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We propose a Deep Texture Encoding Network (Deep-TEN) with a novel Encoding Layer integrated on top of convolutional layers, which ports the entire dictionary learning and encoding pipeline into a single model. Current methods build from distinct components, using standard encoders with separate off-the-shelf features such as SIFT descriptors or pre-trained CNN features for material recognition. Our...
Web applications commonly provide a high attack surface. In today's world of high impact attacks, protecting them against both known and unknown attacks becomes more important than ever. We present an approach of machine learning based anomaly detection to flexibly detect anomalous requests. Our approach leverages long short-term memory (LSTM) neural networks to learn a detailed model of normal requests...
The CRISPR/Cas9 system is a creative and innovative gene editing biotechnology tool in genetic engineering. Although several achievements have been attained using the CRISPR/Cas9 system, it is still a challenge to avoid off-target effects and improve the editing efficacy. Previous efforts on evaluating the efficacy and designing the guide RNA mainly focused on DNA properties. However, some DNA features...
Feature extraction is a key stage in machine learning based VLSI layout hotspot detection flow. Conventional machine learning based methods apply various feature extraction techniques to approximate an original layout structure at nanometer level. However, some important layout pattern information is missed during the approximation process, resulting in performance degradation. In this paper, we present...
With the continuous shrinking of technology nodes, lithography hotspot detection and elimination in the physical verification phase is of great value. Recently machine learning and pattern matching based methods have been extensively studied to overcome runtime overhead problem of expensive full-chip lithography simulation. However, there is still much room for improvement in terms of accuracy and...
In this paper, we describe our system for object tracking over a multiple-camera network task in BigMM Challenge in conjunction with the first IEEE International Conference on Multimedia Big Data (BigMM 2015). We focus on the detection and tracking of pedestrians and vehicles. Based on background modeling, we use HOG and SVM to detect pedestrian and morphological processing to detect vehicle in single...
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