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Accurate positioning technology is the foundation for industry and business applications. Although indoor and outdoor positioning techniques have been well studied separately, positioning performance in the intermediate period of changing the positioning environment is still challenging. This paper proposed a digital twin-aided positioning correction method for seamless positioning focusing on improving the receiver's outdoor positioning performance in urban areas, where the change of the positioning environment usually happens. The proposed algorithm will simulate the positioning solution for virtual receivers in a grid-based digital twin. Based on the simulated positioning solutions, a statistical model will be used to study the positioning characteristics and generate a correction information database for real receivers to improve their positioning performance. This algorithm has a low computation load on the receiver side and does not require a specially designed antenna, making it implementable for small-sized devices., Comment: 7 pages conference paper in indoor positioning and indoor navigation 2024

We propose a feasibility study for real-time automated data standardization leveraging Large Language Models (LLMs) to enhance seamless positioning systems in IoT environments. By integrating and standardizing heterogeneous sensor data from smartphones, IoT devices, and dedicated systems such as Ultra-Wideband (UWB), our study ensures data compatibility and improves positioning accuracy using the Extended Kalman Filter (EKF). The core components include the Intelligent Data Standardization Module (IDSM), which employs a fine-tuned LLM to convert varied sensor data into a standardized format, and the Transformation Rule Generation Module (TRGM), which automates the creation of transformation rules and scripts for ongoing data standardization. Evaluated in real-time environments, our study demonstrates adaptability and scalability, enhancing operational efficiency and accuracy in seamless navigation. This study underscores the potential of advanced LLMs in overcoming sensor data integration complexities, paving the way for more scalable and precise IoT navigation solutions., Comment: Accepted at IPIN 2024. To be published in IEEE Xplore

In multi-agent cooperative tasks, the presence of heterogeneous agents is familiar. Compared to cooperation among homogeneous agents, collaboration requires considering the best-suited sub-tasks for each agent. However, the operation of multi-agent systems often involves a large amount of complex interaction information, making it more challenging to learn heterogeneous strategies. Related multi-agent reinforcement learning methods sometimes use grouping mechanisms to form smaller cooperative groups or leverage prior domain knowledge to learn strategies for different roles. In contrast, agents should learn deeper role features without relying on additional information. Therefore, we propose QTypeMix, which divides the value decomposition process into homogeneous and heterogeneous stages. QTypeMix learns to extract type features from local historical observations through the TE loss. In addition, we introduce advanced network structures containing attention mechanisms and hypernets to enhance the representation capability and achieve the value decomposition process. The results of testing the proposed method on 14 maps from SMAC and SMACv2 show that QTypeMix achieves state-of-the-art performance in tasks of varying difficulty., Comment: 16 pages, 8 figures

The execution of flight missions by unmanned aerial vehicles (UAV) primarily relies on navigation. In particular, the navigation pipeline has traditionally been divided into positioning and control, operating in a sequential loop. However, the existing navigation pipeline, where the positioning and control are decoupled, struggles to adapt to ubiquitous uncertainties arising from measurement noise, abrupt disturbances, and nonlinear dynamics. As a result, the navigation reliability of the UAV is significantly challenged in complex dynamic areas. For example, the ubiquitous global navigation satellite system (GNSS) positioning can be degraded by the signal reflections from surrounding high-rising buildings in complex urban areas, leading to significantly increased positioning uncertainty. An additional challenge is introduced to the control algorithm due to the complex wind disturbances in urban canyons. Given the fact that the system positioning and control are highly correlated with each other, this research proposes a **tightly joined positioning and control model (JPCM) based on factor graph optimization (FGO)**. In particular, the proposed JPCM combines sensor measurements from positioning and control constraints into a unified probabilistic factor graph. Specifically, the positioning measurements are formulated as the factors in the factor graph. In addition, the model predictive control (MPC) is also formulated as the additional factors in the factor graph. By solving the factor graph contributed by both the positioning-related factors and the MPC-based factors, the complementariness of positioning and control can be deeply exploited. Finally, we validate the effectiveness and resilience of the proposed method using a simulated quadrotor system which shows significantly improved trajectory following performance.

