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Temporal Latent Space Modeling for Community Prediction [chapter]

Hossein Fani, Ebrahim Bagheri, Weichang Du
<span title="">2020</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
We propose a temporal latent space model for user community prediction in social networks, whose goal is to predict future emerging user communities based on past history of users' topics of interest.  ...  The model allows each user to adjust its location in the latent space as her topics of interest evolve over time.  ...  Different approaches based on matrix factorization and deep neural networks [1, [31] [32] [33] have been proposed to learn the latent space model of users in the social network.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-030-45439-5_49">doi:10.1007/978-3-030-45439-5_49</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5nwwmjuprbgdxh6fu22dzbt3pe">fatcat:5nwwmjuprbgdxh6fu22dzbt3pe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200509220245/https://link.springer.com/content/pdf/10.1007%2F978-3-030-45439-5_49.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/31/d9/31d9776e46e5ea08320b2dad9fe43116aaf0fb04.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-030-45439-5_49"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

CnGAN: Generative Adversarial Networks for Cross-network user preference generation for non-overlapped users

Dilruk Perera, Roger Zimmermann
<span title="">2019</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s4hirppq3jalbopssw22crbwwa" style="color: black;">The World Wide Web Conference on - WWW &#39;19</a> </i> &nbsp;
The proposed model synthetically generates source network user preferences for non-overlapped users by learning the mapping from target to source network preference manifolds.  ...  our solution by generating user preferences on the Twitter source network for recommendations on the YouTube target network.  ...  We gratefully acknowledge the support of NVIDIA Corporation with the donation of a Titan Xp GPU used for this research.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3308558.3313733">doi:10.1145/3308558.3313733</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/www/PereraZ19.html">dblp:conf/www/PereraZ19</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/l3zqgnwhzjb3znhxixkf5ogeem">fatcat:l3zqgnwhzjb3znhxixkf5ogeem</a> </span>
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Sense-Based Topic Word Embedding Model for Item Recommendation

Ya Xiao, Zhijie Fan, Chengxiang Tan, Qian Xu, Wenye Zhu, Fujia Cheng
<span title="">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
By combining topic distribution, social relationships, and users' interests and interactions, we propose a time-aware probabilistic model to profile a user's preference score on items.  ...  Different experiments on real-world datasets are deployed to prove the feasibility and efficiency of sense-based short text topic assignment and item recommendation in multiple languages.  ...  In the second module, a user profile evolution model is proposed based on the topic generated in the first module, and the user interest, social links and preference score in the SIT network.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2909578">doi:10.1109/access.2019.2909578</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wg72fh7fl5bmre2n7akd24scem">fatcat:wg72fh7fl5bmre2n7akd24scem</a> </span>
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Exploring social influence for recommendation

Mao Ye, Xingjie Liu, Wang-Chien Lee
<span title="">2012</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ibcfmixrofb3piydwg5wvir3t4" style="color: black;">Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval - SIGIR &#39;12</a> </i> &nbsp;
Based on SIS, we mine the social influence between linked friends and the personal preferences of users through statistical inference.  ...  In this paper, we argue that social influence between friends can be captured quantitatively and propose a probabilistic generative model, called social influenced selection(SIS), to model the decision  ...  for each user, (2) the distribution of personal preference over the latent topics for each user, (3) the distribution of generated items for each topic, (4) the distribution of generated content for each  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2348283.2348373">doi:10.1145/2348283.2348373</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/sigir/YeLL12.html">dblp:conf/sigir/YeLL12</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rlieros7lrerjffvz5jarseity">fatcat:rlieros7lrerjffvz5jarseity</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190216183710/https://static.aminer.org/pdf/20170130/pdfs/sigir/4qyxefa0nz2flmkt6bv8gqwkla5emwus.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/92/85/9285f3c70c6cab770d2f7d4ccb32ecae0397b134.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2348283.2348373"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Conversational Speech Recognition By Learning Conversation-level Characteristics [article]

Kun Wei, Yike Zhang, Sining Sun, Lei Xie, Long Ma
<span title="2022-02-17">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
First, a latent variational module (LVM) is attached to a conformer-based encoder-decoder ASR backbone to learn role preference and topical coherence.  ...  The highlights of the proposed model are twofold.  ...  The intermediate representation vectors of role preference and topic consistency are generated by the same text encoder: Table 1 . 1 CER comparation of different end-to-end models on two Mandarin datasets  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2202.07855v2">arXiv:2202.07855v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/u7wloqqhvfbfrcnnwtobr6twyu">fatcat:u7wloqqhvfbfrcnnwtobr6twyu</a> </span>
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Efficient Topic Level Opinion Mining and Sentiment Analysis Algorithm using Latent Dirichlet Allocation Model

