We consider the Gaussian kernel density estimator with bandwidth $\beta^{-\frac12}$ of $n$ iid Gaussian samples. Using the Kac-Rice formula and an Edgeworth expansion, we prove that the expected number of modes on the real line scales as $\Theta(\sqrt{\beta\log\beta})$ as $\beta,n\to\infty$ provided $n^c\lesssim \beta\lesssim n^{2-c}$ for some constant $c>0$. An impetus behind this investigation is to determine the number of clusters to which Transformers are drawn in a metastable state.

Rep. Chris Smith, R-N.J., opens up about the aerial systems spotted in the Garden State on'The Story.' A U.S. Coast Guard official said one of its vessels was trailed by dozens of drones off the coast of New Jersey recently, according to Rep. Chris Smith, R-N.J. Smith, a guest on "The Story with Martha MacCallum" Tuesday, said he spent Monday night on the beach in Ocean County and spoke to several people, including a U.S. Coast Guard commanding officer stationed in Barnegat Light. Smith learned from the Coast Guard commander that the night before, "one of their 47-foot vessels, boats, was trailed very closely by more than a dozen of these drones." "Now, that to me, is very, very, not just suspicious, provocative, and this could be a foreign power, whether it be [Vladimir] Putin, or it could be Xi Jinping in China, or the Middle East, we can't rule any of that out," the congressman said. Photos taken in the Bay Shore section of Toms River of what appear to be large drones hovering in the area at high altitudes in New Jersey on Sunday, Dec. 8, 2024.

At least five people have been killed in Israeli attacks on several towns in southern Lebanon, the country's Health Ministry has said, amid a fragile ceasefire between Israel and Hezbollah. "An Israeli enemy drone strike on the town of Ainata killed one person and wounded another," the ministry said. An "Israeli strike on the town of Bint Jbeil killed three people," while a third "on Beit Lif killed one person", it added. There was no immediate comment from the Israeli military on the attacks. Israel's army escalated its attacks on Lebanon in late September after more than 11 months of cross-border exchanges of fire with the Lebanese armed group Hezbollah, which began firing rockets towards Israel after the Palestinian group Hamas's attack on southern Israel on October 7, 2023.

Can a Big Tech company be sued in Kenya for alleged abuses at an outsourcing company working on its behalf? That's the question at the heart of two lawsuits that are attempting to set a new precedent in Kenya, which is the prime destination for tech companies looking to farm out digital work to the African continent. The two-year legal battle stems from allegations of human rights violations at an outsourced Meta content moderation facility in Nairobi, where employees hired by a contractor were paid as little as 1.50 per hour to view traumatic content, such as videos of rapes, murders, and war crimes. The suits claim that despite the workers being contracted by an outsourcing company, called Sama, Meta essentially supervised and set the terms for the work, and designed and managed the software required for the task. Both companies deny wrongdoing and Meta has challenged the Kenyan courts' jurisdiction to hear the cases.

Apple has begun rolling iOS 18.2 and iPadOS 18.2 to iPhones and iPads. The updates bring with them major enhancements to the company's suite of AI features, and are likely the final software releases Apple has planned for 2024. More Apple Intelligence features are available through macOS 15.2. However, note access to all of the AI features mentioned below is limited to users in the US, Australia, Canada, New Zealand, South Africa and the UK for now, with support additionally limited to devices with their language set to English. Provided you own an iPhone 15 Pro, 16 or 16 Pro, one of the highlights of iOS 18.2 is Image Playground, which is available both as a standalone app and Messages extension.

Hosted by Ben Byford, The Machine Ethics Podcast brings together interviews with academics, authors, business leaders, designers and engineers on the subject of autonomous algorithms, artificial intelligence, machine learning, and technology's impact on society. In this episode we're chatting to Caitlin about gender and AI, that technology isn't neutral, using technology for good, diversity creation and exploitation, lived experience expertise, co-creating technologies and AI life cycle, importance of success metrics, international treaties on AI, and more… Alliance is a leader of the UN's Generation Equality Action Coalition Technology & Innovation for Gender Equality. Caitlin was co-chair of the Expert Group for the UN Commission on the Status of Women (CSW67) in 2023 with its first ever priority theme of Technology & Innovation. Caitlin leads the Human Rights Toolbox initiative, an educational platform that supports a global community working for a human rights-based approach to AI – with equity & inclusion at the core of the code. Women at the Table are a leader of the fr feminist AI research Network, with Hubs in Latin America & the Caribbean, Middle East & North Africa, SouthEastAsia, and sister network in Africa, and serves as Civil Society lead for the World Benchmarking Alliance's Collective Impact Coalition for Ethical AI.

