scholarly journals Similarity structures among vowels

1974 ◽  
Vol 55 (S1) ◽  
pp. S54-S54
Author(s):  
Jared Bernstein
Keyword(s):  
Similarity Structures

Pairwise partitioning: A nonmetric algorithm for identifying feature-based similarity structures

Psychometrika ◽  
1997 ◽  
Vol 62 (1) ◽  
pp. 85-117 ◽  
Author(s):  
J. Wesley Hutchinson ◽  
Amitabh Mungale
Keyword(s):  
Feature Based ◽  
Similarity Structures

scholarly journals System alignment supports cross-domain learning and zero-shot generalisation

2021 ◽  
Author(s):  
Kaarina Aho ◽  
Brett Roads ◽  
Bradley C. Love
Keyword(s):  
Human Performance ◽  
Learning Task ◽  
Associate Learning ◽  
Cross Domain ◽  
Representation Systems ◽  
Spatial Systems ◽  
Spatial Dimensions ◽  
Alignment Mechanism ◽  
Conceptual Relationships ◽  
Similarity Structures

Recent findings suggest conceptual relationships hold across modalities. For instance, if two concepts occur in similar linguistic contexts, they also likely occur in similar visual contexts. These similarity structures may provide a valuable signal for system alignment when learning to map between domains, such as when learning the names of objects. To assess this possibility, we conducted a paired-associate learning experiment in which participants mapped objects that varied on two visual features to locations that varied along two spatial dimensions. We manipulated whether the featural and spatial systems were \textit{aligned} or \textit{misaligned}. Although system alignment was not required to complete this supervised learning task, we found that participants learned more efficiently when systems aligned and that aligned systems facilitated zero-shot generalisation. We fit a variety of models to individuals' responses and found that models which included an offline unsupervised alignment mechanism best accounted for human performance. Our results provide empirical evidence that people align entire representation systems to accelerate learning, even when learning seemingly arbitrary associations between two domains.

Similarity and Group Performance

2007 ◽  
Vol 70 (3) ◽  
pp. 262-271 ◽  
Author(s):  
Nicole H.W. Civettini
Keyword(s):  
Problem Solving ◽  
Group Performance ◽  
Balance Theory ◽  
Similar Group ◽  
Group Members ◽  
Similarity Structures ◽  
Degree Of Similarity ◽  
Problem Solving Task ◽  
Do So

This paper investigates the effects that different patterns of similarity among group members have on a group's performance on a problem-solving task. I discuss and test hypotheses on the effects of similarity on group performance derived from two literatures: balance theory and research on homophily. In an experiment I found that the relative balance of the pattern of similarity was more important in predicting how quickly groups establish norms of interaction and complete a task than how similar group members were to each other. Neither balance nor the degree of similarity had a significant effect on the quality of the groups' work. I conclude that groups with balanced similarity structures produce task solutions that approximate the quality of those from other groups, but they do so in significantly less time. That is, balanced groups are more efficient than unbalanced groups.

scholarly journals Adaptive Collaborative Similarity Learning for Unsupervised Multi-view Feature Selection

2018 ◽  
Author(s):  
Xiao Dong ◽  
Lei Zhu ◽  
Xuemeng Song ◽  
Jingjing Li ◽  
Zhiyong Cheng
Keyword(s):  
Feature Selection ◽  
Optimization Problem ◽  
Research Problem ◽  
Similarity Learning ◽  
Effective Solution ◽  
Unified Framework ◽  
Rank Constraint ◽  
Similarity Structure ◽  
Similarity Structures

In this paper, we investigate the research problem of unsupervised multi-view feature selection. Conventional solutions first simply combine multiple pre-constructed view-specific similarity structures into a collaborative similarity structure, and then perform the subsequent feature selection. These two processes are separate and independent. The collaborative similarity structure remains fixed during feature selection. Further, the simple undirected view combination may adversely reduce the reliability of the ultimate similarity structure for feature selection, as the view-specific similarity structures generally involve noises and outlying entries. To alleviate these problems, we propose an adaptive collaborative similarity learning (ACSL) for multi-view feature selection. We propose to dynamically learn the collaborative similarity structure, and further integrate it with the ultimate feature selection into a unified framework. Moreover, a reasonable rank constraint is devised to adaptively learn an ideal collaborative similarity structure with proper similarity combination weights and desirable neighbor assignment, both of which could positively facilitate the feature selection. An effective solution guaranteed with the proved convergence is derived to iteratively tackle the formulated optimization problem. Experiments demonstrate the superiority of the proposed approach.

Interesting Solvent Area in Crystal Structures of Two Natural Ergot Alkaloids

2005 ◽  
Vol 70 (1) ◽  
pp. 41-50 ◽  
Author(s):  
Blanka Klepetářová ◽  
Jan Čejka ◽  
Bohumil Kratochvíl ◽  
Svetlana Pakhomova ◽  
Ivana Císařová ◽  
...  
Keyword(s):  
Crystal Structures ◽  
Three Dimensional ◽  
Ergot Alkaloids ◽  
Unit Cell ◽  
Dimensional Structure ◽  
Chemical Similarity ◽  
Unit Cell Parameters ◽  
Cell Parameters ◽  
Benzene Solvate ◽  
Similarity Structures

The structures of ergotamine bis(benzene) solvate (1) and ergocristine bis(benzene) solvate (2) are reported. Both structures crystallise in theP212121space group with cell parameters:1,a= 14.2968(3) Å,b= 15.4700(2) Å,c= 17.8123(4) Å, andV= 3939.57(13) Å3;2,a= 11.8358(2) Å,b= 17.6469(3) Å,c= 19.7125(3) Å, andV= 4117.25(12) Å3. Unexpectedly, despite the chemical similarity, structures of1and2significantly differ not only in the unit cell parameters, but also in the packing. Whereas in1solvent cavities are separated, there is only one unusual continuous solvent area in2filled with benzene, forming independent three-dimensional structure.

Cultural, Gender, and Individual Differences in Perceptual and Semantic Structures of Basic Colors in Chinese and English

2002 ◽  
Vol 2 (1) ◽  
pp. 1-28 ◽  
Author(s):  
Carmella Moore ◽  
A. Kimball Romney ◽  
Ti-Lien Hsia
Keyword(s):  
Gender Differences ◽  
Individual Differences ◽  
Paired Comparison ◽  
Linguistic Relativity ◽  
Semantic Structure ◽  
Comparison Task ◽  
Percent Difference ◽  
Similarity Structures ◽  
Chinese And English

AbstractIn this paper we examine the judged similarity among the eight basic focal colors, and their names, among female and male Chinese (n = 68) and English (n = 52) speaking respondents. The major findings are: (1) all respondents share approximately sixty percent of their knowledge of the judged similarity structures of both semantic and perceptual tasks, (2) there are genuine individual differences among respondents that account for about fourteen percent of their knowledge on average, (3) there are small but statistically significant gender differences that come to about three percent on average, (4) there are small but statistically significant differences between Chinese and English respondents of about one-and-a-half percent, (5) there are differences in the semantic structure of the names of colors as compared to the judgments of the color samples that amounts to about five percent, and (6) there is about a three percent difference in the paired comparison task and the triads task. The results place strong constraints on theories relating to individual differences, linguistic relativity, and the relation of perceptual and semantic structures for colors.

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