March 2025 arXiv papers — page 59

Nathan Darjana, Ryo Fujii, Hideo Saito, Hiroki Kajita

Egocentric open-surgery videos capture rich, fine-grained details essential for accurately modeling surgical procedures and human behavior in the operating room. A detailed, pixel-level understanding of hands and surgical tools is crucial for interpreting a surgeon's actions and intentions. We introduce EgoSurgery-HTS, a new dataset with pixel-wise annotatio

Qin Wang, Alessio Quercia, Benjamin Bruns, Abigail Morrison

Self-supervised learning (SSL) methods have achieved remarkable success in learning image representations allowing invariances in them - but therefore discarding transformation information that some computer vision tasks actually require. While recent approaches attempt to address this limitation by learning equivariant features using linear operators in fea

Der-Hau Lee

A robust control strategy for autonomous vehicles can improve system stability, enhance riding comfort, and prevent driving accidents. This paper presents a novel interpolation-tube-based constrained iterative linear quadratic regulator (itube-CILQR) algorithm for autonomous computer-vision-based vehicle lane-keeping. The goal of the algorithm is to enhance

Wesley Scivetti, Nathan Schneider

Construction Grammar hypothesizes that knowledge of a language consists chiefly of knowledge of form-meaning pairs (''constructions'') that include vocabulary, general grammar rules, and even idiosyncratic patterns. Recent work has shown that transformer language models represent at least some constructional patterns, including ones where the construction is

Le Qiu, Zelai Xu, Qixin Tan, Wenhao Tang

Assessing the safety of autonomous driving policy is of great importance, and reinforcement learning (RL) has emerged as a powerful method for discovering critical vulnerabilities in driving policies. However, existing RL-based approaches often struggle to identify vulnerabilities that are both effective-meaning the autonomous vehicle is genuinely responsibl

Igor Uljarević, Jun Zhang

We introduce the notions of partial contact quasi-state and contact quasi-measure. Using the contact spectral invariant from the work by Djordjevi\'c-Uljarevi\'c-Zhang, one can construct partial contact quasi-states and contact quasi-measures on each contact manifold fillable by a Liouville domain with non-vanishing and $\mathbb{Z}$-graded symplectic homolog

Erez Nesharim, Uri Shapira, Noy Soffer Aranov

Every Laurent series in $\mathbb{F}_q\left(\left(t^{-1}\right)\right)$ has a continued fraction expansion whose partial quotients are polynomials. De Mathan and Teuli\'e proved that the degrees of the partial quotients of the left-shifts of every quadratic Laurent series are unbounded. Shapira and Paulin and Kemarsky improved this by showing that certain seq

Florian Rupp, Manuel Eberhardinger, Kai Eckert

The balancing process for game levels in competitive two-player contexts involves a lot of manual work and testing, particularly for non-symmetrical game levels. In this work, we frame game balancing as a procedural content generation task and propose an architecture for automatically balancing of tile-based levels within the PCGRL framework (procedural cont

Zhibo Liu, Akira Watanabe

The gravitational form factor (GFF) of the kaon is investigated in a bottom-up holographic QCD model, in which the strange quark mass breaks the SU(3) flavor symmetry. The probe energy ($Q^2$) dependence of the kaon GFF is explicitly shown and compared to that of the pion. It is presented that our result shows the $1/Q^2$ behavior in the high energy region,

Yifei Zhang, Chang Liu, Jin Wei, Xiaomeng Yang

Text images are unique in their dual nature, encompassing both visual and linguistic information. The visual component encompasses structural and appearance-based features, while the linguistic dimension incorporates contextual and semantic elements. In scenarios with degraded visual quality, linguistic patterns serve as crucial supplements for comprehension

Deng Wang, Olga Mena, Eleonora Di Valentino, Stefano Gariazzo

Cosmological neutrino mass and abundance measurements are reaching unprecedented precision. Testing their stability versus redshift and scale is a crucial issue, as it can serve as a guide for optimizing ongoing and future searches. Here, we perform such analyses, considering a number of redshift, scale, and redshift-and-scale nodes. Concerning the $k$-space

Lingyue Liu, Sebastian Gassenmeier, Erin Koos

Hypothesis: Anisotropic rod particles in capillary suspensions form complex network structures with distinctive orientation patterns and rheological properties that differ significantly from spherical particle systems. By identifying the orientation of individual particles, we are able to acquire invaluable experimental insight into the bulk particle orienta

