We will demonstrate a prototype of sqlcheck, a holistic toolchain for automatically finding and fixing anti-patterns in database applications. The advent of modern database-as-a-service platforms has made it easy for developers to quickly create scalable applications. However, it is still challenging for developers to design performant, maintainable, and accurate applications. This is because developers may unknowingly introduce anti-patterns in the application's SQL statements. These anti-patterns are design decisions that are intended to solve a problem, but often lead to other problems by violating fundamental design principles. sqlcheck leverages techniques for automatically: (1) detecting anti-patterns with high accuracy, (2) ranking them based on their impact on performance, maintainability, and accuracy of applications, and (3) suggesting alternative queries and changes to the database design to fix these anti-patterns. We will demonstrate that sqlcheck enables developers to create more performant, maintainable, and accurate applications. We will show the prevalence of these anti-patterns in a large collection of queries and databases collected from open-source repositories.

Urban material recognition in remote sensing imagery is a challenging problem due to the difficulty of obtaining human annotations, especially on low resolution satellite images. To this end, we propose an unsupervised domain adaptation-based approach using adversarial learning. We aim to harvest information from smaller quantities of high resolution data (source domain) and utilize the same to super-resolve low resolution imagery (target domain). This can potentially aid in semantic as well as material label transfer from a richly annotated source to a target domain.

A LAser Detection And Ranging (LADAR) apparatus obtains range information from a three dimensional scene by emitting laser beams and collecting the reflected rays from target objects in the region of interest. The Agile Beam LADAR concept makes the measurement and interpretation process more efficient by a software-defined architecture that leverages Computational Imaging principles to this end. Using these techniques, we show that, the process of object identification and scene understanding can be accurately performed in the LADAR measurement domain thereby rendering the efforts of pixel based scene reconstruction superfluous.

We explore the effectiveness of deep features extracted by Convolutional Neural Networks(CNNs) in the Discrete Cosine Transform(DCT) domain for various image classification tasks such as pedestrian and face detection, material identification and object recognition. We perform the DCT operation on the feature maps generated by convolutional layers in CNNs. We compare the performance of the same network on the same datasets, with the same hyper-parameters with or without the DCT step. Our results indicate that a DCT operation incorporated into the network after convolution+thresholding and before pooling can have certain advantages such as convergence over fewer training epochs and sparser weight matrices that are more conducive to pruning and hashing techniques.