Meng Wang - Bonn University, for the H1 and ZEUS collaborations

Meng Wang
Are you Meng Wang?

Claim your profile, edit publications, add additional information:

Contact Details

Meng Wang
Bonn University, for the H1 and ZEUS collaborations

Pubs By Year

Pub Categories

Physics - Superconductivity (11)
Computer Science - Computer Vision and Pattern Recognition (10)
Physics - Instrumentation and Detectors (10)
High Energy Physics - Experiment (8)
Physics - Strongly Correlated Electrons (7)
Nuclear Experiment (5)
Computer Science - Learning (4)
Statistics - Machine Learning (3)
Mathematics - Analysis of PDEs (3)
Instrumentation and Methods for Astrophysics (3)
Mathematics - Information Theory (3)
Computer Science - Information Theory (3)
Quantum Physics (2)
Statistics - Theory (2)
Mathematics - Statistics (2)
Physics - Materials Science (2)
Cosmology and Nongalactic Astrophysics (1)
High Energy Physics - Phenomenology (1)
Statistics - Methodology (1)
Physics - Accelerator Physics (1)
Mathematics - Numerical Analysis (1)

Publications Authored By Meng Wang

Full-energy peak (FEP) efficiencies of a HPGe detector equipped with an ultra-low background shield system are calibrated with the Monte Carlo method and further examined using summing peaks in a numerical way. Radionuclides $^{241}$Am, $^{137}$Cs, $^{60}$Co, $^{133}$Ba and $^{152}$Eu are used to construct the simulation model with the toolkit GEANT4. True summing \mbox{coincidence} factors (TSCFs) of $^{60}$Co, $^{133}$Ba and $^{152}$Eu are calculated and result in an improvement up to about 20\% in the FEP efficiency curve. Read More

Despite the promising progress made in recent years, person re-identification (re-ID) remains a challenging task due to the complex variations in human appearances from different camera views. For this challenging problem, a large variety of algorithms have been developed in the fully-supervised setting, requiring access to a large amount of labeled training data. However, the main bottleneck for fully-supervised re-ID is the limited availability of labeled training samples. Read More

We use polarized inelastic neutron scattering to study the temperature and energy dependence of spin space anisotropies in the optimally hole-doped iron pnictide Ba$_{0.67}$K$_{0.33}$Fe$_{2}$As$_{2}$ ($T_{{\rm c}}=38$ K). Read More

The task of flux calibration for LAMOST (Large sky Area Multi-Object Spectroscopic Telescope) spectra is difficult due to many factors. For example, the lack of standard stars, flat fielding for large field of view, and variation of reddening between different stars especially at low galactic latitudes etc. Poor selection, bad spectral quality, or extinction uncertainty of standard stars not only might induce errors to the calculated spectral response curve (SRC), but also might lead to failures in producing final 1D spectra. Read More

New constraints are presented on the spin-dependent WIMP-nucleon interaction from the PandaX-II experiment, using a data set corresponding to a total exposure of 3.3$\times10^4$ kg-days. Assuming a standard axial-vector spin-dependent WIMP interaction with $^{129}$Xe and $^{131}$Xe nuclei, the most stringent upper limits on WIMP-neutron cross sections for WIMPs with masses above 10 GeV/c$^{2}$ are set in all dark matter direct detection experiments. Read More

Low-rank learning has attracted much attention recently due to its efficacy in a rich variety of real-world tasks, e.g., subspace segmentation and image categorization. Read More

Series elastic actuators (SEAs) are growingly important in physical human-robot interaction (HRI) due to their inherent safety and compliance. Cable-driven SEAs also allow flexible installation and remote torque transmission, etc. However, there are still challenges for the impedance control of cable-driven SEAs, such as the reduced bandwidth caused by the elastic component, and the performance balance between reference tracking and robustness. Read More

