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arXiv 2021年

作者： Li, Yunxiang Li, Jingxiong Dan, Ruilong Wang, Shuai Jin, Kai Zeng, Guodong Wang, Jun Pan, Xiangji Zhang, Qianni Zhou, Huiyu Jin, Qun Wang, Li Wang, Yaqi College of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Artificial Intelligence and Biomedical Image Analysis Lab Westlake University Hangzhou China School of Mechanical Electrical and Information Engineering Shandong University Weihai China Department of Ophthalmology Hospital of Zhejiang University Hangzhou China sitem Center for Translational Medicine and Biomedical Entrepreneurship University of Bern Bern Switzerland School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom School of Computing and Mathematical Sciences University of Leicester United Kingdom Department of Human Informatics and Cognitive Sciences Faculty of Human Sciences Waseda University Tokyo Japan Department of Radiology and Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill United States College of Media Engineering Communication University of Zhejiang Hangzhou China

Accurate segmentation is a crucial step in medical image analysis and applying supervised machine learning to segment the organs or lesions has been substantiated effective. However, it is costly to perform data annotation that provides ground truth labels for training the supervised algorithms, and the high variance of data that comes from different domains tends to severely degrade system performance over cross-site or cross-modality datasets. To mitigate this problem, a novel unsupervised domain adaptation (UDA) method named dispensed Transformer network (DTNet) is introduced in this paper. Our novel DTNet contains three modules. First, a dispensed residual transformer block is designed, which realizes global attention by dispensed interleaving operation and deals with the excessive computational cost and GPU memory usage of the Transformer. Second, a multi-scale consistency regularization is proposed to alleviate the loss of details in the low-resolution output for better feature alignment. Finally, a feature ranking discriminator is introduced to automatically assign different weights to domain-gap features to lessen the feature distribution distance, reducing the performance shift of two domains. The proposed method is evaluated on large fluorescein angiography (FA) retinal nonperfusion (RNP) cross-site dataset with 676 images and a wide used cross-modality dataset from the MM-WHS challenge. Extensive results demonstrate that our proposed network achieves the best performance in comparison with several state-of-the-art techniques. © 2021, CC BY.

关键词： Supervised learning

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Slow electron-phonon relaxation controls the dynamics of the superconducting resistive transition

arXiv

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arXiv 2022年

作者： Baeva, E.M. Kolbatova, A.I. Titova, N.A. Saha, S. Boltasseva, A. Bogdanov, S. Shalaev, V.M. Semenov, A.V. Levchenko, A. Goltsman, G.N. Khrapai, Vadim S. Moscow Pedagogical State University Moscow119435 Russia National Research University Higher School of Economics 20 Myasnitskaya Street Moscow101000 Russia Laboratory of Photonic Gas University of Science and Technology MISIS Moscow119049 Russia Birck Nanotechnology Center Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Holonyak Micro and Nanotechnology Lab University of Illinois at Urbana-Champaign UrbanaIL61801 United States Illinois Quantum Information Science and Technology Center University of Illinois at Urbana-Champaign UrbanaIL61801 United States Moscow Institute of Physics and Technology Dolgoprudny141701 Russia Department of Physics University of Wisconsin-Madison Madison Wisconsin MadisonWI53706 United States Russian Quantum Center Moscow Russia Osipyan Institute of Solid State Physics Russian Academy of Sciences Chernogolovka Russia

We investigate the temporal and spatial scales of resistance fluctuations (R-fluctuations) at the superconducting resistive transition accessed through voltage fluctuations measurements in thin epitaxial TiN films. This material is characterized by a slow electron-phonon relaxation, which puts it far beyond the applicability range of the textbook scenario of superconducting fluctuations. The measured Lorentzian spectrum of the R-fluctuations identifies their correlation time, which is nearly constant across the transition region and has no relation to the conventional Ginzburg-Landau time scale. Instead, the correlation time coincides with the energy relaxation time determined by a combination of the electron-phonon relaxation and the relaxation via diffusion into reservoirs. Our data is quantitatively consistent with the model of spontaneous temperature fluctuations and highlight the lack of understanding of the resistive transition in materials with slow electron-phonon relaxation. Copyright © 2022, The Authors. All rights reserved.

