229,217 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Zhao-Papachristos-Appendix-Annals-687-SAGE – Supplemental material for Network Position and Police Who Shoot
No full textSupplemental material, Zhao-Papachristos-Appendix-Annals-687-SAGE for Network Position and Police Who Shoot by Linda Zhao and Andrew V. Papachristos in The ANNALS of the American Academy of Political and Social Science</p
Developing Magnesium Alloys with a Combination of Strength and Ductility Based on Friction Stir-Based Technologies
No full textSome of the key data for the thesis "Developing Magnesium Alloys with a Combination of Strength and Ductility Based on Friction Stir-Based Technologies", including SEM/EDS/EBSD, and TEM data. They are stored in the .oipx format which can be opened by Aztec, .cpr which can be opened by AztecCrystal, and .csv and . tiff as spreadsheets and images.
Part of the data is associated with the following publications:
[1] X. Zhao, X. Zeng, L. Yuan, J. Gandra, Q. Hayat, M. Bai, W.M. Rainforth, D. Guan, A novel approach for producing Mg-3Al-1Zn-0.2 Mn alloy wire with a promising combination of strength and ductility using CoreFlowTM, Scripta Materialia 227 (2023) 115301.
[2] X. Zhao, Y. Xie, J. Gandra, M. Murphy, W.M. Rainforth, D. Guan, A Succinct Method to Recycle WE43 Mg Alloys—From Wasted Chips to Consolidated Billets, TMS Annual Meeting & Exhibition, Springer, (2024), 151-153.
[3] X. Zhao, D. Olden, B. Williams, A. Pariyar, D. Zhang, M. Murphy, P. Reed, P. Allison, B. Jordon, J. Qi, W. M. Rainforth, D. Guan, Grain growth stagnation at 525° C by nanoparticles in a solid-state additively manufactured Mg-4Y-3RE alloy, Journal of Magnesium and Alloys (2024), 4976-4987.
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Automaattinen hermoverkko-oppiminen ihmisen käyttäytymisen ymmärtämiseksi
No full textAbstractUnderstanding human behavior is one of the most pivotal steps toward real-world Artificial Intelligence (AI) or even Artificial general intelligence (AGI). However, this task is challenging as human social attributes make human beings unique, leading to various and complicated behaviors. Moreover, real-life behavior data are normally high-dimensional with dynamic changes or even non-Euclidean structures, involving multiple modalities. Currently, one of the first alternatives to addressing these challenges is using deep neural networks or deep learning, which has brought revolutionary changes in data computation and computer sciences. Nevertheless, expert knowledge of both neural architecture design and human behavior analysis is expected more than ever before in this interdisciplinary research field. All these issues spur the current deep learning studies towards automatic deep neural network learning, which could automatically sketch a neural architecture for a given behavior analysis task. In line with this topic, this thesis explores the automatic neural network learning approach for human behavior understanding from the most representative behaviors, including human facial expression and actions, step by step. First, manually designed computational models are proposed for human facial expression and actions with dynamic information and graph structures. Based on this, to free humans from the exhausting process, more advanced methods, i.e., automatic neural network learning, are presented. Extensive experiments on benchmark facial expression datasets and action recognition datasets are conducted and comparison results prove the effectiveness of the proposed methods.Original papersOriginal papers are not included in the electronic version of the dissertation.Peng, W., Hong, X., Xu, Y., & Zhao, G. (2019). A boost in revealing subtle facial expressions: A consolidated eulerian framework. 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), 1–5. https://doi.org/10.1109/FG.2019.8756541Self-archived versionPeng, W., Hong, X., Zhao, G., & Cambria, E. (2021). Adaptive modality distillation for separable multimodal sentiment analysis. IEEE Intelligent Systems, 36(3), 82–89. https://doi.org/10.1109/MIS.2021.3057757Self-archived versionXia, Z., Peng, W., Khor, H.