Loop optimization for tensor network renormalization

We introduce a tensor renormalization group scheme for coarse-graining a two-dimensional tensor network that can be successfully applied to both classical and quantum systems on and off criticality. The key innovation in our scheme is to deform a 2D tensor network into small loops and then optimize the tensors on each loop. In this way, we remove short-range entanglement at each iteration step and significantly improve the accuracy and stability of the renormalization flow. We demonstrate our algorithm in the classical Ising model and a frustrated 2D quantum model.Comment: 15 pages, 11 figures, accepted version for Phys. Rev. Let

Auxiliary-Variable Adaptive Control Barrier Functions for Safety Critical Systems

This paper studies safety guarantees for systems with time-varying control bounds. It has been shown that optimizing quadratic costs subject to state and control constraints can be reduced to a sequence of Quadratic Programs (QPs) using Control Barrier Functions (CBFs). One of the main challenges in this method is that the CBF-based QP could easily become infeasible under tight control bounds, especially when the control bounds are time-varying. The recently proposed adaptive CBFs have addressed such infeasibility issues, but require extensive and non-trivial hyperparameter tuning for the CBF-based QP and may introduce overshooting control near the boundaries of safe sets. To address these issues, we propose a new type of adaptive CBFs called Auxiliary-Variable Adaptive CBFs (AVCBFs). Specifically, we introduce an auxiliary variable that multiplies each CBF itself, and define dynamics for the auxiliary variable to adapt it in constructing the corresponding CBF constraint. In this way, we can improve the feasibility of the CBF-based QP while avoiding extensive parameter tuning with non-overshooting control since the formulation is identical to classical CBF methods. We demonstrate the advantages of using AVCBFs and compare them with existing techniques on an Adaptive Cruise Control (ACC) problem with time-varying control bounds.Comment: 8 pages, 4 figure

Bright 22 μ\mum Excess Candidates from WISE All-Sky Catalog and Hipparcos Main Catalog

In this paper we present a catalog which includes 141 bright candidates ($\leq10.27$ mag, V band) showing the infrared (IR) excess at 22 $\mu$m. Of which, 38 stars are known IR excess stars or disk, 23 stars are double or multiple stars and 4 are Be stars. While the remaining more than 70 stars are identified as the 22 $\mu$m excess candidates in our work. The criterion of selecting candidates is $K_s-[22]_{\mu m}$. All these candidates are selected from \emph{WISE} All-sky data cross-correlated with \emph{Hipparcos} Main Catalog and the likelihood-ratio technique is employed. Considering the effect of background, we introduce the \emph{IRAS} 100 $\mu$m level to exclude the high background. We also estimated the coincidence probability of these sources. In addition, we presented the optical to mid-infrared SEDs and optical images of all the candidates, and gave the observed optical spectra of 6 stars with NAOC's 2.16-m telescope. To measure for the dust amount around each star, the fractional luminosity is also provided. We also test whether our method of selecting IR excess stars can be used to search for extra-solar planets, we cross-matched our catalog with known IR-excess stars having planets but none is matched. Finally, we give the fraction of stars showing IR-excess for different spectral type of main-sequence stars.Comment: 45 pages, 16 figures, 4 tables. Accepted for publication in ApJ

DualTable: A Hybrid Storage Model for Update Optimization in Hive

Hive is the most mature and prevalent data warehouse tool providing SQL-like interface in the Hadoop ecosystem. It is successfully used in many Internet companies and shows its value for big data processing in traditional industries. However, enterprise big data processing systems as in Smart Grid applications usually require complicated business logics and involve many data manipulation operations like updates and deletes. Hive cannot offer sufficient support for these while preserving high query performance. Hive using the Hadoop Distributed File System (HDFS) for storage cannot implement data manipulation efficiently and Hive on HBase suffers from poor query performance even though it can support faster data manipulation.There is a project based on Hive issue Hive-5317 to support update operations, but it has not been finished in Hive's latest version. Since this ACID compliant extension adopts same data storage format on HDFS, the update performance problem is not solved. In this paper, we propose a hybrid storage model called DualTable, which combines the efficient streaming reads of HDFS and the random write capability of HBase. Hive on DualTable provides better data manipulation support and preserves query performance at the same time. Experiments on a TPC-H data set and on a real smart grid data set show that Hive on DualTable is up to 10 times faster than Hive when executing update and delete operations.Comment: accepted by industry session of ICDE201

Big networks : a survey

A network is a typical expressive form of representing complex systems in terms of vertices and links, in which the pattern of interactions amongst components of the network is intricate. The network can be static that does not change over time or dynamic that evolves through time. The complication of network analysis is different under the new circumstance of network size explosive increasing. In this paper, we introduce a new network science concept called a big network. A big networks is generally in large-scale with a complicated and higher-order inner structure. This paper proposes a guideline framework that gives an insight into the major topics in the area of network science from the viewpoint of a big network. We first introduce the structural characteristics of big networks from three levels, which are micro-level, meso-level, and macro-level. We then discuss some state-of-the-art advanced topics of big network analysis. Big network models and related approaches, including ranking methods, partition approaches, as well as network embedding algorithms are systematically introduced. Some typical applications in big networks are then reviewed, such as community detection, link prediction, recommendation, etc. Moreover, we also pinpoint some critical open issues that need to be investigated further. © 2020 Elsevier Inc