March 2025 arXiv papers — page 150

Helge Ritter, Otthein Herzog, Kurt Rothermel, Anthony G. Cohn

We attempt to take a comprehensive look at the challenges of representing the spatio-temporal structures and dynamic processes defining a city's overall characteristics. For the task of urban planning and urban operation, we take the stance that even if the necessary representations of these structures and processes can be achieved, the most important repres

S. S. Islam, V. Sazgari, J. N. Graham, O. Gerguri

The cuprate superconductor La$_{2-x}$Ba$_x$CuO$_4$ (LBCO) near $x=0.125$ is a striking example of intertwined electronic orders, where 3D superconductivity is anomalously suppressed, allowing spin and charge stripe order to develop, in a manner consistent with the emergence of a pair-density-wave (PDW) state. Understanding this interplay remains a key challe

Duc Hoang Cao

We prove sharp upper bounds for eigenvalues of Schr\"odinger operators on quantum graphs with $\delta$-coupling (also known as Robin) conditions at all vertices. The bounds depend on the geometry of the graph, on the potential, and the strength of the couplings, and as the coupling strengths grow, the dependence on the topology gets weaker, answering a quest

A. Sophie Aiken, Rayssa Caju, Jesse Ratzkin, Almir Silva Santos

We produce many new complete, constant Q-curvature metrics on finitely punctured spheres by gluing together known examples. In our construction we truncate one end of each summand and glue the two summands together "end-to-end," where we've truncated them. We use this construction to show that the unmarked moduli space of solutions with a fixed number of pun

Shuang Su

In this paper, we study currents that have full mass intersection with respect to given currents in the mixed setting on a compact K\"ahler manifold. We compare their singularities by using Lelong numbers. Our main theorems generalize some results of Vu.

Zhe-Geng Chen, Rui-Jing Lu, Zhi-Fu Chen, Wen-Qiang Liang

Some optically selected quasars exhibit Mg II assoicated absorption lines (AALs), and its origin remains unclear. In this paper, we compile a sample of 1769 quasars, with or without Mg II AALs. Of which 1689 are Far-Infrared (FIR) detected quasars and the rest are not detected in FIR. For the FIR undetected quasars, we obtain stacks for both with and without

Serena Fattori, Rino Persiani, Ugo Abundo

This study focuses on a fuel cell composed of Lithium Deuteride (LiD) in a spherical geometry, in which isotropic monoenergetic neutrons of 0.025 eV (thermal neutrons) are generated at the center. The objective is to investigate the production of Tritium via interactions with Lithium-6. The physics of the process has been modeled and analyzed for thirteen di

Bouhamidi Abderrahman, El Harraki Imad, Melouani Yassine

This paper presents a model for tumor growth using nonlocal velocity. We establish some results on the existence and uniqueness of the solution for a nonlocal tumor growth model. Many experiences show that tumor spheroid can be invariant by rotation and can guard the shape of the spheroid during the growth process in some particular cases. Here, we assume th

Samuel Fiorini, Stefan Kober, Michał T. Seweryn, Abhinav Shantanam

A {\em rooted graph} is a graph together with a designated vertex subset, called the {\em roots}. In this paper, we consider rooted graphs embedded in a fixed surface. A collection of faces of the embedding is a {\em face cover} if every root is incident to some face in the collection. We prove that every $3$-connected, rooted graph that has no rooted $K_{2,

Thi Thanh Mai Ta, Quang Huy Nguyen, Phi Hung Pham

In this paper, we employ a space-time finite element method to discretize the parabolic initial-boundary value problem and extend its error analysis with refined estimates on unstructured space-time meshes. We establish higher-order estimates in three different norms, thereby supplementing existing research. Moreover, we obtain an optimal estimate in a norm

Andrzej Dulny, Farzad Jabbarigargari, Andreas Hotho, Laura Maria Schreiber

We propose a 3D U-Net model to predict the spatial distribution of electromagnetic fields inside a radio-frequency (RF) coil with a subject present, using the phase, amplitude, and position of the coils, along with the density, permittivity, and conductivity of the surrounding medium as inputs. To improve accuracy, we introduce a physics-augmented variant, U

Samuel M. Corson

In this short note we answer some questions of Bergman regarding homomorphic images of (ultra)products of groups.

