Associative recall in verbal-discrimination learning as related to pronunciation and degree of practice.
Associative recall in verbal-discrimination learning as related to pronunciation and degree of practice
PRACTICE↗
SEARCH · Engineering Papers
Results for “DISCRIMINATION”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
The discrimination of stimulus duration by pigeons.
Discrimination of visual stimulus duration by pigeons
VISUAL STIMULI↗
Human discrimination of visual direction of motion with and without smooth pursuit eye movements
It has long been known that ocular pursuit of a moving target has a major influence on its perceived speed (Aubert, 1886; Fleischl, 1882). However, little is known about the effect of smooth pursuit on the perception of target direction. Here we compare the precision of human visual-direction judgments under two oculomotor conditions (pursuit vs. fixation). We also examine the impact of stimulus duration (200 ms vs. ~800 ms) and absolute direction (cardinal vs. oblique). Our main finding is that direction discrimination thresholds in the fixation and pursuit conditions are indistinguishable. Furthermore, the two oculomotor conditions showed oblique effects of similar magnitudes. These data suggest that the neural direction signals supporting perception are the same with or without pursuit, despite remarkably different retinal stimulation. During fixation, the stimulus information is restricted to large, purely peripheral retinal motion, while during steady-state pursuit, the stimulus information consists of small, unreliable foveal retinal motion and a large efference-copy signal. A parsimonious explanation of our findings is that the signal limiting the precision of direction judgments is a neural estimate of target motion in head-centered (or world-centered) coordinates (i.e., a combined retinal and eye motion signal) as found in the medial superior temporal area (MST), and not simply an estimate of retinal motion as found in the middle temporal area (MT).
Saccades/physiology↗
Discriminability measures for predicting readability of text on textured backgrounds
Several discriminability measures were examined for their ability to predict reading search times for three levels of text contrast and a range of backgrounds (plain, a periodic texture, and four spatial-frequency-filtered textures created from the periodic texture). Search times indicate that these background variations only affect readability when the text contrast is low, and that spatial frequency content of the background affects readability. These results were not well predicted by the single variables of text contrast (Spearman rank correlation = -0.64) and background RMS contrast (0.08), but a global masking index and a spatial-frequency-selective masking index led to better predictions (-0.84 and -0.81, respectively). c2000 Optical Society of America.
NASA Discipline Space Human Factors↗
Image discrimination models predict detection in fixed but not random noise
By means of a two-interval forced-choice procedure, contrast detection thresholds for an aircraft positioned on a simulated airport runway scene were measured with fixed and random white-noise masks. The term fixed noise refers to a constant, or unchanging, noise pattern for each stimulus presentation. The random noise was either the same or different in the two intervals. Contrary to simple image discrimination model predictions, the same random noise condition produced greater masking than the fixed noise. This suggests that observers seem unable to hold a new noisy image for comparison. Also, performance appeared limited by internal process variability rather than by external noise variability, since similar masking was obtained for both random noise types.
NASA Center ARC↗
High Resolution Scatterometry by Simultaneous Range/Doppler Discrimination
Several spaceborne scatterometer missions have been successfully flown in the past decade. In this paper, the application of simultaneous range/Doppler discrimination techniques are proposed as way of improving the single-pass resolution of conically scaning pencil-beam scatterometer systems.
scatterometry↗
Linear Discriminant Analysis-Based Machine Learning and All-Atom Molecular Dynamics Simulations for Probing Electro-Osmotic Transport in Cationic-Polyelectrolyte-Brush-Grafted Nanochannels
Deciphering the correct mechanisms governing certain phenomena in polyelectrolyte (PE) brush grafted systems, revealed through atomistic simulations, is an extremely challenging problem. In a recent study, our all-atom molecular dynamics (MD) simulations revealed a non-linearly large electroosmotic (EOS) flow (in the presence of an applied electric field) in nanochannels grafted with PMETAC [Poly(2-(methacryloyloxy)ethyl trimethylammonium chloride] brushes. Given the lack of any formal procedure that would have directed us to identify the correct factors responsible for such an occurrence, we needed to spend several months and devote significant analyses to unravel the involved mechanisms. In this paper, we propose a Linear Discriminant Analysis (LDA) based Machine Learning (ML) approach to address this gap. At first, we obtain data on certain basic features from the all-atom MD data. These basic features represent the number of atoms of certain species around one atom of another (or same) species. Here, we obtain such data on basic features for a reference case (case of an EOS flow in PMETAC-brush-grafted nanochannels with a smaller electric field) and a perturbed case (case of an EOS flow in PMETAC-brush-grafted nanochannels with a larger electric field) in bins in which the nanochannel half height has been divided into. These datasets are high-dimensional dataset, to which the LDA is applied. This leads to the projection of the data (between the reference and the perturbed states) in a highly separated form on a 1D line. From such LDA calculations, we are able to identify the relative importance of the different basic features in ensuring this separation of the data (between the reference and the perturbed states) on the 1D line. This relative importance of the different basic features is quantified as “importance scores” for the different features, which in turn tell us what to study and where to study. Such knowledge enables us to rapidly identify the key factors responsible for the non-linearly large EOS transport in PMETAC-brush-grafted nanochannels.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Discrimination of Hexane Isomers by Temperature Swing Adsorption in a Rigid Aluminum Metal–Organic Framework
The efficient separation of alkane isomers with similar physicochemical properties remains a persistent challenge for the petrochemical industry. Adsorptive separation using metal− organic frameworks (MOFs) offers an energy-efficient alternative to conventional distillation. Herein, we report temperature swing discrimination of hexane isomers with different degrees of branching using MIL-120, a rigid aluminum pyromellitate-based MOF. MIL-120 features uniform one-dimensional channels with an aperture of ∼5.5 Å. At 30 °C, it selectively adsorbs linear and monobranched hexanes while excluding the dibranched isomer. Upon heating to 120 °C, both mono- and dibranched isomers are completely excluded, whereas linear hexane remains strongly adsorbed. Breakthrough experiments validate the temperature swing separation performance. Adsorption heat analysis combined with ab initio calculations provides a quantitative measure of distinct differences in adsorption enthalpies, binding energies, and diffusion barriers responsible for the observed separation efficiency, highlighting the potential of this MOF for efficient separation of alkane isomers via temperature swing adsorption.
Adsorption↗
Discriminative versus generative approaches to simulation-based inference
Most of the fundamental, emergent, and phenomenological parameters of particle and nuclear physics are determined through parametric template fits. Simulations are used to populate histograms which are then matched to data. This approach is inherently lossy, since histograms are binned and low-dimensional. Deep learning has enabled unbinned and high-dimensional parameter estimation through neural likelihood(-ratio) estimation. We compare two approaches for neural simulation-based inference (NSBI): one based on discriminative learning (classification) and one based on generative modeling. These two approaches are directly evaluated on the same datasets, with a similar level of hyperparameter optimization in both cases. In addition to a Gaussian dataset, we study NSBI using a Higgs boson dataset from the FAIR Universe Challenge. We find that both the direct likelihood and likelihood ratio estimation are able to effectively extract parameters with reasonable uncertainties. For the numerical examples and within the set of hyperparameters studied, we found that the likelihood ratio method is more accurate and/or precise. Both methods have a significant spread from the network training and would require ensembling or other mitigation strategies in practice.
high energy physics↗
Pulse Shape Discrimination in JSNS 2
JSNS 2 (J-PARC Sterile Neutrino Search at J-PARC Spallation Neutron Source) is an experiment that is searching for sterile neutrinos via the observation of $\overline{ν}$ μ → $\overline{ν}$ e appearance oscillations using neutrinos from muon decay-at-rest. For this search, rejecting cosmic-ray-induced neutron events by Pulse Shape Discrimination (PSD) is essential because the JSNS2 detector is located above ground, on the third floor of the building. We have achieved 94.95% ± 0.15% rejection of neutron events while keeping 92.82% ± 1.77% of signal, electron-like events using a data-driven likelihood method. This article will report the PSD technique using the full fiducial volume of the JSNS2 detector.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Prototype reactor-antineutrino detector based on 6 Li-doped pulse-shape-discriminating plastic scintillator
An aboveground 60-kg reactor-antineutrino detector prototype, comprising a two-dimensional array of 36 6 Li-doped pulse-shape-sensitive plastic scintillator bars, is described. Each bar is 50 cm long with a square cross section of 5.5 cm. Doped with 6 Li at 0.1% by mass, the detector is capable of identifying correlated energy depositions for the detection of reactor antineutrinos via the inverse-𝛽-decay (IDB) reaction. Each bar is wrapped with a specular reflector that directs photons toward photomultiplier tubes mounted at both ends of the bar. Here, this paper highlights the construction, key features, and main performance characteristics of the system. The system, which relies on multiple observables such as pulse-shape discrimination, energy, position, and timing, is capable of detecting IBD-like neutron-correlated backgrounds, long-lived decay chains, and cosmogenic isotopes.
Physics↗
Performance of a second generation of the Fermilab Constant Fraction Discriminator ASIC for time-stamping long-strip AC-LGAD sensors.
