arXiv subjects

Shuai Fu

Publications and source records attributed to Shuai Fu.

31 records · Page 2Linked to original sources

arXiv2024

Harnessing van der Waals CrPS4 and Surface Oxides for non-monotonic pre-set field induced Exchange Bias in Fe3GeTe2

Two-dimensional van der Waals (vdW) heterostructures are an attractive platform for studying exchange bias due to their defect free and atomically flat interfaces. Chromium thiophosphate (CrPS4), an antiferromagnetic material, possesses uncompensated magnetic spins in a single layer, rendering it a promising candidate for exploring exchange bias phenomena. Recent findings have highlighted that naturally oxidized vdW ferromagnetic Fe3GeTe2 exhibits exchange bias, attributed to the antiferromagnetic coupling of its ultrathin surface oxide layer (O-FGT) with the underlying unoxidized Fe3GeTe2. Anomalous Hall measurements are employed to scrutinize the exchange bias within the CrPS4/(O-FGT)/Fe3GeTe2 heterostructure. This analysis takes into account the contributions from both the perfectly uncompensated interfacial CrPS4 layer and the interfacial oxide layer. Intriguingly, a distinct and non-monotonic exchange bias trend is observed as a function of temperature below 140 K. The occurrence of exchange bias induced by a 'pre-set field' implies that the prevailing phase in the polycrystalline surface oxide is ferrimagnetic Fe3O4. Moreover, the exchange bias induced by the ferrimagnetic Fe3O4 is significantly modulated by the presence of the van der Waals antiferromagnetic CrPS4 layer, forming a heterostructure, along with additional iron oxide phases within the oxide layer. These findings underscore the intricate and complex nature of exchange bias in van der Waals heterostructures, highlighting their potential for tailored manipulation and control.

cond-mat.mtrl-sci↗

arXiv2023

Does Misclassifying Non-confounding Covariates as Confounders Affect the Causal Inference within the Potential Outcomes Framework?

The Potential Outcome Framework (POF) plays a prominent role in the field of causal inference. Most causal inference models based on the POF (CIMs-POF) are designed for eliminating confounding bias and default to an underlying assumption of Confounding Covariates. This assumption posits that the covariates consist solely of confounders. However, the assumption of Confounding Covariates is challenging to maintain in practice, particularly when dealing with high-dimensional covariates. While certain methods have been proposed to differentiate the distinct components of covariates prior to conducting causal inference, the consequences of treating non-confounding covariates as confounders remain unclear. This ambiguity poses a potential risk when conducting causal inference in practical scenarios. In this paper, we present a unified graphical framework for the CIMs-POF, which greatly enhances the comprehension of these models' underlying principles. Using this graphical framework, we quantitatively analyze the extent to which the inference performance of CIMs-POF is influenced when incorporating various types of non-confounding covariates, such as instrumental variables, mediators, colliders, and adjustment variables. The key findings are: in the task of eliminating confounding bias, the optimal scenario is for the covariates to exclusively encompass confounders; in the subsequent task of inferring counterfactual outcomes, the adjustment variables contribute to more accurate inferences. Furthermore, extensive experiments conducted on synthetic datasets consistently validate these theoretical conclusions.

stat.ME↗

arXiv2023

A Cu3BHT-Graphene van der Waals Heterostructure with Strong Interlayer Coupling

Two dimensional van der Waals heterostructures (2D are of significant interest due to their intriguing physical properties that are critically defined by the constituent monolayers and their interlayer coupling . However, typical inorganic 2 D vdWhs fall into the weakly coupled region, limiting efficient interfacial charge flow crucial for developing high performance quantum opto electronics. Here, we demonstrate strong interlayer coupling in Cu3 BHT (BHT = benzenehexathiol) graphene vdWhs an organic inorganic bilayer characterized by prominent interlayer charge transfer Monolayer Cu3 BHT with a Kagome lattice is synthesized on the water surface and then coupled with graphene to produce a cm2 scale 2D vdWh. Spectroscopic and electrical studies, along with theoretical calculation s show significant hole transfer from monolayer Cu3 BHT to graphene upon contact , being characteristic fingerprints for strong interlayer coupling This study unveils the great potential of integrating highly pi-conjugated 2D coordination polymers (2DCPs) into 2D vdWhs to explor e intriguing physical phenomena.

