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  1. This Review highlights basic and transition metal conducting and semiconducting oxides. We discuss their material and electronic properties with an emphasis on the crystal, electronic, and band structures. The goal of this Review is to present a current compilation of material properties and to summarize possible uses and advantages in device applications. We discuss Ga 2 O 3 , Al 2 O 3 , In 2 O 3 , SnO 2 , ZnO, CdO, NiO, CuO, and Sc 2 O 3 . We outline the crystal structure of the oxides, and we present lattice parameters of the stable phases and a discussion of the metastable polymorphs. We highlight electrical properties such as bandgap energy, carrier mobility, effective carrier masses, dielectric constants, and electrical breakdown field. Based on literature availability, we review the temperature dependence of properties such as bandgap energy and carrier mobility among the oxides. Infrared and Raman modes are presented and discussed for each oxide providing insight into the phonon properties. The phonon properties also provide an explanation as to why some of the oxide parameters experience limitations due to phonon scattering such as carrier mobility. Thermal properties of interest include the coefficient of thermal expansion, Debye temperature,more »thermal diffusivity, specific heat, and thermal conductivity. Anisotropy is evident in the non-cubic oxides, and its impact on bandgap energy, carrier mobility, thermal conductivity, coefficient of thermal expansion, phonon modes, and carrier effective mass is discussed. Alloys, such as AlGaO, InGaO, (Al x In y Ga 1− x− y ) 2 O 3 , ZnGa 2 O 4 , ITO, and ScGaO, were included where relevant as they have the potential to allow for the improvement and alteration of certain properties. This Review provides a fundamental material perspective on the application space of semiconducting oxide-based devices in a variety of electronic and optoelectronic applications.« less
    Free, publicly-accessible full text available March 1, 2023
  2. Deep learning (DL) is growing in popularity for many data analytics applications, including among enterprises. Large business-critical datasets in such settings typically reside in RDBMSs or other data systems. The DB community has long aimed to bring machine learning (ML) to DBMS-resident data. Given past lessons from in-DBMS ML and recent advances in scalable DL systems, DBMS and cloud vendors are increasingly interested in adding more DL support for DB-resident data. Recently, a new parallel DL model selection execution approach called Model Hopper Parallelism (MOP) was proposed. In this paper, we characterize the particular suitability of MOP for DL on data systems, but to bring MOP-based DL to DB-resident data, we show that there is no single "best" approach, and an interesting tradeoff space of approaches exists. We explain four canonical approaches and build prototypes upon Greenplum Database, compare them analytically on multiple criteria (e.g., runtime efficiency and ease of governance) and compare them empirically with large-scale DL workloads. Our experiments and analyses show that it is non-trivial to meet all practical desiderata well and there is a Pareto frontier; for instance, some approaches are 3x-6x faster but fare worse on governance and portability. Our results and insights can helpmore »DBMS and cloud vendors design better DL support for DB users. All of our source code, data, and other artifacts are available at« less