scholarly journals A Facet Theory Analysis of the Structure of Cognitive Performance in New Zealand Robins (Petroica longipes)

2019 ◽  
Vol 32 ◽  
Author(s):  
Paul M. W. Hackett ◽  
Rachael C. Shaw ◽  
Neeltje J. Boogert ◽  
Nicola. S. Clayton
Keyword(s):  
New Zealand ◽  
Cognitive Performance ◽  
Small Sample Size ◽  
Small Sample ◽  
Facet Theory ◽  
Data Set ◽  
Avian Cognition ◽  
Test Items ◽  
Component Solution ◽  
Mapping Sentence

In this report we analyse the cognitive performance of New Zealand Robins (Petroica longipes) using facet theory, smallest space analysis (SSA) and partial order scalogram analysis (POSA). The data set we analyse was originally subjected to principle component analysis in order to develop a test-battery for avian cognitive performance. We extend these analyses by proposing a two facet rather than a single component solution using SSA and we characterize individual birds by their scores on all tasks using POSA. We note problems with the small sample size and call for our exploratory analyses to be replicated using a larger sample of birds and for the development of further test items using the facet theory’s tool the mapping sentence. We suggest that facet theory and the mapping sentences are research approaches suitable for conceiving, designing, analysing and developing theory that may be used within avian cognitive research. We conclude by proposing a mapping sentence for avian cognition, which forms an adaptable template for future avian cognition research.

A Multi-Linear Statistical Method for Discriminant Analysis of 2D Frontal Face Images

2012 ◽  
pp. 18-33 ◽  
Author(s):  
Carlos Eduardo Thomaz ◽  
Vagner do Amaral ◽  
Gilson Antonio Giraldi ◽  
Edson Caoru Kitani ◽  
João Ricardo Sato ◽  
...  
Keyword(s):  
Small Sample Size ◽  
Face Image ◽  
Small Sample ◽  
High Dimensional ◽  
Data Set ◽  
Linear Discriminant ◽  
Face Images ◽  
Linear Framework ◽  
Facial Changes ◽  
2D Data

This chapter describes a multi-linear discriminant method of constructing and quantifying statistically significant changes on human identity photographs. The approach is based on a general multivariate two-stage linear framework that addresses the small sample size problem in high-dimensional spaces. Starting with a 2D data set of frontal face images, the authors determine a most characteristic direction of change by organizing the data according to the patterns of interest. These experiments on publicly available face image sets show that the multi-linear approach does produce visually plausible results for gender, facial expression and aging facial changes in a simple and efficient way. The authors believe that such approach could be widely applied for modeling and reconstruction in face recognition and possibly in identifying subjects after a lapse of time.

scholarly journals SSMD: a semi-supervised approach for a robust cell type identification and deconvolution of mouse transcriptomics data

2020 ◽  
Author(s):  
Xiaoyu Lu ◽  
Szu-Wei Tu ◽  
Wennan Chang ◽  
Changlin Wan ◽  
Jiashi Wang ◽  
...  
Keyword(s):  
Small Sample Size ◽  
Cell Types ◽  
Small Sample ◽  
Training Data ◽  
Mouse Tissue ◽  
Marker Genes ◽  
Specific Cell ◽  
Cell Type ◽  
Data Set ◽  
Transcriptomics Data

Abstract Deconvolution of mouse transcriptomic data is challenged by the fact that mouse models carry various genetic and physiological perturbations, making it questionable to assume fixed cell types and cell type marker genes for different data set scenarios. We developed a Semi-Supervised Mouse data Deconvolution (SSMD) method to study the mouse tissue microenvironment. SSMD is featured by (i) a novel nonparametric method to discover data set-specific cell type signature genes; (ii) a community detection approach for fixing cell types and their marker genes; (iii) a constrained matrix decomposition method to solve cell type relative proportions that is robust to diverse experimental platforms. In summary, SSMD addressed several key challenges in the deconvolution of mouse tissue data, including: (i) varied cell types and marker genes caused by highly divergent genotypic and phenotypic conditions of mouse experiment; (ii) diverse experimental platforms of mouse transcriptomics data; (iii) small sample size and limited training data source and (iv) capable to estimate the proportion of 35 cell types in blood, inflammatory, central nervous or hematopoietic systems. In silico and experimental validation of SSMD demonstrated its high sensitivity and accuracy in identifying (sub) cell types and predicting cell proportions comparing with state-of-the-arts methods. A user-friendly R package and a web server of SSMD are released via https://github.com/xiaoyulu95/SSMD.

