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The study of magnetic resonance linewidth is crucial in magnetic resonance physics and its applications. Previous studies focused on the linewidth of alkali metal atoms within the spin-exchange relaxation-free regime near zero magnetic field and in strong magnetic fields where Zeeman resonances are well resolved due to the quadratic Zeeman effect. However, the linewidth in the unresolved Zeeman resonance regime, which is prevalent in various magnetometer and comagnetometer applications, is not well understood. To address this, we developed a theoretical framework based on the master equation for alkali metal atoms and solved it under the rotating wave approximation and weak driving conditions. Our numerical calculations and analytical expressions reveal that the light-narrowing effect occurs only when the ratio of the spin exchange rate to the spin destruction rate exceeds a critical value. Additionally, we show that the linewidth in the unresolved Zeeman resonance regime is significantly influenced by the mutual coupling of quantum coherence between different Zeeman sublevels. These findings provide a theoretical tool for understanding spin relaxation in alkali-metal atoms and optimizing the performance of atomic magnetometers and comagnetometers operating in this regime., Comment: 10 pages, 7 figures

This report introduces FunAudioLLM, a model family designed to enhance natural voice interactions between humans and large language models (LLMs). At its core are two innovative models: SenseVoice, which handles multilingual speech recognition, emotion recognition, and audio event detection; and CosyVoice, which facilitates natural speech generation with control over multiple languages, timbre, speaking style, and speaker identity. SenseVoice-Small delivers exceptionally low-latency ASR for 5 languages, and SenseVoice-Large supports high-precision ASR for over 50 languages, while CosyVoice excels in multi-lingual voice generation, zero-shot in-context learning, cross-lingual voice cloning, and instruction-following capabilities. The models related to SenseVoice and CosyVoice have been open-sourced on Modelscope and Huggingface, along with the corresponding training, inference, and fine-tuning codes released on GitHub. By integrating these models with LLMs, FunAudioLLM enables applications such as speech-to-speech translation, emotional voice chat, interactive podcasts, and expressive audiobook narration, thereby pushing the boundaries of voice interaction technology. Demos are available at https://fun-audio-llm.github.io, and the code can be accessed at https://github.com/FunAudioLLM., Comment: Work in progress. Authors are listed in alphabetical order by family name

The optimal fingerprint method serves as a potent approach for detecting and attributing climate change. However, its experimental validation encounters challenges due to the intricate nature of climate systems. Here, we experimentally examine the optimal fingerprint method simulated by a precisely controlled magnetic resonance system of spins. The spin dynamic under an applied deterministic driving field and a noise field is utilized to emulate the complex climate system with external forcing and internal variability. Our experimental results affirm the theoretical prediction regarding the existence of an optimal detection direction which maximizes the signal-to-noise ratio, thereby validating the optimal fingerprint method. This work offers direct empirical verification of the optimal fingerprint method, crucial for comprehending climate change and its societal impacts.

Magnetic field inhomogeneity is usually detrimental to magnetic resonance (MR) experiments. It is widely recognized that a nonuniform magnetic field can lead to an increase in the resonance line width, as well as a reduction in sensitivity and spectral resolution. However, nonuniform magnetic field can also cause shift in resonance frequency, which received far less attention. In this work, we investigate the frequency shift under arbitrary nonuniform magnetic field and boundary relaxation by applying perturbation theory to the Torrey equation. Several compact frequency shift formulas are reported. We find that the frequency shift is mainly determined by $B_z$ distribution (rather than the transverse field components in previous study) and has important dependence on boundary relaxation. Furthermore, due to the difference of boundary relaxation and high order perturbation correction, this frequency shift is spin-species dependent, which implies a systematic error in many MR based precision measurements such as NMR gyroscope and comagnetometers. This insight provides a potential tool for understanding the unexplained isotope shifts in recent NMR gyroscope and new physics searching experiments that utilize comagnetometers. Finally, we propose a new tool for wall interaction research based on the frequency shift's dependency on boundary relaxation.

