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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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Improvements in computing system security updates and purchasing policies could save the Army as much as 85 million kWh and $5 million per year when applied to the Army’s more than one million computers. This represents the largest and highest-priority plug load energy-and cost-saving measure identified by a recent study focused on Army buildings.
Abstract Efficiently extracting data from tables in the scientific literature is pivotal for building large‐scale databases. However, the tables reported in materials science papers exist in highly diverse forms; thus, rule‐based extractions are an ineffective approach. To overcome this challenge, the study presents MaTableGPT, which is a GPT‐based table data extractor from the materials science literature. MaTableGPT features key strategies of table data representation and table splitting for better GPT comprehension and filtering hallucinated information through follow‐up questions. When applied to a vast volume of water splitting catalysis literature, MaTableGPT achieves an extraction accuracy (total F1 score) of up to 96.8%. Through comprehensive evaluations of the GPT usage cost, labeling cost, and extraction accuracy for the learning methods of zero‐shot, few‐shot, and fine‐tuning, the study presents a Pareto‐front mapping where the few‐shot learning method is found to be the most balanced solution owing to both its high extraction accuracy (total F1 score >95%) and low cost (GPT usage cost of 5.97 US dollars and labeling cost of 10 I/O paired examples). The statistical analyses conducted on the database generated by MaTableGPT revealed valuable insights into the distribution of the overpotential and elemental utilization across the reported catalysts in the water splitting literature.
Here, the storyline of MITRE’s common weakness enumeration framework illustrates how the security and privacy technical community can collaborate/cooperate with policy makers to advance policy, giving it specifics and filling gaps of technical knowledge to improve security and resilience of critical infrastructure.
After completing this module, you should be able to: Describe the role of Nuclear Material Accounting and Control (NMAC) in comprehensive nuclear security at a facility; Describe purpose of NMAC; Identify differences between the use of NMAC for IAEA safeguards and for facility nuclear security; List NMAC elements and measures; and Describe process for resolution of irregularities
The SSC work arose from VOLTTRON’s PNNL campus deployment and resulted in a Threat Profile containing critical assets, prioritized threats, and controls for mitigating those threats. The Threat Profile outlines controls already in place, controls not yet in place, and recommendations for implementing controls. As a companion to the software, the Threat Profile shows due diligence in cybersecurity risk management in terms of security requirements, security recommendations, security budgeting, justification, and prioritization. This report describes the SSC offerings in general and the specific offerings applied to VOLTTRON.
Biopharmaceutical production systems and processes are vulnerable to cyberattacks from sophisticated adversaries. Consequently, it is imperative to start building biopharmaceutical manufacturing systems that offer verifiable formalism and transform the current state of security across all production stages.
In a secure collaborative environment, tera-bytes of data generated from powerful scientific instruments are used to train secure machine learning (ML) models on exascale computing systems, which are then securely shared with internal or external collaborators as cloud-based services. Devising such a secure platform is necessary for seamless scientific knowledge sharing without compromising individual, or institute-level, intellectual property and privacy details. By enabling new computing opportunities with sensitive data, we envision a secure collaborative environment that will play a significant role in accelerating scientific discovery. Several recent technological advancements have made it possible to realize these capabilities. In this paper, we present our efforts at ORNL toward developing a secure computation platform. We present a use case where scientific data generated from complex instruments, like those at the Spallation Neutron Source (SNS), are used to train a differential privacy enabled deep learning (DL) network on Summit, which is then hosted as a secure multi-party computation (MPC) service on ORNL’s Compute and Data Environment for Science (CADES) cloud computing platform for third-party inference. In this feasibility study, we discuss the challenges involved, elaborate on leveraged technologies, analyze relevant performance results and present the future vision of our work to establish secure collaboration capabilities within and outside of ORNL.
