March 2025 arXiv papers — page 75

Vinicius Hernandes, Thomas Spriggs, Saqar Khaleefah, Eliska Greplova

Neural quantum states (NQS) have emerged as a powerful tool for approximating quantum wavefunctions using deep learning. While these models achieve remarkable accuracy, understanding how they encode physical information remains an open challenge. In this work, we introduce adiabatic fine-tuning, a scheme that trains NQS across a phase diagram, leading to str

Ivan Chajda, Helmut Länger

The aim of the present paper is to extend the concept of a congruence from lattices to posets. We use an approach different from that used by the first author and V. Sn\'a\v{s}el. By using our definition we show that congruence classes are convex. If the poset in question satisfies the Ascending Chain Condition as well as the Descending Chain Condition, then

Anca-Simona Horvath, Alina Elena Voinea, Radu Arieşan

This study documents a three-week workshop with architecture students, where we designed and 3D printed various minimal surfaces using wood-based filaments, and used them as molds in which to grow mycelium. We detail the design process and the growth of the mycelium in different shapes, together with participants' experiences of working with a living materia

Abdul Ahad Mamun, Muhammad Anisuzzaman Talukder

Advanced theoretical investigations are crucial for understanding the structural growth mechanisms, optoelectronic properties, and photocatalytic activity of photoelectrodes for efficient photoelectrochemical water splitting. In this work, we conducted first-principles calculations aimed at designing $\alpha$-Fe2O3 photoelectrodes incorporating mono-dopants

Léo Meynent, Ivan Melev, Konstantin Schürholt, Göran Kauermann

The weights of neural networks (NNs) have recently gained prominence as a new data modality in machine learning, with applications ranging from accuracy and hyperparameter prediction to representation learning or weight generation. One approach to leverage NN weights involves training autoencoders (AEs), using contrastive and reconstruction losses. This allo

Heng Guo, Kun Tian, Fengxia Liu, Zhiyong Zheng

In 2002, Johnson et al. posed an open problem at the Cryptographers' Track of the RSA Conference: how to construct a secure homomorphic signature on a semigroup, rather than on a group. In this paper, we introduce, for the first time, a semigroup-homomorphic signature scheme. Under certain conditions, we prove that the security of this scheme is based on the

Brihi Joshi, Sriram Venkatapathy, Mohit Bansal, Nanyun Peng

Evaluating creative text such as human-written stories using language models has always been a challenging task -- owing to the subjectivity of multi-annotator ratings. To mimic the thinking process of humans, chain of thought (CoT) generates free-text explanations that help guide a model's predictions and Self-Consistency (SC) marginalizes predictions over

Yuganthi R. Liyanage, Gerardo Chowell, Gleb Pogudin, Necibe Tuncer

Phenomenological models are highly effective tools for forecasting disease dynamics using real world data, particularly in scenarios where detailed knowledge of disease mechanisms is limited. However, their reliability depends on the model parameters' structural and practical identifiability. In this study, we systematically analyze the identifiability of si

Zhaohua Tian, Qi Liu, Yu Tian, Ying Gu

By applying phase modulation across different frequencies, metasurfaces possess the ability to manipulate the temporal dimension of photons at the femtosecond scale. However, there remains a fundamental challenge to shape the single wavepacket at the nanosecond scale by using of metasurfaces. Here, we propose that the single photon temporal shape can be conv

Morgan MacLeod, Abraham Loeb

Heartbeat (HB) stars exhibit pulses in their light curves once per orbit due to ellipsoidal distortions from strong tides at periapse. We analyze the population of HB stars in the Magellanic Clouds captured by the OGLE survey, and provide broadband spectral energy distribution fitting to estimate physical properties of the HB stars. The HB stars span a wide

Siyuan Yang, Shilin Lu, Shizheng Wang, Meng Hwa Er

This paper explores the promising interplay between spiking neural networks (SNNs) and event-based cameras for privacy-preserving human action recognition (HAR). The unique feature of event cameras in capturing only the outlines of motion, combined with SNNs' proficiency in processing spatiotemporal data through spikes, establishes a highly synergistic compa

Thomas Willwacher

We show that a smaller version of the Kontsevich graph complex spanned by triconnected graphs is quasi-isomorphic to the full Kontsevich graph complex.