physics.app-ph↗

arXiv2023

ChameleonIDE: Untangling Type Errors Through Interactive Visualization and Exploration

Dynamically typed programming languages are popular in education and the software industry. While presenting a low barrier to entry, they suffer from run-time type errors and longer-term problems in code quality and maintainability. Statically typed languages, while showing strength in these aspects, lack in learnability and ease of use. In particular, fixing type errors poses challenges to both novice users and experts. Further, compiler-type error messages are presented in a static way that is biased toward the first occurrence of the error in the program code. To help users resolve such type errors, we introduce ChameleonIDE, a type debugging tool that presents type errors to the user in an unbiased way, allowing them to explore the full context of where the errors could occur. Programmers can interactively verify the steps of reasoning against their intention. Through three studies involving real programmers, we showed that ChameleonIDE is more effective in fixing type errors than traditional text-based error messages. This difference is more significant in harder tasks. Further, programmers actively using ChameleonIDE's interactive features are shown to be more efficient in fixing type errors than passively reading the type error output.

cs.HC↗

arXiv2022

First report of a solar energetic particle event observed by China's Tianwen-1 mission in transit to Mars

Solar energetic particles (SEPs) associated with flares and/or coronal mass ejection (CME)-driven shocks can impose acute radiation hazards to space explorations. To measure energetic particles in near-Mars space, the Mars Energetic Particle Analyzer (MEPA) instrument onboard China's Tianwen-1 (TW-1) mission was designed. Here, we report the first MEPA measurements of the widespread SEP event occurring on 29 November 2020 when TW-1 was in transit to Mars. This event occurred when TW-1 and Earth were magnetically well connected, known as the Hohmann-Parker effect, thus offering a rare opportunity to understand the underlying particle acceleration and transport process. Measurements from TW-1 and near-Earth spacecraft show similar double-power-law spectra and a radial dependence of the SEP peak intensities. Moreover, the decay phases of the time-intensity profiles at different locations clearly show the reservoir effect. We conclude that the double-power-law spectrum is likely generated at the acceleration site, and that a small but finite cross-field diffusion is crucial to understand the formation of the SEP reservoir phenomenon. These results provide insight into particle acceleration and transport associated with CME-driven shocks, which may contribute to the improvement of relevant physical models.

astro-ph.SR↗

arXiv2021

Comparison of Anomalous and Galactic Cosmic Ray Oxygen at 1 au during 1997-2020

Using quiet-time measurements of element oxygen within the energy range 7.3--237.9 MeV nuc$^{-1}$ from the ACE spacecraft at 1 au, we compare the energy spectra and intensities of anomalous and Galactic cosmic rays (ACRs and GCRs, respectively) during 1997--2020. Our analysis shows that the transition from ACR-dominated spectrum to GCR-dominated spectrum occurs at energies $\sim$15 to $\sim$35 MeV nuc$^{-1}$, and the transition energy $E_t$ is found to be well anticorrelated with varying solar activity. This is the first study of ACR-GCR transition energy dependence on the solar cycle variation. At energies below $E_t$, the index of the power-law ACR-dominated spectrum ($γ_1$) ranges from -2.0 to -0.5, whereas the GCR-dominated spectrum has a power-law index ($γ_2$) changing from 0.3 to 0.8 at energies ranging from $E_t$ to 237.9 MeV nuc$^{-1}$. Both $γ_1$ and $γ_2$ are positively correlated with solar activity. In addition, during the solar cycle 24/25 minimum period, the peak GCR intensity observed by ACE spacecraft is about 8\% above its 2009 value, setting a new record since the space age, while the peak ACR intensity is almost similar to that of the previous two solar cycles with the same pattern of solar magnetic polarity, indicating a different modulation mechanism between ACRs and GCRs.