Tommaso Antonelli, Xavier Calmet

The Weak Gravity Conjecture states that in any consistent theory of quantum gravity in the landscape of string theory, the repulsive force mediated by a U(1) gauge field must be stronger than the attractive force of gravity. In this work, we calculate quantum gravitational corrections to the charge-to-mass ratio of extremal Reissner-Nordstr\"om black holes,

Omer Noy Klein, Alihan Hüyük, Ron Shamir, Uri Shalit

Randomized Controlled Trials (RCTs) are the gold standard for evaluating the effect of new medical treatments. Treatments must pass stringent regulatory conditions in order to be approved for widespread use, yet even after the regulatory barriers are crossed, real-world challenges might arise: Who should get the treatment? What is its true clinical utility?

Xuxiang Sun, Xianglin Shan, Yilang Liu, Weiwei Zhang

This study focuses on the numerical simulation of high Reynolds number separated flows and proposes a data-driven approach to improve the predictive capability of the SA turbulence model. First, data assimilation was performed on two typical airfoils with high angle-of-attack separated flows to obtain a high-fidelity flow field dataset. Based on this dataset

A. Tutone, A. Anitra, E. Ambrosi, R. La Placa

We present a novel approach using neural networks to recover X-ray spectral model parameters and quantify uncertainties, balancing accuracy and computational efficiency against traditional frequentist and Bayesian methods. Frequentist techniques often fall into local minima, compromising parameter estimation, while Bayesian methods, though more reliable, suf

Tian Tang, Zhixing Tian, Zhenyu Zhu, Chenyang Wang

Query and product relevance prediction is a critical component for ensuring a smooth user experience in e-commerce search. Traditional studies mainly focus on BERT-based models to assess the semantic relevance between queries and products. However, the discriminative paradigm and limited knowledge capacity of these approaches restrict their ability to compre

Hannah Kniesel, Pedro Hermosilla, Timo Ropinski

Recent advances in conditional image generation from diffusion models have shown great potential in achieving impressive image quality while preserving the constraints introduced by the user. In particular, ControlNet enables precise alignment between ground truth segmentation masks and the generated image content, allowing the enhancement of training datase

Fabio R. Llorella, José A. Cebrián

This study explores the use of symbolic regression (SR) combined with genetic algorithms (GA) to classify astronomical objects. Using the SDSS17 dataset from Kaggle, which includes 100,000 observations of stars, galaxies, and quasars, we applied SR to 10\% of the data to derive a mathematical expression capable of distinguishing these classes. A genetic algo

Xinyi Yang, Runzhe Zhan, Derek F. Wong, Shu Yang

Investigating bias in large language models (LLMs) is crucial for developing trustworthy AI. While prompt-based through prompt engineering is common, its effectiveness relies on the assumption that models inherently understand biases. Our study systematically analyzed this assumption using the BBQ and StereoSet benchmarks on both open-source models as well a

Jie He, Simon Yu, Deyi Xiong, Víctor Gutiérrez-Basulto

Recent advancements of in-context learning (ICL) show language models can significantly improve their performance when demonstrations are provided. However, little attention has been paid to model calibration and prediction confidence of ICL in cross-lingual scenarios. To bridge this gap, we conduct a thorough analysis of ICL for cross-lingual sentiment clas

Fengjie Li, Jiajun Jiang, Jiajun Sun, Hongyu Zhang

LLM-based automated program repair methods have attracted significant attention for their state-of-the-art performance. However, they were primarily evaluated on a few well known datasets like Defects4J, raising questions about their effectiveness on new datasets. In this study, we evaluate 11 top-performing LLMs on DEFECTS4J-TRANS, a new dataset derived fro

Zesheng Li, Wei Yang, Yan Su, Yiran Zhu

Postoperative prognostic prediction for colorectal cancer liver metastasis (CRLM) remains challenging due to tumor heterogeneity, dynamic evolution of the hepatic microenvironment, and insufficient multimodal data fusion. To address these issues, we propose 4D-ACFNet, the first framework that synergistically integrates lightweight spatiotemporal modeling, cr

Shuya Xing, Zhongxu Wei, Xu Chen, Junming Zhang

The simultaneous presence of polarity and metallicity or superconductivity in a material signifies the exotic polar metallic or superconducting (SC) state, while such materials are extremely rare due to their exclusive nature. Recently, the interweaved CDW and antipolar charge orders have been discovered in a metallic superatomic crystal of Au6Te12Se8 (ATS),

Jian Zhu, Zhengyu Jia, Tian Gao, Jiaxin Deng

Advanced end-to-end autonomous driving systems predict other vehicles' motions and plan ego vehicle's trajectory. The world model that can foresee the outcome of the trajectory has been used to evaluate the autonomous driving system. However, existing world models predominantly emphasize the trajectory of the ego vehicle and leave other vehicles uncontrollab

Phillip W. K. Jensen, Gustav Stausbøll Hedemark, Karl Michael Ziems, Erik Rosendahl Kjellgren

