arXiv subjects

Peng Yin

Publications and source records attributed to Peng Yin.

70 records · Page 4Linked to original sources

arXiv2019

MRS-VPR: a multi-resolution sampling based global visual place recognition method

Place recognition and loop closure detection are challenging for long-term visual navigation tasks. SeqSLAM is considered to be one of the most successful approaches to achieving long-term localization under varying environmental conditions and changing viewpoints. It depends on a brute-force, time-consuming sequential matching method. We propose MRS-VPR, a multi-resolution, sampling-based place recognition method, which can significantly improve the matching efficiency and accuracy in sequential matching. The novelty of this method lies in the coarse-to-fine searching pipeline and a particle filter-based global sampling scheme, that can balance the matching efficiency and accuracy in the long-term navigation task. Moreover, our model works much better than SeqSLAM when the testing sequence has a much smaller scale than the reference sequence. Our experiments demonstrate that the proposed method is efficient in locating short temporary trajectories within long-term reference ones without losing accuracy compared to SeqSLAM.

cs.RO↗

arXiv2018

Proton spectral functions in finite nuclei based on the extended Brueckner-Hartree-Fock approach

We have calculated the proton spectral functions in finite nuclei based on the local density approximation where the properties of finite nuclei and nuclear matter are calculated by the Skyrme-Hartree-Fock method and the extended Brueckner-Hartree-Fock approach, respectively. The scaled spectral function from our calculation is in good agreement with experimental results at small momenta while the difference between them becomes apparent at high momenta. Besides, a target dependence of the scaled proton spectral function is also obtained in our calculation as was observed in experiment. A further investigation indicates that the proportion of the high density region of the proton has a significant contribution to this target-dependent behavior since the spectral function in asymmetric nuclear matter increases significantly as a function of density.

nucl-th↗

arXiv2018

Synchronous Adversarial Feature Learning for LiDAR based Loop Closure Detection

Loop Closure Detection (LCD) is the essential module in the simultaneous localization and mapping (SLAM) task. In the current appearance-based SLAM methods, the visual inputs are usually affected by illumination, appearance and viewpoints changes. Comparing to the visual inputs, with the active property, light detection and ranging (LiDAR) based point-cloud inputs are invariant to the illumination and appearance changes. In this paper, we extract 3D voxel maps and 2D top view maps from LiDAR inputs, and the former could capture the local geometry into a simplified 3D voxel format, the later could capture the local road structure into a 2D image format. However, the most challenge problem is to obtain efficient features from 3D and 2D maps to against the viewpoints difference. In this paper, we proposed a synchronous adversarial feature learning method for the LCD task, which could learn the higher level abstract features from different domains without any label data. To the best of our knowledge, this work is the first to extract multi-domain adversarial features for the LCD task in real time. To investigate the performance, we test the proposed method on the KITTI odometry dataset. The extensive experiments results show that, the proposed method could largely improve LCD accuracy even under huge viewpoints differences.

cs.RO↗

arXiv2018

Experimentally Robust Self-testing for Bipartite and Tripartite Entangled States

Self-testing refers to a method with which a classical user can certify the state and measurements of quantum systems in a device-independent way. Especially, the self-testing of entangled states is of great importance in quantum information process. A comprehensible example is that violating the CHSH inequality maximally necessarily implies the bipartite shares a singlet. One essential question in self-testing is that, when one observes a non-maximum violation, how close is the tested state to the target state (which maximally violates certain Bell inequality)? The answer to this question describes the robustness of the used self-testing criterion, which is highly important in a practical sense. Recently, J. Kaniewski predicts two analytic self-testing bounds for bipartite and tripartite systems. In this work, we experimentally investigate these two bounds with high quality two-qubit and three-qubit entanglement sources. The results show that these bounds are valid for various of entangled states we prepared, and thus, we implement robust self-testing processes which improve the previous results significantly.

quant-ph↗

arXiv2018

Coulomb Excitation of Deuteron in Peripheral Collisions with a Heavy Ion

We develop an ab initio, non-perturbative, time-dependent Basis Function (tBF) method to solve the nuclear structure and scattering problems in a unified manner. We apply this method to a test problem: the Coulomb excitation of a trapped deuteron by an impinging heavy ion. The states of the deuteron system are obtained by the ab initio nuclear structure calculation implementing a realistic inter-nucleon interaction with a weak external trap to localize the center of mass and to discretize the continuum. The evolution of the internal state of the deuteron system is directly solved using the equation of motion for the scattering. We analyze the excitation mechanism of the deuteron system by investigating its internal transition probabilities and observables as functions of the exposure time and the incident speed. In this investigation, the dynamics of the Coulomb excitation are revealed by the time evolution of the system's internal charge distribution.