Anibal M. Medina-Mardones, Ling Zhou

We establish the foundations of the theory of persistent cohomology operations, derive decomposition formulas for wedge sums and products, and prove their Gromov-Hausdorff stability. We use these results to construct pairs of Riemannian pseudomanifolds for which the Gromov-Hausdorff estimates derived from persistent cohomology operations are strictly sharper

Omar Hussein, Yuri Mishin

We propose a model of a polycrystalline alloy combining the Potts model for grain orientations with a lattice-gas model for solute thermodynamics and diffusion. The alloy evolution with this model is implemented by kinetic Monte Carlo simulations with nonlinear transition barriers between microstates. The model is applied to investigate the long-standing que

Jonas Biechteler, Connor Heimig, Thomas Weber, Dmytro Gryb

Van der Waals (vdW) materials have garnered growing interest for use as nanophotonic building blocks that offer precise control over light-matter interaction at the nanoscale, such as optical metasurfaces hosting sharp quasi-bound states in the continuum resonances. However, traditional fabrication strategies often rely on lift-off processes, which inherentl

José Edson Sampaio

Budur, Fernandes de Bobadilla, Le and Nguyen (2022) conjectured that if two germs of holomorphic functions are topologically equivalent, then the Milnor fibres of their initial forms are homotopy equivalent. In this note, we give affirmative answers to this conjecture in the case of plane curves. We show also that a positive answer to this conjecture implies

John Joon Young Chung, Vishakh Padmakumar, Melissa Roemmele, Yuqian Sun

As creative writing tasks do not have singular correct answers, large language models (LLMs) trained to perform these tasks should be able to generate diverse valid outputs. However, LLM post-training often focuses on improving generation quality but neglects to facilitate output diversity. Hence, in creative writing generation, we investigate post-training

Chan Kim, Seung-Woo Seo, Seong-Woo Kim

Deep Reinforcement Learning (DRL) has demonstrated strong performance in robotic control but remains susceptible to out-of-distribution (OOD) states, often resulting in unreliable actions and task failure. While previous methods have focused on minimizing or preventing OOD occurrences, they largely neglect recovery once an agent encounters such states. Altho

Tobias F. Maier, Hans Peter Büchler, Nicolai Lang

The bottom-up design of strongly interacting quantum materials with prescribed ground state properties is a highly nontrivial task, especially if only simple constituents with realistic two-body interactions are available on the microscopic level. Here we study two- and three-dimensional structures of two-level systems that interact via a simple blockade pot

Jonas Mirlach, Lei Wan, Andreas Wiedholz, Hannan Ejaz Keen

In autonomous driving, the integration of roadside perception systems is essential for overcoming occlusion challenges and enhancing the safety of Vulnerable Road Users(VRUs). While LiDAR and visual (RGB) sensors are commonly used, thermal imaging remains underrepresented in datasets, despite its acknowledged advantages for VRU detection in extreme lighting

Jae-Ho Han, SungBin Lee

The Van Hove singularities (VHSs), where the electronic density of states diverges due to saddle points in the band structure, play a crucial role in enhancing electronic correlations and driving various instabilities. In particular, VHS-induced superconductivity has earned significant attention due to its potential to achieve high transition temperatures an

Pablo Andújar Guerrero, Pantelis E. Eleftheriou, Rosario Mennuni

We analyse domination between invariant types in o-minimal expansions of ordered groups, showing that the domination poset decomposes as the direct product of two posets: the domination poset of an o-minimal expansion of a real closed field, and one derived from a linear o-minimal structure. We prove that if the Morley product is well-defined on the former p

Saray Bakker, Rodrigo Pérez-Dattari, Cosimo Della Santina, Wendelin Böhmer

Using the language of dynamical systems, Imitation learning (IL) provides an intuitive and effective way of teaching stable task-space motions to robots with goal convergence. Yet, IL techniques are affected by serious limitations when it comes to ensuring safety and fulfillment of physical constraints. With this work, we solve this challenge via TamedPUMA,

Haoyu Shang, Jiawei Chen, Rongzhe Hu, Xin Zhen

We reveal nuclear many-body effects on short range correlations by ab initio no-core shell model calculations of the scaling factor a2. The factor a2 characterizes the abundance of SRC pairs and is linearly related to the EMC effect. Our study employs the fifth-order N4LO chiral nuclear force without softening, enabling to distinguish the influences of nucle

Jade Preston, William Basener

Hyperspectral unmixing is the analytical process of determining the pure materials and estimating the proportions of such materials composed within an observed mixed pixel spectrum. We can unmix mixed pixel spectra using linear and nonlinear mixture models. Ordinary least squares (OLS) regression serves as the foundation for many linear mixture models employ

