arXiv subjects

Xiao Liang

Publications and source records attributed to Xiao Liang.

At least 181 records · Page 10Linked to original sources

arXiv2023

Dual-resonance nanostructures for colour down-conversion of colloidal quantum emitters

Linear colour conversion is a process where an emitter absorbs a photon and then emits another photon with either higher or lower energy, corresponding to up- or down conversion, respectively. In this regard, the presence of a volumetric cavity plays a crucial role in enhancing absorption and photoluminescence (PL), as it allows for large volumes of interaction between the exciting photons and the emissive materials, maximising the colour conversion efficiency. Here, we present a dual resonance nanostructure made of a titanium dioxide (TiO2) subwavelength grating to enhance the colour down-conversion efficiency of green light at ~530 nm emitted by gradient alloyed CdxZn1-xSeyS1-y colloidal quantum dots (QDs) when excited with a blue light at ~460 nm. A large mode volume can be created within the QD layer by the hybridisation of the grating resonances and waveguide modes. This allows increasing mode overlap between the resonances and the QDs, resulting in large absorption and tailored emission enhancements. Particularly, we achieved polarized light emission with maximum photoluminescence enhancement of ~140 times at a specific angular direction, and a total enhancement of ~34 times within 0.55 numerical aperture (NA) of the collecting objective. The enhancement encompasses absorption enhancement, Purcell enhancement and directionality enhancement (i.e., outcoupling). We achieved total absorption of 35% for green QDs with a remarkably thin colour conversion layer of ~ 400 nm (inclusive of the TiO2 layer). This work provides a guideline for designing large-volume cavities for practical application in absorption/fluorescence enhancement, such as down colour conversion in microLED displays, detectors or photovoltaics.

physics.optics↗

arXiv2023

Achieving Autonomous Cloth Manipulation with Optimal Control via Differentiable Physics-Aware Regularization and Safety Constraints

Cloth manipulation is a category of deformable object manipulation of great interest to the robotics community, from applications of automated laundry-folding and home organizing and cleaning to textiles and flexible manufacturing. Despite the desire for automated cloth manipulation, the thin-shell dynamics and under-actuation nature of cloth present significant challenges for robots to effectively interact with them. Many recent works omit explicit modeling in favor of learning-based methods that may yield control policies directly. However, these methods require large training sets that must be collected and curated. In this regard, we create a framework for differentiable modeling of cloth dynamics leveraging an Extended Position-based Dynamics (XPBD) algorithm. Together with the desired control objective, physics-aware regularization terms are designed for better results, including trajectory smoothness and elastic potential energy. In addition, safety constraints, such as avoiding obstacles, can be specified using signed distance functions (SDFs). We formulate the cloth manipulation task with safety constraints as a constrained optimization problem, which can be effectively solved by mainstream gradient-based optimizers thanks to the end-to-end differentiability of our framework. Finally, we assess the proposed framework for manipulation tasks with various safety thresholds and demonstrate the feasibility of result trajectories on a surgical robot. The effects of the regularization terms are analyzed in an additional ablation study.

cs.RO↗

arXiv2023

High Performance GPU Accelerated MuST Software

The MuST package is a computational software designed for ab initio electronic structure calculations for solids. The Locally Self-consistent Multiple Scattering (LSMS) method implemented in MuST allows to perform the electronic structure calculation for systems with a large number of atoms per unit cell. For the LSMS method with muffin-tin potential approximation, the major computational challenge is the matrix inverse for the scattering matrix calculation, which could take more than 90\% of the computing time. However, the matrix inverse can be significantly accelerated by modern graphical-processing-units (GPUs). In this paper, we discuss our approach to the code acceleration by offloading the matrix inverse tasks to the GPUs through a Fortran-C interface from the Fortran code to the CUDA code. We report our performance results showing significant speedup ratio achieved to the calculations of NiAu alloy, a candidate for thermoelectric materials.

physics.comp-ph↗

arXiv2023

Thermodynamics and evaporation of perfect fluid dark matter black hole in phantom background

