Dong Wang - PKU

Dong Wang
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Dong Wang

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Computer Science - Computation and Language (15)
Quantum Physics (9)
Computer Science - Learning (8)
Computer Science - Neural and Evolutionary Computing (6)
Physics - Accelerator Physics (5)
Computer Science - Sound (4)
Mathematics - Mathematical Physics (3)
Computer Science - Computer Vision and Pattern Recognition (3)
Physics - Instrumentation and Detectors (3)
Mathematical Physics (3)
Cosmology and Nongalactic Astrophysics (3)
Computer Science - Artificial Intelligence (3)
General Relativity and Quantum Cosmology (3)
High Energy Physics - Theory (3)
Mathematics - Probability (3)
Physics - Materials Science (2)
Mathematics - Classical Analysis and ODEs (1)
Statistics - Applications (1)
Physics - Physics and Society (1)
Earth and Planetary Astrophysics (1)
Statistics - Machine Learning (1)
Statistics - Methodology (1)
Nuclear Experiment (1)
High Energy Physics - Phenomenology (1)
Mathematics - Numerical Analysis (1)
Statistics - Theory (1)
Statistics - Computation (1)
Nonlinear Sciences - Exactly Solvable and Integrable Systems (1)
Physics - Computational Physics (1)
Physics - Chemical Physics (1)
Mathematics - Statistics (1)
Physics - Strongly Correlated Electrons (1)
Computer Science - Architecture (1)
Physics - Medical Physics (1)
Quantitative Biology - Populations and Evolution (1)
Physics - Optics (1)

Publications Authored By Dong Wang

Deep neural models, particularly the LSTM-RNN model, have shown great potential in language identification (LID). However, the phonetic information has been largely overlooked by most of existing neural LID methods, although this information has been used in the conventional phonetic LID systems with a great success. We present a phonetic temporal neural model for LID, which is an LSTM-RNN LID system but accepts phonetic features produced by a phone-discriminative DNN as the input, rather than raw acoustic features. Read More

In this letter, we investigate how to enhance quantum entanglement under an open Dirac system with Hawking effect in Schwarzschild space-time. Specifically, we explore the scenario that particle A hold by Alice undergoes generalized amplitude damping noise in a flat space-time and another particle B by Bob entangled with A is in the Schwarzschild space-time. Then, we put forward a feasible physical scheme for recovering quantum entanglement by prior weak measurement on subsystem A before the interaction with the dissipative environment followed by post filtering operation. Read More

Recently deep neural networks (DNNs) have been used to learn speaker features. However, the quality of the learned features is not sufficiently good, so a complex back-end model, either neural or probabilistic, has to be used to address the residual uncertainty when applied to speaker verification, just as with raw features. This paper presents a convolutional time-delay deep neural network structure (CT-DNN) for speaker feature learning. Read More

It has been shown that Chinese poems can be successfully generated by sequence-to-sequence neural models, particularly with the attention mechanism. A potential problem of this approach, however, is that neural models can only learn abstract rules, while poem generation is a highly creative process that involves not only rules but also innovations for which pure statistical models are not appropriate in principle. This work proposes a memory-augmented neural model for Chinese poem generation, where the neural model and the augmented memory work together to balance the requirements of linguistic accordance and aesthetic innovation, leading to innovative generations that are still rule-compliant. Read More

In this work, there are two parties, Alice on Earth and Bob on the satellite, which initially share an entangled state, and some open problems, which emerge during quantum steering that Alice remotely steers Bob, are investigated. Our analytical results indicate that all entangled pure states and maximally entangled evolution states (EESs) are steerable, and not every entangled evolution state is steerable and some steerable states are only locally correlated. Besides, quantum steering from Alice to Bob experiences a "sudden death" with increasing decoherence strength. Read More

Pure acoustic neural models, particularly the LSTM-RNN model, have shown great potential in language identification (LID). However, the phonetic information has been largely overlooked by most of existing neural LID models, although this information has been used in the conventional phonetic LID systems with a great success. We present a phone-aware neural LID architecture, which is a deep LSTM-RNN LID system but accepts output from an RNN-based ASR system. Read More

