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At least 109 records · Page 6

NeuroSymbolic Approaches as a Vector for Assured Artificial Intelligence

The deployment of artificial intelligence systems in critical applications requires higher levels of assurance for safety, security, and interpretability. While neurosymbolic (NESY) approaches combining neural networks with symbolic reasoning offer potential advantages for assured AI, existing differentiable neurosymbolic frameworks face significant limitations including computational overhead and performance constraints. This report investigates the ISED (InferSampleEstimateDescend) framework as an alternative approach that enables neurosymbolic learning without requiring endtoend differentiability. We evaluate ISED’s utility for geointelligence applications by comparing neurosymbolic models against standard neural networks on aircraft classification tasks using the RarePlanes and MTARSI imagery datasets. Our results demonstrate that while ISEDbased models achieve slightly lower accuracy (89.7% vs 92.1% on RarePlanes; 91.1% vs 92.5% on MTARSI), they provide critical explainability capabilities that enable tracing incorrect predictions back to specific attribute misclassifications. We also present an automated pipeline that generates both attributeclass mappings and neurosymbolic model architectures from natural language descriptions, significantly reducing the manual effort required for NESY model deployment. These findings suggest that ISED offers a promising direction for developing assured AI systems where interpretability and reasoning transparency are prioritized alongside performance.

97 MATHEMATICS AND COMPUTING↗

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science [Slides]

Machine intelligence has the potential to revolutionize materials science, enabling autonomous synthesis, self-driving characterization, and accelerated modeling. However, despite the promise, successful implementation of these methods in day-to-day research remains a challenge. This talk will delve into the reasons behind this, exploring how truly intelligent experiments are hindered by opaque experiment control, a lack of domain-specific models, and human-centric design. Through a focus on the characterization of next-generation microelectronics and energy storage materials, I will share insights from both successful and failed attempts to implement machine intelligence. We will then explore the next steps necessary to unlock the full potential of machine intelligence in materials science, creating a future where intelligent systems work seamlessly alongside researchers to drive innovation and discovery.

36 MATERIALS SCIENCE↗

TIME-RESOLVED EMISSION FROM BRIGHT HOT PIXELS OF AN ACTIVE REGION OBSERVED IN THE EUV BAND WITH SDO/AIA AND MULTI-STRANDED LOOP MODELING

Evidence of small amounts of very hot plasma has been found in active regions and might be an indication of impulsive heating released at spatial scales smaller than the cross-section of a single loop. We investigate the heating and substructure of coronal loops in the core of one such active region by analyzing the light curves in the smallest resolution elements of solar observations in two EUV channels (94 and 335 Å) from the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory. We model the evolution of a bundle of strands heated by a storm of nanoflares by means of a hydrodynamic 0D loop model (EBTEL). The light curves obtained from a random combination of those of single strands are compared to the observed light curves either in a single pixel or in a row of pixels, simultaneously in the two channels, and using two independent methods: an artificial intelligent system (Probabilistic Neural Network) and a simple cross-correlation technique. We explore the space of the parameters to constrain the distribution of the heat pulses, their duration, their spatial size, and, as a feedback on the data, their signatures on the light curves. From both methods the best agreement is obtained for a relatively large population of events (1000) with a short duration (less than 1 minute) and a relatively shallow distribution (power law with index 1.5) in a limited energy range (1.5 decades). The feedback on the data indicates that bumps in the light curves, especially in the 94 Å channel, are signatures of a heating excess that occurred a few minutes before.

79 ASTRONOMY AND ASTROPHYSICS↗

Meeting Report: Metagenomics, Metadata and MetaAnalysis (M3) at ISMB 2010

This report summarizes the proceedings of the first day of the Metagenomics, Metadata and MetaAnalysis (M3) workshop held at the Intelligent Systems for Molecular Biology 2010 conference. The second day, which was dedicated to the inaugural meeting of the BioSharing initiative is presented in a separate report. The Genomic Standards Consortium (GSC) hosted the first day of this Special Interest Group (SIG) at ISMB to continue exploring the bottlenecks and emerging solutions for obtaining biological insights through large-scale comparative analysis of metagenomic datasets. The M3 SIG included invited and selected talks and a panel discussion at the end of the day involving the plenary speakers. Further information about the GSC and its range of activities can be found at http://gensc.org. Information about the newly established BioSharing effort can be found at http://biosharing.org/.

