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Ning Wang

Publications and source records attributed to Ning Wang.

At least 199 records · Page 11Linked to original sources
arXiv2023

Superconducting Diode Effect and Large Magnetochiral Anisotropy in T$_d$-MoTe$_2$ Thin Film

In the absence of time-reversal invariance, metals without inversion symmetry may exhibit nonreciprocal charge transport -- a magnetochiral anisotropy that manifests as unequal electrical resistance for opposite current flow directions. If superconductivity also sets in, the charge transmission may become dissipationless in one direction while remaining dissipative in the opposite, thereby realizing a superconducting diode. Through both direct-current and alternating-current measurements, we study the nonreciprocal effects in thin films of the noncentrosymmetric superconductor T$_d$-MoTe\textsubscript{2} with disorders. We observe nonreciprocal superconducting critical currents with a diode efficiency close to 20\%~, and a large magnetochiral anisotropy coefficient up to $\SI{5.9e8}{\per\tesla\per\ampere}$, under weak out-of-plane magnetic field in the millitesla range. The great enhancement of rectification efficiency under out-of-plane magnetic field is likely abscribed to the vortex ratchet effect, which naturally appears in the noncentrosymmetric superconductor with disorders. Intriguingly, unlike the finding in Rashba systems, the strongest in-plane nonreciprocal effect does not occur when the field is perpendicular to the current flow direction. We develop a phenomenological theory to demonstrate that this peculiar behavior can be attributed to the asymmetric structure of spin-orbit coupling in T$_d$-MoTe\textsubscript{2}. Our study highlights how the crystallographic symmetry critically impacts the nonreciprocal transport, and would further advance the research for designing the superconducting diode with the best performance.

cond-mat.supr-con↗
arXiv2023

Detrive: Imitation Learning with Transformer Detection for End-to-End Autonomous Driving

This Paper proposes a novel Transformer-based end-to-end autonomous driving model named Detrive. This model solves the problem that the past end-to-end models cannot detect the position and size of traffic participants. Detrive uses an end-to-end transformer based detection model as its perception module; a multi-layer perceptron as its feature fusion network; a recurrent neural network with gate recurrent unit for path planning; and two controllers for the vehicle's forward speed and turning angle. The model is trained with an on-line imitation learning method. In order to obtain a better training set, a reinforcement learning agent that can directly obtain a ground truth bird's-eye view map from the Carla simulator as a perceptual output, is used as teacher for the imitation learning. The trained model is tested on the Carla's autonomous driving benchmark. The results show that the Transformer detector based end-to-end model has obvious advantages in dynamic obstacle avoidance compared with the traditional classifier based end-to-end model.

cs.RO↗
arXiv2023

Exploring the relationship between response time sequence in scale answering process and severity of insomnia: a machine learning approach

Objectives: The study aims to investigate the relationship between insomnia and response time. Additionally, it aims to develop a machine learning model to predict the presence of insomnia in participants using response time data. Methods: A mobile application was designed to administer scale tests and collect response time data from 2729 participants. The relationship between symptom severity and response time was explored, and a machine learning model was developed to predict the presence of insomnia. Results: The result revealed a statistically significant difference (p<.001) in the total response time between participants with or without insomnia symptoms. A correlation was observed between the severity of specific insomnia aspects and response times at the individual questions level. The machine learning model demonstrated a high predictive accuracy of 0.743 in predicting insomnia symptoms based on response time data. Conclusions: These findings highlight the potential utility of response time data to evaluate cognitive and psychological measures, demonstrating the effectiveness of using response time as a diagnostic tool in the assessment of insomnia.

cs.LG↗
arXiv2023

Probing the fractional quantum Hall phases in valley-layer locked bilayer MoS$_{2}$

