arXiv subjects

Peng Zhang

Publications and source records attributed to Peng Zhang.

At least 649 records · Page 36Linked to original sources

arXiv2020

Rolling Waves with Non-Paraxial Phonon Spins

We demonstrate a new class of elastic waves in the bulk: When longitudinal and transverse components propagate at the same speed, rolling waves with a spin that is not parallel to the wave vector can emerge. First, we give a general definition of spin for traveling waves. Then, since rolling waves cannot exist in isotropic solids, we derive conditions for anisotropic media and proceed to design architected materials capable of hosting rolling waves. Numerically, we show spin manipulations by reflection. Structures reported in this work can be fabricated using available techniques, opening new possibilities for spin technologies in acoustics, mechanics and phononics.

cond-mat.mes-hall↗

arXiv2020

Calculation of Feynman loop integration and phase-space integration via auxiliary mass flow

We extend the auxiliary-mass-flow (AMF) method originally developed for Feynman loop integration to calculate integrals involving also phase-space integration. Flow of the auxiliary mass from the boundary ($\infty$) to the physical point ($0^+$) is obtained by numerically solving differential equations with respective to the auxiliary mass. For problems with two or more kinematical invariants, the AMF method can be combined with traditional differential equation method by providing systematical boundary conditions and highly nontrivial self-consistent check. The method is described in detail with a pedagogical example of $e^+e^-\rightarrow γ^* \rightarrow t\bar{t}+X$ at NNLO. We show that the AMF method can systematically and efficiently calculate integrals to high precision.

hep-ph↗

arXiv2020

Noise Reduction Technique for Raman Spectrum using Deep Learning Network

In a normal indoor environment, Raman spectrum encounters noise often conceal spectrum peak, leading to difficulty in spectrum interpretation. This paper proposes deep learning (DL) based noise reduction technique for Raman spectroscopy. The proposed DL network is developed with several training and test sets of noisy Raman spectrum. The proposed technique is applied to denoise and compare the performance with different wavelet noise reduction methods. Output signal-to-noise ratio (SNR), root-mean-square error (RMSE) and mean absolute percentage error (MAPE) are the performance evaluation index. It is shown that output SNR of the proposed noise reduction technology is 10.24 dB greater than that of the wavelet noise reduction method while the RMSE and the MAPE are 292.63 and 10.09, which are much better than the proposed technique.

eess.SP↗

arXiv2020

Non-axisymmetric flow characteristics in Head-on Collision of Spinning Droplets

Effects of spinning motion on the bouncing and coalescence between a spinning droplet and a non-spinning droplet undergoing the head-on collision were numerically studied by using a Volume-of-Fluid method. A prominent discovery is that the spinning droplet can induce significant non-axisymmetric flow features for the head-on collision of equal-size droplets composed of the same liquid. Specifically, a non-axisymmetric bouncing was observed, and it is caused by the conversion of the spinning angular momentum into the orbital angular momentum. This process is accompanied by the rotational kinetic energy loss due to the interaction between the rotational and radial flows of the droplets. A non-axisymmetric internal flow and a delayed separation after temporary coalescence were also observed, and they are caused by the enhanced interface oscillation and internal-flow-induced viscous dissipation. The spinning motion can also promote the mass interminglement of droplets because the locally non-uniform mass exchange occurs at the early collision stage by non-axisymmetric flow and is further stretched along the filament at later collision stages. In addition, it is found that the non-axisymmetric flow features increase with increasing the orthogonality of the initial translational motion and the spinning motion of droplets.

physics.flu-dyn↗

arXiv2020

TensorCoder: Dimension-Wise Attention via Tensor Representation for Natural Language Modeling

Transformer has been widely-used in many Natural Language Processing (NLP) tasks and the scaled dot-product attention between tokens is a core module of Transformer. This attention is a token-wise design and its complexity is quadratic to the length of sequence, limiting its application potential for long sequence tasks. In this paper, we propose a dimension-wise attention mechanism based on which a novel language modeling approach (namely TensorCoder) can be developed. The dimension-wise attention can reduce the attention complexity from the original $O(N^2d)$ to $O(Nd^2)$, where $N$ is the length of the sequence and $d$ is the dimensionality of head. We verify TensorCoder on two tasks including masked language modeling and neural machine translation. Compared with the original Transformer, TensorCoder not only greatly reduces the calculation of the original model but also obtains improved performance on masked language modeling task (in PTB dataset) and comparable performance on machine translation tasks.

cs.CL↗

arXiv2020

Approximate Reconstruction of Torsional Potential Energy Surface based on Voronoi Tessellation

