Searcharxiv⌕ Search

arXiv subjects

Yun Wang

Publications and source records attributed to Yun Wang.

At least 181 records · Page 10Linked to original sources
arXiv2023

GeoCamera: Telling Stories in Geographic Visualizations with Camera Movements

In geographic data videos, camera movements are frequently used and combined to present information from multiple perspectives. However, creating and editing camera movements requires significant time and professional skills. This work aims to lower the barrier of crafting diverse camera movements for geographic data videos. First, we analyze a corpus of 66 geographic data videos and derive a design space of camera movements with a dimension for geospatial targets and one for narrative purposes. Based on the design space, we propose a set of adaptive camera shots and further develop an interactive tool called GeoCamera. This interactive tool allows users to flexibly design camera movements for geographic visualizations. We verify the expressiveness of our tool through case studies and evaluate its usability with a user study. The participants find that the tool facilitates the design of camera movements.

cs.HC↗
arXiv2023

Theoretical investigation on potential of zero free charge of (111) and (100) surfaces of Group 10 and 11 metals

The potential of zero free charge (PZFC) value is a crucial parameter in electrochemistry. However, the evaluations of PZFC have traditionally been difficult. To overcome this challenge, we applied a hybrid solvation method that incorporates, both an explicit water layer next to the metal surface and an implicit water layer, combined with density functional theory (DFT) to simplify the PZFC evaluation. Using the (111) and (100) surfaces of Group 10 and 11 metals as model systems, we calculated their PZFC values, which showed excellent agreement with the reported data. This great match validates the accuracy and reliability of our theoretical approach. Notably, we observed that the surface structure and the orientation of water molecules have a significant influence on the PZFC values of the metals. Our study, therefore, paves the way for efficiently and accurately calculating the PZFC values of materials, which can greatly benefit their practical applications.

cond-mat.mtrl-sci↗
arXiv2023

Rigidity of Steady Solutions to the Navier-Stokes Equations in High Dimensions and its applications

Solutions with scaling-invariant bounds such as self-similar solutions, play an important role in the understanding of the regularity and asymptotic structures of solutions to the Navier-Stokes equations. In this paper, we prove that any steady solution satisfying $|\Bu(x)|\leq C/|x|$ for any constant $C$ in $\mathbb{R}^n\setminus \{0\}$ with $ n \geq 4$, must be zero. Our main idea is to analyze the velocity field and the total head pressure via weighted energy estimates with suitable multipliers so that the proof is pretty elementary and short. These results not only give the Liouville-type theorem for steady solutions in higher dimensions with neither smallness nor self-similarity type assumptions, but also help to remove a class of singularities of solutions and give the optimal asymptotic behaviors of solutions at infinity in the exterior domains.

math.AP↗
arXiv2023

Exploring the Mutual Influence between Self-Supervised Single-Frame and Multi-Frame Depth Estimation

Although both self-supervised single-frame and multi-frame depth estimation methods only require unlabeled monocular videos for training, the information they leverage varies because single-frame methods mainly rely on appearance-based features while multi-frame methods focus on geometric cues. Considering the complementary information of single-frame and multi-frame methods, some works attempt to leverage single-frame depth to improve multi-frame depth. However, these methods can neither exploit the difference between single-frame depth and multi-frame depth to improve multi-frame depth nor leverage multi-frame depth to optimize single-frame depth models. To fully utilize the mutual influence between single-frame and multi-frame methods, we propose a novel self-supervised training framework. Specifically, we first introduce a pixel-wise adaptive depth sampling module guided by single-frame depth to train the multi-frame model. Then, we leverage the minimum reprojection based distillation loss to transfer the knowledge from the multi-frame depth network to the single-frame network to improve single-frame depth. Finally, we regard the improved single-frame depth as a prior to further boost the performance of multi-frame depth estimation. Experimental results on the KITTI and Cityscapes datasets show that our method outperforms existing approaches in the self-supervised monocular setting.

cs.CV↗
arXiv2023

Data Player: Automatic Generation of Data Videos with Narration-Animation Interplay

