arXiv subjects

Xiaoli Ma

Publications and source records attributed to Xiaoli Ma.

At least 37 records · Page 2Linked to original sources

arXiv2021

Pressure-enhanced interlayer exciton in WS2/MoSe2 van der Waals heterostructure

The atomic-level vdW heterostructures have been one of the most interesting quantum material systems, due to their exotic physical properties. The interlayer coupling in these systems plays a critical role to realize novel physical observation and enrich interface functionality. However, there is still lack of investigation on the tuning of interlayer coupling in a quantitative way. A prospective strategy to tune the interlayer coupling is to change the electronic structure and interlayer distance by high pressure, which is a well-established method to tune the physical properties. Here, we construct a high-quality WS2/MoSe2 heterostructure in a DAC and successfully tuned the interlayer coupling through hydrostatic pressure. Typical photoluminescence spectra of the monolayer MoSe2 (ML-MoSe2), monolayer WS2 (ML-WS2) and WS2/MoSe2 heterostructure have been observed and it's intriguing that their photoluminescence peaks shift with respect to applied pressure in a quite different way. The intralayer exciton of ML-MoSe2 and ML-WS2 show blue shift under high pressure with a coefficient of 19.8 meV/GPa and 9.3 meV/GPa, respectively, while their interlayer exciton shows relative weak pressure dependence with a coefficient of 3.4 meV/GPa. Meanwhile, external pressure helps to drive stronger interlayer interaction and results in a higher ratio of interlayer/intralayer exciton intensity, indicating the enhanced interlayer exciton behavior. The first-principles calculation reveals the stronger interlayer interaction which leads to enhanced interlayer exciton behavior in WS2/MoSe2 heterostructure under external pressure and reveals the robust peak of interlayer exciton. This work provides an effective strategy to study the interlayer interaction in vdW heterostructures, which could be of great importance for the material and device design in various similar quantum systems.

cond-mat.str-el↗

arXiv2021

Monoclinic EuSn$_2$As$_2$: A Novel High-Pressure Network Structure

The layered crystal of EuSn$_2$As$_2$ has a Bi$_2$Te$_3$-type structure in rhombohedral ($R\bar{3}m$) symmetry and has been confirmed to be an intrinsic magnetic topological insulator at ambient conditions. Combining {\it ab initio} calculations and \emph{in-situ} x-ray diffraction measurements, we identify a new monoclinic EuSn$_2$As$_2$ structure in $C2/m$ symmetry above $\sim$14 GPa. It has a three-dimensional network made up of honeycomb-like Sn sheets and zigzag As chains, transformed from the layered EuSn$_2$As$_2$ via a two-stage reconstruction mechanism with the connecting of Sn-Sn and As-As atoms successively between the buckled SnAs layers. Its dynamic structural stability has been verified by phonon mode analysis. Electrical resistance measurements reveal an insulator-metal-superconductor transition at low temperature around 5 and 15 GPa, respectively, according to the structural conversion, and the superconductivity with a \textit{T}${\rm {_C}}$ value of $\sim 4$ K is observed up to 30.8 GPa. These results establish a high-pressure EuSn$_2$As$_2$ phase with intriguing structural and electronic properties and expand our understandings about the layered magnetic topological insulators.

cond-mat.mtrl-sci↗

arXiv2021

Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition

We propose a novel decentralized feature extraction approach in federated learning to address privacy-preservation issues for speech recognition. It is built upon a quantum convolutional neural network (QCNN) composed of a quantum circuit encoder for feature extraction, and a recurrent neural network (RNN) based end-to-end acoustic model (AM). To enhance model parameter protection in a decentralized architecture, an input speech is first up-streamed to a quantum computing server to extract Mel-spectrogram, and the corresponding convolutional features are encoded using a quantum circuit algorithm with random parameters. The encoded features are then down-streamed to the local RNN model for the final recognition. The proposed decentralized framework takes advantage of the quantum learning progress to secure models and to avoid privacy leakage attacks. Testing on the Google Speech Commands Dataset, the proposed QCNN encoder attains a competitive accuracy of 95.12% in a decentralized model, which is better than the previous architectures using centralized RNN models with convolutional features. We also conduct an in-depth study of different quantum circuit encoder architectures to provide insights into designing QCNN-based feature extractors. Neural saliency analyses demonstrate a correlation between the proposed QCNN features, class activation maps, and input spectrograms. We provide an implementation for future studies.

