pwc-archive/papers-with-abstracts huggingface dataset:
SDN-based Resource Allocation in Edge and Cloud Computing Systems: An Evolutionary Stackelberg Differential Game Approach
Authors: Jun Du, Chunxiao Jiang, Abderrahim Benslimane, Song Guo, Yong Ren
Date: 2021-09-26 00:00:00
Tasks: Cloud Computing, Edge-computing, Management

Recently, the boosting growth of computation-heavy applications raises great challenges for the Fifth Generation (5G) and future wireless networks. As responding, the hybrid edge and cloud computing (ECC) system has been expected as a promising solution to handle the increasing computational applications with low-latency and on-demand services of computation offloading, which requires new computing resource sharing and access control technology paradigms. This work establishes a software-defined networking (SDN) based architecture for edge/cloud computing services in 5G heterogeneous networks (HetNets), which can support efficient and on-demand computing resource management to optimize resource utilization and satisfy the time-varying computational tasks uploaded by user devices. In addition, resulting from the information incompleteness, we design an evolutionary game based service selection for users, which can model the replicator dynamics of service subscription. Based on this dynamic access model, a Stackelberg differential game based cloud computing resource sharing mechanism is proposed to facilitate the resource trading between the cloud computing service provider (CCP) and different edge computing service providers (ECPs). Then we derive the optimal pricing and allocation strategies of cloud computing resource based on the replicator dynamics of users' service selection. These strategies can promise the maximum integral utilities to all computing service providers (CPs), meanwhile the user distribution can reach the evolutionary stable state at this Stackelberg equilibrium. Furthermore, simulation results validate the performance of the designed resource sharing mechanism, and reveal the convergence and equilibrium states of user selection, and computing resource pricing and allocation.
On the Scheduling Policy for Multi-process WNCS under Edge Computing
Authors: Yifei Qiu, Shaohua Wu, Ying Wang
Date: 2021-09-26 00:00:00
Tasks: Edge-computing, Scheduling

This paper considers a multi-process and multi-controller wireless networked control system (WNCS). There are $N$ independent linear time-invariant processes in the system plant which represent different kinds of physical processes. By considering the edge computing, the controllers are played by edge server and cloud server. Each process is measured by a sensor, and the status updates is sent to controller to generate the control command. The link delay of cloud server is longer than that of edge server. The processing time of status update depends on the characteristic of servers and processes. By taking into account such conditions, we mainly investigate how to choose the destination of status updates to minimize the system's average Mean Square Error (MSE), edge server or cloud server? To address this issue, we formulate an infinite horizon average cost Markov Decision Process (MDP) problem and obtain the optimal scheduling policy. The monotonicity of the value function in MDP is characterized and then used to show the threshold structure properties of the optimal scheduling policy. To overcome the curse of dimensionality, we propose a low-complexity suboptimal policy by using additive separable structure of value function. Furthermore, the processing preemption mechanism is considered to handle the status updates more flexible, and the consistency property is proved. On this basis, a numerical example is provided. The simulation results illustrate that the controller selection is related to the timeliness of process and show the performance of the suboptimal policy. We also find that the optimal policy will become a static policy in which the destination of status update is fixed when the wireless channel is error free.
Approaching the Transient Stability Boundary of a Power System: Theory and Applications
Authors: Peng Yang, Feng Liu, Wei Wei, Zhaojian Wang
Date: 2021-09-26 00:00:00

Estimating the stability boundary is a fundamental and challenging problem in transient stability studies. It is known that a proper level set of a Lyapunov function or an energy function can provide an inner approximation of the stability boundary, and the estimation can be expanded by trajectory reversing methods. In this paper, we streamline the theoretical foundation of the expansion methodology, and generalize it by relaxing the request that the initial guess should be a subset of the stability region. We investigate topological characteristics of the expanded boundary, showing how an initial guess can approach the exact stability boundary locally or globally. We apply the theory to transient stability assessment, and propose expansion algorithms to improve the well-known Potential Energy Boundary Surface (PEBS) and Boundary of stability region based Controlling Unstable equilibrium point (BCU) methods. Case studies on the IEEE 39-bus system well verify our results and demonstrate that estimations of the stability boundary and the critical clearing time can be significantly improved with modest computational cost.
System Identification in Multi-Actuator Hard Disk Drives with Colored Noises using Observer/Kalman Filter Identification (OKID) Framework
Authors: Nikhil Potu Surya Prakash, Zhi Chen, Roberto Horowitz
Date: 2021-09-25 00:00:00
Tasks: Time Series, Time Series Analysis

