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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.
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bacterial-classification is a sub repository of the Intelligent Immunity project specific to training and testing machine learning models for bacterial classification (Gram-positive, Gram-negative, and non-bacterial samples). Specifically, this repository aims to compare ML classification performance across different dataset curation strategies.
In the past decade, transportation network companies (TNCs) such as Uber, Lyft, and Via have established themselves as a viable transportation alternative to other modes. However, the popularity of these services has come with a fair share of criticism for their negative externalities such as increasing vehicle miles traveled and congestion in cities. Pooled ride-hailing trips, in which all or a part of two individual (or group) trips are combined in and served by a single vehicle, have the potential to reduce these externalities. Pooling of rides is an effective solution to reduce congestion and travel cost, but pooled rides still represent a small percentage of the total trips served (and miles driven) by TNCs relative to single-occupancy (and without customer) vehicle miles. Both TNCs and cities alike will benefit from understanding what factors encourage or deter pooling a ride-hailing trip. In this study, newly available Chicago transportation network provider data were explored to identify the extent to which different socioeconomic, spatiotemporal, and trip characteristics affect willingness to pool (WTP) in ride-hailing trips. Furthermore, multivariate linear regression and machine-learning models were employed to understand and predict WTP based on location, time, and trip factors. The results show intuitive trends, with income level at drop-off and pickup locations and airport trips as the most important predictors of WTP. Results from this study can help TNCs and cities devise strategies that increase pooled ride-hailing, thereby reducing adverse transportation and energy impacts from ride-hailing modes.
CRISPR/Cas9 technology has become an important tool to generate targeted, highly specific genome mutations. The technology has great potential for crop improvement, as crop genomes are tailored to optimize specific traits over generations of breeding. Many crops have highly complex and polyploid genomes, particularly those used for bioenergy or bioproducts. The majority of tools currently available for designing and evaluating gRNAs for CRISPR experiments were developed based on mammalian genomes that do not share the characteristics or design criteria for crop genomes. We have developed an open source tool for genome-wide design and evaluation of gRNA sequences for CRISPR experiments, CROPSR. The genome-wide approach provides a significant decrease in the time required to design a CRISPR experiment, including validation through PCR, at the expense of an overhead compute time required once per genome, at the first run. To better cater to the needs of crop geneticists, restrictions imposed by other packages on design and evaluation of gRNA sequences were lifted. A new machine learning model was developed to provide scores while avoiding situations in which the currently available tools sometimes failed to provide guides for repetitive, A/T-rich genomic regions. We show that our gRNA scoring model provides a significant increase in prediction accuracy over existing tools, even in non-crop genomes. CROPSR provides the scientific community with new methods and a new workflow for performing CRISPR/Cas9 knockout experiments. CROPSR reduces the challenges of working in crops, and helps speed gRNA sequence design, evaluation and validation. We hope that the new software will accelerate discovery and reduce the number of failed experiments.
The dataset includes the survey results about farmers’ perceptions of marginal land availability and the likelihood of a land pixel being marginal based on a machine learning model trained from the survey. Two spreadsheet files are the farmer and farm characteristics (marginal_land_survey_data_shared.xlsx), and the existing land use of marginal lands (land_use_info_sharing.xlsx). Note: the blank cells in these two spreadsheets mean missing values in the survey response. The GeoTiff file includes two bands, one the marginal land likelihood in the Midwestern states (0-1), the other the dominant reason of land marginality (0-5; 0 for farm size, 1 for growing season precipitation, 2 for root zone soil water capacity, 3 for average slope, 4 for growing season mean temperature, and 5 for growing season diurnal range of temperature). To read the data, please use a GIS software such as ArcGIS or QGIS.