Leveraging multiple sensors enhances complex environmental perception and increases resilience to varying luminance conditions and high-speed motion patterns, achieving precise localization and mapping. This paper proposes, ECMD, an event-centric multisensory dataset containing 81 sequences and covering over 200 km of various challenging driving scenarios including high-speed motion, repetitive scenarios, dynamic objects, etc. ECMD provides data from two sets of stereo event cameras with different resolutions (640*480, 346*260), stereo industrial cameras, an infrared camera, a top-installed mechanical LiDAR with two slanted LiDARs, two consumer-level GNSS receivers, and an onboard IMU. Meanwhile, the ground-truth of the vehicle was obtained using a centimeter-level high-accuracy GNSS-RTK/INS navigation system. All sensors are well-calibrated and temporally synchronized at the hardware level, with recording data simultaneously. We additionally evaluate several state-of-the-art SLAM algorithms for benchmarking visual and LiDAR SLAM and identifying their limitations. The dataset is available at https://arclab-hku.github.io/ecmd/.

Neural networks have been able to generate high-quality single-sentence speech. However, it remains a challenge concerning audio-book speech synthesis due to the intra-paragraph correlation of semantic and acoustic features as well as variable styles. In this paper, we propose a highly expressive paragraph speech synthesis system with a multi-step variational autoencoder, called EP-MSTTS. EP-MSTTS is the first VITS-based paragraph speech synthesis model and models the variable style of paragraph speech at five levels: frame, phoneme, word, sentence, and paragraph. We also propose a series of improvements to enhance the performance of this hierarchical model. In addition, we directly train EP-MSTTS on speech sliced by paragraph rather than sentence. Experiment results on the single-speaker French audiobook corpus released at Blizzard Challenge 2023 show EP-MSTTS obtains better performance than baseline models., Comment: accepted at Interspeech 2024

In multivariate time series analysis, spectral coherence measures the linear dependency between two time series at different frequencies. However, real data applications often exhibit nonlinear dependency in the frequency domain. Conventional coherence analysis fails to capture such dependency. The quantile coherence, on the other hand, characterizes nonlinear dependency by defining the coherence at a set of quantile levels based on trigonometric quantile regression. This paper introduces a new estimation technique for quantile coherence. The proposed method is semi-parametric, which uses the parametric form of the spectrum of a vector autoregressive (VAR) model to approximate the quantile coherence, combined with nonparametric smoothing across quantiles. At a given quantile level, we compute the quantile autocovariance function (QACF) by performing the Fourier inverse transform of the quantile periodograms. Subsequently, we utilize the multivariate Durbin-Levinson algorithm to estimate the VAR parameters and derive the estimate of the quantile coherence. Finally, we smooth the preliminary estimate of quantile coherence across quantiles using a nonparametric smoother. Numerical results show that the proposed estimation method outperforms nonparametric methods. We show that quantile coherence-based bivariate time series clustering has advantages over the ordinary VAR coherence. For applications, the identified clusters of financial stocks by quantile coherence with a market benchmark are shown to have an intriguing and more informative structure of diversified investment portfolios that may be used by investors to make better decisions., Comment: 39 pages, 11 figures

Many multi-sensor navigation systems urgently demand accurate positioning initialization from global navigation satellite systems (GNSSs) in challenging static scenarios. However, ground blockages against line-of-sight (LOS) signal reception make it difficult for GNSS users. Steering local codes in GNSS basebands is a desiring way to correct instantaneous signal phase misalignment, efficiently gathering useful signal power and increasing positioning accuracy. Besides, inertial navigation systems (INSs) have been used as a well-complementary dead reckoning (DR) sensor for GNSS receivers in kinematic scenarios resisting various interferences since early. But little work focuses on the case of whether the INS can improve GNSS receivers in static scenarios. Thus, this paper proposes an enhanced navigation system deeply integrated with low-cost INS solutions and GNSS high-accuracy carrier-based positioning. First, an absolute code phase is predicted from base station information, and integrated solution of the INS DR and real-time kinematic (RTK) results through an extended Kalman filter (EKF). Then, a numerically controlled oscillator (NCO) leverages the predicted code phase to improve the alignment between instantaneous local code phases and received ones. The proposed algorithm is realized in a vector-tracking GNSS software-defined radio (SDR). Real-world experiments demonstrate the proposed SDR regarding estimating time-of-arrival (TOA) and positioning accuracy., Comment: 27 pages, 18 figures

