Locality Preserving Composite Kernel Feature Extraction for Multi-Source Geospatial Image Analysis
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Yuhang Zhang↗
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Results for “Analysis preservation”
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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Quantitative risk assessment system (QRAS)
A quantitative risk assessment system (QRAS) builds a risk model of a system for which risk of failure is being assessed, then analyzes the risk of the system corresponding to the risk model. The QRAS performs sensitivity analysis of the risk model by altering fundamental components and quantifications built into the risk model, then re-analyzes the risk of the system using the modifications. More particularly, the risk model is built by building a hierarchy, creating a mission timeline, quantifying failure modes, and building/editing event sequence diagrams. Multiplicities, dependencies, and redundancies of the system are included in the risk model. For analysis runs, a fixed baseline is first constructed and stored. This baseline contains the lowest level scenarios, preserved in event tree structure. The analysis runs, at any level of the hierarchy and below, access this baseline for risk quantitative computation as well as ranking of particular risks. A standalone Tool Box capability exists, allowing the user to store application programs within QRAS.
Weinstock, Robert M↗
NDMAS: Data storage, visualization, analysis, delivery, and more
Slides representing Nuclear Data Management and Analysis System (NDMAS) Data for properly preserving our publicly funded data, including DOE Public Access Plan and the users of NDMAS. From Capture, Storage, Delivery & Analysis, Archival, and Qualification of Fuel fabrication, PIE, ART Experiment monitoring and operations, and processes for recording work and for looking ahead.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Anonymization of Network Traces Data through Condensation-based Differential Privacy
Network traces are considered a primary source of information to researchers, who use them to investigate research problems such as identifying user behavior, analyzing network hierarchy, maintaining network security, classifying packet flows, and much more. However, most organizations are reluctant to share their data with a third party or the public due to privacy concerns. Therefore, data anonymization prior to sharing becomes a convenient solution to both organizations and researchers. Although several anonymization algorithms are available, few of them allow sufficient privacy (organization need), acceptable data utility (researcher need), and efficient data analysis at the same time. This article introduces a condensation-based differential privacy anonymization approach that achieves an improved tradeoff between privacy and utility compared to existing techniques and produces anonymized network trace data that can be shared publicly without lowering its utility value. Our solution also does not incur extra computation overhead for the data analyzer. A prototype system has been implemented, and experiments have shown that the proposed approach preserves privacy and allows data analysis without revealing the original data even when injection attacks are launched against it. When anonymized datasets are given as input to graph-based intrusion detection techniques, they yield almost identical intrusion detection rates as the original datasets with only a negligible impact.
97 MATHEMATICS AND COMPUTING↗
Modification of Experimental Protocols for a Space Shuttle Flight and Applications for the Analysis of Cytoskeletal Structures During Fertilization, Cell Division , and Development in Sea Urchin Embryos
To explore the role of microgravity on cytoskeletal organization and skeletal calcium deposition during fertilization, cell division, and early development, the sea urchin was chosen as a model developmental system. Methods were developed to employ light, immunofluorescence, and electron microscopy on cultures being prepared for flight on the Space Shuttle. For analysis of microfilaments, microtubules, centrosomes, and calcium-requiring events, our standard laboratory protocols had to be modified substantially for experimentation on the Space Shuttle. All manipulations were carried out in a closed culture chamber containing 35 ml artificial sea water as a culture fluid. Unfertilized eggs stored for 24 hours in these chambers were fertilized with sperm diluted in sea water and fixed with concentrated fixatives for final fixation in formaldehyde, taxol, EGTA, and MgCl2(exp -6)H2O for 1 cell to 16 cell stages to preserve cytoskeletal structures for simultaneous analysis with light, immunofluorescence, and electron microscopy, and 1.5 percent glutaraldehyde and 0.4 percent formaldehyde for blastula and plueus stages. The fixed samples wre maintained in chambers without degradation for up to two weeks after which the specimens were processed and analyzed with routine methods. Since complex manipulations are not possible in the closed chambers, the fertilization coat was removed from fixation using 0.5 percent freshly prepared sodium thioglycolate solution at pH 10.0 which provided reliable immunofluorescence staining for microtubules. Sperm/egg fusion, mitosis, cytokinesis, and calcium deposition during spicule formatin in early embryogenesis were found to be without artificial alterations when compared to cells fixed fresh and processed with conventional methods.
