Experimentally detecting a quantum change point via Bayesian inference

Jan 23, 2018

Shang Yu, Chang-Jiang Huang, Jian-Shun Tang, Zhih-Ahn Jia, Yi-Tao Wang, Zhi-Jin Ke, Wei Liu, Xiao Liu, Zong-Quan Zhou, Ze-Di Cheng, Jin-Shi Xu, Yu-Chun Wu, Yuan-Yuan Zhao, Guo-Yong Xiang, Chuan-Feng Li, Guang-Can Guo, Gael Sentís, Ramon Muñoz-Tapia

Detecting a change point is a crucial task in statistics that has been recently extended to the quantum realm. A source state generator that emits a series of single photons in a default state suffers an alteration at some point and starts to emit photons in a mutated state. The problem consists in identifying the point where the change took place. In this work, we consider a learning agent that applies Bayesian inference on experimental data to solve this problem. This learning machine adjusts the measurement over each photon according to the past experimental results finds the change position in an online fashion. Our results show that the local-detection success probability can be largely improved by using such a machine learning technique. This protocol provides a tool for improvement in many applications where a sequence of identical quantum states is required.
Jan 23, 2018

Shang Yu, Chang-Jiang Huang, Jian-Shun Tang, Zhih-Ahn Jia, Yi-Tao Wang, Zhi-Jin Ke, Wei Liu, Xiao Liu, Zong-Quan Zhou, Ze-Di Cheng, Jin-Shi Xu, Yu-Chun Wu, Yuan-Yuan Zhao, Guo-Yong Xiang, Chuan-Feng Li, Guang-Can Guo, Gael Sentís, Ramon Muñoz-Tapia

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Advancing PICO Element Detection in Medical Text via Deep Neural Networks

Oct 30, 2018

Di Jin, Peter Szolovits

Oct 30, 2018

Di Jin, Peter Szolovits

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Hierarchical Neural Networks for Sequential Sentence Classification in Medical Scientific Abstracts

Aug 19, 2018

Di Jin, Peter Szolovits

Aug 19, 2018

Di Jin, Peter Szolovits

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Image Tag Completion by Low-rank Factorization with Dual Reconstruction Structure Preserved

Jun 09, 2014

Xue Li, Yu-Jin Zhang, Bin Shen, Bao-Di Liu

Jun 09, 2014

Xue Li, Yu-Jin Zhang, Bin Shen, Bao-Di Liu

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High-throughput, high-resolution registration-free generated adversarial network microscopy

Oct 03, 2018

Hao Zhang, Xinlin Xie, Chunyu Fang, Yicong Yang, Di Jin, Peng Fei

Oct 03, 2018

Hao Zhang, Xinlin Xie, Chunyu Fang, Yicong Yang, Di Jin, Peng Fei

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Deep Hybrid Scattering Image Learning

Sep 19, 2018

Mu Yang, Zheng-Hao Liu, Ze-Di Cheng, Jin-Shi Xu, Chuan-Feng Li, Guang-Can Guo

Sep 19, 2018

Mu Yang, Zheng-Hao Liu, Ze-Di Cheng, Jin-Shi Xu, Chuan-Feng Li, Guang-Can Guo

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Short utterance compensation in speaker verification via cosine-based teacher-student learning of speaker embeddings

Oct 25, 2018

Jee-weon Jung, Hee-soo Heo, Hye-jin Shim, Ha-jin Yu

Oct 25, 2018

Jee-weon Jung, Hee-soo Heo, Hye-jin Shim, Ha-jin Yu

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Automatic Three-Dimensional Cephalometric Annotation System Using Three-Dimensional Convolutional Neural Networks

Nov 19, 2018

Sung Ho Kang, Kiwan Jeon, Hak-Jin Kim, Jin Keun Seo, Sang-Hwy Lee

Nov 19, 2018

Sung Ho Kang, Kiwan Jeon, Hak-Jin Kim, Jin Keun Seo, Sang-Hwy Lee

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Replay spoofing detection system for automatic speaker verification using multi-task learning of noise classes

Oct 25, 2018

Hye-Jin Shim, Jee-weon Jung, Hee-Soo Heo, Sunghyun Yoon, Ha-Jin Yu

Oct 25, 2018

Hye-Jin Shim, Jee-weon Jung, Hee-Soo Heo, Sunghyun Yoon, Ha-Jin Yu

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New Movement and Transformation Principle of Fuzzy Reasoning and Its Application to Fuzzy Neural Network

Nov 10, 2018

Chung-Jin Kwak, Son-Il Kwak, Dae-Song Kang, Song-Il Choe, Jin-Ung Kim, Hyok-Gi Chea

Nov 10, 2018

Chung-Jin Kwak, Son-Il Kwak, Dae-Song Kang, Song-Il Choe, Jin-Ung Kim, Hyok-Gi Chea

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Independent Component Analysis via Energy-based and Kernel-based Mutual Dependence Measures

May 17, 2018

Ze Jin, David S. Matteson

We apply both distance-based (Jin and Matteson, 2017) and kernel-based (Pfister et al., 2016) mutual dependence measures to independent component analysis (ICA), and generalize dCovICA (Matteson and Tsay, 2017) to MDMICA, minimizing empirical dependence measures as an objective function in both deflation and parallel manners. Solving this minimization problem, we introduce Latin hypercube sampling (LHS) (McKay et al., 2000), and a global optimization method, Bayesian optimization (BO) (Mockus, 1994) to improve the initialization of the Newton-type local optimization method. The performance of MDMICA is evaluated in various simulation studies and an image data example. When the ICA model is correct, MDMICA achieves competitive results compared to existing approaches. When the ICA model is misspecified, the estimated independent components are less mutually dependent than the observed components using MDMICA, while they are prone to be even more mutually dependent than the observed components using other approaches.
May 17, 2018

Ze Jin, David S. Matteson

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Generating Markov Equivalent Maximal Ancestral Graphs by Single Edge Replacement

Jul 04, 2012

Jin Tian

Jul 04, 2012

Jin Tian

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Parameter Transfer Extreme Learning Machine based on Projective Model

Sep 14, 2018

Chao Chen, Boyuan Jiang, Xinyu Jin

Sep 14, 2018

Chao Chen, Boyuan Jiang, Xinyu Jin

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Consideration on Example 2 of "An Algorithm of General Fuzzy InferenceWith The Reductive Property"

Dec 13, 2017

Son-Il Kwak, Oh-Chol Gwon, Chung-Jin Kwak

In this paper, we will show that (1) the results about the fuzzy reasoning algoritm obtained in the paper "Computer Sciences Vol. 34, No.4, pp.145-148, 2007" according to the paper "IEEE Transactions On systems, Man and cybernetics, 18, pp.1049-1056, 1988" are correct; (2) example 2 in the paper "An Algorithm of General Fuzzy Inference With The Reductive Property" presented by He Ying-Si, Quan Hai-Jin and Deng Hui-Wen according to the paper "An approximate analogical reasoning approach based on similarity measures" presented by Tursken I.B. and Zhong zhao is incorrect; (3) the mistakes in their paper are modified and then a calculation example of FMT is supplemented.
Dec 13, 2017

Son-Il Kwak, Oh-Chol Gwon, Chung-Jin Kwak

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