arXiv subjects

Pengfei Yang

Publications and source records attributed to Pengfei Yang.

49 records · Page 3Linked to original sources

arXiv2021

Reconstructing shock front of unstable detonations based on multi-layer perceptron

The dynamics of frontal and transverse shocks in gaseous detonation waves is a complex phenomenon bringing many difficulties to both numerical and experimental research. Advanced laser-optical visualization of detonation structure may provide certain information of its reactive front, but the corresponding lead shock needs to be reconstructed building the complete flow field. Using the Multi-Layer Perceptron (MLP) approach, we propose in this study a shock front reconstruction method which can predict evolution of the lead shock wavefront from the state of the reactive front. The method is verified through the numerical results of one- and two-dimensional unstable detonations based on the reactive Euler equations with a one-step irreversible chemical reaction model. Results show that the accuracy of the proposed method depends on the activation energy of the reactive mixture, which influences prominently the cellular detonation instability and hence, the distortion of the lead shock surface. To select the input variables for training and evaluate their influence on the effectiveness of the proposed method, five groups, one with six variables and the other with four variables, are tested and analyzed in the MLP model. The trained MLP is tested in the cases with different activation energies, demonstrates the inspiring generalization capability. This paper offers a universal framework for predicting detonation frontal evolution and provides a novel way to interpret numerical and experimental results of detonation waves.

physics.flu-dyn↗

arXiv2021

Analyzing Deep Neural Networks with Symbolic Propagation: Towards Higher Precision and Faster Verification

Deep neural networks (DNNs) have been shown lack of robustness for the vulnerability of their classification to small perturbations on the inputs. This has led to safety concerns of applying DNNs to safety-critical domains. Several verification approaches have been developed to automatically prove or disprove safety properties of DNNs. However, these approaches suffer from either the scalability problem, i.e., only small DNNs can be handled, or the precision problem, i.e., the obtained bounds are loose. This paper improves on a recent proposal of analyzing DNNs through the classic abstract interpretation technique, by a novel symbolic propagation technique. More specifically, the values of neurons are represented symbolically and propagated forwardly from the input layer to the output layer, on top of abstract domains. We show that our approach can achieve significantly higher precision and thus can prove more properties than using only abstract domains. Moreover, we show that the bounds derived from our approach on the hidden neurons, when applied to a state-of-the-art SMT based verification tool, can improve its performance. We implement our approach into a software tool and validate it over a few DNNs trained on benchmark datasets such as MNIST, etc.

cs.LG↗

arXiv2021

Asymptotically Optimal One- and Two-Sample Testing with Kernels

We characterize the asymptotic performance of nonparametric one- and two-sample testing. The exponential decay rate or error exponent of the type-II error probability is used as the asymptotic performance metric, and an optimal test achieves the maximum rate subject to a constant level constraint on the type-I error probability. With Sanov's theorem, we derive a sufficient condition for one-sample tests to achieve the optimal error exponent in the universal setting, i.e., for any distribution defining the alternative hypothesis. We then show that two classes of Maximum Mean Discrepancy (MMD) based tests attain the optimal type-II error exponent on $\mathbb R^d$, while the quadratic-time Kernel Stein Discrepancy (KSD) based tests achieve this optimality with an asymptotic level constraint. For general two-sample testing, however, Sanov's theorem is insufficient to obtain a similar sufficient condition. We proceed to establish an extended version of Sanov's theorem and derive an exact error exponent for the quadratic-time MMD based two-sample tests. The obtained error exponent is further shown to be optimal among all two-sample tests satisfying a given level constraint. Our work hence provides an achievability result for optimal nonparametric one- and two-sample testing in the universal setting. Application to off-line change detection and related issues are also discussed.

cs.IT↗

arXiv2020

Improving Neural Network Verification through Spurious Region Guided Refinement

We propose a spurious region guided refinement approach for robustness verification of deep neural networks. Our method starts with applying the DeepPoly abstract domain to analyze the network. If the robustness property cannot be verified, the result is inconclusive. Due to the over-approximation, the computed region in the abstraction may be spurious in the sense that it does not contain any true counterexample. Our goal is to identify such spurious regions and use them to guide the abstraction refinement. The core idea is to make use of the obtained constraints of the abstraction to infer new bounds for the neurons. This is achieved by linear programming techniques. With the new bounds, we iteratively apply DeepPoly, aiming to eliminate spurious regions. We have implemented our approach in a prototypical tool DeepSRGR. Experimental results show that a large amount of regions can be identified as spurious, and as a result, the precision of DeepPoly can be significantly improved. As a side contribution, we show that our approach can be applied to verify quantitative robustness properties.

