Crawl Semantic Scholar Open Research Corpus Accepted
Changes: 177
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Add Genetic programming - on the programming of computers by means of natural selection
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- Genetic programming - on the programming of computers by means of natural selection
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- 1993-01-01
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- Background on genetic algorithms, LISP, and genetic programming hierarchical problem-solving introduction to automatically-defined functions - the two-boxes problem problems that straddle the breakeven point for computational effort Boolean parity functions determining the architecture of the program the lawnmower problem the bumblebee problem the increasing benefits of ADFs as problems are scaled up finding an impulse response function artificial ant on the San Mateo trail obstacle-avoiding robot the minesweeper problem automatic discovery of detectors for letter recognition flushes and four-of-a-kinds in a pinochle deck introduction to biochemistry and molecular biology prediction of transmembrane domains in proteins prediction of omega loops in proteins lookahead version of the transmembrane problem evolutionary selection of the architecture of the program evolution of primitives and sufficiency evolutionary selection of terminals evolution of closure simultaneous evolution of archi
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- https://semanticscholar.org/paper/505c58c2c100e7512b7f7d906a9d4af72f6e8415
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- no value
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Add The NIST Definition of Cloud Computing
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- The NIST Definition of Cloud Computing
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- 2011-01-01
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- Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model promotes availability and is composed of five essential characteristics, three service models, and four deployment models.
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- https://semanticscholar.org/paper/379f897fa771cc8fecddac7e982c6fb0a3fffb86
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- DOI: 10.6028/NIST.SP.800-145
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Add Mineral Nutrition of Higher Plants
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- Mineral Nutrition of Higher Plants
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- 1986-01-01
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- Nutritional Physiology: Introduction, Definition, and Classification of Mineral Nutrients. Ion Uptake Mechanisms of Individual Cells and Roots: Short Distance Transport. Long-Distance Transport in the Xylem and Phloem and its Regulation. Uptake and Release of Mineral Elements by Leaves and Other Aerial Plant Parts. Yield and the Source-Sink Relationships. Mineral Nutrition and Yield Response. Nitrogen Fixation. Functions of Mineral Nutrients: Macronutrients. Function of Mineral Nutrients: Micronutrients. Beneficial Mineral Elements. Relationship between Mineral Nutrition and Plant Diseases and Pests. Diagnosis of Deficiency and Toxicity of Mineral Nutrients. Plant-Soil Relationships: Nutrient Availability in Soils. Effect of Internal and External Factors on Root Growth and Development. The Soil-Root Interface (Rhizosphere) in Relation to Mineral Nutrition. Adaptation of Plants to Adverse Chemical Soil Conditions. References. Subject Index.
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- https://semanticscholar.org/paper/a194bb9c7ea9b48fc9ed1cc2602e7152829f0f7f
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- DOI: 10.2307/2260650
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Add A More Accurate Method To Estimate Glomerular Filtration Rate from Serum Creatinine: A New Prediction Equation
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- A More Accurate Method To Estimate Glomerular Filtration Rate from Serum Creatinine: A New Prediction Equation
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- 1999-01-01
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- The glomerular filtration rate (GFR) is traditionally considered the best overall index of renal function in health and disease (1). Because GFR is difficult to measure in clinical practice, most clinicians estimate the GFR from the serum creatinine concentration. However, the accuracy of this estimate is limited because the serum creatinine concentration is affected by factors other than creatinine filtration (2, 3). To circumvent these limitations, several formulas have been developed to estimate creatinine clearance from serum creatinine concentration, age, sex, and body size (4-12). Despite more recent studies that have related serum creatinine concentration to GFR (13-24), no formula is more widely used to predict creatinine clearance than that proposed by Cockcroft and Gault (4). This formula is used to detect the onset of renal insufficiency, to adjust the dose of drugs excreted by the kidney, and to evaluate the effectiveness of therapy for progressive renal disease.
