Reviewerly/exHarmony huggingface dataset:
Automatic segmentation of skeletal muscles from MR images using modified U-Net and a novel data augmentation approach

Authors:
raw_author_name: Zhicheng Lin
raw_affiliation_string: Department of Automatic Control and Systems Engineering, United Kingdom
raw_author_name: William H. Henson
raw_affiliation_string: Department of Mechanical Engineering, United Kingdom
raw_author_name: Enrico Dall’Ara
raw_affiliation_string: Department of Oncology and Metabolism, United Kingdom; Insigneo, United Kingdom
raw_author_name: Lingzhong Guo
raw_affiliation_string: Department of Automatic Control and Systems Engineering, United Kingdom; Insigneo, United Kingdom

Abstract: Rapid and accurate muscle segmentation is essential for the diagnosis and monitoring of many musculoskeletal diseases. As gold standard, manual annotation suffers from intensive labor and high inter-operator reproducibility errors. In this study, deep learning (DL) based automatic muscle segmentation from MR scans is investigated for post-menopausal women, who normally experience a decline in muscle volume. The performance of four Deep Learning (DL) models was evaluated: U-Net and UNet++ and two modified U-Net networks, which combined feature fusion and attention mechanisms (Feature-Fusion-UNet, FFU, and Attention-Feature-Fusion-UNet, AFFU). The models were tested for automatic segmentation of 16-lower limb muscles from MRI scans of two cohorts of post-menopausal women (11 subjects in PMW-1, 8 subjects in PMW-2; from two different studies so considered independent datasets) and 10 obese post-menopausal women (PMW-OB). Furthermore, a novel data augmentation approach is proposed to enlarge the training dataset. The results were assessed and compared by using the Dice similarity coefficient (DSC), relative volume error (RVE), and Hausdorff distance (HD). The best performance among all four DL models was achieved by AFFU (PMW-1: DSC 0.828 ± 0.079, 1-RVE 0.859 ± 0.122, HD 29.9 mm ± 26.5 mm; PMW-2: DSC 0.833 ± 0.065, 1-RVE 0.873 ± 0.105, HD 25.9 mm ± 27.9 mm; PMW-OB: DSC 0.862 ± 0.048, 1-RVE 0.919 ± 0.076, HD 34.8 mm ± 46.8 mm). Furthermore, the augmentation of data significantly improved the DSC scores of U-Net and AFFU for all 16 tested muscles (between 0.23% and 2.17% (DSC), 1.6%–1.93% (1-RVE), and 9.6%–19.8% (HD) improvement). These findings highlight the feasibility of utilizing DL models for automatic segmentation of muscles in post-menopausal women and indicate that the proposed augmentation method can enhance the performance of models trained on small datasets.
Exceptional Entanglement and Quantum Sensing with a Parity‐Time‐Symmetric Two‐Qubit System

Authors:
raw_author_name: Ji Zhang
raw_affiliation_string: Institute for Quantum Science and Technology College of Science National University of Defense Technology Changsha 410073 China; Key Laboratory of Low‐Dimension Quantum Structures and Quantum Control of Ministry of Education Synergetic Innovation Center for Quantum Effects and Applications XJ‐Laboratory and Department of Physics Hunan Normal University Changsha 410081 China
raw_author_name: Yan‐Li Zhou
raw_affiliation_string: Institute for Quantum Science and Technology College of Science National University of Defense Technology Changsha 410073 China
raw_author_name: Ping‐Xing Chen
raw_affiliation_string: Institute for Quantum Science and Technology College of Science National University of Defense Technology Changsha 410073 China
raw_author_name: Hui Jing
raw_affiliation_string: Key Laboratory of Low‐Dimension Quantum Structures and Quantum Control of Ministry of Education Synergetic Innovation Center for Quantum Effects and Applications XJ‐Laboratory and Department of Physics Hunan Normal University Changsha 410081 China; Synergetic Innovation Academy for Quantum Science and Technology Zhengzhou University of Light Industry Zhengzhou 450002 China
raw_author_name: Le‐Man Kuang
raw_affiliation_string: Key Laboratory of Low‐Dimension Quantum Structures and Quantum Control of Ministry of Education Synergetic Innovation Center for Quantum Effects and Applications XJ‐Laboratory and Department of Physics Hunan Normal University Changsha 410081 China; Synergetic Innovation Academy for Quantum Science and Technology Zhengzhou University of Light Industry Zhengzhou 450002 China

