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6,467 results for “process”
Sparse Inverse Gaussian Process Regression with Application to Climate Network Discovery
Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. Gaussian Process regression is a popular technique for modeling the input-output relations of a set of variables under the assumption that the weight vector has a Gaussian prior. However, it is challenging to apply Gaussian Process regression to large data sets since prediction based on the learned model requires inversion of an order n kernel matrix. Approximate solutions for sparse Gaussian Processes have been proposed for sparse problems. However, in almost all cases, these solution techniques are agnostic to the input domain and do not preserve the similarity structure in the data. As a result, although these solutions sometimes provide excellent accuracy, the models do not have interpretability. Such interpretable sparsity patterns are very important for many applications. We propose a new technique for sparse Gaussian Process regression that allows us to compute a parsimonious model while preserving the interpretability of the sparsity structure in the data. We discuss how the inverse kernel matrix used in Gaussian Process prediction gives valuable domain information and then adapt the inverse covariance estimation from Gaussian graphical models to estimate the Gaussian kernel. We solve the optimization problem using the alternating direction method of multipliers that is amenable to parallel computation. We demonstrate the performance of our method in terms of accuracy, scalability and interpretability on a climate data set.
Distilling the Verification Process for Prognostics Algorithms
The goal of prognostics and health management (PHM) systems is to ensure system safety, and reduce downtime and maintenance costs. It is important that a PHM system is verified and validated before it can be successfully deployed. Prognostics algorithms are integral parts of PHM systems. This paper investigates a systematic process of verification of such prognostics algorithms. To this end, first, this paper distinguishes between technology maturation and product development. Then, the paper describes the verification process for a prognostics algorithm as it moves up to higher maturity levels. This process is shown to be an iterative process where verification activities are interleaved with validation activities at each maturation level. In this work, we adopt the concept of technology readiness levels (TRLs) to represent the different maturity levels of a prognostics algorithm. It is shown that at each TRL, the verification of a prognostics algorithm depends on verifying the different components of the algorithm according to the requirements laid out by the PHM system that adopts this prognostics algorithm. Finally, using simplified examples, the systematic process for verifying a prognostics algorithm is demonstrated as the prognostics algorithm moves up TRLs.
Stable and Efficient Gaussian Process Calculations
The use of Gaussian processes can be an effective approach to prediction in a supervised learning environment. For large data sets, the standard Gaussian process approach requires solving very large systems of linear equations and approximations are required for the calculations to be practical. We will focus on the subset of regressors approximation technique. We will demonstrate that there can be numerical instabilities in a well known implementation of the technique. We discuss alternate implementations that have better numerical stability properties and can lead to better predictions. Our results will be illustrated by looking at an application involving prediction of galaxy redshift from broadband spectrum data.
Mars Atmosphere and Volatile Evolution (MAVEN) Imaging Ultraviolet Spectrograph (IUVS) Processed-level Data Product Bundle
Mars Atmosphere and Volatile Evolution (MAVEN) Imaging Ultraviolet Spectrograph (IUVS) Processed-level Data Product Bundle
MER2 APXS Xray Spectra (Partially Processed) Data Bundle
This bundle contains Xray Spectra (Partially Processed) data from the Alpha Particle X-ray Spectrometer on Mars Exploration Rover 2 (Spirit). Raw APXS data have been converted to ASCII tables and spectra from previous integrations have been removed.
VG2 NEPTUNE IMAGING SCIENCE SUBSYSTEM PROCESSED V1.0
This data set contains all of the Voyager images from Uranus in cleaned, calibrated and geometrically corrected forms. It is derived from data set VG2-N-ISS-2-EDR-V1.0.
BRIC-23 GeneLab Process Verification Test: Bacillus subtilis transcriptomic, proteomic, and metabolomic data
Microbes interact with humans in complex ways and understanding how they respond to the spaceflight environment is important to the success of future manned spaceflight missions. The BRIC-23 mission was designed to measure the response of Bacillus subtilis and Staphylococcus aureus to the spaceflight environment. This experiment aimed to produce high quality omics data from B. subtilis and S. aureus grown aboard the International Space Station (ISS) to allow comparison to matched ground controls. There were two primary objectives for this experiment: (1) Demonstrate all post-flight processes and operations required for successful completion of GeneLab Reference Missions conducted on ISS, and (2) Generate high quality GeneLab Reference Mission omics data sets for two prokaryotic model organisms, Bacillus subtilis and Staphylococcus aureus. Freezing Control Experiment: The BRIC hardware has significant thermal inertia, thus the freezing rate of samples placed at -80 C is quite slow. This could affect RNA-sequencing, proteomic and metabolic data sets. In an effort to understand how slow freezing could affect these data sets, a control experiment was designed in which B. subtilis and S. aureus were grown in petri plates and either slow frozen to -80 C at a rate matching the BRIC-23 spaceflight samples or processed immediately to harvest RNA and protein. S. aureus omics data is deposited in GLDS-145.
SPATIALLY ADAPTIVE SEMI-SUPERVISED LEARNING WITH GAUSSIAN PROCESSES FOR HYPERSPECTRAL DATA ANALYSIS
SPATIALLY ADAPTIVE SEMI-SUPERVISED LEARNING WITH GAUSSIAN PROCESSES FOR HYPERSPECTRAL DATA ANALYSIS GOO JUN * AND JOYDEEP GHOSH* Abstract. A semi-supervised learning algorithm for the classification of hyperspectral data, Gaussian process expectation maximization (GP-EM), is proposed. Model parameters for each land cover class is first estimated by a supervised algorithm using Gaussian process regressions to find spatially adaptive parameters, and the estimated parameters are then used to initialize a spatially adaptive mixture-of-Gaussians model. The mixture model is updated by expectationmaximization iterations using the unlabeled data, and the spatially adaptive parameters for unlabeled instances are obtained by Gaussian process regressions with soft assignments. Two sets of hyperspectral data taken from the Botswana area by the NASA EO-1 satellite are used for experiments. Empirical evaluations show that the proposed framework performs significantly better than baseline algorithms that do not use spatial information, and the results are also better than any previously reported results by other algorithms on the same data.
