Reviewerly/exHarmony huggingface dataset:
Autonomous error correction of a single logical qubit using two transmons

Large-scale quantum computers will inevitably need quantum error correction to protect information against decoherence. Traditional error correction typically requires many qubits, along with high-efficiency error syndrome measurement and real-time feedback. Autonomous quantum error correction instead uses steady-state bath engineering to perform the correction in a hardware-efficient manner. In this work, we develop a new autonomous quantum error correction scheme that actively corrects single-photon loss and passively suppresses low-frequency dephasing, and we demonstrate an important experimental step towards its full implementation with transmons. Compared to uncorrected encoding, improvements are experimentally witnessed for the logical zero, one, and superposition states. Our results show the potential of implementing hardware-efficient autonomous quantum error correction to enhance the reliability of a transmon-based quantum information processor.
S2S-Sim: A Benchmark Dataset for Ship Cooperative 3D Object Detection

The rapid development of vehicle cooperative 3D object-detection technology has significantly improved the perception capabilities of autonomous driving systems. However, ship cooperative perception technology has received limited research attention compared to autonomous driving, primarily due to the lack of appropriate ship cooperative perception datasets. To address this gap, this paper proposes S2S-sim, a novel ship cooperative perception dataset. Ship navigation scenarios were constructed using Unity3D, and accurate ship models were incorporated while simulating sensor parameters of real LiDAR sensors to collect data. The dataset comprises three typical ship navigation scenarios, including ports, islands, and open waters, featuring common ship classes such as container ships, bulk carriers, and cruise ships. It consists of 7000 frames with 96,881 annotated ship bounding boxes. Leveraging this dataset, we assess the performance of mainstream vehicle cooperative perception models when transferred to ship cooperative perception scenes. Furthermore, considering the characteristics of ship navigation data, we propose a regional clustering fusion-based ship cooperative 3D object-detection method. Experimental results demonstrate that our approach achieves state-of-the-art performance in 3D ship object detection, indicating its suitability for ship cooperative perception.
Shortcuts to Adiabaticity in Krylov Space

Shortcuts to adiabaticity provide fast protocols for quantum state preparation. A new way to construct the auxiliary controls for guiding the system's dynamics boosts their application to many-body systems.
A framework for demonstrating practical quantum advantage: comparing quantum against classical generative models

Abstract Generative modeling has seen a rising interest in both classical and quantum machine learning, and it represents a promising candidate to obtain a practical quantum advantage in the near term. In this study, we build over an existing framework for evaluating the generalization performance of generative models, and we establish the first quantitative comparative race towards practical quantum advantage (PQA) between classical and quantum generative models, namely Quantum Circuit Born Machines (QCBMs), Transformers (TFs), Recurrent Neural Networks (RNNs), Variational Autoencoders (VAEs), and Wasserstein Generative Adversarial Networks (WGANs). After defining four types of PQAs scenarios, we focus on what we refer to as potential PQA, aiming to compare quantum models with the best-known classical algorithms for the task at hand. We let the models race on a well-defined and application-relevant competition setting, where we illustrate and demonstrate our framework on 20 variables (qubits) generative modeling task. Our results suggest that QCBMs are more efficient in the data-limited regime than the other state-of-the-art classical generative models. Such a feature is highly desirable in a wide range of real-world applications where the available data is scarce.
Medical image segmentation based on dynamic positioning and region-aware attention

Transformer has already proven its ability to model long-distance dependencies. However, medical images have strong local structures. Directly using Transformer to extract features would not only contain redundant information increasing the computational effort, but also be detrimental to extracting local details. Given these issues, we propose a network based on dynamic positioning and region-aware attention, which adopts a two-stage feature extraction strategy. In the shallow layer, we design Dynamic Positioning Attention (DPA). It will localize to the key feature information and construct a variable window for it, then perform attention calculation. DPA improves the learning ability of local details, reduces the amount of computation. At the deep level, Bi-Level Routing Attention (BRA) is used to discard irrelevant key–value pairs, achieve content-aware sparse attention for the deep dispersed semantic information, and improve computational efficiency. After several experiments, the results show that our method achieves advanced performance on different types of datasets.
Limitations of Linear Cross-Entropy as a Measure for Quantum Advantage

