Reviewerly/exHarmony huggingface dataset:
Pulse variational quantum eigensolver on cross-resonance-based hardware

State-of-the-art noisy digital quantum computers can only execute short-depth quantum circuits. Variational algorithms are a promising route to unlock the potential of noisy quantum computers since the depth of the corresponding circuits can be kept well below hardware-imposed limits. Typically, the variational parameters correspond to virtual ${R}_{Z}$ gate angles, implemented by phase changes of calibrated pulses. By encoding the variational parameters directly as hardware pulse amplitudes and durations, we succeed in further shortening the pulse schedule and overall circuit duration. This decreases the impact of qubit decoherence and gate noise. As a demonstration, we apply our pulse-based variational algorithm to the calculation of the ground state of different hydrogen-based systems (${\mathrm{H}}_{2}, {\mathrm{H}}_{3}$, and ${\mathrm{H}}_{4}$) using IBM cross-resonance-based hardware. We observe a reduction in schedule duration of up to $5\ifmmode\times\else\texttimes\fi{}$ compared to cnot-based Ans\"atze, while also reducing the measured energy. In particular, we observe a sizable improvement of the minimal energy configuration of ${\mathrm{H}}_{3}$ compared to a cnot-based variational form. Finally, we discuss possible future developments including error-mitigation schemes and schedule optimizations, which will enable further improvements of our approach, paving the way towards the simulation of larger systems on noisy quantum devices.
Probabilistic Solar Proxy Forecasting With Neural Network Ensembles

Abstract Space weather indices are used commonly to drive forecasts of thermosphere density, which affects objects in low‐Earth orbit (LEO) through atmospheric drag. One commonly used space weather proxy, F 10.7cm , correlates well with solar extreme ultra‐violet (EUV) energy deposition into the thermosphere. Currently, the USAF contracts Space Environment Technologies (SET), which uses a linear algorithm to forecast F 10.7cm . In this work, we introduce methods using neural network ensembles with multi‐layer perceptrons (MLPs) and long‐short term memory (LSTMs) to improve on the SET predictions. We make predictions only from historical F 10.7cm values. We investigate data manipulation methods (backwards averaging and lookback) as well as multi step and dynamic forecasting. This work shows an improvement over the popular persistence and the operational SET model when using ensemble methods. The best models found in this work are ensemble approaches using multi step or a combination of multi step and dynamic predictions. Nearly all approaches offer an improvement, with the best models improving between 48% and 59% on relative MSE with respect to persistence. Other relative error metrics were shown to improve greatly when ensembles methods were used. We were also able to leverage the ensemble approach to provide a distribution of predicted values; allowing an investigation into forecast uncertainty. Our work found models that produced less biased predictions at elevated and high solar activity levels. Uncertainty was also investigated through the use of a calibration error score metric (CES), our best ensemble reached similar CES as other work.
YOLO-SSFS: A Method Combining SPD-Conv/STDL/IM-FPN/SIoU for Outdoor Small Target Vehicle Detection

As a vital part of autonomous driving, vehicle detection, especially for outdoor small target vehicles, has attracted great attention from researchers during recent years. To ameliorate the difficulty in accurately identifying outdoor small vehicle targets in dense environments, this paper proposes a new structure named YOLO-SSFS, in which SPD-Conv, a small target detection layer (STDL), the Improved Feature Pyramid Network structure (IM-FPN), and the SCYLLA-IoU (SIoU) loss function are introduced. Firstly, the multi-scale fusion module of the original algorithm is improved by adding a detection layer for smaller targets. This detection layer preserves shallow semantic information, which helps to refine the algorithm’s detection accuracy for small targets. Then, a new Convolutional Neural Network (CNN) building block named SPD-Conv is constructed to replace the pooling layers and convolutional layers in the YOLOv5 algorithm, which reduces information loss, ensures the original fine-grained details of the image and improves the learning ability. Afterwards, a new FPN structure is created to retain more information conducive to small target detection during the feature fusion process so as to enhance the robustness of the method. Finally, to speed up the convergence of the loss function, the SIoU loss function is introduced to replace Complete-IoU (CIoU) in the original algorithm. In order to verify the authenticity of the improved algorithm, we conduct a series of experiments on the VisDrone dataset and perform comparative analyses of the results. The results obtained demonstrate that compared with the original YOLOv5, the proposed model performs better in small target detection. The mean average precision (mAP) is 83.07%, which is 7.63% higher than that for YOLOv5, while the detection speed reaches 52 frames per second (FPS), meeting the requirements for real-time detection.
A Secure, Reliable and Low-Cost Distributed Storage Scheme Based on Blockchain and IPFS for Firefighting IoT Data

