Tootfinder

RealDrive: Retrieval-Augmented Driving with Diffusion Models
Wenhao Ding, Sushant Veer, Yuxiao Chen, Yulong Cao, Chaowei Xiao, Marco Pavone
https://arxiv.org/abs/2505.24808

RealDrive: Retrieval-Augmented Driving with Diffusion Models
Learning-based planners generate natural human-like driving behaviors by learning to reason about nuanced interactions from data, overcoming the rigid behaviors that arise from rule-based planners. Nonetheless, data-driven approaches often struggle with rare, safety-critical scenarios and offer limited controllability over the generated trajectories. To address these challenges, we propose RealDrive, a Retrieval-Augmented Generation (RAG) framework that initializes a diffusion-based planning po…

The Density Distribution of Compressively-Forced Supersonic Turbulence Depends on the Driving Correlation Time
Philipp Grete, Evan Scannapieco, Marcus Br\"uggen, Liubin Pan
https://arxiv.org/abs/2505.23898

The Density Distribution of Compressively-Forced Supersonic Turbulence Depends on the Driving Correlation Time
Supersonic turbulence plays a critical role in shaping astrophysical systems, from molecular clouds to the circumgalactic medium. Key properties of this turbulence include the Mach number, driving scale, and nature of the driving mechanism, which can be solenoidal (divergence-free), compressive (curl-free), or a mix of the two. A less studied property is the correlation time of the driving accelerations, $τ_{\rm a}.$ While this timescale has a minimal impact on solenoidally-driven turbulence, …

Percheron Driven Cart Sponsored by Wegmans Supermarket in Ithaca Festival Parade
#photo #photography #ithaca

Sparsity-Driven Parallel Imaging Consistency for Improved Self-Supervised MRI Reconstruction
Ya\c{s}ar Utku Al\c{c}alar, Mehmet Ak\c{c}akaya
https://arxiv.org/abs/2505.24136

Sparsity-Driven Parallel Imaging Consistency for Improved Self-Supervised MRI Reconstruction
Physics-driven deep learning (PD-DL) models have proven to be a powerful approach for improved reconstruction of rapid MRI scans. In order to train these models in scenarios where fully-sampled reference data is unavailable, self-supervised learning has gained prominence. However, its application at high acceleration rates frequently introduces artifacts, compromising image fidelity. To mitigate this shortcoming, we propose a novel way to train PD-DL networks via carefully-designed perturbation…

Strongly driven transmon as an incoherent noise source
Linda Greggio, R\'emi Robin, Mazyar Mirrahimi, Alexandru Petrescu
https://arxiv.org/abs/2505.24549

Strongly driven transmon as an incoherent noise source
Under strong drives, which are becoming necessary for fast high-fidelity operations, transmons can be structurally unstable. Due to chaotic effects, the computational manifold is no longer well separated from the remainder of the spectrum, which correlates with enhanced offset-charge sensitivity and destructive effects in readout. We show here that these detrimental effects can further propagate to other degrees of freedom, for example to neighboring qubits in a multi-qubit system. Specifically…

nature is healing
https://www.drivingeco.com/en/Starlink-Checkmate-is-destroying-space-satellites-within-a-few-days-of-launch./

A Reward-driven Automated Webshell Malicious-code Generator for Red-teaming
Yizhong Ding
https://arxiv.org/abs/2505.24252 https://arx…

A Reward-driven Automated Webshell Malicious-code Generator for Red-teaming
Frequent cyber-attacks have elevated WebShell exploitation and defense to a critical research focus within network security. However, there remains a significant shortage of publicly available, well-categorized malicious-code datasets organized by obfuscation method. Existing malicious-code generation methods, which primarily rely on prompt engineering, often suffer from limited diversity and high redundancy in the payloads they produce. To address these limitations, we propose \textbf{RAWG}, a…

Enhancing LLM-Based Code Generation with Complexity Metrics: A Feedback-Driven Approach
Melika Sepidband, Hamed Taherkhani, Song Wang, Hadi Hemmati
https://arxiv.org/abs/2505.23953

Enhancing LLM-Based Code Generation with Complexity Metrics: A Feedback-Driven Approach
Automatic code generation has gained significant momentum with the advent of Large Language Models (LLMs) such as GPT-4. Although many studies focus on improving the effectiveness of LLMs for code generation, very limited work tries to understand the generated code's characteristics and leverage that to improve failed cases. In this paper, as the most straightforward characteristic of code, we investigate the relationship between code complexity and the success of LLM generated code. Using a la…

Initial proof of life out of the flash: using normal x1 operation, it appears to be fully functional when driven by the MCU over APB.
So far, I haven't tested actually reading/writing data, but it's doing everything I would expect from a fully functional, correctly soldered flash. Not sure why Vivado doesn't like it.
It's definitely hooked up to the right pins since I'm using the STARTUPE3 hard IP to interface with it.

This https://arxiv.org/abs/2411.03328 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Foundation Models for Rapid Autonomy Validation
We are motivated by the problem of autonomous vehicle performance validation. A key challenge is that an autonomous vehicle requires testing in every kind of driving scenario it could encounter, including rare events, to provide a strong case for safety and show there is no edge-case pathological behavior. Autonomous vehicle companies rely on potentially millions of miles driven in realistic simulation to expose the driving stack to enough miles to estimate rates and severity of collisions. To …