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. 

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.

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 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.

Affinity Propagation Clustering Algorithm Based on PCA

2014 ◽  
Vol 590 ◽  
pp. 688-692
Author(s):  
Bei Chen ◽  
Kun Song
Keyword(s):  
Clustering Algorithm ◽  
Dimensional Space ◽  
Original Data ◽  
New Method ◽  
Affinity Propagation ◽  
High Dimensional ◽  
Redundant Information ◽  
Affinity Propagation Clustering ◽  
Components Analysis ◽  
Low Dimensional

Overlap information usually exits in the high-dimensional data. Misclassified points may be more when affinity propagation clustering is applied to these data. Concerning this problem, a new method combining principal components analysis and affinity propagation clustering is proposed. In this method, dimensionality of the original data is reduced on the premise of reserving most information of the variables. Then, affinity propagation clustering is implemented in the low-dimensional space. Thus, because the redundant information is deleted, the classification is accurate. Experiment is done by using this new method, the results of the experiment explain that this method is effective.

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.

Visual Evoked-Response Correlates of Speechreading Performance in Normal-Hearing Adults

1983 ◽  
Vol 26 (1) ◽  
pp. 2-9 ◽  
Author(s):  
Vincent J. Samar ◽  
Donald G. Sims
Keyword(s):  
Multiple Correlation Coefficient ◽  
Evoked Response ◽  
Multiple Correlation ◽  
Potential Significance ◽  
Visual Evoked Response ◽  
Latency Measure ◽  
Two Measures ◽  
Components Analysis ◽  
The Relationship ◽  
Visual Evoked

The relationship between the latency of the negative peak occurring at approximately 130 msec in the visual evoked-response (VER) and speechreading scores was investigated. A significant product-moment correlation of -.58 was obtained between the two measures, which confirmed the fundamental effect but was significantly weaker than that previously reported in the literature (-.90). Principal components analysis of the visual evoked-response waveforms revealed a previously undiscovered early VER component, statistically independent of the latency measure, which in combination with two other components predicted speechreading with a multiple correlation coefficient of S4. The potential significance of this new component for the study of individual differences in speechreading ability is discussed.

scholarly journals A Generative-Discriminative Framework that Integrates Imaging, Genetic, and Diagnosis into Coupled Low Dimensional Space

NeuroImage ◽  
2021 ◽  
pp. 118200
Author(s):  
Sayan Ghosal ◽  
Qiang Chen ◽  
Giulio Pergola ◽  
Aaron L. Goldman ◽  
William Ulrich ◽  
...  
Keyword(s):  
Dimensional Space ◽  
Low Dimensional

The Relationship between Mobility and COVID-19 in Germany: Modeling Case Occurrence using Apple's Mobility Trends Data

2021 ◽  
Author(s):  
Mark David Walker ◽  
Mihály Sulyok
Keyword(s):  
Generalized Additive Models ◽  
Information Criterion ◽  
Additive Models ◽  
Online Data ◽  
German Government ◽  
Explanatory Variables ◽  
Mobility Data ◽  
Community Mobility ◽  
The Relationship ◽  
Potential Use

Abstract Background Restrictions on social interaction and movement were implemented by the German government in March 2020 to reduce the transmission of coronavirus disease 2019 (COVID-19). Apple's “Mobility Trends” (AMT) data details levels of community mobility; it is a novel resource of potential use to epidemiologists. Objective The aim of the study is to use AMT data to examine the relationship between mobility and COVID-19 case occurrence for Germany. Is a change in mobility apparent following COVID-19 and the implementation of social restrictions? Is there a relationship between mobility and COVID-19 occurrence in Germany? Methods AMT data illustrates mobility levels throughout the epidemic, allowing the relationship between mobility and disease to be examined. Generalized additive models (GAMs) were established for Germany, with mobility categories, and date, as explanatory variables, and case numbers as response. Results Clear reductions in mobility occurred following the implementation of movement restrictions. There was a negative correlation between mobility and confirmed case numbers. GAM using all three categories of mobility data accounted for case occurrence as well and was favorable (AIC or Akaike Information Criterion: 2504) to models using categories separately (AIC with “driving,” 2511. “transit,” 2513. “walking,” 2508). Conclusion These results suggest an association between mobility and case occurrence. Further examination of the relationship between movement restrictions and COVID-19 transmission may be pertinent. The study shows how new sources of online data can be used to investigate problems in epidemiology.

scholarly journals A Model of Stress Change under the First COVID-19 Pandemic among the General Public in Japanese Major Cities and Rural Areas

2021 ◽  
Vol 13 (3) ◽  
pp. 1207
Author(s):  
Misato Uehara ◽  
Makoto Fujii ◽  
Kazuki Kobayashi
Keyword(s):  
Rural Areas ◽  
Healthcare Workers ◽  
Explanatory Variable ◽  
Large City ◽  
Stress Change ◽  
Multiple Factor Analysis ◽  
Explanatory Variables ◽  
Private Research ◽  
The Difference ◽  
The Relationship

Research on stress related to the COVID-19 pandemic has been dominated by the cases of healthcare workers, students, patients, and their stress during the COVID-19 pandemic. This study examined the relationship between the amount of stress change under the COVID-19 pandemic and demographic factors (age, sex, occupation, etc.) in residents of a large city and a rural area of Japan. A total of 1331 valid responses were received in June 2020 from residents of Tokyo, Osaka, and Nagano registered with a private research firm. We were able to identify 15 statistically significant variables out of 36 explanatory variables, which explained the significant increase in stress compared to the pre-pandemic period. Multiple-factor analysis showed that the relationship with people is a more significant explanatory variable for the level of increase in stress than the difference in environment between big cities (Tokyo, Osaka) and rural areas (Nagano), the type of housing, and the decrease in income compared to the pre-pandemic period.

scholarly journals Potential mechanisms involving the immobilization of Cd, As and Cr during swine manure composting

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hao-Nan Guo ◽  
Li-Xia Wang ◽  
Hong-Tao Liu
Keyword(s):  
Cation Exchange Capacity ◽  
Physicochemical Parameters ◽  
Redundancy Analysis ◽  
Critical Role ◽  
Swine Manure ◽  
Exchange Capacity ◽  
Bulking Agent ◽  
Initial Carbon ◽  
Acid Ratio ◽  
The Relationship

Abstract This study aims to investigate the relationship between key physicochemical parameters related to composting process and bioavailability of Cd, As and Cr during swine manure composting through regulating different initial carbon to nitrogen (C/N) ratios (15:1, 20:1, 25:1) and bulking agent types (straw, green waste). Results showed that higher initial C/N ratio of 20:1 or 25:1 and straw as bulking agent were optimal to reduce the bioavailability of Cd, As and Cr (62.4%, 20.6% and 32.2% reduction, respectively). Redundancy analysis implied that the bioavailability of Cd was significantly associated with total phosphorus and total nitrogen, deducing the formation of phosphate precipitation and biosorption might participated in the reaction process, while that of As and Cr were mainly influenced by organic matter (OM), cation exchange capacity (CEC) and OM, CEC, electric conductivity, respectively. A total of 48.5%, 64.6% and 62.2% of Cd, As and Cr redistribution information could be explained by the above parameters. Further correlation analysis revealed that bioavailable As and Cr were negatively correlated with humic acid to fulvic acid ratio. In summary, this study confirms that the mechanisms of phosphate precipitation, biosorption and humification played critical role in reducing Cd, As and Cr bioavailability during swine manure composting.

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