Reviewerly/exHarmony huggingface dataset:
Pulse-efficient quantum machine learning

Quantum machine learning algorithms based on parameterized quantum circuits are promising candidates for near-term quantum advantage. Although these algorithms are compatible with the current generation of quantum processors, device noise limits their performance, for example by inducing an exponential flattening of loss landscapes. Error suppression schemes such as dynamical decoupling and Pauli twirling alleviate this issue by reducing noise at the hardware level. A recent addition to this toolbox of techniques is pulse-efficient transpilation, which reduces circuit schedule duration by exploiting hardware-native cross-resonance interaction. In this work, we investigate the impact of pulse-efficient circuits on near-term algorithms for quantum machine learning. We report results for two standard experiments: binary classification on a synthetic dataset with quantum neural networks and handwritten digit recognition with quantum kernel estimation. In both cases, we find that pulse-efficient transpilation vastly reduces average circuit durations and, as a result, significantly improves classification accuracy. We conclude by applying pulse-efficient transpilation to the Hamiltonian Variational Ansatz and show that it delays the onset of noise-induced barren plateaus.
Adaptive Quantum State Tomography with Active Learning

Recently, tremendous progress has been made in the field of quantum science and technologies: different platforms for quantum simulation as well as quantum computing, ranging from superconducting qubits to neutral atoms, are starting to reach unprecedentedly large systems. In order to benchmark these systems and gain physical insights, the need for efficient tools to characterize quantum states arises. The exponential growth of the Hilbert space with system size renders a full reconstruction of the quantum state prohibitively demanding in terms of the number of necessary measurements. Here we propose and implement an efficient scheme for quantum state tomography using active learning. Based on a few initial measurements, the active learning protocol proposes the next measurement basis, designed to yield the maximum information gain. We apply the active learning quantum state tomography scheme to reconstruct different multi-qubit states with varying degree of entanglement as well as to ground states of the XXZ model in 1D and a kinetically constrained spin chain. In all cases, we obtain a significantly improved reconstruction as compared to a reconstruction based on the exact same number of measurements and measurement configurations, but with randomly chosen basis configurations. Our scheme is highly relevant to gain physical insights in quantum many-body systems as well as for benchmarking and characterizing quantum devices, e.g. for quantum simulation, and paves the way for scalable adaptive protocols to probe, prepare, and manipulate quantum systems.
A data-driven framework for clinical decision support applied to pneumonia management

Despite their long history, it can still be difficult to embed clinical decision support into existing health information systems, particularly if they utilise machine learning and artificial intelligence models. Moreover, when such tools are made available to healthcare workers, it is important that the users can understand and visualise the reasons for the decision support predictions. Plausibility can be hard to achieve for complex pathways and models and perceived "black-box" functionality often leads to a lack of trust. Here, we describe and evaluate a data-driven framework which moderates some of these issues and demonstrate its applicability to the in-hospital management of community acquired pneumonia, an acute respiratory disease which is a leading cause of in-hospital mortality world-wide. We use the framework to develop and test a clinical decision support tool based on local guideline aligned management of the disease and show how it could be used to effectively prioritise patients using retrospective analysis. Furthermore, we show how this tool can be embedded into a prototype clinical system for disease management by integrating metrics and visualisations. This will assist decision makers to examine complex patient journeys, risk scores and predictions from embedded machine learning and artificial intelligence models. Our results show the potential of this approach for developing, testing and evaluating workflow based clinical decision support tools which include complex models and embedding them into clinical systems.
Analysis of the Output Characteristics of a Vertical Photovoltaic System Based on Operational Data: A Case Study in Republic of Korea

The proliferation of renewable energy sources to achieve carbon neutrality has rapidly increased the adoption of photovoltaic (PV) systems. Consequently, specialized solar PV systems have emerged for various installation purposes. This study focuses on grid connecting vertically installed bifacial PV modules facing east and west by establishing a test bed within Republic of Korea. Based on weather and generation data collected in Republic of Korea, located in the middle of latitude 34.98° N, from January to July 2023, we analyzed and compared the generation patterns, peak generation, peak hours, and total generation of conventional and vertical PV systems. Moreover, PVsyst was used to model the solar PV generation and analyze the consistency and viability of vertical PV generation by comparing actual operational data with simulation results. The vertical PV system demonstrated a peak power generation of 89.1% compared with the conventional PV system with bifacial modules. Based on operational data from January to July, the power generation output of the vertical PV system decreased to 65.7% compared with that of the conventional system with bifacial modules. This corresponded to 78.8% to 80.2% based on the PVsyst simulation results. In particular, the investigations related to the peak generation levels and occurrence times of vertical PV systems provide insights into the practicality of vertical solar PV systems and their potential for improving the PV hosting capacity.
Integrity Authentication Based on Blockchain and Perceptual Hash for Remote-Sensing Imagery

