arXiv subjects

Rui Hu

Publications and source records attributed to Rui Hu.

At least 109 records · Page 6Linked to original sources

arXiv2022

TransEM:Residual Swin-Transformer based regularized PET image reconstruction

Positron emission tomography(PET) image reconstruction is an ill-posed inverse problem and suffers from high level of noise due to limited counts received. Recently deep neural networks especially convolutional neural networks(CNN) have been successfully applied to PET image reconstruction. However, the local characteristics of the convolution operator potentially limit the image quality obtained by current CNN-based PET image reconstruction methods. In this paper, we propose a residual swin-transformer based regularizer(RSTR) to incorporate regularization into the iterative reconstruction framework. Specifically, a convolution layer is firstly adopted to extract shallow features, then the deep feature extraction is accomplished by the swin-transformer layer. At last, both deep and shallow features are fused with a residual operation and another convolution layer. Validations on the realistic 3D brain simulated low-count data show that our proposed method outperforms the state-of-the-art methods in both qualitative and quantitative measures.

eess.IV↗

arXiv2022

Robust Optimal Designs when Missing Data Happen at Random

In this article, we investigate the robust optimal design problem for the prediction of response when the fitted regression models are only approximately specified, and observations might be missing completely at random. The intuitive idea is as follows: We assume that data are missing at random, and the complete case analysis is applied. To account for the occurrence of missing data, the design criterion we choose is the mean, for the missing indicator, of the averaged (over the design space) mean squared errors of the predictions. To describe the uncertainty in the specification of the real underlying model, we impose a neighborhood structure on the deterministic part of the regression response and maximize, analytically, the \textbf{M}ean of the averaged \textbf{M}ean squared \textbf{P}rediction \textbf{E}rrors (MMPE), over the entire neighborhood. The maximized MMPE is the ``worst'' loss in the neighborhood of the fitted regression model. Minimizing the maximum MMPE over the class of designs, we obtain robust ``minimax'' designs. The robust designs constructed afford protection from increases in prediction errors resulting from model misspecifications.

stat.ME↗

arXiv2022

Axisymmetric pulsar magnetosphere revisited

We present a global kinetic plasma simulation of an axisymmetric pulsar magnetosphere with self-consistent $e^\pm$ pair production. We use the particle-in-cell method and log-spherical coordinates with a grid size $4096\times 4096$. This allows us to achieve a high voltage induced by the pulsar rotation and investigate pair creation in a young pulsar far from the death line. We find the following. (1) The energy release and $e^\pm$ creation are strongly concentrated in the thin, Y-shaped current sheet, with a peak localized in a small volume at the Y-point. (2) The Y-point is shifted inward from the light cylinder by $\sim 15\%$, and "breathes" with a small amplitude. (3) The dense $e^\pm$ cloud at the Y-point is in ultra-relativistic rotation, which we call super-rotation, because it exceeds co-rotation with the star. The cloud receives angular momentum flowing from the star along the poloidal magnetic lines. (4) Gamma-ray emission peaks at the Y-point and is collimated in the azimuthal direction, tangent to the Y-point circle. (5) The separatrix current sheet between the closed magnetosphere and the open magnetic field lines is sustained by the electron backflow from the Y-point cloud. Its thickness is self-regulated to marginal charge starvation. (6) Only a small fraction of dissipation occurs in the separatrix inward of the Y-point. A much higher power is released in the equatorial plane, especially at the Y-point where the created dense $e^\pm$ plasma is spun up and intermittently ejected through the nozzle between the two open magnetic fluxes.

