In this paper, we analyze maximum Sharpe ratio when the number of assets in a portfolio is larger than its time span. One obstacle in this large dimensional setup is the singularity of the sample covariance matrix of the excess asset returns. To solve this issue, we benefit from a technique called nodewise regression, which was developed by Meinshausen and Buhlmann (2006). It provides a sparse/weakly sparse and consistent estimate of the precision matrix, using the Lasso method. We analyze three issues. One of the key results in our paper is that mean-variance efficiency for the portfolios in large dimensions is established. Then tied to that result, we also show that the maximum out-of-sample Sharpe ratio can be consistently estimated in this large portfolio of assets. Furthermore, we provide convergence rates and see that the number of assets slow down the convergence up to a logarithmic factor. Then, we provide consistency of maximum Sharpe Ratio when the portfolio weights add up to one, and also provide a new formula and an estimate for constrained maximum Sharpe ratio. Finally, we provide consistent estimates of the Sharpe ratios of global minimum variance portfolio and Markowitz's (1952) mean variance portfolio. In terms of assumptions, we allow for time series data. Simulation and out-of-sample forecasting exercise shows that our new method performs well compared to factor and shrinkage based techniques.

Automatic music generation is an interdisciplinary research topic that combines computational creativity and semantic analysis of music to create automatic machine improvisations. An important property of such a system is allowing the user to specify conditions and desired properties of the generated music. In this paper we designed a model for composing melodies given a user specified symbolic scenario combined with a previous music context. We add manual labeled vectors denoting external music quality in terms of chord function that provides a low dimensional representation of the harmonic tension and resolution. Our model is capable of generating long melodies by regarding 8-beat note sequences as basic units, and shares consistent rhythm pattern structure with another specific song. The model contains two stages and requires separate training where the first stage adopts a Conditional Variational Autoencoder (C-VAE) to build a bijection between note sequences and their latent representations, and the second stage adopts long short-term memory networks (LSTM) with structural conditions to continue writing future melodies. We further exploit the disentanglement technique via C-VAE to allow melody generation based on pitch contour information separately from conditioning on rhythm patterns. Finally, we evaluate the proposed model using quantitative analysis of rhythm and the subjective listening study. Results show that the music generated by our model tends to have salient repetition structures, rich motives, and stable rhythm patterns. The ability to generate longer and more structural phrases from disentangled representations combined with semantic scenario specification conditions shows a broad application of our model.

The world's tech powers are sending giant sums of money spinning into Canada, but while many see this as a sign of success, others are worried about researchers and intellectual property being swallowed wholesale. The country is in the midst of an artificial intelligence (AI) boom, with Google, Microsoft, Facebook, Huawei and other global heavyweights spending millions or even hundreds of millions of dollars on research hubs in Quebec, Ontario and Alberta. Canadian doors are open – some fear too open. Jim Hinton, an IP lawyer and founder of the Own Innovation consultancy, reckons that more than half of all AI patents in Canada end up being owned by foreign companies. What we need to be doing is getting money out of our ideas ourselves, instead of seeing foreign talent scoop it all up," said Hinton. "Otherwise we'll never have a Canadian champion." The country is home to hundreds of fledgling AI companies, including much-talked-about start-ups like Element AI and Deep Genomics, but they remain relatively small. "They don't have a strong market position yet," Hinton says. Deep learning pioneers such as Yoshua Bengio and Geoffrey Hinton (no relation to Jim) have nurtured top-notch talent in AI in Canada for years, back when AI was an emerging field. But despite Canadian inheriting this brilliant AI lead from the country's AI "godfathers", big foreign players have an unassailable advantage over homegrown efforts, Hinton said. "It's not an easy go for the average company to make a business out of AI.

When a fifth of the people living in the city of Wakayama faced a three-day water stoppage last month to fix a 60-year-old pipe network, they rushed to get ready, only to learn that the repairs could be made without a shutdown. Some 3,000 complaints were filed with city officials, who said they had no way of knowing until they dug up the pipes. Cities across the world are facing similar challenges in dealing with deteriorating infrastructure because of a lack of precision in where and when to fix aging water pipelines. Now, some cash-strapped cities are embracing new technology to make water repairs more efficient, with the goal of cutting construction costs and lowering utility bills. The need is pressing, as global climate change, with an increasing frequency of floods, droughts and warmer weather, is overloading water systems.

