Meanwhile its Audacious Project --a funding initiative that gives mature nonprofits the opportunity to pitch "moonshot" plans to a coalition of philanthropists--has raised over $1 billion in each of the last two years, in an epic Robin Hood operation for a handful of large-scale projects on climate, health, education, and criminal justice: The Audacious recipients here this year are taking this brief break from their work preventing 16 million unsafe abortions, helping governments in 20 countries prevent lead poisoning, or intercepting 5 percent of the world's river-borne plastic before it reaches the ocean.

For all authors... (a) Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope? While this could potentially guide practitioners to improve classification and mixture proportion estimation in applications where negative unlabeled data is not available but unlabeled data is abundant, we do not believe that it will fundamentally impact how machine learning is used in a way that could conceivably be socially salient. If you used crowdsourcing or conducted research with human subjects... (a) Did you include the full text of instructions given to participants and screenshots, if applicable? [N/A] (b) Did you describe any potential participant risks, with links to Institutional Review Board (IRB) approvals, if applicable? [N/A] (c) Did you include the estimated hourly wage paid to participants and the total amount spent on participant compensation? The proof primarily involves using DKW inequality [15] on pqupcqand pqppcqto show convergence to their respective means qupcqand qppcq. The main idea of the proof is to use the confidence bound derived in Lemma 1 at pcand use the fact that pcminimizes the upper confidence bound. The proof is split into two parts.

Given only positive examples and unlabeled examples (from both positive and negative classes), we might hope nevertheless to estimate an accurate positiveversus-negative classifier. Formally, this task is broken down into two subtasks: (i) Mixture Proportion Estimation(MPE)--determining the fraction of positive examples in the unlabeled data; and (ii)PU-learning--given such an estimate, learning the desired positive-versus-negative classifier.

"Eddington" is a slog, but a slog with ambitions--and its director and screenwriter, Ari Aster, is savvy enough to cultivate an air of mystery about what those ambitions are. His earlier chillers, "Hereditary" (2018) and "Midsommar" (2019), had their labyrinthine ambiguities, too, but they also had propulsive craft and cunning, plus a resolute commitment to scaring us stupid. Then came the ungainly "Beau Is Afraid" (2023), a cavalcade of Oedipal neuroses both showy and coy, in which Aster didn't seem to lose focus so much as sacrifice it on the altar of auteurism. With "Eddington," his high-minded unravelling continues. No longer a horror wunderkind, Aster, at thirty-nine, yearns to be an impish anatomist of the body politic.

In today's digital environment, the rapid propagation of fake news via social networks poses significant social challenges. Most existing detection methods either employ traditional classification models, which suffer from low interpretability and limited generalization capabilities, or craft specific prompts for large language models (LLMs) to produce explanations and results directly, failing to leverage LLMs' reasoning abilities fully. Inspired by the saying that "truth becomes clearer through debate," our study introduces a novel multi-agent system with LLMs named TruEDebate (TED) to enhance the interpretability and effectiveness of fake news detection. TED employs a rigorous debate process inspired by formal debate settings. Central to our approach are two innovative components: the DebateFlow Agents and the InsightFlow Agents. The DebateFlow Agents organize agents into two teams, where one supports and the other challenges the truth of the news. These agents engage in opening statements, cross-examination, rebuttal, and closing statements, simulating a rigorous debate process akin to human discourse analysis, allowing for a thorough evaluation of news content. Concurrently, the InsightFlow Agents consist of two specialized sub-agents: the Synthesis Agent and the Analysis Agent. The Synthesis Agent summarizes the debates and provides an overarching viewpoint, ensuring a coherent and comprehensive evaluation. The Analysis Agent, which includes a role-aware encoder and a debate graph, integrates role embeddings and models the interactions between debate roles and arguments using an attention mechanism, providing the final judgment.

Emotion recognition in conversation (ERC) has been attracting attention by methods for modeling multi-turn contexts. The multi-turn input to a pretraining model implicitly assumes that the current turn and other turns are distinguished during the training process by inserting special tokens into the input sequence. This paper proposes a priority-based attention method to distinguish each turn explicitly by adding dialogue features into the attention mechanism, called Turn Emphasis with Dialogue (TED). It has a priority for each turn according to turn position and speaker information as dialogue features. It takes multi-head self-attention between turn-based vectors for multi-turn input and adjusts attention scores with the dialogue features. We evaluate TED on four typical benchmarks. The experimental results demonstrate that TED has high overall performance in all datasets and achieves state-of-the-art performance on IEMOCAP with numerous turns.

Symbolic regression is a machine learning technique, and it has seen many advancements in recent years, especially in genetic programming approaches (GPSR). Furthermore, it has been known for many years that constant optimization of parameters, during the evolutionary search, greatly increases GPSR performance However, different authors approach such tasks differently and no consensus exists regarding which methods perform best. In this work, we evaluate eight different parameter optimization methods, applied during evolutionary search, over ten known benchmark problems, in two different scenarios. We also propose using an under-explored metric called Tree Edit Distance (TED), aiming to identify symbolic accuracy. In conjunction with classical error measures, we develop a combined analysis of model performance in symbolic regression. We then show that different constant optimization methods perform better in certain scenarios and that there is no overall best choice for every problem. Finally, we discuss how common metric decisions may be biased and appear to generate better models in comparison.

When Chinese developer Game Science revealed its debut console game Black Myth: Wukong last year, it immediately caused a stir. Inspired by the great 16th-century Chinese novel, Journey to the West, the action-packed footage featured the titular mythological monkey Sun Wukong battling Buddhist-folklore demons and sword-wielding anthropomorphic foxes in lusciously rendered forests. Smartphone games are inordinately popular in China, but console game developers are still few and far between, and the excitement for Wukong in Game Science's homeland reached fever pitch. Within 24 hours, the trailer racked up 2m views on YouTube and more than 10m on Chinese video sharing site Bilibili, much to its creators' shock and delight. One excited fan even broke into the developer's office, desperate for more info on the game.