arXiv subjects

Lin Wang

Publications and source records attributed to Lin Wang.

At least 361 records · Page 20Linked to original sources

arXiv2022

Condensation Droplet Sieve

Large droplets emerging during dropwise condensation impair surface properties such as anti-fogging/frosting ability and heat transfer efficiency. How to spontaneously detach massive randomly distributed droplets with controlled sizes has remained a great challenge. Herein, we present a general solution called condensation droplet sieve, through fabricating microscale thin-walled lattice (TWL) structures coated with a superhydrophobic layer. Growing droplets were observed to jumped off this TWL surface with 100% probability once becoming slightly larger than the lattices. The maximum radius and residual volume of droplets were strictly confined to 16 μm and 3.2 nl/mm2 respectively, greatly surpassing the current state of the art. We reveal that this extremely efficient jumping is attributed to the large tolerance of coalescence mismatch and effective isolation of droplets between neighbouring lattices. Our work provides a new perspective for the design and fabrication of high-performance anti-dew materials.

cond-mat.mtrl-sci↗

arXiv2022

Data-driven discovery of high performance layered van der Waals piezoelectric NbOI2

Using high-throughput first-principles calculations to search for layered van der Waals materials with the largest piezoelectric stress coefficients, we discover NbOI2 to be the one among 2940 monolayers screened. The piezoelectric performance of NbOI2 is independent of thickness, and its electromechanical coupling factor of near unity is a hallmark of optimal interconversion between electrical and mechanical energy. Laser scanning vibrometer studies on bulk and few-layer NbOI2 crystals verify their huge piezoelectric responses, which exceed internal references such as In2Se3 and CuInP2S6. Furthermore, we provide insights into the atomic origins of anti-correlated piezoelectric and ferroelectric responses in NbOX2 (X = Cl, Br, I), based on bond covalency and structural distortions in these materials. Our discovery that NbOI2 has the largest piezoelectric stress coefficients among 2D materials calls for the development of NbOI2-based flexible nanoscale piezoelectric devices.

cond-mat.mtrl-sci↗

arXiv2022

The unusual emission from PSR B1859+07 with FAST

We present simultaneous broad-band radio observations on the abnormal emission mode from PSR B1859$+$07 using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). This pulsar shows peculiar emission phenomena, which are occasional shifts of emission to an early rotational phase and mode change of emission at the normal phase. We confirm all these three emission modes with our datasets, including the B (burst) and Q (quiet) modes of the non-shift pulses and the emission shift mode with a quasi-periodicity of 155 pulses. We also identify a new type of emission shift event, which has emission at the normal phase during the event. We studied polarisation properties of these emission modes in details, and found that they all have similar polarisation angle (PA) curve, indicating the emission of all these three modes are from the same emission height.

astro-ph.HE↗

arXiv2022

Social physics

Recent decades have seen a rise in the use of physics methods to study different societal phenomena. This development has been due to physicists venturing outside of their traditional domains of interest, but also due to scientists from other disciplines taking from physics the methods that have proven so successful throughout the 19th and the 20th century. Here we dub this field 'social physics' and pay our respect to intellectual mavericks who nurtured it to maturity. We do so by reviewing the current state of the art. Starting with a set of topics that are at the heart of modern human societies, we review research dedicated to urban development and traffic, the functioning of financial markets, cooperation as the basis for our evolutionary success, the structure of social networks, and the integration of intelligent machines into these networks. We then shift our attention to a set of topics that explore potential threats to society. These include criminal behaviour, large-scale migrations, epidemics, environmental challenges, and climate change. We end the coverage of each topic with promising directions for future research. Based on this, we conclude that the future for social physics is bright. Physicists studying societal phenomena are no longer a curiosity, but rather a force to be reckoned with. Notwithstanding, it remains of the utmost importance that we continue to foster constructive dialogue and mutual respect at the interfaces of different scientific disciplines.

