arXiv subjects

Wei Li

Publications and source records attributed to Wei Li.

At least 1,063 records · Page 59Linked to original sources

arXiv2020

Exact solutions to the nonlinear Schrödinger equation with time-dependent coefficients

In this paper, the trial function method is employed to find the exact solutions for high-order nonlinear Schrödinger equations with time-dependent coefficients. This system describes the propagation of ultrashort light pulses in nonlinear fibers, with self-steepening and self-frequency shift effects. As a result, we derive a range of exact solutions which include Jacobi elliptic function solutions, solitary wave solutions, and rational function solutions.

nlin.PS↗

arXiv2020

Evidence of the Berezinskii-Kosterlitz-Thouless Phase in a Frustrated Magnet

The Berezinskii-Kosterlitz-Thouless (BKT) mechanism, building upon proliferation of topological defects in 2D systems, is the first example of phase transition beyond the Landau-Ginzburg paradigm of symmetry breaking. Such a topological phase transition has long been sought yet undiscovered directly in magnetic materials. Here, we pin down two transitions that bound a BKT phase in an ideal 2D frustrated magnet TmMgGaO$_4$, via nuclear magnetic resonance under in-plane magnetic fields, which do not disturb the low-energy electronic states and allow BKT fluctuations to be detected sensitively. Moreover, by applying out-of-plane fields, we find a critical scaling behaviour of the magnetic susceptibility expected for the BKT transition. The experimental findings can be explained by quantum Monte Carlo simulations applied on an accurate triangular-lattice Ising model of the compound which hosts a BKT phase. These results provide a concrete example for the BKT phase and offer an ideal platform for future investigations on the BKT physics in magnetic materials.

cond-mat.str-el↗

arXiv2020

Task Admission Control and Boundary Analysis of Cognitive Cloud Data Centers

A novel cloud data center (DC) model is studied here with cognitive capabilities for real-time (or online) flow compared to the batch tasks. Here, a DC can determine the cost of using resources and an online user or the user with batch tasks may decide whether or not to pay for getting the services. The online service tasks have a higher priority in getting the service over batch tasks. Both types of tasks need a certain number of virtual machines (VM). By targeting on the maximization of total discounted reward, an optimal policy for admitting task tasks is finally verified to be a state-related control limit policy. Next, a lower and an upper bound for such an optimal policy are derived, respectively, for the estimation and utilization in reality. Finally, a comprehensive set of experiments on the various cases to validate this proposed model and the solution is conducted. As a demonstration, the machine learning method is adopted to show how to obtain the optimal values by using a feed-forward neural network model. The results achieved in this paper will be expectedly utilized in various cloud data centers with cognitive characteristics in an economically optimal strategy.

eess.SY↗

arXiv2020

Evidence for the topological order in a kagome antiferromagnet

A Z2 quantum spin liquid hosts one of the simplest topological orders and exhibits many exotic properties due to long-range quantum entanglements. Its elementary excitations are anyons such as spinons carrying fractionalized spin quantum number and visons carrying emergent Z2 gauge flux. However, experimental detection of these anyons remains elusive. The difficulties lie not only in the fact that there exists few candidates for Z2 quantum spin liquids but also in that visons are magnetically inert hence immune to available experimental techniques. Here we have studied the spin excitations and specific heats of kagome-lattice antiferromagnet Cu$_4$(OH)$_6$FBr and Cu$_3$Zn(OH)$_6$FBr, which consists of two-dimensional Cu$^{2+}$ kagome layers with either Cu$^{2+}$ or Zn$^{2+}$ ions in between. By combining the first principle calculations and inelastic neutron scattering data in the former, we show that the dominate couplings in Cu$_4$(OH)$_6$FBr are between the nearest neighbor spins within the kagome planes, and the kagome and interlayer spin systems are essentially decoupled above the antiferromagnetic transition temperature. The intrinsic spin excitations and specific heats of the kagome layers for Cu$_3$Zn(OH)$_6$FBr are thus derived by removing the contributions from the residual interlayer Cu$^{2+}$ magnetic impurities. Accordingly, the kagome spin system exhibits spin continuum with momentum-dependent spin gap and a large magnetic entropy at low temperature that is insensitive to magnetic field, which can be understood as the evidences of spinons and visons in this kagome quantum spin liquid candidate. Our results suggest the existence of the Z2 anyons in the material, and therefore provide a comprehensive set of evidences for the Z2 topological order in kagome quantum spin liquid and bring their choreographed entanglement dances to the stage of real materials.