cs.SD↗

arXiv2020

Dimensional Crossover Tuned by Pressure in Layered Magnetic NiPS3

Layered magnetic transition-metal thiophosphate NiPS3 has unique two-dimensional (2D) magnetic properties and electronic behavior. The electronic band structure and corresponding magnetic state are expected to sensitive to the interlayer interaction, which can be tuned by external pressure. Here, we report an insulator-metal transition accompanied with magnetism collapse during the 2D-3D crossover in structure induced by hydrostatic pressure. A two-stage phase transition from monoclinic (C2=m) to trigonal (P-31m) lattice is identified by ab initio simulation and confirmed by high-pressure XRD and Raman data, corresponding to a layer by layer slip mechanism along the a-axis. Temperature dependence resistance measurements and room temperature infrared spectroscopy show that the insulator-metal transition occurs near 20 GPa as well as magnetism collapse, which is further confirmed by low temperature Raman measurement and theoretical calculation. These results establish a strong correlation among the structural change, electric transport, and magnetic phase transition and expand our understandings about the layered magnetic materials.

cond-mat.str-el↗

arXiv2020

On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression

In this paper, we exploit the properties of mean absolute error (MAE) as a loss function for the deep neural network (DNN) based vector-to-vector regression. The goal of this work is two-fold: (i) presenting performance bounds of MAE, and (ii) demonstrating new properties of MAE that make it more appropriate than mean squared error (MSE) as a loss function for DNN based vector-to-vector regression. First, we show that a generalized upper-bound for DNN-based vector- to-vector regression can be ensured by leveraging the known Lipschitz continuity property of MAE. Next, we derive a new generalized upper bound in the presence of additive noise. Finally, in contrast to conventional MSE commonly adopted to approximate Gaussian errors for regression, we show that MAE can be interpreted as an error modeled by Laplacian distribution. Speech enhancement experiments are conducted to corroborate our proposed theorems and validate the performance advantages of MAE over MSE for DNN based regression.

eess.AS↗

arXiv2020

Analyzing Upper Bounds on Mean Absolute Errors for Deep Neural Network Based Vector-to-Vector Regression

In this paper, we show that, in vector-to-vector regression utilizing deep neural networks (DNNs), a generalized loss of mean absolute error (MAE) between the predicted and expected feature vectors is upper bounded by the sum of an approximation error, an estimation error, and an optimization error. Leveraging upon error decomposition techniques in statistical learning theory and non-convex optimization theory, we derive upper bounds for each of the three aforementioned errors and impose necessary constraints on DNN models. Moreover, we assess our theoretical results through a set of image de-noising and speech enhancement experiments. Our proposed upper bounds of MAE for DNN based vector-to-vector regression are corroborated by the experimental results and the upper bounds are valid with and without the "over-parametrization" technique.

cs.LG↗

arXiv2020

Variational Quantum Circuits for Deep Reinforcement Learning

The state-of-the-art machine learning approaches are based on classical von Neumann computing architectures and have been widely used in many industrial and academic domains. With the recent development of quantum computing, researchers and tech-giants have attempted new quantum circuits for machine learning tasks. However, the existing quantum computing platforms are hard to simulate classical deep learning models or problems because of the intractability of deep quantum circuits. Thus, it is necessary to design feasible quantum algorithms for quantum machine learning for noisy intermediate scale quantum (NISQ) devices. This work explores variational quantum circuits for deep reinforcement learning. Specifically, we reshape classical deep reinforcement learning algorithms like experience replay and target network into a representation of variational quantum circuits. Moreover, we use a quantum information encoding scheme to reduce the number of model parameters compared to classical neural networks. To the best of our knowledge, this work is the first proof-of-principle demonstration of variational quantum circuits to approximate the deep $Q$-value function for decision-making and policy-selection reinforcement learning with experience replay and target network. Besides, our variational quantum circuits can be deployed in many near-term NISQ machines.

