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Medical image registration and its application in retinal images:a review

Visual computing for Industry,Biomedicine,and Art 2024年第1期7卷 142-164页

作者： Qiushi Nie Xiaoqing Zhang Yan Hu Mingdao Gong Jiang Liu Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Singapore Eye Research Institute Singapore 169856Singapore State Key Laboratory of Ophthalmology Optometry and Visual ScienceEye HospitalWenzhou Medical UniversityWenzhou 325027China

Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse informa-tion of images,which may be captured under different times,angles,or *** several surveys have reviewed the development of medical image registration,they have not systematically summarized the existing med-ical image registration *** this end,a comprehensive review of these methods is provided from traditional and deep-learning-based perspectives,aiming to help audiences quickly understand the development of medical image *** particular,we review recent advances in retinal image registration,which has not attracted much *** addition,current challenges in retinal image registration are discussed and insights and prospects for future research provided.

关键词： computer-aided diagnosis Medical image registration Deep learning Generative model Transformer Retina

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Solving 2 by 2 Grid Sudoku Problem using Grover's Algorithm with Intel Quantum SDK 13

Solving 2 by 2 Grid Sudoku Problem using Grover's Algorithm ...

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13th International Conference on Advanced computer Information Technologies, ACIT 2023

作者： Shinde, Tejas Gambo, Yaknan John Rasch, Kevin Liebelt, Helena Li, Rui Deggendorf Institute of Technology High Performance Computing and Quantum Computing Deggendorf Germany Quantum Computing Research Intel Inc HillsboroOR United States Deggendorf Institute of Technology Department of Applied Computer Science Deggendorf Germany

Quantum technologies are moving towards enabling real world uses and as these technologies develop. The most important thing that differs Quantum from classical computers is that Quantum computers can do 'multiple' tasks at the same time as opposed to the classical computers doing a single task at a time. This phenomenon has led to the discovery of a few algorithms that are faster than regular computers. One such algorithm is the Grover's Algorithm[l]. This algorithm has shown a few real-world applications such as Quantum Search Algorithm, Travelling salesman, etc[2]. In this paper, Grover's algorithm is introduced, and a 2 x 2 Sudoku example is solved using Intel Quantum Software Development Kit[3]. The Intel Quantum SDK is quite new and in this paper we show how a simple Grover's Problem can be implemented in the SDK. Sudoku is a popular number-based puzzle game played on a grid consisting of nine squares subdivided into smaller 3x3 grids. The objective is to fill the grid with numbers from 1 to 9, ensuring that each row, column, and 3x3 grid contains all numbers exactly once. © 2023 IEEE.

关键词： Qubits

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Recent Advances in In-Memory computing:Exploring Memristor and Memtransistor Arrays with 2D Materials

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Nano-Micro Letters 2024年第7期16卷 1-30页

作者： Hangbo Zhou Sifan Li Kah-Wee Ang Yong-Wei Zhang Institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)1 Fusionopolis Way#16-16 ConnexisSingapore 138632Republic of Singapore Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117583Republic of Singapore Institute of Materials Research and Engineering Agency for ScienceTechnology and Research(A*STAR)2 Fusionopolis WaySingapore 138634Republic of Singapore

The conventional computing architecture faces substantial chal-lenges,including high latency and energy consumption between memory and processing *** response,in-memory computing has emerged as a promising alternative architecture,enabling computing operations within memory arrays to overcome these *** devices have gained significant attention as key components for in-memory computing due to their high-density arrays,rapid response times,and ability to emulate biological *** these devices,two-dimensional(2D)material-based memristor and memtransistor arrays have emerged as particularly promising candidates for next-generation in-memory computing,thanks to their exceptional performance driven by the unique properties of 2D materials,such as layered structures,mechanical flexibility,and the capability to form *** review delves into the state-of-the-art research on 2D material-based memristive arrays,encompassing critical aspects such as material selection,device perfor-mance metrics,array structures,and potential ***,it provides a comprehensive overview of the current challenges and limitations associated with these arrays,along with potential *** primary objective of this review is to serve as a significant milestone in realizing next-generation in-memory computing utilizing 2D materials and bridge the gap from single-device characterization to array-level and system-level implementations of neuromorphic computing,leveraging the potential of 2D material-based memristive devices.

关键词： 2D materials Memristors Memtransistors Crossbar array In-memory computing

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PEPC: Parallel and Extensible PC Implementation for Causal Structure Learning 23

PEPC: Parallel and Extensible PC Implementation for Causal S...

