scholarly journals Using Data-Compressors for Classification Hunting Behavioral Sequences in Rodents as “Ethological Texts”

Mathematics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 579
Author(s):  
Jan Levenets ◽  
Anna Novikovskaya ◽  
Sofia Panteleeva ◽  
Zhanna Reznikova ◽  
Boris Ryabko
Keyword(s):  
Small Mammals ◽  
Structural Characteristics ◽  
Pairwise Comparisons ◽  
Evolutionary Analysis ◽  
Hunting Behavior ◽  
Homogeneity Testing ◽  
Association Coefficients ◽  
Behavioral Sequences ◽  
Using Data

One of the main problems in comparative studying animal behavior is searching for an adequate mathematical method for evaluating the similarities and differences between behavioral patterns. This study aims to propose a new tool to evaluate ethological differences between species. We developed the new compression-based method for the homogeneity testing and classification to investigate hunting behavior of small mammals. A distinction of this approach is that it belongs to the framework of mathematical statistics and allows one to compare the structural characteristics of any texts in pairwise comparisons. To validate a new method, we compared the hunting behaviors of different species of small mammals as ethological “texts.” To do this, we coded behavioral elements with different letters. We then tested the hypothesis whether the behavioral sequences of different species as “texts” are generated either by a single source or by different ones. Based on association coefficients obtained from pairwise comparisons, we built a new classification of types of hunting behaviors, which brought a unique insight into how particular elements of hunting behavior in rodents changed and evolved. We suggest the compression-based method for homogeneity testing as a relevant tool for behavioral and evolutionary analysis.

Heteroscedastic Methods for Performing All Pairwise Comparisons of Regression Lines Associated With J Independent Groups

Methodology ◽  
2015 ◽  
Vol 11 (3) ◽  
pp. 110-115 ◽  
Author(s):  
Rand R. Wilcox ◽  
Jinxia Ma
Keyword(s):  
Least Squares ◽  
Pairwise Comparisons ◽  
Simple Extension ◽  
Type I ◽  
Type I Errors ◽  
Well Elderly ◽  
Using Data

Abstract. The paper compares methods that allow both within group and between group heteroscedasticity when performing all pairwise comparisons of the least squares lines associated with J independent groups. The methods are based on simple extension of results derived by Johansen (1980) and Welch (1938) in conjunction with the HC3 and HC4 estimators. The probability of one or more Type I errors is controlled using the improvement on the Bonferroni method derived by Hochberg (1988) . Results are illustrated using data from the Well Elderly 2 study, which motivated this paper.

scholarly journals UAV Image Multi-Labeling with Data-Efficient Transformers

2021 ◽  
Vol 11 (9) ◽  
pp. 3974
Author(s):  
Laila Bashmal ◽  
Yakoub Bazi ◽  
Mohamad Mahmoud Al Rahhal ◽  
Haikel Alhichri ◽  
Naif Al Ajlan
Keyword(s):  
Data Augmentation ◽  
Feature Representation ◽  
Aerial Image ◽  
Remote Sensing Images ◽  
Training Set ◽  
Proposed Model ◽  
Class Labels ◽  
Using Data ◽  
Uav Image

In this paper, we present an approach for the multi-label classification of remote sensing images based on data-efficient transformers. During the training phase, we generated a second view for each image from the training set using data augmentation. Then, both the image and its augmented version were reshaped into a sequence of flattened patches and then fed to the transformer encoder. The latter extracts a compact feature representation from each image with the help of a self-attention mechanism, which can handle the global dependencies between different regions of the high-resolution aerial image. On the top of the encoder, we mounted two classifiers, a token and a distiller classifier. During training, we minimized a global loss consisting of two terms, each corresponding to one of the two classifiers. In the test phase, we considered the average of the two classifiers as the final class labels. Experiments on two datasets acquired over the cities of Trento and Civezzano with a ground resolution of two-centimeter demonstrated the effectiveness of the proposed model.

Classification of hepatitis C virus into major types and subtypes based on molecular evolutionary analysis

1995 ◽  
Vol 36 (2-3) ◽  
pp. 201-214 ◽  
Author(s):  
Ken-ichi Ohba ◽  
Masashi Mizokami ◽  
Tomoyoshi Ohno ◽  
Kaoru Suzuki ◽  
Etsuro Orito ◽  
...  
Keyword(s):  
Hepatitis C Virus ◽  
Hepatitis C ◽  
Evolutionary Analysis ◽  
Molecular Evolutionary Analysis

scholarly journals Studying of Cultural Properties of Pyricularia oryzae Cav. Strains in the South of Russia

2021 ◽  
Vol 12 (1) ◽  
pp. 21-28
Author(s):  
Dmitry Nartymov ◽  
Evgeny Kharitonov ◽  
Elena Dubina ◽  
Sergey Garkusha ◽  
Margarita Ruban ◽  
...  
Keyword(s):  
Pyricularia Oryzae ◽  
Blast Disease ◽  
The South ◽  
Directional Growth ◽  
Rice Plants ◽  
Affected Plant ◽  
Cultural Traits ◽  
Plant Form ◽  
Using Data

