• 제목/요약/키워드: Representation of the generalization

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COMBINATORIAL WEBS OF QUANTUM LIE SUPERALGEBRA sl(1|1)

  • Kim, Dong-Seok
    • East Asian mathematical journal
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    • 제25권4호
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    • pp.469-479
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    • 2009
  • Temperley-Lieb algebras had been generalized to web spaces for rank 2 simple Lie algebras which led us to link invariants for these Lie algebras as a generalization of Jones polynomial. Recently, Geer found a new generalization of Jones polynomial for some Lie superalgebras. In this paper, we study the quantum sl(1|1) representation theory using the web space and find a finite presentation of the representation category (for generic q) of the quantum sl(1|1).

Factorization Models and Other Representation of Independence

  • Lee, Yong-Goo
    • Journal of the Korean Statistical Society
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    • 제19권1호
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    • pp.45-53
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    • 1990
  • Factorization models are a generalization of hierarchical loglinear models which apply equally to discrete and continuous distributions. In regular (strictly positive) cases the intersection of two factorization models is another factorization model whose representation is obtained by a simple algorithm. Failure of this result in an irregular case is related to a theorem of Basu on ancillary statistics.

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MANY VALUED LOGIC AND INTUITIONISTIC FUZZY SETS: A STONE THEOREM GENERALIZATION

  • AMROUNE, ABDELAZIZ;DAVVAZ, BIJAN
    • 호남수학학술지
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    • 제37권3호
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    • pp.269-279
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    • 2015
  • Atanassov introduced another fuzzy object, called intu- itionistic fuzzy set as a generalization of the concept of fuzzy subset. The aim of this paper is the elaboration of a representation theory of involutive interval-valued Łukasiewicz-Moisil algebras by using the notion of intuitionistic fuzzy sets.

자기 정규화를 통한 도메인 불변 특징 학습 (Learning Domain Invariant Representation via Self-Rugularization)

  • 현재국;이찬용;김호성;유현정;고은진
    • 한국군사과학기술학회지
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    • 제24권4호
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    • pp.382-391
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    • 2021
  • Unsupervised domain adaptation often gives impressive solutions to handle domain shift of data. Most of current approaches assume that unlabeled target data to train is abundant. This assumption is not always true in practices. To tackle this issue, we propose a general solution to solve the domain gap minimization problem without any target data. Our method consists of two regularization steps. The first step is a pixel regularization by arbitrary style transfer. Recently, some methods bring style transfer algorithms to domain adaptation and domain generalization process. They use style transfer algorithms to remove texture bias in source domain data. We also use style transfer algorithms for removing texture bias, but our method depends on neither domain adaptation nor domain generalization paradigm. The second regularization step is a feature regularization by feature alignment. Adding a feature alignment loss term to the model loss, the model learns domain invariant representation more efficiently. We evaluate our regularization methods from several experiments both on small dataset and large dataset. From the experiments, we show that our model can learn domain invariant representation as much as unsupervised domain adaptation methods.

ALL GENERALIZED PETERSEN GRAPHS ARE UNIT-DISTANCE GRAPHS

  • Zitnik, Arjana;Horvat, Boris;Pisanski, Tomaz
    • 대한수학회지
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    • 제49권3호
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    • pp.475-491
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    • 2012
  • In 1950 a class of generalized Petersen graphs was introduced by Coxeter and around 1970 popularized by Frucht, Graver and Watkins. The family of $I$-graphs mentioned in 1988 by Bouwer et al. represents a slight further albeit important generalization of the renowned Petersen graph. We show that each $I$-graph $I(n,j,k)$ admits a unit-distance representation in the Euclidean plane. This implies that each generalized Petersen graph admits a unit-distance representation in the Euclidean plane. In particular, we show that every $I$-graph $I(n,j,k)$ has an isomorphic $I$-graph that admits a unit-distance representation in the Euclidean plane with a $n$-fold rotational symmetry, with the exception of the families $I(n,j,j)$ and $I(12m,m,5m)$, $m{\geq}1$. We also provide unit-distance representations for these graphs.

