MINE-based Geometric Constellation Shaping in AWGN Channel

The use of high-order constellation modulations is imperative to improve the spectral efficiency, for both radio frequency/laser-based satellite systems and optical wireless communications. The geometric shaping (GS) optimization as one typical constellation shaping method drives the improvement of communication capacity and system performance. This paper presents a novel mutual information neural estimation (MINE)- based GS method to optimize the high-order constellations in pure additive white Gaussian noise (AWGN) channel, which uses the deep neural network (DNN) to estimate the mutual information (MI) value and maximize the MI to approach the AWGN capacity asymptotically. The proposed system trains both the encoder and MINE networks by back propagation, and does not need to train a decoder for optimization and thus can avoid the loss caused by the decoder. Simulation results show that the MINE-based shaping design outperforms the unshaped M-ary quadrature amplitude modulation (QAM) in terms of MI values. Note that the capacity gain increases slightly as the order M increases. Furthermore, the proposed scheme is promising for constellation design in various channel models, such as the phase noise and the fading channels, once the channel model used in MINE is matched, which can be a future research topic

Improvement of the separation evolution law and separation position determination method of mining overburden strata

In view of the shortcomings of the traditional method of determining the position of the separation layer, according to the actual movement of the pressure balance arch and the rock layer in the overburden, the concept of the triangular separation domain is proposed, and the relationship between the range of the triangular separation domain and the mining distance and the mining fracture angle is established. The calculation method of rock load is modified. By analyzing the stress mode in the pressure arch of the separation zone, the mechanical model of the deflection calculation of each rock stratum is established. Combining the triangular separation zone with the new mechanical model, the calculation model of the dynamic evolution analysis of the separation zone and the calculation model of the composite beam in the separation zone are established. The traditional method and the improved method are used to determine the separation position in an actual mining example. The field exploration proves that the results of the improved method described in this paper are more accurate. The model predicts the position of overburden separation by comparing the deflection between adjacent strata on the same horizontal contact surface, and can accurately predict the position of separation above the working face

Up-Regulation of Mitochondrial Activity and Acquirement of Brown Adipose Tissue-Like Property in the White Adipose Tissue of Fsp27 Deficient Mice

Fsp27, a member of the Cide family proteins, was shown to localize to lipid droplet and promote lipid storage in adipocytes. We aimed to understand the biological role of Fsp27 in regulating adipose tissue differentiation, insulin sensitivity and energy balance. Fsp27−/− mice and Fsp27/lep double deficient mice were generated and we examined the adiposity, whole body metabolism, BAT and WAT morphology, insulin sensitivity, mitochondrial activity, and gene expression changes in these mouse strains. Furthermore, we isolated mouse embryonic fibroblasts (MEFs) from wildtype and Fsp27−/− mice, followed by their differentiation into adipocytes in vitro. We found that Fsp27 is expressed in both brown adipose tissue (BAT) and white adipose tissue (WAT) and its levels were significantly elevated in the WAT and liver of leptin-deficient ob/ob mice. Fsp27−/− mice had increased energy expenditure, lower levels of plasma triglycerides and free fatty acids. Furthermore, Fsp27−/− and Fsp27/lep double-deficient mice are resistant to diet-induced obesity and display increased insulin sensitivity. Moreover, white adipocytes in Fsp27−/− mice have reduced triglycerides accumulation and smaller lipid droplets, while levels of mitochondrial proteins, mitochondrial size and activity are dramatically increased. We further demonstrated that BAT-specific genes and key metabolic controlling factors such as FoxC2, PPAR and PGC1α were all markedly upregulated. In contrast, factors inhibiting BAT differentiation such as Rb, p107 and RIP140 were down-regulated in the WAT of Fsp27−/− mice. Remarkably, Fsp27−/− MEFs differentiated in vitro show many brown adipocyte characteristics in the presence of the thyroid hormone triiodothyronine (T3). Our data thus suggest that Fsp27 acts as a novel regulator in vivo to control WAT identity, mitochondrial activity and insulin sensitivity

The role of connectivity in significant bandgap narrowing for fused-pyrene based non-fullerene acceptors toward high-efficiency organic solar cells

Great attention has been paid to developing low bandgap non-fullerene acceptors (NFAs) for matching wide bandgap donor polymers to increase the photocurrent and therefore the power conversion efficiencies (PCEs) of NFA organic solar cells, while pyrene-core based acceptor-donor-acceptor (A-D-A) NFAs have been mainly reported via the 2,9-position connection due to their bisthieno[3′,2′-b']thienyl[a,h]pyrene fused via a five-membered ring bridge at the ortho-position of pyrene as the representative one named FPIC5, which has prohibited further narrowing their energy gap. Herein, an acceptor FPIC6 was exploited by creating the 1,8-position connection through fusing as bisthieno[3′,2′-b′]thienyl[f-g,m-n]pyrene linked at the bay-position via a six-membered bridge, with enhanced push-pull characteristics within such A-D-A structure. As a structural isomer of FPIC5, FPIC6 exhibited a much lower bandgap of 1.42 eV (1.63 eV for FPIC5). Therefore, the photocurrent and PCE of PTB7-Th:FPIC6 cells were improved to 21.50 mA cm-2 and 11.55%, respectively, due to the balanced mobilities, better photoluminescence quenching efficiency and optimized morphology, which are both ∼40% better than those of PTB7-Th:FPIC5 cells. Our results clearly proved that a pyrene fused core with 1,8-position connection with electron-withdrawing end groups instead of 2,9-position connection is an efficient molecular design strategy to narrow the optical bandgap and improve the photovoltaic performance of NFA based OSCs

