Reviewerly/exHarmony huggingface dataset:
Theory of overparametrization in quantum neural networks

The prospect of achieving quantum advantage with Quantum Neural Networks (QNNs) is exciting. Understanding how QNN properties (e.g., the number of parameters $M$) affect the loss landscape is crucial to the design of scalable QNN architectures. Here, we rigorously analyze the overparametrization phenomenon in QNNs with periodic structure. We define overparametrization as the regime where the QNN has more than a critical number of parameters $M_c$ that allows it to explore all relevant directions in state space. Our main results show that the dimension of the Lie algebra obtained from the generators of the QNN is an upper bound for $M_c$, and for the maximal rank that the quantum Fisher information and Hessian matrices can reach. Underparametrized QNNs have spurious local minima in the loss landscape that start disappearing when $M\geq M_c$. Thus, the overparametrization onset corresponds to a computational phase transition where the QNN trainability is greatly improved by a more favorable landscape. We then connect the notion of overparametrization to the QNN capacity, so that when a QNN is overparametrized, its capacity achieves its maximum possible value. We run numerical simulations for eigensolver, compilation, and autoencoding applications to showcase the overparametrization computational phase transition. We note that our results also apply to variational quantum algorithms and quantum optimal control.
High-Resolution Optical Flow from 1D Attention and Correlation

Optical flow is inherently a 2D search problem, and thus the computational complexity grows quadratically with respect to the search window, making large displacements matching infeasible for high-resolution images. In this paper, we take inspiration from Transformers and propose a new method for high-resolution optical flow estimation with significantly less computation. Specifically, a 1D attention operation is first applied in the vertical direction of the target image, and then a simple 1D correlation in the horizontal direction of the attended image is able to achieve 2D correspondence modeling effect. The directions of attention and correlation can also be exchanged, resulting in two 3D cost volumes that are concatenated for optical flow estimation. The novel 1D formulation empowers our method to scale to very high-resolution input images while maintaining competitive performance. Extensive experiments on Sintel, KITTI and real-world 4K (2160 × 3840) resolution images demonstrated the effectiveness and superiority of our proposed method. Code and models are available at https://github.com/haofeixu/flow1d.
Summarize and Search: Learning Consensus-aware Dynamic Convolution for Co-Saliency Detection

Humans perform co-saliency detection by first summarizing the consensus knowledge in the whole group and then searching corresponding objects in each image. Previous methods usually lack robustness, scalability, or stability for the first process and simply fuse consensus features with image features for the second process. In this paper, we propose a novel consensus-aware dynamic convolution model to explicitly and effectively perform the "summarize and search" process. To summarize consensus image features, we first summarize robust features for every single image using an effective pooling method and then aggregate cross-image consensus cues via the self-attention mechanism. By doing this, our model meets the scalability and stability requirements. Next, we generate dynamic kernels from consensus features to encode the summarized consensus knowledge. Two kinds of kernels are generated in a supplementary way to summarize fine-grained image-specific consensus object cues and the coarse group-wise common knowledge, respectively. Then, we can effectively perform object searching by employing dynamic convolution at multiple scales. Besides, a novel and effective data synthesis method is also proposed to train our network. Experimental results on four benchmark datasets verify the effectiveness of our proposed method. Our code and saliency maps are available at https://github.com/nnizhang/CADC.
Hybrid embedding-based text representation for hierarchical multi-label text classification

Many real-world text classification tasks often deal with a large number of closely related categories organized in a hierarchical structure or taxonomy. Hierarchical multi-label text classification (HMTC) has become rather challenging when it requires handling large sets of closely related categories. The structural features of all categories in the entire hierarchy and the word semantics of their category labels are very helpful in improving text classification accuracy over large sets of closely related categories, which has been neglected in most of existing HMTC approaches. In this paper, we present a hybrid embedding-based text representation for HMTC with high accuracy. First, the hybrid embedding consists of both graph embedding of categories in the hierarchy and their word embedding of category labels. The Structural Deep Network Embedding-based graph embedding model is used to simultaneously encode the global and local structural features of a given category in the whole hierarchy for making the category structurally discriminable. We further use the word embedding technique to encode the word semantics of each category label in the hierarchy for making different categories semantically discriminable. Second, we presented a level-by-level HMTC approach based on the bidirectional Gated Recurrent Unit network model together with the hybrid embedding that is used to learn the representation of the text level-by-level. Last but not least, extensive experiments were made over five large-scale real-world datasets in comparison with the state-of-the-art hierarchical and flat multi-label text classification approaches, and the experimental results show that our approach is very competitive to the state-of-the-art approaches in classification accuracy, in particular maintaining computational costs while achieving superior performance.
LoFGAN: Fusing Local Representations for Few-shot Image Generation

