Tyler O'Neal, Staff Editor SYSTEMS

UCL Antikythera Research Team recreates a mechanical Cosmos for the world's first computer

Researchers at UCL have solved a major piece of the puzzle that makes up the ancient Greek astronomical calculator known as the Antikythera Mechanism, a hand-powered mechanical device that was used to predict astronomical events.

Known to many as the world's first analog computer, the Antikythera Mechanism is the most complex piece of engineering to have survived from the ancient world. The 2,000-year-old device was used to predict the positions of the Sun, Moon, and the planets as well as lunar and solar eclipses.

Published in Scientific Reports, the paper from the multidisciplinary UCL Antikythera Research Team reveals a new display of the ancient Greek order of the Universe (Cosmos), within a complex gearing system at the front of the Mechanism.

Lead author Professor Tony Freeth (UCL Mechanical Engineering) explained: "Ours is the first model that conforms to all the physical evidence and matches the descriptions in the scientific inscriptions engraved on the Mechanism itself.

"The Sun, Moon, and planets are displayed in an impressive tour de force of ancient Greek brilliance."

The Antikythera Mechanism has generated both fascination and intense controversy since its discovery in a Roman-era shipwreck in 1901 by Greek sponge divers near the small Mediterranean island of Antikythera.

The astronomical calculator is a bronze device that consists of a complex combination of 30 surviving bronze gears used to predict astronomical events, including eclipses, phases of the moon, positions of the planets, and even dates of the Olympics.

Whilst great progress has been made over the last century to understand how it worked, studies in 2005 using 3D X-rays and surface imaging enabled researchers to show how the Mechanism predicted eclipses and calculated the variable motion of the Moon.

However, until now, a full understanding of the gearing system at the front of the device has eluded the best efforts of researchers. Only about a third of the Mechanism has survived and is split into 82 fragments - creating a daunting challenge for the UCL team.

The biggest surviving fragment, known as Fragment A, displays features of bearings, pillars, and a block. Another, known as Fragment D, features an unexplained disk, 63-tooth gear, and plate.

Previous research had used X-ray data from 2005 to reveal thousands of text characters hidden inside the fragments, unread for nearly 2,000 years. Inscriptions on the back cover include a description of the cosmos display, with the planets moving on rings and indicated by marker beads. It was this display that the team worked to reconstruct.

Two critical numbers in the X-rays of the front cover, of 462 years and 442 years, accurately represent cycles of Venus and Saturn respectively. When observed from Earth, the planets' cycles sometimes reverse their motions against the stars. Experts must track these variable cycles over long time periods to predict their positions.

"The classic astronomy of the first millennium BC originated in Babylon, but nothing in this astronomy suggested how the ancient Greeks found the highly accurate 462-year cycle for Venus and 442-year cycle for Saturn," explained a Ph.D. candidate and UCL Antikythera Research Team member Aris Dacanalis.

Using an ancient Greek mathematical method described by the philosopher Parmenides, the UCL team not only explained how the cycles for Venus and Saturn were derived but also managed to recover the cycles of all the other planets, where the evidence was missing.

Ph.D. candidate and team member David Higgon explained: "After considerable struggle, we managed to match the evidence in Fragments A and D to a mechanism for Venus, which exactly models its 462-year planetary period relation, with the 63-tooth gear playing a crucial role."

Professor Freeth added: "The team then created innovative mechanisms for all of the planets that would calculate the new advanced astronomical cycles and minimize the number of gears in the whole system so that they would fit into the tight spaces available."

"This is a key theoretical advance on how the Cosmos was constructed in the Mechanism," added co-author, Dr. Adam Wojcik (UCL Mechanical Engineering). "Now we must prove its feasibility by making it with ancient techniques. A particular challenge will be the system of nested tubes that carried the astronomical outputs."

Danish computer scientist has developed a superb algorithm for shortest path problem

Tyler O'Neal, Staff Editor SYSTEMS

One of the most classic algorithmic problems deals with calculating the shortest path between two points. A more complicated variant of the problem is when the route traverses a changing network--whether this is a road network or the internet. For 40 years, an algorithm has been sought to provide an optimal solution to this problem. Now, computer scientist Christian Wulff-Nilsen of the University of Copenhagen and two research colleagues have come up with a recipe.

