Reviewerly/exHarmony huggingface dataset:
Multi-compound Transformer for Accurate Biomedical Image Segmentation

The recent vision transformer (i.e. for image classification) learns non-local attentive interaction of different patch tokens. However, prior arts miss learning the cross-scale dependencies of different pixels, the semantic correspondence of different labels, and the consistency of the feature representations and semantic embeddings, which are critical for biomedical segmentation. In this paper, we tackle the above issues by proposing a unified transformer network, termed Multi-Compound Transformer (MCTrans), which incorporates rich feature learning and semantic structure mining into a unified framework. Specifically, MCTrans embeds the multi-scale convolutional features as a sequence of tokens, and performs intra- and inter-scale self-attention, rather than single-scale attention in previous works. In addition, a learnable proxy embedding is also introduced to model semantic relationship and feature enhancement by using self-attention and cross-attention, respectively. MCTrans can be easily plugged into a UNet-like network, and attains a significant improvement over the state-of-the-art methods in biomedical image segmentation in six standard benchmarks. For example, MCTrans outperforms UNet by 3.64%, 3.71%, 4.34%, 2.8%, 1.88%, 1.57% in Pannuke, CVC-Clinic, CVC-Colon, Etis, Kavirs, ISIC2018 dataset, respectively. Code is available at https://github.com/JiYuanFeng/MCTrans.
Enhanced Jaya algorithm: A simple but efficient optimization method for constrained engineering design problems

Jaya algorithm (JAYA) is a new metaheuristic algorithm, which has a very simple structure and only requires population size and terminal condition for optimization. Given the two features, JAYA has been widely used to solve various types of optimization problems. However, JAYA may easily get trapped in local optima for solving complex optimization problems due to its single learning strategy with little population information. To improve the global search ability of JAYA, this work proposes an enhanced Jaya algorithm (EJAYA) for global optimization. In EJAYA, the local exploitation is based on defined upper and lower local attractors and global exploration is guided by historical population. Like JAYA, EJAYA does notneed any effort for fine tuning initial parameters. To check the performance of the proposed EJAYA, EJAYA is first used to solve 45 test functions extracted from the well-known CEC 2014 and CEC 2015 test suites. Then EJAYA is employed to solve seven challenging real-world engineering design optimization problems. Experimental results support the strong ability of EJAYA to escape from the local optimum for solving complex optimization problems and the effectively of the introduced improved strategies to JAYA. Note that, the source codes of the proposed EJAYA are publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/88877-enhanced-jaya-algorithm-for-global-optimization.
Toward Human–AI Interfaces to Support Explainability and Causability in Medical AI

Our concept of causability is a measure of whether and to what extent humans can understand a given machine explanation. We motivate causability with a clinical case from cancer research. We argue for using causability in medical artificial intelligence (AI) to develop and evaluate future human–AI interfaces.
CR-Fill: Generative Image Inpainting with Auxiliary Contextual Reconstruction

Recent deep generative inpainting methods use attention layers to allow the generator to explicitly borrow feature patches from the known region to complete a missing region. Due to the lack of supervision signals for the correspondence between missing regions and known regions, it may fail to find proper reference features, which often leads to artifacts in the results. Also, it computes pair-wise similarity across the entire feature map during inference bringing a significant computational overhead. To address this issue, we propose to teach such patch-borrowing behavior to an attention-free generator by joint training of an auxiliary contextual reconstruction task, which encourages the generated output to be plausible even when reconstructed by surrounding regions. The auxiliary branch can be seen as a learnable loss function, i.e. named as contextual reconstruction (CR) loss, where query-reference feature similarity and reference-based reconstructor are jointly optimized with the inpainting generator. The auxiliary branch ( i.e. CR loss) is required only during training, and only the inpainting generator is required during the inference. Experimental results demonstrate that the proposed inpainting model compares favourably against the state-of-the-art in terms of quantitative and visual performance. Code is available at https://github.com/zengxianyu/crfill.
Explaining in Style: Training a GAN to explain a classifier in StyleSpace