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- https://semanticscholar.org/paper/7c521546c5312ec96bd17af8d84ee08cbfbd3848
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- DOI: 10.7326/0003-4819-130-6-199903160-00002
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Add The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research
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- The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research
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- 1989-01-01
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- Despite the prevalence of sleep complaints among psychiatric patients, few questionnaires have been specifically designed to measure sleep quality in clinical populations. The Pittsburgh Sleep Quality Index (PSQI) is a self-rated questionnaire which assesses sleep quality and disturbances over a 1-month time interval. Nineteen individual items generate seven "component" scores: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. The sum of scores for these seven components yields one global score. Clinical and clinimetric properties of the PSQI were assessed over an 18-month period with "good" sleepers (healthy subjects, n = 52) and "poor" sleepers (depressed patients, n = 54; sleep-disorder patients, n = 62). Acceptable measures of internal homogeneity, consistency (test-retest reliability), and validity were obtained.
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- https://semanticscholar.org/paper/6889e5a22598521d8af5bbc50d5dd44b2cde92a4
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- DOI: 10.1016/0165-1781(89)90047-4
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Add Principles of fluorescence spectroscopy
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- Principles of fluorescence spectroscopy
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- 1983-01-01
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- Fluorescence methods are being used increasingly in biochemical, medical, and chemical research. This is because of the inherent sensitivity of this technique. and the favorable time scale of the phenomenon of fluorescence. 8 Fluorescence emission occurs about 10- sec (10 nsec) after light absorp tion. During this period of time a wide range of molecular processes can occur, and these can effect the spectral characteristics of the fluorescent compound. This combination of sensitivity and a favorable time scale allows fluorescence methods to be generally useful for studies of proteins and membranes and their interactions with other macromolecules. This book describes the fundamental aspects of fluorescence. and the biochemical applications of this methodology. Each chapter starts with the -theoreticalbasis of each phenomenon of fluorescence, followed by examples which illustrate the use of the phenomenon in the study of biochemical problems. The book contains numerous figures.
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- https://semanticscholar.org/paper/a42d6065d0b1a31c597bcc3b7c0252881a2a2e33
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- DOI: 10.5860/choice.44-3876
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Add ImageNet Large Scale Visual Recognition Challenge
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- ImageNet Large Scale Visual Recognition Challenge
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- 2015-01-01
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- The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the 5 years of the challenge, and propose future directions and improvements.
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- https://semanticscholar.org/paper/e74f9b7f8eec6ba4704c206b93bc8079af3da4bd
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- DOI: 10.1007/s11263-015-0816-y
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Add Molecular cloning: A laboratory manual 2nd ed. Edited by J. . Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, 198
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- Molecular cloning: A laboratory manual 2nd ed. Edited by J. . Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, 198
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- 1990-01-01
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- no value
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- https://semanticscholar.org/paper/adacf2ec25db64cb84404e8fd0915a518da671b0
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Add The Protein Data Bank
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- The Protein Data Bank
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- 2000-01-01
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- The Protein Data Bank [PDB; Berman, Westbrook et al. (2000), Nucleic Acids Res. 28, 235-242; http://www.pdb.org/] is the single worldwide archive of primary structural data of biological macromolecules. Many secondary sources of information are derived from PDB data. It is the starting point for studies in structural bioinformatics. This article describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information and plans for the future development of the resource. The reader should come away with an understanding of the scope of the PDB and what is provided by the resource.
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- https://semanticscholar.org/paper/548fe25043edbe4539c68550383875466ed0d777
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- DOI: 10.1093/nar/28.1.235
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Add Going deeper with convolutions
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- Going deeper with convolutions
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- 2015-01-01
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- We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.
