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SMEC:Scene Mining for E-Commerce

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Journal of Computer Science & Technology 2024年第1期39卷 192-210页

作者：王罡李翔郭子义殷大伟马帅 State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Data Science and Engineering East China Normal UniversityShanghai 200062China *** Inc.Beijing 100176China Baidu Inc.Beijing 100085China

Scene-based recommendation has proven its usefulness in E-commerce,by recommending commodities based on a given ***,scenes are typically unknown in advance,which necessitates scene discovery for *** this article,we study scene discovery for E-commerce *** first formalize a scene as a set of commodity cate-gories that occur simultaneously and frequently in real-world situations,and model an E-commerce platform as a heteroge-neous information network(HIN),whose nodes and links represent different types of objects and different types of rela-tionships between objects,*** then formulate the scene mining problem for E-commerce as an unsupervised learning problem that finds the overlapping clusters of commodity categories in the *** solve the problem,we pro-pose a non-negative matrix factorization based method SMEC(Scene Mining for E-Commerce),and theoretically prove its *** six real-world E-commerce datasets,we finally conduct an extensive experimental study to evaluate SMEC against 13 other methods,and show that SMEC consistently outperforms its competitors with regard to various evaluation measures.

关键词： graph clustering E-commerce heterogeneous information network(HIN) scene mining

Changes in microorganisms and metabolites during long-term fermentation of commercially manufactured anchovy sauce

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Food Science and Human Wellness 2024年

作者： Daehyeon Kim Seong-Eun Park Eun-Ju Kim Seung-Ho Seo Kwang-Moon Cho Sun Jae Kwon Hong-Seok Son Department of Biotechnology College of Life Sciences and Biotechnology Korea University Sonlab inc. 3. AccuGene inc.

This study was performed to investigate the changes in microbial communities and metabolites during the long-term fermentation of commercially manufactured anchovy sauce. Samples of commercial anchovy sauce were collected from large-scale fermentation tanks with fermentation periods of up to 7.87 years. The complex bacterial community was simplified to two genera， Tetragenococcus and Halanaerobium， after approximately 0.55 years of fermentation. Although genera， such as Saccharomyces， Cladosporium， Candida， and Aspergillus， were relatively dominant， no clear pattern was identified in fungal community analysis. The longitudinal metabolite profile demonstrated that approximately half (55.8%) of the metabolites present in anchovy sauce were produced within a year of fermentation due to rapid fermentation. Despite the static microbial community， the contents of several metabolites inc.uding amino acids and biogenic amines changed continuously during the long-term fermentation of anchovy sauce. This study provides novel insights into the changes in microbiota and metabolites in fish sauce produced without any starter inoculation.

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Optimal SOC Headroom of Pump Storage Hydropower for Maximizing Joint Revenue from Day-ahead and Real-time Markets Under Regional Transmission Organization Dispatch

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Journal of Modern Power Systems and Clean Energy 2024年第1期12卷 238-250页

作者： Yikui Liu Bing Huang Yang Lin Yonghong Chen Lei Wu Electrical and Computer Engineering Department Stevens Institute of TechnologyHobokenNJ 07030USA Midcontinent Independent System Operator Inc.CarmelIN 46032USA

In response to the inc.easing penetration of volatile and uncertain renewable energy,the regional transmission organizations(RTOs)have been recently focusing on enhancing the models of pump storage hydropower(PSH)plants,which are one of the key flexibility assets in the day-ahead(DA)and real-time(RT)markets,to further boost their flexibility provision *** by the recent research works that explored the potential benefits of excluding PSHs’cost-related terms from the objective functions of the DA market clearing model,this paper completes a rolling RT market scheme that is compatible with the DA ***,with the vision that PSHs could be permitted to submit state-of-charge(SOC)headrooms in the DA market and to release them in the RT market,this paper uncovers that PSHs could inc.ease the total revenues from the two markets by optimizing their SOC headrooms,assisted by the proposed tri-level optimal SOC headroom ***,in the proposed tri-level model,the middle and lower levels respectively mimic the DA and RT scheduling processes of PSHs,and the upper level determines the optimal headrooms to be submitted to the RTO for maximizing the total revenue from the two *** case studies quantify the profitability of the optimal SOC headroom submissions as well as the associated financial risks.