Searching for the Neutrinoless Double Beta Decay (NLDBD) is now regarded as the topmost promising technique to explore the nature of neutrinos after the discovery of neutrino masses in oscillation experiments. PandaX-III (Particle And Astrophysical Xenon Experiment III) will search for the NLDBD of $^{136}$Xe at the China Jin Ping underground Laboratory (CJPL). In the first phase of the experiment, a high pressure gas Time Projection Chamber (TPC) will contain 200 kg, 90% $^{136}$Xe enriched gas operated at 10 bar. Read More

We have developed a technique to tune the carrier density in graphene using a lithium-ion-based solid electrolyte. We demonstrate that the solid electrolyte can be used as both a substrate to support graphene and a back gate.It can induce a change in the carrier density as large as 1*10^14/cm^2, which is much larger than that induced with oxide-film dielectrics, and it is comparable with that induced by liquid electrolytes. Read More

Plastic scintillation detectors for Time-of-Flight (TOF) measurements are almost essential for event-by-event identification of relativistic rare isotopes. In this work, a pair of plastic scintillation detectors of 50 $\times$ 50 $\times$ 3$^{t}$ mm$^3$ and 80 $\times$ 100 $\times$ 3$^{t}$ mm$^3$ have been set up at the external target facility (ETF), Institute of Modern Physics. Their time, energy and position responses are measured with $^{18}$O primary beam at 400 MeV/nucleon. Read More

Inelastic neutron scattering measurements have been performed to investigate the spin waves of the quasi-one-dimensional antiferromagnetic ladder compound BaFe$_2$S$_3$, where a superconducting transition was observed under pressure [H. Takahashi {\it et al.}, Nat. Read More

Using polarization-resolved electronic Raman scattering we study under-doped, optimally-doped and over-doped Ba$_{1-x}$K$_{x}$Fe$_2$As$_2$ samples in the normal and superconducting states. We show that low-energy nematic fluctuations are universal for all studied doping range. In the superconducting state, we observe two distinct superconducting pair breaking peaks corresponding to one large and one small superconducting gaps. Read More

In this paper, we propose a non-convex formulation to recover the authentic structure from the corrupted real data. Typically, the specific structure is assumed to be low rank, which holds for a wide range of data, such as images and videos. Meanwhile, the corruption is assumed to be sparse. Read More

Online selection of dynamic features has attracted intensive interest in recent years. However, existing online feature selection methods evaluate features individually and ignore the underlying structure of feature stream. For instance, in image analysis, features are generated in groups which represent color, texture and other visual information. Read More

This paper presents a new framework of identifying a series of cyber data attacks on power system synchrophasor measurements. We focus on detecting "unobservable" cyber data attacks that cannot be detected by any existing method that purely relies on measurements received at one time instant. Leveraging the approximate low-rank property of phasor measurement unit (PMU) data, we formulate the identification problem of successive unobservable cyber attacks as a matrix decomposition problem of a low-rank matrix plus a transformed column-sparse matrix. Read More

We report an inelastic neutron scattering study of the spin waves of the one-dimensional antiferromagnetic spin ladder compound RbFe$_2$Se$_3$. The results reveal that the products, $SJ$'s, of the spin $S$ and the magnetic exchange interactions $J$'s along the antiferromagnetic (leg) direction and the ferromagnetic (rung) direction are comparable with those for the stripe ordered phase of the parent compounds of the iron-based superconductors. The universality of the $SJ$'s implies nearly universal spin wave dynamics and the irrelevance of the fermiology for the existence of the stripe antiferromagnetic order among various Fe-based materials. Read More

We use neutron scattering to study spin excitations in single crystals of LiFe$_{0.88}$Co$_{0.12}$As, which is located near the boundary of the superconducting phase of LiFe$_{1-x}$Co$_{x}$As and exhibits non-Fermi-liquid behavior indicative of a quantum critical point. Read More

