Miracle-dz/UniFAR-FTUs huggingface dataset:
How Mega Is the Mega? Exploring the Spillover Effects of WeChat Using Graphical Model

WeChat, an instant messaging app, is considered a mega app due to its dominance in terms of usage among Chinese smartphone users. Nevertheless, little is known about its externality in regard to the broader app market. Our work estimates the spillover effects of WeChat on the other top-50 most frequently used apps in China through data on users’ weekly app usage. Given the challenge of determining causal inference from observational data, we apply a graphical model and econometrics to estimate the spillover effects through two steps: (1) we determine the causal structure by estimating a partially ancestral diagram, using a Fast Causal Inference (FCI) algorithm; (2) given the causal structure, we find a valid adjustment set and estimate the causal effects by an econometric model with the adjustment set as controlling non-causal effects. Our findings show that the spillover effects of WeChat are limited; in fact, only two other apps, Tencent News and Taobao, receive positive spillover effects from WeChat. In addition, we show that, if researchers fail to account for the causal structure that we determined from the graphical model, it is easy to fall into the trap of confounding bias and selection bias when estimating causal effects.
DEM Extraction from ALS Point Clouds in Forest Areas via Graph Convolution Network

It is difficult to extract a digital elevation model (DEM) from an airborne laser scanning (ALS) point cloud in a forest area because of the irregular and uneven distribution of ground and vegetation points. Machine learning, especially deep learning methods, has shown powerful feature extraction in accomplishing point cloud classification. However, most of the existing deep learning frameworks, such as PointNet, dynamic graph convolutional neural network (DGCNN), and SparseConvNet, cannot consider the particularity of ALS point clouds. For large-scene laser point clouds, the current data preprocessing methods are mostly based on random sampling, which is not suitable for DEM extraction tasks. In this study, we propose a novel data sampling algorithm for the data preparation of patch-based training and classification named T-Sampling. T-Sampling uses the set of the lowest points in a certain area as basic points with other points added to supplement it, which can guarantee the integrity of the terrain in the sampling area. In the learning part, we propose a new convolution model based on terrain named Tin-EdgeConv that fully considers the spatial relationship between ground and non-ground points when constructing a directed graph. We design a new network based on Tin-EdgeConv to extract local features and use PointNet architecture to extract global context information. Finally, we combine this information effectively with a designed attention fusion module. These aspects are important in achieving high classification accuracy. We evaluate the proposed method by using large-scale data from forest areas. Results show that our method is more accurate than existing algorithms.
Identifying and Quantifying the Abundance of Economically Important Palms in Tropical Moist Forest Using UAV Imagery

Sustainable management of non-timber forest products such as palm fruits is crucial for the long-term conservation of intact forest. A major limitation to expanding sustainable management of palms has been the need for precise information about the resources at scales of tens to hundreds of hectares, while typical ground-based surveys only sample small areas. In recent years, small unmanned aerial vehicles (UAVs) have become an important tool for mapping forest areas as they are cheap and easy to transport, and they provide high spatial resolution imagery of remote areas. We developed an object-based classification workflow for RGB UAV imagery which aims to identify and delineate palm tree crowns in the tropical rainforest by combining image processing and GIS functionalities using color and textural information in an integrative way to show one of the potential uses of UAVs in tropical forests. Ten permanent forest plots with 1170 reference palm trees were assessed from October to December 2017. The results indicate that palm tree crowns could be clearly identified and, in some cases, quantified following the workflow. The best results were obtained using the random forest classifier with an 85% overall accuracy and 0.82 kappa index.
Mapping Evapotranspiration, Vegetation and Precipitation Trends in the Catchment of the Shrinking Lake Poopó