-Q., Feng, X., & Zhao, G. (2020). Revealing the invisible with model and data shrinking for composite-database micro-expression recognition. IEEE Transactions on Image Processing, 29, 8590–8605. https://doi.org/10.1109/TIP.2020.3018222Self-archived versionPeng, W., Hong, X., & Zhao, G. (2019). Video action recognition via neural architecture searching. 2019 IEEE International Conference on Image Processing (ICIP), 11–15. https://doi.org/10.1109/ICIP.2019.8802919Self-archived versionPeng, W., Shi, J., & Zhao, G. (2021). Spatial temporal graph deconvolutional network for skeleton-based human action recognition. IEEE Signal Processing Letters, 28, 244–248. https://doi.org/10.1109/LSP.2021.3049691Self-archived versionPeng, W., Hong, X., & Zhao, G. (2021). Tripool: Graph triplet pooling for 3D skeleton-based action recognition. Pattern Recognition, 115, 107921. https://doi.org/10.1016/j.patcog.2021.107921Self-archived versionPeng, W., Shi, J., Varanka, T., & Zhao, G. (2021). Rethinking the ST-GCNs for 3D skeleton-based human action recognition. Neurocomputing, 454, 45–53. https://doi.org/10.1016/j.neucom.2021.05.004Self-archived versionPeng, W., Varanka, T., Mostafa, A., Shi, H., & Zhao, G. (2021). Hyperbolic deep neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/TPAMI.2021.3136921Self-archived versionPeng, W., Hong, X., Chen, H., & Zhao, G. (2020). Learning graph convolutional network for skeleton-based human action recognition by neural searching. Proceedings of the AAAI Conference on Artificial Intelligence, 34(03), 2669–2676. https://doi.org/10.1609/aaai.v34i03.5652Self-archived versionPeng, W., Shi, J., Xia, Z., & Zhao, G. (2020). Mix dimension in poincaré geometry for 3d skeleton-based action recognition. Proceedings of the 28th ACM International Conference on Multimedia, 1432–1440. https://doi.org/10.1145/3394171.3413910Self-archived versionTiivistelmäIhmisten käyttäytymisen ymmärtäminen on yksi keskeisistä askeleista kohti todellista tekoälyä (AI) tai jopa yleistä tekoälyä (AGI). Tämä tehtävä on kuitenkin haastava, sillä ihmisen sosiaaliset ominaisuudet tekevät ihmisistä ainutlaatuisia, mikä johtaa erilaisiin ja monimutkaisiin käyttäytymismalleihin. Lisäksi tosielämän käyttäytymisdata on yleensä korkeaulotteinen, ja siinä on dynaamisia muutoksia tai jopa ei-euklidisia rakenteita, joihin liittyy useita modaliteetteja. Tällä hetkellä yksi ensimmäisistä vaihtoehdoista haasteisiin vastaamiseksi on syvän neuroverkon tai syväoppimisen käyttö, joka on tuonut mukanaan Vallankumoukselliset muutokset tietojen laskennassa ja tietojenkäsittelytieteissä. Asiantuntijatietoa sekä hermoarkkitehtuurin suunnittelusta että ihmisen käyttäytymisen analysoinnista odotetaan kuitenkin enemmän kuin koskaan aiemmin tällä tieteidenvälisellä tutkimusalueella. Kaikki nämä kysymykset kannustavat nykyisiä syväoppimistutkimuksia kohti automaattista syvän neuroverkon oppimista, joka voisi automaattisesti luonnostella hermoarkkitehtuurin tietylle käyttäytymisanalyysille Tämän aiheen mukaisesti opinnäytetyössä tutkitaan vaihe vaiheelta automaattista neuroverk- kooppimisen lähestymistapaa ihmisen käyttäytymisen ymmärtämiseen edustavimmista käyttäytymismalleista, mukaan lukien ihmisen ilmeet ja toiminnot. Ensin ehdotetaan manuaalisesti suunniteltuja laskennallisia malleja ihmisen ilmeille ja toiminnalle dynaamisilla tiedoilla ja graafirakenteilla, joiden pohjalta ihmisen uuvuttavasta edistymisestä vapauttamiseksi esitetään edistyneempiä menetelmiä, ie, automaattinen hermoverkkooppiminen. Kattavia kokeita benchmark ME-tietosarjoista ja toiminnantunnistustietosarjoista tehdään ja vertailutulokset osoittavat ehdotettujen menetelmien tehokkuuden.OsajulkaisutOsajulkaisut eivät sisälly väitöskirjan elektroniseen versioon.Peng, W., Hong, X., Xu, Y., & Zhao, G. (2019). A boost in revealing subtle facial expressions: A consolidated eulerian framework. 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), 1–5. https://doi.org/10.1109/FG.2019.8756541Rinnakkaistallennettu versioPeng, W., Hong, X., Zhao, G., & Cambria, E. (2021). Adaptive modality distillation for separable multimodal sentiment analysis. IEEE Intelligent Systems, 36(3), 82–89. https://doi.org/10.1109/MIS.2021.3057757Rinnakkaistallennettu versioXia, Z., Peng, W., Khor, H.-Q., Feng, X., & Zhao, G. (2020). Revealing the invisible with model and data shrinking for composite-database micro-expression recognition. IEEE Transactions on Image Processing, 29, 8590–8605. https://doi.org/10.1109/TIP.2020.3018222Rinnakkaistallennettu versioPeng, W., Hong, X., & Zhao, G. (2019). Video action recognition via neural architecture searching. 