In this paper, we study joint queue-aware and channel-aware scheduling of arbitrarily bursty traffic over multi-state time-varying channels, where the bursty packet arrival in the network layer, the backlogged queue in the data link layer, and the power adaptive transmission with fixed modulation in the physical layer are jointly considered from a cross-layer perspective. To achieve minimum queueing delay given a power constraint, a probabilistic cross-layer scheduling policy is proposed, and characterized by a Markov chain model. To describe the delay-power tradeoff, we formulate a non-linear optimization problem, which however is very challenging to solve. Read More

We report a combined study of the spin resonances and superconducting gaps for underdoped ($T_c=19$ K), optimally doped ($T_c=25$ K), and overdoped ($T_c=19$ K) Ba(Fe$_{1-x}$Co$_x$)$_2$As$_2$ single crystals with inelastic neutron scattering and angle resolved photoemission spectroscopy. We find a quasi two dimensional spin resonance whose energy scales with the superconducting gap in all three compounds. In addition, anisotropic low energy spin excitation enhancements in the superconducting state have been deduced and characterized for the under and optimally doped compounds. Read More

During a long period of time we are combating over-fitting in the CNN training process with model regularization, including weight decay, model averaging, data augmentation, etc. In this paper, we present DisturbLabel, an extremely simple algorithm which randomly replaces a part of labels as incorrect values in each iteration. Although it seems weird to intentionally generate incorrect training labels, we show that DisturbLabel prevents the network training from over-fitting by implicitly averaging over exponentially many networks which are trained with different label sets. Read More

The concept of isochronous mass spectrometry (IMS) applying two time-of-flight (TOF) detectors originated many years ago at GSI. However, the corresponding method for data analysis has never been discussed in detail. Recently, two TOF detectors have been installed at CSRe and the new working mode of the ring is under test. Read More

In this paper, we present direct mass measurements of neutron-rich $^{86}$Kr projectile fragments conducted at the HIRFL-CSR facility in Lanzhou by employing the Isochronous Mass Spectrometry (IMS) method. The new mass excesses of $^{52-54}$Sc nuclides are determined to be -40492(82), -38928(114), -34654(540) keV, which show a significant increase of binding energy compared to the reported ones in the Atomic Mass Evaluation 2012 (AME12). In particular, $^{53}$Sc and $^{54}$Sc are more bound by 0. Read More

We propose a new and simple discretization, named the Modified Virtual Grid Difference (MVGD), for numerical approximation of the Laplace-Beltrami (LB) operator on manifolds sampled by point clouds. The key observation is that both the manifold and a function defined on it can both be parametrized in a local Cartesian coordinate system and approximated using least squares. Based on the above observation, we first introduce a local virtual grid with a scale adapted to the sampling density centered at each point. Read More

We present a two-step technique for the synthesis of superconducting CuxBi2Se3. Cu0.15Bi2Se3 single crystals were synthesized using the melt-growth method. Read More

In this paper, we consider the Landau-Lifshitz equation of the ferromagnetic spin chain from $\R^2$ to the unit sphere $S^2$ under the general Oseen-Frank energy. We obtain global existence and uniqueness of weak solutions for large energy data; moreover, the number of singular points is finite. Read More

We describe the electronics and data acquisition system used in the first phase of the PandaX experiment -- a 120 kg dual-phase liquid xenon dark matter direct detection experiment in the China Jin-Ping Underground Laboratory. This system utilized 180 channels of commercial flash ADC waveform digitizers. This system achieved low trigger threshold ($<$1 keV electron-equivalent energy) and low deadtime data acquistion during the entire experimental run. Read More

Cyber data attacks are the worst-case interacting bad data to power system state estimation and cannot be detected by existing bad data detectors. In this paper, we for the first time analyze the likelihood of cyber data attacks by characterizing the actions of a malicious intruder. We propose to use Markov decision process to model an intruder's strategy, where the objective is to maximize the cumulative reward across time. Read More