Demonstrating quantum advantage requires experimental implementation of a computational task that is hard to achieve using state-of-the-art classical systems. One approach is to perform sampling from a probability distribution associated with a certain class of highly entangled many-body wave functions. It has been suggested that such a quantum advantage can be certified with the linear cross-entropy benchmark (XEB). We critically examine this notion. First, we consider a “benign” setting, where an honest implementation of a noisy quantum circuit is assumed, and characterize the conditions under which the XEB approximates the fidelity of quantum dynamics. Second, we assume an “adversarial” setting, where all possible classical algorithms are considered for comparisons, and show that achieving relatively high XEB values does not imply faithful simulation of quantum dynamics. Specifically, we present an efficient classical algorithm that achieves high XEB values, namely 5–12% of those obtained in the state-of-the-art experiments, within just a few seconds using a single GPU machine. This is made possible by identifying and exploiting several vulnerabilities of the XEB, which allows us to achieve high XEB values without simulating a full quantum circuit. Remarkably, our algorithm features better scaling with the system size than a noisy quantum device for commonly studied random circuit ensembles in various architecture. We quantitatively explain the success of our algorithm and the limitations of the XEB by using a theoretical framework, in which the dynamics of the average XEB and fidelity are mapped to classical statistical mechanics models. Using this framework, we illustrate the relation between the XEB and the fidelity for quantum circuits in various architectures, with different choices of gate sets, and in the presence of noise. Taken together, our results demonstrate that XEB’s utility as a proxy for fidelity hinges on several conditions, which should be independently checked in the benign setting, but cannot be assumed in the general adversarial setting. Therefore, the XEB on its own has a limited utility as a benchmark for quantum advantage. We briefly discuss potential ways to overcome these limitations.6 MoreReceived 28 January 2022Revised 24 July 2023Accepted 20 November 2023DOI:https://doi.org/10.1103/PRXQuantum.5.010334Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum benchmarkingQuantum chaosQuantum computationQuantum protocolsQuantum verificationQuantum Information, Science & Technology
Anomaly detection in the presence of irrelevant features

A bstract Experiments at particle colliders are the primary source of insight into physics at microscopic scales. Searches at these facilities often rely on optimization of analyses targeting specific models of new physics. Increasingly, however, data-driven model-agnostic approaches based on machine learning are also being explored. A major challenge is that such methods can be highly sensitive to the presence of many irrelevant features in the data. This paper presents Boosted Decision Tree (BDT)-based techniques to improve anomaly detection in the presence of many irrelevant features. First, a BDT classifier is shown to be more robust than neural networks for the Classification Without Labels approach to finding resonant excesses assuming independence of resonant and non-resonant observables. Next, a tree-based probability density estimator using copula transformations demonstrates significant stability and improved performance over normalizing flows as irrelevant features are added. The results make a compelling case for further development of tree-based algorithms for more robust resonant anomaly detection in high energy physics.
Particle Swarm Intelligence

This comprehensive exploration delves into the integration of particle swarm intelligence (PSO) within the domain of supply chain management, emphasizing the optimization of supply chain network design. The importance of efficient supply chain network design has been underscored, with a focus on resource allocation, decision-making, and adaptability as key drivers of long-term competitiveness and sustainability. The study navigates through the fundamental components and working mechanisms of the PSO algorithm, highlighting its capacity to optimize complex solution spaces, and compares its advantages with other optimization techniques. Challenges within supply chain network design, encompassing the complexities of modern networks and factors influencing their design, have been examined.
Embracing the future of Artificial Intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in modern education

Abstract The present discussion examines the transformative impact of Artificial Intelligence (AI) in educational settings, focusing on the necessity for AI literacy, prompt engineering proficiency, and enhanced critical thinking skills. The introduction of AI into education marks a significant departure from conventional teaching methods, offering personalized learning and support for diverse educational requirements, including students with special needs. However, this integration presents challenges, including the need for comprehensive educator training and curriculum adaptation to align with societal structures. AI literacy is identified as crucial, encompassing an understanding of AI technologies and their broader societal impacts. Prompt engineering is highlighted as a key skill for eliciting specific responses from AI systems, thereby enriching educational experiences and promoting critical thinking. There is detailed analysis of strategies for embedding these skills within educational curricula and pedagogical practices. This is discussed through a case-study based on a Swiss university and a narrative literature review, followed by practical suggestions of how to implement AI in the classroom.
Exploring the current status and future opportunities of blockchain technology adoption and application in supply chain management

