arXiv subjects

Pan Wang

Publications and source records attributed to Pan Wang.

At least 55 records · Page 3Linked to original sources

arXiv2024

FairSkin: Fair Diffusion for Skin Disease Image Generation

Image generation is a prevailing technique for clinical data augmentation for advancing diagnostic accuracy and reducing healthcare disparities. Diffusion Model (DM) has become a leading method in generating synthetic medical images, but it suffers from a critical twofold bias: (1) The quality of images generated for Caucasian individuals is significantly higher, as measured by the Frechet Inception Distance (FID). (2) The ability of the downstream-task learner to learn critical features from disease images varies across different skin tones. These biases pose significant risks, particularly in skin disease detection, where underrepresentation of certain skin tones can lead to misdiagnosis or neglect of specific conditions. To address these challenges, we propose FairSkin, a novel DM framework that mitigates these biases through a three-level resampling mechanism, ensuring fairer representation across racial and disease categories. Our approach significantly improves the diversity and quality of generated images, contributing to more equitable skin disease detection in clinical settings.

cs.CV↗

arXiv2024

A Robust and Efficient Multi-physics Numerical System for Intensive Blast Wave Propagation in Complex Environments

We establish a high-resolution, high-performance, and high-confidence compressible multiphysics system in a Cartesian grid with irregular boundary topologies to simulate intensive blast waves propagating in large-scale and extremely complex environments. The multiphysics system is modeled by a multi-component model solved using a generalized Godunov method and a classical material point method in a combination of Lagrangian particles and a rigid material model. An artificial neural network equation of state (EOS) is proposed based on experimental data to simulate the intensive explosion products and real gas under extreme pressure and temperature. To improve computational accuracy and efficiency, a deepMTBVD reconstruction scheme of our previous work is extended to the multiphysics system. With the aid of high-performance parallel computation, several large-scale blast wave applications, such as blast wave propagating in a local and entire urban city, are simulated in a reasonable time period, which can validate numerical schemes and lead to more practical engineering applications.

physics.comp-ph↗

arXiv2024

AIM 2024 Sparse Neural Rendering Challenge: Methods and Results

This paper reviews the challenge on Sparse Neural Rendering that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2024. This manuscript focuses on the competition set-up, the proposed methods and their respective results. The challenge aims at producing novel camera view synthesis of diverse scenes from sparse image observations. It is composed of two tracks, with differing levels of sparsity; 3 views in Track 1 (very sparse) and 9 views in Track 2 (sparse). Participants are asked to optimise objective fidelity to the ground-truth images as measured via the Peak Signal-to-Noise Ratio (PSNR) metric. For both tracks, we use the newly introduced Sparse Rendering (SpaRe) dataset and the popular DTU MVS dataset. In this challenge, 5 teams submitted final results to Track 1 and 4 teams submitted final results to Track 2. The submitted models are varied and push the boundaries of the current state-of-the-art in sparse neural rendering. A detailed description of all models developed in the challenge is provided in this paper.

cs.CV↗

arXiv2024

Active control of excitonic strong coupling and electroluminescence in electrically driven plasmonic nanocavities

Enhancement and active control of light-matter interactions at the atomic scale is important for developing next-generation nanophotonic and quantum optical devices. Here, we demonstrate electric control of both excitonic strong coupling and electroluminescence by integrating semiconductor monolayers into a nanometer gap of electrically driven nanocube-on-mirror plasmonic nanocavities. Particularly, in a strongly-coupled system of nanocavity plasmons and WSe2 excitons, the ultra-strong electric field generated in the nanocavity gap enables a reversible modulation of the Rabi splitting between ~102 and 80 meV with a bias below 2.5 V. In the quantum tunnelling regime, by injecting carriers into a nanocavity-integrated WS2 monolayer, bias-controlled spectrally tunable electroluminescence from charged or neutral excitons is achieved with an external quantum efficiency reaching ~3.5%. These results underline practical approaches to electric control of atomic-scale light-matter interactions for applications including nanoscale light sources, ultrafast electro-optic modulation, quantum information processing and sensing.