cond-mat.str-el↗

arXiv2020

Hidden Markov Models for Pipeline Damage Detection Using Piezoelectric Transducers

Oil and gas pipeline leakages lead to not only enormous economic loss but also environmental disasters. How to detect the pipeline damages including leakages and cracks has attracted much research attention. One of the promising leakage detection method is to use lead zirconate titanate (PZT) transducers to detect the negative pressure wave when leakage occurs. PZT transducers can generate and detect guided stress waves for crack detection also. However, the negative pressure waves or guided stress waves may not be easily detected with environmental interference, e.g., the oil and gas pipelines in offshore environment. In this paper, a Gaussian mixture model based hidden Markov model (GMM-HMM) method is proposed to detect the pipeline leakage and crack depth in changing environment and time-varying operational conditions. Leakages in different sections or crack depths are considered as different states in hidden Markov models (HMM). Laboratory experiments show that the GMM-HMM method can recognize the crack depth and leakage of pipeline such as whether there is a leakage, where the leakage is.

eess.SP↗

arXiv2020

Exploring In$_2$(Se$_{1-x}$Te$_x$)$_3$ alloys as photovoltaic materials

In$_2$Se$_3$ in the three-dimensional (3D) hexagonal crystal structure with space group $P6_1$ ($γ$-In$_2$Se$_3$) has a direct band gap of $\sim$1.8 eV and high absorption coefficient, making it a promising semiconductor material for optoelectronics. Incorporating Te allows for tuning the band gap, adding flexibility to device design and extending the application range. Here we report the growth and characterization of $γ$-In$_2$Se$_3$ thin films, and results of hybrid density functional theory calculations to assess the electronic and optical properties of $γ$-In$_2$Se$_3$ and $γ$-In$_2$(Se$_{1-x}$Te$_x$)$_3$ alloys. The calculated band gap of 1.84 eV for $γ$-In$_2$Se$_3$ is in good agreement with data from the absorption spectrum, and the absorption coefficient is found to be as high as that of direct band gap conventional III-V and II-VI semiconductors. Incorporation of Te in the form of $γ$-In$_2$(Se$_{1-x}$Te$_x$)$_3$ alloys is an effective way to tune the band gap from 1.84 eV down to 1.23 eV, thus covering the optimal band gap range for solar cells. We also discuss band gap bowing and mixing enthalpies, aiming at adding $γ$-In$_2$Se$_3$ and $γ$-In$_2$(Se$_{1-x}$Te$_x$)$_3$ alloys to the available toolbox of materials for solar cells and other optoelectronic devices.

cond-mat.mtrl-sci↗

arXiv2020

Timing analysis of the black hole candidate EXO 1846-031 with Insight-HXMT monitoring

We present the observational results from a detailed timing analysis of the black hole candidate EXO 1846-031 during its outburst in 2019 with the observations of Insight-HXMT, NICER and MAXI. This outburst can be classfied roughly into four different states. Type-C quasi-periodic oscillations (QPOs) observed by NICER (about 0.1-6Hz) and Insight-HXMT (about 0.7-8Hz) are also reported in this work. Meanwhile, we study various physical quantities related to QPO frequency.The QPO rms-frequency relationship in three energy band 1-10 keV indicates that there is a turning pointing in frequency around 2 Hz,which is similar to that of GRS 1915+105. A possible hypothesis for the relationship above may be related to the inclination of the source, which may require a high inclination to explain it. The relationships between QPO frequency and QPO rms,hardness,total fractional rms and count rate have also been found in other transient sources, which can indicate that the origin of type-C QPOs is non-thermal.