Lake Poopo is located in the Andean Mountain Range Plateau or Altiplano. A general decline in the lake water level has been observed in the last two decades, coinciding roughly with an intensification of agriculture exploitation, such as quinoa crops. Several factors have been linked with the shrinkage of the lake, including climate change, increased irrigation, mining extraction and population growth. Being an endorheic catchment, evapotranspiration (ET) losses are expected to be the main water output mechanism and previous studies demonstrated ET increases using Earth observation (EO) data. In this study, we seek to build upon these earlier findings by analyzing an ET time series dataset of higher spatial and temporal resolution, in conjunction with land cover and precipitation data. More specifically, we performed a spatio-temporal analysis, focusing on wet and dry periods, that showed that ET changes occur primarily in the wet period, while the dry period is approximately stationary. An analysis of vegetation trends performed using 500 MODIS vegetation index products (NDVI) also showed an overall increasing trend during the wet period. Analysis of NDVI and ET across land cover types showed that only croplands had experienced an increase in NDVI and ET losses, while natural covers showed either constant or decreasing NDVI trends together with increases in ET. The larger increase in vegetation and ET losses over agricultural regions, strongly suggests that cropping practices exacerbated water losses in these areas. This quantification provides essential information for the sustainable planning of water resources and land uses in the catchment. Finally, we examined the spatio-temporal trends of the precipitation using the newly available Climate Hazards Group Infrared Precipitation with Stations (CHIRPS-v2) product, which we validated with onsite rainfall measurements. When integrated over the entire catchment, precipitation and ET showed an average increasing trend of 5.2 mm yr−1 and 4.3 mm yr−1, respectively. This result suggests that, despite the increased ET losses, the catchment-wide water storage should have been offset by the higher precipitation. However, this result is only applicable to the catchment-wide water balance, and the location of water may have been altered (e.g., by river abstractions or by the creation of impoundments) to the detriment of the Lake Poopo downstream.
Numerical Focusing of a Wide-Field-Angle Earth Radiation Budget Imager Using an Artificial Neural Network

Narrow field-of-view scanning thermistor bolometer radiometers have traditionally been used to monitor the earth’s radiant energy budget from low earth orbit (LEO). Such instruments use a combination of cross-path scanning and along-path spacecraft motion to obtain a patchwork of punctual observations which are ultimately assembled into a mosaic. Monitoring has also been achieved using non-scanning instruments operating in a push-broom mode in LOE and imagers operating in geostationary orbit. The current contribution considers a fourth possibility, that of an imager operating in LEO. The system under consideration consists of a Ritchey-Chrétien telescope illuminating a plane two-dimensional microbolometer array. At large field angles, the focal length of the candidate instrument is field-angle dependent, resulting in a blurred image in the readout plane. Presented is a full-field focusing algorithm based on an artificial neural network (ANN). Absorbed power distributions on the microbolometer array produced by discretized scenes are obtained using a high-fidelity Monte Carlo ray-trace (MCRT) model of the imager. The resulting readout array/scene pairs are then used to train an ANN. We demonstrate that a properly trained ANN can be used to convert the readout power distribution into an accurate image of the corresponding discretized scene. This opens the possibility of using an ANN based on a high-fidelity imager model for numerical focusing of an actual imager.
Nonparametric Tensor Completion Based on Gradient Descent and Nonconvex Penalty

Existing tensor completion methods all require some hyperparameters. However, these hyperparameters determine the performance of each method, and it is difficult to tune them. In this paper, we propose a novel nonparametric tensor completion method, which formulates tensor completion as an unconstrained optimization problem and designs an efficient iterative method to solve it. In each iteration, we not only calculate the missing entries by the aid of data correlation, but consider the low-rank of tensor and the convergence speed of iteration. Our iteration is based on the gradient descent method, and approximates the gradient descent direction with tensor matricization and singular value decomposition. Considering the symmetry of every dimension of a tensor, the optimal unfolding direction in each iteration may be different. So we select the optimal unfolding direction by scaled latent nuclear norm in each iteration. Moreover, we design formula for the iteration step-size based on the nonconvex penalty. During the iterative process, we store the tensor in sparsity and adopt the power method to compute the maximum singular value quickly. The experiments of image inpainting and link prediction show that our method is competitive with six state-of-the-art methods.
Designing and Evaluating a User-Oriented Calibration Field for the Target-Based Self-Calibration of Panoramic Terrestrial Laser Scanners

