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Xin Cheng

Publications and source records attributed to Xin Cheng.

At least 91 records · Page 5Linked to original sources
arXiv2024

Unveiling the Initiation Route of Coronal Mass Ejections through their Slow Rise Phase

Understanding the early evolution of coronal mass ejections (CMEs), in particular their initiation, is the key to forecasting solar eruptions and induced disastrous space weather. Although many initiation mechanisms have been proposed, a full understanding of CME initiation, which is identified as a slow rise of CME progenitors in kinematics before the impulsive acceleration, remains elusive. Here, with a state-of-the-art thermal-magnetohydrodynamics simulation, we determine a complete CME initiation route in which multiple mainstream mechanisms occur in sequence yet are tightly coupled. The slow rise is first triggered and driven by the developing hyperbolic flux tube (HFT) reconnection. Subsequently, the slow rise continues as driven by the coupling of the HFT reconnection and the early development of torus instability. The end of the slow rise, i.e., the onset of the impulsive acceleration, is induced by the start of the fast magnetic reconnection coupled with the torus instability. These results unveil that the CME initiation is a complicated process involving multiple physical mechanisms, thus being hardly resolved by a single initiation mechanism.

astro-ph.SR↗
arXiv2024

Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models

We introduce Generalized Instruction Tuning (called GLAN), a general and scalable method for instruction tuning of Large Language Models (LLMs). Unlike prior work that relies on seed examples or existing datasets to construct instruction tuning data, GLAN exclusively utilizes a pre-curated taxonomy of human knowledge and capabilities as input and generates large-scale synthetic instruction data across all disciplines. Specifically, inspired by the systematic structure in human education system, we build the taxonomy by decomposing human knowledge and capabilities to various fields, sub-fields and ultimately, distinct disciplines semi-automatically, facilitated by LLMs. Subsequently, we generate a comprehensive list of subjects for every discipline and proceed to design a syllabus tailored to each subject, again utilizing LLMs. With the fine-grained key concepts detailed in every class session of the syllabus, we are able to generate diverse instructions with a broad coverage across the entire spectrum of human knowledge and skills. Extensive experiments on large language models (e.g., Mistral) demonstrate that GLAN excels in multiple dimensions from mathematical reasoning, coding, academic exams, logical reasoning to general instruction following without using task-specific training data of these tasks. In addition, GLAN allows for easy customization and new fields or skills can be added by simply incorporating a new node into our taxonomy.

cs.CL↗
arXiv2023

Lift Yourself Up: Retrieval-augmented Text Generation with Self Memory

With direct access to human-written reference as memory, retrieval-augmented generation has achieved much progress in a wide range of text generation tasks. Since better memory would typically prompt better generation~(we define this as primal problem). The traditional approach for memory retrieval involves selecting memory that exhibits the highest similarity to the input. However, this method is constrained by the quality of the fixed corpus from which memory is retrieved. In this paper, by exploring the duality of the primal problem: better generation also prompts better memory, we propose a novel framework, selfmem, which addresses this limitation by iteratively employing a retrieval-augmented generator to create an unbounded memory pool and using a memory selector to choose one output as memory for the subsequent generation round. This enables the model to leverage its own output, referred to as self-memory, for improved generation. We evaluate the effectiveness of selfmem on three distinct text generation tasks: neural machine translation, abstractive text summarization, and dialogue generation, under two generation paradigms: fine-tuned small model and few-shot LLM. Our approach achieves state-of-the-art results in four directions in JRC-Acquis, XSum (50.3 ROUGE-1), and BigPatent (62.9 ROUGE-1), demonstrating the potential of self-memory in enhancing retrieval-augmented generation models. Furthermore, we conduct thorough analyses of each component in the selfmem framework to identify bottlenecks and provide insights for future research.

cs.CL↗
arXiv2023

RWKV: Reinventing RNNs for the Transformer Era

Transformers have revolutionized almost all natural language processing (NLP) tasks but suffer from memory and computational complexity that scales quadratically with sequence length. In contrast, recurrent neural networks (RNNs) exhibit linear scaling in memory and computational requirements but struggle to match the same performance as Transformers due to limitations in parallelization and scalability. We propose a novel model architecture, Receptance Weighted Key Value (RWKV), that combines the efficient parallelizable training of transformers with the efficient inference of RNNs. Our approach leverages a linear attention mechanism and allows us to formulate the model as either a Transformer or an RNN, thus parallelizing computations during training and maintains constant computational and memory complexity during inference. We scale our models as large as 14 billion parameters, by far the largest dense RNN ever trained, and find RWKV performs on par with similarly sized Transformers, suggesting future work can leverage this architecture to create more efficient models. This work presents a significant step towards reconciling trade-offs between computational efficiency and model performance in sequence processing tasks.

