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HVQ-VAE: Variational auto-encoder with hyperbolic vector quantization

Computer Vision and Image Understanding 2025年 258卷

作者： Chen, Shangyu Fang, Pengfei Harandi, Mehrtash Le, Trung Cai, Jianfei Phung, Dinh Faculty of Information Technology Department of Data Science and AI Monash University Exhibition Walk ClaytonVIC3168 Australia School of Computer Science and Engineering Southeast University Nanjing210096 China Faculty of Engineering Department of Electrical and Computer Systems Engineering Monash University Exhibition Walk ClaytonVIC3168 Australia

Vector quantized-variational autoencoder (VQ-VAE) and its variants have made significant progress in creating discrete latent space via learning a codebook. Previous works on VQ-VAE have focused on discrete latent spaces in Euclidean or in spherical spaces. This paper studies the geometric prior of hyperbolic spaces as a way to improve the learning capacity of VQ-VAE. That being said, working with the VQ-VAE in the hyperbolic space is not without difficulties, and the benefits of using hyperbolic space as the geometric prior for the latent space have never been studied in VQ-VAE. We bridge this gap by developing the VQ-VAE with hyperbolic vector quantization. To this end, we propose the hyperbolic VQ-VAE (HVQ-VAE), which learns the latent embedding of data and the codebook in the hyperbolic space. Specifically, we endow the discrete latent space in the Poincaré ball, such that the clustering algorithm can be formulated and optimized in the Poincaré ball. Thorough experiments against various baselines are conducted to evaluate the superiority of the proposed HVQ-VAE empirically. We show that HVQ-VAE enjoys better image reconstruction, effective codebook usage, and fast convergence than baselines. We also present evidence that HVQ-VAE outperforms VQ-VAE in low-dimensional latent space. © 2025 Elsevier Inc.

关键词： Unsupervised learning

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Single-cell transcriptomics uncovers EGFR signaling-mediated gastric progenitor cell differentiation in stomach homeostasis (vol 14, 3750, 2023)

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NATURE COMMUNICATIONS 2023年第1期14卷 1-16页

作者： Takada, Hitomi Sasagawa, Yohei Yoshimura, Mika Tanaka, Kaori Iwayama, Yoshimi Hayashi, Tetsutaro Isomura-Matoba, Ayako Nikaido, Itoshi Kurisaki, Akira Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan Master’s/Doctoral Program in Life Science Innovation (Bioinformatics) Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan

Defects in gastric progenitor cell differentiation are associated with various gastric disorders, including atrophic gastritis, intestinal metaplasia, and gastric cancer. However, the mechanisms underlying the multilineage differentiation of gastric progenitor cells during healthy homeostasis remain poorly understood. Here, using a single-cell RNA sequencing method, Quartz-Seq2, we analyzed the gene expression dynamics of progenitor cell differentiation toward pit cell, neck cell, and parietal cell lineages in healthy adult mouse corpus tissues. Enrichment analysis of pseudotime-dependent genes and a gastric organoid assay revealed that EGFR-ERK signaling promotes pit cell differentiation, whereas NF-κB signaling maintains gastric progenitor cells in an undifferentiated state. In addition, pharmacological inhibition of EGFR in vivo resulted in a decreased number of pit cells. Although activation of EGFR signaling in gastric progenitor cells has been suggested as one of the major inducers of gastric cancers, our findings unexpectedly identified that EGFR signaling exerts a differentiation-promoting function, not a mitogenic function, in normal gastric homeostasis.

