Learning Discrete Weights and Activations Using the Local Reparameterization Trick

In computer vision and machine learning, a crucial challenge is to lower the computation and memory demands for neural network inference. A commonplace solution to address this challenge is through the use of binarization. By binarizing the network weights and activations, one can significantly reduce computational complexity by substituting the computationally expensive floating operations with faster bitwise operations. This leads to a more efficient neural network inference that can be deployed on low-resource devices. In this work, we extend previous approaches that trained networks with discrete weights using the local reparameterization trick to also allow for discrete activations. The original approach optimized a distribution over the discrete weights and uses the central limit theorem to approximate the pre-activation with a continuous Gaussian distribution. Here we show that the probabilistic modeling can also allow effective training of networks with discrete activation as well. This further reduces runtime and memory footprint at inference time with state-of-the-art results for networks with binary activations

Raman Spectra of Titanium Carbide MXene from Machine-Learning Force Field Molecular Dynamics

MXenes represent one of the largest class of 2D materials with promising applications in many fields and their properties tunable by the surface group composition. Raman spectroscopy is expected to yield rich information about the surface composition, but the interpretation of measured spectra has proven challenging. The interpretation is usually done via comparison to simulated spectra, but there are large discrepancies between the experimental and earlier simulated spectra. In this work, we develop a computational approach to simulate Raman spectra of complex materials that combines machine-learning force-field molecular dynamics and reconstruction of Raman tensors via projection to pristine system modes. The approach can account for the effects of finite temperature, mixed surfaces, and disorder. We apply our approach to simulate Raman spectra of titanium carbide MXene and show that all these effects must be included in order to properly reproduce the experimental spectra, in particular the broad features. We discuss the origin of the peaks and how they evolve with surface composition, which can then be used to interpret experimental results

Shrinking the Quadratic Estimator

We study a regression characterization for the quadratic estimator of weak lensing, developed by Hu and Okamoto (2001,2002), for cosmic microwave background observations. This characterization motivates a modification of the quadratic estimator by an adaptive Wiener filter which uses the robust Bayesian techniques described in Strawderman (1971) and Berger (1980). This technique requires the user to propose a fiducial model for the spectral density of the unknown lensing potential but the resulting estimator is developed to be robust to misspecification of this model. The role of the fiducial spectral density is to give the estimator superior statistical performance in a "neighborhood of the fiducial model" while controlling the statistical errors when the fiducial spectral density is drastically wrong. Our estimate also highlights some advantages provided by a Bayesian analysis of the quadratic estimator

On digital bioprocessing for manufacturing intelligence: Application of process analytical technology (PAT) and process data analytics (PDA) for upstream process development and intensification

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Controlled defect production in monolayer MoS2 via electron irradiation at ultralow accelerating voltages

Control on spatial location and density of defects in 2D materials can be achieved using electron beam irradiation. Conversely, ultralow accelerating voltages (less than or equal to 5kV) are used to measure surface morphology, with no expected defect creation. We find clear signatures of defect creation in monolayer (ML) MoS2 at these voltages. Evolution of E' and A1' Raman modes with electron dose, and appearance of defect activated peaks indicate defect formation. To simulate Raman spectra of MoS2 at realistic defect distributions, while retaining density-functional theory accuracy, we combine machine-learning force fields for phonons and eigenmode projection approach for Raman tensors. Simulated spectra agree with experiments, with sulphur vacancies as suggested defects. We decouple defects, doping and carbonaceous contamination using control (hBN covered and encapsulated MoS2) samples. We observe cryogenic PL quenching and defect peaks, and find that carbonaceous contamination does not affect defect creation. These studies have applications in photonics and quantum emitters.Comment: 35 pages, 19 figures, 4 table

Cataclysmic Variables from SDSS II. The Second Year

The first full year of operation following the commissioning year of the Sloan Digital Sky Survey has revealed a wide variety of newly discovered cataclysmic variables. We show the SDSS spectra of forty-two cataclysmic variables observed in 2002, of which thirty-five are new classifications, four are known dwarf novae (CT Hya, RZ Leo, T Leo and BZ UMa), one is a known CV identified from a previous quasar survey (Aqr1) and two are known ROSAT or FIRST discovered CVs (RX J09445+0357, FIRST J102347.6+003841). The SDSS positions, colors and spectra of all forty-two systems are presented. In addition, the results of follow-up studies of several of these objects identify the orbital periods, velocity curves and polarization that provide the system geometry and accretion properties. While most of the SDSS discovered systems are faint (>18th mag) with low accretion rates (as implied from their spectral characteristics), there are also a few bright objects which may have escaped previous surveys due to changes in the mass transfer rate.Comment: Accepted for publication in The Astronomical Journal, Vol. 126, Sep. 2003, 44 pages, 25 figures (now with adjacent captions), AASTeX v5.

Implementation of Clinical Practice Guidelines for Hospitalized Patients With COVID-19 in Academic Medical Centers

This survey study assesses the rate at which US academic medical centers have adopted evidenced-based guidelines for the management of COVID-19 into practice

A Mixed Blessing: Market-Mediated Religious Authority in Neopaganism

This research explores how marketplace dynamics affect religious authority in the context of Neopagan religion. Drawing on an interpretivist study of Wiccan practitioners in Italy, we reveal that engagement with the market may cause considerable, ongoing tensions, based on the inherent contradictions that are perceived to exist between spirituality and commercial gain. As a result, market success is a mixed blessing that can increase religious authority and influence, but is just as likely to decrease authority and credibility. Using an extended case study method, we propose a theoretical framework that depicts the links between our informants’ situated experiences and the macro-level factors affecting religious authority as it interacts with market-mediated dynamics at the global level. Overall, our study extends previous work in macromarketing that has looked at religious authority in the marketplace) and how the processes of globalization are affecting religion