114,578 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
Raw data for figures in Hippo pathway and Bonus control developmental cell fate decisions in the Drosophila eye (Zhao et al. 2023).
No full textRaw data for figures in Hippo pathway and Bonus control developmental cell fate decisions in the Drosophila eye (Zhao et al. 2023)
Mekonglema Zhao & Li 2020
No full textGenus Mekonglema Zhao & Li, 2020 Type species. Mekonglema bailang Zhao & Li, 2020 from China.Published as part of Lin, Yejie, Zhao, Huifeng, Koh, Joseph K H & Li, Shuqiang, 2022, Taxonomy notes on twenty-eight spider species (Arachnida: Araneae) from Asia, pp. 198-270 in Zoological Systematics 47 (3) on page 240, DOI: 10.11865/zs.2022303, http://zenodo.org/record/717585
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
Milema Zhao & Li 2022, gen. nov.
No full textMilema Zhao & Li, gen. nov. Type species. Milema nuichua Zhao & Li, sp. nov. Diagnosis. This genus can be distinguished from Telema by the following characters: the ratio of the length of embolus/carapace 0.25–0.30 (vs. 0.50–0.65), the presence of a prolateral cymbial apophysis (vs. absence), belt-shaped tibial glands (vs. plate-shaped), the ratio of the length of embolus/bulb 0.38–1.10 (vs. smaller than 0.32); spermatheca sac-like or globular (vs. cane shaped). Description. Total length 0.90–1.15 in male, 0.95–1.20 in female. Carapace 0.40–0.51 long, Carapace pear shaped, pale or dark brown. Six eyes encircled by black or absent. Tibia I 0.52–0.94. Leg formula 1243, leg glands belt shaped. Abdomen blue or for males, bulb small relative to carapace, the length ratio of bulb/carapace 0.25–0.30. Length of cymbium> femur> tibia> patella; cymbial apophysis present prolaterally. Distribution. Southern Vietnam and Southern Thailand. Etymology. The generic name is a combination of the first two letters of millet (referring to the small size) and the latter four letters of Telema (type genus of the family); feminine in gender. Species included. Milema lorkor Zhao & Li, sp. nov., Milema nuichua Zhao & Li, sp. nov. and Milema sai Zhao & Li, sp. nov. Biology. Habitats of this genus are diverse. The type species, M. nuichua Zhao & Li, sp. nov. inhabits leaf litter; M. sai Zhao & Li, sp. nov. is found at cave entrances; and M. lorkor Zhao & Li, sp. nov. is found deep in caves. These spiders have particular morphological characters adapting to their diverse habitats.Published as part of Lin, Yejie, Zhao, Huifeng, Koh, Joseph K H & Li, Shuqiang, 2022, Taxonomy notes on twenty-eight spider species (Arachnida: Araneae) from Asia, pp. 198-270 in Zoological Systematics 47 (3) on page 245, DOI: 10.11865/zs.2022303, http://zenodo.org/record/717585
Burmalema Zhao & Li 2022, gen. nov.
