February 2025 arXiv papers — page 4

Eduard Eiben, Robert Ganian, Iyad Kanj, Ramanujan M. Sridharan

We study a variant of the Coordinated Motion Planning problem on undirected graphs, referred to herein as the \textsc{Coordinated Sliding-Motion Planning} (CSMP) problem. In this variant, we are given an undirected graph $G$, $k$ robots $R_1,\dots,R_k$ positioned on distinct vertices of $G$, $p\leq k$ distinct destination vertices for robots $R_1,\dots,R_p$,

Murat Manguoğlu, Volker Mehrmann

We propose a new class of multi-layer iterative schemes for solving sparse linear systems in saddle point structure. The new scheme consist of an iterative preconditioner that is based on the (approximate) nullspace method, combined with an iterative least squares approach and an iterative projection method. We present a theoretical analysis and demonstrate

J. P. Wendoloski, J. Hillier, S. D. Liles, M. Rendell

The quality of the silicon-oxide interface plays a crucial role in fabricating reproducible silicon spin qubits. In this work we characterize interface quality by performing mobility measurements on silicon Hall bars. We find a peak electron mobility of nearly $40,000\,\text{cm}^2/\text{Vs}$ in a device with a $21\,\text{nm}$ oxide layer, and a peak hole mob

Anja Seegebrecht, Tanja Schilling

Recently, the concept of minimal dissipation has been brought forward as a means to define work performed on open quantum systems [Phys. Rev. A 105, 052216 (2022)]. We discuss this concept from the point of view of projection operator formalisms in classical statistical physics. We analyse an autonomous composite system which consists of a system and an envi

Martin Bladt, Eric C. K. Cheung, Oscar Peralta, Jae-Kyung Woo

We introduce a novel class of bivariate common-shock discrete phase-type (CDPH) distributions to describe dependencies in loss modeling, with an emphasis on those induced by common shocks. By constructing two jointly evolving terminating Markov chains that share a common evolution up to a random time corresponding to the common shock component, and then proc

Walid El Maouaki, Nouhaila Innan, Alberto Marchisio, Taoufik Said

Quantum federated learning (QFL) merges the privacy advantages of federated systems with the computational potential of quantum neural networks (QNNs), yet its vulnerability to adversarial attacks remains poorly understood. This work pioneers the integration of adversarial training into QFL, proposing a robust framework, quantum federated adversarial learnin

Guillaume Carlier, Maxime Sylvestre

We consider a class of adversarial classification problems in the form of zero-sum games between a classifier and an adversary. The latter is able to corrupt data, at the expense of some optimal transport cost. We show that quite general assumptions on the loss functions of the classifier and the transport cost functions of the adversary ensure the existence

Dasuni N. Rathnaweera, Xudong Huai, K. Ramesh Kumar, Michal J. Winiarski

Magnetic skyrmions are topologically protected spin states enabling high-density, low-power spin electronics. Despite growing efforts to find new skyrmion host systems, the microscopic mechanisms leading to skyrmion phase transitions at specific temperatures and magnetic fields remain elusive. Here, we systematically study the isostructural centrosymmetric m

Yeojin Park, Osman Aycan, Lyes Kadem

This study evaluates the fluid dynamics inside and outside transcatheter blood pump positioned in the aorta. We focus on the pump's impact on blood component damage and arterial wall stress. CFD simulations were performed for rotational speeds ranging from 6000 to 15000 rpm, with a blood flow rate of 1.6 L/min. Results show that significant blood damage may

Muhammed Yusuf Satici, Jianxun Wang, David L. Roberts

Curriculum learning is a training method in which an agent is first trained on a curriculum of relatively simple tasks related to a target task in an effort to shorten the time required to train on the target task. Autonomous curriculum design involves the design of such curriculum with no reliance on human knowledge and/or expertise. Finding an efficient an

Thomas Rocke, James Kermode

The problem of constructing a dataset for MLIP development which gives the maximum quality in the minimum amount of compute time is complex, and can be approached in a number of ways. We introduce a ``Bayesian selection" approach for selecting from a candidate set of structures, and compare the effectiveness of this method against other common approaches in

Christian Komusiewicz, Diptapriyo Majumdar, Frank Sommer

Enumeration kernelization for parameterized enumeration problems was defined by Creignou et al. [Theory Comput. Syst. 2017] and was later refined by Golovach et al. [J. Comput. Syst. Sci. 2022, STACS 2021] to polynomial-delay enumeration kernelization. We consider ENUM LONG-PATH, the enumeration variant of the Long-Path problem, from the perspective of enume