physics.optics↗

arXiv2024

Physics-informed Data-driven Cavitation Model for a Specific MG EOS

We present a novel one-fluid cavitation model of a specific Mie-Grüneisen equation of state(EOS), named polynomial EOS, based on an artificial neural network. Not only the physics-informed equation but also the experimental data are embedded into the proposed model by an optimization problem. The physics-informed data-driven model provides the concerned pressure within the cavitation region, where the density tends to zero when the pressure falls below the saturated pressure. The present model is then applied to computing the challenging compressible multi-phase flow simulation, such as nuclear and underwater explosions. Numerical simulations show that our model in application agrees well with the corresponding experimental data, ranging from one dimension to three dimensions with the $h-$adaptive mesh refinement algorithm and load balance techniques in the structured and unstructured grid.

physics.flu-dyn↗

arXiv2024

FrameNeRF: A Simple and Efficient Framework for Few-shot Novel View Synthesis

We present a novel framework, called FrameNeRF, designed to apply off-the-shelf fast high-fidelity NeRF models with fast training speed and high rendering quality for few-shot novel view synthesis tasks. The training stability of fast high-fidelity models is typically constrained to dense views, making them unsuitable for few-shot novel view synthesis tasks. To address this limitation, we utilize a regularization model as a data generator to produce dense views from sparse inputs, facilitating subsequent training of fast high-fidelity models. Since these dense views are pseudo ground truth generated by the regularization model, original sparse images are then used to fine-tune the fast high-fidelity model. This process helps the model learn realistic details and correct artifacts introduced in earlier stages. By leveraging an off-the-shelf regularization model and a fast high-fidelity model, our approach achieves state-of-the-art performance across various benchmark datasets.

cs.CV↗

arXiv2023

RadOcc: Learning Cross-Modality Occupancy Knowledge through Rendering Assisted Distillation

3D occupancy prediction is an emerging task that aims to estimate the occupancy states and semantics of 3D scenes using multi-view images. However, image-based scene perception encounters significant challenges in achieving accurate prediction due to the absence of geometric priors. In this paper, we address this issue by exploring cross-modal knowledge distillation in this task, i.e., we leverage a stronger multi-modal model to guide the visual model during training. In practice, we observe that directly applying features or logits alignment, proposed and widely used in bird's-eyeview (BEV) perception, does not yield satisfactory results. To overcome this problem, we introduce RadOcc, a Rendering assisted distillation paradigm for 3D Occupancy prediction. By employing differentiable volume rendering, we generate depth and semantic maps in perspective views and propose two novel consistency criteria between the rendered outputs of teacher and student models. Specifically, the depth consistency loss aligns the termination distributions of the rendered rays, while the semantic consistency loss mimics the intra-segment similarity guided by vision foundation models (VLMs). Experimental results on the nuScenes dataset demonstrate the effectiveness of our proposed method in improving various 3D occupancy prediction approaches, e.g., our proposed methodology enhances our baseline by 2.2% in the metric of mIoU and achieves 50% in Occ3D benchmark.

cs.CV↗

arXiv2023

Replica symmetry breaking in 1D Rayleigh scattering system: theory and validations

Spin glass theory, as a paradigm for describing disordered magnetic systems, constitutes a prominent subject of study within statistical physics. Replica symmetry breaking (RSB), as one of the pivotal concepts for the understanding of spin glass theory, means that, under identical conditions disordered systems can yield distinct states with nontrivial correlations. Random fiber laser (RFL) based on Rayleigh scattering (RS) is a complex disordered system, owing to the disorder and stochasticity of RS. In this work, for the first time, we elaborate a precise theoretical model for studying the photonic phase transition via the platform of RS-based RFL, in which we clearly reveal that, apart from the pump power, the photon phase variation in RFL is also an analogy to the temperature term in spin glass phase transition, leading to a novel insight into the intrinsic mechanisms of photonic phase transition. In addition, based on this model and real-time high-fidelity detection spectral evolution, we theoretically predict and experimentally observe the mode-asymmetric characteristics of photonic phase transition in RS-based RFL. This finding contributes to a deeper understanding of the photonic RSB regime and the dynamics of RS-based RFL.

