G. L. Lin - LKB

G. L. Lin
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G. L. Lin
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LKB
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Computer Science - Computer Vision and Pattern Recognition (11)
 
High Energy Physics - Experiment (10)
 
Computer Science - Data Structures and Algorithms (6)
 
Physics - Instrumentation and Detectors (6)
 
High Energy Physics - Phenomenology (5)
 
Physics - Chemical Physics (4)
 
Mathematics - Numerical Analysis (4)
 
Physics - Strongly Correlated Electrons (3)
 
Physics - Materials Science (3)
 
Nuclear Experiment (3)
 
Solar and Stellar Astrophysics (2)
 
High Energy Astrophysical Phenomena (2)
 
Instrumentation and Methods for Astrophysics (1)
 
Statistics - Theory (1)
 
Physics - Superconductivity (1)
 
Physics - Data Analysis; Statistics and Probability (1)
 
Mathematics - Statistics (1)
 
Computer Science - Learning (1)
 
Mathematics - Optimization and Control (1)
 
Computer Science - Discrete Mathematics (1)
 
Computer Science - Human-Computer Interaction (1)
 
Physics - Atomic Physics (1)
 
Mathematics - Complex Variables (1)
 
Mathematics - Probability (1)
 
Statistics - Methodology (1)

Publications Authored By G. L. Lin

Training a Fully Convolutional Network (FCN) for semantic segmentation requires a large number of pixel-level masks, which involves a large amount of human labour and time for annotation. In contrast, image-level labels are much easier to obtain. In this work, we propose a novel method for weakly supervised semantic segmentation with only image-level labels. Read More

We study regularity and numerical methods for two-sided fractional diffusion equations with a lower-order term. We show that the regularity of the solution in weighted Sobolev spaces can be greatly improved compared to that in standard Sobolev spaces. With this regularity, we improve higher-order convergence of a spectral Galerkin method. Read More

We study Lorentz violation effects to flavor transitions of high energy astrophysical neutrinos. It is shown that the appearance of Lorentz violating Hamiltonian can drastically change the flavor transition probabilities of astrophysical neutrinos. Predictions of Lorentz violation effects to flavor compositions of astrophysical neutrinos arriving on Earth are compared with IceCube flavor composition measurement which analyzes astrophysical neutrino events in the energy range between $25~{\rm TeV}$ and $2. 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

In this paper, a deep domain adaptation based method for video smoke detection is proposed to extract a powerful feature representation of smoke. Due to the smoke image samples limited in scale and diversity for deep CNN training, we systematically produced adequate synthetic smoke images with a wide variation in the smoke shape, background and lighting conditions. Considering that the appearance gap (dataset bias) between synthetic and real smoke images degrades significantly the performance of the trained model on the test set composed fully of real images, we build deep architectures based on domain adaptation to confuse the distributions of features extracted from synthetic and real smoke images. Read More

We propose a new approach to image segmentation, which exploits the advantages of both conditional random fields (CRFs) and decision trees. In the literature, the potential functions of CRFs are mostly defined as a linear combination of some pre-defined parametric models, and then methods like structured support vector machines (SSVMs) are applied to learn those linear coefficients. We instead formulate the unary and pairwise potentials as nonparametric forests---ensembles of decision trees, and learn the ensemble parameters and the trees in a unified optimization problem within the large-margin framework. Read More

We consider univariate distributions with finite moments of all positive orders. The moment problem is to determine whether or not a given distribution is uniquely determined by the sequence of its moments. There is a huge literature on this classical topic. Read More

The studying of anomalous diffusion by pulsed field gradient (PFG) diffusion technique still faces challenges. Two different research groups have proposed modified Bloch equation for anomalous diffusion. However, these equations have different forms and, therefore, yield inconsistent results. Read More

Recognizing human activities in a sequence is a challenging area of research in ubiquitous computing. Most approaches use a fixed size sliding window over consecutive samples to extract features---either handcrafted or learned features---and predict a single label for all samples in the window. Two key problems emanate from this approach: i) the samples in one window may not always share the same label. Read More

The {\em maximum duo-preservation string mapping} ({\sc Max-Duo}) problem is the complement of the well studied {\em minimum common string partition} ({\sc MCSP}) problem, both of which have applications in many fields including text compression and bioinformatics. $k$-{\sc Max-Duo} is the restricted version of {\sc Max-Duo}, where every letter of the alphabet occurs at most $k$ times in each of the strings, which is readily reduced into the well known {\em maximum independent set} ({\sc MIS}) problem on a graph of maximum degree $\Delta \le 6(k-1)$. In particular, $2$-{\sc Max-Duo} can then be approximated arbitrarily close to $1. Read More

We study the {\em maximum duo-preservation string mapping} ({\sc Max-Duo}) problem, which is the complement of the well studied {\em minimum common string partition} ({\sc MCSP}) problem. Both problems have applications in many fields including text compression and bioinformatics. Motivated by an earlier local search algorithm, we present an improved approximation and show that its performance ratio is no greater than ${35}/{12} < 2. Read More

