Reviewerly/exHarmony huggingface dataset:
Uncertainty-aware Consistency Learning for Cold-Start Item Recommendation

Graph Neural Network (GNN)-based models have become the mainstream approach for recommender systems. Despite the effectiveness, they are still suffering from the cold-start problem, i.e., recommend for few-interaction items. Existing GNN-based recommendation models to address the cold-start problem mainly focus on utilizing auxiliary features of users and items, leaving the user-item interactions under-utilized. However, embeddings distributions of cold and warm items are still largely different, since cold items' embeddings are learned from lower-popularity interactions, while warm items' embeddings are from higher-popularity interactions. Thus, there is a seesaw phenomenon, where the recommendation performance for the cold and warm items cannot be improved simultaneously. To this end, we proposed a Uncertainty-aware Consistency learning framework for Cold-start item recommendation (shorten as UCC) solely based on user-item interactions. Under this framework, we train the teacher model (generator) and student model (recommender) with consistency learning, to ensure the cold items with additionally generated low-uncertainty interactions can have similar distribution with the warm items. Therefore, the proposed framework improves the recommendation of cold and warm items at the same time, without hurting any one of them. Extensive experiments on benchmark datasets demonstrate that our proposed method significantly outperforms state-of-the-art methods on both warm and cold items, with an average performance improvement of 27.6%.
Unleashing the Power of Python in Autodesk Maya

Coding empowers automation. Scripts can handle mundane and repetitive tasks in an efficient and precise manner. This course will offer will use an hands-on interactive format to walk attendees through representative scripting projects, selected to be useful for everyday workflows. It is intended to be an intermediate course. The goal is to cover provide enough information for attendees to build on later. Python scripting can automate many tasks in Maya, from running simple commands to developing plug-ins. Attendees will learn how to automate a simple task using the magic of scripting, through a hands-on projects. The course will placing objects in a scene through scripting MASH (motion graphic) networks. Attendees should walk away with a solid understanding of the power Python scripting and Maya commands provide, and the the ability to conceive their own advance projects for Maya. Attendees should have programming experience, preferably in Python, but a solid grasp of the foundational programming constructs should suffice. Attendees should have Autodesk Maya, Python, and Visual Studio Code pre-loaded on their devices if they intend to follow along.
Sketching Pipelines for Ephemeral Immersive Spaces

This hands-on class will allow artists to use open-source tools to create interactive and immersive experiences. These tools have been created and incubated at the Society for Arts and Technology (SAT), a unique non-profit organization in Canada whose mission is to democratize technologies to enable people to experience and author multisensory immersions. During the class we invite participants to use their favorite software on platforms they are already familiar with, to interface with our tools. The toolset will include transmission protocols, video mapping tools, sound spatialization software, and gestural control using pose detection. The class will be organized in two parts: a presentation of the tools and context involving the development and applications, and a hands-on session with an ephemeral immersive space. This event is designed for art researchers, artists, designers, content creators, and other creatives interested in creating immersive spaces using research-developed tools. Participants will learn how to employ open-source tools for different artistic tasks so that they will be able to deploy their own immersive spaces after the class.
Non-Linear Optimization for Enhanced Parameter Retrieval in MR Fingerprinting

We investigate non-linear optimization to produce quantitative parameter maps in MR Fingerprinting. Our Monte Carlo analysis shows non-linear optimization to be robust with respect to noise. We also find non-linear optimization to yield consistent results when initialized by dictionary matching using either sparse or densely populated dictionaries. Our research outcomes suggest non-linear optimization is an effective enhancement to the MR Fingerprinting pipeline, allowing for smaller dictionary sizes and more accurate parameter retrieval. Future work will include enhancements to, and analysis of, the computational efficiency of non-linear optimization.
Adaptive k-Space Sampling in Magnetic Resonance Imaging Using Reinforcement Learning