Teacher coaching in New Zealand secondary schools: an exploratory study

2019 ◽  
Vol 8 (2) ◽  
pp. 70-85
Author(s):  
Paul Nathan Bennett
Keyword(s):  
New Zealand ◽  
Secondary Schools ◽  
Small Sample Size ◽  
Small Sample ◽  
Social Construct ◽  
Content Type ◽  
Evaluation Strategies ◽  
Development Approach ◽  
The Individual ◽  
Teacher Coaching

Purpose The purpose of this paper is to explore how teacher coaching is being implemented in New Zealand secondary schools. Design/methodology/approach A pragmatic mixed methods approach was identified as the most suitable. A dominant qualitative approach, using a sequential design, incorporating triangulation of methods and perspectives across time, provided an appropriate research design framework. Findings The findings indicate that teacher coaching is a popular professional development approach that has been enthusiastically implemented throughout New Zealand secondary schools. The four factors of purpose, evaluation, training and funding have been shown to be interrelated factors operating in New Zealand teacher coaching programmes. These factors are perceived to have an influence on teacher coaching programmes achieving their stated objectives. Research limitations/implications A limitation of this study is that it provides a snapshot of teacher coaching in New Zealand secondary schools, and the snapshot presented is constantly changing. A methodological limitation of the study related to the 28 per cent response rate of the questionnaire and the small sample size used for the interview phases. Practical implications This study encourages school leaders to consider if they have defined teacher coaching in the context of their programmes and articulated their objectives. They are persuaded to think about how they could design robust evaluation strategies and targeted training. Social implications The findings show the concept of teacher coaching is a social construct that is influenced not only by unique environmental contexts but also the individual perceptions of all those involved. Originality/value This study provides new knowledge in relation to how and why teacher coaching is being used and the factors that influence whether programme objectives are achieved.

scholarly journals Deep generative models in DataSHIELD

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Stefan Lenz ◽  
Moritz Hess ◽  
Harald Binder
Keyword(s):  
Genetic Variant ◽  
Small Sample Size ◽  
Synthetic Data ◽  
Routine Data ◽  
Original Data ◽  
Generative Models ◽  
Small Sample ◽  
Generative Adversarial Networks ◽  
Artificial Data ◽  
Data Set

Abstract Background The best way to calculate statistics from medical data is to use the data of individual patients. In some settings, this data is difficult to obtain due to privacy restrictions. In Germany, for example, it is not possible to pool routine data from different hospitals for research purposes without the consent of the patients. Methods The DataSHIELD software provides an infrastructure and a set of statistical methods for joint, privacy-preserving analyses of distributed data. The contained algorithms are reformulated to work with aggregated data from the participating sites instead of the individual data. If a desired algorithm is not implemented in DataSHIELD or cannot be reformulated in such a way, using artificial data is an alternative. Generating artificial data is possible using so-called generative models, which are able to capture the distribution of given data. Here, we employ deep Boltzmann machines (DBMs) as generative models. For the implementation, we use the package “BoltzmannMachines” from the Julia programming language and wrap it for use with DataSHIELD, which is based on R. Results We present a methodology together with a software implementation that builds on DataSHIELD to create artificial data that preserve complex patterns from distributed individual patient data. Such data sets of artificial patients, which are not linked to real patients, can then be used for joint analyses. As an exemplary application, we conduct a distributed analysis with DBMs on a synthetic data set, which simulates genetic variant data. Patterns from the original data can be recovered in the artificial data using hierarchical clustering of the virtual patients, demonstrating the feasibility of the approach. Additionally, we compare DBMs, variational autoencoders, generative adversarial networks, and multivariate imputation as generative approaches by assessing the utility and disclosure of synthetic data generated from real genetic variant data in a distributed setting with data of a small sample size. Conclusions Our implementation adds to DataSHIELD the ability to generate artificial data that can be used for various analyses, e.g., for pattern recognition with deep learning. This also demonstrates more generally how DataSHIELD can be flexibly extended with advanced algorithms from languages other than R.

