Reviewerly/exHarmony huggingface dataset:
Sustainable deployment of clinical prediction tools—a 360° approach to model maintenance

Authors:
raw_author_name: Sharon E Davis
raw_affiliation_string: Department of Biomedical Informatics, Vanderbilt University Medical Center , Nashville, TN 37203, United States
raw_author_name: Michael E Matheny
raw_affiliation_string: Department of Biomedical Informatics, Vanderbilt University Medical Center , Nashville, TN 37203, United States; Department of Biostatistics, Vanderbilt University Medical Center , Nashville, TN 37203, United States; Department of Medicine, Vanderbilt University Medical Center , Nashville, TN 37232, United States; Geriatric Research, Education, and Clinical Care, Tennessee Valley Healthcare System VA Medical Center, Veterans Health Administration , Nashville, TN 37212, United States

Abstract: As the enthusiasm for integrating artificial intelligence (AI) into clinical care grows, so has our understanding of the challenges associated with deploying impactful and sustainable clinical AI models. Complex dataset shifts resulting from evolving clinical environments strain the longevity of AI models as predictive accuracy and associated utility deteriorate over time.Responsible practice thus necessitates the lifecycle of AI models be extended to include ongoing monitoring and maintenance strategies within health system algorithmovigilance programs. We describe a framework encompassing a 360° continuum of preventive, preemptive, responsive, and reactive approaches to address model monitoring and maintenance from critically different angles.We describe the complementary advantages and limitations of these four approaches and highlight the importance of such a coordinated strategy to help ensure the promise of clinical AI is not short-lived.
What is a blink? Classifying and characterizing blinks in eye openness signals

Authors:
raw_author_name: Marcus Nyström
raw_affiliation_string: Lund University Humanities Lab, Box 201, SE–221 00, Lund, Sweden
raw_author_name: Richard Andersson
raw_affiliation_string: Tobii AB, Box 743, SE–182 17, Danderyd, Sweden
raw_author_name: Diederick C. Niehorster
raw_affiliation_string: Lund University Humanities Lab and Department of Psychology, Box 201, SE–221 00, Lund, Sweden
raw_author_name: Roy S. Hessels
raw_affiliation_string: Experimental Psychology, Helmholtz Institute, Utrecht University, Heidelberglaan 1, 3584, CS, Utrecht, The Netherlands
raw_author_name: Ignace T. C. Hooge
raw_affiliation_string: Experimental Psychology, Helmholtz Institute, Utrecht University, Heidelberglaan 1, 3584, CS, Utrecht, The Netherlands

Abstract: Blinks, the closing and opening of the eyelids, are used in a wide array of fields where human function and behavior are studied. In data from video-based eye trackers, blink rate and duration are often estimated from the pupil-size signal. However, blinks and their parameters can be estimated only indirectly from this signal, since it does not explicitly contain information about the eyelid position. We ask whether blinks detected from an eye openness signal that estimates the distance between the eyelids (EO blinks) are comparable to blinks detected with a traditional algorithm using the pupil-size signal (PS blinks) and how robust blink detection is when data quality is low. In terms of rate, there was an almost-perfect overlap between EO and PS blink (F1 score: 0.98) when the head was in the center of the eye tracker's tracking range where data quality was high and a high overlap (F1 score 0.94) when the head was at the edge of the tracking range where data quality was worse. When there was a difference in blink rate between EO and PS blinks, it was mainly due to data loss in the pupil-size signal. Blink durations were about 60 ms longer in EO blinks compared to PS blinks. Moreover, the dynamics of EO blinks was similar to results from previous literature. We conclude that the eye openness signal together with our proposed blink detection algorithm provides an advantageous method to detect and describe blinks in greater detail.
Fuzzy cognitive maps for municipal governance improvement

Authors:
raw_author_name: Federico Pablo-Martí
raw_affiliation_string: SCCS Research Group, University of Alcala, Alcalá de Henares, Spain

