They’re Crying in the All-Gender Bathroom: Navigating Belonging in Higher Education While First Generation and Nonbinary

Maintaining the sociocultural and interpersonal supports needed to succeed in higher education as a first-generation student can be very difficult due to a lack of familiarity with what brings success. When this identity intersects with a nonbinary gender identity, it further complicates higher education’s challenges and may make solutions impossible to come by. My experience sits at the intersection of these two identities and their gradual collision and connection with success in higher education. Through this narrative, I seek to unpack potential difficulties and nuances for the increasingly diverse body of first generation students and bring attention to the barriers in our social systems which may be blocking current and future students from achieving their full potential

Destination directed packet switch architecture for a 30/20 GHz FDMA/TDM geostationary communication satellite network

Emphasis is on a destination directed packet switching architecture for a 30/20 GHz frequency division multiplex access/time division multiplex (FDMA/TDM) geostationary satellite communication network. Critical subsystems and problem areas are identified and addressed. Efforts have concentrated heavily on the space segment; however, the ground segment was considered concurrently to ensure cost efficiency and realistic operational constraints

Currency Market Reactions to Good and Bad News During the Asian Crisis

There is considerable disagreement among analysts about the extent to which the spread of the Asian crisis was based on reasonable changes in expectations about fundamentals versus pure contagion effects resulting from imperfections in the behavior of currency and financial markets. In this paper we focus specifically on the behavior of the foreign exchange market for the five Asian countries. We find little support for the hypothesis that the Asian currency crisis was dominated by panic in the markets such that investors and speculators reacted much more strongly to bad than to good news. While the strongest reactions were to home news, there were also a number of significant cross effects. Almost all of these were of the same sign, suggesting that investors typically assumed that what was good for one country was good for all. Again, there was no systematic evidence of stronger reactions to bad than to good news. The markets may have overreacted in general, pushing currencies below the levels justified by the fundamentals, but, if so, this did not undercut the markets ability to respond to good as well as bad news, nor do these responses appear to have been systematically smaller to good than to bad news. The symptoms of the blind panic that has so often been alleged do not appear in the data.

Rectangular Hierarchical Cartograms for Socio-Economic Data

We present rectangular hierarchical cartograms for mapping socio-economic data. These density-normalising cartograms size spatial units by population, increasing the ease with which data for densely populated areas can be visually resolved compared to more conventional cartographic projections. Their hierarchical nature enables the study of spatial granularity in spatial hierarchies, hierarchical categorical data and multivariate data through false hierarchies. They are space-filling representations that make efficient use of space and their rectangular nature (which aims to be as square as possible) improves the ability to compare the sizes (therefore population) of geographical units. We demonstrate these cartograms by mapping the Office for National Statistics Output Area Classification (OAC) by unit postcode (1.52 million in Great Britain) through the postcode hierarchy, using these to explore spatial variation. We provide rich and detailed spatial summaries of socio-economic characteristics of population as types of treemap, exploring the effects of reconfiguring them to study spatial and non-spatial aspects of the OAC classification

Destination-directed, packet-switching architecture for 30/20-GHz FDMA/TDM geostationary communications satellite network

A destination-directed packet switching architecture for a 30/20-GHz frequency division multiple access/time division multiplexed (FDMA/TDM) geostationary satellite communications network is discussed. Critical subsystems and problem areas are identified and addressed. Efforts have concentrated heavily on the space segment; however, the ground segment has been considered concurrently to ensure cost efficiency and realistic operational constraints

Λ\LambdaCDM or self-interacting neutrinos? - how CMB data can tell the two models apart

Of the many proposed extensions to the $\Lambda$CDM paradigm, a model in which neutrinos self-interact until close to the epoch of matter-radiation equality has been shown to provide a good fit to current cosmic microwave background (CMB) data, while at the same time alleviating tensions with late-time measurements of the expansion rate and matter fluctuation amplitude. Interestingly, CMB fits to this model either pick out a specific large value of the neutrino interaction strength, or are consistent with the extremely weak neutrino interaction found in $\Lambda$CDM, resulting in a bimodal posterior distribution for the neutrino self-interaction cross section. In this paper, we explore why current cosmological data select this particular large neutrino self-interaction strength, and by consequence, disfavor intermediate values of the self-interaction cross section. We show how it is the $\ell \gtrsim 1000$ CMB temperature anisotropies, most recently measured by the Planck satellite, that produce this bimodality. We also establish that smaller scale temperature data, and improved polarization data measuring the temperature-polarization cross-correlation, will best constrain the neutrino self-interaction strength. We forecast that the upcoming Simons Observatory should be capable of distinguishing between the models.Comment: 7 pages, 7 figures, comments welcome, references added, version submitted to PR

Detecting Distracted Driving with Deep Learning

© Springer International Publishing AG 2017Driver distraction is the leading factor in most car crashes and near-crashes. This paper discusses the types, causes and impacts of distracted driving. A deep learning approach is then presented for the detection of such driving behaviors using images of the driver, where an enhancement has been made to a standard convolutional neural network (CNN). Experimental results on Kaggle challenge dataset have confirmed the capability of a convolutional neural network (CNN) in this complicated computer vision task and illustrated the contribution of the CNN enhancement to a better pattern recognition accuracy.Peer reviewe