Semiconducting transition-metal dichalcogenides (TMDs) exhibit high mobility, strong spin-orbit coupling, and large effective masses, which simultaneously leads to a rich wealth of Landau quantizations and inherently strong electronic interactions. However, in spite of their extensively explored Landau levels (LL) structure, probing electron correlations in the fractionally filled LL regime has not been possible due to the difficulty of reaching the quantum limit. Here, we report evidence for fractional quantum Hall (FQH) states at filling fractions 4/5 and 2/5 in the lowest LL of bilayer MoS$_{2}$, manifested in fractionally quantized transverse conductance plateaus accompanied by longitudinal resistance minima. We further show that the observed FQH states sensitively depend on the dielectric and gate screening of the Coulomb interactions. Our findings establish a new FQH experimental platform which are a scarce resource: an intrinsic semiconducting high mobility electron gas, whose electronic interactions in the FQH regime are in principle tunable by Coulomb-screening engineering, and as such, could be the missing link between atomically thin graphene and semiconducting quantum wells.

cond-mat.mes-hall↗
arXiv2023

Robin: A Novel Method to Produce Robust Interpreters for Deep Learning-Based Code Classifiers

Deep learning has been widely used in source code classification tasks, such as code classification according to their functionalities, code authorship attribution, and vulnerability detection. Unfortunately, the black-box nature of deep learning makes it hard to interpret and understand why a classifier (i.e., classification model) makes a particular prediction on a given example. This lack of interpretability (or explainability) might have hindered their adoption by practitioners because it is not clear when they should or should not trust a classifier's prediction. The lack of interpretability has motivated a number of studies in recent years. However, existing methods are neither robust nor able to cope with out-of-distribution examples. In this paper, we propose a novel method to produce \underline{Rob}ust \underline{in}terpreters for a given deep learning-based code classifier; the method is dubbed Robin. The key idea behind Robin is a novel hybrid structure combining an interpreter and two approximators, while leveraging the ideas of adversarial training and data augmentation. Experimental results show that on average the interpreter produced by Robin achieves a 6.11\% higher fidelity (evaluated on the classifier), 67.22\% higher fidelity (evaluated on the approximator), and 15.87x higher robustness than that of the three existing interpreters we evaluated. Moreover, the interpreter is 47.31\% less affected by out-of-distribution examples than that of LEMNA.

cs.SE↗
arXiv2023

QoS-aware User Association and Transmission Scheduling for Millimeter-Wave Train-ground Communications

With the development of wireless communication, people have put forward higher requirements for train-ground communications in the high-speed railway (HSR) scenarios. With the help of mobile relays (MRs) installed on the roof of the train, the application of Millimeter-Wave (mm-wave) communication which has rich spectrum resources to the train-ground communication system can realize high data rate, so as to meet users' increasing demand for broad-band multimedia access. Also, full-duplex (FD) technology can theoretically double the spectral efficiency. In this paper, we formulate the user association and transmission scheduling problem in the mm-wave train-ground communication system with MR operating in the FD mode as a nonlinear programming problem. In order to maximize the system throughput and the number of users meeting quality of service (QoS) requirements, we propose an algorithm based on coalition game to solve the challenging NP-hard problem, and also prove the convergence and Nash-stable structure of the proposed algorithm. Extensive simulation results demonstrate that the proposed coalition game based algorithm can effectively improve the system throughput and meet the QoS requirements of as many users as possible, so that the communication system has a certain QoS awareness.

cs.IT↗
arXiv2023

Robust Train-to-Train Transmission Scheduling in mmWave Band for High Speed Train Communication Systems

Demands for data traffic in high-speed railway (HSR) has increased drastically. The increasing entertainment needs of passengers, safety control information exchanges of trains, etc., make train-to-train (T2T) communications face the challenge of achieving high-capacity and high-quality data transmissions. In order to greatly increase the communication capacity, it is urgent to introduce millimeter wave (mmWave) technology. Faced with the problem that mmWave link is easy to be blocked, this paper leverages the existing equipment to assist relay, and proposes an effective transmission scheduling scheme to improve the robustness of T2T communication systems. First of all, we formulate a mixed integer nonlinear programming (MINLP) optimization problem the transmission scheduling in T2T communication systems where mobile relays (MRs) are all working in the full-duplex (FD) mode. Then we propose a low complexity heuristic algorithm to solve the optimization problem, which consists of three components: relay selection, transmission mode selection, and transmission scheduling. The simulation results show that the proposed algorithm can greatly improve the number of completed flows and system throughput. Finally, we analyze the influence of different design parameters on the system performance. The results show that the proposed algorithm can achieve more data flows and system throughput within a reasonable communication distance threshold in T2T communication with obstacles in different orbits. It can balance the computational complexity and system performance to achieve an efficient and robust data transmission.