Timestamped relational datasets consisting of records (or connections) between pairs of entities are ubiquitous in network science. For applications like peer-to-peer communication, email, various social network interactions, and computer network security, it is useful to organize these records into groups based on how and when they are occurring. Weighted line graphs offer a natural way to model how records are related in such datasets but for large real-world graph topologies, building and utilizing the line graph is prohibitively expensive. Here, we present the framework to cluster the edges of a dynamic graph via the associated line graph that contains two major contributions. The first is a method to work with the line graph implicitly and the second is a distributed scale implementation of an agglomerative hierarchical graph clustering algorithm. We outline a novel hierarchical dynamic graph edge clustering approach that efficiently breaks massive relational datasets into small sets of edges containing events at various timescales. This is in stark contrast to traditional graph clustering algorithms that prioritize highly connected (clique-like) community structures. Our approach relies on constructing a sufficient subgraph of a weighted line graph and applying a hierarchical agglomerative clustering. This approach is related to scalable techniques from spatial clustering, nonlinear-dimension reduction, topological data analysis, and draws particular inspiration from HDBSCAN. As an edge clustering, this method yields an overlapping node clustering. Our algorithm is parallelizable and we demonstrate efficient clustering of a billion-scale, real-world dynamic graph into small edge sets that correlate in topology and time. The entire clustering process for a graph with tens of billions of edges takes just a few minutes of run time on 256 nodes of a distributed compute environment. We argue how the output of the edge clustering is useful for a multitude of data visualization and powerful machine learning tasks, both involving the original massive dynamic graph data and metadata associated with the nodes and edges. Finally, we describe how this approach can be extended to dynamic hypergraphs and dynamic graphs/hypergraphs with unstructured data living on vertices and edges.
This software computes different metrics that quantify energy supply security. It computes metrics for the amount of energy self-production, autonomy, system adequacy, self-sufficiency and autarky. The software has been developed to compute metrics that are introduced in a journal manuscript, and the software is intended to be published to make the underlying metric calculation accessible and reproducible to the reader of the article. Potential use of the software is for policymakers, energy planners and researchers to guide energy system infrastructure decisions.
NP-hard problems such as the maximum clique or minimum vertex cover problems, two of Karp’s 21 NP-hard problems, have several applications in computational chemistry, biochemistry and computer network security. Adiabatic quantum annealers can search for the optimum value of such NP-hard optimization problems, given the problem can be embedded on their hardware. However, this is often not possible due to certain limitations of the hardware connectivity structure of the annealer. This paper studies a general framework for a decomposition algorithm for NP-hard graph problems aiming to identify an optimal set of vertices. Our generic algorithm allows us to recursively divide an instance until the generated subproblems can be embedded on the quantum annealer hardware and subsequently solved. Furthermore, the framework is applied to the maximum clique and minimum vertex cover problems, and we propose several pruning and reduction techniques to speed up the recursive decomposition. The performance of both algorithms is assessed in a detailed simulation study.
With the fast growing popularity of the Internet, many organizations are racing to get onto the on-ramp to the Information Superhighway. However, with frequent headlines such as 'Hackers' break in at General Electric raises questions about the Net's Security', 'Internet Security Imperiled - Hackers steal data that could threaten computers world-wide' and 'Stanford Computer system infiltrated; Security fears grow', organizations find themselves rethinking their approach to the on-ramp. Is the Internet safe? What do I need to do to protect my organization? Will hackers try to break into my systems? These are questions many organizations are asking themselves today. In order to safely travel along the Information Superhighway, organizations need a strong security framework. Developing such a framework for a computer site, whether it be just a few dozen hosts or several thousand hosts is not an easy task. The security infrastructure for a site is often developed piece-by-piece in response to security incidents which have affected that site over time. Or worse yet, no coordinated effort has been dedicated toward security. The end result is that many sites are still poorly prepared to handle the security dangers of the Internet. This paper presents guidelines for building a successful security infrastructure. The problem is addressed in a cookbook style method. First is a discussion on how to identify your assets and evaluate the threats to those assets; next are suggestions and tips for identifying the weak areas in your security armor. Armed with this information we can begin to think about what you really need for your site and what you can afford. In this stage of the process we examine the different categories of security tools and products that are available and then present some tips for deciding what is best for your site.