Accurate and smooth global navigation satellite system (GNSS) positioning for pedestrians in urban canyons is still a challenge due to the multipath effects and the non-light-of-sight (NLOS) receptions caused by the reflections from surrounding buildings. The recently developed factor graph optimization (FGO) based GNSS positioning method opened a new window for improving urban GNSS positioning by effectively exploiting the measurement redundancy from the historical information to resist the outlier measurements. Unfortunately, the FGO-based GNSS standalone positioning is still challenged in highly urbanized areas. As an extension of the previous FGO-based GNSS positioning method, this paper exploits the potential of the pedestrian dead reckoning (PDR) model in FGO to improve the GNSS standalone positioning performance in urban canyons. Specifically, the relative motion of the pedestrian is estimated based on the raw acceleration measurements from the onboard smartphone inertial measurement unit (IMU) via the PDR algorithm. Then the raw GNSS pseudorange, Doppler measurements, and relative motion from PDR are integrated using the FGO. Given the context of pedestrian navigation with a small acceleration most of the time, a novel soft motion model is proposed to smooth the states involved in the factor graph model. The effectiveness of the proposed method is verified step-by-step through two datasets collected in dense urban canyons of Hong Kong using smartphone-level GNSS receivers. The comparison between the conventional extended Kalman filter, several existing methods, and FGO-based integration is presented. The results reveal that the existing FGO-based GNSS standalone positioning is highly complementary to the PDR's relative motion estimation. Both improved positioning accuracy and trajectory smoothness are obtained with the help of the proposed method., Comment: 11 pages, 14 figures

GNSS and LiDAR odometry are complementary as they provide absolute and relative positioning, respectively. Their integration in a loosely-coupled manner is straightforward but is challenged in urban canyons due to the GNSS signal reflections. Recent proposed 3D LiDAR-aided (3DLA) GNSS methods employ the point cloud map to identify the non-line-of-sight (NLOS) reception of GNSS signals. This facilitates the GNSS receiver to obtain improved urban positioning but not achieve a sub-meter level. GNSS real-time kinematics (RTK) uses carrier phase measurements to obtain decimeter-level positioning. In urban areas, the GNSS RTK is not only challenged by multipath and NLOS-affected measurement but also suffers from signal blockage by the building. The latter will impose a challenge in solving the ambiguity within the carrier phase measurements. In the other words, the model observability of the ambiguity resolution (AR) is greatly decreased. This paper proposes to generate virtual satellite (VS) measurements using the selected LiDAR landmarks from the accumulated 3D point cloud maps (PCM). These LiDAR-PCM-made VS measurements are tightly-coupled with GNSS pseudorange and carrier phase measurements. Thus, the VS measurements can provide complementary constraints, meaning providing low-elevation-angle measurements in the across-street directions. The implementation is done using factor graph optimization to solve an accurate float solution of the ambiguity before it is fed into LAMBDA. The effectiveness of the proposed method has been validated by the evaluation conducted on our recently open-sourced challenging dataset, UrbanNav. The result shows the fix rate of the proposed 3DLA GNSS RTK is about 30% while the conventional GNSS-RTK only achieves about 14%. In addition, the proposed method achieves sub-meter positioning accuracy in most of the data collected in challenging urban areas.

Accurate and safety-quantifiable localization is of great significance for safety-critical autonomous systems, such as unmanned ground vehicles (UGV) and unmanned aerial vehicles (UAV). The visual odometry-based method can provide accurate positioning in a short period but is subjected to drift over time. Moreover, the quantification of the safety of the localization solution (the error is bounded by a certain value) is still a challenge. To fill the gaps, this paper proposes a safety-quantifiable line feature-based visual localization method with a prior map. The visual-inertial odometry provides a high-frequency local pose estimation which serves as the initial guess for the visual localization. By obtaining a visual line feature pair association, a foot point-based constraint is proposed to construct the cost function between the 2D lines extracted from the real-time image and the 3D lines extracted from the high-precision prior 3D point cloud map. Moreover, a global navigation satellite systems (GNSS) receiver autonomous integrity monitoring (RAIM) inspired method is employed to quantify the safety of the derived localization solution. Among that, an outlier rejection (also well-known as fault detection and exclusion) strategy is employed via the weighted sum of squares residual with a Chi-squared probability distribution. A protection level (PL) scheme considering multiple outliers is derived and utilized to quantify the potential error bound of the localization solution in both position and rotation domains. The effectiveness of the proposed safety-quantifiable localization system is verified using the datasets collected in the UAV indoor and UGV outdoor environments.