Continuum of spin excitations observed in inelastic neutron scattering experiments are often considered as a strong evidence of quantum spin liquid formation. When quantum spin liquid is indeed the ground state of a disorder-free magnetic compound, the elementary excitation is no longer the conventional spin waves (magnons). Instead, the magnons fractionalize into spinons, leaving only a two-spinon continuum detectable in inelastic neutron scattering experiments. For a clean ordered antiferromagnet, it was unclear if we can observe a continuous spectrum similar to the ones in a quantum spin liquid state. Here we show that the magnetically ordered state in Na$_2$BaCo(PO$_4$)$_2$ is able to host a spin excitation continuum induced by strong quantum fluctuations. Thus, a second thought is necessary when concluding such continuum as signature of quantum spin liquid in new material explorations., Comment: 22 pages,9 figures

The complex interplay of spin frustration and quantum fluctuations in low-dimensional quantum materials leads to a variety of intriguing phenomena. This research focuses on a detailed analysis of the magnetic behavior exhibited by NdZnPO, a bilayer spin-1/2 triangular lattice antiferromagnet. The investigation employs magnetization, specific heat, and powder neutron scattering measurements. At zero field, a long-range magnetic order is observed at $T_{\rm N}=1.64~\rm K$. Powder neutron diffraction experiments show the Ising-like magnetic moments along the $c$-axis, revealing a stripe-like magnetic structure with three equivalent magnetic propagation vectors. Application of a magnetic field along the $c$-axis suppresses the antiferromagnetic order, leading to a fully polarized ferromagnetic state above $B_{\rm c}=4.5~\rm T$. This transition is accompanied by notable enhancements in the nuclear Schottky contribution. Moreover, the absence of spin frustration and expected field-induced plateau-like phases are remarkable observations. Detailed calculations of magnetic dipolar interactions revealed complex couplings reminiscent of a honeycomb lattice, suggesting the potential emergence of Kitaev-like physics within this system. This comprehensive study of the magnetic properties of NdZnPO highlights unresolved intricacies, underscoring the imperative for further exploration to unveil the underlying governing mechanisms., Comment: 11 pages, 6 figures

Shared bicycles have emerged as a transformative force in urban transportation, effectively addressing the perennial 'last mile' challenge faced by commuters. The limitations of station-based bike-sharing systems, constrained by point-to-point travel, have spurred the popularity of the dockless model, offering flexible rentals and eliminating docking infrastructure constraints. However, the rapid growth of the sharing economy has introduced new challenges, notably an imbalance between supply and demand, leading to issues like the unavailability of bicycles and insufficient parking spaces during peak hours. To address these challenges, this study introduces a novel variable, Congestion Density (C), to quantitatively measure dynamic congestion levels in dockless bicycle-sharing systems. Leveraging real-time shared bike information from Xiamen, China, we present a sophisticated clustering framework for congested spots, identifying 563 congested spots categorized into Over-crowded, Semi-crowded, and Light-crowded clusters. Strikingly, these clusters align with established subway lines and bus stops, revealing a prevalent trend of integration between subway/bus services and bike-sharing. Overall, this study proposes parking lot management plans and policy recommendations based on the dynamics of crowded parking spaces, geographical characteristics, and land functional attributes. Our findings provide crucial insights for implementing bike-sharing electric fences and understanding urban mobility patterns, contributing to sustainable urban transportation., Comment: 17 pages, 8 figures

The Grushin spaces, as one of the most important models in the Carnot-Carath\'eodory space, are a class of locally compact and geodesic metric spaces which admit a dilation. Function spaces on Grushin spaces and some related geometric problems are always the research hotspots in this field. Firstly, we investigate two classes of Besov type spaces based on the Grushin semigroup and the fractional Grushin semigroup, respectively, and prove some important properties of these two Besov type spaces. Moreover, we also reveal the relationship between them. Secondly, we establish the isoperimetric inequality for the fractional perimeter, which is defined by the Grushin-Laplace operator on Grushin spaces. Finally, we combine the semigroup theory with a nonlocal calculus for the Grushin-Laplace operator to obtain the Sobolev type inequality. As a corollary, we also obtain the embedding theorem for Besov type spaces.

