arXiv subjects

Rui Chen

Publications and source records attributed to Rui Chen.

At least 415 records · Page 23Linked to original sources

arXiv2020

Distinguishing between dynamical and static Rashba effects in hybrid perovskite nanocrystals using transient absorption spectroscopy

The dynamical and static Rashba effects in hybrid methylammonium (MA) lead halide perovskites have recently been theoretically predicted. However, only the static effect was experimentally confirmed so far. Here we report on the dynamical Rashba effect observed using snapshot transient absorption spectral imaging with 400 nm pumping for a fully encapsulated film of 20-nm-sized 3D MAPbBr3 nanocrystals. The effect causes a 240 meV splitting of the lowest-energy absorption bleaching band, initially appearing over sub-ps timescale and progressively stabilizing to 60 meV during 500 ps. The integrated intensities of the split subbands demonstrate a photon-helicity-dependent asymmetry, thus proving the Rashba-type splitting and providing direct experimental evidence for the Rashba spin-split edge states in lead halide perovskite materials. The ultrafast dynamics is governed by the relaxation of two-photon-excited electrons in the Rashba spin-split system caused by a built-in electric field originating from dynamical charge separation in the entire MAPbBr3 nanocrystal.

physics.app-ph↗

arXiv2020

Probing new types of $P_c$ states inspired by the interaction between an $S$-wave charmed baryon and an anticharmed meson in a $\bar T$ doublet state

Inspired by the observations of three $P_c$ states, we systematically investigate interactions between an $S$-wave charmed baryon $\mathcal{B}_{c}^{(*)}=Λ_c/Σ_c/Σ_c^{*}$ and an anticharmed meson $\bar T=\bar D_1/\bar D_2^*$ with the one-pion-exchange potential model and the one-boson-exchange potential model, and search for possible new types of $P_c$ states with the structures of $\mathcal{B}_{c}^{(*)}\bar T$. Both $S$-$D$ wave mixing and coupled channel effects are considered. Our results suggest that in some $\mathcal{B}_{c}^{(*)}\bar T$ systems there are ideal candidates of new types of $P_c$ states, i.e., the $Σ_c\bar{D}_1$ state with $I(J^P)=1/2(1/2^+)$, the $Σ_c\bar{D}_2^*$ state with $I(J^P)=1/2(3/2^+)$, the $Σ_c^*\bar{D}_1$ state with $I(J^P)=1/2(1/2^+)$, and the $Σ_c^*\bar{D}_2^*$ states with $I(J^P)=1/2(1/2^+, 3/2^+)$, and we suggest that these predicted new types of $P_c$ states can be detected in the process $Λ_b^0 \to ψ(2S) p π^{-}$. Meanwhile, we also extend our study to the interactions between an $S$-wave charmed baryon and a charmed meson in a $T$ doublet, and we predict a series of double-charm molecular pentaquarks.

hep-ph↗

arXiv2020

Developing Multi-Task Recommendations with Long-Term Rewards via Policy Distilled Reinforcement Learning

With the explosive growth of online products and content, recommendation techniques have been considered as an effective tool to overcome information overload, improve user experience, and boost business revenue. In recent years, we have observed a new desideratum of considering long-term rewards of multiple related recommendation tasks simultaneously. The consideration of long-term rewards is strongly tied to business revenue and growth. Learning multiple tasks simultaneously could generally improve the performance of individual task due to knowledge sharing in multi-task learning. While a few existing works have studied long-term rewards in recommendations, they mainly focus on a single recommendation task. In this paper, we propose {\it PoDiRe}: a \underline{po}licy \underline{di}stilled \underline{re}commender that can address long-term rewards of recommendations and simultaneously handle multiple recommendation tasks. This novel recommendation solution is based on a marriage of deep reinforcement learning and knowledge distillation techniques, which is able to establish knowledge sharing among different tasks and reduce the size of a learning model. The resulting model is expected to attain better performance and lower response latency for real-time recommendation services. In collaboration with Samsung Game Launcher, one of the world's largest commercial mobile game platforms, we conduct a comprehensive experimental study on large-scale real data with hundreds of millions of events and show that our solution outperforms many state-of-the-art methods in terms of several standard evaluation metrics.

