arXiv subjects

Linlin Li

Publications and source records attributed to Linlin Li.

At least 37 records · Page 2Linked to original sources

arXiv2023

Identification of Carbon Stars from LAMOST DR7

Carbon stars are excellent kinematic tracers of galaxies and play important roles in understanding the evolution of the Galaxy. Therefore, it is worthwhile to search for them in a large amount of spectra. In this work, we build a new carbon star catalog based on the LAMOST DR7 spectra. The catalog contains 4542 spectra of 3546 carbon stars, identified through line index and near-infrared color-color diagrams. Through visual inspection of the spectra, we further subclassify them into 925 C--H, 384 C--R, 608 C--N, and 1292 Ba stars. However, 437 stars could not be sub-classified due to their low signal-to-noise. Moreover, by comparing with LAMOST DR7 pipeline we find 567 more carbon stars and visually sub-classify them. We find that on the $J-H$ vs. $H-K_{\rm s}$ two-color diagram, C--N stars can be reliably distinguished from the other three sub-types. Additionally, by utilizing the Gaia distance, we study the distribution of carbon stars in the H-R diagram and identify 258 dwarf carbon stars by the criterion $M_{\rm G}>$5.0\,mag. Finally, we present the spatial distribution in Galactic coordinates of the 3546 carbon stars. The majority of C-N, C-R, and Ba stars are distributed at low Galactic latitudes, while most C--H and dC stars distribute at high Galactic latitudes.

astro-ph.GA↗

arXiv2023

The measurement of masses of OB-type stars from LAMOST DR5

The measurements of masses and luminosities of massive stars play an important role in understanding the formation and evolution of their host galaxies. In this work, we present the measurement of masses and luminosities of 2,946 OB-type stars, including 78 O-type stars and 2,868 B-type stars, based on their stellar parameters (effective temperature, surface gravity, and metallicity) and PARSEC isochrones model. Our results show that the median mass and luminosity of the 2,946 OB-type stars are 5.4 M$_{\odot}$ and log(L/L$_{\odot}$)=3.2 with the median relative error of 21.4$\%$ and 71.1$\%$, respectively. A good agreement between our results estimated by using our method and those derived by using the orbital motions of binary stars from the literature is found for some B-type stars. In addition, we also fit the mass-luminosity relation of B-type stars by using our derived mass and the luminosity from $Gaia$ DR3.

astro-ph.SR↗

arXiv2023

Identification of Blue Horizontal-Branch Stars From LAMOST DR5

We construct a new catalog of the blue horizontal-branch (BHB) stars from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) DR5 dataset, which contains 5355+81 BHB stars at high Galactic latitude (($|Glat|>20^{\circ}$). We combine the spectral line indices with a set of Balmer line profile selection criteria to identify the BHB stars. During the selection process, we use the line index of \ion{Ca}{2}\,K to exclude the metal-rich A-type dwarfs. We obtain their atmospheric parameters by cross-matching our BHB stars with the catalog provided by \citet{Xiang2022}. The results show that our sample is consistent with the theoretical $T_{\rm eff}$-log\,$g$ evolutionary tracks of the BHB stars, indicating that our method is robust for identifying BHB stars from the LAMOST spectra. Their spatial distribution indicates that most of our BHB stars are located in the inner halo or the disk of the Milky Way. Combined with other BHB samples from the literature, the BHB stars can cover a large Galactic volume, which makes it a better probe for studying the kinematics, dynamics, and structural characteristics of the Milky Way.

