arXiv subjects

Rui Chen

Publications and source records attributed to Rui Chen.

At least 397 records · Page 22Linked to original sources

arXiv2020

Analytical solution for the surface states of antiferromagnetic topological insulator MnBi$_2$Te$_4$

Recently, the intrinsic magnetic topological insulator MnBi$_2$Te$_4$ has attracted great attention. It has an out-of-plane antiferromagnetic order, which is believed to open a sizable energy gap in the surface states. This gap, however, was not always observable in the latest angle-resolved photoemission spectroscopy (ARPES) experiments. To address this issue, we analytically derive an effective model for the two-dimensional (2D) surface states by starting from a three-dimensional (3D) Hamiltonian for bulk MnBi$_2$Te$_4$ and taking into account the spatial profile of the bulk magnetization. Our calculations suggest that the diminished surface gap may be caused by a much smaller and more localized intralayer ferromagnetic order. In addition, we calculate the spatial distribution and penetration depth of the surface states, which indicates that the surface states are mainly embedded in the first two septuple layers from the terminating surface. From our analytical results, the influence of the bulk parameters on the surface states can be found explicitly. Furthermore, we derive a $\bf{k}\cdot \bf{p}$ model for MnBi$_2$Te$_4$ thin films and show the oscillation of the Chern number between odd and even septuple layers. Our results will be helpful for the ongoing explorations of the MnBi$_x$Te$_y$ family.

cond-mat.mes-hall↗

arXiv2020

Offline Meta-level Model-based Reinforcement Learning Approach for Cold-Start Recommendation

Reinforcement learning (RL) has shown great promise in optimizing long-term user interest in recommender systems. However, existing RL-based recommendation methods need a large number of interactions for each user to learn a robust recommendation policy. The challenge becomes more critical when recommending to new users who have a limited number of interactions. To that end, in this paper, we address the cold-start challenge in the RL-based recommender systems by proposing a meta-level model-based reinforcement learning approach for fast user adaptation. In our approach, we learn to infer each user's preference with a user context variable that enables recommendation systems to better adapt to new users with few interactions. To improve adaptation efficiency, we learn to recover the user policy and reward from only a few interactions via an inverse reinforcement learning method to assist a meta-level recommendation agent. Moreover, we model the interaction relationship between the user model and recommendation agent from an information-theoretic perspective. Empirical results show the effectiveness of the proposed method when adapting to new users with only a single interaction sequence. We further provide a theoretical analysis of the recommendation performance bound.

cs.LG↗

arXiv2020

$Z_{cs}(3985)^-$: a strange hidden-charm tetraquark resonance or not?

Inspired by the newly $Z_{cs}(3985)^-$ reported by the BESIII Collaboration in the $K^+$ recoil-mass spectrum of the of $e^+e^-\to (D^{*0}D_s^-/D^0D_s^{*-})K^+$ processes, we perform a dynamical study on the $D^{(*)0}D_s^{*-}$ interactions by adopting a one-boson-exchange model and considering the coupled channel effect. After producing the phase shifts for all the discussed channels, our results exclude the newly $Z_{cs}(3985)^-$ as a $D^{*0}D_s^{-}/D^{0}D_s^{*-}/D^{*0}D_s^{*-}$ resonance with $I(J^P)=1/2(1^+, 0^-, 1^-, 2^-)$.

hep-ph↗

arXiv2020

Higher-Order topological insulator in a dodecagonal quasicrystal

Higher-order topological insulators (HOTIs) are a newly discovered class of topological insulators which exhibit unconventional bulk-boundary correspondence. Very recently, the concept of HOTIs has been extended to aperiodic quasicrystalline systems, where the topological band theory fails to describe topological phases. More importantly, a novel HOTI phase protected by an eightfold rotational symmetry, not found in crystalline materials, was identified in the Ammann-Beenker tiling quasicrystals. Here we report the discovery of a quasicrystalline HOTI in a dodecagonal quasicrystal. The quasicrystalline HOTI supports twelve in-gap zero-energy modes symmetrically distributed at the corners of the quasicrystal dodecagon. These zero-energy corner modes are protected by a combination of a twelvefold rotational symmetry and mirror symmetry, as well as particle-hole symmetry, which has no crystalline counterpart.

