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At least 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

DSCOVR/EPIC-derived global hourly and daily downward shortwave and photosynthetically active radiation data at 0.1° × 0.1° resolution

Downward shortwave radiation (SW) and photosynthetically active radiation (PAR) play crucial roles in Earth system dynamics. Spaceborne remote sensing techniques provide a unique means for mapping accurate spatiotemporally continuous SW–PAR, globally. However, any individual polar-orbiting or geostationary satellite cannot satisfy the desired high temporal resolution (sub-daily) and global coverage simultaneously, while integrating and fusing multisource data from complementary satellites/sensors is challenging because of co-registration, intercalibration, near real-time data delivery and the effects of discrepancies in orbital geometry. The Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR), launched in February 2015, offers an unprecedented possibility to bridge the gap between high temporal resolution and global coverage and characterize the diurnal cycles of SW–PAR globally. In this study, we adopted a suite of well-validated data-driven machine-learning models to generate the first global land products of SW–PAR, from June 2015 to June 2019, based on DSCOVR/EPIC data. The derived products have high temporal resolution (hourly) and medium spatial resolution (0.1°×0.1°), and they include estimates of the direct and diffuse components of SW–PAR. We used independently widely distributed ground station data from the Baseline Surface Radiation Network (BSRN), the Surface Radiation Budget Network (SURFRAD), NOAA's Global Monitoring Division and the U.S. Department of Energy's Atmospheric System Research (ASR) program to evaluate the performance of our products, and we further analyzed and compared the spatiotemporal characteristics of the derived products with the benchmarking Clouds and the Earth's Radiant Energy System Synoptic (CERES) data. We found both the hourly and daily products to be consistent with ground-based observations (e.g., hourly and daily total SWs have low biases of -3.96 and -0.71 W m -2 and root-mean-square errors (RMSEs) of 103.50 and 35.40 W m -2 , respectively). The developed products capture the complex spatiotemporal patterns well and accurately track substantial diurnal, monthly, and seasonal variations in SW–PAR when compared to CERES data. They provide a reliable and valuable alternative for solar photovoltaic applications worldwide and can be used to improve our understanding of the diurnal and seasonal variabilities of the terrestrial water, carbon and energy fluxes at various spatial scales.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

GRAPEVINE: Grids about anything by Poisson's equation in a visually interactive networking environment

A proven 3-D multiple-block elliptic grid generator, designed to run in 'batch mode' on a supercomputer, is improved by the creation of a modern graphical user interface (GUI) running on a workstation. The two parts are connected in real time by a network. The resultant system offers a significant speedup in the process of preparing and formatting input data and the ability to watch the grid solution converge by replotting the grid at each iteration step. The result is a reduction in user time and CPU time required to generate the grid and an enhanced understanding of the elliptic solution process. This software system, called GRAPEVINE, is described, and certain observations are made concerning the creation of such software.

Sorenson, Reese L.↗
NASA NTRS1992

Mars

The present volume on Mars discusses visual, photographic and polarimetric telescopic observations, spacecraft exploration of Mars, the origin and thermal evolution of Mars, and the bulk composition, mineralogy, and internal structure of the planet. Attention is given to Martian gravity and topography, stress and tectonics on Mars, long-term orbital and spin dynamics of Mars, and Martian geodesy and cartography. Topics addressed include the physical volcanology of Mars, the canyon system on planet, Martian channels and valley networks, and ice in the Martian regolith. Also discussed are Martian aeolian processes, sediments, and features, polar deposits of Mars, dynamics of the Martian atmosphere, and the seasonal behavior of water on Mars.

