Tootfinder

Every user: "We want #Firefox to be a real agent for the user, that doesn't give us AI slop and won't sell out our privacy."
#Mozilla: "On Firefox for iOS you can now get an "AI" to summarize a page by shaking your phone"
🤦
https://blog.mozilla.org/en/firefox/shake-to-summarize/

Does @… have a status monitoring page / outage update site / blog / something? Would be nice to know what’s going on.
Also, remind me to figure out how to financially support them when they’re back up!

[2025-09-09 Tue (UTC), 5 new articles found for math.CT Category Theory]
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[2025-09-09 Tue (UTC), 7 new articles found for cs.PL Programming Languages]
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looking at an old rfc i was reminded of how pointless “security considerations” sections used to be
which made me wonder what rule the authors were supposed to be obeying
the first security considerations section was in RFC 1060 which set a bad example that almost everyone followed
https://www.r…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[5/5]:
- Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applicat...
Yuan Zhang, Xinfeng Zhang, Xiaoming Qi, Xinyu Wu, Feng Chen, Guanyu Yang, Huazhu Fu

Rankings for Week 2 https://www.espn.com/fantasy/football/story/_/page/FFWeeklyPlayerRank25main-46028352/fantasy-football-rankings-2025-nfl-week-qb-rb-wr-te-dst

[2025-09-09 Tue (UTC), 6 new articles found for math.AT Algebraic Topology]
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[2025-09-09 Tue (UTC), 3 new articles found for cs.LO Logic in Computer Science]
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[2025-09-09 Tue (UTC), 20 new articles found for math.OC Optimization and Control]
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Performance Characterization of a Point-Cloud-Based Path Planner in Off-Road Terrain
Casey D. Majhor, Jeremy P. Bos
https://arxiv.org/abs/2509.07321 https://

Performance Characterization of a Point-Cloud-Based Path Planner in Off-Road Terrain
We present a comprehensive evaluation of a point-cloud-based navigation stack, MUONS, for autonomous off-road navigation. Performance is characterized by analyzing the results of 30,000 planning and navigation trials in simulation and validated through field testing. Our simulation campaign considers three kinematically challenging terrain maps and twenty combinations of seven path-planning parameters. In simulation, our MUONS-equipped AGV achieved a 0.98 success rate and experienced no failure…

mmBERT: A Modern Multilingual Encoder with Annealed Language Learning
Marc Marone, Orion Weller, William Fleshman, Eugene Yang, Dawn Lawrie, Benjamin Van Durme
https://arxiv.org/abs/2509.06888

mmBERT: A Modern Multilingual Encoder with Annealed Language Learning
Encoder-only languages models are frequently used for a variety of standard machine learning tasks, including classification and retrieval. However, there has been a lack of recent research for encoder models, especially with respect to multilingual models. We introduce mmBERT, an encoder-only language model pretrained on 3T tokens of multilingual text in over 1800 languages. To build mmBERT we introduce several novel elements, including an inverse mask ratio schedule and an inverse temperature…

[2025-09-09 Tue (UTC), 36 new articles found for math.CO Combinatorics]
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[2025-09-09 Tue (UTC), 23 new articles found for cs.CY Computers and Society]
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[2025-09-09 Tue (UTC), 18 new articles found for astro-ph.SR Solar and Stellar Astrophysics]
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[2025-09-09 Tue (UTC), 19 new articles found for math.NT Number Theory]
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[2025-09-09 Tue (UTC), 9 new articles found for cs.DC Distributed, Parallel, and Cluster Computing]
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[2025-09-09 Tue (UTC), 4 new articles found for cs.CC Computational Complexity]
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Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/7]:
- Unlocking Dataset Distillation with Diffusion Models
Brian B. Moser, Federico Raue, Sebastian Palacio, Stanislav Frolov, Andreas Dengel

There's a site called "Lyrics Layers" (or "Lyric Slayers") that has got to be all AI generated bullshit about song lyrics.
I stumbled across it last week to look up a Devo song and it just felt... generic. I checked a few other songs and they all have the same feel.
I could find no info or about page, or anything suggesting humans are involved.
I don't want some goddamn machine with an LLM to guess what song lyrics are about.
I want real li…

[2025-09-09 Tue (UTC), 27 new articles found for hep-th High Energy Physics - Theory]
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[2025-09-09 Tue (UTC), 23 new articles found for math.AG Algebraic Geometry]
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[2025-09-09 Tue (UTC), 7 new articles found for cs.DS Data Structures and Algorithms]
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Two-dimensional transducers
Fosco Loregian
https://arxiv.org/abs/2509.06769 https://arxiv.org/pdf/2509.06769

Two-dimensional transducers
We define a bicategory $\mathbf{2TDX}$ whose 1-cells provide a categorification of transducers, computational devices extending finite-state automata with output capabilities. This bicategory is a mathematically interesting object: its objects are categories $\mathcal{A},\mathcal{B},\dots$ and its 1-cells $(\mathcal{Q}, t) : \mathcal{A} \to \mathcal{B}$ consist of a category $\mathcal{Q}$ of `states', and a profunctor $$ t : \mathcal{A} \times \mathcal{Q}^\text{op}\times\mathcal{Q} \times (\mat…

Mechanized Metatheory of Forward Reasoning for End-to-End Linearizability Proofs
Zachary Kent, Ugur Y. Yavuz, Siddhartha Jayanti, Stephanie Balzer, Guy Blelloch
https://arxiv.org/abs/2509.06872

Mechanized Metatheory of Forward Reasoning for End-to-End Linearizability Proofs
In the past decade, many techniques have been developed to prove linearizability, the gold standard of correctness for concurrent data structures. Intuitively, linearizability requires that every operation on a concurrent data structure appears to take place instantaneously, even when interleaved with other operations. Most recently, Jayanti et al. presented the first sound and complete "forward reasoning" technique for proving linearizability that relates the behavior of a concurrent data stru…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/5]:
- ER-LoRA: Effective-Rank Guided Adaptation for Weather-Generalized Depth Estimation
Weilong Yan, Xin Zhang, Robby T. Tan

Refined Transfer
J. C. Becker
https://arxiv.org/abs/2509.06737 https://arxiv.org/pdf/2509.06737

Refined Transfer
A refined transfer is defined for the purpose of defining a refined version of the families torsion of Dwyer, Weiss, and Williams.

