Tootfinder

Happy International #Coffee Day!

AFFORD2ACT: Affordance-Guided Automatic Keypoint Selection for Generalizable and Lightweight Robotic Manipulation
Anukriti Singh, Kasra Torshizi, Khuzema Habib, Kelin Yu, Ruohan Gao, Pratap Tokekar
https://arxiv.org/abs/2510.01433

AFFORD2ACT: Affordance-Guided Automatic Keypoint Selection for Generalizable and Lightweight Robotic Manipulation
Vision-based robot learning often relies on dense image or point-cloud inputs, which are computationally heavy and entangle irrelevant background features. Existing keypoint-based approaches can focus on manipulation-centric features and be lightweight, but either depend on manual heuristics or task-coupled selection, limiting scalability and semantic understanding. To address this, we propose AFFORD2ACT, an affordance-guided framework that distills a minimal set of semantic 2D keypoints from a…

A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs
Dong Yan, Nanyi Zhang, Junyi Guo
https://arxiv.org/abs/2509.02267

A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs
In this paper, we first conduct a study of the portfolio selection problem, incorporating both exogenous (proportional) and endogenous (resulting from liquidity risk, characterized by a stochastic process) transaction costs through the utility-based approach. We also consider the intrinsic relationship between these two types of costs. To address the associated nonlinear two-dimensional Hamilton-Jacobi-Bellman (HJB) equation, we propose an innovative deep learning-driven policy iteration scheme…

Photos from the Fort Wayne Actuarial and Data Science Picnic 🍔 🌭 , on the #PurdueFortWayne 🐘 campus today.
https://photos.app.goo.gl/nNZZ5RiwYEpAVDw48

A Spatio-Temporal Feature Fusion EEG Virtual Channel Signal Generation Network and Its Application in Anxiety Assessment
Shangqing Yuan, Wenshuang Zhai, Shengwen Guo
https://arxiv.org/abs/2509.19334

A Spatio-Temporal Feature Fusion EEG Virtual Channel Signal Generation Network and Its Application in Anxiety Assessment
To address the issue of limited channels and insufficient information collection in portable EEG devices, this study explores an EEG virtual channel signal generation network using a novel spatio-temporal feature fusion strategy. Based on the EEG signals from four frontal lobe channels, the network aims to generate virtual channel EEG signals for other 13 important brain regions. The architecture of the network is a two-dimensional convolutional neural network and it includes a parallel module …

Just read this long post on "the current state of Cara" https://cara.app/post/2e0d29d3-a57a-4b70-b0d0-9cefc15176ca
🤔 "Not having an algorithmically recommended feed is the biggest reason why people dropped off from Cara after we went viral. In a chronolog…

Jingna Zhang (@zemotion) | Cara - Artist Social & Portfolio Platform
About the current state of Cara— Hi all, I’d like to address some users' concerns about Cara’s stability, algorithms and reach, and why we don’t actively promote Cara and recruit new users. I have touched on most of these before through other posts, but I’ll like to go over the main points and put them in one place since they often come up as recurring questions: — Cara’s longevity/stability > "A few weeks ago, after learning how much Jingna was struggling with her personal l…

Good job now peertube looks shit on mobile devices.
I need to call ublock origin to make it usable. Make these element go away if I don't click them after sometime or make them smaller.
Open in app ? Seriously?
@… #peertube

Discovery of carbon monoxide emission from five debris disks around young A-type stars
A. Mo\'or, P. \'Abrah\'am, \'A, K\'osp\'al, G. Cataldi, A. M. Hughes, S. Marino, Q. Kral, J. Milli, N. Pawellek
https://arxiv.org/abs/2509.16104

Discovery of carbon monoxide emission from five debris disks around young A-type stars
Over the past fifteen years, surveys mainly at millimeter wavelengths have led to the discovery of $\sim$20 gas-bearing debris disks, most of them surrounding young intermediate-mass stars. Exploring the properties and origin of this gas could be fundamental to better understanding the transition between the protoplanetary and debris disk phases, the evolution of icy planetesimal belts, and the formation of planetary atmospheres. To expand the list of known CO-bearing debris disks and to improv…

High-resolution simulations of disc tearing in the GW Orionis triple system
Alison K. Young
https://arxiv.org/abs/2509.09317 https://arxiv.org/pdf/2509.093…

High-resolution simulations of disc tearing in the GW Orionis triple system
The disc around the pre-main-sequence triple star system GW Orionis is known from observations to be warped and broken. Theoretical modelling has produced conflicting results regarding the mechanism responsible for breaking the disc. Analytical predictions for the measured parameters of GW Ori suggest the disc is only marginally stable to tearing. We present new high-resolution simulations of GW Ori that replicate the wavelike regime expected in thick, low-turbulence protoplanetary discs for th…

Latent Interpolation Learning Using Diffusion Models for Cardiac Volume Reconstruction
Niklas Bubeck, Suprosanna Shit, Chen Chen, Can Zhao, Pengfei Guo, Dong Yang, Georg Zitzlsberger, Daguang Xu, Bernhard Kainz, Daniel Rueckert, Jiazhen Pan
https://arxiv.org/abs/2508.13826

Latent Interpolation Learning Using Diffusion Models for Cardiac Volume Reconstruction
Cardiac Magnetic Resonance (CMR) imaging is a critical tool for diagnosing and managing cardiovascular disease, yet its utility is often limited by the sparse acquisition of 2D short-axis slices, resulting in incomplete volumetric information. Accurate 3D reconstruction from these sparse slices is essential for comprehensive cardiac assessment, but existing methods face challenges, including reliance on predefined interpolation schemes (e.g., linear or spherical), computational inefficiency, an…