Data Augmentation Vision Transformer for Fine-grained Image Classification

Recently, the vision transformer (ViT) has made breakthroughs in image recognition. Its self-attention mechanism (MSA) can extract discriminative labeling information of different pixel blocks to improve image classification accuracy. However, the classification marks in their deep layers tend to ignore local features between layers. In addition, the embedding layer will be fixed-size pixel blocks. Input network Inevitably introduces additional image noise. To this end, we study a data augmentation vision transformer (DAVT) based on data augmentation and proposes a data augmentation method for attention cropping, which uses attention weights as the guide to crop images and improve the ability of the network to learn critical features. Secondly, we also propose a hierarchical attention selection (HAS) method, which improves the ability of discriminative markers between levels of learning by filtering and fusing labels between levels. Experimental results show that the accuracy of this method on the two general datasets, CUB-200-2011, and Stanford Dogs, is better than the existing mainstream methods, and its accuracy is 1.4\% and 1.6\% higher than the original ViT, respectivelyComment: IEEE Signal Processing Letter

A Lightweight Reconstruction Network for Surface Defect Inspection

Currently, most deep learning methods cannot solve the problem of scarcity of industrial product defect samples and significant differences in characteristics. This paper proposes an unsupervised defect detection algorithm based on a reconstruction network, which is realized using only a large number of easily obtained defect-free sample data. The network includes two parts: image reconstruction and surface defect area detection. The reconstruction network is designed through a fully convolutional autoencoder with a lightweight structure. Only a small number of normal samples are used for training so that the reconstruction network can be A defect-free reconstructed image is generated. A function combining structural loss and $\mathit{L}1$ loss is proposed as the loss function of the reconstruction network to solve the problem of poor detection of irregular texture surface defects. Further, the residual of the reconstructed image and the image to be tested is used as the possible region of the defect, and conventional image operations can realize the location of the fault. The unsupervised defect detection algorithm of the proposed reconstruction network is used on multiple defect image sample sets. Compared with other similar algorithms, the results show that the unsupervised defect detection algorithm of the reconstructed network has strong robustness and accuracy.Comment: Journal of Mathematical Imaging and Vision(JMIV

Autonomous wind turbine inspection using a quadrotor

There has been explosive growth of wind farm installations in recent years due to the fact that wind energy is gaining worldwide popularity. However, the maintenance of these offshore or onshore wind turbines, especially in remote areas, remains a challenging task. In this work, vision-based autonomous wind turbine inspection using a quadrotor is designed based on realistic assumptions. Both simulation and Hardware-In-the-Loop (HIL) testing results have shown the effectiveness of the proposed approach

Hypoxia associated multi-omics molecular landscape of tumor tissue in patients with hepatocellular carcinoma

The present study was designed to update the knowledge about hypoxia-related multi-omic molecular landscape in hepatocellular carcinoma (HCC) tissues. Large-size HCC datasets from multiple centers were collected. The hypoxia exposure of tumor tissue from patients in 10 HCC cohorts was estimated using a novel HCC-specific hypoxia score system constructed in our previous study. A comprehensive bioinformatical analysis was conducted to compare hypoxia-associated multi-omic molecular features in patients with a high hypoxia score to a low hypoxia score. We found that patients with different exposure to hypoxia differed significantly in transcriptomic, genomic, epigenomic, and proteomic alterations, including differences in mRNA, microRNA (miR), and long non-coding RNA (lncRNA) expression, differences in copy number alterations (CNAs), differences in DNA methylation levels, differences in RNA alternative splicing events, and differences in protein levels. HCC survival-associated molecular events were identified. The potential correlation between molecular features related to hypoxia has also been explored, and various networks have been constructed. We revealed a particularly comprehensive hypoxia-related molecular landscape in tumor tissues that provided novel evidence and perspectives to explain the role of hypoxia in HCC. Clinically, the data obtained from the present study may enable the development of individualized treatment or management strategies for HCC patients with different levels of hypoxia exposure.</p

Efficient induction of CD25- iTreg by co-immunization requires strongly antigenic epitopes for T cells

Background: We previously showed that co-immunization with a protein antigen and a DNA vaccine coding for the same antigen induces CD40(low) IL-10(high) tolerogenic DCs, which in turn stimulates the expansion of antigenspecific CD4(+)CD25(-)Foxp3(+) regulatory T cells (CD25(-) iTreg). However, it was unclear how to choose the antigen sequence to maximize tolerogenic antigen presentation and, consequently, CD25(-) iTreg induction. Results: In the present study, we demonstrated the requirement of highly antigenic epitopes for CD25(-) iTreg induction. Firstly, we showed that the induction of CD25(-) iTreg by tolerogenic DC can be blocked by anti-MHC-II antibody. Next, both the number and the suppressive activity of CD25(-) iTreg correlated positively with the overt antigenicity of an epitope to activate T cells. Finally, in a mouse model of dermatitis, highly antigenic epitopes derived from a flea allergen not only induced more CD25(-) iTreg, but also more effectively prevented allergenic reaction to the allergen than did weakly antigenic epitopes. Conclusions: Our data thus indicate that efficient induction of CD25- iTreg requires highly antigenic peptide epitopes. This finding suggests that highly antigenic epitopes should be used for efficient induction of CD25- iTreg for clinical applications such as flea allergic dermatitis