Torsional modes within a complex molecule containing various functional groups are often strongly coupled so that the harmonic approximation and one-dimensional torsional treatment are inaccurate to evaluate their partition functions. A family of multi-structural approximation methods have been proposed and applied in recent years to deal with the torsional anharmonicity.However, these methods approximate the exact "almost periodic" potential energy as a summation of local periodic functions with symmetric barrier positions and heights. In the present theoretical study, we illustrated that the approximation is inaccurate when torsional modes present non-uniformly distributed local minima. Thereby, we proposed an improved method to reconstruct approximate potential to replace the periodic potential by using information of the local minima and their Voronoi tessellation. First, we established asymmetric barrier heights by introducing two periodicity parameters and assuming that the exact barrier positions are at the boundaries of Voronoi cells. Second, we used multiplicatively weighted Voronoi tessellation to refine the barrier heights and positions by defining a structure-related distance metric. The proposed method has been tested for a few higher-dimensional cases, all of which show promising improved accuracy.

physics.comp-ph↗

arXiv2020

Magnetic topological insulator MnBi6Te10 with zero-field ferromagnetic state and gapped Dirac surface states

Magnetic topological insulators (TIs) with nontrivial topological electronic structure and broken time-reversal symmetry exhibit various exotic topological quantum phenomena. The realization of such exotic phenomena at high temperature is one of central topics in this area. We reveal that MnBi6Te10 is a magnetic TI with an antiferromagnetic ground state below 10.8 K whose nontrivial topology is manifested by Dirac-like surface states. The ferromagnetic axion insulator state with Z4 = 2 emerges once spins polarized at field as low as 0.1 T, accompanied with saturated anomalous Hall resistivity up to 10 K. Such a ferromagnetic state is preserved even external field down to zero at 2 K. Theoretical calculations indicate that the few-layer ferromagnetic MnBi6Te10 is also topologically nontrivial with a non-zero Chern number. Angle-resolved photoemission spectroscopy experiments further reveal three types of Dirac surface states arising from different terminations on the cleavage surfaces, one of which has insulating behavior with an energy gap of ~ 28 meV at the Dirac point. These outstanding features suggest that MnBi6Te10 is a promising system to realize various topological quantum effects at zero field and high temperature.

cond-mat.mtrl-sci↗

arXiv2020

On skin friction in wall-bounded turbulence

In this paper, we derive mathematical formulas for the skin friction coefficient in wall-bounded turbulence based on the Reynolds averaged streamwise momentum equation and the total stress. Specially, with the theoretical or empirical relation of the total stress, the skin friction coefficient is expressed in terms of the mean velocity and the Reynolds shear stress in an arbitrary wall-normal region $[h_0, h_1]$. The formulas are validated using the direct numerical simulation data of turbulent channel and boundary layer flows, and the results show that our formulas estimate the skin friction coefficient very accurately with an error less than $2\%$. We believe that the present integral formula can be used to determine the skin friction in turbulent channel and boundary layer flows at high Reynolds numbers where the near wall statistics are very difficult to measure accurately.

physics.flu-dyn↗

arXiv2020

Thermal Rectification of Solid-liquid Phase Change Thermal Diode under the Effect of Supercooling

Thermal rectification ratios of solid-liquid phase change thermal diodes (SL-PCTDs) are not sustainable beyond a certain temperature bias, necessitating reasonably compatible designs. Manipulating the heat transfer in the forward and reverse directions of the SL-PCTD is of great practical interest to achieve persistent performance. Herein, a SL-PCTD prototype is investigated under the supercooling effect of a phase change material (calcium chloride hexahydrate). In the forward direction, supercooling effect plays a significant role in sustaining natural convection far below the melting temperature (30 C), which leads to robust heat transfer within a temperature bias of 10-40 C. While in the reverse direction, dislodging the supercooling via manual supercooling release (MSR) within a large temperature range of 30-7 C tends to greatly inhibit heat transfer. As a consequence, thermal rectification ratio of 2.95 is achieved, which is competitively sustainable at large temperature bias compared to the SL-PCTD without supercooling effect. To address the effect of supercooling on thermal rectification comprehensively, thermal resistance approach is applied to theoretically model the SL-PCTD, which shows a good agreement with experimental data. Such a device with supercooling control in only one direction is further named as the SL-PCTD with gating functionality, where manipulating supercooling effect of phase change material opens up a feasible avenue for enabling generalized thermal diode.

physics.app-ph↗

arXiv2020

Universal Free Energy Landscape Produces Efficient and Reversible Electron Bifurcation