关键词： Pump storage hydropower energy market state-of-charge(SOC) headroom market revenue tri-level problem

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Graph-decomposed k-NN searching algorithm on road network

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Frontiers of Computer Science 2024年第3期18卷 243-245页

作者： Wei JIANG Bo NING Guanyu LI Mei BAI Xiao JIA Fangliang WEI Faculty of Information Science and Technology Dalian Maritime UniversityDalian 116026China Jiangxing Intelligence inc. Shenzhen 518100China

1 Introduction The k-NN(k Nearest Neighbors)searching problem on road network constitutes a crucial aspect of location-oriented services,which has boasted the practical implications.

关键词： services searching network

Modeling displacement flow inside a full-length casing string for well cementing

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Petroleum Research 2024年第1期9卷 1-16页

作者： Hu Dai Ali Eslami Jason Schneider Gefei Liu Fred Schwering Pegasus Vertex Inc.USA Laval University Canada Sanjel Energy Services Canada Athabasca Oil Corporation Canada

While computer modeling of annular displacement efficiency is widely applied in cementing engineering,modeling the displacement flow inside a casing or drill string for cementing operations has received less *** predicting displacement efficiency inside a full-length pipe is desired by cementing engineers,the attempt of developing a model with both efficiency and accuracy faces *** to computer simulators for this purpose is *** with annular flow,the displacement flow inside pipe,although within a simpler geometry and without eccentricity effect,is not simpler in physics,modelling strategy and predictability,because a variety of flow patterns and flow instabilities can develop to create complicated fluid *** this paper,we present an integrated numerical model developed to simulate displacement flows inside a full-length pipe,which connects an existing annulus model to enable complete displacement simulations of cementing *** model uses three-dimensional grid to solve fluid concentrations with degrees of mixing,and inc.rporates flow instability detection and flow regime *** in cementing,the model accounts for effects of pumping rate,well inc.ination,pipe rotation,fluid densities,rheological parameters and *** computationally efficient model does not rely on high-resolution mesh as often required by conventional Computational Fluid Dynamics models,thus it is suitable to be implemented in a cementing software for daily use by well cementing *** methodology of the model is discussed in detail in this *** validate the model,we examine simulation results against experimental results obtained in our laboratory tests and CFD simulations;acceptable agreement is found under different testing *** also presented two case studies of real cementing jobs with cement evaluation logs compared to simulation results,showing that the model can predict consistent displacement efficie

关键词： Inside pipe displacement Displacement efficiency Numerical model Primary cementing Displacement flow

TextFormer: A Query-based End-to-end Text Spotter with Mixed Supervision

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Machine Intelligence Research 2024年第4期21卷 704-717页

作者： Yukun Zhai Xiaoqiang Zhang Xiameng Qin Sanyuan Zhao Xingping Dong Jianbing Shen School of Computer Science Beijing Institute of TechnologyBeijing 100081China Department of Computer Vision Technology Baidu Inc.Beijing 100193China

End-to-end text spotting is a vital computer vision task that aims to integrate scene text detection and recognition into a unified *** methods heavily rely on region-of-interest(Rol)operations to extract local features and complex post-processing steps to produce final *** address these limitations,we propose TextFormer,a query-based end-to-end text spotter with a transformer ***,using query embedding per text instance,TextFormer builds upon an image encoder and a text decoder to learn a joint semantic understanding for multitask *** allows for mutual training and optimization of classification,segmentation and recognition branches,resulting in deeper feature sharing without sacrificing flexibility or ***,we design an adaptive global aggregation(AGG)module to transfer global features into sequential features for reading arbitrarilyshaped texts,which overcomes the suboptimization problem of Rol ***,potential corpus information is utilized from weak annotations to full labels through mixed supervision,further improving text detection and end-to-end text spotting *** experiments on various bilingual(i.e.,English and Chinese)benchmarks demonstrate the superiority of our *** on the TDA-ReCTS dataset,TextFormer surpasses the state-of-the-art method in terms of 1-NED by 13.2%.

