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Researcher profile

Jie Yu

Jie Yu contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
astro-ph.SRComputation and Languageastro-ph.GAastro-ph.EPcond-mat.mtrl-sciInformation RetrievalMachine Learningphysics.chem-phArtificial Intelligenceastro-ph.IMeess.SPmath.OCphysics.plasm-ph

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Published work

23 published item(s)

preprint2026arXiv

More Than Can Be Said: A Benchmark and Framework for Pre-Question Scientific Ideation

AI research agents have shown strong potential in automating literature search and manuscript refinement, yet most assume a clear and actionable initial input, operating only after a research question has been made explicit. In contrast, human research often begins with tacit friction, a sense of misalignment before a question can be formed. We introduce InciteResearch, a multi-agent framework designed to make a researcher's implicit understanding explicit, inspectable, and actionable. InciteResearch decomposes the logical chain of Socratic questioning and distributes it across the entire pipeline that: (1) Elicits a structured five-dimensional researcher profile state anchored by specific friction points from vague, even domain-unrelated inputs; (2) Violates hidden assumptions by maximizing the feasibility-novelty product with enforcing a 7-stage causal derivation trace; and (3) check whether the proposed method is a Necessary consequence of the reframed insight. We further introduce TF-Bench, the first benchmark for tacit-to-explicit research assistance that distinguishes domain-related from domain-unrelated inspirations across four scientific modes. On TF-Bench, InciteResearch achieves leapfrogging gains over a prompt-based baseline (novelty/impact from 3.671/3.806 to 4.250/4.397), shifting generated proposals from recombination to architectural insight. Our work demonstrates that AI can serve as an extension of thinking itself, rather than merely automating downstream execution.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Context-Aware Approach for Textual Adversarial Attack through Probability Difference Guided Beam Search

Textual adversarial attacks expose the vulnerabilities of text classifiers and can be used to improve their robustness. Existing context-aware methods solely consider the gold label probability and use the greedy search when searching an attack path, often limiting the attack efficiency. To tackle these issues, we propose PDBS, a context-aware textual adversarial attack model using Probability Difference guided Beam Search. The probability difference is an overall consideration of all class label probabilities, and PDBS uses it to guide the selection of attack paths. In addition, PDBS uses the beam search to find a successful attack path, thus avoiding suffering from limited search space. Extensive experiments and human evaluation demonstrate that PDBS outperforms previous best models in a series of evaluation metrics, especially bringing up to a +19.5% attack success rate. Ablation studies and qualitative analyses further confirm the efficiency of PDBS.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Two-Phase Paradigm for Joint Entity-Relation Extraction

An exhaustive study has been conducted to investigate span-based models for the joint entity and relation extraction task. However, these models sample a large number of negative entities and negative relations during the model training, which are essential but result in grossly imbalanced data distributions and in turn cause suboptimal model performance. In order to address the above issues, we propose a two-phase paradigm for the span-based joint entity and relation extraction, which involves classifying the entities and relations in the first phase, and predicting the types of these entities and relations in the second phase. The two-phase paradigm enables our model to significantly reduce the data distribution gap, including the gap between negative entities and other entities, as well as the gap between negative relations and other relations. In addition, we make the first attempt at combining entity type and entity distance as global features, which has proven effective, especially for the relation extraction. Experimental results on several datasets demonstrate that the spanbased joint extraction model augmented with the two-phase paradigm and the global features consistently outperforms previous state-of-the-art span-based models for the joint extraction task, establishing a new standard benchmark. Qualitative and quantitative analyses further validate the effectiveness the proposed paradigm and the global features.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Ages of Main-Sequence Turn-Off Stars from the GALAH Survey

