Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation

We present a discriminative nonparametric latent feature relational model (LFRM) for link prediction to automatically infer the dimensionality of latent features. Under the generic RegBayes (regularized Bayesian inference) framework, we handily incorporate the prediction loss with probabilistic inference of a Bayesian model; set distinct regularization parameters for different types of links to handle the imbalance issue in real networks; and unify the analysis of both the smooth logistic log-loss and the piecewise linear hinge loss. For the nonconjugate posterior inference, we present a simple Gibbs sampler via data augmentation, without making restricting assumptions as done in variational methods. We further develop an approximate sampler using stochastic gradient Langevin dynamics to handle large networks with hundreds of thousands of entities and millions of links, orders of magnitude larger than what existing LFRM models can process. Extensive studies on various real networks show promising performance.Comment: Accepted by AAAI 201

Minimum Entangling Power is Close to Its Maximum

Given a quantum gate $U$ acting on a bipartite quantum system, its maximum (average, minimum) entangling power is the maximum (average, minimum) entanglement generation with respect to certain entanglement measure when the inputs are restricted to be product states. In this paper, we mainly focus on the 'weakest' one, i.e., the minimum entangling power, among all these entangling powers. We show that, by choosing von Neumann entropy of reduced density operator or Schmidt rank as entanglement measure, even the 'weakest' entangling power is generically very close to its maximal possible entanglement generation. In other words, maximum, average and minimum entangling powers are generically close. We then study minimum entangling power with respect to other Lipschitiz-continuous entanglement measures and generalize our results to multipartite quantum systems. As a straightforward application, a random quantum gate will almost surely be an intrinsically fault-tolerant entangling device that will always transform every low-entangled state to near-maximally entangled state.Comment: 26 pages, subsection III.A.2 revised, authors list updated, comments are welcom

Bis[4-(2-aza­niumyleth­yl)piperazin-1-ium] di-μ-sulfido-bis­[disulfido­germanate(II)]

In the title compound, (C6H17N3)2[Ge2S6], the dimeric [Ge2S6]4− anion is formed by two edge-sharing GeS4 tetra­hedral units. The average terminal and bridging Ge—S bond lengths are 2.164 (2) and 2.272 (8) Å, respectively. The dimeric inorganic anions and the organic piperazinium cations are organized into a three-dimensional network by N—H⋯S hydrogen bonds

Jointly Modeling Topics and Intents with Global Order Structure

Modeling document structure is of great importance for discourse analysis and related applications. The goal of this research is to capture the document intent structure by modeling documents as a mixture of topic words and rhetorical words. While the topics are relatively unchanged through one document, the rhetorical functions of sentences usually change following certain orders in discourse. We propose GMM-LDA, a topic modeling based Bayesian unsupervised model, to analyze the document intent structure cooperated with order information. Our model is flexible that has the ability to combine the annotations and do supervised learning. Additionally, entropic regularization can be introduced to model the significant divergence between topics and intents. We perform experiments in both unsupervised and supervised settings, results show the superiority of our model over several state-of-the-art baselines.Comment: Accepted by AAAI 201

Thermodynamic analysis of a dual-loop organic Rankine cycle (ORC) for waste heat recovery of a petrol engine

Huge amounts of low-grade heat energy are discharged to the environment by vehicular engines. Considering the large number of vehicles in the world, such waste energy has a great impact on our environment globally. The Organic Rankine Cycle (ORC), which uses an organic fluid with a low boiling point as the working medium, is considered to be the most promising technology to recover energy from low-grade waste heat. In this study, a dual-loop ORC is presented to simultaneously recover energy from both the exhaust gases and the coolant of a petrol engine. A high-temperature (HT) ORC loop is used to recover heat from the exhaust gases, while a low-temperature (LT) ORC loop is used to recover heat from the coolant and the condensation heat of the HT loop. Figure 1 shows the schematic of the dual-loop ORC. Differing from previous research, two more environmentally friendly working fluids are used, and the corresponding optimisation is conducted. First, the system structure and operating principle are described. Then, a mathematical model of the designed dual-loop ORC is established. Next, the performance of the dual-loop cycle is analysed over the entire engine operating region. Furthermore, the states of each point along the cycle and the heat load of each component are compared with the results of previous research. The results show that the dual-loop ORC can effectively recover the waste heat from the petrol engine, and that the effective thermal efficiency can be improved by about 20 ~ 24%, 14~20%, and 30% in the high-speed, medium-speed, and low-speed operation regions, respectively. The designed dual-loop ORC can achieve a higher system efficiency than previous ORCs of this structure. Therefore, it is a good choice for waste heat recovery from vehicle engines