The Zero Marginal Cost Society: The Internet of Things, the Collaborative Commons, and the Eclipse of Capitalism
by Jeremy Rifkin · 31 Mar 2014 · 565pp · 151,129 words
experiment with a new way of storing data that could eventually drop the marginal cost to near zero. In January 2013 scientists at the European Bioinformatics Institute in Cambridge, England, announced a revolutionary new method of storing massive electronic data by embedding it in synthetic DNA. Two researchers, Nick Goldman and
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code is high and the time it takes to decode information is substantial. Researchers, however, are reasonably confident that an exponential rate of change in bioinformatics will drive the marginal cost to near zero over the next several decades. A near zero marginal cost communication/energy infrastructure for the Collaborative Age
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. The intensification of genetic-Commons advocacy comes at a time when new IT and computing technology is speeding up genetic research. The new field of bioinformatics has fundamentally altered the nature of biological research just as IT, computing, and Internet technology did in the fields of renewable-energy generation and 3D
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, and agribusiness. While the dual movements shared common philosophical ground, they also began to share technological ground with the birth of the new field of bioinformatics. Researchers began using computing technology to decipher, download, catalog, store, and reconfigure genetic information, creating a new kind of genetic capital for the Bioindustrial Age
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accomplish the task. Titans in the computer field like Bill Gates and Wall Street insiders like Michael Milken poured funds into the new field of bioinformatics in hopes of advancing the collaborative partnership of the information and Life Sciences. Computers are not only being used to decipher and store genetic information
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other genomes, are the “common heritage” of evolution and therefore cannot be enclosed as private property.29 Boyle sensed that while the new field of “bioinformatics blurs the line between computer modeling and biological research,” it might be possible that open-source genomics could liberate biological research from narrow corporate interests
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replacing human labor, 121, 129, 267 and social media, 199–200 UPS uses, 11–12 and Watson, 130 bike sharing, 227 biocapacity, 274–275, 286 bioinformatics, 86, 169–171, 182 biosphere lifestyle, 297–303 Biosphere Politics (Rifkin), 167 The Biotech Century (Rifkin), 170 bitcoin, 262 Bok, Bernhard, 215 Botsman, Rachel, 234
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New World View (Rifkin), 100 environmentalist(s), 170–172, 187–188 Environmental Movement, 173, 182, 185 era of transparency, 75–77 Etsy, 91, 262 European Bioinformatics Institute, 86 European Commission, 11, 76–77 European enclosures, and birth of the market economy, 29–38 exponential curves, 79– 81 “extreme productivity,” 3, 70
Data Mining: Concepts and Techniques: Concepts and Techniques
by Jiawei Han, Micheline Kamber and Jian Pei · 21 Jun 2011
abnormalities in their data. The list continues, with cybersecurity and computer network intrusion detection; monitoring of the energy consumption of household appliances; pattern analysis in bioinformatics and pharmaceutical data; financial and business intelligence data; spotting trends in blogs, Twitter, and many more. Storage is inexpensive and getting even less so, as
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data and multimedia data (e.g., pictures and videos) on web pages, graph data like web graphs, and map data on some web sites. In bioinformatics, genomic sequences, biological networks, and 3-D spatial structures of genomes may coexist for certain biological objects. Mining multiple data sources of complex data often
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. It is impossible to enumerate all applications where data mining plays a critical role. Presentations of data mining in knowledge-intensive application domains, such as bioinformatics and software engineering, require more in-depth treatment and are beyond the scope of this book. To demonstrate the importance of applications as a major
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development contributes significantly to the success of data mining and its extensive applications. ■ Data mining has many successful applications, such as business intelligence, Web search, bioinformatics, health informatics, finance, digital libraries, and digital governments. ■ There are many challenging issues in data mining research. Areas include mining methodology, user interaction, efficiency and
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)? 1.10 Outline the major research challenges of data mining in one specific application domain, such as stream/sensor data analysis, spatiotemporal data analysis, or bioinformatics. 1.10. Bibliographic Notes The book Knowledge Discovery in Databases, edited by Piatetsky-Shapiro and Frawley [P-SF91], is an early collection of research papers
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: Exploring Hyperlinks, Contents, and Usage Data by Liu [Liu06]; Data Mining: Introductory and Advanced Topics by Dunham [Dun03]; and Data Mining: Multimedia, Soft Computing, and Bioinformatics by Mitra and Acharya [MA03]. There are also books that contain collections of papers or chapters on particular aspects of knowledge discovery—for example, Relational
