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Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining
When can reliable inference be drawn in the ‘‘Big Data’’ context? This article presents a framework for answering this fundamental question in the context of correlation mining, with implications for general large-scale inference. In large-scale data applications like genomics, connectomics, and eco-informatics, the data set is often variable rich but sample starved: a regime where the number n of acquired samples (statistical replicates) is far fewer than the number p of observed variables (genes, neurons, voxels, or chemical constituents). Much of recent work has focused on understanding the computational complexity of proposed methods for ‘‘Big Data.’’ Sample complexity, however, has received relatively less attention, especially in the setting when the sample size n is fixed, and the dimension p grows without bound. To address this gap, we develop a unified statistical framework that explicitly quantifies the sample complexity of various inferential tasks. Sampling regimes can be divided into several categories: 1) the classical asymptotic regime where the variable dimension is fixed and the sample size goes to infinity; 2) the mixed asymptotic regime where both variable dimension and sample size go to infinity at comparable rates; and 3) the purely high-dimensional asymptotic regime where the variable dimension goes to infinity and the sample size is fixed. Each regime has its niche but only the latter regime applies to exa-scale data dimension. We illustrate this high-dimensional framework for the problem of correlation mining, where it is the matrix of pairwise and partial correlations among the variables that are of interest. Correlation mining arises in numerous applications and subsumes the regression context as a special case. We demonstrate various regimes of correlation mining based on the unifying perspective of high-dimensional learning rates and sample complexity for different structured covariance models and different inference tasks.
Further Refinements to the Foundations of Cyber Zone Defense.
Abstract not provided.
Foundations for the Future Power System: Inverter-Based Resource Interconnection Standards
As power systems evolve to become reliant on solar, wind, batteries, and other inverter-based resources (IBRs), it is essential that those resources meet certain minimum performance and capability criteria designed to ensure the power system operates stably and reliably. Because those criteria, as enshrined in interconnection standards, take years to develop and are very long lived, they need to account for not only the present state of the power system but also its expected future state over the lifetime of the power plants to which they will apply. In addition, they need to be specific enough to ensure reliability without over specifying and, thereby, impeding innovation. Because power systems are shifting rapidly from a state where IBRs make up a small to medium portion of generation to one where the generation will, at times, come predominantly from IBRs, interconnection standards are especially challenging to develop today. Good interconnection standards can make the power system more reliable and less costly to operate, whereas poorly designed standards can lead to major problems, like the famous German 50.2-Hz problem, which introduced a risk of losing many gigawatts of solar on a frequency excursion and resulted in many IBRs being retrofitted at great cost to mitigate a major system reliability risk. Readying the power system by specifying forward-looking technical minimum functional capabilities for IBRs can be an effective approach to avoid future retrofits.
Long-Term Stability Analysis of Power Systems With Wind Power Based on Stochastic Differential Equations: Model Development and Foundations
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Choice reaction time and grip strength as predictors of cardiovascular mortality in middle‐aged and elderly Japanese: from the Radiation Effects Research Foundation Adult Health study
Abstract Background Cognitive function and physical function are important predictors of mortality. Aim To investigate whether or not reaction time (RT) as a cognitive function and grip strength (GS) as a physical function were associated, alone or in combination, with mortality from heart disease or stroke. Methods The subjects included 4901 Adult Health Study participants in Hiroshima who had undergone RT and GS measurements, were 35–74 years old at baseline (1970–1972) and were followed until the end of 2007. Results After adjustment for other potential risk factors, RT was positively and GS was negatively associated with mortality from both heart disease and stroke. These associations were persistent in the model when adjusting simultaneously for RT, GS and other factors, but hazard ratios were attenuated. When we evaluated the associations by baseline age and gender, we found the greater hazard ratios for RT in the younger cohort, but no clear modification by age for GS. The interaction between RT and GS was statistically significant ( P = 0.012) for stroke mortality. In the stratified analyses divided using the age‐specific median value of RT or GS, the estimated hazard ratio of stroke mortality for RT was significant in participants with weak or strong GS but greater in the former, and for GS, it was only significant in participants with slow RT. Conclusion RT and GS, alone and in combination, predicted heart disease and stroke mortalities. Interventions for both cognitive function and physical function may be beneficial for the prevention of cardiovascular disease mortality.