cs.AI↗

arXiv2020

A CNN-LSTM Hybrid Framework for Wrist Kinematics Estimation Using Surface Electromyography

Convolutional neural network (CNN) has been widely exploited for simultaneous and proportional myoelectric control due to its capability of deriving informative, representative and transferable features from surface electromyography (sEMG). However, muscle contractions have strong temporal dependencies but conventional CNN can only exploit spatial correlations. Considering that long short-term memory neural network (LSTM) is able to capture long-term and non-linear dynamics of time-series data, in this paper we propose a CNNLSTM hybrid framework to fully explore the temporal-spatial information in sEMG. Firstly, CNN is utilized to extract deep features from sEMG spectrum, then these features are processed via LSTM-based sequence regression to estimate wrist kinematics. Six healthy participants are recruited for the participatory collection and motion analysis under various experimental setups. Estimation results in both intra-session and inter-session evaluations illustrate that CNN-LSTM significantly outperforms CNN and conventional machine learning approaches, particularly when complex wrist movements are activated.

eess.SP↗

arXiv2019

A general criterion for solid instability and its application to creases

A general force-perturbation-based criterion for solid instability is proposed, which can predict instability including crease without priori knowledge of instability configuration. The crease instability is analyzed in detail, we found that the occurrence of solid instability does not always correspond to the non-positive definiteness of global stiffness matrix. An element stiffness-based criterion based on material stiffness is proposed as a stronger criterion in order to fast determine the occurrence of instability. This criterion has been shown to degenerate into the criterion for judging instability of certain known phenomena, such as necking and shear band phenomena. Besides, instability in strongly anisotropic materials is also predicted by the element stiffness-based criterion.

cond-mat.soft↗

arXiv2019

Chaos Phase Induced Mass-producible Monolayer Two-dimensional Material

Crystal phase is well studied and presents a periodical atom arrangement in three dimensions lattice, but the "amorphous phase" is poorly understood. Here, by starting from cage-like bicyclocalix[2]arene[2]triazines building block, a brand-new 2D MOF is constructed with extremely weak interlaminar interaction existing between two adjacent 2D-crystal layer. Inter-layer slip happens under external disturbance and leads to the loss of periodicity at one dimension in the crystal lattice, resulting in an interim phase between the crystal and amorphous phase - the chaos phase, non-periodical in microscopic scale but orderly in mesoscopic scale. This chaos phase 2D MOF is a disordered self-assembly of black-phosphorus like 3D-layer, which has excellent mechanical-strength and a thickness of 1.15 nm. The bulky 2D-MOF material is readily to be exfoliated into monolayer nanosheets in gram-scale with unprecedented evenness and homogeneity, as well as previously unattained lateral size (>10 um), which present the first mass-producible monolayer 2D material and can form wafer-scale film on substrate.

cond-mat.mtrl-sci↗

arXiv2019

High-numerical-aperture and long-working-distance objectives for single-atom experiments

We present two long-working-distance objective lenses with numerical apertures (NA) of 0.29 and 0.4 for single-atom experiments. The objective lenses are assembled entirely by the commercial on-catalog $Φ$1'' singlets. Both the objectives are capable to correct the spherical aberrations due to the standard flat vacuum glass windows with various thickness. The working distances of NA$=0.29$ and NA$=0.4$ objectives are 34.6 mm and 18.2 mm, respectively, at the design wavelength of 852 nm with 5-mm thick silica window. In addition, the objectives can also be optimized to work at diffraction limit at single wavelength in the entire visible and near infrared regions by slightly tuning the distance between the first two lenses. The diffraction limited fields of view for NA$=0.29$ and NA$=0.4$ objectives are 0.62 mm and 0.61 mm, and the spatial resolutions are 1.8 $μ$m and 1.3 $μ$m at the design wavelength. The performances are simulated by the commercial ray-tracing software and confirmed by imaging the resolution chart and a 1.18 $μ$m pinhole. The two objectives can be used for trapping and manipulating single atoms of various species.

