Towards computational awareness in autonomous robots: an empirical study of computational kernels

Abstract The potential impact of autonomous robots on everyday life is evident in emerging applications such as precision agriculture, search and rescue, and infrastructure inspection. However, such applications necessitate operation in unknown and unstructured environments with a broad and sophisticated set of objectives, all under strict computation and power limitations. We therefore argue that the computational kernels enabling robotic autonomy must be scheduled and optimized to guarantee timely and correct behavior, while allowing for reconfiguration of scheduling parameters at runtime. In this paper, we consider a necessary first step towards this goal of computational awareness in autonomous robots: an empirical study of a base set of computational kernels from the resource management perspective. Specifically, we conduct a data-driven study of the timing, power, and memory performance of kernels for localization and mapping, path planning, task allocation, depth estimation, and optical flow, across three embedded computing platforms. We profile and analyze these kernels to provide insight into scheduling and dynamic resource management for computation-aware autonomous robots. Notably, our results show that there is a correlation of kernel performance with a robot’s operational environment, justifying the notion of computation-aware robots and why our work is a crucial step towards this goal

Clinical course and outcome of patients with COVID-19 in Mumbai City: an observational study

Objective To understand the outcome of hospitalised patients from Mumbai City, which had the highest number of COVID-19 cases in India.Design Observational study with follow-up.Setting Data extraction from medical records of patients with COVID-19 admitted to Nair Hospital & TN Medical College, Mumbai, India.Participants 689 patients with COVID-19 were admitted in the hospital from 26 March 2020 to 11 May 2020.Primary and secondary outcome measures In-hospital mortality; joint effect of comorbidity and age on the risk of dying.Results A total of 689 patients (median age 44 years) admitted with RT-PCR-confirmed COVID-19 were included in the study. Of these, 77.36% of patients were discharged alive while 22.64% died. 11.61% required some kind of oxygen support while 2.8% of patients required intensive care unit admissions. Older age (HR 2.88, 95% CI 2.09 to 3.98), presence of comorbidities (HR 2.56, 95% CI 1.84 to 3.55), history of hypertension (HR 3.19, 95% CI 1.67 to 6.08), and presence of symptoms at the time of admission (HR 3.21, 95% CI 1.41 to 7.26) were associated with increased risk of in-hospital mortality. Treatment with a combination of azithromycin with hydroxychloroquine, antiviral or steroid compared with no treatment did not alter the disease course and in-hospital mortality. The combined effect of old age and presence of comorbid conditions was more pronounced in women than men.Conclusions In-hospital patients were younger, less symptomatic with lesser need of ventilators and oxygen support as compared with many western countries.Trial registration Not applicable (observational study, not a clinical trial)