astro-ph.GA↗

arXiv2023

A study on fault diagnosis in nonlinear dynamic systems with uncertainties

In this draft, fault diagnosis in nonlinear dynamic systems is addressed. The objective of this work is to establish a framework, in which not only model-based but also data-driven and machine learning based fault diagnosis strategies can be uniformly handled. Instead of the well-established input-output and the associated state space models, stable image and kernel representations are adopted in our work as the basic process model forms. Based on it, the nominal system dynamics can then be modelled as a lower-dimensional manifold embedded in the process data space. To achieve a reliable fault detection as a classification problem, projection technique is a capable tool. For nonlinear dynamic systems, we propose to construct projection systems in the well-established framework of Hamiltonian systems and by means of the normalised image and kernel representations. For nonlinear dynamic systems, process data form a non-Euclidean space. Consequently, the norm-based distance defined in Hilbert space is not suitable to measure the distance from a data vector to the manifold of the nominal dynamics. To deal with this issue, we propose to use a Bregman divergence, a measure of difference between two points in a space, as a solution. Moreover, for our purpose of achieving a performance-oriented fault detection, the Bregman divergences adopted in our work are defined by Hamiltonian functions. This scheme not only enables to realise the performance-oriented fault detection, but also uncovers the information geometric aspect of our work. The last part of our work is devoted to the kernel representation based fault detection and uncertainty estimation that can be equivalently used for fault estimation. It is demonstrated that the projection onto the manifold of uncertainty data, together with the correspondingly defined Bregman divergence, is also capable for fault detection.

eess.SY↗

arXiv2023

Which is a better programming assistant? A comparative study between chatgpt and stack overflow

Programmers often seek help from Q\&A websites to resolve issues they encounter during programming. Stack Overflow has been a widely used platform for this purpose for over a decade. Recently, revolutionary AI-powered platforms like ChatGPT have quickly gained popularity among programmers for their efficient and personalized programming assistance via natural language interactions. Both platforms can offer valuable assistance to programmers, but it's unclear which is more effective at enhancing programmer productivity. In our paper, we conducted an exploratory user study to compare the performance of Stack Overflow and ChatGPT in enhancing programmer productivity. Two groups of students with similar programming abilities were instructed to use the two platforms to solve three different types of programming tasks: algorithmic challenges, library usage, and debugging. During the experiments, we measured and compared the quality of code produced and the time taken to complete tasks for the two groups. The results show that, concerning code quality, ChatGPT outperforms Stack Overflow significantly in helping complete algorithmic and library-related tasks, while Stack Overflow is better for debugging tasks. Regarding task completion speed, the ChatGPT group is obviously faster than the Stack Overflow group in the algorithmic challenge, but the two groups have a similar performance in the other two tasks. Additionally, we conducted a post-experiment survey with the participants to understand how the platforms have helped them complete the programming tasks. We analyzed the questionnaires to summarize ChatGPT and Stack Overflow's strengths and weaknesses pointed out by the participants. By comparing these, we identified the reasons behind the two platforms' divergent performances in programming assistance.

cs.SE↗

arXiv2023

Control theoretically explainable application of autoencoder methods to fault detection in nonlinear dynamic systems

This paper is dedicated to control theoretically explainable application of autoencoders to optimal fault detection in nonlinear dynamic systems. Autoencoder-based learning is a standard machine learning method and widely applied for fault (anomaly) detection and classification. In the context of representation learning, the so-called latent (hidden) variable plays an important role towards an optimal fault detection. In ideal case, the latent variable should be a minimal sufficient statistic. The existing autoencoder-based fault detection schemes are mainly application-oriented, and few efforts have been devoted to optimal autoencoder-based fault detection and explainable applications. The main objective of our work is to establish a framework for learning autoencoder-based optimal fault detection in nonlinear dynamic systems. To this aim, a process model form for dynamic systems is firstly introduced with the aid of control theory, which also leads to a clear system interpretation of the latent variable. The major efforts are made on the development of a control theoretic solution to the optimal fault detection problem, in which an analog concept to minimal sufficient statistic, the so-called lossless information compression, is introduced and proven for dynamic systems and fault detection specifications. In particular, the existence conditions for such a latent variable are derived, based on which a loss function and further a learning algorithm are developed. This learning algorithm enables optimally training of autoencoders to achieve an optimal fault detection in nonlinear dynamic systems. A case study on three-tank system is given at the end of this paper to illustrate the capability of the proposed autoencoder-based fault detection and to explain the essential role of the latent variable in the proposed fault detection system.

eess.SY↗

arXiv2023

An alternative paradigm of fault diagnosis in dynamic systems: orthogonal projection-based methods

