pwc-archive/papers-with-abstracts huggingface dataset:
DeepTPI: Test Point Insertion with Deep Reinforcement Learning
Authors: Zhengyuan Shi, Min Li, Sadaf Khan, Liuzheng Wang, Naixing Wang, Yu Huang, Qiang Xu
Date: 2022-06-07 00:00:00
Tasks: Deep Reinforcement Learning, Graph Neural Network, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Test point insertion (TPI) is a widely used technique for testability enhancement, especially for logic built-in self-test (LBIST) due to its relatively low fault coverage. In this paper, we propose a novel TPI approach based on deep reinforcement learning (DRL), named DeepTPI. Unlike previous learning-based solutions that formulate the TPI task as a supervised-learning problem, we train a novel DRL agent, instantiated as the combination of a graph neural network (GNN) and a Deep Q-Learning network (DQN), to maximize the test coverage improvement. Specifically, we model circuits as directed graphs and design a graph-based value network to estimate the action values for inserting different test points. The policy of the DRL agent is defined as selecting the action with the maximum value. Moreover, we apply the general node embeddings from a pre-trained model to enhance node features, and propose a dedicated testability-aware attention mechanism for the value network. Experimental results on circuits with various scales show that DeepTPI significantly improves test coverage compared to the commercial DFT tool. The code of this work is available at https://github.com/cure-lab/DeepTPI.
Resource Allocation for Compression-aided Federated Learning with High Distortion Rate
Authors: Xuan-Tung Nguyen, Minh-Duong Nguyen, Quoc-Viet Pham, Vinh-Quang Do, Won-Joo Hwang
Date: 2022-06-02 00:00:00
Tasks: Federated Learning, Model Compression, Quantization, Vocal Bursts Intensity Prediction

Recently, a considerable amount of works have been made to tackle the communication burden in federated learning (FL) (e.g., model quantization, data sparsification, and model compression). However, the existing methods, that boost the communication efficiency in FL, result in a considerable trade-off between communication efficiency and global convergence rate. We formulate an optimization problem for compression-aided FL, which captures the relationship between the distortion rate, number of participating IoT devices, and convergence rate. Following that, the objective function is to minimize the total transmission time for FL convergence. Because the problem is non-convex, we propose to decompose it into sub-problems. Based on the property of a FL model, we first determine the number of IoT devices participating in the FL process. Then, the communication between IoT devices and the server is optimized by efficiently allocating wireless resources based on a coalition game. Our theoretical analysis shows that, by actively controlling the number of participating IoT devices, we can avoid the training divergence of compression-aided FL while maintaining the communication efficiency.
Edge Graph Neural Networks for Massive MIMO Detection
Authors: Hongyi Li, Junxiang Wang, Yongchao Wang
Date: 2022-05-22 00:00:00
Tasks: Graph Neural Network

Massive Multiple-Input Multiple-Out (MIMO) detection is an important problem in modern wireless communication systems. While traditional Belief Propagation (BP) detectors perform poorly on loopy graphs, the recent Graph Neural Networks (GNNs)-based method can overcome the drawbacks of BP and achieve superior performance. Nevertheless, direct use of GNN ignores the importance of edge attributes and suffers from high computation overhead using a fully connected graph structure. In this paper, we propose an efficient GNN-inspired algorithm, called the Edge Graph Neural Network (EGNN), to detect MIMO signals. We first compute graph edge weights through channel correlation and then leverage the obtained weights as a metric to evaluate the importance of neighbors of each node. Moreover, we design an adaptive Edge Drop (ED) scheme to sparsify the graph such that computational cost can be significantly reduced. Experimental results demonstrate that our proposed EGNN achieves better or comparable performance to popular MIMO detection methods for different modulation schemes and costs the least detection time compared to GNN-based approaches.
A deterministic view on explicit data-driven (M)PC
Authors: Manuel Klädtke, Dieter Teichrib, Nils Schlüter, Moritz Schulze Darup
Date: 2022-06-14 00:00:00
Tasks: Model Predictive Control

