An artificial colour-changing material inspired by the skins of chameleons can be used as a chemical sensor to determine whether seafood is fresh, a study found. Developed by experts from China, the device switches from pink to green in the presence of the amine vapours released by microbes when fish and shrimp spoil. The novel material could also find applications in the development of anticounterfeit technology, camouflage for robots and stretchable electronics, the team said. Panther chameleons are colour-changing reptiles native to the island of Madagascar in the Indian Ocean. Males of the species -- which are more brightly coloured than their female counterparts and change hue when asserting their dominance -- can grow to around 8 inches (20 cm) in length.

In 1949, at the dawn of the computer age, the French philosopher Gabriel Marcel warned of the danger of naively applying technology to solve life's problems. Life, Marcel wrote in Being and Having, cannot be fixed the way you fix a flat tire. Any fix, any technique, is itself a product of that same problematic world, and is therefore problematic, and compromised. Marcel's admonition is often summarized in a single memorable phrase: "Life is not a problem to be solved, but a mystery to be lived." Despite that warning, seventy years later, artificial intelligence is the most powerful expression yet of humans' urge to solve or improve upon human life with computers. But what are these computer systems? As Marcel would have urged, one must ask where they come from, whether they embody the very problems they would purport to solve. Ethics in AI is essentially questioning, constantly investigating, and never taking for granted the technologies that are being rapidly imposed upon human life. That questioning is made all the more urgent because of scale. AI systems are reaching tremendous size in terms of the compute power they require, and the data they consume. And their prevalence in society, both in the scale of their deployment and the level of responsibility they assume, dwarfs the presence of computing in the PC and Internet eras. At the same time, increasing scale means many aspects of the technology, especially in its deep learning form, escape the comprehension of even the most experienced practitioners. Ethical concerns range from the esoteric, such as who is the author of an AI-created work of art; to the very real and very disturbing matter of surveillance in the hands of military authorities who can use the tools with impunity to capture and kill their fellow citizens. Somewhere in the questioning is a sliver of hope that with the right guidance, AI can help solve some of the world's biggest problems. The same technology that may propel bias can reveal bias in hiring decisions. The same technology that is a power hog can potentially contribute answers to slow or even reverse global warming. The risks of AI at the present moment arguably outweigh the benefits, but the potential benefits are large and worth pursuing. As Margaret Mitchell, formerly co-lead of Ethical AI at Google, has elegantly encapsulated, the key question is, "what could AI do to bring about a better society?" Mitchell's question would be interesting on any given day, but it comes within a context that has added urgency to the discussion. Mitchell's words come from a letter she wrote and posted on Google Drive following the departure of her co-lead, Timnit Gebru, in December.

This paper studies linear convergence of the subspace constrained mean shift (SCMS) algorithm, a well-known algorithm for identifying a density ridge defined by a kernel density estimator. By arguing that the SCMS algorithm is a special variant of a subspace constrained gradient ascent (SCGA) algorithm with an adaptive step size, we derive linear convergence of such SCGA algorithm. While the existing research focuses mainly on density ridges in the Euclidean space, we generalize density ridges and the SCMS algorithm to directional data. In particular, we establish the stability theorem of density ridges with directional data and prove the linear convergence of our proposed directional SCMS algorithm.

SpaceX successfully launches NASA astronauts from Kennedy Space Center into space. The International Space Station's population swelled to 11 on Saturday with the jubilant arrival of SpaceX's third crew capsule in less than a year. All of the astronauts -- representing the U.S., Russia, Japan and France -- managed to squeeze into camera view for a congratulatory call from the leaders of their space agencies. This image provided by NASA, astronauts from SpaceX join the astronauts of the International Space Station for an interview on Saturday, April 24, 2021. A recycled SpaceX capsule carrying four astronauts has arrived at the International Space Station, a day after launching from Florida.

Causality analysis is an important problem lying at the heart of science, and is of particular importance in data science and machine learning. An endeavor during the past 16 years viewing causality as real physical notion so as to formulate it from first principles, however, seems to go unnoticed. This study introduces to the community this line of work, with a long-due generalization of the information flow-based bivariate time series causal inference to multivariate series, based on the recent advance in theoretical development. The resulting formula is transparent, and can be implemented as a computationally very efficient algorithm for application. It can be normalized, and tested for statistical significance. Different from the previous work along this line where only information flows are estimated, here an algorithm is also implemented to quantify the influence of a unit to itself. While this forms a challenge in some causal inferences, here it comes naturally, and hence the identification of self-loops in a causal graph is fulfilled automatically as the causalities along edges are inferred. To demonstrate the power of the approach, presented here are two applications in extreme situations. The first is a network of multivariate processes buried in heavy noises (with the noise-to-signal ratio exceeding 100), and the second a network with nearly synchronized chaotic oscillators. In both graphs, confounding processes exist. While it seems to be a huge challenge to reconstruct from given series these causal graphs, an easy application of the algorithm immediately reveals the desideratum. Particularly, the confounding processes have been accurately differentiated. Considering the surge of interest in the community, this study is very timely.

