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Nan Li

Publications and source records attributed to Nan Li.

At least 199 records · Page 11Linked to original sources
arXiv2023

CSST WL preparation I: forecast the impact from non-Gaussian covariances and requirements on systematics-control

The precise estimation of the statistical errors and accurate removal of the systematical errors are the two major challenges for the stage IV cosmic shear surveys. We explore their impact for the China Space-Station Telescope (CSST) with survey area $\sim17,500°^2$ up to redshift $\sim4$. We consider statistical error contributed from Gaussian covariance, connected non-Gaussian covariance and super-sample covariance. We find the non-Gaussian covariances, which is dominated by the super-sample covariance, can largely reduce the signal-to-noise of the two-point statistics for CSST, leading to a $\sim1/3$ loss in the figure-of-merit for the matter clustering properties ($σ_8-Ω_m$ plane) and $1/6$ in the dark energy equation-of-state ($w_0-w_a$ plane). We further put requirements of systematics-mitigation on: intrinsic alignment of galaxies, baryonic feedback, shear multiplicative bias, and bias in the redshift distribution, for an unbiased cosmology. The $10^{-2}$ to $10^{-3}$ level requirements emphasize strong needs in related studies, to support future model selections and the associated priors for the nuisance parameters.

astro-ph.CO↗
arXiv2023

FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources

In settings such as e-recruitment and online dating, recommendation involves distributing limited opportunities, calling for novel approaches to quantify and enforce fairness. We introduce \emph{inferiority}, a novel (un)fairness measure quantifying a user's competitive disadvantage for their recommended items. Inferiority complements \emph{envy}, a fairness notion measuring preference for others' recommendations. We combine inferiority and envy with \emph{utility}, an accuracy-related measure of aggregated relevancy scores. Since these measures are non-differentiable, we reformulate them using a probabilistic interpretation of recommender systems, yielding differentiable versions. We combine these loss functions in a multi-objective optimization problem called \texttt{FEIR} (Fairness through Envy and Inferiority Reduction), applied as post-processing for standard recommender systems. Experiments on synthetic and real-world data demonstrate that our approach improves trade-offs between inferiority, envy, and utility compared to naive recommendations and the baseline methods.

cs.IR↗
arXiv2023

Image Restoration with Point Spread Function Regularization and Active Learning

Large-scale astronomical surveys can capture numerous images of celestial objects, including galaxies and nebulae. Analysing and processing these images can reveal intricate internal structures of these objects, allowing researchers to conduct comprehensive studies on their morphology, evolution, and physical properties. However, varying noise levels and point spread functions can hamper the accuracy and efficiency of information extraction from these images. To mitigate these effects, we propose a novel image restoration algorithm that connects a deep learning-based restoration algorithm with a high-fidelity telescope simulator. During the training stage, the simulator generates images with different levels of blur and noise to train the neural network based on the quality of restored images. After training, the neural network can directly restore images obtained by the telescope, as represented by the simulator. We have tested the algorithm using real and simulated observation data and have found that it effectively enhances fine structures in blurry images and increases the quality of observation images. This algorithm can be applied to large-scale sky survey data, such as data obtained by LSST, Euclid, and CSST, to further improve the accuracy and efficiency of information extraction, promoting advances in the field of astronomical research.

astro-ph.IM↗
arXiv2023

A challenge-based survey of e-recruitment recommendation systems

E-recruitment recommendation systems recommend jobs to job seekers and job seekers to recruiters. The recommendations are generated based on the suitability of the job seekers for the positions as well as the job seekers' and the recruiters' preferences. Therefore, e-recruitment recommendation systems could greatly impact job seekers' careers. Moreover, by affecting the hiring processes of the companies, e-recruitment recommendation systems play an important role in shaping the companies' competitive edge in the market. Hence, the domain of e-recruitment recommendation deserves specific attention. Existing surveys on this topic tend to discuss past studies from the algorithmic perspective, e.g., by categorizing them into collaborative filtering, content based, and hybrid methods. This survey, instead, takes a complementary, challenge-based approach, which we believe might be more practical to developers facing a concrete e-recruitment design task with a specific set of challenges, as well as to researchers looking for impactful research projects in this domain. We first identify the main challenges in the e-recruitment recommendation research. Next, we discuss how those challenges have been studied in the literature. Finally, we provide future research directions that we consider promising in the e-recruitment recommendation domain.