Mother knows best: exploring the matrilineal influence in family tourism among Filipinos living in New Zealand

2018 ◽  
Vol 19 (2) ◽  
pp. 218-236
Author(s):  
Robert Charles Capistrano ◽  
Maria Aurora Correa Bernardo
Keyword(s):  
New Zealand ◽  
Filial Piety ◽  
Complex Dynamics ◽  
Small Sample Size ◽  
The Philippines ◽  
Small Sample ◽  
Final Decision ◽  
Content Type ◽  
Intergenerational Ties ◽  
Culturally Influenced

Purpose This paper aims to examine the personal meanings of hosting experiences of first-generation immigrant families, particularly Filipino mothers in New Zealand, with their visiting relatives (VRs) from the Philippines by using the conceptual lens of hospitality. Design/methodology/approach Through a qualitative approach, a multi-sited fieldwork was carried out to examine kinship ties that bind immigrant-host families in New Zealand with their VRs from the Philippines. Results of in-depth interviews of immigrant-host mothers on their recollections of family visits were thematically analysed. Findings The main drivers that shape the hosting experiences of the research participants are modelling filial piety, fulfilling cultural expectations and strengthening family bonds. These main drivers enable sustaining intergenerational ties that unite the mother’s families in the Philippines and those in New Zealand. Research limitations/implications The study elucidates the complex dynamics of culturally connected and motivated domestic hospitality, where the mother is the main protagonist and orchestrator. This dominance is often subdued, and thus, marketing for family often misses the mark. While the study has a small sample size and therefore lacks representativeness, qualitative accounts have produced an enriched cognitive schema that would enable an interesting way of examining the phenomenon. Practical implications This study reveals that matrilineal influence on family tourism among migrant Filipinos in New Zealand is strong and culturally influenced. Further studies may be done with families from other cultures and families. From a practical perspective, the findings suggest the importance of marketing tourism or hospitality products that facilitate visiting friends and relatives’ travel through domestic hospitality. Social implications This research calls for reforms in the way family tourism is marketed. While commercial imperatives did not drive this research, findings indicate that certain cultures adhere to the wisdom of mothers on making the final decision on how hospitality has to be extended and manifested. Originality/value In the context of family tourism, inadequate research has been undertaken to examine the perspectives of women and their role as host in the travel of VFR. This study purports to fill in the gap in literature related to hosting experiences of women in the context of family tourism and VFR travel and to consider the voices of women in their new homeland.

scholarly journals Autoregressive Prediction with Rolling Mechanism for Time Series Forecasting with Small Sample Size

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Zhihua Wang ◽  
Yongbo Zhang ◽  
Huimin Fu
Keyword(s):  
Time Series ◽  
Sample Size ◽  
Small Sample Size ◽  
Computational Effort ◽  
Small Sample ◽  
Grey Theory ◽  
Data Set ◽  
Rolling Mechanism ◽  
Short Term Forecasting ◽  
Prediction Approach

Reasonable prediction makes significant practical sense to stochastic and unstable time series analysis with small or limited sample size. Motivated by the rolling idea in grey theory and the practical relevance of very short-term forecasting or 1-step-ahead prediction, a novel autoregressive (AR) prediction approach with rolling mechanism is proposed. In the modeling procedure, a new developed AR equation, which can be used to model nonstationary time series, is constructed in each prediction step. Meanwhile, the data window, for the next step ahead forecasting, rolls on by adding the most recent derived prediction result while deleting the first value of the former used sample data set. This rolling mechanism is an efficient technique for its advantages of improved forecasting accuracy, applicability in the case of limited and unstable data situations, and requirement of little computational effort. The general performance, influence of sample size, nonlinearity dynamic mechanism, and significance of the observed trends, as well as innovation variance, are illustrated and verified with Monte Carlo simulations. The proposed methodology is then applied to several practical data sets, including multiple building settlement sequences and two economic series.

scholarly journals Hand Hygiene in the Era of Big Data: We Can Now See What We Have Been Missing

