Vision Large Language Models (VLLMs) exhibit promising potential for multi-modal understanding, yet their application to video-based emotion recognition remains limited by insufficient spatial and contextual awareness. Traditional approaches, which prioritize isolated facial features, often neglect critical non-verbal cues such as body language, environmental context, and social interactions, leading to reduced robustness in real-world scenarios. To address this gap, we propose Set-of-Vision-Text Prompting (SoVTP), a novel framework that enhances zero-shot emotion recognition by integrating spatial annotations (e.g., bounding boxes, facial landmarks), physiological signals (facial action units), and contextual cues (body posture, scene dynamics, others’ emotions) into a unified prompting strategy. SoVTP preserves holistic scene information while enabling fine-grained analysis of facial muscle movements and interpersonal dynamics. Extensive experiments show that SoVTP achieves substantial improvements over existing visual prompting methods, demonstrating its effectiveness in enhancing VLLMs’ video emotion recognition capabilities.

Meta maintains additional capacity within its infrastructure to ensure high availability for business workloads, accommodating user growth, temporal traffic variations, and unforeseen regional failures. However, this strategic choice inherently leads to underutilization of resources. We employ oversubscription as an effective strategy to mitigate infrastructure underutilization.

We introduce OpenDebateEvidence, a comprehensive dataset for argument miningand summarization sourced from the American Competitive Debate community.This dataset includes over 3.5 million documents with rich metadata, making itone of the most extensive collections of debate evidence. OpenDebateEvidencecaptures the complexity of arguments in high school and college debates, pro-viding valuable resources for training and evaluation. Our extensive experimentsdemonstrate the efficacy of fine-tuning state-of-the-art large language models forargumentative abstractive summarization across various methods, models, anddatasets. By providing this comprehensive resource, we aim to advance com-putational argumentation and support practical applications for debaters, edu-cators, and researchers. OpenDebateEvidence is publicly available to supportfurther research and innovation in computational argumentation. Access it here:https://huggingface.co/datasets/Yusuf5/OpenCaselist.

Exploring large corpora can generate useful discoveries but is time-consuming for humans. We formulate a new task, D5, that automatically discovers differences between two large corpora in a goal-driven way. The task input is a problem comprising a user-specified research goal (“*comparing the side effects of drug A and drug*”) and a corpus pair (two large collections of patients' self-reported reactions after taking each drug). The output is a goal-related description (discovery) of how these corpora differ (patients taking drug A “*mention feelings of paranoia*” more often). We build a D5 system, and to quantitatively evaluate its performance, we 1) build a diagnostic benchmark, SynD5, to test whether it can recover known differences between two synthetic corpora, and 2) contribute a meta-dataset, OpenD5, aggregating 675 open-ended problems ranging across business, social sciences, humanities, machine learning, and health. With both synthetic and real datasets, we confirm that language models can leverage the user-specified goals to propose more relevant candidate discoveries, and they sometimes produce discoveries previously unknown to the authors, including demographic differences in discussion topics, political stances in speech, insights in commercial reviews, and error patterns in NLP models. Finally, we discuss the limitations of the current D5 system, which discovers correlation rather than causation and has the potential to reinforce societal biases when misused; therefore, practitioners should treat the outputs of our system with caution.

SUMMARY: The NCI Transcriptional Pharmacodynamics Workbench (NCI TPW) is an extensive compilation of directly measured transcriptional responses to anti-cancer agents across the well-characterized NCI-60 cancer cell lines. The NCI TPW data are publicly available through a web interface that allows limited user interaction with the data. We developed 'TPWshiny' as a standalone, easy to install, R application to facilitate more interactive data exploration.With no programming skills required, TPWshiny provides an intuitive and comprehensive graphical interface to help researchers understand the response of tumor cell lines to 15 therapeutic agents. The data are presented in interactive scatter plots, heatmaps, time series and Venn diagrams. Data can be queried by drug concentration, time point, gene and tissue type. Researchers can download the data for further analysis. AVAILABILITY AND IMPLEMENTATION: Users can download the ready-to-use, self-extracting package for Windows or macOS, and R source code from the project website (https://brb.nci.nih.gov/TPWshiny/). TPWshiny documentation and additional information can be found on the project website.