We show that the explicit realization of data-driven predictive control (DPC) for linear deterministic systems is more tractable than previously thought. To this end, we compare the optimal control problems (OCP) corresponding to deterministic DPC and classical model predictive control (MPC), specify its close relation, and systematically eliminate ambiguity inherent in DPC. As a central result, we find that the explicit solutions to these types of DPC and MPC are of exactly the same complexity. We illustrate our results with two numerical examples highlighting features of our approach.
Distributed Coordination of Charging Stations Considering Aggregate EV Power Flexibility
Authors: Dongxiang Yan, Chengbin Ma, Yue Chen
Date: 2022-06-14 00:00:00
Tasks: Management

In recent years, electric vehicle (EV) charging stations have witnessed a rapid growth. However, effective management of charging stations is challenging due to individual EV owners' privacy concerns, competing interests of different stations, and the coupling distribution network constraints. To cope with this challenge, this paper proposes a two-stage scheme. In the first stage, the aggregate EV power flexibility region is derived by solving an optimization problem. We prove that any trajectory within the obtained region corresponds to at least one feasible EV dispatch strategy. By submitting this flexibility region instead of the detailed EV data to the charging station operator, EV owners' privacy can be preserved and the computational burden can be reduced. In the second stage, a distributed coordination mechanism with a clear physical interpretation is developed with consideration of AC power flow constraints. We prove that the proposed mechanism is guaranteed to converge to the centralized optimum. Case studies validate the theoretical results. Comprehensive performance comparisons are carried out to demonstrate the advantages of the proposed scheme.
Stochastic Event-triggered Variational Bayesian Filtering
Authors: Xiaoxu Lv, Peihu Duan, Zhisheng Duan, Guanrong Chen, Ling Shi
Date: 2022-06-14 00:00:00
Tasks: State Estimation

This paper proposes an event-triggered variational Bayesian filter for remote state estimation with unknown and time-varying noise covariances. After presetting multiple nominal process noise covariances and an initial measurement noise covariance, a variational Bayesian method and a fixed-point iteration method are utilized to jointly estimate the posterior state vector and the unknown noise covariances under a stochastic event-triggered mechanism. The proposed algorithm ensures low communication loads and excellent estimation performances for a wide range of unknown noise covariances. Finally, the performance of the proposed algorithm is demonstrated by tracking simulations of a vehicle.
Time Optimization of Constrained Control for a Thermoelectric Solid System with a Peltier Element
Authors: Alexander Gavrikov, Georgy Kostin
Date: 2022-06-14 00:00:00

A solid system consisting of two heat conducting cylinders with a thermoelectric converter (Peltier element) between them is considered. A nonlinear model, which was previously verified by authors, is used to design a constrained control law that allows us to achieve a steady-state distribution of the temperature in one of the cylinders in much less time than the characteristic time of transient processes. The initial-boundary value problem is exactly linearized over temperature by means of feedback linearization. Although the resulting system is nonlinear in a control function, it is possible to construct a finite-dimensional approximation based on analytical solution of the corresponding eigenproblem for a constant control signal. The time-optimal control problem is studied numerically by using this eigenfunction decomposition. To construct admissible control laws, an auxiliary unconstrained optimization problem is introduced. Its cost functional represents a weighted sum of temperature deviation from the desired zero distribution and a penalty for violating an electric power constraint. The control time interval is split into several parts, and on each subinterval the control signal is taken constant. The optimal piecewise constant feedforward control is found numerically by applying the gradient descent method. We analyze the proposed control law with respect to the shortest admissible time of the process.
A Neural Network-Based Energy Management System for PV-Battery Based Microgrids
Authors: Yusuf Gupta, Mohammad Amin
Date: 2022-06-14 00:00:00
Tasks: energy management, Load Forecasting, Management

