Margaret Munro is a Vancouver-based journalist. My father was preparing breakfast when his blood pressure dropped and he blacked out. Keeling over backward, he hit his head so hard it punched a hole in the wall. "Good thing I didn't hit the stud," he said in the emergency room at Nanaimo Regional General Hospital. He was stable, but the wobbly lines running across a monitor wired to his chest showed the critical state of his 92-year-old heart. It had been repaired before, but now doctors offered something more – a pacemaker to keep it beating steadily. Hundreds of thousands of Canadians have the ingenious devices, and many of them, like my father, likely had them implanted without considering all the implications. A cardiologist stayed late, after his scheduled surgeries, to wire Dad's heart with a German-designed Biotronik pacemaker that would restore a healthy heart rhythm. The procedure, done under local anesthetic, took less than 30 minutes.

It has been more than two years in the making, but 13 new astronauts have finally joined NASA under the mission that will bring the first female to the moon -and some may be the first humans to step on Mars. The candidates, who have been training since 2017, participated in the first public graduation ceremony for astronauts on Friday at the American space Agency's Johnson Space Center in Houston. The group includes six women and seven men, two of them were Canadian Space Agency (CSA) astronauts, and all were chosen from record-setting pool of more than 18,000 applicants. During the ceremony, each of the bright-eyed graduates were given a silver pin that symbolizes the Mercury 7 – NASA's first astronaut group that was selected in 1959. They will then be awarded a gold pin once they completed their first spaceflights.

For the past decades we have experienced an enormous expansion of the accumulated data that humanity produces. Daily a numerous number of smart devices, usually interconnected over internet, produce vast, real-values datasets. Time series representing datasets from completely irrelevant domains such as finance, weather, medical applications, traffic control etc. become more and more crucial in human day life. Analyzing and clustering these time series, or in general any kind of curves, could be critical for several human activities. In the current paper, the new Curves Clustering Using Common Patterns (3CP) methodology is introduced, which applies a repeated pattern detection algorithm in order to cluster sequences according to their shape and the similarities of common patterns between time series, data curves and eventually any kind of discrete sequences. For this purpose, the Longest Expected Repeated Pattern Reduced Suffix Array (LERP-RSA) data structure has been used in combination with the All Repeated Patterns Detection (ARPaD) algorithm in order to perform highly accurate and efficient detection of similarities among data curves that can be used for clustering purposes and which also provides additional flexibility and features.

Abstract--Space-time adaptive processing (ST AP) algorithms with coprime arrays can provide good clutter suppression po - tential with low cost in airborne radar systems as compared with their uniform linear arrays counterparts. However, th e performance of these algorithms is limited by the training samples support in practical applications. T o address this issue, a robust two-stage reduced-dimension (RD) sparsity-aware S T AP algorithm is proposed in this work. In the first stage, an RD virtual snapshot is constructed using all spatial channels but only m adjacent Doppler channels around the target Doppler frequency to reduce the slow-time dimension of the signal. In the second stage, an RD sparse measurement modeling is formulated based on the constructed RD virtual snapshot, wh ere the sparsity of clutter and the prior knowledge of the clutte r ridge are exploited to formulate an RD overcomplete diction ary. Moreover, an orthogonal matching pursuit (OMP)-like metho d is proposed to recover the clutter subspace. In order to set the stopping parameter of the OMP-like method, a robust clutter rank estimation approach is developed. Compared wi th recently developed sparsity-aware ST AP algorithms, the si ze of the proposed sparse representation dictionary is much smal ler, resulting in low complexity. Simulation results show that t he proposed algorithm is robust to prior knowledge errors and can provide good clutter suppression performance in low sam ple support. Index T erms--Robust space-time adaptive processing, coprime arrays, prior knowledge, reduced-dimension, sparsity-aw are.