We present the first implementation and computation of electron spin resonance isotropic hyperfine coupling constants (HFCs) on quantum hardware. As illustrative test cases, we compute the HFCs for the hydroxyl radical (OH$^{\bullet}$), nitric oxide (NO$^{\bullet}$), and the triplet hydroxyl cation (OH$^{+}$). Our approach integrates the qubit-ADAPT method w

Mohammad Al-Turany, David Chamont, Davide Costanzo, Caterina Doglioni

The scientific communities of nuclear, particle, and astroparticle physics are continuing to advance and are facing unprecedented software challenges due to growing data volumes, complex computing needs, and environmental considerations. As new experiments emerge, software and computing needs must be recognised and integrated early in design phases. This doc

Piyush Gulati, Arianna Marchetti, Victoria Sevcenko, Phanish Puranam

A common view holds that generative AI (GenAI) automates cognitive tasks, reshaping roles to emphasize social skills over cognitive ones. Drawing on the framework we develop in this paper, we argue that other outcomes are theoretically possible. We analyze seven million job postings from 595 U.S. public firms that adopted GenAI in 2022-2024, estimating diffe

Marius Jahrens, Thomas Martinetz

This paper elucidates that current state-of-the-art Large Language Models (LLMs) are fundamentally incapable of making decisions or developing "thoughts" within the feature space due to their architectural constraints. We establish a definition of "thought" that encompasses traditional understandings of that term and adapt it for application to LLMs. We demo

Šimon Bräuer, Tomáš Opatrný, Petr Marek

Quantum systems can be prepared in an infinite continuum of states, but only some of them can be used as resources for quantum technologies. Discerning whether a specific quantum state falls into this class, is often a challenging task. We show that it can be performed by looking at the squeezing of the quantum states - a scenario in which the variance of so

Urs Frauenfelder

Time-dependent Stark-Zeeman systems describe the motion of an electron attracted by a proton subject to a magnetic and a time-dependent electric field. For instance the study of the dynamics of a gateway around the moon which is subject to the joint attraction of the moon, the earth and the sun leads to time-dependent Stark-Zeeman systems. In the time-depend

Bouhamidi Abderrahman, El Harraki Imad, Melouani Yassine

This paper presents a mathematical framework for optimizing drug delivery in cancer treatment using a nonlocal model of solid tumor growth. We present a coupled system of partial differential equations that incorporate long-range cellular interactions through integral terms and drug-induced cell death. The model accounts for spatial heterogeneity in both tum

Minseok Kim

In this paper, we study the $p$-Selmer groups in the family of $p$-twists of an elliptic curve $E$ over a number field $K$. We prove that if $E/K$ is an elliptic curve over a number field $K$, and if $d$ is congruent to the dimension of the Selmer group of $E/K$ modulo $2$ and is greater than that dimension, then there exist infinitely many characters $\chi

Xiuwen Fang, Mang Ye, Bo Du

This paper studies a challenging robust federated learning task with model heterogeneous and data corrupted clients, where the clients have different local model structures. Data corruption is unavoidable due to factors such as random noise, compression artifacts, or environmental conditions in real-world deployment, drastically crippling the entire federate

Ali Vosoughi, Dimitra Emmanouilidou, Hannes Gamper

Integrating audio and visual data for training multimodal foundational models remains a challenge. The Audio-Video Vector Alignment (AVVA) framework addresses this by considering AV scene alignment beyond mere temporal synchronization, and leveraging Large Language Models (LLMs) for data curation. AVVA implements a scoring mechanism for selecting aligned tra

S. A. Bhat, S. Jehangir, G. H. Bhat, J. A. Sheikh

The possibility of observing wobbling mode in the even-even systems of 76Ge, 112Ru, 188,192Os, 192Pt and 232Th is explored using the triaxial projected shell model approach. These nuclei are known to have {\gamma}-bands whose odd-spin members are lower than the average of the neighbouring even-spin states. It is shown through a detailed analysis of the excit

Shuguang Chu, Zebin Huang, Yutong Li, Mingwei Lin

This work presents the MarineGym, a high-performance reinforcement learning (RL) platform specifically designed for underwater robotics. It aims to address the limitations of existing underwater simulation environments in terms of RL compatibility, training efficiency, and standardized benchmarking. MarineGym integrates a proposed GPU-accelerated hydrodynami

Falko Helm, Nico Daheim, Iryna Gurevych

Many applications of large language models (LLMs) require long-context understanding, but models continue to struggle with such tasks. We hypothesize that conventional next-token prediction training could contribute to this, because each token is assigned equal weight. Yet, intuitively, the amount of context needed to predict the next token accurately varies

