Algorithms for Extracting Frequent Episodes in the Process of Temporal Data Mining

An important aspect in the data mining process is the discovery of patterns having a great influence on the studied problem. The purpose of this paper is to study the frequent episodes data mining through the use of parallel pattern discovery algorithms. Parallel pattern discovery algorithms offer better performance and scalability, so they are of a great interest for the data mining research community. In the following, there will be highlighted some parallel and distributed frequent pattern mining algorithms on various platforms and it will also be presented a comparative study of their main features. The study takes into account the new possibilities that arise along with the emerging novel Compute Unified Device Architecture from the latest generation of graphics processing units. Based on their high performance, low cost and the increasing number of features offered, GPU processors are viable solutions for an optimal implementation of frequent pattern mining algorithmsFrequent Pattern Mining, Parallel Computing, Dynamic Load Balancing, Temporal Data Mining, CUDA, GPU, Fermi, Thread

Using utilization profiles in allocation and partitioning for multiproscessor systems

technical reportThe problems of multiprocessor partitioning and program allocation are interdependent and critical to the performance of multiprocessor systems?? Minimizing resource partitions for parallel programs on partitionable multiprocessors facilitates greater processor utilization and throughput?? The pro cessing resource requirements of parallel programs vary during program execution and are allocation dependent?? Optimal resource utilization requires that resource requirements be modeled as variable over time?? This paper investigates the use of program pro les in allocating programs and parti tioning multiprocessor systems?? An allocation method is discussed?? The goals of this method are to minimize program execution time minimize the total number of processors used characterize variation in processor requirements over the lifetime of a program to accurately predict the impact on run time of the number of processors available at any point in time and to minimize uctuations in processor requirements to facilitate e cient sharing of processors between partitions on a partitionable multiprocessor?? An application to program partitioning is discussed that improves partition run times compared to other methods?

Using utilization profiles in allocation and partitioning for multiprocessor systems

Journal ArticleThe problems of multiprocessor partitioning and program allocation are interdependent and critical to the performance of multiprocessor systems. Minimizing resource partitions for parallel programs on partitionable multiprocessors facilitates greater processor utilization and throughput. The processing resource requirements of parallel programs vary during program, execution and are allocation dependent. Optimal resource utilization requires that resource requirements be modeled as variable over time. This paper investigates the use of program profiles in allocating programs and partitioning multiprocessor systems. An allocation method is discussed. The goals of this method are to (1) minimize program execution time, (2) minimize t h e total number of processors used, (3) characterize variation in processor requirements over the lifetime of a program, (4) to accurately predict the impact on run time of the number of processors available at any point in time and (5) to minimize fluctuations in processor requirements to facilitate efficient sharing of processors between partitions on a partitionable multiprocessor. An application to program partitioning is discussed that improves partition run times compared to other methods

Algorithms for Hierarchical and Semi-Partitioned Parallel Scheduling

We propose a model for scheduling jobs in a parallel machine setting that takes into account the cost of migrations by assuming that the processing time of a job may depend on the specific set of machines among which the job is migrated. For the makespan minimization objective, the model generalizes classical scheduling problems such as unrelated parallel machine scheduling, as well as novel ones such as semi-partitioned and clustered scheduling. In the case of a hierarchical family of machines, we derive a compact integer linear programming formulation of the problem and leverage its fractional relaxation to obtain a polynomial-time 2-approximation algorithm. Extensions that incorporate memory capacity constraints are also discussed

Deep Evolutionary Generative Molecular Modeling for RNA Aptamer Drug Design

Deep Aptamer Evolutionary Model (DAPTEV Model). Typical drug development processes are costly, time consuming and often manual with regard to research. Aptamers are short, single-stranded oligonucleotides (RNA/DNA) that bind to, and inhibit, target proteins and other types of molecules similar to antibodies. Compared with small-molecule drugs, these aptamers can bind to their targets with high affinity (binding strength) and specificity (designed to uniquely interact with the target only). The typical development process for aptamers utilizes a manual process known as Systematic Evolution of Ligands by Exponential Enrichment (SELEX), which is costly, slow, and often produces mild results. The focus of this research is to create a deep learning approach for the generating and evolving of aptamer sequences to support aptamer-based drug development. These sequences must be unique, contain at least some level of structural complexity, and have a high level of affinity and specificity for the intended target. Moreover, after training, the deep learning system, known as a Variational Autoencoder, must possess the ability to be queried for new sequences without the need for further training. Currently, this research is applied to the SARS-CoV-2 (Covid-19) spike protein’s receptor-binding domain (RBD). However, careful consideration has been placed in the intentional design of a general solution for future viral applications. Each individual run took five and a half days to complete. Over the course of two months, three runs were performed for three different models. After some sequence, score, and statistical comparisons, it was observed that the deep learning model was able to produce structurally complex aptamers with strong binding affinities and specificities to the target Covid-19 RBD. Furthermore, due to the nature of VAEs, this model is indeed able to be queried for new aptamers of similar quality based on previous training. Results suggest that VAE-based deep learning methods are capable of optimizing aptamer-target binding affinities and specificities (multi-objective learning), and are a strong tool to aid in aptamer-based drug development