Instability of multistage compressor K1501

The K1501 compressor, driven by a steam turbine, is used to transport synthetic gas in fertilizer plants of 1000 tons daily production. The turbo-compressor set, which had been in operation since 1982, vibrated rather intensely, and its maximum load was only about 95 percent of the normal value. Damaging vibration to pads and gas-sealing labyrinths occurred three times from 1982 to 1983 and resulted in considerable economic loss. From the characteristics of the vibration, we suspected its cause to be rotor instability due to labyrinth-seal excitation. But, for lack of experience, the problem was not addressed for two years. Finally, we determined that the instability was indeed produced by labyrinth-seal excitation and corrected this problem by injecting gas into the middle-diaphragm labyrinths. This paper primarily discusses the failure and the remedy described above

Dynamic evolution of cross-correlations in the Chinese stock market

We study the dynamic evolution of cross-correlations in the Chinese stock market mainly based on the random matrix theory (RMT). The correlation matrices constructed from the return series of 367 A-share stocks traded on the Shanghai Stock Exchange from January 4, 1999 to December 30, 2011 are calculated over a moving window with a size of 400 days. The evolutions of the statistical properties of the correlation coefficients, eigenvalues, and eigenvectors of the correlation matrices are carefully analyzed. We find that the stock correlations are significantly increased in the periods of two market crashes in 2001 and 2008, during which only five eigenvalues significantly deviate from the random correlation matrix, and the systemic risk is higher in these volatile periods than calm periods. By investigating the significant contributors of the deviating eigenvectors in different moving windows, we observe a dynamic evolution behavior in business sectors such as IT, electronics, and real estate, which lead the rise (drop) before (after) the crashes

Constraining the Equation of State of Neutron Stars through GRB X-Ray Plateaus

The unknown equation of state (EoS) of neutron stars (NSs) is puzzling because of rich non-perturbative effects of strong interaction there. A method to constrain the EoS by using the detected X-ray plateaus of gamma-ray bursts (GRBs) is proposed in this paper. Observations show some GRB X-ray plateaus may be powered by strongly magnetized millisecond NSs. The properties of these NSs should then satisfy: (i) the spin-down luminosity of these NSs should be brighter than the observed luminosity of the X-ray plateaus; (ii) the total rotational energy of these NSs should be larger than the total energy of the X-ray plateaus. Through the case study of GRB 170714A, the moment of inertia of NSs is constrained as $I>1.0\times 10^{45}\left ( \frac{P_{\rm cri}}{1\;\rm ms} \right )^{2} \;\rm g\cdot cm^{2}$, where $P_{\rm cri}$ is the critical rotational period that an NS can achieve. The constraint of the radii of NSs according to GRB 080607 is shown in Table 1.Comment: 6 pages, 2 figute, The Astrophysical Journal, 886:87, 2019 December 1, https://doi.org/10.3847/1538-4357/ab490

Spin transverse force and intrinsic quantum transverse transport

The spin-orbit coupling may generate spin transverse force on moving electron spin, which gives a heuristic picture for the quantum transverse transport of electron. A relation between the spin and anomalous Hall conductance and spin force was established, and applied to several systems. It was predicted that the sign change of anomalous Hall conductance can occur in diluted magnetic semiconductors of narrow band and can be applied to identify intrinsic mechanism experimentally

IsoBN: Fine-Tuning BERT with Isotropic Batch Normalization

Fine-tuning pre-trained language models (PTLMs), such as BERT and its better variant RoBERTa, has been a common practice for advancing performance in natural language understanding (NLU) tasks. Recent advance in representation learning shows that isotropic (i.e., unit-variance and uncorrelated) embeddings can significantly improve performance on downstream tasks with faster convergence and better generalization. The isotropy of the pre-trained embeddings in PTLMs, however, is relatively under-explored. In this paper, we analyze the isotropy of the pre-trained [CLS] embeddings of PTLMs with straightforward visualization, and point out two major issues: high variance in their standard deviation, and high correlation between different dimensions. We also propose a new network regularization method, isotropic batch normalization (IsoBN) to address the issues, towards learning more isotropic representations in fine-tuning by dynamically penalizing dominating principal components. This simple yet effective fine-tuning method yields about 1.0 absolute increment on the average of seven NLU tasks.Comment: AAAI 202