ALICE Collaboration

Correlations among conserved quantum numbers, such as the net-electric charge, the net-baryon, and the net-strangeness in heavy-ion collisions, are crucial for exploring the QCD phase diagram. In this paper, these correlations are investigated using net-proton number (as a proxy for the net-baryon), net-kaon number (for the net-strangeness), and net-charged

Sebastian Tewes, Yufan Chen, Omar Moured, Jiaming Zhang

Document Layout Analysis (DLA) is a fundamental task in document understanding. However, existing DLA and adaptation methods often require access to large-scale source data and target labels. This requirements severely limiting their real-world applicability, particularly in privacy-sensitive and resource-constrained domains, such as financial statements, me

Okuto Morikawa, Shoya Ogawa

We study quasi-stationary states in quantum mechanics using the exact Wentzel--Kramers--Brillouin (WKB) analysis as a nonperturbative framework. Whereas previous works focused mainly on stable systems, we explore unstable states such as resonances. As a concrete example, we analyze the inverted Rosen--Morse potential, which exhibits barrier resonance. This m

Sichao Qu, Min Yang, Sibo Huang, Shuohan Liu

Causality constraints are known to bind sound absorption to a limit that can only be achieved by optimizing the system bandwidth for a specific material thickness. This limit is defined on the assumption of a one-port system, generally causing duality symmetry to be overlooked. Here, we define a generalized causality constraint of sound absorption by investi

Fleurianne Bertrand, Maximilian Brodbeck, Tim Ricken, Henrik Schneider

This paper presents a unified Least-Squares framework for solving nonlinear partial differential equations by recasting the governing system as a residual minimisation problem. A Least-Squares functional is formulated and the corresponding Gauss-Newton method derived, which approximates simultaneously primal and dual variables. We derive conditions under whi

Abdul Qayyum, Moona Mazher, Devran Ugurlu, Jose Alonso Solis Lemus

Whole-heart segmentation from CT and MRI scans is crucial for cardiovascular disease analysis, yet existing methods struggle with modality-specific biases and the need for extensive labeled datasets. To address these challenges, we propose a foundation model for whole-heart segmentation using a self-supervised learning (SSL) framework based on a student-teac

Chengbo Yuan, Suraj Joshi, Shaoting Zhu, Hang Su

Visual augmentation has become a crucial technique for enhancing the visual robustness of imitation learning. However, existing methods are often limited by prerequisites such as camera calibration or the need for controlled environments (e.g., green screen setups). In this work, we introduce RoboEngine, the first plug-and-play visual robot data augmentation

Yongsoo Jho, Seong Chan Park, Chang Sub Shin

We propose an explanation for the recently reported ultra-high-energy neutrino signal at KM3NeT, which shows no clear association with known astrophysical sources. While decaying dark matter in the Galactic Center is a natural candidate, the observed arrival direction strongly suggests an extragalactic origin. We introduce a multicomponent dark matter scenar

Bamidele Ajayi, Basel Barakat, Ken McGarry

This paper assesses the performance of five machine learning classifiers: Decision Tree, Naive Bayes, LightGBM, Logistic Regression, and Random Forest using latent representations learned by a Variational Autoencoder from malware datasets. Results from the experiments conducted on different training-test splits with different random seeds reveal that all the

Sima Farshbaf, Narges Dialami, Miguel Cervera

Additively manufactured auxetic structures offer desirable qualities like lightweight, good energy absorption, excellent indentation resistance, high shear stiffness and fracture toughness among others. A wide range of materials from polymers to metals can be used to fabricate these structures. In contrast to conventional materials, auxetic structures exhibi

Imran Aziz, Yasin Alekajbaf, Dragos Dancila, Kristiaan Pelckmans

The Wireless Allowing Data and Power Transmission (WADAPT) proposal was formed to study the feasibility of wireless technologies in HEP experiments. A strong motivation for using wireless data transmission at the ATLAS detector is the absence of wires and connectors to reduce the passive material. However, the tracking layers are almost hermetic, acting as a

Rafael A. Macedo, Patrick Andriolo, Santiago Zamora, Davide Poderini

Non-stabilizerness, or magic, is a fundamental resource for quantum computation, enabling quantum algorithms to surpass classical capabilities. Despite its importance, characterizing magic remains challenging due to the intricate geometry of stabilizer polytopes and the difficulty of simulating non-stabilizer states. In this work, we reveal an unexpected con

Simon Goorney, Eleni Karydi, Borja Munoz, Otto Santesson

The rapid advancement of Quantum Technology (QT) has created a growing demand for a specialized workforce, spanning across academia and industry. This study presents a quantitative analysis of the QT job market by systematically extracting and classifying 3641 job postings worldwide. The classification pipeline leverages large language models (LLMs) whilst i

