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SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments. This Perspective describes the development and capabilities of SciPy 1.0, an open source scientific computing library for the Python programming language.

\"Computational Fluid-Structure Interaction is a complete, self-contained reference that takes the reader from the fundamentals of computational fluid and solid mechanics all the way to the state-of-the-art in CFSI research\"--

Background Biomolecular interactions that modulate biological processes occur mainly in cavities throughout the surface of biomolecular structures. In the data science era, structural biology has benefited from the increasing availability of biostructural data due to advances in structural determination and computational methods. In this scenario, data-intensive cavity analysis demands efficient scripting routines built on easily manipulated data structures. To fulfill this need, we developed pyKVFinder, a Python package to detect and characterize cavities in biomolecular structures for data science and automated pipelines. Results pyKVFinder efficiently detects cavities in biomolecular structures and computes their volume, area, depth and hydropathy, storing these cavity properties in NumPy arrays. Benefited from Python ecosystem interoperability and data structures, pyKVFinder can be integrated with third-party scientific packages and libraries for mathematical calculations, machine learning and 3D visualization in automated workflows. As proof of pyKVFinder’s capabilities, we successfully identified and compared ADRP substrate-binding site of SARS-CoV-2 and a set of homologous proteins with pyKVFinder, showing its integrability with data science packages such as matplotlib, NGL Viewer, SciPy and Jupyter notebook. Conclusions We introduce an efficient, highly versatile and easily integrable software for detecting and characterizing biomolecular cavities in data science applications and automated protocols. pyKVFinder facilitates biostructural data analysis with scripting routines in the Python ecosystem and can be building blocks for data science and drug design applications.

Geospatial artificial intelligence (GeoAI) has emerged as a subfield of GIScience that uses artificial intelligence approaches and machine learning techniques for geographic knowledge discovery. The non-regularity of data structures has recently led to different variants of graph neural networks in the field of computer science, with graph convolutional neural networks being one of the most prominent that operate on non-euclidean structured data where the numbers of nodes connections vary and the nodes are unordered. These networks use graph convolution – commonly known as filters or kernels – in place of general matrix multiplication in at least one of their layers. This paper suggests spatial regression graph convolutional neural networks (SRGCNNs) as a deep learning paradigm that is capable of handling a wide range of geographical tasks where multivariate spatial data needs modeling and prediction. The feasibility of SRGCNNs lies in the feature propagation mechanisms, the spatial locality nature, and a semi-supervised training strategy. In the experiments, this paper demonstrates the operation of SRGCNNs with social media check-in data in Beijing and house price data in San Diego. The results indicate that a well-trained SRGCNN model is capable of learning from samples and performing reasonable predictions for unobserved locations. The paper also presents the effectiveness of incorporating the idea of geographically weighted regression for handling heterogeneity between locations in the model approach. Compared to conventional spatial regression approaches, SRGCNN-based models tend to generate much more accurate and stable results, especially when the sampling ratio is low. This study offers to bridge the methodological gap between graph deep learning and spatial regression analytics. The proposed idea serves as an example to illustrate how spatial analytics can be combined with state-of-the-art deep learning models, and to enlighten future research at the front of GeoAI.

A Lexicographic Data Pulling Structure for Online Dictionaries. In the field of lexicography the transition from printed to online dictionaries has had a significant influence on numerous aspects of both lexicographic theory and the lexicographic practice. In the continued development of lexicographic theory this influence has to be formulated in order to present guidelines for the optimal application of the resulting adaptations in the lexicographic practice. Dictionary structures should be investigated to determine which structures can be maintained in the new medium, which structures need to be adapted and which new structures are coming to the fore. The focus in this article is on adaptations in lexicographic structures. Reference is made to structures of which the adaptations have already been discussed in metalexicography. The main emphasis is on different types of data distribution structures in online dictionaries. Provision is made for a comprehensive data distribution structure that can be employed in dictionary portals to give the user access to dictionary-external sources. The need of users for more freedom to select their required data, leads to proposals for a new structure, namely the data pulling structure. By employing this structure users can access the internet as lexicographic corpus from any point in an online dictionary to retrieve from the data there the information they require in a specific situation of use. The data pulling structure confirms the status of dictionaries as integrated information instruments and puts them within the scope of an over-arching data structure.