physics.soc-ph↗

arXiv2022

Medical Matting: A New Perspective on Medical Segmentation with Uncertainty

It is difficult to accurately label ambiguous and complex shaped targets manually by binary masks. The weakness of binary mask under-expression is highlighted in medical image segmentation, where blurring is prevalent. In the case of multiple annotations, reaching a consensus for clinicians by binary masks is more challenging. Moreover, these uncertain areas are related to the lesions' structure and may contain anatomical information beneficial to diagnosis. However, current studies on uncertainty mainly focus on the uncertainty in model training and data labels. None of them investigate the influence of the ambiguous nature of the lesion itself.Inspired by image matting, this paper introduces alpha matte as a soft mask to represent uncertain areas in medical scenes and accordingly puts forward a new uncertainty quantification method to fill the gap of uncertainty research for lesion structure. In this work, we introduce a new architecture to generate binary masks and alpha mattes in a multitasking framework, which outperforms all state-of-the-art matting algorithms compared. The proposed uncertainty map is able to highlight the ambiguous regions and a novel multitasking loss weighting strategy we presented can improve performance further and demonstrate their concrete benefits. To fully-evaluate the effectiveness of our proposed method, we first labelled three medical datasets with alpha matte to address the shortage of available matting datasets in medical scenes and prove the alpha matte to be a more efficient labeling method than a binary mask from both qualitative and quantitative aspects.

cs.CV↗

arXiv2021

Deconvolutional Density Network: Modeling Free-Form Conditional Distributions

Conditional density estimation (CDE) is the task of estimating the probability of an event conditioned on some inputs. A neural network (NN) can also be used to compute the output distribution for continuous-domain, which can be viewed as an extension of regression task. Nevertheless, it is difficult to explicitly approximate a distribution without knowing the information of its general form a priori. In order to fit an arbitrary conditional distribution, discretizing the continuous domain into bins is an effective strategy, as long as we have sufficiently narrow bins and very large data. However, collecting enough data is often hard to reach and falls far short of that ideal in many circumstances, especially in multivariate CDE for the curse of dimensionality. In this paper, we demonstrate the benefits of modeling free-form conditional distributions using a deconvolution-based neural net framework, coping with data deficiency problems in discretization. It has the advantage of being flexible but also takes advantage of the hierarchical smoothness offered by the deconvolution layers. We compare our method to a number of other density-estimation approaches and show that our Deconvolutional Density Network (DDN) outperforms the competing methods on many univariate and multivariate tasks. The code of DDN is available at https://github.com/NBICLAB/DDN.

cs.LG↗

arXiv2021

SphereSR: 360° Image Super-Resolution with Arbitrary Projection via Continuous Spherical Image Representation

The 360°imaging has recently gained great attention; however, its angular resolution is relatively lower than that of a narrow field-of-view (FOV) perspective image as it is captured by using fisheye lenses with the same sensor size. Therefore, it is beneficial to super-resolve a 360°image. Some attempts have been made but mostly considered the equirectangular projection (ERP) as one of the way for 360°image representation despite of latitude-dependent distortions. In that case, as the output high-resolution(HR) image is always in the same ERP format as the low-resolution (LR) input, another information loss may occur when transforming the HR image to other projection types. In this paper, we propose SphereSR, a novel framework to generate a continuous spherical image representation from an LR 360°image, aiming at predicting the RGB values at given spherical coordinates for super-resolution with an arbitrary 360°image projection. Specifically, we first propose a feature extraction module that represents the spherical data based on icosahedron and efficiently extracts features on the spherical surface. We then propose a spherical local implicit image function (SLIIF) to predict RGB values at the spherical coordinates. As such, SphereSR flexibly reconstructs an HR image under an arbitrary projection type. Experiments on various benchmark datasets show that our method significantly surpasses existing methods.

cs.CV↗

arXiv2021

BIPS: Bi-modal Indoor Panorama Synthesis via Residual Depth-aided Adversarial Learning

