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Physiological masking in the peripheral auditory system. iii. effect of varying test-click intensity.
Physiological masking in peripheral auditory system - effect of varying test-click intensity in cats
Observations on some experiments on visual masking.
Review and interpretation of experiments on masking of visual stimuli
Single unit analysis of backward visual masking and metacontrast in the cat lateral geniculate nucleus.
Single unit analysis of backward visual masking and metacontrast in lateral geniculate nucleus in cats
Astronomical Near-neighbor Detection with a Four-quadrant Phase Mask (FQPM) Coronagraph
Direct detection of planets around nearby stars requires the development of high-contrast imaging techniques, because of their very different respective fluxes. We thus investigated the innovative coronagraphic approach based on the use of a four-quadrant phase mask (FQPM). Simulations showed that, combined with high-level wavefront correction on an unobscured off-axis section of a large telescope, this method allows high-contrast imaging very close to stars, with detection capability superior to that of a traditional coronagraph. A FQPM instrument was thus built to test the feasibility of near-neighbor observations with our new off-axis approach on a ground-based telescope. In June 2005, we deployed our instrument to the Palomar 200-inch telescope, using existing facilities as much as possible for rapid implementation. In these initial observations, using data processing techniques specific to FQPM coronagraphs, we reached extinction levels of the order of 200:1. Here we discuss our simulations and on-sky results obtained so far.
An Off-Axis Four-Quadrant Phase Mask (FQPM) Coronagraph for Palomar: High-Contrast Near Bright Stars Imager
Direct detection of planets around nearby stars requires the development of high-contrast imaging techniques because of the high difference between their respective fluxes. This led us to test a new coronagraphic approach based on the use of phase mask instead of dark occulting ones. Combined with high-level wavefront correction on an unobscured off-axis section of a large telescope, this method allows imaging very close to the star. Calculations indicate that for a given ground-based on-axis telescope, use of such an off-axis coronagraph provides a near-neighbor detection capability superior to that of a traditional coronagraph utilizing the full telescope aperture. Setting up a laboratory experiment working in near infrared allowed us to demonstrate the principle of the method, and a rejection of 2000:1 has already been achieved.
Characterizing Fit Factor of a One Size Fits-Most Emergency Mask using Subjects with Smaller Neck Circumferences
The Emergency Mask (EM) is an integral part of fire response on ISS, Orion and Gateway. The EM is a hood type protection respirator sealing around the subject’s neck, with an additional nose cup covering the subject’s oral nasal region. When integrated with a pair of Fire Cartridges (FCs), this system can be used to provide safe breathing air in a post-fire environment. The initial EM was certified as a ‘one size fits most’, however a recent requirements scrub discovered that the neck circumferences of the smallest possible crew population were not included in the initial requirement set. The test described in this paper uses the same slightly modified industry standard respirator test called a ‘Fit Factor’ used to certify the existing EM for larger subjects. Tests were performed on eight test subjects with neck circumference smaller than the initial EM requirement to determine if the current EM provided adequate protection during post-fire scenarios. The test included subjects donning an EM and then performing a number of head movements in a tent filled with approximately 50,000 particles/cm3 of smoke particulate. During these motions, particulate measurements were obtained from within the EM and compared with particulate measurements in the surrounding environment to determine the protection the EM provided in a simulated post-fire environment. This paper provides descriptions of the test hardware, methodology and results, which have provided rationale to design and build an EM to accommodate subjects with smaller necks.
Is tokenization needed for masked particle modeling?
In this work, we significantly enhance masked particle modeling (MPM), a self-supervised learning scheme for constructing highly expressive representations of unordered sets relevant to developing foundation models for high-energy physics. In MPM, a model is trained to recover the missing elements of a set, a learning objective that requires no labels and can be applied directly to experimental data. We achieve significant performance improvements over previous work on MPM by addressing inefficiencies in the implementation and incorporating a more powerful decoder. We compare several pre-training tasks and introduce new reconstruction methods that utilize conditional generative models without data tokenization or discretization. We show that these new methods outperform the tokenized learning objective from the original MPM on a new test bed for foundation models for jets, which includes using a wide variety of downstream tasks relevant to jet physics, such as classification, secondary vertex finding, and track identification.
Particle trajectory representation learning with masked point modeling
Liquid argon time projection chambers (LArTPCs) offer millimeter-scale 3D images of particle trajectories, enabling precision studies of neutrino oscillation, detection of supernova and solar neutrinos, searches for exotic dark matter, and proton decay. Current approaches utilize supervised machine learning models, requiring extensive simulations of particle physics and detector response that can introduce bias. Self-supervised learning (SSL), a machine learning approach that learns useful representations of unlabeled data from the data itself, has significantly advanced how large datasets are utilized for representation learning; however, its potential for applications to sensory data in high precision particle physics experiments remains largely unexplored. We introduce the Point-based liquid argon masked autoencoder (PoLAr-MAE), a self-supervised framework that learns physically meaningful representations directly from unlabeled LArTPC images. PoLAr-MAE achieves remarkable data efficiency for a point-level segmentation task, outperforming fully supervised methods in low data regimes. Linear classifiers on model outputs demonstrate robust performance across multiple downstream tasks. Our results position sensor-level SSL as a practical foundation model strategy for LArTPCs.