We present a novel interpretation of the thermodynamics of perfect fluid dark matter (PFDM) black hole based on Misner-Sharp energy, and then investigate its evaporation behavior. We find that the ratio between dark sector initial density and black hole horizon radius significantly influences black hole evaporation behaviors. We demonstrate that the presence of the dark sector can significantly extend the lifetime of a black hole which is similar to the Reissner-Nordstrom case. Our work reformulates the thermodynamics of PFDM black holes and points out the existence of long-lived black holes in the presence of the dark sector.

gr-qc↗

arXiv2023

High-Resolution Vision Transformers for Pixel-Level Identification of Structural Components and Damage

Visual inspection is predominantly used to evaluate the state of civil structures, but recent developments in unmanned aerial vehicles (UAVs) and artificial intelligence have increased the speed, safety, and reliability of the inspection process. In this study, we develop a semantic segmentation network based on vision transformers and Laplacian pyramids scaling networks for efficiently parsing high-resolution visual inspection images. The massive amounts of collected high-resolution images during inspections can slow down the investigation efforts. And while there have been extensive studies dedicated to the use of deep learning models for damage segmentation, processing high-resolution visual data can pose major computational difficulties. Traditionally, images are either uniformly downsampled or partitioned to cope with computational demands. However, the input is at risk of losing local fine details, such as thin cracks, or global contextual information. Inspired by super-resolution architectures, our vision transformer model learns to resize high-resolution images and masks to retain both the valuable local features and the global semantics without sacrificing computational efficiency. The proposed framework has been evaluated through comprehensive experiments on a dataset of bridge inspection report images using multiple metrics for pixel-wise materials detection.

cs.CV↗

arXiv2023

TransFusion: A Practical and Effective Transformer-based Diffusion Model for 3D Human Motion Prediction

Predicting human motion plays a crucial role in ensuring a safe and effective human-robot close collaboration in intelligent remanufacturing systems of the future. Existing works can be categorized into two groups: those focusing on accuracy, predicting a single future motion, and those generating diverse predictions based on observations. The former group fails to address the uncertainty and multi-modal nature of human motion, while the latter group often produces motion sequences that deviate too far from the ground truth or become unrealistic within historical contexts. To tackle these issues, we propose TransFusion, an innovative and practical diffusion-based model for 3D human motion prediction which can generate samples that are more likely to happen while maintaining a certain level of diversity. Our model leverages Transformer as the backbone with long skip connections between shallow and deep layers. Additionally, we employ the discrete cosine transform to model motion sequences in the frequency space, thereby improving performance. In contrast to prior diffusion-based models that utilize extra modules like cross-attention and adaptive layer normalization to condition the prediction on past observed motion, we treat all inputs, including conditions, as tokens to create a more lightweight model compared to existing approaches. Extensive experimental studies are conducted on benchmark datasets to validate the effectiveness of our human motion prediction model.

cs.RO↗

arXiv2023

DE-TGN: Uncertainty-Aware Human Motion Forecasting using Deep Ensembles

Ensuring the safety of human workers in a collaborative environment with robots is of utmost importance. Although accurate pose prediction models can help prevent collisions between human workers and robots, they are still susceptible to critical errors. In this study, we propose a novel approach called deep ensembles of temporal graph neural networks (DE-TGN) that not only accurately forecast human motion but also provide a measure of prediction uncertainty. By leveraging deep ensembles and employing stochastic Monte-Carlo dropout sampling, we construct a volumetric field representing a range of potential future human poses based on covariance ellipsoids. To validate our framework, we conducted experiments using three motion capture datasets including Human3.6M, and two human-robot interaction scenarios, achieving state-of-the-art prediction error. Moreover, we discovered that deep ensembles not only enable us to quantify uncertainty but also improve the accuracy of our predictions.

cs.RO↗

arXiv2023

Near-Unity Emitting, Widely Tailorable and Stable Exciton Concentrators Built from Doubly Gradient 2D Semiconductor Nanoplatelets

