Reviewerly/exHarmony huggingface dataset:
Autofocusing+: Noise-Resilient Motion Correction in Magnetic Resonance Imaging

Image corruption by motion artifacts is an ingrained problem in Magnetic Resonance Imaging (MRI). In this work, we propose a neural network-based regularization term to enhance Autofocusing, a classic optimization-based method to remove rigid motion artifacts. The method takes the best of both worlds: the optimization-based routine iteratively executes the blind demotion and deep learning-based prior penalizes for unrealistic restorations and speeds up the convergence. We validate the method on three models of motion trajectories, using synthetic and real noisy data. The method proves resilient to noise and anatomic structure variation, outperforming the state-of-the-art motion correction methods.
Time complexity analysis of quantum difference methods for linear high dimensional and multiscale partial differential equations

We investigate time complexities of finite difference methods for solving the high-dimensional linear heat equation, the high-dimensional linear hyperbolic equation and the multiscale hyperbolic heat system with quantum algorithms (hence referred to as the “quantum difference methods”). For the heat and linear hyperbolic equations we study the impact of explicit and implicit time discretizations on quantum advantages over the classical difference method. For the multiscale problem, we find the time complexity of both the classical treatment and quantum treatment for the explicit scheme scales as O(1/ε), where ε is the scaling parameter, while the scaling for the multiscale Asymptotic-Preserving (AP) schemes does not depend on ε. This indicates that it is still of great importance to develop AP schemes for multiscale problems in quantum computing.
Design and Construction of an Interactive Intelligent Learning System for English Learners in Higher Education Institutions

At present, the traditional teaching mode of English teaching in higher vocational institutions is not compatible with the characteristics and teaching objectives of higher vocational education under the new situation. The rapid development of interactive multimedia technology has brought a new teaching style and experience to English basic education. Interactive teaching mode breaks the shackles of traditional teaching mode and focuses on cultivating students’ English language application ability and communicative ability. By using interactive multimedia technology, to begin with, students can improve their English communicative ability more effectively in several aspects, including stimulating their interest in speaking, creating an English-speaking environment, and mobilizing their subjective initiative, and can organically integrate with the learning of professional knowledge to lay a foundation for employment. This paper proposes an interactive intelligent learning system based on artificial intelligence algorithms. This new English teaching system with the interaction theory realizes the transformation of a new student-centered teaching mode, which is a great contribution to English classroom learning in higher education institutions.
WavThruVec: Latent speech representation as intermediate features for neural speech synthesis

Recent advances in neural text-to-speech research have been dominated by two-stage pipelines utilizing low-level intermediate speech representation such as mel-spectrograms. However, such predetermined features are fundamentally limited, because they do not allow to exploit the full potential of a data-driven approach through learning hidden representations. For this reason, several end-to-end methods have been proposed. However, such models are harder to train and require a large number of high-quality recordings with transcriptions. Here, we propose WavThruVec - a two-stage architecture that resolves the bottleneck by using high-dimensional Wav2Vec 2.0 embeddings as intermediate speech representation. Since these hidden activations provide high-level linguistic features, they are more robust to noise. That allows us to utilize annotated speech datasets of a lower quality to train the first-stage module. At the same time, the second-stage component can be trained on large-scale untranscribed audio corpora, as Wav2Vec 2.0 embeddings are already time-aligned. This results in an increased generalization capability to out-of-vocabulary words, as well as to a better generalization to unseen speakers. We show that the proposed model not only matches the quality of state-of-the-art neural models, but also presents useful properties enabling tasks like voice conversion or zero-shot synthesis.
MC-GAT: multi-layer collaborative generative adversarial transformer for cholangiocarcinoma classification from hyperspectral pathological images

Accurate histopathological analysis is the core step of early diagnosis of cholangiocarcinoma (CCA). Compared with color pathological images, hyperspectral pathological images have advantages for providing rich band information. Existing algorithms of HSI classification are dominated by convolutional neural network (CNN), which has the deficiency of distorting spectral sequence information of HSI data. Although vision transformer (ViT) alleviates this problem to a certain extent, the expressive power of transformer encoder will gradually decrease with increasing number of layers, which still degrades the classification performance. In addition, labeled HSI samples are limited in practical applications, which restricts the performance of methods. To address these issues, this paper proposed a multi-layer collaborative generative adversarial transformer termed MC-GAT for CCA classification from hyperspectral pathological images. MC-GAT consists of two pure transformer-based neural networks including a generator and a discriminator. The generator learns the implicit probability of real samples and transforms noise sequences into band sequences, which produces fake samples. These fake samples and corresponding real samples are mixed together as input to confuse the discriminator, which increases model generalization. In discriminator, a multi-layer collaborative transformer encoder is designed to integrate output features from different layers into collaborative features, which adaptively mines progressive relations from shallow to deep encoders and enhances the discriminating power of the discriminator. Experimental results on the Multidimensional Choledoch Datasets demonstrate that the proposed MC-GAT can achieve better classification results than many state-of-the-art methods. This confirms the potentiality of the proposed method in aiding pathologists in CCA histopathological analysis from hyperspectral imagery.
Multi-Label Noise Robust Collaborative Learning for Remote Sensing Image Classification

