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Towards Long-Form Video Understanding [article]

Chao-Yuan Wu, Philipp Krähenbühl
2021 arXiv   pre-print
Chao-Yuan was supported by the Facebook PhD Fellowship.  ... 
arXiv:2106.11310v1 fatcat:vxw7ugggwbeibd43ubkaavuw3y

Video Compression through Image Interpolation [article]

Chao-Yuan Wu, Nayan Singhal, Philipp Krähenbühl
2018 arXiv   pre-print
An ever increasing amount of our digital communication, media consumption, and content creation revolves around videos. We share, watch, and archive many aspects of our lives through them, all of which are powered by strong video compression. Traditional video compression is laboriously hand designed and hand optimized. This paper presents an alternative in an end-to-end deep learning codec. Our codec builds on one simple idea: Video compression is repeated image interpolation. It thus benefits
more » ... from recent advances in deep image interpolation and generation. Our deep video codec outperforms today's prevailing codecs, such as H.261, MPEG-4 Part 2, and performs on par with H.264.
arXiv:1804.06919v1 fatcat:7jddy5hjjfh2bnbetjl4aghq24

Transition to chaos in small-world dynamical network

Wu-Jie Yuan, Xiao-Shu Luo, Pin-Qun Jiang, Bing-Hong Wang, Jin-Qing Fang
2008 Chaos, Solitons & Fractals  
More interesting, the numerical results show that the measurement 1 R of the transition ability from non-chaos to chaos approximately obeys power-law forms as 1 R $ p Àr 1 and 1 R $ N Àr 2 .  ...  The theoretical analysis of the transition from non-chaos to chaos in complex dynamical networks Condition of the transition from non-chaos to chaos Here, we consider an isolated node being an n-dimensional  ...  In this paper, we investigate the transition from non-chaos to chaos in a small-world dynamical network.  ... 
doi:10.1016/j.chaos.2006.09.077 fatcat:3d5fui2z7raafjhnapghont6je

Lossless Image Compression through Super-Resolution [article]

Sheng Cao, Chao-Yuan Wu, Philipp Krähenbühl
2020 arXiv   pre-print
We introduce a simple and efficient lossless image compression algorithm. We store a low resolution version of an image as raw pixels, followed by several iterations of lossless super-resolution. For lossless super-resolution, we predict the probability of a high-resolution image, conditioned on the low-resolution input, and use entropy coding to compress this super-resolution operator. Super-Resolution based Compression (SReC) is able to achieve state-of-the-art compression rates with
more » ... runtimes on large datasets. Code is available online at https://github.com/caoscott/SReC.
arXiv:2004.02872v1 fatcat:ocwhfs5f55ht3o2dc3w3lfkcbe

Light-Driven Pitch Tuning of Self-Assembled Hierarchical Gratings

Yuan-Hang Wu, Sai-Bo Wu, Chao Liu, Qing-Gui Tan, Rui Yuan, Jing-Ge Wang, Ling-Ling Ma, Wei Hu
2021 Crystals  
The authors gratefully appreciate Hao Qian (Nanjing Tech University), Cong-Long Yuan (East China University of Science and Technology), Chao-Yi Li (Nanjing University) and Yi-Heng Zhang (Nanjing University  ... 
doi:10.3390/cryst11040326 fatcat:54bv2f7lvrdfhpp6gj2mf36mim

Two new brown rot polypores from tropical China

Meng Zhou, Chao-Ge Wang, Ying-Da Wu, Shun Liu, Yuan Yuan
2021 MycoKeys  
Dai, Chao G. Wang & Yuan Yuan, sp. nov. MycoBank No: 839360 Figs 5, 6 Fomitopsis roseoalba A.M.S. Soares and F. subtropica B.K. Cui & Hai J. Li are potentially the most closely related.  ...  The number of brown rot fungi is remarkably smaller compared to white rot fungi (Zhang 2003; Wu et al. 2020) .  ... 
doi:10.3897/mycokeys.82.68299 pmid:34475802 pmcid:PMC8390457 fatcat:of6ijr2ssbfebgs44rju4r4a7a

Spectral Methods for Nonparametric Models [article]

