Reviewerly/exHarmony huggingface dataset:
Impact of Spectators on a Two-Qubit Gate in a Tunable Coupling Superconducting Circuit

Cross-resonance (CR) gate has emerged as a promising scheme for fault-tolerant quantum computation with fixed-frequency qubits. We experimentally implement entangling CR gate by using a microwave-only control in a tunable coupling superconducting circuit, where the tunable coupler provides extra degrees of freedom to verify optimal condition for constructing CR gate. By developing three-qubit CR Hamiltonian tomography protocol, we systematically investigate the dependency of gate fidelities on spurious qubit interactions and present the first experimental approach to the evaluation of the perturbation impact arising from spectator qubits. Our results reveal that the spectator qubits lead to reductions in CR gate fidelity dependent on ZZ interaction and particular frequency detunings between spectator and gate qubits, demonstrating a more serious impact from the target spectator than from the control spectator. Our experiments uncover optimal CR operation regime and provide insight into an improvement of the CR gate by suppression of unwanted qubit interactions.
Robustly correlated key‐medical image for DNA‐chaos based encryption

Medical images include confidential and sensitive information about patients. Hence, ensuring the security of these images is a crucial requirement. This paper proposes an efficient and secure medical image encryption-decryption scheme based on deoxyribonucleic acid (DNA), one-dimensional chaotic maps (tent and logistic maps), and hash functions (SHA-256 and MD5). The first part of the proposed scheme is the key generation based on the hash functions of the image and its metadata. The key then is highly related and intensely sensitive to the original image. The second part is the rotation and permutation of the first two MSB bit-plans of the medical image to reduce its black background that produces redundant DNA encoded sequences. The third part is the DNA encoding-decoding using dynamically chosen DNA rules for every 2-bit pixel value through the logistic map. Meanwhile, the confusion-diffusion is performed using the tent map and XOR operation. Simulation results and security analysis prove the good encryption effects of the proposed scheme compared to the state-of-art methods with a correlation of 6.66617e-7 and a very large key space of 2624. Furthermore, the proposed system has a strong ability to resist various common attacks such as chosen/known-plaintext attacks and cropping/noise attacks.
Training saturation in layerwise quantum approximate optimization

The quantum approximate optimization algorithm (QAOA) is the most studied gate-based variational quantum algorithm today. We train QAOA one layer at a time to maximize overlap with an $n$ qubit target state. Doing so we discovered that such training always saturates---called training saturation---at some depth ${p}^{*}$, meaning that past a certain depth, overlap cannot be improved by adding subsequent layers. We formulate necessary conditions for saturation. Numerically, we find layerwise QAOA reaches its maximum overlap at depth ${p}^{*}=n$ for the problem of state preparation. The addition of coherent dephasing errors to training removes saturation, recovering robustness to layerwise training. This study sheds new light on the performance limitations and prospects of QAOA.
Privacy and Confidentiality in Process Mining: Threats and Research Challenges

Privacy and confidentiality are very important prerequisites for applying process mining in order to comply with regulations and keep company secrets. This paper provides a foundation for future research on privacy-preserving and confidential process mining techniques. Main threats are identified and related to an motivation application scenario in a hospital context as well as to the current body of work on privacy and confidentiality in process mining. A newly developed conceptual model structures the discussion that existing techniques leave room for improvement. This results in a number of important research challenges that should be addressed by future process mining research.
Frequency estimation under local differential privacy

Private collection of statistics from a large distributed population is an important problem, and has led to large scale deployments from several leading technology companies. The dominant approach requires each user to randomly perturb their input, leading to guarantees in the local differential privacy model. In this paper, we place the various approaches that have been suggested into a common framework, and perform an extensive series of experiments to understand the tradeoffs between different implementation choices. Our conclusion is that for the core problems of frequency estimation and heavy hitter identification, careful choice of algorithms can lead to very effective solutions that scale to millions of users.
Stimuli-Aware Visual Emotion Analysis

