arXiv subjects

Yifei Jin

Publications and source records attributed to Yifei Jin.

At least 37 records · Page 2Linked to original sources

arXiv2025

Clumpy Starburst in a Local Dwarf Galaxy, NGC 1522

To investigate the star-forming process in nearby dwarf galaxies, we present Integral Field Units (IFU) observation of the star-forming dwarf galaxy, NGC 1522, with the Very Large Telescope (VLT)/Multi Unit Spectroscopic Explorer (MUSE) as a part of Dwarf Galaxy Integral Survey (DGIS). Our observation reveals the presence of a star-forming clumpy ring in its central region. We identify nine distinct star-forming clumps based on extinction-corrected H$α$ emission-line map, with the total star formation rate (SFR) of about 0.1 $M_\odot$ yr$^{-1}$. The nine clumps are considered to be starbursts, which represent an extreme case in the local universe, without invoking major merging. We investigate the properties of ionized gas using the strong emission lines and `BPT' diagrams, in conjunction with the velocity mapping. Our analysis unveils intriguing patterns, including the positive metallicity gradient and low N/O abundance ratio. This peculiar distribution of metallicity may signify external gas accretion. Our results suggest that the ongoing star formation in NGC 1522 might be triggered and sustained by the inflow of external metal-poor gas.

astro-ph.GA↗

arXiv2025

Desmearing two-dimensional small-angle neutron scattering data by central moment expansions

Resolution smearing is a critical challenge in the quantitative analysis of two-dimensional small-angle neutron scattering (SANS) data, particularly in studies of soft matter flow and deformation using SANS. We present the central moment expansion technique to address smearing in anisotropic scattering spectra, offering a model-free desmearing methodology. By accounting for directional variations in resolution smearing and enhancing computational efficiency, this approach reconstructs desmeared intensity distributions from smeared experimental data. Computational benchmarks using interacting hard-sphere fluids and Gaussian chain models validate the accuracy of the method, while simulated noise analyses confirm its robustness under experimental conditions. Furthermore, experimental validation using the rheo-SANS data of shear-induced micellar structures demonstrates the practicality and effectiveness of the proposed algorithm. The desmearing technique provides a powerful tool for advancing the quantitative analysis of anisotropic scattering patterns, enabling precise insights into the interplay between material microstructure and macroscopic flow behavior.

cond-mat.soft↗

arXiv2025

Estimating Voltage Drop: Models, Features and Data Representation Towards a Neural Surrogate

Accurate estimation of voltage drop (IR drop) in modern Application-Specific Integrated Circuits (ASICs) is highly time and resource demanding, due to the growing complexity and the transistor density in recent technology nodes. To mitigate this challenge, we investigate how Machine Learning (ML) techniques, including Extreme Gradient Boosting (XGBoost), Convolutional Neural Network (CNN), and Graph Neural Network (GNN) can aid in reducing the computational effort and implicitly the time required to estimate the IR drop in Integrated Circuits (ICs). Traditional methods, including commercial tools, require considerable time to produce accurate approximations, especially for complicated designs with numerous transistors. ML algorithms, on the other hand, are explored as an alternative solution to offer quick and precise IR drop estimation, but in considerably less time. Our approach leverages ASICs' electrical, timing, and physical to train ML models, ensuring adaptability across diverse designs with minimal adjustments. Experimental results underscore the superiority of ML models over commercial tools, greatly enhancing prediction speed. Particularly, GNNs exhibit promising performance with minimal prediction errors in voltage drop estimation. The incorporation of GNNs marks a groundbreaking advancement in accurate IR drop prediction. This study illustrates the effectiveness of ML algorithms in precisely estimating IR drop and optimizing ASIC sign-off. Utilizing ML models leads to expedited predictions, reducing calculation time and improving energy efficiency, thereby reducing environmental impact through optimized power circuits.

