arXiv subjects

Li Tang

Publications and source records attributed to Li Tang.

At least 55 records · Page 3Linked to original sources

arXiv2021

Machine learning forecasts of the cosmic distance duality relation with strongly lensed gravitational wave events

We use simulated strongly lensed gravitational wave events from the Einstein Telescope to demonstrate how the luminosity and angular diameter distances, $d_L(z)$ and $d_A(z)$ respectively, can be combined to test in a model independent manner for deviations from the cosmic distance duality relation and the standard cosmological model. In particular, we use two machine learning approaches, the Genetic Algorithms and Gaussian Processes, to reconstruct the mock data and we show that both approaches are capable of correctly recovering the underlying fiducial model and can provide percent-level constraints at intermediate redshifts when applied to future Einstein Telescope data.

astro-ph.CO↗

arXiv2021

DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation

Shared-account Cross-domain Sequential recommendation (SCSR) is the task of recommending the next item based on a sequence of recorded user behaviors, where multiple users share a single account, and their behaviours are available in multiple domains. Existing work on solving SCSR mainly relies on mining sequential patterns via RNN-based models, which are not expressive enough to capture the relationships among multiple entities. Moreover, all existing algorithms try to bridge two domains via knowledge transfer in the latent space, and the explicit cross-domain graph structure is unexploited. In this work, we propose a novel graph-based solution, namely DA-GCN, to address the above challenges. Specifically, we first link users and items in each domain as a graph. Then, we devise a domain-aware graph convolution network to learn user-specific node representations. To fully account for users' domain-specific preferences on items, two novel attention mechanisms are further developed to selectively guide the message passing process. Extensive experiments on two real-world datasets are conducted to demonstrate the superiority of our DA-GCN method.

cs.IR↗

arXiv2021

Reinforcing increase of ΔTC in MgB2 smart meta-superconductors by adjusting the concentration of inhomogeneous phases

Incorporating with inhomogeneous phases with high electroluminescence (EL) intensity to prepare smart meta-superconductors (SMSCs) is an effective method of increasing the superconducting transition temperature (Tc) and has been confirmed in both MgB2 and Bi(Pb)SrCaCuO systems. However, the increase of ΔTc has been quite small because of the low optimal concentrations of inhomogeneous phases. In this work, three kinds of MgB2 raw materials, namely, aMgB2, bMgB2, and cMgB2, were prepared with particle sizes decreasing in order. Inhomogeneous phases, Y2O3:Eu3+ and Y2O3:Eu3+/Ag, were also prepared and doped into MgB2 to study the influence of doping concentration on the ΔTc of MgB2 with different particle sizes. Results show that reducing the MgB2 particle size increases the optimal doping concentration of inhomogeneous phases, thereby increasing ΔTc. The optimal doping concentrations for aMgB2, bMgB2, and cMgB2 are 0.5%, 0.8%, and 1.2%, respectively. The corresponding ΔTc values are 0.4, 0.9, and 1.2 K, respectively. This work open a new approach to reinforcing increase of ΔTc in MgB2 SMSCs.

cond-mat.supr-con↗

arXiv2020

Model-independently calibrating the luminosity correlations of gamma-ray bursts using deep learning

Gamma-ray bursts (GRBs) detected at high redshift can be used to trace the Hubble diagram of the Universe. However, the distance calibration of GRBs is not as easily as that of type Ia supernovae (SNe Ia). For the calibrating method based on the empirical luminosity correlations, there is an underlying assumption that the correlations should be universal over the whole redshift range. In this paper, we investigate the possible redshift dependence of six luminosity correlations with a completely model-independent deep learning method. We construct a network combining the Recurrent Neural Networks (RNN) and the Bayesian Neural Networks (BNN), where RNN is used to reconstruct the distance-redshift relation by training the network with the Pantheon compilation, and BNN is used to calculate the uncertainty of the reconstruction. Using the reconstructed distance-redshift relation of Pantheon, we test the redshift dependence of six luminosity correlations by dividing the full GRB sample into two subsamples (low-$z$ and high-$z$ subsamples), and find that only the $E_p-E_γ$ relation has no evidence for redshift dependence. We use the $E_p-E_γ$ relation to calibrate GRBs, and the calibrated GRBs give tight constraint on the flat $Λ$CDM model, with the best-fitting parameter $Ω_{\rm M}$=0.307$^{+0.065}_{-0.073}$.

astro-ph.CO↗

arXiv2020

Improving the generalization of network based relative pose regression: dimension reduction as a regularizer