Yuheng Jia, Wesley Armour

Visible-Infrared Person Re-Identification (VI-ReID) plays a crucial role in applications such as search and rescue, infrastructure protection, and nighttime surveillance. However, it faces significant challenges due to modality discrepancies, varying illumination, and frequent occlusions. To overcome these obstacles, we propose \textbf{AMINet}, an Adaptive M

Adtian Atienza, Jakob E. Bardram, Sadasivan Puthusserypady

Wearable sensing devices, such as Holter monitors, will play a crucial role in the future of digital health. Unsupervised learning frameworks such as Self-Supervised Learning (SSL) are essential to map these single-lead electrocardiogram (ECG) signals with their anticipated clinical outcomes. These signals are characterized by a tempo-variant component whose

Ting-Ting Sun, Yusuke Tanimura, Hiroyuki Sagawa, Emiko Hiyama

We study the charge symmetry breaking (CSB) effect in the binding energy of mirror hypernuclei in the mass region $A=7\sim 48$ in relativistic mean field (RMF) models introducing $NN$ and $\Lambda N$ interactions. The phenomenological $\Lambda N$ CSB interaction is introduced and the strength parameter is fitted to reproduce the experimental binding energy d

Aleksei Reutov

Quantum key distribution (QKD) provides a theoretically secure method for cryptographic key exchange by leveraging quantum mechanics, but practical implementations face vulnerabilities such as Trojan horse attack on phase modulators. This work analyzes the security of QKD systems under such attacks, considering both ideal and imperfect state preparation scen

Theo Saporiti, Oleg Kaikov, Vasily Sazonov, Mohamed Tamaazousti

Mitigation of quantum errors is critical for current NISQ devices. In the present work, we address this task by treating the execution of quantum algorithms as the time evolution of an idealized physical system. We use knowledge of its physics to assist the mitigation of the quantum noise produced on the real device. In particular, the time evolution of the

Danial Elyassirad, Benyamin Gheiji, Mahsa Vatanparast, Amir Mahmoud Ahmadzadeh

Background and Purpose: Glioma segmentation is crucial for clinical decisions and treatment planning. Uncertainty quantification methods, including conformal prediction (CP), can enhance segmentation models reliability. This study aims to use CP in glioma segmentation. Methods: We used the UCSF and UPenn glioma datasets, with the UCSF dataset split into trai

Alexander Mielke

We recall the systematic formulation of Eulerian mechanics in terms of Lie derivatives along the vector field of the material points. Using the abstract properties of Lie derivatives we show that the transport via Lie derivatives generates in a natural way a Poisson structure on the chosen phase space. The evolution equations for thermo-viscoelastic-viscopla

Arthur Azevedo de Amorim, Amal Ahmed, Marco Gaboardi

We introduce Cryptis, an extension of the Iris separation logic that can be used to verify cryptographic components using the symbolic model of cryptography. The combination of separation logic and cryptographic reasoning allows us to prove the correctness of a protocol and later reuse this result to verify larger systems that rely on the protocol. To make t

Giuliano Gagliardi, Johannes Hofscheier, Heath Pearson

We prove the generalised Mukai conjecture for $\mathbb{Q}$-factorial spherical Fano varieties. In this case, a stronger inequality holds featuring an extra term - the minimum absolute complexity of a log Calabi-Yau pair - which measures how close the Fano variety is to being toric.

Zijian Kang, Yueyang Li, Shengyu Gong, Weiming Zeng

Emotional Recognition in Conversation (ERC) is valuable for diagnosing health conditions such as autism and depression, and for understanding the emotions of individuals who struggle to express their feelings. Current ERC methods primarily rely on semantic, audio and visual data but face significant challenges in integrating physiological signals such as Ele

Qi Hou, Laurent Saloff-Coste

This work provides an extension of parts of the classical finite dimensional sub-elliptic theory in the context of infinite dimensional compact connected metrizable groups. Given a well understood and well behaved bi-invariant Laplacian, $\Delta$, and a sub-Laplacian, $L$, to which intrinsic distances, $d_\Delta$, $d_L$, are naturally attached, we show that

Hugo Chateau-Laurent, Rufin VanRullen

We present a model inspired by the Global Workspace Theory that integrates specialized modules to perform a sequential reasoning task. A controller selectively routes information between modules through the workspace using a gating mechanism. This approach allows the model to chain operations by iteratively broadcasting information between specialized domain

