Large language models (LLMs) have recently showcased remarkable capabilities, spanning a wide range of tasks and applications, including those in the medical domain. Models like GPT-4 excel in medical question answering but may face challenges in the lack of interpretability when handling complex tasks in real clinical settings. We thus introduce the diagnostic reasoning dataset for clinical notes (DiReCT), aiming at evaluating the reasoning ability and interpretability of LLMs compared to human doctors. It contains 511 clinical notes, each meticulously annotated by physicians, detailing the diagnostic reasoning process from observations in a clinical note to the final diagnosis. Additionally, a diagnostic knowledge graph is provided to offer essential knowledge for reasoning, which may not be covered in the training data of existing LLMs. Evaluations of leading LLMs on DiReCT bring out a significant gap between their reasoning ability and that of human doctors, highlighting the critical need for models that can reason effectively in real-world clinical scenarios.

Virtual Screening is an essential technique in the early phases of drug discovery, aimed at identifying promising drug candidates from vast molecular libraries. Recently, ligand-based virtual screening has garnered significant attention due to its efficacy in conducting extensive database screenings without relying on specific protein-binding site information.Obtaining binding affinity data for complexes is highly expensive, resulting in a limited amount of available data that covers a relatively small chemical space. Moreover, these datasets contain a significant amount of inconsistent noise. It is challenging to identify an inductive bias that consistently maintains the integrity of molecular activity during data augmentation. To tackle these challenges, we propose S-MolSearch, the first framework to our knowledge, that leverages molecular 3D information and affinity information in semi-supervised contrastive learning for ligand-based virtual screening.

How the data center boom plunged one small Pennsylvania town into chaos. Valley View Estates is set to be surrounded by data centers. Get your news from a source that's not owned and controlled by oligarchs. "I don't like to see anyone upset," said Nick Farris of Provident Real Estate Advisors. He was sitting in the front of a crowd of roughly 150 inside Valley View High School's auditorium in Archbald, a town of about 7,500, huddled between two mountain ranges in Pennsylvania's Lackawanna Valley. Farris was there to represent the developer for Project Scott, one of many data center campuses coming to town. "I think that this is the best data center site in this area of the country, by far." The audience had been fairly quiet, bundled in thick coats against the late January cold. But as Farris spoke about data centers as a boon for communities, they began to laugh, drawing a rebuke from town officials. "What about the children?" someone shouted from the crowd. The children were watching from the walls; long banners of Valley View Performing Arts students hanging around the auditorium like championship pennants. Project Scott and four other data facilities will sit just a few thousand feet from the middle and high schools. He was referring to Lockheed Martin's 350,000-square-foot Missiles and Fire Control facility directly next to the high school, parts of which are highly contaminated . "That sucks too!" another attendee yelled back.

Welcome to our latest roundup of what's going on in the indie game space. There are a whole bunch of neat new games out this week, as well as updates on some interesting upcoming projects. In case you missed it, the Steam Spring Sale is under way. There are lots of solid deals here, and my credit card is already screaming at me. I've picked up a bunch of games from my wishlist.

The new paradigm of fine-tuning-as-a-service introduces a new attack surface for Large Language Models (LLMs): a few harmful data uploaded by users can easily trick the fine-tuning to produce an alignment-broken model. We conduct an empirical analysis and uncovera \textit{harmful embedding drift} phenomenon, showing a probable cause of the alignment-broken effect. Inspired by our findings, we propose Vaccine, a perturbation-aware alignment technique to mitigate the security risk of users fine-tuning. The core idea of Vaccine is to produce invariant hidden embeddings by progressively adding crafted perturbation to them in the alignment phase. This enables the embeddings to withstand harmful perturbation from un-sanitized user data in the fine-tuning phase. Our results on open source mainstream LLMs (e.g., Llama2, Opt, Vicuna) demonstrate that Vaccine can boost the robustness of alignment against harmful prompts induced embedding drift while reserving reasoning ability towards benign prompts.

