Fast Low-rank Representation based Spatial Pyramid Matching for Image Classification

Spatial Pyramid Matching (SPM) and its variants have achieved a lot of success in image classification. The main difference among them is their encoding schemes. For example, ScSPM incorporates Sparse Code (SC) instead of Vector Quantization (VQ) into the framework of SPM. Although the methods achieve a higher recognition rate than the traditional SPM, they consume more time to encode the local descriptors extracted from the image. In this paper, we propose using Low Rank Representation (LRR) to encode the descriptors under the framework of SPM. Different from SC, LRR considers the group effect among data points instead of sparsity. Benefiting from this property, the proposed method (i.e., LrrSPM) can offer a better performance. To further improve the generalizability and robustness, we reformulate the rank-minimization problem as a truncated projection problem. Extensive experimental studies show that LrrSPM is more efficient than its counterparts (e.g., ScSPM) while achieving competitive recognition rates on nine image data sets.Comment: accepted into knowledge based systems, 201

Learning latent features with infinite non-negative binary matrix tri-factorization

Non-negative Matrix Factorization (NMF) has been widely exploited to learn latent features from data. However, previous NMF models often assume a fixed number of features, saypfeatures, wherepis simply searched by experiments. Moreover, it is even difficult to learn binary features, since binary matrix involves more challenging optimization problems. In this paper, we propose a new Bayesian model called infinite non-negative binary matrix tri-factorizations model (iNBMT), capable of learning automatically the latent binary features as well as feature number based on Indian Buffet Process (IBP). Moreover, iNBMT engages a tri-factorization process that decomposes a nonnegative matrix into the product of three components including two binary matrices and a non-negative real matrix. Compared with traditional bi-factorization, the tri-factorization can better reveal the latent structures among items (samples) and attributes (features). Specifically, we impose an IBP prior on the two infinite binary matrices while a truncated Gaussian distribution is assumed on the weight matrix. To optimize the model, we develop an efficient modified maximization-expectation algorithm (ME-algorithm), with the iteration complexity one order lower than another recently-proposed Maximization-Expectation-IBP model[9]. We present the model definition, detail the optimization, and finally conduct a series of experiments. Experimental results demonstrate that our proposed iNBMT model significantly outperforms the other comparison algorithms in both synthetic and real data

Explainable Tensorized Neural Ordinary Differential Equations forArbitrary-step Time Series Prediction

We propose a continuous neural network architecture, termed Explainable Tensorized Neural Ordinary Differential Equations (ETN-ODE), for multi-step time series prediction at arbitrary time points. Unlike the existing approaches, which mainly handle univariate time series for multi-step prediction or multivariate time series for single-step prediction, ETN-ODE could model multivariate time series for arbitrary-step prediction. In addition, it enjoys a tandem attention, w.r.t. temporal attention and variable attention, being able to provide explainable insights into the data. Specifically, ETN-ODE combines an explainable Tensorized Gated Recurrent Unit (Tensorized GRU or TGRU) with Ordinary Differential Equations (ODE). The derivative of the latent states is parameterized with a neural network. This continuous-time ODE network enables a multi-step prediction at arbitrary time points. We quantitatively and qualitatively demonstrate the effectiveness and the interpretability of ETN-ODE on five different multi-step prediction tasks and one arbitrary-step prediction task. Extensive experiments show that ETN-ODE can lead to accurate predictions at arbitrary time points while attaining best performance against the baseline methods in standard multi-step time series prediction

Methyl salicylate 2-O-β-D-lactoside, a novel salicylic acid analogue, acts as an anti-inflammatory agent on microglia and astrocytes

<p>Abstract</p> <p>Background</p> <p>Neuroinflammation has been known to play a critical role in the pathogenesis of Alzheimer's disease (AD). Activation of microglia and astrocytes is a characteristic of brain inflammation. Epidemiological studies have shown that long-term use of non-steroidal anti-inflammatory drugs (NSAIDs) delays the onset of AD and suppresses its progression. Methyl salicylate-2-<it>O</it>-<it>β</it>-<smcaps/><smcaps>D</smcaps>-lactoside (DL0309) is a new molecule chemically related to salicylic acid. The present study aimed to evaluate the anti-inflammatory effects of DL0309.</p> <p>Findings</p> <p>Our studies show that DL0309 significantly inhibits lipopolysaccharide (LPS)-induced release of the pro-inflammatory cytokines IL-6, IL-1β, and TNF-α; and the expression of the inflammation-related proteins iNOS, COX-1, and COX-2 by microglia and astrocytes. At a concentration of 10 μM, DL0309 prominently inhibited LPS-induced activation of NF-κB in glial cells by blocking phosphorylation of IKK and p65, and by blocking IκB degradation.</p> <p>Conclusions</p> <p>We demonstrate here for the first time that DL0309 exerts anti-inflammatory effects in glial cells by suppressing different pro-inflammatory cytokines and iNOS/NO. Furthermore, it also regulates the NF-κB signaling pathway by blocking IKK and p65 activation and IκB degradation. DL0309 also acts as a non-selective COX inhibitor in glial cells. These studies suggest that DL0309 may be effective in the treatment of neuroinflammatory disorders, including AD.</p

mmHawkeye: Passive UAV Detection with a COTS mmWave Radar

Small Unmanned Aerial Vehicles (UAVs) are becoming potential threats to security-sensitive areas and personal privacy. A UAV can shoot photos at height, but how to detect such an uninvited intruder is an open problem. This paper presents mmHawkeye, a passive approach for UAV detection with a COTS millimeter wave (mmWave) radar. mmHawkeye doesn't require prior knowledge of the type, motions, and flight trajectory of the UAV, while exploiting the signal feature induced by the UAV's periodic micro-motion (PMM) for long-range accurate detection. The design is therefore effective in dealing with low-SNR and uncertain reflected signals from the UAV. mmHawkeye can further track the UAV's position with dynamic programming and particle filtering, and identify it with a Long Short-Term Memory (LSTM) based detector. We implement mmHawkeye on a commercial mmWave radar and evaluate its performance under varied settings. The experimental results show that mmHawkeye has a detection accuracy of 95.8% and can realize detection at a range up to 80m.Comment: 9 pages, 14 figures, IEEE SECON202