arXiv subjects

Zheng Chen

Publications and source records attributed to Zheng Chen.

At least 253 records · Page 14Linked to original sources

arXiv2021

Theoretical analysis on the transient ignition of premixed expanding flame in a quiescent mixture

The present theory considers the transient evolution of the temperature and fuel mass fraction distributions, and it can determine the critical heating power and minimum ignition energy for successful ignition. The flame initiation process subject to central heating can be approximately decomposed into four stages, i.e., the fast establishment of the ignition kernel, the ignition-energy-supported flame kernel propagation, unsteady transition of the flame kernel, and quasi-steady spherical flame propagation. Comparison between present transient theory and previous quasi-steady theory indicates that the unsteady effects directly lead to the appearance of flame kernel establishing stage and considerably affect the subsequent flame kernel development by lowering the flame propagation speed. Time scale analysis is conducted, and it is found that the transient formulation completely degenerates to the quasi-steady theory either in the neighborhood of stationary flame ball or as approaching the planar flame. Previous quasi-steady theory shows that the critical heating power for successful ignition is proportional to the cube of the critical flame radius. However, the critical heating power predicted by the present transient formulation deviates this scaling law, which can be attributed to the unsteady thermal conduction from heating center to the flame front. Furthermore, in the transient formulation the central heating with finite duration is considered and the minimum ignition energy is obtained. For the first time, the memory effect that persistently drives flame propagation subsequent to central heating switching-off is examined. It is found that as the heating power grows, the memory effect becomes increasingly important and it can greatly reduce the minimum ignition energy.

physics.flu-dyn↗

arXiv2021

Atomic-scale investigation of the irradiation-resistant effect of symmetric tilt grain boundaries of Fe-Ni-Cr alloy

In this paper, the Fe-20Ni-25Cr alloy that is used for fuel cladding or pressure vessels with various grain boundaries (GBs) was investigated by employing molecular dynamics simulations. The bi-crystals comprised of Σ3(111), Σ3(112), Σ9(114), Σ11(113), Σ19(116), and Σ17(223) types GBs were considered to systematically examine the interplay between irradiation defects, irradiation microstructure evolution under stress, and irradiation mechanical properties with irradiation intensity, coincidence site lattice parameter, tilt angle, and GB thickness. It is found that irradiated vacancies and interstitials are annihilated by competitive GB absorption and recombination. Bias absorption of interstitials is observed for most bi-crystals except Σ3(111) and Σ11(113) at 15 keV incident energy, and results in abundant residual vacancies clusters in grain interior. In addition, different GBs exhibit quite diverse irradiation defect sink ability, and the number of residual vacancies is inversely related to the GB thickness, where Σ3(111) and Σ11(113) GBs with narrow GB thickness are weak in defect absorption and the others are strong. Furthermore, uniaxial tensile simulations perpendicular to the GB reveal that all of the mechanical performance of bi-crystals deteriorates after irradiation, which originates from dislocation propagation facilitated by irradiation defect clusters. In particular, regardless of whether the irradiation is applied, the maximum tensile strain, toughness, and Youngs modulus are monotonically correlated with GB tilt angle, while the ultimate tensile strength is stable for larger GB CSL parameter. Finally, on the basis of the evolution of the irradiation defects, microstructures, and mechanical performances, we proposed guidelines of rational design of irradiation-resistant Fe-Ni-Cr alloy.

cond-mat.mtrl-sci↗

arXiv2021

Time-Optimal Guidance for Intercepting Moving Targets with Impact-Angle Constraints

The minimum-time path for intercepting a moving target with a prescribed impact angle is studied in the paper. The candidate paths from Pontryagin's maximum principle are analyzed, so that each candidate is related to a zero of a real-valued function. It is found that the real-valued functions or their first-order derivatives can be converted to polynomials of at most fourth degree. As a result, each canidate path can be computed within a constant time by embedding a standard polynomial solver into the typical bisection method. The control strategy along the shortest candidate eventually gives rise to the time-optimal guidance law. Finally, the developments of the paper is illustrated and verified by three numerical examples.

math.OC↗

arXiv2021

Consensus-Based Distributed Computation of Link-Based Network Metrics

Average consensus algorithms have wide applications in distributed computing systems where all the nodes agree on the average value of their initial states by only exchanging information with their local neighbors. In this letter, we look into link-based network metrics which are polynomial functions of pair-wise node attributes defined over the links in a network. Different from node-based average consensus, such link-based metrics depend on both the distribution of node attributes and the underlying network topology. We propose a general algorithm using the weighted average consensus protocol for the distributed computation of link-based network metrics and provide the convergence conditions and convergence rate analysis.

