scholarly journals Unsupervised Cluster Matching for Content Model

2020 ◽  
Vol 176 (20) ◽  
pp. 39-41
Author(s):  
E. Suchitha ◽  
N. Venkata ◽  
Prasanta Kumar
Keyword(s):  
Unsupervised Cluster ◽  
Content Model ◽  
Cluster Matching

Topic Models for Unsupervised Cluster Matching

2018 ◽  
Vol 30 (4) ◽  
pp. 786-795 ◽  
Author(s):  
Tomoharu Iwata ◽  
Tsutomu Hirao ◽  
Naonori Ueda
Keyword(s):  
Topic Models ◽  
Unsupervised Cluster ◽  
Cluster Matching

Robust unsupervised cluster matching for network data

2017 ◽  
Vol 31 (4) ◽  
pp. 1132-1154 ◽  
Author(s):  
Tomoharu Iwata ◽  
Katsuhiko Ishiguro
Keyword(s):  
Network Data ◽  
Unsupervised Cluster ◽  
Cluster Matching

Animals as Social Objects

2016 ◽  
Vol 21 (3) ◽  
pp. 206-217 ◽  
Author(s):  
Verónica Sevillano ◽  
Susan T. Fiske
Keyword(s):  
Social Perception ◽  
Animal Species ◽  
Social Groups ◽  
Social Responses ◽  
Content Model ◽  
Stereotype Content ◽  
Nonhuman Animals ◽  
Behavioral Tendencies ◽  
Similarities And Differences ◽  
Sphere Of Influence

Abstract. Nonhuman animals are typically excluded from the scope of social psychology. This article presents animals as social objects – targets of human social responses – overviewing the similarities and differences with human targets. The focus here is on perceiving animal species as social groups. Reflecting the two fundamental dimensions of humans’ social cognition – perceived warmth (benign or ill intent) and competence (high or low ability), proposed within the Stereotype Content Model ( Fiske, Cuddy, Glick, & Xu, 2002 ) – animal stereotypes are identified, together with associated prejudices and behavioral tendencies. In line with human intergroup threats, both realistic and symbolic threats associated with animals are reviewed. As a whole, animals appear to be social perception targets within the human sphere of influence and a valid topic for research.

Stereotypes of Social Groups in Germany in Terms of Warmth and Competence

2010 ◽  
Vol 41 (2) ◽  
pp. 76-81 ◽  
Author(s):  
Frank Asbrock
Keyword(s):  
Gay Men ◽  
Social Groups ◽  
Social Judgment ◽  
Cultural Stereotypes ◽  
Student Sample ◽  
Content Model ◽  
Stereotype Content ◽  
German Student ◽  
Stable Clusters ◽  
Relevant Social Groups

The stereotype content model says that warmth and competence are fundamental dimensions of social judgment. This brief report analyzes the cultural stereotypes of relevant social groups in a German student sample (N = 82). In support of the model, stereotypes of 29 societal groups led to five stable clusters of differing warmth and competence evaluations. As expected, clusters cover all four possible combinations of warmth and competence. The study also reports unique findings for the German context, for example, similarities between the perceptions of Turks and other foreigners. Moreover, it points to different stereotypes of lesbians and gay men.

Brightness In The Photopic Range: Psychophysical Modelling With Blue-sensitive Retinal Signals

2020 ◽  
pp. 9-24
Author(s):  
Peter Bodrogi ◽  
Xue Guo ◽  
Tran Quoc Khanh
Keyword(s):  
Ganglion Cells ◽  
Light Stimulus ◽  
Spectral Radiance ◽  
Psychophysical Experiment ◽  
Retinal Ganglion ◽  
Reasonable Accuracy ◽  
Brightness Perception ◽  
Content Model ◽  
The Difference ◽  
Cone Signal

The brightness perception of a large (41°) uniform visual field was investigated in a visual psychophysical experiment. Subjects assessed the brightness of 20 light source spectra of different chromaticities at two luminance levels, Lv=267.6 cd/m2 and Lv=24.8 cd/m2. The resulting mean subjective brightness scale values were modelled by a combination of the signals of retinal mechanisms: S-cones, rods, intrinsically photosensitive retinal ganglion cells (ipRGCs) and the difference of the L-cone signal and the M-cone signal. A new quantity, “relative spectral blue content”, was also considered for modelling. This quantity was defined as “the spectral radiance of the light stimulus integrated with the range (380–520) nm, relative to luminance”. The “relative spectral blue content” model could describe the subjective brightness perception of the observers with reasonable accuracy.

scholarly journals Symptom Cluster-Matching Antidepressant Treatment: A Case Series Pilot Study

2021 ◽  
Vol 14 (6) ◽  
pp. 526
Author(s):  
Sławomir Murawiec ◽  
Marek Krzystanek
Keyword(s):  
Depressive Symptoms ◽  
Pilot Study ◽  
Antidepressant Treatment ◽  
Case Series ◽  
Symptom Cluster ◽  
Study Results ◽  
Cluster Matching ◽  
The Individual ◽  
Meta Analyses ◽  
Effectiveness Studies

Despite treating depression with antidepressants, their effectiveness is often insufficient. Comparative effectiveness studies and meta-analyses show the effectiveness of antidepressants; however, they do not provide clear indications as to the choice of a specific antidepressant. The rational choice of antidepressants may be based on matching their mechanisms of action to the symptomatic profiles of depression, reflecting the heterogeneity of symptoms in different patients. The authors presented a series of cases of patients diagnosed with depression in whom at least one previous antidepressant treatment was shown to be ineffective before drug targeted symptom cluster-matching treatment (SCMT). The presented pilot study shows for the first time the effectiveness of SCMT in the different clusters of depressive symptoms. All the described patients obtained recovery from depressive symptoms after introducing drug-targeted SCMT. Once validated in clinical trials, SCMT might become an effective and rational method of selecting an antidepressant according to the individual profile of depressive symptoms, the mechanism of their formation, and the mechanism of drug action. Although the study results are preliminary, SCMT can be a way to personalize treatment, increasing the likelihood of improvement even in patients who meet criteria for treatment-resistant depression.

Unsupervised domain adaptation based on cluster matching and Fisher criterion for image classification

2021 ◽  
Vol 91 ◽  
pp. 107041
Author(s):  
Heyou Chang ◽  
Fanlong Zhang ◽  
Shuai Ma ◽  
Guangwei Gao ◽  
Hao Zheng ◽  
...  
Keyword(s):  
Image Classification ◽  
Domain Adaptation ◽  
Fisher Criterion ◽  
Unsupervised Domain Adaptation ◽  
Cluster Matching
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