Searcharxiv⌕ Search

arXiv subjects

Lei Gao

Publications and source records attributed to Lei Gao.

At least 73 records · Page 4Linked to original sources
arXiv2023

Antiferromagnetic to Ferrimagnetic Phase Transition and Possible Phase Coexistence in Polar Magnets (Fe$_{1-x}$Mn$_x$)$_2$Mo$_3$O$_8$

In the present work, magnetic properties of single crystal (Fe$_{1-x}$Mn$_x$)$_2$Mo$_3$O$_8$ ($0 0.3$. Meanwhile, sizeable electric polarization of spin origin is commonly observed in all samples, no matter what the magnetic state is. For the samples hosting a ferrimagnetic state, square-like magnetic hysteresis loops are revealed, while the remnant magnetization and coercive field can be tuned drastically by simply varying the Mn-content or temperature. Possible coexistence of the antiferromagnetic and ferrimagnetic phases is proposed to be responsible for the remarkable modulation of magnetic properties in the samples.

cond-mat.mtrl-sci↗
arXiv2023

Magnetic Properties and Spin-orbit Coupling induced Semiconductivity in LK-99

Recent reports of a possible room-temperature superconductor called LK-99 have generated a lot of attention worldwide. In just a few days, a large amount of experimental works attempted to reproduce this sample and verify its properties. At the same time a large amount of theoretical works have also been reported. However, many experiments have drawn different conclusions, and many theoretical results are not consistent with experimental results. For one of the structures of LK-99 with the chemical formula as Pb9Cu(PO4)6O, many first-principles calculations did not consider spin-orbit coupling and concluded that it is a flat band metal. However, spin-orbit coupling is often not negligible in systems with heavy elements, and LK-99 contains a large amount of heavy element Pb. We performed calculations of electronic structure of Pb9Cu(PO4)6O with spin-orbit coupling, and the results show that it's not a metal but a semiconductor. This is consistent with many experimental results. In the ferromagnetic state Pb9Cu(PO4)6O is an indirect-bandgap semiconductor with a bandgap of 292 meV. Moreover, its conduction band is a flat band. At an electron doping level of 0.5 e/unit cell, Pb9Cu(PO4)6O becomes metallic and has a flat band with a width of only 25 meV at the Fermi level in the ferromagnetic state. While in the antiferromagnetic-A state, Pb9Cu(PO4)6O is a direct-bandgap semiconductor with a bandgap of 300 meV. As a magnetic narrowband semiconductor, Pb9Cu(PO4)6O may have potential application value in the field of optoelectronic device, photocatalytic, photodetector and spintronics device.

cond-mat.supr-con↗
arXiv2023

Interpretability of Machine Learning: Recent Advances and Future Prospects

The proliferation of machine learning (ML) has drawn unprecedented interest in the study of various multimedia contents such as text, image, audio and video, among others. Consequently, understanding and learning ML-based representations have taken center stage in knowledge discovery in intelligent multimedia research and applications. Nevertheless, the black-box nature of contemporary ML, especially in deep neural networks (DNNs), has posed a primary challenge for ML-based representation learning. To address this black-box problem, the studies on interpretability of ML have attracted tremendous interests in recent years. This paper presents a survey on recent advances and future prospects on interpretability of ML, with several application examples pertinent to multimedia computing, including text-image cross-modal representation learning, face recognition, and the recognition of objects. It is evidently shown that the study of interpretability of ML promises an important research direction, one which is worth further investment in.

cs.MM↗
arXiv2023

TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Training

In cross-device Federated Learning (FL) environments, scaling synchronous FL methods is challenging as stragglers hinder the training process. Moreover, the availability of each client to join the training is highly variable over time due to system heterogeneities and intermittent connectivity. Recent asynchronous FL methods (e.g., FedBuff) have been proposed to overcome these issues by allowing slower users to continue their work on local training based on stale models and to contribute to aggregation when ready. However, we show empirically that this method can lead to a substantial drop in training accuracy as well as a slower convergence rate. The primary reason is that fast-speed devices contribute to many more rounds of aggregation while others join more intermittently or not at all, and with stale model updates. To overcome this barrier, we propose TimelyFL, a heterogeneity-aware asynchronous FL framework with adaptive partial training. During the training, TimelyFL adjusts the local training workload based on the real-time resource capabilities of each client, aiming to allow more available clients to join in the global update without staleness. We demonstrate the performance benefits of TimelyFL by conducting extensive experiments on various datasets (e.g., CIFAR-10, Google Speech, and Reddit) and models (e.g., ResNet20, VGG11, and ALBERT). In comparison with the state-of-the-art (i.e., FedBuff), our evaluations reveal that TimelyFL improves participation rate by 21.13%, harvests 1.28x - 2.89x more efficiency on convergence rate, and provides a 6.25% increment on test accuracy.