cond-mat.dis-nn↗

arXiv2023

Large-area, freestanding single-crystal gold of single nanometer thickness

Two-dimensional single-crystal metals are highly sought after for next-generation technologies. Here, we report large-area (>10^4 μm2), single-crystal two-dimensional gold with thicknesses down to a single-nanometer level, employing an atomic-level-precision chemical etching approach. The ultrathin thickness and single-crystal quality endow two-dimensional gold with unique properties including significantly quantum-confinement-augmented optical nonlinearity, low sheet resistance, high transparency and excellent mechanical flexibility. By patterning the two-dimensional gold into nanoribbon arrays, extremely-confined near-infrared plasmonic resonances are further demonstrated with quality factors up to 5. The freestanding nature of two-dimensional gold allows its straightforward manipulation and transfer-printing for integration with other structures. The developed two-dimensional gold provides an emerging platform for fundamental studies in various disciplines and opens up new opportunities for applications in high-performance ultrathin optoelectronic, photonic and quantum devices.

physics.optics↗

arXiv2023

FedEdge AI-TC: A Semi-supervised Traffic Classification Method based on Trusted Federated Deep Learning for Mobile Edge Computing

As a typical entity of MEC (Mobile Edge Computing), 5G CPE (Customer Premise Equipment)/HGU (Home Gateway Unit) has proven to be a promising alternative to traditional Smart Home Gateway. Network TC (Traffic Classification) is a vital service quality assurance and security management method for communication networks, which has become a crucial functional entity in 5G CPE/HGU. In recent years, many researchers have applied Machine Learning or Deep Learning (DL) to TC, namely AI-TC, to improve its performance. However, AI-TC faces challenges, including data dependency, resource-intensive traffic labeling, and user privacy concerns. The limited computing resources of 5G CPE further complicate efficient classification. Moreover, the "black box" nature of AI-TC models raises transparency and credibility issues. The paper proposes the FedEdge AI-TC framework, leveraging Federated Learning (FL) for reliable Network TC in 5G CPE. FL ensures privacy by employing local training, model parameter iteration, and centralized training. A semi-supervised TC algorithm based on Variational Auto-Encoder (VAE) and convolutional neural network (CNN) reduces data dependency while maintaining accuracy. To optimize model light-weight deployment, the paper introduces XAI-Pruning, an AI model compression method combined with DL model interpretability. Experimental evaluation demonstrates FedEdge AI-TC's superiority over benchmarks in terms of accuracy and efficient TC performance. The framework enhances user privacy and model credibility, offering a comprehensive solution for dependable and transparent Network TC in 5G CPE, thus enhancing service quality and security.

cs.CR↗

arXiv2023

Contrast-augmented Diffusion Model with Fine-grained Sequence Alignment for Markup-to-Image Generation

The recently rising markup-to-image generation poses greater challenges as compared to natural image generation, due to its low tolerance for errors as well as the complex sequence and context correlations between markup and rendered image. This paper proposes a novel model named "Contrast-augmented Diffusion Model with Fine-grained Sequence Alignment" (FSA-CDM), which introduces contrastive positive/negative samples into the diffusion model to boost performance for markup-to-image generation. Technically, we design a fine-grained cross-modal alignment module to well explore the sequence similarity between the two modalities for learning robust feature representations. To improve the generalization ability, we propose a contrast-augmented diffusion model to explicitly explore positive and negative samples by maximizing a novel contrastive variational objective, which is mathematically inferred to provide a tighter bound for the model's optimization. Moreover, the context-aware cross attention module is developed to capture the contextual information within markup language during the denoising process, yielding better noise prediction results. Extensive experiments are conducted on four benchmark datasets from different domains, and the experimental results demonstrate the effectiveness of the proposed components in FSA-CDM, significantly exceeding state-of-the-art performance by about 2%-12% DTW improvements. The code will be released at https://github.com/zgj77/FSACDM.

cs.CV↗

arXiv2023

High performance artificial visual system with plasmon-enhanced 2D material neural network

