March 2025 arXiv papers — page 68

Chenxiao Hu, Meng Gai, Guoping Wang, Sheng Li

We present a real-time global illumination approach along with a pipeline for dynamic 3D Gaussian models and meshes. Building on a formulated surface light transport model for 3D Gaussians, we address key performance challenges with a fast compound stochastic ray-tracing algorithm and an optimized 3D Gaussian rasterizer. Our pipeline integrates multiple real

Hong Zheng, Yucheng Chen, Nan Mu, Xiaoning Li

Accurate segmentation of the ventricles from cardiac magnetic resonance images (CMRIs) is crucial for enhancing the diagnosis and analysis of heart conditions. Deep learning-based segmentation methods have recently garnered significant attention due to their impressive performance. However, these segmentation methods are typically good at partitioning regula

Yizheng Liu, Shane M. W. Witsell, John F. Conley, Sriram Krishnamoorthy

This work reports the demonstration of ALD-deposited NiO/\b{eta}-Ga2O3 heterojunction diodes (HJDs) on low doped drift layer and highly doped (001) & (100) n+ substrates with experimental observation of a parallel-plane junction electric field as high as 7.5 MV/cm, revealing a crystal orientation dependence in \b{eta}-Ga2O3. We use a novel metalorganic precu

Maria Nareklishvili, Nicholas Polson, Vadim Sokolov

We propose generative learner for estimating heterogeneous treatment effects and characterizing the full distribution of causal effects. The learner takes the form of a multi-head feed-forward neural network with three jointly estimated subnetworks, propensity score, baseline outcome, and heterogeneous treatment effects, where the treatment-effect subnetwork

Rongcui Dong, Sreepathi Pai

Performance analysis is critical for GPU programs with data-dependent behavior, but models like Roofline are not very useful for them and interpreting raw performance counters is tedious. In this work, we present an analytical model for shared memory atomics (\emph{fetch-and-op} and \emph{compare-and-swap} instructions on NVIDIA Volta and Ampere GPU) that al

Elżbieta Pol, Roman Pol, Mirosława Reńska

We prove that if a separable metrizable $X$ is a union of two disjoint 0-dimensional sets $E$, $F$, $E$ is absolutely $G_{\delta}$ and $F$ is absolutely $F_{\sigma\delta}$ then there is a closed embedding $h$ into the union of countable products of the irrationals and the rationals with $E$ being the preimage under $h$ of the countable product of the irratio

F. Nisa Bostancı, Oğuzhan Canpolat, Ataberk Olgun, İsmail Emir Yüksel

DRAM chips are vulnerable to read disturbance phenomena (e.g., RowHammer and RowPress), where repeatedly accessing or keeping open a DRAM row causes bitflips in nearby rows. Attackers leverage RowHammer bitflips in real systems to take over systems and leak data. Consequently, many prior works propose defenses, including recent DDR specifications introducing

Eliza M. B. Guedes, Herondy Mota

In this work, we investigate the quantum Brownian motion of a point charge arising as a consequence of two fluctuating point-like boundaries. The study considers Dirichlet, Neumann, and mixed boundary conditions imposed on a real massless scalar field. Additionally, we analyze the effects of a fluctuating compactification length on the random motion of the p

Anindya Sen, Sunil Chebolu

Suppose you drop a coin from 10 feet above the ground. How long does it take to reach the ground? This routine exercise is well-known to every AP physics and calculus student: the answer is given by a formula that assumes constant acceleration due to gravity. But what if you ask the same question in the more realistic scenario of non-constant acceleration fo

Shalin Parekh

We revisit a result of Hairer-Shen on polymer-type approximations for the stochastic heat equation with a multiplicative noise (SHE) in $d=1$. We consider a general class of polymer models with strongly mixing environment in space and time, and we prove convergence to the It\^o solution of the SHE (modulo shear). The environment is not assumed to be Gaussian

Peter G. Jacobs, Wade Hilts, Robert Dodier, Joseph Leitschuh

Current closed-loop insulin delivery algorithms need to be informed of carbohydrate intake disturbances. This can be a burden on people using these systems. Pramlintide is a hormone that delays gastric emptying, which enables insulin kinetics to align with the kinetics of carbohydrate absorption. Integrating pramlintide into an automated insulin delivery sys

Yufeng Yang, Hassan Taherian, Vahid Ahmadi Kalkhorani, DeLiang Wang

Despite the tremendous success of automatic speech recognition (ASR) with the introduction of deep learning, its performance is still unsatisfactory in many real-world multi-talker scenarios. Speaker separation excels in separating individual talkers but, as a frontend, it introduces processing artifacts that degrade the ASR backend trained on clean speech.

