March 2025 arXiv papers — page 214

Arnau Diebra, Santiago Llorens, Emili Bagan, Gael Sentís

Quantum state exclusion is the task of determining which states from a given set a system was not prepared in. We provide a complete solution to optimal quantum state exclusion for arbitrary sets of pure states generated by finite groups, establishing necessary and sufficient conditions for perfect (zero-error conclusive) exclusion. When perfect exclusion is

Yue Hu, Xue Bai, Baoqi Shi, Jiahao Sun

Integrated photonics has revolutionized optical communication, sensing, and computation, offering miniaturized and lightweight solutions for spacecraft with limited size and payload. Novel chip-scale instruments based on ultralow-loss integrated photonic platforms, including lasers, frequency combs and atomic traps, have been developed for space applications

Florian Jaehn, Niklas Jost

Hub Covering Problems are a subclass of Hub Location Problems. The objective is to select a set of hubs that enable paths between given origin-destination delivery tasks, while minimizing the total setup cost of the hubs. Two constraints must be satisfied: each path must include one or two hubs, and depending on the problem variant, the total path length or

Hazel Olsen, Pierre Devillard, Gianni Aupetit-Diallo, Patrizia Vignolo

We investigate the Lieb-Liniger model of one-dimensional bosons subjected to periodic kicks. In both the non-interacting and strongly interacting limits, the system undergoes dynamical localization, leading to energy saturation at long times. However, for finite interactions, we reveal an interaction-driven transition from an insulating to a metallic phase a

Meng Zhang, Zhihui Li, Zhibin Yu

In the context of global warming, even relatively cooler countries like the UK are experiencing a rise in cooling demand, particularly in southern regions such as London. This growing demand, especially during the summer months, presents significant challenges for energy management systems. Accurately predicting cooling demand in urban domestic buildings is

Tanveer Khan, Fahad Sohrab, Antonis Michalas, Moncef Gabbouj

The COVID-19 pandemic has profoundly affected the normal course of life -- from lock-downs and virtual meetings to the unprecedentedly swift creation of vaccines. To halt the COVID-19 pandemic, the world has started preparing for the global vaccine roll-out. In an effort to navigate the immense volume of information about COVID-19, the public has turned to s

Pavlo Sai, Vadym V. Korotyeyev, Dmytro B. But, Maksym Dub

We present a novel approach to enhance THz nonlinearity by the resonant excitation of two-dimensional plasmons in grating-gate plasmonic crystals. Using a high-electric-field THz pump-THz probe technique, we investigate the nonlinear interaction of spectrally narrow THz pulses with plasmon oscillations in a two-dimensional electron gas on an AlGaN/GaN interf

Yubing Du, Guoshuai Du, Zhixi Zhu, Jiaohui Yan

The extensive applications of cubic silicon in flexible transistors and infrared detectors are much hindered by its intrinsic properties. Metastable silicon phases, such as Si-III, IV and XII prepared using extreme pressure method, provide a unique "genetic bank" with diverse structures and exotic characteristics, however, exploration on their inherent physi

Janmejoy Sarkar, Soumya Roy, A N Ramaprakash, Rushikesh Deogaonkar

The Solar Ultraviolet Imaging Telescope (SUIT) is one of the seven payloads on board Aditya-L1 mission of the Indian Space Research Organization (ISRO). SUIT provides full and partial disk images of the Sun in the 200-400 nm wavelength range. This would help us probe the solar atmosphere at different heights and understand the mass and energy transfer proces

Marin Matsumoto, Ai Nozaki, Hideki Takase, Masato Oguchi

Fully homomorphic encryption (FHE) is a technique that enables statistical processing and machine learning while protecting data, including sensitive information collected by single board computers (SBCs), on a cloud server. Among FHE schemes, the TFHE scheme is capable of homomorphic NAND operations and, unlike other FHE schemes, can perform various operati

Zeqing Wang, Han Fang, Yihong Xu, Yutong Ban

Minimally invasive procedures have been advanced rapidly by the robotic laparoscopic surgery. The latter greatly assists surgeons in sophisticated and precise operations with reduced invasiveness. Nevertheless, it is still safety critical to be aware of even the least tissue deformation during instrument-tissue interactions, especially in 3D space. To addres

Nikolaus Huber, Susanne Graf, Philipp Rümmer, Wang Yi

This paper introduces the Mimosa language, a programming language for the design and implementation of asynchronous reactive systems, describing them as a collection of time-triggered processes which communicate through FIFO buffers. Syntactically, Mimosa builds upon the Lustre data-flow language, augmenting it with a new semantics to allow for the expressio