A Globalized Semismooth Newton Method for Prox-regular Optimization Problems
Yuqia Wu, Pengcheng Wu, Yaohua Hu, Shaohua Pan, Xiaoqi Yang
https://arxiv.org/abs/2509.05765 https:/…

A Globalized Semismooth Newton Method for Prox-regular Optimization Problems
We are concerned with a class of nonconvex and nonsmooth composite optimization problems, comprising a twice differentiable function and a prox-regular function. We establish a sufficient condition for the proximal mapping of a prox-regular function to be single-valued and locally Lipschitz continuous. By virtue of this property, we propose a hybrid of proximal gradient and semismooth Newton methods for solving these composite optimization problems, which is a globalized semismooth Newton metho…

UNH at CheckThat! 2025: Fine-tuning Vs Prompting in Claim Extraction
Joe Wilder, Nikhil Kadapala, Benji Xu, Mohammed Alsaadi, Aiden Parsons, Mitchell Rogers, Palash Agarwal, Adam Hassick, Laura Dietz
https://arxiv.org/abs/2509.06883

UNH at CheckThat! 2025: Fine-tuning Vs Prompting in Claim Extraction
We participate in CheckThat! Task 2 English and explore various methods of prompting and in-context learning, including few-shot prompting and fine-tuning with different LLM families, with the goal of extracting check-worthy claims from social media passages. Our best METEOR score is achieved by fine-tuning a FLAN-T5 model. However, we observe that higher-quality claims can sometimes be extracted using other methods, even when their METEOR scores are lower.

Chromatic numbers with open and nonzero local modular constraints
Daniel Herden, Jonathan Meddaugh, Mark R. Sepanski, William Clark, Adam Kraus, Ellie Matter, Kingsley Michael, Mitchell Minyard, Maricela Ramirez, Kyle Rosengartner, Elyssa Stephens, John Stephens
https://arxiv.org/abs/2509.05822

Chromatic numbers with open and nonzero local modular constraints
In this paper, we explore chromatic numbers subject to various local modular constraints. For fixed $n$, we consider proper integer colorings of a graph $G$ for which the closed and open neighborhood sums have nonzero remainders modulo $n$ and provide bounds for the associated chromatic numbers $χ_n(G)$ and $χ_{(n)}(G)$, respectively. In addition, we provide bounds for $χ_{(n,k)}(G)$, the minimal order of a proper integer coloring of $G$ with open neighborhood sums congruent to $k\mod n$ (wh…

Restricted divisor functions and half Appell sums in higher-level
Meng-Juan Tian, Nian Hong Zhou
https://arxiv.org/abs/2509.06032 https://arxiv.org/pdf/250…

Restricted divisor functions and half Appell sums in higher-level
We examine the value distributions of coefficients in certain $q$-series related to half Appell sums in higher-level and the first moment of the Garvan's $k$-rank of partitions. We prove that these coefficients equal certain restricted divisor functions and can take any nonnegative integer value infinitely many times. As applications, we confirm a conjecture of Xiong on the coefficients of a half Lerch sum and a conjecture of Garvan and Jennings-Shaffer on the nonnegativity of spt-crank-type pa…

On the Bit Size of Sum-of-Squares Proofs for Symmetric Formulations
Alex Bortolotti, Monaldo Mastrolilli, Marilena Palomba, Luis Felipe Vargas
https://arxiv.org/abs/2509.06928 h…

On the Bit Size of Sum-of-Squares Proofs for Symmetric Formulations
The Sum-of-Squares (SoS) hierarchy is a powerful framework for polynomial optimization and proof complexity, offering tight semidefinite relaxations that capture many classical algorithms. Despite its broad applicability, several works have revealed fundamental limitations to SoS automatability. (i) While low-degree SoS proofs are often desirable for tractability, recent works have revealed they may require coefficients of prohibitively large bit size, rendering them computationally infeasible.…

IM-PIR: In-Memory Private Information Retrieval
Mpoki Mwaisela, Peterson Yuhala, Pascal Felber, Valerio Schiavoni
https://arxiv.org/abs/2509.06514 https://…

IM-PIR: In-Memory Private Information Retrieval
Private information retrieval (PIR) is a cryptographic primitive that allows a client to securely query one or multiple servers without revealing their specific interests. In spite of their strong security guarantees, current PIR constructions are computationally costly. Specifically, most PIR implementations are memory-bound due to the need to scan extensive databases (in the order of GB), making them inherently constrained by the limited memory bandwidth in traditional processor-centric compu…

Classification of standard Manin triples in dimension 4 4
Ladislav Hlavat\'y, Petr Novotn\'y, Ivo Petr
https://arxiv.org/abs/2509.05638 https://arx…

Classification of standard Manin triples in dimension 4+4
Four- and six-dimensional Drinfeld doubles were classified in the past in terms of Manin triples. We provide an important step towards classification of eight-dimensional Drinfeld doubles by presenting an extensive list of Manin triples formed by pairs of four-dimensional Lie algebras. Due to the high complexity of the classification we focus on Manin triples formed by algebras in a certain standard form. The list contains 188 non-isomorphic Manin triples plus their duals. To apply the result…

MIO: Multiverse Debugging in the Face of Input/Output -- Extended Version with Additional Appendices
Tom Lauwaerts, Maarten Steevens, Christophe Scholliers
https://arxiv.org/abs/2509.06845

MIO: Multiverse Debugging in the Face of Input/Output -- Extended Version with Additional Appendices
Debugging non-deterministic programs on microcontrollers is notoriously challenging, especially when bugs manifest in unpredictable, input-dependent execution paths. A recent approach, called multiverse debugging, makes it easier to debug non-deterministic programs by allowing programmers to explore all potential execution paths. Current multiverse debuggers enable both forward and backward traversal of program paths, and some facilitate jumping to any previously visited states, potentially bra…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/5]:
- CoreMark: Toward Robust and Universal Text Watermarking Technique
Jiale Meng, Yiming Li, Zheming Lu, Zewei He, Hao Luo, Tianwei Zhang

On the integral cohomology of real toric manifolds
Feifei Fan
https://arxiv.org/abs/2509.06059 https://arxiv.org/pdf/2509.06059

On the integral cohomology of real toric manifolds
Real toric manifolds are the real loci of nonsingular complete toric varieties. In this paper, we calculate the integral cohomology groups of real toric manifolds in terms of the combinatorial data contained in the underlying simplicial fans, generalizing a formula due to Cai and Choi. We also present a combinatorial formula to describe the ring structure, modulo the ideal consisting of elements of order $2$. As an application, a combinatorial-algebraic criterion is established for when a real …

Replaced article(s) found for math.CT. https://arxiv.org/list/math.CT/new
[1/1]:
- Categorical semantics of compositional reinforcement learning
Georgios Bakirtzis, Michail Savvas, Ufuk Topcu

Relaxation of stochastic dominance constraints via optimal mass transport
Darinka Dentcheva, Yunxuan Yi
https://arxiv.org/abs/2509.05744 https://arxiv.org/…

Relaxation of stochastic dominance constraints via optimal mass transport
Optimization problems with stochastic dominance constraints provide a possibility to shape risk by selecting a benchmark random outcome with a desired distribution. The comparison of the relevant random outcomes to the respective benchmarks requires functional inequalities between the distribution functions or their transforms. A difficulty arises when no feasible decision results in a distribution that dominates the benchmark. Our paper addresses the problem of choosing a tight relaxation of t…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[5/6]:
- The Good, the Bad and the Constructive: Automatically Measuring Peer Review's Utility for Authors
Abdelrahman Sadallah, Tim Baumg\"artner, Iryna Gurevych, Ted Briscoe