The integrity of remote-sensing image data is susceptible to corruption during storage and transmission. Perceptual hashing is a non-destructive data integrity-protection technique suitable for high-accuracy requirements of remote-sensing image data. However, the existing remote-sensing image perceptual hash-authentication algorithms face security issues in storing and transmitting the original perceptual hash value. This paper proposes a remote-sensing image integrity authentication method based on blockchain and perceptual hash to address this problem. The proposed method comprises three parts: perceptual hash value generation, secure blockchain storage and transmission, and remote-sensing image integrity authentication. An NSCT-based perceptual hashing algorithm that considers the multi-band characteristics of remote-sensing images is proposed. A Perceptual Hash Secure Storage and Transmission Framework (PH-SSTF) is designed by combining Hyperledger Fabric and InterPlanetary File System (IPFS). The experimental results show that the method can effectively verify remote-sensing image integrity and tamper with the location. The perceptual hashing algorithm exhibits strong robustness and sensitivity. Meanwhile, the comparison results of data-tampering identification for multiple landscape types show that the algorithm has stronger stability and broader applicability compared with existing perceptual hash algorithms. Additionally, the proposed method provides secure storage, transmission, and privacy protection for the perceptual hash value.
Digital Gold or Digital Security? Unravelling the Legal Fabric of Decentralised Digital Assets

This paper offers an in-depth exploration into the intricate world of decentralized digital assets (DDAs), shedding light on their categorization as currencies, commodities, or securities. Building on foundational cases such as SEC v. Howey, the analysis delves into the current controversies surrounding assets like XRP and LBC, exploring the nuances in their classification. By highlighting the challenges of defining categories of DDAs within traditional legal frameworks, this study emphasizes the need for a simple taxonomy that encapsulates the dynamism of digital currencies while permitting flexibility. A proposed framework aims to simplify the categorization process while respecting recent jurisprudence, ensuring regulatory clarity for developers and users of DDAs.
Human Resource Training and Its Effect on the Integration and Success of Organizational Change

HR trainings play a pivotal role in the successful implementation of organizational change. These trainings equip employees with the necessary skills, knowledge, and mindset to adapt to new processes, technologies, or organizational structures. By providing employees with the tools and resources they need to navigate change effectively, HR trainings help to minimize resistance and increase engagement during periods of transition. In this perspective, this research study explores the relationship between human resource (HR) training and its impact on the integration and success of organizational change. The objective is to gain insights from experts in the HR departments of multinational companies through interviews. The study aims to understand how HR training initiatives contribute to the successful implementation of organizational change strategies and promote employee integration during these transitions.
Cloud-based Development Environment: A Case Study of Teaching a Cross-Platform Mobile App Course

This workshop aims to share the experiences of using a cloud-based development environment for teaching a cross-platform mobile app requiring full-stack technologies. This approach brings several benefits to instructors and students: 1) Instructors can be free from trouble shootings in their students' local desktop-based development environments. 2) Students can focus on the main concepts and skills of the course rather than the environment. Cloud-based development environments can be accessed from any device with an internet connection. It provides flexibility and convenience for the student. 3) Instructors can save their grading time by accessing the cloud-based development environment of each student. Furthermore, 4) students are exposed to the power of cloud computing with built-in version control systems.
EST-YOLOv5s: SAR Image Aircraft Target Detection Model Based on Improved YOLOv5s

Due to the diversity of aircraft target scale and interference of background strong scattering in synthetic aperture radar (SAR) images, it is a challenge for target detection tasks. In response to these problems, this paper proposes a new SAR image aircraft target detection model named EST-YOLOv5s. The proposed model integrates the Efficient Channel Attention (ECA) mechanism into the C3 module of the backbone network, which enhances the scattering features of aircraft targets and suppresses irrelevant background information without increasing the number of parameters. Secondly, replace the bottleneck module in the last C3 module in the backbone network with the Swin Transformer Block. By using the shifted window partitioning approach to obtain the global perception ability, the problem of missed detection of small objects is improved. Finally, the Task-Specific Context Decoupling (TSCODE) head is used to balance the relationship between classification and regression so that different contextual details can be better utilized. In this paper, the SAR Aircraft Detection Dataset (SADD) is used as the experimental data set to compare with the baseline model YOLOv5s. The experimental outcomes indicate that the recall of the EST-YOLOv5s model reached 94.2%, the precision reached 97.3%, and the mAP@50 reached 97.8%, which were 2.3%, 1.7%, and 1.7% higher than YOLOv5s respectively. Furthermore, our model also meets the real-time requirements in terms of speed and exhibits strong anti-interference ability.
On Propagation Rules for Entanglement-Assisted Quantum Codes

