Tootfinder

This https://arxiv.org/abs/2506.01921 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

MedEBench: Revisiting Text-instructed Image Editing
Text-guided image editing has seen rapid progress in natural image domains, but its adaptation to medical imaging remains limited and lacks standardized evaluation. Clinically, such editing holds promise for simulating surgical outcomes, creating personalized teaching materials, and enhancing patient communication. To bridge this gap, we introduce \textbf{MedEBench}, a comprehensive benchmark for evaluating text-guided medical image editing. It consists of 1,182 clinically sourced image-prompt …

This https://arxiv.org/abs/2501.07544 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

$C^*$-supports and abnormalities of operator systems
Let $S$ be a concrete operator system represented on some Hilbert space $H$. A $C^*$-support of $S$ is the $C^*$-algebra generated (via the Choi--Effros product) by $S$ inside an injective operator system acting on $H$. By leveraging Hamana's theory, we show that such a $C^*$-support is unique precisely when $C^*(S)$ is contained in every copy of the injective envelope of $S$ that acts on $H$. Further, we demonstrate how the uniqueness of certain $C^*$-supports can be used to give new character…

This https://arxiv.org/abs/2506.02774 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGR_…

Voyager: Real-Time Splatting City-Scale 3D Gaussians on Your Phone
3D Gaussian Splatting (3DGS) is an emerging technique for photorealistic 3D scene rendering. However, rendering city-scale 3DGS scenes on mobile devices, e.g., your smartphones, remains a significant challenge due to the limited resources on mobile devices. A natural solution is to offload computation to the cloud; however, naively streaming rendered frames from the cloud to the client introduces high latency and requires bandwidth far beyond the capacity of current wireless networks. In this…

This https://arxiv.org/abs/2506.02880 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

On the rate of convergence in the CLT for LSS of large-dimensional sample covariance matrices
This paper investigates the rate of convergence for the central limit theorem of linear spectral statistic (LSS) associated with large-dimensional sample covariance matrices. We consider matrices of the form ${\mathbf B}_n=\frac{1}{n}{\mathbf T}_p^{1/2}{\mathbf X}_n{\mathbf X}_n^*{\mathbf T}_p^{1/2},$ where ${\mathbf X}_n= (x_{i j} ) $ is a $p \times n$ matrix whose entries are independent and identically distributed (i.i.d.) real or complex variables, and ${\mathbf T} _p$ is a $p\times p$ nonr…

This https://arxiv.org/abs/2503.01117 has been replaced.
initial toot: https://mastoxiv.page/@arXi…

Gulf Stream drives Kuroshio behind the recent abnormal ocean warming
Recent unprecedented ocean warming has produced coherent sea surface temperature (SST) anomalies across the Northern Hemisphere extratropics. Despite extensive research on inter-basin interactions, the causal mechanisms linking North Pacific and North Atlantic variability remain poorly understood. Here we show that internal SST variability in the Gulf Stream region accounts for 16% of internal SST variability in the Kuroshio region in climate model simulations, while no significant reverse infl…

Spending hours manually updating your Excel dashboards and reports? Copying, pasting, reformatting... 😩 It's tedious, time-consuming, and leaves room for error. What if your key visuals updated automatically when the source data changes?
Excel has a powerful, often overlooked feature perfect for this: the Camera Tool! 📸
It lets you create dynamic, linked visuals from different sheets onto one central summary page. 👉

This https://arxiv.org/abs/2403.05322 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Direct-search methods in the year 2025: Theoretical guarantees and algorithmic paradigms
Optimizing a function without using derivatives is a challenging paradigm, that precludes from using classical algorithms from nonlinear optimization, and may thus seem intractable other than by using heuristics. Nevertheless, the field of derivative-free optimization has succeeded in producing algorithms that do not rely on derivatives and yet are endowed with convergence guarantees. One class of such methods, called direct-search methods, is particularly popular thanks to its simplicity of im…

This https://arxiv.org/abs/2506.02606 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Multi Layered Autonomy and AI Ecologies in Robotic Art Installations
Symbiosis of Agents is a large-scale installation by Baoyang Chen that embeds AI-driven robots in an immersive, mirror-lined arena, probing the tension between machine agency and artistic authorship. Drawing on early cybernetics, rule-based conceptual art, and seminal robotic works, it orchestrates fluid exchanges among robotic arms, quadruped machines, their environment, and the public. A three tier faith system pilots the ecology: micro-level adaptive tactics, meso-level narrative drives, and…

[2025-06-06 Fri (UTC), 1 new article found for cs.SC Symbolic Computation]
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[2025-06-05 Thu (UTC), 2 new articles found for q-bio.BM Biomolecules]
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This https://arxiv.org/abs/2506.02040 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Beyond the Protocol: Unveiling Attack Vectors in the Model Context Protocol Ecosystem
The Model Context Protocol (MCP) is an emerging standard designed to enable seamless interaction between Large Language Model (LLM) applications and external tools or resources. Within a short period, thousands of MCP services have already been developed and deployed. However, the client-server integration architecture inherent in MCP may expand the attack surface against LLM Agent systems, introducing new vulnerabilities that allow attackers to exploit by designing malicious MCP servers. In th…

This https://arxiv.org/abs/2506.02658 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Computational Thinking Reasoning in Large Language Models
While large language models (LLMs) have demonstrated remarkable reasoning capabilities, they often struggle with complex tasks that require specific thinking paradigms, such as divide-and-conquer and procedural deduction, \etc Previous researches integrate external, reliable tools to alleviate logical inconsistencies and hallucinations in LLMs' problem-solving processes. However, we argue that the root challenge is more profound: LLMs lack the complex thinking paradigms (\ie, computational thin…

In May, Microsoft launched Signal, a 120-page print magazine focused on its tech and distributed to business leaders every few months, to counter digital noise (Tom Warren/The Verge)
https://www.theverge.com/notepad-microsoft

Why Microsoft has created its own print magazine
Microsoft has created its own print magazine, named Signal. It’s a reaction to the digital noise and ephemeral content.