cond-mat.mes-hall↗

arXiv2020

Effective Magnetic Hamiltonian at Finite Temperatures for Rare Earth Chalcogenides

Alkali metal rare-earth chalcogenide $ARECh2$ (A=alkali or monovalent metal, RE=rare earth, Ch=O, S, Se, Te), is a large family of quantum spin liquid (QSL) candidates we discovered recently. Unlike $YbMgGaO4$, most members in the family except for the oxide ones, have relatively small crystalline electric-field (CEF) excitation levels, particularly the first ones. This makes the conventional Curie-Weiss analysis at finite temperatures inapplicable and CEF excitations may play an essential role in understanding the low-energy spin physics. Here we considered an effective magnetic Hamiltonian incorporating CEF excitations and spin-spin interactions, to accurately describe thermodynamics in such a system. By taking $NaYbSe2$ as an example, we were able to analyze magnetic susceptibility, magnetization under pulsed high fields and heat capacity in a systematic and comprehensive way. The analysis allows us to produce accurate anisotropic exchange coupling energies and unambiguously determine a crossover temperature ($\sim$25 K in the case of $NaYbSe2$), below which CEF effects fade away and pure spin-spin interactions stand out. We further validated the effective picture by successfully explaining the anomalous temperature dependence of electron spin resonance (ESR) spectral width. The effective scenario in principle can be generalized to other rare-earth spin systems with small CEF excitations.

cond-mat.str-el↗

arXiv2020

Can the newly $P_{cs}(4459)$ be a strange hidden-charm $Ξ_c\bar{D}^{*}$ molecular pentaquarks?

Stimulated by the $P_{cs}(4459)$ reported by the LHCb Collaboration, we perform a single $Ξ_c\bar{D}^*$ channel and a coupled $Ξ_c\bar{D}^*/Ξ_c^*\bar{D}/Ξ_c^{\prime}\bar{D}^*/Ξ_c^*\bar{D}^*$ channel analysis by using a one-boson-exchange model. Our results indicate that the newly $P_{cs}(4459)$ cannot be a pure $Ξ_c\bar{D}^*$ molecular state, but a coupled $Ξ_c\bar{D}^*/Ξ_c^*\bar{D}/Ξ_c^{\prime}\bar{D}^*/Ξ_c^*\bar{D}^*$ bound state with $I(J^P)=0(3/2^-)$, where the $Ξ_c\bar{D}^*$ and $Ξ_c^*\bar{D}$ components are dominant. Meanwhile, we find the interactions from the $Ξ_c^{\prime}\bar{D}^*$ system with $0(1/2^-)$, the $Ξ_c^{*}\bar{D}$ system with $1(3/2^-)$, and the $Ξ_c^{*}\bar{D}^*$ system with $1(1/2^-)$ are strongly attractive, where one can expect possible strange hidden-charm molecular or resonant structures near the these thresholds with the assigned quantum numbers.

hep-ph↗

arXiv2020

Grounding Implicit Goal Description for Robot Indoor Navigation Via Recursive Belief Update

Natural language-based robotic navigation remains a challenging problem due to the human knowledge of navigation constraints, and destination is not directly compatible with the robot knowledge base. In this paper, we aim to translate natural destination commands into high-level robot navigation plans given a map of interest. We identify grammatically associated segments of destination description and recursively apply each of them to update a belief distribution of an area over the given map.We train a destination grounding model using a dataset of single-step belief update for precise, proximity, and directional modifier types. We demonstrate our method on real-world navigation task in an office consisting of 80 areas. Offline experimental results show that our method can directly extract goal destination from unheard, long, and composite text commands asked by humans. This enables users to specify their destination goals for the robot in general and natural form. Hardware experiment results also show that the designed model brings much convenience for specifying a navigation goal to a service robot.