2019 IEEE International Conference on Image Processing (ICIP), 11–15. https://doi.org/10.1109/ICIP.2019.8802919Rinnakkaistallennettu versioPeng, W., Shi, J., & Zhao, G. (2021). Spatial temporal graph deconvolutional network for skeleton-based human action recognition. IEEE Signal Processing Letters, 28, 244–248. https://doi.org/10.1109/LSP.2021.3049691Rinnakkaistallennettu versioPeng, W., Hong, X., & Zhao, G. (2021). Tripool: Graph triplet pooling for 3D skeleton-based action recognition. Pattern Recognition, 115, 107921. https://doi.org/10.1016/j.patcog.2021.107921Rinnakkaistallennettu versioPeng, W., Shi, J., Varanka, T., & Zhao, G. (2021). Rethinking the ST-GCNs for 3D skeleton-based human action recognition. Neurocomputing, 454, 45–53. https://doi.org/10.1016/j.neucom.2021.05.004Rinnakkaistallennettu versioPeng, W., Varanka, T., Mostafa, A., Shi, H., & Zhao, G. (2021). Hyperbolic deep neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/TPAMI.2021.3136921Rinnakkaistallennettu versioPeng, W., Hong, X., Chen, H., & Zhao, G. (2020). Learning graph convolutional network for skeleton-based human action recognition by neural searching. Proceedings of the AAAI Conference on Artificial Intelligence, 34(03), 2669–2676. https://doi.org/10.1609/aaai.v34i03.5652Rinnakkaistallennettu versioPeng, W., Shi, J., Xia, Z., & Zhao, G. (2020). Mix dimension in poincaré geometry for 3d skeleton-based action recognition. Proceedings of the 28th ACM International Conference on Multimedia, 1432–1440. https://doi.org/10.1145/3394171.3413910Rinnakkaistallennettu versioAcademic dissertation to be presented with the assent of the Doctoral Training Committee of Technology and Natural Sciences of the University of Oulu for public defence in the OP auditorium (L10), Linnanmaa, on 14 April 2022, at 12 noonAbstract
Understanding human behavior is one of the most pivotal steps toward real-world Artificial Intelligence (AI) or even Artificial general intelligence (AGI). However, this task is challenging as human social attributes make human beings unique, leading to various and complicated behaviors. Moreover, real-life behavior data are normally high-dimensional with dynamic changes or even non-Euclidean structures, involving multiple modalities. Currently, one of the first alternatives to addressing these challenges is using deep neural networks or deep learning, which has brought revolutionary changes in data computation and computer sciences. Nevertheless, expert knowledge of both neural architecture design and human behavior analysis is expected more than ever before in this interdisciplinary research field. All these issues spur the current deep learning studies towards automatic deep neural network learning, which could automatically sketch a neural architecture for a given behavior analysis task. In line with this topic, this thesis explores the automatic neural network learning approach for human behavior understanding from the most representative behaviors, including human facial expression and actions, step by step. First, manually designed computational models are proposed for human facial expression and actions with dynamic information and graph structures. Based on this, to free humans from the exhausting process, more advanced methods, i.e., automatic neural network learning, are presented. Extensive experiments on benchmark facial expression datasets and action recognition datasets are conducted and comparison results prove the effectiveness of the proposed methods.Tiivistelmä
Ihmisten käyttäytymisen ymmärtäminen on yksi keskeisistä askeleista kohti todellista tekoälyä (AI) tai jopa yleistä tekoälyä (AGI). Tämä tehtävä on kuitenkin haastava, sillä ihmisen sosiaaliset ominaisuudet tekevät ihmisistä ainutlaatuisia, mikä johtaa erilaisiin ja monimutkaisiin käyttäytymismalleihin. Lisäksi tosielämän käyttäytymisdata on yleensä korkeaulotteinen, ja siinä on dynaamisia muutoksia tai jopa ei-euklidisia rakenteita, joihin liittyy useita modaliteetteja. Tällä hetkellä yksi ensimmäisistä vaihtoehdoista haasteisiin vastaamiseksi on syvän neuroverkon tai syväoppimisen käyttö, joka on tuonut mukanaan Vallankumoukselliset muutokset