cs.LG↗
arXiv2023

Classification of Lung Pathologies in Neonates using Dual Tree Complex Wavelet Transform

Annually 8500 neonatal deaths are reported in the US due to respiratory failure. Recently, Lung Ultrasound (LUS), due to its radiation free nature, portability, and being cheaper is gaining wide acceptability as a diagnostic tool for lung conditions. However, lack of highly trained medical professionals has limited its use especially in remote areas. To address this, an automated screening system that captures characteristics of the LUS patterns can be of significant assistance to clinicians who are not experts in lung ultrasound (LUS) images. In this paper, we propose a feature extraction method designed to quantify the spatially-localized line patterns and texture patterns found in LUS images. Using the dual-tree complex wavelet transform (DTCWT) and four types of common image features we propose a method to classify the LUS images into 6 common neonatal lung conditions. These conditions are normal lung, pneumothorax (PTX), transient tachypnea of the newborn (TTN), respiratory distress syndrome (RDS), chronic lung disease (CLD) and consolidation (CON) that could be pneumonia or atelectasis. The proposed method using DTCWT decomposition extracted global statistical, grey-level co-occurrence matrix (GLCM), grey-level run length matrix (GLRLM) and linear binary pattern (LBP) features to be fed to a linear discriminative analysis (LDA) based classifier. Using 15 best DTCWT features along with 3 clinical features the proposed approach achieved a per-image classification accuracy of 92.78% with a balanced dataset containing 720 images from 24 patients and 74.39% with the larger unbalanced dataset containing 1550 images from 42 patients. Likewise, the proposed method achieved a maximum per-subject classification accuracy of 81.53% with 43 DTCWT features and 3 clinical features using the balanced dataset and 64.97% with 13 DTCWT features and 3 clinical features using the unbalanced dataset.

eess.IV↗
arXiv2022

Electronic properties of monolayer copper selenide with one-dimensional moiré patterns

Strain engineering is a vital way to manipulate the electronic properties of two-dimensional (2D) materials. As a typical representative of transition metal mono-chalcogenides (TMMs), a honeycomb CuSe monolayer features with one-dimensional (1D) moiré patterns owing to the uniaxial strain along one of three equivalent orientations of Cu(111) substrates. Here, by combining low-temperature scanning tunneling microscopy/spectroscopy (STM/S) experiments and density functional theory (DFT) calculations, we systematically investigate the electronic properties of the strained CuSe monolayer on the Cu(111) substrate. Our results show the semiconducting feature of CuSe monolayer with a band gap of 1.28 eV and the 1D periodical modulation of electronic properties by the 1D moiré patterns. Except for the uniaxially strained CuSe monolayer, we observed domain boundary and line defects in the CuSe monolayer, where the biaxial-strain and strain-free conditions can be investigated respectively. STS measurements for the three different strain regions show that the first peak in conduction band will move downward with the increasing strain. DFT calculations based on the three CuSe atomic models with different strain inside reproduced the peak movement. The present findings not only enrich the fundamental comprehension toward the influence of strain on electronic properties at 2D limit, but also offer the benchmark for the development of 2D semiconductor materials.

