arXiv subjects

Yun Zhang

Publications and source records attributed to Yun Zhang.

At least 73 records · Page 4Linked to original sources

arXiv2024

BYORP and Dissipation in Binary Asteroids: Lessons from DART

The Near-Earth binary asteroid Didymos was the target of a planetary defense demonstration mission DART in September 2022. The smaller binary component, Dimorphos, was impacted by the spacecraft in order to measure momentum transfer in kinetic impacts into rubble piles. DART and associated Earth-based observation campaigns have provided a wealth of scientific data on the Didymos-Dimorphos binary. DART revealed a largely oblate and ellipsoidal shape of Dimorphos before the impact, while the post-impact observations suggest that Dimorphos now has a prolate shape. Here we add those data points to the known properties of small binary asteroids and propose new paradigms of the radiative binary YORP (BYORP) effect as well as tidal dissipation in small binaries. We find that relatively spheroidal bodies like Dimorphos made of small debris may experience a weaker and more size-dependent BYORP effect than previously thought. This could explain the observed values of period drift in several well-characterized binaries. We also propose that energy dissipation in small binaries is dominated by relatively brief episodes of large-scale movement of (likely surface) materials, rather than long-term steady-state tidal dissipation. We propose that one such episode was triggered on Dimorphos by the DART impact. Depending on the longevity of this high-dissipation regime, it is possible that Dimorphos will be more dynamically relaxed in time for the Hera mission than it was in the weeks following the impact.

astro-ph.EP↗

arXiv2024

The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report

This paper provides a comprehensive review of the NTIRE 2024 challenge, focusing on efficient single-image super-resolution (ESR) solutions and their outcomes. The task of this challenge is to super-resolve an input image with a magnification factor of x4 based on pairs of low and corresponding high-resolution images. The primary objective is to develop networks that optimize various aspects such as runtime, parameters, and FLOPs, while still maintaining a peak signal-to-noise ratio (PSNR) of approximately 26.90 dB on the DIV2K_LSDIR_valid dataset and 26.99 dB on the DIV2K_LSDIR_test dataset. In addition, this challenge has 4 tracks including the main track (overall performance), sub-track 1 (runtime), sub-track 2 (FLOPs), and sub-track 3 (parameters). In the main track, all three metrics (ie runtime, FLOPs, and parameter count) were considered. The ranking of the main track is calculated based on a weighted sum-up of the scores of all other sub-tracks. In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking. In sub-track 2, the number of FLOPs was considered. The score calculated based on the corresponding FLOPs was used to determine the ranking. In sub-track 3, the number of parameters was considered. The score calculated based on the corresponding parameters was used to determine the ranking. RLFN is set as the baseline for efficiency measurement. The challenge had 262 registered participants, and 34 teams made valid submissions. They gauge the state-of-the-art in efficient single-image super-resolution. To facilitate the reproducibility of the challenge and enable other researchers to build upon these findings, the code and the pre-trained model of validated solutions are made publicly available at https://github.com/Amazingren/NTIRE2024_ESR/.

cs.CV↗

arXiv2024

Data Upcycling Knowledge Distillation for Image Super-Resolution

Knowledge distillation (KD) compresses deep neural networks by transferring task-related knowledge from cumbersome pre-trained teacher models to compact student models. However, current KD methods for super-resolution (SR) networks overlook the nature of SR task that the outputs of the teacher model are noisy approximations to the ground-truth distribution of high-quality images (GT), which shades the teacher model's knowledge to result in limited KD effects. To utilize the teacher model beyond the GT upper-bound, we present the Data Upcycling Knowledge Distillation (DUKD), to transfer the teacher model's knowledge to the student model through the upcycled in-domain data derived from training data. Besides, we impose label consistency regularization to KD for SR by the paired invertible augmentations to improve the student model's performance and robustness. Comprehensive experiments demonstrate that the DUKD method significantly outperforms previous arts on several SR tasks.

cs.CV↗

arXiv2024

Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution

Knowledge distillation (KD) is a promising yet challenging model compression technique that transfers rich learning representations from a well-performing but cumbersome teacher model to a compact student model. Previous methods for image super-resolution (SR) mostly compare the feature maps directly or after standardizing the dimensions with basic algebraic operations (e.g. average, dot-product). However, the intrinsic semantic differences among feature maps are overlooked, which are caused by the disparate expressive capacity between the networks. This work presents MiPKD, a multi-granularity mixture of prior KD framework, to facilitate efficient SR model through the feature mixture in a unified latent space and stochastic network block mixture. Extensive experiments demonstrate the effectiveness of the proposed MiPKD method.

