Embracing Complexity: Cognitive Scientific Computing
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Hittinger, Jeffrey A. F. [Lawrence Livermore Natio↗
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A multi-scale cognitive interaction model of instrument operations at the Linac Coherent Light Source
The Linac Coherent Light Source (LCLS) is the world’s first x-ray free electron laser. It is a scientific user facility operated by the SLAC National Accelerator Laboratory, at Stanford, for the U.S. Department of Energy. As beam time at LCLS is extremely valuable and limited, experimental efficiency—getting the most high quality data in the least time—is critical. Our overall project employs cognitive engineering methodologies with the goal of improving experimental efficiency and increasing scientific productivity at LCLS by refining experimental interfaces and workflows, simplifying tasks, reducing errors, and improving operator safety and stress. Here, in this study, we describe a multi-agent, multi-scale computational cognitive interaction model of instrument operations at LCLS. Our model simulates the aspects of human cognition at multiple cognitive and temporal scales, ranging from seconds to hours, and among agents playing multiple roles, including instrument operator, real time data analyst, and experiment manager. The model can roughly predict impacts stemming from proposed changes to operational interfaces and workflows. Example results demonstrate the model’s potential in guiding modifications to improve operational efficiency. We discuss the implications of our effort for cognitive engineering in complex experimental settings and outline future directions for research. The model is open source, and the videos of the supplementary material provide extensive detail.
47 OTHER INSTRUMENTATION↗
Emerging materials intelligence ecosystems propelled by machine learning
We report that the age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its successes and promises, several AI ecosystems are blossoming, many of them within the domain of materials science and engineering. These materials intelligence ecosystems are being shaped by several independent developments. Machine learning (ML) algorithms and extant materials data are utilized to create surrogate models of materials properties and performance predictions. Materials data repositories, which fuel such surrogate model development, are mushrooming. Automated data and knowledge capture from the literature (to populate data repositories) using natural language processing approaches is being explored. The design of materials that meet target property requirements and of synthesis steps to create target materials appear to be within reach, either by closed-loop active-learning strategies or by inverting the prediction pipeline using advanced generative algorithms. AI and ML concepts are also transforming the computational and physical laboratory infrastructural landscapes used to create materials data in the first place. Surrogate models that can outstrip physics-based simulations (on which they are trained) by several orders of magnitude in speed while preserving accuracy are being actively developed. Automation, autonomy and guided high-throughput techniques are imparting enormous efficiencies and eliminating redundancies in materials synthesis and characterization. The integration of the various parts of the burgeoning ML landscape may lead to materials-savvy digital assistants and to a human-machine partnership that could enable dramatic efficiencies, accelerated discoveries and increased productivity. Here, we review these emergent materials intelligence ecosystems and discuss the imminent challenges and opportunities. The materials research landscape is being transformed by the infusion of approaches based on machine learning. This Review discusses the emerging materials intelligence ecosystems and the potential of human-machine partnerships for fast and efficient virtual materials screening, development and discovery.
36 MATERIALS SCIENCE↗
Cognitive analysis of metabolomics data for systems biology
Cognitive computing is revolutionizing the way big data are processed and integrated, with artificial intelligence (AI) natural language processing (NLP) platforms helping researchers to efficiently search and digest the vast scientific literature. Most available platforms have been developed for biomedical researchers, but new NLP tools are emerging for biologists in other fields and an important example is metabolomics. NLP provides literature-based contextualization of metabolic features that decreases the time and expert-level subject knowledge required during the prioritization, identification and interpretation steps in the metabolomics data analysis pipeline. Here, we describe and demonstrate four workflows that combine metabolomics data with NLP-based literature searches of scientific databases to aid in the analysis of metabolomics data and their biological interpretation. Additionally, the four procedures can be used in isolation or consecutively, depending on the research questions. The first, used for initial metabolite annotation and prioritization, creates a list of metabolites that would be interesting for follow-up. The second workflow finds literature evidence of the activity of metabolites and metabolic pathways in governing the biological condition on a systems biology level. The third is used to identify candidate biomarkers, and the fourth looks for metabolic conditions or drug-repurposing targets that the two diseases have in common. The protocol can take 1–4 h or more to complete, depending on the processing time of the various software used.
