arXiv subjects

Chun Wang

Publications and source records attributed to Chun Wang.

At least 55 records · Page 3Linked to original sources

arXiv2024

Machina Economicus: A New Paradigm for Prosumers in the Energy Internet of Smart Cities

Energy Internet (EI) is emerging as new share economy platform for flexible local energy supplies in smart cities. Empowered by the Internet-of-Things (IoT) and Artificial Intelligence (AI), EI aims to unlock peer-to-peer energy trading and sharing among prosumers, who can adeptly switch roles between providers and consumers in localized energy markets with rooftop photovoltaic panels, vehicle-to-everything technologies, packetized energy management, etc. The integration of prosumers in EI, however, will encounter many challenges in modelling, analyzing, and designing an efficient, economic, and social-optimal platform for energy sharing, calling for advanced AI/IoT-based solutions to resource optimization, information exchange, and interaction protocols in the context of the share economy. In this study, we aim to introduce a recently emerged paradigm, Machina Economicus, to investigate the economic rationality in modelling, analysis, and optimization of AI/IoT-based EI prosumer behaviors. The new paradigm, built upon the theory of machine learning and mechanism design, will offer new angles to investigate the selfishness of AI through a game-theoretic perspective, revealing potential competition and collaborations resulting from the self-adaptive learning and decision-making capacity. This study will focus on how the introduction of AI will reshape prosumer behaviors on the EI, and how this paradigm will reveal new research questions and directions when AI meets the share economy. With an extensive case analysis in the literature, we will also shed light on potential solutions for advancements of AI in future smart cities.

cs.CY↗

arXiv2024

Variational Estimation for Multidimensional Generalized Partial Credit Model

Multidimensional item response theory (MIRT) models have generated increasing interest in the psychometrics literature. Efficient approaches for estimating MIRT models with dichotomous responses have been developed, but constructing an equally efficient and robust algorithm for polytomous models has received limited attention. To address this gap, this paper presents a novel Gaussian variational estimation algorithm for the multidimensional generalized partial credit model (MGPCM). The proposed algorithm demonstrates both fast and accurate performance, as illustrated through a series of simulation studies and two real data analyses.

stat.ME↗

arXiv2024

Coordinating Guidance, Matching, and Charging Station Selection for Electric Vehicle Ride-Hailing Services through Data-Driven Stochastic Optimization

Electric vehicles (EVs) play a pivotal role in sustainable ride-hailing services primarily due to their potential in reducing carbon emissions and enhancing environmental protection. Despite their significance, current research in the realm of EV batched matching frequently overlooks critical aspects such as rider demand uncertainty and charging station (CS) selection, leading to inefficiencies like decreased matching rates and prolonged waiting times for both riders and EV drivers. To fill the research gap, we propose a data-driven optimization framework that incorporates two inter-connected stochastic optimization models to address the challenges. The first model aims to relocate the idle EVs under satisfied conditions to the designated regions based on the probabilistic rider demand forecasting result before the real rider demand is revealed. Taking the solutions of the first model as the input, the second model optimizes the batched matching results by minimizing the rider's average waiting time and EV charging waiting time at CS. This integrated framework not only elevates the matching rate through the incorporation of rider demand uncertainties in the guidance module but also substantially curtails both rider and EV charging waiting times by synergizing guidance with CS selection choices. Empirical validation of our framework was conducted through an extensive case study in New York City, utilizing real-world data sets. The validation results demonstrate that the proposed data-driven optimization framework outperforms the benchmark models in terms of the proposed evaluation metrics. Most importantly, when deploying our framework, the charging waiting time of the EVs with low SOC can be reduced up to 73.6% compared to the benchmark model without CS selection.

math.OC↗

arXiv2023

Wireless Regional Imaging through Reconfigurable Intelligent Surfaces: Passive Mode

