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[Display omitted] • A feature extraction module is proposed to extract the geometric and semantic features of each point efficiently. Then, the attention mechanism is deployed to aggregate the learned features. • The proposed ResDLPS-Net is optimized by joint training of residual connections and dense convolutional connections. • Experiments demonstrate that ResDLPS-Net outperforms the state-of-the-art deep learning networks on the indoor dataset S3DIS and the outdoor large-scale dataset Toronto-3D. Significantly, the Mean Intersection over Union (mIoU) of ResDLPS-Net on the Toronto-3D dataset is 80.27%. Semantic segmentation methods based on three-dimensional (3D) point clouds are mostly limited to input point clouds that have been divided into blocks for training. This is mainly attributed to the requirement of constant trade-offs between computational resources and accuracy for directly processing large-scale point clouds. Specifically, the block dividing strategy will add the data preprocessing time to some extent and may disturb the complete geometry of the object. Therefore, this paper proposes a large-scale point cloud semantic segmentation network without block dividing operation, referred to as ResDLPS-Net. This network can take the complete point cloud of the whole large scene as input and process up to nearly a million points on one single GPU. In particular, a novel feature extraction module is designed to efficiently extract neighbor, geometric, and semantic features. The learned features are then aggregated through the attention mechanism to form local feature descriptors. In addition, the proposed ResDLPS-Net is jointly trained by residual connections and dense convolutional connections to optimize the feature aggregation operation. As a result, the ResDLPS-Net performs brilliantly on multiple objects, such as windows, road markings, fences, etc. For example, the Mean Intersection over Union (mIoU) of road markings on the Toronto-3D dataset is 37.76% higher than the state-of-the-art algorithm. Moreover, this paper outperforms most deep learning methods on three well-known benchmark datasets, including the indoor dataset S3DIS and the outdoor large-scale scene datasets Semantic3D and Toronto-3D. The proposed ResDLPS-Net achieves the best performance on the S3DIS dataset. The average accuracy (mA) and overall accuracy (OA) are 82.3% and 88.1%, respectively. Notably, the proposed ResDLPS-Net attains a mIoU of 80.27% on the Toronto-3D dataset, which is 6.00% higher than the best results published currently. [ABSTRACT FROM AUTHOR]

A heavy rainfall event over a 2-h period on 8 July 2013 caused significant flash flooding in the city of Toronto and produced 126 mm of rain accumulation at a gauge located near the Toronto Pearson International Airport. This paper evaluates the quantitative precipitation estimates from the nearby King City C-band dual-polarized radar (WKR). Horizontal reflectivity Z and differential reflectivity ZDR were corrected for attenuation using a modified ZPHI rain profiling algorithm, and rain rates R were calculated from R( Z) and R( Z, ZDR) algorithms. Specific differential phase KDP was used to compute rain rates from three R( KDP) algorithms, one modified to use positive and negative KDP, and an R( KDP, ZDR) algorithm. Additionally, specific attenuation at horizontal polarization A was used to calculate rates from the R( A) algorithm. High-temporal-resolution rain gauge data at 44 locations measured the surface rainfall every 5 min and produced total rainfall accumulations over the affected area. The nearby NEXRAD S-band dual-polarized radar at Buffalo, New York, provided rain-rate and storm accumulation estimates from R( Z) and S-band dual-polarimetric algorithm. These two datasets were used as references to evaluate the C-band estimates. Significant radome attenuation at WKR overshadowed the attenuation correction techniques and resulted in poor rainfall estimates from the R( Z) and R( Z, ZDR) algorithms. Rainfall estimation from the Brandes et al. R( KDP) and R( A) algorithms were superior to the other methods, and the derived storm total accumulation gave biases of 2.1 and −6.1 mm, respectively, with correlations of 0.94. The C-band estimates from the Brandes et al. R( KDP) and R( A) algorithms were comparable to the NEXRAD S-band estimates. [ABSTRACT FROM AUTHOR]

Microsimulation-based models, increasingly used in the transportation domain, require richer datasets than traditional models. Precisely enumerated population data being usually unavailable, transportation researchers generate their statistical equivalent through population synthesis. While various synthesizers are proposed to optimize the accuracy of synthetic populations, no insight is given regarding the impact of the geographic resolution on population synthesis quality. In this paper, we synthesize populations for the Census Metropolitan Areas of Montreal, Toronto, and Vancouver at various geographic resolutions using the enhanced iterative proportional updating algorithm. We define accuracy (representativeness of the sociodemographic characteristics of the entire population) and precision (representativeness of the real population's spatial heterogeneity) as metrics of synthetic populations' quality and measure the impact of the reference resolution on them. Moreover, we assess census targets' harmonization and double geographic resolution control as means of quality improvement. We find that with a less aggregate reference resolution, the gain in precision is higher than the loss in accuracy. The most disaggregate resolution is thus found to be the best choice. Harmonization proves to further optimize synthetic populations while double control harms their quality. Hence, synthesizing at the Dissemination Area resolution using harmonized census targets is found to yield optimal synthetic populations. [ABSTRACT FROM AUTHOR]

The article discusses the highlights of research papers on short read sequencing and algorithms presented at the 2008 Intelligent Systems in Molecular Biology conference held in Toronto, Ontario. Keynote address was delivered by Dr. Edwin Cuppen of the Hubrecht Laboratory. Topics discussed included the advent of high-throughput sequencing, RNA sequencing and advantage of short read sequencing.