关键词： Electron-phonon interactions

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Density peaks based clustering for single-cell interpretation via multikernel learning

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Procedia computer Science 2019年 147卷 71-76页

作者： Samina Kausar Rashid Mehmood Muhammad Shahid Iqbal Rongfang Bie Shujaat Ali Yasir Shabir School of Computer Engineering and Science Shanghai University Shanghai 200444 China Department of Software Engineering University of Kotli AJ & K Pakistan School of Computer Science and Technology Anhui University Hefei China College of Information Science and Technology Beijing Normal University Beijing 100875 China

The development in single-cell technology has enabled to quantify the high throughput gene expressions of individual cell, and it became possible to discover heterogeneity at cell level. To detect heterogeneity within cell population remains challenging in presence of outliers, biological noise, and dropouts. SIMLR (single-cell interpretation via multikernel learning) has been proposed to measure cell to cell similarity, dimensional reduction, clustering, and visualization of scRNA-seq data. SIMLR uses K-means to organize the cells into the predefined number of types, which is a significant drawback of SIMLR toward adaptive analysis of scRNA-seq data. In this paper, we introduced density peaks based clustering for single-cell interpretation via multikernel learning (DP-SIMLR), an adaptive approach to discover biological meaningful heterogeneity within the individual cell population. The DP-SIMLR is an extension of SIMLR, where the concept of density peaks is employed to discover heterogeneity within the cell population, adaptively. We have evaluated the DP-SIMLR on four scRNA-seq datasets and the results are compared with SIMLR.

关键词： Data mining single cell RNA-seq clustering

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Efficient spatialtemporal context modeling for action recognition?

arXiv

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arXiv 2021年

作者： Cao, Congqi Lu, Yue Zhang, Yifan Jiang, Dongmei Zhang, Yanning Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Lab on Speech and Image Information Processing School of Computer Science Northwestern Poly-technical University Xi'an710129 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing100190 China

Contextual information plays an important role in action recognition. Local operations have difficulty to model the relation between two elements with a long-distance interval. However, directly modeling the contextual information between any two points brings huge cost in computation and memory, especially for action recognition, where there is an additional temporal dimension. Inspired from 2D criss-cross attention used in segmentation task, we propose a recurrent 3D criss-cross attention (RCCA-3D) module to model the dense long-range spatiotemporal contextual information in video for action recognition. The global context is factorized into sparse relation maps. We model the relationship between points in the same line along the direction of horizon, vertical and depth at each time, which forms a 3D criss-cross structure, and duplicate the same operation with recurrent mechanism to transmit the relation between points in a line to a plane finally to the whole spatiotemporal space. Compared with the non-local method, the proposed RCCA-3D module reduces the number of parameters and FLOPs by 25% and 30% for video context modeling. We evaluate the performance of RCCA-3D with two latest action recognition networks on three datasets and make a thorough analysis of the architecture, obtaining the optimal way to factorize and fuse the relation maps. Comparisons with other state-of-the-art methods demonstrate the effectiveness and efficiency of our model. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Discovering high utility-occupancy patterns from uncertain data

arXiv

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arXiv 2020年

作者： Chen, Chien-Ming Chen, Lili Gan, Wensheng Qiu, Lina Ding, Weiping College of Computer Science and Engineering Shandong University of Science and Technology Qingdao Shandong266590 China College of Cyber Security Jinan University Guangzhou Guangdong510632 China School of Software South China Normal University Foshan Guangdong528200 China School of Information Science and Technology Nantong University Nantong Jiangsu226019 China

It is widely known that there is a lot of useful information hidden in big data, leading to a new saying that"data is money." Thus, it is prevalent for individuals to mine crucial information for utilization in many real-world applications. In the past, studies have considered frequency. Unfortunately, doing so neglects other aspects, such as utility, interest, or risk. Thus, it is sensible to discover high-utility itemsets (HUIs) in transaction databases while utilizing not only the quantity but also the predefined utility. To find patterns that can represent the supporting transaction, a recent study was conducted to mine high utility-occupancy patterns whose contribution to the utility of the entire transaction is greater than a certain value. Moreover, in realistic applications, patterns may not exist in transactions but be connected to an existence probability. In this paper, a novel algorithm, called High-Utility-Occupancy Pattern Mining in Uncertain databases (UHUOPM), is proposed. The patterns found by the algorithm are called Potential High Utility Occupancy Patterns (PHUOPs). This algorithm divides user preferences into three factors, including support, probability, and utility occupancy. To reduce memory cost and time consumption and to prune the search space in the algorithm as mentioned above, probability-utility-occupancy list (PUO-list) and probability-frequency-utility table (PFU-table) are used, which assist in providing the downward closure property. Furthermore, an original tree structure, called support count tree (SC-tree), is constructed as the search space of the algorithm. Finally, substantial experiments were conducted to evaluate the performance of proposed UHUOPM algorithm on both real-life and synthetic datasets, particularly in terms of effectiveness and efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Probability

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Utilization of Feature-Model in Polio Eradication using ICT in Healthcare

Utilization of Feature-Model in Polio Eradication using ICT ...

