\{kappa}HGCN: Tree-likeness Modeling via Continuous and Discrete Curvature Learning

The prevalence of tree-like structures, encompassing hierarchical structures and power law distributions, exists extensively in real-world applications, including recommendation systems, ecosystems, financial networks, social networks, etc. Recently, the exploitation of hyperbolic space for tree-likeness modeling has garnered considerable attention owing to its exponential growth volume. Compared to the flat Euclidean space, the curved hyperbolic space provides a more amenable and embeddable room, especially for datasets exhibiting implicit tree-like architectures. However, the intricate nature of real-world tree-like data presents a considerable challenge, as it frequently displays a heterogeneous composition of tree-like, flat, and circular regions. The direct embedding of such heterogeneous structures into a homogeneous embedding space (i.e., hyperbolic space) inevitably leads to heavy distortions. To mitigate the aforementioned shortage, this study endeavors to explore the curvature between discrete structure and continuous learning space, aiming at encoding the message conveyed by the network topology in the learning process, thereby improving tree-likeness modeling. To the end, a curvature-aware hyperbolic graph convolutional neural network, \{kappa}HGCN, is proposed, which utilizes the curvature to guide message passing and improve long-range propagation. Extensive experiments on node classification and link prediction tasks verify the superiority of the proposal as it consistently outperforms various competitive models by a large margin.Comment: KDD 202

Hyperbolic Graph Representation Learning: A Tutorial

Graph-structured data are widespread in real-world applications, such as social networks, recommender systems, knowledge graphs, chemical molecules etc. Despite the success of Euclidean space for graph-related learning tasks, its ability to model complex patterns is essentially constrained by its polynomially growing capacity. Recently, hyperbolic spaces have emerged as a promising alternative for processing graph data with tree-like structure or power-law distribution, owing to the exponential growth property. Different from Euclidean space, which expands polynomially, the hyperbolic space grows exponentially which makes it gains natural advantages in abstracting tree-like or scale-free graphs with hierarchical organizations. In this tutorial, we aim to give an introduction to this emerging field of graph representation learning with the express purpose of being accessible to all audiences. We first give a brief introduction to graph representation learning as well as some preliminary Riemannian and hyperbolic geometry. We then comprehensively revisit the hyperbolic embedding techniques, including hyperbolic shallow models and hyperbolic neural networks. In addition, we introduce the technical details of the current hyperbolic graph neural networks by unifying them into a general framework and summarizing the variants of each component. Moreover, we further introduce a series of related applications in a variety of fields. In the last part, we discuss several advanced topics about hyperbolic geometry for graph representation learning, which potentially serve as guidelines for further flourishing the non-Euclidean graph learning community.Comment: Accepted as ECML-PKDD 2022 Tutoria

UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models

Diffusion probabilistic models (DPMs) have demonstrated a very promising ability in high-resolution image synthesis. However, sampling from a pre-trained DPM is time-consuming due to the multiple evaluations of the denoising network, making it more and more important to accelerate the sampling of DPMs. Despite recent progress in designing fast samplers, existing methods still cannot generate satisfying images in many applications where fewer steps (e.g., $<$10) are favored. In this paper, we develop a unified corrector (UniC) that can be applied after any existing DPM sampler to increase the order of accuracy without extra model evaluations, and derive a unified predictor (UniP) that supports arbitrary order as a byproduct. Combining UniP and UniC, we propose a unified predictor-corrector framework called UniPC for the fast sampling of DPMs, which has a unified analytical form for any order and can significantly improve the sampling quality over previous methods, especially in extremely few steps. We evaluate our methods through extensive experiments including both unconditional and conditional sampling using pixel-space and latent-space DPMs. Our UniPC can achieve 3.87 FID on CIFAR10 (unconditional) and 7.51 FID on ImageNet 256$\times$256 (conditional) with only 10 function evaluations. Code is available at https://github.com/wl-zhao/UniPC.Comment: Accepted by NeurIPS 2023. Project page: https://unipc.ivg-research.xy