Understanding human activity is very challenging even with the recently developed 3D/depth sensors. To solve this problem, this work investigates a novel deep structured model, which adaptively decomposes an activity instance into temporal parts using the convolutional neural networks (CNNs). Our model advances the traditional deep learning approaches in two aspects. Read More

By combining a parameterized Hermitian matrix, the realignment matrix of the bipartite density matrix $\rho$ and the vectorization of its reduced density matrices, we present a family of separability criteria, which are stronger than the computable cross norm or realignment (CCNR) criterion. With linear contraction methods, the proposed criteria can be used to detect the multipartite entangled states that are biseparable under any bipartite partitions. Moreover, we show by examples that the presented multipartite separability criteria can be more efficient than the corresponding multipartite realignment criterion based on CCNR, multipartite correlation tensor criterion and multipartite covariance matrix criterion. Read More

Detecting and recognizing objects interacting with humans lie in the center of first-person (egocentric) daily activity recognition. However, due to noisy camera motion and frequent changes in viewpoint and scale, most of the previous egocentric action recognition methods fail to capture and model highly discriminative object features. In this work, we propose a novel pipeline for first-person daily activity recognition, aiming at more discriminative object feature representation and object-motion feature fusion. Read More

While sparse coding-based clustering methods have shown to be successful, their bottlenecks in both efficiency and scalability limit the practical usage. In recent years, deep learning has been proved to be a highly effective, efficient and scalable feature learning tool. In this paper, we propose to emulate the sparse coding-based clustering pipeline in the context of deep learning, leading to a carefully crafted deep model benefiting from both. Read More

The complex interdigitated phases have greatly frustrated attempts to document the basic features of the superconductivity in the alkali metal intercalated iron chalcogenides. Here, using elastic neutron scattering, energy-dispersive x-ray spectroscopy, and resistivity measurements, we elucidate the relations of these phases in Rb$_{1-\delta}$Fe$_y$Se$_{2-z}$S$_z$. We find: i) the iron content is crucial in stabilizing the stripe antiferromagnetic (AF) phase with rhombic iron vacancy order ($y\approx1. Read More

The scan statistic is by far the most popular method for anomaly detection, being popular in syndromic surveillance, signal and image processing, and target detection based on sensor networks, among other applications. The use of the scan statistics in such settings yields a hypothesis testing procedure, where the null hypothesis corresponds to the absence of anomalous behavior. If the null distribution is known, then calibration of a scan-based test is relatively easy, as it can be done by Monte Carlo simulation. Read More

A combination of neutron diffraction and angle-resolved photoemission spectroscopy measurements on a pure antiferromagnetic stripe Rb$_{1-\delta}$Fe$_{1.5-\sigma}$S$_2$ is reported. A neutron diffraction experiment on a powder sample shows that a 98$\%$ volume fraction of the sample is in the antiferromagnetic stripe phase with rhombic iron vacancy order and a refined composition of Rb$_{0. Read More