Stephan Felber, Bernardo Hummes Flores, Hugo Rincon Galeana

We introduce a sheaf-theoretic characterization of task solvability in general distributed computing models, unifying distinct approaches to message-passing models. We establish cellular sheaves as a natural mathematical framework for analyzing the global consistency requirements of local computations. Our main contribution is a task sheaf construction that

Deepika Raman, Nada Madkour, Evan R. Murphy, Krystal Jackson

Frontier AI models -- highly capable foundation models at the cutting edge of AI development -- may pose severe risks to public safety, human rights, economic stability, and societal value in the coming years. These risks could arise from deliberate adversarial misuse, system failures, unintended cascading effects, or simultaneous failures across multiple mo

Alejandro Almodóvar, Tobias Galla, Cristóbal López

We study a system of self-propelled, proliferating finite-size disks with game-theoretical interactions, where growth rates depend on local population composition. We analyze how these interactions influence spatial distribution, coexistence, and extinction. Three scenarios emerge: (i) stable coexistence with well-mixed distributions, (ii) bistability, where

Amir Aghabiglou, Chung San Chu, Chao Tang, Arwa Dabbech

The R2D2 Deep Neural Network (DNN) series was recently introduced for image formation in radio interferometry. It can be understood as a learned version of CLEAN, whose minor cycles are substituted with DNNs. We revisit R2D2 on the grounds of series convergence, training methodology, and DNN architecture, improving its robustness in terms of generalizability

Ying Wang, Lasha Ephremidze, Ronaldo Garcıa Reyes, Pedro Valdes-Sosa

Spectral factorization is a powerful mathematical tool with diverse applications in signal processing and beyond. The Janashia-Lagvilava method has emerged as a leading approach for matrix spectral factorization. In this paper, we extend a central equation of the method to the non-commutative case, enabling polynomial coefficients to be represented in block

Fabian Domberg, Georg Schildbach

Learning-based controllers are often purposefully kept out of real-world applications due to concerns about their safety and reliability. We explore how state-of-the-art world models in Model-Based Reinforcement Learning can be utilized beyond the training phase to ensure a deployed policy only operates within regions of the state-space it is sufficiently fa

Omkar Kokane, Adam Teman, Anushka Jha, Guru Prasath SL

Artificial intelligence necessitates adaptable hardware accelerators for efficient high-throughput million operations. We present pipelined architecture with CORDIC block for linear MAC computations and nonlinear iterative Activation Functions (AF) such as $tanh$, $sigmoid$, and $softmax$. This approach focuses on a Reconfigurable Processing Engine (RPE) bas

Giulia Meglioli, Fabio Punzo

We investigate uniqueness of solutions to certain classes of elliptic and parabolic equations posed on metric graphs. In particular, we address the linear Schr\"odinger equation with a potential, and the heat equation with a variable density. We assume suitable growth conditions on the solutions, which are related to the behaviour at infinity of the potentia

Cunchi Lv, Xiao Shi, Dong Liang, Wenting Tan

Deep Learning (DL), especially with Large Language Models (LLMs), brings benefits to various areas. However, DL training systems usually yield prominent idling GPU resources due to many factors, such as resource allocation and collective communication. To improve GPU utilization, we present SpecInF, which adopts a Speculative Inference Filling method to expl

Grzegorz Skorupko, Fotios Avgoustidis, Carlos Martín-Isla, Lidia Garrucho

The nnU-Net framework has played a crucial role in medical image segmentation and has become the gold standard in multitudes of applications targeting different diseases, organs, and modalities. However, so far it has been used primarily in a centralized approach where the collected data is stored in the same location where nnU-Net is trained. This centraliz

Rashmi R. Nayak, Kamal L. Panigrahi, Manoranjan Samal, Balbeer Singh

This work explores the (non)-integrability and chaotic dynamics of classical strings in the background of a D3-brane with a non-commutative parameter, within the framework of the AdS/CFT correspondence. Using the Polyakov action, we derive the equations of motion and constraints for pulsating strings and analyze their stability through perturbation theory. I

Jayarshi Bhattacharya, Gautam Gangopadhyay, Sunandan Gangopadhyay

This paper explores the dynamics of current and the quantum transport factor in a fermionic system with a central oscillator interacting with two fermionic reservoirs at different temperatures. We derive the master equation for the system density matrix, accounting for energy exchange between the system and the reservoirs. The current is analyzed in relation

Sheng Shang, Chenglong Zhao, Ruixin Zhang, Jianlong Jin

Palm vein recognition is an emerging biometric technology that offers enhanced security and privacy. However, acquiring sufficient palm vein data for training deep learning-based recognition models is challenging due to the high costs of data collection and privacy protection constraints. This has led to a growing interest in generating pseudo-palm vein data

