Pattern Recognition of Subconscious Underpinnings of Cognition using Ultrametric Topological Mapping of Thinking and Memory

2014 ◽  
Vol 8 (4) ◽  
pp. 1-16 ◽  
Author(s):  
Fionn Murtagh
Keyword(s):  
Case Studies ◽  
Theory And Practice ◽  
Large Set ◽  
Thought Processes ◽  
Unconscious Thought ◽  
Text Data ◽  
Data Set ◽  
Ultrametric Topology ◽  
Textual Data

The author reviews the theory and practice of determining what parts of a data set are ultrametric. He describes the potential relevance of ultrametric topology as a framework for unconscious thought processes. This view of ultrametric topology as a framework that complements metric-based, conscious, Aristotelian logical reasoning comes from the work of the Chilean psychoanalyst, Ignacio Matte Blanco. Taking text data, the author develops an algorithm for finding local ultrametricity in such data. He applies that in two case studies. The first relates to a large set of dream reports, and therefore can possibly recall traces of unconscious thought processes. The second case study uses Twitter social media, and has the aim of picking up underlying associations. The author's case studies are selective in regard to names of people and objects, and are focused in order to highlight the principle of his approach, which is one of particular pattern finding in textual data.

scholarly journals Using Model Performance to Assess the Representativeness of Data for Model Development and Calibration in Financial Institutions

Risks ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 204
Author(s):  
Chamay Kruger ◽  
Willem Daniel Schutte ◽  
Tanja Verster
Keyword(s):  
Case Studies ◽  
Model Development ◽  
Model Performance ◽  
Data Set ◽  
Pooled Data ◽  
External Data ◽  
First Case ◽  
Multivariate Prediction ◽  
Credit Data

This paper proposes a methodology that utilises model performance as a metric to assess the representativeness of external or pooled data when it is used by banks in regulatory model development and calibration. There is currently no formal methodology to assess representativeness. The paper provides a review of existing regulatory literature on the requirements of assessing representativeness and emphasises that both qualitative and quantitative aspects need to be considered. We present a novel methodology and apply it to two case studies. We compared our methodology with the Multivariate Prediction Accuracy Index. The first case study investigates whether a pooled data source from Global Credit Data (GCD) is representative when considering the enrichment of internal data with pooled data in the development of a regulatory loss given default (LGD) model. The second case study differs from the first by illustrating which other countries in the pooled data set could be representative when enriching internal data during the development of a LGD model. Using these case studies as examples, our proposed methodology provides users with a generalised framework to identify subsets of the external data that are representative of their Country’s or bank’s data, making the results general and universally applicable.

Innovating the customer loyalty program with social media

2019 ◽  
Vol 32 (5) ◽  
pp. 807-823 ◽  
Author(s):  
Wu He ◽  
Xin Tian ◽  
Feng-Kwei Wang
Keyword(s):  
Social Media ◽  
Customer Loyalty ◽  
Theory And Practice ◽  
Loyalty Programs ◽  
Loyalty Program ◽  
Data Set ◽  
Content Type ◽  
Media Technologies ◽  
Customer Loyalty Programs

Purpose Few academic studies specifically investigate how businesses can use social media to innovate customer loyalty programs. The purpose of this paper is to present an in-depth case study of the Shop Your Way (SYW) program, which is regarded as one of the most successful customer loyalty programs with social media. Design/methodology/approach This paper uses case study research as the methodology to uncover innovative features associated with the SYW customer loyalty program. The authors collected the data from SYW’s social media forums and tweets. The data set was analyzed using social media analytics tools including the R package and Lexicon. Findings Based on the research results, the authors summarize innovative social media features identified from SYW. The authors also provide insights and recommendations for businesses that are seeking to innovate their customer loyalty programs using social media technologies. Originality/value The results of this case study set a good example for businesses which want to innovate and improve their customer loyalty programs using social media technologies. This is the first in-depth case study on the SYW program, one of the most successful customer loyalty programs with social media. The results shed light on how social media can innovate customer loyalty programs in both theory and practice.