In this paper, we propose a multi-RIS-aided wireless imaging framework in 3D facing the distributed placement of multi-sensor networks. The system creates a randomized reflection pattern by adjusting the RIS phase shift, enabling the receiver to capture signals within the designated space of interest (SoI). Firstly, a multi-RIS-aided linear imaging channel modeling is proposed. We introduce a theoretical framework of computational imaging to recover the signal strength distribution of the SOI. For the RIS-aided imaging system, the impact of multiple parameters on the performance of the imaging system is analyzed. The simulation results verify the correctness of the proposal. Furthermore, we propose an amplitude-only imaging algorithm for the RIS-aided imaging system to mitigate the problem of phase unpredictability. Finally, the performance verification of the imaging algorithm is carried out by proof of concept experiments under reasonable parameter settings.

eess.IV↗

arXiv2023

A Note on Improving Variational Estimation for Multidimensional Item Response Theory

Survey instruments and assessments are frequently used in many domains of social science. When the constructs that these assessments try to measure become multifaceted, multidimensional item response theory (MIRT) provides a unified framework and convenient statistical tool for item analysis, calibration, and scoring. However, the computational challenge of estimating MIRT models prohibits its wide use because many of the extant methods can hardly provide results in a realistic time frame when the number of dimensions, sample size, and test length are large. Instead, variational estimation methods, such as Gaussian Variational Expectation Maximization (GVEM) algorithm, have been recently proposed to solve the estimation challenge by providing a fast and accurate solution. However, results have shown that variational estimation methods may produce some bias on discrimination parameters during confirmatory model estimation, and this note proposes an importance weighted version of GVEM (i.e., IW-GVEM) to correct for such bias under MIRT models. We also use the adaptive moment estimation method to update the learning rate for gradient descent automatically. Our simulations show that IW-GVEM can effectively correct bias with modest increase of computation time, compared with GVEM. The proposed method may also shed light on improving the variational estimation for other psychometrics models.

stat.ME↗

arXiv2023

Comprehensive performance comparison among different types of features in data-driven battery state of health estimation

Battery state of health (SOH), which informs the maximal available capacity of the battery, is a key indicator of battery aging failure. Accurately estimating battery SOH is a vital function of the battery management system that remains to be addressed. In this study, a physics-informed Gaussian process regression (GPR) model is developed for battery SOH estimation, with the performance being systematically compared with that of different types of features and machine learning (ML) methods. The method performance is validated based on 58826 cycling data units of 118 cells. Experimental results show that the physics-driven ML generally estimates more accurate SOH than other non-physical features under different scenarios. The physical features-based GPR predicts battery SOH with the errors being less than 1.1% based on 10 to 20 mins' relaxation data. And the high robustness and generalization capability of the methodology is also validated against different ratios of training and test data under unseen conditions. Results also highlight the more effective capability of knowledge transfer between different types of batteries with the physical features and GPR. This study demonstrates the excellence of physical features in indicating the state evolution of complex systems, and the improved indication performance of these features by combining a suitable ML method.

eess.SY↗

arXiv2023

Precise Masses, Ages of ~1.0 million RGB and RC stars observed by the LAMOST

We construct a catalogue of stellar masses and ages for 696,680 red giant branch (RGB) stars, 180,436 primary red clump (RC) stars, and 120,907 secondary RC stars selected from the LAMOST\,DR8. The RGBs, primary RCs, and secondary RCs are identified with the large frequency spacing ($Δν$) and period spacing ($ΔP$), estimated from the LAMOST spectra with spectral SNRs $> 10$ by the neural network method supervised with the seismologic information from LAMOST-Kepler sample stars. The purity and completeness of both RGB and RC samples are better than 95\% and 90\%, respectively. The mass and age of RGBs and RCs are determined again with the neural network method by taking the LAMOST-Kepler giant stars as the training set. The typical uncertainties of stellar mass and age are, respectively, 10\% and 30\% for the RGB stellar sample. For RCs, the typical uncertainties of stellar mass and age are 9\% and 24\%, respectively. The RGB and RC stellar samples cover a large volume of the Milky Way (5 $< R < 20$\,kpc and $|Z| <$\,5\,kpc), which are valuable data sets for various Galactic studies.