关键词： Adult stem cells Stem-cell differentiation Stem-cell niche

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Using machine learning and satellite data from multiple sources to analyze mining,water management,and preservation of cultural heritage

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地球空间信息科学学报（英文版） 2024年第3期27卷 552-571页

作者： Joaquim J.Sousa Jiahui Lin Qun Wang Guang Liu Jinghui Fan Shibiao Bai Hongli Zhao Hongyu Pan Wenjing Wei Vanessa Rittlinger Peter Mayrhofer Ruth Sonnenschein Stefan Steger Luís Paulo Reis School of Science and Technology University of Trás-Os-Montes E Alto DouroVila RealPortugal Centre for Robotics in Industry and Intelligent Systems(CRIIS) INESC Technology and SciencePortoPortugal Aerospace Information Research Institute Chinese Academy of SciencesBeijingChina International Research Center of Big Data for Sustainable Development Goals BeijingChina College of Resources and Environment University of Chinese Academy of SciencesBeijingChina Application system Department Ⅰ China Siwei Surveying and Mapping Technology Co.LtdBeijingChina China Aero Geophysical Survey and Remote Sensing Center for Natural Resources BeijingChina College of Marine Science and Engineering Nanjing Normal UniversityNanjingChina Institute of Mountain Hazards and Environment Chinese Academy of SciencesChengduChina College of Marine Science and Engineering Nanjing Normal UniversityNanjingChina Institute for Earth Observation Eurac ResearchBolzanoItaly Faculty of Engineering University of PortoPortoPortugal

Remote sensing,particularly satellite-based,can play a valuable role in monitoring areas prone to *** high spatial and temporal coverage provided by satellite data can be used to reconstruct past events and continuously monitor sensitive areas for potential *** paper presents a range of techniques and methods that were applied for in-depth analysis and utilization of Earth observation data,with a particular emphasis on:(1)detecting mining sub-sidence,where a novel approach is proposed by combining an improved U-Net model and Interferometry Synthetic Aperture Radar(InSAR)*** results showed that the Efficient Channel Attention(ECA)U-Net model performed better than the U-Net(baseline)model in terms of Mean Intersection over Union(MIoU)and Intersection over Union(loU)indicators;(2)monitor-ing water conservancy and hydropower *** Xiaolangdi multipurpose dam complex was monitored using Small BAsline Subsets(SBAS)InSAR method on Sentinel-1 time series data and four small regions with high deformation rates were identified on the slope of the reservoir bank on the north *** dam body also showed obvious deformation with a velocity exceeding 60 mm/a;(3)the evaluation of the potential of InSAR results to integrate monitoring and warning systems for valuable heritage and architectural *** overall outcome of these methods showed that the use of Artificial Intelligence(AI)techniques in combination with InSAR data leads to more efficient analysis and interpretation,resulting in improved accuracy and prompt identification of potential hazards;and(4)finally,this study also presents a method for detecting landslides in mountainous regions,using optical *** new temporal landslide detection method is evaluated over a 7-year analysis period and unlike conventional bi-temporal change detection methods,this approach does not depend on any prior-knowledge and can potentially detect landslides over extended periods of time

关键词： Landslide detection InSAR time series analysis machine learning heritage preservation mining subsidence water conservancy infrastructures

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Fabric4show: real-time vision system for fabric defect detection and post-processing

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Visual Intelligence 2024年第1期2卷 1-13页

作者： Lin, Huaizhou Cai, Dan Xu, Zengmin Wu, Jinsong Sun, Lixian Jia, Haibin School of Materials Science and Engineering Guangxi Key Laboratory of Information Guilin University of Electronic Technology Guilin China School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Center for Applied Mathematics of Guangxi (GUET) Guilin China Anview.ai Guilin China School of Artificial Intelligence Guilin University of Electronic Technology Guilin China Department of Electrical Engineering University of Chile Santiago Chile

