arXiv subjects

Nan Zhao

Publications and source records attributed to Nan Zhao.

At least 55 records · Page 3Linked to original sources

arXiv2022

Stable Atomic Magnetometer in Parity-Time Symmetry Broken Phase

Random motion of spins is usually detrimental in magnetic resonance experiments. The spin diffusion in non-uniform magnetic fields causes broadening of the resonance and limits the sensitivity and the spectral resolution in applications like magnetic resonance spectroscopy. Here, by observation of the parity-time ($PT$) phase transition of diffusive spins in gradient magnetic fields, we show that the spatial degrees of freedom of atoms could become a resource, rather than harmfulness, for high-precision measurement of weak signals. In the normal phase with zero or low gradient fields, the diffusion results in dissipation of spin precession. However, by increasing the field gradient, the spin system undergoes a $PT$ transition, and enters the $PT$ symmetry broken phase. In this novel phase, the spin precession frequency splits due to spatial localization of the eigenmodes. We demonstrate that, using these spatial-motion-induced split frequencies, the spin system can serve as a stable magnetometer, whose output is insensitive to the inevitable long-term drift of control parameters. This opens a door to detect extremely weak signals in imperfectly controlled environment.

quant-ph↗

arXiv2022

JSRNN: Joint Sampling and Reconstruction Neural Networks for High Quality Image Compressed Sensing

Most Deep Learning (DL) based Compressed Sensing (DCS) algorithms adopt a single neural network for signal reconstruction, and fail to jointly consider the influences of the sampling operation for reconstruction. In this paper, we propose unified framework, which jointly considers the sampling and reconstruction process for image compressive sensing based on well-designed cascade neural networks. Two sub-networks, which are the sampling sub-network and the reconstruction sub-network, are included in the proposed framework. In the sampling sub-network, an adaptive full connected layer instead of the traditional random matrix is used to mimic the sampling operator. In the reconstruction sub-network, a cascade network combining stacked denoising autoencoder (SDA) and convolutional neural network (CNN) is designed to reconstruct signals. The SDA is used to solve the signal mapping problem and the signals are initially reconstructed. Furthermore, CNN is used to fully recover the structure and texture features of the image to obtain better reconstruction performance. Extensive experiments show that this framework outperforms many other state-of-the-art methods, especially at low sampling rates.

cs.CV↗

arXiv2022

Audio Tampering Detection Based on Shallow and Deep Feature Representation Learning

Digital audio tampering detection can be used to verify the authenticity of digital audio. However, most current methods use standard electronic network frequency (ENF) databases for visual comparison analysis of ENF continuity of digital audio or perform feature extraction for classification by machine learning methods. ENF databases are usually tricky to obtain, visual methods have weak feature representation, and machine learning methods have more information loss in features, resulting in low detection accuracy. This paper proposes a fusion method of shallow and deep features to fully use ENF information by exploiting the complementary nature of features at different levels to more accurately describe the changes in inconsistency produced by tampering operations to raw digital audio. The method achieves 97.03% accuracy on three classic databases: Carioca 1, Carioca 2, and New Spanish. In addition, we have achieved an accuracy of 88.31% on the newly constructed database GAUDI-DI. Experimental results show that the proposed method is superior to the state-of-the-art method.

