Dataset for neutron and gamma-ray pulse shape discrimination

The publicly accessible dataset includes neutron and gamma-ray pulse signals for conducting pulse shape discrimination experiments. Several traditional and recently proposed pulse shape discrimination algorithms are utilized to evaluate the performance of pulse shape discrimination under raw pulse signals and noise-enhanced datasets. These algorithms comprise zero-crossing (ZC), charge comparison (CC), falling edge percentage slope (FEPS), frequency gradient analysis (FGA), pulse-coupled neural network (PCNN), ladder gradient (LG), and het-erogeneous quasi-continuous spiking cortical model (HQC-SCM). In addition to the pulse signals, this dataset includes the source code for all the aforementioned pulse shape discrimination methods. Moreover, the dataset provides the source code for schematic pulse shape discrimination performance evaluation and anti-noise performance evaluation. This feature enables researchers to evaluate the performance of these methods using standard procedures and assess their anti-noise ability under various noise conditions. In conclusion, this dataset offers a comprehensive set of resources for conducting pulse shape discrimination experiments and evaluating the performance of various pulse shape discrimination methods under different noise scenarios.Comment: 11 pages,10 figure

Physiologically-Based Vision Modeling Applications and Gradient Descent-Based Parameter Adaptation of Pulse Coupled Neural Networks

In this research, pulse coupled neural networks (PCNNs) are analyzed and evaluated for use in primate vision modeling. An adaptive PCNN is developed that automatically sets near-optimal parameter values to achieve a desired output. For vision modeling, a physiologically motivated vision model is developed from current theoretical and experimental biological data. The biological vision processing principles used in this model, such as spatial frequency filtering, competitive feature selection, multiple processing paths, and state dependent modulation are analyzed and implemented to create a PCNN based feature extraction network. This network extracts luminance, orientation, pitch, wavelength, and motion, and can be cascaded to extract texture, acceleration and other higher order visual features. Theorized and experimentally confirmed cortical information linking schemes, such as state dependent modulation and temporal synchronization are used to develop a PCNN-based visual information fusion network. The network is used to fuse the results of several object detection systems for the purpose of enhanced object detection accuracy. On actual mammograms and FLIR images, the network achieves an accuracy superior to any of the individual object detection systems it fused. Last, this research develops the first fully adaptive PCNN. Given only an input and a desired output, the adaptive PCNN will find all parameter values necessary to approximate that desired output

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction towards cognition and human-level intelligence. In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models, and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning, are reviewed. We further explore several emerging topics, including meta relational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of datasets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions

Spectral feature fusion networks with dual attention for hyperspectral image classification

Recent progress in spectral classification is largely attributed to the use of convolutional neural networks (CNN). While a variety of successful architectures have been proposed, they all extract spectral features from various portions of adjacent spectral bands. In this paper, we take a different approach and develop a deep spectral feature fusion method, which extracts both local and interlocal spectral features, capturing thus also the correlations among non-adjacent bands. To our knowledge, this is the first reported deep spectral feature fusion method. Our model is a two-stream architecture, where an intergroup and a groupwise spectral classifiers operate in parallel. The interlocal spectral correlation feature extraction is achieved elegantly, by reshaping the input spectral vectors to form the socalled non-adjacent spectral matrices. We introduce the concept of groupwise band convolution to enable efficient extraction of discriminative local features with multiple kernels adopting to the local spectral content. Another important contribution of this work is a novel dual-channel attention mechanism to identify the most informative spectral features. The model is trained in an end-to-end fashion with a joint loss. Experimental results on real data sets demonstrate excellent performance compared to the current state-of-the-art