52 research outputs found
RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification
Graph classification is a crucial task in many real-world multimedia
applications, where graphs can represent various multimedia data types such as
images, videos, and social networks. Previous efforts have applied graph neural
networks (GNNs) in balanced situations where the class distribution is
balanced. However, real-world data typically exhibit long-tailed class
distributions, resulting in a bias towards the head classes when using GNNs and
limited generalization ability over the tail classes. Recent approaches mainly
focus on re-balancing different classes during model training, which fails to
explicitly introduce new knowledge and sacrifices the performance of the head
classes. To address these drawbacks, we propose a novel framework called
Retrieval Augmented Hybrid Network (RAHNet) to jointly learn a robust feature
extractor and an unbiased classifier in a decoupled manner. In the feature
extractor training stage, we develop a graph retrieval module to search for
relevant graphs that directly enrich the intra-class diversity for the tail
classes. Moreover, we innovatively optimize a category-centered supervised
contrastive loss to obtain discriminative representations, which is more
suitable for long-tailed scenarios. In the classifier fine-tuning stage, we
balance the classifier weights with two weight regularization techniques, i.e.,
Max-norm and weight decay. Experiments on various popular benchmarks verify the
superiority of the proposed method against state-of-the-art approaches.Comment: Accepted by the ACM International Conference on Multimedia (MM) 202
ALEX: Towards Effective Graph Transfer Learning with Noisy Labels
Graph Neural Networks (GNNs) have garnered considerable interest due to their
exceptional performance in a wide range of graph machine learning tasks.
Nevertheless, the majority of GNN-based approaches have been examined using
well-annotated benchmark datasets, leading to suboptimal performance in
real-world graph learning scenarios. To bridge this gap, the present paper
investigates the problem of graph transfer learning in the presence of label
noise, which transfers knowledge from a noisy source graph to an unlabeled
target graph. We introduce a novel technique termed Balance Alignment and
Information-aware Examination (ALEX) to address this challenge. ALEX first
employs singular value decomposition to generate different views with crucial
structural semantics, which help provide robust node representations using
graph contrastive learning. To mitigate both label shift and domain shift, we
estimate a prior distribution to build subgraphs with balanced label
distributions. Building on this foundation, an adversarial domain discriminator
is incorporated for the implicit domain alignment of complex multi-modal
distributions. Furthermore, we project node representations into a different
space, optimizing the mutual information between the projected features and
labels. Subsequently, the inconsistency of similarity structures is evaluated
to identify noisy samples with potential overfitting. Comprehensive experiments
on various benchmark datasets substantiate the outstanding superiority of the
proposed ALEX in different settings.Comment: Accepted by the ACM International Conference on Multimedia (MM) 202
Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts
Graph classification, aiming at learning the graph-level representations for
effective class assignments, has received outstanding achievements, which
heavily relies on high-quality datasets that have balanced class distribution.
In fact, most real-world graph data naturally presents a long-tailed form,
where the head classes occupy much more samples than the tail classes, it thus
is essential to study the graph-level classification over long-tailed data
while still remaining largely unexplored. However, most existing long-tailed
learning methods in visions fail to jointly optimize the representation
learning and classifier training, as well as neglect the mining of the
hard-to-classify classes. Directly applying existing methods to graphs may lead
to sub-optimal performance, since the model trained on graphs would be more
sensitive to the long-tailed distribution due to the complex topological
characteristics. Hence, in this paper, we propose a novel long-tailed
graph-level classification framework via Collaborative Multi-expert Learning
(CoMe) to tackle the problem. To equilibrate the contributions of head and tail
classes, we first develop balanced contrastive learning from the view of
representation learning, and then design an individual-expert classifier
training based on hard class mining. In addition, we execute gated fusion and
disentangled knowledge distillation among the multiple experts to promote the
collaboration in a multi-expert framework. Comprehensive experiments are
performed on seven widely-used benchmark datasets to demonstrate the
superiority of our method CoMe over state-of-the-art baselines.Comment: Accepted by IEEE Transactions on Big Data (TBD 2024
A Survey on Graph Neural Networks in Intelligent Transportation Systems
Intelligent Transportation System (ITS) is vital in improving traffic
congestion, reducing traffic accidents, optimizing urban planning, etc.
