First discovered 20 years ago, the rare fossil combo reveals a Cretaceous meal in the making. Breakthroughs, discoveries, and DIY tips sent six days a week. A rare dinosaur fossil on display at the Museum of the Rockies in Bozeman, Montana, tells a gory story. The skull from a large plant-eating has a tooth lodged into it, indicating that it may have met its final moments as a meal. The tooth in question belongs to one of the most famous dinosaurs on earth-- .

As libraries explore large language models (LLMs) for use in virtual reference services, a key question arises: Can LLMs serve all users equitably, regardless of demographics or social status? While they offer great potential for scalable support, LLMs may also reproduce societal biases embedded in their training data, risking the integrity of libraries' commitment to equitable service. To address this concern, we evaluate whether LLMs differentiate responses across user identities by prompting six state-of-the-art LLMs to assist patrons differing in sex, race/ethnicity, and institutional role. We find no evidence of differentiation by race or ethnicity, and only minor evidence of stereotypical bias against women in one model. LLMs demonstrate nuanced accommodation of institutional roles through the use of linguistic choices related to formality, politeness, and domain-specific vocabularies, reflecting professional norms rather than discriminatory treatment. These findings suggest that current LLMs show a promising degree of readiness to support equitable and contextually appropriate communication in academic library reference services.

Local news stations are often considered to be reliable sources of non-politicized information, particularly local concerns that residents care about. Because these stations are trusted news sources, viewers are particularly susceptible to the information they report. The Sinclair Broadcast group is a broadcasting company that has acquired many local news stations in the last decade. We investigate the effects of local news stations being acquired by Sinclair: how does coverage change? We use computational methods to investigate changes in internet content put out by local news stations before and after being acquired by Sinclair and in comparison to national news outlets. We find that there is clear evidence that local news stations report more frequently on national news at the expense of local topics, and that their coverage of polarizing national topics increases.

Use any tokenizer you want as long it as the same API.""" In order to convert the dataset to NER format we suggest tokenizing Tweet text and utilizing the character offsets to identify mention tokens. This approach works as long as the tokenizer returned offsets correspond to the offset of the phrase in the original text, i.e. See example code in listing 1. Metric Description strong_mention_match strong_mention_match is a micro-averaged evaluation of entity mentions. A system span must match a gold span exactly to be counted as correct.strong_all_match

While progress has been made in identifying common genetic variants associated with human diseases, for most of common complex diseases, the identified genetic variants only account for a small proportion of heritability. Challenges remain in finding additional unknown genetic variants predisposing to complex diseases. With the advance in next-generation sequencing technologies, sequencing studies have become commonplace in genetic research. The ongoing exome-sequencing and whole-genome-sequencing studies generate a massive amount of sequencing variants and allow researchers to comprehensively investigate their role in human diseases. The discovery of new disease-associated variants can be enhanced by utilizing powerful and computationally efficient statistical methods. In this paper, we propose a functional analysis of variance (FANOVA) method for testing an association of sequence variants in a genomic region with a qualitative trait. The FANOVA has a number of advantages: (1) it tests for a joint effect of gene variants, including both common and rare; (2) it fully utilizes linkage disequilibrium and genetic position information; and (3) allows for either protective or risk-increasing causal variants. Through simulations, we show that FANOVA outperform two popularly used methods - SKAT and a previously proposed method based on functional linear models (FLM), - especially if a sample size of a study is small and/or sequence variants have low to moderate effects. We conduct an empirical study by applying three methods (FANOVA, SKAT and FLM) to sequencing data from Dallas Heart Study. While SKAT and FLM respectively detected ANGPTL 4 and ANGPTL 3 associated with obesity, FANOVA was able to identify both genes associated with obesity.

Retrieval-Augmented Generation (RAG) has become a popular technique for enhancing the reliability and utility of Large Language Models (LLMs) by grounding responses in external documents. Traditional RAG systems rely on Optical Character Recognition (OCR) to first process scanned documents into text. However, even state-of-the-art OCRs can introduce errors, especially in degraded or complex documents. Recent vision-language approaches, such as ColPali, propose direct visual embedding of documents, eliminating the need for OCR. This study presents a systematic comparison between a vision-based RAG system (ColPali) and more traditional OCR-based pipelines utilizing Llama 3.2 (90B) and Nougat OCR across varying document qualities. Beyond conventional retrieval accuracy metrics, we introduce a semantic answer evaluation benchmark to assess end-to-end question-answering performance. Our findings indicate that while vision-based RAG performs well on documents it has been fine-tuned on, OCR-based RAG is better able to generalize to unseen documents of varying quality. We highlight the key trade-offs between computational efficiency and semantic accuracy, offering practical guidance for RAG practitioners in selecting between OCR-dependent and vision-based document retrieval systems in production environments.