Abstract: This paper applies Fuzzy Cognitive Maps (FCMs) to understand the diverse behavior of municipal governments in Ecuador to find common elements that influence the well-being of citizens in the short and long term. Information gathering was conducted in two stages: in the first one, a group of 16 national experts was consulted to develop the initial FCM; in the second stage, local experts from 220 municipalities were interviewed to collect information on the general validity of initial FCMs and specific values given to concepts and relationships in their municipalities. Results show the importance of certain concepts for long-term municipal performance, such as the need for a competitive entrepreneurial sector, improving human resources in the municipality, and, particularly, having a competent mayor with leadership skills and a forward-looking vision that enables the development of municipal projects required to reach an efficient and equitable coverage of goods and services throughout the city. Through the application of genetic algorithms, the FCM was calibrated to ascertain the long-term dynamics of municipal development and the optimal values of the concepts that would optimize the attainment of the set objectives. The derived outcomes suggest the desirability of the maintenance of, in principle, unwanted structures like financial transfers from the central government and the need to exploit natural resources to attain urban development.
Klasik ve Bulanık Kenar Algılama Yöntemlerinin Karşılaştırılması

Authors:
raw_author_name: Gulcihan OZDEMİR
raw_affiliation_string: İSTANBUL TEKNİK ÜNİVERSİTESİ

Abstract: Edge detection is one of the challenging problems in image processing. Four different classical edge detection methods—Sobel, Prewitt, Roberts, and Canny—and type-1 and type-2 fuzzy logic-based edge detection methods are applied to analyze two separate datasets with various properties. The datasets are STARE which contains medical images of the retina and BIPED which contains images of the street. Furthermore, two separate hybrid fuzzy logic methods are implemented. The type-1 and type-2 fuzzy inference techniques are combined to produce the hybrid-1 and hybrid-2 approaches, using the "AND" and "OR" logic operators. We compare the simulation results for each technique using three different image quality metrics. These are Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR), and Structural Similarity Index (SSIM). The type-2 fuzzy technique outperformed the hybrid-1 fuzzy method in visual quality metrics comparison, demonstrating superior blood vessel recognition on the STARE retinal image dataset—a dataset that more closely resembles the human visual system. Using the BIPED street image dataset, the hybrid-1 fuzzy approach outperformed the Roberts method. The hybrid-1 fuzzy technique showed good results in the second order for both kinds of datasets. Any data and general applications can take advantage of it.
Generalization of Deep-Learning Models for Classification of Local Distance Earthquakes and Explosions across Various Geologic Settings

Authors:
raw_author_name: Brandon Schmandt
raw_affiliation_string: 2Department of Earth and Planetary Sciences, University of New Mexico, Albuquerque, New Mexico, U.S.A.
raw_author_name: Ruijia Wang
raw_affiliation_string: 3Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen, People’s Republic of China
raw_author_name: Qingkai Kong
raw_affiliation_string: 4Lawrence Livermore National Laboratory, Livermore, California, U.S.A.
raw_author_name: Pedro Sanchez
raw_affiliation_string: 1Department of Earth Science and Environmental Change, University of Illinois at Urbana-Champaign, Urbana, Illinois, U.S.A.

Abstract: Abstract Although accurately classifying signals from earthquakes and explosions at local distance (&amp;lt;250 km) remains an important task for seismic network operations, the growing volume of available seismic data presents a challenge for analysts using traditional source discrimination techniques. In recent years, deep-learning models have proven effective at discriminating between low-magnitude earthquakes and explosions measured at local distances, but it is not clear how well these models are capable of generalizing across different geological settings. To address the issue of generalization between regions, we train deep-learning models (convolutional neural networks [CNNs]) on time–frequency representations (scalograms) of three-component earthquake and explosion signals from eight different regions in the continental United States. We explore scenarios where models are trained on data from all regions, individual regions, or all but one region. We find that although CNN models trained on individual regions do not necessarily generalize well across different settings, models trained on multiple regions that include diverse path coverage generalize to new regions, with station-level accuracy of up to 90% or more for data sets from unseen regions. In general, CNN-based discrimination models significantly outperform models based on uncorrected P/S ratio (measured in the 10–18 Hz frequency band), even when CNN models are tested on data from entirely unseen regions.
Constructing polynomial libraries for reservoir computing in nonlinear dynamical system forecasting

