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The application of big data, radiomics, machine learning, and artificial intelligence (AI) algorithms in radiology requires access to large data sets containing personal health information. Because machine learning projects often require collaboration between different sites or data transfer to a third party, precautions are required to safeguard patient privacy. Safety measures are required to prevent inadvertent access to and transfer of identifiable information. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI Ethical and Legal standing committee with the mandate to guide the medical imaging community in terms of best practices in data management, access to health care data, de-identification, and accountability practices. Part 2 of this article will inform CAR members on the practical aspects of medical imaging de-identification, strengths and limitations of de-identification approaches, list of de-identification software and tools available, and perspectives on future directions. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) software that analyzes medical images is becoming increasingly prevalent. Unlike earlier generations of AI software, which relied on expert knowledge to identify imaging features, machine learning approaches automatically learn to recognize these features. However, the promise of accurate personalized medicine can only be fulfilled with access to large quantities of medical data from patients. This data could be used for purposes such as predicting disease, diagnosis, treatment optimization, and prognostication. Radiology is positioned to lead development and implementation of AI algorithms and to manage the associated ethical and legal challenges. This white paper from the Canadian Association of Radiologists provides a framework for study of the legal and ethical issues related to AI in medical imaging, related to patient data (privacy, confidentiality, ownership, and sharing); algorithms (levels of autonomy, liability, and jurisprudence); practice (best practices and current legal framework); and finally, opportunities in AI from the perspective of a universal health care system. [ABSTRACT FROM AUTHOR]

Since individual neural networks have limited deep expressiveness and effectiveness, many learning frameworks face difficulties in the availability and balance of sample selection. As a result, in change detection, it is difficult to upgrade the hit rate of a high-performance model on both positive and negative pixels. Therefore, supposing that the sacrificed components coincide perfectly with the important evaluation objectives, such as positives, it would lose more than gain. To address this issue, in this paper, we propose a multi-visual collaborative deep network (MV-CDN) served by three collaborative network members that consists of three subdivision approaches, the CDN with one collaborator (CDN-C), CDN with two collaborators (CDN-2C), and CDN with three collaborators (CDN-3C). The purpose of the collaborator is to re-evaluate the feature elements in the network transmission, and thus to translate the group-thinking into a more robust field of vision. We use three sets of public double-temporal hyperspectral images taken by the AVIRIS and HYPERION sensors to show the feasibility of the proposed schema. The comparison results have confirmed that our proposed schema outperforms the existing state-of-the-art algorithms on the three tested datasets, which demonstrates the broad adaptability and progressiveness of the proposal. [ABSTRACT FROM AUTHOR]

Floating Catchment Area (FCA) methods are a popular tool to investigate accessibility to public facilities, in particular health care services. FCA approaches are attractive because, unlike other accessibility measures, they take into account the potential for congestion of facilities. This is done by 1) considering the population within the catchment area of a facility to calculate a variable that measures level of service, and then 2) aggregating the level of service by population centers subject to catchment area constraints. In this paper we discuss an effect of FCA approaches, an artifact that we term demand and level of service inflation. These artifacts are present in previous implementations of FCA methods. We argue that inflation makes interpretation of estimates of accessibility difficult, which has possible deleterious consequences for decision making. Next, we propose a simple and intuitive approach to proportionally allocate demandand and level of service in FCA calculations. The approach is based on a standardization of the impedance matrix, similar to approaches popular in the spatial statistics and econometrics literature. The result is a more intiuitive measure of accessibility that 1) provides a local version of the provider-to-population ratio; and 2) preserves the level of demand and the level of supply in a system. We illustrate the relevant issues with some examples, and then empirically by means of a case study of accessibility to family physicians in the Hamilton Census Metropolitan Area (CMA), in Ontario, Canada. Results indicate that demand and supply inflation/deflation affect the interpretation of accessibility analysis using existing FCA methods, and that the proposed adjustment can lead to more intuitive results. [ABSTRACT FROM AUTHOR]

Freely-available satellite data streams and the ability to process these data on cloud-computing platforms such as Google Earth Engine have made frequent, large-scale landcover mapping at high resolution a real possibility. In this paper we apply these technologies, along with machine learning, to the mapping of peatlands–a landcover class that is critical for preserving biodiversity, helping to address climate change impacts, and providing ecosystem services, e.g., carbon storage–in the Boreal Forest Natural Region of Alberta, Canada. We outline a data-driven, scientific framework that: compiles large amounts of Earth observation data sets (radar, optical, and LiDAR); examines the extracted variables for suitability in peatland modelling; optimizes model parameterization; and finally, predicts peatland occurrence across a large boreal area (397, 958 km2) of Alberta at 10 m spatial resolution (equalling 3.9 billion pixels across Alberta). The resulting peatland occurrence model shows an accuracy of 87% and a kappa statistic of 0.57 when compared to our validation data set. Differentiating peatlands from mineral wetlands achieved an accuracy of 69% and kappa statistic of 0.37. This data-driven approach is applicable at large geopolitical scales (e.g., provincial, national) for wetland and landcover inventories that support long-term, responsible resource management. [ABSTRACT FROM AUTHOR]

