First-principles calculations of phase transition, elasticity, and thermodynamic properties for TiZr alloy

tructural transformation, pressure dependent elasticity behaviors, phonon, and thermodynamic properties of the equiatomic TiZr alloy are investigated by using first-principles density-functional theory. Our calculated lattice parameters and equation of state for $\alpha$ and $\omega$ phases as well as the phase transition sequence of $\alpha$$\mathtt{\rightarrow}$$\omega$$\mathtt{\rightarrow}$$\beta$ are consistent well with experiments. Elastic constants of $\alpha$ and $\omega$ phases indicate that they are mechanically stable. For cubic $\beta$ phase, however, it is mechanically unstable at zero pressure and the critical pressure for its mechanical stability is predicted to equal to 2.19 GPa. We find that the moduli, elastic sound velocities, and Debye temperature all increase with pressure for three phases of TiZr alloy. The relatively large $B/G$ values illustrate that the TiZr alloy is rather ductile and its ductility is more predominant than that of element Zr, especially in $\beta$ phase. Elastic wave velocities and Debye temperature have abrupt increase behaviors upon the $\alpha$$\mathtt{\rightarrow}$$\omega$ transition at around 10 GPa and exhibit abrupt decrease feature upon the $\omega$$\mathtt{\rightarrow}$$\beta$ transition at higher pressure. Through Mulliken population analysis, we illustrate that the increase of the \emph{d}-band occupancy will stabilize the cubic $\beta$ phase. Phonon dispersions for three phases of TiZr alloy are firstly presented and the $\beta$ phase phonons clearly indicate its dynamically unstable nature under ambient condition. Thermodynamics of Gibbs free energy, entropy, and heat capacity are obtained by quasiharmonic approximation and Debye model.Comment: 9 pages, 10 figure

Privacy-preserving communication and power injection over vehicle networks and 5G smart grid slice

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Self-organization and phase transition in financial markets with multiple choices

Market confidence is essential for successful investing. By incorporating multi-market into the evolutionary minority game, we investigate the effects of investor beliefs on the evolution of collective behaviors and asset prices. When there exists another investment opportunity, market confidence, including overconfidence and under-confidence, is not always good or bad for investment. The roles of market confidence is closely related to market impact. For low market impact, overconfidence in a particular asset makes an investor become insensitive to losses and a delayed strategy adjustment leads to a decline in wealth, and thereafter, one's runaway from the market. For high market impact, under-confidence in a particular asset makes an investor over-sensitive to losses and one's too frequent strategy adjustment leads to a large fluctuation in asset prices, and thereafter, a decrease in the number of agents. At an intermediate market impact, the phase transition occurs. No matter what the market impact is, an equilibrium between different markets exists, which is reflected in the occurrence of similar price fluctuations in different markets. A theoretical analysis indicates that such an equilibrium results from the coupled effects of strategy updating and shift in investment. The runaway of the agents trading a specific asset will lead to a decline in the asset price volatility and such a decline will be inhibited by the clustering of the strategies. A uniform strategy distribution will lead to a large fluctuation in asset prices and such a fluctuation will be suppressed by the decrease in the number of agents in the market. A functional relationship between the price fluctuations and the numbers of agents is found

A Simple and Effective Self-Supervised Contrastive Learning Framework for Aspect Detection

Unsupervised aspect detection (UAD) aims at automatically extracting interpretable aspects and identifying aspect-specific segments (such as sentences) from online reviews. However, recent deep learning-based topic models, specifically aspect-based autoencoder, suffer from several problems, such as extracting noisy aspects and poorly mapping aspects discovered by models to the aspects of interest. To tackle these challenges, in this paper, we first propose a self-supervised contrastive learning framework and an attention-based model equipped with a novel smooth self-attention (SSA) module for the UAD task in order to learn better representations for aspects and review segments. Secondly, we introduce a high-resolution selective mapping (HRSMap) method to efficiently assign aspects discovered by the model to aspects of interest. We also propose using a knowledge distilling technique to further improve the aspect detection performance. Our methods outperform several recent unsupervised and weakly supervised approaches on publicly available benchmark user review datasets. Aspect interpretation results show that extracted aspects are meaningful, have good coverage, and can be easily mapped to aspects of interest. Ablation studies and attention weight visualization also demonstrate the effectiveness of SSA and the knowledge distilling method