La protection des données personnelles des patients face à la modernisation de notre système de santé

With the Internet and the artificial intelligence, the collection of personal data is becoming a strategic element for the digital economy. The collection of information touching the health or the body must respect the patients’ rights. The GDPR came into effect on May 25th, 2018. The healthcare professionals who are established in a Member state, must be careful because they can base their exercise on clinical or biological elements of their patients.     

A multidimensional examination of marital conflict and subjective health over 16 years

Guided by stress process perspectives, this study conceptualizes marital conflict as a multidimensional stressor to assess how three aspects of conflict—frequency of disagreements, breadth of disagreements, and cumulative disagreements—impact subjective health. Longitudinal data of married couples spanning 16 years (n = 373 couples) were analyzed using multilevel modeling. For husbands, more frequent disagreements than usual within a given year were associated with poorer subjective health. For wives, the greater cumulative effects of disagreements over 16 years were harmful for subjective health. We discuss how gendered self‐representations and relationship power issues help explain the findings. This research demonstrated the importance of examining multiple aspects of marital conflict to reveal that their subjective health consequences function differently for wives and husbands.     

Kernel principal component analysis combining rotation forest method for linearly inseparable data

Rotation forest (RoF) is an ensemble classifier combining linear analysis theories and decision tree algorithms. In recent existing works, RoF was widely applied to various fields with outstanding performance compared to traditional machine learning techniques, given that a reasonable number of base classifiers is provided. However, the conventional RoF algorithm suffers from classifying linearly inseparable datasets. In this study, a hybrid algorithm integrating kernel principal component analysis (KPCA) and the conventional RoF algorithm is proposed to overcome the classification difficulty for linearly inseparable datasets. The radial basis function (RBF) is selected as the kernel for the KPCA method to establish the nonlinear mapping for linearly inseparable data. Moreover, we evaluate various kernel parameters for better performance. Experimental results show that our algorithm improves the performance of RoF with linearly inseparable datasets, and therefore provides higher classification accuracy rates compared with other ensemble machine learning methods.     

长时间相处是亲子关系的保护性因素吗？——疫情背景下基于微博大数据的质性分析

目的：采用大数据技术与质性分析相结合的方式,探索疫情期间青少年长时间与父母相处的条件下,亲子冲突的演变特征。方法：利用Python3.8编写程序,以“亲子冲突、初中生、高中生、青少年、冲突、父母、与父母冲突”等为关键词在微博平台上搜索个人发布的内容,经数据整理,选择286条微博文本纳入文本分析,用NVivo12质性分析软件辅助编码。结果：亲子冲突的表达包含了冲突诱因、冲突类型、冲突领域,和冲突管理策略等四个方面的特征。结论：研究建构了冲突诱因、冲突领域、冲突类型和冲突应对策略之间的关系模型。亲子冲突的发生是在社会文化背景下,由于家庭功能失调引发的亲子互动方式异常。亲子冲突发生以后,取积极应对策略有助于家庭功能的良性调节。     

A latent topic model with Markov transition for process data

We propose a latent topic model with a Markov transition for process data, which consists of time-stamped events recorded in a log file. Such data are becoming more widely available in computer-based educational assessment with complex problem-solving items. The proposed model can be viewed as an extension of the hierarchical Bayesian topic model with a hidden Markov structure to accommodate the underlying evolution of an examinee's latent state. Using topic transition probabilities along with response times enables us to capture examinees' learning trajectories, making clustering/classification more efficient. A forward-backward variational expectation-maximization (FB-VEM) algorithm is developed to tackle the challenging computational problem. Useful theoretical properties are established under certain asymptotic regimes. The proposed method is applied to a complex problem-solving item in the 2012 version of the Programme for International Student Assessment (PISA).     

The Performance of Multivariate Methods for Two-Group Comparisons with Small Samples and Incomplete Data

Abstract

In intervention studies having multiple outcomes, researchers often use a series of univariate tests (e.g., ANOVAs) to assess group mean differences. Previous research found that this approach properly controls Type I error and generally provides greater power compared to MANOVA, especially under realistic effect size and correlation combinations. However, when group differences are assessed for a specific outcome, these procedures are strictly univariate and do not consider the outcome correlations, which may be problematic with missing outcome data. Linear mixed or multivariate multilevel models (MVMMs), implemented with maximum likelihood estimation, present an alternative analysis option where outcome correlations are taken into account when specific group mean differences are estimated. In this study, we use simulation methods to compare the performance of separate independent samples t tests estimated with ordinary least squares and analogous t tests from MVMMs to assess two-group mean differences with multiple outcomes under small sample and missingness conditions. Study results indicated that a MVMM implemented with restricted maximum likelihood estimation combined with the Kenward–Roger correction had the best performance. Therefore, for intervention studies with small N and normally distributed multivariate outcomes, the Kenward–Roger procedure is recommended over traditional methods and conventional MVMM analyses, particularly with incomplete data.     

