Tootfinder

Google says users created 100M videos using its AI filmmaking tool Flow since its May launch; Flow leverages Veo 3 and focuses on maintaining visual consistency (Katelyn Chedraoui/CNET)
https://www.cnet.com/tech/services-and-sof

Google's AI Filmmaker Program Flow Helped Creators Make 100 Million Videos
Google confirmed the 100 million milestone exclusively to CNET. Here's what's coming next for Flow and its AI-assisted creators.

It’s been a long wait, but @…’s book is finally out and I just got my copy. Architecture for Flow: Adaptive Systems with Domain-Driven Design, Wardley Mapping, and Team Topologies - I’ve seen her talks on this subject and it’s a powerful combination of tools for getting large teams organized and productive.

Quantum Flow Matching
Zidong Cui, Pan Zhang, Ying Tang
https://arxiv.org/abs/2508.12413 https://arxiv.org/pdf/2508.12413

Quantum Flow Matching
Flow matching has rapidly become a dominant paradigm in classical generative modeling, offering an efficient way to interpolate between two complex distributions. We extend this idea to the quantum realm and introduce Quantum Flow Matching (QFM)-a fully quantum-circuit realization that offers efficient interpolation between two density matrices. QFM offers systematic preparation of density matrices and generation of samples for accurately estimating observables, and can be realized on a quantum…

Pipeline attack stops Russian oil flow to Hungary, Slovakia: https://benborges.xyz/2025/08/19/pipeline-attack-stops-russian-oil.html

Flow Matching-Based Generative Modeling for Efficient and Scalable Data Assimilation
Taos Transue, Bohan Chen, So Takao, Bao Wang
https://arxiv.org/abs/2508.13313 https://

Flow Matching-Based Generative Modeling for Efficient and Scalable Data Assimilation
Data assimilation (DA) is the problem of sequentially estimating the state of a dynamical system from noisy observations. Recent advances in generative modeling have inspired new approaches to DA in high-dimensional nonlinear settings, especially the ensemble score filter (EnSF). However, these come at a significant computational burden due to slow sampling. In this paper, we introduce a new filtering framework based on flow matching (FM) -- called the ensemble flow filter (EnFF) -- to accelera…

🇺🇦 #NowPlaying on KEXP's #SeekAndDestroy
Ulcerate:
🎵 To Flow Through Ashen Hearts
#Ulcerate
https://ulcerate.bandcamp.com/track/to-flow-through-ashen-hearts
https://open.spotify.com/track/1SwE2eFabx4yP0g3ISrTkT

{dtrack} makes documentation of data wrangling part of the analysis and creates pretty flow charts: #rstats

Green's function and volume noncollapsing estimates for the K\"ahler-Ricci flow
Weiqi Zhang, Yashan Zhang
https://arxiv.org/abs/2508.13646 https://

Green's function and volume noncollapsing estimates for the Kähler-Ricci flow
We prove a local volume noncollapsing property for the long-time Kähler-Ricci flow on a minimal Kähler manifold, improving previous results of Guo-Phong-Song-Sturm, Guedj-Tô and Vu. Similar result is also proved for a twisted Kähler-Ricci flow on non-minimal Kähler manifolds. In the course of proof, we develop new Green's function estimates for a general Kähler family under the $L^{p>1}$ and $L^1(\log L)^{q>n}$ volume density conditions.

Impacts of Blade Camber on Cross-Flow Turbine Performance and Loading
Ari Athair, Caelan Consing, Jennifer A. Franck, Owen Williams
https://arxiv.org/abs/2509.14416 https://

Impacts of Blade Camber on Cross-Flow Turbine Performance and Loading
Cross-flow turbines show promise for renewable energy generation from wind and tidal sources. The rotating reference frame of cross-flow turbine blades results in virtual camber and incidence due to streamline curvature, altering the lift, drag and pitching moment of the blades. Adding geometric camber is therefore likely to alter performance and loading, however there is little consensus regarding the direction of camber that might be most favorable. This study compares 2% concave-in and conca…

Structure-preserving parametric finite element methods for two-phase Stokes flow based on Lagrange multiplier approaches
Harald Garcke, Dennis Trautwein, Ganghui Zhang
https://arxiv.org/abs/2508.12326 …

Structure-preserving parametric finite element methods for two-phase Stokes flow based on Lagrange multiplier approaches
We present a novel formulation for parametric finite element methods to approximate two-phase Stokes flow. The new formulation is based on the classical Stokes equation in the bulk and a novel choice of interface conditions with additional Lagrange multipliers. This new Lagrange multiplier approach ensures that the numerical methods exactly preserve two physical structures of two-phase Stokes flow at the fully discrete level: (i) the energy-decaying and (ii) the volume-preserving properties. Mo…

Kinematic flow from the flow of cuts
Ross Glew, Andrzej Pokraka
https://arxiv.org/abs/2508.11568 https://arxiv.org/pdf/2508.11568

Kinematic flow from the flow of cuts
The wavefunction coefficients of conformally coupled scalars in power-law FRW cosmologies satisfy differential equations governed by a set of simple combinatorial rules known as the kinematic flow. In this paper we derive the kinematic flow, expressed using a set of differential forms referred to as the cut basis, from a geometric perspective, relying solely on the cosmological hyperplane arrangement and without invoking bulk physics. Each element of the cut basis corresponds to the positive ge…

The latest #homeassistant has #sankey diagrams for energy flow. I obviously need more energy tracking #sensors for the house ;-)

CTFlow: Video-Inspired Latent Flow Matching for 3D CT Synthesis
Jiayi Wang, Hadrien Reynaud, Franciskus Xaverius Erick, Bernhard Kainz
https://arxiv.org/abs/2508.12900 https://

CTFlow: Video-Inspired Latent Flow Matching for 3D CT Synthesis
Generative modelling of entire CT volumes conditioned on clinical reports has the potential to accelerate research through data augmentation, privacy-preserving synthesis and reducing regulator-constraints on patient data while preserving diagnostic signals. With the recent release of CT-RATE, a large-scale collection of 3D CT volumes paired with their respective clinical reports, training large text-conditioned CT volume generation models has become achievable. In this work, we introduce CTFlo…

MeanFlowSE: one-step generative speech enhancement via conditional mean flow
Duojia Li, Shenghui Lu, Hongchen Pan, Zongyi Zhan, Qingyang Hong, Lin Li
https://arxiv.org/abs/2509.14858

MeanFlowSE: one-step generative speech enhancement via conditional mean flow
Multistep inference is a bottleneck for real-time generative speech enhancement because flow- and diffusion-based systems learn an instantaneous velocity field and therefore rely on iterative ordinary differential equation (ODE) solvers. We introduce MeanFlowSE, a conditional generative model that learns the average velocity over finite intervals along a trajectory. Using a Jacobian-vector product (JVP) to instantiate the MeanFlow identity, we derive a local training objective that directly sup…

Efficient and accurate solution of wind-integrated optimal power flow based on enhanced second-order cone relaxation with rolling cutting plane technique
Zhaojun Ruan, Botao Gao, Libao Shi
https://arxiv.org/abs/2508.12351

Efficient and accurate solution of wind-integrated optimal power flow based on enhanced second-order cone relaxation with rolling cutting plane technique
The integration of large-scale renewable energy sources, such as wind power, poses significant challenges for the optimal operation of power systems owing to their inherent uncertainties. This paper proposes a solution framework for wind-integrated optimal power flow (OPF) that leverages an enhanced second-order cone relaxation (SOCR), supported by a rolling cutting plane technique. Initially, the wind generation cost, arising from discrepancies between scheduled and actual wind power outputs, …

SDAP-based QoS Flow Multiplexing Support in Simu5G for 5G NR Simulation
Mohamed Seliem, Utz Roedig, Cormac Sreenan, Dirk Pesch
https://arxiv.org/abs/2508.12785 https://