This https://arxiv.org/abs/2303.07289 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

The polarized degree of irrationality of $K3$ surfaces
Given a polarized variety $(X,L)$, we construct and study projections of low degree $ X\dashrightarrow \mathbb{P}(H^0(L^\vee)) \dashrightarrow \mathbb P ^n $ using the associated kernel bundles. As an application, we can show that the degree of irrationality of a very general $(1,6)$ abelian surface, as well as that of a very general $K3$ surface of genus $6$ is $3$. We also give new upper bounds for $K3$ surfaces of any genus. Moreover, in the case of surfaces, this observation can be used to …

[2025-06-05 Thu (UTC), 2 new articles found for cs.CE Computational Engineering, Finance, and Science]
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This https://arxiv.org/abs/2505.16865 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

LARES: Latent Reasoning for Sequential Recommendation
Sequential recommender systems have become increasingly important in real-world applications that model user behavior sequences to predict their preferences. However, existing sequential recommendation methods predominantly rely on non-reasoning paradigms, which may limit the model's computational capacity and result in suboptimal recommendation performance. To address these limitations, we present LARES, a novel and scalable LAtent REasoning framework for Sequential recommendation that enhance…

This https://arxiv.org/abs/2503.17319 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

The algebraic internal groupoid model of Martin-Löf type theory
We extend the model structure on the category $\mathbf{Cat}(\mathcal{E})$ of internal categories studied by Everaert, Kieboom and Van der Linden to an algebraic model structure. Moreover, we show that it restricts to the category of internal groupoids. We show that in this case, the algebraic weak factorisation system that consists of the algebraic trivial cofibrations and algebraic fibrations forms a model of Martin-Löf type theory. Taking $\mathcal{E} = \mathbf{Set}$ and forgetting the algeb…

This https://arxiv.org/abs/2504.12483 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Beta function without UV divergences
In this paper, we construct the beta function in the functorial formulation of two-dimensional quantum field theories (FQFT). A key feature of this approach is the absence of ultraviolet divergences. We show that, nevertheless, in the FQFT perturbation theory, the local observables of deformed theories acquire logarithmic dimension, leading to a conformal anomaly. The beta function arises in the functorial approach as an infinitesimal transformation of the partition function under the variation…

This https://arxiv.org/abs/2411.16331 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMM_…

Sonic: Shifting Focus to Global Audio Perception in Portrait Animation
The study of talking face generation mainly explores the intricacies of synchronizing facial movements and crafting visually appealing, temporally-coherent animations. However, due to the limited exploration of global audio perception, current approaches predominantly employ auxiliary visual and spatial knowledge to stabilize the movements, which often results in the deterioration of the naturalness and temporal inconsistencies.Considering the essence of audio-driven animation, the audio signal…

This https://arxiv.org/abs/2503.02769 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSD_…

InSerter: Speech Instruction Following with Unsupervised Interleaved Pre-training
Recent advancements in speech large language models (SpeechLLMs) have attracted considerable attention. Nonetheless, current methods exhibit suboptimal performance in adhering to speech instructions. Notably, the intelligence of models significantly diminishes when processing speech-form input as compared to direct text-form input. Prior work has attempted to mitigate this semantic inconsistency between speech and text representations through techniques such as representation and behavior align…

This https://arxiv.org/abs/2505.03447 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Asymptotic formula for the sum of a prime and a square-full number in intervals shorter than $X^{1/2}$
Let $R(N)$ be the number of representations of $N$ as a sum of a prime and a square-full number weighted with logarithmic function. In $2024$, the author and Y. Suzuki obtained an asymptotic formula for the sum of $R(N)$ over positive integers $N$ in a short interval ($X$, $X+H$] for $X^{\frac{1}{2}+\varepsilon} \le H < X^{1-\varepsilon}$. In this article, we improve the range of $H$, that is, we prove the same asymptotic formula for $X^{\frac{32-4\sqrt{15}}{49}+\varepsilon} \le H \le X^{1- \va…

This https://arxiv.org/abs/2502.17826 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Feedback-Free Resource Scheduling for Flexible Multi-BS Cooperation in FD-RAN
Flexible cooperation among base stations (BSs) is critical to improve resource utilization efficiency and meet personalized user demands. However, its practical implementation is hindered by the current radio access network (RAN), which relies on the coupling of uplink and downlink transmissions and channel state information feedback with inherent issues such as overheads and delays. To overcome these limitations, we consider the fully-decoupled RAN (FD-RAN), in which uplink and downlink functi…

This https://arxiv.org/abs/2506.00891 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Uneven Event Modeling for Partially Relevant Video Retrieval
Given a text query, partially relevant video retrieval (PRVR) aims to retrieve untrimmed videos containing relevant moments, wherein event modeling is crucial for partitioning the video into smaller temporal events that partially correspond to the text. Previous methods typically segment videos into a fixed number of equal-length clips, resulting in ambiguous event boundaries. Additionally, they rely on mean pooling to compute event representations, inevitably introducing undesired misalignment…

This https://arxiv.org/abs/2410.17407 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Morita theory for dynamical von Neumann algebras
Given a locally compact quantum group $\mathbb{G}$ and two $\mathbb{G}$-$W^*$-algebras $α: A\curvearrowleft \mathbb{G}$ and $β: B\curvearrowleft \mathbb{G}$, we study the notion of equivariant $W^*$-Morita equivalence $(A, α)\sim_{\mathbb{G}} (B, β)$, which is an equivariant version of Rieffel's notion of $W^*$-Morita equivalence. We prove that important dynamical properties of $\mathbb{G}$-$W^*$-algebras, such as (inner) amenability, are preserved under equivariant Morita equivalence. For …

This https://arxiv.org/abs/2504.03980 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGR_…

Virtual Reality Lensing for Surface Approximation in Feature-driven DVR
We present a novel lens technique to support the identification of heterogeneous features in direct volume rendering (DVR) visualizations. In contrast to data-centric transfer function (TF) design, our image-driven approach enables users to specify target features directly within the visualization using deformable quadric surfaces. The lens leverages quadrics for their expressive yet simple parametrization, enabling users to sculpt feature approximations by composing multiple quadric lenses. By…

This https://arxiv.org/abs/2505.10229 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Strong and weak convergence rates for fully coupled multiscale stochastic differential equations driven by $α$-stable processes
We first establish strong convergence rates for multiscale systems driven by $α$-stable processes, with analyses constructed in two distinct scaling regimes. When addressing weak convergence rates of this system, we derive four averaged equations with respect to four scaling regimes. Notably, under sufficient Hölder regularity conditions on the time-dependent drifts of slow process, the strong convergence orders are related to the known optimal strong convergence order $1-\frac{1}α$, and the…

This https://arxiv.org/abs/2506.01950 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

DualMap: Online Open-Vocabulary Semantic Mapping for Natural Language Navigation in Dynamic Changing Scenes
We introduce DualMap, an online open-vocabulary mapping system that enables robots to understand and navigate dynamically changing environments through natural language queries. Designed for efficient semantic mapping and adaptability to changing environments, DualMap meets the essential requirements for real-world robot navigation applications. Our proposed hybrid segmentation frontend and object-level status check eliminate the costly 3D object merging required by prior methods, enabling effi…

