of Results. We establish that several problems are highly parallelizable. For each of these problems, we design an optimal O (loglogn ) time parallel algorithm on the Common CRCW PRAM model which is the weakest among the CRCW PRAM models. These problems include: g all nearest smaller values, g preprocessing for answering range maxima queries, g several problems in Computational Geometry, g stri...

Existing methods of parameter and structure learning of Bayesian Networks (BNs) from a database assume that the database is complete. If there are missing values, they are assumed to be missing at random. This paper incorporates the concepts used in Dempster-Shafer theory of belief functions to learn both the parameters and structure of BNs. Instead of filling the missing values by their estima...

Consider a random vector $(V_{1}, \dots , V_{n})$ where $\{V_{k}\}_{k=1, n}$ are the first $n$ components of two-parameter Poisson-Dirichlet distribution $PD(\alpha \theta )$. In this paper, we derive decomposition for vector, and propose an exact simulation algorithm to sample from vector. Moreover, special case arises when $\theta /\alpha $ is positive integer, which present very fast modifie...

In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN). In training, Random Erasing randomly selects a rectangle region in an image and erases its pixels with random values. In this process, training images with various levels of occlusion are generated, which reduces the risk of over-fitting and makes the model robust to o...