We present Hybrid Infused Reranking for Passages Retrieval (HYRR), a framework for training rerankers based on a hybrid of BM25 and neural retrieval models. Retrievers based on hybrid models have been shown to outperform both BM25 and neural models alone. Our approach exploits this improved performance when training a reranker, leading to a robust reranking model. The reranker, a cross-attention neural model, is shown to be robust to different first-stage retrieval systems, achieving better performance than rerankers simply trained upon the first-stage retrievers in the multi-stage systems. We present evaluations on a supervised passage retrieval task using MS MARCO and zero-shot retrieval tasks using BEIR. The empirical results show strong performance on both evaluations.

Major League Baseball will "likely" introduce an Automated Strike Zone System starting in 2024, commissioner Rob Manfred told ESPN. These robot umpires may call all balls and strikes then relay the information to a plate umpire, or be part of a replay review system that allows managers to challenge calls. The comments come following outrage over umpires' missed calls in recent games, including a brutal low strike error during a Detroit Tigers and Minnesota Twins game. MLB has been experimenting with robo-umpires in the Atlantic Triple-A minor league since 2019, using similar technology to golf speed-measurement devices. There may be other benefits to introducing the tech.

Major League Baseball will "likely" introduce an Automated Strike Zone System starting in 2024, commissioner Rob Manfred told ESPN. The so-called robot umpires may call all balls and strikes then relay the information to a plate umpire, or be part of a replay review system that allows managers to challenge calls. "We have an automated strike zone system that works," Manfred said. The comments come in the wake of fan outrage over umpire's missed calls in recent games, including a brutal low strike error during a Detroit Tigers and Minnesota Twins tilt. Give me robo umps already," tweeted Grand Rapids ABC sports director Jamal Spencer. MLB has been experimenting with robo umps in minor league Atlantic Triple-A league since 2019.

Player performance prediction is a serious problem in every sport since it brings valuable future information for managers to make important decisions. In baseball industries, there already existed variable prediction systems and many types of researches that attempt to provide accurate predictions and help domain users. However, it is a lack of studies about the predicting method or systems based on deep learning. Deep learning models had proven to be the greatest solutions in different fields nowadays, so we believe they could be tried and applied to the prediction problem in baseball. Hence, the predicting abilities of deep learning models are set to be our research problem in this paper. As a beginning, we select numbers of home runs as the target because it is one of the most critical indexes to understand the power and the talent of baseball hitters. Moreover, we use the sequential model Long Short-Term Memory as our main method to solve the home run prediction problem in Major League Baseball. We compare models' ability with several machine learning models and a widely used baseball projection system, sZymborski Projection System. Our results show that Long Short-Term Memory has better performance than others and has the ability to make more exact predictions. We conclude that Long Short-Term Memory is a feasible way for performance prediction problems in baseball and could bring valuable information to fit users' needs.

The 2019 Major League Baseball (MLB) postseason is here after an exhilarating regular season in which fans saw many exciting new developments. MLB and Amazon Web Services (AWS) teamed up to develop and deliver three new, real-time machine learning (ML) stats to MLB games: Stolen Base Success Probability, Shift Impact, and Pitcher Similarity Match-up Analysis. These features are giving fans a deeper understanding of America's pastime through Statcast AI, MLB's state-of-the-art technology for collecting massive amounts of baseball data and delivering more insights, perspectives, and context to fans in every way they're consuming baseball games. This post looks at the role machine learning plays in providing fans with deeper insights into the game. We also provide code snippets that show the training and deployment process behind these insights on Amazon SageMaker.

Everybody digs the long ball. If you struck out that many times back in the day, your (butt) would be back in the minor leagues,

J. Do RAN 433 22 Chromosome classification and segmentation as exercises in knowing what to expect.

Machine intelligence problems are sometimes defined as those problems which (i) computers can't yet do, and (ii) humans can. We shall further consider how much "knowledge" about a finite mathematical function can, on certain assumptions, be credited to a computer program. Although our approach is quite general, we are really only interested in programs which evaluate "semi-hard" functions, believing that the evaluation of such functions constitutes the defining aspiration of machine intelligence work. If a function is less hard than "semi-hard," then we can evaluate it by pure algorithm (trading space for time) or by pure look-up (making the opposite trade), with no need to talk of knowledge, advice, machine intelligence, or any of those things. We call such problems "standard." If however the function is "semi-hard," then we will be driven to construct some form of artful compromise between the two representations: without such a compromise the function will not be evaluable within practical resource limits. If the function is harder than "semi-hard," i.e. is actually "hard," then no amount of compromise can ever make feasible its evaluation by any terrestrial device. "Hard" problems In a recent lecture Knuth (1976) called attention to the notion of a "hard" problem as one for which solutions are computable in the theoretical sense but 151 MEASUREMENT OF KNOWLEDGE For illustration he referred to the task, studied by Meyer and Stockmeyer, of determining the truth-values of statements about whole numbers expressed in a restricted logical symbolism, for example Vx Vy(y. But is the problem nevertheless in some important sense "hard?" Meyer and Stockmeyer showed that if we allow input expressions to be as long as only 617 symbols then the answer is "yes," reckoning "hardness" as follows: find an evaluation algorithm expressed as an electrical network of gates and registers such as to minimise the number of components; if this number exceeds the number of elementary particles in the observable Universe (say, 10125), then the problem is "hard."