Théo Bodrito, Olivier Flasseur, Julien Mairal, Jean Ponce

The search for exoplanets is an active field in astronomy, with direct imaging as one of the most challenging methods due to faint exoplanet signals buried within stronger residual starlight. Successful detection requires advanced image processing to separate the exoplanet signal from this nuisance component. This paper presents a novel statistical model tha

Matteo Pozzi, Jacopo Marconi, Shobhit Jain, Mingwu Li

This work presents an optimization framework for tailoring the nonlinear dynamic response of lightly damped mechanical systems using Spectral Submanifold (SSM) reduction. We derive the SSM-based backbone curve and its sensitivity with respect to parameters up to arbitrary polynomial orders, enabling efficient and accurate optimization of the nonlinear freque

Luca Rossetto, Werner Bailer, Duc-Tien Dang-Nguyen, Graham Healy

Egocentric video has seen increased interest in recent years, as it is used in a range of areas. However, most existing datasets are limited to a single perspective. In this paper, we present the CASTLE 2024 dataset, a multimodal collection containing ego- and exo-centric (i.e., first- and third-person perspective) video and audio from 15 time-aligned source

Neha Kuntewar, Jayalal Sarma

Given a circuit $C : \{0,1\}^n \to \{0,1\}^m$ from a circuit class $F$, with $m > n$, finding a $y \in \{0,1\}^m$ such that $\forall x \in \{0,1\}^n$, $C(x) \ne y$, is the range avoidance problem (denoted by $F$-$avoid$). Deterministic polynomial time algorithms (even with access to $NP$ oracles) solving this problem is known to imply explicit constructions

Vittorio Pagni, Sigurd Huber, Michael Epping, Michael Felderer

We present an improved version of a quantum amplitude encoding scheme that encodes the $N$ entries of a unit classical vector $\vec{v}=(v_1,..,v_N)$ into the amplitudes of a quantum state. Our approach has a quadratic speed-up with respect to the original one. We also describe several generalizations, including to complex entries of the input vector and a pa

Hussein Houdrouge, Babak Miraftab, Pat Morin

We give a constructive proof of the fact that the treewidth of a graph $G$ is bounded by a linear function of the separation number of $G$.

Mikhail Kiselev

During last several years, our research team worked on development of a spiking neural network (SNN) architecture, which could be used in the wide range of supervised learning classification tasks. It should work under the condition, that all participating signals (the classified object description, correct class label and SNN decision) should have spiking n

Robin Hesse, Doğukan Bağcı, Bernt Schiele, Simone Schaub-Meyer

Deep learning has become an essential part of computer vision, with deep neural networks (DNNs) excelling in predictive performance. However, they often fall short in other critical quality dimensions, such as robustness, calibration, or fairness. While existing studies have focused on a subset of these quality dimensions, none have explored a more general f

Yuanmin Tang, Jing Yu, Keke Gai, Jiamin Zhuang

Zero-Shot Composed Image Retrieval (ZS-CIR) involves diverse tasks with a broad range of visual content manipulation intent across domain, scene, object, and attribute. The key challenge for ZS-CIR tasks is to modify a reference image according to manipulation text to accurately retrieve a target image, especially when the reference image is missing essentia

Marwan Najjar

We study the decomposition of the holographic 4d $\mathcal{N}=1$ $SU(M)$ gauge theory with in the Klebanov-Strassler set-up. In particular, we propose a consistent framework for defining a modified instanton sum and a 4-group structure for the SYM theory, derived from its $AdS/CFT$ construction. To achieve this, we analyse symmetry topological operators asso

Davide Antonio Mura, Michela Pinna, Lorenzo Putzu, Andrea Loddo

The detection of blood disorders often hinges upon the quantification of specific blood cell types. Variations in cell counts may indicate the presence of pathological conditions. Thus, the significance of developing precise automatic systems for blood cell enumeration is underscored. The investigation focuses on a novel approach termed DE-ViT. This methodol

Yizhe Liu, Tong Jia, Da Cai, Hao Wang

Transparent and specular objects are frequently encountered in daily life, factories, and laboratories. However, due to the unique optical properties, the depth information on these objects is usually incomplete and inaccurate, which poses significant challenges for downstream robotics tasks. Therefore, it is crucial to accurately restore the depth informati

Marco Usai, Andrea Loddo, Alessandra Perniciano, Maurizio Atzori

Gastric cancer ranks as the fifth most common and fourth most lethal cancer globally, with a dismal 5-year survival rate of approximately 20%. Despite extensive research on its pathobiology, the prognostic predictability remains inadequate, compounded by pathologists' high workload and potential diagnostic errors. Thus, automated, accurate histopathological

