February 2025 arXiv papers — page 14

Toon Vandendriessche, Mathieu De Coster, Annelies Lejon, Joni Dambre

Isolated Sign Language Recognition (ISLR) is crucial for scalable sign language technology, yet language-specific approaches limit current models. To address this, we propose a one-shot learning approach that generalises across languages and evolving vocabularies. Our method involves pretraining a model to embed signs based on essential features and using a

Siqi Liu, Ian Gemp, Luke Marris, Georgios Piliouras

Evaluation has traditionally focused on ranking candidates for a specific skill. Modern generalist models, such as Large Language Models (LLMs), decidedly outpace this paradigm. Open-ended evaluation systems, where candidate models are compared on user-submitted prompts, have emerged as a popular solution. Despite their many advantages, we show that the curr

Roger A. Sauer

A new arbitrary Lagrangian-Eulerian (ALE) formulation for Navier-Stokes flow on self-evolving surfaces is presented. It is based on a general curvilinear surface parameterization that describes the motion of the ALE frame. Its in-plane part becomes fully arbitrary, while its out-of-plane part follows the material motion of the surface. This allows for the de

Maria Krinner, Elie Aljalbout, Angel Romero, Davide Scaramuzza

Reinforcement learning (RL) is a powerful approach for robot learning. However, model-free RL (MFRL) requires a large number of environment interactions to learn successful control policies. This is due to the noisy RL training updates and the complexity of robotic systems, which typically involve highly non-linear dynamics and noisy sensor signals. In contr

Allen Schmaltz

We address the neural network robustness problem by adding Similarity (i.e., correctly predicted depth-matches into training)-awareness and Distance-to-training-distribution-awareness to the existing output Magnitude (i.e., decision-boundary)-awareness of the softmax function. The resulting SDM activation function provides strong signals of the relative epis

Maninder Kaur, R. C. Verma

This paper is the extension of our previous work [arXiv:2108.03296] entitled Searching a systematics for nonfactorizable contributions to B hadronic decays. In order to realize the full impact of isospin analysis, and to relate decays of strange bottom meson with those of nonstrange bottom mesons, we generalize it to the SU(3) flavor symmetry to investigate

Daciberg Lima Goncalves, Robert Skiba, P. Christopher Staecker

This paper concerns various models of ``at-most-$n$-valued maps''. That is, multivalued maps $f:X\multimap Y$ for which $f(x)$ has cardinality at most $n$ for each $x$. We consider 4 classes of such maps which have appeared in the literature: $\mathcal U$, the set of exactly $n$-valued maps, or unions of such; $\mathcal F$, the set of $n$-fold maps defined b

Jiaqi Niu, Jingquan Liu, Bin Yang

Achieving intrinsic optical chirality requires breaking all mirror symmetries of an object, and maximum chirality, which allows interaction with only one helicity of light, is particularly promising for applications such as chiral sensing, emission, and lasing. Traditionally, designing maximum chirality in dielectric metasurfaces has relied on precise engine

Aristotelis Ballas, Christos Diou

Domain Generalization (DG) research has gained considerable traction as of late, since the ability to generalize to unseen data distributions is a requirement that eludes even state-of-the-art training algorithms. In this paper we observe that the initial iterations of model training play a key role in domain generalization effectiveness, since the loss land

Yichi Zhang, Zhihao Duan, Yuning Huang, Fengqing Zhu

Learned image compression (LIC) using deep learning architectures has seen significant advancements, yet standard rate-distortion (R-D) optimization often encounters imbalanced updates due to diverse gradients of the rate and distortion objectives. This imbalance can lead to suboptimal optimization, where one objective dominates, thereby reducing overall com

Jing Song, Wei-Hong Liang, Eulogio Oset

We study the scattering parameters of the \(K^+\bar{K}^0\) system through the analysis of the \(D^+\to\bar{K}^0\pi^+\eta\) reaction, aiming at determining the scattering length \(a\) and effective range \(r_0\) of the \(K^+\bar{K}^0\) interaction. These parameters are extracted by analyzing and fitting the mass distributions of the pairs in the final \(\bar{

A. Buciulea, E. Isufi, G. Leus, A. G. Marques

Graphs are ubiquitous to model the irregular (non-Euclidean) structure of complex data, but they are limited to pairwise relationships and fail to model the complexities of the datasets exhibiting higher-order interactions. In that context, simplicial complexes (SCs) are emerging as a tractable candidate to handle such domains. The first step in using SC-bas

