Flows in inkjet-printed aqueous rivulets

We used optical microscopy to investigate flows inside water rivulets that were inkjet-printed onto different surfaces and under different ambient conditions. The acquired fluid dynamics videos were submitted to the 2013 Gallery of Fluid Motion.Comment: This article accompanies a fluid dynamics video submitted to the 2013 Gallery of Fluid Motion of the 66th Annual Meeting of the APS Division of Fluid Dynamic

Valuation of knowledge and ignorance in mesolimbic reward circuitry

The pursuit of knowledge is a basic feature of human nature. However, in domains ranging from health to finance people sometimes choose to remain ignorant. Here, we show that valence is central to the process by which the human brain evaluates the opportunity to gain information, explaining why knowledge may not always be preferred. We reveal that the mesolimbic reward circuitry selectively treats the opportunity to gain knowledge about future favorable outcomes, but not unfavorable outcomes, as if it has positive utility. This neural coding predicts participants’ tendency to choose knowledge about future desirable outcomes more often than undesirable ones, and to choose ignorance about future undesirable outcomes more often than desirable ones. Strikingly, participants are willing to pay both for knowledge and ignorance as a function of the expected valence of knowledge. The orbitofrontal cortex (OFC), however, responds to the opportunity to receive knowledge over ignorance regardless of the valence of the information. Connectivity between the OFC and mesolimbic circuitry could contribute to a general preference for knowledge that is also modulated by valence. Our findings characterize the importance of valence in information seeking and its underlying neural computation. This mechanism could lead to suboptimal behavior, such as when people reject medical screenings or monitor investments more during bull than bear markets

Woods, Tiger; and Yamaguchi, Kristi.

Contributions by Howard J. Bromberg to Great Lives from History: Asian and Pacific Islander Americans, a collection of short biographical essays

Trump, Donald: Environmental Policy of,

Businessman and US. president Donald John Trump was born in Queens, New York, to Frederick (Fred) Trump and Mary MacLeod. Fred Trump, a real estate developer, brought Donald into the family real estate business. Through his business operations, Trump became a billionaire. Donald also became a television celebrity with the reality show The Apprentice. In one of the most unpredictable elections in American history, Trump became the 45th president of the United States. His administration aggressively promoted development of oil, gas, mineral, and coal resources. In doing so, he revoked numerous environmental protections

Aeterni Patris; Infallibility; O\u27Connor, Flannery; Papal Documents

Encyclopedia entries written by Howard J. Bromberg

The IBMAP approach for Markov networks structure learning

In this work we consider the problem of learning the structure of Markov networks from data. We present an approach for tackling this problem called IBMAP, together with an efficient instantiation of the approach: the IBMAP-HC algorithm, designed for avoiding important limitations of existing independence-based algorithms. These algorithms proceed by performing statistical independence tests on data, trusting completely the outcome of each test. In practice tests may be incorrect, resulting in potential cascading errors and the consequent reduction in the quality of the structures learned. IBMAP contemplates this uncertainty in the outcome of the tests through a probabilistic maximum-a-posteriori approach. The approach is instantiated in the IBMAP-HC algorithm, a structure selection strategy that performs a polynomial heuristic local search in the space of possible structures. We present an extensive empirical evaluation on synthetic and real data, showing that our algorithm outperforms significantly the current independence-based algorithms, in terms of data efficiency and quality of learned structures, with equivalent computational complexities. We also show the performance of IBMAP-HC in a real-world application of knowledge discovery: EDAs, which are evolutionary algorithms that use structure learning on each generation for modeling the distribution of populations. The experiments show that when IBMAP-HC is used to learn the structure, EDAs improve the convergence to the optimum