This paper describes a collection of 272 three-dimensional laser scans gathered at two unique planetary analogue rover test facilities in Canada, which offer emulated planetary terrain at manageable scales for algorithmic development. This dataset is subdivided into four individual subsets, each gathered using panning laser rangefinders on different mobile rover platforms. This data should be of interest to field robotics researchers developing rover navigation algorithms suitable for use in three-dimensional, unstructured, natural terrain. All of the data are presented in human-readable text files, and are accompanied by Matlab parsing scripts to facilitate use thereof. This paper provides an overview of the available data. [ABSTRACT FROM PUBLISHER]

To deal with the large volume of data produced by hyperspectral sensors, the Canadian Space Agency (CSA) has developed and patented two near lossless data compression algorithms for use onboard a hyperspectral satellite: successive approximation multi-stage vector quantization (SAMVQ) and hierarchical self-organizing cluster vector quantization (HSOCVQ). This paper describes the two compression algorithms and demonstrates their near lossless feature. The compression error introduced by the two compression algorithms was compared with the intrinsic noise of the original data that is caused by the instrument noise and other noise sources such as calibration and atmospheric correction errors. The experimental results showed that the compression error was not larger than the intrinsic noise of the original data when a test data set was compressed at a compression ratio of 20:1. The overall noise in the reconstructed data that contains both the intrinsic noise and the compression error is even smaller than the intrinsic noise when the data is compressed using SAMVQ. A multi-disciplinary user acceptability study has been carried out in order to evaluate the impact of the two compression algorithms on hyperspectral data applications. This paper briefly summarizes the evaluation results of the user acceptability study. A prototype hardware compressor that implements the two compression algorithms has been built using field programmable gate arrays (FPGAs) and benchmarked. The compression ratio and fidelity achieved by the hardware compressor are similar to those obtained by software simulation. [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]

Since mid-February 2013, the ensemble Kalman filter (EnKF) in operation at the Canadian Meteorological Centre (CMC) has been using a 600 × 300 global horizontal grid and 74 vertical levels. This yields 5.4 × 107 model coordinates. The EnKF has 192 members and uses seven time levels, spaced 1 h apart, for the time interpolation in the 6-h assimilation window. It follows that over 7 × 1010 values are required to specify an ensemble of trial field trajectories. This paper focuses on numerical and computational aspects of the EnKF. In response to the increasing computational challenge posed by the ever more ambitious configurations, an ever larger fraction of the EnKF software system has gradually been parallelized over the past decade. In a strong scaling experiment, the way in which the execution time decreases as larger numbers of processes are used is investigated. In fact, using a substantial fraction of one of the CMC's computers, very short execution times are achieved. As it would thus appear that the CMC's computers can handle more demanding configurations, weak scaling experiments are also performed. Here, both the size of the problem and the number of processes are simultaneously increased. The parallel algorithm responds well to an increase in either the number of ensemble members or the number of model coordinates. A substantial increase (by an order of magnitude) in the number of assimilated observations would, however, be more problematic. Thus, to the extent that this depends on computational aspects, it appears that the meteorological quality of the Canadian operational EnKF can be further improved. [ABSTRACT FROM AUTHOR]

This paper compares three existing Palmer Drought Severity Index (PDSI) formulations for simulating summer moisture variability in western Canada and a preliminary analysis of climate change impacts on summer moisture anomalies. The three models considered are Palmer's original algorithm (orPDSI), the self-calibrating PDSI (scPDSI), and a version modified for Canadian Prairie conditions (cpPDSI). In all formulations, potential evapotranspiration was parameterized by the Penman-Monteith method instead of the traditional Thornthwaite method. The scPDSI was used as a benchmark for evaluation as it is more appropriate for comparing drought severity of diverse climates. The results confirm that orPDSI produces inflated drought statistics as compared to scPDSI, whereas cpPDSI produced more conservative drought statistics than scPDSI. On the basis of results from scPDSI, historical moisture availability in the Canadian Prairies has shown a significant downward trend since 1950 at the 5% level, whereas southern British Columbia has shown a significant increasing trend. No discernible trend was found in the northern parts of the study area. These results were corroborated by trends in annual precipitation and summer temperature over the respective regions. When scPDSI parameters were calibrated using historical climate data, simulations for the 2050s using climate change scenarios from the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) showed increases in summer moisture deficit relative to the 1961-90 baseline. However, projecting the extent to which the frequency of extreme drought and/or wet spell categories will change is not trivial since the computation of scPDSI is tied to the definition of the frequency of extreme events. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes an algorithm that has been developed to process field data from two upward looking sonars, one high (546kHz) and one low (235kHz) frequency, a 2MHz current profiler and a monitoring station, to measure surface ice characteristics on the North Saskatchewan River in the north east of Edmonton, AB, Canada, during the 2009/2010 freeze-up season. The algorithm used to calculate pans/rafts drafts and lengths, and the surface ice concentration is described. The validity and the accuracy of this technique were tested and computed surface ice conditions are presented. For the purpose of examining the effects of hydro-meteorological conditions on measured pan characteristics, the freeze-up period was divided into three distinct stages depending on the measured surface ice concentration. Pan/raft drafts were found to range from 0.1 to 1.0m and pan/raft lengths from 0.6 to 8.0m. The sonar proved to be very accurate in detecting the exact surface ice conditions locally above the sonar beam. However, interpretation of the results can sometimes be challenging, especially when physical processes such as bridging affect local ice conditions. Therefore, additional visual observations (e.g. time lapse photography) of surface ice conditions are recommended to aid in the interpretation of sonar measurements. [Copyright &y& Elsevier]

