C. H. Li - Department of Astronomy, Peking University, Beijing, China

C. H. Li
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Name
C. H. Li
Affiliation
Department of Astronomy, Peking University, Beijing, China
City
Beijing
Country
China

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High Energy Physics - Experiment (7)
 
Computer Science - Learning (6)
 
Computer Science - Computer Vision and Pattern Recognition (4)
 
Physics - Materials Science (4)
 
Mathematics - Algebraic Geometry (3)
 
Physics - Mesoscopic Systems and Quantum Hall Effect (3)
 
Mathematics - Numerical Analysis (3)
 
Quantum Physics (3)
 
Statistics - Machine Learning (3)
 
Nonlinear Sciences - Exactly Solvable and Integrable Systems (2)
 
Physics - Superconductivity (2)
 
Physics - Instrumentation and Detectors (2)
 
Solar and Stellar Astrophysics (2)
 
Mathematics - Analysis of PDEs (2)
 
High Energy Physics - Phenomenology (2)
 
Computer Science - Information Retrieval (1)
 
Mathematics - Information Theory (1)
 
Quantitative Biology - Quantitative Methods (1)
 
Quantitative Biology - Neurons and Cognition (1)
 
Nuclear Experiment (1)
 
Computer Science - Information Theory (1)
 
Mathematical Physics (1)
 
Mathematics - Number Theory (1)
 
Computer Science - Networking and Internet Architecture (1)
 
Mathematics - Differential Geometry (1)
 
Computer Science - Sound (1)
 
Mathematics - Combinatorics (1)
 
Mathematics - Quantum Algebra (1)
 
Mathematics - Mathematical Physics (1)
 
Computer Science - Computation and Language (1)
 
Mathematics - Spectral Theory (1)

Publications Authored By C. H. Li

Large-scale kernel approximation is an important problem in machine learning research. Approaches using random Fourier features have become increasingly popular [Rahimi and Recht, 2007], where kernel approximation is treated as empirical mean estimation via Monte Carlo (MC) or Quasi-Monte Carlo (QMC) integration [Yang et al., 2014]. Read More

In this paper, we prove a $\mathcal C^{2,\alpha}$-estimate for the solution to the complex Monge-Amp\`ere equation $\det(u_{i\bar{j}})=f$ with $0< f\in \mathcal C^{\alpha}$, under the assumption that $u\in \mathcal C^{1,\beta }$ for some $\beta <1$ which depends on $n$ and $\alpha $. Read More

We study the electric transport properties of Lix(NH3)yFe2(TezSe1-z)2 single crystals with z = 0 and 0.6 in the mixed state. Thermally-activated flux-flow, vortex glass and flux-flow Hall effect (FFHE) behaviors are observed. Read More

Generative moment matching network (GMMN) is a deep generative model that differs from Generative Adversarial Network (GAN) by replacing the discriminator in GAN with a two-sample test based on kernel maximum mean discrepancy (MMD). Although some theoretical guarantees of MMD have been studied, the empirical performance of GMMN is still not as competitive as that of GAN on challenging and large benchmark datasets. The computational efficiency of GMMN is also less desirable in comparison with GAN, partially due to its requirement for a rather large batch size during the training. Read More

In this paper, we study the stochastic gradient descent method in analyzing nonconvex statistical optimization problems from a diffusion approximation point of view. Using the theory of large deviation of random dynamical system, we prove in the small stepsize regime and the presence of omnidirectional noise the following: starting from a local minimizer (resp.~saddle point) the SGD iteration escapes in a number of iteration that is exponentially (resp. Read More

Many infrastructure-free indoor positioning systems rely on fine-grained location-dependent fingerprints to train models for localization. The site survey process to collect fingerprints is laborious and is considered one of the major obstacles to deploying such systems. In this paper, we propose TuRF, a fast path-based fingerprint collection mechanism for site survey. Read More

