Circuit complexity of linear functions: gate elimination and feeble security

2012 ◽  
Vol 188 (1) ◽  
pp. 35-46
Author(s):  
A. P. Davydow ◽  
S. I. Nikolenko
Keyword(s):  
Circuit Complexity ◽  
Linear Functions ◽  
Gate Elimination

PENGARUH NILAI PENGANTAR AKUNTANSI TERHADAP KEMAMPUAN MAHASISWA MEMAHAMI KOMPUTER AKUNTANSI PADA PROGRAM STUDI PERBANKAN DAN KEUANGAN POLITEKNIK NEGERI MEDAN

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Jantianus Jantianus ◽  
Khairul Khairul
Keyword(s):  
Linear Regression ◽  
Regression Equation ◽  
Linear Functions ◽  
First Semester ◽  
Normality Test ◽  
Introductory Accounting ◽  
Linearity Test

Ease of understanding Accounting Computers in principle is influenced by mastery of Introduction to Accounting in a systematic manner, assuming that it is capable of operating computers properly. To find out the magnitude of the influence in this study taken a sample of introductory Accounting values from a number of first semester 2017 students and the same data sample for students of Computer Accounting (Accurate) courses when they are in the fourth semester 2018. Feasibility until the data is tested by the normality test to find out the distribution of data and by linearity test to obtain linear functions. The data that has been obtained and tested for its feasibility is processed by Linear Regression using SPSS 24. From the results of the research that has been done obtained a regression equation: Y = 67,953 0.35X, which describes each increase in the value of introductory Accounting one unit will affect 0.35 to Computer Accounting value, but in testing the hypothesis that the value of Introduction to Accounting obtained by students does not affect their ability to obtain Computer Accounting values, one of the causes of this is due to the lack of skills of students to operate computers.Keywords: influence, value, ability

Variability of radiosonde-observed precipitable water in the Baltic region*

2005 ◽  
Vol 36 (4-5) ◽  
pp. 423-433 ◽  
Author(s):  
E. Jakobson ◽  
H. Ohvril ◽  
O. Okulov ◽  
N. Laulainen
Keyword(s):  
Water Vapour ◽  
Precipitable Water ◽  
Linear Functions ◽  
Water Vapour Pressure ◽  
Baltic Region ◽  
Geographical Latitude ◽  
Linear Formula ◽  
Annual Means ◽  
The Baltic ◽  
Two Parameters

The total mass of columnar water vapour (precipitable water, W) is an important parameter of atmospheric thermodynamic and radiative models. In this work more than 60 000 radiosonde observations from 17 aerological stations in the Baltic region over 14 years, 1989–2002, were used to examine the variability of precipitable water. A table of monthly and annual means of W for the stations is given. Seasonal means of W are expressed as linear functions of the geographical latitude degree. A linear formula is also derived for parametrisation of precipitable water as a function of two parameters – geographical latitude and surface water vapour pressure.

scholarly journals Students’ agency, creative reasoning, and collaboration in mathematical problem solving

2021 ◽  
Author(s):  
Ellen Kristine Solbrekke Hansen
Keyword(s):  
Problem Solving ◽  
Driving Forces ◽  
Mathematical Reasoning ◽  
Mathematical Problem ◽  
Linear Functions ◽  
Shared Understanding ◽  
Interaction Patterns ◽  
Collaborative Processes ◽  
Mathematical Properties ◽  
Shared Agency

AbstractThis paper aims to give detailed insights of interactional aspects of students’ agency, reasoning, and collaboration, in their attempt to solve a linear function problem together. Four student pairs from a Norwegian upper secondary school suggested and explained ideas, tested it out, and evaluated their solution methods. The student–student interactions were studied by characterizing students’ individual mathematical reasoning, collaborative processes, and exercised agency. In the analysis, two interaction patterns emerged from the roles in how a student engaged or refrained from engaging in the collaborative work. Students’ engagement reveals aspects of how collaborative processes and mathematical reasoning co-exist with their agencies, through two ways of interacting: bi-directional interaction and one-directional interaction. Four student pairs illuminate how different roles in their collaboration are connected to shared agency or individual agency for merging knowledge together in shared understanding. In one-directional interactions, students engaged with different agencies as a primary agent, leading the conversation, making suggestions and explanations sometimes anchored in mathematical properties, or, as a secondary agent, listening and attempting to understand ideas are expressed by a peer. A secondary agent rarely reasoned mathematically. Both students attempted to collaborate, but rarely or never disagreed. The interactional pattern in bi-directional interactions highlights a mutual attempt to collaborate where both students were the driving forces of the problem-solving process. Students acted with similar roles where both were exercising a shared agency, building the final argument together by suggesting, accepting, listening, and negotiating mathematical properties. A critical variable for such a successful interaction was the collaborative process of repairing their shared understanding and reasoning anchored in mathematical properties of linear functions.

