Gradient Coordination for Quantifying and Maximizing Knowledge Transference in Multi-Task Learning

Multi-task learning (MTL) has been widely applied in online advertising and recommender systems. To address the negative transfer issue, recent studies have proposed optimization methods that thoroughly focus on the gradient alignment of directions or magnitudes. However, since prior study has proven that both general and specific knowledge exist in the limited shared capacity, overemphasizing on gradient alignment may crowd out task-specific knowledge, and vice versa. In this paper, we propose a transference-driven approach CoGrad that adaptively maximizes knowledge transference via Coordinated Gradient modification. We explicitly quantify the transference as loss reduction from one task to another, and then derive an auxiliary gradient from optimizing it. We perform the optimization by incorporating this gradient into original task gradients, making the model automatically maximize inter-task transfer and minimize individual losses. Thus, CoGrad can harmonize between general and specific knowledge to boost overall performance. Besides, we introduce an efficient approximation of the Hessian matrix, making CoGrad computationally efficient and simple to implement. Both offline and online experiments verify that CoGrad significantly outperforms previous methods.Comment: 5 pages, 3 figure

Assessing Income-Related Inequality on Health Service Utilization among Chinese Rural Migrant Workers with New Co-Operative Medical Scheme: A Multilevel Approach

Background: Eliminating inequality in health service utilization is an explicit goal of China's health system. Rural migrant workers with New Rural Cooperative Medical Insurance (NCMS) still face the dilemma of limited health service; however, there is a lack of analysis or measurement on the income-related inequality of health service utilization. Method: The nationally representative data of the China Labor-Force Dynamic Survey in 2016 were used for analysis. Multilevel regressions were used to obtain robust estimates and to account for various covariates associated with health service utilization of rural migrant workers with NCMS. The concentration index and its decomposition method were applied to quantify the income-related inequality of health service utilization of rural migrant workers. Result: The multilevel model analysis indicated that influencing factors of health service utilization were diversified, including gender, city service quality index, type of industry, the per capita annual income, marital status, health self-assessment, the community health index and the number of friends. The concentration indices of the total cost of inpatient and OOP cost of inpatient were 0.102 (95%CI: 0.031, 0.149), and the CI of OOP cost of inpatient was 0.094 (95%CI: 0.007, 0.119), respectively. The horizontal inequality indices of the total cost of inpatient and OOP cost of inpatient were 0.051 and 0.009, respectively. Conclusion: Our study presented a unique opportunity to examine the potential influence factors of health service utilization of rural migrant workers with NCMS, and highlighted that unequal health service utilization is evident among rural migrant workers with NCMS. This study provides important corroborative evidence to take full account of the contribution of each determinant to the inequality and health service needs among rural migrant workers with NCMS, in order to improve the basic medical insurance and social security systems-particularly for some marginal groups in China

Eelgrass detritus as a food source for the sea cucumber Apostichopus japonicus Selenka (Echinidermata: Holothuroidea) in coastal waters of North China: an experimental study in flow-through systems.

Eelgrass ecosystems have a wide variety of ecological functions in which living tissues and detritus may be a food source for many marine animals. In this study, we conducted a laboratory simulating experiment to understand the trophic relationship between the eelgrass Zostera marina L and the sea cucumber Apostichopus japonicus. A mixture of decaying eelgrass debris and seafloor surface muddy sediments was used as food to feed A. japonicus, and then specific growth rates (SGR) and fecal production rates (FPR) were measured. According to the proportion of eelgrass debris, we designed five treatment diets, i.e., ES0, ES10, ES20, ES40, and ES100, with eelgrass debris accounting for 0%, 10%, 20%, 40%, and 100% in dry weight, respectively. Results showed that diet composition had a great influence on the growth of A. japonicus. Sea cucumbers could use decaying eelgrass debris as their food source; and when the organic content of a mixture of eelgrass debris and sediment was 19.6% (ES40), a relatively high SGR (1.54%·d(-1)) and FPR (1.31 g·ind.(-1) d(-1)) of A. japonicus were obtained. It is suggested that eelgrass beds can not only provide habitat for the sea cucumber A. japonicus but can also provide an indirect food source for the deposit feeder. This means that the restoration and reconstruction of eelgrass beds, especially in coastal waters of China, would be a potential and effective measure for sea-cucumber fisheries, in respect to both resource restoration and aquaculture of this valuable species

Determinants of Differences in Health Service Utilization between Older Rural-to-Urban Migrant Workers and Older Rural Residents: Evidence from a Decomposition Approach