Visual localization occupies an important position in many areas such as Augmented Reality, robotics and 3D reconstruction. The state-of-the-art visual localization methods perform pose estimation using geometry based solver within the RANSAC framework. However, these methods require accurate pixel-level matching at high image resolution, which is hard to satisfy under significant changes from appearance, dynamics or perspective of view. End-to-end learning based regression networks provide a solution to circumvent the requirement for precise pixel-level correspondences, but demonstrate poor performance towards cross-scene generalization. In this paper, we explicitly add a learnable matching layer within the network to isolate the pose regression solver from the absolute image feature values, and apply dimension regularization on both the correlation feature channel and the image scale to further improve performance towards generalization and large viewpoint change. We implement this dimension regularization strategy within a two-layer pyramid based framework to regress the localization results from coarse to fine. In addition, the depth information is fused for absolute translational scale recovery. Through experiments on real world RGBD datasets we validate the effectiveness of our design in terms of improving both generalization performance and robustness towards viewpoint change, and also show the potential of regression based visual localization networks towards challenging occasions that are difficult for geometry based visual localization methods.

cs.CV↗

arXiv2020

Radar-on-Lidar: metric radar localization on prior lidar maps

Radar and lidar, provided by two different range sensors, each has pros and cons of various perception tasks on mobile robots or autonomous driving. In this paper, a Monte Carlo system is used to localize the robot with a rotating radar sensor on 2D lidar maps. We first train a conditional generative adversarial network to transfer raw radar data to lidar data, and achieve reliable radar points from generator. Then an efficient radar odometry is included in the Monte Carlo system. Combining the initial guess from odometry, a measurement model is proposed to match the radar data and prior lidar maps for final 2D positioning. We demonstrate the effectiveness of the proposed localization framework on the public multi-session dataset. The experimental results show that our system can achieve high accuracy for long-term localization in outdoor scenes.

cs.RO↗

arXiv2020

Strongly lensed gravitational waves as the probes to test the cosmic distance duality relation

The cosmic distance relation (DDR) associates the angular diameters distance ($D_A$) and luminosity distance ($D_L$) by a simple formula, i.e., $D_L=(1+z)^2D_A$. The strongly lensed gravitational waves (GWs) provide a unique way to measure $D_A$ and $D_L$ simultaneously to the GW source, hence can be used as probes to test DDR. In this paper, we prospect the use of strongly lensed GW events from the future Einstein Telescope to test DDR. We write the possible deviation of DDR as $(1+z)^2D_A/D_L=η(z)$, and consider two different parametrizations of $η(z)$, namely, $η_1(z)=1+η_0 z$ and $η_2(z)=1+η_0 z/(1+z)$. Numerical simulations show that, with about 100 strongly lensed GW events observed by ET, the parameter $η_0$ can be constrained at $1.3\%$ and $3\%$ levels for the first and second parametrizations, respectively.

gr-qc↗

arXiv2020

Chimbuko: A Workflow-Level Scalable Performance Trace Analysis Tool

Because of the limits input/output systems currently impose on high-performance computing systems, a new generation of workflows that include online data reduction and analysis is emerging. Diagnosing their performance requires sophisticated performance analysis capabilities due to the complexity of execution patterns and underlying hardware, and no tool could handle the voluminous performance trace data needed to detect potential problems. This work introduces Chimbuko, a performance analysis framework that provides real-time, distributed, in situ anomaly detection. Data volumes are reduced for human-level processing without losing necessary details. Chimbuko supports online performance monitoring via a visualization module that presents the overall workflow anomaly distribution, call stacks, and timelines. Chimbuko also supports the capture and reduction of performance provenance. To the best of our knowledge, Chimbuko is the first online, distributed, and scalable workflow-level performance trace analysis framework, and we demonstrate the tool's usefulness on Oak Ridge National Laboratory's Summit system.

cs.DC↗

arXiv2020

Straggler-resistant distributed matrix computation via coding theory

The current BigData era routinely requires the processing of large scale data on massive distributed computing clusters. Such large scale clusters often suffer from the problem of "stragglers", which are defined as slow or failed nodes. The overall speed of a computational job on these clusters is typically dominated by stragglers in the absence of a sophisticated assignment of tasks to the worker nodes. In recent years, approaches based on coding theory (referred to as "coded computation") have been effectively used for straggler mitigation. Coded computation offers significant benefits for specific classes of problems such as distributed matrix computations (which play a crucial role in several parts of the machine learning pipeline). The essential idea is to create redundant tasks so that the desired result can be recovered as long as a certain number of worker nodes complete their tasks. In this survey article, we overview recent developments in the field of coding for straggler-resilient distributed matrix computations.