Abstract: Performance and economical efficiency of the combined cooling, heating and power (CCHP) system mainly depend on the system structure, operation strategy and choice of facility capacity. This paper proposes a structural configuration of the CCHP system with hybrid chillers, consisting of a combined electric and absorption chiller, whose electric cooling to cool load ratio varies according to different electric and thermal loads in every hour. A new operation strategy, based on the variational electric cooling to cool load ratio, for the CCHP system with unlimited and limited power generation unit (PGU) capacity is investigated. Given the proposed operation strategy, an optimization algorithm is adopted to determine the optimal PGU capacity. In addition, a case study of a hypothetical hotel in Victoria, BC, Canada is conducted to verify the feasibility of the proposed CCHP system structure and the corresponding optimal operation strategy. [Copyright &y& Elsevier]

Abstract: This paper will discuss TRIPLEX-Flux, a process-based model that estimates net ecosystem production (NEP) as well as analyzing the level of sensitivity of the model''s response by simulating CO2 flux in an existing old black spruce BOREAS site in central Canada. The research objectives were: (1) to test the TRIPLEX-Flux model simulations against flux tower measurements; and (2) to examine parameter and input variable effects on model response via sensitivity analysis. Validation of NEP data at 30min intervals derived from tower and chamber measurements showed that the NEP data from the model corresponded well with the measured NEP from the BOREAS site (R 2 >0.65). Sensitivity analysis demonstrated different levels of sensitivity between morning and noon periods and from the current to doubled atmospheric CO2 concentrations. Additionally, the comparison of different algorithms to calculate stomatal conductance showed that the NEP predicted by the model using the iteration algorithm was consistent with the results using a constant C i/C a of 0.74 for current and 0.81 for doubled CO2 concentrations. Varying parameter and input variable values by ±10% resulted in a similar model response between morning and noon periods (less than or equal to 27.6% and 27.4%, respectively). Most parameters were more sensitive at noon than they were in the morning except for those that were correlated with air temperature, suggesting that air temperature has considerable influence over model sensitivity for these parameters/variables. Air temperature effect was greater under doubled than it was under current atmospheric CO2 concentrations. In contrast, model sensitivity to CO2 decreased under doubled CO2 concentrations. [Copyright &y& Elsevier]

The Western Arctic Linkage Experiment (WALE) is aimed at understanding the role of high-latitude terrestrial ecosystems in the response of the Arctic system to global change through collection and comparison of climate datasets and model results. In this paper, a spatiotemporal approach is taken to compare and validate model results from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) with commonly used analysis and reanalysis datasets for monthly averages of temperature and precipitation in 1992–2000 and for a study area at 55°–65°N, 160°–110°W in northwestern Canada and Alaska. Objectives include a quantitative assessment of similarity between datasets and climate model fields, and identification of geographic areas and seasons that are problematic in modeling, with potential causes that may aid in model improvement. These are achieved by application of algebraic similarity mapping, a simple yet effective method for synoptic analysis of many (here, 45) different spatial datasets, maps, and models. Results indicate a dependence of model–data similarity on seasonality, on climate variable, and on geographic location. In summary, 1) similarity of data and models is better for temperature than for precipitation; and 2) modeling of summer precipitation fields, and to a lesser extent, temperature fields, appears more problematic than that of winter fields. The geographic distribution of areas with best and worst agreement shifts throughout the year, with generally better agreement between maps and models in the northeastern and northern inland areas than in topographically complex and near-coastal areas. The study contributes to an understanding of the geographic complexity of the Arctic system and modeling its diverse climate. [ABSTRACT FROM AUTHOR]

This paper examines the problems of routing freight traffic, scheduling train services and allocating classification work between yards on a rail network. It describes a general optimization model which takes into account the interactions between these activities and develops global strategies for their management over a medium term planning horizon. A heuristic algorithm developed to solve this nonlinear, mixed-integer multicommodity flow problem is also presented with some computational results based on an example at the Canadian National Railroads. [ABSTRACT FROM AUTHOR]