Tootfinder

Empirical dependencies for irregular dwarf galaxies
K. Tillaboev, I. Tadjibaev, N. Otojanova
https://arxiv.org/abs/2509.08751 https://arxiv.org/pdf/2509.08…

Empirical dependencies for irregular dwarf galaxies
In this work, we search for empirical relationships for irregular dwarf galaxies using a collection of observational data published in the NASA and HyperLeda databases. Us- ing a complex statistical analysis, we calculated correlation coefficients between various physical parameters of irregular dwarf galaxies. We found strong correlations between mass and distance, apparent stellar magnitude and distance, mass and absolute mag- nitude, redshift, and mass for all types of irregular dwarf galaxi…

Liouville CFT, Matrix Models and constrained WZW
Babak Haghighat
https://arxiv.org/abs/2508.06901 https://arxiv.org/pdf/2508.06901

Liouville CFT, Matrix Models and constrained WZW
In this paper we study matrix model realizations of Liouville conformal blocks with degenerate and irregular operators. The corresponding matrix model is Hermitian with a $β$-deformed measure and the degree of the potential corresponds to the degree of the irregular operator in the CFT conformal block. We then show how such matrix integrals can be obtained from an $SL(2,\mathbb{C})$ WZW model with a conformal constraint giving rise to Liouville theory. The corresponding conformal blocks satisf…

Multi-Modal Robust Enhancement for Coastal Water Segmentation: A Systematic HSV-Guided Framework
Zhen Tian, Christos Anagnostopoulos, Qiyuan Wang, Zhiwei Gao
https://arxiv.org/abs/2509.08694

Multi-Modal Robust Enhancement for Coastal Water Segmentation: A Systematic HSV-Guided Framework
Coastal water segmentation from satellite imagery presents unique challenges due to complex spectral characteristics and irregular boundary patterns. Traditional RGB-based approaches often suffer from training instability and poor generalization in diverse maritime environments. This paper introduces a systematic robust enhancement framework, referred to as Robust U-Net, that leverages HSV color space supervision and multi-modal constraints for improved coastal water segmentation. Our approach …

Intermittent chaos in an optomechanical resonator
Yue Huo, Zhe Wang, Zhenning Yang, Xiaohe Tang, Deng-Wei Zhang, Qianchuan Zhao, Wenjie Wan, Yu-xi Liu, Xin-You L\"u, Guangming Zhao, Liang Lu, Jing Zhang
https://arxiv.org/abs/2509.09258

Intermittent chaos in an optomechanical resonator
Chaos is a fundamental phenomenon in nonlinear dynamics, manifesting as irregular and unpredictable behavior across various physical systems. Among the diverse routes to chaos, intermittent chaos is a distinct transition pathway, characterized by the temporal or spatial alternation between periodic and chaotic motions. Here, we experimentally demonstrate, for the first time, optomechanically induced intermittent chaos in an optical whispering-gallery-mode microresonator. Specifically, the syste…

Lyapunov stability analysis of the chaotic flow past two square cylinders
Sidhartha Sahu, George Papadakis
https://arxiv.org/abs/2508.05892 https://arxiv.o…

Lyapunov stability analysis of the chaotic flow past two square cylinders
We investigate the stability of the flow past two side-by-side square cylinders (at Reynolds number 200 and gap ratio 1) using tools from dynamical systems theory. The flow is highly irregular due to the complex interaction between the flapping jet emanating from the gap and the vortices shed in the wake. We first perform Spectral Proper Orthogonal Decomposition (SPOD) to understand the flow characteristics. We then conduct Lyapunov stability analysis by linearizing the Navier-Stokes equations …

Robotic Manipulation Framework Based on Semantic Keypoints for Packing Shoes of Different Sizes, Shapes, and Softness
Yi Dong, Yangjun Liu, Jinjun Duan, Yang Li, Zhendong Dai
https://arxiv.org/abs/2509.06048

Robotic Manipulation Framework Based on Semantic Keypoints for Packing Shoes of Different Sizes, Shapes, and Softness
With the rapid development of the warehousing and logistics industries, the packing of goods has gradually attracted the attention of academia and industry. The packing of footwear products is a typical representative paired-item packing task involving irregular shapes and deformable objects. Although studies on shoe packing have been conducted, different initial states due to the irregular shapes of shoes and standard packing placement poses have not been considered. This study proposes a robo…

On some $q$-analog of an initial value problem with infinite order irregular singularity and Mahler transforms
Alberto Lastra, Stephane Malek
https://arxiv.org/abs/2509.06146 ht…

On some $q$-analog of an initial value problem with infinite order irregular singularity and Mahler transforms
A family of $q$-difference-differential equations in two complex variables is studied, under the action of a so-called Mahler transform on time variable. The appearance of a leading formal $q$-difference operator of irregular type in the equation guarantees the existence of a formal solution to the main problem in time variable, which turns out to be obtained after a $q$-analog of Borel-Laplace procedure. Such formal solution to the main problem is $G_q$-summable along certain directions. The $…

Tracking the economy at high frequency
Freddy Garc\'ia-Alb\'an, Juan Jarr\'in
https://arxiv.org/abs/2507.07450 https://ar…

Tracking the economy at high frequency
This paper develops a high-frequency economic indicator using a Bayesian Dynamic Factor Model estimated with mixed-frequency data. The model incorporates weekly, monthly, and quarterly official indicators, and allows for dynamic heterogeneity and stochastic volatility. To ensure temporal consistency and avoid irregular aggregation artifacts, we introduce a pseudo-week structure that harmonizes the timing of observations. Our framework integrates dispersed and asynchronous official statistics in…

Electronic rotons and Wigner crystallites in a two-dimensional dipole liquid
Soobin Park, Minjae Huh, Chris Jozwiak, Eli Rotenberg, Aaron Bostwick, Keun Su Kim
https://arxiv.org/abs/2507.07506

Electronic rotons and Wigner crystallites in a two-dimensional dipole liquid
A key concept proposed by Landau to explain superfluid liquid helium is the elementary excitation of quantum particles called rotons. The irregular arrangement of atoms in a liquid forms the aperiodic dispersion of rotons that played a pivotal role in understanding fractional quantum Hall liquid (magneto-rotons) and the supersolidity of Bose-Einstein condensates. Even for a two-dimensional electron or dipole liquid in the absence of a magnetic field, their repulsive interactions were predicted …

Irregular double-phase evolution problem: existence and global regularity
Rakesh Arora, Sergey Shmarev
https://arxiv.org/abs/2507.04924 https://

Irregular double-phase evolution problem: existence and global regularity
We investigate the homogeneous Dirichlet problem for the irregular double-phase evolution equation \[ u_t-\operatorname{div} \left( a(z)|\nabla u|^{p(z)-2} \nabla u + b(z)|\nabla u|^{q(z)-2} \nabla u\right)=f(z),\quad z=(x,t)\in Q_T:=Ω\times (0,T), \] where $Ω\subset \mathbb{R}^N$, $N \geq 2$ is a bounded domain, $T>0$, The non-differentiable coefficients $a(z)$, $b(z)$, the free term $f$, and the variable exponents $p$, $q$ are given functions. The coefficients $a$ and …

DeepGraphLog for Layered Neurosymbolic AI
Adem Kikaj, Giuseppe Marra, Floris Geerts, Robin Manhaeve, Luc De Raedt
https://arxiv.org/abs/2509.07665 https://…

DeepGraphLog for Layered Neurosymbolic AI
Neurosymbolic AI (NeSy) aims to integrate the statistical strengths of neural networks with the interpretability and structure of symbolic reasoning. However, current NeSy frameworks like DeepProbLog enforce a fixed flow where symbolic reasoning always follows neural processing. This restricts their ability to model complex dependencies, especially in irregular data structures such as graphs. In this work, we introduce DeepGraphLog, a novel NeSy framework that extends ProbLog with Graph Neural …

Semi-parametric Functional Classification via Path Signatures Logistic Regression
Pengcheng Zeng, Siyuan Jiang
https://arxiv.org/abs/2507.06637 https://