Image classification models can depend on multiple different semantic attributes of the image. An explanation of the decision of the classifier needs to both discover and visualize these properties. Here we present StylEx, a method for doing this, by training a generative model to specifically explain multiple attributes that underlie classifier decisions. A natural source for such attributes is the StyleSpace of StyleGAN, which is known to generate semantically meaningful dimensions in the image. However, because standard GAN training is not dependent on the classifier, it may not represent those attributes which are important for the classifier decision, and the dimensions of StyleSpace may represent irrelevant at-tributes. To overcome this, we propose a training procedure for a StyleGAN, which incorporates the classifier model, in order to learn a classifier-specific StyleSpace. Explanatory attributes are then selected from this space. These can be used to visualize the effect of changing multiple attributes per image, thus providing image-specific explanations. We apply StylEx to multiple domains, including animals, leaves, faces and retinal images. For these, we show how an image can be modified in different ways to change its classifier output. Our results show that the method finds attributes that align well with semantic ones, generate meaningful image-specific explanations, and are human-interpretable as measured in user-studies. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
EGO-Swarm: A Fully Autonomous and Decentralized Quadrotor Swarm System in Cluttered Environments

This paper presents a decentralized and asynchronous systematic solution for multi-robot autonomous navigation in unknown obstacle-rich scenes using merely onboard resources. The planning system is formulated under gradient-based local planning framework, where collision avoidance is achieved by formulating the collision risk as a penalty of a nonlinear optimization problem. In order to improve robustness and escape local minima, we incorporate a lightweight topological trajectory generation method. Then agents generate safe, smooth, and dynamically feasible trajectories in only several milliseconds using an unreliable trajectory sharing network. Relative localization drift among agents is corrected by using agent detection in depth images. Our method is demonstrated in both simulation and real-world experiments. The source code is released for the reference of the community.
Deep High-Resolution Representation Learning for Cross-Resolution Person Re-Identification

Person re-identification (re-ID) tackles the problem of matching person images with the same identity from different cameras. In practical applications, due to the differences in camera performance and distance between cameras and persons of interest, captured person images usually have various resolutions. This problem, named Cross-Resolution Person Re-identification, presents a great challenge for the accurate person matching. In this paper, we propose a Deep High-Resolution Pseudo-Siamese Framework (PS-HRNet) to solve the above problem. Specifically, we first improve the VDSR by introducing existing channel attention (CA) mechanism and harvest a new module, i.e., VDSR-CA, to restore the resolution of low-resolution images and make full use of the different channel information of feature maps. Then we reform the HRNet by designing a novel representation head, HRNet-ReID, to extract discriminating features. In addition, a pseudo-siamese framework is developed to reduce the difference of feature distributions between low-resolution images and high-resolution images. The experimental results on five cross-resolution person datasets verify the effectiveness of our proposed approach. Compared with the state-of-the-art methods, the proposed PS-HRNet improves the Rank-1 accuracy by 3.4%, 6.2%, 2.5%,1.1% and 4.2% on MLR-Market-1501, MLR-CUHK03, MLR-VIPeR, MLR-DukeMTMC-reID, and CAVIAR datasets, respectively, which demonstrates the superiority of our method in handling the Cross-Resolution Person Re-ID task. Our code is available at https://github.com/zhguoqing.
Phrase dependency relational graph attention network for Aspect-based Sentiment Analysis

Aspect-based Sentiment Analysis (ABSA) is a subclass of sentiment analysis, which aims to identify the sentiment polarity such as positive, negative, or neutral for specific aspects or attributes that appear in a sentence. Previous studies have focused on extracting aspect-sentiment polarity pairs based on dependency trees, ignoring edge labels and phrase information. In this paper, we instead propose a phrase dependency graph attention network (PD-RGAT) on the ABSA task, which is a relational graph attention network constructed based on the phrase dependency graph, aggregating directed dependency edges and phrase information. We perform experiments with two pre-training models, GloVe and BERT. Experimental results on the benchmarking datasets (i.e., Twitter, Restaurant, and Laptop) demonstrate that our proposed PD-RGAT has comparable effectiveness to a range of state-of-the-art models and further illustrate that the graph convolutional structure based on the phrase dependency graph can capture both syntactic information and short long-range word dependencies. It also shows that incorporating directed edge labels and phrase information can enhance the capture of aspect-sentiment polarities on the ABSA task.
CRIS: CLIP-Driven Referring Image Segmentation