astro-ph.GA↗

arXiv2023

Spatial metallicity variations of mono-temperature stellar populations revealed by early-type stars in LAMOST

We investigate the radial metallicity gradients and azimuthal metallicity distributions on the Galactocentric $X$--$Y$ plane using mono-temperature stellar populations selected from LAMOST MRS young stellar sample. The estimated radial metallicity gradient ranges from $-$0.015\,dex/kpc to $-$0.07\,dex/kpc, which decreases as effective temperature decreases (or stellar age increases) at $7500 < T_{\rm eff} < 12500$\,K ($τ< $1.5 Gyr). The azimuthal metallicity excess (metallicity after subtracting radial metallicity gradient, $Δ$\,[M/H]) distributions exhibit inhomogeneities with dispersions of 0.04\,dex to 0.07\,dex, which decrease as effective temperature decreases. We also identify five potential metal-poor substructures with large metallicity excess dispersions. The metallicity excess distributions of these five metal-poor substructures suggest that they contain a larger fraction of metal-poor stars compared to other control samples. These metal-poor substructures may be associated with high-velocity clouds that infall into the Galactic disk from the Galactic halo, which are not quickly well-mixed with the pre-existing ISM of the Galactic disk. As a result, these high-velocity clouds produce some metal-poor stars and the observed metal-poor substructures. The variations of metallicity inhomogeneities with different stellar populations indicate that high-velocity clouds are not well mixed with the pre-existing Galactic disk ISM within 0.3\,Gyr.

astro-ph.GA↗

arXiv2023

Linear pretraining in recurrent mixture density networks

We present a method for pretraining a recurrent mixture density network (RMDN). We also propose a slight modification to the architecture of the RMDN-GARCH proposed by Nikolaev et al. [2012]. The pretraining method helps the RMDN avoid bad local minima during training and improves its robustness to the persistent NaN problem, as defined by Guillaumes [2017], which is often encountered with mixture density networks. Such problem consists in frequently obtaining "Not a number" (NaN) values during training. The pretraining method proposed resolves these issues by training the linear nodes in the hidden layer of the RMDN before starting including non-linear node updates. Such an approach improves the performance of the RMDN and ensures it surpasses that of the GARCH model, which is the RMDN's linear counterpart.

cs.LG↗

arXiv2022

ScrollTest: Evaluating Scrolling Speed and Accuracy

Scrolling is an essential interaction technique enabling users to display previously off-screen content. Existing evaluation models for scrolling are often entangled with the selection of content, e.g., when scrolling on the phone for reading. Furthermore, some evaluation models overlook whether the user knows the target position. We have developed ScrollTest, a general-purpose evaluation tool for scrolling speed and accuracy that avoids the need for selection. We tested it across four dimensions: 11 different scrolling techniques/devices, 5 frame heights, 13 scrolling distances, and 2 scrolling conditions (i.e., with or without knowing the target position). The results show that flicking and two-finger scrolling are the fastest; flicking is also relatively precise for scrolling to targets already onscreen, but pressing arrow buttons on the scrollbar is the most accurate for scrolling to nearby targets. Mathematical models of scrolling are highly linear when the target position is unknown but like Fitts' law when known.

