Harmonizing Output Imbalance for semantic segmentation on extremely-imbalanced input data

Semantic segmentation is a high level computer vision task that assigns a label for each pixel of an image. It is challenging to deal with extremely-imbalanced data in which the ratio of target pixels to background pixels is lower than 1:1000. Such severe input imbalance leads to output imbalance for poor model training. This paper considers three issues for extremely-imbalanced data: inspired by the region-based Dice loss, an implicit measure for the output imbalance is proposed, and an adaptive algorithm is designed for guiding the output imbalance hyperparameter selection; then it is generalized to distribution-based loss for dealing with output imbalance; and finally a compound loss with our adaptive hyperparameter selection algorithm can keep the consistency of training and inference for harmonizing the output imbalance. With four popular deep architectures on our private dataset from three different input imbalance scales and three public datasets, extensive experiments demonstrate the competitive/promising performance of the proposed method.Comment: 18 pages, 13 figures, 2 appendixe

Msb r‐cnn: A multi‐stage balanced defect detection network

Deep learning networks are applied for defect detection, among which Cascade R‐CNN is a multi‐stage object detection network and is state of the art in terms of accuracy and efficiency. However, it is still a challenge for Cascade R‐CNN to deal with complex and diverse defects, as the widely varied shapes of defects lead to inefficiency for the traditional convolution filter to extract features. Additionally, the imbalance in features, losses and samples cause lower accuracy. To address the above challenges, this paper proposes a multi‐stage balanced R‐CNN (MSB R‐CNN) for defect detection based on Cascade R‐CNN. Firstly, deformable convolution is adopted in different stages of the backbone network to improve its adaptability to the varying shapes of the defect. Then, the features obtained by the backbone network are refined and enhanced by the balanced feature pyramid. To overcome the imbalance of classification and regression loss, the balanced L1 loss is applied at different stages to correct it. Finally, for the sample selection, the interaction of union (IoU) balanced sampler and the online hard example mining (OHEM) sampler are combined at different stages to make the sampling more reasonable, which can bring a better accuracy and convergence effect to the model. The results of our experiments on the DAGM2007 dataset has shown that our network (MSB R‐CNN) can achieve a mean average precision (mAP) of 67.5%, an increase of 1.5% mAP, compared to Cascade R‐CNN

Edge-Assisted V2X Motion Planning and Power Control Under Channel Uncertainty

Edge-assisted vehicle-to-everything (V2X) motion planning is an emerging paradigm to achieve safe and efficient autonomous driving, since it leverages the global position information shared among multiple vehicles. However, due to the imperfect channel state information (CSI), the position information of vehicles may become outdated and inaccurate. Conventional methods ignoring the communication delays could severely jeopardize driving safety. To fill this gap, this paper proposes a robust V2X motion planning policy that adapts between competitive driving under a low communication delay and conservative driving under a high communication delay, and guarantees small communication delays at key waypoints via power control. This is achieved by integrating the vehicle mobility and communication delay models and solving a joint design of motion planning and power control problem via the block coordinate descent framework. Simulation results show that the proposed driving policy achieves the smallest collision ratio compared with other benchmark policies

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

A Sterol and Spiroditerpenoids from a Penicillium sp. Isolated from a Deep Sea Sediment Sample

A new polyoxygenated sterol, sterolic acid (1), three new breviane spiroditerpenoids, breviones I–K (2–4), and the known breviones (5–8), were isolated from the crude extract of a Penicillium sp. obtained from a deep sea sediment sample that was collected at a depth of 5115 m. The structures of 1–4 were elucidated primarily by NMR experiments, and 1 was further confirmed by X-ray crystallography. The absolute configurations of 2 and 3 were deduced by comparison of their CD spectra with those of the model compounds. Compounds 2 and 5 showed significant cytotoxicity against MCF-7 cells, which is comparable to the positive control cisplatin

The impact of immunoglobulin G N-glycosylation level on COVID-19 outcome: evidence from a Mendelian randomization study

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has exerted a profound influence on humans. Increasing evidence shows that immune response is crucial in influencing the risk of infection and disease severity. Observational studies suggest an association between COVID‐19 and immunoglobulin G (IgG) N-glycosylation traits, but the causal relevance of these traits in COVID-19 susceptibility and severity remains controversial.MethodsWe conducted a two-sample Mendelian randomization (MR) analysis to explore the causal association between 77 IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity using summary-level data from genome-wide association studies (GWAS) and applying multiple methods including inverse-variance weighting (IVW), MR Egger, and weighted median. We also used Cochran’s Q statistic and leave-one-out analysis to detect heterogeneity across each single nucleotide polymorphism (SNP). Additionally, we used the MR-Egger intercept test, MR-PRESSO global test, and PhenoScanner tool to detect and remove SNPs with horizontal pleiotropy and to ensure the reliability of our results.ResultsWe found significant causal associations between genetically predicted IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity. Specifically, we observed reduced risk of COVID-19 with the genetically predicted increased IgG N-glycan trait IGP45 (OR = 0.95, 95% CI = 0.92–0.98; FDR = 0.019). IGP22 and IGP30 were associated with a higher risk of COVID-19 hospitalization and severity. Two (IGP2 and IGP77) and five (IGP10, IGP14, IGP34, IGP36, and IGP50) IgG N-glycosylation traits were causally associated with a decreased risk of COVID-19 hospitalization and severity, respectively. Sensitivity analyses did not identify any horizontal pleiotropy.ConclusionsOur study provides evidence that genetically elevated IgG N-glycosylation traits may have a causal effect on diverse COVID-19 outcomes. Our findings have potential implications for developing targeted interventions to improve COVID-19 outcomes by modulating IgG N-glycosylation levels