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Hexagonal Convolutional Neural Network for Spatial Transcriptomics Classification

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Hexagonal Convolutional Neural Network for Spatial Transcrip...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jing Gao Fa Zhang Kai Hu Xuefeng Cui Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China Institute of Computing Technology Chinese Academy of Sciences Beijing China Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Xiangnan University Chenzhou China School of Medical Technology Beijing Institute of Technology Beijing China School of Computer Science and Technology Shandong University Qingdao China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Recent advances in spatial transcriptomics have enabled the comprehensive measurement of transcriptional profiles while retaining the spatial contextual information. Identifying spatial domains is a critical step in the analysis of spatially resolved transcriptomics. Existing unsupervised methods perform poorly on this task owing to the large amount of noise and dropout events in the transcriptomic profiles. To address this problem, we first extend an unsupervised algorithm to a supervised learning method that can identify useful features and reduce noise hindrance. Second, inspired by the classical convolution in convolutional neural networks (CNNs), we designed a regular hexagonal convolution to compensate for the missing gene expression patterns from adjacent nodes. Compared with the graph convolution in graph neural networks (GNNs), our hexagonal convolution can preserve the relative spatial location information of different nodes in graph-structured data. Third, based on the hexagonal convolution, a novel hexagonal Convolutional Neural Network (hexCNN) is proposed for spatial transcriptomics classification. Finally, we compared the proposed hexCNN with existing methods on the DLPFC dataset. The results show that hexCNN achieves a classification accuracy of 87.2% and an average Rand index (ARI) of 78.2% (1.9% and 3.3% higher than those of GNNs).

关键词： Visualization Convolution Supervised learning Interference Spatial databases Graph neural networks Convolutional neural networks

Study on Gene Splicing Site Recognition Based on Particle Swarm Optimization Twin Support Vector Machine Algorithm for Smart Healthcare

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Wireless Communications and Mobile computing 2023年第1期2023卷

作者： Zhang, Fuquan Wang, Yiou Mei, Peng Dai, Aibing Wang, Bo Liu, Laiyang Xia, Yong Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350117 China Institute of Science and Technology Information Beijing Academy of Science and Technology Beijing100089 China Beijing100089 China Industrial Digital Finance Department Huaxia Bank Beijing100020 China Digital Performance and Simulation Technology Lab Beijing Institute of Technology Beijing100081 China Fuzhou Maternity and Child Health Care Hospital Fuzhou350005 China Fujian Medical University Fuzhou350122 China

Gene splicing site recognition is a very important research topic in smart healthcare. Gene splicing site recognition is of great significance, not only for the large-scale and high-quality computational annotation of genomes but also for the analysis and recognition of the gene sequences evolutionary process. It is urgent to study a reliable and effective algorithm for gene splice site recognition. Traditional Twin Support Vector Machine (TWSVM) algorithm has advantages in solving small-sample, nonlinear, and high-dimensional problems, but it cannot deal with parameter selection well. To avoid the blindness of parameter selection, particle swarm optimization algorithm was used to find the optimal parameters of twin support vector machine. Therefore, a Particle Swarm Optimization Twin Support Vector Machine (PSO-TWSVM) algorithm for gene splicing site recognition was proposed in this paper. The proposed algorithm was compared with traditional Support Vector Machine algorithm, TWSVM algorithm, and Least Squares Support Vector Machine algorithm. The comparison results show that the positive sample recognition rate, negative sample recognition rate, and correlation coefficient (CC) of the proposed algorithm are the best among the four different support vector machine algorithms. The proposed algorithm effectively improves the recognition rate and the accuracy of splice sites. The comparison experiments verify the feasibility of the proposed algorithm. © 2023 Fuquan Zhang et al.

关键词： Genes

A3lign-DFER: Pioneering Comprehensive Dynamic Affective Alignment for Dynamic Facial Expression Recognition with CLIP

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arXiv 2024年

作者： Tao, Zeng Wang, Yan Lin, Junxiong Wang, Haoran Mai, Xinji Yu, Jiawen Tong, Xuan Zhou, Ziheng Yan, Shaoqi Zhao, Qing Han, Liyuan Zhang, Wenqiang Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China School of Information Science and Technology Fudan University Shanghai China Institute of Automation Chinese Academy of Sciences Beijing China Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

