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The Lagrangian remainder of Taylor’s series, distinguishes O(f(x)) time complexities to polynomials or not

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arXiv 2020年

作者： Bakas, Nikolaos P. Kosmatopoulos, Elias Chatzichristofis, Savvas A. Computation-based Science and Technology Research Center Cyprus Institute 20 Konstantinou Kavafi Street Aglantzia Nicosia2121 Cyprus Democritus University of Thrace Electrical and Computer engineering Intelligent Systems Lab Department of Computer Science Neapolis University Pafos 2 Danais Avenue Pafos8042 Cyprus

The purpose of this letter is to investigate the time complexity consequences of the truncated Taylor series, known as Taylor Polynomials [1–3]. In particular, it is demonstrated that the examination of the P = NP equality, is associated with the determination of whether the nth derivative of a particular solution is bounded or not. Accordingly, in some cases, this is not true, and hence in general. Copyright © 2020, The Authors. All rights reserved.

关键词： Taylor series

Human action recognition in drone videos using a few aerial training examples

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arXiv 2019年

作者： Sultani, Waqas Shah, Mubarak Intelligent Machine Lab Information Technology University Lahore Pakistan Center for Research in Computer Vision University of Central Florida Orlando United States

Drones are enabling new forms of human actions surveillance due to their low cost and fast mobility. However, using deep neural networks for automatic aerial action recognition is difficult due to the need for a large number of training aerial human action videos. Collecting a large number of human action aerial videos is costly, time-consuming, and difficult. In this paper, we explore two alternative data sources to improve aerial action classification when only a few training aerial examples are available. As a first data source, we resort to video games. We collect plenty of aerial game action videos using two gaming engines. For the second data source, we leverage conditional Wasserstein Generative Adversarial Networks to generate aerial features from ground videos. Given that both data sources have some limitations, e.g. game videos are biased towards specific actions categories (fighting, shooting, etc.,), and it is not easy to generate good discriminative GAN-generated features for all types of actions, we need to efficiently integrate two dataset sources with few available real aerial training videos. To address this challenge of the heterogeneous nature of the data, we propose to use a disjoint multitask learning framework. We feed the network with real and game, or real and GAN-generated data in an alternating fashion to obtain an improved action classifier. We validate the proposed approach on two aerial action datasets and demonstrate that features from aerial game videos and those generated from GAN can be extremely useful for an improved action recognition in real aerial videos when only a few real aerial training examples are available. Copyright © 2019, The Authors. All rights reserved.

关键词： Antennas

When counterpoint meets chinese folk melodies 20

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When counterpoint meets chinese folk melodies

Proceedings of the 34th International Conference on Neural Information Processing systems

作者： Nan Jiang Sheng Jin Zhiyao Duan Changshui Zhang Institute for Artificial Intelligence Tsinghua University (THUAI) and State Key Lab of Intelligent Technologies and Systems and Beijing National Research Center for Information Science and Technology (BNRist) and Department of Automation Tsinghua University Beijing China Department of Electrical and Computer Engineering University of Rochester

ISBN: (纸本)9781713829546

Counterpoint is an important concept in Western music theory. In the past century, there have been significant interests in incorporating counterpoint into Chinese folk music composition. In this paper, we propose a reinforcement learning-based system, named FolkDuet, towards the online countermelody generation for Chinese folk melodies. With no existing data of Chinese folk duets, FolkDuet employs two reward models based on out-of-domain data i.e., Bach chorales, and monophonic Chinese folk melodies. An interaction reward model is trained on the duets formed from outer parts of Bach chorales to model counterpoint interaction, while a style reward model is trained on monophonic melodies of Chinese folk songs to model melodic patterns. With both rewards, the generator of FolkDuet is trained to generate countermelodies while maintaining the Chinese folk style. The entire generation process is performed in an online fashion, allowing real-time interactive human-machine duet improvisation. Experiments show that the proposed algorithm achieves better subjective and objective results than the baselines.