Arastoo Zibaeirad, Marco Vieira

Automating software vulnerability detection (SVD) remains a critical challenge in an era of increasingly complex and interdependent software systems. Despite significant advances in Large Language Models (LLMs) for code analysis, prevailing evaluation methodologies often lack the \textbf{context-aware robustness} necessary to capture real-world intricacies a

Timothy Duff

Motivated by applications of algebraic geometry, we introduce the Galois width, a quantity characterizing the complexity of solving algebraic equations in a restricted model of computation allowing only field arithmetic and adjoining polynomial roots. We explain why practical heuristics such as monodromy give (at least) lower bounds on this quantity, and dis

Ivan Damnjanović

The spectral radius of a graph is the largest modulus of an eigenvalue of its adjacency matrix. Let $\mathcal{C}_{n, e}$ be the set of all the connected simple graphs with $n$ vertices and $n - 1 + e$ edges. Here, we solve the spectral radius maximization problem on $\mathcal{C}_{n, e}$ when $e \le 130$ or $n \ge e + 2 + 13\sqrt{e}$.

Bryan Lim, Roman Huerta, Alejandro Sotelo, Anthonie Quintela

Sophisticated phishing attacks have emerged as a major cybersecurity threat, becoming more common and difficult to prevent. Though machine learning techniques have shown promise in detecting phishing attacks, they function mainly as "black boxes" without revealing their decision-making rationale. This lack of transparency erodes the trust of users and dimini

Shengyun Si, Xinpeng Wang, Guangyao Zhai, Nassir Navab

Recent advancements in large language models (LLMs) have demonstrated that fine-tuning and human alignment can render LLMs harmless. In practice, such "harmlessness" behavior is mainly achieved by training models to reject harmful requests, such as "Explain how to burn down my neighbor's house", where the model appropriately declines to respond. However, thi

Alonso Castillo-Ramirez, Eduardo Veliz-Quintero

For a group $G$ and a finite set $A$, a cellular automaton is a transformation of the configuration space $A^G$ defined via a finite neighborhood and a local map. Although neighborhoods are not unique, every CA admits a unique minimal neighborhood, which consists on all the essential cells in $G$ that affect the behavior of the local map. An active transitio

A. Almasi, A. Moradpouri, M. Shahbazi

We examine the conjecture for the complete monotonicity of certain curvature invariants for quantum black holes. In this note, we study a class of quantum regular black holes that are static, spherically symmetric, and characterized only by their mass. Additionally, this class of black holes reduces to the Schwarzschild solution in the classical limit $\hbar

Do Tran Van, Susovan Pal, Benjamin Eltzner, Stephan F. Huckemann

We consider a quotient of a complete Riemannian manifold modulo an isometrically and properly acting Lie group and lifts of the quotient to the manifolds in optimal position to a reference point on the manifold. With respect to the pushed forward Riemannian volume onto the quotient we derive continuity and uniqueness a.e. and smoothness to large extents also

Raj Kumar Das, Arpan Krishna Mitra

We present a novel formulation for the Hubble parameter derived from Newtonian cosmology, incorporating non-commutative fluid dynamics through a deformed Poisson bracket structure. This approach introduces a new cosmological parameter, denoted by $\sigma$, which emerges naturally from the underlying non-commutative framework. It gives rise to a source term i

Samira Alkaee Taleghan, Andrew P. Barrett, Walter N. Meier, Farnoush Banaei-Kashani

Sea ice plays a critical role in the global climate system and maritime operations, making timely and accurate classification essential. However, traditional manual methods are time-consuming, costly, and have inherent biases. Automating sea ice type classification addresses these challenges by enabling faster, more consistent, and scalable analysis. While b

Kaiwen Zuo, Jing Tang, Hanbing Qin, Binli Luo

Recent advancements in Large Language Models (LLMs) have marked significant progress in understanding and responding to medical inquiries. However, their performance still falls short of the standards set by professional consultations. This paper introduces a novel framework for medical consultation, comprising two main modules: Terminology-Enhanced Informat

David M. Berry

This paper develops a critical theory of artificial intelligence, within a historical constellation where computational systems increasingly generate cultural content that destabilises traditional distinctions between human and machine production. Through this analysis, I introduce the concept of the algorithmic condition, a cultural moment when machine-gene

Zhenyuan Zhang, Shuang Wu, Dumitru Călugăru, Haoyu Hu

Topological heavy fermion models[1-5] describe the flat bands in magic-angle twisted bilayer graphene (MATBG) as arising from the hybridization between localized flat-band orbitals (f-electrons) and nearly-free conduction bands (c-electrons). The interplay between these f-electrons and c-electrons is theorized to give rise to emergent phenomena, including un