A neural network-based energy management system (NN-EMS) has been proposed in this paper for islanded ac microgrids fed by multiple PV-battery based distributed generators (DG). The stochastic and unequal irradiation results in unequal PV output, which causes an unequal state-of-charge (SoC) among the batteries of the DGs. This effect may cause the difference in the SoCs to increase considerably over time, leading to some batteries reaching their SoC limits. These batteries would no longer be able to control the dc-link of the hybrid grid forming DG. The proposed NN-EMS ensures SoC balancing by learning an optimal state-action mapping using the outputs of an optimal power flow (OPF). The training dataset has been generated by executing a mixed-integer linear programming based OPF for droop-based island microgrids considering a practical generation-load profile. The resultant NN-EMS controller inherits the information of optimal states and the network behaviour. Compared to traditional time-ahead centralized methods, the proposed strategy does not require accurate generation-load forecasting. Further, it can also respond to the variations in the PV power in near-real-time without resorting to solving an OPF. The proposed NN-EMS controller has been validated by case studies on a CIGRE LV microgrid containing PV-battery hybrid DGs. The proposed concept can also be extended to synthesize decentralized controllers that can cooperate among themselves to achieve a global objective without communication.
Multiband Massive IoT: A Learning Approach to Infrastructure Deployment
Authors: Enes Krijestorac, Ghaith Hattab, Petar Popovski, Danijela Cabric
Date: 2022-06-13 00:00:00

We consider a novel ultra-narrowband (UNB) low-power wide-area network (LPWAN) architecture design for uplink transmission of a massive number of Internet of Things (IoT) devices over multiple multiplexing bands. An IoT device can randomly choose any of the multiplexing bands to transmit its packet. Due to hardware constraints, a base station (BS) is able to listen to only one multiplexing band. Our main objective is to maximize the packet decoding probability (PDP) by optimizing the placement of the BSs and frequency assignment of BSs to multiplexing bands. We develop two online approaches that adapt to the environment based on the statistics of (un)successful packets at the BSs. The first approach is based on a predefined model of the environment, while the second approach is measurement-based model-free approach, which is applicable to any environment. The benefit of the model-based approach is a lower training complexity, at the risk of a poor fit in a model-incompatible environment. The simulation results show that our proposed approaches to band assignment and BS placement offer significant improvement in PDP over baseline random approaches and perform closely to the theoretical upper bound.
Artificial Neural Network For Transient Stability Assessment: A Review
Authors: Umair Shahzad
Date: 2022-06-13 00:00:00

Integration of large-scale renewable energy sources and increasing uncertainty has drastically changed the dynamics of power system and has consequently brought various challenges. Rapid transient stability assessment of modern power system is a vital requirement for accurate power system planning and operation. The conventional methods are unable to fulfil this requirement. Therefore, novel approaches are required in this regard. Machine leaning approaches such as artificial neural network can play a significant role in this regard. Therefore, this paper aims to review the application of artificial neural network for transient stability assessment of power systems. It is believed that this work will provide a solid foundation for researchers in the domain of machine learning applications to power system security and stability.
A Note on Low-Pass Filter Conditioning for Current-Mode Control
Authors: Xiaofan Cui, Al-Thaddeus Avestruz
Date: 2022-06-12 00:00:00

The low-pass filter is a classic control conditioning approach for high-frequency current-mode control. However, no existing literature discusses the large-signal stability criterion for the current-mode control with low-pass filters. This paper provides a mathematically rigorous large-signal stability criterion. The result can directly benefit the practical engineering implementation of the low-pass filter in high-frequency current-mode control.
Microfounding GARCH Models and Beyond: A Kyle-inspired Model with Adaptive Agents
Authors: Michele Vodret, Iacopo Mastromatteo, Bence Toth, Michael Benzaquen
Date: 2022-06-14 00:00:00
Tasks: Clustering