Many real-world problems can be formalized as predicting links in a partially observed network. Examples include Facebook friendship suggestions, consumer-product recommendations, and the identification of hidden interactions between actors in a crime network. Several link prediction algorithms, notably those recently introduced using network embedding, are capable of doing this by just relying on the observed part of the network. Often, the link status of a node pair can be queried, which can be used as additional information by the link prediction algorithm. Unfortunately, such queries can be expensive or time-consuming, mandating the careful consideration of which node pairs to query. In this paper we estimate the improvement in link prediction accuracy after querying any particular node pair, to use in an active learning setup. Specifically, we propose ALPINE (Active Link Prediction usIng Network Embedding), the first method to achieve this for link prediction based on network embedding. To this end, we generalized the notion of V -optimality from experimental design to this setting, as well as more basic active learning heuristics originally developed in standard classification settings. Empirical results on real data show that ALPINE is scalable, and boosts link prediction accuracy with far fewer queries.

SINGAPORE--(BUSINESS WIRE)--Jumio, the leading provider of AI-powered end-to-end identity verification and authentication solutions, has partnered with CIMB Bank Philippines' all-digital bank to provide a simple, hassle-free and convenient digital onboarding solution to Filipinos. In its first full year of formal operations, CIMB Bank Philippines signed in almost 2 million Filipinos via the CIMB Bank PH digital platform, 30% of which are first-time bankers, making CIMB Bank PH the fastest-growing all-digital bank in the Philippines and ASEAN, and a winner of eight banking awards in 2019. Driving financial inclusion, the all-digital, mobile-first bank offers the best-in-market savings interest rates of 4% with zero transaction fees and minimum balance, and seamless account opening and personal loan applications. CIMB Bank PH's mobile app integrates Jumio's AI-driven identity verification technology to provide a safe, secure and fast digital onboarding experience -- what used to take 15 minutes with a video KYC process now takes less than five minutes, resulting in an increase in conversions and happier customers. Jumio's identity verification solution uses machine learning, AI, certified liveness detection and face-based biometrics to ensure the person behind a digital transaction is who they say they are by matching a user's live selfie with the photo shown on their government-issued ID. "Our partnership with Jumio has been integral in achieving our milestones so far as an all-digital bank in the Philippines to deliver a safe and secure banking experience," said Vijay Manoharan, CIMB Bank PH CEO.

One goal in Bayesian machine learning is to encode prior knowledge into prior distributions, to model data efficiently. We consider prior knowledge from systems of linear (partial and ordinary) differential equations together with their boundary conditions. We construct multi-output Gaussian process priors with realizations dense in the solution set of such systems, in particular any solution (and only such solutions) can be represented to arbitrary precision by Gaussian process regression. The construction is fully algorithmic via Gr\"obner bases and it does not employ any approximation. It builds these priors combining two parametrizations via a pullback: the first parametrizes the solutions for the system of differential equations and the second parametrizes all functions adhering to the boundary conditions.

In the space of a single year, South African agritech enterprise Aerobotics has won numerous awards and made strategic inroads into the massively competitive US agriculture industry. Propelled by world-leading technology, the South African success story is poised to mushroom into a truly global data and analytics software company serving the entire agriculture value chain. Aerobotics, which as little as a year ago was nominated as one of South Africa's most exciting startups, turns imagery into actionable data so that any issues on the farm, or elsewhere in the value chain, can be identified and resolved before they become problems. In essence, Aerobotics exposes what the naked eye cannot see in order to solve problems and make accurate projections, translating into improved yields and profitability. The company's CEO, James Paterson, says the business is ready to build on its highly successful launch in the US and strategically drop further roots and extend services in numerous regions around the world.

Artificial Intelligence (AI) in healthcare is expected to revolutionise the sector. Expectations are high and since 2015 the number of medical algorithms approved by the FDA has grown exponentially. We can see it in this interesting infographic designed by Dr. Bertalan Mesko (@Berci), known as the Medical Futurist, which shows that in 2014 only AliveCor's algorithm for the detection of atrial fibrillation was approved, and then in recent years dozens of algorithms have burst onto the scene with the go-ahead of the FDA, among them, products from Apple and Verily. However, the hype in which AI has been involved has left us some disappointments. But in recent years, a great number of startups and healthcare organisations are getting tangible results in AI applied to different medical fields.

An underreported aspect of the climate crisis is that archaeological sites, cultural landscapes, biodiversity, and distributions of flora and fauna--much of which modern people will never even know about--are disappearing at an alarming rate. I'm an archaeologist, and while I don't know how to solve the climate crisis, I do know what I want to contribute to our shared legacy: a comprehensive digital map of the surface of the planet and everything on it. Such a project will serve both as a record of the state of the planet as it exists now, to help scientists better understand how it is changing, and as a "virtual planet" that can serve as a precious gift for future generations. In June, I and other like-minded scientists launched the Earth Archive: a massive scientific effort aiming to scan the entire solid surface of the planet, starting with the areas most threatened, at a resolution smaller than a meter. This effort aims to use lidar technology, or light detection and ranging technology, which can map both the vegetation and the ground beneath it in three dimensions from the vantage point of a plane, helicopter, or drone.