The cephalopod's disappearing act could help your next sunscreen blend in. Breakthroughs, discoveries, and DIY tips sent every weekday. Cephalopods like octopuses, squid, and cuttlefish have the mesmerizing ability to change the color of their skin to camouflage into the surrounding environment. Multiple biological processes involving a natural pigment called xanthommatin drives this unique ability. As such, various industries are interested in using xanthommatin in products such as paint and natural sunscreen, but the pigment has been hard to research.

The centuries can leave their mark on oil paintings as wear and tear and natural ageing produce cracks, discoloration and patches where pieces of pigment have flaked off. Repairing the damage can take conservators years, so the effort is reserved for the most valuable works, but a fresh approach promises to transform the process by restoring aged artworks in hours. The technique draws on artificial intelligence and other computer tools to create a digital reconstruction of the damaged painting. This is then printed on to a transparent polymer sheet that is carefully laid over the work. To demonstrate the technique, Alex Kachkine, a graduate researcher at Massachusetts Institute of Technology, restored a damaged oil-on-panel work attributed to the Master of the Prado Adoration, a Dutch painter whose name has been lost, as a late 15th-century painting after Martin Schongauer.

Breakthroughs, discoveries, and DIY tips sent every weekday. For being the world's oldest known synthetic pigment, the original recipes for Egyptian blue remain a mystery. The approximately 5,000-year-old dye wasn't a single color, but instead encompassed a range of hues, from deep blues to duller grays and greens. Artisans first crafted Egyptian blue during the Fourth Dynasty (roughly 2613 to 2494 BCE) from recipes reliant on calcium-copper silicate. These techniques were later adopted by Romans in lieu of more expensive materials like lapis lazuli and turquoise.

In this contribution, we define (and test) a pipeline to perform virtual painting recolouring using raw data of X-Ray Fluorescence (XRF) analysis on pictorial artworks. To circumvent the small dataset size, we generate a synthetic dataset, starting from a database of XRF spectra; furthermore, to ensure a better generalisation capacity (and to tackle the issue of in-memory size and inference time), we define a Deep Variational Embedding network to embed the XRF spectra into a lower dimensional, K-Means friendly, metric space. We thus train a set of models to assign coloured images to embedded XRF images. We report here the devised pipeline performances in terms of visual quality metrics, and we close on a discussion on the results.

Authenticating works of art is far from an exact science, but a madonna and child painting has sparked a furious row, being dubbed "the battle of the AIs", after two separate scientific studies arrived at contradictory conclusions. Both studies used state-of-the art AI technology. Months after one study proclaimed that the so-called de Brécy Tondo, currently on display at Bradford council's Cartwright Hall Art Gallery, is "undoubtedly" by Raphael, another has found that it cannot be by the Renaissance master. In January, research teams from the universities of Nottingham and Bradford announced the findings of facial recognition technology, which compared the faces in the Tondo with those in Raphael's Sistine Madonna altarpiece, commissioned in 1512. Having used "millions of faces to train an algorithm to recognise and compare facial features", they stated: "The similarity between the madonnas was found to be 97%, while comparison of the child in both paintings produced an 86% similarity."

In the last decade, we have witnessed a truly remarkable rise of Artificial Intelligence, Statistical and Deep learning methods (for a non exhaustive list of papers on the history of deep learning, see [1, 2, 3], and references therein). Inspired by the incredible results obtained thanks to the application of such methods to scientific problems, its adoption in the field of nuclear imaging applied to Cultural Heritage (CH) has begun (see, e.g., [4, 5, 6, 7, 8], especially the nice overview [9], and, of course, the references therein), also in the field of X-ray fluorescence Macro mapping (MA-XRF) [10, 11, 12, 13, 14, 15]. In MA-XRF, the imaging apparatus produces a data cube which, for each pixel, is formed by a spectrum containing fluorescence lines associated with the element composition of the pigment present in the pictorial layers. MA-XRF data cubes offer an ideal framework for application of unsupervised statistical learning methods [13], due to the huge number of pixel XRF histogram w.r.t. the relatively small number of employed pigment palettes. Unfortunately, in the realm of supervised statistical (deep) learning applied to CH-based analysis, the situation is flipped [14], since the data cube production is slow, obtaining a small dataset for the complexity of the various task at hand (like automatic pigment identification [14, 15], element recognition [16], and even colour association [11, 12]). This justifies the emphasis put on the creation of ad hoc synthetic MA-XRF dataset [14].

