Personalization is one of the most sought out and popular methods for brand recognition and consumer attraction. The usage of deep reinforcement learning due to its' ability to learn actions the way humans learn f...
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In the task of simultaneous localization and mapping (SLAM), a mobile robot should be able to recognize the location that it has previously visited. This is termed as the loop closure detection. Current loop closure d...
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Introduction: The Industrial Internet of Things (IIoT) is a technology that connects devices to collect data and conduct in-depth analysis to provide value-added services to industries. The integration of the physical...
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Graph Neural Networks (GNNs), as the dominant approach in Molecular Representation Learning (MRL), have exhibited remarkable efficacy in diverse tasks, including molecular property prediction and drug discovery. Consi...
Graph Neural Networks (GNNs), as the dominant approach in Molecular Representation Learning (MRL), have exhibited remarkable efficacy in diverse tasks, including molecular property prediction and drug discovery. Considering the dynamic and diverse nature of test molecules in real-world contexts, the previous independently and identically distributed (i.i.d.) assumption for training and test molecules is not align with the requirements of practical applications in chemistry. Rationalization has been proposed to enhance the Out-of-Distribution (OOD) generalization of GNN, but a critical concern remains unexplored: low efficiency in rationale discovery. We consider this bottleneck is due to large search space and overly flexible modeling. Here, we introduce a framework called Molecule Stratifier and Invariant Rational Improver (MSIRI) to overcome the challenge. Specifically, MSIRI adopt vector quantization to obtain invariant rationales and the search space is narrowed by substructure and two assumptions based on subgraph matching. Experimental results on ten datasets demonstrate that our approach both achieves significant improvements across various GNN backbones and outperforms other six Out-of-Distribution method, clearly demonstrating its effectiveness in addressing the OOD challenges in MRL.
Arithmetical operations are fundamental in computing models. But arithmetic operations in membrane computing are restricted in integer field. In this paper, we present fraction arithmetic $\mathbf{P}$ systems for pe...
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Arithmetical operations are fundamental in computing models. But arithmetic operations in membrane computing are restricted in integer field. In this paper, we present fraction arithmetic $\mathbf{P}$ systems for performing addition, subtraction, multiplication, division on fractions through designing the rules with priority. Some examples are given to illustrate how to compute the arithmetical fractions in these systems and show that the designed rules can carry out correct arithmetic computations of fractions.
While large visual models (LVM) demonstrated significant potential in image understanding, due to the application of large-scale pre-training, the Segment Anything Model (SAM) has also achieved great success in the fi...
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An enhanced optimal velocity model(EOVM)that considers driving safety is established to alleviate traffic congestion and ensure driving *** headway is introduced as a criterion for determining whether the car is *** t...
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An enhanced optimal velocity model(EOVM)that considers driving safety is established to alleviate traffic congestion and ensure driving *** headway is introduced as a criterion for determining whether the car is *** the time headway is less discussed to ensure the model's safety and maintain the following state.A stability analysis of the model was carried out to determine than the minimum time headway(TH_(min))or more than the most comfortable time headway(TH_(com)),the acceleration constraints are the stability conditions of the *** EOVM is compared with the optimal velocity model(OVM)and fuzzy car-following model using the real *** show that the EOVM model has the smallest error in average,maximum and median with the real *** confirm the model's safety,design fleet simulation experiments were conducted for three actual scenarios of starting,stopping and uniform process.
The potential for exploitation of AI models has increased due to the rapid advancement of Artificial Intelligence (AI) and the widespread use of platforms like Model Zoo for sharing AI models. Attackers can embed malw...
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Numerous service providers rely on crowdsourcing from service users, rather than a less-specific, more public group, to provide better services to the users themselves. In the majority of studies on the incentive mech...
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Numerous service providers rely on crowdsourcing from service users, rather than a less-specific, more public group, to provide better services to the users themselves. In the majority of studies on the incentive mechanism for crowdsourcing, however, the users' intrinsic desire/demand for better service, which could have been exploited to enhance their involvement in crowdsourcing, has been largely overlooked. Therefore, conclusions are limited regarding the optimal incentives and the benefits of crowdsourcing on the service quality and thus the market share. In this paper, we study the incentive mechanism for crowdsourcing that combines a financial reward in the form of service price discounts and users' intrinsic demand for better service. Our focus is on leveraging the users dual role, i.e., the interdependence between the service and the users. We show that the dynamic market converges to a unique equilibrium under mild conditions, with the consideration of varying service usage levels and privacy concerns of the users. Besides, counter-intuitively, failure to take into account the users' intrinsic reward leads to too little extrinsic incentive. Moreover, our results showed how the competition reshapes the markets, which cannot be intuitively or trivially predicted without a thorough analysis. IEEE
Deep learning full waveform inversion (DL-FWI) is attracting broad attention due to its powerful feature extraction and nonlinear mapping capabilities. It learns stratigraphic features from seismic data without the ne...
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