A Prototype Scalable Readout System for Micro-pattern Gas Detectors

A scalable readout system (SRS) is designed to provide a general solution for different micro-pattern gas detectors. The system mainly consists of three kinds of modules: the ASIC card, the Adapter card and the Front-End Card (FEC). The ASIC cards, mounted with particular ASIC chips, are designed for receiving detector signals. The Adapter card is in charge of digitizing the output signals from several ASIC cards. The FEC, edged-mounted with the Adapter, has a FPGA-based reconfigurable logic and I/O interfaces, allowing users to choose various ASIC cards and Adapters for different types of detectors. The FEC transfers data through Gigabit Ethernet protocol realized by a TCP processor (SiTCP) IP core in field-programmable gate arrays (FPGA). The readout system can be tailored to specific sizes to adapt to the experiment scales and readout requirements. In this paper, two kinds of multi-channel ASIC chips, VA140 and AGET, are applied to verify the concept of this SRS architecture. Based on this VA140 or AGET SRS, one FEC covers 8 ASIC (VA140) cards handling 512 detector channels, or 4 ASIC (AGET) cards handling 256 detector channels. More FECs can be assembled in chassis to handle thousands of detector channels.Comment: 6 pages, 7 figures, 2 table

Covariance, correlation matrix and the multi-scale community structure of networks

Empirical studies show that real world networks often exhibit multiple scales of topological descriptions. However, it is still an open problem how to identify the intrinsic multiple scales of networks. In this article, we consider detecting the multi-scale community structure of network from the perspective of dimension reduction. According to this perspective, a covariance matrix of network is defined to uncover the multi-scale community structure through the translation and rotation transformations. It is proved that the covariance matrix is the unbiased version of the well-known modularity matrix. We then point out that the translation and rotation transformations fail to deal with the heterogeneous network, which is very common in nature and society. To address this problem, a correlation matrix is proposed through introducing the rescaling transformation into the covariance matrix. Extensive tests on real world and artificial networks demonstrate that the correlation matrix significantly outperforms the covariance matrix, identically the modularity matrix, as regards identifying the multi-scale community structure of network. This work provides a novel perspective to the identification of community structure and thus various dimension reduction methods might be used for the identification of community structure. Through introducing the correlation matrix, we further conclude that the rescaling transformation is crucial to identify the multi-scale community structure of network, as well as the translation and rotation transformations.Comment: 10 pages, 7 figure

A Novel Method of Encoded Multiplexing Readout for Micro-pattern Gas Detectors

The requirement of a large number of electronic channels poses a big challenge for Micro-pattern Gas Detector (MPGD) to achieve good spatial resolution. By using the redundancy that at least two neighboring strips record the signal of a particle, a novel method of encoded multiplexing readout for MPGDs is presented in this paper. The method offers a feasible and easily-extensible way of encoding and decoding, and can significantly reduce the number of readout channels. A verification test was carried out on a 5*5 cm2 Thick Gas Electron Multiplier (THGEM) detector using a 8 keV Cu X-ray source with 100um slit, where 166 strips are read out by 21 encoded readout channels. The test results show a good linearity in its position response, and the spatial resolution root-mean-square (RMS) of the test system is about 260 {\mu}m. This method has an attractive potential to build large area detectors and can be easily adapted to other detectors like MPGDs

Cryptanalysis of a multi-party quantum key agreement protocol with single particles

Recently, Sun et al. [Quant Inf Proc DOI: 10.1007/s11128-013-0569-x] presented an efficient multi-party quantum key agreement (QKA) protocol by employing single particles and unitary operations. The aim of this protocol is to fairly and securely negotiate a secret session key among $N$ parties with a high qubit efficiency. In addition, the authors claimed that no participant can learn anything more than his/her prescribed output in this protocol, i.e., the sub-secret keys of the participants can be kept secret during the protocol. However, here we points out that the sub-secret of a participant in Sun et al.'s protocol can be eavesdropped by the two participants next to him/her. In addition, a certain number of dishonest participants can fully determine the final shared key in this protocol. Finally, we discuss the factors that should be considered when designing a really fair and secure QKA protocol.Comment: 7 page

Online Multi-Object Tracking Using CNN-based Single Object Tracker with Spatial-Temporal Attention Mechanism

In this paper, we propose a CNN-based framework for online MOT. This framework utilizes the merits of single object trackers in adapting appearance models and searching for target in the next frame. Simply applying single object tracker for MOT will encounter the problem in computational efficiency and drifted results caused by occlusion. Our framework achieves computational efficiency by sharing features and using ROI-Pooling to obtain individual features for each target. Some online learned target-specific CNN layers are used for adapting the appearance model for each target. In the framework, we introduce spatial-temporal attention mechanism (STAM) to handle the drift caused by occlusion and interaction among targets. The visibility map of the target is learned and used for inferring the spatial attention map. The spatial attention map is then applied to weight the features. Besides, the occlusion status can be estimated from the visibility map, which controls the online updating process via weighted loss on training samples with different occlusion statuses in different frames. It can be considered as temporal attention mechanism. The proposed algorithm achieves 34.3% and 46.0% in MOTA on challenging MOT15 and MOT16 benchmark dataset respectively.Comment: Accepted at International Conference on Computer Vision (ICCV) 201

Hinge solitons in three-dimensional second-order topological insulators

A second-order topological insulator in three dimensions refers to a topological insulator with gapless states localized on the hinges, which is a generalization of a traditional topological insulator with gapless states localized on the surfaces. Here we theoretically demonstrate the existence of stable solitons localized on the hinges of a second-order topological insulator in three dimensions when nonlinearity is involved. By means of systematic numerical study, we find that the soliton has strong localization in real space and propagates along the hinge unidirectionally without changing its shape. We further construct an electric network to simulate the second-order topological insulator. When a nonlinear inductor is appropriately involved, we find that the system can support a bright soliton for the voltage distribution demonstrated by stable time evolution of a voltage pulse.Comment: 11 pages, 6 figure

Optimality of Graphlet Screening in High Dimensional Variable Selection

Consider a linear regression model where the design matrix X has n rows and p columns. We assume (a) p is much large than n, (b) the coefficient vector beta is sparse in the sense that only a small fraction of its coordinates is nonzero, and (c) the Gram matrix G = X'X is sparse in the sense that each row has relatively few large coordinates (diagonals of G are normalized to 1). The sparsity in G naturally induces the sparsity of the so-called graph of strong dependence (GOSD). We find an interesting interplay between the signal sparsity and the graph sparsity, which ensures that in a broad context, the set of true signals decompose into many different small-size components of GOSD, where different components are disconnected. We propose Graphlet Screening (GS) as a new approach to variable selection, which is a two-stage Screen and Clean method. The key methodological innovation of GS is to use GOSD to guide both the screening and cleaning. Compared to m-variate brute-forth screening that has a computational cost of p^m, the GS only has a computational cost of p (up to some multi-log(p) factors) in screening. We measure the performance of any variable selection procedure by the minimax Hamming distance. We show that in a very broad class of situations, GS achieves the optimal rate of convergence in terms of the Hamming distance. Somewhat surprisingly, the well-known procedures subset selection and the lasso are rate non-optimal, even in very simple settings and even when their tuning parameters are ideally set