March 2025 arXiv papers — page 49

Tianfeng Feng, Vlatko Vedral, Chiara Marletto

It is known that an entanglement-based witness of non-classicality can be applied to testing quantum effects in gravity. Specifically, if a system can create entanglement between two quantum probes by local means only, then it must be non-classical. Recently, claims have been made that collapse-based models of classical gravity, i.e. Di\'osi-Penrose model, c

Martin Mäck, Riley J. Preston, Michael Thoss, Samuel L. Rudge

Understanding the current-induced vibrational dynamics in molecular nanojunctions is critical for gaining insight into the stability of such systems. While it is well known that Joule heating at higher bias voltages plays an important role for the stability of the nanojunction, a different mechanism caused by current-induced nonconservative forces has been r

E. A. Meier Valdés, B. -O. Demory, H. Diamond-Lowe, J. M. Mendonça

Terrestrial exoplanets orbiting nearby small, cool stars known as M dwarfs are well suited for atmospheric characterisation. Given the strong XUV irradiation from M dwarf host stars, orbiting exoplanets are thought to be unable to retain primordial H/He-dominated atmospheres. However, the survivability of heavier secondary atmospheres is currently unknown. T

Yannic Talavera, Bernd Ulmann

The aim of this paper is to give an overview of brain organoid computing, its characteristics, challenges, as well as possible advantages for future applications in the field of artificial intelligence. An important part is the extensive bibliography covering all relevant aspects and questions on this topic. Brain organoids - three-dimensional in vitro neura

Suzhe Xu, Jialin Peng, Chengyuan Zhang

Segmentation is a fundamental task in computer vision, with prompt-driven methods gaining prominence due to their flexibility. The Segment Anything Model (SAM) excels at point-prompted segmentation, while text-based models, often leveraging powerful multimodal encoders like BEIT-3, provide rich semantic understanding. However, effectively combining these com

Shreyas Bhat, Dave Dykstra

Fermilab is transitioning authentication and authorization for grid operations to using bearer tokens based on the WLCG Common JWT (JSON Web Token) Profile. One of the functionalities that Fermilab experimenters rely on is the ability to automate batch job submission, which in turn depends on the ability to securely refresh and distribute the necessary crede

Valerii A. Zuev, Elena G. Salmagambetova, Stepan N. Djakov, Lev V. Utkin

Epilepsy is typically diagnosed through electroencephalography (EEG) and long-term video-EEG (vEEG) monitoring. The manual analysis of vEEG recordings is time-consuming, necessitating automated tools for seizure detection. Recent advancements in machine learning have shown promise in real-time seizure detection and prediction using EEG and video data. Howeve

Davide Luparello

This technical note provides comprehensive derivations of fundamental equations in two-level nested and sequential logit models for analyzing hierarchical choice structures. We present derivations of the Berry (1994) inversion formula, nested inclusive values computation, and multi-level market share equations, complementing existing literature. While concep

Martina Halousková, Štefan Lyócsa

Macroeconomic variables are known to significantly impact equity markets, but their predictive power for price fluctuations has been underexplored due to challenges such as infrequency and variability in timing of announcements, changing market expectations, and the gradual pricing in of news. To address these concerns, we estimate the public's attention and

Manuel Esser, Anna Kraut

We consider a stochastic individual-based model of adaptive dynamics for an asexually reproducing population with mutation. Biologically motivated by the influence of seasons or the variation of drug concentration during medical treatment, the model parameters vary over time as piecewise constant and periodic functions. We study the typical evolutionary beha

Leandro Arosio, Håkan Samuelsson Kalm, Erlend F. Wold

We show that any compact smooth real $n$-dimensional manifold $M$ with $n\leq 11$ can be smoothly embedded into $\mathbb{C}^{n+1}$ as a polynomially convex set. In general, there is no such embedding into $\mathbb{C}^n$. This solves a problem by Izzo and Stout for $n\leq 11$. Additionally, we show that the image $\widetilde{M}$ of $M$ in $\mathbb{C}^{n+1}$ i

Alexander Gambashidze, Konstantin Sobolev, Andrey Kuznetsov, Ivan Oseledets

Can Visual Language Models (VLMs) effectively capture human visual preferences? This work addresses this question by training VLMs to think about preferences at test time, employing reinforcement learning methods inspired by DeepSeek R1 and OpenAI O1. Using datasets such as ImageReward and Human Preference Score v2 (HPSv2), our models achieve accuracies of 6

Christina Kassab, Sacha Morin, Martin Büchner, Matías Mattamala

3D scene understanding has been transformed by open-vocabulary language models that enable interaction via natural language. However, at present the evaluation of these representations is limited to datasets with closed-set semantics that do not capture the richness of language. This work presents OpenLex3D, a dedicated benchmark for evaluating 3D open-vocab