astro-ph.HE↗

arXiv2021

Variations of the Galactic Cosmic Rays in the Recent Solar Cycles

In this paper, we study the galactic cosmic ray (GCR) variations over the solar cycles 23 and 24, with measurements from the NASA's ACE/CRIS instrument and the ground-based neutron monitors (NMs). The results show that the maximum GCR intensities of heavy nuclei (nuclear charge 5-28, 50-500 MeV/nuc) at 1 AU during the solar minimum in 2019-2020 break their previous records, exceeding those recorded in 1997 and 2009 by ~25% and ~6%, respectively, and are at the highest levels since the space age. However, the peak NM count rates are lower than those in late 2009. The difference between GCR intensities and NM count rates still remains to be explained. Furthermore, we find that the GCR modulation environment during the solar minimum P24/25 are significantly different from previous solar minima in several aspects, including remarkably low sunspot numbers, extremely low inclination of the heliospheric current sheet, rare coronal mass ejections, weak interplanetary magnetic field and turbulence. These changes are conducive to reduce the level of solar modulation, providing a plausible explanation for the record-breaking GCR intensities in interplanetary space.

astro-ph.SR↗

arXiv2020

Long-Lived Charge Separation Following Pump-Energy Dependent Ultrafast Charge Transfer in Graphene/WS$_2$ Heterostructures

Van der Waals heterostructures consisting of graphene and transition metal dichalcogenides (TMDCs) have recently shown great promise for high-performance optoelectronic applications. However, an in-depth understanding of the critical processes for device operation, namely interfacial charge transfer (CT) and recombination, has so far remained elusive. Here, we investigate these processes in graphene-WS$_2$ heterostructures, by complementarily probing the ultrafast terahertz photoconductivity in graphene and the transient absorption dynamics in WS$_2$ following photoexcitation. We find that CT across graphene-WS$_2$ interfaces occurs via photo-thermionic emission for sub-A-exciton excitation, and direct hole transfer from WS$_2$ to the valence band of graphene for above-A-exciton excitation. Remarkably, we observe that separated charges in the heterostructure following CT live extremely long: beyond 1 ns, in contrast to ~1 ps charge separation reported in previous studies. This leads to efficient photogating of graphene. These findings provide relevant insights to optimize further the performance of optoelectronic devices, in particular photodetection.

cond-mat.mtrl-sci↗

arXiv2020

An ACE/CRIS-observation-based Galactic Cosmic Rays heavy nuclei spectra model II

An observation-based Galactic Cosmic Ray (GCR) spectral model for heavy nuclei is developed. Zhao and Qin (J. Geophys. Res. Space Phys.118, 1837(2013)) proposed an empirical elemental GCR spectra model for nuclear charge 5-28 over the energy range from 30 to 500 MeV/nuc, which is proved to be successful in predicting yearly averaged GCR heavy nuclei spectra.Based on the latest highly statistically precise measurements from ACE/CRIS,a further elemental GCR model with monthly averaged spectra is presented. The model can reproduce the past and predict the futureGCR intensity monthly by correlating model parameters with thecontinuous sunspot number (SSN) record. The effects of solar activity on GCR modulation are considered separately for odd and even solar cycles. Compared with other comprehensive GCR models, our modeling results are satisfyingly consistent with the GCR spectral measurements from ACE/SIS and IMP-8, and have comparable prediction accuracy as the Badhwar & O'Neill 2014 model.A detailed error analysis is also provided.Finally, the GCR carbon and iron nuclei fluxes for the subsequent two solar cycles (SC 25 and 26) are predicted and they show a potential trend in reduced flux amplitude, which is suspected to be relevant to possible weak solar cycles.

astro-ph.IM↗

arXiv2020

Modeling the 10 September 2017 solar energetic particle event using the iPATH model

On September 10 2017, a fast coronal mass ejection (CME) erupted from the active region (AR) 12673, leading to a ground level enhancement (GLE) event at Earth. Using the 2D improved Particle Acceleration and Transport in the Heliosphere (iPATH) model, we model the large solar energetic particle (SEP) event of 10 September 2017 observed at Earth, Mars and STEREO-A. Based on observational evidence,we assume that the CME-driven shock experienced a large lateral expansion shortly after the eruption, which is modelled by a double Gaussian velocity profile in this simulation. We use the in-situ shock arrival times and the observed CME speeds at multiple spacecraft near Earth and Mars as constraints to adjust the input model parameters. The modelled time intensity profiles and fluence for energetic protons are then compared with observations. Reasonable agreements with observations at Mars and STEREO-A are found. The simulated results at Earth differ from observations of GOES-15. Instead, the simulated results at a heliocentric longitude 20 degree west to Earth fit reasonably well with the GOES observation. This can be explained if the pre-event solar wind magnetic field at Earth is not described by a nominal Parker field. Our results suggest that a large lateral expansion of the CME-driven shock and a distorted interplanetary magnetic field due to previous events can be important in understanding this GLE event.