Referring image segmentation aims to segment a referent via a natural linguistic expression. Due to the distinct data properties between text and image, it is challenging for a network to well align text and pixel-level features. Existing approaches use pretrained models to facilitate learning, yet separately transfer the language/vision knowledge from pretrained models, ignoring the multi-modal corresponding information. Inspired by the recent advance in Contrastive Language-Image Pretraining (CLIP), in this paper, we propose an end-to-end CLIP-Driven Referring Image Segmen-tation framework (CRIS). To transfer the multi-modal knowledge effectively, CRIS resorts to vision-language decoding and contrastive learning for achieving the text-to-pixel alignment. More specifically, we design a vision-language decoder to propagate fine-grained semantic information from textual representations to each pixel-level activation, which promotes consistency between the two modalities. In addition, we present text-to-pixel contrastive learning to explicitly enforce the text feature similar to the related pixel-level features and dissimilar to the irrelevances. The experimental results on three benchmark datasets demonstrate that our proposed framework significantly outperforms the state-of-the-art performance without any post-processing.
Almost-linear time decoding algorithm for topological codes

In order to build a large scale quantum computer, one must be able to correct errors extremely fast. We design a fast decoding algorithm for topological codes to correct for Pauli errors and erasure and combination of both errors and erasure. Our algorithm has a worst case complexity of $O(n \alpha(n))$, where $n$ is the number of physical qubits and $\alpha$ is the inverse of Ackermann's function, which is very slowly growing. For all practical purposes, $\alpha(n) \leq 3$. We prove that our algorithm performs optimally for errors of weight up to $(d-1)/2$ and for loss of up to $d-1$ qubits, where $d$ is the minimum distance of the code. Numerically, we obtain a threshold of $9.9\%$ for the 2d-toric code with perfect syndrome measurements and $2.6\%$ with faulty measurements.
Augmented reality in vocational training: A systematic review of research and applications

Augmented reality (AR) technology is widely used in various fields. However, there are few systematic reviews on the application of AR in vocational training. To fill this research gap, the current study reviewed the application of AR technology in the training of various industries over a 20 year period (2000–2021). Through cross-referencing and abstract reading, 80 relevant studies were selected for the final analysis from two perspectives: the improvement of vocational skills (including application area, target audience, training objectives, and effects) and AR training technology (including AR application, AR training system, and device). Furthermore, CiteSpaceV was employed to analyze the research hotspots and trends of AR vocational training. The results indicated that AR training is frequently applied in the industry, vocational education and medical fields. Among these industries, AR has been most frequently used in medical training, industrial maintenance, and assembly. Furthermore, AR glasses, simulators, the Unity3D game engine, 360° panorama, AR systems and apps are becoming widely used for training tasks. The benefits of these systems have been identified. We also explored the impact of AR on vocational training results. Based on 17 empirical studies, this study summarized the results and advantages of AR vocational training. This verified that AR has a high promotion effect on vocational training when the meta-analysis method is used. Future researchers can study how vocational skills education can be combined with these new intelligent technologies to design more mature teaching practice cases.
A Novel Group Recommendation Model With Two-Stage Deep Learning

Group recommendation has recently drawn a lot of attention to the recommender system community. Currently, several deep learning-based approaches are leveraged to learn preferences of groups for items and predict next items in which groups may be interested. Yet, their recommendation performance is still unsatisfactory due to sparse group–item interactions. To address this challenge, this study presents a novel model, called group recommendation model with two-stage deep learning (GRMTDL), which encompasses two sequential stages: 1) group representation learning (GRL) and 2) group preference learning (GPL). In GRL, we first construct an undirected tripartite graph over group–user–item interactions, and then employ it to accurately learn group semantic features through a spatial-based variational graph autoencoder network. While in GPL, we first introduce a dual PL-network that contains two structure-sharing subnetworks: 1) group PL-network employed for GPL and 2) user PL-network utilized for user preference learning. Then, we design a novel layered transfer learning (LTL) method to learn group preferences by alternately optimizing these two subnetworks. In particular, it can effectively absorb knowledge of user preferences into the process of GPL. Furthermore, extensive experiments on four real-world datasets demonstrate that the proposed GRMTDL model outperforms the state-of-the-art baselines for group recommendation.
Facial expression recognition based on deep learning

