308 research outputs found
Relational Self-Supervised Learning on Graphs
Over the past few years, graph representation learning (GRL) has been a
powerful strategy for analyzing graph-structured data. Recently, GRL methods
have shown promising results by adopting self-supervised learning methods
developed for learning representations of images. Despite their success,
existing GRL methods tend to overlook an inherent distinction between images
and graphs, i.e., images are assumed to be independently and identically
distributed, whereas graphs exhibit relational information among data
instances, i.e., nodes. To fully benefit from the relational information
inherent in the graph-structured data, we propose a novel GRL method, called
RGRL, that learns from the relational information generated from the graph
itself. RGRL learns node representations such that the relationship among nodes
is invariant to augmentations, i.e., augmentation-invariant relationship, which
allows the node representations to vary as long as the relationship among the
nodes is preserved. By considering the relationship among nodes in both global
and local perspectives, RGRL overcomes limitations of previous contrastive and
non-contrastive methods, and achieves the best of both worlds. Extensive
experiments on fourteen benchmark datasets over various downstream tasks
demonstrate the superiority of RGRL over state-of-the-art baselines. The source
code for RGRL is available at https://github.com/Namkyeong/RGRL.Comment: CIKM 202
Single-cell RNA-seq data imputation using Feature Propagation
While single-cell RNA sequencing provides an understanding of the
transcriptome of individual cells, its high sparsity, often termed dropout,
hampers the capture of significant cell-cell relationships. Here, we propose
scFP (single-cell Feature Propagation), which directly propagates features,
i.e., gene expression, especially in raw feature space, via cell-cell graph.
Specifically, it first obtains a warmed-up cell-gene matrix via Hard Feature
Propagation which fully utilizes known gene transcripts. Then, we refine the
k-Nearest Neighbor(kNN) of the cell-cell graph with a warmed-up cell-gene
matrix, followed by Soft Feature Propagation which now allows known gene
transcripts to be further denoised through their neighbors. Through extensive
experiments on imputation with cell clustering tasks, we demonstrate our
proposed model, scFP, outperforms various recent imputation and clustering
methods. The source code of scFP can be found at
https://github.com/Junseok0207/scFP.Comment: ICML 2023 Workshop on Computational Biology (Contributed Talk
Haptic Stylus and Empirical Studies on Braille, Button, and Texture Display
This paper presents a haptic stylus interface with a
built-in compact tactile display module and an impact module
as well as empirical studies on Braille, button, and texture
display. We describe preliminary evaluations verifying the
tactile display's performance indicating that it can
satisfactorily represent Braille numbers for both the normal
and the blind. In order to prove haptic feedback capability of
the stylus, an experiment providing impact feedback mimicking
the click of a button has been conducted. Since the developed
device is small enough to be attached to a force feedback
device, its applicability to combined force and tactile
feedback display in a pen-held haptic device is also
investigated. The handle of pen-held haptic interface was
replaced by the pen-like interface to add tactile feedback
capability to the device. Since the system provides
combination of force, tactile and impact feedback, three
haptic representation methods for texture display have been
compared on surface with 3 texture groups which differ in
direction, groove width, and shape. In addition, we evaluate
its capacity to support touch screen operations by providing
tactile sensations when a user rubs against an image displayed
on a monitor
Heterogeneous Graph Learning for Multi-modal Medical Data Analysis
Routine clinical visits of a patient produce not only image data, but also
non-image data containing clinical information regarding the patient, i.e.,
medical data is multi-modal in nature. Such heterogeneous modalities offer
different and complementary perspectives on the same patient, resulting in more
accurate clinical decisions when they are properly combined. However, despite
its significance, how to effectively fuse the multi-modal medical data into a
unified framework has received relatively little attention. In this paper, we
propose an effective graph-based framework called HetMed (Heterogeneous Graph
Learning for Multi-modal Medical Data Analysis) for fusing the multi-modal
medical data. Specifically, we construct a multiplex network that incorporates
multiple types of non-image features of patients to capture the complex
relationship between patients in a systematic way, which leads to more accurate
clinical decisions. Extensive experiments on various real-world datasets
demonstrate the superiority and practicality of HetMed. The source code for
HetMed is available at https://github.com/Sein-Kim/Multimodal-Medical.Comment: AAAI 202
Nanohertz Gravitational Waves from Axion Domain Walls Coupled to QCD
We show that the recently reported NANOGrav, EPTA, PPTA, and CPTA data
suggesting the existence of stochastic gravitational waves in the nanohertz
region can be explained by axion domain walls coupled to QCD. In this scenario,
the non-perturbative effects of QCD generate a temperature-dependent bias for
the domain wall around the QCD phase transition, leading to an immediate
collapse of the domain walls. We perform dedicated lattice simulations of the
axion domain walls, taking into account the temperature dependence of the bias,
to estimate the gravitational waves emitted during the domain wall annihilation
process. We also discuss the future prospects for accelerator-based searches
for the axion and the potential for the formation and detection of primordial
black holes.Comment: 17 pages, 4figure
Conditional Graph Information Bottleneck for Molecular Relational Learning
Molecular relational learning, whose goal is to learn the interaction
behavior between molecular pairs, got a surge of interest in molecular sciences
due to its wide range of applications. Recently, graph neural networks have
recently shown great success in molecular relational learning by modeling a
molecule as a graph structure, and considering atom-level interactions between
two molecules. Despite their success, existing molecular relational learning
methods tend to overlook the nature of chemistry, i.e., a chemical compound is
composed of multiple substructures such as functional groups that cause
distinctive chemical reactions. In this work, we propose a novel relational
learning framework, called CGIB, that predicts the interaction behavior between
a pair of graphs by detecting core subgraphs therein. The main idea is, given a
pair of graphs, to find a subgraph from a graph that contains the minimal
sufficient information regarding the task at hand conditioned on the paired
graph based on the principle of conditional graph information bottleneck. We
argue that our proposed method mimics the nature of chemical reactions, i.e.,
the core substructure of a molecule varies depending on which other molecule it
interacts with. Extensive experiments on various tasks with real-world datasets
demonstrate the superiority of CGIB over state-of-the-art baselines. Our code
is available at https://github.com/Namkyeong/CGIB.Comment: ICML 202
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