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Mathematical model of brain tumour growth with drug resistance

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arXiv 2020年

作者： Trobia, José Tian, Kun Batista, Antonio M. Grebogi, Celso Ren, Hai-Peng Santos, Moises S. Protachevicz, Paulo R. Borges, Fernando S. Szezech, José D. Viana, Ricardo L. Caldas, Iberê L. Iarosz, Kelly C. Department of Mathematics and Statistics State University of Ponta Grossa Ponta GrossaPR84030-900 Brazil Graduate in Science Program - Physics State University of Ponta Grossa Ponta GrossaPR84030-900 Brazil Shaanxi Key Lab of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’anPR710048 China Institute of Physics University of São Paulo São PauloSP05508-900 Brazil Institute for Complex Systems and Mathematical Biology University of Aberdeen AberdeenAB24 3UE United Kingdom Xi’an Technological University Xi’anPR710021 China Center for Mathematics Computation and Cognition Federal University of ABC São Bernardo do CampoSP09606-045 Brazil Department of Physics Federal University of Paraná CuritibaPR80060-000 Brazil Faculdade de Telêmaco Borba FATEB Telêmaco BorbaPR84266-010 Brazil

Brain tumours are masses of abnormal cells that can grow in an uncontrolled way in the brain. There are different types of malignant brain tumours. Gliomas are malignant brain tumours that grow from glial cells and are identified as astrocytoma, oligodendroglioma, and ependymoma. We study a mathematical model that describes glia-neuron interaction, glioma, and chemotherapeutic agent. In this work, we consider drug sensitive and resistant glioma cells. We show that continuous and pulsed chemotherapy can kill glioma cells with a minimal loss of neurons. © 2020, CC BY.

关键词： Tumors

Learning ReLU networks on linearly separable data: Algorithm, optimality, and generalization

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arXiv 2018年

作者： Wang, Gang Giannakis, Georgios B. Chen, Jie Digital Technology Center Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States State Key Lab of Intelligent Control Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Tongji University Shanghai200092 China

Neural networks with REctified Linear Unit (ReLU) activation functions (a.k.a. ReLU networks) have achieved great empirical success in various domains. Nonetheless, existing results for learning ReLU networks either pose assumptions on the underlying data distribution being e.g. Gaussian, or require the network size and/or training size to be sufficiently large. In this context, the problem of learning a two-layer ReLU network is approached in a binary classification setting, where the data are linearly separable and a hinge loss criterion is adopted. Leveraging the power of random noise perturbation, this paper presents a novel stochastic gradient descent (SGD) algorithm, which can provably train any single-hidden-layer ReLU network to attain global optimality, despite the presence of infinitely many bad local minima, maxima, and saddle points in general. This result is the first of its kind, requiring no assumptions on the data distribution, training/network size, or initialization. Convergence of the resultant iterative algorithm to a global minimum is analyzed by establishing both an upper bound and a lower bound on the number of non-zero updates to be performed. Moreover, generalization guarantees are developed for ReLU networks trained with the novel SGD leveraging classic compression bounds. These guarantees highlight a key difference (at least in the worst case) between reliably learning a ReLU network as well as a leaky ReLU network in terms of sample complexity. Numerical tests using both synthetic data and real images validate the effectiveness of the algorithm and the practical merits of the theory. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

Research on Social Marketing Strategies with An Agent-based Propagation Model

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IFAC-PapersOnLine 2017年第1期50卷 13581-13586页

作者： Zeng, Shuai Li, Juanjuan Ni, Xiaochun Yuan, Yong Wang, Fei-Yue The State Key Lab of Management Control for Complex Systems Institute of Automation Chinese Academy of Sciences China Qingdao Academy of Intelligent Industries BeijingShandong China Research Center of Military Computational Experiments and Parallel Systems National University of Defense Technology Changsha China

Considering the increasing complexity of social networks and user behaviors, it is very challenging for advertisers to formulate their strategies of selecting proper initial seed users in their social marketing efforts. In this paper, we tackle this challenge by proposing an agent-based propagation model and injecting it into typical social networks with three types of structures, i.e., Erds-Rényi random graph, Watt-Strogatz small world graph, and Barabási-Albert scale-free graph. We instantiate this agent-based model with demographic characteristics extracted from real-world census data collected in China. By investigating the diffusion process of advertising information in these social networks, we can analyze and compare the performance of advertisers’ targeting and influencer strategies. Our experimental results indicate that advertisers adopting influencer strategies should manipulate the initial well-connected seeds to deliver information only to the potential customers instead of a wide range of generic users. © 2017