cs.CL↗
arXiv2023

A Statistical Study of Soft X-ray Flares on Solar-type Stars

The statistical characteristic of stellar flares at optical bands has received an extensive study, but it remains to be studied at soft X-ray bands, in particular for solar-type stars. Here, we present a statistical study of soft X-ray flares on solar-type stars, which can help understand multi-wavelength behaviors of stellar flares. We mainly use Chandra Source Catalog Release 2.0, which includes a number of flaring stars with denoted variability, and Gaia Data Release 3, which includes necessary information for classifying stars. We also develop a set of methods for identifying and classifying stellar soft X-ray flares and estimating their properties. A detailed statistical investigation for 129 flare samples on 103 nearby solar-type stars as selected yields the following main results. (1) The flare energy emitted at the soft X-ray band in our sample ranges from $\sim 10^{33}$ to $\sim 10^{37} \ \mathrm{erg}$, and the majority of them are superflares with the most energetic one having energy of $6.0_{-4.7}^{+3.2} \times 10^{37} \ \mathrm{erg}$. (2) The flare duration is related to its energy as formulated by $T_\mathrm{duration,SXR} \propto E_\mathrm{flare,SXR}^{\ 0.201 \pm 0.024}$, which is different from those derived at optical and NIR bands, indicating distinct radiation mechanisms at different bands. (3) The frequency distribution of stellar flares as a function of energy is formulated as $\mathrm{d} N_\mathrm{flare} / \mathrm{d} E_\mathrm{flare,SXR} \propto E_\mathrm{flare,SXR}^{\ -1.77}$, which is similar to the results found at other bands and on other types of stars, indicating that the energy emitted at the soft X-ray band could be a constant fraction of the full-band bolometric energy.

astro-ph.SR↗
arXiv2023

Observations of a Failed Solar Filament Eruption Involving External Reconnection

We report a failed solar filament eruption that involves external magnetic reconnection in a quadrupolar magnetic configuration. The evolution exhibits three kinematic evolution phases: a slow-rise phase, an acceleration phase, and a deceleration phase. In the early slow rise, extreme-ultraviolet (EUV) brightenings appear at the expected null point above the filament and are connected to the outer polarities by the hot loops, indicating the occurrence of a breakout reconnection. Subsequently, the filament is accelerated outward, accompanied by the formation of low-lying high-temperature post-flare loops ($>$ 15 MK), complying with the standard flare model. However, after 2--3 minutes, the erupting filament starts to decelerate and is finally confined in the corona. The important finding is that the confinement is closely related to an external reconnection as evidenced by the formation of high-lying large-scale hot loops ($>$ 10 MK) with their brightened footpoints at the outer polarities, the filament fragmentation and subsequent falling along the newly formed large-scale loops, as well as a hard X-ray source close to one of the outer footpoint brightenings. We propose that, even though the initial breakout reconnection and subsequent flare reconnection commence and accelerate the filament eruption, the following external reconnection between the erupting flux rope and overlying field, as driven by the upward filament eruption, makes the eruption finally failed, as validated by the numerical simulation of a failed flux rope eruption.

astro-ph.SR↗
arXiv2023

Regression with Cost-based Rejection

Learning with rejection is an important framework that can refrain from making predictions to avoid critical mispredictions by balancing between prediction and rejection. Previous studies on cost-based rejection only focused on the classification setting, which cannot handle the continuous and infinite target space in the regression setting. In this paper, we investigate a novel regression problem called regression with cost-based rejection, where the model can reject to make predictions on some examples given certain rejection costs. To solve this problem, we first formulate the expected risk for this problem and then derive the Bayes optimal solution, which shows that the optimal model should reject to make predictions on the examples whose variance is larger than the rejection cost when the mean squared error is used as the evaluation metric. Furthermore, we propose to train the model by a surrogate loss function that considers rejection as binary classification and we provide conditions for the model consistency, which implies that the Bayes optimal solution can be recovered by our proposed surrogate loss. Extensive experiments demonstrate the effectiveness of our proposed method.