引用

International Conference on Wavelet Active Media technology and information Processing (ICWAMTIP)

作者： Huaiyu Wen Amin Ul Haq Muhammad Inamullah Sana Ullah Gulzar Alam Institute of Information Science and Engineering Chengdu University Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu Department of Computer Science and &Information Technology University of Malakand Lower Dir Pakistan School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu Department of Information and Computer Science KFUPM Saudi Arabia

ISBN: (数字)9781728142425

ISBN: (纸本)9781728142432

Poliomyelitis is a disease which is spread through virus called enter virus. Its treatment is difficult but can be trim down through prevention strategies like on proper vaccination and management. For this purpose community awareness and mobilization is much needed factor, This disease is eradicated by the develop countries but still present in under develop countries due to the poor hygiene life style and lack of proper awareness about polio disease. Currently, Pakistan, Nigeria and Afghanistan are facing huge challenges to eradicate polio. It mostly affect children under age five and cause paraplegics condition to some part of the body, to cope with this problem a feature model has been proposed to resolve most of the network management issues required to communicate among the polio eradication health centers, like collecting the exact number of affected, missing and refusal children, launching a campaign in a proper hierarchical approach.

关键词： Diseases Vaccines Immune system Analytical models Viruses (medical) Pediatrics Mathematical model

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Preface

Lecture Notes in Electrical Engineering

引用

Lecture Notes in Electrical engineering 2022年 808 LNEE卷 v页

作者： Liu, Qi Liu, Xiaodong Chen, Bo Zhang, Yiming Peng, Jiansheng School of Computer and Software Nanjing University of Information Science and Technology Nanjing Jiangsu China School of Computing Edinburgh Napier University Edinburgh United Kingdom State Key Laboratory of Radar Signal Processing Xidian University Xi’an Shaanxi China School of Civil Engineering and Transportation Hebei University of Technology Tianjin Tianjin China Hechi Universtiy Guangxi Hechi China

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Learning spatio-appearance memory network for high-performance visual tracking

arXiv

引用

arXiv 2020年

作者： Xie, Fei Yang, Wankou Zhang, Kaihua Liu, Bo Xue, Wanli Zuo, Wangmeng School of Automation Southeast University Nanjin210096 China Nanjing University of Information Science and Technology Nanjing China JD Finance America Corporation Mountain ViewCA94089 United States School of Computer Science and Engineering Tianjin University of Technology Tianjin China School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Peng Cheng Lab Shenzhen China

Existing visual object tracking usually learns a bounding-box based template to match the targets across frames, which cannot accurately learn a pixel-wise representation, thereby being limited in handling severe appearance variations caused by large-scale deformation, severe scale variation, and heavy occlusion, etc. To address these issues, much effort has been made on segmentation-based tracking, which learns a pixel-wise object-aware template and can achieve higher accuracy than bounding-box template based tracking. However, existing segmentation-based trackers are ineffective in learning the spatio-temporal correspondence across frames due to no use of the rich temporal information. To overcome this issue, this paper presents a novel segmentation-based tracking architecture, which is equipped with a spatio-appearance memory network to learn accurate spatio-temporal correspondence. Among it, an appearance memory network explores spatio-temporal non-local similarity to learn the dense correspondence between the segmentation mask and the current frame, which can effectively model long-range dependency to capture stable appearance information. Meanwhile, a spatial memory network is modeled as discriminative correlation filter to learn the mapping between feature map and spatial map. The appearance memory network helps to filter out the noisy samples in the spatial memory network while the latter provides the former with more accurate target geometrical center. This mutual promotion greatly boosts the tracking performance. Without bells and whistles, our simple-yet-effective tracking architecture sets new state-of-the-arts on the VOT2016, VOT2018, VOT2019, GOT-10K, TrackingNet, and VOT2020 benchmarks, respectively, especially achieving the EAO scores of 0.535 and 0.506 respectively on VOT2016 and VOT2018. Besides, our tracker outperforms the leading segmentation-based trackers SiamMask and D3S on two video object segmentation benchmarks DAVIS16 and DAVIS17 by a larg

关键词： Pixels

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Bilingual Word Embedding with Sentence Combination CNN for 1-to-N Sentence Alignment 20

Bilingual Word Embedding with Sentence Combination CNN for 1...