cs.CV↗

arXiv2024

Enhancing Recommender Systems: A Strategy to Mitigate False Negative Impact

In implicit collaborative filtering (CF) task of recommender systems, recent works mainly focus on model structure design with promising techniques like graph neural networks (GNNs). Effective and efficient negative sampling methods that suit these models, however, remain underdeveloped. One challenge is that existing hard negative samplers tend to suffer from severer over-fitting in model training. In this work, we first study the reason behind the over-fitting, and illustrate it with the incorrect selection of false negative instances with the support of experiments. In addition, we empirically observe a counter-intuitive phenomenon, that is, polluting hard negative samples' embeddings with a quite large proportional of positive samples' embeddings will lead to remarkable performance gains for prediction accuracy. On top of this finding, we present a novel negative sampling strategy, i.e., positive-dominated negative synthesizing (PDNS). Moreover, we provide theoretical analysis and derive a simple equivalent algorithm of PDNS, where only a soft factor is added in the loss function. Comprehensive experiments on three real-world datasets demonstrate the superiority of our proposed method in terms of both effectiveness and robustness.

cs.IR↗

arXiv2024

Soften to Defend: Towards Adversarial Robustness via Self-Guided Label Refinement

Adversarial training (AT) is currently one of the most effective ways to obtain the robustness of deep neural networks against adversarial attacks. However, most AT methods suffer from robust overfitting, i.e., a significant generalization gap in adversarial robustness between the training and testing curves. In this paper, we first identify a connection between robust overfitting and the excessive memorization of noisy labels in AT from a view of gradient norm. As such label noise is mainly caused by a distribution mismatch and improper label assignments, we are motivated to propose a label refinement approach for AT. Specifically, our Self-Guided Label Refinement first self-refines a more accurate and informative label distribution from over-confident hard labels, and then it calibrates the training by dynamically incorporating knowledge from self-distilled models into the current model and thus requiring no external teachers. Empirical results demonstrate that our method can simultaneously boost the standard accuracy and robust performance across multiple benchmark datasets, attack types, and architectures. In addition, we also provide a set of analyses from the perspectives of information theory to dive into our method and suggest the importance of soft labels for robust generalization.

cs.LG↗

arXiv2024

Direct $N$-body simulations of satellite formation around small asteroids: insights from DART's encounter with the Didymos system

We explore binary asteroid formation by spin-up and rotational disruption considering the NASA DART mission's encounter with the Didymos-Dimorphos binary, which was the first small binary visited by a spacecraft. Using a suite of $N$-body simulations, we follow the gravitational accumulation of a satellite from meter-sized particles following a mass-shedding event from a rapidly rotating primary. The satellite's formation is chaotic, as it undergoes a series of collisions, mergers, and close gravitational encounters with other moonlets, leading to a wide range of outcomes in terms of the satellite's mass, shape, orbit, and rotation state. We find that a Dimorphos-like satellite can form rapidly, in a matter of days, following a realistic mass-shedding event in which only ${\sim}2-3\%$ of the primary's mass is shed. Satellites can form in synchronous rotation due to their formation near the Roche limit. There is a strong preference for forming prolate (elongated) satellites, although some simulations result in oblate spheroids like Dimorphos. The distribution of simulated secondary shapes is broadly consistent with other binary systems, measured through radar or lightcurves. Unless Dimorphos's shape is an outlier, and considering the observational bias against lightcurve-based determination of secondary elongations for oblate bodies, we suggest there could be a significant population of oblate secondaries. If these satellites initially form with elongated shapes, a yet-unidentified pathway is needed to explain how they become oblate. Finally, we show that this chaotic formation pathway occasionally forms asteroid pairs and stable triples, including co-orbital satellites and satellites in mean motion resonances.

astro-ph.EP↗

arXiv2023

An efficient numerical approach to modeling the effects of particle shape on rubble-pile dynamics