Zineb Sordo, Eric Chagnon, Daniela Ushizima

This review surveys the state-of-the-art in text-to-image and image-to-image generation within the scope of generative AI. We provide a comparative analysis of three prominent architectures: Variational Autoencoders, Generative Adversarial Networks and Diffusion Models. For each, we elucidate core concepts, architectural innovations, and practical strengths

Charles Garcion, Sukhjovan S. Gill, Magdalena Misslisch, Alexander Heidt

The DESIRE project aims to test chameleon field theories as potential candidates for dark energy. The chameleon field is a light scalar field that is subject to screening mechanisms in dense environments making them hardly detectable. The project is designed to overcome this challenge. To this end, a specially designed source mass generates periodic gravitat

Zhuo Chen, Jun Jie Miao

Let $\boldsymbol{X}=\{X_k\}_{k=0}^\infty$ be a sequence of compact metric spaces $X_{k}$ and $\boldsymbol{T}=\{T_k\}_{k=0}^\infty$ a sequence of continuous mappings $T_{k}: X_{k} \to X_{k+1}$. The pair $(\boldsymbol{X},\boldsymbol{T})$ is called a nonautonomous dynamical system. In this paper, we study measure-theoretic entropies and pressures, Bowen and pac

Mason Hooten, Het Patel, Yiwei Shao, Rishabh Kumar Singh

This review article discusses some common enhanced sampling methods in relation to the process of self-assembly of biomolecules. An introduction to self-assembly and its challenges is covered followed by a brief overview of the methods and analysis for replica-exchange molecular dynamics, umbrella sampling, metadynamics, and machine learning based techniques

Eli Verwimp, Guy Hacohen, Tinne Tuytelaars

Continual learning aims to enable models to adapt to new datasets without losing performance on previously learned data, often assuming that prior data is no longer available. However, in many practical scenarios, both old and new data are accessible. In such cases, good performance on both datasets is typically achieved by abandoning the model trained on th

Abdallah Alalem Albustami, Ahmad F. Taha, Elias Bou-Harb

Smart grids are inherently susceptible to various types of malicious cyberattacks that have all been documented in the recent literature. Traditional cybersecurity research on power systems often utilizes simplified models that fail to capture the interactions between dynamic and steady-state behaviors, potentially underestimating the impact of cyber threats

L. M. Nieto, S. Zarrinkamar

The one-dimensional harmonic vibronic model, which is a generalization of the so-called linear Landau-Zener model and appears in the form of coupled Schr\"{o}dinger equations, is revisited. After decoupling the components, the resulting fourth-order equation is shown to have a hidden $sl(2)$ algebra. The so-called exceptional part of the spectrum is then exp

Andrii Ilienko, Ilya Molchanov, Tommaso Visonà

We obtain a complete characterization of planar monotone $\sigma$-continuous valuations taking integer values, without assuming invariance under any group of transformations. We further investigate the consequences of dropping monotonicity or $\sigma$-continuity and give a full classification of line valuations. We also introduce a construction of the produc

Hyungi Lee, Seungyoo Lee, Juho Lee

The success of deep learning requires large datasets and extensive training, which can create significant computational challenges. To address these challenges, pseudo-coresets, small learnable datasets that mimic the entire data, have been proposed. Bayesian Neural Networks, which offer predictive uncertainty and probabilistic interpretation for deep neural

Joshua Chen

The program of internal type theory seeks to develop the categorical model theory of dependent type theory using the language of dependent type theory itself. In the present work we study internal homotopical type theory by relaxing the notion of a category with families (cwf) to that of a wild, or precoherent higher cwf, and determine coherence conditions t

Léopold Maytié, Roland Bertin Johannet, Rufin VanRullen

Humans leverage rich internal models of the world to reason about the future, imagine counterfactuals, and adapt flexibly to new situations. In Reinforcement Learning (RL), world models aim to capture how the environment evolves in response to the agent's actions, facilitating planning and generalization. However, typical world models directly operate on the

Tom Görges, Magnus Ørberg Rove, Paul Sharp, Christian Vedel

We examine how railway expansion shaped Denmark's nineteenth-century economic transformation and the diffusion of civic engagement in the form of Grundtvigian institutions. Using a new parish-level panel (1,589 parishes) and a difference-in-differences design that accounts for staggered adoption, we find that railroad connection increased local population by

Yuchao Wang, Yimin Wei

Power amplifiers (PAs) are essential components in wireless communication systems, and the design of their behavioral models has been an important research topic for many years. The widely used generalized memory polynomial (GMP) model suffers from rapid growth in the number of parameters with increasing memory depths and nonlinearity order, which leads to a

