Efficient Spatial Keyword Search in Trajectory Databases

An increasing amount of trajectory data is being annotated with text descriptions to better capture the semantics associated with locations. The fusion of spatial locations and text descriptions in trajectories engenders a new type of top-$k$ queries that take into account both aspects. Each trajectory in consideration consists of a sequence of geo-spatial locations associated with text descriptions. Given a user location $\lambda$ and a keyword set $\psi$, a top-$k$ query returns $k$ trajectories whose text descriptions cover the keywords $\psi$ and that have the shortest match distance. To the best of our knowledge, previous research on querying trajectory databases has focused on trajectory data without any text description, and no existing work has studied such kind of top-$k$ queries on trajectories. This paper proposes one novel method for efficiently computing top-$k$ trajectories. The method is developed based on a new hybrid index, cell-keyword conscious B$^+$-tree, denoted by \cellbtree, which enables us to exploit both text relevance and location proximity to facilitate efficient and effective query processing. The results of our extensive empirical studies with an implementation of the proposed algorithms on BerkeleyDB demonstrate that our proposed methods are capable of achieving excellent performance and good scalability.Comment: 12 page

Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning

Recent progress has been made in using attention based encoder-decoder framework for video captioning. However, most existing decoders apply the attention mechanism to every generated word including both visual words (e.g., "gun" and "shooting") and non-visual words (e.g. "the", "a"). However, these non-visual words can be easily predicted using natural language model without considering visual signals or attention. Imposing attention mechanism on non-visual words could mislead and decrease the overall performance of video captioning. To address this issue, we propose a hierarchical LSTM with adjusted temporal attention (hLSTMat) approach for video captioning. Specifically, the proposed framework utilizes the temporal attention for selecting specific frames to predict the related words, while the adjusted temporal attention is for deciding whether to depend on the visual information or the language context information. Also, a hierarchical LSTMs is designed to simultaneously consider both low-level visual information and high-level language context information to support the video caption generation. To demonstrate the effectiveness of our proposed framework, we test our method on two prevalent datasets: MSVD and MSR-VTT, and experimental results show that our approach outperforms the state-of-the-art methods on both two datasets

Fixed-time command filtered output feedback control for twin-roll inclined casting system with prescribed performance

The article investigates the issue of fixed-time control with adaptive output feedback for a twin-roll inclined casting system (TRICS) with disturbance. First, by using the mean value theorem, the nonaffine functions are decoupled to simplify the system. Second, radial basis function neural networks (RBFNNs) are introduced to approximate an unknown term, and a nonlinear neural state observer is created to handle the effects of unmeasured states. Then, the backstepping design framework is combined with prescribed performance and command filtering techniques to demonstrate that the scheme proposed in this article guarantees system performance within a fixed-time. The control design parameters determine the upper bound of settling time, regardless of the initial state of the system. Meanwhile, it ensures that all signals in the closed-loop system (CLS) remain bounded, and it can also maintain the tracking error within a predefined range within a fixed time. Finally, simulation results assert the effectiveness of the method

Processing long queries against short text: Top-k advertisement matching in news stream applications

© 2017 ACM. Many real applications in real-time news stream advertising call for efficient processing of long queries against short text. In such applications, dynamic news feeds are regarded as queries to match against an advertisement (ad) database for retrieving the k most relevant ads. The existing approaches to keyword retrieval cannot work well in this search scenario when queries are triggered at a very high frequency. To address the problem, we introduce new techniques to significantly improve search performance. First, we devise a two-level partitioning for tight upper bound estimation and a lazy evaluation scheme to delay full evaluation of unpromising candidates, which can bring three to four times performance boosting in a database with 7 million ads. Second, we propose a novel rank-aware block-oriented inverted index to further improve performance. In this index scheme, each entry in an inverted list is assigned a rank according to its importance in the ad. Then, we introduce a block-at-a-time search strategy based on the index scheme to support amuch tighter upper bound estimation and a very early termination. We have conducted experiments with real datasets, and the results show that the rank-aware method can further improve performance by an order of magnitude

g-C3N4-Stabilised Organic–Inorganic Halide Perovskites for Efficient Photocatalytic Selective Oxidation of Benzyl Alcohol