Many inferential and learning tasks can be accomplished efficiently by means of distributed optimization algorithms where the network topology plays a critical role in driving the local interactions among neighboring agents. There is a large body of literature examining the effect of the graph structure on the performance of optimization strategies. In this article, we examine the inverse problem and consider the reverse question: How much information does observing the behavior at the nodes convey about the underlying network structure used for optimization? Over large-scale networks, the difficulty of addressing such inverse questions (or problems) is compounded by the fact that usually only a limited portion of nodes can be probed, giving rise to a second important question: Despite the presence of several unobserved nodes, are partial and local observations still sufficient to discover the graph linking the probed nodes? The article surveys recent advances on this inverse learning problem and related questions. Examples of applications are provided to illustrate how the interplay between graph learning and distributed optimization arises in practice, e.g., in cognitive engineered systems such as distributed detection, or in other real-world problems such as the mechanism of opinion formation over social networks and the mechanism of coordination in biological networks. A unifying framework for examining the reconstruction error will be described, which allows to devise and examine various estimation strategies enabling successful graph learning. The relevance of specific network attributes, such as sparsity versus density of connections, and node degree concentration, is discussed in relation to the topology inference goal. It is shown how universal (i.e., data-driven) clustering algorithms can be exploited to solve the graph learning problem.

Life sciences companies are likely to begin experimenting further with AI in their workflows in the coming years, but they face challenges in AI adoption due to strict regulations. Machine learning has a "black box" problem, meaning that it's in many cases impossible to know how a machine learning algorithm comes to its conclusions. An AI application that detects cancer, for example, may not be able to show an oncologist how it determined the presence of cancer in a patient's body. As a result, if the oncologist used the application to diagnose a patient, they wouldn't be able to explain to the patient what makes them sure they have cancer. This issue relegates AI applications in life sciences to experiments and pilots, and widespread adoption, although likely inevitable, may not come for a while as public opinion shifts toward accepting that its diagnoses are informed by decision-making artificial intelligence and regulations evolve to match.

We present a scoring approach for speaker verification that mimics the standard PLDA-based backend process used in most current speaker verification systems. However, unlike the standard backends, all parameters of the model are jointly trained to optimize the binary cross-entropy for the speaker verification task. We further integrate the calibration stage inside the model, making the parameters of this stage depend on metadata vectors that represent the conditions of the signals. We show that the proposed backend has excellent out-of-the-box calibration performance on most of our test sets, making it an ideal approach for cases in which the test conditions are not known and development data is not available for training a domain-specific calibration model.

Research models use data from Toronto's Don River and Calgary's Bow River TORONTO, November 11, 2019 – Using complex models based on artificial intelligence (AI) and data from the Don River in Toronto and Bow River in Calgary, researchers at the Lassonde School of Engineering can now predict the water levels in rivers days in advance of floods. "We've created methods to predict real-time flood risk," says Usman T. Khan, professor in the Department of Civil Engineering at York's Lassonde School of Engineering. "These results outline an approach that can be used to create models with higher accuracy and lower data requirements, which translates to improved flood early warning systems. Early warning systems are considered the most effective way to mitigate flood induced hazards." The study, led by Khan, was published today in the Journal of Hydrology.

What happens when the Fourth Industrial Revolution collides with the need and desire to improve the state of the world? To be more specific: What impact will artificial intelligence (AI) have on the social sector? The answer depends on the reply to a bigger, deeper question: What ultimately does AI need to solve? The social sector may be defined as an ecosystem where resources are shared for the purpose of helping others rather than only for the benefit or profit of one person or a group. Actors in the sector are expected to ensure that people create and share resources equitably or fairly to the broadest extent possible.

With the increasing complexity of modern power systems, conventional dynamic load modeling with ZIP and induction motors (ZIP + IM) is no longer adequate to address the current load characteristic transitions. In recent years, the WECC composite load model (WECC CLM) has shown to effectively capture the dynamic load responses over traditional load models in various stability studies and contingency analyses. However, a detailed WECC CLM model typically has a high degree of complexity, with over one hundred parameters, and no systematic approach to identifying and calibrating these parameters. Enabled by the wide deployment of PMUs and advanced deep learning algorithms, proposed here is a double deep Q-learning network (DDQN)-based, two-stage load modeling framework for the WECC CLM. This two-stage method decomposes the complicated WECC CLM for more efficient identification and does not require explicit model details. In the first stage, the DDQN agent determines an accurate load composition. In the second stage, the parameters of the WECC CLM are selected from a group of Monte-Carlo simulations. The set of selected load parameters is expected to best approximate the true transient responses. The proposed framework is verified using an IEEE 39-bus test system on commercial simulation platforms.