SDAP-based QoS Flow Multiplexing Support in Simu5G for 5G NR Simulation
The Service Data Adaptation Protocol (SDAP) plays a central role in 5G New Radio (NR), acting as a bridge between the core and radio networks, by enabling QoS Flow multiplexing over shared Data Radio Bearers (DRBs). However, most 5G simulation frameworks, including the popular OMNet++-based Simu5G, lack SDAP support, limiting their ability to model realistic QoS behavior. This paper presents a modular, standardscompliant SDAP extension for Simu5G. The implementation includes core elements such …

This is a lovely review of my latest Buildings&Cities paper on clinic layouts at Moorfields Eye Hospital NHS Trust
https://sustainableconstructionreview.com/2025/06/12/moorfields-eye-hospital-redesigns-pa…

Moorfields Eye Hospital Redesigns Patient Flow with Architectural Innovation
In the bustling corridors of Moorfields Eye Hospital, a silent revolution is underway, one that could reshape how we think about patient flow and clinic design. At the heart of this transformation is…

AUTOVR: Automated UI Exploration for Detecting Sensitive Data Flow Exposures in Virtual Reality Apps
John Y. Kim, Chaoshun Zuo, Yanjie Zhao, Zhiqiang Lin
https://arxiv.org/abs/2508.12187

AUTOVR: Automated UI Exploration for Detecting Sensitive Data Flow Exposures in Virtual Reality Apps
The rise of Virtual Reality (VR) has provided developers with an unprecedented platform for creating games and applications (apps) that require distinct inputs, different from those of conventional devices like smartphones. The Meta Quest VR platform, driven by Meta, has democratized VR app publishing and attracted millions of users worldwide. However, as the number of published apps grows, there is a notable lack of robust headless tools for user interface (UI) exploration and user event testi…

Stability threshold of Couette flow for Boussinesq equations in $\mathbb{R}^2$
Yubo Chen, Wendong Wang, Guoxu Yang
https://arxiv.org/abs/2508.11908 https://

Stability threshold of Couette flow for Boussinesq equations in $\mathbb{R}^2$
This paper establishes the asymptotic stability threshold for the Couette flow $(y,0)$ under the 2D Boussinesq system in $\mathbb{R}^2$. It was proved that for initial perturbations in Sobolev spaces with controlled low horizontal frequencies, the stability threshold is at most $\left\{\frac{1}{3}+, \frac{2}{3}+\right\}$, extending the known threshold results from the periodic case $\mathbb{T}_x \times \mathbb{R}_y$ to the whole space. The core innovations are twofold: First, the $\langle D_x^{…

BEV-ODOM2: Enhanced BEV-based Monocular Visual Odometry with PV-BEV Fusion and Dense Flow Supervision for Ground Robots
Yufei Wei, Wangtao Lu, Sha Lu, Chenxiao Hu, Fuzhang Han, Rong Xiong, Yue Wang
https://arxiv.org/abs/2509.14636

BEV-ODOM2: Enhanced BEV-based Monocular Visual Odometry with PV-BEV Fusion and Dense Flow Supervision for Ground Robots
Bird's-Eye-View (BEV) representation offers a metric-scaled planar workspace, facilitating the simplification of 6-DoF ego-motion to a more robust 3-DoF model for monocular visual odometry (MVO) in intelligent transportation systems. However, existing BEV methods suffer from sparse supervision signals and information loss during perspective-to-BEV projection. We present BEV-ODOM2, an enhanced framework addressing both limitations without additional annotations. Our approach introduces: (1) dens…

Spectral Deformation Flow and Global Classification of Simply-Connected Closed Manifolds
Anton Alexa
https://arxiv.org/abs/2508.11652 https://arxiv.org/pdf…

Spectral Deformation Flow and Global Classification of Simply-Connected Closed Manifolds
We study the evolution of a spectral deformation flow for amplitudes $C_n(τ)$, governed by a second-order self-adjoint operator $\hat{C}$ on a compact domain. This flow leads to exponential stabilization $C_n(τ) \to π$, encoded in the spectral multi-function $C(v,τ,n)$, which describes the geometric deformation across scales. Building on prior results on spectral rigidity, completeness, and asymptotics of $\hat{C}$, we analyze conditions under which this convergence determines the full topo…

I think the Vibecoding reddit has accidentally stumbled on the best description of vibecoding:
It's "roleplay for guys [it is always guys] who want to feel like hackers without doing the hard part".
(Source: https://www.reddit.com/r/vibecoding/com…

Controlling the Flow of Information in Optical Metrology
Maximilian Weimar, Huanli Zhou, Luca Neubacher, Thomas A. Grant, Jakob H\"upfl, Kevin F. MacDonald, Stefan Rotter, Nikolay I. Zheludev
https://arxiv.org/abs/2508.13640

Controlling the Flow of Information in Optical Metrology
Optical metrology has progressed beyond the Abbe-Rayleigh limit, unlocking (sub)atomic precision by leveraging nonlinear phenomena, statistical accumulation, and AI-estimators trained on measurand variations. Here, we reveal that Fisher information, which defines the fundamental precision limit, can be viewed as a physical entity that propagates through space, and we derive the wave equation for sensitivity fields defining the flow of Fisher information, which can resonate, diffract, and interf…

LUNDIsim: model meshes for flow simulation and scientific data compression benchmarks
Laurent Duval, Fr\'ed\'eric Payan, Christophe Preux, Lauriane Bouard
https://arxiv.org/abs/2508.13636

LUNDIsim: model meshes for flow simulation and scientific data compression benchmarks
The volume of scientific data produced for and by numerical simulation workflows is increasing at an incredible rate. This raises concerns either in computability, interpretability, and sustainability. This is especially noticeable in earth science (geology, meteorology, oceanography, and astronomy), notably with climate studies. We highlight five main evaluation issues: efficiency, discrepancy, diversity, interpretability, availability. Among remedies, lossless and lossy compression techn…

Strengthening Programming Comprehension in Large Language Models through Code Generation
Xiaoning Ren, Qiang Hu, Wei Ma, Yan Li, Yao Zhang, Lingxiao Jiang, Yinxing Xue
https://arxiv.org/abs/2508.12620 …

Strengthening Programming Comprehension in Large Language Models through Code Generation
Large language models (LLMs) have recently shown impressive results on diverse code-related tasks, benefiting from large-scale training and instruction tuning. However, studies reveal that their grasp of fundamental programming concepts, such as data flow and control flow, remains shallow, leading to fragile performance when code requires deeper reasoning. This limitation restricts the practical adoption of LLMs in real-world software development. To address this issue, this work introduces a c…

On the foundations of signed graphs I: chain groups, frame matroid, and bivariate flow polynomial
Beifang Chen
https://arxiv.org/abs/2508.11288 https://arx…

On the foundations of signed graphs I: chain groups, frame matroid, and bivariate flow polynomial
This paper studies signed graphs with possible outer-edges. We introduce and investigate the chain group, the boundary operator, the co-boundary operator, the flow group, the tension group, the homology group, the cohomology group, with coefficients in an abelian group. We also introduce and investigate the bivariate flow polynomial for signed graphs. The guiding principle is the correspondence between representable matroids over $\mathbb{R}$ on a ground set $E$ of edges and the subspaces of th…

Data-Driven Contextual Optimization with Gaussian Mixtures: Flow-Based Generalization, Robust Models, and Multistage Extensions
YoungChul Yoon, Grani A. Hanasusanto, Yijie Wang
https://arxiv.org/abs/2509.14557