Tursunali Xamidov, Mirzabek Alloqulov, Sanjar Shaymatov

The particle dynamics and the electric Penrose process for the five-dimensional weakly charged Schwarzschild black hole are studied. Firstly, the horizon structure and the effective potential for the test particle are explored. The radial profile of the effective potential is plotted for different values of the BH charge. Then, we studied energy efficiency u

Mohammad Kazemi, Tolga M. Duman

We consider binary input deletion/substitution channels, which model certain types of synchronization errors encountered in practice. Specifically, we focus on the regime of small deletion and substitution probabilities, and by extending an approach developed for the deletion-only channel, we obtain an asymptotic characterization of the channel capacity for

Petr Fulin, Veronika Gajdosova, Ivana Sloufova, Jiri Hodan

We have collected 21 different formulations of ultrahigh molecular weight polyethylene (UHMWPE), which have been employed as liners in contemporary total knee replacements (TKR). The UHMWPE liners were bought from the most important manufacturers on the orthopedic market in the Czech Republic as of 2020. The collected liners represented a broad range of both

Adrian Padellaro, Sanjaye Ramgoolam, Rak-Kyeong Seong

Character tables of finite groups and closely related commutative algebras have been investigated recently using new perspectives arising from the AdS/CFT correspondence and low-dimensional topological quantum field theories. Two important elements in these new perspectives are physically motivated definitions of quantum complexity for the algebras and a not

Xin Song, Xiaochen Li, Jinxin Hu, Hong Wen

With the rapid growth of user historical behavior data, user interest modeling has become a prominent aspect in Click-Through Rate (CTR) prediction, focusing on learning user intent representations. However, this complexity poses computational challenges, requiring a balance between model performance and acceptable response times for online services. Traditi

LiquidO Collaboration, J. Apilluelo, L. Asquith, E. F. Bannister

Light-based detectors have been widely used in fundamental research and industry since their inception in the 1930s. The energy particles deposit in these detectors is converted to optical signals via the Cherenkov and scintillation mechanisms that are then propagated through transparent media to photosensors placed typically on the detector's periphery, som

Jiamin Xing, Yong Li, Shuguan Ji

In this paper, we present an averaging method for obtaining quasi-periodic response solutions in perturbed, real analytic, quasi-periodic systems with Diophantine frequency vectors. Under the assumptions that the averaged system possesses a non-degenerate equilibrium and that the eigenvalues of its linearized matrix are pairwise distinct, we show that the or

Yiyun Zhou, Zheqi Lv, Shengyu Zhang, Jingyuan Chen

In the realm of Intelligent Tutoring System (ITS), the accurate assessment of students' knowledge states through Knowledge Tracing (KT) is crucial for personalized learning. However, due to data bias, $\textit{i.e.}$, the unbalanced distribution of question groups ($\textit{e.g.}$, concepts), conventional KT models are plagued by cognitive bias, which tends

María Pereira Martínez, Xabier Cid Vidal, Pietro Vischia

An automatic optimisation procedure is proposed for some operational parameters of a Parallel-Plate Avalanche Counter with Optical Readout, a detector designed for heavy-ion tracking and imaging. Exploiting differentiable programming and automatic differentiation, we model the reconstruction of the position of impinging 5.5 MeV alpha particles for different

Deval Mehta, Yiwen Jiang, Catherine L Jan, Mingguang He

Recent advancements in deep learning have shown significant potential for classifying retinal diseases using color fundus images. However, existing works predominantly rely exclusively on image data, lack interpretability in their diagnostic decisions, and treat medical professionals primarily as annotators for ground truth labeling. To fill this gap, we imp

Zhen Yang, Guibao Shen, Minyang Li, Liang Hou

Diffusion models have achieved remarkable progress across various visual generation tasks. However, their performance significantly declines when generating content at resolutions higher than those used during training. Although numerous methods have been proposed to enable high-resolution generation, they all suffer from inefficiency. In this paper, we prop

Thomas Kahle, Hal Schenck, Bernd Sturmfels, Maximilian Wiesmann

An arrangement of hypersurfaces in projective space is strict normal crossing (SNC) if and only if its Euler discriminant is nonzero. We study the critical loci of arbitrary Laurent monomials in the equations of the smooth hypersurfaces. The family of these loci forms an irreducible variety in the product of two projective spaces, known in algebraic statisti