2017May
Authors: BESIII Collaboration, M. Ablikim, M. N. Achasov, S. Ahmed, M. Albrecht, M. Alekseev, A. Amoroso, F. F. An, Q. An, J. Z. Bai, Y. Bai, O. Bakina, R. Baldini Ferroli, Y. Ban, D. W. Bennett, J. V. Bennett, N. Berger, M. Bertani, D. Bettoni, J. M. Bian, F. Bianchi, E. Boger, I. Boyko, R. A. Briere, H. Cai, X. Cai, O. Cakir, A. Calcaterra, G. F. Cao, S. A. Cetin, J. Chai, J. F. Chang, G. Chelkov, G. Chen, H. S. Chen, J. C. Chen, M. L. Chen, S. J. Chen, X. R. Chen, Y. B. Chen, X. K. Chu, G. Cibinetto, H. L. Dai, J. P. Dai, A. Dbeyssi, D. Dedovich, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, Y. Ding, C. Dong, J. Dong, L. Y. Dong, M. Y. Dong, O. Dorjkhaidav, Z. L. Dou, S. X. Du, P. F. Duan, J. Fang, S. S. Fang, X. Fang, Y. Fang, R. Farinelli, L. Fava, S. Fegan, F. Feldbauer, G. Felici, C. Q. Feng, E. Fioravanti, M. Fritsch, C. D. Fu, Q. Gao, X. L. Gao, Y. Gao, Y. G. Gao, Z. Gao, B. Garillon, I. Garzia, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, M. Greco, M. H. Gu, S. Gu, Y. T. Gu, A. Q. Guo, L. B. Guo, R. P. Guo, Y. P. Guo, Z. Haddadi, S. Han, X. Q. Hao, F. A. Harris, K. L. He, X. Q. He, F. H. Heinsius, T. Held, Y. K. Heng, T. Holtmann, Z. L. Hou, C. Hu, H. M. Hu, T. Hu, Y. Hu, G. S. Huang, J. S. Huang, S. H. Huang, X. T. Huang, X. Z. Huang, Z. L. Huang, T. Hussain, W. Ikegami Andersson, Q. Ji, Q. P. Ji, X. B. Ji, X. L. Ji, X. S. Jiang, X. Y. Jiang, J. B. Jiao, Z. Jiao, D. P. Jin, S. Jin, Y. Jin, T. Johansson, A. Julin, N. Kalantar-Nayestanaki, X. L. Kang, X. S. Kang, M. Kavatsyuk, B. C. Ke, T. Khan, A. Khoukaz, P. Kiese, R. Kliemt, L. Koch, O. B. Kolcu, B. Kopf, M. Kornicer, M. Kuemmel, M. Kuhlmann, A. Kupsc, W. Kühn, J. S. Lange, M. Lara, P. Larin, L. Lavezzi, H. Leithoff, C. Leng, C. Li, Cheng Li, D. M. Li, F. Li, F. Y. Li, G. Li, H. B. Li, H. J. Li, J. C. Li, Jin Li, K. Li, K. Li, K. J. Li, Lei Li, P. L. Li, P. R. Li, Q. Y. Li, T. Li, W. D. Li, W. G. Li, X. L. Li, X. N. Li, X. Q. Li, Z. B. Li, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, D. X. Lin, B. Liu, B. J. Liu, C. X. Liu, D. Liu, F. H. Liu, Fang Liu, Feng Liu, H. B. Liu, H. H. Liu, H. H. Liu, H. M. Liu, J. B. Liu, J. Y. Liu, K. Liu, K. Y. Liu, Ke Liu, L. D. Liu, P. L. Liu, Q. Liu, S. B. Liu, X. Liu, Y. B. Liu, Z. A. Liu, Zhiqing Liu, Y. F. Long, X. C. Lou, H. J. Lu, J. G. Lu, Y. Lu, Y. P. Lu, C. L. Luo, M. X. Luo, X. L. Luo, X. R. Lyu, F. C. Ma, H. L. Ma, L. L. Ma, M. M. Ma, Q. M. Ma, T. Ma, X. N. Ma, X. Y. Ma, Y. M. Ma, F. E. Maas, M. Maggiora, Q. A. Malik, Y. J. Mao, Z. P. Mao, S. Marcello, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, J. Min, T. J. Min, R. E. Mitchell, X. H. Mo, Y. J. Mo, C. Morales Morales, G. Morello, N. Yu. Muchnoi, H. Muramatsu, A. Mustafa, Y. Nefedov, F. Nerling, I. B. Nikolaev, Z. Ning, S. Nisar, S. L. Niu, X. Y. Niu, S. L. Olsen, Q. Ouyang, S. Pacetti, Y. Pan, M. Papenbrock, P. Patteri, M. Pelizaeus, J. Pellegrino, H. P. Peng, K. Peters, J. Pettersson, J. L. Ping, R. G. Ping, A. Pitka, R. Poling, V. Prasad, H. R. Qi, M. Qi, T. . Y. Qi, S. Qian, C. F. Qiao, N. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, K. H. Rashid, C. F. Redmer, M. Richter, M. Ripka, M. Rolo, G. Rong, Ch. Rosner, A. Sarantsev, M. Savrié, C. Schnier, K. Schoenning, W. Shan, M. Shao, C. P. Shen, P. X. Shen, X. Y. Shen, H. Y. Sheng, M. R. Shepherd, J. J. Song, W. M. Song, X. Y. Song, S. Sosio, C. Sowa, S. Spataro, G. X. Sun, J. F. Sun, L. Sun, S. S. Sun, X. H. Sun, Y. J. Sun, Y. K Sun, Y. Z. Sun, Z. J. Sun, Z. T. Sun, C. J. Tang, G. Y. Tang, X. Tang, I. Tapan, M. Tiemens, B. T. Tsednee, I. Uman, G. S. Varner, B. Wang, B. L. Wang, D. Wang, D. Y. Wang, Dan Wang, K. Wang, L. L. Wang, L. S. Wang, M. Wang, P. Wang, P. L. Wang, W. P. Wang, X. F. Wang, Y. Wang, Y. D. Wang, Y. F. Wang, Y. Q. Wang, Z. Wang, Z. G. Wang, Z. H. Wang, Z. Y. Wang, Z. Y. Wang, T. Weber, D. H. Wei, J. H. Wei, P. Weidenkaff, S. P. Wen, U. Wiedner, M. Wolke, L. H. Wu, L. J. Wu, Z. Wu, L. Xia, Y. Xia, D. Xiao, H. Xiao, Y. J. Xiao, Z. J. Xiao, X. H. Xie, Y. G. Xie, Y. H. Xie, X. A. Xiong, Q. L. Xiu, G. F. Xu, J. J. Xu, L. Xu, Q. J. Xu, Q. N. Xu, X. P. Xu, L. Yan, W. B. Yan, W. C. Yan, Y. H. Yan, H. J. Yang, H. X. Yang, L. Yang, Y. H. Yang, Y. X. Yang, M. Ye, M. H. Ye, J. H. Yin, Z. Y. You, B. X. Yu, C. X. Yu, J. S. Yu, C. Z. Yuan, Y. Yuan, A. Yuncu, A. A. Zafar, Y. Zeng, Z. Zeng, B. X. Zhang, B. Y. Zhang, C. C. Zhang, D. H. Zhang, H. H. Zhang, H. Y. Zhang, J. Zhang, J. L. Zhang, J. Q. Zhang, J. W. Zhang, J. Y. Zhang, J. Z. Zhang, K. Zhang, L. Zhang, S. Q. Zhang, X. Y. Zhang, Y. Zhang, Y. Zhang, Y. H. Zhang, Y. T. Zhang, Yu Zhang, Z. H. Zhang, Z. P. Zhang, Z. Y. Zhang, G. Zhao, J. W. Zhao, J. Y. Zhao, J. Z. Zhao, Lei Zhao, Ling Zhao, M. G. Zhao, Q. Zhao, S. J. Zhao, T. C. Zhao, Y. B. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, J. P. Zheng, W. J. Zheng, Y. H. Zheng, B. Zhong, L. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, J. Zhu, K. Zhu, K. J. Zhu, S. Zhu, S. H. Zhu, X. L. Zhu, Y. C. Zhu, Y. S. Zhu, Z. A. Zhu, J. Zhuang, B. S. Zou, J. H. Zou

We present first evidence for the process $e^+e^-\to \gamma\eta_c(1S)$ at six center-of-mass energies between 4.01 and 4.60~GeV using data collected by the BESIII experiment operating at BEPCII. Read More

We introduce a scheme for remote entanglement generation for the photon polarization. The technique is based on transferring the initial frequency correlations to specific polarization-frequency correlations by local dephasing and their subsequent removal by frequency up-conversion. On fundamental level, our theoretical results show how to create and transfer entanglement, to particles which never interact, by means of local operations. Read More

W. Zhang's arithmetic fundamental lemma (AFL) is a conjectural identity between the derivative of an orbital integral on a symmetric space with an arithmetic intersection number on a unitary Rapoport-Zink space. In the minuscule case, Rapoport-Terstiege-Zhang have verified the AFL conjecture via explicit evaluation of both sides of the identity. Read More

2017May
Authors: N. Dash, S. Bahinipati, V. Bhardwaj, K. Trabelsi, I. Adachi, H. Aihara, S. Al Said, D. M. Asner, V. Aulchenko, T. Aushev, R. Ayad, V. Babu, I. Badhrees, A. M. Bakich, V. Bansal, E. Barberio, B. Bhuyan, J. Biswal, A. Bobrov, A. Bondar, G. Bonvicini, A. Bozek, M. Bracko, F. Breibeck, T. E. Browder, D. Cervenkov, M. -C. Chang, V. Chekelian, A. Chen, B. G. Cheon, K. Chilikin, K. Cho, Y. Choi, D. Cinabro, S. Di Carlo, Z. Dolezal, Z. Drasal, D. Dutta, S. Eidelman, D. Epifanov, H. Farhat, J. E. Fast, T. Ferber, B. G. Fulsom, V. Gaur, N. Gabyshev, A. Garmash, R. Gillard, P. Goldenzweig, J. Haba, T. Hara, K. Hayasaka, H. Hayashii, M. T. Hedges, W. -S. Hou, T. Iijima, K. Inami, A. Ishikawa, R. Itoh, Y. Iwasaki, W. W. Jacobs, I. Jaegle, H. B. Jeon, Y. Jin, D. Joffe, K. K. Joo, T. Julius, J. Kahn, A. B. Kaliyar, G. Karyan, P. Katrenko, T. Kawasaki, C. Kiesling, D. Y. Kim, H. J. Kim, J. B. Kim, K. T. Kim, M. J. Kim, S. H. Kim, Y. J. Kim, K. Kinoshita, P. Kodys, S. Korpar, D. Kotchetkov, P. Krizan, P. Krokovny, T. Kuhr, R. Kulasiri, R. Kumar, T. Kumita, A. Kuzmin, Y. -J. Kwon, J. S. Lange, 11 I. S. Lee, C. H. Li, L. Li, Y. Li, L. Li Gioi, J. Libby, D. Liventsev, M. Lubej, T. Luo, M. Masuda, D. Matvienko, M. Merola, K. Miyabayashi, H. Miyata, R. Mizuk, G. B. Mohanty, S. Mohanty, H. K. Moon, T. Mori, R. Mussa, E. Nakano, M. Nakao, T. Nanut, K. J. Nath, Z. Natkaniec, M. Nayak, M. Niiyama, N. K. Nisar, S. Nishida, S. Ogawa, S. Okuno, H. Ono, P. Pakhlov, G. Pakhlova, B. Pal, S. Pardi, C. -S. Park, H. Park, S. Paul, T. K. Pedlar, L. Pesantez, R. Pestotnik, L. E. Piilonen, K. Prasanth, M. Ritter, A. Rostomyan, H. Sahoo, Y. Sakai, S. Sandilya, L. Santelj, T. Sanuki, Y. Sato, V. Savinov, O. Schneider, G. Schnell, C. Schwanda, A. J. Schwartz, Y. Seino, K. Senyo, M. E. Sevior, V. Shebalin, C. P. Shen, T. -A. Shibata, J. -G. Shiu, B. Shwartz, F. Simon, A. Sokolov, E. Solovieva, M. Staric, J. F. Strube, J. Stypula, K. Sumisawa, T. Sumiyoshi, M. Takizawa, U. Tamponi, K. Tanida, F. Tenchini, M. Uchida, T. Uglov, Y. Unno, S. Uno, P. Urquijo, Y. Usov, C. Van Hulse, G. Varner, V. Vorobyev, A. Vossen, E. Waheed, C. H. Wang, M. -Z. Wang, P. Wang, M. Watanabe, Y. Watanabe, E. Widmann, K. M. Williams, E. Won, Y. Yamashita, H. Ye, J. Yelton, Y. Yook, C. Z. Yuan, Y. Yusa, Z. P. Zhang, V. Zhilich, V. Zhukova, V. Zhulanov, A. Zupanc