Yating Yu, Congqi Cao, Yifan Zhang, Yanning Zhang

Leveraging the effective visual-text alignment and static generalizability from CLIP, recent video learners adopt CLIP initialization with further regularization or recombination for generalization in open-vocabulary action recognition in-context. However, due to the static bias of CLIP, such video learners tend to overfit on shortcut static features, thereb

Luis Ferroni, Alex Fink

It is possible to write the indicator function of any matroid polytope as an integer combination of indicator functions of Schubert matroid polytopes. In this way, every matroid on $n$ elements of rank $r$ can be thought of as a lattice point in the space having a coordinate for each Schubert matroid on $n$ elements of rank $r$. We study the convex hull of a

Yifan Jia, Xingda Yu, Zhengyang Ji, Songning Lai

Immunohistochemistry (IHC) staining plays a significant role in the evaluation of diseases such as breast cancer. The H&E-to-IHC transformation based on generative models provides a simple and cost-effective method for obtaining IHC images. Although previous models can perform digital coloring well, they still suffer from (i) coloring only through the pixel

Francesco Camilli, Emanuele Mingione, Godwin Osabutey

We study the thermodynamic properties of the generalized non-convex multispecies Curie-Weiss model, where interactions among different types of particles (forming the species) are encoded in a generic matrix. For spins with a generic prior distribution, we compute the pressure in the thermodynamic limit using simple interpolation techniques. For Ising spins,

Jiageng Zhong, Ming Li, Armin Gruen, Konrad Schindler

Corals serve as the foundational habitat-building organisms within reef ecosystems, constructing extensive structures that extend over vast distances. However, their inherent fragility and vulnerability to various threats render them susceptible to significant damage and destruction. The application of advanced 3D reconstruction technologies for high-quality

Mingwei Deng, Ville Kyrki, Dominik Baumann

Transferring knowledge from one environment to another is an essential ability of intelligent systems. Nevertheless, when two environments are different, naively transferring all knowledge may deteriorate the performance, a phenomenon known as negative transfer. In this paper, we address this issue within the framework of multi-armed bandits from the perspec

Yuri Malykhin, Konstantin Ryutin

We describe the set of parameters $(p_1,p_2,q_1,q_2)$ such that the balls $B_{q_1,q_2}^{s,b}$ are rigid in $\ell_{q_1,q_2}^{s,b}$ metric i.e. they are poorly approximated by linear subspaces of dimension $\le (1-\varepsilon)sb$, for large $s, b$. Thus we have settled an important qualitative case in the problem of estimating widths of balls in mixed norms. T

Jan Bok, Jiří Fiala, Nikola Jedličková, Jan Kratochvíl

The notion of graph covers (also referred to as locally bijective homomorphisms) plays an important role in topological graph theory and has found its computer science applications in models of local computation. For a fixed target graph $H$, the {\sc $H$-Cover} problem asks if an input graph $G$ allows a graph covering projection onto $H$. Despite the fact

Zihao Zeng, Chubo Liu, Xin He, Juan Hu

Transformer-based large language models (LLMs) have demonstrated exceptional capabilities in sequence modeling and text generation, with improvements scaling proportionally with model size. However, the limitations of GPU memory have restricted LLM training accessibility for many researchers. Existing heterogeneous training methods significantly expand the s

Céline Guervilly, Emmanuel Dormy

Convection is the main heat transport mechanism in the Earth's liquid core and is thought to power the dynamo that generates the geomagnetic field. Core convection is strongly constrained by rotation while being turbulent. Given the difficulty in modelling these conditions, some key properties of core convection are still debated, including the dominant ener

Ulrich Bauer, Fabian Lenzen

The conformation space of cyclooctane, a ringlike organic molecule comprising eight carbon atoms, is a two-dimensional algebraic variety, which has been studied extensively for more than 90 years. We propose a cell structure representing this space, which arises naturally by partitioning the space into subsets of conformations that admit particular symmetrie

Sima Soltanpour, Rachel Utama, Arnold Chang, Md Taufiq Nasseef

Skull stripping is a common preprocessing step that is often performed manually in Magnetic Resonance Imaging (MRI) pipelines, including functional MRI (fMRI). This manual process is time-consuming and operator dependent. Automating this process is challenging for preclinical data due to variations in brain geometry, resolution, and tissue contrast. While ex

Adam Wesołowski, Jinge Bao

The problems of computing eccentricity, radius, and diameter are fundamental to graph theory. These parameters are intrinsically defined based on the distance metric of the graph. In this work, we propose quantum algorithms for the diameter and radius of undirected, weighted graphs in the adjacency list model. The algorithms output diameter and radius with t

