A Local-Global LDA Model for Discovering Geographical Topics from Social Media

Micro-blogging services can track users' geo-locations when users check-in their places or use geo-tagging which implicitly reveals locations. This "geo tracking" can help to find topics triggered by some events in certain regions. However, discovering such topics is very challenging because of the large amount of noisy messages (e.g. daily conversations). This paper proposes a method to model geographical topics, which can filter out irrelevant words by different weights in the local and global contexts. Our method is based on the Latent Dirichlet Allocation (LDA) model but each word is generated from either a local or a global topic distribution by its generation probabilities. We evaluated our model with data collected from Weibo, which is currently the most popular micro-blogging service for Chinese. The evaluation results demonstrate that our method outperforms other baseline methods in several metrics such as model perplexity, two kinds of entropies and KL-divergence of discovered topics

Optimal Stochastic Package Delivery Planning with Deadline: A Cardinality Minimization in Routing

Vehicle Routing Problem with Private fleet and common Carrier (VRPPC) has been proposed to help a supplier manage package delivery services from a single depot to multiple customers. Most of the existing VRPPC works consider deterministic parameters which may not be practical and uncertainty has to be taken into account. In this paper, we propose the Optimal Stochastic Delivery Planning with Deadline (ODPD) to help a supplier plan and optimize the package delivery. The aim of ODPD is to service all customers within a given deadline while considering the randomness in customer demands and traveling time. We formulate the ODPD as a stochastic integer programming, and use the cardinality minimization approach for calculating the deadline violation probability. To accelerate computation, the L-shaped decomposition method is adopted. We conduct extensive performance evaluation based on real customer locations and traveling time from Google Map.Comment: 7 pages, 6 figures, Vehicular Technology Conference (VTC fall), 2017 IEEE 86t

Spatial-Temporal Residue Network Based In-Loop Filter for Video Coding

Deep learning has demonstrated tremendous break through in the area of image/video processing. In this paper, a spatial-temporal residue network (STResNet) based in-loop filter is proposed to suppress visual artifacts such as blocking, ringing in video coding. Specifically, the spatial and temporal information is jointly exploited by taking both current block and co-located block in reference frame into consideration during the processing of in-loop filter. The architecture of STResNet only consists of four convolution layers which shows hospitality to memory and coding complexity. Moreover, to fully adapt the input content and improve the performance of the proposed in-loop filter, coding tree unit (CTU) level control flag is applied in the sense of rate-distortion optimization. Extensive experimental results show that our scheme provides up to 5.1% bit-rate reduction compared to the state-of-the-art video coding standard.Comment: 4 pages, 2 figures, accepted by VCIP201

Effects of laser fluence on silicon modification by four-beam laser interference

This paper discusses the effects of laser fluence on silicon modification by four-beam laser interference. In this work, four-beam laser interference was used to pattern single crystal silicon wafers for the fabrication of surface structures, and the number of laser pulses was applied to the process in air. By controlling the parameters of laser irradiation, different shapes of silicon structures were fabricated. The results were obtained with the single laser fluence of 354 mJ/cm, 495 mJ/cm, and 637 mJ/cm, the pulse repetition rate of 10 Hz, the laser exposure pulses of 30, 100, and 300, the laser wavelength of 1064 nm, and the pulse duration of 7-9 ns. The effects of the heat transfer and the radiation of laser interference plasma on silicon wafer surfaces were investigated. The equations of heat flow and radiation effects of laser plasma of interfering patterns in a four-beam laser interference distribution were proposed to describe their impacts on silicon wafer surfaces. The experimental results have shown that the laser fluence has to be properly selected for the fabrication of well-defined surface structures in a four-beam laser interference process. Laser interference patterns can directly fabricate different shape structures for their corresponding applications

Analysis of miRNAs and their target genes associated with lipid metabolism in duck liver

Citation: He, J. et al. Analysis of miRNAs and their target genes associated with lipid metabolism in duck liver. Sci. Rep. 6, 27418; doi: 10.1038/srep27418 (2016).Fat character is an important index in duck culture that linked to local flavor, feed cost and fat intake for costumers. Since the regulation networks in duck lipid metabolism had not been reported very clearly, we aimed to explore the potential miRNA-mRNA pairs and their regulatory roles in duck lipid metabolism. Here, Cherry-Valley ducks were selected and treated with/without 5% oil added in feed for 2 weeks, and then fat content determination was performed on. The data showed that the fat contents and the fatty acid ratios of C17:1 and C18:2 were up-regulated in livers of oil-added ducks, while the C12:0 ratio was down-regulated. Then 21 differential miRNAs, including 10 novel miRNAs, were obtain from the livers by sequencing, and 73 target genes involved in lipid metabolic processes of these miRNAs were found, which constituted 316 miRNA-mRNA pairs. Two miRNA-mRNA pairs including one novel miRNA and one known miRNA, N-miR-16020-FASN and gga-miR-144-ELOVL6, were selected to validate the miRNA-mRNA negative relation. And the results showed that N-mir-16020 and gga-miR-144 could respectively bind the 3?-UTRs of FASN and ELOVL6 to control their expressions. This study provides new sights and useful information for future research on regulation network in duck lipid metabolism

Thompson Sampling for Combinatorial Semi-Bandits

We study the application of the Thompson sampling (TS) methodology to the stochastic combinatorial multi-armed bandit (CMAB) framework. We analyze the standard TS algorithm for the general CMAB, and obtain the first distribution-dependent regret bound of $O(mK_{\max}\log T / \Delta_{\min})$, where $m$ is the number of arms, $K_{\max}$ is the size of the largest super arm, $T$ is the time horizon, and $\Delta_{\min}$ is the minimum gap between the expected reward of the optimal solution and any non-optimal solution. We also show that one cannot directly replace the exact offline oracle with an approximation oracle in TS algorithm for even the classical MAB problem. Then we expand the analysis to two special cases: the linear reward case and the matroid bandit case. When the reward function is linear, the regret of the TS algorithm achieves a better bound $O(m\sqrt{K_{\max}}\log T / \Delta_{\min})$. For matroid bandit, we could remove the independence assumption across arms and achieve a regret upper bound that matches the lower bound for the matroid case. Finally, we use some experiments to show the comparison between regrets of TS and other existing algorithms like CUCB and ESCB