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94 records · Page 6

All sources NASA NTRS DOE OSTI

NASA NTRS2019

Understanding Machine Learning in Earth Science: A Natural Language Processing Approach

Machine learning (ML) is being increasingly utilized in Earth science research. Benefits of ML include efficiency, reduction of human error, and ability to extract hidden patterns within data. However, the mutual lack of each other’s domain knowledge by ML and Earth science stands as a barrier to timely and effective implementation. Earth science, in particular, faces challenges in generating sample data, compared to those of traditional ML problems such as face recognition or stock predictions, where data is abundant and not lacking in ground truth, which is necessary for labeling. Earth science data are more varying in formats, such as HDF5 and image resolutions, and are not standardized across instruments, even within a given Earth science discipline. Previous studies have been done to outline the specific challenges that Earth science faces with ML, while others have focused on using existing publications to mine information efficiently. Other resources such as Scikit-Learn have developed decision trees for choosing appropriate machine learning algorithms, but application within Earth science subjects becomes much more complex. For the current study, we propose a methodology and tool that aids in implementation of ML in Earth science using natural language processing (NLP). Our work comprises three main parts: (1) analyzing existing publications related to ML and Earth science, using natural language processing: (2) extracting from the publications information on ML models subjects in Earth Science: and (3) visualizing the extracted relationships as a network graph. The resulting network graph should aid the Earth science communities in applying optimal ML algorithms and guiding data preparation through visualization of similar studies. The network graph and analysis of document similarity will be the basis of our next step, which is to develop a decision tree for selecting optimal machine learning methodologies for specified Earth science applications.

Zheng, Laura↗

NASA NTRS2004

Path perception during rotation: influence of instructions, depth range, and dot density

How do observers perceive their direction of self-motion when traveling on a straight path while their eyes are rotating? Our previous findings suggest that information from retinal flow and extra-retinal information about eye movements are each sufficient to solve this problem for both perception and active control of self-motion [Vision Res. 40 (2000) 3873; Psych. Sci. 13 (2002) 485]. In this paper, using displays depicting translation with simulated eye rotation, we investigated how task variables such as instructions, depth range, and dot density influenced the visual system's reliance on retinal vs. extra-retinal information for path perception during rotation. We found that path errors were small when observers expected to travel on a straight path or with neutral instructions, but errors increased markedly when observers expected to travel on a curved path. Increasing depth range or dot density did not improve path judgments. We conclude that the expectation of the shape of an upcoming path can influence the interpretation of the ambiguous retinal flow. A large depth range and dense motion parallax are not essential for accurate path perception during rotation, but reference objects and a large field of view appear to improve path judgments.

Space Perception/physiology↗

NASA NTRS1999

Deblurring

In most instances, traditional EEG methodology provides insufficient spatial detail to identify relationships between brain electrical events and structures and functions visualized by magnetic resonance imaging or positron emission tomography. This article describes a method called Deblurring for increasing the spatial detail of the EEG and for fusing neurophysiologic and neuroanatomic data. Deblurring estimates potentials near the outer convexity of the cortex using a realistic finite element model of the structure of a subject's head determined from their magnetic resonance images. Deblurring is not a source localization technique and thus makes no assumptions about the number or type of generator sources. The validity of Deblurring has been initially tested by comparing deblurred data with potentials measured with subdural grid recordings. Results suggest that deblurred topographic maps, registered with a subject's magnetic resonance imaging and rendered in three dimensions, provide better spatial detail than has heretofore been obtained with scalp EEG recordings. Example results are presented from research studies of somatosensory stimulation, movement, language, attention and working memory. Deblurred ictal EEG data are also presented, indicating that this technique may have future clinical application as an aid to seizure localization and surgical planning.

Non-NASA Center↗

NASA NTRS1994

Full-wave and half-wave rectification in second-order motion perception

Microbalanced stimuli are dynamic displays which do not stimulate motion mechanisms that apply standard (Fourier-energy or autocorrelational) motion analysis directly to the visual signal. In order to extract motion information from microbalanced stimuli, Chubb and Sperling [(1988) Journal of the Optical Society of America, 5, 1986-2006] proposed that the human visual system performs a rectifying transformation on the visual signal prior to standard motion analysis. The current research employs two novel types of microbalanced stimuli: half-wave stimuli preserve motion information following half-wave rectification (with a threshold) but lose motion information following full-wave rectification; full-wave stimuli preserve motion information following full-wave rectification but lose motion information following half-wave rectification. Additionally, Fourier stimuli, ordinary square-wave gratings, were used to stimulate standard motion mechanisms. Psychometric functions (direction discrimination vs stimulus contrast) were obtained for each type of stimulus when presented alone, and when masked by each of the other stimuli (presented as moving masks and also as nonmoving, counterphase-flickering masks). RESULTS: given sufficient contrast, all three types of stimulus convey motion. However, only one-third of the population can perceive the motion of the half-wave stimulus. Observers are able to process the motion information contained in the Fourier stimulus slightly more efficiently than the information in the full-wave stimulus but are much less efficient in processing half-wave motion information. Moving masks are more effective than counterphase masks at hampering direction discrimination, indicating that some of the masking effect is interference between motion mechanisms, and some occurs at earlier stages. When either full-wave and Fourier or half-wave and Fourier gratings are presented simultaneously, there is a wide range of relative contrasts within which the motion directions of both gratings are easily determinable. Conversely, when half-wave and full-wave gratings are combined, the direction of only one of these gratings can be determined with high accuracy. CONCLUSIONS: the results indicate that three motion computations are carried out, any two in parallel: one standard ("first order") and two non-Fourier ("second-order") computations that employ full-wave and half-wave rectification.

Motion Perception/physiology↗