In the fire scene investigation, the firefighting Internet of Things (IoT) data is the key electronic evidence for event analysis and responsibility determination. However, the traditional centralized storage method leads to data easy to be tampered with and damaged. To solve these problems, this paper designs and implements a secure, reliable and low-cost distributed firefighting IoT data storage scheme based on the Fabric framework, combining blockchain technology, Interplanetary File System (IPFS) and Practical Byzantine Fault Tolerance (PBFT) consensus algorithm to provide a strong support for fire accident traceability. This scheme mainly includes the storage model, key algorithms and Fabric construction and improvement. IPFS stores the complete firefighting IoT data, as the off-chain storage system of the blockchain, and the blockchain only stores the storage address (IPFS hash) of data returned by IPFS, thus reducing the storage space overhead of the blockchain and ensuring data security. Further, we adopt the Fabric framework as the blockchain platform for firefighting IoT data, and embed the PBFT consensus algorithm into the framework to ensure the reliability of consensus nodes in Fabric, thus improving the availability of the blockchain. In addition, we use the AES and RSA algorithms to ensure the security of firefighting IoT data storage and transmission. Through system analysis and experimental testing, the proposed scheme meets the need for secure storage and traceability of firefighting IoT data. Compared with the storage scheme using only blockchain, the blockchain combined with IPFS technology has advantages in storage space occupation, significantly improved throughput, and lower latency overhead. Meanwhile, compared with the official Fabric, the improved Fabric supports Byzantine fault tolerance and has better security.
Generative AI for medical imaging analysis and applications

Generative AI plays a pivotal role in medical imaging analysis, enabling precise diagnosis, treatment planning and disease monitoring. Techniques like generative adversarial networks (GANs) and variational autoencoders (VAEs) enhance medical imaging by generating synthetic images, improving reconstruction, segmentation and facilitating disease diagnosis and treatment planning. Nonetheless, ethical, legal and regulatory concerns arise regarding patient privacy, data protection and fairness. This paper offers an overview of generative AI in medical imaging analysis, highlighting applications, challenges and case studies. It compares results with traditional methods and examines potential implications on healthcare policies. The paper concludes with recommendations for responsible implementation and suggests future research and development directions.
Free-Space and Fiber-Integrated Measurement-Device-Independent Quantum Key Distribution under High Background Noise

Measurement-device-independent quantum key distribution (MDI QKD) provides immunity against all attacks targeting measurement devices. It is essential to implement MDI QKD in the future global-scale quantum communication network. Toward this goal, we demonstrate a robust MDI QKD fully covering daytime, overcoming the high background noise that prevents BB84 protocol even when using a perfect single-photon source. Based on this, we establish a hybrid quantum communication network that integrates free-space and fiber channels through Hong-Ou-Mandle (HOM) interference. Additionally, we investigate the feasibility of implementing HOM interference with moving satellites. Our results serve as a significant cornerstone for future integrated space-ground quantum communication networks that incorporate measurement-device-independent security.
Clustering microbiome data using mixtures of logistic normal multinomial models

Discrete data such as counts of microbiome taxa resulting from next-generation sequencing are routinely encountered in bioinformatics. Taxa count data in microbiome studies are typically high-dimensional, over-dispersed, and can only reveal relative abundance therefore being treated as compositional. Analyzing compositional data presents many challenges because they are restricted to a simplex. In a logistic normal multinomial model, the relative abundance is mapped from a simplex to a latent variable that exists on the real Euclidean space using the additive log-ratio transformation. While a logistic normal multinomial approach brings flexibility for modeling the data, it comes with a heavy computational cost as the parameter estimation typically relies on Bayesian techniques. In this paper, we develop a novel mixture of logistic normal multinomial models for clustering microbiome data. Additionally, we utilize an efficient framework for parameter estimation using variational Gaussian approximations (VGA). Adopting a variational Gaussian approximation for the posterior of the latent variable reduces the computational overhead substantially. The proposed method is illustrated on simulated and real datasets.
Blockchain Technology and Related Security Risks: Towards a Seven-Layer Perspective and Taxonomy