Building a better foundation: improving root-trait measurements to understand and model plant and ecosystem processes
Trait-based approaches provide a useful framework to investigate plant strategies for resource acquisition, growth, and competition, as well as plant impacts on ecosystem processes. Despite significant progress capturing trait variation within and among stems and leaves, identification of trait syndromes within fine-root systems and between fine roots and other plant organs is limited. Here we discuss three underappreciated areas where focused measurements of fine-root traits can make significant contributions to ecosystem science. These include assessment of spatiotemporal variation in fine-root traits, integration of mycorrhizal fungi into fine-root-trait frameworks, and the need for improved scaling of traits measured on individual roots to ecosystem-level processes. Progress in each of these areas is providing opportunities to revisit how below-ground processes are represented in terrestrial biosphere models. Targeted measurements of fine-root traits with clear linkages to ecosystem processes and plant responses to environmental change are strongly needed to reduce empirical and model uncertainties. Further identifying how and when suites of root and whole-plant traits are coordinated or decoupled will ultimately provide a powerful tool for modeling plant form and function at local and global scales.
Theoretical foundations of detection of terahertz radiation in laser-plasma interactions
A theory is developed enabling one to calculate the temporal profile and spectrum of a terahertz wave packet from the energy of the second harmonic of optical radiation generated during the nonlinear interaction between terahertz and circularly polarized laser pulses in the skin layer of an overdense plasma. It is shown that the spectral and temporal characteristics of the envelope of the second harmonic of optical radiation coincide with those of the terahertz pulse only at small durations of the detecting laser radiation. For long laser pulses, the temporal profile and spectrum of the second harmonic are mainly determined by the characteristics of optical radiation at the carrier frequency.
Foundations of automatic feature extraction at LHC–point clouds and graphs
Abstract Deep learning algorithms will play a key role in the upcoming runs of the Large Hadron Collider (LHC), helping bolster various fronts ranging from fast and accurate detector simulations to physics analysis probing possible deviations from the Standard Model. The game-changing feature of these new algorithms is the ability to extract relevant information from high-dimensional input spaces, often regarded as “replacing the expert” in designing physics-intuitive variables. While this may seem true at first glance, it is far from reality. Existing research shows that physics-inspired feature extractors have many advantages beyond improving the qualitative understanding of the extracted features. In this review, we systematically explore automatic feature extraction from a phenomenological viewpoint and the motivation for physics-inspired architectures. We also discuss how prior knowledge from physics results in the naturalness of the point cloud representation and discuss graph-based applications to LHC phenomenology.
ChatHPC: Building the Foundations for a Productive and Trustworthy AI-Assisted HPC Ecosystem
ChatHPC democratizes large language models for the high-performance computing (HPC) community by providing the infrastructure, ecosystem, and knowledge needed to apply modern generative AI technologies to rapidly create specific capabilities for critical HPC components while using relatively modest computational resources. Our divide-and-conquer approach focuses on creating a collection of reliable, highly specialized, and optimized AI assistants for HPC based on the cost-effective and fast Code Llama fine-tuning processes and expert supervision. We target major components of the HPC software stack, including programming models, runtimes, I/O, tooling, and math libraries. Thanks to AI, ChatHPC provides a more productive HPC ecosystem by boosting important tasks related to portability, parallelization, optimization, scalability, and instrumentation, among others. With relatively small datasets (on the order of KB), the AI assistants, which are created in a few minutes by using one node with two NVIDIA H100 GPUs and the ChatHPC library, can create new capabilities with Meta’s 7-billion parameter Code Llama base model to produce high-quality software with a level of trustworthiness of up to 90% higher than the 1.8-trillion parameter OpenAI ChatGPT-4o model for critical programming tasks in the HPC software stack.
