arXiv subjects

Lei Lei

Publications and source records attributed to Lei Lei.

At least 73 records · Page 4Linked to original sources

arXiv2024

Digging into the ultraviolet luminosity functions of galaxies at high redshifts: galaxies evolution, reionization, and cosmological parameters

Thanks to the successful performance of the James Webb Space Telescope, our understanding of the epoch of reionization of the Universe has been advanced. The ultraviolet luminosity functions (UV LFs) of galaxies span a wide range of redshift, not only revealing the connection between galaxies and dark matter (DM) halos but also providing the information during reionization. In this work, we develop a model connecting galaxy counts and apparent magnitude based on UV LFs, which incorporates redshift-dependent star formation efficiency (SFE) and corrections for dust attenuation. By synthesizing some observations across the redshift range $4\le z \le 10$ from various galaxy surveys, we discern the evolving SFE with increasing redshift and DM halo mass through model fitting. Subsequent analyses indicate that the Thomson scattering optical depth was $τ_{\rm e} = 0.054^{+0.001}_{-0.003}$ and the epoch of reionization started (ended) at $z=18.8^{+7.2}_{-6.0}$ ($z=5.3^{+0.8}_{-1.0}$) which is insensitive to the choice of the truncated magnitude of the UV LFs. Incorporating additional dataset and some reasonable constraints, the amplitude of matter perturbation is found to be $σ_8=0.80\pm0.05$, which is consistent with the standard $Λ$CDM model. Future galaxy surveys and the dynamical simulations of galaxy evolution will break the degeneracy between SFE and cosmological parameters, improving the accuracy and the precision of the UV LF model further.

astro-ph.CO↗

arXiv2024

LeGEND: A Top-Down Approach to Scenario Generation of Autonomous Driving Systems Assisted by Large Language Models

Autonomous driving systems (ADS) are safety-critical and require comprehensive testing before their deployment on public roads. While existing testing approaches primarily aim at the criticality of scenarios, they often overlook the diversity of the generated scenarios that is also important to reflect system defects in different aspects. To bridge the gap, we propose LeGEND, that features a top-down fashion of scenario generation: it starts with abstract functional scenarios, and then steps downwards to logical and concrete scenarios, such that scenario diversity can be controlled at the functional level. However, unlike logical scenarios that can be formally described, functional scenarios are often documented in natural languages (e.g., accident reports) and thus cannot be precisely parsed and processed by computers. To tackle that issue, LeGEND leverages the recent advances of large language models (LLMs) to transform textual functional scenarios to formal logical scenarios. To mitigate the distraction of useless information in functional scenario description, we devise a two-phase transformation that features the use of an intermediate language; consequently, we adopt two LLMs in LeGEND, one for extracting information from functional scenarios, the other for converting the extracted information to formal logical scenarios. We experimentally evaluate LeGEND on Apollo, an industry-grade ADS from Baidu. Evaluation results show that LeGEND can effectively identify critical scenarios, and compared to baseline approaches, LeGEND exhibits evident superiority in diversity of generated scenarios. Moreover, we also demonstrate the advantages of our two-phase transformation framework, and the accuracy of the adopted LLMs.

cs.SE↗

arXiv2024

The Spatial Distribution of $\rm CH_4$ and $\rm CO_2$ Ice around Protostars IRAS 16253-2429 and IRAS 23385+6053

The origin and evolution of organic molecules represent a pivotal issue in the fields of astrobiology and astrochemistry, potentially shedding light on the origins of life. The James Webb Space Telescope (JWST), with its exceptional sensitivity and spectral resolution, is well suitable to observe molecules such as methane ($\rm CH_4$). Our analysis focused on the distribution of $\rm CH_4$, $\rm CO_2$, $\rm H_2O$, $\rm{CH_3OH+NH_4^+}$ ice and silicate absorption dips at approximately 7.7, 15.0, 6.0, 6.7 and 10.0 micrometres in two protostars: IRAS 16253-2429 and IRAS 23385+6053. We extract the $\rm CH_4$, $\rm CO_2$, $\rm H_2O$, $\rm{CH_3OH+NH_4^+}$ ice equivalent width (EW) maps and silicate extinction maps of the two sources. Our results reveal that the spatial distribution of $\rm CH_4$ in the protostellar system IRAS 16253-2429 closely mirrors that of its $\rm CO_2$ ice, forming a surrounded distribution that encircles the central protostar. This alignment suggests a common formation mechanism and subsequent trapping within the protostellar envelope, which is consistent with the "Classical" dark-cloud chemistry with ion-molecule reaction. In contrast, the spatial distributions of various molecules in the system IRAS 23385+6053 exhibit low similarities, which may be attributed to the dynamic influences of outflows or accretion processes. These discrepancies highlight the complex interplay between physical processes and chemical evolution in protostellar environments.

