Although Sentinel-2 based land use and land cover (LULC) classification is critical for various environmental monitoring applications, it is a very difficult task due to some key data challenges (e.g., spatial heterogeneity, context information, signature ambiguity). This paper presents a novel Multitask Glocal OBIA-Mamba (MSOM) for enhanced Sentinel-2 classification with the following contributions. First, an object-based image analysis (OBIA) Mamba model (OBIA-Mamba) is designed to reduce redundant computation without compromising fine-grained details by using superpixels as Mamba tokens. Second, a global-local (GLocal) dual-branch convolutional neural network (CNN)-mamba architecture is designed to jointly model local spatial detail and global contextual information. Third, a multitask optimization framework is designed to employ dual loss functions to balance local precision with global consistency. The proposed approach is tested on Sentinel-2 imagery in Alberta, Canada, in comparison with several advanced classification approaches, and the results demonstrate that the proposed approach achieves higher classification accuracy and finer details that the other state-of-the-art methods.

Prediction of angler behaviors, such as catch rates and angler pressure, is essential to maintaining fish populations and ensuring angler satisfaction. Angler behavior can partly be tracked by online platforms and mobile phone applications that provide fishing activities reported by recreational anglers. Moreover, angler behavior is known to be driven by local site attributes. Here, the prediction of citizen-reported angler behavior was investigated by machine-learning methods using auxiliary data on the environment, socioeconomics, fisheries management objectives, and events at a freshwater body. The goal was to determine whether auxiliary data alone could predict the reported behavior. Different spatial and temporal extents and temporal resolutions were considered. Accuracy scores averaged 88% for monthly predictions at single water bodies and 86% for spatial predictions on a day in a specific region across Canada. At other resolutions and scales, the models only achieved low prediction accuracy of around 60%. The study represents a first attempt at predicting angler behavior in time and space at a large scale and establishes a foundation for potential future expansions in various directions.

Summary: Using ECG data, a new machine learning algorithm was able to predict death within 5 years of a patient being admitted to hospital with 87% accuracy. The AI was able to sort patients into 5 categories ranging from low to high risk of death. If you've ever been admitted to hospital or visited an emergency department, you've likely had an electrocardiogram, or ECG, a standard test involving tiny electrodes taped to your chest that checks your heart's rhythm and electrical activity. Hospital ECGs are usually read by a doctor or nurse at your bedside, but now researchers are using artificial intelligence to glean even more information from those results to improve your care and the health-care system all at once. In recently published findings, the research team built and trained machine learning programs based on 1.6 million ECGs done on 244,077 patients in northern Alberta between 2007 and 2020.

Pandemic outbreaks such as COVID-19 occur unexpectedly, and need immediate action due to their potential devastating consequences on global health. Point-of-care routine assessments such as electrocardiogram (ECG), can be used to develop prediction models for identifying individuals at risk. However, there is often too little clinically-annotated medical data, especially in early phases of a pandemic, to develop accurate prediction models. In such situations, historical pre-pandemic health records can be utilized to estimate a preliminary model, which can then be fine-tuned based on limited available pandemic data. This study shows this approach -- pre-train deep learning models with pre-pandemic data -- can work effectively, by demonstrating substantial performance improvement over three different COVID-19 related diagnostic and prognostic prediction tasks. Similar transfer learning strategies can be useful for developing timely artificial intelligence solutions in future pandemic outbreaks.

Electrocardiogram (ECG) abnormalities are linked to cardiovascular diseases, but may also occur in other non-cardiovascular conditions such as mental, neurological, metabolic and infectious conditions. However, most of the recent success of deep learning (DL) based diagnostic predictions in selected patient cohorts have been limited to a small set of cardiac diseases. In this study, we use a population-based dataset of >250,000 patients with >1000 medical conditions and >2 million ECGs to identify a wide range of diseases that could be accurately diagnosed from the patient's first in-hospital ECG. Our DL models uncovered 128 diseases and 68 disease categories with strong discriminative performance.

Join the audience for an AI in Medical Physics Week live webinar at 3 p.m. BST on 23 June 2022 based on IOP Publishing's special issue, Focus on Machine Learning Models in Medical Imaging Want to take part in this webinar? An overview will be given of the role of artificial intelligence (AI) in automatic delineation (contouring) of organs in preclinical cancer research models. It will be shown how AI can increase efficiency in preclinical research. Speaker: Frank Verhaegen is head of radiotherapy physics research at Maastro Clinic, and also professor at the University of Maastricht, both located in the Netherlands. He is also a co-founder of the company SmART Scientific Solutions BV, which develops research software for preclinical cancer research.

Mphasis accelerates the world-leading Quantum Computing Ecosystem in partnership with the University of Calgary and the Government of Alberta. The Quantum Lab is set to accelerate the development of quantum skills in the city to enable job creation. CALGARY, AB, June 9, 2022 – Mphasis, (BSE: 526299; NSE: MPHASIS), an Information Technology (IT) solutions provider specializing in cloud and cognitive services, today joined the Government of Alberta and the University of Calgary to announce the launch of the world-leading Quantum City – Canada. Quantum city will further establish Alberta as a leading technology hub and will accelerate the development of the quantum ecosystem in Calgary. The partnership aims to utilize the synergy between academia, industry, and government to put the process of ideation to market at the forefront.

We are currently looking to identify a Psychiatry Postdoctoral Fellow (PDF) to cover our Research Coordinator for the MIRA, Mental Health Virtual Assistant project for the period of 12 months (maternity leave) - starting July 1st, 2022 (somewhat negotiable). They will be working on a multi-disciplinary team, inclusive of 2 computing science Master students, 2 psychiatry Postdoctoral Fellows (one working hand-in-hand with this Fellow on the expansion of services to additional groups and provinces (supporting French language translation and service expansion to Quebec, among other responsibilities), and one supporting the expansion of services to children and youth), and 1 Indigenous studies PhD student, supporting the co-creation of MIRA with Indigenous communities. Funding is secured to fully support this position. The PDF would be first offered a 6-month contract, with the opportunity for another 6 month extension following a review. The PDF would be working under the supervision of Drs.

In support of the Alberta Technology and Innovation Strategy (ATIS) and in partnership with AltaML, a leading Canadian artificial intelligence company, the AI lab (named GovLab.ai) AltaML will work alongside government staff and post-secondary students and graduates as they work to develop smart products and models that leverage AI to solve complex, real-world problems. The lab will create opportunities for Alberta's public and private sectors to create intellectual property while accelerating Alberta's recovery and economic diversification. "Alberta is a world leader in AI and machine learning research. With the launch of GovLab.ai, Ultimately this will help Alberta's government offer better services, better results and better value to Albertans."

He has been involved in many areas that make use of AI and ML at EA, particularly AI for games development and verification. He started out with game development, but is now with the AI support team, which supports all of the company's teams.