arXiv subjects

Nan Zhang

Publications and source records attributed to Nan Zhang.

At least 127 records · Page 7Linked to original sources

arXiv2023

Leverage classifier: Another look at support vector machine

Support vector machine (SVM) is a popular classifier known for accuracy, flexibility, and robustness. However, its intensive computation has hindered its application to large-scale datasets. In this paper, we propose a new optimal leverage classifier based on linear SVM under a nonseparable setting. Our classifier aims to select an informative subset of the training sample to reduce data size, enabling efficient computation while maintaining high accuracy. We take a novel view of SVM under the general subsampling framework and rigorously investigate the statistical properties. We propose a two-step subsampling procedure consisting of a pilot estimation of the optimal subsampling probabilities and a subsampling step to construct the classifier. We develop a new Bahadur representation of the SVM coefficients and derive unconditional asymptotic distribution and optimal subsampling probabilities without giving the full sample. Numerical results demonstrate that our classifiers outperform the existing methods in terms of estimation, computation, and prediction.

stat.ME↗

arXiv2023

EdgeMA: Model Adaptation System for Real-Time Video Analytics on Edge Devices

Real-time video analytics on edge devices for changing scenes remains a difficult task. As edge devices are usually resource-constrained, edge deep neural networks (DNNs) have fewer weights and shallower architectures than general DNNs. As a result, they only perform well in limited scenarios and are sensitive to data drift. In this paper, we introduce EdgeMA, a practical and efficient video analytics system designed to adapt models to shifts in real-world video streams over time, addressing the data drift problem. EdgeMA extracts the gray level co-occurrence matrix based statistical texture feature and uses the Random Forest classifier to detect the domain shift. Moreover, we have incorporated a method of model adaptation based on importance weighting, specifically designed to update models to cope with the label distribution shift. Through rigorous evaluation of EdgeMA on a real-world dataset, our results illustrate that EdgeMA significantly improves inference accuracy.

cs.CV↗

arXiv2023

Acoustic diagnostics of femtosecond laser filamentation

The promising application of femtosecond laser filamentation in atmospheric remote sensing brings imperative demand for diagnosing the spatiotemporal dynamics of filamentation. Acoustic emission (AE) during filamentation opens a door to give the insight into the dynamic evolution of filaments in air. In particular, the frequency features of the acoustic emission provide relevant information on the conversion of laser energy to acoustic energy. Here, the acoustic emission of femtosecond laser filament manipulated by energy and the focal lengths was measured quantitatively by a broadband microphone, and the acoustic parameters were compared and analyzed. Our results showed that the acoustic power presents a squared dependence on the laser energy and the bandwidth of the acoustic spectrum showed a significant positive correlation with laser energy deposition. It was found that the spectrum of the acoustic pulse emitted from the middle of the filament has a larger bandwidth compared to those emitted from the ends of the filament and the spectrum of the acoustic pulse is also an indicator of the filament intensity distribution. These findings are helpful for studying the plasma filament properties and complex dynamic processes through acoustic parameters and allow the optimization of remote applications.

physics.optics↗

arXiv2023

Analysis of laser polarization state on remote induced plasma luminescence characteristics of filament in air

The femtosecond laser filamentation is the result of the dynamic interplay between plasma self-focusing and defocusing generated by the multiphoton/tunnel ionization of air molecules. This equilibrium allows the filament to stably propagate over long distances at high power densities, making it a promising tool for remote sensing in chemical and biological applications, detection of air pollution, and lightning control, which has attracted wide attention. Laser-induced filamentation is a highly nonlinear process, and initial conditions such as changes in polarization state can affect the fluorescence emission of N2 molecules excited by the filament in air. In this article, we analyze in detail the effect of polarization state changes on the 337 nm fluorescence signal excited by the filament at a distance of 30 m. It was found that the fluorescence signal intensity of the linear polarization state was higher than that of the circular polarization state. Through analysis of these phenomena, we explore the mechanism of plasma fluorescence emission during the long-range filamentation process of femtosecond laser, including the electron collision model and the polarization effect on the critical power for filamentation. These findings are important for the understanding of the stimulated radiation from filaments and may find applications in remote sensing of electric field and THz radiation.

physics.optics↗

arXiv2023

Shoggoth: Towards Efficient Edge-Cloud Collaborative Real-Time Video Inference via Adaptive Online Learning

This paper proposes Shoggoth, an efficient edge-cloud collaborative architecture, for boosting inference performance on real-time video of changing scenes. Shoggoth uses online knowledge distillation to improve the accuracy of models suffering from data drift and offloads the labeling process to the cloud, alleviating constrained resources of edge devices. At the edge, we design adaptive training using small batches to adapt models under limited computing power, and adaptive sampling of training frames for robustness and reducing bandwidth. The evaluations on the realistic dataset show 15%-20% model accuracy improvement compared to the edge-only strategy and fewer network costs than the cloud-only strategy.

