Renormalization of tensor-network states

We have discussed the tensor-network representation of classical statistical or interacting quantum lattice models, and given a comprehensive introduction to the numerical methods we recently proposed for studying the tensor-network states/models in two dimensions. A second renormalization scheme is introduced to take into account the environment contribution in the calculation of the partition function of classical tensor network models or the expectation values of quantum tensor network states. It improves significantly the accuracy of the coarse grained tensor renormalization group method. In the study of the quantum tensor-network states, we point out that the renormalization effect of the environment can be efficiently and accurately described by the bond vector. This, combined with the imaginary time evolution of the wavefunction, provides an accurate projection method to determine the tensor-network wavfunction. It reduces significantly the truncation error and enable a tensor-network state with a large bond dimension, which is difficult to be accessed by other methods, to be accurately determined.Comment: 18 pages 23 figures, minor changes, references adde

PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module

LIDAR point clouds and RGB-images are both extremely essential for 3D object detection. So many state-of-the-art 3D detection algorithms dedicate in fusing these two types of data effectively. However, their fusion methods based on Birds Eye View (BEV) or voxel format are not accurate. In this paper, we propose a novel fusion approach named Point-based Attentive Cont-conv Fusion(PACF) module, which fuses multi-sensor features directly on 3D points. Except for continuous convolution, we additionally add a Point-Pooling and an Attentive Aggregation to make the fused features more expressive. Moreover, based on the PACF module, we propose a 3D multi-sensor multi-task network called Pointcloud-Image RCNN(PI-RCNN as brief), which handles the image segmentation and 3D object detection tasks. PI-RCNN employs a segmentation sub-network to extract full-resolution semantic feature maps from images and then fuses the multi-sensor features via powerful PACF module. Beneficial from the effectiveness of the PACF module and the expressive semantic features from the segmentation module, PI-RCNN can improve much in 3D object detection. We demonstrate the effectiveness of the PACF module and PI-RCNN on the KITTI 3D Detection benchmark, and our method can achieve state-of-the-art on the metric of 3D AP.Comment: 8 pages, 5 figure

Chromosome-Level Genome Assembly for Acer pseudosieboldianum and Highlights to Mechanisms for Leaf Color and Shape Change

Acer pseudosieboldianum (Pax) Komarov is an ornamental plant with prominent potential and is naturally distributed in Northeast China. Here, we obtained a chromosome-scale genome assembly of A. pseudosieboldianum combining HiFi and Hi-C data, and the final assembled genome size was 690.24 Mb and consisted of 287 contigs, with a contig N50 value of 5.7 Mb and a BUSCO complete gene percentage of 98.4%. Genome evolution analysis showed that an ancient duplication occurred in A. pseudosieboldianum. Phylogenetic analyses revealed that Aceraceae family could be incorporated into Sapindaceae, consistent with the present Angiosperm Phylogeny Group system. We further construct a gene-to-metabolite correlation network and identified key genes and metabolites that might be involved in anthocyanin biosynthesis pathways during leaf color change. Additionally, we identified crucial teosinte branched1, cycloidea, and proliferating cell factors (TCP) transcription factors that might be involved in leaf morphology regulation of A. pseudosieboldianum, Acer yangbiense and Acer truncatum. Overall, this reference genome is a valuable resource for evolutionary history studies of A. pseudosieboldianum and lays a fundamental foundation for its molecular breeding

Integrated Metabolomics and Proteomics Analysis Revealed Second Messenger System Disturbance in Hippocampus of Chronic Social Defeat Stress Rat

Depression is a common and disabling mental disorder characterized by high disability and mortality, but its physiopathology remains unclear. In this study, we combined a non-targeted gas chromatography-mass spectrometry (GC-MS)-based metabolomic approach and isobaric tags for relative and absolute quantitation (iTRAQ)-based proteomic analysis to elucidate metabolite and protein alterations in the hippocampus of rat after chronic social defeat stress (CSDS), an extensively used animal model of depression. Ingenuity pathway analysis (IPA) was conducted to integrate underlying relationships among differentially expressed metabolites and proteins. Twenty-five significantly different expressed metabolites and 234 differentially expressed proteins were identified between CSDS and control groups. IPA canonical pathways and network analyses revealed that intracellular second messenger/signal transduction cascades were most significantly altered in the hippocampus of CSDS rats, including cyclic adenosine monophosphate (cAMP), phosphoinositol, tyrosine kinase, and arachidonic acid systems. These results provide a better understanding of biological mechanisms underlying depression, and may help identify potential targets for novel antidepressants

