Lightweight Image Super-Resolution via Weighted Multi-Scale Residual Network

The tradeoff between efficiency and model size of the convolutional neural network(CNN)is an essential issue for applications of CNN-based algorithms to diverse real-world tasks.Although deep learning-based methods have achieved significant improvements in image super-resolution(SR),current CNNbased techniques mainly contain massive parameters and a high computational complexity,limiting their practical applications.In this paper,we present a fast and lightweight framework,named weighted multi-scale residual network(WMRN),for a better tradeoff between SR performance and computational efficiency.With the modified residual structure,depthwise separable convolutions(DS Convs)are employed to improve convolutional operations’efficiency.Furthermore,several weighted multi-scale residual blocks(WMRBs)are stacked to enhance the multi-scale representation capability.In the reconstruction subnetwork,a group of Conv layers are introduced to filter feature maps to reconstruct the final high-quality image.Extensive experiments were conducted to evaluate the proposed model,and the comparative results with several state-of-the-art algorithms demonstrate the effectiveness of WMRN.

NPIPVis:A visualization system involving NBA visual analysis and integrated learning model prediction

Background Data-driven event analysis has gradually become the backbone of modern competitive sports analysis. Competitive sports data analysis tasks increasingly use computer vision and machine-learning models for intelligent data analysis. Existing sports visualization systems focus on the player–team data visualization, which is not intuitive enough for team season win–loss data and game time-series data visualization and neglects the prediction of all-star players. Methods This study used an interactive visualization system designed with parallel aggregated ordered hypergraph dynamic hypergraphs, Calliope visualization data story technology,and i Storyline narrative visualization technology to visualize the regular statistics and game time data of players and teams. NPIPVis includes dynamic hypergraphs of a team’s wins and losses and game plot narrative visualization components. In addition, an integrated learning-based all-star player prediction model, SRR-voting, which starts from the existing minority and majority samples, was proposed using the synthetic minority oversampling technique and Random Under Sampler methods to generate and eliminate samples of a certain size to balance the number of allstar and average players in the datasets. Next, a random forest algorithm was introduced to extract and construct the features of players and combined with the voting integrated model to predict the all-star players, using GridSearch CV, to optimize the hyperparameters of each model in integrated learning and then combined with five-fold cross-validation to improve the generalization ability of the model. Finally, the SHapley Additive ex Planations(SHAP) model was introduced to enhance the interpretability of the model. Results The experimental results of comparing the SRR-voting model with six common models show that the accuracy, F1-score, and recall metrics are significantly improved, which verifies the effectiveness and practicality of the SRR-voting model. Conclusions This study combines data visualization and machine learning to design a National Basketball Association data visualization system to help the general audience visualize game data and predict all-star players;this can also be extended to other sports events or related fields.

Simultaneous Feammox and anammox process facilitated by activated carbon as an electron shuttle for autotrophic biological nitrogen removal

Ferric iron reduction coupled with anaerobic ammonium oxidation(Feammox)is a novel ferric-dependent autotrophic process for biological nitrogen removal(BNR)that has attracted increasing attention due to its low organic carbon requirement.However,extracellular electron transfer limits the nitrogen transformation rate.In this study,activated carbon(AC)was used as an electron shuttle and added into an integrated autotrophic BNR system consisting of Feammox and anammox processes.The nitrogen removal performance,nitrogen transformation pathways and microbial communities were investigated during 194 days of operation.During the stable operational period(days 126–194),the total nitrogen(TN)removal efficiency reached 82.9%±6.8%with a nitrogen removal rate of 0.46±0.04 kg-TN/m^(3)/d.The contributions of the Feammox,anammox and heterotrophic denitrification pathways to TN loss accounted for 7.5%,89.5%and 3.0%,respectively.Batch experiments showed that AC was more effective in accelerating the Feammox rate than the anammox rate.X-ray photoelectron spectroscopy(XPS)analyses showed the presence of ferric iron(Fe(III))and ferrous iron(Fe(II))in secondary minerals.X-ray diffraction(XRD)patterns indicated that secondary iron species were formed on the surface of iron-AC carrier(Fe/AC),and Fe(III)was primarily reduced by ammonium in the Feammox process.The phyla Anaerolineaceae(0.542%)and Candidatus Magasanikbacteria(0.147%)might contribute to the Feammox process,and Candidatus Jettenia(2.10%)and Candidatus Brocadia(1.18%)were the dominative anammox phyla in the bioreactor.Overall,the addition of AC provided an effective way to enhance the autotrophic BNR process by integrating Feammox and anammox.

