There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

In this paper, we discuss consensus problems for networks of dynamic agents with fixed and switching topologies. We analyze three cases: 1) directed networks with fixed topology; 2) directed networks with switching topology; and 3) undirected networks with communication time-delays and fixed topology. We introduce two consensus protocols for networks with and without time-delays and provide a convergence analysis in all three cases. We establish a direct connection between the algebraic connectivity (or Fiedler eigenvalue) of the network and the performance (or negotiation speed) of a linear consensus protocol. This required the generalization of the notion of algebraic connectivity of undirected graphs to digraphs. It turns out that balanced digraphs play a key role in addressing average-consensus problems. We introduce disagreement functions for convergence analysis of consensus protocols. A disagreement function is a Lyapunov function for the disagreement network dynamics. We proposed a simple disagreement function that is a common Lyapunov function for the disagreement dynamics of a directed network with switching topology. A distinctive feature of this work is to address consensus problems for networks with directed information flow. We provide analytical tools that rely on algebraic graph theory, matrix theory, and control theory. Simulations are provided that demonstrate the effectiveness of our theoretical results.

/pdf/consensus-problems-in-networks-of-agents-with-switching-25c3hqvtd8.pdf

Consensus problems in networks of agents with switching topology and time-delays

In this paper we consider task scheduling when sensing and controlling over a network with packet dropping links. We find optimum ways of allocating limited computing resources for estimation and control of a number of linear dynamical systems with different characteristics over communication links with different qualities. We find theoretical expressions relating the optimum sampling rates of the dynamical systems to the characteristics of the communication links and dynamics of the plants. We derive optimum ways of task scheduling for two performance metrics: decay rate and the asymptotic value. The work lays the theoretical foundations for considering the impact of both limited computing and communication resources on estimation and control.

Optimum allocation of computing resources in networked sensing and control

Given a differentially flat system of ODEs. flat outputs that depend only on original variables but not on their derivatives are called zero-flat outputs and systems possessing such outputs are called zero-flat. In this paper we present a theory of zero-flatness for a system of two one-forms in arbitrary number of variables (t, x1. …. χN). Our approach splits the task of finding zero-flat outputs into two parts. First part involves solving for distributions that satisfy a set of algebraic conditions. The second part involves finding an integrable distribution from the solution set of the first part. Typically this part involves solving PDEs. Our results are also applicable in determining if a control alfine system in n states and n−2 controls has flat outputs that depend only on states. We illustrate our method by examples.

http://ieeexplore.ieee.org/iel7/7055173/7081895/07082469.pdf

Differential flatness of two one-forms in arbitrary number of variables

Cyber-physical systems refer to a new genre of engineered systems consisting of a tight coupling between computation, communication and physical entities. The main focus of the seminar was to discuss issues related to the reliable development of cyber-physical systems by using formal verification. This is a multi-disciplinary area requiring collaboration between areas focusing discrete systems analysis and continuous systems analysis. To this end, the seminar brought together researchers working in the fields of formal methods, control theory and hybrid systems to identify and discuss potential issues and research questions which require collaboration between the communities. This report documents the program and the outcomes of Dagstuhl Seminar 14122 "Verification of Cyber-Physical Systems".

Verification of Cyber-Physical Systems (Dagstuhl Seminar 14122)

Robot Dynamics and Control

Motivated by current technological advances in the design of real-time embedded systems, this work deals with the digital control of a continuous-time linear time-invariant (LTI) system whose output can be sampled at a high frequency. Since a typical sampled-data controller operating at a high sampling frequency needs heavy (high-precision) computation to alleviate its sensitivity to measurement and computational errors, the objective is to design a robust hybrid controller for high-frequency applications with limited computational power. To this end, we exploit our recent results on delay-based controller design and propose a digital-control scheme that can implement every continuous-time stabilizing (LTI) controller. This robust hybrid controller, which consists of an ideal sampler, a digital controller, a number of modified second-order holds and possibly a unity feedback, can operate at arbitrarily high sampling frequencies without requiring expensive, high-precision computation. We also discuss how to find a continuous-time LTI controller satisfying prescribed design specifications so that its corresponding digital controller requires the least processing time.

/pdf/synthesis-of-embedded-control-systems-with-high-sampling-29qz1721dj.pdf

Richard M. Murray

Papers

Optimum allocation of computing resources in networked sensing and control

Differential flatness of two one-forms in arbitrary number of variables

Verification of Cyber-Physical Systems (Dagstuhl Seminar 14122)

Robot Dynamics and Control

Synthesis of embedded control systems with high sampling frequencies