Authors: Fengpeng An, Guangpeng An, Qi An, Vito Antonelli, Eric Baussan, John Beacom, Leonid Bezrukov, Simon Blyth, Riccardo Brugnera, Margherita Buizza Avanzini, Jose Busto, Anatael Cabrera, Hao Cai, Xiao Cai, Antonio Cammi, Guofu Cao, Jun Cao, Yun Chang, Shaomin Chen, Shenjian Chen, Yixue Chen, Davide Chiesa, Massimiliano Clemenza, Barbara Clerbaux, Janet Conrad, Davide D'Angelo, Herve De Kerret, Zhi Deng, Ziyan Deng, Yayun Ding, Zelimir Djurcic, Damien Dornic, Marcos Dracos, Olivier Drapier, Stefano Dusini, Stephen Dye, Timo Enqvist, Donghua Fan, Jian Fang, Laurent Favart, Richard Ford, Marianne Goger-Neff, Haonan Gan, Alberto Garfagnini, Marco Giammarchi, Maxim Gonchar, Guanghua Gong, Hui Gong, Michel Gonin, Marco Grassi, Christian Grewing, Mengyun Guan, Vic Guarino, Gang Guo, Wanlei Guo, Xin-Heng Guo, Caren Hagner, Ran Han, Miao He, Yuekun Heng, Yee Hsiung, Jun Hu, Shouyang Hu, Tao Hu, Hanxiong Huang, Xingtao Huang, Lei Huo, Ara Ioannisian, Manfred Jeitler, Xiangdong Ji, Xiaoshan Jiang, Cecile Jollet, Li Kang, Michael Karagounis, Narine Kazarian, Zinovy Krumshteyn, Andre Kruth, Pasi Kuusiniemi, Tobias Lachenmaier, Rupert Leitner, Chao Li, Jiaxing Li, Weidong Li, Weiguo Li, Xiaomei Li, Xiaonan Li, Yi Li, Yufeng Li, Zhi-Bing Li, Hao Liang, Guey-Lin Lin, Tao Lin, Yen-Hsun Lin, Jiajie Ling, Ivano Lippi, Dawei Liu, Hongbang Liu, Hu Liu, Jianglai Liu, Jianli Liu, Jinchang Liu, Qian Liu, Shubin Liu, Shulin Liu, Paolo Lombardi, Yongbing Long, Haoqi Lu, Jiashu Lu, Jingbin Lu, Junguang Lu, Bayarto Lubsandorzhiev, Livia Ludhova, Shu Luo, Vladimir Lyashuk, Randolph Mollenberg, Xubo Ma, Fabio Mantovani, Yajun Mao, Stefano M. Mari, William F. McDonough, Guang Meng, Anselmo Meregaglia, Emanuela Meroni, Mauro Mezzetto, Lino Miramonti, Thomas Mueller, Dmitry Naumov, Lothar Oberauer, Juan Pedro Ochoa-Ricoux, Alexander Olshevskiy, Fausto Ortica, Alessandro Paoloni, Haiping Peng, Jen-Chieh Peng, Ezio Previtali, Ming Qi, Sen Qian, Xin Qian, Yongzhong Qian, Zhonghua Qin, Georg Raffelt, Gioacchino Ranucci, Barbara Ricci, Markus Robens, Aldo Romani, Xiangdong Ruan, Xichao Ruan, Giuseppe Salamanna, Mike Shaevitz, Valery Sinev, Chiara Sirignano, Monica Sisti, Oleg Smirnov, Michael Soiron, Achim Stahl, Luca Stanco, Jochen Steinmann, Xilei Sun, Yongjie Sun, Dmitriy Taichenachev, Jian Tang, Igor Tkachev, Wladyslaw Trzaska, Stefan van Waasen, Cristina Volpe, Vit Vorobel, Lucia Votano, Chung-Hsiang Wang, Guoli Wang, Hao Wang, Meng Wang, Ruiguang Wang, Siguang Wang, Wei Wang, Yi Wang, Yi Wang, Yifang Wang, Zhe Wang, Zheng Wang, Zhigang Wang, Zhimin Wang, Wei Wei, Liangjian Wen, Christopher Wiebusch, Bjorn Wonsak, Qun Wu, Claudia-Elisabeth Wulz, Michael Wurm, Yufei Xi, Dongmei Xia, Yuguang Xie, Zhi-zhong Xing, Jilei Xu, Baojun Yan, Changgen Yang, Chaowen Yang, Guang Yang, Lei Yang, Yifan Yang, Yu Yao, Ugur Yegin, Frederic Yermia, Zhengyun You, Boxiang Yu, Chunxu Yu, Zeyuan Yu, Sandra Zavatarelli, Liang Zhan, Chao Zhang, Hong-Hao Zhang, Jiawen Zhang, Jingbo Zhang, Qingmin Zhang, Yu-Mei Zhang, Zhenyu Zhang, Zhenghua Zhao, Yangheng Zheng, Weili Zhong, Guorong Zhou, Jing Zhou, Li Zhou, Rong Zhou, Shun Zhou, Wenxiong Zhou, Xiang Zhou, Yeling Zhou, Yufeng Zhou, Jiaheng Zou

