Production (computer science)

Abstract: “Meaning” may be assigned to a string in a context-free language by defining “attributes” of the symbols in a derivation tree for that string. The attributes can be defined by functions associated with each production in the grammar. This paper examines the implications of this process when some of the attributes are “synthesized”, i.e., defined solely in terms of attributes of thedescendants of the corresponding nonterminal symbol, while other attributes are “inherited”, i.e., defined in terms of attributes of theancestors of the nonterminal symbol. An algorithm is given which detects when such semantic rules could possibly lead to circular definition of some attributes. An example is given of a simple programming language defined with both inherited and synthesized attributes, and the method of definition is compared to other techniques for formal specification of semantics which have appeared in the literature.

Abstract: Reheating after inflation occurs due to particle production by the oscillating inflaton field. In this paper we briefly describe the perturbative approach to reheating, and then concentrate on effects beyond the perturbation theory. They are related to the stage of parametric resonance, which we call preheating. It may occur in an expanding universe if the initial amplitude of oscillations of the inflaton field is large enough. We investigate a simple model of a massive inflaton field $\ensuremath{\varphi}$ coupled to another scalar field $\ensuremath{\chi}$ with the interaction term ${g}^{2}{\ensuremath{\varphi}}^{2}{\ensuremath{\chi}}^{2}$. Parametric resonance in this model is very broad. It occurs in a very unusual stochastic manner, which is quite different from parametric resonance in the case when the expansion of the universe is neglected. Quantum fields interacting with the oscillating inflaton field experience a series of kicks which, because of the rapid expansion of the universe, occur with phases uncorrelated to each other. Despite the stochastic nature of the process, it leads to exponential growth of fluctuations of the field $\ensuremath{\chi}$. We call this process stochastic resonance. We develop the theory of preheating taking into account the expansion of the universe and back reaction of produced particles, including the effects of rescattering. This investigation extends our previous study of reheating after inflation. We show that the contribution of the produced particles to the effective potential $V(\ensuremath{\varphi})$ is proportional not to ${\ensuremath{\varphi}}^{2}$, as is usually the case, but to $|\ensuremath{\varphi}|$. The process of preheating can be divided into several distinct stages. In the first stage the back reaction of created particles is not important. In the second stage back reaction increases the frequency of oscillations of the inflaton field, which makes the process even more efficient than before. Then the effects related to scattering of $\ensuremath{\chi}$ particles on the oscillating inflaton field terminate the resonance. We calculate the number density of particles ${n}_{\ensuremath{\chi}}$ produced during preheating and their quantum fluctuations $〈{\ensuremath{\chi}}^{2}〉$ with all back reaction effects taken into account. This allows us to find the range of masses and coupling constants for which one can have efficient preheating. In particular, under certain conditions this process may produce particles with a mass much greater than the mass of the inflaton field.

Abstract: The D0 collaboration reports on a search for the Standard Model top quark in p{bar p} collisions at {radical}s = 1.8 TeV at the Fermilab Tevatron, with an integrated luminosity of approximately 50 pb{sup {minus}1}. We have searched for t{bar t} production in the dilepton and single lepton decay channels, with and without tagging of b quark jets. We observe 17 events with an expected background of 3.8 {plus_minus} 0.6 events. The probability for an upward fluctuation of the background to produce the observed signal is 2 {times} 10{sup {minus}6} (equivalent to 4.6 standard deviations). The kinematic properties of the excess events are consistent with top quark decay. We conclude that we have observed the top quark and measure its mass to be 199{sub {minus}21}{sup +19} (stat.) {plus_minus}22 (syst.) GeV/c{sup 2} and its production cross section to be 6.4 {plus_minus} 2.2 pb.

