Reviewerly/exHarmony huggingface dataset:
Multiscale Carbonate Rock Reconstruction Using a Hybrid WGAN-GP and Super-Resolution

Abstract The X-ray micro-Computed Tomography (μ-CT) is the primary tool for digital rock imaging, which provides the foundation for numerically studying petrophysical properties of reservoir rocks at the pore scale. However, the finite resolution of μ-CT imaging cannot capture the micro-porosity at the sub-micrometer scale in carbonate rocks. The tradeoff between the resolution and field of view (FOV) is a persisting challenge in the industry. The machine-learning-based single-image super-resolution techniques has rapidly developed in the past few years. It is becoming a promising approach to "super-resolve" low-resolution carbonate rock images. In this study, we present a fast super-resolution generative adversarial network to enhance the image resolution of carbonate rocks. A pre-trained VGG network is implemented to extract important high-level features, from which the perceptual similarity is evaluated between the generated and ground-truth images. The novelty of this study is two-fold. First, the generator is significantly simplified with a fast super-resolution convolutional neural network. On the other hand, the spatial and channel squeeze-and excitation block is applied to recalibrate nonlinear feature mapping so that the quality of super-resolved images is promising even with much fewer residual blocks. To quantify the quality of the super-resolution images, we compare difference maps between the generated and ground-truth images. Numerical results indicate that the proposed network shows excellent potential in enhancing the resolution of heterogeneous carbonate rocks. In particular, the pixel errors are minor, and the super-resolution images exhibit clear and sharp edges and dissolved mineral texture. This study provides a novel machine-learning-based method using a simple generative adversarial network with squeeze and excitation blocks to super-resolve μ-CT images of carbonate rocks.
How do people distribute their attention while observing <i>The Night Watch</i>?

This study explored how people look at The Night Watch (1642), Rembrandt's masterpiece. Twenty-one participants each stood in front of the painting for 5 min, while their eyes were recorded with a mobile eye-tracker and their thoughts were verbalized with a think-aloud method. We computed a heatmap of the participants' attentional distribution using a novel markerless mapping method. The results showed that the participants' attention was mainly directed at the faces of the two central figures, the bright mascot girl in the painting, and detailed elements such as the apparel of the key figures. The eye-movement analysis and think-aloud data also showed that participants' attention shifted from the faces of the key figures to other elements of the scene over the course of the 5 min. Our analyses are consistent with the theory that Rembrandt used light and texture to capture the viewer's attention. Finally, the robustness of the eye-tracking method was demonstrated by replicating the study on a smaller replica.
FVC: An End-to-End Framework towards Deep Video Compression in Feature Space

Deep video compression is attracting increasing attention from both deep learning and video processing community. Recent learning-based approaches follow the hybrid coding paradigm to perform pixel space operations for reducing redundancy along both spatial and temporal dimentions, which leads to inaccurate motion estimation or less effective motion compensation. In this work, we propose a feature-space video coding framework (FVC), which performs all major operations (i.e., motion estimation, motion compression, motion compensation and residual compression) in the feature space. Specifically, a new deformable compensation module, which consists of motion estimation, motion compression and motion compensation, is proposed for more effective motion compensation. In our deformable compensation module, we first perform motion estimation in the feature space to produce the motion information (i.e., the offset maps). Then the motion information is compressed by using the auto-encoder style network. After that, we use the deformable convolution operation to generate the predicted feature for motion compensation. Finally, the residual information between the feature from the current frame and the predicted feature from the deformable compensation module is also compressed in the feature space. Motivated by the conventional codecs, in which the blocks with different sizes are used for motion estimation, we additionally propose two new modules called resolution-adaptive motion coding (RaMC) and resolution-adaptive residual coding (RaRC) to automatically cope with different types of motion and residual patterns at different spatial locations. Comprehensive experimental results demonstrate that our proposed framework achieves the state-of-the-art performance on three benchmark datasets including HEVC, UVG and MCL-JCV.
Fusing Syntactic Structure Information and Lexical Semantic Information for End-to-End Aspect-Based Sentiment Analysis

The aspect-based sentiment analysis (ABSA) consists of two subtasks-aspect term extraction and aspect sentiment prediction. Most methods conduct the ABSA task by handling the subtasks in a pipeline manner, whereby problems in performance and real application emerge. In this study, we propose an end-to-end ABSA model, namely, SSi-LSi, which fuses the syntactic structure information and the lexical semantic information, to address the limitation that existing end-to-end methods do not fully exploit the text information. Through two network branches, the model extracts syntactic structure information and lexical semantic information, which integrates the part of speech, sememes, and context, respectively. Then, on the basis of an attention mechanism, the model further realizes the fusion of the syntactic structure information and the lexical semantic information to obtain higher quality ABSA results, in which way the text information is fully used. Subsequent experiments demonstrate that the SSi-LSi model has certain advantages in using different text information.
Pathological Digital Biomarkers: Validation and Application

