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Ultra-wide bandgap (UWBG) Al0.65Ga0.35N channel high electron mobility transistors (HEMTs) were deposited using a close-coupled showerhead metal-organic chemical vapor deposition reactor on AlN-on-sapphire templates to investigate the effect of transport properties of the two-dimensional electron gas (2DEG) on the epitaxial structure design. The impact of various scattering phenomena on AlGaN channel HEMTs was analyzed with respect to the channel, buffer, and AlN interlayer design, revealing that the alloy disorder and ionized impurity scattering mechanisms were predominant, limiting the mobility of 2DEG up to 180 cm2/Vs for a sheet charge density of 1.1 × 1013 cm−2. A surface roughness of <1 nm (2 μm × 2 μm atomic force microscopy scan) was achieved for the epitaxial structures demonstrating superior crystalline quality. The fabricated HEMT device showed state-of-the-art contact resistivity (ρc = 8.35 × 10^−6 Ω · cm2), low leakage current (<10^−6 A/mm), high ION/IOFF ratio (>10^5), a breakdown voltage of 2.55 kV, and a Baliga's figure of merit of 260 MW/cm2. This study demonstrates the optimization of the structural design of UWBG AlGaN channel HEMTs and its effect on transport properties to obtain state-of-the-art device performance. 

Thin Si-doped Al-rich (xAl > 0.85) regrown Al(Ga)N layers were deposited on AlN on sapphire template using metal-organic chemical vapor deposition (MOCVD) techniques. The optimization of the deposition conditions, such as temperature (1150 °C), V/III ratio (750), deposition rate (0.7 Å/s), and Si concentration (6 × 10^19/cm3), resulted in a high charge carrier concentration (> 10^15 cm−3) in the Si-doped Al-rich Al(Ga)N films. A pulsed deposition condition with pulsed triethylgallium and a continuous flow of trimethylaluminum and ammonia was employed to achieve a controllable Al composition xAl > 0.95 and to prevent unintended Ga incorporation in the AlGaN material deposited using the close-coupled showerhead reactor. Also, the effect of unintentional Si incorporation on free charge carrier concentration at the regrowth interface was studied by varying the thickness of the regrown Al(Ga)N layer from 65 to < 300 nm. A maximum charge carrier concentration of 4.8 × 10^16 and 7.5 × 10^15/cm3 was achieved for Al0.97Ga0.03N and AlN films with thickness <300 nm compared to previously reported n-Al(Ga)N films with thickness ≥400 nm deposited using MOCVD technique. 

We present an online post-hoc calibration method, called Online Platt Scaling (OPS), which combines the Platt scaling technique with online logistic regression. We demonstrate that OPS smoothly adapts between i.i.d. and non-i.i.d. settings with distribution drift. Further, in scenarios where the best Platt scaling model is itself miscalibrated, we enhance OPS by incorporating a recently developed technique called calibeating to make it more robust. Theoretically, our resulting OPS+calibeating method is guaranteed to be calibrated for adversarial outcome sequences. Empirically, it is effective on a range of synthetic and real-world datasets, with and without distribution drifts, achieving superior performance without hyperparameter tuning. Finally, we extend all OPS ideas to the beta scaling method. 

We study the problem of making calibrated probabilistic forecasts for a binary sequence generated by an adversarial nature. Following the seminal paper of Foster and Vohra (1998), nature is often modeled as an adaptive adversary who sees all activity of the forecaster except the randomization that the forecaster may deploy. A number of papers have proposed randomized forecasting strategies that achieve an ϵ-calibration error rate of O(1/sqrt T), which we prove is tight in general. On the other hand, it is well known that it is not possible to be calibrated without randomization, or if nature also sees the forecaster's randomization; in both cases the calibration error could be Ω(1). Inspired by the equally seminal works on the "power of two choices" and imprecise probability theory, we study a small variant of the standard online calibration problem. The adversary gives the forecaster the option of making two nearby probabilistic forecasts, or equivalently an interval forecast of small width, and the endpoint closest to the revealed outcome is used to judge calibration. This power of two choices, or imprecise forecast, accords the forecaster with significant power -- we show that a faster ϵ-calibration rate of O(1/T) can be achieved even without deploying any randomization. 

