Recently, scientist Gagandeep Kang had to forcefully remind a room full of senior colleagues -- all men -- that she was the chair and that they should speak only when their turn comes. This kind of thing happens all the time, and you become so inured to it that you don't realise it," she says. Kang is the first Indian woman to be elected as a fellow of the Royal Society, but even that, evidently, does not protect you from microaggressions from men. It is a reminder of the kind of bias that women in science have to deal with. Prejudice at many levels is one reason why there are far fewer women scientists than men in the higher echelons of science in India. A 2016-17 report, "Status of Women in Science Among Select Institutions in India: Policy Implications", supported by NITI Aayog, found that while women constitute over a third of science graduates and postgraduates, they make up only 15-20% of tenured faculty across research institutions and universities in India. "As a group, it is not easy for women to stay in science. Only 14% of scientists are women," science writers Nandita Jayaraj and Aashima Dogra write in their recent book, 31 Fantastic Adventures in Science: Women Scientists in India. However, there are women who have beaten odds and shattered stereotypes and glass ceilings. This special feature looks at nine such women who are doing critical work in science and technology in India. They work on an array of complex problems -- in fields ranging from quantum computation to paleoecology. Neuroscientist Vidita Vaidya is looking to decode how experiences and the environment affect the circuits in our brain, which might offer a clue to how we develop psychiatric disorders. Aditi Sen De, the first woman to receive the Shanti Swarup Bhatnagar Prize in physical sciences, is working on different aspects of quantum communication, a field that uses the laws of quantum physics to protect data. This is by no means an exhaustive list of exceptional women scientists, but they are representative of the brilliant minds that have striven and made it to the top and become exemplars. As Kang says, "If you see role models, you see areas you can aspire to.

Artificial intelligence is everywhere but it is considered in a wholly ahistorical way. To understand the impact AI will have on our lives, it is vital to appreciate the context in which the field was established. After all, statistics and state control have evolved hand in hand for hundreds of years. Its origins have been traced not only to analytic philosophy, pure mathematics and Alan Turing, but perhaps surprisingly, to the history of public administration. In "The Government Machine: A Revolutionary History of the Computer" from 2003, Jon Agar of University College London charts the development of the British civil service as it ballooned from 16,000 employees in 1797 to 460,000 by 1999.

The assignment of individual fish to its stock of origin is important for reliable stock assessment and fisheries management. Otolith shape is commonly used as the marker of distinct stocks in discrimination studies. Our literature review showed that the application and comparison of alternative statistical classifiers to discriminate fish stocks based on otolith shape is limited. Therefore, we compared the performance of two traditional and four machine learning classifiers based on Fourier analysis of otolith shape using selected stocks of Atlantic cod (Gadus morhua) in the southern Baltic and Atlantic herring (Clupea harengus) in the western Norwegian Sea, Skagerrak and the southern Baltic Sea. Our results showed that the stocks can be successfully discriminated based on their otolith shapes. We observed significant differences in the accuracy obtained by the tested classifiers.

Mainali's expertise is already being shared outside of the Chesapeake Bay watershed, including a project concerning water flow management in Colorado in an area where water is a scarce resource. To help manage the demands of water for agriculture and wildlife, Mainali wrote algorithms that optimize the water flow and release from reservoirs by various agencies. These algorithms will be used for informed decision making about sharing the responsibility of water release from multiple reservoirs on a daily basis.

Artificial intelligence is coming for America's high-paid professions as it creates winners and losers across the labor market like never before. White-collar jobs and better-educated occupations along with production workers are among the most susceptible to AI's spread into the economy, according to a Brookings Institution report Wednesday that draws on a new analysis of patent data by Stanford University graduate student Michael Webb. "Webb's modeling suggests that just as the impacts of robotics and software tend to be sizable and negative on exposed middle- and low-skill occupations, so AI's inroads are projected to negatively impact higher-skill occupations," researchers Mark Muro, Jacob Whiton and Robert Maxim wrote, noting that their analysis shows potential impacts can be both positive and negative. Workers with graduate or professional degrees will be almost four times as exposed to AI as workers with just a high school degree, the report showed. The researchers also concluded that AI appears most likely to affect men, prime-age and white and Asian American workers.

