Recent developments in the field of robot grasping have shown great improvements in the grasp success rates when dealing with unknown *** this work we improve on one of the most promising approaches,the Grasp Quality ...
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Recent developments in the field of robot grasping have shown great improvements in the grasp success rates when dealing with unknown *** this work we improve on one of the most promising approaches,the Grasp Quality convolutional Neural network(GQ-CNN) trained on the DexNet 2.0 *** propose a new GG-CNN architecture for DexNet,provide a new way for dataset generation for the GG-CNN and describe practical improvements that increase the model validation accuracy and other performance aspects of the whole system.
We propose a latent multi-factor asset pricing model that estimates risk exposure based on firm characteristics and connectivity between assets. To handle connected high-dimensional characteristics, we adopted a graph...
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We propose a latent multi-factor asset pricing model that estimates risk exposure based on firm characteristics and connectivity between assets. To handle connected high-dimensional characteristics, we adopted a graph convolutional network while estimating the connectivity between assets from the correlation of asset returns. Unlike recent literature involving the deep-learningbased latent factor model, we propose a forward stagewise additive factor modeling architecture that constructs latent factors sequentially to maintain the previous stage's factors. Our empirical results on individual U.S. equities show that the proposed graph factor model outperforms other benchmark models in terms of explanatory power and the Sharpe ratio of the factor tangency portfolio.
Gait recognition is becoming one of the promising methods for biometric authentication owing to its self-effacing nature. Contemporary approaches of joint position-based gait recognition generally model gait features ...
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Tourism demand forecasting is a crucial prerequisite for effective and efficient tourism management. This study develops a novel model based on deep learning methods for precise demand forecasting, namely, spatial-tem...
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Tourism demand forecasting is a crucial prerequisite for effective and efficient tourism management. This study develops a novel model based on deep learning methods for precise demand forecasting, namely, spatial-temporal fused graph convolutional network (ST-FGCN). ST-FGCN generates forecasts based on spatial effects extracted using graph convolutional network and temporal dependency captured through long short-term memory. A data-driven spatial matrix is used in our model to strengthen forecasting performance further. Two markedly different forecasting experiments verify the effectiveness of our model. Empirical results suggest that incorporating spatial effects can remarkably reduce forecasting errors. Furthermore, our model shows good applicability for data with different time granularity and different periods: before and during the COVID-19 pandemic. (c) 2022 Elsevier Ltd. All rights reserved.
Subjective cognitive decline is potentially the earliest symptom of Alzheimer's disease, whose objective neurological basis remains elusive. To explore the potential biomarkers for subjective cognitive decline, we...
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Subjective cognitive decline is potentially the earliest symptom of Alzheimer's disease, whose objective neurological basis remains elusive. To explore the potential biomarkers for subjective cognitive decline, we developed a novel deep learning method based on multiscale dynamical brain functional networks to identify subjective cognitive declines. We retrospectively constructed an internal data set (with 112 subjective cognitive decline and 64 healthy control subjects) to develop and internally validate the deep learning model. Conventional deep learning methods based on static and dynamic brain functional networks are compared. After the model is established, we prospectively collect an external data set (26 subjective cognitive decline and 12 healthy control subjects) for testing. Meanwhile, our method provides monitoring of the transitions between normal and abnormal (subjective cognitive decline-related) dynamical functional network states. The features of abnormal dynamical functional network states are quantified by network and variability metrics and associated with individual cognitions. Our method achieves an area under the receiver operating characteristic curve of 0.807 +/- 0.046 in the internal validation data set and of 0.707 (P = 0.007) in the external testing data set, which shows improvements compared to conventional methods. The method further suggests that, at the local level, the abnormal dynamical functional network states are characterized by decreased connectivity strength and increased connectivity variability at different spatial scales. At the network level, the abnormal states are featured by scale-specifically altered modularity and all-scale decreased efficiency. Low tendencies to stay in abnormal states and high state transition variabilities are significantly associated with high general, language and executive functions. Overall, our work supports the deficits in multiscale brain dynamical functional networks detected by the deep lea
The COVID-19 pandemic has underscored the importance of accurate stock prediction in the tourism industry, particularly for hotels. Despite the growing interest in leveraging consumer reviews for stock performance for...
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Intercity population movement has been extensively studied since it is closely related to human society. Currently, city industry structures play dominant roles in the direction of population movement. Yet, the extent...
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Intercity population movement has been extensively studied since it is closely related to human society. Currently, city industry structures play dominant roles in the direction of population movement. Yet, the extent to which different kinds of industry proximity influence human mobility remains unclear. In this study, we introduce the concept of intercity industry proximity, regarded as economic distances, to forecast intercity population movement using a relational graph convolutional network. Our findings demonstrate the effectiveness of this framework in learning information from 18 industry proximity networks. Using this framework, we investigate the impact of distinct industries on population movement by traversing each industry as input separately. Results show that while all industries exhibit favorable predictive performance, slight differences exist. Specifically, the primary industry emerges as the most influential predictor of population movement, followed by secondary industries, whereas certain tertiary industries exert comparatively minimal effects. We also examine the influence of proximity thresholds for graph-generating on model performance. Theoretical explanations concerning face-to-face interactions for the diffusion of tacit knowledge are discussed, and policy implications are provided to enrich the current understanding of population movement.
作者：
Lisi WeiLibo ZhaoXiaoli ZhangCollege of Computer Science and Technology
Jilin University China College of Artificial Intelligence and Big Data Hulunbuir University China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Technology
Jilin University China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China
Due to the limitations of imaging sensors, obtaining a medical image that simultaneously captures both functional metabolic data and structural tissue details remains a significant challenge in clinical diagnosis. To ...
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Due to the limitations of imaging sensors, obtaining a medical image that simultaneously captures both functional metabolic data and structural tissue details remains a significant challenge in clinical diagnosis. To address this, Multimodal Medical Image Fusion (MMIF) has emerged as an effective technique for integrating complementary information from multimodal source images, such as CT, PET, and SPECT, which is critical for providing a comprehensive understanding of both anatomical and functional aspects of the human body. One of the key challenges in MMIF is how to exchange and aggregate this multimodal information. This paper rethinks MMIF by addressing the harmony of modality gaps and proposes a novel Modality-Aware Interaction network (MAINet), which leverages cross-modal feature interaction and progressively fuses multiple features in graph space. Specifically, we introduce two key modules: the Cascade Modality Interaction (CMI) module and the Dual-graph Learning (DGL) module. The CMI module, integrated within a multi-scale encoder with triple branches, facilitates complementary multimodal feature learning and provides beneficial feedback to enhance discriminative feature learning across modalities. In the decoding process, the DGL module aggregates hierarchical features in two distinct graph spaces, enabling global feature interactions. Moreover, the DGL module incorporates a bottom-up guidance mechanism, where deeper semantic features guide the learning of shallower detail features, thus improving the fusion process by enhancing both scale diversity and modality awareness for visual fidelity results. Experimental results on medical image datasets demonstrate the superiority of the proposed method over existing fusion approaches in both subjective and objective evaluations. We also validated the performance of the proposed method in applications such as infrared-visible image fusion and medical image segmentation.
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