A graph energy conjecture through the lenses of semidefinite programming
Aida Abiad, Gabriel Coutinho, Emanuel Juliano, Luuk Reijnders
https://arxiv.org/abs/2509.05814 https://

A graph energy conjecture through the lenses of semidefinite programming
Let $G$ be a graph on $n$ vertices with independence number $α(G)$. Let $\mathcal{E}(G)$ be the energy of a graph, defined as the sum of the absolute values of the adjacency eigenvalues of $G$. Using Graffiti, Fajtlowicz conjectured in the 1980s that $$\frac{1}{2}\mathcal{E}(G) \geq n - α(G).$$ In this paper we derive a semidefinite program formulation of the graph energy, and we use it to obtain several results that constitute a first step towards proving this conjecture. In particular, we s…

Tribonacci-Lucas numbers that are palindromic concatenations of two distinct repdigits
Mahadi Ddamulira
https://arxiv.org/abs/2509.05984 https://arxiv.org/…

Tribonacci-Lucas numbers that are palindromic concatenations of two distinct repdigits
The Tribonacci-Lucas sequence $\{S_n\}_{n\ge 0}$ is defined by the linear recurrence relation $S_{n+3} = S_{n+2} + S_{n+1} + S_n$, for $ n\ge 0 $, with the initial conditions $S_0 =S_2= 3$ and $S_1 = 1$. A palindromic number is a number that remains the same when its digits are reversed. This paper uses Baker's theory for nozero lower bounds for linear forms in logarithms of algebraic numbers, and reduction methods involving the theory of continued fraction to determine all Tribonacci-Lucas num…

MaaSO: SLO-aware Orchestration of Heterogeneous Model Instances for MaaS
Mo Xuan, Zhang yue, Wu Weigang
https://arxiv.org/abs/2509.06362 https://arxiv.org/…

MaaSO: SLO-aware Orchestration of Heterogeneous Model Instances for MaaS
Model-as-a-Service (MaaS) platforms face diverse Service Level Objective (SLO) requirements stemming from various large language model (LLM) applications, manifested in contextual complexity, first-token latency, and between-token latency. On the other hand, an LLM instance, when configured with different parallelism strategies and inference batch sizes, exhibits distinct performance characteristics and can thus be used to serve different SLO requirements. However, current LLM inference systems…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/5]:
- CHIRLA: Comprehensive High-resolution Identification and Re-identification for Large-scale Analysis
Bessie Dominguez-Dager, Felix Escalona, Francisco Gomez-Donoso, Miguel Cazorla

Representation stability for ordered Hurwitz spaces
Zachary Himes, Jeremy Miller, Jennifer C. H. Wilson
https://arxiv.org/abs/2509.05516 https://arxiv.org/…

Representation stability for ordered Hurwitz spaces
In this paper, we study the topology of ordered Hurwitz space. These are moduli spaces of branched covers with a choice of ordering on the branched points. Answering a question of Ellenberg, we prove that the homology of ordered Hurwitz spaces exhibit representation stability.

Crosslisted article(s) found for math.CT. https://arxiv.org/list/math.CT/new
[1/1]:
- Classification of silted algebras for two quivers of Dynkin type $\mathbb{D}_{n}$
Houjun Zhang

Dato: A Task-Based Programming Model for Dataflow Accelerators
Shihan Fang, Hongzheng Chen, Niansong Zhang, Jiajie Li, Han Meng, Adrian Liu, Zhiru Zhang
https://arxiv.org/abs/2509.06794

Dato: A Task-Based Programming Model for Dataflow Accelerators
Recent deep learning workloads increasingly push computational demand beyond what current memory systems can sustain, with many kernels stalling on data movement rather than computation. While modern dataflow accelerators incorporate on-chip streaming to mitigate off-chip bandwidth limitations, existing programming models struggle to harness these capabilities effectively. Low-level interfaces provide fine-grained control but impose significant development overhead, whereas high-level tile-base…

Frequencies of letters in infinite $k$-balanced sequences
Lubom\'ira Dvo\v{r}\'akov\'a, Edita Pelantov\'a
https://arxiv.org/abs/2509.05812 https://

Frequencies of letters in infinite $k$-balanced sequences
Frequency of letters in a symbolic sequence ${\bf u}$ over a finite alphabet is one of the basic characteristics of ${\bf u}$. The notion of $k$-balancedness captures the property that the number of any letter occurring in two arbitrary factors of ${\bf u}$ of equal length differs at most by $k$. For a fixed integer $k$ and alphabet size $d\in \mathbb N$, we discuss possible frequencies of letters in $k$-balanced $d$-ary sequences. For the size $d$ of the alphabet, we introduce the notion of ba…

Uniform subconvexity bounds for $GL(2)\times GL(2)$ $L$-functions in the spectral aspect
Zhao Xu
https://arxiv.org/abs/2509.05968 https://arxiv.org/pdf/250…

Uniform subconvexity bounds for $GL(2)\times GL(2)$ $L$-functions in the spectral aspect
In this paper, we study the second moment for $GL(2)\times GL(2)$ $L$-functions $L(\frac{1}{2},f\times g)$, which leads to a uniform subconvexity bound in the spectral aspect. In particular, if either $f$ or $g$ is a dihedral Maass newform, or if one of them has level $1$, we obtain a Burgess-type bound that is uniform in both $t_f$ and $t_g$, where $t_f$, $t_g$ denote the spectral parameters of $f$, $g$. As an application, we also establish a shrinking result for QUE in the case of dihedral Ma…

FineServe: Precision-Aware KV Slab and Two-Level Scheduling for Heterogeneous Precision LLM Serving
Kyungmin Bin, Seungbeom Choi, Jimyoung Son, Jieun Choi, Daseul Bae, Daehyeon Baek, Kihyo Moon, Minsung Jang, Hyojung Lee
https://arxiv.org/abs/2509.06261

FineServe: Precision-Aware KV Slab and Two-Level Scheduling for Heterogeneous Precision LLM Serving
Recent advances in Post-Training Quantization (PTQ) techniques have significantly increased demand for serving quantized large language models (LLMs), enabling higher throughput and substantially reduced memory usage with minimal accuracy loss. Quantized models address memory constraints in LLMs and enhance GPU resource utilization through efficient GPU sharing. However, quantized models have smaller KV block sizes than non-quantized models, causing limited memory efficiency due to memory fragm…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/5]:
- Representation-Centric Survey of Skeletal Action Recognition and the ANUBIS Benchmark
Liu, Yang, Perera, Ji, Kim, Xu, Wang, Anwar, Gedeon, Wang, Qin

Categorical Tiling Theory: Constructing Directed Planar Tilings via Edge Reversal
Catherine DiLeo, Preston Sessoms, Brandon T. Shapiro
https://arxiv.org/abs/2509.06363 https://