Calibration of terrestrial laser scanners (TLSs) is one of the fundamental tasks for assuring the high measurement accuracy required by an increasing number of end-users. Nevertheless, the development of user-oriented calibration approaches is still an active topic of research. The calibration fields for the target-based self-calibration of TLSs described in the literature are based on the quasi-random distribution of a high number of targets, and they rely on heavy redundancy. This redundancy assures highly accurate calibration results, however, with the price of reduced efficiency. In contrast, this work follows the design, implementation, and validation of a user-oriented, cost-efficient calibration field intended for TLS calibration prior to measurement campaigns. Multiple goals and constraints are placed upon the design of the calibration field, such as comprehensive calibration for high-end panoramic TLSs considering all relevant mechanical misalignments, delivering stable and reusable calibration parameters, increasing calibration efficiency by minimizing calibration-field assembly, measurement acquisition and processing time through reducing the number of targets and scanner stations, as well as estimating calibration parameters with predefined quality criteria. The calibration field design was derived through a series of simulation experiments and it was compared with the current state of the art. The simulations indicate comparable calibration results, with eight times smaller number of targets (14 instead of 120). The implemented calibration field was tested on a range of instruments, successfully improving the measurement quality, both in situ and in the subsequent applications.
Evaluating Thermal Attribute Mapping Strategies for Oblique Airborne Photogrammetric System AOS-Tx8

Thermal imagery is widely used in various fields of remote sensing. In this study, a novel processing scheme is developed to process the data acquired by the oblique airborne photogrammetric system AOS-Tx8 consisting of four thermal cameras and four RGB cameras with the goal of large-scale area thermal attribute mapping. In order to merge 3D RGB data and 3D thermal data, registration is conducted in four steps: First, thermal and RGB point clouds are generated independently by applying structure from motion (SfM) photogrammetry to both the thermal and RGB imagery. Next, a coarse point cloud registration is performed by the support of georeferencing data (global positioning system, GPS). Subsequently, a fine point cloud registration is conducted by octree-based iterative closest point (ICP). Finally, three different texture mapping strategies are compared. Experimental results showed that the global image pose refinement outperforms the other two strategies at registration accuracy between thermal imagery and RGB point cloud. Potential building thermal leakages in large areas can be fast detected in the generated texture mapping results. Furthermore, a combination of the proposed workflow and the oblique airborne system allows for a detailed thermal analysis of building roofs and facades.
Retrieval of Particulate Backscattering Using Field and Satellite Radiometry: Assessment of the QAA Algorithm

Particulate optical backscattering (bbp) is a crucial parameter for the study of ocean biology and oceanic carbon estimations. In this work, bbp retrieval, by the quasi-analytical algorithm (QAA), is assessed using a large in situ database of matched bbp and remote-sensing reflectance (Rrs). The QAA is also applied to satellite Rrs (ESA OC-CCI project) as well, after their validation against in situ Rrs. Additionally, the effect of Raman Scattering on QAA retrievals is studied. Results show negligible biases above random noise when QAA-derived bbp is compared to in situ bbp. In addition, Rrs from the CCI archive shows good agreement with in situ data. The QAA’s functional form of spectral backscattering slope, as derived from in situ radiometry, is validated. Finally, we show the importance of correcting for Raman Scattering over clear waters prior to semi-analytical retrieval. Overall, this work demonstrates the high efficiency of QAA in the bbp detection in case of both in situ and ocean color data, but it also highlights the necessity to increase the number of observations that are severely under-sampled in respect to others environmental parameters.
Comparison of Different Multispectral Sensors for Photosynthetic and Non-Photosynthetic Vegetation-Fraction Retrieval