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a small number of rows (also called transactions or tuples, e.g., samples). This is useful in applications like the analysis of gene expressions in bioinformatics, for example, where we often need to analyze microarray data that contain a large number of genes (e.g., 10,000 to 100,000) but
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mining frequent patterns in various situations, many applications have hidden patterns that are tough to mine, due mainly to their immense length or size. Consider bioinformatics, for example, where a common activity is DNA or microarray data analysis. This involves mapping and analyzing very long DNA and protein sequences. Researchers are
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[LHC97]; Silberschatz and Tuzhilin [ST96]; and Srikant, Vu, and Agrawal [SVA97]. Traditional pattern mining methods encounter challenges when mining high-dimensional patterns, with applications like bioinformatics. Pan, Cong, Tung, et al. [PCT+03] proposed CARPENTER, a method for finding closed patterns in high-dimensional biological data sets, which integrates the advantages
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multiple genes, or cluster genes into groups. For example, we may find a group of genes that express themselves similarly, which is highly interesting in bioinformatics, such as in finding pathways. ■ When analyzing in the sample/condition dimension, we treat each sample/condition as an object and treat the genes as
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may find the differences in gene expression by comparing a group of tumor samples and nontumor samples. Gene expression Gene expression matrices are popular in bioinformatics research and development. For example, an important task is to classify a new gene using the expression data of the gene and that of other
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gene can participate in multiple clusters) nor exhaustive (e.g., where a gene may not participate in any cluster). Biclustering is useful not only in bioinformatics, but also in other applications as well. Consider recommender systems as an example. Using biclustering for a recommender system AllElectronics collects data from customers' evaluations
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and social developments. Because biological sequences carry very complicated semantic meaning and pose many challenging research issues, most investigations are conducted in the field of bioinformatics. Sequential pattern mining has focused extensively on mining symbolic sequences. A sequential pattern is a frequent subsequence existing in a single sequence or a set
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refer to sequences of nucleotides or amino acids. Biological sequence analysis compares, aligns, indexes, and analyzes biological sequences and thus plays a crucial role in bioinformatics and modern biology. Sequence alignment is based on the fact that all living organisms are related by evolution. This implies that the nucleotide (DNA, RNA
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than sets, sequences, lattices, and trees. There is a broad range of graph applications on the Web and in social networks, information networks, biological networks, bioinformatics, chemical informatics, computer vision, and multimedia and text retrieval. Hence, graph and network mining have become increasingly important and heavily researched. We overview the following
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proteomic data). Robust and dedicated analysis methods are needed for handling spatiotemporal data, biological data, related concept hierarchies, and complex semantic relationships. For example, in bioinformatics, a research problem is to identify regulatory influences on genes. Gene regulation refers to how genes in a cell are switched on (or off) to
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Meeting of the Association for Computational Linguistics and Int. Conf. Computational Linguistics (COLING-ACL’98) Montreal, Quebec, Canada. (Aug. 1998). [BB01] Baldi, P.; Brunak, S., Bioinformatics: The Machine Learning Approach. 2nd ed. (2001) MIT Press, Cambridge, MA . [BB02] Borgelt, C.; Berthold, M.R., Mining molecular fragments: Finding relevant substructures of molecules
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.; Niblett, T., A further comparison of splitting rules for decision-tree induction, Machine Learning 8 (1992) 75–85. [BO04] Baxevanis, A.; Ouellette, B.F.F., Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins. 3rd ed. (2004) John Wiley & Sons . [BP92] Bezdek, J.C.; Pal, S.K., Fuzzy Models
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data mining: A report on the KDD-98 panel, SIGKDD Explorations 1 (1999) 6–8. [JP04] Jones, N.C.; Pevzner, P.A., An Introduction to Bioinformatics Algorithms. (2004) MIT Press, Cambridge, MA . [JSD+10] Ji, M.; Sun, Y.; Danilevsky, M.; Han, J.; Gao, J., Graph regularized transductive classification on heterogeneous information
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, M., The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling. 2nd ed. (2002) John Wiley & Sons . [KR03] Krane, D.; Raymer, R., Fundamental Concepts of Bioinformatics. (2003) Benjamin Cummings . [Kre02] Krebs, V., Mapping networks of terrorist cells, Connections 24 (2002) 43–52; (Winter). [KRR+00] Kumar, R.; Raghavan, P.; Rajagopalan, S
