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Xin Zhou

Publications and source records attributed to Xin Zhou.

At least 343 records · Page 19Linked to original sources
arXiv2021

Screen2Words: Automatic Mobile UI Summarization with Multimodal Learning

Mobile User Interface Summarization generates succinct language descriptions of mobile screens for conveying important contents and functionalities of the screen, which can be useful for many language-based application scenarios. We present Screen2Words, a novel screen summarization approach that automatically encapsulates essential information of a UI screen into a coherent language phrase. Summarizing mobile screens requires a holistic understanding of the multi-modal data of mobile UIs, including text, image, structures as well as UI semantics, motivating our multi-modal learning approach. We collected and analyzed a large-scale screen summarization dataset annotated by human workers. Our dataset contains more than 112k language summarization across $\sim$22k unique UI screens. We then experimented with a set of deep models with different configurations. Our evaluation of these models with both automatic accuracy metrics and human rating shows that our approach can generate high-quality summaries for mobile screens. We demonstrate potential use cases of Screen2Words and open-source our dataset and model to lay the foundations for further bridging language and user interfaces.

cs.HC↗
arXiv2021

Examinations of CO completeness based on three independent CO surveys

We report the global properties recovered by an ongoing CO survey of the Milky Way Imaging Scroll Painting (MWISP) toward the Galactic outskirts. %To better understand the observational biases, Our results are also compared to those extracted by a uniform decomposition method from the CfA 1.2 m CO survey and the FCRAO 14 m outer Galaxy survey (OGS). We find that more extended and unseen structures are present in the MWISP data. The total flux across the disk recovered by the MWISP survey is 1.6 times larger than those recovered by the CfA and OGS surveys in the case of the same resolution. The discrepancies are scaling with distance. For example, in the outermost OSC arm, the flux ratios for MWISP-to-CfA and MWISP-to-OGS increase up to 43.8 and 7.4, respectively. Nonetheless, the census of molecular gas in our Galaxy is still far from complete by the MWISP, with flux completeness of $<$58%. The total mass ratios of the tabulated molecular clouds between different surveys are similar to the CO flux ratio. The application of these ratios to the total H$_{\rm 2}$ mass of our Galaxy yields a correction factor of at least 1.4, meaning that the H$_{\rm 2}$ mass of our Galaxy should be at least 40% more massive than previously determined. Including the completeness correction, an even more significant fraction of the matter should be contributed by baryonic matter. The mass spectrum in the outer Galactic plane is better described by a non-truncating power-law with $γ$=$-$1.83$\pm$0.05, and an upper mass of $M_0$=(1.3$\pm$0.5)$\times$10$^{\rm 6}$ $M_\odot$.

astro-ph.GA↗
arXiv2021

swdpwr: A SAS Macro and An R Package for Power Calculation in Stepped Wedge Cluster Randomized Trials

Background and objective: The stepped wedge cluster randomized trial is a study design increasingly used for public health intervention evaluations. Most previous literature focuses on power calculations for this particular type of cluster randomized trials for continuous outcomes, along with an approximation to this approach for binary outcomes. Although not accurate for binary outcomes, it has been widely used. To improve the approximation for binary outcomes, two new methods for stepped wedge designs (SWDs) of binary outcomes have recently been published. However, these new methods have not been implemented in publicly available software. The objective of this paper is to present power calculation software for SWDs in various settings for both continuous and binary outcomes. Methods: We have developed a SAS macro %swdpwr and an R package swdpwr for power calculation in SWDs. Different scenarios including cross-sectional and cohort designs, binary and continuous outcomes, marginal and conditional models, three link functions, with and without time effects are accommodated in this software. Results: swdpwr provides an efficient tool to support investigators in the design and analysis of stepped wedge cluster randomized trails. swdpwr addresses the implementation gap between newly proposed methodology and their application to obtain more accurate power calculations in SWDs. Conclusions: This user-friendly software makes the new methods more accessible and incorporates as many variations as currently available, which were not supported in other related packages. swdpwr is implemented under two platforms: SAS and R, satisfying the needs of investigators from various backgrounds.

stat.AP↗
arXiv2021

Feature Combination Meets Attention: Baidu Soccer Embeddings and Transformer based Temporal Detection

With rapidly evolving internet technologies and emerging tools, sports related videos generated online are increasing at an unprecedentedly fast pace. To automate sports video editing/highlight generation process, a key task is to precisely recognize and locate the events in the long untrimmed videos. In this tech report, we present a two-stage paradigm to detect what and when events happen in soccer broadcast videos. Specifically, we fine-tune multiple action recognition models on soccer data to extract high-level semantic features, and design a transformer based temporal detection module to locate the target events. This approach achieved the state-of-the-art performance in both two tasks, i.e., action spotting and replay grounding, in the SoccerNet-v2 Challenge, under CVPR 2021 ActivityNet workshop. Our soccer embedding features are released at https://github.com/baidu-research/vidpress-sports. By sharing these features with the broader community, we hope to accelerate the research into soccer video understanding.

