Angular Momentum of a Brane-world Model

In this paper we discuss the properties of the general covariant angular momentum of a five-dimensional brane-world model. Through calculating the total angular momentum of this model, we are able to analyze the properties of the total angular momentum in the inflationary RS model. We show that the space-like components of the total angular momentum of are all zero while the others are non-zero, which agrees with the results from ordinary RS model.Comment: 8 pages; accepted by Chinese Physics

Constraining the Skyrme effective interactions and the neutron skin thickness of nuclei using isospin diffusion data from heavy ion collisions

Recent analysis of the isospin diffusion data from heavy-ion collisions based on an isospin- and momentum-dependent transport model with in-medium nucleon-nucleon cross sections has led to the extraction of a value of $L=88\pm 25$ MeV for the slope of the nuclear symmetry energy at saturation density. This imposes stringent constraints on both the parameters in the Skyrme effective interactions and the neutron skin thickness of heavy nuclei. Among the 21 sets of Skyrme interactions commonly used in nuclear structure studies, the 4 sets SIV, SV, G$_\sigma$, and R$_\sigma$ are found to give $L$ values that are consistent with the extracted one. Further study on the correlations between the thickness of the neutron skin in finite nuclei and the nuclear matter symmetry energy in the Skyrme Hartree-Fock approach leads to predicted thickness of the neutron skin of $0.22\pm 0.04$ fm for $^{208}$Pb, $0.29\pm 0.04$ fm for $^{132}$Sn, and $0.22\pm 0.04$ fm for $^{124}$Sn.Comment: 10 pages, 4 figures, 1 Table, Talk given at 1) International Conference on Nuclear Structure Physics, Shanghai, 12-17 June, 2006; 2) 11th China National Nuclear Structure Physics Conference, Changchun, Jilin, 13-18 July, 200

Realizing Hopf Insulators in Dipolar Spin Systems

The Hopf insulator represents a topological state of matter that exists outside the conventional ten-fold way classification of topological insulators. Its topology is protected by a linking number invariant, which arises from the unique topology of knots in three dimensions. We predict that three-dimensional arrays of driven, dipolar-interacting spins are a natural platform to experimentally realize the Hopf insulator. In particular, we demonstrate that certain terms within the dipolar interaction elegantly generate the requisite non-trivial topology, and that Floquet engineering can be used to optimize dipolar Hopf insulators with large gaps. Moreover, we show that the Hopf insulator's unconventional topology gives rise to a rich spectrum of edge mode behaviors, which can be directly probed in experiments. Finally, we present a detailed blueprint for realizing the Hopf insulator in lattice-trapped ultracold dipolar molecules; focusing on the example of ${}^{40}$K$^{87}$Rb, we provide quantitative evidence for near-term experimental feasibility.Comment: 6 + 7 pages, 3 figure

A convergence and diversity guided leader selection strategy for many-objective particle swarm optimization

Recently, particle swarm optimizer (PSO) is extended to solve many-objective optimization problems (MaOPs) and becomes a hot research topic in the field of evolutionary computation. Particularly, the leader particle selection (LPS) and the search direction used in a velocity update strategy are two crucial factors in PSOs. However, the LPS strategies for most existing PSOs are not so efficient in high-dimensional objective space, mainly due to the lack of convergence pressure or loss of diversity. In order to address these two issues and improve the performance of PSO in high-dimensional objective space, this paper proposes a convergence and diversity guided leader selection strategy for PSO, denoted as CDLS, in which different leader particles are adaptively selected for each particle based on its corresponding situation of convergence and diversity. In this way, a good tradeoff between the convergence and diversity can be achieved by CDLS. To verify the effectiveness of CDLS, it is embedded into the PSO search process of three well-known PSOs. Furthermore, a new variant of PSO combining with the CDLS strategy, namely PSO/CDLS, is also presented. The experimental results validate the superiority of our proposed CDLS strategy and the effectiveness of PSO/CDLS, when solving numerous MaOPs with regular and irregular Pareto fronts (PFs)

Analytical technique for simplification of the encoder-decoder circuit for a perfect five-qubit error correction

Simpler encoding and decoding networks are necessary for more reliable quantum error correcting codes (QECCs). The simplification of the encoder-decoder circuit for a perfect five-qubit QECC can be derived analytically if the QECC is converted from its equivalent one-way entanglement purification protocol (1-EPP). In this work, the analytical method to simplify the encoder-decoder circuit is introduced and a circuit that is as simple as the existent simplest circuits is presented as an example. The encoder-decoder circuit presented here involves nine single- and two-qubit unitary operations, only six of which are controlled-NOT (CNOT) gates

Preliminary procedural guide for estimating water and sediment yield from roads in forest

CER76-77DBS-RML-LYS21.Prepared for USDA Forest Service, Rocky Mountain Forest and Range Experiment Station.Includes bibliographical references (page 120).November 1976

Mutual-learning sequence-level knowledge distillation for automatic speech recognition

Automatic speech recognition (ASR) is a crucial technology for man-machine interaction. End-to-end models have been studied recently in deep learning for ASR. However, these models are not suitable for the practical application of ASR due to their large model sizes and computation costs. To address this issue, we propose a novel mutual-learning sequence-level knowledge distillation framework enjoying distinct student structures for ASR. Trained mutually and simultaneously, each student learns not only from the pre-trained teacher but also from its distinct peers, which can improve the generalization capability of the whole network, through making up for the insufficiency of each student and bridging the gap between each student and the teacher. Extensive experiments on the TIMIT and large LibriSpeech corpuses show that, compared with the state-of-the-art methods, the proposed method achieves an excellent balance between recognition accuracy and model compression

3D-TDC: A 3D temporal dilation convolution framework for video action recognition

Video action recognition is a vital area of computer vision. By adding temporal dimension into convolution structure, 3D convolution neural network owns the capacity to extract spatio-temporal features from videos. However, due to computing constraints, it is hard to input the whole video into the convolution network at one time, resulting in a limited temporal receptive field of the network. To address this issue, we propose a novel 3D temporal dilation convolution (3D-TDC) framework, to extract spatio-temporal features of actions from videos. First, we deploy the 3D temporal dilation convolution as the shallow temporal compression layer, enabling an effective capture of spatio-temporal information in a larger time domain with the reduced computational load. Then, an action recognition framework is constructed by integrating two networks with different temporal receptive fields to balance the long-short time difference. We conduct extensive experiments on three widely-used public datasets (UCF-101, HMDB-51, and Kinetics-400) for performance evaluation, and the experimental results demonstrate the effectiveness of our proposed framework in video action recognition with low computational load