Faraz Hussain

The design of SafeJML, a specification language for SCJ with support for WCET specification

Safety-Critical Java (SCJ) is a dialect of Java that allows programmers to implement safety-critical systems, such as software to control airplanes, medical devices, and nuclear power plants. SafeJML extends the Java Modeling Language (JML) to allow specification and checking of both functional and timing constraints for SCJ programs. When our design of the SafeJML is implemented, it will help check the correctness of detailed designs, including timing for real-time systems written in SCJ.

Parameter discovery for stochastic biological models against temporal behavioral specifications using an SPRT based Metric for simulated annealing

Stochastic models are often used to study the behavior of biochemical systems and biomedical devices. While the structure of such models is often readily available from first principles, several quantitative features of the model are not easily determined. These quantitative features are often incorporated into the model as parameters. The algorithmic discovery of parameter values from experimentally observed facts (including extreme-scale data) remains a challenge for the computational systems biology community. In this paper, we present a new parameter discovery algorithm based on Walds sequential probability ratio test (SPRT). Our algorithm uses a combination of simulated annealing and sequential hypothesis testing to reduce the number of samples required for parameter discovery of stochastic models. We use probabilistic bounded linear temporal logic (PBLTL) to express the desired behavioral specification of a model. We also present theoretical results on the correctness of our algorithm, and demonstrate the effectiveness of our algorithm by studying a detailed model of glucose and insulin metabolism.

Decision procedure based discovery of rare behaviors in Stochastic Differential Equation models of biological systems

Stochastic Differential Equation (SDE) models are often used to model the dynamics of complex biological systems. The stochastic nature of these models means that some behaviors are more likely than others. It is often the case that a models primary purpose is to study rare but interesting or important behaviors, such as the formation of a tumor, or the failure of a cyber-physical system. Unfortunately, due to the limited availability of analytic methods for SDEs, stochastic simulations are the most common means for estimating (or bounding) the probability of rare behaviors. Naturally, the cost of stochastic simulations increases with the rarity of the behavior under consideration. To address this problem, we introduce a new algorithm, RESERCHE, that is specifically designed to quantify the likelihood of rare but interesting behaviors in SDE models. Our approach relies on the use of temporal logics for specifying rare behaviors of possible interest, and on the ability of bit-vector decision procedures to reason exhaustively about fixed precision arithmetic. We also compute the probability of an observed behavior under the assumption of Gaussian noise.

temporaljmlc: A JML Runtime Assertion Checker Extension for Specification and Checking of Temporal Properties

Most mainstream specification languages primarily deal with a program’s functional behavior. However, for many common problems, besides the system’s functionality, it is necessary to be able to express its temporal properties, such as the necessity of calling methods in a certain order. We have developed temporaljmlc, a tool that performs runtime assertion checking of temporal properties specified in an extension of the Java Modeling Language (JML). The benefit of temporaljmlc is that it allows succinct specification of temporal properties that would otherwise be tedious and difficult to specify.

Integrating symbolic and statistical methods for testing intelligent systems: Applications to machine learning and computer vision

Embedded intelligent systems ranging from tiny implantable biomedical devices to large swarms of autonomous unmanned aerial systems are becoming pervasive in our daily lives. While we depend on the flawless functioning of such intelligent systems, and often take their behavioral correctness and safety for granted, it is notoriously difficult to generate test cases that expose subtle errors in the implementations of machine learning algorithms. Hence, the validation of intelligent systems is usually achieved by studying their behavior on representative data sets, using methods such as cross-validation and bootstrapping. In this paper, we present a new testing methodology for studying the correctness of intelligent systems. Our approach uses symbolic decision procedures coupled with statistical hypothesis testing to validate machine learning algorithms. We show how we have employed our technique to successfully identify subtle bugs (such as bit flips) in implementations of the k-means algorithm. Such errors are not readily detected by standard validation methods such as randomized testing. We also use our algorithm to analyze the robustness of a human detection algorithm built using the OpenCV open-source computer vision library. We show that the human detection implementation can fail to detect humans in perturbed video frames even when the perturbations are so small that the corresponding frames look identical to the naked eye.