We present an approach for the inclusion of non-spherical constituents in high-resolution N-body discrete element method (DEM) simulations. We use aggregates composed of bonded spheres to model non-spherical components. Though the method may be applied more generally, we detail our implementation in the existing N-body code pkdgrav. It has long been acknowledged that non-spherical grains confer additional shear strength and resistance to flow when compared with spheres. As a result, we expect that rubble-pile asteroids will also exhibit these properties and may behave differently than comparable rubble piles composed of idealized spheres. Since spherical particles avoid some significant technical challenges, most DEM gravity codes have used only spherical particles, or have been confined to relatively low resolutions. We also discuss the work that has gone into improving performance with non-spherical grains, building on pkdgrav's existing leading-edge computational efficiency among DEM gravity codes. This allows for the addition of non-spherical shapes while maintaining the efficiencies afforded by pkdgrav's tree implementation and parallelization. As a test, we simulated the gravitational collapse of 25,000 non-spherical bodies in parallel. In this case, the efficiency improvements allowed for an increase in speed by nearly a factor of three when compared with the naive implementation. Without these enhancements, large runs with non-spherical components would remain prohibitively expensive. Finally, we present the results of several small-scale tests: spinup due to the YORP effect, tidal encounters, and the Brazil-nut Effect. In all cases, we find that the inclusion of non-spherical constituents has a measurable impact on simulation outcomes.

astro-ph.EP↗

arXiv2023

A Closer Look at the Security Risks in the Rust Ecosystem

Rust is an emerging programming language designed for the development of systems software. To facilitate the reuse of Rust code, crates.io, as a central package registry of the Rust ecosystem, hosts thousands of third-party Rust packages. The openness of crates.io enables the growth of the Rust ecosystem but comes with security risks by severe security advisories. Although Rust guarantees a software program to be safe via programming language features and strict compile-time checking, the unsafe keyword in Rust allows developers to bypass compiler safety checks for certain regions of code. Prior studies empirically investigate the memory safety and concurrency bugs in the Rust ecosystem, as well as the usage of unsafe keywords in practice. Nonetheless, the literature lacks a systematic investigation of the security risks in the Rust ecosystem. In this paper, we perform a comprehensive investigation into the security risks present in the Rust ecosystem, asking ``what are the characteristics of the vulnerabilities, what are the characteristics of the vulnerable packages, and how are the vulnerabilities fixed in practice?''. To facilitate the study, we first compile a dataset of 433 vulnerabilities, 300 vulnerable code repositories, and 218 vulnerability fix commits in the Rust ecosystem, spanning over 7 years. With the dataset, we characterize the types, life spans, and evolution of the disclosed vulnerabilities. We then characterize the popularity, categorization, and vulnerability density of the vulnerable Rust packages, as well as their versions and code regions affected by the disclosed vulnerabilities. Finally, we characterize the complexity of vulnerability fixes and localities of corresponding code changes, and inspect how practitioners fix vulnerabilities in Rust packages with various localities.

cs.CR↗

arXiv2023

Software Architecture in Practice: Challenges and Opportunities

Software architecture has been an active research field for nearly four decades, in which previous studies make significant progress such as creating methods and techniques and building tools to support software architecture practice. Despite past efforts, we have little understanding of how practitioners perform software architecture related activities, and what challenges they face. Through interviews with 32 practitioners from 21 organizations across three continents, we identified challenges that practitioners face in software architecture practice during software development and maintenance. We reported on common software architecture activities at software requirements, design, construction and testing, and maintenance stages, as well as corresponding challenges. Our study uncovers that most of these challenges center around management, documentation, tooling and process, and collects recommendations to address these challenges.

cs.SE↗

arXiv2023

LSCD: A Large-Scale Screen Content Dataset for Video Compression

Multimedia compression allows us to watch videos, see pictures and hear sounds within a limited bandwidth, which helps the flourish of the internet. During the past decades, multimedia compression has achieved great success using hand-craft features and systems. With the development of artificial intelligence and video compression, there emerges a lot of research work related to using the neural network on the video compression task to get rid of the complicated system. Not only producing the advanced algorithms, but researchers also spread the compression to different content, such as User Generated Content(UGC). With the rapid development of mobile devices, screen content videos become an important part of multimedia data. In contrast, we find community lacks a large-scale dataset for screen content video compression, which impedes the fast development of the corresponding learning-based algorithms. In order to fulfill this blank and accelerate the research of this special type of videos, we propose the Large-scale Screen Content Dataset(LSCD), which contains 714 source sequences. Meanwhile, we provide the analysis of the proposed dataset to show some features of screen content videos, which will help researchers have a better understanding of how to explore new algorithms. Besides collecting and post-processing the data to organize the dataset, we also provide a benchmark containing the performance of both traditional codec and learning-based methods.

