The task of Knowledge Graph Completion (KGC) is to infer missing links for Knowledge Graphs (KGs) by analyzing graph structures. However, with increasing sparsity in KGs, this task becomes increasingly challenging. In...
The task of Knowledge Graph Completion (KGC) is to infer missing links for Knowledge Graphs (KGs) by analyzing graph structures. However, with increasing sparsity in KGs, this task becomes increasingly challenging. In this paper, we propose VEM $$^2$$ L, a joint learning framework that incorporates structure and relevant text information to supplement insufficient features for sparse KGs. We begin by training two pre-existing KGC models: one based on structure and the other based on text. Our ultimate goal is to fuse knowledge acquired by these models. To achieve this, we divide knowledge within the models into two non-overlapping parts: expressive power and generalization ability. We then propose two different joint learning methods that co-distill these two kinds of knowledge respectively. For expressive power, we allow each model to learn from and exchange knowledge mutually on training examples. For the generalization ability, we propose a novel co-distillation strategy using the Variational EM algorithm on unobserved queries. Our proposed joint learning framework is supported by both detailed theoretical evidence and qualitative experiments, demonstrating its effectiveness.
Time-To-event prediction is an important analytical approach in medical research and personalized medicine that aims to predict the timing of clinically relevant occurrences and find associated risk variables. In the ...
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Dear editor,Infrared and visible image fusion(IVIF)technologies are to extract complementary information from source images and generate a single fused result[1],which is widely applied in various high-level visual ta...
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Dear editor,Infrared and visible image fusion(IVIF)technologies are to extract complementary information from source images and generate a single fused result[1],which is widely applied in various high-level visual tasks such as segmentation and object detection[2].
In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh env...
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In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.
Autonomous Vehicles (AVs) are advancing rapidly, with Level-4 AVs already operating in real-world conditions. Current AVs, however, still lag behind human drivers in adaptability and performance, often exhibiting over...
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Automatic object detection by satellite remote sensing images is of great significance for resource exploration and natural disaster assessment. To solve existing problems in remote sensing image detection, this artic...
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DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in...
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DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in the direction of Imaginative Intelligence(II),i.e.,something similar to automatic wordsto-videos generation or intelligent digital movies/theater technology that could be used for conducting new“Artificiofactual Experiments”[2]to replace conventional“Counterfactual Experiments”in scientific research and technical development for both natural and social studies[2]-[6].Now we have OpenAI’s Sora,so soon,but this is not the final,actually far away,and it is just the beginning.
It is known that the reflectivity of semiconductor materials at terahertz frequencies is sensitive to changes in free carriers. This phenomenon has been rigorously studied with semiconductor wafers and offline semicon...
It is known that the reflectivity of semiconductor materials at terahertz frequencies is sensitive to changes in free carriers. This phenomenon has been rigorously studied with semiconductor wafers and offline semiconductor devices. Despite this capability for sensing free carriers, terahertz probing of semiconductor devices in operation is nearly non-existent. Here we observe a PN-junction diode in operation, using terahertz waves in reflection. The Shockley diode equation and Drude model are leveraged to explain the observation. We anticipate that this work will be fundamental in establishing the capability to monitor semiconductor devices in operation, potentially unlocking a new wave of terahertz applications in the semiconductor industry and beyond
Fog computing, as a distributed paradigm, offers cloud-like services at the edge of the network with low latency and high-access bandwidth to support a diverse range of IoT application scenarios. To fully utilize the ...
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Modeling route representation aims to obtain contextual representations of an entire route for various traffic-related tasks. In reality, spatial-temporal data often exhibits multi-scale characteristics, which are uti...
Modeling route representation aims to obtain contextual representations of an entire route for various traffic-related tasks. In reality, spatial-temporal data often exhibits multi-scale characteristics, which are utilized by many studies to enhance their performance. However, there is still a lack of in-depth research on how to effectively incorporate the multi-scale spatial-temporal information into transformer structure to adequately model route representation. In this paper, we propose a novel hierarchical route representation framework called RouteMT, which effectively captures multi-scale spatial-temporal characteristics of routes and leverages a mixed-scale transformer architecture to fuse intra and interroute features. Experiments on real data confirm RouteMT’s superior performance and versatility.
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