An extended application ‘Brain Q’ processing EEG and MEG data of finger stimulation extended from ‘Zeffiro’ based on machine learning and signal processing

GoalTo apply signal processing and machine learning skills and knowledge in processing the EEG and MEG signal and further localize and evaluate the source of the finger stimulation.MethodsCognitive control is usually applied in information processing and behavioral response. In the preprocessing, baseline correction is implemented to analyze the pre-stimuli, combining ERP to mark the event related potential, studying the time-locked only behavior. Z-score transform, coherence and spec trum are calculated and analyzed in the functional connectivity analysis.In addition to the functional analysis, Bayes Optimizer evaluates the neuro imaging according to the hierarchical Bayes. The introduction of the application is described from both user and developer’s prospects. Results: Introduction of both user and developers aspects, on its modules from pre-processing, functional analysis and results visualization and evaluation is conducted with one specific clinical data case, including the correlation is higher especially on gamma band and the MVAR coherence on the whole source space depicting the relation between different regions, especially on somatosensory (compared by thalamus) when stimulated by finger activity, phase-lock property of the E/MEG signal and etc. Compared to a manual selection, the scaling parameter prediction can be improved with support vector machine (SVM). The evaluation results with Bayes Optimization, location prediction is superior in the somatosensory area and in the thalamus, the total reconstructed source space is larger, one of the realization of cognitive system comparing different kernels and classifiers. The SVM and discriminant classifier gives similar results evaluating the dipole localization and the parameter choice related as well to the shape parameter, noise level, hyperprior and etc.ConclusionApproaches of Brain Q are found to be suitable for pre-processing for the EEG and MEG data. The system is capable of functional analysis including coherence and spectral related computation. Machine learning techniques are conducted as well to analyze and evaluate the result of the dipole reconstruction and help to predict the better model parameters and the localization of the origin dipoles. A case on finger stimulation clinical data is conducted and the results of the analysis temporarily and spatially manifests its functionality for users and potential extensions for developers.     

Religion and state entanglement and interstate armed conflict initiation, 1990–2010

ABSTRACT

The relationship of religion to armed conflict initiation, as opposed to occurrence, remains understudied and undertheorised. The same is true for the relationship of religion to armed conflicts between states, cf. intra-state or extra-state conflict. This contribution adds to the understanding of both of these conflict outcomes. It examines the relationship to interstate armed conflict initiation of state religion, operationalised as government involvement in religion in the Religion and State dataset of Jonathan Fox. We find sufficient evidence to conclude that religion–state entanglement does raise states’ propensities towards first use of military force. Official religion and official support are both strongly correlated with that outcome. Legislative support to the majority religion is also well correlated with it. State-level discrimination against minority religions is weakly but positively correlated, but heavy regulation of majority religions is not correlated. We further find evidence that minority discrimination, majority regulation and legislative support wield their effects in tandem more strongly than they do individually. The foregoing supports the theory that official religious preference does make states more militant.     

Historically recontextualizing Sidman's Tactics: How behavior analysis avoided psychology's methodological Ouroboros

Psychology is undergoing major cultural changes methodologically, with efforts to redefine how psychologists analyze and report their data. Davidson (2018) argued that psychology's methodological crises stem from mechanical objectivity involving the adoption of an analytic tool as source of dependable knowledge. This has led to institutionalization, and eventually uncritical ritualistic use, such as happened with null hypothesis statistical testing. Davidson invoked the mythological symbol of the Ouroboros to represent the endless churning of statistical fads. Sidman (1960), in his Tactics of Scientific Research provided a shield from these problems in terms of the premium he placed on the experience, expertise, judgement, and decision-making of the scientist, that appear to be absent in psychology's ritualized processes.     

Visual cognitive algorithms for high-dimensional data and super-intelligence challenges

In the long run the cognitive algorithms intend to make super-intelligent machines and super-intelligent humans. This paper presents a technical process to reach specific aspects of super-intelligence that are out of the current human cognitive abilities. These aspects are inabilities to discover patterns in large numeric multidimensional data with a naked eye. This is a long-standing problem in Data Science and Modeling in general. The major obstacle is in human inability to see n-D data by a naked eye and our needs in visualization means to represent n-D data in 2-D losslessly. While these means exist their number and abilities are limited. This paper expands the class of such lossless visual methods, by further developing a new concept of Generalized Shifted Paired Coordinates. It shows the advantages of proposed reversible lossless technique by representing real data and by proving mathematical properties.