Quiet Ego and Well-Being: The What, Why, and How -- An Investigation of the Implications of the Quiet Ego for Psychological Well-Being

Ego is that which constructs and evaluates the concept of self in that it processes information and interprets objects (e.g., people, experiences) and labels them as part of the self (or not). To put it another way, ego is an active experiencer, perceiver, and doer that constructs, maintains, and regulates our sense of self and our relationships with others. Ego processes information in different modes. The mode that has been most extensively studied is the egotistical-narcissistic one because it fits well with the predominant cultural ideology of being individualistic and being motivated by self-interest. Thus, what has largely been ignored is an ego that is not predominantly motivated by self-interest. The quiet ego refers to a self-understanding that transcends egotism and identifies with a less defensive and growth-oriented stance toward the self and others. As a relatively new construct, its validity has been examined in domains related to balance, compassion, or growth. Its validity, however, has rarely been examined with respect to other aspects of self-identity that are both conceptually similar and have implications for well-being. In the dissertation studies, I first evaluated and established construct validity of the quiet ego with respect to the domains of self-perception (self-concept clarity or SCC), other-perception (theory of mind or ToM), and emotional intelligence (or EI) (Chapter 2). Building on these studies, I further examined the associations between the quiet ego and well-being through the lenses of SCC, ToM, and EI, demonstrating that the quiet ego predicts enhanced psychological well-being and self-esteem (Chapter 3.1), enriching interpersonal relations (Chapter 3.2), improved subjective well-being and attenuated stress (Chapter 3.3) via its associations with SCC, ToM, and EI, respectively. Finally, to further explore the nature of the association between the quiet ego and well-being, in Chapter 4, I investigated the causal link between the quiet ego and well-being using a longitudinal, randomized experiment. I found that a quiet ego contemplation improved participants’ subjective well-being, diminished their stress, and elevated their psychological flourishing. Taken together, these studies established the importance and validity of the quiet ego, and the results may have significant implications in applied, real-world contexts

Three Essays on Causal Inference With Model Averaging

This dissertation contains essays on causal inference with model averaging. The first essay presents a theoretical derivation of a model-averaging-based average treatment effect estimator. The second essay provides comparison of predictability of treated counterfactual outcome between model averaging and other methods. The third essay is an empirical study evaluating the economic impact of Ukraine\u27s 2013 conflict. The first essay constructs a new average treatment effect estimator based on model averaging in a panel data setting. The estimator is shown to be asymptotically unbiased and consistent. Its asymptotic distribution is derived, which turns out to be non-normal and non- standard. A subsampling procedure is then applied to obtain valid inference. Simulation results show that the proposed estimator compares favorably with alternative estimators in out- of-sample prediction accuracy under a common factor structure. The second essay further compares predictability of treated counterfactual outcome between model averaging and other methods under more general set-ups. The simulations show that the model averaging and penalized regression methods yield more accurate counterfactual prediction than the model selection methods. We also find evidences that if the predictors (e.g., control units\u27 outcomes) are more correlated, the model averaging methods have more accurate prediction than the penalized regression, and vice versa. The third essay evaluates the economic impact of Ukraine\u27s 2013 conflict using a comparative case study. A modified synthetic control method is applied to account for potential spillover from the conflict on Ukraine\u27s neighbouring countries. The results show that Ukraine\u27s real GDP was reduced by 29.7% from late-2013 to the end of 2015. The spillover effects are detected in every quarter since the conflict began. Furthermore, negative spillover effects are found in countries selected by the modified synthetic control

Early career patterns : a comparison of Great Britain and West Germany

BACKGROUND: When it comes to corporate decision making, the traditional rational model suggests that deliberative analysis yields good results. Thus, when contemplating strategic moves, executives are “required” to conduct deliberative analyses. As today’s business environment is becoming increasingly complex and fast-paced, however, executives often face the dilemma of having to make carefully considered strategic decisions on the one hand and not having enough time on the other hand. Intuition offers an efficient solution in this situation. PURPOSE: The purpose of this study is to investigate how corporate executives employ both rationality and intuition in making strategic decisions under uncertain, complex and time-pressured circumstances. RESEARCH METHOD: We conducted three face-to-face interviews with executives from three companies in Sweden. Each interview lasted around one hour.    RESULTS: Drawing on previous psychological and managerial research, we argue that rationality and intuition are better viewed as being complementary rather than separate. Findings from the study suggest that intuition could serve as an effective and efficient means for managers to make strategic decisions; and that intuition indeed plays a role in strategic decision making under complex, uncertain and time limited contexts

Generation of Chinese classical poetry based on pre-trained model

In order to test whether artificial intelligence can create qualified classical poetry like humans, the author proposes a study of Chinese classical poetry generation based on a pre-trained model. This paper mainly tries to use BART and other pre training models, proposes FS2TEXT and RR2TEXT to generate metrical poetry text and even specific style poetry text, and solves the problem that the user's writing intention gradually reduces the relevance of the generated poetry text. In order to test the model's results, the authors selected ancient poets, by combining it with BART's poetic model work, developed a set of AI poetry Turing problems, it was reviewed by a group of poets and poetry writing researchers. There were more than 600 participants, and the final results showed that, high-level poetry lovers can't distinguish between AI activity and human activity, this indicates that the author's working methods are not significantly different from human activities. The model of poetry generation studied by the author generalizes works that cannot be distinguished from those of advanced scholars. The number of modern Chinese poets has reached 5 million. However, many modern Chinese poets lack language ability and skills as a result of their childhood learning. However, many modern poets have no creative inspiration, and the author's model can help them. They can look at this model when they choose words and phrases and they can write works based on the poems they already have, and they can write their own poems. The importance of poetry lies in the author's thoughts and reflections. It doesn't matter how good AI poetry is. The only thing that matters is for people to see and inspire them.Comment: 8 pages,2 figure