Main sequence turn-off (MSTO) stars are good tracers of Galactic populations since their ages can be reliably estimated from atmospheric parameters. Based on the GALAH survey, we use the Yale Rotation Evolution Code to determine ages of 2926 MSTO stars with mean age uncertainty $\sim$10\% considering the variation of C and O abundances. Ages of CO-poor stars are systematically affected by $\sim$10\% due to the C and O abundances, globally shifting to $\sim$0.5 Gyr older compared to the results using solar metal-mixture. Of the stars with \mbox{[Fe/H] $\sim$0.3-0.5} or \mbox{[O/Fe]~$\leq$~-0.25}, many have fractional age differences~$\geq$~20\%, and even reach up to 36\%. The age-metallicity relation appears to possibly exist two distinct sequences: a young sequence of stars with age mostly $<$ 7 Gyr, and a relatively older sequence of stars with age mostly $>$ 7 Gyr, overlapping at 5 Gyr $\leq$~age~$\leq$ 7 Gyr. Moreover, the trends of abundances to age ratios show two corresponding sequences, especially in [O/Fe]-age plane. We also find that [Y/Mg] is a good chemical clock in disk populations. The young sequence and the old sequence can not be separated based on chemistry or kinematics, therefore stellar age is an important parameter to distinguish these two sequences in our sample.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Approximating Nash Equilibrium for Production Control with Sticky Price

We study a mean field game problem arising from the production control for multiple firms with price stickiness in the commodity market. The price dynamics for each firm is described as a (controlled) jump-diffusion process with mean-field interaction. Each firm aims to maximize her expectation of cumulative net profit coupled with each other through price processes. By solving the limiting control problem and a fixed-point problem, we construct an explicit approximating Nash equilibrium when the number of firms grows large.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Astroconformer: Inferring Surface Gravity of Stars from Stellar Light Curves with Transformer

We introduce Astroconformer, a Transformer-based model to analyze stellar light curves from the Kepler mission. We demonstrate that Astrconformer can robustly infer the stellar surface gravity as a supervised task. Importantly, as Transformer captures long-range information in the time series, it outperforms the state-of-the-art data-driven method in the field, and the critical role of self-attention is proved through ablation experiments. Furthermore, the attention map from Astroconformer exemplifies the long-range correlation information learned by the model, leading to a more interpretable deep learning approach for asteroseismology. Besides data from Kepler, we also show that the method can generalize to sparse cadence light curves from the Rubin Observatory, paving the way for the new era of asteroseismology, harnessing information from long-cadence ground-based observations.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Boosting Span-based Joint Entity and Relation Extraction via Squence Tagging Mechanism

Span-based joint extraction simultaneously conducts named entity recognition (NER) and relation extraction (RE) in text span form. Recent studies have shown that token labels can convey crucial task-specific information and enrich token semantics. However, as far as we know, due to completely abstain from sequence tagging mechanism, all prior span-based work fails to use token label in-formation. To solve this problem, we pro-pose Sequence Tagging enhanced Span-based Network (STSN), a span-based joint extrac-tion network that is enhanced by token BIO label information derived from sequence tag-ging based NER. By stacking multiple atten-tion layers in depth, we design a deep neu-ral architecture to build STSN, and each atten-tion layer consists of three basic attention units. The deep neural architecture first learns seman-tic representations for token labels and span-based joint extraction, and then constructs in-formation interactions between them, which also realizes bidirectional information interac-tions between span-based NER and RE. Fur-thermore, we extend the BIO tagging scheme to make STSN can extract overlapping en-tity. Experiments on three benchmark datasets show that our model consistently outperforms previous optimal models by a large margin, creating new state-of-the-art results.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypes

Few-shot named entity recognition (NER) enables us to build a NER system for a new domain using very few labeled examples. However, existing prototypical networks for this task suffer from roughly estimated label dependency and closely distributed prototypes, thus often causing misclassifications. To address the above issues, we propose EP-Net, an Entity-level Prototypical Network enhanced by dispersedly distributed prototypes. EP-Net builds entity-level prototypes and considers text spans to be candidate entities, so it no longer requires the label dependency. In addition, EP-Net trains the prototypes from scratch to distribute them dispersedly and aligns spans to prototypes in the embedding space using a space projection. Experimental results on two evaluation tasks and the Few-NERD settings demonstrate that EP-Net consistently outperforms the previous strong models in terms of overall performance. Extensive analyses further validate the effectiveness of EP-Net.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Incorporation of density scaling constraint in density functional design via contrastive representation learning