cs.DC↗

arXiv2020

Time-Optimal Guidance to Intercept Moving Targets by Dubins Vehicles

This paper is concerned with a Minimum-Time Intercept Problem (MTIP), for which a Dubins vehicle is guided from a position with a prescribed initial orientation angle to intercept a moving target in minimum time. Some geometric properties for the solution of the MTIP are presented, showing that the solution path must lie in a sufficient family of 4 candidates. In addition, necessary and sufficient conditions for optimality of each candidate are established. When the target's velocity is constant, by employing the geometric properties, those 4 candidates are transformed to a class of sufficiently smooth real-valued functions. In order to compute all the 4 candidates, an efficient and robust algorithm to find all the zeros of sufficiently smooth real-valued functions is developed. Since the MTIP with a constant target's velocity is equivalent to the path planning problem of Dubins vehicle in a constant drift field, developing such an algorithm also enables efficiently finding the shortest Dubins path in a constant drift field. Finally, some numerical examples are presented, demonstrating and verifying the developments of the paper.

math.OC↗

arXiv2020

Observation of two-dimensional superconductivity at the LaAlO3/KTaO3(110) heterointerface

We report on the observation of a Tc ~0.9 K superconductivity at the interface between LaAlO3 film and the 5d transition-metal oxide KTaO3(110) single crystal. The interface shows a large anisotropy of the upper critical field, and its superconducting transition is consistent with a Berezinskii-Kosterlitz-Thouless transition. Both facts suggest that the superconductivity is two-dimensional (2D) in nature. The carrier density measured at 5 K is ~7 time 10^13 cm-2. The superconducting layer thickness and coherence length are estimated to be ~8 and ~30 nm, respectively. Our result provides a new platform for the study of 2D superconductivity at oxide interfaces.

cond-mat.supr-con↗

arXiv2020

SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving

Multi-agent interaction is a fundamental aspect of autonomous driving in the real world. Despite more than a decade of research and development, the problem of how to competently interact with diverse road users in diverse scenarios remains largely unsolved. Learning methods have much to offer towards solving this problem. But they require a realistic multi-agent simulator that generates diverse and competent driving interactions. To meet this need, we develop a dedicated simulation platform called SMARTS (Scalable Multi-Agent RL Training School). SMARTS supports the training, accumulation, and use of diverse behavior models of road users. These are in turn used to create increasingly more realistic and diverse interactions that enable deeper and broader research on multi-agent interaction. In this paper, we describe the design goals of SMARTS, explain its basic architecture and its key features, and illustrate its use through concrete multi-agent experiments on interactive scenarios. We open-source the SMARTS platform and the associated benchmark tasks and evaluation metrics to encourage and empower research on multi-agent learning for autonomous driving. Our code is available at https://github.com/huawei-noah/SMARTS.

cs.MA↗

arXiv2020

The use of restricted mean time lost under competing risks data

Background: Under competing risks, the commonly used sub-distribution hazard ratio (SHR) is not easy to interpret clinically and is valid only under the proportional sub-distribution hazard (SDH) assumption. This paper introduces an alternative statistical measure: the restricted mean time lost (RMTL). Methods: First, the definition and estimation methods of the measures are introduced. Second, based on the differences in RMTLs, a basic difference test (Diff) and a supremum difference test (sDiff) are constructed. Then, the corresponding sample size estimation method is proposed. The statistical properties of the methods and the estimated sample size are evaluated using Monte Carlo simulations, and these methods are also applied to two real examples. Results: The simulation results show that sDiff performs well and has relatively high test efficiency in most situations. Regarding sample size calculation, sDiff exhibits good performance in various situations. The methods are illustrated using two examples. Conclusions: RMTL can meaningfully summarize treatment effects for clinical decision making, which can then be reported with the SDH ratio for competing risks data. The proposed sDiff test and the two calculated sample size formulas have wide applicability and can be considered in real data analysis and trial design.

stat.ME↗

arXiv2020

TADOC: Text Analytics Directly on Compression

This article provides a comprehensive description of Text Analytics Directly on Compression (TADOC), which enables direct document analytics on compressed textual data. The article explains the concept of TADOC and the challenges to its effective realizations. Additionally, a series of guidelines and technical solutions that effectively address those challenges, including the adoption of a hierarchical compression method and a set of novel algorithms and data structure designs, are presented. Experiments on six data analytics tasks of various complexities show that TADOC can save 90.8% storage space and 87.9% memory usage, while halving data processing times.