Dimension reduction (DR) is an important and widely studied technique in exploratory data analysis. However, traditional DR methods are not applicable to datasets with with a contrastive structure, where data are split into a foreground group of interest (case or treatment group), and a background group (control group). This type of data, common in biomedical studies, necessitates contrastive dimension reduction (CDR) methods to effectively capture information unique to or enriched in the foreground group relative to the background group. Despite the development of various CDR methods, two critical questions remain underexplored: when should these methods be applied, and how can the information unique to the foreground group be quantified? In this work, we address these gaps by proposing a hypothesis test to determine the existence of contrastive information, and introducing a contrastive dimension estimator (CDE) to quantify the unique components in the foreground group. We provide theoretical support for our methods and validate their effectiveness through extensive simulated, semi-simulated, and real experiments involving images, gene expressions, protein expressions, and medical sensors, demonstrating their ability to identify the unique information in the foreground group.

Before deploying outputs from foundation models in high-stakes tasks, it is imperative to ensure that they align with human values.For instance, in radiology report generation, reports generated by a vision-language model must align with human evaluations before their use in medical decision-making. This paper presents Conformal Alignment, a general framework for identifying units whose outputs meet a user-specified alignment criterion. It is guaranteed that on average, a prescribed fraction of selected units indeed meet the alignment criterion, regardless of the foundation model or the data distribution. Given any pre-trained model and new units with model-generated outputs, Conformal Alignment leverages a set of reference data with ground-truth alignment status to train an alignment predictor. It then selects new units whose predicted alignment scores surpass a data-dependent threshold, certifying their corresponding outputs as trustworthy. Through applications to question answering and radiology report generation, we demonstrate that our method is able to accurately identify units with trustworthy outputs via lightweight training over a moderate amount of reference data. En route, we investigate the informativeness of various features in alignment prediction and combine them with standard models to construct the alignment predictor.

Models for student reading performance can empower educators and institutions to proactively identify at-risk students, thereby enabling early and tailored instructional interventions. However, there are no suitable publicly available educational datasets for modeling and predicting future reading performance. In this work, we introduce the Enhanced Core Reading Instruction (ECRI) dataset, a novel large-scale longitudinal tabular dataset collected across 44 schools with 6,916 students and 172 teachers. We leverage the dataset to empirically evaluate the ability of state-of-the-art machine learning models to recognize early childhood educational patterns in multivariate and partial measurements. Specifically, we demonstrate a simple self-supervised strategy in which a Multi-Layer Perception (MLP) network is pre-trained over masked inputs to outperform several strong baselines while generalizing over diverse educational settings. To facilitate future developments in precise modeling and responsible use of models for individualized and early intervention strategies, our data and code are available at https://ecri-data.github.io/.

Trump administration'unlocks' 140MILLION barrels of precious Iranian oil with major policy change to fight back against'hoarding' China... here's what it means for your wallet Buffy the Vampire Slayer star Nicholas Brendon dead at 54 as'heartbroken' family reveal cause of death Joseph Duggar's wife Kendra is arrested for allegedly endangering welfare of a minor as he faces new charges Behind closed doors, the Duggar family's next nightmare began long before Joseph's arrest: Insiders reveal what they knew and how they plan to recover America is about to be torn apart by a financial tsunami - and it's not just an oil crisis to fear. However, it seems not every corner of Britain is quite so beautiful - as a survey has revealed the least scenic locations. Voters on the Scenic Or Not survey awarded the top spot to Basingstoke's Newbury Road. This unappealing location received the lowest possible score, with just one out of 10 for'scenicness'. And while Cornwall might be renowned for its beautiful scenery, a rather less attractive part of the county - the Electricity Station in Landulph - joins Basingstoke at the bottom of the pile.

Will Gulf states join war? One police officer has been killed in a drone strike by "outlaw groups" on the headquarters of the Iraqi National Intelligence Service in the heart of capital Baghdad. "A drone targeted the headquarters of the Iraqi National Intelligence Service in the Mansour district" at about 10am local time (07:00 GMT), General Saad Maan, head of the Iraqi government's security media unit, said in a brief statement on Saturday. Another drone, filming the operation, crashed into a private members ' sports club popular with the Iraqi elite and foreign diplomats, according to the same source. The drone attack on the headquarters of the National Intelligence Service came hours after another attack on the US military complex.