Haipeng An, Haoming Nie

The scattering of light dark matter (DM) off thermal electrons within the Sun generates a ``fast'' sub-component of the DM flux that can be detected in underground direct detection experiments. This ``fast'' sub-component has a specific origin-namely, from the Sun. In this study, we demonstrate that in detectors composed of single crystals, like in Bragg sca

Chinnadurai Muthu, A. N. Resmi, Avija Ajayakumar, N. E. Aswathi Ravindran

In the quest for advanced memristor technologies, this study introduces the synthesis of delta-formamidinium lead iodide nanoparticles and their self-assembly into nanorods.

Jong Ho Jhee, Alberto Megina, Pacôme Constant Dit Beaufils, Matilde Karakachoff

Background: With the increasing availability of healthcare data, predictive modeling finds many applications in the biomedical domain, such as the evaluation of the level of risk for various conditions, which in turn can guide clinical decision making. However, it is unclear how knowledge graph data representations and their embedding, which are competitive

Alexander Meiners, Hannes Uecker

Combining local bifurcation analysis with numerical continuation and bifurcation methods we study bifurcations from cylindrical vesicles described by the Helfrich equation with volume and area constraints, with a prescribed periodicity along the cylindrical axis. The bifurcating solutions are in two main classes, axisymmetric (pearling), and non-axisymmetric

Hester Graves

The usual division algorithms on $\mathbb{Z}$ and $\mathbb{Z}[i]$ measure the size of remainders using the norm function. These rings are Euclidean with respect to several functions. The pointwise minimum of all Euclidean functions $f: R \setminus 0 \rightarrow \mathbb{N}$ on a Euclidean domain $R$ is itself a Euclidean function, called the minimal Euclidean

Xinyu Tian, Zixuan Zhang, Lixiu Guan, Xiaobiao Liu

Exploring the coupling between layer, magnetism, valley, and topology in two-dimensional (2D) materials is an important approach to deepen our understanding of materials properties. We propose 27 stable ferromagnetic semiconductor monolayers of Janus XYZH (X = Sc, Y, La, Y = Cl, Br, I, and Z = S, Se, Te). All these monolayers exhibit spontaneous valley polar

Nanshan Deng, Weitao Zhou, Bo Zhang, Junze Wen

Current autonomous vehicles operate primarily within limited regions, but there is increasing demand for broader applications. However, as models scale, their limited capacity becomes a significant challenge for adapting to novel scenarios. It is increasingly difficult to improve models for new situations using a single monolithic model. To address this issu

Giovanni Guerrieri

ATLAS Open Data for Education delivers proton--proton collision data from the ATLAS experiment at CERN to the public along with open-access resources for education and outreach. To date ATLAS has released a substantial amount of data from 8 TeV and 13 TeV collisions in an easily-accessible format and supported by dedicated documentation, software, and tutori

Xinyu Yuan, Zichen Wang, Marcus Collins, Huzefa Rangwala

Recent years have witnessed a surge in the development of protein structural tokenization methods, which chunk protein 3D structures into discrete or continuous representations. Structure tokenization enables the direct application of powerful techniques like language modeling for protein structures, and large multimodal models to integrate structures with p

Benjamin Winkel, Fabio Giovanardi, Michael Lindqvist

In recent years, the utilisation of the radio spectrum has dramatically increased. Digital telecommunication applications, be it terrestrial cell-phone networks or new-space low-earth orbit satellite constellations, have not only acquired unprecedented amounts of spectrum but also use their frequencies everywhere on Earth. The consequences for radio astronom

Vincent C. Müller

Hilary Putnam's biography and philosophical development reflect the history of Anglo-Saxon philosophy over the last 40 years. Putnam has influenced this history significantly for almost as long. In this introduction, the main aim is to present the context in which Putnam stands and from which his philosophical contributions can be understood. In the context

Jiuyang Dong, Junjun Jiang, Kui Jiang, Jiahan Li

Although multi-instance learning (MIL) has succeeded in pathological image classification, it faces the challenge of high inference costs due to processing numerous patches from gigapixel whole slide images (WSIs). To address this, we propose HDMIL, a hierarchical distillation multi-instance learning framework that achieves fast and accurate classification b