Changhui Yuan, Shishun Zhao, Shuwei Li, Xinyuan Song

Deep neural networks (DNNs) have become powerful tools for modeling complex data structures through sequentially integrating simple functions in each hidden layer. In survival analysis, recent advances of DNNs primarily focus on enhancing model capabilities, especially in exploring nonlinear covariate effects under right censoring. However, deep learning met

Jorge Fandinno, Yuliya Lierler

Splitting a logic program allows us to reduce the task of computing its stable models to similar tasks for its subprograms. This can be used to increase solving performance and prove program correctness. We generalize the conditions under which this technique is applicable, by considering not only dependencies between predicates but also their arguments and

David Turton, Alexander Tyukov

Holographic duality provides a microscopic interpretation of asymptotically Anti-de Sitter supergravity solutions. The dual states of the field theory give rise to expectation values of light operators. These expectation values correspond to coefficients in the asymptotic expansion of gauge-invariant combinations of supergravity fields. We consider the duali

Omar Amer, Shouvanik Chakrabarti, Kaushik Chakraborty, Shaltiel Eloul

Certified randomness can be generated with untrusted remote quantum computers using multiple known protocols, one of which has been recently realized experimentally. Unlike the randomness sources accessible on today's classical computers, the output of these protocols can be certified to be random under certain computational hardness assumptions, with no tru

Yifei Wang, Yingfei Gu

We propose a novel, distillation-free scheme for the fault-tolerant implementation of non-Clifford gates at the logical level, thereby completing the universal gate set. Our approach exploits generalized lattice surgery to integrate two quantum error-correcting (QEC) codes. Specifically, non-Clifford gates are executed transversally on one QEC code and then

Maciej Skorski, Alina Landowska, Krzysztof Rajda

The volatility and unpredictability of emerging technologies, such as artificial intelligence (AI), generate significant uncertainty, which is widely discussed on social media. This study examines anticipatory discourse surrounding technological futures by analysing 1.5 million posts from 400 key opinion leaders (KOLs) published on the X platform (from 2021

Zhi Hou, Tianyi Zhang, Yuwen Xiong, Haonan Duan

While recent vision-language-action models trained on diverse robot datasets exhibit promising generalization capabilities with limited in-domain data, their reliance on compact action heads to predict discretized or continuous actions constrains adaptability to heterogeneous action spaces. We present Dita, a scalable framework that leverages Transformer arc

Satoshi Komuro

While prior studies have examined the influence of information diffusion on epidemic dynamics, the role of affective polarisation--driven by digital media usage--remains less understood. This study introduces a mathematical framework to quantify the interplay between affective polarisation and epidemic spread, revealing contrasting effects depending on trans

Haoyu Fu, Diankun Zhang, Zongchuang Zhao, Jianfeng Cui

End-to-end (E2E) autonomous driving methods still struggle to make correct decisions in interactive closed-loop evaluation due to limited causal reasoning capability. Current methods attempt to leverage the powerful understanding and reasoning abilities of Vision-Language Models (VLMs) to resolve this dilemma. However, the problem is still open that few VLMs

Nikolai Nikolov, Pascal J. Thomas

The (unbounded version of the) Lempert function $l_D$ on a domain $D\subset\Bbb C^d$ does not usually satisfy the triangle inequality, but on bounded $\mathcal C^2$-smooth strictly pseudoconvex domains, it satisfies a quasi triangle inequality: $l_D(a,c)\le C( l_D(a,b)+l_D(b,c))$. We show that pseudoconvexity is necessary for this property as soon as $D$ has

Chuanzhi Xu, Haoxian Zhou, Haodong Chen, Vera Chung

Event cameras have gained increasing attention for 3D reconstruction due to their high temporal resolution, low latency, and high dynamic range. They capture per-pixel brightness changes asynchronously, allowing accurate reconstruction under fast motion and challenging lighting conditions. In this survey, we provide a comprehensive review of event-driven 3D

Lewis Newsham, Ryan Hyland, Daniel Prince

This paper presents SANDMAN, an architecture for cyber deception that leverages Language Agents to emulate convincing human simulacra. Our 'Deceptive Agents' serve as advanced cyber decoys, designed for high-fidelity engagement with attackers by extending the observation period of attack behaviours. Through experimentation, measurement, and analysis, we demo

L. Barbieri-Viale

For any scheme which is algebraic over a subfield of the complex numbers we here construct an homological regulator from Suslin homology to period homology and a higher cycle class map from Bloch's higher Chow group to the period Borel-Moore homology. Over algebraic numbers, making use of the motivic Albanese, we provide a purely geometric description of the