Providing omnidirectional depth along with RGB information is important for numerous applications, eg, VR/AR. However, as omnidirectional RGB-D data is not always available, synthesizing RGB-D panorama data from limited information of a scene can be useful. Therefore, some prior works tried to synthesize RGB panorama images from perspective RGB images; however, they suffer from limited image quality and can not be directly extended for RGB-D panorama synthesis. In this paper, we study a new problem: RGB-D panorama synthesis under the arbitrary configurations of cameras and depth sensors. Accordingly, we propose a novel bi-modal (RGB-D) panorama synthesis (BIPS) framework. Especially, we focus on indoor environments where the RGB-D panorama can provide a complete 3D model for many applications. We design a generator that fuses the bi-modal information and train it with residual-aided adversarial learning (RDAL). RDAL allows to synthesize realistic indoor layout structures and interiors by jointly inferring RGB panorama, layout depth, and residual depth. In addition, as there is no tailored evaluation metric for RGB-D panorama synthesis, we propose a novel metric to effectively evaluate its perceptual quality. Extensive experiments show that our method synthesizes high-quality indoor RGB-D panoramas and provides realistic 3D indoor models than prior methods. Code will be released upon acceptance.

cs.CV↗

arXiv2021

A Case for a Programmable Edge Storage Middleware

Edge computing is a fast-growing computing paradigm where data is processed at the local site where it is generated, close to the end-devices. This can benefit a set of disruptive applications like autonomous driving, augmented reality, and collaborative machine learning, which produce incredible amounts of data that need to be shared, processed and stored at the edge to meet low latency requirements. However, edge storage poses new challenges due to the scarcity and heterogeneity of edge infrastructures and the diversity of edge applications. In particular, edge applications may impose conflicting constraints and optimizations that are hard to be reconciled on the limited, hard-to-scale edge resources. In this vision paper we argue that a new middleware for constrained edge resources is needed, providing a unified storage service for diverse edge applications. We identify programmability as a critical feature that should be leveraged to optimize the resource sharing while delivering the specialization needed for edge applications. Following this line, we make a case for eBPF and present the design for Griffin - a flexible, lightweight programmable edge storage middleware powered by eBPF.

cs.DC↗

arXiv2021

Dual Transfer Learning for Event-based End-task Prediction via Pluggable Event to Image Translation

Event cameras are novel sensors that perceive the per-pixel intensity changes and output asynchronous event streams with high dynamic range and less motion blur. It has been shown that events alone can be used for end-task learning, e.g., semantic segmentation, based on encoder-decoder-like networks. However, as events are sparse and mostly reflect edge information, it is difficult to recover original details merely relying on the decoder. Moreover, most methods resort to pixel-wise loss alone for supervision, which might be insufficient to fully exploit the visual details from sparse events, thus leading to less optimal performance. In this paper, we propose a simple yet flexible two-stream framework named Dual Transfer Learning (DTL) to effectively enhance the performance on the end-tasks without adding extra inference cost. The proposed approach consists of three parts: event to end-task learning (EEL) branch, event to image translation (EIT) branch, and transfer learning (TL) module that simultaneously explores the feature-level affinity information and pixel-level knowledge from the EIT branch to improve the EEL branch. This simple yet novel method leads to strong representation learning from events and is evidenced by the significant performance boost on the end-tasks such as semantic segmentation and depth estimation.

cs.CV↗

arXiv2021

EvDistill: Asynchronous Events to End-task Learning via Bidirectional Reconstruction-guided Cross-modal Knowledge Distillation