Given only a few available images for a novel unseen category, few-shot image generation aims to generate more data for this category. Previous works attempt to globally fuse these images by using adjustable weighted coefficients. However, there is a serious semantic misalignment between different images from a global perspective, making these works suffer from poor generation quality and diversity. To tackle this problem, we propose a novel Local-Fusion Generative Adversarial Network (LoFGAN) for fewshot image generation. Instead of using these available images as a whole, we first randomly divide them into a base image and several reference images. Next, LoFGAN matches local representations between the base and reference images based on semantic similarities, and replaces the local features with the closest related local features. In this way, LoFGAN can produce more realistic and diverse images at a more fine-grained level, and simultaneously enjoy the characteristic of semantic alignment. Furthermore, a local reconstruction loss is also proposed, which can provide better training stability and generation quality. We conduct extensive experiments on three datasets, which successfully demonstrates the effectiveness of our proposed method for few-shot image generation and downstream visual applications with limited data. Code is available at https://github.com/edward3862/LoFGAN-pytorch.
Densely Guided Knowledge Distillation using Multiple Teacher Assistants

With the success of deep neural networks, knowledge distillation which guides the learning of a small student network from a large teacher network is being actively studied for model compression and transfer learning. However, few studies have been performed to resolve the poor learning issue of the student network when the student and teacher model sizes significantly differ. In this paper, we propose a densely guided knowledge distillation using multiple teacher assistants that gradually decreases the model size to efficiently bridge the large gap between the teacher and student networks. To stimulate more efficient learning of the student network, we guide each teacher assistant to every other smaller teacher assistants iteratively. Specifically, when teaching a smaller teacher assistant at the next step, the existing larger teacher assistants from the previous step are used as well as the teacher network. Moreover, we design stochastic teaching where, for each mini-batch, a teacher or teacher assistants are randomly dropped. This acts as a regularizer to improve the efficiency of teaching of the student network. Thus, the student can always learn salient distilled knowledge from the multiple sources. We verified the effectiveness of the proposed method for a classification task using CIFAR-10, CIFAR-100, and ImageNet. We also achieved significant performance improvements with various backbone architectures such as ResNet, WideResNet, and VGG. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control

In recent years, the photovoltaic generation installed capacity has been steadily growing thanks to its inexhaustible and non-polluting characteristics. However, solar generators are strongly dependent on intermittent weather parameters, increasing power systems' uncertainty level. Forecasting models have arisen as a feasible solution to decreasing photovoltaic generators' uncertainty level, as they can produce accurate predictions. Traditionally, the vast majority of research studies have focused on the development of accurate prediction point forecasters. However, in recent years some researchers have suggested the concept of prediction interval forecasting, where not only an accurate prediction point but also the confidence level of a given prediction are computed to provide further information. This paper develops a new model for predicting photovoltaic generators' output power confidence interval 10 min ahead, based on deep learning, mathematical probability density functions and meteorological parameters. The model's accuracy has been validated with a real data series collected from Spanish meteorological stations. In addition, two error metrics, prediction interval coverage percentage and Skill score, are computed at a 95% confidence level to examine the model's accuracy. The prediction interval coverage percentage values are greater than the chosen confidence level, which means, as stated in the literature, the proposed model is well-founded.
Digital reframing: The design thinking of redesigning traditional products into innovative digital products

Abstract The redesign of traditional products into innovative digital products is a profound form of digital innovation. It is imperative for both digitally native and traditional firms to understand and effectively manage this form of digital innovation to survive and thrive amid expected disruptions when traditional products are driven out of business by digital products. Current insights into this form of digital innovation are limited, as previous research has focused on the design of innovative digital technologies. However, if we break everyday digital products down, we often find centuries‐old physical products and digital technologies that have existed for years, if not decades. Therefore, a more comprehensive understanding of digital innovation is needed to attend to how the digital and the physical are combined, and how such a combination makes digital products innovative even when it reuses existing digital technologies. To this end, this study addresses the research question, “How does the reuse of digital technologies enable the redesign of a traditional product into an innovative digital product?” Adopting the “design thinking as reframing” perspective, we conducted a grounded theory study of the design thinking acted out by the doing of the design of an innovative digital theatre. This study inductively develops a theory explaining the design thinking of redesigning a traditional product into an innovative digital product as a digital reframing process, where designers take the frames of digital technologies and rethink existing products (frame taking), merge the frames into the product under design (frame merging), and propose a new frame to interpret the resultant product (frame giving). Our study has important implications for both research and practice.
Conditional Directed Graph Convolution for 3D Human Pose Estimation