cs.LG↗
arXiv2023

Identifying Footpoints of Pre-eruptive and Coronal Mass Ejection Flux Ropes with Sunspot Scars

The properties of pre-eruptive structures and coronal mass ejections (CMEs) are characterized by those of their footpoints, the latter of which thus attract great interest. However, how to identify the footpoints of pre-eruptive structures and how to identify the footpoints with ground-based instruments, still remain elusive. In this work, we study an arc-shaped structure intruding in the sunspot umbra. It is located close to the (pre-)eruptive flux rope footpoint and is thus expected to help identify the footpoint. We analyse this arc-shaped structure, which we name as "sunspot scar", in a CME event on 2012 July 12 and in two CME events in observationally-inspired MHD simulations performed by OHM and MPI-AMRVAC. The sunspot scar has a more inclined magnetic field with a weaker vertical component and a stronger horizontal component relative to that in the surrounding umbra and manifests as a light bridge in the white light passband. The hot field lines anchored in the sunspot scar are spatially at the transition between the flux rope and the background coronal loops, and temporally in the process of the slipping reconnection which builds up the flux rope. The sunspot scar and its related light bridge mark the edge of the CME flux rope footpoint, and especially, the edge of the pre-eruptive flux rope footpoint in the framework of "pre-eruptive structures being flux ropes". Therefore, they provide a new perspective for the identification of pre-eruptive and CME flux rope footpoints, and also new methods for studying the properties and evolution of pre-eruptive structures and CMEs with photospheric observations only.

astro-ph.SR↗
arXiv2023

SCALE: Synergized Collaboration of Asymmetric Language Translation Engines

In this paper, we introduce SCALE, a collaborative framework that connects compact Specialized Translation Models (STMs) and general-purpose Large Language Models (LLMs) as one unified translation engine. By introducing translation from STM into the triplet in-context demonstrations, SCALE unlocks refinement and pivoting ability of LLM, thus mitigating language bias of LLM and parallel data bias of STM, enhancing LLM speciality without sacrificing generality, and facilitating continual learning without expensive LLM fine-tuning. Our comprehensive experiments show that SCALE significantly outperforms both few-shot LLMs (GPT-4) and specialized models (NLLB) in challenging low-resource settings. Moreover, in Xhosa to English translation, SCALE experiences consistent improvement by a 4 BLEURT score without tuning LLM and surpasses few-shot GPT-4 by 2.5 COMET score and 3.8 BLEURT score when equipped with a compact model consisting of merely 600M parameters. SCALE could also effectively exploit the existing language bias of LLMs by using an English-centric STM as a pivot for translation between any language pairs, outperforming few-shot GPT-4 by an average of 6 COMET points across eight translation directions. Furthermore we provide an in-depth analysis of SCALE's robustness, translation characteristics, and latency costs, providing solid foundation for future studies exploring the potential synergy between LLMs and more specialized, task-specific models.

cs.CL↗
arXiv2023

Decouple knowledge from parameters for plug-and-play language modeling

Pre-trained language models(PLM) have made impressive results in various NLP tasks. It has been revealed that one of the key factors to their success is the parameters of these models implicitly learn all kinds of knowledge during pre-training. However, encoding knowledge implicitly in the model parameters has two fundamental drawbacks. First, the knowledge is neither editable nor scalable once the model is trained, which is especially problematic in that knowledge is consistently evolving. Second, it lacks interpretability and prevents humans from understanding which knowledge PLM requires for a certain problem. In this paper, we introduce PlugLM, a pre-training model with differentiable plug-in memory(DPM). The key intuition is to decouple the knowledge storage from model parameters with an editable and scalable key-value memory and leverage knowledge in an explainable manner by knowledge retrieval in the DPM. To justify this design choice, we conduct evaluations in three settings including: (1) domain adaptation. PlugLM obtains 3.95 F1 improvements across four domains on average without any in-domain pre-training. (2) knowledge update. PlugLM could absorb new knowledge in a training-free way after pre-training is done. (3) in-task knowledge learning. PlugLM could be further improved by incorporating training samples into DPM with knowledge prompting.

cs.CL↗
arXiv2023

Spectral Features of the Solar Transition Region and Chromospheric Lines at Flare Ribbons Observed with IRIS