Data-Driven Contextual Optimization with Gaussian Mixtures: Flow-Based Generalization, Robust Models, and Multistage Extensions
Contextual optimization enhances decision quality by leveraging side information to improve predictions of uncertain parameters. However, existing approaches face significant challenges when dealing with multimodal or mixtures of distributions. The inherent complexity of such structures often precludes an explicit functional relationship between the contextual information and the uncertain parameters, limiting the direct applicability of parametric models. Conversely, while non-parametric model…

On the home stretch for getting ngscopeclient v0.1 out the door!
MacOS binary packaging is now mostly done (thanks Lain), although we don't have DMG's being produced in the CI flow yet.
After several hours of fighting with Windows, the Windows executable has an icon and behaves sanely wrt logging (logs to stdout if launched from a shell, but doesn't spawn a terminal if launched from the start menu).
Still need to do a few tweaks to the MSI installer as well as t…

is AI real life
is it just fantasy
caught up in the hype
no escape from reality
#ai #llm #vibecoding

from my link log —
The simple essence of overloading: making ad-hoc polymorphism more algebraic with flow-based variational type-checking.
https://se.informatik.uni-tuebingen.de/publications/benes2025overloading.pdf
saved 2025-09-11

Accurate measurement of energy relaxation via flux-flow instability
E. M. Baeva, N. A. Titova, M. A. Kirsanova, S. A. Evlashin, A. V. Semenov, D. Yu. Vodolazov, A. I. Kolbatova, G. N. Goltsman
https://arxiv.org/abs/2509.15067

Accurate measurement of energy relaxation via flux-flow instability
In this paper, we investigate flux-flow instability (FFI) in a superconducting single-crystal titanium nitride (TiN) film with negligible volume pinning. By studying the critical current density in 12-nm thick TiN strips of varying widths, we accurately identify the experimental parameters at which the FFI regime occurs. A comprehensive analysis of critical velocity measurements allows us to determine the quasiparticle energy relaxation time, $τ_E$. By comparing our results with the $τ_E$ val…

Multi-Objective Loss Balancing in Physics-Informed Neural Networks for Fluid Flow Applications
Afrah Farea, Saiful Khan, Mustafa Serdar Celebi
https://arxiv.org/abs/2509.14437 h…

Multi-Objective Loss Balancing in Physics-Informed Neural Networks for Fluid Flow Applications
Physics-Informed Neural Networks (PINNs) have emerged as a promising machine learning approach for solving partial differential equations (PDEs). However, PINNs face significant challenges in balancing multi-objective losses, as multiple competing loss terms such as physics residuals, boundary conditions, and initial conditions must be appropriately weighted. While various loss balancing schemes have been proposed, they have been implemented within neural network architectures with fixed activa…

Activity-Cycle Variations of Convection Scales in Subsurface Layers of the Sun
Alexander V. Getling, Alexander G. Kosovichev
https://arxiv.org/abs/2508.12465 https://

Activity-Cycle Variations of Convection Scales in Subsurface Layers of the Sun
We use subsurface-flow velocity maps inferred by time--distance helioseismology from Doppler measurements with the Helioseismic and Magnetic Imager (HMI) of the Solar Dynamics Observatory (SDO) to investigate variations of large-scale convection during Solar Cycles 24 and 25 in the 19-Mm-deep layer. The spatial power spectra of the horizontal-flow divergence reveal well-defined characteristic scales of solar supergranulation in the upper 4 Mm layer, while the giant-cell scale is prominent below…

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

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

Exploring Major Transitions in the Evolution of Biological Cognition With Artificial Neural Networks
Konstantinos Voudouris, Andrew Barron, Marta Halina, Colin Klein, Matishalin Patel
https://arxiv.org/abs/2509.13968

Exploring Major Transitions in the Evolution of Biological Cognition With Artificial Neural Networks
Transitional accounts of evolution emphasise a few changes that shape what is evolvable, with dramatic consequences for derived lineages. More recently it has been proposed that cognition might also have evolved via a series of major transitions that manipulate the structure of biological neural networks, fundamentally changing the flow of information. We used idealised models of information flow, artificial neural networks (ANNs), to evaluate whether changes in information flow in a network ca…

Flow Straight and Fast in Hilbert Space: Functional Rectified Flow
Jianxin Zhang, Clayton Scott
https://arxiv.org/abs/2509.10384 https://arxiv.org/pdf/2509…

Flow Straight and Fast in Hilbert Space: Functional Rectified Flow
Many generative models originally developed in finite-dimensional Euclidean space have functional generalizations in infinite-dimensional settings. However, the extension of rectified flow to infinite-dimensional spaces remains unexplored. In this work, we establish a rigorous functional formulation of rectified flow in an infinite-dimensional Hilbert space. Our approach builds upon the superposition principle for continuity equations in an infinite-dimensional space. We further show that this …

Modeling wind farm noise emission and propagation: effects of flow layout
J. Colas, A. Emmanuelli, D. Dragna, R. J. A. M. Stevens
https://arxiv.org/abs/2508.13128 https://

Modeling wind farm noise emission and propagation: effects of flow layout
This study demonstrates how wind farm flow physics influence noise generation and downstream propagation through numerical simulations. The flow field is modeled using large-eddy simulations (LES), and the time-averaged output serves as input to acoustic models that predict wind turbine noise. In the first turbine row, turbulence-induced noise (TIN) and trailing edge noise (TEN) contribute equally, with TIN dominating at low frequencies and TEN at higher frequencies. Downstream, TEN decreases d…

Lipshitz--Sarkar stable homotopy type for certain planar trivalent graphs with perfect matchings
Nilangshu Bhattacharyya
https://arxiv.org/abs/2508.12272 https://

Lipshitz--Sarkar stable homotopy type for certain planar trivalent graphs with perfect matchings
We develop a space-level refinement of the $2$-factor homology by constructing a stable homotopy type associated to a certain family $\mathscr{G}$ of planar trivalent graphs equipped with perfect matchings. Specifically, we define a cover functor from the $2$-factor flow category $\mathscr{C}(Γ_{M})$ to the cube flow category $\mathscr{C}_{C}(n)$, where the perfect matching graph $Γ_{M}$ represents a planar trivalent graph $G$ together with a perfect matching $M$, such that $(G,M) \in \mathsc…

Real-Time Streaming Mel Vocoding with Generative Flow Matching
Simon Welker, Tal Peer, Timo Gerkmann
https://arxiv.org/abs/2509.15085 https://arxiv.org/pdf…

Real-Time Streaming Mel Vocoding with Generative Flow Matching
The task of Mel vocoding, i.e., the inversion of a Mel magnitude spectrogram to an audio waveform, is still a key component in many text-to-speech (TTS) systems today. Based on generative flow matching, our prior work on generative STFT phase retrieval (DiffPhase), and the pseudoinverse operator of the Mel filterbank, we develop MelFlow, a streaming-capable generative Mel vocoder for speech sampled at 16 kHz with an algorithmic latency of only 32 ms and a total latency of 48 ms. We show real-ti…

Google introduces Veo 3.1, with improved audio output and stronger prompt adherence, and rolls out new updates to its AI video editor Flow (The Keyword)
https://blog.google/technology/ai/veo-updates-flow/

Introducing Veo 3.1 and advanced capabilities in Flow
Today, we’re introducing new and enhanced creative capabilities to edit your clips.

