Miracle-dz/UniFAR-FTUs huggingface dataset:
Early Prediction of Heart Disease via LSTM-XGBoost

With the development of information and technology, especially with the boom in big data, healthcare support systems are becoming much better. However, an early diagnosis is not an easy task because it is hard to find out the hidden patterns in the disease. The symptoms of heart failure are usually non-specific. Therefore, this study aims to devise an early prediction model for incident heart disease based on machine learning algorithms. In this study, we compared several powerful models, including support vector machines (SVM), k-nearest neighbor (KNN), decision tree, extreme gradient boosting (XGBoost), random forest, gradient boosting decision tree (GBDT), categorical boosting (CatBoost), light gradient boosting machine (LightGBM), and choose two best models, namely, XGBoost and LSTM as our prediction model. The XGBoost algorithm was used to extract a subset from the data of patients, which will be fed into the LSTM-XGBoost model for training. An experiment comparison is conducted based on the data from Kaggle. Experimental results demonstrate that the LSTM-XGBoost model can operate effectively with a prediction accuracy achieving 0.9896 and a loss rate achieving 0.0105, which is much better than XGBoost or LSTM, respectively.
SOC Estimation of Lithium-ion Battery Based on Attention Mechanism of EMD-Bi-LSTM Improving by Bayesian Optimization

Data-driven methods are widely applied to predict state-of-charge (SOC) of lithium-ion batteries, as they do not require the detailed knowledge in terms of complex processes occurring inside the battery, but merely learn the underlying nonlinear link between SOC and outside measurements. However, the method has two limitations: (1) Feature information is not fully utilized; (2) Model parameters are numerous and difficult to determine; To address these challenges, this paper proposes a new deep learning approach using feature enhancement and adaptive optimization. Firstly, the empirical mode decomposition (EMD) is used to predict the features of the present situation, eliminating noise points of the data and improving the dimension of the input data to extract the effective decomposition characteristics. Secondly, the feature enhancement technology for box-cox is used to enhance the correlation between features and SOC. With these methods, the potential of features can be made full use of. Then, a two-way long-short-term memory (Bi-LSTM) network combined with dual-stage attention mechanism is used to establish the SOC prediction model. The input attention method independently chooses valuable characteristics as input at each time step in the first stage. In the second stage, the temporal attention mechanism fully considers the correlation of time series and selects the hidden layer state to accurately estimate SOC. Finally, the model's parameters are improved via Bayesian optimization (BO). The experimental findings demonstrate that the model's accuracy is superior than that of other prediction techniques. The average absolute error can be less than 0.50%, and the prediction accuracy can reach 99.91%.
CopyFormer: A Transformer Text Generation model combined with Pointer Network

Industrial Internet Research Institute Wuhan Huazhong Numerical Control Co., Ltd.Wuhan, 430074, China In the Pointer Generation Network, the model uses the attention mechanism in Seq2Seq [1]to selectively point to the text that needs to be copied, thus effectively alleviating the problem of out of vocabulary (OOV). However, Seq2Seq has some defects, so Google proposed a new Encoder-Decoder architecture Transformer [2], to replace the traditional Seq2Seq. This paper proposes a Pointer Generation Network model based on Transformer, which can effectively improve the generation ability of the model by using the powerful model of Transformer combined with Pointer Generation Network. In addition, considering that the Pointer Generation Network does not effectively use the relevant information in the input sequence, this paper selectively combines the information in the input sequence to the Decoder, so as to improve the effect of the model. Finally, experiments on Similar Text Generation tasks verify the effectiveness of the method.
Environment Matting and Compositing

This paper introduces a new process, environment matting, which captures not just a foreground object and its traditional opacity matte from a real-world scene, but also a description of how that object refracts and reflects light, which we call an environment matte. The foreground object can then be placed in a new environment, using environment compositing, where it will refract and reflect light from that scene. Objects captured in this way exhibit not only specular but glossy and translucent effects, as well as selective attenuation and scattering of light according to wavelength. Moreover, the environment compositing process, which can be performed largely with texture mapping operations, is fast enough to run at interactive speeds on a desktop PC. We compare our results to photos of the same objects in real scenes. Applications of this work include the relighting of objects for virtual and augmented reality, more realistic 3D clip art, and interactive lighting design.
Comparisons of feature extraction algorithm based on unmanned aerial vehicle image

