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316 Treffer

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  1. Detailansicht für Treffer 1 öffnen
    CFN-ESA: A Cross-Modal Fusion Network with Emotion-Shift Awareness for Dialogue Emotion Recognition
    Li, Jiang ; Liu, Yingjian ; et al.
    2023
    academicJournal
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  2. Detailansicht für Treffer 2 öffnen
    CFN-ESA: A Cross-Modal Fusion Network With Emotion-Shift Awareness for Dialogue Emotion Recognition
    Li, Jiang ; Wang, Xiaoping ; et al.
    In: IEEE Transactions on Affective Computing ; page 1-16 ; ISSN 1949-3045 2371-9850, 2024
    Online academicJournal
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    Volltext
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  3. Detailansicht für Treffer 3 öffnen
    The strength estimation of the S-CFN epoxy joint on cold-formed steel beam using CZM approach
    Anwar, Siti Nur Rahmah ; Anshari, Buan ; et al.
    In: THE 6TH INTERNATIONAL CONFERENCE ON SCIENCE AND TECHNOLOGY (ICST21): Challenges and Opportunities for Innovation Research on Science Materials, 2023
    Konferenz
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  4. Detailansicht für Treffer 4 öffnen
    CFN-ESA: A Cross-Modal Fusion Network with Emotion-Shift Awareness for Dialogue Emotion Recognition ...
    Li, Jiang ; Wang, Xiaoping ; et al.
    2023
    academicJournal
    Wie komme ich dran?
  5. Detailansicht für Treffer 5 öffnen
    CFNS Ad-Hoc meeting on Radiative Corrections Whitepaper
    Afanasev, Andrei ; Ahmed, Jaseer ; et al.
    2020
    academicJournal
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  6. Detailansicht für Treffer 6 öffnen
    The Pfaffian Structure of CFN Phylogenetic Networks ...
    Cummings, Joseph ; Gross, Elizabeth ; et al.
    2023
    academicJournal
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  7. Detailansicht für Treffer 7 öffnen
    Using CFN for Uniform Sampling of Cloud-Native Datacenters
    Yehezkel, Aviv ; Elyashiv, Eyal ; et al.
    In: 2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC, 2022
    Online Konferenz
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  8. Detailansicht für Treffer 8 öffnen
    CFN-dyncast: Load Balancing the Edges via the Network
    Liu, Bing ; Mao, Jianwei ; et al.
    In: 2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW, 2021
    Online Konferenz
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  9. Detailansicht für Treffer 9 öffnen
    Facile synthesis, structure and infrared properties of CoFe2O4 ferrite nanoparticles (CFN)
    Bajaj, Shakti N. ; Raut, A. V. ; et al.
    In: NATIONAL CONFERENCE ON PHYSICS AND CHEMISTRY OF MATERIALS: NCPCM2020 ; AIP Conference Proceedings ; ISSN 0094-243X, 2021
    Konferenz
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  10. Detailansicht für Treffer 10 öffnen
    SPYGLASS. II. The Multigenerational and Multiorigin Star Formation History of Cepheus Far North
    Kerr, Ronan ; Kraus, Adam L. ; et al.
    2022
    Online academicJournal
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  11. Detailansicht für Treffer 11 öffnen
    CFN: A Complex-Valued Fuzzy Network for Sarcasm Detection in Conversations
    Zhang, Yazhou ; Liu, Yaochen ; et al.
    In: IEEE Transactions on Fuzzy Systems ; volume 29, issue 12, page 3696-3710 ; ISSN 1063-6706 1941-0034, 2021
    Online academicJournal
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  12. Detailansicht für Treffer 12 öffnen
    CFN: A Complex-Valued Fuzzy Network for Sarcasm Detection in Conversations
    Zhang, Yazhou ; Liu, Yaochen ; et al.
    2021
    Online academicJournal
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  13. Detailansicht für Treffer 13 öffnen
    Erratum: Spelling error in citation in CFN 127(1):79
    Callaghan, Carolyn C. ; New York Botanical Garden, LuEsther T. Mertz Library
    2013
    academicJournal
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  14. Detailansicht für Treffer 14 öffnen
    Effects of Macromolecular Crowding on Nanoparticle Diffusion: New Insights from Mössbauer Spectroscopy
    Aleksey A. Nikitin (5070638) ; Anton Yu. Yurenya (11147162) ; et al.
