Tyler O'Neal, Staff Editor INTERCONNECTS

Tokyo Tech builds a novel processor that solves a notoriously complex mathematical problem

Scientists at the Tokyo Institute of Technology have designed a novel processor architecture that can solve combinatorial optimization problems much faster than existing ones. Combinatorial optimization are complex problems that show up across many different fields of science and engineering and are difficult for conventional computers to handle, making specialized processor architectures very important.

The power of applied mathematics can be seen in the advancements of engineering and other sciences. However, often the mathematical problems used in these applications involve complex calculations that are beyond the capacities of modern computers in terms of time and resources. This is the case for combinatorial optimization problems.

Combinatorial optimization consists in locating an optimal object or solution in a finite set of possible ones. Such problems ubiquitously manifest in the real world across different fields. For example, combinatorial optimization problems show up in finance as portfolio optimization, in logistics as the well-known "traveling salesman problem", in machine learning, and in drug discovery. However, current computers cannot cope with these problems when the number of variables is high. Research overview of STATICA, a novel processor architecture.{module INSIDE STORY}

Fortunately, a team of researchers from the Tokyo Institute of Technology, in collaboration with Hitachi Hokkaido University Laboratory, and the University of Tokyo, have designed a novel processor architecture to specifically solve combinatorial optimization problems expressed in the form of an Ising model. The Ising model was originally used to describe the magnetic states of atoms (spins) in magnetic materials. However, this model can be used as an abstraction to solve combinatorial optimization problems because the evolution of the spins, which tends to reach the so-called lowest-energy state, mirrors how an optimization algorithm searches for the best solution. In fact, the state of the spins in the lowest-energy state can be directly mapped to the solution of a combinatorial optimization problem.

The proposed processor architecture, called STATICA, is fundamentally different from existing processors that calculate Ising models, called annealers. One limitation of most reported annealers is that they only consider spin interactions between neighboring particles. This allows for faster calculation but limits their possible applications. In contrast, STATICA is fully connected and all spin-to-spin interactions are considered. While STATICA's processing speed is lower than those of similar annealers, its calculation scheme is better: it uses parallel updating.

In most annealers, the evolution of spins (updating) is calculated iteratively. This process is inherently serial, meaning that spin switchings are calculated one by one because the switching of one spin affects all the rest in the same iteration. In STATICA, the updating process is carried out in parallel using what is known as stochastic cell automata. Instead of calculating spin states using the spins themselves, STATICA creates replicas of the spins and spin-to-replica interactions are used, allowing for parallel calculation. This saves a tremendous amount of time due to the reduced number of steps needed. "We have proven that conventional approaches and STATICA derive the same solution under certain conditions, but STATICA does so in N times fewer steps, where N is the number of spins in the model," remarks Prof. Masato Motomura, who led this project. Furthermore, the research team implemented an approach called delta-driven spin updating. Because only spins that changed in the previous iteration are important when calculating the following one, a selector circuit is used to only involve spins that flipped in each iteration.

STATICA offers reduced power consumption, higher processing speed, and better accuracy than other annealers. "STATICA aims at revolutionizing annealing processors by solving optimization problems based on the mathematical model of stochastic cell automata. Our initial evaluations have provided strong results," concludes Prof. Motomura. Further refinements will make STATICA an attractive choice for combinatorial optimization.

The Galaxy platform enables the free, transparent overview of coronavirus genome information

Tyler O'Neal, Staff Editor INTERCONNECTS

Dr. Wolfgang Maier and Dr. Björn Grüning from the University of Freiburg, together with researchers from universities in Belgium, Australia, and the USA, have reviewed the previously available data on sequences of the novel coronavirus and published their analyses on the open-source platform Galaxy. The two Freiburg bioinformaticians hope that this will facilitate the exchange of data between authorities, institutes, and laboratories dealing with the virus. The Freiburg researchers have documented their approach and results on the bioRxiv portal.

The Galaxy platform is suitable for big data analysis in life sciences. Public servers provide scientists with free access to analysis tools and reproducible evaluation procedures. Maier, Grüning and their colleagues have used Galaxy to re-analyze all publicly available COVID-19 genome data for their study. Previous publications often lacked transparency with regard to data analysis, explains Grüning. For example, only one of four studies on the COVID-19 genome published at the beginning of February contained clear information on the raw data used, says Grüning. “And the analyses were also not well documented and not reproducible.” As a result, it was not possible to understand or verify the respective statements.