Visual emotion analysis (VEA) has attracted great attention recently, due to the increasing tendency of expressing and understanding emotions through images on social networks. Different from traditional vision tasks, VEA is inherently more challenging since it involves a much higher level of complexity and ambiguity in human cognitive process. Most of the existing methods adopt deep learning techniques to extract general features from the whole image, disregarding the specific features evoked by various emotional stimuli. Inspired by the Stimuli-Organism-Response (S-O-R) emotion model in psychological theory, we proposed a stimuli-aware VEA method consisting of three stages, namely stimuli selection (S), feature extraction (O) and emotion prediction (R). First, specific emotional stimuli (i. e., color, object, face) are selected from images by employing the off-the-shelf tools. To the best of our knowledge, it is the first time to introduce stimuli selection process into VEA in an end-to-end network. Then, we design three specific networks, i. e., Global-Net, Semantic-Net and Expression-Net, to extract distinct emotional features from different stimuli simultaneously. Finally, benefiting from the inherent structure of Mikel's wheel, we design a novel hierarchical cross-entropy loss to distinguish hard false examples from easy ones in an emotion-specific manner. Experiments demonstrate that the proposed method consistently outperforms the state-of-the-art approaches on four public visual emotion datasets. Ablation study and visualizations further prove the validity and interpretability of our method.
Differential evolution outside the box

This paper investigates how often the popular configurations of Differential Evolution generate solutions outside the feasible domain. Following previous publications in the field, we argue that what the algorithm does with such solutions and how often this has to happen is important for the overall performance of the algorithm and interpretation of results. Based on observations therein, we conclude that significantly more solutions than what is usually assumed by practitioners need to undergo some sort of 'correction' to conform with the definition of the problem's search domain. A wide range of popular Differential Evolution configurations is considered in this study. Conclusions are made regarding the effect the Differential Evolution components and parameter settings have on the distribution of proportions of infeasible solutions generated in a series of independent runs. Results shown in this study suggest strong dependencies between proportions of generated infeasible solutions and every aspect mentioned above. Further investigation of the distribution of proportions of generated infeasible solutions is required.
CBO-IE: A Data Mining Approach for Healthcare IoT Dataset Using Chaotic Biogeography-Based Optimization and Information Entropy

Data mining is mostly utilized for a huge variety of applications in several fields like education, medical, surveillance, and industries. The clustering is an important method of data mining, in which data elements are divided into groups (clusters) to provide better quality data analysis. The Biogeography-Based Optimization (BO) is the latest metaheuristic approach, which is applied to resolve several complex optimization problems. Here, a Chaotic Biogeography-Based Optimization approach using Information Entropy (CBO-IE) is implemented to perform clustering over healthcare IoT datasets. The main objective of CBO-IE is to provide proficient and precise data point distribution in datasets by using Information Entropy concepts and to initialize the population by using chaos theory. Both Information Entropy and chaos theory are facilitated to improve the convergence speed of BO in global search area for selecting the cluster heads and cluster members more accurately. The CBO-IE is implemented to a MATLAB 2021a tool over eight healthcare IoT datasets, and the results illustrate the superior performance of CBO-IE based on F-Measure, intracluster distance, running time complexity, purity index, statistical analysis, root mean square error, accuracy, and standard deviation as compared to previous techniques of clustering like K-Means, GA, PSO, ALO, and BO approaches.
Physics-Based Shadow Image Decomposition for Shadow Removal

We propose a novel deep learning method for shadow removal. Inspired by physical models of shadow formation, we use a linear illumination transformation to model the shadow effects in the image that allows the shadow image to be expressed as a combination of the shadow-free image, the shadow parameters, and a matte layer. We use two deep networks, namely SP-Net and M-Net, to predict the shadow parameters and the shadow matte respectively. This system allows us to remove the shadow effects from images. We then employ an inpainting network, I-Net, to further refine the results. We train and test our framework on the most challenging shadow removal dataset (ISTD). Our method improves the state-of-the-art in terms of mean absolute error (MAE) for the shadow area by 20%. Furthermore, this decomposition allows us to formulate a patch-based weakly-supervised shadow removal method. This model can be trained without any shadow- free images (that are cumbersome to acquire) and achieves competitive shadow removal results compared to state-of-the-art methods that are trained with fully paired shadow and shadow-free images. Last, we introduce SBU-Timelapse, a video shadow removal dataset for evaluating shadow removal methods.
(H)DPGMM: a hierarchy of Dirichlet process Gaussian mixture models for the inference of the black hole mass function

ABSTRACT We introduce (H)DPGMM, a hierarchical Bayesian non-parametric method based on the Dirichlet process Gaussian mixture model, designed to infer data-driven population properties of astrophysical objects without being committal to any specific physical model. We investigate the efficacy of our model on simulated data sets and demonstrate its capability to reconstruct correctly a variety of population models without the need of fine-tuning of the algorithm. We apply our method to the problem of inferring the black hole mass function given a set of gravitational wave observations from LIGO and Virgo, and find that the (H)DPGMM infers a binary black hole mass function that is consistent with previous estimates without the requirement of a theoretically motivated parametric model. Although the number of systems observed is still too small for a robust inference, (H)DPGMM confirms the presence of at least two distinct modes in the observed merging black hole mass function, hence suggesting in a model-independent fashion the presence of at least two classes of binary black hole systems.
Students' perceptions of augmented reality integrated into a mobile learning environment

