arXiv subjects

Peng Zhang

Publications and source records attributed to Peng Zhang.

At least 721 records · Page 40Linked to original sources

arXiv2019

Vortex-dynamical Interpretation of Anti-phase and In-phase Flickering of Dual Buoyant Diffusion Flames

Anti-phase and in-phase flickering modes of dual buoyant diffusion flames were numerically investigated and theoretically analyzed in this study. Inspired by the flickering mechanism of a single buoyant diffusion flame, for which the deformation, stretching, or even pinch-off of the flame surface result from the formation and evolution of the toroidal vortices, we attempted to understand the anti-phase and in-phase flickering of dual buoyant diffusion flames from the perspective of vortex dynamics. The interaction between the inner-side shear layers of the two flames was identified to be responsible for the different flickering modes. Specifically, the transition between anti-phase and in-phase flickering modes can be predicted by a unified regime nomogram of the normalized flickering frequency versus a characteristic Reynolds number, which accounts for the viscous effect on vorticity diffusion between the two inner-side shear layers. Physically, the transition of the vortical structures from symmetric (in-phase) to staggered (anti-phase) in a dual-flame system can be interpreted as being similar to the mechanism causing flow transition in the wake of a bluff body and forming the Karman vortex street.

physics.flu-dyn↗

arXiv2019

Predicting tongue motion in unlabeled ultrasound videos using convolutional LSTM neural network

A challenge in speech production research is to predict future tongue movements based on a short period of past tongue movements. This study tackles speaker-dependent tongue motion prediction problem in unlabeled ultrasound videos with convolutional long short-term memory (ConvLSTM) networks. The model has been tested on two different ultrasound corpora. ConvLSTM outperforms 3-dimensional convolutional neural network (3DCNN) in predicting the 9\textsuperscript{th} frames based on 8 preceding frames, and also demonstrates good capacity to predict only the tongue contours in future frames. Further tests reveal that ConvLSTM can also learn to predict tongue movements in more distant frames beyond the immediately following frames. Our codes are available at: https://github.com/shuiliwanwu/ConvLstm-ultrasound-videos.

cs.CV↗

arXiv2019

Clustering Bioactive Molecules in 3D Chemical Space with Unsupervised Deep Learning

Unsupervised clustering has broad applications in data stratification, pattern investigation and new discovery beyond existing knowledge. In particular, clustering of bioactive molecules facilitates chemical space mapping, structure-activity studies, and drug discovery. These tasks, conventionally conducted by similarity-based methods, are complicated by data complexity and diversity. We ex-plored the superior learning capability of deep autoencoders for unsupervised clustering of 1.39 mil-lion bioactive molecules into band-clusters in a 3-dimensional latent chemical space. These band-clusters, displayed by a space-navigation simulation software, band molecules of selected bioactivity classes into individual band-clusters possessing unique sets of common sub-structural features beyond structural similarity. These sub-structural features form the frameworks of the literature-reported pharmacophores and privileged fragments. Within each band-cluster, molecules are further banded into selected sub-regions with respect to their bioactivity target, sub-structural features and molecular scaffolds. Our method is potentially applicable for big data clustering tasks of different fields.

q-bio.BM↗

arXiv2019

A Generalized Language Model in Tensor Space

In the literature, tensors have been effectively used for capturing the context information in language models. However, the existing methods usually adopt relatively-low order tensors, which have limited expressive power in modeling language. Developing a higher-order tensor representation is challenging, in terms of deriving an effective solution and showing its generality. In this paper, we propose a language model named Tensor Space Language Model (TSLM), by utilizing tensor networks and tensor decomposition. In TSLM, we build a high-dimensional semantic space constructed by the tensor product of word vectors. Theoretically, we prove that such tensor representation is a generalization of the n-gram language model. We further show that this high-order tensor representation can be decomposed to a recursive calculation of conditional probability for language modeling. The experimental results on Penn Tree Bank (PTB) dataset and WikiText benchmark demonstrate the effectiveness of TSLM.

cs.CL↗

arXiv2019

Signal Shaping for Generalized Spatial Modulation and Generalized Quadrature Spatial Modulation