In a data-driven paradigm, machine learning (ML) is the central component for developing accurate and universal exchange-correlation (XC) functionals in density functional theory (DFT). It is well known that XC functionals must satisfy several exact conditions and physical constraints, such as density scaling, spin scaling, and derivative discontinuity. In this work, we demonstrate that contrastive learning is a computationally efficient and flexible method to incorporate a physical constraint in ML-based density functional design. We propose a schematic approach to incorporate the uniform density scaling property of electron density for exchange energies by adopting contrastive representation learning during the pretraining task. The pretrained hidden representation is transferred to the downstream task to predict the exchange energies calculated by DFT. The electron density encoder transferred from the pretraining task based on contrastive learning predicts exchange energies that satisfy the scaling property, while the model trained without using contrastive learning gives poor predictions for the scaling-transformed electron density systems. Furthermore, the model with pretrained encoder gives a satisfactory performance with only small fractions of the whole augmented dataset labeled, comparable to the model trained from scratch using the whole dataset. The results demonstrate that incorporating exact constraints through contrastive learning can enhance the understanding of density-energy mapping using neural network (NN) models with less data labeling, which will be beneficial to generalizing the application of NN-based XC functionals in a wide range of scenarios that are not always available experimentally but theoretically justified. This work represents a viable pathway toward the machine learning design of a universal density functional via representation learning.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Modeling Stellar Oscillations and Granulation in Radial Velocity Time Series: A Fourier-based Method

Tens of thousands of solar-like oscillating stars have been observed by space missions. Their photometric variability in the Fourier domain can be parameterized by a sum of two super-Lorentizian functions for granulation and a Gaussian-shaped power excess for oscillation. The photometric granulation/oscillation parameters scale with stellar parameters and they can also make predictions for corresponding parameters in radial velocity measurements. Based on scaling relations, we simulate realistic radial velocity time series and examine how the root-mean-square scatter of radial velocity measurements varies with stellar parameters and different observation strategies such as the length of integration time and gaps in the time series. Using stars with extensive spectroscopic observations from the spectrographs (SONG and HARPS), we measure the granulation amplitude and timescale from the power spectrum of the radial velocity time series. We compare these measurements with literature values based on Kepler photometry. We find that the granulation amplitude in radial velocity can be well predicted from the photometry and scaling relations. Both granulation timescales in radial velocity agree with those predicted from photometry for giants and sub-giants. However, for main-sequence stars, only one granulation timescale in radial velocity is in agreement with the photometric-based values, while the other timescale generally lies at lower frequencies compared to the result of photometry. In conclusion, we show the photometric scaling relations from Kepler photometry and the scaling relationship to Doppler observations can be very useful for predicting the photometric and radial velocity stellar variabilities due to stellar granulation and oscillation.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

SummScore: A Comprehensive Evaluation Metric for Summary Quality Based on Cross-Encoder

Text summarization models are often trained to produce summaries that meet human quality requirements. However, the existing evaluation metrics for summary text are only rough proxies for summary quality, suffering from low correlation with human scoring and inhibition of summary diversity. To solve these problems, we propose SummScore, a comprehensive metric for summary quality evaluation based on CrossEncoder. Firstly, by adopting the original-summary measurement mode and comparing the semantics of the original text, SummScore gets rid of the inhibition of summary diversity. With the help of the text-matching pre-training Cross-Encoder, SummScore can effectively capture the subtle differences between the semantics of summaries. Secondly, to improve the comprehensiveness and interpretability, SummScore consists of four fine-grained submodels, which measure Coherence, Consistency, Fluency, and Relevance separately. We use semi-supervised multi-rounds of training to improve the performance of our model on extremely limited annotated data. Extensive experiments show that SummScore significantly outperforms existing evaluation metrics in the above four dimensions in correlation with human scoring. We also provide the quality evaluation results of SummScore on 16 mainstream summarization models for later research.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Topic-Grained Text Representation-based Model for Document Retrieval

Document retrieval enables users to find their required documents accurately and quickly. To satisfy the requirement of retrieval efficiency, prevalent deep neural methods adopt a representation-based matching paradigm, which saves online matching time by pre-storing document representations offline. However, the above paradigm consumes vast local storage space, especially when storing the document as word-grained representations. To tackle this, we present TGTR, a Topic-Grained Text Representation-based Model for document retrieval. Following the representation-based matching paradigm, TGTR stores the document representations offline to ensure retrieval efficiency, whereas it significantly reduces the storage requirements by using novel topicgrained representations rather than traditional word-grained. Experimental results demonstrate that compared to word-grained baselines, TGTR is consistently competitive with them on TREC CAR and MS MARCO in terms of retrieval accuracy, but it requires less than 1/10 of the storage space required by them. Moreover, TGTR overwhelmingly surpasses global-grained baselines in terms of retrieval accuracy.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Wall stabilization of the rigid ballooning $m=1$ mode in a long-thin mirror trap