cs.IT↗
arXiv2023

RIS-assisted High-Speed Railway Integrated Sensing and Communication System

One technology that has the potential to improve wireless communications in years to come is integrated sensing and communication (ISAC). In this study, we take advantage of reconfigurable intelligent surface's (RIS) potential advantages to achieve ISAC while using the same frequency and resources. Specifically, by using the reflecting elements, the RIS dynamically modifies the radio waves' strength or phase in order to change the environment for radio transmission and increase the ISAC systems' transmission rate. We investigate a single cell downlink communication situation with RIS assistance. Combining the ISAC base station's (BS) beamforming with RIS's discrete phase shift optimization, while guaranteeing the sensing signal, The aim of optimizing the sum rate is specified. We take advantage of alternating maximization to find practical solutions with dividing the challenge into two minor issues. The first power allocation subproblem is non-convex that CVX solves by converting it to convex. A local search strategy is used to solve the second subproblem of phase shift optimization. According to the results of the simulation, using RIS with adjusted phase shifts can significantly enhance the ISAC system's performance.

eess.SP↗
arXiv2023

Joint User Association and Transmission Scheduling in Integrated mmWave Access and Terahertz Backhaul Networks

Terahertz wireless backhaul is expected to meet the high-speed backhaul requirements of future ultra-dense networks using millimeter-wave (mmWave) base stations (BSs). In order to achieve higher network capacity with limited resources and meet the quality of service (QoS) requirements of more users in the integrated mmWave access and terahertz backhaul network, this paper formulates a problem of maximizing the number of users successfully served in both the access and backhaul links. Since the problem is a non-linear integer optimization problem, a minimum rate ratio user association and transmission scheduling algorithm is proposed to obtain a suboptimal solution. The proposed algorithm takes the minimum rate ratio as the user association criterion and schedules first the users with fewer backhaul transmission slots. In addition, the algorithm will update the number of access transmission slots allocated to users and the access scheduling results after the backhaul scheduling phase. Numerical results show that the proposed algorithm outperforms several benchmark algorithms in terms of the number of served users and system throughput, and it can cope with a large number of bursty user requests.

cs.IT↗
arXiv2023

Joint Design of Access and Backhaul in Densely Deployed MmWave Small Cells

With the rapid growth of mobile data traffic, the shortage of radio spectrum resource has become increasingly prominent. Millimeter wave (mmWave) small cells can be densely deployed in macro cells to improve network capacity and spectrum utilization. Such a network architecture is referred to as mmWave heterogeneous cellular networks (HetNets). Compared with the traditional wired backhaul, The integrated access and backhaul (IAB) architecture with wireless backhaul is more flexible and cost-effective for mmWave HetNets. However, the imbalance of throughput between the access and backhaul links will constrain the total system throughput. Consequently, it is necessary to jointly design of radio access and backhaul link. In this paper, we study the joint optimization of user association and backhaul resource allocation in mmWave HetNets, where different mmWave bands are adopted by the access and backhaul links. Considering the non-convex and combinatorial characteristics of the optimization problem and the dynamic nature of the mmWave link, we propose a multi-agent deep reinforcement learning (MADRL) based scheme to maximize the long-term total link throughput of the network. The simulation results show that the scheme can not only adjust user association and backhaul resource allocation strategy according to the dynamics in the access link state, but also effectively improve the link throughput under different system configurations.

cs.IT↗
arXiv2023

Joint Optimization of Resource Allocation and User Association in Multi-Frequency Cellular Networks Assisted by RIS

