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We apply semantic boosting to the Segment Anything Model (SAM) to obtain microstructure segmentation for transmission electron microscopy. Our booster, SAM-I-Am, extracts geometric and textural features of various intermediate masks to perform mask removal and mask merging operations
When using five climate model simulations of the response to an abrupt quadrupling of CO 2 , the authors perform the first simultaneous model intercomparison of cloud feedbacks and rapid radiative adjustments with cloud masking effects removed, partitioned among changes in cloud types and gross cloud properties. After CO 2 quadrupling, clouds exhibit a rapid reduction in fractional coverage, cloud-top pressure, and optical depth, with each contributing equally to a 1.1 W m -2 net cloud radiative adjustment, primarily from shortwave radiation. Rapid reductions in midlevel clouds and optically thick clouds are important in reducing planetary albedo in every model. As the planet warms, clouds become fewer, higher, and thicker, and global mean net cloud feedback is positive in all but one model and results primarily from increased trapping of longwave radiation. As was true for earlier models, high cloud changes are the largest contributor to intermodel spread in longwave and shortwave cloud feedbacks, but low cloud changes are the largest contributor to the mean and spread in net cloud feedback. The importance of the negative optical depth feedback relative to the amount feedback at high latitudes is even more marked than in earlier models. Furthermore, the authors show that the negative longwave cloud adjustment inferred in previous studies is primarily caused by a 1.3 W m -2 cloud masking of CO 2 forcing. Properly accounting for cloud masking increases net cloud feedback by 0.3 W m -2 K -1 , whereas accounting for rapid adjustments reduces by 0.14 W m -2 K -1 the ensemble mean net cloud feedback through a combination of smaller positive cloud amount and altitude feedbacks and larger negative optical depth feedbacks.
Here, quantitative wavefront measurements are demonstrated using a Hartmann mask re-imaged onto a camera. The wavefront is reconstructed using standard algorithms applied to the difference of beamlet centroids determined from fluence distributions obtained for two different longitudinal locations of the mask. The wavefront of the optical wave in the object plane is measured independently of imaging-system collimation. Apodization obtained with spatially dithered distributions of small transparent or opaque pixels improves the measurement accuracy by reducing the spatial-frequency content of the mask holes. Simulations and experiments demonstrate the excellent accuracy of this diagnostic over a wide range of parameters, making it suitable, for example, to characterize laser systems.
Here, we report on the fabrication and the characterization of a wedged multilayer Laue lens for x-ray nanofocusing. The lens was fabricated using a sputtering deposition technique, in which a specially designed mask was employed to introduce a thickness gradient in the lateral direction of the multilayer. X-ray characterization shows an efficiency of 27% and a focus size of 26 nm at 14.6 keV, in a good agreement with theoretical calculations. Our results indicate that the desired wedging is achieved in the fabricated structure. Furthermore, we anticipate that continuous development on wedged MLLs will advance x-ray nanofocusing optics to new frontiers and enrich capabilities and opportunities for hard X-ray microscopy.
Chronic active multiple sclerosis (MS) lesions have a rim of activated microglia/macrophages (m/M) leading to ongoing tissue damage, and thus represent a potential treatment target. Activation of this innate immune response in MS has been visualized and quantified using PET imaging with [ 11C]-(R)-PK11195 (PK). Accurate identification of m/M activation in chronic MS lesions requires the sensitivity to detect lower levels of activity within a small tissue volume. We assessed the ability of kinetic modeling of PK PET data to detect m/M activity in different central nervous system (CNS) tissue regions of varying sizes and in chronic MS lesions. Ten patients with MS underwent a single brain MRI and two PK PET scans 2 hours apart. Volume of interest (VOI) masks were generated for the white matter (WM), cortical gray matter (CGM), and thalamus (TH). The distribution volume (VT) was calculated with the Logan graphical method (LGM-VT) utilizing an image-derived input function (IDIF). The binding potential (BPND) was calculated with the reference Logan graphical method (RLGM) utilizing a supervised clustering algorithm (SuperPK) to determine the non-specific binding region. Masks of varying volume were created in the CNS to assess the impact of region size on the various metrics among high and low uptake regions. Chronic MS lesions were also evaluated and individual lesion masks were generated. The highest PK uptake occurred the TH and lowest within the WM, as demonstrated by the mean time activity curves. In the TH, both reference and IDIF based methods resulted in estimates that did not significantly depend on VOI size. However, in the WM, the test-retest reliability of BPND was significantly lower in the smallest VOI, compared to the estimates of LGM-VT. These observations were consistent for all chronic MS lesions examined. In this study, we demonstrate that BPND and LGM-VT are both reliable for quantifying m/M activation in regions of high uptake, however with blood input function LGM-VT is preferred to assess longitudinal m/M activation in regions of relatively low uptake, such as chronic MS lesions.
