Reviewerly/exHarmony huggingface dataset:
Building blocks of a flip-chip integrated superconducting quantum processor

Abstract We have integrated single and coupled superconducting transmon qubits into flip-chip modules. Each module consists of two chips—one quantum chip and one control chip—that are bump-bonded together. We demonstrate time-averaged coherence times exceeding 90 μs, single-qubit gate fidelities exceeding 99.9%, and two-qubit gate fidelities above 98.6%. We also present device design methods and discuss the sensitivity of device parameters to variation in interchip spacing. Notably, the additional flip-chip fabrication steps do not degrade the qubit performance compared to our baseline state-of-the-art in single-chip, planar circuits. This integration technique can be extended to the realisation of quantum processors accommodating hundreds of qubits in one module as it offers adequate input/output wiring access to all qubits and couplers.
Blockchain Technology for Supply Chain Management: A Comprehensive Review

Firms are eager to adopt new technologies, such as Artificial Intelligence (A.I.), Cloud Computing, Big Data, etc., as they witness successful business applications. As one of the disruptive technologies, Blockchain technology (BCT) has been drawing attention stemming from cryptocurrency proliferation (e.g., Bitcoin and Ethereum), for which Blockchain serves as the backbone. However, the public is haunted by the bewilderment between cryptocurrencies and BCT. Furthermore, the burgeoning of Metaverse and non-fungible tokens (NFT) has raised BCT to another notch. This study conducts a holistic literature review on BCT features, implementations, and business implications. In particular, by reviewing and analyzing 2265 up-to-date articles that reveal BCT’s applications across various fields, this Blockchain-centered study reveals the research status and delineates future research directions. It is shown that, among various characteristics of BCT, traceability is the main characteristic fueling BCT’s application in supply chain management (SCM). We further find that the BCT-related research has been extremely growing in SCM, healthcare, and government, while declining in the areas of banking and cyber security. Geographically, the top countries with BCT-related publications are China, U.S., and India. Finally, it is emphasized that BCT-related research in environmental sciences and agriculture have potential to be explored.
Machine learning generalizability across healthcare settings: insights from multi-site COVID-19 screening

As patient health information is highly regulated due to privacy concerns, most machine learning (ML)-based healthcare studies are unable to test on external patient cohorts, resulting in a gap between locally reported model performance and cross-site generalizability. Different approaches have been introduced for developing models across multiple clinical sites, however less attention has been given to adopting ready-made models in new settings. We introduce three methods to do this-(1) applying a ready-made model "as-is" (2); readjusting the decision threshold on the model's output using site-specific data and (3); finetuning the model using site-specific data via transfer learning. Using a case study of COVID-19 diagnosis across four NHS Hospital Trusts, we show that all methods achieve clinically-effective performances (NPV > 0.959), with transfer learning achieving the best results (mean AUROCs between 0.870 and 0.925). Our models demonstrate that site-specific customization improves predictive performance when compared to other ready-made approaches.
Neuromorphic Photonic Memory Devices Using Ultrafast, Non‐Volatile Phase‐Change Materials

The search for ultrafast photonic memory devices is inspired by the ever-increasing number of cloud-computing, supercomputing, and artificial-intelligence applications, together with the unique advantages of signal processing in the optical domain such as high speed, large bandwidth, and low energy consumption. By embracing silicon photonics with chalcogenide phase-change materials (PCMs), non-volatile integrated photonic memory is developed with promising potential in photonic integrated circuits and nanophotonic applications. While conventional PCMs suffer from slow crystallization speed, scandium-doped antimony telluride (SST) has been recently developed for ultrafast phase-change random-access memory applications. An ultrafast non-volatile photonic memory based on an SST thin film with a 2 ns write/erase speed is demonstrated, which is the fastest write/erase speed ever reported in integrated phase-change photonic devices. SST-based photonic memories exhibit multilevel capabilities and good stability at room temperature. By mapping the memory level to the biological synapse weight, an artificial neural network based on photonic memory devices is successfully established for image classification. Additionally, a reflective nanodisplay application using SST with optoelectronic modulation capabilities is demonstrated. Both the optical and electrical changes in SST during the phase transition and the fast-switching speed demonstrate their potential for use in photonic computing, neuromorphic computing, nanophotonics, and optoelectronic applications.
Financial development and institutional quality among emerging economies

