scholarly journals Meaning and structural dynamics in poetry: a computational perspective

2022 ◽  
Author(s):  
Alex Gomez-Marin
Keyword(s):  
Computational Linguistics ◽  
Dimensional Space ◽  
Mathematical Structure ◽  
Connectivity Matrix ◽  
Future Studies ◽  
The Past ◽  
Large Correlation ◽  
Low Dimensional ◽  
Main Ideas ◽  
The Relationship

This work addresses Sri Aurobindo’s mantric poem, Savitri, with a computational linguistics approach. This is one of the longest poems ever written in English. We build the connectivity matrix between all main word pairs and analyse its structure. Concepts emerge as directions that better explain the variance of the data in the hyperspace of words. When projected to the low dimensional space of concepts, the vector of attention as the reader moves through the text shows a large correlation across sections of the poem, thus acting the future and the past over again. These findings suggest that the mathematical structure of Savitri is and reflects a substrate for the author’s main ideas, facilitating the reader’s understanding of the poem’s meaning via its long-range dynamical correlations. Acknowledging an irreducible essence to poetry, future studies on the relationship between words and sounds, and sounds and ideas may provide invaluable hints of the origin of language and its intimate relationship with the evolution of human consciousness.

scholarly journals How relevant is linguistics to computational linguistics?

2011 ◽  
Vol 6 ◽  
Author(s):  
Mark Johnson
Keyword(s):  
Machine Learning ◽  
Computational Linguistics ◽  
Linguistic Theory ◽  
Statistical Techniques ◽  
Engineering Applications ◽  
The Past ◽  
Statistical Parsing ◽  
Types Of Information ◽  
The Relationship ◽  
Scientific Fields

I start by explaining what I take computational linguistics to be, and discuss the relationship between its scientific side and its engineering applications. Statistical techniques have revolutionised many scientific fields in the past two decades, including computational linguistics. I describe the evolution of my own research in statistical parsing and how that lead me away from focusing on the details of any specific linguistic theory, and to concentrate instead on discovering which types of information (i.e., features) are important for specific linguistic processes, rather than on the details of exactly how this information should be formalised. I end by describing some of the ways that ideas from computational linguistics, statistics and machine learning may have an impact on linguistics in the future.

scholarly journals Fructosamine and Hemoglobin A1c Correlations in HIV-Infected Adults in Routine Clinical Care: Impact of Anemia and Albumin Levels

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Luisa Duran ◽  
Carla Rodriguez ◽  
Dan Drozd ◽  
Robin M. Nance ◽  
J. A. Chris Delaney ◽  
...  
Keyword(s):  
Hemoglobin A1c ◽  
Diabetes Management ◽  
Clinical Care ◽  
Future Studies ◽  
Routine Clinical Care ◽  
Large Correlation ◽  
Persons Living With Hiv ◽  
Living With Hiv ◽  
The Relationship ◽  
Hba1c Levels

Fructosamine is an alternative method to hemoglobin A1c (HbA1c) for determining average glycemia. However, its use has not been extensively evaluated in persons living with HIV (PLWH). We examined the relationship between HbA1c and fructosamine values, specifically focusing on anemia (which can affect HbA1c) and albumin as a marker of liver disease. We included 345 PLWH from two sites. We examined Spearman rank correlations between fructosamine and HbA1c and performed linear test for trends to compare fructosamine and HbA1c correlations by hemoglobin and albumin quartiles. We examined discrepant individuals with values elevated only on one test. We found a correlation of 0.70 between fructosamine and HbA1c levels. Trend tests for correlations between fructosamine and HbA1c were significant for both albumin (p=0.05) and hemoglobin (p=0.01) with the lowest correlations in the lowest hemoglobin quartile. We identified participants with unremarkable HbA1c values but elevated fructosamine values. These discrepant individuals had lower mean hemoglobin levels than those elevated by both tests. We demonstrated a large correlation between HbA1c and fructosamine across a range of hemoglobin and albumin levels. There were discrepant cases particularly among those with lower hemoglobin levels. Future studies are needed to clarify the use of fructosamine for diabetes management in PWLH.

scholarly journals Binary Linear Compression for Multi-label Classification

2017 ◽  
Author(s):  
Wen-Ji Zhou ◽  
Yang Yu ◽  
Min-Ling Zhang
Keyword(s):  
Dimensional Space ◽  
Classification Problem ◽  
Complex Model ◽  
Linear Mapping ◽  
Regression Problem ◽  
Low Dimensional ◽  
The Relationship ◽  
Lower Dimensional Space ◽  
Lower Dimensional ◽  
Linear Compression

In multi-label classification tasks, labels are commonly related with each other. It has been well recognized that utilizing label relationship is essential to multi-label learning. One way to utilizing label relationship is to map labels to a lower-dimensional space of uncorrelated labels, where the relationship could be encoded in the mapping. Previous linear mapping methods commonly result in regression subproblems in the lower-dimensional label space. In this paper, we disclose that mappings to a low-dimensional multi-label regression problem can be worse than mapping to a classification problem, since regression requires more complex model than classification. We then propose the binary linear compression (BILC) method that results in a binary label space, leading to classification subproblems. Experiments on several multi-label datasets show that, employing classification in the embedded space results in much simpler models than regression, leading to smaller structure risk. The proposed methods are also shown to be superior to some state-of-the-art approaches.

scholarly journals The Spatial Numerical Association of Response Codes effect and math skills: why related?