The prospect of stabilization of the $m=1$ ``rigid&#39;&#39; ballooning mode in an open axially symmetric long-thin trap with the help of a conducting lateral wall surrounding a column of isotropic plasma is studied. It is found that for effective wall stabilization, the beta parameter must exceed $70\%$. The dependence of the critical beta on the mirror ratio, the radial pressure profile, and the axial profile of the vacuum magnet has been studied. It is shown that when a conductive lateral wall is combined with conductive end plates simulating attachment of the end MHD stabilizers to the central cell of an open trap, there are two critical beta values and two stability zones that can merge, making stable the entire range of allowable beta values $0<β<1$.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Win-Win Cooperation: Bundling Sequence and Span Models for Named Entity Recognition

For Named Entity Recognition (NER), sequence labeling-based and span-based paradigms are quite different. Previous research has demonstrated that the two paradigms have clear complementary advantages, but few models have attempted to leverage these advantages in a single NER model as far as we know. In our previous work, we proposed a paradigm known as Bundling Learning (BL) to address the above problem. The BL paradigm bundles the two NER paradigms, enabling NER models to jointly tune their parameters by weighted summing each paradigm&#39;s training loss. However, three critical issues remain unresolved: When does BL work? Why does BL work? Can BL enhance the existing state-of-the-art (SOTA) NER models? To address the first two issues, we implement three NER models, involving a sequence labeling-based model--SeqNER, a span-based NER model--SpanNER, and BL-NER that bundles SeqNER and SpanNER together. We draw two conclusions regarding the two issues based on the experimental results on eleven NER datasets from five domains. We then apply BL to five existing SOTA NER models to investigate the third issue, consisting of three sequence labeling-based models and two span-based models. Experimental results indicate that BL consistently enhances their performance, suggesting that it is possible to construct a new SOTA NER system by incorporating BL into the current SOTA system. Moreover, we find that BL reduces both entity boundary and type prediction errors. In addition, we compare two commonly used labeling tagging methods as well as three types of span semantic representations.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Age Determination of Galaxy Merger Remnant Stars using Asteroseismology

The Milky Way was shaped by the mergers with several galaxies in the past. We search for remnant stars that were born in these foreign galaxies and assess their ages in an effort to put upper limits on the merger times and thereby better understand the evolutionary history of our Galaxy. Using 6D-phase space information from Gaia eDR3 and chemical information from APOGEE DR16, we kinematically and chemically select $23$ red giant stars belonging to former dwarf galaxies that merged with the Milky Way. With added asteroseismology from Kepler and K2, we determine the ages of the $23$ ex-situ stars and $55$ in-situ stars with great precision. We find that all the ex-situ stars are consistent with being older than $8$ Gyr. While it is not possible to associate all the stars with a specific dwarf galaxy we classify eight of them as Gaia-Enceladus/Sausage stars, which is one of the most massive mergers in our Galaxy&#39;s history. We determine their mean age to be $9.5^{+1.2}_{-1.3}$ Gyr consistent with a merger time of $8$-$10$ Gyr ago. The rest of the stars are possibly associated with Kraken, Thamnos, Sequoia, or another extragalactic progenitor. The age determination of ex-situ stars paves the way to more accurately pinning down when the merger events occurred and hence provide tight constraints useful for simulating how these events unfolded.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Asteroseismology of luminous red giants with Kepler I: Long Period Variables with radial and non-radial modes