This dataset provides the tabular summary of analysis of an alluvial floodplain reach of the East River, downstream of Gothic, CO near Crested Butte. The measurements include bank erosion and accretion rates, channel width, bank curvature, and the aspect/orientation of the river banks. The measurements were derived from binary masks of the location of the river channel from aerial and satellite imagery collected on seven dates over a sixty-year timespan: 1955, 1973, 1983, 1990, 2001, 2011, and 2015. The masks were analyzed using the Spatially Continuous Riverbank Erosion and Accretion Measurements (SCREAM) software detailed in Rowland et al. 2016. The masks used in this analysis can be found an accompanying dataset (DOI: ). Rates of change along the East River were measured over a total of 8-time intervals: 1955-1973; 1955-2015; 1973-1983; 1983-1990; 1990-2001; 2001-2011; 2001-2015; and 2011-2015.In files with “summary” in the name, the data is provided at a pixel level, where each mapped bank pixel has an associated erosion or accretion value, a channel width, a curvature value, and an aspect each river and time period will have an individual file. Files with “Segments” in name provide data that is averaged along segments of the rivers. These segments are approximately 10 channel widths in length. In addition to erosion and accretion rates, the segment-based results include area measurements of erosion and accretion, islands, and channels. The number of islands is also included.
Seismic waveform data are generally contaminated by noise from various sources. Suppressing this noise effectively so that the remaining signal of interest can be successfully exploited remains a fundamental problem for the seismological community. To date, the most common noise suppression methods have been based on frequency filtering. These methods, however, are less effective when the signal of interest and noise share similar frequency bands. Inspired by source separation studies in the field of music information retrieval (Jansson et al., 2017) and a recent study in seismology (Zhu et al., 2019), we implemented a seismic denoising method that uses a trained deep convolutional neural network (CNN) model to decompose an input waveform into a signal of interest and noise. In our approach, the CNN provides a signal mask and a noise mask for an input signal. The short-time Fourier transform (STFT) of the estimated signal is obtained by multiplying the signal mask with the STFT of the input signal. To build and test the denoiser, we used carefully compiled signal and noise datasets of seismograms recorded by the University of Utah Seismograph Stations network. Results of test runs involving more than 9000 constructed waveforms suggest that on average the denoiser improves the signal-to-noise ratios (SNRs) by ~5 db, and that most of the recovered signal waveforms have high similarity with respect to the target waveforms (average correlation coefficient of ~0.80) and suffer little distortion. Application to real data suggests that our denoiser achieves on average a factor of up to ~2-5 improvement in SNR over band-pass filtering and can suppress many types of noise that band-pass filtering cannot. For individual waveforms, the improvement can be as high as ~15 db.
