arXiv subjects

Pei Zhao

Publications and source records attributed to Pei Zhao.

At least 19 recordsLinked to original sources

arXiv2025

The Value of Patience in Online Grocery Shopping

Since the COVID-19 pandemic, online grocery shopping has rapidly reshaped consumer behavior worldwide, fueled by ever-faster delivery promises aimed at maximizing convenience. Yet, this growth has also substantially increased urban traffic congestion, emissions, and pollution. Despite extensive research on urban delivery optimization, little is known about the trade-off between individual convenience and these societal costs. In this study, we investigate the value of marginal extensions in delivery times, termed customer patience, in mitigating the traffic burden caused by grocery deliveries. We first conceptualize the problem and present a mathematical model that highlights a convex relationship between patience and traffic congestion. The theoretical predictions are confirmed by an extensive, network-science based analysis leveraging two large-scale datasets encompassing over 8 million grocery orders in Dubai. Our findings reveal that allowing just five additional minutes in delivery time reduces daily delivery mileage by approximately 30 percent and life-cycle CO2 emissions by 20 percent. Beyond ten minutes of added patience, however, marginal benefits diminish significantly. These results highlight that modest increases in consumer patience can deliver substantial gains in traffic reduction and sustainability, offering a scalable strategy to balance individual convenience with societal welfare in urban delivery systems.

cs.SI↗

arXiv2024

Diffusion Models Meet Network Management: Improving Traffic Matrix Analysis with Diffusion-based Approach

Due to network operation and maintenance relying heavily on network traffic monitoring, traffic matrix analysis has been one of the most crucial issues for network management related tasks. However, it is challenging to reliably obtain the precise measurement in computer networks because of the high measurement cost, and the unavoidable transmission loss. Although some methods proposed in recent years allowed estimating network traffic from partial flow-level or link-level measurements, they often perform poorly for traffic matrix estimation nowadays. Despite strong assumptions like low-rank structure and the prior distribution, existing techniques are usually task-specific and tend to be significantly worse as modern network communication is extremely complicated and dynamic. To address the dilemma, this paper proposed a diffusion-based traffic matrix analysis framework named Diffusion-TM, which leverages problem-agnostic diffusion to notably elevate the estimation performance in both traffic distribution and accuracy. The novel framework not only takes advantage of the powerful generative ability of diffusion models to produce realistic network traffic, but also leverages the denoising process to unbiasedly estimate all end-to-end traffic in a plug-and-play manner under theoretical guarantee. Moreover, taking into account that compiling an intact traffic dataset is usually infeasible, we also propose a two-stage training scheme to make our framework be insensitive to missing values in the dataset. With extensive experiments with real-world datasets, we illustrate the effectiveness of Diffusion-TM on several tasks. Moreover, the results also demonstrate that our method can obtain promising results even with $5\%$ known values left in the datasets.

cs.NI↗

arXiv2024

Traffic Matrix Estimation based on Denoising Diffusion Probabilistic Model

The traffic matrix estimation (TME) problem has been widely researched for decades of years. Recent progresses in deep generative models offer new opportunities to tackle TME problems in a more advanced way. In this paper, we leverage the powerful ability of denoising diffusion probabilistic models (DDPMs) on distribution learning, and for the first time adopt DDPM to address the TME problem. To ensure a good performance of DDPM on learning the distributions of TMs, we design a preprocessing module to reduce the dimensions of TMs while keeping the data variety of each OD flow. To improve the estimation accuracy, we parameterize the noise factors in DDPM and transform the TME problem into a gradient-descent optimization problem. Finally, we compared our method with the state-of-the-art TME methods using two real-world TM datasets, the experimental results strongly demonstrate the superiority of our method on both TM synthesis and TM estimation.

cs.LG↗

arXiv2024

A Real-Time Voice Activity Detection Based On Lightweight Neural

Voice activity detection (VAD) is the task of detecting speech in an audio stream, which is challenging due to numerous unseen noises and low signal-to-noise ratios in real environments. Recently, neural network-based VADs have alleviated the degradation of performance to some extent. However, the majority of existing studies have employed excessively large models and incorporated future context, while neglecting to evaluate the operational efficiency and latency of the models. In this paper, we propose a lightweight and real-time neural network called MagicNet, which utilizes casual and depth separable 1-D convolutions and GRU. Without relying on future features as input, our proposed model is compared with two state-of-the-art algorithms on synthesized in-domain and out-domain test datasets. The evaluation results demonstrate that MagicNet can achieve improved performance and robustness with fewer parameter costs.