Zhixuan Wen, Tian Yu, Fan Feng

Recently there have been extensive theoretical, numerical and experimental works on curved-fold origami. However, we notice that a unified and complete geometric framework for describing the geometry and mechanics of curved-fold origami, especially those with nontrivial Gaussian curvature at the crease (non-Euclidean crease), is still absent. Herein we provi

Rintaro Eto, Matthias Gohlke, Jairo Sinova, Masahito Mochizuki

Quasiparticles are central to condensed matter physics, but their stability can be undermined by quantum many-body interactions. Magnons, quasiparticles in quantum magnets, are particularly intriguing because their properties are governed by both real and spin space. While crystal symmetries may be low, spin interactions often remain approximately isotropic,

Md Isha Ali, Utpal Chattopadhyay, Dilip Kumar Ghosh, N Rajeev

The flavor-changing neutral current semileptonic decays of $B$ mesons provide an excellent platform for indirectly probing New Physics (NP) beyond the Standard Model (SM). Recent measurements of lepton flavor universality (LFU) ratios such as $R_K$, $R_{K^*}$, and $R_\phi$ are consistent with SM predictions, thereby reducing earlier hints of LFU violation. N

Arthur Pignet, John Klein, Genevieve Robin, Antoine Olivier

Deploying digital pathology models across medical centers is challenging due to distribution shifts. Recent advances in domain generalization improve model transferability in terms of aggregated performance measured by the Area Under Curve (AUC). However, clinical regulations often require to control the transferability of other metrics, such as prescribed s

Ji-Yao Chen, Yi Tan, Sylvain Capponi, Didier Poilblanc

Projected entangled-pair states (PEPS) have proven effective in capturing chiral spin liquid ground states, yet the presence of long-range ``gossamer'' correlation tails raises concerns about their ability to accurately describe bulk gaps. Here, we address this challenge and demonstrate that PEPS can reliably characterize gapped bulk excitations in chiral to

Zihao Chen, Chi-Heng Lin, Ran Liu, Jingyun Xiao

Despite the success of contrastive learning (CL) in vision and language, its theoretical foundations and mechanisms for building representations remain poorly understood. In this work, we build connections between noise contrastive estimation losses widely used in CL and distribution alignment with entropic optimal transport (OT). This connection allows us t

Spiros Cotsakis

We show that the late-time acceleration of the universe can be understood as a codimension-one bifurcation of the Friedmann dynamical system in the variables $(H,\Omega)$. At a critical value of the density-parameter combination, a saddle-node bifurcation occurs; beyond the saddle-node, trajectories are globally attracted to a new accelerating fixed point. W

Jozsef Konczer

This paper introduces a framework for finite non-cooperative games where each player faces a globally uncertain parameter with no common prior. Every player chooses both a mixed strategy and projects an emergent subjective prior to the uncertain parameters. We define an "Extended Equilibrium" by requiring that no player can improve her expected utility via a

Max M. Lang, Sol Eskenazi

Telephone surveys remain a valuable tool for gathering insights but typically require substantial resources in training and coordinating human interviewers. This work presents an AI-driven telephone survey system integrating text-to-speech (TTS), a large language model (LLM), and speech-to-text (STT) that mimics the versatility of human-led interviews (full-

J. Schmidinger, S. Vogel, V. Barkov, A. -D. Pham

Digital soil mapping (DSM) relies on a broad pool of statistical methods, yet determining the optimal method for a given context remains challenging and contentious. Benchmarking studies on multiple datasets are needed to reveal strengths and limitations of commonly used methods. Existing DSM studies usually rely on a single dataset with restricted access, l

Anjali Abirami Kugarajh, Marisol Traforetti, Andrea Maselli, Sabino Matarrese

Scalar-Induced Gravitational Waves (SIGWs), second-order tensor modes sourced by first-order scalar fluctuations in General Relativity (GR), are expected to contribute to the Stochastic Gravitational Wave Background (SGWB) potentially detectable by current and future gravitational wave interferometers. In the framework of GR, this SGWB represents an unavoida

Marko Shuntov, Shuowen Jin, Wilfried Mercier, S. Jeyhan Kartaltepe

We report the spectroscopic confirmation of the background source of the most distant Einstein ring known to date, the COSMOS-Web ring. This system consists of a complete Einstein ring at $z=5.1$, lensed by a massive early-type galaxy at $z\sim2$. The redshift $z=5.1043\pm0.0004$ is unambiguously identified with our NOEMA and Keck/MOSFIRE spectroscopy, where