The past decade has witnessed a groundbreaking rise of machine learning for human language analysis, with current methods capable of automatically accurately recovering various aspects of syntax and semantics - including sentence structure and grounded word meaning - from large data collections. Recent research showed the promise of such tools for analyzing acoustic communication in nonhuman species. We posit that machine learning will be the cornerstone of future collection, processing, and analysis of multimodal streams of data in animal communication studies, including bioacoustic, behavioral, biological, and environmental data. Cetaceans are unique non-human model species as they possess sophisticated acoustic communications, but utilize a very different encoding system that evolved in an aquatic rather than terrestrial medium. Sperm whales, in particular, with their highly-developed neuroanatomical features, cognitive abilities, social structures, and discrete click-based encoding make for an excellent starting point for advanced machine learning tools that can be applied to other animals in the future. This paper details a roadmap toward this goal based on currently existing technology and multidisciplinary scientific community effort. We outline the key elements required for the collection and processing of massive bioacoustic data of sperm whales, detecting their basic communication units and language-like higher-level structures, and validating these models through interactive playback experiments. The technological capabilities developed by such an undertaking are likely to yield cross-applications and advancements in broader communities investigating non-human communication and animal behavioral research.

Deep learning-based models have recently outperformed state-of-the-art seasonal forecasting models, such as for predicting El Ni\~no-Southern Oscillation (ENSO). However, current deep learning models are based on convolutional neural networks which are difficult to interpret and can fail to model large-scale atmospheric patterns. In comparison, graph neural networks (GNNs) are capable of modeling large-scale spatial dependencies and are more interpretable due to the explicit modeling of information flow through edge connections. We propose the first application of graph neural networks to seasonal forecasting. We design a novel graph connectivity learning module that enables our GNN model to learn large-scale spatial interactions jointly with the actual ENSO forecasting task. Our model, \graphino, outperforms state-of-the-art deep learning-based models for forecasts up to six months ahead. Additionally, we show that our model is more interpretable as it learns sensible connectivity structures that correlate with the ENSO anomaly pattern.

Transmetrics' demand forecasting and predictive optimization platform is powered by artificial intelligence and machine learning algorithms. With four decades of experience and a strong operational presence in Egypt, KSA, UAE and Sudan, Transmar has built a solid reputation in the market, founded on family values that drive the company's ambition to offer the best in class service to its customers. Transmar owns and operates a large fleet of both dry and refrigerated containers, serves thousands of customers, and moves hundreds of commodities throughout the Middle East. "We strongly believe in the power of Data. Transmetrics' AI solution helps us leverage our 4 decades of operational experience, to make decisions both faster and smarter. As a regionally focused carrier we are more exposed to volatility. We're excited about the capabilities Transmetrics will provide by helping see up to 12 weeks into the future, ensuring we have optimum planning and repositioning plans" said Ahmed el Ahwal, Commercial Manager at Transmar.

TC is characterised by warm core, and a low and availability of huge data, new models using Artificial pressure system with a large vortex in the atmosphere. TC Neural Networks (ANNs) have been increasingly used to brings strong winds, heavy precipitation and high tides in forecast track and intensity of cyclones (Leroux et al. 2018; coastal areas and resulted in huge economic and human loss. Alemany et al. 2018; Giffard-Roisin et al. 2020; Moradi Kordmahalleh, Over the years, many destructive TCs have originated in the Gorji Sefidmazgi, and Homaifar 2016). North Indian Ocean (NIO), consisting of the Bay of Bengal The most important prediction about a TC is its arrival at and the Arabian Sea. In 2008, Nargis, one of the disastrous land, known as landfall of a cyclone. The accurate prediction TC in recent times, originated in the Bay of Bengal and resulted about the location and time of the landfall, and intensity of in 13,800 casualties alone in Myanmar and caused the cyclone at the landfall will hugely help authorities to take US$15.4 billion economic loss (Fritz et al. 2009). In 2018, preventive measures and reduce material and human loss. In Fani cyclone caused 89 causalities in India and Bangladesh, this work, we attempt to predict intensity, location, and time and US$9.1 billion economic loss (Kumar, Lal, and Kumar of the landfall of a TC at any instance of time during the 2020).

Landfall of a tropical cyclone is the event when it moves over the land after crossing the coast of the ocean. It is important to know the characteristics of the landfall in terms of location and time, well advance in time to take preventive measures timely. In this article, we develop a deep learning model based on the combination of a Convolutional Neural network and a Long Short-Term memory network to predict the landfall's location and time of a tropical cyclone in six ocean basins of the world with high accuracy. We have used high-resolution spacial reanalysis data, ERA5, maintained by European Center for Medium-Range Weather Forecasting (ECMWF). The model takes any 9 hours, 15 hours, or 21 hours of data, during the progress of a tropical cyclone and predicts its landfall's location in terms of latitude and longitude and time in hours. For 21 hours of data, we achieve mean absolute error for landfall's location prediction in the range of 66.18 - 158.92 kilometers and for landfall's time prediction in the range of 4.71 - 8.20 hours across all six ocean basins. The model can be trained in just 30 to 45 minutes (based on ocean basin) and can predict the landfall's location and time in a few seconds, which makes it suitable for real time prediction.