cs.SD↗

arXiv2024

Ferroelectric topological superconductor

Two-dimensional topological superconductor (TSC) represents an exotic quantum material with quasiparticle excitation manifesting in dispersive Majorana mode (DMM) at the boundaries. A domain-wall DMM can arise at the boundary between two TSC domains with opposite Chern numbers or with a $π$-phase shift in their pairing gap, which can only be tuned by magnetic field. Here we propose the concept of ferroelectric (FE) TSC, which not only enriches the domain-wall DMMs but also significantly makes them electrically tunable. The $π$-phase shift of the pairing gap is shown to be attained between two TSC domains of opposite FE polarization, and switchable by reversing FE polarizations. In combination with ferromagnetic (FM) polarization, the domain wall can host helical, doubled chiral, and fused DMMs, which can be transferred into each other by changing the direction of electrical and/or magnetic field. Furthermore, based on first-principles calculations, we demonstrate $α$-In$_2$Se$_3$ to be a promising FE TSC candidate in proximity with a FM layer and a superconductor substrate. We envision that FE TSC will significantly ease the manipulation of DMM by electrical field to realize fault-tolerant quantum computation.

cond-mat.supr-con↗

arXiv2023

Elemental Ferroelectric Topological Insulator in $ψ$-bismuthene

Ferroelectric quantum spin Hall insulator (FEQSHI) exhibits coexisting ferroelectricity and time-reversal symmetry protected edge states, holding fascinating prospects for inviting both scientific and application advances, especially in two dimensions. However, all of the previously demonstrated FEQSHIs consist two or more constituent elements. We herein propose the $ψ$-bismuthene, an uncharted allotrope of bilayer Bi (110), to be the first example of 2D elemental FEQSHI. It is demonstrated that $ψ$-bismuthene harbors measurable ferroelectric polarization and nontrivial band gap with moderate switching barrier, which are highly beneficial for the detection and observation of the ferroelectric topologically insulating states. In addition, all-angle auxetic behavior with giant negative Poisson's ratio and ferroelectric controllable persistent spin helix in $ψ$-bismuthene are also discussed. The emergent elemental FEQSHI represents a novel domain for both fundamental physics and technological innovation.

cond-mat.mtrl-sci↗

arXiv2023

Ferroelectric Antiferromagnetic Quantum Anomalous Hall Insulator in TwoDimensional van der Waals Materials

Ferroelectricity, anti-ferromagnetism (AFM) and quantum anomalous Hall effect (QAHE) are three fundamental phenomena in the field of condensed matter physics, which could enable the realization of novel devices and thus attracts great attention. Here, we show theoretical evidence that twodimensional (2D) even-layer MnBi2Te4 allows for the simultaneous presence of intercorrelated ferroelectricity, AFM, and QAHE. Importantly, through rational van der Waals sliding, these exotic properties are strongly coupled. Such coupling could demonstrate many distinctive physics, for example, ferroelectric control of itinerant AFM phase and the sign of quantized anomalous Hall plateau.The explored phenomena and mechanism would not only enrich the research in 2D ferroelectricity and topological magnets, but also guide the design of low-consumption high-speed quantum devices.

physics.comp-ph↗

arXiv2023

Unconventional ferroelectricity in half-filling states of antiparallel stacking of twisted WSe2

Abstract: We report on emergence of an abnormal electronic polarization in twisted double bilayer WSe2 in antiparallel interface stacking geometry, where local centrosymmetry of atomic registries at the twist interface does not favor the spontaneous electronic polarizations as recently observed in the parallel interface stacking geometry. The unconventional ferroelectric behaviors probed by electronic transport measurement occur at half filling insulating states at 1.5 K and gradually disappear at about 40 K. Single band Hubbard model based on the triangular moiré lattice and the interlayer charge transfer controlled by insulating phase transition are proposed to interpret the formation of electronic polarization states near half filling in twisted WSe2 devices. Our work highlights the prominent role of many-body electronic interaction in fostering novel quantum states in moiré-structured systems.

cond-mat.mes-hall↗

arXiv2020

Universal superlattice potential for 2D materials from twisted interface inside h-BN substrate