Tommaso Zajac, Gloria Menegaz, Marco Pizzolato

We propose Microscopic Propagator Imaging (MPI) as a novel method to retrieve the indices of the microscopic propagator which is the probability density function of water displacements due to diffusion within the nervous tissue microstructures. Unlike the Ensemble Average Propagator indices or the Diffusion Tensor Imaging metrics, MPI indices are independent

Tristan Robert, Younes Zine

We study a stochastic complex Ginzburg-Landau equation (SCGL) on compact surfaces with magnetic Laplacian and polynomial nonlinearity, forced by a space-time white noise. After renormalizing the equation in a suitable manner, we show that the dynamics is locally well-posed. Moreover, we prove deterministic global well-posedness for the defocusing SCGL in the

Adtian Atienza, Gouthamaan Manimaran, Jakob E. Bardram, Sadasivan Puthusserypady

Wearable sensing devices, such as Electrocardiogram (ECG) heart-rate monitors, will play a crucial role in the future of digital health. This continuous monitoring leads to massive unlabeled data, incentivizing the development of unsupervised learning frameworks. While Masked Data Modelling (MDM) techniques have enjoyed wide use, their direct application to

Alessandro Riccardi, Luca Laurenti, Bart De Schutter

Partitioning is a fundamental challenge for non-centralized control of large-scale systems, such as hierarchical, decentralized, distributed, and coalitional strategies. The problem consists of finding a decomposition of a network of dynamical systems into system units for which local controllers can be designed. Unfortunately, despite its critical role, a g

Parthasarathi Sahu, Sashikanta Barik, Koushik Ghosh, Hemachander Subramanian

Genome length varies widely among organisms, from compact genomes of prokaryotes to vast and complex genomes of eukaryotes. In this study, we theoretically identify the evolutionary pressures that may have driven this divergence in genome length. We use a parameter-free model to study genome length evolution under selection pressure to minimize replication t

Priya Singh, Manasa Manasa, Mohammad Azam, Tatiana Zajarniuk

FeSe(11) family has a simple crystal structure belonging to iron-based superconductors (FBS) and has many stable phases including hexagonal and tetragonal structures, but only the tetragonal phase exhibits the superconductivity. In this study, we have investigated the effects of chemical pressure induced by As-doping at Se-sites in the FeSe system by prepari

Tobias Kehrer, Christoph Bruder, Parvinder Solanki

Quantum synchronization has been a subject of intensive research in the last decade. In this work, we propose a quantum Li\'enard system whose classical equivalent features two limit cycles to one of which the system will converge. In the quantum case, both limit cycles coexist in a single steady state. Each of these limit cycles localizes to a distinct phas

Federico Librino, Paolo Santi

The challenging applications envisioned for the future Internet of Things networks are making it urgent to develop fast and scalable resource allocation algorithms able to meet the stringent reliability and latency constraints typical of the Ultra Reliable, Low Latency Communications (URLLC). However, there is an inherent tradeoff between complexity and perf

Yunfan Lu, Xiaogang Xu, Hao Lu, Yanlin Qian

Event cameras, with a high dynamic range exceeding $120dB$, significantly outperform traditional embedded cameras, robustly recording detailed changing information under various lighting conditions, including both low- and high-light situations. However, recent research on utilizing event data has primarily focused on low-light image enhancement, neglecting

Katherine Ormerod, Joris Witstok, Renske Smit, Anna de Graaff

Dust is a fundamental component of the interstellar medium within galaxies, as dust grains are highly efficient absorbers of ultraviolet (UV) and optical photons. Accurately quantifying this obscuration is crucial for interpreting galaxy spectral energy distributions (SEDs). The extinction curves in the Milky Way (MW) and Large Magellanic Cloud exhibit a str

Chanaprom Cholsuk, Asli Cakan, Volker Deckert, Sujin Suwanna

Point defects in solid-state quantum systems are vital for enabling single-photon emission at specific wavelengths, making their precise identification essential for advancing applications in quantum technologies. However, pinpointing the microscopic origins of these defects remains a challenge. In this work, we propose Raman spectroscopy as a robust strateg

Theofanis P. Raptis, Andrea Passarella, Marco Conti

Wireless edge networks in smart industrial environments increasingly operate using advanced sensors and autonomous machines interacting with each other and generating huge amounts of data. Those huge amounts of data are bound to make data management (e.g., for processing, storing, computing) a big challenge. Current data management approaches, relying primar

Filip Tronarp

In this article, the two filter formula is re-examined in the setting of partially observed Gauss--Markov models. It is traditionally formulated as a filter running backward in time, where the Gaussian density is parametrized in ``information form''. However, the quantity in the backward recursion is strictly speaking not a distribution, but a likelihood. Ta