cs.MM↗

arXiv2023

Electric-field-induced formation and annihilation of skyrmions in two-dimensional magnet

Electric manipulation of skyrmions in 2D magnetic materials has garnered significant attention due to the potential in energy-efficient spintronic devices. In this work, using first-principles calculations and Monte Carlo simulations, we report the electric-field-tunable magnetic skyrmions in MnIn2Te4 monolayer. By adjusting the magnetic parameters, including the Heisenberg exchange interaction, DMI, and MAE, through applying an electric field, the formation or annihilation of skyrmions can be achieved. Our work suggests a platform for experimental realization of the electric-field-tunable magnetic skyrmions in 2D magnets.

cond-mat.mtrl-sci↗

arXiv2023

Effect of AC current annealing on the microstructure, magnetism and magnetoimpedance of CoFeSiBNb$_3$ microfibers

This paper systematically studies the changes in the microstructure and magnetic properties of CoFeSiBNb$_3$ metallic microfibers before and after AC annealing. The influence of current intensity on the magneto-impedance (MI) effect of the microfibers was analyzed and the microstructure changes of the microfibers before and after annealing were explored by means of high-resolution transmission electron microscopy. Using this data, the mechanism of current annealing to improve the MI characteristics was further revealed. The results show that the surface of the CoFeSiBNb$_3$ metallic microfibers after AC current annealing is smooth and continuous; its general magnetic properties and MI ratio increase at first with current intensity and then decrease at higher intensities. During the current annealing process, Joule heat eliminates residual stresses in the microfibers while forming atomically ordered micro-domains, which improves the degree of its organizational order. Meanwhile, a stable toroidal magnetic field is generated, which promotes the distribution of the magnetic domain structure of the microfibers, thereby improving the MI effect.

physics.app-ph↗

arXiv2023

Momentum Transfer from the DART Mission Kinetic Impact on Asteroid Dimorphos

The NASA Double Asteroid Redirection Test (DART) mission performed a kinetic impact on asteroid Dimorphos, the satellite of the binary asteroid (65803) Didymos, at 23:14 UTC on September 26, 2022 as a planetary defense test. DART was the first hypervelocity impact experiment on an asteroid at size and velocity scales relevant to planetary defense, intended to validate kinetic impact as a means of asteroid deflection. Here we report the first determination of the momentum transferred to an asteroid by kinetic impact. Based on the change in the binary orbit period, we find an instantaneous reduction in Dimorphos's along-track orbital velocity component of 2.70 +/- 0.10 mm/s, indicating enhanced momentum transfer due to recoil from ejecta streams produced by the impact. For a Dimorphos bulk density range of 1,500 to 3,300 kg/m$^3$, we find that the expected value of the momentum enhancement factor, $β$, ranges between 2.2 and 4.9, depending on the mass of Dimorphos. If Dimorphos and Didymos are assumed to have equal densities of 2,400 kg/m$^3$, $β$= 3.61 +0.19/-0.25 (1 $σ$). These $β$ values indicate that significantly more momentum was transferred to Dimorphos from the escaping impact ejecta than was incident with DART. Therefore, the DART kinetic impact was highly effective in deflecting the asteroid Dimorphos.