In this paper, we propose a new paradigm of fault diagnosis in dynamic systems as an alternative to the well-established observer-based framework. The basic idea behind this work is to (i) formulate fault detection and isolation as projection of measurement signals onto (system) subspaces in Hilbert space, and (ii) solve the resulting problems by means of projection methods with orthogonal projection operators and gap metric as major tools. In the new framework, fault diagnosis issues are uniformly addressed both in the model-based and data-driven fashions. Moreover, the design and implementation of the projection-based fault diagnosis systems, from residual generation to threshold setting, can be unifiedly handled. Thanks to the well-defined distance metric for projections in Hilbert subspaces, the projection-based fault diagnosis systems deliver optimal fault detectability. In particular, a new type of residual-driven thresholds is proposed, which significantly increases the fault detectability. In this work, various design schemes are proposed, including a basic projection-based fault detection scheme, fault detection schemes for feedback control systems, fault classification as well as two modified fault detection schemes. As a part of our study, relations to the existing observer-based fault detection systems are investigated, which showcases that, with comparable online computations, the proposed projection-based detection methods offer improved detection performance.

eess.SY↗

arXiv2023

High-resolution tomographic reconstruction of optical absorbance through scattering media using neural fields

Light scattering imposes a major obstacle for imaging objects seated deeply in turbid media, such as biological tissues and foggy air. Diffuse optical tomography (DOT) tackles scattering by volumetrically recovering the optical absorbance and has shown significance in medical imaging, remote sensing and autonomous driving. A conventional DOT reconstruction paradigm necessitates discretizing the object volume into voxels at a pre-determined resolution for modelling diffuse light propagation and the resulting spatial resolution of the reconstruction is generally limited. We propose NeuDOT, a novel DOT scheme based on neural fields (NF) to continuously encode the optical absorbance within the volume and subsequently bridge the gap between model accuracy and high resolution. Comprehensive experiments demonstrate that NeuDOT achieves submillimetre lateral resolution and resolves complex 3D objects at 14 mm-depth, outperforming the state-of-the-art methods. NeuDOT is a non-invasive, high-resolution and computationally efficient tomographic method, and unlocks further applications of NF involving light scattering.

physics.optics↗

arXiv2022

Efficient Image Captioning for Edge Devices

Recent years have witnessed the rapid progress of image captioning. However, the demands for large memory storage and heavy computational burden prevent these captioning models from being deployed on mobile devices. The main obstacles lie in the heavyweight visual feature extractors (i.e., object detectors) and complicated cross-modal fusion networks. To this end, we propose LightCap, a lightweight image captioner for resource-limited devices. The core design is built on the recent CLIP model for efficient image captioning. To be specific, on the one hand, we leverage the CLIP model to extract the compact grid features without relying on the time-consuming object detectors. On the other hand, we transfer the image-text retrieval design of CLIP to image captioning scenarios by devising a novel visual concept extractor and a cross-modal modulator. We further optimize the cross-modal fusion model and parallel prediction heads via sequential and ensemble distillations. With the carefully designed architecture, our model merely contains 40M parameters, saving the model size by more than 75% and the FLOPs by more than 98% in comparison with the current state-of-the-art methods. In spite of the low capacity, our model still exhibits state-of-the-art performance on prevalent datasets, e.g., 136.6 CIDEr on COCO Karpathy test split. Testing on the smartphone with only a single CPU, the proposed LightCap exhibits a fast inference speed of 188ms per image, which is ready for practical applications.

cs.CV↗

arXiv2022

Controllable Image Captioning via Prompting

Despite the remarkable progress of image captioning, existing captioners typically lack the controllable capability to generate desired image captions, e.g., describing the image in a rough or detailed manner, in a factual or emotional view, etc. In this paper, we show that a unified model is qualified to perform well in diverse domains and freely switch among multiple styles. Such a controllable capability is achieved by embedding the prompt learning into the image captioning framework. To be specific, we design a set of prompts to fine-tune the pre-trained image captioner. These prompts allow the model to absorb stylized data from different domains for joint training, without performance degradation in each domain. Furthermore, we optimize the prompts with learnable vectors in the continuous word embedding space, avoiding the heuristic prompt engineering and meanwhile exhibiting superior performance. In the inference stage, our model is able to generate desired stylized captions by choosing the corresponding prompts. Extensive experiments verify the controllable capability of the proposed method. Notably, we achieve outstanding performance on two diverse image captioning benchmarks including COCO Karpathy split and TextCaps using a unified model.

