A study of three transition metal compounds and their applications

The La5/8-yPryCa3/8MnO3 with colossal magnetoresistance (CMR) effect has a rich phase diagram and is well studied for its electronic phase separation phenomenon, where the ferromagnetic (FM) metal order co-exists and competes with the charge-ordered (CO) insulator phase. High Pr doping will favor the CO order, leading a sharp FM to CO dominated phase transition around Pr concentration of 0.3 and above. Hydrostatic pressure favors FM metallic order without damaging the sample and can be tuned continuously. In this study, pressurized-magnetic and resistivity measurements was done on a La0.25Pr0.375Ca0.375MnO3 single crystal. The sample, at first sitting in CO dominated phase, changed into FM upon a small amount of pressure. This transition was verified both by magnetic and resistivity measurement results. FeSe1-x is one of the newly discovered iron-based superconductors. As a binary transition metal compound, it is of great research interest due to the simple stacking 2d-layered structure. The itinerant or localized nature of the electrons in Fe2+ ion has been debated but not concluded. In this research, Raman scattering measurements on FeSe0.97 were applied within a temperature range from 5 K to 300 K. The excitation near 185 cm-1 was assigned to B1g phonon excitation. Broad and intensive excitation peaks were found in a wide region between 200 cm-1 and 700 cm-1, and they are classified as the Fe2+ crystal field excitations. These excitations suggest a low Hund coupling constant and thus support the itinerant nature of 3d electrons in Fe2+ ion indirectly. Evanescent wave was discovered to be able to tunnel through a negative reflectance index material and gets strengthened inside an alternating metal-high K material 1d photonic crystal structure. Where the regular light eventually fails in sub-micron photolithography due to diffraction limit, evanescent wave can carry the information of small structure below diffraction limit. In our study, HfO2, a transitional metal oxide widely used in IC fabrication, was used as the high-K material to construct a sub-wavelength length imaging device for nano scale photolithography applications

A critical comparison of formative feedback and final examination

Assessment as a process of collecting and discussing information from diverse sources to develop an understanding of what learners can do with their knowledge is crucial in education. This paper critically compares two methods of assessment, formative feedback and final examination. The two methods of assessment discussed in this paper are aimed at helping teachers and students meet the essential in education since they determine if the objectives of education

Document Clustering Based On Max-Correntropy Non-Negative Matrix Factorization

Nonnegative matrix factorization (NMF) has been successfully applied to many areas for classification and clustering. Commonly-used NMF algorithms mainly target on minimizing the $l_2$ distance or Kullback-Leibler (KL) divergence, which may not be suitable for nonlinear case. In this paper, we propose a new decomposition method by maximizing the correntropy between the original and the product of two low-rank matrices for document clustering. This method also allows us to learn the new basis vectors of the semantic feature space from the data. To our knowledge, we haven't seen any work has been done by maximizing correntropy in NMF to cluster high dimensional document data. Our experiment results show the supremacy of our proposed method over other variants of NMF algorithm on Reuters21578 and TDT2 databasets.Comment: International Conference of Machine Learning and Cybernetics (ICMLC) 201

Multi-Task Learning for Email Search Ranking with Auxiliary Query Clustering

User information needs vary significantly across different tasks, and therefore their queries will also differ considerably in their expressiveness and semantics. Many studies have been proposed to model such query diversity by obtaining query types and building query-dependent ranking models. These studies typically require either a labeled query dataset or clicks from multiple users aggregated over the same document. These techniques, however, are not applicable when manual query labeling is not viable, and aggregated clicks are unavailable due to the private nature of the document collection, e.g., in email search scenarios. In this paper, we study how to obtain query type in an unsupervised fashion and how to incorporate this information into query-dependent ranking models. We first develop a hierarchical clustering algorithm based on truncated SVD and varimax rotation to obtain coarse-to-fine query types. Then, we study three query-dependent ranking models, including two neural models that leverage query type information as additional features, and one novel multi-task neural model that views query type as the label for the auxiliary query cluster prediction task. This multi-task model is trained to simultaneously rank documents and predict query types. Our experiments on tens of millions of real-world email search queries demonstrate that the proposed multi-task model can significantly outperform the baseline neural ranking models, which either do not incorporate query type information or just simply feed query type as an additional feature.Comment: CIKM 201

One-sample aggregate data meta-analysis of medians

An aggregate data meta-analysis is a statistical method that pools the summary statistics of several selected studies to estimate the outcome of interest. When considering a continuous outcome, typically each study must report the same measure of the outcome variable and its spread (e.g., the sample mean and its standard error). However, some studies may instead report the median along with various measures of spread. Recently, the task of incorporating medians in meta-analysis has been achieved by estimating the sample mean and its standard error from each study that reports a median in order to meta-analyze the means. In this paper, we propose two alternative approaches to meta-analyze data that instead rely on medians. We systematically compare these approaches via simulation study to each other and to methods that transform the study-specific medians and spread into sample means and their standard errors. We demonstrate that the proposed median-based approaches perform better than the transformation-based approaches, especially when applied to skewed data and data with high inter-study variance. In addition, when meta-analyzing data that consists of medians, we show that the median-based approaches perform considerably better than or comparably to the best-case scenario for a transformation approach: conducting a meta-analysis using the actual sample mean and standard error of the mean of each study. Finally, we illustrate these approaches in a meta-analysis of patient delay in tuberculosis diagnosis