Processing Multiple Inputs of Arbitrary Size

Author(s):  
Marco Morazán
Keyword(s):  
Science ◽  
2013 ◽  
Vol 340 (6137) ◽  
pp. 1205-1208 ◽  
Author(s):  
Michael Walter ◽  
Brent Doran ◽  
David Gross ◽  
Matthias Christandl

Entangled many-body states are an essential resource for quantum computing and interferometry. Determining the type of entanglement present in a system usually requires access to an exponential number of parameters. We show that in the case of pure, multiparticle quantum states, features of the global entanglement can already be extracted from local information alone. This is achieved by associating any given class of entanglement with an entanglement polytope—a geometric object that characterizes the single-particle states compatible with that class. Our results, applicable to systems of arbitrary size and statistics, give rise to local witnesses for global pure-state entanglement and can be generalized to states affected by low levels of noise.


2018 ◽  
Vol 2018 (7) ◽  
pp. 121-1-121-8 ◽  
Author(s):  
Clement Fuji Tsang ◽  
Jessica Fridrich
Keyword(s):  

2002 ◽  
Vol 34 (03) ◽  
pp. 469-483
Author(s):  
Ximo Gual-Arnau ◽  
Luis M. Cruz-Orive

Geometric sampling, and local stereology in particular, often require observations at isotropic random directions on the sphere, and some sort of systematic design on the sphere becomes necessary on grounds of efficiency and practical applicability. Typically, the relevant probes are of nucleator type, in which several rays may be contained in a sectioning plane through a fixed point (e.g. through a nucleolus within a biological cell). The latter requirement considerably reduces the choice of design in practice; in this paper, we concentrate on a nucleator design based on splitting the sphere into regions of equal area, but not of identical shape; this design is pseudosystematic rather than systematic in a strict sense. Firstly, we obtain useful exact representations of the variance of an estimator under pseudosystematic sampling on the sphere. Then we adopt a suitable covariogram model to obtain a variance predictor from a single sample of arbitrary size, and finally we examine the prediction accuracy by way of simulation on a synthetic particle model.


10.14311/1636 ◽  
2012 ◽  
Vol 52 (5) ◽  
Author(s):  
Ivan Halupka ◽  
Ján Kollár ◽  
Emília Pietriková

This paper presents our proposal and the implementation of an algorithm for automated refactoring of context-free grammars. Rather than operating under some domain-specific task, in our approach refactoring is perfomed on the basis of a refactoring task defined by its user. The algorithm and the corresponding refactoring system are called mARTINICA. mARTINICA is able to refactor grammars of arbitrary size and structural complexity. However, the computation time needed to perform a refactoring task with the desired outcome is highly dependent on the size of the grammar. Until now, we have successfully performed refactoring tasks on small and medium-size grammars of Pascal-like languages and parts of the Algol-60 programming language grammar. This paper also briefly introduces the reader to processes occurring in grammar refactoring, a method for describing desired properties that a refactored grammar should fulfill, and there is a discussion of the overall significance of grammar refactoring.


Mathematics ◽  
2022 ◽  
Vol 10 (2) ◽  
pp. 181
Author(s):  
Artem E. Starkov ◽  
Leonid B. Sokolinsky

This paper presents a two-dimensional mathematical model of compound eye vision. Such a model is useful for solving navigation issues for autonomous mobile robots on the ground plane. The model is inspired by the insect compound eye that consists of ommatidia, which are tiny independent photoreception units, each of which combines a cornea, lens, and rhabdom. The model describes the planar binocular compound eye vision, focusing on measuring distance and azimuth to a circular feature with an arbitrary size. The model provides a necessary and sufficient condition for the visibility of a circular feature by each ommatidium. On this basis, an algorithm is built for generating a training data set to create two deep neural networks (DNN): the first detects the distance, and the second detects the azimuth to a circular feature. The hyperparameter tuning and the configurations of both networks are described. Experimental results showed that the proposed method could effectively and accurately detect the distance and azimuth to objects.


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