cs.AR↗

arXiv2024

Generalizing soft actor-critic algorithms to discrete action spaces

ATARI is a suite of video games used by reinforcement learning (RL) researchers to test the effectiveness of the learning algorithm. Receiving only the raw pixels and the game score, the agent learns to develop sophisticated strategies, even to the comparable level of a professional human games tester. Ideally, we also want an agent requiring very few interactions with the environment. Previous competitive model-free algorithms for the task use the valued-based Rainbow algorithm without any policy head. In this paper, we change it by proposing a practical discrete variant of the soft actor-critic (SAC) algorithm. The new variant enables off-policy learning using policy heads for discrete domains. By incorporating it into the advanced Rainbow variant, i.e., the ``bigger, better, faster'' (BBF), the resulting SAC-BBF improves the previous state-of-the-art interquartile mean (IQM) from 1.045 to 1.088, and it achieves these results using only replay ratio (RR) 2. By using lower RR 2, the training time of SAC-BBF is strictly one-third of the time required for BBF to achieve an IQM of 1.045 using RR 8. As a value of IQM greater than one indicates super-human performance, SAC-BBF is also the only model-free algorithm with a super-human level using only RR 2. The code is publicly available on GitHub at https://github.com/lezhang-thu/bigger-better-faster-SAC.

cs.LG↗

arXiv2024

A strong He II $λ$1640 emitter with extremely blue UV spectral slope at $z=8.16$: presence of Pop III stars?

Cosmic hydrogen reionization and cosmic production of first metals are major phase transitions of the universe occurring during the first billion years after the Big Bang, however these are still underexplored observationally. Using the JWST NIRSpec prism spectroscopy, we report the discovery of a sub-$L_\ast$ galaxy at $z_{\rm spec}=8.1623\pm0.0007$, dubbed RXJ2129-z8HeII, via the detection of a series of strong rest-frame UV/optical nebular emission lines and the clear Lyman break. RXJ2129-z8HeII shows a pronounced UV continuum with an extremely steep (i.e. blue) spectral slope of $β=-2.53_{-0.07}^{+0.06}$, the steepest amongst all spectroscopically confirmed galaxies at $z_{\rm spec}\gtrsim7$, in support of its very hard ionizing spectrum that could lead to a significant leakage of its ionizing flux. Therefore, RXJ2129-z8HeII is representative of the key galaxy population driving the cosmic reionization. More importantly, we detect a strong He II $λ$1640 emission line in its spectrum, one of the highest redshifts at which such a line is robustly detected. Its high rest-frame equivalent width (${\rm EW}=21\pm4$ Angstrom) and extreme flux ratios with respect to UV metal and Balmer lines raise the possibility that part of RXJ2129-z8HeII's stellar populations could be Pop III-like. Through careful photoionization modeling, we show that the physically calibrated phenomenological models of the ionizing spectra of Pop III stars with strong mass loss can successfully reproduce the emission line flux ratios observed in RXJ2129-z8HeII. Assuming the Eddington limit, the total mass of the Pop III stars within this system is estimated to be $7.8\pm1.4\times10^5 M_\odot$. To date, this galaxy presents the most compelling case in the early universe where trace Pop III stars might coexist with metal-enriched populations.

astro-ph.GA↗

arXiv2024

Open World Learning Graph Convolution for Latency Estimation in Routing Networks

Accurate routing network status estimation is a key component in Software Defined Networking. However, existing deep-learning-based methods for modeling network routing are not able to extrapolate towards unseen feature distributions. Nor are they able to handle scaled and drifted network attributes in test sets that include open-world inputs. To deal with these challenges, we propose a novel approach for modeling network routing, using Graph Neural Networks. Our method can also be used for network-latency estimation. Supported by a domain-knowledge-assisted graph formulation, our model shares a stable performance across different network sizes and configurations of routing networks, while at the same time being able to extrapolate towards unseen sizes, configurations, and user behavior. We show that our model outperforms most conventional deep-learning-based models, in terms of prediction accuracy, computational resources, inference speed, as well as ability to generalize towards open-world input.

