binary representation
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2022 ◽  
pp. 1-7
Author(s):  
Alexandr Usachev

Abstract The paper deals with the sets of numbers from [0,1] such that their binary representation is almost convergent. The aim of the study is to compute the Hausdorff dimensions of such sets. Previously, the results of this type were proved for a single summation method (e.g. Cesàro, Abel, Toeplitz). This study extends the results to a wide range of matrix summation methods.


2022 ◽  
Author(s):  
Kyungtaek Jun

Abstract With the advent of quantum computers, many quantum computing algorithms are being developed. Solving linear systems is one of the most fundamental problems in almost all of science and engineering. Harrow-Hassidim-Lloyd algorithm, a monumental quantum algorithm for solving linear systems on the gate model quantum computers, was invented and several advanced variations have been developed. For a given square matrix A∈R(n×n) and a vector b∈R(n), we will find unconstrained binary optimization (QUBO) models for a vector x∈R(n) that satisfies Ax=b. To formulate QUBO models for a linear system solving problem, we make use of a linear least-square problem with binary representation of the solution. We validate those QUBO models on the D-Wave system and discuss the results. For a simple system, We provide a python code to calculate the matrix characterizing the relationship between the variables and to print the test code that can be used directly in D-Wave system.


Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 68
Author(s):  
Serhii Haliuk ◽  
Oleh Krulikovskyi ◽  
Dmytro Vovchuk ◽  
Fernando Corinto

This paper suggests an approach to generate pseudo-random sequences based on the discrete-time model of the simple memristive chaotic system. We show that implementing Euler’s and Runge–Kutta’s methods for the simulation solutions gives the possibility of obtaining chaotic sequences that maintain general properties of the original chaotic system. A preliminary criterion based on the binary sequence balance estimation is proposed and applied to separate any binary representation of the chaotic time sequences into random and non-random parts. This gives us the possibility to delete obviously non-random sequences prior to the post-processing. The investigations were performed for arithmetic with both fixed and floating points. In both cases, the obtained sequences successfully passed the NIST SP 800-22 statistical tests. The utilization of the unidirectional asymmetric coupling of chaotic systems without full synchronization between them was suggested to increase the performance of the chaotic pseudo-random number generator (CPRNG) and avoid identical sequences on different outputs of the coupled systems. The proposed CPRNG was also implemented and tested on FPGA using Euler’s method and fixed-point arithmetic for possible usage in different applications. The FPGA implementation of CPRNG supports a generation speed up to 1.2 Gbits/s for a clock frequency of 50 MHz. In addition, we presented an example of the application of CPRNG to symmetric image encryption, but nevertheless, one is suitable for the encryption of any binary source.


Federalism ◽  
2021 ◽  
Vol 26 (4) ◽  
pp. 75-88
Author(s):  
N. Yu. Korotina

The complexity of the economic aspects of federal relations and the multidimensional nature of management tasks predetermines the need to comprehend the essence of the system of federalism. Therefore, the purpose of this study is to substantiate a model that, on the one hand, considers federalism as the concept of the creation and functioning of the state system and as a way of managing the economy of the federal state on the other. Application of an evolutionary methodological approach allowed the author to divide the fundamental theories of federalism into two groups: the one examines federalism as a power paradigm, focuses on the federal principles of building a state, political and legal status The other examines federalism as a mechanism for coordinating the economic interests of its participants from the position of providing resources for fulfilling the assigned state functions at each level of the federal structure. The first group of fundamental works allows us to single out the essential features of federal relations. The second group of works made it possible to determine the economic principles of the functioning of federalism relations. Based on the highlighted features and principles of economic relations of federalism the article presents the author’s view of the dual subject essence of the state. Firstly, as a carrier of federal relations as a construct that structures and formats the territorial-state structure, as a mechanism of management and organization that sets the formal conditions for the reproduction of the subjects of the federal state based on the possession of power. Secondly, as an actor, one of the participants in the economic cycle of reproduction of the gross regional product based on the resources of the public sector. The proposed binary representation of the state allows us to show not only its creating role in the system of economic federalism, but also includes the goals of the regional economy in the federal system.


Agronomy ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 43
Author(s):  
Juan Manuel Ponce ◽  
Arturo Aquino ◽  
Diego Tejada ◽  
Basil Mohammed Al-Hadithi ◽  
José Manuel Andújar

The popularisation of aerial remote sensing using unmanned aerial vehicles (UAV), has boosted the capacities of agronomists and researchers to offer farmers valuable data regarding the status of their crops. This paper describes a methodology for the automated detection and individual delineation of tree crowns in aerial representations of crop fields by means of image processing and analysis techniques, providing accurate information about plant population and canopy coverage in intensive-farming orchards with a row-based plant arrangement. To that end, after pre-processing initial aerial captures by means of photogrammetry and morphological image analysis, a resulting binary representation of the land plot surveyed is treated at connected component-level in order to separate overlapping tree crown projections. Then, those components are morphologically transformed into a set of seeds with which tree crowns are finally delineated, establishing the boundaries between them when they appear overlapped. This solution was tested on images from three different orchards, achieving semantic segmentations in which more than 94% of tree canopy-belonging pixels were correctly classified, and more than 98% of trees were successfully detected when assessing the methodology capacities for estimating the overall plant population. According to these results, the methodology represents a promising tool for automating the inventorying of plants and estimating individual tree-canopy coverage in intensive tree-based orchards.