The Global Change Analysis Model (GCAM) simulates complex interactions between the coupled Earth and human systems, providing valuable insights into the co-evolution of land, water, and energy sectors under different future scenarios. Understanding the sensitivities and drivers of this multisectoral system can lead to more robust understanding of the different pathways to particular outcomes. The interactions and complexity of the coupled human-Earth systems make GCAM simulations costly to run at scale - a requirement for large ensemble experiments which explore uncertainty in model parameters and outputs. A differentiable emulator with similar predictive power, but greater efficiency, could provide novel scenario discovery and analysis of GCAM and its outputs, requiring fewer runs of GCAM. As a first use case, we train a neural network on an existing large ensemble that explores a range of GCAM inputs related to different relative contributions of energy production sources, with a focus on wind and solar. We complement this existing ensemble with interpolated input values and a wider selection of outputs, predicting 22,528 GCAM outputs across time, sectors, and regions. We report a median $R^2$ score of 0.998 for the emulator's predictions and an $R^2$ score of 0.812 for its input-output sensitivity.

In recent years, large language models (LLMs) have demonstrated remarkable progress in common-sense reasoning tasks. This ability is fundamental to understanding social dynamics, interactions, and communication. However, the potential of integrating computers with these social capabilities is still relatively unexplored. However, the potential of integrating computers with these social capabilities is still relatively unexplored. This paper introduces MuSA, a multimodal LLM-based agent that analyzes text-rich social content tailored to address selected human-centric content analysis tasks, such as question answering, visual question answering, title generation, and categorization. It uses planning, reasoning, acting, optimizing, criticizing, and refining strategies to complete a task. Our approach demonstrates that MuSA can automate and improve social content analysis, helping decision-making processes across various applications. We have evaluated our agent's capabilities in question answering, title generation, and content categorization tasks. MuSA performs substantially better than our baselines.

Wind power forecasting plays a critical role in modern energy systems, facilitating the integration of renewable energy sources into the power grid. Accurate prediction of wind energy output is essential for managing the inherent intermittency of wind power, optimizing energy dispatch, and ensuring grid stability. This paper proposes the use of Deep Neural Network (DNN)-based predictive models that leverage climate datasets, including wind speed, atmospheric pressure, temperature, and other meteorological variables, to improve the accuracy of wind power simulations. In particular, we focus on the Coupled Model Intercomparison Project (CMIP) datasets, which provide climate projections, as inputs for training the DNN models. These models aim to capture the complex nonlinear relationships between the CMIP-based climate data and actual wind power generation at wind farms located in Germany. Our study compares various DNN architectures, specifically Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM) networks, and Transformer-enhanced LSTM models, to identify the best configuration among these architectures for climate-aware wind power simulation. The implementation of this framework involves the development of a Python package (CADNN) designed to support multiple tasks, including statistical analysis of the climate data, data visualization, preprocessing, DNN training, and performance evaluation. We demonstrate that the DNN models, when integrated with climate data, significantly enhance forecasting accuracy. This climate-aware approach offers a deeper understanding of the time-dependent climate patterns that influence wind power generation, providing more accurate predictions and making it adaptable to other geographical regions.

This paper presents OG-RAG, an Ontology-Grounded Retrieval Augmented Generation method designed to enhance LLM-generated responses by anchoring retrieval processes in domain-specific ontologies. While LLMs are widely used for tasks like question answering and search, they struggle to adapt to specialized knowledge, such as industrial workflows or knowledge work, without expensive fine-tuning or sub-optimal retrieval methods. Existing retrieval-augmented models, such as RAG, offer improvements but fail to account for structured domain knowledge, leading to suboptimal context generation. Ontologies, which conceptually organize domain knowledge by defining entities and their interrelationships, offer a structured representation to address this gap. OG-RAG constructs a hypergraph representation of domain documents, where each hyperedge encapsulates clusters of factual knowledge grounded using domain-specific ontology. An optimization algorithm then retrieves the minimal set of hyperedges that constructs a precise, conceptually grounded context for the LLM. This method enables efficient retrieval while preserving the complex relationships between entities. OG-RAG applies to domains where fact-based reasoning is essential, particularly in tasks that require workflows or decision-making steps to follow predefined rules and procedures. These include industrial workflows in healthcare, legal, and agricultural sectors, as well as knowledge-driven tasks such as news journalism, investigative research, consulting and more. Our evaluations demonstrate that OG-RAG increases the recall of accurate facts by 55% and improves response correctness by 40% across four different LLMs. Additionally, OG-RAG enables 30% faster attribution of responses to context and boosts fact-based reasoning accuracy by 27% compared to baseline methods.