scholarly journals Style at the Scale of the Canon. A Stylometric Analysis of 100 Romanian Novels Published between 1920 and 1940

2020 ◽  
Vol 6 (2) ◽  
pp. 48-63 ◽  
Author(s):  
Emanuel Modoc ◽  
Daiana Gârdan
Keyword(s):  
Statistical Analysis ◽  
Computational Methods ◽  
Test Cases ◽  
Distant Reading ◽  
Pilot Experiment ◽  
Relevant Result ◽  
Computational Stylistics ◽  
Recent Advances

The present study proposes an experimental exploration of the Romanian novel written between 1920 and 1940 through the use of stylometry, a method of distant reading employed for the statistical analysis of style. Drawing from the most recent advances in the field of computational stylistics, we select a formal standpoint from which we seek to investigate the relation between the Romanian novelistic canon and minor, tertiary novels published in the same. In our test cases, we will attempt to establish some of the more promising aspects of stylometric analysis, as well as single out the experiments that yield no relevant result. Because of the relative novelty of the method, the purpose of our investigations is to offer a kind of pilot experiment that can illustrate the benefits of using computational methods on Romanian literary corpora.

EFFECT OF SURFACE TOPOGRAPHY ON SEISMIC MAPPING

Geophysics ◽  
1946 ◽  
Vol 11 (3) ◽  
pp. 362-372 ◽  
Author(s):  
M. B. Widess
Keyword(s):  
Statistical Analysis ◽  
Surface Topography ◽  
Rough Surface ◽  
Computational Methods ◽  
Surface Relief ◽  
Lateral Variations ◽  
Source Of Error ◽  
Gathering Data ◽  
Effect Of Surface ◽  
Approximate Magnitude

The presence of rough surface topography in a prospect frequently constitutes a source of error in seismic mapping and poses the question of what computational methods can be applied by which seismic maps may be freed of the effect of surface relief. Various aspects of the problem are described. The use of a plane datum‐horizon is generally adequate as a solution of the problem. For greater refinement, the structural map may be modified to account for the overburden effect, the approximate magnitude of which is considered. Further modification may be required when lateral variations in subweathering velocity occur. Statistical analysis for determining the degree of conformity between surface topography and mapped structure at depth is useful in gathering data on the influence of surface topography.

Recent Advances in the Machine Learning-Based Drug-Target Interaction Prediction

2019 ◽  
Vol 20 (3) ◽  
pp. 194-202 ◽  
Author(s):  
Wen Zhang ◽  
Weiran Lin ◽  
Ding Zhang ◽  
Siman Wang ◽  
Jingwen Shi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Drug Target ◽  
Prediction Models ◽  
Prediction Methods ◽  
Crucial Issue ◽  
Target Interaction ◽  
Interaction Prediction ◽  
Recent Advances ◽  
Drug Similarity

Background:The identification of drug-target interactions is a crucial issue in drug discovery. In recent years, researchers have made great efforts on the drug-target interaction predictions, and developed databases, software and computational methods.Results:In the paper, we review the recent advances in machine learning-based drug-target interaction prediction. First, we briefly introduce the datasets and data, and summarize features for drugs and targets which can be extracted from different data. Since drug-drug similarity and target-target similarity are important for many machine learning prediction models, we introduce how to calculate similarities based on data or features. Different machine learningbased drug-target interaction prediction methods can be proposed by using different features or information. Thus, we summarize, analyze and compare different machine learning-based prediction methods.Conclusion:This study provides the guide to the development of computational methods for the drug-target interaction prediction.

Computational Methods and Algorithms for Mass Spectrometry Based Differential Proteomics: Recent Advances, Perspectives and Open Problems

2012 ◽  
Vol 9 (3) ◽  
pp. 143-159
Author(s):  
Vasiliki Spyropoulou ◽  
Maria Anna Rapsomaniki ◽  
Konstantinos Theofilatos ◽  
Stergios Papadimitriou ◽  
Spiros Likothanassis ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Computational Methods ◽  
Differential Proteomics ◽  
Open Problems ◽  
Recent Advances

scholarly journals Computational Methods in Social Neuroscience: Recent Advances, New Tools, and Future Directions

2021 ◽  
Author(s):  
Carolyn Parkinson
Keyword(s):  
Computational Methods ◽  
Spatial Scales ◽  
Brain Regions ◽  
Neural Response ◽  
Response Patterns ◽  
Social Environments ◽  
Social Worlds ◽  
Recent Advances ◽  
Social Decisions ◽  
And Behavior

Abstract Recent years have seen a surge of exciting developments in the computational tools available to social neuroscientists. This paper highlights and synthesizes recent advances that have been enabled by the application of such tools, as well as methodological innovations likely to be of interest and utility to social neuroscientists, but that have been concentrated in other sub-fields. Papers in this special issue are emphasized, many of which contain instructive materials (e.g., tutorials, code) for researchers new to the highlighted methods. These include approaches for modeling social decisions, characterizing multivariate neural response patterns at varying spatial scales, using decoded neurofeedback to draw causal links between specific neural response patterns and psychological and behavioral phenomena, examining time-varying patterns of connectivity between brain regions, and characterizing the social networks in which social thought and behavior unfold in everyday life. By combining computational methods for characterizing participants’ rich social environments – at the levels of stimuli, paradigms, and the webs of social relationships that surround people – with those for capturing the psychological processes that undergird social behavior and the wealth of information contained in neuroimaging datasets, social neuroscientists can gain new insights into how people create, understand, and navigate their complex social worlds.

Advances in Assessment of Students' Intuitive Understanding of Physics through Gameplay Data

2013 ◽  
Vol 5 (4) ◽  
pp. 1-16 ◽  
Author(s):  
Mario M. Martinez-Garza ◽  
Douglas Clark ◽  
Brian Nelson
Keyword(s):  
Statistical Analysis ◽  
Learning Environment ◽  
Learning Outcomes ◽  
Computational Methods ◽  
Educational Game ◽  
Qualitative Assessment ◽  
Digital Game ◽  
Learning In Games ◽  
Assessment Of Learning ◽  
Computationally Intensive

In this paper, the authors present advances in analyzing gameplay data as evidence of learning outcomes using computational methods of statistical analysis. These analyses were performed on data gathered from the SURGE learning environment (Martinez-Garza, Clark, & Nelson, 2010). SURGE is a digital game designed to help students articulate their intuitive concepts of motion physics and organize them toward a more normative scientific understanding. Various recurring issues of assessment, which pervade assessment of learning in games more generally, prompted the authors to consider whether gameplay (actions of learners in the context of the game) can be analyzed to produce evidence of learning. The authors describe their approach to the analysis of game play in terms of qualitative assessment that the authors believe may lay the groundwork for the application of similar computationally-intensive techniques in other educational game contexts.

Sign in / Sign up

Export Citation Format

Share Document