Aurelia Butler

Archaeologists at Northern Arizona University are hoping a new technology they helped pioneer will change the way scientists study the broken pieces left behind by ancient societies.
The team from NAU’s Department of Anthropology have succeeded in teaching computers to perform a complex task many scientists who study ancient societies have long dreamt of: rapidly and consistently sorting thousands of pottery designs into multiple stylistic categories. By using a form of machine learning known as Convolutional Neural Networks (CNNs), the archaeologists created a computerized method that roughly emulates the thought processes of the human mind in analyzing visual information.
“Now, using digital photographs of pottery, computers can accomplish what used to involve hundreds of hours of tedious, painstaking and eye-straining work by archaeologists who physically sorted pieces of broken pottery into groups, in a fraction of the time and with greater consistency,” said Leszek Pawlowicz, adjunct faculty in the Department of Anthropology. He and anthropology professor Chris Downum began researching the feasibility of using a computer to accurately classify broken pieces of pottery, known as sherds, into known pottery types in 2016. Results of their research are reported in the June issue of the peer-reviewed publication Journal of Archaeological Science.
“On many of the thousands of archaeological sites scattered across the American Southwest, archaeologists will often find broken fragments of pottery known as sherds. Many of these sherds will have designs that can be sorted into previously-defined stylistic categories, called ‘types,’ that have been correlated with both the general time period they were manufactured and the locations where they were made” Downum said. “These provide archaeologists with critical information about the time a site was occupied, the cultural group with which it was associated and other groups with whom they interacted.”
The research relied on recent breakthroughs in the use of machine learning to classify images by type, specifically CNNs. CNNs are now a mainstay in computer image recognition, being used for everything from X-ray images for medical conditions and matching images in search engines to self-driving cars. Pawlowicz and Downum reasoned that if CNNs can be used to identify things like breeds of dogs and products a consumer might like, why not apply this approach to the analysis of ancient pottery?
Until now, the process of recognizing diagnostic design features on pottery has been difficult and time-consuming. It could involve months or years of training to master and correctly apply the design categories to tiny pieces of a broken pot. Worse, the process was prone to human error because expert archaeologists often disagree over which type is represented by a sherd, and might find it difficult to express their decision-making process in words. An anonymous peer reviewer of the article called this “the dirty secret in archaeology that no one talks about enough.”
Determined to create a more efficient process, Pawlowicz and Downum gathered thousands of pictures of pottery fragments with a specific set of identifying physical characteristics, known as Tusayan White Ware, common across much of northeast Arizona and nearby states. They then recruited four of the Southwest’s top pottery experts to identify the pottery design type for every sherd and create a ‘training set’ of sherds from which the machine can learn. Finally, they trained the machine to learn pottery types by focusing on the pottery specimens the archaeologists agreed on. More

The University of Kent’s School of Physical Sciences, in collaboration with the Science and Technology Facilities Council (STFC) and the Universities of Cardiff, Durham and Leeds, have developed an algorithm to train computers to analyse signals from subatomic particles embedded in advanced electronic materials.
The particles, called muons, are produced in large particle accelerators and are implanted inside samples of materials in order to investigate their magnetic properties. Muons are uniquely useful as they couple magnetically to individual atoms inside the material and then emit a signal detectable by researchers to obtain information on that magnetism.
This ability to examine magnetism on the atomic scale makes muon-based measurements one of the most powerful probes of magnetism in electronic materials, including “quantum materials” such as superconductors and other exotic forms of matter.
As it is not possible to deduce what is going on in the material by simple examination of the signal, researchers normally compare their data to generic models. In contrast, the present team adapted a data-science technique called Principal Component Analysis (PCA), frequently employed in Face Recognition.
The PCA technique involves a computer being fed many related but distinct images and then running an algorithm identifying a small number “archetypal” images that can be combined to reproduce, with great accuracy, any of the original images. An algorithm trained in this way can then go on to perform tasks such as recognising whether a new image matches a previously-seen one.
Researchers adapted the PCA technique to analyse the signals sent out by muons embedded in complex materials, training the algorithm for a variety of quantum materials using experimental data obtained at the ISIS Neutron and Muon source of the STFC Rutherford Appleton Laboratory.
The results showed the new technique is equally as proficient as the standard method at detecting phase transitions and in some cases could detect transitions beyond the capabilities of standard analyses.
Dr Jorge Quintanilla, Senior Lecturer in Condensed Matter Theory at Kent and leader of the Physics of Quantum Materials research group said: ‘Our research results are exceptional, as this was achieved by an algorithm that knew nothing about the physics of the materials being investigated. This suggests that the new approach might have very broad application and, as such, we have made our algorithms available for use by the worldwide research community.’
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Materials provided by University of Kent. Original written by Sam Wood. Note: Content may be edited for style and length. More