cs.DS↗

arXiv2020

Electric field control of disorder-tunable superconductivity and the emergence of quantum metal at an oxide interface

We report on an extraordinary field effect of the superconducting LaAlO3/KTaO3(111) interface with Tc ~2 K. By applying a gate voltage (VG) across KTaO3, the interface can be continuously tuned from superconducting into insulating states, yielding a dome-shaped Tc-VG dependence. The electric gating has only a minor effect on carrier density as evidenced in the Hall-effect measurement, while it changes spatial profile of the carriers in the interface, hence the carrier's disorder level significantly. As temperature is decreased, the resistance saturates at lowest temperature in both superconducting and insulating sides, despite an initial dramatic dropping or increasing, which suggests an emergence of quantum metallic state associated with failed superconductor and/or fragile insulator. A VG-modulation of the magnetic-field-driven superconductor to insulator quantum phase transition reveals a non-universal criticality.

cond-mat.supr-con↗

arXiv2020

Hybrid Ranking Network for Text-to-SQL

In this paper, we study how to leverage pre-trained language models in Text-to-SQL. We argue that previous approaches under utilize the base language models by concatenating all columns together with the NL question and feeding them into the base language model in the encoding stage. We propose a neat approach called Hybrid Ranking Network (HydraNet) which breaks down the problem into column-wise ranking and decoding and finally assembles the column-wise outputs into a SQL query by straightforward rules. In this approach, the encoder is given a NL question and one individual column, which perfectly aligns with the original tasks BERT/RoBERTa is trained on, and hence we avoid any ad-hoc pooling or additional encoding layers which are necessary in prior approaches. Experiments on the WikiSQL dataset show that the proposed approach is very effective, achieving the top place on the leaderboard.

cs.CL↗

arXiv2020

Three-dimensional topological semimetal phase in layered TaNiTe5 probed by de Haas-van Alphen effect

Layered three-dimensional (3D) topological semimetals have attracted intensively attention due to the exotic phenomena and abundantly tunable properties. Here we report the experimental evidence for the 3D topological semimetal phase in layered material TaNiTe5 single crystals through quantum oscillations. Strong quantum oscillations have been observed with diamagnetism background in TaNiTe5. By analyzing the de Haas-van Alphen oscillations, multi-periodic oscillations were extracted, in content with magnetotransport measurements. Moreover, nontrivial "π" Berry phase with 3D Fermi surface is identified, indicating the topologically nontrivial feature in TaNiTe5. Additionally, we demonstrated the thin-layer of TaNiTe5 crystals is highly feasible by the mechanical exfoliation, which offers a platform to explore exotic properties in low dimensional topological semimetal and paves the way for potential applications in nanodevices.

cond-mat.str-el↗

arXiv2020

Theoretical analysis on droplet vaporization at elevated temperatures and pressures

Droplet vaporization has a great impact on combustion of liquid fuels and might greatly affects the engine performance. Physical understanding and theoretical interpretation of the droplet vaporization behavior are decisive to constitute appropriate models for spray combustion. In this work, we proposed a fully transient formulation for the droplet vaporization at wide ranges of temperature and pressure. The governing equations are solved analytically by means of suitable coordinate transformations. The droplet surface temperature and mass fraction of fuel vapor are determined by the matching conditions at the liquid-gas interface. Accordingly, a theoretical model, characterizing the time change of droplet size during its vaporization, is proposed in terms of the unsteadiness factor and droplet vaporization lifetime. The theoretical model successfully reveals the conventional recognition of droplet vaporization characteristics, and its predicted droplet size history agrees well with experimental results reported in the literature. Besides, based on the present theoretical analysis, the experimentally observed dual effect of pressure upon droplet vaporization is analyzed with help of an explicit formula for enthalpy of vaporization indicating its temperature-dependence.

