Progress Toward Zero Entry Mining: Automation Enabling Safer, More Efficient Mining

2021 ◽  
pp. 2-9
Author(s):  
Peter Knights ◽  
Gavin Yeates
Keyword(s):  
Zero Entry

Technology of differentiated training bases of algorithmization and programming on the first course of the university on the basis of the DL.GSU.BY system of distance learning

2021 ◽  
pp. 60-66
Author(s):  
M. S. Dolinsky ◽  
M. A. Dolinskaya
Keyword(s):  
Programming Languages ◽  
Goal Setting ◽  
Practical Experience ◽  
Educational Process ◽  
Basic Language ◽  
Teaching Text ◽  
Zero Entry ◽  
Effectiveness Of Training ◽  
Differentiated Learning ◽  
The University

The article describes the technology of teaching text-based programming on the basis of the DL.GSU.BY website. The main advantages of the technology include: “zero entry threshold”, training adapted to the student, many years of practical experience, efficiency, scalability. The following issues are consistently considered in the article: idealized goal setting; the use by students of a programming language of their choice from a variety of modern programming languages when performing practical tasks in the discipline; a clear verification of goal achievement; blended learning; effective personalization of the educational process; non-standard organizational and technical decisions; effectiveness of training. The idealized goal setting includes the need to teach students the following: algorithmic reformulation of the problem statement; knowledge of a set of basic language constructs, as well as basic builtin procedures and functions; the ability to use basic algorithms on one-dimensional and two-dimensional arrays, sets of plane points, lines, queues; the ability to develop and debug new algorithms. Effective personalization of the educational process is provided with the help of such techniques: at each lesson, the student is offered a choice of activities that correspond to current level of his training; automatic verification of solutions is provided with the test assignment service; the system of automatic differentiated learning is used.

Criteria for a Hadamard Matrix to be Skew-Equivalent

1976 ◽  
Vol 28 (6) ◽  
pp. 1216-1223 ◽  
Author(s):  
Judith Q. Longyear
Keyword(s):  
Hadamard Matrix ◽  
Hadamard Matrices ◽  
Permutation Matrix ◽  
Type I ◽  
Signed Permutation ◽  
Zero Entry

A matrix H of order n = 4t with all entries from the set ﹛1, —1﹜ is Hadamard if HHt = 4tI. The set of Hadamard matrices is . A matrix is of type I or is skew-Hadamard if H = S — I where St = —S (some authors also use H = S + I). The set of type I members is . A matrix P is a signed permutation matrix if each row and each column has exactly one non-zero entry, and that entry is from the set ﹛1, —1﹜.

New design of no-zero-entry single symbol ML decodable STBCs based on group precoding

2011 ◽  
Vol 18 (4) ◽  
pp. 70-75
Author(s):  
Van-bien PHAM ◽  
Wei-xing SHENG ◽  
Xiao-feng MA ◽  
Hao WANG
Keyword(s):  
Zero Entry

A Zero-Entry Cyber Range Environment for Future Learning Ecosystems

2018 ◽  
pp. 93-109
Author(s):  
Elaine M. Raybourn ◽  
Michael Kunz ◽  
David Fritz ◽  
Vince Urias
Keyword(s):  
Zero Entry ◽  
Cyber Range ◽  
Future Learning

scholarly journals Analysis of hard-thresholding for distributed compressed sensing with one-bit measurements

2019 ◽  
Vol 9 (2) ◽  
pp. 455-471
Author(s):  
Johannes Maly ◽  
Lars Palzer
Keyword(s):  
Compressed Sensing ◽  
Signal Recovery ◽  
Hard Thresholding ◽  
Distributed Compressed Sensing ◽  
Numerical Evidence ◽  
Common Support ◽  
Zero Entry ◽  
Theoretical Considerations

Abstract A simple hard-thresholding operation is shown to be able to uniformly recover $L$ signals $\textbf{x}_1,...,\textbf{x}_L \in{\mathbb{R}}^n$ that share a common support of size $s$ from $m = \mathscr{O}(s)$ one-bit measurements per signal if $L \geqslant \ln (en/s)$. This result improves the single signal recovery bounds with $m = \mathscr{O}(s\ln (en/s))$ measurements in the sense that asymptotically fewer measurements per non-zero entry are needed. Numerical evidence supports the theoretical considerations.