Due to the development of communication technology and the rise of user network demand, a reasonable resource allocation for wireless networks is the key to guaranteeing regular operation and improving system performance. Various frequency bands exist in the natural network environment, and heterogeneous cellular network (HCN) has become a hot topic for current research. Meanwhile, Reconfigurable Intelligent Surface (RIS) has become a key technology for developing next-generation wireless networks. By modifying the phase of the incident signal arriving at the RIS surface, RIS can improve the signal quality at the receiver and reduce co-channel interference. In this paper, we develop a RIS-assisted HCN model for a multi-base station (BS) multi-frequency network, which includes 4G, 5G, millimeter wave (mmwave), and terahertz networks, and considers the case of multiple network coverage users, which is more in line with the realistic network characteristics and the concept of 6G networks. We propose the optimization objective of maximizing the system sum rate, which is decomposed into two subproblems, i.e., the user resource allocation and the phase shift optimization problem of RIS components. Due to the NP-hard and coupling relationship, we use the block coordinate descent (BCD) method to alternately optimize the local solutions of the coalition game and the local discrete phase search algorithm to obtain the global solution. In contrast, most previous studies have used the coalition game algorithm to solve the resource allocation problem alone. Simulation results show that the algorithm performs better than the rest of the algorithms, effectively improves the system sum rate, and achieves performance close to the optimal solution of the traversal algorithm with low complexity.

cs.IT↗
arXiv2023

Intrinsic nonlinear Hall effect and gate-switchable Berry curvature sliding in twisted bilayer graphene

Though the observation of the quantum anomalous Hall effect and nonlocal transport response reveals nontrivial band topology governed by the Berry curvature in twisted bilayer graphene, some recent works reported nonlinear Hall signals in graphene superlattices that are caused by the extrinsic disorder scattering rather than the intrinsic Berry curvature dipole moment. In this work, we report a Berry curvature dipole induced intrinsic nonlinear Hall effect in high-quality twisted bilayer graphene devices. We also find that the application of the displacement field substantially changes the direction and amplitude of the nonlinear Hall voltages, as a result of a field-induced sliding of the Berry curvature hotspots. Our work not only proves that the Berry curvature dipole could play a dominant role in generating the intrinsic nonlinear Hall signal in graphene superlattices with low disorder densities, but also demonstrates twisted bilayer graphene to be a sensitive and fine-tunable platform for second harmonic generation and rectification.

cond-mat.mes-hall↗
arXiv2023

Oxide perovskite BaSnO3: A promising high-temperature thermoelectric material for transparent conducting oxides

The new technology of energy conversion must be developed to ensure energy sustainability. Thermoelectric (TE) materials provide an effective means to solve the energy crisis. As a potential TE candidate, the TE properties of perovskite have received extensively attention. We here investigate the TE transport properties of the transparent conducting oxide (TCO) BaSnO3 by first-principles calculations. We find that the BaSnO3 perovskite exhibits outstanding dynamic and thermal stabilities, which provide excellent electronic and thermal transport properties simultaneously. These properties contribute to the remarkable Seebeck coefficient and power factor, which gives rise to the ZT of n-0.37 and p-1.52 at 900 K. Additionally, doping and nanostructure open prospects for effectively improving the TE properties of BaSnO3. Our work provides a basis for further optimizing the TE transport properties of cubic BaSnO3 and may have worthwhile practical significance for applying cubic perovskite to the high-temperature thermoelectric field.

cond-mat.mtrl-sci↗
arXiv2023

Statistical analysis for a penalized EM algorithm in high-dimensional mixture linear regression model

The expectation-maximization (EM) algorithm and its variants are widely used in statistics. In high-dimensional mixture linear regression, the model is assumed to be a finite mixture of linear regression and the number of predictors is much larger than the sample size. The standard EM algorithm, which attempts to find the maximum likelihood estimator, becomes infeasible for such model. We devise a group lasso penalized EM algorithm and study its statistical properties. Existing theoretical results of regularized EM algorithms often rely on dividing the sample into many independent batches and employing a fresh batch of sample in each iteration of the algorithm. Our algorithm and theoretical analysis do not require sample-splitting, and can be extended to multivariate response cases. The proposed methods also have encouraging performances in numerical studies.