cs.RO↗

arXiv2020

Growth and Properties of Dislocated Two-dimensional Layered Materials

Two-dimensional (2D) layered materials hosting dislocations have attracted considerable research attention in recent years. In particular, screw dislocations can result in a spiral topology and an interlayer twist in the layered materials, significantly impacting the stacking order and symmetry of the layers. Moreover, the dislocations with large strain and heavily distorted atomic registry can result in a local modification of the structures around the dislocation. The dislocations thus provide a useful route to engineering optical, electrical, thermal, mechanical and catalytic properties of the 2D layered materials, which show great potential to bring new functionalities. This article presents a comprehensive review of the experimental and theoretical progress on the growth and properties of the dislocated 2D layered materials. It also offers an outlook on the future works in this promising research field.

cond-mat.mtrl-sci↗

arXiv2020

Explainable Recommender Systems via Resolving Learning Representations

Recommender systems play a fundamental role in web applications in filtering massive information and matching user interests. While many efforts have been devoted to developing more effective models in various scenarios, the exploration on the explainability of recommender systems is running behind. Explanations could help improve user experience and discover system defects. In this paper, after formally introducing the elements that are related to model explainability, we propose a novel explainable recommendation model through improving the transparency of the representation learning process. Specifically, to overcome the representation entangling problem in traditional models, we revise traditional graph convolution to discriminate information from different layers. Also, each representation vector is factorized into several segments, where each segment relates to one semantic aspect in data. Different from previous work, in our model, factor discovery and representation learning are simultaneously conducted, and we are able to handle extra attribute information and knowledge. In this way, the proposed model can learn interpretable and meaningful representations for users and items. Unlike traditional methods that need to make a trade-off between explainability and effectiveness, the performance of our proposed explainable model is not negatively affected after considering explainability. Finally, comprehensive experiments are conducted to validate the performance of our model as well as explanation faithfulness.

cs.LG↗

arXiv2020

Probing hidden-charm decay properties of $P_c$ states in a molecular scenario

The $P_c(4312)$, $P_c(4440)$, and $P_c(4457)$ observed by the LHCb Collaboration are very likely to be $S$-wave $Σ_c\bar{D}^{(*)}$ molecular candidates due to their near-threshold character. In this work, we study the hidden-charm decay modes of these $P_c$ states, $P_c\to J/ψp(η_cp)$, using a quark interchange model. The decay mechanism for the $P_c\to J/ψp(η_cp)$ processes arises from the quark-quark interactions, where all parameters are determined by the mass spectra of mesons. We present our results in two scenarios. In scenario I, we perform the dynamical calculations and treat the $P_c$ states as pure $Σ_c \bar D^{(*)}$ molecules. In scenario II, after considering the coupled channel effect between different flavor configurations $Σ^{(*)}_c\bar D^{(*)}$, we calculate these partial decay widths again. The decay patterns in these two scenarios can help us to explore the molecular assignment and the inner flavor configurations for the $P_c$ states. In particular, the decay widths of $Γ(P_c(4312)\toη_cp)$ are comparable to the $J/ψp$ decay widths in both of these two scenarios. Future experiments like LHCb may confirm the existence of the $P_c(4312)$ in the $η_cp$ channel.

hep-ph↗

arXiv2020

Multi-mode OAM Radio Waves: Generation, Angle of Arrival Estimation and Reception With UCAs

Orbital angular momentum (OAM) at radio frequency (RF) provides a novel approach of multiplexing a set of orthogonal modes on the same frequency channel to achieve high spectrum efficiencies. However, there are still big challenges in the multi-mode OAM generation, OAM antenna alignment and OAM signal reception. To solve these problems, we propose an overall scheme of the line-of-sight multi-carrier and multi-mode OAM (LoS MCMM-OAM) communication based on uniform circular arrays (UCAs). First, we verify that UCA can generate multi-mode OAM radio beam with both the RF analog synthesis method and the baseband digital synthesis method. Then, for the considered UCA-based LoS MCMM-OAM communication system, a distance and AoA estimation method is proposed based on the two-dimensional ESPRIT (2-D ESPRIT) algorithm. A salient feature of the proposed LoS MCMM-OAM and LoS MCMM-OAM-MIMO systems is that the channel matrices are completely characterized by three parameters, namely, the azimuth angle, the elevation angle and the distance, independent of the numbers of subcarriers and antennas, which significantly reduces the burden by avoiding estimating large channel matrices, as traditional MIMO-OFDM systems. After that, we propose an OAM reception scheme including the beam steering with the estimated AoA and the amplitude detection with the estimated distance. At last, the proposed methods are extended to the LoS MCMM-OAM-MIMO system equipped with uniform concentric circular arrays (UCCAs). Both mathematical analysis and simulation results validate that the proposed OAM reception scheme can eliminate the effect of the misalignment error of a practical OAM channel and approaches the performance of an ideally aligned OAM channel.