For decades, it was unknown how electron bifurcating systems in Nature prevented energy-wasting short-circuiting reactions that have large driving forces, so synthetic electron bifurcating molecular machines could not be designed and built. The underpinning free energy landscapes for electron bifurcation were also enigmatic. We predict that a simple and universal free energy landscape enables electron bifurcation, and we show that it enables high-efficiency bifurcation with limited short-circuiting (the EB-scheme). The landscape relies on steep free energy slopes in the two redox branches to insulate against short-circuiting without relying on nuanced changes in the microscopic rate constants for the short-circuiting reactions. The EB-scheme thus provides a blueprint for future campaigns to establish synthetic electron bifurcating machines.

physics.bio-ph↗

arXiv2020

Encoding word order in complex embeddings

Sequential word order is important when processing text. Currently, neural networks (NNs) address this by modeling word position using position embeddings. The problem is that position embeddings capture the position of individual words, but not the ordered relationship (e.g., adjacency or precedence) between individual word positions. We present a novel and principled solution for modeling both the global absolute positions of words and their order relationships. Our solution generalizes word embeddings, previously defined as independent vectors, to continuous word functions over a variable (position). The benefit of continuous functions over variable positions is that word representations shift smoothly with increasing positions. Hence, word representations in different positions can correlate with each other in a continuous function. The general solution of these functions is extended to complex-valued domain due to richer representations. We extend CNN, RNN and Transformer NNs to complex-valued versions to incorporate our complex embedding (we make all code available). Experiments on text classification, machine translation and language modeling show gains over both classical word embeddings and position-enriched word embeddings. To our knowledge, this is the first work in NLP to link imaginary numbers in complex-valued representations to concrete meanings (i.e., word order).

cs.CL↗

arXiv2020

Revisiting Regex Generation for Modeling Industrial Applications by Incorporating Byte Pair Encoder

Regular expression is important for many natural language processing tasks especially when used to deal with unstructured and semi-structured data. This work focuses on automatically generating regular expressions and proposes a novel genetic algorithm to deal with this problem. Different from the methods which generate regular expressions from character level, we first utilize byte pair encoder (BPE) to extract some frequent items, which are then used to construct regular expressions. The fitness function of our genetic algorithm contains multi objectives and is solved based on evolutionary procedure including crossover and mutation operation. In the fitness function, we take the length of generated regular expression, the maximum matching characters and samples for positive training samples, and the minimum matching characters and samples for negative training samples into consideration. In addition, to accelerate the training process, we do exponential decay on the population size of the genetic algorithm. Our method together with a strong baseline is tested on 13 kinds of challenging datasets. The results demonstrate the effectiveness of our method, which outperforms the baseline on 10 kinds of data and achieves nearly 50 percent improvement on average. By doing exponential decay, the training speed is approximately 100 times faster than the methods without using exponential decay. In summary, our method possesses both effectiveness and efficiency, and can be implemented for the industry application.

cs.NE↗

arXiv2020

Signal Shaping for Non-Uniform Beamspace Modulated mmWave Hybrid MIMO Communications

This paper investigates adaptive signal shaping methods for millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications based on the maximizing the minimum Euclidean distance (MMED) criterion. In this work, we utilize the indices of analog precoders to carry information and optimize the symbol vector sets used for each analog precoder activation state. Specifically, we firstly propose a joint optimization based signal shaping (JOSS) approach, in which the symbol vector sets used for all analog precoder activation states are jointly optimized by solving a series of quadratically constrained quadratic programming (QCQP) problems. JOSS exhibits good performance, however, with a high computational complexity. To reduce the computational complexity, we then propose a full precoding based signal shaping (FPSS) method and a diagonal precoding based signal shaping (DPSS) method, where the full or diagonal digital precoders for all analog precoder activation states are optimized by solving two small-scale QCQP problems. Simulation results show that the proposed signal shaping methods can provide considerable performance gain in reliability in comparison with existing mmWave transmission solutions.