We relax the strong rationality assumption for the agents in the paradigmatic Kyle model of price formation, thereby reconciling the framework of asymmetrically informed traders with the Adaptive Market Hypothesis, where agents use inductive rather than deductive reasoning. Building on these ideas, we propose a stylised model able to account parsimoniously for a rich phenomenology, ranging from excess volatility to volatility clustering. While characterising the excess-volatility dynamics, we provide a microfoundation for GARCH models. Volatility clustering is shown to be related to the self-excited dynamics induced by traders' behaviour, and does not rely on clustered fundamental innovations. Finally, we propose an extension to account for the fragile dynamics exhibited by real markets during flash crashes.
Effect of Non-local Grazing on Dry-land Vegetation Dynamics
Authors: Mrinal Kanti Pal, Swarup Poria
Date: 2022-06-14 00:00:00

Dry-land ecosystem has turned into a matter of grave concern, due to growing threat of land degradation and bioproductivity-loss. Self-organized vegetation patterns are a remarkable characteristic of these ecosystems; apart from being visually captivating, patterns modulate the system-response to increasing environmental stress. Empirical studies hinted that herbivory is one the key regulatory mechanism behind pattern formation and overall ecosystem functioning. However most of the mathematical models have taken a mean-field strategy to grazing; foraging has been considered to be independent of spatial distribution of vegetation. To this end, an extended version of the celebrated plant-water model due to Klausmeier, has been taken as the base here. To encompass the effect of heterogeneous vegetation distribution on foraging intensity and subsequent impact on entire ecosystem, grazing is considered here to depend on spatially weighted average vegetation density, instead of density at a particular point. Moreover, varying influence of vegetation at any location over gazing elsewhere, is incorporated by choosing suitable averaging function. A comprehensive analysis demonstrates that inclusion of spatial non-locality, alters the understanding of system dynamics significantly. The grazing ecosystem is found to be more resilient to increasing aridity than it was anticipated to be in earlier studies on non-local grazing. The system-response to rising environmental pressure is also observed to vary depending on the grazer. Obtained results also suggest possibility of multi-stability due the history-dependence of system-response. Overall, this work indicates that the spatial heterogeneity in grazing intensity has a decisive role to play in the functioning of water-limited ecosystems.
Toward Dynamic Stability Assessment of Power Grid Topologies using Graph Neural Networks
Authors: Christian Nauck, Michael Lindner, Konstantin Schürholt, Frank Hellmann
Date: 2022-06-10 00:00:00
Tasks: Transfer Learning

To mitigate climate change, the share of renewable energies in power production needs to be increased. Renewables introduce new challenges to power grids regarding the dynamic stability due to decentralization, reduced inertia, and volatility in production. Since dynamic stability simulations are intractable and exceedingly expensive for large grids, graph neural networks (GNNs) are a promising method to reduce the computational effort of analyzing the dynamic stability of power grids. As a testbed for GNN models, we generate new, large datasets of dynamic stability of synthetic power grids, and provide them as an open-source resource to the research community. We find that GNNs are surprisingly effective at predicting the highly non-linear targets from topological information only. For the first time, performance that is suitable for practical use cases is achieved. Furthermore, we demonstrate the ability of these models to accurately identify particular vulnerable nodes in power grids, so-called troublemakers. Last, we find that GNNs trained on small grids generate accurate predictions on a large synthetic model of the Texan power grid, which illustrates the potential for real-world applications.
A new algorithm for structural restrictions in Bayesian vector autoregressions
Authors: Dimitris Korobilis
Date: 2022-06-14 00:00:00
Tasks: Form, parameter estimation

A comprehensive methodology for inference in vector autoregressions (VARs) using sign and other structural restrictions is developed. The reduced-form VAR disturbances are driven by a few common factors and structural identification restrictions can be incorporated in their loadings in the form of parametric restrictions. A Gibbs sampler is derived that allows for reduced-form parameters and structural restrictions to be sampled efficiently in one step. A key benefit of the proposed approach is that it allows for treating parameter estimation and structural inference as a joint problem. An additional benefit is that the methodology can scale to large VARs with multiple shocks, and it can be extended to accommodate non-linearities, asymmetries, and numerous other interesting empirical features. The excellent properties of the new algorithm for inference are explored using synthetic data experiments, and by revisiting the role of financial factors in economic fluctuations using identification based on sign restrictions.
Nowcasting the Portuguese GDP with Monthly Data
Authors: João B. Assunção, Pedro Afonso Fernandes
Date: 2022-06-14 00:00:00
Tasks: Missing Values