Foundation Models for Zero-Shot Segmentation of Scientific Images without AI-Ready Data
Zero-shot and prompt-based models have excelled at visual reasoning tasks by leveraging large-scale natural image corpora, but they often fail on sparse and domain-specific scientific image data. We introduce Zenesis, a no-code interactive computer vision platform designed to reduce data readiness bottlenecks in scientific imaging workflows. Zenesis integrates lightweight multimodal adaptation for zero-shot inference on raw scientific data, human-in-the-loop refinement, and heuristic-based temporal enhancement. We validate our approach on Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) datasets of catalyst-loaded membranes. Zenesis outperforms baselines, achieving an average accuracy of 0.947, Intersection over Union (IoU) of 0.858, and Dice score of 0.923 on amorphous catalyst samples; and 0.987 accuracy, 0.857 IoU, and 0.923 Dice on crystalline samples. These results represent a significant performance gain over conventional methods such as Otsu thresholding and standalone models like the Segment Anything Model (SAM). Zenesis enables effective image segmentation in domains where annotated datasets are limited, offering a scalable solution for scientific discovery.
New Foundations of Newman’s Theory for Solid Electrolytes: Thermodynamics and Transient Balances
Not provided.
Supporting Documentation for "An Exploratory Data Mining Study of Re-entry Events: Foundations for Multi-Modality Sensing and Data Fusion"
This dataset represents the tables generated as part of this report.
Hygrothermal Simulations of Foundations: Part 1 - Soil Material Properties
Hygrothermal performance of soils coupled to buildings is a complicated process. The computational approach for heat transfer via the ground is well defined (EN-ISO-13370:, 2007) together with simplified methods (Staszczuk, Radon, & Holm). Though the soil moisture transfer is generally ignored, it is proven not negligible (Janssen, Carmeliet, & Hens, 2004). Even though reliable material properties of soils are required to perform realistic hygrothermal calculations of soils coupled to buildings, such material properties have not been well defined in hygrothermal calculations tools. Typical building constructions which are greatly influenced by soils are basements, crawl spaces and slab on grade and reliable hygrothermal performance of such construction are highly requested; as it is ranked within the top 10 Building America Enclosure Research Ideas according to Enclosures STC - Residential Energy Efficiency Stakeholder Meeting, February 29, 2012 Austin, TX. There exists an extensive amount of measurements on soil properties in Soil Science though this information must be gathered as well as adapted to be applicable in Building Science and for hygrothermal simulation purposes. Soil properties are important when analyzing and designing both new building constructions and retrofitting measures, where the outer boundary of the buildings enclosure consists of soil materials. Concerning basement energy retrofits, interior solutions of improving the energy demand has to cooperate with the existing soil properties and must therefore be designed thereafter. In concerns of exterior retrofits, the soil material can be replaced, if needed, with a more suitable filling material, though this approach applies only for basement walls. The soil material beneath the basement floor can naturally not be replaced hence the soil properties of this part of the buildings enclosure still must be taken into consideration. This study is divided into several parts. The intention of the first part is to gather, comprehend and adapt soil properties from soil science. The obtained information must be applicable for Building Science related tasks and validated in hygrothermal calculation tools hence the second part of this study will focus on validation of the implemented soil properties. Basic changes in the software code may be requested as well. Different basement constructions will be created with a hygrothermal calculation tool, WUFI, from which simulations will be compared with existing or on-going measurements. The final outcome of the study is to enable an evaluation of several soil types in several climate zones combined with a number of basement assemblies. The study will define which type of soil together with a certain building construction which is considered most and least reliable in concerns of energy consumption and moisture safety. Further, what influences different soils will have on the total energy loss via the ground and if the performance of a different soils can be measured by a comparison of soil properties solely.