59 BASIC BIOLOGICAL SCIENCES↗

Optimizing Batch Crystallization with Model-based Design of Experiments

Adaptive and self-optimizing intelligent systems such as digital twins are increasingly important in science and engineering. Digital twins utilize mathematical models to provide added precision to decision-making. However, physics-informed models are challenging to build, calibrate, and validate with existing data science methods. Model-based design of experiments (MBDoE) is a popular framework for optimizing data collection to maximize parameter precision in mathematical models and digital twins. In this work, we apply MBDoE, facilitated by the open-source package Pyomo.DoE, to train and validate mathematical models for batch crystallization. We quantitatively examined the estimability of the model parameters for experiments with different cooling rates. This analysis provides a quantitative explanation for the heuristic of using multiple experiments at different cooling rates.

Lynch, Hailey↗

ISMB Conference Proceedings

This special issue comprises the papers accepted for presentation at the 19th Annual International Conference on Intelligent Systems for Molecular Biology, joint with the 10th European Conference on Computational Biology, an official conference of the International Society for Computational Biology (ISCB; http://www.iscb.org). ISMB/ECCB 2011 (http://www.iscb.org/ismb2011/) will take place in Vienna, Austria, from July 17 through July 19, 2011; preceded during July 14–16 by eight 1- or 2- day Special Interest Group (SIG) meetings, three satellite meetings and nine half-day tutorials; and followed by two additional satellite meetings. The 48 papers in this volume were selected from 258 submitted papers. Submitted papers were assigned to 13 areas. Area Chairs led each topic area by selecting their area's program committee and overseeing the reviewing process. Many Area Chairs were new compared to 2010, and two completely new areas were added in 2011, ‘Data Visualization’ and ‘Mass Spectrometry and Proteomics’. Six papers for which Area Chairs were in conflict were reviewed under a ‘Conflicts Management’ section headed by the Proceedings Chairs; one such paper was accepted in ‘Bioimaging’. Areas, co-chairs and acceptance information are listed in Table 1. Compared to prior years, five mature topic areas had steady submissions, ‘Evolution and Comparative Genomics’, ‘Gene Regulation and Transcriptomics’, ‘Protein Structure and Function’, ‘Sequence Analysis’, ‘Text Mining’. Two areas newer to ISMB were underrepresented this year, ‘Bioimaging’ and ‘Disease Models and Epidemiology’. One area doubled, ‘Applied Bioinformatics’, renamed from last year's ‘Other Bioinformatics Applications’; and one tripled, ‘Protein Interactions and Molecular Networks’. Across the areas, 326 members of the bioinformatics community provided reviews. Most papers received three reviews and several received four or more. There was significant discussion of the merits of the papers first between referees and the Area Chairs, and then between Area Chairs and the proceedings chairs. Initial decisions to accept papers were made during a comprehensive conference call with the Area Chairs, and several papers underwent a further evaluation by additional Area Chairs before acceptance decisions were finalized. We appreciate that in several cases reviewers' opinions might have changed with additional input or further clarifications from the authors; because the ISMB/ECCB 2011 timeline did not allow for a second round of reviews, however, only papers where referees suggested minor revisions could be accepted. For several papers where referee comments might have been unclear to the authors, Area Chairs added additional comments and explanations. In total, 48 papers were conditionally accepted, pending revision. After acceptance notices were sent, the authors had 2 weeks to modify their papers according to the suggestions made by the reviewers and to respond to the reviewers' comments. Modified papers and authors' responses were reexamined during the next week to ensure each paper was modified appropriately in response to the reviewers' comments. We were pleased that all 48 conditionally accepted papers were finally accepted for the conference. The final acceptance rate was 19%, similar to the rates of 20% and 19% in the two prior years. Collectively, the 48 accepted papers had 185 authors from 18 countries across 6 continents, with 82 authors from North America, 54 from Europe, 25 from Asia (excluding Israel), 10 from Israel, 9 from Australia, 4 from South America and 1 author from Africa. We thank the area chairs and reviewers for their hard work and dedication to maintaining a professional review process. We thank all authors of submitted papers and authors of accepted papers for their diligences in responding to reviewer comments within 2 weeks. We thank Steven Leard's team for extensive technical support with the EasyChair submission and reviewing system; Mona Singh and Joel S. Bader for sharing their experience from last year; the team at Oxford University Press for typesetting the papers; Conference Chairs Burkhard Rost, Michal Linial, Peter Schuster and Kurt Zatloukal, as well as the ISMB/ECCB 2011 Steering Committee, for their valuable input; and Steven Leard for helping us oversee the process.

