47 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
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
Antinociceptive Effect of Ghrelin in a Rat Model of Irritable Bowel Syndrome Involves TRPV1/Opioid Systems
Background/Aims: Irritable bowel syndrome (IBS), defined as recurrent abdominal pain and changes in bowel habits, seriously affects quality of life and ability to work. Ghrelin is a brain-gut hormone, which has been reported to show antinociceptive effects in peripheral pain. We investigated the effect of ghrelin on visceral hypersensitivity and pain in a rat model of IBS. Methods: Maternal deprivation (MD) was used to provide a stress-induced model of IBS in Wistar rats. Colorectal distension (CRD) was used to detect visceral sensitivity, which was evaluated by abdominal withdrawal reflex (AWR) scores. Rats that were confirmed to have visceral hypersensitivity after MD were injected with ghrelin (10 ”g/kg) subcutaneously twice a week from weeks 7 to 8. [D-Lys3]-GHRP-6 (100 nmol/L) and naloxone (100 nmol/L) were administered subcutaneously to block growth hormone secretagogue receptor 1α (GHS-R1α) and opioid receptors, respectively. Expression of transient receptor potential vanilloid type 1 (TRPV1) and ” and Îș opioid receptors (MOR and KOR) in colon, dorsal root ganglion (DRG) and cerebral cortex tissues were detected by western blotting, quantitative real-time polymerase chain reaction (qRT-PCR), immunohistochemical analyses and immunofluorescence. Results: Ghrelin treatment increased expression of opioid receptors and inhibited expression of TRPV1 in colon, dorsal root ganglion (DRG) and cerebral cortex. The antinociceptive effect of ghrelin in the rat model of IBS was partly blocked by both the ghrelin antagonist [D-Lys3]-GHRP-6 and the opioid receptor antagonist naloxone. Conclusion: The results indicate that ghrelin exerted an antinociceptive effect, which was mediated via TRPV1/opioid systems, in IBS-induced visceral hypersensitivity. Ghrelin might potentially be used as a new treatment for IBS