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

The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V−SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online (http://bioinf.spbau.ru/spades). It is distributed as open source software.

/pdf/spades-a-new-genome-assembly-algorithm-and-its-applications-35zec7ea02.pdf

SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing

We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin.

Semi-Supervised Classification with Graph Convolutional Networks

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

We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning (or deep learning) from sequences of words to graphs.DeepWalk uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences. We demonstrate DeepWalk's latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that DeepWalk outperforms challenging baselines which are allowed a global view of the network, especially in the presence of missing information. DeepWalk's representations can provide F1 scores up to 10% higher than competing methods when labeled data is sparse. In some experiments, DeepWalk's representations are able to outperform all baseline methods while using 60% less training data.DeepWalk is also scalable. It is an online learning algorithm which builds useful incremental results, and is trivially parallelizable. These qualities make it suitable for a broad class of real world applications such as network classification, and anomaly detection.

/pdf/deepwalk-online-learning-of-social-representations-4qo03z5zdj.pdf

DeepWalk: online learning of social representations

We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning (or deep learning) from sequences of words to graphs. DeepWalk uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences. We demonstrate DeepWalk's latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that DeepWalk outperforms challenging baselines which are allowed a global view of the network, especially in the presence of missing information. DeepWalk's representations can provide $F_1$ scores up to 10% higher than competing methods when labeled data is sparse. In some experiments, DeepWalk's representations are able to outperform all baseline methods while using 60% less training data. DeepWalk is also scalable. It is an online learning algorithm which builds useful incremental results, and is trivially parallelizable. These qualities make it suitable for a broad class of real world applications such as network classification, and anomaly detection.

DeepWalk: Online Learning of Social Representations

....The most comprehensive guide to designing practical and efficient algorithms!.... The Algorithm Design Manual, Second Edition "...the book is an algorithm-implementation treasure trove, and putting all of these implementations in one place was no small feat. The list of implementations [and] extensive bibliography make the book an invaluable resource for everyone interested in the subject." --ACM Computing Reviews "It has all the right ingredients: rich contents, friendly, personal language, subtle humor, the right references, and a plethora of pointers to resources."-- P. Takis Metaxas, Wellesley College "This is the most approachable book on algorithms I have." -- Megan Squire, Elon University, USA This newly expanded and updated second edition of the best-selling classic continues to take the "mystery" out of designing algorithms, and analyzing their efficacy and efficiency. Expanding onthe first edition, the book now serves as the primary textbook of choice for algorithm design courses while maintaining its status as the premier practical reference guide to algorithms for programmers, researchers, and students. The reader-friendly Algorithm Design Manual provides straightforward access to combinatorial algorithms technology, stressing design over analysis. The first part, Techniques, provides accessible instructionon methods for designing and analyzing computer algorithms. The second part, Resources, is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations and an extensive bibliography. NEW to the second edition: Doubles the tutorial material and exercises over the first edition Provides full online support for lecturers, and a completely updated and improved website component with lecture slides, audio and video Contains a unique catalog identifying the 75 algorithmic problems that arise most often in practice, leading the reader down the right path to solve them Includes several NEW "war stories" relating experiences from real-world applications Provides up-to-date links leading to the very best algorithm implementations available in C, C++, and Java ADDITIONAL Learning Tools: Exercises include "job interview problems" from major software companies Highlighted take-home lesson boxes emphasize essential concepts Provides comprehensive references to both survey articles and the primary literature Exercises points to relevant programming contest challenge problems Many algorithms presented with actual code (written in C) as well as pseudo-code A full set of lecture slides and additional material available at www.algorist.com Written by a well-known algorithms researcher who received the IEEE Computer Science and Engineering Teaching Award, this new edition of The Algorithm Design Manual is an essential learning tool for students needing a solid grounding in algorithms, as well as a special text/reference for professionals who need an authoritative and insightful guide. Professor Skiena is also author of the popular Springer text, Programming Challenges: The Programming Contest Training Manual.

http://www.r-5.org/files/books/computers/algo-list/common/Steven_Skiena-The_Algorithm_Design_Manual-EN.pdf

The Algorithm Design Manual

Permutations and Combinations Permutations Permutation Groups Inversions and Inversion Vectors Special Classes of Permutations Combinations Exercises and Research Problems * Partitions, Compositions, and Young Tableaux Partitions Compositions Young Tableaux Exercises and Research Problems * Representing Graphs Data Structures for Graphs Elementary Graph Operations Graph Embeddings Storage Formats Exercises and Research Problems * Generating Graphs Regular Structures Trees Random Graphs Relations and Functional Graphs Exercises and Research Problems * Properties of Graphs Connectivity Graph Isomorphism Cycles in Graphs Partial Orders Graph Coloring Cliques, Vertex Covers, and Independent Sets Exercises and Research Problems * Algorithmic Graph Theory Shortest Paths Minimum Spanning Trees Network Flow Matching Planar Graphs Exercises and Research Problems

Implementing Discrete Mathematics: Combinatorics And Graph Theory With Mathematica

As a result of the redundancy of the genetic code, adjacent pairs of amino acids can be encoded by as many as 36 different pairs of synonymous codons. A species-specific "codon pair bias" provides that some synonymous codon pairs are used more or less frequently than statistically predicted. We synthesized de novo large DNA molecules using hundreds of over-or underrepresented synonymous codon pairs to encode the poliovirus capsid protein. Underrepresented codon pairs caused decreased rates of protein translation, and polioviruses containing such amino acid-independent changes were attenuated in mice. Polioviruses thus customized were used to immunize mice and provided protective immunity after challenge. This "death by a thousand cuts" strategy could be generally applicable to attenuating many kinds of viruses.

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Steven Skiena

Papers

DeepWalk: online learning of social representations

DeepWalk: Online Learning of Social Representations

The Algorithm Design Manual

Implementing Discrete Mathematics: Combinatorics And Graph Theory With Mathematica

Virus attenuation by genome-scale changes in codon pair bias.