Vamshi Krishna. B, JNTUA, Anantapuramu, India
<span title="2019-10-15">2019</span> <i title="The World Academy of Research in Science and Engineering"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/naqzxq5hurh2bp2pnvwitnnx44" style="color: black;">International Journal of Advanced Trends in Computer Science and Engineering</a> </i> &nbsp;
This paper discusses an efficient algorithm for topic level opinion mining and sentiment analysis of online text reviews by using unsupervised topic model, latent dirichlet allocation (LDA) for topic extraction  ...  The model accuracy is validated on twitter data by evaluating parameters perplexity and loglikelihood and compared with earlier models.  ...  et al. proposed a deep neural network based on attention mechanism to identify different aspect categories of a given review sentence based on different topics [17] .Vamshi et al. proposed topic model-based  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.30534/ijatcse/2019/105852019">doi:10.30534/ijatcse/2019/105852019</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6pmm2iccnbb7npjzq7srfuikwm">fatcat:6pmm2iccnbb7npjzq7srfuikwm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220228212313/http://www.warse.org/IJATCSE/static/pdf/file/ijatcse105852019.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/cf/15/cf158d3f86b967272f7ab19610463bd2b9c93060.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.30534/ijatcse/2019/105852019"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

LDA based integrated document recommendation model for e-learning systems

Rohit Nagori, G. Aghila
<span title="">2011</span> <i title="IEEE"> 2011 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC) </i> &nbsp;
We propose a personalized integrated model for elearning systems that consists of two steps: a) Using Latent Dirichlet Allocation topic modeling technique to make topic analysis on corpus, b) Introducing  ...  a similarity measurement to content-based recommendation approach.  ...  In this paper, we propose an integrated personalized document recommendation model based on Latent Dirichlet Allocation (LDA) [1] , which is originally proposed as a probabilistic document-topic model  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/etncc.2011.6255892">doi:10.1109/etncc.2011.6255892</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kkqepkxofrb4xhubgkzjg7gf2m">fatcat:kkqepkxofrb4xhubgkzjg7gf2m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170808173000/http://www.ugcfrp.ac.in/images/userfiles/42756-lda.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/47/82/4782f078a22efe216adea61324f9eaf63ac18ed2.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/etncc.2011.6255892"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Deep Learning for Latent Events Forecasting in Twitter Aided Caching Networks [article]

Zhong Yang, Yuanwei Liu, Yue Chen, Joey Tianyi Zhou
<span title="2021-01-04">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Firstly, we propose a latent Dirichlet allocation (LDA) model for latent events forecasting taking advantage of the superiority of the LDA model in natural language processing (NLP).  ...  information of the adjacent base stations (BSs).  ...  what to cache based on the public preferences.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2101.01149v1">arXiv:2101.01149v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mnpkxqdcnvbmdm26ruwkfl7jqu">fatcat:mnpkxqdcnvbmdm26ruwkfl7jqu</a> </span>
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Dynamic collaborative filtering based on user preference drift and topic evolution

Charinya Wangwatcharakul, Sartra Wongthanavasu
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
the associated topic evolution of review texts by using topic modeling based on the dynamic environment.  ...  We propose a model to capture the user preference dynamics in the rating matrix by using a joint decomposition method to extract user latent transition patterns and combine latent factors together with  ...  For example, consider the K FIGURE 2 . 2 Illustration of topic model on Automotive reviews output. FIGURE 3 . 3 The graphical illustration of the proposed model based on NMF.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2993289">doi:10.1109/access.2020.2993289</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3ugvw7vqybeztjhvjlwzlc75be">fatcat:3ugvw7vqybeztjhvjlwzlc75be</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201108115845/https://ieeexplore.ieee.org/ielx7/6287639/8948470/09090188.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c3/21/c321dfe1823ccf377c0654e82b6e53d745060c52.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2993289"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

HBGG: a Hierarchical Bayesian Geographical Model for Group Recommendation [chapter]

Ziyu Lu, Hui Li, Nikos Mamoulis, David W. Cheung
<span title="2017-06-09">2017</span> <i title="Society for Industrial and Applied Mathematics"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/viwc2ys5x5a47ogpdlftfzj5fm" style="color: black;">Proceedings of the 2017 SIAM International Conference on Data Mining</a> </i> &nbsp;
First, a generative group geographical topic model (GG) which exploits group membership, group mobility regions and group preferences is proposed.  ...  In this paper, we study the problem of recommending venues to groups of users and propose a Hierarchical Bayesian Model (HBGG) for this purpose.  ...  Conclusions In this paper, we propose a generative group geographical topic model (GG) based on group membership and group mobility regions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1137/1.9781611974973.42">doi:10.1137/1.9781611974973.42</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/sdm/LuLMC17.html">dblp:conf/sdm/LuLMC17</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nw3ydogj2rbkhm6orkrywahmyu">fatcat:nw3ydogj2rbkhm6orkrywahmyu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190227102738/http://pdfs.semanticscholar.org/7eb4/31471b72a8b5b7d62dfc645a5743852ef24b.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/7e/b4/7eb431471b72a8b5b7d62dfc645a5743852ef24b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1137/1.9781611974973.42"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

