Mid- and Long-Wave Infrared Optoelectronics via Intraband Transitions in PbS Colloidal Quantum Dots.

Optical sensing in the mid- and long-wave infrared (MWIR, LWIR) is of paramount importance for a large spectrum of applications including environmental monitoring, gas sensing, hazard detection, food and product manufacturing inspection, and so forth. Yet, such applications to date are served by costly and complex epitaxially grown HgCdTe quantum-well and quantum-dot infrared photodetectors. The possibility of exploiting low-energy intraband transitions make colloidal quantum dots (CQD) an attractive low-cost alternative to expensive low bandgap materials for infrared applications. Unfortunately, fabrication of quantum dots exhibiting intraband absorption is technologically constrained by the requirement of controlled heavy doping, which has limited, so far, MWIR and LWIR CQD detectors to mercury-based materials. Here, we demonstrate intraband absorption and photodetection in heavily doped PbS colloidal quantum dots in the 5-9 µm range, beyond the PbS bulk band gap, with responsivities on the order of 10-4 A/W at 80 K. We have further developed a model based on quantum transport equations to understand the impact of electron population of the conduction band in the performance of intraband photodetectors and offer guidelines toward further performance improvement.

Single-Exciton Gain and Stimulated Emission Across the Infrared Telecom Band from Robust Heavily Doped PbS Colloidal Quantum Dots.

Materials with optical gain in the infrared are of paramount importance for optical communications, medical diagnostics, and silicon photonics. The current technology is based either on costly III-V semiconductors that are not monolithic to silicon CMOS technology or Er-doped fiber technology that does not make use of the full fiber transparency window. Colloidal quantum dots (CQDs) offer a unique opportunity as an optical gain medium in view of their tunable bandgap, solution processability, and CMOS compatibility. The 8-fold degeneracy of infrared CQDs based on Pb-chalcogenides has hindered the demonstration of low-threshold optical gain and lasing, at room temperature. We demonstrate room-temperature, infrared, size-tunable, band-edge stimulated emission with a line width of ∼14 meV. Leveraging robust electronic doping and charge-exciton interactions in PbS CQD thin films, we reach a gain threshold at the single exciton regime representing a 4-fold reduction from the theoretical limit of an 8-fold degenerate system, with a net modal gain in excess of 100 cm-1.

Cue the volatility spillover in the cryptocurrency markets during the COVID-19 pandemic: evidence from DCC-GARCH and wavelet analysis.

This study investigates the dynamic mechanism of financial markets on volatility spillovers across eight major cryptocurrency returns, namely Bitcoin, Ethereum, Stellar, Ripple, Tether, Cardano, Litecoin, and Eos from November 17, 2019, to January 25, 2021. The study captures the financial behavior of investors during the COVID-19 pandemic as a result of national lockdowns and slowdown of production. Three different methods, namely, EGARCH, DCC-GARCH, and wavelet, are used to understand whether cryptocurrency markets have been exposed to extreme volatility. While GARCH family models provide information about asset returns at given time scales, wavelets capture that information across different frequencies without losing inputs from the time horizon. The overall results show that three cryptocurrency markets (i.e., Bitcoin, Ethereum, and Litecoin) are highly volatile and mutually dependent over the sample period. This result means that any kind of shock in one market leads investors to act in the same direction in the other market and thus indirectly causes volatility spillovers in those markets. The results also imply that the volatility spillover across cryptocurrency markets was more influential in the second lockdown that started at the beginning of November 2020. Finally, to calculate the financial risk, two methods-namely, value-at-risk (VaR) and conditional value-at-risk (CVaR)-are used, along with two additional stock indices (the Shanghai Composite Index and S&P 500). Regardless of the confidence level investigated, the selected crypto assets, with the exception of the USDT were found to have substantially greater downside risk than SSE and S&P 500.

UVC-LED-based face mask design and efficacy against common germs.

