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Noise-Tolerant Learning for Audio-Visual Action Recognition

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arXiv 2022年

作者： Han, Haochen Zheng, Qinghua Luo, Minnan Miao, Kaiyao Tian, Feng Chen, Yan The National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an710049 China The School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China The Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Ministry of Education Xi’an710049 China The School of Cyber Science and Engineering Xi’an Jiaotong University Xi’an710049 China

Recently, video recognition is emerging with the help of multi-modal learning, which focuses on integrating distinct modalities to improve the performance or robustness of the model. Although various multi-modal learning methods have been proposed and offer remarkable recognition results, almost all of these methods rely on high-quality manual annotations and assume that modalities among multi-modal data provide semantically relevant information. Unfortunately, the widely used video datasets are usually coarse-annotated or collected from the Internet. Thus, it inevitably contains a portion of noisy labels and noisy correspondence. To address this challenge, we use the audio-visual action recognition task as a proxy and propose a noise-tolerant learning framework to find anti-interference model parameters against both noisy labels and noisy correspondence. Specifically, our method consists of two phases that aim to rectify noise by the inherent correlation between modalities. First, a noise-tolerant contrastive training phase is performed to make the model immune to the possible noisy-labeled data. Despite the benefits brought by contrastive training, it would overfit the noisy correspondence and thus provide false supervision. To alleviate the influence of noisy correspondence, we propose a cross-modal noise estimation component to adjust the consistency between different modalities. As the noisy correspondence existed at the instance level, we further propose a category-level contrastive loss to reduce its interference. Second, in the hybrid-supervised training phase, we calculate the distance metric among features to obtain corrected labels, which are used as complementary supervision to guide the training. Furthermore, due to the lack of suitable datasets, we establish a benchmark of real-world noisy correspondence in audio-visual data by relabeling the Kinetics dataset. Extensive experiments on a wide range of noisy levels demonstrate that our method significant

关键词： Modal analysis

B-doped MCP detector developed for neutron resonance imaging at Back-n white neutron source

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Nuclear science and Techniques 2024年第8期35卷 1-11页

作者： Li, Qiang Wang, Li-Jiao Tang, Jing-Yu Qiu, Xiang-Biao Chen, Zhen Zhao, Mao-Yuan Ning, Chang-Jun Pan, Kai Xu, Wei Li, Tao Lu, Su-Peng Yi, Han Fan, Rui-Rui Feng, Chang-Qing Zhang, Rong Sun, Xiao-Yang An, Qi Bai, Hao-Fan Bai, Jiang-Bo Bao, Jie Cao, Ping Chen, Qi-Ping Chen, Yong-Hao Cui, Zeng-Qi Fan, An-Chuan Feng, Fan-Zhen Gu, Min-Hao Han, Chang-Cai Han, Zi-Jie He, Guo-Zhu He, Yong-Cheng Hong, Yang Hu, Yi-Wei Huang, Han-Xiong Jiang, Wei Jiang, Zhi-Jie Jin, Zheng-Yao Kang, Ling Li, Bo Li, Gong Li, Xiao Li, Yang Liu, Jie Liu, Rong Liu, Shu-Bin Liu, Yi-Na Luan, Guang-Yuan Ren, Jie Ren, Zhi-Zhou Ruan, Xi-Chao Song, Zhao-Hui Sun, Kang Tan, Zhi-Xin Tang, Sheng-Da Wang, Jin-Cheng Wang, Peng-Cheng Wang, Zhao-Hui Wen, Zhong-Wei Wu, Xiao-Guang Wu, Xuan Xia, Cong Yu, Yong-Ji Zhang, Guo-Hui Zhang, Hang-Chang Zhang, Lin-Hao Zhang, Qi-Wei Zhang, Xian-Peng Zhang, Yu-Liang Zhang, Yue Zhang, Zhi-Yong Zhou, Zhi-Hao Zhu, Ke-Jun Zou, Chong Institute of High Energy Physics Chinese Academy of Sciences (CAS) Beijing China Spallation Neutron Source Science Center Dongguan China State Key Laboratory of Particle Detection and Electronics Institute of High Energy Physics Chinese Academy of Sciences Beijing China Dongguan Songshan Lake Future School Dongguan China School of Nuclear Science and Technology University of Science and Technology of China Hefei China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China Hefei China North Night Vision Science and Technology (Nanjing) Research Institute Co. Ltd Nanjing China Department of Modern Physics University of Science and Technology of China Hefei China State Key Laboratory of Nuclear Physics and Technology School of Physics Peking University Beijing China Institute of Modern Physics Fudan University Shanghai China Key Laboratory of Nuclear Data China Institute of Atomic Energy Beijing China China Academy of Engineering Physics Institute of Nuclear Physics and Chemistry Mianyang China USTC archaeometry lab University of Science and Technology of China Hefei China Northwest Institute of Nuclear Technology Xi’an China University of Chinese Academy of Sciences Beijing China China Shipbuilding Trading Co. Ltd Beijing China