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7th International Conference on high performance Compilation, computing and Communications, HP3C 2023

作者： Feng, Yongquan Xu, Liyang Huang, Wanrong Yang, Wenjing Lu, Yutong Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9781450399883

Learning the causal relationships among variables from observational data has been a significant problem in statistics and data mining, and one such algorithm is the PC algorithm. However, the existing CPU-based implementations are difficult to support Causal Structure Learning(CSL) on complex observational data due to inefficiency. In this paper, we propose an efficient CPU-based parallel implementation of the PC algorithm, called PEPC. It has an extensible framework, and some optimizations are adopted to reduce the CSL computation of the PC algorithm. Moreover, our PEPC supports efficient multi-thread parallelism. The experimental results demonstrate that PEPC outperforms existing methods in serial efficiency and multi-thread parallelism. In serial tests, our method achieves the maximum speedup ratios of 577.26, 116.74, and 134.68 over three existing approaches, Stable, ***, and Tetrad. In parallel experiments, it achieves 10.66X speedup when running the PC algorithm with 12 threads. © 2023 ACM.

关键词： Learning algorithms

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Bioinspired activation of silent synapses in layered materials for extensible neuromorphic computing

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Journal of Materiomics 2023年第4期9卷 787-797页

作者： Yan Kang Yabo Chen Yinlong Tan Hao Hao Cheng Li Xiangnan Xie Weihong Hua Tian Jiang College of Advanced Interdisciplinary Studies National University of Defense TechnologyChangsha410073China Institute for Quantum Information&State Key Laboratory of High Performance Computing College of ComputerNational University of Defense TechnologyChangsha410073China Institute for Quantum Science and Technology College of ScienceNational University of Defense TechnologyChangsha410073China

Activation of silent synapses is of great significance for the extension of neural plasticity related to learning and *** by the activation of silent synapses via receptor insertion in neural synapses,we propose an efficient method for activating artificial synapses through the intercalation of Sn in layered a-MoO_(3).Sn intercalation is capable of switching on the response of layered a-MoO_(3)to the stimuli of visible and near infrared light by decreasing the *** mimics the receptor insertion process in silent neural *** Sn-intercalated MoO_(3)(Sn-MoO_(3))exhibits persistent photoconductivity due to the donor impurity induced by Sn *** enables the two-terminal Sn-MoO_(3)device promising optoelectronic synapse with an ultrahigh paired pulse facilitation(PPF)up to 199.5%.On-demand activation and tunable synaptic plasticity endow the device great potentials for extensible neuromorphic *** performance of the extensible artificial neural network(ANN)based on the Sn-MoO_(3)synapses are demonstrated in pattern ***,the recognition accuracy increases from 89.7%to 94.8%by activating more nodes into the *** is consistent with the recognition process of physical neural network during brain *** intercalation engineering of MoO_(3)may provide inspirations for the design of high-performance neuromorphic computing architectures.

关键词： Activation of silent synapse Intercalation Layered materials Neuromorphic computing

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Accumulative Time Based Ranking Method to Reputation Evaluation in Information Networks

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Journal of computer science & Technology 2022年第4期37卷 960-974页

作者： Hao Liao Qi-Xin Liu Ze-Cheng Huang Ke-Zhong Lu Chi Ho Yeung Yi-Cheng Zhang National Engineering Laboratory on Big Data System Computing Technology College of Computer Science and SoftwareEngineeringShenzhen UniversityShenzhen 518060China Guangdong Province Key Laboratory of Popular High Performance Computers Shenzhen UniversityShenzhen 518060China Guangdong Province Engineering Center of China-Made High Performance Data Computing System College of ComputerScience and Software EngineeringShenzhen UniversityShenzhen 518060China Institute of Big Data Intelligent Management and Decision Shenzhen UniversityShenzhen 518060China Department of Science and Environmental Studies The Education University of Hong KongHong Kong 999077China Department of Physics University of FribourgFribourg 1700Switzerland

Due to over-abundant information on the Web, information filtering becomes a key task for online users to obtain relevant suggestions and how to extract the most related item is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed Accumulative Time Based Ranking (ATR) algorithm, we take into account the growth record of the network to identify the evolution of the reputation of users and the quality of items, by incorporating two behavior weighting factors which can capture the hidden facts on reputation and quality dynamics for each user and item respectively. Our proposed ATR algorithm mainly combines the iterative approach to rank user reputation and item quality with temporal dependence compared with other reputation evaluation methods. We show that our algorithm outperforms other benchmark ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Therefore, our proposed method has the capability to effectively evaluate user reputation and item quality.