Prevalence of Deliberate Self-harm Among Chinese Patients With Heroin Dependence: A Meta-Analysis

Background: There is paucity of data regarding the prevalence and methods of deliberate self-harm (DSH) in patients with heroin dependence in international literature. In China, there have been a few studies investigating the prevalence of DSH in heroin-dependent patients (HDPs), but their rates varied widely. We thus conducted a meta-analysis of studies assessing the prevalence of DSH among Chinese HDPs.Methods: Relevant studies were retrieved from major Chinese databases (China National Knowledge Infrastructure, Wanfang data, and SinoMed) and western databases (PubMed, EMBASE, and PsycInfo). Two authors independently identified eligible studies and extracted data. Studies that included a representative sample of Chinese HDPs and ascertained DSH caseness in a reliable way were considered as high quality. Statistical analysis was performed using R software.Results: In total, 15 eligible studies with a total of 37,243 Chinese HDPs were included. All included studies were conducted in heroin detoxification settings. Only two studies were rated as high quality. The pooled prevalence of DSH in Chinese HDPs was 4.4% (95%CI: 2.9, 6.2%), but the heterogeneity of prevalence rates across studies was significant (I2 = 98%, P < 0.001). Studies rated as high quality had significantly higher prevalence of DSH than those rated as low quality (13.2 vs. 3.4%, P < 0.001). Swallowing foreign objects was the most common method of DSH, with a combined prevalence of 2.7% (95%CI: 1.6, 4.4%). Extreme DSH methods such as cutting off fingers and jumping from height were also not uncommon in this patient population.Conclusion: Due to methodological problems in available studies, we find a relatively low prevalence of DSH among Chinese HDPs receiving detoxification treatment. Nevertheless, the self-harmers of Chinese HDPs are more likely to harm themselves in a dangerous or life-threatening way. Restricting the availability of DSH methods may be an effective way to prevent or reduce DSH in China's detoxification treatment settings

ECOLOGICAL CHARACTERISTICS AND SUITABILITY EVALUATION OF FRITILLARIA CIRRHOSA D. DON BASED ON MAXENT MODEL

Background: As a rare and endangered medicinal plant growing in plateaus, Fritillaria cirrhosa D. Don is a scant resource in terms of quantity and planting regions. However, there is limited knowledge on predicting the potential ecological suitability of regions for the species with the climate factors. This paper evaluates the ecological suitability of F. cirrhosa D. Don on a global scale using Maxent. Materials and Methods: The ecologically suitable regions for F. cirrhosa D. Don around the world were evaluated using the maximum entropy model (Maxent), based on 127 known occurrence records and specific environmental predictor variables. Results: The Maxent model was highly accurate, with a statistically significant Area Under the Receiving Operator Curve (AUC) value of 0.993, and the most suitable areas and the suitable areas for F. cirrhosa D. Don were approximately 450,000 and 700,000 sq. km., respectively, including China, Pakistan, Nepal, and Bhutan. A quantitative study of the climatic characteristics of F. cirrhosa D. Don indicated that the period from May to October was critical for plant growth and development. Thus, the stable precipitation-temperature ratios (0.59 to 2.42) during this period could serve as a feature indicator for the geographical distribution of the plant. Conclusion: This work should be beneficial for the introduction and resource protection of F. cirrhosa D. Don, meanwhile, the analytical method could be expanded to predict the potential distribution of other medicinal plants

Experimental study of THGEM detector with mini-rim

The gas gain and energy resolution of single and double THGEM detectors (5{\times}5cm2 effective area) with mini-rims (rim is less than 10{\mu}m) were studied. The maximum gain can reach 5{\times}103 and 2{\times}105 for single and double THGEM respectively, while the energy resolution of 5.9 keV X-ray varied from 18% to 28% for both single and double THGEM detectors of different hole sizes and thicknesses.All the experiments were investigated in mixture of noble gases(argon,neon) and small content of other gases(iso-butane,methane) at atmospheric pressure.Comment: 4pages,6figures, it has been submitted to Chinese Physics