This paper investigates generic signal shaping methods for multiple-data-stream generalized spatial modulation (GenSM) and generalized quadrature spatial modulation (GenQSM) based on the maximizing the minimum Euclidean distance (MMED) criterion. Three cases with different channel state information at the transmitter (CSIT) are considered, including no CSIT, statistical CSIT and perfect CSIT. A unified optimization problem is formulated to find the optimal transmit vector set under size, power and sparsity constraints. We propose an optimization-based signal shaping (OBSS) approach by solving the formulated problem directly and a codebook-based signal shaping (CBSS) approach by finding sub-optimal solutions in discrete space. In the OBSS approach, we reformulate the original problem to optimize the signal constellations used for each transmit antenna combination (TAC). Both the size and entry of all signal constellations are optimized. Specifically, we suggest the use of a recursive design for size optimization. The entry optimization is formulated as a non-convex large-scale quadratically constrained quadratic programming (QCQP) problem and can be solved by existing optimization techniques with rather high complexity. To reduce the complexity, we propose the CBSS approach using a codebook generated by quadrature amplitude modulation (QAM) symbols and a low-complexity selection algorithm to choose the optimal transmit vector set. Simulation results show that the OBSS approach exhibits the optimal performance in comparison with existing benchmarks. However, the OBSS approach is impractical for large-size signal shaping and adaptive signal shaping with instantaneous CSIT due to the demand of high computational complexity. As a low-complexity approach, CBSS shows comparable performance and can be easily implemented in large-size systems.

eess.SP↗

arXiv2019

Transformed $\ell_1$ Regularization for Learning Sparse Deep Neural Networks

Deep neural networks (DNNs) have achieved extraordinary success in numerous areas. However, to attain this success, DNNs often carry a large number of weight parameters, leading to heavy costs of memory and computation resources. Overfitting is also likely to happen in such network when the training data are insufficient. These shortcomings severely hinder the application of DNNs in resource-constrained platforms. In fact, many network weights are known to be redundant and can be removed from the network without much loss of performance. To this end, we introduce a new non-convex integrated transformed $\ell_1$ regularizer to promote sparsity for DNNs, which removes both redundant connections and unnecessary neurons simultaneously. To be specific, we apply the transformed $\ell_1$ to the matrix space of network weights and utilize it to remove redundant connections. Besides, group sparsity is also employed as an auxiliary to remove unnecessary neurons. An efficient stochastic proximal gradient algorithm is presented to solve the new model at the same time. To the best of our knowledge, this is the first work to utilize a non-convex regularizer in sparse optimization based method to promote sparsity for DNNs. Experiments on several public datasets demonstrate the effectiveness of the proposed method.

cs.CV↗

arXiv2018

Scalings in Coalescence of Liquid Droplets

This letter presents a scaling theory of the coalescence of two viscous spherical droplets. An initial value problem was formulated and analytically solved for the evolution of the radius of a liquid neck formed upon droplet coalescence. Two asymptotic solutions of the initial value problem reproduce the well-known scaling relations in the viscous and inertial regimes. The viscous-to-inertial crossover experimentally observed by Paulsen et al. [Phys. Rev. Lett. 106, 114501 (2011)] manifests in the theory, and their fitting relation, which shows collapse of data of different viscosities onto a single curve, is an approximation to the general solution of the initial value problem.

physics.flu-dyn↗

arXiv2018

Two-dimensional Ca-Cl crystals under ambient conditions observed directly by cryo-electron microscopy

Recently, we report the direct observation, under ambient conditions, of Na2Cl and Na3Cl as two-dimensional (2D) Na-Cl crystals, together with regular NaCl, on reduced graphene oxide membranes and on the surfaces of natural graphite powders from salt solutions far below the saturated concentration. However, what are these abnormal stoichiometries for high valence ions, such as calcium ions and copper ions still remain unknown. Here, using cryo-electron microscopy, we report the direct observation of two-dimensional (2D) Ca-Cl crystals on reduced graphene oxide (rGO) membranes, in which the calcium ions are only monovalent (i.e. +1). Remarkably, metallic properties rather than insulating are displayed by those CaCl crystals. We note that such CaCl crystals are obtained by simply incubating rGO membranes in salt solutions below the saturated concentration, under ambient conditions. Theoretical studies show that the formation of those abnormal crystals is attributed to the strong cation-pi interactions of the Ca2+ ions with the aromatic rings in the graphitic surfaces. Since strong cation-pi interactions also exist between other metal ions (such as Mg2+, Fe2+, Co2+, Cu2+, Cd2+, Cr2+ and Pb2+) and graphitic surfaces, similar 2D crystals with abnormal valence state of the metal cations and corresponding abnormal properties are highly expected. The 2D crystals with monovalent calcium ions show unusual electronic properties, and can be applicated in catalyzer, hydrogen storage, high-performance conducting electrodes and sensors. These findings also produce functionalized graphene including compact "graphene-metallic CaCl-insulating CaCl2" junction that can serve as transistors down to the atomic scale, and other devices for magnetic, optical and mechanical applications.