cond-mat.mtrl-sci↗
arXiv2022

Scaling of energy and power in a large quantum battery-charger model

We investigate a multi-qubit quantum battery-charger model, focusing on its potential emulation on a superconducting qubit chip. Using a large-spin representation, we first obtain the analytical form of the energy $E_B(t)$, power $P_B(t)$ and their maximum values, $E_B^{\rm max}$ and $P_B^{\rm max}$, of the battery part by means of the antiferromagnetic Holstein-Primakoff (AFM-HP) transformation within the low-energy approximation. In this case, our results show that superextensive scaling behavior of $P_B^{\rm max}$ ensues. By further combining these with the ones obtained via exact diagonalization (ED), we classify the dynamics of various physical quantities, including the entanglement between the battery and charger parts for system sizes encompassing over 10,000 qubits. Finally, by checking a diverse set of system configurations, including either a fixed battery size with growing number of charger qubits, or when both parts simultaneously grow, we classify the system size scalings of $E_B^{\rm max}$ and $P_B^{\rm max}$, relating it with the entanglement entropy in the system. In agreement with the analytical results, robust superextensive behavior of $P_B^{\rm max}$ is also observed in this case. Our work provides an overall guide for expected features in experiments of quantum batteries emulated in superconducting qubit platforms, in particular ones that exhibit long-range couplings.

quant-ph↗
arXiv2022

A GPU-accelerated Algorithm for Distinct Discriminant Canonical Correlation Network

Currently, deep neural networks (DNNs)-based models have drawn enormous attention and have been utilized to different domains widely. However, due to the data-driven nature, the DNN models may generate unsatisfying performance on the small scale data sets. To address this problem, a distinct discriminant canonical correlation network (DDCCANet) is proposed to generate the deep-level feature representation, producing improved performance on image classification. However, the DDCCANet model was originally implemented on a CPU with computing time on par with state-of-the-art DNN models running on GPUs. In this paper, a GPU-based accelerated algorithm is proposed to further optimize the DDCCANet algorithm. As a result, not only is the performance of DDCCANet guaranteed, but also greatly shortens the calculation time, making the model more applicable in real tasks. To demonstrate the effectiveness of the proposed accelerated algorithm, we conduct experiments on three database with different scales. Experimental results validate the superiority of the proposed accelerated algorithm on given examples.

eess.IV↗
arXiv2022

Early systems change necessary for catalyzing long-term sustainability in a post-2030 agenda

Progress to-date towards the Sustainable Development Goals (SDGs) has fallen short of expectations and is unlikely to fully meet 2030 targets. Despite the little chance of imminent success, past assessments have mostly focused on short- and medium-term evaluations, limiting the ability to explore the longer-term effects of systemic interactions with time lags and delay. Here we undertake global systems modelling with a longer-term view than previous assessments to explore the drivers of sustainability progress and how they could emerge by 2030, 2050, and 2100 under different development pathways and towards quantitative targets. We find that early planning for systems change to shift from business-as-usual to more sustainable pathways is important for accelerating progress towards increasingly ambitious targets by 2030, 2050, and 2100. These findings indicate the importance of adopting longer-term timeframes and pathways to ensure that the necessary pre-conditions are in place for sustainability beyond the current 2030 Agenda.

econ.GN↗
arXiv2022

On the existence of pure, broadband toroidal sources in electrodynamics

Multipoles are paramount for describing electromagnetic fields in many areas of nanoscale optics, playing an essential role for the design of devices in plasmonics and all-dielectric nanophotonics. Challenging the traditional division into electric and magnetic moments, toroidal moments have been proposed as a physically distinct family of multipoles with significant contributions to the properties of matter. However, the apparent impossibility of separately measuring their response sheds doubt on their true physical significance. Here, we confirm the possibility of selectively exciting toroidal moments without any other multipole. We develop a set of general conditions that any current distribution must fulfill to be entirely described by toroidal moments, and prove our results in an analytically solvable case. Our new theory allows us to design and verify experimentally for the first time an artificial structure supporting a pure, broadband toroidal dipole response in the complete absence of the electric dipole and other 'ordinary' multipole contributions. In addition, we propose a structure capable of supporting a novel type of nonradiating source, a 'toroidal anapole', originating from the destructive interference of the toroidal dipole with the unconventional electromagnetic sources known as mean square radii. The results in this work provide conclusive evidence on the independent excitation of toroidal moments in electrodynamics.

physics.class-ph↗
arXiv2022

Wafer-scale epitaxial growth of the thickness-controllable van der Waals ferromagnet CrTe2 for reliable magnetic memory applications

