Tag Archives: Germany

It’s a very ‘carbony’ time: graphene jacket, graphene-skinned airplane, and schwarzite

In August 2018, I been stumbled across several stories about graphene-based products and a new form of carbon.

Graphene jacket

The company producing this jacket has as its goal “… creating bionic clothing that is both bulletproof and intelligent.” Well, ‘bionic‘ means biologically-inspired engineering and ‘intelligent‘ usually means there’s some kind of computing capability in the product. This jacket, which is the first step towards the company’s goal, is not bionic, bulletproof, or intelligent. Nonetheless, it represents a very interesting science experiment in which you, the consumer, are part of step two in the company’s R&D (research and development).

Onto Vollebak’s graphene jacket,

Courtesy: Vollebak

From an August 14, 2018 article by Jesus Diaz for Fast Company,

Graphene is the thinnest possible form of graphite, which you can find in your everyday pencil. It’s purely bi-dimensional, a single layer of carbon atoms that has unbelievable properties that have long threatened to revolutionize everything from aerospace engineering to medicine. …

Despite its immense promise, graphene still hasn’t found much use in consumer products, thanks to the fact that it’s hard to manipulate and manufacture in industrial quantities. The process of developing Vollebak’s jacket, according to the company’s cofounders, brothers Steve and Nick Tidball, took years of intensive research, during which the company worked with the same material scientists who built Michael Phelps’ 2008 Olympic Speedo swimsuit (which was famously banned for shattering records at the event).

The jacket is made out of a two-sided material, which the company invented during the extensive R&D process. The graphene side looks gunmetal gray, while the flipside appears matte black. To create it, the scientists turned raw graphite into something called graphene “nanoplatelets,” which are stacks of graphene that were then blended with polyurethane to create a membrane. That, in turn, is bonded to nylon to form the other side of the material, which Vollebak says alters the properties of the nylon itself. “Adding graphene to the nylon fundamentally changes its mechanical and chemical properties–a nylon fabric that couldn’t naturally conduct heat or energy, for instance, now can,” the company claims.

The company says that it’s reversible so you can enjoy graphene’s properties in different ways as the material interacts with either your skin or the world around you. “As physicists at the Max Planck Institute revealed, graphene challenges the fundamental laws of heat conduction, which means your jacket will not only conduct the heat from your body around itself to equalize your skin temperature and increase it, but the jacket can also theoretically store an unlimited amount of heat, which means it can work like a radiator,” Tidball explains.

He means it literally. You can leave the jacket out in the sun, or on another source of warmth, as it absorbs heat. Then, the company explains on its website, “If you then turn it inside out and wear the graphene next to your skin, it acts like a radiator, retaining its heat and spreading it around your body. The effect can be visibly demonstrated by placing your hand on the fabric, taking it away and then shooting the jacket with a thermal imaging camera. The heat of the handprint stays long after the hand has left.”

There’s a lot more to the article although it does feature some hype and I’m not sure I believe Diaz’s claim (August 14, 2018 article) that ‘graphene-based’ hair dye is perfectly safe ( Note: A link has been removed),

Graphene is the thinnest possible form of graphite, which you can find in your everyday pencil. It’s purely bi-dimensional, a single layer of carbon atoms that has unbelievable properties that will one day revolutionize everything from aerospace engineering to medicine. Its diverse uses are seemingly endless: It can stop a bullet if you add enough layers. It can change the color of your hair with no adverse effects. [emphasis mine] It can turn the walls of your home into a giant fire detector. “It’s so strong and so stretchy that the fibers of a spider web coated in graphene could catch a falling plane,” as Vollebak puts it in its marketing materials.

Not unless things have changed greatly since March 2018. My August 2, 2018 posting featured the graphene-based hair dye announcement from March 2018 and a cautionary note from Dr. Andrew Maynard (scroll down ab out 50% of the way for a longer excerpt of Maynard’s comments),

Northwestern University’s press release proudly announced, “Graphene finds new application as nontoxic, anti-static hair dye.” The announcement spawned headlines like “Enough with the toxic hair dyes. We could use graphene instead,” and “’Miracle material’ graphene used to create the ultimate hair dye.”

From these headlines, you might be forgiven for getting the idea that the safety of graphene-based hair dyes is a done deal. Yet having studied the potential health and environmental impacts of engineered nanomaterials for more years than I care to remember, I find such overly optimistic pronouncements worrying – especially when they’re not backed up by clear evidence.

These studies need to be approached with care, as the precise risks of graphene exposure will depend on how the material is used, how exposure occurs and how much of it is encountered. Yet there’s sufficient evidence to suggest that this substance should be used with caution – especially where there’s a high chance of exposure or that it could be released into the environment.

The full text of Dr. Maynard’s comments about graphene hair dyes and risk can be found here.

Bearing in mind  that graphene-based hair dye is an entirely different class of product from the jacket, I wouldn’t necessarily dismiss risks; I would like to know what kind of risk assessment and safety testing has been done. Due to their understandable enthusiasm, the brothers Tidball have focused all their marketing on the benefits and the opportunity for the consumer to test their product (from graphene jacket product webpage),

While it’s completely invisible and only a single atom thick, graphene is the lightest, strongest, most conductive material ever discovered, and has the same potential to change life on Earth as stone, bronze and iron once did. But it remains difficult to work with, extremely expensive to produce at scale, and lives mostly in pioneering research labs. So following in the footsteps of the scientists who discovered it through their own highly speculative experiments, we’re releasing graphene-coated jackets into the world as experimental prototypes. Our aim is to open up our R&D and accelerate discovery by getting graphene out of the lab and into the field so that we can harness the collective power of early adopters as a test group. No-one yet knows the true limits of what graphene can do, so the first edition of the Graphene Jacket is fully reversible with one side coated in graphene and the other side not. If you’d like to take part in the next stage of this supermaterial’s history, the experiment is now open. You can now buy it, test it and tell us about it. [emphasis mine]

How maverick experiments won the Nobel Prize

While graphene’s existence was first theorised in the 1940s, it wasn’t until 2004 that two maverick scientists, Andre Geim and Konstantin Novoselov, were able to isolate and test it. Through highly speculative and unfunded experimentation known as their ‘Friday night experiments,’ they peeled layer after layer off a shaving of graphite using Scotch tape until they produced a sample of graphene just one atom thick. After similarly leftfield thinking won Geim the 2000 Ig Nobel prize for levitating frogs using magnets, the pair won the Nobel prize in 2010 for the isolation of graphene.

Should you be interested, in beta-testing the jacket, it will cost you $695 (presumably USD); order here. One last thing, Vollebak is based in the UK.

Graphene skinned plane

An August 14, 2018 news item (also published as an August 1, 2018 Haydale press release) by Sue Keighley on Azonano heralds a new technology for airplans,

Haydale, (AIM: HAYD), the global advanced materials group, notes the announcement made yesterday from the University of Central Lancashire (UCLAN) about the recent unveiling of the world’s first graphene skinned plane at the internationally renowned Farnborough air show.

The prepreg material, developed by Haydale, has potential value for fuselage and wing surfaces in larger scale aero and space applications especially for the rapidly expanding drone market and, in the longer term, the commercial aerospace sector. By incorporating functionalised nanoparticles into epoxy resins, the electrical conductivity of fibre-reinforced composites has been significantly improved for lightning-strike protection, thereby achieving substantial weight saving and removing some manufacturing complexities.

Before getting to the photo, here’s a definition for pre-preg from its Wikipedia entry (Note: Links have been removed),

Pre-preg is “pre-impregnated” composite fibers where a thermoset polymer matrix material, such as epoxy, or a thermoplastic resin is already present. The fibers often take the form of a weave and the matrix is used to bond them together and to other components during manufacture.

Haydale has supplied graphene enhanced prepreg material for Juno, a three-metre wide graphene-enhanced composite skinned aircraft, that was revealed as part of the ‘Futures Day’ at Farnborough Air Show 2018. [downloaded from https://www.azonano.com/news.aspx?newsID=36298]

A July 31, 2018 University of Central Lancashire (UCLan) press release provides a tiny bit more (pun intended) detail,

The University of Central Lancashire (UCLan) has unveiled the world’s first graphene skinned plane at an internationally renowned air show.

Juno, a three-and-a-half-metre wide graphene skinned aircraft, was revealed on the North West Aerospace Alliance (NWAA) stand as part of the ‘Futures Day’ at Farnborough Air Show 2018.

The University’s aerospace engineering team has worked in partnership with the Sheffield Advanced Manufacturing Research Centre (AMRC), the University of Manchester’s National Graphene Institute (NGI), Haydale Graphene Industries (Haydale) and a range of other businesses to develop the unmanned aerial vehicle (UAV), which also includes graphene batteries and 3D printed parts.

Billy Beggs, UCLan’s Engineering Innovation Manager, said: “The industry reaction to Juno at Farnborough was superb with many positive comments about the work we’re doing. Having Juno at one the world’s biggest air shows demonstrates the great strides we’re making in leading a programme to accelerate the uptake of graphene and other nano-materials into industry.

“The programme supports the objectives of the UK Industrial Strategy and the University’s Engineering Innovation Centre (EIC) to increase industry relevant research and applications linked to key local specialisms. Given that Lancashire represents the fourth largest aerospace cluster in the world, there is perhaps no better place to be developing next generation technologies for the UK aerospace industry.”

Previous graphene developments at UCLan have included the world’s first flight of a graphene skinned wing and the launch of a specially designed graphene-enhanced capsule into near space using high altitude balloons.

UCLan engineering students have been involved in the hands-on project, helping build Juno on the Preston Campus.

Haydale supplied much of the material and all the graphene used in the aircraft. Ray Gibbs, Chief Executive Officer, said: “We are delighted to be part of the project team. Juno has highlighted the capability and benefit of using graphene to meet key issues faced by the market, such as reducing weight to increase range and payload, defeating lightning strike and protecting aircraft skins against ice build-up.”

David Bailey Chief Executive of the North West Aerospace Alliance added: “The North West aerospace cluster contributes over £7 billion to the UK economy, accounting for one quarter of the UK aerospace turnover. It is essential that the sector continues to develop next generation technologies so that it can help the UK retain its competitive advantage. It has been a pleasure to support the Engineering Innovation Centre team at the University in developing the world’s first full graphene skinned aircraft.”

The Juno project team represents the latest phase in a long-term strategic partnership between the University and a range of organisations. The partnership is expected to go from strength to strength following the opening of the £32m EIC facility in February 2019.

The next step is to fly Juno and conduct further tests over the next two months.

Next item, a new carbon material.

Schwarzite

I love watching this gif of a schwarzite,

The three-dimensional cage structure of a schwarzite that was formed inside the pores of a zeolite. (Graphics by Yongjin Lee and Efrem Braun)

An August 13, 2018 news item on Nanowerk announces the new carbon structure,

The discovery of buckyballs [also known as fullerenes, C60, or buckminsterfullerenes] surprised and delighted chemists in the 1980s, nanotubes jazzed physicists in the 1990s, and graphene charged up materials scientists in the 2000s, but one nanoscale carbon structure – a negatively curved surface called a schwarzite – has eluded everyone. Until now.

University of California, Berkeley [UC Berkeley], chemists have proved that three carbon structures recently created by scientists in South Korea and Japan are in fact the long-sought schwarzites, which researchers predict will have unique electrical and storage properties like those now being discovered in buckminsterfullerenes (buckyballs or fullerenes for short), nanotubes and graphene.

An August 13, 2018 UC Berkeley news release by Robert Sanders, which originated the news item, describes how the Berkeley scientists and the members of their international  collaboration from Germany, Switzerland, Russia, and Italy, have contributed to the current state of schwarzite research,

The new structures were built inside the pores of zeolites, crystalline forms of silicon dioxide – sand – more commonly used as water softeners in laundry detergents and to catalytically crack petroleum into gasoline. Called zeolite-templated carbons (ZTC), the structures were being investigated for possible interesting properties, though the creators were unaware of their identity as schwarzites, which theoretical chemists have worked on for decades.

