“[Squishy circuits] are literally child’s play putty, that is also conductive” describes Dmitry Kireev, assistant professor of biomedical engineering and senior author on the paper.

The conductive squishy circuits — whether homemade or store-bought- are made of flour, water, salt, cream of tartar and vegetable oil. “Salt is what makes it conductive,” Kireev explains. As a child’s toy, this modeling clay is a maleable way to add lights to an art projectby connecting them to a power source as a way to teach kids about circuits. Now, Kireev and his team have demonstrated that the material has more potential.

“We used the squishy circuits as an interface to measure electricity or measure bioelectrical potentials from a human body,” he says. They found that, compared to commercially available gel electrodes, these squishy circuits effectively captured various electrophysiology measurements: electroencephalogram (EEG) for brain activity, electrocardiogram (ECG) for heart recordings, electrooculogram (EOG) for tracking eye movement and electromyography (EMG) for muscle contraction.

“What makes one electrode material better than another in terms of the quality of the measurements is impedance,” he explains. Impedance is a measure that describes the quality of conductivity between two materials. “The lower the impedance between the electrode and the tissue, the better the conductivity in between and the better your ability to measure those bioelectrical potentials.”

The study found that the impedance for the squishy circuit electrode was on par with one of the commercially available gel electrodes and twice as better as a second comparison electrode.

Kireev highlights several benefits to this material. First is cost: Even using pre-made putty, the cost per electrode was about 1cent. Typical electrodes cost on average between $0.25 and $1.

Also, the material is resilient: it can be formed and reformed, molded to the contours of the skin, combined with more putty to make it bigger, reused and easily reconnected if it comes apart. Other comparable state-of-the-art wearable bioelectronics have been made of carbon nanotubes, graphene, silver nanowires and organic polymers. While highly conductive, these materials can be expensive, difficult to handle or make, single use or fragile.

Kireev also highlights the availability of these materials. “It’s something you can do at home or in high school laboratories, for example, if needed,” he says. “You can democratize these applications [so it’s] more widespread.”

He gives credit to his research team of undergraduate students (some of whom have since graduated and are continuing with graduate studies at UMass): Alexandra Katsoulakis, Favour Nakyazze, Max Mchugh, Sean Morris, Monil Bhavsar and Om Tank.

“This is an amazingly lucky ‘galactic line-up’ — a chance alignment of multiple galaxies across a line-of-sight spanning most of the observable universe,” said David Schlegel, a co-author of the study and a senior scientist in Berkeley Lab’s Physics Division. “Finding one such alignment is a needle in the haystack. Finding all of these is like eight needles precisely lined up inside that haystack.”

The Carousel Lens is an alignment consisting of one foreground galaxy cluster (the ‘lens’) and seven background galaxies spanning immense cosmic distances and seen through the gravitationally distorted space-time around the lens. In the dramatic image below:

• The lensing cluster, located 5 billion light years away from Earth, is shown by its four brightest and most massive galaxies (indicated by La, Lb, Lc, and Ld), and these constitute the foreground of the image.
• Seven unique galaxies (numbered 1 through 7), appear through the lens. These are located far beyond, at distances from 7.6 to 12 billion light years away from Earth, approaching the limit of the observable universe.
• Each galaxy’s repeated appearances (indicated by each number’s letter index, e.g., a through d) show differences in shape that are curved and stretched into multiple “fun house mirror” iterations caused by the warped space-time around the lens.
• Of particular interest is the discovery of an Einstein Cross — the largest known to date — shown in galaxy number 4’s multiple appearances (indicated by 4a, 4b, 4c, and 4d). This rare configuration of multiple images around the center of the lens is an indication of the symmetrical distribution of the lens’ mass (dominated by invisible dark matter) and plays a key role in the lens-modeling process.

