Microsoft Build--at the opening keynote by CEO and Chairman, Satya Nadella, announced the final judging for the 2022 Imagine Cup global winner. After the judging, V Bionic took top honours today, May 24. Their platform solution, ExoHeal, combines a therapeutic exoskeleton hand device with sensors, and extensive intuitive app -- that helps patients with hand paralysis to experience a faster, more comfortable, inexpensive, three stage rehabilitation process to improve patients physical and mental health. The V BIONIC team is achieving international recognition through competitions and award programs including: Global Finalists in the Google Science Fair and Social Innovation Award winners at the Diamond Challenge, and today as World Champions of Imagine Cup as key gems in their crown towards success. Their hard work and passion is founded on the inspiration to do more for humanity and by implementing tech-for-good.
The global Automotive Cybersecurity Market size is projected to grow from USD 2.0 billion in 2021 to USD 5.3 billion by 2026, at a CAGR of 21.3%. Increasing incidents of cyber-attacks on vehicles and massive vehicles recalls by OEMs have increased awareness about automotive cybersecurity among OEMs globally. Moreover, increasing government mandates on incorporating several safety features, such as rear-view camera, automatic emergency braking, lane departure warning system, and electronic stability control, have further opened new opportunities for automotive cybersecurity service providers globally. As a result, there are various start-ups present in the automotive cybersecurity ecosystem. Government initiatives toward building an intelligent transport system have also further escalated the demand for cybersecurity solutions all over the world.
A head pops out of the toilet, a woman gets pregnant from birth control pills -- South Korean Booker Prize nominee Bora Chung's short stories are full of horror, inspired by her own lonely life. An academic specialising in Slavic literature, Chung was considered a "genre writer" and excluded from South Korea's mainstream literary scene. Until recently, she was relatively unknown to local readers. Her stories -- which combine science fiction, horror and fantasy -- are not considered "pure" literature by Seoul's cultural elite. But her life took a dramatic turn when her 2017 collection "Cursed Bunny" caught the eye of translator Anton Hur. Hur's English edition of the book, released by British publisher Honford Star, has been named a finalist for this year's International Booker Prize.
Fragility hip fracture increases morbidity and mortality in older adult patients, especially within the first year. Identification of patients at high risk of death facilitates modification of associated perioperative factors that can reduce mortality. Various machine learning algorithms have been developed and are widely used in healthcare research, particularly for mortality prediction. This study aimed to develop and internally validate 7 machine learning models to predict 1-year mortality after fragility hip fracture. This retrospective study included patients with fragility hip fractures from a single center (Siriraj Hospital, Bangkok, Thailand) from July 2016 to October 2018. A total of 492 patients were enrolled. They were randomly categorized into a training group (344 cases, 70%) or a testing group (148 cases, 30%). Various machine learning techniques were used: the Gradient Boosting Classifier (GB), Random Forests Classifier (RF), Artificial Neural Network Classifier (ANN), Logistic Regression Classifier (LR), Naive Bayes Classifier (NB), Support Vector Machine Classifier (SVM), and K-Nearest Neighbors Classifier (KNN). All models were internally validated by evaluating their performance and the area under a receiver operating characteristic curve (AUC). For the testing dataset, the accuracies were GB model = 0.93, RF model = 0.95, ANN model = 0.94, LR model = 0.91, NB model = 0.89, SVM model = 0.90, and KNN model = 0.90. All models achieved high AUCs that ranged between 0.81 and 0.99. The RF model also provided a negative predictive value of 0.96, a positive predictive value of 0.93, a specificity of 0.99, and a sensitivity of 0.68. Our machine learning approach facilitated the successful development of an accurate model to predict 1-year mortality after fragility hip fracture. Several machine learning algorithms (eg, Gradient Boosting and Random Forest) had the potential to provide high predictive performance based on the clinical parameters of each patient. The web application is available at www.hipprediction.com . External validation in a larger group of patients or in different hospital settings is warranted to evaluate the clinical utility of this tool. Thai Clinical Trials Registry (22 February 2021; reg. no. TCTR20210222003 ).
Researchers from Google, Amazon Web Services, UC Berkeley, Shanghai Jiao Tong University, Duke University and Carnegie Mellon University have published a paper titled "Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning" at OSDI 2022. The paper introduces a new method for automating the complex process of parallelising a model with only one line of code. So how does Alpa work? Data parallelism is a technique where model weights are duplicated across accelerators while only partitioning and distributing the training data. The dataset is split into'N' parts in data parallelism with'N' being the quantity of GPUs.
Robots are not limited to industrial works anymore! Thanks to the integration of artificial intelligence and voice recognition, robots are slowly invading our smart homes embedded with devices like wireless security cameras, Smart TVs, Amazon's Alexa, Amazon Echo, Google Assistant, Philips Hue lightbulbs, Ecobee4, etc. And it is not a secret that machine learning software development is on rise now. A lot of clients are coming to develop personalized ML solutions for their businesses. ABI Research predicts that this integration will grow, and by 2024 that over 79 million homes in the world will have a robot in the house.
A type of artificial intelligence called machine learning can help scale up manufacturing of perovskite solar cells. Perovskite materials would be superior to silicon in PV cells, but manufacturing such cells at scale is a huge hurdle. Perovskites are a family of materials that are currently the leading contender to replace the silicon-based solar photovoltaics that are in broad use today. They carry the promise of panels that are far lighter and thinner, that could be made in large volumes with ultra-high throughput at room temperature instead of at hundreds of degrees, and that are easier and cheaper to transport and install. But bringing these materials from small laboratory experiments into a product that can be manufactured competitively has been a protracted struggle.
Microsoft's Think about Cup is one thing I sit up for yearly. The scholars and younger entrepreneurs who submit their extraordinarily early stage initiatives to this world competitors are just like the seeds of future startups and doubtlessly world-changing initiatives. This yr's winner, V Bionic, created a robotic glove to assist sufferers with neurological harm recuperate quicker at a fraction of the value of different choices. The crew, from Saudi Arabia, was led by Zain Samdani, who though he's a pupil has been researching and inventing issues within the robotics class for years. The remainder of the crew are equally on the begins of fascinating careers within the trade.
Artificial Intelligence (AI) powered solutions may soon make roads in India safer to drive. A unique AI approach that uses the predictive power of AI to identify risks on the road, and a collision alert system to communicate timely alerts to drivers, to make several improvements related to road safety, is being implemented in Nagpur City with the objective of bringing a significant reduction in the number of accidents. A project, 'Intelligent Solutions for Road Safety through Technology and Engineering' (iRASTE), has been launched to identify potential accident-causing scenarios while driving a vehicle and alert drivers about the same with the help of the Advanced Driver Assistance System (ADAS). The project will also identify'grey spots', i.e., by data analysis and mobility analysis by continuously monitoring dynamic risks on the entire road network. Grey spots are locations on roads, left unaddressed could become black spots (locations with fatal accidents).