Carlo Gasparetto, Filippo Paiano, Bozhidar Velichkov

We establish an epsilon-regularity theorem at points in the free boundary of almost-minimizers of the energy $\mathrm{Per}_{w}(E)=\int_{\partial^*E}w\,\mathrm{d} {\mathscr{H}}^{n-1}$, where $w$ is a weight asymptotic to $d(\cdot,\mathbb{R}^n\setminus\Omega)^a$ near $\partial\Omega$ and $a>0$. This implies that the boundaries of almost-minimizers are $C^{1,\g

Hocheol Lim, Hyein Cho, Jeonghoon Kim

Efficient CO2 capture is vital for mitigating climate change, with amine-based solvents being widely used due to their strong reactivity with CO2. However, optimizing key properties such as basicity, viscosity, and absorption capacity remains challenging, as traditional methods rely on labor-intensive experimentation and predefined chemical databases, limiti

Nikolaos Biniskos, Manuel dos Santos Dias, Stefano Agrestini, David Sviták

Altermagnets, a unique class of magnetic materials that combines features of both ferromagnets and antiferromagnets, have garnered attention for their potential in spintronics and magnonics. While the electronic properties of altermagnets have been well studied, characterizing their magnon excitations is essential for fully understanding their behavior and e

Dimitri Ognibene, Gregor Donabauer, Emily Theophilou, Cansu Koyuturk

The use of large language model (LLM)-powered chatbots, such as ChatGPT, has become popular across various domains, supporting a range of tasks and processes. However, due to the intrinsic complexity of LLMs, effective prompting is more challenging than it may seem. This highlights the need for innovative educational and support strategies that are both wide

Taiki Goto, Shunsuke Nomura, Tomohiko G. Sano

Knots across various length scales, from micro to macro-scales, such as polymers, DNA, shoelaces, and surgery, serving their unique mechanical properties. The shape of ideal knots has been extensively studied in the context of knot theory, while that of physical knots has been discussed very recently. The complex interplay of elasticity and geometry, such as

Joanna M. Urban, Michael S. Spencer, Maximilian Frenzel, Gaëlle Trippé- Allard

Metal-halide perovskites (MHPs) emerged as a family of novel semiconductors with outstanding optoelectronic properties for applications in photovoltaics and light emission. Recently, they also attract interest as promising candidates for spintronics. In materials lacking inversion symmetry, spin-orbit coupling (SOC) leads to the Rashba-Dresselhaus effect, of

T. Nakano, S. Ajimura, Y. Asano, S. Dat'e

We present prospects for the $\Theta^+$ pentaquark baryon search using the newly constructed LEPS2 facility at SPring-8. The LEPS2 detector system features significant improvements in acceptance for multi-particle final states compared to previous experiments. Our search employs two complementary strategies: direct production in the $\gamma n \to K^-\Theta^+

E. Pouliasis, A. Ruiz, I. Georgantopoulos, A. Akylas

X-rays provide a robust method in identifying AGN. However, in the high-redshift Universe, their space density is relatively low, and, in combination with the small areas covered by X-ray surveys, the selected AGN are poorly sampled. Deep optical/infrared data are essential for locating counterparts and determining redshifts. In this work, we leverage the XM

Devon Jarvis, Sebastian Lee, Clémentine Carla Juliette Dominé, Andrew M Saxe

Prior work has demonstrated a consistent tendency in neural networks engaged in continual learning tasks, wherein intermediate task similarity results in the highest levels of catastrophic interference. This phenomenon is attributed to the network's tendency to reuse learned features across tasks. However, this explanation heavily relies on the premise that

Martin Cooney, Alexey Vinel

"Magic" is referred to here and there in the robotics literature, from "magical moments" afforded by a mobile bubble machine, to "spells" intended to entertain and motivate children--but what exactly could this concept mean for designers? Here, we present (1) some theoretical discussion on how magic could inform interaction designs based on reviewing the lit

Jotin Gogoi, Mrinal Kumar Das

In this work we have realized texture zero structures of neutrino mass matrix through our study of neutrino phenomenology and dark matter. For analysing these processes, we have constructed a model in minimal inverse seesaw, ISS(2,3) by using $A_4$ discrete symmetry. The particle content of ISS(2.3) has been augmented by a scalar triplet $\eta=(\eta_1,\eta_2

Maïri Souza Oliveira, Maxime Lenormand, Sandra Luque, Nelson A. Zamora

Secondary forests now dominate tropical landscapes and play a crucial role in achieving COP15 conservation objectives. This study develops a replicable national approach to identifying and characterising forest ecosystems, with a focus on the role of secondary forests. We hypothesised that dominant tree species in the forest canopy serve as reliable indicato

