Measuring and Modeling Group Dynamics in Open-Source Software Development: A Tensor Decomposition Approach

Many open-source software projects depend on a few core developers, who take over both the bulk of coordination and programming tasks. They are supported by peripheral developers, who contribute either via discussions or programming tasks, often for a limited time. It is unclear what role these peripheral developers play in the programming and communication efforts, as well as the temporary task-related sub-groups in the projects. We mine code-repository data and mailing-list discussions to model the relationships and contributions of developers in a social network and devise a method to analyze the temporal collaboration structures in communication and programming, learning about the strength and stability of social sub-groups in open-source software projects. Our method uses multi-modal social networks on a series of time windows. Previous work has reduced the network structure representing developer collaboration to networks with only one type of interaction, which impedes the simultaneous analysis of more than one type of interaction. We use both communication and version-control data of open-source software projects and model different types of interaction over time. To demonstrate the practicability of our measurement and analysis method, we investigate 10 substantial and popular open-source software projects and show that, if sub-groups evolve, modeling these sub-groups helps predict the future evolution of interaction levels of programmers and groups of developers. Our method allows maintainers and other stakeholders of open-source software projects to assess instabilities and organizational changes in developer interaction and can be applied to different use cases in organizational analysis, such as understanding the dynamics of a specific incident or discussion.

State and fault estimation based on fuzzy observer for a class of Takagi-Sugeno singular models

Singular nonlinear systems have received wide attention in recent years, and can be found in various applications of engineering practice. On the basis of the Takagi-Sugeno (T-S) formalism, which represents a powerful tool allowing the study and the treatment of nonlinear systems, many control and diagnostic problems have been treated in the literature. In this work, we aim to present a new approach making it possible to estimate simultaneously both non-measurable states and unknown faults in the actuators and sensors for a class of continuous-time Takagi-Sugeno singular model (CTSSM). Firstly, the considered class of CTSSM is represented in the case of premise variables which are non-measurable, and is subjected to actuator and sensor faults. Secondly, the suggested observer is synthesized based on the decomposition approach. Next, the observer’s gain matrices are determined using the Lyapunov theory and the constraints are defined as linear matrix inequalities (LMIs). Finally, a numerical simulation on an application example is given to demonstrate the usefulness and the good performance of the proposed dynamic system.

The Role of Public Spending in CO2 Emissions Reduction in Polish Regions: An LMDI Decomposition Approach

Global climate change and air pollution are critical environmental problems in the modern world. Therefore, the reduction of CO2 emissions has long been a crucial challenge for individual countries. In this area, numerous technological, legal, and economic solutions are used. The purpose of this article is to examine the impact of public spending on the level of CO2 emissions in Polish regions. The study uses the logarithmic mean Divisia index (LMDI) method, proposing a unique proprietary set of factors that influence the level of CO2 emissions. The results of the study confirm that public expenditure contributes to reducing CO2 emissions at the regional level, while environmental expenditure is counterproductive. This tendency is observed mainly in regions with a high level of socioeconomic development, higher energy consumption, and high carbon emissions. The failure of environmental spending to reduce carbon dioxide emissions in Polish regions is explained by the “green paradox” hypothesis.

A Robust Tensor-Based Submodule Clustering for Imaging Data Using l12 Regularization and Simultaneous Noise Recovery via Sparse and Low Rank Decomposition Approach

The massive generation of data, which includes images and videos, has made data management, analysis, information extraction difficult in recent years. To gather relevant information, this large amount of data needs to be grouped. Real-life data may be noise corrupted during data collection or transmission, and the majority of them are unlabeled, allowing for the use of robust unsupervised clustering techniques. Traditional clustering techniques, which vectorize the images, are unable to keep the geometrical structure of the images. Hence, a robust tensor-based submodule clustering method based on l12 regularization with improved clustering capability is formulated. The l12 induced tensor nuclear norm (TNN), integrated into the proposed method, offers better low rankness while retaining the self-expressiveness property of submodules. Unlike existing methods, the proposed method employs a simultaneous noise removal technique by twisting the lateral image slices of the input data tensor into frontal slices and eliminates the noise content in each image, using the principles of the sparse and low rank decomposition technique. Experiments are carried out over three datasets with varying amounts of sparse, Gaussian and salt and pepper noise. The experimental results demonstrate the superior performance of the proposed method over the existing state-of-the-art methods.

Quantum Hydrodynamics of Spinning Particles in Electromagnetic and Torsion Fields

We develop a many-particle quantum-hydrodynamical model of fermion matter interacting with the external classical electromagnetic and gravitational/inertial and torsion fields. The consistent hydrodynamical formulation is constructed for the many-particle quantum system of Dirac fermions on the basis of the nonrelativistic Pauli-like equation obtained via the Foldy–Wouthuysen transformation. With the help of the Madelung decomposition approach, the explicit relations between the microscopic and macroscopic fluid variables are derived. The closed system of equations of quantum hydrodynamics encompasses the continuity equation, and the dynamical equations of the momentum balance and the spin density evolution. The possible experimental manifestations of the torsion in the dynamics of spin waves is discussed.

Carbon and Energy Intensity of the USA and Germany. A LMDI Decomposition Approach and Decoupling Analysis.

The present study investigates the driving factors of energy and carbon intensity of the great economies of the USA and Germany, being two economies with different structures and dependencies on energy while they are among the world’s economic and geopolitical leading players. Both Decoupling and Decomposition Analysis are applied to identify and rank factors defining Carbon and Energy Intensity during the period from 2000 to 2017, with the so-called financial crisis being within this period. The decoupling analysis denotes that these advanced economies should enhance their decoupling efforts, while decomposition analysis confirms the leading role of energy intensity in CO2 emissions, next the income and the energy mix emerge as important factors with population trend to be an additional factor only in the USA. Finally, conclusions and relevant policy suggestions are presented.

Concurrent calculation of radiative transfer in the atmospheric simulation in ECHAM-6.3.05p2

​​​​​​​The scalability of the atmospheric model ECHAM6 at low resolution, as used in palaeoclimate simulations, suffers from the limited number of grid points. As a consequence, the potential of current high-performance computing architectures cannot be used at full scale for such experiments, particularly within the available domain decomposition approach. Radiation calculations are a relatively expensive part of the atmospheric simulations, taking up to approximately 50 % or more of the total runtime. This current level of cost is achieved by calculating the radiative transfer only once in every 2 h of simulation. In response, we propose extending the available concurrency within the model further by running the radiation component in parallel with other atmospheric processes to improve scalability and performance. This paper introduces the concurrent radiation scheme in ECHAM6 and presents a thorough analysis of its impact on the performance of the model. It also evaluates the scientific results from such simulations. Our experiments show that ECHAM6 can achieve a speedup of over 1.9× using the concurrent radiation scheme. By performing a suite of stand-alone atmospheric experiments, we evaluate the influence of the concurrent radiation scheme on the scientific results. The simulated mean climate and internal climate variability by the concurrent radiation generally agree well with the classical radiation scheme, with minor improvements in the mean atmospheric circulation in the Southern Hemisphere and the atmospheric teleconnection to the Southern Annular Mode. This empirical study serves as a successful example that can stimulate research on other concurrent components in atmospheric modelling whenever scalability becomes challenging.