physics.chem-ph↗

arXiv2018

Robust Visual Tracking using Multi-Frame Multi-Feature Joint Modeling

It remains a huge challenge to design effective and efficient trackers under complex scenarios, including occlusions, illumination changes and pose variations. To cope with this problem, a promising solution is to integrate the temporal consistency across consecutive frames and multiple feature cues in a unified model. Motivated by this idea, we propose a novel correlation filter-based tracker in this work, in which the temporal relatedness is reconciled under a multi-task learning framework and the multiple feature cues are modeled using a multi-view learning approach. We demonstrate the resulting regression model can be efficiently learned by exploiting the structure of blockwise diagonal matrix. A fast blockwise diagonal matrix inversion algorithm is developed thereafter for efficient online tracking. Meanwhile, we incorporate an adaptive scale estimation mechanism to strengthen the stability of scale variation tracking. We implement our tracker using two types of features and test it on two benchmark datasets. Experimental results demonstrate the superiority of our proposed approach when compared with other state-of-the-art trackers. project homepage http://bmal.hust.edu.cn/project/KMF2JMTtracking.html

cs.CV↗

arXiv2018

Viscous Dissipation, Helicity and Enstrophy of Bouncing Droplets undergoing Off-center Collision

The off-center collision of binary bouncing droplets of equal size was studied numerically by a volume-of-fluid (VOF) method with two marker functions, which has been validated by comparing with available experimental results. A non-monotonic kinetic energy recovery with varying impact parameters was found based on the energy budget analysis. This can be explained by the prolonged entanglement time and the enhanced internal-flow-induced viscous dissipation for bouncing droplets at intermediate impact parameters, compared with those at smaller or larger impact parameters. The universality of this non-monotonicity was numerically verified, and thereby an approximate fitting formula was proposed to correlate the kinetic energy dissipation factor with the impact parameter for various Weber numbers and Ohnesorge numbers. From the vortex dynamics perspective, a helicity analysis of droplet internal flow identifies a strong three-dimensional interaction between the "ring-shaped" vortices and the "line-shaped" shear layers for off-center collisions. Furthermore, we demonstrated theoretically and verified numerically that the equivalence between the total enstrophy and the total viscous dissipation, which holds for a single-phase flow system confined by stationary boundaries, is not generally satisfied for the two-phase flow system containing gas-liquid interfaces. This is attributed to the work done by the unbalanced viscous stresses, which results from the interfacial flow and the vorticity associated with the movement of the oscillating interface.

physics.flu-dyn↗

arXiv2018

Experimental wavelength-space division multiplexing of quantum key distribution with classical optical communication over multicore fiber

We demonstrate quantum key distribution (QKD) with classical signals in a seven-core fiber using dense wavelength division multiplexing. Quantum signals are transmitted in an outer core separately and intercore crosstalk (IC-XT) is the main impairment of them. In order to alleviate IC-XT, we propose a quantum-classical interleave scheme. Then the properties of IC-XT are analyzed based on the measurement results which indicate counter-propagation is a better co-existence method than co-propagation. Finally, we perform QKD experiments in the presence of two classical channels with a channel spacing of 100 GHz between quantum channel and the nearest classical channels. The experiment results prove counter-propagation almost immune to IC-XT, which is consistent with our analysis. Also, the feasibility of the transmission over the range of metropolitan area networks is validated with our scheme.

quant-ph↗

arXiv2018

Supercooling-enabled giant and tunable thermal rectification ratio of a phase change thermal diode