The outstanding optoelectronic performance and facile synthetic approach of metal halide perovskites has inspired additional applications well beyond efficient solar cells and light emitting diodes (LEDs). Herein, we present an alternative option available for the optimisation of selective and efficient oxidation of benzylic alcohols through photocatalysis. The materials engineering of hybrids based on formamidine lead bromide (FAPbBr3) and graphic carbon nitride (g-C3N4) is achieved via facile anti-solvent approach. The photocatalytic performance of the hybrids is highly reliant on weight ratio between FAPbBr3 and g-C3N4. Besides, the presence of g-C3N4 dramatically enhances the long-term stability of the hybrids, compared to metal oxides hybrids. Detailed optical, electrical and thermal studies reveal the proposed novel photocatalytic and stability behaviours arising in FAPbBr3 and g-C3N4 hybrid materials

One network for multi-domains:domain adaptive hashing with intersectant generative adversarial networks

With the recent explosive increase of digital data, image recognition and retrieval become a critical practical application. Hashing is an effective solution to this problem, due to its low storage requirement and high query speed. However, most of past works focus on hashing in a single (source) domain. Thus, the learned hash function may not adapt well in a new (target) domain that has a large distributional difference with the source domain. In this paper, we explore an end-to-end domain adaptive learning framework that simultaneously and precisely generates discriminative hash codes and classifies target domain images. Our method encodes two domains images into a semantic common space, followed by two independent generative adversarial networks arming at crosswise reconstructing two domains' images, reducing domain disparity and improving alignment in the shared space. We evaluate our framework on {four} public benchmark datasets, all of which show that our method is superior to the other state-of-the-art methods on the tasks of object recognition and image retrieval.Comment: Accepted by IJCAI 201

Perceptual Pyramid Adversarial Networks for Text-to-Image Synthesis

Generating photo-realistic images conditioned on semantic text descriptions is a challenging task in computer vision field. Due to the nature of hierarchical representations learned in CNN, it is intuitive to utilize richer convolutional features to improve text-to-image synthesis. In this paper, we propose Perceptual Pyramid Adversarial Network (PPAN) to directly synthesize multi-scale images conditioned on texts in an adversarial way. Specifically, we design one pyramid generator and three independent discriminators to synthesize and regularize multi-scale photo-realistic images in one feed-forward process. At each pyramid level, our method takes coarse-resolution features as input, synthesizes highresolution images, and uses convolutions for up-sampling to finer level. Furthermore, the generator adopts the perceptual loss to enforce semantic similarity between the synthesized image and the ground truth, while a multi-purpose discriminator encourages semantic consistency, image fidelity and class invariance. Experimental results show that our PPAN sets new records for text-to-image synthesis on two benchmark datasets: CUB (i.e., 4.38 Inception Score and .290 Visual-semantic Similarity) and Oxford-102 (i.e., 3.52 Inception Score and .297 Visual-semantic Similarity)

Type II Homo-Type Bi₂O₂Se Nanosheet/InSe Nanoflake Heterostructures for Self-Driven Broadband Visible-Near-Infrared Photodetectors

Bi2O2Se nanosheets, an emerging ternary non-van der Waals two-dimensional (2D) material, have garnered considerable research attention in recent years owing to their robust air stability, narrow indirect bandgap, high mobility, and diverse intriguing properties. However, most of them show high dark current and relatively low light on/off ratio and slow response speed because of the large charge carrier concentration and bolometric effect, hindering their further application in low-energy-consuming optoelectronics. Herein, a homotype van der Waals heterostructure based on exfoliated n-InSe integrated with chemical vapor deposition (CVD)-grown n-Bi2O2Se nanosheets that have type II band alignment was fabricated. The efficient interfacial charge separation, strong interlayer coupling, and effective built-in electric field across the heterointerface demonstrated excellent, stable, and broadband self-driven photodetection in the range 400-1064 nm. Specifically, a high responsivity (R) of 75.2 mA·W-1 and a high specific detectivity (D*) of 1.08 × 1012 jones were achieved under 405 nm illumination. Additionally, a high R of 13.3 mA·W-1 and a high D* of 2.06 × 1011 jones were achieved under 980 nm illumination. Meanwhile, an ultrahigh Ilight/Idark ratio over 105 and a fast response time of 5.8/15 ms under 405 nm illumination confirmed the excellent photosensitivity and fast response behavior. Furthermore, R could be enhanced to 13.6 and 791 mA·W-1 under 405 and 980 nm illumination at a drain-source voltage (Vds) of 1 V, respectively, originating from a lower potential barrier. This study suggested that the Bi2O2Se nanosheet/InSe nanoflake homotype heterojunction can offer potential applications in next-generation broadband photodetectors that consume low energy and exhibit high performance.</p