Authors:
raw_author_name: Yu-Long Bai
raw_affiliation_string: College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, Gansu 730070, China
raw_author_name: Lin Ding
raw_affiliation_string: College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, Gansu 730070, China

Abstract: Reservoir computing is an effective model for learning and predicting nonlinear and chaotic dynamical systems; however, there remains a challenge in achieving a more dependable evolution for such systems. Based on the foundation of Koopman operator theory, considering the effectiveness of the sparse identification of nonlinear dynamics algorithm to construct candidate nonlinear libraries in the application of nonlinear data, an alternative reservoir computing method is proposed, which creates the linear Hilbert space of the nonlinear system by including nonlinear terms in the optimization process of reservoir computing, allowing for the application of linear optimization. We introduce an implementation that incorporates a polynomial transformation of arbitrary order when fitting the readout matrix. Constructing polynomial libraries with reservoir-state vectors as elements enhances the nonlinear representation of reservoir states and more easily captures the complexity of nonlinear systems. The Lorenz-63 system, the Lorenz-96 system, and the Kuramoto-Sivashinsky equation are used to validate the effectiveness of constructing polynomial libraries for reservoir states in the field of state-evolution prediction of nonlinear and chaotic dynamical systems. This study not only promotes the theoretical study of reservoir computing, but also provides a theoretical and practical method for the prediction of nonlinear and chaotic dynamical system evolution.
Readout-Induced Suppression and Enhancement of Superconducting Qubit Lifetimes

Authors:
raw_author_name: Ted Thorbeck
raw_affiliation_string: IBM Quantum, IBM T. J. Watson Research Center, Yorktown Heights, New York 10598, USA
raw_author_name: Zhihao Xiao
raw_affiliation_string: Department of Physics and Applied Physics, University of Massachusetts, Lowell, Massachusetts 01854, USA
raw_author_name: Archana Kamal
raw_affiliation_string: Department of Physics and Applied Physics, University of Massachusetts, Lowell, Massachusetts 01854, USA
raw_author_name: Luke C. G. Govia
raw_affiliation_string: IBM Quantum, IBM Almaden Research Center, San Jose, California 95120, USA

Abstract: It has long been known that the lifetimes of superconducting qubits suffer during readout, increasing readout errors. We show that this degradation is due to the anti-Zeno effect, as readout-induced dephasing broadens the qubit so that it overlaps "hot spots" of strong dissipation, likely due to two-level systems in the qubit's bath. Using a flux-tunable qubit to probe the qubit's frequency-dependent loss, we accurately predict the change in lifetime during readout with a new self-consistent master equation that incorporates the modification to qubit relaxation due to measurement-induced dephasing. Moreover, we controllably demonstrate both the Zeno and anti-Zeno effects, which can explain both suppression and the rarer enhancement of qubit lifetimes during readout.
Image Division Using Threshold Schemes with Privileges

Authors:
raw_author_name: Marek R. Ogiela
raw_affiliation_string: Cryptography and Cognitive Informatics Laboratory, AGH University of Krakow, 30 Mickiewicza Ave., 30-059 Krakow, Poland
raw_author_name: Lidia Ogiela
raw_affiliation_string: Faculty of Computer Science, AGH University of Krakow, 30 Mickiewicza Ave., 30-059 Krakow, Poland

Abstract: Threshold schemes are used among cryptographic techniques for splitting visual data. Such methods allow the generation of a number of secret shares, a certain number of which need to be assembled in order to reconstruct the original image. Traditional techniques for partitioning secret information generate equal shares, i.e., each share has the same value when reconstructing the original secret. However, it turns out that it is possible to develop and use partitioning protocols that allow the generation of privileged shares, i.e., those that allow the reconstruction of secret data in even smaller numbers. This paper will therefore describe new information sharing protocols that create privileged shares, which will also use visual authorization codes based on subject knowledge to select privileged shares for secret restoration. For the protocols described, examples of their functioning will be presented, and their complexity and potential for use in practical applications will be determined.
Impressive predictive model for Breast Cancer based on Machine Learning

Authors:
raw_author_name: Saravanan Thangavel
raw_affiliation_string: GITAM University
raw_author_name: M Prabhakaran
raw_affiliation_string: Alliance University