cs.LG↗

arXiv2020

A General Difficulty Control Algorithm for Proof-of-Work Based Blockchains

Designing an efficient difficulty control algorithm is an essential problem in Proof-of-Work (PoW) based blockchains because the network hash rate is randomly changing. This paper proposes a general difficulty control algorithm and provides insights for difficulty adjustment rules for PoW based blockchains. The proposed algorithm consists a two-layer neural network. It has low memory cost, meanwhile satisfying the fast-updating and low volatility requirements for difficulty adjustment. Real data from Ethereum are used in the simulations to prove that the proposed algorithm has better performance for the control of the block difficulty.

cs.CR↗

arXiv2020

Pressure induced metallization and possible unconventional superconductivity in spin liquid $NaYbSe_{2}$

Beyond the conventional electron pairing mediated by phonons, high-temperature superconductivity in cuprates is believed to stem from quantum spin liquid (QSL). The unconventional superconductivity by doping a spin liquid/Mott insulator, is a long-sought goal but a principal challenge in condensed matter physics because of the lack of an ideal QSL platform. Here we report the pressure induced metallization and possible unconventional superconductivity in $NaYbSe_{2}$, which belongs to a large and ideal family of triangular lattice spin liquid we revealed recently and is evidenced to possess a QSL ground state. The charge gap of NaYbSe2 is gradually reduced by applying pressures, and at ~20 GPa the crystal jumps into a superconducting (SC) phase with Tc ~ 5.8 K even before the insulating gap is completely closed. The metallization is confirmed by further high-pressure experiments but the sign of superconductivity is not well repeated. No symmetry breaking accompanies the SC transition, as indicated by X-ray diffraction and low-temperature Raman experiments under high pressures. This intrinsically connects QSL and SC phases, and suggests an unconventional superconductivity developed from QSL. We further observed the magnetic-field-tuned superconductor-insulator transition which is analogous to that found in the underdoped cuprate superconductor $La_{2-x}Sr_{x}CuO_{4}$. The study is expected to inspire interest in exploring new types of superconductors and sheds light into the intriguing physics from a spin liquid/Mott insulator to a superconductor.

cond-mat.supr-con↗

arXiv2020

Deep Learning Based FDD Non-Stationary Massive MIMO Downlink Channel Reconstruction

This paper proposes a model-driven deep learning-based downlink channel reconstruction scheme for frequency division duplexing (FDD) massive multi-input multi-output (MIMO) systems. The spatial non-stationarity, which is the key feature of the future extremely large aperture massive MIMO system, is considered. Instead of the channel matrix, the channel model parameters are learned by neural networks to save the overhead and improve the accuracy of channel reconstruction. By viewing the channel as an image, we introduce You Only Look Once (YOLO), a powerful neural network for object detection, to enable a rapid estimation process of the model parameters, including the detection of angles and delays of the paths and the identification of visibility regions of the scatterers. The deep learning-based scheme avoids the complicated iterative process introduced by the algorithm-based parameter extraction methods. A low-complexity algorithm-based refiner further refines the YOLO estimates toward high accuracy. Given the efficiency of model-driven deep learning and the combination of neural network and algorithm, the proposed scheme can rapidly and accurately reconstruct the non-stationary downlink channel. Moreover, the proposed scheme is also applicable to widely concerned stationary systems and achieves comparable reconstruction accuracy as an algorithm-based method with greatly reduced time consumption.

cs.IT↗

arXiv2020

Enhanced Adversarial Strategically-Timed Attacks against Deep Reinforcement Learning

Recent deep neural networks based techniques, especially those equipped with the ability of self-adaptation in the system level such as deep reinforcement learning (DRL), are shown to possess many advantages of optimizing robot learning systems (e.g., autonomous navigation and continuous robot arm control.) However, the learning-based systems and the associated models may be threatened by the risks of intentionally adaptive (e.g., noisy sensor confusion) and adversarial perturbations from real-world scenarios. In this paper, we introduce timing-based adversarial strategies against a DRL-based navigation system by jamming in physical noise patterns on the selected time frames. To study the vulnerability of learning-based navigation systems, we propose two adversarial agent models: one refers to online learning; another one is based on evolutionary learning. Besides, three open-source robot learning and navigation control environments are employed to study the vulnerability under adversarial timing attacks. Our experimental results show that the adversarial timing attacks can lead to a significant performance drop, and also suggest the necessity of enhancing the robustness of robot learning systems.

