Scallop: A Language for Neurosymbolic Programming

We present Scallop, a language which combines the benefits of deep learning and logical reasoning. Scallop enables users to write a wide range of neurosymbolic applications and train them in a data- and compute-efficient manner. It achieves these goals through three key features: 1) a flexible symbolic representation that is based on the relational data model; 2) a declarative logic programming language that is based on Datalog and supports recursion, aggregation, and negation; and 3) a framework for automatic and efficient differentiable reasoning that is based on the theory of provenance semirings. We evaluate Scallop on a suite of eight neurosymbolic applications from the literature. Our evaluation demonstrates that Scallop is capable of expressing algorithmic reasoning in diverse and challenging AI tasks, provides a succinct interface for machine learning programmers to integrate logical domain knowledge, and yields solutions that are comparable or superior to state-of-the-art models in terms of accuracy. Furthermore, Scallop's solutions outperform these models in aspects such as runtime and data efficiency, interpretability, and generalizability

MDB: Interactively Querying Datasets and Models

As models are trained and deployed, developers need to be able to systematically debug errors that emerge in the machine learning pipeline. We present MDB, a debugging framework for interactively querying datasets and models. MDB integrates functional programming with relational algebra to build expressive queries over a database of datasets and model predictions. Queries are reusable and easily modified, enabling debuggers to rapidly iterate and refine queries to discover and characterize errors and model behaviors. We evaluate MDB on object detection, bias discovery, image classification, and data imputation tasks across self-driving videos, large language models, and medical records. Our experiments show that MDB enables up to 10x faster and 40\% shorter queries than other baselines. In a user study, we find developers can successfully construct complex queries that describe errors of machine learning models

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

Shifting trends of lung tumours and its diagnosis by lung biopsy: a study of 78 cases

Background: The objective of the study was to study the spectrum of pathological lesions in patients with lung mass and to study correlation between clinical findings, histopathological pattern and immunohistochemical stains in various biopsy specimen for differentiation and typing of tumors.Methods: This retrospective study was done for the period of three years at Department of Pathology, New Civil Hospital, Surat, India, which is a tertiary health care Centre. Here we studied 78 cases of lung biopsy received in formalin, which were subjected to histopathological examination. Immunohistochemistry was performed as and when required.Results: Total 78 lung biopsy specimens were examined. Out of which, 59 cases (75.6%) were neoplastic, 12 cases(15.4%) were non-neoplastic and 7 cases (9%) were inconclusive. The commonest histological type of malignancy was adenocarcinoma which is associated with peripheral mass lesion, female gender and in non-smokers. Commonest non-neoplastic lesion was tuberculosis.  Malignancy was seen quite common in patients presented with lung masses in our institute.Conclusions: Lung tumours are quite common in patients presented with mass lesion. Similar to global trend, adenocarcinoma is the commonest histological type now and associated with change in incidence among women, in non-smokers, molecular alteration and prognosis which need further investigation. Immunohistochemistry is helpful in cases which are not accurately subtyped by histomorphology alone.

Effective Interactive Resolution of Static Analysis Alarms

We propose an interactive approach to resolve static analysis alarms. Our approach synergistically combines a sound but imprecise analysis with precise but unsound heuristics, through user interaction. In each iteration, it solves an optimization problem to find a set of questions for the user such that the expected payoff is maximized. We have implemented our approach in a tool, Ursa, that enables interactive alarm resolution for any analysis specified in the declarative logic programming language Datalog. We demonstrate the effectiveness of Ursa on a state-of-the-art static datarace analysis using a suite of 8 Java programs comprising 41-194 KLOC each. Ursa is able to eliminate 74% of the false alarms per benchmark with an average payoff of 12 per question. Moreover, Ursa prioritizes user effort effectively by posing questions that yield high payoffs earlier

Automated concolic testing of smartphone apps

We present an algorithm and a system for generating input events to exercise smartphone apps. Our approach is based on concolic testing and generates sequences of events automatically and systematically. It alleviates the path-explosion problem by checking a condition on program executions that identifies subsumption between different event sequences. We also describe our implementation of the approach for Android, the most popular smartphone app platform, and the results of an evaluation that demonstrates its effectiveness on five Android apps

Measurement of (n,γ) reaction cross section of 186W-isotope at neutron energy of 20.02±0.58 MeV

The cross-section of 186W(n,γ)187W reaction has been measured at an average neutron energy of 20.02±0.58 MeV by using activation technique. The 27Al(n,α)24Na and 115In(n,n´)115mIn reactions have been used for absolute neutron flux measurement. Theoretically the reaction cross-sections have been calculated by using the TALYS-1.9 code. The results from the present work and the EXFOR based literature data have been compared with the evaluated data and calculated data from TALYS-1.9 code