Purpose This study examines how institutional quality influences variability in financial development among economies in Sub-Saharan Africa (SSA). Design/methodology/approach Empirical estimations verifying various relationships are performed using the limited information maximum likelihood (LIML) estimation technique. Findings The results suggest that institutional quality enhances the pace of financial development among economies in the sub-region all things being equal. In a further micro-level analysis where components of institutional quality index are examined separately, the study’s results suggest that effective governance, regulatory quality, rule of law and accountability tend to have a significant positive impact on financial sector development. Research limitations/implications Findings of the study suggest that policies geared towards improving governance and regulatory institutions can augment development of the financial sector among economies in SSA; governments and policymakers are therefore encouraged to resource noted institutions to play effective roles for the development of the financial sector. Originality/value Compared to related studies, this study reorients existing paradigm, which emphasizes the role of governance and institutional variables in the economic growth discourse. The authors’ empirical inquiry rather focuses on how governance and institutional structures influence regional financial development dynamics. Specifically, this study differs from most macro-level studies found in literature because it examines the impact of hitherto unexamined governance and institutional variables on financial development among economies in SSA.
Apportionment and location of heavy metal(loid)s pollution sources for soil and dust using the combination of principal component analysis, Geodetector, and multiple linear regression of distance

The accurate identification of sources for soil heavy metal(loid) is difficult, especially for multi-functional parks, which include multiple pollution sources. Aiming to identify the apportionment and location of heavy metal(loid)s pollution sources, this study established a method combining principal component analysis (PCA), Geodetector, and multiple linear regression of distance (MLRD) in soil and dust, taking a multi-functional industrial park in Anhui Province, China, as an example. PCA and Geodetector were used to determine the type and possible location of the source. Source apportionment of individual elements is achieved by MLRD. The detection results quantified the spatial explanatory power (0.21 ≤ q ≤ 0.51) of the potential source targets (e.g., river and mining area) for the PCA factors. A comparative analysis of the regression equation (Model 1 and Model 3) indicated that the river (0.50 ≤ R2 ≤0.78), main road (0.47 ≤ R2 ≤ 0.81), and mine (0.14 ≤ R2 ≤ 0.92) (p < 0.01) were the main sources. Different from the traditional source apportionment methods, the current method could obtain the exact contributing sources, not just the type of source (e.g., industrial activities), which could be useful for pollution control in areas with multiple sources.
CNN-Based Deep Learning Network for Human Activity Recognition During Physical Exercise from Accelerometer and Photoplethysmographic Sensors

Wearable device advances such as electrocardiography (ECG), surface electromyography (sEMG), electroencephalography (EEG), photoplethysmography (PPG), and inertial data sensors have resulted in a complicated, massive, and diverse biometric analysis of data. Consequently, PPG sensors have gained popularity in wearable appliances as the primary method for tracking heart rate daily. Unlike the ECG and sEMG signals which must be collected using adhesive metal electrodes across the skin, the PPG signal could be collected at peripheral body locations and requires a more superficial physical contact. In this study, we aim to employ a learning technique to the PPG signal for the recognition enhancement of human activity. A new deep learning model named SE-DeepConvNet is proposed for accurately classifying physical exercise patterns. To assess the recognition performance of deep learning networks, including our proposed network, we utilize a benchmark dataset called the PPG dataset. The SE-DeepConvNet surpasses CNN standard deep learning network regarding accuracy (99.32%) and F1-score (99.32%).
Intelligent dual stream CNN and echo state network for anomaly detection

Traditional video surveillance systems detect abnormal events via human involvement, which is exhausting and erroneous, while computer vision-based automated anomaly detection techniques replace human intervention for secure video surveillance applications. Automated anomaly detection in real-world scenarios is challenging due to diverse nature, complex, and infrequent occurrence of anomalous events. Therefore, in this paper, we propose an intelligent dual stream convolution neural network-based framework for accurate anomalous events detection in real-world surveillance scenarios. The proposed framework comprises two phases: in first phase, we develop a 2D CNN as an autoencoder, followed by a 3D visual features extraction machanism in the second phase. Autoencoder extracts spatial optimal features and forward them to echo state network to acquire a single spatial and temporal information-aware feature vector that is fused with 3D convolutional features for events patterns learning. The fused feature vector is used for anomalous events detection via a trained classifier. The proposed dual stream framework achieves significantly enhanced performance on challenging surveillance and non-surveillance anomaly and violence detection datasets.
Extended reality applications in industry 4.0. – A systematic literature review