Daniel Groll, Thilo Hahn, Paweł Machnikowski, Tilmann Kuhn

This tutorial provides a joint theoretical and experimental overview of heterodyne wave mixing spectroscopy, focusing mainly on four-wave mixing (FWM). This powerful and versatile time-resolved nonlinear optical spectroscopy technique enables the investigation of individual localized single photon emitters, as well as microscopy of extended samples, e.g., tw

Pavan A. Uttarkar, Ryan M. Shannon, Kelly Gourdji, Adam T. Deller

Fast radio bursts (FRBs) are luminous, dispersed pulses of extra-galactic origin. The physics of the emission mechanism, the progenitor environment, and their origin are unclear. Some repeating FRBs are observed to have frequency-dependent exponential suppression in linear polarisation fraction. This has been attributed to multipath propagation in a surround

Shefra Shah, Farah Hussaini, Dumitru Mazilu, Eric E. Bennett

In CT-based evaluation of the extent of cystic changes in the lungs of patients with cystic lung diseases, such as Lymphangioleiomyomatosis (LAM), there is a lack of a lung phantom containing air-filled cavities that mimic pulmonary cysts to calibrate the measurement of cystic volumes from CT scans. Here we describe a simple, easy-to-replicate cystic lung ph

Miguel Albaladejo, Alejandro Canoa, Juan Nieves, Jose Ramón Peláez

We show that the use of realistic $\pi K$ interactions, obtained from a dispersive analysis of scattering data, as well as relativistic corrections, are essential to describe recently observed $\pi^\pm K_S$ femtoscopic correlations. We demonstrate that the spontaneous chiral symmetry breaking dynamics and the non-ordinary features of the $\kappa/K^*_0(700)$

Lucia Härer, Thibault Vieu, Brian Reville

Context: The environments of young star clusters are shaped by the interactions of the powerful winds of massive stars, and their feedback on the cluster birth cloud. Several such clusters show diffuse gamma-ray emission on the degree scale, which hints at ongoing particle acceleration. Aims: To date, particle acceleration and transport in star-cluster envir

Tomohiro Fujita, Ryodai Kawaguchi, Misao Sasaki, Yuichiro Tada

We consider models of inflation that contain a transient non-slow-roll stage and investigate the conditions under which a dip appears in the power spectrum of the curvature perturbation. Using the $\delta N$ formalism, we derive a general relation between the comoving curvature perturbation ${\cal{R}}$ and the scalar field perturbation $\delta\varphi$ and it

Alberto M. Campos, Tertuliano Franco, Markus Heydenreich, Marcel Schrocke

We establish a hydrodynamical limit for the averaging process on the complete graph with N vertices, showing that, after a timescale of order N, the empirical distribution of opinions converges to a unique measure. Moreover, if the initial distribution is absolutely continuous concerning the Lebesgue measure, the limiting measure remains absolutely continuou

Haoran Yin, Anna V. Kononova, Thomas Bäck, Niki van Stein

We study how large language models can be used in combination with evolutionary computation techniques to automatically discover optimization algorithms for the design of photonic structures. Building on the Large Language Model Evolutionary Algorithm (LLaMEA) framework, we introduce structured prompt engineering tailored to multilayer photonic problems such

Bastian Löhrer, Rolf Krause, Jochen Fröhlich

The paper presents a constraint-based collision model for Cosserat rods, able to handle dynamic or static contact between a large number of highly flexible structures. The model provides the required collision impulses prior to updating the solution of the rods, with the impulses accounted for as external loads. The procedure avoids the need to modify the st

Chengan Che, Chao Wang, Tom Vercauteren, Sophia Tsoka

Traditional open-access datasets focusing on surgical procedures are often limited by their small size, typically consisting of fewer than 100 videos and less than 30 hours of footage, which leads to poor model generalization. To address this data limitation, a new dataset called LEMON has been compiled using a novel aggregation pipeline that collects high-r

Pihai Sun, Junjun Jiang, Yuanqi Yao, Youyu Chen

Image-event joint depth estimation methods leverage complementary modalities for robust perception, yet face challenges in generalizability stemming from two factors: 1) limited annotated image-event-depth datasets causing insufficient cross-modal supervision, and 2) inherent frequency mismatches between static images and dynamic event streams with distinct

Yingqing Chen, Christos G. Cassandras

This paper develops an Optimal Safe Sequencing (OSS) control framework for Connected and Automated Vehicles (CAVs) navigating a single-lane roundabout in mixed traffic, where both CAVs and Human-Driven Vehicles (HDVs) coexist. The framework jointly optimizes vehicle sequencing and motion control to minimize travel time, energy consumption, and discomfort whi