Qingyang Zhang, Rose E. Wang, Ana T. Ribeiro, Dora Demszky

Educator attention is critical for student success, yet how educators distribute their attention across students remains poorly understood due to data and methodological constraints. This study presents the first large-scale computational analysis of educator attention patterns, leveraging over 1 million educator utterances from virtual group tutoring sessio

Itay Benou, Tammy Riklin-Raviv

Modern deep neural networks have now reached human-level performance across a variety of tasks. However, unlike humans they lack the ability to explain their decisions by showing where and telling what concepts guided them. In this work, we present a unified framework for transforming any vision neural network into a spatially and conceptually interpretable

Tea Štrekelj, Aljaž Zalar

An $n\times n$ symmetric matrix $A$ is copositive if the quadratic form $x^TAx$ is nonnegative on the nonnegative orthant $\mathbb{R}^{n}_{\geq 0}$. The cone of copositive matrices contains the cone of matrices which are the sum of a positive semidefinite matrix and a nonnegative one and the latter contains the cone of completely positive matrices. These are

Parthiban Loganathan, Elias Zea, Ricardo Vinuesa, Evelyn Otero

Accurate regional climate forecast calls for high-resolution downscaling of Global Climate Models (GCMs). This work presents a deep-learning-based multi-model evaluation and downscaling framework ranking 32 Coupled Model Intercomparison Project Phase 6 (CMIP6) models using a Deep Learning-TOPSIS (DL-TOPSIS) mechanism and so refines outputs using advanced dee

Qiang Ji, Lin Cheng, Zeng Liang, Yingrui Zhuang

To address the lack of energy-carbon efficiency evaluation and the underutilization of low-temperature waste heat in traditional direct reduction iron (DRI) production, this paper proposes a novel zero-carbon hydrogen metallurgy system that integrates the recovery and utilization of low-temperature and high-temperature waste heat, internal energy, and cold e

Thomas Norrenbrock, Timo Kaiser, Sovan Biswas, Ramesh Manuvinakurike

Understanding the classifications of deep neural networks, e.g. used in safety-critical situations, is becoming increasingly important. While recent models can locally explain a single decision, to provide a faithful global explanation about an accurate model's general behavior is a more challenging open task. Towards that goal, we introduce the Quadratic Pr

Yifan Zhang, Wenyu Du, Dongming Jin, Jie Fu

Chain-of-thought (CoT) significantly enhances the performance of large language models (LLMs) across a wide range of tasks, and prior research shows that CoT can theoretically increase expressiveness. However, there is limited mechanistic understanding of the algorithms that Transformer+CoT can learn. Our key contributions are: (1) We evaluate the state trac

Lin Zhang, Yi Tian, XiYun Wang, Wanru Xu

The complex application scenarios have raised critical requirements for precise and generalizable gaze estimation methods. Recently, the pre-trained CLIP has achieved remarkable performance on various vision tasks, but its potentials have not been fully exploited in gaze estimation. In this paper, we propose a novel Differential Contrastive Training strategy

Zexiong Ma, Chao Peng, Pengfei Gao, Xiangxin Meng

Mainstream issue-resolving frameworks predominantly rely on commercial models, leading to high costs and privacy concerns. Existing training approaches for issue resolving struggle with poor generalization and fail to fully leverage open-source development resources. We propose Subtask-oriented Reinforced Fine-Tuning (SoRFT), a novel training approach to enh

Tue Nhi Tran

Throughout the history from pre-industry 4.0 to post-industry 4.0, cybersecurity at banks has undergone significant changes. Pre-industry 4.0 cyber security at banks relied on individual security methods that were highly manual and had low accuracy. When moving to post-industry 4.0, cybersecurity at banks had a major turning point with security methods that

Sotiris Anagnostidis, Gregor Bachmann, Yeongmin Kim, Jonas Kohler

Despite their remarkable performance, modern Diffusion Transformers are hindered by substantial resource requirements during inference, stemming from the fixed and large amount of compute needed for each denoising step. In this work, we revisit the conventional static paradigm that allocates a fixed compute budget per denoising iteration and propose a dynami

Ingeborg Wenger, Peter Eberhard, Henrik Ebel

A contextual anomaly detection method is proposed and applied to the physical motions of a robot swarm executing a coverage task. Using simulations of a swarm's normal behavior, a normalizing flow is trained to predict the likelihood of a robot motion within the current context of its environment. During application, the predicted likelihood of the observed

Yujie Li, Guannan Lai, Xin Yang, Yonghao Li

Open-World Continual Learning (OWCL) is a challenging paradigm where models must incrementally learn new knowledge without forgetting while operating under an open-world assumption. This requires handling incomplete training data and recognizing unknown samples during inference. However, existing OWCL methods often treat open detection and continual learning