关键词： temporal network behavior dynamics reputation evaluation ranking algorithm

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How to Steal CPU Idle Time When Synchronous I/O Mode Becomes Promising 24

How to Steal CPU Idle Time When Synchronous I/O Mode Becomes...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Wu, Chun-Feng Chang, Yuan-Hao Yang, Ming-Chang Kuo, Tei-Wei Department of Computer Science National Yang Ming Chiao Tung University Taiwan Institue of Information Science Academia Sinica Taiwan Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Computer Science and Information Engineering National Taiwan University Taiwan High Performance and Scientific Computing Center National Taiwan University Taiwan Delta Electronics Taiwan

ISBN: (纸本)9798400706011

The advent of Ultra-Low-Latency storage devices has narrowed the performance gap between storage and CPU in computing platforms, facilitating synchronous I/O adoption. Yet, this approach introduces substantial busy waiting time and underutilizes computing units. To address this, we propose a light-weighted Idle-Time-Stealing (ITS) design. This involves a self-improving thread conducting prefetching for high-priority processes during synchronous I/O, and an I/O-waiting process continuing subsequent instruction executions when justifiable. Another thread, the self-sacrificing thread, proactively switches low-priority process I/O requests from synchronous to asynchronous mode, prioritizing high-priority executions. Experimental results demonstrate the effectiveness of our ITS design in reducing CPU idle time. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Data storage equipment

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Proteins Transcription Factor Prediction Using Graph Neural Networks 19th

Proteins Transcription Factor Prediction Using Graph Neural ...

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19th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2024

作者： Amato, Domenico Calderaro, Salvatore Lo Bosco, Giosué Vella, Filippo Rizzo, Riccardo Department of Mathematics and Computer Science University of Palermo Palermo90123 Italy Institute for High-Performance Computing and Networking National Research Council of Italy Palermo90146 Italy

ISBN: (纸本)9783031897030

A transcription factor is a regulatory protein that modulates the transcriptional velocity of genetic information from DNA to mRNA. This protein orchestrates the activation and deactivation of specific genes, thereby regulating their expression to coincide precisely with requisite phases and quantities throughout the cellular lifecycle. This paper introduces an innovative deep-learning methodology designed to ascertain the presence of transcription factors within protein sequences. This method, through a graph-based representation of the sequence, employs a Graph Neural Network for protein classification. We evaluated this novel approach across four datasets derived from the Swiss-Prot database;we achieved promising results that surpass those obtained through traditional fixed-length encodings and conventional machine-learning techniques and are comparable with state-of-the-art deep models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Transcription factors

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Experimental review on Majorana zero-modes in hybrid nanowires

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science China(Physics,Mechanics & Astronomy) 2021年第10期64卷 38-53页

作者： Ji-Bang Fu Bin Li Xin-Fang Zhang Guang-Zheng Yu Guang-Yao Huang Ming-Tang Deng Institute for Quantum Information&State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China

As the condensed matter analog of Majorana fermion, the Majorana zero-mode is well known as a building block of fault-tolerant topological quantum computing. This review focuses on the recent progress of Majorana experiments, especially experiments about semiconductor-superconductor hybrid devices. We first sketch Majorana zero-mode formation from a bottom-up view,which is more suitable for beginners and experimentalists. Then, we survey the status of zero-energy state signatures reported recently, from zero-energy conductance peaks, the oscillations, the quantization, and the interactions with extra degrees of freedom. We also give prospects of future experiments for advancing one-dimensional semiconductor nanowire-superconductor hybrid materials and devices.

关键词： electronic transport in nanoscale materials and structures quantum wires proximity efect superconducting low-dimensional structures

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Transformers for Interpretable Classification of Histopathological Images 18th

Transformers for Interpretable Classification of Histopath...

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18th International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2023

作者： Amato, Domenico Calderaro, Salvatore Lo Bosco, Giosué Marino, Giuseppe Vella, Filippo Rizzo, Riccardo Department of Mathematics and Computer Science University of Palermo Palermo90123 Italy Institute for High-Performance Computing and Networking National Research Council of Italy Palermo90146 Italy

ISBN: (纸本)9783031907135

Recent studies have shown how machine learning techniques can help and support the analysis of expert physicians’ digitalized biopsy images. In particular, these techniques can improve the process related to some repetitive tasks, provide information, and speed up physician decision-making. Classifiers based on neural networks, particularly those that use Vision Transformers, have proven to be state-of-the-art in several classification tasks. In this paper, we apply vision transformers to classify histological images provided in the BreakHis and GasHisSDB datasets, which collect images from breast and gastric biopsies, respectively. The transformer architecture allows classification performances close to the state-of-the-art, also providing an explainable mechanism based on attention maps that localize and highlight the image regions relevant to the classifier’s decision. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Biopsy

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