C. K. Louis, A. Loh, P. Zarka, L. Lamy

The search for radio signals from exoplanets or star-planet interactions is a topic of major scientific interest, as it is likely the best way to detect and measure a planetary magnetic field and, therefore, to probe the inner structure of exoplanets. However, detecting these radio emissions is challenging, since they are anisotropic by nature, sporadic, and

Thomas Weikert, Eljas Roellin, Monica Pérez-Serrano, Elisa Du

A significant and rising proportion of the global population suffer from non-communicable diseases, such as neurological disorders. Neurorehabilitation aims to restore function and independence of neurological patients through providing interdisciplinary therapeutic interventions. Computational neurorehabilitation, an automated simulation approach to dynamic

Chris Pedersen, Laure Zanna, Joan Bruna

Autoregressive surrogate models (or \textit{emulators}) of spatiotemporal systems provide an avenue for fast, approximate predictions, with broad applications across science and engineering. At inference time, however, these models are generally unable to provide predictions over long time rollouts due to accumulation of errors leading to diverging trajector

Hongkuan Zhou, Stefan Schmid, Yicong Li, Lavdim Halilaj

The autonomous driving field has seen remarkable advancements in various topics, such as object recognition, trajectory prediction, and motion planning. However, current approaches face limitations in effectively comprehending the complex evolutions of driving scenes over time. This paper proposes FM4SU, a novel methodology for training a symbolic foundation

Eman Alashwali

Users share a vast amount of data while using web and mobile applications. Most service providers such as email and social media providers provide users with privacy controls, which aim to give users the means to control what, how, when, and with whom, users share data. Nevertheless, it is not uncommon to hear users say that they feel they have lost control

Hassan Irshad Bhatti

Precise temperature measurement at micro/nanoscale is crucial across various domains including physical sciences, chemical processes, industrial production, medical diagnosis, weather forecasting, electronics, and biology. Micro/nanoscale thermal mapping requires precise techniques such as thermocouples, resistance-based devices, infrared thermography, optic

David Chodounský, Monroe Eskew, Thilo Weinert

We investigate big Ramsey degrees of finite substructures of the universal countable homogeneous meet-tree and its binary variant. We prove that structures containing antichains have infinite big Ramsey degrees, and the big Ramsey degree of a 2-element chain is at least 8 and 7 for the binary variant. We deduce that the generic C-relation does not have finit

Paul Meffle

In this paper we define the pro-\'etale homotopy type of a scheme and prove some of its expected properties. Our definition is similar to the definition of the \'etale homotopy type by Michael Artin and Barry Mazur. We prove that for a qcqs scheme the pro-\'etale homotopy type is profinite, determined by a single split affine weakly contractible hypercoverin

Zimin Xia, Alexandre Alahi

We propose a novel fine-grained cross-view localization method that estimates the 3 Degrees of Freedom pose of a ground-level image in an aerial image of the surroundings by matching fine-grained features between the two images. The pose is estimated by aligning a point plane generated from the ground image with a point plane sampled from the aerial image. T

Veronika Solopova

The rise of Artificial Intelligence (AI) presents unprecedented opportunities and challenges for journalism, fact-checking and media regulation. While AI offers tools to combat disinformation and enhance media practices, its unregulated use and associated risks necessitate clear policies and collaborative efforts. This policy paper explores the implications

Davide Faccialà, Matteo Bonanomi, Bruno Nunes Cabral Tenorio, Lorenzo Avaldi

We report a study of the electronic and nuclear relaxation dynamics of the photoexcited RNA base uracil in the gas phase, using time-resolved core level photoelectron spectroscopy together with high level calculations. The dynamics was investigated by trajectory surface-hopping calculations, and the core ionization energies were calculated for geometries sam

Shixiang Liu, Yanhang Zhang, Zhifan Li, Jianxin Yin

Multi-sourced datasets are common in studies of variable interactions, for example, individual-level fMRI integration, cross-domain recommendation, etc, where each source induces a related but distinct dependency structure. Joint learning of multiple graphical models (i.e., multiple precision matrices) has emerged as an important tool in analyzing such data.