Object density reconstruction from projections containing scattered radiation and noise is of critical importance in many applications. Existing scatter correction and density reconstruction methods may not provide the high accuracy needed in many applications and can break down in the presence of unmodeled or anomalous scatter and other experimental artifacts. Incorporating machine-learning models could prove beneficial for accurate density reconstruction, particularly in dynamic imaging, where the time evolution of the density fields could be captured by partial differential equations or by learning from hydrodynamics simulations. In this work, we demonstrate the ability of learned deep neural networks to perform artifact removal in noisy density reconstructions, where the noise is imperfectly characterized. Here, we use a Wasserstein generative adversarial network (WGAN), where the generator serves as a denoiser that removes artifacts in densities obtained from traditional reconstruction algorithms. We train the networks from large density time-series datasets, with noise simulated according to parametric random distributions that may mimic noise in experiments. The WGAN is trained with noisy density frames as generator inputs, to match the generator outputs to the distribution of clean densities (time series) from simulations. A supervised loss is also included in the training, which leads to an improved density restoration performance. In addition, we employ physics-based constraints such as mass conservation during the network training and application to further enable highly accurate density reconstructions. Our preliminary numerical results show that the models trained in our frameworks can remove significant portions of unknown noise in density time-series data.
This study presents significant advancements in computational cannula microscopy for live imaging of cellular dynamics in poplar wood tissues. Leveraging machine-learning models such as pix2pix for image reconstruction, we achieved high-resolution imaging with a field of view of 55µm using a 50µm-core diameter probe. Our method allows for real-time image reconstruction at 0.29 s per frame with a mean absolute error of 0.07. We successfully captured cellular-level dynamics in vivo , demonstrating morphological changes at resolutions as small as 3µm. We implemented two types of probabilistic neural network models to quantify confidence levels in the reconstructed images. This approach facilitates context-aware, human-in-the-loop analysis, which is crucial for in vivo imaging where ground-truth data is unavailable. Using this approach we demonstrated deep in vivo computational imaging of living plant tissue with high confidence (disagreement score ⪅0.2). This work addresses the challenges of imaging live plant tissues, offering a practical and minimally invasive tool for plant biologists.
A neural-network machine learning model is developed to control a bimorph adaptive mirror to achieve and preserve aberration-free coherent X-ray wavefronts at synchrotron radiation and free electron laser beamlines. The controller is trained on a mirror actuator response directly measured at a beamline with a real-time single-shot wavefront sensor, which uses a coded mask and wavelet-transform analysis. The system has been successfully tested on a bimorph deformable mirror at the 28-ID IDEA beamline of the Advanced Photon Source at Argonne National Laboratory. It achieved a response time of a few seconds and maintained desired wavefront shapes (e.g., a spherical wavefront) with sub-wavelength accuracy at 20 keV of X-ray energy. This result is significantly better than what can be obtained using a linear model of the mirror’s response. The developed system has not been tailored to a specific mirror and can be applied, in principle, to different kinds of bending mechanisms and actuators.
Perovskite oxides (ternary chemical formula ABO3) are a diverse class of materials with applications including heterogeneous catalysis, solid-oxide fuel cells, thermochemical conversion, and oxygen transport membranes. However, their multicomponent chemical space is underexplored due to the immense number of possible compositions. To expand the number of computed compounds we report a dataset of 66,516 theoretical multinary oxides, 59,708 of which are perovskites. First, the AA'BB'O6 compositions were generated in the a-b+a- Glazer tilting mode using the computationally-inexpensive Structure Prediction and Diagnostic Software (SPuDS) program. Next, we optimized these structures with DFT using parameters compatible with the Materials Project (MP) database. Our dataset contains these optimized structures and their formation (ΔHf) and decomposition enthalpies (ΔHd) computed relative to MP tabulated elemental references and competing phases, respectively. This dataset can be mined, used to train machine learning models, and rapidly and systematically expanded by optimizing more SPuDS-generated AA'BB'O6 perovskite structures using MP-compatible DFT calculations
A slow extraction system is being developed for the Fermilab’s Delivery Ring to deliver protons to the Mu2e experiment. During the extraction, the beam on target experiences small intensity variations owing to many factors. Various adaptive learning algorithms will be employed for beam regulation to achieve the required spill quality. We discuss here preliminary results of the slow and fast regulation algorithms validation through the computer simulations before their implementation in the FPGA. Particle tracking with sextupole resonance was used to determine the fine shape of the spill profile. Fast semi-analytical simulation schemes and Machine Learning models were used to optimize the fast regulation loop.