cs.SD↗

arXiv2022

High-performance non-Fermi-liquid metallic thermoelectric materials

Searching for high-performance thermoelectric (TE) materials in the paradigm of narrow-bandgap semiconductors has lasted for nearly 70 years and is obviously hampered by a bottleneck of research now. Here we report on the discovery of a few metallic compounds, TiFexCu2x-1Sb and TiFe1.33Sb, showing the thermopower exceeding many TE semiconductors and the dimensionless figure of merits comparable with the state-of-the-art TE materials. A quasi-linear temperature (T) dependence of electrical resistivity in 2 K - 700 K and the logarithmic T-dependent electronic specific heat at low temperature are also observed to coexist with the high thermopower, highlighting the strong intercoupling of the non-Fermi-liquid (NFL) quantum critical behavior of electrons with TE transports. Electronic structure analysis reveals the existence of fluctuating Fe-eg-related local magnetic moments, Fe-Fe antiferromagnetic (AFM) interaction at the nearest 4c-4d sites, and two-fold degenerate eg orbitals antiferromagnetically coupled with the dual-type itinerant electrons close to the Fermi level, all of which infer to a competition between the AFM ordering and Kondo-like spin compensation as well as a parallel two-channel Kondo effect. These effects are both strongly meditated by the structural disorder due to the random filling of Fe/Cu at the equivalent 4c/4d sites of the Heusler crystal lattice. The magnetic susceptibility deviates from ideal antiferromagnetism but can be fitted well by x(T) = 1/(θ + BTα), seemingly being consistent with the quantum critical scenario of strong local correlation as discussed before. Our work not only breaks the dilemma that the promising TE materials should be heavily-doped semiconductors, but also demonstrates the correlation among high TE performance, NFL quantum criticality, and magnetic fluctuation, which opens up new directions for future research.

cond-mat.str-el↗

arXiv2022

Spatio-Temporal Representation Learning Enhanced Source Cell-phone Recognition from Speech Recordings

The existing source cell-phone recognition method lacks the long-term feature characterization of the source device, resulting in inaccurate representation of the source cell-phone related features which leads to insufficient recognition accuracy. In this paper, we propose a source cell-phone recognition method based on spatio-temporal representation learning, which includes two main parts: extraction of sequential Gaussian mean matrix features and construction of a recognition model based on spatio-temporal representation learning. In the feature extraction part, based on the analysis of time-series representation of recording source signals, we extract sequential Gaussian mean matrix with long-term and short-term representation ability by using the sensitivity of Gaussian mixture model to data distribution. In the model construction part, we design a structured spatio-temporal representation learning network C3D-BiLSTM to fully characterize the spatio-temporal information, combine 3D convolutional network and bidirectional long short-term memory network for short-term spectral information and long-time fluctuation information representation learning, and achieve accurate recognition of cell-phones by fusing spatio-temporal feature information of recording source signals. The method achieves an average accuracy of 99.03% for the closed-set recognition of 45 cell-phones under the CCNU\_Mobile dataset, and 98.18% in small sample size experiments, with recognition performance better than the existing state-of-the-art methods. The experimental results show that the method exhibits excellent recognition performance in multi-class cell-phones recognition.

cs.SD↗

arXiv2022

Abs-CAM: A Gradient Optimization Interpretable Approach for Explanation of Convolutional Neural Networks

The black-box nature of Deep Neural Networks (DNNs) severely hinders its performance improvement and application in specific scenes. In recent years, class activation mapping-based method has been widely used to interpret the internal decisions of models in computer vision tasks. However, when this method uses backpropagation to obtain gradients, it will cause noise in the saliency map, and even locate features that are irrelevant to decisions. In this paper, we propose an Absolute value Class Activation Mapping-based (Abs-CAM) method, which optimizes the gradients derived from the backpropagation and turns all of them into positive gradients to enhance the visual features of output neurons' activation, and improve the localization ability of the saliency map. The framework of Abs-CAM is divided into two phases: generating initial saliency map and generating final saliency map. The first phase improves the localization ability of the saliency map by optimizing the gradient, and the second phase linearly combines the initial saliency map with the original image to enhance the semantic information of the saliency map. We conduct qualitative and quantitative evaluation of the proposed method, including Deletion, Insertion, and Pointing Game. The experimental results show that the Abs-CAM can obviously eliminate the noise in the saliency map, and can better locate the features related to decisions, and is superior to the previous methods in recognition and localization tasks.

cs.CV↗

arXiv2021

The JDDC 2.0 Corpus: A Large-Scale Multimodal Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service