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- https://semanticscholar.org/paper/e15cf50aa89fee8535703b9f9512fca5bfc43327
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- DOI: 10.1109/CVPR.2015.7298594
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Add Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources
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- Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources
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- 2008-01-01
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- DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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- https://semanticscholar.org/paper/57e7a7323f58a35f5e2cc33bf17d4ac9cdcafdd4
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- DOI: 10.1038/nprot.2008.211
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Add Elements of Information Theory
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- Elements of Information Theory
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- 1991-01-01
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- Preface to the Second Edition. Preface to the First Edition. Acknowledgments for the Second Edition. Acknowledgments for the First Edition. 1. Introduction and Preview. 1.1 Preview of the Book. 2. Entropy, Relative Entropy, and Mutual Information. 2.1 Entropy. 2.2 Joint Entropy and Conditional Entropy. 2.3 Relative Entropy and Mutual Information. 2.4 Relationship Between Entropy and Mutual Information. 2.5 Chain Rules for Entropy, Relative Entropy, and Mutual Information. 2.6 Jensen's Inequality and Its Consequences. 2.7 Log Sum Inequality and Its Applications. 2.8 Data-Processing Inequality. 2.9 Sufficient Statistics. 2.10 Fano's Inequality. Summary. Problems. Historical Notes. 3. Asymptotic Equipartition Property. 3.1 Asymptotic Equipartition Property Theorem. 3.2 Consequences of the AEP: Data Compression. 3.3 High-Probability Sets and the Typical Set. Summary. Problems. Historical Notes. 4. Entropy Rates of a Stochastic Process. 4.1 Markov Chains. 4.2 Entropy Rate. 4.
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- https://semanticscholar.org/paper/7dbdb4209626fd92d2436a058663206216036e68
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- DOI: 10.1002/0471200611
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Add Deep Residual Learning for Image Recognition
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- Deep Residual Learning for Image Recognition
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- 2016-01-01
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- Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks.
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- https://semanticscholar.org/paper/2c03df8b48bf3fa39054345bafabfeff15bfd11d
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- DOI: 10.1109/cvpr.2016.90
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Add A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye bi
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- A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye bi
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- 1976-01-01
- Description
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- Abstract A protein determination method which involves the binding of Coomassie Brilliant Blue G-250 to protein is described. The binding of the dye to protein causes a shift in the absorption maximum of the dye from 465 to 595 nm, and it is the increase in absorption at 595 nm which is monitored. This assay is very reproducible and rapid with the dye binding process virtually complete in approximately 2 min with good color stability for 1 hr. There is little or no interference from cations such as sodium or potassium nor from carbohydrates such as sucrose. A small amount of color is developed in the presence of strongly alkaline buffering agents, but the assay may be run accurately by the use of proper buffer controls. The only components found to give excessive interfering color in the assay are relatively large amounts of detergents such as sodium dodecyl sulfate, Triton X-100, and commercial glassware detergents.
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- https://semanticscholar.org/paper/f1a9ceb8c3d1bc0569fc3708e76ae635b8e70ddb
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- DOI: 10.1016/0003-2697(76)90527-3
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Add Cleavage of Structural Proteins during the Assembly of the Head of Bacteriophage T4
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- Cleavage of Structural Proteins during the Assembly of the Head of Bacteriophage T4
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- 1970-01-01
- Description
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- Using an improved method of gel electrophoresis, many hitherto unknown proteins have been found in bacteriophage T4 and some of these have been identified with specific gene products. Four major components of the head are cleaved during the process of assembly, apparently after the precursor proteins have assembled into some large intermediate structure.
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- https://semanticscholar.org/paper/77ff0bb96b1d085fe284c0a5d87d1d109001afcd
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- DOI: 10.1038/227680a0
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Add Data Mining Concepts and Techniques
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- Data Mining Concepts and Techniques
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- 2010-01-01
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- Understand the need for analyses of large, complex, information-rich data sets. Identify the goals and primary tasks of the data-mining process. Describe the roots of data-mining technology. Recognize the iterative character of a data-mining process and specify its basic steps. Explain the influence of data quality on a data-mining process. Establish the relation between data warehousing and data mining. Data mining is an iterative process within which progress is defined by discovery, through either automatic or manual methods. Data mining is most useful in an exploratory analysis scenario in which there are no predetermined notions about what will constitute an "interesting" outcome. Data mining is the search for new, valuable, and nontrivial information in large volumes of data. It is a cooperative effort of humans and computers. Best results are achieved by balancing the knowledge of human experts in describing problems and goals with the search capabilities of computers.