Facial expression recognition technology will play an increasingly important role in our daily life. Autonomous driving, virtual reality and all kinds of robots integrated into our life depend on the development of facial expression recognition technology. Many tasks in the field of computer vision are based on deep learning technology and convolutional neural network. The paper proposes an occluded expression recognition model based on the generated countermeasure network. The model is divided into two modules, namely, occluded face image restoration and face recognition.Firstly, this paper summarizes the research status of deep facial expression recognition methods in recent ten years and the development of related facial expression database. Then, the current facial expression recognition methods based on deep learning are divided into two categories: Static facial expression recognition and dynamic facial expression recognition. The two methodswill be introduced and summarized respectively. Aiming at the advanced deep expression recognition algorithms in the field, the performance of these algorithms on common expression databases is compared, and the strengths and weaknesses of these algorithms are analyzed in detail.As the task of facial expression recognition is gradually transferred from the controlled laboratory environment to the challenging real-world environment, with the rapid development of deep learning technology, deep neural network can learn discriminative features, and is gradually applied to automatic facial expression recognition task. The current deep facial expression recognition system is committed to solve the following two problems: (1) Overfitting due to lack of sufficient training data; (2) In the real world environment, other variables that have nothing to do with expression bring interference problems.From the perspective of algorithm, combining other expression models, such as facial action unit model and pleasure arousal dimension model, as well as other multimodal models, such as audio mode, 3D face depth information and human physiological information, can make expression recognition more practical.
MAT: Motion-aware multi-object tracking

Modern multi-object tracking (MOT) systems usually build trajectories through associating per-frame detections. However, facing the challenges of camera motion, fast motion, and occlusion, it is difficult to ensure the quality of long-range tracking or even the tracklet purity, especially for small objects. Most of tracking frameworks depend heavily on the performance of re-identification (ReID) for the data association. Unfortunately, the ReID-based association is not only unreliable and time-consuming, but still cannot address the false negatives for occluded and blurred objects, due to noisy partial-detections, similar appearances, and lack of temporal-spatial constraints. In this paper, we propose an enhanced MOT paradigm, namely Motion-Aware Tracker (MAT). Our MAT is a plug-and-play solution, it mainly focuses on high-performance motion-based prediction, reconnection, and association. First, the nonrigid pedestrian motion and rigid camera motion are blended seamlessly to develop the Integrated Motion Localization (IML) module. Second, the Dynamic Reconnection Context (DRC) module is devised to guarantee the robustness for long-range motion-based reconnection. The core ideas in DRC are the motion-based dynamic-window and cyclic pseudo-observation trajectory filling strategy, which can smoothly fill in the tracking fragments caused by occlusion or blur. At last, we present the 3D Integral Image (3DII) module to efficiently cut off useless track-detection association connections using temporal-spatial constraints. Extensive experiments are conducted on the MOT16&17 challenging benchmarks. The results demonstrate that our MAT can achieve superior performance and surpass other state-of-the-art trackers by a large margin with high efficiency.
Machine learning in the quantum realm: The state-of-the-art, challenges, and future vision

Machine learning has become a ubiquitous and effective technique for data processing and classification. Furthermore, due to the superiority and progress of quantum computing in many areas (e.g., cryptography, machine learning, healthcare), a combination of classical machine learning and quantum information processing has established a new field, called, quantum machine learning. One of the most frequently used applications of quantum computing is machine learning. This paper aims to present a comprehensive review of state-of-the-art advances in quantum machine learning. Besides, this paper outlines recent works on different architectures of quantum deep learning, and illustrates classification tasks in the quantum domain as well as encoding methods and quantum subroutines. Furthermore, this paper examines how the concept of quantum computing enhances classical machine learning. Two methods for improving the performance of classical machine learning are presented. Finally, this work provides a general review of challenges and the future vision of quantum machine learning. • Organize the most recent research works to pave the way for QML researchers. • Demonstrate the commonly used methods in the classification of real problems. • Provide readers with various quantum methods to enhance classical ML. • Present some of the challenges and future directions of QML.
An integrated model of rolling force for extra-thick plate by combining theoretical model and neural network model

To solve the problem of low precision of the existing theoretical model in predicting the rolling force of extra-thick plate, the genetic algorithm (GA) is used as an enhancing means to improve the global searching ability of the BP model, and a GA-BP model with high precision is firstly established. Furthermore, in order to solve the black box problem of the model, an integrated model is ultimately obtained by combining a theoretical model and the established neural network model. During the modeling, 1000 groups of production data of extra-thick plate rolling are selected and normalized as the data set. The optimal network structure of the GA-BP neural network is determined based on the method of trial and error, and the initial weight and threshold of the BP neural network is solved iteratively with the genetic algorithm. On this basis, an integrated model is ultimately obtained according to the principle of multiplication compensation of average error. It is shown that the maximum prediction error of the original BP model is 7.51%, while the value of the GA-BP model is down to 3.95%. This integrated model has not only inherited the rigorous mathematical structure of the theoretical model, but also occupies the high precision that comes from the GA-BP model. Therefore, the present integrated model is more suitable for the process optimization of extra-thick plate rolling.