cs.CV↗

arXiv2023

Coupled air lasing gain and Mie scattering loss: aerosol effect in filament-induced plasma spectroscopy

Femtosecond laser filament-induced plasma spectroscopy (FIPS) demonstrates great potentials in the remote sensing for identifying atmospheric pollutant molecules. Due to the widespread aerosols in atmosphere, the remote detection based on FIPS would be affected from both the excitation and the propagation of fingerprint fluorescence, which still remain elusive. Here the physical model of filament-induced aerosol fluorescence is established to reveal the combined effect of Mie scattering and amplification spontaneous emission, which is then proved by the experimental results, the dependence of the backward fluorescence on the interaction length between filament and aerosols. These findings provide an insight into the complicated aerosol effect in the overall physical process of FIPS including propagation, excitation and emission, paving the way to its practical application in atmospheric remote sensing.

physics.optics↗

arXiv2023

Filament based Ionizing Radiation Sensing Technology

Accidental exposure to overdose ionizing radiation will inevitably lead to severe biological damage, thus detecting and localizing radiation is essential. Traditional measurement techniques are generally restricted to the limited detection range of few centimeters, posing a great risk to operators. The potential in remote sensing makes femtosecond laser filament technology great candidates for constructively address this challenge. Here we propose a novel filament-based ionizing radiation sensing technology (FIRST), and clarify the interaction mechanism between filaments and ionizing radiation. Specifically, it is demonstrated that the energetic electrons and ions produced by α radiation in air can be effectively accelerated within the filament, serving as seed electrons, which will markedly enhance nitrogen fluorescence. The extended nitrogen fluorescence lifetime of ~1 ns is also observed. These findings provide insights into the intricate interaction among ultra-strong light filed, plasma and energetic particle beam, and pave the way for the remote sensing of ionizing radiation.

physics.plasm-ph↗

arXiv2023

Remote Sensing of Trace Element in Sea Salt Aerosol with Sensitivity Level of 10 pg/m3

Sea salt aerosols composed mainly of micrometer-sized sodium chloride particles not only pose a potential threat to human health and traffic safety, but also directly affect climate prediction. The long-range and high-precision sensing of sea salt aerosols remains a challenge for existing composition analysis methods. As the development of ultrashort laser technology, femtosecond laser filamentation provides a new opportunity for molecular remote sensing in complex environments. However, the accuracy at long-distance of this method is still hard to meet the demand (<10 ng/m3) for the remote aerosol monitoring. To solve this problem, we built a remote detection system for sea salt aerosol fluorescence spectroscopy and obtained a very high system sensitivity by introducing a terawatt-class high-performance femtosecond laser and optimizing the filament and aerosol interaction length. The system achieves a Na+ detection limit of 0.015 ng/m3 at a detection distance of 30 m, and 0.006 ng/m3 when supplemented with a deep processing learning algorithm.

physics.ins-det↗

arXiv2023

Temperature-Dependent and Magnetism-Controlled Fermi Surface Changes in Magnetic Weyl Semimetals

The coupling between band structure and magnetism can lead to intricate Fermi surface modifications. Here we report on the comprehensive study of the Shubnikov-de Haas (SdH) effect in two rare-earth-based magnetic Weyl semimetals, NdAlSi and CeAlSi$_{0.8}$Ge$_{0.2}$. The results show that the temperature evolution of topologically nontrivial Fermi surfaces strongly depends on magnetic configurations. In NdAlSi, the SdH frequencies vary with temperature in both the paramagnetic state and the magnetically ordered state with a chiral spin texture, but become temperature independent in the high-field fully polarized state. In CeAlSi$_{0.8}$Ge$_{0.2}$, SdH frequencies are temperature-dependent only in the ferromagnetic state with magnetic fields applied along the $c$ axis. First-principles calculations suggest that the notable temperature and magnetic-configuration dependence of Fermi surface morphology can be attributed to strong exchange coupling between the conduction electrons and local magnetic moments.

cond-mat.str-el↗

arXiv2023

Sub-ppb aerosol detection at a distance of 30 meters by millijoule femtosecond laser pulse filamentation in air