Digital pathology offers powerful tools for biomarker discovery, analysis, and translation. Despite its advantages, the clinical adoption of digital pathology has been slow. A clinical and methodological validation is required for novel digital pathological biomarkers. Four steps are required to validate a novel pathological digital biomarker for clinical use: sample collection and processing, analytical validation, clinical validation, and clinical utility. The digital biomarkers and their diagnostic, monitoring, pharmacodynamic response, predictive, prognostic, safety, and risk assessment applications are discussed. Adopting pathological digital biomarkers can be used in conjunction with other diagnostic technologies to select the most appropriate patient treatment, thereby reducing patient suffering and healthcare costs.
A novel assessment framework for learning-based deepfake detectors in realistic conditions

Detecting manipulations in facial images and video has become an increasingly popular topic in media forensics community. At the same time, deep convolutional neural networks have achieved exceptional results on deepfake detection tasks. Despite the remarkable progress, the performance of such detectors is often evaluated on benchmarks under constrained and non-realistic situations. In fact, current assessment and ranking approaches employed in related benchmarks or competitions are unreliable. The impact of conventional distortions and processing operations found in image and video processing workflows, such as compression, noise, and enhancement, is not sufficiently evaluated. This paper proposes a more rigorous framework to assess the performance of learning-based deepfake detectors in more realistic situations. This framework can serve as a broad benchmarking approach for both general model performance assessment and the ranking of proponents in a competition. In addition, a stochastic degradation-based data augmentation strategy driven by realistic processing operations is designed, which significantly improves the generalization ability of two deepfake detectors.
Quadratic and higher-order unconstrained binary optimization of railway rescheduling for quantum computing

As consequences of disruptions in railway traffic affect passenger experience/satisfaction, appropriate rerouting and/or rescheduling is necessary. These problems are known to be NP-hard, given the numerous restrictions of traffic nature. With the recent advances in quantum technologies, quantum annealing has become an alternative method to solve such optimization problems. To use quantum annealing, the problem needs to be encoded in QUBO (quadratic unconstrained binary optimization) or HOBO (higher-order binary optimization) formulation that can be recast as a QUBO. This paper introduces QUBO and HOBO representations for rescheduling problems of railway traffic management; the latter is a new approach up to our knowledge. This new approach takes into account not only the single-track lines but also the double- and multi-track lines, as well as stations composed of tracks and switches. We consider the conditions of minimal headway between trains, minimal stay on stations, track occupation, and rolling stock circulation. Furthermore, a hybrid quantum-classical procedure is presented that includes rerouting. We demonstrate the proof of concept implementation on the D-Wave Quantum Processing Unit and D-Wave hybrid solver.
Prospects of the Post-Digital University: Analysis of Program Documents in the Field of Education

Introduction. COVID-19 has fundamentally changed the architectonics of the global educational process. While many countries are still analyzing the consequences of the pandemic for education, the world’s largest institutions and organizations are already thinking about the future of education. The purpose of the article is to identify and summarize the prospects for the development of a post-digital university on the basis of this analysis. Materials and Methods. This article presents an analysis of a number of important documents of world organizations on the future prospects of the higher education system during and after the COVID-19 pandemic, as well as official documents on education systems in the Russian Federation and the Republic of Kazakhstan. The main methods of the proposed research are the analysis of program documents in the field of education, as well as a review of the philosophical literature of 2020‒2022 on the trends of the post-digital university. Results. In the worldʼs leading documents on the future of the education system it is shown that education is the main driving force of sustainable development. However, the COVID-19 pandemic has led to an unprecedented destabilization of the entire world education system. The analytical documents, in general, contains the following ideas: increasing the viability of education systems for sustainable development; rethinking education and accelerating positive transformations in teaching and learning. Based on the analysis of the situation, the documents suggested a number of effective measures, is the mains among which: digital transformation of universities; measures to support the research activities of universities. The article suggests that the digitalization of the educational process itself, described in the leading documents on education, will not lead to any results, unless it is accompanied by serious transformations in the content of education. Discussion and Conclusion. In a post-digital university, a radical paradigm shift is needed, the rejection of the neoliberal, standardized, algorithmic structure of the university, its transition to a socially significant, critical, responsible device. New technologies, as well as digitalization in education, should not be a goal, but a means. Only then can progress in education and significant social transformations be possible. The practical significance of this research of the authors lies in the analysis and generalization of strategically important program documents in the field of education on the prospects for the development of a post-digital university.
Molecular-excited-state calculations with the qubit-excitation-based adaptive variational quantum eigensolver protocol

