More Like this

1. Inference of regulatory networks through temporally sparse data

   https://doi.org/10.3389/fcteg.2022.1017256  

   Alali, Mohammad; Imani, Mahdi (December 2022, Frontiers in Control Engineering)

   A major goal in genomics is to properly capture the complex dynamical behaviors of gene regulatory networks (GRNs). This includes inferring the complex interactions between genes, which can be used for a wide range of genomics analyses, including diagnosis or prognosis of diseases and finding effective treatments for chronic diseases such as cancer. Boolean networks have emerged as a successful class of models for capturing the behavior of GRNs. In most practical settings, inference of GRNs should be achieved through limited and temporally sparse genomics data. A large number of genes in GRNs leads to a large possible topology candidate space, which often cannot be exhaustively searched due to the limitation in computational resources. This paper develops a scalable and efficient topology inference for GRNs using Bayesian optimization and kernel-based methods. Rather than an exhaustive search over possible topologies, the proposed method constructs a Gaussian Process (GP) with a topology-inspired kernel function to account for correlation in the likelihood function. Then, using the posterior distribution of the GP model, the Bayesian optimization efficiently searches for the topology with the highest likelihood value by optimally balancing between exploration and exploitation. The performance of the proposed method is demonstrated through comprehensive numerical experiments using a well-known mammalian cell-cycle network. 

   more » « less
2. Identification of Substation Configurations in Modern Power Systems using Artificial Intelligence

   Madurasinghe, Dulip; Venayagamoorthy, Ganesh K (July 2022, 11TH BULK POWER SYSTEMS DYNAMICS AND CONTROL SYMPOSIUM (IREP 2022))

   Power system transmission network topology is utilized in energy management system applications. Substation configurations are fundamental to transmission network topology processing. Modern power systems consisting of renewable energy sources require reliable and fast network topology processing due to the variable nature of wind and solar power plants. Currently used transmission network topology processing, which is based on the relay signals communicated through SCADA is not highly reliable or highly accurate. Substation configuration identification (SCI) for different substation arrangements including main and transfer bus arrangement (MTBA), ring bus arrangement (RBA), and single bus arrangement (SBA) is investigated. Synchrophasor measurement based SCI for functional arrangements (FA) using artificial intelligence (AI) approaches is proposed in this paper. This method improves monitoring FA. Typical results for MTBA, RBA and SBA substation configuration identification is presented. A modified two-area four-machine power system model with two grid connected solar PV plants consisting of MTBA, RBA and SBA is simulated on real-time digital simulator. AI based SCI is shown to accurately identify all possible FAs for the three substation arrangements under any operating condition. 

   more » « less
3. 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. 

   more » « less
4. DIRECTED NETWORK TOPOLOGY INFERENCE VIA GRAPH FILTER IDENTIFICATION

   https://doi.org/10.1109/DSW.2018.8439888  

   Shafipour, Rasoul; Segarra, Santiago; Marques, Antonio G.; Mateos, Gonzalo (June 2018, 2018 IEEE Data Science Workshop (DSW))

   We address the problem of inferring a directed network from nodal observations of graph signals generated by linear diffusion dynamics on the sought graph. Observations are modeled as the outputs of a linear graph filter (i.e., a polynomial on a local diffusion graph-shift operator encoding the unknown graph topology), excited with an ensemble of independent graph signals with arbitrarily-correlated nodal components. In this context, we first rely on observations of the output signals along with prior statistical information on the inputs to identify the diffusion filter. Such problem entails solving a system of quadratic matrix equations, which we recast as a smooth quadratic minimization subject to Stiefel manifold constraints. Subsequent identification of the network topology given the graph filter estimate boils down to finding a sparse and structurally admissible shift that commutes with the given filter, thus forcing the latter to be a polynomial in the sought graph-shift operator. Preliminary numerical tests corroborating the effectiveness of the proposed algorithms in recovering synthetic and real-world digraphs are provided. 

   more » « less
5. Identifying Bottleneck Nodes using Packet Delay Statistics

   https://doi.org/10.1109/MILCOM47813.2019.9021045  

   Marker, Joshua; Shea, John M.; Wong, Tan F.; Graves, Eric; Yu, Paul L. (November 2019, Proceedings of the IEEE Military Communications Conference (MILCOM))

   null (Ed.)

   An algorithm to identify the bottleneck nodes linking two component networks in a simple network of networks (NoN) configuration is proposed. The proposed bottleneck identification algorithm is based on applying a support vector machine on clustered packet delay measurements. This algorithm has the advantage that it requires almost no information about the topology of the underlying NoN. Simulation results show that this algorithm can provide very good detection performance when the component networks of the NoN are not too small in size, or when the connectivity between nodes within the component networks is not too sparse. 

   more » « less