cs.LG↗

arXiv2020

Crystalline Electric-Field Excitations in Quantum Spin Liquids Candidate $NaYbSe_{2}$

Very recently we revealed a large family of triangular lattice quantum spin liquid candidates named rare-earth chalcogenides, which features a high-symmetry structure without structural/charge disorders and spin impurities, and may serve as an ideal platform exploring spin liquid physics. The knowledge of crystalline electric-field (CEF) excitations is an essential step to explore the fundamental magnetism of rare-earth spin systems. Here we employed inelastic neutron scattering (INS) and Raman scattering (RS) to carry out a comprehensive CFE investigation on $NaYbSe_{2}$, a promising representative of the family. By comparison with its nonmagnetic compound $NaLuSe_{2}$, we are able to identify the CEF excitations at 15.8, 24.3 and 30.5 meV at 5K. The selected cuts of the INS spectra are well re-produced with a large anisotropy of $g$ factors ($g_{ab}:g_{c}\sim3:1$). Further, the CEF excitations are explained well by our calculations based on the point charge model. Interestingly, $NaYbSe_{2}$ exhibits an unusual CEF shift to higher energies with increasing temperatures, and the Raman mode close to the first CEF excitation shows an anomalously large softening with decreasing temperatures. The absence of the anomalies in $NaLuSe_{2}$ clearly demonstrates a CEF-phonon coupling not reported in the family. It can be understood in term of the weaker electronegativity of Se. The fact that the smallest first CEF excitation in the sub-family of $NaYbCh_{2}$ is $\sim$ 180K (Ch=O, S, Se), guarantees that the sub-family can be strictly described with an effective S=1/2 picture at sufficiently low temperatures. Interestingly the CEF-phonon coupling revealed here may present alternative possibilities to manipulate the spin systems.

cond-mat.str-el↗

arXiv2019

Topological phase transition between distinctWeyl semimetal states in MoTe2

We present experimental evidence of an intriguing phase transition between distinct topological states in the type-II Weyl semimetal MoTe2. We observe anomalies in the Raman phonon frequencies and linewidths as well as electronic quasielastic peaks around 70 K, which, together with structural, thermodynamic measurements, and electron-phonon coupling calculations, demonstrate a temperature-induced transition between two topological phases previously identified by contrasting spectroscopic measurements. An analysis of experimental data suggests electron-phonon coupling as the main driving mechanism for the change of key topological characters in the electronic structure of MoTe2.We also find the phase transition to be sensitive to sample conditions distinguished by synthesis methods. These discoveries of temperature and material condition-dependent topological phase evolutions and transitions in MoTe2 advance the fundamental understanding of the underlying physics and enable an effective approach to tuning Weyl semimetal states for technological applications.

cond-mat.mtrl-sci↗

arXiv2019

Probing the direct factor for superconductivity in FeSe-Based Superconductors by Raman Scattering

The FeSe-based superconductors exhibit a wide range of critical temperature Tc under a variety of material and physical conditions, but extensive studies to date have yet to produce a consensus view on the underlying mechanism. Here we report on a systematic Raman scattering work on intercalated FeSe superconductors Lix(NH3)yFe2Se2 and (Li,Fe)OHFeSe compared to pristine FeSe. All three crystals show an anomalous power-law temperature dependence of phonon linewidths, deviating from the standard anharmonic behavior. This intriguing phenomenon is attributed to electron-phonon coupling effects enhanced by electron correlation, as evidenced by the evolution of the A1g Raman mode. Meanwhile, an analysis of the B1g mode, which probes the out-of-plane vibration of Fe, reveals a lack of influence by previously suggested structural parameters, and instead indicates a crucial role of the joint density of states in determining Tc. These findings identify carrier doping as the direct factor driving and modulating superconductivity in FeSe-based compounds.