Neutron resonance imaging (NRI) has recently emerged as an appealing technique for neutron radiography. Its complexity surpasses that of conventional transmission imaging, as it requires a high demand for both a neutron source and detector. Consequently, the progression of NRI technology has been sluggish since its inception in the 1980s, particularly considering the limited studies analyzing the neutron energy range above keV. The white neutron source (Back-n) at the China Spallation Neutron Source (CSNS) provides favorable beam conditions for the development of the NRI technique over a wide neutron energy range from eV to MeV. Neutron-sensitive microchannel plates (MCP) have emerged as a cutting-edge tool in the field of neutron detection owing to their high temporal and spatial resolutions, high detection efficiency, and low noise. In this study, we report the development of a $$^{10}$$ B-doped MCP detector, along with its associated electronics, data processing system, and NRI experiments at the Back-n. Individual heavy elements such as gold, silver, tungsten, and indium can be easily identified in the transmission images by their characteristic resonance peaks in the 1–100 eV energy range; the more difficult medium-weight elements such as iron, copper, and aluminum with resonance peaks in the 1–100 keV energy range can also be identified. In particular, results in the neutron energy range of dozens of keV (Aluminum) are reported here for the first time.

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Active self-paced learning for cost-effective and progressive face identification

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arXiv 2017年

作者： Lin, Liang Wang, Keze Meng, Deyu Zuo, Wangmeng Zhang, Lei School of Data and Computer Science Sun Yat-sen University Guangzhou China Engineering Research Center Advanced Computing Engineering Software of Ministry of Education China School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi'an Jiaotong University China School of Computer Science and Technology Harbin Institute of Technology Harbin China Dept. of Computing Hong Kong Polytechnic University Hong Kong

This paper aims to develop a novel cost-effective framework for face identification, which progressively maintains a batch of classifiers with the increasing face images of different individuals. By naturally combining two recently rising techniques: active learning (AL) and self-paced learning (SPL), our framework is capable of automatically annotating new instances and incorporating them into training under weak expert recertification. We first initialize the classifier using a few annotated samples for each individual, and extract image features using the convolutional neural nets. Then, a number of candidates are selected from the unannotated samples for classifier updating, in which we apply the current classifiers ranking the samples by the prediction confidence. In particular, our approach utilizes the high-confidence and low-confidence samples in the self-paced and the active user-query way, respectively. The neural nets are later fine-tuned based on the updated classifiers. Such heuristic implementation is formulated as solving a concise active SPL optimization problem, which also advances the SPL development by supplementing a rational dynamic curriculum constraint. The new model finely accords with the "instructor-student-collaborative" learning mode in human education. The advantages of this proposed framework are two-folds: i) The required number of annotated samples is significantly decreased while the comparable performance is guaranteed. A dramatic reduction of user effort is also achieved over other state-of-the-art active learning techniques. ii) The mixture of SPL and AL effectively improves not only the classifier accuracy compared to existing AL/SPL methods but also the robustness against noisy data. We evaluate our framework on two challenging datasets, which include hundreds of persons under diverse conditions, and demonstrate very promising results. Please find the code of this project at: http://***/projects/aspl/ Copyright © 201