The performance of CLIP in dynamic facial expression recognition (DFER) task doesn’t yield exceptional results as observed in other CLIP-based classification tasks. While CLIP’s primary objective is to achieve alignment between images and text in the feature space, DFER poses challenges due to the abstract nature of text and the dynamic nature of video, making lab.l representation limited and perfect alignment difficult. To address this issue, we have designed A3lign-DFER, which introduces a new DFER lab.ling paradigm to comprehensively achieve alignment, thus enhancing CLIP’s suitability for the DFER task. Specifically, our A3lign-DFER method is designed with multiple modules that work together to obtain the most suitable expanded-dimensional embeddings for classification and to achieve alignment in three key aspects: affective, dynamic, and bidirectional. We replace the input lab.l text with a learnable Multi-Dimensional Alignment Token (MAT), enabling alignment of text to facial expression video samples in both affective and dynamic dimensions. After CLIP feature extraction, we introduce the Joint Dynamic Alignment Synchronizer (JAS), further facilitating synchronization and alignment in the temporal dimension. Additionally, we implement a Bidirectional Alignment Training Paradigm (BAP) to ensure gradual and steady training of parameters for both modalities. Our insightful and concise A3lign-DFER method achieves state-of-the-art results on multiple DFER datasets, including DFEW, FERV39k, and MAFW. Extensive ablation experiments and visualization studies demonstrate the effectiveness of A3lign-DFER. The code will be availab.e in the future. © 2024, CC BY.

关键词： Alignment

dugMatting: Decomposed-Uncertainty-Guided Matting

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arXiv 2023年

作者： Wu, Jiawei Zhang, Changqing Li, Zuoyong Fu, Huazhu Peng, Xi Zhou, Joey Tianyi College of Mechanical and Electrical Engineering Fujian Agriculture and Forestry University Fuzhou China College of Intelligence and Computing Tianjin University Tianjin China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China Institute of High Performance Computing Agency for Science Technology and Research Singapore College of Computer Science Sichuan University Chengdu China Singapore

Cutting out an object and estimating its opacity mask, known as image matting, is a key task in image and video editing. Due to the highly ill-posed issue, additional inputs, typically user-defined trimaps or scribbles, are usually needed to reduce the uncertainty. Although effective, it is either time consuming or only suitable for experienced users who know where to place the strokes. In this work, we propose a decomposed-uncertainty-guided matting (dugMatting) algorithm, which explores the explicitly decomposed uncertainties to efficiently and effectively improve the results. Basing on the characteristic of these uncertainties, the epistemic uncertainty is reduced in the process of guiding interaction (which introduces prior knowledge), while the aleatoric uncertainty is reduced in modeling data distribution (which introduces statistics for both data and possible noise). The proposed matting framework relieves the requirement for users to determine the interaction areas by using simple and efficient lab.ling. Extensively quantitative and qualitative results validate that the proposed method significantly improves the original matting algorithms in terms of both efficiency and efficacy. Copyright © 2023, The Authors. All rights reserved.

关键词： Uncertainty analysis

Quantifying robustness to adversarial word substitutions

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arXiv 2022年

作者： Yang, Yuting Huang, Pei Ma, Fei Fei Cao, Juan Zhang, Meishan Zhang, Jian Li, Jintao Key Lab Of Intelligent Information Processing Institute Of Computing Technology Chinese Academy Of Sciences Beijing100190 China University Of Chinese Academy Of Sciences Beijing100049 China Beijing100190 China Laboratory Of Parallel Software And Computational Science ISCAS Beijing100190 China China

Deep-learning-based NLP models are found to be vulnerable to word substitution perturbations. Before they are widely adopted, the fundamental issues of robustness need to be addressed. Along this line, we propose a formal framework to evaluate word-level robustness. First, to study safe regions for a model, we introduce robustness radius which is the boundary where the model can resist any perturbation. As calculating the maximum robustness radius is computationally hard, we estimate its upper and lower bound. We repurpose attack methods as ways of seeking upper bound and design a pseudo-dynamic programming algorithm for a tighter upper bound. Then verification method is utilized for a lower bound. Further, for evaluating the robustness of regions outside a safe radius, we reexamine robustness from another view: quantification. A robustness metric with a rigorous statistical guarantee is introduced to measure the quantification of adversarial examples, which indicates the model's susceptibility to perturbations outside the safe radius. The metric helps us figure out why state-of-the-art models like BERT can be easily fooled by a few word substitutions, but generalize well in the presence of real-world noises. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamic programming

Staggered Grid Scheme for the FFT-Based Methods

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Chinese Journal of Electronics 2019年第5期28卷 1066-1072页

作者： XIE Jiaye KONG Weibin PANG Lili SONG Weiju HUANG Zhixiang WU Xianliang Industrial Center Nanjing Institute of Technology State Key Laboratory of Millimeter Waves College of Information Engineering Yancheng Institute of Technology Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University

A staggered grid scheme is proposed to reduce both the total memory requirement and the CPU time of generating the corrected near matrix in the FFTbased methods. Two sets of Cartesian grids are used to project the source points and the field points, *** proposed method does not lower the efficiency of computing far matrix-vector products, compared with the traditional uniform Cartesian grid scheme. Some numerical experiments are provided to demonstrate both the correctness and the efficiency of the proposed method.