Semi-parametric Functional Classification via Path Signatures Logistic Regression
We propose Path Signatures Logistic Regression (PSLR), a semi-parametric framework for classifying vector-valued functional data with scalar covariates. Classical functional logistic regression models rely on linear assumptions and fixed basis expansions, which limit flexibility and degrade performance under irregular sampling. PSLR overcomes these issues by leveraging truncated path signatures to construct a finite-dimensional, basis-free representation that captures nonlinear and cross-channe…

Irregular artificial vision optimization strategies based on transformer saliency detection https://ieeexplore.ieee.org/document/10836670 I expect better results from visual-to-auditory sensory substitution (The vOICe) in combination with AI-based scene description (Google Gemini Live, Ch…

Proton-proton scattering on a quantum computer
Ratna Khadka, Gautam Rupak
https://arxiv.org/abs/2508.07986 https://arxiv.org/pdf/2508.07986

Proton-proton scattering on a quantum computer
Scattering of charged particles is ubiquitous in nuclear physics. We calculate the proton-proton $s$-wave phase shift at low energy relevant to solar physics. The phase shift is calculated from the ratio of the regular and irregular solutions to the radial Schrödinger equation on a hard spherical wall boundary for the ground state. The ground state energy is calculated using a hybrid quantum-classical variational algorithm. A theory with short-ranged nuclear interaction in the presence of the …

Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity
Niloofar Asefi, Leonard Lupin-Jimenez, Tianning Wu, Ruoying He, Ashesh Chattopadhyay
https://arxiv.org/abs/2507.06479

Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity
Reconstructing ocean dynamics from observational data is fundamentally limited by the sparse, irregular, and Lagrangian nature of spatial sampling, particularly in subsurface and remote regions. This sparsity poses significant challenges for forecasting key phenomena such as eddy shedding and rogue waves. Traditional data assimilation methods and deep learning models often struggle to recover mesoscale turbulence under such constraints. We leverage a deep learning framework that combines neural…

Crosscoding Through Time: Tracking Emergence & Consolidation Of Linguistic Representations Throughout LLM Pretraining
Deniz Bayazit, Aaron Mueller, Antoine Bosselut
https://arxiv.org/abs/2509.05291

Crosscoding Through Time: Tracking Emergence & Consolidation Of Linguistic Representations Throughout LLM Pretraining
Large language models (LLMs) learn non-trivial abstractions during pretraining, like detecting irregular plural noun subjects. However, it is not well understood when and how specific linguistic abilities emerge as traditional evaluation methods such as benchmarking fail to reveal how models acquire concepts and capabilities. To bridge this gap and better understand model training at the concept level, we use sparse crosscoders to discover and align features across model checkpoints. Using this…

Dualities of Gaudin models with irregular singularities for general linear Lie (super)algebras
Wan Keng Cheong, Ngau Lam
https://arxiv.org/abs/2507.00730 h…

Dualities of Gaudin models with irregular singularities for general linear Lie (super)algebras
We prove an equivalence between the actions of the Gaudin algebras with irregular singularities for $\mathfrak{gl}_d$ and $\mathfrak{gl}_{p+m|q+n}$ on the Fock space of $d(p+m)$ bosonic and $d(q+n)$ fermionic oscillators. This establishes a duality of $(\mathfrak{gl}_d, \mathfrak{gl}_{p+m|q+n})$ for Gaudin models. As an application, we show that the Gaudin algebra with irregular singularities for $\mathfrak{gl}_{p+m|q+n}$ acts cyclically on each weight space of a certain class of…

Irregular bundles on Hopf surfaces
Edoardo Ballico, Elizabeth Gasparim
https://arxiv.org/abs/2508.21814 https://arxiv.org/pdf/2508.21814

Irregular bundles on Hopf surfaces
We discuss the hypersurfaces of the moduli spaces of rank $2$ vector bundles on a classical Hopf surface formed by irregular bundles.

Regular $K_3$-irregular graphs
Artem Hak, Sergiy Kozerenko, Andrii Serdiuk
https://arxiv.org/abs/2507.18776 https://arxiv.org/pdf/2507.18776

Regular $K_3$-irregular graphs
We address the problem proposed by Chartrand, Erdős and Oellermann (1988) about the existence of regular $K_3$-irregular graphs. We first establish bounds on the $K_3$-degrees of such graphs and use them to prove that there are no such graphs with regularities at most $7$. For the regularity $8$, we narrow down the bounds on the order of such graphs to six possible values. We then present an explicit example of a $9$-regular $K_3$-irregular graph. Finally, we discuss an evolutionary algorithm …

Crystalline liftability of irregular weights
Hanneke Wiersema
https://arxiv.org/abs/2506.21637 https://arxiv.org/pdf/2506.21637

Crystalline liftability of irregular weights
Let $p$ be an odd prime. Let $K/\mathbb{Q}_p$ be a finite unramified extension. Let $ρ: G_K \to GL_2(\overline{\mathbb{F}}_p)$ be a continuous representation. We prove that $ρ$ has a crystalline lift of small irregular weight if and only if it has multiple crystalline lifts of certain specified regular weights. The inspiration for this result comes from work of Diamond-Sasaki on geometric Serre weight conjectures. Our result provides a way to translate results currently formulated only for re…

An Approximate Maximum Likelihood Estimator for Discretely Observed Linear Birth-and-Death Processes
Xiaochen Long, Marek Kimmel
https://arxiv.org/abs/2508.07527 https://…

An Approximate Maximum Likelihood Estimator for Discretely Observed Linear Birth-and-Death Processes
Linear birth-and-death processes (LBDPs) are foundational stochastic models in population dynamics, evolutionary biology, and hematopoiesis. Estimating parameters from discretely observed data is computationally demanding due to irregular sampling, noise, and missing values. We propose a novel approximate maximum likelihood estimator (MLE) for LBDPs based on a Gaussian approximation to transition probabilities. The approach transforms estimation into a univariate optimization problem, achieving…

Self-Attention Based Multi-Scale Graph Auto-Encoder Network of 3D Meshes
Saqib Nazir (UNICAEN), Olivier L\'ezoray (UNICAEN), S\'ebastien Bougleux (UNICAEN)
https://arxiv.org/abs/2507.05304

Self-Attention Based Multi-Scale Graph Auto-Encoder Network of 3D Meshes
3D meshes are fundamental data representations for capturing complex geometric shapes in computer vision and graphics applications. While Convolutional Neural Networks (CNNs) have excelled in structured data like images, extending them to irregular 3D meshes is challenging due to the non-Euclidean nature of the data. Graph Convolutional Networks (GCNs) offer a solution by applying convolutions to graph-structured data, but many existing methods rely on isotropic filters or spectral decompositio…

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining
Marc Neu, Isabel Haide, Timo Justinger, Till R\"adler, Valdrin Dajaku, Torben Ferber, J\"urgen Becker
https://arxiv.org/abs/2507.05099

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining
Graph-based Point Cloud Networks (PCNs) are powerful tools for processing sparse sensor data with irregular geometries, as found in high-energy physics detectors. However, deploying models in such environments remains challenging due to stringent real-time requirements for both latency, and throughput. In this work, we present a deeply pipelined dataflow architecture for executing graph-based PCNs on FPGAs. Our method supports efficient processing of dynamic, sparse point clouds while meeting h…

Efficient Probabilistic Parity Shaping for Irregular Repeat-Accumulate LDPC Codes
Diego Lentner, Thomas Wiegart, Richard D. Wesel
https://arxiv.org/abs/2508.19696 https://

Efficient Probabilistic Parity Shaping for Irregular Repeat-Accumulate LDPC Codes
Algorithms are presented that efficiently shape the parity bits of systematic irregular repeat-accumulate (IRA) low-density parity-check (LDPC) codes by following the sequential encoding order of the accumulator. Simulations over additive white Gaussian noise (AWGN) channels with on-off keying show a gain of up to 0.9 dB over uniform signaling.