No full textBurmalema Zhao & Li, gen. nov. Type species. Burmalema shan Zhao & Li, sp. nov. Diagnosis. The new genus resembles Telema Simon, 1882 by lacking a cymbial apophysis, but it can be distinguished by the following: belt-shaped tibial glands (vs. plate-shaped); ratio of embolus/bulb lengths ca. 0.80 (vs. less than 0.42), and twisted embolus (vs. triangular or nearly needle-shaped); females can be distinguished by the L-shaped endogyne with long and sclerotized tubes (vs. cane shaped, with membranous tubes). The new genus can be distinguished from all the other genera of Telemidae by the absence of a cymbial apophysis (vs. presence). Description. Total length 1.25–1.53, carapace 0.52–0.90 long. Eyes vestigial. Carapace, sternum, endites, labium and legs light brown. Endites longer than wide; labium wider than long. Tibia I 0.90–1.00. Leg formula 1243. In male, length of cymbium> femur> tibia> patella; prolateral cymbial apophysis absent; embolus spiraled and long relative to bulb. Endogyne simple, with tube inside, expended distally. Distribution. Myanmar. Etymology. The generic name is derived from “ Burma ”, referring the name of type locality, Myanmar, and “-lema” is a convention from Telema, the type genus of the family; feminine in gender. Species included. Burmalema shan sp. nov.Published as part of Lin, Yejie, Zhao, Huifeng, Koh, Joseph K H & Li, Shuqiang, 2022, Taxonomy notes on twenty-eight spider species (Arachnida: Araneae) from Asia, pp. 198-270 in Zoological Systematics 47 (3) on page 238, DOI: 10.11865/zs.2022303, http://zenodo.org/record/717585
Ihmisele ja mikroeleanalyysi: tietojoukot, menetelmät ja sovellukset
No full textAbstractExploring the possibility of using machines to achieve body gesture-based activity recognition, and even emotion understanding is a promising topic and drives this research. To facilitate research on this topic with computer vision methods, this thesis makes related contributions via four stages: regular body gesture recognition, micro-gesture dataset and analysis, 3D body gesture transfer and generation, and specific applications.For regular human gestures, two analysis methods are proposed that aim at temporal segmentation and recognition tasks. The first work proposes a novel temporal hierarchical dictionary for hidden Markov model transition with deep neural networks. Then, the second work extends the proposed temporal hierarchical dictionary to a more robust online segmentation and recognition of gesture dynamics.Next, we explore the possibility of emotion understanding from human gestures. In the field of psychology, a specific group of body gestures, called micro-gestures (MGs), are used to interpret the inner feelings of humans. To fill the gap in the research of spontaneous emotional gestures, we collect the first spontaneous MG dataset. A comprehensive analysis of MGs is then conducted, leading to interesting insights.Body gestures transfer and generation is another main research direction in this thesis. We try to achieve the 3D human body gesture transfer that can endow target 3D human models with desired MGs. Then, we research how to learn the disentanglement of 3D human pose and shape in an unsupervised setting. Furthermore, we research the generation of animated 3D sequences of a target human body model by directly taking the driving sequences as inputs.Lastly, we present an application for collaborative learning with gesture analysis in the education field. Specifically, we present an interdisciplinary work that introduces an explainable AI prototype for collaborative learning that seeks to provide interpretable insights with machine learning-based models.In summary, we illustrate the contributions of the work and conclude the advantages and limitations of the current work. Potential future work plans are also discussed.Original papersOriginal papers are not included in the electronic version of the dissertation.Chen, H., Liu, X., & Zhao, G. (2018). Temporal hierarchical dictionary with HMM for fast gesture recognition. In 2018 24th International Conference on Pattern Recognition (ICPR), 3378-3383. IEEE. https://doi.org/10.1109/icpr.2018.8546245Self-archived versionChen, H., Liu, X., Shi, J., & Zhao, G. (2020). Temporal hierarchical dictionary guided decoding for online gesture segmentation and recognition. IEEE Transactions on Image Processing, 29, 9689–9702. https://doi.org/10.1109/TIP.2020.3028962Self-archived versionChen, H., Liu, X., Li, X., Shi, H., & Zhao, G. (2019). Analyze spontaneous gestures for emotional stress state recognition: A micro-gesture dataset and analysis with deep learning. 