The intrinsic charge transport of stanene is investigated by using density function theory and density function perturbation theory coupled with Boltzmann transport equations from first principles. The accurate Wannier interpolations are applied to calculate the charge carrier scatterings with all branches of phonons with dispersion contribution. The intrinsic carrier mobilities are predicted to be 2~3$\times10^3$ cm$^2$/(V s) at 300 K, and we find that the intervalley scatterings from the out-of-plane and transverse acoustic phonon modes dominate the carrier relaxation. Read More

In this paper we study the probability distribution of the position of a tagged particle in the $q$-deformed Totally Asymmetric Zero Range Process ($q$-TAZRP) with site dependent jumping rates. For a finite particle system, it is derived from the transition probability previously obtained by Wang and Waugh. We also provide the probability distribution formula for a tagged particle in the $q$-TAZRP with the so-called step initial condition in which infinitely many particles occupy one single site and all other sites are unoccupied. Read More

The uncertainty relation is a fundamental limit in quantum mechanics and is of great importance to quantum information processing as it relates to quantum precision measurement. Due to interactions with the surrounding environment, a quantum system will unavoidably suffer from decoherence. Here, we investigate the dynamic behaviors of the entropic uncertainty relation of an atom-cavity interacting system under a bosonic reservoir during the crossover between Markovian and non-Markovian regimes. Read More

Principal component analysis (PCA) is fundamental to statistical machine learning. It extracts latent principal factors that contribute to the most variation of the data. When data are stored across multiple machines, however, communication cost can prohibit the computation of PCA in a central location and distributed algorithms for PCA are thus needed. Read More

The free electron laser (FEL), as the new generation light source, is an attractive tool in scientific frontier research, because of its advantages of full coherence, ultra-short pulse and controllable polarization. Generally, the soft X-ray FEL facilities require a precise measurement of polarization and X-ray energy spectrum. In this paper, based on the soft X-ray FEL user facility under construction at Shanghai, a numerical model in the framework of Geant4 was developed for simulating electron time of flight (e-TOF) based polarimeter and spectrometer. Read More

We have performed a detailed investigation of the new `breathing' pyrochlore compound LiInCr$_4$O$_8$ through Rh substitution with measurements of magnetic susceptibility, specific heat, and x-ray powder diffraction. The antiferromagnetic phase of LiInCr$_4$O$_8$ is found to be slowly suppressed with increasing Rh, up to the critical concentration of $x$ = 0.1 where the antiferromagnetic phase is still observed with the peak in specific heat $T_p$ = 12. Read More

In this paper, a simple method is proposed to extend the photon energy range of a soft x-ray self-seeding free-electron laser (FEL). A normal monochromator is first applied to purify the FEL spectrum and provide a coherent seeding signal. This coherent signal then interacts with the electron beam in the following reverse tapered undulator section to generate strong coherent microbunchings while maintain the good quality of the electron beam. Read More

Migration is a key mechanism for expansion of communities. In spatially heterogeneous environments, rapidly gaining knowledge about the local environment is key to the evolutionary success of a migrating population. For historical human migration, environmental heterogeneity was naturally asymmetric in the north-south (NS) and east-west (EW) directions. Read More

We extend the matter bounce scenario to a more general theory in which the background dynamics and cosmological perturbations are generated by a $k$-essence scalar field with an arbitrary sound speed. When the sound speed is small, the curvature perturbation is enhanced, and the tensor-to-scalar ratio, which is excessively large in the original model, can be sufficiently suppressed to be consistent with observational bounds. Then, we study the primordial three-point correlation function generated during the matter-dominated contraction stage and find that it only depends on the sound speed parameter. Read More

We identify certain classes of 1D symmetry-protected topological (SPT) phases in which almost every state is universal for measurement based quantum computation (MBQC) in 1D. By developing a general scheme to extract the useful entanglement from SPT ordered states, we can associate to each of these phases a Lie group of executable gates. We then show how to determine this Lie group from the cohomological description of SPT phases, thereby affirming the compelling link between SPT order and the computational power of 1D resource states. Read More

