Reviewerly/exHarmony huggingface dataset:
Improving the Spatial Resolution of Solar Images Based on an Improved Conditional Denoising Diffusion Probability Model

Abstract The quality of solar images plays an important role in the analysis of small events in solar physics. Therefore, the improvement of image resolution based on super-resolution (SR) reconstruction technology has aroused the interest of many researchers. In this paper, an improved conditional denoising diffusion probability model (ICDDPM) based on the Markov chain is proposed for the SR reconstruction of solar images. This method reconstructs high-resolution (HR) images from low-resolution images by learning a reverse process that adds noise to HR images. To verify the effectiveness of the method, images from the Goode Solar Telescope at the Big Bear Solar Observatory and the Helioseismic and Magnetic Imager (HMI) on the Solar Dynamics Observatory are used to train a network, and the spatial resolution of reconstructed images is 4 times that of the original HMI images. The experimental results show that the performance based on ICDDPM is better than the previous work in subject judgment and object evaluation indexes. The reconstructed images of this method have higher subjective vision quality and better consistency with the HMI images. And the structural similarity and rms index results are also higher than the compared method, demonstrating the success of the resolution improvement using ICDDPM.
A multi-layer memory sharing network for video captioning

Over the past several years, video captioning has received much attention in computer vision and machine learning communities. Many models utilize an RNN-based decoder to generate sentences describing the content of a video. They have achieved much progress; however, few methods adopt a decoder with more than three layers because an RNN-based model with more layers may become hard to train, time-consuming or even deteriorate at a certain depth. To address the limitation, we propose a Multi-layer memory sharing Network, MesNet for short, which allows more layers to be stacked without compromising performance. In MesNet, we construct a novel memory sharing structure to strengthen the connections between layers and make the model easier to train. More specifically, we design an Enhanced Gated Recurrent Unit (En-GRU) and stack it to construct a deeper network. Unlike traditional RNN-based multi-layer networks, the memory states of all layers in MesNet are cross-used at each iteration to mimic the brain's complex connections. Extensive experiments on MSVD and MSR-VTT demonstrate that our method performs well and outperforms some state-of-the-art methods significantly. Our code is available at https://github.com/nbbb/MesNet.
Channel attention generative adversarial network for super-resolution of glioma magnetic resonance image

Glioma is the most common primary craniocerebral tumor caused by the cancelation of glial cells in the brain and spinal cord, with a high incidence and cure rate. Magnetic resonance imaging (MRI) is a common technique for detecting and analyzing brain tumors. Due to improper hardware and operation, the obtained brain MRI images are low-resolution, making it difficult to detect and grade gliomas accurately. However, super-resolution reconstruction technology can improve the clarity of MRI images and help experts accurately detect and grade glioma.We propose a glioma magnetic resonance image super-resolution reconstruction method based on channel attention generative adversarial network (CGAN). First, we replace the base block of SRGAN with a residual dense block based on the channel attention mechanism. Second, we adopt a relative average discriminator to replace the discriminator in standard GAN. Finally, we add the mean squared error loss to the training, consisting of the mean squared error loss, the L1 norm loss, and the generator's adversarial loss to form the generator loss function.On the Set5, Set14, Urban100, and glioma datasets, compared with the state-of-the-art algorithms, our proposed CGAN method has improved peak signal-to-noise ratio and structural similarity, and the reconstructed glioma images are more precise than other algorithms.The experimental results show that our CGAN method has apparent improvements in objective evaluation indicators and subjective visual effects, indicating its effectiveness and superiority.
Real-time multiple target segmentation with multimodal few-shot learning

Deep learning-based target segmentation requires a big training dataset to achieve good results. In this regard, few-shot learning a model that quickly adapts to new targets with a few labeled support samples is proposed to tackle this issue. In this study, we introduce a new multimodal few-shot learning [e.g., red-green-blue (RGB), thermal, and depth] for real-time multiple target segmentation in a real-world application with a few examples based on a new squeeze-and-attentions mechanism for multiscale and multiple target segmentation. Compared to the state-of-the-art methods (HSNet, CANet, and PFENet), the proposed method demonstrates significantly better performance on the PST900 dataset with 32 time-series sets in both Hand-Drill, and Survivor classes.
Depth-based pseudo-terrain-following navigation for cruising AUVs

