Sewing states of quantum field theory. (arXiv:1911.11153v1 [hep-th])

Consider an $n$-partite system and denote by $\omega^{(i)}$ the local density matrix at site $A_i$. We say a pure $n$-partite state $|\Omega\rangle$ sews $\omega^{(i)}$ together if it reduces to $\omega^{(i)}$ on $A_i$ for all $i$. In finite quantum systems, density matrices can be sewn together only if their eigenvalues satisfy polygon inequalities. We show that…

Machine-learned metrics for predicting thelikelihood of success in materials discovery. (arXiv:1911.11201v1 [cond-mat.mtrl-sci])

Materials discovery is often compared to the challenge of finding a needle in a haystack. While much work has focused on accurately predicting the properties of candidate materials with machine learning (ML), which amounts to evaluating whether a given candidate is a piece of straw or a needle, less attention has been paid to a…

Energy-efficient stochastic computing with superparamagnetic tunnel junctions. (arXiv:1911.11204v1 [cs.ET])

Superparamagnetic tunnel junctions have emerged as a competitive, realistic nanotechnology to support novel forms of stochastic computation in CMOS-compatible platforms. One of their applications is to generate random bitstreams suitable for use in stochastic computing implementations. We describe a method for digitally programmable bitstream generation based on pre-charge sense amplifiers which is more energy efficient…

Measurements of Sub-Surface Velocity Fields in Faraday Flows. (arXiv:1911.11208v1 [physics.flu-dyn])

Faraday waves are capillary ripples that form on the surface of a fluid being subject to vertical shaking. Although it is well known that the form and shape of the waves pattern depends on driving amplitude and frequency, only recent studies discovered the existence of a horizontal velocity field at the surface, called Faraday flow,…

Machine Learning Enabled Lineshape Analysis in Optical Two-Dimensional Coherent Spectroscopy. (arXiv:1911.11215v1 [physics.optics])

Optical two-dimensional (2D) coherent spectroscopy excels in studying coupling and dynamics in complex systems. The dynamical information can be learned from lineshape analysis to extract the corresponding linewidth. However, it is usually challenging to fit a 2D spectrum, especially when the homogeneous and inhomogeneous linewidths are comparable. We implemented a machine learning algorithm to analyze…

A new set of cluster driven composite development indicators. (arXiv:1911.11226v1 [econ.GN])

Composite development indicators used in policy making often subjectively aggregate a restricted set of indicators. We show, using dimensionality reduction techniques, including Principal Component Analysis (PCA) and for the first time information filtering and hierarchical clustering, that these composite indicators miss key information on the relationship between different indicators. In particular, the grouping of indicators…

Diagnostics of ultra-intense laser pulses using tunneling ionization. (arXiv:1911.11233v1 [physics.atom-ph])

We revisit a recently proposed scheme [M.F. Ciappina et al 2019 Phys. Rev. A 99 043405] for accurate measurement of electromagnetic radiation intensities in a focus of high-power laser beams. The method is based on the observation of multiple sequential tunneling ionization of atoms and suggests the determination of the peak intensity value from the…

Identifying Model Weakness with Adversarial Examiner. (arXiv:1911.11230v1 [cs.CV])

Machine learning models are usually evaluated according to the average case performance on the test set. However, this is not always ideal, because in some sensitive domains (e.g. autonomous driving), it is the worst case performance that matters more. In this paper, we are interested in systematic exploration of the input data space to identify…

RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds. (arXiv:1911.11236v1 [cs.CV])

We study the problem of efficient semantic segmentation for large-scale 3D point clouds. By relying on expensive sampling techniques or computationally heavy pre/post-processing steps, most existing approaches are only able to be trained and operate over small-scale point clouds. In this paper, we introduce RandLA-Net, an efficient and lightweight neural architecture to directly infer per-point…