We investigate a single machine rescheduling problem that arises from an unexpected machine unavailability, after the given set of jobs has already been scheduled to minimize the total weighted completion time. Such a disruption is represented as an unavailable time interval and is revealed to the production planner before any job is processed; the production planner wishes to reschedule the jobs to minimize the alteration to the originally planned schedule, which is measured as the maximum time deviation between the original and the new schedules for all the jobs. The objective function in this rescheduling problem is to minimize the sum of the total weighted completion time and the weighted maximum time deviation, under the constraint that the maximum time deviation is bounded above by a given value. Read More

Semantic image segmentation is a fundamental task in image understanding. Per-pixel semantic labelling of an image benefits greatly from the ability to consider region consistency both locally and globally. However, many Fully Convolutional Network based methods do not impose such consistency, which may give rise to noisy and implausible predictions. Read More

Anomalous diffusion has been investigated in many systems. Pulsed field gradient (PFG) anomalous diffusion is much more complicated than PFG normal diffusion. There have been many theoretical and experimental studies for PFG isotropic anomalous diffusion, but there are very few theoretical treatments reported for anisotropic anomalous diffusion. Read More

Equilibrium molecular dynamics (EMD) simulations along with the Green-Kubo formula have been widely used to calculate lattice thermal conductivities. Previous studies, however, focused primarily on the calculated thermal conductivities, with the uncertainty of the thermal conductivities remaining poorly understood. In this paper, we study the quantification of the uncertainty by using solid argon, silicon, and germanium as model material systems, and examine the origin of the observed uncertainty. Read More

Superradiant decay is accompanied by two kinds of collective lineshifts, an induced shift and the spontaneous "collective Lamb shift." Both form as sum of dipole-dipole interaction-induced level shifts between atoms in the system. We have developed a procedure to obtain numerical results on this model that self-consistently incorporates the shifts. Read More

We give sharp and explicit upper bounds for the first positive eigenvalue $\lambda_1(\Box_b)$ of the Kohn-Laplacian on compact strictly pseudoconvex hypersurfaces in $\mathbb{C}^{n+1}$ in terms of their defining functions. As an application, we show that in the family of real ellipsoids, $\lambda_1(\Box_b)$ has a unique maximum value at the CR sphere. Read More

Recognizing the identities of people in everyday photos is still a very challenging problem for machine vision, due to non-frontal faces, changes in clothing, location, lighting and similar. Recent studies have shown that rich relational information between people in the same photo can help in recognizing their identities. In this work, we propose to model the relational information between people as a sequence prediction task. Read More

Based on regularities in rotational splittings, we seek possible multiplets for the observed frequencies of CoRoT 102749568. Twenty-one sets of multiplets are identified, including four sets of multiplets with $l=1$, nine sets of multiplets with $l=2$, and eight sets of multiplets with $l=3$. In particular, there are three complete triplets ($f_{10}$, $f_{12}$, $f_{14}$), ($f_{31}$, $f_{34}$, $f_{35}$), and ($f_{41}$, $f_{43}$, $f_{44}$). Read More

Recently, very deep convolutional neural networks (CNNs) have shown outstanding performance in object recognition and have also been the first choice for dense classification problems such as semantic segmentation. However, repeated subsampling operations like pooling or convolution striding in deep CNNs lead to a significant decrease in the initial image resolution. Here, we present RefineNet, a generic multi-path refinement network that explicitly exploits all the information available along the down-sampling process to enable high-resolution prediction using long-range residual connections. Read More

Ensemble smoother (ES) has been widely used in high-dimensional inverse modeling. However, its application is limited to problems where uncertain parameters approximately follow Gaussian distributions. For problems with multimodal distributions, using ES directly would be problematic. Read More