One approach to accelerate MRI scans is to acquire fewer k-space samples. Commonly, the sampling pattern is selected before the scan, ignoring the sequential nature of the sampling process. A field of machine learning addressing sequential decision processes is reinforcement learning (RL). We present an approach for creating adaptive two-dimensional (2D) k-space trajectories using RL and the so-called action space shaping. The trained RL algorithm adapts to a variety of basic 2D shapes outperforming simple baseline trajectories. By shaping the action space of the RL agent we achieve better generalization and interpretability of the agent.
Protocol adaptive Stacked transfer learning (STL) U-NET with small dataset training for soft tissue segmentation in dynamic speech MRI

We develop a protocol adaptive Stacked transfer learning (STL) U-NET for soft tissue segmentation in dynamic speech MRI. Our approach leverages knowledge from large open-source datasets, and only needs to be trained on small number of protocol specific images (of the order of 20 images). We demonstrate the utility of STL U-NET in efficiently segmenting soft-tissue articulators from three different protocols with different field strengths, vendors, acquisition, reconstruction. Using the DICE similarity metric, we demonstrate segmentation accuracies with our approach to be at the level of manual segmentation.
Intra- and intersubject synthesis of cardiac MR images using a VAE and GAN

We propose a method for synthesizing cardiac MR images with plausible heart shape and realistic appearance. It breaks down the synthesis into labels deformation and label-to-image translation. The former is achieved via latent space interpolation in a VAE model, while the latter is accomplished via a conditional GAN model. We synthesize 32 short-axis slices within each subject (intrasubject), as well as eight intermediary generated subjects between two dissimilar real subjects (intersubject) that have different anatomies. Such method could provide a solution to enrich a database and to pave the way for the development of generalizable DL based algorithms.
Split-and-Merge Segmentation in Magnetic Resonance Imaging Based on Graph Wedgelets

Graph wedgelets are a novel tool for the fast decomposition of images in geometrically meaningful, wedge-shaped subregions. In this work, we study the usage of graph wedgelets as a promising splitting method in a split-and-merge segmentation scheme for Magnetic Resonance Imaging. We combine adaptive wedgelet splits of MRI images with a simple and classical merging strategy for subregions and obtain in this way an efficient and robust segmentation of diagnostic-relevant subdomains in MRI data.
Accelerated volumetric vocal tract MRI using model based deep learning

3D MRI is a powerful tool to safely visualize the various vocal-tract configurations during voice production [1-3]. Current accelerated 3D vocal-tract MRI schemes based on spatial total variation transform (SPTV) regularization are susceptible to non-trivial artifacts (e.g., blurring at air-tissue boundaries, patchy representation of small structures such as the epiglottis, glottis). In this work, we apply a model based deep learning reconstruction scheme that can significantly accelerate 3D vocal-tract imaging. We demonstrate it to produce images with high-spatial fidelity, natural-looking like contrast, and significantly robust to artifacts seen with current SPTV based schemes.
The More the Merrier? - On the Number of Trainable Parameters in Iterative Neural Networks for Image Reconstruction

Iterative neural networks (INNs) currently define the state-of-the-art for image reconstruction methods. With these methods, the obtained regularizers are not only optimally adapted to the employed physical model but also tailored to the reconstruction method the network implicitly defines. However, comparing the performance of different INNs-based methods is often challenging because of the black-box character of neural networks. In this work we construct an example which highlights the importance of keeping the number of trainable parameters approximately fixed when comparing INNs-based methods. If this aspect is not taken into consideration, wrong conclusions could be drawn.
Brain Tumor Segmentation Using 3D CMM-Net with Limited and Accessible MR Images

In this work, we investigate the possibility of using a limited number of medical MR images for brain tumor segmentation. This can assist to perform the segmentation task using limited and accessible data in clinical practice. We observed that the FLAIR and T1CE are the most suitable images that maintain comparable segmentation performances similar to the case of using more data (i.e., T1, T2, FLAIR, and T1CE). To further improve the segmentation results, we augment the selected image pair and generate an additional fusion map using the Singular Value Decomposition (SVD).
Model Pruning for Generalisability Across MRI Imaging Sites