scholarly journals The use of jet penetrators for movement in the lunar soil

2019 ◽  
Vol 11 (S) ◽  
pp. 221-230
Author(s):  
Vladimir A. ZAGOVORCHEV ◽  
Olga V. TUSHAVINA
Keyword(s):  
Penetration Depth ◽  
Lunar Soil ◽  
Sufficient Accuracy ◽  
The Moon ◽  
Depth Of Penetration ◽  
Soil Movement ◽  
Subsurface Layers ◽  
Zero Entry ◽  
Entry Speed ◽  
Static Resistance

The possibility of using penetrators for researching the subsurface layers of the moon is considered. Possible options for launching such penetrators are indicated, from the way the launch is carried out depends on the depth of penetration into the regolith. It was found that when the propulsion system has less traction than the static resistance of the lunar soil, movement does not occur if the launch of the penetrator is accomplished from the surface with zero entry speed. The dependences are given that permit calculating with sufficient accuracy the penetrator mass, penetration depth and the resulting overloads. The depth of penetration of the inertial penetrator depends on its mass-dimensional qualities and the speed of entry into the soil, which is limited by the level of permissible overloads. The use of a solid fuel engine on the penetrator facilitates increasing the allowable speed of the penetrator into the ground by reducing the overloads acting on it, and thereby increasing the penetration depth.

scholarly journals Penerapan Sparse Principal Component Analysis dalam Menghasilkan Matriks Loading yang Sparse

2018 ◽  
Vol 15 (2) ◽  
pp. 44
Author(s):  
Georgina M. Tinungki ◽  
Nurtiti Sunusi
Keyword(s):  
Principal Component Analysis ◽  
Sparse Matrix ◽  
Principal Component ◽  
Component Analysis ◽  
Sparse Principal Component Analysis ◽  
Sparse Pca ◽  
Variable Reduction ◽  
Zero Entry ◽  
Pca Method ◽  
New Variables

Abstract Sparse Principal Component Analysis (Sparse PCA) is one of the development of  PCA. Sparse PCA modifies new variables as a linier combination of  p old variables (original variable) which  is yielded by PCA method. Modifying new variables is conducted by producing a loading yang sparse matrix, such that old variable which is not effective (value of loading is zero) able be exit from PCA.  In this study, Sparse PCA method was applied on data of Indonesia Poverty population in 2015, that contains 13 variables and 34 observation with variable reduction such that yields 4 (four) new variables, which can explain 80.1% of total variance data. This study show, the loading matrix that has been yielded by using Sparse PCA method to become sparse with there exist 11 elements (loading value) zero entry of matrix, such that the model that has been produced to be simpler and easy to be interpreted. Keywords:  Principal Component Analysis, Sparse Principal Component Analysis, reduksi dimensi, matriks loading yang sparse Abstrak Sparse Principal Component Analysis (Sparse PCA) merupakan salah satu pengembangan dari metode PCA. Sparse PCA memodifikasi variabel-variabel baru yang merupakan kombinasi linear dari  variabel lama (variabel asli) yang dihasilkan oleh metode PCA. Pemodifikasian variabel baru ini dilakukan dengan dengan menghasilkan matriks loading yang sparse sehingga variabel lama yang tidak efektif (memiliki nilai loading sama dengan nol) dapat dikeluarkan dari model PCA. Pada penelitian ini, metode Sparse PCA diterapkan pada data Indikator Kemiskinan Penduduk Indonesia Tahun 2015 yang memuat 13 variabel dan 34 observasi dengan reduksi variabel menghasilkan 4 (empat) variabel baru yang telah mampu menjelaskan 80,1% dari total variansi data. Hasil penelitian menunjukkan, matriks loading yang dihasilkan menggunakan metode Sparse PCA menjadi sparse dengan terdapat 11 elemen (nilai loading) matriks bernilai nol sehingga model yang dihasilkan menjadi lebih sederhana dan mudah untuk diinterpretasikan. Kata Kunci: Principal Component Analysis, Sparse Principal Component Analysis, reduksi dimensi, matriks loading yang sparse

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