Multi-Task Recommendations with Reinforcement Learning

In recent years, Multi-task Learning (MTL) has yielded immense success in Recommender System (RS) applications. However, current MTL-based recommendation models tend to disregard the session-wise patterns of user-item interactions because they are predominantly constructed based on item-wise datasets. Moreover, balancing multiple objectives has always been a challenge in this field, which is typically avoided via linear estimations in existing works. To address these issues, in this paper, we propose a Reinforcement Learning (RL) enhanced MTL framework, namely RMTL, to combine the losses of different recommendation tasks using dynamic weights. To be specific, the RMTL structure can address the two aforementioned issues by (i) constructing an MTL environment from session-wise interactions and (ii) training multi-task actor-critic network structure, which is compatible with most existing MTL-based recommendation models, and (iii) optimizing and fine-tuning the MTL loss function using the weights generated by critic networks. Experiments on two real-world public datasets demonstrate the effectiveness of RMTL with a higher AUC against state-of-the-art MTL-based recommendation models. Additionally, we evaluate and validate RMTL's compatibility and transferability across various MTL models.Comment: TheWebConf202

Beyond Mean-Field Microkinetics: Toward Accurate and Efficient Theoretical Modeling in Heterogeneous Catalysis

Kinetics as the link between atomic scale properties and macroscopic functionalities is indispensable in describing surface chemical reactions and computation-based rational design of catalysts. Kinetic Monte Carlo (KMC) on the explicit lattice can resolve events taking place on the catalytic surfaces at the atomic level. It can explicitly account for spatial correlations due to lateral interactions among adsorbates, which have been proved to significantly affect the surface chemical reactions. However, the disparity in time scales of various processes (e.g., adsorption/desorption, diffusion, and reaction) usually makes brute force KMC simulations impractical. Here, we propose a method, namely XPK, to extend the phenomenological kinetics (PK) for the accurate and efficient microkinetic modeling of heterogeneous catalysis. XPK is achieved through a hybrid between the diffusion-only KMC on the explicit lattice to evaluate the reaction propensities and later an implicit lattice KMC in the PK form to evolve the coverages and calculate the final rates. XPK is tested against the explicit lattice KMC using model systems and is applied to describe the volcano curve of ammonia decomposition on the close-packed surfaces of transition metals with lateral interactions among adsorbates being introduced. The results demonstrate the accuracy of XPK, show the significant influences of the lateral interactions on both the shape of the volcano curve and the position of the volcano top, and highlight the usefulness of XPK in describing complex catalytic kinetics of practical interest and predictive capability in the computation-based rational design of catalysts

Changing cancer survival in China during 2003–15: a pooled analysis of 17 population-based cancer registries

Summary: Background: From 2003 to 2005, standardised 5-year cancer survival in China was much lower than in developed countries and varied substantially by geographical area. Monitoring population-level cancer survival is crucial to the understanding of the overall effectiveness of cancer care. We therefore aimed to investigate survival statistics for people with cancer in China between 2003 and 2015. Methods: We used population-based data from 17 cancer registries in China. Data for the study population was submitted by the end of July 31, 2016, with follow-up data on vital status obtained on Dec 31, 2015. We used anonymised, individual cancer registration records of patients (aged 0–99 years) diagnosed with primary, invasive cancers from 2003 to 2013. Patients eligible for inclusion had data for demographic characteristics, date of diagnosis, anatomical site, morphology, behaviour code, vital status, and last date of contact. We analysed 5-year relative survival by sex, age, and geographical area, for all cancers combined and 26 different cancer types, between 2003 and 2015. We stratified survival estimates by calendar period (2003–05, 2006–08, 2009–11, and 2012–15). Findings: There were 678 842 records of patients with invasive cancer who were diagnosed between 2003 and 2013. Of these records, 659 732 (97·2%) were eligible for inclusion in the final analyses. From 2003–05 to 2012–15, age-standardised 5-year relative survival increased substantially for all cancers combined, for both male and female patients, from 30·9% (95% CI 30·6–31·2) to 40·5% (40·3–40·7). Age-standardised 5-year relative survival also increased for most cancer types, including cancers of the uterus (average change per calendar period 5·5% [95% CI 2·5–8·5]), thyroid (5·4% [3·2–7·6]), cervix (4·5% [2·9–6·2]), and bone (3·2% [2·1–4·4]). In 2012–15, age-standardised 5-year survival for all patients with cancer was higher in urban areas (46·7%, 95% CI 46·5–47·0) than in rural areas (33·6%, 33·3–33·9), except for patients with oesophageal or cervical cancer; but improvements in survival were greater for patients residing in rural areas than in urban areas. Relative survival decreased with increasing age. The increasing trends in survival were consistent with the upward trends of medical expenditure of the country during the period studied. Interpretation: There was a marked overall increase in cancer survival from 2003 to 2015 in the population covered by these cancer registries in China, possibly reflecting advances in the quality of cancer care in these areas. The survival gap between urban and rural areas narrowed over time, although geographical differences in cancer survival remained. Insight into these trends will help prioritise areas that need increased cancer care. Funding: National Key R&D Program of China, PUMC Youth Fund and the Fundamental Research Funds for the Central Universities, and Major State Basic Innovation Program of the Chinese Academy of Medical Sciences