1,720,968 research outputs found
Leveraging a Hybrid Quantum-Classical Framework for Subsurface Target Detection in Radar Sounding System: Challenges and Opportunities
No full textIn this article, we explore the potential of quantum machine learning (QML) for subsurface feature extractions from radar sounder (RS) signals. We propose a hybrid quantum-classical (HQC) learning paradigm that leverages parameterized quantum circuits (PQCs) to generate probability amplitudes based on quantum properties such as superposition and entanglement. These amplitudes are synergistically integrated with the classical deep neural networks that are efficient in learning high-dimensional contextual features for downstream prediction tasks. The present research work is structured around two objectives. First, we investigate the role of quantum circuits in the latent space for transferring back-and-forth rich discriminative spatial context from the encoder to the decoder for segmentation. Second, we investigate how the probabilistic amplitudes derived from quantum circuits are significant in integrating into the classical models to provide new insights for RS signals segmentation. The performance of the hybrid architectures has been studied in small-scale settings by simulating the expected behavior of the quantum circuits on a classical machine. The experimental results have demonstrated the viability of QML frameworks on MCoRDS-1 and MCoRDS-3 datasets for RS signal segmentation. Qualitatively, they are capable of delineating the spatial extent of the bedrock from noise. Additionally, we conduct a comparative analysis between the Qiskit Aer Simulator and the IBM FakeBackend Simulator to highlight the computational trade-offs and validate fidelity of two simulators for scalable experimentation. Therefore, our work opens up new avenues of research for future RS data analysis leading to more precise and efficient subsurface target segmentation
A Hybrid Quantum-Classical CNN Architecture for Semantic Segmentation of Radar Sounder Data
No full textThe article presents for the first time a hybrid quantum-classical architecture in the context of subsurface target detection in the radar sounder signal. We enhance the classical convolutional neural network (CNN) based architecture by integrating a quantum layer in the latent space. We investigate two quantum circuits with the classical neural networks by exploiting fundamental properties of quantum mechanics such as entanglement and superposition. The proposed hybrid architecture is used for the downstream task of patch-wise semantic segmentation of radar sounder subsurface images. Experimental results on the MCoRDS and MCoRDS3 datasets demonstrated the capability of the hybrid quantum-classical approach for radar sounder information extraction
A CNN Architecture Tailored For Quantum Feature Map-Based Radar Sounder Signal Segmentation
No full textThis article presents a hybrid quantum-classical framework by incorporating quantum feature maps into a classical Convolutional Neural Network (CNN) architecture for detecting different subsurface targets in radar sounder signals. The quantum feature maps are generated by quantum circuits to utilize spatially-bound input information from the training samples. The associated spectral probabilistic amplitudes of the feature maps are further fed into the classical CNN-based network to classify the subsurface targets in the radargram. Experimental results on the MCoRDS and MCoRDS3 datasets demonstrated the capability of enhancing the classical architecture through quantum feature maps for characterizing radar sounder data
Local Binary and Multiclass SVMs Trained on a Quantum Annealer
Get PDFSupport vector machines (SVMs) are widely used machine learning models, with formulations for both classification and regression tasks. In the last years, with the advent of working quantum annealers, hybrid SVM models characterised by quantum training and classical execution have been introduced. These models have demonstrated comparable performance to their classical counterparts. However, they are limited in the training set size due to the restricted connectivity of the current quantum annealers. Hence, to take advantage of large datasets, a strategy is required. In the classical domain, local SVMs, namely, SVMs trained on the data samples selected by a k -nearest neighbors model, have already proven successful. Here, the local application of quantum-trained SVM models is proposed and empirically assessed. In particular, this approach allows overcoming the constraints on the training set size of the quantum-trained models while enhancing their performance. In practice, the Fast Local Kernel Support Vector Machine (FaLK-SVM) method, designed for efficient local SVMs, has been combined with quantum-trained SVM models for binary and multiclass classification. In addition, for comparison, FaLK-SVM has been interfaced for the first time with a classical single-step multiclass SVM model (CS SVM). Concerning the empirical evaluation, D-Wave's quantum annealers and real-world datasets taken from the remote sensing domain have been employed. The results have shown the effectiveness and scalability of the proposed approach, but also its practical applicability in a real-world large-scale scenario
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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