Large Language Models (LLMs) have made remarkable strides in various tasks. Whether LLMs are competitive few-shot solvers for information extraction (IE) tasks, however, remains an open problem. In this work, we aim to provide a thorough answer to this question. Through extensive experiments on nine datasets across four IE tasks, we demonstrate that current advanced LLMs consistently exhibit inferior performance, higher latency, and increased budget requirements compared to fine-tuned SLMs under most settings. Therefore, we conclude that LLMs are not effective few-shot information extractors in general. Nonetheless, we illustrate that with appropriate prompting strategies, LLMs can effectively complement SLMs and tackle challenging samples that SLMs struggle with. And moreover, we propose an adaptive filter-then-rerank paradigm to combine the strengths of LLMs and SLMs. In this paradigm, SLMs serve as filters and LLMs serve as rerankers. By prompting LLMs to rerank a small portion of difficult samples identified by SLMs, our preliminary system consistently achieves promising improvements (2.4% F1-gain on average) on various IE tasks, with an acceptable time and cost investment.

The immune response is a dynamic process by which the body determines whether an antigen is self or nonself. The state of this dynamic process is defined by the relative balance and population of inflammatory and regulatory actors which comprise this decision making process. The goal of immunotherapy as applied to, e.g. Rheumatoid Arthritis (RA), then, is to bias the immune state in favor of the regulatory actors - thereby shutting down autoimmune pathways in the response. While there are several known approaches to immunotherapy, the effectiveness of the therapy will depend on how this intervention alters the evolution of this state. Unfortunately, this process is determined not only by the dynamics of the process, but the state of the system at the time of intervention - a state which is difficult if not impossible to determine prior to application of the therapy. To identify such states we consider a mouse model of RA (Collagen-Induced Arthritis (CIA)) immunotherapy; collect high dimensional data on T cell markers and populations of mice after treatment with a recently developed immunotherapy for CIA; and use feature selection algorithms in order to select a lower dimensional subset of this data which can be used to predict both the full set of T cell markers and populations, along with the efficacy of immunotherapy treatment.

In January 2020, privacy journalist Kashmir Hill published an article in The New York Times describing Clearview AI--a company that purports to help U.S. law enforcement match photos of unknown people to their online presence through a facial recognition model trained by scraping millions of publicly available face images online.a In 2021, police departments in many different U.S. cities were reported to have used Clearview AI to, for example, identify Black Lives Matter protestors.b In 2022, a California-based artist found that photos she thought to be in her private medical record were included, without her knowledge or consent, in the LAION training dataset that has been used to train Stable Diffusion and Google Imagen.c The artist has a rare medical condition she prefers to keep private and expressed concern about the abuse potential of generative AI technologies having access to her photos. In January 2023, Twitch streamer QTCinderella made an emphatic plea to her followers on Twitter to stop spreading links to an illicit website hosting AI-generated "deep fake" pornography of her and other women influencers.

My wife is a physician, so we often find ourselves in social engagements with other physicians. When asked what I do, many are intrigued by what it means to be a computer science professor. My graduate students do that now. Do you teach people how to program? No, though some of my colleagues do.

A U.S. Navy warship in the northern Red Sea on Thursday shot down three cruise missiles and several drones launched from Yemen that the Pentagon said might have been headed toward Israel. "We cannot say for certain what these missiles and drones were targeting, but they were launched from Yemen heading north along the Red Sea, potentially towards targets in Israel," Brig. Gen. Patrick Ryder, the Pentagon spokesman, told reporters. The missiles and drones were launched by pro-Iranian Houthi rebels in Yemen amid a flurry of drone attacks against American troops in Iraq and Syria over the past three days, General Ryder said. The incidents underscored the risks that the conflict between Israel and the Palestinian group Hamas could spiral into a wider war.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The Philippine defense chief has ordered all defense personnel and the 163,000-member military to refrain from using digital applications that harness artificial intelligence to generate personal portraits, saying they could pose security risks. Defense Secretary Gilberto Teodoro Jr. issued the order in an Oct. 14 memorandum, as Philippine forces have been working to weaken decades-old communist and Muslim insurgencies and defend territorial interests in the disputed South China Sea. The Department of National Defense on Friday confirmed the authenticity of the memo, which has been circulating online in recent days, but did not provide other details, including what prompted Teodoro to issue the prohibition.