Quantum information technology offers the potential to realize unprecedented computational resources via secure channels distributing entanglement between quantum computers. Diamond, as a host to optically-accessible spin qubits, is a leading platform to realize quantum memory nodes needed to extend such quantum links. Photonic crystal (PhC) cavities enhance light-matter interaction and are essential for an efficient interface between spins and photons that are used to store and communicate quantum information respectively. Here, we demonstrate one- and two-dimensional PhC cavities fabricated in thin-film diamonds, featuring quality factors (Q) of 1.8 × 10 5 and 1.6 × 10 5 , respectively, the highest Qs for visible PhC cavities realized in any material. Importantly, our fabrication process is simple and high-yield, based on conventional planar fabrication techniques, in contrast to the previous with complex undercut processes. We also demonstrate fiber-coupled 1D PhC cavities with high photon extraction efficiency, and optical coupling between a single SiV center and such a cavity at 4 K achieving a Purcell factor of 18. The demonstrated photonic platform may fundamentally improve the performance and scalability of quantum nodes and expedite the development of related technologies.
In this issue, we review quantum computing: what has been accomplished and what is its future. We introduce a collection of opinions about the realistic challenges facing the future of quantum computing.
Virtual Power Plants (VPPs) aggregate distributed energy resources (DERs) to provide grid services traditionally delivered by centralized power plants. This article reviews the current state of VPP deployment, highlighting business models, compensation mechanisms, and global pilot projects. While VPPs offer benefits such as grid flexibility, resilience, and cost savings, challenges remain in communication infrastructure, regulatory frameworks, market access, and customer engagement. To address these, we propose a scalable, privacy-preserving hierarchical VPP architecture that coordinates with distribution utilities and preserves customer data. We also present the Integrated T&D Control Room of the Future as a key test bed for validating and accelerating VPP adoption. These innovations can help transition VPPs from pilot programs to integral components of a modern, reliable power grid.
There is a lack of scientific testing of commercially available malware detectors, especially those that boast accurate classification of never-before-seen (i.e., zero-day) files using machine learning (ML). Consequently, efficacy of malware detectors is opaque, inhibiting end users from making informed decisions and researchers from targeting gaps in current detectors. In this paper, we present a scientific evaluation of four prominent commercial malware detection tools to assist an organization with two primary questions: To what extent do ML-based tools accurately classify previously and never-before-seen files? Is purchasing a network-level malware detector worth the cost? To investigate, we tested each tool against 3,536 total files (2,554 or 72% malicious, 982 or 28% benign) of a variety of file types, including hundreds of malicious zero-days, polyglots, and APT-style files, delivered on multiple protocols. We present statistical results on detection time and accuracy, consider complementary analysis (using multiple tools together), and provide two novel applications of the recent cost-benefit evaluation procedure of Iannacone & Bridges. Although the ML-based tools are more effective at detecting zero-day files and executables, the signature-based tool might still be an overall better option. Both network-based tools provide substantial (simulated) savings when paired with either host tool, yet both show poor detection rates on protocols other than HTTP or SMTP. Our results show that all four tools have near-perfect precision but alarmingly low recall, especially on file types other than executables and office files—37% of malware, including all polyglot files, were undetected. Priorities for researchers and takeaways for end users are given. Code for future use of the cost model is provided.
Computer System Security: The software is used to perform remote data integrity checking without relying on a single third party. In order to remove the trusted third party our tool uses distributed ledger (Blockchain) technology. The ledger is used to store evidences (in the form of hash values). These values are used as an input in the integrity checking process.
We discuss the challenges and approaches to securing numeric computation against adversaries who may want to discover hidden parameters or values used by the algorithm. We discuss techniques that are both cryptographic and non-cryptographic in nature. Cryptographic solutions are either not yet algorithmically feasible or currently require more computational resources than are reasonable to have in a deployed setting. Non-cryptographic solutions may be computationally faster, but these cannot stop a determined adversary. For one such non-cryptographic solution, mixed Boolean arithmetic, we suggest a number of improvements that may protect the obfuscated calculation against current automated deobfuscation methods.
A previously obscure area of cryptography known as Secure Multiparty Computation (MPC) is enjoying increased attention in the field of privacy-preserving machine learning (ML), because ML models implemented using MPC can be uniquely resistant to capture or reverse engineering by an adversary. In particular, an adversary who captures a share of a distributed MPC model provably cannot recover the model itself, nor data evaluated by the model, even by observing the model in operation. We report on our small project to survey current MPC software and judge its practicality for fielding mission-relevant distributed machine learning models.