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arXiv 2023年

作者： Wu, Haoyi Hui, Wenyang Chen, Yezeng Wu, Weiqi Tu, Kewei Zhou, Yi School of Information Science and Technology ShanghaiTech University China Shanghai Engineering Research Center of Intelligent Vision and Imaging China School of Information Science and Technology University of Science and Technology of China China National Engineering Laboratory for Brain-inspired Intelligence Technology and Application China Key Laboratory of Brain Cognition and Education Sciences Ministry of Education China Shanghai Innovation Center for Processor Technologies China Department of Computer Science and Engineering Shanghai Jiao Tong University China

Mathematical understanding and reasoning are crucial tasks for assessing the capabilities of artificial intelligence (ai). However, existing benchmarks either require just a few steps of reasoning, or only contain a small amount of data in one specific topic, making it hard to analyse ai's behaviour with reference to different problems within a specific topic in detail. In this work, we propose CONIC10K, a challenging math problem dataset on conic sections in Chinese senior high school education. Our dataset contains various problems with different reasoning depths, while only the knowledge from conic sections is required. Since the dataset only involves a narrow range of knowledge, it is easy to separately analyse the knowledge a model possesses and the reasoning ability it has. For each problem, we provide a high-quality formal representation, the reasoning steps, and the final solution. Experiments show that existing large language models, including GPT-4, exhibit weak performance on complex reasoning. We hope that our findings could inspire more advanced techniques for precise natural language understanding and reasoning. Our dataset and codes are available at https://***/whyNLP/Conic10K. © 2023, CC BY.

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Correction: ai content detection in the emerging information ecosystem: new obligations for media and tech companies

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Ethics and Information Technology 2024年第4期26卷 1-2页

作者： Knott, Alistair Pedreschi, Dino Jitsuzumi, Toshiya Leavy, Susan Eyers, David Chakraborti, Tapabrata Trotman, Andrew Sundareswaran, Sundar Baeza-Yates, Ricardo Biecek, Przemyslaw Weller, Adrian Teal, Paul D. Basu, Subhadip Haklidir, Mehmet Morini, Virginia Russell, Stuart Bengio, Yoshua Social Media Governance Project Global Partnership on AI Montreal Canada School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand University of Pisa Pisa Italy Chuo University Tokyo Japan Insight SFI Research Centre for Data Analytics School of Information and Communication University College Dublin Dublin Ireland School of Computing University of Otago Dunedin New Zealand Alan Turing Institute London United Kingdom University College London London United Kingdom Institute for Experiential AI Northeastern University Silicon Valley USA Warsaw University of Technology Warsaw Poland University of Cambridge Cambridge United Kingdom Computer Science and Engineering Department Jadavpur University Kolkata India Artificial Intelligence Institute Tubitak Bilgem Gebze Türkiye Center for Human-Compatible AI UC Berkeley Berkeley USA Mila - Quebec AI Institute Montreal Canada University of Montreal Montreal Canada

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SinCWIm: An imputation method for single-cell RNA sequence dropouts using weighted alternating least squares

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computers in Biology and Medicine 2024年 171卷 108225-108225页

作者： Gong, Lejun Cui, Xiong Liu, Yang Lin, Cai Gao, Zhihong Jiangsu Key Lab of Big Data Security & Intelligent Processing School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Department of Burn Wound Repair and Regenerative Medicine Center The First Affiliated Hospital of Wenzhou Medical University Zhejiang Wenzhou325000 China Zhejiang Engineering Research Center of Intelligent Medicine The First Affiliated Hospital of Wenzhou Medical University Wenzhou325000 China

