Growing evidence suggests that social determinants of health (SDoH), a set of nonmedical factors, affect individuals' risks of developing Alzheimer's disease (AD) and related dementias. Nevertheless, the etiological mechanisms underlying such relationships remain largely unclear, mainly due to difficulties in collecting relevant information. This study presents a novel, automated framework that leverages recent advancements of large language model (LLM) and natural language processing techniques to mine SDoH knowledge from extensive literature and integrate it with AD-related biological entities extracted from the general-purpose knowledge graph PrimeKG. Utilizing graph neural networks, we performed link prediction tasks to evaluate the resultant SDoH-augmented knowledge graph. Our framework shows promise for enhancing knowledge discovery in AD and can be generalized to other SDoH-related research areas, offering a new tool for exploring the impact of social determinants on health outcomes. Our code is available at: https://github.com/hwq0726/SDoHenPKG

Detecting multimodal misinformation, especially in the form of image-text pairs, is crucial. Obtaining large-scale, high-quality real-world fact-checking datasets for training detectors is costly, leading researchers to use synthetic datasets generated by AI technologies. However, the generalizability of detectors trained on synthetic data to real-world scenarios remains unclear due to the distribution gap. To address this, we propose learning from synthetic data for detecting real-world multimodal misinformation through two model-agnostic data selection methods that match synthetic and real-world data distributions. Experiments show that our method enhances the performance of a small MLLM (13B) on real-world fact-checking datasets, enabling it to even surpass GPT-4V~\cite{GPT-4V}.

We consider fact-checking approaches that aim to predict the veracity of assertions in knowledge graphs. Five main categories of fact-checking approaches for knowledge graphs have been proposed in the recent literature, of which each is subject to partially overlapping limitations. In particular, current text-based approaches are limited by manual feature engineering. Path-based and rule-based approaches are limited by their exclusive use of knowledge graphs as background knowledge, and embedding-based approaches suffer from low accuracy scores on current fact-checking tasks. We propose a hybrid approach -- dubbed HybridFC -- that exploits the diversity of existing categories of fact-checking approaches within an ensemble learning setting to achieve a significantly better prediction performance. In particular, our approach outperforms the state of the art by 0.14 to 0.27 in terms of Area Under the Receiver Operating Characteristic curve on the FactBench dataset. Our code is open-source and can be found at https://github.com/dice-group/HybridFC.

Background and Aims: This study evaluates the medical reasoning performance of large language models (LLMs) and vision language models (VLMs) in gastroenterology. Methods: We used 300 gastroenterology board exam-style multiple-choice questions, 138 of which contain images to systematically assess the impact of model configurations and parameters and prompt engineering strategies utilizing GPT-3.5. Next, we assessed the performance of proprietary and open-source LLMs (versions), including GPT (3.5, 4, 4o, 4omini), Claude (3, 3.5), Gemini (1.0), Mistral, Llama (2, 3, 3.1), Mixtral, and Phi (3), across different interfaces (web and API), computing environments (cloud and local), and model precisions (with and without quantization). Finally, we assessed accuracy using a semiautomated pipeline. Results: Among the proprietary models, GPT-4o (73.7%) and Claude3.5-Sonnet (74.0%) achieved the highest accuracy, outperforming the top open-source models: Llama3.1-405b (64%), Llama3.1-70b (58.3%), and Mixtral-8x7b (54.3%). Among the quantized open-source models, the 6-bit quantized Phi3-14b (48.7%) performed best. The scores of the quantized models were comparable to those of the full-precision models Llama2-7b, Llama2--13b, and Gemma2-9b. Notably, VLM performance on image-containing questions did not improve when the images were provided and worsened when LLM-generated captions were provided. In contrast, a 10% increase in accuracy was observed when images were accompanied by human-crafted image descriptions. Conclusion: In conclusion, while LLMs exhibit robust zero-shot performance in medical reasoning, the integration of visual data remains a challenge for VLMs. Effective deployment involves carefully determining optimal model configurations, encouraging users to consider either the high performance of proprietary models or the flexible adaptability of open-source models.

Substance use disorders (SUDs) are a growing concern globally, necessitating enhanced understanding of the problem and its trends through data-driven research. Social media are unique and important sources of information about SUDs, particularly since the data in such sources are often generated by people with lived experiences. In this paper, we introduce Reddit-Impacts, a challenging Named Entity Recognition (NER) dataset curated from subreddits dedicated to discussions on prescription and illicit opioids, as well as medications for opioid use disorder. The dataset specifically concentrates on the lesser-studied, yet critically important, aspects of substance use--its clinical and social impacts. We collected data from chosen subreddits using the publicly available Application Programming Interface for Reddit. We manually annotated text spans representing clinical and social impacts reported by people who also reported personal nonmedical use of substances including but not limited to opioids, stimulants and benzodiazepines. Our objective is to create a resource that can enable the development of systems that can automatically detect clinical and social impacts of substance use from text-based social media data. The successful development of such systems may enable us to better understand how nonmedical use of substances affects individual health and societal dynamics, aiding the development of effective public health strategies. In addition to creating the annotated data set, we applied several machine learning models to establish baseline performances. Specifically, we experimented with transformer models like BERT, and RoBERTa, one few-shot learning model DANN by leveraging the full training dataset, and GPT-3.5 by using one-shot learning, for automatic NER of clinical and social impacts. The dataset has been made available through the 2024 SMM4H shared tasks.