cs.NI↗

arXiv2024

Are We Wasting Time? A Fast, Accurate Performance Evaluation Framework for Knowledge Graph Link Predictors

The standard evaluation protocol for measuring the quality of Knowledge Graph Completion methods - the task of inferring new links to be added to a graph - typically involves a step which ranks every entity of a Knowledge Graph to assess their fit as a head or tail of a candidate link to be added. In Knowledge Graphs on a larger scale, this task rapidly becomes prohibitively heavy. Previous approaches mitigate this problem by using random sampling of entities to assess the quality of links predicted or suggested by a method. However, we show that this approach has serious limitations since the ranking metrics produced do not properly reflect true outcomes. In this paper, we present a thorough analysis of these effects along with the following findings. First, we empirically find and theoretically motivate why sampling uniformly at random vastly overestimates the ranking performance of a method. We show that this can be attributed to the effect of easy versus hard negative candidates. Second, we propose a framework that uses relational recommenders to guide the selection of candidates for evaluation. We provide both theoretical and empirical justification of our methodology, and find that simple and fast methods can work extremely well, and that they match advanced neural approaches. Even when a large portion of true candidates for a property are missed, the estimation barely deteriorates. With our proposed framework, we can reduce the time and computation needed similar to random sampling strategies while vastly improving the estimation; on ogbl-wikikg2, we show that accurate estimations of the full, filtered ranking can be obtained in 20 seconds instead of 30 minutes. We conclude that considerable computational effort can be saved by effective preprocessing and sampling methods and still reliably predict performance accurately of the true performance for the entire ranking procedure.

cs.AI↗

arXiv2023

The MAGPI Survey: Effects of Spiral Arms on Different Tracers of the Interstellar Medium and Stellar Populations at z~0.3

Spiral structures are important drivers of the secular evolution of disc galaxies, however, the origin of spiral arms and their effects on the development of galaxies remain mysterious. In this work, we present two three-armed spiral galaxies at z~0.3 in the Middle Age Galaxy Properties with Integral Field Spectroscopy (MAGPI) survey. Taking advantage of the high spatial resolution (~0.6'') of the Multi-Unit Spectroscopic Unit (MUSE), we investigate the two-dimensional distributions of different spectral parameters: Halpha, gas-phase metallicity, and D4000. We notice significant offsets in Halpha (~0.2 dex) as well as gas-phase metallicities (~0.05 dex) among the spiral arms, downstream and upstream of MAGPI1202197197 (SG1202). This observational signature suggests the spiral structure in SG1202 is consistent with arising from density wave theory. No azimuthal variation in Halpha or gas-phase metallicities is observed in MAGPI1204198199 (SG1204), which can be attributed to the tighter spiral arms in SG1204 than SG1202, coming with stronger mixing effects in the disc. The absence of azimuthal D4000 variation in both galaxies suggests the stars at different ages are well-mixed between the spiral arms and distributed around the disc regions. The different azimuthal distributions in Halpha and D4000 highlight the importance of time scales traced by various spectral parameters when studying 2D distributions in spiral galaxies. This work demonstrates the feasibility of constraining spiral structures by tracing interstellar medium (ISM) and stellar population at z~0.3, with a plan to expand the study to the full MAGPI survey.

astro-ph.GA↗

arXiv2023

CAHA/PPAK Integral-field Spectroscopic Observations of M81. II. Testing Photoionization Models in A Spatially-resolved LINER

