Layer Construction of Three-Dimensional Z2 Monopole Charge Nodal Line Semimetals and prediction of the abundant candidate materials

The interplay between symmetry and topology led to the concept of symmetry-protected topological states, including all non-interacting and weakly interacting topological quantum states. Among them, recently proposed nodal line semimetal states with space-time inversion ($\mathcal{PT}$) symmetry which are classified by the Stiefel-Whitney characteristic class associated with real vector bundles and can carry a nontrivial $\mathbb{Z}_2$ monopole charge have attracted widespread attention. However, we know less about such 3D $\mathbb{Z}_2$ nodal line semimetals and do not know how to construct them. In this work, we first extend the layer construction previously used to construct topological insulating states to topological semimetallic systems. We construct 3D $\mathbb{Z}_2$ nodal line semimetals by stacking of 2D $\mathcal{PT}$-symmetric Dirac semimetals via nonsymmorphic symmetries. Based on our construction scheme, effective model and combined with first-principles calculations, we predict two types of candidate electronic materials for $\mathbb{Z}_2$ nodal line semimetals, namely 14 Si and Ge structures and 108 transition metal dichalcogenides $MX_2$ ($M$=Cr, Mo, W, $X$=S, Se, Te). Our theoretical construction scheme can be directly applied to metamaterials and circuit systems. Our work not only greatly enriches the candidate materials and deepens the understanding of $\mathbb{Z}_2$ nodal line semimetal states but also significantly extends the application scope of layer construction

Selective Refinement Network for High Performance Face Detection

High performance face detection remains a very challenging problem, especially when there exists many tiny faces. This paper presents a novel single-shot face detector, named Selective Refinement Network (SRN), which introduces novel two-step classification and regression operations selectively into an anchor-based face detector to reduce false positives and improve location accuracy simultaneously. In particular, the SRN consists of two modules: the Selective Two-step Classification (STC) module and the Selective Two-step Regression (STR) module. The STC aims to filter out most simple negative anchors from low level detection layers to reduce the search space for the subsequent classifier, while the STR is designed to coarsely adjust the locations and sizes of anchors from high level detection layers to provide better initialization for the subsequent regressor. Moreover, we design a Receptive Field Enhancement (RFE) block to provide more diverse receptive field, which helps to better capture faces in some extreme poses. As a consequence, the proposed SRN detector achieves state-of-the-art performance on all the widely used face detection benchmarks, including AFW, PASCAL face, FDDB, and WIDER FACE datasets. Codes will be released to facilitate further studies on the face detection problem.Comment: The first two authors have equal contributions. Corresponding author: Shifeng Zhang ([email protected]

Design, Fabrication, and Properties of 2-2 Connectivity Cement/Polymer based Piezoelectric Composites with Varied Piezoelectric Phase Distribution

The laminated 2-2 connectivity cement/polymer based piezoelectric composites with variedpiezoelectric phase distribution were fabricated by employing Lead Zirconium Titanate ceramicas active phase, and mixture of cement powder, epoxy resin, and hardener as matrix phase with a mass proportion of 4:4:1. The dielectric, piezoelectric, and electromechanical coupling properties of the composites were studied. The composites with large total volume fraction ofpiezoelectric phase have large piezoelectric strain constant and relative permittivity, and thepiezoelectric and dielectric properties of the composites are independent of the dimensional variations of the piezoelectric ceramic layer. The composites with small total volume fraction of piezoelectric phase have large piezoelectric voltage constant, but also large dielectric loss. The composite with gradually increased dimension of piezoelectric ceramic layer has the smallest dielectric loss, and that with the gradually increased dimension of matrix layer has the largest piezoelectric voltage constant. The novel piezoelectric composites show potential applications in fabricating ultrasonic transducers with varied surface vibration amplitude of thetransducer

Research on Embedded Sensors for Concrete Health Monitoring Based on Ultrasonic Testing

In this article, embedded ultrasonic sensors were prepared using 1–3-type piezoelectric composite and piezoelectric ceramic as the piezoelectric elements, respectively. The frequency bandwidth of the novel embedded ultrasonic sensors was investigated. To obtain the relationship between the receiving ultrasonic velocity and compressive strength, as well as their response signals to crack damage, the sensors were fabricated and embedded into the cement mortar before testing. The results demonstrated that the piezoelectric composite sensor had wider frequency bandwidth than the piezoelectric ceramic sensor. The compressive strength and ultrasonic velocity had a positive linear relationship, with a correlation coefficient of 0.9216. The head wave amplitude of the receiving ultrasonic signal was sensitive to the changing crack damage and gradually decayed with the increasing degree of cement damage. Thus, the novel embedded ultrasonic sensors are suitable for concrete health monitoring via ultrasonic non-destructive testing

