In-situ Broadband Cryogenic Calibration for Two-port Superconducting Microwave Resonators

We introduce an improved microwave calibration method for use in a cryogenic environment, based on a traditional three-standard calibration, the Thru-Reflect-Line (TRL) calibration. The modified calibration method takes advantage of additional information from multiple measurements of an ensemble of realizations of a superconducting resonator, as a new pseudo-Open standard, to correct errors in the TRL calibration. We also demonstrate an experimental realization of this in-situ broadband cryogenic calibration system utilizing cryogenic switches. All calibration measurements are done in the same thermal cycle as the measurement of the resonator (requiring only an additional 20 minutes), thus avoiding 4 additional thermal cycles for traditional TRL calibration (which would require an additional 12 days). The experimental measurements on a wave-chaotic microwave billiard verify that the new method significantly improves the measured scattering matrix of a high-quality-factor superconducting resonator.Comment: 9 pages, 8 figure

A first-principles model of time-dependent variations in transmission through a fluctuating scattering environment

Fading is the time-dependent variation in transmitted signal strength through a complex medium, due to interference or temporally evolving multipath scattering. In this paper we use random matrix theory (RMT) to establish a first-principles model for fading, including both universal and non-universal effects. This model provides a more general understanding of the most common statistical models (Rayleigh fading and Rice fading) and provides a detailed physical basis for their parameters. We also report experimental tests on two ray-chaotic microwave cavities. The results show that our RMT model agrees with the Rayleigh/Rice models in the high loss regime, but there are strong deviations in low-loss systems where the RMT approach describes the data well.Comment: 4 pages, 2 figure

Hot electron heatsinks for microwave attenuators below 100 mK

We demonstrate improvements to the cooling power of broad bandwidth (10 GHz) microwave attenuators designed for operation at temperatures below 100 mK. By interleaving 9-$\mu$m thick conducting copper heatsinks in between 10-$\mu$m long, 70-nm thick resistive nichrome elements, the electrical heat generated in the nichrome elements is conducted more readily into the heatsinks, effectively decreasing the thermal resistance between the hot electrons and cold phonons. For a 20 dB attenuator mounted at 20 mK, a minimum noise temperature of $T_{n} \sim$ 50 mK was obtained for small dissipated powers ($P_d <$ 1 nW) in the attenuator. For higher dissipated powers we find $T_n \propto P_{d}^{(1/4.4)}$, with $P_{d} =$ 100 nW corresponding to a noise temperature of 90 mK. This is in good agreement with thermal modeling of the system and represents nearly a factor of 20 improvement in cooling power, or a factor of 1.8 reduction in $T_n$ for the same dissipated power, when compared to a previous design without interleaved heatsinks.Comment: 5 Pages, 3 figure

Predicting the statistics of wave transport through chaotic cavities by the Random Coupling Model: a review and recent progress

In this review, a model (the Random Coupling Model) that gives a statistical description of the coupling of radiation into and out of large enclosures through localized and/or distributed channels is presented. The Random Coupling Model combines both deterministic and statistical phenomena. The model makes use of wave chaos theory to extend the classical modal description of the cavity fields in the presence of boundaries that lead to chaotic ray trajectories. The model is based on a clear separation between the universal statistical behavior of the isolated chaotic system, and the deterministic coupling channel characteristics. Moreover, the ability of the random coupling model to describe interconnected cavities, aperture coupling, and the effects of short ray trajectories is discussed. A relation between the random coupling model and other formulations adopted in acoustics, optics, and statistical electromagnetics, is examined. In particular, a rigorous analogy of the random coupling model with the Statistical Energy Analysis used in acoustics is presented.Comment: 32 pages, 9 figures, submitted to 'Wave Motion', special issue 'Innovations in Wave Model

Experimental Examination of the Effect of Short Ray Trajectories in Two-port Wave-Chaotic Scattering Systems

Predicting the statistics of realistic wave-chaotic scattering systems requires, in addition to random matrix theory, introduction of system-specific information. This paper investigates experimentally one aspect of system-specific behavior, namely the effects of short ray trajectories in wave-chaotic systems open to outside scattering channels. In particular, we consider ray trajectories of limited length that enter a scattering region through a channel (port) and subsequently exit through a channel (port). We show that a suitably averaged value of the impedance can be computed from these trajectories and that this can improve the ability to describe the statistical properties of the scattering systems. We illustrate and test these points through experiments on a realistic two-port microwave scattering billiard.Comment: 14 pages, 9 figure

Updates in Molecular Profiling of Pancreatic Ductal Adenocarcinoma

Outcomes from pancreatic ductal adenocarcinoma (PDAC) remain poor and better methods of prognostication and therapeutic approaches are needed. Recent advances in cancer genomics have led to the development of molecular subtypes of PDAC associated with clinical outcomes. Current evidence also suggests that the subtypes have differential response to first-line chemotherapy regimens. PDAC is also characterized by different stroma and immune environments. Further work is needed to confirm the utility of these subtypes to predicting response to different systemic therapies

Learning Deep Latent Spaces for Multi-Label Classification

Multi-label classification is a practical yet challenging task in machine learning related fields, since it requires the prediction of more than one label category for each input instance. We propose a novel deep neural networks (DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this task. Aiming at better relating feature and label domain data for improved classification, we uniquely perform joint feature and label embedding by deriving a deep latent space, followed by the introduction of label-correlation sensitive loss function for recovering the predicted label outputs. Our C2AE is achieved by integrating the DNN architectures of canonical correlation analysis and autoencoder, which allows end-to-end learning and prediction with the ability to exploit label dependency. Moreover, our C2AE can be easily extended to address the learning problem with missing labels. Our experiments on multiple datasets with different scales confirm the effectiveness and robustness of our proposed method, which is shown to perform favorably against state-of-the-art methods for multi-label classification.Comment: published in AAAI-201