Artificial visual systems (AVS) have gained tremendous momentum because of its huge potential in areas such as autonomous vehicles and robotics as part of artificial intelligence (AI) in recent years. However, current machine visual systems composed of complex circuits based on complementary metal oxide semiconductor (CMOS) platform usually contains photosensor array, format conversion, memory and processing module. The large amount of redundant data shuttling between each unit, resulting in large latency and high power consumption, which greatly limits the performance of the AVS. Here, we demonstrate an AVS based on a new design concept, which consists of hardware devices connected in an artificial neural network (ANN) that can simultaneously sense, pre-process and recognize optical images without latency. The Ag nanograting and the two-dimensional (2D) heterostructure integrated plasmonic phototransistor array (PPTA) constitute the hardware ANN, and its synaptic weight is determined by the adjustable regularized photoresponsivity matrix. The eye-inspired pre-processing function of the device under photoelectric synergy ensures the considerable improvement of the efficiency and accuracy of subsequent image recognition. The comprehensive performance of the proof-of-concept device demonstrates great potential for machine vision applications in terms of large dynamic range (180 dB), high speed (500 ns) and ultralow energy consumption per spike (2.4e(-17) J).

physics.optics↗

arXiv2023

Fast Fabrication of WS2/Bi2Se3 Heterostructures for High Performance Photodetection

Two-dimensional (2D) material heterostructures have attracted considerable attention owing to their interesting and novel physical properties, which expand the possibilities for future optoelectronic, photovoltaic, and nanoelectronic applications. A portable, fast, and deterministic transfer technique is highly needed for the fabrication of heterostructures. Herein, we report a fast half wet poly(dimethylsiloxane) (PDMS) transfer process utilizing the change of adhesion energy with the help of micron-sized water droplets. Using this method, a vertical stacking of the WS2/Bi2Se3 heterostructure with a straddling band configuration is successfully assembled on a fluorophlogopite substrate. Thanks to the complementary band gaps and high efficiency of interfacial charge transfer, the photodetector based on the heterostructure exhibits a superior responsivity of 109.9 A/W for a visible incident light at 473 nm and 26.7 A/W for a 1064 nm near-infrared illumination. Such high photoresponsivity of the heterostructure demonstrates that our transfer method not only owns time efficiency but also ensures high quality of the heterointerface. Our study may open new pathways to the fast and massive fabrication of various vertical 2D heterostructures for applications in twistronics/valleytronics and other band engineering devices.

cond-mat.mes-hall↗

arXiv2023

Implicit and Efficient Point Cloud Completion for 3D Single Object Tracking

The point cloud based 3D single object tracking has drawn increasing attention. Although many breakthroughs have been achieved, we also reveal two severe issues. By extensive analysis, we find the prediction manner of current approaches is non-robust, i.e., exposing a misalignment gap between prediction score and actually localization accuracy. Another issue is the sparse point returns will damage the feature matching procedure of the SOT task. Based on these insights, we introduce two novel modules, i.e., Adaptive Refine Prediction (ARP) and Target Knowledge Transfer (TKT), to tackle them, respectively. To this end, we first design a strong pipeline to extract discriminative features and conduct the matching with the attention mechanism. Then, ARP module is proposed to tackle the misalignment issue by aggregating all predicted candidates with valuable clues. Finally, TKT module is designed to effectively overcome incomplete point cloud due to sparse and occlusion issues. We call our overall framework PCET. By conducting extensive experiments on the KITTI and Waymo Open Dataset, our model achieves state-of-the-art performance while maintaining a lower computational cost.

cs.CV↗

arXiv2023

Multi-scale multi-modal micro-expression recognition algorithm based on transformer