astro-ph.HE↗

arXiv2022

FasterX: Real-Time Object Detection Based on Edge GPUs for UAV Applications

Real-time object detection on Unmanned Aerial Vehicles (UAVs) is a challenging issue due to the limited computing resources of edge GPU devices as Internet of Things (IoT) nodes. To solve this problem, in this paper, we propose a novel lightweight deep learning architectures named FasterX based on YOLOX model for real-time object detection on edge GPU. First, we design an effective and lightweight PixSF head to replace the original head of YOLOX to better detect small objects, which can be further embedded in the depthwise separable convolution (DS Conv) to achieve a lighter head. Then, a slimmer structure in the Neck layer termed as SlimFPN is developed to reduce parameters of the network, which is a trade-off between accuracy and speed. Furthermore, we embed attention module in the Head layer to improve the feature extraction effect of the prediction head. Meanwhile, we also improve the label assignment strategy and loss function to alleviate category imbalance and box optimization problems of the UAV dataset. Finally, auxiliary heads are presented for online distillation to improve the ability of position embedding and feature extraction in PixSF head. The performance of our lightweight models are validated experimentally on the NVIDIA Jetson NX and Jetson Nano GPU embedded platforms.Extensive experiments show that FasterX models achieve better trade-off between accuracy and latency on VisDrone2021 dataset compared to state-of-the-art models.

cs.CV↗

arXiv2022

Data-based analysis of Forward-Backward Asymmetry in $B^\pm \to K^\pm K^\mp K^\pm$

An analysis of the Forward-Backward Asymmetry (FBA) in the decay $B^\pm \to K^\pm K^\mp K^\pm$ is carried out based on the LHCb data. It is found that the large FBA observed for the invariant mass of the $K^+ K^-$ pair around 1.5 GeV can be explained by the interference of the amplitudes between the resonances with even and odd spins, where the former can be the spin-0 $f_0(1500)$ resonance plus a non-resonance $s$-wave, while the latter is a spin-1 resonance which is probably $ρ^0 (1450)$. The analysis shows the existence of the decay channel $B^\pm\to ρ^0(1450) K^\pm$, with $C\!P$ asymmetry of $A_{CP}(B^\pm\to ρ^0(1450) K^\pm)=(-3.4\pm3.0)\%$. This is in contradiction with the conclusion of BaBar in Phys. Rev. D 85, 112010, according to which the analysis showed no signal of the spin-1 resonance $ρ^0(1450)$. We suggest our experimental colleagues to perform a closer analysis to this channel. We also suggest to perform the measurements of the FBAs (as well as the FB-$C\!P$As) in other three-body decay channels of beauty and charmed mesons, as it is helpful for resonance analysis.

hep-ph↗

arXiv2022

Strengthened Fractional Sobolev Type Inequalities in Besov Spaces

The purpose of this article is twofold. The first is to strengthen fractional Sobolev type inequalities in Besov spaces via the classical Lorentz space. In doing so, we show that the Sobolev inequality in Besov spaces is equivalent to the fractional Hardy inequality and the iso-capacitary type inequality. Secondly, we will strengthen fractional Sobolev type inequalities in Besov spaces via capacitary Lorentz spaces associated with Besov capacities. For this purpose, we first study the embedding of the associated capacitary Lorentz space to the classical Lorentz space. Then, the embedding of the Besov space to the capacitary Lorentz space is established. Meanwhile, we show that these embeddings are closely related to the iso-capacitary type inequalities in terms of a new-introduced fractional $(β, p, q)$-perimeter. Moreover, characterizations of more general Sobolev type inequalities in Besov spaces have also been established.

math.AP↗

arXiv2022

Fractional Besov Trace/Extension Type Inequalities via the Caffarelli-Silvestre extension