Introduction

2021 ◽  
pp. 1-8
Author(s):  
Daniel Roth
Keyword(s):  
Case Studies ◽  
Theory And Practice ◽  
Third Party ◽  
Rabbinic Literature ◽  
Historical Accounts ◽  
Key Terms

This introductory chapter defines the key terms and methodology through which the book will be analyzed. It begins with defining “third-party peacemaking and Jewish “rabbinic literature.” Then lays out the flow and structure of the various chapters of the book. The chapter then defines the various types of case studies to be examined in the book, consisting of “legends,” “historical accounts,” and ‘stories.” Finally, the chapter concludes with defining the three-layered methodology through which each case study will be analyzed: “text,” “theory,” and ‘practice.”

Estimating Lightning NOx Production Using NO2 Columns from the TROPOMI Instrument and Flashes from the Geostationary Lightning Mappers

2020 ◽  
Author(s):  
Kenneth Pickering ◽  
Dale Allen ◽  
Eric Bucsela ◽  
Jos van Geffen ◽  
Henk Eskes ◽  
...  
Keyword(s):  
United States ◽  
Case Studies ◽  
The United States ◽  
Data Set ◽  
Case Study Analysis ◽  
Convective Systems ◽  
Convective Clouds ◽  
Deep Convective Clouds ◽  
The One

<p>Nitric oxide (NO) is produced in lightning channels and quickly comes into equilibrium with nitrogen dioxide (NO<sub>2</sub>) in the atmosphere.  The production of NO<sub>x</sub> (NO + NO<sub>2</sub>) leads to subsequent increases in the concentrations of ozone (O<sub>3</sub>) and the hydroxyl radical (OH) and decreases in the concentration of methane (CH<sub>4</sub>), thus impacting the climate system.  Global production of NO<sub>x</sub> from lightning is uncertain by a factor of four.  NO<sub>x</sub> production by lightning will be examined using NO<sub>2</sub> columns from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Copernicus Sentinel-5 Precursor Satellite with an overpass time of approximately 1330 LT and flash rates from the Geostationary Lightning Mapper (GLM) on board the NOAA GOES-16 (75.2° W) and GOES-17 (137.2° W) satellites.  Where there is overlap in coverage of the two GLM instruments, the greater of the two flash counts is used.  Two approaches have been undertaken for this analysis:  a series of case studies of storm systems over the United States, and a gridded analysis over the entire contiguous United States, Central America, northern South America, and surrounding oceans.  A modified Copernicus Sentinel 5P TROPOMI NO<sub>2</sub> data set is used here for the case-study analysis to improve data coverage over deep convective clouds.  In both approaches, only TROPOMI pixels with cloud fraction > 0.95 and cloud pressure < 500 hPa are used.  The stratospheric column is removed from the total slant column, and the result is divided by air mass factors appropriate for deep convective clouds containing lightning NO<sub>x</sub> (LNO<sub>x</sub>).  Case studies have been selected from deep convective systems over and near the United States during the warm seasons of 2018 and 2019.  For each of these systems, NO<sub>x</sub> production per flash is determined by multiplying a TROPOMI-based estimate of the mean tropospheric column of LNO<sub>x</sub> over each system by the storm area and then dividing by a GLM-based estimate of the flashes that contribute to the column.  In the large temporal and spatial scale analysis, the TROPOMI data are aggregated on a 0.5 x 0.5 degree grid and converted to moles LNO<sub>x</sub>*.  GLM flash counts during the one-hour period before TROPOMI overpass are similarly binned. A tropospheric background of LNO<sub>x</sub>* is estimated from grid cells without lightning and subtracted from LNO<sub>x</sub>* in cells with lightning to yield an estimate of freshly produced lightning NO<sub>x</sub>, designated LNO<sub>x</sub>.  Results of the two approaches are compared and discussed with respect to previous LNO<sub>x</sub> per flash estimates.</p><p> </p>

Preventing supplier non-conformance: extending the agency theory perspective

2020 ◽  
Vol 40 (3) ◽  
pp. 315-340
Author(s):  
Anton Shevchenko ◽  
Mark Pagell ◽  
Moren Lévesque ◽  
David Johnston
Keyword(s):  
Agency Theory ◽  
Case Studies ◽  
Methodological Approach ◽  
Theory And Practice ◽  
Third Party ◽  
Content Type ◽  
Agent Based ◽  
Qualitative Case Studies ◽  
Effective Path