Beam availability has increased at the SNS, however, the targeted availability is greater than 95 %, while the SNS has failed to meet lower targets in the past. The HVCM used to power the linac klystrons have been one source of lost beam time and was chosen to explore using AI/ML techniques to improve reliability. Among the possibilities being explored are automating the tuning of HVCMs and predicting component failures such as capacitor aging, rectifier assemblies containing hundreds of diodes, and insulating oil degradation. The methodology pursued includes data cleaning, de-noising, post-analysis data labeling, and machine learning model development. We explore using Long Short-Term Memory and autoencoders for anomaly detection and prognostication used to schedule maintenance. We evaluate the use of model regularizers and constraints to improve the performance of the model and investigate methods to estimate the uncertainty of the models to provide a robust prediction with statistical interoperability. This paper describes the operational experience and known failures of the HVCMs and the proposed ML methodology and the preliminary results of training the AI/ML algorithms.
Field emission is one of the most important issues that limits the performance of the superconducting radio frequency (SRF) systems and leads to SRF cavity trips at the Continuous Electron Beam Accelerator Facility at Jefferson Lab. Studies have confirmed that particulates are the dominant source of field emitters and the particulates can be transported into a cavity from other parts of the accelerator. To monitor the transportation of the particulates, a prototype of a novel, non-invasive laser particulate counter (LPC) has been developed and tested. Experiments have been done to validate the capability of the LPC. We are developing autonomous event detection system to continuously monitor the readout from the LPC and to recognize real events generated by particulates from noises using machine learning model. In this report, we will present how the data are prepared and how the model is trained. We will also discuss the performance of the model.
During the operation of CEBAF, one or more unstable superconducting radio-frequency (SRF) cavities often cause beam loss trips while the unstable cavities themselves do not necessarily trip off. Identifying an unstable cavity out of the hundreds of cavities installed at CEBAF is difficult and time-consuming. The present RF controls for the legacy cavities report at only 1 Hz, which is too slow to detect fast transient instabilities. A fast data acquisition system for the legacy SRF cavities is being developed which samples and reports at 5 kHz to allow for detection of transients. A prototype chassis has been installed and tested in CEBAF. An autoencoder based machine learning model is being developed to identify anomalous SRF cavity behavior. The model is presently being trained on the slow (1 Hz) data that is currently available, and a separate model will be developed and trained using the fast (5 kHz) DAQ data once it becomes available. This paper will discuss the present status of the new fast data acquisition system and results of testing the prototype chassis. This paper will also detail the initial performance metrics of the autoencoder model.
We introduce **DeepBench**, a python library that generates simple simulated image data from first principles, such as basic geometric shapes and astronomical objects. These data are highly valuable for developing (calibration, testing, and benchmarking) statistical and machine learning models because they make it possible to connect the final data product to physically interpretable inputs. This software includes tools to curate and store the datasets to maximize reproducibility.
With the vast data-collecting capabilities of current and future high-energy collider experiments, there is an increasing demand for computationally efficient simulations. Generative machine learning models enable fast event generation, yet so far these approaches are largely constrained to fixed data structures and rigid detector geometries. In this paper, we introduce EPiC-GAN - equivariant point cloud generative adversarial network - which can produce point clouds of variable multiplicity. This flexible framework is based on deep sets and is well suited for simulating sprays of particles called jets. The generator and discriminator utilize multiple EPiC layers with an interpretable global latent vector. Crucially, the EPiC layers do not rely on pairwise information sharing between particles, which leads to a significant speed-up over graph- and transformer-based approaches with more complex relation diagrams. We demonstrate that EPiC-GAN scales well to large particle multiplicities and achieves high generation fidelity on benchmark jet generation tasks.
The measurements performed by particle physics experiments must account for the imperfect response of the detectors used to observe the interactions. One approach, unfolding, statistically adjusts the experimental data for detector effects. Recently, generative machine learning models have shown promise for performing unbinned unfolding in a high number of dimensions. However, all current generative approaches are limited to unfolding a fixed set of observables, making them unable to perform full-event unfolding in the variable dimensional environment of collider data. A novel modification to the variational latent diffusion model (VLD) approach to generative unfolding is presented, which allows for unfolding of high- and variable-dimensional feature spaces. The performance of this method is evaluated in the context of semi-leptonic t\bar{t} t t ‾ production at the Large Hadron Collider.