physics.optics↗

arXiv2019

Realization of nonlinear optical nonreciprocity on a few-photon level based on atoms strongly coupled to an asymmetric cavity

Optical nonreciprocity is important in photonic information processing to route the optical signal or prevent the reverse flow of noise. By adopting the strong nonlinearity associated with a few atoms in a strongly coupled cavity QED system and an asymmetric cavity configuration, we experimentally demonstrate the nonreciprocal transmission between two counterpropagating light fields with extremely low power. This nonreciprocity can even occur on a few-photon level due to the high optical nonlinearity of the system. The working power can be flexibly tuned by changing the effective number of atoms strongly coupled to the cavity. The idea and result can be applied to optical chips as optical diodes by using fiber-based cavity QED systems. Our work opens up new perspectives for realizing optical nonreciprocity on a few-photon level based on the nonlinearities of atoms strongly coupled to an optical cavity.

quant-ph↗

arXiv2019

Universal Hypothesis Testing with Kernels: Asymptotically Optimal Tests for Goodness of Fit

We characterize the asymptotic performance of nonparametric goodness of fit testing. The exponential decay rate of the type-II error probability is used as the asymptotic performance metric, and a test is optimal if it achieves the maximum rate subject to a constant level constraint on the type-I error probability. We show that two classes of Maximum Mean Discrepancy (MMD) based tests attain this optimality on $\mathbb R^d$, while the quadratic-time Kernel Stein Discrepancy (KSD) based tests achieve the maximum exponential decay rate under a relaxed level constraint. Under the same performance metric, we proceed to show that the quadratic-time MMD based two-sample tests are also optimal for general two-sample problems, provided that kernels are bounded continuous and characteristic. Key to our approach are Sanov's theorem from large deviation theory and the weak metrizable properties of the MMD and KSD.

stat.ML↗

arXiv2017

Surface Plasmon Enhanced Strong Exciton-Photon Coupling in Hybrid Inorganic-Organic Perovskites Nanowires

Manipulating strong light-matter interaction in semiconductor microcavities is crucial for developing high-performance exciton polariton devices with great potentials in next-generation all-solid state quantum technologies. In this work, we report surface plasmon enhanced strong exciton-photon interaction in CH3NH3PbBr3 perovskite nanowires. Characteristic anti-crossing behaviors, indicating Rabi splitting energy up to ~ 560 meV, are observed near exciton resonance in hybrid semiconductor-insulator-metallic waveguide cavity at room temperature. An exciton-photon coupling strength enhancement factor of ~ 1.4 times is evaluated, which is mainly attributed to surface plasmon induced localized excitation field redistribution. Further, systematic studies on nanowires and insulator dimension dependence of exciton-photon interaction are presented. These results provide new avenues to achieve extremely high coupling strengths and push forward the development of electrically pumped and ultra-low threshold small lasers.

cond-mat.mes-hall↗

arXiv2017

Robust Kullback-Leibler Divergence and Universal Hypothesis Testing for Continuous Distributions

Universal hypothesis testing refers to the problem of deciding whether samples come from a nominal distribution or an unknown distribution that is different from the nominal distribution. Hoeffding's test, whose test statistic is equivalent to the empirical Kullback-Leibler divergence (KLD), is known to be asymptotically optimal for distributions defined on finite alphabets. With continuous observations, however, the discontinuity of the KLD in the distribution functions results in significant complications for universal hypothesis testing. This paper introduces a robust version of the classical KLD, defined as the KLD from a distribution to the L'evy ball of a known distribution. This robust KLD is shown to be continuous in the underlying distribution function with respect to the weak convergence. The continuity property enables the development of a universal hypothesis test for continuous observations that is shown to be asymptotically optimal for continuous distributions in the same sense as that of the Hoeffding's test for discrete distributions.

cs.IT↗

arXiv2017

Distribution-based bisimulation for labelled Markov processes

In this paper we propose a (sub)distribution-based bisimulation for labelled Markov processes and compare it with earlier definitions of state and event bisimulation, which both only compare states. In contrast to those state-based bisimulations, our distribution bisimulation is weaker, but corresponds more closely to linear properties. We construct a logic and a metric to describe our distribution bisimulation and discuss linearity, continuity and compositional properties.

cs.LO↗