2016Oct
Authors: Daya Bay Collaboration, F. P. An, A. B. Balantekin, H. R. Band, M. Bishai, S. Blyth, D. Cao, G. F. Cao, J. Cao, W. R. Cen, Y. L. Chan, J. F. Chang, L. C. Chang, Y. Chang, H. S. Chen, Q. Y. Chen, S. M. Chen, Y. X. Chen, Y. Chen, J. -H. Cheng, J. Cheng, Y. P. Cheng, Z. K. Cheng, J. J. Cherwinka, M. C. Chu, A. Chukanov, J. P. Cummings, J. de Arcos, Z. Y. Deng, X. F. Ding, 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, M. Y. Guan, L. Guo, X. H. Guo, Z. Guo, R. W. Hackenburg, R. Han, S. Hans, M. He, K. M. Heeger, Y. K. Heng, A. Higuera, Y. K. Hor, Y. B. Hsiung, B. Z. Hu, T. Hu, W. Hu, E. C. Huang, H. X. Huang, X. T. Huang, P. Huber, W. Huo, G. Hussain, D. E. Jaffe, P. Jaffke, K. L. Jen, S. Jetter, X. P. Ji, X. L. Ji, J. B. Jiao, R. A. Johnson, D. Jones, J. Joshi, L. Kang, S. H. Kettell, S. Kohn, M. Kramer, K. K. Kwan, M. W. Kwok, T. 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, 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, D. W. Liu, J. L. Liu, J. C. Liu, C. W. Loh, C. Lu, H. Q. Lu, J. S. Lu, K. B. Luk, Z. Lv, Q. M. Ma, X. Y. Ma, X. B. Ma, Y. Q. Ma, Y. Malyshkin, D. A. Martinez Caicedo, K. T. McDonald, R. D. McKeown, I. Mitchell, M. Mooney, Y. Nakajima, J. Napolitano, D. Naumov, E. Naumova, H. Y. Ngai, Z. Ning, 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, N. Raper, J. Ren, R. Rosero, B. Roskovec, X. C. Ruan, H. Steiner, G. X. Sun, 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. Y. Xu, J. L. Xu, Y. Xu, T. Xue, C. G. Yang, H. Yang, L. Yang, M. S. Yang, M. T. Yang, M. Ye, Z. Ye, M. Yeh, B. L. Young, Z. Y. Yu, S. Zeng, L. Zhan, C. Zhang, H. H. Zhang, J. W. Zhang, Q. M. Zhang, X. T. Zhang, Y. M. Zhang, Y. X. Zhang, Y. M. Zhang, Z. J. Zhang, Z. Y. Zhang, Z. P. Zhang, J. Zhao, Q. W. Zhao, Y. B. Zhao, W. L. Zhong, L. Zhou, N. Zhou, H. L. Zhuang, J. H. Zou

A measurement of electron antineutrino oscillation by the Daya Bay Reactor Neutrino Experiment is described in detail. Six 2.9-GW$_{\rm th}$ nuclear power reactors of the Daya Bay and Ling Ao nuclear power facilities served as intense sources of $\overline{\nu}_{e}$'s. Read More

The experiment of Krasznahorkay \textit{et al} observed the transition of a $\rm{^{8}Be}$ excited state to its ground state and accompanied by an emission of $e^{+}e^{-}$ pair with 17 MeV invariant mass. This 6.8$\sigma$ anomaly can be fitted by a new light gauge boson. Read More

Pulsed field gradient (PFG) has been increasingly employed to study anomalous diffusions in Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI). However, the analysis of PFG anomalous diffusion is complicated. In this paper, a fractal derivative model based modified Gaussian phase distribution method is proposed to describe PFG anomalous diffusion. Read More

Anomalous diffusion exists widely in polymer and biological systems. Pulsed field gradient (PFG) techniques have been increasingly used to study anomalous diffusion in NMR and MRI. However, the interpretation of PFG anomalous diffusion is complicated. Read More

Given a vertex-weighted connected graph $G = (V, E)$, the maximum weight internal spanning tree (MwIST for short) problem asks for a spanning tree $T$ of $G$ such that the total weight of the internal vertices in $T$ is maximized. The un-weighted variant, denoted as MIST, is NP-hard and APX-hard, and the currently best approximation algorithm has a proven performance ratio $13/17$. The currently best approximation algorithm for MwIST only has a performance ratio $1/3 - \epsilon$, for any $\epsilon > 0$. Read More

2016Aug
Authors: F. P. An, A. B. Balantekin, H. R. Band, M. Bishai, S. Blyth, D. Cao, G. F. Cao, J. Cao, W. R. Cen, Y. L. Chan, J. F. Chang, L. C. Chang, Y. Chang, H. S. Chen, Q. Y. Chen, S. M. Chen, Y. X. Chen, Y. Chen, J. -H. Cheng, J. Cheng, Y. P. Cheng, Z. K. Cheng, J. J. Cherwinka, M. C. Chu, A. Chukanov, J. P. Cummings, J. de Arcos, Z. Y. Deng, X. F. Ding, 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, M. Y. Guan, L. Guo, X. H. Guo, Z. Guo, R. W. Hackenburg, R. Han, S. Hans, M. He, K. M. Heeger, Y. K. Heng, A. Higuera, Y. K. Hor, Y. B. Hsiung, B. Z. Hu, T. Hu, W. Hu, E. C. Huang, H. X. Huang, X. T. Huang, P. Huber, W. Huo, G. Hussain, D. E. Jaffe, P. Jaffke, K. L. Jen, S. Jetter, X. P. Ji, X. L. Ji, J. B. Jiao, R. A. Johnson, J. Joshi, L. Kang, S. H. Kettell, S. Kohn, M. Kramer, K. K. Kwan, M. W. Kwok, T. 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, 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, D. W. Liu, J. L. Liu, J. C. Liu, C. W. Loh, C. Lu, H. Q. Lu, J. S. Lu, K. B. Luk, Z. Lv, Q. M. Ma, X. Y. Ma, X. B. Ma, Y. Q. Ma, Y. Malyshkin, D. A. Martinez Caicedo, R. D. McKeown, I. Mitchell, M. Mooney, Y. Nakajima, J. Napolitano, D. Naumov, E. Naumova, H. Y. Ngai, Z. Ning, 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, N. Raper, J. Ren, R. Rosero, B. Roskovec, X. C. Ruan, H. Steiner, G. X. Sun, 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. Y. Xu, J. L. Xu, Y. Xu, T. Xue, C. G. Yang, H. Yang, L. Yang, M. S. Yang, M. T. Yang, M. Ye, Z. Ye, M. Yeh, B. L. Young, Z. Y. Yu, S. Zeng, L. Zhan, C. Zhang, H. H. Zhang, J. W. Zhang, Q. M. Zhang, X. T. Zhang, Y. M. Zhang, Y. X. Zhang, Y. M. Zhang, Z. J. Zhang, Z. Y. Zhang, Z. P. Zhang, J. Zhao, Q. W. Zhao, Y. B. Zhao, W. L. Zhong, L. Zhou, N. Zhou, H. L. Zhuang, J. H. Zou