Erik J Schlicht

Consumption of misinformation can lead to negative consequences that impact the individual and society. To help mitigate the influence of misinformation on human beliefs, algorithmic labels providing context about content accuracy and source reliability have been developed. Since the linguistic features used by algorithms to estimate information accuracy can

Sulagna Ghosh, Nikolaos Ignatiadis, Frederic Koehler, Amber Lee

Given a collection of observed signals corrupted with Gaussian noise, how can we learn to optimally denoise them? This fundamental problem arises in both empirical Bayes and generative modeling. In empirical Bayes, the predominant approach is via nonparametric maximum likelihood estimation (NPMLE), while in generative modeling, score matching (SM) methods ha

Tergel Munkhbat, Namgyu Ho, Seo Hyun Kim, Yongjin Yang

Chain-of-thought (CoT) reasoning has enabled large language models (LLMs) to utilize additional computation through intermediate tokens to solve complex tasks. However, we posit that typical reasoning traces contain many redundant tokens, incurring extraneous inference costs. Upon examination of the output distribution of current LLMs, we find evidence on th

Jeremy Kalfoun, Guillaume Pierrot, John Cagnol

Iterative algorithms are instrumental in modern numerical simulation for solving systems arising from the discretization of PDEs. They face however significant challenges in industrial applications, such as slow convergence, limit cycle oscillations, or iterations blow-up. An ideal preconditioner is rarely available and naive approaches such as Richardson it

QingYuan Jiang, Longfei Huang, Yang Yang

Multimodal learning (MML) is significantly constrained by modality imbalance, leading to suboptimal performance in practice. While existing approaches primarily focus on balancing the learning of different modalities to address this issue, they fundamentally overlook the inherent disproportion in model classification ability, which serves as the primary caus

Jeripothula Prudviraj, Vikram Jamwal

Understanding the stroke-based evolution of visual artworks is useful for advancing artwork learning, appreciation, and interactive display. While the stroke sequence of renowned artworks remains largely unknown, formulating this sequence for near-natural image drawing processes can significantly enhance our understanding of artistic techniques. This paper i

Jin-Fu Chen

We develop a Lindblad framework for quantum stochastic thermodynamics to study the nonequilibrium thermodynamics of open quantum systems. Our approach adopts the local quantum detailed balance condition, ensuring thermodynamic consistency and leading to a joint fluctuation theorem of quantum work and heat. Instead of solving the full evolution of the density

Evans Kiptoo Korir, Zsolt Vizi

Understanding how age-specific social contact patterns and susceptibility influence infectious disease transmission is crucial for accurate epidemic modeling. This study presents an eigenvector-based sensitivity analysis framework to quantify the impact of age-structured interactions on disease spread. By applying perturbation analysis to the Next Generation

Luiz O. R. Solak, Ciro M. Diniz, Daniel Z. Rossatto, Antonio S. M. de Castro

We propose an optical model in which both quantum and quasi-classical states can be ideally stored using coupled resonators. The protocol is based on a time-dependent coupling between two cavities, carefully modulated to allow the complete transfer of an external propagating field from one cavity to another. The system maintains high storage efficiency (abov

Ambroise Heurtebise, Omar Chehab, Pierre Ablin, Alexandre Gramfort

Causal discovery is a difficult problem that typically relies on strong assumptions on the data-generating model, such as non-Gaussianity. In practice, many modern applications provide multiple related views of the same system, which has rarely been considered for causal discovery. Here, we leverage this multi-view structure to achieve causal discovery with

Prasun Dutta, Koustab Ghosh, Rajat K. De

The most popular technique to train a neural network is backpropagation. Recently, the Forward-Forward technique has also been introduced for certain learning tasks. However, in real life, human learning does not follow any of these techniques exclusively. The way a human learns is basically a combination of forward learning, backward propagation and coopera

Abhik Roychoudhury, Andreas Zeller

Artificial Intelligence (AI) technology such as Large Language Models (LLMs) have become extremely popular in creating code. This has led to the conjecture that future software jobs will be exclusively conducted by LLMs, and the software industry will cease to exist. But software engineering is much more than producing code -- notably, \emph{maintaining} lar

D. Serafini, A. Leso, A. Arzenton, S. Spadano

Targeted Radionuclide Therapy (TRT) is a well-established technique for cancer treatment. In this approach, radionuclides are bound to specific drugs that selectively transport them to the tumor site. Within the ISOLPHARM project, a radiopharmaceutical for TRT based on the innovative radionuclide Ag-111 is currently under development. Ag-111 has a half-life