astro-ph.HE↗

arXiv2020

Discovery of oscillations above 200 keV in a black hole X-ray binary with Insight-HXMT

Low-frequency quasi-periodic oscillations (LFQPOs) are commonly found in black hole X-ray binaries, and their origin is still under debate. The properties of LFQPOs at high energies (above 30 keV) are closely related to the nature of the accretion flow in the innermost regions, and thus play a crucial role in critically testing various theoretical models. The Hard X-ray Modulation Telescope (Insight-HXMT) is capable of detecting emissions above 30 keV, and is therefore an ideal instrument to do so. Here we report the discovery of LFQPOs above 200 keV in the new black hole MAXI J1820+070 in the X-ray hard state, which allows us to understand the behaviours of LFQPOs at hundreds of kiloelectronvolts. The phase lag of the LFQPO is constant around zero below 30 keV, and becomes a soft lag (that is, the high-energy photons arrive first) above 30 keV. The soft lag gradually increases with energy and reaches ~0.9s in the 150-200 keV band. The detection at energies above 200 keV, the large soft lag and the energy-related behaviors of the LFQPO pose a great challenge for most currently existing models, but suggest that the LFQPO probably originates from the precession of a small-scale jet.

astro-ph.HE↗

arXiv2020

Discovery of dissipative microwave photonic solitons

Dissipative solitons rely on the double balance between nonlinearity and dispersion as well as gain and loss have attracted a lot of attention in optics, since it gives rise to ultrashort pulses and broadband frequency combs with good stability and smooth spectral envelopes. Here we observe a novel dissipative solitons in microwave photonics that gives rise to wideband tunable frequency hopping microwave signals with fast frequency switching speed. The dissipative microwave photonic solitions are achieved through the double balance between nonlinear gain saturation and linear filtering as well as gain and loss in a microwave photonic resonant cavity. The generation of dissipative solitons with different pulse width, repletion rate and number of solitons per round-trip time are observed, together with the corresponding wideband tunable frequency hopping microwave signals. This work opens new avenues for signal generation, processing and control based on the principle of solitons in microwave photonics, and has great potential in many applications such as modern radars, electronic warfare systems, and telecommunications.

physics.optics↗

arXiv2020

Integrated Cooling (i-Cool) Textile of Heat Conduction and Sweat Transportation for Personal Perspiration Management

Perspiration evaporation plays an indispensable role in human body heat dissipation. However, conventional textiles show limited perspiration management capability in moderate/profuse perspiration scenarios, i.e. low evaporation ability, ineffective evaporative cooling effect, and resultant human body dehydration and electrolyte disorder. Here, we propose a novel concept of integrated cooling (i-Cool) textile of heat conduction and sweat transportation for personal perspiration management based on unique functional structure design. By integrating heat conductive pathways and water transport channels decently, this textile not only shows the capability of liquid water wicking, but also exhibits superior evaporation rate than traditional textiles. Furthermore, compared with cotton, about 2.8 $^\circ$C cooling effect causing less than one third amount of dehydration has also been demonstrated on the artificial sweating skin platform with feedback control loop simulating human body perspiration situation. Moreover, the practical application feasibility of the i-Cool textile design principles has been validated as well. Owing to its exceptional personal perspiration management performance in liquid water wicking, fast evaporation, efficient cooling effect and reduced human body dehydration/electrolyte loss, we expect this i-Cool textile provides promising design guidelines for next-generation personal perspiration management textile.