Kieffer, Hugh H.↗
NASA NTRS2002

INTEX-NA: Intercontinental Chemical Transport Experiment - North America

INTEX-NA is an integrated atmospheric chemistry field experiment to be performed over North America using the NASA DC-8 and P-3B aircraft as its primary platforms. It seeks to understand the exchange of chemicals and aerosols between continents and the global troposphere. The constituents of interest are ozone and its precursors (hydrocarbons, NOX and HOX), aerosols, and the major greenhouse gases (CO2, CH4, N2O). INTEX-NA will provide the observational database needed to quantify inflow, outflow, and transformations of chemicals over North America. INTEX-NA is to be performed in two phases. Phase A will take place during the period of May-August 2004 and Phase B during March-June 2006. Phase A is in summer when photochemistry is most intense and climatic issues involving aerosols and carbon cycle are most pressing, and Phase B is in spring when Asian transport to North America is at its peak. INTEX-NA will coordinate its activities with concurrent measurement programs including satellites (e. g. Terra, Aura, Envisat), field activities undertaken by the North American Carbon Program (NACP), and other U.S. and international partners. However, it is being designed as a 'stand alone' mission such that its successful execution is not contingent on other programs. Synthesis of the ensemble of observation from surface, airborne, and space platforms, with the help of global/regional models is an important It is anticipated that approximately 175 flight hours for each of the aircraft (DC-8 and P-3B) will be required for each Phase. Principal operational sites are tentatively selected to be Bangor, ME; Wallops Island, VA; Seattle, WA; Rhinelander, WI; Lancaster, CA; and New Orleans, LA. These coastal and continental sites can support large missions and are suitable for INTEX-NA objectives. The experiment will be supported by forecasts from meteorological and chemical models, satellite observations, surface networks, and enhanced O3,-sonde releases. In addition to characterizing Atlantic-outflow and Pacific-inflow, INTEX-NA will characterize air masses transported between the U.S., Canada, and Mexico. INTEX-NA will be the first continental scale inflow, outflow, and transformation experiment to be performed over North America. It will provide the most comprehensive observational data set to date to understand the O3/NOX/HOX/aerosol photochemical system and the carbon cycle. One of the critical needs of the carbon cycle research is to obtain large-scale vertical and horizontal concentration gradients of CO2, throughout the troposphere over continental source/sink regions. INTEX-NA is ideally suited to perform this role. Coastal and continental operational sites will allow us to develop a curtain profile of greenhouse gases (e. g. CO2,) and other key pollutants across North America. Such information is central to our quantitative understanding of chemical budgets on the continental scale. We expect to provide a number of satellite under-flights over land and water to test and validate observations from the appropriate satellite platform (e. g. Aura). We plan to develop strong collaborations with other national and international observational programs. Results from INTEX-NA should directly benefit the development of environmental policy for air quality and climate change.

Singh, Hanwant B.↗
NASA NTRS2010

Data Visualization and Analysis Tools for the Global Precipitation Measurement (GPM) Validation Network

The Validation Network (VN) prototype for the Global Precipitation Measurement (GPM) Mission compares data from the Tropical Rainfall Measuring Mission (TRMM) satellite Precipitation Radar (PR) to similar measurements from U.S. and international operational weather radars. This prototype is a major component of the GPM Ground Validation System (GVS). The VN provides a means for the precipitation measurement community to identify and resolve significant discrepancies between the ground radar (GR) observations and similar satellite observations. The VN prototype is based on research results and computer code described by Anagnostou et al. (2001), Bolen and Chandrasekar (2000), and Liao et al. (2001), and has previously been described by Morris, et al. (2007). Morris and Schwaller (2009) describe the PR-GR volume-matching algorithm used to create the VN match-up data set used for the comparisons. This paper describes software tools that have been developed for visualization and statistical analysis of the original and volume matched PR and GR data.

Morris, Kenneth R.↗
NASA NTRS2010

Data Visualization and Analysis Tools for the Global Precipitation Measurement (GPM) Validation Network

The Validation Network (VN) prototype for the Global Precipitation Measurement (GPM) mission compares data from the Tropical Rainfall Measuring Mission (TRMM) satellite Precipitation Radar (PR) to similar measurements from U.S. and international operational weather radars. This prototype is a major component of the GPM Ground Validation System (GVS). The VN provides a means for the precipitation measurement community to identify and resolve significant discrepancies between the ground radar (GR) observations and similar satellite observations. The VN prototype is based on research results and computer code described by Anagnostou et al. (2001), Bolen and Chandrasekar (2000), and Liao et al. (2001), and has previously been described by Morris, et al. (2007). Morris and Schwaller (2009) describe the PR-GR volume-matching algorithm used to create the VN match-up data set used for the comparisons. This paper describes software tools that have been developed for visualization and statistical analysis of the original and volume matched PR and GR data.