We report a study of the decay $D^0 \to K^0_S K^0_S$ using 921~fb$^{-1}$ of data collected at or near the $\Upsilon(4S)$ and $\Upsilon(5S)$ resonances with the Belle detector at the KEKB asymmetric-energy $e^+e^-$ collider. The measured time-integrated CP asymmetry is $ A_{CP}(D^0 \to K^0_S K^0_S) = (-0.02 \pm 1. Read More

We propose and analyse a new class of Littlest Seesaw models, with two right-handed neutrinos in their diagonal mass basis, based on preserving the first column of the Golden Ratio mixing matrix. We perform an exhaustive analysis of all possible remnant symmetries of the group $A_5$ which can be used to enforce various vacuum alignments for the flavon controlling solar mixing, for two simple cases of the atmospheric flavon vacuum alignment. The solar and atmospheric flavon vacuum alignments are enforced by {\em different} remnant symmetries. Read More

In this short article, we state a Hopf type lemma for fractional equations and the outline of its proof. We believe that it will become a powerful tool in applying the method of moving planes on fractional equations to obtain qualitative properties of solutions. Read More

In this paper, we consider nonlinear equations involving the fractional p-Laplacian $$ (-\lap)_p^s u(x)) \equiv C_{n,s,p} PV \int_{\mathbb{R}^n} \frac{|u(x)-u(y)|^{p-2}[u(x)-u(y)]}{|x-z|^{n+ps}} dz= f(x,u).$$ We prove a {\em maximum principle for anti-symmetric functions} and obtain other key ingredients for carrying on the method of moving planes, such as {\em a key boundary estimate lemma}. Then we establish radial symmetry and monotonicity for positive solutions to semilinear equations involving the fractional p-Laplacian in a unit ball and in the whole space. Read More

2017May
Authors: M. Ablikim, M. N. Achasov, X. C. Ai, O. Albayrak, M. Albrecht, D. J. Ambrose, A. Amoroso, F. F. An, Q. An, J. Z. Bai, R. Baldini Ferroli, Y. Ban, D. W. Bennett, J. V. Bennett, M. Bertani, D. Bettoni, J. M. Bian, F. Bianchi, E. Boger, I. Boyko, R. A. Briere, H. Cai, X. Cai, O. Cakir, A. Calcaterra, G. F. Cao, S. A. Cetin, J. F. Chang, G. Chelkov, G. Chen, H. S. Chen, H. Y. Chen, J. C. Chen, M. L. Chen, S. J. Chen, X. Chen, X. R. Chen, Y. B. Chen, H. P. Cheng, X. K. Chu, G. Cibinetto, H. L. Dai, J. P. Dai, A. Dbeyssi, D. Dedovich, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, Y. Ding, C. Dong, J. Dong, L. Y. Dong, M. Y. Dong, S. X. Du, P. F. Duan, E. E. Eren, J. Z. Fan, J. Fang, S. S. Fang, X. Fang, Y. Fang, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, E. Fioravanti, M. Fritsch, C. D. Fu, Q. Gao, X. Y. Gao, Y. Gao, Z. Gao, I. Garzia, C. Geng, K. Goetzen, W. X. Gong, W. Gradl, M. Greco, M. H. Gu, Y. T. Gu, Y. H. Guan, A. Q. Guo, L. B. Guo, Y. Guo, Y. P. Guo, Z. Haddadi, A. Hafner, S. Han, Y. L. Han, X. Q. Hao, F. A. Harris, K. L. He, Z. Y. He, T. Held, Y. K. Heng, Z. L. Hou, C. Hu, H. M. Hu, J. F. Hu, T. Hu, Y. Hu, G. M. Huang, G. S. Huang, H. P. Huang, J. S. Huang, X. T. Huang, Y. Huang, T. Hussain, Q. Ji, Q. P. Ji, X. B. Ji, X. L. Ji, L. L. Jiang, L. W. Jiang, X. S. Jiang, X. Y. Jiang, J. B. Jiao, Z. Jiao, D. P. Jin, S. Jin, T. Johansson, A. Julin, N. Kalantar-Nayestanaki, X. L. Kang, X. S. Kang, M. Kavatsyuk, B. C. Ke, P. Kiese, R. Kliemt, B. Kloss, O. B. Kolcu, B. Kopf, M. Kornicer, W. Kuehn, A. Kupsc, J. S. Lange, M. Lara, P. Larin, C. Leng, C. Li, C. H. Li, Cheng Li, D. M. Li, F. Li, G. Li, H. B. Li, J. C. Li, Jin Li, K. Li, K. Li, Lei Li, P. R. Li, T. Li, W. D. Li, W. G. Li, X. L. Li, X. M. Li, X. N. Li, X. Q. Li, Z. B. Li, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, D. X. Lin, B. J. Liu, C. X. Liu, F. H. Liu, Fang Liu, Feng Liu, H. B. Liu, H. H. Liu, H. H. Liu, H. M. Liu, J. Liu, J. B. Liu, J. P. Liu, J. Y. Liu, K. Liu, K. Y. Liu, L. D. Liu, P. L. Liu, Q. Liu, S. B. Liu, X. Liu, X. X. Liu, Y. B. Liu, Z. A. Liu, Zhiqiang Liu, Zhiqing Liu, H. Loehner, X. C. Lou, H. J. Lu, J. G. Lu, R. Q. Lu, Y. Lu, Y. P. Lu, C. L. Luo, M. X. Luo, T. Luo, X. L. Luo, M. Lv, X. R. Lyu, F. C. Ma, H. L. Ma, L. L. Ma, Q. M. Ma, T. Ma, X. N. Ma, X. Y. Ma, F. E. Maas, M. Maggiora, Y. J. Mao, Z. P. Mao, S. Marcello, J. G. Messchendorp, J. Min, T. J. Min, R. E. Mitchell, X. H. Mo, Y. J. Mo, C. Morales Morales, K. Moriya, N. Yu. Muchnoi, H. Muramatsu, Y. Nefedov, F. Nerling, I. B. Nikolaev, Z. Ning, S. Nisar, S. L. Niu, X. Y. Niu, S. L. Olsen, Q. Ouyang, S. Pacetti, P. Patteri, M. Pelizaeus, H. P. Peng, K. Peters, J. Pettersson, J. L. Ping, R. G. Ping, R. Poling, V. Prasad, Y. N. Pu, M. Qi, S. Qian, C. F. Qiao, L. Q. Qin, N. Qin, X. S. Qin, Y. Qin, Z. H. Qin, J. F. Qiu, K. H. Rashid, C. F. Redmer, H. L. Ren, M. Ripka, G. Rong, Ch. Rosner, X. D. Ruan, V. Santoro, A. Sarantsev, M. Savrié, K. Schoenning, S. Schumann, W. Shan, M. Shao, C. P. Shen, P. X. Shen, X. Y. Shen, H. Y. Sheng, W. M. Song, X. Y. Song, S. Sosio, S. Spataro, G. X. Sun, J. F. Sun, S. S. Sun, Y. J. Sun, Y. Z. Sun, Z. J. Sun, Z. T. Sun, C. J. Tang, X. Tang, I. Tapan, E. H. Thorndike, M. Tiemens, M. Ullrich, I. Uman, G. S. Varner, B. Wang, B. L. Wang, D. Wang, D. Y. Wang, K. Wang, L. L. Wang, L. S. Wang, M. Wang, P. Wang, P. L. Wang, S. G. Wang, W. Wang, X. F. Wang, Y. D. Wang, Y. F. Wang, Y. Q. Wang, Z. Wang, Z. G. Wang, Z. H. Wang, Z. Y. Wang, T. Weber, D. H. Wei, J. B. Wei, P. Weidenkaff, S. P. Wen, U. Wiedner, M. Wolke, L. H. Wu, Z. Wu, L. G. Xia, Y. Xia, D. Xiao, H. Xiao, Z. J. Xiao, Y. G. Xie, Q. L. Xiu, G. F. Xu, L. Xu, Q. J. Xu, Q. N. Xu, X. P. Xu, L. Yan, W. B. Yan, W. C. Yan, Y. H. Yan, H. J. Yang, H. X. Yang, L. Yang, Y. Yang, Y. X. Yang, H. Ye, M. Ye, M. H. Ye, J. H. Yin, B. X. Yu, C. X. Yu, H. W. Yu, J. S. Yu, C. Z. Yuan, W. L. Yuan, Y. Yuan, A. Yuncu, A. A. Zafar, A. Zallo, Y. Zeng, B. X. Zhang, B. Y. Zhang, C. Zhang, C. C. Zhang, D. H. Zhang, H. H. Zhang, H. Y. Zhang, J. J. Zhang, J. L. Zhang, J. Q. Zhang, J. W. Zhang, J. Y. Zhang, J. Z. Zhang, K. Zhang, L. Zhang, S. H. Zhang, X. Y. Zhang, Y. Zhang, Y. N. Zhang, Y. H. Zhang, Y. T. Zhang, Yu Zhang, Z. H. Zhang, Z. P. Zhang, Z. Y. Zhang, G. Zhao, J. W. Zhao, J. Y. Zhao, J. Z. Zhao, Lei Zhao, Ling Zhao, M. G. Zhao, Q. Zhao, Q. W. Zhao, S. J. Zhao, T. C. Zhao, Y. B. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, J. P. Zheng, W. J. Zheng, Y. H. Zheng, B. Zhong, L. Zhou, Li Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, K. Zhu, K. J. Zhu, S. Zhu, X. L. Zhu, Y. C. Zhu, Y. S. Zhu, Z. A. Zhu, J. Zhuang, L. Zotti, B. S. Zou, J. H. Zou