On non-flow suppression in an MLE-based flow analysis
Chong Ye, Wei-Liang Qian, Cesar A. Bernardes, Sandra S. Padula, Rui-Hong Yue, Yutao Xing, Takeshi Kodama
https://arxiv.org/abs/2508.11113

On non-flow suppression in an MLE-based flow analysis
We show that the maximum likelihood estimator (MLE) is an effective tool for mitigating non-flow effects in flow analysis. To this end, one constructs two toy models that simulate non-flow contributions corresponding to particle decay and energy-momentum conservation, respectively. The performance of MLE is analyzed by comparing it against standard approaches such as particle correlation and event plane methods. For both cases, MLE is observed to provide a reasonable estimate of the underlying …

Precision and cost of feedback cooling
Andreas Dechant, Jakob H\"upfl, Shuta Kobayashi, Sosuke Ito, Stefan Rotter
https://arxiv.org/abs/2508.12875 https://

Precision and cost of feedback cooling
We investigate the consequences of information exchange between a system and a measurement-feedback apparatus that cools the system below the environmental temperature. A quantitative relationship between entropy pumping and information acquired about the system is derived, showing that, independent of the concrete realization of the feedback, the latter exceeds the former by a positive amount of excess information flow. This excess information flow satisfies a trade-off relation with the preci…

Joy and Relief as Lotus Flowers Bloom Again in Kashmiri Lake After Three Decades https://www.goodnewsnetwork.org/lotus-flowers-bloom-on-india-lake-for-first-time-in-33-years-like-watching-history-breathe/

Joy and Relief as Lotus Flowers Bloom Again in Kashmiri Lake After Three Decades
"Lotus stem connects our food to the land … now that it’s back, we’re preparing dishes the way our grandmothers did: slow, simple,

Pathways to Elastic Turbulence in Giant Micelles Through Curvature Ratios in Taylor-Couette Flow
Xiaoxiao Yang, Darius Marin, Charlotte Py, Olivier Cardoso, Anke Lindner, Sandra Lerouge
https://arxiv.org/abs/2509.13743

Pathways to Elastic Turbulence in Giant Micelles Through Curvature Ratios in Taylor-Couette Flow
In the past fifteen years, flow instabilities reminiscent of the Taylor-like instabilities driven by hoop stresses, have been observed in wormlike micelles based on surfactant molecules. In particular, purely elastic instabilities and turbulence have been shown to develop on top of shear banding, a type of flow specific to the semi-dilute and concentrated regimes. These instabilities have been identified as the origin of the large body of data showing complex spatio-temporal fluctuations, colle…

um
https://en.wikipedia.org/wiki/DARPA_LifeLog

Holography for stress-energy tensor flows
Xi-Yang Ran, Feng Hao, Hao Ouyang
https://arxiv.org/abs/2508.12275 https://arxiv.org/pdf/2508.12275

Holography for stress-energy tensor flows
We study the holographic description for general stress-energy tensor deformations in arbitrary dimensions using the metric flow approach. Mixed boundary conditions corresponding to these deformations emerge from solutions to the metric flow equations. To test this proposal, we analyze planar AdS black holes with such boundary conditions and find that the deformed energies satisfy flow equations consistent with the field theory interpretation. We further derive the commuting condition for stres…

Weekend Reads
* Cloud flow logs for security
https://www.sei.cmu.edu/blog/enhancing-security-with-cloud-flow-logs/
* Supply chain compromises retrospective

Enhancing Security with Cloud Flow Logs
The SEI has a history of support for flow log analysis, including its 2025 releases (for Azure or AWS) of open-source scripts to facilitate cloud flow log analysis. This blog explores challenges with correlating events across multiple CSPs.

Flow Matching-Based Active Learning for Radio Map Construction with Low-Altitude UAVs
Hao Sun, Shicong Liu, Xianghao Yu, Ying Sun
https://arxiv.org/abs/2509.13822 https://

Flow Matching-Based Active Learning for Radio Map Construction with Low-Altitude UAVs
The employment of unmanned aerial vehicles (UAVs) in the lowaltitude economy necessitates precise and real-time radio maps for reliable communication and safe navigation. However, constructing such maps is hindered by the infeasibility of exhaustive measurements due to UAVs' limited flight endurance. To address this, we propose a novel active learning framework for low-altitude radio map construction based on limited measurements. First, a Plug-and-Play (PnP)-refined flow matching algorithm is …

Laplace comparison on K\"ahler Ricci flow and convergence
Gang Tian, Qi S. Zhang, Zhenlei Zhang, Meng Zhu, Xiaohua Zhu
https://arxiv.org/abs/2509.14820 https://

Laplace comparison on Kähler Ricci flow and convergence
We first prove a uniform integral Laplace comparison result for the Kähler Ricci flow on Fano manifolds which depends only on the initial metric. As an application, using Cheeger-Colding theory and previous results by some of the authors, we give a direct and independent proof of the Hamilton-Tian conjecture on convergence of Kähler-Ricci flows, modulo a codimension 4 singular set. We also expounded on some existing literature on this conjecture.

Cross-Lingual F5-TTS: Towards Language-Agnostic Voice Cloning and Speech Synthesis
Qingyu Liu, Yushen Chen, Zhikang Niu, Chunhui Wang, Yunting Yang, Bowen Zhang, Jian Zhao, Pengcheng Zhu, Kai Yu, Xie Chen
https://arxiv.org/abs/2509.14579

Cross-Lingual F5-TTS: Towards Language-Agnostic Voice Cloning and Speech Synthesis
Flow-matching-based text-to-speech (TTS) models have shown high-quality speech synthesis. However, most current flow-matching-based TTS models still rely on reference transcripts corresponding to the audio prompt for synthesis. This dependency prevents cross-lingual voice cloning when audio prompt transcripts are unavailable, particularly for unseen languages. The key challenges for flow-matching-based TTS models to remove audio prompt transcripts are identifying word boundaries during training…

High-Precision Scale Setting with the Omega-Baryon Mass and Gradient Flow
Alexei Bazavov, Claude W. Bernard, David A. Clarke, Carleton DeTar, Aida X. El-Khadra, Elvira G\'amiz, Steven Gottlieb, Anthony V. Grebe, Urs M. Heller, Leon Hostetler, William I. Jay, Hwancheol Jeong, Andreas S. Kronfeld, Yin Lin, Shaun Lahert, Jack Laiho, Michael Lynch, Andrew T. Lytle, Aaron S. Meyer, Ethan T. Neil, Curtis T. Peterson, James N. Simone, Jacob W. Sitison, Ruth S. Van de Water, Alejandro Vaqu…

High-Precision Scale Setting with the Omega-Baryon Mass and Gradient Flow
The gradient-flow scale $w_0$ in lattice QCD is determined using the mass of the $Ω^-$ baryon to set the physical scale. Nine ensembles using the highly improved staggered quark (HISQ) action with lattice spacings of 0.15 fm down to 0.04 fm are used, seven of which have nearly physical light-quark masses. Electromagnetic corrections to the $Ω^-$ mass are defined in order to compute a pure-QCD $Ω$ mass. The final result is $w_0 = 0.17187(68)$ fm, corresponding to a relative uncertainty of 0.4…

INFNet: A Task-aware Information Flow Network for Large-Scale Recommendation Systems
Kaiyuan Li, Dongdong Mao, Yongxiang Tang, Yanhua Cheng, Yanxiang Zeng, Chao Wang, Xialong Liu, Peng Jiang
https://arxiv.org/abs/2508.11565

INFNet: A Task-aware Information Flow Network for Large-Scale Recommendation Systems
Feature interaction has long been a cornerstone of ranking models in large-scale recommender systems due to its proven effectiveness in capturing complex dependencies among features. However, existing feature interaction strategies face two critical challenges in industrial applications: (1) The vast number of categorical and sequential features makes exhaustive interaction computationally prohibitive, often resulting in optimization difficulties. (2) Real-world recommender systems typically in…

Fluid Dynamics and Domain Reconstruction from Noisy Flow Images Using Physics-Informed Neural Networks and Quasi-Conformal Mapping
Han Zhang, Xue-Cheng Tai, Jean-Michel Morel, Raymond H. Chan
https://arxiv.org/abs/2508.11216