Based on this theoretical work, chemists predict that schwarzites will have unique electronic, magnetic and optical properties that would make them useful as supercapacitors, battery electrodes and catalysts, and with large internal spaces ideal for gas storage and separation.

UC Berkeley postdoctoral fellow Efrem Braun and his colleagues identified these ZTC materials as schwarzites based of their negative curvature, and developed a way to predict which zeolites can be used to make schwarzites and which can’t.

“We now have the recipe for how to make these structures, which is important because, if we can make them, we can explore their behavior, which we are working hard to do now,” said Berend Smit, an adjunct professor of chemical and biomolecular engineering at UC Berkeley and an expert on porous materials such as zeolites and metal-organic frameworks.

Smit, the paper’s corresponding author, Braun and their colleagues in Switzerland, China, Germany, Italy and Russia will report their discovery this week in the journal Proceedings of the National Academy of Sciences. Smit is also a faculty scientist at Lawrence Berkeley National Laboratory.

Playing with carbon

Diamond and graphite are well-known three-dimensional crystalline arrangements of pure carbon, but carbon atoms can also form two-dimensional “crystals” — hexagonal arrangements patterned like chicken wire. Graphene is one such arrangement: a flat sheet of carbon atoms that is not only the strongest material on Earth, but also has a high electrical conductivity that makes it a promising component of electronic devices.

schwarzite carbon cage

The cage structure of a schwarzite that was formed inside the pores of a zeolite. The zeolite is subsequently dissolved to release the new material. (Graphics by Yongjin Lee and Efrem Braun)

Graphene sheets can be wadded up to form soccer ball-shaped fullerenes – spherical carbon cages that can store molecules and are being used today to deliver drugs and genes into the body. Rolling graphene into a cylinder yields fullerenes called nanotubes, which are being explored today as highly conductive wires in electronics and storage vessels for gases like hydrogen and carbon dioxide. All of these are submicroscopic, 10,000 times smaller than the width of a human hair.

To date, however, only positively curved fullerenes and graphene, which has zero curvature, have been synthesized, feats rewarded by Nobel Prizes in 1996 and 2010, respectively.

In the 1880s, German physicist Hermann Schwarz investigated negatively curved structures that resemble soap-bubble surfaces, and when theoretical work on carbon cage molecules ramped up in the 1990s, Schwarz’s name became attached to the hypothetical negatively curved carbon sheets.

“The experimental validation of schwarzites thus completes the triumvirate of possible curvatures to graphene; positively curved, flat, and now negatively curved,” Braun added.

Minimize me

Like soap bubbles on wire frames, schwarzites are topologically minimal surfaces. When made inside a zeolite, a vapor of carbon-containing molecules is injected, allowing the carbon to assemble into a two-dimensional graphene-like sheet lining the walls of the pores in the zeolite. The surface is stretched tautly to minimize its area, which makes all the surfaces curve negatively, like a saddle. The zeolite is then dissolved, leaving behind the schwarzite.

soap bubble schwarzite structure

A computer-rendered negatively curved soap bubble that exhibits the geometry of a carbon schwarzite. (Felix Knöppel image)

“These negatively-curved carbons have been very hard to synthesize on their own, but it turns out that you can grow the carbon film catalytically at the surface of a zeolite,” Braun said. “But the schwarzites synthesized to date have been made by choosing zeolite templates through trial and error. We provide very simple instructions you can follow to rationally make schwarzites and we show that, by choosing the right zeolite, you can tune schwarzites to optimize the properties you want.”

Researchers should be able to pack unusually large amounts of electrical charge into schwarzites, which would make them better capacitors than conventional ones used today in electronics. Their large interior volume would also allow storage of atoms and molecules, which is also being explored with fullerenes and nanotubes. And their large surface area, equivalent to the surface areas of the zeolites they’re grown in, could make them as versatile as zeolites for catalyzing reactions in the petroleum and natural gas industries.

Braun modeled ZTC structures computationally using the known structures of zeolites, and worked with topological mathematician Senja Barthel of the École Polytechnique Fédérale de Lausanne in Sion, Switzerland, to determine which of the minimal surfaces the structures resembled.

The team determined that, of the approximately 200 zeolites created to date, only 15 can be used as a template to make schwarzites, and only three of them have been used to date to produce schwarzite ZTCs. Over a million zeolite structures have been predicted, however, so there could be many more possible schwarzite carbon structures made using the zeolite-templating method.

Other co-authors of the paper are Yongjin Lee, Seyed Mohamad Moosavi and Barthel of the École Polytechnique Fédérale de Lausanne, Rocio Mercado of UC Berkeley, Igor Baburin of the Technische Universität Dresden in Germany and Davide Proserpio of the Università degli Studi di Milano in Italy and Samara State Technical University in Russia.

Here’s a link to and a citation for the paper,

Generating carbon schwarzites via zeolite-templating by Efrem Braun, Yongjin Lee, Seyed Mohamad Moosavi, Senja Barthel, Rocio Mercado, Igor A. Baburin, Davide M. Proserpio, and Berend Smit. PNAS August 14, 2018. 201805062; published ahead of print August 14, 2018. https://doi.org/10.1073/pnas.1805062115

This paper appears to be open access.

Build nanoparticles using techniques from the ancient Egyptians

Great Pyramid of Giza and Sphinx [downloaded from http://news.ifmo.ru/en/science/photonics/news/7731/]

Russian and German scientists have taken a closer look at the Great Pyramid as they investigate better ways of designing sensors and solar cells. From a July 30, 2018 news item on Nanowerk,

An international research group applied methods of theoretical physics to investigate the electromagnetic response of the Great Pyramid to radio waves. Scientists predicted that under resonance conditions the pyramid can concentrate electromagnetic energy in its internal chambers and under the base. The research group plans to use these theoretical results to design nanoparticles capable of reproducing similar effects in the optical range. Such nanoparticles may be used, for example, to develop sensors and highly efficient solar cells.

A July 30, 2018 ITMO University press release, which originated the news item,  expands on the theme,

While Egyptian pyramids are surrounded by many myths and legends, we have little scientifically reliable information about their physical properties. As it turns out, sometimes this information proves to be more fascinating than any fiction. This idea found confirmation in a new joint study undertaken by scientists from ITMO University and the Laser Zentrum Hannover. The physicists took an interest in how the Great Pyramid would interact with electromagnetic waves of a proportional, or resonant, length. Calculations showed that in the resonant state the pyramid can concentrate electromagnetic energy in its internal chambers as well as under its base, where the third unfinished chamber is located.

These conclusions were derived on the basis of numerical modeling and analytical methods of physics. The researchers first estimated that resonances in the pyramid can be induced by radio waves with a length ranging from 200 to 600 meters. Then they made a model of the electromagnetic response of the pyramid and calculated the extinction cross section. This value helps to estimate which part of the incident wave energy can be scattered or absorbed by the pyramid under resonant conditions. Finally, for the same conditions, the scientists obtained the electromagnetic fields distribution inside the pyramid.

3D model of the pyramid. Credit: cheops.SU
3D model of the pyramid. Credit: cheops.SU

In order to explain the results, the scientists conducted a multipole analysis. This method is widely used in physics to study the interaction between a complex object and electromagnetic field. The object scattering the field is replaced by a set of simpler sources of radiation: multipoles. The collection of multipoles radiation coincides with the field scattering by an entire object. Therefore, by knowing the type of each multipole, it is possible to predict and explain the distribution and configuration of the scattered fields in the whole system.

The Great Pyramid attracted the researchers’ attention while they were studying the interaction between light and dielectric nanoparticles. The scattering of light by nanoparticles depends on their size, shape, and refractive index of the source material. By varying these parameters, it is possible to determine the resonance scattering regimes and use them to develop devices for controlling light at the nanoscale.

“Egyptian pyramids have always attracted great attention. We as scientists were interested in them as well, and so we decided to look at the Great Pyramid as a particle resonantly dissipating radio waves. Due to the lack of information about the physical properties of the pyramid, we had to make some assumptions. For example, we assumed that there are no unknown cavities inside, and the building material has the properties of an ordinary limestone and is evenly distributed in and out of the pyramid. With these assumptions, we obtained interesting results that can have important practical applications,” says Andrey Evlyukhin, DSc, scientific supervisor and coordinator of the research.

Now the scientists plan to use the results to reproduce similar effects at the nanoscale.

Polina Kapitanova
Polina Kapitanova

“By choosing a material with suitable electromagnetic properties, we can obtain pyramidal nanoparticles with a potential for practical application in nanosensors and effective solar cells,” says Polina Kapitanova, PhD, associate at the Faculty of Physics and Engineering of ITMO University.

The research was supported by the Russian Science Foundation and the Deutsche Forschungsgemeinschaft (grants № 17-79-20379 and №16-12-10287).

Here’s a link to and a citation for the paper,

Electromagnetic properties of the Great Pyramid: First multipole resonances and energy concentration featured by Mikhail Balezin, Kseniia V. Baryshnikova, Polina Kapitanova, and Andrey B. Evlyukhin. Journal of Applied Physics 124, 034903 (2018) https://doi.org/10.1063/1.5026556 or Journal of Applied Physics, Volume 124, Issue 3. 10.1063/1.5026556 Published Online 20 July 2018

This paper is behind a paywall..

Artificial intelligence (AI) brings together International Telecommunications Union (ITU) and World Health Organization (WHO) and AI outperforms animal testing

Following on my May 11, 2018 posting about the International Telecommunications Union (ITU) and the 2018 AI for Good Global Summit in mid- May, there’s an announcement. My other bit of AI news concerns animal testing.

Leveraging the power of AI for health

A July 24, 2018 ITU press release (a shorter version was received via email) announces a joint initiative focused on improving health,

Two United Nations specialized agencies are joining forces to expand the use of artificial intelligence (AI) in the health sector to a global scale, and to leverage the power of AI to advance health for all worldwide. The International Telecommunication Union (ITU) and the World Health Organization (WHO) will work together through the newly established ITU Focus Group on AI for Health to develop an international “AI for health” standards framework and to identify use cases of AI in the health sector that can be scaled-up for global impact. The group is open to all interested parties.

“AI could help patients to assess their symptoms, enable medical professionals in underserved areas to focus on critical cases, and save great numbers of lives in emergencies by delivering medical diagnoses to hospitals before patients arrive to be treated,” said ITU Secretary-General Houlin Zhao. “ITU and WHO plan to ensure that such capabilities are available worldwide for the benefit of everyone, everywhere.”

The demand for such a platform was first identified by participants of the second AI for Good Global Summit held in Geneva, 15-17 May 2018. During the summit, AI and the health sector were recognized as a very promising combination, and it was announced that AI-powered technologies such as skin disease recognition and diagnostic applications based on symptom questions could be deployed on six billion smartphones by 2021.

The ITU Focus Group on AI for Health is coordinated through ITU’s Telecommunications Standardization Sector – which works with ITU’s 193 Member States and more than 800 industry and academic members to establish global standards for emerging ICT innovations. It will lead an intensive two-year analysis of international standardization opportunities towards delivery of a benchmarking framework of international standards and recommendations by ITU and WHO for the use of AI in the health sector.

“I believe the subject of AI for health is both important and useful for advancing health for all,” said WHO Director-General Tedros Adhanom Ghebreyesus.

The ITU Focus Group on AI for Health will also engage researchers, engineers, practitioners, entrepreneurs and policy makers to develop guidance documents for national administrations, to steer the creation of policies that ensure the safe, appropriate use of AI in the health sector.

“1.3 billion people have a mobile phone and we can use this technology to provide AI-powered health data analytics to people with limited or no access to medical care. AI can enhance health by improving medical diagnostics and associated health intervention decisions on a global scale,” said Thomas Wiegand, ITU Focus Group on AI for Health Chairman, and Executive Director of the Fraunhofer Heinrich Hertz Institute, as well as professor at TU Berlin.

He added, “The health sector is in many countries among the largest economic sectors or one of the fastest-growing, signalling a particularly timely need for international standardization of the convergence of AI and health.”

Data analytics are certain to form a large part of the ITU focus group’s work. AI systems are proving increasingly adept at interpreting laboratory results and medical imagery and extracting diagnostically relevant information from text or complex sensor streams.