K. Urbanowski

We analyze the uncertainty relation for the sum of variances, which is called in some papers, the stronger uncertainty relation for all incompatible observables. We show that this uncertainty relation for the sum of variances of the observables $A$ and $B$ calculated for the eigenstate of one of these observables, (say of $B$), contrary to the suggestions pr

Lei Liu, Yuchao Lu, Ling An, Huajie Liang

As human activities intensify, environmental systems such as aquatic ecosystems and water treatment systems face increasingly complex pressures, impacting ecological balance, public health, and sustainable development, making intelligent anomaly monitoring essential. However, traditional monitoring methods suffer from delayed responses, insufficient data pro

David P. Hofmeyr

In this paper we introduce a simple and intuitive adaptive k nearest neighbours classifier, and explore its utility within the context of bootstrap aggregating ("bagging"). The approach is based on finding discriminant subspaces which are computationally efficient to compute, and are motivated by enhancing the discrimination of classes through nearest neighb

Giovanni Bocchi, Patrizio Frosini, Alessandra Micheletti, Alessandro Pedretti

Group Equivariant Non-Expansive Operators (GENEOs) have emerged as mathematical tools for constructing networks for Machine Learning and Artificial Intelligence. Recent findings suggest that such models can be inserted within the domain of eXplainable Artificial Intelligence (XAI) due to their inherent interpretability. In this study, we aim to verify this c

Yuxuan Liang, Haomin Wen, Yutong Xia, Ming Jin

Spatio-Temporal (ST) data science, which includes sensing, managing, and mining large-scale data across space and time, is fundamental to understanding complex systems in domains such as urban computing, climate science, and intelligent transportation. Traditional deep learning approaches have significantly advanced this field, particularly in the stage of S

Benoit Lange, Nancy Rodriguez, William Puech, Herve Rey

This paper deals with a 3D visualization technique proposed to analyze and manage energy efficiency from a data center. Data are extracted from sensors located in the IBM Green Data Center in Montpellier France. These sensors measure different information such as hygrometry, pressure and temperature. We want to visualize in real-time the large among of data

Zicheng Zhang, Haoning Wu, Ziheng Jia, Weisi Lin

Image quality scoring and interpreting are two fundamental components of Image Quality Assessment (IQA). The former quantifies image quality, while the latter enables descriptive question answering about image quality. Traditionally, these two tasks have been addressed independently. However, from the perspective of the Human Visual System (HVS) and the Perc

Federico Grasselli, Sanggyu Chong, Venkat Kapil, Silvia Bonfanti

The widespread adoption of machine learning surrogate models has significantly improved the scale and complexity of systems and processes that can be explored accurately and efficiently using atomistic modeling. However, the inherently data-driven nature of machine learning models introduces uncertainties that must be quantified, understood, and effectively

Naoki Kitazawa

Refined algebraic domains are regions in the plane surrounded by finitely many non-singular real algebraic curves which may intersect with normal crossing. We are interested in shapes of such regions with surrounding real algebraic curves. Poincar'e-Reeb Graphs of them are graphs the regions naturally collapse to respecting the projection to a straight line.

Xudong Sun, Alex Markham, Pratik Misra, Carsten Marr

Unbiased data synthesis is crucial for evaluating causal discovery algorithms in the presence of unobserved confounding, given the scarcity of real-world datasets. A common approach, implicit parameterization, encodes unobserved confounding by modifying the off-diagonal entries of the idiosyncratic covariance matrix while preserving positive definiteness. Wi

J. N. Graham, T. J. Hicken, R. B. Regmi, M. Janoschek

Muon spin rotation (${\mu}$SR), combined with muon stopping site and local field analysis, was used to investigate the magnetic properties of cobalt intercalated 2H-NbSe$_2$ (Co$_{1/4}$NbSe$_2$). Co$_{1/4}$NbSe$_2$ is predicted to be an altermagnet, and whilst neutron diffraction has proposed its magnetic structure, microscopic details such as the magnetic v

Chuanyin Wang, Yifei Zhang, Neng Gao, Qiang Luo

Personalized federated learning is extensively utilized in scenarios characterized by data heterogeneity, facilitating more efficient and automated local training on data-owning terminals. This includes the automated selection of high-performance model parameters for upload, thereby enhancing the overall training process. However, it entails significant risk

Alberto Coffrini, Paolo Barsocchi, Francesco Furfari, Antonino Crivello

Indoor navigation presents unique challenges due to complex layouts and the unavailability of GNSS signals. Existing solutions often struggle with contextual adaptation, and typically require dedicated hardware. In this work, we explore the potential of a Large Language Model (LLM), i.e., ChatGPT, to generate natural, context-aware navigation instructions fr

