2,961 research outputs found
Deep Landscape Forecasting for Real-time Bidding Advertising
The emergence of real-time auction in online advertising has drawn huge
attention of modeling the market competition, i.e., bid landscape forecasting.
The problem is formulated as to forecast the probability distribution of market
price for each ad auction. With the consideration of the censorship issue which
is caused by the second-price auction mechanism, many researchers have devoted
their efforts on bid landscape forecasting by incorporating survival analysis
from medical research field. However, most existing solutions mainly focus on
either counting-based statistics of the segmented sample clusters, or learning
a parameterized model based on some heuristic assumptions of distribution
forms. Moreover, they neither consider the sequential patterns of the feature
over the price space. In order to capture more sophisticated yet flexible
patterns at fine-grained level of the data, we propose a Deep Landscape
Forecasting (DLF) model which combines deep learning for probability
distribution forecasting and survival analysis for censorship handling.
Specifically, we utilize a recurrent neural network to flexibly model the
conditional winning probability w.r.t. each bid price. Then we conduct the bid
landscape forecasting through probability chain rule with strict mathematical
derivations. And, in an end-to-end manner, we optimize the model by minimizing
two negative likelihood losses with comprehensive motivations. Without any
specific assumption for the distribution form of bid landscape, our model shows
great advantages over previous works on fitting various sophisticated market
price distributions. In the experiments over two large-scale real-world
datasets, our model significantly outperforms the state-of-the-art solutions
under various metrics.Comment: KDD 2019. The reproducible code and dataset link is
https://github.com/rk2900/DL
Spatial-temporal traffic modeling with a fusion graph reconstructed by tensor decomposition
Accurate spatial-temporal traffic flow forecasting is essential for helping
traffic managers to take control measures and drivers to choose the optimal
travel routes. Recently, graph convolutional networks (GCNs) have been widely
used in traffic flow prediction owing to their powerful ability to capture
spatial-temporal dependencies. The design of the spatial-temporal graph
adjacency matrix is a key to the success of GCNs, and it is still an open
question. This paper proposes reconstructing the binary adjacency matrix via
tensor decomposition, and a traffic flow forecasting method is proposed. First,
we reformulate the spatial-temporal fusion graph adjacency matrix into a
three-way adjacency tensor. Then, we reconstructed the adjacency tensor via
Tucker decomposition, wherein more informative and global spatial-temporal
dependencies are encoded. Finally, a Spatial-temporal Synchronous Graph
Convolutional module for localized spatial-temporal correlations learning and a
Dilated Convolution module for global correlations learning are assembled to
aggregate and learn the comprehensive spatial-temporal dependencies of the road
network. Experimental results on four open-access datasets demonstrate that the
proposed model outperforms state-of-the-art approaches in terms of the
prediction performance and computational cost.Comment: 11 pages, 8 figure
Epileptic focus localization using transfer learning on multi-modal EEG
The standard treatments for epilepsy are drug therapy and surgical resection. However, around 1/3 of patients with intractable epilepsy are drug-resistant, requiring surgical resection of the epileptic focus. To address the issue of drug-resistant epileptic focus localization, we have proposed a transfer learning method on multi-modal EEG (iEEG and sEEG). A 10-fold cross-validation approach was applied to validate the performance of the pre-trained model on the Bern-Barcelona and Bonn datasets, achieving accuracy rates of 94.50 and 97.50%, respectively. The experimental results have demonstrated that the pre-trained model outperforms the competitive state-of-the-art baselines in terms of accuracy, sensitivity, and negative predictive value. Furthermore, we fine-tuned our pre-trained model using the epilepsy dataset from Chongqing Medical University and tested it using the leave-one-out cross-validation method, obtaining an impressive average accuracy of 90.15%. This method shows significant feature differences between epileptic and non-epileptic channels. By extracting data features using neural networks, accurate classification of epileptic and non-epileptic channels can be achieved. Therefore, the superior performance of the model has demonstrated that the proposed method is highly effective for localizing epileptic focus and can aid physicians in clinical localization diagnosis
Patient-specific approach using data fusion and adversarial training for epileptic seizure prediction
Epilepsy is the second common neurological disorder after headache, accurate and reliable prediction of seizures is of great clinical value. Most epileptic seizure prediction methods consider only the EEG signal or extract and classify the features of EEG and ECG signals separately, the improvement of prediction performance from multimodal data is not fully considered. In addition, epilepsy data are time-varying, with differences between each episode in a patient, making it difficult for traditional curve-fitting models to achieve high accuracy and reliability. In order to improve the accuracy and reliability of the prediction system, we propose a novel personalized approach based on data fusion and domain adversarial training to predict epileptic seizures using leave-one-out cross-validation, which achieves an average accuracy, sensitivity and specificity of 99.70, 99.76, and 99.61%, respectively, with an average error alarm rate (FAR) of 0.001. Finally, the advantage of this approach is demonstrated by comparison with recent relevant literature. This method will be incorporated into clinical practice to provide personalized reference information for epileptic seizure prediction
Local relational embeddedness and subsidiaries’ innovative performance
We examine how foreign subsidiaries’ local networks with clients, suppliers, and research institutes in emerging markets affect their innovative performance by incorporating resource- based view with local embeddedness perspective. Utilizing a survey dataset of 381 multinational corporation subsidiaries in China, we explore both quality and quantity of subsidiaries’ relations with local clients, suppliers, and research institutes and their impact on subsidiaries’ innovative performance. Specifically, we find that high-quality relations with local clients, broader network with local suppliers, and collaboration with local research institutes all contribute to subsidiaries’ innovative performance. The follow-up on-site interviews with senior executives of foreign subsidiaries provide strong support to all of our empirical findings. This study provides theoretical and practical implications in understanding subsidiaries’ innovative performance
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