Morris, K. Robert↗
NASA NTRS2018

Toward Long-Term Aquatic Science Products from Heritage Landsat Missions

This paper aims at generating a long-term consistent record of Landsat-derived remote sensing reflectance (Rrs) products, which are central for producing downstream aquatic science products (e.g., concentrations of total suspended solids). The products are derived from Landsat-5 and Landsat-7 observations leading to Landsat-8 era to enable retrospective analyses of inland and nearshore coastal waters. In doing so, the data processing was built into the SeaWiFS Data Analysis System (SeaDAS) followed by vicariously calibrating Landsat-7 and -5 data using reference in situ measurements and near-concurrent ocean color products, respectively. The derived Rrs products are then validated using (a) matchups using the Aerosol Robotic Network (AERONET) data measured by in situ radiometers, i.e., AERONET-OC, and (b) ocean color products at select sites in North America. Following the vicarious calibration adjustments, it is found that the overall biases in Rrs products are significantly reduced. The root-mean-square errors (RMSE), however, indicate noticeable uncertainties due to random and systematic noise. Long-term (since 1984) seasonal Rrs composites over 12 coastal and inland systems are further evaluated to explore the utility of Landsat archive processed via SeaDAS. With all the qualitative and quantitative assessments, it is concluded that with careful algorithm developments, it is possible to discern natural variability in historic water quality conditions using heritage Landsat missions. This requires the changes in Rrs exceed maximum expected uncertainties, i.e., 0.0015 [1/sr], estimated from mean RMSEs associated with the matchups and intercomparison analyses. It is also anticipated that Landsat-5 products will be less susceptible to uncertainties in turbid waters with Rrs(660) > 0.004 [1/sr], which is equivalent of ~1.2% reflectance. Overall, end-users may utilize heritage Rrs products with "fitness-for-purpose" concept in mind, i.e., products could be valuable for one application but may not be viable for another. Further research should be dedicated to enhancing atmospheric correction to account for non-negligible near-infrared reflectance in CDOM-rich and extremely turbid waters.

Pahlevan, Nima↗
NASA NTRS2024

Soil Salinity Level Assessment and Prediction Integrating UAV-borne Hyperspectral Imaging and Machine Learning Algorithms to Combat Desertification

In response to the ongoing global food crisis, the United Nations has identified “Zero Hunger” as one of its Sustainable Development Goals. A central contributor to the crisis is the process in which agricultural lands go through desertification. Research has shown a direct correlation between soil salinity and desertification - increased salinity levels indicate a higher risk for desertification. Furthermore, researchers have explored various techniques to map soil salinity, but these methods are oftentimes inefficient and don’t address future salinity predictions. To improve desertification monitoring, soil salinity can be observed via hyperspectral imaging on unmanned aerial vehicles (UAVs) to predict the risk of agricultural desertification using artificial intelligence (AI) and machine learning (ML) techniques. A significant gap exists in past research that applies ML and imaging techniques to soil salinity: convolutional neural networks (CNNs) and regression models are rarely leveraged together, despite the efficiency and accuracy of these models. To compensate for this gap, the proposed system leverages the use of these AI and ML models to improve soil assessment and prediction techniques. This approach involves three steps - data collection, image analysis, and future prediction. Using hyperspectral cameras on UAVs to collect the data from the region, a trained CNN model will output estimated soil salinity levels at a specific time. The estimations will then be analyzed by a regression model to assess the accuracy of future soil salinity predictions. The proposed system will identify regions at risk of desertification to help farmers mitigate agricultural loss, in turn helping alleviate the food crisis.