Abstract: Abstract Parity‐time (PT) symmetric systems with gain‐loss balanced structures can have entirely real eigenenergy spectra despite their non‐Hermitian nature, leading to peculiar discoveries such as single‐mode lasers, wireless power transfer, and non‐reciprocal light propagation. Recently, the study of PT‐symmetry has extended to the quantum realm, inspiring a search for PT‐symmetric quantum effects as well as their unique applications. Here, quantum entanglement and sensing with a PT‐symmetric system of two qubits are studied with Ising‐type interaction. It is shown that exceptional points (EPs) can induce exceptional entanglement phenomena such as collapse‐revival and steady‐state entanglement on the two sides of EPs. The entanglement in the PT‐symmetric system is also found to be generated more quickly than that in the Hermitian counterpart. In addition, a scheme for realizing EP‐enhanced quantum sensing is proposed by calculating the quantum Fisher information to estimate the parameters of the system. The work provides new insights into EP‐engineered quantum effects and can benefit a wide range of applications based on non‐Hermitian quantum devices.
Matrix Factorization Recommendation Algorithm Based on Attention Interaction

Authors:
raw_author_name: Chengzhi Mao
raw_affiliation_string: School of Information Technology Engineering, Tianjin University of Technology and Education, Hexi District, Tianjin 300222, China
raw_author_name: Zhifeng Wu
raw_affiliation_string: School of Information Technology Engineering, Tianjin University of Technology and Education, Hexi District, Tianjin 300222, China
raw_author_name: Yingjie Liu
raw_affiliation_string: School of Information Technology Engineering, Tianjin University of Technology and Education, Hexi District, Tianjin 300222, China
raw_author_name: Zhiwei Shi
raw_affiliation_string: School of Computer Science, Shaanxi Normal University, 620 West Chang’an Street, Xi’an 710119, China

Abstract: Recommender systems are widely used in e-commerce, movies, music, social media, and other fields because of their personalized recommendation functions. The recommendation algorithm is used to capture user preferences, item characteristics, and the items that users are interested in are recommended to users. Matrix factorization is widely used in collaborative filtering algorithms because of its simplicity and efficiency. However, the simple dot-product method cannot establish a nonlinear relationship between user latent features and item latent features or make full use of their personalized information. The model of a neural network combined with an attention mechanism can effectively establish a nonlinear relationship between the potential features of users and items and improve the recommendation accuracy of the model. However, it is difficult for the general attention mechanism algorithm to solve the problem of attention interaction when the number of features between the users and items is not the same. To solve the above problems, this paper proposes an attention interaction matrix factorization (AIMF) model. The AIMF model adopts a symmetric structure using MLP calculation. This structure can simultaneously extract the nonlinear features of user latent features and item latent features, thus reducing the computation time of the model. In addition, an improved attention algorithm named slide-attention is included in the model. The algorithm uses the sliding query method to obtain the user’s attention to the latent features of the item and solves the interaction problem among different dimensions of the user, and the latent features of the item.
Text-Vision Relationship Alignment for Referring Image Segmentation

Authors:
raw_author_name: Bing Luo
raw_affiliation_string: School of Computer and Software Engineering, Xihua University, Chengdu, 610039, China
raw_author_name: Chao Zhang
raw_affiliation_string: Key Laboratory of Intelligent Policing, Sichuan Police College, Luzhou, 646000, China
raw_author_name: Li Xu
raw_affiliation_string: School of Science, Xihua University, Chengdu, 610039, China
raw_author_name: Fayou Xu
raw_affiliation_string: School of Computer and Software Engineering, Xihua University, Chengdu, 610039, China
raw_author_name: Mingming Kong
raw_affiliation_string: School of Computer and Software Engineering, Xihua University, Chengdu, 610039, China

