331 research outputs found
From Simple to Complex: A Progressive Framework for Document-level Informative Argument Extraction
Document-level Event Argument Extraction (EAE) requires the model to extract
arguments of multiple events from a single document. Considering the underlying
dependencies between these events, recent efforts leverage the idea of
"memory", where the results of already predicted events are cached and can be
retrieved to help the prediction of upcoming events. These methods extract
events according to their appearance order in the document, however, the event
that appears in the first sentence does not mean that it is the easiest to
extract. Existing methods might introduce noise to the extraction of upcoming
events if they rely on an incorrect prediction of previous events. In order to
provide more reliable memory, we propose a simple-to-complex progressive
framework for document-level EAE. Specifically, we first calculate the
difficulty of each event and then, we conduct the extraction following a
simple-to-complex order. In this way, the memory will store the most certain
results, and the model could use these reliable sources to help the prediction
of more difficult events. Experiments on WikiEvents show that our model
outperforms SOTA by 1.4% in F1, indicating the proposed simple-to-complex
framework is useful in the EAE task.Comment: Accepted to the Findings of EMNLP 2023 (Long Paper
Spatial-temporal Transformers for EEG Emotion Recognition
Electroencephalography (EEG) is a popular and effective tool for emotion
recognition. However, the propagation mechanisms of EEG in the human brain and
its intrinsic correlation with emotions are still obscure to researchers. This
work proposes four variant transformer frameworks~(spatial attention, temporal
attention, sequential spatial-temporal attention and simultaneous
spatial-temporal attention) for EEG emotion recognition to explore the
relationship between emotion and spatial-temporal EEG features. Specifically,
spatial attention and temporal attention are to learn the topological structure
information and time-varying EEG characteristics for emotion recognition
respectively. Sequential spatial-temporal attention does the spatial attention
within a one-second segment and temporal attention within one sample
sequentially to explore the influence degree of emotional stimulation on EEG
signals of diverse EEG electrodes in the same temporal segment. The
simultaneous spatial-temporal attention, whose spatial and temporal attention
are performed simultaneously, is used to model the relationship between
different spatial features in different time segments. The experimental results
demonstrate that simultaneous spatial-temporal attention leads to the best
emotion recognition accuracy among the design choices, indicating modeling the
correlation of spatial and temporal features of EEG signals is significant to
emotion recognition
Stock Market Prediction via Deep Learning Techniques: A Survey
The stock market prediction has been a traditional yet complex problem
researched within diverse research areas and application domains due to its
non-linear, highly volatile and complex nature. Existing surveys on stock
market prediction often focus on traditional machine learning methods instead
of deep learning methods. Deep learning has dominated many domains, gained much
success and popularity in recent years in stock market prediction. This
motivates us to provide a structured and comprehensive overview of the research
on stock market prediction focusing on deep learning techniques. We present
four elaborated subtasks of stock market prediction and propose a novel
taxonomy to summarize the state-of-the-art models based on deep neural networks
from 2011 to 2022. In addition, we also provide detailed statistics on the
datasets and evaluation metrics commonly used in the stock market. Finally, we
highlight some open issues and point out several future directions by sharing
some new perspectives on stock market prediction
Research and Design of a Routing Protocol in Large-Scale Wireless Sensor Networks
无线传感器网络,作为全球未来十大技术之一,集成了传感器技术、嵌入式计算技术、分布式信息处理和自组织网技术,可实时感知、采集、处理、传输网络分布区域内的各种信息数据,在军事国防、生物医疗、环境监测、抢险救灾、防恐反恐、危险区域远程控制等领域具有十分广阔的应用前景。 本文研究分析了无线传感器网络的已有路由协议,并针对大规模的无线传感器网络设计了一种树状路由协议,它根据节点地址信息来形成路由,从而简化了复杂繁冗的路由表查找和维护,节省了不必要的开销,提高了路由效率,实现了快速有效的数据传输。 为支持此路由协议本文提出了一种自适应动态地址分配算——ADAR(AdaptiveDynamicAddre...As one of the ten high technologies in the future, wireless sensor network, which is the integration of micro-sensors, embedded computing, modern network and Ad Hoc technologies, can apperceive, collect, process and transmit various information data within the region. It can be used in military defense, biomedical, environmental monitoring, disaster relief, counter-terrorism, remote control of haz...学位:工学硕士院系专业:信息科学与技术学院通信工程系_通信与信息系统学号:2332007115216
Measurement of CP asymmetries and branching fraction ratios of B− decays to two charm mesons
The asymmetries of seven decays to two charm mesons are measured using data corresponding to an integrated luminosity of of proton-proton collisions collected by the LHCb experiment. Decays involving a or meson are analysed by reconstructing only the or decay products. This paper presents the first measurement of and , and the most precise measurement of the other five asymmetries. There is no evidence of violation in any of the analysed decays. Additionally, two ratios between branching fractions of selected decays are measured.The CP asymmetries of seven B decays to two charm mesons are measured using data corresponding to an integrated luminosity of 9 fb of proton-proton collisions collected by the LHCb experiment. Decays involving a D or meson are analysed by reconstructing only the D or decay products. This paper presents the first measurement of (B→D) and (B→D), and the most precise measurement of the other five CP asymmetries. There is no evidence of CP violation in any of the analysed decays. Additionally, two ratios between branching fractions of selected decays are measured.[graphic not available: see fulltext]The asymmetries of seven decays to two charm mesons are measured using data corresponding to an integrated luminosity of of proton-proton collisions collected by the LHCb experiment. Decays involving a or meson are analysed by reconstructing only the or decay products. This paper presents the first measurement of and , and the most precise measurement of the other five asymmetries. There is no evidence of violation in any of the analysed decays. Additionally, two ratios between branching fractions of selected decays are measured
