Industry-level Total-factor Energy Efficiency in Developed Countries

This study computes and analyzes the total-factor energy efficiency (TFEE) of 11 industries in 14 developed countries during the period of 1995-2005 using the data envelopment analysis (DEA) approach. There are four inputs: labor, capital stock, intermediate inputs other than energy, and energy. The value added is the only output. The most inefficient industry is the metal industry, which has an average TFEE of 40.6%. Australia is the most inefficient country, with the lowest weighted TFEE in every year except for 1996 and 1998. The most efficient countries are the United States from 1995 to 1998, Denmark from 1999 to 2002, and Netherlands from 2003 to 2005. Given that the number of efficient industries decreases over time, it is clear that most industries have room to improve their energy efficiency as time goes by. Moreover, based on the total-factor framework, this study finds no support for the convergence of energy efficiency levels.Data envelopment analysis (DEA); Total-factor energy efficiency; Industry-level analysis

Probability-preserving evolution in a non-Hermitian two-band model

A non-Hermitian PT-symmetric system can have full real spectrum but does not ensure probability preserving time evolution, in contrast to that of a Hermitian system. We present a non-Hermitian two-band model, which is comprised of dimerized hopping terms and staggered imaginary on-site potentials, and study the dynamics in the exact PT-symmetric phase based on the exact solution. It is shown that an initial state, which does not involve two equal-momentum-vector eigenstates in different bands, obeys perfectly probability-preserving time evolution in terms of the Dirac inner product. Beyond this constriction, the quasi-Hermitian dynamical behaviors, such as non-spreading propagation and fractional revival of a Gaussian wave packet, are also observed.Comment: 8 pages, 14 figure

Design method for quasi-isotropic transformation materials based on inverse Laplace's equation with sliding boundaries

The deformation method of transformation optics has been demonstrated to be a useful tool, especially in designing arbitrary and nonsingular transformation materials. Recently, there are emerging demands for isotropic material parameters, arising from the broadband requirement of the designed devices. In this work, the deformation method is further developed to design quasi-isotropic/isotropic transformation materials. The variational functional of the inverse Laplace's equation is investigated and found to involve the smooth and quasi-conformal nature of coordinate transformation. Together with the sliding boundary conditions, the inverse Laplace's equation can be utilized to give transformations which are conformal or quasi-conformal, depending on functionalities of interest. Examples of designing an arbitrary carpet cloak and a waveguide with arbitrary cross sections are given to validate the proposed idea. Compared with other quasi-conformal methods based on grid generation tools, the proposed method unifies the design and validation of transformation devices, and thus is much convenient.Comment: 8 pages, 4 figure

Representation Learning with Fine-grained Patterns

With the development of computational power and techniques for data collection, deep learning demonstrates a superior performance over most of existing algorithms on benchmark data sets. Many efforts have been devoted to studying the mechanism of deep learning. One important observation is that deep learning can learn the discriminative patterns from raw materials directly in a task-dependent manner. Therefore, the representations obtained by deep learning outperform hand-crafted features significantly. However, those patterns are often learned from super-class labels due to a limited availability of fine-grained labels, while fine-grained patterns are desired in many real-world applications such as visual search in online shopping. To mitigate the challenge, we propose an algorithm to learn the fine-grained patterns sufficiently when only super-class labels are available. The effectiveness of our method can be guaranteed with the theoretical analysis. Extensive experiments on real-world data sets demonstrate that the proposed method can significantly improve the performance on target tasks corresponding to fine-grained classes, when only super-class information is available for training

Promoting cold-start items in recommender systems

As one of major challenges, cold-start problem plagues nearly all recommender systems. In particular, new items will be overlooked, impeding the development of new products online. Given limited resources, how to utilize the knowledge of recommender systems and design efficient marketing strategy for new items is extremely important. In this paper, we convert this ticklish issue into a clear mathematical problem based on a bipartite network representation. Under the most widely used algorithm in real e-commerce recommender systems, so-called the item-based collaborative filtering, we show that to simply push new items to active users is not a good strategy. To our surprise, experiments on real recommender systems indicate that to connect new items with some less active users will statistically yield better performance, namely these new items will have more chance to appear in other users' recommendation lists. Further analysis suggests that the disassortative nature of recommender systems contributes to such observation. In a word, getting in-depth understanding on recommender systems could pave the way for the owners to popularize their cold-start products with low costs.Comment: 6 pages, 6 figure