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1. Online Graph Learning under Smoothness Priors

   https://doi.org/10.23919/EUSIPCO54536.2021.9616123  

   Saboksayr, Seyed Saman; Mateos, Gonzalo; Cetin, Mujdat (August 2021, 2021 29th European Signal Processing Conference (EUSIPCO))

   The growing success of graph signal processing (GSP) approaches relies heavily on prior identification of a graph over which network data admit certain regularity. However, adaptation to increasingly dynamic environments as well as demands for real-time processing of streaming data pose major challenges to this end. In this context, we develop novel algorithms for online network topology inference given streaming observations assumed to be smooth on the sought graph. Unlike existing batch algorithms, our goal is to track the (possibly) time-varying network topology while maintaining the memory and computational costs in check by processing graph signals sequentially-in-time. To recover the graph in an online fashion, we leverage proximal gradient (PG) methods to solve a judicious smoothness-regularized, time-varying optimization problem. Under mild technical conditions, we establish that the online graph learning algorithm converges to within a neighborhood of (i.e., it tracks) the optimal time-varying batch solution. Computer simulations using both synthetic and real financial market data illustrate the effectiveness of the proposed algorithm in adapting to streaming signals to track slowly-varying network connectivity. 

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2. Online Topology Inference from Streaming Stationary Graph Signals with Partial Connectivity Information

   https://doi.org/10.3390/a13090228  

   Shafipour, Rasoul; Mateos, Gonzalo (September 2020, Algorithms)

   null (Ed.)

   We develop online graph learning algorithms from streaming network data. Our goal is to track the (possibly) time-varying network topology, and affect memory and computational savings by processing the data on-the-fly as they are acquired. The setup entails observations modeled as stationary graph signals generated by local diffusion dynamics on the unknown network. Moreover, we may have a priori information on the presence or absence of a few edges as in the link prediction problem. The stationarity assumption implies that the observations’ covariance matrix and the so-called graph shift operator (GSO—a matrix encoding the graph topology) commute under mild requirements. This motivates formulating the topology inference task as an inverse problem, whereby one searches for a sparse GSO that is structurally admissible and approximately commutes with the observations’ empirical covariance matrix. For streaming data, said covariance can be updated recursively, and we show online proximal gradient iterations can be brought to bear to efficiently track the time-varying solution of the inverse problem with quantifiable guarantees. Specifically, we derive conditions under which the GSO recovery cost is strongly convex and use this property to prove that the online algorithm converges to within a neighborhood of the optimal time-varying batch solution. Numerical tests illustrate the effectiveness of the proposed graph learning approach in adapting to streaming information and tracking changes in the sought dynamic network. 

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3. Online proximal gradient for learning graphs from streaming signals

   https://doi.org/10.23919/Eusipco47968.2020.9287550  

   Shafipour, Rasoul; Mateos, Gonzalo (January 2021, 2020 28th European Signal Processing Conference (EUSIPCO))

   null (Ed.)

   We leverage proximal gradient iterations to develop an online graph learning algorithm from streaming network data. Our goal is to track the (possibly) time-varying network topology, and effect memory and computational savings by processing the data on-the-fly as they are acquired. The setup entails observations modeled as stationary graph signals generated by local diffusion dynamics on the unknown network. Moreover, we may have a priori information on the presence or absence of a few edges as in the link prediction problem. The stationarity assumption implies that the observations' covariance matrix and the so-called graph shift operator (GSO - a matrix encoding the graph topology) commute under mild requirements. This motivates formulating the topology inference task as an inverse problem, whereby one searches for a (e.g., sparse) GSO that is structurally admissible and approximately commutes with the observations' empirical covariance matrix. For streaming data said covariance can be updated recursively, and we show online proximal gradient iterations can be brought to bear to efficiently track the time-varying solution of the inverse problem with quantifiable guarantees. Specifically, we derive conditions under which the GSO recovery cost is strongly convex and use this property to prove that the online algorithm converges to within a neighborhood of the optimal time-varying batch solution. Preliminary numerical tests illustrate the effectiveness of the proposed graph learning approach in adapting to streaming information and tracking changes in the sought dynamic network. 

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4. Epistemic Uncertainty Quantification in State-space LPV Model Identification Using Bayesian Neural Networks

   Bao, Yajie; Mohammadpour Velni, Javad; Shahbakhti, Mahdi (January 2020, IEEE control systems letters)

   This paper presents a variational Bayesian inference Neural Network (BNN) approach to quantify uncertainties in matrix function estimation for the state-space linear parameter-varying (LPV) model identification problem using only inputs/outputs data. The proposed method simultaneously estimates states and posteriors of matrix functions given data. In particular, states are estimated by reaching a consensus between an estimator based on past system trajectory and an estimator by recurrent equations of states; posteriors are approximated by minimizing the Kullback–Leibler (KL) divergence between the parameterized posterior distribution and the true posterior of the LPV model parameters. Furthermore, techniques such as transfer learning are explored in this work to reduce computational cost and prevent convergence failure of Bayesian inference. The proposed data-driven method is validated using experimental data for identification of a control-oriented reactivity controlled compression ignition (RCCI) engine model. 

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5. An Efficient Temporal Modeling Approach for Speech Emotion Recognition by Mapping Varied Duration Sentences into Fixed Number of Chunks

   https://doi.org/10.21437/Interspeech.2020-2636  

   Lin, Wei-Cheng; Busso, Carlos (October 2020, Interspeech 2020)

   null (Ed.)

   Speech emotion recognition (SER) plays an important role in multiple fields such as healthcare, human-computer interaction (HCI), and security and defense. Emotional labels are often annotated at the sentence-level (i.e., one label per sentence), resulting in a sequence-to-one recognition problem. Traditionally, studies have relied on statistical descriptions, which are com- puted over time from low level descriptors (LLDs), creating a fixed dimension sentence-level feature representation regardless of the duration of the sentence. However sentence-level features lack temporal information, which limits the performance of SER systems. Recently, new deep learning architectures have been proposed to model temporal data. An important question is how to extract emotion-relevant features with temporal infor- mation. This study proposes a novel data processing approach that extracts a fixed number of small chunks over sentences of different durations by changing the overlap between these chunks. The approach is flexible, providing an ideal frame- work to combine gated network or attention mechanisms with long short-term memory (LSTM) networks. Our experimental results based on the MSP-Podcast dataset demonstrate that the proposed method not only significantly improves recognition accuracy over alternative temporal-based models relying on LSTM, but also leads to computational efficiency. 

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