UAV systems↗
DOE OSTI2024

Collective neural network behavior in a dynamically driven disordered system of superconducting loops

Collective properties of complex systems composed of many interacting components such as neurons in our brain can be modeled by artificial networks based on disordered systems. We show that a disordered neural network of superconducting loops with Josephson junctions can exhibit computational properties like categorization and associative memory in the time evolution of its state in response to information from external excitations. Superconducting loops can trap multiples of fluxons in many discrete memory configurations defined by the local free energy minima in the configuration space of all possible states. A memory state can be updated by exciting the Josephson junctions to fire or allow the movement of fluxons through the network as the current through them surpasses their critical current thresholds. Simulations performed with a lumped element circuit model of a 4-loop network show that information written through excitations is translated into stable states of trapped flux and their time evolution. Experimental implementation on a high-Tc superconductor YBCO-based 4-loop network shows dynamically stable flux flow in each pathway characterized by the correlations between junction firing statistics. Neural network behavior is observed as energy barriers separating state categories in simulations in response to multiple excitations, and experimentally as junction responses characterizing different flux flow patterns in the network. The state categories that produce these patterns have different temporal stabilities relative to each other and the excitations. This provides strong evidence for time-dependent (short-to-long-term) memories, that are dependent on the geometrical and junction parameters of the loops, as described with a network model.

Josephson junctions↗
NASA NTRS2013

The NASA Fireball Network

In the summer of 2008, the NASA Meteoroid Environments Office (MEO) began to establish a video fireball network, based on the following objectives: (1) determine the speed distribution of cm size meteoroids, (2) determine the major sources of cm size meteoroids (showers/sporadic sources), (3) characterize meteor showers (numbers, magnitudes, trajectories, orbits), (4) determine the size at which showers dominate the meteor flux, (5) discriminate between re-entering space debris and meteors, and 6) locate meteorite falls. In order to achieve the above with the limited resources available to the MEO, it was necessary that the network function almost fully autonomously, with very little required from humans in the areas of upkeep or analysis. With this in mind, the camera design and, most importantly, the ASGARD meteor detection software were adopted from the University of Western Ontario's Southern Ontario Meteor Network (SOMN), as NASA has a cooperative agreement with Western's Meteor Physics Group. 15 cameras have been built, and the network now consists of 8 operational cameras, with at least 4 more slated for deployment in calendar year 2013. The goal is to have 15 systems, distributed in two or more groups east of automatic analysis; every morning, this server also automatically generates an email and a web page (http://fireballs.ndc.nasa.gov) containing an automated analysis of the previous night's events. This analysis provides the following for each meteor: UTC date and time, speed, start and end locations (longitude, latitude, altitude), radiant, shower identification, light curve (meteor absolute magnitude as a function of time), photometric mass, orbital elements, and Tisserand parameter. Radiant/orbital plots and various histograms (number versus speed, time, etc) are also produced. After more than four years of operation, over 5,000 multi-station fireballs have been observed, 3 of which potentially dropped meteorites. A database containing data on all these events, including the videos and calibration information, has been developed and is being modified to include data from the SOMN and other camera networks.

Cooke, William J.↗
DOE OSTI2024

Quartz Dissolution Effects on Flow Channelization and Transport Behavior in Three‐Dimensional Fracture Networks

We perform a set of reactive transport simulations in three-dimensional fracture networks to characterize the impact of geochemical reactions on flow channelization. Flow channelization, a frequently observed phenomenon in porous and fractured subsurface rock formations, results from the spatially variable hydraulic resistance offered by a geological structure. In addition to geo-structural features such as network connectivity, geometry, and hydraulic resistance, geochemical reactions, for example, dissolution and precipitation, can dynamically inhibit or enhance flow channelization. These geochemical processes can change the fracture permeability leading to increased flow channelization, which are localized connected regions of high volumetric flow rates that are seemingly ubiquitous in the subsurface. In our simulations, fractures partially filled with quartz are gradually dissolved until quasi-steady state conditions are obtained. We compare the flow field's initial unreacted and final dissolved states in terms of flow and transport observations. We observe that the dissolved fracture networks provide less resistance to flow and exhibit increased flow channelization when compared to their unreacted counterparts. However, there is substantial variability in the magnitude of these changes which implies that the channelization strongly depends on the network structure. In turn, we identify the interplay between the particular network structure and the impact of geochemical dissolution on flow channelization. The presented results indicate that geological systems that have been weathering or reactive for longer times in older landscapes are likely to have increased flow channelization compared to their equivalent but younger counterparts, which implies a time dependence on flow channelization in fractured media.