Purpose This study was based on situated learning, by combining mobile learning and augmented reality, so that students could not only access information content in a real environment but also obtain such information via augmented reality, to support mobile learning. Design/methodology/approach The research included development of an augmented reality system combined with situational learning, used by students to learn about campus plants as part of the college life technology curriculum. Students took part in mobile learning, and an investigation was conducted into the computer learning behaviour of notebook users. College students were used as the experimental subjects. Data were collected using questionnaire surveys and were evaluated in order to identify the behavioural intentions of learners in outdoor learning activities. Findings The questionnaire survey covered environmental interactivity, system quality and textbook content. It was found that learners who used mobile learning augmented reality (MLAR) generally managed to browse all the contents of the textbook at each learning location, without spending too much time looking for information, and learners could quickly integrate this into the learning situation. Learners who used MLAR had a strong motivation to study plants at the learning site because they wanted to use the augmented reality technology to observe virtual plant models. Learners who used MLAR in their field learning liked using augmented reality for further learning, for example, using a magic wand to interact with the technology. Originality/value This study adopted a new approach to deliver elements of the life technology curriculum, integrating augmented reality into mobile learning. All participating students gave positive reviews of six aspects of the proposed system: their behavioural intentions, cognitive usefulness, cognitive ease of use, environmental interactivity, system quality and textbook content.
SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs

Entity alignment, aiming to identify equivalent entities across different knowledge graphs (KGs), is a fundamental problem for constructing Web-scale KGs. Over the course of its development, the label supervision has been considered necessary for accurate alignments. Inspired by the recent progress of self-supervised learning, we explore the extent to which we can get rid of supervision for entity alignment. Commonly, the label information (positive entity pairs) is used to supervise the process of pulling the aligned entities in each positive pair closer. However, our theoretical analysis suggests that the learning of entity alignment can actually benefit more from pushing unlabeled negative pairs far away from each other than pulling labeled positive pairs close. By leveraging this discovery, we develop the self-supervised learning objective for entity alignment. We present SelfKG with efficient strategies to optimize this objective for aligning entities without label supervision. Extensive experiments on benchmark datasets demonstrate that SelfKG without supervision can match or achieve comparable results with state-of-the-art supervised baselines. The performance of SelfKG suggests that self-supervised learning offers great potential for entity alignment in KGs. The code and data are available at https://github.com/THUDM/SelfKG.
Pattern recognition with neuromorphic computing using magnetic field–induced dynamics of skyrmions

Nonlinear phenomena in physical systems can be used for brain-inspired computing with low energy consumption. Response from the dynamics of a topological spin structure called skyrmion is one of the candidates for such a neuromorphic computing. However, its ability has not been well explored experimentally. Here, we experimentally demonstrate neuromorphic computing using nonlinear response originating from magnetic field–induced dynamics of skyrmions. We designed a simple-structured skyrmion-based neuromorphic device and succeeded in handwritten digit recognition with the accuracy as large as 94.7% and waveform recognition. Notably, there exists a positive correlation between the recognition accuracy and the number of skyrmions in the devices. The large degrees of freedom of skyrmion systems, such as the position and the size, originate from the more complex nonlinear mapping, the larger output dimension, and, thus, high accuracy. Our results provide a guideline for developing energy-saving and high-performance skyrmion neuromorphic computing devices.
A Survey of Vision-Language Pre-Trained Models

As transformer evolves, pre-trained models have advanced at a breakneck pace in recent years. They have dominated the mainstream techniques in natural language processing (NLP) and computer vision (CV). How to adapt pre-training to the field of Vision-and-Language (V-L) learning and improve downstream task performance becomes a focus of multimodal learning. In this paper, we review the recent progress in Vision-Language Pre-Trained Models (VL-PTMs). As the core content, we first briefly introduce several ways to encode raw images and texts to single-modal embeddings before pre-training. Then, we dive into the mainstream architectures of VL-PTMs in modeling the interaction between text and image representations. We further present widely-used pre-training tasks, and then we introduce some common downstream tasks. We finally conclude this paper and present some promising research directions. Our survey aims to provide researchers with synthesis and pointer to related research.
Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging

The collection and curation of large-scale medical datasets from multiple institutions is essential for training accurate deep learning models, but privacy concerns often hinder data sharing. Federated learning (FL) is a promising solution that enables privacy-preserving collaborative learning among different institutions, but it generally suffers from performance deterioration due to heterogeneous data distributions and a lack of quality labeled data. In this paper, we present a robust and label-efficient self-supervised FL framework for medical image analysis. Our method introduces a novel Transformer-based self-supervised pre-training paradigm that pre-trains models directly on decentralized target task datasets using masked image modeling, to facilitate more robust representation learning on heterogeneous data and effective knowledge transfer to downstream models. Extensive empirical results on simulated and real-world medical imaging non-IID federated datasets show that masked image modeling with Transformers significantly improves the robustness of models against various degrees of data heterogeneity. Notably, under severe data heterogeneity, our method, without relying on any additional pre-training data, achieves an improvement of 5.06%, 1.53% and 4.58% in test accuracy on retinal, dermatology and chest X-ray classification compared to the supervised baseline with ImageNet pre-training. In addition, we show that our federated self-supervised pre-training methods yield models that generalize better to out-of-distribution data and perform more effectively when fine-tuning with limited labeled data, compared to existing FL algorithms. The code is available at https://github.com/rui-yan/SSL-FL.
Reconstructing Personalized Semantic Facial NeRF Models from Monocular Video

We present a novel semantic model for human head defined with neural radiance field. The 3D-consistent head model consist of a set of disentangled and interpretable bases, and can be driven by low-dimensional expression coefficients. Thanks to the powerful representation ability of neural radiance field, the constructed model can represent complex facial attributes including hair, wearings, which can not be represented by traditional mesh blendshape. To construct the personalized semantic facial model, we propose to define the bases as several multi-level voxel fields. With a short monocular RGB video as input, our method can construct the subject's semantic facial NeRF model with only ten to twenty minutes, and can render a photo-realistic human head image in tens of miliseconds with a given expression coefficient and view direction. With this novel representation, we apply it to many tasks like facial retargeting and expression editing. Experimental results demonstrate its strong representation ability and training/inference speed. Demo videos and released code are provided in our project page: https://ustc3dv.github.io/NeRFBlendShape/
Outsourced Analysis of Encrypted Graphs in the Cloud with Privacy Protection

Huge diagrams have unique properties for organizations and research, such as client linkages in informal organizations and customer evaluation lattices in social channels. They necessitate a lot of financial assets to maintain because they are large and frequently continue to expand. Owners of large diagrams may need to use cloud resources due to the extensive arrangement of open cloud resources to increase capacity and computation flexibility. However, the cloud's accountability and protection of schematics have become a significant issue. In this study, we consider calculations for security savings for essential graph examination practices: schematic extraterrestrial examination for outsourcing graphs in the cloud server. We create the security-protecting variants of the two proposed Eigen decay computations. They are using two cryptographic algorithms: additional substance homomorphic encryption (ASHE) strategies and some degree homomorphic encryption (SDHE) methods. Inadequate networks also feature a distinctively confidential info adaptation convention to allow the trade-off between secrecy and data sparseness. Both dense and sparse structures are investigated. According to test results, calculations with sparse encoding can drastically reduce information. SDHE-based strategies have reduced computing time, while ASHE-based methods have reduced stockpiling expenses.
A 3D Image Quality Assessment Method Based on Vector Information and SVD of Quaternion Matrix under Cloud Computing Environment

With the increasing demands of end-users to the visual perception in three-dimension (3D) image, quality assessment for 3D imageis dominantly required as the feedback information for multimedia transmission systems. In this paper, a novel full-reference quality assessment method by considering the depth and integral color information of 3D image under cloud computing environment is proposed. Based on the property of the depth information in 3D image, the depth map is firstly separated into different planes according to the perception of human visual system (HVS). Then, after express the image pixels of every separated plane through quaternions, the structural and energy information are separated by quaternion singular value decomposition (QSVD). The distortion of structural and energy in every plane are calculated in various formulas respectively. The final result is calculated in terms of the global score, which synthesizes the structural and energy distortion scores in every individual depth plane. It should be pointed out that the chrominance information is employed in our mechanism to evaluate the color image quality because of its useful characteristic for 3D color image quality assessment, and its spatial correlation is used for calculating structural distortion through vector cross-product. Our experimental results confirm that the proposed method has achieves better performance under cloud computing environments compared with other existing 3D image quality assessment methods.