Author(s):  
Travis Wagner

This paper examines two examples of archival visual information with potentially transgender and non-binary representation to interrogate the descriptive challenges latent within such materials. By using gender theory and queer historiography, this paper deploys a critical case study to consider the particularities of naming gender when contextual evidence provides little to no authoritative guidance. By talking through the way gender makes itself visible within visual information, the paper guides readers through the way transgender or non-binary identity might exist within both pieces of visual information. The paper then provides suggestions on how to provide respectful and inclusive descriptive records that attend to the complexities of a still-evolving queer history. By offering both a statement on the impossibility of naming identity within intersecting forms of queer embodiment alongside reference points for methods of discussing potential gendered identities, the paper offers practical approaches to describing transgender and non-binary identities for information professionals.


Author(s):  
Trinh Quang Kien

In recent years with the explosion of research in artificial intelligence, deep learning models based on convolutional neural networks (CNNs) are one of the promising architectures for practical applications thanks to their reasonably good achievable accuracy. However, CNNs characterized by convolutional layers often have a large number of parameters and computational workload, leading to large energy consumption for training and network inference. The binarized neural network (BNN) model has been recently proposed to overcome that drawback. The BNNs use binary representation for the inputs and weights, which inherently reduces memory requirements and simplifies computations while still maintaining acceptable accuracy. BNN thereby is very suited for the practical realization of Edge-AI application on resource- and energy-constrained devices such as embedded or mobile devices. As CNN and BNN both compose linear transformations layers,  they can be fooled by adversarial attack patterns. This topic has been actively studied recently but most of them are for CNN. In this work, we examine the impact of the adversarial attack on BNNs and propose a solution to improve the accuracy of BNN against this type of attack. Specifically, we use an Enhanced Fast Adversarial Training (EFAT) method to train the network that helps the BNN be more robust against major adversarial attack models with a very short training time. Experimental results with Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attack models on our trained BNN network with MNIST dataset increased accuracy from 31.34% and 0.18% to 96.96% and 85.08%, respectively.


2021 ◽  
Vol 49 (1) ◽  
Author(s):  
Toufik Datsi ◽  
◽  
Khalid Aznag ◽  
Ahmed El Oirrak ◽  
◽  
...  

Current artificial neural network image recognition techniques use all the pixels of an image as input. In this paper, we present an efficient method for handwritten digit recognition that involves extracting the characteristics of a digit image by coding each row of the image as a decimal value, i.e., by transforming the binary representation into a decimal value. This method is called the decimal coding of rows. The set of decimal values calculated from the initial image is arranged as a vector and normalized; these values represent the inputs to the artificial neural network. The approach proposed in this work uses a multilayer perceptron neural network for the classification, recognition, and prediction of handwritten digits from 0 to 9. In this study, a dataset of 1797 samples were obtained from a digit database imported from the Scikit-learn library. Backpropagation was used as a learning algorithm to train the multilayer perceptron neural network. The results show that the proposed approach achieves better performance than two other schemes in terms of recognition accuracy and execution time.


Author(s):  
Rajeev Colaço ◽  
Stephanie Watson-Grant

The global data community has made—and is continuing to make—enormous strides toward collecting, analyzing, and using sex-disaggregated data to improve international development programs. Historically, however, sex-disaggregation has been—and largely continues to be—a binary representation of cisgender female and cisgender male populations. This binary interpretation excludes transgender and gender-nonconforming people and further perpetuates marginalization and discrimination of these populations. In a world where disparities are increasing, it is paramount to highlight and share the experiences of marginalized populations so we are better able to serve all beneficiary needs and end disparities. To this end, we call for a paradigm shift from binary sex-disaggregation to multinomial gender-disaggregation, which is more inclusive and equitable. This call to action is aimed particularly at surveyors, researchers, program implementors, policy makers, and gender rights advocates in both resource-sufficient and resource-constrained settings. The lack of adequate gender-disaggregated data is a universal problem.


2021 ◽  
Author(s):  
Jayanta Pal ◽  
Soumen Ghosh ◽  
Bansibadan Maji ◽  
Dilip Kumar Bhattacharya

Abstract Similarity/dissimilarity study of protein and genome sequences remains a challenging task and selection of techniques and descriptors to be adopted, plays an important role in computational biology. Again, genome sequence comparison is always preferred to protein sequence comparison due the presence of 20 amino acids in protein sequence compared to only 4 nucleotides in genome sequence. So it is important to consider suitable representation that is both time and space efficient and also equally applicable to protein sequences of equal and unequal lengths. In the binary form of representation, Fourier transform of a protein sequence reduces to the transformation of 20 simple binary sequences in Fourier domain, where in each such sequence, Perseval’s Identity gives a very simple computable form of power spectrum. This gives rise to readily acceptable forms of moments of different degrees. Again such moments, when properly normalized, show a monotonically descending trend with the increase in the degrees of the moments. So it is better to stick to moments of smaller degrees only. In this paper, descriptors are taken as 20 component vectors, where each component corresponds to a general second order moment of one of the 20 simple binary sequences. Then distance matrices are obtained by using Euclidean distance as the distance measure between each pair of sequence. Phylogenetic trees are obtained from the distance matrices using UPGMA algorithm. In the present paper, the datasets used for similarity/dissimilarity study are 9 ND4, 16 ND5, 9 ND6, 24 TF proteins and 12 Baculovirus proteins. It is found that the phylogenetic trees produced by the present method are at par with those produced by the earlier methods adopted by other authors and also their known biological references. Further it takes less computational time and also it is equally applicable to sequences of equal and unequal lengths.


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