As part of this, the ITU Focus Group for AI for Health will also produce an assessment framework to standardize the evaluation and validation of AI algorithms — including the identification of structured and normalized data to train AI algorithms. It will develop open benchmarks with the aim of these becoming international standards.

The ITU Focus Group for AI for Health will report to the ITU standardization expert group for multimedia, Study Group 16.

I got curious about Study Group 16 (from the Study Group 16 at a glance webpage),

Study Group 16 leads ITU’s standardization work on multimedia coding, systems and applications, including the coordination of related studies across the various ITU-T SGs. It is also the lead study group on ubiquitous and Internet of Things (IoT) applications; telecommunication/ICT accessibility for persons with disabilities; intelligent transport system (ITS) communications; e-health; and Internet Protocol television (IPTV).

Multimedia is at the core of the most recent advances in information and communication technologies (ICTs) – especially when we consider that most innovation today is agnostic of the transport and network layers, focusing rather on the higher OSI model layers.

SG16 is active in all aspects of multimedia standardization, including terminals, architecture, protocols, security, mobility, interworking and quality of service (QoS). It focuses its studies on telepresence and conferencing systems; IPTV; digital signage; speech, audio and visual coding; network signal processing; PSTN modems and interfaces; facsimile terminals; and ICT accessibility.

I wonder which group deals with artificial intelligence and, possibly, robots.

Chemical testing without animals

Thomas Hartung, professor of environmental health and engineering at Johns Hopkins University (US), describes in his July 25, 2018 essay (written for The Conversation) on phys.org the situation where chemical testing is concerned,

Most consumers would be dismayed with how little we know about the majority of chemicals. Only 3 percent of industrial chemicals – mostly drugs and pesticides – are comprehensively tested. Most of the 80,000 to 140,000 chemicals in consumer products have not been tested at all or just examined superficially to see what harm they may do locally, at the site of contact and at extremely high doses.

I am a physician and former head of the European Center for the Validation of Alternative Methods of the European Commission (2002-2008), and I am dedicated to finding faster, cheaper and more accurate methods of testing the safety of chemicals. To that end, I now lead a new program at Johns Hopkins University to revamp the safety sciences.

As part of this effort, we have now developed a computer method of testing chemicals that could save more than a US$1 billion annually and more than 2 million animals. Especially in times where the government is rolling back regulations on the chemical industry, new methods to identify dangerous substances are critical for human and environmental health.

Having written on the topic of alternatives to animal testing on a number of occasions (my December 26, 2014 posting provides an overview of sorts), I was particularly interested to see this in Hartung’s July 25, 2018 essay on The Conversation (Note: Links have been removed),

Following the vision of Toxicology for the 21st Century, a movement led by U.S. agencies to revamp safety testing, important work was carried out by my Ph.D. student Tom Luechtefeld at the Johns Hopkins Center for Alternatives to Animal Testing. Teaming up with Underwriters Laboratories, we have now leveraged an expanded database and machine learning to predict toxic properties. As we report in the journal Toxicological Sciences, we developed a novel algorithm and database for analyzing chemicals and determining their toxicity – what we call read-across structure activity relationship, RASAR.

This graphic reveals a small part of the chemical universe. Each dot represents a different chemical. Chemicals that are close together have similar structures and often properties. Thomas Hartung, CC BY-SA

To do this, we first created an enormous database with 10 million chemical structures by adding more public databases filled with chemical data, which, if you crunch the numbers, represent 50 trillion pairs of chemicals. A supercomputer then created a map of the chemical universe, in which chemicals are positioned close together if they share many structures in common and far where they don’t. Most of the time, any molecule close to a toxic molecule is also dangerous. Even more likely if many toxic substances are close, harmless substances are far. Any substance can now be analyzed by placing it into this map.

If this sounds simple, it’s not. It requires half a billion mathematical calculations per chemical to see where it fits. The chemical neighborhood focuses on 74 characteristics which are used to predict the properties of a substance. Using the properties of the neighboring chemicals, we can predict whether an untested chemical is hazardous. For example, for predicting whether a chemical will cause eye irritation, our computer program not only uses information from similar chemicals, which were tested on rabbit eyes, but also information for skin irritation. This is because what typically irritates the skin also harms the eye.

How well does the computer identify toxic chemicals?

This method will be used for new untested substances. However, if you do this for chemicals for which you actually have data, and compare prediction with reality, you can test how well this prediction works. We did this for 48,000 chemicals that were well characterized for at least one aspect of toxicity, and we found the toxic substances in 89 percent of cases.

This is clearly more accurate that the corresponding animal tests which only yield the correct answer 70 percent of the time. The RASAR shall now be formally validated by an interagency committee of 16 U.S. agencies, including the EPA [Environmental Protection Agency] and FDA [Food and Drug Administration], that will challenge our computer program with chemicals for which the outcome is unknown. This is a prerequisite for acceptance and use in many countries and industries.

The potential is enormous: The RASAR approach is in essence based on chemical data that was registered for the 2010 and 2013 REACH [Registration, Evaluation, Authorizations and Restriction of Chemicals] deadlines [in Europe]. If our estimates are correct and chemical producers would have not registered chemicals after 2013, and instead used our RASAR program, we would have saved 2.8 million animals and $490 million in testing costs – and received more reliable data. We have to admit that this is a very theoretical calculation, but it shows how valuable this approach could be for other regulatory programs and safety assessments.

In the future, a chemist could check RASAR before even synthesizing their next chemical to check whether the new structure will have problems. Or a product developer can pick alternatives to toxic substances to use in their products. This is a powerful technology, which is only starting to show all its potential.

It’s been my experience that these claims having led a movement (Toxicology for the 21st Century) are often contested with many others competing for the title of ‘leader’ or ‘first’. That said, this RASAR approach seems very exciting, especially in light of the skepticism about limiting and/or making animal testing unnecessary noted in my December 26, 2014 posting.it was from someone I thought knew better.

Here’s a link to and a citation for the paper mentioned in Hartung’s essay,

Machine learning of toxicological big data enables read-across structure activity relationships (RASAR) outperforming animal test reproducibility by Thomas Luechtefeld, Dan Marsh, Craig Rowlands, Thomas Hartung. Toxicological Sciences, kfy152, https://doi.org/10.1093/toxsci/kfy152 Published: 11 July 2018

This paper is open access.

Quantum back action and devil’s play

I always appreciate a reference to James Clerk Maxwell’s demon thought experiment (you can find out about it in the Maxwell’s demon Wikipedia entry). This time it comes from physicist  Kater Murch in a July 23, 2018 Washington University in st. Louis (WUSTL) news release (published July 25, 2018 on EurekAlert) written by Brandie Jefferson (offering a good explanation of the thought experiment and more),

Thermodynamics is one of the most human of scientific enterprises, according to Kater Murch, associate professor of physics in Arts & Sciences at Washington University in St. Louis.

“It has to do with our fascination of fire and our laziness,” he said. “How can we get fire” — or heat — “to do work for us?”

Now, Murch and colleagues have taken that most human enterprise down to the intangible quantum scale — that of ultra low temperatures and microscopic systems — and discovered that, as in the macroscopic world, it is possible to use information to extract work.

There is a catch, though: Some information may be lost in the process.

“We’ve experimentally confirmed the connection between information in the classical case and the quantum case,” Murch said, “and we’re seeing this new effect of information loss.”

The results were published in the July 20 [2018] issue of Physical Review Letters.

The international team included Eric Lutz of the University of Stuttgart; J. J. Alonzo of the University of Erlangen-Nuremberg; Alessandro Romito of Lancaster University; and Mahdi Naghiloo, a Washington University graduate research assistant in physics.

That we can get energy from information on a macroscopic scale was most famously illustrated in a thought experiment known as Maxwell’s Demon. [emphasis mine] The “demon” presides over a box filled with molecules. The box is divided in half by a wall with a door. If the demon knows the speed and direction of all of the molecules, it can open the door when a fast-moving molecule is moving from the left half of the box to the right side, allowing it to pass. It can do the same for slow particles moving in the opposite direction, opening the door when a slow-moving molecule is approaching from the right, headed left. ­

After a while, all of the quickly-moving molecules are on the right side of the box. Faster motion corresponds to higher temperature. In this way, the demon has created a temperature imbalance, where one side of the box is hotter. That temperature imbalance can be turned into work — to push on a piston as in a steam engine, for instance. At first the thought experiment seemed to show that it was possible create a temperature difference without doing any work, and since temperature differences allow you to extract work, one could build a perpetual motion machine — a violation of the second law of thermodynamics.

“Eventually, scientists realized that there’s something about the information that the demon has about the molecules,” Murch said. “It has a physical quality like heat and work and energy.”

His team wanted to know if it would be possible to use information to extract work in this way on a quantum scale, too, but not by sorting fast and slow molecules. If a particle is in an excited state, they could extract work by moving it to a ground state. (If it was in a ground state, they wouldn’t do anything and wouldn’t expend any work).

But they wanted to know what would happen if the quantum particles were in an excited state and a ground state at the same time, analogous to being fast and slow at the same time. In quantum physics, this is known as a superposition.

“Can you get work from information about a superposition of energy states?” Murch asked. “That’s what we wanted to find out.”

There’s a problem, though. On a quantum scale, getting information about particles can be a bit … tricky.

“Every time you measure the system, it changes that system,” Murch said. And if they measured the particle to find out exactly what state it was in, it would revert to one of two states: excited, or ground.

This effect is called quantum backaction. To get around it, when looking at the system, researchers (who were the “demons”) didn’t take a long, hard look at their particle. Instead, they took what was called a “weak observation.” It still influenced the state of the superposition, but not enough to move it all the way to an excited state or a ground state; it was still in a superposition of energy states. This observation was enough, though, to allow the researchers track with fairly high accuracy, exactly what superposition the particle was in — and this is important, because the way the work is extracted from the particle depends on what superposition state it is in.

To get information, even using the weak observation method, the researchers still had to take a peek at the particle, which meant they needed light. So they sent some photons in, and observed the photons that came back.

“But the demon misses some photons,” Murch said. “It only gets about half. The other half are lost.” But — and this is the key — even though the researchers didn’t see the other half of the photons, those photons still interacted with the system, which means they still had an effect on it. The researchers had no way of knowing what that effect was.

They took a weak measurement and got some information, but because of quantum backaction, they might end up knowing less than they did before the measurement. On the balance, that’s negative information.

And that’s weird.

“Do the rules of thermodynamics for a macroscopic, classical world still apply when we talk about quantum superposition?” Murch asked. “We found that yes, they hold, except there’s this weird thing. The information can be negative.

“I think this research highlights how difficult it is to build a quantum computer,” Murch said.

“For a normal computer, it just gets hot and we need to cool it. In the quantum computer you are always at risk of losing information.”

Here’s a link to and a citation for the paper,

Information Gain and Loss for a Quantum Maxwell’s Demon by M. Naghiloo, J. J. Alonso, A. Romito, E. Lutz, and K. W. Murch. Phys. Rev. Lett. 121, 030604 (Vol. 121, Iss. 3 — 20 July 2018) DOI:https://doi.org/10.1103/PhysRevLett.121.030604 Published 17 July 2018

© 2018 American Physical Society

This paper is behind a paywall.

Brainy and brainy: a novel synaptic architecture and a neuromorphic computing platform called SpiNNaker

I have two items about brainlike computing. The first item hearkens back to memristors, a topic I have been following since 2008. (If you’re curious about the various twists and turns just enter  the term ‘memristor’ in this blog’s search engine.) The latest on memristors is from a team than includes IBM (US), École Politechnique Fédérale de Lausanne (EPFL; Swizterland), and the New Jersey Institute of Technology (NJIT; US). The second bit comes from a Jülich Research Centre team in Germany and concerns an approach to brain-like computing that does not include memristors.

Multi-memristive synapses

In the inexorable march to make computers function more like human brains (neuromorphic engineering/computing), an international team has announced its latest results in a July 10, 2018 news item on Nanowerk,

Two New Jersey Institute of Technology (NJIT) researchers, working with collaborators from the IBM Research Zurich Laboratory and the École Polytechnique Fédérale de Lausanne, have demonstrated a novel synaptic architecture that could lead to a new class of information processing systems inspired by the brain.

