Anti-POLL polyclonal antibody

Two current members of the U.S. Supreme Court took their seats despite allegations of sexual harassment (Clarence Thomas) and sexual assault (Brett Kavanaugh) leveled against them during their confirmation hearings. In each instance, the Senate vote was close and split mainly along party lines: Republicans for and Democrats against. Polls showed that a similar division existed among party supporters in the electorate. There are, however, differences among rank-and-file partisans that help shape their views on the issues raised by these two controversial appointments to the nation's highest court. Using data from a national survey of registered voters, we examine the factors associated with citizens' attitudes about the role of women in politics, the extent to which sexism is a problem in society, the recent avalanche of sexual harassment charges made against elected officials and other political (as well as entertainment, business, and academic) figures, and the #MeToo movement. We are particularly interested in whether a strong sense of partisanidentityadds significantly to our understanding of people's attitudes on these matters. In addition, our experimental evidence allows us to determine whether shared partisanship overrides other factors when an elected official from one's own party is accused of sexual misbehavior.

In this letter, we study federated learning in a cellular system with a base station (BS) and a large number of users with local data sets. We show that multichannel random access can provide a better performance than sequential polling when some users are unable to compute local updates (due to other tasks) or in dormant state. In addition, for better aggregation in federated learning, the access probabilities of users can be optimized for given local updates. To this end, we formulate an optimization problem and show that a distributed approach can be used within federated learning to adaptively decide the access probabilities.