Assessing change in the Earth's land surface albedo with moderate resolution satellite imagery

Land surface albedo describes the proportion of incident solar radiant flux that is reflected from the Earth's surface and therefore is a crucial parameter in modeling and monitoring attempts to capture the current climate, hydrological, and biogeochemical cycles and predict future scenarios. Due to the temporal variability and spatial heterogeneity of land surface albedo, remote sensing offers the only realistic method of monitoring albedo on a global scale. While the distribution of bright, highly reflective surfaces (clouds, snow, deserts) govern the vast majority of the fluctuation, variations in the intrinsic surface albedo due to natural and human disturbances such as urban development, fire, pests, harvesting, grazing, flooding, and erosion, as well as the natural seasonal rhythm of vegetation phenology, play a significant role as well. The development of times series of global snow-free and cloud-free albedo from remotely sensed observations over the past decade and a half offers a unique opportunity to monitor and assess the impact of these alterations to the Earth's land surface. By utilizing multiple satellite records from the MODerate-resolution Imaging Spectroradiometer (MODIS), the Multi-angle Imaging Spectroradiometer (MISR) and the Visible Infrared Imaging Radiometer Suite (VIIRS) instruments, and developing innovative spectral conversion coefficients and temporal gap-filling strategies, it has been possible to utilize the strengths of the various sensors to improve the spatial and temporal coverage of global land surface albedo retrievals. The availability of these products is particularly important in tropical regions where cloud cover obscures the forest for significant periods. In the Amazon, field ecologists have noted that some areas of the forest ecosystem respond rapidly with foliage growth at the beginning of the dry season, when sunlight can finally penetrate fully to the surface and have suggested this phenomenon can continue until reductions in water availability (particularly in times of drought) impact the growth cycle. While it has been difficult to capture this variability from individual optical satellite sensors, the temporally gap-filled albedo products developed during this research are used in a case study to monitor the Amazon during the dry season and identify the extent of these regions of foliage growth

Predominant contribution of cis-regulatory divergence in the evolution of mouse alternative splicing

Divergence of alternative splicing represents one of the major driving forces to shape phenotypic diversity during evolution. However, the extent to which these divergences could be explained by the evolving cis-regulatory versus trans-acting factors remains unresolved. To globally investigate the relative contributions of the two factors for the first time in mammals, we measured splicing difference between C57BL/6J and SPRET/EiJ mouse strains and allele-specific splicing pattern in their F1 hybrid. Out of 11,818 alternative splicing events expressed in the cultured fibroblast cells, we identified 796 with significant difference between the parental strains. After integrating allele-specific data from F1 hybrid, we demonstrated that these events could be predominately attributed to cis-regulatory variants, including those residing at and beyond canonical splicing sites. Contrary to previous observations in Drosophila, such predominant contribution was consistently observed across different types of alternative splicing. Further analysis of liver tissues from the same mouse strains and reanalysis of published datasets on other strains showed similar trends, implying in general the predominant contribution of cis-regulatory changes in the evolution of mouse alternative splicing

ANALYSIS OF TEACHER APTITUDE IMPACT TO PRE-SERVICE TEACHERS TRAINING

Teacher aptitude as self-preparation for people engaging in teaching services, can promote higher professional quality, boost professional achievements and lead them to aim higher. This analytical study aims at analyzing the professional characteristics of teacher aptitude, vice its operation in Chinese trainee teachers’ preparation then suggest strategies to improve it. The authors divide teacher aptitude into four aspects: having professional teaching values, interest, ability, personality. Furthermore, the study suggests that both evaluation and cultivation of teacher aptitude should be considered. Therefore, pre-service teacher preparation should base on the aptitude as personal willingness by also adding aptitude assessment during the enrollment, provide enough educational theory& practice training programs, and construct adequate pre-service teacher training mechanisms for non-education majors’ students. Article visualizations

The Influences of Key Factors on the Consequences Following the Natural Gas Leakage from Pipeline