Breast cancer diagnosis using multiple activation deep neural network

2021 ◽  
pp. 1063293X2110251
Author(s):  
K Vijayakumar ◽  
Vinod J Kadam ◽  
Sudhir Kumar Sharma
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Neural Network ◽  
Breast Cancer Diagnosis ◽  
Activation Function ◽  
Linear Functions ◽  
Cancer Data ◽  
Final Layer ◽  
Improved Performance ◽  
Hidden Layer

Deep Neural Network (DNN) stands for multilayered Neural Network (NN) that is capable of progressively learn the more abstract and composite representations of the raw features of the input data received, with no need for any feature engineering. They are advanced NNs having repetitious hidden layers between the initial input and the final layer. The working principle of such a standard deep classifier is based on a hierarchy formed by the composition of linear functions and a defined nonlinear Activation Function (AF). It remains uncertain (not clear) how the DNN classifier can function so well. But it is clear from many studies that within DNN, the AF choice has a notable impact on the kinetics of training and the success of tasks. In the past few years, different AFs have been formulated. The choice of AF is still an area of active study. Hence, in this study, a novel deep Feed forward NN model with four AFs has been proposed for breast cancer classification: hidden layer 1: Swish, hidden layer, 2:-LeakyReLU, hidden layer 3: ReLU, and final output layer: naturally Sigmoidal. The purpose of the study is twofold. Firstly, this study is a step toward a more profound understanding of DNN with layer-wise different AFs. Secondly, research is also aimed to explore better DNN-based systems to build predictive models for breast cancer data with improved accuracy. Therefore, the benchmark UCI dataset WDBC was used for the validation of the framework and evaluated using a ten-fold CV method and various performance indicators. Multiple simulations and outcomes of the experimentations have shown that the proposed solution performs in a better way than the Sigmoid, ReLU, and LeakyReLU and Swish activation DNN in terms of different parameters. This analysis contributes to producing an expert and precise clinical dataset classification method for breast cancer. Furthermore, the model also achieved improved performance compared to many established state-of-the-art algorithms/models.

scholarly journals A framework for reducing the overhead of the quantum oracle for use with Grover’s algorithm with applications to cryptanalysis of SIKE

2020 ◽  
Vol 15 (1) ◽  
pp. 143-156
Author(s):  
Jean-François Biasse ◽  
Benjamin Pring
Keyword(s):  
Search Algorithm ◽  
Circuit Complexity ◽  
Quantum Circuit ◽  
Grover’S Algorithm ◽  
Search Problems ◽  
Trade Offs ◽  
Single Target ◽  
Grover's Algorithm ◽  
Quantum Oracle ◽  
The Cost

AbstractIn this paper we provide a framework for applying classical search and preprocessing to quantum oracles for use with Grover’s quantum search algorithm in order to lower the quantum circuit-complexity of Grover’s algorithm for single-target search problems. This has the effect (for certain problems) of reducing a portion of the polynomial overhead contributed by the implementation cost of quantum oracles and can be used to provide either strict improvements or advantageous trade-offs in circuit-complexity. Our results indicate that it is possible for quantum oracles for certain single-target preimage search problems to reduce the quantum circuit-size from $O\left(2^{n/2}\cdot mC\right)$ (where C originates from the cost of implementing the quantum oracle) to $O(2^{n/2} \cdot m\sqrt{C})$ without the use of quantum ram, whilst also slightly reducing the number of required qubits.This framework captures a previous optimisation of Grover’s algorithm using preprocessing [21] applied to cryptanalysis, providing new asymptotic analysis. We additionally provide insights and asymptotic improvements on recent cryptanalysis [16] of SIKE [14] via Grover’s algorithm, demonstrating that the speedup applies to this attack and impacting upon quantum security estimates [16] incorporated into the SIKE specification [14].