Background and objectives: Single-cell RNA sequencing (scRNA-seq) provides a powerful tool for exploring cellular heterogeneity, discovering novel or rare cell types, distinguishing between tissue-specific cellular composition, and understanding cell differentiation during development. However, due to technological limitations, dropout events in scRNA-seq can mistakenly convert some entries in the real data to zero. This is equivalent to introducing noise into the data of cell gene expression entries. The data is contaminated, which affects the performance of downstream analyses, including clustering, cell annotation, differential gene expression analysis, and so on. Therefore, it is a crucial work to accurately determine which zeros are due to dropout events and perform imputation operations on them. Methods: Considering the different confidence levels of different zeros in the gene expression matrix, this paper proposes a SinCWIm method for dropout events in scRNA-seq based on weighted alternating least squares (WALS). The method utilizes Pearson correlation coefficient and hierarchical clustering to quantify the confidence of zero entries. It is then combined with WALS for matrix decomposition. And the imputation result is made close to the actual number by outlier removal and data correction operations. Results: A total of eight single-cell sequencing datasets were used for comparative experiments to demonstrate the overall superiority of SinCWIm over state-of-the-art models. SinCWIm was applied to cluster the data to obtain an adjusted RAND index evaluation, and the Usoskin, Pollen and Bladder datasets scored 94.46%, 96.48% and 76.74%, respectively. In addition, significant improvements were made in the retention of differential expression genes and visualization. Conclusions: SinCWIm provides a valuable imputation method for handling dropout events in single-cell sequencing data. In comparison to advanced methods, SinCWIm demonstrates excellent performance in

关键词： RNA

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Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization

arXiv

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arXiv 2023年

作者： Huang, Zhuo Zhu, Miaoxi Xia, Xiaobo Shen, Li Yu, Jun Gong, Chen Han, Bo Du, Bo Liu, Tongliang Syndey Ai Centre The University of Sydney Australia National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China Jd Explore Academy China Department of Automation University of Science and Technology of China China Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China Department of Computer Science Hong Kong Baptist University Hong Kong

Robust generalization aims to tackle the most challenging data distributions which are rare in the training set and contain severe noises, i.e., photon-limited corruptions. Common solutions such as distributionally robust optimization (DRO) focus on the worst-case empirical risk to ensure low training error on the uncommon noisy distributions. However, due to the over-parameterized model being optimized on scarce worst-case data, DRO fails to produce a smooth loss landscape, thus struggling on generalizing well to the test set. Therefore, instead of focusing on the worst-case risk minimization, we propose SharpDRO by penalizing the sharpness of the worst-case distribution, which measures the loss changes around the neighbor of learning parameters. Through worst-case sharpness minimization, the proposed method successfully produces a flat loss curve on the corrupted distributions, thus achieving robust generalization. Moreover, by considering whether the distribution annotation is available, we apply SharpDRO to two problem settings and design a worst-case selection process for robust generalization. Theoretically, we show that SharpDRO has a great convergence guarantee. Experimentally, we simulate photon-limited corruptions using CIFAR10/100 and ImageNet30 datasets and show that SharpDRO exhibits a strong generalization ability against severe corruptions and exceeds well-known baseline methods with large performance gains. Copyright © 2023, The Authors. All rights reserved.

关键词： Optimization

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Integration of Droplet Microfluidic Tools for Single-cell Functional Metagenomics:An engineering Head Start

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Genomics, Proteomics & Bioinformatics 2021年第3期19卷 504-518页

作者： David Conchouso Amani Al-Ma’abadi Hayedeh Behzad Mohammed Alarawi Masahito Hosokawa Yohei Nishikawa Haruko Takeyama Katsuhiko Mineta Takashi Gojobori Department of Industrial and Mechanical Engineering Universidad de las Américas PueblaPuebla 72810Mexico Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Biological and Environmental Sciences and Engineering Division King Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Research Organization for Nano&Life Innovation Waseda UniversityTokyo 162-0041Japan Department of Life Science and Medical Bioscience Waseda UniversityTokyo 162-8480Japan Institute for Advanced Research of Biosystem Dynamics Waseda Research Institute for Science and EngineeringWaseda UniversityTokyo 169-8555Japan Computational Bio Big-Data Open Innovation Laboratory AIST-Waseda UniversityTokyo 169–0072Japan Computer Electricaland Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia

Droplet microfluidic techniques have shown promising outcome to study single cells at high ***,their adoption in laboratories studying“-omics”sciences is still irrelevant due to the complex and multidisciplinary nature of the *** facilitate their use,here we provide engineering details and organized protocols for integrating three droplet-based microfluidic technologies into the metagenomic pipeline to enable functional screening of bioproducts at high ***,a device encapsulating single cells in droplets at a rate of~250 Hz is described considering droplet size and cell ***,we expand on previously reported fluorescence-activated droplet sorting systems to integrate the use of 4 independent fluorescence-exciting lasers(i.e.,405,488,561,and 637 nm)in a single platform to make it compatible with different fluorescence-emitting *** this sorter,both hardware and software are provided and optimized for effortlessly sorting droplets at 60 ***,a passive droplet merger is also integrated into our pipeline to enable adding new reagents to already-made droplets at a rate of 200 ***,we provide an optimized recipe for manufacturing these chips using silicon dry-etching *** of the overall integration and the technical details presented here,our approach allows biologists to quickly use microfluidic technologies and achieve both single-cell resolution and high-throughput capability(>50,000 cells/day)for mining and bioprospecting metagenomic data.

关键词： Droplet microfluidics Single cell Metagenomics Droplet sorter Biotechnology

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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

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Procedia computer science 2023年 218卷 24-34页

作者： Seifedine Kadry Rubén González Crespo Enrique Herrera-Viedma Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Andalusian Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai 602105 Tamil Nadu India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores.

关键词： Breast cancer Thermal imaging Firefly algorithm Feature optimization Classification

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Face Recognition using SIFT and LBPH-based Hybrid Learning Model

Face Recognition using SIFT and LBPH-based Hybrid Learning M...

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International Symposium on Advanced Electrical and Communication Technologies (ISAECT)

作者： Hemanta Kumar Bhuyan Rahul Peddipaga Biswajit Brahma Wael Suliman Vinayakumar Ravi Department of Information Technology and Computer Application Vignan's Foundation for Science Technology & Research (Deemed to be University) Andhra Pradesh India McKesson Corporation San Francisco CA USA Department of Electrical Engineering Prince Mohammad Bin Fahd University Khobar Saudi Arabia Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia

ISBN: (数字)9798331529987

ISBN: (纸本)9798331529994

The face recognition has been focused on object detection from images using various deep learning and machine learning models. This work proposes a deep learning model using multiple testing platforms to identify different face images from either videos or images. Two feature extraction methods, Scale-Invariant Feature Transform (SIFT) and Local Binary Patterns Histogram (LBPH) and a deep learning model (ResNet) is considered in the proposed model. LBPH features and ResNet models are considered for face recognition through videos, whereas the SIFT model is used for images. Since both deep learning and machine learning models have been considered for face recognition, their performance varied as per model when we used different videos and images. We have demonstrated our proposed model using real-time videos and images using the above technical approaches. LBPH efficiently detected and categorize local characteristics with an accuracy of 99.9%, whereas SIFT consistently maintained an accuracy rate of 88.9%. As our comparative observation, we found that the ResNet model performed well compared to other models.

关键词： Deep learning Histograms Accuracy Image recognition Face recognition Lighting Transforms Feature extraction Real-time systems Videos

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Deep Learning Model-based Approach for Lung Cancer Detection

Deep Learning Model-based Approach for Lung Cancer Detection

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International Symposium on Advanced Electrical and Communication Technologies (ISAECT)

作者： Hemanta Kumar Bhuyan Subbarao Peram Wael Suliman Vinayakumar Ravi Biswajit Brahma Department of Information Technology and Computer Application Vignan's Foundation for Science Technology & Research (Deemed to be University) Andhra Pradesh India Department of Electrical Engineering Prince Mohammad Bin Fahd University Khobar Saudi Arabia Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia McKesson Corporation San Francisco CA USA

ISBN: (数字)9798331529987

ISBN: (纸本)9798331529994

Cancer disease detection is a critical task in healthcare systems when different technologies or tools are used to detect the exact disease in a patient. Although various cancer diseases are considered to be analyzed in the healthcare system, lung cancer disease is one of them. A hazardous cancer disease must be detected earlier to save patient life. Traditional methods for lung cancer detection are very tedious. Thus, this paper proposed a deep learning (DL) model with various techniques to design individual models and experiments accordingly. Different techniques such as ResNet 152, DenseNet 169, Xception, and Autoencoders are considered for testing lung cancer disease data set through computed tomography (CT) images. The proposed models have demonstrated impressive performance across various image identification tasks. The model trains various algorithms using a benchmark lung cancer dataset. The experimental comparative analysis is considered for better performance between the proposed and existing models, which helped to determine the appropriate method with the correct model. The DenseNet 169 model has better performance in both training accuracy (94.60%) and testing accuracy (92.00%).

