Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme

Joint extraction of entities and relations is an important task in information extraction. To tackle this problem, we firstly propose a novel tagging scheme that can convert the joint extraction task to a tagging problem. Then, based on our tagging scheme, we study different end-to-end models to extract entities and their relations directly, without identifying entities and relations separately. We conduct experiments on a public dataset produced by distant supervision method and the experimental results show that the tagging based methods are better than most of the existing pipelined and joint learning methods. What's more, the end-to-end model proposed in this paper, achieves the best results on the public dataset

An Iterative Classification and Semantic Segmentation Network for Old Landslide Detection Using High-Resolution Remote Sensing Images

Huge challenges exist for old landslide detection because their morphology features have been partially or strongly transformed over a long time and have little difference from their surrounding. Besides, small-sample problem also restrict in-depth learning. In this paper, an iterative classification and semantic segmentation network (ICSSN) is developed, which can greatly enhance both object-level and pixel-level classification performance by iteratively upgrading the feature extractor shared by two network. An object-level contrastive learning (OCL) strategy is employed in the object classification sub-network featuring a siamese network to realize the global features extraction, and a sub-object-level contrastive learning (SOCL) paradigm is designed in the semantic segmentation sub-network to efficiently extract salient features from boundaries of landslides. Moreover, an iterative training strategy is elaborated to fuse features in semantic space such that both object-level and pixel-level classification performance are improved. The proposed ICSSN is evaluated on the real landslide data set, and the experimental results show that ICSSN can greatly improve the classification and segmentation accuracy of old landslide detection. For the semantic segmentation task, compared to the baseline, the F1 score increases from 0.5054 to 0.5448, the mIoU improves from 0.6405 to 0.6610, the landslide IoU improved from 0.3381 to 0.3743, and the object-level detection accuracy of old landslides is enhanced from 0.55 to 0.9. For the object classification task, the F1 score increases from 0.8846 to 0.9230, and the accuracy score is up from 0.8375 to 0.8875

A Hyper-pixel-wise Contrastive Learning Augmented Segmentation Network for Old Landslide Detection Using High-Resolution Remote Sensing Images and Digital Elevation Model Data

As a harzard disaster, landslide often brings tremendous losses to humanity, so it's necessary to achieve reliable detection of landslide. However, the problems of visual blur and small-sized dataset cause great challenges for old landslide detection task when using remote sensing data. To reliably extract semantic features, a hyper-pixel-wise contrastive learning augmented segmentation network (HPCL-Net) is proposed, which augments the local salient feature extraction from the boundaries of landslides through HPCL and fuses the heterogeneous infromation in the semantic space from High-Resolution Remote Sensing Images and Digital Elevation Model Data data. For full utilization of the precious samples, a global hyper-pixel-wise sample pair queues-based contrastive learning method, which includes the construction of global queues that store hyper-pixel-wise samples and the updating scheme of a momentum encoder, is developed, reliably enhancing the extraction ability of semantic features. The proposed HPCL-Net is evaluated on a Loess Plateau old landslide dataset and experiment results show that the model greatly improves the reliablity of old landslide detection compared to the previous old landslide segmentation model, where mIoU metric is increased from 0.620 to 0.651, Landslide IoU metric is increased from 0.334 to 0.394 and F1-score metric is increased from 0.501 to 0.565

Past 140-year environmental record in the northern South China Sea: Evidence from coral skeletal trace metal variations

About 140-year changes in the trace metals in Porites coral samples from two locations in the northern South China Sea were investigated. Results of PCA analyses suggest that near the coast, terrestrial input impacted behavior of trace metals by 28.4%, impact of Sea Surface Temperature (SST) was 19.0%, contribution of war and infrastructure were 14.4% and 15.6% respectively. But for a location in the open sea, contribution of War and SST reached 33.2% and 16.5%, while activities of infrastructure and guano exploration reached 13.2% and 14.7%. While the spatiotemporal change model of Cu, Cd and Pb in seawater of the north area of South China Sea during 1986-1997 were reconstructed. It was found that in the sea area Cu and Cd contaminations were distributed near the coast while areas around Sanya, Hainan had high Pb levels because of the well-developed tourism related activities. (C) 2013 Elsevier Ltd. All rights reserved

