In modelling risk management strategies (i.e., within-lot batch testing, statistical process control), two basic assumptions have been normally made: that the true concentration of microorganisms are log-normally distributed within a batch, and that the variance of the samples is the same for a little or highly contaminated lot. These assumptions were evaluated by characterising the between-bat...

Batch normalization (BN) is comprised of a component followed by an affine transformation and has become essential for training deep neural networks. Standard initialization each BN in network sets the scale shift to 1 0, respectively. However, after we have observed that these parameters do not alter much from their initialization. Furthermore, noticed process can still yield overly large valu...

A batch machine is a machine that can process a number of jobs simultaneously as a batch, and the processing time of a batch is equal to the longest processing time of the jobs assigned to it. In this paper we present a polynomial time approximation scheme (PTAS) for scheduling a batch machine to minimize the total completion time with job release dates. Also, we present a fully polynomial time...

A batch processing machine can simultaneously process several jobs forming a batch. This paper considers the problem of scheduling jobs with non-identical capacity requirements, on a single-batch processing machine of a given capacity, to minimize the makespan. The processing time of a batch is equal to the largest processing time of any job in the batch. We present some dominance properties fo...

The most natural way to speed-up the training of large networks is to use dataparallelism on multiple GPUs. To scale Stochastic Gradient (SG) based methods to more processors, one need to increase the batch size to make full use of the computational power of each GPU. However, keeping the accuracy of network with increase of batch size is not trivial. Currently, the state-of-the art method is t...

We study the properties of the endpoint of stochastic gradient descent (SGD). By approximating SGD as a stochastic differential equation (SDE) we consider the Boltzmann-Gibbs equilibrium distribution of that SDE under the assumption of isotropic variance in loss gradients. Through this analysis, we find that three factors – learning rate, batch size and the variance of the loss gradients – cont...

This document defines a MIME content type suitable for tunneling an ESMTP [RFC-821, RFC-1869] transaction through any MIME-capable transport. This type can be used for a variety of purposes, including: Extending end-to-end MIME-based security services (e.g., [RFC-1847]) to cover message envelope information as well as message content. Making it possible to use specific SMTP extensions such as N...

The paper at hand deals with an approach for the automatic generation of dynamic nonlinear process models. These models are obtained from experimental process data as well as from theoretical biological and chemical reeections using the ideas of Genetic Programming for the supervision and coordination of the symbolic model structure during the automatic development.