Junjian Mi, Sheng Xu, Shuxiang Li, Chenxi Jiang

We performed the detailed magnetotransport measurements and first principle calculations to study the electronic properties of the transition metal dipnictides ZrAs2, which is a topological nodal-line semimetal. Extremely large unsaturated magnetoresistance (MR) which is up to 1.9 * 10^4 % at 2 K and 14 T was observed with magnetic field along the c-axis. Th

Krzysztof Podlaski, Michal Beczkowski, Katharina Simbeck, Katrin Dziergwa

Soft and future skills are in high demand in the modern job market. These skills are required for both technical and non-technical people. It is difficult to teach these competencies in a classical academic environment. The paper presents a possible approach to teaching in soft and future skills in a short, intensive joint project. In our case, it is a proje

S. P. Bommanaboyena, C. Müller, M. Jarošová, K. Wolk

The recent discovery of altermagnetism has sparked renewed interest in the growth of epitaxial films of the NiAs-phase polymorph of CrSb. This paper describes the magnetron sputtering-based fabrication and characterization of high-quality single crystalline CrSb(0001) thin films supported by an isostructural nonmagnetic PtSb buffer. X-ray diffraction and sca

Mattia Birti, Andrea Maurino, Francesco Osborne

The integration of Environmental, Social, and Governance (ESG) factors into corporate decision-making is a fundamental aspect of sustainable finance. However, ensuring that business practices align with evolving regulatory frameworks remains a persistent challenge. AI-driven solutions for automatically assessing the alignment of sustainability reports and no

Mohammad Nasimuzzaman Mishuk, Mouli Hazra, Anand Kumar, Peter Dannberg

Single photon emitters in hexagonal boron nitride are based on fluorescent point-like defects. These defects typically have exceptional photophysical properties and therefore been the focus of extensive research due to their potential to advance photonic quantum technologies. However, achieving scalable integration of these emitters to arbitrary platforms wi

Charles Dawson, Van Tran, Max Z. Li, Chuchu Fan

Increased deployment of autonomous systems in fields like transportation and robotics have seen a corresponding increase in safety-critical failures. These failures can be difficult to model and debug due to the relative lack of data: compared to tens of thousands of examples from normal operations, we may have only seconds of data leading up to the failure.

Marina D'Amato, Jeroen van der Laak, Francesco Ciompi

Accurate tumor detection in digital pathology whole-slide images (WSIs) is crucial for cancer diagnosis and treatment planning. Multiple Instance Learning (MIL) has emerged as a widely used approach for weakly-supervised tumor detection with large-scale data without the need for manual annotations. However, traditional MIL methods often depend on classificat

Lie Meng Pang, Hisao Ishibuchi

When we manually design an evolutionary optimization algorithm, we implicitly or explicitly assume a set of target optimization problems. In the case of automated algorithm design, target optimization problems are usually explicitly shown. Recently, the use of large language models (LLMs) for the design of evolutionary multi-objective optimization (EMO) algo

Angelo Ziletti, Leonardo D'Ambrosi

Clinical cohort definition is crucial for patient recruitment and observational studies, yet translating inclusion/exclusion criteria into SQL queries remains challenging and manual. We present an automated system utilizing large language models that combines criteria parsing, two-level retrieval augmented generation with specialized knowledge bases, medical

Youngjin Yoo, Bogdan Georgescu, Yanbo Zhang, Sasa Grbic

Recent advancements in AI and medical imaging offer transformative potential in emergency head CT interpretation for reducing assessment times and improving accuracy in the face of an increasing request of such scans and a global shortage in radiologists. This study introduces a 3D foundation model for detecting diverse neuro-trauma findings with high accura

Max van Haren, Lennart Blanken, Tom Oomen

The increasing demands for high accuracy in mechatronic systems necessitate the incorporation of parameter variations in feedforward control. The aim of this paper is to develop a data-driven approach for direct learning of parameter-varying feedforward control to increase tracking performance. The developed approach is based on kernel-regularized function e

Philip Döbler, David Álvarez, Lucas J. Menger, Thomas Lippert

The OmpSs-2 programming model is used in HPC programs to parallelize code and offload code to accelerators. In this work, we extend the offloading capability to quantum computers. We explain the necessary changes to the Clang compiler and the Nanos6 runtime, which are both part of OmpSs-2. In addition, we develop a simulator that simulates a quantum computer