关键词： Training Deep learning Analytical models Computational modeling Lungs Computed tomography Autoencoders Lung cancer Medical services Benchmark testing

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Spatial Attention-Guided Generative Adversarial Network for Synthesizing Contrast-enhanced Computed Tomography Images

Spatial Attention-Guided Generative Adversarial Network for ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Yulin Yang Yinhao Li Qingqing Chen Xian-Hua Han Jing Liu Lanfen Lin Hongjie Hu Yen-Wei Chen Gradate School of Information Science and Engineering Ritsumeikan University Shiga Japan College of Information Science and Engineering Ritsumeikan University Shiga Japan Department of Radiology Sir Run Run Shaw Hospital Zhejiang University School of Medicine Hangzhou China Artificial Intelligence Research Center Yamaguchi University Japan Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China

Compared to non-contrast computed tomography (NC-CT) scans, contrast-enhanced (CE) CT scans provide more abundant information about focal liver lesions (FLLs), which play a crucial role in the FLLs diagnosis. However, CE-CT scans require patient to inject contrast agent into the body, which increase the physical and economic burden of the patient. In this paper, we propose a spatial attention-guided generative adversarial network (SAG-GAN), which can directly obtain corresponding CE-CT images from the patient’s NC-CT images. In the SAG-GAN, we devise a spatial attention-guided generator, which utilize a lightweight spatial attention module to highlight synthesis task-related areas in NC-CT image and neglect unrelated areas. To assess the performance of our approach, we test it on two tasks: synthesizing CE-CT images in arterial phase and portal venous phase. Both qualitative and quantitative results demonstrate that SAG-GAN is superior to existing GANs-based image synthesis methods.

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R-Bench: Are your Large Multimodal Model Robust to Real-world Corruptions?

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IEEE Journal on Selected Topics in Signal Processing 2025年

作者： Li, Chunyi Zhang, Jianbo Zhang, Zicheng Wu, Haoning Tian, Yuan Sun, Wei Lu, Guo Min, Xiongkuo Liu, Xiaohong Lin, Weisi Zhang, Xiao-Ping Zhai, Guangtao Shanghai Jiao Tong University Institute of Image Communication and Network Engineering Shanghai200240 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Shanghai AI Lab Shanghai200232 China East China Normal University School of Communication and Electronic Engineering Shanghai200240 China Shanghai Jiao Tong University John Hopcroft Center Shanghai200240 China Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering TorontoONM5B 2K3 Canada

The outstanding performance of Large Multimodal Models (LMMs) has made them widely applied in vision-related tasks. However, various corruptions in the real world mean that images will not be as ideal as in simulations, presenting significant challenges for the practical application of LMMs. To address this issue, we introduce R-Bench, a benchmark focused on the Real-world Robustness of LMMs. Specifically, we: (a) model the complete link from user capture to LMMs reception, comprising 33 corruption dimensions, including 7 steps according to the corruption sequence, and 7 groups based on low-level attributes;(b) collect reference/distorted image dataset before/after corruption, including 2,970 question-answer pairs with human labeling;(c) propose comprehensive evaluation for absolute/relative robustness and benchmark 20 mainstream LMMs. Results show that while LMMs can correctly handle the original reference images, their performance is not stable when faced with distorted images, and there is a significant gap in robustness compared to the human visual system. We hope that R-Bench will inspire improving the robustness of LMMs, extending them from experimental simulations to the real-world application. © 2007-2012 IEEE.

关键词： ai-Generated Content Image Compression Perceptual Quality Ultra-Low Bitrate

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