A Non-Dominated Sorting Evolutionary Algorithm Updating When Required
Lucas R. C. Farias, Abimael J. F. Santos, Matheus R. B. Nobre
https://arxiv.org/abs/2507.03864

A Non-Dominated Sorting Evolutionary Algorithm Updating When Required
The NSGA-III algorithm relies on uniformly distributed reference points to promote diversity in many-objective optimization problems. However, this strategy may underperform when facing irregular Pareto fronts, where certain vectors remain unassociated with any optimal solutions. While adaptive schemes such as A-NSGA-III address this issue by dynamically modifying reference points, they may introduce unnecessary complexity in regular scenarios. This paper proposes NSGA-III with Update when Requ…

Not Just What, But When: Integrating Irregular Intervals to LLM for Sequential Recommendation
Wei-Wei Du, Takuma Udagawa, Kei Tateno
https://arxiv.org/abs/2507.23209 https://

Not Just What, But When: Integrating Irregular Intervals to LLM for Sequential Recommendation
Time intervals between purchasing items are a crucial factor in sequential recommendation tasks, whereas existing approaches focus on item sequences and often overlook by assuming the intervals between items are static. However, dynamic intervals serve as a dimension that describes user profiling on not only the history within a user but also different users with the same item history. In this work, we propose IntervalLLM, a novel framework that integrates interval information into LLM and inco…

A Systematic Search for Main-Sequence Dipper Stars Using the Zwicky Transient Facility
Anastasios Tzanidakis, James R. A. Davenport, Neven Caplar, Eric C. Bellm, Wilson Beebe, Doug Branton, Sandro Campos, Andrew J. Connolly, Melissa DeLucchi, Konstantin Malanchev, Sean McGuire
https://arxiv.org/abs/2508.03964…

A Systematic Search for Main-Sequence Dipper Stars Using the Zwicky Transient Facility
Main-sequence dipper stars, characterized by irregular and often aperiodic luminosity dimming events, offer a unique opportunity to explore the variability of circumstellar material and its potential links to planet formation, debris disks, and broadly star-planet interactions. The advent of all-sky time-domain surveys has enabled the rapid discovery of these unique systems. We present the results of a large systematic search for main-sequence dipper stars, conducted across a sample of 63 milli…

FUTransUNet-GradCAM: A Hybrid Transformer-U-Net with Self-Attention and Explainable Visualizations for Foot Ulcer Segmentation
Akwasi Asare, Mary Sagoe, Justice Williams Asare
https://arxiv.org/abs/2508.03758

FUTransUNet-GradCAM: A Hybrid Transformer-U-Net with Self-Attention and Explainable Visualizations for Foot Ulcer Segmentation
Automated segmentation of diabetic foot ulcers (DFUs) plays a critical role in clinical diagnosis, therapeutic planning, and longitudinal wound monitoring. However, this task remains challenging due to the heterogeneous appearance, irregular morphology, and complex backgrounds associated with ulcer regions in clinical photographs. Traditional convolutional neural networks (CNNs), such as U-Net, provide strong localization capabilities but struggle to model long-range spatial dependencies due to…

Comparison of Path Planning Algorithms for Autonomous Vehicle Navigation Using Satellite and Airborne LiDAR Data
Chang Liu, Zhexiong Xue, Tamas Sziranyi
https://arxiv.org/abs/2507.05884

Comparison of Path Planning Algorithms for Autonomous Vehicle Navigation Using Satellite and Airborne LiDAR Data
Autonomous vehicle navigation in unstructured environments, such as forests and mountainous regions, presents significant challenges due to irregular terrain and complex road conditions. This work provides a comparative evaluation of mainstream and well-established path planning algorithms applied to weighted pixel-level road networks derived from high-resolution satellite imagery and airborne LiDAR data. For 2D road-map navigation, where the weights reflect road conditions and terrain difficul…

FlashCommunication V2: Bit Splitting and Spike Reserving for Any Bit Communication
Qingyuan Li, Bo Zhang, Hui Kang, Tianhao Xu, Yulei Qian, Yuchen Xie, Lin Ma
https://arxiv.org/abs/2508.03760

FlashCommunication V2: Bit Splitting and Spike Reserving for Any Bit Communication
Nowadays, communication bottlenecks have emerged as a critical challenge in the distributed training and deployment of large language models (LLMs). This paper introduces FlashCommunication V2, a novel communication paradigm enabling efficient cross-GPU transmission at arbitrary bit widths. Its core innovations lie in the proposed bit splitting and spike reserving techniques, which address the challenges of low-bit quantization. Bit splitting decomposes irregular bit widths into basic units, en…

Sloppy daisy in soft focus from the patch near some beehives and just near the road in Shindagin Hollow State Forest
#flowers

Making Graphs Irregular through Irregularising Walks
Julien Bensmail, Romain Bourneuf, Paul Colinot, Samuel Humeau, Timoth\'ee Martinod
https://arxiv.org/abs/2506.21254

Making Graphs Irregular through Irregularising Walks
The 1-2-3 Conjecture, introduced by Karoński, Łuczak, and Thomason in 2004, was recently solved by Keusch. This implies that, for any connected graph $G$ different from $K_2$, we can turn $G$ into a locally irregular multigraph $M(G)$, i.e., in which no two adjacent vertices have the same degree, by replacing some of its edges with at most three parallel edges. In this work, we introduce and study a restriction of this problem under the additional constraint that edges added to $G$ to reach $…

Reduction of Feynman Integrals in the Parametric Representation IV: Integrals with Irregular Integration Regions
Wen Chen
https://arxiv.org/abs/2508.18419 https://

Reduction of Feynman Integrals in the Parametric Representation IV: Integrals with Irregular Integration Regions
Parametric Feynman integrals with the regions of integration defined by some polynomials are considered in this paper. It is shown that integrals with irregular integration regions can be converted to standard parametric integrals, for which a reduction method is known. An application of this method to the analytic calculation of three-point energy correlators is presented. In principle, this method applies to more general event shapes and even jet observables.

Derivative Estimation from Coarse, Irregular, Noisy Samples: An MLE-Spline Approach
Konstantin E. Avrachenkov, Leonid B. Freidovich
https://arxiv.org/abs/2507.22176 https://

Derivative Estimation from Coarse, Irregular, Noisy Samples: An MLE-Spline Approach
We address numerical differentiation under coarse, non-uniform sampling and Gaussian noise. A maximum-likelihood estimator with $L_2$-norm constraint on a higher-order derivative is obtained, yielding spline-based solution. We introduce a non-standard parameterization of quadratic splines and develop recursive online algorithms. Two formulations -- quadratic and zero-order -- offer tradeoff between smoothness and computational speed. Simulations demonstrate superior performance over high-gain o…

Mapping Sparse Triangular Solves to GPUs via Fine-grained Domain Decomposition
Atharva Gondhalekar, Kjetil Haugen, Thomas Gibson, Wu-chun Feng
https://arxiv.org/abs/2508.04917 h…

Mapping Sparse Triangular Solves to GPUs via Fine-grained Domain Decomposition
Sparse linear systems are typically solved using preconditioned iterative methods, but applying preconditioners via sparse triangular solves introduces bottlenecks due to irregular memory accesses and data dependencies. This work leverages fine-grained domain decomposition to adapt triangular solves to the GPU architecture. We develop a fine-grained domain decomposition strategy that generates non-overlapping subdomains, increasing parallelism in the application of preconditioner at the expense…

Gaussian process surrogate with physical law-corrected prior for multi-coupled PDEs defined on irregular geometry
Pucheng Tang, Hongqiao Wang, Wenzhou Lin, Qian Chen, Heng Yong
https://arxiv.org/abs/2509.02617

Gaussian process surrogate with physical law-corrected prior for multi-coupled PDEs defined on irregular geometry
Parametric partial differential equations (PDEs) are fundamental mathematical tools for modeling complex physical systems, yet their numerical evaluation across parameter spaces remains computationally intensive when using conventional high-fidelity solvers. To address this challenge, we propose a novel physical law-corrected prior Gaussian process (LC-prior GP) surrogate modeling framework that effectively integrates data-driven learning with underlying physical constraints to flexibly handle …

Semiregular and strongly irregular boundary points for nonlocal Dirichlet problems
Anders Bj\"orn, Jana Bj\"orn, Minhyun Kim
https://arxiv.org/abs/2506.23188