Using a data set of 2.93 fb$^{-1}$ taken at a center-of-mass energy $\sqrt{s}$ = 3.773 GeV with the BESIII detector at the BEPCII collider, we perform a search for an extra U(1) gauge boson, also denoted as a dark photon. Read More

While both the data volume and heterogeneity of the digital music content is huge, it has become increasingly important and convenient to build a recommendation or search system to facilitate surfacing these content to the user or consumer community. Most of the recommendation models fall into two primary species, collaborative filtering based and content based approaches. Variants of instantiations of collaborative filtering approach suffer from the common issues of so called "cold start" and "long tail" problems where there is not much user interaction data to reveal user opinions or affinities on the content and also the distortion towards the popular content. Read More

To locate all eigenvalues of a matrix more precisely, we exclude some sets which do not include any eigenvalue of the matrix from the well-known Brauer set to give two new Brauer-type eigenvalue inclusion sets. And it is also shown that the new sets are contained in the Brauer set. Read More

We state a precise conjectural isomorphism between localizations of the equivariant quantum K-theory ring of a flag variety and the equivariant K-homology ring of the affine Grassmannian, in particular relating their Schubert bases and structure constants. This generalizes Peterson's isomorphism in (co)homology. We prove a formula for the Pontryagin structure constants in the K-homology ring, and we use it to check our conjecture in few situations. Read More

Visual data such as videos are often sampled from complex manifold. We propose leveraging the manifold structure to constrain the deep action feature learning, thereby minimizing the intra-class variations in the feature space and alleviating the over-fitting problem. Considering that manifold can be transferred, layer by layer, from the data domain to the deep features, the manifold priori is posed from the top layer into the back propagation learning procedure of convolutional neural network (CNN). Read More

2017May
Authors: Belle Collaboration, C. -L. Hsu, D. Dossett, M. E. Sevior, I. Adachi, H. Aihara, S. Al Said, D. M. Asner, H. Atmacan, V. Aulchenko, T. Aushev, I. Badhrees, A. M. Bakich, E. Barberio, P. Behera, M. Berger, V. Bhardwaj, B. Bhuyan, J. Biswal, T. Bloomfield, A. Bondar, G. Bonvicini, A. Bozek, M. Bračko, T. E. Browder, V. Chekelian, A. Chen, K. Chilikin, R. Chistov, K. Cho, Y. Choi, D. Cinabro, N. Dash, S. Di Carlo, Z. Doležal, Z. Drásal, S. Eidelman, H. Farhat, J. E. Fast, B. G. Fulsom, V. Gaur, N. Gabyshev, A. Garmash, P. Goldenzweig, B. Golob, O. Grzymkowska, E. Guido, T. Hara, K. Hayasaka, H. Hayashii, M. T. Hedges, W. -S. Hou, K. Inami, G. Inguglia, A. Ishikawa, W. W. Jacobs, I. Jaegle, H. B. Jeon, Y. Jin, D. Joffe, K. K. Joo, T. Julius, A. B. Kaliyar, K. H. Kang, P. Katrenko, T. Kawasaki, C. Kiesling, D. Y. Kim, H. J. Kim, J. B. Kim, K. T. Kim, M. J. Kim, S. H. Kim, P. Kodyš, S. Korpar, D. Kotchetkov, P. Križan, P. Krokovny, T. Kuhr, R. Kulasiri, A. Kuzmin, Y. -J. Kwon, Y. -T. Lai, J. S. Lange, C. H. Li, L. Li, L. Li Gioi, J. Libby, D. Liventsev, M. Lubej, T. Luo, M. Masuda, T. Matsuda, D. Matvienko, K. Miyabayashi, H. Miyata, R. Mizuk, G. B. Mohanty, T. Mori, R. Mussa, E. Nakano, M. Nakao, T. Nanut, K. J. Nath, Z. Natkaniec, M. Nayak, N. K. Nisar, S. Nishida, S. Ogawa, S. Okuno, H. Ono, Y. Onuki, G. Pakhlova, B. Pal, C. -S. Park, C. W. Park, H. Park, S. Paul, L. Pesántez, L. E. Piilonen, A. Rostomyan, Y. Sakai, S. Sandilya, T. Sanuki, Y. Sato, V. Savinov, O. Schneider, G. Schnell, C. Schwanda, Y. Seino, K. Senyo, V. Shebalin, T. -A. Shibata, J. -G. Shiu, B. Shwartz, F. Simon, E. Solovieva, M. Starič, J. F. Strube, K. Sumisawa, M. Takizawa, F. Tenchini, M. Uchida, T. Uglov, Y. Unno, S. Uno, P. Urquijo, Y. Usov, C. Van Hulse, G. Varner, K. E. Varvell, V. Vorobyev, E. Waheed, C. H. Wang, M. -Z. Wang, M. Watanabe, Y. Watanabe, E. Widmann, K. M. Williams, E. Won, Y. Yamashita, H. Ye, Z. P. Zhang, V. Zhilich, V. Zhukova, V. Zhulanov, A. Zupanc

