Reviewerly/exHarmony huggingface dataset:
Research on Personalized Book Recommendation Based on Improved Similarity Calculation and Data Filling Collaborative Filtering Algorithm

(Purpose/Significance). This paper aims at the problems of inaccurate recommendation effect caused by data sparseness and cold start in the traditional collaborative filtering-based book personalized recommendation algorithm. So this paper proposes a collaborative filtering recommendation algorithm which improves the similarity solution method and the filling method of missing data. (Method/Process). By considering the influence of the user’s common rating book collection on the similarity calculation, the average rating value of all books is used as the threshold, and the user’s common rating weight is introduced into the user’s similarity calculation. As for data filling, according to the user’s average rating, the basic attributes such as the age and gender of users are coded, and then Euclidean distance is initially calculated, making hierarchical clustering on users. What’s more, Shope-one algorithm is used to calculate the filling value of the former m similar users，and add the weight value of the degree simultaneously to get the final filling value, so as to improve the data filling method. (Result/Conclusion). Experiments were carried out with the data set of Book-Crossing Data set through Python. The experimental results show that the improved collaborative filtering algorithm has a significantly improvement in the accuracy and quality of book recommendation.
Features of distance learning at the stage of basic general education

This article examines the features of the use of information technology at the stage of basic general education in a distance format. The aim of the study is to substantiate the advantages of introducing distance learning at the stage of basic general education. Particular attention is paid to the consideration of age-related physiological characteristics of pupils of this age period. The interests, motives, and goals of pupils in this age range are presented. The results of the study can be used both in practical pedagogical activities and in further research on the feasibility of using distance learning forms at the stage of basic general education.
Weakly Supervised MR-TRUS Image Synthesis for Brachytherapy of Prostate Cancer

Prostate magnetic resonance imaging (MRI) offers accurate details of structures and tumors for prostate cancer brachytherapy. However, it is unsuitable for routine treatment since MR images differ significantly from trans-rectal ultrasound (TRUS) images conventionally used for radioactive seed implants in brachytherapy. TRUS imaging is fast, convenient, and widely available in the operation room but is known for its low soft-tissue contrast and tumor visualization capability in the prostate area. Conventionally, practitioners usually rely on prostate segmentation to fuse the two imaging modalities with non-rigid registration. However, prostate delineation is often not available on diagnostic MR images. Besides, the high non-linear intensity relationship between two imaging modalities poses a challenge to non-rigid registration. Hence, we propose a method to generate a TRUS-styled image from a prostate MR image to replace the role of the TRUS image in radiation therapy dose pre-planning. We propose a structural constraint to handle non-linear projections of anatomical structures between MR and TRUS images. We further include an adversarial mechanism to enforce the model to preserve anatomical features in an MR image (such as prostate boundary and dominant intraprostatic lesion (DIL)) while synthesizing the TRUS-styled counterpart image. The proposed method is compared with other state-of-art methods with real TRUS images as the reference. The results demonstrate that the TRUS images synthesized by our method can be used for brachytherapy treatment planning for prostate cancer.
RL Based Unsupervised Video Summarization Framework for Ultrasound Imaging

The need for summarizing long medical scan videos for automatic triage in Emergency Departments and transmission of the summarized videos for telemedicine has gained significance during the COVID-19 pandemic. However, supervised learning schemes for summarizing videos are infeasible as manual labeling of scans for large datasets is impractical by frontline clinicians. This work presents a methodology to summarize ultrasound videos using completely unsupervised learning schemes and is validated on Lung Ultrasound videos. A Convolutional Autoencoder and a Transformer decoder is trained in an unsupervised reinforcement learning setup i.e., without supervised labels in the whole workflow. Novel precision and recall computation for ultrasound videos is also presented employing which high Precision and $$F_{1}$$ scores of 64.36% and 35.87% with an average video compression rate of 78% is obtained when validated against clinically annotated cases. Even though demonstrated using lung ultrasound videos, our approach can be readily extended to other imaging modalities.
Unsupervised Deep Non-rigid Alignment by Low-Rank Loss and Multi-input Attention