cs.IR↗
arXiv2023

SkillGPT: a RESTful API service for skill extraction and standardization using a Large Language Model

We present SkillGPT, a tool for skill extraction and standardization (SES) from free-style job descriptions and user profiles with an open-source Large Language Model (LLM) as backbone. Most previous methods for similar tasks either need supervision or rely on heavy data-preprocessing and feature engineering. Directly prompting the latest conversational LLM for standard skills, however, is slow, costly and inaccurate. In contrast, SkillGPT utilizes a LLM to perform its tasks in steps via summarization and vector similarity search, to balance speed with precision. The backbone LLM of SkillGPT is based on Llama, free for academic use and thus useful for exploratory research and prototype development. Hence, our cost-free SkillGPT gives users the convenience of conversational SES, efficiently and reliably.

cs.CL↗
arXiv2023

FEcMD: A multi-physics and multi-scale computational program for electron emission characteristics dynamically coupled with atomic structure in metal nano-emitters

Field emission coupled with molecular dynamics simulation (FEcMD) software package is a computational tool for studying the electron emission characteristics and the atomic structure evolution of micro- and nano-protrusions made of pure metals or multi-component alloys by means of multi-physics and multi-scale methodology. The implementations of molecular dynamics, the electrodynamics, and the heat conduction in FEcMD program are addressed. For molecular dynamics simulation, the Lennard-Jones potentials, embedded atomic method (EAM), and moment tensor potentials (MTP) are fully supported for both alloys and pure metals. In the electrodynamics, the FEcMD program incorporates the space charge fields (space charge potential and exchange-correlation effects) in the Wentzel-Kramers-Brillouin-Jeffreys (WKBJ) approximation to evaluate the field emission current density more reliably for nano-gaps between two metal electrodes. Additionally, the advanced two-temperature heat conduction model is implemented in FEcMD program, and which provides more reliable descriptions for the temperature evolutions of electron and phonon subsystems under the radiofrequency (RF) or pulse electric fields. Comprehensive benchmark tests are performed for each module in FEcMD software to validate the numerical results, and also to access the accuracy and efficiency of the implemented algorithms. Finally, some typical applications of FEcMD program are also demonstrated for understanding the evolution of temperature and electric field coupled with the dynamic changing of atomic structures for metal micro- and nano-protrusions.

physics.comp-ph↗
arXiv2023

ScaleLat: A chemical structure matching algorithm for mapping atomic structure of multi-phase system and high entropy alloys

ScaleLat (Scale Lattice) is a computer program written in C for performing the atomic structure analysis of multi-phase system or high entropy alloys (HEAs). The program implements an atomic cluster extraction algorithm to obtain all independent and symmetry-reduced characteristic chemical structures for the complex atomic configurations which are usually obtained from molecular dynamics or kinetic Monte-Carlo simulations for supercell containing more than 104 atoms. ScaleLat employes an efficient and unique chemical structure matching algorithm to map all extracted atomic clusters from a large supercell (>10^4 atoms) to a representative small one (~ 10^3 or less), providing the possibility to directly use the highly accurate quantum mechanical methods to study the electronic, magnetic, and mechanical properties of multi-component alloys with complex microstructures. We demonstrate the capability of ScaleLat code by conducting both the atomic structure analysis and chemical structure matching procedure for Fe-12.8 at.% Cr binary alloy and equiatomic CrFeCoNiCu high entropy alloy, and by successfully obtaining the representatively supercells containing 10^2~10^3 atoms of the two alloys. Overall, ScaleLat program provides a universal platform to efficiently project all essential chemical structures of large complex atomic structures to a relatively easy-handling small supercell for quantum mechanical calculations of various user interested properties.

cond-mat.mtrl-sci↗
arXiv2023

Revisiting Galaxy Evolution in Morphology in the COSMOS field (COSMOS-ReGEM):I. Merging Galaxies