We report a study of the charmless hadronic decay of the charged $B$ meson to the three-body final state $K^+ K^- \pi^+$. The results are based on a data sample that contains $772\times10^6$ $B \bar{B}$ pairs collected at the $\Upsilon(4S)$ resonance with the Belle detector at the KEKB asymmetric-energy $e^+ e^-$ collider. The measured inclusive branching fraction and the direct $CP$ asymmetry are $(5. Read More

2017May
Authors: T. Julius1, M. E. Sevior2, G. B. Mohanty3, I. Adachi4, H. Aihara5, S. Al Said6, D. M. Asner7, V. Aulchenko8, T. Aushev9, R. Ayad10, V. Babu11, I. Badhrees12, A. M. Bakich13, V. Bansal14, E. Barberio15, M. Barrett16, M. Berger17, V. Bhardwaj18, B. Bhuyan19, J. Biswal20, T. Bloomfield21, A. Bobrov22, A. Bondar23, G. Bonvicini24, A. Bozek25, M. Bračko26, T. E. Browder27, D. Červenkov28, M. -C. Chang29, Y. Chao30, V. Chekelian31, A. Chen32, B. G. Cheon33, K. Chilikin34, K. Cho35, Y. Choi36, D. Cinabro37, N. Dash38, S. Di Carlo39, Z. Doležal40, D. Dossett41, Z. Drásal42, D. Dutta43, S. Eidelman44, H. Farhat45, J. E. Fast46, T. Ferber47, B. G. Fulsom48, V. Gaur49, N. Gabyshev50, A. Garmash51, R. Gillard52, P. Goldenzweig53, J. Haba54, T. Hara55, K. Hayasaka56, H. Hayashii57, W. -S. Hou58, C. -L. Hsu59, T. Iijima60, K. Inami61, A. Ishikawa62, R. Itoh63, Y. Iwasaki64, W. W. Jacobs65, I. Jaegle66, Y. Jin67, D. Joffe68, K. K. Joo69, J. Kahn70, G. Karyan71, P. Katrenko72, T. Kawasaki73, C. Kiesling74, D. Y. Kim75, H. J. Kim76, J. B. Kim77, K. T. Kim78, M. J. Kim79, S. H. Kim80, Y. J. Kim81, K. Kinoshita82, P. Kodyš83, S. Korpar84, D. Kotchetkov85, P. Križan86, P. Krokovny87, T. Kuhr88, R. Kulasiri89, A. Kuzmin90, Y. -J. Kwon91, J. S. Lange92, I. S. Lee93, C. H. Li94, L. Li95, Y. Li96, L. Li Gioi97, J. Libby98, D. Liventsev99, T. Luo100, J. MacNaughton101, M. Masuda102, T. Matsuda103, M. Merola104, K. Miyabayashi105, H. Miyata106, R. Mizuk107, H. K. Moon108, T. Mori109, R. Mussa110, E. Nakano111, M. Nakao112, T. Nanut113, K. J. Nath114, Z. Natkaniec115, M. Nayak116, N. K. Nisar117, S. Nishida118, S. Ogawa119, H. Ono120, P. Pakhlov121, G. Pakhlova122, B. Pal123, S. Pardi124, C. -S. Park125, H. Park126, L. Pesántez127, R. Pestotnik128, L. E. Piilonen129, C. Pulvermacher130, M. Ritter131, H. Sahoo132, Y. Sakai133, M. Salehi134, S. Sandilya135, L. Santelj136, T. Sanuki137, Y. Sato138, V. Savinov139, O. Schneider140, G. Schnell141, C. Schwanda142, A. J. Schwartz143, Y. Seino144, K. Senyo145, V. Shebalin146, T. -A. Shibata147, J. -G. Shiu148, B. Shwartz149, A. Sokolov150, E. Solovieva151, M. Starič152, T. Sumiyoshi153, U. Tamponi154, K. Tanida155, F. Tenchini156, K. Trabelsi157, M. Uchida158, S. Uehara159, T. Uglov160, Y. Unno161, S. Uno162, P. Urquijo163, Y. Usov164, C. Van Hulse165, G. Varner166, K. E. Varvell167, A. Vossen168, E. Waheed169, C. H. Wang170, M. -Z. Wang171, P. Wang172, M. Watanabe173, Y. Watanabe174, E. Widmann175, K. M. Williams176, E. Won177, Y. Yamashita178, H. Ye179, C. Z. Yuan180, Y. Yusa181, Z. P. Zhang182, V. Zhilich183, V. Zhulanov184, A. Zupanc185
Affiliations: 1The Belle Collaboration, 2The Belle Collaboration, 3The Belle Collaboration, 4The Belle Collaboration, 5The Belle Collaboration, 6The Belle Collaboration, 7The Belle Collaboration, 8The Belle Collaboration, 9The Belle Collaboration, 10The Belle Collaboration, 11The Belle Collaboration, 12The Belle Collaboration, 13The Belle Collaboration, 14The Belle Collaboration, 15The Belle Collaboration, 16The Belle Collaboration, 17The Belle Collaboration, 18The Belle Collaboration, 19The Belle Collaboration, 20The Belle Collaboration, 21The Belle Collaboration, 22The Belle Collaboration, 23The Belle Collaboration, 24The Belle Collaboration, 25The Belle Collaboration, 26The Belle Collaboration, 27The Belle Collaboration, 28The Belle Collaboration, 29The Belle Collaboration, 30The Belle Collaboration, 31The Belle Collaboration, 32The Belle Collaboration, 33The Belle Collaboration, 34The Belle Collaboration, 35The Belle Collaboration, 36The Belle Collaboration, 37The Belle Collaboration, 38The Belle Collaboration, 39The Belle Collaboration, 40The Belle Collaboration, 41The Belle Collaboration, 42The Belle Collaboration, 43The Belle Collaboration, 44The Belle Collaboration, 45The Belle Collaboration, 46The Belle Collaboration, 47The Belle Collaboration, 48The Belle Collaboration, 49The Belle Collaboration, 50The Belle Collaboration, 51The Belle Collaboration, 52The Belle Collaboration, 53The Belle Collaboration, 54The Belle Collaboration, 55The Belle Collaboration, 56The Belle Collaboration, 57The Belle Collaboration, 58The Belle Collaboration, 59The Belle Collaboration, 60The Belle Collaboration, 61The Belle Collaboration, 62The Belle Collaboration, 63The Belle Collaboration, 64The Belle Collaboration, 65The Belle Collaboration, 66The Belle Collaboration, 67The Belle Collaboration, 68The Belle Collaboration, 69The Belle Collaboration, 70The Belle Collaboration, 71The Belle Collaboration, 72The Belle Collaboration, 73The Belle Collaboration, 74The Belle 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182The Belle Collaboration, 183The Belle Collaboration, 184The Belle Collaboration, 185The Belle Collaboration

We measure the branching fraction and $CP$-violating asymmetry in the decay $B^{0}\to \pi^{0}\pi^{0}$, using a data sample of $752\times 10^{6}$ $B\bar{B}$ pairs collected at the $\Upsilon(4S)$ resonance with the Belle detector at the KEKB $e^{+}e^{-}$ collider. The obtained branching fraction and direct $CP$ asymmetry are $ \mathcal{B}(B\to \pi^{0}\pi^{0}) = [1.31 \pm 0. Read More

