A troubling trend has emerged in schools across the United States, with young students falling victim to the increasing use of artificial intelligence (AI)-powered "nudify" apps that have the power to create fake pornography of classmates. "Nudify" is an umbrella term referring to a plethora of widely available apps and websites that allow users to alter photos of full-dressed individuals and virtually undress them. Some apps can create nude images with just a headshot of the victim. Don Austin, the superintendent of the Palo Alto Unified School District, told Fox News Digital that this type of online harassment can be more relentless compared to traditional in-person bullying. "It used to be that a bully had to come over and push you. Palo Alto is not a community where people are going to come push anybody into a locker. But it's not immune from online bullying," Austin said.

Stanley Zhong had a 4.42 grade point average, a nearly perfect SAT score, had bested adults in competitive coding competitions and started his own electronic signing service all while still in high school. When it came time to apply to colleges, Zhong's family wasn't overly concerned about his prospects even amid an increasingly competitive admissions environment. But, by the end of his senior year in Palo Alto in 2023, Zhong received rejection letters to 16 of the 18 colleges where he applied, including five University of California campuses that his father had figured would be safety schools. "It was surprise upon surprise upon surprise, and then it turned into frustration and, eventually, anger," his father, Nan Zhong, told The Times in a recent interview. "And I think both Stanley and I felt the same way, that something is really funky here."

Code: We release a PyPI package, stark-qa (https://pypi.org/project/stark-qa/). The Croissant metadata for our dataset is available for viewing and downloading at https://stark.stanford.edu/files/croissant_metadata.json. We provide a persistent dereferenceable identifier DOI: https://doi.org/10.57967/hf/2530. RK retrieval datasets are under license CC-BY-4.0 as stated in our website. And our released code is under MIT license, as stated in the GitHub repository. We plan to update our website with the most recent document and Python package. We will maintain our GitHub repository will pull requests and open issues. We hereby confirm that we bear all responsibility for any violation of rights that may occur in the use or distribution of the data and content presented in this work. We affirm that we have obtained all necessary permissions and licenses for the data and content included in this work. We confirm that the use of this data complies with all relevant laws and regulations, and we take full responsibility for addressing any claims or disputes that may arise regarding rights violations or licensing issues.

OGB datasets are large-scale, encompass multiple important graph ML tasks, and cover a diverse range of domains, ranging from social and information networks to biological networks, molecular graphs, source code ASTs, and knowledge graphs. For each dataset, we provide a unified evaluation protocol using meaningful application-specific data splits and evaluation metrics. In addition to building the datasets, we also perform extensive benchmark experiments for each dataset. Our experiments suggest that OGB datasets present significant challenges of scalability to large-scale graphs and out-of-distribution generalization under realistic data splits, indicating fruitful opportunities for future research. Finally, OGB provides an automated end-to-end graph ML pipeline that simplifies and standardizes the process of graph data loading, experimental setup, and model evaluation. OGB will be regularly updated and welcomes inputs from the community. OGB datasets as well as data loaders, evaluation scripts, baseline code, and leaderboards are publicly available at https://ogb.stanford.edu.

The detailed description of each data point's entries is as follows. " query ": " What is the weather in Palo Alto?", In this example, the query asks about the current weather in Palo Alto. Here's an example JSON data for the parallel function-calling category, i.e., the user's query contains " query ": " Find the sum of all the multiples of 3 and 5 " description ": " Find the sum of all multiples of " description ": " The numbers to find multiples of.", " description ": " Find the product of the first n prime This step helps to filter out poorly formatted or incomplete data points. B.1 Generator LLM Prompt Example Prompt for the Generator to Generate Parallel Function-Calling Data """ You are a data labeler.

Our dataset consists of a novel task suite comprising summarization, perception (recall, tracking), visual reasoning (spatial, temporal, predictive, causal, counterfactual), and navigation (room-to-room, object retrieval) tasks. HourVideo includes 500 manually curated egocentric videos from the Ego4D dataset, spanning durations of 20 to 120 minutes, and features 12,976 high-quality, five-way multiple-choice questions. Benchmarking results reveal that multimodal models, including GPT-4 and LLaVA-NeXT, achieve marginal improvements over random chance. In stark contrast, human experts significantly outperform the state-of-the-art long-context multimodal model, Gemini Pro 1.5 (85.0% vs. 37.3%), highlighting a substantial gap in multimodal capabilities. Our benchmark, evaluation toolkit, prompts, and documentation are available at hourvideo.stanford.edu.

This paper addresses the critical challenge of optimizing electric vehicle charging station placement through a novel data-driven methodology employing causal discovery techniques. While traditional approaches prioritize economic factors or power grid constraints, they often neglect empirical charging patterns that ultimately determine station utilization. We analyze extensive charging data from Palo Alto and Boulder (337,344 events across 100 stations) to uncover latent relationships between station characteristics and utilization. Applying structural learning algorithms (NOTEARS and DAGMA) to this data reveals that charging demand is primarily determined by three factors: proximity to amenities, EV registration density, and adjacency to high-traffic routes. These findings, consistent across multiple algorithms and urban contexts, challenge conventional infrastructure distribution strategies. We develop an optimization framework that translates these insights into actionable placement recommendations, identifying locations likely to experience high utilization based on the discovered dependency structures. The resulting site selection model prioritizes strategic clustering in high-amenity areas with substantial EV populations rather than uniform spatial distribution. Our approach contributes a framework that integrates empirical charging behavior into infrastructure planning, potentially enhancing both station utilization and user convenience. By focusing on data-driven insights instead of theoretical distribution models, we provide a more effective strategy for expanding charging networks that can adjust to various stages of EV market development.

All the datasets used in our paper are publicly available and are to be used for research purposes. To the best of our knowledge, they do not have any personally identifiable information or offensive content. Table 1 gives the download links and licenses of these datasets. Use is restricted to non-commercial research and educational purposes CUB-Families (2) https://github.com/HCPLab-SYSU/HS Use is restricted to non-commercial research and educational purposes Tiny ImageNet http://cs231n.stanford.edu/tiny-imagenet-200.zip Use is restricted to non-commercial research and educational purposes We use the samples collected in (3) as the image repository for CUB and CUB-Families.