We propose a deep low-rank alignment network that can simultaneously perform non-rigid alignment and noise decomposition for multiple images despite severe noise and sparse corruptions. To address this challenging task, we introduce a low-rank loss in deep learning under the assumption that a set of well-aligned, well-denoised images should be linearly correlated, and thus, that a matrix consisting of the images should be low-rank. This allows us to remove the noise and corruption from input images in a self-supervised learning manner (i.e., without requiring supervised data). In addition, we introduce multi-input attention modules into Siamese U-nets in order to aggregate the corruption information from the set of images. To the best of our knowledge, this is the first attempt to introduce a low-rank loss for deep learning-based non-rigid alignment. Experiments using both synthetic data and real medical image data demonstrate the effectiveness of the proposed method. The code will be publicly available in https://github.com/asanomitakanori/Unsupervised-Deep-Non-Rigid-Alignment-by-Low-Rank-Loss-and-Multi-Input-Attention .
What Can We Learn About a Generated Image Corrupting Its Latent Representation?

Generative adversarial networks (GANs) offer an effective solution to the image-to-image translation problem, thereby allowing for new possibilities in medical imaging. They can translate images from one imaging modality to another at a low cost. For unpaired datasets, they rely mostly on cycle loss. Despite its effectiveness in learning the underlying data distribution, it can lead to a discrepancy between input and output data. The purpose of this work is to investigate the hypothesis that we can predict image quality based on its latent representation in the GANs bottleneck. We achieve this by corrupting the latent representation with noise and generating multiple outputs. The degree of differences between them is interpreted as the strength of the representation: the more robust the latent representation, the fewer changes in the output image the corruption causes. Our results demonstrate that our proposed method has the ability to i) predict uncertain parts of synthesized images, and ii) identify samples that may not be reliable for downstream tasks, e.g., liver segmentation task.
Identifying Phenotypic Concepts Discriminating Molecular Breast Cancer Sub-Types

Molecular breast cancer sub-types derived from core-biopsy are central for individual outcome prediction and treatment decisions. Determining sub-types by non-invasive imaging procedures would benefit early assessment. Furthermore, identifying phenotypic traits of sub-types may inform our understanding of disease processes as we become able to monitor them longitudinally. We propose a model to learn phenotypic appearance concepts of four molecular sub-types of breast cancer. A deep neural network classification model predicts sub-types from multi-modal, multi-parametric imaging data. Intermediate representations of the visual information are clustered, and clusters are scored based on testing with concept activation vectors to assess their contribution to correctly discriminating sub-types. The proposed model can predict sub-types with competitive accuracy from simultaneous $${}^{18}$$ F-FDG PET/MRI, and identifies visual traits in the form of shared and discriminating phenotypic concepts associated with the sub-types.
Characterizing Continual Learning Scenarios for Tumor Classification in Histopathology Images

Recent years have seen great advancements in the development of deep-learning models for histopathology image analysis in digital pathology (DP) applications, evidenced by the increasingly common deployment of these models in both research and clinical settings. Although such models have shown unprecedented performance in solving fundamental computational tasks in DP applications, they suffer from catastrophic forgetting when adapted to unseen data with transfer learning. With an increasing need for deep-learning models to handle ever-changing data distributions, including evolving patient population and new diagnosis assays, continual learning (CL) models that alleviate model forgetting need to be introduced in DP-based analysis. However, to our best knowledge, there’s no systematic study of such models for DP-specific applications. Here, we propose CL scenarios in DP settings, where histopathology image data from different sources/distributions arrive sequentially, the knowledge of which is integrated into a single model without training all the data from scratch. We then established an augmented dataset for colorectal cancer H &E classification to simulate shifts of image appearance and evaluated CL model performance in the proposed CL scenarios. We leveraged a breast tumor H &E dataset along with the colorectal cancer to evaluate CL from different tumor types. In addition, we evaluated CL methods in an online few-shot setting under the constraints of annotation and computational resources. We revealed promising results of CL in DP applications, potentially paving the way for application of these methods in clinical practice.
Self-Knowledge Distillation for First Trimester Ultrasound Saliency Prediction