It is very difficult and complex to acquire photosynthetic vegetation (PV) and non-PV (NPV) fractions (fPV and fNPV) using multispectral satellite sensors because estimations of fPV and fNPV are influenced by many factors, such as background-noise interference of pixel-, spatial-, and spectral-scale effects. In this study, comparisons between Sentinel-2A Multispectral Instrument (S2 MSI), Landsat-8 Operational Land Imager (L8 OLI), and GF1 Wide Field View (GF1 WFV) sensors for retrieving sparse photosynthetic and non-photosynthetic vegetation coverage are presented. The analysis employed a linear spectral-mixture model (LSMM) and nonlinear spectral-mixture model (NSMM) to unmix pixels with different spectral and spatial resolution images based on field endmembers; the estimated endmember fractions were later validated with reference to fraction measurements. The results demonstrated that: (1) with higher spatial and spectral resolution, the S2 MSI sensor had a clear advantage for retrieving PV and NPV fractions compared to L8 OLI and GF1 WFV sensors; (2) through incorporating more red edge (RE) and near-infrared (NIR) bands, the accuracy of NPV fraction estimation could be greatly improved; (3) nonlinear spectral mixing effects were not obvious on the 10–30 m spatial scale for desert vegetation; (4) in arid regions, a shadow endmember is a significant factor for sparse vegetation coverage estimated with remote-sensing data. The estimated NPV fractions were especially affected by the shadow effects and could increase root mean square by 50%. The utilized approaches in the study could effectively assess the performance of major multispectral sensors to extract fPV and fNPV through the novel method of spectral-mixture analysis.
Feasibility of Burned Area Mapping Based on ICESAT-2 Photon Counting Data

Accurately mapping burned areas is crucial for the analysis of carbon emissions and wildfire risk as well as understanding the effects of climate change on forest structure. Burned areas have predominantly been mapped using optical remote sensing images. However, the structural changes due to fire also offer opportunities for mapping burned areas using three-dimensional (3D) datasets such as Light detection and ranging (LiDAR). This study focuses on the feasibility of using photon counting LiDAR data from National Aeronautics and Space Administration’s (NASA) Ice, Cloud, and land Elevation Satellite-2 (ICESat−2) mission to differentiate vegetation structure in burned and unburned areas and ultimately classify burned areas along mapped ground tracks. The ICESat−2 mission (launched in September 2018) provides datasets such as geolocated photon data (ATL03), which comprises precise latitude, longitude and elevation of each point where a photon interacts with land surface, and derivative products such as the Land Water Vegetation Elevation product (ATL08), which comprises estimated terrain and canopy height information. For analysis, 24 metrics such as the average, median and standard deviation of canopy height were derived from ATL08 data over forests burned by recent fires in 2018 in northern California and western New Mexico. A reference burn map was derived from Sentinel−2 images based on the differenced Normalized Burn Ratio (dNBR) index. A landcover map based on Sentinel−2 images was employed to remove non-forest classes. Landsat 8 based dNBR image and landcover map were also used for comparison. Next, ICESat−2 data of forest samples were classified into burned and unburned ATL08 100-m segments by both Random Forest classification and logistic regression. Both Sentinel−2 derived and Landsat 8 derived ATL08 samples got high classification accuracy, 83% versus 76%. Moreover, the resulting classification accuracy by Random Forest and logistic regression reached 83% and 74%, respectively. Among the 24 ICESat−2 metrics, apparent surface reflectance and the number of canopy photons were the most important. Furthermore, burn severity of each ATL08 segment was also estimated with Random Forest regression. R2 of predicted burn severity to observed dNBR is 0.61 with significant linear relationship and moderate correlation (r = 0.78). Overall, the reasonably high accuracies achieved in this study demonstrate the feasibility of employing ICESat−2 data in burned forest classification, opening avenues for improved estimation of burned biomass and carbon emissions from a 3D perspective.
Anomalous elastic buckling of layered crystalline materials in the absence of structure slenderness