Let $u(\cdot,\cdot)$ be the Caffarelli-Silvestre extension of $f.$ The first goal of this article is to establish the fractional trace type inequalities involving the Caffarelli-Silvestre extension $u(\cdot,\cdot)$ of $f.$ In doing so, firstly, we establish the fractional Sobolev/ logarithmic Sobolev/ Hardy trace inequalities in terms of $\nabla_{(x,t)}u(x,t).$ Then, we prove the fractional anisotropic Sobolev/ logarithmic Sobolev/ Hardy trace inequalities in terms of $ {\partial_{t} u(x,t)}$ or $(-Δ)^{-γ/2}u(x,t)$ only. Moreover, based on an estimate of the Fourier transform of the Caffarelli-Silvestre extension kernel and the sharp affine weighted $L^p$ Sobolev inequality, we prove that the $\dot{H}^{-β/2}(\mathbb{R}^n)$ norm of $f(x)$ can be controlled by the product of the weighted $L^p-$affine energy and the weighted $L^p-$norm of ${\partial_{t} u(x,t)}.$ The second goal of this article is to characterize non-negative measures $μ$ on $\mathbb{R}^{n+1}_+$ such that the embeddings $$\|u(\cdot,\cdot)\|_{L^{q_0,p_0}_μ(\mathbb{R}^{n+1})}\lesssim \|f\|_{\dotΛ^{p,q}_β(\mathbb{R}^n)}$$ hold for some $p_0$ and $q_0$ depending on $p$ and $q$ which are classified in three different cases: (1). $p=q\in (n/(n+β),1];$ (2) $(p,q)\in (1,n/β)\times (1,\infty);$ (3). $(p,q)\in (1,n/β)\times\{\infty\}.$ For case (1), the embeddings can be characterized in terms of an analytic condition of the variational capacity minimizing function, the iso-capacitary inequality of open balls, and other weak type inequalities. For cases (2) and (3), the embeddings are characterized by the iso-capacitary inequality for fractonal Besov capacity of open sets.

math.AP↗

arXiv2021

Embeddings of Function Spaces via the Caffarelli-Silvestre Extension, Capacities and Wolff potentials

Let $P_α f(x,t)$ be the Caffarelli-Silvestre extension of a smooth function $f(x): \mathbb{R}^n \rightarrow \mathbb{R}^{n+1}_+:=\mathbb{R}^n\times (0,\infty).$ The purpose of this article is twofold. Firstly, we want to characterize a nonnegative measure $μ$ on $\mathbb{R}^{n+1}_+$ such that $f(x)\rightarrow P_α f(x,t)$ induces bounded embeddings from the Lebesgue spaces $L^p(\mathbb{R}^n)$ to the $L^q(\mathbb{R}^{n+1}_+,μ).$ On one hand, these embeddings will be characterized by using a newly introduced $L^p-$capacity associated with the Caffarelli-Silvestre extension. In doing so, the mixed norm estimates of $P_α f(x,t),$ the dual form of the $L^p-$capacity, the $L^p-$capacity of general balls, and a capacitary strong type inequality will be established, respectively. On the other hand, when $p>q>1,$ these embeddings will also be characterized in terms of the Hedberg-Wolff potential of $μ.$ Secondly, we characterize a nonnegative measure $μ$ on $\mathbb{R}^{n+1}_+$ such that $f(x)\rightarrow P_α f(x,t)$ induces bounded embeddings from the homogeneous Sobolev spaces $\dot{W}^{β,p}(\mathbb{R}^n)$ to the $L^q(\mathbb{R}^{n+1}_+,μ)$ in terms of the fractional perimeter of open sets for endpoint cases and the fractional capacity for general cases.

math.AP↗

arXiv2021

When Creators Meet the Metaverse: A Survey on Computational Arts

The metaverse, enormous virtual-physical cyberspace, has brought unprecedented opportunities for artists to blend every corner of our physical surroundings with digital creativity. This article conducts a comprehensive survey on computational arts, in which seven critical topics are relevant to the metaverse, describing novel artworks in blended virtual-physical realities. The topics first cover the building elements for the metaverse, e.g., virtual scenes and characters, auditory, textual elements. Next, several remarkable types of novel creations in the expanded horizons of metaverse cyberspace have been reflected, such as immersive arts, robotic arts, and other user-centric approaches fuelling contemporary creative outputs. Finally, we propose several research agendas: democratising computational arts, digital privacy, and safety for metaverse artists, ownership recognition for digital artworks, technological challenges, and so on. The survey also serves as introductory material for artists and metaverse technologists to begin creations in the realm of surrealistic cyberspace.

cs.CY↗

arXiv2021

Understanding and Testing Generalization of Deep Networks on Out-of-Distribution Data