Multi Actuator Technology in Hard Disk drives (HDDs) equips drives with two dual stage actuators (DSA) each comprising of a voice coil motor (VCM) actuator and a piezoelectric micro actuator (MA) operating on the same pivot point. Each DSA is responsible for controlling half of the drive's arms. As both the DSAs operate independently on the same pivot timber, the control forces and torques generated by one affect the operation of the other. The feedback controllers might not completely reject these transferred disturbances and a need to design feedforward controllers arises, which require a good model of the disturbance process. The usual system identification techniques produce a biased estimate because of the presence of the runout which is a colored noise. In this paper, we use the OKID framework to estimate this disturbance cross transfer function from the VCM control input of one DSA to the output of the other DSA from the collected time series data corrupted by colored noise.
Accelerated consensus in multi-agent networks via memory of local averages
Authors: Aditya Bhaskar, Shriya Rangarajan, Vikram Shree, Mark Campbell, Francesca Parise
Date: 2021-09-25 00:00:00

Classical mathematical models of information sharing and updating in multi-agent networks use linear operators. In the paradigmatic DeGroot model, agents update their states with linear combinations of their neighbors' current states. In prior work, an accelerated averaging model employing the use of memory has been suggested to accelerate convergence to a consensus state for undirected networks. There, the DeGroot update on the current states is followed by a linear combination with the previous states. We propose a modification where the DeGroot update is applied to the current and previous states and is then followed by a linear combination step. We show that this simple modification applied to undirected networks permits convergence even for periodic networks. Further, it allows for faster convergence than the DeGroot and accelerated averaging models for suitable networks and model parameters.
Automated Multi-domain Engineering Design through Linear Graph and Genetic Programming
Authors: Eric McCormick, Haoxiang Lang, Clarence W. de Silva
Date: 2021-09-25 00:00:00

This paper proposes a methodology of integrating the Linear Graph (LG) approach with Genetic Programming (GP) for generating an automated multi-domain engineering design approach by using the in-house developed LG MATLAB toolbox and the GP toolbox in MATLAB. The necessary background for the development are presented, and the methodology used in this work to facilitate the construction and evaluation of filter circuits, using LG models, is described. Designing electronic filter circuits through an evolution from electronic components to the completed circuits is demonstrated. The topology and component values of three types of filter circuits: low pass, high pass, and band pass, are designed through this evolutionary approach, for various cut-off frequencies. Furthermore, the paper demonstrates through examples of these evolved filter circuits, the combined GP and LG approach is successful in constructing high order filter circuits that are capable of attenuating undesired frequencies while maintaining desirable ones. The work presented in the paper is a key step towards the integration of LG modeling, through the use of the LGtheory MATLAB Toolbox, with machine learning techniques for the automated design of dynamic multi-domain mechatronic systems.
Automaton-based Implicit Controlled Invariant Set Computation for Discrete-Time Linear Systems
Authors: Zexiang Liu, Tzanis Anevlavis, Necmiye Ozay, Paulo Tabuada
Date: 2021-09-25 00:00:00
Tasks: Form

In this paper, we derive closed-form expressions for implicit controlled invariant sets for discrete-time controllable linear systems with measurable disturbances. In particular, a disturbance-reactive (or disturbance feedback) controller in the form of a parameterized finite automaton is considered. We show that, for a class of automata, the robust positively invariant sets of the corresponding closed-loop systems can be expressed by a set of linear inequality constraints in the joint space of system states and controller parameters. This leads to an implicit representation of the invariant set in a lifted space. We further show how the same parameterization can be used to compute invariant sets when the disturbance is not available for measurement.
Suboptimal nonlinear model predictive control with input move-blocking
Authors: Artemi Makarow, Christoph Rösmann, Torsten Bertram
Date: 2021-09-25 00:00:00
Tasks: Blocking, Model Predictive Control

This paper deals with the integration of input move-blocking into the framework of suboptimal model predictive control. The blocked input parameterization is explicitly considered as a source of suboptimality. A straightforward integration approach is to hold back a manually generated stabilizing fallback solution in some buffer for the case that the optimizer does not find a better input move-blocked solution. An extended approach superimposes the manually generated stabilizing warm-start by the move-blocked control sequence and enables a stepwise improvement of the control performance. In addition, this contribution provides a detailed review of the literature on input move-blocked model predictive control and combines important results with the findings of suboptimal model predictive control. A numerical example supports the theoretical results and shows the effectiveness of the proposed approach.
A Primal Decomposition Approach to Globally Coupled Aggregative Optimization over Networks
Authors: Yuanhanqing Huang, Jianghai Hu
Date: 2021-09-25 00:00:00