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), 1–8. https://doi.org/10.1109/FG.2019.8756513Self-archived versionChen, H., Tang, H., Yu, Z., Sebe, N., & Zhao, G. (2022). Geometry-contrastive transformer for generalized 3D pose transfer. The AAAI Conference on Artificial Intelligence (AAAI). Manuscript submitted for publication.Chen, H., Tang, H., Shi, H., Peng, W., Sebe, N, & Zhao, G. (2021). Intrinsic-extrinsic preserved GANs for unsupervised 3D pose transfer. In Proceedings of the IEEE International Conference on Computer Vision (ICCV). Manuscript submitted for publication.Chen, H, Tang, H., Sebe, N., & Zhao, G. (2021). AniFormer: Data-driven 3D animation with transformer. In Proceedings of the British Machine Vision Conference (BMVC), 2021. Manuscript submitted for publication.Chen, H., Tan, E., Lee, Y., Praharaj, S., Specht, M., & Zhao, G. (2020). Developing AI into explanatory supporting models: An explanation-visualized deep learning prototype for computer supported collaborative learning. In Gresalfi, M. and Horn, I. S. (Eds.), The Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS) 2020, Volume 2 (pp. 1133-1140). Nashville, Tennessee: International Society of the Learning Sciences.Self-archived versionTiivistelmäIhmisillä on synnynnäinen kyky välittää ja ymmärtää monipuolista tietoa kehonliikkeiden avulla. Tällainen viestintä on läsnä lähes kaikkialla arjen elämässä. Tässä tutkimuksessa tarkastelemme koneen opettamista tunnistamaan toimia ja jopa ymmärtämään tunteita kehon eleiden perusteella. Väitöskirjatutkimuksessa tarkastelemme aihetta konenäkömenetelmillä ja jaamme tulokset neljään kategoriaan: tavanomaisten kehon eleiden tunnistus, mikroeleiden tietoaineisto ja analyysi, kehon eleiden siirtäminen kolmiulotteiseen malliin ja tuottaminen sillä sekä erityiset sovellukset.Tavanomaisten eleiden analyysia varten ehdotamme kahta menetelmää ajalliseen segmentointiin ja tunnistustoimintoihin. Ensimmäisessä työssä ehdotamme uutta, syviä neuroverkostoja hyödyntävää ajallis-hierarkkista sanastoa Markovin piilomallin siirtymille. Toisessa työssä laajennamme ehdotettua ajallis-hierarkkista sanastoa tehokkaammalla verkkopohjaisella segmentoinnilla ja eledynamiikan tunnistamisella. Viitekehys perustuu tila-ajalliseen tarkkaavaisuusverkostoon. Se hyödyntää Lien ryhmien monimuotoisia esityksiä ja oppii kuviot iteratiivisesti.Seuraavaksi tutkimme tunteiden ymmärtämistä ihmiseleistä. Psykologiassa kutsutaan mikroeleiksi tietynlaisten, tunteita ilmentävien ruumiineleiden ryhmää. Mikroeleet, kuten nenän koskettaminen, ovat hienovaraisia, spontaaneja ruumiineleitä, jotka voivat tahattomasti välittää tietoa piilotetuista tunteista. Spontaanien tunne-eleiden tutkimuksen aukon täyttämiseksi kokoamme ensimmäisen spontaaneihin mikroeleisiin keskittyvän tietoaineiston. Seuraavaksi suoritamme mikroeleiden kattavan analyysin, joka johtaa mielenkiintoisiin tuloksiin.Väitöskirjan toinen tärkeä tutkimussuunta on kehon eleiden siirtäminen ja tuottaminen. Yritämme siirtää eleitä kolmiulotteiseen ihmiskehon malliin mahdollistaaksemme haluttujen mikroeleiden tuottamisen. Tämän jälkeen tutkimme koneen opettamista erottamaan kolmiulotteiset asennot ja muodot valvomattomassa ympäristössä. Lisäksi tutkimme animoitujen kolmiulotteisten sekvenssien tuottamista ihmiskehon mallilla käyttämällä ajojaksoja suorina syötteinä.Lopuksi esittelemme eleiden analysointia hyödyntävän yhteistoiminnallisen oppimisen koulutussovelluksen. Tarkemmin sanottuna tarkastelemme poikkitieteellistä työtä, jossa luomme yhteistoiminnalliseen oppimiseen soveltuvan tekoälyn prototyypin, jonka tarkoitus on tuottaa ymmärrettävää tietoa koneoppimiseen perustuvien mallien avulla.Tiivistelmäosiossa havainnollistamme työn tuloksia ja pohdimme nykyisen tutkimuksen etuja ja