Within a few days, the team was able to apply identical workflows to each of the available sequences and make them publicly accessible via Galaxy. As a result, researchers worldwide now have access to the network of Galaxy servers in Europe, the USA, and Australia, not only for the evaluation of the data but also as the scientific infrastructure for their own work with COVID-19 data. This means that scientists will be able to analyze new COVID-19 datasets on public servers within hours after their release through the same workflows used to analyze the current data.

The researchers agree that there is currently a lack of data exchange in research on COVID-19, says Maier. This should change with the publications on Galaxy. “Global cooperation, which is necessary to deal with public health emergencies such as the COVID-19 outbreak, ultimately requires unrestricted access to data, analytical tools, and computational infrastructure.”

The Galaxy project was initiated at Penn State University in the USA and further developed at the University of Freiburg in the Collaborative Research Centre “Medical Epigenetics” and as part of the German Network for Bioinformatics Infrastructure (de.NBI). The European server is located in the IT Services department at the University of Freiburg and is designed as a community project. The data is freely accessible online. Scientists who wish to use the server do not need to have any programming skills. All analyses can be set up through a graphical user interface. The team at the University of Freiburg led by Prof. Dr. Rolf Backofen from the Department of Computer Science is responsible for Galaxy’s further development.

Dr. David uses prevalent technologies, 'Internet of Things' data for atmospheric science

Tyler O'Neal, Staff Editor INTERCONNECTS

The use of prevalent technologies and crowdsourced data may benefit weather forecasting and atmospheric research, according to a new paper authored by Dr. Noam David, a Visiting Scientist at the Laboratory of Associate Professor Yoshihide Sekimoto at the Institute of Industrial Science, The University of Tokyo, Japan. The paper, published in Advances in Atmospheric Sciences, reviews a number of research works on the subject and points to the potential of this innovative approach.

Specialized instruments for environmental monitoring are often limited as a result of technical and practical constraints. Existing technologies, including remote sensing systems and ground-level tools, may suffer from obstacles such as low spatial representativity (in situ sensors, for example) or lack of accuracy when measuring near the Earth's surface (satellites). These constraints often limit the ability to carry out representative observations and, as a result, the capacity to deepen our existing understanding of atmospheric processes. Multi-systems and IoT (Internet of Things) technologies have become increasingly distributed as they are embedded into our environment. As they become more widely deployed, these technologies generate unprecedented data volumes with immense coverage, immediacy and availability. As a result, a growing opportunity is emerging to complement state-of-the-art monitoring techniques with the large streams of data produced. Notably, these resources were originally designed for purposes other than environmental monitoring and are naturally not as precise as dedicated sensors. Therefore, they should be treated as complementary tools and not as a substitute. However, in the many cases where dedicated instruments are not deployed in the field, these newly available 'environmental sensors' can provide some response which is often invaluable. Dr. Noam David, a Visiting Scientist at the Laboratory of Associate Professor Yoshihide Sekimoto at the Institute of Industrial Science, The University of Tokyo, Japan.{module In-article}

Smartphones, for example, contain weather-sensitive sensors and recent works indicate the ability to use the data collected by these devices on a multisource basis to monitor atmospheric pressure and temperature. Data shared as an open source in social networks can provide vital environmental information reported by thousands of 'human observers' directly from an area of interest. Wireless communication links that form the basis for transmitting data between cellular communication base stations serve as an additional example. Weather conditions affect the signal strength on these links and this effect can be measured. As a result the links can be utilized as an environmental monitoring facility. A variety of studies on the subject point to the ability to monitor rainfall and other hydrometeors including fog, water vapor, dew and even the precursors of air pollution using the data generated by these systems.

Notably, the data from these new 'sensors' could be assimilated into high-resolution numerical prediction models, and thus may lead to improvements in forecasting capabilities. Put to use, this novel approach could provide the groundwork for developing new early-warning systems against natural hazards, and generate a variety of products necessary for a wide range of fields. The contribution to public health and safety as a result of these could potentially be of significant value.