关键词： Marketing Autonomous agents Behavioral research Commerce Computational methods Population statistics Agent based model Demographic characteristics Information diffusion Potential customers Propagation modeling Seeding strategies Small world graphs Social marketings

Effects of drug resistance in the tumour-immune system with chemotherapy treatment

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arXiv 2020年

作者： Trobia, José Gabrick, Enrique C. Seifert, Evandro G. Borges, Fernando S. Protachevicz, Paulo R. Szezech, José D. Iarosz, Kelly C. Santos, Moises S. Caldas, Iberê L. Tian, Kun Ren, Hai-Peng Grebogi, Celso Batista, Antonio M. Graduate Program in Science - Physics State University of Ponta Grossa Ponta GrossaPR84030-900 Brazil Department of Mathematics and Statistics State University of Ponta Grossa Ponta GrossaPR84030-900 Brazil Department of Physics State University of Ponta Grossa Ponta GrossaPR84030-900 Brazil Center for Mathematics Computation and Cognition Federal University of ABC São Bernardo do CampoSP09606-045 Brazil Institute of Physics University of São Paulo São PauloSP05508-900 Brazil Faculty of Telêmaco Borba FATEB Telêmaco BorbaPR84266-010 Brazil Graduate Program in Chemical Engineering Federal Technological University of Paraná Ponta Grossa Paraná84016-210 Brazil Shaanxi Key Lab of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China Xi’an Technological University Xi’an710021 China Institute for Complex Systems and Mathematical Biology University of Aberdeen AberdeenAB24 3UE United Kingdom

Cancer is a term used to refer to a large set of diseases. The cancerous cells grow and divide and, as a result, they form tumours that grow in size. The immune system recognise the cancerous cells and attack them, though, it can be weakened by the cancer. One type of cancer treatment is chemotherapy, which uses drugs to kill cancer cells. Clinical, experimental, and theoretical research has been developed to understand the dynamics of cancerous cells with chemotherapy treatment, as well as the interaction between tumour growth and immune system. We study a mathematical model that describes the cancer growth, immune system response, and chemotherapeutic agents. The immune system is composed of resting cells that are converted to hunting cells to combat the cancer. In this work, we consider drug sensitive and resistant cancer cells. We show that the tumour growth can be controlled not only by means of different chemotherapy protocols, but also by the immune system that attacks both sensitive and resistant cancer cells. Furthermore, for all considered protocols, we demonstrate that the time delay from resting to hunting cells plays a crucial role in the combat against cancer cells. Copyright © 2020, The Authors. All rights reserved.

关键词： Chemotherapy

Analysis of security in cyber-physical systems

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Science China. Technological Sciences 2017年第12期60卷 1975-1977页

作者： Chen Jie Zhang Fan Sun Jian School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081

In recent years, cyber-physical systems (CPSs) have received much attention from both the academic world and the industrial world, which refer to a deep integration and coordination of physical and computational resources [1,2]. Typical examples of CPSs can be found in smart grids, smart transportation systems, industrial control systems, water supply systems, and so on. Furthermore, many military systems are also CPSs. The key characteristic of CPSs is the integration of computing, control and communication. The increased interconnection between the cyber and physical spaces make CPSs vulnerable to various malicious attacks. A well-known example of an attack of CPSs is the Stuxnet which infected the control system of nuclear-fuel centrifuges of Bushehr nuclear power plant in Iran. Stuxnet makes people beware of the grave consequences of a cyber-attack on a CPS. Since many national critical infrastructures are applications of CPS, ensuring security and safety of such systems is of great importance.

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Adaptive optimal control for a class of uncertain systems with saturating actuators and external disturbance using integral reinforcement learning

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Adaptive optimal control for a class of uncertain systems wi...

