Tootfinder

Nearly 8 years later I’ll be speaking again about why it’s not to late to upstream your legacy Linux based platform in 2 weeks time at #EOSS24 #ELCNA @ Seattle !

Room-temperature photonic quantum computing in integrated silicon photonics with germanium-silicon single-photon avalanche diodes
Neil Na, Chou-Yun Hsu, Erik Chen, Richard Soref
https://arxiv.org/abs/2405.04763

Room-temperature photonic quantum computing in integrated silicon photonics with germanium-silicon single-photon avalanche diodes
Most, if not all, photonic quantum computing (PQC) relies upon superconducting nanowire single-photon detectors (SNSPDs) based on Nb operated at a temperature < 4 K. This paper proposes and analyzes 300 K Si-waveguide-integrated GeSi single-photon avalanche diodes (SPADs) based on the recently demonstrated normal-incidence GeSi SPADs operated at room temperature, and shows that their performance is competitive against that of SNSPDs in a series of metrics for PQC with a reasonable time-gating w…

Watermark-based Detection and Attribution of AI-Generated Content
Zhengyuan Jiang, Moyang Guo, Yuepeng Hu, Neil Zhenqiang Gong
https://arxiv.org/abs/2404.04254

Watermark-based Detection and Attribution of AI-Generated Content
Several companies--such as Google, Microsoft, and OpenAI--have deployed techniques to watermark AI-generated content to enable proactive detection. However, existing literature mainly focuses on user-agnostic detection. Attribution aims to further trace back the user of a generative-AI service who generated a given content detected as AI-generated. Despite its growing importance, attribution is largely unexplored. In this work, we aim to bridge this gap by providing the first systematic study o…

SA development is so slow and trust-based that I can’t imagine us ever getting an ‘over the transom’ submission of LLM-generated code and paying any attention to it.
Rules are a different story, as those just need to prove themselves in QA. We've already got some rules that are algorithmically generated, but no one claims that to be “AI" and a trusted human does the commit before RuleQA gets to put them to the test.

Prediction techniques for dynamic imaging with online primal-dual methods
Neil Dizon, Jyrki Jauhiainen, Tuomo Valkonen
https://arxiv.org/abs/2405.02497 htt…

Prediction techniques for dynamic imaging with online primal-dual methods
Online optimisation facilitates the solution of dynamic inverse problems, such as image stabilisation, fluid flow monitoring, and dynamic medical imaging. In this paper, we improve upon previous work on predictive online primal-dual methods on two fronts. Firstly, we provide a more concise analysis that symmetrises previously unsymmetric regret bounds, and relaxes previous restrictive conditions on the dual predictor. Secondly, based on the latter, we develop several improved dual predictors. W…

This https://arxiv.org/abs/2402.04551 has been replaced.
link: https://scholar.google.com/scholar?q=a

Gamma-ray Bursts as Distance Indicators by a Statistical Learning Approach
Gamma-ray bursts (GRBs) can be probes of the early universe, but currently, only 26% of GRBs observed by the Neil Gehrels Swift Observatory GRBs have known redshifts ($z$) due to observational limitations. To address this, we estimated the GRB redshift (distance) via a supervised statistical learning model that uses optical afterglow observed by Swift and ground-based telescopes. The inferred redshifts are strongly correlated (a Pearson coefficient of 0.93) with the observed redshifts, thus pro…

Optimization-based Prompt Injection Attack to LLM-as-a-Judge
Jiawen Shi, Zenghui Yuan, Yinuo Liu, Yue Huang, Pan Zhou, Lichao Sun, Neil Zhenqiang Gong
https://arxiv.org/abs/2403.17710

Optimization-based Prompt Injection Attack to LLM-as-a-Judge
LLM-as-a-Judge is a novel solution that can assess textual information with large language models (LLMs). Based on existing research studies, LLMs demonstrate remarkable performance in providing a compelling alternative to traditional human assessment. However, the robustness of these systems against prompt injection attacks remains an open question. In this work, we introduce JudgeDeceiver, a novel optimization-based prompt injection attack tailored to LLM-as-a-Judge. Our method formulates a p…

U.S. Election Hardens Hate Universe
Akshay Verma, Richard Sear, Neil F. Johnson
https://arxiv.org/abs/2405.00459 https://arxiv.org/pd…

U.S. Election Hardens Hate Universe
Local or national politics can trigger potentially dangerous hate in someone. But with a third of the world's population eligible to vote in elections in 2024 alone, we lack understanding of how individual-level hate multiplies up to hate behavior at the collective global scale. Here we show, based on the most recent U.S. election, that offline events are associated with a rapid adaptation of the global online hate universe that hardens (strengthens) both its network-of-networks structure and t…

Physics-informed generative model for drug-like molecule conformers
David C. Williams, Neil Imana
https://arxiv.org/abs/2403.07925 https://

Physics-informed generative model for drug-like molecule conformers
We present a diffusion-based, generative model for conformer generation. Our model is focused on the reproduction of bonded structure and is constructed from the associated terms traditionally found in classical force fields to ensure a physically relevant representation. Techniques in deep learning are used to infer atom typing and geometric parameters from a training set. Conformer sampling is achieved by taking advantage of recent advancements in diffusion-based generation. By training on la…

A Transfer Attack to Image Watermarks
Yuepeng Hu, Zhengyuan Jiang, Moyang Guo, Neil Gong
https://arxiv.org/abs/2403.15365 https://arx…

A Transfer Attack to Image Watermarks
Watermark has been widely deployed by industry to detect AI-generated images. The robustness of such watermark-based detector against evasion attacks in the white-box and black-box settings is well understood in the literature. However, the robustness in the no-box setting is much less understood. In particular, multiple studies claimed that image watermark is robust in such setting. In this work, we propose a new transfer evasion attack to image watermark in the no-box setting. Our transfer at…