Code Generation for Big Data Processing in the Web using WebAssembly

Traditional clusters for cloud computing are quite hard to configure and setup, and the number of cluster nodes is limited by the available hardware in the cluster. We hence envision the concept of a Browser Cloud: One just has to visit with his/her web browser a certain webpage in order to connect his/her computer to the Browser Cloud. In this way the setup of the Browser Cloud is much easier than those of traditional clouds. Furthermore, the Browser Cloud has a much larger number of potential nodes, as any computer running a browser may connect to and be integrated in the Browser Cloud. New challenges arise when setting up a cloud by web browsers: Data is processed within the browser, which requires to use the technologies offered by the browser for this purpose. The typically used JavaScript runtime environment may be too slow, because JavaScript is an interpreted language. Hence we investigate the possibilities for computing the work-intensive part of the query processing inside a virtual machine of the web browser. The technology WebAssemby for virtual machines is recently supported by all major browsers and promises high speedups in comparison with JavaScript. Recent approaches to efficient Big Data processing generate code for the data processing steps of queries. To run the generated code in a WebAssembly virtual machine, an online compiler is needed to generate the WebAssembly bytecode from the generated code. Hence our main contribution is an online compiler to WebAssembly bytecode especially developed to run in the web browser and for Big Data processing based on code generation of the processing steps. In our experiments, the runtimes of Big Data processing using JavaScript is compared with running WebAssembly technologies in the major web browsers

Non-adiabatic effects in periodically driven-dissipative open quantum systems

We present a general method to calculate the quasi-stationary state of a driven-dissipative system coupled to a transmission line (and more generally, to a reservoir) under periodic modulation of its parameters. Using Floquet's theorem, we formulate the differential equation for the system's density operator which has to be solved for a single period of modulation. On this basis we also provide systematic expansions in both the adiabatic and high-frequency regime. Applying our method to three different systems -- two- and three-level models as well as the driven nonlinear cavity -- we propose periodic modulation protocols of parameters leading to a temporary suppression of effective dissipation rates, and study the arising non-adiabatic features in the response of these systems.Comment: 12 pages, 8 figure

Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics

The identification of HLA peptides by mass spectrometry is non-trivial. Here, the authors extended and used the wealth of data from the ProteomeTools project to improve the prediction of non-tryptic peptides using deep learning, and show their approach enables a variety of immunological discoveries

A pragmatic randomized feasibility trial of influenza vaccines

Background The relative vaccine effectiveness (rVE) of high-dose quadrivalent influenza vaccines (QIV-HD) versus standard-dose quadrivalent influenza vaccines (QIV-SD) against hospitalizations and mortality in the general older population has not been evaluated in an individually randomized trial. Because of the large sample size required, such a trial will need to incorporate innovative, pragmatic elements. Methods We conducted a pragmatic, open-label, active-controlled, randomized feasibility trial in Danish citizens aged 65 to 79 years during the 2021–2022 influenza season. Participants were randomly assigned 1:1 to receive QIV-HD or QIV-SD. Randomization was integrated into routine vaccination practice, and the trial relied solely on nationwide administrative health registries for data collection. Outcomes consisted of a feasibility assessment and descriptive rVE estimates. Results We invited 34,000 persons to participate. A total of 12,477 randomly assigned participants were included in the final analyses. Mean (±SD) age was 71.7±3.9 years, and 5877 (47.1%) were women. Registry-based data collection was feasible, with complete follow-up data for 99.9% of participants. Baseline characteristics were comparable to those of the overall Danish population aged 65 to 79 years. The incidence of hospitalization for influenza or pneumonia was 10 (0.2%) of 6245 in the QIV-HD group and 28 (0.4%) of 6232 in the QIV-SD group (rVE, 64.4%; 95% confidence interval, 24.4 to 84.6). All-cause death occurred in 21 (0.3%) and 41 (0.7%) participants in the QIV-HD and QIV-SD groups, respectively (rVE, 48.9%; 95% confidence interval, 11.5 to 71.3). Conclusions Conducting a pragmatic randomized trial of QIV-HD versus QIV-SD using existing infrastructure and registry-based data collection was feasible. The findings of lower incidence of hospitalization for influenza or pneumonia and all-cause mortality in the QIV-HD group compared with the QIV-SD group require replication in a future, fully powered trial. (Funded by Sanofi; ClinicalTrials.gov number, NCT05048589.