Submarine groundwater discharge (SGD) in high volcanic islands can be an important source of freshwater and nutrients to coral reefs. High inorganic nutrient content is generally thought augment primary production coastal systems but when this delivered via a vector as the case with SGD study, effects on productivity are unclear. In current literature, there limited evidence for direct associat...

In this paper, we study the stochastic gradient descent (SGD) method for the nonconvex nonsmooth optimization, and propose an accelerated SGD method by combining the variance reduction technique with Nesterov’s extrapolation technique. Moreover, based on the local error bound condition, we establish the linear convergence of our method to obtain a stationary point of the nonconvex optimization....

Neutrophil specific granule deficiency (SGD) is a rare congenital disorder of unknown cause associated with an impaired inflammatory response and an absence of neutrophil secondary granules. Reduced levels of several neutrophil proteins have led to the suggestion that the defect may lie at the level of transcription, a hypothesis that is supported by abnormally low levels of lactoferrin message...

Stochastic Gradient Descent (SGD) is the standard numerical method used to solve the core optimization problem for the vast majority of machine learning (ML) algorithms. In the context of large scale learning, as utilized by many Big Data applications, efficient parallelization of SGD is in the focus of active research. Recently, we were able to show that the asynchronous communication paradigm...

With the fast development of deep learning, people have started to train very big neural networks using massive data. Asynchronous Stochastic Gradient Descent (ASGD) is widely used to fulfill this task, which, however, is known to suffer from the problem of delayed gradient. That is, when a local worker adds the gradient it calculates to the global model, the global model may have been updated ...

Neural networks exhibit good generalization behavior in the over-parameterized regime, where the number of network parameters exceeds the number of observations. Nonetheless, current generalization bounds for neural networks fail to explain this phenomenon. In an attempt to bridge this gap, we study the problem of learning a two-layer over-parameterized neural network, when the data is generate...

Crystal River/Kings Bay is a spring-fed estuarine system located on the west coast of the Florida peninsula. During 2008-2009, a field investigation was conducted to measure submarine groundwater discharges (SGDs) from numerous spring vents in Kings Bay. Based on directly measured real-time SGD data, an empirical relationship that links SGD with tides in Kings Bay and the groundwater level meas...

BACKGROUND In the management of Asian patients with acute coronary syndrome (ACS), the comparative cost-effectiveness of ticagrelor and prasugrel, referenced to generic clopidogrel, is unknown. OBJECTIVE To assess the cost-effectiveness of ticagrelor and prasugrel as compared with generic clopidogrel in patients with ACS in Singapore. METHODS A Markov model simulating a typical cohort of 62...

Uniform sampling of training data has been commonly used in traditional stochastic optimization algorithms such as Proximal Stochastic Gradient Descent (prox-SGD) and Proximal Stochastic Dual Coordinate Ascent (prox-SDCA). Although uniform sampling can guarantee that the sampled stochastic quantity is an unbiased estimate of the corresponding true quantity, the resulting estimator may have a ra...

Mercury (Hg) is a potent toxin that biomagnifies in aquatic food webs. Large fish generally have higher Hg concentrations than small fish of the same species. However, models predict that fish that grow large faster should have lower Hg concentrations than small, slow-growing fish due to somatic growth dilution (SGD). We examined the relationship between Hg concentrations and growth rate in fis...