Cosmic ray muon has strong penetrating power and no ionizing radiation hazards, which make cosmic ray muon an ideal probe to detect the special nuclear materials (SNM). However, the existing muon tomography experiments have the disadvantages of long imaging time and poor imaging accuracy, due to the low event rate of muons and small interaction cross section between muons and material nucleus. To optimize the imaging quality and imaging time, high spatial resolution muon tomography facility should be investigated more deeply. Micromegas with its high spatial resolution and large detection area is one of the suitable detectors for the muon tomography facility. In this paper, a high spatial muon tomography prototype was presented. The Micromegas detector was based on thermal bonding technique, which was easy to manufacture and can achieve good performance. A novel multiplexing method base on position encoding was introduced in this research to reduce the channels in an order of magnitude. Then, this paper carried out the research of a general and scalable muon imaging readout system, which employed a discrete architecture of front-end and back-end electronics and can be adapted to different scales of muon tomography experiments. Finally, a tomography prototype system was designed and implemented, including eight Micromegas detectors, four front-end electronics cards and a data acquisition board. Test results showed that this prototype can image objects with 2cm size and distinguish different materials.
The Fourier transform spectrometer (FTS) is a core instrument for solar observation with high spectral resolution, especially in the infrared. The Infrared System for the Accurate Measurement of Solar Magnetic Field (AIMS), working at 10-13 $mu m$, will use a FTS to observe the solar spectrum. The Bruker IFS-125HR, which meets the spectral resolution requirement of AIMS but just equips with a point source detector, is employed to carry out preliminary experiment for AIMS. A sun-light feeding experimental system is further developed. Several experiments are taken with them during 2018 and 2019 to observe the solar spectrum in the visible and near infrared wavelength, respectively. We also proposed an inversion method to retrieve the solar spectrum from the observed interferogram and compared it with the standard solar spectrum atlas. Although there is a wavelength limitation due to the present sun-light feeding system, the results in the wavelength band from 0.45-1.0 $mu m$ and 1.0-2.2 $mu m$ show a good consistence with the solar spectrum atlas, indicating the validity of our observing configuration, the data analysis method and the potential to work in longer wavelength. The work provided valuable experience for the AIMS not only for the operation of a FTS but also for the development of its scientific data processing software.
We present quantum algorithms, for Hamiltonians of linear combinations of local unitary operators, for Hamiltonian matrix-vector products and for preconditioning with the inverse of shifted reduced Hamiltonian operator that contributes to the diagonal matrix elements only. The algorithms implement a convergent series of approximations towards the exact solution of the full CI (configuration interaction) problem. The algorithm scales with O(m^5 ), with m the number of one-electron orbitals in the case of molecular electronic structure calculations. Full CI results can be obtained with a scaling of O(nm^5 ), with n the number of electrons and a prefactor on the order of 10 to 20. With low orders of Hamiltonian matrix-vector products, a whole repertoire of approximations widely used in modern electronic structure theory, including various orders of perturbation theory and/or truncated CI at different orders of excitations can be implemented for quantum computing for both routine and benchmark results at chemical accuracy. The lowest order matrix-vector product with preconditioning, basically the second-order perturbation theory, is expected to be a leading algorithm for demonstrating quantum supremacy for Ab Initio simulations, one of the most anticipated real world applications. The algorithm is also applicable for the hybrid variational quantum eigensolver.
End-to-end Spoken Language Understanding (SLU) models are made increasingly large and complex to achieve the state-ofthe-art accuracy. However, the increased complexity of a model can also introduce high risk of over-fitting, which is a major challenge in SLU tasks due to the limitation of available data. In this paper, we propose an attention-based SLU model together with three encoder enhancement strategies to overcome data sparsity challenge. The first strategy focuses on the transferlearning approach to improve feature extraction capability of the encoder. It is implemented by pre-training the encoder component with a quantity of Automatic Speech Recognition annotated data relying on the standard Transformer architecture and then fine-tuning the SLU model with a small amount of target labelled data. The second strategy adopts multitask learning strategy, the SLU model integrates the speech recognition model by sharing the same underlying encoder, such that improving robustness and generalization ability. The third strategy, learning from Component Fusion (CF) idea, involves a Bidirectional Encoder Representation from Transformer (BERT) model and aims to boost the capability of the decoder with an auxiliary network. It hence reduces the risk of over-fitting and augments the ability of the underlying encoder, indirectly. Experiments on the FluentAI dataset show that cross-language transfer learning and multi-task strategies have been improved by up to 4:52% and 3:89% respectively, compared to the baseline.