Abstract: Abstract Referring image segmentation aims to segment object in an image based on a referring expression. Its difficulty lies in aligning expression semantics with visual instances. The existing methods based on semantic reasoning are limited by the performance of external syntax parser and do not explicitly explore the relationships between visual instances. This article proposes an end-to-end method for referring image segmentation by aligning ’linguistic relationship’ with ’visual relationships’. This method does not rely on external syntax parser for expression parsing. In this paper, the expression is adaptively and structurally parsed into three components: ’subject’, ’object’, and ’linguistic relationship’ by the Semantic Component Parser (SCP) in a learnable manner. Instances Activation Map Module (IAM) locates multiple visual instances based on the subject and object. In addition, the Relationship Based Visual Localization Module (RBVL) firstly enables each instance of the image to learn global knowledge, then decodes the visual relationships between these visual instances, and finally aligns the visual relationships with the linguistic relationships to further accurately locate the target object. The experimental results show that the proposed method improves performance by 4– 9% compared with baseline method on multiple referring image segmentation datasets.
Comprehensive study of driver behavior monitoring systems using computer vision and machine learning techniques

Authors:
raw_author_name: Borko Furht
raw_affiliation_string: Department of Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, FL, 33431, United States
raw_author_name: Mehrdad Nojoumian
raw_affiliation_string: Department of Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, FL, 33431, United States

Abstract: Abstract The flourishing realm of advanced driver-assistance systems (ADAS) as well as autonomous vehicles (AVs) presents exceptional opportunities to enhance safe driving. An essential aspect of this transformation involves monitoring driver behavior through observable physiological indicators, including the driver’s facial expressions, hand placement on the wheels, and the driver’s body postures. An artificial intelligence (AI) system under consideration alerts drivers about potentially unsafe behaviors using real-time voice notifications. This paper offers an all-embracing survey of neural network-based methodologies for studying these driver bio-metrics, presenting an exhaustive examination of their advantages and drawbacks. The evaluation includes two relevant datasets, separately categorizing ten different in-cabinet behaviors, providing a systematic classification for driver behaviors detection. The ultimate aim is to inform the development of driver behavior monitoring systems. This survey is a valuable guide for those dedicated to enhancing vehicle safety and preventing accidents caused by careless driving. The paper’s structure encompasses sections on autonomous vehicles, neural networks, driver behavior analysis methods, dataset utilization, and final findings and future suggestions, ensuring accessibility for audiences with diverse levels of understanding regarding the subject matter.
Finding Regions of Counterfactual Explanations via Robust Optimization

Authors:
raw_author_name: Donato Maragno
raw_affiliation_string: Amsterdam Business School, University of Amsterdam, 1018 TV Amsterdam, Netherlands
raw_author_name: Jannis Kurtz
raw_affiliation_string: Amsterdam Business School, University of Amsterdam, 1018 TV Amsterdam, Netherlands
raw_author_name: Tabea E. Röber
raw_affiliation_string: Amsterdam Business School, University of Amsterdam, 1018 TV Amsterdam, Netherlands
raw_author_name: Ş. İlker Birbil
raw_affiliation_string: Amsterdam Business School, University of Amsterdam, 1018 TV Amsterdam, Netherlands
raw_author_name: Dick den Hertog
raw_affiliation_string: Amsterdam Business School, University of Amsterdam, 1018 TV Amsterdam, Netherlands

Abstract: Counterfactual explanations (CEs) play an important role in detecting bias and improving the explainability of data-driven classification models. A CE is a minimal perturbed data point for which the decision of the model changes. Most of the existing methods can only provide one CE, which may not be achievable for the user. In this work, we derive an iterative method to calculate robust CEs (i.e., CEs that remain valid even after the features are slightly perturbed). To this end, our method provides a whole region of CEs, allowing the user to choose a suitable recourse to obtain a desired outcome. We use algorithmic ideas from robust optimization and prove convergence results for the most common machine learning methods, including decision trees, tree ensembles, and neural networks. Our experiments show that our method can efficiently generate globally optimal robust CEs for a variety of common data sets and classification models. History: Accepted by Andrea Lodi, Area Editor for Design &amp; Analysis of Algorithms—Discrete. Funding: This work was supported by the Nederlandse Organisatie voor Wetenschappelijk Onderzoek [Grant OCENW.GROOT.2019.015, Optimization for and with Machine Learning (OPTIMAL)]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2023.0153 .
The Temporal and Spatial Characteristics of Wind–Photovoltaic–Hydro Hybrid Power Output Based on a Cloud Model and Copula Function

