Assume, Guarantee or Repair - A Regular Framework for Non Regular Properties

We present Assume-Guarantee-Repair (AGR) - a novel framework which verifies that a program satisfies a set of properties and also repairs the program in case the verification fails. We consider communicating programs - these are simple C-like programs, extended with synchronous actions over communication channels. Our method, which consists of a learning-based approach to assume-guarantee reasoning, performs verification and repair simultaneously: in every iteration, AGR either makes another step towards proving that the (current) system satisfies the required properties, or alters the system in a way that brings it closer to satisfying the properties. To handle infinite-state systems we build finite abstractions, for which we check the satisfaction of complex properties that contain first-order constraints, using both syntactic and semantic-aware methods. We implemented AGR and evaluated it on various communication protocols. Our experiments present compact proofs of correctness and quick repairs

Assume, Guarantee or Repair

We present Assume-Guarantee-Repair (AGR) – a novel framework which not only verifies that a program satisfies a set of properties, but also repairs the program in case the verification fails. We consider communicating programs – these are simple C-like programs, extended with synchronous communication actions over communication channels. Our method, which consists of a learning-based approach to assume-guarantee reasoning, performs verification and repair simultaneously. In every iteration, AGR either makes another step towards proving that the (current) system satisfies the specification, or alters the system in a way that brings it closer to satisfying the specification. We manage handling infinite-state systems by using a finite abstract representation, and reduce the semantic problems in hand – satisfying complex specifications that also contain first-order constraints – to syntactic ones, namely membership and equivalence queries for regular languages. We implemented our algorithm and evaluated it on various examples. Our experiments present compact proofs of correctness and quick repairs

Automated Program Repair Using Formal Verification Techniques

We focus on two different approaches to automatic program repair, based on formal verification methods. Both repair techniques consider infinite-state C-like programs, and consist of a generate-validate loop, in which potentially repaired programs are repeatedly generated and verified. Both approaches are incremental – partial information gathered in previous verification attempts is used in the next steps. However, the settings of both approaches, including their techniques for finding repairs, are quite distinct. The first approach uses syntactic mutations to repair sequential programs with respect to assertions in the code. It is based on a reduction to the problem of finding unsatisfiable sets of constraints, which is addressed using an interplay between SAT and SMT solvers. A novel notion of must-fault-localization enables efficient pruning of the search space, without losing any potential repair. The second approach uses an Assume-Guarantee (AG) style reasoning in order to verify large programs, composed of two concurrent components. The AG reasoning is based on automata-learning techniques. When verification fails, the procedure repeatedly repairs one of the components, until a correct repair is found. Several different repair methods are considered, trading off precision and convergence to a correct repair

HIV-1 infection among women in Israel, 2010–2018

Introduction Although women comprise 33% of the HIV-1-carriers in Israel, they have not previously been considered a risk group requiring special attention. Immigration waves from countries in Africa and in East Europe may have changed the local landscape of women diagnosed with HIV-1. Here, we aimed to assess viral and demographic characteristics of HIV-1-positive women identified in Israel between 2010 and 2018. Methods All > 16 year-old, HIV-1-infected women, diagnosed in Israel in 2010–2018, (n = 763) registered in the National HIV reference laboratory were included in this cross-sectional study. Demographic and clinical characteristics were extracted from the database. Viral subtypes and transmitted drug resistance mutations (TDRM) were determined in 337 (44.2%) randomly selected samples collected from treatment-naive women. Results Median age at diagnosis was 38 years. Most (73.3%) women were immigrants from the former Soviet Union (FSU) (41.2%, 314) or sub-Saharan Africa (SSA) (32.2%, 246) and carried subtype A (79.7%) or C (90.3%), respectively. Only 11.4% (87) were Israeli-born women. Over the years, the prevalence of women from SSA decreased while that of women from FSU increased significantly (p < 0.001). The median CD4+ cell count was 263 cells/mm3, and higher (391 cells/mm3) in Israeli-born women. TDRM were identified in 10.4% of the tested samples; 1.8, 3 and 7.1% had protease inhibitors (PI), nucleotide reverse transcriptase inhibitors (NRTI) and non-nucleoside reverse transcriptase inhibitors (NNRTI) TDRM, respectively. The prevalence of women with NNRTI TDRM significantly increased from 4.9% in 2010–2012 to 13.3% in 2016–2018. Israeli-born women had the highest prevalence (16.3%) of NNRTI TDRM (p = 0.014). NRTI A62 (5.6%), NNRTI E138 and K103 (5.6 and 4.2%, respectively) were the most prominent mutated sites. Conclusions Most HIV-1-positive women diagnosed in Israel in 2010–2018 were immigrants, with the relative ratio of FSU immigrants increasing in recent years. The high proportion of women diagnosed with resistance mutations, particularly, the yearly increase in the frequency of NNRTI mutations, support the national policy of resistance testing at baseline

An Automata-Theoretic Approach to Model-Checking Systems and Specifications Over Infinite Data Domains