With the development of the mobile communication network, mobile learning is becoming a hot spot in the world. According to the idea of system engineering, this paper describes the teaching design process based on WeChat, which is divided into three parts, i.e. teaching preparation, teaching implementation and teaching evaluation. It also elaborates on the specific activities of each stage. The result shows that teaching activities based on the WeChat platform can promote students’ learning and have certain practical significance.

Bloom filter is a compact memory-efficient probabilistic data structure supporting membership testing, i.e., to check whether an element is in a given set. However, as Bloom filter maps each element with random hash functions, little flexibility is provided even if the information of negative keys (elements are not in the set) is available, especially when the misidentification of negative keys brings different costs. The problem worsens when the hash functions are non-uniform, i.e., mapping each element into Bloom filter non-uniformly. To address the above problem, we propose a new hash adaptive Bloom filter (HABF) that supports customizing hash functions for keys. Besides, we propose a filter family, including f-HABF (fast hashing version), c-HABF (cache-friendly version), and s-HABF (stacked version). We show that HABF family is Pareto optimal among all comparison filters in terms of accuracy and query latency. We conduct extensive experiments on representative datasets, and the results show that HABF family outperforms the standard Bloom filter and its cutting-edge variants on the whole in terms of accuracy, construction/query time, and memory space consumption. All the source codes are available in our source codes ( https://github.com/njulands/HashAdaptiveBF ).

Earth Observation (EO) data is a source of a wide range of information, in vegetation, oceanography, land use, land cover and many more applications. To uncover the hidden information in the data, unsupervised learning techniques like clustering is used popularly. With technological advancements, the amount of data received through satellites rises exponentially, possessing the properties of Big Data. Traditional implementations of clustering algorithms have processing limitations based on the memory capability of the system. In general, applying any clustering algorithm directly to the large EO raster data requires a considerably large amount of time, due to the spatio-temporal nature of the data. The data generated by remote sensors have significant redundant values, so we have made an attempt to use compressed raster data for clustering without decompressing it. In this work, we present the compact data structure, known as k2-raster, based technique for clustering raster data. k2-raster preserves the spatial context in the image and provides time efficient and lossless compression. We have applied this technique to OCM2-NDVI, in GeoTIFF format, single and stacked image to develop a compressed dataset for efficient representation. As partition based clustering algorithms are widely used in clustering EO data because of their simplicity and time efficiency, we have examined the results on k-means and mini-batch k-means. We have also assessed the performance of our algorithm on model based clustering algorithms. It has been demonstrated that, for datasets with larger numbers of raster values, the developed compact data structure based approach works very well in terms of compact representation of data as well as, with both the partition based and model based clustering techniques making the clustering scalable.

In this work, we put forward a space-preserving data structure for isogeometric topology optimization in B-splines space, exploiting the Bėzier extraction operator of B-splines. According to the space-preserving nature of Bernstein basis function space within an individual isogeometric analysis element, we derive the standard elemental stiffness matrix for all Bėzier elements. With the aid of Bėzier extraction matrix obtained from the knot insertion algorithm, all the elemental stiffness matrices of B-spline elements can be equivalently expressed by the aforementioned standard Bėzier stiffness matrix. The data processing arrays are put forward for B-spline and Bėzier isogeometric analysis meshes, which constitute the space-preserving data structure for isogeometric topology optimization. Three numerical examples are used to validate the effectiveness of the proposed space-preserving data structures for isogeometric topology optimization, where the maximum memory burden is decreased by four orders of magnitude, and the maximum computational efficiency is improved by two orders of magnitude, involved in storing and computing the essential data for the stiffness matrices of all B-spline elements, in comparison with the conventional space-varying data structure. Therefore, the proposed space-preserving data structure is a promising way of implementing isogeometric topology optimization method.

Graph is a well known data structure to represent the associated relationships in a variety of applications, e.g., data science and machine learning. Despite a wealth of existing efforts on developing graph processing systems for improving the performance and/or energy efficiency on traditional architectures, dedicated hardware solutions, also referred to as graph processing accelerators, are essential and emerging to provide the benefits significantly beyond what those pure software solutions can offer. In this paper, we conduct a systematical survey regarding the design and implementation of graph processing accelerators. Specifically, we review the relevant techniques in three core components toward a graph processing accelerator: preprocessing, parallel graph computation, and runtime scheduling. We also examine the benchmarks and results in existing studies for evaluating a graph processing accelerator. Interestingly, we find that there is not an absolute winner for all three aspects in graph acceleration due to the diverse characteristics of graph processing and the complexity of hardware configurations. We finally present and discuss several challenges in details, and further explore the opportunities for the future research.