We report on the spectral features of the Si IV 1402.77 Å, C II 1334.53 Å, and Mg II h or k lines, formed in the layers from the transition region to the chromosphere, in three two-ribbon flares (with X-, M-, and C-class) observed with IRIS. All the three lines show significant redshifts within the main flare ribbons, which mainly originate from the chromospheric condensation during the flares. The average redshift velocities of the Si IV line within the main ribbons are 56.6, 25.6, and 10.5 km s$^{-1}$ for the X-, M-, and C-class flares, respectively, which show a decreasing tendency with the flare class. The C II and Mg II lines show a similar tendency but with smaller velocities compared to the Si IV line. Additionally, the Mg II h or k line shows a blue-wing enhancement in the three flares in particular at the flare ribbon fronts, which is supposed to be caused by an upflow in the upper chromosphere due to the heating of the atmosphere. Moreover, the Mg II h or k line exhibits a central reversal at the flare ribbons, but turns to pure emission shortly after 1--4 minutes. Correspondingly, the C II line also shows a central reversal but in a smaller region. However, for the Si IV line, the central reversal is only found in the X-class flare, but not in the other two flares. As usual, the central reversal of these lines can be caused by the opacity effect. This implies that in addition to the optically thick lines (C II and Mg II lines), the Si IV line can become optically thick in a strong flare, which is likely related to the nonthermal electron beam heating.

astro-ph.SR↗
arXiv2023

Three-dimensional Turbulent Reconnection within Solar Flare Current Sheet

Solar flares can release coronal magnetic energy explosively and may impact the safety of near-earth space environments. Their structures and properties on macroscale have been interpreted successfully by the generally-accepted two-dimension standard model invoking magnetic reconnection theory as the key energy conversion mechanism. Nevertheless, some momentous dynamical features as discovered by recent high-resolution observations remain elusive. Here, we report a self-consistent high-resolution three-dimension magnetohydrodynamical simulation of turbulent magnetic reconnection within a flare current sheet. It is found that fragmented current patches of different scales are spontaneously generated with a well-developed turbulence spectrum at the current sheet, as well as at the flare loop-top region. The close coupling of tearing-mode and Kelvin-Helmholtz instabilities plays a critical role in developing turbulent reconnection and in forming dynamical structures with synthetic observables in good agreement with realistic observations. The sophisticated modeling makes a paradigm shift from the traditional to three-dimension turbulent reconnection model unifying flare dynamical structures of different scales.

astro-ph.SR↗
arXiv2023

Leveraging Optical Communication Fiber and AI for Distributed Water Pipe Leak Detection

Detecting leaks in water networks is a costly challenge. This article introduces a practical solution: the integration of optical network with water networks for efficient leak detection. Our approach uses a fiber-optic cable to measure vibrations, enabling accurate leak identification and localization by an intelligent algorithm. We also propose a method to access leak severity for prioritized repairs. Our solution detects even small leaks with flow rates as low as 0.027 L/s. It offers a cost-effective way to improve leak detection, enhance water management, and increase operational efficiency.

eess.SY↗
arXiv2023

The Structure of Coronal Mass Ejections Recorded by the K-Coronagraph at Mauna Loa Solar Observatory

Previous survey studies reported that coronal mass ejections (CMEs) can exhibit various structures in white-light coronagraphs, and $\sim$30\% of them have the typical three-part feature in the high corona (e.g., 2--6 $R_\odot$), which has been taken as the prototypical structure of CMEs. It is widely accepted that CMEs result from eruption of magnetic flux ropes (MFRs), and the three-part structure can be understood easily by means of the MFR eruption. It is interesting and significant to answer why only $\sim$30\% of CMEs have the three-part feature in previous studies. Here we conduct a synthesis of the CME structure in the field of view (FOV) of K-Coronagraph (1.05--3 $R_\odot$). In total, 369 CMEs are observed from 2013 September to 2022 November. After inspecting the CMEs one by one through joint observations of the AIA, K-Coronagraph and LASCO/C2, we find 71 events according to the criteria: 1) limb event; 2) normal CME, i.e., angular width $\geq$ 30$^{\circ}$; 3) K-Coronagraph caught the early eruption stage. All (or more than 90\% considering several ambiguous events) of the 71 CMEs exhibit the three-part feature in the FOV of K-Coronagraph, while only 30--40\% have the feature in the C2 FOV (2--6 $R_\odot$). For the first time, our studies show that 90--100\% and 30--40\% of normal CMEs possess the three-part structure in the low and high corona, respectively, which demonstrates that many CMEs can lose the three-part feature during their early evolutions, and strongly supports that most (if not all) CMEs have the MFR structures.