astro-ph.GA↗

arXiv2024

NTIRE 2024 Challenge on Night Photography Rendering

This paper presents a review of the NTIRE 2024 challenge on night photography rendering. The goal of the challenge was to find solutions that process raw camera images taken in nighttime conditions, and thereby produce a photo-quality output images in the standard RGB (sRGB) space. Unlike the previous year's competition, the challenge images were collected with a mobile phone and the speed of algorithms was also measured alongside the quality of their output. To evaluate the results, a sufficient number of viewers were asked to assess the visual quality of the proposed solutions, considering the subjective nature of the task. There were 2 nominations: quality and efficiency. Top 5 solutions in terms of output quality were sorted by evaluation time (see Fig. 1). The top ranking participants' solutions effectively represent the state-of-the-art in nighttime photography rendering. More results can be found at https://nightimaging.org.

cs.CV↗

arXiv2024

Rapidly growing primordial black holes as seeds of the massive high-redshift JWST Galaxies

A group of massive galaxies at redshifts of $z\gtrsim 7$ have been recently detected by the James Webb Space Telescope (JWST), which were unexpected to form so early within the framework of standard Big Bang cosmology. In this work, we propose that this puzzle can be explained by the presence of some primordial black holes (PBHs) with a mass of $\sim 1000 M_\odot$. These PBHs, clothed in dark matter halo and undergoing super-Eddington accretion, serve as seeds for the early galaxy formation with masses of $\sim 10^{8}-10^{10}~M_\odot$ at high redshift, thus accounting for the JWST observations. Using a hierarchical Bayesian inference framework to constrain the PBH mass distribution models, we find that the Lognormal model with $M_{\rm c}\sim 750M_\odot$ is preferred over other hypotheses. These rapidly growing BHs are expected to emit strong radiation and may appear as high-redshift compact objects, similar to those recently discovered by JWST. Although we focuse on PBHs in this work, the bound on the initial mass of the seed black holes remains robust even if they were formed through astrophysical channels.

astro-ph.CO↗

arXiv2024

MIPI 2024 Challenge on Nighttime Flare Removal: Methods and Results

The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems. However, the scarcity of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). Building on the achievements of the previous MIPI Workshops held at ECCV 2022 and CVPR 2023, we introduce our third MIPI challenge including three tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Nighttime Flare Removal track on MIPI 2024. In total, 170 participants were successfully registered, and 14 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Nighttime Flare Removal. More details of this challenge and the link to the dataset can be found at https://mipi-challenge.org/MIPI2024/.

cs.CV↗

arXiv2024

RAAMove: A Corpus for Analyzing Moves in Research Article Abstracts

Move structures have been studied in English for Specific Purposes (ESP) and English for Academic Purposes (EAP) for decades. However, there are few move annotation corpora for Research Article (RA) abstracts. In this paper, we introduce RAAMove, a comprehensive multi-domain corpus dedicated to the annotation of move structures in RA abstracts. The primary objective of RAAMove is to facilitate move analysis and automatic move identification. This paper provides a thorough discussion of the corpus construction process, including the scheme, data collection, annotation guidelines, and annotation procedures. The corpus is constructed through two stages: initially, expert annotators manually annotate high-quality data; subsequently, based on the human-annotated data, a BERT-based model is employed for automatic annotation with the help of experts' modification. The result is a large-scale and high-quality corpus comprising 33,988 annotated instances. We also conduct preliminary move identification experiments using the BERT-based model to verify the effectiveness of the proposed corpus and model. The annotated corpus is available for academic research purposes and can serve as essential resources for move analysis, English language teaching and writing, as well as move/discourse-related tasks in Natural Language Processing (NLP).

cs.CL↗

arXiv2024

Self-Supervised High Dynamic Range Imaging with Multi-Exposure Images in Dynamic Scenes

