To investigate the contextual and temporal distribution of false positives (FPs) in a state-of-the-art deep learning (DL)-based atrial fibrillation (AF) detection algorithm when applied to an electrocardiogram (ECG) dataset collected under free-living ambulatory conditions. We hypothesize that under such conditions, the FPs detected by a DL model might have some correlations with the patient’s ambulatory contexts. First, a DL model is trained and evaluated on three public arrhythmia datasets from PhysioNet. It is ensured that the model has state-of-the-art performance on these public datasets. Thereafter, the same model is applied to a 215-days long contextualized single-channel ECG dataset collected under free-living ambulatory conditions. Through a manual examination of the model’s output, ground truth is obtained and the correlations between the patient’s ambulatory contexts and the true/false positive rate are analyzed. Nearly 62% of the segments marked as AF by the model were ≤50 seconds in length, and 99.9% of them were FPs. Among these non-trivial short segments of FPs, almost 78% were mainly associated with three specific contextual events; change in activity, change in body position (especially during the night), and sudden movement acceleration. Moreover, the number of FPs detected by the DL model are higher in female than in male participants. Finally, true positive (TP) AF segments are found more in the morning and late evening. These findings may have significant implications for the current use and future design of DL models for AF detection, and help understand the role of context information in reducing the FP rate in real-time AF detection under free-living conditions.

Memory hierarchies in modern computer systems are complex; often, they include multi-level caches and multiple memory controllers on the same chip. Without careful design, programs suffer from unnecessary data movement between caches and memory, degrading performance and increasing energy consumption. Array regrouping can significantly improve data locality by improving spatial reuse of data and reducing cache contention. However, existing techniques for identifying opportunities for array regrouping are lacking in three ways. First, they provide inadequate information to guide regrouping. Second, the cost of monitoring employed by prior tools to identify regrouping opportunities limits the use of these methods in practice. Third, existing metrics for quantifying the benefits of array regrouping can lead to inappropriate transformations that hurt performance. In this paper, we describe ArrayTool - a lightweight profiler that guides array regrouping. ArrayTool has three unique capabilities. First, it focuses attention on arrays with significant access latency. Second, it identifies the feasibility and quantifies the benefits of regrouping arrays with lightweight array-centric profiling. Third, it works on both shared-memory and distributed-memory parallel programs. To illustrate the utility of ArrayTool, we employ it to analyze three benchmarks. Using the guidance it provides, we regroup program arrays, improving performance from 25% to a factor of two.

As mobile devices evolve, end users have ever increasing demand for ubiquitous network access. WiFi, being now commonplace, has the potential to fulfill this demand. Apart from WiFi hotspots deployed by ISPs, home users start showing interest in sharing bandwidth with others (e.g. Fon bases its business model on users sharing open access points.) However, all existing WiFi sharing approaches are unsecured due to the difficulty of distributing access keys, discouraging potential users.