Multiscale Latent-Guided Entropy Model for LiDAR Point Cloud Compression

The non-uniform distribution and extremely sparse nature of the LiDAR point cloud (LPC) bring significant challenges to its high-efficient compression. This paper proposes a novel end-to-end, fully-factorized deep framework that encodes the original LPC into an octree structure and hierarchically decomposes the octree entropy model in layers. The proposed framework utilizes a hierarchical latent variable as side information to encapsulate the sibling and ancestor dependence, which provides sufficient context information for the modelling of point cloud distribution while enabling the parallel encoding and decoding of octree nodes in the same layer. Besides, we propose a residual coding framework for the compression of the latent variable, which explores the spatial correlation of each layer by progressive downsampling, and model the corresponding residual with a fully-factorized entropy model. Furthermore, we propose soft addition and subtraction for residual coding to improve network flexibility. The comprehensive experiment results on the LiDAR benchmark SemanticKITTI and MPEG-specified dataset Ford demonstrates that our proposed framework achieves state-of-the-art performance among all the previous LPC frameworks. Besides, our end-to-end, fully-factorized framework is proved by experiment to be high-parallelized and time-efficient and saves more than 99.8% of decoding time compared to previous state-of-the-art methods on LPC compression

Visit-to-visit variability in triglyceride-glucose index and diabetes:A 9-year prospective study in the Kailuan Study

Instruction/Aims: It is unknown whether variability in the triglyceride-glucose index (TyG-index) is associated with the risk of diabetes. Here, we sought to characterize the relationship between TyG-index variability and incident diabetes. Methods: We performed a prospective study of 48,013 participants in the Kailuan Study who did not have diabetes. The TyG-index was calculated as ln [triglyceride (TG, mg/dL) concentration × fasting blood glucose concentration (FBG, mg/dL)/2]. The TyG-index variability was assessed using the standard deviation (SD) of three TyG-index values that were calculated during 2006/07, 2008/09, and 2010/11. We used the Cox proportional hazard models to analyze the effect of TyG-index variability on incident diabetes. Results: A total of 4,055 participants were newly diagnosed with diabetes during the study period of 8.95 years (95% confidence interval (CI) 8.48–9.29 years). After adjustment for confounding factors, participants in the highest and second-highest quartiles had significantly higher risks of new-onset diabetes versus the lowest quartile, with hazard ratios (95% CIs) of 1.18 (1.08–1.29) and 1.13 (1.03–1.24), respectively (P trend< 0.05). These higher risks remained after further adjustment for the baseline TyG-index. Conclusions: A substantial fluctuation in TyG-index is associated with a higher risk of diabetes in the Chinese population, implying that it is important to maintain a normal and consistent TyG-index

Transformation of animal utilization strategies from the late Neolithic to the Han Dynasty in the Hexi Corridor, northwest China: Zooarchaeological and stable isotopic evidence

The trajectory and influencing factors for changes to ancient human livelihoods in the Hexi Corridor of northwest China have been intensively discussed. The Hexi Corridor is a key crossroads for trans-Eurasian exchange in both the prehistoric and historical periods. Although most studies have focused on the reconstruction of human paleodiet and plant subsistence, the diachronic change of animal utilization strategies spanning the prehistoric and historical periods remains unclear, due to the absence of zooarchaeological and isotopic studies, especially in Han Dynasty (202 BCE–220 CE). Here we report new zooarchaeological, stable isotope, and radiocarbon dating data from the Heishuiguo Cemetery of the Han Dynasty in the Hexi Corridor, indicating that humans mainly used domestic chickens, pigs and sheep as funerary objects, with other buried livestock including cattle, horses and dogs. Stable carbon and nitrogen isotope data suggest humans might have fed chickens, pigs and dogs more C4 foods (likely millets or their byproducts) than herbivorous livestock in the Heishuiguo during the Han Dynasty. Compared to other prehistoric zooarchaeological and isotopic studies in the Hexi Corridor, we detected an increasing significance of herbivorous livestock in animal utilization strategies compared with omnivorous livestock, and a basic declining weight of C4 foods in fodders from ∼2,300 to 200 BCE, which was probably induced by long-distance exchange and climate fluctuation. However, the trend was reversed during the Han Dynasty in the Hexi Corridor, primarily due to the control of the area by the Han Empire and the subsequent massive immigration from the Yellow River valley of north China

Progress on the Co-Pyrolysis of Coal and Biomass

In this chapter, the synergistic mechanism and the resulting influence during co-pyrolysis of coal and biomass, are summarized. The properties of coal and biomass, the release and migration of alkali and alkaline earth metals (AAEMs), the interaction between volatile and char, the characteristics of the resulting volatiles, and the physicochemical structure and reactivity of co-pyrolysis char, are also analyzed. In addition, the influence of AAEMs on the properties of the co-pyrolysis products is reviewed. Moreover, the analysis of the co-pyrolysis industry demonstration is also mentioned. Finally, this chapter also proposes some additional possibilities, based on further literature research

The migration of acetochlor from feed to milk

Acetochlor has been widely used globally for its effective weed control, but the dietary intake of associated residues by people has become a major concern nowadays. Milk is regarded as the best solvent to dissolve pesticides due to its fat-rich characteristic. In this study, we aimed to evaluate the transfer of acetochlor from feed to raw milk. Twenty lactating Australian Holstein cows were randomly chosen and divided into 1 control group and 3 treatment groups, feeding acetochlor at the dosages of 0, 0.45, 1.35 and 4.05 g per day during the treatment period. The concentration of acetochlor residues in raw milk was detected by QuEChERS together with a gas chromatography-mass spectrometry (GC-MS) method. The results showed that the highest concentrations of acetochlor residues in raw milk for the three treatment groups had a positive correlation with the dosage levels and the transfer efficiency of the low dose group was only 0.080%, higher than those of the other two groups. Besides, the national estimated daily intake (NEDI) of acetochlor from milk is 1.67 × 10(−5) mg kg(−1), which is 0.08% of the ADI. Overall, we concluded that the risk of acetochlor residues in milk was low, but high-dose acetochlor had a larger impact on milk quality and low-dose acetochlor had potential risks