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. 2005 ACM SIGSOFT Symp. Foundations of Software Engineering (FSE’05) Lisbon, Portugal. (Sept. 2005). [MA03] Mitra, S.; Acharya, T., Data Mining: Multimedia, Soft Computing, and Bioinformatics. (2003) John Wiley & Sons . [MAE05] Metwally, A.; Agrawal, D.; El Abbadi, A., Efficient computation of frequent and top-k elements in data streams, In: Proc
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22 (1989) 2191–2204. [MO04] Madeira, S.C.; Oliveira, A.L., Biclustering algorithms for biological data analysis: A survey, IEEE/ACM Trans. Computational Biology and Bioinformatics 1 (1) (2004) 24–25. [MP69] Minsky, M.L.; Papert, S., Perceptrons: An Introduction to Computational Geometry. (1969) MIT Press, Cambridge, MA . [MRA95] Metha, M
Data Mining: Concepts, Models, Methods, and Algorithms
by Mehmed Kantardzić · 2 Jan 2003 · 721pp · 197,134 words
years, including causal feature selection and Relief. The book contains real-world case studies from a variety of areas, including text classification, web mining, and bioinformatics. Saul, L. K., et al., Spectral Methods for Dimensionality Reduction, in Semisupervised Learning, B. Schööelkopf, O. Chapelle and A. Zien eds., MIT Press, Cambridge, MA
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mapping does not improve the SVM performance. Using the linear kernel is good enough, and C is the only tuning parameter. Many microarray data in bioinformatics and collection of electronic documents for classification are examples of this data set type. As the number of features is smaller, and the number of
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. Other interesting application areas for SVMs are in text mining and categorization of large collection of documents, and in the analysis of genome sequences in bioinformatics. Furthermore, the SVM has been successfully used in a study of text and data for marketing applications. As kernel methods and maximum margin methods including
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analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine-learning
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example, are patterns in a real-valued time series that may be of interest. Similarly, in symbolic sequences, regular expressions represent well-defined patterns. In bioinformatics, genes are known to appear as local patterns interspersed between chunks of noncoding DNA. Matching and discovery of such patterns are very useful in many
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applications, not only in bioinformatics. Due to their readily interpretable structure, patterns play a particularly dominant role in data mining. There have been many techniques used to model global or
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. It encompasses different forms of databases, including data warehouses, data cubes, tabular or relational data, and many applications, among which are music warehouses, video mining, bioinformatics, semantic Web and data streams. Li, H. X., V. C. Yen, Fuzzy Sets and Fuzzy Decision-Making, CRC Press, Inc., Boca Raton, 1995. The book
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, New York, 1997. Thuraisingham, B., Data Mining: Technologies, Techniques, Tools, and Trends, CRC Press LLC, Boca Raton, FL, 1999. Tsur, S., Data Mining in the Bioinformatics Domain, Proceedings of the 26th YLDB Conference, Cairo, Egypt, 2000, pp. 711–714. Two Crows Corp., Introduction to Data Mining and Knowledge Discovery, Two Crows
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. Wang, Y., F. Makedon, Application of Relief-F Feature Filtering Algorithm to Selecting Informative Genes for Cancer Classification Using Microarray Data, 2004 IEEE Computational Systems Bioinformatics Conference (CSB'04), Stanford, CA, August 2004. Weiss, S. M., N. Indurkhya, Predictive Data Mining: A Practical Guide, Morgan Kaufman Publishers, Inc., San Francisco, CA
Artificial Intelligence: A Modern Approach
by Stuart Russell and Peter Norvig · 14 Jul 2019 · 2,466pp · 668,761 words
et al., 2003), inferring cellular networks (Friedman, 2004), genetic linkage analysis to locate disease-related genes (Silberstein et al., 2013), and many other tasks in bioinformatics. We could go on, but instead we’ll refer you to Pourret et al. (2008), a 400-page guide to applications of Bayesian networks. Published
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victory in the 2001 KDD Cup data mining competition for a Bayes net learning method (Cheng et al., 2002). (The specific task here was a bioinformatics problem with 139,351 features!) A structure-learning approach based on maximizing likelihood was developed by Cooper and Herskovits (1992) and improved by Heckerman et
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et al., 2015) provides parse trees for a 3-million-word corpus of English. Many of the n-gram model techniques are also used in bioinformatics problems. Biostatistics and probabilistic NLP are coming closer together, as each deals with long, structured sequences chosen from an alphabet. Early part-of-speech (POS
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., Li, P., Krishnan, A., and Liu, J. (2011). Large-scale dynamic gene regulatory network inference combining differential equation models with local dynamic Bayesian network analysis. Bioinformatics, 27 19, 2686–91. Liang, P., Jordan, M. I., and Klein, D. (2011). Learning dependency–based compositional semantics. arXiv:1109.6841. Liang, P. and Potts