We revisit the evolution of galaxy morphology in the COSMOS field over the redshift range $0.2\leq z \leq 1$, using a large and complete sample of 33,605 galaxies with a stellar mass of log($M_{\ast}$/M$_{\odot} )>9.5$ with significantly improved redshifts and comprehensive non-parametric morphological parameters. Our sample has 13,881 ($\sim41.3\%$) galaxies with reliable spectroscopic redshifts and has more accurate photometric redshifts with a $σ_{\rm NMAD} \sim 0.005$. This paper is the first in a series that investigates merging galaxies and their properties. We identify 3,594 major merging galaxies through visual inspection and find 1,737 massive galaxy pairs with log($M_\ast$/M$_\odot$)$>10.1$. Among the family of non-parametric morphological parameters including $C$, $A$, $S$, $Gini$, $M_{\rm 20}$, $A_{\rm O}$, and $D_{\rm O}$, we find that the outer asymmetry parameter $A_{\rm O}$ and the second-order momentum parameter $M_{\rm 20}$ are the best tracers of merging features than other combinations. Hence, we propose a criterion for selecting candidates of violently star-forming mergers: $M_{\rm 20}> -3A_{\rm O}+3$ at $0.2 -6A_{\rm O}+3.7$ at $0.6<z<1.0$. Furthermore, we show that both the visual merger sample and the pair sample exhibit a similar evolution in the merger rate at $z<1$, with $\Re \sim(1+z)^{1.79 \pm 0.13}$ for the visual merger sample and $\Re \sim(1+z)^{2.02\pm 0.42}$ for the pair sample. The visual merger sample has a specific star formation rate that is about 0.16\,dex higher than that of non-merger galaxies, whereas no significant star formation excess is observed in the pair sample. This suggests that the effects of mergers on star formation differ at different merger stages.

astro-ph.GA↗
arXiv2023

LLM4Jobs: Unsupervised occupation extraction and standardization leveraging Large Language Models

Automated occupation extraction and standardization from free-text job postings and resumes are crucial for applications like job recommendation and labor market policy formation. This paper introduces LLM4Jobs, a novel unsupervised methodology that taps into the capabilities of large language models (LLMs) for occupation coding. LLM4Jobs uniquely harnesses both the natural language understanding and generation capacities of LLMs. Evaluated on rigorous experimentation on synthetic and real-world datasets, we demonstrate that LLM4Jobs consistently surpasses unsupervised state-of-the-art benchmarks, demonstrating its versatility across diverse datasets and granularities. As a side result of our work, we present both synthetic and real-world datasets, which may be instrumental for subsequent research in this domain. Overall, this investigation highlights the promise of contemporary LLMs for the intricate task of occupation extraction and standardization, laying the foundation for a robust and adaptable framework relevant to both research and industrial contexts.

cs.CL↗
arXiv2023

FractalAD: A simple industrial anomaly detection method using fractal anomaly generation and backbone knowledge distillation

Although industrial anomaly detection (AD) technology has made significant progress in recent years, generating realistic anomalies and learning priors of normal remain challenging tasks. In this study, we propose an end-to-end industrial anomaly detection method called FractalAD. Training samples are obtained by synthesizing fractal images and patches from normal samples. This fractal anomaly generation method is designed to sample the full morphology of anomalies. Moreover, we designed a backbone knowledge distillation structure to extract prior knowledge contained in normal samples. The differences between a teacher and a student model are converted into anomaly attention using a cosine similarity attention module. The proposed method enables an end-to-end semantic segmentation network to be used for anomaly detection without adding any trainable parameters to the backbone and segmentation head, and has obvious advantages over other methods in training and inference speed.. The results of ablation studies confirmed the effectiveness of fractal anomaly generation and backbone knowledge distillation. The results of performance experiments showed that FractalAD achieved competitive results on the MVTec AD dataset and MVTec 3D-AD dataset compared with other state-of-the-art anomaly detection methods.

