The most potent weapon against COVID-19 is a vaccine based on messenger RNA (mRNA). The first of these vaccines authorized for use was developed by the German company BioNTech in cooperation with Pfizer, closely followed by the (U.S.-produced) Moderna vaccine. These vaccines send a piece of mRNA into cells of a host. The mRNA instructs the cells to produce masses of the same spike protein that also occurs on the shell of the real coronavirus. The immune system responds by learning to destroy anything showing that protein: if the real virus arrives, the immune system will attack it immediately. This much has been reported widely by the media. But important questions remain. How is mRNA actually synthesized as a transcription of the spike-producing segment of the virus' RNA? How is the selection and replication done? How does mRNA enter a host cell, and how long will it stay there? Will it produce the spike protein forever? Is it perhaps dangerous? And the biggest question of all: How does the immune system record the structure of the foreign protein, how does it recognize the invader, and how is the immune response cranked up? To answer these questions, we bring you a conversation between Ubiquity editor Walter Tichy and his daughter Dr. Evelyn Tichy, an infectious disease expert.

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Student mobility is a shared goal of the member states of the European Union. One ingredient that makes student mobility possible is a universal student ID card that is accepted everywhere and can be coded with services at the institutions visited. The European Student Card (ESC) is such a universal ID card, standardized in Europe. A team of students from the Karlsruhe Institute of Technology developed the software for it. It works as follows: A student with an ESC simply walks up to a self-service kiosk, presents the card to a reader, and then selects the desired services, such as cafeteria, library, lab access, etc. In this interview, the development team will explain how they made this work smoothly, including the security considerations. This project is another example of how undergraduate students can build impressive software if given a challenge, the right tools, and some supervision.

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Vehicle emissions tests used to be done entirely in the laboratory. However, certain car manufacturers cheated on those tests. In response, the European Union introduced emissions tests in real traffic. To make such tests meaningful, they must be performed on routes that meet certain criteria, such as the difference in elevation between start and end points and the proportion of urban and country roads. Finding suitable routes is a complex search problem. Undergraduate students from Karlsruhe Institute of Technology, Germany, developed the first fully automatic solution for finding such routes. In this interview, they share how they did it.

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The purpose of the Mars rover is in its name---to rove, explore, study Martian geology, look for signs of water, look for signs of life (past or present), etc. However, achieving these and other objectives requires putting the rover down on a suitable landing site, i.e. a site suitable for searching for the desired information and safe to land and function without hindrance or breaking down.

The data for making these decisions comes from prior Mars missions. Selecting a suitable landing site is a complex process typically taking several years. Researchers at MIT's Kavli Institute for Astrophysics and Space Research prototyped a new software that can help NASA mission planners to more rapidly and reliably find landing sites, potentially reducing the total time required to weeks. In this interview, Victor Pankratius, leader of the research team, shares some insight into the project.

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Natural language processing, an area of artificial intelligence (AI), has attained remarkable successes. Digital assistants such as Siri and Alexa respond to spoken commands, and understand several languages. Google has demonstrated a machine can call up a restaurant and make a reservation in a manner that is indistinguishable from a human. Automated translation services are used around the world in over a hundred languages. This interview discusses a new and surprising application of language processing in politics. Though the AI software analyzes texts in German, it could be adapted to any language. The underlying technology has wider applications in text analysis, including legal tech, contracting, and others. Here is a summary.

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In this interview, computer scientist Catherine McGeoch demystifies quantum computing and introduces us to a new world of computational thinking.

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In an earlier interview (April 2016), Ubiquity spoke with John Gustafson about the unum, a new format for floating point numbers. The unique property of unums is that they always know how many digits of accuracy they have. Now Gustafson has come up with yet another format that, like the unum 1.0, always knows how accurate it is. But it also allows an almost arbitrary mapping of bit patterns to the reals. In doing so, it paves the way for custom number systems that squeeze the maximum accuracy out of a given number of bits. This new format could have prime applications in deep learning, big data, and exascale computing.

