This paper proposes a reinforcement learning-based control education platform utilizing python and light-weight rapid control prototyping (LW-RCP). The platform employs the Sim-to-Real technique, in which neural netwo...
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This paper presents opstool, a python package designed to enhance the pre- and post-processing capabilities of OpenSees and OpenSeesPy. It simplifies structural analysis workflows by automating tasks such as mesh gene...
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This paper presents opstool, a python package designed to enhance the pre- and post-processing capabilities of OpenSees and OpenSeesPy. It simplifies structural analysis workflows by automating tasks such as mesh generation, data management, and data visualization. The package efficiently manages large-scale simulation results, enabling the structured extraction of system, nodal, and element responses. In addition, it integrates adaptive iteration algorithms to improve convergence issues in nonlinear static and dynamic response analyses. By reducing manual modeling effort and enhancing model accuracy, opstool improves workflow efficiency and enables researchers and practitioners to conduct more effective computational simulations using OpenSees and OpenSeesPy, which further supports various task forces in earthquake engineering, such as performance-based design of new structures and regional seismic risk assessment of existing infrastructure systems.
In recent years, python has become widely used language for data processing in machine learning and deep learning. However, the dynamic typing of python can lead to errors caused by array shape mismatches that are onl...
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In recent years, python has become widely used language for data processing in machine learning and deep learning. However, the dynamic typing of python can lead to errors caused by array shape mismatches that are only detected at runtime. To increase development efficiency, we propose a static method to check python code that can detect shape errors before execution. Existing research activities such as Pytropos provide the capability to manually annotate the shape types of arrays and external datasets with python's type hint feature. However, manual annotation decreases code flexibility and can cause problems, such as incorrect marking due to human error, and wasted labor and time costs. To address these issues, we propose a method that leverages abstract interpretation and abstract syntax tree analysis to statically check code for array and dataset shape types, thus reducing the need for manual annotation and improving code flexibility. We give an implementation of the proposed method named ShapeChecker for the widely-used library NumPy as an example. ShapeChecker extracts the shape type of NumPy arrays and automatically reads external datasets to obtain shape type information, accelerating the checking process and outputting the cause of shape errors when detected. We compared ShapeChecker with existing solutions in various scenarios and obtained promising results, demonstrating the tool's usefulness in improving efficiency and reducing runtime errors. To further enhance its functionality, we plan to extend ShapeChecker's support to more packages and address known issues. Overall, our proposed method and ShapeChecker tool provide a static approach to detecting array shape errors that can improve code quality and improve development efficiency.
With CUDA computational model in mind, we introduce MCTS-NC (Monte Carlo Tree ***). It contains four, fast-operating and thoroughly parallel, variants of the MCTS algorithm. The design of MCTS-NC combines three parall...
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With CUDA computational model in mind, we introduce MCTS-NC (Monte Carlo Tree ***). It contains four, fast-operating and thoroughly parallel, variants of the MCTS algorithm. The design of MCTS-NC combines three parallelization levels (leaf/root/tree parallelizations). Additionally, all algorithmic stages-selections, expansions, playouts, backups-employ multiple GPU threads. We apply suitable reduction patterns to carry out summations or max/ argmax operations. The implementation uses very few device-host memory transfers, no atomic operations (is lock-free), and takes advantage of threads cooperation. In the mathematical part of this article, we demonstrate how the confidence bounds on estimated action values become tightened by both the number of independent concurrent playouts and the number of independent concurrent trees. The experimental part reports the performance of MCTS-NC on two game examples: Connect4 and Gomoku. All computational results can be exactly reproduced.
Drought is a hazard that causes great economic, ecological, and human loss. With an ever-growing risk of climate change, their frequency and magnitude are expected to increase. While there are many indices and metrics...
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Drought is a hazard that causes great economic, ecological, and human loss. With an ever-growing risk of climate change, their frequency and magnitude are expected to increase. While there are many indices and metrics available for the analysis of droughts, assessing their impacts represents one of the best ways to understand their magnitude and extent. However, there are no systematic records outlining these impacts. To help in their ongoing creation, we present a software framework that leverages raw newspaper articles, identifies any drought-related ones, and automatically classifies them according to a set of socioeconomic impacts. The information is provided to the user in a structured format, including geographical coordinates and their date of reporting. Our approach employs state-of-the-art Transformer-based Natural Language Processing (NLP) techniques, which achieve great accuracy. We currently support newspaper articles in the Spanish language within Spain, but our framework can be expanded to other countries and languages.
Brain-computer interfaces (BCIs) establish a connection between the human brain and external devices, facilitating novel forms of communication and control. Although BCIs possess significant potential, there is a need...
