Tootfinder

Non-Relativistic Intersecting Branes, Newton-Cartan Geometry and AdS/CFT
Neil Lambert, Joseph Smith
https://arxiv.org/abs/2405.06552 https://

OMG my long time online friend @… has just made a contribution to Gaiman/Pratchett canon on the FIRST PAGE of GOOD OMENS
Neil Gaiman on Tumblr: (first off: I'm not an astronomer, and I may be wildly off, but…) I had always assumed that "This too was incorrect. By almost a...

This https://arxiv.org/abs/2306.05855 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Probing electromagnetic-gravitational wave emission coincidence in type I binary-driven hypernova family of long GRBs at very-high redshift
The repointing time of the XRT instrument on the Neil Gehrels Swift Observatory satellite has posed challenges in observing and studying the early X-ray emissions within $\approx40$ s after a gamma-ray burst (GRB) trigger. To address this issue, we adopt a novel approach that capitalizes on the cosmological time dilation in GRBs with redshifts ranging from $3$ to $9$. Applying this strategy to Swift/XRT data, we investigate the earliest X-ray emissions of $368$ GRBs from the Swift catalog, incl…

Visual Hallucinations of Multi-modal Large Language Models
Wen Huang, Hongbin Liu, Minxin Guo, Neil Zhenqiang Gong
https://arxiv.org/abs/2402.14683 https:/…

Visual Hallucinations of Multi-modal Large Language Models
Visual hallucination (VH) means that a multi-modal LLM (MLLM) imagines incorrect details about an image in visual question answering. Existing studies find VH instances only in existing image datasets, which results in biased understanding of MLLMs' performance under VH due to limited diversity of such VH instances. In this work, we propose a tool called VHTest to generate a diverse set of VH instances. Specifically, VHTest finds some initial VH instances in existing image datasets (e.g., COCO)…

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.

Solving Data-centric Tasks using Large Language Models
Shraddha Barke, Christian Poelitz, Carina Suzana Negreanu, Benjamin Zorn, Jos\'e Cambronero, Andrew D. Gordon, Vu Le, Elnaz Nouri, Nadia Polikarpova, Advait Sarkar, Brian Slininger, Neil Toronto, Jack Williams
https://arxiv.org/abs/2402.11734

Solving Data-centric Tasks using Large Language Models
Large language models (LLMs) are rapidly replacing help forums like StackOverflow, and are especially helpful for non-professional programmers and end users. These users are often interested in data-centric tasks, such as spreadsheet manipulation and data wrangling, which are hard to solve if the intent is only communicated using a natural-language description, without including the data. But how do we decide how much data and which data to include in the prompt? This paper makes two contributi…

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 …

Don't mention it: An approach to assess challenges to using software mentions for citation and discoverability research
Stephan Druskat, Neil P. Chue Hong, Sammie Buzzard, Olexandr Konovalov, Patrick Kornek
https://arxiv.org/abs/2402.14602

Don't mention it: An approach to assess challenges to using software mentions for citation and discoverability research
Datasets collecting software mentions from scholarly publications can potentially be used for research into the software that has been used in the published research, as well as into the practice of software citation. Recently, new software mention datasets with different characteristics have been published. We present an approach to assess the usability of such datasets for research on research software. Our approach includes sampling and data preparation, manual annotation for quality and men…

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…

Affleck-Dine Dirac Leptogenesis
Neil D. Barrie, Chengcheng Han
https://arxiv.org/abs/2402.15245 https://arxiv.org/pdf/2402.15245

Affleck-Dine Dirac Leptogenesis
We present a minimal framework that realises successful Dirac Leptogenesis through the Affleck-Dine mechanism. A single right-handed neutrino and a neutrinophillic Higgs doublet are introduced to the Standard Model, which couple via a Yukawa interaction. The inflationary setting is induced by a combination of the two Higgs doublets, with their global symmetry violating interactions leading to a net charge generation via the Affleck-Dine mechanism. This simple Standard Model extension exhibits a…

This https://arxiv.org/abs/2402.06347 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Optical and soft X-ray light-curve analysis during the 2022 eruption of U Scorpii: structural changes in the accretion disk
We present our optical photometric observations of the 2022 eruption of the recurrent nova U Scorpii (U Sco) using 49,152 data points over 70 d following the optical peak. We have also analyzed its soft X-ray (0.3--1 keV) light curve by the Neil Gehrels Swift Observatory. During the 2022 eruption, the optical plateau stage started 13.8--15.0 d and ended 23.8--25.0 d after the optical peak. The soft X-ray stage started 14.6--15.3 d and ended 38.7--39.5 d after the optical peak. Both stages start…

Solving Data-centric Tasks using Large Language Models
Shraddha Barke, Christian Poelitz, Carina Suzana Negreanu, Benjamin Zorn, Jos\'e Cambronero, Andrew D. Gordon, Vu Le, Elnaz Nouri, Nadia Polikarpova, Advait Sarkar, Brian Slininger, Neil Toronto, Jack Williams
https://arxiv.org/abs/2402.11734

Solving Data-centric Tasks using Large Language Models
Large language models (LLMs) are rapidly replacing help forums like StackOverflow, and are especially helpful for non-professional programmers and end users. These users are often interested in data-centric tasks, such as spreadsheet manipulation and data wrangling, which are hard to solve if the intent is only communicated using a natural-language description, without including the data. But how do we decide how much data and which data to include in the prompt? This paper makes two contributi…

Determination of the dynamic Young's modulus of quantum materials in piezoactuator-driven uniaxial pressure cells using a low-frequency a.c. method
Caitlin I. O'Neil, Zhenhai Hu, Naoki Kikugawa, Dmitry A. Sokolov, Andrew P. Mackenzie, Hilary M. L. Noad, Elena Gati
https://arxiv.org/abs/2403.17519

Determination of the dynamic Young's modulus of quantum materials in piezoactuator-driven uniaxial pressure cells using a low-frequency a.c. method
We report on a new technique for measuring the dynamic Young's modulus, $E$, of quantum materials at low temperatures as a function of static tuning strain, $ε$, in piezoactuator-driven pressure cells. In addition to a static tuning of stress and strain, we apply a small-amplitude, finite-frequency a.c. (1 Hz$ \lesssim ω\lesssim $1000 Hz) uniaxial stress, $σ_{ac}$, to the sample and measure the resulting a.c. strain, $ε_{ac}$, using a capacitive sensor to obtain the associated modulus $E$. …

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…

Solving Data-centric Tasks using Large Language Models
Shraddha Barke, Christian Poelitz, Carina Suzana Negreanu, Benjamin Zorn, Jos\'e Cambronero, Andrew D. Gordon, Vu Le, Elnaz Nouri, Nadia Polikarpova, Advait Sarkar, Brian Slininger, Neil Toronto, Jack Williams
https://arxiv.org/abs/2402.11734

Solving Data-centric Tasks using Large Language Models
Large language models (LLMs) are rapidly replacing help forums like StackOverflow, and are especially helpful for non-professional programmers and end users. These users are often interested in data-centric tasks, such as spreadsheet manipulation and data wrangling, which are hard to solve if the intent is only communicated using a natural-language description, without including the data. But how do we decide how much data and which data to include in the prompt? This paper makes two contributi…