60 APPLIED LIFE SCIENCES↗

Big Data, Big Solutions

Data—lots of data—generated in seconds and piling up on the internet, streaming and stored in countless databases. Big data is important for commerce, society and our nation’s security. Yet the volume, velocity, variety and veracity of data is simply too great for any single analyst to make sense of alone. It requires advanced, data-intensive computing. Simply put, data-intensive computing is the use of sophisticated computers to sort through mounds of information and present analysts with solutions in the form of graphics, scenarios, formulas, new hypotheses and more. This scientific capability is foundational to PNNL’s energy, environment and security missions. Senior Scientist and Division Director Bill Pike and his team are developing analytic tools that are used to solve important national challenges, including cyber systems defense, power grid control systems, intelligence analysis, climate change and scientific exploration.

97 MATHEMATICS AND COMPUTING↗

167-PFlops deep learning for electron microscopy: from learning physics to atomic manipulation

An artificial intelligence system called MENNDL, which used 25,200 NVIDIA Volta GPUs on Oak Ridge National Laboratory's Summit machine, automatically designed an optimal deep learning network in order to extract structural information from raw atomic-resolution microscopy data. In a few hours, MENNDL creates and evaluates millions of networks using a scalable, parallel, asynchronous genetic algorithm augmented with a support vector machine to automatically find a superior deep learning network topology and hyper-parameter set than a human expert can find in months. For the application of electron microscopy, the system furthers the goal of improving our understanding of the electron-beam-matter interactions and real-time image-based feedback, which enables a huge step beyond human capacity towards nanofabricating materials automatically. MENNDL has been scaled to the 4,200 available nodes of Summit achieving a measured 152.5 PFlops, with an estimated sustained performance of 167 PFlops when the entire machine is available.

Patton, Robert↗

Noise-Based Selection of Robust Inherited Model for Accurate Continual Learning

There is a growing demand for an intelligent system to continually learn knowledge from a data stream. Continual learning requires both the preservation of previous knowledge (i.e., avoiding catastrophic forgetting) and the acquisition of new knowledge. Different from previous works that focus only on model adaptation (e.g., regularization, network expansion, memory rehearsal, etc.), we propose a novel training scheme named acquisitive learning (AL), which emphasizes both the knowledge inheritance and knowledge acquisition. AL starts from an elaborately selected model with pre-trained knowledge (the inherited model) and then adapts it to new data using segmented training. The selection is achieved by injecting random noise to various inherited models for better model robustness, which promises higher accuracy in further knowledge acquisition. The approach is validated by the visualization of the loss landscape and quantitative roughness measurement. The combination of the selective inherited model and knowledge acquisition reduces catastrophic forgetting by 10X on the CIFAR-100 dataset.

Du, Xiaocong↗

The Rise of Intelligent Materials Science: Unleashing the Power of Machine Intelligence in Characterization

Machine intelligence has the potential to revolutionize materials science, enabling autonomous synthesis, self-driving characterization, and accelerated modeling. However, despite the promise, successful implementation of these methods in day-to-day research remains a challenge. This talk will delve into the reasons behind this, exploring how truly intelligent experiments are hindered by opaque experiment control, a lack of domain-specific models, and human-centric design. Through a focus on the characterization of next-generation microelectronics and energy storage materials, I will share insights from both successful and failed attempts to implement machine intelligence. We will then explore the next steps necessary to unlock the full potential of machine intelligence in materials science, creating a future where intelligent systems work seamlessly alongside researchers to drive innovation and discovery.

autonomous↗

Artificial Intelligence for Earth System Predictability (AI4ESP) (2021 Workshop Report)

In October 2021, the U.S. Department of Energy (DOE) welcomed participants to the Artificial Intelligence for Earth System Predictability (AI4ESP) Workshop, hosted by the Office of Biological and Environmental Research (BER)—Advanced Scientific Computing Research (ASCR). The workshop is part of BER-ASCR’s ambition to more radically and aggressively advance prediction capabilities in the climate, Earth, and environmental sciences through the use of modern data analytics and artificial intelligence (AI). Advances in these capabilities are needed to improve predictions of climate change and extreme events that provide actionable information for planning and building resilience to their impacts.