Rui Xu, Jiachen Lu, Shihao Deng, Ligong Bian

We explore the effects of low-scale cosmological first-order phase transitions on Big Bang Nucleosynthesis (BBN) and Cosmic Microwave Background (CMB) anisotropy and distortion. We examine two scenarios: the distribution of the phase-transition energy in the cosmic background and the phase transition as an additional source of energy injection. Our analysis

Zixue Zeng, Xiaoyan Zhao, Matthew Cartier, Xin Meng

3D ultrasound delivers high-resolution, real-time images of soft tissues, which is essential for pain research. However, manually distinguishing various tissues for quantitative analysis is labor-intensive. To streamline this process, we developed and validated GRN+, a novel multi-model framework that automates layer segmentation with minimal annotated data.

Zixue Zeng, Matthew Cartier, Xiaoyan Zhao, Pengyu Chen

Available studies on chronic lower back pain (cLBP) typically focus on one or a few specific tissues rather than conducting a comprehensive layer-by-layer analysis. Since three-dimensional (3-D) images often contain hundreds of slices, manual annotation of these anatomical structures is both time-consuming and error-prone. We aim to develop and validate a no

Ubong Sam Idiong, Unanaowo Nyong Bassey

The search for spectral shift functions of operators remains an open area of research. In this paper, the Kre\u{\i}n's spectral shift functions are computed for the Lam\'e operator in the Weierstrass form and the Brioschi-Halphen operator through Green functions obtained by applying the technique of Fourier transform of distributions.

Paweł Zyblewski, Szymon Wojciechowski

The successes achieved by deep neural networks in computer vision tasks have led in recent years to the emergence of a new research area dubbed Multi-Dimensional Encoding (MDE). Methods belonging to this family aim to transform tabular data into a homogeneous form of discrete digital signals (images) to apply convolutional networks to initially unsuitable pr

Nenad Vukmirović

Present calculations of electrical transport properties of materials require evaluations of electron-phonon coupling constants on dense predefined grids of electron and phonon momenta and performing the sums over these momenta. In this work, we present the methodology for calculation of carrier relaxation times and electrical transport properties without the

Junhyuk So, Jiwoong Shin, Chaeyeon Jang, Eunhyeok Park

Recently, diffusion models have achieved significant advances in vision, text, and robotics. However, they still face slow generation speeds due to sequential denoising processes. To address this, a parallel sampling method based on Picard iteration was introduced, effectively reducing sequential steps while ensuring exact convergence to the original output.

Yuli Zhou, Yawei Li, Yuqian Fu, Luca Benini

Video camouflaged object segmentation (VCOS), aiming at segmenting camouflaged objects that seamlessly blend into their environment, is a fundamental vision task with various real-world applications. With the release of SAM2, video segmentation has witnessed significant progress. However, SAM2's capability of segmenting camouflaged videos is suboptimal, espe

Francesca Cantor, Julia D'Amico, Florian Frick, Eric Myzelev

We develop a novel topological framework that yields results constraining the distribution of zeros of certain zero mean real-valued maps, namely those obtained from composing a fixed equivariant map with linear functionals. We use this framework to establish upper bounds for the topology of set systems in the domain where (multivariate) trigonometric polyno

Flaurent Heully-Alary, Nadia Ben Amor, Nicolas Suaud, Laura Messio

Herbertsmithite is known as the archetype of a S=1/2 nearest-neighbor Heisenberg antiferromagnet on the Kagom\'e lattice, theoretically presumed to be a quantum gapless spin liquid. However, more and more experiments reveal that the model suffers from deviations from the ideal one, evidenced at very low temperatures. This detailed ab initio study focuses on

Zhen Huang, Denis Golež, Hugo U. R. Strand, Jason Kaye

We present an efficient separation of variables algorithm for the evaluation of imaginary time Feynman diagrams appearing in the bold pseudo-particle strong coupling expansion of the Anderson impurity model. The algorithm uses a fitting method based on AAA rational approximation and numerical optimization to obtain a sum-of-exponentials expansion of the hybr

Nobuyuki Okuma

A fractional Chern insulator is thought to emerge from the competition between one-particle band topology and strong repulsive interactions. As an attempt to study lattice models of fractional Chern insulators, we introduce a biorthogonal basis constructed from coherent-like states on the von Neumann lattice. Focusing on the fact that this basis is diagonal

XiaoXu Zhang, Yang Li, Yu Shang, MingYue Zhao

The phase transition in CrN epitaxial films is substantially suppressed by epitaxial constraint. Here, we propose that nitrogen (N) vacancies can be taken as a knob to regulate the phase transition of CrN(111) epitaxial films. To validate this concept, a series of CrN(111) films with controlled N concentrations (approximately from 0.0 to 5.0 at.%) were epita