Blockchain technology can be a useful tool to address issues related to sustainability. From its initial foundation based on cryptocurrency to the development of smart contracts, blockchain technology promises significant business benefits for various industry sectors, including the potential to offer more trustworthy modes of governance, reducing the risks for environmental and economic crises. Notwithstanding its known benefits, and despite having some protective measures and security features, this emerging technology still faces significant security challenges within its different abstract layers. This paper classifies the critical cybersecurity threats and vulnerabilities inherent in smart contracts based on an in-depth literature review and analysis. From the perspective of architectural layering, each layer of the blockchain has its own corresponding security issues. In order to have a detailed look at the source of security vulnerabilities within the blockchain, a seven-layer architecture is used, whereby the various components of each layer are set out, highlighting the related security risks and corresponding countermeasures. This is followed by a taxonomy that establishes the inter-relationships between the vulnerabilities and attacks in a smart contract. A specific emphasis is placed on the issues caused by centralisation within smart contracts, whereby a “one-owner” controls access, thus threatening the very decentralised nature that blockchain is based upon. This work offers two main contributions: firstly, a general taxonomy that compiles the different vulnerabilities, types of attacks, and related countermeasures within each of the seven layers of the blockchain; secondly, a specific focus on one layer of the blockchain namely, the contract layer. A model application is developed that depicts, in more detail, the security risks within the contract layer, while enlisting the best practices and tools to use to mitigate against these risks. The findings point to future research on developing countermeasures to alleviate the security risks and vulnerabilities inherent to one-owner control in smart contracts.
Bounds on the smallest sets of quantum states with special quantum nonlocality

An orthogonal set of states in multipartite systems is called to be strong quantum nonlocality if it is locally irreducible under every bipartition of the subsystems \cite{Halder19}. In this work, we study a subclass of locally irreducible sets: the only possible orthogonality preserving measurement on each subsystems are trivial measurements. We call the set with this property is locally stable. We find that in the case of two qubits systems locally stable sets are coincide with locally indistinguishable sets. Then we present a characterization of locally stable sets via the dimensions of some states depended spaces. Moreover, we construct two orthogonal sets in general multipartite quantum systems which are locally stable under every bipartition of the subsystems. As a consequence, we obtain a lower bound and an upper bound on the size of the smallest set which is locally stable for each bipartition of the subsystems. Our results provide a complete answer to an open question (that is, can we show strong quantum nonlocality in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="double-struck">C</mml:mi></mml:mrow><mml:mrow class="MJX-TeXAtom-ORD"><mml:msub><mml:mi>d</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:msup><mml:mo>&amp;#x2297;</mml:mo><mml:msup><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="double-struck">C</mml:mi></mml:mrow><mml:mrow class="MJX-TeXAtom-ORD"><mml:msub><mml:mi>d</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:msup><mml:mo>&amp;#x2297;</mml:mo><mml:mo>&amp;#x22EF;</mml:mo><mml:mo>&amp;#x2297;</mml:mo><mml:msup><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="double-struck">C</mml:mi></mml:mrow><mml:mrow class="MJX-TeXAtom-ORD"><mml:msub><mml:mi>d</mml:mi><mml:mi>N</mml:mi></mml:msub></mml:mrow></mml:msup></mml:math> for any <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>d</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>&amp;#x2265;</mml:mo><mml:mn>2</mml:mn></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>1</mml:mn><mml:mo>&amp;#x2264;</mml:mo><mml:mi>i</mml:mi><mml:mo>&amp;#x2264;</mml:mo><mml:mi>N</mml:mi></mml:math>?) raised in a recent paper \cite{Shi22N}. Compared with all previous relevant proofs, our proof here is quite concise.
Information and Media Literacy in the Age of AI: Options for the Future

The concepts of information and media literacy have been central components of digital literacy since the digitization of information began. However, the increasing influence of artificial intelligence on how individuals locate, evaluate, and create content has significant implications for what it means to be information and media literate. This paper begins by exploring the role artificial intelligence plays at the various stages of information retrieval and creation processes. Following this, the paper reviews existing digital literacy frameworks to ascertain their definitions of information and media literacy and the potential impact of artificial intelligence on them. We find that digital literacy frameworks have been slow to react to artificial intelligence and its repercussions, and we recommend a number of strategies for the future. These strategies center around a more agile, responsive, and participatory approach to digital literacy framework development and maintenance.
Toward a Comprehensive Framework for Ensuring Security and Privacy in Artificial Intelligence

The rapid expansion of artificial intelligence poses significant challenges in terms of data security and privacy. This article proposes a comprehensive approach to develop a framework to address these issues. First, previous research on security and privacy in artificial intelligence is reviewed, highlighting the advances and existing limitations. Likewise, open research areas and gaps that require attention to improve current frameworks are identified. Regarding the development of the framework, data protection in artificial intelligence is addressed, explaining the importance of safeguarding the data used in artificial intelligence models and describing policies and practices to guarantee their security, as well as approaches to preserve the integrity of said data. In addition, the security of artificial intelligence is examined, analyzing the vulnerabilities and risks present in artificial intelligence systems and presenting examples of potential attacks and malicious manipulations, together with security frameworks to mitigate these risks. Similarly, the ethical and regulatory framework relevant to security and privacy in artificial intelligence is considered, offering an overview of existing regulations and guidelines.
Encoding-independent optimization problem formulation for quantum computing