Izaskun Jimenez-Serra, Claudio Codella, Arnaud Belloche

Thanks to the advent of sensitive and broad bandwidth instrumentation, complex organic molecules (COMs) have been found in a wide variety of interstellar environments, not only in our Galaxy but also in external galaxies up to a redshift of 0.89. The detection of COMs in cold environments such as starless or prestellar cores has challenged our understanding

Eduardo Abi Jaber, Paul Gassiat, Dimitri Sotnikov

We study the martingale property and moment explosions of a signature volatility model, where the volatility process of the log-price is given by a linear form of the signature of a time-extended Brownian motion. Excluding trivial cases, we demonstrate that the price process is a true martingale if and only if the order of the linear form is odd and a correl

Shun-Cai Zhao, Yi-Meng Huang, Yi-Fan Yang, Zi-Ran Zhao

Machine learning simulations of open quantum dynamics often rely on recursive predictors that accumulate error. We develop a non-recursive convolutional neural networks (CNNs) that maps system parameters and a redundant time encoding directly to excitation-energy-transfer populations in the Fenna-Matthews-Olson complex. The encoding-modified logistic plus $\

Thomas Schnappinger, Markus Kowalewski

Polaritonic chemistry offers the possibility of modifying molecular properties and even influencing chemical reactivity through strong coupling between vibrational transitions and confined light modes in optical cavities. Despite considerable theoretical progress and due to the complexity of the coupled light-matter system, the fundamental mechanism how and

Jun Lu, Tianyi Xu, Bill Ding, David Li

In this paper, we tackle the critical challenge of compressing large language models (LLMs) to facilitate their practical deployment and broader adoption. We introduce a novel post-training compression paradigm that focuses on low-rank decomposition of LLM weights. Our analysis identifies two main challenges in this task: the variability in LLM activation di

Kaixin Du, Min Meng, Xiaoming Hu

Motivated by the increasing attention to overall social benefits in networked multi-agent systems, this paper investigates an optimization problem building on noncooperative games under high-level regulation, which can be formulated in a bilevel structure. Specifically, the low level consists of a noncooperative game, where each player competes to minimize i

D. J. Scheeres

This work analyzes the energetics of asteroid rubble piles in order to understand what asteroid morphologies should naturally arise from their formation and evolution process. In doing this, a phase diagram is developed that maps out the range of final minimum energy states that a collapsing gravitational aggregate can achieve as a function of total angular

Sovan Ghosh, Bheema Lingam Chittari

We investigated the electronic structure, Fermi surface topology and the emergence of valley imbalance in rhombohedral trilayer graphene (RTG) induced by the topological proximity and the electric fields. We show that, a strong proximity strength isolates the unperturbed low energy bands at the charge neutrality and the isolated topological bands show metall

Boyuan Zheng, Shouyi Lu, Renbo Huang, Minqing Huang

We introduce R2LDM, an innovative approach for generating dense and accurate 4D radar point clouds, guided by corresponding LiDAR point clouds. Instead of utilizing range images or bird's eye view (BEV) images, we represent both LiDAR and 4D radar point clouds using voxel features, which more effectively capture 3D shape information. Subsequently, we propose

Ruiyang Ha, Songyi Jiang, Bin Li, Bikang Pan

Conventional person re-identification (ReID) research is often limited to single-modality sensor data from static cameras, which fails to address the complexities of real-world scenarios where multi-modal signals are increasingly prevalent. For instance, consider an urban ReID system integrating stationary RGB cameras, nighttime infrared sensors, and UAVs eq

Kwan Yun, Chaelin Kim, Hangyeul Shin, Junyong Noh

Recent 3D face editing methods using masks have produced high-quality edited images by leveraging Neural Radiance Fields (NeRF). Despite their impressive performance, existing methods often provide limited user control due to the use of pre-trained segmentation masks. To utilize masks with a desired layout, an extensive training dataset is required, which is

Agastya Raj, Dan Kilper, Marco Ruffini

The gain spectrum of an Erbium-Doped Fiber Amplifier (EDFA) has a complex dependence on channel loading, pump power, and operating mode, making accurate modeling difficult to achieve. Machine Learning (ML) based modeling methods can achieve high accuracy, but they require comprehensive data collection. We present a novel ML-based Semi-Supervised, Self-Normal

Johan Edstedt, André Mateus, Alberto Jaenal

Structure-from-Motion (SfM) is the task of estimating 3D structure and camera poses from images. We define Collaborative SfM (ColabSfM) as sharing distributed SfM reconstructions. Sharing maps requires estimating a joint reference frame, which is typically referred to as registration. However, there is a lack of scalable methods and training datasets for reg