cs.CV↗
arXiv2023

Introducing Shape Prior Module in Diffusion Model for Medical Image Segmentation

Medical image segmentation is critical for diagnosing and treating spinal disorders. However, the presence of high noise, ambiguity, and uncertainty makes this task highly challenging. Factors such as unclear anatomical boundaries, inter-class similarities, and irrational annotations contribute to this challenge. Achieving both accurate and diverse segmentation templates is essential to support radiologists in clinical practice. In recent years, denoising diffusion probabilistic modeling (DDPM) has emerged as a prominent research topic in computer vision. It has demonstrated effectiveness in various vision tasks, including image deblurring, super-resolution, anomaly detection, and even semantic representation generation at the pixel level. Despite the robustness of existing diffusion models in visual generation tasks, they still struggle with discrete masks and their various effects. To address the need for accurate and diverse spine medical image segmentation templates, we propose an end-to-end framework called VerseDiff-UNet, which leverages the denoising diffusion probabilistic model (DDPM). Our approach integrates the diffusion model into a standard U-shaped architecture. At each step, we combine the noise-added image with the labeled mask to guide the diffusion direction accurately towards the target region. Furthermore, to capture specific anatomical a priori information in medical images, we incorporate a shape a priori module. This module efficiently extracts structural semantic information from the input spine images. We evaluate our method on a single dataset of spine images acquired through X-ray imaging. Our results demonstrate that VerseDiff-UNet significantly outperforms other state-of-the-art methods in terms of accuracy while preserving the natural features and variations of anatomy.

eess.IV↗
arXiv2023

MQENet: A Mesh Quality Evaluation Neural Network Based on Dynamic Graph Attention

With the development of computational fluid dynamics, the requirements for the fluid simulation accuracy in industrial applications have also increased. The quality of the generated mesh directly affects the simulation accuracy. However, previous mesh quality metrics and models cannot evaluate meshes comprehensively and objectively. To this end, we propose MQENet, a structured mesh quality evaluation neural network based on dynamic graph attention. MQENet treats the mesh evaluation task as a graph classification task for classifying the quality of the input structured mesh. To make graphs generated from structured meshes more informative, MQENet introduces two novel structured mesh preprocessing algorithms. These two algorithms can also improve the conversion efficiency of structured mesh data. Experimental results on the benchmark structured mesh dataset NACA-Market show the effectiveness of MQENet in the mesh quality evaluation task.

cs.CE↗
arXiv2023

Quantitative estimates on the $C^2$-singular sets in Alexandrov spaces

The total disaster may be controllable if not preventable. We will explore this phenomenon for singularities in metric spaces. A point in an $n$-dimensional Alexandrov space is called regular if its tangent cone is isometric to $\mathbb R^n$. Examples show that not every regular point is smooth, and the non-smooth points, away from the boundary, can have co-dimension 1. In this paper, we define a non-negative function $\mathcal K(x)$, which quantitatively measures the extent of the point $x$ from being $C^2$. The so-called $C^2$-singular points are identified as the set where $\mathcal K>0$. We show that $\int_{B_r(p)} \mathcal K(x)\, \operatorname d\mathcal H^{n-1}\le c(n,κ,ν)r^{n-2}$ for any $n$-dimensional Alexandrov space $(X,p)$ with curv $\ge κ$ and $\operatorname{Vol}\left(B_1(p)\right)\geν>0$. This leads to the Hausdorff dimension estimate $\dim_\mathcal H\{\mathcal K>0\}\le n-1$, and the quantitative Hausdorff measure estimate $\mathcal H^{n-1}\left(\{\mathcal K>ε\}\cap B_r(p)\right)\le ε^{-1}\cdot c(n,ν)r^{n-2}$. These results also make progress on Naber's conjecture on the convergence of curvature measures. The measure $\mathcal K(x)\, \operatorname d\mathcal H^{n-1}$ on Alexandrov spaces can be viewed as the counterpart of the curvature measure $scal \,\operatorname d {vol}_{g}$ on smooth manifolds. We also show that if $n$-dimensional Alexandrov spaces $X_i$ Gromov-Hausdorff converge to a smooth manifold with no boundary without collapsing, then $\mathcal K_i\, \operatorname d\mathcal H^{n-1}\to 0$ as a measure.

math.DG↗
arXiv2023

The Joule--Thomson and Joule--Thomson-like effects of the black holes in a cavity

