Causally Regularized Learning with Agnostic Data Selection Bias

Most of previous machine learning algorithms are proposed based on the i.i.d. hypothesis. However, this ideal assumption is often violated in real applications, where selection bias may arise between training and testing process. Moreover, in many scenarios, the testing data is not even available during the training process, which makes the traditional methods like transfer learning infeasible due to their need on prior of test distribution. Therefore, how to address the agnostic selection bias for robust model learning is of paramount importance for both academic research and real applications. In this paper, under the assumption that causal relationships among variables are robust across domains, we incorporate causal technique into predictive modeling and propose a novel Causally Regularized Logistic Regression (CRLR) algorithm by jointly optimize global confounder balancing and weighted logistic regression. Global confounder balancing helps to identify causal features, whose causal effect on outcome are stable across domains, then performing logistic regression on those causal features constructs a robust predictive model against the agnostic bias. To validate the effectiveness of our CRLR algorithm, we conduct comprehensive experiments on both synthetic and real world datasets. Experimental results clearly demonstrate that our CRLR algorithm outperforms the state-of-the-art methods, and the interpretability of our method can be fully depicted by the feature visualization.Comment: Oral paper of 2018 ACM Multimedia Conference (MM'18

P−v{\cal P}-v Criticality in Gauged Supergravities

AdS black holes show richer transition behaviors in extended phase space by assuming the cosmological constant and its conjugate quantity to behave like thermodynamic pressure and thermodynamic volume. We study the extended thermodynamics of charged dilatonic AdS black holes in a class of Einstein-Maxwell-dilaton theories that can be embedded in gauged supergravities in various dimensions. We find that the transition behaviors of higher dimensional dilatonic AdS black holes are different from the four dimensional counterparts, and new transition behaviors emerges in higher dimensions. First, there exists standard Van der Waals transition only in a five dimensional dilatonic AdS black hole with two equal charges. Second, there emerge a new phase transition branch in negative pressure region in six and seven dimensional dilatonic black holes with two equal charges. Third, there emerge transition behaviors in higher dimensional black hole with single charge cases, which are absent in four dimensions.Comment: Latex, 18 pages, 8 figures; published versio

Beyond a Passive Conduit: Implications of Lymphatic Biology for Kidney Diseases

The kidney contains a network of lymphatic vessels that clear fluid, small molecules, and cells from the renal interstitium. Through modulating immune responses and via crosstalk with surrounding renal cells, lymphatic vessels have been implicated in the progression and maintenance of kidney disease. In this Review, we provide an overview of the development, structure, and function of lymphatic vessels in the healthy adult kidney. We then highlight the contributions of lymphatic vessels to multiple forms of renal pathology, emphasizing CKD, transplant rejection, and polycystic kidney disease and discuss strategies to target renal lymphatics using genetic and pharmacologic approaches. Overall, we argue the case for lymphatics playing a fundamental role in renal physiology and pathology and treatments modulating these vessels having therapeutic potential across the spectrum of kidney disease