The findings are an important step toward building more energy-efficient computing systems that also are capable of learning and adaptation in the real world. …

A July 10, 2018 NJIT news release (also on EurekAlert) by Tracey Regan, which originated by the news item, adds more details,

The researchers, Bipin Rajendran, an associate professor of electrical and computer engineering, and S. R. Nandakumar, a graduate student in electrical engineering, have been developing brain-inspired computing systems that could be used for a wide range of big data applications.

Over the past few years, deep learning algorithms have proven to be highly successful in solving complex cognitive tasks such as controlling self-driving cars and language understanding. At the heart of these algorithms are artificial neural networks – mathematical models of the neurons and synapses of the brain – that are fed huge amounts of data so that the synaptic strengths are autonomously adjusted to learn the intrinsic features and hidden correlations in these data streams.

However, the implementation of these brain-inspired algorithms on conventional computers is highly inefficient, consuming huge amounts of power and time. This has prompted engineers to search for new materials and devices to build special-purpose computers that can incorporate the algorithms. Nanoscale memristive devices, electrical components whose conductivity depends approximately on prior signaling activity, can be used to represent the synaptic strength between the neurons in artificial neural networks.

While memristive devices could potentially lead to faster and more power-efficient computing systems, they are also plagued by several reliability issues that are common to nanoscale devices. Their efficiency stems from their ability to be programmed in an analog manner to store multiple bits of information; however, their electrical conductivities vary in a non-deterministic and non-linear fashion.

In the experiment, the team showed how multiple nanoscale memristive devices exhibiting these characteristics could nonetheless be configured to efficiently implement artificial intelligence algorithms such as deep learning. Prototype chips from IBM containing more than one million nanoscale phase-change memristive devices were used to implement a neural network for the detection of hidden patterns and correlations in time-varying signals.

“In this work, we proposed and experimentally demonstrated a scheme to obtain high learning efficiencies with nanoscale memristive devices for implementing learning algorithms,” Nandakumar says. “The central idea in our demonstration was to use several memristive devices in parallel to represent the strength of a synapse of a neural network, but only chose one of them to be updated at each step based on the neuronal activity.”

Here’s a link to and a citation for the paper,

Neuromorphic computing with multi-memristive synapses by Irem Boybat, Manuel Le Gallo, S. R. Nandakumar, Timoleon Moraitis, Thomas Parnell, Tomas Tuma, Bipin Rajendran, Yusuf Leblebici, Abu Sebastian, & Evangelos Eleftheriou. Nature Communications volume 9, Article number: 2514 (2018) DOI: https://doi.org/10.1038/s41467-018-04933-y Published 28 June 2018

This is an open access paper.

Also they’ve got a couple of very nice introductory paragraphs which I’m including here, (from the June 28, 2018 paper in Nature Communications; Note: Links have been removed),

The human brain with less than 20 W of power consumption offers a processing capability that exceeds the petaflops mark, and thus outperforms state-of-the-art supercomputers by several orders of magnitude in terms of energy efficiency and volume. Building ultra-low-power cognitive computing systems inspired by the operating principles of the brain is a promising avenue towards achieving such efficiency. Recently, deep learning has revolutionized the field of machine learning by providing human-like performance in areas, such as computer vision, speech recognition, and complex strategic games1. However, current hardware implementations of deep neural networks are still far from competing with biological neural systems in terms of real-time information-processing capabilities with comparable energy consumption.

One of the reasons for this inefficiency is that most neural networks are implemented on computing systems based on the conventional von Neumann architecture with separate memory and processing units. There are a few attempts to build custom neuromorphic hardware that is optimized to implement neural algorithms2,3,4,5. However, as these custom systems are typically based on conventional silicon complementary metal oxide semiconductor (CMOS) circuitry, the area efficiency of such hardware implementations will remain relatively low, especially if in situ learning and non-volatile synaptic behavior have to be incorporated. Recently, a new class of nanoscale devices has shown promise for realizing the synaptic dynamics in a compact and power-efficient manner. These memristive devices store information in their resistance/conductance states and exhibit conductivity modulation based on the programming history6,7,8,9. The central idea in building cognitive hardware based on memristive devices is to store the synaptic weights as their conductance states and to perform the associated computational tasks in place.

The two essential synaptic attributes that need to be emulated by memristive devices are the synaptic efficacy and plasticity. …

It gets more complicated from there.

Now onto the next bit.

SpiNNaker

At a guess, those capitalized N’s are meant to indicate ‘neural networks’. As best I can determine, SpiNNaker is not based on the memristor. Moving on, a July 11, 2018 news item on phys.org announces work from a team examining how neuromorphic hardware and neuromorphic software work together,

A computer built to mimic the brain’s neural networks produces similar results to that of the best brain-simulation supercomputer software currently used for neural-signaling research, finds a new study published in the open-access journal Frontiers in Neuroscience. Tested for accuracy, speed and energy efficiency, this custom-built computer named SpiNNaker, has the potential to overcome the speed and power consumption problems of conventional supercomputers. The aim is to advance our knowledge of neural processing in the brain, to include learning and disorders such as epilepsy and Alzheimer’s disease.

A July 11, 2018 Frontiers Publishing news release on EurekAlert, which originated the news item, expands on the latest work,

“SpiNNaker can support detailed biological models of the cortex–the outer layer of the brain that receives and processes information from the senses–delivering results very similar to those from an equivalent supercomputer software simulation,” says Dr. Sacha van Albada, lead author of this study and leader of the Theoretical Neuroanatomy group at the Jülich Research Centre, Germany. “The ability to run large-scale detailed neural networks quickly and at low power consumption will advance robotics research and facilitate studies on learning and brain disorders.”

The human brain is extremely complex, comprising 100 billion interconnected brain cells. We understand how individual neurons and their components behave and communicate with each other and on the larger scale, which areas of the brain are used for sensory perception, action and cognition. However, we know less about the translation of neural activity into behavior, such as turning thought into muscle movement.

Supercomputer software has helped by simulating the exchange of signals between neurons, but even the best software run on the fastest supercomputers to date can only simulate 1% of the human brain.

“It is presently unclear which computer architecture is best suited to study whole-brain networks efficiently. The European Human Brain Project and Jülich Research Centre have performed extensive research to identify the best strategy for this highly complex problem. Today’s supercomputers require several minutes to simulate one second of real time, so studies on processes like learning, which take hours and days in real time are currently out of reach.” explains Professor Markus Diesmann, co-author, head of the Computational and Systems Neuroscience department at the Jülich Research Centre.

He continues, “There is a huge gap between the energy consumption of the brain and today’s supercomputers. Neuromorphic (brain-inspired) computing allows us to investigate how close we can get to the energy efficiency of the brain using electronics.”

Developed over the past 15 years and based on the structure and function of the human brain, SpiNNaker — part of the Neuromorphic Computing Platform of the Human Brain Project — is a custom-built computer composed of half a million of simple computing elements controlled by its own software. The researchers compared the accuracy, speed and energy efficiency of SpiNNaker with that of NEST–a specialist supercomputer software currently in use for brain neuron-signaling research.

“The simulations run on NEST and SpiNNaker showed very similar results,” reports Steve Furber, co-author and Professor of Computer Engineering at the University of Manchester, UK. “This is the first time such a detailed simulation of the cortex has been run on SpiNNaker, or on any neuromorphic platform. SpiNNaker comprises 600 circuit boards incorporating over 500,000 small processors in total. The simulation described in this study used just six boards–1% of the total capability of the machine. The findings from our research will improve the software to reduce this to a single board.”

Van Albada shares her future aspirations for SpiNNaker, “We hope for increasingly large real-time simulations with these neuromorphic computing systems. In the Human Brain Project, we already work with neuroroboticists who hope to use them for robotic control.”

Before getting to the link and citation for the paper, here’s a description of SpiNNaker’s hardware from the ‘Spiking neural netowrk’ Wikipedia entry, Note: Links have been removed,

Neurogrid, built at Stanford University, is a board that can simulate spiking neural networks directly in hardware. SpiNNaker (Spiking Neural Network Architecture) [emphasis mine], designed at the University of Manchester, uses ARM processors as the building blocks of a massively parallel computing platform based on a six-layer thalamocortical model.[5]

Now for the link and citation,

Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model by
Sacha J. van Albada, Andrew G. Rowley, Johanna Senk, Michael Hopkins, Maximilian Schmidt, Alan B. Stokes, David R. Lester, Markus Diesmann, and Steve B. Furber. Neurosci. 12:291. doi: 10.3389/fnins.2018.00291 Published: 23 May 2018

As noted earlier, this is an open access paper.

Cellulose and natural nanofibres

Specifically, the researchers are describing these as cellulose nanofibrils. On the left of the image, the seed look mores like an egg waiting to be fried for breakfast but the image on the right is definitely fibrous-looking,

Through contact with water, the seed of Neopallasia pectinata from the family of composite plants forms a slimy sheath. The white cellulose fibres anchor it to the seed surface. Courtesy: Kiel University (CAU)

A December 18, 2018 news item on Nanowerk describes the research into seeds and cellulose,

The seeds of some plants such as basil, watercress or plantain form a mucous envelope as soon as they come into contact with water. This cover consists of cellulose in particular, which is an important structural component of the primary cell wall of green plants, and swelling pectins, plant polysaccharides.

In order to be able to investigate its physical properties, a research team from the Zoological Institute at Kiel University (CAU) used a special drying method, which gently removes the water from the cellulosic mucous sheath. The team discovered that this method can produce extremely strong nanofibres from natural cellulose. In future, they could be especially interesting for applications in biomedicine.

A December 18, 2018 Kiel University press release, which originated the news item, offers further details about the work,

Thanks to their slippery mucous sheath, seeds can slide through the digestive tract of birds undigested. They are excreted unharmed, and can be dispersed in this way. It is presumed that the mucous layer provides protection. “In order to find out more about the function of the mucilage, we first wanted to study the structure and the physical properties of this seed envelope material,” said Zoology Professor Stanislav N. Gorb, head of the “Functional Morphology and Biomechanics” working group at the CAU. In doing so they discovered that its properties depend on the alignment of the fibres that anchor them to the seed surface

Diverse properties: From slippery to sticky

The pectins in the shell of the seeds can absorb a large quantity of water, and thus form a gel-like capsule around the seed in a few minutes. It is anchored firmly to the surface of the seed by fine cellulose fibres with a diameter of just up to 100 nanometres, similar to the microscopic adhesive elements on the surface of highly-adhesive gecko feet. So in a sense, the fibres form the stabilising backbone of the mucous sheath.

The properties of the mucous change, depending on the water concentration. “The mucous actually makes the seeds very slippery. However, if we reduce the water content, it becomes sticky and begins to stick,” said Stanislav Gorb, summarising a result from previous studies together with Dr Agnieszka Kreitschitz. The adhesive strength is also increased by the forces acting between the individual vertically-arranged nanofibres of the seed and the adhesive surface.

Specially dried

In order to be able to investigate the mucous sheath under a scanning electron microscope, the Kiel research team used a particularly gentle method, so-called critical-point drying (CPD). They dehydrated the mucous sheath of various seeds step-by-step with liquid carbon dioxide – instead of the normal method using ethanol. The advantage of this method is that evaporation of liquid carbon dioxide can be controlled under certain pressure and temperature conditions, without surface tension developing within the sheath. As a result, the research team was able to precisely remove water from the mucous, without drying out the surface of the sheath and thereby destroying the original cell structure. Through the highly-precise drying, the structural arrangement of the individual cellulose fibres remained intact.

Almost as strongly-adhesive as carbon nanotubes

The research team tested the dried cellulose fibres regarding their friction and adhesion properties, and compared them with those of synthetically-produced carbon nanotubes. Due to their outstanding properties, such as their tensile strength, electrical conductivity or their friction, these microscopic structures are interesting for numerous industrial applications of the future.