Data visualizations and narratives are often integrated to convey data stories effectively. Among various data storytelling formats, data videos have been garnering increasing attention. These videos provide an intuitive interpretation of data charts while vividly articulating the underlying data insights. However, the production of data videos demands a diverse set of professional skills and considerable manual labor, including understanding narratives, linking visual elements with narration segments, designing and crafting animations, recording audio narrations, and synchronizing audio with visual animations. To simplify this process, our paper introduces a novel method, referred to as Data Player, capable of automatically generating dynamic data videos with narration-animation interplay. This approach lowers the technical barriers associated with creating data videos rich in narration. To enable narration-animation interplay, Data Player constructs references between visualizations and text input. Specifically, it first extracts data into tables from the visualizations. Subsequently, it utilizes large language models to form semantic connections between text and visuals. Finally, Data Player encodes animation design knowledge as computational low-level constraints, allowing for the recommendation of suitable animation presets that align with the audio narration produced by text-to-speech technologies. We assessed Data Player's efficacy through an example gallery, a user study, and expert interviews. The evaluation results demonstrated that Data Player can generate high-quality data videos that are comparable to human-composed ones.

cs.HC↗
arXiv2023

Key Gene Mining in Transcriptional Regulation for Specific Biological Processes with Small Sample Sizes Using Multi-network pipeline Transformer

Gene mining is an important topic in the field of life sciences, but traditional machine learning methods cannot consider the regulatory relationships between genes. Deep learning methods perform poorly in small sample sizes. This study proposed a deep learning method, called TransGeneSelector, that can mine critical regulatory genes involved in certain life processes using a small-sample transcriptome dataset. The method combines a WGAN-GP data augmentation network, a sample filtering network, and a Transformer classifier network, which successfully classified the state (germinating or dry seeds) of Arabidopsis thaliana seed in a dataset of 79 samples, showing performance comparable to that of Random Forests. Further, through the use of SHapley Additive exPlanations method, TransGeneSelector successfully mined genes involved in seed germination. Through the construction of gene regulatory networks and the enrichment analysis of KEGG, as well as RT-qPCR quantitative analysis, it was confirmed that these genes are at a more upstream regulatory level than those Random Forests mined, and the top 11 genes that were uniquely mined by TransGeneSelector were found to be related to the KAI2 signaling pathway, which is of great regulatory importance for germination-related genes. This study provides a practical tool for life science researchers to mine key genes from transcriptome data.

q-bio.QM↗
arXiv2023

Wakey-Wakey: Animate Text by Mimicking Characters in a GIF

With appealing visual effects, kinetic typography (animated text) has prevailed in movies, advertisements, and social media. However, it remains challenging and time-consuming to craft its animation scheme. We propose an automatic framework to transfer the animation scheme of a rigid body on a given meme GIF to text in vector format. First, the trajectories of key points on the GIF anchor are extracted and mapped to the text's control points based on local affine transformation. Then the temporal positions of the control points are optimized to maintain the text topology. We also develop an authoring tool that allows intuitive human control in the generation process. A questionnaire study provides evidence that the output results are aesthetically pleasing and well preserve the animation patterns in the original GIF, where participants were impressed by a similar emotional semantics of the original GIF. In addition, we evaluate the utility and effectiveness of our approach through a workshop with general users and designers.

cs.HC↗
arXiv2023

A broken "$α$-intensity" relation caused by the evolving photosphere emission and the nature of the extraordinarily bright GRB 230307A

GRB 230307A is one of the brightest gamma-ray bursts detected so far. With the excellent observation of GRB 230307A by Fermi-GBM, we can reveal the details of prompt emission evolution. As found in high-time-resolution spectral analysis, the early low-energy spectral indices ($α$) of this burst exceed the limit of synchrotron radiation ($α=-2/3$), and gradually decreases with the energy flux ($F$). A tight $E_{\rm p}\propto F^{0.54}$ correlation anyhow holds within the whole duration of the burst, where $E_{\rm p}$ is the spectral peak energy. Such evolution pattern of $α$ and $E_{\rm p}$ with intensity is called ``double tracking". For the $α-F$ relation, we find a log Bayes factor $\sim$ 210 in favor of a smoothly broken power-law function over a linear function in log-linear space. We call this particular $α-F$ relation as broken ``$α$-intensity", and interpret it as the evolution of the ratio of thermal and non-thermal components, which is also the evolution of the photosphere. GRB 230307A with a duration of $\sim 35~\rm s$, if indeed at a redshift of $z=0.065$, is likely a neutron star merger event (i.e., it is intrinsically ``short"). Intriguingly, different from GRB 060614 and GRB 211211A, this long event is not composed of a hard spike followed by a soft tail, suggesting that the properties of the prompt emission light curves are not a good tracer of the astrophysical origins of the bursts. The other possibility of $z=3.87$ would point toward very peculiar nature of both GRB 230307A and its late time thermal-like emission.