Hannes Gernandt, Friedrich Philipp, Till Preuster, Manuel Schaller

The solution of constrained linear partial-differential equations can be described via parametric representations of linear relations. To study these representations, we provide a novel definition of boundary triplets for linear relations in range representations where the associated boundary map is defined on the domain of the parameterizing operators rathe

Ebtihal Abdulrahman, Suhair Alshehri, Ali Alzubaidy, Asma Cherif

Recently, the Internet of Things (IoT) environment has become increasingly fertile for malicious users to break the security and privacy of IoT users. Access control is a paramount necessity to forestall illicit access. Traditional access control mechanisms are designed and managed in a centralized manner, thus rendering them unfit for decentralized IoT syst

Parthapratim Mahapatra, Debatri Chattopadhyay, Anuradha Gupta, Fabio Antonini

Black holes (BHs) with masses between $\sim 3-5M_{\odot}$, produced by a binary neutron star (BNS) merger, can further pair up with a neutron star or BH and merge again within a Hubble time. However, the astrophysical environments in which this can happen and the rate of such mergers are open questions in astrophysics. Gravitational waves may play an importa

Pranavi Kolouju, Eric Xing, Robert Pless, Nathan Jacobs

Composed image retrieval (CIR) enables users to search images using a reference image combined with textual modifications. Recent advances in vision-language models have improved CIR, but dataset limitations remain a barrier. Existing datasets often rely on simplistic, ambiguous, or insufficient manual annotations, hindering fine-grained retrieval. We introd

Xiaochen Du, Mengren Liu, Jiayu Peng, Hoje Chun

Electrochemical interfaces are crucial in catalysis, energy storage, and corrosion, where their stability and reactivity depend on complex interactions between the electrode, adsorbates, and electrolyte. Predicting stable surface structures remains challenging, as traditional surface Pourbaix diagrams tend to either rely on expert knowledge or costly $\texti

H. Mete Soner, Josef Teichmann, Qinxin Yan

We analyze an algorithm to numerically solve the mean-field optimal control problems by approximating the optimal feedback controls using neural networks with problem specific architectures. We approximate the model by an $N$-particle system and leverage the exchangeability of the particles to obtain substantial computational efficiency. In addition to sever

Benjamin J. B. Deutschmann, Erik Leitinger, Klaus Witrisal

Reciprocity-based beamforming-most commonly employed in time-division duplexing-uses noisy, estimated (i.e., measured) channel state information (CSI) acquired on the uplink. While computationally efficient, reciprocity-based beamforming suffers severe losses under (i) low signal-to-noise ratio (SNR) and (ii) user mobility because it ignores the underlying p

Yongyi Zang, Qiuqiang Kong

Accurate and efficient simulation of room impulse responses is crucial for spatial audio applications. However, existing acoustic ray-tracing tools often operate as black boxes and only output impulse responses (IRs), providing limited access to intermediate data or spatial fidelity. To address those problems, this paper presents GSound-SIR, a novel Python-b

Ruijia Zhang, Siliang Zeng, Chenliang Li, Alfredo Garcia

The goal of the Inverse reinforcement learning (IRL) task is to identify the underlying reward function and the corresponding optimal policy from a set of expert demonstrations. While most IRL algorithms' theoretical guarantees rely on a linear reward structure, we aim to extend the theoretical understanding of IRL to scenarios where the reward function is p

Yujie Yang, Lingfeng Xiang, Peiran Du, Zhen Lin

Memory disaggregation is an emerging technology that decouples memory from traditional memory buses, enabling independent scaling of compute and memory. Compute Express Link (CXL), an open-standard interconnect technology, facilitates memory disaggregation by allowing processors to access remote memory through the PCIe bus while preserving the shared-memory

Preetha Ramiah, David I. Hastie, Oliver Bunnin, Silvia Liverani

In this paper we demonstrate a new advance in causal Bayesian graphical modelling combined with Adversarial Risk Analysis. This research aims to support strategic analyses of various defensive interventions to counter the threat arising from plots of an adversary. These plots are characterised by a sequence of preparatory phases that an adversary must necess

Andrew Rosemberg, Michael Klamkin, Pascal Van Hentenryck

The growing scale of power systems and the increasing uncertainty introduced by renewable energy sources necessitates novel optimization techniques that are significantly faster and more accurate than existing methods. The AC Optimal Power Flow (AC-OPF) problem, a core component of power grid optimization, is often approximated using linearized DC Optimal Po