Abstract: The nuclear shell model predicts that the next doubly magic shell closure beyond ${}^{208}\mathrm{Pb}$ is at a proton number between $Z=114$ and 126 and at a neutron number $N=184.$ The outstanding aim of experimental investigations is the exploration of this region of spherical superheavy elements (SHE's). This article describes the experimental methods that led to the identification of elements 107 to 112 at GSI, Darmstadt. Excitation functions were measured for the one-neutron evaporation channel of cold-fusion reactions using lead and bismuth targets. The maximum cross section was measured at beam energies well below a fusion barrier estimated in one dimension. These studies indicate that the transfer of nucleons is an important process for the initiation of fusion. The recent efforts at JINR, Dubna, to investigate the hot-fusion reaction for the production of SHE's using actinide targets are also presented. First results were obtained on the synthesis of neutron-rich isotopes of elements 112 and 114. However, the most surprising result was achieved in 1999 at LBNL, Berkeley. In a study of the reaction ${}^{86}{\mathrm{K}\mathrm{r}+}^{208}{\mathrm{Pb}\ensuremath{\rightarrow}}^{294}{118}^{*},$ three decay chains were measured and assigned to the superheavy nucleus ${}^{293}118.$ The decay data reveal that, for the heaviest elements, the dominant decay mode is alpha emission, not fission. The results are discussed in the framework of theoretical models. This article also presents plans for the further development of the experimental setup and the application of new techniques. At a higher sensitivity, the exploration of the region of spherical SHE's now seems to be feasible, more than 30 years after its prediction.