A micro-expression is a spontaneous unconscious facial muscle movement that can reveal the true emotions people attempt to hide. Although manual methods have made good progress and deep learning is gaining prominence. Due to the short duration of micro-expression and different scales of expressed in facial regions, existing algorithms cannot extract multi-modal multi-scale facial region features while taking into account contextual information to learn underlying features. Therefore, in order to solve the above problems, a multi-modal multi-scale algorithm based on transformer network is proposed in this paper, aiming to fully learn local multi-grained features of micro-expressions through two modal features of micro-expressions - motion features and texture features. To obtain local area features of the face at different scales, we learned patch features at different scales for both modalities, and then fused multi-layer multi-headed attention weights to obtain effective features by weighting the patch features, and combined cross-modal contrastive learning for model optimization. We conducted comprehensive experiments on three spontaneous datasets, and the results show the accuracy of the proposed algorithm in single measurement SMIC database is up to 78.73% and the F1 value on CASMEII of the combined database is up to 0.9071, which is at the leading level.

cs.CV↗

arXiv2023

Reconstruction of compressed spectral imaging based on global structure and spectral correlation

In this paper, a convolutional sparse coding method based on global structure characteristics and spectral correlation is proposed for the reconstruction of compressive spectral images. The spectral data is regarded as the convolution sum of the convolution kernel and the corresponding coefficients, using the convolution kernel operates the global image information, preserving the structure information of the spectral image in the spatial dimension. To take full exploration of the constraints between spectra, the coefficients corresponding to the convolution kernel are constrained by the L_(2,1)norm to improve spectral accuracy. And, to solve the problem that convolutional sparse coding is insensitive to low frequency, the global total-variation (TV) constraint is added to estimate the low-frequency components. It not only ensures the effective estimation of the low-frequency but also transforms the convolutional sparse coding into a de-noising process, which makes the reconstructing process simpler. Simulations show that compared with the current mainstream optimization methods, the proposed method can improve the reconstruction quality by up to 4 dB in PSNR and 10% in SSIM, and has a great improvement in the details of the reconstructed image.

cs.CV↗

arXiv2022

Radiation build-up and dissipation in random fiber laser

Random fiber laser (RFL) is a complex physical system that arises from the distributed amplification and the intrinsic stochasticity of the fiber scattering. There has been widespread interest in analyzing the underlying lightwave kinetics at steady state. However, the transient state, such as the RFL build-up and dissipation, is also particularly important for unfolding lightwave interaction process. Here, we investigate for the first time the RFL dynamics at transient state, and track the RFL temporal and spectral evolution theoretically and experimentally. Particularly, with the contribution of randomly distributed feedback, the build-up of RFL shows continuous Verhulst logistic growth curves without cavity-related features, which is significantly different from the step-like growth curve of conventional fiber lasers. Furthermore, the radiation build-up duration is inversely related to the pump power, and the spectral evolution of RFL undergoes two phases from spectral density increase to spectral broadening. From steady-state to pump switch-off state, the RFL output power dissipates immediately, and the remaining Stokes lightwave from the Rayleigh scattering will gradually disappear after one round-trip. This work provides new insights into the transient dynamics features of the RFL.

physics.optics↗

arXiv2022

Experimental Performance Evaluation of Cell-free Massive MIMO Systems Using COTS RRU with OTA Reciprocity Calibration and Phase Synchronization

Downlink coherent multiuser transmission is an essential technique for cell-free massive multiple-input multiple output (MIMO) systems, and the availability of channel state information (CSI) at the transmitter is a basic requirement. To avoid CSI feedback in a time-division duplex system, the uplink channel parameters should be calibrated to obtain the downlink CSI due to the radio frequency circuit mismatch of the transceiver. In this paper, a design of a reference signal for over-the-air reciprocity calibration is proposed. The frequency domain generated reference signals can make full use of the flexible frame structure of the fifth generation (5G) new radio, which can be completely transparent to commercial off-the-shelf (COTS) remote radio units (RRUs) and commercial user equipments. To further obtain the calibration of multiple RRUs, an interleaved RRU grouping with a genetic algorithm is proposed, and an averaged Argos calibration algorithm is also presented. We develop a cell-free massive MIMO prototype system with COTS RRUs, demonstrate the statistical characteristics of the calibration error and the effectiveness of the calibration algorithm, and evaluate the impact of the calibration delay on the different cooperative transmission schemes.

cs.IT↗