Hsiao-Yu Fish Tung and Chao-Yuan Wu and Manzil Zaheer and Alexander J. Smola
2017 arXiv   pre-print
Nonparametric models are versatile, albeit computationally expensive, tool for modeling mixture models. In this paper, we introduce spectral methods for the two most popular nonparametric models: the Indian Buffet Process (IBP) and the Hierarchical Dirichlet Process (HDP). We show that using spectral methods for the inference of nonparametric models are computationally and statistically efficient. In particular, we derive the lower-order moments of the IBP and the HDP, propose spectral
more » ... s for both models, and provide reconstruction guarantees for the algorithms. For the HDP, we further show that applying hierarchical models on dataset with hierarchical structure, which can be solved with the generalized spectral HDP, produces better solutions to that of flat models regarding likelihood performance.
arXiv:1704.00003v1 fatcat:4yje76xc5vacbef6eqvellmnue

Revealing the Heavy Quasiparticles in the Heavy-Fermion Superconductor CeCu2Si2 [article]

Zhongzheng Wu, Yuan Fang, Hang Su, Wu Xie, Peng Li, Yi Wu, Yaobo Huang, Dawei Shen, Balasubramanian Thiagarajan, Johan Adell, Chao Cao, Huiqiu Yuan (+2 others)
2021 arXiv   pre-print
The superconducting order parameter of the first heavy-fermion superconductor CeCu2Si2 is currently under debate. A key ingredient to understand its superconductivity and physical properties is the quasiparticle dispersion and Fermi surface, which remains elusive experimentally. Here we present measurements from angle-resolved photoemission spectroscopy. Our results emphasize the key role played by the Ce 4f electrons for the low-temperature Fermi surface, highlighting a band-dependent
more » ... n-f electron hybridization. In particular, we find a very heavy quasi-two-dimensional electron band near the bulk X point and moderately heavy three-dimensional hole pockets near the Z point. Comparison with theoretical calculations reveals the strong local correlation in this compound, calling for further theoretical studies. Our results provide the electronic basis to understand the heavy fermion behavior and superconductivity; implications for the enigmatic superconductivity of this compound are also discussed.
arXiv:2105.12394v2 fatcat:exbjrs5zv5bctgc4jlx3qrk7km

Statin use and breast cancer survival and risk: a systematic review and meta-analysis

Qi-Jun Wu, Chao Tu, Yuan-Yuan Li, Jingjing Zhu, Ke-Qing Qian, Wen-Jing Li, Lang Wu
2015 OncoTarget  
Data extraction and quality assessment A pair of investigators independently carried out the abstract screening, full text screening, and data extraction (Chao Tu and Lang Wu).  ...  GRANT SUPPORT This study was supported by the Younger research fund of Shengjing Hospital (Grant 2014sj09 for Qi-Jun Wu).  ... 
doi:10.18632/oncotarget.5557 pmid:26472026 pmcid:PMC4767486 fatcat:u67ypkarvjadfm5o4ufi6avzwa

Parity and endometrial cancer risk: a meta-analysis of epidemiological studies

Qi-Jun Wu, Yuan-Yuan Li, Chao Tu, Jingjing Zhu, Ke-Qing Qian, Tong-Bao Feng, Changwei Li, Lang Wu, Xiao-Xin Ma
2015 Scientific Reports  
Acknowledgements This work was supported by The National Natural Science Foundation of China (81472438 and 81272874 for Xiao-Xin Ma), and the Younger research fund of Shengjing Hospital (Grant 2014sj09 for Qi-Jun Wu  ... 
doi:10.1038/srep14243 pmid:26373341 pmcid:PMC4642705 fatcat:7vxngl47zfgqblot5zpk5iayy4

Efficient parallelized particle filter design on CUDA

Min-An Chao, Chun-Yuan Chu, Chih-Hao Chao, An-Yeu Wu
2010 2010 IEEE Workshop On Signal Processing Systems  
Particle filtering is widely used in numerous nonlinear applications which require reconfigurability, fast prototyping, and online parallel signal processing. The emerging computing platform, CUDA, may be regarded as the most appealing platform for such implementation. However, there are not yet literatures exploring how to utilize CUDA for particle filters. This parer aims to provide two design techniques, A) finiteredraw importance-maximizing (FRIM) prior editing and B) localized resampling,
more » ... or efficient implementation of particle filters on CUDA, which can be verified to reduce global operations and provide significant speedup. The modifications on algorithm and architectural mapping are evaluated with conceptual and quantitative analysis. From the classic bearingsonly tracking experiments, the proposed design is 5.73 times faster than the direct implementation on GeForce 9400m.
doi:10.1109/sips.2010.5624805 fatcat:i5dlvkqcbbfs3its7n363vqbsy