Insoluble carbonaceous materials as electron shuttles enhance the anaerobic/anoxic bioremediation of redox pollutants:Recent advances

Carbonaceous materials can accelerate extracellular electron transfer for the biotransformation of many recalcitrant,redox-sensitive contaminants and have received considerable attention in fields related to anaerobic bioremediation.As important electron shuttles(ESs),carbonaceous materials effectively participate in redox biotransformation processes,especially microbially-driven Fe reduction or oxidation coupled with pollutions transformation and anaerobic fermentation for energy and by-product recovery.The related bioprocesses are reviewed here to show that carbonaceous ESs can facilitate electron transfer between microbes and extracellular substrates.The classification and characteristics of carbon-containing ESs are summarized,with an emphasis on activated carbon,graphene,carbon nanotubes and carbonbased immobilized mediators.The influencing factors,including carbon material properties(redox potential,electron transfer capability and solubility)and environmental factors(temperature,p H,substrate concentration and microbial species),on pollution catalytic efficiency are discussed.Furthermore,we briefly describe the prospects of carbonaceous ESs in the field of microbial-driven environmental remediation.

WaveLines:towards effective visualization and analysis of stability in power grid simulation

Closely related to the safety and stability of power grids,stability analysis has long been a core topic in the electric industry.Conventional approaches employ computational simulation to make the quantitative judgement of the grid stability under distinctive conditions.The lack of in-depth data analysis tools has led to the difficulty in analytical tasks such as situation-aware analysis,instability reasoning and pattern recognition.To facilitate visual exploration and reasoning on the simulation data,we introduce WaveLines,a visual analysis approach which supports the supervisory control of multivariate simulation time series of power grids.We design and implement an interactive system that supports a set of analytical tasks proposed by domain experts and experienced operators.Experiments have been conducted with domain experts to illustrate the usability and effectiveness of WaveLines.

Example-based web page recoloring method

1 Introduction The user interface(UI)is very important for a software product,and a well-designed UI will attract users and make a good impression.It has been shown that color plays an important role in invoking emotional reactions[1],influencing usability[2],forming first impressions[3],and affecting the energy cost of the UI[4].Generating satisfying color schemes has high requirements for experience,aesthetic,and creative inspiration of designers[5].

Combined UAMP and MF Message Passing Algorithm for Multi-Target Wideband DOA Estimation with Dirichlet Process Prior

When estimating the direction of arrival (DOA) of wideband signals from multiple sources, the performance of sparse Bayesian methods is influenced by the frequency bands occupied by signals in different directions. This is particularly true when multiple signal frequency bands overlap. Message passing algorithms (MPA) with Dirichlet process (DP) prior can be employed in a sparse Bayesian learning (SBL) framework with high precision. However, existing methods suffer from either high complexity or low precision. To address this, we propose a low-complexity DOA estimation algorithm based on a factor graph. This approach introduces two strong constraints via a stretching transformation of the factor graph. The first constraint separates the observation from the DP prior, enabling the application of the unitary approximate message passing (UAMP) algorithm for simplified inference and mitigation of divergence issues. The second constraint compensates for the deviation in estimation angle caused by the grid mismatch problem. Compared to state-of-the-art algorithms, our proposed method offers higher estimation accuracy and lower complexity.