astro-ph.HE↗
arXiv2023

Creating Emordle: Animating Word Cloud for Emotion Expression

We propose emordle, a conceptual design that animates wordles (compact word clouds) to deliver their emotional context to the audiences. To inform the design, we first reviewed online examples of animated texts and animated wordles, and summarized strategies for injecting emotion into the animations. We introduced a composite approach that extends an existing animation scheme for one word to multiple words in a wordle with two global factors: the randomness of text animation (entropy) and the animation speed (speed). To create an emordle, general users can choose one predefined animated scheme that matches the intended emotion class and fine-tune the emotion intensity with the two parameters. We designed proof-of-concept emordle examples for four basic emotion classes, namely happiness, sadness, anger, and fear. We conducted two controlled crowdsourcing studies to evaluate our approach. The first study confirmed that people generally agreed on the conveyed emotions from well-crafted animations, and the second one demonstrated that our identified factors helped fine-tune the delivered emotion extent. We also invited general users to create emordles on their own based on our proposed framework. Through this user study, we confirmed the effectiveness of the approach. We concluded with implications for future research opportunities of supporting emotion expression in visualizations.

cs.HC↗
arXiv2023

Uniqueness and uniform structural stability of Poiseuille flows with large fluxes in two-dimensional strips

In this paper, we prove the uniform nonlinear structural stability of Poiseuille flows with suitably large flux for the steady Navier-Stokes system in a two-dimensional strip with arbitrary period. Furthermore, the well-posedness theory for the Navier-Stokes system is also proved even when the $L^2$-norm of the external force is large. In particular, if the vertical velocity is suitably small where the smallness is independent of the flux, then Poiseuille flow is the unique solution of the steady Navier-Stokes system in the periodic strip. The key point is to establish uniform a priori estimates for the corresponding linearized problem via the boundary layer analysis, where we explore the particular features of odd and even stream functions. The analysis for the even stream function is new, which not only generalizes the previous study for the symmetric flows in \cite{Rabier1}, but also provides an explicit relation between the flux and period.

math.AP↗
arXiv2023

Phenomenological power spectrum models for H$α$ emission line galaxies from the Nancy Grace Roman Space Telescope

The High Latitude Spectroscopic Survey (HLSS) is the reference baseline spectroscopic survey for NASA's Nancy Grace Roman space telescope, measuring redshifts of $\sim 10$M H$α$ emission line galaxies over a $2000$ deg$^2$ footprint at $z=1-2$. In this work, we use a realistic Roman galaxy mock catalogue to explore optimal phenomenological modeling of the measured power spectrum. We consider two methods for modeling the redshift-space distortions (Kaiser squashing and another with a window function on $β$ that selects out the coherent radial infall pairwise velocities, $M_A$ and $M_B$, respectively), two models for the nonlinear impact of baryons that smears the BAO signal (a fixed ratio between the smearing scales in the perpendicular and parallel dimensions and another where these smearing scales are kept as a free parameters, P$_{dw}(k|k_*)$ and P$_{dw}(k|Σ_\perp,Σ_\parallel)$, respectively), and two analytical emulations of nonlinear growth (one employing the halo model and another formulated from simulated galaxy clustering of a semi-analytical model, $F_{HM}$ and $F_{SAM}$, respectively). We find that the best model combination employing $F_{HM}$ is $P_{dw}(k|k_*)*F_{HM}*M_B$, while the best combination employing $F_{SAM}$ is $P_{dw}(k|k_*)*F_{SAM}*M_B$, which leads to unbiased measurements of cosmological parameters. We compare these to the Effective Field Theory of Large-Scale Structure perturbation theory model $P_{EFT}(k|Θ)$, and find that our simple phenomenological models are comparable across the entire redshift range for $k_{max}=0.25$ and $0.3$ $h$/Mpc. We expect the tools that we have developed to be useful in probing dark energy and testing gravity using Roman in an accurate and robust manner.

