Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering

Graph collaborative filtering, which learns user and item representations through message propagation over the user-item interaction graph, has been shown to effectively enhance recommendation performance. However, most current graph collaborative filtering models mainly construct the interaction graph on a single behavior domain (e.g. click), even though users exhibit various types of behaviors on real-world platforms, including actions like click, cart, and purchase. Furthermore, due to variations in user engagement, there exists an imbalance in the scale of different types of behaviors. For instance, users may click and view multiple items but only make selective purchases from a small subset of them. How to alleviate the behavior imbalance problem and utilize information from the multiple behavior graphs concurrently to improve the target behavior conversion (e.g. purchase) remains underexplored. To this end, we propose IMGCF, a simple but effective model to alleviate behavior data imbalance for multi-behavior graph collaborative filtering. Specifically, IMGCF utilizes a multi-task learning framework for collaborative filtering on multi-behavior graphs. Then, to mitigate the data imbalance issue, IMGCF improves representation learning on the sparse behavior by leveraging representations learned from the behavior domain with abundant data volumes. Experiments on two widely-used multi-behavior datasets demonstrate the effectiveness of IMGCF.Comment: accepted by ICDM2023 Worksho

Maternal ambient air pollution exposure with spatial-temporal variations and preterm birth risk assessment during 2013-2017 in Zhejiang Province, China.

Preterm birth (PTB) can give rise to significant neonatal morbidity and mortality, as well as children's long-term health defects. Many studies have illustrated the associations between ambient air pollution exposure during gestational periods and PTB risks, but most of them only focused on one single air pollutant, such as PM2.5. In this population-based environmental-epidemiology study, we recruited 6275 pregnant mothers in Zhejiang Province, China, and evaluated their gestational exposures to various air pollutants during 2013-2017. Time-to-event logistic regressions were performed to estimate risk associations after adjusting all confounders, and Quasi-AQI model and PCA-GLM analysis were applied to resolve the collinearity issues in multi-pollutant regression models. It was found that gestational exposure to ambient air pollutants was significantly associated with the occurrence of PTB, and SO2 was the largest contributor with a proportion of 29.4%. Three new variables, prime factor (a combination of PM2.5, PM10, SO2, and NO2), carbon factor (CO), and ozone factor (O3), were generated by PCA integration, contributing 63.4%, 17.1%, and 19.5% to PTB risks, respectively. The first and third trimester was the most crucial exposure window, suggesting the pregnant mothers better to avoid severe air pollution exposures during these sensitive periods

Estimating household air pollution exposures and health impacts from space heating in rural China

Exposure to and the related burden of diseases caused by pollution from solid fuel cooking, known as household air pollution (HAP), has been incorporated in the assessment of the Global Burden of Diseases (GBD) project. In contrast, HAP from space heating using solid fuels, prevalent in countries at middle or high altitudes, is less studied and missing from the GBD assessment. China is an ideal example to estimate the bias of exposure and burden of diseases assessment when space heating is neglected, considering its remarkably changing demands for heating from the north to the south and a large solid-fuel-dependent rural population. In this study, based on a meta-analysis of 27 field measurement studies in rural China, we derive the indoor PM2.5 (fine particulate matter with an aerodynamic diameter smaller than 2.5 μm) concentration for both the heating and non-heating seasons. Combining this dataset with time-activity patterns and percentage of households using solid fuels, we assess the population-weighted annual mean exposure to PM2.5 (PWE) and the health impacts associated with HAP in mainland rural China by county for the year 2010. We find that ignoring heating impacts leads to an underestimation in PWE estimates by 38 μg/m3 for the nationwide rural population (16 to 40 as interquartile range) with substantial negative bias in northern provinces. Correspondingly, premature deaths and disability-adjusted life years will be underestimated by approximately 30 × 103 and 60 × 104 in 2010, respectively. Our study poses the need for incorporating heating effects into HAP risk assessments in China as well as globally

Terahertz nonlinear hall rectifiers based on spin-polarized topological electronic states in 1T-CoTe2

The zero-magnetic-field nonlinear Hall effect (NLHE) refers to the second-order transverse current induced by an applied alternating electric field; it indicates the topological properties of inversion-symmetry-breaking crystals. Despite several studies on the NLHE induced by the Berry-curvature dipole in Weyl semimetals, the direct current conversion by rectification is limited to very low driving frequencies and cryogenic temperatures. The nonlinear photoresponse generated by the NLHE at room temperature can be useful for numerous applications in communication, sensing, and photodetection across a high bandwidth. In this study, observations of the second-order NLHE in type-II Dirac semimetal CoTe2 under time-reversal symmetry are reported. This is determined by the disorder-induced extrinsic contribution on the broken-inversion-symmetry surface and room-temperature terahertz rectification without the need for semiconductor junctions or bias voltage. It is shown that remarkable photoresponsivity over 0.1 A W−1, a response time of approximately 710 ns, and a mean noise equivalent power of 1 pW Hz−1/2 can be achieved at room temperature. The results open a new pathway for low-energy photon harvesting via nonlinear rectification induced by the NLHE in strongly spin–orbit-coupled and inversion-symmetry-breaking systems, promising a considerable impact in the field of infrared/terahertz photonicsPID2019–109525RB-I00, CEX2018-000805-M, EU’s H2020 NFFA-Europe (n. 654360), and NFFA-Europe-Pilot (10100741

Substantial transition to clean household energy mix in rural China

The household energy mix has significant impacts on human health and climate, as it contributes greatly to many health- and climate-relevant air pollutants. Compared to the well-established urban energy statistical system, the rural household energy statistical system is incomplete and is often associated with high biases. Via a nationwide investigation, this study revealed high contributions to energy supply from coal and biomass fuels in the rural household energy sector, while electricity comprised ∼20%. Stacking (the use of multiple sources of energy) is significant, and the average number of energy types was 2.8 per household. Compared to 2012, the consumption of biomass and coals in 2017 decreased by 45% and 12%, respectively, while the gas consumption amount increased by 204%. Increased gas and decreased coal consumptions were mainly in cooking, while decreased biomass was in both cooking (41%) and heating (59%). The time-sharing fraction of electricity and gases (E&G) for daily cooking grew, reaching 69% in 2017, but for space heating, traditional solid fuels were still dominant, with the national average shared fraction of E&G being only 20%. The non-uniform spatial distribution and the non-linear increase in the fraction of E&G indicated challenges to achieving universal access to modern cooking energy by 2030, particularly in less-developed rural and mountainous areas. In some non-typical heating zones, the increased share of E&G for heating was significant and largely driven by income growth, but in typical heating zones, the time-sharing fraction was <5% and was not significantly increased, except in areas with policy intervention. The intervention policy not only led to dramatic increases in the clean energy fraction for heating but also accelerated the clean cooking transition. Higher income, higher education, younger age, less energy/stove stacking and smaller family size positively impacted the clean energy transition

The effect of the state sector on wage inequality in urban China: 1988–2007

This paper examines the effect of the public sector and state-owned enterprises (SOEs) on wage inequality in urban China using China Household Income Project data. It applies quantile regression analysis, the Machado and Mata decomposition to investigate how urban wage inequality was affected by the changes in wage structure and employment shares of the public sector and SOEs. We find that since the radical state sector reforms designed to reduce overstaffing and improve efficiency in the late 1990s, urban wage gaps were narrowed due to the reduction in the employment share of the state sector; the wage premium of the state sector in comparison with the non-state sector increased significantly; and changes in the wage structure of the labour market caused the rise in urban wage inequality