The Arctic is warming and feedbacks in the coupled Earth system may be driving the Arctic to tipping events that could have critical downstream impacts for the rest of the globe. In this project we have focused on analyzing sea ice variability and loss in the coupled Earth system Summer sea ice loss is happening rapidly and although the loss may be smooth and reversible, it has significant consequences for other Arctic systems as well as geopolitical and economic implications. Accurate seasonal predictions of sea ice minimum extent and long-term estimates of timing for a seasonally ice-free Arctic depend on a better understanding of the factors influencing sea ice dynamics and variation in this strongly coupled system. Under this project we have investigated the most influential factors in accurate predictions of September Arctic sea ice extent using machine learning models trained separately on observational data and on simulation data from five E3SM historical ensembles. Monthly averaged data from June, July, and August for a selection of ice, ocean, and atmosphere variables were used to train a random forest regression model. Gini importance measures were computed for each input feature with the testing data. We found that sea ice volume is most important earlier in the season (June) and sea ice extent became a more important predictor closer to September. Results from this study provide insight into how feature importance changes with forecast length and illustrates differences between observational data and simulated Earth system data. We have additionally performed a global sensitivity analysis (GSA) using a fully coupled ultra- low resolution configuration E3SM. To our knowledge, this is the first global sensitivity analysis involving the fully-coupled E3SM Earth system model. We have found that parameter variations show significant impact on the Arctic climate state and atmospheric parameters related to cloud parameterizations are the most significant. We also find significant interactions between parameters from different components of E3SM. The results of this study provide invaluable insight into the relative importance of various parameters from the sea ice, atmosphere and ocean components of the E3SM (including cross-component parameter interactions) on various Arctic-focused quantities of interest (QOIs).
Transition metal dichalcogenides (TMDs) such as MoX 2 are known to undergo a structural phase transformation as well as a change in the electronic conductivity upon Li intercalation. These properties make them candidates for charge tunable ion-insertion materials that could be used in electro-chemical devices for neuromorphic computing applications. In this work we study the phase stability and electronic structure of Li-intercalated bilayer MoX 2 with X=S, Se or Te. Using first-principles calculations in combination with classical and machine learning modeling approaches we find that the energy needed to stabilize the conductive phase decreases with increasing atomic mass of the chalcogen atom X. A similar decreasing trend is found in the threshold Li concentration where the structural phase transition takes place. While the electronic conductivity increases with increasing ion concentration at low concentrations, we do not observe a conductivity jump at the phase transition point.
In international nuclear safeguards, the International Atomic Energy Agency (IAEA) is tasked with inspecting and verifying nuclear facilities and their activities. Data analytics and machine learning to support inspections require large amounts of data that nuclear facility operators may consider proprietary or sensitive, so the IAEA may not have full access. Allowing computation over private data without compromising its security therefore has value for safeguards inspections and analysis. Privacy-preserving machine learning (PPML) consists of security-focused techniques that allow data analytics and machine learning algorithms to run on sensitive data without revealing it. This includes ideas like homomorphic encryption (HE), secure multiparty computation (SMPC), and secure enclaves. HE allows algorithms and mathematical operations to be conducted directly on the encrypted data instead of first decrypting it. With SMPC, multiple entities collaboratively compute over distributed data such that no party is able to directly view any others’ original data. Secure enclaves allow computation to take place in a separate and heavily blocked-off section of a CPU. Techniques like these allow for several potential use cases in which the security of data is essential. With SMPC, machine learning models can be trained over the input data from multiple entities, resulting in a model that all users can benefit from without leaking the input data from any particular entity. With SMPC or a zero-knowledge proof (ZKP), an algorithm returning some single answer or truth value can be run on someone else’s data without ever needing to see that data, potentially allowing for verification or proof of some underlying question. HE can allow for outsourcing computation on data to a hostile or untrusted environment. Although most of the research in this field resides within the health and financial domains, tools from PPML may have similar applications in nuclear safeguards. Allowing the IAEA to compute over proprietary information, such as process models and raw sensor data using PPML techniques, provides the baseline for running complex analytics without needing direct unencrypted access to the underlying data, maintaining its privacy. Important limitations to consider for these techniques include the efficiency and level of security required. The security of HE and SMPC come at the cost of speed—the significant amount of overhead means that algorithms implemented in these protocols and encryption schemes are slower than when run on plaintext. Additionally, several important parameters determine what techniques or protocols are used based on the security requirements. SMPC protocols may need to be selected for resistance against a party that attempts to deviate from the protocol to distort the result or gain access to additional information, and a protocol secure against these attacks may further increase the overhead of the algorithm.