We consider a class of multi-agent optimization problems, where each agent has a local objective function that depends on its own decision variables and the aggregate of others, and is willing to cooperate with other agents to minimize the sum of the local objectives. After associating each agent with an auxiliary variable and the related local estimates, we conduct primal decomposition to the globally coupled problem and reformulate it so that it can be solved distributedly. Based on the Douglas-Rachford method, an algorithm is proposed which ensures the exact convergence to a solution of the original problem. The proposed method enjoys desirable scalability by only requiring each agent to keep local estimates whose number grows linearly with the number of its neighbors. We illustrate our proposed algorithm by numerical simulations on a commodity distribution problem over a transport network.
Distributed Computation of Stochastic GNE with Partial Information: An Augmented Best-Response Approach
Authors: Yuanhanqing Huang, Jianghai Hu
Date: 2021-09-25 00:00:00

In this paper, we focus on the stochastic generalized Nash equilibrium problem (SGNEP) which is an important and widely-used model in many different fields. In this model, subject to certain global resource constraints, a set of self-interested players aim to optimize their local objectives that depend on their own decisions and the decisions of others and are influenced by some random factors. We propose a distributed stochastic generalized Nash equilibrium seeking algorithm in a partial-decision information setting based on the Douglas-Rachford operator splitting scheme, which relaxes assumptions in the existing literature. The proposed algorithm updates players' local decisions through augmented best-response schemes and subsequent projections onto the local feasible sets, which occupy most of the computational workload. The projected stochastic subgradient method is applied to provide approximate solutions to the augmented best-response subproblems for each player. The Robbins-Siegmund theorem is leveraged to establish the main convergence results to a true Nash equilibrium using the proposed inexact solver. Finally, we illustrate the validity of the proposed algorithm via two numerical examples, i.e., a stochastic Nash-Cournot distribution game and a multi-product assembly problem with the two-stage model.
Constrained Attack-Resilient Estimation of Stochastic Cyber-Physical Systems
Authors: Wenbin Wan, Hunmin Kim, Naira Hovakimyan, Petros Voulgaris
Date: 2021-09-25 00:00:00
Tasks: State Estimation

In this paper, a constrained attack-resilient estimation algorithm (CARE) is developed for stochastic cyber-physical systems. The proposed CARE can simultaneously estimate the compromised system states and attack signals. It has improved estimation accuracy and attack detection performance when physical constraints and operational limitations are available. In particular, CARE is designed for simultaneous input and state estimation that provides minimum-variance unbiased estimates, and these estimates are projected onto the constrained space restricted by inequality constraints subsequently. We prove that the estimation errors and their covariances from CARE are less than those from unconstrained algorithms and confirm that this property can further reduce the false negative rate in attack detection. We show that estimation errors of CARE are practically exponentially stable in mean square. Finally, an illustrative example of attacks on a vehicle is given to demonstrate the improved estimation accuracy and detection performance compared to an existing unconstrained algorithm.
Development of Safety Monitoring System of Connected and Automated Vehicles considering the Trade-off between Communication Efficiency and Data Reliability
Authors: Sehyun Tak, Seongjin Choi
Date: 2021-09-25 00:00:00

The safety of urban transportation systems is considered a public health issue worldwide, and many researchers have contributed to improving it. Connected automated vehicles (CAVs) and cooperative intelligent transportation systems (C-ITSs) are considered solutions to ensure urban transportation systems' safety using various sensors and communication devices. However, it is found difficult to deploy the C-ITS framework in South Korea, because CAVs produce a massive amount of data every minute but it cannot be transmitted via existing communication network. Thus, raw data must be sampled to reduce the size of the data over communication network and transmitted to the server for further processing. On the other hand, the sampled data must be highly accurate to ensure the safety of different agents in C-ITS. Thus, in this study, we designed and developed a C-ITS architecture and data flow, including messages and protocols for the safety monitoring system of CAVs, and determined the optimal sampling interval for data transmission while considering the trade-off between communication efficiency and reliability of safety performance indicators. Three safety performance indicators were introduced: severe deceleration, lateral position variance, and inverse time to collision. A field test is conducted to collect data from various sensors installed in the CAV, determining the optimal sampling interval. Kolmogorov-Smirnov test is conducted to ensure statistical consistency between sampled and raw datasets. The effects of the sampling interval on message delay, data accuracy, and communication efficiency were analyzed.
Data-driven control via Petersen's lemma
Authors: Andrea Bisoffi, Claudio De Persis, Pietro Tesi
Date: 2021-09-24 00:00:00
Tasks: LEMMA