This https://arxiv.org/abs/2506.00541 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

3D Trajectory Reconstruction of Moving Points Based on Asynchronous Cameras
Photomechanics is a crucial branch of solid mechanics. The localization of point targets constitutes a fundamental problem in optical experimental mechanics, with extensive applications in various missions of UAVs. Localizing moving targets is crucial for analyzing their motion characteristics and dynamic properties. Reconstructing the trajectories of points from asynchronous cameras is a significant challenge. It encompasses two coupled sub-problems: trajectory reconstruction and camera synchr…

[2025-06-05 Thu (UTC), no new articles found for cs.SC Symbolic Computation]
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[2025-06-05 Thu (UTC), 1 new article found for physics.ao-ph Atmospheric and Oceanic Physics]
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STELLA: Towards Protein Function Prediction with Multimodal LLMs Integrating Sequence-Structure Representations
Hongwang Xiao, Wenjun Lin, Xi Chen, Hui Wang, Kai Chen, Jiashan Li, Yuancheng Sun, Sicheng Dai, Boya Wu, Qiwei Ye
https://arxiv.org/abs/2506.03800

STELLA: Towards Protein Function Prediction with Multimodal LLMs Integrating Sequence-Structure Representations
Protein biology focuses on the intricate relationships among sequences, structures, and functions. Deciphering protein functions is crucial for understanding biological processes, advancing drug discovery, and enabling synthetic biology applications. Since protein sequences determine tertiary structures, which in turn govern functions, integrating sequence and structure information is essential for accurate prediction of protein functions. Traditional protein language models (pLMs) have advance…

This https://arxiv.org/abs/2502.08852 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Controlling discrete semilinear wave equations toward flocks
In this work, we initiate the research on controlling nonlinear waves propagating on lattices from a completely new perspective. We consider nonlinear waves on a lattice as a system of interacting particles and study their collective flocking behavior. By designing suitable feedback controls, we show that any admissible flock can be reached within a finite amount of time. Finally, we highlight the connection between our flocking problem and a minimal-time problem in the framework of nonlinear H…

[2025-06-05 Thu (UTC), 1 new article found for math.OA Operator Algebras]
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This https://arxiv.org/abs/2506.00433 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Latent Wavelet Diffusion: Enabling 4K Image Synthesis for Free
High-resolution image synthesis remains a core challenge in generative modeling, particularly in balancing computational efficiency with the preservation of fine-grained visual detail. We present Latent Wavelet Diffusion (LWD), a lightweight framework that enables any latent diffusion model to scale to ultra-high-resolution image generation (2K to 4K) for free. LWD introduces three key components: (1) a scale-consistent variational autoencoder objective that enhances the spectral fidelity of la…

This https://arxiv.org/abs/2503.16424 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGR_…

Bézier Splatting for Fast and Differentiable Vector Graphics Rendering
Differentiable vector graphics (VGs) are widely used in image vectorization and vector synthesis, while existing representations are costly to optimize and struggle to achieve high-quality rendering results for high-resolution images. This work introduces a new differentiable VG representation, dubbed Bézier Splatting, that enables fast yet high-fidelity VG rasterization. Bézier Splatting samples 2D Gaussians along Bézier curves, which naturally provide positional gradients at object boundar…

This https://arxiv.org/abs/2502.00514 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A Proof of The Changepoint Detection Threshold Conjecture in Preferential Attachment Models
We investigate the problem of detecting and estimating a changepoint in the attachment function of a network evolving according to a preferential attachment model on $n$ vertices, using only a single final snapshot of the network. Bet et al.~\cite{bet2023detecting} show that a simple test based on thresholding the number of vertices with minimum degrees can detect the changepoint when the change occurs at time $n-Ω(\sqrt{n})$. They further make the striking conjecture that detection becomes im…

This https://arxiv.org/abs/2505.24198 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Learning Gentle Humanoid Locomotion and End-Effector Stabilization Control
Can your humanoid walk up and hand you a full cup of beer, without spilling a drop? While humanoids are increasingly featured in flashy demos like dancing, delivering packages, traversing rough terrain, fine-grained control during locomotion remains a significant challenge. In particular, stabilizing a filled end-effector (EE) while walking is far from solved, due to a fundamental mismatch in task dynamics: locomotion demands slow-timescale, robust control, whereas EE stabilization requires rap…

[2025-06-06 Fri (UTC), no new articles found for physics.ao-ph Atmospheric and Oceanic Physics]
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This https://arxiv.org/abs/2505.24718 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Reinforcing Video Reasoning with Focused Thinking
Recent advancements in reinforcement learning, particularly through Group Relative Policy Optimization (GRPO), have significantly improved multimodal large language models for complex reasoning tasks. However, two critical limitations persist: 1) they often produce unfocused, verbose reasoning chains that obscure salient spatiotemporal cues and 2) binary rewarding fails to account for partially correct answers, resulting in high reward variance and inefficient learning. In this paper, we propos…

Beyond Worst-Case Analysis for Symbolic Computation: Root Isolation Algorithms
Alperen A. Erg\"ur, Josu\'e Tonelli-Cueto, Elias Tsigaridas
https://arxiv.org/abs/2506.04436

Beyond Worst-Case Analysis for Symbolic Computation: Root Isolation Algorithms
We introduce beyond-worst-case analysis into symbolic computation. This is an extensive field which almost entirely relies on worst-case bit complexity, and we start from a basic problem in the field: isolating the real roots of univariate polynomials. This is a fundamental problem in symbolic computation and it is arguably one of the most basic problems in computational mathematics. The problem has a long history decorated with numerous ingenious algorithms and furnishes an active area of rese…

UniSim: A Unified Simulator for Time-Coarsened Dynamics of Biomolecules
Ziyang Yu, Wenbing Huang, Yang Liu
https://arxiv.org/abs/2506.03157 https://…

UniSim: A Unified Simulator for Time-Coarsened Dynamics of Biomolecules
Molecular Dynamics (MD) simulations are essential for understanding the atomic-level behavior of molecular systems, giving insights into their transitions and interactions. However, classical MD techniques are limited by the trade-off between accuracy and efficiency, while recent deep learning-based improvements have mostly focused on single-domain molecules, lacking transferability to unfamiliar molecular systems. Therefore, we propose \textbf{Uni}fied \textbf{Sim}ulator (UniSim), which levera…

