Metamorphosis

None provided

Selfies of Twitter Data Stream through the Lens of Information Theory: A Comparative Case Study of Tweet-trails with Healthcare Hashtags

Little research in information system has been carried out on the subject of user’s choice of different components when composing a tweet through the analytical lens of information theory. This study employs a comparative case study approach to examine the use of hashtags of medical-terminology versus lay-language in tweet-trails and (1) introduces a novel H(x) index to reveal the complexity in the statistical structure and the variety in the composition of a tweet-trail, (2) applies radar graph and scatter plot as intuitive data visualization aids, and (3) proposes a methodological framework for structural analysis of Twitter data stream as a supplemental tool for profile analysis of Twitter users and content analysis of tweets. This systematic framework is capable of unveiling patterns in the structure of tweet-trails and providing quick and preliminary snap shots (selfies) of Twitter data stream because it’s an automatic and objective approach which requires no human intervention

青少年學習與適應模式之驗證

[[issue]]1

An Economy-wide Analysis of Impacts of WTO Tiered Formula for Tariff Reduction on Taiwan

In this study we use Taiwan as a case study to provide an economy-wide analysis of impacts on Taiwan of WTO tariff reduction schemes with different combinations of thresholds and reduction rates. The model we utilized in this study is Taiwan General Equilibrium Model with a WTO module (TAIGEM-WTO, hereafter) that is a multi-sectoral computable general equilibrium (CGE) model of the Taiwan's economy derived from Australian ORANI model (Dixon, Parmenter, Sutton and Vincent, 1982). Simulation results show that results are more sensitive to the scheme of tariff-reduction (i.e., Category 1, 2, and 3) than the tiered levels (i.e., A, B, C, and D) and as a strategy we should pay more attention to the arguments related to the amounts of tariff-reduction. Moreover, changes in nominal average tariff rates are more sensitive and shocks to the economy are more severe when we change the tariff reduction categories rather than the tiered levels. This conclusion also applies to the tiered reduction case when only sensitive products are considered. Finally, simulations with sector's bound rate calculated using arithmetic means have bigger effects than those using import values as weights. Therefore, sector's bound rate using import values as weights would be preferred.International Relations/Trade,

Development and Validations of a 3-D Numerical Wave Model in Cartesian Grid System Using Level Set Method

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchive

Deep-Q Learning with Hybrid Quantum Neural Network on Solving Maze Problems

Quantum computing holds great potential for advancing the limitations of machine learning algorithms to handle higher dimensions of data and reduce overall training parameters in deep learning (DL) models. This study uses a trainable variational quantum circuit (VQC) on a gate-based quantum computing model to investigate the potential for quantum benefit in a model-free reinforcement learning problem. Through a comprehensive investigation and evaluation of the current model and capabilities of quantum computers, we designed and trained a novel hybrid quantum neural network based on the latest Qiskit and PyTorch framework. We compared its performance with a full-classical CNN with and without an incorporated VQC. Our research provides insights into the potential of deep quantum learning to solve a maze problem and, potentially, other reinforcement learning problems. We conclude that reinforcement learning problems can be practical with reasonable training epochs. Moreover, a comparative study of full-classical and hybrid quantum neural networks is discussed to understand these two approaches' performance, advantages, and disadvantages to deep-Q learning problems, especially on larger-scale maze problems larger than 4x4

Predicting protein-protein interactions in unbalanced data using the primary structure of proteins

<p>Abstract</p> <p>Background</p> <p>Elucidating protein-protein interactions (PPIs) is essential to constructing protein interaction networks and facilitating our understanding of the general principles of biological systems. Previous studies have revealed that interacting protein pairs can be predicted by their primary structure. Most of these approaches have achieved satisfactory performance on datasets comprising equal number of interacting and non-interacting protein pairs. However, this ratio is highly unbalanced in nature, and these techniques have not been comprehensively evaluated with respect to the effect of the large number of non-interacting pairs in realistic datasets. Moreover, since highly unbalanced distributions usually lead to large datasets, more efficient predictors are desired when handling such challenging tasks.</p> <p>Results</p> <p>This study presents a method for PPI prediction based only on sequence information, which contributes in three aspects. First, we propose a probability-based mechanism for transforming protein sequences into feature vectors. Second, the proposed predictor is designed with an efficient classification algorithm, where the efficiency is essential for handling highly unbalanced datasets. Third, the proposed PPI predictor is assessed with several unbalanced datasets with different positive-to-negative ratios (from 1:1 to 1:15). This analysis provides solid evidence that the degree of dataset imbalance is important to PPI predictors.</p> <p>Conclusions</p> <p>Dealing with data imbalance is a key issue in PPI prediction since there are far fewer interacting protein pairs than non-interacting ones. This article provides a comprehensive study on this issue and develops a practical tool that achieves both good prediction performance and efficiency using only protein sequence information.</p