Automating the Formal Verification of Software

Formally verified correctness is one of the most desirable properties of software systems. Despite great progress made toward verification via interactive proof assistants, such as Coq and Isabelle/HOL, such verification remains one of the most effort-intensive (and often prohibitively difficult) software development activities. Recent work has created tools that automatically synthesize proofs either through reasoning using precomputed facts or using machine learning to model proofs and then perform biased search through the proof space. However, models in existing tools fail to capture the richness present in proofs, such as the information the programmer has access to when writing proofs and the natural language contained within variable names. Furthermore, these prior models do not make use of variations in the learning process and advances in large language models. In this dissertation, I develop tools to improve proof synthesis and to enable fully automating more verification. I first present TacTok, a proof-synthesis tool that models proofs using both the partial proof written thus far and the semantics of the proof state. I then present Diva, a proof-synthesis tool that controls the learning process to produce a diverse set of models and, due to the unique nature of proof synthesis (the existence of the theorem prover, an oracle that infallibly judges a proof’s correctness), efficiently combines these models to improve the overall proving power. I then present Passport, a proof-synthesis tool that systematically explores different ways of encoding identifiers in proofs to improve synthesis. Finally, I present Baldur, a proof-synthesis tool that uses transformer-based pretrained large language models fine-tuned on proofs to generate and repair whole proofs at once, rather than one step at a time. This dissertation contributes new ideas for improving automated proof synthesis and empirically demonstrates that the improvement is significant on large benchmarks consisting of open-source software projects

Magmatic Environments and Timescales: Experimental Studies on Martian Basalt and Terrestrial Dacite.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Getting More out of Large Language Models for Proofs

Large language models have the potential to simplify formal theorem proving and make it more accessible. But how to get the most out of these models is still an open question. To answer this question, we take a step back and explore the failure cases of these models using common prompting-based techniques. Our talk will discuss these failure cases and what they can teach us about how to get more out of these models

Passport: Improving Automated Formal Verification Using Identifiers

Formally verifying system properties is one of the most effective ways of improving system quality, but its high manual effort requirements often render it prohibitively expensive. Tools that automate formal verification, by learning from proof corpora to suggest proofs, have just begun to show their promise. These tools are effective because of the richness of the data the proof corpora contain. This richness comes from the stylistic conventions followed by communities of proof developers, together with the logical systems beneath proof assistants. However, this richness remains underexploited, with most work thus far focusing on architecture rather than making the most of the proof data. In this paper, we develop Passport, a fully-automated proof-synthesis tool that systematically explores how to most effectively exploit one aspect of that proof data: identifiers. Passport enriches a predictive Coq model with three new encoding mechanisms for identifiers: category vocabulary indexing, subword sequence modeling, and path elaboration. We compare Passport to three existing base tools which Passport can enhance: ASTactic, Tac, and Tok. In head-to-head comparisons, Passport automatically proves 29% more theorems than the best-performing of these base tools. Combining the three Passport-enhanced tools automatically proves 38% more theorems than the three base tools together, without Passport's enhancements. Finally, together, these base tools and Passport-enhanced tools prove 45% more theorems than the combined base tools without Passport's enhancements. Overall, our findings suggest that modeling identifiers can play a significant role in improving proof synthesis, leading to higher-quality software

Positive mental health in schizophrenia and healthy comparison groups: relationships with overall health and biomarkers

ObjectivePositive psychological factors (PPFs) have been reported to have a significant impact on health in the general population. However, little is known about the relationship of these factors with mental and physical health in schizophrenia.MethodOne hundred and thirty-five outpatients with schizophrenia and 127 healthy comparison subjects (HCs), aged 26-65 years, were evaluated with scales of resilience, optimism, happiness, and perceived stress. Measures of mental and physical health were also obtained. Regression analyses examined associations of a PPF composite with health variables.ResultsRelative to the HCs, the schizophrenia group had lower levels of PPFs. However, there was considerable heterogeneity, with over one-third of schizophrenia participants having values within the 'normative' range. The PPF composite was positively related to mental and physical health variables and with biomarkers of inflammation and insulin resistance. The relationship between PPFs and mental health was particularly strong for individuals with schizophrenia.ConclusionA sizable minority of adults with chronic schizophrenia have levels of resilience, optimism, happiness, and perceived stress similar to HCs. Psychosocial interventions to enhance PPFs should be tested in patients with serious mental illnesses, with the goal of improving their mental health (beyond controlling symptoms of psychosis) and their physical health

