53 research outputs found
Minimum Weight Perfect Matching via Blossom Belief Propagation
Max-product Belief Propagation (BP) is a popular message-passing algorithm
for computing a Maximum-A-Posteriori (MAP) assignment over a distribution
represented by a Graphical Model (GM). It has been shown that BP can solve a
number of combinatorial optimization problems including minimum weight
matching, shortest path, network flow and vertex cover under the following
common assumption: the respective Linear Programming (LP) relaxation is tight,
i.e., no integrality gap is present. However, when LP shows an integrality gap,
no model has been known which can be solved systematically via sequential
applications of BP. In this paper, we develop the first such algorithm, coined
Blossom-BP, for solving the minimum weight matching problem over arbitrary
graphs. Each step of the sequential algorithm requires applying BP over a
modified graph constructed by contractions and expansions of blossoms, i.e.,
odd sets of vertices. Our scheme guarantees termination in O(n^2) of BP runs,
where n is the number of vertices in the original graph. In essence, the
Blossom-BP offers a distributed version of the celebrated Edmonds' Blossom
algorithm by jumping at once over many sub-steps with a single BP. Moreover,
our result provides an interpretation of the Edmonds' algorithm as a sequence
of LPs
Hierarchical Graph Generation with -trees
Generating graphs from a target distribution is a significant challenge
across many domains, including drug discovery and social network analysis. In
this work, we introduce a novel graph generation method leveraging -tree
representation which was originally designed for lossless graph compression.
Our motivation stems from the ability of the -trees to enable compact
generation while concurrently capturing the inherent hierarchical structure of
a graph. In addition, we make further contributions by (1) presenting a
sequential -tree representation that incorporates pruning, flattening, and
tokenization processes and (2) introducing a Transformer-based architecture
designed to generate the sequence by incorporating a specialized tree
positional encoding scheme. Finally, we extensively evaluate our algorithm on
four general and two molecular graph datasets to confirm its superiority for
graph generation.Comment: 22 pages (10 appendices
Symmetric Exploration in Combinatorial Optimization is Free!
Recently, deep reinforcement learning (DRL) has shown promise in solving
combinatorial optimization (CO) problems. However, they often require a large
number of evaluations on the objective function, which can be time-consuming in
real-world scenarios. To address this issue, we propose a "free" technique to
enhance the performance of any deep reinforcement learning (DRL) solver by
exploiting symmetry without requiring additional objective function
evaluations. Our key idea is to augment the training of DRL-based combinatorial
optimization solvers by reward-preserving transformations. The proposed
algorithm is likely to be impactful since it is simple, easy to integrate with
existing solvers, and applicable to a wide range of combinatorial optimization
tasks. Extensive empirical evaluations on NP-hard routing optimization,
scheduling optimization, and de novo molecular optimization confirm that our
method effortlessly improves the sample efficiency of state-of-the-art DRL
algorithms. Our source code is available at
https://github.com/kaist-silab/sym-rd.Comment: 20 pages (including 9 pages of the appendix), 12 figure
EPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost
Graph-based models have become increasingly important in various domains, but
the limited size and diversity of existing graph datasets often limit their
performance. To address this issue, we propose EPIC (Edit Path Interpolation
via learnable Cost), a novel interpolation-based method for augmenting graph
datasets. Our approach leverages graph edit distance to generate new graphs
that are similar to the original ones but exhibit some variation in their
structures. To achieve this, we learn the graph edit distance through a
comparison of labeled graphs and utilize this knowledge to create graph edit
paths between pairs of original graphs. With randomly sampled graphs from a
graph edit path, we enrich the training set to enhance the generalization
capability of classification models. We demonstrate the effectiveness of our
approach on several benchmark datasets and show that it outperforms existing
augmentation methods in graph classification tasks
Learning Debiased Classifier with Biased Committee
Neural networks are prone to be biased towards spurious correlations between
classes and latent attributes exhibited in a major portion of training data,
which ruins their generalization capability. We propose a new method for
training debiased classifiers with no spurious attribute label. The key idea is
to employ a committee of classifiers as an auxiliary module that identifies
bias-conflicting data, i.e., data without spurious correlation, and assigns
large weights to them when training the main classifier. The committee is
learned as a bootstrapped ensemble so that a majority of its classifiers are
biased as well as being diverse, and intentionally fail to predict classes of
bias-conflicting data accordingly. The consensus within the committee on
prediction difficulty thus provides a reliable cue for identifying and
weighting bias-conflicting data. Moreover, the committee is also trained with
knowledge transferred from the main classifier so that it gradually becomes
debiased along with the main classifier and emphasizes more difficult data as
training progresses. On five real-world datasets, our method outperforms prior
arts using no spurious attribute label like ours and even surpasses those
relying on bias labels occasionally.Comment: Conference on Neural Information Processing Systems (NeurIPS), New
Orleans, 202
Diffusion Tensor Imaging: Exploring the Motor Networks and Clinical Applications
With the advances in diffusion magnetic resonance (MR) imaging techniques, diffusion tensor imaging (DTI) has been applied to a number of neurological conditions because DTI can demonstrate microstructures of the brain that are not assessable with conventional MR imaging. Tractography based on DTI offers gross visualization of the white matter fiber architecture in the human brain in vivo. Degradation of restrictive barriers and disruption of the cytoarchitecture result in changes in the diffusion of water molecules in various pathological conditions, and these conditions can also be assessed with DTI. Yet many factors may influence the ability to apply DTI clinically, so these techniques have to be used with a cautious hand
Morbidity and related factors among elderly people in South Korea: results from the Ansan Geriatric (AGE) cohort study
BACKGROUND: A thorough examination of the morbidity and comorbidity profiles among the elderly and an evaluation of the related factors are required to improve the delivery of health care to the elderly and to estimate the cost of that care. In South Korea where the aged population is rapidly increasing, however, to date only one study using a limited sample (84 subjects) has provided information on morbidity and related factors among the elderly. Using a large, stratified, random sample (2,767 subjects) from the population-based Ansan Geriatric study, the present study sought to assess the morbidity and comorbidity, and to determine the relationships of these variables with sociodemographic and health characteristics in elderly people in South Korea. METHODS: A total of 2,767 subjects (1,215 men and 1,552 women) aged 60–84 years were randomly selected from September 2002 to August 2003 in Ansan, South Korea. Data on sociodemographic and health characteristics, and clinical diagnosis were collected using questionnaires. When available, the medical records and medications taken by the subjects were also cross-checked. RESULTS: Of the total subjects, 78.0% reported diagnosed disease, 11.0% had been cured, and 46.8% had been diagnosed with more than two diseases. The mean number of morbidities per person among elderly Koreans was 1.62 ± 1.35 (mean ± standard deviation), and women had a greater number of diseases per person than did men. The most common morbidities were chronic diseases such as hypertension, arthritis, and diabetes mellitus. In women, osteoporosis and arthritis were the second and third most prevalent diseases, respectively. Morbidity was significantly associated with gender, employment, household income, alcohol intake, self-assessed health status, and worries about health. CONCLUSION: These data will enhance understanding of the patterns of health problems among elderly Koreans and will contribute to the application of appropriate intervention strategies
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