Evaluation of InSAR Tropospheric Delay Correction Methods in a Low-Latitude Alpine Canyon Region
Tropospheric delay error must be reduced during interferometric synthetic aperture radar (InSAR) measurement. Depending on different geographical environments, an appropriate correction method should be selected to improve the accuracy of InSAR deformation monitoring. In this study, surface deformation monitoring was conducted in a high mountain gorge region in Yunnan Province, China, using Sentinel-1A images of ascending and descending tracks. The tropospheric delay in the InSAR interferogram was corrected using the Linear, Generic Atmospheric Correction Online Service for InSAR (GACOS) and ERA-5 meteorological reanalysis data (ERA5) methods. The correction effect was evaluated by combining phase standard deviation, semi-variance function, elevation correlation, and global navigation satellite system (GNSS) deformation monitoring results. The mean value of the phase standard deviation (Aver) of the linear correction interferogram and the threshold value (sill) of the semi-variogram were reduced by –20.98% and –41%, respectively, while the accuracy of the InSAR deformation points near the GNSS site was increased by 58%. The results showed that the three methods reduced the tropospheric delay error of InSAR deformation monitoring by different degrees in low-latitude mountains and valleys. Linear correction was the best at alleviating the tropospheric delay, followed by GACOS, while ERA5 had poor correction stability
Future Projection for Climate Suitability of Summer Maize in the North China Plain
Climate change has and will continue to exert significant effects on social economy, natural environment, and human life. Research on the climatic suitability of crops is critical for mitigating and adapting to the negative impacts of climate change on crop production. In the study, we developed the climate suitability model of maize and investigated the climate suitability of summer maize during the base period (1981–2010) and two future periods of 2031–2060 (2040s) and 2071–2100 (2080s) in the North China Plain (NCP) based on BCC-CSM2-MR model (BCC) from the Coupled Model Comparison Program (CMIP6) under two Shared Socioeconomic Pathways (SSP) 245 and SSP585. The phenological shift of maize under future climate scenarios was simulated by the Agricultural Production Systems Simulator (APSIM). The results showed that the root mean square errors (RMSE) between observations and projections for sunshine suitability (SS), temperature suitability (ST), precipitation suitability (SP), and integrated climate suitability (SZ) during the whole growth period were 0.069, 0.072, 0.057, and 0.040, respectively. Overall, the BCC projections for climate suitability were in suitable consistency with the observations in the NCP. During 1981–2010, the SP, ST, and SZ were high in the north of the NCP and low in the south. The SP, ST, and SZ showed a downward trend under all the future climate scenarios in most areas of NCP while the SS increased. Therein, the change range of SP and SS was 0–0.1 under all the future climate scenarios. The ST declined by 0.1–0.2 in the future except for the decrease of more than 0.3 under the SSP585 scenario in the 2080s. The decrease in SZ in the 2040s and 2080s under both SSP scenarios varied from 0 to 0.2. Moreover, the optimum area decreases greatly under future scenarios while the suitable area increases significantly. Adjusting sowing data (SD) would have essential impacts on climate suitability. To some extent, delaying SD was beneficial to improve the climate suitability of summer maize in the NCP, especially under the SSP585 scenario in the 2080s. Our findings can not only provide data support for summer maize production to adapt to climate change but also help to propose agricultural management measures to cope with future climate change
Study of Material Composition Effects on the Mechanical Properties of Soil-Rock Mixtures
Soil-rock mixtures are often seen in geological deposits. Mechanical properties of these mixtures are controlled by microstructural characteristics such as rock size distribution, rock shape, locations, and content. The effects of material composition on soil-rock mechanical properties were studied in the laboratory. The soil-rock material was screened into different size categories. Medium-scale shearing and triaxial experiments were used to study the relationships among macrodeformation, strength, content, size, and random location of rocks. The medium-scale triaxial shearing instrument included the computer control system, EDC control system, and sensor response. The stress-strain curve of soil-rock mixtures was found as a hardening curve which is approximately hyperbolic, and there was no obvious peak intensity value. When the Mohr–Coulomb criterion was used to depict the curve under a shear strain of 0.15, cohesion first increased and then decreased, a finding opposite to the internal friction angle with a decrease in particle size. Elastic modulus increased with an increase in rock size, but Poisson’s ratio remained constant. In similar conditions, the random location of rocks can lead to a variation range of 4 degree of the internal friction angle, and cohesion values can change in a large range than the mean value
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