This study comprises a systematic literature review that offers a thorough analysis of the present state-of-the-art knowledge regarding the adoption and application of blockchain technology within the context of supply chain management. Based on an extensive review of 133 articles from highly-regarded journals, we developed a framework where we highlight the integration of the transition from blockchain technology adoption to its application. This transition process is explained by two theories: the Technology Acceptance Model and the Diffusion of Innovation Theory. In the adoption phase, we identified nine internal drivers and seven external drivers, together with comprehensive barriers. Our framework also integrates and examines the blockchain technology application stage, focusing not only the current development of benefits of blockchain technology implementation but also the challenges that impede the successful implementation of blockchain technology in supply chain management. The future development prospects are also highlighted which include standardization, platform development, regulation system development, cost reduction, and talent cultivation. These future developments were critical yet were missing in the current academic literature. By providing an in-depth analysis of these challenges, our study can assist supply chain practitioners in making informed decisions about blockchain-enabled supply chain management initiatives.
Revolutionizing Health Data Management With Blockchain Technology

This chapter thoroughly investigates the revolutionary use of blockchain technology in the administration of medical data. Blockchain integration has become a disruptive force in the era of digital health records, with the potential to completely revolutionize how healthcare organizations manage, trade, and safeguard data. The main objective is to analyze blockchain's complex role in transforming healthcare data management. The foundations of blockchain in healthcare data management are among the important subjects addressed; this section introduces the technology's basic ideas, composition, and operation. Examining how blockchain improves data security and privacy in healthcare takes up much of this chapter. The analysis focuses on how blockchain breaks down the conventional boundaries of data silos to enable smooth data interchange across heterogeneous healthcare systems. Examining the difficulties and real-world applications of implementing blockchain technology in healthcare rounds off the chapter.
Relationships between Pedagogical Practices and Affective States for Effective Teaching during the COVID-19 Pandemic: Insights from University Professors

Objectives: This study aimed to analyze how pedagogical practices and affective states during emergency remote teaching influence professors’ perspectives on their capabilities and the professor-student relationship. Method: We used a nonexperimental quantitative design, collecting data through an online survey during the first year of the pandemic. Participants were recruited using a non-probability sampling method. A total of 636 university professors from Chilean universities participated. We performed descriptive and correlation analyses between variables. Also, to gain a deeper understanding of the factors influencing perceptions of the professors’ competence and the professor–student relationship, we conducted linear regression models. Results: A higher perception of pedagogical competence was related to male professors reporting a high evaluation of teaching quality and a high diversification of assessment strategies. A better professor–student relationship was related to female professors reporting a high level of tutoring activities and activities in which students work collaboratively. Additionally, unpleasant affective states were experienced at a higher rate by younger and female professors. Conclusions: Variables that make up the perception of professor competence and the professor–student relationship were identified, relating to gender and pedagogical practices, such as diversification of assessment strategies, tutoring, and collaborative activities. Implications: The results can guide future institutional actions to improve conditions and establish guidelines for quality virtual education in the post-pandemic scenario.
SExpSMA-based T5: Serial exponential-slime mould algorithm based T5 model for question answer and distractor generation

Generally, multiple choice questions are an effective and extensive form used in standard tests in order to evaluate the learner’s skills and knowledge. Nonetheless, the multiple-choice question composition particularly the distractor construction is quite difficult. The distracters are needed to be both plausible and inappropriate and adequate to mystify the learners who did not master the information. Thus, the distractor generation emergence is important that can help several standard tests in an extensive range of domain. In this research, question-answer generation system is developed with a distractor model by developing an optimized T5 model. At first, BERT tokenization is used to pre-process the passage/context and question, which are given as the input to train the approach. Then, the question and answer generation is performed by utilizing the T5 approach that is trained by proposed Serial Exponential-Slime Mould approach (SExpSMA). Exponential weighted moving average is extended to Serial Exponential weighted moving average and incorporated in Slime Mould Algorithm (SMA) to propose SExpSMA. In addition, the proposed SExpSMA-based T5 model is employed to generate distractors for the questions. Eventually, experimentation analysis exhibits that proposed SExpSMA-based T5 model achieves better outcomes regarding the metrics, like ROUGE, BLEU, and METEOR with the values of 0.919, 0.918, 0.488, respectively.
Grammar and Technology: A Bibliometric Exploration of Research Trends and Influential Works