The strength of electrostatic interactions (EI) between electrons and holes within semiconductor nanocrystals profoundly impact the performance of their optoelectronic systems, and different optoelectronic devices demand distinct EI strength of the active medium. However, achieving a broad range, fine-tuning of the EI strength for specific optoelectronic applications is a daunting challenge, especially in quasi 2-dimensional core-shell semiconductor nanoplatelets (NPLs), as the epitaxial growth of the inorganic shell along the direction of the thickness that solely contributes to the quantum confined effect significantly undermines the strength of the EI. Herein we propose and demonstrate a novel doubly-gradient (DG) core-shell architecture of semiconductor NPLs for on-demand tailoring of the EI strength by controlling the localized exciton concentration via in-plane architectural modulation, demonstrated by a wide tuning of radiative recombination rate and exciton binding energy. Moreover, these exciton-concentration-engineered DG NPLs also exhibit a near-unity quantum yield, remarkable thermal and photo stability, as well as considerably suppressed self-absorption. As proof-of-concept demonstrations, highly efficient color converters and high-performance light-emitting diodes (external quantum efficiency: 16.9%, maximum luminance: 43,000 cd/m2) have been achieved based on the DG NPLs. This work thus opens up new avenues for developing high-performance colloidal optoelectronic device applications.

physics.optics↗

arXiv2023

ORRN: An ODE-based Recursive Registration Network for Deformable Respiratory Motion Estimation with Lung 4DCT Images

Deformable Image Registration (DIR) plays a significant role in quantifying deformation in medical data. Recent Deep Learning methods have shown promising accuracy and speedup for registering a pair of medical images. However, in 4D (3D + time) medical data, organ motion, such as respiratory motion and heart beating, can not be effectively modeled by pair-wise methods as they were optimized for image pairs but did not consider the organ motion patterns necessary when considering 4D data. This paper presents ORRN, an Ordinary Differential Equations (ODE)-based recursive image registration network. Our network learns to estimate time-varying voxel velocities for an ODE that models deformation in 4D image data. It adopts a recursive registration strategy to progressively estimate a deformation field through ODE integration of voxel velocities. We evaluate the proposed method on two publicly available lung 4DCT datasets, DIRLab and CREATIS, for two tasks: 1) registering all images to the extreme inhale image for 3D+t deformation tracking and 2) registering extreme exhale to inhale phase images. Our method outperforms other learning-based methods in both tasks, producing the smallest Target Registration Error of 1.24mm and 1.26mm, respectively. Additionally, it produces less than 0.001\% unrealistic image folding, and the computation speed is less than 1 second for each CT volume. ORRN demonstrates promising registration accuracy, deformation plausibility, and computation efficiency on group-wise and pair-wise registration tasks. It has significant implications in enabling fast and accurate respiratory motion estimation for treatment planning in radiation therapy or robot motion planning in thoracic needle insertion.

eess.IV↗

arXiv2023

Continuity in law for solutions of SPDEs with space-time homogeneous Gaussian noise

In this article, we study the continuity in law of the solutions of two linear multiplicative SPDEs (the parabolic Anderson model and the hyperbolic Anderson model) with respect to the spatial parameter of the noise. The solution is interpreted in the Skorohod sense, using Malliavin calculus. We consider two cases: (i) the regular noise, whose spatial covariance is given by the Riesz kernel of order $α\in (0,d)$, in spatial dimension $d\geq 1$; (ii) the rough noise, which is fractional in space with Hurst index $H<1/2$, in spatial dimension $d=1$. We assume that the noise is colored in time. The similar problem for the white noise in time was considered in Bezdek (2016) and Giordano, Jolis and Quer-Sardanyons (2020).

math.PR↗

arXiv2023

LayoutDiffuse: Adapting Foundational Diffusion Models for Layout-to-Image Generation

Layout-to-image generation refers to the task of synthesizing photo-realistic images based on semantic layouts. In this paper, we propose LayoutDiffuse that adapts a foundational diffusion model pretrained on large-scale image or text-image datasets for layout-to-image generation. By adopting a novel neural adaptor based on layout attention and task-aware prompts, our method trains efficiently, generates images with both high perceptual quality and layout alignment, and needs less data. Experiments on three datasets show that our method significantly outperforms other 10 generative models based on GANs, VQ-VAE, and diffusion models.

cs.CV↗

arXiv2023

New starting point registration method for tagged MRI tongue motion estimation

Accurate tongue motion estimation is essential for tongue function evaluation. The harmonic phase processing (HARP) method and the phase vector incompressible registration algorithm (PVIRA) based on HARP can generate motion estimates from tagged MRI images, but they suffer from tag jumping due to large motions. This paper proposes a new registration method by combining the stationary velocity fields produced by PVIRA between successive time frames as a new initialization of the final registration stage to avoid tag jumping. The experiment results demonstrate the proposed method can avoid tag jumping and outperform the existing methods on tongue motion estimates.