In this work, sub-ppb aerosol detection is achieved by femtosecond laser filament with a single pulse energy of 4 mJ at a distance of 30 m. A concave mirror with an open aperture of 41.4 cm is employed in an off-axis optical system to focus the femtosecond laser beam and collect the fluorescence of NaCl aerosol. The simulation and experimental results show that the astigmatism can be greatly reduced when femtosecond laser beam is incident non-symmetrically on the concave mirror. Compared with the case that femtosecond laser strikes at the center of the concave mirror, the intensity of the optical filament is increased by 69.5 times, and the detection of limit of sodium chloride aerosol is reduced by 86%, which is down to 0.32 ppb. The improved excitation scheme in this work utilizes the nonsymmetrical beam spot on the concave mirror to compensate the non-symmetry induced by the off-axis setup, reducing the astigmatism of the focusing laser beam and improving the aerosol's detection of limit.

physics.optics↗

arXiv2023

For Intelligent and Higher Spectrum Efficiency: A Variable Packing Ratio Transmission System Based on Faster-than-Nyquist and Deep Learning

With the rapid development of various services in wireless communications, spectrum resource has become increasingly valuable. Faster than Nyquist (FTN) signaling, proposed in the 1970s, is a promising paradigm for improving spectrum utilization. This paper proposes intelligent variable-packing-ratio (VPR)-based transmissions for high spectrum efficiency (SE) and security, respectively. Aided by deep learning (DL)-based estimation, the proposed scheme for high SE can achieve a higher capacity with negligible modification to existing communication paradigms (e.g., spectrum allocation or frame structure). Also, for VPR-based secure transmission, a dynamic generation scheme is proposed to produce randomly distributed positions to switch the packing ratio, which can effectively avoid detections and attacks. In addition, we propose a simplified DL-based packing ratio estimation for both of these two scenarios so that the receiver can estimate the packing ratio without any in-band or out-band control messages. Simulation results show that the proposed simplified estimation achieves nearly the same accuracy and convergence speed as the original multi-branch fully-connected structure with a complexity reduction of 20 folds. Finally, we derive the closed-form SE of the proposed VPR transmission under different channels. The numerical results validate the correctness of the derivation and demonstrate the SE gains of the VPR scheme beyond conventional Nyquist transmission.

eess.SP↗

arXiv2022

Boosting COVID-19 Severity Detection with Infection-aware Contrastive Mixup Classification

This paper presents our solution for the 2nd COVID-19 Severity Detection Competition. This task aims to distinguish the Mild, Moderate, Severe, and Critical grades in COVID-19 chest CT images. In our approach, we devise a novel infection-aware 3D Contrastive Mixup Classification network for severity grading. Specifcally, we train two segmentation networks to first extract the lung region and then the inner lesion region. The lesion segmentation mask serves as complementary information for the original CT slices. To relieve the issue of imbalanced data distribution, we further improve the advanced Contrastive Mixup Classification network by weighted cross-entropy loss. On the COVID-19 severity detection leaderboard, our approach won the first place with a Macro F1 Score of 51.76%. It significantly outperforms the baseline method by over 11.46%.

eess.IV↗

arXiv2022

Coordinating Cross-modal Distillation for Molecular Property Prediction

In recent years, molecular graph representation learning (GRL) has drawn much more attention in molecular property prediction (MPP) problems. The existing graph methods have demonstrated that 3D geometric information is significant for better performance in MPP. However, accurate 3D structures are often costly and time-consuming to obtain, limiting the large-scale application of GRL. It is an intuitive solution to train with 3D to 2D knowledge distillation and predict with only 2D inputs. But some challenging problems remain open for 3D to 2D distillation. One is that the 3D view is quite distinct from the 2D view, and the other is that the gradient magnitudes of atoms in distillation are discrepant and unstable due to the variable molecular size. To address these challenging problems, we exclusively propose a distillation framework that contains global molecular distillation and local atom distillation. We also provide a theoretical insight to justify how to coordinate atom and molecular information, which tackles the drawback of variable molecular size for atom information distillation. Experimental results on two popular molecular datasets demonstrate that our proposed model achieves superior performance over other methods. Specifically, on the largest MPP dataset PCQM4Mv2 served as an "ImageNet Large Scale Visual Recognition Challenge" in the field of graph ML, the proposed method achieved a 6.9% improvement compared with the best works. And we obtained fourth place with the MAE of 0.0734 on the test-challenge set for OGB-LSC 2022 Graph Regression Task. We will release the code soon.

cs.LG↗

arXiv2022

CMC v2: Towards More Accurate COVID-19 Detection with Discriminative Video Priors

