自然言語処理における数値推論：数学的同等性の課題、革新、および対処戦略

京都大学新制・課程博士博士(情報学)甲第24929号情博第840号京都大学大学院情報学研究科知能情報学専攻(主査)特定教授 黒橋 禎夫, 教授 河原 達也, 教授 西野 恒学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDFA

3D Reconstruction of Optical Building Images Based on Improved 3D-R2N2 Algorithm

Three-dimensional reconstruction technology is a key element in the construction of urban geospatial models. Addressing the current shortcomings in reconstruction accuracy, registration results convergence, reconstruction effectiveness, and convergence time of 3D reconstruction algorithms, we propose an optical building object 3D reconstruction method based on an improved 3D-R2N2 algorithm. The method inputs preprocessed optical remote sensing images into a Convolutional Neural Network (CNN) with dense connections for encoding, converting them into a low-dimensional feature matrix and adding a residual connection between every two convolutional layers to enhance network depth. Subsequently, 3D Long Short-Term Memory (3D-LSTM) units are used for transitional connections and cyclic learning. Each unit selectively adjusts or maintains its state, accepting feature vectors computed by the encoder. These data are further passed into a Deep Convolutional Neural Network (DCNN), where each 3D-LSTM hidden unit partially reconstructs output voxels. The DCNN convolutional layer employs an equally sized 3 3 3 convolutional kernel to process these feature data and decode them, thereby accomplishing the 3D reconstruction of buildings. Simultaneously, a pyramid pooling layer is introduced between the feature extraction module and the fully connected layer to enhance the performance of the algorithm. Experimental results indicate that, compared to the 3D-R2N2 algorithm, the SFM-enhanced AKAZE algorithm, the AISI-BIM algorithm, and the improved PMVS algorithm, the proposed algorithm improves the reconstruction effect by 5.3%, 7.8%, 7.4%, and 1.0% respectively. Furthermore, compared to other algorithms, the proposed algorithm exhibits higher efficiency in terms of registration result convergence and reconstruction time, with faster computational speed. This research contributes to the enhancement of building 3D reconstruction technology, laying a foundation for future research in deep learning applications in the architectural field

ComSearch: Equation Searching with Combinatorial Strategy for Solving Math Word Problems with Weak Supervision

17th Conference of the European Chapter of the Association for Computational Linguistics, May 2-6, 2023Previous studies have introduced a weakly-supervised paradigm for solving math word problems requiring only the answer value annotation. While these methods search for correct value equation candidates as pseudo labels, they search among a narrow sub-space of the enormous equation space. To address this problem, we propose a novel search algorithm with combinatorial strategy ComSearch, which can compress the search space by excluding mathematically equivalent equations. The compression allows the searching algorithm to enumerate all possible equations and obtain high-quality data. We investigate the noise in the pseudo labels that hold wrong mathematical logic, which we refer to as the false-matching problem, and propose a ranking model to denoise the pseudo labels. Our approach holds a flexible framework to utilize two existing supervised math word problem solvers to train pseudo labels, and both achieve state-of-the-art performance in the weak supervision task

Relation Extraction with Weighted Contrastive Pre-training on Distant Supervision

17th Conference of the European Chapter of the Association for Computational Linguistics, May 2-6, 2023Contrastive pre-training on distant supervision has shown remarkable effectiveness in improving supervised relation extraction tasks. However, the existing methods ignore the intrinsic noise of distant supervision during the pre-training stage. In this paper, we propose a weighted contrastive learning method by leveraging the supervised data to estimate the reliability of pre-training instances and explicitly reduce the effect of noise. Experimental results on three supervised datasets demonstrate the advantages of our proposed weighted contrastive learning approach compared to two state-of-the-art non-weighted baselines. Our code and models are available at: https://github.com/YukinoWan/WCL