Li Liu, Shuzhou Sun, Shuaifeng Zhi, Fan Shi

While recent debiasing methods for Scene Graph Generation (SGG) have shown impressive performance, these efforts often attribute model bias solely to the long-tail distribution of relationships, overlooking the more profound causes stemming from skewed object and object pair distributions. In this paper, we employ causal inference techniques to model the cau

Yazmin Cote, Carlos Uzcátegui-Aylwin

This article explores the connections between graph-theoretical and topological approaches in the study of the Jordan curve theorem for grids. Building on the foundational work of Rosenfeld, who developed adjacency-based concepts on $\mathbb{Z}^2$, and the subsequent introduction of the topological digital plane $\mathbb{K}^2$ with the Khalimsky topology by

Felix Faltings, Wei Wei, Yujia Bao

Traditional retrieval methods rely on transforming user queries into vector representations and retrieving documents based on cosine similarity within an embedding space. While efficient and scalable, this approach often fails to handle complex queries involving logical constructs such as negations, conjunctions, and disjunctions. In this paper, we propose a

M. Khirk, L. V. Grigorenko, E. Yu. Nikolskii, P. G. Sharov

The unbound nucleus $^{7}$He was recently studied in the $^2$H($^{6}$He,$^1$H)$^{7}$He reaction at 29 $A\,$MeV beam energy in Ref.[M.S. Golovkov et al., Phys. Rev. C 109, L061602 (2024)]. The excitation spectrum of $^{7}$He was measured up to $E_T=8$ MeV ($E_T$ is energy above the $^{6}$He-$n$ threshold). Angular distribution for the $^{6}$He-$n$ decay of th

Jack Buttcane

The purpose of this article is to verify the conjectures of the previous paper in the particular case of $GL(4)$. We accomplish this in general, but observe two failures of the conjectures: First, that the Strong Interchange of Integrals conjecture is perhaps false for a single Weyl element $w_{2,2}$, though we prove the Weak Interchange of Integrals still h

Volker Betz, Andreas Klippel, Julian Nauth

We revisit and extend results by Ueltschi [19] on the application of reflection positivity to loop models with $\theta \in \mathbb{N}_{\geq 2}$. By exploiting additional flexibility in the method, we prove the existence of long loops over a broader range of parameters $u$ and $\theta$, and establish new lower bounds for connection probabilities and the criti

Radu Beche, Sergiu Nedevschi

The development of aerial holistic scene understanding algorithms is hindered by the scarcity of comprehensive datasets that enable both semantic and geometric reconstruction. While synthetic datasets offer an alternative, existing options exhibit task-specific limitations, unrealistic scene compositions, and rendering artifacts that compromise real-world ap

Andrei V. Konstantinov, Lev V. Utkin

There are many approaches for training decision trees. This work introduces a novel gradient-based method for constructing decision trees that optimize arbitrary differentiable loss functions, overcoming the limitations of heuristic splitting rules. Unlike traditional approaches that rely on heuristic splitting rules, the proposed method refines predictions

Mihai Marian

We prove a conjecture about the concordance invariant $\vartheta$, defined in a recent paper by Lewark and Zibrowius. This result simplifies the relation between $\vartheta$ and Rasmussen's $s$-invariant. The proof relies on Bar-Natan's tangle version of Khovanov homology or, more precisely, on its distillation in the case of 4-ended tangles into the immerse

Thomás Jung Spier

The polynomial reconstruction problem, introduced by Cvetkovi\'c in 1973, asks whether the characteristic polynomial $\phi^G$ of a graph $G$ with at least $3$ vertices can be reconstructed from the polynomial deck $\{\phi^{G \setminus i}\}_{i \in V(G)}$. In this work, we prove that $\phi^G \pmod{4}$ can be reconstructed from the polynomial deck if the number

Calder D. Sheagren, Brenden T. Kadota, Jaykumar H. Patel, Mark Chiew

Cardiac parametric mapping is useful for evaluating cardiac fibrosis and edema. Parametric mapping relies on single-shot heartbeat-by-heartbeat imaging, which is susceptible to intra-shot motion during the imaging window. However, reducing the imaging window requires undersampled reconstruction techniques to preserve image fidelity and spatial resolution. Th

V. K. Oikonomou, Ardit Gkioni, Iason Sdranis, Pyotr Tsyba

We study the inflationary phenomenology of a rescaled Einstein-Gauss-Bonnet gravity. In this framework, the gravitational constant of the Einstein-Hilbert term is rescaled due to effective terms active in the high curvature era. Basically, the total theory is an $F(R,G,\phi)$ theory with the Gauss-Bonnet part contributing only a non-minimal coupling to the s