2019 ◽  
Author(s):  
Krzysztof Cipora ◽  
Yunfeng He ◽  
Hans-Christoph Nuerk
Keyword(s):  
Number Processing ◽  
Fundamental Question ◽  
Snarc Effect ◽  
Math Skills ◽  
Model Framework ◽  
Future Studies ◽  
The Past ◽  
Elementary Number ◽  
The Relationship

Evidence from multiple studies conducted in the past few decades converges on the conclusion that numerical properties can be associated with specific directions in space. Such Spatial-Numerical Associations (SNAs), as a signature of elementary number processing, seem to be a likely correlate of math skills. Nevertheless, almost three decades of research on the Spatial Numerical Association of Response Codes (SNARC) effect, the hallmark of SNAs, has not provided conclusive results on whether there is a relation with math skills. Here, going beyond reviewing the existing literature on the topic, we try to answer a more fundamental question about WHY the SNARC effect should (and should not) be related to math skills. We propose a multi-route model framework for a SNARC-math skills relationship. We conclude that the relationship is not straightforward and that several other factors should be considered, which under certain circumstances or in certain groups, can cause effects of opposite directions. The model can account for conflicting results, and thus may be helpful for deriving predictions in future studies.

Deep context interaction network based on attention mechanism for click-through rate prediction

2021 ◽  
pp. 1-16
Author(s):  
Ling Yuan ◽  
Zhuwen Pan ◽  
Ping Sun ◽  
Yinzhen Wei ◽  
Haiping Yu
Keyword(s):  
Online Advertising ◽  
Dimensional Space ◽  
Interaction Network ◽  
High Order ◽  
Attention Mechanism ◽  
Feature Interaction ◽  
Feature Interactions ◽  
Low Dimensional ◽  
Click Through Rate ◽  
The Relationship

Click-through rate (CTR) prediction, which aims to predict the probability of a user clicking on an ad, is a critical task in online advertising systems. The problem is very challenging since(1) an effective prediction relies on high-order combinatorial features, and(2)the relationship to auxiliary ads that may impact the CTR. In this paper, we propose Deep Context Interaction Network on Attention Mechanism(DCIN-Attention) to process feature interaction and context at the same time. The context includes other ads in the current search page, historically clicked and unclicked ads of the user. Specifically, we use the attention mechanism to learn the interactions between the target ad and each type of auxiliary ad. The residual network is used to model the feature interactions in the low-dimensional space, and with the multi-head self-attention neural network, high-order feature interactions can be modeled. Experimental results on Avito dataset show that DCIN outperform several existing methods for CTR prediction.

scholarly journals Word Play

2007 ◽  
Vol 33 (4) ◽  
pp. 443-467
Author(s):  
Lauri Karttunen
Keyword(s):  
Computational Linguistics ◽  
Final Section ◽  
Question Answering ◽  
Word Play ◽  
The Past ◽  
Finite State Transducers ◽  
Finite State ◽  
History Of ◽  
Commercial Applications ◽  
The Relationship

This article is a perspective on some important developments in semantics and in computational linguistics over the past forty years. It reviews two lines of research that lie at opposite ends of the field: semantics and morphology. The semantic part deals with issues from the 1970s such as discourse referents, implicative verbs, presuppositions, and questions. The second part presents a brief history of the application of finite-state transducers to linguistic analysis starting with the advent of two-level morphology in the early 1980s and culminating in successful commercial applications in the 1990s. It offers some commentary on the relationship, or the lack thereof, between computational and paper-and-pencil linguistics. The final section returns to the semantic issues and their application to currently popular tasks such as textual inference and question answering.

scholarly journals The impact of ischemic stroke on connectivity gradients

2018 ◽  
Author(s):  
Şeyma Bayrak ◽  
Ahmed A. Khalil ◽  
Kersten Villringer ◽  
Jochen B. Fiebach ◽  
Arno Villringer ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Three Dimensional ◽  
Connectivity Matrix ◽  
Continuous Space ◽  
Functional Deficits ◽  
Stroke Research ◽  
Low Dimensional ◽  
The Impact ◽  
The Relationship ◽  
The Brain