Visual-Inertial Odometry (VIO) usually suffers from drifting over long-time runs, the accuracy is easily affected by dynamic objects. We propose DynaVIG, a navigation and object tracking system based on the integration of Monocular Vision, Inertial Navigation System (INS), and Global Navigation Satellite System (GNSS). Our system aims to provide an accurate global estimation of the navigation states and object poses for the automated ground vehicle (AGV) in dynamic scenes. Due to the scale ambiguity of the object, a prior height model is proposed to initialize the object pose, and the scale is continuously estimated with the aid of GNSS and INS. To precisely track the object with complex moving, we establish an accurate dynamics model according to its motion state. Then the multi-sensor observations are optimized in a unified framework. Experiments on the KITTI dataset demonstrate that the multisensor fusion can effectively improve the accuracy of navigation and object tracking, compared to state-of-the-art methods. In addition, the proposed system achieves good estimation of the objects that change speed or direction.

A nonparametric method is proposed for estimating the quantile spectra and cross-spectra introduced in Li (2012; 2014) as bivariate functions of frequency and quantile level. The method is based on the quantile discrete Fourier transform (QDFT) defined by trigonometric quantile regression and the quantile series (QSER) defined by the inverse Fourier transform of the QDFT. A nonparametric spectral estimator is constructed from the autocovariance and cross-covariance functions of the QSER using the lag-window (LW) approach. Various quantile smoothing techniques are employed further to reduce the statistical variability of the estimator across quantiles, among which is a new technique called spline quantile regression (SQR). The performance of the proposed estimation method is evaluated through a simulation study.

There is often a trade-off between performance and latency in streaming automatic speech recognition (ASR). Traditional methods such as look-ahead and chunk-based methods, usually require information from future frames to advance recognition accuracy, which incurs inevitable latency even if the computation is fast enough. A causal model that computes without any future frames can avoid this latency, but its performance is significantly worse than traditional methods. In this paper, we propose corresponding revision strategies to improve the causal model. Firstly, we introduce a real-time encoder states revision strategy to modify previous states. Encoder forward computation starts once the data is received and revises the previous encoder states after several frames, which is no need to wait for any right context. Furthermore, a CTC spike position alignment decoding algorithm is designed to reduce time costs brought by the revision strategy. Experiments are all conducted on Librispeech datasets. Fine-tuning on the CTC-based wav2vec2.0 model, our best method can achieve 3.7/9.2 WERs on test-clean/other sets, which is also competitive with the chunk-based methods and the knowledge distillation methods., Comment: Accepted by Interspeech 2022

Neutron star mergers are cosmic catastrophes that produce some of the most energetic observed phenomena: short gamma-ray bursts, gravitational wave signals, and kilonovae. The latter are optical transients, powered by radioactive nuclides which are synthesized when the neutron-rich ejecta of a disrupted neutron star undergoes decompression. We model this decompression phase using data from simulations of post-merger accretion disk winds. We use smoothed particle hydrodynamics with realistic nuclear heating to model the expansion over multiple scales, from initially several thousand km to billions of km. We then render a realistic image of a kilonova ejecta as it would appear for a nearby observer. This is the first time such a visualization is performed using input from state-of-the-art accretion disk simulations, nuclear physics and atomic physics. The volume rendering in our model computes an opacity transfer function on the basis of the physical opacity, varying significantly with the inhomogeneity of the neutron richness in the ejecta. Other physical quantities such as temperature or electron fraction can be visualized using an independent color transfer function. We discuss several difficulties with the ParaView application that we encountered during the visualization process, and give descriptions of our solutions and workarounds which could be used for future improvements., Comment: Presented at the Scientific Visualization Supercomputing Conference 2021, St. Louis, MO