Abstract Feature point extraction technology has become a research hotspot in the photogrammetry and computer vision. The commonly used point feature extraction operators are SIFT operator, Forstner operator, Harris operator and Moravec operator, etc. With the high spatial resolution characteristics, UAV image is different from the traditional aviation image. Based on these characteristics of the unmanned aerial vehicle (UAV), this paper uses several operators referred above to extract feature points from the building images, grassland images, shrubbery images, and vegetable greenhouses images. Through the practical case analysis, the performance, advantages, disadvantages and adaptability of each algorithm are compared and analyzed by considering their speed and accuracy. Finally, the suggestions of how to adapt different algorithms in diverse environment are proposed.
Efficient Source Inference in Large-Scale Networks based on Community Detection

Propagation of rumors is a big challenge for public safety, thus limiting the spread of rumors can help maintain social harmony and stability. Rumor source detection is one of the solutions to this problem, which controls the spread of rumors by locating the source node. However such algorithms usually have the problem of high time complexity. This paper reduces the time complexity by introducing a community detection algorithm so that it can be used for larger networks, and proposes a heuristic method to improve its accuracy. To verify the effectiveness of the algorithm, experiments were conducted on different network scales according to the susceptible-infected (SI) model. Results show that the proposed algorithm converges faster than the benchmark method without reducing accuracy, while the heuristic method further reduces errors in the proposed algorithm. The proposed algorithm is expected to be useful in various fields such as public safety, intelligence analysis and emergency response, providing an effective method to identify and prevent the spread of false information.
Carbon-Efficient Design Optimization for Computing Systems

The world's push toward an environmentally sustainable society is highly dependent on the semiconductor industry, due to carbon footprints of global-scale sources such as computing systems for virtual and extended reality applications (VR and XR). Despite previous carbon modeling efforts for such computing systems, there lacks a wide range of design tools to optimize total life cycle carbon footprint (during manufacturing and also during day-to-day operation), while meeting application-level constraints (power, performance, area). To address this need, we have developed a carbon-aware design framework that optimizes carbon efficiency of computing systems---quantified by metrics such as total Carbon Delay Product (tCDP: the product of total carbon and total application execution time)---while also identifying key design parameters for improving carbon efficiency. As a case study, we demonstrate the effectiveness of our framework to improve tCDP of hardware accelerators for artificial intelligence (AI) and XR applications. We show: (1) optimizing for carbon efficiency (tCDP) instead of energy efficiency (Energy-Delay Product or EDP) improves carbon efficiency by up to 6.9×---i.e., optimizing for EDP is insufficient; (2) for multi-core CPUs inside production VR headsets, optimizing number of cores (from 8 to 4) improves tCDP by 1.25× (over their entire lifetime); (3) leveraging an advanced three-dimensional integration (3D) technique (3D stacking of separately-fabricated logic and memory chips) can improve tCDP by 6.9× vs. conventional systems (no 3D stacking).
Research on the Method Matching of Manufacturing Service Resources in a Cloud Manufacturing Environment

In order to quickly and accurately search for manufacturing service resources that meet the demand from the cloud manufacturing(CMfg) platform. In this paper, we first define a multigranularity manufacturing service resource description model. On this basis, a two-stage service resource matching method based on semantic similarity-based hierarchical matching and service quality evaluation is proposed, in which the matching degree of each granularity of manufacturing resource supply and demand is first calculated to generate a candidate set of manufacturing service resources that satisfy the conditions. The service quality evaluation index is then used to match the service providers in the candidate set with service quality. The final set of manufacturing service resources that meet user requirements is obtained, and the effectiveness and feasibility of the method are verified by examples.
Mean Value Coordinates for Closed Triangular Meshes

Constructing a function that interpolates a set of values defined at vertices of a mesh is a fundamental operation in computer graphics. Such an interpolant has many uses in applications such as shading, parameterization and deformation. For closed polygons, mean value coordinates have been proven to be an excellent method for constructing such an interpolant. In this paper, we generalize mean value coordinates from closed 2D polygons to closed triangular meshes. Given such a mesh P, we show that these coordinates are continuous everywhere and smooth on the interior of P. The coordinates are linear on the triangles of P and can reproduce linear functions on the interior of P. To illustrate their usefulness, we conclude by considering several interesting applications including constructing volumetric textures and surface deformation.
Effects of acute rejection, infection, and chronic rejection on the perfusion of single lung grafts as assessed by 133-XE radiospirometry