We report the design and test results of a beam monitor developed for online monitoring in hadron therapy. The beam monitor uses eight silicon pixel sensors, \textit{Topmetal-${II}^-$}, as the anode array. \textit{Topmetal-${II}^-$} is a charge sensor designed in a CMOS 0. Read More

In this letter, the dynamic behavior of Einstein-Podolsky-Rosen (EPR) steering and its redistribution under the relativistic motion are investigated. The investigation results have shown that EPR steering from Alice to Bob-I experiences a sudden death with increasing acceleration parameter \beta\ when \alpha\ is approximately less than 0.6. Read More

\textit{Topmetal-${II}^-$} is a low noise CMOS pixel direct charge sensor with a pitch of 83$\mu m$. CdZnTe is an excellent semiconductor material for radiation detection. The combination of CdZnTe and the sensor makes it possible to build a detector with high spatial resolution. Read More

Convolutional neural networks (CNNs) have been widely employed in many applications such as image classification, video analysis and speech recognition. Being compute-intensive, CNN computations are mainly accelerated by GPUs with high power dissipations. Recently, studies were carried out exploiting FPGA as CNN accelerator because of its reconfigurability and energy efficiency advantage over GPU, especially when OpenCL-based high-level synthesis tools are now available providing fast verification and implementation flows. Read More

Besides the original seeded undulator line, in the Soft X-ray free-electron laser (SXFEL) user facility at Shanghai, a second undulator line based on self-amplified spontaneous emission is proposed to achieve 2 nm laser pulse with extremely high brightness. In this paper, the beam energy deviation induced by the undulator wakefields is numerically obtained, and it is verified to have a good agreement between 3D and 2D simulation results. The beam energy loss along the undulator degrades the expected FEL output performance. Read More

Deriving single or few cycle terahertz pulse (THz) by intense femtosecond laser through cascaded optical rectification in electro-optic crystals is a crucial technique in cutting-edge time-resolved spectroscopy to characterize micro-scale structures and ultrafast dynamics. In the past decade, lithium niobate (LN) crystal implementation of wave-front tilt scheme has been prevalently used, while painstaking efforts have been invested in order to achieve higher THz conversion efficiency. In this research we developed a brand new type of LN crystal possessing dual-face-cut and Brewster coupling, and conducted experimental and simulative investigation systematically to optimize the multi-dimensionally entangled parameters in THz generation, predicting the extreme conversion efficiency of 10% is potentially promising at the THz absorption coefficient of 0. Read More

In finance, economics and many other fields, observations in a matrix form are often observed over time. For example, many economic indicators are obtained in different countries over time. Various financial characteristics of many companies are reported over time. Read More

We review matter bounce scenarios where the matter content is dark matter and dark energy. These cosmologies predict a nearly scale-invariant power spectrum with a slightly red tilt for scalar perturbations and a small tensor-to-scalar ratio. Importantly, these models predict a positive running of the scalar index, contrary to the predictions of the simplest inflationary and ekpyrotic models, and hence could potentially be falsified by future observations. Read More

Research on multilingual speech recognition remains attractive yet challenging. Recent studies focus on learning shared structures under the multi-task paradigm, in particular a feature sharing structure. This approach has been found effective to improve performance on each individual language. Read More

We present the OC16-CE80 Chinese-English mixlingual speech database which was released as a main resource for training, development and test for the Chinese-English mixlingual speech recognition (MixASR-CHEN) challenge on O-COCOSDA 2016. This database consists of 80 hours of speech signals recorded from more than 1,400 speakers, where the utterances are in Chinese but each involves one or several English words. Based on the database and another two free data resources (THCHS30 and the CMU dictionary), a speech recognition (ASR) baseline was constructed with the deep neural network-hidden Markov model (DNN-HMM) hybrid system. Read More

PLDA is a popular normalization approach for the i-vector model, and it has delivered state-of-the-art performance in speaker verification. However, PLDA training requires a large amount of labeled development data, which is highly expensive in most cases. A possible approach to mitigate the problem is various unsupervised adaptation methods, which use unlabeled data to adapt the PLDA scattering matrices to the target domain. Read More