This https://arxiv.org/abs/2502.05221 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Blackout DIFUSCO
This study explores the integration of Blackout Diffusion into the DIFUSCO framework for combinatorial optimization, specifically targeting the Traveling Salesman Problem (TSP). Inspired by the success of discrete-time diffusion models (D3PM) in maintaining structural integrity, we extend the paradigm to a continuous-time framework, leveraging the unique properties of Blackout Diffusion. Continuous-time modeling introduces smoother transitions and refined control, hypothesizing enhanced solutio…

This https://arxiv.org/abs/2412.15969 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

On cutoff via rigidity for high dimensional curved diffusions
We consider overdamped Lanvegin diffusions in Euclidean space, with curvature equal to the spectral gap. This includes the Ornstein-Uhlenbeck process as well as non Gaussian and non product extensions with convex interaction, such as the Dyson process from random matrix theory. We show that a cutoff phenomenon or abrupt convergence to equilibrium occurs in high-dimension, at a critical time equal to the logarithm of the dimension divided by twice the spectral gap. This cutoff holds for Wasserst…

Transportation cost and contraction coefficient for channels on von Neumann algebras
Roy Araiza, Marius Junge, Peixue Wu
https://arxiv.org/abs/2506.04197 h…

Transportation cost and contraction coefficient for channels on von Neumann algebras
We present a noncommutative optimal transport framework for quantum channels acting on von Neumann algebras. Our central object is the Lipschitz cost measure, a transportation-inspired quantity that evaluates the minimal cost required to move between quantum states via a given channel. Accompanying this is the Lipschitz contraction coefficient, which captures how much the channel contracts the Wasserstein-type distance between states. We establish foundational properties of these quantities, in…

This https://arxiv.org/abs/2505.20573 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Collision- and Reachability-Aware Multi-Robot Control with Grounded LLM Planners
Large language models (LLMs) have demonstrated strong performance in various robot control tasks. However, their deployment in real-world applications remains constrained. Even state-ofthe-art LLMs, such as GPT-o4mini, frequently produce invalid action plans that violate physical constraints, such as directing a robot to an unreachable location or causing collisions between robots. This issue primarily arises from a lack of awareness of these physical constraints during the reasoning process. T…

This https://arxiv.org/abs/2505.24380 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

SASP: Strip-Aware Spatial Perception for Fine-Grained Bird Image Classification
Fine-grained bird image classification (FBIC) is not only of great significance for ecological monitoring and species identification, but also holds broad research value in the fields of image recognition and fine-grained visual modeling. Compared with general image classification tasks, FBIC poses more formidable challenges: 1) the differences in species size and imaging distance result in the varying sizes of birds presented in the images; 2) complex natural habitats often introduce strong ba…

[2025-06-05 Thu (UTC), 3 new articles found for cs.GR Graphics]
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Improving Post-Processing for Quantitative Precipitation Forecasting Using Deep Learning: Learning Precipitation Physics from High-Resolution Observations
ChangJae Lee, Heecheol Yang, Byeonggwon Kim
https://arxiv.org/abs/2506.03842

Improving Post-Processing for Quantitative Precipitation Forecasting Using Deep Learning: Learning Precipitation Physics from High-Resolution Observations
Accurate quantitative precipitation forecasting (QPF) remains one of the main challenges in numerical weather prediction (NWP), primarily due to the difficulty of representing the full complexity of atmospheric microphysics through parameterization schemes. This study introduces a deep learning-based post-processing model, DL-QPF, which diagnoses precipitation fields from meteorological forecasts by learning directly from high-resolution radar estimates precipitation. The DL-QPF model is constr…

This https://arxiv.org/abs/2505.24139 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

S4-Driver: Scalable Self-Supervised Driving Multimodal Large Language Modelwith Spatio-Temporal Visual Representation
The latest advancements in multi-modal large language models (MLLMs) have spurred a strong renewed interest in end-to-end motion planning approaches for autonomous driving. Many end-to-end approaches rely on human annotations to learn intermediate perception and prediction tasks, while purely self-supervised approaches--which directly learn from sensor inputs to generate planning trajectories without human annotations often underperform the state of the art. We observe a key gap in the input re…

This https://arxiv.org/abs/2411.05441 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Sticky diffusions on star graphs : characterization and It{ô} formula
We investigate continuous diffusions on star graphs with sticky behavior at the vertex. These are Markov processes with continuous paths having a positive occupation time at the vertex. We characterize sticky diffusions as time-changed nonsticky diffusions by adapting the classical technique of It{ô} and McKean. We prove a form of It{ô} formula, also known as Freidlin-Sheu formula, for this type of process. As an intermediate step, we also obtain a stochastic differential equation satisfied b…

This https://arxiv.org/abs/2501.10306 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Micro-Macro Decomposition of Particle Swarm Optimization Methods
Solving non-convex minimization problems using multi-particle metaheuristic derivative-free optimization methods is still an active area of research. Popular methods are Particle Swarm Optimization (PSO) methods, that iteratively update a population of particles according to dynamics inspired by social interactions between individuals. We present a modification to include constrained minimization problems using exact penalization. Additionally, we utilize the hierarchical structure of PSO to in…

This https://arxiv.org/abs/2505.20290 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

EgoZero: Robot Learning from Smart Glasses
Despite recent progress in general purpose robotics, robot policies still lag far behind basic human capabilities in the real world. Humans interact constantly with the physical world, yet this rich data resource remains largely untapped in robot learning. We propose EgoZero, a minimal system that learns robust manipulation policies from human demonstrations captured with Project Aria smart glasses, $\textbf{and zero robot data}$. EgoZero enables: (1) extraction of complete, robot-executable ac…

[2025-06-06 Fri (UTC), no new articles found for math.OA Operator Algebras]
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SplArt: Articulation Estimation and Part-Level Reconstruction with 3D Gaussian Splatting
Shengjie Lin, Jiading Fang, Muhammad Zubair Irshad, Vitor Campagnolo Guizilini, Rares Andrei Ambrus, Greg Shakhnarovich, Matthew R. Walter
https://arxiv.org/abs/2506.03594

SplArt: Articulation Estimation and Part-Level Reconstruction with 3D Gaussian Splatting
Reconstructing articulated objects prevalent in daily environments is crucial for applications in augmented/virtual reality and robotics. However, existing methods face scalability limitations (requiring 3D supervision or costly annotations), robustness issues (being susceptible to local optima), and rendering shortcomings (lacking speed or photorealism). We introduce SplArt, a self-supervised, category-agnostic framework that leverages 3D Gaussian Splatting (3DGS) to reconstruct articulated ob…

This https://arxiv.org/abs/2410.20426 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Exact Temporal Variation for Fractional Stochastic Heat Equation Driven by Space-Time White Noise
In this paper, we consider the exact fractional variation for the temporal process of the solution to the fractional stochastic heat equation on $\mathbb{R}$ driven by a space-time white noise, and as an application we give the estimate of drift parameter.