eess.SP↗

arXiv2020

Towards Deeper Graph Neural Networks with Differentiable Group Normalization

Graph neural networks (GNNs), which learn the representation of a node by aggregating its neighbors, have become an effective computational tool in downstream applications. Over-smoothing is one of the key issues which limit the performance of GNNs as the number of layers increases. It is because the stacked aggregators would make node representations converge to indistinguishable vectors. Several attempts have been made to tackle the issue by bringing linked node pairs close and unlinked pairs distinct. However, they often ignore the intrinsic community structures and would result in sub-optimal performance. The representations of nodes within the same community/class need be similar to facilitate the classification, while different classes are expected to be separated in embedding space. To bridge the gap, we introduce two over-smoothing metrics and a novel technique, i.e., differentiable group normalization (DGN). It normalizes nodes within the same group independently to increase their smoothness, and separates node distributions among different groups to significantly alleviate the over-smoothing issue. Experiments on real-world datasets demonstrate that DGN makes GNN models more robust to over-smoothing and achieves better performance with deeper GNNs.

cs.LG↗

arXiv2020

Normal Assisted Stereo Depth Estimation

Accurate stereo depth estimation plays a critical role in various 3D tasks in both indoor and outdoor environments. Recently, learning-based multi-view stereo methods have demonstrated competitive performance with a limited number of views. However, in challenging scenarios, especially when building cross-view correspondences is hard, these methods still cannot produce satisfying results. In this paper, we study how to leverage a normal estimation model and the predicted normal maps to improve the depth quality. We couple the learning of a multi-view normal estimation module and a multi-view depth estimation module. In addition, we propose a novel consistency loss to train an independent consistency module that refines the depths from depth/normal pairs. We find that the joint learning can improve both the prediction of normal and depth, and the accuracy & smoothness can be further improved by enforcing the consistency. Experiments on MVS, SUN3D, RGBD, and Scenes11 demonstrate the effectiveness of our method and state-of-the-art performance.

cs.CV↗

arXiv2020

Deep Shape from Polarization

This paper makes a first attempt to bring the Shape from Polarization (SfP) problem to the realm of deep learning. The previous state-of-the-art methods for SfP have been purely physics-based. We see value in these principled models, and blend these physical models as priors into a neural network architecture. This proposed approach achieves results that exceed the previous state-of-the-art on a challenging dataset we introduce. This dataset consists of polarization images taken over a range of object textures, paints, and lighting conditions. We report that our proposed method achieves the lowest test error on each tested condition in our dataset, showing the value of blending data-driven and physics-driven approaches.

cs.CV↗

arXiv2020

How to Evaluate Proving Grounds for Self-Driving? A Quantitative Approach

Proving ground has been a critical component in testing and validation for Connected and Automated Vehicles (CAV). Although quite a few world-class testing facilities have been under construction over the years, the evaluation of proving grounds themselves as testing approaches has rarely been studied. In this paper, we present the first attempt to systematically evaluate CAV proving grounds and contribute to a generative sample-based approach to assessing the representation of traffic scenarios in proving grounds. Leveraging typical use cases extracted from naturalistic driving events, we establish a strong link between proving ground testing results of CAVs and their anticipated public street performance. We present benchmark results of our approach on three world-class CAV testing facilities: Mcity, Almono (Uber ATG), and Kcity. We successfully show the overall evaluation of these proving grounds in terms of their capability to accommodate real-world traffic scenarios. We believe that when the effectiveness of a testing ground itself is validated, the testing results would grant more confidence for CAV public deployment.

