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arXiv 2023年

作者： Zhao, Yiming Zhou, Tao Gu, Yunqi Zhou, Yi Zhang, Yizhe Wu, Ye Fu, Huazhu PCA Lab The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The School of Computer Science and Engineering Southeast University Nanjing211189 China The Institute of High Performance Computing A*STAR Singapore

Polyp segmentation plays a vital role in accurately locating polyps at an early stage, which holds significant clinical importance for the prevention of colorectal cancer. Various polyp segmentation methods have been developed using fully-supervised deep learning techniques. However, pixel-wise annotation for polyp images by physicians during the diagnosis is both time-consuming and expensive. Moreover, visual foundation models such as the Segment Anything Model (SAM) have shown remarkable performance. Nevertheless, directly applying SAM to medical segmentation may not produce satisfactory results due to the inherent absence of medical knowledge. In this paper, we propose a novel SAM-guided Collaborative Learning Network (SAM-CLNet) for scribble-supervised polyp segmentation, enabling a collaborative learning process between our segmentation network and SAM to boost the model performance. Specifically, we first propose a Cross-level Enhancement and Aggregation Network (CEA-Net) for weakly-supervised polyp segmentation. Within CEA-Net, we propose a Cross-level Enhancement Module (CEM) that integrates the adjacent features to enhance the representation capabilities of different resolution features. Additionally, a Feature Aggregation Module (FAM) is employed to capture richer features across multiple levels. Moreover, we present a box-augmentation strategy that combines the segmentation maps generated by CEA-Net with scribble annotations to create more precise prompts. These prompts are then fed into SAM, generating segmentation SAM-guided masks, which can provide additional supervision to train CEA-Net effectively. Furthermore, we present an Image-level Filtering Mechanism to filter out unreliable SAM-guided masks. Extensive experimental results show that our SAM-CLNet outperforms state-of-the-art weakly-supervised segmentation methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Diagnosis

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LSA-PINN: Linear Boundary Connectivity Loss for Solving PDEs on Complex Geometry

arXiv

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arXiv 2023年

作者： Wong, Jian Cheng Chiu, Pao-Hsiung Ooi, Chinchun Dao, My Ha Ong, Yew-Soon Institute of High Performance Computing Agency for Science Technology and Research Singapore School of Computer Science and Engineering Nanyang Technological University Singapore

We present a novel loss formulation for efficient learning of complex dynamics from governing physics, typically described by partial differential equations (PDEs), using physics-informed neural networks (PINNs). In our experiments, existing versions of PINNs are seen to learn poorly in many problems, especially for complex geometries, as it becomes increasingly difficult to establish appropriate sampling strategy at the near boundary region. Overly dense sampling can adversely impede training convergence if the local gradient behaviors are too complex to be adequately modelled by PINNs. On the other hand, if the samples are too sparse, existing PINNs tend to overfit the near boundary region, leading to incorrect solution. To prevent such issues, we propose a new Boundary Connectivity (BCXN) loss function which provides linear local structure approximation (LSA) to the gradient behaviors at the boundary for PINN. Our BCXN-loss implicitly imposes local structure during training, thus facilitating fast physics-informed learning across entire problem domains with order of magnitude sparser training samples. This LSA-PINN method shows a few orders of magnitude smaller errors than existing methods in terms of the standard L2-norm metric, while using dramatically fewer training samples and iterations. Our proposed LSA-PINN does not pose any requirement on the differentiable property of the networks, and we demonstrate its benefits and ease of implementation on both multi-layer perceptron and convolutional neural network versions as commonly used in current PINN literature. © 2023, CC BY.