astro-ph.CO↗
arXiv2023

Very-High-Energy Gamma-Ray Afterglows of GRB 201015A and GRB 201216C

Gamma-ray bursts (GRBs) 201015A and 201216C are valuable cases with detection of very high energy (VHE) gamma-ray afterglows. By analysing their prompt emission data, we find that GRB 201216C is an extremely energetic long GRB with a hard gamma-ray spectrum, while GRB 201015A is a relative sub-energetic, soft spectrum GRB. Attributing their radio-optical-X-ray afterglows to the synchrotron radiation of the relativistic electrons accelerated in their jets, we fit their afterglow lightcurves with the standard external shock model and infer their VHE afterglows from the synchrotron self-Compton scattering process of the electrons. It is found that the jet of GRB 201015A is mid-relativistic ($Γ_0=44$) surrounded by a very dense medium ($n=1202$ cm$^{-3}$) and the jet of GRB 201216C is ultra-relativistic ($Γ_0=331$) surrounded by a moderate dense medium ($n=5$ cm$^{-3}$). The inferred peak luminosity of the VHE gamma-ray afterglows of GRB 201216C is approximately $10^{-9}$ erg cm$^{-2}$ s$^{-1}$ at $57-600$ seconds after the GRB trigger, making it can be detectable with the MAGIC telescopes at a high confidence level, even the GRB is at a redshift of 1.1. Comparing their intrinsic VHE gamma-ray lightcurves and spectral energy distributions with GRBs~180720B, 190114C, and 190829A, we show that their intrinsic peak luminosity of VHE gamma-ray afterglows at $10^{4}$ seconds post the GRB trigger is variable from $10^{45}$ to $5\times 10^{48}$ erg s$^{-1}$, and their kinetic energy, initial Lorentz factor, and medium density are diverse among bursts.

astro-ph.HE↗
arXiv2023

Exploiting Implicit Rigidity Constraints via Weight-Sharing Aggregation for Scene Flow Estimation from Point Clouds

Scene flow estimation, which predicts the 3D motion of scene points from point clouds, is a core task in autonomous driving and many other 3D vision applications. Existing methods either suffer from structure distortion due to ignorance of rigid motion consistency or require explicit pose estimation and 3D object segmentation. Errors of estimated poses and segmented objects would yield inaccurate rigidity constraints and in turn mislead scene flow estimation. In this paper, we propose a novel weight-sharing aggregation (WSA) method for feature and scene flow up-sampling. WSA does not rely on estimated poses and segmented objects, and can implicitly enforce rigidity constraints to avoid structure distortion in scene flow estimation. To further exploit geometric information and preserve local structure, we design a deformation degree module aim to keep the local region invariance. We modify the PointPWC-Net and integrate the proposed WSA and deformation degree module into the enhanced PointPWC-Net to derive an end-to-end scene flow estimation network, called WSAFlowNet. Extensive experimental results on the FlyingThings3D and KITTI datasets demonstrate that our WSAFlowNet achieves the state-of-the-art performance and outperforms previous methods by a large margin. We will release the source code at https://github.com/wangyunlhr/WSAFlowNet.git.

cs.CV↗
arXiv2023

TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs

Artificial Intelligence (AI) has made incredible progress recently. On the one hand, advanced foundation models like ChatGPT can offer powerful conversation, in-context learning and code generation abilities on a broad range of open-domain tasks. They can also generate high-level solution outlines for domain-specific tasks based on the common sense knowledge they have acquired. However, they still face difficulties with some specialized tasks because they lack enough domain-specific data during pre-training or they often have errors in their neural network computations on those tasks that need accurate executions. On the other hand, there are also many existing models and systems (symbolic-based or neural-based) that can do some domain-specific tasks very well. However, due to the different implementation or working mechanisms, they are not easily accessible or compatible with foundation models. Therefore, there is a clear and pressing need for a mechanism that can leverage foundation models to propose task solution outlines and then automatically match some of the sub-tasks in the outlines to the off-the-shelf models and systems with special functionalities to complete them. Inspired by this, we introduce TaskMatrix.AI as a new AI ecosystem that connects foundation models with millions of APIs for task completion. Unlike most previous work that aimed to improve a single AI model, TaskMatrix.AI focuses more on using existing foundation models (as a brain-like central system) and APIs of other AI models and systems (as sub-task solvers) to achieve diversified tasks in both digital and physical domains. As a position paper, we will present our vision of how to build such an ecosystem, explain each key component, and use study cases to illustrate both the feasibility of this vision and the main challenges we need to address next.