In this article, we present a method to forecast the Portuguese gross domestic product (GDP) in each current quarter (nowcasting). It combines bridge equations of the real GDP on readily available monthly data like the Economic Sentiment Indicator (ESI), industrial production index, cement sales or exports and imports, with forecasts for the jagged missing values computed with the well-known Hodrick and Prescott (HP) filter. As shown, this simple multivariate approach can perform as well as a Targeted Diffusion Index (TDI) model and slightly better than the univariate Theta method in terms of out-of-sample mean errors.
On the number of non-isomorphic choices on four elements
Authors: Alfio Giarlotta, Angelo Petralia, Stephen Watson
Date: 2022-06-14 00:00:00

We use a combinatorial approach to compute the number of non-isomorphic choices on four elements that can be explained by models of bounded rationality.
Combining Image Space and q-Space PDEs for Lossless Compression of Diffusion MR Images
Authors: Ikram Jumakulyyev, Thomas Schultz
Date: 2022-06-14 00:00:00
Tasks: Diffusion  MRI

Diffusion MRI is a modern neuroimaging modality with a unique ability to acquire microstructural information by measuring water self-diffusion at the voxel level. However, it generates huge amounts of data, resulting from a large number of repeated 3D scans. Each volume samples a location in q-space, indicating the direction and strength of a diffusion sensitizing gradient during the measurement. This captures detailed information about the self-diffusion, and the tissue microstructure that restricts it. Lossless compression with GZIP is widely used to reduce the memory requirements. We introduce a novel lossless codec for diffusion MRI data. It reduces file sizes by more than 30% compared to GZIP, and also beats lossless codecs from the JPEG family. Our codec builds on recent work on lossless PDE-based compression of 3D medical images, but additionally exploits smoothness in q-space. We demonstrate that, compared to using only image space PDEs, q-space PDEs further improve compression rates. Moreover, implementing them with Finite Element Methods and a custom acceleration significantly reduces computational expense. Finally, we show that our codec clearly benefits from integrating subject motion correction, and slightly from optimizing the order in which the 3D volumes are coded.
Fully Automated Assessment of Cardiac Coverage in Cine Cardiovascular Magnetic Resonance Images using an Explainable Deep Visual Salient Region Detection Model
Authors: Shahabedin Nabavi, Mohammad Hashemi, Mohsen Ebrahimi Moghaddam, Ahmad Ali Abin, Alejandro F. Frangi
Date: 2022-06-14 00:00:00
Tasks: Prognosis

Cardiovascular magnetic resonance (CMR) imaging has become a modality with superior power for the diagnosis and prognosis of cardiovascular diseases. One of the essential basic quality controls of CMR images is to investigate the complete cardiac coverage, which is necessary for the volumetric and functional assessment. This study examines the full cardiac coverage using a 3D convolutional model and then reduces the number of false predictions using an innovative salient region detection model. Salient regions are extracted from the short-axis cine CMR stacks using a three-step proposed algorithm. Combining the 3D CNN baseline model with the proposed salient region detection model provides a cascade detector that can reduce the number of false negatives of the baseline model. The results obtained on the images of over 6,200 participants of the UK Biobank population cohort study show the superiority of the proposed model over the previous state-of-the-art studies. The dataset is the largest regarding the number of participants to control the cardiac coverage. The accuracy of the baseline model in identifying the presence/absence of basal/apical slices is 96.25\% and 94.51\%, respectively, which increases to 96.88\% and 95.72\% after improving using the proposed salient region detection model. Using the salient region detection model by forcing the baseline model to focus on the most informative areas of the images can help the model correct misclassified samples' predictions. The proposed fully automated model's performance indicates that this model can be used in image quality control in population cohort datasets and also real-time post-imaging quality assessments.