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Rui Li

Publications and source records attributed to Rui Li.

At least 343 records · Page 19Linked to original sources
arXiv2023

Wake effects of offshore wind farm clusters revealed by SAR and WRF

Wake effects, i.e. the reduced momentum and increased turbulence caused by the upstream wind farm, have a significant adverse impact on downstream wind farms. However, due to the lack of ground truth for flow scenarios without wind farms in place (as the wind farm has already been constructed on site), it is extremely difficult to quantify the real impact caused by the presence of upstream wind farms for the downstream area. This paper seeks to develop a potential solution by taking advantage of both SAR and WRF. Specifically, the real-world wind speed with wind farms is retrieved from the SAR images using the C-band model, while the scenario without wind farms is simulated by the WRF model. By combining these two technologies, the potential impact of long-distance wind farm wakes is revealed and analysed.

physics.ao-ph↗
arXiv2023

Toward a stellar population catalog in the Kilo Degree Survey: the impact of stellar recipes on stellar masses and star formation rates

The Kilo Degree Survey (KiDS) is currently the only sky survey providing optical ($ugri$) plus near-infrared (NIR, $ZYHJK_S$) seeing matched photometry over an area larger than 1000 $\rm deg^2$. This is obtained by incorporating the NIR data from the VISTA Kilo Degree Infrared Galaxy (VIKING) survey, covering the same KiDS footprint. As such, the KiDS multi-wavelength photometry represents a unique dataset to test the ability of stellar population models to return robust photometric stellar mass ($M_*$) and star-formation rate (SFR) estimates. Here we use a spectroscopic sample of galaxies for which we possess $u g r i Z Y J H K_s$ ``gaussianized'' magnitudes from KiDS data release 4. We fit the spectral energy distribution from the 9-band photometry using: 1) three different popular libraries of stellar {population} templates, 2) single burst, simple and delayed exponential star-formation history models, and 3) a wide range of priors on age and metallicity. As template fitting codes we use two popular softwares: LePhare and CIGALE. We investigate the variance of the stellar masses and the star-formation rates from the different combinations of templates, star formation recipes and codes to assess the stability of these estimates and define some ``robust'' median quantities to be included in the upcoming KiDS data releases. As a science validation test, we derive the mass function, the star formation rate function, and the SFR-$M_*$ relation for a low-redshift ($z<0.5$) sample of galaxies, that result in excellent agreement with previous literature data. The final catalog, containing $\sim290\,000$ galaxies with redshift $0.01<z<0.9$, is made publicly available.

astro-ph.GA↗
arXiv2023

Power System Capacity Planning Considering Seasonal Hydrogen Storage by Salt Caverns

In China, air conditioning in summer and electric heating in winter lead to seasonal volatility in load power. Therefore, it is urgent to develop economic and efficient long-term energy storage systems to enhance peak regulation. Power-to-hydrogen technology is a perspective solution to balance seasonal power fluctuation. However, current hydrogen storage methods have shortcomings such as small storage capacity, high levelized cost and low operation safety, which the salt cavern hydrogen storage could overcome. This paper considers the use of hydrogen storage in salt caverns as a means of peak shaving. To minimize the overall operating cost, a comprehensive power system capacity planning model is proposed with the consideration of hydrogen storage in salt caverns, which is implemented by adopting an improved fast unit commitment method. Considering the seasonal characteristics of the load power in Jiangsu Province, the capacity of the power system in 2050 has been planned. According to the case study, after the optimal deployment of the salt cavern hydrogen storage system (SCHSS), the construction capacity of renewable units (especially wind power) will be significantly increased with environmental friendliness and lower costs. Compared with the current energy storage method, the overall cost of SCHSS-incorporated power system will be reduced by 22.2% with a carbon emission reduction of 24.4%, and the amount of curtailed wind and solar power will be reduced by 27.0% and 13.6%, respectively.

eess.SY↗
arXiv2023

DenseLens -- Using DenseNet ensembles and information criteria for finding and rank-ordering strong gravitational lenses,

Convolutional neural networks (CNNs) are the state-of-the-art technique for identifying strong gravitational lenses. Although they are highly successful in recovering genuine lens systems with a high true-positive rate, the unbalanced nature of the data set (lens systems are rare), still leads to a high false positive rate. For these techniques to be successful in upcoming surveys (e.g. with Euclid) most emphasis should be set on reducing false positives, rather than on reducing false negatives. In this paper, we introduce densely connected neural networks (DenseNets) as the CNN architecture in a new pipeline-ensemble model containing an ensemble of classification CNNs and regression CNNs to classify and rank-order lenses, respectively. We show that DenseNets achieve comparable true positive rates but considerably lower false positive rates (when compared to residual networks; ResNets). Thus, we recommend DenseNets for future missions involving large data sets, such as Euclid, where low false positive rates play a key role in the automated follow-up and analysis of large numbers of strong gravitational lens candidates when human vetting is no longer feasible