cs.LG↗

arXiv2019

Higher-Order Topological Insulators in Quasicrystals

Current understanding of higher-order topological insulators (HOTIs) is based primarily on crystalline materials. Here, we propose that HOTIs can be realized in quasicrystals. Specifically, we show that two distinct types of second-order topological insulators (SOTIs) can be constructed on the quasicrystalline lattices (QLs) with different tiling patterns. One is derived by using a Wilson mass term to gap out the edge states of the quantum spin Hall insulator on QLs. The other is the quasicrystalline quadrupole insulator (QI) with a quantized quadrupole moment. We reveal some unusual features of the corner states (CSs) in the quasicrystalline SOTIs. We also show that the quasicrystalline QI can be simulated by a designed electrical circuit, where the CSs can be identified by measuring the impedance resonance peak. Our findings not only extend the concept of HOTIs into quasicrystals but also provide a feasible way to detect the topological property of quasicrystals in experiments.

cond-mat.mes-hall↗

arXiv2019

GRIP: Generative Robust Inference and Perception for Semantic Robot Manipulation in Adversarial Environments

Recent advancements have led to a proliferation of machine learning systems used to assist humans in a wide range of tasks. However, we are still far from accurate, reliable, and resource-efficient operations of these systems. For robot perception, convolutional neural networks (CNNs) for object detection and pose estimation are recently coming into widespread use. However, neural networks are known to suffer overfitting during training process and are less robust within unseen conditions, which are especially vulnerable to adversarial scenarios. In this work, we propose Generative Robust Inference and Perception (GRIP) as a two-stage object detection and pose estimation system that aims to combine relative strengths of discriminative CNNs and generative inference methods to achieve robust estimation. Our results show that a second stage of sample-based generative inference is able to recover from false object detection by CNNs, and produce robust estimations in adversarial conditions. We demonstrate the efficacy of GRIP robustness through comparison with state-of-the-art learning-based pose estimators and pick-and-place manipulation in dark and cluttered environments.

cs.RO↗

arXiv2019

Orbital Angular Momentum Waves: Generation, Detection and Emerging Applications

Orbital angular momentum (OAM) has aroused a widespread interest in many fields, especially in telecommunications due to its potential for unleashing new capacity in the severely congested spectrum of commercial communication systems. Beams carrying OAM have a helical phase front and a field strength with a singularity along the axial center, which can be used for information transmission, imaging and particle manipulation. The number of orthogonal OAM modes in a single beam is theoretically infinite and each mode is an element of a complete orthogonal basis that can be employed for multiplexing different signals, thus greatly improving the spectrum efficiency. In this paper, we comprehensively summarize and compare the methods for generation and detection of optical OAM, radio OAM and acoustic OAM. Then, we represent the applications and technical challenges of OAM in communications, including free-space optical communications, optical fiber communications, radio communications and acoustic communications. To complete our survey, we also discuss the state of art of particle manipulation and target imaging with OAM beams.

physics.app-ph↗

arXiv2019

Time and Energy-Optimal Lane Change Maneuvers for Cooperating Connected Automated Vehicles

We derive optimal control policies for a Connected and Automated Vehicle (CAV) cooperating with neighboring CAVs to implement a highway lane change maneuver. We optimize the maneuver time and subsequently minimize the associated energy consumption of all cooperating vehicles in this maneuver. We prove structural properties of the optimal policies which simplify the solution derivations and lead to analytical optimal control expressions. The solutions, when they exist, are guaranteed to satisfy safety constraints for all vehicles involved in the maneuver. Simulation results show the effectiveness of the proposed solution and significant performance improvements compared to maneuvers performed by human-driven vehicles.

eess.SY↗

arXiv2019

DeLottery: A Novel Decentralized Lottery System Based on Blockchain Technology

In this paper, we design DeLottery, a decentralized lottery system based on block chain technology and smart contracts. Lottery is a classical form of entertainment and charity for centuries. Facing the bottleneck of the combination between lottery and information technology, we use smart contracts and blockchain in decentralized, intelligent, and secure systems for lottery industries. Moreover, we are inspired by the algorithm of RANDAO, an outstanding way of random number generation in blockchain scenario. The components and the functions of the novel system are described in details. We implement DeLottery in a blockchain network and show functioning procedure and security of the proposed lottery system.

cs.DC↗

arXiv2019

S4G: Amodal Single-view Single-Shot SE(3) Grasp Detection in Cluttered Scenes

Grasping is among the most fundamental and long-lasting problems in robotics study. This paper studies the problem of 6-DoF(degree of freedom) grasping by a parallel gripper in a cluttered scene captured using a commodity depth sensor from a single viewpoint. We address the problem in a learning-based framework. At the high level, we rely on a single-shot grasp proposal network, trained with synthetic data and tested in real-world scenarios. Our single-shot neural network architecture can predict amodal grasp proposal efficiently and effectively. Our training data synthesis pipeline can generate scenes of complex object configuration and leverage an innovative gripper contact model to create dense and high-quality grasp annotations. Experiments in synthetic and real environments have demonstrated that the proposed approach can outperform state-of-the-arts by a large margin.