rajoituksia. Lisäksi tarkastelemme mahdollisia jatkotutkimussuunnitelmia, kuten luotettavien tunnemallien hyödyntämistä ihmiseleiden analysoinnissa sekä 3D-teknologian yhdistämistä affektiiviseen laskentaan.OsajulkaisutOsajulkaisut eivät sisälly väitöskirjan elektroniseen versioon.Chen, H., Liu, X., & Zhao, G. (2018). Temporal hierarchical dictionary with HMM for fast gesture recognition. In 2018 24th International Conference on Pattern Recognition (ICPR), 3378-3383. IEEE. https://doi.org/10.1109/icpr.2018.8546245Rinnakkaistallennettu versioChen, H., Liu, X., Shi, J., & Zhao, G. (2020). Temporal hierarchical dictionary guided decoding for online gesture segmentation and recognition. IEEE Transactions on Image Processing, 29, 9689–9702. https://doi.org/10.1109/TIP.2020.3028962Rinnakkaistallennettu versioChen, H., Liu, X., Li, X., Shi, H., & Zhao, G. (2019). Analyze spontaneous gestures for emotional stress state recognition: A micro-gesture dataset and analysis with deep learning. 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), 1–8. https://doi.org/10.1109/FG.2019.8756513Rinnakkaistallennettu versioChen, H., Tang, H., Yu, Z., Sebe, N., & Zhao, G. (2022). Geometry-contrastive transformer for generalized 3D pose transfer. The AAAI Conference on Artificial Intelligence (AAAI). Manuscript submitted for publication.Chen, H., Tang, H., Shi, H., Peng, W., Sebe, N, & Zhao, G. (2021). Intrinsic-extrinsic preserved GANs for unsupervised 3D pose transfer. In Proceedings of the IEEE International Conference on Computer Vision (ICCV). Manuscript submitted for publication.Chen, H, Tang, H., Sebe, N., & Zhao, G. (2021). AniFormer: Data-driven 3D animation with transformer. In Proceedings of the British Machine Vision Conference (BMVC), 2021. Manuscript submitted for publication.Chen, H., Tan, E., Lee, Y., Praharaj, S., Specht, M., & Zhao, G. (2020). Developing AI into explanatory supporting models: An explanation-visualized deep learning prototype for computer supported collaborative learning. In Gresalfi, M. and Horn, I. S. (Eds.), The Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS) 2020, Volume 2 (pp. 1133-1140). Nashville, Tennessee: International Society of the Learning Sciences.Rinnakkaistallennettu versioAcademic dissertation to be presented, with the assent of the Doctoral Training Committee of Information Technology and Electrical Engineering of the University of Oulu, for public defence in the Oulun Puhelin auditorium (L5), Linnanmaa, on 18 March 2022, at 12 noonAbstract
Exploring the possibility of using machines to achieve body gesture-based activity recognition, and even emotion understanding is a promising topic and drives this research. To facilitate research on this topic with computer vision methods, this thesis makes related contributions via four stages: regular body gesture recognition, micro-gesture dataset and analysis, 3D body gesture transfer and generation, and specific applications.
For regular human gestures, two analysis methods are proposed that aim at temporal segmentation and recognition tasks. The first work proposes a novel temporal hierarchical dictionary for hidden Markov model transition with deep neural networks. Then, the second work extends the proposed temporal hierarchical dictionary to a more robust online segmentation and recognition of gesture dynamics.
Next, we explore the possibility of emotion understanding from human gestures. In the field of psychology, a specific group of body gestures, called micro-gestures (MGs), are used to interpret the inner feelings of humans. To fill the gap in the research of spontaneous emotional gestures, we collect the first spontaneous MG dataset. A comprehensive analysis of MGs is then conducted, leading to interesting insights.
Body gestures transfer and generation is another main research direction in this thesis. We try to achieve the 3D human body gesture transfer that can endow target 3D human models with desired MGs. Then, we research how to learn the disentanglement of 3D human pose and shape in an unsupervised setting. Furthermore, we research the generation of animated 3D sequences of a target human body model by directly taking the driving sequences as inputs.