A. P. Lednov

We consider the problem of damping a control system with delay, described by first-order functional-differential equations on a temporal star graph. The delay in the system is time-proportional and propagates through the internal vertex. We study the variational problem of minimizing the energy functional, taking into account the probabilities the of scenari

Daniel Abode, Pedro Maia de Sant Ana, Ramoni Adeogun, Alexander Artemenko

Subnetworks are expected to enhance wireless pervasiveness for critical applications such as wireless control of plants, however, they are interference-limited due to their extreme density. This paper proposes a goal-oriented joint power and multiple sub-bands allocation policy for interference coordination in 6G in-factory subnetworks. Current methods for i

Chang Ruan, Junzhi Wang, Chao Ou, Juan Li

The abundance ratio of $^{14}$N$/^{15}$N is, in principle, a powerful tool for tracing stellar nucleosynthesis. This work aims to measure and analyze ($^{14}$N/$^{15}$N)$\times$($^{13}$C/$^{12}$C) and $^{14}$N$/^{15}$N abundance ratios in massive star-forming regions across a range of galactocentric distances to provide constraints on galactic chemical evolu

Xingzuo Li, Kehai Chen, Yunfei Long, Xuefeng Bai

Large language model (LLM) agents typically adopt a step-by-step reasoning framework, in which they interleave the processes of thinking and acting to accomplish the given task. However, this paradigm faces a deep-rooted one-pass issue whereby each generated intermediate thought is plugged into the trajectory regardless of its correctness, which can cause ir

Katarzyna Chęć, Bartosz Uniejewski, Rafał Weron

Recent studies provide evidence that decomposing the electricity price into the long-term seasonal component (LTSC) and the remaining part, predicting both separately, and then combining their forecasts can bring significant accuracy gains in day-ahead electricity price forecasting. However, not much attention has been paid to predicting the LTSC, and the la

Hugo Thomas, Julien Hébraud, Bertrand Georgeot, Gabriel Lemarié

We investigate coherent multiple scattering effects in the random quantum kicked rotor model. By changing the starting time of the Floquet period, two new classes of models can be introduced that exhibit similar interference structures. For one of the two classes, these structures appear on top of a non-trivial background, which we describe in detail. Its or

Satoshi Tajima, Marco Dentz

The combined effect of tidal forcing and aquifer heterogeneity leads to intricate transport patterns in coastal aquifers that impact both on solute residence times and mixing dynamics. We study these patterns through detailed numerical simulations of density-dependent flow and transport in a three-dimensional heterogeneous coastal aquifer under tidal forcing

Nhat A. Nghiem

Demonstrating quantum advantage has been a pressing challenge in the field. Most claimed quantum speedups rely on a subroutine in which classical information can be accessed in a coherent quantum manner, which imposes a crucial constraint on the implementability of these quantum algorithms. It has even been shown that without such an access, the quantum comp

Andrea Cosso, Laura Perelli

In this article, we study the classical finite-horizon optimal stopping problem for multidimensional diffusions through an approach that differs from what is typically found in the literature. More specifically, we first prove a key equality for the value function from which a series of results easily follow. This equality enables us to prove that the classi

Guanyu Cui, Hanzhi Wang, Zhewei Wei

We study the problem of efficiently approximating the \textit{effective resistance} (ER) on undirected graphs, where ER is a widely used node proximity measure with applications in graph spectral sparsification, multi-class graph clustering, network robustness analysis, graph machine learning, and more. Specifically, given any nodes $s$ and $t$ in an undirec

Mehran Hosseini, Alessio Lomuscio, Nicola Paoletti

We present a framework for verifying Memoryful Neural Multi-Agent Systems (MN-MAS) against full Linear Temporal Logic (LTL) specifications. In MN-MAS, agents interact with a non-deterministic, partially observable environment. Examples of MN-MAS include multi-agent systems based on feed-forward and recurrent neural networks or state-space models. Different f

Oliver Grainge, Michael Milford, Indu Bodala, Sarvapali D. Ramchurn

Visual Place Recognition (VPR) localizes a query image by matching it against a database of geo-tagged reference images, making it essential for navigation and mapping in robotics. Although Vision Transformer (ViT) solutions deliver high accuracy, their large models often exceed the memory and compute budgets of resource-constrained platforms such as drones

Mustafa Majeed Abd Zaid, Ahmed Abed Mohammed, Putra Sumari

This study investigates the classification of aerial images depicting transmission towers, forests, farmland, and mountains. To complete the classification job, features are extracted from input photos using a Convolutional Neural Network (CNN) architecture. Then, the images are classified using Softmax. To test the model, we ran it for ten epochs using a ba