Self-knowledge distillation (SKD) is a recent and promising machine learning approach where a shallow student network is trained to distill its own knowledge. By contrast, in traditional knowledge distillation a student model distills its knowledge from a large teacher network model, which involves vast computational complexity and a large storage size. Consequently, SKD is a useful approach to model medical imaging problems with scarce data. We propose an original SKD framework to predict where a sonographer should look next using a multi-modal ultrasound and gaze dataset. We design a novel Wide Feature Distillation module, which is applied to intermediate feature maps in the form of transformations. The module applies a more refined feature map filtering which is important when predicting gaze for the fetal anatomy variable in size. Our architecture design includes ReSL loss that enables a student network to learn useful information whilst discarding the rest. The proposed network is validated on a large multi-modal ultrasound dataset, which is acquired during routine first trimester fetal ultrasound scanning. Experimental results show the novel SKD approach outperforms alternative state-of-the-art architectures on all saliency metrics.
The influence of students’ ability on the continuous intention of blended learning

Blended learning has grown in importance in colleges since the COVID-19 pandemic because it is a creative and effective extension of traditional education. The aim of this paper is to characterize how students' ability affects their desire for continuous learning in a blended learning environment. In order to do this, a proposed and tested integral model of students' flow in blended learning environments is constructed. This is achieved by a survey resulting in a sample of 344 valid questionnaires. The theoretical model was tested and validated using the standard methodological procedure based on exploratory and confirmatory analyses. According to the results, the students' web skills and social skills significantly impact the flow experience in a positive way, and metacognitive regulating ability has a significant negative impact on flow experience, which in return significantly impact the continuous learning intention. In conclusion, some suggestions for teachers and the online teaching systems are put forward to improve students' continuous learning intention.
All that glitters is not gold: Relational events models with spurious events

Abstract As relational event models are an increasingly popular model for studying relational structures, the reliability of large-scale event data collection becomes more and more important. Automated or human-coded events often suffer from non-negligible false-discovery rates in event identification. And most sensor data are primarily based on actors’ spatial proximity for predefined time windows; hence, the observed events could relate either to a social relationship or random co-location. Both examples imply spurious events that may bias estimates and inference. We propose the Relational Event Model for Spurious Events (REMSE), an extension to existing approaches for interaction data. The model provides a flexible solution for modeling data while controlling for spurious events. Estimation of our model is carried out in an empirical Bayesian approach via data augmentation. Based on a simulation study, we investigate the properties of the estimation procedure. To demonstrate its usefulness in two distinct applications, we employ this model to combat events from the Syrian civil war and student co-location data. Results from the simulation and the applications identify the REMSE as a suitable approach to modeling relational event data in the presence of spurious events.
Identification of Cospectral Simple Jointed Kinematic Chains using Characteristic Polynomial of Degree Matrix and Structural Matrix

Abstract Different methods to address the isomorphism in kinematic chains existed earlier and is well reported. In all the methods, link-adjacency matrix (LAM) represents the structural information of a given kinematic chain. Having gathered a large collection of co-spectral chains belonging to a large number of chain categories possessing (containing up to 20 links and up to 13 degrees of freedom), the present work aims at addressing the isomorphism by developing and implementing a methodology for isomorphism based on characteristic coefficients of the degree matrix and structural matrix representations of a specified Simple Jointed Kinematic Chain (SJKC) being computed and comparing. While the above two tests proved to be successful in most cases, two instances of cospectral cases were discovered wherein the above polynomial-based tests failed to detect dissimilarity in the chain structures. These cases are reported in the paper.
Position-Aware Anti-Aliasing Filters for 3D Medical Image Analysis

Maximum pooling, average pooling, and strided convolution are three widely adopted down-sampling approaches in deep learning based 3D medical image analysis. However, these methods have their own pros and cons. Maximum pooling and strided convolution are advantageous in capturing the discriminative features but often lead to the aliasing problem. In comparison, average pooling anti-aliases the representations but produces less discriminative representations. To address such shortcoming, anti-aliased maximum pooling (MaxBlurPool) uses low-pass filters to mitigate the aliasing effect. However, these filters are designed to be fixed, making it difficult to adapt to various spatial positions. In this paper, we propose position-aware anti-aliasing filters (PASS) to learn spatially adaptive low-pass filters. Compared to maximum pooling, PASS integrates a one-layer local attention module, whose computational cost is minimal. Thus, PASS can be incorporated into existing network architecture with minor efforts. In comparison to previous anti-aliased counterparts, PASS brings consistent and clear performance gains on brain tumor segmentation, pulmonary nodule detection, and cerebral hemorrhage detection. Besides, PASS also greatly improves the model robustness under adversarial attack.
Estimating Structurally Similar Graphical Models

