Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Liberal magazine The New Republic (TNR) garnered the scorn of critics after publishing an article Friday celebrating "petromasculinity" being rejected by younger generations, specifically those using online dating apps. In the piece, headlined "'Petromasculinity' Is Becoming Toxic, Too--at Least to Online Daters," TNR praised what appeared to be a shift in online daters preferring a potential partner who cares about climate change and "rejecting petromasculinity: the climate denial, authoritarian politics, and sexism that are too often inextricably linked." The dating app Tinder is shown on an Apple iPhone in this photo illustration taken February 10, 2016.

Professor and CADScor System inventor will present on the AI-based CADScor System, a rapid assessment of coronary artery disease. The session will be live broadcast on this link: https://us02web.zoom.us/j/86991641341?pwd WGx2V0c1aXkvbVhBQTdhdWhESGZiUT09. The recording and the presentations will also be available after the event. The information was provided, through the agency of the above contact person, for publication at the time specified by the company's news distributor, GlobeNewswire. About Acarix: Acarix is a Swedish medical device company that innovates solutions for rapid AI-based rule out of Coronary Artery Disease (CAD).

OmniML's solution enables users to design, optimize and deploy advanced machine learning models to hardware devices at the network edge.

Sygno, the provider of Automated Machine Learning (AutoML) transaction monitoring models, has reportedly joined the recent cohort of scale-ups that …

Deep learning models have strong generalisation abilities and can automatically … on domain experts in traditional machine learning algorithms.

… growth in the amount of money invested in artificial intelligence. … embryonic A.I. and machine-learning startups received $212.8 million.

Snowflake is placing big bets to make its platform friendly for machine-learning experts as it tries to catch rival Databricks, even paying $800 …

Nextracker technologies to play key role in achieving Sudair energy production and low-cost solar power production objectives.

Nowadays, detecting anomalous communities in networks is an essential task in research, as it helps discover insights into community-structured networks. Most of the existing methods leverage either information regarding attributes of vertices or the topological structure of communities. In this study, we introduce the Co-Membership-based Generic Anomalous Communities Detection Algorithm (referred as to CMMAC), a novel and generic method that utilizes the information of vertices co-membership in multiple communities. CMMAC is domain-free and almost unaffected by communities' sizes and densities. Specifically, we train a classifier to predict the probability of each vertex in a community being a member of the community. We then rank the communities by the aggregated membership probabilities of each community's vertices. The lowest-ranked communities are considered to be anomalous. Furthermore, we present an algorithm for generating a community-structured random network enabling the infusion of anomalous communities to facilitate research in the field. We utilized it to generate two datasets, composed of thousands of labeled anomaly-infused networks, and published them. We experimented extensively on thousands of simulated, and real-world networks, infused with artificial anomalies. CMMAC outperformed other existing methods in a range of settings. Additionally, we demonstrated that CMMAC can identify abnormal communities in real-world unlabeled networks in different domains, such as Reddit and Wikipedia.

Advances in the field of artificial intelligence resulted in incorporation of these technologies into the process of scientific research and in the field of photonics. Such methods as machine learning and deep learning have become popular design tools for development of photonic devices. Design in this case implies prediction of a physical response of a given structure (forward design) as well as the reverse process of finding parameters of a structure required to provide a desired response (inverse design). While design procedures arguably remain the most widespread implementation of machine learning in photonics, novel applications begin to emerge leading to evolvement of a new research area of intelligent photonics.