International comparison of land development rights' transfer

2008-2009 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Experimental observation of chiral phonons in monolayer WSe2

Chirality characterizes an object that is not identical to its mirror image. In condensed matter physics, Fermions have been demonstrated to obtain chirality through structural and time-reversal symmetry breaking. These systems display unconventional electronic transport phenomena such as the quantum Hall effect and Weyl semimetals. However, for bosonic collective excitations in atomic lattices, chirality was only theoretically predicted and has never been observed. We experimentally show that phonons can exhibit intrinsic chirality in monolayer tungsten diselenide, whose lattice breaks the inversion symmetry and enables inequivalent electronic K and -K valley states. The time-reversal symmetry is also broken when we selectively excite the valley polarized holes by circularly polarized light. Brillouin-zone-boundary phonons are then optically created by the indirect infrared absorption through the hole-phonon interactions. The unidirectional intervalley transfer of holes ensures that only the phonon modes in one valley are excited. We found that such photons are chiral through the transient infrared circular dichroism, which proves the valley phonons responsible to the indirect absorption has non-zero pseudo-angular momentum. From the spectrum we further deduce the energy transferred to the phonons that agrees with both the first principle calculation and the double-resonance Raman spectroscopy. The chiral phonons have significant implications for electron-phonon coupling in solids, lattice-driven topological states, and energy efficient information processing

A macroscopic approach to the lane formation phenomenon in pedestrian counterflow

We simulate pedestrian counterflow by adopting an optimal path-choice strategy and a recently observed speed-density relationship. Although the whole system is symmetric, the simulation demonstrates the segregation and formation of many walking lanes for two groups of pedestrians. The symmetry breaking is most likely triggered by a small numerical viscosity or "noise", and the segregation is associated with the minimization of travel time. The underlying physics can be compared with the "optimal self- organization" mechanism in Helbing's social force model, by which driven entities in an open system tend to minimize their interaction to enable them to reach some ordering state. © 2011 Chinese Physical Society and IOP Publishing Ltd.postprin

Temporal and Spatial Expression of Tissue Inhibitors of Metalloproteinases 1 and 2 (TIMP-1 and -2) in the Bovine Corpus Luteum

The matrix metalloproteinases (MMPs) and their endogenous inhibitors, tissue inhibitors of metalloproteinases (TIMPs), may mediate the dramatic structural and functional changes in the corpus luteum (CL) over the course of its life span. In addition to regulating MMP activity, TIMPs are also involved in a variety of cellular processes, including cell proliferation and steroidogenesis. In a series of initial studies, we determined that matrix metalloproteinase inhibitory activity was present in protein extracts from early (4 days old, estrus = day 0), mid (10–12 days old) and late (16 days old) CL (n = 3 for each stage). Reverse zymography revealed four metalloproteinase inhibitory protein bands with relative molecular masses that are consistent with those reported for TIMP-1 to -4. In order to gain a better understanding of TIMPs and their role in luteal function, we further characterized this inhibitory activity with a particular focus on the temporal and spatial expression of TIMP-1 and TIMP-2 in the bovine CL. Northern blotting revealed that the TIMP-1 transcript (0.9 kb) was expressed at a higher (p < 0.05) level in early and mid cycle CL than in the late stage. In contrast, two TIMP-2 mRNA species, one major 1 kb species and one minor 3.5 kb species, were significantly (p < 0.05) increased in the mid and late cycle CL than in the early. Western blotting analyses demonstrated no differences in TIMP-1 (29 kDa) protein levels between early and mid stages, while its levels decreased (p < 0.05) from the mid to late stage CL. Conversely, TIMP-2 (22 kDa) protein was detected at a low level in the early CL, but significantly (p < 0.05) increased in the mid and late stages. Immunohistochemistry revealed that both TIMP-1 and -2 were localized to large luteal cells from all three ages of CL. TIMP-1 was also localized in capillary smooth muscle cells, while TIMP-2 was restricted to the endothelial cells in the capillary compartment. In conclusion, the different temporal expression patterns of TIMP-1 and TIMP-2 suggest that TIMP-1 may be important for luteal formation and development, while TIMP-2 may play significant roles during luteal development and maintenance. Furthermore, the distinct localization of these two inhibitors in the vascular compartment indicates that they may serve diverse physiological functions during different stages of luteal angiogenesis