A^3NCF: An Adaptive Aspect Attention Model for Rating Prediction

Zhiyong Cheng, Ying Ding, Xiangnan He, Lei Zhu, Xuemeng Song, Mohan Kankanhalli
<span title="">2018</span> <i title="International Joint Conferences on Artificial Intelligence Organization"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vfwwmrihanevtjbbkti2kc3nke" style="color: black;">Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence</a> </i> &nbsp;
Specifically, we design a new topic model to extract user preferences and item characteristics from review texts.  ...  They are then used to 1) guide the representation learning of users and items, and 2) capture a user's special attention on each aspect of the targeted item with an attention network.  ...  Algorithm 1: Generation Process of Our Topic Model. Based on these assumptions, we develop a new topic model, which is a generative probabilistic model as shown in Fig. 2 .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.24963/ijcai.2018/521">doi:10.24963/ijcai.2018/521</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/ijcai/ChengD0ZSK18.html">dblp:conf/ijcai/ChengD0ZSK18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ykagpwrxjvgtlmwovhmv5ehcs4">fatcat:ykagpwrxjvgtlmwovhmv5ehcs4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190217140027/http://pdfs.semanticscholar.org/057a/da50135eedd6980de60737af020c0decabd1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/05/7a/057ada50135eedd6980de60737af020c0decabd1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.24963/ijcai.2018/521"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Customer Reviews Analysis with Deep Neural Networks for E-Commerce Recommender Systems

Babak Maleki Shoja, Nasseh Tabrizi
<span title="">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
To do so, we developed a glossary of features for each product category and evaluated them for removing irrelevant terms using Latent Dirichlet Allocation.  ...  An essential prerequisite of an effective recommender system is providing helpful information regarding users and items to generate high-quality recommendations.  ...  It learns the latent topics from reviews and ratings without having the constraint of a one-to-one mapping between latent factors and latent topics.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2937518">doi:10.1109/access.2019.2937518</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kmqunio5jrahnkgpupin5lfbta">fatcat:kmqunio5jrahnkgpupin5lfbta</a> </span>
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Personalised Visual Art Recommendation by Learning Latent Semantic Representations [article]

Bereket Abera Yilma, Najib Aghenda, Marcelo Romero, Yannick Naudet, Herve Panetto
<span title="2020-07-24">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Specifically, we trained a Latent Dirichlet Allocation (LDA) model on textual descriptions of paintings.  ...  To address these limitations, we introduce an approach for Personalised Recommendation of Visual arts based on learning latent semantic representation of paintings.  ...  PROPOSED APPROACH A. LDA Model Latent Dirichlet allocation (LDA) is an unsupervised generative probabilistic model for collections of text corpora proposed by [9] .  ... 
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Modeling Group Dynamics for Personalized Group-Event Recommendation [chapter]

Sanjay Purushotham, C. -C. Jay Kuo
<span title="">2015</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
We show that modeling group dynamics learns the group preferences better than aggregating individual user preferences, and that our approach out-performs popular state-of-the-art group recommender systems  ...  In this paper, we propose collaborative-filtering based Bayesian models which captures group dynamics such as user interactions, usergroup membership etc., for personalized group-event recommendations.  ...  We use topic models based on Latent Dirichlet Allocation (LDA) [1] to model the descriptions of events and the groups, and we use matrix factorization to match the latent features of group to the latent  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-16268-3_51">doi:10.1007/978-3-319-16268-3_51</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/w7u3yoqi7bhyflc3blk72x4ttu">fatcat:w7u3yoqi7bhyflc3blk72x4ttu</a> </span>
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Natural Language Processing via LDA Topic Model in Recommendation Systems [article]

Hamed Jelodar, Yongli Wang, Mahdi Rabbani, SeyedValyAllah Ayobi
<span title="2019-09-20">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Topic modeling based on LDA, is a powerful technique for semantic mining and perform topic extraction.  ...  In this paper, we present taxonomy of recommendation systems and applications based on LDA.  ...  Recently, researchers proposed various methods based on probabilistic topic modeling methods Bleiet al. (2003) .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1909.09551v1">arXiv:1909.09551v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ok3piccvx5agfds6adyiny2aom">fatcat:ok3piccvx5agfds6adyiny2aom</a> </span>
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