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  1. Using low-temperature cathodoluminescence spectroscopy, we study the properties of N- and Al-polar AlN layers grown by molecular beam epitaxy on bulk AlN{0001}. Compared with the bulk AlN substrate, layers of both polarities feature a suppression of deep-level luminescence, a total absence of the prevalent donor with an exciton binding energy of 28 meV, and a much increased intensity of the emission from free excitons. The dominant donor in these layers is characterized by an associated exciton binding energy of 13 meV. The observation of excited exciton states up to the exciton continuum allows us to directly extract the Γ5 free exciton binding energy of 57 meV. 
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    Free, publicly-accessible full text available August 1, 2024
  2. Abstract We report the growth of α -Ga 2 O 3 on m -plane α -Al 2 O 3 by conventional plasma-assisted molecular-beam epitaxy and In-mediated metal–oxide-catalyzed epitaxy (MOCATAXY). We report a growth rate diagram for α -Ga 2 O 3 ( 10 1 ¯ 0 ), and observe (i) a growth rate increase, (ii) an expanded growth window, and (iii) reduced out-of-lane mosaic spread when MOCATAXY is employed for the growth of α -Ga 2 O 3 . Through the use of In-mediated catalysis, growth rates over 0.2 μ m h −1 and rocking curves with full width at half maxima of Δ ω ≈ 0.45° are achieved. Faceting is observed along the α -Ga 2 O 3 film surface and explored through scanning transmission electron microscopy. 
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  3. A D-band (110‒170 GHz) SiC substrate-integrated waveguide (SIW) is characterized on-wafer by two different vector network analyzers (VNAs): a 220-GHz single-sweep VNA and an 110-GHz VNA with WR8 (90‒140 GHz) and WR5 (140‒220 GHz) frequency extenders. To facilitate probing, the SIW input and output are transitioned to grounded coplanar waveguides (GCPWs). Two-tier calibration is used to de-embed the SIW-GCPW transitions as well as to extract the intrinsic SIW characteristics. In general, the two VNAs are in agreement and both result in an ultra-low insertion loss of approximately 0.2 dB/mm for the same SIW, despite stitching errors at band edges. 
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  4. A D-band (110‒170 GHz) SiC substrate-integrated waveguide (SIW) is characterized on-wafer by two different vector network analyzers (VNAs): a 220-GHz single-sweep VNA and an 110-GHz VNA with WR8 (90‒140 GHz) and WR5 (140‒220 GHz) frequency extenders. To facilitate probing, the SIW input and output are transitioned to grounded coplanar waveguides (GCPWs). Two-tier calibration is used to de-embed the SIWGCPW transitions as well as to extract the intrinsic SIW characteristics. In general, the two VNAs are in agreement and both result in an ultra-low insertion loss of approximately 0.2 dB/mm for the same SIW, despite stitching errors at band edges. 
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  5. We report controlled silicon doping of Ga2O3 grown in plasma-assisted molecular beam epitaxy. Adding an endplate to the Si effusion cell enables the control of the mobile carrier density, leading to over 5-orders of magnitude change in the electrical resistivity. Room temperature mobilities >100  cm2/V s are achieved, with a peak value >125  cm2/V s at a doping density of low-1017/cm3. Temperature-dependent Hall effect measurements exhibit carrier freeze out for samples doped below the Mott criterion. A mobility of 390  cm2/V s is observed at 97  K.

     
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  6. Free, publicly-accessible full text available August 1, 2024
  7. Free, publicly-accessible full text available July 1, 2024
  8. Despite considerable advancements with deep neural language models (LMs), neural text generation still suffers from degeneration: the generated text is repetitive, generic, selfcontradictory, and often lacks commonsense. Our analyses on sentence-level attention patterns in LMs reveal that neural degeneration may be associated with insufficient learning of task-specific characteristics by the attention mechanism. This finding motivates onthe-fly attention modulation1– a simple but effective method that enables the injection of priors into attention computation during inference. Automatic and human evaluation results on three text generation benchmarks demonstrate that attention modulation helps LMs generate text with enhanced fluency, creativity, and commonsense reasoning, in addition to significantly reduce sentence-level repetition. 
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