Nexus between green financing, economic risk, political risk and environment: evidence from China

This study provides fresh evidence regarding the dynamic association that is believed to exist in relation to green finance (GF), economic growth (GDP), political risk (PR), economic risk (ER), and carbon dioxide (CO2) emissions. It therefore uses the dataset pertaining to China from most recent time-series – covering the period spanning from the years of 1990 to 2020, by employing the Morlet Wavelet Analysis technique. The empirical findings of the wavelet power spectrum reveal that green finance GF and ER are vulnerable in the short- and long-run, and the short-run, respectively. At the same time, no vulnerability has been observed in the GDP, PR, and CO2 emissions. In addition to this, the wavelet coherence also reveals the bidirectional causal association that exists between GF-CO2 and ER-CO2, but only in the short run. It must also be taken into consideration that the causal influence of CO2 is deemed to be greater than the GF and ER, respectively. Besides this, a bidirectional causal nexus also exists between the GDP and CO2 emissions, only in the long run. Furthermore, the association between the economic growths follows both the phase and antiphase associations. Moreover, the study also reveals that there is no significant causal link between the PR and CO2 emissions. The results emphasize that the significance of green finance investment will tend to increase with strict policy implications, stabilization or minimization of economic risk and political risk. The same will also take place while promoting environmentally friendly production via economic growth, so as to reduce CO2 emission in the region taken into account

Being Comes from Not-being: Open-vocabulary Text-to-Motion Generation with Wordless Training

Text-to-motion generation is an emerging and challenging problem, which aims to synthesize motion with the same semantics as the input text. However, due to the lack of diverse labeled training data, most approaches either limit to specific types of text annotations or require online optimizations to cater to the texts during inference at the cost of efficiency and stability. In this paper, we investigate offline open-vocabulary text-to-motion generation in a zero-shot learning manner that neither requires paired training data nor extra online optimization to adapt for unseen texts. Inspired by the prompt learning in NLP, we pretrain a motion generator that learns to reconstruct the full motion from the masked motion. During inference, instead of changing the motion generator, our method reformulates the input text into a masked motion as the prompt for the motion generator to ``reconstruct'' the motion. In constructing the prompt, the unmasked poses of the prompt are synthesized by a text-to-pose generator. To supervise the optimization of the text-to-pose generator, we propose the first text-pose alignment model for measuring the alignment between texts and 3D poses. And to prevent the pose generator from overfitting to limited training texts, we further propose a novel wordless training mechanism that optimizes the text-to-pose generator without any training texts. The comprehensive experimental results show that our method obtains a significant improvement against the baseline methods. The code is available at https://github.com/junfanlin/oohmg

Prompt-Matched Semantic Segmentation

The objective of this work is to explore how to effectively and efficiently adapt pre-trained visual foundation models to various downstream tasks of semantic segmentation. Previous methods usually fine-tuned the entire networks for each specific dataset, which will be burdensome to store massive parameters of these networks. A few recent works attempted to insert some extra trainable parameters into the frozen networks to learn visual prompts for parameter-efficient tuning. However, these works showed poor generality as they were designed specifically for Transformers. Moreover, using limited information in these schemes, they exhibited a poor capacity to learn beneficial prompts. To alleviate these issues, we propose a novel Stage-wise Prompt-Matched Framework for generic and effective visual prompt tuning. Specifically, to ensure generality, we divide the pre-trained backbone with frozen parameters into multiple stages and perform prompt learning between different stages, which makes the proposed scheme applicable to various architectures of CNN and Transformer. For effective tuning, a lightweight Semantic-aware Prompt Matcher (SPM) is designed to progressively learn reasonable prompts with a recurrent mechanism, guided by the rich information of interim semantic maps. Working as deep matched filter of representation learning, the proposed SPM can well transform the output of the previous stage into a desirable input for the next stage, thus achieving the better matching/stimulating for the pre-trained knowledge. Extensive experiments on four benchmarks demonstrate that the proposed scheme can achieve a promising trade-off between parameter efficiency and performance effectiveness. Our code and models will be released

Extraction of differential expressing aphid-resistance genes of sorghum (Sorghum bicolor L. Monech) and construction of suppression subtractive hybridization (SSH) library