AbstractUnderstanding the relationship between localized anatomical damage, reorganization, and functional deficits is a major challenge in stroke research. Previous work has shown that localized lesions cause widespread functional connectivity alterations in structurally intact areas, thereby affecting a whole network of interconnected regions. Recent advances suggest an alternative to discrete functional networks by describing a connectivity space based on a low-dimensional embedding of the full connectivity matrix. The dimensions of this space, described as connectivity gradients, capture the similarity of areas’ connections along a continuous space. Here, we defined a three-dimensional connectivity space template based on functional connectivity data from healthy controls. By projecting lesion locations into this space, we demonstrate that ischemic strokes resulted in dimension-specific alterations in functional connectivity over the first week after symptoms onset. Specifically, changes in functional connectivity were captured along connectivity Gradients 1 and 3. The degree of change in functional connectivity was determined by the distance from the lesion along these connectivity gradients regardless of the anatomical distance from the lesion. Together, these results provide a novel framework to study reorganization after stroke and suggest that, rather than only impacting on anatomically proximate areas, the indirect effects of ischemic strokes spread along the brain relative to the space defined by its connectivity.

scholarly journals Microplastics in Terrestrial Ecosystems: A Scientometric Analysis

2020 ◽  
Vol 12 (20) ◽  
pp. 8739
Author(s):  
Donghui He ◽  
Keith Bristow ◽  
Vilim Filipović ◽  
Jialong Lv ◽  
Hailong He
Keyword(s):  
Web Of Science ◽  
Terrestrial Ecosystems ◽  
Economic Effects ◽  
Current Status ◽  
Scientometric Analysis ◽  
Abundance Distribution ◽  
Future Studies ◽  
The Past ◽  
The Relationship ◽  
Total Environment

Microplastics, as an emerging contaminant, have been shown to threaten the sustainability of ecosystems, and there is also concern about human exposure, as microplastic particles tend to bioaccumulate and biomagnify through the food chain. While microplastics in marine environments have been extensively studied, research on microplastics in terrestrial ecosystems is just starting to gain momentum. In this paper, we used scientometric analysis to understand the current status of microplastic research in terrestrial systems. The global scientific literature on microplastics in terrestrial ecosystems, based on data from the Web of Science between 1986 and 2020, was explored with the VOSviewer scientometric software. Co-occurrence visualization maps and citation analysis were used to identify the relationship among keywords, authors, organizations, countries, and journals focusing on the issues of terrestrial microplastics. The results show that research on microplastics in terrestrial systems just started in the past few years but is increasing rapidly. Science of the Total Environment ranks first among the journals publishing papers on terrestrial microplastics. In addition, we also highlighted the desire to establish standards/protocols for extracting and quantifying microplastics in soils. Future studies are recommended to fill the knowledge gaps on the abundance, distribution, ecological and economic effects, and toxicity of microplastics.

scholarly journals Same or different? A multi-method review on the relationships between processes underlying executive control

2020 ◽  
Author(s):  
Monica N. Toba ◽  
Tal Seidel Malkinson ◽  
Henrietta Howells ◽  
Melissa Ann Mackie ◽  
Alfredo Spagna
Keyword(s):  
Working Memory ◽  
Executive Control ◽  
Dimensional Space ◽  
Reciprocal Interactions ◽  
Behavioral Expression ◽  
Lesion Studies ◽  
Multi Method Approach ◽  
Low Dimensional ◽  
The Relationship ◽  
Method Approach

Attention, working memory, and executive control are commonly considered distinct cognitive functions with important reciprocal interactions. Lesion studies pioneered by Donald Stuss have demonstrated both overlap and dissociation in their behavioral expression and anatomical underpinnings. Here, we provide an overview of cognitive models as well as recent data from lesion studies and both invasive and noninvasive multimodal neuroimaging and brain stimulation, in order to provide an updated perspective on the relationship between attention, working memory, and executive control. Specifically, we address the functional and anatomical correspondence between these processes, toward the goal of identifying whether a lower dimensional theoretical framework should be employed to understand executive control (Karolis et al., 2019). We conclude by emphasizing that one avenue for moving the field, pioneered by Donald Stuss, forward consists of studying this low-dimensional space with a multi-method approach to identify converging evidence regarding the interaction between subfunctions, allowing to construct a model of executive control as the emergent consequence of efficient implementation of these processes.

scholarly journals On the Use of Redundancy Analysis to Study the Property Crime in Poland

2018 ◽  
Vol 6 (332) ◽  
pp. 99-109 ◽  
Author(s):  
Małgorzata Misztal
Keyword(s):  
Redundancy Analysis ◽  
Dimensional Space ◽  
Property Crime ◽  
Explanatory Variables ◽  
Ordination Techniques ◽  
Exploratory Data ◽  
Components Analysis ◽  
Low Dimensional ◽  
The Relationship ◽  
Response Variables

Redundancy analysis (RDA) is a canonical form of principal components analysis (PCA) and is one of, so‑called, linear ordination techniques. The goal of ordination is to represent objects and response variables relationships as faithfully as possible in a low‑dimensional space. Redundancy analysis is also a technique of exploratory data analysis. Graphical presentation of the results using the ordination biplots or triplots can facilitate the analysis of the relationship between the variation in the set of the response variables and the variation of the explanatory variables. In the paper, redundancy analysis was applied to assess the relationships between the selected socio‑economic factors and the intensity of the crime against property in Poland. 

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