cs.CV↗

arXiv2022

DyLex: Incorporating Dynamic Lexicons into BERT for Sequence Labeling

Incorporating lexical knowledge into deep learning models has been proved to be very effective for sequence labeling tasks. However, previous works commonly have difficulty dealing with large-scale dynamic lexicons which often cause excessive matching noise and problems of frequent updates. In this paper, we propose DyLex, a plug-in lexicon incorporation approach for BERT based sequence labeling tasks. Instead of leveraging embeddings of words in the lexicon as in conventional methods, we adopt word-agnostic tag embeddings to avoid re-training the representation while updating the lexicon. Moreover, we employ an effective supervised lexical knowledge denoising method to smooth out matching noise. Finally, we introduce a col-wise attention based knowledge fusion mechanism to guarantee the pluggability of the proposed framework. Experiments on ten datasets of three tasks show that the proposed framework achieves new SOTA, even with very large scale lexicons.

cs.CL↗

arXiv2022

PDE-constrained shape registration to characterize biological growth and morphogenesis from imaging data

We propose a PDE-constrained shape registration algorithm that captures the deformation and growth of biological tissue from imaging data. Shape registration is the process of evaluating optimum alignment between pairs of geometries through a spatial transformation function. We start from our previously reported work, which uses 3D tensor product B-spline basis functions to interpolate 3D space. Here, the movement of the B-spline control points, composed with an implicit function describing the shape of the tissue, yields the total deformation gradient field. The deformation gradient is then split into growth and elastic contributions. The growth tensor captures addition of mass, i.e. growth, and evolves according to a constitutive equation which is usually a function of the elastic deformation. Stress is generated in the material due to the elastic component of the deformation alone. The result of the registration is obtained by minimizing a total energy functional which includes: a distance measure reflecting similarity between the shapes, and the total elastic energy accounting for the growth of the tissue. We apply the proposed shape registration framework to study zebrafish embryo epiboly process and tissue expansion during skin reconstruction surgery. We anticipate that our PDE-constrained shape registration method will improve our understanding of biological and medical problems in which tissues undergo extreme deformations over time.

physics.bio-ph↗

arXiv2022

Text is no more Enough! A Benchmark for Profile-based Spoken Language Understanding

Current researches on spoken language understanding (SLU) heavily are limited to a simple setting: the plain text-based SLU that takes the user utterance as input and generates its corresponding semantic frames (e.g., intent and slots). Unfortunately, such a simple setting may fail to work in complex real-world scenarios when an utterance is semantically ambiguous, which cannot be achieved by the text-based SLU models. In this paper, we first introduce a new and important task, Profile-based Spoken Language Understanding (ProSLU), which requires the model that not only relies on the plain text but also the supporting profile information to predict the correct intents and slots. To this end, we further introduce a large-scale human-annotated Chinese dataset with over 5K utterances and their corresponding supporting profile information (Knowledge Graph (KG), User Profile (UP), Context Awareness (CA)). In addition, we evaluate several state-of-the-art baseline models and explore a multi-level knowledge adapter to effectively incorporate profile information. Experimental results reveal that all existing text-based SLU models fail to work when the utterances are semantically ambiguous and our proposed framework can effectively fuse the supporting information for sentence-level intent detection and token-level slot filling. Finally, we summarize key challenges and provide new points for future directions, which hopes to facilitate the research.

cs.CL↗

arXiv2021

A catalogue of 323 cataclysmic variables from LAMOST DR6

In this work, we present a catalog of cataclysmic variables (CVs) identified from the Sixth Data Release (DR6) of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). To single out the CV spectra, we introduce a novel machine-learning algorithm called UMAP to screen out a total of 169,509 H$α$-emission spectra, and obtain a classification accuracy of the algorithm of over 99.6$\%$ from the cross-validation set. We then apply the template matching program PyHammer v2.0 to the LAMOST spectra to obtain the optimal spectral type with metallicity, which helps us identify the chromospherically active stars and potential binary stars from the 169,509 spectra. After visually inspecting all the spectra, we identify 323 CV candidates from the LAMOST database, among them 52 objects are new. We further discuss the new CV candidates in subtypes based on their spectral features, including five DN subtype during outbursts, five NL subtype and four magnetic CVs (three AM Her type and one IP type). We also find two CVs that have been previously identified by photometry, and confirm their previous classification by the LAMOST spectra.