PLDA is a popular normalization approach for the i-vector model, and it has delivered state-of-the-art performance in speaker verification. However, PLDA training requires a large amount of labelled development data, which is highly expensive in most cases. We present a cheap PLDA training approach, which assumes that speakers in the same session can be easily separated, and speakers in different sessions are simply different. Read More

This paper presents a unified model to perform language and speaker recognition simultaneously and altogether. The model is based on a multi-task recurrent neural network where the output of one task is fed as the input of the other, leading to a collaborative learning framework that can improve both language and speaker recognition by borrowing information from each other. Our experiments demonstrated that the multi-task model outperforms the task-specific models on both tasks. Read More

Recurrent neural networks (RNNs) have shown clear superiority in sequence modeling, particularly the ones with gated units, such as long short-term memory (LSTM) and gated recurrent unit (GRU). However, the dynamic properties behind the remarkable performance remain unclear in many applications, e.g. Read More

We present the AP16-OL7 database which was released as the training and test data for the oriental language recognition (OLR) challenge on APSIPA 2016. Based on the database, a baseline system was constructed on the basis of the i-vector model. We report the baseline results evaluated in various metrics defined by the AP16-OLR evaluation plan and demonstrate that AP16-OL7 is a reasonable data resource for multilingual research. Read More

Resource states that contain nontrivial symmetry-protected topological order are identified for universal single-qudit measurement-based quantum computation. Our resource states fall into two classes: one as the qudit generalizations of the 1D qubit cluster state, and the other as the higher-symmetry generalizations of the spin-1 Affleck-Kennedy-Lieb-Tasaki (AKLT) state, namely, with unitary, orthogonal, or symplectic symmetry. The symmetry in cluster states protects information propagation (identity gate), while the higher symmetry in AKLT-type states enables nontrivial gate computation. Read More

Radio frequency deflectors are widely used for time-resolved electron beam energy, emittance and radiation profile measurements in modern free electron laser facilities. In this paper, we present the beam dynamics aspects of the deflecting cavity of SXFEL user facility, which is located at the exit of the undulator. With a targeted time resolution around 10 fs, it is expected to be an important tool for time-resolved commissioning and machine studies for SXFEL user facility. Read More

We propose a new scheme to generate high-brightness and temporal coherent soft x-ray radiation in a seeded free-electron laser. The proposed scheme is based the coherent harmonic generation (CHG) and superradiant principles. A CHG scheme is first used to generate coherent signal at ultra-high harmonics of the seed. Read More

We study a model of nonintersecting Brownian bridges on an interval with either absorbing or reflecting walls at the boundaries, focusing on the point in space-time at which the particles meet the wall. These processes are determinantal, and in different scaling limits when the particles approach the reflecting (resp. absorbing) walls we obtain hard-edge limiting kernels which are the even (resp. Read More

We study the asymptotic behavior of the eigenvalues of Gaussian perturbations of large Hermitian random matrices for which the limiting eigenvalue density vanishes at a singular interior point or vanishes faster than a square root at a singular edge point. First, we show that the singular behavior propagates macroscopically for sufficiently small Gaussian perturbations, and we describe the macroscopic eigenvalue behavior for Gaussian perturbations of critical size. Secondly, for sufficiently small Gaussian perturbations of unitary invariant random matrices, we prove that the microscopic eigenvalue correlations near the singular point are described by the same limiting kernel as in the unperturbed case. Read More

We proposed an efficient iterative thresholding method for multi-phase image segmentation. The algorithm is based on minimizing piecewise constant Mumford-Shah functional in which the contour length (or perimeter) is approximated by a non-local multi-phase energy. The minimization problem is solved by an iterative method. Read More

We study the effects of the non-attractor initial conditions for the canonical single-field inflation. The non-attractor stage can last only several $e$-folding numbers, and should be followed by hilltop inflation. This two-stage evolution leads to large scale suppression in the primordial power spectrum, which is favored by recent observations. Read More