关键词： Partial differential equations

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Emerging theory and phenomena in thermal conduction: A selective review

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science China(Physics,Mechanics & Astronomy) 2022年第11期65卷 2-17页

作者： Jie Chen Jia He Dongkai Pan Xiaotian Wang Nuo Yang Jiaojiao Zhu Shengyuan A.Yang Gang Zhang Center for Phononics and Thermal Energy Science China-EU Joint Lab for NanophononicsMOE Key Laboratory of Advanced Microstructured MaterialsSchool of Physics Science and EngineeringTongji UniversityShanghai 200092China School of Energy and Power Engineering Huazhong University of Science and Technology(HUST)Wuhan 430074China School of Physical Science and Technology Southwest UniversityChongqing 400715China Research Laboratory for Quantum Materials Singapore University of Technology and DesignSingapore 487372Singapore Institute of High Performance Computing Agency for ScienceTechnology and Research(A*STAR)Singapore 138632Singapore

Recently, emerging phonon phenomena have been discovered and rapidly developed, which have become an active hot research topic. In this review article, we present state-of-the-art advances in several fascinating phonon transport phenomena. First, we summarize the recent progress on the wave nature of phonons, including phonon coherence and its effects on thermal conductivity and the topological properties of phonons. Then, we discuss the particle nature of phonons, including the weak coupling of phonons and the high-order phonon anharmonicity. Finally, we present the summary and a brief outlook. This review presents the advanced understanding of some emerging phonon phenomena in solid materials, which provides new opportunities for further advancement in a wide variety of applications.

关键词： phonon transport coherence topological phonon high-order phonon interaction

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Comparison of deep learning classification based methods with hyper parameter tuning on hyperspectral imagery 2

Comparison of deep learning classification based methods wit...

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2nd International Conference on Range Technology, ICORT 2021

作者： Bidari, Indira Chickerur, Satyadhyan Soumya, G S Sushmita, H T Talikoti, Rekha Smita, S K KLE Technological University School of Computer Science and Engineering Hubballi India KLE Technological University Centre for High Performance Computing School of Computer Science and Engineering Hubballi India

ISBN: (纸本)9781665449564

Hyperspectral Imagery (HSI) with classification is the most significant and dominant area in remote sensing. The HSI's profuse information is used for various applications in mineralogy, agriculture, physics, astronomy, chemical imaging, surveillance, and environmental sciences. In this paper, a comparison of classification approaches on the hyperspectral image dataset of Indian Pines is carried out. The three methods considered for the study. First one is CNN-Convolutional Neural Network is used for encoding pixel's spectral and spatial information and conducted the classification. Second is the MLP-Multi-Layer Perceptron is a typical kind of neural network with several layers for classification and third is HybridSN is a spectral-spatial 3D-CNN along with spatial2D-CNN. A comparison of these deep learning classification-based methods is piloted. Hyperparameter tuning is performed to increase the model accuracy for the ideal model architecture. © 2021 IEEE.

关键词： Classification (of information)

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Change detection and classification using hyperspectral imagery 2

Change detection and classification using hyperspectral imag...

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2nd International Conference on Range Technology, ICORT 2021

作者： Bidari, Indira Chickerur, Satyadhyan Kulkarni, Akshay Mahajan, Anish Nikkam, Amogh Akella, Sumanth KLE Technological University School of Computer Science and Engineering Hubballi India KLE Technological University Centre for High Performance Computing School of Computer Science and Engineering Hubballi India

ISBN: (纸本)9781665449564

Hyperspectral Imagery is a field with various applications in the present world. Classification and Change Detection (CD) have been fields of great importance over the years. Powerful tools are built by combining these two approaches, HSI with classification and with change detection. Deep learning-based land-cover classification and change detection algorithms have made significant advancements during recent times. In this paper, a band-specific feature extraction and classification method using 2D and 3D CNN (Hybrid Spectral Net) is being projected, which is computationally more accessible. Also, a change detection algorithm uses the Slow Feature Analysis (SFA) technique and fully connected layers of a neural network to give a binary classification. So initially, we aimed to do the multiclass classification by combining classification and change detection in one module. But a dataset with the ground truth value and bitemporal was required, which was not available, so both classification and change detection have been implemented on different datasets. © 2021 IEEE.