tietojen laskennassa ja tietojenkäsittelytieteissä. Asiantuntijatietoa sekä hermoarkkitehtuurin suunnittelusta että ihmisen käyttäytymisen analysoinnista odotetaan kuitenkin enemmän kuin koskaan aiemmin tällä tieteidenvälisellä tutkimusalueella. Kaikki nämä kysymykset kannustavat nykyisiä syväoppimistutkimuksia kohti automaattista syvän neuroverkon oppimista, joka voisi automaattisesti luonnostella hermoarkkitehtuurin tietylle käyttäytymisanalyysille Tämän aiheen mukaisesti opinnäytetyössä tutkitaan vaihe vaiheelta automaattista neuroverk- kooppimisen lähestymistapaa ihmisen käyttäytymisen ymmärtämiseen edustavimmista käyttäytymismalleista, mukaan lukien ihmisen ilmeet ja toiminnot. Ensin ehdotetaan manuaalisesti suunniteltuja laskennallisia malleja ihmisen ilmeille ja toiminnalle dynaamisilla tiedoilla ja graafirakenteilla, joiden pohjalta ihmisen uuvuttavasta edistymisestä vapauttamiseksi esitetään edistyneempiä menetelmiä, ie, automaattinen hermoverkkooppiminen. Kattavia kokeita benchmark ME-tietosarjoista ja toiminnantunnistustietosarjoista tehdään ja vertailutulokset osoittavat ehdotettujen menetelmien tehokkuuden
Lyophyllum pallidofumosum Y. H. Ma, W. M. Chen & Y. C. Zhao 2022, sp. nov.
No full textLyophyllum pallidofumosum Y. H. Ma, W. M. Chen & Y. C. Zhao sp. nov. (Figs. 1A, 3) MycoBank: MB 844903 Etymology: — pallidofumosum, refers to the morphological similarity of the new species to L. fumosum. Diagnosis:— Lyophyllum pallidofumosum differs from the other species of the L. decastes complex by a greyish orange pileus surface, margin with waves when old; globose to subglobose basidiospores (4.5–5.6 × 4.3–5.4 µm), clavate basidia (25–34 × 7–9 µm); occurring in a mixed forest dominated by Pinus yunnanensis, Pinus armandi, and Quercus fabrei; and the highest growth rate on the YPD medium,. Holotype:— CHINA, Yunnan Province, Xuanwei City, Reshui Town, in a mixed forest, 25°58′17″ N, 103°44′50″ E, elev. 2344m, 09 September 2021, Yuanhao Ma, Weimin Chen, Ping Liu, 21rs0242 (HKAS 124190) Description:—pileus 20–60 mm in diam., Pileus shape convex and margin involute when young, applanate, margin waves when old. Pileus colour slightly desaturated orange (5B3–5), greyish orange (6B5), oak brown (5D5–6). Lamellae adnate or subdecurrent. Lamellae colour white (5A1) to light greyish orange (5B2). Context white to whitish. Stipe cylindrical slightly enlarged towards base, central, solid, tough in texture, 30–80 × 8–15 mm. Stipe colour white (5A1) to light greyish orange (5B2). Basidiospores hyaline, globose to subglobose, smooth, with a single large oil-drop and a small apiculus, (4.26–) 4.55–5.54 (–5.76) × (4.15–) 4.30–5.36 (–5.57) µm, (m/n/p=113/6/2, l m =5.06 ± 0.31µm, w m =4.83 ±0.31µm), Q= (1.00–) 1.01–1.11 (–1.12), Q m =1.05 ±0.32. Basidia 4-spored, clavate, sometimes ventricose, (24.10–) 25.53–33.50 (–35.36) × (6.78–) 7.09–8.78 (–9.63) µm, l m =29.08 ± 2.31µm, w m =7.89 ±0.55µm, Q= (2.93–) 3.15–4.24 (–4.44), Q m =3.70 ±0.34; sterigmata 1.77–3.87 µm long. Some with clamp connections. Lamellae without cheilocystidia and pleurocystidia. Colonies 43–45 mm radius after 24 d at 22℃ in the dark on YPD, white, downy to cottony with loose aerial hyphae; some with clamp connections. Habitat:—solitary, gregarious or cespitose on the ground as saprobic in a mixed forest dominated by Pinus yunnanensis, Pinus armandi, and Quercus fabrei. Additional specimens examined:— CHINA, Yunnan Province, Xuanwei City, Reshui Town, in a mixed forest, 25°58′17″ N, 103°44′50″ E, elev. 2344m, 24 July 2019, YAASM6138 (HKAS 124189, Paratype); ibid., Shangri-La County, 16 October 2020, YAASM6215; ibid., 26 July 2006, yang 4742 (HKAS 50539); ibid., Lanping County, Jinding Town, 15 October 2011, zhao 1334 (HKAS 74287). Ex-type: YAASM6628 and YAASM6629, isolated from the inner tissues of two basidiomata. Ex-type sequences:— YAASM6628/HKAS124190a = LSU: ON834576, ITS: ON680829; YAASM6629/ HKAS124190b = LSU: ON834577, ITS: ON680830.Published as part of Ma, Yuanhao, Liu, Ping, Zhao, Ziyue, Chen, Weimin & Zhao, Yongchang, 2022, Lyophyllum pallidofumosum sp. nov. (Lyophyllaceae, Agaricales), from southwestern China, pp. 173-183 in Phytotaxa 576 (2) on page 178, DOI: 10.11646/phytotaxa.576.2.3, http://zenodo.org/record/746126
Prof. Th. W. Adorno and the author Hans Erich Nossack.
