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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Shi, Zhenning Zheng, Haoshuai Xu, Chen Dong, Changsheng Pan, Bin Xie, Xueshuo He, Along Li, Tao Fu, Huazhu College of Computer Science Nankai University China School of Statistics and Data Science Nankai University China Haihe Lab of ITAI China Institute of High Performance Computing A*STAR Singapore

Recently, research on denoising diffusion models has expanded its application to the field of image restoration. Traditional diffusion-based image restoration methods utilize degraded images as conditional input to effectively guide the reverse generation process, without modifying the original denoising diffusion process. However, since the degraded images already include low-frequency information, starting from Gaussian white noise will result in increased sampling steps. We propose Resfusion, a general framework that incorporates the residual term into the diffusion forward process, starting the reverse process directly from the noisy degraded images. The form of our inference process is consistent with the DDPM. We introduced a weighted residual noise, named resnoise, as the prediction target and explicitly provide the quantitative relationship between the residual term and the noise term in resnoise. By leveraging a smooth equivalence transformation, Resfusion determine the optimal acceleration step and maintains the integrity of existing noise schedules, unifying the training and inference processes. The experimental results demonstrate that Resfusion exhibits competitive performance on ISTD dataset, LOL dataset and Raindrop dataset with only five sampling steps. Furthermore, Resfusion can be easily applied to image generation and emerges with strong versatility. Our code and model are available at https://***/nkicsl/Resfusion. © 2024 Neural information processing systems foundation. All rights reserved.

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Learning in sinusoidal spaces with physics-informed neural networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 985-1000页

作者： Wong, Jian Cheng Ooi, Chin Chun Gupta, Abhishek Ong, Yew Soon Institute of High Performance Computing Agency for Science Technology and Research Singapore138632 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Singapore Institute of Manufacturing Technology Agency for Science Technology and Research Singapore138632 Singapore Agency for Science Technology and Research Singapore138632 Singapore Data Science and Artificial Intelligence Research Centre School of Computer Science and Engineering Nanyang Technological University Singapore138632 Singapore

A physics-informed neural network (PINN) uses physics-Augmented loss functions, e.g., incorporating the residual term from governing partial differential equations (PDEs), to ensure its output is consistent with fundamental physics laws. However, it turns out to be difficult to train an accurate PINN model for many problems in practice. In this article, we present a novel perspective of the merits of learning in sinusoidal spaces with PINNs. By analyzing behavior at model initialization, we first show that a PINN of increasing expressiveness induces an initial bias around flat output functions. Notably, this initial solution can be very close to satisfying many physics PDEs, i.e., falling into a localminimum of the PINN loss that onlyminimizes PDE residuals, while still being far from the true solution that jointly minimizes PDE residuals and the initial and/or boundary conditions. It is difficult for gradient descent optimization to escape from such a local minimum trap, often causing the training to stall. We then prove that the sinusoidalmapping of inputs-in an architecture we label as sf-PINN-is effective to increase input gradient variability, thus avoiding being trapped in such deceptive local minimum. The level of variability can be effectively modulated to match high-frequency patterns in the problem at hand. A key facet of this article is the comprehensive empirical study that demonstrates the efficacy of learning in sinusoidal spaces with PINNs for a wide range of forward and inversemodeling problems spanning multiple physics domains. Impact Statement-Falling under the emerging field of physicsinformed machine learning, PINN models have tremendous potential as a unifying AI framework for assimilating physics theory and measurement data. However, they remain infeasible for broad science and engineering applications due to computational cost and training challenges, especially for more complex problems. Instead of focusing on empirical demonstration of appli

关键词： Gradient methods

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AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis of the Femoral Head Detection 37

AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis o...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Chen, Haoyang Liu, Shuai Lu, Feng Li, Wei Sheng, Bin Li, Mi Jin, Hai Zomaya, Albert Y. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Department of Computer Science and Engineering Shanghai Jiao Tong University China Tongji Hospital Tongji Medical College Huazhong University of Science and Technology China

ISBN: (纸本)9781577358800

Early diagnosis of osteonecrosis of the femoral head (ONFH) can inhibit the progression and improve femoral head preservation. The radiograph difference between early ONFH and healthy ones is not apparent to the naked eye. It is also hard to produce a large dataset to train the classification model. In this paper, we propose Asymmetric-Sensitive Transformer (AsT) to capture the uneven development of the bilateral femoral head to enable robust ONFH detection. Our ONFH detection is realized using the self-attention mechanism to femoral head regions while conferring sensitivity to the uneven development by the attention-shared transformer. The real-world experiment studies show that AsT achieves the best performance of AUC 0.9313 in the early diagnosis of ONFH and can find out misdiagnosis cases firmly. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Large dataset

