Reviewerly/exHarmony huggingface dataset:
Focal plane coincidence method for a multi-view telecentric 3D imaging system

Multi-view microscopic fringe projection systems, which use high-resolution telecentric lenses and the Scheimpflug condition, face challenges in coinciding focal planes accurately, resulting in inconsistent measurements between views. In this Letter, we developed a sharpness evaluation function based on the total power of the line-spread function, which was subsequently used to generate a full-field sharpness distribution map. Then we employed the correlation between the sharpness map and orientation of the focal plane to precisely coincide the focal planes. Experimental results validate the proposed method and demonstrate its improved consistency in 3D reconstruction.
Generative adversarial networks with attentional multimodal for human face synthesis

&lt;p&gt;&lt;span&gt;Face synthesis and editing has increased cumulative consideration by the improvement of generative adversarial networks (GANs). The proposed attentional GAN-deep attentional multimodal similarity modal (AttnGAN-DAMSM) model focus on generating high-resolution images by removing discriminator components and generating realistic images from textual description. The attention model creates the attention map on the image and automatically retrieves the features to produce various sub-areas of the image. The DAMSM delivers fine-grained image-text identical loss to generative networks. This study, first describe text phrases and the model will generate a photorealistic high-resolution image composed of features with high accuracy. Next, model will fine-tune the selected features of face images and it will be left to the control of the user. The result shows that the proposed AttnGAN-DAMSM model delivers the performance metrics like structural similarity index measure (SSIM), feature similarity index measure (FSIM) and frechet inception distance (FID) using CelebA and CUHK face sketch (CUFS) dataset. For CelebFaces attribute (CelebA) dataset, the SSIM achieves 78.82% and for CUFS dataset, the SSIM achieves 81.45% which ensures accurate face synthesis and editing compared with existing methods such as GAN, SuperstarGAN and identity-sensitive GAN (IsGAN) models.&lt;/span&gt;&lt;/p&gt;
Robust Federated Learning for Heterogeneous Model and Data

Data privacy and security is an essential challenge in medical clinical settings, where individual hospital has its own sensitive patients data. Due to recent advances in decentralized machine learning in Federated Learning (FL), each hospital has its own private data and learning models to collaborate with other trusted participating hospitals. Heterogeneous data and models among different hospitals raise major challenges in robust FL, such as gradient leakage, where participants can exploit model weights to infer data. Here, we proposed a robust FL method to efficiently tackle data and model heterogeneity, where we train our model using knowledge distillation and a novel weighted client confidence score on hematological cytomorphology data in clinical settings. In the knowledge distillation, each participant learns from other participants by a weighted confidence score so that knowledge from clean models is distributed other than the noisy clients possessing noisy data. Moreover, we use symmetric loss to reduce the negative impact of data heterogeneity and label diversity by reducing overfitting the model to noisy labels. In comparison to the current approaches, our proposed method performs the best, and this is the first demonstration of addressing both data and model heterogeneity in end-to-end FL that lays the foundation for robust FL in laboratories and clinical applications.
Strategies for secondary use of real-world clinical and administrative data for outcome ascertainment in pragmatic clinical trials

Pragmatic trials are gaining popularity as a cost-effective way to examine treatment effectiveness and generate timely comparative evidence. Incorporating supplementary real-world data is recommended for robust outcome monitoring. However, detailed operational guidelines are needed to inform effective use and integration of heterogeneous databases. Lessons learned from the Veterans Affairs (VA) Diuretic Comparison Project (DCP) are reviewed, providing adaptable recommendations to capture clinical outcomes from real-world data. Non-cancer deaths and major cardiovascular (CV) outcomes were determined using VA, Medicare, and National Death Index (NDI) data. Multiple ascertainment strategies were applied, including claims-based algorithms, natural language processing, and systematic chart review. During a mean follow-up of 2.4 (SD = 1.4) years, 907 CV events were identified within the VA healthcare system. Slight delays (∼1 year) were expected in obtaining Medicare data. An additional 298 patients were found having a CV event outside of the VA in 2016 - 2021, increasing the CV event rate from 3.5 % to 5.7 % (770 of 13,523 randomized). NDI data required ∼2 years waiting period. Such inclusion did not increase the number of deaths identified (all 894 deaths were captured by VA data) but enhanced the accuracy in determining cause of death. Our experience supports the recommendation of integrating multiple data sources to improve clinical outcome ascertainment. While this approach is promising, hierarchical data aggregation is required when facing different acquisition timelines, information availability/completeness, coding practice, and system configurations. It may not be feasible to implement comparable applications and solutions to studies conducted under different constraints and practice. The recommendations provide guidance and possible action plans for researchers who are interested in applying cross-source data to ascertain all study outcomes.
Design methodology for catadioptric zoom panoramic optical systems based on image plane bending matched correction