Calculations of molecular spectral properties, like photodissociation rates and absorption bands, rely on knowledge of the excited state energies of the molecule of interest. Protocols based on the variational quantum eigensolver (VQE) are promising candidates to calculate such energies on emerging noisy intermediate-scale quantum (NISQ) computers. The successful implementation of these protocols on NISQ computers, relies on ans\"atze that can accurately approximate the molecular states and that can be implemented by shallow quantum circuits. We introduce the excited qubit-excitation-based adaptive (e-QEB-ADAPT)-VQE protocol to calculate molecular-excited-state energies. The e-QEB-ADAPT-VQE constructs efficient problem-tailored ans\"atze by iteratively appending evolutions of qubit excitation operators. The e-QEB-ADAPT-VQE also improves on previous ADAPT-VQE protocol in that it is designed to be independent on the choice of initial reference state. We perform classical numerical simulations for LiH and ${\mathrm{BeH}}_{2}$ to benchmark the performance of the e-QEB-ADAPT-VQE. We demonstrate that the e-QEB-ADAPT-VQE can construct highly accurate ans\"atze that require at least an order of magnitude fewer cnot gates than standard fixed unitary coupled-cluster ans\"atze, such as the UCCSD and the GUCCSD. We also show that the e-QEB-ADAPT-VQE is more successful in constructing ans\"atze for excited molecular states than other ADAPT-VQE protocols.
Sports training in virtual reality to improve response behavior in karate kumite with transfer to real world

Virtual reality (VR) training has become valuable in sports to improve motor behavior and train specific situations under standardized conditions. However, studies comparing conventional training with VR training are rare, especially for advanced athletes. Furthermore, it remains unclear whether the performance improvement achieved through VR training can be transferred to the real world (RW). Therefore, we present a study analyzing sports-specific response training using a head-mounted display (HMD) combined with conventional training and its transfer to RW. In ten training sessions over 6 weeks, a VR training group (VRG, n = 15) performed virtual karate training (10 min) combined with a conventional training (80 min), while a conventional training group (CG, n = 12) conducted only conventional training (90 min) at the same time. The VR training consisted of the athlete responding to various karate attacks performed by a virtual opponent in a karate-specific manner. The study design included a pretest, an intermediate test (after 5 training sessions), and a posttest. We analyzed sports-specific response behavior concerning the competition-relevant karate attacks Gyaku-Zuki jodan (GZj) and Kizami-Zuki (KZ) using the parameters ‘response time’, and the “response quality” when the athletes had to react to attacks of a virtual opponent in VR and a real opponent in RW. For the parameter “response time,” improvements were detected only for the VRG in VR concerning GZj and KZ. For the parameter “response quality” for both groups, no improvements could be found. Furthermore, athletes provided positive feedback regarding the integration of VR training into conventional training.
Hybrid meta‐heuristic algorithm for optimal virtual machine placement and migration in cloud computing

Summary Data sharing in cloud computing happens with multiple participants to freely distribute the group data, which focuses on advancing the effectiveness of work in cooperative backgrounds and has attained widespread benefits. The main intent of this article is to accomplish a virtual machines (VMs) placement and migration model using a hybrid meta‐heuristic concept. A new meta‐heuristic algorithm named DJ‐HA is developed for optimal VM placement and migration to reduce the count of active servers, and minimization of makespan, and energy consumption with a faster convergence rate in a cloud background. Then, the VM migration is done based on the multi‐objective function concerning energy consumption and makespan using the same hybrid DJ‐HA. From the result analysis, the energy consumption of the DJ‐HA is correspondingly secured at 4.3%, 3.5%, 31%, and 33% more advanced than PSO, GWO, DHOA, and JA, at the 100th iteration for Experiment 1. Accordingly, the cost function of the suggested DJ‐HA is secured at 88.8%, 89.4%, 33.3%, and 50% increased than PSO, GWO, DHOA, and JA at the 100th iteration for Experiment 4. Hence, it is proved that the suggested VM migration using DJ‐HA is enriched than the other conventional algorithms.
The <i>what</i> , The <i>from</i> , and The <i>to</i> : The Migration Games in Deduplicated Systems