cs.IT↗

arXiv2020

Sharp Hardy-Rellich Type Inequalities Associated with Dunkl Operators

In this paper, we obtained the Dunkl analogy of classical Lp Hardy inequality for $p > N + 2γ$ with sharp constant $\left(\frac{p-N-2γ}{p}\right)^{p}$, where $2γ$ is the degree of weight function associated with Dunkl operators, and $L^p$ Hardy inequalities with distant function in some G-invariant domains. Moreover we proved two sharp Hardy-Rellich type inequalities for Dunkl operators.

math.AP↗

arXiv2020

Funding information in Web of Science: An updated overview

Despite the limitations of funding acknowledgment (FA) data in Web of Science (WoS), studies using FA information have increased rapidly over the last several years. Considering this WoS'recent practice of updating funding data, this paper further investigates the characteristics and distribution of FA data in four WoS journal citation indexes. The research reveals that FA information coverage variances persist cross all four citation indexes by time coverage, language and document type. Our evidence suggests an improvement in FA information collection in humanity and social science research. Departing from previous studies, we argue that FA text (FT) alone no longer seems an appropriate field to retrieve and analyze funding information, since a substantial number of documents only report funding agency or grant number information in respective fields. Articles written in Chinese have a higher FA presence rate than other non-English WoS publications. This updated study concludes with a discussion of new findings and practical guidance for the future retrieval and analysis of funded research.

cs.DL↗

arXiv2019

DeepGoal: Learning to Drive with driving intention from Human Control Demonstration

Recent research on automotive driving developed an efficient end-to-end learning mode that directly maps visual input to control commands. However, it models distinct driving variations in a single network, which increases learning complexity and is less adaptive for modular integration. In this paper, we re-investigate human's driving style and propose to learn an intermediate driving intention region to relax difficulties in end-to-end approach. The intention region follows both road structure in image and direction towards goal in public route planner, which addresses visual variations only and figures out where to go without conventional precise localization. Then the learned visual intention is projected on vehicle local coordinate and fused with reliable obstacle perception to render a navigation score map widely used for motion planning. The core of the proposed system is a weakly-supervised cGAN-LSTM model trained to learn driving intention from human demonstration. The adversarial loss learns from limited demonstration data with one local planned route and enables reasoning of multi-modal behavior with diverse routes while testing. Comprehensive experiments are conducted with real-world datasets. Results show the proposed paradigm can produce more consistent motion commands with human demonstration, and indicates better reliability and robustness to environment change.

cs.RO↗

arXiv2019

Towards navigation without precise localization: Weakly supervised learning of goal-directed navigation cost map

Autonomous navigation based on precise localization has been widely developed in both academic research and practical applications. The high demand for localization accuracy has been essential for safe robot planing and navigation while it makes the current geometric solutions less robust to environmental changes. Recent research on end-to-end methods handle raw sensory data with forms of navigation instructions and directly output the command for robot control. However, the lack of intermediate semantics makes the system more rigid and unstable for practical use. To explore these issues, this paper proposes an innovate navigation framework based on the GPS-level localization, which takes the raw perception data with publicly accessible navigation maps to produce an intermediate navigation cost map that allows subsequent flexible motion planning. A deterministic conditional adversarial network is adopted in our method to generate visual goal-directed paths under diverse navigation conditions. The adversarial loss avoids the pixel-level annotation and enables a weakly supervised training strategy to implicitly learn both of the traffic semantics in image perceptions and the planning intentions in navigation instructions. The navigation cost map is then rendered from the goal-directed path and the concurrently collected laser data, indicating the way towards the destination. Comprehensive experiments have been conducted with a real vehicle running in our campus and the results have verified the robustness to localization error of the proposed navigation system.

cs.RO↗

arXiv2019

Non-parametric reconstruction of dark energy and cosmic expansion from the Pantheon compilation of type Ia supernovae

The equation of state (EoS) of dark energy plays an important role in the evolution of the universe and arouses great interests in recent years. With the progress on observational technique, precise constraint on the EoS of dark energy becomes possible. In this paper, we reconstruct the EoS of dark energy and cosmic expansion using Gaussian processes (GP) from the most up-to-date Pantheon compilation of type Ia supernovae (SNe Ia), which consists of 1048 finely calibrated SNe Ia. The reconstructed EoS of dark energy has large uncertainty due to its dependence on the second order derivative of the construction. Adding the direct measurements of Hubble parameters $H(z)$ as an additional constraint on the first order derivative can partially reduce the uncertainty, but is still not precise enough to distinguish between evolving and constant dark energy. Besides, the results heavily rely on the prior of Hubble constant $H_0$. The $H_0$ value inferred from SNe+$H(z)$ without prior is $H_0=70.5\pm 0.5~{\textrm{km}~\textrm{s}^{-1}~\textrm{Mpc}^{-1}}$. Moreover, the matter density $Ω_M$ has an unnegligible effect on the reconstruction of dark energy. Therefore, more accurate determinations on $H_0$ and $Ω_M$ are needed to tightly constrain the EoS of dark energy.