The disappearance of reactor $\bar{\nu}_e$ observed by the Daya Bay experiment is examined in the framework of a model in which the neutrino is described by a wave packet with a relative intrinsic momentum dispersion $\sigma_\text{rel}$. Three pairs of nuclear reactors and eight antineutrino detectors, each with good energy resolution, distributed among three experimental halls, supply a high-statistics sample of $\bar{\nu}_e$ acquired at nine different baselines. This provides a unique platform to test the effects which arise from the wave packet treatment of neutrino oscillation. Read More

In this work, a second-order approximation of the fractional substantial derivative is presented by considering a modified shifted substantial Gr\"{u}nwald formula and its asymptotic expansion. Moreover, the proposed approximation is applied to a fractional diffusion equation with fractional substantial derivative in time. With the use of the fourth-order compact scheme in space, we give a fully discrete Gr\"{u}nwald-Letnikov-formula-based compact difference scheme and prove its stability and convergence by the energy method under smooth assumptions. Read More

The multi-term time-fractional mixed diffusion-wave equations (TFMDWEs) are considered and the numerical method with its error analysis is presented in this paper. First, a $L2$ approximation is proved with first order accuracy to the Caputo fractional derivative of order $\beta \in (1,2).$ Then the approximation is applied to solve a one-dimensional TFMDWE and an implicit, compact difference scheme is constructed. Read More

2016Jul
Authors: F. P. An, A. B. Balantekin, H. R. Band, M. Bishai, S. Blyth, D. Cao, G. F. Cao, J. Cao, W. R. Cen, Y. L. Chan, J. F. Chang, L. C. Chang, Y. Chang, H. S. Chen, Q. Y. Chen, S. M. Chen, Y. X. Chen, Y. Chen, J. -H. Cheng, J. Cheng, Y. P. Cheng, Z. K. Cheng, J. J. Cherwinka, M. C. Chu, A. Chukanov, J. P. Cummings, J. de Arcos, Z. Y. Deng, X. F. Ding, 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, M. Y. Guan, L. Guo, R. P. Guo, X. H. Guo, Z. Guo, R. W. Hackenburg, R. Han, S. Hans, M. He, K. M. Heeger, Y. K. Heng, A. Higuera, Y. K. Hor, Y. B. Hsiung, B. Z. Hu, T. Hu, W. Hu, E. C. Huang, H. X. Huang, X. T. Huang, P. Huber, W. Huo, G. Hussain, D. E. Jaffe, P. Jaffke, K. L. Jen, S. Jetter, X. P. Ji, X. L. Ji, J. B. Jiao, R. A. Johnson, D. Jones, J. Joshi, L. Kang, S. H. Kettell, S. Kohn, M. Kramer, K. K. Kwan, M. W. Kwok, T. Kwok, T. J. Langford, K. Lau, L. Lebanowski, J. Lee, J. H. C. Lee, R. T. Lei, R. Leitner, C. Li, D. J. Li, F. Li, G. S. Li, Q. J. Li, S. Li, S. C. Li, W. D. Li, X. N. 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, D. W. Liu, J. L. Liu, J. C. Liu, C. W. Loh, C. Lu, H. Q. Lu, J. S. Lu, K. B. Luk, Z. Lv, Q. M. Ma, X. Y. Ma, X. B. Ma, Y. Q. Ma, Y. Malyshkin, D. A. Martinez Caicedo, K. T. McDonald, R. D. McKeown, I. Mitchell, M. Mooney, Y. Nakajima, J. Napolitano, D. Naumov, E. Naumova, H. Y. Ngai, Z. Ning, 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, N. Raper, J. Ren, R. Rosero, B. Roskovec, X. C. Ruan, H. Steiner, G. X. Sun, 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. Y. Xu, J. L. Xu, Y. Xu, T. Xue, C. G. Yang, H. Yang, L. Yang, M. S. Yang, M. T. Yang, M. Ye, Z. Ye, M. Yeh, B. L. Young, Z. Y. Yu, S. Zeng, L. Zhan, C. Zhang, H. H. Zhang, J. W. Zhang, Q. M. Zhang, X. T. Zhang, Y. M. Zhang, Y. X. Zhang, Y. M. Zhang, Z. J. Zhang, Z. Y. Zhang, Z. P. Zhang, J. Zhao, Q. W. Zhao, Y. B. Zhao, W. L. Zhong, L. Zhou, N. Zhou, H. L. Zhuang, J. H. Zou