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- https://semanticscholar.org/paper/d76b49bb538e01576e744ab68033a9f643cc4483
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- DOI: 10.5860/choice.49-3305
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Add Computer Science
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- Computer Science
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- Computer science
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- Computer science is the study of computation and information. Computer science deals with theory of computation, algorithms, computational problems and the design of computer systems hardware, software and applications. Computer science addresses both human-made and natural information processes, such as communication, control, perception, learning and intelligence especially in human-made computing systems and machines. According to Peter Denning, the fundamental question underlying computer science is, What can be automated?Its fields can be divided into theoretical and practical disciplines. Computational complexity theory is highly abstract, while computer graphics and computational geometry emphasizes real-world applications. Algorithmics is called the heart of computer science.
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- https://en.wikipedia.org/?curid=5323
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Add Business
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- Business
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Add Medicine
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- Medicine
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Add Economics
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- Economics
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Add Biology
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- Biology
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Add Mathematics
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- Mathematics
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Add Physics
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- Physics
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Add Engineering
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- Engineering
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Add Chemistry
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- Chemistry
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- Chemistry is the scientific discipline involved with elements and compounds composed of atoms, molecules and ions: their composition, structure, properties, behavior and the changes they undergo during a reaction with other substances.In the scope of its subject, chemistry occupies an intermediate position between physics and biology. It is sometimes called the central science because it provides a foundation for understanding both basic and applied scientific disciplines at a fundamental level. For example, chemistry explains aspects of plant chemistry (botany), the formation of igneous rocks (geology), how atmospheric ozone is formed and how environmental pollutants are degraded (ecology), the properties of the soil on the moon (astrophysics), how medications work (pharmacology), and how to collect DNA evidence at a crime scene (forensics).
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- https://en.wikipedia.org/?curid=5180
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Add Psychology
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- Psychology
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- Psychology is the science of mind and behavior. Psychology includes the study of conscious and unconscious phenomena, as well as feeling and thought. It is an academic discipline of immense scope. Psychologists seek an understanding of the emergent properties of brains, and all the variety of phenomena linked to those emergent properties, joining this way the broader neuro-scientific group of researchers. As a social science, it aims to understand individuals and groups by establishing general principles and researching specific cases.In this field, a professional practitioner or researcher is called a psychologist and can be classified as a social, behavioral, or cognitive scientist. Psychologists attempt to understand the role of mental functions in individual and social behavior, while also exploring the physiological and biological processes that underlie cognitive functions and behaviors.
