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

Regulatory networks are responsible for controlling many aspects of biology including gene expression. Due to the complexity of existing networks many researchers have turned to synthetic networks designed to achieve a desired function. The Elowitz repressilator is a synthetic biological network of three repressors that exhibits oscillating gene expression, though its operation is noisy and lacks robustness in the cellular environment. This project aims to improve the quality of oscillation by decreasing frequency variability by inserting transcriptional time delay between one or more pairs of circuit elements. A test circuit containing twofluorescent proteins (CFP and YFP) behind separate, inducible promoters was constructed and used to characterize several delay fragments, with the fragment being inserted between YFP and its promoter while CFP functioned as an internal standard. A 1.5 kB non-coding (junk) DNA sequence derived from the GAL3 gene in yeast was selected because it did not appear to significantly impact expression and was expected to achieve a time delay of roughly 30 seconds. Baseline characterizations were performed on the repressilator using single-cell timelapse fluorescence microscopy with MATLAB image analysis software. The GAL3-derived fragment was inserted into the repressilator between the Lambda-CI promoter and lacI gene before the delayed repressilator was characterized similarly. Oscillatory behavior with dramatically reduced fluorescence intensity was observed having roughly the same frequency as the repressilator, but without the upward drift in baseline fluorescence.

Optimization of a Gene Oscillator using Transcriptional Time Delay

There is a growing interest in building autonomous systems that interact with complex environments. The difficulty associated with obtaining an accurate model for such environments poses a challenge to the task of assessing and guaranteeing the system's performance. We present a data-driven solution that allows for a system to be evaluated for specification conformance without an accurate model of the environment. Our approach involves learning a conservative reactive bound of the environment's behavior using data and specification of the system's desired behavior. First, the approach begins by learning a conservative reactive bound on the environment's actions that captures its possible behaviors with high probability. This bound is then used to assist verification, and if the verification fails under this bound, the algorithm returns counter-examples to show how failure occurs and then uses these to refine the bound. We demonstrate the applicability of the approach through two case-studies: i) verifying controllers for a toy multi-robot system, and ii) verifying an instance of human-robot interaction during a lane-change maneuver given real-world human driving data.

Counter-example Guided Learning of Bounds on Environment Behavior

Establishing performance metrics is a key part of a systematic design process. In particular, specifying metrics useful for quantifying performance in the ongoing efforts towards biomolecular circuit design is an important problem. Here we address this issue for the design of a fast biomolecular step response that is uniform across different cells and widely different environmental conditions using a combination of simple mathematical models and experimental measurements using single-cell time-lapse microscopy. We evaluate two metrics, the difference of the step response from an ideal step and the relative difference between multiple realizations of the step response, that can provide a single number to measure performance. We use a model of protein productiondegradation to show that these performance metrics correlate with response features of speed and noise. Finally, we work through an experimental methodology to estimate these metrics for step responses that have been acquired for inducible protein expression circuits in E. coli. These metrics will be useful to evaluate biomolecular step responses, as well as for setting similar performance measures for other design goals.

Performance metrics for a biomolecular step response

We consider identifiability of linear systems driven by white noise using a combination of distributional and time series measurements. Specifically, we assume that the system has no control inputs available and can only be observed at stationarity. The user is able to measure the full stationary state distribution as well as observe time correlations for small subsets of the state. We formulate theoretical conditions on identifiability of parameters from distributional information alone. We then give a sufficient condition and an effective necessary condition for identifiability using a combination of distributional and time series measurements. We illustrate the ideas with some simple examples as well as a biologically inspired example of a transcription and degradation process.

/pdf/linear-system-identifiability-from-distributional-and-time-2e8aucaepp.pdf

Linear system identifiability from distributional and time series data

This report describes a number of changes to the ARM AMBA bus case study from Bloem et al. that lead to significant reduction in synthesis time. In addition, it identifies the reason of blowup for the synthesized strategies in earlier studies as lack of binary decision diagram (BDD) reordering during strategy construction. Enabling dynamic BDD reordering with the group sifting algorithm, we synthesized strategies for as many as 18 masters, with both the original and revised specifications. This conclusion is based on detailed experimental measurements that show the changes of BDD sizes over time for the fixpoint and other variables during the nested fixed point computation, including the cumulative time spent on BDD reordering and the total number of BDD nodes. The measurements were obtained for eight different cases, allowing to compare the original with the revised specifications, with strategy reordering enabled or not, and conjoining the weak fairness guarantees or merging them into a single Buchi automaton. The revised specification proposed here is expressed using the open Promela language.

Richard M. Murray

Papers

Optimization of a Gene Oscillator using Transcriptional Time Delay

Counter-example Guided Learning of Bounds on Environment Behavior

Performance metrics for a biomolecular step response

Linear system identifiability from distributional and time series data

Revisiting the AMBA AHB bus case study