Exploiting Pseudo Future Contexts for Emotion Recognition in Conversations

With the extensive accumulation of conversational data on the Internet, emotion recognition in conversations (ERC) has received increasing attention. Previous efforts of this task mainly focus on leveraging contextual and speaker-specific features, or integrating heterogeneous external commonsense knowledge. Among them, some heavily rely on future contexts, which, however, are not always available in real-life scenarios. This fact inspires us to generate pseudo future contexts to improve ERC. Specifically, for an utterance, we generate its future context with pre-trained language models, potentially containing extra beneficial knowledge in a conversational form homogeneous with the historical ones. These characteristics make pseudo future contexts easily fused with historical contexts and historical speaker-specific contexts, yielding a conceptually simple framework systematically integrating multi-contexts. Experimental results on four ERC datasets demonstrate our method's superiority. Further in-depth analyses reveal that pseudo future contexts can rival real ones to some extent, especially in relatively context-independent conversations.Comment: 15 pages, accepted by ADMA 202

Time-frequency analysis framework for understanding non-stationary and multi-scale characteristics of sea-level dynamics

Rising sea level caused by global climate change may increase extreme sea level events, flood low-lying coastal areas, change the ecological and hydrological environment of coastal areas, and bring severe challenges to the survival and development of coastal cities. Hong Kong is a typical economically and socially developed coastal area. However, in such an important coastal city, the mechanisms of local sea-level dynamics and their relationship with climate teleconnections are not well explained. In this paper, Hong Kong tide gauge data spanning 68 years was documented to study the historical sea-level dynamics. Through the analysis framework based on Wavelet Transform and Hilbert Huang Transform, non-stationary and multi-scale features in sea-level dynamics in Hong Kong are revealed. The results show that the relative sea level (RSL) in Hong Kong has experienced roughly 2.5 cycles of high-to-low sea-level transition in the past half-century. The periodic amplitude variation of tides is related to Pacific Decadal Oscillation (PDO) and El Niño-Southern Oscillation (ENSO). RSL rise and fall in eastern Hong Kong often occur in La Niña and El Niño years, respectively. The response of RSL to the PDO and ENSO displays a time lag and spatial heterogeneity in Hong Kong. Hong Kong's eastern coastal waters are more strongly affected by the Pacific climate and current systems than the west. This study dissects the non-stationary and multi-scale characteristics of relative sea-level change and helps to better understand the response of RSL to the global climate system

Clinical profile of Parkinson's disease in the Gumei community of Minhang district, Shanghai

OBJECTIVE: We examined the demographic and clinical profiles of Parkinson's disease in Shanghai, China, to assist in disease management and provide comparative data on Parkinson's disease prevalence, phenotype, and progression among different regions and ethnic groups. METHODS: A door-to-door survey and follow-up clinical examinations identified 180 community-dwelling Han-Chinese Parkinson's disease patients (104 males, 76 females). RESULTS: The average age at onset was 65.16±9.60 years. The most common initial symptom was tremor (112 patients, 62.22%), followed by rigidity (38, 21.11%), bradykinesia (28, 15.56%) and tremor plus rigidity (2, 1.11%). Tremor as the initial symptom usually began in a single limb (83.04% of patients). The average duration from onset to mild Parkinson's disease (Hoehn-Yahr phase 1-2) was 52.74±45.64 months. Progression from mild to moderate/severe Parkinson's disease (phase≥3) was significantly slower (87.07±58.72 months;

Research progress of pretreatment - RO seawater desalination

Reverse osmosis (RO) seawater desalination is an effective way to produce fresh water. However, the effect of scaling, concentrated water treatment and energy consumption limit the further application and development of the technology. Nanofiltration (NF) used as the pre-treatment process of seawater, prevented RO membranes from scaling, reduced energy consumption and also played an important role in concentrated water treatment. This study mainly summarized the research progress of performance of nanofiltration membrane used for RO seawater desalination pretreatment,the pretreatment process of nanolfiltration for RO seawater desalination and the combined technologies of NF - RO with some other desalination technologies

He-Plasma Jet Generation and Its Application for E. coli Sterilization

Atmospheric pressure plasma jet (APPJ) is a promising technique for the sterilization of pathogenic microorganisms in an ambient environment. In this work, a helium-APPJ was generated by double dielectric barrier discharge and applied to the sterilization of model microorganism in air and water. Discharge characteristics (including waveform and frequency of applied voltage), jet properties (such as feed gas flow rate, jet length, thermal effect, and optic emission spectra), and sterilization performance (in terms of clear/sterilized area, size of plaques, and sterilization efficiency) were investigated. Homogeneous helium plasma jet was generated in an energy-efficient way (18 kHz, 6 kV, 0.08 W) with a 19 mm jet and limited heating. The He-APPJ achieved good sterilization performances within very short treatment time (as short as 30 s). For surface sterilization, the area of clear zone and size of the plaque were 1809 mm2 and 48 mm, respectively, within 5 min treatment. For water sterilization, 99.8% sterilization efficiency was achieved within 5 min treatment. The optic emission spectra suggest that active species such as excited molecules, ions, and radicals were produced in the He-APPJ. The as-produced active species played important roles in the sterilization process