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purpose underground liquid scintillator detector, was proposed with the determination of the neutrino mass hierarchy as a primary physics goal. It is also capable of observing neutrinos from terrestrial and extra-terrestrial sources, including supernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos, atmospheric neutrinos, solar neutrinos, as well as exotic searches such as nucleon decays, dark matter, sterile neutrinos, etc. We present the physics motivations and the anticipated performance of the JUNO detector for various proposed measurements. Read More

In this paper, we consider the solutions of the relaxed Q-tensor flow in $\R^3$ with small parameter $\epsilon$. Firstly, we show that the limiting map is the so called harmonic map flow; Secondly, we also present a new proof for the global existence of weak solution for the harmonic map flow in three dimensions as in \cite{struwe88} and \cite{keller}, where Ginzburg-Landau approximation approach was used. Read More

We present a systematic angle-resolved photoemission spectroscopy study of the substitution-dependence of the electronic structure of Rb$_{0.8}$Fe$_{2}$(Se$_{1-z}$S$_z$)$_2$ (z = 0, 0.5, 1), where superconductivity is continuously suppressed into a metallic phase. Read More

We study sequential change-point detection using sketches (linear projections) of high-dimensional signal vectors, by presenting the sketching procedures that are derived based on the generalized likelihood ratio statistic. We consider both fixed and time-varying projections, and derive theoretical approximations to two fundamental performance metrics: the average run length (ARL) and the expected detection delay (EDD); these approximations are shown to be highly accurate by numerical simulations. We also characterize the performance of the procedure when the projection is a Gaussian random projection or a sparse 0-1 matrix (in particular, an expander graph). Read More

We consider the problem of detecting a sparse Poisson mixture. Our results parallel those for the detection of a sparse normal mixture, pioneered by Ingster (1997) and Donoho and Jin (2004), when the Poisson means are larger than logarithmic in the sample size. In particular, a form of higher criticism achieves the detection boundary in the whole sparse regime. Read More

We prove some existence results for the fractional Yamabe problem in the case that the boundary manifold is umbilic, thus covering some of the cases not considered by Gonzalez and Qing. These are inspired by the work of Coda-Marques on the boundary Yamabe problem but, in addition, a careful understanding of the behavior at infinity for asymptotically hyperbolic metrics is required. Read More

Automated scene analysis has been a topic of great interest in computer vision and cognitive science. Recently, with the growth of crowd phenomena in the real world, crowded scene analysis has attracted much attention. However, the visual occlusions and ambiguities in crowded scenes, as well as the complex behaviors and scene semantics, make the analysis a challenging task. Read More

Human activity understanding with 3D/depth sensors has received increasing attention in multimedia processing and interactions. This work targets on developing a novel deep model for automatic activity recognition from RGB-D videos. We represent each human activity as an ensemble of cubic-like video segments, and learn to discover the temporal structures for a category of activities, i. Read More

The Einstein-Podolsky-Rosen (EPR) paradox established a link between entanglement and nonlocality in quantum mechanics. EPR steering is the nonlocality associated with the EPR paradox and has traditionally only been investigated between two parties. Here, we present the first experimental observations of multipartite EPR steering, and of the genuine tripartite continuous variable entanglement of three mesoscopic optical systems. Read More

In this paper, we focus on the ergodic downlink sum-rate performance of a system consisting of a set of heterogeneous users. We study three user selection schemes to group near-orthogonal users for simultaneous transmission. The first scheme is a random selection policy that achieves fairness, but does not exploit multi-user diversity. Read More

A low artificial anisotropy cellular automaton (CA) model is developed for the simulation of microstructure evolution in directional solidification. The CA model's capture rule was modified by a limited neighbor solid fraction (LNSF) method. Various interface curvature calculation methods have been compared. Read More