The Effect of Energy Service Companies on Energy Use in Selected Developing Countries: A Synthetic Control Approach

This study investigates the empirical effect of the service provided by the energy service companies (ESCOs) on the total energy use in thirteen developing countries by employing a transparent and data-driven statistical methodology, the synthetic control method (SCM). This methodology compares the post-treatment total energy use of a treated country, a country that has initiated ESCO activities, with the trajectory of total energy use for a synthetic control unit, a combination of economies being similar to the treated country with the exception of no ESCO activities initiated. The SCM can account for the potential heterogeneity regarding the effect of ESCO activities in various countries. In these thirteen countries, we find that the ESCOs exert a strong energy-saving effect in Colombia, Ghana, Kenya and South Africa; while a robust energy-using effect is found in Chile. No significant energy using or saving effects are found in the rest of treated countries.      Keywords: Energy service companies (ESCOs), Synthetic control method (SCM), Total energy use JEL Classifications: O13, Q43, Q5

Learning Discriminative Shrinkage Deep Networks for Image Deconvolution

Most existing methods usually formulate the non-blind deconvolution problem into a maximum-a-posteriori framework and address it by manually designing kinds of regularization terms and data terms of the latent clear images. However, explicitly designing these two terms is quite challenging and usually leads to complex optimization problems which are difficult to solve. In this paper, we propose an effective non-blind deconvolution approach by learning discriminative shrinkage functions to implicitly model these terms. In contrast to most existing methods that use deep convolutional neural networks (CNNs) or radial basis functions to simply learn the regularization term, we formulate both the data term and regularization term and split the deconvolution model into data-related and regularization-related sub-problems according to the alternating direction method of multipliers. We explore the properties of the Maxout function and develop a deep CNN model with a Maxout layer to learn discriminative shrinkage functions to directly approximate the solutions of these two sub-problems. Moreover, given the fast-Fourier-transform-based image restoration usually leads to ringing artifacts while conjugate-gradient-based approach is time-consuming, we develop the Conjugate Gradient Network to restore the latent clear images effectively and efficiently. Experimental results show that the proposed method performs favorably against the state-of-the-art ones in terms of efficiency and accuracy

A novel regulatory event-based gene set analysis method for exploring global functional changes in heterogeneous genomic data sets

<p>Abstract</p> <p>Background</p> <p>Analyzing gene expression data by assessing the significance of pre-defined gene sets, rather than individual genes, has become a main approach in microarray data analysis and this has promisingly derive new biological interpretations of microarray data. However, the detection power of conventional gene list or gene set-based approaches is limited on highly heterogeneous samples, such as tumors.</p> <p>Results</p> <p>We developed a novel method, the regulatory <b>e</b>vent-based <b>G</b>ene <b>S</b>et <b>A</b>nalysis (eGSA), which considers not only the consistently changed genes but also every gene regulation (event) of each sample to overcome the detection limit. In comparison with conventional methods, eGSA can detect functional changes in heterogeneous samples more precisely and robustly. Furthermore, by utilizing eGSA, we successfully revealed novel functional characteristics and potential mechanisms of very early hepatocellular carcinoma (HCC).</p> <p>Conclusion</p> <p>Our study creates a novel scheme to directly target the major cellular functional changes in heterogeneous samples. All potential regulatory routines of a functional change can be further analyzed by the regulatory event frequency. We also provide a case study on early HCCs and reveal a novel insight at the initial stage of hepatocarcinogenesis. eGSA therefore accelerates and refines the interpretation of heterogeneous genomic data sets in the absence of gene-phenotype correlations.</p

A Practical and Secure Stateless Order Preserving Encryption for Outsourced Databases

Order-preserving encryption (OPE) plays an important role in securing outsourced databases. OPE schemes can be either Stateless or Stateful. Stateful schemes can achieve the ideal security of order-preserving encryption, i.e., “reveal no information about the plaintexts besides order.” However, comparing to stateless schemes, stateful schemes require maintaining some state information locally besides encryption keys and the ciphertexts are mutable. On the other hand, stateless schemes only require remembering encryption keys and thus is more efficient. It is a common belief that stateless schemes cannot provide the same level of security as stateful ones because stateless schemes reveal the relative distance among their corresponding plaintext. In real world applications, such security defects may lead to the leakage of statistical and sensitive information, e.g., the data distribution, or even negates the whole encryption. In this paper, we propose a practical and secure stateless order-preserving encryption scheme. With prior knowledge of the data to be encrypted, our scheme can achieve IND-CCPA (INDistinguishability under Committed ordered Chosen Plaintext Attacks) security for static data set. Though the IND-CCPA security can\u27t be met for dynamic data set, our new scheme can still significantly improve the security in real world applications. Along with the encryption scheme, in this paper we also provide methods to eliminate access pattern leakage in communications and thus prevents some common attacks to OPE schemes in practice

On the Improvement of Wiener Attack on RSA with Small Private Exponent

RSA system is based on the hardness of the integer factorization problem (IFP). Given an RSA modulus N=pq, it is difficult to determine the prime factors p and q efficiently. One of the most famous short exponent attacks on RSA is the Wiener attack. In 1997, Verheul and van Tilborg use an exhaustive search to extend the boundary of the Wiener attack. Their result shows that the cost of exhaustive search is 2r+8 bits when extending the Weiner's boundary r bits. In this paper, we first reduce the cost of exhaustive search from 2r+8 bits to 2r+2 bits. Then, we propose a method named EPF. With EPF, the cost of exhaustive search is further reduced to 2r-6 bits when we extend Weiner's boundary r bits. It means that our result is 214 times faster than Verheul and van Tilborg's result. Besides, the security boundary is extended 7 bits

When Social Influence Meets Item Inference

Research issues and data mining techniques for product recommendation and viral marketing have been widely studied. Existing works on seed selection in social networks do not take into account the effect of product recommendations in e-commerce stores. In this paper, we investigate the seed selection problem for viral marketing that considers both effects of social influence and item inference (for product recommendation). We develop a new model, Social Item Graph (SIG), that captures both effects in form of hyperedges. Accordingly, we formulate a seed selection problem, called Social Item Maximization Problem (SIMP), and prove the hardness of SIMP. We design an efficient algorithm with performance guarantee, called Hyperedge-Aware Greedy (HAG), for SIMP and develop a new index structure, called SIG-index, to accelerate the computation of diffusion process in HAG. Moreover, to construct realistic SIG models for SIMP, we develop a statistical inference based framework to learn the weights of hyperedges from data. Finally, we perform a comprehensive evaluation on our proposals with various baselines. Experimental result validates our ideas and demonstrates the effectiveness and efficiency of the proposed model and algorithms over baselines.Comment: 12 page