This paper presents an approach of depth-based pseudo-terrain-following navigation for underactuated cruising AUVs subjected to near-bottom survey missions. Based on the waypoint-based path planning, our approach provides a set of optimal waypoints by following which a cruising AUV accomplishes an effectual near-bottom flight avoiding bottom collision. In deriving our waypoints, we solve an optimization problem. The problem formulation employs the performance index as a metric for the deviation between the AUV flight trajectory and the along-track altitude envelope of a terrain. An inequality constraint of the minimum allowable altitude is incorporated into our optimization. Moreover, considering the motion-constrained characteristics of a dynamic system, a state–space model describing the motion of a specific vehicle is also incorporated into the optimization. As the solution procedure a GDS-based search algorithm is used. By applying the algorithm, the optimal reference depths of the waypoints predefined within a horizontal plane are obtained. Being a depth-based control approach providing superior angular motion stability, our pseudo-terrain-following navigation is particularly suited to acoustic bottom mapping and imaging. The feasibility and efficacy of our approach are demonstrated through the application of optimal waypoints to an actual near-bottom survey mission using our cruising AUV.
Implementation of optical neural network based on Mach–Zehnder interferometer array

Compared with electrons, photons have the potential to realise ultra-high speed operations because of its unique high speed and high parallelism. In recent years, there have been many researches on neural networks using optical hardware. The Mach–Zehnder interferometer (MZI) and micro-ring resonator (MRR) are commonly used as optical devices to realise linear operation units in optical neural networks (ONN). MZI has the advantages of simple fabrication, high sensitivity, and easy integration, which has attracted the attention of researchers. We summarise the implementation methods of ONN matrix multiplication based on MZI, the implementation methods of non-linear activation, and the on-chip training methods. We first summarise the researches on matrix multiplication of ONN based on MZI. Three kinds of MZI grid decomposition methods, Fast Fourier Transform (FFT) grid structures, and the corresponding derivation processes are introduced, respectively. Then, several experimental implementations of ONN based on MZI are summarised, and the characteristics of optical processors fabricated in these references are analysed. Finally, the realisation methods of non-linear activation and on-chip training of silicon ONN are introduced, respectively.
Social networks and gamification in physical education: A case study

Recent research and studies on training plans in physical education agree on the need to incorporate digital technologies in teaching contexts. At the same time, the introduction of active methodologies, such as gamification, has become one of the major trends of recent years. In this paper we present the perception of teachers and students about a gamified proposal for physical education in secondary education that uses social networks as a way for communication, collaboration, and promotion of physical activity among students. &lt;i&gt;Edmodo&lt;/i&gt; has been used to carry out this experience because, as a vertical social network, it has an attractive interface for students and protects their privacy by not requiring any personal data.&lt;br /&gt; This is a qualitative research, with a case study methodological design, which has used the focus group and group interview as data collection techniques. The sample consisted of 10 teachers, which represents all the teachers participating in the experience, and 56 students. The results show the positive assessment, both by teachers and students, of the &lt;i&gt;Edmodo &lt;/i&gt;social network as a virtual teaching-learning environment for physical education in secondary education, and gamification as an active methodology. Likewise, the influence that the proposal, based on the use of digital technologies and gamification, has had on the promotion of physical activity among pupils has been high.
Hybridization Based Machine Translations for Low-Resource Language with Language Divergence

A hybridised form of direct and rule-based language processing is used in this paper to present a Machine translation system from Sanskrit to Hindi. The divergence between Sanskrit and Hindi is also discussed in this paper, along with a proposition for how to handle it. Sanskrit-Hindi bilingual dictionaries, Grammatical Sanskrit corpus and a Sanskrit analyses rule base, have all been used in the projected system. The projected system's ability to access data from various data vocabularies and rule bases utilised in the system expansion has been improved by the usage of Elasticsearch technique. Additionally, a novel technique that builds a parse tree from the parsing table is presented in this paper. The system processes the input Sanskrit sentence using the parsing approach and the Context Free Grammar in normal form for Sanskrit language processing. No standard Sanskrit-Hindi Grammatical corpora available for Machine Translation which is designed and developed in the proposed work. The specific language sentence is produced using the Grammatical corpora and bilingual dictionaries. The proposed system achieved a Bilingual Evaluation Understudy (BLEU) score of 51.6 percent after being tested using Python's natural language toolkit API. The proposed system performs better than current systems when compared to cutting-edge systems, according to the comparison.
World map of low-layer atmospheric extinction values for solar power tower plants projects