Lastly, we present an application for collaborative learning with gesture analysis in the education field. Specifically, we present an interdisciplinary work that introduces an explainable AI prototype for collaborative learning that seeks to provide interpretable insights with machine learning-based models.
In summary, we illustrate the contributions of the work and conclude the advantages and limitations of the current work. Potential future work plans are also discussed.Tiivistelmä
Ihmisillä on synnynnäinen kyky välittää ja ymmärtää monipuolista tietoa kehonliikkeiden avulla. Tällainen viestintä on läsnä lähes kaikkialla arjen elämässä. Tässä tutkimuksessa tarkastelemme koneen opettamista tunnistamaan toimia ja jopa ymmärtämään tunteita kehon eleiden perusteella. Väitöskirjatutkimuksessa tarkastelemme aihetta konenäkömenetelmillä ja jaamme tulokset neljään kategoriaan: tavanomaisten kehon eleiden tunnistus, mikroeleiden tietoaineisto ja analyysi, kehon eleiden siirtäminen kolmiulotteiseen malliin ja tuottaminen sillä sekä erityiset sovellukset.
Tavanomaisten eleiden analyysia varten ehdotamme kahta menetelmää ajalliseen segmentointiin ja tunnistustoimintoihin. Ensimmäisessä työssä ehdotamme uutta, syviä neuroverkostoja hyödyntävää ajallis-hierarkkista sanastoa Markovin piilomallin siirtymille. Toisessa työssä laajennamme ehdotettua ajallis-hierarkkista sanastoa tehokkaammalla verkkopohjaisella segmentoinnilla ja eledynamiikan tunnistamisella. Viitekehys perustuu tila-ajalliseen tarkkaavaisuusverkostoon. Se hyödyntää Lien ryhmien monimuotoisia esityksiä ja oppii kuviot iteratiivisesti.
Seuraavaksi tutkimme tunteiden ymmärtämistä ihmiseleistä. Psykologiassa kutsutaan mikroeleiksi tietynlaisten, tunteita ilmentävien ruumiineleiden ryhmää. Mikroeleet, kuten nenän koskettaminen, ovat hienovaraisia, spontaaneja ruumiineleitä, jotka voivat tahattomasti välittää tietoa piilotetuista tunteista. Spontaanien tunne-eleiden tutkimuksen aukon täyttämiseksi kokoamme ensimmäisen spontaaneihin mikroeleisiin keskittyvän tietoaineiston. Seuraavaksi suoritamme mikroeleiden kattavan analyysin, joka johtaa mielenkiintoisiin tuloksiin.
Väitöskirjan toinen tärkeä tutkimussuunta on kehon eleiden siirtäminen ja tuottaminen. Yritämme siirtää eleitä kolmiulotteiseen ihmiskehon malliin mahdollistaaksemme haluttujen mikroeleiden tuottamisen. Tämän jälkeen tutkimme koneen opettamista erottamaan kolmiulotteiset asennot ja muodot valvomattomassa ympäristössä. Lisäksi tutkimme animoitujen kolmiulotteisten sekvenssien tuottamista ihmiskehon mallilla käyttämällä ajojaksoja suorina syötteinä.
Lopuksi esittelemme eleiden analysointia hyödyntävän yhteistoiminnallisen oppimisen koulutussovelluksen. Tarkemmin sanottuna tarkastelemme poikkitieteellistä työtä, jossa luomme yhteistoiminnalliseen oppimiseen soveltuvan tekoälyn prototyypin, jonka tarkoitus on tuottaa ymmärrettävää tietoa koneoppimiseen perustuvien mallien avulla.
Tiivistelmäosiossa havainnollistamme työn tuloksia ja pohdimme nykyisen tutkimuksen etuja ja rajoituksia. Lisäksi tarkastelemme mahdollisia jatkotutkimussuunnitelmia, kuten luotettavien tunnemallien hyödyntämistä ihmiseleiden analysoinnissa sekä 3D-teknologian yhdistämistä affektiiviseen laskentaan
Raw data of Zhao et al., 2022, Geoderma
No full textRaw data associated with Zhao et al., 2022, Geoderma. Any use of the data set should be approved by the corresponding author Kai Yue at "[email protected]".</p
Hengconarius Li & Zhao & Zhang & Li 2018, gen. n.
