Sure Screening for Gaussian Graphical Models

We propose {graphical sure screening}, or GRASS, a very simple and computationally-efficient screening procedure for recovering the structure of a Gaussian graphical model in the high-dimensional setting. The GRASS estimate of the conditional dependence graph is obtained by thresholding the elements of the sample covariance matrix. The proposed approach possesses the sure screening property: with very high probability, the GRASS estimated edge set contains the true edge set. Furthermore, with high probability, the size of the estimated edge set is controlled. We provide a choice of threshold for GRASS that can control the expected false positive rate. We illustrate the performance of GRASS in a simulation study and on a gene expression data set, and show that in practice it performs quite competitively with more complex and computationally-demanding techniques for graph estimation

Facial Motion Prior Networks for Facial Expression Recognition

Deep learning based facial expression recognition (FER) has received a lot of attention in the past few years. Most of the existing deep learning based FER methods do not consider domain knowledge well, which thereby fail to extract representative features. In this work, we propose a novel FER framework, named Facial Motion Prior Networks (FMPN). Particularly, we introduce an addition branch to generate a facial mask so as to focus on facial muscle moving regions. To guide the facial mask learning, we propose to incorporate prior domain knowledge by using the average differences between neutral faces and the corresponding expressive faces as the training guidance. Extensive experiments on three facial expression benchmark datasets demonstrate the effectiveness of the proposed method, compared with the state-of-the-art approaches.Comment: VCIP 2019, Oral. Code is available at https://github.com/donydchen/FMPN-FE

Efficient 525nm laser generation in single or double resonant cavity

This paper reports the results of a study into highly efficient sum frequency generation from 792 and 1556 nm wavelength light to 525 nm wavelength light using either a single or double resonant ring cavity based on a periodically poled potassium titanyl phosphate crystal (PPKTP). By optimizing the cavity$'$s parameters, the maximum power achieved for the resultant 525 nm laser was 263 and 373 mW for the single and double resonant cavity, respectively. The corresponding quantum conversion efficiencies were 8 and 77\% for converting 1556 nm photons to 525 nm photons with the single and double resonant cavity, respectively. The measured intra-cavity single pass conversion efficiency for both configurations was about 5\%. The performances of the sum frequency generation in these two configurations were studied and compared in detail. This work will provide guidelines for optimizing the generation of sum frequency generated laser light for a variety of configurations. The high conversion efficiency achieved in this work will help pave the way for frequency up-conversion of non-classical quantum states, such as the squeezed vacuum and single photon states. The proposed green laser source will be used in our future experiments, which includes a plan to generate two-colour entangled photon pairs and achieve the frequency down-conversion of single photons carrying orbital angular momentum.Comment: 7 pages,1 table, 5 figures,36 referenc

Evaluation of the POSSUM, p-POSSUM, o-POSSUM, and APACHE II scoring systems in predicting postoperative mortality and morbidity in gastric cancer patients

SummaryBackground/ObjectiveGastric cancer is the fourth most prevalent cancer worldwide. The ability to accurately predict surgery-related morbidity and mortality is critical in deciding both the timing of surgery and choice of surgical procedure. The aim of this study is to compare the POSSUM, p-POSSUM, o-POSSUM, and APACHE II scoring systems for predicting surgical morbidity and mortality in Chinese gastric cancer patients, as well as to create new scoring systems to achieve better prediction.MethodsData from 612 gastric cancer patients undergoing gastrectomy between January 2007 and December 2011 were included in this study. The predictive abilities of the four scoring systems were compared by examining observed-to-expected (O/E) ratios, the receiver operating characteristic curve, Student t test, and χ2 test results.ResultsThe observed complication rate of 34% (n = 208) did not differ significantly from the rate of 36.6% (n = 208) predicted by the POSSUM scoring system (O/E ratio = 0.93). The observed mortality rate was 2.9% (n = 18). For predicting mortality, POSSUM had an O/E ratio of 0.34 as compared with p-POSSUM (O/E ratio = 0.91), o-POSSUM (O/E ratio = 1.26), and APACHE II (O/E ratio = 0.28).ConclusionThe POSSUM scoring system performed well with respect to predicting morbidity risk following gastric cancer resection. For predicting postoperative mortality, p-POSSUM and o-POSSUM exhibited superior performance relative to POSSUM and APACHE II