A Novel Distance between Vague Sets and Its Applications in Decision Making

A novel distance between vague sets (VSs) is presented after the inadequacies of existing distance measures between vague sets are analyzed by artificial vague sets. The proposed method investigates the assignment of degree of hesitation to the membership and nonmembership degree, and the properties are also discussed. The performances of the new method are illustrated by pattern classification problem. Finally, the proposed method is applied into multicriteria fuzzy decision making, where the linear programming method is taken to generate optimal weights for every criterion and the best alternative is obtained by the weighted sum of distance measures between each alternative and the idea alternative with respect to a set of criteria. The experimental results show the effectiveness of the proposed method

Estimating groundwater recharge for Great Britain

Groundwater plays an important role in supporting public water supplies and sustaining river flows. For example, groundwater supplies up to 80% drinking water in southern England. Groundwater recharge determines how much water is available to groundwater system. It is necessary to develop a groundwater recharge model for Great Britain to support the national water resource management. A national groundwater model was constructed using a SLiM model in this study. SLiM, a distributed rainfall-runoff-recharge model, can objectively estimate groundwater recharge and surface runoff based on climate and catchment characteristics. The datasets of Digital Terrain Model (DTM), daily distributed rainfall, potential evapotranspiration, land-cover and hydrology of soil types were collected in this study. The model was then calibrated using river flow datasets from 102 gauging stations across Great Britain. Nash–Sutcliffe coefficient was used to measure the goodness-of-fit between modelled and observed river flow. After calibration, the Nash–Sutcliffe coefficient values for more than 70% of the gauging stations are larger than 0.5. This means that this model can be used to provide sensible groundwater recharge across Great Britain. The model can produce both long-term-average and daily time variant distributed groundwater recharge in Great Britain. Therefore, it can be used to feed recharge datasets to other groundwater models, such as groundwater flow, groundwater vulnerability assessment, and groundwater pollutant transport models. This can also be a basis to investigate the impacts of climate and land-cover changes on groundwater recharge and hence the groundwater system

Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimation

With contributions from the open-source community, a vast amount of instruction tuning (IT) data has emerged. Given the significant resource allocation required by training and evaluating models, it is advantageous to have an efficient method for selecting high-quality IT data. However, existing methods for instruction data selection have limitations such as relying on fragile external APIs, being affected by biases in GPT models, or reducing the diversity of the selected instruction dataset. In this paper, we propose an industrial-friendly, expert-aligned and diversity-preserved instruction data selection method: Clustering and Ranking (CaR). CaR consists of two steps. The first step involves ranking instruction pairs using a scoring model that is well aligned with expert preferences (achieving an accuracy of 84.25%). The second step involves preserving dataset diversity through a clustering process.In our experiment, CaR selected a subset containing only 1.96% of Alpaca's IT data, yet the underlying AlpaCaR model trained on this subset outperforms Alpaca by an average of 32.1% in GPT-4 evaluations. Furthermore, our method utilizes small models (355M parameters) and requires only 11.2% of the monetary cost compared to existing methods, making it easily deployable in industrial scenarios

HPV E6 induces eIF4E transcription to promote the proliferation and migration of cervical cancer

AbstractIncreasing evidence has placed eukaryotic translation initiation factor 4E (eIF4E) at the hub of tumor development and progression. Several studies have reported that eIF4E is over-expressed in cervical cancer; however, the mechanism remains elusive. The results of this study further confirm over-expression of eIF4E in cervical cancer tumors and cell lines, and we have discovered that the transcription of eIF4E is induced by protein E6 of the human papillomavirus (HPV). Moreover, regulation of eIF4E by E6 significantly influences cell proliferation, the cell cycle, migration, and apoptosis. Therefore, eIF4E emerges as a key player in tumor development and progression and a potential target for CC treatment and prevention

Nuclear phylogeny and insights into whole-genome duplications and reproductive development of Solanaceae plants

Solanaceae, the nightshade family, have ∼2700 species, including the important crops potato and tomato, ornamentals, and medicinal plants. Several sequenced Solanaceae genomes show evidence for whole-genome duplication (WGD), providing an excellent opportunity to investigate WGD and its impacts. Here, we generated 93 transcriptomes/genomes and combined them with 87 public datasets, for a total of 180 Solanaceae species representing all four subfamilies and 14 of 15 tribes. Nearly 1700 nuclear genes from these transcriptomic/genomic datasets were used to reconstruct a highly resolved Solanaceae phylogenetic tree with six major clades. The Solanaceae tree supports four previously recognized subfamilies (Goetzeioideae, Cestroideae, Nicotianoideae, and Solanoideae) and the designation of three other subfamilies (Schizanthoideae, Schwenckioideae, and Petunioideae), with the placement of several previously unassigned genera. We placed a Solanaceae-specific whole-genome triplication (WGT1) at ∼81 million years ago (mya), before the divergence of Schizanthoideae from other Solanaceae subfamilies at ∼73 mya. In addition, we detected two gene duplication bursts (GDBs) supporting proposed WGD events and four other GDBs. An investigation of the evolutionary histories of homologs of carpel and fruit developmental genes in 14 gene (sub)families revealed that 21 gene clades have retained gene duplicates. These were likely generated by the Solanaceae WGT1 and may have promoted fleshy fruit development. This study presents a well-resolved Solanaceae phylogeny and a new perspective on retained gene duplicates and carpel/fruit development, providing an improved understanding of Solanaceae evolution