On F-theory E_6 GUTs

We approach the Minimum Supersymmetric Standard Model (MSSM) from an E_6 GUT by using the spectral cover construction and non-abelian gauge fluxes in F-theory. We start with an E_6 singularity unfolded from an E_8 singularity and obtain E_6 GUTs by using an SU(3) spectral cover. By turning on SU(2) X U(1)^2 gauge fluxes, we obtain a rank 5 model with the gauge group SU(3) X SU(2) X U(1)^2. Based on the well-studied geometric backgrounds in the literature, we demonstrate several models and discuss their phenomenology.Comment: 42 pages, 17 tables; typos corrected, clarifications added, and references adde

The effects of specific commodity taxes on output and location of free entry oligopoly

This paper examines the impact of a specific commodity tax on output and the location decision of undifferentiated oligopolistic firms with free entry. It shows that (1) the optimum output and location of the oligopolistic firm is independent of the specific commodity tax if the demand function is linear (2) an increase in the specific commodity tax will increase (decrease) output per firm and move the plant location toward (away from) the output market if the demand function is concave (convex). These results are consistent with the conventional results based on the non-spatial setting. In the case in which the demand function is linear or concave, it shows that the number of firms and total output of oligopoly may increase. These results are significantly different from the conventional results based on non-spatial setting. It indicates that the effects of the specific tax on total output and the number of firms crucially depend upon transport costs and the location decisions of oligopolistic firms.undifferentiated oligopoly, specific tax, location decision.

Factors of Employee’s E-Learning Effectiveness: A Multi-Level Study Based on Socio-Technical Systems Theory

Application of e-learning in enterprises provides the advantages of lower training cost, richer learning content, higher information consistency, and easier update of content. Despite the fact that enterprises have the intention to introduce e-learning, there is not a complete framework to which they can refer to ensure the benefits of e-learning for employee training or learning and understand which important factors affect employee’s e-learning effectiveness. Relative to the difficulties of introducing e-learning in management practice, the academic achievements in this aspect also seem very limited. Most the existing papers are focused on discussion and survey of e-learning in school, and very few of them are dedicated to empirical research of e-learning in corporate environment. Besides, these studies discuss e-learning only at the technical or the individual level without a comprehensive investigation into the factors affecting e-learning effectiveness with multi-level theoretic framework. This paper applies the socio-technical systems theory to review and integrate theories about employee e-learning from a macro view. To make up the insufficiency of related research, literature review and case research are conducted first. Based on the interview results, an analysis model is constructed to thoroughly explore factors affecting employee’s e-learning effectiveness. Later, through a questionnaire survey on employees’ adoption of e-learning and subsequent multi-level data analysis, hypotheses on the relationship of the influencing factors and the research model are verified. Results show that e-learning effectiveness (usefulness of e-learning, continuance intention to use, and e-learning performance) is simultaneously or alternately affected by direct or moderating factors of the technical system and the social system at the work environment level and the individual level. Compared with the existing research, this paper uses a more comprehensive system view to construct the theoretical model and empirically verify it. The results can be a reference for future researchers and managers of e-learning in enterprises

Masking Improves Contrastive Self-Supervised Learning for ConvNets, and Saliency Tells You Where

While image data starts to enjoy the simple-but-effective self-supervised learning scheme built upon masking and self-reconstruction objective thanks to the introduction of tokenization procedure and vision transformer backbone, convolutional neural networks as another important and widely-adopted architecture for image data, though having contrastive-learning techniques to drive the self-supervised learning, still face the difficulty of leveraging such straightforward and general masking operation to benefit their learning process significantly. In this work, we aim to alleviate the burden of including masking operation into the contrastive-learning framework for convolutional neural networks as an extra augmentation method. In addition to the additive but unwanted edges (between masked and unmasked regions) as well as other adverse effects caused by the masking operations for ConvNets, which have been discussed by prior works, we particularly identify the potential problem where for one view in a contrastive sample-pair the randomly-sampled masking regions could be overly concentrated on important/salient objects thus resulting in misleading contrastiveness to the other view. To this end, we propose to explicitly take the saliency constraint into consideration in which the masked regions are more evenly distributed among the foreground and background for realizing the masking-based augmentation. Moreover, we introduce hard negative samples by masking larger regions of salient patches in an input image. Extensive experiments conducted on various datasets, contrastive learning mechanisms, and downstream tasks well verify the efficacy as well as the superior performance of our proposed method with respect to several state-of-the-art baselines