University of Bridgeport

About: University of Bridgeport is a education organization based out in Bridgeport, Connecticut, United States. It is known for research contribution in the topics: Wireless sensor network & Key distribution in wireless sensor networks. The organization has 1008 authors who have published 1639 publications receiving 22740 citations.

Review of Deep Learning Algorithms and Architectures

Abstract: Deep learning (DL) is playing an increasingly important role in our lives. It has already made a huge impact in areas, such as cancer diagnosis, precision medicine, self-driving cars, predictive forecasting, and speech recognition. The painstakingly handcrafted feature extractors used in traditional learning, classification, and pattern recognition systems are not scalable for large-sized data sets. In many cases, depending on the problem complexity, DL can also overcome the limitations of earlier shallow networks that prevented efficient training and abstractions of hierarchical representations of multi-dimensional training data. Deep neural network (DNN) uses multiple (deep) layers of units with highly optimized algorithms and architectures. This paper reviews several optimization methods to improve the accuracy of the training and to reduce training time. We delve into the math behind training algorithms used in recent deep networks. We describe current shortcomings, enhancements, and implementations. The review also covers different types of deep architectures, such as deep convolution networks, deep residual networks, recurrent neural networks, reinforcement learning, variational autoencoders, and others.

Engineering of human brain organoids with a functional vascular-like system

Abstract: Human cortical organoids (hCOs), derived from human embryonic stem cells (hESCs), provide a platform to study human brain development and diseases in complex three-dimensional tissue. However, current hCOs lack microvasculature, resulting in limited oxygen and nutrient delivery to the inner-most parts of hCOs. We engineered hESCs to ectopically express human ETS variant 2 (ETV2). ETV2-expressing cells in hCOs contributed to forming a complex vascular-like network in hCOs. Importantly, the presence of vasculature-like structures resulted in enhanced functional maturation of organoids. We found that vascularized hCOs (vhCOs) acquired several blood-brain barrier characteristics, including an increase in the expression of tight junctions, nutrient transporters and trans-endothelial electrical resistance. Finally, ETV2-induced endothelium supported the formation of perfused blood vessels in vivo. These vhCOs form vasculature-like structures that resemble the vasculature in early prenatal brain, and they present a robust model to study brain disease in vitro.

Employee proactivity in organizations: A comparative meta‐analysis of emergent proactive constructs

Abstract: This meta-analysis of 103 independent samples provides a comparative evaluation of the relationships associated with four emergent proactive constructs including proactive personality, personal initiative, voice, and taking charge. This study investigates comparative relationships among these focal proactive constructs and key organizational variables (i.e., job performance), personality traits (i.e., the Big Five), and individual variables (i.e., work experience). Results reveal significant correlations between proactivity and performance, satisfaction, affective organizational commitment, and social networking. Results also provide evidence that differentiates focal proactive constructs from the Big Five as well as individual differences in work experience, age, and general mental ability. Current progress towards a more integrative understanding of proactivity research is discussed.