This paper considers the problem of estimating the structure of structurally similar graphical models in high dimensions. This problem is pertinent in multi-modal or multi-domain datasets that consist of multiple information domains, each modeled by one probabilistic graphical model (PGM), e.g., in brain network modeling using different neuroimaging modalities. Induced by an underlying shared causal source, the domains, and subsequently their associated PGMs, can have structural similarities. This paper focuses on Gaussian and Ising models and characterizes the information-theoretic sample complexity of estimating the structures of a pair of PGMs in the degree-bounded and edge-bounded subclasses. The PGMs are assumed to have <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> nodes with distinct and unknown structures. Their similarity is accounted for by assuming that a pre-specified set of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> nodes form identical subgraphs in both PGMs. Necessary and sufficient conditions on the sample complexity for a bounded probability of error are characterized. The necessary conditions are information-theoretic (algorithm-independent), delineating the statistical difficulty of the problem. The sufficient conditions are based on deploying maximum likelihood decoders. While the specifics of the results vary across different subclasses and parameter regimes, one key observation is that in specific subclasses and regimes, the sample complexity varies with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> according to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Theta (\log (p-q))$ </tex-math></inline-formula> . For Ising models, a low complexity, online structure estimation (learning) algorithm based on multiplicative weights is also proposed. Numerical evaluations are also included to illustrate the interplay among different parameters on the sample complexity when the structurally similar graphs are recovered by a maximum likelihood-based graph decoder and the proposed online estimation algorithm.
Wedding Crashers: an Epistemological Model for When Refusal to Provide Service to Protected Groups Is Not Wrongful Discrimination

Abstract The question of the relation between wrongful discrimination and the freedom of conscience and religion has been the subject of many debates over the past decade and has occupied both courts and the public. The most well-known legal case in that regard is likely Masterpiece Cakeshop , in which a Colorado bakery owner refused to bake a wedding cake for a homosexual couple and was sued for violating the State’s Anti-Discrimination law. Recently, the Supreme Court of the U.S has agreed to hear yet another Colorado case, 303 Creative llc v. Elenis , in which a website designer wanted to post a message saying she will not design websites for same-sex weddings. The purpose of our article is to point to a significant distinction between a refusal to serve clients on the basis of their race, gender, sexual orientation, etc., and a refusal to serve them because such service requires the providers to engage in activities or projects to which they deeply oppose. We think the latter case, sometimes, might not at all be discrimination. Importantly, we distinguish between a deep objection to the content of the service or product requested and a rejection of the client because of her characteristics. How can a supplier prove that his or her refusal to serve a client belonging to a “protected class” is based on the content of the product or service requested and not on the client’s characteristics? We formulate a two-prong test that courts in the US and UK have implicitly adopted. We ask, first, whether the supplier would refuse the same service to a client not belonging to the protected class, and second, whether the supplier would serve the same client (belonging to a protected class) with other products and services. If the answer to both questions is positive, then the supplier’s refusal is not wrongful discrimination because it shows an objection to the product or service requested and not a rejection of the client. In practice, this test is not always easy to apply. We therefore developed an epistemological model to substantiate the conditions that may help providers persuade the courts that their refusal to serve a client stems from the content of the request, not from the client’s identity.
A privacy‐preserving recommendation method with clustering and locality‐sensitive hashing

Abstract Nowadays, there is a significant increase in information, resulting in information overload. Recommendation systems have been widely adopted, and they can help users find information relevant to their interests. However, a malicious attacker can infer users' private information via recommendations. To solve problems of data sparseness, enormous high‐dimensional data, the cold start problem and privacy protection in an intelligent recommender system, this study proposes a privacy‐preserving collaborative filtering recommendation method with clustering and locality‐sensitive hashing. First, we cluster users according to their characteristic information to obtain sub‐rating matrices. We use the latent factor model to predict and fill in the missing ratings in those matrices. Second, we combine the sub‐rating matrices into a complete rating matrix, subsequently, we obtained the neighbors of the target user by analyzing the similarity of the users. We use a locality‐sensitive hashing algorithm to reduce the dimensionality of the user rating data and build an index that could quickly obtain the neighbors of the target user. Finally, we predict the target user's ratings and provide recommendations to the target user. Through experiments, our study shows that our method can deal with the problems of data sparseness and cold start problems well and the accuracy of the intelligent recommendation system has been improved. In addition, we use hash techniques to search for the neighbors, which effectively protects the privacy of the user.