cs.CV↗
arXiv2021

Decentralized Spatial-Temporal Trajectory Planning for Multicopter Swarms

Multicopter swarms with decentralized structure possess the nature of flexibility and robustness, while efficient spatial-temporal trajectory planning still remains a challenge. This report introduces decentralized spatial-temporal trajectory planning, which puts a well-formed trajectory representation named MINCO into multi-agent scenarios. Our method ensures high-quality local planning for each agent subject to any constraint from either the coordination of the swarm or safety requirements in cluttered environments. Then, the local trajectory generation is formulated as an unconstrained optimization problem that is efficiently solved in milliseconds. Moreover, a decentralized asynchronous mechanism is designed to trigger the local planning for each agent. A systematic solution is presented with detailed descriptions of careful engineering considerations. Extensive benchmarks and indoor/outdoor experiments validate its wide applicability and high quality. Our software will be released for the reference of the community.

cs.RO↗
arXiv2021

High-Q silicon nitride drum resonators strongly coupled to gates

Silicon nitride (SiN) mechanical resonators with high quality mechanical properties are attractive for fundamental research and applications. However, it is challenging to maintain these mechanical properties while achieving strong coupling to an electrical circuit for efficient on-chip integration. Here, we present a SiN drum resonator covered with an aluminum thin film, enabling large capacitive coupling to a suspended top-gate. Implementing the full electrical measurement scheme, we demonstrate a high quality factor ~ 1E4 (comparable to that of bare drums at room temperature) and present our ability to detect ? 10 mechanical modes at low temperature. The drum resonator is also coupled to a microwave cavity, so that we can perform optomechanical sideband pumping with a fairly good coupling strength G and demonstrate mechanical parametric amplification. This SiN drum resonator design provides efficient electrical integration and exhibits promising features for exploring mode coupling and signal processing.

physics.app-ph↗
arXiv2021

EGO-Swarm: A Fully Autonomous and Decentralized Quadrotor Swarm System in Cluttered Environments

This paper presents a decentralized and asynchronous systematic solution for multi-robot autonomous navigation in unknown obstacle-rich scenes using merely onboard resources. The planning system is formulated under gradient-based local planning framework, where collision avoidance is achieved by formulating the collision risk as a penalty of a nonlinear optimization problem. In order to improve robustness and escape local minima, we incorporate a lightweight topological trajectory generation method. Then agents generate safe, smooth, and dynamically feasible trajectories in only several milliseconds using an unreliable trajectory sharing network. Relative localization drift among agents is corrected by using agent detection in depth images. Our method is demonstrated in both simulation and real-world experiments. The source code is released for the reference of the community.

cs.RO↗
arXiv2021

TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing

Various robustness evaluation methodologies from different perspectives have been proposed for different natural language processing (NLP) tasks. These methods have often focused on either universal or task-specific generalization capabilities. In this work, we propose a multilingual robustness evaluation platform for NLP tasks (TextFlint) that incorporates universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analysis. TextFlint enables practitioners to automatically evaluate their models from all aspects or to customize their evaluations as desired with just a few lines of code. To guarantee user acceptability, all the text transformations are linguistically based, and we provide a human evaluation for each one. TextFlint generates complete analytical reports as well as targeted augmented data to address the shortcomings of the model's robustness. To validate TextFlint's utility, we performed large-scale empirical evaluations (over 67,000 evaluations) on state-of-the-art deep learning models, classic supervised methods, and real-world systems. Almost all models showed significant performance degradation, including a decline of more than 50% of BERT's prediction accuracy on tasks such as aspect-level sentiment classification, named entity recognition, and natural language inference. Therefore, we call for the robustness to be included in the model evaluation, so as to promote the healthy development of NLP technology.

cs.CL↗
arXiv2021

Molecular gas distribution perpendicular to the Galactic plane

We use the ~370 square degrees data from the MWISP CO survey to study the vertical distribution of the molecular clouds (MCs) toward the tangent points in the region of l=[16,52]deg and |b|<5.1deg. The molecular disk consists of two components with the layer thickness (FWHM) of ~85pc and ~280pc, respectively. In the inner Galaxy, the molecular mass in the thin disk is dominant, while the molecular mass traced by the discrete MCs with weak CO emission in the thick disk is probably <10% of the whole molecular disk. For the CO gas in the thick disk, we identified 1055 high-z MCs that are >100pc from the Galactic plane. However, only a few samples (i.e., 32 MCs) are located in the |z|>360pc region. Typically, the discrete MCs of the thick disk population have a median peak temperature of 2.1 K, a median velocity dispersion of 0.8km/s, and a median effective radius of 2.5pc. The median surface density of these MCs is 6.8 Msun/pc^2, indicating very faint CO emission for these high-z MCs. The cloud-cloud velocity dispersion is 4.9+-1.3 km/s and a linear variation with a slope of -0.4 km/s/kpc is obtained in the region of R_GC=2.2-6.4kpc. Assuming that these clouds are supported by their turbulent motions against the gravitational pull of the disk, a model of rho0(R) = 1.28exp(-R/3.2kpc) Msun/pc^3 can be used to describe the distribution of the total mass density in the Galactic midplane.

