Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-11T13:04:44.031625Z
Paper Citation Record · LEDGER
As of 15 August 2026, this Paper Citation Record lists 98 of 98 outbound references and 0 inbound Pith citation observations for arXiv:2412.13541.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-11T13:04:44.031625Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
98 of 98 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation dcaad6fd-f495-4a5d-91a5-41f58826a32c · outbound
Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion Recognition Multi- modal emotion recognition with temporal and semantic consistency,
Reference 1
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Observation df03fbe2-a4d6-4e08-97cf-8447872d2b92 · outbound
Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion Recognition Graphcfc: A directed graph based cross-modal feature complementation approach for multimodal conversational emotion recognition,
Reference 2
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Observation 8933dfbb-315e-4b23-a782-578ca47b9eaf · outbound
Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion Recognition Emocov: Machine learning for emotion detection, analysis and visualization using covid-19 tweets,
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Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion Recognition Recognition of emotions in user-generated videos through frame-level adaptation and emotion intensity learning,
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Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion Recognition Customer preferences extraction for air purifiers based on fine-grained sentiment analysis of online reviews,
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Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion Recognition Intelligent cockpit for intelligent vehicle in metaverse: A case study of empathetic auditory regulation of human emotion,
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Observation 25e62902-ded8-4435-83f1-1603403a2e93 · outbound
Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion Recognition Disturbance rejection in mimo systems with emotional-learning-based controller: Application to variable rotor-speed helicopters,
Reference 10
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Observation 8a191e8a-8a8f-4e62-9ed4-315d681350d4 · outbound
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Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion Recognition Combining a parallel 2d cnn with a self-attention dilated residual network for ctc-based discrete speech emotion recognition,
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Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion Recognition Mlg-ncs: Multimodal local–global neuromorphic computing system for affective video content analysis,
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Observation 5cb8c857-548d-4fbd-a3fb-900b986fef80 · outbound
Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion Recognition Few-shot learning for fine-grained emotion recognition using physiological signals,
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Observation fafa05ad-f86d-46a2-a471-c7bdd2d81967 · outbound
Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion Recognition Challenges in the diagnosis of parkinson’s disease,
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Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion Recognition MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos
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Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion Recognition Mixed feelings: expression of non-basic emotions in a muscle-based talking head,
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Observation 30762f56-392c-4266-9997-f3f2f2bf7368 · outbound
Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion Recognition Convolutional features-based broad learning with lstm for multidimensional facial emotion recognition in human–robot interaction,
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Observation af6362f7-738b-4fac-908f-a2ec6533bde9 · outbound
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Observation 5028ea18-9ba5-49b1-a444-9a2f188aef31 · outbound
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Observation 39a86d04-ea6a-4576-b50a-7d5708c4134b · outbound
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Observation d8477e61-bb0d-4ded-9394-d33b95ade90b · outbound
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Reference 97
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Reference 98
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Reference 99
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Reference 100
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Reference 101
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