Layered crystalline materials, such as graphene, boron nitride, tungsten sulfate, phosphorene, etc., have attracted enormous attentions, due to their unique crystal structures and superior mechanical, thermal, and physical properties. Making use of mechanical buckling is a promising route to control their structural morphology and thus tune their physical properties, giving rise to many novel applications. In this paper, we employ molecular dynamics (MD) simulations and theoretical modeling to study the compressive buckling of a column made of layered crystalline materials with the crystal layers parallel to the compressive direction. We find that the mechanical buckling of the layered crystalline materials exhibits two anomalous and counter-intuitive features as approaching the zero slenderness ratio. First, the critical buckling strain εcr has a finite value that is much lower than the material's elastic limit strain. A continuum mechanics model (by homogenizing the layered materials) is proposed for the εcr, which agrees well with the results of MD simulations. We find that the εcr solely depends on elastic constants without any structural dimension, which appears to be an intrinsic material property and thus is defined as intrinsic buckling strain (IBS), εcr IBS, in this paper. Second, below a certain nanoscale length, l0, in the compressive direction (e.g., about 20 nm for graphite), the critical buckling strain εcr shows a size effect, i.e., increasing as the column length L decreases. To account for the size effect, inspired by our recently developed multi-beam shear model (Liu et al., 2011), a bending energy term of individual crystal layer is introduced in our continuum model. The theoretical model of εcr agrees well with the size effects observed in MD simulations. This study could lay a ground for engineering layered crystalline materials in various nano-materials and nano-devices via mechanical buckling. & 2015 Elsevier Ltd. All rights reserved.
Structural Analyses on the Deamidation of N-Terminal Asn in the Human N-Degron Pathway

The N-degron pathway is a proteolytic system in which a single N-terminal amino acid acts as a determinant of protein degradation. Especially, degradation signaling of N-terminal asparagine (Nt-Asn) in eukaryotes is initiated from its deamidation by N-terminal asparagine amidohydrolase 1 (NTAN1) into aspartate. Here, we have elucidated structural principles of deamidation by human NTAN1. NTAN1 adopts the characteristic scaffold of CNF1/YfiH-like cysteine hydrolases that features an α-β-β sandwich structure and a catalytic triad comprising Cys, His, and Ser. In vitro deamidation assays using model peptide substrates with varying lengths and sequences showed that NTAN1 prefers hydrophobic residues at the second-position. The structures of NTAN1-peptide complexes further revealed that the recognition of Nt-Asn is sufficiently organized to produce high specificity, and the side chain of the second-position residue is accommodated in a hydrophobic pocket adjacent to the active site of NTAN1. Collectively, our structural and biochemical analyses of the substrate specificity of NTAN1 contribute to understanding the structural basis of all three amidases in the eukaryotic N-degron pathway.
New Explanation about Cunkou Pulse

Objective: Pulse Diagnosis of Medicine is an important part. Zhenmai has its strict rules. Successive Doctors have attached great importance. Since the Jin Dynasty and written by WANG Shu-he Mai jing Since the development. The book was regarded as St. Code, has been followed so far. However, according to the modern theory of anatomy. Clock attending the traditional method have a certain error. Inch main vein in the performance of the diagnosis. Anatomy from speaking, Cunkou distribution network is the blind spot of the radial artery, and the real Cunkou pulse diagnosis should be part of the traditional Cunkou site to the ulnar Mobile 0.5 cm around the site. Clinical Practice confirmed that the vast majority of the human body parts touch the traditional pulse-inch less than the pulse. If moving to the ulnar about 0.5 cm can clearly feel To feel the pulse beat. Author of that part of the traditional clock-inch forward 0.5 cm to the ulnar the region, should be the exact location-inch clock. Inch clock is not in the radial artery, but in the branch of the radial artery -Fortune on the artery.