Mengjie Xu, Yitao Zhu, Haotian Jiang, Jiaming Li

Multi-view object tracking (MVOT) offers promising solutions to challenges such as occlusion and target loss, which are common in traditional single-view tracking. However, progress has been limited by the lack of comprehensive multi-view datasets and effective cross-view integration methods. To overcome these limitations, we compiled a Multi-View object Tra

Luigi Piccinelli, Christos Sakaridis, Yung-Hsu Yang, Mattia Segu

Accurate monocular metric depth estimation (MMDE) is crucial to solving downstream tasks in 3D perception and modeling. However, the remarkable accuracy of recent MMDE methods is confined to their training domains. These methods fail to generalize to unseen domains even in the presence of moderate domain gaps, which hinders their practical applicability. We

José Siqueira de Cerqueira, Kai-Kristian Kemell, Rebekah Rousi, Nannan Xi

The rapid proliferation of Large Language Models (LLMs) has raised significant trustworthiness and ethical concerns. Despite the widespread adoption of LLMs across domains, there is still no clear consensus on how to define and operationalise trustworthiness. This study aims to bridge the gap between theoretical discussion and practical implementation by ana

Ziang Guo, Konstantin Gubernatorov, Selamawit Asfaw, Zakhar Yagudin

In autonomous driving, dynamic environment and corner cases pose significant challenges to the robustness of ego vehicle's decision-making. To address these challenges, commencing with the representation of state-action mapping in the end-to-end autonomous driving paradigm, we introduce a novel pipeline, VDT-Auto. Leveraging the advancement of the state unde

Praveen Kumar, Tushar Waghmare, Sudhir Kumar, Rajdeep Banerjee

Two-dimensional (2D) transition metal dichalcogenides like molybdenum diselenide (MoSe$_2$) have shown great potential in optoelectronics and energy storage due to their layer-dependent bandgap. However, producing high-quality 2D MoSe$_2$ layers in a scalable and controlled manner remains challenging. Traditional methods, such as hydrothermal and liquid-phas

Joris J. Weeda, Saray Bakker, Gang Chen, Javier Alonso-Mora

Navigation Among Movable Obstacles (NAMO) poses a challenge for traditional path-planning methods when obstacles block the path, requiring push actions to reach the goal. We propose a framework that enables movability-aware planning to overcome this challenge without relying on explicit obstacle placement. Our framework integrates a global Semantic Visibilit

Xuzheng Yang, Junzhuo Liu, Peng Wang, Guoqing Wang

Referring Expression Comprehension (REC) is a foundational cross-modal task that evaluates the interplay of language understanding, image comprehension, and language-to-image grounding. It serves as an essential testing ground for Multimodal Large Language Models (MLLMs). To advance this field, we introduced a new REC dataset in our previous conference paper

Muhan Luo, Qi Zhou

We prove that under the natural assumption over the dynamical degrees, the saddle periodic points of a H\'enon-like map in any dimension equidistribute with respect to the equilibrium measure. Our work is a generalization of results of Bedford-Lyubich-Smillie, Dujardin and Dinh-Sibony along with improvements of their techniques. We also investigate some fine

Mirjam Weilenmann, Nicolas Gisin, Pavel Sekatski

The role of complex quantities in quantum theory has been puzzling physicists since the beginnings. It is thus natural to ask whether, in order to describe our experiments, the mathematical structure of complex Hilbert spaces it is built on is really necessary. Recently, it was shown that this structure is inevitable in network scenarios with independent sou

Manganaw N'Daam, Tchilabalo Abozou Kpanzou, Edoh Katchekpele

In this paper, we propose a novel method for estimating the long-memory parameter in time series. By combining the multi-resolution framework of wavelets with the robustness of the Least Absolute Deviations (LAD) criterion, we introduce a periodogram providing a robust alternative to classical methods in the presence of non-Gaussian noise. Incorporating this

Gilles Van De Vyver, Aksel Try Lenz, Erik Smistad, Sindre Hellum Olaisen

One of the main challenges in current research on segmentation in cardiac ultrasound is the lack of large and varied labeled datasets and the differences in annotation conventions between datasets. This makes it difficult to design robust segmentation models that generalize well to external datasets. This work utilizes diffusion models to create generative a

Juan L. Gamella, Simon Bing, Jakob Runge

We evaluate methods for causal representation learning (CRL) on a simple, real-world system where these methods are expected to work. The system consists of a controlled optical experiment specifically built for this purpose, which satisfies the core assumptions of CRL and where the underlying causal factors (the inputs to the experiment) are known, providin