The extraordinary properties of the Kitaev model have motivated an intense search for new physics in materials that combine geometrical and bond frustration. In this work, we employ inelastic neutron scattering, spin wave theory, and exact diagonalization to study the spin dynamics in the perfect triangular-lattice antiferromagnet (TLAF) CsCeSe$_2$. This material orders into a stripe phase, which is demonstrated to arise as a consequence of the off-diagonal bond-dependent terms in the spin Hamiltonian. By studying the spin dynamics at intermediate fields, we identify an interaction between the single-magnon state and the two-magnon continuum that causes decay of coherent magnon excitations, level repulsion, and transfer of spectral weight to the continuum that are controlled by the strength of the magnetic field. Our results provide a microscopic mechanism for the stabilization of the stripe phase in TLAF and show how complex many-body physics can be present in the spin dynamics in a magnet with strong Kitaev coupling even in an ordered ground state., Comment: 6 + 10 pages, 4 + 11 figures

The two-dimensional triangular-lattice antiferromagnet (TLAF) is a textbook example of frustrated magnetic systems. Despite its simplicity, the TLAF model exhibits a highly rich and complex magnetic phase diagram, featuring numerous distinct ground states that can be stabilized through frustrated next-nearest-neighbor couplings or anisotropy. In this paper, we report low-temperature magnetic properties of the TLAF material CsCeSe$_2$. The inelastic neutron scattering (INS) together with specific heat measurements and density functional theory calculations of crystalline electric field suggest that the ground state of Ce ions is a Kramers doublet with strong easy-plane anisotropy. Elastic neutron scattering measurements demonstrate the presence of stripe-$yz$ magnetic order that develops below $T_{\rm N} = 0.35$ K, with the zero-field ordered moment of $m_{\rm Ce} \approx 0.65~\mu_{\rm B}$. Application of magnetic field first increases the ordering temperature by about 20% at the intermediate field region and eventually suppresses the stripe order in favor of the field-polarized ferromagnetic state via a continuous quantum phase transition (QPT). The field-induced response demonstrates sizable anisotropy for different in-plane directions, $\mathbf{B}\parallel{}\mathbf{a}$ and $\mathbf{B}\perp{}\mathbf{a}$, which indicates the presence of bond-dependent coupling in the spin Hamiltonian. We further show theoretically that the presence of anisotropic bond-dependent interactions can change the universality class of QPT for $\mathbf{B}\parallel{}\mathbf{a}$ and $\mathbf{B}\perp{}\mathbf{a}$., Comment: 15 pages, 8 figures

Matrix-variate optimization plays a central role in advanced wireless system designs. In this paper, we aim to explore optimal solutions of matrix variables under two special structure constraints using complex matrix derivatives, including diagonal structure constraints and constant modulus constraints, both of which are closely related to the state-of-the-art wireless applications. Specifically, for diagonal structure constraints mostly considered in the uplink multi-user single-input multiple-output (MU-SIMO) system and the amplitude-adjustable intelligent reflecting surface (IRS)-aided multiple-input multiple-output (MIMO) system, the capacity maximization problem, the mean-squared error (MSE) minimization problem and their variants are rigorously investigated. By leveraging complex matrix derivatives, the optimal solutions of these problems are directly obtained in closed forms. Nevertheless, for constant modulus constraints with the intrinsic nature of element-wise decomposability, which are often seen in the hybrid analog-digital MIMO system and the fully-passive IRS-aided MIMO system, we firstly explore inherent structures of the element-wise phase derivatives associated with different optimization problems. Then, we propose a novel alternating optimization (AO) algorithm with the aid of several arbitrary feasible solutions, which avoids the complicated matrix inversion and matrix factorization involved in conventional element-wise iterative algorithms. Numerical simulations reveal that the proposed algorithm can dramatically reduce the computational complexity without loss of system performance.