High-order computational scheme for a dynamic continuum model for bi-directional pedestrian flows

In this article, we present a high-order weighted essentially non-oscillatory (WENO) scheme, coupled with a high-order fast sweeping method, for solving a dynamic continuum model for bi-directional pedestrian flows. We first review the dynamic continuum model for bi-directional pedestrian flows. This model is composed of a coupled system of a conservation law and an Eikonal equation. Then we present the first-order Lax-Friedrichs difference scheme with first-order Euler forward time discretization, the third-order WENO scheme with third-order total variation diminishing (TVD) Runge-Kutta time discretization, and the fast sweeping method, and demonstrate how to apply them to the model under study. We present a comparison of the numerical results of the model from the first-order and high-order methods, and conclude that the high-order method is more efficient than the first-order one, and they both converge to the same solution of the physical model. © 2010 Computer-Aided Civil and Infrastructure Engineering.postprin

Learning preferences for large scale multi-label problems

Despite that the majority of machine learning approaches aim to solve binary classification problems, several real-world applications require specialized algorithms able to handle many different classes, as in the case of single-label multi-class and multi-label classification problems. The Label Ranking framework is a generalization of the above mentioned settings, which aims to map instances from the input space to a total order over the set of possible labels. However, generally these algorithms are more complex than binary ones, and their application on large-scale datasets could be untractable. The main contribution of this work is the proposal of a novel general online preference-based label ranking framework. The proposed framework is able to solve binary, multi-class, multi-label and ranking problems. A comparison with other baselines has been performed, showing effectiveness and efficiency in a real-world large-scale multi-label task

B-spline recurrent neural network and its application to modelling of non-linear dynamic systems

A new recurrent neural network based on B-spline function approximation is presented. The network can be easily trained and its training converges more quickly than that for other recurrent neural networks. Moreover, an adaptive weight updating algorithm for the recurrent network is proposed. It can speed up the training process of the network greatly and its learning speed is more quickly than existing algorithms, e.g., back-propagation algorithm. Examples are presented comparing the adaptive weight updating algorithm and the constant learning rate method, and illustrating its application to modelling of nonlinear dynamic system.published_or_final_versio

State estimation with measurement error compensation using neural network

For a system with redundant sensors, the estimated state from the Kalman filter is biased if sensor mounting error existed. To remove this bias, the mounting errors must be compensated first before using the Kalman filter. It is shown that only the projection part of the sensors errors in the measurement space needs to be compensated. If the state of a system is unavailable, a neurofuzzy network can be used to estimate the compensation term. This method is simpler, as it does not require a model for the errors as that proposed in [2]. A sub-optimal Kalman filter with measurement compensation that restrains each row of the Kalman gain matrix to be in the measurement space is also derived. An example is presented to illustrate the performance of the proposed methods.published_or_final_versio

Fault detection of redundant systems based on B-spline neural network

The fault detection and isolation of redundant sensor systems based on B-spline neural networks is presented in this paper. The network is trained using an algorithm with an adaptive learning rate. To further save computation time, the residual vector is transformed from a multivariate B-spline function to an univariate B-spline function. The detection of abrupt and drifting faults using the proposed method is discusses. The performance of the proposed method is illustrated by an example involving a redundant system consisting of six sensors.published_or_final_versio

Nonlinear observer design with unknown nonlinearity via B-spline network approach

A novel approach is proposed to the state estimation of a class of nonlinear systems which consist of known linear part and unknown nonlinear part. A linear observer is first designed then a nonlinear compensation term in the nonlinear observer is determined using the proposed “deconvolution method”. The B-spline neural network is used to model the estimated compensation term. Three simulation examples are given to compare the effectiveness of the proposed approach and some analytical approaches.published_or_final_versio