On the Function, Subject, and Capacity of the Religious Property System in China

To construct our religious property system, we must first define its purpose and function, and then clarify the connotation, subject, and capacity of religious property, the premise of which is to scientifically understand the nature, purpose and function of religions. The"religious purpose"of the religious property system is different in its appeal to different subjects: The state, religious groups and believers. For different types of property, religious purposes differ in directness and indirectness, but they are unified in the realization of the basic religious policy of the Party and the state

Transcriptome profiling of a common mistletoe species parasitizing four typical host species in urban southwest China

Comparing gene expressions among parasitic plants infecting different host species can have significant implications for understanding host–parasite interactions. Taxillus nigrans is a common hemiparasitic species in Southwest China that parasitizes a variety of host species. However, a lack of nucleotide sequence data to date has hindered transcriptome-level research on T. nigrans. In this study, the transcriptomes of T. nigrans individuals parasitizing four typical host species (Broussonetia papyrifera (Bpap), a broad-leaved tree species; Cryptomeria fortunei (Cfor), a coniferous tree species; Cinnamomum septentrionale (Csep), an evergreen tree species; and Ginkgo biloba (Gbil), a deciduous-coniferous tree species) were sequenced, and the expression profiles and metabolic pathways were compared among hosts. A total of greater than 400 million reads were generated in nine cDNA libraries. These were de novo assembled into 293823 transcripts with an N50 value of 1790 bp. A large number of differentially expressed genes (DEGs) were identified when comparing T. nigrans individuals on different host species: Bpap vs. Cfor (1253 DEGs), Bpap vs. Csep (864), Bpap vs. Gbil (517), Cfor vs. Csep (259), Cfor vs. Gbil (95), and Csep vs. Gbil (40). Four hundred and fifteen unigenes were common to all six pairwise comparisons; these were primarily associated with Cytochrome P450 and environmental adaptation, as determined in a KEGG enrichment analysis. Unique unigenes were also identified, specific to Bpap vs. Cfor (808 unigenes), Bpap vs. Csep (329 unigenes), Bpap vs. Gbil (87 unigenes), Cfor vs. Csep (108 unigenes), Cfor vs. Gbil (32 unigenes), and Csep vs. Gbil comparisons (23 unigenes); partial unigenes were associated with the metabolism of terpenoids and polyketides regarding plant hormone signal transduction. Weighted gene co-expression network analysis (WGCNA) revealed four modules that were associated with the hosts. These results provide a foundation for further exploration of the detailed molecular mechanisms involved in plant parasitism

Using CloudSat observations to evaluate cloud top heights from convection parameterization

How high convective clouds can go is of great importance to climate. Cloud ice and liquid water that detrain near the top of convective cores are important for the formation of anvil clouds and thus impact cloud radiative forcing and the Earth’s radiation budget. This study uses CloudSat observations to evaluate convective cloud top heights in the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM5). Results show that convective cloud top heights in the tropics are much lower than observed by CloudSat, by more than 2 km on average. Temperature and moisture anomalies from climatological means are composited for convective clouds of different heights for both observations and model simulation. It is found that convective environment is warmer and moister, and the anomalies are larger for clouds of higher tops. For a given convective cloud top height, the corresponding atmosphere in CAM5 is more convectively unstable than what the CloudSat observations indicate, suggesting that there is too much entrainment into convective clouds in the model

Text-oriented Modality Reinforcement Network for Multimodal Sentiment Analysis from Unaligned Multimodal Sequences

Multimodal Sentiment Analysis (MSA) aims to mine sentiment information from text, visual, and acoustic modalities. Previous works have focused on representation learning and feature fusion strategies. However, most of these efforts ignored the disparity in the semantic richness of different modalities and treated each modality in the same manner. That may lead to strong modalities being neglected and weak modalities being overvalued. Motivated by these observations, we propose a Text-oriented Modality Reinforcement Network (TMRN), which focuses on the dominance of the text modality in MSA. More specifically, we design a Text-Centered Cross-modal Attention (TCCA) module to make full interaction for text/acoustic and text/visual pairs, and a Text-Gated Self-Attention (TGSA) module to guide the self-reinforcement of the other two modalities. Furthermore, we present an adaptive fusion mechanism to decide the proportion of different modalities involved in the fusion process. Finally, we combine the feature matrices into vectors to get the final representation for the downstream tasks. Experimental results show that our TMRN outperforms the state-of-the-art methods on two MSA benchmarks.Comment: Accepted by CICAI 2023 (Finalist of Best Student Paper Award

Process Knowledge-guided Autonomous Evolutionary Optimization for Constrained Multiobjective Problems

Various real-world problems can be attributed to constrained multi-objective optimization problems. Although there are various solution methods, it is still very challenging to automatically select efficient solving strategies for constrained multi-objective optimization problems. Given this, a process knowledge-guided constrained multi-objective autonomous evolutionary optimization method is proposed. Firstly, the effects of different solving strategies on population states are evaluated in the early evolutionary stage. Then, the mapping model of population states and solving strategies is established. Finally, the model recommends subsequent solving strategies based on the current population state. This method can be embedded into existing evolutionary algorithms, which can improve their performances to different degrees. The proposed method is applied to 41 benchmarks and 30 dispatch optimization problems of the integrated coal mine energy system. Experimental results verify the effectiveness and superiority of the proposed method in solving constrained multi-objective optimization problems.The National Key R&D Program of China, the National Natural Science Foundation of China, Shandong Provincial Natural Science Foundation, Fundamental Research Funds for the Central Universities and the Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4235hj2023Electrical, Electronic and Computer Engineerin

Taxonomic status of Populus wulianensis and P. ningshanica (Salicaceae)

Species delimitation in the genus Populus is particularly challenging due to high levels of intraspecific polymorphism as well as frequent interspecific hybridisation and introgression. In this study, we aimed to examine the taxonomic status of Populus ningshanica and P. wulianensis using an integrative taxonomy that considers multiple operational criteria. We carried out morphometric analyses of leaf traits and genetic examinations (including sequence variations at five barcoding DNAs and polymorphisms at 14 nuclear microsatellite SSR primers) at the population level between them and two closely related species P. adenopoda and P. davidiana. Results suggest that P. wulianensis belongs to the polymorphic species, P. adenopoda and should be considered as a synonym of the latter. P. ningshanica may have arisen as a result on the hybridisation between P. adenopoda and P. davidiana and therefore should be treated as P. × ningshanica. This study highlights the importance of the integrated evidence in taxonomic decisions of the disputed species

Context De-confounded Emotion Recognition

Context-Aware Emotion Recognition (CAER) is a crucial and challenging task that aims to perceive the emotional states of the target person with contextual information. Recent approaches invariably focus on designing sophisticated architectures or mechanisms to extract seemingly meaningful representations from subjects and contexts. However, a long-overlooked issue is that a context bias in existing datasets leads to a significantly unbalanced distribution of emotional states among different context scenarios. Concretely, the harmful bias is a confounder that misleads existing models to learn spurious correlations based on conventional likelihood estimation, significantly limiting the models' performance. To tackle the issue, this paper provides a causality-based perspective to disentangle the models from the impact of such bias, and formulate the causalities among variables in the CAER task via a tailored causal graph. Then, we propose a Contextual Causal Intervention Module (CCIM) based on the backdoor adjustment to de-confound the confounder and exploit the true causal effect for model training. CCIM is plug-in and model-agnostic, which improves diverse state-of-the-art approaches by considerable margins. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our CCIM and the significance of causal insight.Comment: Accepted by CVPR 2023. CCIM is available at https://github.com/ydk122024/CCI