This paper proposes new propagation rules on quantum codes in the entanglement-assisted scenario. The rules lead to new families of such quantum codes whose parameters are demonstrably optimal. To obtain the results, we devise tools to puncture and shorten codes in ways that ensure their Hermitian hulls have certain desirable properties.
Hash-Based Convolutional Deep-thinking Pattern Classifier

To address pattern classification problems without involving complex computations, a Hash-Based Convolutional Deep-Thinking Pattern Classifier (HCDTPC) has been developed. This classifier draws inspiration from the human visual system and human thinking logic. The architecture consists of five layers, each serving a distinct purpose. In the initial convolutional layer, informative and distinct features are extracted, mimicking the functionality of the human visual system. The subsequent clustering layer employs a clustering algorithm to group samples within a class into smaller clusters. This arrangement allows the following layer to compare test samples across different clusters, replicating a human-like classification process where the most similar samples should belong to the same group. The hashing layer of the devised system employs a similarity-preserve hashing technique to transform feature maps into hashed data. This transformation reduces the dimensionality of the data while retaining essential similarity information. In the logical layer, Bayesian inference is employed to classify testing samples, mirroring human thought processes. The simulation section assesses the performance of the HCDTPC, demonstrating its effectiveness and efficiency in pattern classification.
Federated Machine Learning In 5G Smart Healthcare: A Security Perspective Review

Federated learning (also known as collaborative learning) is a decentralized approach to training machine learning models. In 5G smart healthcare, federated machine learning (FML) can potentially improve patient care by offering improved diagnosis, prognosis, and therapy models. Nevertheless, a significant worry regarding FML is its lack of security. Within the context of 5G smart healthcare, this review paper looks at FML from a security point of view, discussing the benefits and risks of using FML in 5G smart healthcare and the possible solutions to these risks. The issues of privacy, adversarial attacks, communication security, and malevolent clients are brought up in the discussion on security challenges. Differential privacy, secure aggregation and training, adversarial training, secure communication, client authentication, and model pruning are some of the solutions that have been suggested. We will be able to protect the privacy of patient data in FML if we take the necessary steps to address these security problems.
Data cleaning survey and challenges – improving outlier detection algorithm in machine learning

Data cleaning, also referred to as data cleansing, constitutes a pivotal phase in data processing subsequent to data collection. Its primary objective is to identify and eliminate incomplete data, duplicates, outdated information, anomalies, missing values, and errors. The influence of data quality on the effectiveness of machine learning (ML) models is widely acknowledged, prompting data scientists to dedicate substantial effort to data cleaning prior to model training. This study accentuates critical facets of data cleaning and the utilization of outlier detection algorithms. Additionally, our investigation encompasses the evaluation of prominent outlier detection algorithms through benchmarking, seeking to identify an efficient algorithm boasting consistent performance. As the culmination of our research, we introduce an innovative algorithm centered on the fusion of Isolation Forest and clustering techniques. By leveraging the strengths of both methods, this proposed algorithm aims to enhance outlier detection outcomes. This work endeavors to elucidate the multifaceted importance of data cleaning, underscored by its symbiotic relationship with ML models. Furthermore, our exploration of outlier detection methodologies aligns with the broader objective of refining data processing and analysis paradigms. Through the convergence of theoretical insights, algorithmic exploration, and innovative proposals, this study contributes to the advancement of data cleaning and outlier detection techniques in the realm of contemporary data-driven environments.
Deep learning based channel estimation method for mine OFDM system

In this paper, we present a channel estimation approach based on deep learning to solve the problem that the orthogonal frequency division multiplexing (OFDM) system channel estimation algorithm cannot accurately obtain the channel state information in the complex environment of the mine, resulting in system performance degradation. First, LS channel estimation matrix is considered as a low-resolution image and the actual channel state information is considered as a high-resolution image. Then the optimization of the LS channel estimation matrix is achieved by the FSRCNN image super-resolution algorithm. We validate the effectiveness of the proposed algorithm by conducting experiments in different channel environments, different number of pilots, and mismatched signal-to-noise ratio scenarios. The simulation results show that the proposed scheme is much better than the traditional LS channel estimation method and the DFT-LS channel estimation method, and the accuracy of the proposed scheme approaches that of the MMSE channel estimation method when the number of pilots is low.
Generalized Quantum Measurements on a Higher-Dimensional System via Quantum Walks