Genetic insights into resting heart rate and its role in cardiovascular disease

Resting heart rate is associated with cardiovascular diseases and mortality in observational and Mendelian randomization studies. The aims of this study are to extend the number of resting heart rate associated genetic variants and to obtain further insights in resting heart rate biology and its clinical consequences. A genome-wide meta-analysis of 100 studies in up to 835,465 individuals reveals 493 independent genetic variants in 352 loci, including 68 genetic variants outside previously identified resting heart rate associated loci. We prioritize 670 genes and in silico annotations point to their enrichment in cardiomyocytes and provide insights in their ECG signature. Two-sample Mendelian randomization analyses indicate that higher genetically predicted resting heart rate increases risk of dilated cardiomyopathy, but decreases risk of developing atrial fibrillation, ischemic stroke, and cardio-embolic stroke. We do not find evidence for a linear or non-linear genetic association between resting heart rate and all-cause mortality in contrast to our previous Mendelian randomization study. Systematic alteration of key differences between the current and previous Mendelian randomization study indicates that the most likely cause of the discrepancy between these studies arises from false positive findings in previous one-sample MR analyses caused by weak-instrument bias at lower P-value thresholds. The results extend our understanding of resting heart rate biology and give additional insights in its role in cardiovascular disease development

Human-environment interaction systems between regional and continental scales in mid-latitude Eurasia during 6000–3000 years ago

The Late Neolithic and Bronze Ages witnessed the extensive expansion of human settlements, along with the dispersal of crops and livestock originating from West and East Asia. These events profoundly reshaped the human-environment relationship in mid-latitude Eurasia and the underlying trans-Eurasian exchange. While the processes and factors that underpin the interaction between human societies and ever-changing environments have been a heated debate in various regions of Eurasia, there is still a lack of synergistic discussion regarding human-environment interactions at regional and continental scales. To this end, we provide a comprehensive review and synthesis of updated radiocarbon dates and archaeobotanical and zooarchaeological data from sites dated between 6000 and 3000 cal. yr BP in mid-latitude Eurasia, coupled with associated archaeological and palaeoclimatic records. The results reveal the emergence and expansion of a number of regional settlement centers along the prehistoric Silk Roads and Eurasian Steppes during the 6th–4th millennium cal. yr BP. The prime drivers include the spread of new technologies, human migration, and climate change. As a result of successful food production and increasing social complexity, many areas have experienced rapid population growth, creating a foundation for subsequent widespread expansion of farming and herding communities across Eurasia. Under this overarching picture, many regional patterns arose due to specific natural and social conditions, weaving into broad spatiotemporal variations across Eurasia. A new conceptual model is proposed to depict this feedback loop of the interaction between human-environment systems at and between regional and continental scales

D-DPCC: Deep Dynamic Point Cloud Compression via 3D Motion Prediction

The non-uniformly distributed nature of the 3D dynamic point cloud (DPC) brings significant challenges to its high-efficient inter-frame compression. This paper proposes a novel 3D sparse convolution-based Deep Dynamic Point Cloud Compression (D-DPCC) network to compensate and compress the DPC geometry with 3D motion estimation and motion compensation in the feature space. In the proposed D-DPCC network, we design a {\it Multi-scale Motion Fusion} (MMF) module to accurately estimate the 3D optical flow between the feature representations of adjacent point cloud frames. Specifically, we utilize a 3D sparse convolution-based encoder to obtain the latent representation for motion estimation in the feature space and introduce the proposed MMF module for fused 3D motion embedding. Besides, for motion compensation, we propose a 3D {\it Adaptively Weighted Interpolation} (3DAWI) algorithm with a penalty coefficient to adaptively decrease the impact of distant neighbors. We compress the motion embedding and the residual with a lossy autoencoder-based network. To our knowledge, this paper is the first work proposing an end-to-end deep dynamic point cloud compression framework. The experimental result shows that the proposed D-DPCC framework achieves an average 76\% BD-Rate (Bjontegaard Delta Rate) gains against state-of-the-art Video-based Point Cloud Compression (V-PCC) v13 in inter mode

Collaborative Filtering Recommendation Using Nonnegative Matrix Factorization in GPU-Accelerated Spark Platform

Nonnegative matrix factorization (NMF) has been introduced as an efficient way to reduce the complexity of data compression and its capability of extracting highly interpretable parts from data sets, and it has also been applied to various fields, such as recommendations, image analysis, and text clustering. However, as the size of the matrix increases, the processing speed of nonnegative matrix factorization is very slow. To solve this problem, this paper proposes a parallel algorithm based on GPU for NMF in Spark platform, which makes full use of the advantages of in-memory computation mode and GPU acceleration. The new GPU-accelerated NMF on Spark platform is evaluated in a 4-node Spark heterogeneous cluster using Google Compute Engine by configuring each node a NVIDIA K80 CUDA device, and experimental results indicate that it is competitive in terms of computational time against the existing solutions on a variety of matrix orders. Furthermore, a GPU-accelerated NMF-based parallel collaborative filtering (CF) algorithm is also proposed, utilizing the advantages of data dimensionality reduction and feature extraction of NMF, as well as the multicore parallel computing mode of CUDA. Using real MovieLens data sets, experimental results have shown that the parallelization of NMF-based collaborative filtering on Spark platform effectively outperforms traditional user-based and item-based CF with a higher processing speed and higher recommendation accuracy