COVID-CBR: a deep learning architecture featuring case-based reasoning for classification of COVID-19 from chest x-ray images

Background and Objectives: This study aims to assist rapid accurate diagnosis of COVID-19 based on chest x-ray (CXR) images to provide supplementary information, leading to screening program for early detection of COVID-19 based on CXR images by developing an interpretable, robust and performant AI system. Methods: A case-based reasoning approach built upon autoencoder deep learning architecture is applied to classify COVID-19 from other non-COVID-19 as well as normal subjects from chest x-ray images. The system integrates the interpretation and decision-making together by producing a set of profiles that in appearance resemble the training samples and hence explain the outcome of classifications. Three classes are studied, which are COVID-19 (n=250), other non-COVID-19 diseases (NCD) (n=384), including TB and ARDS, and normal (n=327). Results: This COVID-CBR system sustains the average sensitivity and specificity of 93.1±3.58% and 96.1±4.10% respectively for classification of these three classes. In comparison with the current state of the art, including COVID-Net, VGG-16 and other explainable AI systems, the developed COVID-CBR system appears to perform similar or better when classifying multi-class categories. Conclusion: This paper presents a case-based reasoning deep learning system for detection of COVID-19 from chest x-ray images. Comparison with several state of the art systems is conducted. Although the improvement tends to be marginal, especially for VGG-16, the novelty of this work manifests its interpretable feature building upon case-based reasoning, leading to revealing this viral insight and hence ascertaining more effective treatment and drugs while maintaining being transparent. Furthermore, different from several other current explainable networks that highlight key regions or the points of an input that activate the network, i.e. heat maps, this work is constructed upon whole training images, i.e. case-based, whereby each training image belongs to one of the case clusters

Modelling of chromatic contrast for retrieval of wallpaper images

Colour remains one of the key factors in presenting an object and consequently has been widely applied in retrieval of images based on their visual contents. However, a colour appearance changes with the change of viewing surroundings, the phenomenon that has not been paid attention yet while performing colour-based image retrieval. To comprehend this effect, in this paper, a chromatic contrast model, CAMcc, is developed for the application of retrieval of colour intensive images, cementing the gap that most of existing colour models lack to fill by taking simultaneous colour contrast into account. Subsequently, the model is applied to the retrieval task on a collection of museum wallpapers of colour-rich images. In comparison with current popular colour models including CIECAM02, HSI, and RGB, with respect to both foreground and background colours, CAMcc appears to outperform the others with retrieved results being closer to query images. In addition, CAMcc focuses more on foreground colours, especially by maintaining the balance between both foreground and background colours, while the rest of existing models take on dominant colours that are perceived the most, usually background tones. Significantly, the contribution of the investigation lies in not only the improvement of the accuracy of colour-based image retrieval, but also the development of colour contrast model that warrants an important place in colour and computer vision theory, leading to deciphering the insight of this age-old topic of chromatic contrast in colour science

Low temperature terahertz spectroscopy of n-InSb through a magnetic field driven metal-insulator transition

We use fiber-coupled photoconductive emitters and detectors to perform terahertz (THz) spectroscopy of lightly-doped n-InSb directly in the cryogenic (1.5 K) bore of a high-field superconducting magnet. We measure transmission spectra from 0.1-1.1 THz as the sample is driven through a metal-insulator transition (MIT) by applied magnetic field. In the low-field metallic state, the data directly reveal the plasma edge and magneto-plasmon modes. With increasing field, a surprisingly broad band (0.3-0.8 THz) of low transmission appears at the onset of the MIT. This band subsequently collapses and evolves into the sharp 1s -> 2p- transition of electrons `frozen' onto isolated donors in the insulating state.Comment: 4 pages, 3 figure

Perturbation theory of von Neumann Entropy

In quantum information theory, von Neumann entropy plays an important role. The entropies can be obtained analytically only for a few states. In continuous variable system, even evaluating entropy numerically is not an easy task since the dimension is infinite. We develop the perturbation theory systematically for calculating von Neumann entropy of non-degenerate systems as well as degenerate systems. The result turns out to be a practical way of the expansion calculation of von Neumann entropy.Comment: 7 page

An approach to exact solutions of the time-dependent supersymmetric two-level three-photon Jaynes-Cummings model

By utilizing the property of the supersymmetric structure in the two-level multiphoton Jaynes-Cummings model, an invariant is constructed in terms of the supersymmetric generators by working in the sub-Hilbert-space corresponding to a particular eigenvalue of the conserved supersymmetric generators. We obtain the exact solutions of the time-dependent Schr\"{o}dinger equation which describes the time-dependent supersymmetric two-level three-photon Jaynes-Cummings model (TLTJCM) by using the invariant-related unitary transformation formulation. The case under the adiabatic approximation is also discussed. Keywords: Supersymmetric Jaynes-Cummings model; exact solutions; invariant theory; geometric phase factor; adiabatic approximationComment: 7 pages, Late

Sensitivity analysis for diffusion processes constrained to an orthant

This paper studies diffusion processes constrained to the positive orthant under infinitesimal changes in the drift. Our first main result states that any constrained function and its (left) drift-derivative is the unique solution to an augmented Skorohod problem. Our second main result uses this characterization to establish a basic adjoint relationship for the stationary distribution of the constrained diffusion process jointly with its left-derivative process.Comment: Published in at http://dx.doi.org/10.1214/13-AAP967 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

A Simultaneous Quantum Secure Direct Communication Scheme between the Central Party and Other M Parties

We propose a simultaneous quantum secure direct communication scheme between one party and other three parties via four-particle GHZ states and swapping quantum entanglement. In the scheme, three spatially separated senders, Alice, Bob and Charlie, transmit their secret messages to a remote receiver Diana by performing a series local operations on their respective particles according to the quadripartite stipulation. From Alice, Bob, Charlie and Diana's Bell measurement results, Diana can infer the secret messages. If a perfect quantum channel is used, the secret messages are faithfully transmitted from Alice, Bob and Charlie to Diana via initially shared pairs of four-particle GHZ states without revealing any information to a potential eavesdropper. As there is no transmission of the qubits carrying the secret message in the public channel, it is completely secure for the direct secret communication. This scheme can be considered as a network of communication parties where each party wants to communicate secretly with a central party or server.Comment: 4 pages, no figur