Rickard Karlsson, Bram van den Akker, Felipe Moraes, Hugo M. Proença

Qini curves are a widely used tool for assessing treatment policies under allocation constraints as they visualize the incremental gain of a new treatment policy versus the cost of its implementation. Standard Qini curve estimation assumes no interference between units: that is, that treating one unit does not influence the outcome of any other unit. In many

Henryk Gzyl

Here we consider an analytically tractable model of a two level quantum system subject to random shocks and prove that it decays asymptotically to a trivial state, that is, to a state in which the two levels have equal probability of occupation. In a two qubit system, if the shocks affect each qubit independently, the equilibrium density matrix becomes a sim

Feng Guo, Luis D. Couto

This study evaluates numerical discretization methods for the Single Particle Model (SPM) used in electrochemical modeling. The methods include the Finite Difference Method (FDM), spectral methods, Pad\'e approximation, and parabolic approximation. Evaluation criteria are accuracy, execution time, and memory usage, aiming to guide method selection for electr

Luigi Martinelli

For any $n \ge 3$, we consider the moduli space $M_n$ of semistable sheaves with Mukai vector $2(1,0,1-n)$ on a K3 surface. The moduli space $M_n$ is singular and lacks a crepant resolution, but might still admit a categorical crepant one. As a preliminary to exploring this possibility, we study the explicit resolution of singularities of $M_n$ constructed b

Gabriel Undeutsch, Maximilian Aigner, Ailton J. Garcia, Johannes Reindl

Photon indistinguishability, entanglement, and antibunching are key ingredients in quantum optics and photonics. Decay cascades in quantum emitters offer a simple method to create entangled photon-pairs with negligible multi-pair generation probability. However, the degree of indistinguishability of the photons emitted in a cascade is intrinsically limited b

Mingjie Wu, Chenggui Yang, Huihua Wang, Chen Xue

The UAV technology is gradually maturing and can provide extremely powerful support for smart agriculture and precise monitoring. Currently, there is no dataset related to green walnuts in the field of agricultural computer vision. Thus, in order to promote the algorithm design in the field of agricultural computer vision, we used UAV to collect remote-sensi

Rita Ferreira, Diogo Gomes, Teruo Tada

This chapter examines monotonicity techniques in the theory of mean-field games(MFGs). Originally, monotonicity ideas were used to establish the uniqueness of solutions for MFGs. Later, monotonicity methods and monotone operators were further exploited to build numerical methods and to construct weak solutions under mild assumptions. Here, after a brief disc

Mack Matlabyana, Thabo Ngoako, Hlengani Siweya

The concept of $P_F$-frames was introduced by Ngoako [24] as a point-free extension of $P_F$-spaces. We observe that the open cozero quotient of a $P_F$-frame is itself a $P_F$-frame. The class of $P_F$-frames contains the class of $P$-frames and is, in turn, contained in the class of $F$-frames. We show that a frame $L$ is a $P_F$-frame if and only if $\bet

Adib Karimi, Mohammad Mehdi Ebadzadeh

We propose a novel Inverse Reinforcement Learning (IRL) method that mitigates the rigidity of fixed reward structures and the limited flexibility of implicit reward regularization. Building on the Maximum Entropy IRL framework, our approach incorporates a squared temporal-difference (TD) regularizer with adaptive targets that evolve dynamically during traini

Xuan Chen, Yuesheng Dai, Shi-Yuan Li, Zong-Guo Si

The $e^+e^-$ annihilation of unpolarized beams is free from initial hadron states or initial anisotropy around the azimuthal angle, hence ideal for studying the correlations of dynamical origin via final state jets. We investigate the planar properties of the multi-jet events employing the relevant event-shape observables at next-to-next-to-leading order ($\

Meng Lou, Yizhou Yu

Top-down attention plays a crucial role in the human vision system, wherein the brain initially obtains a rough overview of a scene to discover salient cues (i.e., overview first), followed by a more careful finer-grained examination (i.e., look closely next). However, modern ConvNets remain confined to a pyramid structure that successively downsamples the f

Tiangang Cui, Karina Koval, Roland Herzog, Robert Scheichl

We propose a novel approach for sequential optimal experimental design (sOED) for Bayesian inverse problems involving expensive models with high-dimensional unknown parameters. This work focuses on designs that maximize the expected information gain (EIG) from prior to posterior, a task that is computationally very challenging in non-Gaussian settings. This

Xingrui Liu, Ying Wang, Yanlong Zhao

This paper investigates system identification problems with Gaussian inputs and quantized observations under fixed thresholds. By reinterpreting the nonlinear effects induced by quantization as the product of the unknown parameter and an unknown nonlinear coefficient, this work establishes a novel weighted least-squares criterion that enables linear estimati