We address the problem of designing a stabilizing closed-loop control law directly from input and state measurements collected in an open-loop experiment. In the presence of noise in data, we have that a set of dynamics could have generated the collected data and we need the designed controller to stabilize such set of data-consistent dynamics robustly. For this problem of data-driven control with noisy data, we advocate the use of a popular tool from robust control, Petersen's lemma. In the cases of data generated by linear and polynomial systems, we conveniently express the uncertainty captured in the set of data-consistent dynamics through a matrix ellipsoid, and we show that a specific form of this matrix ellipsoid makes it possible to apply Petersen's lemma to all of the mentioned cases. In this way, we obtain necessary and sufficient conditions for data-driven stabilization of linear systems through a linear matrix inequality. The matrix ellipsoid representation enables insights and interpretations of the designed control laws. In the same way, we also obtain sufficient conditions for data-driven stabilization of polynomial systems through (convex) sum-of-squares programs. The findings are illustrated numerically.
U(1) dynamics in neuronal activities
Authors: Chia-Ying Lin, Ping-Han Chen, Hsiu-Hau Lin, Wen-Min Huang
Date: 2021-09-26 00:00:00

Neurons convert the external stimuli into action potentials, or spikes, and encode the contained information into the biological nerve system. Despite the complexity of neurons and the synaptic interactions in between, the rate models are often adapted to describe neural encoding with modest success. However, it is not clear whether the firing rate, the reciprocal of the time interval between spikes, is sufficient to capture the essential feature for the neuronal dynamics. Going beyond the usual relaxation dynamics in Ginzburg-Landau theory for statistical systems, we propose the neural activities can be captured by the U(1) dynamics, integrating the action potential and the ``phase" of the neuron together. The gain function of the Hodgkin-Huxley neuron and the corresponding dynamical phase transitions can be described within the U(1) neuron framework. In addition, the phase dependence of the synaptic interactions is illustrated and the mapping to the Kinouchi-Copelli neuron is established. It suggests that the U(1) neuron is the minimal model for single-neuron activities and serves as the building block of the neuronal network for information processing.
Biological logics are restricted
Authors: Thomas M. A. Fink, Ryan Hannam
Date: 2021-09-26 00:00:00

Networks of gene regulation govern morphogenesis, determine cell identity and regulate cell function. But we have little understanding, at the local level, of which logics are biologically preferred or even permitted. To solve this puzzle, we studied the consequences of a fundamental aspect of gene regulatory networks: genes and transcription factors talk to each other but not themselves. Remarkably, this bipartite structure severely restricts the number of logical dependencies that a gene can have on other genes. We developed a theory for the number of permitted logics for different regulatory building blocks of genes and transcription factors. We tested our predictions against a simulation of the 19 simplest building blocks, and found complete agreement. The restricted range of biological logics is a key insight into how information is processed at the genetic level. It constraints global network function and makes it easier to reverse engineer regulatory networks from observed behavior.
Cancer Progression as a Learning Process
Authors: Aseel Shomar, Omri Barak, Naama Brenner
Date: 2021-09-26 00:00:00
Tasks: Learning Theory

Drug resistance and metastasis - the major complications in cancer - both entail adaptation of cancer cells to stress, whether a drug or a lethal new environment. Intriguingly, these adaptive processes share similar features that cannot be explained by a pure Darwinian scheme, including dormancy, increased heterogeneity, and stress-induced plasticity. Here, we propose that learning theory offers a framework to explain these features and may shed light on these two intricate processes. In this framework, learning is performed at the single cell level, by stress-driven exploratory trial-and-error. Such a process is not contingent on pre-existing pathways but on a random search for a state that diminishes the stress. We review underlying mechanisms that may support this search, and show by using a learning model that such exploratory adaptation is feasible in a high dimensional system as the cell. At the population level, we view the tissue as a network of exploring agents that communicate and restrain cancer formation in health. In this view, disease results from the breakdown of homeostasis between cellular exploratory drive and tissue homeostasis.
Immunity-induced criticality of the genotype network of influenza A (H3N2) hemagglutinin
Authors: Blake J. M. Williams, C. Brandon Ogbunugafor, Benjamin M. Althouse, Laurent Hébert-Dufresne
Date: 2021-09-25 00:00:00

Seasonal influenza kills hundreds of thousands every year, with multiple constantly-changing strains in circulation at any given time. A high mutation rate enables the influenza virus to evade recognition by the human immune system, including immunity acquired through past infection and vaccination. Here, we capture the genetic similarity of influenza strains and their evolutionary dynamics with genotype networks. We show that the genotype networks of influenza A (H3N2) hemagglutinin are characterized by heavy-tailed distributions of module sizes and connectivity, suggesting critical-like behavior. We argue that: (i) genotype networks are driven by mutation and host immunity to explore a subspace of networks predictable in structure, and (ii) genotype networks provide an underlying structure necessary to capture the rich dynamics of multistrain epidemic models. In particular, inclusion of strain-transcending immunity in epidemic models is dependent upon the structure of an underlying genotype network. This interplay suggests a self-organized criticality where the epidemic dynamics of influenza locates critical-like regions of its genotype network. We conclude that this interplay between disease dynamics and network structure might be key for future network analysis of pathogen evolution and realistic multistrain epidemic models.