Energy-Utility Function-Based Resource Control for In-Memory Database Systems LIVE

The ever-increasing demand for scalable database systems is limited by their energy consumption, which is one of the major challenges in research today. While existing approaches mainly focused on transaction-oriented disk-based database systems, we are investigating and optimizing the energy consumption and performance of data-oriented scale-up in-memory database systems that make heavy use of the main power consumers, which are processors and main memory. In this demo, we present energy-utility functions as an approach for enabling the operating system to improve the energy efficiency of scalable in-memory database systems. Our highly interactive demo setup mainly allows attendees to switch between multiple DBMS workloads and watch in detail how the system responds by adapting the hardware configuration appropriately

A highly specific and sensitive serological assay detects SARS‑CoV‑2 antibody levels in COVID‑19 patients that correlate with neutralization

Objective The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic challenges national health systems and the global economy. Monitoring of infection rates and seroprevalence can guide public health measures to combat the pandemic. This depends on reliable tests on active and former infections. Here, we set out to develop and validate a specific and sensitive enzyme linked immunosorbent assay (ELISA) for detection of anti SARS-CoV-2 antibody levels. Methods In our ELISA, we used SARS-CoV-2 receptor-binding domain (RBD) and a stabilized version of the spike (S) ectodomain as antigens. We assessed sera from patients infected with seasonal coronaviruses, SARS-CoV-2 and controls. We determined and monitored IgM-, IgA- and IgG-antibody responses towards these antigens. In addition, for a panel of 22 sera, virus neutralization and ELISA parameters were measured and correlated. Results The RBD-based ELISA detected SARS-CoV-2-directed antibodies, did not cross-react with seasonal coronavirus antibodies and correlated with virus neutralization (R2 = 0.89). Seroconversion started at 5 days after symptom onset and led to robust antibody levels at 10 days after symptom onset. We demonstrate high specificity (99.3%; N = 1000) and sensitivity (92% for IgA, 96% for IgG and 98% for IgM; > 10 days after PCR-proven infection; N = 53) in serum. Conclusions With the described RBD-based ELISA protocol, we provide a reliable test for seroepidemiological surveys. Due to high specificity and strong correlation with virus neutralization, the RBD ELISA holds great potential to become a preferred tool to assess thresholds of protective immunity after infection and vaccination

Mobile Robot Mapping in Populated Environments

The problem of learning maps with mobile robots has received considerable attention over the past years. Most of the approaches, however, assume that the environment is static during the data-acquisition phase. In this paper we consider the problem of creating maps with mobile robots in populated environments. Our approach uses a probabilistic method to track multiple people and to incorporate the estimates of the tracking technique into the mapping process. The resulting maps are more accurate since the number of spurious objects is reduced and since the robustness of range registration is improved. Our approach has been implemented and tested on real robots in indoor and outdoor scenarios. We present several experiments illustrating the capabilities of our approach to generate accurate 2d and 3d maps