“Our tests showed that the frictional and adhesive forces of the cellulose fibres are almost as strong as with vertically-arranged carbon nanotubes,” said Dr Clemens Schaber, first author of the study. The structural dimensions of the cellulose nanofibers are similar to the vertically aligned carbon nanotubes. Through the special drying method, they can also vary the adhesive strength in a targeted manner. In Gorb’s working group, the zoologist and biomechanic examines the functioning of biological nanofibres, and the potential to imitate them with technical means. “As a natural raw material, cellulose fibres have distinct advantages over carbon nanotubes, whose health effects have not yet been fully investigated,” continued Schaber. Nanocellulose is primarily found in biodegradable polymer composites, which are used in biomedicine, cosmetics or the food industry.

Here’s a link to and a citation for the paper,

Friction-Active Surfaces Based on Free-Standing Anchored Cellulose Nanofibrils by Clemens F. Schaber, Agnieszka Kreitschitz, and Stanislav N. Gorb. ACS Appl. Mater. Interfaces, 2018, 10 (43), pp 37566–37574 DOI: 10.1021/acsami.8b05972 Publication Date (Web): September 19, 2018

Copyright © 2018 American Chemical Society

This paper is behind a paywall.

Carbon nanotube optics and the quantum

A US-France-Germany collaboration has led to some intriguing work with carbon nanotubes. From a June 18, 2018 news item on ScienceDaily,

Researchers at Los Alamos and partners in France and Germany are exploring the enhanced potential of carbon nanotubes as single-photon emitters for quantum information processing. Their analysis of progress in the field is published in this week’s edition of the journal Nature Materials.

“We are particularly interested in advances in nanotube integration into photonic cavities for manipulating and optimizing light-emission properties,” said Stephen Doorn, one of the authors, and a scientist with the Los Alamos National Laboratory site of the Center for Integrated Nanotechnologies (CINT). “In addition, nanotubes integrated into electroluminescent devices can provide greater control over timing of light emission and they can be feasibly integrated into photonic structures. We are highlighting the development and photophysical probing of carbon nanotube defect states as routes to room-temperature single photon emitters at telecom wavelengths.”

A June 18, 2018 Los Alamos National Laboratory (LANL) news release (also on EurekAlert), which originated the news item, expands on the theme,

The team’s overview was produced in collaboration with colleagues in Paris (Christophe Voisin [Ecole Normale Supérieure de Paris (ENS)]) who are advancing the integration of nanotubes into photonic cavities for modifying their emission rates, and at Karlsruhe (Ralph Krupke [Karlsruhe Institute of Technology (KIT]) where they are integrating nanotube-based electroluminescent devices with photonic waveguide structures. The Los Alamos focus is the analysis of nanotube defects for pushing quantum emission to room temperature and telecom wavelengths, he said.

As the paper notes, “With the advent of high-speed information networks, light has become the main worldwide information carrier. . . . Single-photon sources are a key building block for a variety of technologies, in secure quantum communications metrology or quantum computing schemes.”

The use of single-walled carbon nanotubes in this area has been a focus for the Los Alamos CINT team, where they developed the ability to chemically modify the nanotube structure to create deliberate defects, localizing excitons and controlling their release. Next steps, Doorn notes, involve integration of the nanotubes into photonic resonators, to provide increased source brightness and to generate indistinguishable photons. “We need to create single photons that are indistinguishable from one another, and that relies on our ability to functionalize tubes that are well-suited for device integration and to minimize environmental interactions with the defect sites,” he said.

“In addition to defining the state of the art, we wanted to highlight where the challenges are for future progress and lay out some of what may be the most promising future directions for moving forward in this area. Ultimately, we hope to draw more researchers into this field,” Doorn said.

Here’s a link to and a citation for the paper,

Carbon nanotubes as emerging quantum-light sources by X. He, H. Htoon, S. K. Doorn, W. H. P. Pernice, F. Pyatkov, R. Krupke, A. Jeantet, Y. Chassagneux & C. Voisin. Nature Materials (2018) DOI: https://doi.org/10.1038/s41563-018-0109-2 Published online June 18, 2018

This paper is behind a paywall.

Electrode-filled elastic fiber for wearable electronics and robots

This work comes out of Switzerland. A May 25, 2018 École Polytechnique Fédérale de Lausanne (EPFL) press release (also on EurekAlert) announces their fibers,

EPFL scientists have found a fast and simple way to make super-elastic, multi-material, high-performance fibers. Their fibers have already been used as sensors on robotic fingers and in clothing. This breakthrough method opens the door to new kinds of smart textiles and medical implants.

It’s a whole new way of thinking about sensors. The tiny fibers developed at EPFL are made of elastomer and can incorporate materials like electrodes and nanocomposite polymers. The fibers can detect even the slightest pressure and strain and can withstand deformation of close to 500% before recovering their initial shape. All that makes them perfect for applications in smart clothing and prostheses, and for creating artificial nerves for robots.

The fibers were developed at EPFL’s Laboratory of Photonic Materials and Fiber Devices (FIMAP), headed by Fabien Sorin at the School of Engineering. The scientists came up with a fast and easy method for embedding different kinds of microstructures in super-elastic fibers. For instance, by adding electrodes at strategic locations, they turned the fibers into ultra-sensitive sensors. What’s more, their method can be used to produce hundreds of meters of fiber in a short amount of time. Their research has just been published in Advanced Materials.

Heat, then stretch
To make their fibers, the scientists used a thermal drawing process, which is the standard process for optical-fiber manufacturing. They started by creating a macroscopic preform with the various fiber components arranged in a carefully designed 3D pattern. They then heated the preform and stretched it out, like melted plastic, to make fibers of a few hundreds microns in diameter. And while this process stretched out the pattern of components lengthwise, it also contracted it crosswise, meaning the components’ relative positions stayed the same. The end result was a set of fibers with an extremely complicated microarchitecture and advanced properties.

Until now, thermal drawing could be used to make only rigid fibers. But Sorin and his team used it to make elastic fibers. With the help of a new criterion for selecting materials, they were able to identify some thermoplastic elastomers that have a high viscosity when heated. After the fibers are drawn, they can be stretched and deformed but they always return to their original shape.

Rigid materials like nanocomposite polymers, metals and thermoplastics can be introduced into the fibers, as well as liquid metals that can be easily deformed. “For instance, we can add three strings of electrodes at the top of the fibers and one at the bottom. Different electrodes will come into contact depending on how the pressure is applied to the fibers. This will cause the electrodes to transmit a signal, which can then be read to determine exactly what type of stress the fiber is exposed to – such as compression or shear stress, for example,” says Sorin.

Artificial nerves for robots

Working in association with Professor Dr. Oliver Brock (Robotics and Biology Laboratory, Technical University of Berlin), the scientists integrated their fibers into robotic fingers as artificial nerves. Whenever the fingers touch something, electrodes in the fibers transmit information about the robot’s tactile interaction with its environment. The research team also tested adding their fibers to large-mesh clothing to detect compression and stretching. “Our technology could be used to develop a touch keyboard that’s integrated directly into clothing, for instance” says Sorin.

The researchers see many other potential applications. Especially since the thermal drawing process can be easily tweaked for large-scale production. This is a real plus for the manufacturing sector. The textile sector has already expressed interest in the new technology, and patents have been filed.

There’s a video of the lead researcher discussing the work as he offers some visual aids,

Here’s a link to and a citation for the paper,

Superelastic Multimaterial Electronic and Photonic Fibers and Devices via Thermal Drawing by Yunpeng Qu, Tung Nguyen‐Dang, Alexis Gérald Page, Wei Yan, Tapajyoti Das Gupta, Gelu Marius Rotaru, René M. Rossi, Valentine Dominique Favrod, Nicola Bartolomei, Fabien Sorin. Advanced Materials First published: 25 May 2018 https://doi.org/10.1002/adma.201707251

This paper is behind a paywall.

Being smart about using artificial intelligence in the field of medicine

Since my August 20, 2018 post featured an opinion piece about the possibly imminent replacement of radiologists with artificial intelligence systems and the latest research about employing them for diagnosing eye diseases, it seems like a good time to examine some of the mythology embedded in the discussion about AI and medicine.

Imperfections in medical AI systems

An August 15, 2018 article for Slate.com by W. Nicholson Price II (who teaches at the University of Michigan School of Law; in addition to his law degree he has a PhD in Biological Sciences from Columbia University) begins with the peppy, optimistic view before veering into more critical territory (Note: Links have been removed),

For millions of people suffering from diabetes, new technology enabled by artificial intelligence promises to make management much easier. Medtronic’s Guardian Connect system promises to alert users 10 to 60 minutes before they hit high or low blood sugar level thresholds, thanks to IBM Watson, “the same supercomputer technology that can predict global weather patterns.” Startup Beta Bionics goes even further: In May, it received Food and Drug Administration approval to start clinical trials on what it calls a “bionic pancreas system” powered by artificial intelligence, capable of “automatically and autonomously managing blood sugar levels 24/7.”

An artificial pancreas powered by artificial intelligence represents a huge step forward for the treatment of diabetes—but getting it right will be hard. Artificial intelligence (also known in various iterations as deep learning and machine learning) promises to automatically learn from patterns in medical data to help us do everything from managing diabetes to finding tumors in an MRI to predicting how long patients will live. But the artificial intelligence techniques involved are typically opaque. We often don’t know how the algorithm makes the eventual decision. And they may change and learn from new data—indeed, that’s a big part of the promise. But when the technology is complicated, opaque, changing, and absolutely vital to the health of a patient, how do we make sure it works as promised?

Price describes how a ‘closed loop’ artificial pancreas with AI would automate insulin levels for diabetic patients, flaws in the automated system, and how companies like to maintain a competitive advantage (Note: Links have been removed),

[…] a “closed loop” artificial pancreas, where software handles the whole issue, receiving and interpreting signals from the monitor, deciding when and how much insulin is needed, and directing the insulin pump to provide the right amount. The first closed-loop system was approved in late 2016. The system should take as much of the issue off the mind of the patient as possible (though, of course, that has limits). Running a close-loop artificial pancreas is challenging. The way people respond to changing levels of carbohydrates is complicated, as is their response to insulin; it’s hard to model accurately. Making it even more complicated, each individual’s body reacts a little differently.

Here’s where artificial intelligence comes into play. Rather than trying explicitly to figure out the exact model for how bodies react to insulin and to carbohydrates, machine learning methods, given a lot of data, can find patterns and make predictions. And existing continuous glucose monitors (and insulin pumps) are excellent at generating a lot of data. The idea is to train artificial intelligence algorithms on vast amounts of data from diabetic patients, and to use the resulting trained algorithms to run a closed-loop artificial pancreas. Even more exciting, because the system will keep measuring blood glucose, it can learn from the new data and each patient’s artificial pancreas can customize itself over time as it acquires new data from that patient’s particular reactions.

Here’s the tough question: How will we know how well the system works? Diabetes software doesn’t exactly have the best track record when it comes to accuracy. A 2015 study found that among smartphone apps for calculating insulin doses, two-thirds of the apps risked giving incorrect results, often substantially so. … And companies like to keep their algorithms proprietary for a competitive advantage, which makes it hard to know how they work and what flaws might have gone unnoticed in the development process.

There’s more,

These issues aren’t unique to diabetes care—other A.I. algorithms will also be complicated, opaque, and maybe kept secret by their developers. The potential for problems multiplies when an algorithm is learning from data from an entire hospital, or hospital system, or the collected data from an entire state or nation, not just a single patient. …

The [US Food and Drug Administraiont] FDA is working on this problem. The head of the agency has expressed his enthusiasm for bringing A.I. safely into medical practice, and the agency has a new Digital Health Innovation Action Plan to try to tackle some of these issues. But they’re not easy, and one thing making it harder is a general desire to keep the algorithmic sauce secret. The example of IBM Watson for Oncology has given the field a bit of a recent black eye—it turns out that the company knew the algorithm gave poor recommendations for cancer treatment but kept that secret for more than a year. …

While Price focuses on problems with algorithms and with developers and their business interests, he also hints at some of the body’s complexities.

Can AI systems be like people?