Jürgen Rossmann

The autor considers an initial-boundary value problem for the nonstationary Stokes system in an angle, where Dirichlet and Neumann conditions are prescribed on the diferent sides of the angle. The major part of the paper deals with the parameter-depending problem which arises after the application of the Laplace transform. The author obtains existence, uniqu

Liang Hou, Cong Liu, Mingwu Zheng, Xin Tao

Resolution generalization in image generation tasks enables the production of higher-resolution images with lower training resolution overhead. However, a key obstacle for diffusion transformers in addressing this problem is the mismatch between positional encodings seen at inference and those used during training. Existing strategies such as positional enco

Lijiang Li, Jinglu Wang, Xiang Ming, Yan Lu

In the Generative AI era, safeguarding 3D models has become increasingly urgent. While invisible watermarking is well-established for 2D images with encoder-decoder frameworks, generalizable and robust solutions for 3D remain elusive. The main difficulty arises from the renderer between the 3D encoder and 2D decoder, which disrupts direct gradient flow and c

Somia Atmani, Kheireddine Biroud, Maha Daoud, El-Haj Laamri

In this work, we study the existence and nonexistence of nonnegative solutions to a class of nonlocal elliptic systems set in a bounded open subset of $\mathbb{R}^N$. The diffusion operators are of type $u_i\mapsto d_i(-\Delta)^{s_i}u_i$ where $0<s_1\neq s_2<1$, and the gradients of the unknowns act as source terms. Existence results are obtained by proving

Wen Yang

It is proved that a bijection between two compact hyperbolic surfaces with boundary is an isometry if it and its inverse map each geodesic onto some geodesic.

Murray Skolnick, Salvatore Torquato

Two-phase heterogeneous materials arising in a variety of natural and synthetic situations exhibit a wide-variety of microstructures and thus display a broad-spectrum effective physical properties. Given that such properties of disordered materials generally depend on an infinite-set of microstructural correlation functions that are typically unobtainable, i

Nooshin Bahador, Milad Lankarany

Spectrograms are pivotal in time-frequency signal analysis, widely used in audio processing and computational neuroscience. Chirp-like patterns in electroencephalogram (EEG) spectrograms (marked by linear or exponential frequency sweep) are key biomarkers for seizure dynamics, but automated tools for their detection, localization, and feature extraction are

Philippe Balbiani, Cigdem Gencer

In this note, by integrating ideas concerning terminating tableaux-based procedures in modal logics and finite frame property of intuitionistic modal logic IK, we provide new and simpler decidability proofs for FIK and LIK.

Matteo M. Defranchis, Jorge de Blas, Ankita Mehta, Michele Selvaggi

A scan of the beam energy across the top quark pair ($\mathrm{t\overline{t}}$) production threshold is part of the program of future Higgs, top, and electroweak factory projects. In this paper, we provide projections for the achievable precision in the top quark mass ($m_\mathrm{t}$), width ($\Gamma_\mathrm{t}$), and Yukawa coupling ($y_\mathrm{t}$) at the e

Haozhe Qi, Shaokai Ye, Alexander Mathis, Mackenzie W. Mathis

Understanding human behavior requires measuring behavioral actions. Due to its complexity, behavior is best mapped onto a rich, semantic structure such as language. Emerging multimodal large language models (MLLMs) are promising candidates, but their fine-grained action understanding ability has not been fully examined. In this work, we reformulate EPIC-KITC

Thomas Sugg, Kyle O'Brien, Lekh Poudel, Alex Dumouchelle

This paper introduces ACC-NVS1, a specialized dataset designed for research on Novel View Synthesis specifically for airborne and ground imagery. Data for ACC-NVS1 was collected in Austin, TX and Pittsburgh, PA in 2023 and 2024. The collection encompasses six diverse real-world scenes captured from both airborne and ground cameras, resulting in a total of 14

M. Albaladejo, A. Garcia-Lorenzo, J. Nieves

This work addresses the incorporation of Coulomb interactions into femtoscopy correlation functions (CFs) used to probe hadron interactions. Combining strong contact potentials with Coulomb effects, the derived scattering amplitudes and wave functions are used to compute CFs, accounting for both interactions coherently. Next, we analyze the nature of the cor

Boqi Chen, Cédric Vincent-Cuaz, Lydia A. Schoenpflug, Manuel Madeira

Vision foundation models (FMs) are accelerating the development of digital pathology algorithms and transforming biomedical research. These models learn, in a self-supervised manner, to represent histological features in highly heterogeneous tiles extracted from whole-slide images (WSIs) of real-world patient samples. The performance of these FMs is signific

ALICE Collaboration

According to quantum chromodynamics, at sufficiently high energy, the structure of hadrons reveals a dynamic equilibrium between gluon splitting and gluon recombination -- a phenomenon known as saturation. The process of diffractive photonuclear production of a J/$\psi$ vector meson provides a direct insight into the gluon composition of hadrons. The J/$\psi

