Automated fact-checking is a crucial task in the governance of internet content. Although various studies utilize advanced models to tackle this issue, a significant gap persists in addressing complex real-world rumors and deceptive claims. To address this challenge, this paper explores the novel task of flaw-oriented fact-checking, including aspect generation and flaw identification. We also introduce RefuteClaim, a new framework designed specifically for this task. Given the absence of an existing dataset, we present FlawCheck, a dataset created by extracting and transforming insights from expert reviews into relevant aspects and identified flaws. The experimental results underscore the efficacy of RefuteClaim, particularly in classifying and elucidating false claims.

Forecasting electricity demand plays a critical role in ensuring reliable and cost-efficient operation of the electricity supply. With the global transition to distributed renewable energy sources and the electrification of heating and transportation, accurate load forecasts become even more important. While numerous empirical studies and a handful of review articles exist, there is surprisingly little quantitative analysis of the literature, most notably none that identifies the impact of factors on forecasting performance across the entirety of empirical studies. In this article, we therefore present a Meta-Regression Analysis (MRA) that examines factors that influence the accuracy of short-term electricity load forecasts. We use data from 421 forecast models published in 59 studies. While the grid level (esp. individual vs. aggregated vs. system), the forecast granularity, and the algorithms used seem to have a significant impact on the MAPE, bibliometric data, dataset sizes, and prediction horizon show no significant effect. We found the LSTM approach and a combination of neural networks with other approaches to be the best forecasting methods. The results help practitioners and researchers to make meaningful model choices. Yet, this paper calls for further MRA in the field of load forecasting to close the blind spots in research and practice of load forecasting.

What is the best simulation tool for robotics? This is a hard question to answer because many people (or their companies) specialize in one tool or another. Some simulators are better at one aspect of robotics than at others. When I'm asked to recommend the best simulation tool for robotics I have to find an expert and hope that they are current and across a wide range of simulation tools in order to give me the best advice, which was why I took particular note of the recent review paper from Australia's CSIRO, "A Review of Physics Simulators for Robotics Applications" by Jack Collins, Shelvin Chand, Anthony Vanderkop, and David Howard, published in IEEE Access (Volume: 9). "We have compiled a broad review of physics simulators for use within the major fields of robotics research. More specifically, we navigate through key sub-domains and discuss the features, benefits, applications and use-cases of the different simulators categorised by the respective research communities. Our review provides an extensive index of the leading physics simulators applicable to robotics researchers and aims to assist them in choosing the best simulator for their use case."

What is a genetic algorithm? Bayesian inference ([1] links to particle methods in Bayesian statistics and hidden Markov chain models and [2] a tutorial on genetic particle models) Bioinformatics multiple sequence alignment.[1] SAGA is available on:.[4] Bioinformatics: Motif Discovery.[5] Calculation of bound states and local-density approximations. Code-breaking, using the GA to search large solution spaces of ciphers for the one correct decryption.[8]

You are right that the algorithms in Pluribus are totally different than reinforcement learning or MCTS. At a high level, that is because our settings are 1) games, that is, there is more than one player, and 2) of imperfect information, that is, when a player has to choose an action, the player does not know the entire state of the world. There is no good textbook on solving imperfect-information games. So, to read up on this literature, you will need to read research papers. Below in this post are selected papers from my research group that would be good to read given that you want to learn about this field.

This is a mostly auto-generated list of review articles on machine learning and artificial intelligence that are on arXiv. Although some of them were written for a specific technical audience or application, the techniques described are nonetheless generally relevant. The list is sorted reverse chronologically. It was generated on 2019-01-24. A rememberable short link to this page is https://j.mp/ml-reviews.

This article records the results of an experiment in which a survey of AI and expert systems (ES) literature was attempted using Science Citation Indexes. The survey identified a sample of authors and institutions that have had a significant impact on the historical development of AI and ES. However, it also identified several glaring problems with using Science Citation Indexes as a method of comprehensively studying a body of scientific research. Accordingly, the reader is cautioned against using the results presented here to conclude that author A is a better or worse AI researcher than author B.