수치지형도를 이용한 연속지적도의 지도 일반화 기법 연구 (The Map Generalization Methodology for Korean Cadastral Map using Topographic Map)

  • 박우진;이재은;유기윤
    • Spatial Information Research
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    • 제19권1호
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    • pp.73-82
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    • 2011
  • 최근, 지적도 이용에 대한 요구사항이 공공기관과 민간부문에서 높아지고 있는 추세이다. 지적도를 웹 환경 또는 모바일 환경에서 활용하기 위해서는 원 지도자료를 임의 단계의 축척 별로 압축해 놓은 다축척 공간표현 데이터베이스로 구축되어야 할 필요가 있다. 본 연구에서는 기존의 연속지적도를 다축척 공간표현 데이터베이스로 구축하는데 있어서 지형도와의 중첩과 폴리곤 지도 일반화 기법을 적용하는 방안에 대해서 제안하였다. 이 과정은 크게 지형도 네트워크 데이터 재구조화, 네트워크 위계에 따른 필지경계선 병합, 선형 단순화 기법 적용의 세 단계로 이루어져 있다. 본 연구에서 제안된 일반화 기법을 수원지역의 연속지적도에 적용한 결과, 1:5,000, 1:20,000, 1:100,000 세 축척의 연속지적도로 각각 일반화 되었으며, 데이터 압축률은 각각 15%, 8%, 1% 수준으로 나타났다.

Text-independent Speaker Identification Using Soft Bag-of-Words Feature Representation

  • Jiang, Shuangshuang;Frigui, Hichem;Calhoun, Aaron W.
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제14권4호
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    • pp.240-248
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    • 2014
  • We present a robust speaker identification algorithm that uses novel features based on soft bag-of-word representation and a simple Naive Bayes classifier. The bag-of-words (BoW) based histogram feature descriptor is typically constructed by summarizing and identifying representative prototypes from low-level spectral features extracted from training data. In this paper, we define a generalization of the standard BoW. In particular, we define three types of BoW that are based on crisp voting, fuzzy memberships, and possibilistic memberships. We analyze our mapping with three common classifiers: Naive Bayes classifier (NB); K-nearest neighbor classifier (KNN); and support vector machines (SVM). The proposed algorithms are evaluated using large datasets that simulate medical crises. We show that the proposed soft bag-of-words feature representation approach achieves a significant improvement when compared to the state-of-art methods.

귀납적 일반화를 이용한 형태지식의 습득과 디자인에 관한 연구 (A Study on the Learning Shape Knowledge and Design with Inductive Generalization)

  • 차명열
    • 한국실내디자인학회논문집
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    • 제19권6호
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    • pp.20-29
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    • 2010
  • Art historians and critics have defined the style as common features appeared in a class of objects. Abstract common features from a set of objects have been used as a bench mark for date and location of original works. Commonalities in shapes are identified by relationships as well as physical properties from shape descriptions. This paper will focus on how the computer and human can recognize common shape properties from a class of shape objects to learn design knowledge. Shape representation using schema theory has been explored and possible inductive generalization from shape descriptions has been investigated. Also learned shape knowledge can be used. for new design process as design concept. Several design process such as parametric design, replacement design, analogy design etc. are used for these design processes. Works of Mario Botta and Louis Kahn are analyzed for explicitly clarifying the process from conceptual ideas to final designs. In this paper, theories of computer science, artificial intelligence, cognitive science and linguistics are employed as important bases.

A STUDY OF THE BILATERAL FORM OF THE MOCK THETA FUNCTIONS OF ORDER EIGHT

  • Srivastava, Bhaskar
    • 충청수학회지
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    • 제18권2호
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    • pp.117-129
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    • 2005
  • We give a generalization of bilateral mock theta functions of order eight and show that they are $F_q$-functions. We also give an integral representation for these functions. We give a relation between mock theta functions of the first set and bilateral mock theta functions of the second set.

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