关键词： Green’s function Singularity RFG-FFT Staggered grid scheme Corrected near matrix

Notmetoo: An Autonomous Agent of Preventing Sexual Harassment and Crime

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SSRN

SSRN 2021年

作者： Qiaojuan, Huang Luo, Xudong Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Guangxi Key Lab of MultiSource Information Mining & Security School of Computer Science and Engineering Guangxi Normal University Guilin China

Nowadays more and more cars are around us, offering us great convenience in daily life. For example, Uber taxis in western countries and Didi taxis in China allow passengers to hail a ride anytime, anywhere, cheaper and convenient. However, many female passengers have been sexually harassed and even killed by evil taxi drivers in recent years. As a result, females are so afraid of taking a taxi out. Thus, in this paper, we develop an autonomous agent system to protect females (predominantly female passengers) from sexual Harassment and crime. Specifically, we first build the keyword library of criminal trends based on similar cases reported in the news. Then our system recognises a driver’s crime tendency through what the driver talks about with a female passenger. Once our system recognises a criminal tendency, it automatically takes the corresponding warning or makes distress calls to the police when necessary. Finally, we do many experiments to evaluate our system, showing that the system is effective in crime prevention. © 2021, The Authors. All rights reserved.

关键词： Crime

The Minority Matters: A Diversity-Promoting Collab.rative Metric Learning Algorithm

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arXiv 2022年

作者： Bao, Shilong Xu, Qianqian Yang, Zhiyong He, Yuan Cao, Xiaochun Huang, Qingming State Key Laboratory of Information Security Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China Alibaba Group China School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University China Key Laboratory of Big Data Mining and Knowledge Management CAS China Peng Cheng Laboratory China

Collab.rative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collab.rative Filtering. Following the convention of RS, existing methods exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, we argue that the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collab.rative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to include a multiple set of representations for each user in the system. Based on this embedding paradigm, user preference toward an item is aggregated from different embeddings by taking the minimum item-user distance among the user embedding set. Furthermore, we observe that the diversity of the embeddings for the same user also plays an essential role in the model. To this end, we propose a diversity control regularization term to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could generalize well to unseen test data by tackling the challenge of the annoying operation that comes from the minimum value. Experiments over a range of benchmark datasets speak to the efficacy of DPCML. © 2022, CC BY-NC-SA.

关键词： Embeddings

High information Density and Low Coverage Data Storage in DNA with Efficient Channel Coding Schemes

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arXiv 2024年

作者： Ding, Yi He, Xuan Nguyen, Tuan Thanh Song, Wentu Yakhini, Zohar Yaakobi, Eitan Pan, Linqiang Tang, Xiaohu Cai, Kui Information Coding and Transmission Key Lab of Sichuan Province Southwest Jiaotong University Sichuan Chengdu611756 China Cluster Singapore University of Technology and Design 487372 Singapore Faculty of Computer Science Technion - Israel Institute of Technology Haifa3200003 Israel School of Computer Science RUNI Herzliya4615200 Israel Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

DNA-based data storage has been attracting significant attention due to its extremely high data storage density, low power consumption, and long duration compared to conventional data storage media. Despite the recent advancements in DNA data storage technology, significant challenges remain. In particular, various types of errors can occur during the processes of DNA synthesis, storage, and sequencing, including substitution errors, insertion errors, and deletion errors. Furthermore, the entire oligo may be lost. In this work, we report a DNA-based data storage architecture that incorporates efficient channel coding schemes, including different types of error-correcting codes (ECCs) and constrained codes, for both the inner coding and outer coding for the DNA data storage channel. We also carried out large scale experiments to validate our proposed DNA-based data storage architecture. Specifically, 1.61 and 1.69 MB data were encoded into 30,000 oligos each, with information densities of 1.731 and 1.815, respectively. It has been found that the stored information can be fully recovered without any error at average coverages of 4.5 and 6.0, respectively. This experiment achieved the highest net information density and lowest coverage among existing DNA-based data storage experiments (with standard DNA), with data recovery rates and coverage approaching theoretical optima. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel coding