The exploration of computer vision applications for fabric defect detection has immense potential value. However, current relevant research in this area has primarily focused on detection models that aim for high detection accuracy and algorithmic efficiency, while neglecting the practical industrial production requirements. Therefore, we propose a fabric defect detection and post-processing system that integrates an optimized region with convolutional neural network (CNN) features (i.e., Faster R-CNN) for defect detection, defect localization and detection model evaluation. In addition, the proposed intelligent system incorporates novel approaches, such as a rearranged fabric dataset, anomaly detection, recommended clipping region division, and a replenishment device. This study illustrates an example of artificial intelligence (AI)-driven automated technology in fabric manufacturing. The accuracy and detection speed of different detection models under identical hardware conditions are evaluated and compared with related work. Experimental results demonstrate that the proposed approach achieves comparable performance to other models, while significantly reducing computational resource requirements. The potential efficiency of using two-stage networks on hardware systems for fabric defect detection tasks is highlighted, which is likely to have relevant implications for the textile industry. © The Author(s) 2024.

关键词： Automation Computer vision Defect detection Fabric manufacturing Faster R-CNN

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Search-in-Memory (SiM): Reliable, Versatile, and Efficient data Matching in SSD's NAND Flash Memory Chip for data Indexing Acceleration

arXiv

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arXiv 2024年

作者： Chen, Yun-Chih Chang, Yuan-Hao Kuo, Tei-Wei The Department of Computer Science TU Dortmund Germany The Institute of Information Science Academia Sinica Taipei115 Taiwan Department of Computer Science and Information Engineering High Performance and Scientific Computing Center Center of Data Intelligence: Technologies Applications and Systems of National Taiwan University Taiwan

To index the increasing volume of data, modern data indexes are typically stored on SSDs and cached in DRAM. However, searching such an index has resulted in significant I/O traffic due to limited access locality and inefficient cache utilization. At the heart of index searching is the operation of filtering through vast data spans to isolate a small, relevant subset, which involves basic equality tests rather than the complex arithmetic provided by modern CPUs. We introduce the Search-in-Memory (SiM) chip, which demonstrates the feasibility of performing data filtering directly within a NAND flash memory chip, transmitting only relevant search results rather than complete pages. Instead of adding complex circuits, we propose repurposing existing circuitry for efficient and accurate bitwise parallel matching. We demonstrate how different data structures can use our flexible SIMD command interface to offload index searches. This strategy not only frees up the CPU for more computationally demanding tasks, but it also optimizes DRAM usage for write buffering, significantly lowering energy consumption associated with I/O transmission between the CPU and DRAM. Extensive testing across a wide range of workloads reveals up to a 9X speedup in write-heavy workloads and up to 45% energy savings due to reduced read and write I/O. Furthermore, we achieve significant reductions in median and tail read latencies of up to 89% and 85% respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy utilization

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Real-time behavior recognition of animal: an IoT-based system design using acceleration data

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Multimedia Tools and Applications 2024年 1-26页

作者： Tran, Duc-Nghia Manh, Do Viet Van Thanh, Pham Shankar, A. Achyut Joshi, Kireet Tran, Duc-Tan Institute of Information Technology Vietnam Academy of Science and Technology Hanoi Viet Nam University of Fire Prevention and Fighting Hanoi100000 Viet Nam Department of Cyber Systems Engineering WMG University of Warwick CoventryCV74AL United Kingdom Department of CSE University Centre for Research & ampDevelopment Chandigarh University Punjab Mohali140413 India School of Computer Science Engineering Lovely Professional University Punjab Phagwara144411 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun248002 India Center of Research Impact and Outcome Chitkara University Punjab Chandigarh India Department of Computer Science & amp Engineering Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Faculty of Electrical and Electronic Engineering Phenikaa University Hanoi12116 Viet Nam

