Patch antenna microcavity terahertz sources with enhanced emission

We study the emission properties of an electroluminescent THz frequency quantum cascade structure embedded in an array of patch antenna double-metal microcavities. We show that high photon extraction efficiencies can be obtained by adjusting the active region thickness and array periodicity as well as high Purcell factors (up to 65), leading to an enhanced overall emitted power. Up to a 44-fold increase in power is experimentally observed in comparison with a reference device processed in conventional mesa geometry. Estimation of the Purcell factors using electromagnetic simulations and the theoretical extraction efficiency are in agreement with the observed power enhancement and show that, in these microcavities, the overall enhancement solely depends on the square of the total quality factor

Tomography and state reconstruction with superconducting single-photon detectors

We perform quantum state reconstruction of coherent and thermal states with a detector which has an enhanced multiphoton response. The detector is based on superconducting nanowires, where the bias current sets the dependence of the click probability on the photon number; this bias current is used as tuning parameter in the state reconstruction. The nonlinear response makes our nanowire-based detector superior to the linear detectors that are conventionally used for quantum state reconstruction.Comment: revision of intro compared to V

Waveguide single-photon detectors for integrated quantum photonic circuits

The generation, manipulation and detection of quantum bits (qubits) encoded on single photons is at the heart of quantum communication and optical quantum information processing. The combination of single-photon sources, passive optical circuits and single-photon detectors enables quantum repeaters and qubit amplifiers, and also forms the basis of all-optical quantum gates and of linear-optics quantum computing. However, the monolithic integration of sources, waveguides and detectors on the same chip, as needed for scaling to meaningful number of qubits, is very challenging, and previous work on quantum photonic circuits has used external sources and detectors. Here we propose an approach to a fully-integrated quantum photonic circuit on a semiconductor chip, and demonstrate a key component of such circuit, a waveguide single-photon detector. Our detectors, based on superconducting nanowires on GaAs ridge waveguides, provide high efficiency (20%) at telecom wavelengths, high timing accuracy (60 ps), response time in the ns range, and are fully compatible with the integration of single-photon sources, passive networks and modulators.Comment: 11 pages, 4 figure

Carnitine supplementation has protective and detrimental effects in Saccharomyces cerevisiae that are genetically mediated

l-Carnitine plays a well-documented role in eukaryotic energy homeostasis by acting as a shuttling molecule for activated acyl residues across intracellular membranes. This activity, supported by carnitine acyl-transferases and transporters, is referred to as the carnitine shuttle. However, several pleiotropic and often beneficial effects of carnitine in humans have been reported that appear to be unrelated to shuttling activity, but little conclusive evidence regarding molecular mechanisms exists. We have recently demonstrated a role of carnitine, independent of the carnitine shuttle, in yeast stress protection. Here, we show that carnitine specifically protects against oxidative stress caused by H2O2 and the superoxide-generating agent menadione. Surprisingly, carnitine has a detrimental effect on survival when combined with thiol-modifying agents. Central elements of the oxidative stress response, specifically the transcription factors Yap1p and Skn7p, are shown to be required for carnitine's protective effect, but several downstream effectors are dispensable. A DNA microarray-based analysis identifies Cyc3p, a cytochrome c heme lyase, as being important for carnitine's impact during oxidative stress. These findings establish a direct genetic link to a carnitine-related phenotype that is independent of the shuttle system and suggests that Saccharomyces cerevisiae should provide a useful model for further elucidation of carnitine's physiological roles. © 2010 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd.Articl

A Deep Learning Approach for Breast Invasive Ductal Carcinoma Detection and Lymphoma Multi-Classification in Histological Images

