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

Diabetic Nephropathy: Perspective on Novel Molecular Mechanisms

Diabetes mellitus (DM) is the major cause of end-stage renal disease (ESRD) globally, and novel treatments are urgently needed. Current therapeutic approaches for diabetic nephropathy (DN) are focussing on blood pressure control with inhibitors of the renin-angiotensin-aldosterone system, on glycaemic and lipid control, and life-style changes. In this review, we highlight new molecular insights aiding our understanding of the initiation and progression of DN, including glomerular insulin resistance, dysregulation of cellular substrate utilisation, podocyte-endothelial communication, and inhibition of tubular sodium coupled glucose reabsorption. We believe that these mechanisms offer new therapeutic targets that can be exploited to develop important renoprotective treatments for DN over the next decade