We propose an algorithm to simultaneously prune and train CNNs, leading to networks which have increased generalisability across imaging sites. Through segmentation of data from the ABIDE dataset, we show that through reducing the number of parameters in the network throughout training, we are able to reduce model overfitting, creating a model which is more robust to expect image variations across scanners. We also introduce a novel Targeted Dropout algorithm, which aids the process of model pruning. We demonstrate the approach on a UNet architecture, the basis of nearly all segmentation approaches across medical imaging.
Enhanced T2-weighted images using Luminal Water Imaging and U-Net based segmentation for prostate cancer diagnosis.

Clinical prostate carcinoma (PCa) detection with MRI has focused on qualitative assessment. However, novel Luminal Water Imaging (LWI) provides quantitative information with promising results for detecting PCa. To overcome shortcomings of clinical prostate MRI protocol, we propose using LWI to augment T2-weighted (T2W) images to improve image contrast for PCa detection while maintaining anatomical details needed for radiological diagnosis. Here, we investigate automatic segmentation models and various weighting functions of LWI parameter maps to generate semi-quantitative T2W images that also preserves anatomical detail. Our results show that a combined T2W and LWI parameter image provides enhanced detection of PCa.
Virtual Reality Patient Training for Magnetic Resonance Imaging

For many patients, particularly pediatric patients, MRI exams can be intimidating primarily due to the confined space and loud noises. This can result in failed, non-diagnostic, or degraded exams at cost and even risk to the patient. We present a virtual reality application for mobile devices, such as mobile phones, of a Magnetic Resonance Imaging simulation and training software to serve as a tool for medical professionals to prepare children for MRI scans. Initial evaluations show promising results for reducing movement in test subjects and potential for reducing worry or anxiety among individuals preparing for MRI.
Super Resolution of MR Images with Deep Learning Based k-space Interpolation

Super resolution of MR images can be used to speed up MRI scan time. However, super resolution is a highly ill-posed problem as the low-resolution images lack high frequency spatial information. In this paper, we propose a hybrid dual domain cascaded U-Net to restore the high-resolution images. Firstly, the U-Net operating in k -space domain is used to interpolate the missing k -space data points and then the U-Net operating in image domain provides a refined high-resolution solution image. Experimental results show a successful reconstruction of high resolution images by using only central 6.25% and 25% k -space data.
MRI contrast synthesis from low-rank coefficient images

Synthetic contrasts are commonly derived from parameter maps via Bloch simulation.Typically, model imperfections, in particular partial volume effects, cause artifacts in those images. Recently, it has been proposed to overcome this problem by mapping directly from MR-Fingerprinting data to synthetic contrasts with neural networks. Those methods, however, face the MRF-typical undersampling artifacts, as well as the computational burden of hundreds of input images. We propose to first reconstruct images in a low-rank sub-space, which maintains the correct partial volume contrast, but allows for removal of undersampling artifacts, and to map from this space to synthetic contrasts with a neural network.
Improved Automated Central Vein Sign Assessment by Multi-Level Classification

Central vein sign (CVS) assessment has shown potential to improve differential diagnosis in multiple sclerosis, but automating this task remains non-trivial. As human inter-rater agreement was reported to improve by separating the tasks of lesion exclusion and CVS assessment, we hypothesized that this could also benefit automated CVS assessment. To test this hypothesis, we implemented a novel multi-level classifier for automated CVS assessment and trained and evaluated it in more than 9400 expert-reviewed lesions. The new approach outperforms previous methods, achieving per-class accuracies of 76%&amp;ndash;83% in an unseen testing set and &amp;gt;90% accuracy to identify MS cases.
Understanding domain shift in learned MRI reconstruction: A quantitative analysis on fastMRI knee and neuro sequences

