C/C++ implementation of functions of the class LT0

This report describes an on-going implementation, in C/C++, of the functions and schemes of the formal system LT0, presented in the paper Caporaso, Pani and Covino [1]. The final aim is to be able to effectively construct a "small manageable" Exponential Diophantine Equation which represents (in the sense of Chaitin [2]) an algorithmical random binary sequence

Structure-Property Correlations and Superconductivity in Spinels

In this chapter, alternative views based on the structure have been presented in the spinel superconducting compounds, including the only oxide spinel superconductor, LiTi2O4, and non-oxide superconductors, CuIr2S4 and CuV2S4. Inspection of the atomic arrangements, electronic structures and bonding interactions of spinel superconductor, LiTi2O4 shows that LiTi2O4 can be interpreted as Li-doped TiO2, which is similar with doping Cu into TiSe2 to induce superconductivity. Different from LiTi2O4, the electronic structures of CuIr2S4 and CuV2S4 indicate a distinctive way to understand them in the structural viewpoint. The d6 electron configuration and the octahedral coordination of Ir in CuIr2S4 can be analogous to the d6 in perovskites, which sometimes host a metal-insulator transition. However, the superconductivity in CuV2S4 may be induced from the suppression of charge density waves. This kind of structural views will help chemists understand physical phenomena obviously more straightforward, though not sufficient, as clearly shown by the competition between each other, such as superconductivity and other structural phase transition (CDWs), oxidation fluctuation or magnsetism

An Evaluation of Machine Learning and Deep Learning Models for Drought Prediction using Weather Data

Drought is a serious natural disaster that has a long duration and a wide range of influence. To decrease the drought-caused losses, drought prediction is the basis of making the corresponding drought prevention and disaster reduction measures. While this problem has been studied in the literature, it remains unknown whether drought can be precisely predicted or not with machine learning models using weather data. To answer this question, a real-world public dataset is leveraged in this study and different drought levels are predicted using the last 90 days of 18 meteorological indicators as the predictors. In a comprehensive approach, 16 machine learning models and 16 deep learning models are evaluated and compared. The results show no single model can achieve the best performance for all evaluation metrics simultaneously, which indicates the drought prediction problem is still challenging. As benchmarks for further studies, the code and results are publicly available in a Github repository.Comment: Github link: https://github.com/jwwthu/DL4Climate/tree/main/DroughtPredictio

Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools

Big data has been used widely in many areas including the transportation industry. Using various data sources, traffic states can be well estimated and further predicted for improving the overall operation efficiency. Combined with this trend, this study presents an up-to-date survey of open data and big data tools used for traffic estimation and prediction. Different data types are categorized and the off-the-shelf tools are introduced. To further promote the use of big data for traffic estimation and prediction tasks, challenges and future directions are given for future studies

Network On Network for Tabular Data Classification in Real-world Applications

Tabular data is the most common data format adopted by our customers ranging from retail, finance to E-commerce, and tabular data classification plays an essential role to their businesses. In this paper, we present Network On Network (NON), a practical tabular data classification model based on deep neural network to provide accurate predictions. Various deep methods have been proposed and promising progress has been made. However, most of them use operations like neural network and factorization machines to fuse the embeddings of different features directly, and linearly combine the outputs of those operations to get the final prediction. As a result, the intra-field information and the non-linear interactions between those operations (e.g. neural network and factorization machines) are ignored. Intra-field information is the information that features inside each field belong to the same field. NON is proposed to take full advantage of intra-field information and non-linear interactions. It consists of three components: field-wise network at the bottom to capture the intra-field information, across field network in the middle to choose suitable operations data-drivenly, and operation fusion network on the top to fuse outputs of the chosen operations deeply. Extensive experiments on six real-world datasets demonstrate NON can outperform the state-of-the-art models significantly. Furthermore, both qualitative and quantitative study of the features in the embedding space show NON can capture intra-field information effectively

Determinants of Entry Mode Decision: A Discussion on Firm-specific and Country-specific Factors

In today’s dynamic global economy and competitive environment, it is crucially important for firms to expand their production and services in multiple markets. As many firms attempts to develop and sustain competitive advantages, it has academic and practical value to examine the decision of entry mode choice. This dissertation aims to investigate the determinants of multinational enterprises’ entry mode choice in emerging economics by using China as our empirical setting. Based on four main leading theories regarding to entry mode decision, we narrow down the set of previously-claimed entry mode determinants into two groups—firm-specific and country-specific factors. We then build hypotheses regarding each of the five factors and run an econometrics test basing on data of year 1996. In order to compensate the limitations of quantitative research and generate a more solid conclusion on the determinants of entry mode, we also conduct a qualitative analysis on these factors. The main conclusions of our research are: (1) Both firm-specific and country-specific factors have impacts on multinational enterprises’ entry mode choice; (2) Of all five observed factors, “regional risks and incentive regulations” has the greatest influence, followed by psychic distance, degree of technology intensive, duration of project and the amount of foreign invested capital. This research has important implications not only for research but also for international managers