No abstract

No abstract

The flow of novel coronavirus (COVID-19) has affected almost every aspect of human life around the globe. Being the emerging ground and early sufferer of the virus, Wuhan city-data remains a case of multifold significance. Further, it is of notable importance to explore the impact of unique and unprecedented public health response of Chinese authorities-the extreme lockdown of the city. In this research, we investigate the statistical nature of the viral transmission concerning social distancing, extreme quarantine, and robust lockdown interventions. We observed highly convincing and statistically significant evidences in favor of quarantine and social distancing approaches. These findings might help countries, now facing, or likely to face the wave of the virus. We analyzed Wuhan-based data of "number of deaths" and "confirmed cases," extracted from China CDC weekly database, dated from February 13, 2020, to March 24, 2020. To estimate the underlying group structure, the assembled data is further subdivided into three blocks, each consists of two weeks. Thus, the complete data set is studied in three phases, such as, phase 1 (

We describe a novel theoretical framework for modeling structured drawings which contain one or more patterns of repetition in their constituent elements. We then present PatternSketch, a sketch-based drawing tool built using our framework to allow quick construction of structured drawings. PatternSketch can recognize and beautify drawings containing line segments, polylines, arcs, and circles. Users can employ a series of gestures to identify repetitive elements and create new elements based on automatically inferred patterns. PatternSketch leverages the programming-by-example (PBE) paradigm, enabling it to infer non-trivial patterns from a few examples. We show that PatternSketch, with its sketch-based user interface and a unique pattern inference algorithm, enables efficient and natural construction of structured drawings.

No abstract