Dilaton domination in the MSSM and its singlet extensions

We analyse the current status of the dilaton domination scenario in the MSSM and its singlet extensions taking into account the measured value of the Higgs mass, the relic abundance of dark matter and constraints from SUSY searches at the LHC. We find that in the case of the MSSM the requirement of a dark matter relic abundance in accord with observation severely restricts the allowed parameter space, implying an upper bound on the superpartner masses which makes it fully testable at LHC-14. In singlet extensions with a large singlet-MSSM coupling $\lambda$ as favoured by naturalness arguments the coloured sparticles should again be within the reach of the LHC-14, while for small $\lambda$ it is possible to decouple the MSSM and singlet sectors, achieving the correct dark matter abundance with a singlino LSP while allowing for a heavy MSSM spectrum.Comment: 9 pages, 3 figure

The challenge to democracy III. The family farm in the machine age

The family farm is the most fundamental economic institution in American civilization. It has given character to the whole of American life. This is true of the family farm in all parts of the country. It has stimulated idealism, economic and social reform, nationalism and independence. It has strengthened democracy and individualism. The influence of the family farm in shaping the development of American social institutions hardly can be overestimated. The farm family has been regarded as characteristic of all that is good in family life. It has made important contributions to democracy and to representative government by putting democratic theories into practice on a large scale. The farm family makes democracy a truly national achievement in our country. The importance of the family farm as a fundamental concept of the American way of life is based on two definite and interrelated assumptions: first, that the family farm, as conceived by the founders of the republic, is the comer stone of a democratic rural America; and, second, that it is the tangible expression of a sound philosophy of agriculture without which we cannot have a sound nation. The family farm constitutes today, as it has in the past, the fulfillment of the hopes and the aspirations of millions of people

Social anxiety disorder: A review of environmental risk factors

Social anxiety disorder (SAD) is a debilitating and chronic illness characterized by persistent fear of one or more social or performance situations, with a relatively high lifetime prevalence of 7% to 13% in the general population. Although the last two decades have witnessed enormous growth in the study of biological and dispositional factors underlying SAD, comparatively little attention has been directed towards environmental factors in SAD, even though there has been much ongoing work in the area. In this paper, we provide a recent review and critique of proposed environmental risk factors for SAD, focusing on traditional as well as some understudied and overlooked environmental risk factors: parenting and family environment, adverse life events, cultural and societal factors, and gender roles. We also discuss the need for research design improvements and considerations for future directions

Transferability Metrics for Object Detection

Transfer learning aims to make the most of existing pre-trained models to achieve better performance on a new task in limited data scenarios. However, it is unclear which models will perform best on which task, and it is prohibitively expensive to try all possible combinations. If transferability estimation offers a computation-efficient approach to evaluate the generalisation ability of models, prior works focused exclusively on classification settings. To overcome this limitation, we extend transferability metrics to object detection. We design a simple method to extract local features corresponding to each object within an image using ROI-Align. We also introduce TLogME, a transferability metric taking into account the coordinates regression task. In our experiments, we compare TLogME to state-of-the-art metrics in the estimation of transfer performance of the Faster-RCNN object detector. We evaluate all metrics on source and target selection tasks, for real and synthetic datasets, and with different backbone architectures. We show that, over different tasks, TLogME using the local extraction method provides a robust correlation with transfer performance and outperforms other transferability metrics on local and global level features.Comment: 12 pages, 4 Figure