To harness the intriguing properties of two-dimensional van der Waals (vdW) ferromagnets (FMs) for versatile applications, the key challenge lies in the reliable material synthesis for scalable device production. Here, we demonstrate the epitaxial growth of single-crystalline 1T-CrTe2 thin films on 2-inch sapphire substrates. Benefiting from the uniform surface energy of the dangling bond-free Al2O3(0001) surface, the layer-by-layer vdW growth mode is observed right from the initial growth stage, which warrants precise control of the sample thickness and atomically smooth surface morphology across the entire wafer. Moreover, the presence of the Coulomb interaction at the CrTe2/Al2O3 interface serves as an effective tuning parameter to tailor the anomalous Hall response, and the structural optimization of the CrTe2-based spin-orbit torque device leads to a substantial switching power reduction by 54%. Our results may lay out a general framework for the design of energy-efficient spintronics based on configurable vdW FMs.

cond-mat.mtrl-sci↗
arXiv2022

Intrinsic Spectrum Analysis of Laser Dynamics Based on Fractional Fourier Transform

Intrinsic spectrum that results from the coupling of spontaneous emission in a laser cavity, can determine the energy concentration and coherence of lasers, which is crucial for the optical high-precision measurement. Up to now, it is hard to analyze the intrinsic spectrum in the high-speed laser dynamics process, especially under the condition of fast wavelength sweeping. In this work, a new method to analyze the laser intrinsic spectrum is proposed with the laser energy decomposition to a series of chirp-frequency signals, which is realized by fractional Fourier transform (FRFT) of the coherently reconstructed laser waveform. The new understanding of the energy distribution of lasers contributes to the accurate characterization of laser dynamical parameters in time-frequency domain. In the proof-of-concept experiment, the time-frequency dynamical process of a commercial wavelength swept laser is tested with different wavelength-scanning speeds, and the most suitable measurement time window width required for the FRFT-based narrowest spectrum is also explored. The proposed analysis method of laser dynamical parameters will promote the understanding of laser dynamics, and benefit for the optical precision measurement applications.

physics.optics↗
arXiv2022

Federated Learning for Internet of Things: Applications, Challenges, and Opportunities

Billions of IoT devices will be deployed in the near future, taking advantage of faster Internet speed and the possibility of orders of magnitude more endpoints brought by 5G/6G. With the growth of IoT devices, vast quantities of data that may contain users' private information will be generated. The high communication and storage costs, mixed with privacy concerns, will increasingly challenge the traditional ecosystem of centralized over-the-cloud learning and processing for IoT platforms. Federated Learning (FL) has emerged as the most promising alternative approach to this problem. In FL, training data-driven machine learning models is an act of collaboration between multiple clients without requiring the data to be brought to a central point, hence alleviating communication and storage costs and providing a great degree of user-level privacy. However, there are still some challenges existing in the real FL system implementation on IoT networks. In this paper, we will discuss the opportunities and challenges of FL in IoT platforms, as well as how it can enable diverse IoT applications. In particular, we identify and discuss seven critical challenges of FL in IoT platforms and highlight some recent promising approaches towards addressing them.

cs.LG↗
arXiv2022

Vision Transformer with Convolutions Architecture Search

Transformers exhibit great advantages in handling computer vision tasks. They model image classification tasks by utilizing a multi-head attention mechanism to process a series of patches consisting of split images. However, for complex tasks, Transformer in computer vision not only requires inheriting a bit of dynamic attention and global context, but also needs to introduce features concerning noise reduction, shifting, and scaling invariance of objects. Therefore, here we take a step forward to study the structural characteristics of Transformer and convolution and propose an architecture search method-Vision Transformer with Convolutions Architecture Search (VTCAS). The high-performance backbone network searched by VTCAS introduces the desirable features of convolutional neural networks into the Transformer architecture while maintaining the benefits of the multi-head attention mechanism. The searched block-based backbone network can extract feature maps at different scales. These features are compatible with a wider range of visual tasks, such as image classification (32 M parameters, 82.0% Top-1 accuracy on ImageNet-1K) and object detection (50.4% mAP on COCO2017). The proposed topology based on the multi-head attention mechanism and CNN adaptively associates relational features of pixels with multi-scale features of objects. It enhances the robustness of the neural network for object recognition, especially in the low illumination indoor scene.