We present Deep Speaker, a neural speaker embedding system that maps utterances to a hypersphere where speaker similarity is measured by cosine similarity. The embeddings generated by Deep Speaker can be used for many tasks, including speaker identification, verification, and clustering. We experiment with ResCNN and GRU architectures to extract the acoustic features, then mean pool to produce utterance-level speaker embeddings, and train using triplet loss based on cosine similarity. Read More

In this paper, we construct a new even constrained B(C) type Toda hierarchy and derive its B(C) type Block type additional symmetry. Also we generalize the B(C) type Toda hierarchy to the $N$-component B(C) type Toda hierarchy which is proved to have symmetries of a coupled $\bigotimes^NQT_+ $ algebra ( $N$-folds direct product of the positive half of the quantum torus algebra $QT$). Read More

Multi-label text classification is a popular machine learning task where each document is assigned with multiple relevant labels. This task is challenging due to high dimensional features and correlated labels. Multi-label text classifiers need to be carefully regularized to prevent the severe over-fitting in the high dimensional space, and also need to take into account label dependencies in order to make accurate predictions under uncertainty. Read More

A new method for learning variational autoencoders is developed, based on an application of Stein's operator. The framework represents the encoder as a deep nonlinear function through which samples from a simple distribution are fed. One need not make parametric assumptions about the form of the encoder distribution, and performance is further enhanced by integrating the proposed encoder with importance sampling. Read More

Graphene has hugely increased its quality in nanodevices thanks to hexagonal boron nitride (hBN) acting as a supporting layer. Here, we investigate to which extent hBN and channel length scaling can be exploited in graphene field-effect transistors (GFETs) to get competitive radio-frequency (RF) performances. For such a purpose, we have applied multi-scale physics-based techniques to assess the scalability of the transistor RF performance via reduction of the channel length. Read More

In this article, we consider discrete schemes for a fractional diffusion equation involving a tempered fractional derivative in time. We present a semi-discrete scheme by using the local discontinuous Galerkin (LDG) discretization in the spatial variables. We prove that the semi-discrete scheme is unconditionally stable in $L^2$ norm and convergence with optimal convergence rate $\mathcal{O}(h^{k+1})$. Read More

2017Apr
Authors: BESIII collaboration, M. Ablikim, M. N. Achasov, S. Ahmed, O. Albayrak, M. Albrecht, M. Alekseev, A. Amoroso, F. F. An, Q. An, J. Z. Bai, O. Bakina, R. Baldini Ferroli, Y. Ban, D. W. Bennett, J. V. Bennett, N. Berger, M. Bertani, D. Bettoni, J. M. Bian, F. Bianchi, E. Boger, I. Boyko, R. A. Briere, H. Cai, X. Cai, O. Cakir, A. Calcaterra, G. F. Cao, S. A. Cetin, J. Chai, J. F. Chang, G. Chelkov, G. Chen, H. S. Chen, J. C. Chen, M. L. Chen, P. L. Chen, S. J. Chen, X. R. Chen, Y. B. Chen, X. K. Chu, G. Cibinetto, H. L. Dai, J. P. Dai, A. Dbeyssi, D. Dedovich, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. DeMori, Y. Ding, C. Dong, J. Dong, L. Y. Dong, M. Y. Dong, O. Dorjkhaidav, Z. L. Dou, S. X. Du, P. F. Duan, J. Fang, S. S. Fang, X. Fang, Y. Fang, R. Farinelli, L. Fava, S. Fegan, F. Feldbauer, G. Felici, C. Q. Feng, E. Fioravanti, M. Fritsch, C. D. Fu, Gao, Q. Gao, X. L. Gao, Y. Gao, Z. Gao, I. Garzia, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, M. Greco, M. H. Gu, S. Gu, Y. T. Gu, A. Q. Guo, L. B. Guo, R. P. Guo, Y. P. Guo, Z. Haddadi, A. Hafner, S. Han, X. Q. Hao, F. A. Harris, K. L. He, X. Q. He, F. H. Heinsius, T. Held, Y. K. Heng, T. Holtmann, Z. L. Hou, C. Hu, H. M. Hu, T. Hu, Y. Hu, G. S. Huang, J. S. Huang, X. T. Huang, X. Z. Huang, Z. L. Huang, T. Hussain, W. Ikegami Andersson, Q. Ji, Q. P. Ji, X. B. Ji, X. L. Ji, X. S. Jiang, X. Y. Jiang, J. B. Jiao, Z. Jiao, D. P. Jin, S. Jin, T. Johansson, A. Julin, N. Kalantar-Nayestanaki, X. L. Kang, X. S. Kang, M. Kavatsyuk, B. C. Ke, Tabassum KhanKhan, P. Kiese, R. Kliemt, B. Kloss, L. K. Koch, O. B. Kolcu, B. Kopf, M. Kornicer, M. Kuemmel, M. Kuhlmann, A. Kupsc, W. Kühn, J. S. Lange, M. Lara, P. Larin, L. Lavezzi, H. Leithoff, C. Leng, C. Li, ChengLi, D. M. Li, F. Li, F. Y. Li, G. Li, H. B. Li, H. J. Li, J. C. Li, JinLi, K. Li, K. Li, LeiLi, P. L. Li, P. R. Li, Q. Y. Li, T. Li, W. D. Li, W. G. Li, X. L. Li, X. N. Li, X. Q. Li, Z. B. Li, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, D. X. Lin, B. Liu, B. J. Liu, C. X. Liu, D. Liu, F. H. Liu, FangLiu, FengLiu, H. B. Liu, H. H. Liu, H. H. Liu, H. M. Liu, J. B. Liu, J. P. Liu, J. Y. Liu, K. Liu, K. Y. Liu, KeLiu, L. D. Liu, P. L. Liu, Q. Liu, S. B. Liu, X. Liu, Y. B. Liu, Y. Y. Liu, Z. A. Liu, ZhiqingLiu, Y. F. Long, X. C. Lou, H. J. Lu, J. G. Lu, Y. Lu, Y. P. Lu, C. L. Luo, M. X. Luo, T. Luo, X. L. Luo, X. R. Lyu, F. C. Ma, H. L. Ma, L. L. Ma, M. M. Ma, Q. M. Ma, T. Ma, X. N. Ma, X. Y. Ma, Y. M. Ma, F. E. Maas, M. Maggiora, Q. A. Malik, Y. J. Mao, Z. P. Mao, S. Marcello, J. G. Messchendorp, G. Mezzadri, J. Min, T. J. Min, R. E. Mitchell, X. H. Mo, Y. J. Mo, C. Morales Morales, G. Morello, N. Yu. Muchnoi, H. Muramatsu, P. Musiol, A. Mustafa, Y. Nefedov, F. Nerling, I. B. Nikolaev, Z. Ning, S. Nisar, S. L. Niu, X. Y. Niu, S. L. Olsen, Q. Ouyang, S. Pacetti, Y. Pan, P. Patteri, M. Pelizaeus, J. Pellegrino, H. P. Peng, K. Peters, J. Pettersson, J. L. Ping, R. G. Ping, R. Poling, V. Prasad, H. R. Qi, M. Qi, S. Qian, C. F. Qiao, J. J. Qin, N. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, K. H. Rashid, C. F. Redmer, M. Richter, M. Ripka, G. Rong, Ch. Rosner, X. D. Ruan, A. Sarantsev, M. Savrié, C. Schnier, K. Schoenning, W. Shan, M. Shao, C. P. Shen, P. X. Shen, X. Y. Shen, H. Y. Sheng, J. J. Song, X. Y. Song, S. Sosio, C. Sowa, S. Spataro, G. X. Sun, J. F. Sun, S. S. Sun, X. H. Sun, Y. J. Sun, Y. KSun, Y. Z. Sun, Z. J. Sun, Z. T. Sun, C. J. Tang, X. Tang, I. Tapan, M. Tiemens, TsTsednee, I. Uman, G. S. Varner, B. Wang, B. L. Wang, D. Wang, D. Y. Wang, DanWang, K. Wang, L. L. Wang, L. S. Wang, M. Wang, P. Wang, P. L. Wang, W. P. Wang, X. F. Wang, Y. D. Wang, Y. F. Wang, Y. Q. Wang, Z. Wang, Z. G. Wang, Z. H. Wang, Z. Y. Wang, Z. Y. Wang, T. Weber, D. H. Wei, P. Weidenkaff, S. P. Wen, U. Wiedner, M. Wolke, L. H. Wu, L. J. Wu, Z. Wu, L. Xia, Y. Xia, D. Xiao, Y. J. Xiao, Z. J. Xiao, Y. G. Xie, YuehongXie, X. A. Xiong, Q. L. Xiu, G. F. Xu, J. J. Xu, L. Xu, Q. J. Xu, Q. N. Xu, X. P. Xu, L. Yan, W. B. Yan, W. C. Yan, Y. H. Yan, H. J. Yang, H. X. Yang, L. Yang, Y. H. Yang, Y. X. Yang, M. Ye, M. H. Ye, J. H. Yin, Z. Y. You, B. X. Yu, C. X. Yu, J. S. Yu, C. Z. Yuan, Y. Yuan, A. Yuncu, A. A. Zafar, Y. Zeng, Z. Zeng, B. X. Zhang, B. Y. Zhang, C. C. Zhang, D. H. Zhang, H. H. Zhang, H. Y. Zhang, J. Zhang, J. L. Zhang, J. Q. Zhang, J. W. Zhang, J. Y. Zhang, J. Z. Zhang, K. Zhang, L. Zhang, S. Q. Zhang, X. Y. Zhang, Y. Zhang, Y. Zhang, Y. H. Zhang, Y. T. Zhang, YuZhang, Z. H. Zhang, Z. P. Zhang, Z. Y. Zhang, G. Zhao, J. W. Zhao, J. Y. Zhao, J. Z. Zhao, LeiZhao, LingZhao, M. G. Zhao, Q. Zhao, S. J. Zhao, T. C. Zhao, Y. B. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, J. P. Zheng, W. J. Zheng, Y. H. Zheng, B. Zhong, L. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. X. Zhou, K. Zhu, K. J. Zhu, S. Zhu, S. H. Zhu, X. L. Zhu, Y. C. Zhu, Y. S. Zhu, Z. A. Zhu, J. Zhuang, L. Zotti, B. S. Zou, J. H. Zou

