Text to realistic image generation with attentional concatenation generative adversarial networks.

In this paper, we propose an Attentional Concatenation Generative Adversarial Network (ACGAN) aiming at generating 1024 × 1024 high-resolution images. First, we propose a multilevel cascade structure, for text-to-image synthesis. During training progress, we gradually add new layers and, at the same time, use the results and word vectors from the previous layer as inputs to the next layer to generate high-resolution images with photo-realistic details. Second, the deep attentional multimodal similarity model is introduced into the network, and we match word vectors with images in a common semantic space to compute a fine-grained matching loss for training the generator. In this way, we can pay attention to the fine-grained information of the word level in the semantics. Finally, the measure of diversity is added to the discriminator, which enables the generator to obtain more diverse gradient directions and improve the diversity of generated samples. The experimental results show that the inception scores of the proposed model on the CUB and Oxford-102 datasets have reached 4.48 and 4.16, improved by 2.75% and 6.42% compared to Attentional Generative Adversarial Networks (AttenGAN). The ACGAN model has a better effect on text-generated images, and the resulting image is closer to the real image

Revisiting the globalisation-welfare state Nexus: what about the quality of the social welfare?

A large amount of literature examines the effects of globalisation on the size of the welfare state. Unlike previous papers, this article studies globalisation’s effects on the quality of social welfare. For this purpose, we use the annual panel dataset of 169 countries from 1970 to 2018. The findings indicate that a higher level of globalisation leads to a higher quality of the welfare state. This evidence is valid when the countries are divided according to their income levels, such as low-income, middle-income, and high-income economies. In addition, these results remain robust when various sensitivity analyses are implemented, such as using different indicators of globalisation, utilising different estimation techniques, including various controls, and excluding outliers

VoxelFormer: Bird's-Eye-View Feature Generation based on Dual-view Attention for Multi-view 3D Object Detection

In recent years, transformer-based detectors have demonstrated remarkable performance in 2D visual perception tasks. However, their performance in multi-view 3D object detection remains inferior to the state-of-the-art (SOTA) of convolutional neural network based detectors. In this work, we investigate this issue from the perspective of bird's-eye-view (BEV) feature generation. Specifically, we examine the BEV feature generation method employed by the transformer-based SOTA, BEVFormer, and identify its two limitations: (i) it only generates attention weights from BEV, which precludes the use of lidar points for supervision, and (ii) it aggregates camera view features to the BEV through deformable sampling, which only selects a small subset of features and fails to exploit all information. To overcome these limitations, we propose a novel BEV feature generation method, dual-view attention, which generates attention weights from both the BEV and camera view. This method encodes all camera features into the BEV feature. By combining dual-view attention with the BEVFormer architecture, we build a new detector named VoxelFormer. Extensive experiments are conducted on the nuScenes benchmark to verify the superiority of dual-view attention and VoxelForer. We observe that even only adopting 3 encoders and 1 historical frame during training, VoxelFormer still outperforms BEVFormer significantly. When trained in the same setting, VoxelFormer can surpass BEVFormer by 4.9% NDS point. Code is available at: https://github.com/Lizhuoling/VoxelFormer-public.git

Endophytic Beauveria bassiana promotes plant biomass growth and suppresses pathogen damage by directional recruitment

IntroductionEntomopathogenic fungi (EPF) can colonize and establish symbiotic relationships with plants as endophytes. Recently, EPF have been reported to suppress plant pathogens and induce plant resistance to diseases. However, the potential mechanisms via which EPF as endophytes control major plant diseases in situ remain largely unknown.MethodsPot and field experiments were conducted to investigate the mechanisms via which an EPF, Beauveria bassiana, colonizes tomato, under Botrytis cinerea infection stress. B. bassiana blastospores were inoculated into tomato plants by root irrigation. Tomato resistance to tomato gray mold caused by B. cinerea was evaluated by artificial inoculation, and B. bassiana colonization in plants and rhizosphere soil under B. cinerea infection stress was evaluated by colony counting and quantitative PCR. Furthermore, the expression levels of three disease resistance-related genes (OXO, CHI, and atpA) in tomato leaves were determined to explore the effect of B. bassiana colonization on plant disease resistance performance in pot experiments.ResultsB. bassiana colonization could improve resistance of tomato plants to gray mold caused by B. cinerea. The incidence rate, lesion diameter, and disease index of gray mold decreased in both the pot and field experiments following B. bassiana colonization. B. bassiana was more likely to accumulate in the pathogen infected leaves, while decreasing in the rhizosphere soil, and induced the expression of plant resistance genes, which were up-regulated in leaves.DiscussionThe results indicated that plants could “recruit” B. bassiana from rhizosphere soil to diseased plants as directional effects, which then enhanced plant growth and resistance against pathogens, consequently inhibiting pathogen infection and multiplication in plants. Our findings provide novel insights that enhance our understanding of the roles of EPF during pathogen challenge

Education in inpatient children and young people’s mental health services

<p>As a chronic disease, osteoarthritis (OA) leads to the degradation of both cartilage and subchondral bone, its development being mediated by proinflammatory cytokines like interleukin-1β. In the present study, the anti-inflammatory effect of specnuezhenide (SPN) in OA and its underlying mechanism were studied in vitro and in vivo. The results showed that SPN decreases the expression of cartilage matrix-degrading enzymes and the activation of NF-κB and wnt/β-catenin signaling, and increases chondrocyte-specific gene expression in IL-1β-induced inflammation in chondrocytes. Furthermore, SPN treatment prevents the degeneration of both cartilage and subchondral bone in a rat model of OA. To the best of our knowledge, this study is the first to report that SPN decreases interleukin-1β-induced inflammation in rat chondrocytes by inhibiting the activation of the NF-κB and wnt/β-catenin pathways, and, thus, has therapeutic potential in the treatment of OA.</p