This ebook, based on the latest ZDNet / TechRepublic special feature, examines how driverless cars, trucks, semis, delivery vehicles, drones, and other UAVs are poised to unleash a new level of automation in the enterprise. Few technologies have been more anticipated heading into the 2020s than autonomous vehicles. Tantalizingly close and yet still perhaps decades from market adoption in some use cases, the technology is as promising as it is misunderstood. You've heard the consumer hype, but what gets less ink are the transformative changes that autonomous vehicles will bring -- in some cases already are bringing -- to the enterprise. Affecting sectors as disparate as shipping and logistics, energy, agriculture, transportation, construction, and infrastructure -- to name just a few -- it's hard to overstate the impact of the diverse and versatile set of technologies lumped into the decidedly broad category of'autonomous vehicles'. This guide will help you sort the hype from the business reality and tell you all you need to know about the autonomous vehicle revolution on the ground, in the air, and even at sea. In 1939, General Motors predicted we'd have an autonomous vehicle highway system up and running by the dawn of the 1960s. As with a lot of autonomous vehicle hype, that prediction was a tad premature, but it demonstrates the long history of autonomous vehicle development.

The Mat\'ern covariance function is a popular choice for prediction in spatial statistics and uncertainty quantification literature. A key benefit of the Mat\'ern class is that it is possible to get precise control over the degree of differentiability of the process realizations. However, the Mat\'ern class possesses exponentially decaying tails, and thus may not be suitable for modeling long range dependence. This problem can be remedied using polynomial covariances; however one loses control over the degree of differentiability of the process realizations, in that the realizations using polynomial covariances are either infinitely differentiable or not differentiable at all. We construct a new family of covariance functions using a scale mixture representation of the Mat\'ern class where one obtains the benefits of both Mat\'ern and polynomial covariances. The resultant covariance contains two parameters: one controls the degree of differentiability near the origin and the other controls the tail heaviness, independently of each other. Using a spectral representation, we derive theoretical properties of this new covariance including equivalence measures and asymptotic behavior of the maximum likelihood estimators under infill asymptotics. The improved theoretical properties in predictive performance of this new covariance class are verified via extensive simulations. Application using NASA's Orbiting Carbon Observatory-2 satellite data confirms the advantage of this new covariance class over the Mat\'ern class, especially in extrapolative settings.

A referring expression (RE) is a description that identifies a set of instances unambiguously. Mining REs from data finds applications in natural language generation, algorithmic journalism, and data maintenance. Since there may exist multiple REs for a given set of entities, it is common to focus on the most intuitive ones, i.e., the most concise and informative. In this paper we present REMI, a system that can mine intuitive REs on large RDF knowledge bases. Our experimental evaluation shows that REMI finds REs deemed intuitive by users. Moreover we show that REMI is several orders of magnitude faster than an approach based on inductive logic programming.

Modern weather forecast models perform uncertainty quantification using ensemble prediction systems, which collect nonparametric statistics based on multiple perturbed simulations. To provide accurate estimation, dozens of such computationally intensive simulations must be run. We show that deep neural networks can be used on a small set of numerical weather simulations to estimate the spread of a weather forecast, significantly reducing computational cost. To train the system, we both modify the 3D U-Net architecture and explore models that incorporate temporal data. Our models serve as a starting point to improve uncertainty quantification in current real-time weather forecasting systems, which is vital for predicting extreme events.

We present a new feature extraction method for complex and large datasets, based on the concept of transport operators on graphs. The proposed approach generalizes and extends the many existing data representation methodologies built upon diffusion processes, to a new domain where dynamical systems play a key role. The main advantage of this approach comes from the ability to exploit different relationships than those arising in the context of e.g., Graph Laplacians. Fundamental properties of the transport operators are proved. We demonstrate the flexibility of the method by introducing several diverse examples of transformations. We close the paper with a series of computational experiments and applications to the problem of classification of hyperspectral satellite imagery, to illustrate the practical implications of our algorithm and its ability to quantify new aspects of relationships within complicated datasets.