Onboard Detection of Snow, Ice, Clouds, and Other Processes
The detection of clouds within a satellite image is essential for retrieving surface geophysical parameters from optical and thermal imagery. Even a small percentage of cloud cover within a radiometer pixel can adversely affect the determination of surface variables such as albedo and temperature. Thus, onboard processing of satellite data requires reliable automated cloud detection algorithms that are applicable to a wide range of surface types. Unfortunately cloud-detection, particularly over snow- and ice-covered surfaces, is a problem that plagues the field of remote sensing because of the lack of spectral contrast. This paper discusses preliminary results based on kernel methods for unsupervised discovery of snow, ice, clouds, and other geophysical processes based on data from the MODIS instrument and discusses implementation in computationally constrained environments such as those found on satellites.
VG1/VG2 SATURN IMAGING SCIENCE SUBSYSTEM PROCESSED V1.1
This data set contains all of the Voyager images from Saturn in cleaned, calibrated and geometrically corrected forms. It is derived from data set VG1/VG2-S-ISS-2-EDR-V1.0.
Model-based Prognostics with Concurrent Damage Progression Processes
Model-based prognostics approaches rely on physics-based models that describe the behavior of systems and their components. These models must account for the several different damage processes occurring simultaneously within a component. Each of these damage and wear processes contribute to the overall component degradation. We develop a model-based prognostics methodology that consists of a joint state-parameter estimation problem, in which the state of a system along with parameters describing the damage progression are estimated, followed by a prediction problem, in which the joint state-parameter estimate is propagated forward in time to predict end of life and remaining useful life. The state-parameter estimate is computed using a particle filter, and is represented as a probability distribution, allowing the prediction of end of life and remaining useful life within a probabilistic framework that supports uncertainty management. We also develop a novel variance control algorithm that maintains an uncertainty bound around the unknown parameters to limit the amount of estimation uncertainty and, consequently, reduce prediction uncertainty. We construct a detailed physics-based model of a centrifugal pump that includes damage progression models, to which we apply our model-based prognostics algorithm. We illustrate the operation of the prognostic solution with a number of simulation-based experiments and demonstrate the performance of the approach when multiple damage mechanisms are active.
ARC Code TI: Block-GP: Scalable Gaussian Process Regression
Block GP is a Gaussian Process regression framework for multimodal data, that can be an order of magnitude more scalable than existing state-of-the-art nonlinear regression algorithms. The framework builds local Gaussian Processes on semantically meaningful partitions of the data and provides higher prediction accuracy than a single global model with very high confidence.
Block-GP: Scalable Gaussian Process Regression for Multimodal Data
Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. In many cases, regression algorithms such as linear regression or neural networks attempt to fit the target variable as a function of the input variables without regard to the underlying joint distribution of the variables. As a result, these global models are not sensitive to variations in the local structure of the input space. Several algorithms, including the mixture of experts model, classification and regression trees (CART), and others have been developed, motivated by the fact that a variability in the local distribution of inputs may be reflective of a significant change in the target variable. While these methods can handle the non-stationarity in the relationships to varying degrees, they are often not scalable and, therefore, not used in large scale data mining applications. In this paper we develop Block-GP, a Gaussian Process regression framework for multimodal data, that can be an order of magnitude more scalable than existing state-of-the-art nonlinear regression algorithms. The framework builds local Gaussian Processes on semantically meaningful partitions of the data and provides higher prediction accuracy than a single global model with very high confidence. The method relies on approximating the covariance matrix of the entire input space by smaller covariance matrices that can be modeled independently, and can therefore be parallelized for faster execution. Theoretical analysis and empirical studies on various synthetic and real data sets show high accuracy and scalability of Block-GP compared to existing nonlinear regression techniques.
Dissecting PADI6 function defines oocyte cytoplasmic lattices as regulatory hubs for fundamental cellular processes
GEO Series GSE289965. Mus musculus. 72 samples. Type: Expression profiling by high throughput sequencing.
Procollagen C-propeptide Processing Impacts Bone Matrix
GEO Series GSE279055. Mus musculus. 24 samples. Type: Expression profiling by high throughput sequencing.
Xenophagic process controls the Enteroinvasive Escherichia coli infection of the epithelial cells III
GEO Series GSE93588. Homo sapiens. 16 samples. Type: Expression profiling by array.
Construction of N6 Methyladenosine Atlas in Yak Epididymis Reveals the Process of Sperm Maturation Modification
GEO Series GSE264337. Bos grunniens. 18 samples. Type: Expression profiling by high throughput sequencing; Other.
A modular analysis reveals that kinase-associated gene groups are involved in taurolithocholic acid 3-sulfate (TLC-S)-induced biological processes of AR42J cells
GEO Series GSE63959. Rattus norvegicus. 2 samples. Type: Expression profiling by array.
Selective inhibition of JAK1 with filgotinib reverses pathogenic processes in the DSS preclinical model for IBD
GEO Series GSE105417. Mus musculus. 15 samples. Type: Expression profiling by array.
IRF4, a master transcription factor, regulates genes involved in BCR signaling, antigen processing and presentation, and GC development
GEO Series GSE64270. Homo sapiens. 5 samples. Type: Genome binding/occupancy profiling by genome tiling array; Expression profiling by array.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.