Graph convolutional networks have significantly improved 3D human pose estimation by representing the human skeleton as an undirected graph. However, this representation fails to reflect the articulated characteristic of human skeletons as the hierarchical orders among the joints are not explicitly presented. In this paper, we propose to represent the human skeleton as a directed graph with the joints as nodes and bones as edges that are directed from parent joints to child joints. By so doing, the directions of edges can explicitly reflect the hierarchical relationships among the nodes. Based on this representation, we further propose a spatial-temporal conditional directed graph convolution to leverage varying non-local dependence for different poses by conditioning the graph topology on input poses. Altogether, we form a U-shaped network, named U-shaped Conditional Directed Graph Convolutional Network, for 3D human pose estimation from monocular videos. To evaluate the effectiveness of our method, we conducted extensive experiments on two challenging large-scale benchmarks: Human3.6M and MPI-INF-3DHP. Both quantitative and qualitative results show that our method achieves top performance. Also, ablation studies show that directed graphs can better exploit the hierarchy of articulated human skeletons than undirected graphs, and the conditional connections can yield adaptive graph topologies for different poses.
MMOCR

We present MMOCR---an open-source toolbox which provides a comprehensive pipeline for text detection and recognition, as well as their downstream tasks such as named entity recognition and key information extraction. MMOCR implements 14 state-of-the-art algorithms, which is significantly more than all the existing open-source OCR projects we are aware of to date. To facilitate future research and industrial applications of text recognition-related problems, we also provide a large number of trained models and detailed benchmarks to give insights into the performance of text detection, recognition and understanding. MMOCR is publicly released at https://github.com/open-mmlab/mmocr.
Fast-adapting and privacy-preserving federated recommender system

In the mobile Internet era, recommender systems have become an irreplaceable tool to help users discover useful items, thus alleviating the information overload problem. Recent research on deep neural network (DNN)-based recommender systems have made significant progress in improving prediction accuracy, largely attributed to the widely accessible large-scale user data. Such data is commonly collected from users’ personal devices and then centrally stored in the cloud server to facilitate model training. However, with the rising public concerns on user privacy leakage in online platforms, online users are becoming increasingly anxious over abuses of user privacy. Therefore, it is urgent and beneficial to develop a recommender system that can achieve both high prediction accuracy and strong privacy protection. To this end, we propose a DNN-based recommendation model called PrivRec running on the decentralized federated learning (FL) environment, which ensures that a user’s data is fully retained on her/his personal device while contributing to training an accurate model. On the other hand, to better embrace the data heterogeneity (e.g., users’ data vary in scale and quality significantly) in FL, we innovatively introduce a first-order meta-learning method that enables fast on-device personalization with only a few data points. Furthermore, to defend against potential malicious participants that pose serious security threat to other users, we further develop a user-level differentially private model, namely DP-PrivRec, so attackers are unable to identify any arbitrary user from the trained model. To compensate for the loss by adding noise during model updates, we introduce a two-stage training approach. Finally, we conduct extensive experiments on two large-scale datasets in a simulated FL environment, and the results validate the superiority of both PrivRec and DP-PrivRec.
The Archerfish Hunting Optimizer: A Novel Metaheuristic Algorithm for Global Optimization