With the development of the Internet, more and more people get accustomed to online shopping. When communicating with customer service, users may express their requirements by means of text, images, and videos, which precipitates the need for understanding these multimodal information for automatic customer service systems. Images usually act as discriminators for product models, or indicators of product failures, which play important roles in the E-commerce scenario. On the other hand, detailed information provided by the images is limited, and typically, customer service systems cannot understand the intents of users without the input text. Thus, bridging the gap of the image and text is crucial for the multimodal dialogue task. To handle this problem, we construct JDDC 2.0, a large-scale multimodal multi-turn dialogue dataset collected from a mainstream Chinese E-commerce platform (JD.com), containing about 246 thousand dialogue sessions, 3 million utterances, and 507 thousand images, along with product knowledge bases and image category annotations. We present the solutions of top-5 teams participating in the JDDC multimodal dialogue challenge based on this dataset, which provides valuable insights for further researches on the multimodal dialogue task.

cs.CL↗

arXiv2020

Impact and Calibration of Nonlinear Reciprocity Mismatch in Massive MIMO

Time-division-duplexing massive multiple-input multiple-output (MIMO) systems estimate the channel state information (CSI) by leveraging the uplink-downlink channel reciprocity, which is no longer valid when the mismatch arises from the asymmetric uplink and downlink radio frequency (RF) chains. Existing works treat the reciprocity mismatch as constant for simplicity. However, the practical RF chain consists of nonlinear components, which leads to nonlinear reciprocity mismatch. In this work, we examine the impact and the calibration approach of the nonlinear reciprocity mismatch in massive MIMO systems. To evaluate the impact of the nonlinear mismatch, we first derive the closed-form expression of the ergodic achievable rate. Then, we analyze the performance loss caused by the nonlinear mismatch to show that the impact of the mismatch at the base station (BS) side is much larger than that at the user equipment side. Therefore, we propose a calibration method for the BS. During the calibration, polynomial function is applied to approximate the nonlinear mismatch factor, and over-the-air training is employed to estimate the polynomial coefficients. After that, the calibration coefficients are computed by maximizing the downlink achievable rate. Simulation results are presented to verify the analytical results and to show the performance of the proposed calibration approach.

cs.IT↗

arXiv2020

Anomalous Motion of Particle Levitated by Laguerre-Gaussian beam

Laguerre-Gaussian (LG) beam has orbital angular momentum (OAM). A particle trapped in an LG beam will rotate about the beam axis, due to the transfer of OAM. The rotation of the particle is usually in the same direction as that of the beam OAM. However, we discovere that when the LG beam is strongly focused, the rotation of the particle and the beam OAM might be in the opposite direction. This anomalous effect is caused by the negative torque on the particle exerted by the focused LG beam, which is similar to the optical pulling force in the linear case. We calculated the scattering force distribution of a micro-particle trapped in an optical tweezers formed by the strongly focused LG beam, and showed that there exist stable trajectories of the particle that controlled by the negative torque. We proposed several necessary conditions for observing the counter-intuitive trajectories. Our work reveals that the strongly trapped micro-particle exhibits diversity of motion patterns.

physics.optics↗

arXiv2020

Magnetic Noise Enabled Biocompass

The discovery of magnetic protein provides a new understanding of a biocompass at the molecular level. However, the mechanism by which magnetic protein enables a biocompass is still under debate, mainly because of the absence of permanent magnetism in the magnetic protein at room temperature. Here, based on a widely accepted radical pair model of a biocompass, we propose a microscopic mechanism that allows the biocompass to operate without a finite magnetization of the magnetic protein in a biological environment. With the structure of the magnetic protein, we show that the magnetic fluctuation, rather than the permanent magnetism, of the magnetic protein can enable geomagnetic field sensing. An analysis of the quantum dynamics of our microscopic model reveals the necessary conditions for optimal sensitivity. Our work clarifies the mechanism by which magnetic protein enables a biocompass.

