Oregon State University

About: Oregon State University is a education organization based out in Corvallis, Oregon, United States. It is known for research contribution in the topics: Population & Gene. The organization has 28192 authors who have published 64044 publications receiving 2634108 citations. The organization is also known as: Oregon Agricultural College & OSU.

Learning to Optimize: Training Deep Neural Networks for Interference Management

Abstract: Numerical optimization has played a central role in addressing key signal processing (SP) problems Highly effective methods have been developed for a large variety of SP applications such as communications, radar, filter design, and speech and image analytics, just to name a few However, optimization algorithms often entail considerable complexity, which creates a serious gap between theoretical design/analysis and real-time processing In this paper, we aim at providing a new learning-based perspective to address this challenging issue The key idea is to treat the input and output of an SP algorithm as an unknown nonlinear mapping and use a deep neural network (DNN) to approximate it If the nonlinear mapping can be learned accurately by a DNN of moderate size, then SP tasks can be performed effectively—since passing the input through a DNN only requires a small number of simple operations In our paper, we first identify a class of optimization algorithms that can be accurately approximated by a fully connected DNN Second, to demonstrate the effectiveness of the proposed approach, we apply it to approximate a popular interference management algorithm, namely, the WMMSE algorithm Extensive experiments using both synthetically generated wireless channel data and real DSL channel data have been conducted It is shown that, in practice, only a small network is sufficient to obtain high approximation accuracy, and DNNs can achieve orders of magnitude speedup in computational time compared to the state-of-the-art interference management algorithm

Current characterization methods for cellulose nanomaterials

Abstract: A new family of materials comprised of cellulose, cellulose nanomaterials (CNMs), having properties and functionalities distinct from molecular cellulose and wood pulp, is being developed for applications that were once thought impossible for cellulosic materials. Commercialization, paralleled by research in this field, is fueled by the unique combination of characteristics, such as high on-axis stiffness, sustainability, scalability, and mechanical reinforcement of a wide variety of materials, leading to their utility across a broad spectrum of high-performance material applications. However, with this exponential growth in interest/activity, the development of measurement protocols necessary for consistent, reliable and accurate materials characterization has been outpaced. These protocols, developed in the broader research community, are critical for the advancement in understanding, process optimization, and utilization of CNMs in materials development. This review establishes detailed best practices, methods and techniques for characterizing CNM particle morphology, surface chemistry, surface charge, purity, crystallinity, rheological properties, mechanical properties, and toxicity for two distinct forms of CNMs: cellulose nanocrystals and cellulose nanofibrils.

Influence of a Reflective Explicit Activity-based Approach on Elementary Teachers' Conceptions of Nature of Science.

Abstract: This study assessed the influence of a reflective, explicit, activity-based approach to nature of science (NOS) instruction undertaken in the context of an elementary science methods course on pre- service teachers' views of some aspects of NOS. These aspects included the empirical, tentative, subjec- tive (theory-laden), imaginative and creative, and social and cultural NOS. Two additional aspects were the distinction between observation and inference, and the functions of and relationship between scientif- ic theories and laws. Participants were 25 undergraduate and 25 graduate preservice elementary teachers enrolled in two sections of the investigated course. An open-ended NOS questionnaire coupled with indi- vidual interviews was used to assess participants' NOS views before and at the conclusion of the course. The majority of participants held naive views of the target NOS aspects at the beginning of the study. Dur- ing the first week of class, participants were engaged in specially designed activities that were coupled with explicit NOS instruction. Throughout the remainder of the course, participants were provided with structured opportunities to reflect on their views of the target NOS aspects. Postinstruction assessments in- dicated that participants made substantial gains in their views of some of the target NOS aspects. Less sub- stantial gains were evident in the case of the subjective, and social and cultural NOS. The results of the present study support the effectiveness of explicit, reflective NOS instruction. Such instruction, nonethe- less, might be rendered more effective when integrated within a conceptual change approach. © 2000 John Wiley & Sons, Inc. J Res Sci Teach 37: 295 -317, 2000.

Stratified atmospheric boundary layers

Abstract: Various features of different stability regimes of the stable boundary layer are discussed. Traditional layering is examined in terms of the roughness sublayer, surface layer, local similarity, z-less stratification and the region near the boundary-layer top. In the very stable case, the strongest turbulence may be detached from the surface and generated by shear associated with a low level jet, gravity waves or meandering motions. In this case, similarity theory and the traditional concept of a boundary-layer break down. The elevated turbulence may intermittently recouple to the surface. Inability to adequately measure turbulent fluxes in very stable conditions limits our knowledge of this regime.