astro-ph.SR↗

arXiv2021

Application of the unified control and detection framework to detecting stealthy integrity cyber-attacks on feedback control systems

This draft addresses issues of detecting stealthy integrity cyber-attacks on automatic control systems in the unified control and detection framework. A general form of integrity cyber-attacks that cannot be detected using the well-established observer-based technique is first introduced as kernel attacks. The well-known replay, zero dynamics and covert attacks are special forms of the kernel attacks. Existence conditions for the kernel attacks are presented. It is demonstrated, in the unified framework of control and detection, that all kernel attacks can be structurally detected when not only the observer-based residual, but also the control signal based residual signals are generated and used for the detection purpose. Based on the analytical results, two schemes for detecting the kernel attacks are then proposed, which allow reliable attack detection without loss of control performance. While the first scheme is similar to the well-established moving target method and auxiliary system aided detection scheme, the second detector is realised with encrypted transmissions of control and monitoring signals in the feedback control system that prevents adversary to gain system knowledge by means of eavesdropping attacks. Both schemes are illustrated by examples of detecting replay, zero dynamics and covert attacks and an experimental study on a three-tank control system.

eess.SY↗

arXiv2021

LightMBERT: A Simple Yet Effective Method for Multilingual BERT Distillation

The multilingual pre-trained language models (e.g, mBERT, XLM and XLM-R) have shown impressive performance on cross-lingual natural language understanding tasks. However, these models are computationally intensive and difficult to be deployed on resource-restricted devices. In this paper, we propose a simple yet effective distillation method (LightMBERT) for transferring the cross-lingual generalization ability of the multilingual BERT to a small student model. The experiment results empirically demonstrate the efficiency and effectiveness of LightMBERT, which is significantly better than the baselines and performs comparable to the teacher mBERT.

cs.CL↗

arXiv2020

Improving Task-Agnostic BERT Distillation with Layer Mapping Search

Knowledge distillation (KD) which transfers the knowledge from a large teacher model to a small student model, has been widely used to compress the BERT model recently. Besides the supervision in the output in the original KD, recent works show that layer-level supervision is crucial to the performance of the student BERT model. However, previous works designed the layer mapping strategy heuristically (e.g., uniform or last-layer), which can lead to inferior performance. In this paper, we propose to use the genetic algorithm (GA) to search for the optimal layer mapping automatically. To accelerate the search process, we further propose a proxy setting where a small portion of the training corpus are sampled for distillation, and three representative tasks are chosen for evaluation. After obtaining the optimal layer mapping, we perform the task-agnostic BERT distillation with it on the whole corpus to build a compact student model, which can be directly fine-tuned on downstream tasks. Comprehensive experiments on the evaluation benchmarks demonstrate that 1) layer mapping strategy has a significant effect on task-agnostic BERT distillation and different layer mappings can result in quite different performances; 2) the optimal layer mapping strategy from the proposed search process consistently outperforms the other heuristic ones; 3) with the optimal layer mapping, our student model achieves state-of-the-art performance on the GLUE tasks.

cs.CL↗

arXiv2020

TinyBERT: Distilling BERT for Natural Language Understanding

Language model pre-training, such as BERT, has significantly improved the performances of many natural language processing tasks. However, pre-trained language models are usually computationally expensive, so it is difficult to efficiently execute them on resource-restricted devices. To accelerate inference and reduce model size while maintaining accuracy, we first propose a novel Transformer distillation method that is specially designed for knowledge distillation (KD) of the Transformer-based models. By leveraging this new KD method, the plenty of knowledge encoded in a large teacher BERT can be effectively transferred to a small student Tiny-BERT. Then, we introduce a new two-stage learning framework for TinyBERT, which performs Transformer distillation at both the pretraining and task-specific learning stages. This framework ensures that TinyBERT can capture he general-domain as well as the task-specific knowledge in BERT. TinyBERT with 4 layers is empirically effective and achieves more than 96.8% the performance of its teacher BERTBASE on GLUE benchmark, while being 7.5x smaller and 9.4x faster on inference. TinyBERT with 4 layers is also significantly better than 4-layer state-of-the-art baselines on BERT distillation, with only about 28% parameters and about 31% inference time of them. Moreover, TinyBERT with 6 layers performs on-par with its teacher BERTBASE.

cs.CL↗