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

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

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics. In this work, we provide an overview of the capabilities and development practices of the SciPy library and highlight some recent technical developments.

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SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python

Methods We use both a Monte Carlo (MC) sampling and a Bayesian analysis to find the effects of the photospheric composition and the touchdown radius We apply these two methods to six LMXBs that show PRE XRBs In both methods, we solve the Eddington flux equation and the apparent angular area equation both of which include the correction terms For the MC sampling, we have developed an iterative method in order to solve these two equations more efficiently Results We confirm that the effects of the photospheric composition and the touchdown radius are similar in the statistical and analytical estimation of mass and radius even when the correction terms are considered Furthermore, in all of the six sources, we find that a H-poor photosphere and a large touchdown radius are favored statistically regardless of the statistical method Our Bayesian analysis also hints that touchdown can occur farther from the neutron star surface when the photosphere is more H-poor This correlation could be qualitatively understood with the Eddington flux equation We propose a physical explanation for this correlation between the photospheric composition and the touchdown radius Our results show that when accounting for the uncertainties of the photospheric composition and the touchdown radius, most likely radii of the neutron stars in these six LMXBs are less than 125 km, which is similar to the bounds for the neutron star radius placed with the tidal deformability measured from the gravitational wave signal

Measuring Masses and Radii of Neutron Stars in Low-Mass X-ray Binaries: Effects of Atmospheric Composition and Touchdown Radius.

With H. A. Bethe, G. E. Brown worked on the merger rate of neutron star binaries for the gravitational wave detection. Their prediction has to be modified significantly due to the observations of 2M⊙ neutron stars and the detection of gravitational waves. There still, however, remains a possibility that neutron star-low mass black hole binaries are significant sources of gravitational waves for the ground-based detectors. In this paper, I review the evolution of neutron star binaries with super-Eddington accretion and discuss the future prospect.

Gravitational waves from neutron star binaries

We show that a common evolutionary history can produce the black hole binaries in the Galaxy in which the black holes have masses of ~ 5-10 M_sun. In with low-mass, ~ 3 M_sun are initially "silent" until the companion begins evolving across the Herzsprung gap. We provide evidence that the narrower, shorter period binaries, with companions now in main sequence, are fossil remnants of gamma ray bursters (GRBs). We also show that the GRB is generally accompanied by a hypernova explosion (a very energetic supernova explosion). We further show that the binaries with evolved companions are good models for some of the ultraluminous X-ray sources (ULXs) recently seen by Chandra in other galaxies. The great regularity in our evolutionary history, especially the fact that most of the companions of ZAMS mass <~ 2.5 M_sun remain in main sequences as K or M stars can be explained by the mass loss in common envelope evolution to be Case C; i.g., to occur only after core He burning has finished. Since our argument for Case C mass transfer is not generally understood in the community, we add an appendix, showing that with certain assumptions which we outline we can reproduce the regularities in the evolution of black hole binaries by Case C mass transfer.

On the Theory of Gamma Ray Bursts and Hypernovae: The Black Hole Soft X-ray Transient Sources

Publisher's Note: Kaon Condensation, Black Holes, and Cosmological Natural Selection [Phys. Rev. Lett. 101, 091101 (2008)]

We propose a new method to detect gravitational waves, based on spatial coherence interferometry with stellar light, as opposed to the conventional temporal coherence interferometry with laser sources. The proposed method detects gravitational waves by using two coherent beams of light from a single distant star measured at separate space-based detectors with a long baseline. This method can be applied to either the amplitude or intensity interferometry. This experiment allows for the search of gravitational waves in the lower frequency range of $10^{-6}$ to $10^{-4}$ Hz. In this work, we present the detection sensitivity of the proposed stellar interferometer by taking the detector response and shot and acceleration noises into account. Furthermore, the proposed experimental setup is capable of searching for primordial black holes and studying the size of the target neutron star, which are also discussed in the paper.

Chang-Hwan Lee

Papers

Measuring Masses and Radii of Neutron Stars in Low-Mass X-ray Binaries: Effects of Atmospheric Composition and Touchdown Radius.

Gravitational waves from neutron star binaries

On the Theory of Gamma Ray Bursts and Hypernovae: The Black Hole Soft X-ray Transient Sources

Publisher's Note: Kaon Condensation, Black Holes, and Cosmological Natural Selection [Phys. Rev. Lett. 101, 091101 (2008)]

Stellar interferometry for gravitational waves