Paid autonomous vehicle service is coming to Los Angeles, thanks to a decision by California regulators today to allow Alphabet subsidiary Waymo to operate in the city. Under the new ruling, Waymo is also permitted to launch service in a large section of the San Francisco Peninsula. The decision by the California Public Utilities Commission will likely prove controversial. It comes over the protest of local governments and agencies, including the Los Angeles Department of Transportation, the San Francisco County Transportation Authority, the city of South San Francisco, and the County of San Mateo. All argued that local government and citizens should have more input and oversight over the expanded autonomous taxi service.

Another California community is raising concerns about plans to unleash the Waymo self-driving vehicle in its jurisdiction, following several incidents involving autonomous ride-hailing cars that resulted in injuries. San Mateo County, in the San Francisco Bay Area, has requested more information from state regulators before allowing Google-owned Waymo to operate its driverless vehicles in the county. San Mateo County made the request after Waymo submitted a letter Jan. 19 to the California Public Utilities Commission, asking the agency to approve its proposed expansion of its Automated Vehicle Passenger Services into portions of the San Francisco Peninsula, which includes San Mateo County, as well as the southwest region of Los Angeles County. The company has already been serving a portion of San Francisco, from Lands End to Bernal Heights. The autonomous car began offering rides for a limited time in November in Santa Monica, Century City, West Hollywood, Mid-City Koreatwon and downtown L.A., giving residents a chance at testing the driverless ride.

As Waymo robotaxis plucked up passengers for free this week in Santa Monica and Venice, worry grew among Los Angeles officials about the safety of driverless cars on city streets. Mayor Bass asked regulators Wednesday to increase their scrutiny of automated taxis and said the city should have a say in how they are regulated. The move comes after a Cruise robotaxi dragged a person down a San Francisco street last month and the company allegedly failed to disclose the footage to the state Department of Motor Vehicles. The DMV suspended the General Motors-owned company's permits and Cruise has since announced it will suspend U.S. operations. The incident in San Francisco -- where the two driverless fleets were doing business -- was among several that raised red flags among Los Angeles officials, who have begun to see more and more robotaxis being tested on city streets.

The future of driverless cars in America is a promotional booth with a surfboard and a movie director's clapboard. Robotaxis have officially arrived in Los Angeles, and last week, residents lined up in Santa Monica's main promenade to get a smartphone code needed to ride them. For now, the cars, from the Alphabet-owned start-up Waymo, won't leave the tame streets of Santa Monica. But in the coming months, they'll embark on a multi-month "tour" of the city, heading to West Hollywood, downtown L.A., and several other neighborhoods. For the past decade, the two leading robotaxi companies, Waymo and Cruise, have been focused primarily on San Francisco and Phoenix, where they both already take paid passengers.

Whether we're living in the age of Peak TV or Trough TV, one thing is clear: There's too much TV. Thankfully, not every show has to be watched in its entirety. One of the best things about television is its serialized nature, the continuous thread that strings viewers along from one episode to the next. It's a cliché that prestige television is the new novel precisely because of the way that many dramas develop their characters and plots over many hours of storytelling. But an older virtue of TV is its brevity--the way a scenario can be introduced and resolved within the space of an hour, or half that--and some of the best episodes are less like chapters in a long-running novel than like short stories or short films. There's been no shortage of debate about this question, but for our purposes, we're defining it simply as an episode that stands up on its own, whether or not you've seen the rest of the show. Some are "bottle episodes," which typically confine a small cast to one location to save money. Some are "departure episodes," in which a show abandons its usual format or style to suddenly become, say, silent, animated, a musical, or about a minor character it was never about before. But not all bottle episodes and departure episodes are stand-alones, and vice versa. It's for this reason that you won't find Breaking Bad's celebrated "Fly" on this list: It may be a bottle episode, but it doesn't stand alone, because the best thing about it--how the housefly is a metaphor for everything else going on in the series--is comprehensible only to those who have watched the show. These are English-language selections, and, out of fairness, we have limited ourselves to one episode per series, although some shows are full of stellar contenders. Use these picks--arranged in chronological order, with an admitted bias toward our most recent, and best, era of television--to populate your streaming queue with a feast of bite-sized morsels, each of which could double as either a snackable introduction to a new show or a satisfying meal in itself. If movies made Alfred Hitchcock a name, TV made him a brand. The master of suspense embraced the burgeoning medium in 1955 with Alfred Hitchcock Presents (later renamed The Alfred Hitchcock Hour), an anthology series whose entries began and ended the same way: the titular celebrity providing context to a unique half-hour thriller, typically an adaption of a short story by an esteemed author (John Cheever, Ray Bradbury, many others).