Categorical Tiling Theory: Constructing Directed Planar Tilings via Edge Reversal
Tilings of the plane resemble the simplicial and other complexes from algebraic topology, but have not been studied from this perspective. We construct finite categories corresponding to polygons with labeled directed edges, and introduce the problem of modeling tilings of the Euclidean or hyperbolic plane as presheaves over such a category. Combinatorially, this amounts to choosing an ``alignment'' for a tiling: a direction for every edge and consistent labels for the edges of each polygonal t…

Termination Analysis of Linear-Constraint Programs
Amir M. Ben-Amram, Samir Genaim, Jo\"el Ouaknine, James Worrell
https://arxiv.org/abs/2509.06752 https://

Termination Analysis of Linear-Constraint Programs
This Survey provides an overview of techniques in termination analysis for programs with numerical variables and transitions defined by linear constraints. This subarea of program analysis is challenging due to the existence of undecidable problems, and this Survey systematically explores approaches that mitigate this inherent difficulty. These include foundational decidability results, the use of ranking functions, and disjunctive well-founded transition invariants. The Survey also discusses n…

Centered Moments of Weighted One-Level Densities of $GL(2)$ $L$-Functions
Lawrence Dillon, Xiaoyao Huang, Say-Yeon Kwon, Meiling Laurence, Steven J. Miller, Vishal Muthuvel, Luke Rowen, Pramana Saldin, Steven Zanetti
https://arxiv.org/abs/2509.05810

Centered Moments of Weighted One-Level Densities of $GL(2)$ $L$-Functions
Katz and Sarnak conjectured that the behavior of zeros near the central point of any family of $L$-functions is well-modeled by the behavior of eigenvalues near $1$ of some classical compact group (either the symplectic, unitary, or even, odd, or full orthogonal group). In 2018, Knightly and Reno proved that the symmetry group can vary depending on how the $L$-functions in the family are weighted. They observed both orthogonal and symplectic symmetry in the one-level densities of families of cu…

20 Years in Life of a Smart Building: A retrospective
Karolina Skrivankova, Mark Handley, Stephen Hailes
https://arxiv.org/abs/2509.06229 https://arxiv.org…

20 Years in Life of a Smart Building: A retrospective
Operating an intelligent smart building automation system in 2025 is met with many challenges: hardware failures, vendor obsolescence, evolving security threats and more. None of these have been comprehensibly addressed by the industrial building nor home automation industries, limiting feasibility of operating large, truly smart automation deployments. This paper introduces KaOS, a distributed control platform for constructing robust and evolvable smart building automation systems using afford…

Crosslisted article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/2]:
- Signal-Based Malware Classification Using 1D CNNs
Jack Wilkie, Hanan Hindy, Ivan Andonovic, Christos Tachtatzis, Robert Atkinson

Simplicial Approach to Frobenius Algebras in the Category of Relations
Dominik Lachman
https://arxiv.org/abs/2509.06193 https://arxiv.org/pdf/2509.06193

Simplicial Approach to Frobenius Algebras in the Category of Relations
Frobenius algebras in the category of sets and relations ($\mathbf{Rel}$) serve as a unifying framework for various algebraic and combinatorial structures, including groupoids, effect algebras, and abstract circles. Recently, a nerve construction of simplicial sets for Frobenius algebras in $\mathbf{Rel}$ has been introduced. In this work, we investigate the lifting properties of these simplicial sets, linking them to the algebraic properties of Frobenius algebras. We introduce $ε$-simplicial …

Pacing Types: Safe Monitoring of Asynchronous Streams
Florian Kohn, Arthur Correnson, Jan Baumeister, Bernd Finkbeiner
https://arxiv.org/abs/2509.06724 https://

Pacing Types: Safe Monitoring of Asynchronous Streams
Stream-based monitoring is a real-time safety assurance mechanism for complex cyber-physical systems such as unmanned aerial vehicles. In this context, a monitor aggregates streams of input data from sensors and other sources to give real-time statistics and assessments of the system's health. Since monitors are safety-critical components, it is crucial to ensure that they are free of potential runtime errors. One of the central challenges in designing reliable stream-based monitors is to deal …

Spivey-type recurrence relation for fully degenerate Bell polynomials
Taekyun Kim, Dae San Kim
https://arxiv.org/abs/2509.05761 https://arxiv.org/pdf/2509.…

Spivey-type recurrence relation for fully degenerate Bell polynomials
Spivey's combinatorial method revealed an important identity for Bell numbers, involving Stirling numbers of the second kind. This paper extends his work by deriving Spivey-type recurrence relations for fully degenerate Bell polynomials and degenerate Fubini polynomials. Our derivation uses degenerate Stirling numbers of the second kind and two-variable degenerate Fubini polynomials of order a.

Gathering in Non-Vertex-Transitive Graphs Under Round Robin
Serafino Cicerone, Alessia Di Fonso, Gabriele Di Stefano, Alfredo Navarra
https://arxiv.org/abs/2509.06064 https://…

Gathering in Non-Vertex-Transitive Graphs Under Round Robin
The Gathering problem for a swarm of robots asks for a distributed algorithm that brings such entities to a common place, not known in advance. We consider the well-known OBLOT model with robots constrained to move along the edges of a graph, hence gathering in one vertex, eventually. Despite the classical setting under which the problem has been usually approached, we consider the `hostile' case where: i) the initial configuration may contain multiplicities, i.e. more than one robot may occupy…

Crosslisted article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/2]:
- LocoMamba: Vision-Driven Locomotion via End-to-End Deep Reinforcement Learning with Mamba
Yinuo Wang, Gavin Tao

Dynamical systems as enriched functors
Suddhasattwa Das, Tomoharu Suda
https://arxiv.org/abs/2509.05900 https://arxiv.org/pdf/2509.05900

Dynamical systems as enriched functors
This article presents a general description of dynamical systems using the language of enriched functors and enriched natural transformations. This framework is essential to establish the equivalence of three descriptions of dynamics -- a semigroup action on the domain; a parameterized family of endomorphisms; and a transformation of time-space into the collection of endomorphisms. A collection of categorical axioms are presented that provides a complete categorical language to develop dynamica…

Fixed Parameter Tractable Linearizability Monitoring for Stack, Queue and Anagram Agnostic Data Types
Lee Zheng Han, Umang Mathur
https://arxiv.org/abs/2509.05586 https://

Fixed Parameter Tractable Linearizability Monitoring for Stack, Queue and Anagram Agnostic Data Types
Verifying linearizability of concurrent data structures is NP-hard, even for simple types. We present fixed-parameter tractable algorithms for monitoring stacks, queues, and anagram-agnostic data types (AADTs), parameterized by the maximum concurrency. Our approach leverages frontier graphs and partition states to bound the search space. For AADTs, equivalence of linearizations enables monitoring in log-linear time. For stacks, we introduce a grammar-based method with a sub-cubic reduction to m…

The dimension of well approximable numbers
Victor Beresnevich, Sanju Velani
https://arxiv.org/abs/2509.05687 https://arxiv.org/pdf/2509.05687

