Tootfinder

Edinburgh's low emission zone seems to be working.
https://road.cc/content/news/ediburghs-lez-results-more-cycling-and-less-car-use-314269

Edinburgh sees “statistically significant” shift towards cycling and walking plus reduced car use following introduction of low emission zone, finds study
Within a year, the LEZ has contributed in improving air quality, with the number of non-compliant vehicles entering the zone also decreasing significantly

EIM-TRNG: Obfuscating Deep Neural Network Weights with Encoding-in-Memory True Random Number Generator via RowHammer
Ranyang Zhou, Abeer Matar A. Almalky, Gamana Aragonda, Sabbir Ahmed, Filip Roth Tr{\o}nnes-Christensen, Adnan Siraj Rakin, Shaahin Angizi
https://arxiv.org/abs/2507.02206 …

EIM-TRNG: Obfuscating Deep Neural Network Weights with Encoding-in-Memory True Random Number Generator via RowHammer
True Random Number Generators (TRNGs) play a fundamental role in hardware security, cryptographic systems, and data protection. In the context of Deep NeuralNetworks (DNNs), safeguarding model parameters, particularly weights, is critical to ensure the integrity, privacy, and intel-lectual property of AI systems. While software-based pseudo-random number generators are widely used, they lack the unpredictability and resilience offered by hardware-based TRNGs. In this work, we propose a novel an…

This https://arxiv.org/abs/2409.16967 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Scalable Multi-Robot Informative Path Planning for Target Mapping via Deep Reinforcement Learning
Autonomous robots are widely utilized for mapping and exploration tasks due to their cost-effectiveness. Multi-robot systems offer scalability and efficiency, especially in terms of the number of robots deployed in more complex environments. These tasks belong to the set of Multi-Robot Informative Path Planning (MRIPP) problems. In this paper, we propose a deep reinforcement learning approach for the MRIPP problem. We aim to maximize the number of discovered stationary targets in an unknown 3D …

"Whatever" is a brilliant essay on "AI" by @…:
"But I think the core of what pisses me off is that selling this magic machine requires selling the idea that doing things is worthless. Because if doing something has some value, then it must be somehow better than pushing a button and receiving Whatever for essentially no cost."

The rise of Whatever
This was originally titled “I miss when computers were fun”. But in the course of writing it, I discovered that there is a reason computers became less fun, a dark thread woven through a number of events in recent history. Let me back up a bit.

This is an interesting article about FFOTUS's cell phone.
Apparently many people know the number. And apparently FFOTUS often picks up and answers.
If I had the number I would publish it so that all of us could carry out our First Amendment right to "right to petition the government for redress of grievances".
"The Secret History of Trump’s Private Cellphone"

The Secret History of Trump’s Private Cellphone
“Who’s calling?” the president asks as he answers call after call from numbers he doesn’t know.

My wife currently messages me the increasing number of noise sources at home:
Chainsaw to cut bushes, roadworks with jackhammer and rock cutting saw, and now the garbage truck beeping while reversing in our road.
I get a play-by-play description of what keeps her from sleeping in, and I'm torn between laughing, emphasizing and thinking "What happens if she goes all Michael Douglas in Falling Down?"

This https://arxiv.org/abs/2502.09891 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) has proven effective in integrating external knowledge into large language models (LLMs) for solving question-answer (QA) tasks. The state-of-the-art RAG approaches often use the graph data as the external data since they capture the rich semantic information and link relationships between entities. However, existing graph-based RAG approaches cannot accurately identify the relevant information from the graph and also consume large numbers of tokens in the o…

Two new NERDS papers published at CHIIR 2025
https://nerds.itu.dk/2025/05/02/two-new-nerds-papers-published-at-chiir-2025/

This https://arxiv.org/abs/2503.18616 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

FF-SRL: High Performance GPU-Based Surgical Simulation For Robot Learning
Robotic surgery is a rapidly developing field that can greatly benefit from the automation of surgical tasks. However, training techniques such as Reinforcement Learning (RL) require a high number of task repetitions, which are generally unsafe and impractical to perform on real surgical systems. This stresses the need for simulated surgical environments, which are not only realistic, but also computationally efficient and scalable. We introduce FF-SRL (Fast and Flexible Surgical Reinforcement …

This https://arxiv.org/abs/2504.16571 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

LaSDVS : A Post-Quantum Secure Compact Strong-Designated Verifier Signature
Digital signatures are fundamental cryptographic primitives that ensure the authenticity and integrity of digital communication. However, in scenarios involving sensitive interactions -- such as e-voting or e-cash -- there is a growing need for more controlled signing mechanisms. Strong-Designated Verifier Signature (SDVS) offers such control by allowing the signer to specify and restrict the verifier of a signature. The existing state-of-the-art SDVS are mostly based on number-theoretic hardne…