A Google service that automatically labels images produced starkly different results depending on skin tone on a given image. The company fixed the issue, but the problem is likely much broader. In the fight against the novel coronavirus, many countries ordered that citizens have their temperature checked at train stations or airports. The device needed in such situations, a hand-held thermometer, has risen from a specialist item to a common sight. A branch of Artificial Intelligence known as "computer vision" focuses on automated image labeling.

Bats can learn to mimic specific sounds, which puts them into an elite group of animals capable of this. Studying how bats can copy noises could help us learn more about humans' unique capacity for speech and language. The ability to imitate specific sounds – called vocal production learning – is rare in the animal kingdom. Humans are capable of it, as are some bird species, as well as seals, dolphins, whales and elephants. "It's relatively difficult," says Ella Lattenkamp at the Max Planck Institute for Psycholinguistics in Nijmegen, the Netherlands.

Using a new field of applied mathematics, a computer scientist at The University of Texas at Arlington is working to enhance the perception capabilities of robots. William Beksi, assistant professor of computer science and engineering, is investigating how to effectively process 3-D point cloud data captured from low-cost sensors--information that robots could use to facilitate intelligent tasks in complex scenarios. Beksi's work is funded with a two-year, $175,000 grant from the National Science Foundation. Three-dimensional point clouds are sets of points in space, sometimes with color information, that can be obtained from inexpensive 3-D sensors. However, data generated by these sensors can suffer from anomalies, such as the presence of noise and variation in density of the points. These issues limit the reliability, efficiency and scalability of robotic perception applications that use 3-D point clouds for manipulation, navigation, and object detection and classification.

Data science is one of the most well-payed jobs in the contemporary market. It is even considered as the hottest job of the 21st century. Data science has been a game-changer across every industry. With high-level digitization of processes, the generation of data is at peak and thus data science technology and tools are deployed to drive more productivity across organizations. This tech-field as a whole has a bunch of perks to provide including technologies for Big Data, Data Mining, Machine Learning, Data Analysis, and Data Analytics.

The heart is a specialized muscle that contracts rhythmically around its closed chambers to propel blood. However, this pumping function fluctuates throughout the day as the circulating blood flow adapts to the body's ever-changing metabolic demands1. Understanding the variations in cardiac pump activity with each heartbeat might have relevance for explaining the intricacies of heart function in health and disease. However, the tools for scrutinizing such changes remain imprecise. Writing in Nature, Ouyang et al.2 report the development of a computational platform that uses an artificial-intelligence (AI) approach to assess cardiac ultrasound video and to provide continuous, beat-by-beat measurement of cardiac pump function.

Penguins inhabit one of the most secluded parts of the planet, yet human activity is threatening their existence. Warmer temperatures associated with climate change are melting the Antarctic ice faster than ever, eroding the grounds where penguins live, feed and breed, while commercial overfishing and incidents like oil spills are depleting their food supply. A 2008 World Wildlife Fund study reveals that if the global average temperatures increase by just 2 C – a distinct possibility over the next 40 years – around 50 percent of emperor penguins and 75 percent of Adelie penguins could disappear. Conservation efforts for penguins are easier said than done. Their secluded existence in the Antarctic region means there is very little data available, and manned missions are difficult, especially during the harsh winters.

Researchers say evaluating the'social-connectedness' of regions using Facebook data could give epidemiologists another tool in judging the spread of infectious disease outside of geographic proximity and population density. The study, which appears in the preprint journal ArXiv and is authored by researchers from New York University, found links between two hotspots of the ongoing COVID-19 pandemic - Westchester County, New York and Lodi province in Italy - to areas with correlating connections on the social media platform, Facebook. Using an equation developed by the same researchers in 2017 called the'Social Connectedness Index' the study was able to make correlations between the spread of COVID-19 from Westchester County and Lodi to geographically disparate locations like ski resorts on Florida and vacation spots in Rimini, Italy near the Adriatic sea. Those correlations remained even after controlling for wealth, population density, and geographic proximity according to researchers. Levels of social connectedness didn't always correlate to the disproportionate spread of the virus, however.

This paper intends to understand and to improve the working principle of decomposition-based multi-objective evolutionary algorithms. We review the design of the well-established Moea/d framework to support the smooth integration of different strategies for sub-problem selection, while emphasizing the role of the population size and of the number of offspring created at each generation. By conducting a comprehensive empirical analysis on a wide range of multi-and many-objective combinatorial NK landscapes, we provide new insights into the combined effect of those parameters on the anytime performance of the underlying search process. In particular, we show that even a simple random strategy selecting sub-problems at random outperforms existing sophisticated strategies. We also study the sensitivity of such strategies with respect to the ruggedness and the objective space dimension of the target problem.

Benchmarking unsupervised outlier detection is difficult. Outliers are rare, and existing benchmark data contains outliers with various and unknown characteristics. Fully synthetic data usually consists of outliers and regular instance with clear characteristics and thus allows for a more meaningful evaluation of detection methods in principle. Nonetheless, there have only been few attempts to include synthetic data in benchmarks for outlier detection. This might be due to the imprecise notion of outliers or to the difficulty to arrive at a good coverage of different domains with synthetic data. In this work we propose a generic process for the generation of data sets for such benchmarking. The core idea is to reconstruct regular instances from existing real-world benchmark data while generating outliers so that they exhibit insightful characteristics. This allows both for a good coverage of domains and for helpful interpretations of results. We also describe three instantiations of the generic process that generate outliers with specific characteristics, like local outliers. A benchmark with state-of-the-art detection methods confirms that our generic process is indeed practical.