physics.bio-ph↗

arXiv2020

Sensing individual nuclear spins with a single rare-earth electron spin

Rare-earth related electron spins in crystalline hosts are unique material systems, as they can potentially provide a direct interface between telecom band photons and long-lived spin quantum bits. Specifically, their optically accessible electron spins in solids interacting with nuclear spins in their environment are valuable quantum memory resources. Detection of nearby individual nuclear spins, so far exclusively shown for few dilute nuclear spin bath host systems such as the NV center in diamond or the silicon vacancy in silicon carbide, remained an open challenge for rare-earths in their host materials, which typically exhibit dense nuclear spin baths. Here, we present the electron spin spectroscopy of single Ce$^{3+}$ ions in a yttrium orthosilicate host, featuring a coherence time of $T_{2}=124\,μ$s. This coherent interaction time is sufficiently long to isolate proximal $^{89}$Y nuclear spins from the nuclear spin bath of $^{89}$Y. Furthermore, it allows for the detection of a single nearby $^{29}$Si nuclear spin, native to the host material with ~5% abundance. This study opens the door to quantum memory applications in rare-earth ion related systems based on coupled environmental nuclear spins, potentially useful for quantum error correction schemes.

quant-ph↗

arXiv2019

Sky pixel detection in outdoor imagery using an adaptive algorithm and machine learning

Computer vision techniques enable automated detection of sky pixels in outdoor imagery. In urban climate, sky detection is an important first step in gathering information about urban morphology and sky view factors. However, obtaining accurate results remains challenging and becomes even more complex using imagery captured under a variety of lighting and weather conditions. To address this problem, we present a new sky pixel detection system demonstrated to produce accurate results using a wide range of outdoor imagery types. Images are processed using a selection of mean-shift segmentation, K-means clustering, and Sobel filters to mark sky pixels in the scene. The algorithm for a specific image is chosen by a convolutional neural network, trained with 25,000 images from the Skyfinder data set, reaching 82% accuracy for the top three classes. This selection step allows the sky marking to follow an adaptive process and to use different techniques and parameters to best suit a particular image. An evaluation of fourteen different techniques and parameter sets shows that no single technique can perform with high accuracy across varied Skyfinder and Google Street View data sets. However, by using our adaptive process, large increases in accuracy are observed. The resulting system is shown to perform better than other published techniques.

cs.CV↗

arXiv2019

Robust Federated Learning with Noisy Communication

Federated learning is a communication-efficient training process that alternates between local training at the edge devices and averaging the updated local model at the central server. Nevertheless, it is impractical to achieve a perfect acquisition of the local models in wireless communication due to noise, which also brings serious effects on federated learning. To tackle this challenge, we propose a robust design for federated learning to alleviate the effects of noise in this paper. Considering noise in the two aforementioned steps, we first formulate the training problem as a parallel optimization for each node under the expectation-based model and the worst-case model. Due to the non-convexity of the problem, a regularization for the loss function approximation method is proposed to make it tractable. Regarding the worst-case model, we develop a feasible training scheme which utilizes the sampling-based successive convex approximation algorithm to tackle the unavailable maxima or minima noise condition and the non-convex issue of the objective function. Furthermore, the convergence rates of both new designs are analyzed from a theoretical point of view. Finally, the improvement of prediction accuracy and the reduction of loss function are demonstrated via simulations for the proposed designs.

cs.LG↗

arXiv2019

Computing and Communicating Functions in Disorganized Wireless Networks

For future wireless networks, enormous numbers of interconnections are required, creating a disorganized topology and leading to a great challenge in data aggregation. Instead of collecting data individually, a more efficient technique, computation over multi-access channels (CoMAC), has emerged to compute functions by exploiting the signal-superposition property of wireless channels. However, the implementation of CoMAC in disorganized networks with multiple relays (hops) is still an open problem. In this paper, we combine CoMAC and orthogonal communication in the disorganized network to attain the computation of functions at the fusion center. First, to make the disorganized network more tractable, we reorganize the disorganized network into a hierarchical network with multiple layers that consists of subgroups and groups. In the hierarchical network, we propose multi-layer function computation where CoMAC is applied to each subgroup and orthogonal communication is adopted within each group. By computing and communicating subgroup and group functions over layers, the desired functions are reconstructed at the fusion center. The general computation rate is derived and the performance is further improved through time allocation and power control. The closed-form solutions to optimization are obtained, which suggest that existing CoMAC and orthogonal communication schemes can be generalized.