关键词： Change detection

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Health Care and Management based on Block chain and Machine Learning

Health Care and Management based on Block chain and Machine ...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Bakh, Ruhi Sarkar, VeenaNair Auguskani, Jerlin Priya Lovelin Poornima, Galiveeti Shetty, Monali N. Raj, Meerjumla Govind Dr. Ambedkar Institute of Management Studies and Research India Iihmr University India Department of Nursing College Aldrab Jazan University Saudi Arabia High Performance Computing Lab School of Computer Science and Information Science Presidency University Bengaluru India Computer Science Engineering Fr. Crce India St. Martin and #39 s Engineering College Survey No. 563 Dulapally Kompally Telangana Secunderabad500100 India

ISBN: (纸本)9798350334357

With the coming of technology, we currently have an immense measure of information accessible in each field, making it conceivable to offer responses to many issues. We will utilize machine learning and block chain technology to resolve issues with healthcare information the executives in this review. With the guide of machine learning, separating just the relevant information from the data is plausible. Using prepared calculations, this is finished. The reliability of information trade turns into a test after this information has been put away. This is where block chain technology is valuable. Block chain technology's agreement guarantees that information is credible and exchanges are secure. By putting the patient at the focal point of the healthcare framework and upgrading the security and interoperability of health information, block chain technology can possibly further develop health care organization. This article fundamentally centres around settling issues with healthcare information the board using Block chain technology and including a few urgent highlights utilizing Machine Learning. The freshest technology, block chain, offers a strong and secure groundwork. With these offices, block chain has gotten some momentum in an assortment of use areas, including the food business, store network the board, energy, government support offices, and healthcare. © 2023 IEEE.

关键词： Health care

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An Efficient Sparse LSTM Accelerator on Embedded FPGAs with Bandwidth-Oriented Pruning

An Efficient Sparse LSTM Accelerator on Embedded FPGAs with ...

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International Conference on Field Programmable Logic and Applications

作者： Shiqing Li Shien Zhu Xiangzhong Luo Tao Luo Weichen Liu School of Computer Science and Engineering Nanyang Technological University Singapore Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) Singapore

Long short-term memory (LSTM) networks have been widely used in natural language processing applications. Although over 80% weights can be pruned to reduce the memory requirement with little accuracy loss, the pruned model still cannot be buffered on-chip for small embedded FPGAs. Considering that weights are stored in the off-chip DDR, the performance of LSTM is bounded by the available memory bandwidth. However, current pruning strategies did not consider bandwidth utilization and thus lead to bad performance in this situation. In this work, we propose an efficient sparse LSTM accelerator on embedded FPGAs with bandwidth-oriented pruning. The key idea is that data sequences can be compressed if items can be represented by a linear function of their indices in the sequences. Inspired by this idea, we first propose a column-wise pruning strategy that removes all the column indices and around 75% row indices of the remaining weights. Based on the strategy, we design a dedicated compressed format to fill the bandwidth. Further, we propose a fully pipelined hardware accelerator, which achieves the workload balance and shortens the critical path. Finally, we train the LSTM model using the TIMIT dataset and implement the accelerator on the Xilinx PYNQ-Z1 platform. The experimental result shows that our design achieves around 0.3% accuracy improvement, a 2.18x performance speedup, and a 1.96x power efficiency compared to the state-of-the-art work.

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Identification of Key Genes as Potential Drug Targets for Gastric Cancer

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Tsinghua science and Technology 2023年第4期28卷 649-664页

作者： Md.Tofazzal Hossain Md.Selim Reza Yin Peng Shengzhong Feng Yanjie Wei Center for High Performance Computing Joint Engineering Research Center for Health Big Data Intelligent Analysis TechnologyShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Department of Statistics Bangabandhu Sheikh Mujibur Rahman Science and Technology UniversityGopalganj 8100Bangladesh Guangdong Provincial Key Laboratory for Genome Stability and Disease Prevention and Regional Immunity and Diseases Department of PathologyShenzhen UniversityShenzhen 518060China