When a black hole is enclosed in a cavity in asymptotically flat space, an effective volume can be introduced, and an effective pressure can be further defined as its conjugate variable. By this means, an extended phase space is constructed in a cavity, which resembles that in the anti-de Sitter (AdS) space in many aspects. However, there are still some notable dissimilarities simultaneously. In this work, the Joule--Thomson (JT) effect of the black holes, widely discussed in the AdS space as an isenthalpic (constant-mass) process, is shown to only have cooling region in a cavity. On the contrary, in a constant-thermal-energy process (the JT-like effect), there is only heating region in a cavity. Altogether, different from the AdS case, there is no inversion temperature or inversion curve in a cavity. Our work reveals the subtle discrepancy between the two different extended phase spaces that is sensitive to the specific boundary conditions.

gr-qc↗
arXiv2023

Locate and Beamform: Two-dimensional Locating All-neural Beamformer for Multi-channel Speech Separation

Recently, stunning improvements on multi-channel speech separation have been achieved by neural beamformers when direction information is available. However, most of them neglect to utilize speaker's 2-dimensional (2D) location cues contained in mixture signal, which limits the performance when two sources come from close directions. In this paper, we propose an end-to-end beamforming network for 2D location guided speech separation merely given mixture signal. It first estimates discriminable direction and 2D location cues, which imply directions the sources come from in multi views of microphones and their 2D coordinates. These cues are then integrated into location-aware neural beamformer, thus allowing accurate reconstruction of two sources' speech signals. Experiments show that our proposed model not only achieves a comprehensive decent improvement compared to baseline systems, but avoids inferior performance on spatial overlapping cases.

eess.AS↗
arXiv2023

speech and noise dual-stream spectrogram refine network with speech distortion loss for robust speech recognition

In recent years, the joint training of speech enhancement front-end and automatic speech recognition (ASR) back-end has been widely used to improve the robustness of ASR systems. Traditional joint training methods only use enhanced speech as input for the backend. However, it is difficult for speech enhancement systems to directly separate speech from input due to the diverse types of noise with different intensities. Furthermore, speech distortion and residual noise are often observed in enhanced speech, and the distortion of speech and noise is different. Most existing methods focus on fusing enhanced and noisy features to address this issue. In this paper, we propose a dual-stream spectrogram refine network to simultaneously refine the speech and noise and decouple the noise from the noisy input. Our proposed method can achieve better performance with a relative 8.6% CER reduction.

cs.SD↗
arXiv2023

Spatiotemporal Attention-based Semantic Compression for Real-time Video Recognition

This paper studies the computational offloading of video action recognition in edge computing. To achieve effective semantic information extraction and compression, following semantic communication we propose a novel spatiotemporal attention-based autoencoder (STAE) architecture, including a frame attention module and a spatial attention module, to evaluate the importance of frames and pixels in each frame. Additionally, we use entropy encoding to remove statistical redundancy in the compressed data to further reduce communication overhead. At the receiver, we develop a lightweight decoder that leverages a 3D-2D CNN combined architecture to reconstruct missing information by simultaneously learning temporal and spatial information from the received data to improve accuracy. To fasten convergence, we use a step-by-step approach to train the resulting STAE-based vision transformer (ViT_STAE) models. Experimental results show that ViT_STAE can compress the video dataset HMDB51 by 104x with only 5% accuracy loss, outperforming the state-of-the-art baseline DeepISC. The proposed ViT_STAE achieves faster inference and higher accuracy than the DeepISC-based ViT model under time-varying wireless channel, which highlights the effectiveness of STAE in guaranteeing higher accuracy under time constraints.

cs.CV↗
arXiv2023

On isolation of singular zeros of multivariate analytic systems

We give a separation bound for an isolated multiple root $x$ of a square multivariate analytic system $f$ satisfying that an operator deduced by adding $Df(x)$ and a projection of $D^2f(x)$ in a direction of the kernel of $Df(x)$ is invertible. We prove that the deflation process applied on $f$ and this kind of roots terminates after only one iteration. When $x$ is only given approximately, we give a numerical criterion for isolating a cluster of zeros of $f$ near $x$. We also propose a lower bound of the number of roots in the cluster.

math.NA↗