Lateral superlattices in 2D materials are emerging as a powerful platform for exploring novel quantum phenomena, which can be realized through the proximity coupling in forming moiré pattern with another layer. This approach, however, is invasive, material-specific, and requires small lattice mismatch and suitable band alignment, largely limited to graphene and transition metal dichalcogenides (TMDs). Hexagonal boron nitride (h-BN) of anti-parallel (AA') stacking order has been an indispensable building block, as dielectric substrates and capping layers for realizing high quality van der Waals devices. There is also emerging interest on parallelly aligned h-BN of Bernal (AB) stacking, where the broken inversion and mirror symmetries lead to out-of-plane electrical polarization with sign controlled by interlayer translation. Here we show that the laterally patterned electrical polarization at a nearly parallel interface within the h-BN substrate or capping layer can be exploited to create non-invasively a universal superlattice potential in general 2D materials. The feasibility is demonstrated by first principle calculations for monolayer MoSe2, black phosphorus, and antiferromagnetic MnPSe3 on such h-BN substrate. The potential strength can reach 200 meV, customizable in this range through choice of vertical distance of target material from the interface in h-BN. We also find sizable out-of-plane electric field at the h-BN surface, which can realize superlattice potential for interlayer excitons in TMD bilayers as well as dipolar molecules. The idea is further generalized to AB stacked h-BN film subject to torsion with adjacent layers all twisted with an angle, which allows the potential and field strength to be scaled up with film thickness, saturating to a quasi-periodic one with chiral structure.

cond-mat.mes-hall↗

arXiv2020

Chiral channel network from magnetization textures in 2D MnBi2Te4

When atomically thin van der Waals (vdW) magnet forms long-period moiré pattern with a magnetic substrate, the sensitive dependence of interlayer magnetic coupling on the atomic registries can lead to moiré defined magnetization textures in the two-dimensional (2D) magnets. The recent discovery of 2D magnetic topological insulators such as MnBi2Te4 leads to the interesting possibility to explore the interplay of such magnetization textures with the topological surface states, which we explore here with a minimal model established for 2D MnBi2Te4. The sign flip of the exchange gap across a magnetization domain wall gives rise to a single in-gap chiral channel on each surface. In the periodic magnetization textures, such chiral spin channels at the domain walls couple to form a network and superlattice minibands emerge. We find that in magnetization textures with closed domain wall geometries, the formed superlattice miniband is a gapped Dirac cone featuring orbital magnetization from the current circulation in the close loops of chiral channels, while in magnetization textures with open domain wall geometries, gapless mini-Dirac cone is found instead. The miniband Bloch states feature a spatial texture of spin and local current density, which are clear manifestation of the spin-momentum locked chiral channels at the domain walls. The results suggest a new platform to engineer spin and current flows through the manipulation of magnetization domains for spintronic devices.

cond-mat.mes-hall↗

arXiv2020

Acoustic Echo Cancellation by Combining Adaptive Digital Filter and Recurrent Neural Network

Acoustic Echo Cancellation (AEC) plays a key role in voice interaction. Due to the explicit mathematical principle and intelligent nature to accommodate conditions, adaptive filters with different types of implementations are always used for AEC, giving considerable performance. However, there would be some kinds of residual echo in the results, including linear residue introduced by mismatching between estimation and the reality and non-linear residue mostly caused by non-linear components on the audio devices. The linear residue can be reduced with elaborate structure and methods, leaving the non-linear residue intractable for suppression. Though, some non-linear processing methods have already be raised, they are complicated and inefficient for suppression, and would bring damage to the speech audio. In this paper, a fusion scheme by combining adaptive filter and neural network is proposed for AEC. The echo could be reduced in a large scale by adaptive filtering, resulting in little residual echo. Though it is much smaller than speech audio, it could also be perceived by human ear and would make communication annoy. The neural network is elaborately designed and trained for suppressing such residual echo. Experiments compared with prevailing methods are conducted, validating the effectiveness and superiority of the proposed combination scheme.

cs.SD↗

arXiv2020

A Lite Microphone Array Beamforming Scheme with Maximum Signal-to-Noise Ratio Filter

Since space-domain information can be utilized, microphone array beamforming is often used to enhance the quality of the speech by suppressing directional disturbance. However, with the increasing number of microphone, the complexity would be increased. In this paper, a concise beamforming scheme using Maximum Signal-to-Noise Ratio (SNR) filter is proposed to reduce the beamforming complexity. The maximum SNR filter is implemented by using the estimated direction-of-arrival (DOA) of the speech source localization (SSL) and the solving method of independent vector analysis (IVA). Our experiments show that when compared with other widely-used algorithms, the proposed algorithm obtain higher gain of signal-to-interference and noise ratio (SINR).

cs.SD↗

arXiv2020

Competitive Wakeup Scheme for Distributed Devices

Wakeup is the primary function in voice interaction which is the mainstream scheme in man-machine interaction (HMI) applications for smart home. All devices will response if the same wake-up word is used for all devices. This will bring chaos and reduce user quality of experience (QoE). The only way to solve this problem is to make all the devices in the same wireless local area network (WLAN) competing to wake-up based on the same scoring rule. The one closest to the user would be selected for response. To this end, a competitive wakeup scheme is proposed in this paper with elaborately designed calibration method for receiving energy of microphones. Moreover, the user orientation is assisted to determine the optimal device. Experiments reveal the feasibility and validity of this scheme.