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Research on performance optimization of virtual data space across WAN

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Frontiers of computer science 2024年第6期18卷 167-187页

作者： Jiantong HUO Zhisheng HUO Limin XIAO Zhenxue HE State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China High Performance Computing Center Beihang UniversityBeijing 100191China Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural UniversityBaoding 071001China

For the high-performance computing in a WAN environment,the geographical locations of national supercomputing centers are scattered and the network topology is complex,so it is difficult to form a unified view of *** aggregate the widely dispersed storage resources of national supercomputing centers in China,we have previously proposed a global virtual data space named GVDS in the project of“high performance computing Virtual Data Space”,a part of the National Key Research and Development Program of *** GVDS enables large-scale applications of the high-performance computing to run efficiently across ***,the applications running on the GVDS are often data-intensive,requiring large amounts of data from multiple supercomputing centers across *** this regard,the GVDS suffers from performance bottlenecks in data migration and access across *** solve the above-mentioned problem,this paper proposes a performance optimization framework of GVDS including the multitask-oriented data migration method and the request access-aware IO proxy resource allocation *** a WAN environment,the framework proposed in this paper can make an efficient migration decision based on the amount of migrated data and the number of multiple data sources,guaranteeing lower average migration latency when multiple data migration tasks are running in *** addition,it can ensure that the thread resource of the IO proxy node is fairly allocated among different types of requests(the IO proxy is a module of GVDS),so as to improve the application’s performance across *** experimental results show that the framework can effectively reduce the average data access delay of GVDS while improving the performance of the application greatly.

关键词： storage aggregation across WANs large-scale applications GVDS data migration allocation of IO proxy resource

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Grading retinopathy of prematurity with feedforward network 11th

Grading retinopathy of prematurity with feedforward network

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11th International Conference on Soft computing and Pattern Recognition, SoCPaR 2019, and 11th World Congress on Nature and Biologically Inspired computing, NaBIC 2019

作者： Giraddi, Shantala Chickerur, Satyadhyan Annigeri, Nirmala School of Computer Science and Engineering KLE Technological University HubliKarnataka580031 India Center for High Performance Computing KLE Technological University HubliKarnataka580031 India

ISBN: (纸本)9783030493448

Retinopathy of Prematurity is a disease that affects premature infants having low birth weight. The disease may lead to blindness unless timely treatment is not provided. Because of the high birth rate premature babies and expanded neonatal care, the incidence of ROP is worrying in India today. There is an urgent need to create awareness about disease. The researchers propose a new approach of grading ROP with feed forward networks using second order texture features. Experiments are conducted with six different architectures of Feed Forward Networks. Second order texture features mean, entropy, contrast, correlation, homogeneity, energy from Gray level co-occurrence matrix (GLCM) are considered. The results obtained indicate Feed forward network offers an easy yet effective paradigm for ROP Grading. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Grading

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Automatic facet extraction based on multidimensional semantic index

Automatic facet extraction based on multidimensional semanti...

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2012 8th International Conference on Semantics, Knowledge and Grids, SKG 2012

作者： Wei, Xiao Luo, Xiangfeng Li, Qing School of Computing Engineering and Science High Performance Computing Center Shanghai University Shanghai 200072 China School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai 200235 China City University of Hong Kong Department of Computer Science City University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9780769547947

Faceted search on web pages needs exact facets. However, it is difficult to extract facets exactly from web pages because the web pages are unstructured and lack of facet information. Therefore, facet extraction is a key to faceted search. This paper proposed a method of extracting facets automatically from unstructured web pages to improve the faceted search on web. The Multidimensional Semantic Index (MDSI) of web pages is constructed by mining all kinds of semantic relations among the words from web pages, which creates a semantic-rich index for web pages. In MDSI, the differently dimensional semantic indexes are bridged by mining the semantic mapping between them. Based on the MDSI of web pages, the facets are extracted by analyzing semantic mapping relations in MDSI. To validate the effect of the proposed method, two datasets are constructed and the experimental results show that the proposed method is feasible and comparatively precise. © 2012 IEEE.

关键词： Extraction

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An Investigation into Power Aware Aspects of Rendering 3D Models on Multi-Core Processors

An Investigation into Power Aware Aspects of Rendering 3D Mo...