关键词： Cost effectiveness

Robust background exclusion for salient object detection

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Robust background exclusion for salient object detection

International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Yuming Hu Quan Zhou Guangwei Gao Zhijun Yao Weihua Ou Longin Jan Latecki Key Lab of Ministry of Education for Broad Band Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Nanjing China Nanjing University of Posts and Telecommunications Nanjing CN The 723 Institute of China Shipbuilding Industry Corporation Yangzhou China School of Big Data and Computer Science Guizhou Normal University Guiyang China Department of Computer and Information Science Temple University Philadelphia USA

ISBN: (纸本)9781509028610

Recently, investigating boundary prior to aid other low-level image cues, have gained great attention in salient object detection. Although the salient regions are mostly located in the image center, the inverse might not necessarily be true. In addition, such kind of center-bias assumption is very simple and fragile, especially when salient regions often touch the image boundary or the images are with complex background (e.g., small scale and high contrast patterns). To this end, this paper presents a new framework to address these two issues. First, we propose a robust background descriptor, called boundary connectivity, which is calculated to measure how heavily a region is connected to image boundary. This measure has an intuitive geometrical interpretation that are absent in previous saliency formulation. In order to reduce the effect of imprecise object boundary, we also propose a multiple layer over-segmentation framework to integrate multiple low-level cues, including our background measure, to highlight clean and uniform saliency maps. The experiment results demonstrate that our method achieves state-of-the-art results on MSRA-1000 datasets.

关键词： Image color analysis Object detection Image segmentation Visualization Robustness Image resolution Computers

Seasonal Variation, Gas/Particle Partitioning, Sources and Risks of Polycyclic Aromatic Compounds in Xi’an, Northwest China

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SSRN

SSRN 2023年

作者： Zhang, Jianing Wei, Chong Han, Yongming Lei, Dewen State Key Laboratory of Loess and Quaternary Geology Key Lab of Aerosol Chemistry and Physics Institute of Earth Environment Chinese Academy of Sciences Xi’an710061 China Shanghai Carbon Data Research Center CAS Key Laboratory of Low-Carbon Conversion Science and Engineering Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai201210 China Department of Environmental Science and Technology School of Human Settlements and Civil Engineering Xi’an Jiaotong University Xi’an710049 China University of Chinese Academy of Sciences Beijing100049 China

Polycyclic aromatic compounds (PACs), such as polycyclic aromatic hydrocarbons (PAHs,) and their derivatives (oxygenated PAHs, nitrated PAHs and N-heterocyclic PAHs), have received extensive attention due to their carcinogenicity, teratogenicity, and mutagenicity to humans. In this study, gaseous and PM2.5-bound PACs were analysed in four seasons in 2016 in Xi’an and further evaluated for their gas/particle partition, sources, and health risks. Annual average concentrations (gaseous + particulate) of Σ27PAHs, Σ12OPAHs, Σ14NPAHs, and Σ4AZAs were 445.1±428.0, 45.6±62.4, 4.9±6.5, and 6.2±8.6 ng m−3, respectively, with the highest values in winter and the lowest values in summer. Low-molecular-weight PACs mainly occur in gaseous and high-molecular-weight PACs partition in particles, and the major mechanism is absorption into the atmospheric particles. PACs significantly correlated with combustion-derived air pollutants and carbon fractions, suggesting their common sources and transport pathways. The concentration ratios indicated mixed sources of fossil fuel combustion and biomass burning, while the positive matrix factorization model suggested that coal combustion, vehicle exhaust, and biomass burning accounted for 30.0%, 25.8% and 23.6% of PAHs, respectively, and direct emissions contributed 56.1% of OPAHs. The PACs estimated annual lifetime lung cancer risk is 2.67 × 10−5, exceeding the value advocated by the World Health Organization (10−5). © 2023, The Authors. All rights reserved.

