Social Energy and Trust

Trust is the crucial basis of all interpersonal relationships and is composed of expectation and vulnerability. Its significance within the field of psychology is far-reaching. In 2002, Canavan coined the term social energy to define the distinct psychological phenomenon that occurs when the self (P) and another individual (O) share enthusiasm toward a common object or event (X). In this paper, I investigate the influence of social energy on how individuals rate or rank the trustworthiness of another, as well as the reason for being trusted, in the presence of high and low social energy. The experiment is conducted within a 2X1 ANOVA design by manipulating the different levels of social energy, either high or low. In addition to measuring trust and the reasons for trusting, participants’ feelings and emotions were assessed in three hypothetical situations, measuring whether the trust was warranted. The main finding concludes that participants’ ratings of another individual’s trustworthiness is significantly higher in high social energy conditions, compared to those in low social energy conditions. In addition, the reason for P’s doing so is that “I know in high social energy O thinks in the same way as I do,” which is reinforced by CHI2 Test results. By assessing emotional variables, it is found that gratitude, positive feelings, expectation, and regrets differ significantly with regard to levels of social energy. In addition, the HSE participant liked and believed the other, and was more kindly disposed than the LSE participant, who tended to be more blaming and punitive

Investigating Wage Disparities Based on Sexual Orientation in the German Labor Market

Diese Studie untersucht den Einfluss der sexuellen Orientierung auf das Einkommen im deutschen Arbeitsmarkt. Anhand von Daten aus der Sozio-oekonomischen Panelstudie aus dem Jahr 2020 analysiere ich die Lohnunterschiede zwischen heterosexuellen und nicht-heterosexuellen Arbeitnehmern. Die Ergebnisse zeigen, dass nicht-heterosexuelle Männer etwa 7,7% weniger verdienen als ihre heterosexuellen Kollegen, während lesbische oder bisexuelle Frauen einen Einkommensvorteil von 4,22% erfahren, obwohl dieser statistisch nicht signifikant ist. Weitere Analysen, die sich auf europäisch geborene Vollzeitbeschäftigte konzentrieren, bestätigen diese Trends, wenn auch mit geringerer statistischer Signifikanz. Die Oaxaca-Blinder-Dekomposition hebt Faktoren hervor, die zur Lohnlücke beitragen, wobei Diskriminierung und berufliche Segregation die Haupttreiber hinter den Lohnunterschieden sind. Darüber hinaus betont diese Studie die Notwendigkeit weiterer Forschung und politischer Maßnahmen, um derartige Ungleichheiten auf dem Arbeitsmarkt anzugehen.This study investigates the impact of sexual orientation on earnings in the German labour market. Using data from the Socio-Economic Panel survey from the year 2020, I analyse wage differentials between heterosexual and non-heterosexual workers. The findings reveal that nonheterosexual men earn approximately 7.7% less than their heterosexual counterparts, while lesbian or bisexual women experience a 4.22% income premium, though statistically insignificant. Further analysis focusing on European-born full-time workers confirms these trends, although with reduced statistical significance. Oaxaca-Blinder decomposition highlights factors contributing to the wage gap, with discrimination and occupational sorting being the main drivers behind the wage disparities. Furthermore, this study emphasises the need for further research and policy interventions to address labour market inequalities of this kind

Pricing and Bidding Strategies for Cloud Computing Spot Instances

We consider a cloud service based on spot instances and explore bidding and pricing strategies aimed at optimizing users\u27 utility and provider\u27s revenue, respectively. Our focus is on jobs that are heterogeneous in both valuation and sensitivity to execution delay. Of particular interest is the impact of correlation in these two dimensions. We characterize optimal bidding and pricing strategies under some simplifying assumptions, and more importantly highlight the impact of correlation in determining the benefits of a spot service over an on-demand service. We also provide a preliminary assessment of the results\u27 robustness under more general assumptions

The Role of Biochar in Enhancing Soil Carbon Sequestration for Carbon Neutrality

With the development of industry, carbon emissions are increasing: global temperatures are rising, habitats are shrinking, sea level rises and other issues are emerging one after another, climate change is getting more and more attention, and strategic goals such as carbon neutrality have also been formulated to alleviate global climate change. As a material to mitigate climate change and help achieve the goal of carbon neutrality, biochar can effectively absorb and store carbon and reduce carbon footprints. Through a critical analysis of the role of biochar in achieving carbon neutrality, this paper analyzes the principles of carbon absorption using biochar in agriculture, etc., and points out the existing limitations of biochar, such as high cost and land occupation, and efficacy instability, and gives the existing research based on the limitations. An increasing corpus of research has pinpointed elements like the temperature at which biochar is formed and the kind of biochar that is best for a certain soils and plants. Some solutions and mitigation methods conclude that biochar has a high development potential to help achieve carbon neutrality