Geraldo Botelho, Luis Alberto Garcia, Vinícius C. C. Miranda

Let $E_1, \ldots, E_m$ be (non necessarily Archimedean) Riesz spaces, let $F$ be an Archimedean Riesz space and let $A \colon E_1 \times \cdots \times E_m \to F$ be a regular disjointness preserving $m$-linear operator. We prove that all Arens extensions of $A$ are disjointness preserving if either $A$ has finite lattice rank or the spaces are Banach lattice

Hamed Taghavian, Jens Sjölund

Finding a positive state-space realization with the minimum dimension for a given transfer function is an open problem in control theory. In this paper, we focus on positive realizations in Markov form and propose a linear programming approach that computes them with a minimum dimension. Such minimum dimension of positive Markov realizations is an upper boun

Christopher Leet, Aidan Sciortino, Sven Koenig

A modern smart factory runs a manufacturing procedure using a collection of programmable machines. Typically, materials are ferried between these machines using a team of mobile robots. To embed a manufacturing procedure in a smart factory, a factory operator must a) assign its processes to the smart factory's machines and b) determine how agents should carr

Yukuan Yang, Xucheng Lu, Zhili Zhang, Zepeng Wu

Generating adversarial safety-critical scenarios is a pivotal method for testing autonomous driving systems, as it identifies potential weaknesses and enhances system robustness and reliability. However, existing approaches predominantly emphasize unrestricted collision scenarios, prompting non-player character (NPC) vehicles to attack the ego vehicle indisc

Ganzhao Yuan

This paper proposes {\sf AEPG-SPIDER}, an Adaptive Extrapolated Proximal Gradient (AEPG) method with variance reduction for minimizing composite nonconvex finite-sum functions. It integrates three acceleration techniques: adaptive stepsizes, Nesterov's extrapolation, and the recursive stochastic path-integrated estimator SPIDER. Unlike existing methods t

Jennifer Hu, Felix Sosa, Tomer Ullman

The question of whether large language models (LLMs) possess Theory of Mind (ToM) -- often defined as the ability to reason about others' mental states -- has sparked significant scientific and public interest. However, the evidence as to whether LLMs possess ToM is mixed, and the recent growth in evaluations has not resulted in a convergence. Here, we take

Christof Puhle

In this paper, we present a deep-learning method to filter out effects such as ambient noise, reflections, or source directivity from microphone array data represented as cross-spectral matrices. Specifically, we focus on a generative adversarial network (GAN) architecture designed to transform fixed-size cross-spectral matrices. Theses models were trained u

Kai Ma, Lin-Yun He

Searching for dark matter at high-energy colliders and direct detection experiments can effectively cover nearly the entire mass range from the MeV to the TeV scale. In this paper, we focus on four-fermion contact interactions formulated within the framework of Effective Field Theory. Specifically, we present a detailed analysis of mono-lepton production at

Sajjan Sheoran, Pratibha Dev

The anomalous Hall effect (AHE) is an efficient tool for detecting the N\'eel vector in collinear compensated magnets with spin-split bands, known as altermagnets (AMs). Here, we establish design principles for obtaining non-zero anomalous Hall conductivity in the recently proposed two-dimensional (2D) AMs using spin and magnetic group symmetry analysis. We

Max van Haren, Roy S. Smith, Tom Oomen

Models that contain intersample behavior are important for control design of systems with slow-rate outputs. The aim of this paper is to develop a system identification technique for fast-rate models of systems where only slow-rate output measurements are available, e.g., vision-in-the-loop systems. In this paper, the intersample response is estimated by ide

Jingqiu Zhou, Lue Fan, Linjiang Huang, Xiaoyu Shi

Driving scene reconstruction and rendering have advanced significantly using the 3D Gaussian Splatting. However, most prior research has focused on the rendering quality along a pre-recorded vehicle path and struggles to generalize to out-of-path viewpoints, which is caused by the lack of high-quality supervision in those out-of-path views. To address this i

Francesco Bertolotti, Luca Mari

Predicting the future trajectory of complex and rapidly evolving systems remains a significant challenge, particularly in domains where data is scarce or unreliable. This study introduces a novel approach to qualitative forecasting by leveraging Large Language Models to conduct Delphi studies. The methodology was applied to explore the future evolution of Ge

Jiwei Wang, Simone Baldi, Henk J. van Waarde

The goal of model reference adaptive control (MRAC) is to ensure that the trajectories of an unknown dynamical system track those of a given reference model. This is done by means of a feedback controller that adaptively changes its gains using data collected online from the closed-loop system. One of the approaches to solve the MRAC problem is to impose con