Light traveling from far-distant space can be magnified and curved as it passes through the gravitationally distorted space-time of nearer galaxies or clusters of galaxies. In rare instances, a configuration of objects aligns nearly perfectly to form a strong gravitational lens. Using an abundance of new data from the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys, recent observations from NASA’s Hubble Space Telescope, and the Perlmutter supercomputer at the National Energy Research Scientific Computing Center (NERSC), the research team built on their earlier studies (in May 2020 and Feb 2021) to identify likely strong lens candidates, laying the groundwork for the current discovery.

“Our team has been searching for strong lenses and modeling the most valuable systems,” explains Xiaosheng Huang, a study co-author and member of Berkeley Lab’s Supernova Cosmology Project, and a professor of physics and astronomy at the University of San Francisco. “The Carousel Lens is an incredible alignment of seven galaxies in five groupings that line up nearly perfectly behind the foreground cluster lens. As they appear through the lens, the multiple images of each of the background galaxies form approximately concentric circular patterns around the foreground lens, as in a carousel. It’s an unprecedented discovery, and the computational model generated shows a highly promising prospect for measuring the properties of the cosmos, including those of dark matter and dark energy.”

The study also involved several Berkeley Lab student researchers, including the lead author, William Sheu, an undergraduate student intern with DESI at the beginning of this study, now a PhD student at UCLA and a DESI collaborator.

The Carousel Lens will enable researchers to study dark energy and dark matter in entirely new ways based on the strength of the observational data and its computational model.

“This is an extremely unusual alignment, which by itself will provide a testbed for cosmological studies,” observes Nathalie Palanque-Delabrouille, director of Berkeley Lab’s Physics Division. “It also shows how the imaging done for DESI can be leveraged for other scientific applications,” such as investigating the mysteries of dark matter and the accelerating expansion of the universe, which is driven by dark energy.

While big tech companies have the resources and expertise to meet these demands, they remain beyond the reach of most small and medium-sized enterprises and individuals. To respond to this need, a Concordia-led international team of researchers has developed a new framework to make complex AI tasks more accessible and transparent to users.

The framework, described in an article published in the journal Information Sciences, specializes in providing solutions to deep reinforcement learning (DRL) requests. DRL is a subset of machine learning that combines deep learning, which uses layered neural networks to find patterns in huge data sets, and reinforcement learning, in which an agent learns how to make decisions by interacting with its environment based on a reward/penalty system.

DRL is used in industries as diverse as gaming, robotics, health care and finance.

The framework pairs developers, companies and individuals that have specific but out-of-reach AI needs with service providers who have the resources, expertise and models they require. The service is crowdsourced, built on a blockchain and uses a smart contract — a contract with a pre-defined set of conditions built into the code — to match the users with the appropriate service provider.

“Crowdsourcing the process of training and designing DRL makes the process more transparent and more accessible,” says Ahmed Alagha, a PhD candidate at the Gina Cody School of Engineering and Computer Science and the paper’s lead author.

“With this framework, anyone can sign up and build a history and profile. Based on their expertise, training and ratings, they can be allocated tasks that users are requesting.”

Democratizing DRL

According to his co-author and thesis supervisor Jamal Bentahar, a professor at the Concordia Institute for Information Systems Engineering, this service opens the potential offered by DRL to a much wider population than was previously available.

“To train a DRL model, you need computational resources that are not available to everyone. You also need expertise. This framework offers both,” he says.

The researchers believe that their system’s design will reduce costs and risk by distributing computation efforts via the blockchain. The potentially catastrophic consequences of a server crash or malicious attack are mitigated by having dozens or hundreds of other machines working on the same problem.

“If a centralized server fails, the whole platform goes down,” Alagha explains. “Blockchain gives you distribution and transparency. Everything is logged on it, so it is very difficult to tamper with.”

The difficult and costly process of training a model to work properly can be shortened by having an existing model available that only requires some relatively minor adjustments to fit a user’s particular needs.

“For instance, suppose a large city develops a model that can automate traffic light sequences to optimize traffic flow and minimize accidents. Smaller cities or towns may not have the resources to develop one on their own, but they can use the one the big city developed and adapt it for their own circumstances.”

Hadi Otrok, Shakti Singh and Rabeb Mizouni of Khalifa University in Abu Dhabi contributed this study.