引用

Proceedings of the 4th International Conference on Natural Language Processing and information Retrieval

作者： Xinyuan Ren Xiangling Fu Xuesi Zhou Chunsheng Liu Songfeng Gao Lei Peng School of Software Engineering Beijing University of Posts and Telecommunications Beijing China and Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China Department of Electronic Engineering Tsinghua University Hai Dian Beijing China and Multimedia Signal and Intelligent Information Processing Lab Tsinghua University Hai Dian Beijing China BUPT and Huarong Joint lab of Smart Finance Beijing P. R. China and HuaRong RongTong (Beijing) Technology Co. Ltd Beijing P. R. China

ISBN: (纸本)9781450377607

Sentence alignment, as one of the most active and fundamental tasks in the field of natural language processing (NLP), is usually realized in two categories of methods. One is traditional methods which are firstly proposed, the other, which are adopted later, is based on the Neural Network method. Presently, under the limitation that the existing mainstream data corpora are mostly in the form of 1-to-1, the alignment models with relatively good performance mainly apply to the cases of 1-to-1 sentence alignment. However, under the circumstance that a sentence contains too much information, 1-to-N sentence alignment can actually have a better effect on sentence translation tasks, compared with the 1-to-1 form, since it is more flexible and can reduce the complexity of the original sentence. As a result, we attempt to exploit neural networks with relatively good performance in the cases of 1-to-1 to fit in the cases of 1-to-N. In this paper, a novel 1-N Bilingual word Embedding with Sentence Combination CNN Improved Framework (1-NBESCC) is proposed in order to align 1-to-N sentences more precisely. Experiments show that our proposed model performs as good as the traditional methods such as BLEUALIGN in 1-to-1 situation, but much better in 1-to-N situation.

关键词： Convolutional Neural Network

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Enhancing job recommendations with LLM-based resume completion: A behavior-denoised alignment approach

引用

information Processing & Management 2025年第6期62卷

作者： Chen Zhu Xiao Hu Han Wu Chuan Qin Hengshu Zhu Hui Xiong The School of Management University of Science and Technology of China Hefei 230000 Anhui China Career Science Lab BOSS Zhipin 100015 Beijing China University of Science and Technology of China Hefei 230000 Anhui China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei 230601 Anhui China PBC School of Finance Tsinghua University Beijing 100083 China Computer Network Information Center Chinese Academy of Sciences 100083 Beijing China Thrust of Artificial Intelligence The Hong Kong University of Science and Technology (Guangzhou) Guangzhou 510000 Guangdong China

Personalized job recommender system is pivotal in connecting job-seekers with suitable jobs in online recruitment services, significantly impacting the efficiency of the recruitment process. Despite the remarkable progress of existing job recommendation algorithms, they encounter challenges due to the delayed availability of updated resumes, leading to intricacies in profiling job-seeker preferences. Indeed, recent advancements in Large Language Models (LLMs) for text generation offer a straightforward solution through automatic resume completion for job-seekers. This enables a model-agnostic method to tackle the problem of outdated resumes in job recommendations. To this end, here we propose a user behavior-based preference alignment framework for fine-tuning LLMs to benefit job recommendations through resume completion. Meanwhile, to mitigate noise influence in behavioral data (i.e., bias and variance), we creatively propose a noise-robust LLM alignment method, named Denoised Direct Preference Optimization (Denoised DPO). This method can effectively disentangle genuine user preferences from noisy behavioral data. Specifically, we first design a novel reward function for preference estimation by combining an LLM-based component for real user preference with a regression model for bias disentanglement. Moreover, we develop a Thurstonian-style model for job-seekers’ preference modeling to stabilize data reliability amidst behavior variances. Finally, to evaluate our approach, we have conducted extensive offline/online experiments. In offline experiments, we especially have constructed real-world datasets, which contains more than 1 million users, 40 million jobs, and 500 million interaction records, from one of the largest recruitment platforms in China. And we evaluate our methods on two classic recommendation paradigms and the results proves our method can bring 1%–6% improvement among various metrics (i.e. AUC, NDCG, HR, and MAP). In online experiments, we deploye

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