We observe for the first time the process $e^{+}e^{-} \rightarrow \eta h_c$ with data collected by the BESIII experiment. Significant signals are observed at the center-of-mass energy $\sqrt{s}=4.226$ GeV, and the Born cross section is measured to be $(9. Read More

Current state-of-the-art approaches to skeleton-based action recognition are mostly based on recurrent neural networks (RNN). In this paper, we propose a novel convolutional neural networks (CNN) based framework for both action classification and detection. Raw skeleton coordinates as well as skeleton motion are fed directly into CNN for label prediction. Read More

The goal of this paper is to achieve a computational model and corresponding efficient algorithm for obtaining a sparse representation of the fitting surface to the given scattered data. The basic idea of the model is to utilize the principal shift invariant (PSI) space and the l1 norm minimization. In order to obtain different sparsity of the approximation solution, the problem is represented as a multilevel LASSO (MLASSO) model with different regularization parameters. Read More

In this work, we propose "Residual Attention Network", a convolutional neural network using attention mechanism which can incorporate with state-of-art feed forward network architecture in an end-to-end training fashion. Our Residual Attention Network is built by stacking Attention Modules which generate attention-aware features. The attention-aware features from different modules change adaptively as layers going deeper. Read More

We present here a model of carrier distribution and transport in semiconductor alloys accounting for quantum localization effects in disordered materials. This model is based on the recent development of a mathematical theory of quantum localization which introduces for each type of carrier a spatial function called \emph{localization landscape}. These landscapes allow us to predict the localization regions of electron and hole quantum states, their corresponding energies, and the local densities of states. Read More

Urbach tails in semiconductors are often associated to effects of compositional disorder. The Urbach tail observed in InGaN alloy quantum wells of solar cells and LEDs by biased photocurrent spectroscopy is shown to be characteristic of the ternary alloy disorder. The broadening of the absorption edge observed for quantum wells emitting from violet to green (indium content ranging from 0 to 28\%) corresponds to a typical Urbach energy of 20~meV. Read More

A century after its conception, quantum mechanics still hold surprises that contradict many "common sense" notions. The contradiction is especially sharp in case one consider trajectories of truly quantum objects such as single photons. From a classical point of view, trajectories are well defined for particles, but not for waves. Read More

This paper introduces a novel method to account for quantum disorder effects into the classical drift-diffusion model of semiconductor transport through the localization landscape theory. Quantum confinement and quantum tunneling in the disordered system change dramatically the energy barriers acting on the perpendicular transport of heterostructures. In addition they lead to percolative transport through paths of minimal energy in the 2D landscape of disordered energies of multiple 2D quantum wells. Read More

We study the naturalness, dark matter, and muon anomalous magnetic moment in the Supersymmetric Standard Models (SSMs) with a pseudo-Dirac gluino (PDGSSMs) from hybrid $F-$ and $D-$term supersymmetry (SUSY) breakings. To obtain the observed dark matter relic density and explain the muon anomalous magnetic moment, we find that the low energy fine-tuning measures are larger than about 200 due to strong constraints from the LUX and PANDAX experiments. Thus, to study the natural PDGSSMs, we consider multi-component dark matter and then the relic density of the lighest supersymmetric particle (LSP) neutralino is smaller than the correct value. Read More

High-temperature superconductivity is closely adjacent to a long-range antiferromagnetism, which is called as parent compound. In cuprates, all parent compounds are alike and carrier doping leads to superconductivity, so a unified phase diagram can be drawn. However, the properties of parent compounds for iron-based superconductors show significant diversity and both carrier and isovalent doping can cause superconductivity, which cast doubt on the idea that there is a unified phase diagram for them. Read More

We study two coupled Su-Schrieffer-Heeger (SSH) chains system, which is shown to contain rich quantum phases associated with topological invariants protected by symmetries. In the weak coupling region, the system supports two non-trivial topological insulating phases, characterized by winding number N = +1 or -1, and two types of edge states. The boundary between the two topological phases arises from two band closing points, which exhibit topological characteristics in one-dimensional k space. Read More

The task of next POI recommendation has been studied extensively in recent years. However, developing an unified recommendation framework to incorporate multiple factors associated with both POIs and users remains challenging, because of the heterogeneity nature of these information. Further, effective mechanisms to handle cold-start and endow the system with interpretability are also difficult topics. Read More

In this paper, from the algebraic reductions from the Lie algebra $gl(n,\mathbb C)$ to its commutative subalgebra $Z_n$, we construct the general $Z_n$-Sine-Gordon and $Z_n$-Sinh-Gordon systems which contain many multi-component Sine-Gordon type and Sinh-Gordon type equations. Meanwhile, we give the B\"acklund transformations of the $Z_n$-Sine-Gordon and $Z_n$-Sinh-Gordon equations which can generate new solutions from seed solutions. To see the $Z_n$-systems clearly, we consider the $Z_2$-Sine-Gordon and $Z_3$-Sine-Gordon equations explicitly including their B\"acklund transformations, the nonlinear superposition formula and Lax pairs. Read More