Resting-State Functional Connectivity between Fronto-Parietal and Default Mode Networks in Obsessive-Compulsive Disorder

Background: Obsessive-compulsive disorder (OCD) is characterized by an excessive focus on upsetting or disturbing thoughts, feelings, and images that are internally-generated. Internally-focused thought processes are subserved by the ‘‘default mode network’ ’ (DMN), which has been found to be hyperactive in OCD during cognitive tasks. In healthy individuals, disengagement from internally-focused thought processes may rely on interactions between DMN and a frontoparietal network (FPN) associated with external attention and task execution. Altered connectivity between FPN and DMN may contribute to the dysfunctional behavior and brain activity found in OCD. Methods: The current study examined interactions between FPN and DMN during rest in 30 patients with OCD (17 unmedicated) and 32 control subjects (17 unmedicated). Timecourses from seven fronto-parietal seeds were correlated across the whole brain and compared between groups. Results: OCD patients exhibited altered connectivity between FPN seeds (primarily anterior insula) and several regions of DMN including posterior cingulate cortex, medial frontal cortex, posterior inferior parietal lobule, and parahippocampus. These differences were driven largely by a reduction of negative correlations among patients compared to controls. Patients also showed greater positive connectivity between FPN and regions outside DMN, including thalamus, lateral frontal cortex, and somatosensory/motor regions

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Albiglutide and cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease (Harmony Outcomes): a double-blind, randomised placebo-controlled trial

Background: Glucagon-like peptide 1 receptor agonists differ in chemical structure, duration of action, and in their effects on clinical outcomes. The cardiovascular effects of once-weekly albiglutide in type 2 diabetes are unknown. We aimed to determine the safety and efficacy of albiglutide in preventing cardiovascular death, myocardial infarction, or stroke. Methods: We did a double-blind, randomised, placebo-controlled trial in 610 sites across 28 countries. We randomly assigned patients aged 40 years and older with type 2 diabetes and cardiovascular disease (at a 1:1 ratio) to groups that either received a subcutaneous injection of albiglutide (30–50 mg, based on glycaemic response and tolerability) or of a matched volume of placebo once a week, in addition to their standard care. Investigators used an interactive voice or web response system to obtain treatment assignment, and patients and all study investigators were masked to their treatment allocation. We hypothesised that albiglutide would be non-inferior to placebo for the primary outcome of the first occurrence of cardiovascular death, myocardial infarction, or stroke, which was assessed in the intention-to-treat population. If non-inferiority was confirmed by an upper limit of the 95% CI for a hazard ratio of less than 1·30, closed testing for superiority was prespecified. This study is registered with ClinicalTrials.gov, number NCT02465515. Findings: Patients were screened between July 1, 2015, and Nov 24, 2016. 10 793 patients were screened and 9463 participants were enrolled and randomly assigned to groups: 4731 patients were assigned to receive albiglutide and 4732 patients to receive placebo. On Nov 8, 2017, it was determined that 611 primary endpoints and a median follow-up of at least 1·5 years had accrued, and participants returned for a final visit and discontinuation from study treatment; the last patient visit was on March 12, 2018. These 9463 patients, the intention-to-treat population, were evaluated for a median duration of 1·6 years and were assessed for the primary outcome. The primary composite outcome occurred in 338 (7%) of 4731 patients at an incidence rate of 4·6 events per 100 person-years in the albiglutide group and in 428 (9%) of 4732 patients at an incidence rate of 5·9 events per 100 person-years in the placebo group (hazard ratio 0·78, 95% CI 0·68–0·90), which indicated that albiglutide was superior to placebo (p<0·0001 for non-inferiority; p=0·0006 for superiority). The incidence of acute pancreatitis (ten patients in the albiglutide group and seven patients in the placebo group), pancreatic cancer (six patients in the albiglutide group and five patients in the placebo group), medullary thyroid carcinoma (zero patients in both groups), and other serious adverse events did not differ between the two groups. There were three (<1%) deaths in the placebo group that were assessed by investigators, who were masked to study drug assignment, to be treatment-related and two (<1%) deaths in the albiglutide group. Interpretation: In patients with type 2 diabetes and cardiovascular disease, albiglutide was superior to placebo with respect to major adverse cardiovascular events. Evidence-based glucagon-like peptide 1 receptor agonists should therefore be considered as part of a comprehensive strategy to reduce the risk of cardiovascular events in patients with type 2 diabetes. Funding: GlaxoSmithKline