17,068 research outputs found
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
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
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