A Qualitative Study of the Status of Children’s Play From the Viewpoints of Experts and Suggestions for Promotion Interventions

Background: The latest national census reports the population of Iranian children (1 - 8 years old) about 11 millions. On the other hand, the latest population policies approved by supreme cultural revolution council (SCRC) will make this population increase faster. Childhood development is one of the social determinants of health, of which “child’s play” is a part. Objectives: This study is an effort to identify difficulties and challenges of the plays influential on Iranian children’s health nationwide, in order to present enhancive strategies by utilizing the views of stakeholders and national studies. Patients and Methods: Analyzing children’s play stakeholders, main organizations were identified and views of 13 informed people involved in the field were investigated through deep semi-structured interview. A denaturalized approach was employed in analyzing the data. In addition to descriptions of the state, interventions development, and designing the conceptual model, national reports and studies, and other countries’ experiences were also reviewed. Results: Society’s little knowledge of “children’s plays”, absence of administrators for children’s play, shortage of public facilities for children’s play and improper geographical and demographic availability, absence of policies for Iranian “toy”, and little attention of media to the issue are the five major problems as stated by interviewees. Conclusions: The proposed interventions are presented as “promoting the educational levels of parents and selected administrators for children’s play”, “approving the play and toy policy for Iran 2025”, and “increasing public facilities for children’s play with defined distribution and availability”.

Distributed conditional computation

L'objectif de cette thèse est de présenter différentes applications du programme de recherche de calcul conditionnel distribué. On espère que ces applications, ainsi que la théorie présentée ici, mènera à une solution générale du problème d'intelligence artificielle, en particulier en ce qui a trait à la nécessité d'efficience. La vision du calcul conditionnel distribué consiste à accélérer l'évaluation et l'entraînement de modèles profonds, ce qui est très différent de l'objectif usuel d'améliorer sa capacité de généralisation et d'optimisation. Le travail présenté ici a des liens étroits avec les modèles de type mélange d'experts. Dans le chapitre 2, nous présentons un nouvel algorithme d'apprentissage profond qui utilise une forme simple d'apprentissage par renforcement sur un modèle d'arbre de décisions à base de réseau de neurones. Nous démontrons la nécessité d'une contrainte d'équilibre pour maintenir la distribution d'exemples aux experts uniforme et empêcher les monopoles. Pour rendre le calcul efficient, l'entrainement et l'évaluation sont contraints à être éparse en utilisant un routeur échantillonnant des experts d'une distribution multinomiale étant donné un exemple. Dans le chapitre 3, nous présentons un nouveau modèle profond constitué d'une représentation éparse divisée en segments d'experts. Un modèle de langue à base de réseau de neurones est construit à partir des transformations éparses entre ces segments. L'opération éparse par bloc est implémentée pour utilisation sur des cartes graphiques. Sa vitesse est comparée à deux opérations denses du même calibre pour démontrer le gain réel de calcul qui peut être obtenu. Un modèle profond utilisant des opérations éparses contrôlées par un routeur distinct des experts est entraîné sur un ensemble de données d'un milliard de mots. Un nouvel algorithme de partitionnement de données est appliqué sur un ensemble de mots pour hiérarchiser la couche de sortie d'un modèle de langage, la rendant ainsi beaucoup plus efficiente. Le travail présenté dans cette thèse est au centre de la vision de calcul conditionnel distribué émis par Yoshua Bengio. Elle tente d'appliquer la recherche dans le domaine des mélanges d'experts aux modèles profonds pour améliorer leur vitesse ainsi que leur capacité d'optimisation. Nous croyons que la théorie et les expériences de cette thèse sont une étape importante sur la voie du calcul conditionnel distribué car elle cadre bien le problème, surtout en ce qui concerne la compétitivité des systèmes d'experts.

Optimal piecewise locally linear modeling

Associative memory networks such as Radial Basis Functions, Neurofuzzy and Fuzzy Logic used for modelling nonlinear processes suffer from the curse of dimensionality (COD), in that as the input dimension increases the parameterization, computation cost, training data requirements, etc. increase exponentially. Here a new algorithm is introduced for the construction of a Delaunay input space partitioned optimal piecewise locally linear models to overcome the COD as well as generate locally linear models directly amenable to linear control and estimation algorithms. The training of the model is configured as a new mixture of experts network with a new fast decision rule derived using convex set theory. A very fast simulated reannealing (VFSR) algorithm is utilized to search a global optimal solution of the Delaunay input space partition. A benchmark non-linear time series is used to demonstrate the new approach.

Multi-sensor background subtraction by fusing multiple region-based probabilistic classifiers

In the recent years, the computer vision community has shown great interest on depth-based applications thanks to the performance and flexibility of the new generation of RGB-D imagery. In this paper, we present an efficient background subtraction algorithm based on the fusion of multiple region-based classifiers that processes depth and color data provided by RGB-D cameras. Foreground objects are detected by combining a region-based foreground prediction (based on depth data) with different background models (based on a Mixture of Gaussian algorithm) providing color and depth descriptions of the scene at pixel and region level. The information given by these modules is fused in a mixture of experts fashion to improve the foreground detection accuracy. The main contributions of the paper are the region-based models of both background and foreground, built from the depth and color data. The obtained results using different database sequences demonstrate that the proposed approach leads to a higher detection accuracy with respect to existing state-of-the-art techniques.