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- https://en.wikipedia.org/?curid=22921
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Add John R. Koza
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- John R. Koza
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- https://www.semanticscholar.org/author/1732302
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Add Peter M. Mell
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- Peter M. Mell
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- https://www.semanticscholar.org/author/3320631
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Add Timothy Grance
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- Timothy Grance
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- https://www.semanticscholar.org/author/46190295
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Add Horst Marschner
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- Horst Marschner
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- https://www.semanticscholar.org/author/32143105
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Add S. Gnanapriya
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- S. Gnanapriya
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- https://www.semanticscholar.org/author/35295590
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Add R. Suganya
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- R. Suganya
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- https://www.semanticscholar.org/author/145941609
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Add G. Devi
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- G. Devi
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- https://www.semanticscholar.org/author/153424916
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Add M. Suresh Kumar
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- M. Suresh Kumar
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- https://www.semanticscholar.org/author/48388066
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Add Andrew S. Levey
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- Andrew S. Levey
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- https://www.semanticscholar.org/author/5270617
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Add Juan P. Bosch
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- Juan P. Bosch
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- https://www.semanticscholar.org/author/144903870
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Add Julia Breyer Lewis
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- Julia Breyer Lewis
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- https://www.semanticscholar.org/author/3177950
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Add Tom Greene
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- Tom Greene
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- https://www.semanticscholar.org/author/144782918
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Add Nancy Rogers
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- Nancy Rogers
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- https://www.semanticscholar.org/author/7569194
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Add D Roth
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- D Roth
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- https://www.semanticscholar.org/author/152567875
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Add Daniel J Buysse
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- Daniel J Buysse
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- https://www.semanticscholar.org/author/3570467
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Add Charles F. Reynolds
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- Charles F. Reynolds
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- https://www.semanticscholar.org/author/1743278
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Add Timothy H. Monk
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- Timothy H. Monk
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- https://www.semanticscholar.org/author/4826424
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Add Susan R. Berman
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- Susan R. Berman
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- https://www.semanticscholar.org/author/40168398
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Add David J. Kupfer
- Name
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- Unchanged
- David J. Kupfer
- Bio
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- Unchanged
- no value
- Link
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- Unchanged
- https://www.semanticscholar.org/author/2851840
Author | v1 | current (v1) -
Add Joseph R. Lakowicz
- Name
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- Joseph R. Lakowicz
- Bio
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- no value
- Link
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- https://www.semanticscholar.org/author/93522474
Author | v1 | current (v1) -
Add Yangqing Jia
- Name
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- Unchanged
- Yangqing Jia
- Bio
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- no value
- Link
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- Unchanged
- https://www.semanticscholar.org/author/39978391
Author | v1 | current (v1) -
Add Olga Russakovsky