In this paper, we propose a 3D LiDAR aided global navigation satellite system (GNSS) non-line-of-sight (NLOS) mitigation method caused by both static buildings and dynamic objects. A sliding window map describing the surrounding of the ego-vehicle is first generated, based on real-time 3D point clouds from a 3D LiDAR sensor. Then, NLOS receptions are detected based on the sliding window map using a proposed fast searching method which is free of the initial guess of the position of the GNSS receiver. Instead of directly excluding the detected NLOS satellites from further positioning estimation, this paper rectifies the pseudorange measurement model by (1) correcting the pseudorange measurements if the reflecting point of NLOS signals is detected inside the sliding window map, and (2) remodeling the uncertainty of the NLOS pseudorange measurement using a novel weighting scheme. We evaluated the performance of the proposed method in several typical urban canyons in Hong Kong using an automobile-level GNSS receiver. Moreover, we also evaluate the potential of the proposed NLOS mitigation method in GNSS and inertial navigation systems integration via factor graph optimization., Comment: 13 pages, 11 figures

This paper proposes an improved global navigation satellite system (GNSS) positioning method that explores the time correlation between consecutive epochs of the code and carrier phase measurements which significantly increases the robustness against outlier measurements. Instead of relying on the time difference carrier phase (TDCP) which only considers two neighboring epochs using an extended Kalman filter (EKF) estimator, this paper proposed to employ the carrier-phase measurements inside a window, the so-called window carrier-phase (WCP), to constrain the states inside a factor graph. A left null space matrix is employed to eliminate the shared unknown ambiguity variables and therefore, correlated the associated states inside the WCP. Then the pseudorange, Doppler, and the constructed WCP measurements are integrated simultaneously using factor graph optimization (FGO) to estimate the state of the GNSS receiver. We evaluated the performance of the proposed method in two typical urban canyons in Hong Kong, achieving the mean positioning error of 1.76 meters and 2.96 meters, respectively, using the automobile-level GNSS receiver. Meanwhile, the effectiveness of the proposed method is further evaluated using a low-cost smartphone level GNSS receiver and similar improvement is also obtained, compared with several existing GNSS positioning methods.

Accurate and globally referenced global navigation satellite system (GNSS) based vehicular positioning can be achieved in outlier-free open areas. However, the performance of GNSS can be significantly degraded by outlier measurements, such as multipath effects and non-line-of-sight (NLOS) receptions arising from signal reflections of buildings. Inspired by the advantage of batch historical data in resisting outlier measurements, in this paper, we propose a graduated non-convexity factor graph optimization (FGO-GNC) to improve the GNSS positioning performance, where the impact of GNSS outliers is mitigated by estimating the optimal weightings of GNSS measurements. Different from the existing local solutions, the proposed FGO-GNC employs the non-convex Geman McClure (GM) function to globally estimate the weightings of GNSS measurements via a coarse-to-fine relaxation. The effectiveness of the proposed method is verified through several challenging datasets collected in urban canyons of Hong Kong using automobile level and low-cost smartphone level GNSS receivers.

Global navigation satellite systems (GNSS) are one of the utterly popular sources for providing globally referenced positioning for autonomous systems. However, the performance of the GNSS positioning is significantly challenged in urban canyons, due to the signal reflection and blockage from buildings. Given the fact that the GNSS measurements are highly environmentally dependent and time-correlated, the conventional filtering-based method for GNSS positioning cannot simultaneously explore the time-correlation among historical measurements. As a result, the filtering-based estimator is sensitive to unexpected outlier measurements. In this paper, we present a factor graph-based formulation for GNSS positioning and real-time kinematic (RTK). The formulated factor graph framework effectively explores the time-correlation of pseudorange, carrier-phase, and doppler measurements, and leads to the non-minimal state estimation of the GNSS receiver. The feasibility of the proposed method is evaluated using datasets collected in challenging urban canyons of Hong Kong and significantly improved positioning accuracy is obtained, compared with the filtering-based estimator.

