GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network Embedding

In this paper, we propose GPSP, a novel Graph Partition and Space Projection based approach, to learn the representation of a heterogeneous network that consists of multiple types of nodes and links. Concretely, we first partition the heterogeneous network into homogeneous and bipartite subnetworks. Then, the projective relations hidden in bipartite subnetworks are extracted by learning the projective embedding vectors. Finally, we concatenate the projective vectors from bipartite subnetworks with the ones learned from homogeneous subnetworks to form the final representation of the heterogeneous network. Extensive experiments are conducted on a real-life dataset. The results demonstrate that GPSP outperforms the state-of-the-art baselines in two key network mining tasks: node classification and clustering.Comment: WWW 2018 Poste

NMSSM with generalized deflected mirage mediation

We propose to generate a realistic soft SUSY breaking spectrum for Next-to-Minimal Supersymmetric Standard Model (NMSSM) with a generalized deflected mirage mediation scenario, in which additional Yukawa and gauge mediation contributions are included to deflect the renormalization group equation(RGE) trajectory. Based on the Wilsonian effective action obtained by integrating out the messengers, the NMSSM soft SUSY breaking spectrum can be given analytically at the messenger scale. We find that additional contributions to $m_S^2$ can possibly ameliorate the stringent constraints from the electroweak symmetry breaking (EWSB) and 125 GeV Higgs mass. Constraints from dark matter and fine-tuning are also discussed. The Barbieri-Giudice fine-tuning measure and electroweak fine-tuning measure in our scenario can be as low as ${\cal O}(1)$, which possibly indicates that our scenario is natural.Comment: Published version, minor changes; 28 pages, 6 figure

Construction of a molecular marker linkage map and its use for quantitative trait locus (QTLs) underlying drought tolerance at germination stage in soybean

A backcross inbred line (BIL) population of soybean was examined under polyethylene glycol (PEG) and  well-watered conditions to identify the quantitative trait locus (QTL) controlling the drought-tolerance at germination stage. The recipient, SNWS0048, was a wild soybean with strong drought tolerance and the donor, Jinda73, was a drought-sensitive variety with superior agronomic traits. The molecular genetic linkage map was produced using the technique of simple sequence repeats (SSR) marker and tool of gene mapping, and 120 SSR markers and 2 morphology markers covering 1655.4 cM were produced. The average genetic distance between markers was 17.68 cM. The range of markers per linkage group was from 2 to 9, and the length was from 2.8 to 230.0 cM. Most of the markers among linkages were well distributed. 17 QTLs with additive effects and/or additive × environment interaction effects, involved in drought tolerance of soybean in germination stage, were found on linkage group G2-A2, G10-D2, G11-E. Out of these QTLs, 9 QTLs only were significant in additive effects, 8 QTLs had additive effect and additive effect by PEG treatment. Four tightly linked QTLs (Sat_199-I on MLG G2-A2, I-Satt327 on G2-A2, Satt528-Sat_365 on MLG G10-D2, Satt573-Satt606 on MLG G11-E) controlling drought tolerant traits in germination stage were revealed, and would be useful in future for marker assisted selection programs (MAS) and cultivar improvement.Key words: Soybean, molecular marker linkage map, quantitative trait loci (QTLs), drought tolerance, germination stage

Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification

Quantum neural networks (QNNs) have become an important tool for understanding the physical world, but their advantages and limitations are not fully understood. Some QNNs with specific encoding methods can be efficiently simulated by classical surrogates, while others with quantum memory may perform better than classical classifiers. Here we systematically investigate the problem-dependent power of quantum neural classifiers (QCs) on multi-class classification tasks. Through the analysis of expected risk, a measure that weighs the training loss and the generalization error of a classifier jointly, we identify two key findings: first, the training loss dominates the power rather than the generalization ability; second, QCs undergo a U-shaped risk curve, in contrast to the double-descent risk curve of deep neural classifiers. We also reveal the intrinsic connection between optimal QCs and the Helstrom bound and the equiangular tight frame. Using these findings, we propose a method that uses loss dynamics to probe whether a QC may be more effective than a classical classifier on a particular learning task. Numerical results demonstrate the effectiveness of our approach to explain the superiority of QCs over multilayer Perceptron on parity datasets and their limitations over convolutional neural networks on image datasets. Our work sheds light on the problem-dependent power of QNNs and offers a practical tool for evaluating their potential merit.Comment: Updated version. Published on PR

Multivariate Functional Clustering with Variable Selection and Application to Sensor Data from Engineering Systems

Multi-sensor data that track system operating behaviors are widely available nowadays from various engineering systems. Measurements from each sensor over time form a curve and can be viewed as functional data. Clustering of these multivariate functional curves is important for studying the operating patterns of systems. One complication in such applications is the possible presence of sensors whose data do not contain relevant information. Hence it is desirable for the clustering method to equip with an automatic sensor selection procedure. Motivated by a real engineering application, we propose a functional data clustering method that simultaneously removes noninformative sensors and groups functional curves into clusters using informative sensors. Functional principal component analysis is used to transform multivariate functional data into a coefficient matrix for data reduction. We then model the transformed data by a Gaussian mixture distribution to perform model-based clustering with variable selection. Three types of penalties, the individual, variable, and group penalties, are considered to achieve automatic variable selection. Extensive simulations are conducted to assess the clustering and variable selection performance of the proposed methods. The application of the proposed methods to an engineering system with multiple sensors shows the promise of the methods and reveals interesting patterns in the sensor data.Comment: 30 pages, 7 figure