cs.AI↗
arXiv2023

The Possibility of Modeling the Very High Energy Afterglow of GRB 221009A in a Wind Environment

In this paper, we model the dynamics and radiation physics of the rarity event GRB 221009A afterglow in detail. By introducing a top-hat jet that propagates in an environment dominated by stellar winds, we explain the publicly available observations of afterglow associated with GRB 221009A over the first week. It is predicted that GRB 221009A emits a luminous very high energy (VHE) afterglow based on the synchrotron self-Compton (SSC) process in our model. We show the broadband spectral energy distribution (SED) analysis results of GRB 221009A, and find that the SSC radiation component of GRB 221009A is very bright in the 0.1 - 10 TeV band. The integrated SED shows that the SSC emission in the TeV band has significantly higher than the detection sensitivity of LHASSO, MAGIC and CTA. However, since the release of further observations, deviations from the standard wind environment model gradually show up in data. For example, the late-time multiband afterglow cannot be consistently explained under the standard wind environment scenario. It may be necessary to consider modeling with a structured jet with complex geometry or a partial revision of the standard model. Furthermore, we find that the inclusion of GeV observations could break the degeneracy between model parameters, highlighting the significance of high-energy observations in determining accurate parameters for GRB afterglows.

astro-ph.HE↗
arXiv2023

NetworkNarratives: Data Tours for Visual Network Exploration and Analysis

This paper introduces semi-automatic data tours to aid the exploration of complex networks. Exploring networks requires significant effort and expertise and can be time-consuming and challenging. Distinct from guidance and recommender systems for visual analytics, we provide a set of goal-oriented tours for network overview, ego-network analysis, community exploration, and other tasks. Based on interviews with five network analysts, we developed a user interface (NetworkNarratives) and 10 example tours. The interface allows analysts to navigate an interactive slideshow featuring facts about the network using visualizations and textual annotations. On each slide, an analyst can freely explore the network and specify nodes, links, or subgraphs as seed elements for follow-up tours. Two studies, comprising eight expert and 14 novice analysts, show that data tours reduce exploration effort, support learning about network exploration, and can aid the dissemination of analysis results. NetworkNarratives is available online, together with detailed illustrations for each tour.

cs.HC↗
arXiv2023

Notable: On-the-fly Assistant for Data Storytelling in Computational Notebooks

Computational notebooks are widely used for data analysis. Their interleaved displays of code and execution results (e.g., visualizations) are welcomed since they enable iterative analysis and preserve the exploration process. However, the communication of data findings remains challenging in computational notebooks. Users have to carefully identify useful findings from useless ones, document them with texts and visual embellishments, and then organize them in different tools. Such workflow greatly increases their workload, according to our interviews with practitioners. To address the challenge, we designed Notable to offer on-the-fly assistance for data storytelling in computational notebooks. It provides intelligent support to minimize the work of documenting and organizing data findings and diminishes the cost of switching between data exploration and storytelling. To evaluate Notable, we conducted a user study with 12 data workers. The feedback from user study participants verifies its effectiveness and usability.

cs.HC↗
arXiv2023

Static Replication of Impermanent Loss for Concentrated Liquidity Provision in Decentralised Markets

This article analytically characterizes the impermanent loss of concentrated liquidity provision for automatic market makers in decentralised markets such as Uniswap. We propose two static replication formulas for the impermanent loss by a combination of European calls or puts with strike prices supported on the liquidity provision price interval. It facilitates liquidity providers to hedge permanent loss by trading crypto options in more liquid centralised exchanges such as Deribit. Numerical examples illustrate the astonishing accuracy of the static replication.

q-fin.GN↗