Research works

Precision high-speed quantum logic with holes on a natural silicon foundry platform

Abstract Silicon spin qubits in gate-defined quantum dots leverage established semiconductor infrastructure and offer a scalable path toward transformative quantum technologies. Holes spins in silicon offer compact all-electrical control, whilst retaining the salient features of...

Atomistic machine learning with irreducible Cartesian natural tensors

Abstract Atomistic machine learning is a powerful tool for accurate and efficient investigation of material behavior at the atomic scale. While attempts have been made to construct models directly within Cartesian space, they face challenges in providing a systematic framework ba...

Effects of Internal Migration on the Life Satisfaction of Apprentices

Abstract Migration can have far-reaching implications for individuals’ life satisfaction (LS). Previous research has mostly examined how migration influences LS using broad samples of the adult population. Moreover, it has not sufficiently considered the influence of other import...

Properties and calculation of pythagorean triples

Pythagorean triples, which are sets of three natural numbers (a,b,c) satisfying the equation a2+b2=c2, are among the oldest and most fascinating subjects in number theory. This paper is motivated by the potential of Pythagorean triples as a vehicle for curriculum enrichment at va...

Improving simulation of Earth system variability through weakly coupled ocean data assimilation in E3SM

Abstract. Accurate initialization of ocean states is essential for skillful prediction of Earth system variability across seasonal-to-decadal timescales. In this study, we evaluate the impact of a newly developed four-dimensional ensemble variational (4DEnVar)-based weakly couple...

Testing Higgs CP Properties at the CEPC with an Additional ISR Parameter

We evaluate the experimental sensitivity to the CP -odd admixture in the standard Higgs boson for the process e + e − → HZ . The analysis is performed assuming the future lepton collider CEPC reference detector operating at $$\sqrt s $$ = 240 GeV with statistics of 5.6 ab –1 . Us...

The cognitive architecture of degraded speech intelligibility

Accurate speech understanding is crucial in everyday life. Yet, speech is often degraded by a variety of talker and environmental factors, which listeners must overcome. In this online experiment, we assess how degraded speech intelligibility is supported by “elemental” cognitive...

Unveiling the link between extreme precipitation events and flood disasters in China: from a 3D perspective

Abstract. Extreme precipitation events and their triggered flood disasters have received increasing attention owing to their severe threats to human lives and socioeconomic development. However, there is still a lack of research on their evolutionary characteristics and driving f...

Non-Gaussian Noise Magnetometry Using Local Spin Qubits

Atomic scale qubits, as may be realized in nitrogen vacancy (NV) centers in diamond, offer the opportunity to study magnetic field noise with nanometer scale spatial resolution. Using these spin qubits, one can learn a great deal about the magnetic-field noise correlations, and c...

Deep generative models for enhanced vitreous OCT imaging

Abstract The aim of the work is to evaluate deep learning (DL) models for enhancing vitreous optical coherence tomography (OCT) image quality and potentially reducing acquisition time. Conditional Denoising Diffusion Probabilistic Models (cDDPMs), Brownian Bridge Diffusion Models...

Stochastic Solutions to Abstract Telegraph-Type Equations Involving Fractional Dynamics

Abstract This paper investigates abstract integro-differential hyperbolic equations, focusing on the probabilistic representation of their solutions. Our analysis is based on fractional derivatives and non-local operators, which are powerful tools for modeling anomalous behavior...

The interplay between Bayesian inference and conformal prediction

Conformal prediction (CP) has emerged as a cutting-edge methodology in statistics and machine learning, providing prediction intervals with finite-sample frequentist coverage guarantees. Yet, its interplay with Bayesian statistics-often criticized for lacking frequentist guarante...

Effective integration of Lie type algebras

Abstract This is a short survey on the recent developments made in the integration theory with effective formulas of algebraic structures stronger or higher than Lie algebras. This article is part of the theme issue ‘Derived Lie algebras in geometry and topology’.

Diminished positive affect in social anhedonia: linking anticipation and experience during dyadic interaction

Abstract Social anhedonia is a known risk factor for mental disorders such as depression and schizophrenia. Yet, it is unclear, whether social anhedonia actually encompasses diminished experience of pleasure during social interactions or an increased experience of negative affect...

Exploring Copper’s Role in Metabolic Modulation of Lachancea thermotolerans in Winemaking Conditions

The phenomenon of global warming has been demonstrated to induce the premature ripening of grapes, resulting in a concomitant increase in sugar and a decrease in acidity. This can result in the production of wines that are characterised by an imbalance in terms of alcohol content...

Hydrodynamic classification of a binary density feed with no by-pass

The hydrodynamic classification of two minerals of very different density, silica and hematite, was investigated using a modified REFLUX™ Classifier known as the GradePro™. The primary objective was to achieve efficient classification with no by-pass, meaning no entrainment of re...

Finetuning Foundation Models for Temporal Clinical Transcriptomics Data

BACKGROUND: Timeseries clinical transcriptomic datasets offer the opportunity to gain insights into the dynamics of disease mechanisms/treatment responses. However, their utility in uncovering temporal patterns is often limited by high noise levels and small sample sizes. Leverag...

Simple homotopy theory for Fukaya categories

Abstract We develop a categorical framework for simple homotopy theory in Fukaya categories, based on the fundamental group of the ambient symplectic manifold. When the first Chern class vanishes, we show that any isomorphism in the Fukaya category of a Weinstein manifold has tri...

Digitized counterdiabatic quantum feature extraction

We introduce a Hamiltonian-based quantum feature extraction method that generates complex features via the dynamics of $k$-local many-body spins Hamiltonians, enhancing machine learning performance. Classical feature vectors are embedded into spin-glass Hamiltonians, where both s...

Comparative study of ensemble-based uncertainty quantification methods for neural network interatomic potentials

Abstract Machine learning interatomic potentials (MLIPs) enable atomistic simulations with near first-principles accuracy at substantially reduced computational cost, making them powerful tools for large-scale materials modeling. The accuracy of MLIPs is typically validated on a...