Data-parameterized systems model systems with finite control over an infinite data domain. VLTL is an extension of LTL that uses variables in order to specify properties of computations over infinite data, and as such, VLTL is suitable for specifying properties of data-parameterized systems. We present alternating variable Büchi word automata (AVBWs), a new model of automata over infinite alphabets, capable of modeling a significant fragment of VLTL. While alternating and non-deterministic Büchi automata over finite alphabets have the same expressive power, we show that this is not the case for infinite data domains, as we prove that AVBWs are strictly stronger than the previously defined non-deterministic variable Büchi word automata (NVBWs). However, while the emptiness problem is easy for NVBWs, it is undecidable for AVBWs. We present an algorithm for translating AVBWs to NVBWs in cases where such a translation is possible. Additionally, we characterize the structure of AVBWs that can be translated to NVBWs with our algorithm. We then rely on the natural iterative behavior of our translation algorithm to describe a bounded model-checking procedure for the logic that we consider. Furthermore, we present several fragments of the logic that can be expressed by NVBWs, as well as a fragment that cannot be expressed by NVBWs, yet whose satisfiability is decidable

An Automata-Theoretic Approach to Modeling Systems and Specifications over Infinite Data

Data-parameterized systems model finite state systems over an infinite data domain. VLTL is an extension of LTL that uses variables in order to specify properties of computations over infinite data, and as such VLTL is suitable for specifying properties of data-parameterized systems. We present Alternating Variable Büchi Word Automata (AVBWs), a new model of automata over infinite alphabets, capable of modeling a significant fragment of VLTL. While alternating and non-deterministic Büchi automata over finite alphabets have the same expressive power, we show that this is not the case for infinite data domains, as we prove that AVBWs are strictly stronger than the previously defined Non-deterministic Variable Büchi Word Automata (NVBWs). However, while the emptiness problem is easy for NVBWs, it is undecidable for AVBWs. We present an algorithm for translating AVBWs to NVBWs in cases where such a translation is possible. Additionally, we characterize the structure of AVBWs that can be translated to NVBWs with our algorithm, and identify fragments of VLTL for which a direct NVBW construction exists. Since the emptiness problem is crucial in the automata-theoretic approach to model checking, our results give rise to a model-checking algorithm for a rich fragment of VLTL and systems over infinite data domains

Lifestyle Changes, Emotional Eating, Gender, and Stress during COVID-19 Lockdown

Emotional eating poses health risks. It is associated with adverse weight gain and a higher body mass index and is frequently triggered by stressful situations such as pandemics. The COVID-19 pandemic was found to cause stress as well as lifestyle changes of different magnitudes. This study examined the relationship between lifestyle changes and emotional eating by focusing on the moderating effect of gender and COVID-19-related stressors. A total of 1969 respondents completed online questionnaires during the first COVID-19 lockdown in Israel. The questionnaires assessed COVID-19-related lifestyle changes concerning eating habits, alcohol consumption, sleep quality, physical activity, and cigarette smoking, COVID-19-related stressors, and emotional eating (Dutch Eating Behavior Questionnaire). People with positive and negative COVID-19-related lifestyle changes demonstrated higher emotional eating compared to people with no lifestyle changes. The relationship between lifestyle changes and emotional eating was moderated by gender and COVID-19-related stressors. In conclusion, health officials should consider recommendations about lifestyle changes given to the public in times of unpredictable changes, with special attention to populations at risk of emotional eating. As emotional eating is related to poor emotional regulation skills, public nutrition recommendations should focus on improving these skills rather than adopting better diets

Difference between Minorities and Majorities in the Association between COVID-19-Related Stress and Psychological Distress: A Socio-Ecological Perspective and the Moderating Role of Parenthood

This study introduces a socio-ecological perspective of differences in psychological distress between the Palestinian minority and Jewish majority citizens of Israel during lockdown due to COVID-19. The study examines the association between COVID-19-related stress and psychological distress, and the moderating effect of parenthood. Online questionnaires, completed by 1934 participants (1391 Jews, 552 Palestinians; 1306 parents, 637 without children; 54.86% female, 45.13% male; M age = 40.38, SD = 13.77) assessed COVID-19-related stressors and depression, anxiety, and stress. The Palestinian minority showed a higher level of COVID-19-related stress and psychological distress than the Jewish majority. Parenthood showed a moderating effect on the association between COVID-19-related stress and distress for the Jewish majority but not the Palestinian minority. The results emphasize the significance of considering social status when seeking to understand the differences between minorities and majorities in terms of distress and resilience during pandemic events, and the need for cultural sensitivity and awareness when issuing instructions in such circumstances. Additionally, the results highlight the potential role of parenthood as a resilience factor, depending upon social status

Comparison of the new fully automated extraction platform eMAG to the MagNA PURE 96 and the well-established easyMAG for detection of common human respiratory viruses.

Respiratory viral infections constitute the majority of samples tested in the clinical virology laboratory during the winter season, and are mainly diagnosed using molecular assays, namely real-time PCR (qPCR). Therefore, a high-quality extraction process is critical for successful, reliable and sensitive qPCR results. Here we aimed to evaluate the performance of the newly launched eMAG compared to the fully automated MagNA PURE 96 (Roche, Germany) and to the semi-automated easyMAG (bioMerieux, France) extraction platforms. For this analysis, we assessed and compared the analytic and clinical performance of the three platforms, using 262 archived respiratory samples positive or negative to common viruses regularly examined in our laboratory (influenza A, B, H1N1pdm, Respiratory Syncytial Virus (RSV), human Metapneumovirus (hMPV), parainfluenza-3, adenovirus and negative samples). In addition, quantitated virus controls were used to determine the limit of detection of each extraction method. In all categories tested, eMAG results were comparable to those of the easyMAG and MagNa PURE 96, highly sensitive for all viruses and over 98% clinical specificity and sensitivity for all viruses tested. Together with its high level of automation, the bioMerieux eMAG is a high-quality extraction platform enabling effective molecular analysis and is mostly suitable for medium-sized laboratories