arXiv subjects

Peng Zhang

Publications and source records attributed to Peng Zhang.

At least 667 records · Page 37Linked to original sources

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↗

arXiv2020

Reflecting Modulation

Reconfigurable intelligent surface (RIS) has emerged as a promising technique for future wireless communication networks. How to reliably transmit information in a RIS-based communication system arouses much interest. This paper proposes a reflecting modulation (RM) scheme for RIS-based communications, where both the reflecting patterns and transmit signals can carry information. Depending on that the transmitter and RIS jointly or independently deliver information, RM is further classified into two categories: jointly mapped RM (JRM) and separately mapped RM (SRM). JRM and SRM are naturally superior to existing schemes, because the transmit signal vectors, reflecting patterns, and bit mapping methods of JRM and SRM are more flexibly designed. To enhance transmission reliability, this paper proposes a discrete optimization-based joint signal mapping, shaping, and reflecting (DJMSR) design for JRM and SRM to minimize the bit error rate (BER) with a given transmit signal candidate set and a given reflecting pattern candidate set. To further improve the performance, this paper optimizes multiple reflecting patterns and their associated transmit signal sets in continuous fields for JRM and SRM. Numerical results show that JRM and SRM with the proposed system optimization methods considerably outperform existing schemes in BER.

eess.SP↗

arXiv2020

NbO2-based memristive neurons for burst-based perceptron

Neuromorphic computing using spike-based learning has broad prospects in reducing computing power. Memristive neurons composed with two locally active memristors have been used to mimic the dynamical behaviors of biological neurons. In this work, the dynamic operating conditions of NbO2-based memristive neurons and their transformation boundaries between the spiking and the bursting are comprehensively investigated. Furthermore, the underlying mechanism of bursting is analyzed and the controllability of the number of spikes during each burst period is demonstrated. Finally, pattern classification and information transmitting in a perceptron neural network by using the number of spikes per bursting period to encode information is proposed. The results show a promising approach for the practical implementation of neuristor in spiking neural networks.

cs.ET↗

arXiv2020

Deep Reinforcement Learning (DRL): Another Perspective for Unsupervised Wireless Localization

Location is key to spatialize internet-of-things (IoT) data. However, it is challenging to use low-cost IoT devices for robust unsupervised localization (i.e., localization without training data that have known location labels). Thus, this paper proposes a deep reinforcement learning (DRL) based unsupervised wireless-localization method. The main contributions are as follows. (1) This paper proposes an approach to model a continuous wireless-localization process as a Markov decision process (MDP) and process it within a DRL framework. (2) To alleviate the challenge of obtaining rewards when using unlabeled data (e.g., daily-life crowdsourced data), this paper presents a reward-setting mechanism, which extracts robust landmark data from unlabeled wireless received signal strengths (RSS). (3) To ease requirements for model re-training when using DRL for localization, this paper uses RSS measurements together with agent location to construct DRL inputs. The proposed method was tested by using field testing data from multiple Bluetooth 5 smart ear tags in a pasture. Meanwhile, the experimental verification process reflected the advantages and challenges for using DRL in wireless localization.

cs.LG↗

arXiv2020

Optimizing Streaming Parallelism on Heterogeneous Many-Core Architectures: A Machine Learning Based Approach

This article presents an automatic approach to quickly derive a good solution for hardware resource partition and task granularity for task-based parallel applications on heterogeneous many-core architectures. Our approach employs a performance model to estimate the resulting performance of the target application under a given resource partition and task granularity configuration. The model is used as a utility to quickly search for a good configuration at runtime. Instead of hand-crafting an analytical model that requires expert insights into low-level hardware details, we employ machine learning techniques to automatically learn it. We achieve this by first learning a predictive model offline using training programs. The learnt model can then be used to predict the performance of any unseen program at runtime. We apply our approach to 39 representative parallel applications and evaluate it on two representative heterogeneous many-core platforms: a CPU-XeonPhi platform and a CPU-GPU platform. Compared to the single-stream version, our approach achieves, on average, a 1.6x and 1.1x speedup on the XeonPhi and the GPU platform, respectively. These results translate to over 93% of the performance delivered by a theoretically perfect predictor.

cs.DC↗

arXiv2020

Analytical solution for the spectrum of two ultracold atoms in a completely anisotropic confinement

We study the system of two ultracold atoms in a three-dimensional (3D) or two-dimensional (2D) completely anisotropic harmonic trap. We derive the algebraic equation J_{3D}(E) = 1/a_{3D} (J_{2D}(E) = ln a_{2D}) for the eigen-energy E of this system in the 3D (2D) case, with a_{3D} and a_{2D} being the corresponding s-wave scattering lengths, and provide the analytical expressions of the functions J_{3D}(E) and J_{2D}(E). In previous researches this type of equation was obtained for spherically or axially symmetric harmonic traps (T. Busch, et. al., Found. Phys. 28, 549 (1998); Z. Idziaszek and T. Calarco, Phys. Rev. A 74, 022712 (2006)). However, for our cases with a completely anisotropic trap, only the equation for the ground-state energy of some cases has been derived (J. Liang and C. Zhang, Phys. Scr. 77, 025302 (2008)). Our results in this work are applicable for arbitrary eigen-energy of this system, and can be used for the studies of dynamics and thermal-dynamics of interacting ultracold atoms in this trap, e.g., the calculation of the 2nd virial coefficient or the evolution of two-body wave functions. In addition, our approach for the derivation of the above equations can also be used for other two-body problems of ultracold atoms.

physics.atm-clus↗

arXiv2020

Magnetically Induced Optical Transparency With Ultra-Narrow Spectrum

Magnetically induced optical transparency (MIOT) is a technique to realize the narrow transmission spectrum in a cavity quantum electric dynamics (cavity QED) system, which is demonstrated in the recent experiment of cold 88Sr atoms in an optical cavity [Phys. Rev. Lett. 118, 263601 (2017)]. In this experiment, MIOT induces a new narrow transmission window for the probe beam, which is highly immune to the fluctuation of the cavity mode frequency. The linewidth of this transmission window approaches the decay rate of the electronic 3P1 state (about 2pi*7.5kHz) and is much less than the uncertainty of the cavity mode frequency (about 2pi*150kHz). In this work, we propose an approach to further reduce the linewidth of this MIOT-induced transmission window, with the help of two Raman beams which couples the electronic 3P1 state to the3S1state, and the3S1state to the 3P0 state, respectively. With this approach, one can reduce the transmission linewidth by orders of magnitude. Moreover, the peak value of the relative transmission power or the transmission rate of the probe beam is almost unchanged by the Raman beams. Our results are helpful for the study of precision measurement and other quantum optical processes based on cavity quantum electronic dynamics (cavity-QED).

quant-ph↗

arXiv2020

Enhanced Microgrid Power Flow Incorporating Hierarchical Control

An enhanced microgrid power flow (EMPF) is devised to incorporate hierarchical control effects. The new contributions are threefold: 1) an advanced-hierarchical-control-based Newton approach is established to accurately assess power sharing and voltage regulation effects; 2) a modified Jacobian matrix is derived to incorporate droop control and various secondary control modes; and 3) the secondary adjustment is calculated on top of the droop-control-based power flow results to ensure a robust Newton solution. Case studies validate that EMPF is efficacious and efficient and can serve as a powerful tool for microgrid operation and monitoring, especially for those highly meshed microgrids in urban areas.

eess.SY↗