River networks sustain life and landscapes by carrying and distributing water, sediment, and nutrients throughout ecosystems and communities. At the largest scale, river networks drain continents through tree-like tributary networks. At typically smaller scales, river deltas and braided rivers form loopy, complex distributary river networks via avulsions and bifurcations.In order to model flows through these networks or analyze network structure, the topology, or connectivity, of the network must be resolved. Additionally, morphologic properties of each river channel as well as the direction of flow through the channel inform how fluxes travel through the network’s channels. Riv Graphis a Python package that automates the extraction and characterization of river channel networks from a user-provided binary image, or mask, of a channel network (Fig. 1). Masks may be derived from (typically remotely-sensed) imagery, simulations, or even hand-drawn. RivGraph will create explicit representations of the channel network by resolving river centerlines as links, and junctions as nodes. Flow directions are solved for each link of the network without using auxiliary data, e.g., a digital elevation model (DEM). Morphologic properties are computed as well, including link lengths, widths, sinuosities, branching angles,and braiding indices. If provided,RivGraph will preserve georeferencing information of the mask and will export results as ESRI shapefiles, GeoJSONs, and GeoTIFFs for easy import into GIS software.RivGraph can also return extracted networks as networkx objects for convenient interfacing with the full-featured networkx package (Hagberg et al., 2008). Finally, RivGraph offers a suite of topologic metrics that were specifically designed for river channel network analysis (Tejedor et al., 2015b).
Future weak lensing surveys potentially hold the highest statistical power for constrain ing cosmological parameters compared to other cosmological probes. The statistical power of a weak lensing survey is determined by the sky coverage, the inverse of the noise in shear measurements, and the galaxy number density. The combination of the latter two factors is often expressed in terms of n eff – the “effective number density of galaxies used for weak lensing measurements”. In this work, we estimate n eff for the Large Synoptic Survey Telescope (LSST) project, the most powerful ground-based lensing survey planned for the next two decades. We investigate how the following factors affect the resulting n eff of the survey with detailed simulations: (1) survey time, (2) shear measurement algorithm, (3) algorithm for combining multiple exposures, (4) inclusion of data from multiple filter bands, (5) redshift distribution of the galaxies, and (6) masking and blending. For the first time, we quantify in a general weak lensing analysis pipeline the sensitivity of n eff to the above factors. We find that with current weak lensing algorithms, expected distributions of observing parameters, and all lensing data (r- and i-band, covering 18,000 degree 2 of sky) for LSST, n eff ≈ 37 arcmin -2 before considering blending and masking, n eff ≈ 31 arcmin -2 when rejecting seriously blended galaxies and n eff ≈ 26 arcmin -2 when considering an additional 15% loss of galaxies due to masking. With future improvements in weak lensing algorithms, these values could be expected to increase by up to 20%. Throughout the paper, we also stress the ways in which n eff depends on our ability to understand and control systematic effects in the measurements.
This project has developed components of a waferless, flexible, low-cost tandem multijunction III-V/Si microwire array solar cell technology which combines the efficiency of wafered III-V photovoltaic technologies with the process designed to meet the Sunshot object. The project focused on design of lattice-matched GaAsP/SiGe two junction cell design and lattice-mismatched GaInP/Si tandem cell design. Combined electromagnetic simulation/device physics models using realistic microwire tandem structures were developed that predict >22% conversion efficiency for known material parameters, such as tunnel junction structure, window layer structure, absorber lifetimes and optical absorption and these model indicate a clear path to 30% efficiency for high quality III-V heterostructures. SiGe microwire arrays were synthesized via Cu-catalyzed vapor-liquid-solid (VLS) growth with inexpensive chlorosilane and chlorogermance precursors in an atmospheric pressure reactor. SiGe alloy composition in microwires was found to be limited to a maximum of 12% Ge incorporation during chlorogermane growth, due to the melting of the alloy near the solidus composition. Lattice mismatched InGaP double heterostructures were grown by selective epitaxy with a thermal oxide mask on Si microwire substrates using metallorganic vapor phase epitaxy. Transmission electron microscopy (TEM) analysis confirms the growth of individual step graded layers and a high density of defects near the wire/III-V interface. Selective epitaxy was initiated with a low temperature nucleation scheme under “atomic layer epitaxy” or “flow mediated epitaxy” conditions whereby the Ga and P containing precursors are alternately introduced into the reactor to promote layer-bylayer growth. In parallel to our efforts on conformal GaInP heteroepitaxy on selectively masked Si microwires, we explored direct, axial growth of GaAs on Si wire arrays as another route to a tandem junction architecture. We proposed axial, lattice-mismatched growth of a GaAs segment that extrude out of a Si wire via a self-aligned SiO 2 hollow cylindrical mask. With this growth strategy, misfit dislocations that would normally form at the GaAs/Si interface during thin film epitaxy may bend over to and thus terminate at the sidewall of the SiO 2 tube. A reactive-ion etching technique was employed 1) to remove Si to form a hollow, self-aligned SiO 2 cylindrical tube as a growth template for GaAs epitaxy using a vertical, showerhead, low-pressure metal-organic chemical-vapor deposition reactor that was operated at 0.1 atm. Successful epitaxy of axial GaAs wires on non-polar, <111>-oriented Si wire substrates was found at temperatures of ~850C. This and the other III-V/Si heterojunction wire synthesis strategies described here are promising approaches to realize future III-V/Si tandem solar cell designs.