Álvaro Rodríguez Abella, Leonardo Colombo

A discrete theory for implicit nonholonomic Lagrangian systems undergoing elastic collisions is developed. It is based on the discrete Lagrange-d'Alembert-Pontryagin variational principle and the dynamical equations thus obtained are the discrete nonholonomic implicit Euler-Lagrange equations together with the discrete conditions for the elastic impact. To i

Jakub Vašíček, Dafni Skiadopoulou, Ksenia G. Kuznetsova, Lukas Käll

In mass spectrometry-based proteomics, experts usually project data onto a single set of reference sequences, overlooking the influence of common haplotypes (combinations of genetic variants inherited together from a parent). We recently introduced ProHap, a tool for generating customized protein haplotype databases. Here, we present ProHap Explorer, a visua

Shakiba Rahimiaghdam, Hande Alemdar

Ensuring transparency in machine learning decisions is critically important, especially in sensitive sectors such as healthcare, finance, and justice. Despite this, some popular explainable algorithms, such as Local Interpretable Model-agnostic Explanations (LIME), often produce unstable explanations due to the random generation of perturbed samples. Random

Ritish Khetarpal, Aradhana Singh

The topology of city street networks (SNs) is constrained by spatial embedding, requiring non-crossing links and preventing random node placement or overlap. Here, we analyzed SNs of $33$ Indian cities to explore how the spatial embedding and the planarity jointly shape their topology. Overall, we found that all the studied SNs have small-world properties wi

Yifei Xiong, Ning Jiang, Zhen Pan, Lei Hao

We present a comprehensive analysis of the extended emission line region (EELR) in the host galaxy of the tidal disruption event (TDE) AT2019qiz, utilizing VLT/MUSE integral-field spectroscopy. The high spatial-resolution data reveal a bi-conical emission structure approximately $3.7~\mathrm{kpc}$ in scale within the galactic center, characterized by a promi

Chengjie Ge, Xueyang Fu, Peng He, Kunyu Wang

Leveraging its robust linear global modeling capability, Mamba has notably excelled in computer vision. Despite its success, existing Mamba-based vision models have overlooked the nuances of event-driven tasks, especially in video reconstruction. Event-based video reconstruction (EBVR) demands spatial translation invariance and close attention to local event

R. S. Stroud, C. Reynolds, T. Melichar, J. Haley

VN precipitates used to strengthen ARAFM steels for fusion applications dissolve under high Fe ion irradiation (100 dpa at 10^-3 dpa s^-1, 600 C). This study examined point defects and solute substitutions using atom probe tomography, machine learning interatomic potentials, and density functional theory. Combined with transmission electron microscopy, resul

Natacha Kuete Meli, Vladislav Golyanik, Marcel Seelbach Benkner, Michael Moeller

There is growing interest in solving computer vision problems such as mesh or point set alignment using Adiabatic Quantum Computing (AQC). Unfortunately, modern experimental AQC devices such as D-Wave only support Quadratic Unconstrained Binary Optimisation (QUBO) problems, which severely limits their applicability. This paper proposes a new way to overcome

Yuhong Jin, Andong Cong, Lei Hou, Qiang Gao

Koopman operator theory is a popular candidate for data-driven modeling because it provides a global linearization representation for nonlinear dynamical systems. However, existing Koopman operator-based methods suffer from shortcomings in constructing the well-behaved observable function and its inverse and are inefficient enough when dealing with partial d

Lan-Ye He, Xin-Man Ye, Dao-Xin Yao

We investigate the squared sublattice magnetizations and magnetic excitations of a $S=1/2$ trilayer antiferromagnetic Heisenberg model with interlayer interaction $J_{\bot}$ and intralayer interaction $J_{//}$ by employing stochastic series expansion quantum Monte Carlo (SSE-QMC) and stochastic analytic continuation (SAC) methods. Compared with the bilayer m

Anja Reusch, Yonatan Belinkov

Generative Information Retrieval (GenIR) is a novel paradigm in which a transformer encoder-decoder model predicts document rankings based on a query in an end-to-end fashion. These GenIR models have received significant attention due to their simple retrieval architecture while maintaining high retrieval effectiveness. However, in contrast to established re

Robert Ashmead, Michael B. Hawes, Mary Pritts, Pavel Zhuravlev

To protect the confidentiality of the 2020 Census, the U.S. Census Bureau adopted a statistical disclosure limitation framework based on the principles of differential privacy. A key component was the TopDown Algorithm, which applied differentially-private noise to an extensive series of counts from the confidential 2020 Census data and transformed the resul