Extended reality technologies such as virtual reality, augmented reality, and mixed reality represents a paradigm that enhances and supports industry 4.0 in diverse settings. To spread such a revolutionary environment, this systematic review focuses on analyzing extendend reality essence and application and reporting the assessment of 287 approaches gathered from 2011 to 2022, classified and characterized in the proposed taxonomy. Based on the sample of analyzed works, the results indicate that industry 4.0 has embraced the use of these technologies. Heterogeneous solution proposals in various fields of application and activities were found. Notwithstanding, the research articles report similar advantages and benefits (e.g., high performance on human tasks or robot collaboration, high-quality rates for specific products, among others). In addition, we present the most widespread equipment and devices that are currently preferred to develop extended reality applications, which allows us to identify hardware patterns commonly shared in a variety of fields. Whilst, with the aid of association rules, we reveal further insights among the items of the proposed taxonomy. Furthermore, we also present a thorough analysis of trends and research directions in the extended reality field for industry 4.0. Finally, from our results, we show that the accessibility and accelerated progress in technological devices, incorporating advanced algorithms, ergonomic features, built-in cameras, and sensors, have encouraged a massive adoption and extensive application development in a wide spectrum of industry 4.0 domains.
Deep learning spatial phase unwrapping: a comparative review

Phase unwrapping is an indispensable step for many optical imaging and metrology techniques. The rapid development of deep learning has brought ideas to phase unwrapping. In the past four years, various phase dataset generation methods and deep-learning-involved spatial phase unwrapping methods have emerged quickly. However, these methods were proposed and analyzed individually, using different strategies, neural networks, and datasets, and applied to different scenarios. It is thus necessary to do a detailed comparison of these deep-learning-involved methods and the traditional methods in the same context. We first divide the phase dataset generation methods into random matrix enlargement, Gauss matrix superposition, and Zernike polynomials superposition, and then divide the deep-learning-involved phase unwrapping methods into deep-learning-performed regression, deep-learning-performed wrap count, and deep-learning-assisted denoising. For the phase dataset generation methods, the richness of the datasets and the generalization capabilities of the trained networks are compared in detail. In addition, the deep-learning-involved methods are analyzed and compared with the traditional methods in ideal, noisy, discontinuous, and aliasing cases. Finally, we give suggestions on the best methods for different situations and propose the potential development direction for the dataset generation method, neural network structure, generalization ability enhancement, and neural network training strategy for the deep-learning-involved spatial phase unwrapping methods.
Dual Contrastive Prediction for Incomplete Multi-View Representation Learning

In this article, we propose a unified framework to solve the following two challenging problems in incomplete multi-view representation learning: i) how to learn a consistent representation unifying different views, and ii) how to recover the missing views. To address the challenges, we provide an information theoretical framework under which the consistency learning and data recovery are treated as a whole. With the theoretical framework, we propose a novel objective function which jointly solves the aforementioned two problems and achieves a provable sufficient and minimal representation. In detail, the consistency learning is performed by maximizing the mutual information of different views through contrastive learning, and the missing views are recovered by minimizing the conditional entropy through dual prediction. To the best of our knowledge, this is one of the first works to theoretically unify the cross-view consistency learning and data recovery for representation learning. Extensive experimental results show that the proposed method remarkably outperforms 20 competitive multi-view learning methods on six datasets in terms of clustering, classification, and human action recognition. The code could be accessed from https://pengxi.me.
Central Bank Digital Currencies: Agendas for future research

We use the bibliometric and content analysis of 174 documents retrieved from the Scopus database to present the publication trends in Central Bank Digital Currencies (CBDCs) since 2018 and highlight the top publishing source with the most contributing authors in their affiliated countries. While showing influential studies, important themes covered to date, and the intellectual structure of the CBDC literature, we present recent research trends, gaps, and future research agendas in this domain. Research curiosity on the systematical framework, significance, and structural implication of CBDC as a structural shift in the digitization of the monetary system are major propellants. CBDCs have far-reaching implications for monetary and payment systems, and their development could pave the way for a global common currency. Researchers are making significant efforts to investigate how CBDCs are linked to international trade and assets, particularly cryptocurrencies. There is much room for theoretical development, contextual coverage, and methodology contributions.
Discriminative Manifold Distribution Alignment for Domain Adaptation

Domain adaptation (DA) aims to accomplish tasks on unlabeled target data by learning and transferring knowledge from related source domains. In order to learn a discriminative and domain-invariant model, a critical step is to align source and target data well and thus reduce their distribution divergence. But existing DA methods mainly align the global feature distributions in distorted original space, which neglects their fine-grained local information and intrinsic geometrical structures. Moreover, some methods rely heavily on pseudo-labels to align features, which may undermine adaptation performance and lead to negative transfer. We propose an efficient discriminative manifold distribution alignment (DMDA) approach, which improves feature transferability by aligning both global and local distributions and refines a discriminative model by learning geometrical structures in manifold space. In addition, when learning geometrical structures, DMDA is exempt from the uncertainty and error brought by pseudo-labels of a target domain. It is very concise and efficient to be implemented by integrating learning steps and obtaining solutions directly. Extensive experiments on 68 DA tasks from seven benchmarks and subsequent analyses show that DMDA outperforms the compared methods in both classification accuracy and time efficiency, thus representing a significant advance in the DA field.
Deep learning and statistical methods for short- and long-term solar irradiance forecasting for Islamabad

