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

This paper applies some previously studied extended Kalman filter techniques for planar road geometry estimation to the domain of autonomous navigation of off-highway vehicles. In this work, a clothoid model of the road geometry is constructed and estimated recursively based on road features extracted from single-axis LADAR range measurements. We present a method for feature extraction of the road centerline in the image plane, and describe its application to recursive estimation of the road geometry. We analyze the performance of our method against simulated motion of varied road geometries and against closed-loop detection, tracking and following of desert roads. Our method accommodates full 6 DOF motion of the vehicle as it navigates, constructs consistent estimates of the road geometry with respect to a fixed global reference frame, and requires an estimate of the sensor pose for each range measurement

/pdf/model-based-estimation-of-off-highway-road-geometry-using-223ctnt8iq.pdf

Model-based estimation of off-highway road geometry using single-axis LADAR and inertial sensing

In this paper, we consider a state estimation problem over a bandwidth limited network. A sensor network consisting of N sensors is used to observe the states of M plants, but only p les N sensors can transmit their measurements to a centralized estimator at each time. Therefore a suitable scheme that schedules the proper sensors to access the network at each time so that the total estimation error is minimized is required. We propose four different sensor scheduling schemes. The static and stochastic schemes assume no feedback from the estimator to the scheduler, while the two dynamic schemes, Maximum Error First (MEF) and Maximum Deduction First (MDF) assume such feedback is available. We compare the four schemes via some examples and show MEF and MDF schemes perform better than the static and stochastic schemes, which demonstrates that feedback can play an important role in this remote state estimation problem. We also show that MDF performs better than MEF as MDF considers the total estimation error while MEF considers the individual estimation error.

/pdf/effective-sensor-scheduling-schemes-in-a-sensor-network-by-42z9gok2cu.pdf

Effective Sensor Scheduling Schemes in a Sensor Network by Employing Feedback in the Communication Loop

This paper describes the design and construction of a small ducted fan engine which is being used for experimental research in robust nonlinear control of high-performance vectored thrust aircraft. The fan consists of a high-efficiency electric motor with a 6-inch diameter blade, capable of generating up to 9 Newtons of thrust. Flaps on the fan allow the thrust to be vectored from side to side and even reversed. The engine is mounted on a three degree of freedom stand which allows horizontal and vertical translation as well as unrestricted pitch angle. We give a detailed description of the design and construction of the fan and its analytical and empirical models. Initial PID controllers for altitude and pitch angle stabilization are included to verify the system model and indicate future avenues of research.

/pdf/design-and-construction-of-a-small-ducted-fan-engine-for-3yha9jghii.pdf

Design and construction of a small ducted fan engine for nonlinear control experiments

We present a mathematical programming-based method for optimal control of nonlinear systems subject to temporal logic task specifications. We specify tasks using a fragment of linear temporal logic (LTL) that allows both finite- and infinite-horizon properties to be specified, including tasks such as surveillance, periodic motion, repeated assembly, and environmental monitoring. Our method directly encodes an LTL formula as mixed-integer linear constraints on the system variables, avoiding the computationally expensive process of creating a finite abstraction. Our approach is efficient; for common tasks our formulation uses significantly fewer binary variables than related approaches and gives the tightest possible convex relaxation. We apply our method on piecewise affine systems and certain classes of differentially flat systems. In numerical experiments, we solve temporal logic motion planning tasks for high-dimensional (10\(+\) continuous state) systems.

/pdf/optimal-control-of-nonlinear-systems-with-temporal-logic-jj59nxkw3n.pdf

Optimal Control of Nonlinear Systems with Temporal Logic Specifications

A method, device, framework, and system provide the ability to control an unmanned aerial vehicle (UAV) to avoid obstacle collision. Range data of a real-world scene is acquired using range sensors (that provide depth data to visible objects). The range data is combined into an egospace representation (consisting of pixels in egospace). An apparent size of each of the visible objects is expanded based on a dimension of the UAV. An assigned destination in the real world scene based on world space is received and transformed into egospace coordinates in egospace. A trackable path from the UAV to the assigned destination through egospace that avoids collision with the visible objects (based on the expanded apparent sizes of each of the visible objects) is generated. Inputs that control the UAV to follow the trackable path are identified.

Richard M. Murray

Papers

Model-based estimation of off-highway road geometry using single-axis LADAR and inertial sensing

Effective Sensor Scheduling Schemes in a Sensor Network by Employing Feedback in the Communication Loop

Design and construction of a small ducted fan engine for nonlinear control experiments

Optimal Control of Nonlinear Systems with Temporal Logic Specifications

Controlling unmanned aerial vehicles to avoid obstacle collision