Merging multi-exposure images is a common approach for obtaining high dynamic range (HDR) images, with the primary challenge being the avoidance of ghosting artifacts in dynamic scenes. Recent methods have proposed using deep neural networks for deghosting. However, the methods typically rely on sufficient data with HDR ground-truths, which are difficult and costly to collect. In this work, to eliminate the need for labeled data, we propose SelfHDR, a self-supervised HDR reconstruction method that only requires dynamic multi-exposure images during training. Specifically, SelfHDR learns a reconstruction network under the supervision of two complementary components, which can be constructed from multi-exposure images and focus on HDR color as well as structure, respectively. The color component is estimated from aligned multi-exposure images, while the structure one is generated through a structure-focused network that is supervised by the color component and an input reference (\eg, medium-exposure) image. During testing, the learned reconstruction network is directly deployed to predict an HDR image. Experiments on real-world images demonstrate our SelfHDR achieves superior results against the state-of-the-art self-supervised methods, and comparable performance to supervised ones. Codes are available at https://github.com/cszhilu1998/SelfHDR

cs.CV↗

arXiv2024

Black holes as the source of dark energy: a stringent test with high-redshift JWST AGNs

Studies have proposed that there is evidence for cosmological coupling of black holes (BHs) with an index of $k\approx 3$; hence, BHs serve as the astrophysical source of dark energy. However, the data sample is limited for the redshifts of $\leq 2.5$. In recent years, the James Webb Space Telescope (JWST) has detected many high-redshift active galactic nuclei (AGNs) and quasars. Among the JWST NIRSpec-/NIRCam-resolved AGNs, three are determined to be in early-type host galaxies with a redshift of $z\sim 4.5--7$. However, their $M_{\star}$ and $M_{\rm BH}$ are in tension with the predicted cosmological coupling of black holes with $k = 3$ at a confidence level of $\sim 2σ$, which challenges the hypothesis that BHs serve as the origin of dark energy. Future work on high-redshift AGNs using the JWST will further assess such a hypothesis by identifying more early-type host galaxies in the higher mass range.

astro-ph.CO↗

arXiv2023

Learning Real-World Image De-Weathering with Imperfect Supervision

Real-world image de-weathering aims at removing various undesirable weather-related artifacts. Owing to the impossibility of capturing image pairs concurrently, existing real-world de-weathering datasets often exhibit inconsistent illumination, position, and textures between the ground-truth images and the input degraded images, resulting in imperfect supervision. Such non-ideal supervision negatively affects the training process of learning-based de-weathering methods. In this work, we attempt to address the problem with a unified solution for various inconsistencies. Specifically, inspired by information bottleneck theory, we first develop a Consistent Label Constructor (CLC) to generate a pseudo-label as consistent as possible with the input degraded image while removing most weather-related degradations. In particular, multiple adjacent frames of the current input are also fed into CLC to enhance the pseudo-label. Then we combine the original imperfect labels and pseudo-labels to jointly supervise the de-weathering model by the proposed Information Allocation Strategy (IAS). During testing, only the de-weathering model is used for inference. Experiments on two real-world de-weathering datasets show that our method helps existing de-weathering models achieve better performance. Codes are available at https://github.com/1180300419/imperfect-deweathering.

cs.CV↗

arXiv2023

Kilonova-Targeting Lightcurve Classification for Wide Field Survey Telescope

With the enhancement of sensitivity of Gravitational Wave (GW) detectors and capabilities of large survey facilities, such as Vera Rubin Observatory Legacy Survey of Space and Time (LSST) and 2.5-m Wide Field Survey Telescope (WFST), we now have the potential to detect an increasing number of distant kilonova (KN). However, distinguishing KN from the plethora of detected transients in ongoing and future follow-up surveys presents a significant challenge. In this study, our objective is to establish an efficient classification mechanism tailored for the follow-up survey conducted by WFST, with a specific focus on identifying KN associated with GW. We employ a novel temporal convolutional neural network architecture, trained using simulated multi-band photometry lasting for 3 days by WFST, accompanied by contextual information, i.e. luminosity distance information by GW. By comparison of the choices of contextual information, we can reach 95\% precision, and 94\% recall for our best model. It also performs good validation on photometry data on AT2017gfo and AT2019npv. Furthermore, we investigate the ability of the model to distinguish KN in a GW follow-up survey. We conclude that there is over 80\% probability that we can capture true KN in selected 20 candidates among $\sim 250$ detected astrophysical transients that have passed real-bogus filter and cross-matching.

astro-ph.IM↗

arXiv2023

Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part II: Control-Aware Radio Resource Allocation