A Trajectory-Based Point Tracker Using Chaos Evolutionary Programming [chapter]

Shu-Mei Guo, Chih-Yuan Hsu, Po-Nung Wu, Jason Sheng-Hong Tsai
2009 Lecture Notes in Computer Science  
The chaos optimization algorithm Recently, COA has been proposed and successfully applied in the optimization problem. Chaos often exists in nonlinear systems.  ...  The chaos evolutionary programming algorithm The joint algorithms of the EP and COA are developed as follows.  ... 
doi:10.1007/978-3-642-02568-6_22 fatcat:dv7qyeyx6jb4bgmeehtvzzqpqm

NetPilot

Xin Wu, Daniel Turner, Chao-Chih Chen, David A. Maltz, Xiaowei Yang, Lihua Yuan, Ming Zhang
2012 Computer communication review  
The soaring demands for always-on and fast-response online services have driven modern datacenter networks to undergo tremendous growth. These networks often rely on commodity hardware to reach immense scale while keeping capital expenses under check. The downside is that commodity devices are prone to failures, raising a formidable challenge for network operators to promptly handle these failures with minimal disruptions to the hosted services. Recent research efforts have focused on automatic
more » ... failure localization. Yet, resolving failures still requires significant human interventions, resulting in prolonged failure recovery time. Unlike previous work, NetPilot aims to quickly mitigate rather than resolve failures. NetPilot mitigates failures in much the same way operators do -by deactivating or restarting suspected offending components. NetPilot circumvents the need for knowing the exact root cause of a failure by taking an intelligent trial-and-error approach. The core of NetPilot is comprised of an Impact Estimator that helps guard against overly disruptive mitigation actions and a failure-specific mitigation planner that minimizes the number of trials. We demonstrate that NetPilot can effectively mitigate several types of critical failures commonly encountered in production datacenter networks.
doi:10.1145/2377677.2377759 fatcat:3d6h43yfx5earcuw5eosd3pqpq

Compressed Video Action Recognition [article]

Chao-Yuan Wu, Manzil Zaheer, Hexiang Hu, R. Manmatha, Alexander J. Smola, Philipp Krähenbühl
2018 arXiv   pre-print
Training robust deep video representations has proven to be much more challenging than learning deep image representations. This is in part due to the enormous size of raw video streams and the high temporal redundancy; the true and interesting signal is often drowned in too much irrelevant data. Motivated by that the superfluous information can be reduced by up to two orders of magnitude by video compression (using H.264, HEVC, etc.), we propose to train a deep network directly on the
more » ... d video. This representation has a higher information density, and we found the training to be easier. In addition, the signals in a compressed video provide free, albeit noisy, motion information. We propose novel techniques to use them effectively. Our approach is about 4.6 times faster than Res3D and 2.7 times faster than ResNet-152. On the task of action recognition, our approach outperforms all the other methods on the UCF-101, HMDB-51, and Charades dataset.
arXiv:1712.00636v2 fatcat:xujkjv2yfjb4piiqzwxcpp2zlq

Video Compression Through Image Interpolation [chapter]

Chao-Yuan Wu, Nayan Singhal, Philipp Krähenbühl
2018 Lecture Notes in Computer Science  
0000−0002−5690−8865] , Nayan Singhal [0000−0002−3189−6693] , and Philipp Krähenbühl [0000−0002−9846−4369] Abstract. An ever increasing amount of our digital communication, media consumption, and content creation revolves around videos. We share, watch, and archive many aspects of our lives through them, all of which are powered by strong video compression. Traditional video compression is laboriously hand designed and hand optimized. This paper presents an alternative in an end-to-end deep
more » ... ing codec. Our codec builds on one simple idea: Video compression is repeated image interpolation. It thus benefits from recent advances in deep image interpolation and generation. Our deep video codec outperforms today's prevailing codecs, such as H.261, MPEG-4 Part 2, and performs on par with H.264.
doi:10.1007/978-3-030-01237-3_26 fatcat:g4nusdxzmndvnayyse3xgzffx4
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