The origin of the low-ionization nuclear emission-line region (LINER) prevalent in local galaxies and its relationship with supermassive black holes are debated for decades. We preform a comprehensive evaluation of traditional photoionization models against the circumnuclear ionized gas in M81, for which recent CAHA/PPAK integral-field spectroscopic observations reveal a LINER characteristic out to a galactocentric radius of ~1 kpc. Constructed with the photoionization code cloudy, the models have the novel aspect of their primary parameters being well constrained by extensive observations of a prototypical low-luminosity active galactic nucleus (LLAGN) and an old stellar bulge in M81. Additionally, these models incorporate a reasonably broad range of uncertain nebular properties. It is found that the integrated photoionization by the LLAGN and hot, low-mass stars distributed in the bulge can roughly reproduce the observed radial intensity distributions of the Halpha, Hbeta and [N ii] lines, with the bulge stars dominating the ionizing flux at radii >~200 pc. However, the models generally fail to reproduce a similarly declining profile of the [Oiii] line or an accordingly flat profile of the [O iii]/Hbeta ratio. This clearly points to a deficiency of ionizing photons in the outer regions despite an extended photoionization source. The discrepancy might be alleviated if much of the observed [O iii] line arose from a bulge-filling, low-density gas surrounding a denser, Halpha-emitting disk, or by a higher AGN luminosity in the recent past. The case of M81 has important implications for the ionization mechanism of LINERs and low-ionization emission-line regions in general.

astro-ph.GA↗

arXiv2023

Spatially-Resolved Temperature and Density Structures of Nearby HII regions

Photoionization models frequently assume constant temperature or density within HII regions. We investigate this assumption by measuring the detailed temperature and density structures of four HII regions in the Large Magellanic Cloud and the Small Magellanic Cloud, using integral-field spectroscopic data from the Wide-Field Spectrograph on the ANU 2.3m telescope. We analyse the distribution of emission-lines of low-ionization species, intermediate-ionization species and high-ionization species. We present the complex electron temperature and density structures within HII regions. All four nebulae present a negative gradient in the electron density profile. Both positive and negative temperature gradients are observed in the nebulae. We create a series of nebula models with a constant ISM pressure and varying temperature and density distributions. Comparison of the line ratios between our HII regions and models suggests that none of the simple nebula models can reproduce the observed temperature and density structures. Comparison between the models and the data suggests that the ISM pressure of nebulae in LMC and SMC is between log(P/k)=6-7.5. Complex internal structures of the nebulae highlight the importance of future Monte-Carlo photoionization codes for accurate nebula modeling, which include a comprehensive consideration of arbitrary geometries of HII regions.

astro-ph.GA↗

arXiv2023

Learning Cellular Coverage from Real Network Configurations using GNNs

Cellular coverage quality estimation has been a critical task for self-organized networks. In real-world scenarios, deep-learning-powered coverage quality estimation methods cannot scale up to large areas due to little ground truth can be provided during network design & optimization. In addition they fall short in produce expressive embeddings to adequately capture the variations of the cells' configurations. To deal with this challenge, we formulate the task in a graph representation and so that we can apply state-of-the-art graph neural networks, that show exemplary performance. We propose a novel training framework that can both produce quality cell configuration embeddings for estimating multiple KPIs, while we show it is capable of generalising to large (area-wide) scenarios given very few labeled cells. We show that our framework yields comparable accuracy with models that have been trained using massively labeled samples.

cs.NI↗

arXiv2022

Theoretically Modelling Photoionized Regions with Fractal Geometry in Three Dimensions

We create a photoionization model embedded in the turbulent ISM by using the state-of-the-art Messenger Monte-Carlo MAPPINGS~V code (M$^3$) in conjunction with the CMFGEN stellar atmosphere model. We show that the turbulent ISM causes the inhomogeneity of electron temperature and density within the nebula. The fluctuation in the turbulent ISM creates complex ionization structures seen in nearby nebulae. The inhomogeneous density distribution within the nebula creates a significant scatter on the spatially-resolved standard optical diagnostic diagrams, which cannot be represented by the spherical constant density photoionization model. We analyze the dependence of different optical emission lines on the complexity of nebular geometry, finding that the emission-lines residing on the nebular boundary are highly sensitive to the complexity of nebular geometry, while the emission-lines produced throughout the nebula are sensitive to the density distribution of the ISM within the nebula. Our fractal photoionization model demonstrates that a complex nebular geometry is required for accurate modeling of HII regions and emission-line galaxies, especially for the high-redshift galaxies, where the ISM is highly turbulent based on the increasing observational evidence.