cs.RO↗

arXiv2020

A Modular Interpretation of BBGS Towers

In 2000, based on his procedure for constructing explicit towers of modular curves, Elkies deduced explicit equations of rank-2 Drinfeld modular curves which coincide with the asymptotically optimal towers of curves constructed by Garcia and Stichtenoth. In 2015, Bassa, Beelen, Garcia, and Stichtenoth constructed a celebrated (recursive and good) tower (BBGS-tower for short) of curves and outlined a modular interpretation of the defining equations. Soon after that, Gekeler studied in depth the modular curves coming from sparse Drinfeld modules. In this paper, to establish a link between these existing results, we propose and prove a generalized Elkies' Theorem which tells in detail how to directly describe a modular interpretation of the equations of rank-m Drinfeld modular curves with m>=2.

math.NT↗

arXiv2020

Structural phase transitions and photoluminescence mechanism in a layer of 3D hybrid perovskite nanocrystals

Although the structural phase transitions in single-crystal hybrid methyl-ammonium (MA) lead halide perovskites (MAPbX3, X = Cl, Br, I) are common phenomena, they have never been observed in the corresponding nanocrystals. Here we demonstrate that two-photon-excited photoluminescence (PL) spectroscopy is capable of monitoring the structural phase transitions in MAPbX3 nanocrystals because nonlinear susceptibilities govern the light absorption rates. We provide experimental evidence that the orthorhombic-to-tetragonal structural phase transition in a single layer of 20-nm-sized 3D MAPbBr3 nanocrystals is spread out within the 70 - 140 K range. This structural phase instability range arises because, unlike in single-crystal MAPbX3, free rotations of MA ions in the corresponding nanocrystals are no longer restricted by a long-range MA dipole order. The resulting configurational entropy loss can be even enhanced by the interfacial electric field arising due to charge separation at the MAPbBr3/ZnO heterointerface, extending the orthorhombic-to-tetragonal structural phase instability range from 70 to 230 K. We conclude that the weak sensitivity of conventional one-photon-excited PL spectroscopy to the structural phase transitions in 3D MAPbX3 nanocrystals results from the structural phase instability providing negligible distortions of PbX6 octahedra. In contrast, the intensity of two-photon-excited PL and electric-field-induced one-photon-excited PL still remains sensitive enough to weak structural distortions due to the higher rank tensor nature of nonlinear susceptibilities involved. We also show that room-temperature PL originates from the radiative recombination of the optical-phonon vibrationally excited polaronic quasiparticles with energies might exceed the ground-state Frohlich polaron and Rashba energies due to optical-phonon bottleneck.

physics.app-ph↗

arXiv2020

Sex Differences in Severity and Mortality Among Patients With COVID-19: Evidence from Pooled Literature Analysis and Insights from Integrated Bioinformatic Analysis

Objective: To conduct a meta-analysis of current studies that examined sex differences in severity and mortality in patients with COVID-19, and identify potential mechanisms underpinning these differences. Methods: We performed a systematic review to collate data from observational studies examining associations of sex differences with clinical outcomes of COVID-19. PubMed, Web of Science and four preprint servers were searched for relevant studies. Data were extracted and analyzed using meta-analysis where possible, with summary data presented otherwise. Publicly available bulk RNA sequencing (RNA-seq), single-cell RNA sequencing (scRNA-seq), and chromatin immunoprecipitation sequencing (ChIP-seq) data were analyzed to explore the potential mechanisms underlying the observed association. Results: 39 studies met inclusion criteria, representing 77932 patients, of which 41510 (53.3%) were males. Men were at a markedly increased risk of developing severe cases compared with women. Furthermore, the pooled odds ratio (OR) of mortality for male group compared with the female group indicated significant higher mortality rate for male. Data from scRNA-seq suggest that men have a higher amount of ACE2-expressing pulmonary alveolar type II cells than women. Sex-based immunological differences exist. The expression of androgen receptor (AR) is positively correlated with ACE2, and there is evidence that AR may directly regulate the expression of ACE2. Conclusions: This meta-analysis detected an increased severity and mortality rate in the male populations with COVID-19, which might be attributable to the sex-based differences in cellular compositions and immunological microenvironments of the lung. The host cell receptor ACE2 is likely regulated by AR signaling pathway, which is identified as a potential target for prevention and treatment of SARS-Cov-2 infections in men.

q-bio.PE↗