Semiregular and strongly irregular boundary points for nonlocal Dirichlet problems
In this paper we study nonlocal nonlinear equations of fractional $(s,p)$-Laplacian type on $\mathbf{R}^n$. We show that the irregular boundary points for the Dirichlet problem can be divided into two disjoint classes: semiregular and strongly irregular boundary points, with very different behaviour. Two fundamental tools needed to show this are the Kellogg property (from our previous paper) and a new removability result for solutions in the $V^{s,p}$ Sobolev type space, which we deduce more ge…

ASDR: Exploiting Adaptive Sampling and Data Reuse for CIM-based Instant Neural Rendering
Fangxin Liu, Haomin Li, Bowen Zhu, Zongwu Wang, Zhuoran Song, Habing Guan, Li Jiang
https://arxiv.org/abs/2508.02304

ASDR: Exploiting Adaptive Sampling and Data Reuse for CIM-based Instant Neural Rendering
Neural Radiance Fields (NeRF) offer significant promise for generating photorealistic images and videos. However, existing mainstream neural rendering models often fall short in meeting the demands for immediacy and power efficiency in practical applications. Specifically, these models frequently exhibit irregular access patterns and substantial computational overhead, leading to undesirable inference latency and high power consumption. Computing-in-memory (CIM), an emerging computational parad…

OASIS: Harnessing Diffusion Adversarial Network for Ocean Salinity Imputation using Sparse Drifter Trajectories
Bo Li, Yingqi Feng, Ming Jin, Xin Zheng, Yufei Tang, Laurent Cherubin, Alan Wee-Chung Liew, Can Wang, Qinghua Lu, Jingwei Yao, Shirui Pan, Hong Zhang, Xingquan Zhu
https://arxiv.org/abs/2508.21570…

OASIS: Harnessing Diffusion Adversarial Network for Ocean Salinity Imputation using Sparse Drifter Trajectories
Ocean salinity plays a vital role in circulation, climate, and marine ecosystems, yet its measurement is often sparse, irregular, and noisy, especially in drifter-based datasets. Traditional approaches, such as remote sensing and optimal interpolation, rely on linearity and stationarity, and are limited by cloud cover, sensor drift, and low satellite revisit rates. While machine learning models offer flexibility, they often fail under severe sparsity and lack principled ways to incorporate phys…

Examples of complete Calabi--Yau metrics on affine smoothings of irregular toric Calabi--Yau cones
Ronan J. Conlon, Tran-Trung Nghiem
https://arxiv.org/abs/2506.14118

Examples of complete Calabi--Yau metrics on affine smoothings of irregular toric Calabi--Yau cones
We present new examples of affine Calabi--Yau manifolds of Euclidean volume growth and quadratic curvature decay, whose tangent cones at infinity are irregular and have smooth links. In the process, we demonstrate (and provide the relevant computer code) how to explicitly compute the Reeb field and all Minkowski decompositions of a given toric Calabi--Yau cone with smooth link from the data of its toric polytope. Minkowski decompositions of this polytope into lattice segments and/or triangles g…

Time Resolution Independent Operator Learning
Diab W. Abueidda, Mbebo Nonna, Panos Pantidis, Mostafa E. Mobasher
https://arxiv.org/abs/2507.02524 https://

Time Resolution Independent Operator Learning
Accurately learning solution operators for time-dependent partial differential equations (PDEs) from sparse and irregular data remains a challenging task. Recurrent DeepONet extensions inherit the discrete-time limitations of sequence-to-sequence (seq2seq) RNN architectures, while neural-ODE surrogates cannot incorporate new inputs after initialization. We introduce NCDE-DeepONet, a continuous-time operator network that embeds a Neural Controlled Differential Equation (NCDE) in the branch and a…

"Solar panels in space ‘could provide 80% of Europe’s renewable energy by 2050’"
#SolarPower #energy #Renewables

A symbolic regression-based implicit algebraic stress turbulence model: incorporating the production of non-dimensional Reynolds stress deviatoric tensor
Ziqi Ji, Penghao Duan, Gang Du
https://arxiv.org/abs/2507.04679

A symbolic regression-based implicit algebraic stress turbulence model: incorporating the production of non-dimensional Reynolds stress deviatoric tensor
Turbulence constitutes an exceptionally complex and irregular flow phenomenon that manifests in liquids, gases, and plasma, making it ubiquitous in both natural processes and engineering applications. Given the relatively modest advancements in classical turbulence models over the past half-century, data-driven approaches, such as machine learning, have recently gained considerable traction in turbulence model research. In this study, we introduce a symbolic regression-based implicit algebraic …

Weak Existence for Degenerate Distribution Dependent SDEs with multiplicative Noise -- a pathwise regularization approach
Fabian Harang, Chengcheng Ling, Peter H. C. Pang
https://arxiv.org/abs/2509.03665

Weak Existence for Degenerate Distribution Dependent SDEs with multiplicative Noise -- a pathwise regularization approach
We establish the existence of weak solutions to a class of distribution-dependent stochastic differential equations (DDSDEs) with possibly degenerate multiplicative noise and singular coefficients. Extending the weak existence techniques introduced by Bechtold & Hofmanova 2023 to a distribution-dependent framework, we utilize pathwise averaging and local-time decomposition methods to show how irregular noise effectively regularizes analytical challenges associated with degeneracies in stochasti…

“From beginning to end,
this process is highly irregular.”
–former DOJ inspector general
Michael Bromwich,
on the bogus criminal referral of
Sen. Adam Schiff (D-CA)
by the Federal Housing Finance Authority’s inspector general
https://talki…

Nearly All The Trump II Depredations Run Through DOJ
A lot of things happened. Here are some of the things. This...

Numerical solution of 2D boundary value problems on merged Voronoi-Delaunay meshes
M. M. Chernyshov, P. N. Vabishchevich
https://arxiv.org/abs/2509.00557 https://

Numerical solution of 2D boundary value problems on merged Voronoi-Delaunay meshes
Computational technologies for the approximate solution of multidimensional boundary value problems often rely on irregular computational meshes and finite-volume approximations. In this framework, the discrete problem represents the corresponding conservation law for control volumes associated with the nodes of the mesh. This approach is most naturally and consistently implemented using Delaunay triangulations together with Voronoi diagrams as control volumes. In this paper, we employ meshes w…

Decoupling Geometry from Optimization in 2D Irregular Cutting and Packing Problems: an Open-Source Collision Detection Engine
Jeroen Gardeyn, Tony Wauters, Greet Vanden Berghe
https://arxiv.org/abs/2508.08341

Decoupling Geometry from Optimization in 2D Irregular Cutting and Packing Problems: an Open-Source Collision Detection Engine
Addressing irregular cutting and packing (C&P) optimization problems poses two distinct challenges: the geometric challenge of determining whether or not an item can be placed feasibly at a certain position, and the optimization challenge of finding a good solution according to some objective function. Until now, those tackling such problems have had to address both challenges simultaneously, requiring two distinct sets of expertise and a lot of research & development effort. One way to lower t…

Influence of finite ion Larmor radius on the dynamics of weakly-collisional plasma jets colliding in magnetic arch
Artem V. Korzhimanov, Roman S. Zemskov, Sergey A. Koryagin, Mikhail E. Viktorov
https://arxiv.org/abs/2508.02519

Influence of finite ion Larmor radius on the dynamics of weakly-collisional plasma jets colliding in magnetic arch
The effect of the finite ion Larmor radius on the dynamics of two counterstreaming weakly collisional plasma flows in a magnetic field of an arch configuration is considered. Hybrid numerical simulations show that in a system whose dimensions are close to the ion Larmor radius, more intense interaction dynamics are observed, the magnetic arch experiences a significant expansion with the formation of a region with an irregular character of magnetic lines, in which magnetic reconnection processes…

https://rudhar.com/lingtics/crtvrbir.htm

I haven't been able to get a positive ID on the "sloppy daisies" that grow in Shindagin Hollow State forest but species that look similar are found in Mexico, Hawaii and other places -- these often have missing or irregularly shaped petals
#photo #photography

FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks
Zhaoxuan Kan, Husheng Han, Shangyi Shi, Tenghui Hua, Hang Lu, Xiaowei Li, Jianan Mu, Xing Hu
https://arxiv.org/abs/2506.10399

FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks
Graph Convolutional Neural Networks (GCNs) have gained widespread popularity in various fields like personal healthcare and financial systems, due to their remarkable performance. Despite the growing demand for cloud-based GCN services, privacy concerns over sensitive graph data remain significant. Homomorphic Encryption (HE) facilitates Privacy-Preserving Machine Learning (PPML) by allowing computations to be performed on encrypted data. However, HE introduces substantial computational overhea…

Failure modes of irregular ceramic foam under compression: Development of a new image based strut segmentation strategy
Vinit Vijay Deshpande, Romana Piat
https://arxiv.org/abs/2506.10860

Failure modes of irregular ceramic foam under compression: Development of a new image based strut segmentation strategy
The work investigates the failure modes of the microstructure of an irregular ceramic foam subjected to uniaxial compression loading. The foam material is manufactured using the direct foaming method and has polydispersed pores homogeneously distributed in the microstructure. The effective stress-strain curve and the macroscopic strength of the material differs significantly with the predictions of the Gibson-Ashby model that assumes a regular microstructure. The objective of this work is to de…

From Regular to Irregular: A Unified Origin for Argyres-Douglas Theories
Simone Giacomelli, William Harding, Noppadol Mekareeya, Alessandro Mininno
https://arxiv.org/abs/2507.13434

From Regular to Irregular: A Unified Origin for Argyres-Douglas Theories
We propose that Argyres-Douglas theories of type $D_p(\mathrm{SU}(N))$ and $(A_{p-1}, A_{N-1})$ - both realizable as Type A class $\mathcal{S}$ theories with irregular punctures - can be obtained via a sequence of mass deformations from a common ancestor: a class $\mathcal{S}$ theory with only regular punctures. Building on our previous work, this result establishes that these theories ultimately originate from 6d $\mathcal{N}=(1,0)$ orbi-instanton theories compactified on a torus. The requisit…

Radius-zero Extended Symmetries and Irregular Fibres of $\mathbb{Z}^d$-Substitution Subshifts
\'Alvaro Bustos-Gajardo, Daniel Luz, Neil Ma\~nibo
https://arxiv.org/abs/2506.13392

Radius-zero Extended Symmetries and Irregular Fibres of $\mathbb{Z}^d$-Substitution Subshifts
In this work, we consider $\mathbb{Z}^d$-shifts generated by digit substitutions. For such a shift $\mathbb{X}$, we study the elements of the normaliser of $\mathbb{Z}^d$ in the group of self homeomorphisms (called extended symmetries) whose local maps guaranteed by the generalised Curtis--Hedlund--Lyndon theorem have radius-zero. Using the formalism of minimal sets developed by Lemańczyk, Müllner and Yassawi, we provide an algorithm to compute elements of $\mathcal{N}(\mathbb{X})$ that prese…

Fourier-Orbit Construction of GKZ-Type Systems for Commutative Linear Algebraic Groups
Go Okuyama
https://arxiv.org/abs/2509.03881 https://arxiv.org/pdf/25…

Fourier-Orbit Construction of GKZ-Type Systems for Commutative Linear Algebraic Groups
We introduce the Fourier-orbit construction of GKZ-type D-modules associated with commutative linear algebraic group actions G = TU (where T is an algebraic torus and U a unipotent group) on a vector space V. This framework generalizes the classical toric GKZ system to mixed torus-unipotent settings. We establish generic holonomicity via a parameter-free symbolic moment ideal, develop symbolic tools for rank analysis, and exhibit new families with Airy-type irregular behavior. Our approach reco…

A unified approach to total irregular labeling
Aleams Barra
https://arxiv.org/abs/2506.17922 https://arxiv.org/pdf/2506.17922

A unified approach to total irregular labeling
We present a unified approach to compute the total vertex irregularity strength (tvs) of various graphs, employing a novel technique recently proposed by Barra et al. For graphs such as cycles, paths, prisms, wheels, complete graphs, helm graphs, friendship graphs, and $K_{n,n}$ , we offer simplified and unified proofs of their previously established tvs values. Furthermore, we resolve an open problem by determining the tvs for simple 2-regular graphs.

Hyperasymptotics for linear difference equations with an irregular singularity of rank one: Polynomial coefficients
Gerg\H{o} Nemes
https://arxiv.org/abs/2508.15246 https://

Hyperasymptotics for linear difference equations with an irregular singularity of rank one: Polynomial coefficients
Hyperasymptotics is an analytical method that incorporates exponentially small contributions into asymptotic approximations, thereby expanding their domain of validity, improving accuracy, and providing deeper insight into the underlying singularity structures. It also allows for the computation of problem-specific invariants, such as Stokes multipliers, whose values are often assumed or remain unknown in other approaches. For differential equations, unlike standard asymptotic expansions, hyper…

Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars
Leon Armbruster, Vlad Medvedev, Andreas Rosskopf
https://arxiv.org/abs/2507.23119

Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars
Metasurfaces are innovative planar optical structures capable of manipulating incident light properties. Accurate and computationally efficient modeling of such metasurfaces, particularly those with irregular geometries, remains a challenge for conventional solvers. In this work, we present a mesh-free Physics-Informed PointNet (PIPN) to model electromagnetic scattering from all-dielectric metasurfaces that feature spatially varying nanopillars. Our approach uses the PointNet architecture to di…

Explicit Function-Correcting Code Constructions for Lee Metric Channels
Hareesh K., Rashid Ummer N. T., B. Sundar Rajan
https://arxiv.org/abs/2508.01702 https://

Explicit Function-Correcting Code Constructions for Lee Metric Channels
Function-Correcting Codes (FCCs) are a novel class of codes designed to protect function evaluations of messages against errors while minimizing redundancy. A theoretical framework for systematic FCCs to channels matched to the Lee metric has been studied recently, which introduced function-correcting Lee codes (FCLCs) and also derived upper and lower bounds on their optimal redundancy. In this paper, we propose a Plotkin-like bound for irregular Lee-distance codes, which improves an existing b…

Real Time FPGA Based Transformers & VLMs for Vision Tasks: SOTA Designs and Optimizations
Safa Mohammed Sali, Mahmoud Meribout, Ashiyana Abdul Majeed
https://arxiv.org/abs/2509.04162

Real Time FPGA Based Transformers & VLMs for Vision Tasks: SOTA Designs and Optimizations
Transformers and vision-language models (VLMs) have emerged as dominant architectures in computer vision and multimodal AI, offering state-of-the-art performance in tasks such as image classification, object detection, visual question answering, and caption generation. However, their high computational complexity, large memory footprints, and irregular data access patterns present significant challenges for deployment in latency- and power-constrained environments. Field-programmable gate array…

#Upcoming Twelve years in the making, “The Formless Irregular” chronicles the art of Babs Santini over 5 decades. Contains many never before seen pieces and illuminating commentary in this unique catalogue of (almost) everything Steven Stapleton has created visually! Preorders available via Timeless Edition, July 19th.

Investigating the clumpy star formation in an interacting dwarf irregular galaxy
Augusto E. Lassen, Ana L. Chies-Santos, Rogerio Riffel, Evelyn J. Johnston, Eleazar R. Carrasco, Boris H\"au{\ss}ler, Gabriel M. Azevedo, Jean M. Gomes, Rogemar A. Riffel, Ariel Werle, Rubens E. G. Machado, Daniel Ruschel-Dutra
https://arxiv.or…

Investigating the clumpy star formation in an interacting dwarf irregular galaxy
Clumpy morphologies are more frequent in distant and low-mass star-forming galaxies. Therefore the less numerous nearby galaxies presenting kpc-sized clumps represent unique laboratories from which to address the mechanisms driving clump formation and study why such structures become less common in the local Universe, and why they tend to exhibit smaller sizes and lower star formation rates compared to their high-$z$ counterparts. We use high spatial resolution Integral Field Unit observations …

Fast Regularized 3D Near-Field MIMO Imaging Using Stochastic Proximal Gradient Method
Okyanus Oral
https://arxiv.org/abs/2509.00774 https://arxiv.org/pdf/2…