Fluid Dynamics and Domain Reconstruction from Noisy Flow Images Using Physics-Informed Neural Networks and Quasi-Conformal Mapping
Blood flow imaging provides important information for hemodynamic behavior within the vascular system and plays an essential role in medical diagnosis and treatment planning. However, obtaining high-quality flow images remains a significant challenge. In this work, we address the problem of denoising flow images that may suffer from artifacts due to short acquisition times or device-induced errors. We formulate this task as an optimization problem, where the objective is to minimize the discrep…

A Novel Attention-Augmented Wavelet YOLO System for Real-time Brain Vessel Segmentation on Transcranial Color-coded Doppler
Wenxuan Zhang (, Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China), Shuai Li (, Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China), Xinyi Wang (, Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China), Yu Sun (, Department of Bi…

A Novel Attention-Augmented Wavelet YOLO System for Real-time Brain Vessel Segmentation on Transcranial Color-coded Doppler
The Circle of Willis (CoW), vital for ensuring consistent blood flow to the brain, is closely linked to ischemic stroke. Accurate assessment of the CoW is important for identifying individuals at risk and guiding appropriate clinical management. Among existing imaging methods, Transcranial Color-coded Doppler (TCCD) offers unique advantages due to its radiation-free nature, affordability, and accessibility. However, reliable TCCD assessments depend heavily on operator expertise for identifying …

A reduced-order derivative-informed neural operator for subsurface fluid-flow
Jeongjin (Jayjay), Park, Grant Bruer, Huseyin Tuna Erdinc, Abhinav Prakash Gahlot, Felix J. Herrmann
https://arxiv.org/abs/2509.13620

A reduced-order derivative-informed neural operator for subsurface fluid-flow
Neural operators have emerged as cost-effective surrogates for expensive fluid-flow simulators, particularly in computationally intensive tasks such as permeability inversion from time-lapse seismic data, and uncertainty quantification. In these applications, the fidelity of the surrogate's gradients with respect to system parameters is crucial, as the accuracy of downstream tasks, such as optimization and Bayesian inference, relies directly on the quality of the derivative information. Recent …

ViTAD: Timing Violation-Aware Debugging of RTL Code using Large Language Models
Wenhao Lv, Yingjie Xia, Xiyuan Chen, Li Kuang
https://arxiv.org/abs/2508.13257 https://

ViTAD: Timing Violation-Aware Debugging of RTL Code using Large Language Models
In modern Very Large Scale Integrated (VLSI) circuit design flow, the Register-Transfer Level (RTL) stage presents a critical opportunity for timing optimization. Addressing timing violations at this early stage is essential, as modern systems demand higher speeds, where even minor timing violations can lead to functional failures or system crashes. However, traditional timing optimization heavily relies on manual expertise, requiring engineers to iteratively analyze timing reports and debug. T…

Ok, ich habe jetzt 3 Stunden damit zugebracht, einen #PowerAutomate-Flow ans Laufen zu bekommen, bei dem ein API-Call eine 503 warf. Diese 503 kann in diesem Fall heißen:
"Der Inhaltstyp steht noch nicht zur Verfügung, um hinzugefügt werden zu können."
Es kann aber auch heißen:
"Der Inhaltstyp wurde schon hinzugefügt."
Und beides wirft denselben…

Structural Abstraction and Refinement for Probabilistic Programs
Guanyan Li, Juanen Li, Zhilei Han, Peixin Wang, Hongfei Fu, Fei He
https://arxiv.org/abs/2508.12344 https://

Structural Abstraction and Refinement for Probabilistic Programs
In this paper, we present structural abstraction refinement, a novel framework for verifying the threshold problem of probabilistic programs. Our approach represents the structure of a Probabilistic Control-Flow Automaton (PCFA) as a Markov Decision Process (MDP) by abstracting away statement semantics. The maximum reachability of the MDP naturally provides a proper upper bound of the violation probability, termed the structural upper bound. This introduces a fresh ``structural'' characterizati…

FS-DFM: Neue Apple-KI-Modelle spucken lange Texte sehr schnell aus
Mittels "Few-Step Discrete Flow-Matching" will Apple beim Output großer Sprachmodelle den Turbo einschalten. Kooperationspartner ist eine US-Hochschule.

Flow Engineering, which provides a collaborative hardware development platform for companies like Rivian, raised a $23M Series A led by Sequoia (Allie Garfinkle/Fortune)
https://fortune.com/2025/10/14/exclusive-f…

Exclusive: Flow Engineering raises $23 million Series A led by Sequoia to power hardware’s new era
Flow Engineering has raised a $23 million Series A, led by Sequoia and including the Collisons, Fortune has exclusively learned.

Identification of Sub/Super-Synchronous Control Interaction Paths Using Dissipative Energy Flow
Kaustav Chatterjee, Sameer Nekkalapu, Sayak Mukherjee, Ramij Raja Hossain, Marcelo Elizondo
https://arxiv.org/abs/2508.11561

Identification of Sub/Super-Synchronous Control Interaction Paths Using Dissipative Energy Flow
Sub- and super-synchronous control interactions (SSCIs) are oscillations arising from adverse interactions between inverter-based resource (IBR) controls and the power network. SSCIs often involve multiple frequencies and propagate through complex, interconnected paths, making it difficult for model-based approaches to identify both the sources and the paths of oscillatory energy flow. This paper extends the Dissipative Energy Flow (DEF) method, originally developed for low-frequency electromec…

Optimizing the Interplay Between the Chord-to-Radius Ratio, Camber and Pitch of Cross-Flow Turbine Blades
Ari Athair, Aidan Hunt, Han-Wen Chi, Owen Williams
https://arxiv.org/abs/2509.14484

Optimizing the Interplay Between the Chord-to-Radius Ratio, Camber and Pitch of Cross-Flow Turbine Blades
This study examines the combined impact of the chord-to-radius ratio (c/R), blade camber, and preset pitch on cross-flow turbine performance. While prior research has examined individual effects of c/R and pitch, this work focuses on their interaction with camber and the resulting aerodynamic behaviors. Central to the analysis is the concept of virtual camber and virtual incidence, which arise from the curved trajectory of the blades, and are quantified using a conformal transformation. Using t…

Testing and benchmarking emerging supercomputers via the MFC flow solver
Benjamin Wilfong, Anand Radhakrishnan, Henry A. Le Berre, Tanush Prathi, Stephen Abbott, Spencer H. Bryngelson
https://arxiv.org/abs/2509.13575

Testing and benchmarking emerging supercomputers via the MFC flow solver
Deploying new supercomputers requires testing and evaluation via application codes. Portable, user-friendly tools enable evaluation, and the Multicomponent Flow Code (MFC), a computational fluid dynamics (CFD) code, addresses this need. MFC is adorned with a toolchain that automates input generation, compilation, batch job submission, regression testing, and benchmarking. The toolchain design enables users to evaluate compiler-hardware combinations for correctness and performance with limited s…

Deep Reinforcement Learning for Active Flow Control around a Three-Dimensional Flow-Separated Wing at Re = 1,000
R. Montal\`a, B. Font, P. Su\'arez, J. Rabault, O. Lehmkuhl, R. Vinuesa, I. Rodriguez
https://arxiv.org/abs/2509.10195

Deep Reinforcement Learning for Active Flow Control around a Three-Dimensional Flow-Separated Wing at Re = 1,000
This study explores the use of deep reinforcement learning (DRL) for active flow control (AFC) to reduce flow separation on wings at high angles of attack. Concretely, here the DRL agent controls the flow over the three-dimensional NACA0012 wing section at the Reynolds number Re = 1,000 and angle of attack AoA = 20 degrees, autonomously identifying optimal control actions through real-time flow data and a reward function focused on improving aerodynamic performance. The framework integrates the…

Learning Neural Networks by Neuron Pursuit
Akshay Kumar, Jarvis Haupt
https://arxiv.org/abs/2509.12154 https://arxiv.org/pdf/2509.12154

