We consider a population, partitioned into a set of communities, and study the problem of identifying the largest community within the population via sequential, random sampling of individuals. There are multiple sampling domains, referred to as \emph{boxes}, which also partition the population. Each box may consist of individuals of different communities, and each community may in turn be spread across multiple boxes. The learning agent can, at any time, sample (with replacement) a random individual from any chosen box; when this is done, the agent learns the community the sampled individual belongs to, and also whether or not this individual has been sampled before. The goal of the agent is to minimize the probability of mis-identifying the largest community in a \emph{fixed budget} setting, by optimizing both the sampling strategy as well as the decision rule. We propose and analyse novel algorithms for this problem, and also establish information theoretic lower bounds on the probability of error under any algorithm. In several cases of interest, the exponential decay rates of the probability of error under our algorithms are shown to be optimal up to constant factors. The proposed algorithms are further validated via simulations on real-world datasets.

Possibilistic logic is the most popular approach to represent and reason with uncertain and partially inconsistent knowledge. Regarding normal forms, the encoding of real-world problems does usually not result in a clausal formula and although a possibility nonclausal formula is theoretically equivalent to some possibilistic clausal formula [26, 22], approaches needing clausal form transformations are practically infeasible or have experimentally shown to be highly inefficient as discussed below. Two kinds of clausal form transformation are known: (1) one is based on the repetitive application of the distributive laws to the input non-clausal formula until a logically equivalent clausal formula is obtained; and (2) the other transformation, Tsetin-transformation [59], is based on recursively substituting sub-formulas in the input non-clausal formula by fresh literals until obtaining an equi-satisfiable, but not equivalent, clausal formula.

Introduction: This report is created for the benefit of strategic planners who seek in-depth study of the Global Machine Learning in Medicine Market . It is compiled for the sake of organizations considering Machine Learning in Medicine industry and those who want to boost their market value from their existing investments. With the advent of globalization of the Machine Learning in Medicine industry, market insights about the continents, countries, regions, as well as cities become the most important criteria while prioritizing markets. The consumption patterns, customer and supplier bargaining power and the structural analysis of the application fields is given in the study. This report covers top 200 countries and other entities operating in the market.

Latin america It is one of the world's most advanced areas of open science and this is due to the "need" of its experts to continue a long tradition of research with low resources to develop it. This was stated by Guillermo Anlo, Regional Head of UNESCO's Scientific, Technological and Innovation Policy Programme, in the framework of the International Science Day and at the full celebration in Paris of the General Conference of that UNESCO agency. HIM-HER-ITFrom which global agreements on open science and artificial intelligence are expected to emerge. The Argentine expert explains: "The region has a strong and long tradition in the scientific community, but with few resources and investments, so it has struggled very hard for this synergy and cooperation," which notes that outside Europe and the United States, it is the "great hubs of science," America has advanced Latin "necessarily". Cielo Networks and Latindex or the Clacso Foundation (Latin American Council for the Social Sciences), promoted by UNESCO itself, are examples of shared spaces accessible to the public and open to content that are a milestone in the collaborative sciences of the region.

Introduction: This report is created for the benefit of strategic planners who seek in-depth study of the Global Military Artificial Intelligence (AI) Market . It is compiled for the sake of organizations considering Military Artificial Intelligence (AI) industry and those who want to boost their market value from their existing investments. With the advent of globalization of the Military Artificial Intelligence (AI) industry, market insights about the continents, countries, regions, as well as cities become the most important criteria while prioritizing markets. The consumption patterns, customer and supplier bargaining power and the structural analysis of the application fields is given in the study. This report covers top 200 countries and other entities operating in the market.

The feasibility of making profitable trades on a single asset on stock exchanges based on patterns identification has long attracted researchers. Reinforcement Learning (RL) and Natural Language Processing have gained notoriety in these single-asset trading tasks, but only a few works have explored their combination. Moreover, some issues are still not addressed, such as extracting market sentiment momentum through the explicit capture of sentiment features that reflect the market condition over time and assessing the consistency and stability of RL results in different situations. Filling this gap, we propose the Sentiment-Aware RL (SentARL) intelligent trading system that improves profit stability by leveraging market mood through an adaptive amount of past sentiment features drawn from textual news. We evaluated SentARL across twenty assets, two transaction costs, and five different periods and initializations to show its consistent effectiveness against baselines. Subsequently, this thorough assessment allowed us to identify the boundary between news coverage and market sentiment regarding the correlation of price-time series above which SentARL's effectiveness is outstanding.

We present a novel method for reducing the computational complexity of rigorously estimating the partition functions (normalizing constants) of Gibbs (Boltzmann) distributions, which arise ubiquitously in probabilistic graphical models. A major obstacle to practical applications of Gibbs distributions is the need to estimate their partition functions. The state of the art in addressing this problem is multi-stage algorithms, which consist of a cooling schedule, and a mean estimator in each step of the schedule. While the cooling schedule in these algorithms is adaptive, the mean estimation computations use MCMC as a black-box to draw approximate samples. We develop a doubly adaptive approach, combining the adaptive cooling schedule with an adaptive MCMC mean estimator, whose number of Markov chain steps adapts dynamically to the underlying chain. Through rigorous theoretical analysis, we prove that our method outperforms the state of the art algorithms in several factors: (1) The computational complexity of our method is smaller; (2) Our method is less sensitive to loose bounds on mixing times, an inherent component in these algorithms; and (3) The improvement obtained by our method is particularly significant in the most challenging regime of high-precision estimation. We demonstrate the advantage of our method in experiments run on classic factor graphs, such as voting models and Ising models.

International economics has a long history of improving our understanding of factors causing trade, and the consequences of free flow of goods and services across countries. The recent shocks to the free trade regime, especially trade disputes among major economies, as well as black swan events, such as trade wars and pandemics, raise the need for improved predictions to inform policy decisions. AI methods are allowing economists to solve such prediction problems in new ways. In this manuscript, we present novel methods that predict and associate food and agricultural commodities traded internationally. Association Rules (AR) analysis has been deployed successfully for economic scenarios at the consumer or store level, such as for market basket analysis. In our work however, we present analysis of imports and exports associations and their effects on commodity trade flows. Moreover, Ensemble Machine Learning methods are developed to provide improved agricultural trade predictions, outlier events' implications, and quantitative pointers to policy makers.

Participatory Budgeting (PB) is a process in which voters decide how to allocate a common budget; most commonly it is done by ordinary people -- in particular, residents of some municipality -- to decide on a fraction of the municipal budget. From a social choice perspective, existing research on PB focuses almost exclusively on designing computationally-efficient aggregation methods that satisfy certain axiomatic properties deemed "desirable" by the research community. Our work complements this line of research through a user study (N = 215) involving several experiments aimed at identifying what potential voters (i.e., non-experts) deem fair or desirable in simple PB settings. Our results show that some modern PB aggregation techniques greatly differ from users' expectations, while other, more standard approaches, provide more aligned results. We also identify a few possible discrepancies between what non-experts consider \say{desirable} and how they perceive the notion of "fairness" in the PB context. Taken jointly, our results can be used to help the research community identify appropriate PB aggregation methods to use in practice.

NVIDIA has set the stage for businesses worldwide to design and deploy large language models (LLMs). This design enables them to develop domain-specific chatbots, personal assistants, and other artificial intelligence systems. The firm announced the NVIDIA NeMo Megatron framework for training trillion-parameter language models. In addition, NVIDIA Triton Inference Server offers multi-node distributed inference features for new domains and languages. When used in conjunction with NVIDIA DGX systems, these technologies provide an enterprise-grade solution for simplifying the construction and deployment of massive language models.