eess.SP↗

arXiv2020

High Temperature Virial Expansion to Universal Quench Dynamics

High temperature virial expansion is a powerful tool in equilibrium statistical mechanics. In this letter we generalize the high temperature virial expansion approach to treat far-from-equilibrium quench dynamics. As an application of our framework, we study the dynamics of a Bose gas quenched from non-interacting to unitarity, and we compare our theoretical results with unexplained experimental results by the Cambridge group [Eigen et al., Nature 563, 221 (2018)]. We show that, during the quench dynamics, the momentum distribution decreases for low-momentum part with $k k^*$, where $k^*$ is a characteristic momentum scale separating the low- and the high-momentum regimes. We determine the universal value of $k^*λ$ that agrees perfectly with the experiment, with $λ$ being the thermal de Broglie wave length. We also find a jump of the half-way relaxation time across $k^*λ$ and the non-monotonic behavior of energy distribution, both of which agree with the experiment. Finally, we address the issue whether the long-time steady state thermalizes or not, and we find that this state does thermalize except for the very high momentum tail with $kλ\gg 1$. Our framework can also be applied to quench dynamics in other systems.

cond-mat.quant-gas↗

arXiv2020

The liquid argon detector and measurement of SiPM array at liquid argon temperature

Particle detectors based on liquid argon (LAr) have recently become recognized as an extremely attractive technology for the direct detection of dark matter as well as the measurement of coherent elastic neutrino-nucleus scattering (CE$ν$NS). The Chinese argon group at Institute of High Energy Physics has been studying the LAr detector technology and a LAr detector has been operating steadily. A program of using a dual phase LAr detector to measure the CE$ν$NS at Taishang Nuclear Power Plant has been proposed and the R\&D work is ongoing. Considering the requirements of ultra-low radio-purity and high photon collection efficiency, SiPMs will be a good choice and will be used in the detector. In this proceeding, an introduction of the LAr detector and the measurement results of SiPM array at LAr temperature will be presented.

physics.ins-det↗

arXiv2020

Developing the radium measurement system for the water Cherenkov detector of the Jiangmen Underground Neutrino Observatory

The Jiangmen Underground Neutrino Observatory is proposed to determine neutrino mass hierarchy using a 20~ktonne liquid scintillator detector. Strict radio-purity requirements have been put forward for all the components of the detector. According to the MC simulation results, the radon dissolved in the water Cherenkov detector should be below 200~mBq/m$^3$. Radium, the progenitor of radon, should also be taken seriously into account. In order to measure the radium concentration in water, a radium measurement system, which consists of a radium extraction system, a radon emanation chamber and a radon concentration measurement system, has been developed. In this paper, the updated radon concentration in gas measurement system with a one-day-measurement sensitivity of $\sim$5~mBq/m$^3$, the detail of the development of the radium concentration in water measurement system with a sensitivity of $\sim$23~mBq/m$^3$ as well as the measurement results of Daya Bay water samples will be presented.

astro-ph.IM↗

arXiv2020

KoGuN: Accelerating Deep Reinforcement Learning via Integrating Human Suboptimal Knowledge

Reinforcement learning agents usually learn from scratch, which requires a large number of interactions with the environment. This is quite different from the learning process of human. When faced with a new task, human naturally have the common sense and use the prior knowledge to derive an initial policy and guide the learning process afterwards. Although the prior knowledge may be not fully applicable to the new task, the learning process is significantly sped up since the initial policy ensures a quick-start of learning and intermediate guidance allows to avoid unnecessary exploration. Taking this inspiration, we propose knowledge guided policy network (KoGuN), a novel framework that combines human prior suboptimal knowledge with reinforcement learning. Our framework consists of a fuzzy rule controller to represent human knowledge and a refine module to fine-tune suboptimal prior knowledge. The proposed framework is end-to-end and can be combined with existing policy-based reinforcement learning algorithm. We conduct experiments on both discrete and continuous control tasks. The empirical results show that our approach, which combines human suboptimal knowledge and RL, achieves significant improvement on learning efficiency of flat RL algorithms, even with very low-performance human prior knowledge.

cs.AI↗

arXiv2020

ScenarioSA: A Large Scale Conversational Database for Interactive Sentiment Analysis

Interactive sentiment analysis is an emerging, yet challenging, subtask of the sentiment analysis problem. It aims to discover the affective state and sentimental change of each person in a conversation. Existing sentiment analysis approaches are insufficient in modelling the interactions among people. However, the development of new approaches are critically limited by the lack of labelled interactive sentiment datasets. In this paper, we present a new conversational emotion database that we have created and made publically available, namely ScenarioSA. We manually label 2,214 multi-turn English conversations collected from natural contexts. In comparison with existing sentiment datasets, ScenarioSA (1) covers a wide range of scenarios; (2) describes the interactions between two speakers; and (3) reflects the sentimental evolution of each speaker over the course of a conversation. Finally, we evaluate various state-of-the-art algorithms on ScenarioSA, demonstrating the need of novel interactive sentiment analysis models and the potential of ScenarioSA to facilitate the development of such models.

cs.IR↗