关键词： Polycyclic aromatic hydrocarbons

Non-reciprocal Cavity Polariton with Atoms Strongly Coupled to Optical Cavity

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arXiv 2019年

作者： Yang, Pengfei Li, Ming Han, Xing He, Hai Li, Gang Zou, Chang-Ling Zhang, Pengfei Qian, Yuhua Zhang, Tiancai State Key Laboratory of Quantum Optics and Quantum Optics Devices Institute of Opto-Electronics Shanxi University Taiyuan030006 China Cas Key Laboratory of Quantum Information University of Science and Technology of China Anhui Hefei230026 China Collaborative Innovation Center of Extreme Optics Shanxi University Taiyuan030006 China Institute of Big Data Science and Industry Shanxi University Taiyuan030006 China Key Lab. of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan030006 China

Breaking the time-reversal symmetry of light is of great importance for fundamental physics and has attracted increasing interest in the study of non-reciprocal photonic devices. Here, we experimentally demonstrate a chiral cavity QED system with multiple atoms strongly coupled to a Fabry-Pérot cavity. By polarizing the internal quantum state of the atoms, the time-reversal symmetry of the atom-cavity interaction is broken. The strongly coupled atom-cavity system can be described by non-reciprocal quasiparticles, i.e., the cavity polariton. When it works in the linear regime, the inherent nonreciprocity makes the system work as a single-photon-level optical isolator. Benefiting from the collective enhancement of multiple atoms, an isolation ratio exceeding 30 dB on the single-quanta level (∼ 0.1 photon on average) is achieved. The validity of the non-reciprocal device under zero magnetic field and the reconfigurability of the isolation direction are also experimentally demonstrated. Moreover, when the cavity polariton works in the nonlinear regime, the quantum interference between polaritons with weak anharmonicity induces non-reciprocal nonclassical statistics of cavity transmission from coherent probe light. Copyright © 2019, The Authors. All rights reserved.

关键词： Atoms

Blockchain Assisted Federated Learning over Wireless Channels: Dynamic Resource Allocation and Client Scheduling

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arXiv 2021年

作者： Deng, Xiumei Li, Jun Ma, Chuan Wei, Kang Shi, Long Ding, Ming Chen, Wen Poor, H. Vincent The School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China Zhejiang Lab Hangzhou China Nanjing University of Science and Technology Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China Data61 CSIRO KensingtonWA6152 Australia The Department of Electronics Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Electrical and Computer Engineering Princeton University NJ08544 United States

The blockchain technology has been extensively studied to enable distributed and tamper-proof data processing in federated learning (FL). Most existing blockchain assisted FL (BFL) frameworks have employed a third-party blockchain network to decentralize the model aggregation process. However, decentralized model aggregation is vulnerable to pooling and collusion attacks from the third-party blockchain network. Driven by this issue, we propose a novel BFL framework that features the integration of training and mining at the client side. To optimize the learning performance of FL, we propose to maximize the long-term time average (LTA) training data size under a constraint of LTA energy consumption. To this end, we formulate a joint optimization problem of training client selection and resource allocation (i.e., the transmit power and computation frequency at the client side), and solve the long-term mixed integer non-linear programming based on a Lyapunov technique. In particular, the proposed dynamic resource allocation and client scheduling (DRACS) algorithm can achieve a trade-off of [O(1/V), O(√V)] to balance the maximization of the LTA training data size and the minimization of the LTA energy consumption with a control parameter V. Our experimental results show that the proposed DRACS algorithm achieves better learning accuracy than benchmark client scheduling strategies with limited time or energy consumption. Copyright © 2021, The Authors. All rights reserved.

