Reviewerly/exHarmony huggingface dataset:
Comparative analysis of the hybrid gazelle‐Nelder–Mead algorithm for parameter extraction and optimization of solar photovoltaic systems

Authors:
raw_author_name: Serdar Ekinci
raw_affiliation_string: Department of Computer Engineering Batman University  Batman Turkey
raw_author_name: Davut Izci
raw_affiliation_string: Department of Computer Engineering Batman University  Batman Turkey
raw_author_name: Abdelazim G. Hussien
raw_affiliation_string: Applied Science Research Center Applied Science Private University  Amman Jordan; Department of Computer and Information Science Linköping University  Linköping Sweden; Faculty of Science Fayoum University  Fayoum Egypt; MEU Research Unit Middle East University  Amman Jordan

Abstract: Abstract The pressing need for sustainable energy solutions has driven significant research in optimizing solar photovoltaic (PV) systems which is crucial for maximizing energy conversion efficiency. Here, a novel hybrid gazelle‐Nelder–Mead (GOANM) algorithm is proposed and evaluated. The GOANM algorithm synergistically integrates the gazelle optimization algorithm (GOA) with the Nelder–Mead (NM) algorithm, offering an efficient and powerful approach for parameter extraction in solar PV models. This investigation involves a thorough assessment of the algorithm's performance across diverse benchmark functions, including unimodal, multimodal, fixed‐dimensional multimodal, and CEC2020 benchmark functions. Notably, the GOANM consistently outperforms other optimization approaches, demonstrating enhanced convergence speed, accuracy, and reliability. Furthermore, the application of the GOANM is extended to the parameter extraction of the single diode and double diode models of RTC France solar cell and PV model of Photowatt‐PWP201 PV module. The experimental results consistently demonstrate that the GOANM outperforms other optimization approaches in terms of accurate parameter estimation, low root mean square values, fast convergence, and alignment with experimental data. These results emphasize its role in achieving superior performance and efficiency in renewable energy systems.
Semi-supervised imbalanced multi-label classification with label propagation

Authors:
raw_author_name: Guodong Du
raw_affiliation_string: School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, PR China
raw_author_name: Jia Zhang
raw_affiliation_string: College of Information Science and Technology, Jinan University, Guangzhou 510632, PR China
raw_author_name: Hanrui Wu
raw_affiliation_string: College of Information Science and Technology, Jinan University, Guangzhou 510632, PR China
raw_author_name: Peiliang Wu
raw_affiliation_string: School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, PR China
raw_author_name: Shaozi Li
raw_affiliation_string: Department of Artificial Intelligence, Xiamen University, Xiamen, 361005, PR China

Abstract: Multi-label learning tasks usually encounter the problem of the class-imbalance, where samples and their corresponding labels are non-uniformly distributed over multi-label data space. It has attracted increasing attention during the past decade, however, there is a lack of methods capable of handling the imbalanced problem in a semi-supervised setting. This study proposes a label propagation technique to settle the semi-supervised imbalanced multi-label issue. Specially, we first utilize a collaborative manner to exploit the correlations from labels and instances, and learn a label regularization matrix to overcome the imbalanced problem in the labeled instance. After that, we extend to semi-supervised learning and explore to represent the similarity of instances with weighted graphs on labeled and unlabeled data. Then, the data distribution information and label correlations are fully utilized to design the loss function under the consistency assumption manner. At last, we present an iterative scheme to settle the optimization issue, thereby achieving label propagation to address the imbalanced challenge. Experiments on a variety of multi-label data sets show the favorable performance of the proposed method against related comparing approaches. Notably, the proposed method is also validated to be robust with a limited number of training instances.
Learning efficient decoders for quasichaotic quantum scramblers

Authors:
raw_author_name: Lorenzo Leone
raw_affiliation_string: Physics Department, University of Massachusetts Boston, Massachusetts 02125, USA; Theoretical Division (T-4), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
raw_author_name: Salvatore F. E. Oliviero
raw_affiliation_string: Physics Department, University of Massachusetts Boston, Massachusetts 02125, USA; Theoretical Division (T-4), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
raw_author_name: Seth Lloyd
raw_affiliation_string: Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA; Turing Inc., Brooklyn, New York, USA
raw_author_name: Alioscia Hamma
raw_affiliation_string: Dipartimento di Fisica ‘Ettore Pancini’, Università degli Studi di Napoli Federico II, Via Cintia 80126, Napoli, Italy; INFN, Sezione di Napoli, Italy

Abstract: Scrambling of quantum information is an important feature at the root of randomization and benchmarking protocols, the onset of quantum chaos, and black-hole physics. Unscrambling this information is possible given perfect knowledge of the scrambler [arXiv:1710.03363]. We show that one can retrieve the scrambled information even without any previous knowledge of the scrambler, by a learning algorithm that allows the building of an efficient decoder. Remarkably, the decoder is classical in the sense that it can be efficiently represented on a classical computer as a Clifford operator. It is striking that a classical decoder can retrieve with fidelity one all the information scrambled by a random unitary that cannot be efficiently simulated on a classical computer, as long as there is no full-fledged quantum chaos. This result shows that one can learn the salient properties of quantum unitaries in a classical form and sheds a new light on the meaning of quantum chaos. Furthermore, we obtain results concerning the algebraic structure of $t$-doped Clifford circuits, i.e., Clifford circuits containing $t$ non-Clifford gates, their gate complexity, and learnability that are of independent interest. In particular, we show that a $t$-doped Clifford circuit ${U}_{t}$ can be decomposed into two Clifford circuits ${U}_{0},{U}_{0}^{\ensuremath{'}}$ that sandwich a local unitary operator ${u}_{t}$, i.e., ${U}_{t}={U}_{0}{u}_{t}{U}_{0}^{\ensuremath{'}}$. The local unitary operator ${u}_{t}$ contains $t$ non-Clifford gates and acts nontrivially on at most $t$ qubits. As simple corollaries, the gate complexity of the $t$-doped Clifford circuit ${U}_{t}$ is $O({n}^{2}+{t}^{3})$, and it admits a efficient process tomography using $poly(n,{2}^{t})$ resources.
Autonomous error correction of a single logical qubit using two transmons