Hyman, Jeffrey D.↗
DOE OSTI2023

Deconvoluting experimental decay energy spectra: The O 26 case

In nuclear reaction experiments, the measured decay energy spectra can give insights into the shell structure of decaying systems. However, extracting the underlying physics from the measurements is challenging due to detector resolution and acceptance effects. The Richardson-Lucy (RL) algorithm, a deblurring method that is commonly used in optics and has proven to be a successful technique for restoring images, was applied to our experimental nuclear physics data. The only inputs to the method are the observed energy spectrum and the detector's response matrix also known as the transfer matrix. We demonstrate that the technique can help access information about the shell structure of particle-unbound systems from the measured decay energy spectrum that is not immediately accessible via traditional approaches such as χ-square fitting. For a similar purpose, we developed a machine learning model that uses a deep neural network (DNN) classifier to identify resonance states from the measured decay energy spectrum. We tested the performance of both methods on simulated data and experimental measurements. Then, we applied both algorithms to the decay energy spectrum of 26 O → 24 O + n + n measured via invariant mass spectroscopy. Here, the resonance states restored using the RL algorithm to deblur the measured decay energy spectrum agree with those found by the DNN classifier. Both deblurring and DNN approaches suggest that the raw decay energy spectrum of 26 O exhibits three peaks at approximately 0.15 MeV, 1.50 MeV, and 5.00 MeV, with half-widths of 0.29 MeV, 0.80 MeV, and 1.85 MeV, respectively.

Spectrometers & spectroscopic techniques↗
NASA NTRS1991

Measuring earth orientation with the Global Positioning System

A globally distributed network of high-precision receivers which obtain data from the full Global Positioning System (GPS) configuration of 18 or more satellites may soon become an efficient and economical method for the rapid determination of short-term variations in earth orientation. A covariance analysis has been performed to evaluate the errors associated with GPS monitoring of earth orientation. Earth orientation parameters were modeled either as constants over observing windows of various lengths, or as stochastic process-noise variables. The sensitivity of earth orientation estimates to systematic errors in selected model parameters was also examined. GPS measurements appear to be highly competitive with those from other techniques, and have the potential to generate nearly continuous centimeter-level earth orientation information to aid both spacecraft navigation and the study of high-frequency earth orientation-related processes.

Freedman, Adam P.↗
NASA NTRS1992

Viewing hybrid systems as products of control systems and automata

The purpose of this note is to show how hybrid systems may be modeled as products of nonlinear control systems and finite state automata. By a hybrid system, we mean a network of consisting of continuous, nonlinear control system connected to discrete, finite state automata. Our point of view is that the automata switches between the control systems, and that this switching is a function of the discrete input symbols or letters that it receives. We show how a nonlinear control system may be viewed as a pair consisting of a bialgebra of operators coding the dynamics, and an algebra of observations coding the state space. We also show that a finite automata has a similar representation. A hybrid system is then modeled by taking suitable products of the bialgebras coding the dynamics and the observation algebras coding the state spaces.

Grossman, R. L.↗
NASA NTRS1995

NDSC and JPL stratospheric lidars

The Network for the Detection of Stratospheric Change is an international cooperation providing a set of high-quality, remote-sensing instruments at observing stations around the globe. A brief description of the NDSC and its goals is presented. Lidar has been selected as the NDSC instrument for measurements of stratospheric profiles of ozone, temperature, and aerosol. The Jet Propulsion Laboratory has developed and implemented two stratospheric lidar systems for NDSC. These are located at Table Mountain, California, and at Mauna Loa, Hawaii. These systems, which utilize differential absorption lidar, Rayleigh lidar, raman lidar, and backscatter lidar, to measure ozone, temperature, and aerosol profiles in the stratosphere are briefly described. Examples of results obtained for both long-term and individual profiles are presented.

