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Several sources of delay in an epidemic network might negatively affect the stability and robustness of the entire network. In this paper, a multi-delayed Susceptible-Infectious-Susceptible (SIS) model is applied on a metapopulation network, where the epidemic delays are categorized into local and global delays. While local delays result from intra-population lags such as symptom development duration or recovery period, global delays stem from inter-population lags, e.g., transition duration between subpopulations. The theoretical results for a network of subpopulations with identical linear SIS dynamics and different types of time-delay show that depending on the type of time-delay in the network, different eigenvalues of the underlying graph should be evaluated to obtain the feasible regions of stability. The delay-dependent stability of such epidemic networks has been analytically derived, which eliminates potentially expensive computations required by current algorithms. The effect of time-delay on the H2 norm-based performance of a class of epidemic networks with additive noise inputs and multiple delays is studied and the closed form of their performance measure is derived using the solution of delayed Lyapunov equations. As a case study, the theoretical findings are implemented on a network of United States’ busiest airports.
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Jointly Aligning and Predicting Continuous Emotion Annotations
Time-continuous dimensional descriptions of emotions (e.g., arousal, valence) allow researchers to characterize short-time changes and to capture long-term trends in emotion expression. However, continuous emotion labels are generally not synchronized with the input speech signal due to delays caused by reaction-time, which is inherent in human evaluations. To deal with this challenge, we introduce a new convolutional neural network (multi-delay sinc network) that is able to simultaneously align and predict labels in an end-to-end manner. The proposed network is a stack of convolutional layers followed by an aligner network that aligns the speech signal and emotion labels. This network is implemented using a new convolutional layer that we introduce, the delayed sinc layer. It is a time-shifted low-pass (sinc) filter that uses a gradient-based algorithm to learn a single delay. Multiple delayed sinc layers can be used to compensate for a non-stationary delay that is a function of the acoustic space. We test the efficacy of this system on two common emotion datasets, RECOLA and SEWA, and show that this approach obtains state-of-the-art speech-only results by learning time-varying delays while predicting dimensional descriptors of emotions.
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- Award ID(s):
- 1651740
- PAR ID:
- 10125068
- Date Published:
- Journal Name:
- IEEE Transactions on Affective Computing
- ISSN:
- 2371-9850
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Detection of human emotions is an essential part of affect-aware human-computer interaction (HCI). In daily conversations, the preferred way of describing affects is by using categorical emotion labels (e.g., sad, anger, surprise). In categorical emotion classification, multiple descriptors (with different degrees of relevance) can be assigned to a sample. Perceptual evaluations have relied on primary and secondary emotions to capture the ambiguous nature of spontaneous recordings. Primary emotion is the most relevant category felt by the evaluator. Secondary emotions capture other emotional cues also conveyed in the stimulus. In most cases, the labels collected from the secondary emotions are discarded, since assigning a single class label to a sample is preferred from an application perspective. In this work, we take advantage of both types of annotations to improve the performance of emotion classification. We collect the labels from all the annotations available for a sample and generate primary and secondary emotion labels. A classifier is then trained using multitask learning with both primary and secondary emotions. We experimentally show that considering secondary emotion labels during the learning process leads to relative improvements of 7.9% in F1-score for an 8-class emotion classification task.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/taffc.2019.2917047