Phase change thermal diodes (PCTD) suffer from fairly low thermal rectification ratio, which hampers their widespread utilization as thermal management and control units for cutting-edge technologies, encompassing photovoltaics, thermoelectric modules, batteries and other miniaturized electronic products, etc. It is thus indispensable to explore a high-performance PCTD. Herein, a tunable and scalable PCTD is modeled, theoretically analyzed, fabricated and experimentally executed, accessing an unprecedented giant thermal rectification ratio of 3.0 at ambient temperature. With optimized length ratio and feasible assembly of two phase change terminals, the emerging physical states of thermal media continuously contribute to align the convective-intensified thermal conductive profiles, mandatory for heat flux manipulation within temperature bias of 10~40 °C. The most significant finding discloses the fact that supercooling elongates the overall workable temperature bias range, while manual supercooling release allows to tune the thermal rectification ratio at any temperature bias within 10~33 °C. Integrating the conventional asymmetric thermal transport mechanism with state-specific heat transfer hysteresis helps establish the novel governing mechanism.

physics.app-ph↗

arXiv2018

Generalized Beamspace Modulation Using Multiplexing: A Breakthrough in mmWave MIMO

Spatial multiplexing (SMX) multiple-input multiple-output (MIMO) over the best beamspace was considered as the best solution for millimeter wave (mmWave) communications regarding spectral efficiency (SE), referred as the best beamspace selection (BBS) solution. The equivalent MIMO water-filling (WF-MIMO) channel capacity was treated as an unsurpassed SE upper bound. Recently, researchers have proposed various schemes trying to approach the benchmark and the performance bound. But, are they the real limit of mmWave MIMO systems with reduced radio-frequency (RF) chains? In this paper, we challenge the benchmark and the corresponding bound by proposing a better transmission scheme that achieves higher SE, namely the Generalized Beamspace Modulation using Multiplexing (GBMM). Inspired by the concept of spatial modulation, besides the selected beamspace, the selection operation is used to carry information. We prove that GBMM is superior to BBS in terms of SE and can break through the well known `upper bound'. That is, GBMM renews the upper bound of the SE. We investigate SE-oriented precoder activation probability optimization, fully-digital precoder design, optimal power allocation and hybrid precoder design for GBMM. A gradient ascent algorithm is developed to find the optimal solution, which is applicable in all signal-to-noise-ratio (SNR) regimes. The best solution is derived in the high SNR regime. Additionally, we investigate the hybrid receiver design and deduce the minimum number of receive RF chains configured to gain from GBMM in achievable SE. We propose a coding approach to realize the optimized precoder activation. An extension to mmWave broadband communications is also discussed. Comparisons with the benchmark (i.e., WF-MIMO channel capacity) are made under different system configurations to show the superiority of GBMM.

eess.SP↗

arXiv2018

Semi-analytical calculation of gluon fragmentation into ${^{1}\hspace{-0.6mm}S_{0}^{[1,8]}}$ quarkonia at next-to-leading order

We calculate the NLO corrections for the gluon fragmentation functions to a heavy quark-antiquark pair in ${^{1}\hspace{-0.6mm}S_{0}^{[1]}}$ or ${^{1}\hspace{-0.6mm}S_{0}^{[8]}}$ state within NRQCD factorization. We use integration-by-parts reduction to reduce the original expression to simpler master integrals (MIs), and then set up differential equations for these MIs. After calculating the boundary conditions, MIs can be obtained by solving the differential equations numerically. Our results are expressed in terms of asymptotic expansions at singular points of $z$ (light-cone momentum fraction carried by the quark-antiquark pair), which can not only give FFs results with very high precision at any value of $z$, but also provide fully analytical structure at these singularities. We find that the NLO corrections are significant, with K-factors larger than 2 in most regions. The NLO corrections may have important impact on heavy quarkonia (e.g. $η_c$ and $J/ψ$) production at the LHC.

