A Pairwise Difference Estimator for Partially Linear Spatial Autoregressive Models

Su and Jin (2010) develop for partially linear spatial autoregressive (PL-SAR) model a profile quasimaximum likelihood based estimation procedure. More recently, Su (2011) proposes for this model a semiparametric GMM estimator. However, both of them can be computationally challenging for applied researchers and are not easy to implement in practice. In this article, we propose a computationally simple estimator for the PL-SAR model in the presence of either heteroscedastic or spatially correlated error terms. This estimator blends the essential features of both the GMM estimator for linear SAR model and the pairwise difference estimator for conventional partially linear model. Limiting distribution of the proposed estimator is established and consistent estimator for its asymptotic CV matrix is provided. Monte Carlo studies indicate that our estimator is attractive particularly when one is interested in estimating the finite-dimensional parameters in the model.Spatial autoregression, Partially linear model, Pairwise difference

Studies of epigenetic instability in human normal and diseased vulva skin

Epigenetic modification is another mechanism involved in the cancer development besides classic mutations such as deletion. Aberrant promoter methylation and associated chromatin modification have been frequently reported in various tumors of different clinical stages. Hypermethylation has been frequently observed in tumor suppressor genes and causes reduced transcripts of these genes. Hypomethylation also has been reported to be involved in activation of oncogenes. Inactivation of the X chromosome and the imprinting of gamete DNA depend on the methylation patterns in the promoter region. Understanding methylation mechanisms could be helpful to diagnosis of early stage tumorgenesis or offer molecular markers for detecting cancers. Changes in methylation patterns based on human vulva pathological tissue type of four genes have been studied in the current project. RASSF1A and DAPK-1 are tumor suppressor genes. BRCA2 is considered highly associated with breast and ovarian cancer. And H19 is a maternal imprinted gene. DAPK-1 and BRCA2 have been found to be significantly hypermethylated in Lichen Sclerosis (LS) and Squamous Cell Carcinoma (SCC). And RASSF1A has displayed an interesting methylation pattern in the post transcription region, where the frequency of methylation significantly decreased from normal tissue to LS tissue, but then dramatically increased to SCC. H19 failed to show any changes in the methylation pattern with methods tested. This was most likely due to interference of primer dimers by SYBR-green in the real-time PCR analyses. Instead of using the same promoter sequence reported by previous papers, extended estimated promoter regions and partial transcription regions were obtained from Genome Browser and additional CpG island sites have been studied in current project. Methylation patterns have been detected that differ from the literature in these genes. These results may provide more information to find a more precise active promoter region and epigenetic involved sequences for future research

Bolt Detection Signal Analysis Method Based on ICEEMD

The construction quality of the bolt is directly related to the safety of the project, and as such, it must be tested. In this paper, the improved complete ensemble empirical mode decomposition (ICEEMD) method is introduced to the bolt detection signal analysis. The ICEEMD is used in order to decompose the anchor detection signal according to the approximate entropy of each intrinsic mode function (IMF). The noise of the IMFs is eliminated by the wavelet soft threshold de-noising technique. Based on the approximate entropy, and the wavelet de-noising principle, the ICEEMD-De anchor signal analysis method is proposed. From the analysis of the vibration analog signal, as well as the bolt detection signal, the result shows that the ICEEMD-De method is capable of correctly separating the different IMFs under noisy conditions, and also that the IMF can effectively identify the reflection signal of the end of the bolt

Numerical simulation of the optimal two-mode attacks for two-way continuous-variable quantum cryptography in reverse reconciliation

We analyze the security of the two-way continuous-variable quantum key distribution protocol in reverse reconciliation against general two-mode attacks, which represent all accessible attacks at fixed channel parameters. Rather than against one specific attack model, the expression of secret key rates of the two-way protocol are derived against all accessible attack models. It is found that there is an optimal two-mode attack to minimize the performance of the protocol in terms of both secret key rates and maximal transmission distances. We identify the optimal two-mode attack, give the specific attack model of the optimal two-mode attack and show the performance of the two-way protocol against the optimal two-mode attack. Even under the optimal two-mode attack, the performances of two-way protocol are still better than the corresponding one-way protocol, which shows the advantage of making a double use of the quantum channel and the potential of long-distance secure communication using two-way protocol.Comment: 14 pages, 8 figure

Improvement of two-way continuous-variable quantum key distribution with virtual photon subtraction

We propose a method to improve the performance of two-way continuous-variable quantum key distribution protocol by virtual photon subtraction. The Virtual photon subtraction implemented via non-Gaussian post-selection not only enhances the entanglement of two-mode squeezed vacuum state but also has advantages in simplifying physical operation and promoting efficiency. In two-way protocol, virtual photon subtraction could be applied on two sources independently. Numerical simulations show that the optimal performance of renovated two-way protocol is obtained with photon subtraction only used by Alice. The transmission distance and tolerable excess noise are improved by using the virtual photon subtraction with appropriate parameters. Moreover, the tolerable excess noise maintains a high value with the increase of distance so that the robustness of two-way continuous-variable quantum key distribution system is significantly improved, especially at long transmission distance.Comment: 15 pages, 6 figure

Deep Landscape Forecasting for Real-time Bidding Advertising

The emergence of real-time auction in online advertising has drawn huge attention of modeling the market competition, i.e., bid landscape forecasting. The problem is formulated as to forecast the probability distribution of market price for each ad auction. With the consideration of the censorship issue which is caused by the second-price auction mechanism, many researchers have devoted their efforts on bid landscape forecasting by incorporating survival analysis from medical research field. However, most existing solutions mainly focus on either counting-based statistics of the segmented sample clusters, or learning a parameterized model based on some heuristic assumptions of distribution forms. Moreover, they neither consider the sequential patterns of the feature over the price space. In order to capture more sophisticated yet flexible patterns at fine-grained level of the data, we propose a Deep Landscape Forecasting (DLF) model which combines deep learning for probability distribution forecasting and survival analysis for censorship handling. Specifically, we utilize a recurrent neural network to flexibly model the conditional winning probability w.r.t. each bid price. Then we conduct the bid landscape forecasting through probability chain rule with strict mathematical derivations. And, in an end-to-end manner, we optimize the model by minimizing two negative likelihood losses with comprehensive motivations. Without any specific assumption for the distribution form of bid landscape, our model shows great advantages over previous works on fitting various sophisticated market price distributions. In the experiments over two large-scale real-world datasets, our model significantly outperforms the state-of-the-art solutions under various metrics.Comment: KDD 2019. The reproducible code and dataset link is https://github.com/rk2900/DL