An Off-Grid Turbo Channel Estimation Algorithm for Millimeter Wave Communications

The bandwidth shortage has motivated the exploration of the millimeter wave (mmWave) frequency spectrum for future communication networks. To compensate for the severe propagation attenuation in the mmWave band, massive antenna arrays can be adopted at both the transmitter and receiver to provide large array gains via directional beamforming. To achieve such array gains, channel estimation (CE) with high resolution and low latency is of great importance for mmWave communications. However, classic super-resolution subspace CE methods such as multiple signal classification (MUSIC) and estimation of signal parameters via rotation invariant technique (ESPRIT) cannot be applied here due to RF chain constraints. In this paper, an enhanced CE algorithm is developed for the off-grid problem when quantizing the angles of mmWave channel in the spatial domain where off-grid problem refers to the scenario that angles do not lie on the quantization grids with high probability, and it results in power leakage and severe reduction of the CE performance. A new model is first proposed to formulate the off-grid problem. The new model divides the continuously-distributed angle into a quantized discrete grid part, referred to as the integral grid angle, and an offset part, termed fractional off-grid angle. Accordingly, an iterative off-grid turbo CE (IOTCE) algorithm is proposed to renew and upgrade the CE between the integral grid part and the fractional off-grid part under the Turbo principle. By fully exploiting the sparse structure of mmWave channels, the integral grid part is estimated by a soft-decoding based compressed sensing (CS) method called improved turbo compressed channel sensing (ITCCS). It iteratively updates the soft information between the linear minimum mean square error (LMMSE) estimator and the sparsity combiner. Monte Carlo simulations are presented to evaluate the performance of the proposed method, and the results show that it enhances the angle detection resolution greatly

Secret key generation based on estimated channel state information for TDD-OFDM systems over fading channels

One of the fundamental problems in cryptography is the generation of a common secret key between two legitimate parties to prevent eavesdropping. In this paper, we propose an information-theoretic secret key generation (SKG) method for time division duplexing (TDD)-based orthogonal frequency-division multiplexing (OFDM) systems over multipath fading channels. By exploring physical layer properties of the wireless medium, i.e., the reciprocity, randomness, and privacy features of the radio channel, an SKG method is proposed to maximize the number of secret bits given a target secret key disagreement ratio (SKDR). In the proposed SKG method, the phase information of the estimated channel state information (CSI) is distilled for SKG, and a special guard band (GB) scheme is designed to achieve the target SKDR with a small phase information loss. The proposed GB consists of both the amplitude GB (AGB) and phase GB (PGB), where the AGB is determined by the average signal-to-interference plus noise ratio (SINR), whereas the PGB adapts itself to the instantaneous SINR and thus incurs a smaller phase information loss in the higher SINR region. Analyses show that this GB scheme trades off a small loss of channel phase information for a better SKDR performance, and achieves a much larger number of quantization levels for a given SKDR due to the fact that the PGB decreases quickly as the SINR increases. Based on the performance analysis on the SKDR, the average secret key length, the phase information loss percentage (PILP), and the optimal GB and quantization level of the adaptive quantizor are derived for a given target SKDR. Both analytical and simulation results are presented to demonstrate the superiority of the proposed scheme for TDD-OFDM systems over frequency-selective fading channels

Multi-faceted Graph Attention Network for Radar Target Recognition in Heterogeneous Radar Network

Radar target recognition (RTR), as a key technology of intelligent radar systems, has been well investigated. Accurate RTR at low signal-to-noise ratios (SNRs) still remains an open challenge. Most existing methods are based on a single radar or the homogeneous radar network, which do not fully exploit frequency-dimensional information. In this paper, a two-stream semantic feature fusion model, termed Multi-faceted Graph Attention Network (MF-GAT), is proposed to greatly improve the accuracy in the low SNR region of the heterogeneous radar network. By fusing the features extracted from the source domain and transform domain via a graph attention network model, the MF-GAT model distills higher-level semantic features before classification in a unified framework. Extensive experiments are presented to demonstrate that the proposed model can greatly improve the RTR performance at low SNRs.Comment: 6 pages, 4 figure