Learning Neural Networks by Neuron Pursuit
The first part of this paper studies the evolution of gradient flow for homogeneous neural networks near a class of saddle points exhibiting a sparsity structure. The choice of these saddle points is motivated from previous works on homogeneous networks, which identified the first saddle point encountered by gradient flow after escaping the origin. It is shown here that, when initialized sufficiently close to such saddle points, gradient flow remains near the saddle point for a sufficiently lon…

Scaling Multi-Agent Epistemic Planning through GNN-Derived Heuristics
Giovanni Briglia, Francesco Fabiano, Stefano Mariani
https://arxiv.org/abs/2508.12840 https://

Scaling Multi-Agent Epistemic Planning through GNN-Derived Heuristics
Multi-agent Epistemic Planning (MEP) is an autonomous planning framework for reasoning about both the physical world and the beliefs of agents, with applications in domains where information flow and awareness among agents are critical. The richness of MEP requires states to be represented as Kripke structures, i.e., directed labeled graphs. This representation limits the applicability of existing heuristics, hindering the scalability of epistemic solvers, which must explore an exponential sear…

GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning
Kelin Yu, Sheng Zhang, Harshit Soora, Furong Huang, Heng Huang, Pratap Tokekar, Ruohan Gao
https://arxiv.org/abs/2508.11049

GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning
Recent advances have shown that video generation models can enhance robot learning by deriving effective robot actions through inverse dynamics. However, these methods heavily depend on the quality of generated data and struggle with fine-grained manipulation due to the lack of environment feedback. While video-based reinforcement learning improves policy robustness, it remains constrained by the uncertainty of video generation and the challenges of collecting large-scale robot datasets for tra…

OpenLB-UQ: An Uncertainty Quantification Framework for Incompressible Fluid Flow Simulations
Mingliang Zhong, Adrian Kummerl\"ander, Shota Ito, Mathias J. Krause, Martin Frank, Stephan Simonis
https://arxiv.org/abs/2508.13867

OpenLB-UQ: An Uncertainty Quantification Framework for Incompressible Fluid Flow Simulations
Uncertainty quantification (UQ) is crucial in computational fluid dynamics to assess the reliability and robustness of simulations, given the uncertainties in input parameters. OpenLB is an open-source lattice Boltzmann method library designed for efficient and extensible simulations of complex fluid dynamics on high-performance computers. In this work, we leverage the efficiency of OpenLB for large-scale flow sampling with a dedicated and integrated UQ module. To this end, we focus on non-intr…

Good Vibrations? A Qualitative Study of Co-Creation, Communication, Flow, and Trust in Vibe Coding
Veronica Pimenova, Sarah Fakhoury, Christian Bird, Margaret-Anne Storey, Madeline Endres
https://arxiv.org/abs/2509.12491

Good Vibrations? A Qualitative Study of Co-Creation, Communication, Flow, and Trust in Vibe Coding
Vibe coding, a term coined by Andrej Karpathy in February 2025, has quickly become a compelling and controversial natural language programming paradigm in AI-assisted software development. Centered on iterative co-design with an AI assistant, vibe coding emphasizes flow and experimentation over strict upfront specification. While initial studies have begun to explore this paradigm, most focus on analyzing code artifacts or proposing theories with limited empirical backing. There remains a need …

E-MoFlow: Learning Egomotion and Optical Flow from Event Data via Implicit Regularization
Wenpu Li, Bangyan Liao, Yi Zhou, Qi Xu, Pian Wan, Peidong Liu
https://arxiv.org/abs/2510.12753

E-MoFlow: Learning Egomotion and Optical Flow from Event Data via Implicit Regularization
The estimation of optical flow and 6-DoF ego-motion, two fundamental tasks in 3D vision, has typically been addressed independently. For neuromorphic vision (e.g., event cameras), however, the lack of robust data association makes solving the two problems separately an ill-posed challenge, especially in the absence of supervision via ground truth. Existing works mitigate this ill-posedness by either enforcing the smoothness of the flow field via an explicit variational regularizer or leveraging…

Paris-based Upciti, which offers city operations software and hardware to over 150 municipalities in 17 countries, raised a $20M Series A led by Notion Capital (Tamara Djurickovic/Tech.eu)
https://tech.eu/2025/10/16/upciti-closes-20m-to-dr…

UPCITI closes $20M to drive global growth of its urban operating platform
UPCITI, a Paris-based company building an operating system for cities, has raised $20 million in a Series A funding round. The round was led by Notion Capital, with participation from Point Nine and Chalfen Ventures.UPCITI combines proprietary software with purpose-built hardware to help municipalities run critical operations, such as traffic flow, street management, and public safety, more efficiently and securely, with a clear ROI that simplifies adoption. By owning both the software an…

Euclidean Approach to Green-Wave Theory Applied to Traffic Signal Networks
Melvin H. Friedman, Brian L. Mark, Nathan H. Gartner
https://arxiv.org/abs/2508.12146 https://

Euclidean Approach to Green-Wave Theory Applied to Traffic Signal Networks
Travel on long arterials with signalized intersections can be inefficient if not coordinated properly. As the number of signals increases, coordination becomes more challenging and traditional progression schemes tend to break down. Long progressions save travel time and fuel, reduce pollution and traffic accidents by providing a smoother flow of traffic. This paper introduces a green-wave theory that can be applied to a network of intersecting arterial roads. It enables uninterrupted flow on a…

Scattering theory for Stokes flow in complex branched structures
Haiyang Wang, Fredrik Fryklund, Samuel Potter, Leslie Greengard
https://arxiv.org/abs/2509.12500 https://…

Scattering theory for Stokes flow in complex branched structures
Slow, viscous flow in branched structures arises in many biological and engineering settings. Direct numerical simulation of flow in such complicated multi-scale geometry, however, is a computationally intensive task. We propose a scattering theory framework that dramatically reduces this cost by decomposing networks into components connected by short straight channels. Exploiting the phenomenon of rapid return to Poiseuille flow (Saint-Venant's principle in the context of elasticity), we compu…

Crosslisted article(s) found for math.OC. https://arxiv.org/list/math.OC/new
[1/1]:
- Flow Matching-Based Generative Modeling for Efficient and Scalable Data Assimilation
Taos Transue, Bohan Chen, So Takao, Bao Wang

Sex-Specific Vascular Score: A Novel Perfusion Biomarker from Supervoxel Analysis of 3D pCASL MRI
Sneha Noble, Neelam Sinha, Vaanathi Sundareshan, Thomas Gregor Issac
https://arxiv.org/abs/2508.13173

Sex-Specific Vascular Score: A Novel Perfusion Biomarker from Supervoxel Analysis of 3D pCASL MRI
We propose a novel framework that leverages 3D pseudo-continuous arterial spin labeling (3D pCASL) MRI to compute sex-specific vascular scores that quantify cerebrovascular health and potential disease susceptibility. The brain is parcellated into spatially contiguous regions of homogeneous perfusion using supervoxel clustering, capturing both microvascular and macrovascular contributions. Mean cerebral blood flow (CBF) values are extracted from 186 cognitively healthy participants and used to …

On the Feasibility of Inter-Flow Service Degradation Detection
Balint Bicski, Adrian Pekar
https://arxiv.org/abs/2509.11140 https://arxiv.org/pdf/2509.1114…

On the Feasibility of Inter-Flow Service Degradation Detection
Hardware acceleration in modern networks creates monitoring blind spots by offloading flows to a non-observable state, hindering real-time service degradation (SD) detection. To address this, we propose and formalize a novel inter-flow correlation framework, built on the hypothesis that observable flows can act as environmental sensors for concurrent, non-observable flows. We conduct a comprehensive statistical analysis of this inter-flow landscape, revealing a fundamental trade-off: while the …