Authors:
raw_author_name: Haoling Min
raw_affiliation_string: School of Water Resources and Hydropower Engineering, North China Electric Power University, Beijing 102206, China
raw_author_name: Chunlai Li
raw_affiliation_string: Economic and Technological Research Institute, State Grid Qinghai Electric Power Company, Xining 810000, China
raw_author_name: Libin Yang
raw_affiliation_string: Economic and Technological Research Institute, State Grid Qinghai Electric Power Company, Xining 810000, China

Abstract: In a high proportion of wind–photovoltaic–hydro hybrid power systems, fluctuation and dispersion make it difficult to accurately quantify the output characteristics. Therefore, in this study, a cloud model and copula correlation coefficient matrix were constructed for a hybrid power generation system based on the output data. Multiple backward cloud transformation based on the sampling-with-replacement method was proposed to calculate the improved entropy and hyperentropy to analyze the fluctuation range and dispersion degree quantitatively. A similarity index was proposed to evaluate the similarity between wind power, PV power, and hydropower. A suitable copula function was selected, and the Kendall and Spearman coefficients show the correlation relationships of the hybrid systems. The temporal and spatial characteristics of the hybrid systems were analyzed based on the two models. A typical example in Qinghai proved the effectiveness and applicability of the method. The results show that the correlation between photovoltaic power and hydropower is better and that, in summer, hydropower can be used to adjust the output of renewable energy.
Parameter instance learning with enhanced vision transformers for aerial person re‐identification

Authors:
raw_author_name: Houfu Peng
raw_affiliation_string: School of Big Data and Computer Science Guizhou Normal University  Guiyang China
raw_author_name: Xing Lu
raw_affiliation_string: School of Big Data and Computer Science Guizhou Normal University  Guiyang China
raw_author_name: Lili Xu
raw_affiliation_string: School of Big Data and Computer Science Guizhou Normal University  Guiyang China
raw_author_name: Daoxun Xia
raw_affiliation_string: School of Big Data and Computer Science Guizhou Normal University  Guiyang China
raw_author_name: Xiaoyao Xie
raw_affiliation_string: Guizhou Key Laboratory of Information and Computing Science Guizhou Normal University  Guiyang China

Abstract: Summary In an agnostic space environment, aerial person re‐identification (Re‐ID) is a task that the query person may not occur in the gallery set, it is considered a subordinate task within the domain of open‐world person Re‐ID, and is a more challenging and practical application research. The aerial person images, captured by unmanned aerial vehicles, present more significant challenges such as weak appearance features, fewer individual person samples and occlusion due to variations in camera height and viewing angles compared to ground‐level images. Most state‐of‐the‐arts person Re‐ID methods developed for open‐world datasets rely heavily on local convolutional neural networks but exhibit suboptimal performance when directly applied to aerial person Re‐ID tasks. In this article, a parameter instance learning based on vision transformers (ViT) model is introduced for the design of aerial person Re‐ID. Initially, we employ a self‐supervised paradigm grounded in parameter instance discrimination, aiming to capture feature alignment and instance similarity. Subsequently, using labeled training data, we optimize the network model through the calculation of two types of loss functions. Finally, we employ a feature enhancement strategy utilizing zero‐padding and displacement techniques. This strategy effectively and directly enhances the robustness of the ViT model against issues such as occlusion and misalignment. We conducted experiments on a Re‐ID dataset to validate the effectiveness of the method. Our approach achieves a mean average precision of 57.31% and a Rank‐1 accuracy of 65.29% on the aerial person Re‐ID dataset PRAI‐1581.
Adaptive Dual Aggregation Network with Normalizing Flows for Low-Light Image Enhancement

Authors:
raw_author_name: Jianzhong Cao
raw_affiliation_string: Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China
raw_author_name: Jijiang Huang
raw_affiliation_string: Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China

