Time series similarity computation is a fundamental primitive that underpins many time series data analysis tasks. However, many existing time series similarity measures have a high computation cost. While there has been much research effort for reducing the computational cost, such effort is usually specific to one similarity measure. We proposeNeuTS(Neural metric learning forTimeSeries) to accelerate time series similarity computation in a generic fashion.NeuTScomputes the similarity of a given time series pair in linear time and generic to handle any existing similarity measures.NeuTSsamples a number of seed time series from the given database, and then uses their pair-wise similarities as guidance to approximate the similarity function with a neural metric learning framework.NeuTSfeatures two novel modules to achieve accurate approximation of the similarity function: (1) a local attention memory module that augments existing recurrent neural networks for time series encoding; and (2) a distance-weighted ranking loss that effectively transcribes information from the seed-based guidance. With these two modules,NeuTScan yield high accuracies and fast convergence rates even if the training data is small. Our experiments with five real-life datasets and four similarity measures (Fréchet, Hausdorff, ERP and DTW) show thatNeuTSoutperforms baselines consistently and significantly. Specifically, it achieves over 80 percent accuracies in most settings, while obtaining 50x-1000x speedup over bruteforce methods and 3x-350x speedup over approximate algorithms for top-k similarity search.

Full-duplex (FD) wireless is an attractive PHY technology with high potential to improve the throughput of WLAN due to bidirectional transmissions. However, existing FD MACs fail to fully take advantage of bidirectional transmissions because of neglecting a feature of FD wireless that whether to build bidirectional transmissions relies on the queuing state of peers. In this paper, we design PQ-MAC, the first FD MAC which exploits more bidirectional transmission opportunities by leveraging peers' queuing information. Since PQ-MAC seizes the neglected but important feature, PQ-MAC can improve the performance with a slight transmission overhead. Simulations show that, in a 1-cell FD WLAN, PQ-MAC can achieve higher throughput than existing MACs when the buffer of AP is relatively small (≤200 frames). When the buffer of AP is relatively large (>200 frames), PQ-MAC can reduce the queuing delay without the loss of throughput.