No full textGenus Hengconarius Z. Zhao & S. Li gen. n. Type species. Draconarius exilis ZhaNG, Zhu & WaNG, 2005 Etymology. THe GeNeRIC NAMe IS DeRIVeD FROM THe PINYIN "HeNG", ReFeRRING TO THe HeNGDUAN MOUNTAINS WHeRe THe GeNUS IS DISTRIBUTeD AND THe "- conarius " FROM Draconarius. THe GeNDeR IS MASCULINe. Diagnosis. Hengconarius Z. ZhaO & S. LI gen. n. IS SIMILAR TO Sinodraconarius, Nuconarius Z. ZhaO & S. LI gen. n. AND Draconarius. BUT THeY CAN Be DISTINGUISHeD IN DeTAILS AS FOLLOWS: THe NeW GeNUS CAN Be DISTINGUISHeD FROM Sinodraconarius BY A PATeLLAR APOPHYSIS THAT IS NeVeR BIFURCATe AND A CONDUCTOR WHICH IS BIFURCATe; FROM Nuconarius Z. ZhaO & S. LI gen. n. BY THe MeDIAN APOPHYSIS NOT POINTeD, THe CONDUCTOR WITH BASAL LAMeLLA AND THe ePIGYNe WITH MIDDLe SePTUM, THe ANTeRIOR eDGe OF ePIGYNe WRINKLeD AND SCLeROTIZeD, THe POSTeRIOR eDGe OF ePIGYNe SCLeROTIZeD; FROM Draconarius BY THe CYMBIAL FURROW LeSS THAN ½ THe LeNGTH OF CYMBIUM, THe MeDIAN APOPHYSIS SLICe-SHAPeD, ePIGYNAL TeeTH ABSeNT, AND THe SPeRMATHeCAe SIMPLe AND NOT CONVOLUTeD. Description. SMALL TO MeDIUM-LARGe SIZeD, WITH TOTAL LeNGTHS RANGING FROM 6.28 TO 12.91. CARAPACe YeLLOW TO TAN; RADIAL ReGION WITH GReY-GReeN SHORT SeTAe; CHeLICeRAe WITH 3 PROMARGINAL AND 2 ReTROMARGINAL TeeTH. LeG FORMULA 4> 1> 2> 3. MALe PALP: PATeLLAR APOPHYSIS PReSeNT OR ReDUCeD; TWO TIBIAL APOPHYSeS: ReTROLATeRAL TIBIAL APOPHYSIS BROAD AND slice-shaped; lateral tibial apophysis small, whose length and width about ⅓ of the length and WIDTH OF ReTROLATeRAL TIBIAL APOPHYSIS, ReSPeCTIVeLY; CYMBIAL FURROW SHORT, LeSS THAN ½ THe LeNGTH OF CYMBIUM; eMBOLIC BASe BeGINNING AT ABOUT 8 O’CLOCK POSITION; CONDUCTOR BeFURCATe, WITH A SMALL BASAL LAMeLLA AND A CONDUCTOR’S DORSAL APOPHYSIS; MeDIAN APOPHYSIS THIN, USUALLY SLICe-SHAPeD. EPIGYNe: ePIGYNAL TeeTH ABSeNT; THe ANTeRIOR eDGe OF ePIGYNe WRINKLeD AND SCLeROTIZeD; THe POSTeRIOR eDGe OF ePIGYNe SCLeROTIZeD; THe SHAPe OF ATRIUM VARIABLe; ePIGYNAL HOODS LOCATeD ANTeROLATeRALLY; COPULATORY OPeNINGS LOCATeD CeNTRALLY; COPULATORY DUCT exTeNDS HORIZONTALLY; SPeRMATHeCAe SIMPLe AND SePARATeD, THe DISTANCe BeTWeeN SPeRMATHeCAe SUBeqUAL TO THe DIAMeTeR OF SPeRMATHeCA; SPeRMATHeCAL HeADS LOCATeD AT ANTeROLATeRALLY OR IN THe MIDDLe. Natural history. SPeCIeS OF Hengconarius Z. ZhaO & S. LI gen. n. WeRe ALL COLLeCTeD BY HAND OR TRAPPING MeTHODS. THeY WeRe FOUND IN MOIST AND GLOOMY PLACeS IN THe HIGHLANDS (1881 –4089 M), SUCH AS UNDeR STONeS, ROCKS AND IN LeAF-LITTeR. Composition. SO FAR eIGHT SPeCIeS