30,333 research outputs found

    Evidence of instability in previously-mapped landslides as measured using GPS, optical, and SAR data between 2007 and 2017: A case study in the Portuguese Bend Landslide Complex, California

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    Velocity dictates the destructive potential of a landslide. A combination of synthetic aperture radar (SAR), optical, and GPS data were used to maximize spatial and temporal coverage to monitor continuously-moving portions of the Portuguese Bend landslide complex on the Palos Verdes Peninsula in Southern California. Forty SAR images from the COSMO-SkyMed satellite, acquired between 19 July 2012 and 27 September 2014, were processed using Persistent Scatterer Interferometry (PSI). Eight optical images from the WorldView-2 satellite, acquired between 20 February 2011 and 16 February 2016, were processed using the Co-registration of Optically Sensed Images and Correlation (COSI-Corr) technique. Displacement measurements were taken at GPS monuments between September 2007 and May 2017. Incremental and average deformations across the landslide complex were measured using all three techniques. Velocity measured within the landslide complex ranges from slow (\u3e 1.6 m/year) to extremely slow (\u3c 16 mm/year). COSI-Corr and GPS provide detailed coverage of m/year-scale deformation while PSI can measure extremely slow deformation rates (mm/year-scale), which COSI-Corr and GPS cannot do reliably. This case study demonstrates the applicability of SAR, optical, and GPS data synthesis as a complimentary approach to repeat field monitoring and mapping to changes in landslide activity through time

    Digital image correlation (DIC) analysis of the 3 December 2013 Montescaglioso landslide (Basilicata, Southern Italy). Results from a multi-dataset investigation

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    Image correlation remote sensing monitoring techniques are becoming key tools for providing effective qualitative and quantitative information suitable for natural hazard assessments, specifically for landslide investigation and monitoring. In recent years, these techniques have been successfully integrated and shown to be complementary and competitive with more standard remote sensing techniques, such as satellite or terrestrial Synthetic Aperture Radar interferometry. The objective of this article is to apply the proposed in-depth calibration and validation analysis, referred to as the Digital Image Correlation technique, to measure landslide displacement. The availability of a multi-dataset for the 3 December 2013 Montescaglioso landslide, characterized by different types of imagery, such as LANDSAT 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor), high-resolution airborne optical orthophotos, Digital Terrain Models and COSMO-SkyMed Synthetic Aperture Radar, allows for the retrieval of the actual landslide displacement field at values ranging from a few meters (2–3 m in the north-eastern sector of the landslide) to 20–21 m (local peaks on the central body of the landslide). Furthermore, comprehensive sensitivity analyses and statistics-based processing approaches are used to identify the role of the background noise that affects the whole dataset. This noise has a directly proportional relationship to the different geometric and temporal resolutions of the processed imagery. Moreover, the accuracy of the environmental-instrumental background noise evaluation allowed the actual displacement measurements to be correctly calibrated and validated, thereby leading to a better definition of the threshold values of the maximum Digital Image Correlation sub-pixel accuracy and reliability (ranging from 1/10 to 8/10 pixel) for each processed dataset

    Neural Natural Language Inference Models Enhanced with External Knowledge

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    Modeling natural language inference is a very challenging task. With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve the state-of-the-art performance. Although there exist relatively large annotated data, can machines learn all knowledge needed to perform natural language inference (NLI) from these data? If not, how can neural-network-based NLI models benefit from external knowledge and how to build NLI models to leverage it? In this paper, we enrich the state-of-the-art neural natural language inference models with external knowledge. We demonstrate that the proposed models improve neural NLI models to achieve the state-of-the-art performance on the SNLI and MultiNLI datasets.Comment: Accepted by ACL 201

    Scan matching by cross-correlation and differential evolution

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    Scan matching is an important task, solved in the context of many high-level problems including pose estimation, indoor localization, simultaneous localization and mapping and others. Methods that are accurate and adaptive and at the same time computationally efficient are required to enable location-based services in autonomous mobile devices. Such devices usually have a wide range of high-resolution sensors but only a limited processing power and constrained energy supply. This work introduces a novel high-level scan matching strategy that uses a combination of two advanced algorithms recently used in this field: cross-correlation and differential evolution. The cross-correlation between two laser range scans is used as an efficient measure of scan alignment and the differential evolution algorithm is used to search for the parameters of a transformation that aligns the scans. The proposed method was experimentally validated and showed good ability to match laser range scans taken shortly after each other and an excellent ability to match laser range scans taken with longer time intervals between them.Web of Science88art. no. 85

    Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation

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    Most recent approaches use the sequence-to-sequence model for paraphrase generation. The existing sequence-to-sequence model tends to memorize the words and the patterns in the training dataset instead of learning the meaning of the words. Therefore, the generated sentences are often grammatically correct but semantically improper. In this work, we introduce a novel model based on the encoder-decoder framework, called Word Embedding Attention Network (WEAN). Our proposed model generates the words by querying distributed word representations (i.e. neural word embeddings), hoping to capturing the meaning of the according words. Following previous work, we evaluate our model on two paraphrase-oriented tasks, namely text simplification and short text abstractive summarization. Experimental results show that our model outperforms the sequence-to-sequence baseline by the BLEU score of 6.3 and 5.5 on two English text simplification datasets, and the ROUGE-2 F1 score of 5.7 on a Chinese summarization dataset. Moreover, our model achieves state-of-the-art performances on these three benchmark datasets.Comment: arXiv admin note: text overlap with arXiv:1710.0231
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