astro-ph.GA↗
arXiv2023

A Systematic Review for Transformer-based Long-term Series Forecasting

The emergence of deep learning has yielded noteworthy advancements in time series forecasting (TSF). Transformer architectures, in particular, have witnessed broad utilization and adoption in TSF tasks. Transformers have proven to be the most successful solution to extract the semantic correlations among the elements within a long sequence. Various variants have enabled transformer architecture to effectively handle long-term time series forecasting (LTSF) tasks. In this article, we first present a comprehensive overview of transformer architectures and their subsequent enhancements developed to address various LTSF tasks. Then, we summarize the publicly available LTSF datasets and relevant evaluation metrics. Furthermore, we provide valuable insights into the best practices and techniques for effectively training transformers in the context of time-series analysis. Lastly, we propose potential research directions in this rapidly evolving field.

cs.LG↗
arXiv2023

Meta-Learned Kernel For Blind Super-Resolution Kernel Estimation

Recent image degradation estimation methods have enabled single-image super-resolution (SR) approaches to better upsample real-world images. Among these methods, explicit kernel estimation approaches have demonstrated unprecedented performance at handling unknown degradations. Nonetheless, a number of limitations constrain their efficacy when used by downstream SR models. Specifically, this family of methods yields i) excessive inference time due to long per-image adaptation times and ii) inferior image fidelity due to kernel mismatch. In this work, we introduce a learning-to-learn approach that meta-learns from the information contained in a distribution of images, thereby enabling significantly faster adaptation to new images with substantially improved performance in both kernel estimation and image fidelity. Specifically, we meta-train a kernel-generating GAN, named MetaKernelGAN, on a range of tasks, such that when a new image is presented, the generator starts from an informed kernel estimate and the discriminator starts with a strong capability to distinguish between patch distributions. Compared with state-of-the-art methods, our experiments show that MetaKernelGAN better estimates the magnitude and covariance of the kernel, leading to state-of-the-art blind SR results within a similar computational regime when combined with a non-blind SR model. Through supervised learning of an unsupervised learner, our method maintains the generalizability of the unsupervised learner, improves the optimization stability of kernel estimation, and hence image adaptation, and leads to a faster inference with a speedup between 14.24 to 102.1x over existing methods.

cs.CV↗
arXiv2023

Sobolev regularity for a class of local fractional new maximal operators

This paper is devoted to studying the regularity properties for the new maximal operator $M_φ$ and the fractional new maximal operator $M_{φ,β}$ in the local case. Some new pointwise gradient estimates of $M_{φ,Ω}$ and $M_{φ,β,Ω}$ are given. Moreover, the boundedness of $M_{φ,Ω}$ and $M_{φ,β,Ω}$ on Sobolev space is established. As applications, we also obtain the bounds of the above operators on Sobolev space with zero boundary values.

math.FA↗
arXiv2023

The Qitai Radio Telescope

This study presents a general outline of the Qitai radio telescope (QTT) project. Qitai, the site of the telescope, is a county of Xinjiang Uygur Autonomous Region of China, located in the east Tianshan Mountains at an elevation of about 1800 m. The QTT is a fully steerable, Gregorian type telescope with a standard parabolic main reflector of 110 m diameter. The QTT has adopted an um-brella support, homology-symmetric lightweight design. The main reflector is active so that the deformation caused by gravity can be corrected. The structural design aims to ultimately allow high-sensitivity observations from 150 MHz up to 115 GHz. To satisfy the requirements for early scientific goals, the QTT will be equipped with ultra-wideband receivers and large field-of-view mul-ti-beam receivers. A multi-function signal-processing system based on RFSoC and GPU processor chips will be developed. These will enable the QTT to operate in pulsar, spectral line, continuum and Very Long Baseline Interferometer (VLBI) observing modes. Electromagnetic compatibility (EMC) and radio frequency interference (RFI) control techniques are adopted throughout the system design. The QTT will form a world-class observational platform for the detection of low-frequency (nanoHertz) gravitational waves through pulsar timing array (PTA) techniques, pulsar surveys, the discovery of binary black-hole systems, and exploring dark matter and the origin of life in the universe.

astro-ph.IM↗
arXiv2023

The Robust Semantic Segmentation UNCV2023 Challenge Results

This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023. The challenge was centered around semantic segmentation in urban environments, with a particular focus on natural adversarial scenarios. The report presents the results of 19 submitted entries, with numerous techniques drawing inspiration from cutting-edge uncertainty quantification methodologies presented at prominent conferences in the fields of computer vision and machine learning and journals over the past few years. Within this document, the challenge is introduced, shedding light on its purpose and objectives, which primarily revolved around enhancing the robustness of semantic segmentation in urban scenes under varying natural adversarial conditions. The report then delves into the top-performing solutions. Moreover, the document aims to provide a comprehensive overview of the diverse solutions deployed by all participants. By doing so, it seeks to offer readers a deeper insight into the array of strategies that can be leveraged to effectively handle the inherent uncertainties associated with autonomous driving and semantic segmentation, especially within urban environments.