Vlaho-Josip Štironja, Luka Petrović, Juraj Peršić, Ivan Marković

Accurate ego-motion estimation is a critical component of any autonomous system. Conventional ego-motion sensors, such as cameras and LiDARs, may be compromised in adverse environmental conditions, such as fog, heavy rain, or dust. Automotive radars, known for their robustness to such conditions, present themselves as complementary sensors or a promising alt

Xin Ding, Xin Li, Haotong Qin, Zhibo Chen

Quantization and cache mechanisms are typically applied individually for efficient Diffusion Transformers (DiTs), each demonstrating notable potential for acceleration. However, the promoting effect of combining the two mechanisms on efficient generation remains under-explored. Through empirical investigation, we find that the combination of quantization and

Hangfei Ye, Chenlu Xu, Min Hu, Haifeng Dong

Optically-pumped magnetic gradiometers (OPGs) play a crucial role in applications such as magnetic anomaly detection and bio-magnetic measurements. This study classifies current OPGs into four types based on their differential modes: voltage, frequency, optical rotation, and magnetic field differential modes. We introduce the concept of inherent Common-Mode

Congbin Xu, Chengde Qian, Zhaojun Wang, Changliang Zou

As the volume of data continues to expand, it becomes increasingly common for data to be aggregated from multiple sources. Leveraging multiple sources for model training typically achieves better predictive performance on test datasets. Unsupervised multi-source domain adaptation aims to predict labels of unlabeled samples in the target domain by using label

Shaofei Cai, Zhancun Mu, Anji Liu, Yitao Liang

We aim to develop a goal specification method that is semantically clear, spatially sensitive, domain-agnostic, and intuitive for human users to guide agent interactions in 3D environments. Specifically, we propose a novel cross-view goal alignment framework that allows users to specify target objects using segmentation masks from their camera views rather t

Emese Sziklay, Tamás Jursonovics

This paper presents a summary analysis of the Least Frequently Used (LFU) and Perfect Least Frequently Used (PLFU) cache eviction algorithms on real data, transferred on Content Delivery Nettworks (CDNs), as well as on Zipf distributed samples. In light of the growing emphasis on energy efficiency in CDNs in recent years due to rising energy costs, this pape

Tonghui Li, Yuanfang Guo, Zeming Liu, Heqi Peng

Deepfake detection technologies become vital because current generative AI models can generate realistic deepfakes, which may be utilized in malicious purposes. Existing deepfake detection methods either rely on developing classification methods to better fit the distributions of the training data, or exploiting forgery synthesis mechanisms to learn a more c

Jianghao Chen, Junhong Wu, Yangyifan Xu, Jiajun Zhang

Long-context modeling has drawn more and more attention in the area of Large Language Models (LLMs). Continual training with long-context data becomes the de-facto method to equip LLMs with the ability to process long inputs. However, it still remains an open challenge to measure the quality of long-context training data. To address this issue, we propose a

Michael Björklund, Rickard Cullman, Alexander Fish

This paper introduces and studies the Ehrhart spectrum of a set $E \subseteq \mathbb{Z}^r$, defined as the set of all Ehrhart polynomials of simplices with vertices in $E$, generalizing the notion of volume spectrum. We show that for any $E \subseteq \mathbb{Z}^r$ with positive upper Banach density, there is some $n \in \mathbb{Z}^r$ such that the Ehrhart sp

Yu Zhan, Hanjing Ye, Hong Zhang

Localizing a person from a moving monocular camera is critical for Human-Robot Interaction (HRI). To estimate the 3D human position from a 2D image, existing methods either depend on the geometric assumption of a fixed camera or use a position regression model trained on datasets containing little camera ego-motion. These methods are vulnerable to severe cam

Chen-Gia Tsai, Chia-Wei Li

Groove sensations arise from rhythmic structures that evoke an urge to move in response to music. While syncopation has been extensively studied in groove perception, the neural mechanisms underlying low-frequency groove remain underexplored. This fMRI study examines the role of the mirror neuron system and associated brain regions in processing low-frequenc

Nathan D. Schiele, Olga Gadyatskaya

CONTEXT. Attack treesare a recommended threat modeling tool, but there is no established method to compare them. OBJECTIVE. We aim to establish a method to compare "real" attack trees, based on both the structure of the tree itself and the meaning of the node labels. METHOD. We define four methods of comparison (three novel and one established) and compare t

Milin Patel, Rolf Jung, Marzana Khatun

In the automobile industry, ensuring the safety of automated vehicles equipped with the Automated Driving System (ADS) is becoming a significant focus due to the increasing development and deployment of automated driving. Automated driving depends on sensing both the external and internal environments of a vehicle, utilizing perception sensors and algorithms