The development of accurate methods for multi-label classification (MLC) of remote sensing (RS) images is one of the most important research topics in RS. The MLC methods based on convolutional neural networks (CNNs) have shown strong performance gains in RS. However, they usually require a high number of reliable training images annotated with multiple land-cover class labels. Collecting such data is time-consuming and costly. To address this problem, the publicly available thematic products, which can include noisy labels, can be used to annotate RS images with zero-labeling cost. However, multi-label noise (which can be associated with wrong and missing label annotations) can distort the learning process of the MLC methods. To address this problem, we propose a novel multi-label noise robust collaborative learning (RCML) method to alleviate the negative effects of multi-label noise during the training phase of a CNN model. RCML identifies, ranks, and excludes noisy multi-labels in RS images based on three main modules: 1) the discrepancy module; 2) the group lasso module; and 3) the swap module. The discrepancy module ensures that the two networks learn diverse features, while producing the same predictions. The task of the group lasso module is to detect the potentially noisy labels assigned to multi-labeled training images, while the swap module is devoted to exchange the ranking information between two networks. Unlike the existing methods that make assumptions about noise distribution, our proposed RCML does not make any prior assumption about the type of noise in the training set. The experiments conducted on two multi-label RS image archives confirm the robustness of the proposed RCML under extreme multi-label noise rates. Our code is publicly available at: http://www.noisy-labels-in-rs.org.
Government financial support and financial performance of SMEs: A dual sequential mediator approach

Government financial support (GFS) is an important factor for firms in developing countries, particularly small and medium enterprises (SMEs), to be competitive and perform better. Nonetheless, studies on the relationship between GFS and firm performance have yielded inconclusive results. Researchers' efforts to resolve the inconclusiveness led to an examination of competitive advantage (CA) as a simple mediator. This study contends that CA should not be the first mediator but rather a resource acquired with GFS, such as cloud ERP, which has CA-enabled qualities, as opposed to GFS, which lacks the ability to offer CA to firms. Hence, using 204 Malaysian manufacturing SMEs as a sample, this study investigates the dual sequential mediation of cloud ERP implementation (CERPI) and CA in the GFS-financial performance (FPER) relationship. PLS-SEM was employed as a data analysis method and for hypotheses assessment. Findings reveal that the GFS is not directly associated with FPER. However, GFS is positively related to CERPI, which subsequently enhances CA positively. CA is also positively associated with FPER, and CERPI and CA sequentially mediate the GFS and FPER relationship positively. This study makes a contribution to the literature by providing a more holistic understanding of the complex relationship between GFS and FPER. This study could assist SMEs and policymakers in gaining a better understanding of the process and requirements for realising valuable benefits from GFS.
How often do cancer researchers make their data and code available and what factors are associated with sharing?

Various stakeholders are calling for increased availability of data and code from cancer research. However, it is unclear how commonly these products are shared, and what factors are associated with sharing. Our objective was to evaluate how frequently oncology researchers make data and code available and explore factors associated with sharing.A cross-sectional analysis of a random sample of 306 cancer-related articles indexed in PubMed in 2019 which studied research subjects with a cancer diagnosis was performed. All articles were independently screened for eligibility by two authors. Outcomes of interest included the prevalence of affirmative sharing declarations and the rate with which declarations connected to data complying with key FAIR principles (e.g. posted to a recognised repository, assigned an identifier, data license outlined, non-proprietary formatting). We also investigated associations between sharing rates and several journal characteristics (e.g. sharing policies, publication models), study characteristics (e.g. cancer rarity, study design), open science practices (e.g. pre-registration, pre-printing) and subsequent citation rates between 2020 and 2021.One in five studies declared data were publicly available (59/306, 19%, 95% CI: 15-24%). However, when data availability was investigated this percentage dropped to 16% (49/306, 95% CI: 12-20%), and then to less than 1% (1/306, 95% CI: 0-2%) when data were checked for compliance with key FAIR principles. While only 4% of articles that used inferential statistics reported code to be available (10/274, 95% CI: 2-6%), the odds of reporting code to be available were 5.6 times higher for researchers who shared data. Compliance with mandatory data and code sharing policies was observed in 48% (14/29) and 0% (0/6) of articles, respectively. However, 88% of articles (45/51) included data availability statements when required. Policies that encouraged data sharing did not appear to be any more effective than not having a policy at all. The only factors associated with higher rates of data sharing were studying rare cancers and using publicly available data to complement original research.Data and code sharing in oncology occurs infrequently, and at a lower rate than would be expected given the prevalence of mandatory sharing policies. There is also a large gap between those declaring data to be available, and those archiving data in a way that facilitates its reuse. We encourage journals to actively check compliance with sharing policies, and researchers consult community-accepted guidelines when archiving the products of their research.
Critical assessment of transformer-based AI models for German clinical notes

