Adapting image processing and clustering methods to productive efficiency analysis and benchmarking: A cross disciplinary approach

This dissertation explores the interdisciplinary applications of computational methods in quantitative economics. Particularly, this thesis focuses on problems in productive efficiency analysis and benchmarking that are hardly approachable or solvable using conventional methods. In productive efficiency analysis, null or zero values are often produced due to the wrong skewness or low kurtosis of the inefficiency distribution as against the distributional assumption on the inefficiency term. This thesis uses the deconvolution technique, which is traditionally used in image processing for noise removal, to develop a fully non-parametric method for efficiency estimation. Publications 1 and 2 are devoted to this topic, with focus being laid on the cross-sectional case and panel case, respectively. Through Monte-Carlo simulations and empirical applications to Finnish electricity distribution network data and Finnish banking data, the results show that the Richardson-Lucy blind deconvolution method is insensitive to the distributio-nal assumptions, robust to the data noise levels and heteroscedasticity on efficiency estimation. In benchmarking, which could be the next step of productive efficiency analysis, the 'best practice' target may not perform under the same operational environment with the DMU under study. This would render the benchmarks impractical to follow and adversely affects the managers to make the correct decisions on performance improvement of a DMU. This dissertation proposes a clustering-based benchmarking framework in Publication 3. The empirical study on Finnish electricity distribution network reveals that the proposed framework novels not only in its consideration on the differences of the operational environment among DMUs, but also its extreme flexibility. We conducted a comparison analysis on the different combinations of the clustering and efficiency estimation techniques using computational simulations and empirical applications to Finnish electricity distribution network data, based on which Publication 4 specifies an efficient combination for benchmarking in energy regulation.  This dissertation endeavors to solve problems in quantitative economics using interdisciplinary approaches. The methods developed benefit this field and the way how we approach the problems open a new perspective

Charge Density Wave Instability and Soft Phonon in AAPt3_3P (AA=Ca, Sr, and La)

The electronic and phonon properties of the platinum pnictide superconductors $A$Pt$_3$P ($A$=Ca, Sr, and La) were studied using first-principles calculations. The spin-orbit coupling effect is significant in LaPt$_3$P but negligible in CaPt$_3$P and SrPt$_3$P, although they all share the same anti-pevroskite structure. Moreover, SrPt$_3$P has been demonstrated to exhibit an unexpected weak charge-density-wave(CDW) instability which is neither simply related to the Fermi-surface nesting nor to the momentum-dependent electron-phonon coupling alone. The instability is absent in CaPt$_3$P and can be quickly suppressed by the external pressure, accompanied with gradual decreases in the phonon softening and BCS $T_c$. Our results suggest SrPt$_3$P as a rare example where superconductivity is enhanced by the CDW fluctuations

DPAN: Dynamic Preference-based and Attribute-aware Network for Relevant Recommendations

In e-commerce platforms, the relevant recommendation is a unique scenario providing related items for a trigger item that users are interested in. However, users' preferences for the similarity and diversity of recommendation results are dynamic and vary under different conditions. Moreover, individual item-level diversity is too coarse-grained since all recommended items are related to the trigger item. Thus, the two main challenges are to learn fine-grained representations of similarity and diversity and capture users' dynamic preferences for them under different conditions. To address these challenges, we propose a novel method called the Dynamic Preference-based and Attribute-aware Network (DPAN) for predicting Click-Through Rate (CTR) in relevant recommendations. Specifically, based on Attribute-aware Activation Values Generation (AAVG), Bi-dimensional Compression-based Re-expression (BCR) is designed to obtain similarity and diversity representations of user interests and item information. Then Shallow and Deep Union-based Fusion (SDUF) is proposed to capture users' dynamic preferences for the diverse degree of recommendation results according to various conditions. DPAN has demonstrated its effectiveness through extensive offline experiments and online A/B testing, resulting in a significant 7.62% improvement in CTR. Currently, DPAN has been successfully deployed on our e-commerce platform serving the primary traffic for relevant recommendations. The code of DPAN has been made publicly available

Can Electromagnetic Information Theory Improve Wireless Systems? A Channel Estimation Example

Electromagnetic information theory (EIT) is an emerging interdisciplinary subject that integrates classical Maxwell electromagnetics and Shannon information theory. The goal of EIT is to uncover the information transmission mechanisms from an electromagnetic (EM) perspective in wireless systems. Existing works on EIT are mainly focused on the analysis of degrees-of-freedom (DoF), system capacity, and characteristics of the electromagnetic channel. However, these works do not clarify how EIT can improve wireless communication systems. To answer this question, in this paper, we provide a novel demonstration of the application of EIT. By integrating EM knowledge into the classical MMSE channel estimator, we observe for the first time that EIT is capable of improving the channel estimation performace. Specifically, the EM knowledge is first encoded into a spatio-temporal correlation function (STCF), which we term as the EM kernel. This EM kernel plays the role of side information to the channel estimator. Since the EM kernel takes the form of Gaussian processes (GP), we propose the EIT-based Gaussian process regression (EIT-GPR) to derive the channel estimations. In addition, since the EM kernel allows parameter tuning, we propose EM kernel learning to fit the EM kernel to channel observations. Simulation results show that the application of EIT to the channel estimator enables it to outperform traditional isotropic MMSE algorithm, thus proving the practical values of EIT.Comment: Electromagnetic information theory (EIT) is an emerging interdisciplinary subject, aiming at providing a unified analytical framework for wireless systems as well as guiding practical system design. This paper answers the question: "How can we improve wireless communication systems via EIT"