Event cameras sense per-pixel intensity changes and produce asynchronous event streams with high dynamic range and less motion blur, showing advantages over conventional cameras. A hurdle of training event-based models is the lack of large qualitative labeled data. Prior works learning end-tasks mostly rely on labeled or pseudo-labeled datasets obtained from the active pixel sensor (APS) frames; however, such datasets' quality is far from rivaling those based on the canonical images. In this paper, we propose a novel approach, called \textbf{EvDistill}, to learn a student network on the unlabeled and unpaired event data (target modality) via knowledge distillation (KD) from a teacher network trained with large-scale, labeled image data (source modality). To enable KD across the unpaired modalities, we first propose a bidirectional modality reconstruction (BMR) module to bridge both modalities and simultaneously exploit them to distill knowledge via the crafted pairs, causing no extra computation in the inference. The BMR is improved by the end-tasks and KD losses in an end-to-end manner. Second, we leverage the structural similarities of both modalities and adapt the knowledge by matching their distributions. Moreover, as most prior feature KD methods are uni-modality and less applicable to our problem, we propose to leverage an affinity graph KD loss to boost the distillation. Our extensive experiments on semantic segmentation and object recognition demonstrate that EvDistill achieves significantly better results than the prior works and KD with only events and APS frames.

cs.CV↗

arXiv2021

An Alternative Approach for Computing Discrete Logarithms in Compressed SIDH

Currently, public-key compression of supersingular isogeny Diffie-Hellman (SIDH) and its variant, supersingular isogeny key encapsulation (SIKE) involve pairing computation and discrete logarithm computation. In this paper, we propose novel methods to compute only 3 discrete logarithms instead of 4, in exchange for computing a lookup table efficiently. The algorithms also allow us to make a trade-off between memory and efficiency. Our implementation shows that the efficiency of our algorithms is close to that of the previous work, and our algorithms perform better in some special cases.

cs.CR↗

arXiv2021

Deep Learning for HDR Imaging: State-of-the-Art and Future Trends

High dynamic range (HDR) imaging is a technique that allows an extensive dynamic range of exposures, which is important in image processing, computer graphics, and computer vision. In recent years, there has been a significant advancement in HDR imaging using deep learning (DL). This study conducts a comprehensive and insightful survey and analysis of recent developments in deep HDR imaging methodologies. We hierarchically and structurally group existing deep HDR imaging methods into five categories based on (1) number/domain of input exposures, (2) number of learning tasks, (3) novel sensor data, (4) novel learning strategies, and (5) applications. Importantly, we provide a constructive discussion on each category regarding its potential and challenges. Moreover, we review some crucial aspects of deep HDR imaging, such as datasets and evaluation metrics. Finally, we highlight some open problems and point out future research directions.

eess.IV↗

arXiv2021

All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda

Since the popularisation of the Internet in the 1990s, the cyberspace has kept evolving. We have created various computer-mediated virtual environments including social networks, video conferencing, virtual 3D worlds (e.g., VR Chat), augmented reality applications (e.g., Pokemon Go), and Non-Fungible Token Games (e.g., Upland). Such virtual environments, albeit non-perpetual and unconnected, have bought us various degrees of digital transformation. The term `metaverse' has been coined to further facilitate the digital transformation in every aspect of our physical lives. At the core of the metaverse stands the vision of an immersive Internet as a gigantic, unified, persistent, and shared realm. While the metaverse may seem futuristic, catalysed by emerging technologies such as Extended Reality, 5G, and Artificial Intelligence, the digital `big bang' of our cyberspace is not far away. This survey paper presents the first effort to offer a comprehensive framework that examines the latest metaverse development under the dimensions of state-of-the-art technologies and metaverse ecosystems, and illustrates the possibility of the digital `big bang'. First, technologies are the enablers that drive the transition from the current Internet to the metaverse. We thus examine eight enabling technologies rigorously - Extended Reality, User Interactivity (Human-Computer Interaction), Artificial Intelligence, Blockchain, Computer Vision, IoT and Robotics, Edge and Cloud computing, and Future Mobile Networks. In terms of applications, the metaverse ecosystem allows human users to live and play within a self-sustaining, persistent, and shared realm. Therefore, we discuss six user-centric factors -- Avatar, Content Creation, Virtual Economy, Social Acceptability, Security and Privacy, and Trust and Accountability. Finally, we propose a concrete research agenda for the development of the metaverse.