This https://arxiv.org/abs/2505.16196 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

SEM: Enhancing Spatial Understanding for Robust Robot Manipulation
A key challenge in robot manipulation lies in developing policy models with strong spatial understanding, the ability to reason about 3D geometry, object relations, and robot embodiment. Existing methods often fall short: 3D point cloud models lack semantic abstraction, while 2D image encoders struggle with spatial reasoning. To address this, we propose SEM (Spatial Enhanced Manipulation model), a novel diffusion-based policy framework that explicitly enhances spatial understanding from two com…

This https://arxiv.org/abs/2501.03470 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Positivstellensätze for polynomial matrices with universal quantifiers
This paper investigates Positivstellensätze for polynomial matrices subject to universally quantified polynomial matrix inequality constraints. We first establish a matrix-valued Positivstellensatz under the Archimedean condition, incorporating universal quantifiers. For scalar-valued polynomial objectives, we further develop a sparse Positivstellensatz that leverages correlative sparsity patterns within these quantified constraints. Moving beyond the Archimedean framework, we then derive a se…

This https://arxiv.org/abs/2408.00277 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Stochastic Domination of Exit Times for Random Walks and Brownian Motion with Drift
In this note, by an elementary use of Girsanov's transform we show that the exit time for either a biased random walk or a drifted Brownian motion on a symmetric interval is stochastically monotone with respect to the drift parameter. In the random walk case, this gives an alternative proof of a recent result of E. Peköz and R. Righter in 2024. Our arguments in both discrete and continuous cases are parallel to each other. We also outline a simple SDE proof for the Brownian case based on a sta…

Facial Appearance Capture at Home with Patch-Level Reflectance Prior
Yuxuan Han, Junfeng Lyu, Kuan Sheng, Minghao Que, Qixuan Zhang, Lan Xu, Feng Xu
https://arxiv.org/abs/2506.03478

Facial Appearance Capture at Home with Patch-Level Reflectance Prior
Existing facial appearance capture methods can reconstruct plausible facial reflectance from smartphone-recorded videos. However, the reconstruction quality is still far behind the ones based on studio recordings. This paper fills the gap by developing a novel daily-used solution with a co-located smartphone and flashlight video capture setting in a dim room. To enhance the quality, our key observation is to solve facial reflectance maps within the data distribution of studio-scanned ones. Spec…

This https://arxiv.org/abs/2505.10018 has been replaced.
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LEMON-Mapping: Loop-Enhanced Large-Scale Multi-Session Point Cloud Merging and Optimization for Globally Consistent Mapping
Multi-robot collaboration is becoming increasingly critical and presents significant challenges in modern robotics, especially for building a globally consistent, accurate map. Traditional multi-robot pose graph optimization (PGO) methods ensure basic global consistency but ignore the geometric structure of the map, and only use loop closures as constraints between pose nodes, leading to divergence and blurring in overlapping regions. To address this issue, we propose LEMON-Mapping, a loop-enha…

This https://arxiv.org/abs/2505.21920 has been replaced.
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InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective
The Segment Anything Model (SAM), a vision foundation model, exhibits impressive zero-shot capabilities in general tasks but struggles in specialized domains. Parameter-efficient fine-tuning (PEFT) is a promising approach to unleash the potential of SAM in novel scenarios. However, existing PEFT methods for SAM neglect the domain-invariant relations encoded in the pre-trained model. To bridge this gap, we propose InfoSAM, an information-theoretic approach that enhances SAM fine-tuning by distil…

This https://arxiv.org/abs/2411.15479 has been replaced.
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Sparse Polynomial Matrix Optimization
A polynomial matrix inequality is a formula asserting that a polynomial matrix is positive semidefinite. Polynomial matrix optimization concerns minimizing the smallest eigenvalue of a symmetric polynomial matrix subject to a tuple of polynomial matrix inequalities. This work explores the use of sparsity methods in reducing the complexity of sum-of-squares based methods in verifying polynomial matrix inequalities or solving polynomial matrix optimization. In the unconstrained setting, Newton po…

This https://arxiv.org/abs/2505.21863 has been replaced.
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GETReason: Enhancing Image Context Extraction through Hierarchical Multi-Agent Reasoning
Publicly significant images from events hold valuable contextual information, crucial for journalism and education. However, existing methods often struggle to extract this relevance accurately. To address this, we introduce GETReason (Geospatial Event Temporal Reasoning), a framework that moves beyond surface-level image descriptions to infer deeper contextual meaning. We propose that extracting global event, temporal, and geospatial information enhances understanding of an image's significanc…

This https://arxiv.org/abs/2505.06787 has been replaced.
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Digital-physical testbed for ship autonomy studies in the Marine Cybernetics Laboratory basin
The algorithms developed for Maritime Autonomous Surface Ships (MASS) are often challenging to test on actual vessels due to high operational costs and safety considerations. Simulations offer a cost-effective alternative and eliminate risks, but they may not accurately represent real-world dynamics for the given tasks. Utilizing small-scale model ships and robotic vessels in conjunction with a laboratory basin provides an accessible testing environment for the early stages of validation proces…

Multi-Spectral Gaussian Splatting with Neural Color Representation
Lukas Meyer, Josef Gr\"un, Maximilian Weiherer, Bernhard Egger, Marc Stamminger, Linus Franke
https://arxiv.org/abs/2506.03407

Multi-Spectral Gaussian Splatting with Neural Color Representation
We present MS-Splatting -- a multi-spectral 3D Gaussian Splatting (3DGS) framework that is able to generate multi-view consistent novel views from images of multiple, independent cameras with different spectral domains. In contrast to previous approaches, our method does not require cross-modal camera calibration and is versatile enough to model a variety of different spectra, including thermal and near-infra red, without any algorithmic changes. Unlike existing 3DGS-based frameworks that tre…

[2025-06-06 Fri (UTC), 9 new articles found for math.PR Probability]
#toXiv_bot_toot

This https://arxiv.org/abs/2410.14899 has been replaced.
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Out-of-distribution Robust Optimization
In this paper, we consider the contextual robust optimization problem under an out-of-distribution setting. The contextual robust optimization problem considers a risk-sensitive objective function for an optimization problem with the presence of a context vector (also known as covariates or side information) capturing related information. While the existing works mainly consider the in-distribution setting, and the resultant robustness achieved is in an out-of-sample sense, our paper studies an…