Authors: CBM Collaboration, T. Ablyazimov, A. Abuhoza, R. P. Adak, M. Adamczyk, K. Agarwal, M. M. Aggarwal, Z. Ahammed, F. Ahmad, N. Ahmad, S. Ahmad, A. Akindinov, P. Akishin, E. Akishina, T. Akishina, V. Akishina, A. Akram, M. Al-Turany, I. Alekseev, E. Alexandrov, I. Alexandrov, S. Amar-Youcef, M. Anđelić, O. Andreeva, C. Andrei, A. Andronic, Yu. Anisimov, H. Appelshäuser, D. Argintaru, E. Atkin, S. Avdeev, R. Averbeck, M. D. Azmi, V. Baban, M. Bach, E. Badura, S. Bähr, T. Balog, M. Balzer, E. Bao, N. Baranova, T. Barczyk, D. Bartoş, S. Bashir, M. Baszczyk, O. Batenkov, V. Baublis, M. Baznat, J. Becker, K. -H. Becker, S. Belogurov, D. Belyakov, J. Bendarouach, I. Berceanu, A. Bercuci, A. Berdnikov, Y. Berdnikov, R. Berendes, G. Berezin, C. Bergmann, D. Bertini, O. Bertini, C. Beşliu, O. Bezshyyko, P. P. Bhaduri, A. Bhasin, A. K. Bhati, B. Bhattacharjee, A. Bhattacharyya, T. K. Bhattacharyya, S. Biswas, T. Blank, D. Blau, V. Blinov, C. Blume, Yu. Bocharov, J. Book, T. Breitner, U. Brüning, J. Brzychczyk, A. Bubak, H. Büsching, T. Bus, V. Butuzov, A. Bychkov, A. Byszuk, Xu Cai, M. Cálin, Ping Cao, G. Caragheorgheopol, I. Carević, V. Cătănescu, A. Chakrabarti, S. Chattopadhyay, A. Chaus, Hongfang Chen, LuYao Chen, Jianping Cheng, V. Chepurnov, H. Cherif, A. Chernogorov, M. I. Ciobanu, G. Claus, F. Constantin, M. Csanád, N. D'Ascenzo, Supriya Das, Susovan Das, J. de Cuveland, B. Debnath, D. Dementiev, Wendi Deng, Zhi Deng, H. Deppe, I. Deppner, O. Derenovskaya, C. A. Deveaux, M. Deveaux, K. Dey, M. Dey, P. Dillenseger, V. Dobyrn, D. Doering, Sheng Dong, A. Dorokhov, M. Dreschmann, A. Drozd, A. K. Dubey, S. Dubnichka, Z. Dubnichkova, M. Dürr, L. Dutka, M. Dželalija, V. V. Elsha, D. Emschermann, H. Engel, V. Eremin, T. Eşanu, J. Eschke, D. Eschweiler, Huanhuan Fan, Xingming Fan, M. Farooq, O. Fateev, Shengqin Feng, S. P. D. Figuli, I. Filozova, D. Finogeev, P. Fischer, H. Flemming, J. Förtsch, U. Frankenfeld, V. Friese, E. Friske, I. Fröhlich, J. Frühauf, J. Gajda, T. Galatyuk, G. Gangopadhyay, C. García Chávez, J. Gebelein, P. Ghosh, S. K. Ghosh, S. Gläßel, M. Goffe, L. Golinka-Bezshyyko, V. Golovatyuk, S. Golovnya, V. Golovtsov, M. Golubeva, D. Golubkov, A. Gómez Ramírez, S. Gorbunov, S. Gorokhov, D. Gottschalk, P. Gryboś, A. Grzeszczuk, F. Guber, K. Gudima, M. Gumiński, A. Gupta, Yu. Gusakov, Dong Han, H. Hartmann, Shue He, J. Hehner, N. Heine, A. Herghelegiu, N. Herrmann, B. Heß, J. M. Heuser, A. Himmi, C. Höhne, R. Holzmann, Dongdong