Marilyn Monroe famously sang that diamonds are a girl’s best friend, but they are also very popular with quantum scientists — with two new research breakthroughs poised to accelerate the development of synthetic diamond-based quantum technology, improve scalability, and dramatically reduce manufacturing costs.
While silicon is traditionally used for computer and mobile phone hardware, diamond has unique properties that make it particularly useful as a base for emerging quantum technologies such as quantum supercomputers, secure communications and sensors.
However there are two key problems; cost, and difficulty in fabricating the single crystal diamond layer, which is smaller than one millionth of a metre.
A research team from the ARC Centre of Excellence for Transformative Meta-Optics at the University of Technology Sydney (UTS), led by Professor Igor Aharonovich, has just published two research papers, in Nanoscale and Advanced Quantum Technologies, that address these challenges.
“For diamond to be used in quantum applications, we need to precisely engineer ‘optical defects’ in the diamond devices — cavities and waveguides — to control, manipulate and readout information in the form of qubits — the quantum version of classical computer bits,” said Professor Aharonovich.
“It’s akin to cutting holes or carving gullies in a super thin sheet of diamond, to ensure light travels and bounces in the desired direction,” he said. More

Psychology researchers at UC Santa Cruz have found that playing games in virtual reality creates an effect called “time compression,” where time goes by faster than you think. Grayson Mullen, who was a cognitive science undergraduate at the time, worked with Psychology Professor Nicolas Davidenko to design an experiment that tested how virtual reality’s effects on a game player’s sense of time differ from those of conventional monitors. The results are now published in the journal Timing & Time Perception.
Mullen designed a maze game that could be played in both virtual reality and conventional formats, then the research team recruited 41 UC Santa Cruz undergraduate students to test the game. Participants played in both formats, with researchers randomizing which version of the game each student started with. Both versions were essentially the same, but the mazes in each varied slightly so that there was no repetition between formats.
Participants were asked to stop playing the game whenever they felt like five minutes had passed. Since there were no clocks available, each person had to make this estimate based on their own perception of the passage of time.
Prior studies of time perception in virtual reality have often asked participants about their experiences after the fact, but in this experiment, the research team wanted to integrate a time-keeping task into the virtual reality experience in order to capture what was happening in the moment. Researchers recorded the actual amount of time that had passed when each participant stopped playing the game, and this revealed a gap between participants’ perception of time and the reality.
The study found that participants who played the virtual reality version of the game first played for an average of 72.6 seconds longer before feeling that five minutes had passed than students who started on a conventional monitor. In other words, students played for 28.5 percent more time than they realized in virtual reality, compared to conventional formats.
This time compression effect was observed only among participants who played the game in virtual reality first. The paper concluded this was because participants based their judgement of time in the second round on whatever initial time estimates they made during the first round, regardless of format. But if the time compression observed in the first round is translatable to other types of virtual reality experiences and longer time intervals, it could be a big step forward in understanding how this effect works. More