Authors:
raw_author_name: Ziqian Li
raw_affiliation_string: James Franck Institute, University of Chicago, Chicago, IL, 60637, USA
raw_author_name: Tanay Roy
raw_affiliation_string: James Franck Institute, University of Chicago, Chicago, IL, 60637, USA
raw_author_name: David Rodríguez Pérez
raw_affiliation_string: Department of Physics, Colorado School of Mines, Golden, CO, 80401, USA
raw_author_name: Eliot Kapit
raw_affiliation_string: Department of Physics, Colorado School of Mines, Golden, CO, 80401, USA
raw_author_name: David I. Schuster
raw_affiliation_string: Department of Physics, University of Chicago, Chicago, IL, 60637, USA

Abstract: 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

Authors:
raw_author_name: Wenbin Yang
raw_affiliation_string: School of Computer Engineer and Science, Shanghai University, Shanghai 200444, China
raw_author_name: Xinzhi Wang
raw_affiliation_string: School of Computer Engineer and Science, Shanghai University, Shanghai 200444, China
raw_author_name: Xiangfeng Luo
raw_affiliation_string: School of Computer Engineer and Science, Shanghai University, Shanghai 200444, China
raw_author_name: Shaorong Xie
raw_affiliation_string: School of Computer Engineer and Science, Shanghai University, Shanghai 200444, China
raw_author_name: Junxi Chen
raw_affiliation_string: School of Computer Engineer and Science, Shanghai University, Shanghai 200444, China

Abstract: 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

Authors:
raw_author_name: Kazutaka Takahashi
raw_affiliation_string: Department of Physics Engineering, Faculty of Engineering, Mie University, Mie 514-8507, Japan; Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg, Luxembourg
raw_author_name: Adolfo del Campo
raw_affiliation_string: Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg, Luxembourg; Donostia International Physics Center, E-20018 San Sebastián, Spain

Abstract: 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

Authors:
raw_author_name: Mohamed Hibat-Allah
raw_affiliation_string: Zapata AI Canada Inc., 25 Adelaide St East, Toronto, ON, M5C 3A1, Canada
raw_author_name: Marta Mauri
raw_affiliation_string: Zapata AI Canada Inc., 25 Adelaide St East, Toronto, ON, M5C 3A1, Canada
raw_author_name: Juan Carrasquilla
raw_affiliation_string: Vector Institute, MaRS Centre, Toronto, ON, M5G 1M1, Canada
raw_author_name: Alejandro Perdomo-Ortiz
raw_affiliation_string: Zapata AI Canada Inc., 25 Adelaide St East, Toronto, ON, M5C 3A1, Canada

Abstract: 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

Authors:
raw_author_name: Zhongmiao Huang
raw_affiliation_string: School of Computer Science and Technology, Xinjiang University, Urumqi, 830046, Xinjiang, China
raw_author_name: Shuli Cheng
raw_affiliation_string: School of Computer Science and Technology, Xinjiang University, Urumqi, 830046, Xinjiang, China
raw_author_name: Liejun Wang
raw_affiliation_string: School of Computer Science and Technology, Xinjiang University, Urumqi, 830046, Xinjiang, China

Abstract: 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.
Anomaly detection in the presence of irrelevant features

Authors:
raw_author_name: Marat Freytsis
raw_affiliation_string: Department of Physics, LEPP, Cornell University, Ithaca, NY, 14853, USA

Abstract: 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

Authors:
raw_author_name: M. K. Sharma
raw_affiliation_string: Chaudhary Charan Singh University, India
raw_author_name: M. Sunil Kumar
raw_affiliation_string: Institute of Aeronautical Engineering, Hyderabad, India
raw_author_name: V. Revathi
raw_affiliation_string: Department of Applied Sciences, New Horizon College of Engineering, Bangalore, India
raw_author_name: Joshuva Arockia Dhanraj
raw_affiliation_string: Hindustan Institute of Technology and Science, India

Abstract: 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.
Exploring the current status and future opportunities of blockchain technology adoption and application in supply chain management

Authors:
raw_author_name: Yong Lin
raw_affiliation_string: Birmingham Business School, University of Birmingham, Edgbaston, Birmingham, B15 2TY, UK

Abstract: 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

Authors:
raw_author_name: Muhammad Usman Tariq
raw_affiliation_string: Abu Dhabi University, UAE

Abstract: 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

Authors:
raw_author_name: Carola Bruna
raw_affiliation_string: Universidad de Concepción
raw_author_name: Verónica Villarroel
raw_affiliation_string: Universidad San Sebastián

Abstract: 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.