Improving Resource Efficiency in Cloud Computing

Customers inside the cloud computing market are heterogeneous in several aspects, e.g., willingness to pay and performance requirement. By taking advantage of trade-offs created by these heterogeneities, the service provider can realize a more efficient system. This thesis is concerned with methods to improve the utilization of cloud infrastructure resources, and with the role of pricing in realizing those improvements and leveraging heterogeneity. Towards improving utilization, we explore methods to optimize network usage through traffic engineering. Particularly, we introduce a novel optimization framework to decrease the bandwidth required by inter-data center networks through traffic scheduling and shaping, and then propose algorithms to improve network utilization based on the analytical results derived from the optimization. When considering pricing, we focus on elucidating conditions under which providing a mix of services can increase a service provider\u27s revenue. Specifically, we characterize the conditions under which providing a ``delayed\u27\u27 service can result in a higher revenue for the service provider, and then offer guidelines for both users and providers

LSTM Deep Neural Network Based Power Data Credit Tagging Technology

The value of power data credit reporting in the social credit system continues to increase, and the government, users and the whole society have deep expectations and support for power data credit reporting. This paper will combine the data labeling theory as the support, define the power data label and explain its labeling implementation. Based on the construction of knowledge graph, the method of labeling power data is introduced in detail: demand analysis method, index selection method, data cleaning method and data desensitization method. Use the sorted data labels to establish a label system for power data, and through its system, visualize the comprehensive situation of enterprise power data credit information to meet the development of power data credit business. This paper takes shell enterprises as the main representatives of credit risk enterprises, analyzes the power data in the three stages before and after loans, and builds a value mining model for power credit data. In the future, the data labeling technology and value mining model of the power data credit business will be comprehensively applied, and the power data label library and credit model library will be established and continuously improved, so as to facilitate the evaluation of the operation of the enterprise at different stages

Identity-Obscured Neural Radiance Fields: Privacy-Preserving 3D Facial Reconstruction

Neural radiance fields (NeRF) typically require a complete set of images taken from multiple camera perspectives to accurately reconstruct geometric details. However, this approach raise significant privacy concerns in the context of facial reconstruction. The critical need for privacy protection often leads invidividuals to be reluctant in sharing their facial images, due to fears of potential misuse or security risks. Addressing these concerns, we propose a method that leverages privacy-preserving images for reconstructing 3D head geometry within the NeRF framework. Our method stands apart from traditional facial reconstruction techniques as it does not depend on RGB information from images containing sensitive facial data. Instead, it effectively generates plausible facial geometry using a series of identity-obscured inputs, thereby protecting facial privacy

Smooth Diffusion: Crafting Smooth Latent Spaces in Diffusion Models

Recently, diffusion models have made remarkable progress in text-to-image (T2I) generation, synthesizing images with high fidelity and diverse contents. Despite this advancement, latent space smoothness within diffusion models remains largely unexplored. Smooth latent spaces ensure that a perturbation on an input latent corresponds to a steady change in the output image. This property proves beneficial in downstream tasks, including image interpolation, inversion, and editing. In this work, we expose the non-smoothness of diffusion latent spaces by observing noticeable visual fluctuations resulting from minor latent variations. To tackle this issue, we propose Smooth Diffusion, a new category of diffusion models that can be simultaneously high-performing and smooth. Specifically, we introduce Step-wise Variation Regularization to enforce the proportion between the variations of an arbitrary input latent and that of the output image is a constant at any diffusion training step. In addition, we devise an interpolation standard deviation (ISTD) metric to effectively assess the latent space smoothness of a diffusion model. Extensive quantitative and qualitative experiments demonstrate that Smooth Diffusion stands out as a more desirable solution not only in T2I generation but also across various downstream tasks. Smooth Diffusion is implemented as a plug-and-play Smooth-LoRA to work with various community models. Code is available at https://github.com/SHI-Labs/Smooth-Diffusion.Comment: GitHub: https://github.com/SHI-Labs/Smooth-Diffusio