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Moiré superlattices host a rich variety of correlated electronic phases. However, the moiré potential is fixed by interlayer coupling, and it is dependent on the nature of carriers and valleys. In contrast, it has been predicted that twisted hexagonal boron nitride (hBN) layers can impose a periodic electrostatic potential capable of engineering the properties of adjacent functional layers. Here, we show that this potential is described by a theory of electric polarization originating from the interfacial charge redistribution, validated by its dependence on supercell sizes and distance from the twisted interfaces. This enables controllability of the potential depth and profile by controlling the twist angles between the two interfaces. Employing this approach, we further demonstrate how the electrostatic potential from a twisted hBN substrate impedes exciton diffusion in semiconductor monolayers, suggesting opportunities for engineering the properties of adjacent functional layers using the surface potential of a twisted hBN substrate. 

Carnegie Mellon University, Johns Hopkins University, and New York University created the Project Equity-focused Launch to Empower and Value AGEP Faculty to Thrive in Engineering (ELEVATE) Alliance (National Science Foundation Awards #2149995, #2149798 #2149899 from the Division of Equity for Excellence in STEM in the Directorate for STEM Education) to develop a model to promote the equitable advancement of early career tenure-track engineering faculty from populations of interest to the Alliances for Graduate Education and the Professoriate (AGEP) program. The goal of this AGEP Faculty Career Pathways Alliance Model (FCPAM) is to develop, implement, self-study, and institutionalize a career pathway model that can be adapted for use at other similar institutions for advancing early career engineering faculty who are: African Americans, Hispanic Americans, American Indians, Alaska Natives, Native Hawaiians, and Native Pacific Islanders. This NSF AGEP FCPAM will provide a framework for institutional change at private, highly selective research institutions that will enable all faculty to be members of a collaborative community. Improving the experience of these faculty can lead to increased diversity in the engineering faculty and ultimately result in graduating more engineering students from diverse populations and increasing diversity in the engineering workforce. The Alliance interventions will focus on three major areas, 1) equity-focused institutional change designed to make structural changes that support the advancement of AGEP faculty, 2) identity-affirming mentorship that acknowledges and provides professional support to AGEP faculty holistically, recognizing all parts of their identity and 3) inclusive professional development that equips all engineering faculty and institutional leaders with skills to implement inclusive practices and equips AGEP faculty for career advancement. In this paper, we will discuss the process of creating a leadership team to address these focus areas and assess the processes and procedures that currently exist at the three institutions as we begin to institutionalize these change efforts. We provide an overview of the project and efforts to date. We will also present our process for engaging in our initial self-study evaluation and next steps. 

Recent advances in computer vision has led to a growth of interest in deploying visual analytics model on mobile devices. However, most mobile devices have limited computing power, which prohibits them from running large scale visual analytics neural networks. An emerging approach to solve this problem is to offload the computation of these neural networks to computing resources at an edge server. Efficient computation offloading requires optimizing the trade-off between multiple objectives including compressed data rate, analytics performance, and computation speed. In this work, we consider a “split computation” system to offload a part of the computation of the YOLO object detection model. We propose a learnable feature compression approach to compress the intermediate YOLO features with lightweight computation. We train the feature compression and decompression module together with the YOLO model to optimize the object detection accuracy under a rate constraint. Compared to baseline methods that apply either standard image compression or learned image compression at the mobile and perform image de-compression and YOLO at the edge, the proposed system achieves higher detection accuracy at the low to medium rate range. Furthermore, the proposed system requires substantially lower computation time on the mobile device with CPU only. 

Excited-state catalysis, a process that involves one or more excited catalytic species, has emerged as a powerful tool in organic synthesis because it allows access to the excited-state reaction landscape for the discovery of novel chemical reactivity. Herein, we report the first excited-state palladium-catalyzed 1,2-spin-center shift reaction that enables site-selective functionalization of carbohydrates. The strategy features mild reaction conditions with high levels of regio- and stereoselectivity that tolerate a wide range of functional groups and complex molecular architectures. Mechanistic studies suggest a radical mechanism involving the formation of hybrid palladium species that undergoes a 1,2-spin-center shift followed by the reduction, deuteration, and iodination to afford functionalized 2-deoxy sugars. The new reactivity will provide a general approach for the rapid generation of natural and unnatural carbohydrates.