Deep network models perform excellently on In-Distribution (ID) data, but can significantly fail on Out-Of-Distribution (OOD) data. While developing methods focus on improving OOD generalization, few attention has been paid to evaluating the capability of models to handle OOD data. This study is devoted to analyzing the problem of experimental ID test and designing OOD test paradigm to accurately evaluate the practical performance. Our analysis is based on an introduced categorization of three types of distribution shifts to generate OOD data. Main observations include: (1) ID test fails in neither reflecting the actual performance of a single model nor comparing between different models under OOD data. (2) The ID test failure can be ascribed to the learned marginal and conditional spurious correlations resulted from the corresponding distribution shifts. Based on this, we propose novel OOD test paradigms to evaluate the generalization capacity of models to unseen data, and discuss how to use OOD test results to find bugs of models to guide model debugging.

cs.LG↗

arXiv2021

Data-Driven Modeling of Coarse Mesh Turbulence for Reactor Transient Analysis Using Convolutional Recurrent Neural Networks

Advanced nuclear reactors often exhibit complex thermal-fluid phenomena during transients. To accurately capture such phenomena, a coarse-mesh three-dimensional (3-D) modeling capability is desired for modern nuclear-system code. In the coarse-mesh 3-D modeling of advanced-reactor transients that involve flow and heat transfer, accurately predicting the turbulent viscosity is a challenging task that requires an accurate and computationally efficient model to capture the unresolved fine-scale turbulence. In this paper, we propose a data-driven coarse-mesh turbulence model based on local flow features for the transient analysis of thermal mixing and stratification in a sodium-cooled fast reactor. The model has a coarse-mesh setup to ensure computational efficiency, while it is trained by fine-mesh computational fluid dynamics (CFD) data to ensure accuracy. A novel neural network architecture, combining a densely connected convolutional network and a long-short-term-memory network, is developed that can efficiently learn from the spatial-temporal CFD transient simulation results. The neural network model was trained and optimized on a loss-of-flow transient and demonstrated high accuracy in predicting the turbulent viscosity field during the whole transient. The trained model's generalization capability was also investigated on two other transients with different inlet conditions. The study demonstrates the potential of applying the proposed data-driven approach to support the coarse-mesh multi-dimensional modeling of advanced reactors.

physics.flu-dyn↗

arXiv2021

Relativistic wind from a magnetic monopole rotator

A rotating star with a monopole (or split monopole) magnetic field gives the simplest, prototype model of a rotationally driven stellar wind. Winds from compact objects, in particular neutron stars, carry strong magnetic fields with modest plasma loading, and develop ultra-relativistic speeds. We investigate the relativistic wind launched from a dense, gravitationally bound, atmosphere on the stellar surface. We first examine the problem analytically and then perform global kinetic plasma simulations. Our results show how the wind acceleration mechanism changes from centrifugal (magnetohydrodynamic) to electrostatic (charge-separated) depending on the parameters of the problem. The two regimes give winds with different angular distributions and different scalings with the magnetization parameter.

astro-ph.HE↗

arXiv2021

PLUMENet: Efficient 3D Object Detection from Stereo Images

3D object detection is a key component of many robotic applications such as self-driving vehicles. While many approaches rely on expensive 3D sensors such as LiDAR to produce accurate 3D estimates, methods that exploit stereo cameras have recently shown promising results at a lower cost. Existing approaches tackle this problem in two steps: first depth estimation from stereo images is performed to produce a pseudo LiDAR point cloud, which is then used as input to a 3D object detector. However, this approach is suboptimal due to the representation mismatch, as the two tasks are optimized in two different metric spaces. In this paper we propose a model that unifies these two tasks and performs them in the same metric space. Specifically, we directly construct a pseudo LiDAR feature volume (PLUME) in 3D space, which is then used to solve both depth estimation and object detection tasks. Our approach achieves state-of-the-art performance with much faster inference times when compared to existing methods on the challenging KITTI benchmark.

cs.CV↗

arXiv2021

Investigation of physical dose enhancement in core-shell magnetic gold nanoparticles with TOPAS simulation

