Automatic summarization of mass-emergency events plays a critical role in disaster management. The second edition of CrisisFACTS aims to advance disaster summarization based on multi-stream fact-finding with a focus on web sources such as Twitter, Reddit, Facebook, and Webnews. Here, participants are asked to develop systems that can extract key facts from several disaster-related events, which ultimately serve as a summary. This paper describes our method to tackle this challenging task. We follow previous work and propose to use a combination of retrieval, reranking, and an embarrassingly simple instruction-following summarization. The two-stage retrieval pipeline relies on BM25 and MonoT5, while the summarizer module is based on the open-source Large Language Model (LLM) LLaMA-13b. For summarization, we explore a Question Answering (QA)-motivated prompting approach and find the evidence useful for extracting query-relevant facts. The automatic metrics and human evaluation show strong results but also highlight the gap between open-source and proprietary systems.

Transformer, as one of the most advanced neural network models in Natural Language Processing (NLP), exhibits diverse applications in the field of anomaly detection. To inspire research on Transformer-based anomaly detection, this review offers a fresh perspective on the concept of anomaly detection. We explore the current challenges of anomaly detection and provide detailed insights into the operating principles of Transformer and its variants in anomaly detection tasks. Additionally, we delineate various application scenarios for Transformer-based anomaly detection models and discuss the datasets and evaluation metrics employed. Furthermore, this review highlights the key challenges in Transformer-based anomaly detection research and conducts a comprehensive analysis of future research trends in this domain. The review includes an extensive compilation of over 100 core references related to Transformer-based anomaly detection. To the best of our knowledge, this is the first comprehensive review that focuses on the research related to Transformer in the context of anomaly detection. We hope that this paper can provide detailed technical information to researchers interested in Transformer-based anomaly detection tasks.

Global stability and robustness guarantees in learned dynamical systems are essential to ensure well-behavedness of the systems in the face of uncertainty. We present Extended Linearized Contracting Dynamics (ELCD), the first neural network-based dynamical system with global contractivity guarantees in arbitrary metrics. The key feature of ELCD is a parametrization of the extended linearization of the nonlinear vector field. In its most basic form, ELCD is guaranteed to be (i) globally exponentially stable, (ii) equilibrium contracting, and (iii) globally contracting with respect to some metric. To allow for contraction with respect to more general metrics in the data space, we train diffeomorphisms between the data space and a latent space and enforce contractivity in the latent space, which ensures global contractivity in the data space. We demonstrate the performance of ELCD on the $2$D, $4$D, and $8$D LASA datasets.

National Statistical Organizations (NSOs) increasingly draw on Machine Learning (ML) to improve the timeliness and cost-effectiveness of their products. When introducing ML solutions, NSOs must ensure that high standards with respect to robustness, reproducibility, and accuracy are upheld as codified, e.g., in the Quality Framework for Statistical Algorithms (QF4SA; Yung et al. 2022). At the same time, a growing body of research focuses on fairness as a pre-condition of a safe deployment of ML to prevent disparate social impacts in practice. However, fairness has not yet been explicitly discussed as a quality aspect in the context of the application of ML at NSOs. We employ Yung et al. (2022)'s QF4SA quality framework and present a mapping of its quality dimensions to algorithmic fairness. We thereby extend the QF4SA framework in several ways: we argue for fairness as its own quality dimension, we investigate the interaction of fairness with other dimensions, and we explicitly address data, both on its own and its interaction with applied methodology. In parallel with empirical illustrations, we show how our mapping can contribute to methodology in the domains of official statistics, algorithmic fairness, and trustworthy machine learning.

Seven people, including three children, were killed in a Russian drone attack on a gas station in the Ukrainian city of Kharkiv on Saturday. Russia has lost more than 3,000 tanks in Ukraine - the equivalent of its entire pre-war active inventory - but has enough lower-quality armored vehicles in storage for years of replacements, a leading research center said on Tuesday. Ukraine has also suffered heavy losses since Russia invaded in February 2022, but Western military replenishments have allowed it to maintain inventories while upgrading quality, the International Institute for Strategic Studies said. Even after the loss of so many tanks - including an estimated 1,120 in the past year - Russia still has about twice as many available for combat as Ukraine, according to the IISS's annual Military Balance, a key research tool for defense analysts. Henry Boyd, the institute's senior fellow for military capability, said Russia had been roughly "breaking even" in terms of replacements.

An Israeli drone strike has targeted two journalists in Muraj, north of Rafah, including Al Jazeera Arabic correspondent, Ismail Abu Omar who doctors say is in a critical condition.

It's been just over 21 years since one of the darkest days in NASA's history. On the morning of February 1, 2003, Space Shuttle Columbia disintegrated as it reentered the atmosphere over Texas and Louisiana. The seven astronauts aboard – David Brown, Rick Husband, Laurel Clark, Kalpana Chawla, Michael Anderson, William McCool and Ilan Ramon – all lost their lives. The tragic event is being retold for a BBC Two documentary series airing from this week on BBC Two, 'The Space Shuttle That Fell to Earth'. MailOnline has revealed a step-by-step graphic showing exactly what went wrong on that fateful morning, which changed NASA forever.

This can help make the field stronger. There will also always be a lot of problems to address, and AI can help, but it can only help if there are enough people working on those problems. I really enjoy social gatherings, including going to parties or just being out with people. I also just started writing poetry. I signed up to a poetry class over the summer which I'm looking forward to attending. As well as my mentors and advisor, my friends and community, I've found the following resources helpful during my PhD:

Beverly Hills police drone captures slip and fall. The affluent 90210 zip code is often associated with a hit television show that aired in the 1990s. It is also where an alleged burglar fell off a ladder into a pool. The Beverly Hills Police Department (BHPD) shared drone footage of the incident from Jan. 6 on Instagram with the caption, "Burglar caught in 4K. The video first shows a man crawling out of a home's window before being seen atop a tall ladder over what appears to be a garage.

Comedian Adam Corolla discusses how self-driving cars are causing chaos in San Francisco on'Jesse Watters Primetime.' A former interim mayor of San Francisco announced Tuesday he's running for his previous job, joining a competitive field of candidates who say the city has crumbled under the watch of Mayor London Breed, who is up for reelection this year. Mark Farrell served as interim mayor of San Francisco from January to July 2018, when Breed was elected to finish the term of Ed Lee, who died in office. The lawyer and former city supervisor said he had not planned to return to politics but feels he has the right skills to turn San Francisco around. "It is really painful to watch the city you love and you grew up in maligned across the globe," he said in an interview with The Associated Press.