Using the EM Algorithm to Train Neural Networks: Misconceptions and a New Algorithm for Multiclass Classification

The expectation-maximization (EM) algorithm has been of considerable interest in recent years as the basis for various algorithms in application areas of neural networks such as pattern recognition. However, there exists some misconceptions concerning its application to neural networks. In this paper, we clarify these misconceptions and consider how the EM algorithm can be adopted to train multilayer perceptron (MLP) and mixture of experts (ME) networks in applications to multiclass classification. We identify some situations where the application of the EM algorithm to train MLP networks may be of limited value and discuss some ways of handling the difficulties. For ME networks, it is reported in the literature that networks trained by the EM algorithm using iteratively reweighted least squares (IRLS) algorithm in the inner loop of the M-step, often performed poorly in multiclass classification. However, we found that the convergence of the IRLS algorithm is stable and that the log likelihood is monotonic increasing when a learning rate smaller than one is adopted. Also, we propose the use of an expectation-conditional maximization (ECM) algorithm to train ME networks. Its performance is demonstrated to be superior to the IRLS algorithm on some simulated and real data sets.

Guiding local regression using visualisation

Solving many scientific problems requires effective regression and/or classification models for large high-dimensional datasets. Experts from these problem domains (e.g. biologists, chemists, financial analysts) have insights into the domain which can be helpful in developing powerful models but they need a modelling framework that helps them to use these insights. Data visualisation is an effective technique for presenting data and requiring feedback from the experts. A single global regression model can rarely capture the full behavioural variability of a huge multi-dimensional dataset. Instead, local regression models, each focused on a separate area of input space, often work better since the behaviour of different areas may vary. Classical local models such as Mixture of Experts segment the input space automatically, which is not always effective and it also lacks involvement of the domain experts to guide a meaningful segmentation of the input space. In this paper we addresses this issue by allowing domain experts to interactively segment the input space using data visualisation. The segmentation output obtained is then further used to develop effective local regression models.

DVMS 1.5: A user manual (the data visualization and modeling system)

The data available during the drug discovery process is vast in amount and diverse in nature. To gain useful information from such data, an effective visualisation tool is required. To provide better visualisation facilities to the domain experts (screening scientist, biologist, chemist, etc.),we developed a software which is based on recently developed principled visualisation algorithms such as Generative Topographic Mapping (GTM) and Hierarchical Generative Topographic Mapping (HGTM). The software also supports conventional visualisation techniques such as Principal Component Analysis, NeuroScale, PhiVis, and Locally Linear Embedding (LLE). The software also provides global and local regression facilities . It supports regression algorithms such as Multilayer Perceptron (MLP), Radial Basis Functions network (RBF), Generalised Linear Models (GLM), Mixture of Experts (MoE), and newly developed Guided Mixture of Experts (GME). This user manual gives an overview of the purpose of the software tool, highlights some of the issues to be taken care while creating a new model, and provides information about how to install & use the tool. The user manual does not require the readers to have familiarity with the algorithms it implements. Basic computing skills are enough to operate the software.

Guiding local regression using visualisation

Solving many scientific problems requires effective regression and/or classification models for large high-dimensional datasets. Experts from these problem domains (e.g. biologists, chemists, financial analysts) have insights into the domain which can be helpful in developing powerful models but they need a modelling framework that helps them to use these insights. Data visualisation is an effective technique for presenting data and requiring feedback from the experts. A single global regression model can rarely capture the full behavioural variability of a huge multi-dimensional dataset. Instead, local regression models, each focused on a separate area of input space, often work better since the behaviour of different areas may vary. Classical local models such as Mixture of Experts segment the input space automatically, which is not always effective and it also lacks involvement of the domain experts to guide a meaningful segmentation of the input space. In this paper we addresses this issue by allowing domain experts to interactively segment the input space using data visualisation. The segmentation output obtained is then further used to develop effective local regression models.

Thermodiffusion in a multicomponent lyotropic mixture in the vicinity of the critical micellar concentration by using the Z-scan technique

Thermodiffusion in a lyotropic mixture of water and potassium laurate is investigated by means of an optical technique (Z scan) distinguishing the index variations due to the temperature gradient and the mass gradients. A phenomenological framework allowing for coupled diffusion is developed in order to analyze thermodiffusion in multicomponent systems. An observable parameter relating to the mass gradients is found to exhibit a sharp change around the critical micellar concentration, and thus may be used to detect it. The change in the slope is due to the markedly different values of the Soret coefficients of the surfactant and the micelles. The difference in the Soret coefficients is due to the fact that the micellization process reduces the energy of interaction of the ball of amphiphilic molecules with the solvent.

