Coronavirus can be transmitted through the air by close proximity to infected persons. Commercial aircraft are a likely way to both transmit the virus among passengers and move the virus between locations. The importance of learning about where and how coronavirus has entered the United States will help further our understanding of the disease. Air travelers can come from countries or areas with a high rate of infection and may very well be at risk of being exposed to the virus. Therefore, as they reach the United States, the virus could easily spread. On our analysis, we utilized machine learning to determine if the number of flights into the Washington DC Metro Area had an effect on the number of cases and deaths reported in the city and surrounding area.

TU Wien Informatics will be welcoming Toby Walsh, AI expert and "rock star" of Australia's digital revolution, for the Gödel Lecture 2022. Artificial intelligence is an essential part of our lives – for better or worse. It can be used to influence what we buy, who gets shortlisted for a job, and even how we vote. Without AI, medical technology wouldn't have come so far, we'd still be getting lost on backroads in our GPS-free cars, and smartphones wouldn't be so, well, smart. But as we continue to build more intelligent and autonomous machines, what impact will this have on humanity and the planet?

Officially it is just a research vessel, but China's newly unveiled drone carrier is a clear sign Beijing is rushing to deploy an autonomous swarm of unmanned devices in its push for military supremacy in the Pacific Ocean. State media last month showed the launching of the Zhu Hai Yun -- "Zhu Hai Cloud" -- capable of transporting an unspecified number of flying drones as well as surface and submarine craft, and operating autonomously thanks to artificial intelligence. The 89-metre (292-foot) ship would be operational by year-end with a top speed of 18 knots, vastly increasing China's surveillance potential of the vast Pacific area it considers its zone of influence. "The vessel is not only an unprecedented precision tool at the frontier of marine science, but also a platform for marine disaster prevention and mitigation, seabed precision mapping, marine environment monitoring, and maritime search and rescue," Chen Dake, lab director at the firm that built the carrier, told China Daily. Armies worldwide see drone squadrons as key players in combat, able to overwhelm defence systems by sheer numbers and without putting soldiers' lives at risk, such as with more expensive jets or tanks.

Object tracking --following objects over time --is an essential image analysis technique used to quantify dynamic processes in biosciences. A new application called TrackMate v7 enables scientists to track objects in images easily. TrackMate is a free, open-source tool available as part of the Fiji image analysis platform. In life sciences, tracking is used, for instance, to follow the movement of molecules, subcellular organelles, bacteria, cells, and whole animals. However, due to the sheer diversity of images used in research, no single application can address every tracking challenge.

A new study from the University of Montpellier, France has found that the reef fish humans find 'ugly' are more likely to be endangered than more colourful and aesthetically pleasing species.

Britons' trust in science is at an all-time high after the Covid pandemic, a new report reveals – but misinformation on social media continues to present a'threat to scientific credibility'. The 3M State of Science Index, published on Tuesday, reveals that 90 per cent of UK residents trust science in 2022, compared with 85 per cent in 2019. This stat also compares with 88 per cent of Europeans and 89 per cent of people globally who trust science in 2022. In the UK, 57 per cent of Brits say they are now more appreciative of science after the pandemic, likely due to the efforts of scientists in creating Covid vaccines. However, misinformation'is widespread' on social media and threatens the future of the public's understanding of science, the report says.

An artificial intelligence (AI) tool that can transform famous paintings into different art styles, or create brand new artworks from a text prompt, may work by using a'secret language', experts claim. Text-to-image app DALL-E 2 was released by artificial intelligence lab OpenAI last month, and is able to create multiple realistic images and artwork from a single text prompt. It is also able to add objects into existing images, or even provide different points of view on an existing image. Now researchers believe they may have figured out how the technology works, after discovering that gibberish words produce specific pictures. Computer scientists used DALL-E 2 to generate images that contained text inside them, by asking for'captions' or'subtitles'.

Aspect-based sentiment analysis (ABSA) aims to predict the sentiment expressed in a review with respect to a given aspect. The core of ABSA is to model the interaction between the context and given aspect to extract aspect-related information. In prior work, attention mechanisms and dependency graph networks are commonly adopted to capture the relations between the context and given aspect. And the weighted sum of context hidden states is used as the final representation fed to the classifier. However, the information related to the given aspect may be already discarded and adverse information may be retained in the context modeling processes of existing models. Such a problem cannot be solved by subsequent modules due to two reasons. First, their operations are conducted on the encoder-generated context hidden states, whose value cannot be changed after the encoder. Second, existing encoders only consider the context while not the given aspect. To address this problem, we argue the given aspect should be considered as a new clue out of context in the context modeling process. As for solutions, we design three streams of aspect-aware context encoders: an aspect-aware LSTM, an aspect-aware GCN, and three aspect-aware BERTs. They are dedicated to generating aspect-aware hidden states which are tailored for the ABSA task. In these aspect-aware context encoders, the semantics of the given aspect is used to regulate the information flow. Consequently, the aspect-related information can be retained and aspect-irrelevant information can be excluded in the generated hidden states. We conduct extensive experiments on several benchmark datasets with empirical analysis, demonstrating the efficacies and advantages of our proposed aspect-aware context encoders.

Meiotic maturation is a crucial step of oocyte formation allowing its potential fertilization and embryo development. Elucidating this process is important both for fundamental research and assisted reproductive technology. Few computational tools based on non-invasive measurements are however available to characterize oocyte meiotic maturation. Here, we develop a computational framework to phenotype oocytes based on images acquired in transmitted light. We trained neural networks to segment the contour of oocytes and their zona pellucida using oocytes from diverse species. We defined a comprehensive set of morphological features to describe an oocyte. These steps are implemented in an open-source Fiji plugin. We present a feature based machine learning pipeline to recognize oocyte populations and determine their morphological differences. We first demonstrate its potential to screen oocyte from different strains and automatically identify their morphological characteristics. Its second application is to predict and characterize the maturation potential of oocytes. We identify the texture of the zona pellucida and the cytoplasmic particles size as features to assess mouse oocyte maturation potential and tested whether these features were applicable to human oocyte's developmental potential.

June 6 (Reuters) - When a team of scientists listened to an audio clip recorded underwater off islands in central Indonesia, they heard what sounded like a campfire. Instead, it was a coral reef, teeming with life, according to a study scientists from British and Indonesian universities published last month, in which they used hundreds of such audio clips to train a computer programme to monitor the health of a coral reef by listening to it. A healthy reef has a complex "crackling, campfire-like" sound because of all the creatures living on and in it, while a degraded reef sounds more desolate, life sciences specialist and the team's lead researcher Ben Williams said. The artificial intelligence (AI) system parses data points such as the frequency and loudness of the sound from the audio clips, and can determine with at least 92% accuracy whether the reef is healthy or degraded, according to the team's study published in Ecological Indicators journal. The scientists hope this new AI system will help conservation groups around the world to track reef health more efficiently.