Fast Regularized 3D Near-Field MIMO Imaging Using Stochastic Proximal Gradient Method
Near-field multiple-input multiple-output (MIMO) radar imaging suffers from high computational load inherently due to irregular spatial sampling with distributed antennas. Existing acceleration methods for near-field MIMO imaging typically rely on interpolation or compensation of measurements and are primarily developed for direct reconstruction. This hinders their ease of adoption for different MIMO geometries and requires further modification for regularized inversion. In this study, we addre…

Adaptive Control Attention Network for Underwater Acoustic Localization and Domain Adaptation
Quoc Thinh Vo, Joe Woods, Priontu Chowdhury, David K. Han
https://arxiv.org/abs/2506.17409

Adaptive Control Attention Network for Underwater Acoustic Localization and Domain Adaptation
Localizing acoustic sound sources in the ocean is a challenging task due to the complex and dynamic nature of the environment. Factors such as high background noise, irregular underwater geometries, and varying acoustic properties make accurate localization difficult. To address these obstacles, we propose a multi-branch network architecture designed to accurately predict the distance between a moving acoustic source and a receiver, tested on real-world underwater signal arrays. The network lev…

Modeling Human Spatial Mobility Patterns with the L\'evy Flight Cluster Model
Malcolm Wolff, Adrian Dobra, Anton H. Westveld, Grace S. Chiu
https://arxiv.org/abs/2509.00298 …

Modeling Human Spatial Mobility Patterns with the Lévy Flight Cluster Model
Despite the extensive collection of individual mobility data over the past decade, fueled by the widespread use of GPS-enabled personal devices, the existing statistical literature on estimating human spatial mobility patterns from temporally irregular location data remains limited. In this paper, we introduce the Lévy Flight Cluster Model (LFCM), a hierarchical Bayesian mixture model designed to analyze an individual's activity distribution. The LFCM can be utilized to determine probabilistic…

Hydrodynamic Modeling Improvements for Floating Offshore Wind Turbines with Validation Results
Doyal Sarker, Md Sakif, Tri Ngo, Tuhin Das
https://arxiv.org/abs/2508.01093 https:…

Hydrodynamic Modeling Improvements for Floating Offshore Wind Turbines with Validation Results
This study presents key enhancements in hydrodynamic modeling using the strip-based Morison's equation approach to enable rapid simulations of Floating Offshore Wind Turbines (FOWT). The modeling framework employs the relative form of the Morison equation, incorporating nonlinear irregular wave kinematics, vertical wave stretching, and diffraction corrections based on MacCamy-Fuchs (MCF) theory for large-scale, non-slender structures. Wave kinematics are iteratively applied at dynamically displ…

Replaced article(s) found for math.RA. https://arxiv.org/list/math.RA/new
[1/1]:
- Quadratic Motion Polynomials With Irregular Factorizations
Daren A. Thimm, Zijia Li, Hans-Peter Schr\"ocker, Johannes Siegele

pyFAST: A Modular PyTorch Framework for Time Series Modeling with Multi-source and Sparse Data
Zhijin Wang, Senzhen Wu, Yue Hu, Xiufeng Liu
https://arxiv.org/abs/2508.18891 http…

pyFAST: A Modular PyTorch Framework for Time Series Modeling with Multi-source and Sparse Data
Modern time series analysis demands frameworks that are flexible, efficient, and extensible. However, many existing Python libraries exhibit limitations in modularity and in their native support for irregular, multi-source, or sparse data. We introduce pyFAST, a research-oriented PyTorch framework that explicitly decouples data processing from model computation, fostering a cleaner separation of concerns and facilitating rapid experimentation. Its data engine is engineered for complex scenarios…

Irregular Reminder: Intune is SHIT for macOS Management

Semi-Supervised Bayesian GANs with Log-Signatures for Uncertainty-Aware Credit Card Fraud Detection
David Hirnschall
https://arxiv.org/abs/2509.00931 https://

Semi-Supervised Bayesian GANs with Log-Signatures for Uncertainty-Aware Credit Card Fraud Detection
We present a novel deep generative semi-supervised framework for credit card fraud detection, formulated as time series classification task. As financial transaction data streams grow in scale and complexity, traditional methods often require large labeled datasets, struggle with time series of irregular sampling frequencies and varying sequence lengths. To address these challenges, we extend conditional Generative Adversarial Networks (GANs) for targeted data augmentation, integrate Bayesian i…

Accelerating GNN Training through Locality-aware Dropout and Merge
Gongjian Sun, Mingyu Yan, Dengke Han, Runzhen Xue, Duo Wang, Xiaochun Ye, Dongrui Fan
https://arxiv.org/abs/2506.21414

Accelerating GNN Training through Locality-aware Dropout and Merge
Graph Neural Networks (GNNs) have demonstrated significant success in graph learning and are widely adopted across various critical domains. However, the irregular connectivity between vertices leads to inefficient neighbor aggregation, resulting in substantial irregular and coarse-grained DRAM accesses. This lack of data locality presents significant challenges for execution platforms, ultimately degrading performance. While previous accelerator designs have leveraged on-chip memory and data a…

SparStencil: Retargeting Sparse Tensor Cores to Scientific Stencil Computations via Structured Sparsity Transformation
Qi Li, Kun Li, Haozhi Han, Liang Yuan, Junshi Chen, Yunquan Zhang, Yifeng Chen, Hong An, Ting Cao, Mao Yang
https://arxiv.org/abs/2506.22969

SparStencil: Retargeting Sparse Tensor Cores to Scientific Stencil Computations via Structured Sparsity Transformation
Sparse Tensor Cores offer exceptional performance gains for AI workloads by exploiting structured 2:4 sparsity. However, their potential remains untapped for core scientific workloads such as stencil computations, which exhibit irregular sparsity patterns.This paper presents SparStencil, the first system to retarget sparse TCUs for scientific stencil computations through structured sparsity transformation. SparStencil introduces three key techniques: (1) Adaptive Layout Morphing, which restruct…

Improving SpGEMM Performance Through Matrix Reordering and Cluster-wise Computation
Abdullah Al Raqibul Islam, Helen Xu, Dong Dai, Ayd{\i}n Bulu\c{c}
https://arxiv.org/abs/2507.21253

Improving SpGEMM Performance Through Matrix Reordering and Cluster-wise Computation
Sparse matrix-sparse matrix multiplication (SpGEMM) is a key kernel in many scientific applications and graph workloads. Unfortunately, SpGEMM is bottlenecked by data movement due to its irregular memory access patterns. Significant work has been devoted to developing row reordering schemes towards improving locality in sparse operations, but prior studies mostly focus on the case of sparse-matrix vector multiplication (SpMV). In this paper, we address these issues with hierarchical clusterin…

Optimal Unpredictable Control for Linear Systems
Chendi Qu, Jianping He, Jialun Li, Xiaoming Duan
https://arxiv.org/abs/2508.14686 https://arxiv.org/pdf/25…

Optimal Unpredictable Control for Linear Systems
In this paper, we investigate how to achieve the unpredictability against malicious inferences for linear systems. The key idea is to add stochastic control inputs, named as unpredictable control, to make the outputs irregular. The future outputs thus become unpredictable and the performance of inferences is degraded. The major challenges lie in: i) how to formulate optimization problems to obtain an optimal distribution of stochastic input, under unknown prediction accuracy of the adversary; a…

Dynamic Count Models with Flexible Innovation Processes for Irregular Maritime Migration
Gregor Zens, Jakub Bijak
https://arxiv.org/abs/2508.18716 https://…

Dynamic Count Models with Flexible Innovation Processes for Irregular Maritime Migration
Motivated by the dynamics of weekly sea border crossings in the Mediterranean (2015-2025) and the English Channel (2018-2025), we develop a Bayesian dynamic framework for modeling potentially heteroskedastic count time series. Building on theoretical considerations and empirical stylized facts, our approach specifies a latent log-intensity that follows a random walk driven by either heavy-tailed or stochastic volatility innovations, incorporating an explicit mechanism to separate structural fro…

Inference on Optimal Policy Values and Other Irregular Functionals via Smoothing
Justin Whitehouse, Morgane Austern, Vasilis Syrgkanis
https://arxiv.org/abs/2507.11780