Hu, Guangming Huang, Xinjie Huang, D. Hutter, A. Ierusalimov, E. -M. Ilgenfritz, M. Irfan, D. Ivanischev, M. Ivanov, P. Ivanov, Valery Ivanov, Victor Ivanov, Vladimir Ivanov, A. Ivashkin, K. Jaaskelainen, H. Jahan, V. Jain, V. Jakovlev, T. Janson, Di Jiang, A. Jipa, I. Kadenko, P. Kähler, B. Kämpfer, V. Kalinin, J. Kallunkathariyil, K. -H. Kampert, E. Kaptur, R. Karabowicz, O. Karavichev, T. Karavicheva, D. Karmanov, V. Karnaukhov, E. Karpechev, K. Kasiński, G. Kasprowicz, M. Kaur, A. Kazantsev, U. Kebschull, G. Kekelidze, M. M. Khan, S. A. Khan, A. Khanzadeev, F. Khasanov, A. Khvorostukhin, V. Kirakosyan, M. Kirejczyk, A. Kiryakov, M. Kiš, I. Kisel, P. Kisel, S. Kiselev, T. Kiss, P. Klaus, R. Kłeczek, Ch. Klein-Bösing, V. Kleipa, V. Klochkov, P. Kmon, K. Koch, L. Kochenda, P. Koczoń, W. Koenig, M. Kohn, B. W. Kolb, A. Kolosova, B. Komkov, M. Korolev, I. Korolko, R. Kotte, A. Kovalchuk, S. Kowalski, M. Koziel, G. Kozlov, V. Kozlov, V. Kramarenko, P. Kravtsov, E. Krebs, C. Kreidl, I. Kres, D. Kresan, G. Kretschmar, M. Krieger, A. V. Kryanev, E. Kryshen, M. Kuc, W. Kucewicz, V. Kucher, L. Kudin, A. Kugler, Ajit Kumar, Ashwini Kumar, L. Kumar, J. Kunkel, A. Kurepin, N. Kurepin, A. Kurilkin, P. Kurilkin, V. Kushpil, S. Kuznetsov, V. Kyva, V. Ladygin, C. Lara, P. Larionov, A. Laso García, E. Lavrik, I. Lazanu, A. Lebedev, S. Lebedev, E. Lebedeva, J. Lehnert, J. Lehrbach, Y. Leifels, F. Lemke, Cheng Li, Qiyan Li, Xin Li, Yuanjing Li, V. Lindenstruth, B. Linnik, Feng Liu, I. Lobanov, E. Lobanova, S. Löchner, P. -A. Loizeau, S. A. Lone, J. A. Lucio Martínez, Xiaofeng Luo, A. Lymanets, Pengfei Lyu, A. Maevskaya, S. Mahajan, D. P. Mahapatra, T. Mahmoud, P. Maj, Z. Majka, A. Malakhov, E. Malankin, D. Malkevich, O. Malyatina, H. Malygina, M. M. Mandal, S. Mandal, V. Manko, S. Manz, A. M. Marin Garcia, J. Markert, S. Masciocchi, T. Matulewicz, L. Meder, M. Merkin, V. Mialkovski, J. Michel, N. Miftakhov, L. Mik, K. Mikhailov, V. Mikhaylov, B. Milanović, V. Militsija, D. Miskowiec, I. Momot, T. Morhardt, S. Morozov, W. F. J. Müller, C. Müntz, S. Mukherjee, C. E. Muńoz Castillo, Yu. Murin, R. Najman, C. Nandi, E. Nandy, L. Naumann, T. Nayak, A. Nedosekin, V. S. Negi, W. Niebur, V. Nikulin, D. Normanov, A. Oancea, Kunsu