Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks

Prediction of popularity has profound impact for social media, since it offers opportunities to reveal individual preference and public attention from evolutionary social systems. Previous research, although achieves promising results, neglects one distinctive characteristic of social data, i.e., sequentiality. For example, the popularity of online content is generated over time with sequential post streams of social media. To investigate the sequential prediction of popularity, we propose a novel prediction framework called Deep Temporal Context Networks (DTCN) by incorporating both temporal context and temporal attention into account. Our DTCN contains three main components, from embedding, learning to predicting. With a joint embedding network, we obtain a unified deep representation of multi-modal user-post data in a common embedding space. Then, based on the embedded data sequence over time, temporal context learning attempts to recurrently learn two adaptive temporal contexts for sequential popularity. Finally, a novel temporal attention is designed to predict new popularity (the popularity of a new user-post pair) with temporal coherence across multiple time-scales. Experiments on our released image dataset with about 600K Flickr photos demonstrate that DTCN outperforms state-of-the-art deep prediction algorithms, with an average of 21.51% relative performance improvement in the popularity prediction (Spearman Ranking Correlation).Comment: accepted in IJCAI-1

Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images

In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation hasn't efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address the these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectral-spatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient

Deformable Kernel Expansion Model for Efficient Arbitrary-shaped Scene Text Detection

Scene text detection is a challenging computer vision task due to the high variation in text shapes and ratios. In this work, we propose a scene text detector named Deformable Kernel Expansion (DKE), which incorporates the merits of both segmentation and contour-based detectors. DKE employs a segmentation module to segment the shrunken text region as the text kernel, then expands the text kernel contour to obtain text boundary by regressing the vertex-wise offsets. Generating the text kernel by segmentation enables DKE to inherit the arbitrary-shaped text region modeling capability of segmentation-based detectors. Regressing the kernel contour with some sampled vertices enables DKE to avoid the complicated pixel-level post-processing and better learn contour deformation as the contour-based detectors. Moreover, we propose an Optimal Bipartite Graph Matching Loss (OBGML) that measures the matching error between the predicted contour and the ground truth, which efficiently minimizes the global contour matching distance. Extensive experiments on CTW1500, Total-Text, MSRA-TD500, and ICDAR2015 demonstrate that DKE achieves a good tradeoff between accuracy and efficiency in scene text detection

The Performance Matching of Inverter Room Air Conditioner

Some key parameter like suction superheat, compressor frequency, indoor and outdoor air volume, have been explored using simulation tools for the performance matching of the inverter room air conditioner. It was found that the all above parameters can be further optimized in every matching condition (including intermediate cooling, rated cooling, maximum cooling, middle heating and rated heating, etc.). The optimum range for all above parameter has been found and can be used in the variable frequency control module to ensure optimal overall performance of the system

A solution to persistent RFI in narrowband radio SETI: The MultiBeam Point-source Scanning strategy

Narrowband radio search for extraterrestrial intelligence (SETI) in the 21st century suffers severely from radio frequency interference (RFI), resulting in a high number of false positives, and it could be the major reason why we have not yet received any messages from space. We thereby propose a novel observation strategy, called MultiBeam Point-source Scanning (MBPS), to revolutionize the way RFI is identified in narrowband radio SETI and provide a prominent solution to the current situation. The MBPS strategy is a simple yet powerful method that sequentially scans over the target star with different beams of a telescope, hence creating real-time references in the time domain for cross-verification, thus potentially identifying all long-persistent RFI with a level of certainty never achieved in any previous attempts. By applying the MBPS strategy during the observation of TRAPPIST-1 with the FAST telescope, we successfully identified all 16,645 received signals as RFI using the solid criteria introduced by the MBPS strategy. Therefore we present the MBPS strategy as a promising tool that should bring us much closer to the first discovery of a genuine galactic greeting