PurposeThe supply chain management literature and agency theory suggest that preventing supplier non-conformance—a supplier's failure to conform to the requirements of the buyer—requires monitoring supplier behavior. However, case studies collected to explore how buyers monitored suppliers revealed an unexpected empirical phenomenon. Some buyers believed they could prevent non-conformance by either trusting their suppliers or relying on a third party, without monitoring their behavior. The purpose of this article is to examine conditions when buyers should monitor supplier behavior to prevent non-conformance.Design/methodology/approachThis article employs a mixed-method design by formulating an agent-based simulation grounded in the case-study findings and agency theory to reconcile observed unexpected behaviors with scholarly suggestions.FindingsThe simulation results indicate that buyers facing severe consequences from non-conformance should opt to monitor supplier behavior. Sourcing from trusted suppliers should only be reserved for buyers that lack competence and have a small number of carefully selected suppliers. Moreover, buyers facing minor consequences from non-conformance should generally favor sourcing from trusted suppliers over monitoring their behavior. The results also suggest that having a third-party involved in monitoring suppliers is an effective path to preventing non-conformance.Originality/valueBy combining a simulation with qualitative case studies, this article examines whether buyers were making appropriate decisions, thereby offering contributions to theory and practice that would not have been possible using either methodological approach alone.

scholarly journals Teaching Developmental Theory with Interrupted Video Case Studies

2019 ◽  
Vol 19 (5) ◽  
Author(s):  
Bill Anderson
Keyword(s):  
Human Development ◽  
Case Studies ◽  
Life Story ◽  
Real Life ◽  
Posttest Score ◽  
Developmental Theory ◽  
Theory And Practice ◽  
Student Work ◽  
Post Test

This study sought to determine the usefulness of interrupted case studies, utilizing a progressive disclosure of information over time, to increase critical thinking and student learning in the study of foundational theories in the human development field. Apted’s (2013) Up documentary series, consisting of video interviews over a 49-year period, was used as the interrupted study and successfully provided vicarious, but meaningful, opportunities to consistently and authentically apply course content. Participants (N = 23) were students in three sections of a graduate Human Development course where a pre-/post-test format was utilized. The effect was significant as all participant’s posttest score improved an average of 24.3%, F(3, 19) = 3.55, p = .049. Also, coded student work indicated an increase in complex levels of thinking across the 8-week assignment, further validating post-test scores, t(352) = -3.172, p = .002. Evidence from student work further confirmed that an interrupted video case-study, could address limitations typically associated with case-based instruction and, more importantly, provide the critical case-study qualities needed here. Those included, telling a detailed, ambiguous, and real-life story that provided genuine context to connect theory and practice.

Analysis, Discussion, and Evaluations for the Case Studies

2021 ◽  
pp. 142-150
Keyword(s):  
Colon Cancer ◽  
Case Studies ◽  
Classification Accuracy ◽  
Experimental Results ◽  
Full Data ◽  
Data Set ◽  
Evaluation Measures ◽  
Full Dataset ◽  
Average Accuracy

The purpose of this chapter is to discuss and analyse the results produced in Chapter 5. To evaluate the proposed models, this chapter compares the models with others existing in the literature. Additionally, the chapter discusses the evaluation measures used to validate the experimental results of Chapter 5. For example, from experiments, GA/DT demonstrated the highest average accuracy (92%) for classifying colon cancer, compared with other algorithms. PSO/DT presented 89%, PSO/SVM presented 89%, and IG/DT presented 89%, demonstrating very good classification accuracy. PSO/NB presented 57% and GA/NB presented 58%: less classification accuracy. Table ‎6.1 lists all accuracies resulting from experiments of case study one, as applied to the full data set. There are 45 algorithmic incorporation methods that have accuracy above 80% when applied to the full dataset. One algorithm presents an accuracy of 92%. Nine others scored below 60%.

Interactive Data Visualization to Understand Data Better

2014 ◽  
Vol 4 (2) ◽  
pp. 1-10 ◽  
Author(s):  
Zhecheng Zhu ◽  
Bee Hoon Heng ◽  
Kiok Liang Teow
Keyword(s):  
Case Studies ◽  
Healthcare System ◽  
Data Visualization ◽  
Healthcare Systems ◽  
Data Set ◽  
First Case ◽  
Interactive Data ◽  
Visual Impact ◽  
Visualization Techniques