A new strategy for capturing the 3D shape of the human face draws on data from sibling pairs and leads to identification of novel links between facial shape traits and specific locations within the human genome. Hanne Hoskens of the Department of Human Genetics at Katholieke Universiteit in Leuven, Belgium, and colleagues present these findings in the open-access journal PLOS Genetics.
The ability to capture the 3D shape of the human face — and how it varies between individuals with different genetics — can inform a variety of applications, including understanding human evolution, planning for surgery, and forensic sciences. However, existing tools for linking genetics to physical traits require input of simple measurements, such as distance between the eyes, that do not adequately capture the complexities of facial shape.
Now, Hoskens and colleagues have developed a new strategy for capturing these complexities in a format that can then be studied with existing analytical tools. To do so, they drew on the facial similarities often seen between genetically related siblings. The strategy was initially developed by learning from 3D facial data from a group of 273 pairs of siblings of European ancestry, which revealed 1,048 facial traits that are shared between siblings — and therefore presumably have a genetic basis.
The researchers then applied their new strategy for capturing face shape to 8,246 individuals of European ancestry, for whom they also had genetic information. This produced data on face-shape similarities between siblings that could then be combined with their genetic data and analyzed with existing tools for linking genetics to physical traits. Doing so revealed 218 locations within the human genome, or loci, that were associated with facial traits shared by siblings.
Further examination of the 218 loci showed that some are the sites of genes that have previously been linked to embryonic facial development and abnormal development of head and facial bones.
The authors note that this study could serve as the basis for several different directions of future research, including replication of the findings in larger populations, and investigation of the identified genetic loci in order to better understand the biological processes involved in facial development.
Hoskens adds, “Since siblings are likely to share facial features due to close kinship, traits that are biologically relevant can be extracted from phenotypically similar sibling pairs.”
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Materials provided by PLOS. Note: Content may be edited for style and length. More

Artificial intelligence (AI) learning machines can be trained to solve problems and puzzles on their own instead of using rules that we made for them. But often, researchers do not know what rules the machines make for themselves. Cold Spring Harbor Laboratory (CSHL) Assistant Professor Peter Koo developed a new method that quizzes a machine-learning program to figure out what rules it learned on its own and if they are the right ones.
Computer scientists “train” an AI machine to make predictions by presenting it with a set of data. The machine extracts a series of rules and operations — a model — based on information it encountered during its training. Koo says:
“If you learn general rules about the math instead of memorizing the equations, you know how to solve those equations. So rather than just memorizing those equations, we hope that these models are learning to solve it and now we can give it any equation and it will solve it.”
Koo developed a type of AI called a deep neural network (DNN) to look for patterns in RNA strands that increase the ability of a protein to bind to them. Koo trained his DNN, called Residual Bind (RB), with thousands of RNA sequences matched to protein binding scores, and RB became good at predicting scores for new RNA sequences. But Koo did not know whether the machine was focusing on a short sequence of RNA letters — a motif — that humans might expect, or some other secondary characteristic of the RNA strands that they might not.
Koo and his team developed a new method, called Global Importance Analysis, to test what rules RB generated to make its predictions. He presented the trained network with a carefully designed set of synthetic RNA sequences containing different combinations of motifs and features that the scientists thought might influence RB’s assessments.
They discovered the network considered more than just the spelling of a short motif. It factored in how the RNA strand might fold over and bind to itself, how close one motif is to another, and other features.
Koo hopes to test some key results in a laboratory. But rather than test every prediction in that lab, Koo’s new method acts like a virtual lab. Researchers can design and test millions of different variables computationally, far more than humans could test in a real-world lab.
“Biology is super anecdotal. You can find a sequence, you can find a pattern but you don’t know ‘Is that pattern really important?’ You have to do these interventional experiments. In this case, all my experiments are all done by just asking the neural network.”
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Materials provided by Cold Spring Harbor Laboratory. Original written by Luis Sandoval. Note: Content may be edited for style and length. More