In this work, we investigate the problem of domain shift in the context of state-of-the-art MRI reconstruction networks with respect to variations in training data. We provide visualization tools and support our findings with statistical analysis for the networks evaluated on fastMRI knee and neuro data. We observe that the signal-to-noise ratio of the examined sequences plays an essential role, and we statistically prove the hypothesis that the type/amount of training data is less important for low acceleration factors. Finally, we provide a visualization tool facilitating the examination of the networks&amp;rsquo; performance on each individual subject of the fastMRI data.
StRegA: Unsupervised Anomaly Detection in Brain MRIs using Compact Context-encoding Variational Autoencoder

Deep learning methods are typically trained in a supervised with annotated data for analysing medical images with the motivation of detecting pathologies. In the absence of manually annotated training data, unsupervised anomaly detection can be one of the possible solutions. This work proposes StRegA, an unsupervised anomaly detection pipeline based on a compact ceVAE and shows its applicability in detecting anomalies such as tumours in brain MRIs. The proposed pipeline achieved a Dice score of 0.642&amp;plusmn;0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859&amp;plusmn;0.112 while detecting artificially induced anomalies.
Double High-Order Correlation Preserved Robust Multi-View Ensemble Clustering

Ensemble clustering (EC), utilizing multiple basic partitions (BPs) to yield a robust consensus clustering, has shown promising clustering performance. Nevertheless, most current algorithms suffer from two challenging hurdles: (1) a surge of EC-based methods only focus on pair-wise sample correlation while fully ignoring the high-order correlations of diverse views. (2) they deal directly with the co-association (CA) matrices generated from BPs, which are inevitably corrupted by noise and thus degrade the clustering performance. To address these issues, we propose a novel Double High-Order Correlation Preserved Robust Multi-View Ensemble Clustering (DC-RMEC) method, which preserves the high-order inter-view correlation and the high-order correlation of original data simultaneously. Specifically, DC-RMEC constructs a hypergraph from BPs to fuse high-level complementary information from different algorithms and incorporates multiple CA-based representations into a low-rank tensor to discover the high-order relevance underlying CA matrices, such that double high-order correlation of multi-view features could be dexterously uncovered. Moreover, a marginalized denoiser is invoked to gain robust view-specific CA matrices. Furthermore, we develop a unified framework to jointly optimize the representation tensor and the result matrix. An effective iterative optimization algorithm is designed to optimize our DC-RMEC model by resorting to the alternating direction method of multipliers. Extensive experiments on seven real-world multi-view datasets have demonstrated the superiority of DC-RMEC compared with several state-of-the-art multi-view ensemble clustering methods.
Bipartite and tripartite continuous variable entanglement in a circular array of coupled optical waveguides

Abstract Integrated optical systems have evolved into suitable platforms in the field of photonic quantum technologies. New technologies open up new possibilities for multimode quantum operations. Here we study how circularly coupled waveguide arrays generate bipartite and tripartite continuous-variable (CV) entanglement. We focus on the single-mode squeezed state as input to the circular array of the waveguide system. Our findings suggest that the circularly coupled arrays can be used to generate entangled sources in CV quantum technologies. So the generation of entanglement makes the circular arrays more critical for further investigation and in the applications of photonic CV quantum-information processing.
Analysis of Participants’ Perception of the Pilot Implementation of the Practicum Semester System: With a Focus on Colleges of Education

