Software Impacts最新文献

One of the main causes of Human-induced earthquakes is hydroelectric reservoir impoundment, a phenomenon known as Reservoir Triggered Seismicity (RTS). To assess the role of reservoir impoundment in triggering seismicity of a region, several codes are written in MATLAB based on Green’s function solution of poroelastic equations to simulate stress, pore pressure and change in fault stability. All these codes are embedded into a single user-friendly application software. This application takes various inputs on reservoir dimensions, reservoir loading time history, fault orientation, poroelastic properties of the medium for calculations, and presents results in simple graphics.

One method of learning to drive, like operating a car, is to adopt a simulation approach with a strong focus on game elements. In our research, we developed the ProjectStir application, a car driving simulator that uses this game-centric approach. The app immerses users in a virtual representation of Asia’s urban environments known for their narrow streets. It is able to assess driver performance by tracking the number of checkpoints collected and the level of damage incurred. Additionally, the app features a leaderboard, highlighting that hitting more checkpoints and minimizing damage will result in a better ranking. ProjectStir is compatible with the PC platform and can be operated using a joystick, keyboard or steering wheel.

In response to the NP-hard power scheduling problem, the pymops package is developed as a robust solution. The package operates within a multi-agent simulation environment, where power-generating units are represented as reinforcement learning (RL) agents. The environment is designed to account for a comprehensive range of constraints. It also accommodates thermal valve point effects (VPEs) within cost and emissions functions. Moreover, in cases of constraint violations, the environment makes real-time contextual adjustments. Within the environment, the power scheduling problem is broken down into sequential Markov decision processes (MDPs), which serve as inputs for training a deep RL model aimed at solving the optimization problem.

This paper presents a Python script that automates the estimation of Yang & Zhang’s stock realized volatility proxy for univariate and multivariate cases. Yang & Zhang’s realized volatility is a stock volatility proxy commonly used by financial researchers and practitioners due to its unbiasedness in the continuous limit, independence of the drift, and consistence in dealing with price jumps. The proposed script allows the efficient estimation of Yang & Zhang realized volatility with local data and the use of Yahoo Finance API.

Ant-based Topology Search (ANTS) is a Neural Architecture Search (NAS) inspired by ant colony optimization (ACO). ANTS encodes the neural structure search space within a highly interconnected structure. Optimization agents, like ants, navigate this structure in search of an optimal neural topology. Continuous Ant-based Topology Search (CANTS) builds upon ANTS by replacing the discrete search space with a 3D continuous one. CANTS introduces a fourth dimension for potential neural synaptic weights, transitioning from NAS to NeuroEvolution (NE). This automates artificial neural network design without relying on backpropagation, reducing optimization time and offering a promising approach for machine learning applications.

The implementation of a holistic cybersecurity approach involves engaging multiple functional areas within the organization, each assigned specific actions to achieve strategic cybersecurity objectives. These actions – there can be numerous permutations of them – have associated costs, expertise requirements. Selecting the right combinations requires careful analysis and consideration, leading to time-consuming deliberations and potential conflicts. Identifying inadequate combinations that fail to meet strategic goals also requires significant effort. To streamline this process, we developed FLECO (Fast, Lightweight, and Efficient Cybersecurity Optimization), an adaptable multi-objective genetic algorithm that enables near-instantaneous identification of feasible cross-functional combinations. It serves as a foundation for the cybersecurity workforce to reach a consensus.

FAW-encoding is an open-source C library that facilitates a memory efficient compression of an array of integers. An integer in an array either lags behind or never exceeds its original place in that array after encoded by FAW-encoding. Therefore, FAW-encoding causes in-place compression of an array of integers that never needs memory allocation to store the encoded data. Due to these special properties, FAW-encoding optimizes algorithms, such as, graph mining and joining, that generally produce large intermediate results. Besides encoding, the open-source C library comprises with methods for decoding or searching an encoded array and intersecting and merging encoded arrays.

iPydisp is an open-source, Python-based tool designed to model the spatio-temporal dispersal of insect species such as parasitoids. It provides an accessible interface for interactive spatio-temporal analysis, using cellular automata algorithms for easy data processing and updates. The tool’s main features include intuitive data loading, easy setting of dispersal constraints, and dynamic visualization of dispersal patterns. iPydisp has the potential to improve our understanding of parasitoid dispersal and enhance pest management strategies.

This paper presents a software package developed in Python that allows the application of the technique known as Kernel Logistic Regression (KLR), a Machine Learning (ML) tool, to the problem of transport demand prediction. More concretely, it permits the specification of a series of models using KLR and their estimation by means of a Penalised Maximum Likelihood Estimation (PMLE) procedure providing a set of goodness-of-fit indicators and the application of model validation techniques. Another functionality is that it allows to extract from the model several indicators such as the Willingness to Pay (WTP) or the Value of Time (VOT).

Brain–Computer Interface (BCI) systems allow to perform actions by translating brain activity into commands. Such systems require training a classification algorithm to discriminate between mental states, using specific features from the brain signals. This step is crucial and presents specific constraints in clinical contexts.

HappyFeat is an open-source software making BCI experiments easier in such contexts: effortlessly extracting and selecting adequate features for training, in a single GUI. Novel features based on Functional Connectivity can be used, allowing graph-oriented approaches. We describe HappyFeat’s mechanisms, showing its performances in typical use cases, and showcasing how to compare different types of features.