Abstract: 1 Various Aspects of Memory- 11 On the Purpose and Nature of Biological Memory- 111 Some Fundamental Concepts- 112 The Classical Laws of Association- 113 On Different Levels of Modelling- 12 Questions Concerning the Fundamental Mechanisms of Memory- 121 Where Do the Signals Relating to Memory Act Upon?- 122 What Kind of Encoding is Used for Neural Signals?- 123 What are the Variable Memory Elements?- 124 How are Neural Signals Addressed in Memory?- 13 Elementary Operations Implemented by Associative Memory- 131 Associative Recall- 132 Production of Sequences from the Associative Memory- 133 On the Meaning of Background and Context- 14 More Abstract Aspects of Memory- 141 The Problem of Infinite-State Memory- 142 Invariant Representations- 143 Symbolic Representations- 144 Virtual Images- 145 The Logic of Stored Knowledge- 2 Pattern Mathematics- 21 Mathematical Notations and Methods- 211 Vector Space Concepts- 212 Matrix Notations- 213 Further Properties of Matrices- 214 Matrix Equations- 215 Projection Operators- 216 On Matrix Differential Calculus- 22 Distance Measures for Patterns- 221 Measures of Similarity and Distance in Vector Spaces- 222 Measures of Similarity and Distance Between Symbol Strings- 223 More Accurate Distance Measures for Text- 3 Classical Learning Systems- 31 The Adaptive Linear Element (Adaline)- 311 Description of Adaptation by the Stochastic Approximation- 32 The Perceptron- 33 The Learning Matrix- 34 Physical Realization of Adaptive Weights- 341 Perceptron and Adaline- 342 Classical Conditioning- 343 Conjunction Learning Switches- 344 Digital Representation of Adaptive Circuits- 345 Biological Components- 4 A New Approach to Adaptive Filters- 41 Survey of Some Necessary Functions- 42 On the "Transfer Function" of the Neuron- 43 Models for Basic Adaptive Units- 431 On the Linearization of the Basic Unit- 432 Various Cases of Adaptation Laws- 433 Two Limit Theorems- 434 The Novelty Detector- 44 Adaptive Feedback Networks- 441 The Autocorrelation Matrix Memory- 442 The Novelty Filter- 5 Self-Organizing Feature Maps- 51 On the Feature Maps of the Brain- 52 Formation of Localized Responses by Lateral Feedback- 53 Computational Simplification of the Process- 531 Definition of the Topology-Preserving Mapping- 532 A Simple Two-Dimensional Self-Organizing System- 54 Demonstrations of Simple Topology-Preserving Mappings- 541 Images of Various Distributions of Input Vectors- 542 "The Magic TV"- 543 Mapping by a Feeler Mechanism- 55 Tonotopic Map- 56 Formation of Hierarchical Representations- 561 Taxonomy Example- 562 Phoneme Map- 57 Mathematical Treatment of Self-Organization- 571 Ordering of Weights- 572 Convergence Phase- 58 Automatic Selection of Feature Dimensions- 6 Optimal Associative Mappings- 61 Transfer Function of an Associative Network- 62 Autoassociative Recall as an Orthogonal Projection- 621 Orthogonal Projections- 622 Error-Correcting Properties of Projections- 63 The Novelty Filter- 631 Two Examples of Novelty Filter- 632 Novelty Filter as an Autoassociative Memory- 64 Autoassociative Encoding- 641 An Example of Autoassociative Encoding- 65 Optimal Associative Mappings- 651 The Optimal Linear Associative Mapping- 652 Optimal Nonlinear Associative Mappings- 66 Relationship Between Associative Mapping, Linear Regression, and Linear Estimation- 661 Relationship of the Associative Mapping to Linear Regression- 662 Relationship of the Regression Solution to the Linear Estimator- 67 Recursive Computation of the Optimal Associative Mapping- 671 Linear Corrective Algorithms- 672 Best Exact Solution (Gradient Projection)- 673 Best Approximate Solution (Regression)- 674 Recursive Solution in the General Case- 68 Special Cases- 681 The Correlation Matrix Memory- 682 Relationship Between Conditional Averages and Optimal Estimator- 7 Pattern Recognition- 71 Discriminant Functions- 72 Statistical Formulation of Pattern Classification- 73 Comparison Methods- 74 The Subspace Methods of Classification- 741 The Basic Subspace Method- 742 The Learning Subspace Method (LSM)- 75 Learning Vector Quantization- 76 Feature Extraction- 77 Clustering- 771 Simple Clustering (Optimization Approach)- 772 Hierarchical Clustering (Taxonomy Approach)- 78 Structural Pattern Recognition Methods- 8 More About Biological Memory- 81 Physiological Foundations of Memory- 811 On the Mechanisms of Memory in Biological Systems- 812 Structural Features of Some Neural Networks- 813 Functional Features of Neurons- 814 Modelling of the Synaptic Plasticity- 815 Can the Memory Capacity Ensue from Synaptic Changes?- 82 The Unified Cortical Memory Model- 821 The Laminar Network Organization- 822 On the Roles of Interneurons- 823 Representation of Knowledge Over Memory Fields- 824 Self-Controlled Operation of Memory- 83 Collateral Reading- 831 Physiological Results Relevant to Modelling- 832 Related Modelling- 9 Notes on Neural Computing- 91 First Theoretical Views of Neural Networks- 92 Motives for the Neural Computing Research- 93 What Could the Purpose of the Neural Networks be?- 94 Definitions of Artificial "Neural Computing" and General Notes on Neural Modelling- 95 Are the Biological Neural Functions Localized or Distributed?- 96 Is Nonlinearity Essential to Neural Computing?- 97 Characteristic Differences Between Neural and Digital Computers- 971 The Degree of Parallelism of the Neural Networks is Still Higher than that of any "Massively Parallel" Digital Computer- 972 Why the Neural Signals Cannot be Approximated by Boolean Variables- 973 The Neural Circuits do not Implement Finite Automata- 974 Undue Views of the Logic Equivalence of the Brain and Computers on a High Level- 98 "Connectionist Models"- 99 How can the Neural Computers be Programmed?- 10 Optical Associative Memories- 101 Nonholographic Methods- 102 General Aspects of Holographic Memories- 103 A Simple Principle of Holographic Associative Memory- 104 Addressing in Holographic Memories- 105 Recent Advances of Optical Associative Memories- Bibliography on Pattern Recognition- References