astro-ph.GA↗
arXiv2021

No Need for Interactions: Robust Model-Based Imitation Learning using Neural ODE

Interactions with either environments or expert policies during training are needed for most of the current imitation learning (IL) algorithms. For IL problems with no interactions, a typical approach is Behavior Cloning (BC). However, BC-like methods tend to be affected by distribution shift. To mitigate this problem, we come up with a Robust Model-Based Imitation Learning (RMBIL) framework that casts imitation learning as an end-to-end differentiable nonlinear closed-loop tracking problem. RMBIL applies Neural ODE to learn a precise multi-step dynamics and a robust tracking controller via Nonlinear Dynamics Inversion (NDI) algorithm. Then, the learned NDI controller will be combined with a trajectory generator, a conditional VAE, to imitate an expert's behavior. Theoretical derivation shows that the controller network can approximate an NDI when minimizing the training loss of Neural ODE. Experiments on Mujoco tasks also demonstrate that RMBIL is competitive to the state-of-the-art generative adversarial method (GAIL) and achieves at least 30% performance gain over BC in uneven surfaces.

cs.RO↗
arXiv2021

CMPCC: Corridor-based Model Predictive Contouring Control for Aggressive Drone Flight

In this paper, we propose an efficient, receding horizon, local adaptive low-level planner as the middle layer between our original planner and controller. Our method is named as corridor-based model predictive contouring control (CMPCC) since it builds upon on MPCC and utilizes the flight corridor as hard safety constraints. It optimizes the flight aggressiveness and tracking accuracy simultaneously, thus improving our system's robustness by overcoming unmeasured disturbances. Our method features its online flight speed optimization, strict safety and feasibility, and real-time performance, and will be released as a low-level plugin for a large variety of quadrotor systems.

cs.RO↗
arXiv2020

Distances to molecular clouds in the second Galactic quadrant

We present distances to 76 medium-sized molecular clouds and an extra large-scale one in the second Galactic quadrant ($104.75^\circ <l<150.25^\circ $ and $|b|<5.25^\circ$), 73 of which are accurately measured for the first time. Molecular cloud samples are drawn from $l$-$b$-$V$ space ($-95 < V_{\rm LSR}< 25$ \kms) with the density-based spatial clustering of applications with noise (DBSCAN) algorithm, and distances are measured with the background-eliminated extinction-parallax (BEEP) method using extinctions and Gaia DR2 parallaxes. The range of measured distances to 76 molecular clouds is from 211 to 2631 pc, and the extra large-scale molecular cloud appears to be a coherent structure at about 1 kpc, across about 40° ($\sim$700 pc) in the Galactic longitude.

astro-ph.GA↗
arXiv2020

Semi-supervised Active Learning for Instance Segmentation via Scoring Predictions

Active learning generally involves querying the most representative samples for human labeling, which has been widely studied in many fields such as image classification and object detection. However, its potential has not been explored in the more complex instance segmentation task that usually has relatively higher annotation cost. In this paper, we propose a novel and principled semi-supervised active learning framework for instance segmentation. Specifically, we present an uncertainty sampling strategy named Triplet Scoring Predictions (TSP) to explicitly incorporate samples ranking clues from classes, bounding boxes and masks. Moreover, we devise a progressive pseudo labeling regime using the above TSP in semi-supervised manner, it can leverage both the labeled and unlabeled data to minimize labeling effort while maximize performance of instance segmentation. Results on medical images datasets demonstrate that the proposed method results in the embodiment of knowledge from available data in a meaningful way. The extensive quantitatively and qualitatively experiments show that, our method can yield the best-performing model with notable less annotation costs, compared with state-of-the-arts.

cs.CV↗
arXiv2020

EGO-Planner: An ESDF-free Gradient-based Local Planner for Quadrotors

Gradient-based planners are widely used for quadrotor local planning, in which a Euclidean Signed Distance Field (ESDF) is crucial for evaluating gradient magnitude and direction. Nevertheless, computing such a field has much redundancy since the trajectory optimization procedure only covers a very limited subspace of the ESDF updating range. In this paper, an ESDF-free gradient-based planning framework is proposed, which significantly reduces computation time. The main improvement is that the collision term in the penalty function is formulated by comparing the colliding trajectory with a collision-free guiding path. The resulting obstacle information will be stored only if the trajectory hits new obstacles, making the planner only extract necessary obstacle information. Then, we lengthen the time allocation if dynamical feasibility is violated. An anisotropic curve fitting algorithm is introduced to adjust higher-order derivatives of the trajectory while maintaining the original shape. Benchmark comparisons and real-world experiments verify its robustness and high-performance. The source code is released as ROS packages.