Positioning and sensing over wireless networks are imperative for many emerging applications. However, since traditional wireless channel models over-simplify the user equipment (UE) as a point target, they cannot be used for sensing the attitude of the UE, which is typically described by the spatial orientation. In this paper, a comprehensive electromagnetic propagation modeling (EPM) based on electromagnetic theory is developed to precisely model the near-field channel. For the noise-free case, the EPM model establishes the non-linear functional dependence of observed signals on both the position and attitude of the UE. To address the difficulty in the non-linear coupling, we first propose to divide the distance domain into three regions, separated by the defined Phase ambiguity distance and Spacing constraint distance. Then, for each region, we obtain the closed-form solutions for joint position and attitude estimation with low complexity. Next, to investigate the impact of random noise on the joint estimation performance, the Ziv-Zakai bound (ZZB) is derived to yield useful insights. The expected Cram\'er-Rao bound (ECRB) is further provided to obtain the simplified closed-form expressions for the performance lower bounds. Our numerical results demonstrate that the derived ZZB can provide accurate predictions of the performance of estimators in all signal-to-noise ratio (SNR) regimes. More importantly, we achieve the millimeter-level accuracy in position estimation and attain the 0.1-level accuracy in attitude estimation., Comment: 19 pages, 13 figures. Accepted by IEEE Journal on Selected Areas in Communications

Aiming at providing wireless communication systems with environment-perceptive capacity, emerging integrated sensing and communication (ISAC) technologies face multiple difficulties, especially in balancing the performance trade-off between the communication and radar functions. In this paper, we introduce a reconfigurable intelligent surface (RIS) to assist both data transmission and target detection in a dual-functional ISAC system. To formulate a general optimization framework, diverse communication performance metrics have been taken into account including famous capacity maximization and mean-squared error (MSE) minimization. Whereas the target detection process is modeled as a general likelihood ratio test (GLRT) due to the practical limitations, and the monotonicity of the corresponding detection probability is proved. For the single-user and single-target (SUST) scenario, the minimum transmit power of the ISAC transceiver has been revealed. By exploiting the optimal conditions of the BS design, we validate that the BS is able to realize the maximum power allocation scheme and derive the optimal BS precoder in a semi-closed form. Moreover, an alternating direction method of multipliers (ADMM) based RIS design is proposed to address the optimization of unit-modulus RIS phase shifts. For the sake of further enhancing computational efficiency, we also develop a low-complexity RIS design based on Riemannian gradient descent. Furthermore, the ISAC transceiver design for the multiple-users and multiple-targets (MUMT) scenario is also investigated, where a zero-forcing (ZF) radar receiver is adopted to cancel the interferences. Then optimal BS precoder is derived under the maximum power allocation scheme, and the RIS phase shifts can be optimized by extending the proposed ADMM-based RIS design. Numerical simulation results verify the performance of our proposed transceiver designs., Comment: 30 pages, 8 figures, manuscript submitted to IEEE TCOM

Interactions of collective excitations often lead to rich emergent phenomena in many-particle quantum systems. In ordered magnets, the elementary excitations are spin waves (magnons), which obey Bose-Einstein statistics. Similar to the Cooper pairs in superconductors, magnons can be paired into bound states under attractive interactions. Even more interestingly, the Zeeman coupling to a magnetic field acts as a chemical potential that can tune the particle density through a quantum critical point (QCP), beyond which a ``hidden order'' is predicted to exist. However, experimental confirmation of this QCP and the associated new state of matter remain elusive. Here we report direct observation of the Bose-Einstein condensation (BEC) of the two-magnon bound state in Na$_2$BaNi(PO$_4$)$_2$. Comprehensive thermodynamic measurements confirmed the existence of a two-dimensional BEC-QCP at the saturation field. Inelastic neutron scattering experiments were performed to accurately establish the magnetic exchange model. An exact solution of the model found stable 2-magnon bound states that were further confirmed by an electron spin resonance (ESR) experiment, demonstrating that the QCP is due to the pair condensation and the phase below saturation field is the long-sought-after spin nematic (SN) phase., Comment: 53 pages, 28 figures