54 ENVIRONMENTAL SCIENCES↗

An Intelligent Garbage Sorting System Based on Edge Computing and Visual Understanding of Social Internet of Vehicles

In order to enable Social Internet of Vehicles devices to achieve the purpose of intelligent and autonomous garbage classification in a public environment, while avoiding network congestion caused by a large amount of data accessing the cloud at the same time, it is therefore considered to combine mobile edge computing with Social Internet of Vehicles to give full play to mobile edge computing features of high bandwidth and low latency. At the same time, based on cutting-edge technologies such as deep learning, knowledge graph, and 5G transmission, the paper builds an intelligent garbage sorting system based on edge computing and visual understanding of Social Internet of Vehicles. First of all, for the massive multisource heterogeneous Social Internet of Vehicles big data in the public environment, different item modal data adopts different processing methods, aiming to obtain a visual understanding model. Secondly, using the 5G network, the model is deployed on the edge device and the cloud for cloud-side collaborative management, aiming to avoid the waste of edge node resources, while ensuring the data privacy of the edge node. Finally, the Social Internet of Vehicles devices is used to make intelligent decision-making on the big data of the items. First, the items are judged as garbage, and then the category is judged, and finally the task of grabbing and sorting is realized. The experimental results show that the system proposed in this paper can efficiently process the big data of Social Internet of Vehicles and make valuable intelligent decisions. At the same time, it also has a certain role in promoting the promotion of Social Internet of Vehicles devices.

Shen, Xuehao↗

A Hardware Platform for Studying Naval Power Electronic Power Distribution Systems

Abstract – Future intelligent ship system designs will likely include electric propulsion, numerous highpower sensors, and directed energy weapons. Supply and control of these large nonlinear loads will require a networked, multi-converter power electronic power distribution system. This work presents a hardware platform to emulate a microgrid power system with multiple power converters and a power data communication network. The platform is reconfigurable and can include both AC and DC power distribution zones, representative of shipboard power systems. It also allows for the study of both power control actuation and power data communication delays. Since the platform is based on electric power hardware, spatial and temporal uncertainties are inherently embedded in the system. Specifically, this work examines the control actuation of multiple pulsed power loads in a single microgrid. Several pulse load levels and operating scenarios have been implemented and measured. A framework for control parameter quantification is presented, and various metrics are explored to capture pulse signal characteristics. The sensitivity of pulse load metric parameters is analyzed. Dynamic shipboard, mission-specific load profiles coupled with pulse loads can also be emulated in the hardware platform.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Integration of Solid Oxide Fuel Cell Systems Into Artificial Intelligence Data Centers

This report presents the results of a techno-economic analysis (TEA) that evaluates the economic benefits of integrating solid oxide fuel cell (SOFC) systems with artificial intelligence (AI) data centers. The analysis was completed in two phases: a scoping-level analysis was performed to identify impactful integration opportunities, followed by a more detailed TEA. Results show that, due to their modularity, SOFC can meet the 99.999% availability requirement of data centers with minimal additional costs. Heat integration via absorption chillers decreases data center electricity consumption at the tradeoff of increased water consumption. Higher SOFC exhaust temperatures are important for achieving larger electricity savings. Finally, power electronics integration with SOFC direct current electricity can reduce electricity consumption by 9 percent and reduce water consumption by 6.4 percent.

20 FOSSIL-FUELED POWER PLANTS↗

Resilience Measurement Framework For Post-deployment Artificial Intelligence (ai) Integrated Systems

Resilience is largely defined as the ability to adapt or recover from adverse conditions, stresses, attacks, or compromises on systems that use or are enabled by digital resources. In Artificial Intelligence Management and Research for Advanced Networked Testbed Hub (AMARANTH), resilience is measured in the amount of time it took from the beginning of a testing period for the model to reach predictions outside of the original 95% confidence interval or using the Kullback-Leibler (KL) divergence theorem, the Population Stability Index (PSI), and traditional methods such as root mean squared error (RMSE) threshold. Artificial Intelligence (AI) model drift is of significant concern when deploying AI-integrated systems into critical and/or secure environments. Drift can impact resilience of the AI-integrated system post-deployment and requires consistent maintenance and upkeep to ensure the model is accurate and precise. To quantify model drift and predict the point when a model's drift becomes unacceptable, we describe using Kullback-Leibler (KL) divergence, Population Stability Index (PSI) and/or confidence interval width estimations to determine the point of failure and time to failure of a model post-deployment. Through simple code functions, the KL-divergence, PSI, confidence interval, and root mean squared (RMSE) point of failures can be used to derive when a model needs to be maintained as well as the impact of adversarial action through statistical means.