Juana Valeria Hurtado, Sajad Marvi, Rohit Mohan, Abhinav Valada

Panoptic tracking enables pixel-level scene interpretation of videos by integrating instance tracking in panoptic segmentation. This provides robots with a spatio-temporal understanding of the environment, an essential attribute for their operation in dynamic environments. In this paper, we propose a novel approach for panoptic tracking that simultaneously c

Takahito Kashiwabara

We consider the isoparametric finite element method (FEM) for the Poisson equation in a smooth domain with the homogeneous Dirichlet boundary condition. Because the boundary is curved, standard triangulated meshes do not exactly fit it. Thereby we need to introduce curved elements if better accuracy than linear FEM is desired, which necessitates the use of i

Xavier Vasques, Thibaut Possompes, Herve Rey, Marine Le Touze

Increases in energy prices and the global goal of mitigating CO2 emissions necessitate the development of intelligent Building Management Systems (BMS) that operate on an energy-efficient basis. Data Centers, buildings and/or group of buildings are often responsible for huge energy consumption. One way to monitor and optimize energy consumption is to instrum

Shinhyung Yang, David Georg Reichelt, Reiner Jung, Marcel Hansson

Observability of a software system aims at allowing its engineers and operators to keep the system robust and highly available. With this paper, we present the Kieker Observability Framework Version 2, the successor of the Kieker Monitoring Framework. In this tool artifact paper, we do not just present the Kieker framework, but also a demonstration of its ap

Hilde Bellersen, Michele Guerrini, Caterina Cocchi

Cs-based semiconductors like $\mathrm{Cs_3Sb}$ and $\mathrm{Cs_2Te}$ are currently used as photocathodes in particle accelerators. Their performance as electron sources critically depends on their interaction with intense laser sources. In this work, we investigate from first principles the time-dependent response of $\mathrm{Cs_3Sb}$ and $\mathrm{Cs_2Te}$ t

Qipeng Mei, Dimitri Bulatov, Dorota Iwaszczuk

This study presents a novel approach for roof detail extraction and vectorization using remote sensing images. Unlike previous geometric-primitive-based methods that rely on the detection of corners, our method focuses on edge detection as the primary mechanism for roof reconstruction, while utilizing geometric relationships to define corners and faces. We a

Jian-Jian Jiang, Xiao-Ming Wu, Yi-Xiang He, Ling-An Zeng

Bimanual robotic manipulation is an emerging and critical topic in the robotics community. Previous works primarily rely on integrated control models that take the perceptions and states of both arms as inputs to directly predict their actions. However, we think bimanual manipulation involves not only coordinated tasks but also various uncoordinated tasks th

Yuanyang Zhang, Yijie Lin, Weiqing Yan, Li Yao

Incomplete multi-view clustering (IMVC) has garnered increasing attention in recent years due to the common issue of missing data in multi-view datasets. The primary approach to address this challenge involves recovering the missing views before applying conventional multi-view clustering methods. Although imputation-based IMVC methods have achieved signific

Aidan Ferguson, Perry Gibson, Lara D'Agata, Parker McLeod

The deployment of deep neural networks (DNNs) in privacy-sensitive environments is constrained by computational overheads in fully homomorphic encryption (FHE). This paper explores unstructured sparsity in FHE matrix multiplication schemes as a means of reducing this burden while maintaining model accuracy requirements. We demonstrate that sparsity can be ex

Peihao Sun, Jacopo Baglioni, Beatrice Baraldi, Weilong Chen

We demonstrate a setup combining fast scanning calorimetry with X-ray total scattering at a synchrotron beamline, allowing for \emph{in-situ} characterizations of the nano-scale structure of samples during and after temperature scans. The setup features a portable vacuum chamber giving high signal-to-background ratio even on amorphous samples, which enables

Rainis Haller, Paavo Kuuseok, Märt Põldvere

In a paper published in 2020 in Studia Mathematica, Abrahamsen et al. proved that in the real space $L_1(\mu)$, where $\mu$ is a non-zero $\sigma$-finite (countably additive non-negative) measure, norm-one elements in finite convex combinations of relatively weakly open subsets of the unit ball are interior points of these convex combinations in the relative

Katsumi Takahashi, Koh Takeuchi, Hisashi Kashima

Machine learning models usually assume that a set of feature values used to obtain an output is fixed in advance. However, in many real-world problems, a cost is associated with measuring these features. To address the issue of reducing measurement costs, various methods have been proposed to dynamically select which features to measure, but existing methods

