An Empirical Study of Untangling Patterns of Two-Class Dependency Cycles

Dependency cycles pose a significant challenge to software quality and maintainability. However, there is limited understanding of how practitioners resolve dependency cycles in real-world scenarios. This paper presents an empirical study investigating the recurring patterns employed by software developers to resolve dependency cycles between two classes in practice. We analyzed the data from 38 open-source projects across different domains and manually inspected hundreds of cycle untangling cases. Our findings reveal that developers tend to employ five recurring patterns to address dependency cycles. The chosen patterns are not only determined by dependency relations between cyclic classes, but also highly related to their design context, i.e., how cyclic classes depend on or are depended by their neighbor classes. Through this empirical study, we also discovered three common counterintuitive solutions developers usually adopted during cycles' handling. These recurring patterns and common counterintuitive solutions observed in dependency cycles' practice can serve as a taxonomy to improve developers' awareness and also be used as learning materials for students in software engineering and inexperienced developers. Our results also suggest that, in addition to considering the internal structure of dependency cycles, automatic tools need to consider the design context of cycles to provide better support for refactoring dependency cycles.Comment: Preprint accepted for publication in Empirical Software Engineering, 202

Greedy routing with guaranteed delivery using Ricci flows

Greedy forwarding with geographical locations in a wireless sensor network may fail at a local minimum. In this paper we propose to use conformal mapping to compute a new embedding of the sensor nodes in the plane such that greedy forwarding with the virtual coordinates guarantees delivery. In particular, we extract a planar triangulation of the sensor network with non-triangular faces as holes, by either using the nodes ’ location or using a landmark-based scheme without node location. The conformal map is computed with Ricci flow such that all the non-triangular faces are mapped to perfect circles. Thus greedy forwarding will never get stuck at an intermediate node. The computation of the conformal map and the virtual coordinates is performed at a preprocessing phase and can be implemented by local gossip-style computation. The method applies to both unit disk graph models and quasi-unit disk graph models. Simulation results are presented for these scenarios

Hourglass Charge-Three Weyl Phonons

Unconventional Weyl point with nonlinear dispersion features higher topological charge $|{\cal{C}}|>1$ and multiple topologically protected Fermi arc states at its boundary. As a novel topological state, it has been attracting widespread attention. However, the unconventional Weyl point with $|{\cal{C}}|=3$ has not yet been reported in realistic materials, even though it has been theoretically proposed for more than a decade. In this work, based on first-principles calculations and theoretical analysis, we predict the existing material, $\rm\alpha$-LiIO$_3$ as the first realistic example with this unconventional Weyl point. Particularly, in the phonon spectra of $\rm\alpha$-LiIO$_3$, two Weyl points with ${\cal{C}}=-3$, connected by time-reversal symmetry, appear at the neck crossing-point of a hourglass-type band, leading to two hourglass charge-3 Weyl phonons. The symmetry protection and the associated novel triple- and sextuple-helicoid surface arc states of the hourglass charge-3 Weyl phonons are revealed. Our results uncover a hidden topological character of $\rm\alpha$-LiIO$_3$ and also show that the phonon spectra is a great platform for exploring unconventional topological states

THE EFFECT OF AXIAL COMPRESSION RATIO ON SEISMIC BEHAVIOR OF INFILLED REINFORCED CONCRETE FRAMES WITH PROFILED STEEL SHEET BRACING

Seven infilled reinforced concrete (RC) frames strengthening with profiled steel sheet bracing are researched on the effect of axial compression ratio (0.3~0.9). Hysteretic curves, envelope curves, stiffness degradation curves, ductility and energy dissipation capacity are analysed in the finite element. The results show that profiled steel sheet bracing plays a good role in reinforcing infilled RC frames and the hysteretic curves express plump relatively. With the increase of axial compression ratio, the bearing capacity is improved significantly. The axial compression ratio has little effect on the lateral stiffness of the structure, and the initial stiffness increases slightly with the increase of axial compression ratio. The structure has good ductility when the axial compression ratio is less than 0.6. The ductility is declined with the increase of axial compression ratio. As the displacement increases, the energy dissipation capacity of the specimens increases. However, the energy dissipation capacity is reduced as the increase of axial compression ratio

DeepTrio: a ternary prediction system for protein-protein interaction using mask multiple parallel convolutional neural networks.

Motivation Protein–protein interaction (PPI), as a relative property, is determined by two binding proteins, which brings a great challenge to design an expert model with an unbiased learning architecture and a superior generalization performance. Additionally, few efforts have been made to allow PPI predictors to discriminate between relative properties and intrinsic properties. Results We present a sequence-based approach, DeepTrio, for PPI prediction using mask multiple parallel convolutional neural networks. Experimental evaluations show that DeepTrio achieves a better performance over several state-of-the-art methods in terms of various quality metrics. Besides, DeepTrio is extended to provide additional insights into the contribution of each input neuron to the prediction results. Availability and implementation We provide an online application at http://bis.zju.edu.cn/deeptrio. The DeepTrio models and training data are deposited at https://github.com/huxiaoti/deeptrio.git