No full textProf. Th. W. Adorno and the author Hans Erich Nossack at a reception of Insel Verlag, Buchmesse Frankfurt 1966LB
Gated relational stacked denoising autoencoder with localized author embedding for global citation recommendation
No full textCitation recommendation is an effective and efficient way to facilitate authors finding desired references. This paper presents a novel neural network based model, called gated relational probabilistic stacked denoising autoencoder with localized author (GRSLA) embedding, for global citation recommendation task. Our model is comprised of two modules with different neural network architecture. For each citing and cited papers, we use a gated paper embedding module, which is extended from probabilistic stacked denoising autoencoder (PSDAE) by adding gated units, to obtain their paper vectors. The added gated units are able to utilize text information of cited paper to refine the vector representation of citing paper in multiple semantic levels. For an author in papers, we first apply topic model to obtain his/her semantic neighbors, and then use a localized author embedding (LAE) module to excavate author vector representation from semantic and explicit neighbors. Unlike most graph convolutional network (GCN) based methods, the LAE module is able to avoid computing global Laplacian in whole graph by taking limited neighbors. Moreover, the LAE module can also be stacked to absorb more neighbors, which makes our model have high extendibility. Based on the generation process of GRSLA, we also derive a learning algorithm of our model by maximum a posteriori (MAP) estimation. We conduct experiments on the AAN, DBLP and CORD-19 datasets, and the results show that GRSLA model works well than previous global citation recommendation methods
Sinraptoridae Currie and Zhao 1993
No full textSinraptoridae Currie and Zhao, 19936 Included taxa. Sinraptor dongi Currie and Zhao, 19936; Sinraptor hepingensis (Gao, 1992); Yangchuanosaurus magnus Dong, Zhou and Zhang, 1983; Yangchuanosaurus shangyouensis Dong, Chang, Li and Zhou, 1978. Temporal range. Late Jurassic. Occurrence. Shishugou Formation, Xinjiang, China; Shangshaxiamiao Formation, Sichuan, China. Diagnosis. Promaxillary foramen enlarged and larger than maxillary foramen; large, deep excavatio pneumatica in the ascending process of the maxilla, enclosing one or several pneumatic openings; a further synapomorphy of sinraptorids may be the presence of a medial posterior prong in the jugal; the existence of this prong is known in S. dongi, but its presence cannot be confirmed in the other species because these taxa are based on articulated skulls. Remarks. Although all of the taxa included in the Sinraptoridae have been described as separate species, their morphology seems to be almost identical, as far as can be judged from published accounts (Dong et al. 1983; Gao 1992; Currie and Zhao 19936). Since both species of Yangchuanosaurus are also from the same formation, it seems possible that all the specimens represent only one species. The differences between Sinraptor dongi and Sinraptor hepingensis listed by Currie and Zhao (19936, p. 2039) are very slight and probably lie within the limits of individual variation. However, since I have not examined this material, all described species are provisionally retained as valid taxa, pending a revision of the Sinraptoridae. Given that all character codings within sinraptorids are identical, they are treated as one operational unit.Published as part of Rauhut, Oliver W. M., 2003, The interrelationships and evolution of basal theropod dinosaurs, pp. 1-213 in Special papers in palaeontology 69 on page 27, DOI: 10.5281/zenodo.338257
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