astro-ph.SR↗
arXiv2023

Weakly Supervised Regression with Interval Targets

This paper investigates an interesting weakly supervised regression setting called regression with interval targets (RIT). Although some of the previous methods on relevant regression settings can be adapted to RIT, they are not statistically consistent, and thus their empirical performance is not guaranteed. In this paper, we provide a thorough study on RIT. First, we proposed a novel statistical model to describe the data generation process for RIT and demonstrate its validity. Second, we analyze a simple selection method for RIT, which selects a particular value in the interval as the target value to train the model. Third, we propose a statistically consistent limiting method for RIT to train the model by limiting the predictions to the interval. We further derive an estimation error bound for our limiting method. Finally, extensive experiments on various datasets demonstrate the effectiveness of our proposed method.

cs.LG↗
arXiv2023

Partial-Label Regression

Partial-label learning is a popular weakly supervised learning setting that allows each training example to be annotated with a set of candidate labels. Previous studies on partial-label learning only focused on the classification setting where candidate labels are all discrete, which cannot handle continuous labels with real values. In this paper, we provide the first attempt to investigate partial-label regression, where each training example is annotated with a set of real-valued candidate labels. To solve this problem, we first propose a simple baseline method that takes the average loss incurred by candidate labels as the predictive loss. The drawback of this method lies in that the loss incurred by the true label may be overwhelmed by other false labels. To overcome this drawback, we propose an identification method that takes the least loss incurred by candidate labels as the predictive loss. We further improve it by proposing a progressive identification method to differentiate candidate labels using progressively updated weights for incurred losses. We prove that the latter two methods are model-consistent and provide convergence analyses. Our proposed methods are theoretically grounded and can be compatible with any models, optimizers, and losses. Experiments validate the effectiveness of our proposed methods.

cs.LG↗
arXiv2023

A Model for Confined Solar Eruptions Including External Reconnection

The violent disruption of the coronal magnetic field is often observed to be restricted to the low corona, appearing as a confined eruption. The possible causes of the confinement remain elusive. Here, we model the eruption of a magnetic flux rope in a quadrupolar active region, with the parameters set such that magnetic X-lines exist both below and above the rope. This facilitates the onset of magnetic reconnection in either place but with partly opposing effects on the eruption. The lower reconnection initially adds poloidal flux to the rope, increasing the upward hoop force and supporting the rise of the rope. However, when the flux of the magnetic side lobes enters the lower reconnection, the flux rope is found to separate from the reconnection site and the flux accumulation ceases. At the same time, the upper reconnection begins to reduce the poloidal flux of the rope, decreasing its hoop force; eventually this cuts the rope completely. The relative weight of the two reconnection processes is varied in the model, and it is found that their combined effect and the tension force of the overlying field confine the eruption if the flux ratio of the outer to the inner polarities exceeds a threshold, which is about 1.3 for our Cartesian box and chosen parameters. We hence propose that external reconnection between an erupting flux rope and overlying flux can play a vital role in confining eruptions.

astro-ph.SR↗
arXiv2023

Fine structures of radio bursts from flare star AD Leo with FAST observations

Radio bursts from nearby active M-dwarfs have been frequently reported and extensively studied in solar or planetary paradigms. Whereas, their sub-structures or fine structures remain rarely explored despite their potential significance in diagnosing the plasma and magnetic field properties of the star. Such studies in the past have been limited by the sensitivity of radio telescopes. Here we report the inspiring results from the high time-resolution observations of a known flare star AD Leo with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). We detected many radio bursts in the two days of observations with fine structures in the form of numerous millisecond-scale sub-bursts. Sub-bursts on the first day display stripe-like shapes with nearly uniform frequency drift rates, which are possibly stellar analogs to Jovian S-bursts. Sub-bursts on the second day, however, reveal a different blob-like shape with random occurrence patterns and are akin to solar radio spikes. The new observational results suggest that the intense emission from AD Leo is driven by electron cyclotron maser instability which may be related to stellar flares or interactions with a planetary companion.

astro-ph.SR↗