McDermid, I. Stuart↗
NASA NTRS1999

Nitrogen Oxides and Ozones from B-747 Measurements (NOXAR) during POLINAT 2 and SONEX: Overview and Case-Studies on Continental and Marine Convection

In the framework of the project POLINAT 2 (Pollution in the North Atlantic Flight Corridor) we measured NO(x) (NO and NO2) and ozone on 98 flights through the North Atlantic Flight Corridor (NAFC) with a fully automated system permanently installed aboard an in-service Swissair B-747 airliner in the period of August to November 1997. The averaged NO, concentrations both in the NAFC and at the U.S. east coast were similar to that measured in autumn 1995 with the same system. The patchy occurrence of NO(x), enhancements up to 3000 pptv over several hundred kilometers (plumes), predominately found over the U.S. east coast lead to a log-normal NO(x) probability density function. In three case-studies we examine the origins of such plumes by combining back-trajectories with brightness temperature enhanced (IR) satellite imagery, with lightning observations from the U.S. National Lightning Detection Network (NLDN) or with the Optical Transient Detector (OTD) satellite. For frontal activity above the continental U.S., we demonstrate that the location of NO(x) plumes can be well explained with maps of convective influence. For another case we show that the number of lightning flashes in a cluster of marine thunderstorms is proportional to the NO(x) concentrations observed several hundred kilometers downwind of the anvil outflows and suggest that lightning was the dominant source. From the fact that in autumn the NO, maximum was found several hundred kilometers off the U.S. east coast, it can be inferred that thunderstorms triggered over the warm Gulf Stream current are an important source for the regional upper tropospheric NO(x) budget in autumn.

Jeker, Dominique↗
NASA NTRS2015

Converter Compressor Building, SWMU 089, Hot Spot Areas 1, 2, and 5 Operations, Maintenance, and Monitoring Report, Kennedy Space Center, Florida

This Operations, Maintenance, and Monitoring Report (OMMR) presents the findings, observations, and results from operation of the air sparging (AS) interim measure (IM) for Hot Spot (HS) Areas 1, 2, and 5 at the Converter Compressor Building (CCB) located at Kennedy Space Center (KSC), Florida. The objective of the IM at CCB HS Areas 1, 2, and 5 is to decrease concentrations of volatile organic compounds (VOCs) in groundwater in the treatment zones via AS to levels that will enable a transition to a monitored natural attenuation (MNA) phase. This OMMR presents system operations and maintenance (O&M) information and performance monitoring results since full-scale O&M began in June 2014 (2 months after initial system startup in April 2014), including quarterly performance monitoring events in July and October 2014 and January and May 2015. Based on the results to date, the AS system is operating as designed and is meeting the performance criteria and IM objective. The performance monitoring network is adequately constructed for assessment of IM performance at CCB HS Areas 1, 2, and 5. At the March 2014 KSC Remediation Team (KSCRT) Meeting, team consensus was reached for the design prepared for expansion of the system to treat the HS 4 area, and at the November 2014 KSCRT Meeting, team consensus was reached that HS 3 was adequately delineated horizontally and vertically and for selection of AS for the remedial approach for HS 3. At the July 2015 KSCRT meeting, team consensus was reached to continue IM operations in all zones until HSs 3 and 4 is operational, once HS 3 and 4 zones are operational discontinue operations in HS 1, 2, and 5 zones where concentrations are less than GCTLs to observe whether rebounding conditions occur. Team consensus was also reached to continue quarterly performance monitoring to determine whether operational zones achieve GCTLs and to continue annual IGWM of CCB-MW0012, CCBMW0013, and CCB-MW0056, located south of the treatment area. The next performance monitoring event is scheduled for July 2015.

Converter Compressor Building↗
NASA NTRS2020

Amino Acid Encoding for Deep Learning Applications

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-of-the-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Deep-learning↗