This work presents MicroNAS, an automated neural architecture search tool specifically designed to create models optimized for microcontrollers with small memory resources. The ESP32 microcontroller, with 320 KB of memory, is used as the target platform. The artificial intelligence contribution lies in a novel method for optimizing convolutional neural network and gated recurrent unit architectures by considering the memory size of the target microcontroller as a guide. A comparison is made between memory-driven model optimization and traditional two-stage methods, which use pruning, to show the e ffectiveness of the proposed framework. To demonstrate the engineering application of MicroNAS, a fall detection system (FDS) for lower-limb amputees is developed as a pilot study. A critical challenge in fall detection studies, class imbalance in the dataset, is addressed. The results show that MicroNAS models achieved higher F1-scores than alternative approaches, such as ensemble methods and H2O Automated Machine Learning, presenting a significant step forward in real-time FDS development. Biomechanists using body-worn sensors for activity detection can adopt the open-source code to design machine learning models tailored for microcontroller platforms with limited memory. Keywords: automated machine learning, tiny machine learning, neural architecture search, pruning, class imbalance, fall detection, lower limb amputee, Inertial Measurement Unit (IMU)1. Introduction Falls present a major health risk for individuals with lower limb amputation [1, 2]. Specifically, more than half of lower limb amputees report falling in the previous 12 months. Furthermore, of those reporting a fall, approximately 75% report multiple falls [1]. Falls have the potential for multiple negative sequelae, including fractures, traumatic brain injuries, lacerations, sprains, hematomas, and even death [3]. More commonly, a fall may only result in minor injuries or bruises but can impact the person's confidence in their balance and mobility [3]. Consequently, they may limit their physical activity and social participation, leading to a decline in overall physical and emotional health. Falls also pose a barrier to successful rehabilitation, whether it be physical or emotional injury. The extent to which falls delay or prevent successful rehabilitation of individuals with lower limb amputations is unknown.

Semantic communication systems, which leverage Generative AI (GAI) to transmit semantic meaning rather than raw data, are poised to revolutionize modern communications. However, they are vulnerable to backdoor attacks, a type of poisoning manipulation that embeds malicious triggers into training datasets. As a result, Backdoor attacks mislead the inference for poisoned samples while clean samples remain unaffected. The existing defenses may alter the model structure (such as neuron pruning that potentially degrades inference performance on clean inputs, or impose strict requirements on data formats (such as ``Semantic Shield" that requires image-text pairs). To address these limitations, this work proposes a defense mechanism that leverages semantic similarity to detect backdoor attacks without modifying the model structure or imposing data format constraints. By analyzing deviations in semantic feature space and establishing a threshold-based detection framework, the proposed approach effectively identifies poisoned samples. The experimental results demonstrate high detection accuracy and recall across varying poisoning ratios, underlining the significant effectiveness of our proposed solution.

A panel joins'Fox News @ Night' to weigh in on a voter sentiment poll about the incoming Trump administration, Chinese President Xi Jinping's invitation to the presidential inauguration, and efforts by Trump Cabinet nominees to court senators. President-elect Donald Trump unleashed a slew of nominations on Sunday night, naming several new people to serve in his forthcoming administration. In several Truth Social posts on Sunday, Trump introduced various experts to work in the White House on issues ranging from defense to technology to budgeting. The Republican leader began by naming Stephen Alexander Vaden as his nominee for deputy secretary of the Department of Agriculture. "In my First Term, Stephen was the General Counsel of the Department of Agriculture, and a Member of the Board of the Commodity Credit Corporation, where he won two cases before the United States Supreme Court, relocated and reorganized the Agencies that comprise the Department to better serve Rural America, and engaged in substantial regulatory reform," Trump wrote in a post.