Recently neural architecture search(NAS) has been successfully used in image classification, natural language processing, and automatic speech recognition(ASR) tasks for finding the state-of-the-art(SOTA) architectures than those human-designed architectures. NAS can derive a SOTA and data-specific architecture over validation data from a pre-defined search space with a search algorithm. Inspired by the success of NAS in ASR tasks, we propose a NAS-based ASR framework containing one search space and one differentiable search algorithm called Differentiable Architecture Search(DARTS). Our search space follows the convolution-augmented transformer(Conformer) backbone, which is a more expressive ASR architecture than those used in existing NAS-based ASR frameworks. To improve the performance of our method, a regulation method called Dynamic Search Schedule(DSS) is employed. On a widely used Mandarin benchmark AISHELL-1, our best-searched architecture outperforms the baseline Conform model significantly with about 11% CER relative improvement, and our method is proved to be pretty efficient by the search cost comparisons., Comment: submitted to INTERSPEECH 2021

Robust and precise localization is essential for the autonomous system with navigation requirements. Light detection and ranging (LiDAR) odometry is extensively studied in the past decades to achieve this goal. Satisfactory accuracy can be achieved in scenarios with abundant environmental features using existing LiDAR odometry (LO) algorithms. Unfortunately, the performance of the LiDAR odometry is significantly degraded in urban canyons with numerous dynamic objects and complex environmental structures. Meanwhile, it is still not clear from the existing literature which LO algorithms perform well in such challenging environments. To fill this gap, this paper evaluates an array of popular and extensively studied LO pipelines using the datasets collected in urban canyons of Hong Kong. We present the results in terms of their positioning accuracy and computational efficiency. Three major factors dominating the performance of LO in urban canyons are concluded, including the ego-vehicle dynamic, moving objects, and degree of urbanization. According to our experiment results, point-wise achieves better accuracy in urban canyons while feature-wise achieves cost-efficiency and satisfactory positioning accuracy., Comment: 15 pages, 14 figures

Accurate localization and 3D maps are increasingly needed for various artificial intelligence based IoT applications such as augmented reality, intelligent transportation, crowd monitoring, robotics, etc. This article proposes a novel semantic 3D map change detection and update based on a smartphone visual positioning system (VPS) for the outdoor and indoor environments. The proposed method presents an alternate solution to SLAM for map update in terms of efficiency, cost, availability, and map reuse. Building on existing 3D maps of recent years, a system is designed to use artificial intelligence to identify high-level semantics in images for positioning and map change detection. Then, a virtual LIDAR that estimates the depth of objects in the 3D map is used to generate a compact point cloud to update changes in the scene. We present an excellent performance of localization with respect to other state-of-the-art smartphone positioning solutions to accurately update semantic 3D maps. It is shown that the proposed solution can position users within 1.9m, and update objects with an average error of 2.1m., Comment: 12 pages, 4 figures. arXiv admin note: text overlap with arXiv:2011.10743

Accurate smartphone-based outdoor localization system in deep urban canyons are increasingly needed for various IoT applications such as augmented reality, intelligent transportation, etc. The recently developed feature-based visual positioning system (VPS) by Google detects edges from smartphone images to match with pre-surveyed edges in their map database. As smart cities develop, the building information modeling (BIM) becomes widely available, which provides an opportunity for a new semantic-based VPS. This article proposes a novel 3D city model and semantic-based VPS for accurate and robust pose estimation in urban canyons where global navigation satellite system (GNSS) tends to fail. In the offline stage, a material segmented city model is used to generate segmented images. In the online stage, an image is taken with a smartphone camera that provides textual information about the surrounding environment. The approach utilizes computer vision algorithms to rectify and hand segment between the different types of material identified in the smartphone image. A semantic-based VPS method is then proposed to match the segmented generated images with the segmented smartphone image. Each generated image holds a pose that contains the latitude, longitude, altitude, yaw, pitch, and roll. The candidate with the maximum likelihood is regarded as the precise pose of the user. The positioning results achieves 2.0m level accuracy in common high rise along street, 5.5m in foliage dense environment and 15.7m in alleyway. A 45% positioning improvement to current state-of-the-art method. The estimation of yaw achieves 2.3{\deg} level accuracy, 8 times the improvement to smartphone IMU., Comment: 12 pages, 6 figures

