Effect of Earth's rotation on the trajectories of free-fall bodies in Equivalence Principle Experiment

Owing to Earth's rotation a free-fall body would move in an elliptical orbit rather than along a straight line forward to the center of the Earth. In this paper on the basis of the theory for spin-spin coupling between macroscopic rotating bodies we study violation of the equivalence principle from long-distance free-fall experiments by means of a rotating ball and a non-rotating sell. For the free-fall time of 40 seconds, the difference between the orbits of the two free-fall bodies is of the order of 10^{-9}cm which could be detected by a SQUID magnetometer owing to such a magnetometer can be used to measure displacements as small as 10^{-13} centimeters.Comment: 6 pages, 4 figure

Convergence on Gauss-Seidel iterative methods for linear systems with general H-matrices

It is well known that as a famous type of iterative methods in numerical linear algebra, Gauss-Seidel iterative methods are convergent for linear systems with strictly or irreducibly diagonally dominant matrices, invertible $H-$matrices (generalized strictly diagonally dominant matrices) and Hermitian positive definite matrices. But, the same is not necessarily true for linear systems with nonstrictly diagonally dominant matrices and general $H-$matrices. This paper firstly proposes some necessary and sufficient conditions for convergence on Gauss-Seidel iterative methods to establish several new theoretical results on linear systems with nonstrictly diagonally dominant matrices and general $H-$matrices. Then, the convergence results on preconditioned Gauss-Seidel (PGS) iterative methods for general $H-$matrices are presented. Finally, some numerical examples are given to demonstrate the results obtained in this paper

Improving Pre-trained Language Model Fine-tuning with Noise Stability Regularization

The advent of large-scale pre-trained language models has contributed greatly to the recent progress in natural language processing. Many state-of-the-art language models are first trained on a large text corpus and then fine-tuned on downstream tasks. Despite its recent success and wide adoption, fine-tuning a pre-trained language model often suffers from overfitting, which leads to poor generalizability due to the extremely high complexity of the model and the limited training samples from downstream tasks. To address this problem, we propose a novel and effective fine-tuning framework, named Layerwise Noise Stability Regularization (LNSR). Specifically, we propose to inject the standard Gaussian noise or In-manifold noise and regularize hidden representations of the fine-tuned model. We first provide theoretical analyses to support the efficacy of our method. We then demonstrate the advantages of the proposed method over other state-of-the-art algorithms including L2-SP, Mixout and SMART. While these previous works only verify the effectiveness of their methods on relatively simple text classification tasks, we also verify the effectiveness of our method on question answering tasks, where the target problem is much more difficult and more training examples are available. Furthermore, extensive experimental results indicate that the proposed algorithm can not only enhance the in-domain performance of the language models but also improve the domain generalization performance on out-of-domain data.Comment: Accepted by TNNL

Cloud service-oriented dashboard for work cell management in RFID-enabled ubiquitous manufacturing

This article aims at developing a service-oriented dashboard for operators and supervisors of manufacturing shopfloor work-cells to realize information visibility and traceability effectively with cloud and RFID (radio frequency identification) technologies. The work is based on a case of an illustrative assembly line consisting of a number of work cells. The dashboard is deployed for facilitating assembly operations in ubiquitous manufacturing environment. The utilization of the system leads to significant improvements in work cell productivity and quality, operational flexibility and decision efficiency. © 2013 IEEE.published_or_final_versio

A dynamical approach to identify vertices centrality in complex networks

In this paper, we proposed a dynamical approach to assess vertices centrality according to the synchronization process of the Kuramoto model. In our approach, the vertices dynamical centrality is calculated based on the Difference of vertices Synchronization Abilities (DSA), which are different from traditional centrality measurements that are related to the topological properties. Through applying our approach to complex networks with a clear community structure, we have calculated all vertices' dynamical centrality and found that vertices at the end of weak links have higher dynamical centrality. Meanwhile, we analyzed the robustness and efficiency of our dynamical approach through testing the probabilities that some known vital vertices were recognized. Finally, we applied our dynamical approach to identify community due to its satisfactory performance in assessing overlapping vertices. Our present work provides a new perspective and tools to understand the crucial role of heterogeneity in revealing the interplay between the dynamics and structure of complex networks

Strong Decays of the Radial Excited States B(2S)B(2S) and D(2S)D(2S)

The strong OZI allowed decays of the first radial excited states $B(2S)$ and $D(2S)$ are studied in the instantaneous Bethe-Salpeter method, and by using these OZI allowed channels we estimate the full decay widths: $\Gamma_{B^0(2S)}=24.4$ MeV, $\Gamma_{B^+(2S)}=23.7$ MeV, $\Gamma_{D^0(2S)}=11.3$ MeV and $\Gamma_{D^+(2S)}=11.9$ MeV. We also predict the masses of them: $M_{B^0(2S)}=5.777$ GeV, $M_{B^+(2S)}=5.774$ GeV, $M_{D^0(2S)}=2.390$ GeV and $M_{D^+(2S)}=2.393$ GeV.Comment: 6 pages, 1 figur