In this paper, we study the stability of nuclear magnetic resonance gyroscope (NMRG), which employs Xe nuclear spins to measure inertial rotation rate. The Xe spin polarization is sensed by an in-situ Rb-magnetometer. The Rb-magnetometer works in a parametric oscillation mode (henceforth referred to as the Rb parametric magnetometer, or Rb-PM), in which the Larmor frequency of the Rb spins is modulated and the transverse components of Xe nuclear spin polarization are measured. As the measurement output of the Rb-PM, the phase of the Xe nuclear spin precession is eventually converted to the Xe nuclear magnetic resonance (NMR) frequencies and the inertial rotation rate. Here we provide a comprehensive study of the NMR phase measured by the Rb-PM, and analyze the influence of various control parameters, including the DC magnetic field, the frequency and phase of the modulation field, and the Rb resonance linewidth, on the stability of the NMR phase. Based on these analysis, we propose and implement a self-calibrating method to compensate the NMR phase drift during the Rb-PM measurement. With the self-calibrating Rb-PM, we demonstrate a significant improvement of the bias stability of NMRG., Comment: 16 pages, 11 figures

The rare-earth perovskite TbScO$_3$ has been widely used as a substrate for the growth of epitaxial ferroelectric and multiferroic thin films, while its detailed low-temperature magnetic properties were rarely reported. In this paper, we performed detailed magnetization, specific heat and single crystal neutron scattering measurements, along with the crystalline electric field calculations to study the low-temperature magnetic properties of TbScO$_3$. All our results suggest the magnetic Tb$^{3+}$ has an Ising-like pseudo-doublet ground state at low temperatures. Due to the constrain of local point symmetry, these Tb$^{3+}$ Ising moments are confined in the $ab$ plane with a tilt angle of $\varphi = \pm48^{\mathrm{o}}$ to the $a$ axis. In zero field, the system undergoes an antiferromagnetic phase transition at $T_{\mathrm{N}}=2.53$ K, and forms a $G_xA_y$ noncollinear magnetic structure below $T_{\mathrm{N}}$. We find the dipole-dipole interactions play an important role to determine the magnetic ground state, which are also responsible for the quasi-one-dimensional magnetism in TbScO$_3$. The significant anisotropic diffuse scatterings further confirm the quasi-one-dimensional magnetism along the $c$ axis. The magnetic phase diagram with the field along the easy $b$ axis is well established. In addition to the $G_xA_y$ antiferromagnetic state, there is an exotic field-induced phase emerged near the critical field $B_{\mathrm{c}}\simeq0.7$ T, where three-dimensional magnetic order is suppressed but strong one-dimensional correlations may still exist., Comment: 10 pages, 5 figures

Geometrical frustration and the enhancement of strong quantum fluctuations in two-dimensional triangular antiferromagnets can lead to various intriguing phenomena. Here, we studied the spin-1/2 triangular lattice antiferromagnet NdAuAl$_4$Ge$_2$. Thermodynamic and transport properties, such as magnetization and specific heat together with the resistivity measurements were performed. In zero field, two successive phase transitions were observed at $T_{\rm N1}=1.75\pm 0.02$ K and $T_{\rm N2}=0.49\pm 0.02$ K, respectively. Under magnetic field, $\rm XXZ$-type anisotropy was revealed, with the moments pointing along the easy $c$ axis. For $B\parallel c$, multiple field-induced states were observed, and the magnetic phase diagram was established based on the specific heat and magnetization data. The temperature-dependent resistivity measurements indicate that NdAuAl$_4$Ge$_2$ is a good metal. It is very likely that both the long-range RKKY interactions and the geometrical frustration play an important roles in this case., Comment: 9 pages, 7 figures