There is strong interest among healthcare payers to identify emerging healthcare cost drivers to support early intervention. However, many challenges arise in analyzing large, high dimensional, and noisy healthcare data. In this paper, we propose a systematic approach that utilizes hierarchical search strategies and enhanced statistical process control (SPC) algorithms to surface high impact cost drivers. Our approach aims to provide interpretable, detailed, and actionable insights of detected change patterns attributing to multiple clinical factors. We also proposed an algorithm to identify comparable treatment offsets at the population level and quantify the cost impact on their utilization changes. To illustrate our approach, we apply it to the IBM Watson Health MarketScan Commercial Database and organized the detected emerging drivers into 5 categories for reporting. We also discuss some findings in this analysis and potential actions in mitigating the impact of the drivers., Comment: arXiv admin note: substantial text overlap with arXiv:1907.08237

Magnetic reconnection is a ubiquitous plasma process in which oppositely directed magnetic field lines break and rejoin, resulting in a change of the magnetic field topology. Reconnection generates magnetic islands: regions enclosed by magnetic field lines and separated by reconnection points. Proper identification of these features is important to understand particle acceleration and overall behavior of plasma. We present a contour-tree based visualization for robust and objective identification of islands and reconnection points in two-dimensional (2D) magnetic reconnection simulations. The application of this visualization to a simple simulation has revealed a physical phenomenon previously not reported, resulting in a more comprehensive understanding of magnetic reconnection., Comment: To to published in IEEE Transactions on Visualization and Computer Graphics 2020

LiDAR-based SLAM algorithms are extensively studied to providing robust and accurate positioning for autonomous driving vehicles (ADV) in the past decades. Satisfactory performance can be obtained using high-grade 3D LiDAR with 64 channels, which can provide dense point clouds. Unfortunately, the high price significantly prevents its extensive commercialization in ADV. The cost-effective 3D LiDAR with 16 channels is a promising replacement. However, only limited and sparse point clouds can be provided by the 16 channels LiDAR, which cannot guarantee sufficient positioning accuracy for ADV in challenging dynamic environments. The high-resolution image from the low-cost camera can provide ample information about the surroundings. However, the explicit depth information is not available from the image. Inspired by the complementariness of 3D LiDAR and camera, this paper proposes to make use of the high-resolution images from a camera to enrich the raw 3D point clouds from the low-cost 16 channels LiDAR based on a state-of-the-art deep learning algorithm. An ERFNet is firstly employed to segment the image with the aid of the raw sparse 3D point clouds. Meanwhile, the sparse convolutional neural network is employed to predict the dense point clouds based on raw sparse 3D point clouds. Then, the predicted dense point clouds are fused with the segmentation outputs from ERFnet using a novel multi-layer convolutional neural network to refine the predicted 3D point clouds. Finally, the enriched point clouds are employed to perform LiDAR SLAM based on the state-of-the-art normal distribution transform (NDT). We tested our approach on the re-edited KITTI datasets: (1)the sparse 3D point clouds are significantly enriched with a mean square error of 1.1m MSE. (2)the map generated from the LiDAR SLAM is denser which includes more details without significant accuracy loss.

Testing is one of the most important steps in software development. It ensures the quality of software. Continuous Integration (CI) is a widely used testing system that can report software quality to the developer in a timely manner during the development progress. Performance, especially scalability, is another key factor for High Performance Computing (HPC) applications. Though there are many applications and tools to profile the performance of HPC applications, none of them are integrated into the continuous integration. On the other hand, no current continuous integration tools provide easy-to-use scalability test capabilities. In this work, we propose BeeSwarm, a scalability test system that can be easily applied to the current CI test environment enabling scalability test capability for HPC developers. As a showcase, BeeSwarm is integrated into Travis CI and GitLab CI to execute the scalability test workflow on Chameleon cloud.

The recently proposed factor graph optimization (FGO) is adopted to integrate GNSS/INS attracted lots of attention and improved the performance over the existing EKF-based GNSS/INS integrations. However, a comprehensive comparison of those two GNSS/INS integration schemes in the urban canyon is not available. Moreover, the performance of the FGO-based GNSS/INS integration rely heavily on the size of the window of optimization. Effectively tuning the window size is still an open question. To fill this gap, this paper evaluates both loosely and tightly-coupled integrations using both EKF and FGO via the challenging dataset collected in the urban canyon. The detailed analysis of the results for the advantages of the FGO is also given in this paper by degenerating the FGO-based estimator to an EKF like estimator. More importantly, we analyze the effects of window size against the performance of FGO, by considering both the GNSS pseudorange error distribution and environmental conditions., Comment: 14 pages, 12 Figures