hep-ph↗

arXiv2018

Multiple topological states in iron-based superconductors

Topological insulators and semimetals as well as unconventional iron-based superconductors have attracted major recent attention in condensed matter physics. Previously, however, little overlap has been identified between these two vibrant fields, even though the principal combination of topological bands and superconductivity promises exotic unprecedented avenues of superconducting states and Majorana bound states (MBSs), the central building block for topological quantum computation. Along with progressing laser-based spin-resolved and angle-resolved photoemission spectroscopy (ARPES) towards high energy and momentum resolution, we have resolved topological insulator (TI) and topological Dirac semimetal (TDS) bands near the Fermi level ($E_{\text{F}}$) in the iron-based superconductors Li(Fe,Co)As and Fe(Te,Se), respectively. The TI and TDS bands can be individually tuned to locate close to $E_{\text{F}}$ by carrier doping, allowing to potentially access a plethora of different superconducting topological states in the same material. Our results reveal the generic coexistence of superconductivity and multiple topological states in iron-based superconductors, rendering these materials a promising platform for high-$T_{\text{c}}$ topological superconductivity.

cond-mat.supr-con↗

arXiv2018

Prediction of Coronary Heart Disease Using Routine Blood Tests

Background --The objective of this study was to examine the association of routine blood test results with coronary heart disease (CHD) risk, to incorporate them into coronary prediction models and to compare the discrimination properties of this approach with other prediction functions. Methods and Results --This work was designed as a retrospective, single-center study of a hospital-based cohort. The 5060 CHD patients (2365 men and 2695 women) were 1 to 97 years old at baseline with 8 years (2009-2017) of medical records, 5051 health check-ups and 5075 cases of other diseases. We developed a two-layer Gradient Boosting Decision Tree(GBDT) model based on routine blood data to predict the risk of coronary heart disease, which could identify 86% of people with coronary heart disease. We built a dataset with 15,000 routine blood tests results. Using this dataset, we trained the two-layer GBDT model to classify healthy status, coronary heart disease and other diseases. As a result of the classification after machine learning, we found that the sensitivity of detecting the health data was approximately 93% for all data, and the sensitivity of detecting CHD was 93% for disease data that included coronary heart disease. On this basis, we further visualized the correlation between routine blood results and related data items, and there was an obvious pattern in health and coronary heart disease in all data presentations, which can be used for clinical reference. Finally, we briefly analyzed the results above from the perspective of pathophysiology. Conclusions --Routine blood data provides more information about CHD than what we already know through the correlation between test results and related data items. A simple coronary disease prediction model was developed using a GBDT algorithm, which will allow physicians to predict CHD risk in patients without overt CHD.

physics.med-ph↗

arXiv2018

A Quantum Many-body Wave Function Inspired Language Modeling Approach

The recently proposed quantum language model (QLM) aimed at a principled approach to modeling term dependency by applying the quantum probability theory. The latest development for a more effective QLM has adopted word embeddings as a kind of global dependency information and integrated the quantum-inspired idea in a neural network architecture. While these quantum-inspired LMs are theoretically more general and also practically effective, they have two major limitations. First, they have not taken into account the interaction among words with multiple meanings, which is common and important in understanding natural language text. Second, the integration of the quantum-inspired LM with the neural network was mainly for effective training of parameters, yet lacking a theoretical foundation accounting for such integration. To address these two issues, in this paper, we propose a Quantum Many-body Wave Function (QMWF) inspired language modeling approach. The QMWF inspired LM can adopt the tensor product to model the aforesaid interaction among words. It also enables us to reveal the inherent necessity of using Convolutional Neural Network (CNN) in QMWF language modeling. Furthermore, our approach delivers a simple algorithm to represent and match text/sentence pairs. Systematic evaluation shows the effectiveness of the proposed QMWF-LM algorithm, in comparison with the state of the art quantum-inspired LMs and a couple of CNN-based methods, on three typical Question Answering (QA) datasets.

cs.CL↗

arXiv2018

Thermal conductivity and electrical resistivity of solid iron at Earth's core conditions from first-principles

We compute the thermal conductivity and electrical resistivity of solid hcp Fe to pressures and temperatures of Earth's core. We find significant contributions from electron-electron scattering, usually neglected at high temperatures in transition metals. Our calculations show a quasi-linear relation between electrical resistivity and temperature for hcp Fe at extreme high pressures. We obtain thermal and electrical conductivities that are consistent with experiments considering reasonable error. The predicted thermal conductivity is reduced from previous estimates that neglect electron-electron scattering. Our estimated thermal conductivity for the outer core is 77$\pm$10 W/m/K, and is consistent with a geodynamo driven by thermal convection.

cond-mat.mtrl-sci↗