Deduplication reduces the size of the data stored in large-scale storage systems by replacing duplicate data blocks with references to their unique copies. This creates dependencies between files that contain similar content and complicates the management of data in the system. In this article, we address the problem of data migration, in which files are remapped between different volumes as a result of system expansion or maintenance. The challenge of determining which files and blocks to migrate has been studied extensively for systems without deduplication. In the context of deduplicated storage, however, only simplified migration scenarios have been considered. In this article, we formulate the general migration problem for deduplicated systems as an optimization problem whose objective is to minimize the system’s size while ensuring that the storage load is evenly distributed between the system’s volumes and that the network traffic required for the migration does not exceed its allocation. We then present three algorithms for generating effective migration plans, each based on a different approach and representing a different trade-off between computation time and migration efficiency. Our greedy algorithm provides modest space savings but is appealing thanks to its exceptionally short runtime. Its results can be improved by using larger system representations. Our theoretically optimal algorithm formulates the migration problem as an integer linear programming (ILP) instance. Its migration plans consistently result in smaller and more balanced systems than those of the greedy approach, although its runtime is long and, as a result, the theoretical optimum is not always found. Our clustering algorithm enjoys the best of both worlds: its migration plans are comparable to those generated by the ILP-based algorithm, but its runtime is shorter, sometimes by an order of magnitude. It can be further accelerated at a modest cost in the quality of its results.
BSC-Net: Background Suppression Algorithm for Stray Lights in Star Images

Most background suppression algorithms are weakly robust due to the complexity and fluctuation of the star image’s background. In this paper, a background suppression algorithm for stray lights in star images is proposed, which is named BSC-Net (Background Suppression Convolutional Network) and consist of two parts: “Background Suppression Part” and “Foreground Retention Part”. The former part achieves background suppression by extracting features from various receptive fields, while the latter part achieves foreground retention by merging multi-scale features. Through this two-part design, BSC-Net can compensate for blurring and distortion of the foreground caused by background suppression, which is not achievable in other methods. At the same time, a blended loss function of smooth_L1&amp;Structure Similarity Index Measure (SSIM) is introduced to hasten the network convergence and avoid image distortion. Based on the BSC-Net and the loss function, a dataset consisting of real images will be used for training and testing. Finally, experiments show that BSC-Net achieves the best results and the largest Signal-to-Noise Ratio (SNR) improvement in different backgrounds, which is fast, practical and efficient, and can tackle the shortcomings of existing methods.
Driven anti-Bragg subradiant correlations in waveguide quantum electrodynamics

We study theoretically driven quantum dynamics in periodic arrays of two-level qubits coupled to the waveguide. We demonstrate, that strongly subradiant eigenstates of the master equation for the density matrix emerge under strong coherent driving for arrays with the anti-Bragg periods $d=\lambda/4,3\lambda/4$... This happens even though are no such states at low driving powers. Thus, our findings show that in the quantum nonlinear regime the domain of existence of subradiant states expands beyond conventional subwavelength structures. These driven states are directly manifested in long-living quantum correlations between the qubit excitations.
An improved hand gesture recognition system using keypoints and hand bounding boxes

Hand gesture recognition is a significant problem for human–computer interaction. One form of hand gesture recognition is static hand gestures. This study developed a static hand gesture recognition system, consisting of three modules: Feature extraction Module, Processing Module, and Classification Module. The feature extraction module uses human pose estimation with a top-down method to extract not only the keypoints but also body and hand bounding boxes. After being normalized and processed in the processing module, its output will be used as the input for the classification module in which we proposed an architecture called Two-pipeline architecture. In this module, we also employ different methods to find the most suitable one for this task. Experiments were conducted on three datasets called HANDS, OUHANDS, and SHAPE. Results showed that the proposed Two-pipeline architecture with 2.5 million parameters obtained accuracy of 94%, 98%, and 94% on three datasets. In addition, the lightweight version with 0.22 million parameters also achieved accuracy of 91%, 94%, and 96%.
Traceable and revocable multi-authority attribute-based keyword search for cloud storage

Ciphertext-Policy Attribute-Based Keyword Search (CP-ABKS) provides data privacy and achieves fine-grained access control over encrypted data in the cloud. However, authorized users may misuse the secret key for financial benefits in a multi-user scenario. Thus, tracing those malicious users and revoking them from the system is essential. Alongside this, most existing schemes have only a single authority to generate the secret key, which may lead to misuse of the secret key. To address these problems, this paper proposes a traceable and revocable multi-authority attribute-based keyword search in the cloud. The scheme involves two authorities generating the user secret key to restrict any individual authority’s unauthorized access to cloud data. The scheme also traces malicious users and revokes them from the system. Further, we prove that the scheme is secure against chosen keyword attacks, chosen plaintext attacks, and traceability. And also verify the security against malicious authorities. The performance analysis shows that the proposed scheme is efficient in computation cost compared to the state-of-the-art schemes.