Core Cosmology Library: Precision Cosmological Predictions for LSST

The Core Cosmology Library (CCL) provides routines to compute basic cosmological observables to a high degree of accuracy, which have been verified with an extensive suite of validation tests. Predictions are provided for many cosmological quantities, including distances, angular power spectra, correlation functions, halo bias and the halo mass function through state-of-the-art modeling prescriptions available in the literature. Fiducial specifications for the expected galaxy distributions for the Large Synoptic Survey Telescope (LSST) are also included, together with the capability of computing redshift distributions for a user-defined photometric redshift model. A rigorous validation procedure, based on comparisons between CCL and independent software packages, allows us to establish a well-defined numerical accuracy for each predicted quantity. As a result, predictions for correlation functions of galaxy clustering, galaxy-galaxy lensing and cosmic shear are demonstrated to be within a fraction of the expected statistical uncertainty of the observables for the models and in the range of scales of interest to LSST. CCL is an open source software package written in C, with a python interface and publicly available at https://github.com/LSSTDESC/CCL.Comment: 38 pages, 18 figures, matches ApJS accepted versio

Measurement of the Galaxy Angular Power Spectrum Using Simulated Data From the Legacy Survey of Space and Time

The Legacy Survey of Space and Time (LSST) carried out by the Vera C. Rubin Observatory is one of the most ambitious optical surveys planned in the near future. It will generate $\sim 20$~TB of data every night during its 10 year survey duration. Simulated data sets (``Data Challenges'') have been generated by the LSST Dark Energy Science Collaboration (DESC) to allow development of efficient analysis algorithms, evaluation of their performance and scaling capabilities, and to study the impact of systematic uncertainties. In this work we focus on the analysis of Large Scale Structure using galaxies selected from the DESC Data Challenge 2 (DC2) corresponding to a survey area of 300 square degrees. We develop and perform an end-to-end analysis of the galaxy Large Scale Structure with the DC2 data, validating the extraction of galaxy power spectra and galaxy bias in the presence of complex systematics. We provide a range of strong consistency checks of both the simulated data and the analysis techniques.Based on matching selected galaxies with the DC2 truth catalog, we optimize selection to obtain a clean sample of galaxies. After generating and applying survey masks, we produce galaxy density maps binned in redshift, with six bins from $z = 0.2 - 1.4$. We generate the power spectra measurements from the processed data and fit the data using theoretical predictions to estimate the galaxy bias as a function of redshift. Finally, we study the effect of systematic uncertainties on estimation of the galaxy bias.We find that the relative uncertainty $\sigma_\ell / C_\ell$ in the measured angular power spectra is smaller than 10\% in most cases, and smaller than 4\% at multipoles above 1500.% Changes in the angular power spectra due to systematic uncertainties are found to be on average of order 0.5\%, i.e. $\Delta C_\ell / C_\ell \approx 0.5\%$, with a maximum deviation of 1.5\%. % Systematic effects are small compared to the statistical uncertainty of the measured angular power spectra: $\Delta C_\ell \sim 0.1 \sigma_\ell$ on average with a maximum of $0.25 \sigma_\ell$

ULAN: A Universal Local Adversarial Network for SAR Target Recognition Based on Layer-Wise Relevance Propagation

Recent studies have proven that synthetic aperture radar (SAR) automatic target recognition (ATR) models based on deep neural networks (DNN) are vulnerable to adversarial examples. However, existing attacks easily fail in the case where adversarial perturbations cannot be fully fed to victim models. We call this situation perturbation offset. Moreover, since background clutter takes up most of the area in SAR images and has low relevance to recognition results, fooling models with global perturbations is quite inefficient. This paper proposes a semi-white-box attack network called Universal Local Adversarial Network (ULAN) to generate universal adversarial perturbations (UAP) for the target regions of SAR images. In the proposed method, we calculate the model’s attention heatmaps through layer-wise relevance propagation (LRP), which is used to locate the target regions of SAR images that have high relevance to recognition results. In particular, we utilize a generator based on U-Net to learn the mapping from noise to UAPs and craft adversarial examples by adding the generated local perturbations to target regions. Experiments indicate that the proposed method effectively prevents perturbation offset and achieves comparable attack performance to conventional global UAPs by perturbing only a quarter or less of SAR image areas