cs.RO↗

arXiv2019

Disorder-induced Majorana zero modes in a dimerized Kitaev superconductor chain

Motivated by the recent experimental observation of the topological Anderson insulator in disordered atomic wires based on the Su-Schrieffer-Heeger (SSH) model, we study disorder effects on a dimerized Kitaev superconductor chain which is regarded as the superconductor version of the SSH model. By computing the real-space winding number and the zero-bias differential conductance, we analyze the topological phase transitions occurring in a dimerized Kitaev superconductor chain with disorder. It is found that disorder can induce a topologically nontrivial superconductor phase hosting Majorana zero modes (MZMs). We can regulate the appearance of disorder-induced MZMs by adjusting the dimerization parameter. Finally, we use the self-consistent Born approximation method to verify the numerical results.

cond-mat.mes-hall↗

arXiv2019

$\bar{K}Λ$ molecular explanation to the newly observed $Ξ(1620)^0$

The newly observed $Ξ(1620)^0$ by the Belle Collaboration inspires our interest in performing a systematic study on the interaction of an anti-strange meson $(\bar{K}^{(*)})$ with a strange or doubly strange ground octet baryon $\mathcal{B}$ ($Λ$, $Σ$, and $Ξ$), where the spin-orbit force and the recoil correction are considered in the adopted one-boson-exchange model. Our results indicate that $Ξ(1620)^0$ can be explained as a $\bar{K}Λ$ molecular state with $I(J^P)=1/2(1/2^-)$ and the intermediate force from $σ$ exchange plays an important role. Additionally, we also predict several other possible molecular candidates, i.e., the $\bar{K}Σ$ molecular state with $I(J^P)=1/2(1/2^-)$ and the triply strange $\bar{K}Ξ$ molecular state with $I(J^P)=0(1/2^-)$.

hep-ph↗

arXiv2019

Active Learning for Risk-Sensitive Inverse Reinforcement Learning

One typical assumption in inverse reinforcement learning (IRL) is that human experts act to optimize the expected utility of a stochastic cost with a fixed distribution. This assumption deviates from actual human behaviors under ambiguity. Risk-sensitive inverse reinforcement learning (RS-IRL) bridges such gap by assuming that humans act according to a random cost with respect to a set of subjectively distorted distributions instead of a fixed one. Such assumption provides the additional flexibility to model human's risk preferences, represented by a risk envelope, in safe-critical tasks. However, like other learning from demonstration techniques, RS-IRL could also suffer inefficient learning due to redundant demonstrations. Inspired by the concept of active learning, this research derives a probabilistic disturbance sampling scheme to enable an RS-IRL agent to query expert support that is likely to expose unrevealed boundaries of the expert's risk envelope. Experimental results confirm that our approach accelerates the convergence of RS-IRL algorithms with lower variance while still guaranteeing unbiased convergence.

cs.LG↗

arXiv2019

Learning Lightweight Pedestrian Detector with Hierarchical Knowledge Distillation

It remains very challenging to build a pedestrian detection system for real world applications, which demand for both accuracy and speed. This work presents a novel hierarchical knowledge distillation framework to learn a lightweight pedestrian detector, which significantly reduces the computational cost and still holds the high accuracy at the same time. Following the `teacher--student' diagram that a stronger, deeper neural network can teach a lightweight network to learn better representations, we explore multiple knowledge distillation architectures and reframe this approach as a unified, hierarchical distillation framework. In particular, the proposed distillation is performed at multiple hierarchies, multiple stages in a modern detector, which empowers the student detector to learn both low-level details and high-level abstractions simultaneously. Experiment result shows that a student model trained by our framework, with 6 times compression in number of parameters, still achieves competitive performance as the teacher model on the widely used pedestrian detection benchmark.

cs.CV↗

arXiv2019

Topological Anderson insulator phase in a quasicrystal lattice

Motivated by the recent experimental realization of the topological Anderson insulator and research interest on the topological quasicrystal lattices, we investigate the effects of disorder on topological properties of a two-dimensional Penrose-type quasicrystal lattice that supports the quantum spin Hall insulator (QSHI) and normal insulator (NI) phases in the clean limit. It is shown that the helical edge state of the QSHI phase is robust against weak disorder. Most saliently, it is found that disorder can induce a phase transition from NI to QSHI phase in the quasicrystal system. The numerical results based on a two-terminal device show that a quantized conductance plateau can arise inside the energy gap of NI phase for moderate Anderson disorder strength. Further, it is confirmed that the local current distributions of the disorder-induced quantized conductance plateau are located on the two edges of sample. Finally, we identify this disorder-induced phase as topological Anderson insulator phase by computing the disorder-averaged spin Bott index.