This paper focuses on interactive data visualization techniques and their applications in healthcare systems. Interactive data visualization is a collection of techniques translating data from its numeric format to graphic presentation dynamically for easy understanding and visual impact. Compared to conventional static data visualization techniques, interactive data visualization techniques allow users to self-explore the entire data set by instant slice and dice, quick switching among multiple data sources. Adjustable granularity of interactive data visualization allows for both detailed micro information and aggregated macro information displayed in a single chart. Animated transition adds extra visual impact that describes how system transits from one state to another. When applied to healthcare system, interactive visualization techniques are useful in areas such as information integration, flow or trajectory presentation and location related visualization, etc. In this paper, three case studies are shared to illustrate how interactive data visualization techniques are applied to various aspects of healthcare systems. The first case study shows a pathway visualization representing longitudinal disease progression of a patient cohort. The second case study shows a dashboard profiling different patient cohorts from multiple perspectives. The third case study shows an interactive map illustrating patient geographical distribution at adjustable granularity. All three case studies illustrate that interactive data visualization techniques help quick information access, fast knowledge sharing and better decision making in healthcare system.

scholarly journals Quantifying emotionally grounded discursive knowledge with cognitive-affective maps

2021 ◽  
Author(s):  
Jasmin Luthardt ◽  
Jonathan Howard Morgan ◽  
Inka Bormann ◽  
Tobias Schröder
Keyword(s):  
Quantitative Methods ◽  
Belief Systems ◽  
Visual Representations ◽  
Three Dimensions ◽  
Third Party ◽  
Group Level ◽  
Text Data ◽  
Data Set ◽  
Network Similarity

AbstractBelief systems matter for all kinds of human social interaction. People have individual cognitions and feelings concerning processes in their environment, which is why they may evaluate them differently. Belief systems can be visualized with cognitive-affective maps (CAMs; as reported by Thagard (in: McGregor (ed) EMPATHICA: A computer support system with visual representations for cognitive-affective mapping, AAAI Press, CA, 2010)). However, it is unclear whether CAMs can be constructed in an intersubjective way by different researchers attempting to map the beliefs of a third party based on qualitative text data. To scrutinize this question, we combined qualitative strategies and quantitative methods of text and network analysis in a case study examining belief networks about participation. Our data set consists of 10 sets of two empirical CAMs: the first CAM was created based on participants’ freely associating concepts related to participation in education (N = 10), the second one was created based on given text data which the participants represented as a CAM following a standardized instruction manual (N = 10). Both CAM-types were compared along three dimensions of similarity (network similarity, concept association similarity, affective similarity). On all dimensions of similarity, there was substantially higher intersubjective agreement in the text-based CAMs than in the free CAMs, supporting the viability of cognitive affective mapping as an intersubjective research method for studying the emotional coherence of belief systems and discursive knowledge. In addition, this study highlights the potential for identifying group-level differences based on how participants associate concepts.

Molecular Views of Ice-Embedded Lumbricus Terrestris Erythrocruorin Obtained By Invariant Classification

1990 ◽  
Vol 48 (1) ◽  
pp. 450-451
Author(s):  
Michael schatz ◽  
Joachim Jäger ◽  
Marin van Heel
Keyword(s):  
Image Interpretation ◽  
Lumbricus Terrestris ◽  
Reference Image ◽  
Large Set ◽  
Multivariate Statistical ◽  
Data Set ◽  
Low Contrast ◽  
Negative Stain ◽  
Reference Images ◽  
Statistical Resolution

Lumbricus terrestris erythrocruorin is a giant oxygen-transporting macromolecule in the blood of the common earth worm (worm "hemoglobin"). In our current study, we use specimens (kindly provided by Drs W.E. Royer and W.A. Hendrickson) embedded in vitreous ice (1) to avoid artefacts encountered with the negative stain preparation technigue used in previous studies (2-4).Although the molecular structure is well preserved in vitreous ice, the low contrast and high noise level in the micrographs represent a serious problem in image interpretation. Moreover, the molecules can exhibit many different orientations relative to the object plane of the microscope in this type of preparation. Existing techniques of analysis requiring alignment of the molecular views relative to one or more reference images often thus yield unsatisfactory results.We use a new method in which first rotation-, translation- and mirror invariant functions (5) are derived from the large set of input images, which functions are subsequently classified automatically using multivariate statistical techniques (6). The different molecular views in the data set can therewith be found unbiasedly (5). Within each class, all images are aligned relative to that member of the class which contributes least to the classes′ internal variance (6). This reference image is thus the most typical member of the class. Finally the aligned images from each class are averaged resulting in molecular views with enhanced statistical resolution.

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