A current problem after single lung transplantation is the evaluation of graft function during various pathologic conditions of the transplanted lung. In 10 single lung recipients, of which six had a parenchymal lung disease, and four pulmonary hy.pertension, relative perfusion of the transplanted lung (Qtx) was determined with multidetector 133-Xe radiospirometry. The determinations were performed seven times during acute rejection, nine times during infection, and four times during simultaneous rejection and infection. In the patients with a follow-up period over 5 months, the effect of bronchiolitis obtiterans syndrome on perfusion was assessed as well. Statistically significant decrease in the (~tx was observed during acute rejection, whereas the perfusion change during infection was not significant. The Qtx tended to decrease during long-term follow-up, and the decrease was more prominent in the patients who developed bronchiolitis obliterans syndrome. The assessment of the (~tx with radiospirometry distinguishes acute rejection from infection and can be used for differential diagnostic aid after single lung transplantation.
Mixed-Integer Quadrangulation

We present a novel method for quadrangulating a given triangle mesh. After constructing an as smooth as possible symmetric cross field satisfying a sparse set of directional constraints (to capture the geometric structure of the surface), the mesh is cut open in order to enable a low distortion unfolding. Then a seamless globally smooth parametrization is computed whose iso-parameter lines follow the cross field directions. In contrast to previous methods, sparsely distributed directional constraints are sufficient to automatically determine the appropriate number, type and position of singularities in the quadrangulation. Both steps of the algorithm (cross field and parametrization) can be formulated as a mixed-integer problem which we solve very efficiently by an adaptive greedy solver. We show several complex examples where high quality quad meshes are generated in a fully automatic manner.
Lightweight Fabric Defect Detection Based on Improved YOLOv4

Fabric quality is crucial for subsequent production, so automated detection of different types of defects on fabric surfaces is essential. To improve the applicability of the detection model in textile factories, a lightweight fabric defect detection method based on YOLOv4 is proposed, which firstly addresses the characteristics of large scale transformation of fabric defects and more small defects by combining the shallow features of the 11th layer in YOLOv4 with the deep features to generate a new scale feature layer; secondly, the SPP structure in YOLOv4 is improved to using pooling decomposition to improve the large pooling kernel and increase the model computation speed; finally, the depth-separable convolution is introduced to optimize the model size, making the model detection faster to meet the actual needs of textile mills. The improved network improves the defect detection accuracy, with an average detection accuracy of 79.31%, and its detection speed is also 5 times faster than that of the original YOLOv4, which is more suitable for the actual detection of textile mills than the original YOLOv4.
Node Importance Evaluation in Complex Networks Based on Community Division

Considering the high computational complexity of Closeness Centrality and the structural characteristics of community in complex networks, The BridgeRank (BR) algorithm to evaluate node importance of complex networks based on community division is proposed. The complex network is divided into several communities. The node with the largest local betweenness centrality in each community is extracted as the keynode of the community. The importance ranking of nodes is obtained by adopting the BR algorithm to caculate the sum of the shortest paths from each node to these key nodes in the network. The BR algorithm is achieved based on the Kernighan-Lin, Girvan-Newman, Newman fast and Louvain algorithm respectively. The experimental shows that BR algorithm based on Louvain community division has the highest accuracy in estimating node importance. On the basis that Community Size BridgeRank (CS-BR) and the Local Betweenness Centrality BridgeRank (LBC-BR) are proposed based on the size of the community and the weight of the local betweenness centrality of key nodes respectively. The experiments results on four detasets show that compared with some classic algorithms, the important nodes evaluated by the three proposed methods have stronger spread ability in SIR model, Moreover,the improved CS-BR and LBC-BR algorithm are better than BR.
Dynamic Graph Convolutional Transformer for Short-term Wind Speed Forecasting

Wind speed forecasting is still a challenging problem, especially considering the correlations of spatial and temporal domains. However, the changing properties of spatial dependencies over time are ignored in most existing algorithms. In this paper, we propose a novel spatio-temporal machine learning algorithm, named Dynamic Graph Convolutional Transformer (DGCT), for wind speed forecasting. The key contribution of the proposed method is that graph convolutional networks are embedded into self-attention layers of Transformer to capture spatio-temporal correlations to improve the accuracy of forecasting. For the changing properties of spatial dependencies, we model the spatial dependencies as a mixture of global and localized patterns, which are represented by static and dynamic matrices respectively. Moreover, an auxiliary network is designed to generate the dynamic matrix, which further improve the forecasting accuracy. Experiments on two real-world datasets demonstrate that the proposed method outperformed other existing methods consistently.
A Method for Flow Field Reconstruction based on Fourier Neural Operator Network