Haicheng Liao, Chengyue Wang, Kaiqun Zhu, Yilong Ren

In mixed autonomous driving environments, accurately predicting the future trajectories of surrounding vehicles is crucial for the safe operation of autonomous vehicles (AVs). In driving scenarios, a vehicle's trajectory is determined by the decision-making process of human drivers. However, existing models primarily focus on the inherent statistical pattern

Hasti Narimanzadeh, Takayuki Hiraoka, Mikko Kivelä

Unknown node attributes in complex networks may introduce community structures that are important to distinguish from those driven by known attributes. We propose a block-corrected modularity that discounts given block structures present in the network to reveal communities masked by them. We show analytically how the proposed modularity finds the community

Ning Shang, Li Lyna Zhang, Siyuan Wang, Gaokai Zhang

LongRoPE2 is a novel approach that extends the effective context window of pre-trained large language models (LLMs) to the target length, while preserving the performance on the original shorter context window. This is achieved by three contributions: (1) a hypothesis that insufficient training in higher RoPE dimensions contributes to the persistent out-of-d

Rita Ferreira, Diogo Gomes, Vardan Voskanyan

This paper addresses the crucial question of solution uniqueness in stationary first-order Mean-Field Games (MFGs). Despite well-established existence results, establishing uniqueness, particularly for weaker solutions in the sense of monotone operators, remains an open challenge. Building upon the framework of monotonicity methods, we introduce a linearizat

Djardiel da S. Gomes, Isaac M. Felix, Willian F. Radel, Alexandre C. Dias

Recently synthesized Porous 12-Atom-Wide Armchair Graphene Nanoribbons Nano Lett. 2024, 24, 10718-10723 exhibit tunable properties through periodic porosity, enabling precise control over their electronic, optical, thermal, and mechanical behavior. This work presents a comprehensive theoretical characterization of pristine and porous 12-AGNRs based on densit

G. Younes, S. K. Lander, M. G. Baring, M. L. Bause

We present the timing and spectral analyses of the NICER, NuSTAR, and IXPE observations of the magnetar 1E 1841-045 covering 82 days following its August 2024 bursting activity as well as radio observations utilizing MeerKAT and Effelsberg. We supplement our study with a historical NuSTAR and all 2024 pre-outburst NICER observations. The outburst is marked b

Yufei Wei, Sha Lu, Wangtao Lu, Rong Xiong

Monocular Visual Odometry (MVO) provides a cost-effective, real-time positioning solution for autonomous vehicles. However, MVO systems face the common issue of lacking inherent scale information from monocular cameras. Traditional methods have good interpretability but can only obtain relative scale and suffer from severe scale drift in long-distance tasks.

Zijie Zhou, Zhangshuo Qi, Luqi Cheng, Guangming Xiong

Robust and accurate localization is critical for autonomous driving. Traditional GNSS-based localization methods suffer from signal occlusion and multipath effects in urban environments. Meanwhile, methods relying on high-definition (HD) maps are constrained by the high costs associated with the construction and maintenance of HD maps. Standard-definition (S

Angus McCowan

A prominent report claimed substantial support for two introductions of SARS-CoV-2 into humans using a calculation that combined phylodynamic inferences and epidemic models. Inspection of the calculation identifies an imbalance in the hypothesis testing framework that confounds this result; the single-introduction model was tested against more stringent cond

Daniel Velicka, Ondrej Vysocky, Lubomir Riha

The development of exascale and post-exascale HPC and AI systems integrates thousands of CPUs and specialized accelerators, making energy optimization critical as power costs rival hardware expenses. To reduce consumption, frequency and voltage scaling techniques are widely used, but their effectiveness depends on adapting to application demands in real-time

Jan Verlage, Peter Kratzer

Weakly and strongly interacting quantum many-body systems, namely semiconductors and Mott insulators, are combined into a layered heterostructure. Via the hierarchy of correlations, we derive and match the propagating quasi-particle solutions in the different regions and calculate the transmission coefficients through these layered structures. As a proof of

Haochen Sun, Shuwen Zhang, Lujie Niu, Lei Ren

Large Language Models (LLMs) based agent systems have made great strides in real-world applications beyond traditional NLP tasks. This paper proposes a new LLM-based Multi-Agent System (LLM-MAS) benchmark, Collab-Overcooked, built on the popular Overcooked-AI game with more applicable and challenging tasks in interactive environments. Collab-Overcooked exten