Abstract Objective Healthcare data such as clinical notes are primarily recorded in an unstructured manner. If adequately translated into structured data, they can be utilized for health economics and set the groundwork for better individualized patient care. To structure clinical notes, deep-learning methods, particularly transformer-based models like Bidirectional Encoder Representations from Transformers (BERT), have recently received much attention. Currently, biomedical applications are primarily focused on the English language. While general-purpose German-language models such as GermanBERT and GottBERT have been published, adaptations for biomedical data are unavailable. This study evaluated the suitability of existing and novel transformer-based models for the German biomedical and clinical domain. Materials and Methods We used 8 transformer-based models and pre-trained 3 new models on a newly generated biomedical corpus, and systematically compared them with each other. We annotated a new dataset of clinical notes and used it with 4 other corpora (BRONCO150, CLEF eHealth 2019 Task 1, GGPONC, and JSynCC) to perform named entity recognition (NER) and document classification tasks. Results General-purpose language models can be used effectively for biomedical and clinical natural language processing (NLP) tasks, still, our newly trained BioGottBERT model outperformed GottBERT on both clinical NER tasks. However, training new biomedical models from scratch proved ineffective. Discussion The domain-adaptation strategy’s potential is currently limited due to a lack of pre-training data. Since general-purpose language models are only marginally inferior to domain-specific models, both options are suitable for developing German-language biomedical applications. Conclusion General-purpose language models perform remarkably well on biomedical and clinical NLP tasks. If larger corpora become available in the future, domain-adapting these models may improve performances.
Texture classification for visual data using transfer learning

The texture is the most fundamental aspect of a picture that contributes to its recognition. Computer vision challenges such as picture identification and segmentation are built on the foundation of texture analysis. Various images of satellite, forestry, medical etc. have been identifiable because of textures. This work aims to offer texture classification models that will outperform previously presented methods. In this work, transfer learning was applied to attain this goal. MobileNetV3 and InceptionV3 are the two pre-trained models employed. Brodatz, Kylberg, and Outex texture datasets were used to evaluate the models. The models achieved excellent results and achieved the objective in most cases. Classification accuracy obtained for the Kylberg dataset were 100% and 99.89%. For the Brodatz dataset, the classification accuracy obtained was 99.83% and 99.94%. For the Outex datasets, the classification accuracy obtained was 99.48% and 99.48%. The model outputs the corresponding label of the texture of the image.
Wild Terrestrial Animal Re-Identification Based on an Improved Locally Aware Transformer with a Cross-Attention Mechanism

The wildlife re-identification recognition methods based on the camera trap were used to identify different individuals of the same species using the fur, stripes, facial features and other features of the animal body surfaces in the images, which is an important way to count the individual number of a species. Re-identification of wild animals can provide solid technical support for the in-depth study of the number of individuals and living conditions of rare wild animals, as well as provide accurate and timely data support for population ecology and conservation biology research. However, due to the difficulty of recording the shy wild animals and distinguishing the similar fur of different individuals, only a few papers have focused on the re-identification recognition of wild animals. In order to fill this gap, we improved the locally aware transformer (LA transformer) network structure for the re-identification recognition of wild terrestrial animals. First of all, at the stage of feature extraction, we replaced the self-attention module of the LA transformer with a cross-attention block (CAB) in order to calculate the inner-patch attention and cross-patch attention, so that we could efficiently capture the global information of the animal body's surface and local feature differences of fur, colors, textures, or faces. Then, the locally aware network of the LA transformer was used to fuse the local and global features. Finally, the classification layer of the network realized wildlife individual recognition. In order to evaluate the performance of the model, we tested it on a dataset of Amur tiger torsos and the face datasets of six different species, including lions, golden monkeys, meerkats, red pandas, tigers, and chimpanzees. The experimental results showed that our wildlife re-identification model has good generalization ability and is superior to the existing methods in mAP (mean average precision), and obtained comparable results in the metrics Rank 1 and Rank 5.
Gallery D.C