astro-ph.EP↗

arXiv2023

Exploring the recycling model of Phobos formation: rubble-pile satellites

Phobos is the target of the return sample mission Martian Moons eXploration by JAXA that will analyze in great details the physical and compositional properties of the satellite from orbit, from the surface and in terrestrial laboratories, giving clues about its formation. Some models propose that Phobos and Deimos were formed after a giant impact giving rise to an extended debris disk. Assuming that Phobos formed from a cascade of disruptions and re-accretions of several parent bodies in this disk, and that they are all characterized by a low material cohesion, Hesselbrock & Milton (2017) have showed that a recycling process may happen during the assembling of Phobos, by which Phobos' parents are destroyed into a Roche-interior ring and reaccreted several times. In the current paper we explore in details the recycling model, and pay particular attention to the characteristics of the disk using 1D models of disk/satellite interactions. In agreement with previous studies we confirm that, if Phobos' parents bodies are gravitational aggregates (rubble piles), then the recycling process does occur. However, Phobos should be accompanied today by a Roche-interior ring. Furthermore, the characteristics of the ring are not reconcilable with today`s observations of Mars' environment, which put stringent constraints on the existence of a ring around Mars. The recycling mechanism may or may not have occurred at the Roche limit for an old moon population, depending on their internal cohesion. However, the Phobos we see today cannot be the outcome of such a recycling process.

astro-ph.EP↗

arXiv2022

FastBVP-Net: a lightweight pulse extraction network for measuring heart rhythm via facial videos

Remote photoplethysmography (rPPG) is an attractive camera-based health monitoring method that can measure the heart rhythm from facial videos. Many well-established deep-learning models have been reported to measure heart rate (HR) and heart rate variability (HRV). However, most of these models usually require a 30-second facial video and enormous computational resources to obtain accurate and robust results, which significantly limits their applications in real-world scenarios. Hence, we propose a lightweight pulse extraction network, FastBVP-Net, to quickly measure heart rhythm via facial videos. The proposed FastBVP-Net uses a multi-frequency mode signal fusion (MMSF) mechanism to characterize the different modes of the raw signals in a decompose module and reconstruct the blood volume pulse (BVP) signal under a complex noise environment in a compose module. Meanwhile, an oversampling training scheme is used to solve the over-fitting problem caused by the limitations of the datasets. Then, the HR and HRV can be estimated based on the extracted BVP signals. Comprehensive experiments are conducted on the benchmark datasets to validate the proposed FastBVP-Net. For intra-dataset and cross-dataset testing, the proposed approach achieves better performance for HR and HRV estimation from 30-second facial videos with fewer computational burdens than the current well-established methods. Moreover, the proposed approach also achieves competitive results from 15-second facial videos. Therefore, the proposed FastBVP-Net has the potential to be applied in many real-world scenarios with shorter videos.

cs.CV↗

arXiv2022

Constraining surface properties of asteroid (162173) Ryugu from numerical simulations of Hayabusa2 mission impact experiment

The Hayabusa2 mission impact experiment on asteroid Ryugu created an unexpectedly large crater. The associated regime of low-gravity, low-strength cratering remained largely unexplored so far, because these impact conditions cannot be re-created in laboratory experiments on Earth. Here we show that the target cohesion may be very low and the impact probably occurred in the transitional cratering regime, between strength and gravity. For such conditions, our numerical simulations are able to reproduce the outcome of the impact on Ryugu, including the effects of boulders originally located near the impact point. Consistent with most recent analysis of Ryugu and Bennu, cratering scaling-laws derived from our results suggest that surfaces of small asteroids must be very young. However, our results also show that the cratering efficiency can be strongly affected by the presence of a very small amount of cohesion. Consequently, the varying ages of different geological surface units on Ryugu may be due to the influence of cohesion.

astro-ph.EP↗

arXiv2022

A Survey on Perceptually Optimized Video Coding

To provide users with more realistic visual experiences, videos are developing in the trends of Ultra High Definition (UHD), High Frame Rate (HFR), High Dynamic Range (HDR), Wide Color Gammut (WCG) and high clarity. However, the data amount of videos increases exponentially, which requires high efficiency video compression for storage and network transmission. Perceptually optimized video coding aims to maximize compression efficiency by exploiting visual redundancies. In this paper, we present a broad and systematic survey on perceptually optimized video coding. Firstly, we present problem formulation and framework of the perceptually optimized video coding, which includes visual perception modelling, visual quality assessment and perceptual video coding optimization. Secondly, recent advances on visual factors, computational perceptual models and quality assessment models are presented. Thirdly, we review perceptual video coding optimizations from four key aspects, including perceptually optimized bit allocation, rate-distortion optimization, transform and quantization, filtering and enhancement. In each part, problem formulation, working flow, recent advances, advantages and challenges are presented. Fourthly, perceptual coding performances of the latest coding standards and tools are experimentally analyzed. Finally, challenging issues and future opportunities are identified.

cs.MM↗