Maximum Likelihood Methods in Treating Outliers and Symmetrically Heavy-Tailed Distributions for Nonlinear Structural Equation Models with Missing Data

By means of more than a dozen user friendly packages, structural equation models (SEMs) are widely used in behavioral, education, social, and psychological research. As the underlying theory and methods in these packages are vulnerable to outliers and distributions with longer-than-normal tails, a fundamental problem in the field is the development of robust methods to reduce the influence of outliers and the distributional deviation in the analysis. In this paper we develop a maximum likelihood (ML) approach that is robust to outliers and symmetrically heavy-tailed distributions for analyzing nonlinear SEMs with ignorable missing data. The analytic strategy is to incorporate a general class of distributions into the latent variables and the error measurements in the measurement and structural equations. A Monte Carlo EM (MCEM) algorithm is constructed to obtain the ML estimates, and a path sampling procedure is implemented to compute the observed-data log-likelihood and then the Bayesian information criterion for model comparison. The proposed methodologies are illustrated with simulation studies and an example. The research described herein was fully supported by a grant (CUHK 4243/03H) from the Rearch Grants Council of the Hong Kong Special Administration Region. The authors are thankful to the Editor, the Associate Editor, and anonymous reviewers for valuable comments which improve the paper significantly, and are grateful to ICPSR and the relevant funding agency for allowing the use of their data. Requests for reprints should be sent to S. Y. Lee, Department of Statistics, The Chinese University of Hong Kong, Shatin, N. T., Hong Kong.     

SPIRITUAL TRANSFORMATION AS THE AWAKENING OF GLOBAL CONSCIOUSNESS: A DIMENSIONAL SHIFT IN THE TECHNOLOGY OF MIND

Abstract. Great spiritual and philosophical traditions through the ages have sought to tap and articulate the grammar or logic of the fundamental unified field that is the common generative ground of our diverse worldviews, religions, cultures, ideologies, and disciplinary languages. I suggest that we are in the midst of a profound dimensional shift in our rational capacity to process reality, and I seek to articulate the implications of this evolutionary shift to global reason and awakened consciousness for all aspects of our human and rational enterprise. It is clear that we are in the midst of an unprecedented shift in the human condition—a global renaissance that affects every aspect of our cultural lives, self‐understanding, experience, and world making. This evolutionary transformation, when seen through the dilated global lens, has been emerging through the ages on a global scale. I suggest that this advance in our technology of mind is of an order of magnitude that is so radical and comprehensive that the very concept of a person, of what it means to be human, of our encounter with Reality, and of all our hermeneutical arts including the sciences are likewise taken to a higher, global, dimension. I explore this emergent grammar of spiritual transformation to global, dialogic, integral, and holistic consciousness, the global awakening of reason, scientific knowing, and the holistic worldview.     

算法歧视比人类歧视引起更少道德惩罚欲

算法歧视屡见不鲜, 人们对其有何反应值得关注。6个递进实验比较了不同类型歧视情境下人们对算法歧视和人类歧视的道德惩罚欲, 并探讨其潜在机制和边界条件。结果发现：相对于人类歧视, 人们对算法歧视的道德惩罚欲更少(实验1~6), 潜在机制是人们认为算法(与人类相比)更缺乏自由意志(实验2~4), 且个体拟人化倾向越强或者算法越拟人化, 人们对算法的道德惩罚欲越强(实验5~6)。研究结果有助于更好地理解人们对算法歧视的反应, 并为算法犯错后的道德惩罚提供启示。     

The effects on classifier performance of 2D discrete wavelet transform analysis and whale optimization algorithm for recognition of power quality disturbances

Power Quality (PQ) is becoming more and more important day by day in the electric network. Signal processing, pattern recognition and machine learning are increasingly being studied for the automatic recognition of any disturbances that may occur during the generation, transmission, and distribution of electricity. There are three main steps to identify the PQ disturbances. These include the use of signal processing methods to calculate the features representing the disturbances, the selection of those that are more useful than these feature sets to prevent the creation of a complex classification model, the creating a classification model that recognizes multiple classes using the selected feature subsets. In this study, one-dimensional (1D) PQ disturbances signals are transformed into two-dimensional (2D) signals, 2D discrete wavelet transforms (2D-DWT) are used to extract the features. The features are extracted by using the wavelet families such as Daubechies, Biorthogonal, Symlets, Coiflets and Fejer-Korovkin in 2D-DWT to analyze PQ disturbances. Whale Optimization Algorithm (WOA) and k-nearest neighbor (KNN) classifier determine the feature subsets. Then, WOA and k nearest neighbor (KNN) classifier are used to determine the feature group. By using KNN and Support Vector Machines (SVM) classification methods, Classifier models that distinguish PQ disturbances are formed. The main aim of the study is to determine the features derived from 2D wavelet coefficients for different wavelet families and to determine which of them has a better classification performance to distinguish PQ disturbances signals. At the same time, different classification methods are simulated and a model which can classify PQ disturbances signals with high performance is created. Also, the generated models are analysed for their performance in terms of different noise levels (40 dB, 30 dB, 20 dB). The result of this simulation study shows that the model developed to classify PQ disturbances is superior to conventional models and other 2D signal processing methods in the literature. In addition, it was concluded that the proposed method can cope better with noisy signals by low computational complexity and higher classification rate.     

