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

Safe and robust GC (II) safety mode, if activated, maintains the spacecraft near a safety reference for all time. The algorithm utilizes separate feedforward and feedback components. In standard mode, the feedforward guidance solutions come from a way-point generation algorithm that uses a discrete linear-time-varying dynamics model. This approach provides a convex formulation of the problem (solvable onboard as a second-order cone program) that includes control and state constraints; the safety-mode availability adds a constraint in this standard-mode formulation as well. The feedback guarantees standard-mode resolvability to update the guidance prole in a robust, model-predictive manner. In safety mode, an oine-designed feedforward policy with the added feedback maintains the spacecraft in a hovering state in the proximity of its position at safety-mode activation time; this provides robustness to unexpected stateconstraint changes such as unexpected ground proximity during landing operations. A simulation demonstrating both the standard and safety modes is provided for a spacecraft autonomous-descent scenario toward a small asteroid with an uncertain gravity model and errors in the surface altitude constraint.

Robust Model Predictive Control with a Safety Mode: Applied to Small-Body Proximity Operations

In this paper we consider state estimation carried over a sensor network. A fusion center forms a local multi-hop tree of sensors and fuses the data into a state estimate. A set of sensor trees with desired properties is constructed, and those sensor trees are scheduled in such a way that the network lifetime is maximized. The sensor tree constructio n and scheduling algorithms are shown to have low polynomial time complexity, which leads to efficient implementation in practice. The scheduling algorithm is also shown to return the optimal solution. Examples are provided to demonstrate the algorithms.

Sensor Network Lifetime Maximization Via Sensor Trees Construction and Scheduling

We study the problem of using a small number of mobile sensors to monitor various threats in a geographical area. Using some recent results on stochastic sensor scheduling, we propose a stochastic sensor movement strategy. We present simple conditions under which it is not possible to maintain a bounded estimate error covariance for all the threats. We also study a simple sub-optimal algorithm to generate stochastic trajectories. Simulations are presented to illustrate the results.

/pdf/on-sensor-coverage-by-mobile-sensors-3t68uhd9sp.pdf

On Sensor Coverage by Mobile Sensors

We present an identification framework for biochemical systems that allows multiple candidate models to be compared. This framework is designed to select a model that fits the data while maintaining model simplicity. The model identification task is divided into a parameter estimation stage and a model comparison stage. Model selection is based on calculating Akaike's information criterion, which is a systematic method for determining the model that best represents a set of experimental data. Two case studies are presented: a simulated transcriptional control circuit and a system of oscillators that has been built and characterized in vitro. In both examples the multi-model framework is able to discriminate between model candidates to select the one that best describes the data.

/pdf/a-multi-model-approach-to-identification-of-biosynthetic-yt79mb7eaq.pdf

A Multi-Model Approach to Identification of Biosynthetic Pathways

This manuscript is for review purposes only and may not be reproduced, in whole or in part, without written consent from the author. Permission is granted for individual, non-commercial use.

/pdf/networked-sensing-estimation-and-control-systems-40qe9v35dz.pdf

Richard M. Murray

Papers

Robust Model Predictive Control with a Safety Mode: Applied to Small-Body Proximity Operations

Sensor Network Lifetime Maximization Via Sensor Trees Construction and Scheduling

On Sensor Coverage by Mobile Sensors

A Multi-Model Approach to Identification of Biosynthetic Pathways

Networked Sensing, Estimation and Control Systems