Sandia National Laboratories and the Department of Energy (DOE) have completed on a multi-year program to examine the effects of control theory on increasing power produced by resonant wave energy conversion (WEC) devices. The tank tests have been conducted at the Naval Surface Warfare Center Carderock Division (NSWCCD) Maneuvering and Sea Keeping Basin (MASK) in West Bethesda, MD. This report outlines the "MASK 3 " wave tank test within the Advanced WEC Dynamics and Controls (AWDC) project. This test represents the final test in the AWDC project. The focus of the MASK 3 test was to consider coordinated 3 -degree-of-freedom ( 3 DOF) control of a WEC in a realistic ocean environment. A key aspect of this test was the inclusion of a "self-tunine mechanism which uses an optimization algorithm to update controller gains based on a changing sea state. The successful implementation of the self-tuning mechanism is the last crucial step required for such a controller to be implemented in real ocean environments.
Highly flexible, electrically conductive freestanding graphene membranes hold great promise for vibration-based applications. This study focuses on their integration into mainstream semiconductor manufacturing methods. We designed a two-mask lithography process that creates an array of freestanding graphene-based variable capacitors on 100 mm silicon wafers. The first mask forms long trenches terminated by square wells featuring cone-shaped tips at their centers. The second mask fabricates metal traces from each tip to its contact pad along the trench and a second contact pad opposite the square well. A graphene membrane is then suspended over the square well to form a variable capacitor. The same capacitor structures were also built on 5 mm by 5 mm bare dies containing an integrated circuit underneath. We used atomic force microscopy, optical microscopy, and capacitance measurements in time to characterize the samples.
The increase in neutron flux at the Oak Ridge National Laboratory (ORNL) Spallation Neutron Source (SNS), currently operating at 2.0 MW proton beam power, has created new opportunities for higher-throughput neutron scattering experiments while also increasing the importance of minimizing background scattering and optimizing sample-environment operations. To address these challenges on the Backscattering Silicon Spectrometer (BA-SIS), several upgrades were developed and evaluated, including boron carbide (B₄C) masking for flat-plate sample containers, multi-cell sample holders used with a vertically translating sample stick, and an automated helium pump and purge (HPP) system for closed-cycle refrigerators. Neutron diffraction measurements demonstrate that B₄C masks reduce background scattering by 49–67%, outperforming both borated aluminum and boron nitride masks while introducing no additional Bragg reflections within the instrument’s accessible Q-range. Commissioning tests of a double-cell flat-plate sample contain-er showed no measurable crosstalk between adjacent sample compartments and con-firmed stable thermal performance, enabling multiple samples to be measured without re-peated temperature cycling. In addition, the automated HPP system provided reproducible sample-space gas handling with approximately ±1 mbar precision while reducing the need for operator intervention and supporting remote operation. Together, these developments improve signal-to-noise performance, increase experimental throughput, and enhance operational efficiency at BASIS, supporting the instrument’s continued operation under higher neutron flux conditions.