James Hotchkiss, David Stapleton

We introduce a new stable birational invariant, which takes the form of a functor sending a degenerating variety to the homotopy type of a chain complex. Our invariant is a categorification of the motivic volume of Nicaise and Shinder. From the class of the chain complex in a Grothendieck group, we obtain a motivic obstruction to retract rationality, valued

Maria Chiara Angelini, Saverio Palazzi, Giorgio Parisi, Tommaso Rizzo

We analyze the spin glass transition in a field in finite dimension $D$ below the upper critical dimension directly at zero temperature using a recently introduced perturbative loop expansion around the Bethe lattice solution. The expansion is generated by the so-called $M$-layer construction, and it has $1/M$ as the associated small parameter. Computing ana

ATLAS Collaboration

This paper presents a first measurement of the cross-section for the charged-current Drell-Yan process $pp\rightarrow W^{\pm} \rightarrow \ell^{\pm} \nu$ above the resonance region, where $\ell$ is an electron or muon. The measurement is performed for transverse masses, $m_{\text{T}}^{\text{W}}$, between 200 GeV and 5000 GeV, using a sample of 140 fb$^{-1}$

Hansi Yang, Qi Zhang, Wei Jiang, Jianguo Li

Large language models (LLMs) have shown impressive abilities in answering questions across various domains, but they often encounter hallucination issues on questions that require professional and up-to-date knowledge. To address this limitation, retrieval-augmented generation (RAG) techniques have been proposed, which retrieve relevant information from exte

Özgün Turgut, Felix S. Bott, Markus Ploner, Daniel Rueckert

The success of foundation models in natural language processing and computer vision has motivated similar approaches in time series analysis. While foundational time series models have proven beneficial on a variety of tasks, their effectiveness in medical applications with limited data remains underexplored. In this work, we investigate this question in the

Jing-Yuan Chang

Badminton, known for having the fastest ball speeds among all sports, presents significant challenges to the field of computer vision, including player identification, court line detection, shuttlecock trajectory tracking, and player stroke-type classification. In this paper, we introduce a novel video clipping strategy to extract frames of each player's rac

Javier Cembrano, Golnoosh Shahkarami

In the metric distortion problem, a set of voters and candidates lie in a common metric space, and a committee of $k$ candidates must be elected. The objective is to minimize a social cost, defined as a function of the distances between voters and their chosen representatives, while the voting rule only has access to ordinal preferences. The distortion of a

Ilya A. Kudryavtsev

Diffraction is a phenomenon, discussed for centuries from various points of view. The very simple principle, proposed by Huygens [1] and then modified by Fresnel[2], Stokes [3] and Kirchoff [4], allows us to make calculations, substituting an incident wave by the multitude of waves, radiated by the number of secondary sources with regard for interference. Be

Yuichiro Yoshida, Luca Erhart, Takuma Murokoshi, Rika Nakagawa

We propose using the wave function generated by the quantum selected configuration interaction (QSCI) method as the trial wave function in phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC). In the QSCI framework, electronic configurations are sampled from the quantum state realized on a quantum computer. These configurations serve as basis states for

Federico Librino, Paolo Santi

The current development trend of wireless communications aims at coping with the very stringent reliability and latency requirements posed by several emerging Internet of Things (IoT) application scenarios. Since the problem of realizing Ultra Reliable Low-Latency Communications (URLLC) is becoming more and more important, it has attracted the attention of r

Yifei Xia, Suhan Ling, Fangcheng Fu, Yujie Wang

Generating high-fidelity long videos with Diffusion Transformers (DiTs) is often hindered by significant latency, primarily due to the computational demands of attention mechanisms. For instance, generating an 8-second 720p video (110K tokens) with HunyuanVideo takes about 600 PFLOPs, with around 500 PFLOPs consumed by attention computations. To address this

Titus Pinta

We introduce a new framework for analyzing (Quasi-}Newton type methods applied to non-smooth optimization problems. The source of randomness comes from the evaluation of the (approximation) of the Hessian. We derive, using a variant of Chernoff bounds for stopping times, expectation and probability bounds for the random variable representing the number of it

Sabine Muzellec, Andrea Alamia, Thomas Serre, Rufin VanRullen

Neural synchrony is hypothesized to play a crucial role in how the brain organizes visual scenes into structured representations, enabling the robust encoding of multiple objects within a scene. However, current deep learning models often struggle with object binding, limiting their ability to represent multiple objects effectively. Inspired by neuroscience,