Program Verification with Separation Logic and Rely Guarantee

<p>This thesis explores two kinds of program logics that have become important for modern program verification - separation logic, for reasoning about programs that use pointers to build mutable data structures, and rely guarantee reasoning, for reasoning about shared variable concurrent programs. We look more closely into the motivations for merging these two kinds of logics into a single formalism that exploits the benefits of both approaches - local, modular, and explicit reasoning about interference between threads in a shared memory concurrent program. We discuss in detail two such formalisms - RGSep and Local Rely Guarantee (LRG), in particular we analyse how each formalism models program state and treats the distinction between global state (shared by all threads) and local state (private to a given thread) and how each logic models actions performed by threads on shared state, and look into the proof rules specifically for reasoning about atomic blocks of code. We present full examples of proofs in each logic and discuss their differences. This thesis also illustrates how a weakest precondition semantics for separation logic can be used to carry out calculational proofs. We also note how in essence these proofs are data abstraction proofs showing that a data structure implements some abstract data type, and relate this idea to a classic data abstraction technique by Hoare. Finally, as part of the thesis we also present a survey of tools that are currently available for doing manual or semi-automated proofs as well as program analyses with separation logic and rely guarantee.</p>

Program Verification with Separation Logic and Rely Guarantee

<p>This thesis explores two kinds of program logics that have become important for modern program verification - separation logic, for reasoning about programs that use pointers to build mutable data structures, and rely guarantee reasoning, for reasoning about shared variable concurrent programs. We look more closely into the motivations for merging these two kinds of logics into a single formalism that exploits the benefits of both approaches - local, modular, and explicit reasoning about interference between threads in a shared memory concurrent program. We discuss in detail two such formalisms - RGSep and Local Rely Guarantee (LRG), in particular we analyse how each formalism models program state and treats the distinction between global state (shared by all threads) and local state (private to a given thread) and how each logic models actions performed by threads on shared state, and look into the proof rules specifically for reasoning about atomic blocks of code. We present full examples of proofs in each logic and discuss their differences. This thesis also illustrates how a weakest precondition semantics for separation logic can be used to carry out calculational proofs. We also note how in essence these proofs are data abstraction proofs showing that a data structure implements some abstract data type, and relate this idea to a classic data abstraction technique by Hoare. Finally, as part of the thesis we also present a survey of tools that are currently available for doing manual or semi-automated proofs as well as program analyses with separation logic and rely guarantee.</p>

Symptomatology of COVID-19 - Lessons from a meta-analysis across 13 countries

Background: COVID-19 pandemic has resulted in varying clinical manifestations and mortality rates. There is no consensus on the symptomatology that would guide researchers and clinicians. Aims and Objectives: The objective was to identify symptoms and their frequencies of COVID-19 with a meta-analysis of studies from several countries. Materials and Methods: Data sources: A systematic review using PubMed and Google Scholar data sources and reference tracing were used to identify 7176 articles. Eligibility criteria: Suitable articles were selected manually with selection criteria and 14 original articles included in meta-analysis. Data abstraction and analysis: PRISMA guidelines used for data abstraction and a table was generated by feeding it with numbers and proportions of each symptom described. A meta-analysis was carried out using random effect models on each symptom separately across the studies and their prevalence rates and 95% confident intervals were calculated. Results: Selected 14 studies, either cross-sectional or cohort studies are analyzed. There were 2,660 confirmed cases of COVID-19. The majority were from China (n=2,439, 91.7%) and remainder from the Netherlands, Italy, Korea, and India and one article from Europe. There were a total of 32 symptoms identified from the meta-analysis and additional 7 symptoms were identified from reference searching. The most common symptoms were (prevalence >50%): fever (79.56%, 95% CI: 72.17–86.09%), malaise (63.3%, 95% CI: 53.1–73.0%), cough (56.7%, 95% CI: 48.6–64.6%), and cold (55.6%, 95% CI: 45.2–65.7%). Symptoms of intermediate incidence (5–49%) were anosmia, sneezing, ocular pain, fatigue, sputum production, arthralgia, tachypnea, palpitation, headache, chest tightness, shortness of breath, chills, myalgia, sore throat, anorexia, weakness, diarrhea, rhinorrhea, dizziness, nausea, altered level of consciousness, vomiting, and abdominal pain. Rare symptoms (<5%): tonsil swelling, hemoptysis, conjunctival injection, lymphadenopathy, and rash. Conclusion: We found (25/32, from meta-analysis) symptoms to be present in ≥5% of cases which could be considered as “typical” symptoms of COVID-19. The list of symptoms we identified is different from those documents released by the WHO, CDC, NHS, Chinese CDC, Institute Pasteur and Mayo Clinic. The compiled list would be useful for future researchers to document a comprehensive picture of the illness.

AlphaPilot: autonomous drone racing

This paper presents a novel system for autonomous, vision-based drone racing combining learned data abstraction, nonlinear filtering, and time-optimal trajectory planning. The system has successfully been deployed at the first autonomous drone racing world championship: the 2019 AlphaPilot Challenge. Contrary to traditional drone racing systems, which only detect the next gate, our approach makes use of any visible gate and takes advantage of multiple, simultaneous gate detections to compensate for drift in the state estimate and build a global map of the gates. The global map and drift-compensated state estimate allow the drone to navigate through the race course even when the gates are not immediately visible and further enable to plan a near time-optimal path through the race course in real time based on approximate drone dynamics. The proposed system has been demonstrated to successfully guide the drone through tight race courses reaching speeds up to $${8}\,{\hbox {m}/\hbox {s}}$$ 8 m / s and ranked second at the 2019 AlphaPilot Challenge.