2020 ◽  
Vol 41 (S1) ◽  
pp. s445-s446
Author(s):  
Megan DiGiorgio ◽  
Lori Moore ◽  
Greg Robbins ◽  
Albert Parker ◽  
James Arbogast
Keyword(s):  
Hand Hygiene ◽  
Direct Observation ◽  
Fixed Effects ◽  
Small Sample Size ◽  
Compliance Rate ◽  
Final Analysis ◽  
Small Sample ◽  
Annual Number ◽  
Data Set ◽  
Cast Doubt

Background: Hand hygiene (HH) has long been a focus in the prevention of healthcare-associated infections. The limitations of direct observation, including small sample size (often 20–100 observations per month) and the Hawthorne effect, have cast doubt on the accuracy of reported compliance rates. As a result, hospitals are exploring the use of automated HH monitoring systems (AHHMS) to overcome the limitations of direct observation and to provide a more robust and realistic estimation of HH behaviors. Methods: Data analyzed in this study were captured utilizing a group-based AHHMS installed in a number of North American hospitals. Emergency departments, overflow units, and units with <1 year of data were excluded from the study. The final analysis included data from 58 inpatient units in 10 hospitals. Alcohol-based hand rub and soap dispenses HH events (HHEs) and room entries and exits (HH opportunities (HHOs) were used to calculate unit-level compliance rates. Statistical analysis was performed on the annual number of dispenses and opportunities using a mixed effects Poisson regression with random effects for facility, unit, and year, and fixed effects for unit type. Interactions were not included in the model based on interaction plots and significance tests. Poisson assumptions were verified with Pearson residual plots. Results: Over the study period, 222.7 million HHOs and 99 million HHEs were captured in the data set. There were an average of 18.7 beds per unit. The average number of HHOs per unit per day was 3,528, and the average number of HHEs per unit per day was 1,572. The overall median compliance rate was 35.2 (95% CI, 31.5%–39.3%). Unit-to-unit comparisons revealed some significant differences: compliance rates for medical-surgical units were 12.6% higher than for intensive care units (P < .0001). Conclusions: This is the largest HH data set ever reported. The results illustrate the magnitude of HHOs captured (3,528 per unit per day) by an AHHMS compared to that possible through direct observation. It has been previously suggested that direct observation samples between 0.5% to 1.7% of all HHOs. In healthcare, it is unprecedented for a patient safety activity that occurs as frequently as HH to not be accurately monitored and reported, especially with HH compliance as low as it is in this multiyear, multicenter study. Furthermore, hospitals relying on direct observation alone are likely insufficiently allocating and deploying valuable resources for improvement efforts based on the scant information obtained. AHHMSs have the potential to introduce a new era in HH improvement.Funding: GOJO Industries, Inc., provided support for this study.Disclosures: Lori D. Moore and James W. Arbogast report salary from GOJO.

scholarly journals Assessment of Business Interruption of Flood-Affected Companies Using Random Forests

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 1049 ◽  
Author(s):  
Zakia Sultana ◽  
Tobias Sieg ◽  
Patric Kellermann ◽  
Meike Müller ◽  
Heidi Kreibich
Keyword(s):  
Random Forests ◽  
Small Sample Size ◽  
Economic Loss ◽  
Important Variable ◽  
Small Sample ◽  
Economic Losses ◽  
Data Set ◽  
Variable Importance Measures ◽  
Interruption Costs ◽  
Business Interruption

Losses due to floods have dramatically increased over the past decades, and losses of companies, comprising direct and indirect losses, have a large share of the total economic losses. Thus, there is an urgent need to gain more quantitative knowledge about flood losses, particularly losses caused by business interruption, in order to mitigate the economic loss of companies. However, business interruption caused by floods is rarely assessed because of a lack of sufficiently detailed data. A survey was undertaken to explore processes influencing business interruption, which collected information on 557 companies affected by the severe flood in June 2013 in Germany. Based on this data set, the study aims to assess the business interruption of directly affected companies by means of a Random Forests model. Variables that influence the duration and costs of business interruption were identified by the variable importance measures of Random Forests. Additionally, Random Forest-based models were developed and tested for their capacity to estimate business interruption duration and associated costs. The water level was found to be the most important variable influencing the duration of business interruption. Other important variables, relating to the estimation of business interruption duration, are the warning time, perceived danger of flood recurrence and inundation duration. In contrast, the amount of business interruption costs is strongly influenced by the size of the company, as assessed by the number of employees, emergency measures undertaken by the company and the fraction of customers within a 50 km radius. These results provide useful information and methods for companies to mitigate their losses from business interruption. However, the heterogeneity of companies is relatively high, and sector-specific analyses were not possible due to the small sample size. Therefore, further sector-specific analyses on the basis of more flood loss data of companies are recommended.

