In many applications of black-box optimization, one can evaluate multiple points simultaneously, e.g. when evaluating the performances of several different neural network architectures in a parallel computing environment. In this paper, we develop a novel batch Bayesian optimization algorithm — the parallel knowledge gradient method. By construction, this method provides the one-step Bayes opti...

OBJECTIVE To develop a competency based discriminatory assessment method for physiology practical examination. METHOD Results from 1st professional M.B.B.S. Part I and II of three batches were taken and students performance in traditional and objective structured practical examination (OSPE) were compared. The course objective for practical examination of all three batches were same. However,...

: world population has been continuously raised and therefore due to this fact new source of foods, particularly proteins, are in demand. in recent years, single cell protein has been considered as an accepted substitute for animal and plant proteins. in this study, single cell protein production was studied in a batch submerged culture using aspergillus niger ptcc5012. experimental design was ...

For the operation of the nonstop service systems, some methods have been put to practical use to perform the batch update concurrently with the online entries. However, the whole batch update cannot be executed as a transaction by the conventional methods. So, in the case where a transaction failure occurs in the batch update, there is the problem that the rollback of the update cannot be execu...

The batch is an important technique for delivering video over Internet or VoD. It is a key method to improve effect for video multicast. In this paper, we research the batch strategy of proxy cache for streaming media using dynamic cache, proposed the three kind of cache algorithm for proxy cache: window batch, size batch, efficient batch. These methods increased the length of batch, solved the...

In this paper, we develop a new accelerated stochastic gradient method for efficiently solving the convex regularized empirical risk minimization problem in mini-batch settings. The use of mini-batches is becoming a golden standard in the machine learning community, because mini-batch settings stabilize the gradient estimate and can easily make good use of parallel computing. The core of our pr...

In the paper, we study the mini-batch stochastic ADMMs (alternating direction method of multipliers) for the nonconvex nonsmooth optimization. We prove that, given an appropriate mini-batch size, the mini-batch stochastic ADMM without variance reduction (VR) technique is convergent and reaches the convergence rate of O(1/T ) to obtain a stationary point of the nonconvex optimization, where T de...

Batch Reinforcement Learning has established itself as a valuable alternative to develop learning and adaptive agents. Batch Reinforcement Learning algorithms are characterized by obtaining a policy from a set of collected data. Common methods apply adapted versions of RL update rules, such as QLearning, on the transitions of the batch, building a pattern set. The target values of the pattern r...

In the pharmaceutical industry, a stability study is usually conducted to establish the drug expiration dating period(or shelf-life) of a drug product. The method suggested in the current FDA guidelines does not consider the situation when there is a batch-to-batch variation. Chow and Shao (1991) proposed a model which can take into account the batch-to-batch variation results. In this paper, a...

This paper presents a new measurement-based optimization framework for batch processes whereby optimal operation can be achieved via the tracking of active constraints. It is shown that, under mild assumptions and to a first-order approximation, tracking the necessary conditions of optimality is equivalent to tracking active constraints (both during the batch and at the end of the batch). Thus ...