physics.app-ph↗

arXiv2020

A coupled two-species model for the pair contact process with diffusion

The contact process with diffusion (PCPD) defined by the binary reactions 2 B -> 3 B, 2 B -> 0 and diffusive particle spreading exhibits an unusual active to absorbing phase transition whose universality class has long been disputed. Multiple studies have indicated that an explicit account of particle pair degrees of freedom may be required to properly capture this system's effective long-time, large-scale behavior. We introduce a two-species representation in which single particles B and pairs A are coupled according to the stochastic reactions B + B -> A, A -> A + B, A -> 0, and A -> B + B. Mean-field analysis reveals that the phase transition is driven by competition and balance between both species. We employ Monte Carlo simulations to demonstrate that this model captures the pertinent PCPD features. In the inactive phase, A particles rapidly go extinct, leaving the B species to undergo pure pair annihilation kinetics. At criticality, both A and B densities decay with the same exponents as the PCPD order parameters, and display mean-field scaling above the critical dimension 2. In one dimension, the critical exponents for the B species obtained from seed simulations agree well with previously reported exponent values. We demonstrate that the scaling properties of consecutive particle pairs in the PCPD are identical with that of the A species in the coupled model. This two-species picture resolves the conceptual difficulty for seed simulations in the original PCPD and naturally introduces multiple length and time scales, which cause strong corrections to scaling. The extracted moment ratios from our simulations indicate that our model displays the same temporal crossover behavior as the PCPD, which further corroborates its full dynamical equivalence with our coupled model.

cond-mat.stat-mech↗

arXiv2020

VoiceFilter-Lite: Streaming Targeted Voice Separation for On-Device Speech Recognition

We introduce VoiceFilter-Lite, a single-channel source separation model that runs on the device to preserve only the speech signals from a target user, as part of a streaming speech recognition system. Delivering such a model presents numerous challenges: It should improve the performance when the input signal consists of overlapped speech, and must not hurt the speech recognition performance under all other acoustic conditions. Besides, this model must be tiny, fast, and perform inference in a streaming fashion, in order to have minimal impact on CPU, memory, battery and latency. We propose novel techniques to meet these multi-faceted requirements, including using a new asymmetric loss, and adopting adaptive runtime suppression strength. We also show that such a model can be quantized as a 8-bit integer model and run in realtime.

eess.AS↗

arXiv2020

Temporal optical neurons for serial deep learning

Deep learning is able to functionally simulate the human brain and thus, it has attracted considerable interest. Optics-assisted deep learning is a promising approach to improve the forward-propagation speed and reduce the power consumption. However, present methods are based on a parallel processing approach that is inherently ineffective in dealing with serial data signals at the core of information and communication technologies. Here, we propose and demonstrate a serial optical deep learning concept that is specifically designed to directly process high-speed temporal data. By utilizing ultra-short coherent optical pulses as the information carriers, the neurons are distributed at different time slots in a serial pattern, and interconnected to each other through group delay dispersion. A 4-layer serial optical neural network (SONN) was constructed and trained for classification of both analog and digital signals with simulated accuracy rates of over 90% with proper individuality variance rates. Furthermore, we performed a proof-of-concept experiment of a pseudo-3-layer SONN to successfully recognize the ASCII (American Standard Code for Information Interchange) codes of English letters at a data rate of 12 Gbps. This concept represents a novel one-dimensional realization of artificial neural networks, enabling an efficient application of optical deep learning methods to the analysis and processing of serial data signals, while offering a new overall perspective for the temporal signal processing.

eess.SP↗

arXiv2020

Parallel Rescoring with Transformer for Streaming On-Device Speech Recognition

Recent advances of end-to-end models have outperformed conventional models through employing a two-pass model. The two-pass model provides better speed-quality trade-offs for on-device speech recognition, where a 1st-pass model generates hypotheses in a streaming fashion, and a 2nd-pass model re-scores the hypotheses with full audio sequence context. The 2nd-pass model plays a key role in the quality improvement of the end-to-end model to surpass the conventional model. One main challenge of the two-pass model is the computation latency introduced by the 2nd-pass model. Specifically, the original design of the two-pass model uses LSTMs for the 2nd-pass model, which are subject to long latency as they are constrained by the recurrent nature and have to run inference sequentially. In this work we explore replacing the LSTM layers in the 2nd-pass rescorer with Transformer layers, which can process the entire hypothesis sequences in parallel and can therefore utilize the on-device computation resources more efficiently. Compared with an LSTM-based baseline, our proposed Transformer rescorer achieves more than 50% latency reduction with quality improvement.