Abstract: INTRODUCTION: Breast cancer is a major health concern for women all over the world.&#x0D; OBJECTIVES: In order to reduce mortality rates and provide the most effective treatment, Histopathology image prognosis is essential. When a pathologist examines a biopsy specimen under a microscope, they are engaging in histopathology. The pathologist looks for the picture, determines its type, labels it, and assigns a grade.&#x0D; METHODS: Tissue architecture, cell distribution, and cellular form all play a role in determining whether a histopathological scan is benign or malignant. Manual picture classification is the slowest and most error-prone method. Automated diagnosis based on machine learning is necessary for early and precise diagnosis, but this challenge has prevented it from being addressed thus far. In this study, we apply curvelet transform to a picture that has been segmented using k-means clustering to isolate individual cell nuclei.&#x0D; RESULTS: We analysed data from the Wisconsin Diagnosis Breast Cancer database for this article in the context of similar studies in the literature.&#x0D; CONCLUSION: It is demonstrated that compared to another machine learning algorithm, the IICA-ANN IICA-KNN and IICA-SVM-KNN method using the logistic algorithm achieves 98.04% accuracy.
Assessing non-Markovian dynamics through moments of the Choi state

Authors:
raw_author_name: Bivas Mallick
raw_affiliation_string: S. N. Bose National Centre for Basic Sciences, Block JD, Sector III, Salt Lake, Kolkata 700 106, India
raw_author_name: Saheli Mukherjee
raw_affiliation_string: S. N. Bose National Centre for Basic Sciences, Block JD, Sector III, Salt Lake, Kolkata 700 106, India
raw_author_name: Ananda G. Maity
raw_affiliation_string: Networked Quantum Devices Unit, Okinawa Institute of Science and Technology Graduate University, Onna-son, Okinawa 904-0495, Japan
raw_author_name: A. S. Majumdar
raw_affiliation_string: S. N. Bose National Centre for Basic Sciences, Block JD, Sector III, Salt Lake, Kolkata 700 106, India

Abstract: Non-Markovian effects in open quantum system dynamics usually manifest the backflow of information from the environment to the system, indicating complete-positive divisibility breaking of the dynamics. We provide a criterion for witnessing such non-Markovian dynamics exhibiting information backflow, based on partial moments of Choi matrices. The moment condition determined by the positive semi-definiteness of a matrix does not hold for a Choi state describing non-Markovian dynamics. We then present some explicit examples in support of our proposed non-Markovianity detection scheme. Finally, a moment-based measure of non-Markovianity for unital dynamics is formulated.
Discrimination and certification of unknown quantum measurements

Authors:
raw_author_name: Aleksandra Krawiec
raw_affiliation_string: AstroCeNT, Nicolaus Copernicus Astronomical Center, Polish Academy of Sciences, ul. Rektorska 4, 00-614 Warsaw, Poland
raw_author_name: Łukasz Pawela
raw_affiliation_string: Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, ul. Bałtycka 5, 44-100 Gliwice, Poland
raw_author_name: Zbigniew Puchała
raw_affiliation_string: Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, ul. Bałtycka 5, 44-100 Gliwice, Poland

Abstract: We study the discrimination of von Neumann measurements in the scenario when we are given a reference measurement and some other measurement. The aim of the discrimination is to determine whether the other measurement is the same as the first one. We consider the cases when the reference measurement is given without the classical description and when its classical description is known. Both cases are studied in the symmetric and asymmetric discrimination setups. Moreover, we provide optimal certification schemes enabling us to certify a known quantum measurement against the unknown one.
Classification of Mobile-Based Oral Cancer Images Using the Vision Transformer and the Swin Transformer

Authors:
raw_author_name: Bofan Song
raw_affiliation_string: Wyant College of Optical Sciences, The University of Arizona, Tucson, AZ 85721, USA
raw_author_name: Dharma Raj KC
raw_affiliation_string: Computer Science Department, The University of Arizona, Tucson, AZ 85721, USA
raw_author_name: Rubin Yuchan Yang
raw_affiliation_string: Computer Science Department, The University of Arizona, Tucson, AZ 85721, USA
raw_author_name: Shaobai Li
raw_affiliation_string: Wyant College of Optical Sciences, The University of Arizona, Tucson, AZ 85721, USA
raw_author_name: Chicheng Zhang
raw_affiliation_string: Computer Science Department, The University of Arizona, Tucson, AZ 85721, USA
raw_author_name: Rongguang Liang
raw_affiliation_string: Wyant College of Optical Sciences, The University of Arizona, Tucson, AZ 85721, USA

