Tootfinder

An obvious quick win would be to tunnel the J-Church under the hill from 20th to 22nd St instead of the winding right-of-way it uses now.
-Speedier: The ROW has a 3mph speed limit for safety bc someone could be (illegally) walking on it
-Helps enable two-car service: Liberty St in the middle is one of 2 J stops that don't fit two-car trains
-Bike/walk benefit: ROW becomes a legit trail, making it vastly easier to walk/bike from Noe Valley to Dolores Park/Castro

This https://arxiv.org/abs/2112.11131 has been replaced.
link: https://scholar.google.com/scholar?q=a

Omnisolver: an extensible interface to Ising spin glass solvers
We introduce a new framework for implementing Binary Quadratic Model (BQM) solvers called Omnisolver. The framework provides an out-of-the-box dynamically built command-line interface as well as an input/output system, thus heavily reducing the effort required for implementing new algorithms for solving BQMs. The proposed software should be of benefit for researchers focusing on quantum annealers or discrete optimization algorithms as well as groups utilizing discrete optimization as a part of …

This https://arxiv.org/abs/2402.12777 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Application of Quantum Extreme Learning Machines for QoS Prediction of Elevators' Software in an Industrial Context
Quantum Extreme Learning Machine (QELM) is an emerging technique that utilizes quantum dynamics and an easy-training strategy to solve problems such as classification and regression efficiently. Although QELM has many potential benefits, its real-world applications remain limited. To this end, we present QELM's industrial application in the context of elevators, by proposing an approach called QUELL. In QUELL, we use QELM for the waiting time prediction related to the scheduling software of ele…

Shifting the Lens: Detecting Malware in npm Ecosystem with Large Language Models
Nusrat Zahan, Philipp Burckhardt, Mikola Lysenko, Feross Aboukhadijeh, Laurie Williams
https://arxiv.org/abs/2403.12196

Shifting the Lens: Detecting Malware in npm Ecosystem with Large Language Models
The Gartner 2022 report predicts that 45% of organizations worldwide will encounter software supply chain attacks by 2025, highlighting the urgency to improve software supply chain security for community and national interests. Current malware detection techniques aid in the manual review process by filtering benign and malware packages, yet such techniques have high false-positive rates and limited automation support. Therefore, malware detection techniques could benefit from advanced, more au…