In Part I of this two-part paper (Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part I: Communication-Aware Vehicle Control), we decomposed the multi-timescale control and communications (MTCC) problem in Cellular Vehicle-to-Everything (C-V2X) system into a communication-aware Deep Reinforcement Learning (DRL)-based platoon control (PC) sub-problem and a control-aware DRL-based radio resource allocation (RRA) sub-problem. We focused on the PC sub-problem and proposed the MTCC-PC algorithm to learn an optimal PC policy given an RRA policy. In this paper (Part II), we first focus on the RRA sub-problem in MTCC assuming a PC policy is given, and propose the MTCC-RRA algorithm to learn the RRA policy. Specifically, we incorporate the PC advantage function in the RRA reward function, which quantifies the amount of PC performance degradation caused by observation delay. Moreover, we augment the state space of RRA with PC action history for a more well-informed RRA policy. In addition, we utilize reward shaping and reward backpropagation prioritized experience replay (RBPER) techniques to efficiently tackle the multi-agent and sparse reward problems, respectively. Finally, a sample- and computational-efficient training approach is proposed to jointly learn the PC and RRA policies in an iterative process. In order to verify the effectiveness of the proposed MTCC algorithm, we performed experiments using real driving data for the leading vehicle, where the performance of MTCC is compared with those of the baseline DRL algorithms.

eess.SY↗

arXiv2023

Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part I: Communication-Aware Vehicle Control

An intelligent decision-making system enabled by Vehicle-to-Everything (V2X) communications is essential to achieve safe and efficient autonomous driving (AD), where two types of decisions have to be made at different timescales, i.e., vehicle control and radio resource allocation (RRA) decisions. The interplay between RRA and vehicle control necessitates their collaborative design. In this two-part paper (Part I and Part II), taking platoon control (PC) as an example use case, we propose a joint optimization framework of multi-timescale control and communications (MTCC) based on Deep Reinforcement Learning (DRL). In this paper (Part I), we first decompose the problem into a communication-aware DRL-based PC sub-problem and a control-aware DRL-based RRA sub-problem. Then, we focus on the PC sub-problem assuming an RRA policy is given, and propose the MTCC-PC algorithm to learn an efficient PC policy. To improve the PC performance under random observation delay, the PC state space is augmented with the observation delay and PC action history. Moreover, the reward function with respect to the augmented state is defined to construct an augmented state Markov Decision Process (MDP). It is proved that the optimal policy for the augmented state MDP is optimal for the original PC problem with observation delay. Different from most existing works on communication-aware control, the MTCC-PC algorithm is trained in a delayed environment generated by the fine-grained embedded simulation of C-V2X communications rather than by a simple stochastic delay model. Finally, experiments are performed to compare the performance of MTCC-PC with those of the baseline DRL algorithms.

eess.SY↗

arXiv2023

The nanohertz stochastic gravitational wave background from cosmic string loops and the abundant high redshift massive galaxies

Recently, pulsar timing array (PTA) experiments have provided compelling evidence for the existence of the nanohertz stochastic gravitational wave background (SGWB). In this work, we demonstrated that cosmic string loops generated from cosmic global strings offer a viable explanation for the observed nanohertz SGWB data, requiring a cosmic string tension parameter of $\log(Gμ) \sim -12$ and a loop number density of $\log N \sim 4$. Additionally, we revisited the impact of cosmic string loops on the abundance of massive galaxies at high redshifts. However, our analysis revealed challenges in identifying a consistent parameter space that can concurrently explain both the SGWB data and observations from the James Webb Space Telescope. This indicates the necessity for either extending the existing model employed in this research or acknowledging distinct physical origins for these two phenomena.

astro-ph.HE↗

arXiv2023

Achieving Scalable Capacity in Wireless Mesh Networks

Wireless mesh networks play a critical role in enabling key networking scenarios in beyond-5G (B5G) and 6G networks, including integrated access and backhaul (IAB), multi-hop sidelinks, and V2X. However, it still poses a challenge to deliver scalable per-node throughput via mesh networking, which significantly limits the potential of large-scale deployment of wireless mesh networks. Existing research has achieved $O(1)$ per-node throughput in a dense network, but how to achieve scalability remains an unresolved issue for an extended wireless network where the network size increases with a constant node density. This issue prevents a wireless mesh network from large-scale deployment. To this end, this paper aims to develop a theoretical approach to achieving scalable per-node throughput in wireless mesh networks. First, the key factors that limit the per-node throughput of wireless mesh networks are analyzed, through which two major ones are identified, i.e., link sharing and interference. Next, a multi-tier hierarchical architecture is proposed to overcome the link-sharing issue. The inter-tier interference under this architecture is then mitigated by utilizing orthogonal frequency allocation between adjacent tiers, while the intra-tier interference is reduced by considering two specific transmission schemes, one is MIMO spatial multiplexing with time-division, the other is MIMO beamforming. Theoretical analysis shows that the multi-tier mesh networking architecture can achieve a per-node throughput of $Θ(1)$ in both schemes, as long as certain conditions on network parameters including bandwidth, antenna numbers, and node numbers of each tier are satisfied. A case study on a realistic deployment of 10,000 nodes is then carried out, which demonstrates that a scalable throughput of $Θ(1)$ is achievable with a reasonable assumption on bandwidth and antenna numbers.

