Set (abstract data type)
About: Set (abstract data type) is a research topic. Over the lifetime, 63143 publications have been published within this topic receiving 961716 citations.
Papers published on a yearly basis
01 Jan 1986
TL;DR: Binary Alloy Phase Diagrams, Second Edition, Plus Updates, on CD-ROM offers you the same high-quality, reliable data you'll find in the 3-volume print set published by ASM in 1990.
Abstract: Gives you access to the 4,700 atomic and weight percent graphs included in the reference set Binary Alloy Phase Diagrams, Second Edition, published by ASM in 1990 - plus updates! All the data from the 3,600-page, three-volume set, abstracts of phase diagram evaluations for 3,000 binary alloy systems, special points and crystal structure tables, along with 300 recent updates from the current literature are included on one CD-ROM for ease of use and storage. Binary Alloy Phase Diagrams plus updates on CD-ROM containing all the data from Massalski's world standard, three-volume, 3,600-page Binary Alloy Phase Diagrams, Second Edition, fits in the palm of your hand! This CD includes 4,700 diagrams; abstracts of phase diagram evaluations for 3,000 binary alloy systems; special points; crystal structure tables; plus 300 recent updates from current literature. All in databases and in CD-ROM format, so it's easier to access, more flexible to use, and more efficient for you to store than ever before. Binary Alloy Phase Diagrams, Second Edition, Plus Updates, on CD-ROM, offers you the same high-quality, reliable data you'll find in the 3-volume print set published by ASM in 1990. The over 4,700 diagrams were digitized from original program graphs or redrawn from carefully selected data sources. Each diagram is in accordance to thermodynamic principles and is consistent with melting and phase-transition temperatures of the pure elements. All diagrams met strict quality standards throughout preparation. Now, the CD-ROM format puts this quality information at your fingertips. These are not scanned pages, but true, complete databases of phase diagram and crystallographic information, all in one incredibly small but powerful package, you'll wonder what you ever did without it! This new electronic format allows you to: Search for diagrams, crystal structure data, or text by keying in the alloys. Search the Master Crystal Structure Table for Intermetallic compounds with equivalent structure type, temperature, and phase width criteria. Print diagrams, text, crystal structure. Examine any new data in conjunction with the original data as presented in the print volume. Zoom in on a complicated section of the diagram for a closer look. (Vat payable on UK orders for CD products) Multi-User prices available: Contact Steve French (Customer Services Manager) Telephone: +44 (0)1462 437933; E-Mail: SFrench@ameritech.co.uk
21 Jul 2017
TL;DR: ResNeXt as discussed by the authors is a simple, highly modularized network architecture for image classification, which is constructed by repeating a building block that aggregates a set of transformations with the same topology.
Abstract: We present a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call cardinality (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width. On the ImageNet-1K dataset, we empirically show that even under the restricted condition of maintaining complexity, increasing cardinality is able to improve classification accuracy. Moreover, increasing cardinality is more effective than going deeper or wider when we increase the capacity. Our models, named ResNeXt, are the foundations of our entry to the ILSVRC 2016 classification task in which we secured 2nd place. We further investigate ResNeXt on an ImageNet-5K set and the COCO detection set, also showing better results than its ResNet counterpart. The code and models are publicly available online.
TL;DR: In this article, the authors compare the predictive accuracy of various methods in a set of representative problem domains, including correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods.
Abstract: Collaborative filtering or recommender systems use a database about user preferences to predict additional topics or products a new user might like. In this paper we describe several algorithms designed for this task, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods. We compare the predictive accuracy of the various methods in a set of representative problem domains. We use two basic classes of evaluation metrics. The first characterizes accuracy over a set of individual predictions in terms of average absolute deviation. The second estimates the utility of a ranked list of suggested items. This metric uses an estimate of the probability that a user will see a recommendation in an ordered list. Experiments were run for datasets associated with 3 application areas, 4 experimental protocols, and the 2 evaluation metrics for the various algorithms. Results indicate that for a wide range of conditions, Bayesian networks with decision trees at each node and correlation methods outperform Bayesian-clustering and vector-similarity methods. Between correlation and Bayesian networks, the preferred method depends on the nature of the dataset, nature of the application (ranked versus one-by-one presentation), and the availability of votes with which to make predictions. Other considerations include the size of database, speed of predictions, and learning time.
TL;DR: In this paper, a universally applicable attitude and skill set for computer science is presented, which is a set of skills and attitudes that everyone would be eager to learn and use, not just computer scientists.
Abstract: It represents a universally applicable attitude and skill set everyone, not just computer scientists, would be eager to learn and use.
TL;DR: A significant update to one of the tools in this domain called Enrichr, a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries is presented.
Abstract: Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.
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