Atmospheric extinction is defined as the ability of the atmosphere to attenuate the solar radiation beam. In the case of concentrating solar power tower plants, low-layer atmospheric extinction plays an important role in the assessment of the solar resource by attenuating the radiation on its way from the heliostats to the receivers. The lack of values of the low-layer atmospheric extinction could lead to incorrect decisions in solar projects and investments. In this paper, we present a world map of annual low-layer atmospheric extinction values. The world map has been elaborated by processing the 30-year database provided by MERRA-2 and the use of atmospheric radiative transfer codes. The results show that the atmospheric extinction values are not negligible and reach high values in many areas of the sun belt. Global daily averages of atmospheric extinction are between approximately 3% and 30%, implying the similar values of annual energy losses in solar power tower plants. The southern hemisphere is characterised by low extinction values, while hight attenuation values are found in the northern hemisphere, especially in West and Central Africa, the Arabian Peninsula, Turkmenistan, India, and China, where annual atmospheric extinction exceeds 20%, reaching values of 30% in some places. These results have a major impact on the economic evaluation of solar tower projects. For atmospheric extinction values above 20%, the impact on the Levelized Cost Of Energy implies upward corrections of more than 25%, approaching 50% for values such as those obtained in Chat or Niger. Geographical information of low-layer atmospheric extinction values will help to reduce uncertainty margins in studies related to tower technology solar plants.
A hierarchical estimation of multi-modal distribution programming for regression problems

Estimation of distribution programming is an iterative method to evolve program trees. It estimates the distribution of the most suitable program trees and then produces a new generation of program trees by sampling from the distribution. This paper proposes a hierarchical estimation of multimodal distribution programming (HEMMDP). First, the population is divided into K subpopulations by a clustering algorithm where the distribution of each subpopulation is modified according to an objective function. Then, at each generation, a new subpopulation is generated from the modified distribution. The objective function aims to gradually improve the fitness of the program trees in each subpopulation. Finally, the appropriate program trees are added as new terminal nodes to the terminal set, resulting in a new hierarchy. The best-fitting program trees from each subpopulation with high synergistic value are chosen as basis functions. The proposed approach uses a linear function of the basis functions to solve the regression problem. The proposed method is evaluated on several real-world benchmark datasets. The datasets are divided into four classes: small-difficult, small-easy, large-difficult, and large-easy. The proposed method improves the results of the best methods for the regression problem by 232% and 62% for small difficult data sets and large difficult data sets, respectively.
A general framework for quantifying aleatoric and epistemic uncertainty in graph neural networks

Graph Neural Networks (GNN) provide a powerful framework that elegantly integrates Graph theory with Machine learning for modeling and analysis of networked data. We consider the problem of quantifying the uncertainty in predictions of GNN stemming from modeling errors and measurement uncertainty. We consider aleatoric uncertainty in the form of probabilistic links and noise in feature vector of nodes, while epistemic uncertainty is incorporated via a probability distribution over the model parameters. We propose a unified approach to treat both sources of uncertainty in a Bayesian framework, where Assumed Density Filtering is used to quantify aleatoric uncertainty and Monte Carlo dropout captures uncertainty in model parameters. Finally, the two sources of uncertainty are aggregated to estimate the total uncertainty in predictions of a GNN. Results in the real-world datasets demonstrate that the Bayesian model performs at par with a frequentist model and provides additional information about predictions uncertainty that are sensitive to uncertainties in the data and model.
JMDC: A joint model and data compression system for deep neural networks collaborative computing in edge-cloud networks

Deep Neural Networks (DNNs) have shown exceptional promise in providing Artificial Intelligence (AI) to many computer vision applications. Nevertheless, complex models, intensive computations, and resource constraints hinder the use of DNNs in edge computing scenarios. Existing studies have already focused on edge-cloud collaborative inference, where a DNN model is decoupled at an intermediate layer, and the two parts are sequentially executed at the edge device and the cloud server, respectively. In this work, we examine the status quo approaches of DNN execution, and find that it still takes a lot of time on edge device computation and edge-cloud data transmission. Using this insight, we propose a new edge-cloud DNN collaborative computing framework, JMDC, based on Joint Model and Data Compression. In JMDC, we adopt the attention mechanism to select important model channels for efficient inference computation, and important regions of the intermediate output for transferring to the cloud. We further use the quantization technique to reduce actual bits needed to be transferred. Depending on the specific application requirements on latency or accuracy, JMDC can adaptively determine the optimal partition point and compression strategy under different resource conditions. By extensive experiments based on the Raspberry Pi 4B device and the CIFAR10 dataset, we demonstrate the effectiveness of JMDC in enabling on-demand low-latency DNN inference, and its superiority over other baseline schemes.
Learning representational invariances for data-efficient action recognition