GF-5 Hyperspectral Data for Species Mapping of Mangrove in Mai Po, Hong Kong

Hyperspectral data has been widely used in species discrimination of plants with rich spectral information in hundreds of spectral bands, while the availability of hyperspectral data has hindered its applications in many specific cases. The successful operation of the Chinese satellite, Gaofen-5 (GF-5), provides potentially promising new hyperspectral dataset with 330 spectral bands in visible and near infrared range. Therefore, there is much demand for assessing the effectiveness and superiority of GF-5 hyperspectral data in plants species mapping, particularly mangrove species mapping, to better support the efficient mangrove management. In this study, mangrove forest in Mai Po Nature Reserve (MPNR), Hong Kong was selected as the study area. Four dominant native mangrove species were investigated in this study according to the field surveys. Two machine learning methods, Random Forests and Support Vector Machines, were employed to classify mangrove species with Landsat 8, Simulated Hyperion and GF-5 data sets. The results showed that 97 more bands of GF-5 over Hyperion brought a higher over accuracy of 87.12%, in comparison with 86.82% from Hyperion and 73.89% from Landsat 8. The higher spectral resolution of 5 nm in GF-5 was identified as making the major contribution, especially for the mapping of Aegiceras corniculatum. Therefore, GF-5 is likely to improve the classification accuracy of mangrove species mapping via enhancing spectral resolution and thus has promising potential to improve mangrove monitoring at species level to support mangrove management

Community-based plant diversity monitoring of a dense-canopy and species-rich tropical forest using airborne LiDAR data

Tropical forests are widely regarded as the Earth’s most important ecosystems, yet they are severely threatened by anthropogenic disturbances. Rapid and extensive monitoring of forest structure and biodiversity is crucial for developing ecologically sound conservation and restoration strategies. Airborne light detection and ranging (LiDAR) can effectively monitor three-dimensional forest canopy structures. Traditionally, LiDAR-based plant diversity estimation has relied on individual tree-based and area-based approaches. However, these approaches face significant challenges in tropical forests due to their complex canopy structures and diverse plant compositions. Therefore, by proposing a novel community-based approach, this study aims to examine the relationship between field-derived biodiversity indices and LiDAR-derived canopy structural metrics of plant communities in a species-rich ForestGEO forest dynamic plot in tropical Hong Kong. Our goal is to determine whether canopy structural metrics extracted from airborne LiDAR data can serve as a robust and efficient alternative for expediting plant diversity monitoring in highly dense and diverse tropical forests. Our results indicate that an integration watershed segmentation technique (for automatic patch-scale plant community delineation), LiDAR-derived canopy structural metrics, and machine learning-based random forest regression analysis can provide accurate predictions of community-based species diversity indices. Among various diversity indices, species richness and the Shannon-Wiener index are most accurately estimated using LiDAR-derived metrics. This study reveals that species richness is predominantly influenced by the existence of multi-layered canopy structures, whereas the Shannon-Wiener index is associated with both multi-layered structures and canopy morphologies. Overall, our findings showcase the immense potential of airborne LiDAR data in advancing the monitoring of structure and biodiversity in dense-canopy and species-rich tropical forests in a spatially explicit manner

Spatial variation and driving mechanism of polycyclic aromatic hydrocarbons (PAHs) emissions from vehicles in China

Rapid motorization has made vehicles become one of the major sources of air pollution and poses substantial risk to human health. Accurate estimation of spatiotemporal variation and driving factors of vehicle emissions will be valuable for pollution control and public health protection. Here, taking polycyclic aromatic hydrocarbons (PAHs) as an example, a dynamic vehicle emission model (DVEM) was developed to assess vehicle emissions. The inventories of vehicle emissions from 2002 to 2017 were estimated under different emission standards and vehicle kilometers traveled for all types of vehicles in China. Vehicle PAHs emissions peaked 1586.85 tonne (t) in 2012 and declined by 36% in the follow-up five years because the vehicle growth has been offset by the upgraded emissions standards. There were remarkable variations among different provinces in China. The vehicle emissions are higher in eastern coastal provinces like Guangdong, Shandong, Jiangsu, and Zhejiang, while lower in the western provinces except Xinjiang. Motorcycles (44.1%) and light duty vehicles (17.8%) were the main contributors to PAHs emissions. The higher urbanization within a region, the larger its vehicle emission density. Urban road density may be linked with the number of on-road vehicles, which is the real driving factor of emissions. Therefore, integrated management should be taken by government to reduce the impacts of vehicle PAHs emissions