The STAR experiment at RHIC is planning to upgrade the Time Projection Chamber which lies at the heart of the detector. We have designed an instrument to measure the tension of the wires in the multi-wire proportional chambers (MWPCs) which will be used in the TPC upgrade. The wire tension measurement system causes the wires to vibrate and then it measures the fundamental frequency of the oscillation via a laser based optical platform. Read More

Using the Dirac and the Yang monopole in spinor condensates as examples, we show that interactions can stretch the point singularity of a monopole into an extended manifold, whose shape is strongly influenced by the sign of interaction. The singular manifold will cause the first and second Chern number to assume non-integer values when it intersects the surface on which the Chern numbers are calculated. This leads to a gradual decrease of the Chern numbers as the monopole moves away from the surface of integration, instead of the sudden jump characteristic of a point monopole. Read More

We completely describe the Brill-Noether theory for curves in the primitive linear system on generic abelian surfaces, in the following sense: given integers $d$ and $r$, consider the variety $V^r_d(|H|)$ parametrizing curves $C$ in the primitive linear system $|H|$ together with a torsion-free sheaf on $C$ of degree $d$ and $r+1$ global sections. We give a necessary and sufficient condition for this variety to be non-empty, and show that it is either a disjoint union of Grassmannians, or irreducible. Moreover, we show that, when non-empty, it is of expected dimension. Read More

C-eigenvalues of piezoelectric-type tensors which are real and always exist, are introduced by Chen et al. [1]. And the largest C-eigenvalue for the piezoelectric tensor determines the highest piezoelectric coupling constant. Read More

Recent algorithmic developments have enabled computers to automatically determine and prove the capacity regions of small hypergraph networks under network coding. A structural theory relating network coding problems of different sizes is developed to make best use of this newfound computational capability. A formal notion of network minimality is developed which removes components of a network coding problem that are inessential to its core complexity. Read More

Miniscope calcium imaging is increasingly being used to monitor large populations of neuronal activities in freely behaving animals. However, due to the high background and low signal-to-noise ratio of the single-photon based imaging used in this technique, extraction of neural signals from the large numbers of imaged cells automatically has remained challenging. Here we describe a highly accurate framework for automatically identifying activated neurons and extracting calcium signals from the miniscope imaging data, seeds cleansing Constrained Nonnegative Matrix Factorization (sc-CNMF). Read More

2017Apr
Authors: F. P. An, A. B. Balantekin, H. R. Band, M. Bishai, S. Blyth, D. Cao, G. F. Cao, J. Cao, Y. L. Chan, J. F. Chang, Y. Chang, H. S. Chen, Q. Y. Chen, S. M. Chen, Y. X. Chen, Y. Chen, J. Cheng, Z. K. Cheng, J. J. Cherwinka, M. C. Chu, A. Chukanov, J. P. Cummings, Y. Y. Ding, M. V. Diwan, M. Dolgareva, J. Dove, D. A. Dwyer, W. R. Edwards, R. Gill, M. Gonchar, G. H. Gong, H. Gong, M. Grassi, W. Q. Gu, L. Guo, X. H. Guo, Y. H. Guo, Z. Guo, R. W. Hackenburg, S. Hans, M. He, K. M. Heeger, Y. K. Heng, A. Higuera, Y. B. Hsiung, B. Z. Hu, T. Hu, E. C. Huang, H. X. Huang, X. T. Huang, Y. B. Huang, P. Huber, W. Huo, G. Hussain, D. E. Jaffe, K. L. Jen, X. P. Ji, X. L. Ji, J. B. Jiao, R. A. Johnson, D. Jones, L. Kang, S. H. Kettell, A. Khan, S. Kohn, M. Kramer, K. K. Kwan, M. W. Kwok, T. J. Langford, K. Lau, L. Lebanowski, J. Lee, J. H. C. Lee, R. T. Lei, R. Leitner, J. K. C. Leung, C. Li, D. J. Li, F. Li, G. S. Li, Q. J. Li, S. Li, S. C. Li, W. D. Li, X. N. Li, X. Q. Li, Y. F. Li, Z. B. Li, H. Liang, C. J. Lin, G. L. Lin, S. Lin, S. K. Lin, Y. -C. Lin, J. J. Ling, J. M. Link, L. Littenberg, B. R. Littlejohn, J. L. Liu, J. C. Liu, C. W. Loh, C. Lu, H. Q. Lu, J. S. Lu, K. B. Luk, X. Y. Ma, X. B. Ma, Y. Q. Ma, Y. Malyshkin, D. A. Martinez Caicedo, K. T. McDonald, R. D. McKeown, I. Mitchell, Y. Nakajima, J. Napolitano, D. Naumov, E. Naumova, H. Y. Ngai, J. P. Ochoa-Ricoux, A. Olshevskiy, H. -R. Pan, J. Park, S. Patton, V. Pec, J. C. Peng, L. Pinsky, C. S. J. Pun, F. Z. Qi, M. Qi, X. Qian, R. M. Qiu, N. Raper, J. Ren, R. Rosero, B. Roskovec, X. C. Ruan, H. Steiner, P. Stoler, J. L. Sun, W. Tang, D. Taychenachev, K. Treskov, K. V. Tsang, C. E. Tull, N. Viaux, B. Viren, V. Vorobel, C. H. Wang, M. Wang, N. Y. Wang, R. G. Wang, W. Wang, X. Wang, Y. F. Wang, Z. Wang, Z. Wang, Z. M. Wang, H. Y. Wei, L. J. Wen, K. Whisnant, C. G. White, L. Whitehead, T. Wise, H. L. H. Wong, S. C. F. Wong, E. Worcester, C. -H. Wu, Q. Wu, W. J. Wu, D. M. Xia, J. K. Xia, Z. Z. Xing, J. L. Xu, Y. Xu, T. Xue, C. G. Yang, H. Yang, L. Yang, M. S. Yang, M. T. Yang, Y. Z. Yang, M. Ye, Z. Ye, M. Yeh, B. L. Young, Z. Y. Yu, S. Zeng, L. Zhan, C. Zhang, C. C. Zhang, H. H. Zhang, J. W. Zhang, Q. M. Zhang, R. Zhang, X. T. Zhang, Y. M. Zhang, Y. X. Zhang, Y. M. Zhang, Z. J. Zhang, Z. Y. Zhang, Z. P. Zhang, J. Zhao, L. Zhou, H. L. Zhuang, J. H. Zou

The Daya Bay experiment has observed correlations between reactor core fuel evolution and changes in the reactor antineutrino flux and energy spectrum. Four antineutrino detectors in two experimental halls were used to identify 2.2 million inverse beta decays (IBDs) over 1230 days spanning multiple fuel cycles for each of six 2. Read More

Time-series, multi-color photometry and high-resolution spectra of the short period eclipsing binary V Tri were obtained by observations. The completely covered light and radial velocity curves of the binary system are presented. All times of light minima derived from both photoelectric and CCD photometry were used to calculate the orbital period and new ephemerides of the eclipsing system. Read More

Although extensive experimental and theoretical works have been conducted to understand the ballistic and diffusive phonon transport in nanomaterials recently, direct observation of temperature and thermal nonequilibrium of different phonon modes has not been realized. Herein, we have developed a method within the framework of molecular dynamics to calculate the temperatures of phonon in both real and phase spaces. Taking silicon thin film and graphene as examples, we directly obtained the spectral phonon temperature (SPT) and observed the local thermal nonequilibrium between the ballistic and diffusive phonons. Read More

While deep learning methods have achieved state-of-the-art performance in many challenging inverse problems like image inpainting and super-resolution, they invariably involve problem-specific training of the networks. Under this approach, different problems require different networks. In scenarios where we need to solve a wide variety of problems, e. Read More