Realities and Anxieties to Live With: An In-Depth Inquiry of the Experience of Internationally Educated Professionals in the Bridging Programs at Universities in Ontario

Researchers have identified a trend of brain waste, brain abuse and brain drain on the part of internationally educated professionals (IEPs) after they immigrate to Canada. University-based bridging programs have been implemented in the past decade to meet the need of this population to integrate to the new society. My dissertation attempts to inquire into the experiences, emotional experiences in particular, of the IEPs who studied in the university-based bridging programs in Ontario. I raised four questions: First, how do the IEPs perceive their new realities? Second, what are the anxieties of the IEPs? Third, what realities lead them to those anxieties and how do their anxieties affect their perception of the challenges they face? Fourth, what role does higher education play in mirroring and shaping the way the IEPs perceive and feel the realities? Using in-depth interviewing as the research method, I had three one-hour interviews with each of the five research participants who volunteered in this study. When analyzing the five cases, I focused on the conflicts, contentions and contradictions that the interview transcripts revealed of the participants experiences in relation to others. I resorted to theories in psychoanalysis in order to understand the IEPs anxieties when they encountered various challenges both internally and externally. I find that the IEPs anxieties are partly inherent in the process of immigration, partly reflective of their own modes of learning and the need of external support and partly the side effect of higher education which, questionably, attempts to reproduce the correlation between knowledge and privileges. I argue that the bridging programs and the IEPs need to learn from their anxieties and the social anxieties and engage in a critical exploration of the difficult knowledge of the-self-in-the-changing-world

5-Bromo-N 3-phenyl­pyrazine-2,3-diamine

In the title compound, C10H9BrN4, the dihedral angle between the benzene and pyrazine rings is 61.34 (5)°. Inter­molecular N—H⋯N hydrogen bonds and N—H⋯π inter­actions assemble the mol­ecules into a three-dimensional network structure

Adversarial Deep Structured Nets for Mass Segmentation from Mammograms

Mass segmentation provides effective morphological features which are important for mass diagnosis. In this work, we propose a novel end-to-end network for mammographic mass segmentation which employs a fully convolutional network (FCN) to model a potential function, followed by a CRF to perform structured learning. Because the mass distribution varies greatly with pixel position, the FCN is combined with a position priori. Further, we employ adversarial training to eliminate over-fitting due to the small sizes of mammogram datasets. Multi-scale FCN is employed to improve the segmentation performance. Experimental results on two public datasets, INbreast and DDSM-BCRP, demonstrate that our end-to-end network achieves better performance than state-of-the-art approaches. \footnote{https://github.com/wentaozhu/adversarial-deep-structural-networks.git}Comment: Accepted by ISBI2018. arXiv admin note: substantial text overlap with arXiv:1612.0597

TMDs from Semi-inclusive Energy Correlators

We introduce a novel category of observables known as the Semi-Inclusive Energy Correlators (SIECs), an extension of the recently proposed nucleon energy correlator to integrate a new element, the fragmenting energy correlation function. These SIECs gauge the correlation between the examined hadron and the surrounding radiations, providing a comprehensive tomography of the radiative patterns originating from initial state radiation or parton fragmentation. As such, they could function as the generating functions for numerous kinematic distributions. To illustrate, we find a direct relation between the transverse momentum moments (TMMs) of the transverse momentum-dependent (TMD) distributions and the SIECs. We demonstrate how the TMMs of the TMD parton distributions and the fragmentation functions can be distinctively derived from the nucleon energy correlator and the fragmenting energy correlator, respectively, without enforcing the back-to-back kinematics.Comment: 7 pages, 4 figures + supplemental materials (4 pages

Assessment of Prediction Capabilities of COCOSYS and CFX Code for Simplified Containment

The acceptable accuracy for simulation of severe accident scenarios in containments of nuclear power plants is required to investigate the consequences of severe accidents and effectiveness of potential counter measures. For this purpose, the actual capability of CFX tool and COCOSYS code is assessed in prototypical geometries for simplified physical process-plume (due to a heat source) under adiabatic and convection boundary condition, respectively. Results of the comparison under adiabatic boundary condition show that good agreement is obtained among the analytical solution, COCOSYS prediction, and CFX prediction for zone temperature. The general trend of the temperature distribution along the vertical direction predicted by COCOSYS agrees with the CFX prediction except in dome, and this phenomenon is predicted well by CFX and failed to be reproduced by COCOSYS. Both COCOSYS and CFX indicate that there is no temperature stratification inside dome. CFX prediction shows that temperature stratification area occurs beneath the dome and away from the heat source. Temperature stratification area under adiabatic boundary condition is bigger than that under convection boundary condition. The results indicate that the average temperature inside containment predicted with COCOSYS model is overestimated under adiabatic boundary condition, while it is underestimated under convection boundary condition compared to CFX prediction

Co-occurrence Feature Learning for Skeleton based Action Recognition using Regularized Deep LSTM Networks

Skeleton based action recognition distinguishes human actions using the trajectories of skeleton joints, which provide a very good representation for describing actions. Considering that recurrent neural networks (RNNs) with Long Short-Term Memory (LSTM) can learn feature representations and model long-term temporal dependencies automatically, we propose an end-to-end fully connected deep LSTM network for skeleton based action recognition. Inspired by the observation that the co-occurrences of the joints intrinsically characterize human actions, we take the skeleton as the input at each time slot and introduce a novel regularization scheme to learn the co-occurrence features of skeleton joints. To train the deep LSTM network effectively, we propose a new dropout algorithm which simultaneously operates on the gates, cells, and output responses of the LSTM neurons. Experimental results on three human action recognition datasets consistently demonstrate the effectiveness of the proposed model.Comment: AAAI 2016 conferenc