The application of metal nanoparticles as sensitization materials is a common strategy that is used to study dose enhancement in radiotherapy. Recent in vitro tests have revealed that magnetic gold nanoparticles can be used in cancer therapy under a magnetic field to enhance the synergistic efficiency in radiotherapy and photothermal therapy. However, magnetic gold nanoparticles have rarely been studied as sensitization materials. In this study, we obtained further results of the sensitization properties of magnetic gold nanoparticles using the Monte Carlo method TOPAS and TOPAS-nBio. We analyzed the properties of magnetic gold nanoparticles in monoenergetic photons and brachytherapy, and we investigated whether the magnetic field contributes to the sensitization process. Our results demonstrated that the dose enhancement factor of the magnetic gold nanoparticles was 16.7% lower than that of gold nanoparticles in a single particle irradiated by monoenergetic photons. In the cell model, the difference was less than 8.1% in the cytoplasm. We revealed that the magnetic field has no detrimental effect on radiosensitization. Moreover, the sensitization properties of magnetic gold nanoparticles in a clinical brachytherapy source have been revealed for the first time.

physics.med-ph↗

arXiv2021

Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization

Federated learning (FL) enables distributed agents to collaboratively learn a centralized model without sharing their raw data with each other. However, data locality does not provide sufficient privacy protection, and it is desirable to facilitate FL with rigorous differential privacy (DP) guarantee. Existing DP mechanisms would introduce random noise with magnitude proportional to the model size, which can be quite large in deep neural networks. In this paper, we propose a new FL framework with sparsification-amplified privacy. Our approach integrates random sparsification with gradient perturbation on each agent to amplify privacy guarantee. Since sparsification would increase the number of communication rounds required to achieve a certain target accuracy, which is unfavorable for DP guarantee, we further introduce acceleration techniques to help reduce the privacy cost. We rigorously analyze the convergence of our approach and utilize Renyi DP to tightly account the end-to-end DP guarantee. Extensive experiments on benchmark datasets validate that our approach outperforms previous differentially-private FL approaches in both privacy guarantee and communication efficiency.

cs.LG↗

arXiv2021

MultiXNet: Multiclass Multistage Multimodal Motion Prediction

One of the critical pieces of the self-driving puzzle is understanding the surroundings of a self-driving vehicle (SDV) and predicting how these surroundings will change in the near future. To address this task we propose MultiXNet, an end-to-end approach for detection and motion prediction based directly on lidar sensor data. This approach builds on prior work by handling multiple classes of traffic actors, adding a jointly trained second-stage trajectory refinement step, and producing a multimodal probability distribution over future actor motion that includes both multiple discrete traffic behaviors and calibrated continuous position uncertainties. The method was evaluated on large-scale, real-world data collected by a fleet of SDVs in several cities, with the results indicating that it outperforms existing state-of-the-art approaches.

cs.CV↗

arXiv2021

PolyTransform: Deep Polygon Transformer for Instance Segmentation

In this paper, we propose PolyTransform, a novel instance segmentation algorithm that produces precise, geometry-preserving masks by combining the strengths of prevailing segmentation approaches and modern polygon-based methods. In particular, we first exploit a segmentation network to generate instance masks. We then convert the masks into a set of polygons that are then fed to a deforming network that transforms the polygons such that they better fit the object boundaries. Our experiments on the challenging Cityscapes dataset show that our PolyTransform significantly improves the performance of the backbone instance segmentation network and ranks 1st on the Cityscapes test-set leaderboard. We also show impressive gains in the interactive annotation setting. We release the code at https://github.com/uber-research/PolyTransform.

cs.CV↗

arXiv2020

Multi-Task Multi-Sensor Fusion for 3D Object Detection

In this paper we propose to exploit multiple related tasks for accurate multi-sensor 3D object detection. Towards this goal we present an end-to-end learnable architecture that reasons about 2D and 3D object detection as well as ground estimation and depth completion. Our experiments show that all these tasks are complementary and help the network learn better representations by fusing information at various levels. Importantly, our approach leads the KITTI benchmark on 2D, 3D and BEV object detection, while being real time.

cs.CV↗