The Support Feature Machine: Classification with the Least Number of Features and Application to Neuroimaging Data

2013 ◽  
Vol 25 (6) ◽  
pp. 1548-1584 ◽  
Author(s):  
Sascha Klement ◽  
Silke Anders ◽  
Thomas Martinetz
Keyword(s):  
Feature Selection ◽  
Small Sample Size ◽  
Small Sample ◽  
Biological Data ◽  
Support Vector ◽  
Data Sets ◽  
Universal Method ◽  
Data Set ◽  
Separating Hyperplane ◽  
New Formulation

By minimizing the zero-norm of the separating hyperplane, the support feature machine (SFM) finds the smallest subspace (the least number of features) of a data set such that within this subspace, two classes are linearly separable without error. This way, the dimensionality of the data is more efficiently reduced than with support vector–based feature selection, which can be shown both theoretically and empirically. In this letter, we first provide a new formulation of the previously introduced concept of the SFM. With this new formulation, classification of unbalanced and nonseparable data is straightforward, which allows using the SFM for feature selection and classification in a large variety of different scenarios. To illustrate how the SFM can be used to identify both the smallest subset of discriminative features and the total number of informative features in biological data sets we apply repetitive feature selection based on the SFM to a functional magnetic resonance imaging data set. We suggest that these capabilities qualify the SFM as a universal method for feature selection, especially for high-dimensional small-sample-size data sets that often occur in biological and medical applications.

TIBIAL NERVE PERINEURAL INJECTIONS AT THE POSTERIOR TARSAL TUNNEL

2013 ◽  
Vol 16 (03) ◽  
pp. 1350014
Author(s):  
Oliver C. Joseph ◽  
Oleg Uryasev ◽  
John P. McNamara ◽  
Apostolos P. Dallas
Keyword(s):  
Patient Population ◽  
Tibial Nerve ◽  
Small Sample Size ◽  
Symptomatic Relief ◽  
Small Sample ◽  
Definitive Treatment ◽  
Tarsal Tunnel ◽  
Initial Attempt ◽  
Data Set ◽  
Us Guidance

Introduction: Posterior tarsal tunnel syndrome (PostTTS) refers to compression of the tibial nerve (TN) within this tunnel. PostTTS is most commonly secondary to entrapment with subsequent inflammation. As it is true with other entrapment-type neuropathies, corticosteroids could provide therapeutic relief. To the authors' knowledge, the feasibility of such injections using ultrasound guidance has not been described in the literature. We hypothesize that one can inject the TN perineural space immediately proximal to the posterior tarsal tunnel utilizing ultrasonography US-guidance. Methods: This research was a pilot study using four cadaveric models. US was utilized to image the proximal posterior tarsal tunnel. Perineural injections of methylene blue were performed with subsequent dissection. Injections were designated as accurate (referring to nerve staining) and precise (referring to dye localization). Results: One cadaver was precluded due to pronounced musculoskeletal abnormality. 5-of-6 (83%) injections were accurate and 6-of-6 (100%) precise. Conclusion: Initial attempt was inaccurate and precise, while later injections were both accurate and precise. The most apparent source of error was from one cadaver's pronounced musculoskeletal deformity, which precluded successful injections bilaterally. Of the three cadavers unaffected by musculoskeletal deformity, accuracy was 5-of-6 (83%) and precision was 6-of-6 (100%). While surgery is the definitive treatment for refractory PostTTS, therapeutic effect of corticosteroid injections has not been evaluated in this patient population. Such injections could provide symptomatic relief and postpone surgical intervention. Small sample size not withstanding the results suggest that TN perineural injections are feasible under US-guidance. This study suggests that US-guidance can increase accuracy and precision and is a potential adjunct to the treatment. Future study will expand the initial data set and categorize consistent protocol. Subsequent translational research will then be sought to evaluate therapeutic efficacy in this patient population.

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