Yusen Xie, Zhengmin Huang, Shaojie Shen, Jun Ma

In this paper, we introduce Semi-SMD, a novel metric depth estimation framework tailored for surrounding cameras equipment in autonomous driving. In this work, the input data consists of adjacent surrounding frames and camera parameters. We propose a unified spatial-temporal-semantic fusion module to construct the visual fused features. Cross-attention compo

Sanghyuk Kim, Minsik Seo, Sunwoong Yang, Namwoo Kang

This study presents a rigid-deformation decomposition framework for vehicle collision dynamics that mitigates the spectral bias of implicit neural representations, that is, coordinate-based neural networks that directly map spatio-temporal coordinates to physical fields. We introduce a hierarchical architecture that decouples global rigid-body motion from lo

Sian Gooding, Lucia Lopez-Rivilla, Edward Grefenstette

Open-ended tasks are particularly challenging for LLMs due to the vast solution space, demanding both expansive exploration and adaptable strategies, especially when success lacks a clear, objective definition. Writing, with its vast solution space and subjective evaluation criteria, provides a compelling testbed for studying such problems. In this paper, we

Jin Guang, Xinyun Chen, J. G. Dai, Peter W. Glynn

Inspired by Dai et al. [2023], we develop a novel multi-scaling asymptotic regime for semimartingale reflecting Brownian motion (SRBM). In this regime, we establish the steady-state convergence of SRBM to a product-form limit with exponentially distributed components by assuming the P-reflection matrix and a uniform moment bound condition. We further demonst

Hai-Long Fu, Yong-Hui Lin, Feng-Kun Guo, Hans-Werner Hammer

The Efimov effect is an intriguing three-body quantum phenomenon. Searching for Efimov states within the realms of nuclear and hadronic physics presents a challenge due to the inherent inability of natural physical systems to exhibit adjustable two-body scattering lengths. In this study, we examine the potential existence of Efimov states in the $D^*D^*D^*$

Ilias Stogiannidis, Steven McDonagh, Sotirios A. Tsaftaris

Vision-Language Models (VLMs) have recently emerged as powerful tools, excelling in tasks that integrate visual and textual comprehension, such as image captioning, visual question answering, and image-text retrieval. However, existing benchmarks for VLMs include spatial components, which often fail to isolate spatial reasoning from related tasks such as obj

Martin Perešíni, Ivan Homoliak, Samuel Olekšák, Samuel Slávka

Traditionally, mobile wallets rely on a trusted server that provides them with a current view of the blockchain, and thus, these wallets do not need to validate the header chain or transaction inclusion themselves. If a mobile wallet were to validate a header chain and inclusion of its transactions, it would require significant storage and performance overhe

Jungin Park, Jiyoung Lee, Kwanghoon Sohn

View-invariant representation learning from egocentric (first-person, ego) and exocentric (third-person, exo) videos is a promising approach toward generalizing video understanding systems across multiple viewpoints. However, this area has been underexplored due to the substantial differences in perspective, motion patterns, and context between ego and exo v

Daniel Amankwah, Jakob Björnberg, Sigurdur Örn Stefánsson, Benedikt Stufler

A finite graph embedded in the plane is called a series-parallel map if it can be obtained from a finite tree by repeatedly subdividing and doubling edges. We study the scaling limit of weighted random two-connected series-parallel maps with $n$ edges and show that under some integrability conditions on these weights, the maps with distances rescaled by a fa

Seyed Moein Ayyoubzadeh, Kourosh Shahnazari, Mohammadamin Fazli, Mohammadali Keshtparvar

Exploration of the impact of personality traits on social interactions within anonymous online communities poses a challenge at the interface of networked social sciences and psychology. We analyze whether Myers-Briggs Type Indicator (MBTI) personality types impact the dynamics of interactions on an anonymous chat system with over 288,000 messages from 6,076

Abdulhamid Abubakar, Hamidatu Abdulkadir, Ibrahim Rabiu Abdullahi, Abubakar Auwal Khalid

This paper presents our findings for SemEval 2025 Task 2, a shared task on entity-aware machine translation (EA-MT). The goal of this task is to develop translation models that can accurately translate English sentences into target languages, with a particular focus on handling named entities, which often pose challenges for MT systems. The task covers 10 ta

Ying Liu, Jingjing Xiao, Nianyu Yi, Huihui Cao

Recovery type a posteriori error estimators are popular, particularly in the engineering community, for their computationally inexpensive, easy to implement, and generally asymptotically exactness. Unlike the residual type error estimators, one can not establish upper and lower a posteriori error bounds for the classical recovery type error estimators withou

