Tootfinder

On this day in 2017, the world said goodbye to Neil Long. Many people seeing this probably won't have any idea who this is. He wanted no special mention or remembrance upon his passing, but I'm going to keep ignoring his wishes, because he was a friend, colleague, and a person who deserves to be remembered.
When I met him he was doing #infosec at Oxford University. He was involved in early various botnet and DDoS mitigation activities and maintained darknet services plus a howto page for many years. He was on the board of FIRST.org and was one of the founders of Team Cymru.
One of my fondest memories of him was the way he completely dismantled some poor fellow who posed some challenge or question in a very rude manner. The best part of Neil's retort, was not what he said, but how he said it. In that finely honed, proper, and understated British way, Neil ultimately dismissed him and moved on. I don't think the recipient fully understood he had been completely flattened until hours later.

Swift monitoring of GK Persei during the 2018 dwarf nova outburst
Songpeng Pei, Marina Orio, Xiaowan Zhang
https://arxiv.org/abs/2403.05328 https://…

Swift monitoring of GK Persei during the 2018 dwarf nova outburst
The old nova and intermediate polar (IP) GK Persei underwent one of its recurrent dwarf nova (DN) outbursts in 2018. We proposed monitoring it in UV and X-rays with the Neil Gehrels Swift Observatory, starting less than six days after the eruption, until 16 days after the eruption ended. For the first time we could follow the decay to minimum light UV and X-rays. We present the timing and spectral analysis, comparing the results with the previous outbursts and with the quiescent status. We conf…

Link Stealing Attacks Against Inductive Graph Neural Networks
Yixin Wu, Xinlei He, Pascal Berrang, Mathias Humbert, Michael Backes, Neil Zhenqiang Gong, Yang Zhang
https://arxiv.org/abs/2405.05784

Link Stealing Attacks Against Inductive Graph Neural Networks
A graph neural network (GNN) is a type of neural network that is specifically designed to process graph-structured data. Typically, GNNs can be implemented in two settings, including the transductive setting and the inductive setting. In the transductive setting, the trained model can only predict the labels of nodes that were observed at the training time. In the inductive setting, the trained model can be generalized to new nodes/graphs. Due to its flexibility, the inductive setting is the mo…

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…

Orbital and Atmospheric Characterization of the 1RXS J034231.8 121622 System Using High-Resolution Spectroscopy Confirms That The Companion is a Low-Mass Star
Clarissa R. Do \'O, Ben Sappey, Quinn M. Konopacky, Jean-Baptiste Ruffio, Kelly K. O'Neil, Tuan Do, Gregory Martinez, Travis S. Barman, Jayke S. Nguyen, Jerry W. Xuan, Christopher A. Theissen, Sarah Blunt, William Thompson, Chih-Chun Hsu, Ashley Baker, Randall Bartos, Geoffrey A. Blake, Benjamin Calvin, Sylvain Cetre, Jac…

Orbital and Atmospheric Characterization of the 1RXS J034231.8+121622 System Using High-Resolution Spectroscopy Confirms That The Companion is a Low-Mass Star
The 1RXS J034231.8+121622 system consists of an M dwarf primary and a directly imaged low-mass stellar companion. We use high resolution spectroscopic data from Keck/KPIC to estimate the objects' atmospheric parameters and radial velocities (RVs). Using PHOENIX stellar models, we find that the primary has a temperature of 3460 $\pm$ 50 K a metallicity of 0.16 $\pm$ 0.04, while the secondary has a temperature of 2510 $\pm$ 50 K and a metallicity of $0.13\substack{+0.12 \\ -0.11}$. Recent work su…

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…

The NANOGrav 15 yr Data Set: Looking for Signs of Discreteness in the Gravitational-wave Background
Gabriella Agazie, Paul T. Baker, Bence B\'ecsy, Laura Blecha, Adam Brazier, Paul R. Brook, Lucas Brown, Sarah Burke-Spolaor, J. Andrew Casey-Clyde, Maria Charisi, Shami Chatterjee, Tyler Cohen, James M. Cordes, Neil J. Cornish, Fronefield Crawford, H. Thankful Cromartie, Megan E. DeCesar, Paul B. Demorest, Heling Deng, Timothy Dolch, Elizabeth C. Ferrara, William Fiore, Emmanuel Fons…

The NANOGrav 15 yr Data Set: Looking for Signs of Discreteness in the Gravitational-wave Background
The cosmic merger history of supermassive black hole binaries (SMBHBs) is expected to produce a low-frequency gravitational wave background (GWB). Here we investigate how signs of the discrete nature of this GWB can manifest in pulsar timing arrays through excursions from, and breaks in, the expected $f_{\mathrm{GW}}^{-2/3}$ power-law of the GWB strain spectrum. To do this, we create a semi-analytic SMBHB population model, fit to NANOGrav's 15 yr GWB amplitude, and with 1,000 realizations we st…

Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks
Yichang Xu, Ming Yin, Minghong Fang, Neil Zhenqiang Gong
https://arxiv.org/abs/2403.03149

Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks
Recent studies have revealed that federated learning (FL), once considered secure due to clients not sharing their private data with the server, is vulnerable to attacks such as client-side training data distribution inference, where a malicious client can recreate the victim's data. While various countermeasures exist, they are not practical, often assuming server access to some training data or knowledge of label distribution before the attack. In this work, we bridge the gap by proposing I…

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…

Twenty-nine million Intrinsic Q-factor Monolithic Microresonators on Thin Film Lithium Niobate
Xinrui Zhu, Yaowen Hu, Shengyuan Lu, Hana K. Warner, Xudong Li, Yunxiang Song, Leticia Magalhaes, Amirhassan Shams-Ansari, Neil Sinclair, Marko Loncar
https://arxiv.org/abs/2402.16161

Twenty-nine million Intrinsic Q-factor Monolithic Microresonators on Thin Film Lithium Niobate
The recent emergence of thin-film lithium niobate (TFLN) has extended the landscape of integrated photonics. This has been enabled by the commercialization of TFLN wafers and advanced nanofabrication of TFLN such as high-quality dry etching. However, fabrication imperfections still limit the propagation loss to a few dB/m, restricting the impact of this platform. Here, we demonstrate TFLN microresonators with a record-high intrinsic quality (Q) factor of twenty-nine million, corresponding to an…

Mudjacking: Patching Backdoor Vulnerabilities in Foundation Models
Hongbin Liu, Michael K. Reiter, Neil Zhenqiang Gong
https://arxiv.org/abs/2402.14977 htt…

Mudjacking: Patching Backdoor Vulnerabilities in Foundation Models
Foundation model has become the backbone of the AI ecosystem. In particular, a foundation model can be used as a general-purpose feature extractor to build various downstream classifiers. However, foundation models are vulnerable to backdoor attacks and a backdoored foundation model is a single-point-of-failure of the AI ecosystem, e.g., multiple downstream classifiers inherit the backdoor vulnerabilities simultaneously. In this work, we propose Mudjacking, the first method to patch foundation …