eess.IV↗

arXiv2023

Deep Learning (DL)-based Automatic Segmentation of the Internal Pudendal Artery (IPA) for Reduction of Erectile Dysfunction in Definitive Radiotherapy of Localized Prostate Cancer

Background and purpose: Radiation-induced erectile dysfunction (RiED) is commonly seen in prostate cancer patients. Clinical trials have been developed in multiple institutions to investigate whether dose-sparing to the internal-pudendal-arteries (IPA) will improve retention of sexual potency. The IPA is usually not considered a conventional organ-at-risk (OAR) due to segmentation difficulty. In this work, we propose a deep learning (DL)-based auto-segmentation model for the IPA that utilizes CT and MRI or CT alone as the input image modality to accommodate variation in clinical practice. Materials and methods: 86 patients with CT and MRI images and noisy IPA labels were recruited in this study. We split the data into 42/14/30 for model training, testing, and a clinical observer study, respectively. There were three major innovations in this model: 1) we designed an architecture with squeeze-and-excite blocks and modality attention for effective feature extraction and production of accurate segmentation, 2) a novel loss function was used for training the model effectively with noisy labels, and 3) modality dropout strategy was used for making the model capable of segmentation in the absence of MRI. Results: The DSC, ASD, and HD95 values for the test dataset were 62.2%, 2.54mm, and 7mm, respectively. AI segmented contours were dosimetrically equivalent to the expert physician's contours. The observer study showed that expert physicians' scored AI contours (mean=3.7) higher than inexperienced physicians' contours (mean=3.1). When inexperienced physicians started with AI contours, the score improved to 3.7. Conclusion: The proposed model achieved good quality IPA contours to improve uniformity of segmentation and to facilitate introduction of standardized IPA segmentation into clinical trials and practice.

eess.IV↗

arXiv2023

CyberLoc: Towards Accurate Long-term Visual Localization

This technical report introduces CyberLoc, an image-based visual localization pipeline for robust and accurate long-term pose estimation under challenging conditions. The proposed method comprises four modules connected in a sequence. First, a mapping module is applied to build accurate 3D maps of the scene, one map for each reference sequence if there exist multiple reference sequences under different conditions. Second, a single-image-based localization pipeline (retrieval--matching--PnP) is performed to estimate 6-DoF camera poses for each query image, one for each 3D map. Third, a consensus set maximization module is proposed to filter out outlier 6-DoF camera poses, and outputs one 6-DoF camera pose for a query. Finally, a robust pose refinement module is proposed to optimize 6-DoF query poses, taking candidate global 6-DoF camera poses and their corresponding global 2D-3D matches, sparse 2D-2D feature matches between consecutive query images and SLAM poses of the query sequence as input. Experiments on the 4seasons dataset show that our method achieves high accuracy and robustness. In particular, our approach wins the localization challenge of ECCV 2022 workshop on Map-based Localization for Autonomous Driving (MLAD-ECCV2022).

cs.CV↗

arXiv2022

Cepstral Coefficients for Earthquake Damage Assessment of Bridges Leveraging Deep Learning

Bridges are indispensable elements in resilient communities as essential parts of the lifeline transportation systems. Knowledge about the functionality of bridge structures is crucial, especially after a major earthquake event. In this study, we propose signal processing approaches for automated AI-equipped damage detection of bridges. Mel-scaled filter banks and cepstral coefficients are utilized for training a deep learning architecture equipped with Gated Recurrent Unit (GRU) layers that consider the temporal variations in a signal. The proposed framework has been validated on an RC bridge structure in California. The bridge is subjected to 180 bi-directional ground motion records with sampled scale factors and six different intercept angles. Compared with the benchmark cumulative intensity features, the Mel filter banks resulted in 15.5% accuracy in predicting critical drift ratios. The developed strategy for spatio-temporal analysis of signals enhances the robustness of damage diagnosis frameworks that utilize deep learning for monitoring lifeline structures.

eess.SP↗

arXiv2022

High-Fidelity Visual Structural Inspections through Transformers and Learnable Resizers