- Name
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- Unchanged
- Olga Russakovsky
- Bio
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- no value
- Link
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- Unchanged
- https://www.semanticscholar.org/author/2192178
Author | v1 | current (v1) -
Add Jia Deng
- Name
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- Jia Deng
- Bio
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- no value
- Link
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- Unchanged
- https://www.semanticscholar.org/author/48550120
Author | v1 | current (v1) -
Add Hao Su
- Name
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- Unchanged
- Hao Su
- Bio
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- no value
- Link
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- https://www.semanticscholar.org/author/71309570
Author | v1 | current (v1) -
Add Jonathan Krause
- Name
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- Jonathan Krause
- Bio
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- no value
- Link
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- https://www.semanticscholar.org/author/2285165
Author | v1 | current (v1) -
Add Sanjeev Satheesh
- Name
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- Unchanged
- Sanjeev Satheesh
- Bio
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- no value
- Link
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- Unchanged
- https://www.semanticscholar.org/author/145031342
Author | v1 | current (v1) -
Add Sean Ma
- Name
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- Unchanged
- Sean Ma
- Bio
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- no value
- Link
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- Unchanged
- https://www.semanticscholar.org/author/145423516
Author | v1 | current (v1) -
Add Zhiheng Huang
- Name
-
- Unchanged
- Zhiheng Huang
- Bio
-
- Unchanged
- no value
- Link
-
- Unchanged
- https://www.semanticscholar.org/author/3109481
Author | v1 | current (v1) -
Add Andrej Karpathy
- Name
-
- Unchanged
- Andrej Karpathy
- Bio
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- no value
- Link
-
- Unchanged
- https://www.semanticscholar.org/author/2354728
Author | v1 | current (v1) -
Add Aditya Khosla
- Name
-
- Unchanged
- Aditya Khosla
- Bio
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- Unchanged
- no value
- Link
-
- Unchanged
- https://www.semanticscholar.org/author/2556428
Author | v1 | current (v1) -
Add Michael S. Bernstein
- Name
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- Unchanged
- Michael S. Bernstein
- Bio
-
- Unchanged
- no value
- Link
-
- Unchanged
- https://www.semanticscholar.org/author/145879842
Author | v1 | current (v1) -
Add Alexander C. Berg
- Name
-
- Unchanged
- Alexander C. Berg
- Bio
-
- Unchanged
- no value
- Link
-
- Unchanged
- https://www.semanticscholar.org/author/39668247
Author | v1 | current (v1) -
Add Fei-Fei Li
- Name
-
- Unchanged
- Fei-Fei Li
- Bio
-
- Unchanged
- no value
- Link
-
- Unchanged
- https://www.semanticscholar.org/author/48004138
Author | v1 | current (v1) -
Add Shaoqing Ren
- Name
-
- Unchanged
- Shaoqing Ren
- Bio
-
- Unchanged
- no value
- Link
-
- Unchanged
- https://www.semanticscholar.org/author/3080683
Author | v1 | current (v1) -
Add Kaiming He
- Name
-
- Unchanged
- Kaiming He
- Bio
-
- Unchanged
- no value
- Link
-
- Unchanged
- https://www.semanticscholar.org/author/39353098
Author | v1 | current (v1) -
Add Prescott L Deininger
- Name
-
- Unchanged
- Prescott L Deininger
- Bio
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- Unchanged
- no value
- Link
-
- Unchanged
- https://www.semanticscholar.org/author/2148636
Author | v1 | current (v1) -
Add Helen M. Berman
- Name
-
- Unchanged
- Helen M. Berman
- Bio
-
- Unchanged
- no value
- Link
-
- Unchanged
- https://www.semanticscholar.org/author/1679221
Author | v1 | current (v1) -
Add John D. Westbrook
- Name
-
- Unchanged
- John D. Westbrook
- Bio
-
- Unchanged
- no value
- Link
-
- Unchanged
- https://www.semanticscholar.org/author/34819339
Author | v1 | current (v1) -
Add Zukang Feng
- Name
-
- Unchanged
- Zukang Feng
- Bio
-
- Unchanged
- no value
- Link
-
- Unchanged
- https://www.semanticscholar.org/author/2272834
Author | v1 | current (v1) -
Add Gary Gilliland
- Name
-
- Unchanged
- Gary Gilliland
- Bio
-
- Unchanged
- no value
- Link
-
- Unchanged
- https://www.semanticscholar.org/author/144217390
Author | v1 | current (v1) -
Add T. N. Bhat
- Name
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- Unchanged
- T. N. Bhat
- Bio
-
- Unchanged
- no value
- Link
-
- Unchanged
- https://www.semanticscholar.org/author/145506090
Author | v1 | current (v1) -
Add Helge Weissig
- Name
-
- Unchanged
- Helge Weissig
- Bio
-
- Unchanged
- no value
- Link
-
- Unchanged
- https://www.semanticscholar.org/author/3309680
Author | v1 | current (v1) -
Add Ilya N. Shindyalov
- Name
-
- Unchanged
- Ilya N. Shindyalov
- Bio
-
- Unchanged
- no value
- Link
-
- Unchanged
- https://www.semanticscholar.org/author/2145522
Author | v1 | current (v1) -
Add Philip E. Bourne
- Name
-
- Unchanged
- Philip E. Bourne
- Bio
-
- Unchanged
- no value
- Link
-
- Unchanged
- https://www.semanticscholar.org/author/2048644
Author | v1 | current (v1) -
Add Christian Szegedy
- Name
-
- Unchanged
- Christian Szegedy
- Bio
-
- Unchanged
- no value
- Link
-
- Unchanged
- https://www.semanticscholar.org/author/2574060
Author | v1 | current (v1) -
Add Wei Liu