astro-ph.GA↗

arXiv2022

A Graph Attention Learning Approach to Antenna Tilt Optimization

6G will move mobile networks towards increasing levels of complexity. To deal with this complexity, optimization of network parameters is key to ensure high performance and timely adaptivity to dynamic network environments. The optimization of the antenna tilt provides a practical and cost-efficient method to improve coverage and capacity in the network. Previous methods based on Reinforcement Learning (RL) have shown great promise for tilt optimization by learning adaptive policies outperforming traditional tilt optimization methods. However, most existing RL methods are based on single-cell features representation, which fails to fully characterize the agent state, resulting in suboptimal performance. Also, most of such methods lack scalability, due to state-action explosion, and generalization ability. In this paper, we propose a Graph Attention Q-learning (GAQ) algorithm for tilt optimization. GAQ relies on a graph attention mechanism to select relevant neighbors information, improve the agent state representation, and update the tilt control policy based on a history of observations using a Deep Q-Network (DQN). We show that GAQ efficiently captures important network information and outperforms standard DQN with local information by a large margin. In addition, we demonstrate its ability to generalize to network deployments of different sizes and densities.

cs.LG↗

arXiv2022

First Identification of New X-Ray Spectra of Mo39+, Mo40+, W43+, W44+ and W45+ on EAST

New high-resolution x-ray spectra of Mo39+, Mo40+, W43+, W44+ and W45+ have been carefully confirmed for the first time by use of the x-ray imaging crystal spectrometer (XCS) in Experimental Advanced Superconducting Tokamak (EAST) under various combined auxiliary heating plasmas conditions. Wavelength of these new x-ray spectra is ranged from 3.895 Å to 3.986 Å. When core electron temperature (Te0) reaches 6.0 keV, Mo39+ and Mo40+ lines of 3.9727, 3.9294 and 3.9480 Å can be effectively detected on XCS for EAST; meanwhile, line-integrated brightness of these spectral lines of Mo39+ and Mo40+ is very considerable when electron temperature reaches 12.9 keV. Multi-components spectral lines for W43+, W44+ and W45+ have also been identified when Te0 reaches 6 keV. Parts of spectral lines, such as Zn-1, Cu-2, Cu-4a, Cu-4d and Cu-5 lines of tungsten, are first observed experimentally. When electron temperature reaches 12.9 keV, line-integrated intensity for part of these spectral lines of W43+, W44+ and W45+ are considerable. These experimental results and theoretical predictions from FAC and FLYCHK codes are in good general agreement. These new spectral lines, obtained on XCS for EAST, are vital for deeply uncovering the mechanisms of ion and electron thermal, high-Z impurity and momentum (anomalous) transport to achieve the advanced steady-state operation scenarios for ITER and CFETR.

physics.plasm-ph↗

arXiv2022

Messenger Monte-Carlo MAPPINGS V (M^3) -- A self-consistent three-dimensional photoionization code

The Messenger Interface Monte-Carlo Mappings V (M^3) is a photoionization code adopting the fully self-consistent Monte-Carlo radiative transfer technique, which presents a major advance over previous photoionization models with simple geometries. M^3 is designed for modeling nebulae in arbitrary three-dimensional geometries. In this paper, we describe the Monte-Carlo radiative transfer technique and the microphysics implemented in M^3, including the photoionization, collisional ionization, the free-free and free-bound recombination, and two-photon radiation. We put M^3 through the Lexington/Meudon benchmarks to test the reliability of the new code. We apply M^3 to three HII region models with fiducial geometries, demonstrating that M^3 is capable of dealing with nebulae with complex geometries. M^3 is a promising tool for understanding emission-line behavior in the era of SDSS-V/LVM and JWST, which will provide high-quality data of spatially-resolved nearby HII regions and highly turbulent local and high-redshift HII regions.