cs.RO↗
arXiv2020

Revisiting the distance, environment and supernova properties of SNR G57.2+0.8 that hosts SGR 1935+2154

We have performed a multi-wavelength study of supernova remnant (SNR) G57.2+0.8 and its environment. The SNR hosts the magnetar SGR 1935+2154, which emitted an extremely bright ms-duration radio burst on 2020 Apr 28 (The Chime/Frb Collaboration et al. 2020; Bochenek et al. 2020). We used the 12CO and 13CO J=1-0 data from the Milky Way Image Scroll Painting (MWISP) CO line survey to search for molecular gas associated with G57.2+0.8, in order to constrain the physical parameters (e.g., the distance) of the SNR and its magnetar. We report that SNR G57.2+0.8 is likely impacting the molecular clouds (MCs) at the local standard of rest (LSR) velocity V_{LSR} ~ 30 km/s and excites a weak 1720 MHz OH maser with a peak flux density of 47 mJy/beam. The chance coincidence of a random OH spot falling in the SNR is <12%, and the OH-CO correspondence chance is 7% at the maser spot. This combines to give < 1% false probability of the OH maser, suggesting a real maser detection. The LSR velocity of the MCs places the SNR and magnetar at a kinematic distance of d=6.6 +/- 0.7 kpc. The nondetection of thermal X-ray emission from the SNR and the relatively dense environment suggests G57.2+0.8 be an evolved SNR with an age $t>1.6 \times 10^4$ (d/6.6 kpc) yr. The explosion energy of G57.2+0.8 is lower than $2 \times 10^{51}(n_0/10 cm^{-3})^{1.16} (d/~6.6 kpc)^{3.16}$ erg, which is not very energetic even assuming a high ambient density $n_0$ = 10 cm$^{-3}$. This reinforces the opinion that magnetars do not necessarily result from very energetic supernova explosions.

astro-ph.HE↗
arXiv2020

AI-lead Court Debate Case Investigation

The multi-role judicial debate composed of the plaintiff, defendant, and judge is an important part of the judicial trial. Different from other types of dialogue, questions are raised by the judge, The plaintiff, plaintiff's agent defendant, and defendant's agent would be to debating so that the trial can proceed in an orderly manner. Question generation is an important task in Natural Language Generation. In the judicial trial, it can help the judge raise efficient questions so that the judge has a clearer understanding of the case. In this work, we propose an innovative end-to-end question generation model-Trial Brain Model (TBM) to build a Trial Brain, it can generate the questions the judge wants to ask through the historical dialogue between the plaintiff and the defendant. Unlike prior efforts in natural language generation, our model can learn the judge's questioning intention through predefined knowledge. We do experiments on real-world datasets, the experimental results show that our model can provide a more accurate question in the multi-role court debate scene.

cs.CL↗
arXiv2020

Kalibre: Knowledge-based Neural Surrogate Model Calibration for Data Center Digital Twins

Computational fluid dynamics (CFD) model has been widely used for prototyping data centers. Evolving it to high-fidelity {\em digital twin} is desirable for the management and operations of large-scale data centers. Manually calibrating CFD model parameters to achieve twin-class fidelity by specially trained domain expert is tedious and labor-intensive. To reduce manual efforts, existing automatic calibration approaches developed for various computational models apply heuristics to search model configurations within an empirically defined parameter bound. However, in the context of CFD, each search step requires long-lasting CFD model's iterated solving, rendering these approaches impractical with increased model complexity. This paper presents Kalibre, a knowledge-based neural surrogate approach that performs CFD model calibration by iterating four key steps of i) training a neural surrogate model based on CFD-generated data, ii) finding the optimal parameters at the moment through neural surrogate retraining based on sensor-measured data, iii) configuring the found parameters back to the CFD model, and iv) validating the CFD model using sensor-measured data as the ground truth. Thus, the parameter search is offloaded to the neural surrogate which is ultra-faster than CFD model's iterated solving. To speed up the convergence of Kalibre, we integrate prior knowledge of the twinned data center's thermophysics into the neural surrogate design to improve its learning efficiency. With about five hours computation on a 32-core processor, Kalibre achieves mean absolute errors (MAEs) of $0.81^o$C and $0.75^o$C in calibrating two CFD models for two production data halls hosting thousands of servers each while requires fewer CFD solving processes than existing baseline approaches.

eess.SY↗