nucl-th↗

arXiv2018

Learning to Sequence Robot Behaviors for Visual Navigation

Recent literature in the robotics community has focused on learning robot behaviors that abstract out lower-level details of robot control. To fully leverage the efficacy of such behaviors, it is necessary to select and sequence them to achieve a given task. In this paper, we present an approach to both learn and sequence robot behaviors, applied to the problem of visual navigation of mobile robots. We construct a layered representation of control policies composed of low- level behaviors and a meta-level policy. The low-level behaviors enable the robot to locomote in a particular environment while avoiding obstacles, and the meta-level policy actively selects the low-level behavior most appropriate for the current situation based purely on visual feedback. We demonstrate the effectiveness of our method on three simulated robot navigation tasks: a legged hexapod robot which must successfully traverse varying terrain, a wheeled robot which must navigate a maze-like course while avoiding obstacles, and finally a wheeled robot navigating in the presence of dynamic obstacles. We show that by learning control policies in a layered manner, we gain the ability to successfully traverse new compound environments composed of distinct sub-environments, and outperform both the low-level behaviors in their respective sub-environments, as well as a hand-crafted selection of low-level policies on these compound environments.

cs.RO↗

arXiv2017

Condition directed Multi-domain Adversarial Learning for Loop Closure Detection

Loop closure detection (LCD) is the key module in appearance based simultaneously localization and mapping (SLAM). However, in the real life, the appearance of visual inputs are usually affected by the illumination changes and texture changes under different weather conditions. Traditional methods in LCD usually rely on handcraft features, however, such methods are unable to capture the common descriptions under different weather conditions, such as rainy, foggy and sunny. Furthermore, traditional handcraft features could not capture the highly level understanding for the local scenes. In this paper, we proposed a novel condition directed multi-domain adversarial learning method, where we use the weather condition as the direction for feature inference. Based on the generative adversarial networks (GANs) and a classification networks, the proposed method could extract the high-level weather-invariant features directly from the raw data. The only labels required here are the weather condition of each visual input. Experiments are conducted in the GTAV game simulator, which could generated lifelike outdoor scenes under different weather conditions. The performance of LCD results shows that our method outperforms the state-of-arts significantly.

cs.RO↗

arXiv2017

Towards Stable Adversarial Feature Learning for LiDAR based Loop Closure Detection

Stable feature extraction is the key for the Loop closure detection (LCD) task in the simultaneously localization and mapping (SLAM) framework. In our paper, the feature extraction is operated by using a generative adversarial networks (GANs) based unsupervised learning. GANs are powerful generative models, however, GANs based adversarial learning suffers from training instability. We find that the data-code joint distribution in the adversarial learning is a more complex manifold than in the original GANs. And the loss function that drive the attractive force between synthesis and target distributions is unable for efficient latent code learning for LCD task. To relieve this problem, we combines the original adversarial learning with an inner cycle restriction module and a side updating module. To our best knowledge, we are the first to extract the adversarial features from the light detection and ranging (LiDAR) based inputs, which is invariant to the changes caused by illumination and appearance as in the visual inputs. We use the KITTI odometry datasets to investigate the performance of our method. The extensive experiments results shows that, with the same LiDAR projection maps, the proposed features are more stable in training, and could significantly improve the robustness on viewpoints differences than other state-of-art methods.

cs.RO↗

arXiv2017

Deuteron Coulomb Excitation in Peripheral Collisions with a Heavy Ion

We develop and test an ab initio time-dependent Basis Function (tBF) method to solve non-perturbative and time-dependent problems in quantum mechanics. For our test problem, we apply this method to the Coulomb excitation of the deuteron by an impinging heavy ion. In the tBF method applied to deuterium, we employ wave functions for its bound and excited states to calculate its transition probabilities and the r.m.s. radius during the scattering process. For comparison, corresponding results based on first-order perturbation theory are also provided. For the Coulomb excitation process in a weak and time-varying Coulomb field, where higher-order effects are negligible, we obtain good agreement of the results based on these two methods. The tBF method is then applied to the Coulomb excitation process with stronger external field. The higher-order effects, such as those appearing in the reorientation of the polarization of the deuteron system, are analyzed.

nucl-th↗

arXiv2015

FFLO state with angle dependent Gap in Asymmetric Nuclear Matter

We consider the Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state with an angle dependent-gap (ADG) for the arbitrary angle θ_0 between the direction of the Cooper pair momentum and the symmetry axis of the ADG in asymmetric nuclear matter. We find two kinds of locally stable states, i.e., the FFLO-ADG-orthogonal and FFLO-ADG-parallel states, which correspond to θ_0=π/2 and θ_0=0, respectively. Furthermore, the-FFLO-ADG-orthogonal state is located at small asymmetry, whereas the FFLO-ADG-parallel state is favored for large asymmetry. The critical isospin asymmetry α_c, where the superfluid vanishes, is enhanced largely by considering the Cooper pair momentum with an ADG.

nucl-th↗

arXiv2015

Simulation-based Sensitivity Analysis for Non-ignorable Missing Data

Sensitivity analysis is popular in dealing with missing data problems particularly for non-ignorable missingness. It analyses how sensitively the conclusions may depend on assumptions about missing data e.g. missing data mechanism (MDM). We called models under certain assumptions sensitivity models. To make sensitivity analysis useful in practice we need to define some simple and interpretable statistical quantities to assess the sensitivity models. However, the assessment is difficult when the missing data mechanism is missing not at random (MNAR). We propose a novel approach in this paper on attempting to investigate those assumptions based on the nearest-neighbour (KNN) distances of simulated datasets from various MNAR models. The method is generic and it has been applied successfully to several specific models in this paper including meta-analysis model with publication bias, analysis of incomplete longitudinal data and regression analysis with non-ignorable missing covariates.