Development of the Catfish 250K SNP Array for Genome-Wide Association Studies

Catfish is the primary aquaculture species in the United States. In recent years, the catfish industry has encountered unprecedented challenges including increased feeding costs, devastating diseases and fierce international competition. Traditional selective breeding have been conducted for catfish breeds with improved traits of fast-growth, high feed efficiency and high level of disease resistance. Genomic selection that utilizes whole genome-based markers to assist selective breeding holds premise with increased selection intensity and accuracy. However, genome-scale genetic markers are required for its implementation, which has been a major limitation to most farming animals including aquaculture species. In recent years, next-generation sequencing technologies have enabled efficient and cost-effective identification of genome-scale genetic markers, such as single nucleotide polymorphisms (SNPs). With the availability of a large number of SNPs, the challenge then is how to genotype these SNPs efficiently and economically. One of the most efficient approaches is to design a high-density array that includes hundreds of thousands of SNPs covering the entire genome. Toward genomic selection in catfish, my research, as presented here, encompasses these two major progresses with the generation of genome-scale SNPs and the development of a high-density SNP array in catfish. Using the RNA-Seq approach, over two million gene-associated SNPs were identified from channel catfish and blue catfish, two of the most important catfish species, providing a large pool of SNP resources for designing SNP array. Criteria-based filtering resulted in hundreds of thousands of quality SNPs that are intra-specific in channel catfish, intra-specific in blue catfish, and inter-specific between the two species. This is the first application of next-generation sequencing technology in catfish for genome-wide SNP identification. With the large number of SNPs, it’s important to select SNPs to represent each gene because SNPs within same genes are always linked. In addition, pseudo-SNPs can be detected due to misalignment of paralogous sequences from duplicated genes. To generate the reference gene transcript sequences for SNP selection and to detect potential pseudo-SNPs derived from duplicated genes, the catfish transcriptome assembly and annotation was conducted by RNA-Seq of a doubled haploid channel catfish, which harbors two identical sets of chromosomes and therefore there should be no variations. A comprehensive set of catfish transcript sequences was obtained including over 14,000 full-length transcripts. A set of genes putatively duplicated in catfish genome were identified, which aided the detection of pseudo-SNPs. With these resources, the catfish 250K SNP array was developed with the state-of-the-art Affymetrix Axiom technology with inclusion of over 250,000 SNPs. This is the first high density SNP array developed for catfish, which should be valuable for both the catfish industry and research such as in genomic selection, genome-wide association studies, fine linkage mapping and haplotype analysis

Layout and Location of Water IoT Device Based on Few-Shot Reinforcement Learning

After the traditional water equipment integrates the communication module, IoT (Internet of Things) device is formed. Whether these battery-powered IoT devices can be installed in a certain location depends on whether the power consumption of these IoT devices in these locations can meet the expected life cycle. In this paper, by adopting strategies to save the power consumption of IoT devices when sending data, more locations can be selected to install IoT devices. The process of IoT device sending data packet sequence needs to be aware of the environment, interact with the environment, then make a decision, and then adjust the policy according to the effect of the action. Therefore, in this paper, the process of IoT device sending data packet sequence is modelled as MDP (Markov Sequence Decision Process), and the real-time SINR of channel and the transmission delay of data packet sequence are defined as the state space, and the action space consists of immediate transmission and delayed transmission, with the minimum total power consumption as the objective function. Because IoT devices are very sensitive to power consumption and cannot collect a large amount of data for training, this paper uses the Proximal Policy Optimization algorithm based on prior distribution to conduct few-shot reinforcement learning to quickly obtain the optimal decision sequence of layout and location of IoT devices

Credit-based Pricing for Multi-user Class Transportation Facilities

This paper proposes an innovative arc-based credit (ABC) congestion pricing scheme to improve the system performance in a transportation network. By associating each arc with apositive or negative credit rate, the strategy can accomplish multiple planning goals, such as efficiency, fairness, and public acceptance simultaneously. We first demonstrate that on a one-origin or one-destination network, a pareto-improving, system-optimal and revenue-neutral credit scheme always exists and can be obtained by solving a set of linear equations. Recognizing that such a credit scheme may not exist in a multi-origin network, we then define the maximum-revenue problem with pareto-improving constrains (MRPI): find the maximum possible revenue collected by the credit scheme with optimal arc flows and non-increasing origin-destination (OD) travel costs. We discover that the dual of MRPI is equivalent to a typical Transportation Problem which, therefore, provides a simple way to calculate the revenue by just examining the dual problem. At the end of the paper, a numerical example with a small synthetic network is provided for the comparison of the credit scheme with other existing toll schemes in terms of OD travel disutilities