Chakrabarti, Amitabha↗
Orientation reversal and the Chern-Simons natural boundary
We show that the fundamental property of preservation of relations, underlying resurgent analysis, provides a new perspective on crossing a natural boundary, an important general problem in theoretical and mathematical physics. This reveals a deeper rigidity aspect of resurgence in a quantum field theory path integral. The physical context here is the non-perturbative completion of complex Chern-Simons theory that associates to a 3-manifold a collection of q-series invariants labeled by Spinc structures, for which crossing the natural boundary corresponds to orientation reversal of the 3-manifold. Our new resurgent perspective leads to a practical numerical algorithm that generates q-series which are dual to unary q-series composed of false theta functions. Until recently, these duals were only known in a limited number of cases, essentially based on Ramanujan’s mock theta functions, and the common belief was that the duals might not even exist in the general case. Resurgence analysis identifies as primary objects Mordell integrals: up to changes of variables, they are Laplace transforms of resurgent functions. Their unique Borel summed transseries decomposition on either side of the Stokes line is simply the unique decomposition into real and imaginary parts. In turn, the latter are combinations of unary q-series in terms of q and its modular counterpart $\overset{\sim }{q}$ , and are resurgent by construction. The Mordell integral is analytic across the natural boundary of the q and $\overset{\sim }{q}$ series, and uniqueness of a similar decomposition which preserves algebraic relations on the other side of the boundary defines the unique boundary crossing of the q series. We demonstrate that this continuation can be efficiently implemented numerically. In the cases where unique mock modular identities are known, they are found by this numerical procedure, but the procedure can go well beyond the known list of identities. A particularly interesting feature of the resurgent approach is that it reveals new aspects, and is very different from other known approaches based on indefinite theta series, Appell-Lerch sums, and representation theory of logarithmic vertex operator algebras.
Chern-Simons theories↗
Mitigate: An Adaptive Network Data Anonymization Tool Using Condensation-Based Differential Privacy
Modern network devices collect a large amount of data that can be analyzed to identify bottlenecks, anomalies, cyber-attacks, etc. Therefore, there is often a need to analyze such collections of network data quite often by an external expert or by the research community. However, these collections of data contain sensitive, proprietary information. In order for the network data to be shared, it must first be anonymized. The overall objective of this project is to develop an innovative privacy management tool to anonymize network data and achieve sufficient privacy, acceptable data utility, and efficient data analysis at the same time. No existing anonymization methods can achieve all of these at the same time. The core of this technology is a differential private clustering algorithm that provides strong privacy protection, preserves data properties important for subsequent analysis, and allows the party receiving the anonymized data to conduct analysis directly on anonymized data without the need of decryption or any extra processing. The research carried out was to design, implement and verify a solution to this problem by completing the following tasks: 1) developing the core technology; 2) developing a context based method that automatically recommends fields that must be anonymized; 3) conducted experiments showing superior results using our approach compared to existing tools, and 4) developed an intuitive but basic user interface. The research that was conducted generated novel algorithmic techniques that utilize state-of-the-art methods such as condensation, differential privacy preservation, clustering, automated tuning based on contextual awareness, and recommendation techniques to specify columns to users for anonymization leading to optimal privacy that allows research analysis on the dataset. Experiments were conducted to evaluate the efficacy of these novel algorithmic techniques by performing analysis on original non-anonymized datasets, then conducting analysis on the same yet anonymized datasets and comparing the results of the analyses. Overall, the anonymized analysis results were within 1% of the original results, verifying that the generated technology not only guarantees a high level of privacy but also enables research analysis as if it were conducted on the original dataset. Potential applications of this technology include anonymization of any type of structured network datasets that contain sensitive identifiers, such as IP addresses, that can be used in multiple applications. For example, to create an AI or machine learning model for cyber security, e.g., to detect attacks, or for performance analysis, e.g., identify bottlenecks or predict performance. In addition, a market analysis that was conducted for potential applications of this technology identified a broader range of applications of our anonymization technology beyond the network sector that includes healthcare, banking, insurance, securities, finance (FISB), data brokering, cloud services, ad sales, and government.
