A Survey on Datasets for Decision-making of Autonomous Vehicle

Autonomous vehicles (AV) are expected to reshape future transportation systems, and decision-making is one of the critical modules toward high-level automated driving. To overcome those complicated scenarios that rule-based methods could not cope with well, data-driven decision-making approaches have aroused more and more focus. The datasets to be used in developing data-driven methods dramatically influences the performance of decision-making, hence it is necessary to have a comprehensive insight into the existing datasets. From the aspects of collection sources, driving data can be divided into vehicle, environment, and driver related data. This study compares the state-of-the-art datasets of these three categories and summarizes their features including sensors used, annotation, and driving scenarios. Based on the characteristics of the datasets, this survey also concludes the potential applications of datasets on various aspects of AV decision-making, assisting researchers to find appropriate ones to support their own research. The future trends of AV dataset development are summarized

A cylindrical core-shell-like TiO2 nanotube array anode for flexible fiber-type dye-sensitized solar cells

A versatile anodization method was reported to anodize Ti wires into cylindrical core-shell-like and thermally crystallized TiO2 nanotube (TNT) arrays that can be directly used as the photoanodes for semi- and all-solid fiber-type dye-sensitized solar cells (F-DSSC). Both F-DSSCs showed higher power conversion efficiencies than or competitive to those of previously reported counterparts fabricated by depositing TiO2 particles onto flexible substrates. The substantial enhancement is presumably attributed to the reduction of grain boundaries and defects in the prepared TNT anodes, which may suppress the recombination of the generated electrons and holes, and accordingly lead to more efficient carrier-transfer channels

Lattice and QR decomposition-based algorithms for recursive least squares adaptive nonlinear filters

Journal ArticleThis paper presents a lattice structure for adaptive Volterra systems. The stucture is applicable to arbitrary planes of support of the Volterra kernels. A fast least squares lattice and a fast QR-lattice adaptive nonlinear filtering algorithms based on the lattice structure are also presented. These algorithms share the fast convergence property of fast least squares transversal Volterra filters; however, unlike the transversal filters they do not suffer from numerical instability

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

A new polygenic score for refractive error improves detection of children at risk of high myopia but not the prediction of those at risk of myopic macular degeneration

Background High myopia (HM), defined as a spherical equivalent refractive error (SER) ≤ −6.00 diopters (D), is a leading cause of sight impairment, through myopic macular degeneration (MMD). We aimed to derive an improved polygenic score (PGS) for predicting children at risk of HM and to test if a PGS is predictive of MMD after accounting for SER. Methods The PGS was derived from genome-wide association studies in participants of UK Biobank, CREAM Consortium, and Genetic Epidemiology Research on Adult Health and Aging. MMD severity was quantified by a deep learning algorithm. Prediction of HM was quantified as the area under the receiver operating curve (AUROC). Prediction of severe MMD was assessed by logistic regression. Findings In independent samples of European, African, South Asian and East Asian ancestry, the PGS explained 19% (95% confidence interval 17–21%), 2% (1–3%), 8% (7–10%) and 6% (3–9%) of the variation in SER, respectively. The AUROC for HM in these samples was 0.78 (0.75–0.81), 0.58 (0.53–0.64), 0.71 (0.69–0.74) and 0.67 (0.62–0.72), respectively. The PGS was not associated with the risk of MMD after accounting for SER: OR = 1.07 (0.92–1.24). Interpretation Performance of the PGS approached the level required for clinical utility in Europeans but not in other ancestries. A PGS for refractive error was not predictive of MMD risk once SER was accounted fo

骨关节炎与2型糖尿病的风险:一项双样本孟德尔随机化分析

Abstract Background Physical inactivity is an independent risk factor for type 2 diabetes (T2D). Osteoarthritis (OA) is a common joint disease that limits patients' physical activity, which may increase risk of other chronic diseases including T2D. However, studies evaluating the effect of OA on T2D are scarce. This study aimed to investigate the causal effect of knee and hip OA on risk of T2D from a genetic perspective. Methods We performed two‐sample Mendelian randomization (MR) analyses to obtain nonconfounding estimates of the effect of OA on T2D risk. Single nucleotide polymorphisms (SNPs) from genome‐wide association studies were selected as genetic instruments for radiographic knee and hip OA (ie, Kellgren–Lawrence grade ≥2). The associations of these SNPs with T2D were evaluated in participants from the UK Biobank. Sensitivity analyses were conducted to test the robustness of the MR results. Results Genetic predisposition of knee but not hip OA was significantly associated with an increased risk of T2D (knee OA: odds ratio [OR] 1.18, 95% confidence interval (CI) 1.09–1.27, p <.001; hip OA: OR 1.04, 95% CI 0.94–1.16, p = .425). Sensitivity analyses showed that the main findings are robust. Conclusion The current study provides genetic evidence supporting that knee OA is a potential risk factor for T2D

Additional file 1 of Association of smoking with cartilage loss of knee osteoarthritis: data from two longitudinal cohorts

Additional file

The statistics of Weibo data.

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