A new measurement of the reactor antineutrino flux and energy spectrum by the Daya Bay reactor neutrino experiment is reported. The antineutrinos were generated by six 2.9~GW$_{\mathrm{th}}$ nuclear reactors and detected by eight antineutrino detectors deployed in two near (560~m and 600~m flux-weighted baselines) and one far (1640~m flux-weighted baseline) underground experimental halls. Read More

2016Jul
Authors: The Daya Bay collaboration, F. P. An, A. B. Balantekin, H. R. Band, M. Bishai, S. Blyth, D. Cao, G. F. Cao, J. Cao, W. R. Cen, Y. L. Chan, J. F. Chang, L. C. Chang, Y. Chang, H. S. Chen, Q. Y. Chen, S. M. Chen, Y. X. Chen, Y. Chen, J. -H. Cheng, J. Cheng, Y. P. Cheng, Z. K. Cheng, J. J. Cherwinka, M. C. Chu, A. Chukanov, J. P. Cummings, J. de Arcos, Z. Y. Deng, X. F. Ding, 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, M. Y. Guan, L. Guo, R. P. Guo, X. H. Guo, Z. Guo, R. W. Hackenburg, R. Han, S. Hans, M. He, K. M. Heeger, Y. K. Heng, A. Higuera, Y. K. Hor, Y. B. Hsiung, B. Z. Hu, T. Hu, W. Hu, E. C. Huang, H. X. Huang, X. T. Huang, P. Huber, W. Huo, G. Hussain, D. E. Jaffe, P. Jaffke, K. L. Jen, S. Jetter, X. P. Ji, X. L. Ji, J. B. Jiao, R. A. Johnson, J. Joshi, L. Kang, S. H. Kettell, S. Kohn, M. Kramer, K. K. Kwan, M. W. Kwok, T. 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, 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, D. W. Liu, J. L. Liu, J. C. Liu, C. W. Loh, C. Lu, H. Q. Lu, J. S. Lu, K. B. Luk, Z. Lv, Q. M. Ma, X. Y. Ma, X. B. Ma, Y. Q. Ma, Y. Malyshkin, D. A. Martinez Caicedo, K. T. McDonald, R. D. McKeown, I. Mitchell, M. Mooney, Y. Nakajima, J. Napolitano, D. Naumov, E. Naumova, H. Y. Ngai, Z. Ning, 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, N. Raper, J. Ren, R. Rosero, B. Roskovec, X. C. Ruan, H. Steiner, G. X. Sun, 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. Y. Xu, J. L. Xu, Y. Xu, T. Xue, C. G. Yang, H. Yang, L. Yang, M. S. Yang, M. T. Yang, M. Ye, Z. Ye, M. Yeh, B. L. Young, Z. Y. Yu, S. Zeng, L. Zhan, C. Zhang, H. H. Zhang, J. W. Zhang, Q. M. Zhang, X. T. Zhang, Y. M. Zhang, Y. X. Zhang, Y. M. Zhang, Z. J. Zhang, Z. Y. Zhang, Z. P. Zhang, J. Zhao, Q. W. Zhao, Y. B. Zhao, W. L. Zhong, L. Zhou, N. Zhou, H. L. Zhuang, J. H. Zou

This Letter reports an improved search for light sterile neutrino mixing in the electron antineutrino disappearance channel with the full configuration of the Daya Bay Reactor Neutrino Experiment. With an additional 404 days of data collected in eight antineutrino detectors, this search benefits from 3.6 times the statistics available to the previous publication, as well as from improvements in energy calibration and background reduction. Read More

