Study on Optimal Middle Temperature of Cascade-condenser in CO2/NH3 Cascade Refrigeration Systems with Two Temperature Ranges

This paper analyzes a CO2/NH3 cascade refrigeration system of two temperature range applied in the cold storage. A mathematical model is presented to determine the optimal middle temperatures of the cascade-condenser for obtaining the maximum coefficient of performance (COP) under different operation conditions. Three main parameters including the evaporation temperature in the cold storage, the evaporation temperature in the refrigerated storage and the condensation temperature in the high temperature stage are used to study the optimal middle temperature of CO2 in the cascade-condenser. The results show that the optimal middle temperature increases with the increment of three main parameters. Moreover, under specific conditions, the optimal temperature is equal to the evaporation temperature of refrigerated storage. The results shown in this paper is helpful to the control strategy of CO2/NH3 cascade refrigeration systems for two temperature ranges

Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains

Pre-trained language models have been applied to various NLP tasks with considerable performance gains. However, the large model sizes, together with the long inference time, limit the deployment of such models in real-time applications. One line of model compression approaches considers knowledge distillation to distill large teacher models into small student models. Most of these studies focus on single-domain only, which ignores the transferable knowledge from other domains. We notice that training a teacher with transferable knowledge digested across domains can achieve better generalization capability to help knowledge distillation. Hence we propose a Meta-Knowledge Distillation (Meta-KD) framework to build a meta-teacher model that captures transferable knowledge across domains and passes such knowledge to students. Specifically, we explicitly force the meta-teacher to capture transferable knowledge at both instance-level and feature-level from multiple domains, and then propose a meta-distillation algorithm to learn single-domain student models with guidance from the meta-teacher. Experiments on public multi-domain NLP tasks show the effectiveness and superiority of the proposed Meta-KD framework. Further, we also demonstrate the capability of Meta-KD in the settings where the training data is scarce

Quantification of gas hydrate saturation and morphology based on a generalized effective medium model

Highlights • A modified cementation theory is developed by introducing generalized pressure-dependent normalized contact-cemented radii. • A generalized effective medium model is proposed to merge the effective medium theory and cementation theory. • Modeling and inversion schemes are proposed to quantify hydrate saturation and morphology from laboratory and well-log data. • Hydrates mainly grow as matrix-supporting form (~54%) in sands and as pore-filling form (~59%) in clay-rich marine sediments. Abstract Numerous models have been developed for prediction of gas hydrate saturation based on the microstructural relationship between gas hydrates and sediment grains. However, quantification of hydrate saturation and morphology from elastic properties has been hindered by failing to account for complex hydrate distributions. Here, we develop a generalized effective medium model by applying the modified Hashin-Shtrikman bounds to a newly developed cementation theory. This model is validated by experimental data for synthetic methane and tetrahydrofuran hydrates. Good comparison of model predictions with experimental measurements not only reveals its ability to merge the results of contact cementation theory and effective medium theory, but also indicates its feasibility for characterizing complex morphologies. Moreover, the results of inverting acoustic measurements quantitatively confirm that for synthetic samples in “excess-gas” condition gas hydrates mainly occur as a hybrid-cementing morphology with a low percentage of pore-filling morphology, whereas for pressure-core hydrate-bearing sediments in natural environments they exist as matrix-supporting and pore-filling morphologies with a very low percentage of hybrid-cementing morphology. The hydrate saturations estimated from sonic and density logs in several regions including northern Cascadia margin (Integrated Ocean Drilling Program Expedition 311, Hole U1326D and Hole U1327E), Alaska North Slope (Mount Elbert test well) and Mackenzie Delta (Mallik 5L-38), are comparable to the referenced hydrate saturations derived from core data and resistivity, and/or nuclear magnetic resonance log data, confirming validity and applicability of our model. Furthermore, our results indicate that ~8% hybrid-cementing, ~33% matrix-supporting and ~59% pore-filling hydrates may coexist in the fine-grained and clay-rich marine sediments on the northern Cascadia margin, whereas ~10% hybrid-cementing, ~54% matrix-supporting and ~36% pore-filling hydrates may coexist in the coarse-grained and sand-dominated terrestrial sediments of the Alaska North Slope and Mackenzie Delta

Learning to Expand: Reinforced Pseudo-relevance Feedback Selection for Information-seeking Conversations

