Bicycle is an affordable and environmental friendly alternative to private cars and public transportation. Recently, some big cities in China established the bike-sharing system (BSS) through which people can rent bikes offered by government or commercial companies. However, due to limited parking space, it is often difficult for bikers to park their bicycles in bike stations. This paper envisions approaching this problem by using a self-organized bike redistribution strategy: as time passes by, bike society will form an equilibrium state of bike redistribution.
Imaging in low light is difficult because the number of photons arriving at the sensor is low. Imaging dynamic scenes in low-light environments is even more difficult because as the scene moves, pixels in adjacent frames need to be aligned before they can be denoised. Conventional CMOS image sensors (CIS) are at a particular disadvantage in dynamic low-light settings because the exposure cannot be too short lest the read noise overwhelms the signal. We propose a solution using Quanta Image Sensors (QIS) and present a new image reconstruction algorithm. QIS are single-photon image sensors with photon counting capabilities. Studies over the past decade have confirmed the effectiveness of QIS for low-light imaging but reconstruction algorithms for dynamic scenes in low light remain an open problem. We fill the gap by proposing a student-teacher training protocol that transfers knowledge from a motion teacher and a denoising teacher to a student network. We show that dynamic scenes can be reconstructed from a burst of frames at a photon level of 1 photon per pixel per frame. Experimental results confirm the advantages of the proposed method compared to existing methods.
The Covid-19 pandemic has caused huge losses of lives. Social distancing policies were enacted in an effort to contain the virus. However, they constrained commercial activities, leading to recessions worldwide. Nevertheless, this situation provides an opportunity to investigate how companies' financial measures of liquidity, solvability, activity, and profitability reacted to external risks similar to the pandemic. This paper approaches this issue by collecting data from companies listed on China's Growth Enterprise Market of Shenzhen Stock Exchange. Due to the limited numbers of companies from several industries, only six industries that contain more than 30 companies listed were selected. Several ratios for liquidity, solvability, activity, and profitability were calculated with reported financial data and mapped throughout the studied period. Changes were recorded to determine the sensitivity of these measures. How price changes responded to the increases in the number of covid cases was studied as well. The first finding is that liquidity and solvability ratios were not sensitive to the pandemic for the studied companies. On the contrary, activity and profitability were negatively influenced severely. In addition, prices had a negative relationship with increases in covid cases in general, but the regression result was not statistically significant due to the lack of representations.
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