cs.HC↗

arXiv2022

LAMOST meets Gaia: The Galactic Open Clusters

Open Clusters are born and evolve along the Milky Way plane, on them is imprinted the history of the Galactic disc, including the chemical and dynamical evolution. Chemical and dynamical properties of open clusters can be derived from photometric, spectroscopic, and astrometric data of their member stars. Based on the photometric and astrometric data from the Gaia mission, the membership of stars in more than 2000 Galactic clusters has been identified in the literature. The chemical and kinematical properties, however, are still poorly known for many of these clusters. In synergy with the large spectroscopic survey LAMOST (data release 8) and Gaia (data release 2), we report a new comprehensive catalogue of 386 open clusters. This catalogue has homogeneous parameter determinations of radial velocity, metallicity, and dynamical properties, such as orbit, eccentricity, angular momenta, total energy, and 3D Galactic velocity. These parameters allow the first radial velocity determination and the first spectroscopic [Fe/H] determination for 44 and 137 clusters, respectively. The metallicity distribution of majority clusters shows falling trends in the parameter space of the Galactocentric radius, the total energy, and the Z component of angular momentum -- except for two old groups that show flat tails in their own parameter planes. Cluster populations of ages younger and older than 500 Myrs distribute diversely on the disc. The latter has a spatial consistency with the Galactic disc flare. The 3-D spatial comparison between very young clusters (< 100 Myr) and nearby molecular clouds revealed a wide range of metallicity distribution along the Radcliffe gas cloud wave, indicating a possible inhomogeneous mixing or fast star formation along the wave. This catalogue would serve the community as a useful tool to trace the chemical and dynamical evolution of the Milky Way.

astro-ph.GA↗

arXiv2022

Li-rich Giants in LAMOST Survey. III. The statistical analysis of Li-rich giants

The puzzle of Li-rich giant is still unsolved, contradicting the prediction of the standard stellar models. Although the exact evolutionary stages play a key role in the knowledge of Li-rich giants, a limited number of Li-rich giants have been taken with high-quality asteroseismic parameters to clearly distinguish the stellar evolutionary stages. Based on the LAMOST Data Release 7 (DR7), we applied a data-driven neural network method to derive the parameters for giant stars, which contain the largest number of Li-rich giants. The red giant stars are classified into three stages of Red Giant Branch (RGB), Primary Red Clump (PRC), and Secondary Red Clump (SRC) relying on the estimated asteroseismic parameters. In the statistical analysis of the properties (i.e. stellar mass, carbon, nitrogen, Li-rich distribution, and frequency) of Li-rich giants, we found that: (1) Most of the Li-rich RGB stars are suggested to be the descendants of Li-rich pre-RGB stars and/or the result of engulfment of planet or substellar companions; (2) The massive Li-rich SRC stars could be the natural consequence of Li depletion from the high-mass Li-rich RGB stars. (3) Internal mixing processes near the helium flash can account for the phenomenon of Li-rich on PRC that dominated the Li-rich giants. Based on the comparison of [C/N] distributions between Li-rich and normal PRC stars, the Li-enriched processes probably depend on the stellar mass.

astro-ph.SR↗

arXiv2022

Play Like the Pros? Solving the Game of Darts as a Dynamic Zero-Sum Game

The game of darts has enjoyed great growth over the past decade with the perception of darts moving from that of a pub game to a game that is regularly scheduled on prime-time television in many countries including the U.K., Germany, the Netherlands, and Australia among others. The game of darts involves strategic interactions between two players but to date the literature has ignored these interactions. In this paper, we formulate and solve the game of darts as a dynamic zero-sum-game (ZSG), and to the best of our knowledge we are the first to do so. We also estimate individual skill models using a novel data-set based on darts matches that were played by the top 16 professional players in the world during the 2019 season. Using the fitted skill models and our ZSG problem formulation, we quantify the importance of playing strategically, i.e. taking into account the score and strategy of one's opponent, when computing an optimal strategy. For top professionals we find that playing strategically results in an increase in win-probability of just 0.2% - 0.6% over a single leg but as much as 2.3% over a best-of-35 legs match.