Hospital volume and mortality after transjugular intrahepatic portosystemic shunt creation in the United States

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141487/1/hep29354-sup-0001-suppinfo1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141487/2/hep29354_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141487/3/hep29354.pd

Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning

Graph Active Learning (GAL), which aims to find the most informative nodes in graphs for annotation to maximize the Graph Neural Networks (GNNs) performance, has attracted many research efforts but remains non-trivial challenges. One major challenge is that existing GAL strategies may introduce semantic confusion to the selected training set, particularly when graphs are noisy. Specifically, most existing methods assume all aggregating features to be helpful, ignoring the semantically negative effect between inter-class edges under the message-passing mechanism. In this work, we present Semantic-aware Active learning framework for Graphs (SAG) to mitigate the semantic confusion problem. Pairwise similarities and dissimilarities of nodes with semantic features are introduced to jointly evaluate the node influence. A new prototype-based criterion and query policy are also designed to maintain diversity and class balance of the selected nodes, respectively. Extensive experiments on the public benchmark graphs and a real-world financial dataset demonstrate that SAG significantly improves node classification performances and consistently outperforms previous methods. Moreover, comprehensive analysis and ablation study also verify the effectiveness of the proposed framework.Comment: Accepted by CIKM 202

CA1-projecting subiculum neurons facilitate object-place learning.

Recent anatomical evidence suggests a functionally significant back-projection pathway from the subiculum to the CA1. Here we show that the afferent circuitry of CA1-projecting subicular neurons is biased by inputs from CA1 inhibitory neurons and the visual cortex, but lacks input from the entorhinal cortex. Efferents of the CA1-projecting subiculum neurons also target the perirhinal cortex, an area strongly implicated in object-place learning. We identify a critical role for CA1-projecting subicular neurons in object-location learning and memory, and show that this projection modulates place-specific activity of CA1 neurons and their responses to displaced objects. Together, these experiments reveal a novel pathway by which cortical inputs, particularly those from the visual cortex, reach the hippocampal output region CA1. Our findings also implicate this circuitry in the formation of complex spatial representations and learning of object-place associations

Phenotypic and Physiological Characterization of the Epibiotic Interaction Between TM7x and Its Basibiont Actinomyces

Despite many examples of obligate epibiotic symbiosis (one organism living on the surface of another) in nature, such an interaction has rarely been observed between two bacteria. Here, we further characterize a newly reported interaction between a human oral obligate parasitic bacterium TM7x (cultivated member of Candidatus Saccharimonas formerly Candidate Phylum TM7), and its basibiont Actinomyces odontolyticus species (XH001), providing a model system to study epiparasitic symbiosis in the domain Bacteria. Detailed microscopic studies indicate that both partners display extensive morphological changes during symbiotic growth. XH001 cells manifested as short rods in monoculture, but displayed elongated and hyphal morphology when physically associated with TM7x. Interestingly, these dramatic morphological changes in XH001 were also induced in oxygen-depleted conditions, even in the absence of TM7x. Targeted quantitative real-time PCR (qRT-PCR) analyses revealed that both the physical association with TM7x as well as oxygen depletion triggered up-regulation of key stress response genes in XH001, and in combination, these conditions act in an additive manner. TM7x and XH001 co-exist with relatively uniform cell morphologies under nutrient-replete conditions. However, upon nutrient depletion, TM7x-associated XH001 displayed a variety of cell morphologies, including swollen cell body, clubbed-ends, and even cell lysis, and a large portion of TM7x cells transformed from ultrasmall cocci into elongated cells. Our study demonstrates a highly dynamic interaction between epibiont TM7x and its basibiont XH001 in response to physical association or environmental cues such as oxygen level and nutritional status, as reflected by their morphological and physiological changes during symbiotic growth