Asian control Conference

作者： Jingang Zhao Minggang Gan Jie Chen Dongyang Hou Meng Zhang Yongqiang Bai State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing

ISBN: (纸本)9781509015740;9781509015733

The problem of adaptive optimal control for a class of nonlinear uncertain systems with saturating actuators and external disturbance is investigated in this paper. Considering the saturating actuators, a non-quadratic cost function is adopted. The key of this optimal control problem is to find the solution to the Hamilton Jacobi Bellman equation (HJB). An online intergral reinforcement learning (IRL) algorithm based-Neural Network (NN) is given to approximate the solution. Unlike traditional integral reinforcement learning algorithms, data onto a period of time stored together with current data are used to update the neural network weights in place of persistence of excitation (PE) condition. This method overcomes the shortcomings of the PE condition which is not easy to be checked online. Finally, numerical examples are given to show the effectiveness of the proposed methods.

关键词： Optimal control Actuators Nonlinear systems Approximation algorithms Artificial neural networks System dynamics

Real-Time lane detection and forward collision warning system based on stereo vision 28

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Real-Time lane detection and forward collision warning syste...

28th IEEE intelligent Vehicles Symposium, IV 2017

作者： Song, Wenjie Fu, Mengyin Yang, Yi Wang, Meiling Wang, Xinyu Kornhauser, Alain State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institution of Technology Beijing 100081 China Princeton University United States Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781509048052

This paper presents a real-Time and robust lane detection and forward collision warning technique based on stereo cameras. First, obstacles image is obtained through stereo matching and UV-disparity segmentation algorithm. Then, Inverse Perspective Mapping(IPM) and Sobel filtering are conducted to generate a low-noise top view of the road by fusing the obstacles image and the original image. Next, Hough Transformation for the top view map is completed and the extreme points(poles) are calculated as the detected lanes according to the traffic lanes model. Besides, the host lane is selected or supplemented among all the detected lanes and the nearest obstacle in this host lane is detected for the forward collision warning. Experimental results on the public data set indicate that our method can work effectively and real-Timely in the normal structured environment. © 2017 IEEE.

关键词： Roads Cameras Image segmentation Robustness Real-time systems Automobiles Alarm systems

Compressive phase retrieval via reweighted amplitude flow

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arXiv 2017年

作者： Zhang, Liang Wang, Gang Giannakis, Georgios B. Chen, Jie Digital Technology Center Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China School of Automation State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

The problem of reconstructing a sparse signal vector from magnitude-only measurements (a.k.a., compressive phase retrieval), emerges naturally in diverse applications, but it is NP-hard in general. Building on recent advances in nonconvex optimization, this paper puts forth a new algorithm that is termed compressive reweighted amplitude flow and abbreviated as CRAF, for compressive phase retrieval. Specifically, CRAF operates in two stages. The first stage seeks a sparse initial guess via a new spectral procedure. In the second stage, CRAF implements a few hard thresholding based iterations using reweighted gradients. When there are sufficient measurements, CRAF provably recovers the underlying signal vector exactly with high probability under suitable conditions. Moreover, its sample complexity coincides with that of the state-of-the-art procedures. Finally, substantial simulated tests showcase remarkable performance of the new spectral initialization, as well as improved exact recovery relative to competing alternatives. Copyright © 2017, The Authors. All rights reserved.

关键词： Iterative methods

Energy Consumption Analysis in Multi-legged Robots Based on Gait Planning 35

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Energy Consumption Analysis in Multi-legged Robots Based on ...

第35届中国控制会议

作者： DU Xiaolong WANG Shoukun ZONG Xiaoyan WANG Junzheng State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology

ISBN: (纸本)9781509009107

Currently,the multi-legged robots have to confront the problem of low energy utilization that restricts the moving radius of *** at the problem of energy consumption for mobile robots,this paper provides the analysis in terms of the gait ***,two kinds of gait curves respectively,cycloid curve and Bezier curve are introduced and the main difference of two gait patterns is that the Bezier curve can achieve the swing leg retraction(SLR).The length of step(S),the height of step(H) and the duty ratio of gait cycle(P) are set as the primary research points about the gait of mobile *** this work,we formulate two indexes to quantitatively describe the performance of the quadruped robot and related simulations are carried out for gait ***,the results provide some recommendations for the gait planning of the quadruped robot,that is,the changes of gait parameters influence the index of energy consumption and SLR can effectively reduce energy consumption.

关键词： Energy consumption Multi-legged robot Gait planning SLR Gait parameters