A Hierarchy of Idealized Monsoons in an Intermediate GCM

A hierarchy of idealized monsoons with increased degrees of complexity is built using an intermediate model with simplified physics and idealized land–sea geometry. This monsoon hierarchy helps formulate a basic understanding about the distribution of the surface equivalent potential temperature θ e, which proves to provide a general guide on the monsoon rainfall. The zonally uniform monsoon in the simplest aquaplanet simulations is explained by a linearized model of the meridional distribution of θ e, which is driven by the seasonally varying solar insolation and damped by both the monsoon overturning circulation and the local negative feedback. The heat capacities of the surface and the atmosphere give rise to an intrinsic time scale that causes the monsoon migration to lag behind the sun and reduces the monsoon extent and intensity. Monsoons with a zonally confined continent can be understood based on the zonally uniform monsoon by considering the ocean influence on the land through the westerly jet advection, which reduces the monsoon extent and induces zonal asymmetry. Monsoon responses to more realistic factors such as land geometry, albedo, and ocean heat flux are consistently predicted by their impacts on the surface θ e distribution. The soil moisture effect, however, does not fully fit into the surface θ e argument and provides additional control on monsoon rainfall by inducing regional circulation and rainfall patterns

An parallel information retrieval method for e-commerce

An online transaction always retrieves a large amount of information before making decisions. Currently, the parallel methods for retrieving such information can only provide a similar performance to serial methods. In this paper we first perform an analysis to determine the factors that affect the performance of exiting methods, i.e., HQR and EHQR, and show that the several of these factors are not considered by these methods. Motivated by this, we propose a new dispatch scheme called AEHQR, which takes into account the features of parallel dispatching. In addition, we provide cost models that determine the optimal performance achievable by any parallel dispatching method. Using experimental comparison, we illustrate that the AEHQR is significantly outperforms the HQR and EHQR under all conditions.<br /

Machine-Learning in the Chinese Factor Zoo

We add to the emerging literature on empirical asset pricing in the Chinese stock market by building and analyzing a comprehensive set of factors with 1,160 signals for return prediction. Using various machine learning algorithms, we investigate which signals dominate in the Chinese market, a market characterized by a large proportion of retail investors with speculative motives, state-owned firms, and short-sales restrictions. Contrary to studies for the U.S. market, liquidity and fundamental factors emerge as the most important predictors, while price trend signals are less significant. We find that retail investors' dominating presence positively affects short-term predictability, particularly for small stocks. Another feature that distinguishes the Chinese from the U.S. market is the high predictability of large stocks and state-owned enterprises over longer horizons. Our portfolio analysis shows that this overall increased predictability leads to significantly higher out-of-sample performance than in other markets, which remains economically significant after transaction costs

Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features

The increasing ubiquity of language technology necessitates a shift towards considering cultural diversity in the machine learning realm, particularly for subjective tasks that rely heavily on cultural nuances, such as Offensive Language Detection (OLD). Current understanding underscores that these tasks are substantially influenced by cultural values, however, a notable gap exists in determining if cultural features can accurately predict the success of cross-cultural transfer learning for such subjective tasks. Addressing this, our study delves into the intersection of cultural features and transfer learning effectiveness. The findings reveal that cultural value surveys indeed possess a predictive power for cross-cultural transfer learning success in OLD tasks and that it can be further improved using offensive word distance. Based on these results, we advocate for the integration of cultural information into datasets. Additionally, we recommend leveraging data sources rich in cultural information, such as surveys, to enhance cultural adaptability. Our research signifies a step forward in the quest for more inclusive, culturally sensitive language technologies.Comment: Findings of EMNLP 202

NO Reduction By Propane Over Monolithic Cordierite-based Fe/Al2O3 Catalyst: Reaction Mechanism And Effect Of H2O/SO2

The selective reduction of NO by C3H8and the sensitivity to H2O and SO2have been studied over monolithic cordierite-based Fe/Al2O3catalysts, which were prepared by the sol–gel and impregnation method. The catalysts were investigated by N2 adsorption, X-ray diffraction (XRD), scanning electron microscope (SEM), X-ray photoelectron spectroscopy (XPS) and in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) techniques. Results showed that NO reduction was more than 90% in the absence of oxygen at 500 °C and in the presence of oxygen at 600 °C respectively. In a continues test of 12 h at 600 °C, 0.02% of SO2caused an irrecoverable decrease of NO conversion from 94% to 85% and 2.5% of H2O caused a drop of NO conversion from 86% to 56%, while NO conversion totally recovered when H2O was removed. The catalysts lost 15% of the initial activity after a hydrothermal treatment due to the agglomeration of iron oxide nanorods. Sulphidation treatment caused about a loss of 30% of the initial activity because of the deposited SO42−species. In situ study by DRIFTS indicated that coexisting H2O influenced the formation NO2 ad species and unidentate nitrate, while SO2 slightly inhibited the formation of NO2/NO3−species but promoted the formation of acetate/formate species during NO reduction by C3H8. Based on the results, a preliminary mechanism was proposed and discussed. The results may help understand the fundamental performance of monolithic cordierite-based Fe/Al2O3catalysts and provide some reference for SCR-HC catalyst design

Decipher the sensitivity of urban canopy air temperature to anthropogenic heat flux with a forcing-feedback framework

The sensitivity of urban canopy air temperature (Ta) to anthropogenic heat flux (QAH) is known to vary with space and time, but the key factors controlling such spatiotemporal variabilities remain elusive. To quantify the contributions of different physical processes to the magnitude and variability of ∆Ta/∆QAH (where ∆ represents a change), we develop a forcing-feedback framework based on the energy budget of air within the urban canopy layer and apply it to diagnosing ∆Ta/∆QAH simulated by the Community Land Model Urban (CLMU) over the contiguous United States (CONUS). In summer, the median ∆Ta/∆QAH is around 0.01 K (W m-2)-1over CONUS. Besides the direct effect of QAH on Ta, there are important feedbacks through changes in the surface temperature, the atmosphere-canopy air heat conductance (ca), and the surface-canopy air heat conductance. The positive and negative feedbacks nearly cancel each other and ∆Ta/∆QAH is mostly controlled by the direct effect in summer. In winter, ∆Ta/∆QAH becomes stronger, with the median value increased by about 20% due to weakened negative feedback associated with ca. The spatial and temporal (both seasonal and diurnal) of ∆Ta/∆QAH and the nonlinear response of ∆Ta to ∆QAH are strongly related to the variability of ca, highlighting the importance of correctly parameterizing convective heat transfer in urban canopy models