The study delves into the pivotal role of Grammar in scholarly communication, emphasising the need for a nuanced understanding of evolving grammatical research. Conducting a bibliometric analysis of 381 papers from the Scopus database (2015-2023), it explores grammar teaching and learning via technology. Employing advanced methods, the research identifies influential authors, institutions, and countries, tracks evolving themes, and assesses funding sources. The study utilises tools like Microsoft Excel, VOS Viewer, and Harzing to unveil partnerships, current research areas, and impact assessment. By categorising five clusters, it sheds light on underexplored research areas, offering insights for future investigations into grammar research.&#x0D;
Assessing Gender Differences in Technological Pedagogical Content Knowledge (TPACK) among EFL High School Teachers in Inner Mongolia

This study examines gender differences in Technological Pedagogical Content Knowledge (TPACK) among 437 English as a Foreign Language (EFL) high school teachers in Inner Mongolia, China. Quantitative data revealed significant TPACK disparities between male and female teachers, with males exhibiting higher levels. The findings suggest that male teachers demonstrate greater confidence in integrating technology into their English classrooms. To address the TPACK gender gap, it is recommended that female EFL teachers receive specialized training through mentorship programs, online communities, and workshops. Policy changes are urged to create a collaborative, supportive, and inclusive digital environment for effective technology integration.
Research on error detection in English translation texts using machine learning algorithms

This paper provides a concise overview of a grammatical error correction algorithm based on the encoder-decoder structure. The traditional unidirectional long short-term memory (LSTM) in the encoder was transformed into a bidirectional LSTM. Subsequently, the grammatical error correction algorithm was simulated and experimented with. In the experiments, it was compared with two other error correction algorithms: one based on the LSTM classification model and the other on the traditional unidirectional LSTM translation model. The results indicated that the three error correction algorithms exhibited little difference in detection performance when faced with distinct corpus databases. Furthermore, when dealing with the same corpus database, the bidirectional LSTM algorithm demonstrated the most robust detection performance, followed by the one based on the traditional unidirectional LSTM translation model, and lastly, the one based on the LSTM classification model performed the least effectively.
A study on human perception of document paragraph layout using surveys1

We identify a list of characteristics of a text document that we use as the basis for a study on human perception of what the structure of a document should look like. Our study reveals that the native language dimension has a significant impact on the human perception of some such characteristics, including the average number of paragraphs starting with a verb, as well as other characteristics describing average paragraph proportions. The study results also show what the mean values are, for the different document characteristics, and their distribution across the native language dimension, therefore providing some idea of what a normalised structure of a document should look like. The results of the study have a direct application in a new method for embedding secret messages in text documents that has been recently proposed by the authors and which uses manipulations in the paragraph layout of a document.
B-HPD: Bagging-based hybrid approach for the early diagnosis of Parkinson’s disease1

Parkinson’s disease (PD) is a neurodegenerative condition that affects the neurological, behavioral, and physiological systems of the brain. According to the most recent WHO data, 0.51 percent of all fatalities in India are caused by PD. It is a widely recognized fact that about one million people in the United States suffer from PD, relative to nearly five million people worldwide. Approximately 90% of Parkinson’s patients have speech difficulties. As a result, it is crucial to identify PD early on so that appropriate treatment may be determined. For the early diagnosis of PD, we propose a Bagging-based hybrid (B-HPD) approach in this study. Seven classifiers such as Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), Naïve Bayes (NB), K nearest neighbor (KNN), Random Under-sampling Boost (RUSBoost) and Support Vector Machine (SVM) are considered as base estimators for Bagging ensemble method and three oversampling techniques such as Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic (ADASYN) and SVMSmote are implemented under this research work. Feature Selection (FS) is also used for data preprocessing and further performance enhancement. We obtain the Parkinson’s Disease classification dataset (imbalanced) from the Kaggle repository. Finally, using two performance measures: Accuracy and Area under the curve (AUC), we compare the performance of the model with ALL features and with selected features. Our study suggests bagging with a base classifier: RF is showing the best performance in all the cases (with ALL features: 754, with FS: 500, with three Oversampling techniques) and may be used for PD diagnosis in the healthcare industry.
Student media committee in the information policy system of the Higher School of Folk Arts: concept of activity

The article examines the role of media resources, incl. social networks as the main channels for informing students and positioning higher educational institutions in the modern world. Discusses how universities can use these tools to share knowledge, demonstrate achievements, communicate activities and build their own brand. A concept for the work of the media committee of the Student Council of the Higher School of Folk Arts (Academy) is proposed, which includes improving the quality of content, developing new platforms, high-quality visual design and differentiated content to expand audience coverage. An important aspect is also the formation of an effective team for successful and productive work.