scholarly journals A scalable method for the generation of small test sets

Author(s):  
S. Remersaro ◽  
J. Rajski ◽  
S.M. Reddy ◽  
I. Pomeranz
Keyword(s):  
2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Zheng Zhang ◽  
Guozhi Song ◽  
Jigang Wu

One of the critical issues for facial expression recognition is to eliminate the negative effect caused by variant poses and illuminations. In this paper a two-stage illumination estimation framework is proposed based on three-dimensional representative face and clustering, which can estimate illumination directions under a series of poses. First, 256 training 3D face models are adaptively categorized into a certain amount of facial structure types byk-means clustering to group people with similar facial appearance into clusters. Then the representative face of each cluster is generated to represent the facial appearance type of that cluster. Our training set is obtained by rotating all representative faces to a certain pose, illuminating them with a series of different illumination conditions, and then projecting them into two-dimensional images. Finally the saltire-over-cross feature is selected to train a group of SVM classifiers and satisfactory performance is achieved when estimating a number of test sets including images generated from 64 3D face models kept for testing, CAS-PEAL face database, CMU PIE database, and a small test set created by ourselves. Compared with other related works, our method is subject independent and has less computational complexityO(C×N)without 3D facial reconstruction.


Diagnostica ◽  
2019 ◽  
Vol 65 (4) ◽  
pp. 193-204
Author(s):  
Johannes Baltasar Hessler ◽  
David Brieber ◽  
Johanna Egle ◽  
Georg Mandler ◽  
Thomas Jahn

Zusammenfassung. Der Auditive Wortlisten Lerntest (AWLT) ist Teil des Test-Sets Kognitive Funktionen Demenz (CFD; Cognitive Functions Dementia) im Rahmen des Wiener Testsystems (WTS). Der AWLT wurde entlang neurolinguistischer Kriterien entwickelt, um Interaktionen zwischen dem kognitiven Status der Testpersonen und den linguistischen Eigenschaften der Lernliste zu reduzieren. Anhand einer nach Alter, Bildung und Geschlecht parallelisierten Stichprobe von gesunden Probandinnen und Probanden ( N = 44) und Patientinnen und Patienten mit Alzheimer Demenz ( N = 44) wurde mit ANOVAs für Messwiederholungen überprüft, inwieweit dieses Konstruktionsziel erreicht wurde. Weiter wurde die Fähigkeit der Hauptvariablen des AWLT untersucht, zwischen diesen Gruppen zu unterscheiden. Es traten Interaktionen mit geringer Effektstärke zwischen linguistischen Eigenschaften und der Diagnose auf. Die Hauptvariablen trennten mit großen Effektstärken Patientinnen und Patienten von Gesunden. Der AWLT scheint bei vergleichbarer differenzieller Validität linguistisch fairer als ähnliche Instrumente zu sein.


2018 ◽  
Vol 21 (5) ◽  
pp. 381-387 ◽  
Author(s):  
Hossein Atabati ◽  
Kobra Zarei ◽  
Hamid Reza Zare-Mehrjardi

Aim and Objective: Human dihydroorotate dehydrogenase (DHODH) catalyzes the fourth stage of the biosynthesis of pyrimidines in cells. Hence it is important to identify suitable inhibitors of DHODH to prevent virus replication. In this study, a quantitative structure-activity relationship was performed to predict the activity of one group of newly synthesized halogenated pyrimidine derivatives as inhibitors of DHODH. Materials and Methods: Molecular structures of halogenated pyrimidine derivatives were drawn in the HyperChem and then molecular descriptors were calculated by DRAGON software. Finally, the most effective descriptors for 32 halogenated pyrimidine derivatives were selected using bee algorithm. Results: The selected descriptors using bee algorithm were applied for modeling. The mean relative error and correlation coefficient were obtained as 2.86% and 0.9627, respectively, while these amounts for the leave one out−cross validation method were calculated as 4.18% and 0.9297, respectively. The external validation was also conducted using two training and test sets. The correlation coefficients for the training and test sets were obtained as 0.9596 and 0.9185, respectively. Conclusion: The results of modeling of present work showed that bee algorithm has good performance for variable selection in QSAR studies and its results were better than the constructed model with the selected descriptors using the genetic algorithm method.


1988 ◽  
Vol 3 (3) ◽  
pp. 159-169
Author(s):  
H.G. Pope ◽  
B.M. Cohen ◽  
J.F. Lipinski ◽  
D. Yurgelun-Todd

SummaryWe performed a blind family interview study of 226 first-degree relatives of 63 probands meeting DSM-III criteria for schizophrenia, schizoaffective disorder, and bipolar disorder, as diagnosed by the National Institute ot Mental Health Diagnostic Interview Schedule (DIS). A small test-retest reliability study demonstrated good agreement between the proband interviewer and the principal family interviewer for the major diagnostic categories of psychotic disorders. Excellent compliance was obtained, with 85% of living relatives interviewed personally.Three principal findings emerged front the study. First, as expected, bipolar disorder, as defined by DSM-III, displayed a strong familial comportent, comparable to that found by many studies using criteria other than those of DSM-III. Second, patients meeting DSM-III criteria for schizophrenia and schizoaffective disorder displayed a low familial prevalence of schizophrenia. Although initially suprising, this finding is in agreement with the results of several other recent lantily studies of schizophrenia. Upon comparing our results with those of other recent family studies of schizophrenia, it appears that the familial component in schizophrenia tnay be less than was estimated by earlier studies using older and “broader” definitions of schizophrenia.Third, we found that patients meeting DSM-III criteria for schizophrenia appeared genetically heterogeneous. Those who had displayed a superimposed full affective syndrome at some tinte in the course of their illness, together with those probands meeting DSM-III criteria for schizoaffective disorder, displayed a high familial prevalence of major affective disorder, similar to that found in the families of the bipolar probands. On the other hand, “pure” DSM-III schizophrenie probands, who had never experienced a superimposed full affective syndrome, displayed a low familial prevalence of major affective disorder, similar to that found in the general population. These findings favor the possibility that probands meeting DSM-III criteria for schizophrenia, but displaying a superimposed full affective syndrome, may in sonie cases have a disorder genetically relatcd to major affective disorder.Further prospective family interview studies, using DSM-III criteria and larger samples, will be necessary to test these preliminary impressions.


2020 ◽  
Vol 24 (6) ◽  
pp. 1311-1328
Author(s):  
Jozsef Suto

Nowadays there are hundreds of thousands known plant species on the Earth and many are still unknown yet. The process of plant classification can be performed using different ways but the most popular approach is based on plant leaf characteristics. Most types of plants have unique leaf characteristics such as shape, color, and texture. Since machine learning and vision considerably developed in the past decade, automatic plant species (or leaf) recognition has become possible. Recently, the automated leaf classification is a standalone research area inside machine learning and several shallow and deep methods were proposed to recognize leaf types. From 2007 to present days several research papers have been published in this topic. In older studies the classifier was a shallow method while in current works many researchers applied deep networks for classification. During the overview of plant leaf classification literature, we found an interesting deficiency (lack of hyper-parameter search) and a key difference between studies (different test sets). This work gives an overall review about the efficiency of shallow and deep methods under different test conditions. It can be a basis to further research.


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