The major scientific goals of DArk Matter Particle Explorer (DAMPE) are to study cosmic-ray electrons (including positrons) and gamma rays from 5 GeV to 10 TeV and nuclei from Z = 1 to 26 up to 100 TeV. The deposited energy measured by the Bismuth Germanate Oxide (BGO) calorimeter of DAMPE is affected by fluorescence attenuation in BGO crystals that are 600 mm long. In this work, an in-orbit attenuation calibration method is reported, and energy correction of the sensitive detector unit of the BGO calorimeter is also presented.
This paper proposes a new two-stage network mediation method based on the use of a latent network approach -- model-based eigenvalue decomposition -- for analyzing social network data with nodal covariates. In the decomposition stage of the observed network, no assumption on the metric of the latent space structure is required. In the mediation stage, the most important eigenvectors of a network are used as mediators. This method further offers an innovative way for controlling for the conditional covariates and it only considers the information left in the network. We demonstrate this approach in a detailed tutorial R code provided for four separate cases -- unconditional and conditional model-based eigenvalue decompositions for either a continuous outcome or a binary outcome -- to show its applicability to empirical network data.
For manufacturing Micromegas detectors, the bulk method based on photoetching, was successfully developed and widely used in nuclear and particle physics experiments. However, the complexity of the method requires a considerable number of advanced instruments and processing, limiting the accessibility of this method for production of Micromegas detectors. In view of these limitations with the bulk method, a new method based on thermal bonding technique (TBM) has been developed to manufacture Micromegas detectors in a much simplified and efficient way without etching. This paper describes the TBM in detail and presents performance of the Micromegas detectors built with the TBM. The effectiveness of this method was investigated by testing Micromegas detector prototypes built with the method. Both X-rays and electron beams were used to characterize the prototypes in a gas mixture of argon and CO2 (7%). A typical energy resolution of ~16% (full width at half maximum, FWHM) and an absolute gain greater than 10^4 were obtained with 5.9 keV X-rays. Detection efficiency greater than 98% and a spatial resolution of ~65 {mu}m were achieved using a 5 GeV electron beam at the DESY test-beam facility. The gas gain of a Micromegas detector could reach up to 10^5 with a uniformity of better than 10% when the size of the avalanche gap was optimized thanks to the flexibility of the TBM in defining the gap. Additionally, the TBM facilitates the exploration of new detector structures based on Micromegas owing to the much-simplified operation with the method.
Cross-term spatiotemporal encoding (xSPEN) is a recently introduced imaging approach delivering single-scan 2D NMR images with unprecedented resilience to field inhomogeneities. The method relies on performing a pre-acquisition encoding and a subsequent image read out while using the disturbing frequency inhomogeneities as part of the image formation processes, rather than as artifacts to be overwhelmed by the application of external gradients. This study introduces the use of this new single-shot MRI technique as a diffusion-monitoring tool, for accessing regions that have hitherto been unapproachable by diffusion-weighted imaging (DWI) methods. In order to achieve this, xSPEN MRIs intrinsic diffusion weighting effects are formulated using a customized, spatially-localized b-matrix analysis; with this, we devise a novel diffusion-weighting scheme that both exploits and overcomes xSPENs strong intrinsic weighting effects. The ability to provide reliable and robust diffusion maps in challenging head and brain regions, including the eyes and the optic nerves, is thus demonstrated in humans at 3T; new avenues for imaging other body regions are also briefly discussed.
A BGO electromagnetic calorimeter (ECAL) is built for the DArk Matter Particle Explorer (DAMPE) mission. The effect of temperature on the BGO ECAL was investigated with a thermal vacuum experiment. The light output of a BGO crystal depends on temperature significantly. The temperature coefficient of each BGO crystal bar has been calibrated, and a correction method is also presented in this paper.
The DArk Matter Particle Explorer (DAMPE) is a space experiment designed to search for dark matter indirectly by measuring the spectra of photons, electrons, and positrons up to 10 TeV. The BGO electromagnetic calorimeter (ECAL) is its main sub-detector for energy measurement. In this paper, the instrumentation and development of the BGO ECAL is briefly described. The calibration on the ground, including the pedestal, minimum ionizing particle (MIP) peak, dynode ratio, and attenuation length with the cosmic rays and beam particles is discussed in detail. Also, the energy reconstruction results of the electrons from the beam test are presented.