Sibling rivalry,separation, and change in Austen's Sense and Sensibility

The paper explores a process of growth represented in the interplay of Jane Austen's characterizations of Marianne and Elinor Dashwood in Sense and Sensibility, approaching the text through the lens of psychoanalytic theories on oedipal sibling rivalry, separation, and processes of change. A close reading of Sense and Sensibility tracks Marianne Dashwood's repudiation of any ‘second attachment’ as the surface of an unconscious fantasy, denying a rival for the mother's love. A psychoanalytic view contrasts Marianne's lack of separation from her mother, her use of denial and projection, and her near death after losing the man she loves, with her older sister Elinor Dashwood's capacities for depression, reflection, and greater acceptance of loss and separation. The narrative portrays Mrs. Dashwood's identification with and idealization of her daughter Marianne, which contribute to her oedipal sibling ‘victory’. In the language and structure of the novel, the projections, identifications, aggressions, and separations (conscious and unconscious) of the sisters in the vicissitudes of their adolescent loves and rivalries constitute a process of growth. Austen's novel brings to life, with the vividness and coherence of great literature, forces and fantasies in oedipal sibling rivalries, inspiring renewed attention to their subtle presence in the transference and countertransference of the psychoanalytic process.     

Optimized cuttlefish algorithm for diagnosis of Parkinson’s disease

This paper presents an optimized cuttlefish algorithm for feature selection based on the traditional cuttlefish algorithm, which can be used for diagnosis of Parkinson’s disease at its early stage. Parkinson is a central nervous system disorder, caused due to the loss of brain cells. Parkinson's disease is incurable and could eventually lead to death but medications can help to control symptoms and elongate the patient's life to some extent. The proposed model uses the traditional cuttlefish algorithm as a search strategy to ascertain the optimal subset of features. The decision tree and k-nearest neighbor classifier as a judgment on the selected features. The Parkinson speech with multiple types of sound recordings and Parkinson Handwriting sample’s datasets are used to evaluate the proposed model. The proposed algorithm can be used in predicting the Parkinson’s disease with an accuracy of approximately 94% and help individual to have proper treatment at early stage. The experimental result reveals that the proposed bio-inspired algorithm finds an optimal subset of features, maximizing the accuracy, minimizing number of features selected and is more stable.     

谱聚类算法在不同属性层级结构诊断评估中的应用

聚类分析已成功用于认知诊断评估(CDA)中,使用广泛的聚类分析方法为K-means算法,有研究已证明K-means在CDA中具有较好的聚类效果。而谱聚类算法通常比K-means分类效果更佳,本研究将谱聚类算法引进CDA,探讨了属性层级结构、属性个数、样本量和失误率对该方法的影响。研究发现：（1）谱聚类算法要比K-means提供更好的聚类结果,尤其在实验条件较苛刻时,谱聚类算法更加稳健;（2）线型结构聚类效果最好,收敛型和发散型相近,独立型结构表现较差;（3）属性个数和失误率增加后,聚类效果会下降;（4）样本量增加后,聚类效果有所提升,但K-means方法有时会有反向结果出现。     

Comments on 'the case of Melanie' and the reaction of Eduardo Gastelumendi

Following an attempt to connect the theory of archetypes with the theory of primal phantasies, the commentary refers to how moments of complexity may be differentiated in terms of 'now moments' and concludes with an amplification of the 'black woman' in Melanie's dream related to a black woman in an important Austrian fairy tale.     

Perhaps Unidimensional Is Not Unidimensional

Miller (1956) identified his famous limit of 7 ± 2 items based in part on absolute identification—the ability to identify stimuli that differ on a single physical dimension, such as lines of different length. An important aspect of this limit is its independence from perceptual effects and its application across all stimulus types. Recent research, however, has identified several exceptions. We investigate an explanation for these results that reconciles them with Miller’s work. We find support for the hypothesis that the exceptional stimulus types have more complex psychological representations, which can therefore support better identification. Our investigation uses data sets with thousands of observations for each participant, which allows the application of a new technique for identifying psychological representations: the structural forms algorithm of Kemp and Tenenbaum (2008) . This algorithm supports inferences not possible with previous techniques, such as multidimensional scaling.     

Primal splitting as a basis for emotional and cognitive development in children

This article discusses the importance of good primal splitting as the basis for the child's emotional and cognitive development. A theoretical introduction analyses the possible pathologies of primal splitting, as they were first pointed out by Melanie Klein and then by some post-Kleinian authors, in particular Donald Meltzer. This is followed by some excerpts from the clinical material of the first year of the psychoanalytic psychotherapy of a three-year-old child suffering from eating problems. Starting from the child's play activity, the author tries to identify the splitting problems that underlie the child's view of the world, and to address these with him. The article shows how at times it can be useful to speak also to the child's intelligence, combining the usual analytic work (containment of anxiety and analysis of transference) with the analysis of the child's distortions and cognitive misconceptions. The article also suggests that faulty primal splitting tends to be transmitted from one generation to the next.