84 research outputs found
Posterior propriety and admissibility of hyperpriors in normal hierarchical models
Hierarchical modeling is wonderful and here to stay, but hyperparameter
priors are often chosen in a casual fashion. Unfortunately, as the number of
hyperparameters grows, the effects of casual choices can multiply, leading to
considerably inferior performance. As an extreme, but not uncommon, example use
of the wrong hyperparameter priors can even lead to impropriety of the
posterior. For exchangeable hierarchical multivariate normal models, we first
determine when a standard class of hierarchical priors results in proper or
improper posteriors. We next determine which elements of this class lead to
admissible estimators of the mean under quadratic loss; such considerations
provide one useful guideline for choice among hierarchical priors. Finally,
computational issues with the resulting posterior distributions are addressed.Comment: Published at http://dx.doi.org/10.1214/009053605000000075 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Dissipation enhancement by transport noise for stochastic -Laplace equations
The stochastic -Laplace equation with multiplicative transport noise is
studied on the torus . It is shown that the
dissipation is enhanced by transport noise in both the averaged sense and the
pathwise sense.Comment: 20 pages. We have made some small correction
Crowdsourcing to Smartphones: Incentive Mechanism Design for Mobile Phone Sensing
Mobile phone sensing is a new paradigm which takes advantage of the pervasive smartphones to collect and analyze data beyond the scale of what was previously possible. In a mobile phone sensing system, the platform recruits smartphone users to provide sensing service. Existing mobile phone sensing applications and systems lack good incentive mechanisms that can attract more user participation. To address this issue, we design incentive mechanisms for mobile phone sensing. We consider two system models: the platform-centric model where the platform provides a reward shared by participating users, and the user-centric model where users have more control over the payment they will receive. For the platform-centric model, we design an incentive mechanism using a Stackelberg game, where the platform is the leader while the users are the followers. We show how to compute the unique Stackelberg Equilibrium, at which the utility of the platform is maximized, and none of the users can improve its utility by unilaterally deviating from its current strategy. For the user-centric model, we design an auction-based incentive mechanism, which is computationally efficient, individually rational, profitable, and truthful. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our incentive mechanisms
Terahertz Signal Generation Based on a Dual-Mode 1.5 μm DFB Semiconductor Laser
A novel dual-mode DFB semiconductor diode laser has been demonstrated. Using photomixing techniques, a terahertz signal at ~560 GHz has been generated. The THz signal shows power fluctuations related to mode competition in the laser
Terahertz Signal Generation Based on a Dual-Mode 1.5 μm DFB Semiconductor Laser
A novel dual-mode DFB semiconductor diode laser has been demonstrated. Using photomixing techniques, a terahertz signal at ~560 GHz has been generated. The THz signal shows power fluctuations related to mode competition in the laser
Experience-driven Networking: A Deep Reinforcement Learning based Approach
Modern communication networks have become very complicated and highly
dynamic, which makes them hard to model, predict and control. In this paper, we
develop a novel experience-driven approach that can learn to well control a
communication network from its own experience rather than an accurate
mathematical model, just as a human learns a new skill (such as driving,
swimming, etc). Specifically, we, for the first time, propose to leverage
emerging Deep Reinforcement Learning (DRL) for enabling model-free control in
communication networks; and present a novel and highly effective DRL-based
control framework, DRL-TE, for a fundamental networking problem: Traffic
Engineering (TE). The proposed framework maximizes a widely-used utility
function by jointly learning network environment and its dynamics, and making
decisions under the guidance of powerful Deep Neural Networks (DNNs). We
propose two new techniques, TE-aware exploration and actor-critic-based
prioritized experience replay, to optimize the general DRL framework
particularly for TE. To validate and evaluate the proposed framework, we
implemented it in ns-3, and tested it comprehensively with both representative
and randomly generated network topologies. Extensive packet-level simulation
results show that 1) compared to several widely-used baseline methods, DRL-TE
significantly reduces end-to-end delay and consistently improves the network
utility, while offering better or comparable throughput; 2) DRL-TE is robust to
network changes; and 3) DRL-TE consistently outperforms a state-ofthe-art DRL
method (for continuous control), Deep Deterministic Policy Gradient (DDPG),
which, however, does not offer satisfying performance.Comment: 9 pages, 12 figures, paper is accepted as a conference paper at IEEE
Infocom 201
Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial
Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials.
Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure.
Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen.
Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049
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