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Institution

Iowa State University

EducationAmes, Iowa, United States
About: Iowa State University is a education organization based out in Ames, Iowa, United States. It is known for research contribution in the topics: Population & Gene. The organization has 50151 authors who have published 107716 publications receiving 3355909 citations. The organization is also known as: Iowa State University of Science and Technology & Iowa State College.


Papers
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Journal ArticleDOI
TL;DR: In this article, the limit distributions of the estimator of p and of the regression t test are derived under the assumption that p = ± 1, where p is a fixed constant and t is a sequence of independent normal random variables.
Abstract: Let n observations Y 1, Y 2, ···, Y n be generated by the model Y t = pY t−1 + e t , where Y 0 is a fixed constant and {e t } t-1 n is a sequence of independent normal random variables with mean 0 and variance σ2. Properties of the regression estimator of p are obtained under the assumption that p = ±1. Representations for the limit distributions of the estimator of p and of the regression t test are derived. The estimator of p and the regression t test furnish methods of testing the hypothesis that p = 1.

23,509 citations

Journal ArticleDOI
TL;DR: A description of the ab initio quantum chemistry package GAMESS, which can be treated with wave functions ranging from the simplest closed‐shell case up to a general MCSCF case, permitting calculations at the necessary level of sophistication.
Abstract: A description of the ab initio quantum chemistry package GAMESS is presented. Chemical systems containing atoms through radon can be treated with wave functions ranging from the simplest closed-shell case up to a general MCSCF case, permitting calculations at the necessary level of sophistication. Emphasis is given to novel features of the program. The parallelization strategy used in the RHF, ROHF, UHF, and GVB sections of the program is described, and detailed speecup results are given. Parallel calculations can be run on ordinary workstations as well as dedicated parallel machines. © John Wiley & Sons, Inc.

18,546 citations

Book ChapterDOI
TL;DR: The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue.
Abstract: Publisher Summary This chapter provides an account of different neural network architectures for pattern recognition. A neural network consists of several simple processing elements called neurons. Each neuron is connected to some other neurons and possibly to the input nodes. Neural networks provide a simple computing paradigm to perform complex recognition tasks in real time. The chapter categorizes neural networks into three types: single-layer networks, multilayer feedforward networks, and feedback networks. It discusses the gradient descent and the relaxation method as the two underlying mathematical themes for deriving learning algorithms. A lot of research activity is centered on learning algorithms because of their fundamental importance in neural networks. The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue. It closes with the discussion of performance and implementation issues.

13,033 citations

Journal ArticleDOI
Claude Amsler1, Michael Doser2, Mario Antonelli, D. M. Asner3  +173 moreInstitutions (86)
TL;DR: This biennial Review summarizes much of particle physics, using data from previous editions.

12,798 citations

Journal ArticleDOI
TL;DR: The preparation of a series of normal stages of the chick embryo does not need justification at a time when chick ernbryos are not only widely used in descriptive and experimental embryology but are proving to be increasingly valuable in medical research, as in work on viruses and cancer.
Abstract: FORTY-FIVE FIGURES The preparation of a series of normal stages of the chick embryo does not need justification at a time when chick ernbryos are not only widely used in descriptive and experimental embryology but are proving to be increasingly valuable in medical research, as in work on viruses and cancer. The present series was planned in connection with the preparation of a new edition of Lillie’s DeueZopmerzt of the Chick by the junior author. It is being published separately to make it accessible immediately to a large group of workers. Ever since Aristotle “discovered” the chick embryo as the ideal, object for embryological studies, the embryos have been described in terms of the length of time of incubation, and this arbitrary method is still in general use, except for the first three days of incubation during which more detailed characteristics such as the numbers of somites are applied. The shortcomings of a classification based on chronological age are obvious to every worker in this field, for enormous variations may occur in embryos even though all eggs in a setting are plmaced in the incubator at the same time. Many factors are responsible for the lack of correlation between chronological and structural age. Among these are : genetic differences in the rate of development of different breccls (eg., the embryo of the White Leghorn breed develops more 49

12,079 citations


Authors

Showing all 50392 results

NameH-indexPapersCitations
Feng Zhang1721278181865
Yang Gao1682047146301
Steven N. Blair165879132929
Carlos Bustamante161770106053
Darien Wood1602174136596
Pete Smith1562464138819
Richard J. Davidson15660291414
Mark Raymond Adams1471187135038
H. A. Neal1411903115480
Mitchell Wayne1391810108776
Frank Filthaut1351684103590
Tiziano Rovelli135144190518
Francesco Navarria135153591427
Francesca Romana Cavallo135157192392
Yasar Onel134142492200
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202378
2022549
20213,569
20203,803
20193,787
20183,741