关键词： End-to-end text spotting arbitrarily-shaped texts transformer mixed supervision multitask modeling.

A reinforcement learning approach for thermostat setpoint preference learning

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Building Simulation 2024年第1期17卷 131-146页

作者： Hussein Elehwany Mohamed Ouf Burak Gunay Nunzio Cotrufo Jean-Simon Venne Department of Environmental Engineering Carleton UniversityOttawaCanada Departmentof Building Civiland Environmental EngineeringConcordia UniversityMontrealCanada BrainBox Al inc. MontrealCanada

Occupant-centric controls(OcC)is an indoor climate control approach whereby occupant feedback is used in the sequence of operation of building energy *** OcC has been used in a wide range of building applications,an OcC category that has received considerable research interest is learning occupants'thermal preferences through their thermostat interactions and adapting temperature setpoints *** recent studies used reinforcement learning(RL)as an agent for OcC to optimize energy use and occupant *** studies depended on predicted mean vote(PMV)models or constant comfort ranges to represent comfort,while only few of them used thermostat *** paper addresses this gap by introducing a new off-policy reinforcement learning(RL)algorithm that imitates the occupant behaviour by utilizing unsolicited occupant thermostat *** algorithm is tested with a number of synthetically generated occupant behaviour models implemented via the Python APl of *** simulation results indicate that the RL algorithm could rapidly learn preferences for all tested occupant behaviour scenarios with minimal exploration *** substantial energy savings were observed with most occupant scenarios,the impact on the energy savings varied depending on occupants'preferences and thermostat use behaviour stochasticity.

关键词： reinforcement learning preference learning occupant-centric controls smart thermostats off-policy learning

SAM3D: zero-shot 3D object detection via the segment anything model

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Science China(Information Sciences) 2024年第4期67卷 331-332页

作者： Dingyuan ZHANG Dingkang LIANG Hongcheng YANG Zhikang ZOU Xiaoqing YE Zhe LIU Xiang BAI School of Artificial Intelligence and Automation Huazhong University of Science and Technology Baidu inc. School of Electronic Information and Communication Huazhong University of Science and Technology

In the past few years, foundation models have thrived and succeeded in linguistic and visual tasks, showing astonishing zero-shot and few-shot capabilities. Their advances encourage researchers and industries to extend the boundaries of what artificial intelligence can do and have shown some fantastic products(e.g., ChatGPT [1]) with the potential to change the world.

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Semantic segmentation via pixel-to-center similarity calculation

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 87-100页

作者： Dongyue Wu Zilin Guo Aoyan Li Changqian Yu Nong Sang Changxin Gao National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhanChina Meituan inc. BeijingChina

Sinc. the fully convolutional network has achieved great success in semantic segmentation,lots of works have been proposed to extract discriminative pixel ***,the authors observe that existing methods still suffer from two typical challenges:(i)The intra-class feature variation between different scenes may be large,leading to the difficulty in maintaining the consistency between same-class pixels from different scenes;(ii)The inter-class feature distinc.ion in the same scene could be small,resulting in the limited performance to distinguish different classes in each *** authors first rethink se-mantic segmentation from a perspective of similarity between pixels and class *** weight vector of the segmentation head represents its corresponding semantic class in the whole dataset,which can be regarded as the embedding of the class ***,the pixel-wise classification amounts to computing similarity in the final feature space between pixels and the class *** this novel view,the authors propose a Class Center Similarity(CCS)layer to address the above-mentioned challenges by generating adaptive class centers conditioned on each scenes and supervising the similarities between class *** CCS layer utilises the Adaptive Class Center Module to generate class centers conditioned on each scene,which adapt the large intra-class variation between different *** designed Class Distance Loss(CD Loss)is introduced to control both inter-class and intra-class distances based on the predicted center-to-center and pixel-to-center ***,the CCS layer outputs the processed pixel-to-center similarity as the segmentation *** experiments demonstrate that our model performs favourably against the state-of-the-art methods.

关键词： computer vision deep neural networks image segmentation scene understanding