cs.IT↗

arXiv2023

Implicit Sensing in Traffic Optimization: Advanced Deep Reinforcement Learning Techniques

A sudden roadblock on highways due to many reasons such as road maintenance, accidents, and car repair is a common situation we encounter almost daily. Autonomous Vehicles (AVs) equipped with sensors that can acquire vehicle dynamics such as speed, acceleration, and location can make intelligent decisions to change lanes before reaching a roadblock. A number of literature studies have examined car-following models and lane-changing models. However, only a few studies proposed an integrated car-following and lane-changing model, which has the potential to model practical driving maneuvers. Hence, in this paper, we present an integrated car-following and lane-changing decision-control system based on Deep Reinforcement Learning (DRL) to address this issue. Specifically, we consider a scenario where sudden construction work will be carried out along a highway. We model the scenario as a Markov Decision Process (MDP) and employ the well-known DQN algorithm to train the RL agent to make the appropriate decision accordingly (i.e., either stay in the same lane or change lanes). To overcome the delay and computational requirement of DRL algorithms, we adopt an MEC-assisted architecture where the RL agents are trained on MEC servers. We utilize the highly reputable SUMO simulator and OPENAI GYM to evaluate the performance of the proposed model under two policies; ε-greedy policy and Boltzmann policy. The results unequivocally demonstrate that the DQN agent trained using the ε-greedy policy significantly outperforms the one trained with the Boltzmann policy.

cs.LG↗

arXiv2023

Limiting Magnitudes of the Wide Field Survey Telescope (WFST)

Expected to be of the highest survey power telescope in the northern hemisphere, the Wide Field Survey Telescope (WFST) will begin its routine observations of the northern sky since 2023. WFST will produce a lot of scientific data to support the researches of time-domain astronomy, asteroids and the solar system, galaxy formation and cosmology and so on. We estimated that the 5 $σ$ limiting magnitudes of WFST with 30 second exposure are $u=22.31$ mag, $g=23.42$ mag, $r=22.95$ mag, $i=22.43$ mag, $z=21.50$ mag, $w=23.61$ mag. The above values are calculated for the conditions of $airmass=1.2$, seeing = 0.75 arcsec, precipitable water vapour (PWV) = 2.5 mm and Moon-object separation = $45^{\circ}$ at the darkest New Moon night of the Lenghu site (V=22.30 mag, Moon phase $θ=0^{\circ}$). The limiting magnitudes in different Moon phase conditions are also calculated. The calculations are based on the empirical transmittance data of WFST optics, the vendor provided CCD quantum efficiency, the atmospherical model transmittance and spectrum of the site. In the absence of measurement data such as sky transmittance and spectrum, we use model data.

astro-ph.IM↗

arXiv2023

Beyond Image Borders: Learning Feature Extrapolation for Unbounded Image Composition

For improving image composition and aesthetic quality, most existing methods modulate the captured images by striking out redundant content near the image borders. However, such image cropping methods are limited in the range of image views. Some methods have been suggested to extrapolate the images and predict cropping boxes from the extrapolated image. Nonetheless, the synthesized extrapolated regions may be included in the cropped image, making the image composition result not real and potentially with degraded image quality. In this paper, we circumvent this issue by presenting a joint framework for both unbounded recommendation of camera view and image composition (i.e., UNIC). In this way, the cropped image is a sub-image of the image acquired by the predicted camera view, and thus can be guaranteed to be real and consistent in image quality. Specifically, our framework takes the current camera preview frame as input and provides a recommendation for view adjustment, which contains operations unlimited by the image borders, such as zooming in or out and camera movement. To improve the prediction accuracy of view adjustment prediction, we further extend the field of view by feature extrapolation. After one or several times of view adjustments, our method converges and results in both a camera view and a bounding box showing the image composition recommendation. Extensive experiments are conducted on the datasets constructed upon existing image cropping datasets, showing the effectiveness of our UNIC in unbounded recommendation of camera view and image composition. The source code, dataset, and pretrained models is available at https://github.com/liuxiaoyu1104/UNIC.

cs.CV↗