Inference on Optimal Policy Values and Other Irregular Functionals via Smoothing
Constructing confidence intervals for the value of an optimal treatment policy is an important problem in causal inference. Insight into the optimal policy value can guide the development of reward-maximizing, individualized treatment regimes. However, because the functional that defines the optimal value is non-differentiable, standard semi-parametric approaches for performing inference fail to be directly applicable. Existing approaches for handling this non-differentiability fall roughly int…

Transiting Planetary Debris near the Roche Limit of a White Dwarf on a 4.97$\,$hr Orbit -- and its Vanishing
Joseph A. Guidry, Zachary P. Vanderbosch, J. J. Hermes, Dimitri Veras, Mark A. Hollands, Soumyadeep Bhattacharjee, Ilaria Caiazzo, Kareem El-Badry, Malia L. Kao, Lou Baya Ould Rouis, Antonio C. Rodriguez, Jan van Roestel
https://arx…

Transiting Planetary Debris near the Roche Limit of a White Dwarf on a 4.97$\,$hr Orbit -- and its Vanishing
We present the discovery of deep, irregular, periodic transits towards the white dwarf ZTF$\,$J1944$+$4557 using follow-up time-series photometry and spectroscopy from Palomar, Keck, McDonald, Perkins, and Lowell observatories. We find a predominant period of 4.9704$\,$hr, consistent with an orbit near the Roche limit of the white dwarf, with individual dips over 30$\%$ deep and lasting between 15 and 40 minutes. Similar to the first known white dwarf with transiting debris, WD$\,$1145$+$017, t…

LongSplat: Robust Unposed 3D Gaussian Splatting for Casual Long Videos
Chin-Yang Lin, Cheng Sun, Fu-En Yang, Min-Hung Chen, Yen-Yu Lin, Yu-Lun Liu
https://arxiv.org/abs/2508.14041

LongSplat: Robust Unposed 3D Gaussian Splatting for Casual Long Videos
LongSplat addresses critical challenges in novel view synthesis (NVS) from casually captured long videos characterized by irregular camera motion, unknown camera poses, and expansive scenes. Current methods often suffer from pose drift, inaccurate geometry initialization, and severe memory limitations. To address these issues, we introduce LongSplat, a robust unposed 3D Gaussian Splatting framework featuring: (1) Incremental Joint Optimization that concurrently optimizes camera poses and 3D Gau…

On analysis of problems of mathematical physics with non-Lipschitz boundaries
Anna Rozanova-Pierrat (MICS)
https://arxiv.org/abs/2509.02176 https://arxiv.o…

On analysis of problems of mathematical physics with non-Lipschitz boundaries
We review recent advances in solving problems of mathematical physics on domains with irregular boundaries in Rn. We distinguish two frameworks: a measure-free approach in the image of the trace operator spaces for extension domains and an L2-approach depending on a d-upper regular boundary measure. In both cases, the domains can have boundaries with different Hausdorff dimensions inside the interval (n -- 2, n). The generalization of the Poincar{é}-Steklov/Dirichlet-to-Neumann operator for th…

Sampling Theory of Jointly Bandlimited Time-vertex Graph Signals
Hang Sheng, Hui Feng, Junhao Yu, Feng Ji, Bo Hu
https://arxiv.org/abs/2508.21412 https://a…

Sampling Theory of Jointly Bandlimited Time-vertex Graph Signals
Time-vertex graph signal (TVGS) models describe time-varying data with irregular structures. The bandlimitedness in the joint time-vertex Fourier spectral domain reflects smoothness in both temporal and graph topology. In this paper, we study the critical sampling of three types of TVGS including continuous-time signals, infinite-length sequences, and finite-length sequences in the time domain for each vertex on the graph. For a jointly bandlimited TVGS, we prove a lower bound on sampling densi…

Hybrid Classical-Quantum Rainbow Table Attack on Human Passwords
MA. Khajeian
https://arxiv.org/abs/2507.14600 https://arxiv.org/pdf/…

Hybrid Classical-Quantum Rainbow Table Attack on Human Passwords
Passwords that are long and human-generated pose a challenge for both classical and quantum attacks due to their irregular structure and large search space. In this work, we present an enhanced classical-quantum hybrid attack tailored to this scenario. We build rainbow tables using dictionary-based password generation with transformation rules to better model real user behavior. These tables are then organized into buckets, enabling faster lookup and reduced space complexity. To perform quantum…

A new algorithm for sampling parameters in a structured correlation matrix with application to estimating optimal combinations of muscles to quantify progression in Duchenne muscular dystrophy
Michael K. Kim, Michael J. Daniels, William D. Rooney, Rebecca J. Willcocks, Glenn A. Walter, Krista H. Vandenborne
https://arxiv.org/abs…

A new algorithm for sampling parameters in a structured correlation matrix with application to estimating optimal combinations of muscles to quantify progression in Duchenne muscular dystrophy
The goal of this paper is to estimate an optimal combination of biomarkers for individuals with Duchenne muscular dystrophy (DMD), which provides the most sensitive combinations of biomarkers to assess disease progression (in this case, optimal with respect to standardized response mean (SRM) for 4 muscle biomarkers). The biomarker data is an incomplete (missing and irregular) multivariate longitudinal data. We propose a normal model with structured covariance designed for our setting. To sampl…

Examining MPI and its Extensions for Asynchronous Multithreaded Communication
Jiakun Yan, Marc Snir, Yanfei Guo
https://arxiv.org/abs/2508.18667 https://ar…

Examining MPI and its Extensions for Asynchronous Multithreaded Communication
The increasing complexity of HPC architectures and the growing adoption of irregular scientific algorithms demand efficient support for asynchronous, multithreaded communication. This need is especially pronounced with Asynchronous Many-Task (AMT) systems. This communication pattern was not a consideration during the design of the original MPI specification. The MPI community has recently introduced several extensions to address these evolving requirements. This work evaluates two such extensio…

Am I Playing Better Now? The Effects of G-SYNC in 60Hz Gameplay
Maryam Riahi, Benjamin Watson
https://arxiv.org/abs/2506.19084 https://

Am I Playing Better Now? The Effects of G-SYNC in 60Hz Gameplay
G-SYNC technology matches formerly regular display refreshes to irregular frame updates, improving frame rates and interactive latency. In a previous study of gaming at the 30Hz frame rates common on consoles, players of Battlefield 4 were unable to discern when G-SYNC was in use, but scored higher with G-SYNC and were affected emotionally. We build on that study with the first examination of G-SYNC's effects at the 60Hz frame rate more common in PC gaming and on emerging consoles. Though G-SYN…

🫀 Simulations of beating heart may inform treatments for atrial fibrillation
https://medicalxpress.com/news/2025-07-simulations-heart-treatments-atrial-fibrillation.html

Simulations of beating heart may inform treatments for atrial fibrillation
You may have heard the phrase "my heart skipped a beat" when someone was talking about a romantic encounter. In truth, hearts that beat irregularly are dangerous for your health. Atrial fibrillation (AF) is the most common type of irregular heartbeat, and over time, it can worsen and become a permanent condition, a severe disorder that's the leading preventable cause of ischemic stroke, according to the NIH.

