相关论文: Learning Molecular Chirality via Chiral Determinan…
Deep learning has proven to be a suitable alternative to least-squares (LSQ) fitting for parameter estimation in various quantitative MRI (QMRI) models. However, current deep learning implementations are not robust to changes in MR…
We introduce chiral rotational spectroscopy: a new technique that enables the determination of the orientated optical activity pseudotensor components $B_{XX}$, $B_{YY}$ and $B_{ZZ}$ of chiral molecules, in a manner that reveals the…
Molecular chirality is a key design property for many technologies including bioresponsive imaging, circularly polarized light detection and emission, molecular motors and switches. Imaging and manipulating the primary steps of transient…
The search of chiral magnetic effect (CME) in heavy-ion collisions has attracted long-term attentions. Multiple observables have been proposed but all suffer from obstacles due to large background contaminations. In this Letter, we…
The observation of chirality is ubiquitous in nature. Contrary to intuition, the population of opposite chiralities is surprisingly asymmetric at fundamental levels. Examples range from parity violation in the subatomic weak force to the…
Metasurfaces, the two-dimensional analogues of metamaterials, are ideal platforms for sensing molecular chirality at the nanoscale, e.g. of inclusions of natural optically active molecules, as they offer large accessible areas (they are…
Despite the effectiveness of Convolutional Neural Networks (CNNs) for image classification, our understanding of the relationship between shape of convolution kernels and learned representations is limited. In this work, we explore and…
Controlling optical chirality at the subwavelength scales is essential for many applications of nanophotonic structures in polarization optics, sensing, and nonlinear photonics. Achieving a strong chiroptical response in planar dielectric…
Chirality in condensed matter is now a topic of the utmost importance because of its significant role in the understanding and mastering of a large variety of new fundamental physicals mechanisms. Versatile experimental approaches, capable…
Self-supervised learning (SSL) has emerged as a powerful paradigm for representation learning by optimizing geometric objectives, such as invariance to augmentations, variance preservation, and feature decorrelation, without requiring…
In this paper we investigate and compare different gradient algorithms designed for the domain expression of the shape derivative. Our main focus is to examine the usefulness of kernel reproducing Hilbert spaces for PDE constrained shape…
Accurate simulations of atomistic systems from first principles are limited by computational cost. In high-throughput settings, machine learning can reduce these costs significantly by accurately interpolating between reference…
Metric and kernel learning are important in several machine learning applications. However, most existing metric learning algorithms are limited to learning metrics over low-dimensional data, while existing kernel learning algorithms are…
A novel optical method for distinguishing chiral molecules is proposed and validated within a quantum simulator employing a trapped-ion qudit. This approach correlates the sign disparity of the dipole moment of chiral molecules with…
Chemical reaction networks (CRNs) formally model chemistry in a well-mixed solution. CRNs are widely used to describe information processing occurring in natural cellular regulatory networks, and with upcoming advances in synthetic biology,…
We develop a family of chiral measures to quantify the chirality of a distribution and assign it a handedness. Our measures are built using the tensorial moments of the distribution, which naturally encode its spatial character, not only…
Detecting and controlling the chirality of materials play an essential role in exploring nature, providing new avenues for material creation, discrimination, and manipulation. In such tasks, chiral reagents are essential in defining or…
Chirality is ubiquitous in nature and fundamental in science, from particle physics to metamaterials.The most established technique of chiral discrimination - photoabsorption circular dichroism - relies on the magnetic properties of a…
The predictive accuracy of Machine Learning (ML) models of molecular properties depends on the choice of the molecular representation. Based on the postulates of quantum mechanics, we introduce a hierarchy of representations which meet…
In this study, a scalable online kernel learning framework is proposed for estimating bidirectional causal effects in systems characterized by mutual dependence and heteroskedasticity. Traditional causal inference often focuses on…