scholarly journals Drift Analysis and Evolutionary Algorithms Revisited

2018 ◽  
Vol 27 (4) ◽  
pp. 643-666 ◽  
Author(s):  
J. LENGLER ◽  
A. STEGER
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Random Search ◽  
Optimization Algorithms ◽  
Simple Algorithm ◽  
Linear Functions ◽  
Search Point ◽  
Drift Analysis ◽  
Fixed Constant ◽  
Greedy Optimization

One of the easiest randomized greedy optimization algorithms is the following evolutionary algorithm which aims at maximizing a function f: {0,1}n → ℝ. The algorithm starts with a random search point ξ ∈ {0,1}n, and in each round it flips each bit of ξ with probability c/n independently at random, where c > 0 is a fixed constant. The thus created offspring ξ' replaces ξ if and only if f(ξ') ≥ f(ξ). The analysis of the runtime of this simple algorithm for monotone and for linear functions turned out to be highly non-trivial. In this paper we review known results and provide new and self-contained proofs of partly stronger results.

scholarly journals Surrogate-Based Optimization of Horizontal Axis Hydrokinetic Turbine Rotor Blades

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 4045
Author(s):  
David Menéndez Arán ◽  
Ángel Menéndez
Keyword(s):  
Turbine Blade ◽  
Design Method ◽  
Optimization Method ◽  
Turbine Rotor ◽  
Computational Effort ◽  
Rotor Blades ◽  
Linear Functions ◽  
Cavitation Inception ◽  
Hydrokinetic Turbine ◽  
Blade Geometry

A design method was developed for automated, systematic design of hydrokinetic turbine rotor blades. The method coupled a Computational Fluid Dynamics (CFD) solver to estimate the power output of a given turbine with a surrogate-based constrained optimization method. This allowed the characterization of the design space while minimizing the number of analyzed blade geometries and the associated computational effort. An initial blade geometry developed using a lifting line optimization method was selected as the base geometry to generate a turbine blade family by multiplying a series of geometric parameters with corresponding linear functions. A performance database was constructed for the turbine blade family with the CFD solver and used to build the surrogate function. The linear functions were then incorporated into a constrained nonlinear optimization algorithm to solve for the blade geometry with the highest efficiency. A constraint on the minimum pressure on the blade could be set to prevent cavitation inception.

scholarly journals Circuit complexity in interacting QFTs and RG flows

2018 ◽  
Vol 2018 (10) ◽  
Author(s):  
Arpan Bhattacharyya ◽  
Arvind Shekar ◽  
Aninda Sinha
Keyword(s):  
Circuit Complexity

scholarly journals Cache-Aided General Linear Function Retrieval

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 25
Author(s):  
Kai Wan ◽  
Hua Sun ◽  
Mingyue Ji ◽  
Daniela Tuninetti ◽  
Giuseppe Caire
Keyword(s):  
Linear Function ◽  
Linear Functions ◽  
General Linear ◽  
Linear Combinations ◽  
Past Work ◽  
Single File ◽  
Caching Scheme ◽  
Retrieval Scheme ◽  
Data Points ◽  
Coded Caching

Coded Caching, proposed by Maddah-Ali and Niesen (MAN), has the potential to reduce network traffic by pre-storing content in the users’ local memories when the network is underutilized and transmitting coded multicast messages that simultaneously benefit many users at once during peak-hour times. This paper considers the linear function retrieval version of the original coded caching setting, where users are interested in retrieving a number of linear combinations of the data points stored at the server, as opposed to a single file. This extends the scope of the authors’ past work that only considered the class of linear functions that operate element-wise over the files. On observing that the existing cache-aided scalar linear function retrieval scheme does not work in the proposed setting, this paper designs a novel coded caching scheme that outperforms uncoded caching schemes that either use unicast transmissions or let each user recover all files in the library.

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