President Donald Trump says he will speak with Vladimir Putin "very soon" as the U.S. tracks reports of a suspected pneumonic plague death in Russia's Siberia region.

US'watching closely' after plague researcher dies in Russia, Rubio says To play this video you need to enable JavaScript in your browser. The US says it is closely monitoring the case of a Russian lab technician who died at a plague research centre in Siberia. Secretary of State Marco Rubio said there was concern the incident could have led to the spread of pneumonic plague, but that there was no cause for alarm. Announcing the cause of death as pneumonia of undetermined etiology, Irkutsk's governor said that no micro-organisms linked to the woman's work had been found in her biological samples. Unconfirmed reports had suggested the woman fell ill either after breaking a test tube containing the pathogen or following a field trip to a region where plague is found naturally among wild rodents. No other cases of staff from the plague institute seeking medical attention since her death had been reported, Irkutsk governor Igor Kobzev said, quoting Russia's consumer and health watchdog, Rospotrebnadzor.

Asynchronous Bayesian optimization is widely used for gradient-free optimization in domains with independent parallel experiments and varying evaluation times. Existing methods posit that standard acquisitions lead to redundant and repeated queries, proposing complex solutions to enforce diversity in queries. Challenging this fundamental premise, we show that methods, like the Upper Confidence Bound, can in fact achieve theoretical guarantees essentially equivalent to those of sequential Thompson sampling. A conceptual analysis of asynchronous Bayesian optimization reveals that existing works neglect intermediate posterior updates, which we find to be generally sufficient to avoid redundant queries. Further investigation shows that by penalizing busy locations, diversity-enforcing methods can over-explore in asynchronous settings, reducing their performance. Our extensive experiments demonstrate that simple standard acquisition functions match or outperform purpose-built asynchronous methods across synthetic and real-world tasks.

Monte Carlo stopping rules in a manner that is both sample efficient and robust to estimation error.

Social conflict is a survival mechanism yielding both normal and pathological behaviors. Tounderstand its underlying principles, we collected behavioral and whole-brain neural data from mice advancing through stages of social conflict.

We study adversarial perturbations when the instances are uniformly distributed over{0,1}n.