A multiclass classifier is said to be top-label calibrated if the reported probability for the predicted class -- the top-label -- is calibrated, conditioned on the top-label. This conditioning on the top-label is absent in the closely related and popular notion of confidence calibration, which we argue makes confidence calibration difficult to interpret for decision-making. We propose top-label calibration as a rectification of confidence calibration. Further, we outline a multiclass-to-binary (M2B) reduction framework that unifies confidence, top-label, and class-wise calibration, among others. As its name suggests, M2B works by reducing multiclass calibration to numerous binary calibration problems, each of which can be solved using simple binary calibration routines. We instantiate the M2B framework with the well-studied histogram binning (HB) binary calibrator, and prove that the overall procedure is multiclass calibrated without making any assumptions on the underlying data distribution. In an empirical evaluation with four deep net architectures on CIFAR-10 and CIFAR-100, we find that the M2B + HB procedure achieves lower top-label and class-wise calibration error than other approaches such as temperature scaling. 

Gene regulatory networks underpin stress response pathways in plants. However, parsing these networks to prioritize key genes underlying a particular trait is challenging. Here, we have built the Gene Regulation and Association Network (GRAiN) of rice ( Oryza sativa ). GRAiN is an interactive query-based web-platform that allows users to study functional relationships between transcription factors (TFs) and genetic modules underlying abiotic-stress responses. We built GRAiN by applying a combination of different network inference algorithms to publicly available gene expression data. We propose a supervised machine learning framework that complements GRAiN in prioritizing genes that regulate stress signal transduction and modulate gene expression under drought conditions. Our framework converts intricate network connectivity patterns of 2160 TFs into a single drought score. We observed that TFs with the highest drought scores define the functional, structural, and evolutionary characteristics of drought resistance in rice. Our approach accurately predicted the function of OsbHLH148 TF, which we validated using in vitro protein-DNA binding assays and mRNA sequencing loss-of-function mutants grown under control and drought stress conditions. Our network and the complementary machine learning strategy lends itself to predicting key regulatory genes underlying other agricultural traits and will assist in the genetic engineering of desirable rice varieties. 

Transcription factors (TFs) play a central role in regulating molecular level responses of plants to external stresses such as water limiting conditions, but identification of such TFs in the genome remains a challenge. Here, we describe a network-based supervised machine learning framework that accurately predicts and ranks all TFs in the genome according to their potential association with drought tolerance. We show that top ranked regulators fall mainly into two ‘age’ groups; genes that appeared first in land plants and genes that emerged later in the Oryza clade. TFs predicted to be high in the ranking belong to specific gene families, have relatively simple intron/exon and protein structures, and functionally converge to regulate primary and secondary metabolism pathways. Repeated trials of nested cross-validation tests showed that models trained only on regulatory network patterns, inferred from large transcriptome datasets, outperform models trained on heterogenous genomic features in the prediction of known drought response regulators. A new R/Shiny based web application, called the DroughtApp, provides a primer for generation of new testable hypotheses related to regulation of drought stress response. Furthermore, to test the system we experimentally validated predictions on the functional role of the rice transcription factor OsbHLH148, using RNA sequencing of knockout mutants in response to drought stress and protein-DNA interaction assays. Our study exemplifies the integration of domain knowledge for prioritization of regulatory genes in biological pathways of well-studied agricultural traits. 

UV‐ranged micro‐LEDs are being explored for numerous applications due to their high stability and power efficiency. However, previous reports have shown reduced external quantum efficiency (EQE) and increased leakage current due to the increase in surface‐to‐volume ratio with a decrease in the micro‐LED size. Herein, the size‐related performance for UV‐A micro‐LEDs, ranging from 8 × 8 to 100 × 100 μm², is studied. These devices exhibit reduced leakage current with the implementation of atomic layer deposition‐based sidewall passivation. A systematic EQE comparison is performed with minimal leakage current and a size‐independent on‐wafer EQE of around 5.5% is obtained. Smaller sized devices experimentally show enhanced EQE at high current density due to their improved heat dissipation capabilities. To the best of authors’ knowledge, this is the highest reported on‐wafer EQE demonstrated in <10 μm dimensioned 368 nm UV LEDs.