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, Z. U., Dechter, R., Thompson, E., and Geiger, D. (2013). A system for exact and approximate genetic linkage analysis of SNP data in large pedigrees. Bioinformatics, 29, 197–205. Silva, R., Melo, F. S., and Veloso, M. (2015). Towards table tennis with a quadrotor autonomous learning robot and onboard vision. In
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., 515, 516, 799, 1087, 1111 binding list, 284 Bingham, E., 667, 1087 Binmore, K., 637, 1087 binocular stereopsis, 1009, 1009–1010, 1028 binomial nomenclature, 357 bioinformatics, 903 biological naturalism, 1036 Biran, O., 1060, 1088 Birattari, M., 160, 1093 Birbeck, M., 357, 1085 Bischof, J., 1060, 1087 Bishop, C. M., 160, 473
Forty Signs of Rain
by Kim Stanley Robinson · 29 May 2004 · 362pp · 104,308 words
of fields and really excellent in more than one. As good a scientist as one could find for the rather odd job of running the Bioinformatics Division at NSF, good almost to the point of exaggeration—too precise, too interrogatory—it kept her from pursuing a course of action with drive
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worse.” They laughed. “And you have your journal work too.” “That’s right.” Frank waved at the piles of typescripts: three stacks for Review of Bioinformatics, two for The Journal of Sociobiology. “Always behind. Luckily the other editors are better at keeping up.” Anna nodded. Editing a journal was a privilege
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credit hog, if not worse. It was interesting, then, that Pierzinski had gone down to Torrey Pines to work on a temporary contract, for a bioinformatics researcher whom Frank didn’t know. Perhaps that had been a bid to escape the advisor. But now he was back. Frank dug into the
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, and kept her on the editorial board of The Journal of Statistical Biology, despite the fact that her job at NSF as director of the Bioinformatics Division might be said to be occupying her more than full-time already; but much of that job was administrative, and like the milk pumping
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saps. Inch along. It stayed so bad that Frank realized he was going to be late to work. And this was the morning when his bioinformatics panel was to begin! He needed to get there for the panel to start on time; there was no slack in the schedule. The panel
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eight of them sitting around the long cluttered conference table. Dr. Frank Vanderwal, moderator, NSF (on leave from University of California, San Diego, Department of Bioinformatics). Dr. Nigel Pritchard, Georgia Institute of Technology, Computer Sciences. Dr. Alice Freundlich, Harvard University, Department of Biochemistry. Dr. Habib Ndina, University of Virginia Medical School
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. Some of the proposals brought up interesting problems, and several strong ones in a row made them aware of just how amazing contemporary work in bioinformatics was, and what some of the potential benefits for human health might be, if all this were to come together and make a robust biotechnology
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board with that very same quick satisfied look. Now, in this room, Diane was already on to the next item on her agenda. AFTERWARD, THE bioinformatics group sat in Anna’s and Frank’s rooms on the sixth floor, sipping cold coffee and looking into the atrium. Edgardo came in. “So
Food Allergy: Adverse Reactions to Foods and Food Additives
by Dean D. Metcalfe · 15 Dec 2008 · 623pp · 448,848 words
chapter. Food allergen protein families Based on their shared amino acid sequences and conserved three-dimensional structures, proteins can be classified into families using various bioinformatics tools which form the basis of several protein family databases, one of which is Pfam [8]. Over the past 10 years or so there has
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been an explosion in the numbers of well characterized allergens, which have been sequenced and are being collected into a number of databases to facilitate bioinformatic analysis [9]. We have undertaken this analysis for both plant [1] and animal food allergens [10] along with pollen allergens [2]. They show similar distributions
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high identity of the molecular surfaces that are accessible to IgE and thus offer a molecular explanation for the observed clinical cross-reactivities. A structural bioinformatic analysis of Bet v 1 and its homologous allergens from apple, soybean, and celery showed that conservation of three-dimensional structure plays an important role
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amino acid sequence to those of known allergens and gliadins as one of many assessments performed to evaluate product safety [4,69]. The purpose of bioinformatic analyses is to describe the biological and taxonomical relatedness of a query sequence to other functionally related proteins. In the context of allergy, the goal
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at least 70% identity. Recent published work has led to the harmonization of the methods used for bioinformatic searches and a better understanding of the data generated [73,74] from such studies. An additional bioinformatics approach can be taken by searching for 100% identity matches along short sequences contained in the query