This paper presents our solution for the 2nd COVID-19 Competition, occurring in the framework of the AIMIA Workshop at the European Conference on Computer Vision (ECCV 2022). In our approach, we employ the winning solution last year which uses a strong 3D Contrastive Mixup Classifcation network (CMC v1) as the baseline method, composed of contrastive representation learning and mixup classification. In this paper, we propose CMC v2 by introducing natural video priors to COVID-19 diagnosis. Specifcally, we adapt a pre-trained (on video dataset) video transformer backbone to COVID-19 detection. Moreover, advanced training strategies, including hybrid mixup and cutmix, slicelevel augmentation, and small resolution training are also utilized to boost the robustness and the generalization ability of the model. Among 14 participating teams, CMC v2 ranked 1st in the 2nd COVID-19 Competition with an average Macro F1 Score of 89.11%.

eess.IV↗

arXiv2022

Explainable Human-in-the-loop Dynamic Data-Driven Digital Twins

Digital Twins (DT) are essentially dynamic data-driven models that serve as real-time symbiotic "virtual replicas" of real-world systems. DT can leverage fundamentals of Dynamic Data-Driven Applications Systems (DDDAS) bidirectional symbiotic sensing feedback loops for its continuous updates. Sensing loops can consequently steer measurement, analysis and reconfiguration aimed at more accurate modelling and analysis in DT. The reconfiguration decisions can be autonomous or interactive, keeping human-in-the-loop. The trustworthiness of these decisions can be hindered by inadequate explainability of the rationale, and utility gained in implementing the decision for the given situation among alternatives. Additionally, different decision-making algorithms and models have varying complexity, quality and can result in different utility gained for the model. The inadequacy of explainability can limit the extent to which humans can evaluate the decisions, often leading to updates which are unfit for the given situation, erroneous, compromising the overall accuracy of the model. The novel contribution of this paper is an approach to harnessing explainability in human-in-the-loop DDDAS and DT systems, leveraging bidirectional symbiotic sensing feedback. The approach utilises interpretable machine learning and goal modelling to explainability, and considers trade-off analysis of utility gained. We use examples from smart warehousing to demonstrate the approach.

cs.AI↗

arXiv2022

A Joint Estimation Approach to Sparse Additive Ordinary Differential Equations

Ordinary differential equations (ODEs) are widely used to characterize the dynamics of complex systems in real applications. In this article, we propose a novel joint estimation approach for generalized sparse additive ODEs where observations are allowed to be non-Gaussian. The new method is unified with existing collocation methods by considering the likelihood, ODE fidelity and sparse regularization simultaneously. We design a block coordinate descent algorithm for optimizing the non-convex and non-differentiable objective function. The global convergence of the algorithm is established. The simulation study and two applications demonstrate the superior performance of the proposed method in estimation and improved performance of identifying the sparse structure.

stat.ME↗

arXiv2022

A spherical harmonics method for processing anisotropic X-ray atomic pair distribution functions

A general spherical harmonics method is described for extracting anisotropic pair distribution functions (PDF) in this work. In the structural study of functional crystallized materials, the investigation of the local structures under the application of external stimuli, such as electric field and stress, is in urgent need. A well-established technique for local structure studies is PDF analysis, but the extraction of the X-ray PDF data is usually based on angular integrations of isotropic X-ray structure functions, which is no longer valid for the anisotropic responses of the materials under orientation-dependent stimuli. Therefore, we have developed an advanced spherical harmonics method to transform two-dimensional X-ray diffraction patterns into anisotropic PDF data, based on three-dimensional diffraction geometry and Fourier transform. The electrical-field-induced local structural change in the PbZr0.54Ti0.46O3 ceramics is then presented to demonstrate the method's effectiveness.

cond-mat.mtrl-sci↗

arXiv2022

Dynamical Modeling for non-Gaussian Data with High-dimensional Sparse Ordinary Differential Equations

Ordinary differential equations (ODE) have been widely used for modeling dynamical complex systems. For high-dimensional ODE models where the number of differential equations is large, it remains challenging to estimate the ODE parameters and to identify the sparse structure of the ODE models. Most existing methods exploit the least-square based approach and are only applicable to Gaussian observations. However, as discrete data are ubiquitous in applications, it is of practical importance to develop dynamic modeling for non-Gaussian observations. New methods and algorithms are developed for both parameter estimation and sparse structure identification in high-dimensional linear ODE systems. First, the high-dimensional generalized profiling method is proposed as a likelihood-based approach with ODE fidelity and sparsity-inducing regularization, along with efficient computation based on parameter cascading. Second, two versions of the two-step collocation methods are extended to the non-Gaussian set-up by incorporating the iteratively reweighted least squares technique. Simulations show that the profiling procedure has excellent performance in latent process and derivative fitting and ODE parameter estimation, while the two-step collocation approach excels in identifying the sparse structure of the ODE system. The usefulness of the proposed methods is also demonstrated by analyzing three real datasets from Google trends, stock market sectors, and yeast cell cycle studies.

stat.ME↗