Computer Science & Engineering

To report a unique transforming-growth-factor-β-induced (TGFBI) gene phenotype with Arg124Leu mutation in an Indian family. A family with 5 affected members presented to our hospital and were clinically diagnosed as suffering from Bowman layer dystrophy after examination. Peripheral blood samples were collected in EDTA from all for genomic DNA isolation. Keratoplasty was performed in 2 patients followed by histopathological evaluation of the cornea. DNA was subjected to PCR amplification of TGFBI and tumor-associated calcium signal transducer 2 (TACSTD2) genes followed by direct sequencing of all coding exons to identify the causative mutations. Slitlamp examination of the cornea revealed superficial reticular opacities with diffuse anterior stromal haze suggestive of Bowman layer dystrophy but histopathological examination revealed the presence of both hyaline and amyloid deposits in the cornea. TGFBI sequencing revealed a heterozygous mutation, Arg124Leu (c.418 G→T) in all the affected members while TACSTD2 did not show any changes. Molecular analysis established the diagnosis of a novel TGFBI variant with Arg124Leu mutation. The presence of lattice- like lines clinically and histopathological demonstration of both amyloid and hyaline deposits with the occurrence of Arg124Leu mutation in all the affected family members are an unusual phenomenon and are here described for the first time.

The spatial libraries that have been developed by Microsoft, IBM and Oracle have substantially changed the capabilities of geospatial computing. These libraries implement several functionalities that include intersection, distance, and area for various geospatial objects. These libraries came out to address a wealth of use cases that were challenging in that era. As time goes by, GPS devices and location-aware mobile technologies increased the demand for geospatial computing, in general, and for real time geostreaming, in particular. Existing commercial spatial libraries were originally designed to support operations on stationary objects with limited or no capabilities for moving objects. In this paper, we introduce the RxSpatial library, a real time reactive spatial library for spatiotemporal stream query processing. RxSpatial provides, (1) a front-end, which is a programming interface for developers who are familiar with the Microsoft. NET Reactive framework and the Microsoft SQL Server Spatial Library, and (2) a back-end for processing spatial operations in a streaming fashion. RxSpatial provides the programming convenience at the front end and the query processing efficiency at the back end.

Connected moving objects with location sensors form the world of the Internet of Moving Things. This world includes people, animals, vehicles, drones, and vessels, to name a few. The conventional spatial libraries including Microsoft SQL Server Spatial (SqlSpatial) are primarily developed to evaluate spatial operations on stationary things. When it comes to real-world applications for the Internet of Moving Things that require realtime tracking and processing, the limitations of these libraries float to the surface. Unfortunately, the SqlSpatial library has very limited operations in this domain. This paper presents the Reactive eXtension Spatial (RxSpatial) library developed to provide real-time processing of spatio-temporal operations on moving objects connected through the Internet of Things. The superiority of the RxSpatial over the basic SqlSpatial is demonstrated throughout extensive experimental evaluations on real and synthetic data sets.

Spatial variability has been observed in many geo-phenomena including climatic zones, USDA plant hardiness zones, and terrestrial habitat types (e.g., forest, grasslands, wetlands, and deserts). However, current deep learning methods follow a spatial-one-size-fits-all(OSFA) approach to train single deep neural network models that do not account for spatial variability. In this work, we propose and investigate a spatial-variability aware deep neural network(SVANN) approach, where distinct deep neural network models are built for each geographic area. We evaluate this approach using aerial imagery from two geographic areas for the task of mapping urban gardens. The experimental results show that SVANN provides better performance than OSFA in terms of precision, recall,and F1-score to identify urban gardens.

Given trajectory data with gaps, we analyze them for possible rendezvous regions. Societal applications include improving maritime safety and regulation, epidemiology, homeland security, and public safety. The challenges come from two aspects. If trajectory data are not available around the rendezvous then either linear or shortestpath interpolation may fail to detect the possible rendezvous. Furthermore, the problem is computationally expensive due to large number of gaps and the trajectories associated. In this paper, we first use the plane sweep algorithm as a baseline. Then we propose a new filtering framework using the concept of a space-time grid. Experimental results and case study on real-world maritime trajectory data show that the proposed approach substantially improves the Area Pruning Efficiency over the baseline technique.

Spatial variability is a prominent feature of various geographic phenomena such as climatic zones, USDA plant hardiness zones, and terrestrial habitat types (e.g., forest, grasslands, wetlands, and deserts). However, current deep learning methods follow a spatial-one-size-fits-all (OSFA) approach to train single deep neural network models that do not account for spatial variability. Quantification of spatial variability can be challenging due to the influence of many geophysical factors. In preliminary work, we proposed a spatial variability aware neural network (SVANN-I, formerly called SVANN ) approach where weights are a function of location but the neural network architecture is location independent. In this work, we explore a more flexible SVANN-E approach where neural network architecture varies across geographic locations. In addition, we provide a taxonomy of SVANN types and a physics inspired interpretation model. Experiments with aerial imagery based wetland mapping show t...

Given Spatial Variability Aware Neural Networks (SVANNs), the goal is to investigate mathematical (or computational) models for comparative physical interpretation towards their transparency (e.g., simulatibility, decomposability and algorithmic transparency). This problem is important due to important use-cases such as reusability, debugging, and explainability to a jury in a court of law. Challenges include a large number of model parameters, vacuous bounds on generalization performance of neural networks, risk of overfitting, sensitivity to noise, etc., which all detract from the ability to interpret the models. Related work on either modelspecific or model-agnostic post-hoc interpretation is limited due to a lack of consideration of physical constraints (e.g., mass balance) and properties (e.g., second law of geography). This work investigates physical interpretation of SVANNs using novel comparative approaches based on geographically heterogeneous features. The proposed approac...

Given aggregated mobile device data, the goal is to understand the impact of COVID-19 policy interventions on mobility. This problem is vital due to important societal use cases, such as safely reopening the economy. Challenges include understanding and interpreting questions of interest to policymakers, cross-jurisdictional variability in choice and time of interventions, the large data volume, and unknown sampling bias. The related work has explored the COVID-19 impact on travel distance, time spent at home, and the number of visitors at different points of interest. However, many policymakers are interested in long-duration visits to high-risk business categories and understanding the spatial selection bias to interpret summary reports. We provide an Entity Relationship diagram, system architecture, and implementation to support queries on long-duration visits in addition to fine resolution device count maps to understand spatial bias. We closely collaborated with policymakers to...