Yangjun Zeng, Yiwei Qiu, Liuchao Xu, Chenjia Gu

Renewable power-to-hydrogen (ReP2H) systems require rectifiers to supply power to electrolyzers (ELZs). Two main types of rectifiers, insulated-gate bipolar transistor rectifiers (IGBT-Rs) and thyristor rectifiers (TRs), offer distinct tradeoffs. IGBT-Rs provide flexible reactive power control but are costly, whereas TRs are more affordable with lower power

Marcin Markiewicz, Konrad Schlichtholz

We provide a generalization of the idea of unitary designs to cover finite averaging over much more general operations on quantum states. Namely, we construct finite averaging sets for averaging quantum states over arbitrary reductive Lie groups, on condition that the averaging is performed uniformly over the compact component of the group. Our construction

Xiumin Huang, Jianghui Ji, Chunhui Bao, Dongjie Tan

Given that secular perturbations in a binary system not only excite high orbital eccentricities but also alter the planetary orbital inclination, the classical Keplerian orbital model is no longer applicable for orbital retrieval. The combination of a dynamical model and observational data is essential for characterizing the configuration and planetary mass

Tiarna Lee, Esther Puyol-Antón, Bram Ruijsink, Pier-Giorgio Masci

Artificial intelligence (AI) is increasingly being used for medical imaging tasks. However, there can be biases in AI models, particularly when they are trained using imbalanced training datasets. One such example has been the strong ethnicity bias effect in cardiac magnetic resonance (CMR) image segmentation models. Although this phenomenon has been reporte

Junyuan Gao, Yongpeng Wu, Giuseppe Caire, Wei Yang

This paper considers the unsourced random access (URA) problem with a random and unknown number of active users in multiple-input multiple-output (MIMO) quasi-static Rayleigh fading channels. We derive non-asymptotic achievability bounds on the probability of incorrectly estimating the number of active users, and provide scaling laws on the gap between the e

S. K. Ivanov, S. A. Zhuravitskii, N. S. Kostyuchenko, N. N. Skryabin

Quasicrystals are unique systems that, unlike periodic structures, lack translational symmetry but exhibit long-range order dramatically enriching the system properties. While evolution of light in the bulk of photonic quasicrystals is well studied, experimental evidences of light localization near the edge of truncated photonic quasicrystal structures are p

J. M. Diederik Kruijssen, Nicholas Emmons

Artificial intelligence (AI) systems powered by large language models have become increasingly prevalent in modern society, enabling a wide range of applications through natural language interaction. As AI agents proliferate in our daily lives, their generic and uniform expressiveness presents a significant limitation to their appeal and adoption. Personalit

Mathyn van Dael, Max van Haren, Gert Witvoet, Bas Swinkels

Frequency response function (FRF) measurements are widely used in Gravitational Wave (GW) detectors, e.g., for the design of controllers, calibrating signals and diagnostic problems with system dynamics. The aim of this paper is to present GraFIT: a toolbox that enables fast, inexpensive, and accurate identification of FRF measurements for GW detectors compa

Marek Wolf, Petr Zasche, Miloslav Zejda, Martin Mašek

We present a detailed analysis of the low-mass detached eclipsing binary system BB Persei, which contains two K-type stars in a circular orbit with a short period of 0.4856 d. We used light curves from the Transiting Exoplanet Survey Satellite, which observed BB Per in five sectors, to determine its photometric properties and a precise orbital ephemeris. The

Takami Kuroda, Kyohei Kawaguchi, Masaru Shibata

We present results of numerical relativity simulations for the collapse of rotating magnetized white dwarfs (WDs) in three dimension, aiming at discussing the explosion dynamics and associate multi-messenger signals: gravitational waves (GWs), neutrinos, and electromagnetic counterparts. All WDs initiate gravitational collapse due to electron captures and th

Marco Drewes, Elena Shaposhnikova, Mikhail Shaposhnikov

The FCC program at CERN provides an attractive all-in-one solution to address many of the key questions in particle physics. While we fully support the efforts towards this ambitious path, we believe that it is important to prepare a mitigation strategy in case the program faces unexpected obstacles for geopolitical or other reasons. This approach could be b

Gensheng Pei, Tao Chen, Yujia Wang, Xinhao Cai

The CLIP model has demonstrated significant advancements in aligning visual and language modalities through large-scale pre-training on image-text pairs, enabling strong zero-shot classification and retrieval capabilities on various domains. However, CLIP's training remains computationally intensive, with high demands on both data processing and memory. To a

Agastya Raj, Zehao Wang, Frank Slyne, Tingjun Chen

We introduce a ML-based architecture for network operators to detect impairments from specific OSaaS users while blind to the users' internal spectrum details. Experimental studies with three OSaaS users demonstrate the model's capability to accurately classify the source of impairments, achieving classification accuracy of 94.2%.