Global optimization solves real-world problems numerically or analytically by minimizing their objective functions. Most of the analytical algorithms are greedy and computationally intractable (Gonzalez in Handbook of approximation algorithms and metaheuristics: contemporary and emerging applications, vol. 2. CRC Press, Boca Raton, 2018). Metaheuristics are generally nature-inspired optimization algorithms. They numerically find a near-optimal solution for optimization problems in a reasonable amount of time. We propose a novel metaheuristic algorithm for global optimization. It is based on the shooting and jumping behaviors of the archerfish for hunting aerial insects. We name our proposed algorithm the archerfish hunting optimizer (AHO). The AHO algorithm has two parameters (the swapping angle and the attractiveness rate) to set. We execute the AHO algorithm using five different values for each parameter. In all, we perform 25 simulations for four distinct values of the search space dimension (i.e., 5, 10, 15, and 20). We run the Friedman test to determine the best values of parameters for each dimension. We perform three different comparisons to validate the proposed algorithm's performance. First, AHO is compared to 12 recent metaheuristic algorithms (the accepted algorithms for the 2020's competition on single-objective bound-constrained numerical optimization) on ten test functions of the benchmark CEC 2020 for unconstrained optimization. The experimental results are evaluated using the Wilcoxon signed-rank test. Experimental outcomes show that the AHO algorithm, in terms of robustness, convergence, and quality of the obtained solution, is significantly competitive compared to state-of-the-art methods. Second, the performance of AHO and three recent metaheuristic algorithms is evaluated using five engineering design problems taken from the benchmark CEC 2020 for non-convex constrained optimization. The obtained results are ranked using the ranking scheme detailed in the corresponding paper, and the obtained ranks illustrate that AHO is very competitive when opposed to the considered algorithms. Finally, the performance of AHO in solving five engineering design problems is assessed and compared to several well-established state-of-the-art algorithms. We analyzed the obtained numerical results in detail. These results show that the AHO algorithm is significantly better than, or at least comparable to the considered algorithms with very efficient performance in solving many optimization problems. The statistical indicators illustrate that the AHO algorithm has a high ability to significantly outperform the well-established optimizers.
Effectiveness of Immersive Virtual Reality in Nursing Education

Digital education using immersive virtual reality (VR) technology is available in nursing. Evidence of its effectiveness is essential.This review analyzed the effectiveness of and barriers to using immersive VR in nursing education.A structured search was performed in PubMed, Cumulative Index to Nursing and Allied Health Literature, EMBASE, PsycINFO, Web of Science, and ProQuest Nursing & Allied Health Database. The Medical Education Research Study Quality Instrument was used to assess the quality of studies.The final review was composed of 9 studies of moderate to high quality published between 2018 and 2020. The review indicated that immersive VR increased learning, cognitive, and psychomotor performance. Most participants of the studies preferred using immersive VR in education and with a variety of experiential scenarios. Barriers were associated with technological hardware and software (eg, simulation sickness, lack of visual comfort).The review supports the viability of immersive VR technology in nursing education.
Video Processing Using Deep Learning Techniques: A Systematic Literature Review

Studies show lots of advanced research on various data types such as image, speech, and text using deep learning techniques, but nowadays, research on video processing is also an emerging field of computer vision. Several surveys are present on video processing using computer vision deep learning techniques, targeting specific functionality such as anomaly detection, crowd analysis, activity monitoring, etc. However, a combined study is still unexplored. This paper aims to present a Systematic Literature Review (SLR) on video processing using deep learning to investigate the applications, functionalities, techniques, datasets, issues, and challenges by formulating the relevant research questions (RQs). This systematic mapping includes 93 research articles from reputed databases published between 2011 and 2020. We categorize the deep learning technique for video processing as CNN, DNN, and RNN based. We observe the significant advancements in video processing between 2017 and 2020, primarily due to the advent of AlexNet, ResNet, and LSTM based deep learning techniques. The prominent fields of video processing research are observed as human action recognition, crowd anomaly detection, and behavior analysis. This SLR is a helpful guide for the researchers to explore the recent literature, available datasets, and existing deep learning techniques for video processing.
Rapido: Prototyping Interactive AR Experiences through Programming by Demonstration

Programming by Demonstration (PbD) is a well-known technique that allows non-programmers to describe interactivity by performing examples of the expected behavior, but it has not been extensively explored for AR. We present Rapido, a novel early-stage prototyping tool to create fully interactive mobile AR prototypes from non-interactive video prototypes using PbD. In Rapido, designers use a mobile AR device to record a video prototype to capture context, sketch assets, and demonstrate interactions. They can demonstrate touch inputs, animation paths, and rules to, e.g., have a sketch follow the focus area of the device or the user’s world-space touches. Simultaneously, a live website visualizes an editable overview of all the demonstrated examples and infers a state machine of the user flow. Our key contribution is a method that enables designers to turn a video prototype into an executable state machine through PbD. The designer switches between these representations to interactively refine the final interactive prototype. We illustrate the power of Rapido’s approach by prototyping the main interactions of three popular AR mobile applications.