Vazha Loladze, Arthur Platschorre, Mario Reig

We study the minimal requirements to obtain axion strings for axions with exponentially good quality. These ingredients appear in theories where an axion coming from a higher-form gauge field mixes with the phase of a complex scalar field in a situation that resembles higher-groups. The resulting axion is perturbatively massless and inherits a high-quality s

Chetna Singhal, Yassine Hadjadj-Aoul

Efficient network modeling is essential for resource optimization and network planning in next-generation large-scale complex networks. Traditional approaches, such as queuing theory-based modeling and packet-based simulators, can be inefficient due to the assumption made and the computational expense, respectively. To address these challenges, we propose an

Inseung Hwang, Kiseok Choi, Hyunho Ha, Min H. Kim

Snapshot polarization imaging calculates polarization states from linearly polarized subimages. To achieve this, a polarization camera employs a double Bayer-patterned sensor to capture both color and polarization. It demonstrates low light efficiency and low spatial resolution, resulting in increased noise and compromised polarization measurements. Although

Markus Bachmayr, Henrik Eisenmann, Igor Voulis

Near-optimal computational complexity of an adaptive stochastic Galerkin method with independently refined spatial meshes for elliptic partial differential equations is shown. The method takes advantage of multilevel structure in expansions of random diffusion coefficients and combines operator compression in the stochastic variables with error estimation us

Guanglu Dong, Tianheng Zheng, Yuanzhouhan Cao, Linbo Qing

Recently, deep image deraining models based on paired datasets have made a series of remarkable progress. However, they cannot be well applied in real-world applications due to the difficulty of obtaining real paired datasets and the poor generalization performance. In this paper, we propose a novel Channel Consistency Prior and Self-Reconstruction Strategy

David Ph. Shakouri, Crit Cremers, Niels O. Schiller

This article presents experiments performed using a computational laboratory environment for language acquisition experiments. It implements a multi-agent system consisting of two agents: an adult language model and a daughter language model that aims to learn the mother language. Crucially, the daughter agent does not have access to the internal knowledge o

V. I. Grytsay

A mathematical model of atherosclerosis of a blood vessel is advanced with regard for the entry of low-density lipoproteins (LDLs) into blood. For the first time, the influence of cytokines on the inflammation of a blood vessel at the formation of atherosclerotic plaques is taken into account. With the help of the expansion in a Fourier series and the calcul

Nikolai Beluhov

A leaper is a chess piece which generalises the knight. Given $n$ and a $(p, q)$-leaper $L$, we study the greatest $m$ such that the $m \times m$ grid graph can be embedded into the $n \times n$ leaper graph of $L$. We can assume that $p$ and $q$ are relatively prime. We show that $m \approx n$ when $p$ and $q$ are of opposite parities and $m \approx n/2$ ot

Qiumei Huang, Zhonghua Qiao, Cheng Wang, Huiting Yang

In cancer research, the role of the extracellular matrix (ECM) and its associated matrix-degrading enzyme (MDE) has been a significant area of focus. This study presents a numerical algorithm designed to simulate a previously established tumor model that incorporates various biological factors, including tumor cells, viable cells, necrotic cells, and the dyn

Malek Itani, Tuochao Chen, Arun Raghavan, Gavriel Kohlberg

The conventional wisdom has been that designing ultra-compact, battery-constrained wireless hearables with on-device speech AI models is challenging due to the high computational demands of streaming deep learning models. Speech AI models require continuous, real-time audio processing, imposing strict computational and I/O constraints. We present NeuralAids,

Julia Le Bihan, Bartosz Kołodziejek

This work investigates the tail behavior of solutions to the affine stochastic fixed-point equation of the form $X\stackrel{d}{=}AX+B$, where $X$ and $(A,B)$ are independent. Focusing on the light-tail regime, following [Burdzy et al. (2022), Ann. Appl. Probab.] we introduce a local dependence measure along with an associated Legendre-type transform. These t

Nhat A. Nghiem

It is shown that a quantum computer can test the convexity and monotonicity of a given function exponentially more efficiently than a classical computer. This establishes another prominent example that showcases the potential of quantum computers in function-related problems, which can be practical in functional optimization.