We present the design and performance characterization results of the second generation of the novel Fermilab Constant Fraction Discriminator ASIC (FCFD) developed to readout AC-coupled low gain avalanche detector (AC-LGAD) sensors. This study presents the performance of the ASIC which was optimized specifically for reading out strip AC-LGAD sensors designed for ePIC experiment at EiC. Performance was measured using charge injection and particle beams with prototype AC-LGAD sensors wirebonded to the FCFD ASIC.
Apresyan, A.↗
Bolide Light-curve Analysis and Discrimination Explorer (BLADE)
SAND2025-09601O Bolide Light-curve Analysis and Discrimination Explorer (BLADE) is a robust, high-fidelity framework designed to analyze the light curves of bolides—objects detected from space. It automates the identification of fragmentation events and energy release modes, using advanced techniques like Savitzky-Golay filtering, prominence-based peak detection, and gradient analysis. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Silber, Elizabeth [Sandia National Lab. (SNL-CA), ↗
Evaluating Physics-Informed Neural Network Performance for Seismic Discrimination between Earthquakes and Explosions
In this article, we evaluate adding a weak physics constraint, that is, a physics‐based empirical relationship, to the loss function with a physics‐informed manner in local distance explosion discrimination in the hope of improving the generalization capability of the machine learning (ML) model. We compare the proposed model with the two‐branch model we previously developed, as well as with a pure data‐driven model. Unexpectedly, the proposed model did not consistently outperform the pure data‐driven model. By varying the level of inconsistency in the training data, we find this approach is modulated by the strength of the physics relationship. In conclusion, this result has important implications for how to best incorporate physical constraints in ML models.
58 GEOSCIENCES↗
Track vs Shower Discrimination in the Event Reconstruction of the ICARUS Experiment
The ICARUS collaboration has employed the 760-ton T600 liquid argon TPC detector in a successful three-year physics run at the underground LNGS laboratory, performing a sensitive search for LSND-like anomalous appearance in the CERN Neutrino to Gran Sasso beam, which contributed to the constraints on the allowed neutrino oscillation parameters to a narrow region around 1 eV. After a significant overhaul at CERN, the T600 detector has been installed at Fermilab. The detector commissioning phase lasted until June 2022, then ICARUS moved to data taking for neutrino oscillation physics collecting events from the Booster Neutrino Beam (BNB) and the Neutrinos at the Main Injector (NuMI) beam off-axis. The initial experiment goals are to either confirm or refute the claim by Neutrino-4 short-baseline reactor experiment, perform measurements of neutrino cross sections with the NuMI beam and several Beyond Standard Model searches. Then, ICARUS will jointly search for evidence of sterile neutrinos with the Short-Baseline Near Detector (SBND). In this contribution, we discuss recent changes to the standard TPC event reconstruction that uses Pandora, a pattern recognition software common to liquid argon-based detectors. In particular, we performed a new training of the Boosted Decision Tree (BDT) employed to separate track-like and shower-like reconstructed particles using Monte Carlo simulations of neutrino events from BNB in ICARUS. We compare the discrimination capabilities of the old and new BDT training and discuss further improvements of this algorithm.
43 PARTICLE ACCELERATORS↗
Yet Another Discriminant Analysis (YADA): A Probabilistic Model for Machine Learning Applications
This paper presents a probabilistic model for various machine learning (ML) applications. While deep learning (DL) has produced state-of-the-art results in many domains, DL models are complex and over-parameterized, which leads to high uncertainty about what the model has learned, as well as its decision process. Further, DL models are not probabilistic, making reasoning about their output challenging. In contrast, the proposed model, referred to as Yet Another Discriminate Analysis(YADA), is less complex than other methods, is based on a mathematically rigorous foundation, and can be utilized for a wide variety of ML tasks including classification, explainability, and uncertainty quantification. YADA is thus competitive in most cases with many state-of-the-art DL models. Ideally, a probabilistic model would represent the full joint probability distribution of its features, but doing so is often computationally expensive and intractable. Hence, many probabilistic models assume that the features are either normally distributed, mutually independent, or both, which can severely limit their performance. YADA is an intermediate model that (1) captures the marginal distributions of each variable and the pairwise correlations between variables and (2) explicitly maps features to the space of multivariate Gaussian variables. Numerous mathematical properties of the YADA model can be derived, thereby improving the theoretic underpinnings of ML. Validation of the model can be statistically verified on new or held-out data using native properties of YADA. However, there are some engineering and practical challenges that we enumerate to make YADA more useful.
97 MATHEMATICS AND COMPUTING↗
Method for investighting thresholds of spatial discrimination by the human fingers
Thresholds of spatial discrimination by human fingers
SPATIAL PERCEPTION↗
Kinesthetic spatial discrimination in the practice of sports
Kinesthetic spatial discrimination in sports
DEPTH PERCEPTION↗