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is normally present in food for human consumption derived from plant and microbial sources indicating that the protein has a long history of safe use. Bioinformatic analysis of CP4 EPSPS: A search for amino acid sequence similarity between the CP4 EPSPS protein and known allergens was conducted according to the methods
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Clin Immunol 2000;106:228–38. 73 Thomas K, Bannon G, Hefle S, et al. In silico methods for evaluating human allergenicity to novel proteins. Bioinformatics Workshop Meeting Report, February 23–24, 2005. Toxicol Sci 2005;88:307–10. 74 Ladics GS, Bannon GA, Silvanovich A, Cressman, RF. Comparison of conventional
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. 75 Bannon G, Ogawa T. Evaluation of available IgE-binding epitope data and its utility in bioinformatics. Mol Nutr Food Res 2006;50:638–44. 76 Hileman RE, Silvanovich A, Goodman RE, et al. Bioinformatic methods for allergenicity assessment using a comprehensive allergen database. Int Archives Allergy Immunol 2002;128:280–91
Beautiful Data: The Stories Behind Elegant Data Solutions
by Toby Segaran and Jeff Hammerbacher · 1 Jul 2009
students in many scientific domains are playing the role of the Data Scientist. One of our hires for the Facebook Data team came from a bioinformatics lab where he was building data pipelines and performing offline data analysis of a similar kind. The well-known Large Hadron Collider at CERN generates
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the most inefficient home-brewed language. However, the process of determining the exact order of these 3 billion bases requires a significant effort spanning chemistry, bioinformatics, laboratory procedures, and a lot of spinning disks. The Human Genome Project aimed, for the first time, to sequence every one of these characters. A
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the sequencing data is available, it is stored in two formats in a high-performance Oracle database. While production systems make good use of databases, bioinformatics tools tend to continue to work against flat files on a physical filesystem. To be sure that we cater to all tastes, the vast swaths
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the wider data web via RDF. RON is http://rdf.openmolecules. net, the resource that connects records from DBPedia, Chemical Blogspace, and ChEBI (a European Bioinformatics Institute Chemistry resource). Taking this one step further, we can link our experimental data into a wider discussion on the Web by using RDF from
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and metaphysical naturalism. Pierre Lindenbaum obtained his PhD in virology in 2000, when he studied the virushost interactions. He then switched his professional career to bioinformatics, and after one year at the French National Center of Genotyping (France) he joined the French startup Integragen in 2001. He now works as a
The Wealth of Networks: How Social Production Transforms Markets and Freedom
by Yochai Benkler · 14 May 2006 · 678pp · 216,204 words
computational analysis, more can be organized for peer production. The relevant model here is open bioinformatics. Bioinformatics generally is the practice of pursuing solutions to biological questions using mathematics and information technology. Open bioinformatics is a movement within bioinformatics aimed at developing the tools in an open-source model, and in providing access to
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the tools and the outputs on a free and open basis. Projects like these include the Ensmbl Genome Browser, operated by the European Bioinformatics Institute and the Sanger Centre, or the National Center for Biotechnology Information (NCBI), both of which use computer databases to provide access to data and
The Architecture of Open Source Applications
by Amy Brown and Greg Wilson · 24 May 2011 · 834pp · 180,700 words
has two children and a very old cat. C. Titus Brown (Continuous Integration): Titus has worked in evolutionary modeling, physical meteorology, developmental biology, genomics, and bioinformatics. He is now an Assistant Professor at Michigan State University, where he has expanded his interests into several new areas, including reproducibility and maintainability of
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Computer Science and Evolutionary Biology at Michigan State University. In her copious spare time, she likes to read, hike, travel, and hack on open source bioinformatics software. She blogs at http://www.voidptr.net. Francesco Cesarini (Riak): Francesco Cesarini has used Erlang on a daily basis since 1995, having worked in
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is a contract software developer based in Dublin, Ireland. Currently he is working on tools for electronics design, though in a previous life he developed bioinformatics software. He has many audacious plans for Audacity, and he hopes some, at least, will see the light of day. Chris Davis (Graphite): Chris is
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wild include the use of RCP to monitor the Mars Rover robots developed by NASA at the Jet Propulsion Laboratory, Bioclipse for data visualization of bioinformatics and Dutch Railway for monitoring train performance. The common thread that ran through many of these applications was that these teams decided that they could
