Search

Dark matter direct detection experiments mostly operate at deep underground laboratories. It is necessary to consider shielding effect of the Earth, especially for dark matter particles interacting with a large cross section. We analyzed and simulated the Earth shielding effect for dark matter at the China Jinping Underground Laboratory (CJPL) with a simulation package, CJPL Earth Shielding Simulation code (CJPL\_ESS), which is applicable to other underground locations. The further constraints on the $\chi$-N cross section exclusion regions are derived based on the studies with CDEX experiment data., Comment: 8 pages, 8 figures, 2 tables. Version updated to match PRD version

We present novel constraints on light dark matter (DM) from evaporating primordial black holes (PBHs), using the 205.4 kg$\cdot$day data from China Jinping Underground Laboratory's CDEX-10 p-type point contact germanium detector with 160 eVee analysis threshold. DM from PBHs with masses ranging from 1$\times$10$^{15}$ to 2$\times$10$^{16}$ g is searched in this work. In the presence of PBH abundance compatible with present bounds, our result excludes the DM-nucleon elastic scattering cross section region from 8.2$\times$10$^{-31}$ to 2.0$\times$10$^{-29}$ cm$^{2}$ for DM of 1 keV to 15 MeV from PBHs with masses of 5$\times$10$^{15}$ g, as well as, from 1.7$\times$10$^{-29}$ to 5.0$\times$10$^{-29}$ cm$^{2}$ for DM of 1 keV to 2 MeV from PBHs with masses of 1.8$\times$10$^{16}$ g. If the DM-nucleon elastic scattering cross section can be determined in the future, the abundance of PBH may be severely constrained by DM evaporation., Comment: 7 pages, 6 figures

Tensor rank learning for canonical polyadic decomposition (CPD) has long been deemed as an essential yet challenging problem. In particular, since thetensor rank controls the complexity of the CPD model, its inaccurate learning would cause overfitting to noise or underfitting to the signal sources, and even destroy the interpretability of model parameters. However, the optimal determination of a tensor rank is known to be a non-deterministic polynomial-time hard (NP-hard) task. Rather than exhaustively searching for the best tensor rank via trial-and-error experiments, Bayesian inference under the Gaussian-gamma prior was introduced in the context of probabilistic CPD modeling, and it was shown to be an effective strategy for automatic tensor rank determination. This triggered flourishing research on other structured tensor CPDs with automatic tensor rank learning. On the other side of the coin, these research works also reveal that the Gaussian-gamma model does not perform well for high-rank tensors and/or low signal-to-noise ratios (SNRs). To overcome these drawbacks, in this paper, we introduce a more advanced generalized hyperbolic (GH) prior to the probabilistic CPD model, which not only includes the Gaussian-gamma model as a special case, but also is more flexible to adapt to different levels of sparsity. Based on this novel probabilistic model, an algorithm is developed under the framework of variational inference, where each update is obtained in a closed-form. Extensive numerical results, using synthetic data and real-world datasets, demonstrate the significantly improved performance of the proposed method in learning both low as well as high tensor ranks even for low SNR cases. © 1991-2012 IEEE.

This survey was made to identify, describe, and analyze research related to social work education published between 1960 and 1968, and to provide classified abstracts of the relevant research. In addition, it is hoped that the survey will identify and call attention to major trends and gaps in research in social work education. After an introduction to the survey in Chapter One, Chapter Two presents the methodology of the project. Chapters Three through Six give the content of social work education: (1) history and philosophy; (2) manpower supply; (3) curriculum; and (4) institutional characteristics and inter-institutional relationships. Chapter Seven summarizes the survey, discusses trends and gaps in research, and the limitation of this survey.

Reconfigurable intelligent surface (RIS)-assisted transmission and space shift keying (SSK) appear as promising candidates for future energy-efficient wireless systems. In this article, two RIS-based SSK schemes are proposed to efficiently improve the error and throughput performance of conventional SSK systems, respectively. The first one, termed RIS-SSK with passive beamforming (RIS-SSK-PB), employs an RIS for beamforming and targets the maximization of the minimum squared Euclidean distance between any two decision points. The second one, termed RIS-SSK with Alamouti space-time block coding (RIS-SSK-ASTBC), employs an RIS for ASTBC and enables the RIS to transmit its own Alamouti-coded information while reflecting the incident SSK signals to the destination. A low-complexity beamformer and an efficient maximum-likelihood (ML) detector are designed for RIS-SSK-PB and RIS-SSK-ASTBC, respectively. Approximate expressions for the average bit error probabilities of the source and/or the RIS are derived in closed-form assuming ML detection. Extensive computer simulations are conducted to verify the performance analysis. Results show that RIS-SSK-PB significantly outperforms the existing RIS-free and RIS-based SSK schemes, and RIS-SSK-ASTBC enables highly reliable transmission with throughput improvement. © 2020 IEEE.