Mapping and localization is a critical module of autonomous driving, and significant achievements have been reached in this field. Beyond Global Navigation Satellite System (GNSS), research in point cloud registration, visual feature matching, and inertia navigation has greatly enhanced the accuracy and robustness of mapping and localization in different scenarios. However, highly urbanized scenes are still challenging: LIDAR- and camera-based methods perform poorly with numerous dynamic objects; the GNSS-based solutions experience signal loss and multipath problems; the inertia measurement units (IMU) suffer from drifting. Unfortunately, current public datasets either do not adequately address this urban challenge or do not provide enough sensor information related to mapping and localization. Here we present UrbanLoco: a mapping/localization dataset collected in highly-urbanized environments with a full sensor-suite. The dataset includes 13 trajectories collected in San Francisco and Hong Kong, covering a total length of over 40 kilometers. Our dataset includes a wide variety of urban terrains: urban canyons, bridges, tunnels, sharp turns, etc. More importantly, our dataset includes information from LIDAR, cameras, IMU, and GNSS receivers. Now the dataset is publicly available through the link in the footnote. Dataset Link: https://advdataset2019.wixsite.com/urbanloco., Comment: 7 pages, ICRA2020

In this paper, a new estimation method is introduced for the quantile spectrum, which uses a parametric form of the autoregressive (AR) spectrum coupled with nonparametric smoothing. The method begins with quantile periodograms which are constructed by trigonometric quantile regression at different quantile levels, to represent the serial dependence of time series at various quantiles. At each quantile level, we approximate the quantile spectrum by a function in the form of an ordinary AR spectrum. In this model, we first compute what we call the quantile autocovariance function (QACF) by the inverse Fourier transformation of the quantile periodogram at each quantile level. Then, we solve the Yule-Walker equations formed by the QACF to obtain the quantile partial autocorrelation function (QPACF) and the scale parameter. Finally, we smooth QPACF and the scale parameter across the quantile levels using a nonparametric smoother, convert the smoothed QPACF to AR coefficients, and obtain the AR spectral density function. Numerical results show that the proposed method outperforms other conventional smoothing techniques. We take advantage of the two-dimensional property of the estimators and train a convolutional neural network (CNN) to classify smoothed quantile periodogram of earthquake data and achieve a higher accuracy than a similar classifier using ordinary periodograms., Comment: 24 pages, 3 figures and 5 tables

Nonlinear dynamic volatility has been observed in many financial time series. The recently proposed quantile periodogram offers an alternative way to examine this phenomena in the frequency domain. The quantile periodogram is constructed from trigonometric quantile regression of time series data at different frequencies and quantile levels. It is a useful tool for quantile-frequency analysis (QFA) of nonlinear serial dependence. This paper introduces a number of spectral divergence metrics based on the quantile periodogram for diagnostic checks of financial time series models and model-based discriminant analysis. The parametric bootstrapping technique is employed to compute the $p$-values of the metrics. The usefulness of the proposed method is demonstrated empirically by a case study using the daily log returns of the S\&P 500 index over three periods of time together with their GARCH-type models. The results show that the QFA method is able to provide additional insights into the goodness of fit of these financial time series models that may have been missed by conventional tests. The results also show that the QFA method offers a more informative way of discriminant analysis for detecting regime changes in time series.

There is strong interest among payers to identify emerging healthcare cost drivers to support early intervention. However, many challenges arise in analyzing large, high dimensional, and noisy healthcare data. In this paper, we propose a systematic approach that utilizes hierarchical and multi-resolution search strategies using enhanced statistical process control (SPC) algorithms to surface high impact cost drivers. Our approach aims to provide interpretable, detailed, and actionable insights of detected change patterns attributing to multiple demographic and clinical factors. We also proposed an algorithm to identify comparable treatment offsets at the population level and quantify the cost impact on their utilization changes., Comment: 2019 KDD DS Healthcare workshop

This report describes eighteen projects that explored how commercial cloud computing services can be utilized for scientific computation at national laboratories. These demonstrations ranged from deploying proprietary software in a cloud environment to leveraging established cloud-based analytics workflows for processing scientific datasets. By and large, the projects were successful and collectively they suggest that cloud computing can be a valuable computational resource for scientific computation at national laboratories.