Visual inspection is the predominant technique for evaluating the condition of civil infrastructure. The recent advances in unmanned aerial vehicles (UAVs) and artificial intelligence have made the visual inspections faster, safer, and more reliable. Camera-equipped UAVs are becoming the new standard in the industry by collecting massive amounts of visual data for human inspectors. Meanwhile, there has been significant research on autonomous visual inspections using deep learning algorithms, including semantic segmentation. While UAVs can capture high-resolution images of buildings' façades, high-resolution segmentation is extremely challenging due to the high computational memory demands. Typically, images are uniformly downsized at the price of losing fine local details. Contrarily, breaking the images into multiple smaller patches can cause a loss of global contextual in-formation. We propose a hybrid strategy that can adapt to different inspections tasks by managing the global and local semantics trade-off. The framework comprises a compound, high-resolution deep learning architecture equipped with an attention-based segmentation model and learnable downsampler-upsampler modules designed for optimal efficiency and in-formation retention. The framework also utilizes vision transformers on a grid of image crops aiming for high precision learning without downsizing. An augmented inference technique is used to boost the performance and re-duce the possible loss of context due to grid cropping. Comprehensive experiments have been performed on 3D physics-based graphics models synthetic environments in the Quake City dataset. The proposed framework is evaluated using several metrics on three segmentation tasks: component type, component damage state, and global damage (crack, rebar, spalling).

eess.IV↗

arXiv2022

Deep Learning based Direct Segmentation Assisted by Deformable Image Registration for Cone-Beam CT based Auto-Segmentation for Adaptive Radiotherapy

Cone-beam CT (CBCT)-based online adaptive radiotherapy calls for accurate auto-segmentation to reduce the time cost for physicians to edit contours. However, deep learning (DL)-based direct segmentation of CBCT images is a challenging task, mainly due to the poor image quality and lack of well-labelled large training datasets. Deformable image registration (DIR) is often used to propagate the manual contours on the planning CT (pCT) of the same patient to CBCT. In this work, we undertake solving the problems mentioned above with the assistance of DIR. Our method consists of three main components. First, we use deformed pCT contours derived from multiple DIR methods between pCT and CBCT as pseudo labels for initial training of the DL-based direct segmentation model. Second, we use deformed pCT contours from another DIR algorithm as influencer volumes to define the region of interest for DL-based direct segmentation. Third, the initially trained DL model is further fine-tuned using a smaller set of true labels. We found that DL-based direct segmentation on CBCT trained with pseudo labels and without influencer volumes shows poor performance compared to DIR-based segmentation. However, adding deformed pCT contours as influencer volumes in the direct segmentation network dramatically improves segmentation performance, reaching the accuracy level of DIR-based segmentation. The DL model with influencer volumes can be further improved through fine-tuning using a smaller set of true labels. Experiments showed that 7 out of 19 structures have an at least 0.2 Dice similarity coefficient increase compared to DIR-based segmentation. A DL-based direct CBCT segmentation model can be improved to outperform DIR-based segmentation models by using deformed pCT contours as pseudo labels and influencer volumes for initial training, and by using a smaller set of true labels for model fine tuning.

physics.med-ph↗

arXiv2022

Coexistence of in-plane and out-of-plane exchange Bias in correlated kagome antiferromagnet Mn3- xCrxSn

The materials exhibiting exchange bias (EB) have been extensively investigated mainly due to their great technological applications in magnetic sensors, but its underlying mechanism remains elusive. Here we report the novel coexistence of in-plane and out-of-plane EB in the Cr-doped Mn3Sn, a non-colinear antiferromagnet with a geometrically frustrated Kagome plane of Mn. Field-cooling experiments with the applied field parallel and perpendicular to the frustrated Kagome plane exhibits loop shifts and enhanced coercivities. Interestingly, a maximum EB field of 1090 Oe is observed along out-of-plane direction in the Mn2.58Cr0.42Sn sample, higher than that of in-plane value. Our results indicate that the exchange bias along perpendicular kagome plane is primarily induced by Dzyaloshinskii-Moriya interactions due to the breaking of interfacial symmetry, while the EB along kagome plane is due to the exchange effects at interface of AFM and FM component originating from the net moment. These findings provide a new insight on EB in the kagome AFM materials, which are important and highly potential to the application of antiferromagnetic spintronics.

cond-mat.mtrl-sci↗