stat.AP↗

arXiv2022

Double-Barreled Question Detection at Momentive

Momentive offers solutions in market research, customer experience, and enterprise feedback. The technology is gleaned from the billions of real responses to questions asked on the platform. However, people may create biased questions. A double-barreled question (DBQ) is a common type of biased question that asks two aspects in one question. For example, "Do you agree with the statement: The food is yummy, and the service is great.". This DBQ confuses survey respondents because there are two parts in a question. DBQs impact both the survey respondents and the survey owners. Momentive aims to detect DBQs and recommend survey creators to make a change towards gathering high quality unbiased survey data. Previous research work has suggested detecting DBQs by checking the existence of grammatical conjunction. While this is a simple rule-based approach, this method is error-prone because conjunctions can also exist in properly constructed questions. We present an end-to-end machine learning approach for DBQ classification in this work. We handled this imbalanced data using active learning, and compared state-of-the-art embedding algorithms to transform text data into vectors. Furthermore, we proposed a model interpretation technique propagating the vector-level SHAP values to a SHAP value for each word in the questions. We concluded that the word2vec subword embedding with maximum pooling is the optimal word embedding representation in terms of precision and running time in the offline experiments using the survey data at Momentive. The A/B test and production metrics indicate that this model brings a positive change to the business. To the best of our knowledge, this is the first machine learning framework for DBQ detection, and it successfully differentiates Momentive from the competitors. We hope our work sheds light on machine learning approaches for bias question detection.

cs.CL↗

arXiv2022

A spatially dependent correction of Gaia EDR3 parallax zero-point offset based on 0.3 million LAMOST DR8 giant stars

We have studied the zero-point offset of Gaia early Data Release 3 (EDR3) parallaxes based on a sample of 0.3 million giant stars built from the LAMOST data with distance accuracy better than 8.5\%. The official parallax zero-point corrections largely reduce the global offset in the Gaia EDR3 parallaxes: the global parallax offsets are $-$27.9 $μ$as and $-$26.5 $μ$as (before correction) and $+$2.6 $μ$as and $+$2.9 $μ$as (after correction) for the five- and six-parameter solutions, respectively. The bias of the raw parallax measurements is significantly dependent on the $G$ magnitudes, spectral colors, and positions of stars. The official parallax zero-point corrections could reduce parallax bias patterns with $G$ magnitudes, while could not fully account the patterns in the spaces of the spectral colors and positions. In the current paper, a spatially dependent parallax zero-point correction model for Gaia EDR3 five-parameter solution in the LAMOST footprint is firstly provided with the advantage of huge number of stars in our sample.

astro-ph.SR↗

arXiv2022

The value-added catalogue for LAMOST DR8 low-resolution spectra

We present a value-added catalog containing stellar parameters estimated from 7.10 million low-resolution spectra for 5.16 million unique stars with spectral signal-to-noise ratios (SNRs) higher than 10 obtained by the Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST) Galactic spectroscopic surveys. The catalog presents values of stellar atmospheric parameters (effective temperature $T_{\mathrm{eff}}$, surface gravity $\log g$, metallicity [Fe/H]/[M/H]), $α$-element to metal abundance ratio [$α$/M], carbon and nitrogen to iron abundance ratios [C/Fe] and [N/Fe] and 14 bands' absolute magnitudes deduced from LAMOST spectra using the method of neural network. The spectro-photometric distances of those stars are also provided based on the distance modulus. For stars with spectral SNRs larger than 50, precisions of $T_{\mathrm{eff}}$, $\log g$, [Fe/H], [M/H], [C/Fe], [N/Fe] and [$α$/M] are 85\,K, 0.098\,dex, 0.05\,dex, 0.05\,dex, 0.052\,dex, 0.082\,dex and 0.027\,dex, respectively. The errors of 14 band's absolute magnitudes are 0.16--0.22\,mag for stars with spectral SNRs larger than 50. The spectro-photometric distance is accurate to 8.5\% for stars with spectral SNRs larger than 50, and is more accurate than geometrical distance for stars with distance larger than 2.0\,kpc. Our estimates of [Fe/H] are reliable down to [Fe/H] $\sim -3.5$\,dex, significantly better than previous results. The catalog provide 26,868 unique very metal poor star candidates ([Fe/H] $\leq -2.0$). The catalog would be a valuable data set to study the structure and evolution of the Galaxy, especially the solar-neighbourhood and the outer disc.