Youqing Hua, Shuai Liu, Yiguang Hong, Wei Ren

The dual challenges of prohibitive communication overhead and the impracticality of gradient computation due to data privacy or black-box constraints in distributed systems motivate this work on communication-constrained gradient-free optimization. We propose a stochastic distributed zeroth-order algorithm (Com-DSZO) requiring only two function evaluations p

Saika Wong, Zhentao Chen, Mi Pan, Miroslaw J. Skibniewski

Psychophysiological methods present a promising approach to fostering enhanced mutual communication and collaboration between human workers and robots. Despite their potential, there is still limited understanding of how to effectively integrate psychophysiological methods to improve human-robot collaboration (HRC) in construction. This paper addresses this

Stephan Lachnit

Qualifying new detectors in test beam environments presents a challenging setting that requires stable operation of diverse devices, often employing multiple data acquisition systems. Changes to these setups are frequent, such as using different reference detectors depending on the facility. Managing this complexity necessitates a system capable of controlli

Vincent C. Müller

Data mining is not an invasion of privacy because access to data is only by machines, not by people: this is the argument that is investigated here. The current importance of this problem is developed in a case study of data mining in the USA for counterterrorism and other surveillance purposes. After a clarification of the relevant nature of privacy, it is

Victor Besnier, Mickael Chen, David Hurych, Eduardo Valle

Masked Generative Image Transformers (MaskGIT) have emerged as a scalable and efficient image generation framework, able to deliver high-quality visuals with low inference costs. However, MaskGIT's token unmasking scheduler, an essential component of the framework, has not received the attention it deserves. We analyze the sampling objective in MaskGIT, base

Tiziana Trombetti

The peculiar motion of an observer relative to an ideal reference frame at rest with respect to the cosmic background produces boosting effects which modify and transfer at higher multipoles the frequency spectrum of the isotropic background. To mitigate the computational effort needed for accurate theoretical predictions, analytical solutions of a linear sy

Vittorio Pippi, Fabio Quattrini, Silvia Cascianelli, Alessio Tonioni

Styled Handwritten Text Generation (HTG) has recently received attention from the computer vision and document analysis communities, which have developed several solutions, either GAN- or diffusion-based, that achieved promising results. Nonetheless, these strategies fail to generalize to novel styles and have technical constraints, particularly in terms of

Jonas Wallat, Abdelrahman Abdallah, Adam Jatowt, Avishek Anand

Large Language Models (LLMs) encapsulate a surprising amount of factual world knowledge. However, their performance on temporal questions and historical knowledge is limited because they often cannot understand temporal scope and orientation or neglect the temporal aspect altogether. In this study, we aim to measure precisely how robust LLMs are for question

Agastya Raj, Zehao Wang, Frank Slyne, Tingjun Chen

We implement a ML-based attention framework with component-specific decoders, improving optical power spectrum prediction in multi-span networks. By reducing the need for in-depth training on each component, the framework can be scaled to multi-span topologies with minimal data collection, making it suitable for brown-field scenarios.

Pablo Garcia-Fernandez, Lorenzo Vaquero, Mingxuan Liu, Feng Xue

Open-vocabulary object detection (OvOD) is set to revolutionize security screening by enabling systems to recognize any item in X-ray scans. However, developing effective OvOD models for X-ray imaging presents unique challenges due to data scarcity and the modality gap that prevents direct adoption of RGB-based solutions. To overcome these limitations, we pr

Daniel M. Jimenez-Gutierrez, Mehrdad Hassanzadeh, Aris Anagnostopoulos, Ioannis Chatzigiannakis

Federated learning (FL) allows collaborative machine learning (ML) model training among decentralized clients' information, ensuring data privacy. The decentralized nature of FL deals with non-independent and identically distributed (non-IID) data. This open problem has notable consequences, such as decreased model performance and more significant convergenc

Yufei Shi, Weilong Yan, Gang Xu, Yumeng Li

Video large language models (ViLLMs) excel in general video understanding, e.g., recognizing activities like talking and eating, but struggle with identity-aware comprehension, such as "Wilson is receiving chemotherapy" or "Tom is discussing with Sarah", limiting their applicability in smart healthcare and smart home environments. To address this limitation,

