On abstraction refinement for program analyses in Datalog

A central task for a program analysis concerns how to efficiently find a program abstraction that keeps only information relevant for proving properties of interest. We present a new approach for finding such abstractions for program analyses written in Datalog. Our approach is based on counterexample-guided abstraction refinement: when a Datalog analysis run fails using an abstraction, it seeks to generalize the cause of the failure to other abstractions, and pick a new abstraction that avoids a similar failure. Our solution uses a boolean satisfiability formulation that is general, complete, and optimal: it is independent of the Datalog solver, it generalizes the failure of an abstraction to as many other abstractions as possible, and it identifies the cheapest refined abstraction to try next. We show the performance of our approach on a pointer analysis and a typestate analysis, on eight real-world Java benchmark programs

Assumption Generation for the Verification of Learning-Enabled Autonomous Systems

Providing safety guarantees for autonomous systems is difficult as these systems operate in complex environments that require the use of learning-enabled components, such as deep neural networks (DNNs) for visual perception. DNNs are hard to analyze due to their size (they can have thousands or millions of parameters), lack of formal specifications (DNNs are typically learnt from labeled data, in the absence of any formal requirements), and sensitivity to small changes in the environment. We present an assume-guarantee style compositional approach for the formal verification of system-level safety properties of such autonomous systems. Our insight is that we can analyze the system in the absence of the DNN perception components by automatically synthesizing assumptions on the DNN behaviour that guarantee the satisfaction of the required safety properties. The synthesized assumptions are the weakest in the sense that they characterize the output sequences of all the possible DNNs that, plugged into the autonomous system, guarantee the required safety properties. The assumptions can be leveraged as run-time monitors over a deployed DNN to guarantee the safety of the overall system; they can also be mined to extract local specifications for use during training and testing of DNNs. We illustrate our approach on a case study taken from the autonomous airplanes domain that uses a complex DNN for perception

Lipid profile in oral submucous fibrosis

<p>Abstract</p> <p>Background</p> <p>Changes in lipid profile have long been associated with malignancies as lipids play a key role in maintenance of cell integrity. This study evaluated the alterations in extended lipid profile in untreated patients of oral submucous fibrosis (OSMF) and studied the correlation between lipid levels with tobacco consumption.</p> <p>Materials and methods</p> <p>In this hospital-based study, 65 clinically diagnosed and histopathologically proven patients of OSMF and 42 age and sex matched controls were studied. In these samples serum lipids including: (i) Total cholesterol, (ii) LDL cholesterol (LDLC), (iii) HDL cholesterol (HDLC) (iv) VLDL cholesterol (VLDLC) (v) triglycerides (vi) Apo-A1 (viii) Apo-B and (viii) LPa were analyzed.</p> <p>Results</p> <p>A significant decrease in plasma total cholesterol, HDLC and Apo-A1 was observed in patients with OSMF as compared to the controls. Thus an inverse relationship between plasma lipid levels and patients was found in OSMF.</p> <p>Conclusion</p> <p>The lower levels of plasma cholesterol and other lipid constituents in patients might be due to their increased utilization. The findings strongly warrant an in-depth study of alterations in plasma lipid profile in patients with oral precancerous conditions.</p

Closed-loop Analysis of Vision-based Autonomous Systems : A Case Study

Deep neural networks (DNNs) are increasingly used in safety-critical autonomous systems as perception components processing high-dimensional image data. Formal analysis of these systems is particularly challenging due to the complexity of the perception DNNs, the sensors (cameras), and the environment conditions. We present a case study applying formal probabilistic analysis techniques to an experimental autonomous system that guides airplanes on taxiways using a perception DNN. We address the above challenges by replacing the camera and the network with a compact probabilistic abstraction built from the confusion matrices computed for the DNN on a representative image data set. We also show how to leverage local, DNN-specific analyses as run-time guards to increase the safety of the overall system. Our findings are applicable to other autonomous systems that use complex DNNs for perception

Typilus: Neural Type Hints

Type inference over partial contexts in dynamically typed languages is challenging. In this work, we present a graph neural network model that predicts types by probabilistically reasoning over a program's structure, names, and patterns. The network uses deep similarity learning to learn a TypeSpace -- a continuous relaxation of the discrete space of types -- and how to embed the type properties of a symbol (i.e. identifier) into it. Importantly, our model can employ one-shot learning to predict an open vocabulary of types, including rare and user-defined ones. We realise our approach in Typilus for Python that combines the TypeSpace with an optional type checker. We show that Typilus accurately predicts types. Typilus confidently predicts types for 70% of all annotatable symbols; when it predicts a type, that type optionally type checks 95% of the time. Typilus can also find incorrect type annotations; two important and popular open source libraries, fairseq and allennlp, accepted our pull requests that fixed the annotation errors Typilus discovered.Comment: Accepted to PLDI 202

Comparative study between the Hybrid Capture II test and PCR based assay for the detection of human papillomavirus DNA in oral submucous fibrosis and oral squamous cell carcinoma

<p>Abstract</p> <p>Background</p> <p>Oral malignancy is a major global health problem. Besides the main risk factors of tobacco, smoking and alcohol, infection by human papillomavirus (HPV) and genetic alterations are likely to play an important role in these lesions. The purpose of this study was to compare the efficacy of HC-II assay and PCR for the detection of specific HPV type (HPV 16 E6) in OSMF and OSCC cases as well as find out the prevalence of the high risk HPV (HR-HPV) in these lesions.</p> <p>Methods and materials</p> <p>Four hundred and thirty patients of the potentially malignant and malignant oral lesions were taken from the Department of Otorhinolaryngology, Moti Lal Nehru Medical College, Allahabad, India from Sept 2007-March 2010. Of which 208 cases were oral submucous fibrosis (OSMF) and 222 cases were oral squamous cell carcinoma (OSCC). The HC-II assay and PCR were used for the detection of HR-HPV DNA.</p> <p>Result</p> <p>The overall prevalence of HR-HPV 16 E6 DNA positivity was nearly 26% by PCR and 27.4% by the HC-II assay in case of potentially malignant disorder of the oral lesions such as OSMF. However, in case of malignant oral lesions such as OSCC, 32.4% HPV 16 E6 positive by PCR and 31.4% by the HC-II assay. In case of OSMF, the two test gave concordant result for 42 positive samples and 154 negative samples, with an overall level of agreement of 85.4% (Cohen's kappa = 66.83%, 95% CI 0.553-0.783). The sensitivity and specificity of the test were 73.7% and 92.05% (p < 0.00). In case of OSCC, the two test gave concordant result for 61 positive samples and 152 negative samples, with an overall level of agreement of 88.3% (Cohen's kappa = 79.29, 95% CI 0.769-0.939) and the sensitivity and specificity of the test were 87.14% and 92.76% (p < 0.00).</p> <p>Conclusion</p> <p>This study concluded that slight difference was found between the positivity rate of HR-HPV infection detected by the HC-II and PCR assay in OSMF and OSCC cases and the HC II assay seemed to have better sensitivity in case of OSCC.</p