Technology is rapidly improving, offering new innovations and revolutionary projects at ever greater speeds. Some very sharp minds out there are continuously developing technology that might completely transform our lives, while others come up with innovations that'merely' enable us to do things a little differently or more efficiently. Many of these new developments seem to have been taken straight from science-fiction, whether it's robot fingers with human skin, colour-changing cars, smart pans that help you stick to your weight loss plans, or AI powered toothbrushes that warn you when you're not cleaning your teeth properly. In this article we will introduce you to some weird – but wonderful – recent innovations and technological advancements. Soon, hyper-realistic robots will join us in various sectors, such as the services industry and in medical and nursing care.

On June 25, 1949, the British neurologist Geoffrey Jefferson gave a lecture to the Royal College of Surgeons of England entitled The Mind of Mechanical Man. It may be surprising that machine intelligence was the subject of much debate in Jefferson's time, with some describing the 1904s as the period in which artificial intelligence was born following the development of cybernetics. Jefferson's ideas about the intersection of human and machine were ahead of their time and even impressed the great Alan Turing with their prescience and clarity. "[N]ot until a machine can write a sonnet or a concerto because of thoughts and emotions felt, and not by the chance fall of symbols, could we agree that machine equals brain -- that is, not only write it but know that it had written it," Jefferson said in his lecture. "No mechanism could feel (and not merely artificially signal, an easy contrivance) pleasure at its successes, grief when its valves fuse, be warmed by flattery, be made miserable by its mistakes, be charmed by sex, be angry or miserable when it cannot get what it wants." Whether they know it or not, critics of artificial intelligence's application in the art world -- and by extension, the world of NFTs -- employ a version of Jefferson's argument when they decry that the technology takes something away from the creative "soul" of artists and their work.

The study was published in Nature Communications. Producing flexible electronics consists of expensive tools and several scientific steps many times. Therefore, an easy and versatile fabrication strategy to support the increasing demand for flexible electroluminescence devices in technological and optical applications is needed. Ji Liu and researchers display an approach to fabricating flexible electroluminescence devices through multi-material 3D printing. They formulated ion conducting, electroluminescent and insulating inks suitable for 3D printing, which they could use to create facile, on-demand, flexible, and stretchable electroluminescent devices.

X-ray fluorescence spectroscopy (XRF) plays an important role for elemental analysis in a wide range of scientific fields, especially in cultural heritage. XRF imaging, which uses a raster scan to acquire spectra across artworks, provides the opportunity for spatial analysis of pigment distributions based on their elemental composition. However, conventional XRF-based pigment identification relies on time-consuming elemental mapping by expert interpretations of measured spectra. To reduce the reliance on manual work, recent studies have applied machine learning techniques to cluster similar XRF spectra in data analysis and to identify the most likely pigments. Nevertheless, it is still challenging for automatic pigment identification strategies to directly tackle the complex structure of real paintings, e.g. pigment mixtures and layered pigments. In addition, pixel-wise pigment identification based on XRF imaging remains an obstacle due to the high noise level compared with averaged spectra. Therefore, we developed a deep-learning-based end-to-end pigment identification framework to fully automate the pigment identification process. In particular, it offers high sensitivity to the underlying pigments and to the pigments with a low concentration, therefore enabling satisfying results in mapping the pigments based on single-pixel XRF spectrum. As case studies, we applied our framework to lab-prepared mock-up paintings and two 19th-century paintings: Paul Gauguin's Po\`emes Barbares (1896) that contains layered pigments with an underlying painting, and Paul Cezanne's The Bathers (1899-1904). The pigment identification results demonstrated that our model achieved comparable results to the analysis by elemental mapping, suggesting the generalizability and stability of our model.