Deep learning techniques have achieved specific results in recording device source identification. The recording device source features include spatial information and certain temporal information. However, most recording device source identification methods based on deep learning only use spatial representation learning from recording device source features, which cannot make full use of recording device source information. Therefore, in this paper, to fully explore the spatial information and temporal information of recording device source, we propose a new method for recording device source identification based on the fusion of spatial feature information and temporal feature information by using an end-to-end framework. From a feature perspective, we designed two kinds of networks to extract recording device source spatial and temporal information. Afterward, we use the attention mechanism to adaptively assign the weight of spatial information and temporal information to obtain fusion features. From a model perspective, our model uses an end-to-end framework to learn the deep representation from spatial feature and temporal feature and train using deep and shallow loss to joint optimize our network. This method is compared with our previous work and baseline system. The results show that the proposed method is better than our previous work and baseline system under general conditions., Comment: 20 pages, 5 figures, recording device identification

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., Comment: 6+18 pages, 4+8 figures, to be published in Physical Review Letters

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., Comment: 9 pages, 3 figures

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., Comment: Audio tampering detection, 21 pages, 4 figures

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/({\theta} + BT{\alpha}), 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., Comment: 19 pages with 6 figures

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., Comment: 29 pages, 4 figures

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., Comment: Abs-CAM for Explanation of Convolutional Neural Networks

With the rapid proliferation of smart devices in wireless networks, more powerful technologies are expected to fulfill the network requirements of high throughput, massive connectivity, and diversify quality of service. To this end, rate splitting multiple access (RSMA) is proposed as a promising solution to improve spectral efficiency and provide better fairness for the next-generation mobile networks. In this paper, the outage performance of uplink RSMA transmission with randomly deployed users is investigated, taking both user scheduling schemes and power allocation strategies into consideration. Specifically, the greedy user scheduling (GUS) and cumulative distribution function (CDF) based user scheduling (CUS) schemes are considered, which could maximize the rate performance and guarantee scheduling fairness, respectively. Meanwhile, we re-investigate cognitive power allocation (CPA) strategy, and propose a new rate fairness-oriented power allocation (FPA) strategy to enhance the scheduled users' rate fairness. By employing order statistics and stochastic geometry, an analytical expression of the outage probability for each scheduling scheme combining power allocation is derived to characterize the performance. To get more insights, the achieved diversity order of each scheme is also derived. Theoretical results demonstrate that both GUS and CUS schemes applying CPA or FPA strategy can achieve full diversity orders, and the application of CPA strategy in RSMA can effectively eliminate the secondary user's diversity order constraint from the primary user. Simulation results corroborate the accuracy of the analytical expressions, and show that the proposed FPA strategy can achieve excellent rate fairness performance in high signal-to-noise ratio region., Comment: 38 pages,8 figures

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.

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.

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.

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., Comment: 13 pages, 14 figures

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 $T_{2}=124\,\mu$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., Comment: 8 pages, 8 figures

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.

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., Comment: This revision accepted in Urban Climate. 17 pages, 12 figures

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.

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.

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., Comment: 30 pages, 11 figures

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~$\mu$m, which is much smaller than the corresponding diffusion crosstalk free distance of $\sim$138~$\mu$m. The fundamental limit on the spatial resolution as determined by diffusion and the uncertainty principle is analyzed.

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.

We propose a way to cool the rotation of a nanodiamond, which contains a NV-center and is levitated by an optical tweezer. Following the rotation of the particle, the NV-center electron spin experiences varying external fields and so leads to spin-rotation coupling. By optically pumping the electrons from a higher energy level to a lower level, the rotation energy is dissipated. We give the analytical result for the damping torque exerted on the nanodiamond, and evaluate the final cooling temperature by the fluctuation-dissipation theorem. It's shown that the quantum regime of the rotation can be reached with our scheme., Comment: 12 pages, 4 figures

Using a single quantum probe to sense other quantum objects offers distinct advantages but suffers from some limitations that may degrade the sensing precision severely, especially when the probe-target coupling is weak. Here we propose a strategy to improve the sensing precision by using the quantum probe to engineer the evolution of the target. We consider an exactly solvable model, in which a qubit is used as the probe to sense the frequency of a harmonic oscillator. We show that by applying adaptive periodic quantum control on the qubit, the sensing precision can be enhanced from 1/T scaling with the total time cost T to 1/T^{2} scaling, thus improving the precision by several orders of magnitudes. Such improvement can be achieved without any direct access to the oscillator and the improvement increases with decreasing probe-target coupling. This provides a useful routine to ultrasensitive quantum sensing of weakly coupled quantum objects., Comment: 11 pages, 4 figures