The Impact of Extended Heat Exposure on Rapid Sulphoaluminate Cement Concrete Up To 120°C

This study examined the stability of rapid sulphoaluminate cement concrete (R-SACC) when exposed to heat for extended periods of time. The physicochemical processes present in R-SACC as a function of temperature were determined through various tests. The general behavior of rapid sulphoaluminate cement (R-SAC) at a range of temperatures is summarized. The results show that observing color change could be a simple way to identify deterioration of R-SACC, along with the rebound hammer. The matrix formation of ettringite was broken and the mass of the hydrated product decreased with heat exposure; the major mineral composition of the paste consisted of CaSO4, CaCO3 and β-C2S; and the interface between aggregate and paste in the R-SACC become loosely structured with cracks. Between 50°C and 120°C, the rapid sulphoaluminate cement (R-SAC) paste first expanded and then shrank, and the shrinkage rate of R-SAC was much greater than that of R-SACC

Relational Learning for Joint Head and Human Detection

Head and human detection have been rapidly improved with the development of deep convolutional neural networks. However, these two tasks are often studied separately without considering their inherent correlation, leading to that 1) head detection is often trapped in more false positives, and 2) the performance of human detector frequently drops dramatically in crowd scenes. To handle these two issues, we present a novel joint head and human detection network, namely JointDet, which effectively detects head and human body simultaneously. Moreover, we design a head-body relationship discriminating module to perform relational learning between heads and human bodies, and leverage this learned relationship to regain the suppressed human detections and reduce head false positives. To verify the effectiveness of the proposed method, we annotate head bounding boxes of the CityPersons and Caltech-USA datasets, and conduct extensive experiments on the CrowdHuman, CityPersons and Caltech-USA datasets. As a consequence, the proposed JointDet detector achieves state-of-the-art performance on these three benchmarks. To facilitate further studies on the head and human detection problem, all new annotations, source codes and trained models will be public

3D carbon allotropes: Topological quantum materials with obstructed atomic insulating phases, multiple bulk-boundary correspondences, and real topology

The study of topological phases with unconventional bulk-boundary correspondences and nontrivial real Chern number has garnered significant attention in the topological states of matter. Using the first-principle calculations and theoretical analysis, we perform a high-throughput material screening of the 3D obstructed atomic insulators (OAIs) and 3D real Chern insulators (RCIs) based on the Samara Carbon Allotrope Database (SACADA). Results show that 422 out of 703 3D carbon allotropes are 3D OAIs with multiple bulk-boundary correspondences, including 2D obstructed surface states (OSSs) and 1D hinge states, which are in one dimension and two dimensions lower than the 3D bulk, respectively. The 2D OSSs in these OAIs can be modified when subjected to appropriate boundaries, which benefits the investigation of surface engineering and the development of efficient topological catalysts. These 422 OAIs, which have 2D and 1D boundary states, are excellent platforms for multi-dimensional topological boundaries research. Remarkably, 138 of 422 OAIs are also 3D RCIs, which show a nontrivial real topology in the protection of spacetime inversion symmetry. Our work not only provides a comprehensive list of 3D carbon-based OAIs and RCIs, but also guides their application in various aspects based on multiple bulk-boundary correspondences and real topological phases

Dynamics of Associative Polymers with High Density of Reversible Bonds

We design and synthesize unentangled associative polymers carrying unprecedented high fractions of stickers, up to eight per Kuhn segment, that can form strong pairwise hydrogen bonding of $\sim20k_BT$ without microphase separation. The reversible bonds significantly slow down the polymer dynamics but nearly do not change the shape of linear viscoelastic spectra. Moreover, the structural relaxation time of associative polymers increases exponentially with the fraction of stickers and exhibits a universal yet non-Arrhenius dependence on the distance from polymer glass transition temperature. These results cannot be understood within the framework of the classic sticky-Rouse model but are rationalized by a renormalized Rouse model, which highlights an unexpected influence of reversible bonds on the structural relaxation rather than the shape of viscoelastic spectra for associative polymers with high concentrations of stickers.Comment: 4 figure