Abdul Basit, Nouhaila Innan, Muhammad Haider Asif, Minghao Shao

Large Language Models (LLMs) offer powerful capabilities in code generation, natural language understanding, and domain-specific reasoning. Their application to quantum software development remains limited, in part because of the lack of high-quality datasets both for LLM training and as dependable knowledge sources. To bridge this gap, we introduce \textit{

Philippe Bergault, Olivier Guéant, Hamza Bodor

This paper addresses the trade-off between internalisation and externalisation in the management of stochastic trade flows. We consider agents who must absorb flows and manage risk by deciding whether to warehouse it or hedge in the market, thereby incurring transaction costs and market impact. Unlike market makers, these agents cannot skew their quotes to a

Yujiao Yang, Jing Lian, Linhui Li

Mixture-of-Experts (MoE) enhances model performance while maintaining computational efficiency, making it well-suited for large-scale applications. Conventional mixture-of-experts (MoE) architectures suffer from suboptimal coordination dynamics, where isolated expert operations expose the model to overfitting risks. Moreover, they have not been effectively e

Lei Wang, Xin Liu, Xiaojun Chen

We consider the distributionally robust optimization (DRO) model of principal component analysis (PCA) to account for uncertainty in the underlying probability distribution. The resulting formulation leads to a nonsmooth constrained min-max optimization problem, where the ambiguity set captures the distributional uncertainty by the type-$2$ Wasserstein dista

Savitri Gallego, Uwe Oberlack, Jan Lommler, Christopher M. Karwin

The Compton Spectrometer and Imager (COSI) is a Compton telescope designed to survey the 0.2-5 MeV sky, consisting of a compact array of cross-strip germanium detectors. As part of its development, in 2016 COSI had a successful 46 day flight on board NASA's Super Pressure Balloon platform. This was a precursor to the COSI Small Explorer (COSI-SMEX) satellite

Jerzy Kaczorowski, Alberto Perelli

We present a streamlined account of a recent theorem on the classification of the $L$-functions of degree 2 and conductor 1 from the extended Selberg class. We also present a more general new result dealing with functional equations involving two Dirichlet series. Further, we correct a slip in the original proof of the above theorem, which however does not a

Sebastian Schmidt, Leonard Schenk, Leo Schwinn, Stephan Günnemann

As the data demand for deep learning models increases, active learning (AL) becomes essential to strategically select samples for labeling, which maximizes data efficiency and reduces training costs. Real-world scenarios necessitate the consideration of incomplete data knowledge within AL. Prior works address handling out-of-distribution (OOD) data, while an

Jiale Chen, Wei Wang, Chongyang Shi, Li Dong

Robust Reversible Watermarking (RRW) enables perfect recovery of cover images and watermarks in lossless channels while ensuring robust watermark extraction in lossy channels. Existing RRW methods, mostly non-deep learning-based, face complex designs, high computational costs, and poor robustness, limiting their practical use. This paper proposes Deep Robust

Victor Gaydamachenko, Christoph Kissling, Lukas Grünhaupt

Traveling-wave parametric amplifiers (TWPAs) have become an essential tool for the readout of quantum circuits and the search for dark matter. We report on the implementation of an rf-SQUID-based Josephson TWPA with an average saturation power of -84 dBm, while providing an average power gain of 20 dB from 3.5 to 8.5 GHz. This wide bandwidth is enabled by re

Mikhail Tuzhilin

We introduce new centrality measures, called ksi-centrality and normalized ksi-centrality measure the importance of a node up to the importance of its neighbors. First, we show that normalized ksi-centrality can be rewritten in terms of the Laplacian matrix such that its expression is similar to the local clustering coefficient. After that we introduce avera

Peretz Yafin, Nir Sochen, Iftach Klapp

Due to their affordable, low mass, and small dimensions, uncooled microbolometer-based thermal focal plane arrays (UC-FPAs) are useful for long-wave infrared (LWIR)imaging applications. However, in outdoor conditions typical in agricultural remote sensing, cameras based on UC-FPAs may suffer from drift in offset and gain. To tackle the persistent drift, the

Leonardo Geronzi, Aline Bel-Brunon, Antonio Martinez, Michel Rochette

Objective: we propose a procedure for calibrating 4 parameters governing the mechanical boundary conditions (BCs) of a thoracic aorta (TA) model derived from one patient with ascending aortic aneurysm. The BCs reproduce the visco-elastic structural support provided by the soft tissue and the spine and allow for the inclusion of the heart motion effect. Metho

