivity and final Quality-of-Results (QoR). To strike a good balance across multiple QoR of interest (e.g., performan

Lu Meng, Vadim Baru, Evgeny Epelbaum, Arseniy A. Filin

Lattice QCD has become an essential tool for studying the hadron-hadron interaction from the first principles. However, when extracting infinite-volume scattering parameters from finite-volume energy levels, the traditional L\"uscher formula encounters limitations due to the left-hand cut induced by long-range interactions such as the one-pion exchange. In t

Chenhao Xue, Yi Ren, Jinwei Zhou, Kezhi Li

Multipliers and multiply-accumulators (MACs) are fundamental building blocks for compute-intensive applications such as artificial intelligence. With the diminishing returns of Moore's Law, optimizing multiplier performance now necessitates process-aware architectural innovations rather than relying solely on technology scaling. In this paper, we introduce D

Edit Matyus

This article briefly overviews the scientific activities of the Molecular Quantum (electro-)Dynamics (MQD) Research Group in Budapest. Since its foundation in 2016, the MQD group has worked on molecular spectroscopy and molecular physics topics with primary applications and relevance to high-resolution and precision spectroscopy.

Debin Xiang, Qifan Jiang, Liqiang Lu, Siwei Tan

Constrained binary optimization aims to find an optimal assignment to minimize or maximize the objective meanwhile satisfying the constraints, which is a representative NP problem in various domains, including transportation, scheduling, and economy. Quantum approximate optimization algorithms (QAOA) provide a promising methodology for solving this problem b

Debapratim Banerjee

We consider the Wigner matrix $W_{n}$ of dimension $n \times n$ as $n \to \infty$. The objective of this paper is two folds: first we construct an operator $\mathcal{W}$ on a suitable Hilbert space $\mathcal{H}$ and then define a suitable notion of convergence such that the matrices $W_{n}$ converge in that notion of convergence to $\mathcal{W}$. We further

Kaito Kishi, Junpei Yamaguchi, Tetsuya Izu, Noboru Kunihiro

The discrete logarithm problem (DLP) over finite fields, commonly used in classical cryptography, has no known polynomial-time algorithm on classical computers. However, Shor has provided its polynomial-time algorithm on quantum computers. Nevertheless, there are only few examples simulating quantum circuits that operate on general pairs of modulo $p$ and or

Alejandro Fructuoso-Bonet, Jesús Rodríguez-López

In recent studies, Bibiloni-Femenias, Mi\~{n}ana and Valero characterized the functions that aggregate a family of (quasi-)(pseudo)metric modulars defined over a fixed set $X$ into a single one. In this paper, we adopt a related but different approach to examine those functions that allow us to define a (quasi-)(pseudo)metric modular in the Cartesian product

Geoffrey Compère, Dima Fontaine, Kevin Nguyen

We study the general structure of the electromagnetic field in the vicinity of spatial infinity. Starting from the general solution of the sourced Maxwell equations written in terms of multipole moments as obtained by Iyer and Damour, we derive the expansion of the electromagnetic field perturbatively in the electromagnetic coupling. At leading order, where

Evgenii Evstafev

Large language models (LLMs) demonstrate increasing capabilities in creative text generation, yet systematic evaluations of their humor production remain underexplored. This study presents a comprehensive analysis of 13 state-of-the-art LLMs across five architectural families, evaluating their performance in generating technically relevant humor for software

Alberto Miguel-Gómez

We provide a partial answer to a question asked independently by Kim and d'Elb\'ee and show that, under the assumption of the stable Kim-forking conjecture, every $\mathrm{NSOP}_1$ rosy theory must be simple. We also prove that the theory of a Frobenius field has stable Kim-forking.

Kazunori Takeshita, Yoshikazu Terada

We focus on nonlinear Function-on-Scalar regression, where the predictors are scalar variables, and the responses are functional data. Most existing studies approximate the hidden nonlinear relationships using linear combinations of basis functions, such as splines. However, in classical nonparametric regression, it is known that these approaches lack adapti

Adrián Sánchez-Mompó, Ioannis Mavromatis, Peizheng Li, Konstantinos Katsaros

This study presents an empirical investigation into the energy consumption of Discriminative and Generative AI models within real-world MLOps pipelines. For Discriminative models, we examine various architectures and hyperparameters during training and inference and identify energy-efficient practices. For Generative AI, Large Language Models (LLMs) are asse

Renzhi Tian, Jinjie Wang, Wei Yang, Weizhen Li

Optical Coherence Tomography (OCT) and Optical Coherence Tomography Angiography (OCTA) are key diagnostic tools for clinical evaluation and management of retinal diseases. Compared to traditional OCT, OCTA provides richer microvascular information, but its acquisition requires specialized sensors and high-cost equipment, creating significant challenges for t

Ciarán Furey, O. Grace Telford, Alex de Koter, Frank Backs

Radiation-driven winds heavily influence the evolution and fate of massive stars. Feedback processes from these winds impact the properties of the interstellar medium of their host galaxies. The dependence of mass loss on stellar properties is poorly understood, particularly at low metallicity ($Z$). We aim to characterise stellar and wind properties of mass

Ryan Sweke, Seongwook Shin, Elies Gil-Fuster

There is currently a huge effort to understand the potential and limitations of variational quantum machine learning (QML) based on the optimization of parameterized quantum circuits. Recent proposals toward dequantizing variational QML models for regression problems include approaches based on kernel methods with carefully chosen kernel functions, approxima

Jiankai Tang, Jiacheng Liu, Renling Tong, Kai Zhu

Photoplethysmography (PPG) Sensors, widely deployed in smartwatches, offer a simple and non-invasive authentication approach for daily use. However, PPG authentication faces reliability issues due to motion artifacts from physical activity and physiological variability over time. To address these challenges, we propose MTL-RAPID, an efficient and reliable PP

Nadine du Toit, Kristian K. Müller-Nedebock

A novel field theoretical approach towards modelling dynamic networking in complex systems is presented. An equilibrium networking formalism which utilises Gaussian fields is adapted to model the dynamics of particles that can bind and unbind from one another. Here, \textit{networking} refers to the introduction of instantaneous co-localisation constraints a

Chen-How Huang, Jon Ortuzar, M. A. Cazalilla

Chains of triangular nanographene (triangulene), recently identified as realizing the valence-bond solid phase of a spin-1 chain, offer a promising platform for quantum information processing. We propose a spin-singlet qubit based on these chains grown on a superconducting substrate. Using the numerical renormalization group (NRG), we identify a manifold con

Mishay Naidoo, Stephen Paine, Amit Kumar Mishra, Mohammed Yunus Abdul Gaffar

Road traffic monitoring typically involves the counting and recording of vehicles on public roads over extended periods. The data gathered from such monitoring provides useful information to municipal authorities in urban areas. This paper presents a low-cost, widely deployable sensing subsystem based on Continuous Wave Doppler radar. The proposed system can