Towards Transaction as a Service

This paper argues for decoupling transaction processing from existing two-layer cloud-native databases and making transaction processing as an independent service. By building a transaction as a service (TaaS) layer, the transaction processing can be independently scaled for high resource utilization and can be independently upgraded for development agility. Accordingly, we architect an execution-transaction-storage three-layer cloud-native database. By connecting to TaaS, 1) the AP engines can be empowered with ACID TP capability, 2) multiple standalone TP engine instances can be incorporated to support multi-master distributed TP for horizontal scalability, 3) multiple execution engines with different data models can be integrated to support multi-model transactions, and 4) high performance TP is achieved through extensive TaaS optimizations and consistent evolution. Cloud-native databases deserve better architecture: we believe that TaaS provides a path forward to better cloud-native databases

Fusion of infrared and visible images for remote detection of low-altitude slow-speed small targets.

Detection of the low-altitude and slow-speed small (LSS) targets is one of the most popular research topics in remote sensing. Despite of a few existing approaches, there is still an accuracy gap for satisfying the practical needs. As the LSS targets are too small to extract useful features, deep learning based algorithms can hardly be used. To this end, we propose in this article an effective strategy for determining the region of interest, using a multiscale layered image fusion method to extract the most representative information for LSS-target detection. In addition, an improved self-balanced sensitivity segment model is proposed to detect the fused LSS target, which can further improve both the detection accuracy and the computational efficiency. We conduct extensive ablation studies to validate the efficacy of the proposed LSS-target detection method on three public datasets and three self-collected datasets. The superior performance over the state of the arts has fully demonstrated the efficacy of the proposed approach

Cardiovascular toxicity profiles of immune checkpoint inhibitors with or without angiogenesis inhibitors: a real-world pharmacovigilance analysis based on the FAERS database from 2014 to 2022

BackgroundImmune checkpoint inhibitors (ICIs) combined with angiogenesis inhibitors (AGIs) have become increasingly available for multiple types of cancers, although the cardiovascular safety profiles of this combination therapy in real-world settings have not been elucidated to date. Therefore, we aimed to comprehensively investigate the cardiovascular toxicity profiles of ICIs combined with AGIs in comparison with ICIs alone.MethodsThe Food and Drug Administration Adverse Event Reporting System (FAERS) database from the 1st quarter of 2014 to the 1st quarter of 2022 was retrospectively queried to extract reports of cardiovascular adverse events (AEs) associated with ICIs alone, AGIs alone and combination therapy. To perform disproportionality analysis, the reporting odds ratios (RORs) and information components (ICs) were calculated with statistical shrinkage transformation formulas and a lower limit of the 95% confidence interval (CI) for ROR (ROR025) > 1 or IC (IC025) > 0 with at least 3 reports was considered statistically significant.ResultsA total of 18 854 cardiovascular AE cases/26 059 reports for ICIs alone, 47 168 cases/67 595 reports for AGIs alone, and 3 978 cases/5 263 reports for combination therapy were extracted. Compared to the entire database of patients without AGIs or ICIs, cardiovascular AEs were overreported in patients with combination therapy (IC025/ROR025 = 0.559/1.478), showing stronger signal strength than those taking ICIs alone (IC025/ROR025 = 0.118/1.086) or AGIs alone (IC025/ROR025 = 0.323/1.252). Importantly, compared with ICIs alone, combination therapy showed a decrease in signal strength for noninfectious myocarditis/pericarditis (IC025/ROR025 = 1.142/2.216 vs. IC025/ROR025 = 0.673/1.614), while an increase in signal value for embolic and thrombotic events (IC025/ROR025 = 0.147/1.111 vs. IC025/ROR025 = 0.591/1.519). For outcomes of cardiovascular AEs, the frequency of death and life-threatening AEs was lower for combination therapy than ICIs alone in noninfectious myocarditis/pericarditis (37.7% vs. 49.2%) as well as in embolic and thrombotic events (29.9% vs. 39.6%). Analysis among indications of cancer showed similar findings.ConclusionOverall, ICIs combined with AGIs showed a greater risk of cardiovascular AEs than ICIs alone, mainly due to an increase in embolic and thrombotic events while a decrease in noninfectious myocarditis/pericarditis. In addition, compared with ICIs alone, combination therapy presented a lower frequency of death and life-threatening in noninfectious myocarditis/pericarditis and embolic and thrombotic events

Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Model

End-to-end autonomous driving provides a feasible way to automatically maximize overall driving system performance by directly mapping the raw pixels from a front-facing camera to control signals. Recent advanced methods construct a latent world model to map the high dimensional observations into compact latent space. However, the latent states embedded by the world model proposed in previous works may contain a large amount of task-irrelevant information, resulting in low sampling efficiency and poor robustness to input perturbations. Meanwhile, the training data distribution is usually unbalanced, and the learned policy is hard to cope with the corner cases during the driving process. To solve the above challenges, we present a semantic masked recurrent world model (SEM2), which introduces a latent filter to extract key task-relevant features and reconstruct a semantic mask via the filtered features, and is trained with a multi-source data sampler, which aggregates common data and multiple corner case data in a single batch, to balance the data distribution. Extensive experiments on CARLA show that our method outperforms the state-of-the-art approaches in terms of sample efficiency and robustness to input permutations.Comment: 11 pages, 7 figures, 1 table, submitted to Deep RL Workshop 202