Dae Cheol Kwon, Xinyu Zhang

Although DRL (deep reinforcement learning) has emerged as a powerful tool for making better decisions than existing hand-crafted communication protocols, it faces significant limitations: 1) Selecting the appropriate neural network architecture and setting hyperparameters are crucial for achieving desired performance levels, requiring domain expertise. 2) Th

Masroor Bashir, Nidharssan S, Pravabati Chingangbam, Fazlu Rahman

We carry out a comprehensive hierarchical multi-scale morphological analysis to search for anomalous behaviour in the large scale matter distribution using convergence map provided by the Atacama Cosmology Telescope (ACT) Data Release 6. We use a suite of morphological statistics consisting of Minkowski functionals, contour Minkowski tensor and Betti numbers

Andrew Zimmer

We introduce a class of complex manifolds which we call weakly holomorphic homogeneous regular manifolds (wHHR) manifolds. As the name suggests, this class contains the so-called holomorphic homogeneous regular manifolds but also other classes of complex manifolds such as two dimensional finite type domains and simply connected K\"ahler manifolds with pinche

Yicheng Zhang, Ravan Nazaraliyev, Sankha Baran Dutta, Andres Marquez

Multi-GPU systems are becoming increasingly important in highperformance computing (HPC) and cloud infrastructure, providing acceleration for data-intensive applications, including machine learning workloads. These systems consist of multiple GPUs interconnected through high-speed networking links such as NVIDIA's NVLink. In this work, we explore whether the

Dean Zadok, Oren Salzman, Alon Wolf, Alex M. Bronstein

Building robotic prostheses requires a sensor-based interface designed to provide the robotic hand with the control required to perform hand gestures. Traditional Electromyography (EMG) based prosthetics and emerging alternatives often face limitations such as muscle-activation limitations, high cost, and complex calibrations. In this paper, we present a low

Matthias Herp, Johannes Brachem, Michael Altenbuchinger, Thomas Kneib

Graphical Transformation Models (GTMs) are introduced as a novel approach to effectively model multivariate data with intricate marginals and complex dependency structures semiparametrically, while maintaining interpretability through the identification of varying conditional independencies. GTMs extend multivariate transformation models by replacing the Gau

Dongfang Zhao

We study the problem of approximating Hamming distance in sublinear time under property-preserving hashing (PPH), where only hashed representations of inputs are available. Building on the threshold evaluation framework of Fleischhacker, Larsen, and Simkin (EUROCRYPT 2022), we present a sequence of constructions with progressively improved complexity: a base

Alex Xiaoqin Yan, Honglin Bao, Tom R. Leppard, Andrew P. Davis

This study investigates the social dynamics of knowledge production in American sociology. Departing from traditional approaches focused on citations, co-authorship, and faculty hiring, we introduce a method capturing the dynamics of networks inferred from text to explore which ideas gain traction (a.k.a vogue). Drawing on sociology doctoral dissertations an

Phillip Driscoll, Priyanka Kumar

AI, especially Large Language Models (LLMs) like ChatGPT, have rapidly developed and gained widespread adoption in the past five years, shifting user preference from traditional search engines. However, the generative nature of LLMs raises concerns about presenting misinformation as fact. To address this, we developed a web-based application that helps K-12

Maryam Abdolali, Romina Zakerian, Behnam Roshanfekr, Fardin Ayar

In this paper, we propose a novel framework that combines ensemble learning with augmented graph structures to improve the performance and robustness of semi-supervised node classification in graphs. By creating multiple augmented views of the same graph, our approach harnesses the "wisdom of a diverse crowd", mitigating the challenges posed by noisy graph s

Ali Kurmus, Michal Zajacek, Greg Kestin, Louis Deslauriers

We present an interdisciplinary comparison between binary black hole systems and Radio Frequency (RF) Paul Traps, modeling the gravitational binary system as a rotating saddle near its center. This analogy connects these seemingly unrelated systems through the concept of dynamic stability. The rotating saddle potential is analytically tractable, allowing us

Guilherme L. Pimentel, Chen Yang

We compute correlation functions of the primordial density perturbations when they couple to a gapless, strongly coupled sector of spectator fields -- ``unparticles" -- during inflation. We first derive a four-point function of conformally coupled scalars for all kinematic configurations in de Sitter, which exchanges an unparticle at tree-level, by performin

Fernando García-Muñoz, Cristian Duran-Mateluna

This study introduces adaptive robust optimization (ARO) and adaptive robust stochastic optimization (ARSO) approaches to address long- and short-term uncertainties in the optimal sizing and placement of distributed energy resources in distribution networks. ARO models uncertainty using a Budget of Uncertainty (BoU), while ARSO distinguishes long-term (LT) d

