Hyperspectral images (HSIs) are a kind of optical remote sensing image with a high spectral resolution. Hyperspectral images (HSIs) have attracted much attention recently as they possess unique properties and contain massive information. The newly developed deep learning methods are applied successfully in HSI classification, achieving higher accuracy than traditional methods. The earlier DL-based HSI classification methods were based on fully connected neural networks, such as stacked autoencoders (SAEs) and recursive autoencoders (RAEs). Therefore, they destroyed the spatial structure information of an HSI as they could only handle one-dimensional vectors.

Space robotics startup GITAI and the Japan Aerospace Exploration Agency (JAXA) are teaming up to produce the world's first robotics demonstration in space by a private company. The new agreement under the JAXA Space Innovation through Partnership and Co-creation (J-SPARC) initiative aims to demonstrate the potential for robots to automate of the processing of specific tasks aboard the International Space Station (ISS). Robotics is altering many aspects of our lives in many fields and one where it is particularly attractive is in the exploration and exploitation of space. Ironically, the great strides made in manned spaceflight since the first Vostok mission lifted off in 1961 have shown that not only is supporting astronauts in orbit challenging and expensive, there are also many tasks, like microgravity experiments, where the human touch isn't the best choice. These tasks often require complex, precise, and subtle movements that demand either a highly specialized and expensive bespoke apparatus or a robot.

However, as IDTechEx has reported previously in its article'AI in Medical Diagnostics: Current Status & Opportunities for Improvement', image recognition AI's current value proposition remains below the expectations of most radiologists. Over the next decade, AI image recognition companies serving the medical diagnostics space will need to test and implement a multitude of features to increase the value of their technology to stakeholders across the healthcare setting. Radiologists have a range of imaging methods at their disposal and may need to utilise more than one to detect signs of disease. For example, X-ray and CT scanning are both used to detect respiratory diseases. X-rays are cheaper and quicker, but CT scanning provides more detail about lesion pathology due its ability to form 3D images of the chest.

How is Machine Learning helping to develop TB drugs? Many biologists use machine learning (ML) as a computational tool to analyze a massive amount of data, helping them to recognise potential new drugs. MIT researchers have now integrated a new feature into these types of machine learning algorithms, enhancing their prediction-making ability. Using this new tool allows computer models to account for uncertainty in the data they are testing, MIT researchers detected several promising components that target a protein required by the bacteria that cause tuberculosis (TB). Although computer scientists previously used this technique, they have not taken off in biology.

An innovative artificial intelligence (AI) tool developed by NASA has helped identify a cluster of craters on Mars that formed within the last decade. The new machine-learning algorithm, an automated fresh impact crater classifier, was created by researchers at NASA's Jet Propulsion Laboratory (JPL) in California -- and represents the first time artificial intelligence has been used to identify previously unknown craters on the Red Planet, according to a statement from NASA. Scientists have fed the algorithm more than 112,000 images taken by the Context Camera on NASA's Mars Reconnaissance Orbiter (MRO). The program is designed to scan the photos for changes to Martian surface features that are indicative of new craters. In the case of the algorithm's first batch of finds, scientists think these craters formed from a meteor impact between March 2010 and May 2012.

Microsoft, in collaboration with MITRE, IBM, NVIDIA, and Bosch, has released a new open framework that aims to help security analysts detect, respond to, and remediate adversarial attacks against machine learning (ML) systems. Called the Adversarial ML Threat Matrix, the initiative is an attempt to organize the different techniques employed by malicious adversaries in subverting ML systems. Just as artificial intelligence (AI) and ML are being deployed in a wide variety of novel applications, threat actors can not only abuse the technology to power their malware but can also leverage it to fool machine learning models with poisoned datasets, thereby causing beneficial systems to make incorrect decisions, and pose a threat to stability and safety of AI applications. Indeed, ESET researchers last year found Emotet -- a notorious email-based malware behind several botnet-driven spam campaigns and ransomware attacks -- to be using ML to improve its targeting. Then earlier this month, Microsoft warned about a new Android ransomware strain that included a machine learning model that, while yet to be integrated into the malware, could be used to fit the ransom note image within the screen of the mobile device without any distortion.

Pfizer and IBM researchers claim to have developed a machine learning technique that can predict Alzheimer's disease years before symptoms develop. By analyzing small samples of language data obtained from clinical verbal tests, the team says their approach achieved 71% accuracy when tested against a group of cognitively healthy people. Alzheimer's disease begins with vague, often misinterpreted signs of mild memory loss followed by a slow, progressively serious decline in cognitive ability and quality of life. According to the nonprofit Alzheimer's Association, more than 5 million Americans of all ages have Alzheimer's, and every state is expected to see at least a 14% rise in the prevalence of Alzheimer's between 2017 and 2025. Due to the nature of Alzheimer's disease and how it takes hold in the brain, it's likely that the best way to delay its onset is through early intervention.

The Millennium Institute for Foundational Research on Dataa (IMFD) started its operations in June 2018, funded by the Millennium Science Initiative of the Chilean National Agency of Research and Development.b IMFD is a joint initiative led by Universidad de Chile and Universidad Católica de Chile, with the participation of five other Chilean universities: Universidad de Concepción, Universidad de Talca, Universidad Técnica Federico Santa María, Universidad Diego Portales, and Universidad Adolfo Ibáñez. IMFD aims to be a reference center in Latin America related to state-of-the-art research on the foundational problems with data, as well as its applications to tackling diverse issues ranging from scientific challenges to complex social problems. As tasks of this kind are interdisciplinary by nature, IMFD gathers a large number of researchers in several areas that include traditional computer science areas such as data management, Web science, algorithms and data structures, privacy and verification, information retrieval, data mining, machine learning, and knowledge representation, as well as some areas from other fields, including statistics, political science, and communication studies. IMFD currently hosts 36 researchers, seven postdoctoral fellows, and more than 100 students.

Forests are the major terrestrial ecosystem responsible for carbon sequestration and storage. The Amazon rainforest is the world's largest tropical rainforest encompassing up to 2,124,000 square miles, covering a large area in South America including nine countries. The majority of that area (69%) lies in Brazil. Thus, Amazonia holds about 20% of the total carbon contained in the world's terrestrial vegetation.1,5,7 But the rampant deforestation due to illegal logging, mining, cattle ranching, and soy plantation are examples of threats to the vast region.

Transaction data is like a friendship tie: both parties must respect the relationship and if one party exploits it the relationship sours. As data becomes increasingly valuable, firms must take care not to exploit their users or they will sour their ties. Ethical uses of data cover a spectrum: at one end, using patient data in healthcare to cure patients is little cause for concern. At the other end, selling data to third parties who exploit users is serious cause for concern.2 Between these two extremes lies a vast gray area where firms need better ways to frame data risks and rewards in order to make better legal and ethical choices.