Zoom panoramic optical systems incorporate a panoramic peripheral vision zoom imaging function, enabling swift wide-area coarse and localized precise detection. These systems find applicability in examining pipes and other internal structures. This study centers on the catadioptric zoom panoramic optical system and its associated optical design method. A mathematical model of the front mirror was formulated based on the imaging relationship. Subsequently, the bending of the image plane of the front mirror was computed and simplified through curve fitting. The object surface bending of the zoom subsystem was established correspondingly, leveraging the solution for the mirror bending curve matched correction, culminating in deriving the initial structure of the zoom. Integrating the front mirror and zoom subsystems facilitated the comprehensive design realization of the catadioptric zoom panoramic optical system. A catadioptric zoom panoramic optical system was designed to validate the proposed design method. This study introduces a novel, to the best of our knowledge, conceptual approach to crafting catadioptric zoom panoramic optical systems.
Attention gate and dilation U-shaped network (GDUNet): an efficient breast ultrasound image segmentation network with multiscale information extraction

In recent years, computer-aided diagnosis (CAD) systems have played an important role in breast cancer screening and diagnosis. The image segmentation task is the key step in a CAD system for the rapid identification of lesions. Therefore, an efficient breast image segmentation network is necessary for improving the diagnostic accuracy in breast cancer screening. However, due to the characteristics of blurred boundaries, low contrast, and speckle noise in breast ultrasound images, breast lesion segmentation is challenging. In addition, many of the proposed breast tumor segmentation networks are too complex to be applied in practice.We developed the attention gate and dilation U-shaped network (GDUNet), a lightweight, breast lesion segmentation model. This model improves the inverted bottleneck, integrating it with tokenized multilayer perceptron (MLP) to construct the encoder. Additionally, we introduce the lightweight attention gate (AG) within the skip connection, which effectively filters noise in low-level semantic information across spatial and channel dimensions, thus attenuating irrelevant features. To further improve performance, we innovated the AG dilation (AGDT) block and embedded it between the encoder and decoder in order to capture critical multiscale contextual information.We conducted experiments on two breast cancer datasets. The experiment's results show that compared to UNet, GDUNet could reduce the number of parameters by 10 times and the computational complexity by 58 times while providing a double of the inference speed. Moreover, the GDUNet achieved a better segmentation performance than did the state-of-the-art medical image segmentation architecture.Our proposed GDUNet method can achieve advanced segmentation performance on different breast ultrasound image datasets with high efficiency.
Phase measuring deflectometry for convex aspheric surface measurement

This paper introduces what we believe to be a novel approach to accurately measure the shape of convex aspherical surfaces with large slope gradients. This approach employs a pre-distortion system to enhance the visibility of the structured light pattern that is captured by camera. The data processing involves iterative methods to obtain surface shape data. The initial step in the experimental calibration involves establishing a reference plane, which serves as the starting point for the iterative process. The calculation for slope is subsequently utilized to determine the initial slope of the surface under test, and the height of the tested element is derived by integrating these slopes. Through multiple iterations and continuous updating of the surface height, the precise and authentic true surface height is ultimately achieved. The method’s accuracy is assessed through the measurement of a highly steep convex aspherical area with a diameter of 5.2 mm and a radius of curvature of approximately 7.7 mm. The proposed method demonstrates root mean square accuracy that can reach half a wavelength when compared to the measurement results obtained from high-precision profilers.
Author identification of literary works based on text analysis and deep learning