Haozhe Shu, Mingpei Lin

A unified framework is proposed to quantitatively characterize pitchfork bifurcations and associated symmetry breaking in the elliptic restricted three-body problem (ERTBP). It is known that planar/vertical Lyapunov orbits and Lissajous orbits near the collinear libration points undergo pitchfork bifurcations with varying orbital energy. These bifurcations i

Takuro Morimoto, Harumi Haraguchi

Research on using Large Language Models (LLMs) in system development is expanding, especially in automated code and test generation. While E2E testing is vital for ensuring application quality, most test generation research has focused on unit tests, with limited work on E2E test code. This study proposes a method for automatically generating E2E test code f

Robert Ghrist, Julian Gould, Miguel Lopez, Hans Riess

Modern financial networks involve complex obligations that transcend simple monetary debts: multiple currencies, prioritized claims, supply chain dependencies, and more. We present a mathematical framework that unifies and extends these scenarios by recasting the classical Eisenberg-Noe model of financial clearing in terms of lattice liability networks. Each

A. Tononi, G. E. Astrakharchik

We investigate the phenomenon of Bose-Einstein condensation in ideal bosonic gases confined to axially-symmetric surfaces of revolution. The single-particle Schr\"odinger equation is formulated on a general surface and then explicitly solved in the ellipsoidal and toroidal geometries to determine the one-body energy spectrum. We discuss how the curved geomet

Osvaldo Santillán, Alejandro Morano

In the present work, multiplicative renormalization \cite{dixon} for Yang-Mills theories is reviewed. While this subject is not new, it is suggested that a clear understanding of these methods leads to a systematic way for interpreting the counter terms needed for non multiplicative renormalization of quantum gravity, for models such as \cite{dewitt}-\cite{s

Shu Kanno, Ikko Hamamura, Rudy Raymond, Qi Gao

Randomized algorithms are crucial subroutines in quantum computing, but the requirement to execute many types of circuits on a real quantum device has been challenging to their extensive implementation. In this study, we propose an engineering method to reduce the executing time for randomized algorithms using dynamic circuits, i.e., quantum circuits involvi

Thomas Wolfs

We characterize the biorthogonal ensembles that are both a multiple orthogonal polynomial ensemble and a polynomial ensemble of derivative type (also called a P\'olya ensemble). We focus on the notions of multiplicative and additive derivative type that typically appear in connection with products and sums of random matrices respectively. Essential in the ch

Qingshan Hou, Meng Wang, Peng Cao, Zou Ke

Recent advancements in ophthalmology foundation models such as RetFound have demonstrated remarkable diagnostic capabilities but require massive datasets for effective pre-training, creating significant barriers for development and deployment. To address this critical challenge, we propose FundusGAN, a novel hierarchical feature-aware generative framework sp

Tushin Mallick, Cristina Nita-Rotaru, Ashish Kundu, Ramana Kompella

Classification techniques can be used to analyze system behaviors, network protocols, and cryptographic primitives based on identifiable traits. While useful for defense, such classification can also be leveraged by attackers to infer system configurations, detect vulnerabilities, and tailor attacks such as denial-of-service, key recovery, or downgrade attac

Yang Jing, Lei Li, Jingtong Zhang

The Schr\"{o}dinger Bridge Problem (SBP), which can be understood as an entropy-regularized optimal transport, seeks to compute stochastic dynamic mappings connecting two given distributions. SBP has shown significant theoretical importance and broad practical potential, with applications spanning a wide range of interdisciplinary fields. While theoretical a

Yaoxuan Zeng, Wanying Kang, Glenn R. Flierl, Geoffrey K. Vallis

It remains puzzling why, despite their similar nature, Jupiter and Saturn possess a prograde equatorial jet, whereas Uranus and Neptune have a retrograde one. To understand this discrepancy, we use a two-dimensional quasi-geostrophic model to explore how the jet penetration depth, regulated by Ohmic dissipation, influences the structure and organization of j

Wenxuan Zhu, Bing Li, Cheng Zheng, Jinjie Mai

Multimodal Large Language Models (MLLMs) have demonstrated impressive 2D image/video understanding capabilities. However, there are no publicly standardized benchmarks to assess the abilities of MLLMs in understanding the 4D objects (3D objects with temporal evolution over time). In this paper, we introduce 4D-Bench, the first benchmark to evaluate the capab

Abel Dantas, Carlos Baquero

Virtual presence demands ultra-low latency, a factor that centralized architectures, by their nature, cannot minimize. Local peer-to-peer architectures offer a compelling alternative, but also pose unique challenges in terms of network infrastructure. This paper introduces a prototype leveraging Conflict-Free Replicated Data Types (CRDTs) to enable real-time