Abstract: Oral cancer, a pervasive and rapidly growing malignant disease, poses a significant global health concern. Early and accurate diagnosis is pivotal for improving patient outcomes. Automatic diagnosis methods based on artificial intelligence have shown promising results in the oral cancer field, but the accuracy still needs to be improved for realistic diagnostic scenarios. Vision Transformers (ViT) have outperformed learning CNN models recently in many computer vision benchmark tasks. This study explores the effectiveness of the Vision Transformer and the Swin Transformer, two cutting-edge variants of the transformer architecture, for the mobile-based oral cancer image classification application. The pre-trained Swin transformer model achieved 88.7% accuracy in the binary classification task, outperforming the ViT model by 2.3%, while the conventional convolutional network model VGG19 and ResNet50 achieved 85.2% and 84.5% accuracy. Our experiments demonstrate that these transformer-based architectures outperform traditional convolutional neural networks in terms of oral cancer image classification, and underscore the potential of the ViT and the Swin Transformer in advancing the state of the art in oral cancer image analysis.
Efficient Path Planning Based on Dynamic Bridging Rapidly Exploring Random Tree

Authors:
raw_author_name: Baoquan Li
raw_affiliation_string: School of Control Science and Engineering, Tiangong University, Tianjin 300387, China
raw_author_name: Ruiyang Tong
raw_affiliation_string: School of Control Science and Engineering, Tiangong University, Tianjin 300387, China
raw_author_name: Chuanjing Tang
raw_affiliation_string: School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China

Abstract: In the domain of mobile robotic navigation, the real-time generation of low-cost, executable reference trajectories is crucial. This paper propounds an innovative path planning strategy, termed Dynamic Bridging Rapidly Exploring Random Tree (DBR-RRT), which endeavors to enable safe and expedited path navigation. Initially, a heuristic discrimination method is engaged in the path search phase, whereby the issue of sluggish search velocity is tackled by evaluating whether sampled points reside at “bridging locations” within a free space, and by assessing the spatial–geometric relationships between proximate obstacles and auxiliary points. Subsequently, by leveraging extended speed, additional sampling points are generated in the vicinity of existing points to augment the search’s efficacy. Ultimately, the path is optimized and pruned by synthesizing the local curvature of the sampling points and the proximity to obstacles, assigning varied priorities to nodes, thus ensuring that the path’s quality and smoothness is upheld.
Brain tumor segmentation using synthetic MR images - A comparison of GANs and diffusion models

Authors:
raw_author_name: Muhammad Usman Akbar
raw_affiliation_string: Department of Biomedical Engineering, Linköping University, Linköping, Sweden
raw_author_name: Måns Larsson
raw_affiliation_string: Eigenvision, Malmö, Sweden
raw_author_name: Anders Eklund
raw_affiliation_string: Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden

Abstract: Large annotated datasets are required for training deep learning models, but in medical imaging data sharing is often complicated due to ethics, anonymization and data protection legislation. Generative AI models, such as generative adversarial networks (GANs) and diffusion models, can today produce very realistic synthetic images, and can potentially facilitate data sharing. However, in order to share synthetic medical images it must first be demonstrated that they can be used for training different networks with acceptable performance. Here, we therefore comprehensively evaluate four GANs (progressive GAN, StyleGAN 1-3) and a diffusion model for the task of brain tumor segmentation (using two segmentation networks, U-Net and a Swin transformer). Our results show that segmentation networks trained on synthetic images reach Dice scores that are 80%-90% of Dice scores when training with real images, but that memorization of the training images can be a problem for diffusion models if the original dataset is too small. Our conclusion is that sharing synthetic medical images is a viable option to sharing real images, but that further work is required. The trained generative models and the generated synthetic images are shared on AIDA data hub.