<h2>Abstract</h2> With the development of science, speech, picture, and other analysis, problems have been gradually better solved, but the study of Chinese text has been a complex problem to overcome. Chinese text analysis requires not only statistics but also semantic comprehension analysis. Different text types need other language style feature modeling to obtain good recognition results. In this study, we use the deep learning method to construct an automatic text feature extraction model and classify it with the author as a classification label. This study presents a literature author recognition model based on deep learning, which is mainly divided into three phases: text preprocessing, feature extraction, and classification. Each part consists of several small modules or steps. First, we input the corpus to Word2Vec to generate the new word vector. Then, the improved text feature extractor based on CNN and Attention extracts the text features and uses them as the input of the CNN convolution layer. After convolution, the text is combined with bits to get Window Feature Sequence. It is the text feature vector. Next, based on LSTM and Softmax classification output, Window Feature Sequence is used as the input of LSTM to obtain two one-dimensional vectors spliced by concatenate layer. Finally, the result is classified through the fully connected layer, Batch Normalization layer, and Softmax. The performance of the proposed model in recognizing authors of Chinese literature was evaluated using two datasets. In the research process, the data we collected included works of different forms, such as prose and fiction. The research results show that the proposed model can effectively identify author identity. The classification accuracy of our proposed algorithm is significantly better than that of the benchmark model.
Optimization of track and field training methods based on SSA-BP and its effect on athletes' explosive power

Digitalization and informationization are important trends in the development of the sports industry. The study first introduced the sparrow search algorithm to improve the generalization ability of traditional neural networks, optimizing the assignment of initial weights and thresholds of neural networks; Secondly, the chicken swarm algorithm is introduced to optimize the training combination, period, and intensity of athletes based on the evaluation results, improving the subjective limitations of traditional training methods. The results of model performance analysis show that the sparrow search algorithm is better than other intelligent optimization algorithms in finding fitted parameters, and the solution error is less than 0.50. The evaluation model performs well in terms of accuracy, recall, average relative error, and R2 evaluation indicators. The model has high repeatability and is suitable for evaluating track and field training methods. The accuracy and computational speed of the chicken swarm algorithm are relatively good; Compared with other optimization models, the chicken swarm algorithm has better optimization ability and accuracy. Friedman test found significant differences in the chicken swarm algorithm, and the optimized training method has a significant positive impact on the explosive power of athletes, and the training period and intensity arrangement are reasonable and more helpful to the improvement of athletic performance. This study improves the scientific rationality of the development of track and field training methods, which is conducive to optimizing the training effect of track and field sports, and facilitates the risk management and personalized training of athletes. At the same time, it greatly promotes the integration and development of sports and computer disciplines.
Two-particle Hadamard walk on dynamically percolated line and circle

Abstract Asymptotic dynamics of a Hadamard walk of two non-interacting quantum particles on a dynamically percolated finite line or a circle is investigated. We construct a basis of the attractor space of the corresponding random-unitary dynamics and prove the completeness of our solution. In comparison to the one-particle case, the structure of the attractor space is much more complex, resulting in intriguing asymptotic dynamics. General results are illustrated on two examples. First, for circles of length not divisible by 4 the boundary conditions reduces the number of attractors considerably, allowing for fully analytic solution. Second, we investigate line of length 4 and determine the asymptotic cycle of reduced coin states and position distributions, focusing on the correlations between the two particles. Our results show that a random unitary evolution, which is a combination of quantum dynamics and a classical stochasticity, leads to correlations between initially uncorrelated particles. This is not possible for purely unitary evolution of non-interacting quantum particles. The shared dynamically percolated graph can thus be considered as a weak form of interaction.
PPML-Omics: A privacy-preserving federated machine learning method protects patients’ privacy in omic data

Modern machine learning models toward various tasks with omic data analysis give rise to threats of privacy leakage of patients involved in those datasets. Here, we proposed a secure and privacy-preserving machine learning method (PPML-Omics) by designing a decentralized differential private federated learning algorithm. We applied PPML-Omics to analyze data from three sequencing technologies and addressed the privacy concern in three major tasks of omic data under three representative deep learning models. We examined privacy breaches in depth through privacy attack experiments and demonstrated that PPML-Omics could protect patients' privacy. In each of these applications, PPML-Omics was able to outperform methods of comparison under the same level of privacy guarantee, demonstrating the versatility of the method in simultaneously balancing the privacy-preserving capability and utility in omic data analysis. Furthermore, we gave the theoretical proof of the privacy-preserving capability of PPML-Omics, suggesting the first mathematically guaranteed method with robust and generalizable empirical performance in protecting patients' privacy in omic data.
Digital teaching: Developing a structured digital teaching competency framework for medical teachers

