Learning a Deep Listwise Context Model for Ranking Refinement

Learning to rank has been intensively studied and widely applied in information retrieval. Typically, a global ranking function is learned from a set of labeled data, which can achieve good performance on average but may be suboptimal for individual queries by ignoring the fact that relevant documents for different queries may have different distributions in the feature space. Inspired by the idea of pseudo relevance feedback where top ranked documents, which we refer as the \textit{local ranking context}, can provide important information about the query's characteristics, we propose to use the inherent feature distributions of the top results to learn a Deep Listwise Context Model that helps us fine tune the initial ranked list. Specifically, we employ a recurrent neural network to sequentially encode the top results using their feature vectors, learn a local context model and use it to re-rank the top results. There are three merits with our model: (1) Our model can capture the local ranking context based on the complex interactions between top results using a deep neural network; (2) Our model can be built upon existing learning-to-rank methods by directly using their extracted feature vectors; (3) Our model is trained with an attention-based loss function, which is more effective and efficient than many existing listwise methods. Experimental results show that the proposed model can significantly improve the state-of-the-art learning to rank methods on benchmark retrieval corpora

Unbiased Learning to Rank with Unbiased Propensity Estimation

Learning to rank with biased click data is a well-known challenge. A variety of methods has been explored to debias click data for learning to rank such as click models, result interleaving and, more recently, the unbiased learning-to-rank framework based on inverse propensity weighting. Despite their differences, most existing studies separate the estimation of click bias (namely the \textit{propensity model}) from the learning of ranking algorithms. To estimate click propensities, they either conduct online result randomization, which can negatively affect the user experience, or offline parameter estimation, which has special requirements for click data and is optimized for objectives (e.g. click likelihood) that are not directly related to the ranking performance of the system. In this work, we address those problems by unifying the learning of propensity models and ranking models. We find that the problem of estimating a propensity model from click data is a dual problem of unbiased learning to rank. Based on this observation, we propose a Dual Learning Algorithm (DLA) that jointly learns an unbiased ranker and an \textit{unbiased propensity model}. DLA is an automatic unbiased learning-to-rank framework as it directly learns unbiased ranking models from biased click data without any preprocessing. It can adapt to the change of bias distributions and is applicable to online learning. Our empirical experiments with synthetic and real-world data show that the models trained with DLA significantly outperformed the unbiased learning-to-rank algorithms based on result randomization and the models trained with relevance signals extracted by click models

High Eifficiency Wireless Power Transfer System Robust against Misalignment

The power transfer efficiency of magnetic resonance coupling wireless power transfer (WPT) system is sensitive to the alignment of the transmitter and receiver. In this paper, a multi-coil WPT structure is proposed to enhance power transfer efficiency. It is shown by experimental results that the proposed WPT system can achieve 90.2% efficiency in aligned working conditions. Meanwhile, this WPT system maintains over 70% efficiency from 0 to 55 mm misalignment distance, which is 66% of the length of the Rx board. The proposed WPT system is suitable for the applications of implant devices for biomedical health care and treatment

Identifying Opportunities for Aligning Production and Consumption in the U.S. Fisheries by Considering Seasonality

Seasonality is a natural feature of wild caught fisheries that introduces variation in food supply, and which often is amplified by fisheries management systems. Seasonal timing of landings patterns and linkages to consumption patterns can have a potentially strong impact on income for coastal communities as well as import patterns. This study characterizes the relationship between seasonality in seafood production and consumption in the United States by analyzing monthly domestic fisheries landings and imports and retail sales of farmed and wild seafood from 2017 to 2019. Analyses were conducted for total seafood sales, by product form, by species group, and by region of the United States. The data reveal strong seasonal increases in consumption around December and March. Seasonal increases in consumption in Spring and Summer occurred in parallel with domestic fishing production. Domestic landings vary by region, but most regions have peak fishing seasons between May and October. Alaska has the largest commercial fishery in the United States and seasonal peaks in Alaska (July/August, February/March) strongly influence seasonality in national landings. Misalignment between domestic production and consumption in some seasons and species groups creates opportunities for imports to supplement demand and lost opportunities for domestic producers.publishedVersio

Neurological complications after first dose of COVID-19 vaccines and SARS-CoV-2 infection

Emerging reports of rare neurological complications associated with COVID-19 infection and vaccinations are leading to regulatory, clinical and public health concerns. We undertook a self-controlled case series study to investigate hospital admissions from neurological complications in the 28 days after a first dose of ChAdOx1nCoV-19 (n = 20,417,752) or BNT162b2 (n = 12,134,782), and after a SARS-CoV-2-positive test (n = 2,005,280). There was an increased risk of Guillain–Barré syndrome (incidence rate ratio (IRR), 2.90; 95% confidence interval (CI): 2.15–3.92 at 15–21 days after vaccination) and Bell’s palsy (IRR, 1.29; 95% CI: 1.08–1.56 at 15–21 days) with ChAdOx1nCoV-19. There was an increased risk of hemorrhagic stroke (IRR, 1.38; 95% CI: 1.12–1.71 at 15–21 days) with BNT162b2. An independent Scottish cohort provided further support for the association between ChAdOx1nCoV and Guillain–Barré syndrome (IRR, 2.32; 95% CI: 1.08–5.02 at 1–28 days). There was a substantially higher risk of all neurological outcomes in the 28 days after a positive SARS-CoV-2 test including Guillain–Barré syndrome (IRR, 5.25; 95% CI: 3.00–9.18). Overall, we estimated 38 excess cases of Guillain–Barré syndrome per 10 million people receiving ChAdOx1nCoV-19 and 145 excess cases per 10 million people after a positive SARS-CoV-2 test. In summary, although we find an increased risk of neurological complications in those who received COVID-19 vaccines, the risk of these complications is greater following a positive SARS-CoV-2 test