While long period variables (LPVs) have been extensively investigated, especially with MACHO and OGLE data for the Magellanic Clouds, there still exist open questions in their pulsations regarding the excitation mechanisms, radial order and angular degree assignment. Here, we perform asteroseismic analyses on LPVs observed by the 4-year Kepler mission. Using a cross-correlation method, we detect unambiguous pulsation ridges associated with radial fundamental modes ($n=1$) and overtones ($n\geqslant2$), where the radial order assignment is made by using theoretical frequencies and observed frequencies. Our results confirm that the amplitude variability seen in semiregulars is consistent with oscillations being solar-like. We identify that the dipole modes, $l=1$, are dominant in the radial orders of $3\leq n \leq6$, and that quadrupole modes, $l=2$, are dominant in the first overtone $n=2$. A test of seismic scaling relations using Gaia DR2 parallaxes reveals the possibility that the relations break down when $ν_{\rm max}$ $\lesssim$ 3 $μ$Hz (R $\gtrsim$ 40 R$_{\odot}$, or log $\rm L/L_{\odot}$ $\gtrsim$ 2.6). Our homogeneous measurements of pulsation amplitude and period for 3213 LPVs will be very valuable for probing effects of pulsation on mass loss, in particular in those stars with periods around 60 days, which has been argued as a threshold of substantial pulsation-triggered mass loss.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Asteroseismology of luminous red giants with Kepler. II. Dependence of mass loss on pulsations and radiation

Mass loss by red giants is an important process to understand the final stages of stellar evolution and the chemical enrichment of the interstellar medium. Mass-loss rates are thought to be controlled by pulsation-enhanced dust-driven outflows. Here we investigate the relationships between mass loss, pulsations, and radiation, using 3213 luminous Kepler red giants and 135000 ASAS-SN semiregulars and Miras. Mass-loss rates are traced by infrared colours using 2MASS and WISE and by observed-to-model WISE fluxes, and are also estimated using dust mass-loss rates from literature assuming a typical gas-to-dust mass ratio of 400. To specify the pulsations, we extract the period and height of the highest peak in the power spectrum of oscillation. Absolute magnitudes are obtained from the 2MASS Ks band and the Gaia DR2 parallaxes. Our results follow. (i) Substantial mass loss sets in at pulsation periods above ~60 and ~100 days, corresponding to Asymptotic-Giant-Branch stars at the base of the period-luminosity sequences C&#39; and C. (ii) The mass-loss rate starts to rapidly increase in semiregulars for which the luminosity is just above the Red-Giant-Branch tip and gradually plateaus to a level similar to that of Miras. (iii) The mass-loss rates in Miras do not depend on luminosity, consistent with pulsation-enhanced dust-driven winds. (iv) The accumulated mass loss on the Red Giant Branch consistent with asteroseismic predictions reduces the masses of red-clump stars by 6.3%, less than the typical uncertainty on their asteroseismic masses. Thus mass loss is currently not a limitation of stellar age estimates for galactic archaeology studies.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

CardioLearn: A Cloud Deep Learning Service for Cardiac Disease Detection from Electrocardiogram

Electrocardiogram (ECG) is one of the most convenient and non-invasive tools for monitoring peoples&#39; heart condition, which can use for diagnosing a wide range of heart diseases, including Cardiac Arrhythmia, Acute Coronary Syndrome, et al. However, traditional ECG disease detection models show substantial rates of misdiagnosis due to the limitations of the abilities of extracted features. Recent deep learning methods have shown significant advantages, but they do not provide publicly available services for those who have no training data or computational resources. In this paper, we demonstrate our work on building, training, and serving such out-of-the-box cloud deep learning service for cardiac disease detection from ECG named CardioLearn. The analytic ability of any other ECG recording devices can be enhanced by connecting to the Internet and invoke our open API. As a practical example, we also design a portable smart hardware device along with an interactive mobile program, which can collect ECG and detect potential cardiac diseases anytime and anywhere.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Magnetic Activity of F-, G-, and K-type Stars in the LAMOST-Kepler Field

Monitoring chromospheric and photospheric indexes of magnetic activity can provide valuable information, especially the interaction between different parts of the atmosphere and their response to magnetic fields. We extract chromospheric indexes, S and Rhk+, for 59,816 stars from LAMOST spectra in the LAMOST-Kepler program, and photospheric index, Reff, for 5575 stars from Kepler light curves. The log Reff shows positive correlation with log Rhk+. We estimate the power-law indexes between Reff and Rhk+ for F-, G-, and K-type stars, respectively. We also confirm the dependence of both chromospheric and photospheric activity on stellar rotation. Ca II H and K emissions and photospheric variations generally decrease with increasing rotation periods for stars with rotation periods exceeding a few days. The power-law indexes in exponential decay regimes show different characteristics in the two activity-rotation relations. The updated largest sample including the activity proxies and reported rotation periods provides more information to understand the magnetic activity for cool stars.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Simple hydrogenic estimates for the exchange and correlation energies of atoms and atomic ions, with implications for density functional theory

