A Theorem Proving Approach For Automatically Synthesizing Visualizations Of Flow Cytometry Data

Background: Polychromatic flow cytometry is a popular technique that has wide usage in the medical sciences, especially for studying phenotypic properties of cells. The high-dimensionality of data generated by flow cytometry usually makes it difficult to visualize. The naive solution of simply plotting two-dimensional graphs for every combination of observables becomes impractical as the number of dimensions increases. A natural solution is to project the data from the original high dimensional space to a lower dimensional space while approximately preserving the overall relationship between the data points. The expert can then easily visualize and analyze this low-dimensional embedding of the original dataset. Results: This paper describes a new method, SANJAY, for visualizing high-dimensional flow cytometry datasets. This technique uses a decision procedure to automatically synthesize two-dimensional and three-dimensional projections of the original high-dimensional data while trying to minimize distortion. We compare SANJAY to the popular multidimensional scaling (MDS) approach for visualization of small data sets drawn from a representative set of benchmarks, and our experiments show that SANJAY produces distortions that are 1.44 to 4.15 times smaller than those caused due to MDS. Our experimental results show that SANJAY also outperforms the Random Projections technique in terms of the distortions in the projections. Conclusions: We describe a new algorithmic technique that uses a symbolic decision procedure to automatically synthesize low-dimensional projections of flow cytometry data that typically have a high number of dimensions. Our algorithm is the first application, to our knowledge, of using automated theorem proving for automatically generating highly-accurate, low-dimensional visualizations of high-dimensional data

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

Adversarial Attacks On Computer Vision Algorithms Using Natural Perturbations

Verifying the correctness of intelligent embedded systems is notoriously difficult due to the use of machine learning algorithms that cannot provide guarantees of deterministic correctness. In this paper, our validation efforts demonstrate that the OpenCV Histogram of Oriented Gradients (HOG) implementation for human detection is susceptible to errors due to both malicious perturbations and naturally occurring fog phenomena. To the best of our knowledge, we are the first to explicitly employ a natural perturbation (like fog) as an adversarial attack using methods from computer graphics. Our experimental results show that computer vision algorithms are susceptible to errors under a small set of naturally occurring perturbations even if they are robust to a majority of such perturbations. Our methods and results may be of interest to the designers, developers and validation teams of intelligent cyber-physical systems such as autonomous cars

A theorem proving approach for automatically synthesizing visualizations of flow cytometry data

Abstract Background Polychromatic flow cytometry is a popular technique that has wide usage in the medical sciences, especially for studying phenotypic properties of cells. The high-dimensionality of data generated by flow cytometry usually makes it difficult to visualize. The naive solution of simply plotting two-dimensional graphs for every combination of observables becomes impractical as the number of dimensions increases. A natural solution is to project the data from the original high dimensional space to a lower dimensional space while approximately preserving the overall relationship between the data points. The expert can then easily visualize and analyze this low-dimensional embedding of the original dataset. Results This paper describes a new method, SANJAY, for visualizing high-dimensional flow cytometry datasets. This technique uses a decision procedure to automatically synthesize two-dimensional and three-dimensional projections of the original high-dimensional data while trying to minimize distortion. We compare SANJAY to the popular multidimensional scaling (MDS) approach for visualization of small data sets drawn from a representative set of benchmarks, and our experiments show that SANJAY produces distortions that are 1.44 to 4.15 times smaller than those caused due to MDS. Our experimental results show that SANJAY also outperforms the Random Projections technique in terms of the distortions in the projections. Conclusions We describe a new algorithmic technique that uses a symbolic decision procedure to automatically synthesize low-dimensional projections of flow cytometry data that typically have a high number of dimensions. Our algorithm is the first application, to our knowledge, of using automated theorem proving for automatically generating highly-accurate, low-dimensional visualizations of high-dimensional data