Cardiovascular Disease has long been the number one cause of death in the U.S. and some of the stats are startling: an American will have a heart attack approximately every 40 seconds for a total of 805,000 every year, At the same time, mortality and morbidity rates of CVD are increasing year by year, especially in developing regions. Studies have shown that approximately 80% of CVD-related deaths occur in low- and middle-income countries. Besides, these deaths occur at a younger age than in high-income countries. CVD represents a significant economic cost for society, around $351.2 billion in the US, chronically affecting patients' quality of life. The EU has estimated that the overall yearly cost amounts to €210 billion, allocating around 53% to healthcare costs (€111 billion), with 26% related to productivity losses (€54 billion), and the remaining 21% (€45 billion) to the informal care of people with CVD (European Cardiovascular Disease Statistics 2017).
Fifteen-year-old Jordyne Lewis was stressed out. The high school sophomore from Harrisburg, North Carolina, was overwhelmed with schoolwork, never mind the uncertainty of living in a pandemic that has dragged on for two long years. Despite the challenges, she never turned to her school counselor or sought out a therapist. Instead, she shared her feelings with a robot. Lewis has struggled to cope with the changes and anxieties of pandemic life and for this extroverted teenager, loneliness and social isolation were among the biggest hardships.
In 2022, businesses will be using artificial intelligence (AI) in more innovative ways than ever before. Some of the most popular ways that AI will be used in businesses include: Chatbots will be used to communicate with customers. AI will be used to analyze data and make decisions. Robotics will be used to automate tasks. AI will be used to create new products and services. Virtual assistants will be used to manage tasks. AI will be used to improve customer service. Predictive analytics will be used to make decisions. It allows users to interact with digital objects using their smartphones. While mixed reality combines real world elements with virtual ones. These vehicles can drive on highways and through city streets without assistance from a driver or any human behind the wheel. They also protect organizations against malicious attacks. By 2030, this number is projected to grow to 3.1 billion. These people generate more than 70 percent of all greenhouse gas emissions and over 80 percent of global water usage.
Utilizing the paired multi-modal neuroimaging data has been proved to be effective to investigate human cognitive activities and certain pathologies. However, it is not practical to obtain the full set of paired neuroimaging data centrally since the collection faces several constraints, e.g., high examination costs, long acquisition time, and even image corruption. In addition, most of the paired neuroimaging data are dispersed into different medical institutions and cannot group together for centralized training considering the privacy issues. Under the circumstance, there is a clear need to launch federated learning and facilitate the integration of other unpaired data from different hospitals or data owners. In this paper, we build up a new benchmark for federated multi-modal unsupervised brain image synthesis (termed as FedMed-GAN) to bridge the gap between federated learning and medical GAN. Moreover, based on the similarity of edge information across multi-modal neuroimaging data, we propose a novel edge loss to solve the generative mode collapse issue of FedMed-GAN and mitigate the performance drop resulting from differential privacy. Compared with the state-of-the-art method shown in our built benchmark, our novel edge loss could significantly speed up the generator convergence rate without sacrificing performance under different unpaired data distribution settings.
The selfie is taken on a mobile device and then uploaded ID.me, a third-party identity verification company that will use its own facial recognition to verify the individual US taxpayers will have to submit a video selfie to access certain Internal Revenue Service (IRS) tools and applications starting this summer. The selfie is taken on a mobile device and then uploaded to ID.me, a third-party identity verification company that will use its own facial recognition to verify the individual. Once verified, the taxpayer will be asked to upload their government ID and copies of bills. Users can access basic information on the IRS without logging into ID.me, but the unique sign in will be required to make and view payments, access tax records, view or create payment plans, manage communications preference or view tax professional authorizations. However, this process is not a requirement to file taxes.
EXPRESSING A DISEASE: Want to know whether you have Covid-19 or even Alzheimer's? Artificial intelligence might soon have an answer just by listening to your voice. Leading researchers are developing technology that sorts through evidence of so-called vocal biomarkers to hone in on medical conditions that might not be detectable during routine office visits or exams. "This line might seem to have been lifted from a Star Trek script," said Bertalan Meskó, director of the Medical Futurist Institute. "But we are close to having such conversations with our computers."