关键词：

Imitation learning from observations by minimizing inverse dynamics disagreement

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arXiv 2019年

作者： Yang, Chao Ma, Xiaojian Huang, Wenbing Sun, Fuchun Liu, Huaping Huang, Junzhou Gan, Chuang State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Center for Vision Cognition Learning and Autonomy Department of Computer Science UCLA Tencent AI Lab MIT-IBM Watson AI Lab

This paper studies Learning from Observations (LfO) for imitation learning with access to state-only demonstrations. In contrast to Learning from Demonstration (LfD) that involves both action and state supervision, LfO is more practical in leveraging previously inapplicable resources (e.g. videos), yet more challenging due to the incomplete expert guidance. In this paper, we investigate LfO and its difference with LfD in both theoretical and practical perspectives. We first prove that the gap between LfD and LfO actually lies in the disagreement of inverse dynamics models between the imitator and the expert, if following the modeling approach of GAIL [16]. More importantly, the upper bound of this gap is revealed by a negative causal entropy which can be minimized in a model-free way. We term our method as Inverse-Dynamics-Disagreement-Minimization (IDDM) which enhances the conventional LfO method through further bridging the gap to LfD. Considerable empirical results on challenging benchmarks indicate that our method attains consistent improvements over other LfO counterparts. Copyright © 2019, The Authors. All rights reserved.

关键词： Dynamics

Fruit-CoV: An efficient vision-based framework for speedy detection and diagnosis of SARS-CoV-2 infections through recorded cough sounds (a short report)

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arXiv 2021年

作者： Nguyen, Long H. Pham, Nhat Truong Do, Van Huong Nguyen, Liu Tai Nguyen, Thanh Tin Do, Van Dung Nguyen, Hai Nguyen, Ngoc Duy Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Division of Computational Mechatronics Institute for Computational Science Ton Duc Thang University Ho Chi Minh City Viet Nam ASICLAND Suwon Korea Republic of Human Computer Interaction Lab Sejong University Seoul Korea Republic of Hanoi University of Industry Hanoi Viet Nam Khoury College of Computer Science Northeastern University Boston United States Institute for Intelligent Systems Research and Innovation Deakin University VIC Australia

SARS-CoV-2 is colloquially known as COVID-19 that had an initial outbreak in December 2019. The deadly virus has spread across the world, taking part in the global pandemic disease since March 2020. In addition, a recent variant of SARS-CoV-2 named Delta is intractably contagious and responsible for more than four million deaths over the world. Therefore, it is vital to possess a selftesting service of SARS-CoV-2 at home. In this study, we introduce Fruit-CoV, a two-stage vision framework, which is capable of detecting SARS-CoV-2 infections through recorded cough sounds. Specifically, we convert sounds into Log-Mel Spectrograms and use the EfficientNet-V2 network to extract its visual features in the first stage. In the second stage, we use 14 convolutional layers extracted from the large-scale Pretrained Audio Neural Networks for audio pattern recognition (PANNs) and theWavegram-Log-Mel-CNN to aggregate feature representations of the Log-Mel Spectrograms. Finally, we use the combined features to train a binary classifier. In this study, we use a dataset provided by the AICovidVN 115M Challenge, which includes a total of 7371 recorded cough sounds collected throughout Vietnam, India, and Switzerland. Experimental results show that our proposed model achieves an AUC score of 92.8% and ranks the 1st place on the leaderboard of the AICovidVN Challenge. More importantly, our proposed framework can be integrated into a call center or a VoIP system to speed up detecting SARS-CoV-2 infections through online/recorded cough sounds. © 2021, CC BY.