Oh, Yu. Onishchuk, G. Ososkov, P. Otfinowski, E. Ovcharenko, S. Pal, I. Panasenko, N. R. Panda, S. Parzhitskiy, V. Patel, C. Pauly, M. Penschuck, D. Peshekhonov, V. Peshekhonov, V. Petráček, M. Petri, M. Petriş, A. Petrovici, M. Petrovici, A. Petrovskiy, O. Petukhov, D. Pfeifer, K. Piasecki, J. Pieper, J. Pietraszko, R. Płaneta, V. Plotnikov, V. Plujko, J. Pluta, A. Pop, V. Pospisil, K. Poźniak, A. Prakash, S. K. Prasad, M. Prokudin, I. Pshenichnov, M. Pugach, V. Pugatch, S. Querchfeld, S. Rabtsun, L. Radulescu, S. Raha, F. Rami, R. Raniwala, S. Raniwala, A. Raportirenko, J. Rautenberg, J. Rauza, R. Ray, S. Razin, P. Reichelt, S. Reinecke, A. Reinefeld, A. Reshetin, C. Ristea, O. Ristea, A. Rodriguez Rodriguez, F. Roether, R. Romaniuk, A. Rost, E. Rostchin, I. Rostovtseva, Amitava Roy, Ankhi Roy, J. Rożynek, Yu. Ryabov, A. Sadovsky, R. Sahoo, P. K. Sahu, S. K. Sahu, J. Saini, S. Samanta, S. S. Sambyal, V. Samsonov, J. Sánchez Rosado, O. Sander, S. Sarangi, T. Satława, S. Sau, V. Saveliev, S. Schatral, C. Schiaua, F. Schintke, C. J. Schmidt, H. R. Schmidt, K. Schmidt, J. Scholten, K. Schweda, F. Seck, S. Seddiki, I. Selyuzhenkov, A. Semennikov, A. Senger, P. Senger, A. Shabanov, A. Shabunov, Ming Shao, A. D. Sheremetiev, Shusu Shi, N. Shumeiko, V. Shumikhin, I. Sibiryak, B. Sikora, A. Simakov, C. Simon, C. Simons, R. N. Singaraju, A. K. Singh, B. K. Singh, C. P. Singh, V. Singhal, M. Singla, P. Sitzmann, K. Siwek-Wilczyńska, L. Škoda, I. Skwira-Chalot, I. Som, Guofeng Song, Jihye Song, Z. Sosin, D. Soyk, P. Staszel, M. Strikhanov, S. Strohauer, J. Stroth, C. Sturm, R. Sultanov, Yongjie Sun, D. Svirida, O. Svoboda, A. Szabó, R. Szczygieł, R. Talukdar, Zebo Tang, M. Tanha, J. Tarasiuk, O. Tarassenkova, M. -G. Târzilă, M. Teklishyn, T. Tischler, P. Tlustý, T. Tölyhi, A. Toia, N. Topil'skaya, M. Träger, S. Tripathy, I. Tsakov, Yu. Tsyupa, A. Turowiecki, N. G. Tuturas, F. Uhlig, E. Usenko, I. Valin, D. Varga, I. Vassiliev, O. Vasylyev, E. Verbitskaya, W. Verhoeven, A. Veshikov, R. Visinka, Y. P. Viyogi, S. Volkov, A. Volochniuk, A. Vorobiev, Aleksey Voronin, Alexander Voronin, V. Vovchenko, M. Vznuzdaev, Dong Wang, Xi-Wei Wang, Yaping Wang, Yi Wang, M. Weber, C. Wendisch, J. P. Wessels, M. Wiebusch, J. Wiechula, D. Wielanek, A. Wieloch, A. Wilms, N. Winckler, M. Winter, K. Wiśniewski, Gy. Wolf, Sanguk Won, Ke-Jun Wu, J. Wüstenfeld, Changzhou Xiang, Nu Xu, Junfeng Yang, Rongxing Yang, Zhongbao Yin, In-Kwon Yoo, B. Yuldashev, I. Yushmanov, W. Zabołotny, Yu. Zaitsev, N. I. Zamiatin, Yu. Zanevsky, M. Zhalov, Yifei Zhang, Yu Zhang, Lei Zhao, Jiajun Zheng, Sheng Zheng, Daicui Zhou, Jing Zhou, Xianglei Zhu, A. Zinchenko, W. Zipper, M. Żoładź, P. Zrelov, V. Zryuev, P. Zumbruch, M. Zyzak

