147 research outputs found
Communicative Message Passing for Inductive Relation Reasoning
Relation prediction for knowledge graphs aims at predicting missing
relationships between entities. Despite the importance of inductive relation
prediction, most previous works are limited to a transductive setting and
cannot process previously unseen entities. The recent proposed subgraph-based
relation reasoning models provided alternatives to predict links from the
subgraph structure surrounding a candidate triplet inductively. However, we
observe that these methods often neglect the directed nature of the extracted
subgraph and weaken the role of relation information in the subgraph modeling.
As a result, they fail to effectively handle the asymmetric/anti-symmetric
triplets and produce insufficient embeddings for the target triplets. To this
end, we introduce a \textbf{C}\textbf{o}mmunicative \textbf{M}essage
\textbf{P}assing neural network for \textbf{I}nductive re\textbf{L}ation
r\textbf{E}asoning, \textbf{CoMPILE}, that reasons over local directed subgraph
structures and has a vigorous inductive bias to process entity-independent
semantic relations. In contrast to existing models, CoMPILE strengthens the
message interactions between edges and entitles through a communicative kernel
and enables a sufficient flow of relation information. Moreover, we demonstrate
that CoMPILE can naturally handle asymmetric/anti-symmetric relations without
the need for explosively increasing the number of model parameters by
extracting the directed enclosing subgraphs. Extensive experiments show
substantial performance gains in comparison to state-of-the-art methods on
commonly used benchmark datasets with variant inductive settings.Comment: Accepted by AAAI-202
UrbanFM: Inferring Fine-Grained Urban Flows
Urban flow monitoring systems play important roles in smart city efforts
around the world. However, the ubiquitous deployment of monitoring devices,
such as CCTVs, induces a long-lasting and enormous cost for maintenance and
operation. This suggests the need for a technology that can reduce the number
of deployed devices, while preventing the degeneration of data accuracy and
granularity. In this paper, we aim to infer the real-time and fine-grained
crowd flows throughout a city based on coarse-grained observations. This task
is challenging due to two reasons: the spatial correlations between coarse- and
fine-grained urban flows, and the complexities of external impacts. To tackle
these issues, we develop a method entitled UrbanFM based on deep neural
networks. Our model consists of two major parts: 1) an inference network to
generate fine-grained flow distributions from coarse-grained inputs by using a
feature extraction module and a novel distributional upsampling module; 2) a
general fusion subnet to further boost the performance by considering the
influences of different external factors. Extensive experiments on two
real-world datasets, namely TaxiBJ and HappyValley, validate the effectiveness
and efficiency of our method compared to seven baselines, demonstrating the
state-of-the-art performance of our approach on the fine-grained urban flow
inference problem
Harnessing Geometric Frustration to Form Band Gaps in Acoustic Channel Lattices
We demonstrate both numerically and experimentally that geometric frustration
in two-dimensional periodic acoustic networks consisting of arrays of narrow
air channels can be harnessed to form band gaps (ranges of frequency in which
the waves cannot propagate in any direction through the system). While resonant
standing wave modes and interferences are ubiquitous in all the analyzed
network geometries, we show that they give rise to band gaps only in the
geometrically frustrated ones (i.e. those comprising of triangles and
pentagons). Our results not only reveal a new mechanism based on geometric
frustration to suppress the propagation of pressure waves in specific frequency
ranges, but also opens avenues for the design of a new generation of smart
systems that control and manipulate sound and vibrations
DDT: Dual-branch Deformable Transformer for Image Denoising
Transformer is beneficial for image denoising tasks since it can model
long-range dependencies to overcome the limitations presented by inductive
convolutional biases. However, directly applying the transformer structure to
remove noise is challenging because its complexity grows quadratically with the
spatial resolution. In this paper, we propose an efficient Dual-branch
Deformable Transformer (DDT) denoising network which captures both local and
global interactions in parallel. We divide features with a fixed patch size and
a fixed number of patches in local and global branches, respectively. In
addition, we apply deformable attention operation in both branches, which helps
the network focus on more important regions and further reduces computational
complexity. We conduct extensive experiments on real-world and synthetic
denoising tasks, and the proposed DDT achieves state-of-the-art performance
with significantly fewer computational costs.Comment: The code is avaliable at: https://github.com/Merenguelkl/DD
Enhancing Student Performance Prediction on Learnersourced Questions with SGNN-LLM Synergy
As an emerging education strategy, learnersourcing offers the potential for
personalized learning content creation, but also grapples with the challenge of
predicting student performance due to inherent noise in student-generated data.
While graph-based methods excel in capturing dense learner-question
interactions, they falter in cold start scenarios, characterized by limited
interactions, as seen when questions lack substantial learner responses. In
response, we introduce an innovative strategy that synergizes the potential of
integrating Signed Graph Neural Networks (SGNNs) and Large Language Model (LLM)
embeddings. Our methodology employs a signed bipartite graph to comprehensively
model student answers, complemented by a contrastive learning framework that
enhances noise resilience. Furthermore, LLM's contribution lies in generating
foundational question embeddings, proving especially advantageous in addressing
cold start scenarios characterized by limited graph data interactions.
Validation across five real-world datasets sourced from the PeerWise platform
underscores our approach's effectiveness. Our method outperforms baselines,
showcasing enhanced predictive accuracy and robustness
Deep Learning-Based Human Pose Estimation: A Survey
Human pose estimation aims to locate the human body parts and build human
body representation (e.g., body skeleton) from input data such as images and
videos. It has drawn increasing attention during the past decade and has been
utilized in a wide range of applications including human-computer interaction,
motion analysis, augmented reality, and virtual reality. Although the recently
developed deep learning-based solutions have achieved high performance in human
pose estimation, there still remain challenges due to insufficient training
data, depth ambiguities, and occlusion. The goal of this survey paper is to
provide a comprehensive review of recent deep learning-based solutions for both
2D and 3D pose estimation via a systematic analysis and comparison of these
solutions based on their input data and inference procedures. More than 240
research papers since 2014 are covered in this survey. Furthermore, 2D and 3D
human pose estimation datasets and evaluation metrics are included.
Quantitative performance comparisons of the reviewed methods on popular
datasets are summarized and discussed. Finally, the challenges involved,
applications, and future research directions are concluded. We also provide a
regularly updated project page: \url{https://github.com/zczcwh/DL-HPE
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