Alberto Sciaccaluga, Agnese Costa, Fabrizio Tavecchio, Gianluigi Bodo

Multifrequency polarimetry, recently extended to the X-ray band thanks to the Imaging X-ray Polarimetry Explorer (IXPE) satellite, is an essential tool for understanding blazar jets. High-frequency-peaked BL Lacs (HBLs) and extreme high-frequency-peaked BL Lacs (EHBLs) are especially interesting because the polarimetric properties of their synchrotron emissi

Giulia Cavagnari, Marc Quincampoix

This paper concerns the problem of reachability of a given state for a multiagent control system in $\mathbb{R}^d$. In such a system, at every time each agent can choose his/her velocity which depends both on his/her position and on the position of the whole crowd of agents (modeled by a probability measure on $ \mathbb{R}^d$). The main contribution of the p

Haonan Zhang, Huiyuan Li, Zhimin Zhang

In this paper, we present an efficient fully spectral approximation scheme for exploring the one-dimensional steady-state neutron transport equation. Our methodology integrates the spectral-(Petrov-)Galerkin scheme in the spatial dimension with the Legendre-Gauss collocation scheme in the directional dimension. The directional integral in the original proble

Tamar Bar-On, Nikolay Nikolov

We generalize the notions of composition series and composition factors for profinite groups, and prove a profinite version of the Jordan-Holder Theorem. We apply this to prove a Galois Theorem for infinite prosolvable extensions. In addition, we investigate the connection between the abstract and topological composition factors of a nonstrongly complete pro

Xu Mao, Gang Yu, Hongsheng Ding, Yongmei Zhao

Multi-foci metalenses with uniform focal arrays attract special attention as they enable a single incident beam to focus on the same focal plane and share the identical numerical apertures. In this work, we demonstrate the scalable manufacturing of polarization-insensitive metalenses with high-uniform focal arrays in the visible. To overcome the limitations

Roberta Satariano, Anatoly Fjodorovich Volkov, Halima Giovanna Ahmad, Luigi Di Palma

Magnetic Josephson junctions (MJJs) have emerged as a prominent playground to explore the interplay between superconductivity and ferromagnetism. A series of fascinating experiments have revealed striking phenomena at the Superconductor/Ferromagnet (S/F) interface, pointing to tunable phase transitions and to the generation of unconventional spin-triplet cor

Sihyeong Park, Jemin Lee, Byung-Soo Kim, Seokhun Jeon

With the advancement of Large Language Models (LLMs), the importance of accelerators that efficiently process LLM computations has been increasing. This paper discusses the necessity of LLM accelerators and provides a comprehensive analysis of the hardware and software characteristics of the main commercial LLM accelerators. Based on this analysis, we propos

Francesca Larosa, Fermin Mallor, S. Hoyas, Alberto J. Conejero

We explore the integration of climate action and Sustainable Development Goals (SDGs) in nationally determined contributions (NDCs), revealing persistent synergies and trade-offs across income groups. While high-income countries emphasize systemic challenges like health (SDG3) and inequality (SDG10), low-income nations prioritize the water-energy-food nexus

Athanasios G. Kanatas, Harris K. Armeniakos, Harpreet S. Dhillon, Marco Di Renzo

This paper examines the number of communication modes, that is, the degrees of freedom (DoF) in a wireless line-of-sight channel comprising a small continuous linear intelligent antenna array in the near field of a large one. The framework allows for any orientations between the arrays and any positions in a two-dimensional space assuming that the transmitti

Ermanno Bartoli, Dennis Rotondi, Kai O. Arras, Iolanda Leite

Long-term planning for robots operating in domestic environments poses unique challenges due to the interactions between humans, objects, and spaces. Recent advancements in trajectory planning have leveraged vision-language models (VLMs) to extract contextual information for robots operating in real-world environments. While these methods achieve satisfying

Zhiye Yang, Zheng Wang, Huaning Liu, Keqin Feng

Low Ambiguity Zone (LAZ) sequences play a pivotal role in modern integrated sensing and communication (ISAC) systems. Recently, Wang \textit{et al.} [arXiv:2501.11313] proposed a definition of locally perfect nonlinear functions (LPNFs) and constructed three classes of both periodic and aperiodic LAZ sequence sets with flexible parameters by applying such fu

Vladimir M. Kaganer, Domenik Spallek, Philipp John, Oliver Brandt

Correlations between dislocations in crystals reduce the elastic energy via screening of the strain by the surrounding dislocations. We study the correlations of threading dislocations in GaN epitaxial films with dislocation densities of $5\times10^{8}$ cm$^{-2}$ and $1.8\times10^{10}$ cm$^{-2}$ by X-ray diffraction (XRD) in reciprocal space and by high-reso