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Dave Schrader, known to his friends as Dr. Dave, worked for 24 years in advanced development and marketing at Teradata, a major data warehouse vendor. He actively gives talks on business analytics, and since retiring has spent time exploring the field of sports analytics. In this interview, Schrader discusses how analytics is playing a significant role in professional sports--from Major League Soccer to the NBA.

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Crunching numbers was the prime task of early computers. The common element of these early computers is they all used integer arithmetic. John Gustafson, one of the foremost experts in scientific computing, has proposed a new number format that provides more accurate answers than standard floats, yet saves space and energy. The new format might well revolutionize the way we do numerical calculations.

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In this wide-ranging interview, we will hear from a pioneer in computational biology on where the field stands and on where it is going. The topics stretch from gene sequencing and protein structure prediction, all the way to personalized medicine and cell regulation. We'll find out how bioinformatics uses a data-driven approach and why personal drugs may become affordable. We'll even discuss whether we will be able to download our brains into computers and live forever.

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Fixing bugs manually is expensive, time-consuming, and unpleasant. How about getting the computer to fix the bugs, automatically? Automatically repairing them might save us from misunderstandings, lack of time, carelessness, or plain old laziness. But this brings into question some fundamental limitations. Yet in the past 10 years, a number of young scientists have taken on automatic bug fixing. This interview discusses the approximations currently in use and how far they can take us.

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Multicore CPUs and GPUs have brought parallel computation within reach of any programmer. How can we put the performance potential of these machines to good use? The contributors of the symposium suggest a number of approaches, among them algorithm engineering, parallel programming languages, compilers that target both SIMD and MIMD architectures, automatic detection and repair of data races, transactional memory, automated performance tuning, and automatic parallelizers. The transition from sequential to parallel computing is now perhaps at the half-way point. Parallel programming will eventually become routine, because advances in hardware, software, and programming tools are simplifying the problems of designing and implementing parallel computations. ...

In this interview conducted by Ubiquity editor Walter Tichy, Prof. Thomas Fahringer of the Institute of Computer Science, University of Innsbruck (Austria) discusses the difficulty in predicting the performance of parallel programs, and the subsequent popularity of auto-tuning to automate program optimization. ...

Chips with multiple processors, called multicore chips, have caused a resurgence of interest in parallel computing. Multicores are now available in servers, PCs, laptops, embedded systems, and mobile devices. Because multiprocessors could be mass-produced for the same cost as uniprocessors, parallel programming is no longer reserved for a small elite of programmers such as operating system developers, database system designers, and supercomputer users. Thanks to multicore chips, everyone's computer is a parallel machine. Parallel computing has become ubiquitous. In this symposium, seven authors examine what it means for computing to enter the parallel age. ...

This article is a personal account of the methodological evolution of software engineering research from the 1970s to the present. ...

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Punched cards were already obsolete when I began my studies at the Technical University of Munich in 1971. Instead, we had the luxury of an interactive, line-oriented editor for typing our programs. Doug Engelbart had already invented the mouse, but the device was not yet available. With line editors, users had to identify lines by numbers and type in awkward substitution commands just to add missing semicolons. Though cumbersome by today's standards, it was obvious that line-oriented editors were far better than punched cards. Not long after, screen oriented editors such as Vi and Emacs appeared. Again, these editors were obvious improvements and everybody quickly made the switch. No detailed usability studies were needed. "Try it and you'll like it" was enough. (Brian Reid at CMU likened screen editors to handing out free cocaine in the schoolyard.) Switching from Assembler to Fortran, Algol, or Pascal also was a no-brainer. But in the late '70s, the acceptance of new technologies for building software seemed to slow down, even though more people were building software tools. Debates raged over whether Pascal was superior to C, without a clear winner. Object-oriented programming, invented back in the '60s with Simula, took decades to be widely adopted. Functional programming is languishing to this day. The debate about whether agile methods are better than plan-driven methods has not led to a consensus. Literally hundreds of software development technologies and programming languages have been invented, written about, and demoed over the years, only to be forgotten. What went wrong?

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