Fox News contributor Raymond Arroyo sounds off on Rosie The Pivoter ODonnell for her latest criticism of the Trump administration and the NEA teacher of the years admission that the job is deeply political. President Donald Trump has escalated his long-running feud with Rosie O'Donnell. On Saturday, Trump, 79, floated the idea of revoking the 63-year-old comedian and actress's U.S. citizenship following her move to Ireland earlier this year. "Because of the fact that Rosie O'Donnell is not in the best interests of our Great Country, I am giving serious consideration to taking away her Citizenship," Trump wrote in a post to his social media platform Truth Social. "She is a Threat to Humanity, and should remain in the wonderful Country of Ireland, if they want her. GOD BLESS AMERICA!" he added.

Try to book a train ticket on Amtrak's home page and you might just swear off train travel. A recent attempt to book a journey from New York to Boston, for example, required toggling between windows, looking up obscure station codes, and waiting for slow page repaints. When the order was finally submitted, an error message came up noting that, due to technical difficulties, the request could not be processed. If only Amtrak's Web designers were as attentive as the makers of the railroad's telephone self-service system. That system, which features the digitized voice of an operator named Julie, is a primer on good customer service.

A highly comparative, feature-based approach to time series classification is introduced that uses an extensive database of algorithms to extract thousands of interpretable features from time series. These features are derived from across the scientific time-series analysis literature, and include summaries of time series in terms of their correlation structure, distribution, entropy, stationarity, scaling properties, and fits to a range of time-series models. After computing thousands of features for each time series in a training set, those that are most informative of the class structure are selected using greedy forward feature selection with a linear classifier. The resulting feature-based classifiers automatically learn the differences between classes using a reduced number of time-series properties, and circumvent the need to calculate distances between time series. Representing time series in this way results in orders of magnitude of dimensionality reduction, allowing the method to perform well on very large datasets containing long time series or time series of different lengths. For many of the datasets studied, classification performance exceeded that of conventional instance-based classifiers, including one nearest neighbor classifiers using Euclidean distances and dynamic time warping and, most importantly, the features selected provide an understanding of the properties of the dataset, insight that can guide further scientific investigation.

This paper presents a new method, Minimax Tree Optimization (MMTO), to learn a heuristic evaluation function of a practical alpha-beta search program. The evaluation function may be a linear or non-linear combination of weighted features, and the weights are the parameters to be optimized. To control the search results so that the move decisions agree with the game records of human experts, a well-modeled objective function to be minimized is designed. Moreover, a numerical iterative method is used to nd local minima of the objective function, and more than forty million parameters are adjusted by using a small number of hyper parameters. This method was applied to shogi, a major variant of chess in which the evaluation function must handle a larger state space than in chess. Experimental results show that the large-scale optimization of the evaluation function improves the playing strength of shogi programs, and the new method performs signicantly better than other methods. Implementation of the new method in our shogi program Bonanza made substantial contributions to the program's rst-place nish in the 2013 World Computer Shogi Championship. Additionally, we present preliminary evidence of broader applicability of our method to other two-player games such as chess.