On the Structure and Complexity of Rational Sets of Regular Languages

In a recent thread of papers, we have introduced FQL, a precise specification language for test coverage, and developed the test case generation engine FShell for ANSI C. In essence, an FQL test specification amounts to a set of regular languages, each of which has to be matched by at least one test execution. To describe such sets of regular languages, the FQL semantics uses an automata-theoretic concept known as rational sets of regular languages (RSRLs). RSRLs are automata whose alphabet consists of regular expressions. Thus, the language accepted by the automaton is a set of regular expressions. In this paper, we study RSRLs from a theoretic point of view. More specifically, we analyze RSRL closure properties under common set theoretic operations, and the complexity of membership checking, i.e., whether a regular language is an element of a RSRL. For all questions we investigate both the general case and the case of finite sets of regular languages. Although a few properties are left as open problems, the paper provides a systematic semantic foundation for the test specification language FQL

Relationship between local ski bending curvature, lean angle and radial force in alpine skiing

The deflection of the ski is a prerequisite for carved turns. The more the ski is edged, the more the ski has to deflect and the more radial force has to be realised in order to keep the whole edge in contact with the snow. To verify this relationship, local ski bending curvature, the lean angle and the radial force were correlated with each other. Characteristic curvature patterns as well as very large correlations (r > 0.7) between the variables were identified.Die Durchbiegung des Skis ist eine Voraussetzung für gecarvte Schwünge. Je stärker der Ski gekantet ist, desto mehr muss der Ski durchbiegen und desto mehr Radialkraft muss realisiert werden, um die gesamte Kante in Kontakt mit dem Schnee zu halten. Um diesen Zusammenhang zu verifizieren, wurden die lokale Skikrümmung, der Neigungswinkel und die Radialkraft miteinander korreliert. Es wurden sowohl charakteristische Krümmungsmuster als auch sehr große Korrelationen (r > 0,7) zwischen den Variablen festgestellt

Closure properties and complexity of rational sets of regular languages

This work received funding in part by the National Research Network RiSE on Rigorous Systems Engineering (Austrian Science Fund (FWF): S11403-N23), by the Vienna Science and Technology Fund (WWTF) through grant PROSEED, by an Erwin Schrödinger Fellowship (Austrian Science Fund (FWF): J3696-N26), and by the European Research Council under the European Community's Seventh Framework Programme (FP7/2007–2013)/ERC grant agreement DIADEM no. 246858

LIPIcs

The semantics of concurrent data structures is usually given by a sequential specification and a consistency condition. Linearizability is the most popular consistency condition due to its simplicity and general applicability. Nevertheless, for applications that do not require all guarantees offered by linearizability, recent research has focused on improving performance and scalability of concurrent data structures by relaxing their semantics. In this paper, we present local linearizability, a relaxed consistency condition that is applicable to container-type concurrent data structures like pools, queues, and stacks. While linearizability requires that the effect of each operation is observed by all threads at the same time, local linearizability only requires that for each thread T, the effects of its local insertion operations and the effects of those removal operations that remove values inserted by T are observed by all threads at the same time. We investigate theoretical and practical properties of local linearizability and its relationship to many existing consistency conditions. We present a generic implementation method for locally linearizable data structures that uses existing linearizable data structures as building blocks. Our implementations show performance and scalability improvements over the original building blocks and outperform the fastest existing container-type implementations

R-Flurbiprofen Reduces Neuropathic Pain in Rodents by Restoring Endogenous Cannabinoids

Background: R-flurbiprofen, one of the enantiomers of flurbiprofen racemate, is inactive with respect to cyclooxygenase inhibition, but shows analgesic properties without relevant toxicity. Its mode of action is still unclear. Methodology/Principal Findings: We show that R-flurbiprofen reduces glutamate release in the dorsal horn of the spinal cord evoked by sciatic nerve injury and thereby alleviates pain in sciatic nerve injury models of neuropathic pain in rats and mice. This is mediated by restoring the balance of endocannabinoids (eCB), which is disturbed following peripheral nerve injury in the DRGs, spinal cord and forebrain. The imbalance results from transcriptional adaptations of fatty acid amide hydrolase (FAAH) and NAPE-phospholipase D, i.e. the major enzymes involved in anandamide metabolism and synthesis, respectively. R-flurbiprofen inhibits FAAH activity and normalizes NAPE-PLD expression. As a consequence, R-Flurbiprofen improves endogenous cannabinoid mediated effects, indicated by the reduction of glutamate release, increased activity of the anti-inflammatory transcription factor PPAR gamma and attenuation of microglia activation. Antinociceptive effects are lost by combined inhibition of CB1 and CB2 receptors and partially abolished in CB1 receptor deficient mice. R-flurbiprofen does however not cause changes of core body temperature which is a typical indicator of central effects of cannabinoid-1 receptor agonists. Conclusion: Our results suggest that R-flurbiprofen improves the endogenous mechanisms to regain stability after axonal injury and to fend off chronic neuropathic pain by modulating the endocannabinoid system and thus constitutes an attractive, novel therapeutic agent in the treatment of chronic, intractable pain

The German National Registry of Primary Immunodeficiencies (2012-2017)

Introduction: The German PID-NET registry was founded in 2009, serving as the first national registry of patients with primary immunodeficiencies (PID) in Germany. It is part of the European Society for Immunodeficiencies (ESID) registry. The primary purpose of the registry is to gather data on the epidemiology, diagnostic delay, diagnosis, and treatment of PIDs. Methods: Clinical and laboratory data was collected from 2,453 patients from 36 German PID centres in an online registry. Data was analysed with the software Stata® and Excel. Results: The minimum prevalence of PID in Germany is 2.72 per 100,000 inhabitants. Among patients aged 1–25, there was a clear predominance of males. The median age of living patients ranged between 7 and 40 years, depending on the respective PID. Predominantly antibody disorders were the most prevalent group with 57% of all 2,453 PID patients (including 728 CVID patients). A gene defect was identified in 36% of patients. Familial cases were observed in 21% of patients. The age of onset for presenting symptoms ranged from birth to late adulthood (range 0–88 years). Presenting symptoms comprised infections (74%) and immune dysregulation (22%). Ninety-three patients were diagnosed without prior clinical symptoms. Regarding the general and clinical diagnostic delay, no PID had undergone a slight decrease within the last decade. However, both, SCID and hyper IgE- syndrome showed a substantial improvement in shortening the time between onset of symptoms and genetic diagnosis. Regarding treatment, 49% of all patients received immunoglobulin G (IgG) substitution (70%—subcutaneous; 29%—intravenous; 1%—unknown). Three-hundred patients underwent at least one hematopoietic stem cell transplantation (HSCT). Five patients had gene therapy. Conclusion: The German PID-NET registry is a precious tool for physicians, researchers, the pharmaceutical industry, politicians, and ultimately the patients, for whom the outcomes will eventually lead to a more timely diagnosis and better treatment

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Abstract: Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts