WeaveNet for Approximating Two-sided Matching Problems

Matching, a task to optimally assign limited resources under constraints, is a fundamental technology for society. The task potentially has various objectives, conditions, and constraints; however, the efficient neural network architecture for matching is underexplored. This paper proposes a novel graph neural network (GNN), \textit{WeaveNet}, designed for bipartite graphs. Since a bipartite graph is generally dense, general GNN architectures lose node-wise information by over-smoothing when deeply stacked. Such a phenomenon is undesirable for solving matching problems. WeaveNet avoids it by preserving edge-wise information while passing messages densely to reach a better solution. To evaluate the model, we approximated one of the \textit{strongly NP-hard} problems, \textit{fair stable matching}. Despite its inherent difficulties and the network's general purpose design, our model reached a comparative performance with state-of-the-art algorithms specially designed for stable matching for small numbers of agents

TNF: Tri-branch Neural Fusion for Multimodal Medical Data Classification

This paper presents a Tri-branch Neural Fusion (TNF) approach designed for classifying multimodal medical images and tabular data. It also introduces two solutions to address the challenge of label inconsistency in multimodal classification. Traditional methods in multi-modality medical data classification often rely on single-label approaches, typically merging features from two distinct input modalities. This becomes problematic when features are mutually exclusive or labels differ across modalities, leading to reduced accuracy. To overcome this, our TNF approach implements a tri-branch framework that manages three separate outputs: one for image modality, another for tabular modality, and a third hybrid output that fuses both image and tabular data. The final decision is made through an ensemble method that integrates likelihoods from all three branches. We validate the effectiveness of TNF through extensive experiments, which illustrate its superiority over traditional fusion and ensemble methods in various convolutional neural networks and transformer-based architectures across multiple datasets

3D Point Cloud Registration with Learning-based Matching Algorithm

We present a novel differential matching algorithm for 3D point cloud registration. Instead of only optimizing the feature extractor for a matching algorithm, we propose a learning-based matching module optimized to the jointly-trained feature extractor. We focused on edge-wise feature-forwarding architectures, which are memory-consuming but can avoid the over-smoothing effect that GNNs suffer. We improve its memory efficiency to scale it for point cloud registration while investigating the best way of connecting it to the feature extractor. Experimental results show our matching module's significant impact on performance improvement in rigid/non-rigid and whole/partial point cloud registration datasets with multiple contemporary feature extractors. For example, our module boosted the current SOTA method, RoITr, by +5.4%, and +7.2% in the NFMR metric and +6.1% and +8.5% in the IR metric on the 4DMatch and 4DLoMatch datasets, respectively

Impact of Ambulation Status in Patients with End-stage Renal Disease on Hemodialysis due to Diabetic Nephropathy : The PREDICT Study

Article信州医学雑誌 68(3): 131-138(2020)journal articl

カシノナガキクイムシの繁殖成功に与える坑道作成開始時期の影響

We allowed the oak borer, Platypus quercivorus, to construct galleries in fresh logs of Pasania edulis in an evergreen broad-leaved forest during a period from June to December 1997 and counted the number of adults that emerged from each gallery in the autumn of 1997 and summer of 1998. Attack by this species, namely the start of gallery construction, was observed from early June to early October in 1997. The distribution patterns of entry holes changed from uniform to contagious with the increment of their density. Reproductive success was lower for galleries started after August than those started in June and July, from which new adults emerged in September and October 1997. The galleries started after the end of August did not develop well and failed to produce any new adults. These results suggest that the time of attack is very important for reproductive success of this species and that adults should start gallery construction by the end of July to produce a considerable number of new adult beetles. The construction of galleries by new adults in autumn may contribute little to the population dynamics of this species