Exact density functionals for the exchange and correlation energies are approximated in practical calculations for the ground-state electronic structure of a many-electron system. An important exact constraint for the construction of approximations is to recover the correct non-relativistic large-$Z$ expansions for the corresponding energies of neutral atoms with atomic number $Z$ and electron number $N=Z$, which are correct to leading order ($-0.221 Z^{5/3}$ and $-0.021 Z \ln Z$ respectively) even in the lowest-rung or local density approximation. We find that hydrogenic densities lead to $E_x(N,Z) \approx -0.354 N^{2/3} Z$ (as known before only for $Z \gg N \gg 1$) and $E_c \approx -0.02 N \ln N$. These asymptotic estimates are most correct for atomic ions with large $N$ and $Z \gg N$, but we find that they are qualitatively and semi-quantitatively correct even for small $N$ and for $N \approx Z$. The large-$N$ asymptotic behavior of the energy is pre-figured in small-$N$ atoms and atomic ions, supporting the argument that widely-predictive approximate density functionals should be designed to recover the correct asymptotics. It is shown that the exact Kohn-Sham correlation energy, when calculated from the pure ground-state wavefunction, should have no contribution proportional to $Z$ in the $Z\to \infty$ limit for any fixed $N$.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Solar-type Stars Observed by LAMOST and Kepler

Obtaining measurements of chromospheric and photometric activity of stars with near-solar fundamental parameters and rotation periods is important for a better understanding of solar-stellar connection. We select a sample of 2603 stars with near-solar fundamental parameters from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST)-Kepler field and use LAMOST spectra to measure their chromospheric activity and Kepler light curves to measure their photospheric activity (i.e., the amplitude of the photometric variability). While the rotation periods of 1556 of these stars could not be measured due to the low amplitude of the photometric variability and highly irregular temporal profile of light curves, 254 stars were further identified as having near-solar rotation periods. We show that stars with near-solar rotation periods have chromospheric activities that are systematically higher than stars with undetected rotation periods. Furthermore, while the solar level of photospheric and chromospheric activity appears to be typical for stars with undetected rotation periods, the Sun appears to be less active than most stars with near-solar rotation periods (both in terms of photospheric and chromospheric activity).

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Two-dimensional MX family of Dirac materials with tunable electronic and topological properties

We propose a novel class of two-dimensional (2D) Dirac materials in the MX family (M=Be, Mg, Zn and Cd, X = Cl, Br and I), which exhibit graphene-like band structures with linearly-dispersing Dirac-cone states over large energy scales (0.8~1.8 eV) and ultra-high Fermi velocities comparable to graphene. The electronic and topological properties are found to be highly tunable and amenable to effective modulation via anion-layer substitution and vertical electric field. The electronic structures of several members of the family are shown to host a Van-Hove singularity (VHS) close to the energy of the Dirac node. The enhanced density-of-states associated with these VHSs could provide a mechanism for inducing topological superconductivity. The presence of sizable band gaps, ultra-high carrier mobilities, and small effective masses makes the MX family promising for electronics and spintronics applications.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Very regular high-frequency pulsation modes in young intermediate-mass stars

Asteroseismology is a powerful tool for probing the internal structures of stars by using their natural pulsation frequencies. It relies on identifying sequences of pulsation modes that can be compared with theoretical models, which has been done successfully for many classes of pulsators, including low-mass solar-type stars, red giants, high-mass stars and white dwarfs. However, a large group of pulsating stars of intermediate mass--the so-called delta Scuti stars--have rich pulsation spectra for which systematic mode identification has not hitherto been possible. This arises because only a seemingly random subset of possible modes are excited, and because rapid rotation tends to spoil the regular patterns. Here we report the detection of remarkably regular sequences of high-frequency pulsation modes in 60 intermediate-mass main-sequence stars, allowing definitive mode identification. Some of these stars have space motions that indicate they are members of known associations of young stars, and modelling of their pulsation spectra confirms that these stars are indeed young.

0 citations0 reviewsNo code linkedOpen access