Analysis of traffic noise distribution and influence factors in Chinese urban residential blocks.

To improve the acoustic environment of residential blocks, noise mapping is employed in this study to analyze traffic noise distribution and the influence factors of four types of residential blocks in China. The study shows that high-rise small blocks have the highest average noise level (Lavg) for ground and building facades, followed by small low-rise blocks while modern residential blocks yield the lowest value. An analysis of the standard deviation (STD) of spatial statistical noise level (Ln) shows that the STD of the ground and building façade of two types of small blocks is higher than that of other blocks. The analysis of influence factors indicates that the lot area of residential block has significant negative correlation with ground and building facade average noise level (Lavg), and street coverage ratio (SCR) has significant positive correlation with ground and building facade average noise level (Lavg). In low-rise and high-rise small blocks, ground space index (GSI) has significant negative correlation with ground and building facade average noise level (Lavg); street interface density (SID) has significant positive correlation with the STDs of ground and building facade noise. Floor space index (FSI) shows significant positive correlation with the STDs of ground and building facade noise in low-rise small block

Drought events and their effects on vegetation productivity in China

Many parts of the world have experienced frequent and severe droughts during the last few decades. Most previous studies examined the effects of specific drought events on vegetation productivity. In this study, we characterized the drought events in China from 1982 to 2012 and assessed their effects on vegetation productivity inferred from satellite data. We first assessed the occurrence, spatial extent, frequency, and severity of drought using the Palmer Drought Severity Index (PDSI). We then examined the impacts of droughts on China\u27s terrestrial ecosystems using the Normalized Difference Vegetation Index (NDVI). During the period 1982–2012, China\u27s land area (%) experiencing drought showed an insignificant trend. However, the drought conditions had been more severe over most regions in northern parts of China since the end of the 1990s, indicating that droughts hit these regions more frequently due to the drier climate. The severe droughts substantially reduced annual and seasonal NDVI. The magnitude and direction of the detrended NDVI under drought stress varied with season and vegetation type. The inconsistency between the regional means of PDSI and detrended NDVI could be attributed to different responses of vegetation to drought and the timing, duration, severity, and lag effects of droughts. The negative effects of droughts on vegetation productivity were partly offset by the enhancement of plant growth resulting from factors such as lower cloudiness, warming climate, and human activities (e.g., afforestation, improved agricultural management practices)

On the Saturation Phenomenon of Stochastic Gradient Descent for Linear Inverse Problems

Stochastic gradient descent (SGD) is a promising method for solving large-scale inverse problems due to its excellent scalability with respect to data size. The current mathematical theory in the lens of regularization theory predicts that SGD with a polynomially decaying stepsize schedule may suffer from an undesirable saturation phenomenon; i.e., the convergence rate does not further improve with the solution regularity index when it is beyond a certain range. In this work, we present a refined convergence rate analysis of SGD and prove that saturation actually does not occur if the initial stepsize of the schedule is sufficiently small. Several numerical experiments are provided to complement the analysis

On the convergence of stochastic gradient descent for nonlinear inverse problems

In this work, we analyze the regularizing property of the stochastic gradient descent for the numerical solution of a class of nonlinear ill-posed inverse problems in Hilbert spaces. At each step of the iteration, the method randomly chooses one equation from the nonlinear system to obtain an unbiased stochastic estimate of the gradient and then performs a descent step with the estimated gradient. It is a randomized version of the classical Landweber method for nonlinear inverse problems, and it is highly scalable to the problem size and holds significant potential for solving large-scale inverse problems. Under the canonical tangential cone condition, we prove the regularizing property for a priori stopping rules and then establish the convergence rates under a suitable sourcewise condition and a range invariance condition

Spin correlated interferometry for polarized and unpolarized photons on a beam splitter

Spin interferometry of the 4th order for independent polarized as well as unpolarized photons arriving simultaneously at a beam splitter and exhibiting spin correlation while leaving it, is formulated and discussed in the quantum approach. Beam splitter is recognized as a source of genuine singlet photon states. Also, typical nonclassical beating between photons taking part in the interference of the 4th order is given a polarization dependent explanation.Comment: RevTeX, 19 pages, 1 ps figure, author web page at http://m3k.grad.hr/pavici