Future networks are expected to connect an enormous number of nodes wirelessly using wide-band transmission. This brings great challenges. To avoid collecting a large amount of data from the massive number of nodes, computation over multi-access channel (CoMAC) is proposed to compute a desired function over the air utilizing the signal-superposition property of MAC. Due to frequency selective fading, wide-band CoMAC is more challenging and has never been studied before. In this work, we propose the use of orthogonal frequency division multiplexing (OFDM) in wide-band CoMAC to transmit functions in a similar way to bit sequences through division, allocation and reconstruction of function. An achievable rate without any adaptive resource allocation is derived. To prevent a vanishing computation rate from the increase of the number of nodes, a novel sub-function allocation of sub-carriers is derived. Furthermore, we formulate an optimization problem considering power allocation. A sponge-squeezing algorithm adapted from the classical water-filling algorithm is proposed to solve the optimal power allocation problem. The improved computation rate of the proposed framework and the corresponding allocation has been verified through both theoretical analysis and simulation.

RGB-Thermal (RGB-T) object tracking receives more and more attention due to the strongly complementary benefits of thermal information to visible data. However, RGB-T research is limited by lacking a comprehensive evaluation platform. In this paper, we propose a large-scale video benchmark dataset for RGB-T tracking.It has three major advantages over existing ones: 1) Its size is sufficiently large for large-scale performance evaluation (total frame number: 234K, maximum frame per sequence: 8K). 2) The alignment between RGB-T sequence pairs is highly accurate, which does not need pre- or post-processing. 3) The occlusion levels are annotated for occlusion-sensitive performance analysis of different tracking algorithms.Moreover, we propose a novel graph-based approach to learn a robust object representation for RGB-T tracking. In particular, the tracked object is represented with a graph with image patches as nodes. This graph including graph structure, node weights and edge weights is dynamically learned in a unified ADMM (alternating direction method of multipliers)-based optimization framework, in which the modality weights are also incorporated for adaptive fusion of multiple source data.Extensive experiments on the large-scale dataset are executed to demonstrate the effectiveness of the proposed tracker against other state-of-the-art tracking methods. We also provide new insights and potential research directions to the field of RGB-T object tracking.

We systematically investigate the multi-stability behaviour and cooling of both librational and translational modes of an optically levitated nonspherical nanoparticle. By expanding the trapping potential to the fourth order of both the translational and librational freedom degrees, we deduce the nonlinearity of them and their nonlinear coupling. Through stability analysis, we find that the system presents multi-stability when either the librational or the translational drive is red-detuned. The system will be stabilized if and only if these two drives are both blue-detuned. In the steady state region, we study the synthetic cooling scheme of translational mode by utilising librational mode. We find that matching the driving amplitude of these two modes and appropriate air pressure can optimize synthetic cooling. The synthetic cooling limit can be greatly improved, if we combine the feedback cooling with the synthetic cooling., Comment: 20 pages, 12 figures

Unmanned aerial vehicles (UAVs) have gained great interest for rapid deployment in both civil and military applications. UAV communication has its own distinctive channel characteristics compared with widely used cellular and satellite systems. Thus, accurate channel characterization is crucial for the performance optimization and design of efficient UAV communication systems. However, several challenges exist in UAV channel modeling. For example, propagation characteristics of UAV channels are still less explored for spatial and temporal variations in non\textendash stationary channels. Also, airframe shadowing has not yet been investigated for small size rotary UAVs. This paper provides an extensive survey on the measurement campaigns launched for UAV channel modeling using low altitude platforms and discusses various channel characterization efforts. We also review the contemporary perspective of UAV channel modeling approaches and outline some future research challenges in this domain.