关键词： Blockchain

Deterministic realization of collective measurements via photonic quantum walks

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Deterministic realization of collective measurements via pho...

第十八届全国量子光学学术会议

作者：侯志博 Jun-Feng Tang Yuan Yuan Kang-Da Wu Guo-Yong Xiang Chuan-Feng Li Guang-Can Guo Jiangwei Shang Huangjun Zhu Jian Li 中科大 Key Laboratory of Quantum Information University of Science and Technology of ChinaCAS Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Naturwissenschaftlich-Technische Fakult?t Universit?t Siegen Beijing Key Laboratory of Nanophotonics and Ultrafine Optoelectronic Systems School of PhysicsBeijing Institute of Technology Institute for Theoretical Physics University of Cologne Department of Physics and Center for Field Theory and Particle Physics Fudan University State Key Laboratory of Surface Physics Fudan University Collaborative Innovation Center of Advanced Microstructures Institute of Signal Processing Transmission Nanjing University of Posts and Telecommunications Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and TelecommunicationsMinistry of Education

Collective measurements on identically prepared quantum systems can extract more information than local measurements, thereby enhancing information-processing efficiency. Although this nonclassical phenomenon has been known for two decades, it has remained a challenging task to demonstrate the advantage of collective measurements in experiments. Here, we introduce a general recipe for performing deterministic collective measurements on two identically prepared qubits based on quantum walks. Using photonic quantum walks, we realize experimentally an optimized collective measurement with fidelity 0.9946 without post selection. As an application, we achieve the highest tomographic efficiency in qubit state tomography to date. Our work offers an effective recipe for beating the precision limit of local measurements in quantum state tomography and metrology. In addition, our study opens an avenue for harvesting the power of collective measurements in quantum information processing and for exploring the intriguing physics behind this power.

关键词： Deterministic realization of collective measurements via photonic quantum walks

How to Coadd Images.Ⅱ.Anti-aliasing and PSF Deconvolution

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Research in Astronomy and Astrophysics 2024年第4期24卷 103-113页

作者： Lei Wang Huanyuan Shan Lin Nie Dezi Liu Zhaojun Yan Guoliang Li Cheng Cheng Yushan Xie Han Qu Wenwen Zheng Xi Kang Purple Mountain Observatory Chinese Academy of SciencesNanjing 210023China National Basic Science Data Center Beijing 100190China Zhejiang University-Purple Mountain Observatory Joint Research Center for Astronomy Zhejiang UniversityHangzhou 310030China Shanghai Astronomical Observatory Shanghai 200030China Key Laboratory of Radio Astronomy and Technology Chinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China Department of Information Engineering Wuhan Institute of CityWuhan 430083China South-Westem Institute for Astronomy Research Yunnan UniversityKunming 650500China The Shanghai Key Lab for Astrophysics.Shanghai Normal University Shanghai200234China Chinese Academy of Sciences South America Center for Astronomy National Astronomical ObservatoriesCASBeijing 100101China CAS Key Laboratory of Optical Astronomy National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China

We have developed a novel method for co-adding multiple under-sampled images that combines the iteratively reweighted least squares and divide-and-conquer *** approach not only allows for the anti-aliasing of the images but also enables Point-Spread Function(PSF)deconvolution,resulting in enhanced restoration of extended sources,the highest peak signal-to-noise ratio,and reduced ringing *** test our method,we conducted numerical simulations that replicated observation runs of the China Space Station Telescope/the VLT Survey Telescope(VST)and compared our results to those obtained using previous *** simulation showed that our method outperforms previous approaches in several ways,such as restoring the profile of extended sources and minimizing ringing ***,because our method relies on the inherent advantages of least squares fitting,it is more versatile and does not depend on the local uniformity hypothesis for the ***,the new method consumes much more computation than the other approaches.

关键词： methods:analytical techniques:image processing gravitational lensing:weak (ISM:)cosmic rays