123 research outputs found
Graph Unlearning with Efficient Partial Retraining
Graph Neural Networks (GNNs) have achieved remarkable success in various
real-world applications. However, GNNs may be trained on undesirable graph
data, which can degrade their performance and reliability. To enable trained
GNNs to efficiently unlearn unwanted data, a desirable solution is
retraining-based graph unlearning, which partitions the training graph into
subgraphs and trains sub-models on them, allowing fast unlearning through
partial retraining. However, the graph partition process causes information
loss in the training graph, resulting in the low model utility of sub-GNN
models. In this paper, we propose GraphRevoker, a novel graph unlearning
framework that better maintains the model utility of unlearnable GNNs.
Specifically, we preserve the graph property with graph property-aware sharding
and effectively aggregate the sub-GNN models for prediction with graph
contrastive sub-model aggregation. We conduct extensive experiments to
demonstrate the superiority of our proposed approach.Comment: 8 pages, 3 figures, accepted by The Web Conference 2024 (PhD
Symposium Track
Magnetoelectric domains and their switching mechanism in a Y-type hexaferrite
By employing resonant X-ray microdiffraction, we image the magnetisation and
magnetic polarity domains of the Y-type hexaferrite
BaSrMgFeO. We show that the magnetic polarity
domain structure can be controlled by both magnetic and electric fields, and
that full inversion of these domains can be achieved simply by reversal of an
applied magnetic field in the absence of an electric field bias. Furthermore,
we demonstrate that the diffraction intensity measured in different X-ray
polarisation channels cannot be reproduced by the accepted model for the polar
magnetic structure, known as the 2-fan transverse conical (TC) model. We
propose a modification to this model, which achieves good quantitative
agreement with all of our data. We show that the deviations from the TC model
are large, and may be the result of an internal magnetic chirality, most likely
inherited from the parent helical (non-polar) phase.Comment: 9 figure
Enhancing Graph Collaborative Filtering via Uniformly Co-Clustered Intent Modeling
Graph-based collaborative filtering has emerged as a powerful paradigm for
delivering personalized recommendations. Despite their demonstrated
effectiveness, these methods often neglect the underlying intents of users,
which constitute a pivotal facet of comprehensive user interests. Consequently,
a series of approaches have arisen to tackle this limitation by introducing
independent intent representations. However, these approaches fail to capture
the intricate relationships between intents of different users and the
compatibility between user intents and item properties.
To remedy the above issues, we propose a novel method, named uniformly
co-clustered intent modeling. Specifically, we devise a uniformly contrastive
intent modeling module to bring together the embeddings of users with similar
intents and items with similar properties. This module aims to model the
nuanced relations between intents of different users and properties of
different items, especially those unreachable to each other on the user-item
graph. To model the compatibility between user intents and item properties, we
design the user-item co-clustering module, maximizing the mutual information of
co-clusters of users and items. This approach is substantiated through
theoretical validation, establishing its efficacy in modeling compatibility to
enhance the mutual information between user and item representations.
Comprehensive experiments on various real-world datasets verify the
effectiveness of the proposed framework.Comment: In submissio
Influence of acidic metabolic environment on differentiation of stem cell-derived cardiomyocytes
Stem cell-based myocardial regeneration is a frontier topic in the treatment of myocardial infarction. Manipulating the metabolic microenvironment of stem cells can influence their differentiation into cardiomyocytes, which have promising clinical applications. pH is an important indicator of the metabolic environment during cardiomyocyte development. And lactate, as one of the main acidic metabolites, is a major regulator of the acidic metabolic environment during early cardiomyocyte development. Here, we summarize the progress of research into the influence of pH value and lactate on cardiomyocyte survival and differentiation, as well as related mechanisms
Generating bright-field images of stained tissue slices from Mueller matrix polarimetric images with CycleGAN using unpaired dataset
Recently, Mueller matrix (MM) polarimetric imaging-assisted pathology detection methods are showing great potential in clinical diagnosis. However, since our human eyes cannot observe polarized light directly, it raises a notable challenge for interpreting the measurement results by pathologists who have limited familiarity with polarization images. One feasible approach is to combine MM polarimetric imaging with virtual staining techniques to generate standardized stained images, inheriting the advantages of information-abundant MM polarimetric imaging. In this study, we develop a model using unpaired MM polarimetric images and bright-¯eld images for generating standard hematoxylin and eosin (H&E) stained tissue images. Compared with the existing polarization virtual staining techniques primarily based on the model training with paired images, the proposed Cycle-Consistent Generative Adversarial Networks (CycleGAN)based model simpli¯es data acquisition and data preprocessing to a great extent. The outcomes demonstrate the feasibility of training CycleGAN with unpaired polarization images and their corresponding bright-¯eld images as a viable approach, which provides an intuitive manner for pathologists for future polarization-assisted digital pathology
Observation of Full-Parameter Jones Matrix in Bilayer Metasurface
Metasurfaces, artificial 2D structures, have been widely used for the design
of various functionalities in optics. Jones matrix, a 2*2 matrix with eight
parameters, provides the most complete characterization of the metasurface
structures in linear optics, and the number of free parameters (i.e., degrees
of freedom, DOFs) in the Jones matrix determines the limit to what
functionalities we can realize. Great efforts have been made to continuously
expand the number of DOFs, and a maximal number of six has been achieved
recently. However, the realization of 'holy grail' goal with eight DOFs (full
free parameters) has been proven as a great challenge so far. Here, we show
that by cascading two layer metasurfaces and utilizing the gradient descent
optimization algorithm, a spatially varying Jones matrix with eight DOFs is
constructed and verified numerically and experimentally in optical frequencies.
Such ultimate control unlocks new opportunities to design optical
functionalities that are unattainable with previously known methodologies and
may find wide potential applications in optical fields.Comment: 53 paegs, 4 figure
Efficient Cavity Searching for Gene Network of Influenza A Virus
High order structures (cavities and cliques) of the gene network of influenza
A virus reveal tight associations among viruses during evolution and are key
signals that indicate viral cross-species infection and cause pandemics. As
indicators for sensing the dynamic changes of viral genes, these higher order
structures have been the focus of attention in the field of virology. However,
the size of the viral gene network is usually huge, and searching these
structures in the networks introduces unacceptable delay. To mitigate this
issue, in this paper, we propose a simple-yet-effective model named HyperSearch
based on deep learning to search cavities in a computable complex network for
influenza virus genetics. Extensive experiments conducted on a public influenza
virus dataset demonstrate the effectiveness of HyperSearch over other advanced
deep-learning methods without any elaborated model crafting. Moreover,
HyperSearch can finish the search works in minutes while 0-1 programming takes
days. Since the proposed method is simple and easy to be transferred to other
complex networks, HyperSearch has the potential to facilitate the monitoring of
dynamic changes in viral genes and help humans keep up with the pace of virus
mutations.Comment: work in progres
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