Data Abstraction

This chapter introduces the techniques used to abstract the data from low-level data to features that can be used to visualize and develop models to inform the different stakeholders. These techniques range from (a) knowledge engineering, where expert knowledge is used to develop formulae to compute various measures from low-level data; (b) feature selection, where specific important variables are selected from the list of low-level data variables as they may be deemed more important than other; and (c) feature extraction, where features are computed statistically through combining various measures from low-level data. The chapter also includes labs where, using real game data, you get to apply the discussed techniques.

Pediatric Chronic Critical Illness: Protocol for a Scoping Review

Background Improvements in the delivery of intensive care have increased survival among even the most critically ill children, thereby leading to a growing number of children with chronic complex medical conditions in the pediatric intensive care unit (PICU). Some of these children are at a significant risk of recurrent and prolonged critical illness, with higher morbidity and mortality, making them a unique population described as having chronic critical illness (CCI). To date, pediatric CCI has been understudied and lacks an accepted consensus case definition. Objective This study aims to describe the protocol and methodology used to perform a scoping review that will describe how pediatric CCI has been defined in the literature, including the concept of prolonged PICU admission and the methodologies used to develop any existing definitions. It also aims to describe patient characteristics and outcomes evaluated in the included studies. Methods We will search four electronic databases for studies that evaluated children admitted to any PICU identified with CCI. We will also search for studies describing prolonged PICU admission, as this concept is related to pediatric CCI. Furthermore, we will develop a hybrid crowdsourcing and machine learning (ML) methodology to complete citation screening. Screening and data abstraction will be performed by 2 reviewers independently and in duplicate. Data abstraction will include the details of population definitions, demographic and clinical characteristics of children with CCI, and evaluated outcomes. Results The database search, crowd reviewer recruitment, and ML algorithm development began in March 2021. Citation screening and data abstraction were completed in April 2021. Final data verification is ongoing, with analysis and results anticipated to be completed by fall 2021. Conclusions This scoping review will describe the existing or suggested definitions of pediatric CCI and important demographic and clinical characteristics of patients to whom these definitions have been applied. This review’s results will help inform the development of a consensus case definition for pediatric CCI and set a priority agenda for future research. We will use and demonstrate the validity of crowdsourcing and ML methodologies for improving the efficiency of large scoping reviews. International Registered Report Identifier (IRRID) DERR1-10.2196/30582

Software Tools for Systematic Literature Review in Medicine: A Review and Feature Analysis (Preprint)

BACKGROUND Systematic reviews (SRs) are central to evaluating therapies but have high costs in terms of both time and money. Many software tools exist to assist with SRs, but most tools do not support the full process, and transparency and replicability of SR depends on performing and presenting evidence according to established best practices. OBJECTIVE In order to provide a basis for comparing and selecting between software tools that support SR, we performed a feature-by-feature comparison of SR tools. METHODS We searched for SR tools by reviewing any such tool listed the Systematic Review Toolbox, previous reviews of SR tools, and qualitative Google searching. We included all SR tools that were currently functional, and require no coding and excluded reference managers, desktop applications, and statistical software. The list of features to assess was populated by combining all features assessed in four previous reviews of SR tools; we also added five features (Manual Addition, Screening Automation, Dual Extraction, Living review, Public outputs) that were independently noted as best practices or enhancements of transparency/replicability. Then, two reviewers assigned binary “present/absent” assessments to all SR tools with respect to all features, and a third reviewer adjudicated all disagreements. RESULTS Of 49 SR tools found, 27 were excluded, leaving 22 for assessment. Twenty-eight features were assessed across 6 classes, and the inter-observer agreement was 86.46%. DistillerSR, EPPI-Reviewer Web, and Nested Knowledge support the most features (24/28, 86%), followed by Covidence, SRDB.PRO, SysRev (20/28, 71%). Six tools support fewer than half of all features assessed: SyRF, Data Abstraction Assistant, SWIFT-review, SR-Accelerator, RobotReviewer, and COVID-NMA. Notably, only 9 of 22 tools (41%) support direct search, only four (18%) offer dual extraction, and only 9 (41%) offer living/updatable reviews. CONCLUSIONS DistillerSR, EPPI-Reviewer Web, and Nested Knowledge each offer a high density of SR-focused web-based tools. By transparent comparison and discussion regarding SR tool functionality, the medical community can both choose among existing software offerings and note the areas of growth needed, most notably in the support of living reviews.

ANALISIS METAFORA DALAM LAGU JEPANG BERTEMAKAN BUNUH DIRI

This article aims to examine the metaphor that is used in suicidal-themed Japanese songs. This article uses the theory by Stephen Ullman. The data used in this article are from Japanese songs, that is Nautilus by Yorushika, Yoru ni Kakeru by YOASOBI, Inochi ni Kirawareteiru by Kanzaki Iori, Ruru-chan no Jisatsu Haishin by Shinsei Kamattechan, Kuyamu to Kaite Mirai and Umareta Imi Nado Nakatta by Mafumafu, Aka Pen Onegaishimasu by PowaPowaP, and Ikite Itandayona by Aimyon. The data was collected using simak bebas libat cakap (SBLC) method and analyzed using a descriptive method. The purpose of this article is to find out what kind of metaphors are used by songwriters in their lyrics. The results of research conducted on 8 songs found as many as 27 metaphorical data. Out of the 27 data, abstraction metaphors were mostly found with a total of 16 data, followed by anthropomorphic metaphors with a total of 5 data. Abstraction metaphor and synthetic metaphor have 4 data and 2 data respectively.