During the Covid-19 pandemic, one of the best means of personal protection was using face masks. In this context, the World Health Organization has declared the attempts to produce masks inactivating airborne virus species a welcome initiative. This preliminary study aimed to prove that airborne germs passing through a mask filter cartridge can be destroyed by the rays emitted from UVC LEDs placed in such cartridge. We therefore designed such a face mask and tested the efficiency of UVC LEDs placed in its cartridge against common contaminants, gram-positive Staphylococcus aureus, gram-negative Pseudomonas aeruginosa, and the influenza A/Puerto Rico/8/1934 virus because of its similarity with SARS CoV-2. Eight UVC LEDs with a total power of 75 mW provided sufficient germicidal effect for all three germs. In terms of safety, ozone production released during UVC LED emission was negligible. Our findings are promising, as they show that well-designed UVC-based face masks can be effective against airborne germs, but further research on a greater sample may help us learn more and optimise such face masks.

Comparison of different estimation methods for extreme value distribution.

The extreme value distribution was developed for modeling extreme-order statistics or extreme events. In this study, we discuss the distribution of the largest extreme. The main objective of this paper is to determine the best estimators of the unknown parameters of the extreme value distribution. Thus, both classical and Bayesian methods are used. The classical estimation methods under consideration are maximum likelihood estimators, moment's estimators, least squares estimators, and weighted least squares estimators, percentile estimators, the ordinary least squares estimators, best linear unbiased estimators, L-moments estimators, trimmed L-moments estimators, and Bain and Engelhardt estimators. We also propose new estimators for the unknown parameters. Bayesian estimators of the parameters are derived by using Lindley's approximation and Markov Chain Monte Carlo methods. The asymptotic confidence intervals are considered by using maximum likelihood estimators. The Bayesian credible intervals are also obtained by using Gibbs sampling. The performances of these estimation methods are compared with respect to their biases and mean square errors through a simulation study. The maximum daily flood discharge (annual) data sets of the Meriç River and Feather River are analyzed at the end of the study for a better understanding of the methods presented in this paper.

A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans.

We introduce a new computer aided detection and diagnosis system for lung cancer screening with low-dose CT scans that produces meaningful probability assessments. Our system is based entirely on 3D convolutional neural networks and achieves state-of-the-art performance for both lung nodule detection and malignancy classification tasks on the publicly available LUNA16 and Kaggle Data Science Bowl challenges. While nodule detection systems are typically designed and optimized on their own, we find that it is important to consider the coupling between detection and diagnosis components. Exploiting this coupling allows us to develop an end-to-end system that has higher and more robust performance and eliminates the need for a nodule detection false positive reduction stage. Furthermore, we characterize model uncertainty in our deep learning systems, a first for lung CT analysis, and show that we can use this to provide well-calibrated classification probabilities for both nodule detection and patient malignancy diagnosis. These calibrated probabilities informed by model uncertainty can be used for subsequent risk-based decision making towards diagnostic interventions or disease treatments, as we demonstrate using a probability-based patient referral strategy to further improve our results.

Size- and Temperature-Dependent Intraband Optical Properties of Heavily n-Doped PbS Colloidal Quantum Dot Solid-State Films.

Steady-state access to intraband transitions in colloidal quantum dots (CQDs), via doping, permits exploitation of the electromagnetic spectrum at energies below the band gap. CQD intraband optoelectronics allows envisaging cheap mid- and long-wavelength infrared photodetectors and light-emitting devices, which today employ epitaxial materials. As intraband devices start to emerge, thorough studies of the basic properties of intraband transitions in different CQD materials are needed to guide technological research. In this work, we investigate the size and temperature dependence of the intraband transition in heavily n-doped PbS quantum dot (QD) films. In the studied QD size range (5-8 nm), the intraband energy spans from 209 to 151 meV. We measure the intraband absorption coefficient of heavily doped PbS QD films to be around 2 × 104 cm-1, proving that intraband absorption is as strong as interband absorption. We demonstrate a negative dependence of the intraband energy with temperature, in contrast to the positive dependence of the interband transition. Also opposite to the interband case, the temperature dependence of the intraband energy increases with decreasing size, going from -29 µeV/K to -49 µeV/K in the studied size range.

Copula Based Classifier Fusion Under Statistical Dependence.

We consider the problem of fusing probability scores from a set of classifiers to estimate a final fused probability score. Our interest is in scenarios where the classifiers are statistically dependent. To that end, we propose a new classifier fusion approach that is data driven and founded on the statistical theory of copulas. Numerical results with both simulated and real data show that our copula based classifier fusion approach produces better probability scores than individual classifiers and outperforms existing probability score fusion approaches.