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  1. With a call in recent years to increase safety and enhance the value of emerging high-rise building clusters, skybridges as linking systems are attacking interest by urban designers and could play a key role in the development of our future cities. While the functional and economic benefits of the skybridges are realized, the effects of skybridges on structural systems are not widely understood. Researchers and practitioners in both academia and industry have been investigating the potential of the skybridge serving to increase the resiliency and sustainability of the connected structures. However, there is a gap between engineering science in academiamore »and engineering practice in industry, which has previously limiting the research outcomes from becoming built realities. Partnering with an industry expert in high-rise building design, Skidmore, Owings & Merrill LLP, this study sought to better understand how coupling behaviors between high-rise structures using a skybridge affect various aspects of the individual and the linked structures. In this study, parametric data, including modal information, displacement, shear, and overturning moment were gathered from realistic high-rise structure models to evaluate the structural performance under static and dynamic loading when the skybridge is installed at various locations of the structures.« less
  2. With increasing demands for high performance in structural systems, Smart Structures Technologies (SST) is receiving considerable attention as it has the potential to transform many fields in engineering, including civil, mechanical, aerospace, and geotechnical engineering. Both the academic and industrial worlds are seeking ways to utilize SST, however, there is a significant gap between the engineering science in academia and engineering practice in the industry. To respond to this challenge, San Francisco State University and the University of South Carolina collaborated with industrial partners to establish a Research Experiences for Undergraduates (REU) Site program, focusing on academia-industry collaborations in SST.more »This REU program intends to train undergraduate students to serve as the catalysts to facilitate the research infusion between academic and industrial partners. This student-driven joint venture between academia and industry is expected to establish a virtuous circle for knowledge exchange and contribute to advancing fundamental research and implementation of SST. The program features: formal training, workshops, and supplemental activities in the conduct of research in academia and industry; innovative research experience through engagement in projects with scientific and practical merits in both academic and industrial environments; experience in conducting laboratory experiments; and opportunities to present the research outcomes to the broader community at professional settings. This REU program provides engineering undergraduate students with unique research experience in both academic and industrial settings through cooperative research projects. Experiencing research in both worlds is expected to help students transition from a relatively dependent status to an independent status as their competence level increases. The joint efforts among two institutions and industry partners provide the project team with extensive access to valuable resources, such as expertise to offer a wider-range of informative training workshops, advanced equipment, valuable data sets, experienced undergraduate mentors, and professional connections, that would facilitate a meaningful REU experience. Recruitment of participants targeted 20 collaborating minority and primarily undergraduate institutions (15 of them are Hispanic-Serving Institutions, HSI) with limited science, technology, engineering, and mathematics (STEM) research capabilities. The model developed through this program may help to exemplify the establishment of a sustainable collaboration model between academia and industry that helps address the nation's need for mature, independent, informed, and globally competitive STEM professionals and could be adapted to other disciplines. In this paper, the details of the first-year program will be described. The challenges and lesson-learned on the collaboration between the two participating universities, communications with industrial partners, recruitment of the students, set up of the evaluation plans, and development and implementation of the program will be discussed. The preliminary evaluation results and recommendations will also be shared.« less
  3. Properties of a double-period InAs/GaSb superlattice grown by solid-source molecular beam epitaxy are presented. Precise growth conditions at the InAs/GaSb heterojunction yielded abrupt heterointerfaces and superior material quality as verified by X-ray diffraction and transmission electron microscopy (TEM) analysis. Moreover, high-resolution TEM imaging and elemental composition profiling of the InAs/GaSb heterostructure demonstrated abrupt atomic transitions between each Sb- or As-containing epilayer. An 8 × 8 k · p model is used to compute the electronic band structure of the constituent long- and short-period superlattices, taking into account the effects of conduction and valence band mixing, quantum confinement, pseudomorphic strain, andmore »magnetic field on the calculated dispersions. Magnetotransport measurements over a variable temperature range (390 mK to 294 K) show anisotropic transport exhibiting a striking magnetoresistance and show Shubnikov-de Haas oscillations, the latter being indicative of high quality material synthesis. The measurements also reveal the existence of at least two carrier populations contributing to in-plane conductance in the structure.« less
  4. Convolutional neural networks (CNNs) have been increasingly deployed to Internet of Things (IoT) devices. Hence, many efforts have been made towards efficient CNN inference in resource-constrained platforms. This paper attempts to explore an orthogonal direction: how to conduct more energy-efficient training of CNNs, so as to enable on-device training? We strive to reduce the energy cost during training, by dropping unnecessary computations, from three complementary levels: stochastic mini-batch dropping on the data level; selective layer update on the model level; and sign prediction for low-cost, low-precision back-propagation, on the algorithm level. Extensive simulations and ablation studies, with real energy measurementsmore »from an FPGA board, confirm the superiority of our proposed strategies and demonstrate remarkable energy savings for training. Specifically, when training ResNet-74 on CIFAR-10, we achieve aggressive energy savings of >90% and >60%, while incurring an accuracy loss of only about 2% and 1.2%, respectively. When training ResNet-110 on CIFAR-100, an over 84% training energy saving comes at the small accuracy costs of 2% (top-1) and 0.1% (top-5).« less