The dimension of well approximable numbers
In this survey article, we explore a central theme in Diophantine approximation inspired by a celebrated result of Besicovitch on the Hausdorff dimension of well approximable real numbers. We outline some of the key developments stemming from Besicovitch's result, with a focus on the Mass Transference Principle, Ubiquity and Diophantine approximation on manifolds and fractals. We highlight the subtle yet profound connections between number theory and fractal geometry, and discuss several open p…

H$_{2}$OT: Hierarchical Hourglass Tokenizer for Efficient Video Pose Transformers
Wenhao Li, Mengyuan Liu, Hong Liu, Pichao Wang, Shijian Lu, Nicu Sebe
https://arxiv.org/abs/2509.06956

H$_{2}$OT: Hierarchical Hourglass Tokenizer for Efficient Video Pose Transformers
Transformers have been successfully applied in the field of video-based 3D human pose estimation. However, the high computational costs of these video pose transformers (VPTs) make them impractical on resource-constrained devices. In this paper, we present a hierarchical plug-and-play pruning-and-recovering framework, called Hierarchical Hourglass Tokenizer (H$_{2}$OT), for efficient transformer-based 3D human pose estimation from videos. H$_{2}$OT begins with progressively pruning pose tokens …

Replaced article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- Flexible Coded Distributed Convolution Computing for Enhanced Straggler Resilience and Numerical ...
Shuo Tan, Rui Liu, Xuesong Han, XianLei Long, Kai Wan, Linqi Song, Yong Li

The compact double category $\mathbf{Int}(\mathbf{Poly}_*)$ models control flow and data transformations
Grigory Kondyrev, David I. Spivak
https://arxiv.org/abs/2509.05462 https…

The compact double category $\mathbf{Int}(\mathbf{Poly}_*)$ models control flow and data transformations
Hasegawa showed that control flow in programming languages -- while loops and if-then-else statements -- can be modeled using traced cocartesian categories, such as the category $\mathbf{Set}_*$ of pointed sets. In this paper we define an operad $\mathscr{W}$ of wiring diagrams that provides syntax for categories whose control flow moreover includes data transformations, including deleting, duplicating, permuting, and applying pre-specified functions to variables. In the most basic version, the…

Comparing Methods for the Cross-Level Verification of SystemC Peripherals with Symbolic Execution
Karl Aaron Rudkowski, Sallar Ahmadi-Pour, Rolf Drechsler
https://arxiv.org/abs/2509.05504

Comparing Methods for the Cross-Level Verification of SystemC Peripherals with Symbolic Execution
Virtual Prototypes (VPs) are important tools in modern hardware development. At high abstractions, they are often implemented in SystemC and offer early analysis of increasingly complex designs. These complex designs often combine one or more processors, interconnects, and peripherals to perform tasks in hardware or interact with the environment. Verifying these subsystems is a well-suited task for VPs, as they allow reasoning across different abstraction levels. While modern verification techn…

Interleaving Reasoning for Better Text-to-Image Generation
Wenxuan Huang, Shuang Chen, Zheyong Xie, Shaosheng Cao, Shixiang Tang, Yufan Shen, Qingyu Yin, Wenbo Hu, Xiaoman Wang, Yuntian Tang, Junbo Qiao, Yue Guo, Yao Hu, Zhenfei Yin, Philip Torr, Yu Cheng, Wanli Ouyang, Shaohui Lin
https://arxiv.org/abs/2509.06945

Interleaving Reasoning for Better Text-to-Image Generation
Unified multimodal understanding and generation models recently have achieve significant improvement in image generation capability, yet a large gap remains in instruction following and detail preservation compared to systems that tightly couple comprehension with generation such as GPT-4o. Motivated by recent advances in interleaving reasoning, we explore whether such reasoning can further improve Text-to-Image (T2I) generation. We introduce Interleaving Reasoning Generation (IRG), a framework…

FoMo4Wheat: Toward reliable crop vision foundation models with globally curated data
Bing Han, Chen Zhu, Dong Han, Rui Yu, Songliang Cao, Jianhui Wu, Scott Chapman, Zijian Wang, Bangyou Zheng, Wei Guo, Marie Weiss, Benoit de Solan, Andreas Hund, Lukas Roth, Kirchgessner Norbert, Andrea Visioni, Yufeng Ge, Wenjuan Li, Alexis Comar, Dong Jiang, Dejun Han, Fred Baret, Yanfeng Ding, Hao Lu, Shouyang Liu

FoMo4Wheat: Toward reliable crop vision foundation models with globally curated data
Vision-driven field monitoring is central to digital agriculture, yet models built on general-domain pretrained backbones often fail to generalize across tasks, owing to the interaction of fine, variable canopy structures with fluctuating field conditions. We present FoMo4Wheat, one of the first crop-domain vision foundation model pretrained with self-supervision on ImAg4Wheat, the largest and most diverse wheat image dataset to date (2.5 million high-resolution images collected over a decade a…

BIR-Adapter: A Low-Complexity Diffusion Model Adapter for Blind Image Restoration
Cem Eteke, Alexander Griessel, Wolfgang Kellerer, Eckehard Steinbach
https://arxiv.org/abs/2509.06904

BIR-Adapter: A Low-Complexity Diffusion Model Adapter for Blind Image Restoration
This paper introduces BIR-Adapter, a low-complexity blind image restoration adapter for diffusion models. The BIR-Adapter enables the utilization of the prior of pre-trained large-scale diffusion models on blind image restoration without training any auxiliary feature extractor. We take advantage of the robustness of pretrained models. We extract features from degraded images via the model itself and extend the self-attention mechanism with these degraded features. We introduce a sampling guida…

Intraoperative 2D/3D Registration via Spherical Similarity Learning and Inference-Time Differentiable Levenberg-Marquardt Optimization
Minheng Chen, Youyong Kong
https://arxiv.org/abs/2509.06890

Intraoperative 2D/3D Registration via Spherical Similarity Learning and Inference-Time Differentiable Levenberg-Marquardt Optimization
Intraoperative 2D/3D registration aligns preoperative 3D volumes with real-time 2D radiographs, enabling accurate localization of instruments and implants. A recent fully differentiable similarity learning framework approximates geodesic distances on SE(3), expanding the capture range of registration and mitigating the effects of substantial disturbances, but existing Euclidean approximations distort manifold structure and slow convergence. To address these limitations, we explore similarity le…

Barlow-Swin: Toward a novel siamese-based segmentation architecture using Swin-Transformers
Morteza Kiani Haftlang, Mohammadhossein Malmir, Foroutan Parand, Umberto Michelucci, Safouane El Ghazouali
https://arxiv.org/abs/2509.06885

Barlow-Swin: Toward a novel siamese-based segmentation architecture using Swin-Transformers
Medical image segmentation is a critical task in clinical workflows, particularly for the detection and delineation of pathological regions. While convolutional architectures like U-Net have become standard for such tasks, their limited receptive field restricts global context modeling. Recent efforts integrating transformers have addressed this, but often result in deep, computationally expensive models unsuitable for real-time use. In this work, we present a novel end-to-end lightweight archi…