We review encoding and hardware-independent formulations of optimization problems for quantum computing. Using this generalized approach, an extensive library of optimization problems from the literature and their various derived spin encodings are discussed. Common building blocks that serve as a construction kit for formulating these spin Hamiltonians are provided. This previously introduced approach paves the way toward a fully automatic construction of Hamiltonians for arbitrary discrete optimization problems and this freedom in the problem formulation is a key step for tailoring optimal spin Hamiltonians for different hardware platforms.
Real-time hand interaction and self-directed machine learning agents in immersive learning environments

Integration of extended reality (XR) in education is becoming popular to transform the traditional classroom with immersive learning environments. The adoption of immersive learning is accelerating as an innovative approach for science and engineering subjects. With new powerful interaction techniques in XR and the latest developments in artificial intelligence, interactive and self-directed learning are becoming important. However, there is a lack of research exploring these emerging technologies research with kinesthetic learning or “hands-one learning" as a pedagogical approach using real-time hand interaction and agent-guided learning in immersive environments. This paper proposes a novel approach that uses machine learning agents to facilitate interactive kinesthetic learning in science and engineering education through real-time hand interaction in the virtual world. To implement the following approach, this paper uses a chemistry-related case study and presents a usability evaluation conducted with 15 expert reviewers and 2 subject experts. NASA task load index is used for cognitive workload measurement, and the technology acceptance model is used for measuring perceived ease of use and perceived usefulness in the evaluations. The evaluation with expert reviewers proposed self-directed learning using trained agents can help in the end-user training in learning technical topics and controller-free hand interaction for kinesthetic tasks can improve hands-on learning motivation in virtual laboratories. This success points to a novel research area where agents embodied in an immersive environment using machine learning techniques can forge a new pedagogical approach where they can act as both teacher and assessor.
Does inflation worsen income inequality? A meta-analysis

Despite extensive econometric evidence, the research literature has been unable to draw firm conclusions regarding the effect of inflation on income inequality. In this paper, we apply meta-regression methods to a novel data set of 1767 estimates reported in 124 published studies that investigate the effect of inflation on income inequality. We distinguish between estimates that examine the effect of inflation on levels of income inequality and those that examine the effect of inflation on differences of income inequality. For level estimates, not controlling for moderator variables points to mild publication bias in favor of positive estimates (i.e., the current literature favors publishing studies that find that inflation increases income inequality), but publication selectivity does not hold once we control for a set of moderator variables. For difference estimates, mild publication bias in favor of negative estimates is found only once we control for moderator variables. In addition, our results suggest that inflation has a (small-to-moderate) inequality increasing effect for both level and difference estimates. Furthermore, we show that several factors influence reported estimates, including researcher choices concerning the measurement of inflation and inequality, the characteristics of data and estimation methods, and controlling for other components of inequality.
Comparing Student and Generative Artificial Intelligence Chatbot Responses to Organic Chemistry Writing-to-Learn Assignments

Chemistry education research demonstrates the value of open-ended writing tasks, such as writing-to-learn (WTL) assignments, for supporting students' learning with topics including reasoning about reaction mechanisms. The emergence of generative artificial intelligence (AI) technology, such as chatbots ChatGPT and Bard, raises concerns regarding the value of open-ended writing tasks in the classroom; one concern involves academic integrity and whether students will use these chatbots to produce sufficient responses to open-ended writing tasks. The present study investigates the degree to which generative AI chatbots exhibit mechanistic reasoning in response to organic chemistry WTL assignments. We produced responses from three generative AI chatbots (ChatGPT-3.5, ChatGPT-4, and Bard) to two WTL assignments developed to elicit students' mechanistic reasoning. Using previously reported machine learning models for analyzing student writing in response to the WTL assignments, we analyzed the chatbot responses for the inclusion of features pertinent to mechanistic reasoning. Herein, we report quantitative analyses of (1) the differences between chatbot responses on the two assignments and (2) the differences between chatbot and authentic student responses. Findings indicate that chatbots respond differently to different WTL assignments. Additionally, the chatbots rarely incorporated the discussion of electron movement, a key feature of mechanistic reasoning. Furthermore, the chatbots, in general, do not engage in mechanistic reasoning at the same level as students. We contextualize the results by considering academic integrity with the assumption that students' intentions are to engage in academically honest behavior, and we focus on understanding the ethical uses of generative AI for classroom assignments.