cs.IT↗

arXiv2019

Multi-Antenna Covert Communications with Random Access Protocol

In this paper, we consider the issue of covert communications with random access protocol. We consider that the legitimate user Bob has no priori knowledge about packet arrival time and thus employs data-aided frame detection based on reference sequence. The warden user Willie tries to detect this covert communication by using a radiometer. The detection performance analysis is provided for both Bob and Willie under Rayleigh fading channel. It is demonstrated that the covert performance can benefit from the increase of the frame length or the number of receive antennas from frame detection perspective. Numerical results are provided to verify the proposed studies.

cs.IT↗

arXiv2019

Time-Varying Massive MIMO Channel Estimation: Capturing, Reconstruction and Restoration

On the time-varying channel estimation, the traditional downlink (DL) channel restoration schemes usually require the reconstruction for the covariance of downlink process noise vector, which is dependent on DL channel covariance matrix (CCM). However, the acquisition of the CCM leads to unacceptable overhead in massive MIMO systems. To tackle this problem, in this paper, we propose a novel scheme for the DL channel tracking. First, with the help of virtual channel representation (VCR), we build a dynamic uplink (UL) massive MIMO channel model with the consideration of off-grid refinement. Then, a coordinate-wise maximization based expectation maximization (EM) algorithm is adopted for capturing the model parameters, including the spatial signatures, the time-correlation factors, the off-grid bias, the channel power, and the noise power. Thanks to the angle reciprocity, the spatial signatures, timecorrelation factors and off-grid bias of the DL channel model can be reconstructed with the knowledge of UL ones. However, the other two kinds of model parameters are closely related with the carrier frequency, which cannot be perfectly inferred from the UL ones. Instead of relearning the DL model parameters with dedicated training, we resort to the optimal Bayesian Kalman filter (OBKF) method to accurately track the DL channel with the partially prior knowledge. At the same time, the model parameters will be gradually restored. Specially, the factor-graph and the Metropolis Hastings MCMC are utilized within the OBKF framework. Finally, numerical results are provided to demonstrate the efficiency of our proposed scheme.

cs.IT↗

arXiv2019

Spin image of an atomic vapor cell with a resolution smaller than the diffusion crosstalk free distance

The diffusion crosstalk free distance is an important parameter for spin images in atomic vapor cells and is also regarded as a limit on the spatial resolution. However, by modulating the pumping light both spatially and temporally using a digital micromirror device, a spin image of a vapor cell has been obtained with a distinguishable stripe width of 13.7~$μ$m, which is much smaller than the corresponding diffusion crosstalk free distance of $\sim$138~$μ$m. The fundamental limit on the spatial resolution as determined by diffusion and the uncertainty principle is analyzed.

physics.atom-ph↗

arXiv2018

Computation Over NOMA: Improved Achievable Rate Through Sub-Function Superposition

Massive numbers of nodes will be connected in future wireless networks. This brings great difficulty to collect a large amount of data. Instead of collecting the data individually, computation over multi-access channel (CoMAC) provides an intelligent solution by computing a desired function over the air based on the signal-superposition property of wireless channel. To improve the spectrum efficiency in conventional CoMAC, we propose the use of non-orthogonal multiple access (NOMA) for functions in CoMAC. The desired functions are decomposed into several sub-functions, and multiple sub-functions are selected to be superposed over each resource block (RB). The corresponding achievable rate is derived based on sub-function superposition, which prevents a vanishing computation rate for large numbers of nodes. In order to gain more insights, we further study the limiting case when the number of nodes goes to infinity. An exact expression of the rate is derived which provides a lower bound on the computation rate. Compared with existing CoMAC, the NOMA-based CoMAC (NOMA-CoMAC) not only achieves a higher computation rate, but also provides an improved non-vanishing rate. Furthermore, the diversity order of the computation rate of NOMA-CoMAC is derived, and it shows that the system performance is dominated by the node with the worst channel gain among these sub-functions in each RB.

cs.IT↗