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Abstract Plasmon polaritons, or plasmons, are coupled oscillations of electrons and electromagnetic fields that can confine the latter into deeply subwavelength scales, enabling novel polaritonic devices. While plasmons have been extensively studied in normal metals or semimetals, they remain largely unexplored in correlated materials. In this paper, we report infrared (IR) nano-imaging of thin flakes of CsV₃Sb₅, a prototypical layered Kagome metal. We observe propagating plasmon waves in real-space with wavelengths tunable by the flake thickness. From their frequency-momentum dispersion, we infer the out-of-plane dielectric function$${{{{{{\boldsymbol{\epsilon }}}}}}}_{{{{{{\boldsymbol{c}}}}}}}$$ ${ϵ}_c$that is generally difficult to obtain in conventional far-field optics, and elucidate signatures of electronic correlations when compared to density functional theory (DFT). We propose correlation effects might have switched the real part of$${{{{{{\boldsymbol{\epsilon }}}}}}}_{{{{{{\boldsymbol{c}}}}}}}$$ ${ϵ}_c$from negative to positive values over a wide range of middle-IR frequencies, transforming the surface plasmons into hyperbolic bulk plasmons, and have dramatically suppressed their dissipation. 

Rotating machines, such as pumps and compressors, are critical components in refineries and chemical plants used to transport fluids between processing units. Bearings are often the critical parts of rotating machinery, and their failure could result in economic loss and/or safety issues. Therefore, estimation of the remaining useful life (RUL) of a bearing plays an important role in reducing production losses and avoiding machine damage. Because bearing failure mechanisms tend to be complex and stochastic, data-driven RUL estimation approaches have found more applications. This work proposes a novel RUL estimation method based on systematic feature engineering and extreme learning machine (ELM). The PRONOSTIA dataset is used to demonstrate the effectiveness of the proposed method. 

We report the design and performance of a nonmagnetic drift stable optically pumped cesium magnetometer with a measured sensitivity of 35 fT at 200 s integration time and stability below 50 fT between 70 and 600 s. The sensor is based on the nonlinear magneto-optical rotation effect: in a Bell–Bloom configuration, a higher order polarization moment (alignment) of Cs atoms is created with a pump laser beam in an anti-relaxation coated Pyrex cell under vacuum, filled with Cs vapor at room temperature. The polarization plane of light passing through the cell is modulated due the precession of the atoms in an external magnetic field of 2.1 μT, used to optically determine the Larmor precession frequency. Operation is based on a sequence of optical pumping and observation of freely precessing spins at a repetition rate of 8 Hz. This free precession decay readout scheme separates optical pumping and probing and, thus, ensures a systematically highly clean measurement. Due to the residual offset of the sensor of <15 pT together with negligible crosstalk of adjacent sensors, this device is uniquely suitable for a variety of experiments in low-energy particle physics with extreme precision, here as a highly stable and systematically clean reference probe in search for time-reversal symmetry violating electric dipole moments. 

Electromigration (EM) analysis for complicated interconnects requires the solving of partial differential equations, which is expensive. In this paper, we propose a fast transient hydrostatic stress analysis for EM failure assessment for multi-segment interconnects using generative adversarial networks (GANs). Our work is inspired by the image synthesis and feature of generative deep neural networks. The stress evaluation of multi-segment interconnects, modeled by partial differential equations, can be viewed as time-varying 2D-images-to-image problem where the input is the multi-segment interconnects topology with current densities and the output is the EM stress distribution in those wire segments at the given aging time. We show that the conditional GAN can be exploited to attend the temporal dynamics for modeling the time-varying dynamic systems like stress evolution over time. The resulting algorithm, called {\it EM-GAN}, can quickly give accurate stress distribution of a general multi-segment wire tree for a given aging time, which is important for full-chip fast EM failure assessment. Our experimental results show that the EM-GAN shows 6.6\% averaged error compared to COMSOL simulation results with orders of magnitude speedup. It also delivers $$8.3 \times$$ speedup over state-of-the-art analytic based EM analysis solver.