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2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Konnurmath, Guruprasad Chickerur, Satyadhyan School of Computer Science and Engineering KLE Technological University Hubballi580031 India Centre for High Performance Computing K L e Technological University Hubballi580031 India

Graphics Processing Units (GPUs) are designed to process numerically intensive applications and specially customized with additional computational power to achieve efficient rendering of 3D applications. Shaders are basically the computer programs that run on graphics rendering pipeline and inform about how to process and render each pixel, to perform efficient rendering of 3D applications. Shaders included in our Geometric complexity, texture resolution, per-pixel lighting. These shaders expose GPUs to suffer more power utilization during rendering process. In this paper, initially GPU power usage is investigated at run-time for rendering configuration to define shader parameters on a 3D scene and then power prediction model is proposed at different shader calls on GPU to generate power-aware dynamically reconfigurable rendering model. In the implementation process we integrate the TensorFlow management library routines and dynamic voltage and frequency scheduling (DVFS) mechanism to improve efficiency in rendering process and reach optimal power savings. To analyze the power efficiency of GPUs, shader parameters such as geometric complexity, texture resolution, per-pixel lighting, filtering that expose more towards power consumption are experimented with common GPU workloads during rendering process, then frequency and voltage is altered based on the framerate monitoring. Also in addition to this the frame rate is monitored and maintained in such a way that GPU frequency is adjusted in parallel if the framerate is within the acceptable range so that unnecessary power usage is reduced. During the experimental tests a lower limit of 30 frames per second and upper limit of 60 frames per second is configured. The GPU frequency is reduced if the existing framerate is greater than upper configured limit and frequency range is increased if framerate is below the configured lower limit. Compared with the previous state of art, results show power savings around 40% and imp

关键词： Graphics processing unit

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Grid enabled optimization

Grid enabled optimization

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European Grid Conference on Advances in Grid computing - EGC 2005

作者： Ng, Hee-Khiang Ong, Yew-Soon Hung, Terence Lee, Bu-Sung School of Computer Engineering Nanyang Technological University Nanyang Avenue Singapore 639798 Singapore Institute of High Performance Computing Singapore Science Park II Singapore 117528 Singapore

In this paper, we present a scalable parallel framework, which employs grid computing technologies, for solving computationally expensive and intractable design problems. Using an aerodynamic airfoil design optimization problem as an example the application of the grid computing strategies is discussed. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Distributed computer systems

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Vision Transformers with Hierarchical Attention

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Machine Intelligence Research 2024年第4期21卷 670-683页

作者： Yun Liu Yu-Huan Wu Guolei Sun Le Zhang Ajad Chhatkuli Luc Van Gool Institute for Infocomm Research(I2R) A*STARSingapore 138632Singapore Institute of High Performance Computing(IHPC) A*STARSingapore 138632Singapore Computer Vision Lab ETH ZürichZürich 8092Switzerland School of Information and Communication Engineering University of Electronic Science and Technology of China(UESTC)Chengdu 611731China

This paper tackles the high computational/space complexity associated with multi-head self-attention(MHSA)in vanilla vision *** this end,we propose hierarchical MHSA(H-MHSA),a novel approach that computes self-attention in a hierarchical ***,we first divide the input image into patches as commonly done,and each patch is viewed as a ***,the proposed H-MHSA learns token relationships within local patches,serving as local relationship ***,the small patches are merged into larger ones,and H-MHSA models the global dependencies for the small number of the merged *** last,the local and global attentive features are aggregated to obtain features with powerful representation *** we only calculate attention for a limited number of tokens at each step,the computational load is reduced ***,H-MHSA can efficiently model global relationships among tokens without sacrificing fine-grained *** the H-MHSA module incorporated,we build a family of hierarchical-attention-based transformer networks,namely *** demonstrate the superiority of HAT-Net in scene understanding,we conduct extensive experiments on fundamental vision tasks,including image classification,semantic segmentation,object detection and instance ***,HAT-Net provides a new perspective for vision *** and pretrained models are available at https://***/yun-liu/HAT-Net.

关键词： Vision transformer hierarchical attention global attention local attention scene understanding.

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GECKA3D: A 3D game engine for commonsense knowledge acquisition 29

GECKA3D: A 3D game engine for commonsense knowledge acquisit...

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29th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2016

作者： Cambria, Erik Nguyen, Tam V. Cheng, Brian Kwok, Kenneth Sepulveda, Jose School of Computer Engineering Nanyang Technological University Singapore ARTIC Research Center Singapore Polytechnic Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore

ISBN: (纸本)9781577357568

Commonsense knowledge representation and reasoning is key tor tasks such as artificial intelligence and natural language understanding. Since commonsense consists of information that humans take for granted, gathering it is an extremely difficult task. In this paper, we introduce a novel 3D game engine for commonsense knowledge acquisition (GECKA3D) which aims to collect commonsense from game designers through the development of serious games. GECKA3D integrates the potential of serious games and games with a purpose. This provides a platform for the acquisition of re-usable and multi-purpose knowledge, and also enables the development of games that can provide entertainment value and teach players something meaningful about the actual world they live in. © 2016, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Serious games

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