Experiments were conducted for the extraction of differential expressing aphid-resistance genes of sorghum (Sorghum bicolor L. Monech) in the experimental laboratories and fields of Hebei Agricultural University, China and Shenyang Agricultural University, Liaoning Province, China, during 2010 to 2011 and suppression subtractive hybridization (SSH) library was constructed. The seeds of two sorghum varieties (Henong-16 and Qian-3) were grown and aphids were infested through natural and artificial way on sorghum seedlings (10-day old) with a paint brush. Total mRNA was isolated from fresh leaves samples using Trizol reagent and plant RNA mate (TAKARA). Integrity of RNA was confirmed by 1.2% agarose gel electrophoresis. SSH was performed using PCR-Select cDNA subtraction kit user manual according to the manufacturer’s instruction (Clontech Laboratories, Inc, USA). cDNA that contained specific (differentially expressed) transcripts were denoted as tester and the reference cDNA as driver. Tester and driver cDNAs were hybridized after two rounds of subtractive suppression PCR and the pMD18-T vector (TAKARA, Dalian, China). After preliminary screening by subtractive hybridization, plasmid restriction enzyme digestion, colony PCR for 100 forward and 100 reverse clones were sequenced by two-way hybridization using Mega BACE1000 to obtain better quality of 200 expressed sequence tag (EST) sequences. Cross-match software and ClustalW2 were used to obtain vector sequence shielding and multiple comparisons. Using BLAST at NCBI database for homology comparisons, it was concluded that a number of EST sequences which had different degrees of homology with known proteins or genes and another six EST sequences did not have any significant homology in the database. These sequences might have representation for new and unknown genes, or higher variability of non-coding region cDNA sequences.Key words: Extraction, sorghum, SSH, aphid-resistance genes

Self-Evolved Diverse Data Sampling for Efficient Instruction Tuning

Enhancing the instruction-following ability of Large Language Models (LLMs) primarily demands substantial instruction-tuning datasets. However, the sheer volume of these imposes a considerable computational burden and annotation cost. To investigate a label-efficient instruction tuning method that allows the model itself to actively sample subsets that are equally or even more effective, we introduce a self-evolving mechanism DiverseEvol. In this process, a model iteratively augments its training subset to refine its own performance, without requiring any intervention from humans or more advanced LLMs. The key to our data sampling technique lies in the enhancement of diversity in the chosen subsets, as the model selects new data points most distinct from any existing ones according to its current embedding space. Extensive experiments across three datasets and benchmarks demonstrate the effectiveness of DiverseEvol. Our models, trained on less than 8% of the original dataset, maintain or improve performance compared with finetuning on full data. We also provide empirical evidence to analyze the importance of diversity in instruction data and the iterative scheme as opposed to one-time sampling. Our code is publicly available at https://github.com/OFA-Sys/DiverseEvol.git

Perivascular adipose tissue (PVAT) in atherosclerosis: a double-edged sword

Abstract Perivascular adipose tissue (PVAT), the adipose tissue that surrounds most of the vasculature, has emerged as an active component of the blood vessel wall regulating vascular homeostasis and affecting the pathogenesis of atherosclerosis. Although PVAT characteristics resemble both brown and white adipose tissues, recent evidence suggests that PVAT develops from its own distinct precursors implying a closer link between PVAT and vascular system. Under physiological conditions, PVAT has potent anti-atherogenic properties mediated by its ability to secrete various biologically active factors that induce non-shivering thermogenesis and metabolize fatty acids. In contrast, under pathological conditions (mainly obesity), PVAT becomes dysfunctional, loses its thermogenic capacity and secretes pro-inflammatory adipokines that induce endothelial dysfunction and infiltration of inflammatory cells, promoting atherosclerosis development. Since PVAT plays crucial roles in regulating key steps of atherosclerosis development, it may constitute a novel therapeutic target for the prevention and treatment of atherosclerosis. Here, we review the current literature regarding the roles of PVAT in the pathogenesis of atherosclerosis.https://deepblue.lib.umich.edu/bitstream/2027.42/145729/1/12933_2018_Article_777.pd

Dual Information Enhanced Multi-view Attributed Graph Clustering

Multi-view attributed graph clustering is an important approach to partition multi-view data based on the attribute feature and adjacent matrices from different views. Some attempts have been made in utilizing Graph Neural Network (GNN), which have achieved promising clustering performance. Despite this, few of them pay attention to the inherent specific information embedded in multiple views. Meanwhile, they are incapable of recovering the latent high-level representation from the low-level ones, greatly limiting the downstream clustering performance. To fill these gaps, a novel Dual Information enhanced multi-view Attributed Graph Clustering (DIAGC) method is proposed in this paper. Specifically, the proposed method introduces the Specific Information Reconstruction (SIR) module to disentangle the explorations of the consensus and specific information from multiple views, which enables GCN to capture the more essential low-level representations. Besides, the Mutual Information Maximization (MIM) module maximizes the agreement between the latent high-level representation and low-level ones, and enables the high-level representation to satisfy the desired clustering structure with the help of the Self-supervised Clustering (SC) module. Extensive experiments on several real-world benchmarks demonstrate the effectiveness of the proposed DIAGC method compared with the state-of-the-art baselines.Comment: 11 pages, 4 figure