Automated parameter estimation for biological models using Bayesian statistical model checking

BackgroundProbabilistic models have gained widespread acceptance in the systems biology community as a useful way to represent complex biological systems. Such models are developed using existing knowledge of the structure and dynamics of the system, experimental observations, and inferences drawn from statistical analysis of empirical data. A key bottleneck in building such models is that some system variables cannot be measured experimentally. These variables are incorporated into the model as numerical parameters. Determining values of these parameters that justify existing experiments and provide reliable predictions when model simulations are performed is a key research problem.Domain experts usually estimate the values of these parameters by fitting the model to experimental data. Model fitting is usually expressed as an optimization problem that requires minimizing a cost-function which measures some notion of distance between the model and the data. This optimization problem is often solved by combining local and global search methods that tend to perform well for the specific application domain. When some prior information about parameters is available, methods such as Bayesian inference are commonly used for parameter learning. Choosing the appropriate parameter search technique requires detailed domain knowledge and insight into the underlying system.ResultsUsing an agent-based model of the dynamics of acute inflammation, we demonstrate a novel parameter estimation algorithm by discovering the amount and schedule of doses of bacterial lipopolysaccharide that guarantee a set of observed clinical outcomes with high probability. We synthesized values of twenty-eight unknown parameters such that the parameterized model instantiated with these parameter values satisfies four specifications describing the dynamic behavior of the model.ConclusionsWe have developed a new algorithmic technique for discovering parameters in complex stochastic models of biological systems given behavioral specifications written in a formal mathematical logic. Our algorithm uses Bayesian model checking, sequential hypothesis testing, and stochastic optimization to automatically synthesize parameters of probabilistic biological models.

Parameter discovery in stochastic biological models using simulated annealing and statistical model checking

Stochastic models are increasingly used to study the behaviour of biochemical systems. While the structure of such models is often readily available from first principles, unknown quantitative features of the model are incorporated into the model as parameters. Algorithmic discovery of parameter values from experimentally observed facts remains a challenge for the computational systems biology community. We present a new parameter discovery algorithm that uses simulated annealing, sequential hypothesis testing, and statistical model checking to learn the parameters in a stochastic model. We apply our technique to a model of glucose and insulin metabolism used for in-silico validation of artificial pancreata and demonstrate its effectiveness by developing parallel CUDA-based implementation for parameter synthesis in this model.

SANJAY: Automatically synthesizing visualizations of flow cytometry data using decision procedures

Polychromatic flow cytometry is a widely used technique for gathering and analyzing cellular data. The data generated is high-dimensional, and therefore notoriously difficult to visualize by a human expert. The traditional method of plotting every pair of observables of the original high-dimensional data leads to a combinatorial explosion in the number of visualizations. The usual solution is to project the data into a lower-dimensional space while approximately preserving key properties and relationships among data points. The lower dimensional data can then be easily analyzed with the help of specialized data visualization software.

Parameter discovery for stochastic computational models in systems biology using Bayesian model checking

Parameterized probabilistic complex computational (P2C2) models are being increasingly used in computational systems biology for analyzing biological systems. A key challenge is to build mechanistic P2C2 models by combining prior knowledge and empirical data, given that certain system properties are unknown. These unknown components are incorporated into a model as parameters and determining their values has traditionally been a process of trial and error. We present a new algorithmic procedure for discovering parameters in agent-based models of biological systems against behavioral specifications mined from large data-sets. Our approach uses Bayesian model checking, sequential hypothesis testing, and stochastic optimization to synthesize parameters of P2C2 models. We demonstrate our algorithm by discovering the amount and schedule of doses of bacterial lipopolysaccharide in a clinical agent-based model of the dynamics of acute inflammation that guarantee a set of desired clinical outcomes with high probability.

Putting humpty-dumpty together: Mining causal mechanistic biochemical models from big data

In traditional engineering disciplines, the construction of a system is usually preceded by a formal or informal specification of the design of the system being developed. In biochemical applications, however, a detailed specification of the systems structure and dynamics is usually unavailable. Thus, mechanistic details of biochemical systems must be mined from experimental observations. In this paper, we adopt a formal methods approach towards deriving causal mechanistic models from time-series observations of biochemical systems. The mined model captures causality among multiple biological events and also allows causal relationships between sets of events. We exploit results from trace theory and use the power of powerful constraint solvers to develop a new framework for causality identification and reasoning that captures dynamic relationships among species in biochemical reaction networks.