Batch verification can provide large computational savings when several signatures, or other constructs, are verified together. Several batch verification algorithms have been published in recent years, in particular for both DSA-type and RSA signatures. We describe new attacks on several of these published schemes. A general weakness is explained which applies to almost all known batch verifie...

The Distributed Batch Controller (DBC) supports scientiic batch data processing. The DBC distributes batch jobs to one or more pools of workstations and monitors and controls their execution. The pools themselves may be geographically distributed, and need not be dedicated to processing batch jobs. We describe the use of the DBC in a large scientiic data processing application, namely the gener...

The Distributed Batch Controller DBC supports scienti c batch data processing The DBC dis tributes batch jobs to one or more pools of workstations and monitors and controls their execution The pools themselves may be geographically distributed and need not be dedicated to process ing batch jobs We describe the use of the DBC in a large scienti c data processing application namely the generation...

The pressure swing distillation in different batch column configurations is investigated by rigorous simulation calculations. The calculations are made by a professional flow-sheet simulator for the separation of a minimum (ethanol–toluene) and a maximum boiling (water– ethylene-diamine) azeotropic mixture. Besides studying the well known configurations (rectifier, stripper) we also investigate...

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...