97 MATHEMATICS AND COMPUTING↗
Signal-preserving CMB component separation with machine learning
Analysis of microwave sky signals, such as the cosmic microwave background, often requires component separation using multifrequency methods, whereby different signals are isolated according to their different frequency behaviors. Many so-called blind methods, such as the internal linear combination (ILC), make minimal assumptions about the spatial distribution of the signal or contaminants, and only assume knowledge of the frequency dependence of the signal. The ILC produces a minimum-variance linear combination of the measured frequency maps. In the case of Gaussian, statistically isotropic fields, this is the optimal linear combination, as the variance is the only statistic of interest. However, in many cases the signal we wish to isolate, or the foregrounds we wish to remove, are non-Gaussian and/or statistically anisotropic (in particular for the case of Galactic foregrounds). In such cases, it is possible that machine learning (ML) techniques can be used to exploit the non-Gaussian features of the foregrounds and thereby improve component separation. However, many ML techniques require the use of complex, difficult-to-interpret operations on the data. We propose a hybrid method whereby we train an ML model using only combinations of the data that , and combine the resulting ML-predicted foreground estimate with the ILC solution to reduce the error from the ILC. We demonstrate our methods on simulations of extragalactic temperature and Galactic polarization foregrounds and show that our ML model can exploit non-Gaussian features, such as point sources and spatially varying spectral indices, to produce lower-variance maps than ILC—e.g., reducing the variance of the B-mode residual by factors of up to 5—while preserving the signal of interest in an unbiased manner. Moreover, we often find improved performance even when applying our ML technique to foreground models on which it was not trained. Published by the American Physical Society 2025
McCarthy, Fiona (ORCID:0000000253893565)↗
Cleaning Study of Genesis Sample 60487
The Genesis mission collected solar wind and brought it back to Earth in order to provide precise knowledge of solar isotopic and elemental compositions. The ions in the solar wind were stopped in the collectors at depths on the order of 10 to a few hundred nanometers. This shallow implantation layer is critical for scientific analysis of the composition of the solar wind and must be preserved throughout sample handling, cleaning, processing, distribution, preparation and analysis. Particles of Genesis wafers, brine from the Utah Testing Range and an organic film have deleterious effects on many of the high-resolution instruments that have been developed to analyze the implanted solar wind. We have conducted a correlative microscopic study of the efficacy of cleaning Genesis samples with megasonically activated ultrapure water and UV/ozone cleaning. Sample 60487, the study sample, is a piece of float-zone silicon from the B/C array approximately 4.995mm x 4.145 mm in size
Kuhlman, Kim R.↗
Cellulose Acetate Replica Cleaning Study of Genesis Non-Flight Sample 3CZ00327
The Genesis mission collected solar wind and brought it back to Earth in order to provide precise knowledge of solar isotopic and elemental compositions. The ions in the solar wind were stopped in the collectors at depths on the order of 10 to a few hundred nanometers. This shallow implantation layer is critical for scientific analysis of the composition of the solar wind and must be preserved throughout sample handling, cleaning, processing, distribution, preparation and analysis. We are working interactively with the community of scientists analyzing Genesis samples, using our unique laboratory facilities -- and, where needed, our unique cleaning techniques -- to significantly enhance the science return from the Genesis mission. This work is motivated by the need to understand the submicron contamination on the collectors in the Genesis payload as recovered from the crash site in the Utah desert, and -- perhaps more importantly -- how to remove it. That is, we are evaluating the effectiveness of the wet-chemical "cleaning" steps used by various investigators, to enable them to design improved methods of stripping terrestrial contamination from surfaces while still leaving the solar-wind signal intact.
Kuhlman, K. R.↗
Catastrophic Impact of Silicon on Silicon: Unraveling the Genesis Impact Using Sample 61881
The Genesis mission collected solar wind and brought it back to Earth in order to provide precise knowledge of solar isotopic and elemental compositions. The ions in the solar wind were stopped in the collectors at depths on the order of 10 to a few hundred nanometers. This shallow implantation layer is critical for scientific analysis of the composition of the solar wind and must be preserved throughout sample handling, cleaning, processing, distribution, preparation and analysis. The current work is motivated by the need to understand the interaction of the Genesis payload with contamination during the crash in the Utah desert. Silicon contamination has been found to be notoriously difficult to remove from silicon samples despite multiple cleanings with multiple techniques. However, the question has been posed, "Does the silicon really need to be removed for large area analyses?." If the recalcitrant silicon contamination is all pure silicon from fractured collectors, only a very tiny fraction of that bulk material will contain solar wind, which could skew the analyses. This could be complicated if the silicon trapped other materials and/or gases as it impacted the surface.