关键词： SARS

Dialogue distillation: Open-domain dialogue augmentation using unpaired data

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arXiv 2020年

作者： Zhang, Rongsheng Zheng, Yinhe Shao, Jianzhi Mao, Xiaoxi Xi, Yadong Huang, Minlie Fuxi AI Lab NetEase Inc. Hangzhou China Department of Computer Science and Technology Institute for Artifical Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Beijing China Alibaba Group Hangzhou China

Recent advances in open-domain dialogue systems rely on the success of neural models that are trained on large-scale data. However, collecting large-scale dialogue data is usually time-consuming and labor-intensive. To address this data dilemma, we propose a novel data augmentation method for training open-domain dialogue models by utilizing unpaired data. Specifically, a data-level distillation process is first proposed to construct augmented dialogues where both post and response are retrieved from the unpaired data. A ranking module is employed to filter out low-quality dialogues. Further, a model-level distillation process is employed to distill a teacher model trained on high-quality paired data to augmented dialogue pairs, thereby preventing dialogue models from being affected by the noise in the augmented data. Automatic and manual evaluation indicates that our method can produce high-quality dialogue pairs with diverse contents, and the proposed data-level and model-level dialogue distillation can improve the performance of competitive baselines. Copyright © 2020, The Authors. All rights reserved.

关键词： Speech processing

Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report

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arXiv 2022年

作者： Ignatov, Andrey Timofte, Radu Denna, Maurizio Younes, Abdel Gankhuyag, Ganzorig Huh, Jingang Kim, Myeong Kyun Yoon, Kihwan Moon, Hyeon-Cheol Lee, Seungho Choe, Yoonsik Jeong, Jinwoo Kim, Sungjei Smyl, MacIej Latkowski, Tomasz Kubik, Pawel Sokolski, Michal Ma, Yujie Chao, Jiahao Zhou, Zhou Gao, Hongfan Yang, Zhengfeng Zeng, Zhenbing Zhuge, Zhengyang Li, Chenghua Zhu, Dan Sun, Mengdi Duan, Ran Gao, Yan Kong, Lingshun Sun, Long Li, Xiang Zhang, Xingdong Zhang, Jiawei Wu, Yaqi Pan, Jinshan Yu, Gaocheng Zhang, Jin Zhang, Feng Ma, Zhe Wang, Hongbin Cho, Hojin Kim, Steve Li, Huaen Ma, Yanbo Luo, Ziwei Li, Youwei Yu, Lei Wen, Zhihong Wu, Qi Fan, Haoqiang Liu, Shuaicheng Zhang, Lize Zong, Zhikai Kwon, Jeremy Zhang, Junxi Li, Mengyuan Fu, Nianxiang Ding, Guanchen Zhu, Han Chen, Zhenzhong Li, Gen Zhang, Yuanfan Sun, Lei Zhang, Dafeng Yang, Neo Liu, Fitz Zhao, Jerry Ayazoglu, Mustafa Bilecen, Bahri Batuhan Hirose, Shota Arunruangsirilert, Kasidis Ao, Luo Leung, Ho Chun Wei, Andrew Liu, Jie Liu, Qiang Yu, Dahai Li, Ao Luo, Lei Zhu, Ce Hong, Seongmin Park, Dongwon Lee, Joonhee Lee, Byeong Hyun Lee, Seunggyu Chun, Se Young He, Ruiyuan Jiang, Xuhao Ruan, Haihang Zhang, Xinjian Liu, Jing Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Computer Vision Lab Eth Zürich Switzerland Ai Witchlabs Switzerland University of Würzburg Germany Synaptics Europe Switzerland Korea Republic of Yonsei University Korea Republic of Tcl Research Europe Poland East China Normal University China Institute of Automation Chinese Academy of Sciences China Boe Technology Group Co. Ltd. China Nanjing University of Science and Technology China Ant Group China GenGenAI Korea Republic of Hefei University of Technology China Megvii Technology China China Xidian University China Qingdao Hi-image Technologies Co. Ltd. China Korea Republic of Wuhan University China China Samsung Research China Aselsan Corporation Turkey Waseda University Japan Tcl Corporate Research China University of Electronic Science and Technology China Intelligent Computational Imaging Lab Seoul National University Korea Republic of Bilibili Inc. China Shanghai Jiao Tong University China Assiut University Egypt

Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized image super-resolution solution that can demonstrate a real-time performance on mobile NPUs. The participants were provided with the DIV2K dataset and trained INT8 models to do a high-quality 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper. Copyright © 2022, The Authors. All rights reserved.

