Applying Particle Swarm Optimization to Solve Portfolio Selection Problems

Particle swarm optimization (PSO), introduced by Kennedy and Eberhart in 1995, is a social population-based search algorithm and is generally similar to the evolutionary computation techniques that have been successfully applied to solve various hard optimization problems. The standard Markowitz mean-variance approach to portfolio selection involves tracing out an efficient frontier, a continuous curve illustrating the tradeoff between return and risk. In this paper we applied the particle swarm approach to find an efficient frontier associated with the classical and general (unconstrained and constrained) mean-variance portfolio selection problem. The OR library data sets were tested in our paper and computational results showed that the PSO found better solutions when compared to genetic algorithm (GA), simulated annealing(SA), and tabu search(TS)

Non-reversible Parallel Tempering for Deep Posterior Approximation

Parallel tempering (PT), also known as replica exchange, is the go-to workhorse for simulations of multi-modal distributions. The key to the success of PT is to adopt efficient swap schemes. The popular deterministic even-odd (DEO) scheme exploits the non-reversibility property and has successfully reduced the communication cost from $O(P^2)$ to $O(P)$ given sufficiently many $P$ chains. However, such an innovation largely disappears in big data due to the limited chains and few bias-corrected swaps. To handle this issue, we generalize the DEO scheme to promote non-reversibility and propose a few solutions to tackle the underlying bias caused by the geometric stopping time. Notably, in big data scenarios, we obtain an appealing communication cost $O(P\log P)$ based on the optimal window size. In addition, we also adopt stochastic gradient descent (SGD) with large and constant learning rates as exploration kernels. Such a user-friendly nature enables us to conduct approximation tasks for complex posteriors without much tuning costs.Comment: Accepted by AAAI 202

Web Usage Mining to Extract Knowledge for Modelling Users of Taiwan Travel Recommendation Mobile APP

This work presents the design of a web mining system to understand the navigational behavior of passengers in developed Taiwan travel recommendation mobile app that provides four main functions including recommend by location , hot topic , nearby scenic spots information , my favorite and 2650 scenic spots. To understand passenger navigational patterns, log data from actual cases of app were collected and analysed by web mining system. This system analysed 58981 sessions of 1326 users for the month of June, 2014. Sequential profiles for passenger navigational patterns were captured by applying sequence-based representation schemes in association with Markov models and enhanced K-mean clustering algorithms for sequence behavior mining cluster patterns. The navigational cycle, time, function numbers, and the depth and extent (range) of app were statistically analysed. The analysis results can be used improved the passengers\u27 acceptance of app and help generate potential personalization recommendations for achieving an intelligent travel recommendation service

Global Protein Interactome Exploration Through Mining Genome-Scale Data in \u3ci\u3eArabidopsis thaliana\u3c/i\u3e

Background Many essential cellular processes, such as cellular metabolism, transport, cellular metabolism and most regulatory mechanisms, rely on physical interactions between proteins. Genome-wide protein interactome networks of yeast, human and several other animal organisms have already been established, but this kind of network reminds to be established in the field of plant. Results We first predicted the protein protein interaction in Arabidopsis thaliana with methods, including ortholog, SSBP, gene fusion, gene neighbor, phylogenetic profile, coexpression, protein domain, and used Naïve Bayesian approach next to integrate the results of these methods and text mining data to build a genome-wide protein interactome network. Furthermore, we adopted the data of GO enrichment analysis, pathway, published literature to validate our network, the confirmation of our network shows the feasibility of using our network to predict protein function and other usage. Conclusions Our interactome is a comprehensive genome-wide network in the organism plant Arabidopsis thaliana, and provides a rich resource for researchers in related field to study the protein function, molecular interaction and potential mechanism under different conditions

Customer Behavior Survery for Cultural and Creative Park in Taiwan

Cultural and Creative Park is a recreational campus which usually consists of exhibition, gallery, show room, movie theater, and multi-function facilities to provide the cultural activities. Besides, in the Cultural and Creative Park, restaurants, coffee shops, bookstores, gift shops, and other business units are nearby. How to improve the customer experience in the Cultural and Creative Park is an important research question for the managerial division to promote culture industries. In this research, the questionnaires were developed and performed in one of creative park in Taipei, Taiwan to study customer behavior. This paper addresses the survey result and the insights revealed from the survey