astro-ph.SR↗

arXiv2021

Beyond spectroscopy. I. Metallicities, distances, and age estimates for over twenty million stars from SMSS DR2 and Gaia EDR3

Accurate determinations of stellar parameters and distances for large complete samples of stars are keys for conducting detailed studies of the formation and evolution of our Galaxy. Here we present stellar atmospheric parameters ($T_{\rm eff}$, luminosity classifications, and [Fe/H]) estimates for some 24 million stars determined from the stellar colors of SMSS DR2 and Gaia EDR3, based on training datasets with available spectroscopic measurements from previous high/medium/low-resolution spectroscopic surveys. The number of stars with photometric-metallicity estimates is 4-5 times larger than that collected by the current largest spectroscopic survey to date - LAMOST - over the course of the past decade. External checks indicate that the precision of the photometric-metallicity estimates are quite high, comparable to or slightly better than that derived from spectroscopy, with typical values around 0.05-0.15dex for both dwarf and giant stars with [Fe/H]>$-$1.0, 0.10-0.20dex for giant stars with $-$2.0<[Fe/H]<$-$1.0. and 0.20-0.25dex for giant stars with [Fe/H]<$-$2.0, and include estimates for stars as metal-poor as [Fe/H]~$-$3.5, substantially lower than previous photometric techniques. Photometric-metallicity estimates are obtained for an unprecedented number of metal-poor stars, including a total of over three million metal-poor (MP; [Fe/H] <$-$1.0) stars, over half a million very metal-poor (VMP; [Fe/H]<$-$2.0) stars, and over 25,000 extremely metal-poor (EMP; [Fe/H]<$-$3.0) stars. Moreover, distances are determined for over 20 million stars in our sample. For the over 18 million sample stars with accurate Gaia parallaxes, stellar ages are estimated by comparing with theoretical isochrones. Astrometric information is provided for the stars in our catalog, along with radial velocities for ~10% of our sample stars, taken from completed/ongoing large-scale spectroscopic surveys.

astro-ph.SR↗

arXiv2021

Most "young" $α$-rich stars have high masses but are actually old

Recent observations have revealed a population of $α$-element abundances enhanced giant stars with unexpected high masses ($\gtrsim$1 $M_\odot$) from asteroseismic analysis and spectroscopy. Assuming single-star evolution, their masses imply young ages ($τ<6$Gyr) incompatible with the canonical Galactic chemical evolution scenario. Here we study the chemistry and kinematics of a large sample of such $α$-rich, high-mass red giant branch (RGB) stars drawn from the LAMOST spectroscopic surveys. Using LAMOST and Gaia, we found these stars share the same kinematics as the canonical high-$α$ old stellar population in the Galactic thick disk. The stellar abundances show that these high-$α$ massive stars have $α$- and iron-peak element abundances similar to those of the high-$α$ old thick disk stars. However, a portion of them exhibit higher [(N+C)/Fe] and [Ba/Fe] ratios, which implies they have gained C- and Ba-rich materials from extra sources, presumably asymptotic giant branch (AGB) companions. The results support the previous suggestion that these RGB stars are products of binary evolution. Their high masses thus mimic "young" single stars, yet in fact they belong to an intrinsic old stellar population. To fully explain the stellar abundance patterns of our sample stars, a variety of binary evolution channels, such as, main-sequence (MS) + RGB, MS + AGB, RGB + RGB and RGB + AGB, are required, pointing to diverse formation mechanisms of these seemly rejuvenated cannibals. With this larger sample, our results confirm earlier findings that most, if not all, $α$-rich stars in the Galactic disk seem to be old.

astro-ph.GA↗