cond-mat.supr-con↗

arXiv2019

Channel Estimation for Extremely Large-Scale Massive MIMO Systems

Extremely large-scale massive multiple-input multiple-output (MIMO) has shown considerable potential in future mobile communications. However, the use of extremely large aperture arrays has led to near-field and spatial non-stationary channel conditions, which result in changes to transceiver design and channel state information that should be acquired. This letter focuses on the channel estimation problem and describes the non-stationary channel through mapping between subarrays and scatterers. We propose subarray-wise and scatterer-wise channel estimation methods to estimate the near-field non-stationary channel from the view of subarray and scatterer, respectively. Numerical results demonstrate that subarray-wise method can derive accurate channel estimation results with low complexity, whereas the scatterer-wise method can accurately position the scatterers and identify almost all the mappings between subarrays and scatterers.

cs.IT↗

arXiv2019

Reinforcement Learning based Interconnection Routing for Adaptive Traffic Optimization

Applying Machine Learning (ML) techniques to design and optimize computer architectures is a promising research direction. Optimizing the runtime performance of a Network-on-Chip (NoC) necessitates a continuous learning framework. In this work, we demonstrate the promise of applying reinforcement learning (RL) to optimize NoC runtime performance. We present three RL-based methods for learning optimal routing algorithms. The experimental results show the algorithms can successfully learn a near-optimal solution across different environment states. Reproducible Code: github.com/huckiyang/interconnect-routing-gym

cs.NI↗

arXiv2019

Beamforming Network Optimization for Reducing Channel Time Variation in High-Mobility Massive MIMO

Communications in high-mobility environments have caught a lot of attentions recently. In this paper, fast time-varying channels for massive multiple-input multiple-output (MIMO) systems are addressed. We derive the exact channel power spectrum density (PSD) for the uplink from a high-speed railway (HSR) to a base station (BS) and propose to further reduce the channel time variation via beamforming network optimization. A large-scale uniform linear array (ULA) is equipped at the HSR to separate multiple Doppler shifts in angle domain through high-resolution transmit beamforming. Each branch comprises a dominant Doppler shift, which can be compensated to suppress the channel time variation, and we derive the channel PSD and the Doppler spread to assess the residual channel time variation. Interestingly, the channel PSD can be exactly expressed as the product of a pattern function and a beam-distortion function. The former reflects the impact of array aperture and is the converted radiation pattern of ULA, while the latter depends on the configuration of beamforming directions. Inspired by the PSD analysis, we introduce a common configurable amplitudes and phases (CCAP) parameter to optimize the beamforming network, by partly removing the constant modulus quantized phase constraints of matched filter (MF) beamformers. In this way, the residual Doppler shifts can be ulteriorly suppressed, further reducing the residual channel time variation. The optimal CCAP parameter minimizing the Doppler spread is derived in a closed form. Numerical results are provided to corroborate both the channel PSD analysis and the superiority of beamforming network optimization technique.

eess.SP↗

arXiv2019

When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks

Discovering and exploiting the causality in deep neural networks (DNNs) are crucial challenges for understanding and reasoning causal effects (CE) on an explainable visual model. "Intervention" has been widely used for recognizing a causal relation ontologically. In this paper, we propose a causal inference framework for visual reasoning via do-calculus. To study the intervention effects on pixel-level features for causal reasoning, we introduce pixel-wise masking and adversarial perturbation. In our framework, CE is calculated using features in a latent space and perturbed prediction from a DNN-based model. We further provide the first look into the characteristics of discovered CE of adversarially perturbed images generated by gradient-based methods \footnote{~~https://github.com/jjaacckkyy63/Causal-Intervention-AE-wAdvImg}. Experimental results show that CE is a competitive and robust index for understanding DNNs when compared with conventional methods such as class-activation mappings (CAMs) on the Chest X-Ray-14 dataset for human-interpretable feature(s) (e.g., symptom) reasoning. Moreover, CE holds promises for detecting adversarial examples as it possesses distinct characteristics in the presence of adversarial perturbations.

cs.CV↗