Susan Baxter, a medical writer with over 20 years experience, a PhD in health economics, and author of countless magazine articles and several books, offers a more person-centered approach to the discussion in her July 6, 2018 posting on susanbaxter.com,

The fascination with AI continues to irk, given that every second thing I read seems to be extolling the magic of AI and medicine and how It Will Change Everything. Which it will not, trust me. The essential issue of illness remains perennial and revolves around an individual for whom no amount of technology will solve anything without human contact. …

But in this world, or so we are told by AI proponents, radiologists will soon be obsolete. [my August 20, 2018 post] The adaptational learning capacities of AI mean that reading a scan or x-ray will soon be more ably done by machines than humans. The presupposition here is that we, the original programmers of this artificial intelligence, understand the vagaries of real life (and real disease) so wonderfully that we can deconstruct these much as we do the game of chess (where, let’s face it, Big Blue ate our lunch) and that analyzing a two-dimensional image of a three-dimensional body, already problematic, can be reduced to a series of algorithms.

Attempting to extrapolate what some “shadow” on a scan might mean in a flesh and blood human isn’t really quite the same as bishop to knight seven. Never mind the false positive/negatives that are considered an acceptable risk or the very real human misery they create.

Moravec called it

It’s called Moravec’s paradox, the inability of humans to realize just how complex basic physical tasks are – and the corresponding inability of AI to mimic it. As you walk across the room, carrying a glass of water, talking to your spouse/friend/cat/child; place the glass on the counter and open the dishwasher door with your foot as you open a jar of pickles at the same time, take a moment to consider just how many concurrent tasks you are doing and just how enormous the computational power these ostensibly simple moves would require.

Researchers in Singapore taught industrial robots to assemble an Ikea chair. Essentially, screw in the legs. A person could probably do this in a minute. Maybe two. The preprogrammed robots took nearly half an hour. And I suspect programming those robots took considerably longer than that.

Ironically, even Elon Musk, who has had major production problems with the Tesla cars rolling out of his high tech factory, has conceded (in a tweet) that “Humans are underrated.”

I wouldn’t necessarily go that far given the political shenanigans of Trump & Co. but in the grand scheme of things I tend to agree. …

Is AI going the way of gene therapy?

Susan draws a parallel between the AI and medicine discussion with the discussion about genetics and medicine (Note: Links have been removed),

On a somewhat similar note – given the extent to which genetics discourse has that same linear, mechanistic  tone [as AI and medicine] – it turns out all this fine talk of using genetics to determine health risk and whatnot is based on nothing more than clever marketing, since a lot of companies are making a lot of money off our belief in DNA. Truth is half the time we don’t even know what a gene is never mind what it actually does;  geneticists still can’t agree on how many genes there are in a human genome, as this article in Nature points out.

Along the same lines, I was most amused to read about something called the Super Seniors Study, research following a group of individuals in their 80’s, 90’s and 100’s who seem to be doing really well. Launched in 2002 and headed by Angela Brooks Wilson, a geneticist at the BC [British Columbia] Cancer Agency and SFU [Simon Fraser University] Chair of biomedical physiology and kinesiology, this longitudinal work is examining possible factors involved in healthy ageing.

Turns out genes had nothing to do with it, the title of the Globe and Mail article notwithstanding. (“Could the DNA of these super seniors hold the secret to healthy aging?” The answer, a resounding “no”, well hidden at the very [end], the part most people wouldn’t even get to.) All of these individuals who were racing about exercising and working part time and living the kind of life that makes one tired just reading about it all had the same “multiple (genetic) factors linked to a high probability of disease”. You know, the gene markers they tell us are “linked” to cancer, heart disease, etc., etc. But these super seniors had all those markers but none of the diseases, demonstrating (pretty strongly) that the so-called genetic links to disease are a load of bunkum. Which (she said modestly) I have been saying for more years than I care to remember. You’re welcome.

The fundamental error in this type of linear thinking is in allowing our metaphors (genes are the “blueprint” of life) and propensity towards social ideas of determinism to overtake common sense. Biological and physiological systems are not static; they respond to and change to life in its entirety, whether it’s diet and nutrition to toxic or traumatic insults. Immunity alters, endocrinology changes, – even how we think and feel affects the efficiency and effectiveness of physiology. Which explains why as we age we become increasingly dissimilar.

If you have the time, I encourage to read Susan’s comments in their entirety.

Scientific certainties

Following on with genetics, gene therapy dreams, and the complexity of biology, the June 19, 2018 Nature article by Cassandra Willyard (mentioned in Susan’s posting) highlights an aspect of scientific research not often mentioned in public,

One of the earliest attempts to estimate the number of genes in the human genome involved tipsy geneticists, a bar in Cold Spring Harbor, New York, and pure guesswork.

That was in 2000, when a draft human genome sequence was still in the works; geneticists were running a sweepstake on how many genes humans have, and wagers ranged from tens of thousands to hundreds of thousands. Almost two decades later, scientists armed with real data still can’t agree on the number — a knowledge gap that they say hampers efforts to spot disease-related mutations.

In 2000, with the genomics community abuzz over the question of how many human genes would be found, Ewan Birney launched the GeneSweep contest. Birney, now co-director of the European Bioinformatics Institute (EBI) in Hinxton, UK, took the first bets at a bar during an annual genetics meeting, and the contest eventually attracted more than 1,000 entries and a US$3,000 jackpot. Bets on the number of genes ranged from more than 312,000 to just under 26,000, with an average of around 40,000. These days, the span of estimates has shrunk — with most now between 19,000 and 22,000 — but there is still disagreement (See ‘Gene Tally’).

… the inconsistencies in the number of genes from database to database are problematic for researchers, Pruitt says. “People want one answer,” she [Kim Pruitt, a genome researcher at the US National Center for Biotechnology Information {NCB}] in Bethesda, Maryland] adds, “but biology is complex.”

I wanted to note that scientists do make guesses and not just with genetics. For example, Gina Mallet’s 2005 book ‘Last Chance to Eat: The Fate of Taste in a Fast Food World’ recounts the story of how good and bad levels of cholesterol were established—the experts made some guesses based on their experience. That said, Willyard’s article details the continuing effort to nail down the number of genes almost 20 years after the human genome project was completed and delves into the problems the scientists have uncovered.

Final comments

In addition to opaque processes with developers/entrepreneurs wanting to maintain their secrets for competitive advantages and in addition to our own poor understanding of the human body (how many genes are there anyway?), there are same major gaps (reflected in AI) in our understanding of various diseases. Angela Lashbrook’s August 16, 2018 article for The Atlantic highlights some issues with skin cancer and shade of your skin (Note: Links have been removed),

… While fair-skinned people are at the highest risk for contracting skin cancer, the mortality rate for African Americans is considerably higher: Their five-year survival rate is 73 percent, compared with 90 percent for white Americans, according to the American Academy of Dermatology.

As the rates of melanoma for all Americans continue a 30-year climb, dermatologists have begun exploring new technologies to try to reverse this deadly trend—including artificial intelligence. There’s been a growing hope in the field that using machine-learning algorithms to diagnose skin cancers and other skin issues could make for more efficient doctor visits and increased, reliable diagnoses. The earliest results are promising—but also potentially dangerous for darker-skinned patients.

… Avery Smith, … a software engineer in Baltimore, Maryland, co-authored a paper in JAMA [Journal of the American Medical Association] Dermatology that warns of the potential racial disparities that could come from relying on machine learning for skin-cancer screenings. Smith’s co-author, Adewole Adamson of the University of Texas at Austin, has conducted multiple studies on demographic imbalances in dermatology. “African Americans have the highest mortality rate [for skin cancer], and doctors aren’t trained on that particular skin type,” Smith told me over the phone. “When I came across the machine-learning software, one of the first things I thought was how it will perform on black people.”

Recently, a study that tested machine-learning software in dermatology, conducted by a group of researchers primarily out of Germany, found that “deep-learning convolutional neural networks,” or CNN, detected potentially cancerous skin lesions better than the 58 dermatologists included in the study group. The data used for the study come from the International Skin Imaging Collaboration, or ISIC, an open-source repository of skin images to be used by machine-learning algorithms. Given the rise in melanoma cases in the United States, a machine-learning algorithm that assists dermatologists in diagnosing skin cancer earlier could conceivably save thousands of lives each year.

… Chief among the prohibitive issues, according to Smith and Adamson, is that the data the CNN relies on come from primarily fair-skinned populations in the United States, Australia, and Europe. If the algorithm is basing most of its knowledge on how skin lesions appear on fair skin, then theoretically, lesions on patients of color are less likely to be diagnosed. “If you don’t teach the algorithm with a diverse set of images, then that algorithm won’t work out in the public that is diverse,” says Adamson. “So there’s risk, then, for people with skin of color to fall through the cracks.”

As Adamson and Smith’s paper points out, racial disparities in artificial intelligence and machine learning are not a new issue. Algorithms have mistaken images of black people for gorillas, misunderstood Asians to be blinking when they weren’t, and “judged” only white people to be attractive. An even more dangerous issue, according to the paper, is that decades of clinical research have focused primarily on people with light skin, leaving out marginalized communities whose symptoms may present differently.

The reasons for this exclusion are complex. According to Andrew Alexis, a dermatologist at Mount Sinai, in New York City, and the director of the Skin of Color Center, compounding factors include a lack of medical professionals from marginalized communities, inadequate information about those communities, and socioeconomic barriers to participating in research. “In the absence of a diverse study population that reflects that of the U.S. population, potential safety or efficacy considerations could be missed,” he says.

Adamson agrees, elaborating that with inadequate data, machine learning could misdiagnose people of color with nonexistent skin cancers—or miss them entirely. But he understands why the field of dermatology would surge ahead without demographically complete data. “Part of the problem is that people are in such a rush. This happens with any new tech, whether it’s a new drug or test. Folks see how it can be useful and they go full steam ahead without thinking of potential clinical consequences. …

Improving machine-learning algorithms is far from the only method to ensure that people with darker skin tones are protected against the sun and receive diagnoses earlier, when many cancers are more survivable. According to the Skin Cancer Foundation, 63 percent of African Americans don’t wear sunscreen; both they and many dermatologists are more likely to delay diagnosis and treatment because of the belief that dark skin is adequate protection from the sun’s harmful rays. And due to racial disparities in access to health care in America, African Americans are less likely to get treatment in time.

Happy endings

I’ll add one thing to Price’s article, Susan’s posting, and Lashbrook’s article about the issues with AI , certainty, gene therapy, and medicine—the desire for a happy ending prefaced with an easy solution. If the easy solution isn’t possible accommodations will be made but that happy ending is a must. All disease will disappear and there will be peace on earth. (Nod to Susan Baxter and her many discussions with me about disease processes and happy endings.)

The solutions, for the most part, are seen as technological despite the mountain of evidence suggesting that technology reflects our own imperfect understanding of health and disease therefore providing what is at best an imperfect solution.

Also, we tend to underestimate just how complex humans are not only in terms of disease and health but also with regard to our skills, understanding, and, perhaps not often enough, our ability to respond appropriately in the moment.

There is much to celebrate in what has been accomplished: no more black death, no more smallpox, hip replacements, pacemakers, organ transplants, and much more. Yes, we should try to improve our medicine. But, maybe alongside the celebration we can welcome AI and other technologies with a lot less hype and a lot more skepticism.

SIGGRAPH (Special Interest Group on Computer GRAPHics and Interactive Techniques) and their art gallery from Aug. 12 – 16, 2018 (making the world synthetic) in Vancouver (Canada)

While my main interest is the group’s temporary art gallery, I am providing a brief explanatory introduction and a couple of previews for SIGGRAPH 2018.

Introduction

For anyone unfamiliar with the Special Interest Group on Computer GRAPHics and Interactive Techniques (SIGGRAPH) and its conferences, from the SIGGRAPH Wikipedia entry Note: Links have been removed),

Some highlights of the conference are its Animation Theater and Electronic Theater presentations, where recently created CG films are played. There is a large exhibition floor, where several hundred companies set up elaborate booths and compete for attention and recruits. Most of the companies are in the engineering, graphics, motion picture, or video game industries. There are also many booths for schools which specialize in computer graphics or interactivity.