To explore the core competencies needed on part of the medical and dental teachers to carry out effective digital teaching for their students.It was a qualitative study which employed phenomenological approach. The data were collected from 12 teachers who were purposively selected for semi-structured interviews. They were all expert digital teachers. The data were transcribed verbatim, coded and analyzed thematically. Textural and structural description of the themes helped to develop a new competency framework.The data yielded 47 selective codes with 15 sub-themes and five themes. The emergent themes included general digital competencies, specific digital teaching competencies, mastery of the subject matter, mastery of pedagogical strategies and proficiency in using innovative digital technologies for teaching. The themes 1, 2 and 5 relate to digital competencies only whereas the themes 3 and 4 are generic competencies which apply to both digital and non-digital teaching. These generic competencies form the basis of all kinds of teaching, hence equally important for digital teaching.Medical teachers should possess diverse digital competencies. The competency framework that emerged in the current research encompasses the essential attributes that should be included in any future training program aiming at the digital capacity building of the teachers. This will keep them primed for effective digital teaching. Given its crucial importance, the digital teaching competency should be considered as a cross-cutting competency that applies to almost all of the famous eight roles of medical teacher.
Dilemma and coping strategies of news communication based on artificial intelligence and big data

News dissemination is an important way for people to obtain information. With the development of new technologies, traditional news dissemination models have been impacted. It has problems with information filtering and bias, and has certain limitations in news quality, dissemination efficiency, etc., which makes it difficult to effectively meet people's information needs. In order to improve the quality and efficiency of news dissemination, promote the positive impact of news dissemination on society, this article combined artificial intelligence and big data technology to conduct in-depth research on the difficulties and coping strategies of news dissemination. This article first analyzed the characteristics and functions and influencing factors of news dissemination, then provided an overview of the difficulties and coping strategies in news dissemination. Finally, using association rule algorithms, personalized recommendations for news dissemination are achieved. To verify the effectiveness of artificial intelligence and big data in coping with the dilemma of news dissemination, this article conducted experimental analysis from the perspectives of news content quality, dissemination efficiency, objectivity, and dissemination cost. The experimental results show that under the application of news dissemination strategies based on artificial intelligence and big data, the quality of news content and dissemination efficiency have been improved by 4.76 % and 3.63 %, respectively. The conclusion indicates that artificial intelligence and big data can help improve the quality and dissemination efficiency of news content, and meet the diverse needs of the public for information.
Performance of machine translators in translating French medical research abstracts to English: A comparative study of DeepL, Google Translate, and CUBBITT

Background Non-English speaking researchers may find it difficult to write articles in English and may be tempted to use machine translators (MTs) to facilitate their task. We compared the performance of DeepL , Google Translate , and CUBBITT for the translation of abstracts from French to English. Methods We selected ten abstracts published in 2021 in two high-impact bilingual medical journals ( CMAJ and Canadian Family Physician ) and used nine metrics of Recall-Oriented Understudy for Gisting Evaluation ( ROUGE-1 recall/precision/F1-score , ROUGE-2 recall/precision/F1-score , and ROUGE-L recall/precision/F1-score ) to evaluate the accuracy of the translation (scores ranging from zero to one [= maximum]). We also used the fluency score assigned by ten raters to evaluate the stylistic quality of the translation (ranging from ten [= incomprehensible] to fifty [= flawless English]). We used Kruskal-Wallis tests to compare the medians between the three MTs. For the human evaluation, we also examined the original English text. Results Differences in medians were not statistically significant for the nine metrics of ROUGE (medians: min-max = 0.5246–0.7392 for DeepL , 0.4634–0.7200 for Google Translate , 0.4815–0.7316 for CUBBITT , all p-values &gt; 0.10). For the human evaluation, CUBBITT tended to score higher than DeepL , Google Translate , and the original English text (median = 43 for CUBBITT , vs. 39, 38, and 40, respectively, p-value = 0.003). Conclusion The three MTs performed similarly when tested with ROUGE , but CUBBITT was slightly better than the other two using human evaluation. Although we only included abstracts and did not evaluate the time required for post-editing, we believe that French-speaking researchers could use DeepL , Google Translate , or CUBBITT when writing articles in English.
Visualization Method of Pathophysiology Pathway in Nursing Diagnosis Learning

Setiawan, Henri MSiMed, RN; Hidayat, Nur MM; Richard, Selvia David MN, RN Author Information