Tensor products of strongly graded vertex algebras and their modules

We study strongly graded vertex algebras and their strongly graded modules, which are conformal vertex algebras and their modules with a second, compatible grading by an abelian group satisfying certain grading restriction conditions. We consider a tensor product of strongly graded vertex algebras and its tensor product strongly graded modules. We prove that a tensor product of strongly graded irreducible modules for a tensor product of strongly graded vertex algebras is irreducible, and that such irreducible modules, up to equivalence, exhaust certain naturally defined strongly graded irreducible modules for a tensor product of strongly graded vertex algebras. We also prove that certain naturally defined strongly graded modules for the tensor product strongly graded vertex algebra are completely reducible if and only if every strongly graded module for each of the tensor product factors is completely reducible. These results generalize the corresponding known results for vertex operator algebras and their modules.Comment: 26 pages. For the sake of readability, I quote certain necessary technical definitions from earlier work of Y.-Z. Huang, J. Lepowsky and L. Zhang [arXiv:0710.2687, arXiv:1012.4193, arXiv:math/0609833

Fingerprint of rice paddies in spatial-temporal dynamics of atmospheric methane concentration in monsoon Asia.

Agriculture (e.g., rice paddies) has been considered one of the main emission sources responsible for the sudden rise of atmospheric methane concentration (XCH4) since 2007, but remains debated. Here we use satellite-based rice paddy and XCH4 data to investigate the spatial-temporal relationships between rice paddy area, rice plant growth, and XCH4 in monsoon Asia, which accounts for ~87% of the global rice area. We find strong spatial consistencies between rice paddy area and XCH4 and seasonal consistencies between rice plant growth and XCH4. Our results also show a decreasing trend in rice paddy area in monsoon Asia since 2007, which suggests that the change in rice paddy area could not be one of the major drivers for the renewed XCH4 growth, thus other sources and sinks should be further investigated. Our findings highlight the importance of satellite-based paddy rice datasets in understanding the spatial-temporal dynamics of XCH4 in monsoon Asia

A Novel Model of Atherosclerosis in Rabbits Using Injury to Arterial Walls Induced by Ferric Chloride as Evaluated by Optical Coherence Tomography as well as Intravascular Ultrasound and Histology

This study aim was to develop a new model of atherosclerosis by FeCl3-induced injury to right common carotid arteries (CCAs) of rabbits. Right CCAs were induced in male New Zealand White rabbits (n = 15) by combination of a cholesterol-rich diet and FeCl3-induced injury to arterial walls. The right and left CCAs were evaluated by histology and in vivo intravascular ultrasound (IVUS) and optical coherence tomography (OCT) examinations of 24 hours (n = 3), 8 weeks (n = 6), and 12 weeks (n = 6) after injury. Each right CCA of the rabbits showed extensive white-yellow plaques. At eight and 12 weeks after injury, IVUS, OCT, and histological findings demonstrated that the right CCAs had evident eccentric plaques. Six plaques (50%) with evident positive remodeling were observed. Marked progression was clearly observed in the same plaque at 12 weeks after injury when it underwent repeat OCT and IVUS. We demonstrated, for the first time, a novel model of atherosclerosis induced by FeCl3. The model is simple, fast, inexpensive, and reproducible and has a high success rate. The eccentric plaques and remodeling of plaques were common in this model. We successfully carried out IVUS and OCT examinations twice in the same lesion within a relatively long period of time

Towards Domain-Independent and Real-Time Gesture Recognition Using mmWave Signal

Human gesture recognition using millimeter wave (mmWave) signals provides attractive applications including smart home and in-car interface. While existing works achieve promising performance under controlled settings, practical applications are still limited due to the need of intensive data collection, extra training efforts when adapting to new domains (i.e. environments, persons and locations) and poor performance for real-time recognition. In this paper, we propose DI-Gesture, a domain-independent and real-time mmWave gesture recognition system. Specifically, we first derive the signal variation corresponding to human gestures with spatial-temporal processing. To enhance the robustness of the system and reduce data collecting efforts, we design a data augmentation framework based on the correlation between signal patterns and gesture variations. Furthermore, we propose a dynamic window mechanism to perform gesture segmentation automatically and accurately, thus enable real-time recognition. Finally, we build a lightweight neural network to extract spatial-temporal information from the data for gesture classification. Extensive experimental results show DI-Gesture achieves an average accuracy of 97.92%, 99.18% and 98.76% for new users, environments and locations, respectively. In real-time scenario, the accuracy of DI-Gesutre reaches over 97% with average inference time of 2.87ms, which demonstrates the superior robustness and effectiveness of our system.Comment: The paper is submitted to the journal of IEEE Transactions on Mobile Computing. And it is still under revie