Gastric cancer(GC)is one of the most common cancers and ranks the third in cancer mortality all over the *** goal of this study was to identify potential hub-genes,highlighting their functions,signaling pathways,and candidate drugs for the treatment of GC *** used publicly available next generation sequencing(NGS)data to identify differentially expressed(DE)*** top DE genes were mapped to STRING database to construct the protein-protein interaction(PPI)network and top hub genes were selected for further *** found a total of 1555 DE genes with 870 upregulated and 685 downregulated genes in *** selected the top 400(200 upregulated and 200 downregulated)genes to construct a PPI network and extracted the top 15 hub *** gene ontology(GO)term and kyoto encyclopedia of genes and genomes(KEGG)pathway enrichment analyses of the 15 hub genes exposed some important functions and signaling pathways that were significantly associated with GC *** survival analysis of the hub genes disclosed that the lower expressions of the three hub genes CDH2,COL4A1,and COL5A2 were associated with better survival of GC *** three genes might be the candidate biomarkers for the diagnosis and treatment of ***,we considered 3 key proteins(genomic biomarkers)(COL4A1,CDH2,and CO5A2)as the drug target proteins(receptors),performed their docking analysis with the 102 meta-drug agents,and found Everolimus,Docetaxel,Lanreotide,Venetoclax,Temsirolimus,and Nilotinib as the top ranked 6 candidate drugs with respect to our proposed target proteins for the treatment against GC ***,the proposed drugs might play vital role for the treatment against GC patients.

关键词： gastric cancer hub genes candidate genes molecular docking candidate drugs

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Theoretically guaranteed bidirectional data rectification for robust sequential recommendation 23

Theoretically guaranteed bidirectional data rectification fo...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Yatong Sun Xiaochun Yang Zhu Sun Bin Wang Yan Wang School of Computer Science and Engineering Northeastern University China and School of Computing Macquarie University Australia School of Computer Science and Engineering Northeastern University China Center for Frontier AI Research Institute of High Performance Computing A*STAR Singapore School of Computing Macquarie University Australia

Sequential recommender systems (SRSs) are typically trained to predict the next item as the target given its preceding (and succeeding) items as the input. Such a paradigm assumes that every input-target pair is reliable for training. However, users can be induced to click on items that are inconsistent with their true preferences, resulting in unreliable instances, i.e., mismatched input-target pairs. Current studies on mitigating this issue suffer from two limitations: (i) they discriminate instance reliability according to models trained with unreliable data, yet without theoretical guarantees that such a seemingly contradictory solution can be effective; and (ii) most methods can only tackle either unreliable input or targets but fail to handle both simultaneously. To fill the gap, we theoretically unveil the relationship between SRS predictions and instance reliability, whereby two error-bounded strategies are proposed to rectify unreliable targets and input, respectively. On this basis, we devise a model-agnostic Bidirectional Data Rectification (BirDRec) framework, which can be flexibly implemented with most existing SRSs for robust training against unreliable data. Additionally, a rectification sampling strategy is devised and a self-ensemble mechanism is adopted to reduce the (time and space) complexity of BirDRec. Extensive experiments on four real-world datasets verify the generality, effectiveness, and efficiency of our proposed BirDRec.

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Numerical simulations of attachment-line boundary layer in hypersonic flow, Part II: the features of three-dimensional turbulent boundary layer

arXiv

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arXiv 2024年

作者： Xi, Youcheng Yan, Bowen Yang, Guangwen Song, Fu School of Aerospace Engineering Tsinghua University Beijing100084 China Institute of High Performance Computing Department of Computer Science and Technology Tsinghua University Beijing100084 China

In this study, we investigate the characteristics of three-dimensional turbulent boundary layers influenced by transverse flow and pressure gradients. Our findings reveal that even without assuming an infinite sweep, a fully developed turbulent boundary layer over the present swept blunt body maintains spanwise homogeneity, consistent with infinite sweep assumptions. We critically examine the law-of-the and temperature-velocity relationships, typically applied two-dimensional turbulent boundary layers, in three-dimensional contexts. Results show that with transverse velocity and pressure gradient, streamwise velocity adheres to classical velocity transformation relationships and the predictive accuracy of classical temperature-velocity relationships diminishes because of pressure gradient. We show that near-wall streak structures persist and correspond with energetic structures in the outer region, though three-dimensional effects redistribute energy to align more with the external flow direction. Analysis of shear Reynolds stress and mean flow shear directions reveals in near-wall regions with low transverse flow velocity, but significant deviations at higher transverse velocities. Introduction of transverse pressure gradients together with the transverse velocities alter the velocity profile and mean flow shear directions, with shear Reynolds stress experiencing similar changes but with a lag increasing with transverse. Consistent directional alignment in outer regions suggests a partitioned relationship between shear Reynolds stress and mean flow shear: nonlinear in the inner region and approximately linear in the outer region. © 2024, CC BY.

关键词： Pressure gradient

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