Accurately identifying and categorizing cancer structures/sub-types in histological images is an important clinical task involving a considerable workload and a specific subspecialty of pathologists. Digitizing pathology is a current trend that provides large amounts of visual data allowing a faster and more precise diagnosis through the development of automatic image analysis techniques. Recent studies have shown promising results for the automatic analysis of cancer tissue by using deep learning strategies that automatically extract and organize the discriminative information from the data. This paper explores deep learning methods for the automatic analysis of Hematoxylin and Eosin stained histological images of breast cancer and lymphoma. In particular, a deep learning approach is proposed for two different use cases: the detection of invasive ductal carcinoma in breast histological images and the classification of lymphoma sub-types. Both use cases have been addressed by adopting a residual convolutional neural network that is part of a convolutional autoencoder network (i.e., FusionNet). The performances have been evaluated on the public datasets of digital histological images and have been compared with those obtained by using different deep neural networks (UNet and ResNet). Additionally, comparisons with the state of the art have been considered, in accordance with different deep learning approaches. The experimental results show an improvement of 5.06% in F-measure score for the detection task and an improvement of 1.09% in the accuracy measure for the classification task

Gigapixel Histopathological Image Analysis Using Attention-Based Neural Networks

Although CNNs are widely considered as the state-of-the-art models in various applications of image analysis, one of the main challenges still open is the training of a CNN on high resolution images. Different strategies have been proposed involving either a rescaling of the image or an individual processing of parts of the image. Such strategies cannot be applied to images, such as gigapixel histopathological images, for which a high reduction in resolution inherently effects a loss of discriminative information, and in respect of which the analysis of single parts of the image suffers from a lack of global information or implies a high workload in terms of annotating the training images in such a way as to select significant parts. We propose a method for the analysis of gigapixel histopathological images solely by using weak image-level labels. In particular, two analysis tasks are taken into account: a binary classification and a prediction of the tumor proliferation score. Our method is based on a CNN structure consisting of a compressing path and a learning path. In the compressing path, the gigapixel image is packed into a grid-based feature map by using a residual network devoted to the feature extraction of each patch into which the image has been divided. In the learning path, attention modules are applied to the grid-based feature map, taking into account spatial correlations of neighboring patch features to find regions of interest, which are then used for the final whole slide analysis. Our method integrates both global and local information, is flexible with regard to the size of the input images and only requires weak image-level labels. Comparisons with different methods of the state-of-the-art on two well known datasets, Camelyon16 and TUPAC16, have been made to confirm the validity of the proposed model

A Deep Learning Approach for Voice Disorder Detection for Smart Connected Living Environments

Edge Analytics and Artificial Intelligence are important features of the current smart connected living community. In a society where people, homes, cities, and workplaces are simultaneously connected through various devices, primarily through mobile devices, a considerable amount of data is exchanged, and the processing and storage of these data are laborious and difficult tasks. Edge Analytics allows the collection and analysis of such data on mobile devices, such as smartphones and tablets, without involving any cloud-centred architecture that cannot guarantee real-time responsiveness. Meanwhile, Artificial Intelligence techniques can constitute a valid instrument to process data, limiting the computation time, and optimising decisional processes and predictions in several sectors, such as healthcare. Within this field, in this article, an approach able to evaluate the voice quality condition is proposed. A fully automatic algorithm, based on Deep Learning, classifies a voice as healthy or pathological by analysing spectrogram images extracted by means of the recording of vowel /a/, in compliance with the traditional medical protocol. A light Convolutional Neural Network is embedded in a mobile health application in order to provide an instrument capable of assessing voice disorders in a fast, easy, and portable way. Thus, a straightforward mobile device becomes a screening tool useful for the early diagnosis, monitoring, and treatment of voice disorders. The proposed approach has been tested on a broad set of voice samples, not limited to the most common voice diseases but including all the pathologies present in three different databases achieving F1-scores, over the testing set, equal to 80%, 90%, and 73%. Although the proposed network consists of a reduced number of layers, the results are very competitive compared to those of other "cutting edge" approaches constructed using more complex neural networks, and compared to the classic deep neural networks, for example, VGG-16 and ResNet-50