Boyi Li, Ye Yuan, Wenjun Tan

The MedSAM model, built upon the SAM framework, enhances medical image segmentation through generalizable training but still exhibits notable limitations. First, constraints in the perturbation window settings during training can cause MedSAM to incorrectly segment small tissues or organs together with adjacent structures, leading to segmentation errors. Sec

Muhammad Al-Zafar Khan, Jamal Al-Karaki

In this study, we formulate the drone delivery problem as a control problem and solve it using Model Predictive Control. Two experiments are performed: The first is on a less challenging grid world environment with lower dimensionality, and the second is with a higher dimensionality and added complexity. The MPC method was benchmarked against three popular M

M. Pavez-Herrera, P. Sánchez-Sáez, L. Hernández-García, F. E. Bauer

ALeRCE (Automatic Learning for the Rapid Classification of Events) processes the Zwicky Transient Facility (ZTF) alert stream in preparation for the Vera C. Rubin Observatory, classifying objects using a broad taxonomy. The ALeRCE light curve classifier is a balanced random forest (BRF) algorithm with a two-level scheme that uses variability features from th

Da Eun Kang, Dominika Itrich, Victor F. Ksoll, Leonardo Testi

We introduce an updated version of our deep learning tool that predicts stellar parameters from the optical spectra of young low-mass stars with intermediate spectral resolution. We adopt a conditional invertible neural network (cINN) architecture to infer the posterior distribution of stellar parameters and train our cINN on two Phoenix stellar atmosphere m

Jon Asier Bárcena-Petisco, Luis Martínez, María Merino, Juan Manuel Montoya

In this paper we introduce a family of partitions of the set of natural numbers, Fibonacci-like partitions. In particular, we introduce a Fibonacci-like partition in a number of parts corresponding to the Fibonacci numbers, the standard Fibonacci-like partitions of the first kind. That partition refines the well-known partition in two parts associated to the

Vo Quoc Bao, Phung Ho Hai, Dao Van Thinh

Let X be a smooth projective curve over a field k of characteristic zero. The differential fundamental group of X is defined as the Tannakian dual to the category of vector bundles with (integrable) connections on X. This work investigates the relationship between the de Rham cohomology of a vector bundle with connection and the group cohomology of the corre

Jaeseong Oh, Brendon Rhoades

Let $\mathbf{x}_{k \times p}$ be a $k \times p$ matrix of variables and let $\mathbb{F}[\mathbf{x}_{k \times p}]$ be the polynomial ring in these variables. Given two weak compositions $\alpha,\beta \models_0 n$ of lengths $\ell(\alpha) = k$ and $\ell(\beta) = p$, we study the ideal $I_{\alpha,\beta} \subseteq \mathbb{F}[\mathbf{x}_{k \times \ell}]$ generate

Itay Nakash, Nitay Calderon, Eyal Ben David, Elad Hoffer

Large Language Models (LLMs) have shown impressive versatility as general purpose models. However, their broad applicability comes at a high-cost computational overhead, particularly in auto-regressive decoding where each step requires a forward pass. In domain-specific settings, general-purpose capabilities are unnecessary and can be exchanged for efficienc

André Groß, Birte Richter, Bjarne Thomzik, Britta Wrede

Understanding how scaffolding strategies influence human understanding in human-robot interaction is important for developing effective assistive systems. This empirical study investigates linguistic scaffolding strategies based on negation as an important means that de-biases the user from potential errors but increases processing costs and hesitations as a

Yannick Dengler, Suchita Kulkarni, Axel Maas, Kevin Radl

Dark matter may accumulate in neutron stars given its gravitational interaction and abundance. We investigate the influence of strongly-interacting dark matter, described by a QCD-like one-flavor $G_2$ gauge theory, on neutron stars. This choice allows to test, for the first time, a first-principles-determined non-Abelian dark matter equation of state, which

Bo Leng, Ran Yu, Wei Han, Lu Xiong

Applying reinforcement learning to autonomous driving has garnered widespread attention. However, classical reinforcement learning methods optimize policies by maximizing expected rewards but lack sufficient safety considerations, often putting agents in hazardous situations. This paper proposes a risk-aware reinforcement learning approach for autonomous dri

Uttam Cadambi Padmanaban, Bharathram Ganapathisubramani, Sean Symon

We present a novel framework for assimilating planar PIV experimental data using a variational approach to enhance the predictions of the Spalart-Allmaras RANS turbulence model. Our method applies three-dimensional constraints to the assimilation of mean velocity data, incorporating a corrective forcing term in the momentum equations. The advantages of this