ARe CONSIDeReD IN THe NeW GeNUS: H. dedaensis Z. ZHAO & S. LI sp. n., H. exilis (ZHANG, ZHU & WANG, 2005) comb. n., H. falcatus (XU & LI, 2006) comb. n., H. incertus (WANG, 2003) comb. n., H. latusincertus (WANG, GRISWOLD & MILLeR, 2010) comb. n., H. longipalpus Z. ZHAO & S. LI sp. n., H. longpuensis Z. ZHAO & S. LI sp. n. AND H. pseudobrunneus (WANG, 2003) comb. n. Distribution. SICHUAN, TIBeT AND YUNNAN, CHINA (FIG. 20). Comments. FOR THe DeTAILS OF THe ReLATIONSHIPS IN Hengconarius Z. ZhaO & S. LI gen. n., PLeASe See ZZ630, ZZ913, ZZ929, ZZ 987, SD023 AND SD041 (SOUTHeRN COeLOTeS GROUPS) IN FIGURe 3 AND SUPPLeMeNTARY FIGUReS S4– S 6 OF Z. ZHAO & S. LI (2017).Published as part of Li, Bing, Zhao, Zhe, Zhang, Chuntian & Li, Shuqiang, 2018, Nuconarius gen. n. and Hengconarius gen. n., two new genera of Coelotinae (Araneae, Agelenidae) spiders from Southwest China, pp. 237-263 in Zootaxa 4457 (2) on pages 246-248, DOI: 10.11646/zootaxa.4457.2.2, http://zenodo.org/record/134255
Stenus (Hypostenus) primivenatus Zhao & Zhou 2020, sp. nov.
No full text<i>1.</i> <i>Stenus (Hypostenus) primivenatus</i> Zhao & Zhou, sp. nov. <p> <b>Type Material.</b> <b>Holotype:</b> male, CHINA, Hainan, Jianfengling (180°52′E, 18°48′N), 20.VII. 2004, 650 m, Jie Wu and Yong-jie Chen collected. [Deposited in Institute of Zoology, Chinese Academy of Sciences (IZ-CAS)]</p> <p> <b>Diagnosis.</b> This new species belongs to the <i>cicindeloides</i> group. It can be easily distinguished from <i>S. (H.) cicindeloides</i> (Schaller) and <i>S. (H.) yiae</i> Zhao & Zhou <b>sp. nov.</b> by spots on elytra. It is similar to <i>S. (H.) verticalis</i> Benick, but can be distinguished from the narrow apical part of median lobe and small body. Although the median hooks of <i>S. (H.) primivenatus</i> Zhao & Zhou <b>sp. nov.</b> is not distinct sclerotized like other species of the <i>cicindeloides</i> group, the median hooks are still connected by slightly sclerotized part. Maybe this new species is primitive in this species group. Detailed description and illustrations of the species are provided by Zhao & Zhou (2008).</p>Published as part of <i>Zhao, Cai-Yun & Zhou, Hong-Zhang, 2020, Validation of Stenus (Hypostenus) primivenatus and Stenus (Hypostenus) yiae (Coleoptera, Staphylinidae, Steninae), pp. 591-592 in Zootaxa 4881 (3)</i> on page 591, DOI: 10.11646/zootaxa.4881.3.11, <a href="http://zenodo.org/record/4283876">http://zenodo.org/record/4283876</a>
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