Tootfinder

Towards Modelling AR Sco: Calibration -- Reproducing High-Energy Pulsar Emission and Testing Convergence to Aristotelian Electrodynamics
Louis Du Plessis, Christo Venter, Alice K. Harding, Zorawar Wadiasingh, Constantinos Kalapotharakos
https://arxiv.org/abs/2506.18917

Towards Modelling AR Sco: Calibration -- Reproducing High-Energy Pulsar Emission and Testing Convergence to Aristotelian Electrodynamics
In recent years, kinetic simulations have been crucial to further our understanding of pulsar electrodynamics. Yet, due to the large-scale separation between the gyro-period vs the stellar rotation period, resolving the particle gyration has been computationally unfeasible for realistic pulsar parameters. The main aim of this work is comparing our gyro-phase-resolved model with a gyro-centric pulsar model, where our model solves the general equations of motion with included radiation reaction u…

Analog of theta-lifting for a curve over dual numbers over a finite field
David Kazhdan, Alexander Polishchuk
https://arxiv.org/abs/2506.19036 https://

Analog of theta-lifting for a curve over dual numbers over a finite field
We continue the study of automorphic functions associated with a curve $C$ over the ring $k[ε]/(ε^2)$, where $k$ is a finite field, begun in arXiv:2303.16259. Namely, we study an example of theta-lifting in this framework and show that it can be understood in terms of the orbit decomposition of the space of automorphic functions $\mathcal{S}(\rm{SL}_2(F)\backslash \rm{SL}_2(\mathbb{A}_C))$ introduced in loc.cit. We prove that all strongly cuspidal functions in $\mathcal{S}(\rm{SL}_2(F)\backsl…

This https://arxiv.org/abs/2505.16768 has been replaced.
initial toot: https://mastoxiv.page/@arX…

High-Efficiency Plasma-Based Compressor for Ultrafast Soft X-ray Free-Electron Lasers
The generation of intense, femtosecond-scale X-ray pulses is crucial for probing matter under extreme temporal and field conditions. Current chirped-pulse amplification (CPA) techniques in free-electron lasers (FELs), however, face efficiency limitations in the soft X-ray regime due to the inherent constraints of conventional optical compressors. To address this challenge, we propose a high-efficiency plasma-based compressor utilizing highly ionized noble gas plasma. Exploiting strong refractiv…

Synaptic shot-noise triggers fast and slow global oscillations in balanced neural networks
Denis S. Goldobin, Maria V. Ageeva, Matteo di Volo, Ferdinand Tixidre, Alessandro Torcini
https://arxiv.org/abs/2505.22373

Synaptic shot-noise triggers fast and slow global oscillations in balanced neural networks
Neural dynamics is determined by the transmission of discrete synaptic pulses (synaptic shot-noise) among neurons. However, the neural responses are usually obtained within the diffusion approximation modeling synaptic inputs as continuous Gaussian noise. Here, we present a rigorous mean-field theory that encompasses synaptic shot-noise for sparse balanced inhibitory neural networks driven by an excitatory drive. Our theory predicts new dynamical regimes, in agreement with numerical simulations…

"How can we learn and use AI at the same time?:: Participatory Design of GenAI with High School Students
Isabella Pu, Prerna Ravi, Linh Dieu Dinh, Chelsea Joe, Caitlin Ogoe, Zixuan Li, Cynthia Breazeal, Anastasia K. Ostrowski
https://arxiv.org/abs/2506.15525

ATK: Automatic Task-driven Keypoint Selection for Robust Policy Learning
Yunchu Zhang, Shubham Mittal, Zhengyu Zhang, Liyiming Ke, Siddhartha Srinivasa, Abhishek Gupta
https://arxiv.org/abs/2506.13867

ATK: Automatic Task-driven Keypoint Selection for Robust Policy Learning
Visuomotor policies often suffer from perceptual challenges, where visual differences between training and evaluation environments degrade policy performance. Policies relying on state estimations, like 6D pose, require task-specific tracking and are difficult to scale, while raw sensor-based policies may lack robustness to small visual disturbances.In this work, we leverage 2D keypoints - spatially consistent features in the image frame - as a flexible state representation for robust policy le…

SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies
Nadun Ranawaka Arachchige, Zhenyang Chen, Wonsuhk Jung, Woo Chul Shin, Rohan Bansal, Pierre Barroso, Yu Hang He, Yingyang Celine Lin, Benjamin Joffe, Shreyas Kousik, Danfei Xu
https://arxiv.org/abs/2506.11948

SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies
Offline Imitation Learning (IL) methods such as Behavior Cloning are effective at acquiring complex robotic manipulation skills. However, existing IL-trained policies are confined to executing the task at the same speed as shown in demonstration data. This limits the task throughput of a robotic system, a critical requirement for applications such as industrial automation. In this paper, we introduce and formalize the novel problem of enabling faster-than-demonstration execution of visuomotor p…

Risk-Guided Diffusion: Toward Deploying Robot Foundation Models in Space, Where Failure Is Not An Option
Rohan Thakker, Adarsh Patnaik, Vince Kurtz, Jonas Frey, Jonathan Becktor, Sangwoo Moon, Rob Royce, Marcel Kaufmann, Georgios Georgakis, Pascal Roth, Joel Burdick, Marco Hutter, Shehryar Khattak
https://arxiv.org/abs/2506.1760…

Risk-Guided Diffusion: Toward Deploying Robot Foundation Models in Space, Where Failure Is Not An Option
Safe, reliable navigation in extreme, unfamiliar terrain is required for future robotic space exploration missions. Recent generative-AI methods learn semantically aware navigation policies from large, cross-embodiment datasets, but offer limited safety guarantees. Inspired by human cognitive science, we propose a risk-guided diffusion framework that fuses a fast, learned "System-1" with a slow, physics-based "System-2", sharing computation at both training and inference to couple adaptability …

AMPLIFY: Actionless Motion Priors for Robot Learning from Videos
Jeremy A. Collins, Lor\'and Cheng, Kunal Aneja, Albert Wilcox, Benjamin Joffe, Animesh Garg
https://arxiv.org/abs/2506.14198

AMPLIFY: Actionless Motion Priors for Robot Learning from Videos
Action-labeled data for robotics is scarce and expensive, limiting the generalization of learned policies. In contrast, vast amounts of action-free video data are readily available, but translating these observations into effective policies remains a challenge. We introduce AMPLIFY, a novel framework that leverages large-scale video data by encoding visual dynamics into compact, discrete motion tokens derived from keypoint trajectories. Our modular approach separates visual motion prediction fr…