stat.ME↗

arXiv2013

Three-body force effect on neutrino emissivities of neutron stars within the framework of the Brueckner-Hartree-Fock approach

The three-body force (TBF) effect on the neutrino emissivity in neutron star matter and the total neutrino emissivity of neutron stars have been investigated within the framework of the Brueckner-Hartree-Fock approach by adopting the AV18 two-body interaction plus a microscopic TBF. The neutrino emissivity from the direct Urca process turns out to be much larger than that from the modified Urca process. Inclusion of the TBF reduces strongly the density thresholds of the direct Urca processes involving electrons and muons. The TBF effect on the total neutrino emissivity of neutron stars is shown to be negligibly weak for neutron stars with small masses. For neutron stars with large masses, the TBF effect becomes visible and inclusion of the TBF may enhance the total neutrino emissivity by about 50% for neutron stars with a given mass of $M=1.6M_{\odot}$.

nucl-th↗

arXiv2013

Effect of tensor correlations on the depletion of nuclear Fermi sea within the extended BHF approach

We have investigated the effect of tensor correlations on the depletion of nuclear Fermi sea in symmetric nuclear matter within the framework of the extended Brueckner-Hartree-Fock approach by adopting the AV18 two-body interaction and a microscopic three-body force. The contributions from various partial wave channels including the isospin-singlet T=0 channel, the isospin-triplet T=1 channel and the T=0 tensor 3SD1 channel have been calculated. The T=0 neutron-proton correlations play a dominated role in causing the depletion of nuclear Fermi sea. The T=0 correlation-induced depletion turns out to stem almost completely from the $^3SD_1$ tensor channel. The isospin-singlet T=0 3SD1 tensor correlations is shown to response for most of the depletion, which amounts to more than 70 percent out of the total depletion in the density region considered. The TBF turns out to leads to an enhancement of the depletion at high densities well above the empirical saturation density and its effect increases as a function of density.

nucl-th↗

arXiv2013

Three-body force effect on off-shell mass operator and spectral functions in nuclear matter

Within the framework of the Brueckner theory, the off-shell behaviors of the mass operator $M(k,ω)=V(k,ω)+iW(k,ω)$, i.e., its dependence upon the momentum $k$ and upon the nucleon frequency $ω$, are investigated by including nuclear three-body force (TBF). The first two terms of the hole-line expansion of the mass operator are taken into account. The TBF effects on their off-shell properties are discussed. A comparison is made between the on-shell and off-shell values of $M_{1}$. The nucleon spectral function and nucleon momentum distribution are also calculated, and the calculation shows that they are hardly affected by the TBF effect at the saturation density. At a high density two times greater than the saturation density, inclusion of the TBF may lead to a visible effect on the spectral function and may enhance the depletion of the hole states.

nucl-th↗

arXiv2013

Three-body force effect on nucleon momentum distributions in asymmetric nuclear matter within the framework of the extended BHF approach

We have investigated the three-body force (TBF) effect on the neutron and proton momentum distributions in asymmetric nuclear matter within the framework of the extended Brueckner-Hartree-Fock approach by adopting the $AV18$ two-body interaction plus a microscopic TBF. In asymmetric nuclear matter, it is shown that the neutron and proton momentum distributions become different from their common distribution in symmetric nuclear matter. The predicted depletion of the proton hole states increases while the neutron one decreases as a function of isospin-asymmetry. The TBF effect on the neutron and proton momentum distributions turns out to be negligibly weak at low densities around and below the normal nuclear density. The TBF effect is found to become sizable only at high densities well above the saturation density, and inclusion of the TBF leads to an overall enhancement of the depletion of the neutron and proton Fermi seas.

nucl-th↗

arXiv2013

Active Self-Assembly of Algorithmic Shapes and Patterns in Polylogarithmic Time

We describe a computational model for studying the complexity of self-assembled structures with active molecular components. Our model captures notions of growth and movement ubiquitous in biological systems. The model is inspired by biology's fantastic ability to assemble biomolecules that form systems with complicated structure and dynamics, from molecular motors that walk on rigid tracks and proteins that dynamically alter the structure of the cell during mitosis, to embryonic development where large-scale complicated organisms efficiently grow from a single cell. Using this active self-assembly model, we show how to efficiently self-assemble shapes and patterns from simple monomers. For example, we show how to grow a line of monomers in time and number of monomer states that is merely logarithmic in the length of the line. Our main results show how to grow arbitrary connected two-dimensional geometric shapes and patterns in expected time that is polylogarithmic in the size of the shape, plus roughly the time required to run a Turing machine deciding whether or not a given pixel is in the shape. We do this while keeping the number of monomer types logarithmic in shape size, plus those monomers required by the Kolmogorov complexity of the shape or pattern. This work thus highlights the efficiency advantages of active self-assembly over passive self-assembly and motivates experimental effort to construct general-purpose active molecular self-assembly systems.

cs.DS↗