Luyao Tang, Yuxuan Yuan, Chaoqi Chen, Zeyu Zhang

Although foundation models (FMs) claim to be powerful, their generalization ability significantly decreases when faced with distribution shifts, weak supervision, or malicious attacks in the open world. On the other hand, most domain generalization or adversarial fine-tuning methods are task-related or model-specific, ignoring the universality in practical a

Ugne Miniotaite, Ola K. Forslund, Elisabetta Nocerino, Yuqing Ge

We utilized high-pressure methods to synthesize the oxides AReO$4$ (A=Zn, Mg) and characterized their crystal structures as monoclinic wolframite-type. By combining muon spin spectroscopy ($\mu^+$SR) with DFT calculations for muon stopping sites, we identify two possible magnetic spin structures for both compounds: $\Gamma_3$ with the propagation vector $\ma

Changho Shin, Xinya Yan, Suenggwan Jo, Sungjun Cho

Language models often struggle with temporal misalignment, performance degradation caused by shifts in the temporal distribution of data. Continuously updating models to avoid degradation is expensive. Can models be adapted without updating model weights? We present TARDIS, an unsupervised representation editing method that addresses this challenge. TARDIS e

Anders Malthe Westerkam, Troels Pedersen

We propose a message passing algorithm for tracking of clutter signals in MIMO radar. The method exploits basis expansion to linearise the signal model, to enable mean field approach for tracking the posterior distribution of the clutter as it evolves across time, as well as the mean and precision of the clutter map. The method shows good estimation accuracy

Jake Fillman, Michala N. Gradner, Hannah J. Hendricks

We establish a new and simple criterion that suffices to generate many spectral gaps for periodic word models. This leads to new examples of ergodic Schr\"odinger operators with Cantor spectra having zero Hausdorff dimension that simultaneously may have arbitrarily small supremum norm together with arbitrarily long runs on which the potential vanishes.

Ferenc Iglói, Yu-Cheng Lin

We apply a real-space block renormalization group approach to study the critical properties of the random transverse-field Ising spin chain with multispin interactions. First we recover the known properties of the traditional model with two-spin interactions by applying the renormalization approach for arbitrary size of the block. For the model with three-sp

J. Sampaio, A. Pascaud, E. Quero, A. Thiaville

Van der Waals ferromagnets, such as Fe5GeTe2, offer a promising platform for spintronic devices based on chiral magnetic textures, provided a significant Dzyaloshinskii-Moriya interaction (DMI) can be induced to stabilise the textures. Here, we directly measure DMI in epitaxial Fe5GeTe2 thin films using Brillouin light scattering spectroscopy and observe a c

Yinan Zhang, Huiqi Hu, Xuan Zhou

This study proposes a novel storage engine, SynchroStore, designed to address the inefficiency of update operations in columnar storage systems based on Log-Structured Merge Trees (LSM-Trees) under hybrid workload scenarios. While columnar storage formats demonstrate significant query performance advantages when handling large-scale datasets, traditional col

Erick Silva, Tadeu Freitas, Rehana Yasmin, Ali Shoker

A notable challenge in Electric Vehicle (EV) charging is the time required to fully charge the battery, which can range from 15 minutes to 2-3 hours. This idle period, however, presents an opportunity to offer time-consuming or data-intensive services such as vehicular software updates. ISO 15118 referred to the concept of Value-Added Services (VAS) in the c

E. Khomenko, N. Vitas, M. Collados, M. Modestov

The aim of this paper is to improve our understanding of the heating mechanisms of the solar chromosphere via realistic three-dimensional (3D) modeling of solar magneto-convection, considering the fact that solar plasma contains a significant fraction of neutral gas. For that we performed simulations of the same physically volume of the Sun, namely 5.76x5.76

Nishu Kumari

A diagonally symmetric alternating sign matrix (DSASM) is a symmetric matrix with entries $-1$, $0$ and $1$, where the nonzero entries alternate in sign along each row and column, and the sum of the entries in each row and column equals $1$. An off-diagonally symmetric alternating sign matrix (OSASM) is a DSASM, where the number of nonzero diagonal entries i

Ruiqi Zhu, Endong Sun, Guanhe Huang, Oya Celiktutan

Continual adaptation is essential for general autonomous agents. For example, a household robot pretrained with a repertoire of skills must still adapt to unseen tasks specific to each household. Motivated by this, building upon parameter-efficient fine-tuning in language models, prior works have explored lightweight adapters to adapt pretrained policies, wh

Jie Liu

In \cite{liu2022practical}, a general algorithm is developed to efficiently obtain the best accuracy using the regular refinement. The adaptive refinement allows for obtaining an accuracy with a smaller number of DoFs compared with the regular refinement. In this paper, we investigate the best accuracy when using the adaptive refinement. To this end, we stud