KDP/PEDOT: PSS mixture as a new alternative in the fabrication of pressure sensing devices

In this work we studied the mixture of poly(3,4-ethylenedioxythiophene): poly(styrenesulfonate) (PEDOT:PSS), a commercial polymer, with monobasic potassium phosphate (KDP), a piezoelectric salt, as a possible novel material in the fabrication of a low cost, easy-to-make,flexible pressure sensing device. The mixture between KDP and PEDOT: PSS was painted in a flexible polyester substrate and dried. Afterwards, I x V curves were carried out. The samples containing KDP presented higher values of current in smaller voltages than the PEDOT: PSS without KDP. This can mean a change in the chain arrays. Other results showed that the material responds to directly applied pressure to the sample that can be useful to sensors fabrication. (c) 2008 Elsevier B.V. All rights reserved.

Vulvar Lichen Sclerosus: Efficacy of Photodynamic Therapy Under Conscious Sedation with Inhaled 50% Nitrous Oxide and Oxygen Mixture

Anogenital lichen sclerosus is a chronic, inflammatory, mucocutaneous disorder of significant morbidity. Common symptoms include pruritus, pain, dysuria, and dyspareunia, frequently of difficult control. Photodynamic therapy (PDT) may be an effective therapeutic option in selected cases refractory to first--‐line treatment options. However, procedure--‐related pain is a limiting factor in patient adherence to treatment. Conscious sedation and analgesia with a ready--‐to--‐use gas mixture of nitrous oxide and oxygen is useful in short--‐term procedures. It provides a rapid, effective, and short--‐lived effect, without the need for anesthesiology support. A 75--‐year--‐old woman presented with a highly symptomatic, histologically confirmed vulvar lichen sclerosus, with at least 15 years of evolution. Pain, pruritus, and dysuria were intense and disabling. Treatment with ultrapotent topical corticosteroids proved to be ineffective despite patient compliance. She was then referred for PDT. A total of 3 sessions were performed, held at a mean interval of 9 weeks, and under the analgesic and sedative effect of nitrous oxide/oxygen gas. Response to treatment was evaluated through a daily, self--‐reported pain rating scale. Dysuria remitted completely after the first PDT session. An 80% reduction in pruritus and pain was observed after the third session, and has been sustained for the past six months without further need for topical corticotherapy. Treatment sessions were well tolerated and pain-- free, with no side effects to report. PDT appears to be effective in the symptomatic treatment of vulvar lichen sclerosus. To the authors’ knowledge this is the first case reporting the use of inhaled nitrous oxide/oxygen gas mixture during PDT performed in the genital area. Its analgesic and sedative effects may increase patients’ adherence to this painful procedure. Furthermore, given its safety, it can be easily managed in outpatient clinics by trained dermatologists.

Thermo-compression molding of chitosan with a deep eutectic mixture for biofilms development

Supplementary information available at: http://www.rsc.org/suppdata/c5/gc/c5gc02231b/c5gc02231b1.pdf

Hindsight judgement on ambiguous episodes of suspected infection in critically ill children: poor consensus amongst experts?

Few episodes of suspected infection observed in paediatric intensive care are classifiable without ambiguity by a priori defined criteria. Most require additional expert judgement. Recently, we observed a high variability in antibiotic prescription rates, not explained by the patients' clinical data or underlying diseases. We hypothesised that the disagreement of experts in adjudication of episodes of suspected infection could be one of the potential causes for this variability. During a 5-month period, we included all patients of a 19-bed multidisciplinary, tertiary, neonatal and paediatric intensive care unit, in whom infection was clinically suspected and antibiotics were prescribed ( n=183). Three experts (two senior ICU physicians and a specialist in infectious diseases) were provided with all patient data, laboratory and microbiological findings. All experts classified episodes according to a priori defined criteria into: proven sepsis, probable sepsis (negative cultures), localised infection and no infection. Episodes of proven viral infection and incomplete data sets were excluded. Of the remaining 167 episodes, 48 were classifiable by a priori criteria ( n=28 proven sepsis, n= 20 no infection). The three experts only achieved limited agreement beyond chance in the remaining 119 episodes (kappa = 0.32, and kappa = 0.19 amongst the ICU physicians). The kappa is a measure of the degree of agreement beyond what would be expected by chance alone, with 0 indicating the chance result and 1 indicating perfect agreement. CONCLUSION: agreement of specialists in hindsight adjudication of episodes of suspected infection is of questionable reliability.

The analysis of knowledge retention mechanisms for leaving experts

The goal of this thesis is studying knowledge retention mechanisms used in cases of single experts’ leaving in the case company, analyzing the reason for the mechanisms choice and successfulness of knowledge retention process depending of that choice. The theoretical part discusses the origins of knowledge retention processes in the theoretical studies, the existing knowledge retention mechanisms and practical issues of their implementation. The empirical part of the study is designed as employees’ interview with later discussion of the findings. The empirical findings indicate the following reasons for knowledge retention mechanisms choice: type of knowledge retained, specialty of leaving experts and time and distance issues of a particular case. The following factors influenced the success of a retention process: choice of knowledge retention mechanisms, usage of combination of mechanisms and creation of knowledge retention plans. The results might be useful for those interested in factors influencing knowledge retention processes in cases of experts’ departure.