eess.AS↗

arXiv2020

Kondo holes in the 2D itinerant Ising ferromagnet Fe3GeTe2

Heavy fermion (HF) states emerge in correlated quantum materials due to the interplay between localized magnetic moments and itinerant electrons, but rarely appear in 3d-electron systems due to high itinerancy of d-electrons. Here, an anomalous enhancement of Kondo screening is observed at the Kondo hole of local Fe vacancies in Fe3GeTe2 which is a recently discovered 3d-HF system featuring of Kondo lattice and two-dimensional itinerant ferromagnetism. An itinerant Kondo-Ising model is established to reproduce the experimental results which provides insight of the competition between Ising ferromagnetism and Kondo screening. This work explains the microscopic origin of the d-electron HF states and inspires study of the enriched quantum many-body phenomena with Kondo holes in Ising ferromagnets.

cond-mat.mtrl-sci↗

arXiv2020

Muon conversion to electron in nuclei in Minimal R-symmetric Supersymmetric Standard Model

We analyze the lepton flavor violating process $μ-e$ conversion in the framework of the minimal R-symmetric supersymmetric standard model. The theoretical predictions are determined by considering the experimental constraint on parameter $δ^{12}$ from the lepton flavor violating decay $μ\rightarrow e γ$. The numerical results show that $γ$ penguins and Z penguins dominate the predictions on CR($μ-e$,Nucleus), and the contributions from Higgs penguins and box diagrams are insignificant. The theoretical predictions on conversion rate CR($μ-e$,Nucleus) in a Al or Ti target can be enhanced close to the future experimental sensitivities and are very promising to be observed in near future experiment.

hep-ph↗

arXiv2020

Adversarial Privacy Preserving Graph Embedding against Inference Attack

Recently, the surge in popularity of Internet of Things (IoT), mobile devices, social media, etc. has opened up a large source for graph data. Graph embedding has been proved extremely useful to learn low-dimensional feature representations from graph structured data. These feature representations can be used for a variety of prediction tasks from node classification to link prediction. However, existing graph embedding methods do not consider users' privacy to prevent inference attacks. That is, adversaries can infer users' sensitive information by analyzing node representations learned from graph embedding algorithms. In this paper, we propose Adversarial Privacy Graph Embedding (APGE), a graph adversarial training framework that integrates the disentangling and purging mechanisms to remove users' private information from learned node representations. The proposed method preserves the structural information and utility attributes of a graph while concealing users' private attributes from inference attacks. Extensive experiments on real-world graph datasets demonstrate the superior performance of APGE compared to the state-of-the-arts. Our source code can be found at https://github.com/uJ62JHD/Privacy-Preserving-Social-Network-Embedding.

cs.LG↗

arXiv2020

Physically-Constrained Transfer Learning through Shared Abundance Space for Hyperspectral Image Classification

Hyperspectral image (HSI) classification is one of the most active research topics and has achieved promising results boosted by the recent development of deep learning. However, most state-of-the-art approaches tend to perform poorly when the training and testing images are on different domains, e.g., source domain and target domain, respectively, due to the spectral variability caused by different acquisition conditions. Transfer learning-based methods address this problem by pre-training in the source domain and fine-tuning on the target domain. Nonetheless, a considerable amount of data on the target domain has to be labeled and non-negligible computational resources are required to retrain the whole network. In this paper, we propose a new transfer learning scheme to bridge the gap between the source and target domains by projecting the HSI data from the source and target domains into a shared abundance space based on their own physical characteristics. In this way, the domain discrepancy would be largely reduced such that the model trained on the source domain could be applied on the target domain without extra efforts for data labeling or network retraining. The proposed method is referred to as physically-constrained transfer learning through shared abundance space (PCTL-SAS). Extensive experimental results demonstrate the superiority of the proposed method as compared to the state-of-the-art. The success of this endeavor would largely facilitate the deployment of HSI classification for real-world sensing scenarios.

cs.CV↗