Bit-Vector Representation of Dominance-Based Approximation Space

2011 ◽  
pp. 1-16 ◽  
Author(s):  
Chien-Chung Chan ◽  
Gwo-Hshiung Tzeng
Keyword(s):  
Vector Representation ◽  
Approximation Space ◽  
Bit Vector

A Decision Procedure for Bit-Vector Arithmetic

1998 ◽  
Author(s):  
Clark W. Barrett ◽  
David L. Dill ◽  
Jeremy R. Levitt
Keyword(s):  
Decision Procedure ◽  
Bit Vector

On How Bit-Vector Logic Can Help Verify LTL-based Specifications

2020 ◽  
pp. 1-1
Author(s):  
Mohammad Mehdi Pourhashem Kallehbasti ◽  
Matteo Giovanni Rossi ◽  
Luciano Baresi
Keyword(s):  
Bit Vector

Multi-modal actuation with the activation bit vector machine

2021 ◽  
Vol 66 ◽  
pp. 162-175
Author(s):  
H.R. Schmidtke
Keyword(s):  
Bit Vector

Scholar2vec: Vector Representation of Scholars for Lifetime Collaborator Prediction

2021 ◽  
Vol 15 (3) ◽  
pp. 1-19
Author(s):  
Wei Wang ◽  
Feng Xia ◽  
Jian Wu ◽  
Zhiguo Gong ◽  
Hanghang Tong ◽  
...  
Keyword(s):  
Scientific Collaboration ◽  
Early Stage ◽  
Collaboration Network ◽  
Vector Representation ◽  
Network Embedding ◽  
Machine Learning Methods ◽  
Academic Networks ◽  
Special Relationships ◽  
Real World Datasets ◽  
Vector Representations

While scientific collaboration is critical for a scholar, some collaborators can be more significant than others, e.g., lifetime collaborators. It has been shown that lifetime collaborators are more influential on a scholar’s academic performance. However, little research has been done on investigating predicting such special relationships in academic networks. To this end, we propose Scholar2vec, a novel neural network embedding for representing scholar profiles. First, our approach creates scholars’ research interest vector from textual information, such as demographics, research, and influence. After bridging research interests with a collaboration network, vector representations of scholars can be gained with graph learning. Meanwhile, since scholars are occupied with various attributes, we propose to incorporate four types of scholar attributes for learning scholar vectors. Finally, the early-stage similarity sequence based on Scholar2vec is used to predict lifetime collaborators with machine learning methods. Extensive experiments on two real-world datasets show that Scholar2vec outperforms state-of-the-art methods in lifetime collaborator prediction. Our work presents a new way to measure the similarity between two scholars by vector representation, which tackles the knowledge between network embedding and academic relationship mining.

Crystallochemical classifications of phyllosilicates based on the unified system of projection of chemical composition: I. The mica group

Clay Minerals ◽  
1990 ◽  
Vol 25 (1) ◽  
pp. 73-81 ◽  
Author(s):  
A. Wiewióra
Keyword(s):  
Chemical Composition ◽  
Divalent Cations ◽  
Layer Charge ◽  
Vector Representation ◽  
Similar Formula ◽  
Crystallochemical Formula ◽  
Trivalent Cations ◽  
Image Position ◽  
Composition I ◽  
Unit Vectors

AbstractA unified system of vector representation of chemical composition is proposed for the phyllosilicates based on projection of the composition, as given by crystallochemical formula, onto a field with orthogonal axes chosen for octahedral divalent cations, R2+, and Si (X, Y, respectively), and oblique axes for octahedral trivalent cations, R3+, and vacancies, □, (V, Z, respectively). Point coordinates for each set of axes were used to define the direction and length of the unit vectors for phyllosilicates belonging to different groups. Parallel to these fundamental directions the composition isolines were drawn in the projection fields. Applied to micas, this system enables control of the chemical composition by the general crystallochemical formula covering all varieties of Li-free dioctahedral and trioctahedral micas:where z (number of vacancies) = (y-x+ m)/2; m (layer charge) =1; u+y+z = 3. There is a similar formula for vacancy-free lithian micas:where w = m — x+y;m=1; u+y+w = 3, and for Li-free brittle micas:where z = (y — x+m)/2; m = 2; u+y+z = 3. Projection fields were used to classify micas.

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