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precipitation values to a matplotlib function that generates a scatter plot. Most workflow systems are designed for a specific application area. For example, Taverna targets bioinformatics workflows, and NiPype allows the creation of neuroimaging workflows. While VisTrails supports much of the functionality provided by other workflow systems, it was designed to
In Our Own Image: Savior or Destroyer? The History and Future of Artificial Intelligence
by George Zarkadakis · 7 Mar 2016 · 405pp · 117,219 words
written on the DNA. Cutting-edge research in biology does not take place in vitro in a wet lab, but in silico in a computer. Bioinformatics – the accumulation, tagging, storing, manipulation and mining of digital biological data – is the present, and future, of biology research. The computer metaphor for life is
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computers. Big data are our newfound economic bounty. The big data economy In 2010, I took a contract as External Relations Officer at the European Bioinformatics Institute (EBI) at Hinxton, Cambridge. The Institute is part of the intergovernmental European Molecular Biology Laboratory, and its core mission is to provide an infrastructure
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placed on biological data. Almost everyone understood the potential for driving innovation through this data, and was ready to support the expansion of Europe’s bioinformatics infrastructure, even as Europe was going through the Great Recession. The message was simple and clear: whoever owned the data owned the future. Governments and
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film), robot Andrew 55, 57 big bang of the modern mind 10, 12–15 big data economy 249–55 binary arithmetic 149 binary logic 198 bioinformatics 123, 249 Blade Runner (1982 film) 53–4, 57, 72 Bletchley Park codebreakers 234–6 body, role in consciousness 169–71 body–mind dualism 124
Fifty Degrees Below
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The End of Medicine: How Silicon Valley (And Naked Mice) Will Reboot Your Doctor
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Big Data Analytics: Turning Big Data Into Big Money
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The Singularity Is Near: When Humans Transcend Biology
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Physics of the Future: How Science Will Shape Human Destiny and Our Daily Lives by the Year 2100
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The Elements of Statistical Learning (Springer Series in Statistics)
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100 Plus: How the Coming Age of Longevity Will Change Everything, From Careers and Relationships to Family And
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As the Future Catches You: How Genomics & Other Forces Are Changing Your Work, Health & Wealth
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Explaining Humans: What Science Can Teach Us About Life, Love and Relationships
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Our Posthuman Future: Consequences of the Biotechnology Revolution
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Life at the Speed of Light: From the Double Helix to the Dawn of Digital Life
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Life's Greatest Secret: The Race to Crack the Genetic Code
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Dinosaurs Rediscovered
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Essential Scrum: A Practical Guide to the Most Popular Agile Process
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Protocol: how control exists after decentralization
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Pearls of Functional Algorithm Design
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The New Harvest: Agricultural Innovation in Africa
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Advances in Financial Machine Learning
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Reinventing Discovery: The New Era of Networked Science
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Exploring Everyday Things with R and Ruby
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Running Money
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MacroWikinomics: Rebooting Business and the World
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The Data Journalism Handbook
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HBase: The Definitive Guide
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Algorithms Unlocked
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Mastering Structured Data on the Semantic Web: From HTML5 Microdata to Linked Open Data
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The Invisible Web: Uncovering Information Sources Search Engines Can't See
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Solr 1.4 Enterprise Search Server
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SQL Hacks
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Natural Language Annotation for Machine Learning
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Tools for Computational Finance
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Life on the Edge: The Coming of Age of Quantum Biology
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Superintelligence: Paths, Dangers, Strategies
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Nexus
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