The growing threat of global climate change stemming from the huge carbon footprint left behind by fossil fuels has prompted interest in exploring and utilizing renewable energy resources. Several statistical, Machine and Deep Learning techniques exist and have been used for many years for a range of forecasting problems. This study is based on the data recorded for 4 years and 9 months using precise instruments, in Islamabad, Pakistan. For this purpose we use statistical and Deep Learning architectures for forecasting solar Global Horizontal Irradiance which not only helps in grid management and power distribution, but also brings attention towards the potential of solar power production in Pakistan and its part to play in tackling global climate change. We have used statistical methods such as Seasonal Auto-Regressive Integrated Moving Average Exogenous (SARIMAX) and Prophet, and Machine Learning methods such as Long Short-Term Memory (LSTM) which is an extension of Recurrent Neural Networks (RNN), Convolutional Neural Network (CNN) and Artificial Neural Network (ANN). The selected forecast methods in our study are based on their ability to work with time series data and we have used different models configurations to see which performs best for our dataset. The performance of every model is studied using different error metrics such as Coefficient of Determination (R2), Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The major contribution of this study is the data collected to carry out research towards the goal of renewable energy future, and from the test methods used on the data in this study, it can be intuitively determined that ANN, CNN, and LSTM architectures perform best for short-term forecasts, while SARIMAX and Prophet are efficient for long-term forecasts.
Non-fungible token (NFT) markets on the Ethereum blockchain: temporal development, cointegration and interrelations

The market for non-fungible tokens (NFTs), transferrable and unique digital assets on public blockchains, has received widespread attention and experienced strong growth since early 2021. This study provides an introduction to NFTs and explores the 14 largest submarkets using data from the Ethereum blockchain between June 2017 and May 2021. The analyses rely on (a) the number of NFT sales, (b) the dollar volume of NFT trades and (c) the number of unique blockchain wallets that traded NFTs. Based on the number of transactions and wallets, the Ethereum-based NFT market peaked at the end of 2017 due to the success of the CryptoKitties project. As of 2021, fewer transactions occur but the traded value is much higher. We find that NFT submarkets are cointegrated and feature various causal short-run connections between them. The success or adoption of younger NFT projects is influenced by that of more established markets. At the same time, the success of newer markets has an impact on the more established projects. The results contribute to the overall understanding of the NFT phenomenon as an emerging asset class and suggest that NFT markets are immature or even inefficient.
Pre-Trained Language Models and Their Applications

Pre-trained language models have achieved striking success in natural language processing (NLP), leading to a paradigm shift from supervised learning to pre-training followed by fine-tuning. The NLP community has witnessed a surge of research interest in improving pre-trained models. This article presents a comprehensive review of representative work and recent progress in the NLP field and introduces the taxonomy of pre-trained models. We first give a brief introduction of pre-trained models, followed by characteristic methods and frameworks. We then introduce and analyze the impact and challenges of pre-trained models and their downstream applications. Finally, we briefly conclude and address future research directions in this field.
DSMAC: Privacy-Aware Decentralized Self-Management of Data Access Control Based on Blockchain for Health Data

In recent years, the interest in using wireless communication technologies and mobile devices in the healthcare environment has increased.However, despite increased attention to the security of electronic health records, patient privacy is still at risk for data breaches.Thus, it is quite a challenge to involve an access control system especially if the patients' medical data are accessible by users who have diverse privileges in different situations.Blockchain is a new technology that can be adopted for decentralized access control management issues.Nevertheless, different scalability, security, and privacy challenges affect this technology.To address these issues, we suggest a novel Decentralized Self-Management of data Access Control (DSMAC) system using a blockchain-based Self-Sovereign Identity (SSI) model for privacy-preserving medical data, empowering patients with mechanisms to preserve control over their personal information and allowing them to self-grant access rights to their medical data.DSMAC leverages smart contracts to conduct Role-based Access Control policies and adopts the implementation of decentralized identifiers and verifiable credentials to describe advanced access control techniques for emergency cases.Finally, by evaluating performance and comparing analyses with other schemes, DSMAC can satisfy the privacy requirements of medical systems in terms of privacy, scalability, and sustainability, and offers a new approach for emergency cases.