Tootfinder

Towards Bio-Inspired Robotic Trajectory Planning via Self-Supervised RNN
Miroslav Cibula, Krist\'ina Malinovsk\'a, Matthias Kerzel
https://arxiv.org/abs/2507.02171

Towards Bio-Inspired Robotic Trajectory Planning via Self-Supervised RNN
Trajectory planning in robotics is understood as generating a sequence of joint configurations that will lead a robotic agent, or its manipulator, from an initial state to the desired final state, thus completing a manipulation task while considering constraints like robot kinematics and the environment. Typically, this is achieved via sampling-based planners, which are computationally intensive. Recent advances demonstrate that trajectory planning can also be performed by supervised sequence l…

Track-before-detect in RIS-aided Integrated Sensing and Communication
Georgios Mylonopoulos, Luca Venturino, Emanuele Grossi, Stefano Buzzi, Ciro D'Elia
https://arxiv.org/abs/2507.02352

Track-before-detect in RIS-aided Integrated Sensing and Communication
This study considers a base station equipped with sensing and communication capabilities, which serves a ground user and scans a portion of the sky via a passive reconfigurable intelligent surface. To achieve more favorable system tradeoffs, we utilize a multi-frame radar detector, comprising a detector, a plot-extractor, and a track-before-detect processor. The main idea proposed here is that user spectral efficiency can be enhanced by increasing the number of scans jointly processed by the mu…

Dynamic Strategy Adaptation in Multi-Agent Environments with Large Language Models
Shaurya Mallampati, Rashed Shelim, Walid Saad, Naren Ramakrishnan
https://arxiv.org/abs/2507.02002

Dynamic Strategy Adaptation in Multi-Agent Environments with Large Language Models
Large language models (LLMs) demonstrate strong reasoning abilities across mathematical, strategic, and linguistic tasks, yet little is known about how well they reason in dynamic, real-time, multi-agent scenarios, such as collaborative environments in which agents continuously adapt to each other's behavior, as in cooperative gameplay settings. In this paper, we bridge this gap by combining LLM-driven agents with strategic reasoning and real-time adaptation in cooperative, multi-agent environm…

🇺🇦 #NowPlaying on BBCRadio3's #InTune
Grace Williams, Ostrobothnian Chamber Orchestra & Malin Broman:
🎵 Calm sea in summer (Sea sketches)
#GraceWilliams #OstrobothnianChamberOrchestra #MalinBroman

Series C, Episode 01 - Aftermath
SERVALAN: What's your father's name?
DAYNA: Hal Mellanby.
AVON: Mean anything to you?
SERVALAN: No.
DAYNA: I will tell my father you are here.
AVON: Thank you.
https://blake.torpidity.net/m/301/173 B7B5

🇺🇦 Auf radioeins läuft...
Mélanie Pain:
🎵 7 ou 8 fois
#NowPlaying #MélaniePain
https://melaniepain.bandcamp.com/track/7-ou-8-fois
https://open.spotify.com/track/7DcT5yNAONvfhCRnSh1pR0

Canada’s Boycott Is Freaking MAGA Out
https://charlieangus.substack.com/p/maga-is-freaking-out-over-canadas?r=2m1hl&utm_campaign=post&utm_medium=email&triedRedirect=true

#MeilaniPaul as Bobbie Romero at the train station
Baywatch Nights 1x13 - Payback
#RandomBaywatch #lvdlpx

Covariate balance under Bayesian decision theory
Andr\'e A. F. Fumis, Victor Fossaluza, Rafael B. Stern
https://arxiv.org/abs/2509.01828 https://arxiv.…

Covariate balance under Bayesian decision theory
We study optimal sample allocation between treatment and control groups under Bayesian linear models. We derive an analytic expression for the Bayes risk, which depends jointly on sample size and covariate mean balance across groups. Under a flat conditional prior, the covariate mean balance term simplifies to the Mahalanobis distance. Our results reveal that the optimal allocation does not always correspond to equal sample sizes, and we provide sufficient conditions under which equal allocatio…

🇺🇦 #NowPlaying on BBCRadio3's #ThroughTheNight
Johann Strauss II, RTV Slovenia Symphony Orchestra & Rossen Milanov:
🎵 Roses from the South - waltz, Op 388
#JohannStraussII #RTVSloveniaSymphonyOrchestra #RossenMilanov