Elira Shaska, Tony Shaska

We investigate the relationship between Geometric Invariant Theory (GIT) heights and weighted heights, with a focus on their interaction in weighted projective spaces and their application to binary forms. Building on the weighted height framework developed in previous papers, we relate it to Zhang's GIT height via the Veronese map. For a semistable cycle, w

Kai Wang, Zhen Sun, Bailing Wang, Qilin Fan

With the growing interconnection between In-Vehicle Networks (IVNs) and external environments, intelligent vehicles are increasingly vulnerable to sophisticated external network attacks. This paper proposes ATHENA, the first IVN intrusion detection framework that adopts a vehicle-cloud integrated architecture to achieve better security performance for the re

Gigliola Staffilani, Minh-Binh Tran

We consider in this work a $2$-dimensional $3$-wave kinetic equation describing the dynamics of the thermal cloud outside a Bose-Einstein Condensate. We construct global non-radial mild solutions for the equation. Those mild solutions are the summation of Dirac masses on circles. We prove that in each spatial direction, either Dirac masses at the origin, whi

Merim Dzaferagic, Kevin O'Sullivan, Bruce Richardson, Brendan Ryan

We demonstrate end-to-end 5G Open RAN over PON using off-the-shelf open networking hardware and open source RAN software. The implementation of the Cooperative Transport Interface provides timely synchronisation of PON and RAN schedulers.

Leiming Chen, Patrick Jentsch, Chiu Fan Lee, Ananyo Maitra

We reanalyze the hydrodynamic theory of "flocks" that is, polar ordered "dry" active fluids in two dimensions. For "Malthusian" flocks, in which birth and death cause the density to relax quickly, thereby eliminating density as a hydrodynamic variable, we are able to obtain two exact scaling laws relating the three scaling exponents characterizing the long-d

Poonam Singh, P. Maneesha, Manju Kumari, Abdelkrim Mekki

Aliovalent doping in an oxide material introduces modifications in the valence state of the host cation and often leads to tailoring the oxygen content in the lattice. Moreover, if the dopant cation is larger than the host cation, the lattice strain and disorder may be affected. Such changes are expected to modify the electronic clouds and lead to different

Jan Spěvák

We study topological versions of an independent set in an abelian group and a linearly independent set in a vector space, a {\em topologically independent set} in a topological group and a {\em topologically linearly independent set} in a topological vector space. These counterparts of their algebraic versions are defined analogously and possess similar prop

Mishal Fatima Minhas, Rachmad Vidya Wicaksana Putra, Falah Awwad, Osman Hasan

Neuromorphic Continual Learning (NCL) paradigm leverages Spiking Neural Networks (SNNs) to enable continual learning (CL) capabilities for AI systems to adapt to dynamically changing environments. Currently, the state-of-the-art employ a memory replay-based method to maintain the old knowledge. However, this technique relies on long timesteps and compression

Yujie Liu, Xiaoying Wang, Yuzhou Hao, Xuejie Li

Lattice thermal conductivity ($\kappa_L$) is crucial for efficient thermal management in electronics and energy conversion technologies. Traditional methods for predicting \k{appa}L are often computationally expensive, limiting their scalability for large-scale material screening. Empirical models, such as the Slack model, offer faster alternatives but requi

Yongkang Cheng, Shaoli Huang, Xuelin Chen, Jifeng Ning

Diffusion models have demonstrated remarkable synthesis quality and diversity in generating co-speech gestures. However, the computationally intensive sampling steps associated with diffusion models hinder their practicality in real-world applications. Hence, we present DIDiffGes, for a Decoupled Semi-Implicit Diffusion model-based framework, that can synthe

Gunjan Yadav, Madan Mohan Mahana, Tarak Nath Dey

We investigate the coherent single photon scattering process in a topological waveguide coupled with a driven $\Lambda$ system. We derive an analytical expression for transmittance by using the scattering formalism for three different sublattice sites (A, B, and AB), which couples to the $\Lambda$ system. We have demonstrated that the system's response is to

Michael D. White, Michael D. Atkinson, Adam J. Plowman, Pratheek Shanthraj

Microstructure quantification is an important step towards establishing structure-property relationships in materials. Machine learning-based image processing methods have been shown to outperform conventional image processing techniques and are increasingly applied to microstructure quantification tasks. In this work, we present a 3D variational autoencoder