This article presents the results of the development of a methodology for describing the main morphological and cultural traits of the Pyricularia oryzae Cav. strains widespread in the south of Russia. At the same time, the types of traits are identified and listed, which make it possible to unambiguously determine the uniqueness and variety of the pathogen. The relationships and patterns established using cluster and statistical analysis make it possible to identify the conditions for the development of a pathogen that determine its predominant forms. Thus, research shows that leaf forms of P. oryzae strains isolated from rice plants with leaf form of blast disease have an equally directional growth pattern of a colony with a felt structure, and strains isolated from neck-affected plant form often produce a zone of a colony with a clumpy structure. The classification of cultural traits will make it possible to obtain scientifically grounded and comparable data that can be used in the analysis of the interaction of P. oryzae strains with rice plants on various varieties and in various agro-technological conditions in order to improve and rationalize agricultural activities. The study opens up the possibility of using data in breeding, making it possible to identify forms of a pathogen that infect certain varieties.

DEVELOPMENT OF AN ALGORITHM FOR FINDING ANOMALIES IN WEATHER REPORTS TO IMPROVE THE QUALITY OF FORECASTS

2021 ◽  
pp. 45-52
Author(s):  
K. G. Yashchenkov ◽  
K. S. Dymko ◽  
N. O. Ukhanov ◽  
A. V. Khnykin
Keyword(s):  
Optimal Method ◽  
Implementation Processes ◽  
Advantages And Disadvantages ◽  
Message Exchange ◽  
Text Information ◽  
Preliminary Preparation ◽  
Using Data ◽  
The Moment

The issues of using data analysis methods to find and correct errors in the reports issued by meteorologists are considered. The features of processing various types of meteorological messages are studied. The advantages and disadvantages of existing methods of classification of text information are considered. The classification methods are compared in order to identify the optimal method that will be used in the developed algorithm for analyzing meteorological messages. The prospects of using each of the methods in the developed algorithm are described. An algorithm for processing the source data is proposed, which consists in using syntactic and logical analysis to preclean the data from various kinds of noise and determine format errors for each type of message. After preliminary preparation the classification method correlates the received set of message characteristics with the previously trained model to determine the error of the current weather report and output the corresponding message to the operator in real time. The software tools used in the algorithm development and implementation processes are described. A complete description of the process of processing a meteorological message is presented from the moment when the message is entered in a text editor until the message is sent to the international weather message exchange service. The developed software is demonstrated, in which the proposed algorithm is implemented, which allows to improve the quality of messages and, as a result, the quality of meteorological forecasts. The results of the implementation of the new algorithm are described by comparing the number of messages containing various types of errors before the implementation of the algorithm and after the implementation.

Rule based classification of neurodegenerative diseases using data driven gait features

2018 ◽  
Vol 9 (4) ◽  
pp. 547-560 ◽  
Author(s):  
Kartikay Gupta ◽  
Aayushi Khajuria ◽  
Niladri Chatterjee ◽  
Pradeep Joshi ◽  
Deepak Joshi
Keyword(s):  
Neurodegenerative Diseases ◽  
Data Driven ◽  
Rule Based ◽  
Gait Features ◽  
Using Data

scholarly journals Prima facie duties and the structure of ethical explanations

2019 ◽  
Vol 62 (3) ◽  
pp. 53-64
Author(s):  
Monika Jovanovic
Keyword(s):  
Structural Characteristics ◽  
Prima Facie ◽  
Moral Reasons ◽  
Ethical Theories ◽  
Problematic Case ◽  
Normative Implications

I begin with the thesis that the most appropriate classification of ethical theories pertains to their structural characteristics and give the advantage to the particularism/ generalism dichotomy over the deontological/teleological and act-centered/agent-centered classifications. Subsequently I use the example of Ross?s ethics of prima facie duties to illustrate how this distinction can be properly applied to a seemingly problematic case. In the first part of the paper I aim to show that Ross?s view is, in spite of its use of deontological terminology, essentially particularist. I then examine the specificities of Ross?s pluralism and explore the connection between prima facie duties and normative moral reasons. In the second part of the paper I criticize Audi?s interpretation of Ross?s ethics and show that Ross?s view doesn?t have the normative implications that Audi ascribes to it.

scholarly journals SIPEC: the deep-learning Swiss knife for behavioral data analysis

2020 ◽  
Author(s):  
Markus Marks ◽  
Jin Qiuhan ◽  
Oliver Sturman ◽  
Lukas von Ziegler ◽  
Sepp Kollmorgen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
Animal Behavior ◽  
Home Cage ◽  
Complex Environments ◽  
Or Groups ◽  
Multiple Behaviors ◽  
Freely Moving ◽  
Using Data

ABSTRACTAnalysing the behavior of individuals or groups of animals in complex environments is an important, yet difficult computer vision task. Here we present a novel deep learning architecture for classifying animal behavior and demonstrate how this end-to-end approach can significantly outperform pose estimation-based approaches, whilst requiring no intervention after minimal training. Our behavioral classifier is embedded in a first-of-its-kind pipeline (SIPEC) which performs segmentation, identification, pose-estimation and classification of behavior all automatically. SIPEC successfully recognizes multiple behaviors of freely moving mice as well as socially interacting nonhuman primates in 3D, using data only from simple mono-vision cameras in home-cage setups.

Sign in / Sign up

Export Citation Format

Share Document