Kuhlman, K. R.↗
Small Particulate Contamination Survey Of Genesis Flight Sample 61423
The Genesis mission collected solar wind and brought it back to Earth in order to provide precise knowledge of solar isotopic and elemental compositions. The ions in the solar wind stop in the collectors at depths on the order of 10 to a few hundred nanometers. This shallow implantation layer is critical for scientific analysis of the composition of the solar wind and must be preserved throughout sample handling, cleaning, processing, distribution, preparation and analysis. We continue to work with the community of scientists analyzing Genesis samples using our unique laboratory facilities -- and, where needed, our unique cleaning techniques -- to significantly enhance the science return from the Genesis mission. This work is motivated by the need to understand the submicron contamination on the collectors in the Genesis payload as recovered from the crash site in the Utah desert, and -- perhaps more importantly -- how to remove it. We continue to evaluate the effectiveness of the wet-chemical "cleaning" steps used by various investigators, to enable them to design improved methods of stripping spacecraft and terrestrial contamination from surfaces while still leaving the solar-wind signal intact.
Kuhlman, K. R.↗
dCache: The Storage System of Choice for Data-Intensive Applications
The ever-increasing volumes of data produced by modern scientific facilities like EuXFEL and LHC put significant stress on data management infrastructure operated by laboratories and research centers. The challenges to be addressed span the entire data life cycle, from ingest and efficient data analysis to long-term preservation, typically involving large tape libraries. dCache, a storage system developed in collaboration between the Deutsches Elektronen-Synchrotron (DESY), Fermi National Accelerator Laboratory, and Nordic e-Infrastructure Collaboration (NeIC), is designed to manage a large number of disk servers and to facilitate transparent data migration to and from archival storage. Its multifaceted approach offers a unified method to support a variety of scientific use cases with the same storage infrastructure, including high-throughput data ingest, data sharing over wide area networks, efficient access from HPC clusters, and long-term data preservation on tertiary storage. Initially developed for high energy physics (HEP) experiments, dCache is now used by various scientific communities, including astrophysics, biomedical research, and life sciences, each having specific requirements. This paper presents architecture, deployment strategies, performance and scalability enhancements, and recent advancements in dCache addressing the needs of scientific communities. Finally, we touch on the development and release process, ensuring the software’s high quality.
DCache↗
Single-Walled Zeolitic Nanotube–Poly(oxazoline) Nanocomposites as Heterogeneous Catalysts for Acid–Base Cascade Reactions
Zeolites with a unique, 1-dimensional form factor were recently discovered − zeolite nanotubes (ZNTs). Here we describe the synthesis and characterization of NaH-ZNTpoly( oxazoline) composites targeting liquid-phase acid−base cascade catalysis. NaH-ZNT, a one-dimensional zeolite analogue with mesoporosity (3−4 nm) associated with nanotubes and inherent Brønsted acid sites associated with the microporous zeolite domains, is functionalized with poly(oxazoline)-based triblock copolymers with varying molecular weights (3−17 kDa). The composites are characterized using N2 sorption, STEM, FTIR, and elemental analysis, confirming successful grafting and preservation of the zeolite nanotube structure. The composites’ catalytic performance is evaluated through separate acid and base reactions, followed by a combined cascade of a deacetalization−Knoevenagel condensation for the synthesis of chalcone compounds. High initial reaction rates are demonstrated, but modest overall cascade product formation rates are observed, attributed to interactions between Brønsted acid sites and base amine groups that occur in the polymer-grafted systems. Physical mixtures of NaH-ZNT-SH and poly(oxazoline)s, lacking covalent linkages between ZNT and the polymer, support this supposition. This work demonstrates the potential of NaH-ZNT-poly(oxazoline) composites for liquid-phase cascade catalysis for synthesizing compounds of potential medicinal interest, highlighting the benefits of the grafting-to approach as well as the need for further optimization of the catalytic performance.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Network-Forming Liquids from Metal-Bis(acetamide) Frameworks with Low Melting Temperatures
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99 GENERAL AND MISCELLANEOUS↗
Optical interference for the guidance of cryogenic focused ion beam milling beyond the axial diffraction limit
Cryogenic focused ion beam (Cryo-FIB) milling has become a standard step in the cryogenic electron tomography (Cryo-ET) workflow and is required to thin cells to electron-semitransparency. However, this destructive process removes the vast majority of the cellular material and raises a critical question: what thin section should be preserved for Cryo-ET analysis? Using a tri-coincident cryogenic FIB-SEM-LM system, we identify an interferometric optical response that can be used for targeting lamella production to fluorescently labeled structures with accuracy beyond the diffraction limit. Here we demonstrate this approach using synthetic samples of fluorescent beads embedded in micron-scale droplets of amorphous ice. We then apply the approach to capture virions inside host cells. Successful targeting is confirmed by Cryo-ET revealing clusters of virions in intracellular vesicles. The method does not require any fluorescent fiducials or axial registration and can be performed on any fluorescently labeled structure that is visible in widefield fluorescence microscopy.