Yawei Li, Bin Ren, Jingyun Liang, Rakesh Ranjan

While vision transformers achieve significant breakthroughs in various image restoration (IR) tasks, it is still challenging to efficiently scale them across multiple types of degradations and resolutions. In this paper, we propose Fractal-IR, a fractal-based design that progressively refines degraded images by repeatedly expanding local information into bro

Danisbel Herrera, Eric Gawiser, Barbara Benda, Nicole Firestone

Lyman Alpha Emitters (LAEs) are star-forming galaxies that efficiently probe the spatial distribution of galaxies in the high redshift universe. The spatial clustering of LAEs reflects the properties of their individual host dark matter halos, allowing us to study the evolution of the galaxy-halo connection. We analyze the clustering of 5233, 5220, and 3706

Andrew Jacoby, Samiran Ghosh, Malay Banerjee, Aditi Ghosh

Climate policy and legislation has a significant influence on both domestic and global responses to the pressing environmental challenges of our time. The effectiveness of such climate legislation is closely tied to the complex dynamics among elected officials, a dynamic significantly shaped by the relentless efforts of lobbying. This project aims to develop

Zeyu Jia, Yury Polyanskiy, Alexander Rakhlin

We study the problem of sequential probability assignment under logarithmic loss, both with and without side information. Our objective is to analyze the minimax regret -- a notion extensively studied in the literature -- in terms of geometric quantities, such as covering numbers and scale-sensitive dimensions. We show that the minimax regret for the case of

Sergei Nirenburg, Marjorie McShane, Sanjay Oruganti

For AI agents to emulate human behavior, they must be able to perceive, meaningfully interpret, store, and use large amounts of information about the world, themselves, and other agents. Metacognition is a necessary component of all of these processes. In this paper, we briefly a) introduce content-centric computational cognitive (C4) modeling for next-gener

Tobias Gessler, Tin Dizdarevic, Ani Calinescu, Benjamin Ellis

AI agents hold the potential to transform everyday life by helping humans achieve their goals. To do this successfully, agents need to be able to coordinate with novel partners without prior interaction, a setting known as zero-shot coordination (ZSC). Overcooked has become one of the most popular benchmarks for evaluating coordination capabilities of AI age

Zheng Lin, Nan Zhou, Chen-Xi Du, Deng-Ping Fan

Interactive segmentation aims to segment the specified target on the image with positive and negative clicks from users. Interactive ambiguity is a crucial issue in this field, which refers to the possibility of multiple compliant outcomes with the same clicks, such as selecting a part of an object versus the entire object, a single object versus a combinati

Hanyu Liu, Ying Yu, Hang Xiao, Siyao Li

Sensor-based human activity recognition is a key technology for many human-centered intelligent applications. However, this research is still in its infancy and faces many unresolved challenges. To address these, we propose a comprehensive optimization process approach centered on multi-attention interaction. We first utilize unsupervised statistical feature

Debarshi Basu, Ashish Chandra, Himanshu Chourasiya

This work investigates the nature of mixed state entanglement and correlation in a braneworld cosmological model, where the bulk geometry is described by an eternal BTZ black hole truncated by an end-of-the-world brane representing a Friedmann-Robertson-Walker (FRW) cosmology. We explore the holographic reflected entropy for both adjacent and disjoint subsys

Sipaz Sharma

We investigate in detail the sensitivity of the fourth-order charm fluctuation, calculated on the lattice, to the input bare charm quark mass. We approach its continuum limit by employing four different lines of constant physics. We quantify the cutoff effects arising due to bare charm quark mass for both coarser and finer lattices. Finally, we show that the

T. Nandi, Soumya Chatterjee, Adya P. Mishra, Y. Azuma

In nuclear collisions, nuclear bremsstrahlung can cause nuclear Coulomb excitation via photon exchange in the projectile as well as the target nuclei. Such a process originating in nuclear timescales (zeptoseconds) can also influence the atomic phenomenon, which can be observed if it is delayed at least by a few attoseconds as atomic timescales $\ge$ an atto

Łukasz Rudnicki

I show that a real linear second order ordinary differential equation $u''\left(x\right)+h\left(x\right)u\left(x\right)=0$, with differentiable $h(x)$, locally admits two linearly independent solutions which exist on an open interval around any $x_0\in\mathbb{R}$: \[ u_\mathtt{top}(x)=\exp\left[\int_{x_0}^{x}\!\!d\xi\,\Phi\left(\xi\right)\frac{\Phi'\left(\xi