math.ST↗
arXiv2023

Two-photon-transition superadiabatic passage in an nitrogen-vacancy center in diamond

Reaching a given target quantum state with high fidelity and fast operation speed close to the quantum limit represents an important goal in quantum information science. Here, we experimentally demonstrate superadiabatic quantum driving to achieve population transfer in a three-level solid-state spin system. Starting from traditional stimulated Raman adiabatic passage (STIRAP), our approach implements superadiabatic corrections to the STIRAP Hamiltonians with several paradigmatic pulse shapes. It requires no need of intense microwave pulses or long transfer times and shows enhanced robustness over pulse imperfections. These results might provide a useful tool for quantum information processing and coherent manipulations of quantum systems.

quant-ph↗
arXiv2023

High-dimensional Tensor Response Regression using the t-Distribution

In recent years, promising statistical modeling approaches to tensor data analysis have been rapidly developed. Traditional multivariate analysis tools, such as multivariate regression and discriminant analysis, are generalized from modeling random vectors and matrices to higher-order random tensors. One of the biggest challenges to statistical tensor models is the non-Gaussian nature of many real-world data. Unfortunately, existing approaches are either restricted to normality or implicitly using least squares type objective functions that are computationally efficient but sensitive to data contamination. Motivated by this, we adopt a simple tensor t-distribution that is, unlike the commonly used matrix t-distributions, compatible with tensor operators and reshaping of the data. We study the tensor response regression with tensor t-error, and develop penalized likelihood-based estimation and a novel one-step estimation. We study the asymptotic relative efficiency of various estimators and establish the one-step estimator's oracle properties and near-optimal asymptotic efficiency. We further propose a high-dimensional modification to the one-step estimation procedure and show that it attains the minimax optimal rate in estimation. Numerical studies show the excellent performance of the one-step estimator.

stat.ME↗
arXiv2023

A Dynamic Feature Interaction Framework for Multi-task Visual Perception

Multi-task visual perception has a wide range of applications in scene understanding such as autonomous driving. In this work, we devise an efficient unified framework to solve multiple common perception tasks, including instance segmentation, semantic segmentation, monocular 3D detection, and depth estimation. Simply sharing the same visual feature representations for these tasks impairs the performance of tasks, while independent task-specific feature extractors lead to parameter redundancy and latency. Thus, we design two feature-merge branches to learn feature basis, which can be useful to, and thus shared by, multiple perception tasks. Then, each task takes the corresponding feature basis as the input of the prediction task head to fulfill a specific task. In particular, one feature merge branch is designed for instance-level recognition the other for dense predictions. To enhance inter-branch communication, the instance branch passes pixel-wise spatial information of each instance to the dense branch using efficient dynamic convolution weighting. Moreover, a simple but effective dynamic routing mechanism is proposed to isolate task-specific features and leverage common properties among tasks. Our proposed framework, termed D2BNet, demonstrates a unique approach to parameter-efficient predictions for multi-task perception. In addition, as tasks benefit from co-training with each other, our solution achieves on par results on partially labeled settings on nuScenes and outperforms previous works for 3D detection and depth estimation on the Cityscapes dataset with full supervision.

cs.CV↗
arXiv2023

Recyclable Semi-supervised Method Based on Multi-model Ensemble for Video Scene Parsing

Pixel-level Scene Understanding is one of the fundamental problems in computer vision, which aims at recognizing object classes, masks and semantics of each pixel in the given image. Since the real-world is actually video-based rather than a static state, learning to perform video semantic segmentation is more reasonable and practical for realistic applications. In this paper, we adopt Mask2Former as architecture and ViT-Adapter as backbone. Then, we propose a recyclable semi-supervised training method based on multi-model ensemble. Our method achieves the mIoU scores of 62.97% and 65.83% on Development test and final test respectively. Finally, we obtain the 2nd place in the Video Scene Parsing in the Wild Challenge at CVPR 2023.

eess.IV↗