Humanity faces numerous problems of common-pool resource appropriation.

Efficiently aggregating data from different sources is a challenging problem, particularly when samples from each source are distributed differently.

Defiant Dems receive 24/7 protection from Capitol Police after Trump accused them of'seditious behavior' and threatened them with execution What Meghan's announcements in her pseudo-Royal court get wrong and why they'speak volumes', revealed by experts Presidential hopeful is dragged into criminal probe... as shock texts emerge: 'It will open Pandora's Box' Multiple cast members speak to Daily Mail and hurl ugly allegations at each other... and reveal co-stars they can't stand Family panic as Britney Spears takes'disturbing' measures... after world was shocked by her unrecognizable new look Everybody Loves Raymond stars now unrecognizable as they reunite for sitcom's 30th anniversary Democratic candidate gives bizarre defense after comments that she'hates' Nashville resurface Private school where teacher'had sex with five students as soon as they turned 16' - and it was LEGAL Kansas City Chiefs coach slams Donald Trump in brutal putdown: 'He has no idea what's going on' Anna Kepner's ex-boyfriend claims stepbrother'climbed on top of her' months before cheerleader was found dead on cruise Bruce Willis' daughter Rumer makes heartbreaking confession about famous father's dementia battle Truth about Ariana Grande and Cynthia Erivo's'secret marriage'... and the depressing reason insiders say their friendship could soon be OVER America's most forgiving wife lists enormous $6m NYC apartment she shares with disgraced CEO caught with woman on Coldplay kisscam Kessler twins who worked with Frank Sinatra and wowed Elvis Presley'paid a lot of money' to die together at 89 A lion's roar is undeniably one of the most fearsome sounds across the entire animal kingdom. Now, it turns out these majestic creatures produce not just one, but two distinct types of roar. That's according to researchers from the University of Exeter, who have identified a brand new type of growl in African lions. The animals - often referred to as the'King of the Jungle' - are best known for their full-throated roar, an immensely powerful vocalization that can be heard up to five miles away. However, using AI, the researchers were able to identify a second type of roar, which they've called the'intermediary roar'.

Evaluating the quality of synthetic data remains a persistent challenge in the Android malware domain due to instability and the lack of standardization among existing metrics. Experiments involving ten generative models and five balanced datasets demonstrate that the Super-Metric is more stable and consistent than traditional metrics, exhibiting stronger correlations with the actual performance of classifiers. Synthetic data generation has become an increasingly relevant strategy in cybersecurity [1], [2], [3], particularly as a way to mitigate the scarcity of real, complete, and high-quality datasets that limit the performance and generalization of machine learning models. Despite these advances, assessing the quality of synthetic data remains a complex and largely non-standardized methodological challenge [4], with no clear consensus on which metrics should be used or how to combine them consistently. The literature reports a significant fragmentation in the application of fidelity metrics, with studies identifying more than 65 distinct indicators used independently to assess fidelity [5]. This diversity hinders model-to-model comparison, reduces experimental reproducibility, and complicates the integrated interpretation of data quality.

Introduction: The application of Artificial Intelligence in games has evolved significantly, allowing for dynamic content generation. However, its use as a core gameplay co-creation tool remains underexplored. Objective: This paper proposes SpellForger, a game where players create custom spells by writing natural language prompts, aiming to provide a unique experience of personalization and creativity. Methodology: The system uses a supervised-trained BERT model to interpret player prompts. This model maps textual descriptions to one of many spell prefabs and balances their parameters (damage, cost, effects) to ensure competitive integrity. The game is developed in the Unity Game Engine, and the AI backend is in Python. Expected Results: W e expect to deliver a functional prototype that demonstrates the generation of spells in real time, applied to an engaging gameplay loop, where player creativity is central to the experience, validating the use of AI as a direct gameplay mechanic.

Binary options trading is often marketed as a field where predictive models can generate consistent profits. However, the inherent randomness and stochastic nature of binary options make price movements highly unpredictable, posing significant challenges for any forecasting approach. This study demonstrates that machine learning algorithms struggle to outperform a simple baseline in predicting binary options movements. Using a dataset of EUR/USD currency pairs from 2021 to 2023, we tested multiple models, including Random Forest, Logistic Regression, Gradient Boosting, and k-Nearest Neighbors (kNN), both before and after hyperparameter optimization. Furthermore, several neural network architectures, including Multi-Layer Perceptrons (MLP) and a Long Short-Term Memory (LSTM) network, were evaluated under different training conditions. Despite these exhaustive efforts, none of the models surpassed the ZeroR baseline accuracy, highlighting the inherent randomness of binary options. These findings reinforce the notion that binary options lack predictable patterns, making them unsuitable for machine learning-based forecasting.

The classic Mallows model is a widely-used tool to realize distributions on permutations.

Latent Dirichlet Allocation (LDA) is a very popular model for topic modeling as well as many other problems with latent groups. It is both simple and effective. When the number of topics (or latent groups) is unknown, the Hierarchical Dirichlet Process (HDP) provides an elegant non-parametric extension; however, it is a complex model and it is difficult to incorporate prior knowledge since the distribution over topics is implicit. We propose two new models that extend LDA in a simple and intuitive fashion by directly expressing a distribution over the number of topics. We also propose a new online Bayesian moment matching technique to learn the parameters and the number of topics of those models based on streaming data. The approach achieves higher log-likelihood than batch and online HDP with fixed hyperparameters on several corpora. The code is publicly available at https://github.com/whsu/bmm .

Event datasets involve irregular occurrences of events over the timeline and are prevalent in numerous domains.

Neural Information Processing Systems http://nips.cc/