cs.CV↗
arXiv2023

Beyond Static Datasets: A Deep Interaction Approach to LLM Evaluation

Large Language Models (LLMs) have made progress in various real-world tasks, which stimulates requirements for the evaluation of LLMs. Existing LLM evaluation methods are mainly supervised signal-based which depends on static datasets and cannot evaluate the ability of LLMs in dynamic real-world scenarios where deep interaction widely exists. Other LLM evaluation methods are human-based which are costly and time-consuming and are incapable of large-scale evaluation of LLMs. To address the issues above, we propose a novel Deep Interaction-based LLM-evaluation framework. In our proposed framework, LLMs' performances in real-world domains can be evaluated from their deep interaction with other LLMs in elaborately designed evaluation tasks. Furthermore, our proposed framework is a general evaluation method that can be applied to a host of real-world tasks such as machine translation and code generation. We demonstrate the effectiveness of our proposed method through extensive experiments on four elaborately designed evaluation tasks.

cs.CL↗
arXiv2023

On the chromatic number of some ($P_3\cup P_2$)-free graphs

A hereditary class $\cal G$ of graphs is {\em $χ$-bounded} if there is a {\em $χ$-binding function}, say $f$, such that $χ(G)\le f(ω(G))$ for every $G\in\cal G$, where $χ(G)(ω(G))$ denotes the chromatic (clique) number of $G$. It is known that for every $(P_3\cup P_2)$-free graph $G$, $χ(G)\le \frac{1}{6}ω(G)(ω(G)+1)(ω(G)+2)$ \cite{BA18}, and the class of $(2K_2, 3K_1)$-free graphs does not admit a linear $χ$-binding function\cite{BBS19}. In this paper, we prove that (\romannumeral 1) $χ(G)\le2ω(G)$ if $G$ is ($P_3\cup P_2$, kite)-free, (\romannumeral 2) $χ(G)\leω^2(G)$ if $G$ is ($P_3\cup P_2$, hammer)-free, (\romannumeral 3) $χ(G)\le\frac{3ω^2(G)+ω(G)}{2}$ if $G$ is ($P_3\cup P_2, C_5$)-free. Furthermore, we also discuss $χ$-binding functions for $(P_3\cup P_2, K_4)$-free graphs.

math.CO↗
arXiv2023

Black-box Source-free Domain Adaptation via Two-stage Knowledge Distillation

Source-free domain adaptation aims to adapt deep neural networks using only pre-trained source models and target data. However, accessing the source model still has a potential concern about leaking the source data, which reveals the patient's privacy. In this paper, we study the challenging but practical problem: black-box source-free domain adaptation where only the outputs of the source model and target data are available. We propose a simple but effective two-stage knowledge distillation method. In Stage \uppercase\expandafter{\romannumeral1}, we train the target model from scratch with soft pseudo-labels generated by the source model in a knowledge distillation manner. In Stage \uppercase\expandafter{\romannumeral2}, we initialize another model as the new student model to avoid the error accumulation caused by noisy pseudo-labels. We feed the images with weak augmentation to the teacher model to guide the learning of the student model. Our method is simple and flexible, and achieves surprising results on three cross-domain segmentation tasks.

cs.CV↗
arXiv2023

Ultra-high mobility semiconducting epitaxial graphene on silicon carbide

Graphene nanoelectronics potential was limited by the lack of an intrinsic bandgap[1] and attempts to tailor a bandgap either by quantum confinement or by chemical functionalization failed to produce a semiconductor with a large enough band gap and a sufficient mobility. It is well known that by evaporating silicon from commercial electronics grade silicon carbide crystals an epitaxial graphene layer forms on the surfaces [2]. The first epigraphene layer to form on the silicon terminated face, known as the buffer layer, is insulating. It is chemically bonded to the SiC and spectroscopic measurements [3] have identified semiconducting signatures on the microscopic domains. However, the bonding to the SiC is disordered and the mobilities are small. Here we demonstrate a quasi-equilibrium annealing method that produces macroscopic atomically flat terraces covered with a well ordered epigraphene buffer layer that has a 0.6 eV bandgap. Room temperature mobilities exceed 5000 cm2/Vs which is much larger than silicon and 20 times larger than the phonon scattering imposed limit of current 2D semiconductors. Critical for nanotechnology, its lattice is aligned with the SiC substrate, it is chemically, mechanically, and thermally robust, and it can be conventionally patterned and seamlessly connected to semimetallic epigraphene making semiconducting epigraphene ideally suited for nanoelectronics.

