926 research outputs found
Link Prediction via Matrix Completion
Inspired by practical importance of social networks, economic networks,
biological networks and so on, studies on large and complex networks have
attracted a surge of attentions in the recent years. Link prediction is a
fundamental issue to understand the mechanisms by which new links are added to
the networks. We introduce the method of robust principal component analysis
(robust PCA) into link prediction, and estimate the missing entries of the
adjacency matrix. On one hand, our algorithm is based on the sparsity and low
rank property of the matrix, on the other hand, it also performs very well when
the network is dense. This is because a relatively dense real network is also
sparse in comparison to the complete graph. According to extensive experiments
on real networks from disparate fields, when the target network is connected
and sufficiently dense, whatever it is weighted or unweighted, our method is
demonstrated to be very effective and with prediction accuracy being
considerably improved comparing with many state-of-the-art algorithms
A Dynamical Graph Prior for Relational Inference
Relational inference aims to identify interactions between parts of a
dynamical system from the observed dynamics. Current state-of-the-art methods
fit a graph neural network (GNN) on a learnable graph to the dynamics. They use
one-step message-passing GNNs -- intuitively the right choice since
non-locality of multi-step or spectral GNNs may confuse direct and indirect
interactions. But the \textit{effective} interaction graph depends on the
sampling rate and it is rarely localized to direct neighbors, leading to local
minima for the one-step model. In this work, we propose a \textit{dynamical
graph prior} (DYGR) for relational inference. The reason we call it a prior is
that, contrary to established practice, it constructively uses error
amplification in high-degree non-local polynomial filters to generate good
gradients for graph learning. To deal with non-uniqueness, DYGR simultaneously
fits a ``shallow'' one-step model with shared graph topology. Experiments show
that DYGR reconstructs graphs far more accurately than earlier methods, with
remarkable robustness to under-sampling. Since appropriate sampling rates for
unknown dynamical systems are not known a priori, this robustness makes DYGR
suitable for real applications in scientific machine learning
Statistical Mechanics of Generalization In Graph Convolution Networks
Graph neural networks (GNN) have become the default machine learning model
for relational datasets, including protein interaction networks, biological
neural networks, and scientific collaboration graphs. We use tools from
statistical physics and random matrix theory to precisely characterize
generalization in simple graph convolution networks on the contextual
stochastic block model. The derived curves are phenomenologically rich: they
explain the distinction between learning on homophilic and heterophilic graphs
and they predict double descent whose existence in GNNs has been questioned by
recent work. Our results are the first to accurately explain the behavior not
only of a stylized graph learning model but also of complex GNNs on messy
real-world datasets. To wit, we use our analytic insights about homophily and
heterophily to improve performance of state-of-the-art graph neural networks on
several heterophilic benchmarks by a simple addition of negative self-loop
filters
Scaling and Alpha-Helix Regulation of Protein Relaxation in a Lipid Bilayer
Protein conformation and orientation in the lipid membrane plays a key role in many cellular processes. Here we use molecular dynamics simulation to investigate the relaxation and C-terminus diffusion of a model helical peptide: beta-amyloid (Aβ) in a lipid membrane.We observed that after the helical peptide was initially half-embedded in the extracelluar leaflet of phosphatidylcholine (PC) or PC/cholesterol (PC/CHOL) membrane, the C-terminus diffused across the membrane and anchored to PC headgroups of the cytofacial lipid leaflet. In some cases, the membrane insertion domain of the Aβ was observed to partially unfold. Applying a sigmoidal fit to the process, we found that the characteristic velocity of the C-terminus, as it moved to its anchor site, scaled with θu −4/3, where θu is the fraction of the original helix that was lost during a helix to coil transition. Comparing this scaling with that of bead-spring models of polymer relaxation suggests that the C-terminus velocity is highly regulated by the peptide helical content, but that it is independent of the amino acid type. The Aβ was stabilized by the attachment of the positive Lys28 side chain to the negative phosphate of PC or 3β oxygen of CHOL in the extracellular lipid leaflet and of the C-terminus to its anchor site in the cytofacial lipid leaflet
Molecular Dynamics Simulations Reveal the Protective Role of Cholesterol in β-Amyloid Protein-Induced Membrane Disruptions in Neuronal Membrane Mimics
Interactions of β-amyloid (Aβ) peptides with neuronal membranes have been associated with the pathogenesis of Alzheimer\u27s disease (AD); however, the molecular details remain unclear. We used atomistic molecular dynamics (MD) simulations to study the interactions of Aβ40 and Aβ42 with model neuronal membranes. The differences between cholesterol-enriched and depleted lipid domains were investigated by the use of model phosphatidylcholine (PC) lipid bilayers with and without 40 mol % cholesterol. A total of 16 independent 200 ns simulation replicates were investigated. The surface area per lipid, bilayer thickness, water permeability barrier, and lipid order parameter, which are sensitive indicators of membrane disruption, were significantly altered by the inserted state of the protein. We conclude that cholesterol protects Aβ-induced membrane disruption and inhibits β-sheet formation of Aβ on the lipid bilayer. The latter could represent a two-dimensional (2D) seeding template for the formation of toxic oligomeric Aβ in the pathogenesis of AD
Prompt Sapper: LLM-Empowered Software Engineering Infrastructure for AI-Native Services
Foundation models, such as GPT-4, DALL-E have brought unprecedented AI
"operating system" effect and new forms of human-AI interaction, sparking a
wave of innovation in AI-native services, where natural language prompts serve
as executable "code" directly (prompt as executable code), eliminating the need
for programming language as an intermediary and opening up the door to personal
AI. Prompt Sapper has emerged in response, committed to support the development
of AI-native services by AI chain engineering. It creates a large language
model (LLM) empowered software engineering infrastructure for authoring AI
chains through human-AI collaborative intelligence, unleashing the AI
innovation potential of every individual, and forging a future where everyone
can be a master of AI innovation. This article will introduce the R\&D
motivation behind Prompt Sapper, along with its corresponding AI chain
engineering methodology and technical practices
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