Fangyijie Wang, Kathleen M. Curran, Guénolé Silvestre

Accurate segmentation of ultrasound (US) images of the cervical muscles is crucial for precision healthcare. The demand for automatic computer-assisted methods is high. However, the scarcity of labeled data hinders the development of these methods. Advanced semi-supervised learning approaches have displayed promise in overcoming this challenge by utilizing l

Chang-Yong Yang, Sheng-Qin Feng

We investigate the influence of quark anomalous magnetic moments (AMMs) on the mass spectra of neutral pseudoscalar mesons ($\pi$, $K$, $\eta$, $\eta^{'}$) under external magnetic fields, finite temperatures, and quark chemical potentials using the three-flavor Nambu-Jona-Lasinio model. By incorporating AMMs at the quark level, we reveal that AMMs significan

Julius Stephan Junker, Rong Hu, Ziyue Li, Wolfgang Ketter

This paper addresses the critical challenge of optimizing electric vehicle charging station placement through a novel data-driven methodology employing causal discovery techniques. While traditional approaches prioritize economic factors or power grid constraints, they often neglect empirical charging patterns that ultimately determine station utilization. W

Francesco Cioni, Fabio Taddei

In this paper, we propose an electronic refrigerator based on a ballistic Andreev interferometer that allows to reach a maximum cooling power per channel up to five orders of magnitude larger than that of the conventional normal metal-insulator-superconductor cooler. This effect is achieved by exploiting the destructive interference that occurs when the supe

Lorenzo Malagutti, Alessia Selmi, Laura Bandiera, Vladimir Baryshevsky

Electromagnetic calorimeters used in high-energy physics and astrophysics rely heavily on high-Z inorganic scintillators, such as lead tungstate (PbWO4 or PWO). The crystalline structure and lattice orientation of inorganic scintillators are frequently underestimated in detector design, even though it is known that the crystalline lattice strongly modifies t

Oladiran Johnson Abimbola, Taiwo Adewumi, Musa Abubakar

As the global climate changes, urban heat island (UHI) is a critical factor in ever expanding urban landscape, studying and mitigating the UHI is important for remediating climate change and providing for the human and ecosystem health within the urban area. This study has aimed to study the UHI in Lafia, a tropical city in Nigeria and its other impacted fac

Wei-hao Huang, Hiromichi Matsuyama, Yu Yamashiro

This study proposes a hybrid quantum-classical approach to solving the Capacitated Vehicle Routing Problem (CVRP) by integrating the Column Generation (CG) method with the Quantum Alternating Operator Ansatz (QAOAnsatz). The CG method divides the CVRP into the reduced master problem, which finds the best combination of the routes under the route set, and one

Yuang Feng, Shuyong Gao, Fuzhen Yan, Yicheng Song

Video Camouflaged Object Detection (VCOD) aims to segment objects whose appearances closely resemble their surroundings, posing a challenging and emerging task. Existing vision models often struggle in such scenarios due to the indistinguishable appearance of camouflaged objects and the insufficient exploitation of dynamic information in videos. To address t

Giulia Cavalleri, Alain Miranville

In this paper, we study an optimal control problem for a brain tumor growth model that incorporates lactate metabolism, viscoelastic effects, and tissue damage. The PDE system, introduced in [G. Cavalleri, P. Colli, A. Miranville, E. Rocca, On a Brain Tumor Growth Model with Lactate Metabolism, Viscoelastic Effects, and Tissue Damage (2025)], couples a Fishe

Florian Honz, Bernhard Schrenk

Terrestrial free-space optical (FSO) links are an ideal candidate to extend the bandwidth continuum offered by fiber networks, yet at the expense of unfavorable cost credentials due to highly complex opto-mechanical setups. As a response to this challenge, we present a simple fiber-based focal plane array (FPA) architecture which contributes beamforming func

W. Lokbani, V. Allard, T. Broussolle, CY. Barrey

Osteolytic metastases located in the vertebrae reduce strength and enhance the risk of vertebral fractures. This risk can be predicted by means of validated finite element models, but their reproducibility needs to be assessed. For that purpose, experimental data are requested. The aim of this study was to conduct open-access experiments on vertebrae, with a

Dongsheng Yang, Qianying Liu, Wataru Sato, Takashi Minato

Automatic robotic facial expression generation is crucial for human-robot interaction, as handcrafted methods based on fixed joint configurations often yield rigid and unnatural behaviors. Although recent automated techniques reduce the need for manual tuning, they tend to fall short by not adequately bridging the gap between human preferences and model pred