AbstractThe effects of the environmental dispersion (i.e. atmospheric stability, wind speed, temperature, humidity and ground roughness) and source release factors (i.e. pipeline diameter, length, pressure and release opening area) on the suffocation distance, flammable vapor cloud distance, overpressure distance and thermal radiation distance after the natural gas released from pipeline were evaluated and analyzed. The results show that all the environmental dispersion factors except humidity have an effect on the flammable vapor cloud distance. The more stable atmospheric condition, lower wind speed and smaller ground roughness lead to the longer flammable vapor cloud distance. The atmosphere temperature has a very limited influence on the flammable vapor cloud distance. The higher ambient temperature and larger humidity result in the longer downwind thermal radiation distance, while the atmospheric stability, wind speed and ground roughness nearly does not. All the four source release factors significantly influence the flammable vapor cloud distance and thermal radiation distance, which is due to the different release amount, release rate and initial momentum

Robust Time Series Dissimilarity Measure for Outlier Detection and Periodicity Detection

Dynamic time warping (DTW) is an effective dissimilarity measure in many time series applications. Despite its popularity, it is prone to noises and outliers, which leads to singularity problem and bias in the measurement. The time complexity of DTW is quadratic to the length of time series, making it inapplicable in real-time applications. In this paper, we propose a novel time series dissimilarity measure named RobustDTW to reduce the effects of noises and outliers. Specifically, the RobustDTW estimates the trend and optimizes the time warp in an alternating manner by utilizing our designed temporal graph trend filtering. To improve efficiency, we propose a multi-level framework that estimates the trend and the warp function at a lower resolution, and then repeatedly refines them at a higher resolution. Based on the proposed RobustDTW, we further extend it to periodicity detection and outlier time series detection. Experiments on real-world datasets demonstrate the superior performance of RobustDTW compared to DTW variants in both outlier time series detection and periodicity detection

TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis

Time series anomaly detection is a challenging problem due to the complex temporal dependencies and the limited label data. Although some algorithms including both traditional and deep models have been proposed, most of them mainly focus on time-domain modeling, and do not fully utilize the information in the frequency domain of the time series data. In this paper, we propose a Time-Frequency analysis based time series Anomaly Detection model, or TFAD for short, to exploit both time and frequency domains for performance improvement. Besides, we incorporate time series decomposition and data augmentation mechanisms in the designed time-frequency architecture to further boost the abilities of performance and interpretability. Empirical studies on widely used benchmark datasets show that our approach obtains state-of-the-art performance in univariate and multivariate time series anomaly detection tasks. Code is provided at https://github.com/DAMO-DI-ML/CIKM22-TFAD.Comment: Accepted by the ACM International Conference on Information and Knowledge Management (CIKM 2022

BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition

In real-world scenarios like traffic and energy, massive time-series data with missing values and noises are widely observed, even sampled irregularly. While many imputation methods have been proposed, most of them work with a local horizon, which means models are trained by splitting the long sequence into batches of fit-sized patches. This local horizon can make models ignore global trends or periodic patterns. More importantly, almost all methods assume the observations are sampled at regular time stamps, and fail to handle complex irregular sampled time series arising from different applications. Thirdly, most existing methods are learned in an offline manner. Thus, it is not suitable for many applications with fast-arriving streaming data. To overcome these limitations, we propose \ours: Bayesian Online Multivariate Time series Imputation with functional decomposition. We treat the multivariate time series as the weighted combination of groups of low-rank temporal factors with different patterns. We apply a group of Gaussian Processes (GPs) with different kernels as functional priors to fit the factors. For computational efficiency, we further convert the GPs into a state-space prior by constructing an equivalent stochastic differential equation (SDE), and developing a scalable algorithm for online inference. The proposed method can not only handle imputation over arbitrary time stamps, but also offer uncertainty quantification and interpretability for the downstream application. We evaluate our method on both synthetic and real-world datasets