Determining if a court has applied a bright-line or totality-of-the-circumstances rule for Fourth Amendment cases demonstrates a difficult problem even for human lawyers and justices. Determining the type of test that governs an issue is essential to answering a legal question. Modern natural language processing (NLP) tools, such as transformers, demonstrate the capacity to extract relevant features from unlabelled text. This study demonstrates the effectiveness of the BERT, RoBERTa, and ALBERT transformer models to classify Fourth Amendment cases by bright-line or totality-of-the-circumstances rule. Two approaches are considered in which models are trained with either positive language extracted by a domain-expert or with full texts of cases. Transformers attain up to 92.31% accuracy on full texts, further demonstrating the capability of NLP techniques on domain-specific tasks even without handcrafted features.

High-resolution, low-latency apps in computer vision are ubiquitous in today’s world of mixed-reality devices. These innovations provide a platform that can leverage the improving technology of depth sensors and embedded accelerators to enable higher-resolution, lower-latency processing for 3D scenes using depth-upsampling algorithms. This research demonstrates that filter-based upsampling algorithms are feasible for mixed-reality apps using low-power hardware accelerators. The authors parallelized and evaluated a depth-upsampling algorithm on two different devices: a reconfigurable-logic FPGA embedded within a low-power SoC; and a fixed-logic embedded graphics processing unit. We demonstrate that both accelerators can meet the real-time requirements of 11 ms latency for mixed-reality apps. 1

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