Yockey, Patience [Idaho National Laboratory (INL),↗

Intelligent Leak Detection System

apability of underground carbon dioxide storage to confine and sustain injected CO2 for a very long time is the main concern for geologic CO2 sequestration. If a leakage from a geological CO2 sequestration site occurs, it is crucial to find the approximate amount and the location of the leak in order to implement proper remediation activity. An overwhelming majority of research and development for storage site monitoring has been concentrated on atmospheric, surface or near surface monitoring of the sequestered CO2. This study aims to monitor the integrity of CO2 storage at the reservoir level. This work proposes developing in-situ CO2 Monitoring and Verification technology based on the implementation of Permanent Down-hole Gauges (PDG) or “Smart Wells” along with Artificial Intelligence and Data Mining (AI&DM). The technology attempts to identify the characteristics of the CO2 leakage by de-convolving the pressure signals collected from Permanent Down-hole Gauges (PDG). Citronelle field, a saline aquifer reservoir, located in the U.S. was considered for this study. A reservoir simulation model for CO2 sequestration in the Citronelle field was developed and history matched. The presence of the PDGs were considered in the reservoir model at the injection well and an observation well. High frequency pressure data from sensors were collected based on different synthetic CO2 leakage scenarios in the model. Due to complexity of the pressure signal behaviors, a Machine Learning-based technology was introduced to build an Intelligent Leakage Detection System (ILDS). The ILDS was able to detect leakage characteristics in a short period of time (less than a day) demonstrating the capability of the system in quantifying leakage characteristics subject to complex rate behaviors. The performance of ILDS was examined under different conditions such as multiple well leakages, cap rock leakage, availability of an additional monitoring well, presence of pressure drift and noise in the pressure sensor and uncertainty in the reservoir model.

Mohaghegh, Shahab D.↗

HEPCloud, an Elastic Hybrid HEP Facility using an Intelligent Decision Support System

HEPCloud is rapidly becoming the primary system for provisioning compute resources for all Fermilab-affiliated experiments. In order to reliably meet the peak demands of the next generation of High Energy Physics experiments, Fermilab must plan to elastically expand its computational capabilities to cover the forecasted need. Commercial cloud and allocation-based High Performance Computing (HPC) resources both have explicit and implicit costs that must be considered when deciding when to provision these resources, and at which scale. In order to support such provisioning in a manner consistent with organizational business rules and budget constraints, we have developed a modular intelligent decision support system (IDSS) to aid in the automatic provisioning of resources spanning multiple cloud providers, multiple HPC centers, and grid computing federations. In this paper, we discuss the goals and architecture of the HEPCloud Facility, the architecture of the IDSS, and our early experience in using the IDSS for automated facility expansion both at Fermi and Brookhaven National Laboratory.

Mhashilkar, Parag↗

Surface recogniton for cars: A comprehensive approach for neural networks

This paper explores the viability of neural-networkbased classification of ground surface for vehicles. By classifying road surface in near realtime, improvements in vehicle performance (e.g. braking and cornering) may be possible. Classification performance for many combinations of feature encoding and neural network types are compared. The vehicle used here was an An Audi “S3” with a magnetic suspension system on the sport mode. An NI CompactRIO (or cDAQ) module was used to record from a lowing the cDAQ to communicate with the PCB 352C03 one-axis accelerometer. The accelerometer was firmly attached to the windshield of the car. This work focuses on the classification of four road surfaces (asphalt, dirt, concrete, and sand), though larger target sets were also considered. The most accurate method involved a MATLAB feature extraction package with a back-propagation neural network, yielding an overall accuracy of 97%. Lessons learned from this wide exploration of options may extend to other related classification problems.

33 ADVANCED PROPULSION SYSTEMS↗