By analyzing large amounts of animal behavior data, humans can assess cattle’s condition. It leads to research and development a field using accelerometers and machine learning algorithms to ‘study’ behavior from acceleration data. It is challenging to analyze animal behavior due to the activity complexity. For example, acceleration data of some cow behaviors (e.g., feeding and standing) are similar if these behaviors contain similar gestures. In practical, real-time applications even bring more challenging since it is necessary to minimize a data window for behavior consideration. Using short data window often reduces the performance of classification algorithms. This study proposes a new solution for the mentioned challenges by combining neck-mounted and leg-mounted accelerometers, then using the vector of the dynamic body acceleration (VeDBA) and Mean features of synchronized acceleration data for the Decision Tree algorithm. The proposed method is demonstrated that performs well (0.94 accuracy, 0.91 sensitivity, 0.90 positive predictive value) within a low computational complexity algorithm and short data window (3-second data window, at the rate of one sample per second). It is particularly suited for the case of a real-time IoT-based system. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Accelerometers

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Spatial flood susceptibility mapping using an explainable artificial intelligence(XAI)model

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Geoscience Frontiers 2023年第6期14卷 8-19页

作者： Biswajeet Pradhan Saro Lee Abhirup Dikshit Hyesu Kim Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) School of Civil and Environmental EngineeringFaculty of Engineering&ITUniversity of Technology SydneySydneyNSW 2007Australia Earth Observation Center Institute of Climate ChangeUniversiti Kebangsaan Malaysia43600 UKMBangiSelangorMalaysia Geoscience Data Center Korea Institute of Geoscience and Mineral Resources(KIGAM)124 Gwahang-noYuseong-guDaejeon 34132South Korea Department of Resources Engineering Korea University of Science and Technology217 Gajeong-roYuseong-guDaejeon 34113South Korea Department of Astronomy Space Science and GeologyChungnam National University99 Daehak-roYuseong-guDaejeon 34134South Korea

Floods are natural hazards that lead to devastating financial losses and large displacements of *** susceptibility maps can improve mitigation measures according to the specific conditions of a study *** design of flood susceptibility maps has been enhanced through use of hybrid machine learning and deep learning *** these models have achieved better accuracy than traditional models,they are not widely used by stakeholders due to their black-box *** this study,we propose the application of an explainable artificial intelligence(XAI)model that incorporates the Shapley additive explanation(SHAP)model to interpret the outcomes of convolutional neural network(CNN)deep learning models,and analyze the impact of variables on flood susceptibility *** study was conducted in Jinju Province,South Korea,which has a long history of flood *** performance was evaluated using the area under the receiver operating characteristic curve(AUROC),which showed a prediction accuracy of 88.4%.SHAP plots showed that land use and various soil attributes significantly affected flood susceptibility in the study *** light of these findings,we recommend the use of XAIbased models in future flood susceptibility mapping studies to improve interpretations of model outcomes,and build trust among stakeholders during the flood-related decision-making process.

关键词： Flood susceptibility Explainable AI Deep learning South Korea

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Average Performance Analysis of Multi-Class Classification Based on Error-Correcting Output Codes

Average Performance Analysis of Multi-Class Classification B...

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IEEE International Conference on systems, Man and Cybernetics

作者： Manabu Kobayashi Gendo Kumoi Hideki Yagi Shigeichi Hirasawa Center for Data Science Waseda University Tokyo Japan Department of Information and Management Systems Engineering Nagaoka University of Technology Niigata Japan Department of Computer and Network Engineering University of Electro-Communications Tokyo Japan

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In machine learning, one of the methods to solve multiclass classification problems is a framework called Error-Correcting Output Codes (ECOC), which constructs a multiclass classifier by combining a lot of binary classifiers. ECOC assigns binary codewords to each category, and the multiclass classification performance varies depending on the code. In this study, we treat each element of the codeword as a random variable and evaluate the average performance of ECOC. As a result, for $M$ class classification if the number of binary classifiers is $O(\log M)$ , then the average error probability of various codes approaches that of MAP estimation. We show that the important points are the ratio between the number of binary classifiers and $\log M$ and the difference between the maximum posterior probability and the second highest posterior probability for the categories.