Purpose: This study sought to conduct a qualitative data analysis of the Practicum Semester Systemthat has been implemented as a pilot project to identify issues and challenges facing colleges ofEducation pertaining to the expansion of the system.&#x0D; Methods: To achieve this goal, a package of materials and presentation contents from the resultreporting session regarding the pilot Practicum Semester System project we are analyzed in the firstand second semesters of 2022. The qualitative document analysis drew upon the feedback from fivecolleges of Education that implemented the Practicum Semester System as a pilot project, five partnerschools, college students as the participants in this system, and students at partner schools. A seriesof common issues were derived through in vivo coding and subcoding with the use of a softwareprogram called MAXQDA 2022.&#x0D; Results: Based on the aggregated participant feedback, especially centering around the colleges ofEducation that implemented the pilot project, the following three issues were identified: i) capacitybuilding for the operators of the practicum semesters and related confusion, ii) needs of current issuesassociated with the partnership among colleges of Education, universities, partner schools and localoffices of Education and a strong will to address such issues, and iii) the specific demand for thepracticum semester implementation guidelines and differences between them.&#x0D; Conclusion: Based on these findings, this study presents policy implications for redefining the teachertraining framework, ensuring consistency between the current teacher training framework of collegesof education and the Practicum Semester System, exploring alternatives to the Practicum SemesterSystem’s operating model, reorganizing the curriculum of teacher training institutions and having asufficient workforce in place for the operation of the system.
The Learning Attitudes of Innovative Autonomous Elementary School Students and the Related Variables at Different Levels of Learning Attitudes

Purpose: We analyzed trends in the learning attitudes of students from Innovative AutonomousElementary Schools over the past three years and the related variables at various levels of learningattitudes.&#x0D; Methods: We conducted trend analysis to examine trends in the learning attitudes of students atinnovative autonomous elementary schools over the past three years. We also conducted quantileregression analysis to determine which variables were significantly related to learning attitude at upper,middle, and lower levels.&#x0D; Results: Students’learning attitudes indicated a clear and linear increase over time. With the adventof the COVID-19 pandemic, the growth of learning attitudes slowed, but did not retreat, and showedremarkable growth in the second year of the pandemic. This suggested the resilience of the studentsat the Innovative Autonomous Schools. As a result of the quantile regression analysis, the learningattitude was shown to be improved through the students' autonomous activities and autonomouscompetency. In addition, this study presented an example analysis in which at different places onthe distribution of the dependent variable the effects of predictor variables could be simultaneouslyexamined through quantile regression analysis.&#x0D; Conclusion: This study found that the performance of innovative autonomous schools was positivethrough the trends of elementary school students’learning attitudes, and that their learning attitudescould be improved through students’autonomous activities and autonomous competencies emphasizedin the education of innovative autonomous schools. The research also illustrated the importance ofoperating a curriculum that fosters communication skills between students and students' self-directedness during class. Through the analysis of learning attitudes by level, it was shown thatcustomized education support by level is needed.
A Study on the Evaluation and Selection of Korean Language arts Textbooks of In-service Korean Language Teacher

Purpose: The purpose is to find problems derived from the elements and selection process and waysto improve them. Considering that the 2022 revised Korean language curriculum has further expandedthe type and number of high school elective courses in preparation for the high school credit systemin preparation for the 2015 revised curriculum.&#x0D; Methods: For this purpose, this study conducted Focus Group Interviews (FGIs) with 9 Koreanlanguage teachers who had participated in the evaluation and selection of Korean language textbooksin unit schools based on the revised 2015 Korean language curriculum.&#x0D; Results: 1) Korean language teachers mainly adopted the “overall textbook evaluation approach” dueto the high burden of evaluation in the process of selecting and evaluating Korean language textbooks. 2) Variables influencing the evaluation and selection process of Korean language textbooks included “appropriate sanctions”, “adequate amount of learning content”, “various teaching and learning activitiesand availability of supplementary materials”, as well as “authors and publishers”. 3) The primaryproblems identified in the selection and evaluation of Korean language textbooks under the verificationand approval system were the excessive amount of textbooks to be evaluated, resulting in “excessivecontent and insufficient evaluation time”, and the emergence of “complex and abstract textbookevaluation criteria”.&#x0D; Conclusion: Proposed solutions to address these issues include “securing evaluation time andsimplifying procedures” and “providing and utilizing objective textbook information for each publisher”.