We present a new measurement of the Lyα forest power spectrum at 1.8 < z < 3.4 using 74 Keck/HIRES and VLT/UVES high-resolution, high-signal-to-noise-ratio quasar spectra. We developed a custom pipeline to measure the power spectrum and its uncertainty, which fully accounts for finite resolution and noise and corrects for the bias induced by masking missing data, damped Lyα absorption systems, and metal absorption lines. Our measurement results in unprecedented precision on the small-scale modes $k\gt 0.02\,{\rm{s}}\,{\mathrm{km}}^{-1}$, inaccessible to previous SDSS/BOSS analyses. It is well known that these high-k modes are highly sensitive to the thermal state of the intergalactic medium, but contamination by narrow metal lines is a significant concern. We quantify the effect of metals on the small-scale power and find a modest effect on modes with $k\lt 0.1\,{\rm{s}}\,{\mathrm{km}}^{-1}$. As a result, by masking metals and restricting to $k\lt 0.1\,{\rm{s}}\,{\mathrm{km}}^{-1}$, their impact is completely mitigated. We present an end-to-end Bayesian forward-modeling framework whereby mock spectra with the same noise, resolution, and masking as our data are generated from Lyα forest simulations. These mock spectra are used to build a custom emulator, enabling us to interpolate between a sparse grid of models and perform Markov chain Monte Carlo fits. Our results agree well with BOSS on scales $k\lt 0.02\,{\rm{s}}\,{\mathrm{km}}^{-1}$, where the measurements overlap. The combination of the percent-level low-k precision of BOSS with our 5%–15% high-k measurements results in a powerful new data set for precisely constraining the thermal history of the intergalactic medium, cosmological parameters, and the nature of dark matter. Lastly, the power spectra and their covariance matrices are provided as electronic tables.
Abstract. This study presents a method to identify and distinguish insects, clouds, and precipitation in 35 GHz (Ka-band) vertically pointing polarimetric radar Doppler velocity power spectra and then produce masks indicating the occurrence of hydrometeors (i.e., clouds or precipitation) and insects at each range gate. The polarimetric radar used in this study transmits a linear polarized wave and receives signals in collinear (CoPol) and cross-linear (XPol) polarized channels. The measured CoPol and XPol Doppler velocity spectra are used to calculate linear depolarization ratio (LDR) spectra. The insect–hydrometeor discrimination method uses CoPol and XPol spectral information in two separate algorithms with their spectral results merged and then filtered into single value products at each range gate. The first algorithm discriminates between insects and clouds in the CoPol Doppler velocity power spectra based on the spectra texture, or spectra roughness, which varies due to the scattering characteristics of insects vs. cloud particles. The second algorithm distinguishes insects from raindrops and ice particles by exploiting the larger Doppler velocity spectra LDR produced by asymmetric insects. Since XPol power return is always less than CoPol power return for the same target (i.e., insect or hydrometeor), fewer insects and hydrometeors are detected in the LDR algorithm than the CoPol algorithm, which drives the need for a CoPol based algorithm. After performing both CoPol and LDR detection algorithms, regions of insect and hydrometeor scattering from both algorithms are combined in the Doppler velocity spectra domain and then filtered to produce a binary hydrometeor mask indicating the occurrence of cloud, raindrops, or ice particles at each range gate. Forty-seven summertime days were processed with the insect–hydrometeor discrimination method using US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program Ka-band zenith pointing radar observations in northern Oklahoma, USA. For these 47 d, over 70 % of the hydrometeor mask column bottoms were within ±100 m of simultaneous ceilometer cloud base heights. All datasets and images are available to the public on the DOE ARM repository.
Optical device images the sun on a mask that transmits it or prevents its transmission to a photodetector behind the mask depending on image position on the mask. The device uses a pinhole as the image former to provide a large field of view and diffraction-limited resolution.
GPEDIT compares each character in data record to corresponding character in mask provided by user. Mask indicates that alphabetic or numeric character is allowed or that blank or punctuation mark is anticipated. Mask can also indicate that any data character other than some invalid character is allowed.
A modification of the V mask sequential control chart is proposed. In this modified scheme, a parabolic section is included in the mask to provide better performance when the process undergoes a large change in the mean from goal conditions. It is shown that the modified V mask can be implemented either in conventional graphic form, or in an algorithmic form suitable for a digital computer. Average run lengths are given for a typical range of circumstances. It also is shown that the conventional Shewhart chart is better than a sequential chart for the specific purpose of promptly detecting very large shifts of the mean from goal conditions.