We present a highly scalable self-training framework for incrementally adapting vision-based end-to-end autonomous driving policies in a semi-supervised manner, i.e., over a continual stream of incoming video data. To facilitate large-scale model training (e.g., open web or unlabeled data), we do not assume access to ground-truth labels and instead estimate pseudo-label policy targets for each video. Our framework comprises three key components: knowledge distillation, a sample purification module, and an exploration and knowledge retention mechanism. First, given sequential image frames, we pseudo-label the data and estimate uncertainty using an ensemble of inverse dynamics models. The uncertainty is used to select the most informative samples to add to an experience replay buffer. We specifically select high-uncertainty pseudo-labels to facilitate the exploration and learning of new and diverse driving skills. However, in contrast to prior work in continual learning that assumes ground-truth labeled samples, the uncertain pseudo-labels can introduce significant noise. Thus, we also pair the exploration with a label refinement module, which makes use of consistency constraints to re-label the noisy exploratory samples and effectively learn from diverse data. Trained as a complete never-ending learning system, we demonstrate state-of-the-art performance on training from domain-changing data as well as millions of images from the open web.

Is there "fat" (overheads) in cloud computing infrastructure software that can be trimmed? Would doing so help ameliorate the need for frequent hardware refreshes and extend the life of existing hardware? In this paper, we demonstrate that, indeed, there is "fat" that can be trimmed by using specialized OS-based software stacks. Doing so can allow decade-old computers to be used for critical cloud infrastructure services, potentially yielding 3x improvements in efficiency compared to standard software stacks on newer hardware. The implications of these results raise the possibility of exploiting OS optimizations to reduce server hardware obsolescence. Further, it suggests the importance of addressing the key portability challenges of specialized OS stacks.

The Rust programming language is becoming increasingly popular among systems programmers due to its efficient performance and robust memory safety guarantees. Rust employs an ownership model to ensure these guarantees by allowing each value to be owned by only one identifier at a time. It uses the concept of borrowing and lifetimes to enable other variables to temporarily borrow values. Despite its benefits, security vulnerabilities have been reported in Rust projects, often attributed to the use of “unsafe” Rust code. These vulnerabilities, in part, arise from incorrect lifetime annotations on function signatures. However, existing tools fail to detect these bugs, primarily because such bugs are rare, challenging to detect through dynamic analysis, and require explicit memory models. To overcome these limitations, we characterize incorrect lifetime annotations as a source of memory safety bugs and leverage this understanding to devise a novel static analysis tool,Yuga, to detect potential lifetime annotation bugs.Yugauses a multi-phase analysis approach, starting with a quick pattern-matching algorithm to identify potential buggy components and then conducting a flow and field-sensitive alias analysis to confirm the bugs. We also curate new datasets of lifetime annotation bugs.Yugasuccessfully detects bugs with good precision on these datasets, and we make the code and datasets publicly available.

OBJECTIVES: Describe the change in mobile technology used by an urban Latino population between 2011 and 2014, and compare findings with national estimates. MATERIALS AND METHODS: Patients were surveyed on medical history and mobile technology use. We analyzed specific areas of mobile health capacity stratified by chronic disease, age, language preference, and educational attainment. RESULTS: Of 2144 Latino patients, the percentage that owned a cell phone and texted were in-line with Pew estimates, but app usage was not. Patients with chronic disease had reduced access to mobile devices (P < .001) and lower use of mobile phone functionalities. DISCUSSION: Prior research suggests that Latinos can access mHealth; however, we observed lower rates among Latino patients actively seeking heath care. CONCLUSION: Published national estimates do not accurately reflect the mobile technology use of Latino patients served by our public safety-net facility. The difference is greater for older, less educated patients with chronic disease.