关键词： Image enhancement

Multi-segment Soft Robot Control via Deep Koopman-based Model Predictive Control

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arXiv 2025年

作者： Lv, Lei Liu, Lei Bao, Lei Sun, Fuchun Dong, Jiahong Zhang, Jianwei Shan, Xuemei Sun, Kai Huang, Hao Luo, Yu Department of Computer Science and Technology Tsinghua University China School of Biomedical Engineering Tsinghua University China Beijing Soft Robot Tech Co. Ltd. China School of Clinical Medicine Tsinghua University China Department of Informatics University of Hamburg Germany School of Mechanical Engineering and Automation Beihang University China Huawei Noah’s Ark Lab Hong Kong Shanghai Research Institute for Intelligent Autonomous Systems Tongji University China

Soft robots, compared to regular rigid robots, as their multiple segments with soft materials bring flexibility and compliance, have the advantages of safe interaction and dexterous operation in the environment. However, due to its characteristics of high dimensional, nonlinearity, time-varying nature, and infinite degree of freedom, it has been challenges in achieving precise and dynamic control such as trajectory tracking and position reaching. To address these challenges, we propose a framework of Deep Koopman-based Model Predictive Control (DK-MPC) for handling multi-segment soft robots. We first employ a deep learning approach with sampling data to approximate the Koopman operator, which therefore linearizes the high-dimensional nonlinear dynamics of the soft robots into a finite-dimensional linear representation. Secondly, this linearized model is utilized within a model predictive control framework to compute optimal control inputs that minimize the tracking error between the desired and actual state trajectories. The real-world experiments on the soft robot "Chordata" demonstrate that DK-MPC could achieve high-precision control, showing the potential of DK-MPC for future applications to soft robots. More visualization results can be found at https://***/DKMPC/. © 2025, CC BY.

关键词： Predictive control systems

Uncertainty-Aware Score Distribution Learning for Action Quality Assessment

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Uncertainty-Aware Score Distribution Learning for Action Qua...

Conference on computer vision and Pattern Recognition (CVPR)

作者： Yansong Tang Zanlin Ni Jiahuan Zhou Danyang Zhang Jiwen Lu Ying Wu Jie Zhou Department of Automation Tsinghua University China State Key Lab of Intelligent Technologies and Systems China Beijing National Research Center for Information Science and Technology China Electrical and Computer Engineering Department Northwestern University Tsinghua Shenzhen International Graduate School Tsinghua University China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Assessing action quality from videos has attracted growing attention in recent years. Most existing approaches usually tackle this problem based on regression algorithms, which ignore the intrinsic ambiguity in the score labels caused by multiple judges or their subjective appraisals. To address this issue, we propose an uncertainty-aware score distribution learning (USDL) approach for action quality assessment (AQA). Specifically, we regard an action as an instance associated with a score distribution, which describes the probability of different evaluated scores. Moreover, under the circumstance where finer-grained score labels are available (e.g., difficulty degree of an action or multiple scores from different judges), we further devise a multi-path uncertainty-aware score distribution learning (MUSDL) method to explore the disentangled components of a score. In order to demonstrate the effectiveness of our proposed methods, We conduct experiments on two AQA datasets containing various Olympic actions. Our approaches set new state-of-the-arts under the Spearman's Rank Correlation (i.e., 0.8102 on AQA-7 and 0.9273 on MTL-AQA).

关键词： Videos Uncertainty Games Gaussian distribution Task analysis Quality assessment Training