2016Jul
Authors: Daya Bay, MINOS Collaborations, :, P. Adamson, F. P. An, I. Anghel, A. Aurisano, A. B. Balantekin, H. R. Band, G. Barr, M. Bishai, A. Blake, S. Blyth G. J. Bock, D. Bogert, D. Cao, G. F. Cao, J. Cao, S. V. Cao, T. J. Carroll, C. M. Castromonte, W. R. Cen, Y. L. Chan, J. F. Chang, L. C. Chang, Y. Chang, H. S. Chen, Q. Y. Chen, R. Chen, S. M. Chen, Y. Chen, Y. X. Chen, J. Cheng, J. -H. Cheng, Y. P. Chen, Z. K. Cheng, J. J. Cherwinka, S. Childress, M. C. Chu, A. Chukanov, J. A. B. Coelho, L. Corwin, D. Cronin-Hennessy, J. P. Cummings, J. de Arcos, S. De Rijck, Z. Y. Deng, A. V. Devan, N. E. Devenish, X. F. Ding, Y. Y. Ding, M. V. Diwan, M. Dolgareva, J. Dove, D. A. Dwyer, W. R. Edwards, C. O. Escobar, J. J. Evans, E. Falk, G. J. Feldman, W. Flanagan, M. V. Frohne, M. Gabrielyan, H. R. Gallagher, S. Germani, R. Gill, R. A. Gomes, M. Gonchar, G. H. Gong, H. Gong, M. C. Goodman, P. Gouffon, N. Graf, R. Gran, M. Grassi, K. Grzelak, W. Q. Gu, M. Y. Guan, L. Guo, R. P. Guo, X. H. Guo, Z. Guo, A. Habig, R. W. Hackenburg, S. R. Hahn, R. Han, S. Hans, J. Hartnell, R. Hatcher, M. He, K. M. Heeger, Y. K. Heng, A. Higuera, A. Holin, Y. K. Hor, Y. B. Hsiung, B. Z. Hu, T. Hu, W. Hu, E. C. Huang, H. X. Huang, J. Huang, X. T. Huang, P. Huber, W. Huo, G. Hussain, J. Hylen, G. M. Irwin, Z. Isvan, D. E. Jaffe, P. Jaffke, C. James, K. L. Jen, D. Jensen, S. Jetter, X. L. Ji, X. P. Ji, J. B. Jiao, R. A. Johnson, J. K. de Jong, J. Joshi, T. Kafka, L. Kang, S. M. S. Kasahara, S. H. Kettell, S. Kohn, G. Koizumi, M. Kordosky, M. Kramer, A. Kreymer, 1 K. K. Kwan, M. W. Kwok, T. Kwok, K. Lang, 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, 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, P. J. Litchfield, L. Littenberg, B. R. Littlejohn, D. W. Liu, J. C. Liu, J. L. Liu, C. W. Loh, C. Lu, H. Q. Lu, J. S. Lu, P. Lucas, K. B. Luk, Z. Lv, Q. M. Ma, X. B. Ma, X. Y. Ma, Y. Q. Ma, Y. Malyshkin, W. A. Mann, M. L. Marshak, D. A. Martinez Caicedo, N. Mayer, K. T. McDonald, C. McGivern, R. D. McKeown, M. M. Medeiros, R. Mehdiyev, J. R. Meier, M. D. Messier, W. H. Miller, S. R. Mishra, I. Mitchell, M. Mooney, C. D. Moore, L. Mualem, J. Musser, Y. Nakajima, D. Naples, J. Napolitano, D. Naumov, E. Naumova, J. K. Nelson, H. B. Newman, H. Y. Ngai, R. J. Nichol, Z. Ning, A. Nowak, J. O'Connor, J. P. Ochoa-Ricoux, A. Olshevskiy, M. Orchanian, R., R. B. Pahlka, J. Paley, H. -R. Pan, J. Park, R. B. Patterson, S. Patton, G. Pawloski, V. Pec, J. C. Peng, A. Perch, M. M. Pfutzner, D. D. Phan, S. Phan-Budd, L. Pinsky, R. K. Plunkett, N. Poonthottathil, C. S. J. Pun, F. Z. Qi, M. Qi, X. Qian, X. Qiu, A. Radovic, N. Raper, B. Rebel, J. Ren, C. Rosenfeld, R. Rosero, B. Roskovec, X. C. Ruan, H. A. Rubin, P. Sail, M. C. Sanchez, J. Schneps, A. Schreckenberger, P. Schreiner, R. Sharma, S. Moed Sher, A. Sousa, H. Steiner, G. X. Sun, J. L. Sun, N. Tagg, R. L. Talaga, W. Tang, D. Taychenachev, J. Thomas, M. A. Thomson, X. Tian A. Timmons, J. Todd, S. C. Tognini, R. Toner, D. Torretta, K. Treskov, K. V. Tsang, C. E. Tull, G. Tzanakos, J. Urheim, P. Vahle, 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. M. Wang, R. C. Webb, A. Weber, H. Y. Wei, L. J. Wen, K. Whisnant, C. White, L. Whitehead L. H. Whitehead, T. Wise, S. G. Wojcicki, 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, J. Y. Xu, Y. Xu, T. Xue, C. G. Yang, H. Yang, L. Yang, M. S. Yang, M. T. Yang, M. Ye., Z. Ye, M. Yeh, B. L. Young, Z. Y. Yu, S. Zeng, L. ZhanC. Zhang, H. H. Zhang, J. W. Zhang, Q. M. Zhang, X. T. Zhang, Y. M. Zhang, Y. X. Zhang, Z. J. Zhang, Z. P. Zhang, Z. Y. Zhang, J. Zhao, Q. W. Zhao, Y. B. Zhao, W. L. Zhong, L. Zhou, N. Zhou, H. L. Zhuang, J. H. Zou

Searches for a light sterile neutrino have been performed independently by the MINOS and the Daya Bay experiments using the muon (anti)neutrino and electron antineutrino disappearance channels, respectively. In this Letter, results from both experiments are combined with those from the Bugey-3 reactor neutrino experiment to constrain oscillations into light sterile neutrinos. The three experiments are sensitive to complementary regions of parameter space, enabling the combined analysis to probe regions allowed by the LSND and MiniBooNE experiments in a minimally extended four-neutrino flavor framework. Read More

