We formulate and evaluate the Automated Simulation Analysis Procedure (ASAP), an algorithm for steady-state simulation output analysis based on the method of nonoverlapping batch means (NOBM). ASAP delivers a confidence interval for an expected response that is centered on the sample mean of a portion of a simulation-generated time series and satisfies a user-specified absolute or relative prec...

Batch Normalization (BN) is a milestone technique in the development of deep learning, enabling various networks to train. However, normalizing along the batch dimension introduces problems — BN’s error increases rapidly when the batch size becomes smaller, caused by inaccurate batch statistics estimation. This limits BN’s usage for training larger models and transferring features to computer v...

Parameter settings profoundly impact the performance of machine learning algorithms and laboratory experiments. The classical grid search or trial-error methods are exponentially expensive in large parameter spaces, and Bayesian optimization (BO) offers an elegant alternative for global optimization of black box functions. In situations where the black box function can be evaluated at multiple ...

Forces generated by cells play a vital role in many cellular processes like cell spreading, motility, differentiation and apoptosis. Understanding the mechanics of single cells is essential to delineate the link between cellular force generation/sensing and function. MEMS sensors, because of their small size and fine force/displacement resolution, are ideal for force and displacement sensing at...

in this work, old newspaper fibers (onf) bleached with h2o2, treated with koh and treated with naocl were investigated as potential adsorbents. the characterization of the produced fibers using ft-ir, sem and particle size distribution have been carried out and tested for the removal of ni (ii) from aqueous solutions. the results indicated that the fibers treated with koh give the highest %remo...

Over the last two decades there has been considerable growth in digital communication systems which operate on a slotted system. In several applications, transmission of packets over the network takes place in batches of varying size, and transmission time depends upon the size of the batch. Performance modelling of these systems is usually done using discrete-time queues. In view of this, we c...

This paper addresses a production and outbound distribution scheduling problem in which a set of jobs have to be process on a single machine for delivery to customers or to other machines for further processing. We assume that there is a sufficient number of vehicles and the delivery costs is independent of batch size but it is dependent on each trip. In this paper, we present an Artificial Imm...

In [14] there is proposed an ElGamal-type cryptosystem based on non-maximal imaginary quadratic orders with trapdoor decryption. The trapdoor information is the factorization of the non-fundamental discriminant ∆p = ∆1p . The NICE-cryptosystem (New Ideal Coset En-cryption) [24,12] is an efficient variant thereof, which uses an element g ∈ Ker(φ−1 Cl ) ⊆ Cl(∆p), where k is random and φ−1 Cl : Cl...

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...

We consider processing and shipment scheduling of a batch of size M jobs on a flexible (multifunctional) machine. All jobs in the batch require the same sequence of N operations on the machine. Costs are incurred in the forms of holding costs of jobs waiting for the next operation, setup costs whenever the machine is set up for a new operation, and shipment cost whenever the whole batch or a pa...