cond-mat.mes-hall↗
arXiv2023

A tight linear chromatic bound for ($P_3\cup P_2, W_4$)-free graphs

For two vertex disjoint graphs $H$ and $F$, we use $H\cup F$ to denote the graph with vertex set $V(H)\cup V(F)$ and edge set $E(H)\cup E(F)$, and use $H+F$ to denote the graph with vertex set $V(H)\cup V(F)$ and edge set $E(H)\cup E(F)\cup\{xy\;|\; x\in V(H), y\in V(F)$$\}$. A $W_4$ is the graph $K_1+C_4$. In this paper, we prove that $χ(G)\le 2ω(G)$ if $G$ is a ($P_3\cup P_2, W_4$)-free graph. This bound is tight when $ω=2$ and $3$, and improves the main result of Wang and Zhang. Also, this bound partially generalizes some results of Prashant {\em et al.}.

math.CO↗
arXiv2023

Optimal chromatic bound for ($P_3\cup P_2$, house)-free graphs

Let $G$ and $H$ be two vertex disjoint graphs. The {\em union} $G\cup H$ is the graph with $V(G\cup H)=V(G)\cup V(H)$ and $E(G\cup H)=E(G)\cup E(H)$. We use $P_k$ to denote a {\em path} on $k$ vertices, use {\em house} to denote the complement of $P_5$. In this paper, we show that $χ(G)\le2ω(G)$ if $G$ is ($P_3\cup P_2$, house)-free. Moreover, this bound is optimal when $ω(G)\ge2$.

math.CO↗
arXiv2023

Superconductivity of cerium under pressures up to 54GPa

Cerium is a fascinating element due to its diverse physical properties, which include forming various crystal structures ($γ$, $α$, $α^{'}$, $α^{''}$ and $ε$), mixed valence behavior and superconductivity, making it an ideal platform for investigating the interplay between different electronic states. Here, we present a comprehensive transport study of cerium under hydrostatic pressures up to 54 GPa. Upon applying pressure, cerium undergoes the $α$ $\rightarrow$ $α^{''}$ transition at around 4.9 GPa, which is accompanied by the appearance of superconductivity with $T_{\rm c}$ of 0.4 K, and $T_{\rm c}$ slightly increases to 0.5 K at 11.4 GPa. At 14.3 GPa, $T_{\rm c}$ suddenly increases when the $α^{''}$ phase transforms into the $ε$ phase, reaching a maximum value of 1.25 K at around 17.2 GPa. Upon further increasing the pressure, $T_{\rm c}$ monotonically decreases. Together with the results of previous studies, our findings suggest that the evolution of superconductivity in cerium is closely correlated with the multiple pressure-induced structural transitions and corresponding unusual electronic structures.

cond-mat.supr-con↗
arXiv2023

Learning Fine-Grained Features for Pixel-wise Video Correspondences

Video analysis tasks rely heavily on identifying the pixels from different frames that correspond to the same visual target. To tackle this problem, recent studies have advocated feature learning methods that aim to learn distinctive representations to match the pixels, especially in a self-supervised fashion. Unfortunately, these methods have difficulties for tiny or even single-pixel visual targets. Pixel-wise video correspondences were traditionally related to optical flows, which however lead to deterministic correspondences and lack robustness on real-world videos. We address the problem of learning features for establishing pixel-wise correspondences. Motivated by optical flows as well as the self-supervised feature learning, we propose to use not only labeled synthetic videos but also unlabeled real-world videos for learning fine-grained representations in a holistic framework. We adopt an adversarial learning scheme to enhance the generalization ability of the learned features. Moreover, we design a coarse-to-fine framework to pursue high computational efficiency. Our experimental results on a series of correspondence-based tasks demonstrate that the proposed method outperforms state-of-the-art rivals in both accuracy and efficiency.

cs.CV↗
arXiv2023

Search for ultralight dark matter with a frequency adjustable diamagnetic levitated sensor

Among several dark matter candidates, bosonic ultralight (sub meV) dark matter is well motivated because it could couple to the Standard Model (SM) and induce new forces. Previous MICROSCOPE and Eot Wash torsion experiments have achieved high accuracy in the sub-1 Hz region, but at higher frequencies there is still a lack of relevant experimental research. We propose an experimental scheme based on the diamagnetic levitated micromechanical oscillator, one of the most sensitive sensors for acceleration sensitivity below the kilohertz scale. In order to improve the measurement range, we used the sensor whose resonance frequency could be adjusted from 0.1Hz to 100Hz. The limits of the coupling constant are improved by more than 10 times compared to previous reports, and it may be possible to achieve higher accuracy by using the array of sensors in the future.

astro-ph.CO↗