A New Hybrid Model of Generative Adversarial Network and You Only Look Once Algorithm for Automatic License-Plate Recognition
Behnoud Shafiezadeh, Amir Mashmool, Farshad Eshghi, Manoochehr Kelarestaghi
https://arxiv.org/abs/2509.06868

A New Hybrid Model of Generative Adversarial Network and You Only Look Once Algorithm for Automatic License-Plate Recognition
Automatic License-Plate Recognition (ALPR) plays a pivotal role in Intelligent Transportation Systems (ITS) as a fundamental element of Smart Cities. However, due to its high variability, ALPR faces challenging issues more efficiently addressed by deep learning techniques. In this paper, a selective Generative Adversarial Network (GAN) is proposed for deblurring in the preprocessing step, coupled with the state-of-the-art You-Only-Look-Once (YOLO)v5 object detection architectures for License-Pl…

Matching Shapes Under Different Topologies: A Topology-Adaptive Deformation Guided Approach
Aymen Merrouche, Stefanie Wuhrer, Edmond Boyer
https://arxiv.org/abs/2509.06862 https…

Matching Shapes Under Different Topologies: A Topology-Adaptive Deformation Guided Approach
Non-rigid 3D mesh matching is a critical step in computer vision and computer graphics pipelines. We tackle matching meshes that contain topological artefacts which can break the assumption made by current approaches. While Functional Maps assume the deformation induced by the ground truth correspondences to be near-isometric, ARAP-like deformation-guided approaches assume the latter to be ARAP. Neither assumption holds in certain topological configurations of the input shapes. We are motivated…

Automated Radiographic Total Sharp Score (ARTSS) in Rheumatoid Arthritis: A Solution to Reduce Inter-Intra Reader Variation and Enhancing Clinical Practice
Hajar Moradmand, Lei Ren
https://arxiv.org/abs/2509.06854

Automated Radiographic Total Sharp Score (ARTSS) in Rheumatoid Arthritis: A Solution to Reduce Inter-Intra Reader Variation and Enhancing Clinical Practice
Assessing the severity of rheumatoid arthritis (RA) using the Total Sharp/Van Der Heijde Score (TSS) is crucial, but manual scoring is often time-consuming and subjective. This study introduces an Automated Radiographic Sharp Scoring (ARTSS) framework that leverages deep learning to analyze full-hand X-ray images, aiming to reduce inter- and intra-observer variability. The research uniquely accommodates patients with joint disappearance and variable-length image sequences. We developed ARTSS us…

ToonOut: Fine-tuned Background-Removal for Anime Characters
Matteo Muratori, Jo\"el Seytre
https://arxiv.org/abs/2509.06839 https://arxiv.org/pdf/2509…

ToonOut: Fine-tuned Background-Removal for Anime Characters
While state-of-the-art background removal models excel at realistic imagery, they frequently underperform in specialized domains such as anime-style content, where complex features like hair and transparency present unique challenges. To address this limitation, we collected and annotated a custom dataset of 1,228 high-quality anime images of characters and objects, and fine-tuned the open-sourced BiRefNet model on this dataset. This resulted in marked improvements in background removal accurac…

Evaluating the Impact of Adversarial Attacks on Traffic Sign Classification using the LISA Dataset
Nabeyou Tadessa, Balaji Iyangar, Mashrur Chowdhury
https://arxiv.org/abs/2509.06835

Evaluating the Impact of Adversarial Attacks on Traffic Sign Classification using the LISA Dataset
Adversarial attacks pose significant threats to machine learning models by introducing carefully crafted perturbations that cause misclassification. While prior work has primarily focused on MNIST and similar datasets, this paper investigates the vulnerability of traffic sign classifiers using the LISA Traffic Sign dataset. We train a convolutional neural network to classify 47 different traffic signs and evaluate its robustness against Fast Gradient Sign Method (FGSM) and Projected Gradient De…

Leveraging Generic Foundation Models for Multimodal Surgical Data Analysis
Simon Pezold, J\'er\^ome A. Kurylec, Jan S. Liechti, Beat P. M\"uller, Jo\"el L. Lavanchy
https://arxiv.org/abs/2509.06831

Leveraging Generic Foundation Models for Multimodal Surgical Data Analysis
We investigate how both the adaptation of a generic foundation model via transfer learning and the integration of complementary modalities from the operating room (OR) can support surgical data science. To this end, we use V-JEPA as the single-modality foundation of a multimodal model for minimally invasive surgery support. We analyze how the model's downstream performance can benefit (a) from finetuning on unlabeled surgical video data and (b) from providing additional time-resolved data strea…

Curia: A Multi-Modal Foundation Model for Radiology
Corentin Dancette, Julien Khlaut, Antoine Saporta, Helene Philippe, Elodie Ferreres, Baptiste Callard, Th\'eo Danielou, L\'eo Alberge, L\'eo Machado, Daniel Tordjman, Julie Dupuis, Korentin Le Floch, Jean Du Terrail, Mariam Moshiri, Laurent Dercle, Tom Boeken, Jules Gregory, Maxime Ronot, Fran\c{c}ois Legou, Pascal Roux, Marc Sapoval, Pierre Manceron, Paul H\'erent

Curia: A Multi-Modal Foundation Model for Radiology
AI-assisted radiological interpretation is based on predominantly narrow, single-task models. This approach is impractical for covering the vast spectrum of imaging modalities, diseases, and radiological findings. Foundation models (FMs) hold the promise of broad generalization across modalities and in low-data settings. However, this potential has remained largely unrealized in radiology. We introduce Curia, a foundation model trained on the entire cross-sectional imaging output of a major hos…

Video-Based MPAA Rating Prediction: An Attention-Driven Hybrid Architecture Using Contrastive Learning
Dipta Neogi, Nourash Azmine Chowdhury, Muhammad Rafsan Kabir, Mohammad Ashrafuzzaman Khan
https://arxiv.org/abs/2509.06826

Video-Based MPAA Rating Prediction: An Attention-Driven Hybrid Architecture Using Contrastive Learning
The rapid growth of visual content consumption across platforms necessitates automated video classification for age-suitability standards like the MPAA rating system (G, PG, PG-13, R). Traditional methods struggle with large labeled data requirements, poor generalization, and inefficient feature learning. To address these challenges, we employ contrastive learning for improved discrimination and adaptability, exploring three frameworks: Instance Discrimination, Contextual Contrastive Learning, …

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward
Yufeng Cheng, Wenxu Wu, Shaojin Wu, Mengqi Huang, Fei Ding, Qian He
https://arxiv.org/abs/2509.06818

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward
Recent advancements in image customization exhibit a wide range of application prospects due to stronger customization capabilities. However, since we humans are more sensitive to faces, a significant challenge remains in preserving consistent identity while avoiding identity confusion with multi-reference images, limiting the identity scalability of customization models. To address this, we present UMO, a Unified Multi-identity Optimization framework, designed to maintain high-fidelity identit…