关键词： Codes Error probability Posterior probability Estimation Machine learning Random variables Performance analysis Cybernetics

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No Victim Left Behind: Developing a Dual-Use LEO Satellite System for Real-Time Population Mapping and Everyday Connectivity 23

No Victim Left Behind: Developing a Dual-Use LEO Satellite S...

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23rd International Symposium on Communications and information Technologies, ISCIT 2024

作者： Ohashi, Takumi Noda, Futo Tomura, Takashi Laosunthara, Ampan Suzuki, Koudai Tanaka, Yuno Kishimoto, Maki Seto, Rie Sakamoto, Hiraku Tran, Gia Khanh Shirane, Atsushi Tokyo Institute of Technology Department of Transdisciplinary Science and Engineering Tokyo Japan Chulalongkorn University Disaster and Risk Management Information Systems Research Unit Bangkok Thailand Tokyo Institute of Technology Department of Electrical and Electronic Engineering Tokyo Japan Tokyo Institute of Technology Department of Civil and Environmental Engineering Tokyo Japan Tokyo Institute of Technology Department of Architecture and Building Engineering Tokyo Japan Meteorological Research Institute Department of Observation & Data Assimilation Tsukuba Japan Tokyo Institute of Technology Department of Mechanical Engineering Tokyo Japan Institute of Innovative Research Tokyo Institute of Technology Tokyo Japan

ISBN: (纸本)9798350353952

Disasters often disrupt communication infrastructure, impeding damage assessment and rescue operations. This paper presents a method for real-time population distribution mapping during disasters using the widespread prevalence of smartphones and Low Earth Orbit (LEO) satellites. The system detects signals emitted by smartphones using LEO satellites equipped with directional antennas. By analyzing the received signal strength indicators (RSSI), it generates high-resolution population distribution maps in affected areas, providing critical information for resource allocation and rescue efforts. The system's design allows for functionality during both normal and emergency conditions, offering connectivity and location-based services in daily life while supporting disaster response efforts. A proof-of-concept experiment demonstrates the detection of smartphone signals from tall buildings, confirming the system's ability to distinguish between the presence and absence of smartphones in the observed area based on the detected spectra. This preliminary result suggests the feasibility of estimating population distribution by detecting leaked signals from smartphones during disasters. Further research is needed to address challenges such as precise localization, indoor signal detection, and system scalability. This research explores the integration of satellite technology with mobile devices for effective disaster management, contributing to the development of resilient and efficient emergency response strategies. © 2024 IEEE.

关键词： Directive antennas

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Enhancing 3D Human Pose Estimation Amidst Severe Occlusion with Dual Transformer Fusion

arXiv

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arXiv 2024年

作者： Ghafoor, Mehwish Mahmood, Arif Bilal, Muhammad Department of Computer Science Information Technology University Lahore Pakistan Center of Excellence in Intelligent Engineering Systems Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

In the field of 3D Human Pose Estimation from monocular videos, the presence of diverse occlusion types presents a formidable challenge. Prior research has made progress by harnessing spatial and temporal cues to infer 3D poses from 2D joint observations. This paper introduces a Dual Transformer Fusion (DTF) algorithm, a novel approach to obtain a holistic 3D pose estimation, even in the presence of severe occlusions. Confronting the issue of occlusion-induced missing joint data, we propose a temporal interpolation-based occlusion guidance mechanism. To enable precise 3D Human Pose Estimation, our approach leverages the innovative DTF architecture, which first generates a pair of intermediate views. Each intermediate-view undergoes spatial refinement through a self-refinement schema. Subsequently, these intermediate-views are fused to yield the final 3D human pose estimation. The entire system is end-to-end trainable. Through extensive experiments conducted on the Human3.6M and MPI-INF-3DHP datasets, our method’s performance is rigorously evaluated. Notably, our approach outperforms existing state-of-the-art methods on both datasets, yielding substantial improvements. The code is available here: https://***/MehwishG/DTF. © 2024, CC BY-NC-ND.

关键词： 3D modeling

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