AI can be used for the early detection of virus outbreaks that might result in a pandemic. AI detected the coronavirus long before the world's population really knew what it was. On December 31st, a Toronto-based startup called BlueDot identified the outbreak in Wuhan, several hours after the first cases were diagnosed by local authorities. The BlueDot team confirmed the info its system had relayed and informed their clients that very day, nearly a week before Chinese and international health organisations made official announcements. Thanks to the speed and scale of AI, BlueDot was able to get a head start over everyone else.

We investigate spatio-temporal event analysis using point processes. Inferring the dynamics of event sequences spatiotemporally has many practical applications including crime prediction, social media analysis, and traffic forecasting. In particular, we focus on spatio-temporal Hawkes processes that are commonly used due to their capability to capture excitations between event occurrences. We introduce a novel inference framework based on randomized transformations and gradient descent to learn the process. We replace the spatial kernel calculations by randomized Fourier feature-based transformations. The introduced randomization by this representation provides flexibility while modeling the spatial excitation between events. Moreover, the system described by the process is expressed within closed-form in terms of scalable matrix operations. During the optimization, we use maximum likelihood estimation approach and gradient descent while properly handling positivity and orthonormality constraints. The experiment results show the improvements achieved by the introduced method in terms of fitting capability in synthetic and real datasets with respect to the conventional inference methods in the spatio-temporal Hawkes process literature. We also analyze the triggering interactions between event types and how their dynamics change in space and time through the interpretation of learned parameters.

As the size, complexity, and availability of data continues to grow, scientists are increasingly relying upon black-box learning algorithms that can often provide accurate predictions with minimal a priori model specifications. Tools like random forest have an established track record of off-the-shelf success and even offer various strategies for analyzing the underlying relationships between features and the response. Motivated by recent insights into random forest behavior, here we introduce the idea of augmented bagging (AugBagg), a procedure that operates in an identical fashion to the classical bagging and random forest counterparts but which operates on a larger space containing additional, randomly generated features. Somewhat surprisingly, we demonstrate that the simple act of adding additional random features into the model can have a dramatic beneficial effect on performance, sometimes outperforming even an optimally tuned traditional random forest. This finding that the inclusion of an additional set of features generated independently of the response can considerably improve predictive performance has crucial implications for the manner in which we consider and measure variable importance. Numerous demonstrations on both real and synthetic data are provided.

With the advancements in computing technology and web-based applications, data is increasingly generated in multi-dimensional form. This data is usually sparse due to the presence of a large number of users and fewer user interactions. To deal with this, the Nonnegative Tensor Factorization (NTF) based methods have been widely used. However existing factorization algorithms are not suitable to process in all three conditions of size, density, and rank of the tensor. Consequently, their applicability becomes limited. In this paper, we propose a novel fast and efficient NTF algorithm using the element selection approach. We calculate the element importance using Lipschitz continuity and propose a saturation point based element selection method that chooses a set of elements column-wise for updating to solve the optimization problem. Empirical analysis reveals that the proposed algorithm is scalable in terms of tensor size, density, and rank in comparison to the relevant state-of-the-art algorithms.

Despite Generative Adversarial Networks (GANs) have been widely used in various image-to-image translation tasks, they can be hardly applied on mobile devices due to their heavy computation and storage cost. Traditional network compression methods focus on visually recognition tasks, but never deal with generation tasks. Inspired by knowledge distillation, a student generator of fewer parameters is trained by inheriting the low-level and high-level information from the original heavy teacher generator. To promote the capability of student generator, we include a student discriminator to measure the distances between real images, and images generated by student and teacher generators. An adversarial learning process is therefore established to optimize student generator and student discriminator. Qualitative and quantitative analysis by conducting experiments on benchmark datasets demonstrate that the proposed method can learn portable generative models with strong performance.

Many real-world problems are categorized as large-scale problems, and metaheuristic algorithms as an alternative method to solve large-scale problem; they need the evaluation of many candidate solutions to tackle them prior to their convergence, which is not affordable for practical applications since the most of them are computationally expensive. In other words, these problems are not only large-scale but also computationally expensive, that makes them very difficult to solve. There is no efficient surrogate model to support large-scale expensive global optimization (LSEGO) problems. As a result, the algorithms should address LSEGO problems using a limited computational budget to be applicable in real-world applications. In this paper, we propose a simple novel algorithm called Shahryar Origami Optimization (SOO) algorithm to tackle LSEGO problems with a limited computational budget. Our proposed algorithm benefits from two leading steps, namely, finding the region of interest and then shrinkage of the search space by folding it into the half with exponential speed. One of the main advantages of the proposed algorithm is being free of any control parameters, which makes it far from the intricacies of the tuning process. The proposed algorithm is compared with cooperative co-evolution with delta grouping on 20 benchmark functions with dimension 1000. Also, we conducted some experiments on CEC-2017, D=10, 30, 50, and 100 to investigate the behavior of SOO algorithm in lower dimensions. The results show that SOO is beneficial not only in large-scale problems, but also in low-scale optimization problems.

AI can be used for the early detection of virus outbreaks that might result in a pandemic. AI detected the coronavirus long before the world's population really knew what it was. On December 31st, a Toronto-based startup called BlueDot identified the outbreak in Wuhan, several hours after the first cases were diagnosed by local authorities. The BlueDot team confirmed the info its system had relayed and informed their clients that very day, nearly a week before Chinese and international health organisations made official announcements. Thanks to the speed and scale of AI, BlueDot was able to get a head start over everyone else.

You immigrate to a new country that speaks a different language, and start work with some of the brightest engineers in the world. Now, you're leading teams of people who are 10 or 20 years older than you, working on one of the fastest growing internet companies of the last decade. You have two options: sink or swim. That's the position Simon Eskildsen found himself in early in his career. He left his home in Denmark after high school, and moved to Canada alone to take a pre-college gap year working at Shopify. When he started, Shopify had 150 employees supporting tens of thousands of merchants. Now, it has 5,000 employees and over a million merchants.

AI can be used for the early detection of virus outbreaks that might result in a pandemic. AI detected the coronavirus long before the world's population really knew what it was. On December 31st, a Toronto-based startup called BlueDot identified the outbreak in Wuhan, several hours after the first cases were diagnosed by local authorities. The BlueDot team confirmed the info its system had relayed and informed their clients that very day, nearly a week before Chinese and international health organisations made official announcements. Thanks to the speed and scale of AI, BlueDot was able to get a head start over everyone else.

Many companies in the biometric, digital identity, and cybersecurity space have shared predictions for 2020 with Biometric Update, touching on many of the key themes of the past year, and reflecting the wealth of opportunity, as well as the anxieties at play in the industry. Those predictions most closely thematically related to biometrics and our top news stories are collected below. "The global market for mobile biometrics is forecast to grow at an impressive 31.14 percent CAGR, adding $28.45 billion per year in incremental growth between 2018 and 2023, despite the CAGR decelerating by 22 percent in the period," points out Robert Prigge, CEO of Jumio. "The growth forecasts in the latest set of market analyst reports that indicate widespread adoption of biometrics technology: 22 percent for mobile biometrics, 22 percent for 3D sensors, and 19 percent for healthcare biometrics Facial authentication is impacting the physical security market, cloud-based subscription services are becoming more popular for security, and the Pentagon is expected to remain a source of opportunity for companies offering advanced authentication technologies. Although we are still in the early stages of biometric-based identity proofing and authentication, its development will serve as a viable solution for the growing fraud epidemic."