Cryoelectron microscopy↗
Conditional diffusion machine-learning framework for mapping valence electron distribution from convergent beam electron diffraction
Quantitative convergent beam electron diffraction (CBED) enables determination of aspherical valence electron distributions through refinement of low-order structure factors, which are highly sensitive to chemical bonding and charge density variations. However, conventional quantitative CBED (QCBED) requires solving a highly nonlinear inverse problem with many coupled parameters, and computationally intensive dynamical diffraction calculations, making it time-consuming and difficult to apply to complex systems. More broadly, reconstructing charge density and orbital electron distribution from diffraction data has long been a central challenge in both x-ray and electron crystallography. Here, in this study, we introduce an artificial-intelligence (AI)-based framework that replaces traditional refinement with a data-driven inverse solver. Using a large synthetic CBED dataset generated by Bloch-wave simulations, we train a conditional diffusion model to directly infer crystal structural parameters and multipole density formalism parameters, and hence valence electron distributions, from CBED patterns alone. By learning from forward simulations across realistic parameter space, the model effectively solves the inverse problem. Compared with direct regression approaches, the diffusion-based framework provides posterior parameter distributions for rigorous uncertainty quantification while preserving quantitative fidelity and reducing analysis time by orders of magnitude. By eliminating the need for external single-crystal x-ray diffraction data and complex nonlinear refinement, this approach enables practical, high-throughput, and in situ quantitative CBED, enabling real-time mapping of valence electron distributions and their correlation with functional responses in quantum and energy materials.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Diagnostic Evaluation of the IS1081-Targeted Real-Time PCR for Detection of Mycobacterium bovis DNA in Bovine Milk Samples
The ability of Mycobacterium bovis (M. bovis) to survive in bovine milk has emerged as a serious public health concern. The first objective of this study was to evaluate the diagnostic utility of IS1081-targeted real-time PCR for the detection of M. bovis DNA in different fractions of bovine milk. In a model study, bovine milk samples were spiked with serially diluted M. bovis BCG to investigate the detection limit of M. bovis DNA in whole milk and milk fractions (cream, pellet, and pellet + cream combined) using IS1081 real-time PCR. The assay was then used to detect M. bovis DNA in whole milk and milk fractions from naturally infected animals. The results showed that the IS1081 real-time PCR was more sensitive when detecting M. bovis DNA in the cream layer alone and cream + pellet combined compared to whole milk or the pellet alone. While PCR-based diagnostic assays for the detection of M. bovis in milk samples provide a quicker diagnostic tool for bovine tuberculosis, safe processing, and handling of M. bovis-infected milk samples remain a challenge and pose a human health risk. PrimeStore Molecular Transport Medium (MTM) has been shown to rapidly inactivate infected specimens while preserving nucleic acid for subsequent Molecular analysis. Therefore, the secondary objective of this study was to evaluate the ability of MTM to inactivate M. bovis BCG in spiked milk samples as well as its ability to preserve BCG DNA for the PCR assay. The results showed that MTM can successfully inactivate BCG alone or in spiked milk samples while preserving DNA for the PCR assay. The CT values of M. bovis BCG alone and spiked milk samples aliquoted in MTM and without MTM were similar at various dilutions. Taken together, our results indicate that using DNA extracted from the milk cream fraction alone or combined milk cream and pellet improved the recovery rate of M. bovis DNA in bovine milk samples. MTM has the potential to provide a safe and rapid sample processing tool for M. bovis inactivation in milk samples and preserve DNA for molecular diagnostics.
59 BASIC BIOLOGICAL SCIENCES↗