Samuel Rota Bulò, Nemanja Bartolovic, Lorenzo Porzi, Peter Kontschieder

We present a novel, hardware rasterized rendering approach for ray-based 3D Gaussian Splatting (RayGS), obtaining both fast and high-quality results for novel view synthesis. Our work contains a mathematically rigorous and geometrically intuitive derivation about how to efficiently estimate all relevant quantities for rendering RayGS models, structured with

Yazhou Zhang, Chunwang Zou, Bo Wang, Jing Qin

Sarcasm detection, as a crucial research direction in the field of Natural Language Processing (NLP), has attracted widespread attention. Traditional sarcasm detection tasks have typically focused on single-modal approaches (e.g., text), but due to the implicit and subtle nature of sarcasm, such methods often fail to yield satisfactory results. In recent yea

Shuhao Zhang, Bo Cheng, Jiale Han, Yuli Chen

Text watermarking provides an effective solution for identifying synthetic text generated by large language models. However, existing techniques often focus on satisfying specific criteria while ignoring other key aspects, lacking a unified evaluation. To fill this gap, we propose the Comprehensive Evaluation Framework for Watermark (CEFW), a unified framewo

Danrui Li, Yichao Shi, Yaluo Wang, Ziying Shi

Efficiently searching for relevant case studies is critical in architectural design, as designers rely on precedent examples to guide or inspire their ongoing projects. However, traditional text-based search tools struggle to capture the inherently visual and complex nature of architectural knowledge, often leading to time-consuming and imprecise exploration

Mohsen Abdoli, Ramin G. Youvalari, Frank Plowman, Alexandre Tissier

This paper presents an intra coding tool, named Merge mode for Template-based Intra Mode Derivation (TIMD). TIMD-Merge has been adopted in the 15\textsuperscript{th} version of the Enhanced Compression Model (ECM) software that explores video coding technologies beyond Versatile Video Coding (VVC) standard. This proposed tool operates on top of the regular T

Tianyi Wang, Harry Cheng, Xiao Zhang, Yinglong Wang

Suffering from performance bottlenecks in passively detecting high-quality Deepfake images due to the advancement of generative models, proactive perturbations offer a promising approach to disabling Deepfake manipulations by inserting signals into benign images. However, existing proactive perturbation approaches remain unsatisfactory in several aspects: 1)

Li Qianqian, Wang Dinghuai, Yin Huicheng

There is an interesting open question: for the $n$-D ($n\ge 1$) semilinear wave equation with scale-invariant damping $\partial_t^2u-\Delta u+\frac{\mu}{t}\partial_tu=|u|^p$, where $t\ge 1$, $p>1$ and $\mu>0$, the global small data weak solution $u$ will exist when $p>p_{crit}(n,\mu)=\max\{p_s(n+\mu), p_f(n)\}$ with $p_{s}(n+\mu)=\frac{n+\mu+1+\sqrt{(n+\mu)^

Josef Grus, Claire Hanen, Zdeněk Hanzálek

Periodic messages transfer data from sensors to actuators in cars, planes, and complex production machines. When considering a given routing, the unicast message starts at its source and goes over several dedicated resources to reach its destination. Such unicast message can be represented as a chain of point-to-point communications. Thus, the scheduling of

Feilong Cao, Shao-Bo Lin

The great success of deep learning has stimulated avid research activities in verifying the power of depth in theory, a common consensus of which is that deep net are versatile in approximating and learning numerous functions. Such a versatility certainly enhances the understanding of the power of depth, but makes it difficult to judge which data features ar

Nicola Borri

Blockchain is a technological innovation that has the potential to radically change our financial markets by providing an alternative management approach to the "promise market", which is the foundation of our financial systems. Its disruptive potential also extends to corporate finance, where blockchain is beginning to influence valuation methods and capita

Edoardo De Matteis, Matteo Migliarini, Alessio Sampieri, Indro Spinelli

We introduce Human Motion Unlearning and motivate it through the concrete task of preventing violent 3D motion synthesis, an important safety requirement given that popular text-to-motion datasets (HumanML3D and Motion-X) contain from 7\% to 15\% violent sequences spanning both atomic gestures (e.g., a single punch) and highly compositional actions (e.g., lo

Taeyeop Lee, Bowen Wen, Minjun Kang, Gyuree Kang

We introduce Any6D, a model-free framework for 6D object pose estimation that requires only a single RGB-D anchor image to estimate both the 6D pose and size of unknown objects in novel scenes. Unlike existing methods that rely on textured 3D models or multiple viewpoints, Any6D leverages a joint object alignment process to enhance 2D-3D alignment and metric