cs.CV↗
arXiv2022

Single-shot measurement of frequency-resolved state of polarization dynamics in ultrafast lasers using dispersed division-of-amplitude

Precise measurement of multi-parameters of ultrafast lasers is vital both in scientific investigations and technical applications, such as, optical field manipulation, pulse shaping, sample characteristics test, and biomedical imaging. Tremendous progress in parameter measurement of ultrafast laser has been made, including single-shot spectra acquired by time-stretch dispersive Fourier transform in spectral domain, and pulse magnification or compression realized by time lens in temporal domain. Nevertheless, single-shot measurement of frequency-resolved states of polarization (SOPs) of ultrafast lasers has not been reported so far, and the unregular SOP evolution dynamics in ultrafast pulses is hardly explored. Here, we demonstrate a new single-shot frequency-resolved SOPs measurement system by utilizing division-of-amplitude method under far-field approximation. Large dispersion is utilized to time-stretch the laser pulses, where the spectrum information is mapped into temporal waveform via dispersive Fourier transform. By calibrating system matrix with different wavelengths, the precise frequency-resolved SOPs are obtained together with high speed opto-electron detection. We demonstrate applications in direct measurement of transient mode-locked fiber laser dynamics. We observe complex frequency-dependent SOPs dynamics in the building up of dissipative solitons, and apparent discrepancy of SOPs between sideband and main peak in conventional solitons. Our observations reveal that the SOP plays a far more complex part in mode-locking process, which is different from the traditional viewpoint. Taking advantage of broadband achromatic optical elements, this method can be extended to measurement of much broad pulse lasers, which will pave the way for reliable measurement and precise control of ultrafast lasers with frequency-resolved SOPs structures.

physics.optics↗
arXiv2021

Revealing the role of interfacial heterogeneous nucleation in metastable thin film growth of rare earth nickelates electronic transition materials

Although rare earth nickelates (ReNiO3) exhibit abundant electronic phases and widely adjustable metal to insulator electronic transition properties, their practical electronic applications are largely impeded by their intrinsic meta stability. Apart from elevating oxygen reaction pressures, heterogeneous nucleation is expected as an alternative strategy that enables the crystallization of ReNiO3 at low meta stability. In this work, the respective roles of high oxygen pressure and heterogeneous interface in triggering ReNiO3 thin films growth at metastable state are revealed. The ReNiO3 (Re:Nd, Sm, Eu, Gd, and Dy) heterogeneous thin films growth on LaAlO3 single crystal substrate have an effective crystallization at atmosphere without the necessity to apply high oxygen pressures, suggesting the interfacial bonding between the ReNiO3 and substrates can sufficiently reduce the positive Gibbs formation energy of ReNiO3, which is further verified by the first principles calculations. Nevertheless, the abrupt electronic transitions only appear in ReNiO3 thin films grown at high oxygen pressures, in which cases the oxygen vacancies are effectively eliminated via high oxygen pressure reactions as indicated by near edge X ray absorption fine structure (NEXAFS). This work unveils the synergistic effects of heterogeneous nucleation and high oxygen pressure on the growth of high quality ReNiO3 thin films.

cond-mat.mtrl-sci↗
arXiv2021

Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection

Federated learning can be a promising solution for enabling IoT cybersecurity (i.e., anomaly detection in the IoT environment) while preserving data privacy and mitigating the high communication/storage overhead (e.g., high-frequency data from time-series sensors) of centralized over-the-cloud approaches. In this paper, to further push forward this direction with a comprehensive study in both algorithm and system design, we build FedIoT platform that contains FedDetect algorithm for on-device anomaly data detection and a system design for realistic evaluation of federated learning on IoT devices. Furthermore, the proposed FedDetect learning framework improves the performance by utilizing a local adaptive optimizer (e.g., Adam) and a cross-round learning rate scheduler. In a network of realistic IoT devices (Raspberry PI), we evaluate FedIoT platform and FedDetect algorithm in both model and system performance. Our results demonstrate the efficacy of federated learning in detecting a wider range of attack types occurred at multiple devices. The system efficiency analysis indicates that both end-to-end training time and memory cost are affordable and promising for resource-constrained IoT devices. The source code is publicly available at https://github.com/FedML-AI/FedIoT.

cs.LG↗