astro-ph.GA↗

arXiv2021

DoubleEnsemble: A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data Analysis

Modern machine learning models (such as deep neural networks and boosting decision tree models) have become increasingly popular in financial market prediction, due to their superior capacity to extract complex non-linear patterns. However, since financial datasets have very low signal-to-noise ratio and are non-stationary, complex models are often very prone to overfitting and suffer from instability issues. Moreover, as various machine learning and data mining tools become more widely used in quantitative trading, many trading firms have been producing an increasing number of features (aka factors). Therefore, how to automatically select effective features becomes an imminent problem. To address these issues, we propose DoubleEnsemble, an ensemble framework leveraging learning trajectory based sample reweighting and shuffling based feature selection. Specifically, we identify the key samples based on the training dynamics on each sample and elicit key features based on the ablation impact of each feature via shuffling. Our model is applicable to a wide range of base models, capable of extracting complex patterns, while mitigating the overfitting and instability issues for financial market prediction. We conduct extensive experiments, including price prediction for cryptocurrencies and stock trading, using both DNN and gradient boosting decision tree as base models. Our experiment results demonstrate that DoubleEnsemble achieves a superior performance compared with several baseline methods.

cs.LG↗

arXiv2020

AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment

The multi-factor model is a widely used model in quantitative investment. The success of a multi-factor model is largely determined by the effectiveness of the alpha factors used in the model. This paper proposes a new evolutionary algorithm called AutoAlpha to automatically generate effective formulaic alphas from massive stock datasets. Specifically, first we discover an inherent pattern of the formulaic alphas and propose a hierarchical structure to quickly locate the promising part of space for search. Then we propose a new Quality Diversity search based on the Principal Component Analysis (PCA-QD) to guide the search away from the well-explored space for more desirable results. Next, we utilize the warm start method and the replacement method to prevent the premature convergence problem. Based on the formulaic alphas we discover, we propose an ensemble learning-to-rank model for generating the portfolio. The backtests in the Chinese stock market and the comparisons with several baselines further demonstrate the effectiveness of AutoAlpha in mining formulaic alphas for quantitative trading.

q-fin.CP↗

arXiv2019

GRID: a Student Project to Monitor the Transient Gamma-Ray Sky in the Multi-Messenger Astronomy Era

The Gamma-Ray Integrated Detectors (GRID) is a space mission concept dedicated to monitoring the transient gamma-ray sky in the energy range from 10 keV to 2 MeV using scintillation detectors onboard CubeSats in low Earth orbits. The primary targets of GRID are the gamma-ray bursts (GRBs) in the local universe. The scientific goal of GRID is, in synergy with ground-based gravitational wave (GW) detectors such as LIGO and VIRGO, to accumulate a sample of GRBs associated with the merger of two compact stars and study jets and related physics of those objects. It also involves observing and studying other gamma-ray transients such as long GRBs, soft gamma-ray repeaters, terrestrial gamma-ray flashes, and solar flares. With multiple CubeSats in various orbits, GRID is unaffected by the Earth occultation and serves as a full-time and all-sky monitor. Assuming a horizon of 200 Mpc for ground-based GW detectors, we expect to see a few associated GW-GRB events per year. With about 10 CubeSats in operation, GRID is capable of localizing a faint GRB like 170817A with a 90% error radius of about 10 degrees, through triangulation and flux modulation. GRID is proposed and developed by students, with considerable contribution from undergraduate students, and will remain operated as a student project in the future. The current GRID collaboration involves more than 20 institutes and keeps growing. On August 29th, the first GRID detector onboard a CubeSat was launched into a Sun-synchronous orbit and is currently under test.

astro-ph.IM↗