physics.space-ph↗

arXiv2019

Effect of Star Rotation Rates on the Characteristics of Energetic Particle Events

Recent detection of superflares on solar-type stars by Kepler mission raised a possibility that they can be associated with energetic coronal mass ejections (CMEs) and energetic particle events (SEPs). These space weather events can impact habitability of exoplanets around these stars. Here we use the improved Particle Acceleration and Transport in the Heliosphere (iPATH) model, to model the time intensity profile and spectrum of SEPs accelerated at CME-driven shocks from stars of different ages traced by their rotation rates. We consider a solar-like (G-type) star with 6 different rotation rates varying from 0.5 to 3.0 times rotation speed of the sun. In all 6 cases, a fast CME is launched with the same speed of 1500 km/sec and the resulting time intensity profiles at 3 locations and and energy spectra at 5 locations at 1 AU are obtained. The maximum particle energy at the shock front as a function of r is also shown. Our results suggest that within 0.8 AU the maximum particle energy at the shock front increases with the rotation rate of the star. However, event integrated spectra for the five selected locations along the CME path show complicated patterns. This is because the Parker magnetic field for rapidly rotating stars is more tightly winded. Our results can be used in estimating the radiation environments of terrestrial-type exoplanets around solar-type stars.

astro-ph.SR↗

arXiv2015

An adaptive kriging method for solving nonlinear inverse statistical problems

In various industrial contexts, estimating the distribution of unobserved random vectors Xi from some noisy indirect observations H(Xi) + Ui is required. If the relation between Xi and the quantity H(Xi), measured with the error Ui, is implemented by a CPU-consuming computer model H, a major practical difficulty is to perform the statistical inference with a relatively small number of runs of H. Following Fu et al. (2014), a Bayesian statistical framework is considered to make use of possible prior knowledge on the parameters of the distribution of the Xi, which is assumed Gaussian. Moreover, a Markov Chain Monte Carlo (MCMC) algorithm is carried out to estimate their posterior distribution by replacing H by a kriging metamodel build from a limited number of simulated experiments. Two heuristics, involving two different criteria to be optimized, are proposed to sequentially design these computer experiments in the limits of a given computational budget. The first criterion is a Weighted Integrated Mean Square Error (WIMSE). The second one, called Expected Conditional Divergence (ECD), developed in the spirit of the Stepwise Uncertainty Reduction (SUR) criterion, is based on the discrepancy between two consecutive approximations of the target posterior distribution. Several numerical comparisons conducted over a toy example then a motivating real case-study show that such adaptive designs can significantly outperform the classical choice of a maximin Latin Hypercube Design (LHD) of experiments. Dealing with a major concern in hydraulic engineering, a particular emphasis is placed upon the prior elicitation of the case-study, highlighting the overall feasibility of the methodology. Faster convergences and manageability considerations lead to recommend the use of the ECD criterion in practical applications.

stat.ME↗

arXiv2012

Estimating Discrete Markov Models From Various Incomplete Data Schemes

The parameters of a discrete stationary Markov model are transition probabilities between states. Traditionally, data consist in sequences of observed states for a given number of individuals over the whole observation period. In such a case, the estimation of transition probabilities is straightforwardly made by counting one-step moves from a given state to another. In many real-life problems, however, the inference is much more difficult as state sequences are not fully observed, namely the state of each individual is known only for some given values of the time variable. A review of the problem is given, focusing on Monte Carlo Markov Chain (MCMC) algorithms to perform Bayesian inference and evaluate posterior distributions of the transition probabilities in this missing-data framework. Leaning on the dependence between the rows of the transition matrix, an adaptive MCMC mechanism accelerating the classical Metropolis-Hastings algorithm is then proposed and empirically studied.

stat.CO↗