38,419 research outputs found
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 , where 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
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