This https://arxiv.org/abs/2505.21179 has been replaced.
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Normalized Attention Guidance: Universal Negative Guidance for Diffusion Model
Negative guidance -- explicitly suppressing unwanted attributes -- remains a fundamental challenge in diffusion models, particularly in few-step sampling regimes. While Classifier-Free Guidance (CFG) works well in standard settings, it fails under aggressive sampling step compression due to divergent predictions between positive and negative branches. We present Normalized Attention Guidance (NAG), an efficient, training-free mechanism that applies extrapolation in attention space with L1-based…

This https://arxiv.org/abs/2505.00237 has been replaced.
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Future-Oriented Navigation: Dynamic Obstacle Avoidance with One-Shot Energy-Based Multimodal Motion Prediction
This paper proposes an integrated approach for the safe and efficient control of mobile robots in dynamic and uncertain environments. The approach consists of two key steps: one-shot multimodal motion prediction to anticipate motions of dynamic obstacles and model predictive control to incorporate these predictions into the motion planning process. Motion prediction is driven by an energy-based neural network that generates high-resolution, multi-step predictions in a single operation. The pred…

This https://arxiv.org/abs/2505.20292 has been replaced.
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OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation
Subject-to-Video (S2V) generation aims to create videos that faithfully incorporate reference content, providing enhanced flexibility in the production of videos. To establish the infrastructure for S2V generation, we propose OpenS2V-Nexus, consisting of (i) OpenS2V-Eval, a fine-grained benchmark, and (ii) OpenS2V-5M, a million-scale dataset. In contrast to existing S2V benchmarks inherited from VBench that focus on global and coarse-grained assessment of generated videos, OpenS2V-Eval focuses …

This https://arxiv.org/abs/2410.06307 has been replaced.
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Model Predictive Control is Almost Optimal for Restless Bandit
We consider the discrete time infinite horizon average reward restless markovian bandit (RMAB) problem. We propose a \emph{model predictive control} based non-stationary policy with a rolling computational horizon $τ$. At each time-slot, this policy solves a $τ$ horizon linear program whose first control value is kept as a control for the RMAB. Our solution requires minimal assumptions and quantifies the loss in optimality in terms of $τ$ and the number of arms, $N$. We show that its sub-opt…

This https://arxiv.org/abs/2504.17959 has been replaced.
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CIVIL: Causal and Intuitive Visual Imitation Learning
Today's robots learn new tasks by imitating human examples. However, this standard approach to visual imitation learning is fundamentally limited: the robot observes what the human does, but not why the human chooses those behaviors. Without understanding the features that factor into the human's decisions, robot learners often misinterpret the data and fail to perform the task when the environment changes. We therefore propose a shift in perspective: instead of asking human teachers just to sh…

Myopic non-intersection in a periodic potential
Jonas Arista, Daniel Remenik, Avelio Sep\'ulveda
https://arxiv.org/abs/2506.05246 https://

Myopic non-intersection in a periodic potential
We introduce a class of Markov processes conditioned to avoid intersection over a moving time window of length T>0, a setting we refer to as myopic non-intersection. In particular, we study a system of myopic non-intersecting Brownian motions subject to a periodic potential. Our focus lies in understanding the interplay between the confining effect of the potential and the repulsion induced by the non-intersection constraint. We show that, in the long time limit, and as both T and the strength …

This https://arxiv.org/abs/2505.20156 has been replaced.
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HunyuanVideo-Avatar: High-Fidelity Audio-Driven Human Animation for Multiple Characters
Recent years have witnessed significant progress in audio-driven human animation. However, critical challenges remain in (i) generating highly dynamic videos while preserving character consistency, (ii) achieving precise emotion alignment between characters and audio, and (iii) enabling multi-character audio-driven animation. To address these challenges, we propose HunyuanVideo-Avatar, a multimodal diffusion transformer (MM-DiT)-based model capable of simultaneously generating dynamic, emotion-…

This https://arxiv.org/abs/2412.03293 has been replaced.
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Diffusion-VLA: Generalizable and Interpretable Robot Foundation Model via Self-Generated Reasoning
In this paper, we present DiffusionVLA, a novel framework that seamlessly combines the autoregression model with the diffusion model for learning visuomotor policy. Central to our approach is a next-token prediction objective, enabling the model to reason effectively over the user's query in the context of current observations. Subsequently, a diffusion model is attached to generate robust action outputs. To enhance policy learning through self-reasoning, we introduce a novel reasoning injectio…

This https://arxiv.org/abs/2407.12518 has been replaced.
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Inertial Methods with Viscous and Hessian driven Damping for Non-Convex Optimization
In this paper, we aim to study non-convex minimization problems via second-order (in-time) dynamics, including a non-vanishing viscous damping and a geometric Hessian-driven damping. Second-order systems that only rely on a viscous damping may suffer from oscillation problems towards the minima, while the inclusion of a Hessian-driven damping term is known to reduce this effect without explicit construction of the Hessian in practice. There are essentially two ways to introduce the Hessian-driv…

This https://arxiv.org/abs/2505.19901 has been replaced.
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Dynamic-I2V: Exploring Image-to-Video Generaion Models via Multimodal LLM
Recent advancements in image-to-video (I2V) generation have shown promising performance in conventional scenarios. However, these methods still encounter significant challenges when dealing with complex scenes that require a deep understanding of nuanced motion and intricate object-action relationships. To address these challenges, we present Dynamic-I2V, an innovative framework that integrates Multimodal Large Language Models (MLLMs) to jointly encode visual and textual conditions for a diffus…

Infinitesimal freeness for orthogonally invariant random matrices
Guillaume C\'ebron (Toulouse), James A Mingo (Queen's)
https://arxiv.org/abs/2506.05139

Infinitesimal freeness for orthogonally invariant random matrices
We introduce a new kind of free independence, called real infinitesimal freeness. We show that independent orthogonally invariant with infinitesimal laws are asymptotically real infinitesimally free. We introduce new cumulants, called real infinitesimal cumulants and show that real infinitesimal freeness is equivalent to vanishing of mixed cumulants. We prove the formula for cumulants with products as entries.