Compared to Samsung's Galaxy Z Fold 5, not only does OnePlus' first foldable pack two larger displays, it's also thinner and lighter -- as long as you don't count the hulking camera module. The most impressive thing the Open is doing is putting pressure on the price of big foldables. Starting at $1,700, it costs $100 less than the Z Fold 5 and Google's Pixel Fold -- and that's before you factor in the launch deal that knocks another $200 off with the trade-in of any phone. We put it through its paces in our full review. You can get these reports delivered daily direct to your inbox.

The demo explains how artificial intelligence is used in the app and its features. The chiefs of the FBI and Britain's MI5 have raised concerns about the enormous and terrifying potential artificial intelligence poses for terrorists, saying the technology adds "a level of threat to that we haven't previously encountered." "It's one of those issues where no one has a monopoly of wisdom and trying to have a different form of public-private partnership and, crucially, international partnerships," MI5 Director General Ken McCallum said during the Five Eyes alliance conference in California this week. The conference between the United States, United Kingdom, Canada, Australia and New Zealand focused on the launch of an initiative aimed at finding innovative responses to developing intelligence threats, particularly in the face of new technology such as AI generative platforms. "Emerging technologies are essential to our economic and national security, and America's role as a leading economic power, but they also present new and evolving threats," FBI Director Christopher Wray said ahead of the conference.

Textbooks are one of the main mediums for delivering high-quality education to students. In particular, explanatory and illustrative visuals play a key role in retention, comprehension and general transfer of knowledge. However, many textbooks lack these interesting visuals to support student learning. In this paper, we investigate the effectiveness of vision-language models to automatically enhance textbooks with images from the web. We collect a dataset of e-textbooks in the math, science, social science and business domains. We then set up a text-image matching task that involves retrieving and appropriately assigning web images to textbooks, which we frame as a matching optimization problem. Through a crowd-sourced evaluation, we verify that (1) while the original textbook images are rated higher, automatically assigned ones are not far behind, and (2) the precise formulation of the optimization problem matters. We release the dataset of textbooks with an associated image bank to inspire further research in this intersectional area of computer vision and NLP for education.

Accurate hydrological understanding and water cycle prediction are crucial for addressing scientific and societal challenges associated with the management of water resources, particularly under the dynamic influence of anthropogenic climate change. Existing reviews predominantly concentrate on the development of machine learning (ML) in this field, yet there is a clear distinction between hydrology and ML as separate paradigms. Here, we introduce physics-aware ML as a transformative approach to overcome the perceived barrier and revolutionize both fields. Specifically, we present a comprehensive review of the physics-aware ML methods, building a structured community (PaML) of existing methodologies that integrate prior physical knowledge or physics-based modeling into ML. We systematically analyze these PaML methodologies with respect to four aspects: physical data-guided ML, physics-informed ML, physics-embedded ML, and physics-aware hybrid learning. PaML facilitates ML-aided hypotheses, accelerating insights from big data and fostering scientific discoveries. We first conduct a systematic review of hydrology in PaML, including rainfall-runoff hydrological processes and hydrodynamic processes, and highlight the most promising and challenging directions for different objectives and PaML methods. Finally, a new PaML-based hydrology platform, termed HydroPML, is released as a foundation for hydrological applications. HydroPML enhances the explainability and causality of ML and lays the groundwork for the digital water cycle's realization. The HydroPML platform is publicly available at https://hydropml.github.io/.