    2021
    academicJournal
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  15. Detailansicht für Treffer 15 öffnen
    Design and Synthesis of a Nanopolymer for CO 2 Capture and Wastewater Treatment
    Entesar Al-Hetlani (9310362) ; Narendran Rajendran (818959) ; et al.
    2021
    academicJournal
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  16. Detailansicht für Treffer 16 öffnen
    Figure 2. Signal detection theory shows when majority-based reasoning is incorrect. ; Decision scenarios in which Condorcet will make different kinds of inaccurate predictions about groups of individually-optimal (see Figure 1) decision-makers (a–c), and in which majority voting is sub-optimal (d); prior probability of state + is on x-axis, ratio of cost of false positive to cost of false negative is on y-axis, so values larger than one indicate classifying – as + is relatively worse than classifying + as –, and vice versa. Grey regions show decision scenarios in which Condorcet leads to at least one predictive error: (a) As group size increases Condorcet incorrectly predicts that majority group accuracy will asymptotically increase towards 1, whereas it actually does not (Error Ia, main text); (b) As group size increases Condorcet incorrectly predicts that majority group accuracy will asymptotically increase towards 1, whereas groups actually make worse decisions than individuals (Error Ib, main text); (c) As group size increases Condorcet incorrectly predicts that majority group accuracy will decrease towards zero, whereas it actually converges to a positive level (Error II, main text); (d) Condorcet makes at least one of the errors just described; this plot also corresponds to decision scenarios in which majority decision-making is suboptimal, and should be replaced by a sub- (upper-left area) or super- (lower-right area) majority quorum rule as described in Results. Signal detection analysis for individual decision-makers is described in the Appendix; parameters for the analysis are μ-=0,μ+=σ=1, CTP=CTN=0, CFN=1, CFP=ratio.
    2019
    unknown
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  17. Detailansicht für Treffer 17 öffnen
    Figure 3. How majority voting can be optimally replaced by quorum decisions. ; Illustrative failures of simple majority voting and Condorcet predictions (left column) and their remediation through appropriate use of sub- or super-majority decision quorums (right column); group size increases on x-axis, while group accuracy increases on y-axis; red-dashed line indicates individual decision accuracy, black dot-dash line represents prior probability of state + (panels a and c) or of state – (panel e), solid blue line represents group accuracy. (a) Error Ia: Condorcet predicts that increasing group size will result in group accuracy converging to 1, but it converges to p which is above the individual accuracy level. (b) Choosing a super-majority quorum leads group accuracy to converge to 1 with increasing group size. (c) Error Ib: Condorcet predicts that increasing group size will result in group accuracy converging to 1, but it converges to p which is below the individual accuracy level, hence groups perform worse than individuals. (d) Choosing a super-majority quorum leads group accuracy to converge to 1 with increasing group size. (e) Error II: Condorcet predicts that increasing group size will result in group accuracy converging to 0, but it converges to 1 – p which is below the individual accuracy level. (f) Choosing a sub-majority quorum leads group accuracy to converge to 1 with increasing group size. Simulations comprise 10,000 replicates with individual accuracies derived from signal detection analysis with parameters μ-=0,μ+=σ=1, CTP=CTN=0, CFN=1 and: (a) CFP=4,p=0.9,q=0.5, (b) CFP=4,p=0.9,q=0.7, (c) CFP=1,p=0.7,q=0.5, (d) CFP=1,p=0.7,q=0.75, (e) CFP=10,p=0.7,q=0.5, (f) CFP=10,p=0.7,q=0.035.
    2019
    unknown
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  18. Detailansicht für Treffer 18 öffnen
    New insights into spontaneous pneumothorax: A review
    Louw, EH ; Shaw, JA ; et al.
    In: African Journal of Thoracic and Critical Care Medicine ; volume 27, issue 1, page 18 ; ISSN 2617-0205 2617-0191, 2021
    academicJournal
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  19. Detailansicht für Treffer 19 öffnen
    CFN Lectures on Functional Nanostructures - Volume 2
    Vojta, Matthias ; Röthig, Christian ; et al.
    In: Lecture Notes in Physics ; ISSN 0075-8450 1616-6361 ; ISBN 9783642143755 9783642143762; (2011)
    Buch
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  20. Detailansicht für Treffer 20 öffnen
    Syncope due to tracheal adenoid cystic carcinoma
    Bots, Eva ; van Wyk, AC ; et al.
    In: $2, 2019
    Online academicJournal
    Zugriff:
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    Volltext
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