Wen Li, Chen Liu, Shangshu Yu, Dunqiang Liu

Scene coordinate regression achieves impressive results in outdoor LiDAR localization but requires days of training. Since training needs to be repeated for each new scene, long training times make these methods impractical for time-sensitive applications, such as autonomous driving, drones, and robotics. We identify large coverage areas and vast data in lar

Shue Long Chan

Current frameworks for evaluating security bug severity, such as the Common Vulnerability Scoring System (CVSS), prioritize the ratio of exploitability to impact. This paper suggests that the above approach measures the "known knowns" but inadequately addresses the "known unknowns" especially when there exist multiple possible exploit paths and side effects,

David Pelosi, Fernando Barão, Bruna Bertucci, Emanuele Fiandrini

The study presents an effective approach for deriving and utilizing polarity-based cross-correlation functions to forecast Galactic Cosmic Ray (GCR) fluxes based on solar activity proxies. By leveraging a universal correlation framework calibrated with AMS-02 and PAMELA proton flux data under a numerical model, the methodology incorporates Empirical Mode Dec

Wenqi Pei, Hailing Xu, Hengyuan Zhao, Shizheng Hou

Natural Language to SQL (NL2SQL) has seen significant advancements with large language models (LLMs). However, these models often depend on closed-source systems and high computational resources, posing challenges in data privacy and deployment. In contrast, small language models (SLMs) struggle with NL2SQL tasks, exhibiting poor performance and incompatibil

Farhan Farsi, Parnian Fazel, Sepand Haghighi, Sadra Sabouri

The study of historical languages presents unique challenges due to their complex orthographic systems, fragmentary textual evidence, and the absence of standardized digital representations of text in those languages. Tackling these challenges needs special NLP digital tools to handle phonetic transcriptions and analyze ancient texts. This work introduces Pa

Morgane Austern, Yuanchuan Guo, Zheng Tracy Ke, Tianle Liu

Topic modeling is traditionally applied to word counts without accounting for the context in which words appear. Recent advancements in large language models (LLMs) offer contextualized word embeddings, which capture deeper meaning and relationships between words. We aim to leverage such embeddings to improve topic modeling. We use a pre-trained LLM to conve

L. Friedland, A. G. Shagalov

Autoresonant (continuously phase-locked) two-phase waves of the Korteweg-de-Vries equation are excited and controlled using a two-component, small amplitude, chirped frequency driving. These solutions are analyzed in the weakly nonlinear regime. The theory is based on Whitham's averaged variational principle. The problem is reduced to a fully separated two d

Z. Zarezadeh, N. Zarezadeh

In this paper we consider a new probability sampling methods based on Langevin diffusion dynamics to resolve the problem of existing Monte Carlo algorithms when draw samples from high dimensional target densities. We extent Metropolis-Adjusted Langevin Diffusion algorithm by modelling the stochasticity of precondition matrix as a random matrix. An advantage

Vaibhav Kumar, Marwa Chafii

The upcoming sixth-generation (6G) wireless standard is anticipated to support a variety of applications that will require a seamless integration of sensing and communication services in a single network infrastructure. At the same time, 6G is also expected to support millions of low-powered Internet-of-Things (IoT) devices. Previous studies have demonstrate

Ainara Kazymova, Vaibhav Kumar, Christina Pöpper, Marwa Chafii

Various emerging applications in sixth-generation (6G) wireless demand a seamless integration of communication and sensing services, driving the development of integrated sensing and communication (ISAC) systems. Using a common waveform for both functions introduces additional security challenges, as information-bearing signals are vulnerable to eavesdroppin

Xing Xie, Jiawei Liu, Huijie Fan, Zhi Han

Directly reconstructing 3D CT volume from few-view 2D X-rays using an end-to-end deep learning network is a challenging task, as X-ray images are merely projection views of the 3D CT volume. In this work, we facilitate complex 2D X-ray image to 3D CT mapping by incorporating new view synthesis, and reduce the learning difficulty through view-guided feature a

Giovanni Briglia, Stefano Mariani, Franco Zambonelli

Causal reasoning is increasingly used in Reinforcement Learning (RL) to improve the learning process in several dimensions: efficacy of learned policies, efficiency of convergence, generalisation capabilities, safety and interpretability of behaviour. However, applications of causal reasoning to Multi-Agent RL (MARL) are still mostly unexplored. In this pape

Alexander Armbruster, Fabrizio Grandoni, Edin Husić, Antoine Tinguely

In the Time-Windows Unsplittable Flow on a Path problem (twUFP) we are given a resource whose available amount changes over a given time interval (modeled as the edge-capacities of a given path $G$) and a collection of tasks. Each task is characterized by a demand (of the considered resource), a profit, an integral processing time, and a time window. Our goa