cs.CY↗

arXiv2021

Subsystems with shadowing property for $\mathbb{Z}^{k}$-actions

In this paper, subsystems with shadowing property for $\mathbb{Z}^{k}$-actions are investigated. Let $α$ be a continuous $\mathbb{Z}^{k}$-action on a compact metric space $X$. We introduce the notions of pseudo orbit and shadowing property for $α$ along subsets, particularly subspaces, of $\mathbb{R}^{k}$. Combining with another important property "expansiveness" for subsystems of $α$ which was introduced and systematically investigated by Boyle and Lind, we show that if $α$ has the shadowing property and is expansive along a subspace $V$ of $\mathbb{R}^{k}$, then so does for $α$ along any subspace $W$ of $\mathbb{R}^{k}$ containing $V$. Let $α$ be a smooth $\mathbb{Z}^{k}$-action on a closed Riemannian manifold $M$, $μ$ an ergodic probability measure and $Γ$ the Oseledec set. We show that, under a basic assumption on the Lyapunov spectrum, $α$ has the shadowing property and is expansive on $Γ$ along any subspace $V$ of $\mathbb{R}^{k}$ containing a regular vector; furthermore, $α$ has the quasi-shadowing property on $Γ$ along any 1-dimensional subspace $V$ of $\mathbb{R}^{k}$ containing a first-type singular vector. As an application, we also consider the 1-dimensional subsystems (i.e., flows) with shadowing property for the $\mathbb{R}^{k}$-action on the suspension manifold induced by $α$.

math.DS↗

arXiv2021

SiamEvent: Event-based Object Tracking via Edge-aware Similarity Learning with Siamese Networks

Event cameras are novel sensors that perceive the per-pixel intensity changes and output asynchronous event streams, showing lots of advantages over traditional cameras, such as high dynamic range (HDR) and no motion blur. It has been shown that events alone can be used for object tracking by motion compensation or prediction. However, existing methods assume that the target always moves and is the stand-alone object. Moreover, they fail to track the stopped non-independent moving objects on fixed scenes. In this paper, we propose a novel event-based object tracking framework, called SiamEvent, using Siamese networks via edge-aware similarity learning. Importantly, to find the part having the most similar edge structure of target, we propose to correlate the embedded events at two timestamps to compute the target edge similarity. The Siamese network enables tracking arbitrary target edge by finding the part with the highest similarity score. This extends the possibility of event-based object tracking applied not only for the independent stand-alone moving objects, but also for various settings of the camera and scenes. In addition, target edge initialization and edge detector are also proposed to prevent SiamEvent from the drifting problem. Lastly, we built an open dataset including various synthetic and real scenes to train and evaluate SiamEvent. Extensive experiments demonstrate that SiamEvent achieves up to 15% tracking performance enhancement than the baselines on the real-world scenes and more robust tracking performance in the challenging HDR and motion blur conditions.

cs.CV↗

arXiv2021

Quantitative destruction of invariant circles

For area-preserving twist maps on the annulus, we consider the problem on quantitative destruction of invariant circles with a given frequency $ω$ of an integrable system by a trigonometric polynomial of degree $N$ perturbation $R_N$ with $\|R_N\|_{C^r}<ε$. We obtain a relation among $N$, $r$, $ε$ and the arithmetic property of $ω$, for which the area-preserving map admit no invariant circles with $ω$.

math.DS↗

arXiv2021

Hamilton-Jacobi equations with their Hamiltonians depending Lipschitz continuously on the unknown

We study the Hamilton-Jacobi equations $H(x,Du,u)=0$ in $M$ and $\partial u/\partial t +H(x,D_xu,u)=0$ in $M\times(0,\infty)$, where the Hamiltonian $H=H(x,p,u)$ depends Lipschitz continuously on the variable $u$. In the framework of the semicontinuous viscosity solutions due to Barron-Jensen, we establish the comparison principle, existence theorem, and representation formula as value functions for extended real-valued, lower semicontinuous solutions for the Cauchy problem. We also establish some results on the long-time behavior of solutions for the Cauchy problem and classification of solutions for the stationary problem.

math.AP↗