We first review the univariate and bivariate lack-of-memory properties (LMPs). The univariate LMP is a remarkable characterization of the exponential distribution, while the bivariate LMP is shared by the famous Marshall and Olkin's, Block and Basu's as well as Freund's bivariate exponential distributions. We treat all the bivariate lack-of-memory (BLM) distributions in a unified approach and develop some new general properties of the BLM distributions, including joint moment generating function, product moments and dependence structure. Read More

We consider the single machine scheduling problem with job-dependent machine deterioration. In the problem, we are given a single machine with an initial non-negative maintenance level, and a set of jobs each with a non-preemptive processing time and a machine deterioration. Such a machine deterioration quantifies the decrement in the machine maintenance level after processing the job. Read More

We investigate the maximum happy vertices (MHV) problem and its complement, the minimum unhappy vertices (MUHV) problem. We first show that the MHV and MUHV problems are a special case of the supermodular and submodular multi-labeling (Sup-ML and Sub-ML) problems, respectively, by re-writing the objective functions as set functions. The convex relaxation on the Lov\'{a}sz extension, originally presented for the submodular multi-partitioning (Sub-MP) problem, can be extended for the Sub-ML problem, thereby proving that the Sub-ML (Sup-ML, respectively) can be approximated within a factor of $2 - \frac{2}{k}$ ($\frac{2}{k}$, respectively). Read More

In order to perform probabilistic tsunami hazard assessment (PTHA) based on subduction zone earthquakes, it is necessary to start with a catalog of possible future events along with the annual probability of occurance, or a probability distribution of such events that can be easily sampled. For nearfield events, the distribution of slip on the fault can have a significant effect on the resulting tsunami. We present an approach to defining a probability distribution based on subdividing the fault geometry into many subfaults and prescribing a desired covariance matrix relating slip on one subfault to slip on any other subfault. Read More

A procedure is introduced to recognise sunspots automatically in solar full-disk photosphere images obtained from Huairou Solar Observing Station, National Astronomical Observatories of China. The images are first pre-processed through Gaussian algorithm. Sunspots are then recognised by the morphological Bot-hat operation and Otsu threshold. Read More

Crowd counting is an important task in computer vision, which has many applications in video surveillance. Although the regression-based framework has achieved great improvements for crowd counting, how to improve the discriminative power of image representation is still an open problem. Conventional holistic features used in crowd counting often fail to capture semantic attributes and spatial cues of the image. Read More

A physically-motivated function was developed to accurately determine the total absorption peak in an electromagnetic calorimeter and to overcome biases present in many commonly used methods. The function is the convolution of a detector resolution function with the sum of a delta function, which represents the complete absorption of energy, and a tail function, which describes the partial absorption of energy and depends on the detector materials and structures. Its performance was tested with the simulation of three typical cases. Read More

2016Mar
Authors: Daya Bay Collaboration, F. P. An, A. B. Balantekin, H. R. Band, M. Bishai, S. Blyth, D. Cao, G. F. Cao, J. Cao, W. R. Cen, Y. L. Chan, J. F. Chang, L. C. Chang, Y. Chang, H. S. Chen, Q. Y. Chen, S. M. Chen, Y. X. Chen, Y. Chen, J. H. Cheng, J. -H. Cheng, J. Cheng, Y. P. Cheng, Z. K. Cheng, J. J. Cherwinka, M. C. Chu, A. Chukanov, J. P. Cummings, J. de Arcos, Z. Y. Deng, X. F. Ding, 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, M. Y. Guan, L. Guo, R. P. Guo, X. H. Guo, Z. Guo, R. W. Hackenburg, R. Han, S. Hans, M. He, K. M. Heeger, Y. K. Heng, A. Higuera, Y. K. Hor, Y. B. Hsiung, B. Z. Hu, T. Hu, W. Hu, E. C. Huang, H. X. Huang, X. T. Huang, P. Huber, W. Huo, G. Hussain, D. E. Jaffe, P. Jaffke, K. L. Jen, S. Jetter, X. P. Ji, X. L. Ji, J. B. Jiao, R. A. Johnson, J. Joshi, L. Kang, S. H. Kettell, S. Kohn, M. Kramer, K. K. Kwan, M. W. Kwok, T. 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, 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, D. W. Liu, J. J. Liu, J. L. Liu, J. C. Liu, C. W. Loh, C. Lu, H. Q. Lu, J. S. Lu, K. B. Luk, Z. Lv, Q. M. Ma, X. Y. Ma, X. B. Ma, Y. Q. Ma, Y. Malyshkin, D. A. Martinez Caicedo, K. T. McDonald, R. D. McKeown, I. Mitchell, M. Mooney, Y. Nakajima, J. Napolitano, D. Naumov, E. Naumova, H. Y. Ngai, Z. Ning, 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, N. Raper, J. Ren, R. Rosero, B. Roskovec, X. C. Ruan, H. Steiner, G. X. Sun, J. L. Sun, W. Tang, D. Taychenachev, T. Konstantin, 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, W. 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, D. M. Xia, J. K. Xia, Z. Z. Xing, J. Y. Xu, J. L. Xu, J. Xu, Y. Xu, T. Xue, J. Yan, C. G. Yang, H. Yang, L. Yang, M. S. Yang, M. T. Yang, M. Ye, Z. Ye, M. Yeh, B. L. Young, G. Y. Yu, Z. Y. Yu, L. Zhan, C. Zhang, H. H. Zhang, J. W. Zhang, Q. M. Zhang, X. T. Zhang, Y. M. Zhang, Y. X. Zhang, Y. M. Zhang, Z. J. Zhang, Z. Y. Zhang, Z. P. Zhang, J. Zhao, Q. W. Zhao, Y. F. Zhao, Y. B. Zhao, W. L. Zhong, L. Zhou, N. Zhou, H. L. Zhuang, J. H. Zou