Neutral Mood Induction During Reconsolidation Reduces Accuracy, but Not Vividness and Anxiety, of Emotional Episodic Memories

When consolidated memories are reactivated, they become labile and have to go through reconsolidation to become stabilized.  This property of memory may potentially be used to reduce the impact of highly negative episodic memories.  Because detailed and vivid negative episodic memories are mediated by high arousal, if arousal is lessened during reconsolidation, then memory accuracy and vividness should diminish.  In this study, I examine this hypothesis.  Participants viewed a stressful, suspenseful movie on Day 1 to develop negative episodic memories.  Then, 24 to 29 hours later, they saw a brief reminder of the stressful movie (or not), and then viewed a neutral (or positive) movie.  Another 24 to 29 hours later, I tested the accuracy, vividness, and anxiety associated with their memory of the stressful movie. Participants who watched the reminder and then the neutral movie showed reduced memory accuracy.  Despite the reduction in memory accuracy, their memory vividness and anxiety associated with the stressful movie did not decrease. The results partly supported my hypothesis

Nanostructured Semiconductors for Electrochemical and Photoelectrochemical Water Splitting

Chemical energy storage by water splitting is a promising solution for the utilization of solar energy in numerous applications. Both efficient electrocatalysts and photocatalysts with a facile and scalable synthesis method are indispensable to achieve economic feasibility for water splitting. In the introductory chapter, a comprehensive review of electrochemical and photoelectrochemical (PEC) water splitting is provided and the fundamental concept of the PEC device design is described. Three different systems for water splitting were then explored in the following experimental chapters. In the second chapter, Manganese oxides (Mn3O4, Mn5O8 and Mn2O3) nanoparticles, with a range of specific surface area and crystal structures, were synthesized and compared. The nearly identical morphology of synthesized manganese oxide nanocrystals allows a systematic investigation on the relation between oxidation states and catalytic activities of different manganese oxide nanocrystals. Interesting discoveries regarding surface-specific turnover frequency, mole-specific turnover frequency and catalytic stability were demonstrated. Highly transparent and robust sub-monolayers of Co3O4 nano-islands were subsequently developed, which efficiently catalyse water oxidation with a comparable performance to noble metal-based catalysts. The potential of the Co3O4 nano-islands for photoelectrochemical water splitting has been demonstrated by incorporation of co-catalysts in GaN nanowire photoanodes. The Co3O4-GaN photoanodes reveal significantly reduced onset overpotentials, improved photoresponse and photostability compared to the bare GaN ones. In the last experimental chapter, we developed both physically- and chemically-induced morphology/structure tuning procedures, viz. capillary force-induced self-assembly and corrosion followed by regrowth, drastically increasing the water oxidation photocurrent density of nanostructured hematite photoanode. In addition to morphological changes, structural transformations were obtained by capillary force-induced self-assembly resulting in improved crystallinity of hematite with preferential orientation in the [110] direction. High conductivity of the hematite (001) basal planes contributes to the significantly enhanced photo-electrocatalytic activity. Subsequent dissolution and regrowth of hematite nanostructures further improved the performance, resulting in improved light absorption, more efficient charge separation and surface charge transfer processes

Visible and Near Infrared Image Fusion Based on Texture Information

Multi-sensor fusion is widely used in the environment perception system of the autonomous vehicle. It solves the interference caused by environmental changes and makes the whole driving system safer and more reliable. In this paper, a novel visible and near-infrared fusion method based on texture information is proposed to enhance unstructured environmental images. It aims at the problems of artifact, information loss and noise in traditional visible and near infrared image fusion methods. Firstly, the structure information of the visible image (RGB) and the near infrared image (NIR) after texture removal is obtained by relative total variation (RTV) calculation as the base layer of the fused image; secondly, a Bayesian classification model is established to calculate the noise weight and the noise information and the noise information in the visible image is adaptively filtered by joint bilateral filter; finally, the fused image is acquired by color space conversion. The experimental results demonstrate that the proposed algorithm can preserve the spectral characteristics and the unique information of visible and near-infrared images without artifacts and color distortion, and has good robustness as well as preserving the unique texture.Comment: 10 pages,11 figure

Rebalanced Zero-shot Learning

Zero-shot learning (ZSL) aims to identify unseen classes with zero samples during training. Broadly speaking, present ZSL methods usually adopt class-level semantic labels and compare them with instance-level semantic predictions to infer unseen classes. However, we find that such existing models mostly produce imbalanced semantic predictions, i.e. these models could perform precisely for some semantics, but may not for others. To address the drawback, we aim to introduce an imbalanced learning framework into ZSL. However, we find that imbalanced ZSL has two unique challenges: (1) Its imbalanced predictions are highly correlated with the value of semantic labels rather than the number of samples as typically considered in the traditional imbalanced learning; (2) Different semantics follow quite different error distributions between classes. To mitigate these issues, we first formalize ZSL as an imbalanced regression problem which offers empirical evidences to interpret how semantic labels lead to imbalanced semantic predictions. We then propose a re-weighted loss termed Re-balanced Mean-Squared Error (ReMSE), which tracks the mean and variance of error distributions, thus ensuring rebalanced learning across classes. As a major contribution, we conduct a series of analyses showing that ReMSE is theoretically well established. Extensive experiments demonstrate that the proposed method effectively alleviates the imbalance in semantic prediction and outperforms many state-of-the-art ZSL methods. Our code is available at https://github.com/FouriYe/ReZSL-TIP23.Comment: Accepted to IEEE Transactions on Image Processing (TIP) 202