cond-mat.mes-hall↗

arXiv2019

Relativistic Difference of LIGO Signal

Signal waves of the monotonously increasing frequency detected by LIGO are generally considered to be gravitational waves of spiral binary stars, thus confirming the general theory of relativity. Here we present a universal method for signal wave spectrum analysis, introducing the true conclusions of numerical calculation and image analysis of GW150914 signal wave. Firstly, numerical calculation results of GW150914 signal wave frequency change rate obey the com quantization law which needs to be accurately described by integers, and there is an irreconcilable difference between the results and the generalized relativistic frequency equation of the gravitational wave. Secondly, the assignment of the frequency and frequency change rate of GW10914 signal wave to the generalized relativistic frequency equation of gravitational wave constructs a non-linear equation group about the mass of wave source, and the computer image solution shows that the equation group has no GW10914 signal wave solution. Thirdly, it is not unique to calculate the chirp mass of the wave source from the different frequencies and change rates of the numerical relativistic waveform of the GW150914 signal wave, and the numerical relativistic waveform of the GW150914 signal wave deviates too far from the original waveform actually. Other LIGO signal waveforms do not have obvious characteristics of gravitational frequency variation of spiral binary stars and lack precise data, so they cannot be used for numerical analysis and image solution. Therefore, the generalized relativistic frequency equation of gravitational wave has not been verified by LIGO signal wave up to now. Does this mean that the gravitational wave signals from some spiral binaries that may be detected in future obey the same com quantization law? The answer remains unclear.

physics.gen-ph↗

arXiv2019

Point-Based Multi-View Stereo Network

We introduce Point-MVSNet, a novel point-based deep framework for multi-view stereo (MVS). Distinct from existing cost volume approaches, our method directly processes the target scene as point clouds. More specifically, our method predicts the depth in a coarse-to-fine manner. We first generate a coarse depth map, convert it into a point cloud and refine the point cloud iteratively by estimating the residual between the depth of the current iteration and that of the ground truth. Our network leverages 3D geometry priors and 2D texture information jointly and effectively by fusing them into a feature-augmented point cloud, and processes the point cloud to estimate the 3D flow for each point. This point-based architecture allows higher accuracy, more computational efficiency and more flexibility than cost-volume-based counterparts. Experimental results show that our approach achieves a significant improvement in reconstruction quality compared with state-of-the-art methods on the DTU and the Tanks and Temples dataset. Our source code and trained models are available at https://github.com/callmeray/PointMVSNet .

cs.CV↗

arXiv2019

Floquet Weyl semimetals in light-irradiated type-II and hybrid line-node semimetals

Type-II Weyl semimetals have recently attracted intensive research interest because they host Lorentz-violating massless fermions as quasiparticles. The discovery of type-II Weyl semimetals evokes the study of type-II line-node semimetals (LNSMs) whose linear dispersion is strongly tilted near the nodal ring. We present here a study on the circularly polarized light-induced Floquet states in type-II LNSMs, as well as those in hybrid LNSMs that have a partially over-tilted linear dispersion in the vicinity of the nodal ring. We illustrate that two distinct types of Floquet Weyl semimetal (WSM) states can be induced in periodically driven type-II and hybrid LNSMs, and the type of Floquet WSMs can be tuned by the direction and intensity of the incident light. We construct phase diagrams of light irradiated type-II and hybrid LNSMs which are quite distinct from those of light-irradiated type-I LNSMs. Moreover, we show that the photoinduced Floquet type-I and type-II WSMs can be characterized by the emergence of different anomalous Hall conductivities.

cond-mat.mes-hall↗

arXiv2019

Strong LHCb evidence supporting the existence of the hidden-charm molecular pentaquarks

On March 26th, 2019, at the Rencontres de Moriond QCD conference, the LHCb Collaboration reported the observation of three new pentaquarks, namely $P_c(4312)$, $P_c(4440)$ and $P_c(4457)$, which are consistent with the loosely bound molecular hidden-charm pentaquark states composed of an S-wave charmed baryon $Σ_c$ and an S-wave anti-charmed meson ($\bar{D}, \bar{D}^*$). In this work, we present a direct calculation by the one-boson-exchange (OBE) model and demonstrate explicitly that the $P_c(4312)$, $P_c(4440)$ and $P_c(4457)$ do correspond to the loosely bound $Σ_c\bar{D}$ with $(I=1/2,J^P=1/2^-)$, $Σ_c\bar{D}^*$ with $(I=1/2,J^P=1/2^-)$ and $Σ_c\bar{D}^*$ with $(I=1/2,J^P=3/2^-)$, respectively.

hep-ph↗