To characterize circumstellar systems in high contrast imaging, the fundamental step is to construct a best point spread function (PSF) template for the non-circumstellar signals (i.e., star light and speckles) and separate it from the observation. With existing PSF construction methods, the circumstellar signals (e.g., planets, circumstellar disks) are unavoidably altered by over-fitting and/or self-subtraction, making forward modeling a necessity to recover these signals. We present a forward modeling--free solution to these problems with data imputation using sequential non-negative matrix factorization (DI-sNMF). DI-sNMF first converts this signal separation problem to a "missing data" problem in statistics by flagging the regions which host circumstellar signals as missing data, then attributes PSF signals to these regions. We mathematically prove it to have negligible alteration to circumstellar signals when the imputation region is relatively small, which thus enables precise measurement for these circumstellar objects. We apply it to simulated point source and circumstellar disk observations to demonstrate its proper recovery of them. We apply it to Gemini Planet Imager (GPI) K1-band observations of the debris disk surrounding HR 4796A, finding a tentative trend that the dust is more forward scattering as the wavelength increases. We expect DI-sNMF to be applicable to other general scenarios where the separation of signals is needed.

Deep learning models have been criticized for their lack of easy interpretation, which undermines confidence in their use for important applications. Nevertheless, they are consistently utilized in many applications, consequential to humans' lives, mostly because of their better performance. Therefore, there is a great need for computational methods that can explain, audit, and debug such models. Here, we use flip points to accomplish these goals for deep learning models with continuous output scores (e.g., computed by softmax), used in social applications. A flip point is any point that lies on the boundary between two output classes: e.g. for a model with a binary yes/no output, a flip point is any input that generates equal scores for "yes" and "no". The flip point closest to a given input is of particular importance because it reveals the least changes in the input that would change a model's classification, and we show that it is the solution to a well-posed optimization problem. Flip points also enable us to systematically study the decision boundaries of a deep learning classifier. The resulting insight into the decision boundaries of a deep model can clearly explain the model's output on the individual-level, via an explanation report that is understandable by non-experts. We also develop a procedure to understand and audit model behavior towards groups of people. Flip points can also be used to alter the decision boundaries in order to improve undesirable behaviors. We demonstrate our methods by investigating several models trained on standard datasets used in social applications of machine learning. We also identify the features that are most responsible for particular classifications and misclassifications.

Motivated by the concept of network motifs we construct certain clustering methods (functors) which are parametrized by a given collection of motifs (or representers).

The global artificial intelligence in fashion market accounted for US$ 270.0 Mn in 2018 and is expected to grow at a CAGR of 36.9% over the forecast period 2019-2027, to account for US$ 4,391.7 Mn in 2027. Driving factors such as availability of massive amount of data due to increasing proliferation of digital services across the globe, and real time consumer behavior insights and increased operational efficiency are driving the adoption of AI in fashion industry will drive the market during the forecast period and have a high impact in the short term. However, factors such as concerns related to data privacy and security is anticipated to hinder the market growth in the coming years. AI integration in fashion plays a crucial role in sales, marketing, and customer-focused purposes.Initial adopters point toward the key impacts of technology in improving customer experience and decent growth in company revenue. Elevated customer experience helps the retailer to crack entirely new tactics of customer engagement and communication.With AI integration, the retailers can precisely spot the customers' expected needs at precise times and offer the appropriate product to gain a competitive advantage. Some of the past initiatives taken in the fashion industry sector which has revolutionize the use of AI in the sector are North Face leveraging IBM Watson's ML technology to recommend more personalized apparel to the customers.Further, eBay's AI integration helps their sellers sell more by better inventory management and pricing recommendations.

The graph represents a network of 3,290 Twitter users whose tweets in the requested range contained "govtech", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 23 December 2019 at 07:09 UTC. The requested start date was Monday, 23 December 2019 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 13-day, 9-hour, 49-minute period from Monday, 09 December 2019 at 01:50 UTC to Sunday, 22 December 2019 at 11:39 UTC.

A new study co-authored by an MIT economist shows that improved translation software can significantly boost international trade online -- a notable case of machine learning having a clear impact on economic activity. The research finds that after eBay improved its automatic translation program in 2014, commerce shot up by 10.9 percent among pairs of countries where people could use the new system. To have it be so clear in such a short amount of time really says a lot about the power of this technology," says Erik Brynjolfsson, an MIT economist and co-author of a new paper detailing the results. To put the results in perspective, he adds, consider that physical distance is, by itself, also a significant barrier to global commerce. The 10.9 percent change generated by eBay's new translation software increases trade by the same amount as "making the world 26 percent smaller, in terms of its impact on the goods that we studied," he says. The paper, "Does Machine Translation Affect International Trade?

Rising number of contact centers and necessity for compliance and risk management across several verticals have led the companies to invent solutions in speech analytics which will aid companies to comprehend the changing necessities of customers. Several organizations functioning in diverse industrial domains have been evolving interests for the transcription and analyzing of customers and structural media and uptake rational decisions for the management of business and consumers with the help of speech and text intelligence. This is the main factor that is responsible for the growth of the speech analytics market and a protuberant driving factor in the growing demands for speech analytics in several industrial applications. This rising demand can also be accredited to the burdens on businesses for safeguarding their rational assets for improving agility and competence in business operations via the all-embracing insights quarried in the Voice of Customer (VoC). Speech analytics is used in sectors such as customer experience management, agent performance, business processes, compliance and risk management, and market intelligence.

U.S. spies are no longer being tailed by foreign governments in about 30 different countries because advances in facial recognition, biometrics and artificial intelligence have made it almost impossible for the agents to hide. Whereas governments would once physically follow CIA officers, facial recognition at airports and general CCTV surveillance in those countries makes it far easier to track people. It comes as U.S. intelligence agencies face a growing crisis in intelligence gathering, as developments in technology are making it increasingly more difficult to protect operatives and mask their digital footprints. In one attempt to tackle the crisis, the CIA created a multi-million dollar program called the Station of the Future, intelligence officials revealed to Yahoo News. The program, created over the past decade, was run out of a diplomatic facility in Latin America and involved a team of spies trying to build tools and test techniques that could help the industry battle the digital age.

It's a skillful and encouraging report for governments, manufacturers, advertisements, and residential & business customers to propose their market-centric tactics in the global market. The report contains details of the segments that are thriving in the market together with sub-segments. The performance of the market evaluated in terms of value USD Million over the period 2019 to 2025. Ruling companies in the industry with their profiles, classification, size, cost, business atmosphere, product portfolio, and contact information are added in this report. The market effect factors analysis section highlights market progress/risk, technology progress, substitutes threat, consumer needs/customer preference changes that decides the next strategy. Then the impact survey of both drivers as well as limiting factors is explained in the analysis.

New York, December 30, 2019: The global Artificial Intelligence Platform market is segregated on the basis of Component as Tools and Services. Based on Deployment the global Artificial Intelligence Platform market is segmented in Cloud and On-Premises. Based on End-User Industry the global Artificial Intelligence Platform market is segmented in Manufacturing, Healthcare, BFSI, Research and Academia, Transportation, Retail and Ecommerce, and Others. The global Artificial Intelligence Platform market is expected to exceed more than US$ 10.8 Billion by 2024, at a CAGR of more than 28% in the given forecast period. The global Artificial Intelligence Platform market report provides geographic analysis covering regions, such as North America, Europe, Asia-Pacific, and Rest of the World.