Xuejian Guo, Zhiqiang Tian, Yuehang Wang, Siqi Li

Low-light image enhancement aims to restore the under-exposure image captured in dark scenarios. Under such scenarios, traditional frame-based cameras may fail to capture the structure and color information due to the exposure time limitation. Event cameras are bio-inspired vision sensors that respond to pixel-wise brightness changes asynchronously. Event ca

Enrique García García, Giovanni Guerrieri, Rubén Pérez Mercado, Michael Ryan Zengel

During the ESCAPE project, a pilot analysis facility was developed with a bottom-up approach, in collaboration with all the project partners. As a result, the CERN Virtual Research Environment (VRE) initiative proposes a workspace that facilitates data access from the ESCAPE Data Lake, managed by Rucio, a data management framework, and supports interactive a

Chenyu Wu, Shaoguang Zhang, Changxin Guo, Yufei Zhang

This study aims to enhance the generalizability of Reynolds-averaged Navier-Stokes (RANS) turbulence models, which are crucial for engineering applications. Classic RANS turbulence models often struggle to predict separated flows accurately. Recently, Data-driven machine learning approaches for turbulence modeling have been explored to address this issue. Ho

Junyi Wang, Mubai Du, Ye Wu, Yijie Li

Registration of diffusion MRI tractography is an essential step for analyzing group similarities and variations in the brain's white matter (WM). Streamline-based registration approaches can leverage the 3D geometric information of fiber pathways to enable spatial alignment after registration. Existing methods usually rely on the optimization of the spatial

Marcel Reginatto, Andrés Darío Bermúdez Manjarres, Sebastian Ulbricht

Descriptions of classical mechanics in Hilbert space go back to the work of Koopman and von Neumann in the 1930s. Decades later, van Hove derived a unitary representation of the group of contact transformations which recently has been used to develop a novel formulation of classical mechanics in Hilbert space. This formulation differs from the Koopman-von Ne

Antonio Capolupo, Gabriele Pisacane, Aniello Quaranta, Francesco Romeo

We report a novel neutron interferometry scheme aimed at probing the potential existence of mirror neutrons, which have been proposed as viable dark matter candidates. Our theoretical analysis demonstrates that if mirror neutrons exist, ordinary neutrons would acquire a measurable geometric phase as a result of their mixing with these mirror counterparts.

Shuhan Li, Siyu Song, Peng Lv, Shihao Wang

Recent experiments have synthesized Cs2AgBi2I9 by partially substituting Cs+ with Ag+ at the A-site of Cs3Bi2I9, resulting in enhanced charge transport properties compared to Cs3Bi2I9. However, the atomic-scale mechanisms behind this enhancement remain unclear. In this work, we investigate the carrier transport mechanisms in Cs2A'Bi2I9 (A' = Ag, Cu) using fi

Dario Stein

Two high-level "pictures" of probability theory have emerged: one that takes as central the notion of random variable, and one that focuses on distributions and probability channels (Markov kernels). While the channel-based picture has been successfully axiomatized, and widely generalized, using the notion of Markov category, the categorical semantics of the

Zhengyang Ji, Shang Gao, Li Liu, Yifan Jia

Biomedical visual question answering (VQA) has been widely studied and has demonstrated significant application value and potential in fields such as assistive medical diagnosis. Despite their success, current biomedical VQA models perform multimodal information interaction only at the model level within large language models (LLMs), leading to suboptimal mu

Zahra Mirzaiyan, Michele Girfoglio, Gianluigi Rozza

It is well known that in the computational fluid dynamics simulations related to the cardiovascular system the enforcement of outflow boundary conditions is a crucial point. In fact, they highly affect the computed flow and a wrong setup could lead to unphysical results. In this chapter we discuss the main features of two different ways for the estimation of

Sara Damavandi, Laura Berardi, Sina Abbasi

Food banks can improve food donation administration, provide real-time inventory tracking, and guarantee compliance with food safety regulations by incorporating blockchain technology. The efficiency, openness, and dependability of food bank supply chains are greatly increased by this integration, leading to more sustainable and successful operations. This s

Youri Davydov, Vladimir Rotar

We consider a limit theorem for a triangular array of point processes generated by non-identically distributed random variables, and apply the result for the analysis of the limiting behavior of the Argmaximum of independent random variables, as well as for some step processes.

Yan Chen, Wei-Wei Zhang, Tian-Xi Ren, Xiang Hao

We put forward a physical model of a uniformly accelerated Unruh-DeWitt battery and use quantum work extraction as a probe to witness the thermal nature of the Unruh effect in a high dimensional Minkowski spacetime. By means of the open quantum system approach, we investigate the maximal amount of quantum work extraction with respect to the acceleration-indu