cs.SD↗

arXiv2020

Voice Activity Detection Scheme by Combining DNN Model with GMM Model

Due to the superior modeling ability of deep neural network (DNN), it is widely used in voice activity detection (VAD). However, the performance may degrade if no sufficient data especially for practical data could be used for training, thus, leading to inferior ability of adaption to environment. Moreover, large model structure could not always be used in practical, especially for low cost devices where restricted hardware is used. This is on the contrary for Gaussian mixture model (GMM) where model parameters can be updated in real-time, but, with low modeling ability. In this paper, deeply integrated scheme combining these two models are proposed to improve adaptability and modeling ability. This is done by directly combining the results of models and feeding it back, together with the result of the DNN model, to update the GMM model. Besides, a control scheme is elaborately designed to detect the endpoints of speech. The superior performance by employing this scheme is validated through experiments in practical, which give an insight into the advantage of combining supervised learning and unsupervised learning.

cs.SD↗

arXiv2017

Monotonic quantum-to-classical transition enabled by positively-correlated biphotons

Multiparticle interference is a fundamental phenomenon in the study of quantum mechanics.It was discovered in a recent experiment [Ra, Y.-S. et al, Proc. Natl Acad. Sci. USA \textbf{110}, 1227(2013)] that spectrally uncorrelated biphotons exhibited a nonmonotonic quantum-to-classical transition in a four-photon Hong-Ou-Mandel (HOM) interference. In this work, we consider the same scheme with spectrally correlated photons.By theoretical calculation and numerical simulation, we found the transition not only can be nonmonotonic with negative-correlated or uncorrelated biphotons, but also can be monotonic with positive-correlated biphotons. The fundamental reason for this difference is that the HOM-type multi-photon interference is a differential-frequency interference. Our study may shed new light on understanding the role of frequency entanglement in multi-photon behavior.

quant-ph↗

arXiv2016

Spectrally pure states at telecommunications wavelengths from periodically poled $M$TiO$X$O$_4$ ($M$ = K, Rb, Cs; $X$ = P, As) crystals

Significant successes have recently been reported in the study of the generation of spectrally pure state in group-velocity-matched (GVM) nonlinear crystals. However, the GVM condition can only be realized in limited kinds of crystals and at limited wavelengths. Here, we investigate pure state generation in the isomorphs of PPKTP crystal: i.e., periodically poled RTP, KTA, RTA and CTA crystals. By numerical simulation, we find that these crystals from the KTP family can generate pure photons with high spectral purity (over 0.8), wide tunability (more than 400 nm), reasonable nonlinearity at a variety of wavelengths (from 1300 nm to 2100 nm). It is also discovered that the PPCTA crystal may achieve purity of 0.97 at 1506 nm. This study may provide more and better choices for quantum state engineering at telecom wavelengths.

quant-ph↗

arXiv2016

Strained GaN Quantum-Well FETs on Single Crystal Bulk AlN Substrates

We report the first realization of molecular beam epitaxy grown strained GaN quantum well field-effect transistors on single-crystal bulk AlN substrates. The fabricated double heterostructure FETs exhibit a two- dimensional electron gas (2DEG) density in excess of 2x10^13/cm2. Ohmic contacts to the 2DEG channel were formed by n+ GaN MBE regrowth process, with a contact resistance of 0.13 Ohm-mm. Raman spectroscopy using the quantum well as an optical marker reveals the strain in the quantum well, and strain relaxation in the regrown GaN contacts. A 65-nm-long rectangular-gate device showed a record high DC drain current drive of 2.0 A/mm and peak extrinsic transconductance of 250 mS/mm. Small-signal RF performance of the device achieved current gain cutoff frequency fT~120 GHz. The DC and RF performance demonstrate that bulk AlN substrates offer an attractive alternative platform for strained quantum well nitride transistors for future high-voltage and high-power microwave applications.

cond-mat.mtrl-sci↗

arXiv2015

Dual Optical Marker Raman Characterization of Strained GaN-channels on AlN Using AlN/GaN/AlN Quantum Wells and 15N Isotopes

This work shows that the combination of ultrathin highly strained GaN quantum wells embedded in an AlN matrix, with controlled isotopic concentrations of Nitrogen enables a dual marker method for Raman spectroscopy. By combining these techniques, we demonstrate the effectiveness in studying strain in the vertical direction. This technique will enable the precise probing of properties of buried active layers in heterostructures, and can be extended in the future to vertical devices such as those used for optical emitters, and for power electronics.

cond-mat.mtrl-sci↗