Flow field reconstruction is a key task of aircraft sensing the external environment. Numerical methods like CFD are time-consuming and computationally expensive, so surrogate model-based methods are adopted in the task. However, most kinds of surrogate model only learn the mapping from the input space to the output space. Fourier neural operator (FNO) is a model structure that can learn from the functional input space to output functional space. This paper develops the FNO method to reconstruct the flow field around a wing using pressure sensor measurements on the wing surface. We carried out experiments to validate the developed method, and the result shows our method can achieve high accuracy and noise robustness.
The Digital Michelangelo Project: 3D Scanning of Large Statues

We describe a hardware and software system for digitizing the shape and color of large fragile objects under non-laboratory conditions. Our system employs laser triangulation rangefinders, laser time-of-flight rangefinders, digital still cameras, and a suite of software for acquiring, aligning, merging, and viewing scanned data. As a demonstration of this system, we digitized 10 statues by Michelangelo, including the well-known figure of David, two building interiors, and all 1,163 extant fragments of the Forma Urbis Romae, a giant marble map of ancient Rome. Our largest single dataset is of the David - 2 billion polygons and 7,000 color images. In this paper, we discuss the challenges we faced in building this system, the solutions we employed, and the lessons we learned. We focus in particular on the unusual design of our laser triangulation scanner and on the algorithms and software we developed for handling very large scanned models.
Toolglass and Magic Lenses: The See-Through Interface

ToolglassTM widgets are new user interface tools that can appear, as though on a transparent sheet of glass, between an application and a traditional cursor. They can be positioned with one hand while the other positions the cursor. The widgets provide a rich and concise vocabulary for operating on application objects. These widgets may incorporate visual filters, called Magic Lens<sup>TM</sup> filters, that modify the presentation of application objects to reveal hidden information, to enhance data of interest, or to suppress distracting information. Together, these tools form a see-through interface that offers many advantages over traditional controls. They provide a new style of interaction that better exploits the user's everyday skills. They can reduce steps, cursor motion, and errors. Many widgets can be provided in a user interface, by designers and by users, without requiring dedicated screen space. In addition, lenses provide rich context-dependent feedback and the ability to view details and context simultaneously. Our widgets and lenses can be combined to form operation and viewing macros, and can be used over multiple applications.
CustomHalide – A new plugin of clang for loop optimization

Nowadays, Polyhedral and Halide techniques optimize complex nested loops in deep learning frameworks’ basic operator compilation process. However, faced with many algorithm platforms, a single framework cannot be used for deployment purposes. In some scenarios, the complex deployment environment of the framework is also an important consideration. At this point, users are more interested in whether there is an optimization tool chain that automatically optimizes the simple described algorithm directly to solve the needs of customized deployment while bringing performance benefits. This paper presents an automatic optimization toolchain CustomHalide based on Halide automatic scheduling algorithm and Clang AST. The core of CustomHaide is the tool Loop-convert, which realizes loop parameters’ extraction from source code, converting parameters, calling the Halide auto-scheduler, integration and compilation of optimized code. Loop-convert provides a fully automated flow from C/C++ source code to optimized programs. After testing, CustomHalide has a better effect on identifying and extracting source code than Polly. At the same time, the average performance of CustomHalide is 3% higher than that of Halide both on CPU and GPU in the case of known bound information. The average compilation time of CustomHalide is one third of TVM with the same end-to-end performance. Finally, through the bound setting strategy, CustomHalide supports loop optimization with unknown bound information, and its average performance is about 50% higher than that of the program optimized by clang-O3.
Knowledge Graph Embedding Method Based on Entity Metric Learning

A knowledge graph is a knowledge base of graph structure that stores massive facts and their relations in the form of triplets. Embedding entities and relations in knowledge graph into low-dimensional continuous vector space is of great significance for downstream tasks, whereas most existing methods give an end-to-end fixed representation. Based on deep metric learning, we give different embeddings for the same entity in different relations and positions, thus dynamically representing entities in knowledge graph and effectively modeling complex relations. In addition, a path-based negative sampling method is proposed to widen the distance between similar entities to better deal with the aggregation problem of embedding entities in the representation space under one-to-many relation. Experiments show that the MRR and Hit@1 of this method on the linked prediction dataset FB15k-237 reach 0.360 and 0.267 respectively, which are higher than the mainstream distance models and semantic matching models. Finally, the MRR-Dimension curves demonstrates that our model can perform well in both low and high embedding dimensions and has the potential to be applied to large-scale knowledge graph.