Time reversal Aharonov-Casher effect in mesoscopic rings with Rashba spin-orbital interaction

The time reversal Aharonov-Casher (AC) interference effect in the mesoscopic ring structures, based on the experiment in Phys. Rev. Lett. \textbf{97}, 196803 (2006), is studied theoretically. The transmission curves are calculated from the scattering matrix formalism, and the time reversal AC interference frequency is singled out from the Fourier spectra in numerical simulations. This frequency is in good agreement with analytical result. It is also shown that in the absent of magnetic field, the Altshuler-Aronov-Spivak type (time reversal) AC interference retains under the influence of strong disorder, while the Aharonov-Bohm type AC interference is suppressed.Comment: 5 pages, 4 figures, accepted by Phys. Rev.

Structural damage measure index based on non-probabilistic reliability model

2013-2014 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Constraints on timing of the early-paleoproterozoic magmatism and crustal evolution of the Oulongbuluke microcontinent: U-Pb and Lu-Hf isotope systematics of zircons from Mohe granitic pluton

The Olongbuluke micro-continent is composed of a two-fold basement with cover strata. The lower basement is the medium-to high-grade Delingha complex (DC) and the Dakendaban Group, the upper is the low-grade Wandonggou Group. Zircon LA-ICP-MS dating gives U-Pb isotope of 27 zircon grains from the Mohe granite of the DC, of which 26 zircon grains suffered various degree of Pb loss and defined a Concordia with a upper intercept age of 2470 +19/-18Ma. Hf isotope of 25 zircon grains was measured using LA-MC-ICP-MS technique, of which 23 grains crystallized from Mohe granite show Hf(2470Ma) = 0.28129-0.28140 (weighted average of 0.28134±0.00003). εHf = 2.94-6.95(weighted average of 4.58 + 0.54/-0.76), resident time for the felsic crustal reservoir TcDM=2.54-2.75 Ga with weighted average of 2.66 + 0.04/-0.02Ga. These data indicate that the Mohe granite pluton was derived from partial melting of the mantle, and the intrusion of the Mohe granite pluton implied a crustal accretion event occurred in Olongbuluke micro-continent at ca. 2.5 Ga.欧龙布鲁克微陆块具典型的基底与盖层二元结构，基底自下而上由德令哈杂岩、达肯大坂岩群和万洞沟群三个岩石-构造单元组成。应用LA-ICP-MS测定了德令哈杂岩中的莫河花岗岩体的27个颗锆石的U-Pb同位索成分，其中26颗锆石发生不同程度的放射成因铅同位素丢失，其不一致线上交点年龄为2470+19/-18Ma。应用LA-MC-ICP-MS测定了25颗锆石的Hf同位索成分，其中岩浆结晶成因的23颗锆石的Hf(2470Ma)=0.28129-0.28140，平均值0.28134±0.00003；εHf值的变化范围2.94-6.95，加权平均值4.58+0.54/-0.76，长英质地壳存留年龄TcDM=2.54-2.75Ga，加权平均值2.66+0.04/-0.02Ga。以上数据将该花岗岩的形成年龄约束在2470Ma，其岩浆来源于地幔物质的部分熔融，指示欧龙布鲁克微陆块在-2.5Ga的地壳增生事件。published_or_final_versio

On Solving a Generalized Chinese Remainder Theorem in the Presence of Remainder Errors

In estimating frequencies given that the signal waveforms are undersampled multiple times, Xia et. al. proposed to use a generalized version of Chinese remainder Theorem (CRT), where the moduli are $M_1, M_2, \cdots, M_k$ which are not necessarily pairwise coprime. If the errors of the corrupted remainders are within \tau=\sds \max_{1\le i\le k} \min_{\stackrel{1\le j\le k}{j\neq i}} \frac{\gcd(M_i,M_j)}4, their schemes can be used to construct an approximation of the solution to the generalized CRT with an error smaller than $\tau$. Accurately finding the quotients is a critical ingredient in their approach. In this paper, we shall start with a faithful historical account of the generalized CRT. We then present two treatments of the problem of solving generalized CRT with erroneous remainders. The first treatment follows the route of Wang and Xia to find the quotients, but with a simplified process. The second treatment considers a simplified model of generalized CRT and takes a different approach by working on the corrupted remainders directly. This approach also reveals some useful information about the remainders by inspecting extreme values of the erroneous remainders modulo $4\tau$. Both of our treatments produce efficient algorithms with essentially optimal performance. Finally, this paper constructs a counterexample to prove the sharpness of the error bound $\tau$

Semantic Object Parsing with Graph LSTM

By taking the semantic object parsing task as an exemplar application scenario, we propose the Graph Long Short-Term Memory (Graph LSTM) network, which is the generalization of LSTM from sequential data or multi-dimensional data to general graph-structured data. Particularly, instead of evenly and fixedly dividing an image to pixels or patches in existing multi-dimensional LSTM structures (e.g., Row, Grid and Diagonal LSTMs), we take each arbitrary-shaped superpixel as a semantically consistent node, and adaptively construct an undirected graph for each image, where the spatial relations of the superpixels are naturally used as edges. Constructed on such an adaptive graph topology, the Graph LSTM is more naturally aligned with the visual patterns in the image (e.g., object boundaries or appearance similarities) and provides a more economical information propagation route. Furthermore, for each optimization step over Graph LSTM, we propose to use a confidence-driven scheme to update the hidden and memory states of nodes progressively till all nodes are updated. In addition, for each node, the forgets gates are adaptively learned to capture different degrees of semantic correlation with neighboring nodes. Comprehensive evaluations on four diverse semantic object parsing datasets well demonstrate the significant superiority of our Graph LSTM over other state-of-the-art solutions.Comment: 18 page

Hybrid reliability analysis of structures with multi-source uncertainties

2013-2014 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Reinforcement recommendation with user multi-aspect preference

Formulating recommender system with reinforcement learning (RL) frameworks has attracted increasing attention from both academic and industry communities. While many promising results have been achieved, existing models mostly simulate the environment reward with a unified value, which may hinder the understanding of users' complex preferences and limit the model performance. In this paper, we consider how to model user multi-aspect preferences in the context of RL-based recommender system. More specifically, we base our model on the framework of deterministic policy gradient (DPG), which is effective in dealing with large action spaces. A major challenge for modeling user multi-aspect preferences lies in the fact that they may contradict with each other. To solve this problem, we introduce Pareto optimization into the DPG framework. We assign each aspect with a tailored critic, and all the critics share the same actor. The Pareto optimization is realized by a gradient-based method, which can be easily integrated into the actor and critic learning process. Based on the designed model, we theoretically analyze its gradient bias in the optimization process, and we design a weight-reuse mechanism to lower the upper bound of this bias, which is shown to be effective for improving the model performance. We conduct extensive experiments based on three real-world datasets to demonstrate our model's superiorities