Development of Fe 3 O 4 core–TiO 2 shell nanocomposites and nanoconjugates as a foundation for neuroblastoma radiosensitization
Neuroblastoma is the most common extracranial solid malignancy in childhood which, despite the current progress in radiotherapy and chemotherapy protocols, still has a high mortality rate in high risk tumors. Nanomedicine offers exciting and unexploited opportunities to overcome the shortcomings of conventional medicine. The photocatalytic properties of Fe 3 O 4 core-TiO 2 shell nanocomposites and their potential for cell specific targeting suggest that nanoconstructs produced using Fe 3 O 4 core-TiO 2 shell nanocomposites could be used to enhance radiation effects in neuroblastoma. In this study, we evaluated bare, metaiodobenzylguanidine (MIBG) and 3,4-Dihydroxyphenylacetic acid (DOPAC) coated Fe 3 O4@TiO 2 as potential radiosensitizers for neuroblastoma in vitro. The uptake of bare and MIBG coated nanocomposites modestly sensitized neuroblastoma cells to ionizing radiation. Conversely, cells exposed to DOPAC coated nanocomposites exhibited a five-fold enhanced sensitivity to radiation, increased numbers of radiation induced DNA double-strand breaks, and apoptotic cell death. The addition of a peptide mimic of the epidermal growth factor (EGF) to nanoconjugates coated with MIBG altered their intracellular distribution. Cryo X-ray fluorescence microscopy tomography of frozen hydrated cells treated with these nanoconjugates revealed cytoplasmic as well as nuclear distribution of the nanoconstructs. The intracellular distribution pattern of different nanoconjugates used in this study was different for different nanoconjugate surface molecules. Cells exposed to DOPAC covered nanoconjugates showed the smallest nanoconjugate uptake, with the most prominent pattern of large intracellular aggregates. Interestingly, cells treated with this nanoconjugate also showed the most pronounced radiosensitization effect in combination with the external beam x-ray irradiation. Further studies are necessary to evaluate mechanistic basis for this increased radiosensitization effect. Preliminary studies with the nanoparticles carrying an EGF mimicking peptide showed that this approach to targeting could perhaps be combined with a different approach to radiosensitization – use of nanoconjugates in combination with the radioactive iodine. Much additional work will be necessary in order to evaluate possible benefits of targeted nanoconjugates carrying radionuclides.
Systems 4.0: Systems Foundations for Industry 4.0
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DPSIR-ESA Vulnerability Assessment (DEVA) Framework: Synthesis, Foundational Overview, and Expert Case Studies
Land resources are central to understanding the relationship between humans and their environment. We broadly define a land resource to include all the ecological resources of climate, water, soil, landforms, flora, and fauna, and all the socioeconomic systems that interact with agriculture, forestry, and other land uses within some system boundary. Understanding the vulnerability of land resources to changes in land management or climate forcing is critical to developing sustainable land management strategies. Vulnerability assessments are complex given the multiple uses of the assessments, the multi-disciplinary nature of the problem, limited understanding, the dynamic structure of vulnerability, scale issues, and problems with identifying effective vulnerability indicators. Here, we propose a novel conceptual framework for vulnerability assessments of land resources that combines the driver– pressure–state–impact–response (DPSIR) framework adopted by the European Environment Agency to describe interactions between society and the environment, and the exposure-sensitivity-adaptive capacity (ESA) framework used by the Intergovernmental Panel on Climate Change to assess impacts of climate change. The DPSIR-ESA Vulnerability Assessment (DEVA) framework operationalizes the process of assessing the vulnerability of a target system to external stressors. The DEVA framework includes the following elements: 1) Definition of the target system (Land resource), 2) Description of internal characteristics of the target system (State), 3) Description of target system vulnerability indicators (Adaptive capacity, Sensitivity), 4) Description of stressor characteristics (Drivers, Pressures), 5) Description of stressor vulnerability indicators (Exposure), 6) Description of target system response to stressors (Impacts), and 7) Description of modifications to target systems or stressors (Responses). In stating that they have “applied the DEVA framework”, analysts acknowledge that they have (a) considered the full breadth of each DEVA element, (b) have made conscious decisions to limit the scope and complexity of certain elements, and (c) can communicate both the rationale for these decisions and the impact of these decisions on the vulnerability assessment results and recommendations. The DEVA framework was refined during invited presentations and follow-up discussions from a series of Special Sessions with leading experts at two successive ASABE Annual International Meetings. Six case studies drawn from the sessions elaborate upon the DEVA framework and provide concrete examples of the key concepts. The DEVA approach gives engineers, planners, and analysts a new, flexible framework to apply a broad array of useful tools toward assessment of land resource system vulnerability.