Transformer Masked Autoencoders for RF Device Fingerprinting
Machine learning methods for RF device fingerprinting typically rely on CNN-based models. Transformer-based models have outperformed CNNs for modulation classification tasks, but there are few implementations for device fingerprinting. We train a transformer for device fingerprinting with the largest device count to date and explore several variations of the architecture. Additionally, we demonstrate that pre-training an RF transformer as a Masked Autoencoder improves classification accuracy, as has been observed for CNN fingerprinting models and vision transformers.
Make the Fastest Faster: Importance Mask Synthesis for Interactive Volume Visualization using Reconstruction Neural Networks
Visualizing a large-scale volumetric dataset with high resolution is challenging due to the substantial computational time and space complexity. Recent deep learning-based image inpainting methods significantly improve rendering latency by reconstructing a high-resolution image for visualization in constant time on GPU from a partially rendered image where only a portion of pixels go through the expensive rendering pipeline. However, existing solutions need to render every pixel of either a predefined regular sampling pattern or an irregular sample pattern predicted from a low-resolution image rendering. Both methods require a significant amount of expensive pixel-level rendering. In this work, we provide Importance Mask Learning (IML) and Synthesis (IMS) networks, which are the first attempts to directly synthesize important regions of the regular sampling pattern from the user’s view parameters, to further minimize the number of pixels to render by jointly considering the dataset, user behavior, and the downstream reconstruction neural network. Our solution is a unified framework to handle various types of inpainting methods through the proposed differentiable compaction/decompaction layers. Experiments show our method can further improve the overall rendering latency of state-of-the-art volume visualization methods using reconstruction neural network for free when rendering scientific volumetric datasets. Our method can also directly optimize the off-the-shelf pre-trained reconstruction neural networks without elongated retraining.
A Parameter-masked Mock Data Challenge for Beyond-two-point Galaxy Clustering Statistics
The past few years have seen the emergence of a wide array of novel techniques for analyzing high-precision data from upcoming galaxy surveys, which aim to extend the statistical analysis of galaxy clustering data beyond the linear regime and the canonical two-point (2pt) statistics. We test and benchmark some of these new techniques in a community data challenge named “Beyond-2pt,” initiated during the Aspen 2022 Summer Program “Large-Scale Structure Cosmology beyond 2-Point Statistics,” whose first round of results we present here. The challenge data set consists of high-precision mock galaxy catalogs for clustering in real space, in redshift space, and on a light cone. Participants in the challenge have developed end-to-end pipelines to analyze mock catalogs and extract unknown (“masked”) cosmological parameters of the underlying ΛCDM models with their methods. The methods represented are density-split clustering, nearest neighbor statistics, BACCO power spectrum emulator, void statistics, LEFTfield field-level inference using effective field theory (EFT), and joint power spectrum and bispectrum analyses using both EFT and simulation-based inference. In this work, we review the results of the challenge, focusing on problems solved, lessons learned, and future research needed to perfect the emerging beyond-2pt approaches. The unbiased parameter recovery demonstrated in this challenge by multiple statistics and the associated modeling and inference frameworks supports the credibility of cosmology constraints from these methods. The challenge data set is publicly available, and we welcome future submissions from methods that are not yet represented.
DOE TRACER CSAPR2 Cell Tracking Cloud Mask
C-Band Scanning ARM Precipitation Radar, CF/Radial Formatted, Quality Controlled Tobac Tracking Cloud Mask
Note on binaural masking-level differences at high frequencies.
Quantitative black box model for interpreting data on binaural masking level difference at high frequencies
Auditory masking - A study of its physiological mechanism and of correlations between physiological and psychological observations First quarterly progress report, period ending 30 Jun. 1965
Psychological and physiological study of auditory masking
Visually evoked response correlates of perceptual masking and enhancements.
Perceptual masking and enhancements of two flashes in evoked cortical potentials recorded by electroencephalography
Visually evoked response correlates of perceptual masking and enhancement.
Visually evoked cortical response correlates of perceptual masking and enhancement
Process facilitates photoresist mask alignment on SiC crystals
Growth of silicon dioxide on a silicon carbide crystal ensures proper orientation of photoresist masks on the crystals used for semiconductor devices. The crystal is heated in a water vapor-saturated gas to delineate p-n junctions that intersect the crystal surface.