SURGIN: SURrogate-guided Generative INversion for subsurface multiphase flow with quantified uncertainty
Zhao Feng, Bicheng Yan, Luanxiao Zhao, Xianda Shen, Renyu Zhao, Wenhao Wang, Fengshou Zhang
https://arxiv.org/abs/2509.13189

SURGIN: SURrogate-guided Generative INversion for subsurface multiphase flow with quantified uncertainty
We present a direct inverse modeling method named SURGIN, a SURrogate-guided Generative INversion framework tailed for subsurface multiphase flow data assimilation. Unlike existing inversion methods that require adaptation for each new observational configuration, SURGIN features a zero-shot conditional generation capability, enabling real-time assimilation of unseen monitoring data without task-specific retraining. Specifically, SURGIN synergistically integrates a U-Net enhanced Fourier Neural…

Multi-robot Multi-source Localization in Complex Flows with Physics-Preserving Environment Models
Benjamin Shaffer, Victoria Edwards, Brooks Kinch, Nathaniel Trask, M. Ani Hsieh
https://arxiv.org/abs/2509.14228

Multi-robot Multi-source Localization in Complex Flows with Physics-Preserving Environment Models
Source localization in a complex flow poses a significant challenge for multi-robot teams tasked with localizing the source of chemical leaks or tracking the dispersion of an oil spill. The flow dynamics can be time-varying and chaotic, resulting in sporadic and intermittent sensor readings, and complex environmental geometries further complicate a team's ability to model and predict the dispersion. To accurately account for the physical processes that drive the dispersion dynamics, robots must…

On the planar free elastic flow with small oscillation of curvature
Ben Andrews, Glen Wheeler
https://arxiv.org/abs/2509.11129 https://arxiv.org/pdf/2509.1…

On the planar free elastic flow with small oscillation of curvature
The free elastic flow that begins at any curve exists for all time. If the initial curve is an $ω$-fold covered circle (``$ω$-circle'') the solution expands self-similarly. Very recently, Miura and the second author showed that (topological) $ω$-circles that are close to multiply-covered round circles are asymptotically stable under the planar free elastic flow, which means that upon rescaling the rescaled flow converges smoothly to the stationary (in the rescaled setting) $ω$-circle. Close…

Crosslisted article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- AUTOVR: Automated UI Exploration for Detecting Sensitive Data Flow Exposures in Virtual Reality Apps
John Y. Kim, Chaoshun Zuo, Yanjie Zhao, Zhiqiang Lin

Progressive Flow-inspired Unfolding for Spectral Compressive Imaging
Xiaodong Wang, Ping Wang, Zijun He, Mengjie Qin, Xin Yuan
https://arxiv.org/abs/2509.12079 https://

Progressive Flow-inspired Unfolding for Spectral Compressive Imaging
Coded aperture snapshot spectral imaging (CASSI) retrieves a 3D hyperspectral image (HSI) from a single 2D compressed measurement, which is a highly challenging reconstruction task. Recent deep unfolding networks (DUNs), empowered by explicit data-fidelity updates and implicit deep denoisers, have achieved the state of the art in CASSI reconstruction. However, existing unfolding approaches suffer from uncontrollable reconstruction trajectories, leading to abrupt quality jumps and non-gradual re…

Super resonance: Breaking the bandwidth limit of resonant modes and its application to flow control
Adam R. Harris, Armin Kianfar, David Roca, Daniel Yago, Christoph Brehm, Mahmoud I. Hussein
https://arxiv.org/abs/2509.15142

Super resonance: Breaking the bandwidth limit of resonant modes and its application to flow control
We report the discovery of super resonance--a new regime of resonant behavior in which a mode's out-of-phase response persists far beyond its classical bandwidth. This effect emerges from a coiled phononic structure composed of a locally resonant elastic metamaterial and architected to support multiple internal energy pathways. These pathways converge at a single structural point, enabling extended modal dominance and significantly broadening the frequency range over which a resonant phase is s…

RFM-Editing: Rectified Flow Matching for Text-guided Audio Editing
Liting Gao, Yi Yuan, Yaru Chen, Yuelan Cheng, Zhenbo Li, Juan Wen, Shubin Zhang, Wenwu Wang
https://arxiv.org/abs/2509.14003

RFM-Editing: Rectified Flow Matching for Text-guided Audio Editing
Diffusion models have shown remarkable progress in text-to-audio generation. However, text-guided audio editing remains in its early stages. This task focuses on modifying the target content within an audio signal while preserving the rest, thus demanding precise localization and faithful editing according to the text prompt. Existing training-based and zero-shot methods that rely on full-caption or costly optimization often struggle with complex editing or lack practicality. In this work, we p…

Sigma model renormalisation group flows, singularities and some remarks on cosmology
Georgios Papadopoulos
https://arxiv.org/abs/2509.13092 https://arxiv.o…

Sigma model renormalisation group flows, singularities and some remarks on cosmology
We investigate the properties of the renormalisation group (RG) flow of two-dimensional sigma models with a generic metric coupling by utilising known results for the Ricci flow. We point out that on many occasions the RG flow develops singularities, due to strong coupling behaviour, before it reaches a UV or an IR fixed point. We illustrate our analysis with several examples. We give particular emphasis to type I singularities, where the length of the curvature of the sigma model target space …

Discovering Flow Separation Control Strategies in 3D Wings via Deep Reinforcement Learning
R. Montal\`a, B. Font, P. Su\'arez, J. Rabault, O. Lehmkuhl, R. Vinuesa, I. Rodriguez
https://arxiv.org/abs/2509.10185

Discovering Flow Separation Control Strategies in 3D Wings via Deep Reinforcement Learning
In this work, deep reinforcement learning (DRL) is applied to active flow control (AFC) over a threedimensional SD7003 wing at a Reynolds number of Re = 60,000 and angle of attack of AoA = 14 degrees. In the uncontrolled baseline case, the flow exhibits massive separation and a fully turbulent wake. Using a GPU-accelerated CFD solver and multi-agent training, DRL discovers control strategies that enhance lift (79%), reduce drag (65%), and improve aerodynamic efficiency (408%). Flow visualizatio…

3D FlowMatch Actor: Unified 3D Policy for Single- and Dual-Arm Manipulation
Nikolaos Gkanatsios, Jiahe Xu, Matthew Bronars, Arsalan Mousavian, Tsung-Wei Ke, Katerina Fragkiadaki
https://arxiv.org/abs/2508.11002

3D FlowMatch Actor: Unified 3D Policy for Single- and Dual-Arm Manipulation
We present 3D FlowMatch Actor (3DFA), a 3D policy architecture for robot manipulation that combines flow matching for trajectory prediction with 3D pretrained visual scene representations for learning from demonstration. 3DFA leverages 3D relative attention between action and visual tokens during action denoising, building on prior work in 3D diffusion-based single-arm policy learning. Through a combination of flow matching and targeted system-level and architectural optimizations, 3DFA achieve…

Density-based topology optimization strategy for optimal design of uniform flow manifolds
Sanjay Vermani, Nitish Anand
https://arxiv.org/abs/2509.12996 https://

Density-based topology optimization strategy for optimal design of uniform flow manifolds
Uniform flow distribution across parallel channels directly impacts the performance and efficiency of many fluid and energy systems. However, designing efficient flow manifolds that ensure uniform flow distribution remains a challenge. This issue is even more pronounced in the design of multichannel three-dimensional manifolds. Hence, this study presents a scalable topology optimization framework for the systematic design of multi-channel flow manifolds. The proposed method extends the conventi…

Diagonalization of the Toda flow on conjugacy classes of matrices with simple spectrum
David Mart\'inez Torres, Carlos Tomei
https://arxiv.org/abs/2509.13452 https://…