This https://arxiv.org/abs/2407.00144 has been replaced.
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SCOPE: Stochastic Cartographic Occupancy Prediction Engine for Uncertainty-Aware Dynamic Navigation
This article presents a family of Stochastic Cartographic Occupancy Prediction Engines (SCOPEs) that enable mobile robots to predict the future states of complex dynamic environments. They do this by accounting for the motion of the robot itself, the motion of dynamic objects, and the geometry of static objects in the scene, and they generate a range of possible future states of the environment. These prediction engines are software-optimized for real-time performance for navigation in crowded …

This https://arxiv.org/abs/2505.19746 has been replaced.
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Improving Heart Rejection Detection in XPCI Images Using Synthetic Data Augmentation
Accurate identification of acute cellular rejection (ACR) in endomyocardial biopsies is essential for effective management of heart transplant patients. However, the rarity of high-grade rejection cases (3R) presents a significant challenge for training robust deep learning models. This work addresses the class imbalance problem by leveraging synthetic data generation using StyleGAN to augment the limited number of real 3R images. Prior to GAN training, histogram equalization was applied to sta…

This https://arxiv.org/abs/2407.04562 has been replaced.
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An SDE Perspective on Stochastic Inertial Gradient Dynamics with Time-Dependent Viscosity and Geometric Damping
Our approach is part of the close link between continuous dissipative dynamical systems and optimization algorithms. We aim to solve convex minimization problems by means of stochastic inertial differential equations which are driven by the gradient of the objective function. This will provide a general mathematical framework for analyzing fast optimization algorithms with stochastic gradient input. Our study is a natural extension of our previous work devoted to the first-order in time stochas…

Weak solutions of Stochastic Volterra Equations in convex domains with general kernels
Eduardo Abi Jaber, Aur\'elien Alfonsi, Guillaume Szulda
https://arxiv.org/abs/2506.04911

Weak solutions of Stochastic Volterra Equations in convex domains with general kernels
We establish new weak existence results for $d$-dimensional Stochastic Volterra Equations (SVEs) with continuous coefficients and possibly singular one-dimensional non-convolution kernels. These results are obtained by introducing an approximation scheme and showing its convergence. A particular emphasis is made on the stochastic invariance of the solution in a closed convex set. To do so, we extend the notion of kernels that preserve nonnegativity introduced in \cite{Alfonsi23} to non-convolut…

This https://arxiv.org/abs/2406.02436 has been replaced.
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Safe, Out-of-Distribution-Adaptive MPC with Conformalized Neural Network Ensembles
We present SODA-MPC, a Safe, Out-of-Distribution-Adaptive Model Predictive Control algorithm, which uses an ensemble of learned models for prediction, with a runtime monitor to flag unreliable out-of-distribution (OOD) predictions. When an OOD situation is detected, SODA-MPC triggers a safe fallback control strategy based on reachability, yielding a control framework that achieves the high performance of learning-based models while preserving the safety of reachability-based control. We demonst…

This https://arxiv.org/abs/2505.19028 has been replaced.
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InfoChartQA: A Benchmark for Multimodal Question Answering on Infographic Charts
Understanding infographic charts with design-driven visual elements (e.g., pictograms, icons) requires both visual recognition and reasoning, posing challenges for multimodal large language models (MLLMs). However, existing visual-question answering benchmarks fall short in evaluating these capabilities of MLLMs due to the lack of paired plain charts and visual-element-based questions. To bridge this gap, we introduce InfoChartQA, a benchmark for evaluating MLLMs on infographic chart understand…

This https://arxiv.org/abs/2403.18213 has been replaced.
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Long-Term Open-Pit Mine Planning with Large Neighbourhood Search
We present a Large Neighbourhood Search based approach for solving complex long-term open-pit mine planning problems. An initial feasible solution, generated by a sliding windows heuristic, is improved through repeated solves of a restricted mixed-integer program. Each iteration leaves only a subset of the variables in the planning model free to take on new values. We form these subsets through the use of neighbourhood formation strategies that exploit model structure. We show that our approach…

On Pioneering Works of Albert Shiryaev on Markov Decision Processes and Some Later Developments
Eugene A. Feinberg
https://arxiv.org/abs/2506.04896 https:/…

On Pioneering Works of Albert Shiryaev on Markov Decision Processes and Some Later Developments
This article is dedicated to three fundamental papers on Markov Decision Processes and on control with incomplete observations published by Albert Shiryaev approximately sixty years ago. One of these papers was coauthored with O.V. Viskov. We discuss some of the results and some of many rich ideas presented in these papers and survey some later developments. At the end we mention some recent studies of Albert Shiryaev on Kolmogorov's equations for jump Markov processes and on control of continu…

This https://arxiv.org/abs/2404.19564 has been replaced.
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Time, Travel, and Energy in the Uniform Dispersion Problem
We investigate the algorithmic problem of uniformly dispersing a swarm of robots in an unknown, gridlike environment. In this setting, our goal is to study the relationships between performance metrics and robot capabilities. We introduce a formal model comparing dispersion algorithms based on makespan, traveled distance, energy consumption, sensing, communication, and memory. Using this framework, we classify uniform dispersion algorithms according to their capability requirements and performa…

This https://arxiv.org/abs/2505.18924 has been replaced.
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LLM-Guided Taxonomy and Hierarchical Uncertainty for 3D Point CLoud Active Learning
We present a novel active learning framework for 3D point cloud semantic segmentation that, for the first time, integrates large language models (LLMs) to construct hierarchical label structures and guide uncertainty-based sample selection. Unlike prior methods that treat labels as flat and independent, our approach leverages LLM prompting to automatically generate multi-level semantic taxonomies and introduces a recursive uncertainty projection mechanism that propagates uncertainty across hier…

This https://arxiv.org/abs/2505.16512 has been replaced.
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Beyond Face Swapping: A Diffusion-Based Digital Human Benchmark for Multimodal Deepfake Detection
In recent years, the rapid development of deepfake technology has given rise to an emerging and serious threat to public security: diffusion model-based digital human generation. Unlike traditional face manipulation methods, such models can generate highly realistic videos with consistency through multimodal control signals. Their flexibility and covertness pose severe challenges to existing detection strategies. To bridge this gap, we introduce DigiFakeAV, the first large-scale multimodal digi…

This https://arxiv.org/abs/2309.03765 has been replaced.
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Equivariant Symmetries for Inertial Navigation Systems
This paper investigates the problem of inertial navigation system (INS) filter design through the lens of symmetry. The extended Kalman filter (EKF) and its variants have been the staple of INS filtering for 50 years. However, recent advances in inertial navigation systems have exploited matrix Lie group structure to design stochastic filters and state observers that have been shown to display superior performance compared to classical solutions. In this work, we explore various symmetries of i…