Aman Prakash, Nikumani Choudhury, Anakhi Hazarika, Alekhya Gorrela

LoRaWAN is a low-power long-range protocol that enables reliable and robust communication. This paper addresses the challenge of predicting the spreading factor (SF) in LoRaWAN networks using machine learning (ML) techniques. Optimal SF allocation is crucial for optimizing data transmission in IoT-enabled mobile devices, yet it remains a challenging task due

Na Li, Yang Zhao, Wen-Long Ma, Z. D. Wang

Long-range bipartite entanglement (LBE) and its distribution properties are studied in XXZ spin chains with the exponential and power-law long-range interactions (ELRIs and PLRIs). LBE quantified by two-qubit concurrence decays exponentially along with two-site distance in the infinite chain with ELRIs in the thermodynamic limit, and the long-range behavior

Mauro Bonafini, Giulia Cavagnari, Antonio Marigonda

In this paper, we introduce an optimal control problem for multi-agent systems with non-local cost which favors simultaneous aggregation of particles. This is done introducing a time-dependent notion of multiplicity whose intrinsic dynamical nature differs from more established geometric-like definitions.

Mateusz J. Samsel, Agata Fronczak, Piotr Fronczak

We investigate the full temporal evolution of epidemic outbreaks in complex networks, focusing on the susceptible-infected (SI) model of disease transmission. Combining theoretical analysis with large-scale numerical simulations, we uncover two universal patterns of epidemic growth, determined by the structure of the underlying network. In small-world networ

Aram A. Mkrtchyan, Anastasia S. Netrusova, Mikhail S. Mishevsky, Zohran Ali

High-quality microring resonators (MRRs) have proven to be promising sources of optical combs generated from continuous-wave radiation. In addition to the primary comb that propagates along with the pump, Rayleigh scattering creates a comb that travels in the opposite direction. Normally, the scattering is a very weak, however, in the high-quality-factor MRR

Rischan Mafrur

The integration of blockchain technology with data analytics is essential for extracting insights in the cryptocurrency space. Although academic literature on blockchain data analytics is limited, various industry solutions have emerged to address these needs. This paper provides a comprehensive literature review, drawing from both academic research and indu

Joscha Grüger, Tobias Geyer, Tobias Brix, Michael Storck

This research focuses on evaluating and enhancing data readiness for the development of an Artificial Intelligence (AI)-based Clinical Decision Support System (CDSS) in the context of skin cancer treatment. The study, conducted at the Skin Tumor Center of the University Hospital M\"unster, delves into the essential role of data quality, availability, and ext

J. Fei, A. Yuan, K. Wei, L. Sun

In future high-energy physics experiments, the electromagnetic calorimeter (ECAL) will operate in exceptionally high-luminosity. An ECAL featuring layered readout in the longitudinal direction and precise time-stamped information offers a multi-dimensional view, enriching our comprehension of the showering process of electromagnetic particles in high-luminos

Kostas Sozos, Francesco Da Ros, Senior Member Optica, Metodi Yankov

High order modulation formats constitute the most prominent way for increasing spectral efficiency in transmission systems. Coherent transceivers that support such higher order formats require heavy digital signal processing (DSP), which increases the power consumption of coherent pluggables, well above the intensity modulation and direct detection (IM/DD) c

Jean-Marie Malherbe, Thierry Roudier

We present in this paper an exceptional scientific dataset allowing to investigate the structure and evolution of the interior of solar supergranulation cells. Trees of Fragmenting Granules (TFG) and associated flows were evidenced using Local Correlation Tracking techniques (LCT) from a 24 H duration sequence of Hinode (JAXA/NASA) observations. The treatmen

Daniele Malpetti, Marco Scutari, Francesco Gualdi, Jessica van Setten

Federated learning leverages data across institutions to improve clinical discovery while complying with data-sharing restrictions and protecting patient privacy. This paper provides a gentle introduction to this approach in bioinformatics, and is the first to review key applications in proteomics, genome-wide association studies (GWAS), single-cell and mult

Shunyu Liu, Wenkai Fang, Zetian Hu, Junjie Zhang

Large Language Models (LLMs) have demonstrated unprecedented generative capabilities, yet their alignment with human values remains critical for ensuring helpful and harmless deployments. While Reinforcement Learning from Human Feedback (RLHF) has emerged as a powerful paradigm for aligning LLMs with human preferences, its reliance on complex reward modeling

