Excitonic Effects in Quantum Wires

We review the effects of Coulomb correlation on the linear and non-linear optical properties of semiconductor quantum wires, with emphasis on recent results for the bound excitonic states. Our theoretical approach is based on generalized semiconductor Bloch equations, and allows full three-dimensional multisubband description of electron-hole correlation for arbitrary confinement profiles. In particular, we consider V- and T-shaped structures for which significant experimental advances were obtained recently. Above band gap, a very general result obtained by this approach is that electron-hole Coulomb correlation removes the inverse-square-root single-particle singularity in the optical spectra at band edge, in agreement with previous reports from purely one-dimensional models. Strong correlation effects on transitions in the continuum are found to persist also at high densities of photoexcited carriers. Below bandgap, we find that the same potential- (Coulomb) to kinetic-energy ratio holds for quite different wire cross sections and compositions. As a consequence, we identify a shape- and barrier-independent parameter that governs a universal scaling law for exciton binding energy with size. Previous indications that the shape of the wire cross-section may have important effects on exciton binding are discussed in the light of the present results.Comment: Proc. OECS-5 Conference, G\"ottingen, 1997 (To appear in Phys. Stat. Sol. (b)

Resonant Transport in Nb/GaAs/AlGaAs/GaAs Microstructures

Resonant transport in a hybrid semiconductor-superconductor microstructure grown by MBE on GaAs is presented. This structure experimentally realizes the prototype system originally proposed by de Gennes and Saint-James in 1963 in \emph{all}-metal structures. A low temperature single peak superimposed to the characteristic Andreev-dominated subgap conductance represents the mark of such resonant behavior. Random matrix theory of quantum transport was employed in order to analyze the observed magnetotransport properties and ballistic effects were included by directly solving the Bogoliubov-de Gennes equations.Comment: 7 pages REVTeX, 4 figures, to be published by World Scientific in Proceedings of International Symposium on Mesoscopic Superconductivity and Spintronics (NTT R&D Center Atsugi, Japan, March 2002

Robust reconstruction of the discrete state for a class of nonlinear uncertain switched systems

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Well-width dependence of exciton-phonon scattering in InxGa1 - xAs/GaAs single quantum wells

The temperature and density dependencies of the exciton dephasing time in In0.18Ga0.82As/GaAs single quantum wells with different thicknesses have been measured by degenerate four-wave mixing. The exciton-phonon scattering contribution to the dephasing is isolated by extrapolating the dephasing rate to zero-exciton density. From the temperature dependence of this rate we have deduced the linewidth broadening coefficients for acoustic and optical phonons. We find acoustic-phonon coefficients that increase from 1.6 to 3 μeV/K when increasing the well width from 1 to 4 nm. This is in quantitative agreement with theoretical predictions when the spatial extension of the exciton wave function, strongly penetrating into the GaAs barrier in thin InxGa1-xAs quantum wells, is taken into account. The optical-phonon coefficient does not show a systematic dependence on well thickness, and is comparable with the value for bulk GaAs

A Prognostic Model for Estimating the Time to Virologic Failure in HIV-1 Infected Patients Undergoing a New Combination Antiretroviral Therapy Regimen

<p>Abstract</p> <p>Background</p> <p>HIV-1 genotypic susceptibility scores (GSSs) were proven to be significant prognostic factors of fixed time-point virologic outcomes after combination antiretroviral therapy (cART) switch/initiation. However, their relative-hazard for the time to virologic failure has not been thoroughly investigated, and an expert system that is able to predict how long a new cART regimen will remain effective has never been designed.</p> <p>Methods</p> <p>We analyzed patients of the Italian ARCA cohort starting a new cART from 1999 onwards either after virologic failure or as treatment-naïve. The time to virologic failure was the endpoint, from the 90^thday after treatment start, defined as the first HIV-1 RNA > 400 copies/ml, censoring at last available HIV-1 RNA before treatment discontinuation. We assessed the relative hazard/importance of GSSs according to distinct interpretation systems (Rega, ANRS and HIVdb) and other covariates by means of Cox regression and random survival forests (RSF). Prediction models were validated via the bootstrap and c-index measure.</p> <p>Results</p> <p>The dataset included 2337 regimens from 2182 patients, of which 733 were previously treatment-naïve. We observed 1067 virologic failures over 2820 persons-years. Multivariable analysis revealed that low GSSs of cART were independently associated with the hazard of a virologic failure, along with several other covariates. Evaluation of predictive performance yielded a modest ability of the Cox regression to predict the virologic endpoint (c-index≈0.70), while RSF showed a better performance (c-index≈0.73, p < 0.0001 vs. Cox regression). Variable importance according to RSF was concordant with the Cox hazards.</p> <p>Conclusions</p> <p>GSSs of cART and several other covariates were investigated using linear and non-linear survival analysis. RSF models are a promising approach for the development of a reliable system that predicts time to virologic failure better than Cox regression. Such models might represent a significant improvement over the current methods for monitoring and optimization of cART.</p

A generative adversarial network (GAN) technique for internet of medical things data

The application of machine learning and artificial intelligence techniques in the medical world is growing, with a range of purposes: from the identification and prediction of possible diseases to patient monitoring and clinical decision support systems. Furthermore, the widespread use of remote monitoring medical devices, under the umbrella of the “Internet of Medical Things” (IoMT), has simplified the retrieval of patient information as they allow continuous monitoring and direct access to data by healthcare providers. However, due to possible issues in real-world settings, such as loss of connectivity, irregular use, misuse, or poor adherence to a monitoring program, the data collected might not be sufficient to implement accurate algorithms. For this reason, data augmentation techniques can be used to create synthetic datasets sufficiently large to train machine learning models. In this work, we apply the concept of generative adversarial networks (GANs) to perform a data augmentation from patient data obtained through IoMT sensors for Chronic Obstructive Pulmonary Disease (COPD) monitoring. We also apply an explainable AI algorithm to demonstrate the accuracy of the synthetic data by comparing it to the real data recorded by the sensors. The results obtained demonstrate how synthetic datasets created through a well-structured GAN are comparable with a real dataset, as validated by a novel approach based on machine learning

Metal/III-V diodes engineered by means of Si interlayers: interface reactions versus local interface dipoles

We present studies of Al/n-GaAs(001) and Al/p-GaAs(001) diodes in which the Schottky barrier height was varied by fabricating Si bilayers at the interface under either Ga or Al flux. Comparison of the effect of each interlayer on the n- and p-type barrier height allowed us to rule out any major role of interface reactions and test the predictions of the local interface-dipole model of Schottky barrier tuning. (C) 2001 American Institute of Physics