GUI design is an integral part of software development. The process of designing a mobile application typically starts with the ideation and inspiration search from existing designs. However, existing information-retrieval based, and database-query based methods cannot efficiently gain inspirations in three requirements: design practicality, design granularity and design knowledge discovery. In this paper we propose a web application, called Gallery D.C. that aims to facilitate the process of user interface design through real world GUI component search. Gallery D.C. indexes GUI component designs using reverse engineering and deep learning based computer vision techniques on millions of real world applications. To perform an advanced design search and knowledge discovery, our approach extracts information about size, color, component type, and text information to help designers explore multi-faceted design space and distill higher-order of design knowledge. Gallery D.C. is well received via an informal evaluation with 7 professional designers.
Single Stage Virtual Try-On Via Deformable Attention Flows

Virtual try-on aims to generate a photo-realistic fitting result given an in-shop garment and a reference person image. Existing methods usually build up multi-stage frameworks to deal with clothes warping and body blending respectively, or rely heavily on intermediate parser-based labels which may be noisy or even inaccurate. To solve the above challenges, we propose a single-stage try-on framework by developing a novel Deformable Attention Flow (DAFlow), which applies the deformable attention scheme to multi-flow estimation. With pose keypoints as the guidance only, the self- and cross-deformable attention flows are estimated for the reference person and the garment images, respectively. By sampling multiple flow fields, the feature-level and pixel-level information from different semantic areas is simultaneously extracted and merged through the attention mechanism. It enables clothes warping and body synthesizing at the same time which leads to photo-realistic results in an end-to-end manner. Extensive experiments on two try-on datasets demonstrate that our proposed method achieves state-of-the-art performance both qualitatively and quantitatively. Furthermore, additional experiments on the other two image editing tasks illustrate the versatility of our method for multi-view synthesis and image animation. Code will be made available at https://github.com/OFA-Sys/DAFlow .
A Wushu Posture Recognition System Based on MediaPipe

With the development of information technology such as computer vision and human-computer interaction, online physical education has become an active field of current physical education research. Especially with COVID-19, a significant number of people are restricted to learning and exercising motor skills in small spaces, but most of the movement cannot be carried out in narrow environment. Wushu is exempt from this restriction, so it is often used in online physical education in China. In this context, we propose a Wushu posture recognition system based on camera and MediaPipe for tracking hand, head and body movements of users. According to the Landmarks returned by Mediapipe, we designed recognition algorithms for Fist, Palm, Hook, Tiger Talon, Forward Lunge, Horse Stance and Empty Step. By testing 400 photos, the experimental results show that these algorithms can effectively identify these movements. From there, we built a system using Python to help users perform Wushu training independently, safely, and efficiently without a teacher.
A Selective Federated Reinforcement Learning Strategy for Autonomous Driving

Currently, the complex traffic environment challenges the fast and accurate response of a connected autonomous vehicle (CAV). More importantly, it is difficult for different CAVs to collaborate and share knowledge. To remedy that, this paper proposes a selective federated reinforcement learning (SFRL) strategy to achieve online knowledge aggregation strategy to improve the accuracy and environmental adaptability of the autonomous driving model. First, we propose a federated reinforcement learning framework that allows participants to use the knowledge of other CAVs to make corresponding actions, thereby realizing online knowledge transfer and aggregation. Second, we use reinforcement learning to train local driving models of CAVs to cope with collision avoidance tasks. Third, considering the efficiency of federated learning (FL) and the additional communication overhead it brings, we propose a CAVs selection strategy before uploading local models. When selecting CAVs, we consider the reputation of CAVs, the quality of local models, and time overhead, so as to select as many high-quality users as possible while considering resources and time constraints. With above strategic processes, our framework can aggregate and reuse the knowledge learned by CAVs traveling in different environments to assist in driving decisions. Extensive simulation results validate that our proposal can improve model accuracy and learning efficiency while reducing communication overhead.
Characterizing and Understanding End-to-End Multi-Modal Neural Networks on GPUs

Multi-modal neural networks have become increasingly pervasive in many machine learning application domains due to their superior accuracy by fusing various modalities. However, they present many unique characteristics such as multi-stage execution, frequent synchronization and high heterogeneity, which are not well understood in the system and architecture community. In this article, we first present and characterize a set of multi-modal neural network workloads of different sizes at inference stage. We then explore their important implications from system and architecture aspects. We hope that our work can help guide future software/hardware design and optimization for efficient inference of multi-modal DNN applications.