Hao Feng, Zhi Zuo, Jia-Hui Pan, Ka-Hei Hui

We introduce \textit{WonderVerse}, a simple but effective framework for generating extendable 3D scenes. Unlike existing methods that rely on iterative depth estimation and image inpainting, often leading to geometric distortions and inconsistencies, WonderVerse leverages the powerful world-level priors embedded within video generative foundation models to c

Miao Yu, Fanci Meng, Xinyun Zhou, Shilong Wang

With the rapid evolution of Large Language Models (LLMs), LLM-based agents and Multi-agent Systems (MAS) have significantly expanded the capabilities of LLM ecosystems. This evolution stems from empowering LLMs with additional modules such as memory, tools, environment, and even other agents. However, this advancement has also introduced more complex issues

Andrej Tschalzev, Lennart Purucker, Stefan Lüdtke, Frank Hutter

Data repositories have accumulated a large number of tabular datasets from various domains. Machine Learning researchers are actively using these datasets to evaluate novel approaches. Consequently, data repositories have an important standing in tabular data research. They not only host datasets but also provide information on how to use them in supervised

Ryan Quek Wei Heng, Edoardo Vittori, Keane Ong, Rui Mao

This paper introduces a methodology leveraging Large Language Models (LLMs) for sector-level portfolio allocation through systematic analysis of macroeconomic conditions and market sentiment. Our framework emphasizes top-down sector allocation by processing multiple data streams simultaneously, including policy documents, economic indicators, and sentiment p

Cesare Maria Dalbagno, Manuel de Castro Ribeiro Jardim, Mihnea Angheluţă

The goal of this study is to investigate whether latent space representations of visual stimuli and fMRI data share common information. Decoding and reconstructing stimuli from fMRI data remains a challenge in AI and neuroscience, with significant implications for understanding neural representations and improving the interpretability of Artificial Neural Ne

Andrew Mary Huet de Barochez, Stéphan Plassart, Sébastien Monnet

The growth in computational power and data hungriness of Machine Learning has led to an important shift of research efforts towards the distribution of ML models on multiple machines, leading in even more powerful models. However, there exists many Distributed Artificial Intelligence paradigms and for each of them the platform and algorithm configurations pl

Fufangchen Zhao, Songbai Tan, Xuerui Qiu, Linrui Xun

Existing video large language models (VLLMs) primarily leverage prompt agnostic visual encoders, which extract untargeted facial representations without awareness of the queried information, leading to the loss of task critical cues. To address this challenge, we propose FaVChat, the first VLLM designed for reasoning over subtle visual and dynamic facial cue

Benoît Perthame, Delphine Salort, Clément Rieutord

The time-elapsed model for neural networks is a nonlinear age structured equationwhere the renewal term describes the network activity and influences the dischargerate, possibly with a delay due to the length of connections.We solve a long standing question, namely that an inhibitory network withoutdelay will converge to a steady state and thus the network i

Da Zhao, Wanjie Wang, Jialiang Li

The community detection problem on multilayer networks have drawn much interest. When the nodal covariates ar also present, few work has been done to integrate information from both sources. To leverage the multilayer networks and the covariates, we propose two new algorithms: the spectral clustering on aggregated networks with covariates (SCANC), and the sp

Rami Katz, Giulia Giordano, Michael Margaliot

We consider time-invariant nonlinear $n$-dimensional strongly $2$-cooperative systems, that is, systems that map the set of vectors with up to weak sign variation to its interior. Strongly $2$-cooperative systems enjoy a strong Poincare-Bendixson property: bounded solutions that maintain a positive distance from the set of equilibria converge to a periodic s

Chengshu Zhao, Yunyang Ge, Xinhua Cheng, Bin Zhu

Video body-swapping aims to replace the body in an existing video with a new body from arbitrary sources, which has garnered more attention in recent years. Existing methods treat video body-swapping as a composite of multiple tasks instead of an independent task and typically rely on various models to achieve video body-swapping sequentially. However, these

Chaowei Zhang, Zongling Feng, Zewei Zhang, Jipeng Qiang

The questionable responses caused by knowledge hallucination may lead to LLMs' unstable ability in decision-making. However, it has never been investigated whether the LLMs' hallucination is possibly usable to generate negative reasoning for facilitating the detection of fake news. This study proposes a novel supervised self-reinforced reasoning rectificatio

Bertrand Deroin, Christophe Dupont, Victor Kleptsyn

Let $\mathcal{F}$ be a singular holomorphic foliation on an algebraic complex surface $S$, with hyperbolic singularities and no foliated cycle. We prove a formula for the transverse Hausdorff dimension of the unique harmonic current, involving the Furstenberg entropy and the Lyapunov exponent. In particular, we extend Brunella's inequality to every holomorph