The OpenMolcas Web: A Community-Driven Approach to Advancing Computational Chemistry

The developments of the open-source OpenMolcas chemistry software environment since spring 2020 are described, with a focus on novel functionalities accessible in the stable branch of the package or via interfaces with other packages. These developments span a wide range of topics in computational chemistry and are presented in thematic sections: electronic structure theory, electronic spectroscopy simulations, analytic gradients and molecular structure optimizations, ab initio molecular dynamics, and other new features. This report offers an overview of the chemical phenomena and processes OpenMolcas can address, while showing that OpenMolcas is an attractive platform for state-of-the-art atomistic computer simulations

Significant Correlation Pattern Mining in Smart Homes

[[abstract]]Owing to the great advent of sensor technology, the usage data of appliances in a house can be logged and collected easily today. However, it is a challenge for the residents to visualize how these appliances are used. Thus, mining algorithms are much needed to discover appliance usage patterns. Most previous studies on usage pattern discovery are mainly focused on analyzing the patterns of single appliance rather than mining the usage correlation among appliances. In this article, a novel algorithm, namely Correlation Pattern Miner (CoPMiner), is developed to capture the usage patterns and correlations among appliances probabilistically. CoPMiner also employs four pruning techniques and a statistical model to reduce the search space and filter out insignificant patterns, respectively. Furthermore, the proposed algorithm is applied on a real-world dataset to show the practicability of correlation pattern mining.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

A novel algorithm for mining closed temporal patterns from interval-based data

[[notice]]補正完

Incremental mining of temporal patterns in interval-based database

[[abstract]]In several real-life applications, sequence databases, in general, are updated incrementally with time. Some discovered sequential patterns may be invalidated and some new ones may be introduced by the evolution of the database. When a small set of sequences grow, or when some new sequences are added into the database, re-mining sequential patterns from scratch each time is usually inefficient and thus not feasible. Although there have been several recent studies on the maintenance of sequential patterns in an incremental manner, these works only consider the patterns extracted from time point-based data. Few research efforts have been elaborated on maintaining time interval-based sequential patterns, also called temporal patterns, where each datum persists for a period of time. In this paper, an efficient algorithm, Inc_TPMiner (Incremental Temporal Pattern Miner) is developed to incrementally discover temporal patterns from interval-based data. Moreover, the algorithm employs some optimization techniques to reduce the search space effectively. The experimental results on both synthetic and real datasets indicate that Inc_TPMiner significantly outperforms re-mining with static algorithms in execution time and possesses graceful scalability. Furthermore, we also apply Inc_TPMiner on a real dataset to show the practicability of incremental mining of temporal patterns.[[notice]]補正完