Diagonalization of the Toda flow on conjugacy classes of matrices with simple spectrum
We construct coordinates on conjugacy classes of traceless complex matrices with simple spectrum that diagonalize the non-periodic Toda vector field. By this we mean that the coordinates, defined on an open and dense neighborhood of any diagonal matrix in the conjugacy class, decouple the Toda vector field into a sum of multiples of the Euler and rotational vector field in $\mathbb{C}$. Using Lie theoretic methods, we extend this construction from $\mathfrak{sl}_\mathbb{C}$ to arbitrary complex…

FractalPINN-Flow: A Fractal-Inspired Network for Unsupervised Optical Flow Estimation with Total Variation Regularization
Sara Behnamian, Rasoul Khaksarinezhad, Andreas Langer
https://arxiv.org/abs/2509.08670

FractalPINN-Flow: A Fractal-Inspired Network for Unsupervised Optical Flow Estimation with Total Variation Regularization
We present FractalPINN-Flow, an unsupervised deep learning framework for dense optical flow estimation that learns directly from consecutive grayscale frames without requiring ground truth. The architecture centers on the Fractal Deformation Network (FDN) - a recursive encoder-decoder inspired by fractal geometry and self-similarity. Unlike traditional CNNs with sequential downsampling, FDN uses repeated encoder-decoder nesting with skip connections to capture both fine-grained details and long…

Dynamic stall of a hydrofoil with tubercles in surface gravity waves
Guillaume Ricard, Gunnar Jacobi, Daniele Fiscaletti, Abel-John Buchner
https://arxiv.org/abs/2508.13329 http…

Dynamic stall of a hydrofoil with tubercles in surface gravity waves
The interaction of an object with an unsteady flow is non-trivial and is still far from being fully understood. When an airfoil or hydrofoil, for example, undergoes time-dependent motion, nonlinear flow phenomena such as dynamic stall can emerge. The present work experimentally investigates the interaction between a hydrofoil and surface gravity waves. The waves impose periodic fluctuations of the velocity magnitude and orientation, causing a steadily translating hydrofoil to be susceptible to …

Energy-Constrained Navigation for Planetary Rovers under Hybrid RTG-Solar Power
Tianxin Hu, Weixiang Guo, Ruimeng Liu, Xinhang Xu, Rui Qian, Jinyu Chen, Shenghai Yuan, Lihua Xie
https://arxiv.org/abs/2509.15062

Energy-Constrained Navigation for Planetary Rovers under Hybrid RTG-Solar Power
Future planetary exploration rovers must operate for extended durations on hybrid power inputs that combine steady radioisotope thermoelectric generator (RTG) output with variable solar photovoltaic (PV) availability. While energy-aware planning has been studied for aerial and underwater robots under battery limits, few works for ground rovers explicitly model power flow or enforce instantaneous power constraints. Classical terrain-aware planners emphasize slope or traversability, and trajector…

An $\epsilon$-Regularity Theorem for Non-collapsed Ricci Flow
Harry Fluck, Max Hallgren
https://arxiv.org/abs/2509.14154 https://arxiv.org/pdf/2509.14154…

An $ε$-Regularity Theorem for Non-collapsed Ricci Flow
In this article we prove an $ε$-regularity theorem for non-collapsed Ricci flows, and use this to prove new estimates for singularity models of Fano Kähler-Ricci flows. In the course of our proof, we find a criterion for uniform convergence of solutions to the heat equation along a sequence of $\mathbb{F}$-converging Ricci flows, and apply this to new parabolic regularizations of some natural geometric quantities.

Online Multi-Robot Coordination and Cooperation with Task Precedence Relationships
Walker Gosrich, Saurav Agarwal, Kashish Garg, Siddharth Mayya, Matthew Malencia, Mark Yim, Vijay Kumar
https://arxiv.org/abs/2509.15052

Online Multi-Robot Coordination and Cooperation with Task Precedence Relationships
We propose a new formulation for the multi-robot task allocation problem that incorporates (a) complex precedence relationships between tasks, (b) efficient intra-task coordination, and (c) cooperation through the formation of robot coalitions. A task graph specifies the tasks and their relationships, and a set of reward functions models the effects of coalition size and preceding task performance. Maximizing task rewards is NP-hard; hence, we propose network flow-based algorithms to approximat…

Cooperative Face Liveness Detection from Optical Flow
Artem Sokolov, Mikhail Nikitin, Anton Konushin
https://arxiv.org/abs/2508.10786 https://arxiv.org/pdf…

Cooperative Face Liveness Detection from Optical Flow
In this work, we proposed a novel cooperative video-based face liveness detection method based on a new user interaction scenario where participants are instructed to slowly move their frontal-oriented face closer to the camera. This controlled approaching face protocol, combined with optical flow analysis, represents the core innovation of our approach. By designing a system where users follow this specific movement pattern, we enable robust extraction of facial volume information through neur…

Crosslisted article(s) found for math.DG. https://arxiv.org/list/math.DG/new
[1/1]:
- Spectral Deformation Flow and Global Classification of Simply-Connected Closed Manifolds
Anton Alexa

WaterFlow: Explicit Physics-Prior Rectified Flow for Underwater Saliency Mask Generation
Runting Li, Shijie Lian, Hua Li, Yutong Li, Wenhui Wu, Sam Kwong
https://arxiv.org/abs/2510.12605

WaterFlow: Explicit Physics-Prior Rectified Flow for Underwater Saliency Mask Generation
Underwater Salient Object Detection (USOD) faces significant challenges, including underwater image quality degradation and domain gaps. Existing methods tend to ignore the physical principles of underwater imaging or simply treat degradation phenomena in underwater images as interference factors that must be eliminated, failing to fully exploit the valuable information they contain. We propose WaterFlow, a rectified flow-based framework for underwater salient object detection that innovatively…

Origin of pressure-flow non-linearity in two-phase intermittent flow in porous media
Paolo Botticini, Davide Picchi, Santanu Sinha, Alex Hansen
https://arxiv.org/abs/2510.12588 …

Origin of pressure-flow non-linearity in two-phase intermittent flow in porous media
This study presents a first-principles model to predict the two-phase pressure drop in gas-liquid intermittent flow through round capillaries, which serve as the simplest analogous of a porous medium. Building upon the classical capillary flow theory, we derive a model for a train of elongated bubbles, and rigorously quantify its validity in terms of the dimensionless parameters of the problem (capillary number, number of bubbles, gas volume fraction, and channel length-to-diameter ratio). The …

Flow-Opt: Scalable Centralized Multi-Robot Trajectory Optimization with Flow Matching and Differentiable Optimization
Simon Idoko, Arun Kumar Singh
https://arxiv.org/abs/2510.09204

Flow-Opt: Scalable Centralized Multi-Robot Trajectory Optimization with Flow Matching and Differentiable Optimization
Centralized trajectory optimization in the joint space of multiple robots allows access to a larger feasible space that can result in smoother trajectories, especially while planning in tight spaces. Unfortunately, it is often computationally intractable beyond a very small swarm size. In this paper, we propose Flow-Opt, a learning-based approach towards improving the computational tractability of centralized multi-robot trajectory optimization. Specifically, we reduce the problem to first lear…

Relaminarization of turbulent puffs in pipe flow
Basheer A. Khan, Shai Arogeti, Oriel Shoshani, Alexander Yakhot
https://arxiv.org/abs/2509.12663 https://a…

Relaminarization of turbulent puffs in pipe flow
This study examines the relaminarization of turbulent puffs in pipe flow using highly resolved direct numerical simulations at Reynolds numbers of 1880, 1900, and 1920. The exponential decay of the total energy of streamwise velocity fluctuations and the weak dependence of the decay rate on the Reynolds number were verified. In the cross-section of the laminar-turbulent interface at the trailing edge of the puff, an analysis of the spatio-temporal evolution of the streamline patterns reveals a …