Intelligent personal assistant systems for information-seeking conversations are increasingly popular in real-world applications, especially for e-commerce companies. With the development of research in such conversation systems, the pseudo-relevance feedback (PRF) has demonstrated its effectiveness in incorporating relevance signals from external documents. However, the existing studies are either based on heuristic rules or require heavy manual labeling. In this work, we treat the PRF selection as a learning task and proposed a reinforced learning based method that can be trained in an end-to-end manner without any human annotations. More specifically, we proposed a reinforced selector to extract useful PRF terms to enhance response candidates and a BERT based response ranker to rank the PRF-enhanced responses. The performance of the ranker serves as rewards to guide the selector to extract useful PRF terms, and thus boost the task performance. Extensive experiments on both standard benchmark and commercial datasets show the superiority of our reinforced PRF term selector compared with other potential soft or hard selection methods. Both qualitative case studies and quantitative analysis show that our model can not only select meaningful PRF terms to expand response candidates but also achieve the best results compared with all the baseline methods on a variety of evaluation metrics. We have also deployed our method on online production in an e-commerce company, which shows a significant improvement over the existing online ranking system

Kuaipedia: a Large-scale Multi-modal Short-video Encyclopedia

Online encyclopedias, such as Wikipedia, have been well-developed and researched in the last two decades. One can find any attributes or other information of a wiki item on a wiki page edited by a community of volunteers. However, the traditional text, images and tables can hardly express some aspects of an wiki item. For example, when we talk about ``Shiba Inu'', one may care more about ``How to feed it'' or ``How to train it not to protect its food''. Currently, short-video platforms have become a hallmark in the online world. Whether you're on TikTok, Instagram, Kuaishou, or YouTube Shorts, short-video apps have changed how we consume and create content today. Except for producing short videos for entertainment, we can find more and more authors sharing insightful knowledge widely across all walks of life. These short videos, which we call knowledge videos, can easily express any aspects (e.g. hair or how-to-feed) consumers want to know about an item (e.g. Shiba Inu), and they can be systematically analyzed and organized like an online encyclopedia. In this paper, we propose Kuaipedia, a large-scale multi-modal encyclopedia consisting of items, aspects, and short videos lined to them, which was extracted from billions of videos of Kuaishou (Kwai), a well-known short-video platform in China. We first collected items from multiple sources and mined user-centered aspects from millions of users' queries to build an item-aspect tree. Then we propose a new task called ``multi-modal item-aspect linking'' as an expansion of ``entity linking'' to link short videos into item-aspect pairs and build the whole short-video encyclopedia. Intrinsic evaluations show that our encyclopedia is of large scale and highly accurate. We also conduct sufficient extrinsic experiments to show how Kuaipedia can help fundamental applications such as entity typing and entity linking

A Unified Model for Video Understanding and Knowledge Embedding with Heterogeneous Knowledge Graph Dataset

Video understanding is an important task in short video business platforms and it has a wide application in video recommendation and classification. Most of the existing video understanding works only focus on the information that appeared within the video content, including the video frames, audio and text. However, introducing common sense knowledge from the external Knowledge Graph (KG) dataset is essential for video understanding when referring to the content which is less relevant to the video. Owing to the lack of video knowledge graph dataset, the work which integrates video understanding and KG is rare. In this paper, we propose a heterogeneous dataset that contains the multi-modal video entity and fruitful common sense relations. This dataset also provides multiple novel video inference tasks like the Video-Relation-Tag (VRT) and Video-Relation-Video (VRV) tasks. Furthermore, based on this dataset, we propose an end-to-end model that jointly optimizes the video understanding objective with knowledge graph embedding, which can not only better inject factual knowledge into video understanding but also generate effective multi-modal entity embedding for KG. Comprehensive experiments indicate that combining video understanding embedding with factual knowledge benefits the content-based video retrieval performance. Moreover, it also helps the model generate better knowledge graph embedding which outperforms traditional KGE-based methods on VRT and VRV tasks with at least 42.36% and 17.73% improvement in HITS@10

Grazing weakens competitive interactions between active methanotrophs and nitrifiers modulating greenhouse-gas emissions in grassland soils

This work was financially supported by Natural Science Foundation of China (41977033, 41907026, and 41721001), Fundamental Research Funds for the Central Universities (2019QNA6011), National Key Basic Research Program of China (2014CB138801), Shandong Provincial Natural Science Foundation (ZR2019BD032), China Postdoctoral Science Foundation (2020T130387 and 2019M652448). CG-R was funded by a Royal Society University Research Fellowship (UF150571). Special thanks to ChunMei Meng, Yu Luo, and Yan Zheng for their assistance in laboratory analyses.Peer reviewedPublisher PD