MIORe & VAR-MIORe: Benchmarks to Push the Boundaries of Restoration
George Ciubotariu, Zhuyun Zhou, Zongwei Wu, Radu Timofte
https://arxiv.org/abs/2509.06803 https://…

MIORe & VAR-MIORe: Benchmarks to Push the Boundaries of Restoration
We introduce MIORe and VAR-MIORe, two novel multi-task datasets that address critical limitations in current motion restoration benchmarks. Designed with high-frame-rate (1000 FPS) acquisition and professional-grade optics, our datasets capture a broad spectrum of motion scenarios, which include complex ego-camera movements, dynamic multi-subject interactions, and depth-dependent blur effects. By adaptively averaging frames based on computed optical flow metrics, MIORe generates consistent moti…

SynthDrive: Scalable Real2Sim2Real Sensor Simulation Pipeline for High-Fidelity Asset Generation and Driving Data Synthesis
Zhengqing Chen, Ruohong Mei, Xiaoyang Guo, Qingjie Wang, Yubin Hu, Wei Yin, Weiqiang Ren, Qian Zhang
https://arxiv.org/abs/2509.06798

SynthDrive: Scalable Real2Sim2Real Sensor Simulation Pipeline for High-Fidelity Asset Generation and Driving Data Synthesis
In the field of autonomous driving, sensor simulation is essential for generating rare and diverse scenarios that are difficult to capture in real-world environments. Current solutions fall into two categories: 1) CG-based methods, such as CARLA, which lack diversity and struggle to scale to the vast array of rare cases required for robust perception training; and 2) learning-based approaches, such as NeuSim, which are limited to specific object categories (vehicles) and require extensive multi…

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results
George Ciubotariu, Florin-Alexandru Vasluianu, Zhuyun Zhou, Nancy Mehta, Radu Timofte, Ke Wu, Long Sun, Lingshun Kong, Zhongbao Yang, Jinshan Pan, Jiangxin Dong, Jinhui Tang, Hao Chen, Yinghui Fang, Dafeng Zhang, Yongqi Song, Jiangbo Guo, Shuhua Jin, Zeyu Xiao, Rui Zhao, Zhuoyuan Li, Cong Zhang, Yufeng Peng, Xin Lu, Zhijing Sun, Chengjie Ge, Zihao Li, Zishun Liao, Ziang Zhou, Qiyu Kang, Xueyang Fu, Zheng-Jun Zha, Yuq…

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results
This paper presents a comprehensive review of the AIM 2025 High FPS Non-Uniform Motion Deblurring Challenge, highlighting the proposed solutions and final results. The objective of this challenge is to identify effective networks capable of producing clearer and visually compelling images in diverse and challenging conditions, by learning representative visual cues for complex aggregations of motion types. A total of 68 participants registered for the competition, and 9 teams ultimately submitt…

P3-SAM: Native 3D Part Segmentation
Changfeng Ma, Yang Li, Xinhao Yan, Jiachen Xu, Yunhan Yang, Chunshi Wang, Zibo Zhao, Yanwen Guo, Zhuo Chen, Chunchao Guo
https://arxiv.org/abs/2509.06784

P3-SAM: Native 3D Part Segmentation
Segmenting 3D assets into their constituent parts is crucial for enhancing 3D understanding, facilitating model reuse, and supporting various applications such as part generation. However, current methods face limitations such as poor robustness when dealing with complex objects and cannot fully automate the process. In this paper, we propose a native 3D point-promptable part segmentation model termed P3-SAM, designed to fully automate the segmentation of any 3D objects into components. Inspire…

UrbanTwin: High-Fidelity Synthetic Replicas of Roadside Lidar Datasets
Muhammad Shahbaz, Shaurya Agarwal
https://arxiv.org/abs/2509.06781 https://arxiv.org…

UrbanTwin: High-Fidelity Synthetic Replicas of Roadside Lidar Datasets
This article presents UrbanTwin datasets - high-fidelity, realistic replicas of three public roadside lidar datasets: LUMPI, V2X-Real-IC, and TUMTraf-I. Each UrbanTwin dataset contains 10K annotated frames corresponding to one of the public datasets. Annotations include 3D bounding boxes, instance segmentation labels, and tracking IDs for six object classes, along with semantic segmentation labels for nine classes. These datasets are synthesized using emulated lidar sensors within realistic dig…

D-HUMOR: Dark Humor Understanding via Multimodal Open-ended Reasoning
Sai Kartheek Reddy Kasu, Mohammad Zia Ur Rehman, Shahid Shafi Dar, Rishi Bharat Junghare, Dhanvin Sanjay Namboodiri, Nagendra Kumar
https://arxiv.org/abs/2509.06771

D-HUMOR: Dark Humor Understanding via Multimodal Open-ended Reasoning
Dark humor in online memes poses unique challenges due to its reliance on implicit, sensitive, and culturally contextual cues. To address the lack of resources and methods for detecting dark humor in multimodal content, we introduce a novel dataset of 4,379 Reddit memes annotated for dark humor, target category (gender, mental health, violence, race, disability, and other), and a three-level intensity rating (mild, moderate, severe). Building on this resource, we propose a reasoning-augmented f…

Raw2Event: Converting Raw Frame Camera into Event Camera
Zijie Ning, Enmin Lin, Sudarshan R. Iyengar, Patrick Vandewalle
https://arxiv.org/abs/2509.06767 https://

Raw2Event: Converting Raw Frame Camera into Event Camera
Event cameras offer unique advantages such as high temporal resolution, low latency, and high dynamic range, making them more and more popular for vision tasks under challenging light conditions. However, their high cost, limited resolution, and lack of features such as autofocus hinder their broad adoption, particularly for early-stage development and prototyping. In this work, we present Raw2Event, a complete hardware-software system that enables real-time event generation from low-cost raw f…

Pothole Detection and Recognition based on Transfer Learning
Mang Hu, Qianqian Xia
https://arxiv.org/abs/2509.06750 https://arxiv.org/pdf/2509.06750…

Pothole Detection and Recognition based on Transfer Learning
With the rapid development of computer vision and machine learning, automated methods for pothole detection and recognition based on image and video data have received significant attention. It is of great significance for social development to conduct an in-depth analysis of road images through feature extraction, thereby achieving automatic identification of the pothole condition in new images. Consequently, this is the main issue addressed in this study. Based on preprocessing techniques suc…

Event Spectroscopy: Event-based Multispectral and Depth Sensing using Structured Light
Christian Geckeler, Niklas Neugebauer, Manasi Muglikar, Davide Scaramuzza, Stefano Mintchev
https://arxiv.org/abs/2509.06741

Event Spectroscopy: Event-based Multispectral and Depth Sensing using Structured Light
Uncrewed aerial vehicles (UAVs) are increasingly deployed in forest environments for tasks such as environmental monitoring and search and rescue, which require safe navigation through dense foliage and precise data collection. Traditional sensing approaches, including passive multispectral and RGB imaging, suffer from latency, poor depth resolution, and strong dependence on ambient light - especially under forest canopies. In this work, we present a novel event spectroscopy system that simulta…