Abstract: Low-light image enhancement (LLIE) aims to improve the visual quality of images taken under complex low-light conditions. Recent works focus on carefully designing Retinex-based methods or end-to-end networks based on deep learning for LLIE. However, these works usually utilize pixel-level error functions to optimize models and have difficulty effectively modeling the real visual errors between the enhanced images and the normally exposed images. In this paper, we propose an adaptive dual aggregation network with normalizing flows (ADANF) for LLIE. First, an adaptive dual aggregation encoder is built to fully explore the global properties and local details of the low-light images for extracting illumination-robust features. Next, a reversible normalizing flow decoder is utilized to model real visual errors between enhanced and normally exposed images by mapping images into underlying data distributions. Finally, to further improve the quality of the enhanced images, a gated multi-scale information transmitting module is leveraged to introduce the multi-scale information from the adaptive dual aggregation encoder into the normalizing flow decoder. Extensive experiments on paired and unpaired datasets have verified the effectiveness of the proposed ADANF.
Analogue Quantum Simulation with Fixed-Frequency Transmon Qubits

Authors:
raw_author_name: Adam Smith
raw_affiliation_string: Centre for the Mathematics and Theoretical Physics of Quantum Non-Equilibrium Systems, University of Nottingham, Nottingham, NG7 2RD, UK; School of Physics and Astronomy, University of Nottingham, Nottingham, NG7 2RD, UK
raw_author_name: Florian Mintert
raw_affiliation_string: Helmholtz-Zentrum Dresden-Rossendorf, Bautzner Landstraße 400, 01328 Dresden, Germany; Physics Department, Blackett Laboratory, Imperial College London, Prince Consort Road, SW7 2BW, United Kingdom
raw_author_name: Daniel Malz
raw_affiliation_string: Department of Physics, Technische Universität München, James-Franck-Straße 1, 85748 Garching, Germany; Max-Planck-Institute of Quantum Optics, Hans-Kopfermann-Str. 1, 85748 Garching, Germany

Abstract: We experimentally assess the suitability of transmon qubits with fixed frequencies and fixed interactions for the realization of analogue quantum simulations of spin systems. We test a set of necessary criteria for this goal on a commercial quantum processor using full quantum process tomography and more efficient Hamiltonian tomography. Significant single qubit errors at low amplitudes are identified as a limiting factor preventing the realization of analogue simulations on currently available devices. We additionally find spurious dynamics in the absence of drive pulses, which we identify with coherent coupling between the qubit and a low dimensional environment. With moderate improvements, analogue simulation of a rich family of time-dependent many-body spin Hamiltonians may be possible.
New independent daily global solar radiation estimation models based on the day number of the year

Authors:
raw_author_name: Raphael Hermann Tonsie Djiela
raw_affiliation_string: UR2MSP, Department of Physics, University of Dschang, Dschang, Cameroon; URISIE, University Institute of Technology Fotso Victor, University of Dschang, Bandjoun, Cameroon
raw_author_name: Ghislain Tchuen
raw_affiliation_string: UR2MSP, Department of Physics, University of Dschang, Dschang, Cameroon; URISIE, University Institute of Technology Fotso Victor, University of Dschang, Bandjoun, Cameroon

Abstract: Solar radiation data are necessary information for the analysis and prediction of certain environmental processes and the planning of related energy projects. Many models have been proposed in the literature for their estimation due to their unavailability in some locations, especially in developing countries. However, peoples are currently finding more accurate and simple models. One type of these models is the day of the year-based (DYB) model, which has as merit over others, the fact that it does not depend on any environmental parameter. In this work, nine existing DYB models are calibrated with Levenberg–Marquardt nonlinear curve fitting technique for the estimation of solar radiation in the regional cities of Cameroon. In addition, two new DYB models are proposed. The results show that one of the two proposed models (M11) best estimates the solar radiation in nine sites and is ranked second in one site with the root mean square error in the range [0.2365; 0.3039], the mean absolute percentage error in the range [0.1873; 0.2348], and the correlation coefficient in the range [0.8332; 0.9164]. The new model M11 is therefore recommended for estimating solar radiation of some sites of Cameroon and in regions with similar climate.