Mainak Dutta, Partha Nandi, Bibhas Ranjan Majhi

We study the impact of thermal effects on gravity-induced entanglement (GIE) in a system of quantum harmonic oscillators interacting with classical linearly polarized gravitational waves (GWs). Specifically, we model the endpoints of interferometer arms in LIGO-like detectors as two-dimensional oscillators. Following the thermofield dynamics (TFD) approach,

M. Abhishek, Ankit Dhanuka, Deb Sankar Banerjee, Madan Rao

Activity and renewability are distinctive features of living matter, and constitute a new class of materials that we term renewable active matter. A striking example is the cell cytoskeleton, where myosin filaments bind to the actin meshwork, apply contractile stresses and undergo continual stress/strain dependent turnover, thus acting as both force generato

Guy Fowler, Emanuele Tron

Let $E_1, E_2 / \mathbb{C}$ be non-isomorphic elliptic curves with complex multiplication. We prove that the pair $(E_1, E_2)$ is characterised, up to isomorphism, by the difference $j(E_1) - j(E_2)$ of the respective $j$-invariants. In other words, we show that if $x_1, x_2, x_3, x_4$ are singular moduli such that $x_1 - x_2 = x_3 - x_4$, then either $(x_1,

Roberto Montemanni, Derek H. Smith

The Maximum Flow Problem with Conflict Constraints is a generalization that adds conflict constraints to a classical optimization problem on networks used to model several real-world applications. In the last few years several approaches, both heuristic and exact, have been proposed to attack the problem. In this paper we consider a mixed integer linear prog

Jan Ellmenreich, Matteo Giacomini, Antonio Huerta, Philip L. Lederer

In this work we introduce the concept of characteristic boundary conditions (CBCs) within the framework of Hybridizable Discontinuous Galerkin (HDG) methods, including both the Navier-Stokes characteristic boundary conditions (NSCBCs) and a novel approach to generalized characteristic relaxation boundary conditions (GRCBCs). CBCs are based on the characteris

Andrii Yermakov, Jan Cech, Jiri Matas

This paper tackles the challenge of detecting partially manipulated facial deepfakes, which involve subtle alterations to specific facial features while retaining the overall context, posing a greater detection difficulty than fully synthetic faces. We leverage the Contrastive Language-Image Pre-training (CLIP) model, specifically its ViT-L/14 visual encoder

Brian C. Hall, Sorawit Eaknipitsari

We consider a family $b_{s,\tau}$ of free multiplicative Brownian motions labeled by a real variance parameter $s$ and a complex covariance parameter $\tau$. We then consider the element $xb_{s,\tau}$, where $x$ is non-negative and freely independent of $b_{s,\tau}$. Our goal is to identify the support of the Brown measure of $xb_{s,\tau}$. In the case $\tau

Purba Chatterjee, Eleni Katifori

Network remodeling, or adaptation, in the presence of periodically driven forcings has hereto remained largely unexplored, despite the fact that a broad class of biological transport networks, e.g. animal vasculature, depends on periodic driving (pulsatility of the heart) to maintain flow. Short-term pulsatile dynamics of compliant vessels affects the long-t

Jan Schwientek, Katrin Teichert, Jan Schröder, Johannes Höller

Model-based process design and operation involves here-and-now and wait-and-see decisions. Here-and-now decisions include design variables like the size of heat exchangers or the height of distillation columns, whereas wait-and-see decisions are directed towards operational variables like reflux and split ratios. In this contribution, we describe how to deal

Daniele Malafarina, Hrishikesh Chakrabarty, Ilia Musco

We establish for the first time the conditions that must be imposed on the action for a magnetic universe in a theory of non-linear electrodynamics in order to have an asymptotically de Sitter initial state followed by a slow roll inflationary phase. We show that models so far proposed in the literature do not allow for a prolonged inflationary phase consist

Yuetong Fang

Let $(X,\omega)$ be a compact Hermitian manifold of dimension $n$. We show that all $(\omega,m)$-subharmonic functions are $L^p$ integrable on $X$, for any $p < \frac{n}{n-m}$.

Niketa Penumajji

This paper explores the application of Convolutional Neural Networks CNNs for classifying emotions in speech through Mel Spectrogram representations of audio files. Traditional methods such as Gaussian Mixture Models and Hidden Markov Models have proven insufficient for practical deployment, prompting a shift towards deep learning techniques. By transforming

Xianke Qiang, Zheng Chang, Geyong Min

To leverage the vast amounts of onboard data while ensuring privacy and security, federated learning (FL) is emerging as a promising technology for supporting a wide range of vehicular applications. Although FL has great potential to improve the architecture of intelligent vehicular networks, challenges arise due to vehicle mobility, wireless channel instabi