arXiv subjects

Tong Ma

Publications and source records attributed to Tong Ma.

6 recordsLinked to original sources

arXiv2026

Counterintuitive inverse superconducting transition beyond 4He-cooling limit

Thermally driven quantum-orders observed in exceptional instances may redefine the role of thermal-fluctuation from a source of decoherence to a resource for coherent-state engineering. While preliminary signs of counterintuitive temperature-rise-triggered superconductivity manifested in CeCu2Si2, ErRh4B4, Ho1.2Mo6S8 and (La,Ce)Al2, their critical-temperatures (Tc-inv) remain below Kelvin-range, precluding substantial applications. Here, we report field-modulated inverse-superconducting-transitions above 4He-cooling-limit in Eu-based infinite-layer nickelates (EuxNd1-xNiO2 and EuxPr1-xNiO2) grown on a substrate under both overdoped and underdoped regimes. Paradigmatically, superconductivity with zero-resistance is confined between Tc-inv (2.6-5.4 K) and another higher normal-Tc, rising and decreasing with applied magnetic-field, respectively. Starting from the resistive-state below Tc-inv, the inverse-superconducting-transition is driven by not only temperature-rising, but also current-density, while superconductivity further vanishes at higher temperature and current thresholds. The Kelvin-range inverse superconducting transition is plausibly explained by temperature-induced alternating dominance of effective magnetic-fields arising from Eu2+4f7 related compensations relative to the upper-critical-field. Furthermore, an extended-phenomenological-framework is also supported by reemerged superconductivity below 300 mK under magnetic-field, giving rise to an unprecedented temperature-induced reentrant superconductivity. Our findings establish magnetic-interaction-reconfigured high-Tc systems as fertile platforms for exploring quantum phenomena that reverse thermal-decoherence paradigm, also enabling antithetical-designs to unlock untapped application-scenarios for quantum-phase-transition devices.

cond-mat.supr-con↗

arXiv2025

A chemical avenue to manipulate field-reentrant superconducting rivalries in infinite layer nickelates

Recently, preliminary magnetic field-reentrant superconductivity manifested in high-temperature (Tc) Eu-doped infinite-layer (IL) nickelates, beyond analogous discoveries exclusively in low-Tc systems. This evokes intriguing fundamental issues about potential quantum-phase boundary and criticality between unconventional superconductivity and field-reentrant-one, which are inexplicable owing to formidable challenges in growing IL-nickelates towards later-series rare-earths. Herein, we open up chemical avenues to enable effective growth of (RE1-yRE'y)1-xEuxNiO2 (RE/RE': Pr, Nd, Sm, Gd, Dy), giving rise to discoveries of RE-4f-related quantum competition between high-Tc and reentrant superconductivity. Robust magnetic-field-reentrant superconductivity with uniaxial anisotropy is observed at superconducting-dome boundaries, stemming from Eu2+-4f7 associated competition between magnetic-fluctuation promoted pairing and exchange-field interactions. Their quantum-criticality is further modulable via RE(RE')-magnetism, which either reinforces reentrancy or elevates Tc (40.1 K) with more robust critical-current-density (~266 kA/cm2 at 2 K) beyond Sr-/Ca-doped counterparts. Our synthetic route enables the establishment of an ideal platform via IL-nickelates for studying 4f-related unconventional superconductivity and quantum-criticality.

cond-mat.supr-con↗

arXiv2025

ATLAS: A Layered Constraint-Guided Framework for Structured Artifact Generation in LLM-Assisted MDE