Optimizing Vision Transformers for Medical Image Segmentation

For medical image semantic segmentation (MISS), Vision Transformers have emerged as strong alternatives to convolutional neural networks thanks to their inherent ability to capture long-range correlations. However, existing research uses off-the-shelf vision Transformer blocks based on linear projections and feature processing which lack spatial and local context to refine organ boundaries. Furthermore, Transformers do not generalize well on small medical imaging datasets and rely on large-scale pre-training due to limited inductive biases. To address these problems, we demonstrate the design of a compact and accurate Transformer network for MISS, CS-Unet, which introduces convolutions in a multi-stage design for hierarchically enhancing spatial and local modeling ability of Transformers. This is mainly achieved by our well-designed Convolutional Swin Transformer (CST) block which merges convolutions with Multi-Head Self-Attention and Feed-Forward Networks for providing inherent localized spatial context and inductive biases. Experiments demonstrate CS-Unet without pre-training outperforms other counterparts by large margins on multi-organ and cardiac datasets with fewer parameters and achieves state-of-the-art performance. Our code is available at Github

Delocalization of d-electrons induced by cation coupling in ultrathin Chevrel-phase NiMo₃S₄ nanosheets for efficient electrochemical water splitting

Chevrel-phase metal sulfides are known to be promising materials for energy conversion and storage applications. However, a detailed understanding of the intrinsic kinetic mechanisms of electrocatalytic bifunctional hydrogen and oxygen evolution reactions (HER/OER) on NiMo3S4-based Chevrel-phases is lacking. Herein, novel ultrathin self-assembled nanosheets of NiMo3S4 are coupled with transition metal atoms (M/N-NiMo3S4; where M = Co, Fe, and Cu) were formed by a facile hydrothermal approach. Notably, the Co/N-NiMo3S4 electrocatalyst exhibits excellent performance in terms of ultralow overpotentials of 78, 208, 282, and 307 mV at 10, 100, 500, and 1000 mA cm−2 for the HER; and 186, 204, and 225 mV at 50, 100, and 300 mA cm−2 for the OER, respectively. Experimental and first principle calculations demonstrate that Co atoms coupling with edge Ni atoms results in d‐electron delocalization on Co/N-NiMo3S4, signifying the efficient charge transfer to improve overall water electrolysis. In addition, an upshift in the d‐band center of Co/N-NiMo3S4 can optimize the free energies of a variety of reaction intermediates for water adsorption and dissociation; thereby facilitating the robust alkaline overall water electrolysis at 1.47 V. This work therefore greatly deepens the understanding of the bifunctional hydrogen and oxygen evolution reaction of Chevrel-phase electrocatalysts.</p

Delocalization of d-electrons induced by cation coupling in ultrathin Chevrel-phase NiMo₃S₄ nanosheets for efficient electrochemical water splitting

Chevrel-phase metal sulfides are known to be promising materials for energy conversion and storage applications. However, a detailed understanding of the intrinsic kinetic mechanisms of electrocatalytic bifunctional hydrogen and oxygen evolution reactions (HER/OER) on NiMo3S4-based Chevrel-phases is lacking. Herein, novel ultrathin self-assembled nanosheets of NiMo3S4 are coupled with transition metal atoms (M/N-NiMo3S4; where M = Co, Fe, and Cu) were formed by a facile hydrothermal approach. Notably, the Co/N-NiMo3S4 electrocatalyst exhibits excellent performance in terms of ultralow overpotentials of 78, 208, 282, and 307 mV at 10, 100, 500, and 1000 mA cm−2 for the HER; and 186, 204, and 225 mV at 50, 100, and 300 mA cm−2 for the OER, respectively. Experimental and first principle calculations demonstrate that Co atoms coupling with edge Ni atoms results in d‐electron delocalization on Co/N-NiMo3S4, signifying the efficient charge transfer to improve overall water electrolysis. In addition, an upshift in the d‐band center of Co/N-NiMo3S4 can optimize the free energies of a variety of reaction intermediates for water adsorption and dissociation; thereby facilitating the robust alkaline overall water electrolysis at 1.47 V. This work therefore greatly deepens the understanding of the bifunctional hydrogen and oxygen evolution reaction of Chevrel-phase electrocatalysts.</p