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Brain-computer interfaces (BCIs) establish a connection between the human brain and external devices, facilitating novel forms of communication and control. Although BCIs possess significant potential, there is a need for performance improvements. Integrating neural networks into BCIs is crucial for enhancing functionality and promoting broader adoption. This study examines 1,867 articles from the Web of Science core database, covering the period from 1996 to 2024, to identify contemporary hotspots and trends in the application of neural networks within BCIs. This study employs bibliometric methods and python for analysis, examining collaborative relationships, citation networks, keyword bursts, and clustering with visual representations of the findings. The results indicated that current study hotspots predominantly center on "P300," "Long Short-Term Memory," "Motor Imagery," "Epilepsy," "Emotion Recognition," "Feature Extraction," and "Transfer Learning." Future development directions encompass: (1) the establishment of public BCI datasets;(2) the exploration of diverse feature extraction and fusion methods;(3) the enhancement of machine learning and deep learning integration for improved performance and real-time processing;(4) the expansion of application scenarios and the development of portable devices;(5) the optimization of transfer learning algorithms to mitigate performance challenges arising from individual differences. This study provides an overview of the current research landscape and identifies potential future research directions. Furthermore, it assists practitioners in recognizing additional business opportunities and acts as a resource for the formulation of government policy.
Two-dimensional (2D) van der Waals (vdWs) structures are the subject of extensive research in materials science, celebrated for their unique physical properties and potential technological applications. However, the d...
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Two-dimensional (2D) van der Waals (vdWs) structures are the subject of extensive research in materials science, celebrated for their unique physical properties and potential technological applications. However, the diversity of stacking modes in 2D vdWs structures poses a challenge for research. In response to the complexity of the stacking process for these layered structures, we have developed a python package, PyHTStack2D, specifically designed to support High-Throughput Stacking of 2D materials research. The package provides two primary functionalities: Firstly, it facilitates the batch stacking of homo-and heterostructures, with careful consideration of specific sequences and patterns, such as those observed in the 1T/2H phase transitions of transition metal dichalcogenides;Secondly, it aids in the efficient creation of computational directories and the generation of requisite shell scripts for the batch computation submissions of the stacked structures. By employing this package, we performed high-throughput computational simulations of properties such as electronic energy band structures and magnetic ground states of bilayers composed of 2H-TMDHs. These results have enabled us to identify the types of electronic band structures within these systems, providing critical insights into their potential applications in optoelectronics and photocatalysis. Furthermore, preliminary findings indicate the potential feasibility of generating bipolar magnetic semiconductors via the stacking of magnetic monolayers. The PyHTStack2D package provides an opportunity to perform efficient high-throughput calculations of 2D vdWs homo/heterostructures.
The action-rules package provides an efficient method for mining action rules using the Action-Apriori algorithm, a modification of the traditional Apriori algorithm tailored specifically for action rule mining. Desig...
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The action-rules package provides an efficient method for mining action rules using the Action-Apriori algorithm, a modification of the traditional Apriori algorithm tailored specifically for action rule mining. Designed to generate counterfactual explanations, this python package enables researchers and practitioners to discover actionable insights by integrating user-defined parameters directly into the rule generation process, reducing computational overhead. The action-rules package supports optional GPU acceleration to further speedup processing on large datasets. The package provides a user-friendly API, as well as a command-line interface for versatile use. The package supports the customization of stable and flexible attributes, as well as separate minimum support and confidence thresholds for both the desired and undesired components of the rules. Comprehensive documentation, including a Jupyter Notebook example, is provided to facilitate ease of use for both novice and expert users.
PokerKit is an open-source python library designed to overcome the restrictions of existing Poker game simulation and hand evaluation tools, which typically support only a handful of Poker variants and lack flexibilit...
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PokerKit is an open-source python library designed to overcome the restrictions of existing Poker game simulation and hand evaluation tools, which typically support only a handful of Poker variants and lack flexibility in game state control. In contrast, PokerKit significantly expands this scope by supporting an extensive array of Poker variants and it provides a flexible architecture for users to define their custom games. This article details the design and implementation of PokerKit, including its intuitive programmatic API, multivariant game support, and a unified hand evaluation suite across different hand types. The flexibility of PokerKit allows for applications in diverse areas, such as Poker AI development, tool creation, and online Poker casino implementation. PokerKit's reliability has been established through static type checking, extensive doctests, and unit tests, achieving 99% code coverage. The introduction of PokerKit represents a significant contribution to the field of computer Poker, fostering future research and advanced AI development for a wide variety of Poker games.
We present a converter software program that automatically translates python-based machine learning algorithms into Structured Text codes. This tool empowers engineers to efficiently generate machine learning models i...
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We present a converter software program that automatically translates python-based machine learning algorithms into Structured Text codes. This tool empowers engineers to efficiently generate machine learning models in a programming language widely used in industrial controllers. It supports the conversion of decision tree and multilayer perceptron models built using scikit-learn library. Moreover, the generated Structure Text code is compatible with ABB's Industrial IT 800xA DCS syntax. A practical example demonstrates the effectiveness of this converter software program and its potential to enhance the integration of machine learning models into industrial automation systems.
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