However, due to the complexity of the traffic network, traditional machine
learning and statistical methods are relegated to the background. With the
advent of the artificial intelligence era, many deep learning frameworks have
made remarkable progress in various fields and are now considered effective
methods in many areas. As a deep learning method, Graph Neural Networks (GNNs)
have emerged as a highly competitive method in the ITS field since 2019 due to
their strong ability to model graph-related problems. As a result, more and
more scholars pay attention to the applications of GNNs in transportation
domains, which have shown excellent performance. However, most of the research
in this area is still concentrated on traffic forecasting, while other ITS
domains, such as autonomous vehicles and urban planning, still require more
attention. This paper aims to review the applications of GNNs in six
representative and emerging ITS domains: traffic forecasting, autonomous
vehicles, traffic signal control, transportation safety, demand prediction, and
parking management. We have reviewed extensive graph-related studies from 2018
to 2023, summarized their methods, features, and contributions, and presented
them in informative tables or lists. Finally, we have identified the challenges
of applying GNNs to ITS and suggested potential future directions
Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation
Modeling customer shopping intentions is a crucial task for e-commerce, as it
directly impacts user experience and engagement. Thus, accurately understanding
customer preferences is essential for providing personalized recommendations.
Session-based recommendation, which utilizes customer session data to predict
their next interaction, has become increasingly popular. However, existing
session datasets have limitations in terms of item attributes, user diversity,
and dataset scale. As a result, they cannot comprehensively capture the
spectrum of user behaviors and preferences. To bridge this gap, we present the
Amazon Multilingual Multi-locale Shopping Session Dataset, namely Amazon-M2. It
is the first multilingual dataset consisting of millions of user sessions from
six different locales, where the major languages of products are English,
German, Japanese, French, Italian, and Spanish. Remarkably, the dataset can
help us enhance personalization and understanding of user preferences, which
can benefit various existing tasks as well as enable new tasks. To test the
potential of the dataset, we introduce three tasks in this work: (1)
next-product recommendation, (2) next-product recommendation with domain
shifts, and (3) next-product title generation. With the above tasks, we
benchmark a range of algorithms on our proposed dataset, drawing new insights
for further research and practice. In addition, based on the proposed dataset
and tasks, we hosted a competition in the KDD CUP 2023 and have attracted
thousands of users and submissions. The winning solutions and the associated
workshop can be accessed at our website https://kddcup23.github.io/.Comment: Accepted by NeurIPS 2023, Track on Datasets and Benchmarks; Dataset
for KDD Cup 2023, https://kddcup23.github.io
Real-time Monitoring for the Next Core-Collapse Supernova in JUNO
Core-collapse supernova (CCSN) is one of the most energetic astrophysical
events in the Universe. The early and prompt detection of neutrinos before
(pre-SN) and during the SN burst is a unique opportunity to realize the
multi-messenger observation of the CCSN events. In this work, we describe the
monitoring concept and present the sensitivity of the system to the pre-SN and
SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is
a 20 kton liquid scintillator detector under construction in South China. The
real-time monitoring system is designed with both the prompt monitors on the
electronic board and online monitors at the data acquisition stage, in order to
ensure both the alert speed and alert coverage of progenitor stars. By assuming
a false alert rate of 1 per year, this monitoring system can be sensitive to
the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos
up to about 370 (360) kpc for a progenitor mass of 30 for the case
of normal (inverted) mass ordering. The pointing ability of the CCSN is
evaluated by using the accumulated event anisotropy of the inverse beta decay
interactions from pre-SN or SN neutrinos, which, along with the early alert,
can play important roles for the followup multi-messenger observations of the
next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure
Effective Sorting of Fractional Optical Vortex Modes
Mode sorter is the crucial component of the communication systems based on orbital angular momentum (OAM). However, schemes proposed so far can only effectively sort integer OAM (IOAM) modes. Here, we demonstrate the effective sorting of fractional OAM (FOAM) modes by utilizing the coordinate transformation method, which can convert FOAM modes to IOAM modes. The transformed IOAM modes are subsequently sorted by using a mode conversion method called topological charge matching. The validation of our scheme is verified by implementing two sorting processes and corresponding mode purity analyses, both theoretically and experimentally. This new sorting method exhibits a huge potential of implementing a highly confidential and high-capacity FOAM-based communication system, which may inspire further applications in both classical and quantum regimes
Influences of Species and Density on the Horizontal Flame Spread Behavior of Densified Wood
Densified wood possesses outstanding mechanical properties and serves as a desired construction material for modern timber buildings. However, the limited research on its flame behavior hinders its broader applications. The authors of this paper experimentally and analytically investigated the influence of wood species and density on horizontal flame spread behavior. Densified oak and densified fir were tested. The flame spread rate decreased with wood density in both densified wood types. Their values were close at the same density. The mass loss rate (mË) of the densified wood decreased with the increase in wood density. The densified oak had higher mË due to its lower lignin content. Dimensionless correlations between the mË and density were obtained which agree with the experiments. The flame heights (Lf) of the densified wood also decreased with the increase in wood density. The densified oak had higher Lf due to its higher mË. As the densified wood density increased, the radiation (and conduction) was reduced (and enhanced), but the convection remained constant. The densified oak had lower convection, lower conduction, and higher radiation than the densified fir at the same density. Gas-phase heat transfer was dominant in the flame spread of the densified wood, but conduction was also significant as its contribution can be as high as 70% of gaseous heat transfer
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