gr-qc↗

arXiv2019

LiDAR-Camera Calibration under Arbitrary Configurations: Observability and Methods

LiDAR-camera calibration is a precondition for many heterogeneous systems that fuse data from LiDAR and camera. However, the constraint from common field of view and the requirement for strict time synchronization make the calibration a challenging problem. In this paper, we propose a novel LiDAR-camera calibration method aiming to eliminate these two constraints. Specifically, we capture a scan of 3D LiDAR when both the environment and the sensors are stationary, then move the camera to reconstruct the 3D environment using the sequentially obtained images. Finally, we align 3D visual points to the laser scan based on tightly couple graph optimization method to calculate the extrinsic parameters between LiDAR and camera. Under this design, the configuration of these two sensors are free from the common field of view constraint owing to the extended view from the moving camera. And we also eliminate the requirement for strict time synchronization as we only use the single scan of laser data when the sensors are stationary. We theoretically derive the conditions of minimal observability for our method and prove that the accuracy of calibration is improved by collecting more observations from multiple scattered calibration targets. We validate our method on both simulation platform and real-world datasets. Experiments show that our method achieves higher accuracy than other comparable methods, which is in accordance with our theoretical analysis. In addition, the proposed method is beneficial to not only plane measurement error based chessboard, but also other point measurement error based calibration targets, such as boxes and polygonal boards.

cs.RO↗

arXiv2019

Communication constrained cloud-based long-term visual localization in real time

Visual localization is one of the primary capabilities for mobile robots. Long-term visual localization in real time is particularly challenging, in which the robot is required to efficiently localize itself using visual data where appearance may change significantly over time. In this paper, we propose a cloud-based visual localization system targeting at long-term localization in real time. On the robot, we employ two estimators to achieve accurate and real-time performance. One is a sliding-window based visual inertial odometry, which integrates constraints from consecutive observations and self-motion measurements, as well as the constraints induced by localization on the cloud. This estimator builds a local visual submap as the virtual observation which is then sent to the cloud as new localization constraints. The other one is a delayed state Extended Kalman Filter to fuse the pose of the robot localized from the cloud, the local odometry and the high-frequency inertial measurements. On the cloud, we propose a longer sliding-window based localization method to aggregate the virtual observations for larger field of view, leading to more robust alignment between virtual observations and the map. Under this architecture, the robot can achieve drift-free and real-time localization using onboard resources even in a network with limited bandwidth, high latency and existence of package loss, which enables the autonomous navigation in real-world environment. We evaluate the effectiveness of our system on a dataset with challenging seasonal and illuminative variations. We further validate the robustness of the system under challenging network conditions.

cs.RO↗

arXiv2019

Universally Decodable Matrices for Distributed Matrix-Vector Multiplication

Coded computation is an emerging research area that leverages concepts from erasure coding to mitigate the effect of stragglers (slow nodes) in distributed computation clusters, especially for matrix computation problems. In this work, we present a class of distributed matrix-vector multiplication schemes that are based on codes in the Rosenbloom-Tsfasman metric and universally decodable matrices. Our schemes take into account the inherent computation order within a worker node. In particular, they allow us to effectively leverage partial computations performed by stragglers (a feature that many prior works lack). An additional main contribution of our work is a companion matrix-based embedding of these codes that allows us to obtain sparse and numerically stable schemes for the problem at hand. Experimental results confirm the effectiveness of our techniques.

cs.IT↗

arXiv2018

Testing the anisotropy of the Universe with the distance duality relation

The distance duality relation (DDR) is valid in Riemannian spacetime. The astronomical data hint that the universe may have certain preferred direction. If the universe is described by anisotropic cosmological models based on Riemannian spacetime, then DDR still valid. If the anisotropy universe is described by other models which are not based on Riemannian spacetime, then DDR is violated. Thus, DDR could be used to test the validity of these anisotropic cosmological models. In this paper, we perform anisotropic DDR parametrization with the dipolar structures. The DDR is tested by comparing the luminosity distance from type-Ia supernovae (Union 2.1 and JLA compilations) and the angular diameter distance from strong gravitational lensing (SL) systems at the same redshift. It is shown that, the DDR is valid with the Union2.1 compilation, while is violated more than 1$σ$ confidence level with the JLA compilation. Additionally, we verify the statistical signification of our method with Monte Carlo simulations. Due to the large uncertainty of available data, no strong evidence is found to violate the DDR in the anisotropic models.

gr-qc↗