When heading somewhere new, most of us leave it to computer algorithms to help us find the best route, whether by using a car's GPS, or public transport and map apps on our phone. Still, there are times when a proposed route doesn't quite align with reality. This is because road networks, public transportation networks, and other networks aren't static. The best route can suddenly be the slowest, e.g. because a queue has formed due to roadworks or an accident.

People probably don't think about the complicated math behind routing suggestions in these types of situations. The software being used is trying to solve a variant for the classic algorithmic "shortest path" problem, the shortest path in a dynamic network. For 40 years, researchers have been working to find an algorithm that can optimally solve this mathematical conundrum. Now, Christian Wulff-Nilsen of the University of Copenhagen's Department of Computer Science has succeeded in cracking the nut along with two colleagues.

"We have developed an algorithm, for which we now have mathematical proof, that it is better than every other algorithm up to now--and the closest thing to optimal that will ever be, even if we look 1000 years into the future," says Associate Professor Wulff-Nilsen. The results were presented at the FOCS 2020 conference. Christian Wulff-Nilsen

Optimally, in this context, refers to an algorithm that spends as little time and as little computer memory as possible to calculate the best route in a given network. This is not just true of road and transportation networks, but also the internet or any other type of network.

Networks as graphs

The researchers represent a network as a so-called dynamic graph". In this context, a graph is an abstract representation of a network consisting of edges, roads for example, and nodes, representing intersections, for example. When a graph is dynamic, it means that it can change over time. The new algorithm handles changes consisting of deleted edges--for example if the equivalent of a stretch of a road suddenly becomes inaccessible due to roadworks.

"The tremendous advantage of seeing a network as an abstract graph is that it can be used to represent any type of network. It could be the internet, where you want to send data via as short a route as possible, a human brain, or the network of friendship relations on Facebook. This makes graph algorithms applicable in a wide variety of contexts," explains Christian Wulff-Nilsen.

Traditional algorithms assume that a graph is static, which is rarely true in the real world. When these kinds of algorithms are used in a dynamic network, they need to be rerun every time a small change occurs in the graph--which wastes time.

More data necessitates better algorithms

Finding better algorithms is not just useful when traveling. It is necessary for virtually any area where data is produced, as Christian Wulff-Nilsen points out:

"We are living in a time when volumes of data grow at a tremendous rate and the development of hardware simply can't keep up. To manage all of the data we produce, we need to develop smarter software that requires less running time and memory. That's why we need smarter algorithms," he says.

He hopes that it will be possible to use this algorithm or some of the techniques behind it in practice, but stresses that this is theoretical evidence and first requires experimentation.

Multiple factors synergistically drive socioeconomic disparities in flu burden

Tyler O'Neal, Staff Editor SYSTEMS

Supercomputational modeling identifies areas where inequities are most severe and overlooked

A comprehensive modeling study sheds new light on socioeconomic-based mechanisms that drive disparities in influenza burden across the U.S. Casey Zipfel of Georgetown University in Washington D.C. and colleagues present this analysis in the open-access journal PLOS Computational Biology.

People of lower socioeconomic status experience an increased burden of influenza. Past studies have identified various factors that underlie this health inequity, including decreased flu vaccination, lack of access to paid sick leave, lack of healthcare access, increased susceptibility to infection, and different exposure patterns. However, no previous study has considered all of these factors at once. Flu CREDIT Flockine, Pixabay

For the new study, Zipfel, and colleagues considered how multiple underlying factors independently and synergistically drive health disparities in influenza burden. They combined large-scale disease datasets and observations from past studies to develop data-driven computational models, enabling them to explore how various factors impact influenza transmission and burden for people of varying socioeconomic status across the U.S.

The analysis showed that people of lower socioeconomic status bear a disproportionate burden of influenza infection in the U.S., and this disparity arises from the synergistic combination of multiple social-economic and healthcare factors. The researchers also identified geographic regions where disparities are most severe and where existing systems to track influenza tend to overlook flu cases among people of low socioeconomic status.

"As the divide in health disparities grows wider across the world, it is imperative that we continue to understand how social determinants impact health, and how this is reflected geographically," Zipfel says. "Our work spotlights inequities in respiratory disease transmission, currently on display due to the COVID-19 pandemic."

The new findings could help inform efforts to eliminate public health disparities due to socioeconomic status and systemic racism. Meanwhile, the researchers note the need to collect better data on healthcare access and usage among people of low socioeconomic status to validate their model findings and inform future research and public health efforts.

Freely available article in PLOS Computational Biology: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008642