Random dynamics and invariant measures for a class of non-Newtonian fluids of differential type on 2D and 3D Poincar\'e domains
Kush Kinra, Fernanda Cipriano
https://arxiv.org/abs/2506.04801

Random dynamics and invariant measures for a class of non-Newtonian fluids of differential type on 2D and 3D Poincaré domains
In this article, we consider a class of incompressible stochastic third-grade fluids (non-Newtonian fluids) equations on two- as well as three-dimensional Poincaré domains $\mathcal{O}$ (which may be bounded or unbounded). Our aims are to study the well-posedness and asymptotic analysis for the solutions of the underlying system. Firstly, we prove that the underlying system defined on $\mathcal{O}$ has a unique weak solution (in the analytic sense) under Dirichlet boundary condition and it als…

This https://arxiv.org/abs/2505.16452 has been replaced.
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CMRINet: Joint Groupwise Registration and Segmentation for Cardiac Function Quantification from Cine-MRI
Accurate and efficient quantification of cardiac function is essential for the estimation of prognosis of cardiovascular diseases (CVDs). One of the most commonly used metrics for evaluating cardiac pumping performance is left ventricular ejection fraction (LVEF). However, LVEF can be affected by factors such as inter-observer variability and varying pre-load and after-load conditions, which can reduce its reproducibility. Additionally, cardiac dysfunction may not always manifest as alterations…

Finitary codings and stochastic domination for Poisson representable processes
Yinon Spinka
https://arxiv.org/abs/2506.04797 https://…

Finitary codings and stochastic domination for Poisson representable processes
Construct a random set by independently selecting each finite subset of the integers with some probability depending on the set up to translations and taking the union of the selected sets. We show that when the only sets selected with positive probability are pairs, such a random set is a finitary factor of an IID process, answering a question of Forsström, Gantert and Steif. More generally, we show that this is the case whenever the distribution induced by the size of the selected sets has s…

This https://arxiv.org/abs/2505.15173 has been replaced.
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AvatarShield: Visual Reinforcement Learning for Human-Centric Video Forgery Detection
The rapid advancement of Artificial Intelligence Generated Content (AIGC) technologies, particularly in video generation, has led to unprecedented creative capabilities but also increased threats to information integrity, identity security, and public trust. Existing detection methods, while effective in general scenarios, lack robust solutions for human-centric videos, which pose greater risks due to their realism and potential for legal and ethical misuse. Moreover, current detection approach…

Total progeny for spectrally negative branching L{\'e}vy processes with absorption
Christophe Profeta (LaMME)
https://arxiv.org/abs/2506.04783 https://…

Total progeny for spectrally negative branching L{é}vy processes with absorption
We consider a spectrally negative branching L{é}vy process in which particles are killed upon crossing below zero. It is known that such a process becomes extinct almost surely if the drift toward -$\infty$ is sufficiently strong to counterbalance the reproduction rate. In this note, we study the tail asymptotics of the number of particles absorbed at the boundary during the lifetime of the process, in both the subcritical and critical regimes.

This https://arxiv.org/abs/2505.11282 has been replaced.
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MTevent: A Multi-Task Event Camera Dataset for 6D Pose Estimation and Moving Object Detection
Mobile robots are reaching unprecedented speeds, with platforms like Unitree B2, and Fraunhofer O3dyn achieving maximum speeds between 5 and 10 m/s. However, effectively utilizing such speeds remains a challenge due to the limitations of RGB cameras, which suffer from motion blur and fail to provide real-time responsiveness. Event cameras, with their asynchronous operation, and low-latency sensing, offer a promising alternative for high-speed robotic perception. In this work, we introduce MTeve…

Universality of convergence rate of rightmost eigenvalue of complex IID random matrices
Yutao Ma, Xinchen Hu
https://arxiv.org/abs/2506.04560 https://

Universality of convergence rate of rightmost eigenvalue of complex IID random matrices
Let $X$ be an $n\times n$ matrix with independent and identically distributed (i.i.d.) entries $x_{ij} \stackrel{\text { d }}{=} n^{-1 / 2} ξ$ with $ξ$ being a complex random variable of mean zero and variance one. Let $\{σ_i\}_{1\le i\le n}$ be the eigenvalues of $X,$ and $R_n:=\max_i \Re σ_i$ and $Z_n$ be some scaled version of $R_n.$ It was proved that $Z_n$ converges weakly to the Gumbel distribution $Λ$ under certain moment conditions on $ξ.$ We further prove that for a complex rando…

A phase transition in the Bakry-\'Emery gradient estimate for Dyson Brownian motion
Kohei Suzuki, Kenshiro Tashiro
https://arxiv.org/abs/2506.04424 htt…

A phase transition in the Bakry-Émery gradient estimate for Dyson Brownian motion
In this paper, we find a gap between the lower bound of the Bakry-Émery $N$-Ricci tensor ${\rm Ric}_N$ and the Bakry-Émery gradient estimate ${\sf BE}$ in the space associated with the finite-particle Dyson Brownian motion (DBM) with inverse temperature $0<β<1$. Namely, we prove that, for the weighted space $(\mathbb R^n, w_β)$ with $w_β=\prod_{i

Object-centric 3D Motion Field for Robot Learning from Human Videos
Zhao-Heng Yin, Sherry Yang, Pieter Abbeel
https://arxiv.org/abs/2506.04227 https://

Object-centric 3D Motion Field for Robot Learning from Human Videos
Learning robot control policies from human videos is a promising direction for scaling up robot learning. However, how to extract action knowledge (or action representations) from videos for policy learning remains a key challenge. Existing action representations such as video frames, pixelflow, and pointcloud flow have inherent limitations such as modeling complexity or loss of information. In this paper, we propose to use object-centric 3D motion field to represent actions for robot learning …

Pseudo-Simulation for Autonomous Driving
Wei Cao, Marcel Hallgarten, Tianyu Li, Daniel Dauner, Xunjiang Gu, Caojun Wang, Yakov Miron, Marco Aiello, Hongyang Li, Igor Gilitschenski, Boris Ivanovic, Marco Pavone, Andreas Geiger, Kashyap Chitta
https://arxiv.org/abs/2506.04218

Pseudo-Simulation for Autonomous Driving
Existing evaluation paradigms for Autonomous Vehicles (AVs) face critical limitations. Real-world evaluation is often challenging due to safety concerns and a lack of reproducibility, whereas closed-loop simulation can face insufficient realism or high computational costs. Open-loop evaluation, while being efficient and data-driven, relies on metrics that generally overlook compounding errors. In this paper, we propose pseudo-simulation, a novel paradigm that addresses these limitations. Pseudo…