Artificial intelligence already is part of our everyday lives: in our web searches, in our interactions with digital assistants, and even helping us decide what movies and TV shows to watch. In the world of worker safety, AI is providing "great opportunities." "Not only will it be in the fabric of the future of work, but it's going to be in the fabric of solutions to the future of work as well," Vietas said during a webinar hosted by the agency in June. Some of the benefits AI is providing to the safety field: deeper insights, continuous observations and real-time alerts to help employees avoid unsafe situations and organizations respond to incidents quicker. Experts say making use of AI requires collaborative efforts between safety professionals and other departments, namely information technology, to ensure transparency as well as alleviate privacy concerns and other issues workers may have.
Petropoulos, Fotios, Apiletti, Daniele, Assimakopoulos, Vassilios, Babai, Mohamed Zied, Barrow, Devon K., Taieb, Souhaib Ben, Bergmeir, Christoph, Bessa, Ricardo J., Bijak, Jakub, Boylan, John E., Browell, Jethro, Carnevale, Claudio, Castle, Jennifer L., Cirillo, Pasquale, Clements, Michael P., Cordeiro, Clara, Oliveira, Fernando Luiz Cyrino, De Baets, Shari, Dokumentov, Alexander, Ellison, Joanne, Fiszeder, Piotr, Franses, Philip Hans, Frazier, David T., Gilliland, Michael, Gönül, M. Sinan, Goodwin, Paul, Grossi, Luigi, Grushka-Cockayne, Yael, Guidolin, Mariangela, Guidolin, Massimo, Gunter, Ulrich, Guo, Xiaojia, Guseo, Renato, Harvey, Nigel, Hendry, David F., Hollyman, Ross, Januschowski, Tim, Jeon, Jooyoung, Jose, Victor Richmond R., Kang, Yanfei, Koehler, Anne B., Kolassa, Stephan, Kourentzes, Nikolaos, Leva, Sonia, Li, Feng, Litsiou, Konstantia, Makridakis, Spyros, Martin, Gael M., Martinez, Andrew B., Meeran, Sheik, Modis, Theodore, Nikolopoulos, Konstantinos, Önkal, Dilek, Paccagnini, Alessia, Panagiotelis, Anastasios, Panapakidis, Ioannis, Pavía, Jose M., Pedio, Manuela, Pedregal, Diego J., Pinson, Pierre, Ramos, Patrícia, Rapach, David E., Reade, J. James, Rostami-Tabar, Bahman, Rubaszek, Michał, Sermpinis, Georgios, Shang, Han Lin, Spiliotis, Evangelos, Syntetos, Aris A., Talagala, Priyanga Dilini, Talagala, Thiyanga S., Tashman, Len, Thomakos, Dimitrios, Thorarinsdottir, Thordis, Todini, Ezio, Arenas, Juan Ramón Trapero, Wang, Xiaoqian, Winkler, Robert L., Yusupova, Alisa, Ziel, Florian
Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.
Artificial intelligence already is part of our everyday lives: in our web searches, in our interactions with digital assistants, and even helping us decide what movies and TV shows to watch. "Not only will it be in the fabric of the future of work, but it's going to be in the fabric of solutions to the future of work as well," Vietas said during a webinar hosted by the agency in June. Some of the benefits AI is providing to the safety field: deeper insights, continuous observations and real-time alerts to help employees avoid unsafe situations and organizations respond to incidents quicker. Experts say making use of AI requires collaborative efforts between safety professionals and other departments, namely information technology, to ensure transparency as well as alleviate privacy concerns and other issues workers may have. "Our recommendation is, basically, try to understand AI and try to see how it can work for you," said Houshang Darabi, a professor at the University of Illinois Chicago and co-director of the occupational safety program at the school's Great Lakes Center for Occupational Health and Safety.
There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.