Dozens of research papers are presented each year, and SIGGRAPH is widely considered the most prestigious forum for the publication of computer graphics research.[1] The recent paper acceptance rate for SIGGRAPH has been less than 26%.[2] The submitted papers are peer-reviewed in a single-blind process.[3] There has been some criticism about the preference of SIGGRAPH paper reviewers for novel results rather than useful incremental progress.[4][5] …

This is the third SIGGRAPH Vancouver has hosted; the others were in 2011 and 2014.  The theme for the 2018 iteration is ‘Generations’; here’s more about it from an Aug. 2, 2018 article by Terry Flores for Variety,

While its focus is firmly forward thinking, SIGGRAPH 2018, the computer graphics, animation, virtual reality, games, digital art, mixed reality, and emerging technologies conference, is also tipping its hat to the past thanks to its theme this year: Generations. The conference runs Aug. 12-16 in Vancouver, B.C.

“In the literal people sense, pioneers in the computer graphics industry are standing shoulder to shoulder with researchers, practitioners and the future of the industry — young people — mentoring them, dabbling across multiple disciplines to innovate, relate, and grow,” says SIGGRAPH 2018 conference chair Roy C. Anthony, VP of creative development and operations at software and technology firm Ventuz. “This is really what SIGGRAPH has always been about. Generations really seemed like a very appropriate way of looking back and remembering where we all came from and how far we’ve come.”

SIGGRAPH 2018 has a number of treats in store for attendees, including the debut of Disney’s first VR film, the short “Cycles”; production sessions on the making of “Blade Runner 2049,” “Game of Thrones,” “Incredibles 2” and “Avengers: Infinity War”; as well as sneak peeks of Disney’s upcoming “Ralph Breaks the Internet: Wreck-It Ralph 2” and Laika’s “Missing Link.”

That list of ‘treats’ in the last paragraph makes the conference seem more like an iteration of a ‘comic-con’ than a technology conference.

Previews

I have four items about work that will be presented at SIGGRAPH 2018, First up, something about ‘redirected walking’ from a June 18, 2018 Association for Computing Machinery news release on EurekAlert,

CHICAGO–In the burgeoning world of virtual reality (VR) technology, it remains a challenge to provide users with a realistic perception of infinite space and natural walking capabilities in the virtual environment. A team of computer scientists has introduced a new approach to address this problem by leveraging a natural human phenomenon: eye blinks.

All humans are functionally blind for about 10 percent of the time under normal circumstances due to eye blinks and saccades, a rapid movement of the eye between two points or objects. Eye blinks are a common and natural cause of so-called “change blindness,” which indicates the inability for humans to notice changes to visual scenes. Zeroing in on eye blinks and change blindness, the team has devised a novel computational system that effectively redirects the user in the virtual environment during these natural instances, all with undetectable camera movements to deliver orientation redirection.

“Previous RDW [redirected walking] techniques apply rotations continuously while the user is walking. But the amount of unnoticeable rotations is limited,” notes Eike Langbehn, lead author of the research and doctoral candidate at the University of Hamburg. “That’s why an orthogonal approach is needed–we add some additional rotations when the user is not focused on the visuals. When we learned that humans are functionally blind for some time due to blinks, we thought, ‘Why don’t we do the redirection during eye blinks?'”

Human eye blinks occur approximately 10 to 20 times per minute, about every 4 to 19 seconds. Leveraging this window of opportunity–where humans are unable to detect major motion changes while in a virtual environment–the researchers devised an approach to synchronize a computer graphics rendering system with this visual process, and introduce any useful motion changes in virtual scenes to enhance users’ overall VR experience.

The researchers’ experiments revealed that imperceptible camera rotations of 2 to 5 degrees and translations of 4 to 9 cm of the user’s viewpoint are possible during a blink without users even noticing. They tracked test participants’ eye blinks by an eye tracker in a VR head-mounted display. In a confirmatory study, the team validated that participants could not reliably detect in which of two eye blinks their viewpoint was manipulated while walking a VR curved path. The tests relied on unconscious natural eye blinking, but the researchers say redirection during blinking could be carried out consciously. Since users can consciously blink multiple times a day without much effort, eye blinks provide great potential to be used as an intentional trigger in their approach.

The team will present their work at SIGGRAPH 2018, held 12-16 August in Vancouver, British Columbia. The annual conference and exhibition showcases the world’s leading professionals, academics, and creative minds at the forefront of computer graphics and interactive techniques.

“RDW is a big challenge since current techniques still need too much space to enable unlimited walking in VR,” notes Langbehn. “Our work might contribute to a reduction of space since we found out that unnoticeable rotations of up to five degrees are possible during blinks. This means we can improve the performance of RDW by approximately 50 percent.”

The team’s results could be used in combination with other VR research, such as novel steering algorithms, improved path prediction, and rotations during saccades, to name a few. Down the road, such techniques could some day enable consumer VR users to virtually walk beyond their living room.

Langbehn collaborated on the work with Frank Steinicke of University of Hamburg, Markus Lappe of University of Muenster, Gregory F. Welch of University of Central Florida, and Gerd Bruder, also of University of Central Florida. For the full paper and video, visit the team’s project page.

###

About ACM, ACM SIGGRAPH, and SIGGRAPH 2018

ACM, the Association for Computing Machinery, is the world’s largest educational and scientific computing society, uniting educators, researchers, and professionals to inspire dialogue, share resources, and address the field’s challenges. ACM SIGGRAPH is a special interest group within ACM that serves as an interdisciplinary community for members in research, technology, and applications in computer graphics and interactive techniques. SIGGRAPH is the world’s leading annual interdisciplinary educational experience showcasing the latest in computer graphics and interactive techniques. SIGGRAPH 2018, marking the 45th annual conference hosted by ACM SIGGRAPH, will take place from 12-16 August at the Vancouver Convention Centre in Vancouver, B.C.

They have provided an image illustrating what they mean (I don’t find it especially informative),

Caption: The viewing behavior of a virtual reality user, including fixations (in green) and saccades (in red). A blink fully suppresses visual perception. Credit: Eike Langbehn

Next up (2), there’s Disney Corporation’s first virtual reality (VR) short, from a July 19, 2018  Association for Computing Machinery news release on EurekAlert,

Walt Disney Animation Studios will debut its first ever virtual reality short film at SIGGRAPH 2018, and the hope is viewers will walk away feeling connected to the characters as equally as they will with the VR technology involved in making the film.

Cycles, an experimental film directed by Jeff Gipson, centers around the true meaning of creating a home and the life it holds inside its walls. The idea for the film is personal, inspired by Gipson’s childhood spending time with his grandparents and creating memories in their home, and later, having to move them to an assisted living residence.

“Every house has a story unique to the people, the characters who live there,” says Gipson. “We wanted to create a story in this single place and be able to have the viewer witness life happening around them. It is an emotionally driven film, expressing the real ups and downs, the happy and sad moments in life.”

For Cycles, Gipson also drew from his past life as an architect, having spent several years designing skate parks, and from his passion for action sports, including freestyle BMX. In Los Angeles, where Gipson lives, it is not unusual to find homes with an empty swimming pool reserved for skating or freestyle biking. Part of the pitch for Cycles came out of Gipson’s experience riding in these empty pools and being curious about the homes attached to them, the families who lived there, and the memories they made.

SIGGRAPH attendees will have the opportunity to experience Cycles at the Immersive Pavilion, a new space for this year’s conference. The Pavilion is devoted exclusively to virtual, augmented, and mixed reality and will contain: the VR Theater, a storytelling extravaganza that is part of the Computer Animation Festival; the Vrcade, a space for VR, AR, and MR games or experiences; and the well-known Village, for showcasing large-scale projects. SIGGRAPH 2018, held 12-16 August in Vancouver, British Columbia, is an annual gathering that showcases the world’s leading professionals, academics, and creative minds at the forefront of computer graphics and interactive techniques.

The production team completed Cycles in four months with about 50 collaborators as part of a professional development program at the studio. A key difference in VR filmmaking includes getting creative with how to translate a story to the VR “screen.” Pre-visualizing the narrative, for one, was a challenge. Rather than traditional storyboarding, Gipson and his team instead used a mix of Quill VR painting techniques and motion capture to “storyboard” Cycles, incorporating painters and artists to generate sculptures or 3D models of characters early on and draw scenes for the VR space. The creators also got innovative with the use of light and color saturation in scenes to help guide the user’s eyes during the film.

“What’s cool for VR is that we are really on the edge of trying to figure out what it is and how to tell stories in this new medium,” says Gipson. “In VR, you can look anywhere and really be transported to a different world, experience it from different angles, and see every detail. We want people watching to feel alive and feel emotion, and give them a true cinematic experience.”

This is Gipson’s VR directorial debut. He joined Walt Disney Animation Studios in 2013, serving as a lighting artist on Disney favorites like Frozen, Zootopia, and Moana. Of getting to direct the studio’s first VR short, he says, “VR is an amazing technology and a lot of times the technology is what is really celebrated. We hope more and more people begin to see the emotional weight of VR films, and with Cycles in particular, we hope they will feel the emotions we aimed to convey with our story.”

Apparently this is a still from the ‘short’,

Caption: Disney Animation Studios will present ‘Cycles’ , its first virtual reality (VR) short, at ACM SIGGRAPH 2018. Credit: Disney Animation Studios

There’s also something (3) from Google as described in a July 26, 2018 Association of Computing Machinery news release on EurekAlert,

Google has unveiled a new virtual reality (VR) immersive experience based on a novel system that captures and renders high-quality, realistic images from the real world using light fields. Created by a team of leading researchers at Google, Welcome to Light Fields is the tech giant’s splash into the nascent arena of light fields VR experiences, an exciting corner of VR video technology gaining traction for its promise to deliver extremely high-quality imagery and experiences in the virtual world.

Google released Welcome to Light Fields earlier this year as a free app on Steam VR for HTC Vive, Oculus Rift, and Windows Mixed Reality headsets. The creators will demonstrate the VR experience at SIGGRAPH 2018, in the Immersive Pavilion, a new space for this year’s conference. The Pavilion is devoted exclusively to virtual, augmented, and mixed reality and will contain: the Vrcade, a space for VR, AR, and MR games or experiences; the VR Theater, a storytelling extravaganza that is part of the Computer Animation Festival; and the well-known Village, for showcasing large-scale projects. SIGGRAPH 2018, held 12-16 August in Vancouver, British Columbia, is an annual gathering that showcases the world’s leading professionals, academics, and creative minds at the forefront of computer graphics and interactive techniques.

Destinations in Welcome to Light Fields include NASA’s Space Shuttle Discovery, delivering to viewers an astronaut’s view inside the flight deck, which has never been open to the public; the pristine teak and mahogany interiors of the Gamble House, an architectural treasure in Pasadena, CA; and the glorious St. Stephen’s Church in Granada Hills, CA, home to a stunning wall of more than 14,000 pieces of glimmering stained glass.

“I love that light fields in VR can teleport you to exotic places in the real world, and truly make you believe you are there,” says Ryan Overbeck, software engineer at Google who co-led the project. “To me, this is magic.”

To bring this experience to life, Overbeck worked with a team that included Paul Debevec, senior staff engineer at Google, who managed the project and led the hardware piece with engineers Xueming Yu, Jay Busch, and Graham Fyffe. With Overbeck, Daniel Erickson and Daniel Evangelakos focused on the software end. The researchers designed a comprehensive system for capturing and rendering high-quality, spherical light field still images from footage captured in the real world. They developed two easy-to-use light field camera rigs, based on the GoPro Hero4action sports camera, that efficiently capture thousands of images on the surface of a sphere. Those images were then passed through a cloud-based light-field-processing pipeline.

Among other things, explains Overbeck, “The processing pipeline uses computer vision to place the images in 3D and generate depth maps, and we use a modified version of our vp9 video codec

to compress the light field data down to a manageable size.” To render a light field dataset, he notes, the team used a rendering algorithm that blends between the thousands of light field images in real-time.

