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