Variations of topological theory and ergodic theory via gap function in non-uniform specification
Wanshan Lin, Xueting Tian, Chenwei Yu
https://arxiv.org/abs/2508.17800 https://…

Variations of topological theory and ergodic theory via gap function in non-uniform specification
In [44], we qualitatively studied some classical results implied by the specification property for dynamical systems with non-uniform specification. In this paper, we perform quantitative studies on how properties of topological theory and ergodic theory vary with the gap function in non-uniform specification from different points of view. Firstly, we establish a lower estimation for Bowen topological entropy of irregular sets, determined by the lower asymptotic linear growth rate of the gap fu…

Subset Random Sampling and Reconstruction of Finite Time-Vertex Graph Signals
Hang Sheng, Qinji Shu, Hui Feng, Bo Hu
https://arxiv.org/abs/2508.21415 https://

Subset Random Sampling and Reconstruction of Finite Time-Vertex Graph Signals
Finite time-vertex graph signals (FTVGS) provide an efficient representation for capturing spatio-temporal correlations across multiple data sources on irregular structures. Although sampling and reconstruction of FTVGS with known spectral support have been extensively studied, the case of unknown spectral support requires further investigation. Existing random sampling methods may extract samples from any vertex at any time, but such strategies are not friendly in practice, where sampling is t…

A Data-Aware Fourier Neural Operator for Modeling Spatiotemporal Electromagnetic Fields
Zaifan Wu, Yue You, Xian Zhou, Fan Zhang
https://arxiv.org/abs/2508.17238 https://…

A Data-Aware Fourier Neural Operator for Modeling Spatiotemporal Electromagnetic Fields
Neural operators have recently emerged as a powerful tool for solving partial differential equations (PDEs), including Maxwell's equations in computational electromagnetics. Prior machine learning models often fail to capture both temporal field evolution and generalization to irregular geometries. Here, we introduce a Data-Aware Fourier Neural Operator (DA-FNO) as a surrogate solver. Applied autoregressively, it predicts the temporal evolution of all field components while monitoring energy dy…

Replaced article(s) found for math.CA. https://arxiv.org/list/math.CA/new
[1/1]:
- Quantitative Besicovitch projection theorem for irregular sets of directions
Damian D\k{a}browski

Efficient Estimation of Temporal Exceeding Probability for Ship Responses in Broadband Wave Fields
Shayesteh Hafezi, Xianliang Gong, Yulin Pan
https://arxiv.org/abs/2508.20271 h…

Efficient Estimation of Temporal Exceeding Probability for Ship Responses in Broadband Wave Fields
In this paper, we develop an efficient method to evaluate the temporal exceeding probability of ship motion (percentage of time for the ship motion to be above a given high threshold) in an irregular wave field. Our method builds on our previous work of Gong et al. (Applied Ocean Research, 2022), which converts the calculation into a sampling problem in the space of wave group parameters, within which an acquisition-based sequential sampling method is developed to reduce the number of required …

Latent Factor Point Processes for Patient Representation in Electronic Health Records
Parker Knight, Doudou Zhou, Zongqi Xia, Tianxi Cai, Junwei Lu
https://arxiv.org/abs/2508.20327

Latent Factor Point Processes for Patient Representation in Electronic Health Records
Electronic health records (EHR) contain valuable longitudinal patient-level information, yet most statistical methods reduce the irregular timing of EHR codes into simple counts, thereby discarding rich temporal structure. Existing temporal models often impose restrictive parametric assumptions or are tailored to code level rather than patient-level tasks. We propose the latent factor point process model, which represents code occurrences as a high-dimensional point process whose conditional in…

SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity
Xingyang Li, Jie Jiang, Yu Feng, Yiming Gan, Jieru Zhao, Zihan Liu, Jingwen Leng, Minyi Guo
https://arxiv.org/abs/2507.21499

SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity
Rendering is critical in fields like 3D modeling, AR/VR, and autonomous driving, where high-quality, real-time output is essential. Point-based neural rendering (PBNR) offers a photorealistic and efficient alternative to conventional methods, yet it is still challenging to achieve real-time rendering on mobile platforms. We pinpoint two major bottlenecks in PBNR pipelines: LoD search and splatting. LoD search suffers from workload imbalance and irregular memory access, making it inefficient on …

Man clears hurdles at Trackapalooza 2025
#photo #photography #sports #photo

Whole-Body Control Framework for Humanoid Robots with Heavy Limbs: A Model-Based Approach
Tianlin Zhang, Linzhu Yue, Hongbo Zhang, Lingwei Zhang, Xuanqi Zeng, Zhitao Song, Yun-Hui Liu
https://arxiv.org/abs/2506.14278

Whole-Body Control Framework for Humanoid Robots with Heavy Limbs: A Model-Based Approach
Humanoid robots often face significant balance issues due to the motion of their heavy limbs. These challenges are particularly pronounced when attempting dynamic motion or operating in environments with irregular terrain. To address this challenge, this manuscript proposes a whole-body control framework for humanoid robots with heavy limbs, using a model-based approach that combines a kino-dynamics planner and a hierarchical optimization problem. The kino-dynamics planner is designed as a mode…

When Secure Aggregation Falls Short: Achieving Long-Term Privacy in Asynchronous Federated Learning for LEO Satellite Networks
Mohamed Elmahallawy, Tie Luo
https://arxiv.org/abs/2508.13425

When Secure Aggregation Falls Short: Achieving Long-Term Privacy in Asynchronous Federated Learning for LEO Satellite Networks
Secure aggregation is a common technique in federated learning (FL) for protecting data privacy from both curious internal entities (clients or server) and external adversaries (eavesdroppers). However, in dynamic and resource-constrained environments such as low Earth orbit (LEO) satellite networks, traditional secure aggregation methods fall short in two aspects: (1) they assume continuous client availability while LEO satellite visibility is intermittent and irregular; (2) they consider priv…

COSMIC-L: A Photometric Catalog of Observed Stars in the Large MagellanIc Cloud
A. Franco, A. A. Nucita, F. De Paolis. F. Strafella
https://arxiv.org/abs/2506.16896

COSMIC-L: A Photometric Catalog of Observed Stars in the Large MagellanIc Cloud
The Magellanic Clouds are two nearby dwarf irregular galaxies whose study can help us in understanding galaxy and stellar evolution. In particular, the Large Magellanic Cloud, the larger one, contains approximately 30 billion stars at various evolutionary stages. In this work, we present an SDSS $gri$-bands photometric analysis based on multiple images acquired with DECam, the Dark Energy Camera, installed on the Blanco telescope at Cerro Tololo Inter-American Observatory (Chile). We performed …

GALE: Leveraging Heterogeneous Systems for Efficient Unstructured Mesh Data Analysis
Guoxi Liu, Thomas Randall, Rong Ge, Federico Iuricich
https://arxiv.org/abs/2507.15230

GALE: Leveraging Heterogeneous Systems for Efficient Unstructured Mesh Data Analysis
Unstructured meshes present challenges in scientific data analysis due to irregular distribution and complex connectivity. Computing and storing connectivity information is a major bottleneck for visualization algorithms, affecting both time and memory performance. Recent task-parallel data structures address this by precomputing connectivity information at runtime while the analysis algorithm executes, effectively hiding computation costs and improving performance. However, existing approaches…

Smooth Flow Matching
Jianbin Tan, Anru R. Zhang
https://arxiv.org/abs/2508.13831 https://arxiv.org/pdf/2508.13831…

Smooth Flow Matching
Functional data, i.e., smooth random functions observed over a continuous domain, are increasingly available in areas such as biomedical research, health informatics, and epidemiology. However, effective statistical analysis for functional data is often hindered by challenges such as privacy constraints, sparse and irregular sampling, infinite dimensionality, and non-Gaussian structures. To address these challenges, we introduce a novel framework named Smooth Flow Matching (SFM), tailored for g…

Profile-Guided Temporal Prefetching
Mengming Li, Qijun Zhang, Yichuan Gao, Wenji Fang, Yao Lu, Yongqing Ren, Zhiyao Xie
https://arxiv.org/abs/2506.15985 ht…

Profile-Guided Temporal Prefetching
Temporal prefetching shows promise for handling irregular memory access patterns, which are common in data-dependent and pointer-based data structures. Recent studies introduced on-chip metadata storage to reduce the memory traffic caused by accessing metadata from off-chip DRAM. However, existing prefetching schemes struggle to efficiently utilize the limited on-chip storage. An alternative solution, software indirect access prefetching, remains ineffective for optimizing temporal prefetching.…

Urban RIS-Assisted HAP Networks: Performance Analysis Using Stochastic Geometry
Islam M. Tanash, Ayush Kumar Dwivedi, Taneli Riihonen
https://arxiv.org/abs/2506.15338

Urban RIS-Assisted HAP Networks: Performance Analysis Using Stochastic Geometry
This paper studies a high-altitude platform (HAP) network supported by reconfigurable intelligent surfaces (RISs). The practical irregular placement of HAPs and RISs is modeled using homogeneous Poisson point processes, while buildings that cause blockages in urban areas are modeled as a Boolean scheme of rectangles. We introduce a novel approach to characterize the statistical channel based on generalized Beta prime distribution. Analytical expressions for coverage probability and ergodic capa…