Quantum measurements play a fundamental role in quantum mechanics. Especially, generalized quantum measurements provide a powerful and versatile tool to extract information from quantum systems. However, how to realize them on an arbitrary higher-dimensional quantum system remains a challenging task. Here we propose a simple recipe for the implementation of a general positive-operator valued measurement (POVM) on a higher-dimensional quantum system via a one-dimensional discrete-time quantum walk with a two-dimensional coin. Furthermore, using single photons and linear optics, we realize experimentally a symmetric, informationally complete (SIC) POVM on a three-dimensional system with high fidelity. As an application, we realize a qutrit state tomography with SIC-POVM and confirm that the infidelity scaling achieved by the qutrit SIC-POVM is as good as that based on mutually unbiased bases. Our study paves the way to explore physics and information in higher-dimensional quantum systems and finds applications in various quantum information processing tasks that rely on generalized quantum measurements.
IPSO-LSTM hybrid model for predicting online public opinion trends in emergencies

When emergencies are widely discussed and shared, it may lead to conflicting opinions and negative emotions among internet users. Accurately predicting sudden network public opinion events is of great importance. Therefore, this paper constructs a hybrid forecasting model to solve this problem. First, this model introduces an improved inertia weight and an adaptive variation operation to enhance the Particle Swarm Optimization (PSO) algorithm. Then, the improved PSO (IPSO) algorithm optimizes the parameters of the Long Short-Term Memory (LSTM) neural network. Finally, the IPSO-LSTM hybrid prediction model is constructed to forecast and analyze emergency public opinion dissemination trends. The experimental outcomes indicate that the IPSO-LSTM model surpasses others and has high prediction accuracy. In the four emergency predictions we select, the MAPE value of IPSO-LSTM is 74.27% better than that of BP, 33.96% better than that of LSTM, and 13.59% better than that of PSO-LSTM on average. This study aims to assist authorities in quickly identifying potential public opinion crises, developing effective strategies, and promoting sustainable and positive growth in the network environment.
Open-Sourcing Highly Capable Foundation Models: An Evaluation of Risks, Benefits, and Alternative Methods for Pursuing Open-Source Objectives

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A novel camera calibration method based on circle projection model

Camera calibration is one of the important factors affecting measurement accuracy in the field of visual measurement, and the camera calibration method with high precision and strong robustness is the prerequisite for ensuring high-precision visual measurement. Among the commonly used calibration methods, target-based calibration methods have high accuracy and good flexibility. However, the feature extraction of checkerboard targets is prone to noise interference. Circle targets have higher feature extraction accuracy, but will introduce eccentricity errors in the center of the circle projection. In order to eliminate the eccentricity error of circle targets, this paper proposes a camera calibration method based on the circle projection model, which does not rely on the central projection coordinates, but directly constructs the mapping between the circle equation on the target and the elliptic equation in the camera image, and solves the optimal mapping parameters through nonlinear optimization algorithm. Through simulation and experiments, it has been proven that the calibration method based on the circle projection model has high calibration accuracy and anti-noise interference ability, providing a guarantee for high-precision visual measurement.
Scientific Computing with Diffractive Optical Neural Networks

Diffractive optical neural networks (DONNs) are emerging as high‐throughput and energy‐efficient hardware platforms to perform all‐optical machine learning (ML) in machine vision systems. However, the current demonstrated applications of DONNs are largely image classification tasks, which undermine the prospect of developing and utilizing such hardware for other ML applications. Herein, the deployment of an all‐optical reconfigurable DONNs system for scientific computing is demonstrated numerically and experimentally, including guiding two‐dimensional quantum material synthesis, predicting the properties of two‐dimensional quantum materials and small molecular cancer drugs, predicting the device response of nanopatterned integrated photonic power splitters, and the dynamic stabilization of an inverted pendulum with reinforcement learning. Despite a large variety of input data structures, a universal feature engineering approach is developed to convert categorical input features to images that can be processed in the DONNs system. The results open up new opportunities for employing DONNs systems for a broad range of ML applications.
Preparing a Fast Pauli Decomposition for Variational Quantum Solving Linear Equations

Abstract Solving linear equations is a basic and significant mathematical task, and it can be executed by variational quantum algorithm (VQA) with quantum advantages by leveraging near‐term quantum device and classical optimizer. In the above algorithm, the coefficient matrix should be decomposed first but slowly with the traditional method, for realizing an effective quantum circuit. In this paper, a general framework for preparing a fast Pauli decomposition for solving arbitrary linear equations using VQA is proposed. This method has a simpler form and reduces the complexity of matrix decomposition compared with the traditional one. Moreover, the concrete tables of two and three qubits cases are given for looking up quickly and the instances of Toeplitz matrix, Yule–Walker, and arbitrary equations are demonstrated. Finally, numerical simulations are given to verify this method. This work provides a more convenient and faster preparatory phase for solving linear equations using VQA.