The team relied on Google’s talented pool of engineers in computer vision, graphics, video compression, and machine learning to overcome the unique challenges posed in light fields technology. They also collaborated closely with the WebM team (who make the vp9 video codec) to develop the high-quality light field compression format incorporated into their system, and leaned heavily on the expertise of the Jump VR team to help pose the images and generate depth maps. (Jump is Google’s professional VR system for achieving 3D-360 video production at scale.)

Indeed, with Welcome to Light Fields, the Google team is demonstrating the potential and promise of light field VR technology, showcasing the technology’s ability to provide a truly immersive experience with a level of unmatched realism. Though light fields technology has been researched and explored in computer graphics for more than 30 years, practical systems for actually delivering high-quality light field experiences has not yet been possible.

Part of the team’s motivation behind creating this VR light field experience is to invigorate the nascent field.

“Welcome to Light Fields proves that it is now possible to make a compelling light field VR viewer that runs on consumer-grade hardware, and we hope that this knowledge will encourage others to get involved with building light field technology and media,” says Overbeck. “We understand that in order to eventually make compelling consumer products based on light fields, we need a thriving light field ecosystem. We need open light field codecs, we need artists creating beautiful light field imagery, and we need people using VR in order to engage with light fields.”

I don’t really understand why this image, which looks like something belongs on advertising material, would be chosen to accompany a news release on a science-based distribution outlet,

Caption: A team of leading researchers at Google, will unveil the new immersive virtual reality (VR) experience “Welcome to Lightfields” at ACM SIGGRAPH 2018. Credit: Image courtesy of Google/Overbeck

Finally (4), ‘synthesizing realistic sounds’ is announced in an Aug. 6, 2018 Stanford University (US) news release (also on EurekAlert) by Taylor Kubota,

Advances in computer-generated imagery have brought vivid, realistic animations to life, but the sounds associated with what we see simulated on screen, such as two objects colliding, are often recordings. Now researchers at Stanford University have developed a system that automatically renders accurate sounds for a wide variety of animated phenomena.

“There’s been a Holy Grail in computing of being able to simulate reality for humans. We can animate scenes and render them visually with physics and computer graphics, but, as for sounds, they are usually made up,” said Doug James, professor of computer science at Stanford University. “Currently there exists no way to generate realistic synchronized sounds for complex animated content, such as splashing water or colliding objects, automatically. This fills that void.”

The researchers will present their work on this sound synthesis system as part of ACM SIGGRAPH 2018, the leading conference on computer graphics and interactive techniques. In addition to enlivening movies and virtual reality worlds, this system could also help engineering companies prototype how products would sound before being physically produced, and hopefully encourage designs that are quieter and less irritating, the researchers said.

“I’ve spent years trying to solve partial differential equations – which govern how sound propagates – by hand,” said Jui-Hsien Wang, a graduate student in James’ lab and in the Institute for Computational and Mathematical Engineering (ICME), and lead author of the paper. “This is actually a place where you don’t just solve the equation but you can actually hear it once you’ve done it. That’s really exciting to me and it’s fun.”

Predicting sound

Informed by geometry and physical motion, the system figures out the vibrations of each object and how, like a loudspeaker, those vibrations excite sound waves. It computes the pressure waves cast off by rapidly moving and vibrating surfaces but does not replicate room acoustics. So, although it does not recreate the echoes in a grand cathedral, it can resolve detailed sounds from scenarios like a crashing cymbal, an upside-down bowl spinning to a stop, a glass filling up with water or a virtual character talking into a megaphone.

Most sounds associated with animations rely on pre-recorded clips, which require vast manual effort to synchronize with the action on-screen. These clips are also restricted to noises that exist – they can’t predict anything new. Other systems that produce and predict sounds as accurate as those of James and his team work only in special cases, or assume the geometry doesn’t deform very much. They also require a long pre-computation phase for each separate object.

“Ours is essentially just a render button with minimal pre-processing that treats all objects together in one acoustic wave simulation,” said Ante Qu, a graduate student in James’ lab and co-author of the paper.

The simulated sound that results from this method is highly detailed. It takes into account the sound waves produced by each object in an animation but also predicts how those waves bend, bounce or deaden based on their interactions with other objects and sound waves in the scene.

Challenges ahead

In its current form, the group’s process takes a while to create the finished product. But, now that they have proven this technique’s potential, they can focus on performance optimizations, such as implementing their method on parallel GPU hardware, that should make it drastically faster.

And, even in its current state, the results are worth the wait.

“The first water sounds we generated with the system were among the best ones we had simulated – and water is a huge challenge in computer-generated sound,” said James. “We thought we might get a little improvement, but it is dramatically better than previous approaches even right out of the box. It was really striking.”

Although the group’s work has faithfully rendered sounds of various objects spinning, falling and banging into each other, more complex objects and interactions – like the reverberating tones of a Stradivarius violin – remain difficult to model realistically. That, the group said, will have to wait for a future solution.

Timothy Langlois of Adobe Research is a co-author of this paper. This research was funded by the National Science Foundation and Adobe Research. James is also a professor, by courtesy, of music and a member of Stanford Bio-X.

Researchers Timothy Langlois, Doug L. James, Ante Qu and Jui-Hsien Wang have created a video featuring highlights of animations with sounds synthesized using the Stanford researchers’ new system.,

The researchers have also provided this image,

By computing pressure waves cast off by rapidly moving and vibrating surfaces – such as a cymbal – a new sound synthesis system developed by Stanford researchers can automatically render realistic sound for computer animations. (Image credit: Timothy Langlois, Doug L. James, Ante Qu and Jui-Hsien Wang)

It does seem like we’re synthesizing the world around us, eh?

The SIGGRAPH 2018 art gallery

Here’s what SIGGRAPH had to say about its 2018 art gallery in Vancouver and the themes for the conference and the gallery (from a May 18, 2018 Associating for Computing Machinery news release on globalnewswire.com (also on this 2018 SIGGRAPH webpage),

SIGGRAPH 2018, the world’s leading showcase of digital art created using computer graphics and interactive techniques, will present a special Art Gallery, entitled “Origins,” and historic Art Papers in Vancouver, B.C. The 45th SIGGRAPH conference will take place 12–16 August at the Vancouver Convention Centre. The programs will also honor the generations of creators that have come before through a special, 50th anniversary edition of the Leonard journal. To register for the conference, visit S2018.SIGGRAPH.ORG.

The SIGGRAPH 2018 ART GALLERY is a curated exhibition, conceived as a dialogical space that enables the viewer to reflect on man’s diverse cultural values and rituals through contemporary creative practices. Building upon an exciting and eclectic selection of creative practices mediated through technologies that represent the sophistication of our times, the SIGGRAPH 2018 Art Gallery will embrace the narratives of the indigenous communities based near Vancouver and throughout Canada as a source of inspiration. The exhibition will feature contemporary media artworks, art pieces by indigenous communities, and other traces of technologically mediated Ludic practices.

Andrés Burbano, SIGGRAPH 2018 Art Gallery chair and professor at Universidad de los Andes, said, “The Art Gallery aims to articulate myth and technology, science and art, the deep past and the computational present, and will coalesce around a theme of ‘Origins.’ Media and technological creative expressions will explore principles such as the origins of the cosmos, the origins of life, the origins of human presence, the origins of the occupation of territories in the Americas, and the origins of people living in the vast territories of the Arctic.”

He continued, “The venue [in Vancouver] hopes to rekindle the original spark that ignited the collaborative spirit of the SIGGRAPH community of engineers, scientists, and artists, who came together to create the very first conference in the early 1970s.”

Highlights from the 2018 Art Gallery include:

Transformation Mask (Canada) [Technology Based]
Shawn Hunt, independent; and Microsoft Garage: Andy Klein, Robert Butterworth, Jonathan Cobb, Jeremy Kersey, Stacey Mulcahy, Brendan O’Rourke, Brent Silk, and Julia Taylor-Hell, Microsoft Vancouver

TRANSFORMATION MASK is an interactive installation that features the Microsoft HoloLens. It utilizes electronics and mechanical engineering to express a physical and digital transformation. Participants are immersed in spatial sounds and holographic visuals.

Somnium (U.S.) [Science Based]
Marko Peljhan, Danny Bazo, and Karl Yerkes, University of California, Santa Barbara

Somnium is a cybernetic installation that provides visitors with the ability to sensorily, cognitively, and emotionally contemplate and experience exoplanetary discoveries, their macro and micro dimensions, and the potential for life in our Galaxy. Some might call it “space telescope.”

Ernest Edmonds Retrospective – Art Systems 1968-2018 (United Kingdom) [History Based]
Ernest Edmonds, De Montfort University

Celebrating one of the pioneers of computer graphics-based art since the early 1970s, this Ernest Edmonds career retrospective will showcase snapshots of Edmonds’ work as it developed over the years. With one piece from each decade, the retrospective will also demonstrate how vital the Leonardo journal has been throughout the 50-year journey.

In addition to the works above, the Art Gallery will feature pieces from notable female artists Ozge Samanci, Ruth West, and Nicole L’Hullier. For more information about the Edmonds retrospective, read THIS POST ON THE ACM SIGGRAPH BLOG.

The SIGGRAPH 2018 ART PAPERS program is designed to feature research from artists, scientists, theorists, technologists, historians, and more in one of four categories: project description, theory/criticism, methods, or history. The chosen work was selected by an international jury of scholars, artists, and immersive technology developers.

To celebrate the 50th anniversary of LEONARDO (MIT Press), and 10 years of its annual SIGGRAPH issue, SIGGRAPH 2018 is pleased to announce a special anniversary edition of the journal, which will feature the 2018 art papers. For 50 years, Leonardo has been the definitive publication for artist-academics. To learn more about the relationship between SIGGRAPH and the journal, listen to THIS EPISODE OF THE SIGGRAPH SPOTLIGHT PODCAST.

“In order to encourage a wider range of topics, we introduced a new submission type, short papers. This enabled us to accept more content than in previous years. Additionally, for the first time, we will introduce sessions that integrate the Art Gallery artist talks with Art Papers talks, promoting richer connections between these two creative communities,” said Angus Forbes, SIGGRAPH 2018 Art Papers chair and professor at University of California, Santa Cruz.

Art Papers highlights include:

Alienating the Familiar with CGI: A Recipe for Making a Full CGI Art House Animated Feature [Long]
Alex Counsell and Paul Charisse, University of Portsmouth

This paper explores the process of making and funding an art house feature film using full CGI in a marketplace where this has never been attempted. It explores cutting-edge technology and production approaches, as well as routes to successful fundraising.

Augmented Fauna and Glass Mutations: A Dialogue Between Material and Technique in Glassblowing and 3D Printing [Long]
Tobias Klein, City University of Hong Kong

The two presented artworks, “Augmented Fauna” and “Glass Mutations,” were created during an artist residence at the PILCHUCK GLASS SCHOOL. They are examples of the qualities and methods established through a synthesis between digital workflows and traditional craft processes and thus formulate the notion of digital craftsmanship.

Inhabitat: An Imaginary Ecosystem in a Children’s Science Museum [Short]
Graham Wakefield, York University, and Haru Hyunkyung Ji, OCAD University

“Inhabitat” is a mixed reality artwork in which participants become part of an imaginary ecology through three simultaneous perspectives of scale and agency; three distinct ways to see with other eyes. This imaginary world was exhibited at a children’s science museum for five months, using an interactive projection-augmented sculpture, a large screen and speaker array, and a virtual reality head-mounted display.

What’s the what?

My father used to say that and I always assumed it meant summarize the high points, if you need to, and get to the point—fast. In that spirit, I am both fascinated and mildly appalled. The virtual, mixed, and augmented reality technologies, as well as, the others being featured at SIGGRAPH 2018 are wondrous in many ways but it seems we are coming ever closer to a world where we no longer interact with nature or other humans directly. (see my August 10, 2018 posting about the ‘extinction of experience’ for research that encourages more direct interaction with nature) I realize that SIGGRAPH is intended as a primarily technical experience but I think a little more content questioning these technologies and their applications (social implications) might be in order. That’s often the artist’s role but I can’t see anything in the art gallery descriptions that hint at any sort of fundamental critique.