Co-Seg: Mutual Prompt-Guided Collaborative Learning for Tissue and Nuclei Segmentation
Qing Xu, Wenting Duan, Zhen Chen
https://arxiv.org/abs/2509.06740 https://

Co-Seg: Mutual Prompt-Guided Collaborative Learning for Tissue and Nuclei Segmentation
Histopathology image analysis is critical yet challenged by the demand of segmenting tissue regions and nuclei instances for tumor microenvironment and cellular morphology analysis. Existing studies focused on tissue semantic segmentation or nuclei instance segmentation separately, but ignored the inherent relationship between these two tasks, resulting in insufficient histopathology understanding. To address this issue, we propose a Co-Seg framework for collaborative tissue and nuclei segmenta…

Zero-shot 3D-Aware Trajectory-Guided image-to-video generation via Test-Time Training
Ruicheng Zhang, Jun Zhou, Zunnan Xu, Zihao Liu, Jiehui Huang, Mingyang Zhang, Yu Sun, Xiu Li
https://arxiv.org/abs/2509.06723

Zero-shot 3D-Aware Trajectory-Guided image-to-video generation via Test-Time Training
Trajectory-Guided image-to-video (I2V) generation aims to synthesize videos that adhere to user-specified motion instructions. Existing methods typically rely on computationally expensive fine-tuning on scarce annotated datasets. Although some zero-shot methods attempt to trajectory control in the latent space, they may yield unrealistic motion by neglecting 3D perspective and creating a misalignment between the manipulated latents and the network's noise predictions. To address these challenge…

MRI-Based Brain Tumor Detection through an Explainable EfficientNetV2 and MLP-Mixer-Attention Architecture
Mustafa Yurdakul, \c{S}akir Ta\c{s}demir
https://arxiv.org/abs/2509.06713

MRI-Based Brain Tumor Detection through an Explainable EfficientNetV2 and MLP-Mixer-Attention Architecture
Brain tumors are serious health problems that require early diagnosis due to their high mortality rates. Diagnosing tumors by examining Magnetic Resonance Imaging (MRI) images is a process that requires expertise and is prone to error. Therefore, the need for automated diagnosis systems is increasing day by day. In this context, a robust and explainable Deep Learning (DL) model for the classification of brain tumors is proposed. In this study, a publicly available Figshare dataset containing 3,…

Cortex-Synth: Differentiable Topology-Aware 3D Skeleton Synthesis with Hierarchical Graph Attention
Mohamed Zayaan S
https://arxiv.org/abs/2509.06705 https://

Cortex-Synth: Differentiable Topology-Aware 3D Skeleton Synthesis with Hierarchical Graph Attention
We present Cortex Synth, a novel end-to-end differentiable framework for joint 3D skeleton geometry and topology synthesis from single 2D images. Our architecture introduces three key innovations: (1) A hierarchical graph attention mechanism with multi-scale skeletal refinement, (2) Differentiable spectral topology optimization via Laplacian eigen decomposition, and (3) Adversarial geometric consistency training for pose structure alignment. The framework integrates four synergistic modules: a …

STAGE: Segmentation-oriented Industrial Anomaly Synthesis via Graded Diffusion with Explicit Mask Alignment
Xichen Xu, Yanshu Wang, Jinbao Wang, Qunyi Zhang, Xiaoning Lei, Guoyang Xie, Guannan Jiang, Zhichao Lu
https://arxiv.org/abs/2509.06693

STAGE: Segmentation-oriented Industrial Anomaly Synthesis via Graded Diffusion with Explicit Mask Alignment
Segmentation-oriented Industrial Anomaly Synthesis (SIAS) plays a pivotal role in enhancing the performance of downstream anomaly segmentation, as it provides an effective means of expanding abnormal data. However, existing SIAS methods face several critical limitations: (i) the synthesized anomalies often lack intricate texture details and fail to align precisely with the surrounding background, and (ii) they struggle to generate fine-grained, pixel-level anomalies. To address these challenges…

BioLite U-Net: Edge-Deployable Semantic Segmentation for In Situ Bioprinting Monitoring
Usman Haider, Lukasz Szemet, Daniel Kelly, Vasileios Sergis, Andrew C. Daly, Karl Mason
https://arxiv.org/abs/2509.06690

BioLite U-Net: Edge-Deployable Semantic Segmentation for In Situ Bioprinting Monitoring
Bioprinting is a rapidly advancing field that offers a transformative approach to fabricating tissue and organ models through the precise deposition of cell-laden bioinks. Ensuring the fidelity and consistency of printed structures in real-time remains a core challenge, particularly under constraints imposed by limited imaging data and resource-constrained embedded hardware. Semantic segmentation of the extrusion process, differentiating between nozzle, extruded bioink, and surrounding backgrou…

VIM-GS: Visual-Inertial Monocular Gaussian Splatting via Object-level Guidance in Large Scenes
Shengkai Zhang, Yuhe Liu, Guanjun Wu, Jianhua He, Xinggang Wang, Mozi Chen, Kezhong Liu
https://arxiv.org/abs/2509.06685

VIM-GS: Visual-Inertial Monocular Gaussian Splatting via Object-level Guidance in Large Scenes
VIM-GS is a Gaussian Splatting (GS) framework using monocular images for novel-view synthesis (NVS) in large scenes. GS typically requires accurate depth to initiate Gaussian ellipsoids using RGB-D/stereo cameras. Their limited depth sensing range makes it difficult for GS to work in large scenes. Monocular images, however, lack depth to guide the learning and lead to inferior NVS results. Although large foundation models (LFMs) for monocular depth estimation are available, they suffer from cro…

Online Clustering of Seafloor Imagery for Interpretation during Long-Term AUV Operations
Cailei Liang, Adrian Bodenmann, Sam Fenton, Blair Thornton
https://arxiv.org/abs/2509.06678

Online Clustering of Seafloor Imagery for Interpretation during Long-Term AUV Operations
As long-endurance and seafloor-resident AUVs become more capable, there is an increasing need for extended, real-time interpretation of seafloor imagery to enable adaptive missions and optimise communication efficiency. Although offline image analysis methods are well established, they rely on access to complete datasets and human-labelled examples to manage the strong influence of environmental and operational conditions on seafloor image appearance-requirements that cannot be met in real-time…

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery
Cailei Liang, Adrian Bodenmann, Emma J Curtis, Samuel Simmons, Kazunori Nagano, Stan Brown, Adam Riese, Blair Thornton
https://arxiv.org/abs/2509.06660

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery
High-throughput interpretation of robotically gathered seafloor visual imagery can increase the efficiency of marine monitoring and exploration. Although recent research has suggested that location metadata can enhance self-supervised feature learning (SSL), its benefits across different SSL strategies, models and seafloor image datasets are underexplored. This study evaluates the impact of location-based regularisation on six state-of-the-art SSL frameworks, which include Convolutional Neural …