- Name
-
- Unchanged
- Wei Liu
- Bio
-
- Unchanged
- no value
- Link
-
- Unchanged
- https://www.semanticscholar.org/author/46641766
Author | v1 | current (v1) -
Add Pierre Sermanet
- Name
-
- Unchanged
- Pierre Sermanet
- Bio
-
- Unchanged
- no value
- Link
-
- Unchanged
- https://www.semanticscholar.org/author/3142556
Author | v1 | current (v1) -
Add Scott Reed
- Name
-
- Unchanged
- Scott Reed
- Bio
-
- Unchanged
- no value
- Link
-
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- https://www.semanticscholar.org/author/48840704
Author | v1 | current (v1) -
Add Dragomir Anguelov
- Name
-
- Unchanged
- Dragomir Anguelov
- Bio
-
- Unchanged
- no value
- Link
-
- Unchanged
- https://www.semanticscholar.org/author/1838674
Author | v1 | current (v1) -
Add Dumitru Erhan
- Name
-
- Unchanged
- Dumitru Erhan
- Bio
-
- Unchanged
- no value
- Link
-
- Unchanged
- https://www.semanticscholar.org/author/1761978
Author | v1 | current (v1) -
Add Vincent Vanhoucke
- Name
-
- Unchanged
- Vincent Vanhoucke
- Bio
-
- Unchanged
- no value
- Link
-
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- https://www.semanticscholar.org/author/2657155
Author | v1 | current (v1) -
Add Andrew Rabinovich
- Name
-
- Unchanged
- Andrew Rabinovich
- Bio
-
- Unchanged
- no value
- Link
-
- Unchanged
- https://www.semanticscholar.org/author/39863668
Author | v1 | current (v1) -
Add Da Wei Huang
- Name
-
- Unchanged
- Da Wei Huang
- Bio
-
- Unchanged
- no value
- Link
-
- Unchanged
- https://www.semanticscholar.org/author/31121189
Author | v1 | current (v1) -
Add Brad T. Sherman
- Name
-
- Unchanged
- Brad T. Sherman
- Bio
-
- Unchanged
- no value
- Link
-
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Author | v1 | current (v1) -
Add Richard A. Lempicki
- Name
-
- Unchanged
- Richard A. Lempicki
- Bio
-
- Unchanged
- no value
- Link
-
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Author | v1 | current (v1) -
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- Name
-
- Unchanged
- Thomas M. Cover
- Bio
-
- Unchanged
- no value
- Link
-
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Author | v1 | current (v1) -
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- Name
-
- Unchanged
- Joy A. Thomas
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-
- Unchanged
- no value
- Link
-
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Author | v1 | current (v1) -
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- Name
-
- Unchanged
- Xiangyu Zhang
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-
- Unchanged
- no value
- Link
-
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Author | v1 | current (v1) -
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- Name
-
- Unchanged
- Jian Sun
- Bio
-
- Unchanged
- no value
- Link
-
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Author | v1 | current (v1) -
Add M M Bradford
- Name
-
- Unchanged
- M M Bradford
- Bio
-
- Unchanged
- no value
- Link
-
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Author | v1 | current (v1) -
Add U. K. Laemmli
- Name
-
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- U. K. Laemmli
- Bio
-
- Unchanged
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- Link
-
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Author | v1 | current (v1) -
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- Current
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Topic to resource relation | v1 -
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Topic to resource relation | v1 -
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Topic to resource relation | v1 -
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Topic to resource relation | v1 -
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Topic to resource relation | v1 -
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Topic to resource relation | v1 -
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Topic to resource relation | v1 -
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Topic to resource relation | v1 -
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Topic to resource relation | v1 -
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Topic to resource relation | v1 -
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Topic to resource relation | v1 -
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Topic to resource relation | v1 -
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Topic to resource relation | v1 -
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Topic to resource relation | v1 -
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Topic to resource relation | v1 -
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Topic to resource relation | v1 -
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Topic to resource relation | v1 -
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Topic to resource relation | v1 -
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Author to resource relation | v1 -
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Author to resource relation | v1 -
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Author to resource relation | v1 -
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Author to resource relation | v1 -
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Author to resource relation | v1 -
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Author to resource relation | v1 -
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