Background: The widening gap in health service utilization between different groups in mainland China has become an important issue that cannot be avoided. Our study explored the existence of differences and the causes of the differences in the health service utilization of older rural-to-urban migrant workers in comparison to older rural dwellers. Further, our study explored socioeconomic differences in health service utilization. Methods: The data from the China Labor-Force Dynamic Survey in 2016, the data from the Urban Statistical Yearbook in 2016, and the Statistical Bulletin were used. Our study applied the latest Andersen Model according to China’s current situation. Before we studied health service utilization, we used Coarsened Exact Matching to control the confounding factors. After matching, 2314 respondents were successfully matched (859 older rural-to-urban migrant workers and 1455 older rural dwellers). The Fairlie decomposition method was used to analyze the differences and the sources of health service utilization between older rural-to-urban migrant workers and their rural counterparts. Results: After matching, the probability two-weeks outpatient for older rural-to-urban migrant workers (5.59%) was significantly lower than older rural dwellers (7.57%). The probability of inpatient for older rural-to-urban migrant workers (5.59%) was significantly lower than older rural dwellers (9.07%). Overall, 17.98% of the total difference for two-week outpatient utilization was due to the observed influence factors. Moreover, 71.88% of total difference in inpatient utilization was due to the observed influence factors. Income quantiles (49.57%), health self-assessments (80.91%), and the sex ratio in the community (−102.29%) were significant in the differences in inpatient utilization. Conclusions: The findings provide important insights into the socioeconomic differences in health service utilization among older rural-to-urban migrant workers and older rural residents in China. These insights urge the government to take full account of the heterogeneity in designing health security system reform and public health interventions targeting vulnerable groups

Herbicide leakage into seawater impacts primary productivity and zooplankton globally

Abstract Predicting the magnitude of herbicide impacts on marine primary productivity remains challenging because the extent of worldwide herbicide pollution in coastal waters and the concentration-response relationships of phytoplankton communities to multiple herbicides are unclear. By analyzing the spatiotemporal distribution of herbicides at 661 bay and gulf stations worldwide from 1990 to 2022, we determined median, third quartile and maximum concentrations of 12 triazine herbicides of 0.18 nmol L−1, 1.27 nmol L−1 and 29.50 nmol L−1 (95%Confidence Interval: CI 1.06, 1.47), respectively. Under current herbicide stress, phytoplankton primary productivity was inhibited by more than 5% at 25% of the sites and by more than 10% at 10% of the sites (95%CI 3.67, 4.34), due to the inhibition of highly abundant sensitive species, community structure/particle size succession (from Bacillariophyta to Dinophyceae and from nano-phytoplankton to micro-phytoplankton), and resulting growth rate reduction. Concurrently, due to food chain cascade effects, the dominant micro-zooplankton population shifted from larger copepod larvae to smaller unicellular ciliates, which might prolong the transmission process in marine food chain and reduce the primary productivity transmission efficiency. As herbicide application rates on farmlands worldwide are correlated with residues in their adjacent seas, a continued future increase in herbicide input may seriously affect the stability of coastal waters

AdaSparse: Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction

Click-through rate (CTR) prediction is a fundamental technique in recommendation and advertising systems. Recent studies have proved that learning a unified model to serve multiple domains is effective to improve the overall performance. However, it is still challenging to improve generalization across domains under limited training data, and hard to deploy current solutions due to their computational complexity. In this paper, we propose a simple yet effective framework AdaSparse for multi-domain CTR prediction, which learns adaptively sparse structure for each domain, achieving better generalization across domains with lower computational cost. In AdaSparse, we introduce domain-aware neuron-level weighting factors to measure the importance of neurons, with that for each domain our model can prune redundant neurons to improve generalization. We further add flexible sparsity regularizations to control the sparsity ratio of learned structures. Offline and online experiments show that AdaSparse outperforms previous multi-domain CTR models significantly

Mean fecal production rates (FPR; g·ind.

<p>^−<b>1</b><b> d</b>^−<b>1</b><b>) of </b><b><i>Apostichopus japonicus</i></b><b> during the experimental period.</b> Means (n = 4) with different letters denoting significant differences (<i>p</i><0.05), and bars representing standard deviations of the means.</p

Initial and final wet weight (g·ind.⁻¹) of <i>A. japonicus</i> for five diet treatments.

<p>Note: values with different letters in the same row were significantly different from each other (n = 4, <i>p</i><0.05).</p

Assimilation efficiency (AE) of <i>Apostichopus japonicus</i>.

<p>Bars represent standard deviations of the means.</p