Substantial experimental and theoretical efforts worldwide are devoted to explore the phase diagram of strongly interacting matter. At LHC and top RHIC energies, QCD matter is studied at very high temperatures and nearly vanishing net-baryon densities. There is evidence that a Quark-Gluon-Plasma (QGP) was created at experiments at RHIC and LHC. Read More

We propose a scheme to modulate the entanglement between two oscillators separated in space via the squeezing cavity field generated by the optical parametric amplifier instead of injecting the squeezing field directly with the assistance of Coulomb interaction. We show that the Coulomb interaction between the oscillators is the essential reason for the existence of entanglement. Due to the gain of the optical parametric amplifier and the phase of the pump driving the optical parametric amplifier can simultaneously modulate the squeezing cavity field, the radiation pressure interaction between the cavity field and the oscillator is modulated accordingly. Read More

Recent progress in neural learning demonstrated that machines can do well in regularized tasks, e.g., the game of Go. Read More

A strong analog classical simulation of general quantum evolution is proposed, which serves as a novel scheme in quantum computation and simulation. The scheme employs the approach of geometric quantum mechanics and quantum informational technique of quantum tomography, which applies broadly to cases of mixed states, nonunitary evolution, and infinite dimensional systems. The simulation provides an intriguing classical picture to probe quantum phenomena, namely, a coherent quantum dynamics can be viewed as a globally constrained classical Hamiltonian dynamics of a collection of coupled particles or strings. Read More

The existence of gender differences in the structure and composition of social networks is a well established finding in the social and behavioral sciences, but researchers continue to debate whether structural, dispositional, or life course factors are the primary driver of these differences. In this paper we extend work on gender differences in social networks to patterns of interaction, propinquity, and connectivity captured via a social sensing platform comprised of an ensemble of individuals' phone calls, text messaging, face-to-face interactions, and traces of their mobility activities. We attempt to isolate dispositional from other factors by focusing on a relatively homogeneous population on a relatively closed setting at the same stage in the life course. Read More

We study the physical properties of double-cavity optomechanical system in which the mechanical resonator interacts with one of the coupled cavities and another cavity is used as an auxiliary cavity. The model can be expected to achieve the strong optomechanical coupling strength and overcome the optomechanical cavity decay, simultaneously. Through the coherent auxiliary cavity interferences, the steady-state squeezing of mechanical resonator can be generated in highly unresolved sideband regime. Read More

Learning and generating Chinese poems is a charming yet challenging task. Traditional approaches involve various language modeling and machine translation techniques, however, they perform not as well when generating poems with complex pattern constraints, for example Song iambics, a famous type of poems that involve variable-length sentences and strict rhythmic patterns. This paper applies the attention-based sequence-to-sequence model to generate Chinese Song iambics. Read More

Disequilibrium species have been used previously to probe the deep water abundances and the eddy diffusion coefficient for giant planets. In this paper, we present a diffusion-kinetics code that predicts the abundances of disequilibrium species in the tropospheres of Jupiter and Saturn with updated thermodynamic and kinetic data. The dependence on the deep water abundance and the eddy diffusion coefficient is investigated. Read More

Learning a good distance metric in feature space potentially improves the performance of the KNN classifier and is useful in many real-world applications. Many metric learning algorithms are however based on the point estimation of a quadratic optimization problem, which is time-consuming, susceptible to overfitting, and lack a natural mechanism to reason with parameter uncertainty, an important property useful especially when the training set is small and/or noisy. To deal with these issues, we present a novel Bayesian metric learning method, called Bayesian NCA, based on the well-known Neighbourhood Component Analysis method, in which the metric posterior is characterized by the local label consistency constraints of observations, encoded with a similarity graph instead of independent pairwise constraints. Read More

For text-independent short-utterance speaker recognition (SUSR), the performance often degrades dramatically. This paper presents a combination approach to the SUSR tasks with two phonetic-aware systems: one is the DNN-based i-vector system and the other is our recently proposed subregion-based GMM-UBM system. The former employs phone posteriors to construct an i-vector model in which the shared statistics offers stronger robustness against limited test data, while the latter establishes a phone-dependent GMM-UBM system which represents speaker characteristics with more details. Read More

Although highly correlated, speech and speaker recognition have been regarded as two independent tasks and studied by two communities. This is certainly not the way that people behave: we decipher both speech content and speaker traits at the same time. This paper presents a unified model to perform speech and speaker recognition simultaneously and altogether. Read More

This paper proposes a novel framework to alleviate the model drift problem in visual tracking, which is based on paced updates and trajectory selection. Given a base tracker, an ensemble of trackers is generated, in which each tracker's update behavior will be paced and then traces the target object forward and backward to generate a pair of trajectories in an interval. Then, we implicitly perform self-examination based on trajectory pair of each tracker and select the most robust tracker. Read More