physics.flu-dyn↗

arXiv2020

Neural Stochastic Block Model & Scalable Community-Based Graph Learning

This paper proposes a novel scalable community-based neural framework for graph learning. The framework learns the graph topology through the task of community detection and link prediction by optimizing with our proposed joint SBM loss function, which results from a non-trivial adaptation of the likelihood function of the classic Stochastic Block Model (SBM). Compared with SBM, our framework is flexible, naturally allows soft labels and digestion of complex node attributes. The main goal is efficient valuation of complex graph data, therefore our design carefully aims at accommodating large data, and ensures there is a single forward pass for efficient evaluation. For large graph, it remains an open problem of how to efficiently leverage its underlying structure for various graph learning tasks. Previously it can be heavy work. With our community-based framework, this becomes less difficult and allows the task models to basically plug-in-and-play and perform joint training. We currently look into two particular applications, the graph alignment and the anomalous correlation detection, and discuss how to make use of our framework to tackle both problems. Extensive experiments are conducted to demonstrate the effectiveness of our approach. We also contributed tweaks of classic techniques which we find helpful for performance and scalability. For example, 1) the GAT+, an improved design of GAT (Graph Attention Network), the scaled-cosine similarity, and a unified implementation of the convolution/attention based and the random-walk based neural graph models.

cs.SI↗

arXiv2020

Highly flexible electromagnetic interference shielding films based on ultrathin Ni/Ag composites on paper substrates

Highly flexible electromagnetic interference (EMI) shielding material with excellent shielding performance is of great significance to practical applications in next-generation flexible devices. However, most EMI materials suffer from insufficient flexibility and complicated preparation methods. In this study, we propose a new scheme to fabricate a magnetic Ni particle/Ag matrix composite ultrathin film on a paper surface. For a ~2 micro meter thick film on paper, the EMI shielding effectiveness (SE) was found to be 46.2 dB at 8.1 GHz after bending 200,000 times over a radius of ~2 mm. The sheet resistance (Rsq) remained lower than 2.30 Ohm after bending 200,000 times. Contrary to the change in Rsq, the EMI SE of the film generally increased as the weight ratio of Ag to Ni increased, in accordance with the principle that EMI SE is positively related with an increase in electrical conductivity. Desirable EMI shielding ability, ultrahigh flexibility, and simple processing provide this material with excellent application prospects.

physics.app-ph↗

arXiv2020

On Dubins Paths to a Circle

This paper is concerned with characterizing the shortest path of a Dubins vehicle from a position with a prescribed heading angle to a target circle with the final heading tangential to the target circle. Such a shortest path is of significant importance as it is usually required in real-world scenarios, such as taking a snapshot of a forbidden region or loitering above a ground sensor to collect data by a fixed-wing unmanned aerial vehicle in a minimum time. By applying Pontryagin's maximum principle, some geometric properties for the shortest path are established {without considering any assumption on the relationship between the minimum turning radius and radius of the target circle}, showing that the shortest path must lie in a sufficient family of 12 types. By employing those geometric properties, the analytical solution to each type is devised so that the length of each type can be computed in a constant time. In addition, some properties depending on problem parameters are found so that the shortest path can be computed without checking all the 12 types. Finally, some numerical simulations are presented, illustrating and validating the developments of the paper.

math.OC↗

arXiv2020

Pre-Training for Query Rewriting in A Spoken Language Understanding System

Query rewriting (QR) is an increasingly important technique to reduce customer friction caused by errors in a spoken language understanding pipeline, where the errors originate from various sources such as speech recognition errors, language understanding errors or entity resolution errors. In this work, we first propose a neural-retrieval based approach for query rewriting. Then, inspired by the wide success of pre-trained contextual language embeddings, and also as a way to compensate for insufficient QR training data, we propose a language-modeling (LM) based approach to pre-train query embeddings on historical user conversation data with a voice assistant. In addition, we propose to use the NLU hypotheses generated by the language understanding system to augment the pre-training. Our experiments show pre-training provides rich prior information and help the QR task achieve strong performance. We also show joint pre-training with NLU hypotheses has further benefit. Finally, after pre-training, we find a small set of rewrite pairs is enough to fine-tune the QR model to outperform a strong baseline by full training on all QR training data.

cs.CL↗

arXiv2020

An Ontology-driven Treatment Article Retrieval System for Precision Oncology

This paper presents an ontology-driven treatment article retrieval system developed and experimented using the data and ground truths provided by the TREC 2017 precision medicine track. The key aspects of our system include: meaningful integration of various disease, gene, and drug name ontologies, training of a novel perceptron model for article relevance labeling, a ranking module that considers additional factors such as journal impact and article publication year, and comprehensive query matching rules. Experimental results demonstrate that our proposed system considerably outperforms the results of the best participating system of the TREC 2017 precision medicine challenge.

cs.IR↗