Displacement detection of a sphere particle in focused laser beams with quadrant photodetector (QPD) provides a fast and high precision way to determine the particle location. In contrast to the traditional Gaussian beams, the sensitivity of displacement detection using various doughnut beams are investigated. The sensitivity improvement for large sphere particles along the longitudinal direction is reported. With appropriate vortex charge $l$ of the doughnut beams, they can outperform the Gaussian beam to get more than one order higher sensitivity and thus have potential applications in various high precision measurement. By using the levitating doughnut beam itself to detect the particle displacement, the result will also facilitate the recent proposal of levitating a particle in doughnut beams to suppress the light absorption., Comment: 6 pages, 6 figures

We study the coherence dynamics of a qubit coupled to a harmonic oscillator with both linear and quadratic interactions. As long as the linear coupling strength is much smaller than the oscillator frequency, the long time behavior of the coherence is dominated by the quadratic coupling strength $g_2$. The coherence decays and revives at a period $2\pi/g_2$, with the width of coherence peak decreases as the temperature increases, hence providing a way to measure $g_2$ precisely without cooling. Unlike the case of linear coupling, here the coherence dynamics never reduces to the classical limit in which the oscillator is classical. Finally, the validity of linear coupling approximation is discussed and the coherence under Hahn-echo is evaluated, Comment: 11 pages, 2 figures

We investigate the enhanced electric dipole-dipole interaction by surface plasmon polaritons (SPPs) supported by the planar metal film waveguide. By taking two nitrogen-vacancy (NV) center electric-dipoles in diamond as an example, both the coupling strength and collective relaxation of two dipoles are studied with numerical Green Function method. Compared to the two-dipole coupling on planar surface, metal film provides stronger and tunable coupling coefficients. Enhancement of the interaction between coupled NV center dipoles could have applications in both quantum information and energy transfer investigation. Our investigation provides a systematical result for experimental applications based on dipole-dipole interaction mediated with SPPs on planar metal film., Comment: 8 pages, 7 figures

The two-stage precoding has been proposed to reduce the overhead of both the channel training and the channel state information (CSI) feedback for the massive multiple-input multiple-output (MIMO) system. But the overlap of the angle-spreading-ranges (ASR) for different user clusters may seriously degrade the performance of the two-stage precoding. In this letter, we propose one ASR overlap mitigating scheme through the base station (BS) selection. Firstly, the BS selection is formulated as a sum signal-to-interference-plus-noise ratio (SINR) maximization problem. Then, the problem is solved by a low-complex algorithm through maximizing signal-to-leakage-plus-noise ratio (SLNR). In addition, we propose one low-overhead algorithm with the lower bound on the average SLNR as the objective function. Finally, we demonstrate the efficacy of the proposed schemes through the numerical simulations., Comment: Submitted to IEEE Wireless Communications Letters

As a revolutionary wireless transmission strategy, interference alignment (IA) can improve the capacity of the cell-edge users. However, the acquisition of the global channel state information (CSI) for IA leads to unacceptable overhead in the massive MIMO systems. To tackle this problem, in this paper, we propose an IA and soft-space-reuse (IA-SSR) based cooperative transmission scheme under the two-stage precoding framework. Specifically, the cell-center and the cell-edge users are separately treated to fully exploit the spatial degrees of freedoms (DoF). Then, the optimal power allocation policy is developed to maximize the sum-capacity of the network. Next, a low-cost channel estimator is designed for the proposed IA-SSR framework. Some practical issues in IA-SSR implementation are also discussed. Finally, plenty of numerical results are presented to show the efficiency of the proposed algorithm., Comment: Submitted to IEEE Transactions on Wireless Communications

We systematically investigate the bistable behavior and squeezing property of the librational mode of a levitated nonspherical nanoparticle trapped by laser beams. By expanding the librational potential to the forth order of the librational angle $\theta$, we find that the nonlinear coefficient of this mode is dependent only on the size and material of nanoparticle, but independent of trapping potential shape. The bistability and hysteresis are displayed when the driving frequency is red-detuned to the librational mode. In the blue-detuned region, we have studied squeezing of the variance of librational mode in detail, which has potential application for measurement of angle and angular momentum., Comment: 16 pages, 6 figures, accepted for Phys. Rev. A