Juncen Guo, Siao Liu, Xiaoguang Zhu, Lianlong Sun

With the proliferation of multi-modal data in large-scale visual recognition systems, enabling models to continuously acquire knowledge from evolving data streams while preserving prior information has become increasingly critical. Class-Continual Learning (CCL) addresses this challenge by incrementally incorporating new class knowledge without revisiting hi

Yihan Chen, Wenfei Yang, Huan Ren, Shifeng Zhang

Relative pose estimation provides a promising way for achieving object-agnostic pose estimation. Despite the success of existing 3D correspondence-based methods, the reliance on explicit feature matching suffers from small overlaps in visible regions and unreliable feature estimation for invisible regions. Inspired by humans' ability to assemble two object p

Mohammad H. Zhoolideh Haghighi, Alireza Ghasrimanesh, Habib Khosroshahi

For monitoring the night sky conditions, wide-angle all-sky cameras are used in most astronomical observatories to monitor the sky cloudiness. In this manuscript, we apply a deep-learning approach for automating the identification of precipitation clouds in all-sky camera data as a cloud warning system. We construct our original training and test sets using

Shubhi Bansal, Kushaan Gowda, Anupama Sureshbabu K, Chirag Kothari

The exponential growth of user-generated content on social media platforms has precipitated significant challenges in information management, particularly in content organization, retrieval, and discovery. Hashtags, as a fundamental categorization mechanism, play a pivotal role in enhancing content visibility and user engagement. However, the development of

Aditya Sai Ellendula, Arun K Pujari, Vikas Kumar, Venkateswara Rao Kagita

This paper presents an efficient preference elicitation framework for uncertain matroid optimization, where precise weight information is unavailable, but insights into possible weight values are accessible. The core innovation of our approach lies in its ability to systematically elicit user preferences, aligning the optimization process more closely with d

Himal Acharya, Max Aker, Dominic Batzler, Armen Beglarian

Neutrinos are the most abundant fundamental matter particles in the Universe and play a crucial role in particle physics and cosmology. Neutrino oscillation, discovered about 25 years ago, reveals that the three known species mix with each other. Anomalous results from reactor and radioactive-source experiments suggest a possible fourth neutrino state, the s

Haoyu Wang, Christopher M. Poskitt, Jun Sun

Agents built on LLMs are increasingly deployed across diverse domains, automating complex decision-making and task execution. However, their autonomy introduces safety risks, including security vulnerabilities, legal violations, and unintended harmful actions. Existing mitigation methods, such as model-based safeguards and early enforcement strategies, fall

Bingchen Miao, Yang Wu, Minghe Gao, Qifan Yu

The development of Generalist Virtual Agents (GVAs) has shown significant promise in autonomous task execution. However, current training paradigms face critical limitations, including reliance on outcome supervision and labor-intensive human annotations. To address these challenges, we propose Similar, a Step-Wise Multi-Dimensional Generalist Reward Model,

Vito Crismale, Manuel Friedrich, Joscha Seutter

We provide an adaptive finite element approximation for a model of quasi-static crack growth in dimension two. The discrete setting consists of integral functionals that are defined on continuous, piecewise affine functions, where the triangulation is a part of the unknown of the problem and adaptive in each minimization step. The limit passage is conducted

Nada Almalki, Siddharth Gupta, Othon Michail, Andreas Padalkin

Autonomous reconfiguration of agent-based systems is a key challenge in the study of programmable matter, distributed robotics, and molecular self-assembly. While substantial prior work has focused on size-preserving transformations, much less is known about size-changing transformations. Such transformations find application in natural processes, active sel

A. Algaba, M. C. Domínguez-Moreno, M. Merino, A. J. Rodríguez-Luis

In this work we consider an unfolding of a normal form of the Lorenz system near a triple-zero singularity. We are interested in the analysis of double-zero bifurcations emerging from that singularity. Their local study provide partial results that are extended by means of numerical continuation methods. Specifically, a curve of heteroclinic connections is d

Tao Meng, Shuo Shan, Hongen Shao, Yuntao Shou

Entity alignment aims to use pre-aligned seed pairs to find other equivalent entities from different knowledge graphs (KGs) and is widely used in graph fusion-related fields. However, as the scale of KGs increases, manually annotating pre-aligned seed pairs becomes difficult. Existing research utilizes entity embeddings obtained by aggregating single structu