Annual 30-m big Lake Maps of the Tibetan Plateau in 1991–2018

Lake systems on the Tibetan Plateau (TP) are important for the supply and storage of fresh water to billions of people. However, previous studies on the dynamics of these lakes focused on monitoring on multi-year scales and therefore lack sufficient temporal information. Here we present a new dataset comprising annual maps of big lakes (>10 km2) on the TP for 1991–2018, generated by utilizing all available Landsat images in conjunction with Google Earth Engine. The annual lake maps with high overall accuracy (~96%) highlight distinctive lake distribution and lake changes: (1) about 70% number and area of lakes concentrated in the Inner basin; (2) generally increasing trends in both the area (by 33%) and number (by 30%) of lakes from 1991 to 2018; (3) the total area changes were dominated by larger lakes (>50 km2) while more fluctuations in the lake number changes were found in medium lakes (10−50 km2). Our dataset infills temporal gaps in long-term inter-annual variations of big lakes, contributing towards enhanced knowledge of TP lake systems

Mapping forests in monsoon Asia with ALOS PALSAR 50-m mosaic images and MODIS imagery in 2010.

Extensive forest changes have occurred in monsoon Asia, substantially affecting climate, carbon cycle and biodiversity. Accurate forest cover maps at fine spatial resolutions are required to qualify and quantify these effects. In this study, an algorithm was developed to map forests in 2010, with the use of structure and biomass information from the Advanced Land Observation System (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) mosaic dataset and the phenological information from MODerate Resolution Imaging Spectroradiometer (MOD13Q1 and MOD09A1) products. Our forest map (PALSARMOD50 m F/NF) was assessed through randomly selected ground truth samples from high spatial resolution images and had an overall accuracy of 95%. Total area of forests in monsoon Asia in 2010 was estimated to be ~6.3 × 10(6 )km(2). The distribution of evergreen and deciduous forests agreed reasonably well with the median Normalized Difference Vegetation Index (NDVI) in winter. PALSARMOD50 m F/NF map showed good spatial and areal agreements with selected forest maps generated by the Japan Aerospace Exploration Agency (JAXA F/NF), European Space Agency (ESA F/NF), Boston University (MCD12Q1 F/NF), Food and Agricultural Organization (FAO FRA), and University of Maryland (Landsat forests), but relatively large differences and uncertainties in tropical forests and evergreen and deciduous forests

A global moderate resolution dataset of gross primary production of vegetation for 2000–2016

Ye

Integrating remote sensing and geospatial big data for urban land use mapping: a review

Remote Sensing (RS) has been used in urban mapping for a long time; however, the complexity and diversity of urban functional patterns are difficult to be captured by RS only. Emerging Geospatial Big Data (GBD) are considered as the supplement to RS data, and help to contribute to our understanding of urban lands from physical aspects (i.e., urban land cover) to socioeconomic aspects (i.e., urban land use). Integrating RS and GBD could be an effective way to combine physical and socioeconomic aspects with great potential for high-quality urban land use classification. In this study, we reviewed the existing literature and focused on the state-of-the-art and perspective of the urban land use categorization by integrating RS and GBD. Specifically, the commonly used RS features (e.g., spectral, textural, temporal, and spatial features) and GBD features (e.g., spatial, temporal, semantic, and sequence features) were identified and analyzed in urban land use classification. The integration strategies for RS and GBD features were categorized into feature-level integration (FI) and decision-level integration (DI). To be more specific, the FI method integrates the RS and GBD features and classifies urban land use types using the integrated feature sets; the DI method processes RS and GBD independently and then merges the classification results based on decision rules. We also discussed other critical issues, including analysis unit setting, parcel segmentation, parcel labeling of land use types, and data integration. Our findings provide a retrospect of different features from RS and GBD, strategies of RS and GBD integration, and their pros and cons, which could help to define the framework for future urban land use mapping and better support urban planning, urban environment assessment, urban disaster monitoring and urban traffic analysis

Decision-level and feature-level integration of remote sensing and geospatial big data for urban land use mapping

Information about urban land use is important for urban planning and sustainable development. The emergence of geospatial big data (GBD), increased the availability of remotely sensed (RS) data and the development of new methods for data integration to provide new opportunities for mapping types of urban land use. However, the modes of RS and GBD integration are diverse due to the differences in data, study areas, classifiers, etc. In this context, this study aims to summarize the main methods of data integration and evaluate them via a case study of urban land use mapping in Hangzhou, China. We first categorized the RS and GBD integration methods into decision-level integration (DI) and feature-level integration (FI) and analyzed their main differences by reviewing the existing literature. The two methods were then applied for mapping urban land use types in Hangzhou city, based on urban parcels derived from the OpenStreetMap (OSM) road network, 10 m Sentinel-2A images, and points of interest (POI). The corresponding classification results were validated quantitatively and qualitatively using the same testing dataset. Finally, we illustrated the advantages and disadvantages of both approaches via bibliographic evidence and quantitative analysis. The results showed that: (1) The visual comparison indicates a generally better performance of DI-based classification than FI-based classification; (2) DI-based urban land use mapping is easy to implement, while FI-based land use mapping enables the mixture of features; (3) DI-based and FI-based methods can be used together to improve urban land use mapping, as they have different performances when classifying different types of land use. This study provides an improved understanding of urban land use mapping in terms of the RS and GBD integration strategy