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

Bilinear Graph Neural Network with Neighbor Interactions

Graph Neural Network (GNN) is a powerful model to learn representations and make predictions on graph data. Existing efforts on GNN have largely defined the graph convolution as a weighted sum of the features of the connected nodes to form the representation of the target node. Nevertheless, the operation of weighted sum assumes the neighbor nodes are independent of each other, and ignores the possible interactions between them. When such interactions exist, such as the co-occurrence of two neighbor nodes is a strong signal of the target node's characteristics, existing GNN models may fail to capture the signal. In this work, we argue the importance of modeling the interactions between neighbor nodes in GNN. We propose a new graph convolution operator, which augments the weighted sum with pairwise interactions of the representations of neighbor nodes. We term this framework as Bilinear Graph Neural Network (BGNN), which improves GNN representation ability with bilinear interactions between neighbor nodes. In particular, we specify two BGNN models named BGCN and BGAT, based on the well-known GCN and GAT, respectively. Empirical results on three public benchmarks of semi-supervised node classification verify the effectiveness of BGNN -- BGCN (BGAT) outperforms GCN (GAT) by 1.6% (1.5%) in classification accuracy.Codes are available at: https://github.com/zhuhm1996/bgnn.Comment: Accepted by IJCAI 2020. SOLE copyright holder is IJCAI (International Joint Conferences on Artificial Intelligence), all rights reserve

HDIdx: High-Dimensional Indexing for Efficient Approximate Nearest Neighbor Search

Fast Nearest Neighbor (NN) search is a fundamental challenge in large-scale data processing and analytics, particularly for analyzing multimedia contents which are often of high dimensionality. Instead of using exact NN search, extensive research efforts have been focusing on approximate NN search algorithms. In this work, we present "HDIdx", an efficient high-dimensional indexing library for fast approximate NN search, which is open-source and written in Python. It offers a family of state-of-the-art algorithms that convert input high-dimensional vectors into compact binary codes, making them very efficient and scalable for NN search with very low space complexity

How to Retrain Recommender System? A Sequential Meta-Learning Method

Practical recommender systems need be periodically retrained to refresh the model with new interaction data. To pursue high model fidelity, it is usually desirable to retrain the model on both historical and new data, since it can account for both long-term and short-term user preference. However, a full model retraining could be very time-consuming and memory-costly, especially when the scale of historical data is large. In this work, we study the model retraining mechanism for recommender systems, a topic of high practical values but has been relatively little explored in the research community. Our first belief is that retraining the model on historical data is unnecessary, since the model has been trained on it before. Nevertheless, normal training on new data only may easily cause overfitting and forgetting issues, since the new data is of a smaller scale and contains fewer information on long-term user preference. To address this dilemma, we propose a new training method, aiming to abandon the historical data during retraining through learning to transfer the past training experience. Specifically, we design a neural network-based transfer component, which transforms the old model to a new model that is tailored for future recommendations. To learn the transfer component well, we optimize the "future performance" -- i.e., the recommendation accuracy evaluated in the next time period. Our Sequential Meta-Learning(SML) method offers a general training paradigm that is applicable to any differentiable model. We demonstrate SML on matrix factorization and conduct experiments on two real-world datasets. Empirical results show that SML not only achieves significant speed-up, but also outperforms the full model retraining in recommendation accuracy, validating the effectiveness of our proposals. We release our codes at: https://github.com/zyang1580/SML.Comment: Appear in SIGIR 202

An optimal full frequency control strategy for the modular multilevel matrix converter based on predictive control

The modular multilevel matrix converter (M3C) is a promising topology for high-voltage high-power applications. Recent researches have proved its significant advantages for adjustable-speed motor drives compared with the back-to-back modular multilevel converter (MMC). However, the branch energy balancing in the M3C presents great challenge especially at critical-frequency points where the output frequency is close to zero or grid-side frequency. Generally, this balancing control depends on the appropriate injection of inner circulating currents and the common-mode voltage (CMV) whereas their values are hard to determine and optimize. In this paper, an optimization based predictive control method is proposed to calculate the required circulating currents and the CMV. The proposed method features a broad-frequency range balancing of capacitor-voltages and no reactive power in the grid side. For operation at critical-frequency points, there is no increase on branch voltage stresses and limited increase on branch current stresses. A downscaled M3C system with 27 cells is designed and experiment results with the R-L load and induction motor load are presented to verify the proposed control method

A branch current reallocation based energy balancing strategy for the modular multilevel matrix converter operating around equal frequency

The Modular multilevel matrix converter (M3C) is a promising topology for medium-voltage, high-power applications. Due to the modular structure, it is scalable, produces high quality output waveforms and can be fault tolerant. However, the M3C suffers from capacitor-voltage fluctuation if the output frequency is similar to the input frequency. This problem could limit the circuit’s application in the adjustable speed drives (ASD). This paper introduces a theoretical analysis in the phasor-domain to find the branch energy equilibrium point of the M3C when operating with equal input and output frequencies. On the basis of this equilibrium point, a branch current reallocation based energy balancing control method is proposed to equalize the energy stored in the nine converter branches. With this novel control method, the M3C can effectively overcome the capacitor voltage fluctuation without using balancing techniques based on common mode voltage or applying reactive power at the input side

Development of a SYBR Green I real-time PCR for the detection of the orf virus

Orf is a non-systemic, ubiquitous disease of sheep and goats caused by the orf virus (ORFV). ORFV occasionally causes cutaneous lesions in humans in contact with infected animals. In the present study, a real-time PCR method was established for detection of ORFV using the fluorescent chimeric dye SYBR Green I. Specific primers were designed to target a highly conserved region of the ORFV B2L gene. This method was able to detect a minimum of 20 copies of ORFV genomic DNA. The results showed no cross-reactions with other common DNA viruses. The time required for the test was approximately 1.5 h. Clinical test samples showed that this method was faster and had a higher sensitivity than traditional PCR. In conclusion, this novel, real-time PCR-based assay provides a rapid, sensitive, and specific method for ORFV detection. This test provides improved technical support for studies regarding the clinical diagnosis and epidemiology of ORFV