This article reports an improved independent measurement of neutrino mixing angle $\theta_{13}$ at the Daya Bay Reactor Neutrino Experiment. Electron antineutrinos were identified by inverse $\beta$-decays with the emitted neutron captured by hydrogen, yielding a data-set with principally distinct uncertainties from that with neutrons captured by gadolinium. With the final two of eight antineutrino detectors installed, this study used 621 days of data including the previously reported 217-day data set with six detectors. Read More

State-of-the-art semantic image segmentation methods are mostly based on training deep convolutional neural networks (CNNs). In this work, we proffer to improve semantic segmentation with the use of contextual information. In particular, we explore `patch-patch' context and `patch-background' context in deep CNNs. Read More

In this paper, we aim to learn a mapping (or embedding) from images to a compact binary space in which Hamming distances correspond to a ranking measure for the image retrieval task. We make use of a triplet loss because this has been shown to be most effective for ranking problems. However, training in previous works can be prohibitively expensive due to the fact that optimization is directly performed on the triplet space, where the number of possible triplets for training is cubic in the number of training examples. Read More

The neutrino mass hierarchy is one of the neutrino fundamental properties yet to be determined. We introduce a method to determine neutrino mass hierarchy by comparing the interaction rate of neutral current (NC) interactions, $\nu(\hat{\nu}) + p\rightarrow\nu(\hat{\nu}) + p$, and inverse beta decays (IBD), $\bar{\nu}_e + p\rightarrow n + e^+$, of supernova neutrinos in scintillation detectors. Neutrino flavor conversions inside the supernova are sensitive to neutrino mass hierarchy. Read More

2-Dimensional (2D) CrPS4 single crystals have been grown by the chemical vapor transport method. The crystallographic, magnetic, electronic and thermal transport properties of the single crystals were investigated by the room-temperature X-ray diffraction, electrical resistivity \r{ho}(T), specific heat CP(T) and the electronic spin response (ESR) measurements. CrPS4 crystals crystallize into a monoclinic structure. Read More

Hashing methods aim to learn a set of hash functions which map the original features to compact binary codes with similarity preserving in the Hamming space. Hashing has proven a valuable tool for large-scale information retrieval. We propose a column generation based binary code learning framework for data-dependent hash function learning. Read More

Jinping Neutrino Experiment (Jinping) is proposed to significantly improve measurements on solar neutrinos and geoneutrinos in China Jinping Laboratory - a lab with a number of unparalleled features, thickest overburden, lowest reactor neutrino background, etc., which identify it as the world-best low-energy neutrino laboratory. The proposed experiment will have target mass of 4 kilotons of liquid scintillator or water-based liquid scintillator, with a fiducial mass of 2 kilotons for neutrino-electron scattering events and 3 kilotons for inverse-beta interaction events. Read More

We measured the magnetoresistvity (MR) properties of the 1T`-MoTe2 single crystal under the magnetic field up to 33 T.By analyzing the Shubnikov de Haas oscillations of MR at the low temperature, single Fermi surface is revealed.From the strong oscillatory component of the longitudinal resistance {\Delta}Rxx,a linear dependence of the Landau index n on 1/B is obtained. Read More

Recent works on deep conditional random fields (CRF) have set new records on many vision tasks involving structured predictions. Here we propose a fully-connected deep continuous CRF model for both discrete and continuous labelling problems. We exemplify the usefulness of the proposed model on multi-class semantic labelling (discrete) and the robust depth estimation (continuous) problems. Read More

More than two decades ago, we studied heavy-flavor-conserving weak decays of heavy baryons within the framework that incorporates both heavy-quark and chiral symmetries. In view of the first observation of $\Xi_b^-\to\Lambda_b^0\pi^-$ by LHCb recently, we have reexamined these decays and presented updated predictions. The predicted rates for $\Xi_b^-\to\Lambda_b^0\pi^-$ in the MIT bag and diquark models are consistent with experiment. Read More