ATLAS is a constraint-guided generation framework for structured engineering artifacts whose outputs must satisfy explicit schemas, domain rules, and audit requirements. Rather than treating a large language model as a standalone generator, ATLAS places generation inside a model-driven workflow that separates domain representation, constraint compilation, and post-generation validation. ATLAS combines three components. A metamodel-integration stage builds a typed representation of domain entities and relations; in this study, it operates over authoritative AUTOSAR meta-model assets. An Integrated Constraint Model (ICM) compiles heterogeneous requirements into two operational layers: generation-time structural constraints and post-generation semantic/logical obligations. Constraint-Guided, Validation-Backed Generation (CVG) then combines Layer~1 constrained decoding, Layer~2 backend validation, and audit-guided repair. In the AUTOSAR instantiation, these Layer~2 obligations are realized through SHACL/SMT-style checks, illustrating how the same ICM can be connected to domain-specific validation backends. We evaluate ATLAS on AUTOSAR artifact generation at both single-file and multi-file scales. In the evaluated AUTOSAR setting, ATLAS consistently produces schema-valid single-file outputs and preserves perfect file completeness and XSD validity at multi-file scale, while SHACL/SMT checks and result analysis continue to expose residual system-level defects. The empirical picture is therefore one of bounded automation: ATLAS secures structural validity and turns higher-level failures into explicit, diagnosable objects within the generation workflow.

cs.SE↗

arXiv2021

Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems

Time series forecasting remains a central challenge problem in almost all scientific disciplines. We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system using Dynamic Mode Decomposition (DMD) in time delay coordinates. Central to this approach is the insight that grid load, like many observables on complex real-world systems, has an "almost-periodic" character, i.e., a continuous Fourier spectrum punctuated by dominant peaks, which capture regular (e.g., daily or weekly) recurrences in the dynamics. The forecasting method presented takes advantage of this property by (i) regressing to a deterministic linear model whose eigenspectrum maps onto those peaks, and (ii) simultaneously learning a stochastic Gaussian process regression (GPR) process to actuate this system. Our forecasting algorithm is compared against state-of-the-art forecasting techniques not using additional explanatory variables and is shown to produce superior performance. Moreover, its use of linear intrinsic dynamics offers a number of desirable properties in terms of interpretability and parsimony. Results are presented for a test case using load data from an electrical grid. Load forecasting is an essential challenge in power systems engineering, with major implications for real-time control, pricing, maintenance, and security decisions.

physics.soc-ph↗

arXiv2020

Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids

Real-time state estimation and forecasting is critical for efficient operation of power grids. In this paper, a physics-informed Gaussian process regression (PhI-GPR) method is presented and used for probabilistic forecasting and estimating the phase angle, angular speed, and wind mechanical power of a three-generator power grid system using sparse measurements. In standard data-driven Gaussian process regression (GPR), parameterized models for the prior statistics are fit by maximizing the marginal likelihood of observed data, whereas in PhI-GPR, we compute the prior statistics by solving stochastic equations governing power grid dynamics. The short-term forecast of a power grid system dominated by wind generation is complicated by the stochastic nature of the wind and the resulting uncertain mechanical wind power. Here, we assume that the power-grid dynamic is governed by the swing equations, and we treat the unknown terms in the swing equations (specifically, the mechanical wind power) as random processes, which turns these equations into stochastic differential equations. We solve these equations for the mean and variance of the power grid system using the Monte Carlo simulations method. We demonstrate that the proposed PhI-GPR method can accurately forecast and estimate both observed and unobserved states, including the mean behavior and associated uncertainty. For observed states, we show that PhI-GPR provides a forecast comparable to the standard data-driven GPR, with both forecasts being significantly more accurate than the autoregressive integrated moving average (ARIMA) forecast. We also show that the ARIMA forecast is much more sensitive to observation frequency and measurement errors than the PhI-GPR forecast.

stat.ML↗

arXiv2019

Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression

We propose a new forecasting method for predicting load demand and generation scheduling. Accurate week-long forecasting of load demand and optimal power generation is critical for efficient operation of power grid systems. In this work, we use a synthetic data set describing a power grid with 700 buses and 134 generators over a 365-days period with data synthetically generated at an hourly rate. The proposed approach for week-long forecasting is based on the Gaussian process regression (GPR) method, with prior covariance matrices of the quantities of interest (QoI) computed from ensembles formed by up to twenty preceding weeks of QoI observations. Then, we use these covariances within the GPR framework to forecast the QoIs for the following week. We demonstrate that the the proposed ensemble GPR (EGPR) method is capable of accurately forecasting weekly total load demand and power generation profiles. The EGPR method is shown to outperform traditional forecasting methods including the standard GPR and autoregressive integrated moving average (ARIMA) methods.

cs.LG↗