Data augmentation is a ubiquitous technique for improving image classification when labeled data is scarce. Constraining the model predictions to be invariant to diverse data augmentations effectively injects the desired representational invariances to the model (e.g., invariance to photometric variations) and helps improve accuracy. Compared to image data, the appearance variations in videos are far more complex due to the additional temporal dimension. Yet, data augmentation methods for videos remain under-explored. This paper investigates various data augmentation strategies that capture different video invariances, including photometric, geometric, temporal, and actor/scene augmentations. When integrated with existing semi-supervised learning frameworks, we show that our data augmentation strategy leads to promising performance on the Kinetics-100/400, Mini-Something-v2, UCF-101, and HMDB-51 datasets in the low-label regime. We also validate our data augmentation strategy in the fully supervised setting and demonstrate improved performance.
Integrated digital image correlation for mechanical characterization of carbon fiber-reinforced polymer plates

We use integrated digital image correlation (IDIC) with gradient-free optimization to identify the in-plane, linear-elastic material parameters of carbon fiber-reinforced polymer (CFRP) plates. With IDIC, a monolithic least-squares problem is formulated, based on the video capture of a tension/compression test, with the material parameters as unknowns. Transverse deformations are induced by using holeplate specimens, so that all parameters are identified with one tensile test. We also demonstrate how an invariant-based CFRP model, parameterized by the Tsai modulus, further enhances the robustness of IDIC characterization.
TRL: Transformer based refinement learning for hybrid-supervised semantic segmentation

This paper studies a new yet practical setting of semi-supervised semantic segmentation, i.e., hybrid-supervised semantic segmentation, where a small number of pixel-level (strong) annotations and a large number of image-level (weak) annotations are provided. It is a common practice to utilize pseudo labels to mitigate the issue of lacking strong annotations. However, most of the existing works focus on improving the model representation with unlabeled data, while ignoring the quality of pseudo labels, leading to poor segmentation performance. It is difficult to directly learn a model with limited images to produce high-quality pseudo labels. To address this problem, we propose a novel learning method, i.e., Transformer based Refinement Learning (TRL), which explores a learning process under the assistance of weak annotations and the supervision of strong annotations. TRL progressively refines heat maps from the poor qualities to the better ones to obtain satisfactory pseudo labels. Specifically, we propose a Dual-Cross Transformer Network (DCTN) to perform the refinement learning. DCTN extracts the features from both images and heat maps by a dual-stream network. The cross attentions inside DCTN hierarchically fuse the dual-stream features. The experiments on the PASCAL VOC and COCO datasets show that TRL outperforms the state-of-the-art methods for hybrid-supervised semantic segmentation.
Fairness constraint efficiency optimization for multiresource allocation in a cluster system serving internet of things

Summary Massive, diverse, and high‐frequency Internet of Things (IoT) applications pose challenges to the operation of cluster systems that serve it. Fair and efficient multidimensional resource allocation is of great significance to the sustainable operation of these systems. However, most of the existing cluster multiresource allocation optimization researches focus too much on the fairness of resource allocation and ignore the efficiency. The unbalanced use of multidimensional system resources reduces the effective utilization of system resources, which seriously affects the service quality of IoT applications. In this paper, we define the multiresource fair and efficient sharing optimization as a fairness‐constrained efficiency optimization problem, which is from dynamics, discrete resources, and heterogeneous perspectives according to the characteristics of cluster system in practical. Moreover, we present a dynamic efficiency‐aware multiresource fair allocation algorithm, DEF, which can improve the ability of the cluster system to serve diverse IoT applications. In the algorithm, large jobs schedule to the servers that expect the least remaining resources. Simulations performed using Google cluster‐usage traces show that DEF can improve system resource utilization and guarantee the fairness of sharing among users.
Quantum approximate optimization for combinatorial problems with constraints

The Quantum Approximate Optimization Algorithm (QAOA) is an algorithmic framework for finding approximate solutions to combinatorial optimization problems, derived from an approximation to the Quantum Adiabatic Algorithm (QAA). In solving combinatorial optimization problems with constraints in the context of QAOA, one needs to find a way to encode problem constraints into the scheme. In this paper, we propose and discuss several QAOA-based algorithms to solve combinatorial optimization problems with equality and/or inequality constraints. We formalize the encoding method of different types of constraints, and demonstrate the effectiveness and efficiency of the proposed scheme by providing examples and results for some well-known NP optimization problems. Compared to previous constraint-encoding methods, we argue our work leads to a more generalized framework for finding, in the context of QAOA, higher-quality approximate solutions to combinatorial problems with various types of constraints.