59 BASIC BIOLOGICAL SCIENCES↗
Examination of Haines Jump in Microfluidic Experiments via Evolution Graphs and Interface Tracking
This work examines a type of rapid pore-filling event in multiphase flow through permeable media that is better known as Haines Jump. While existing microfluidic experiments on Haines Jump mostly seek to maintain quasi-steady states through very low bulk flow rates over long periods of time, this work explores the combined use of a highly structured microscale transport network, high-speed fluorescent microscopy, displacement front segmentation algorithms, and a tracking algorithm to build evolution graphs that track displacement fronts as they evolve through high-speed video recording. The resulting evolution graph allows the segmentation of a high-speed recording in both space and time, potentially facilitating topology-cognitive computation on the transport network. Occurrences of Haines Jump are identified in the microfluidic displacement experiments and their significance in bulk flow rates is qualitatively analyzed. The bulk flow rate has little effect on the significance of Haines Jump during merging and splitting, but large bulk flow rates may obscure small bursts at the narrowest part of the throat.
permeable media↗
Livermore Computing Integrates Advanced Cognitive Simulation Resource
With the integration of the first production Cerebras CS-1 system into Lassen, Livermore Computing (LC) is preparing the way for advanced cognitive simulation (CogSim) capabilities integrated into leadership HPC systems to support Artificial Intelligence (AI) driven computational needs of the Stockpile Stewardship Program.
97 MATHEMATICS AND COMPUTING↗
Cognitive impacts of computer vision-based decision support for international nuclear safeguards-relevant visual analysis tasks .
Abstract not provided.
Gastelum, Zoe Nellie↗
Cognitive impacts of computer vision-based decision support for international nuclear safeguards-relevant visual analysis tasks.
Abstract not provided.
Gastelum, Zoe Nellie↗
A review of non-cognitive applications for neuromorphic computing
Abstract Though neuromorphic computers have typically targeted applications in machine learning and neuroscience (‘cognitive’ applications), they have many computational characteristics that are attractive for a wide variety of computational problems. In this work, we review the current state-of-the-art for non-cognitive applications on neuromorphic computers, including simple computational kernels for composition, graph algorithms, constrained optimization, and signal processing. We discuss the advantages of using neuromorphic computers for these different applications, as well as the challenges that still remain. The ultimate goal of this work is to bring awareness to this class of problems for neuromorphic systems to the broader community, particularly to encourage further work in this area and to make sure that these applications are considered in the design of future neuromorphic systems.
97 MATHEMATICS AND COMPUTING↗
Cognitive IoT and Edge Computing for Intrusion Detection with Federated TinyML
Internet of Things (IoT) and Edge Computing (EC) are rapidly becoming an integral part of the modern society. By 2030, there is estimated to be over 40 billion active and connected IoT devices [1]. This rapid progress also comes with a significant implication on cybersecurity. Back-end infrastructure and systems have a much broader attack than they did previously due to vulnerable IoT/EC devices being connected to wireless networks. This expanding attack surface is a growing concern because IoT/EC are increasingly being used in critical systems such as power grids, health care, and smart homes. To effectively address a problem of this scale, cognitive cyber methods—which can autonomously detect and react to cyber attacks as they develop—are needed. To address this, we bring Artificial Intelligence (AI) and Machine Learning (ML) to IoT/EC devices, using tinyML to monitor voluminous IoT data against cyber threats, and using Federated Learning (FL) to share local detection knowledge across the system while preserving privacy. We propose a novel three-layer architecture: (1) an IoT layer for tinyML-based inference, (2) an edge layer for ML model training, and (3) a cloud layer for FL operations. Using the publicly available 11-class N-BaIoT dataset [2], we demonstrate that this architecture mitigates resource constraints at the IoT layer while improving detection accuracy over standard two-layer designs. An outlier-resistant scaler, feature reduction, and quantization enable the tinyML model to maintain detection accuracy with a reduced model size. Additionally, federated learning that only utilizes the intersection (across heterogenous devices) of the reduced feature set achieves superior detection accuracy compared to locally trained models.
Li, Mingyan [ORNL] (ORCID:0009000569532640)↗
MIDAS: Modeling Individual Differences using Advanced Statistics
This research explores novel methods for extracting relevant information from EEG data to characterize individual differences in cognitive processing. Our approach combines expertise in machine learning, statistics, and cognitive science, advancing the state-of-the art in all three domains. Specifically, by using cognitive science expertise to interpret results and inform algorithm development, we have developed a generalizable and interpretable machine learning method that can accurately predict individual differences in cognition. The output of the machine learning method revealed surprising features of the EEG data that, when interpreted by the cognitive science experts, provided novel insights to the underlying cognitive task. Additionally, the outputs of the statistical methods show promise as a principled approach to quickly find regions within the EEG data where individual differences lie, thereby supporting cognitive science analysis and informing machine learning models. This work lays methodological ground work for applying the large body of cognitive science literature on individual differences to high consequence mission applications.
97 MATHEMATICS AND COMPUTING↗
RADAMS: Resilient and adaptive alert and attention management strategy against Informational Denial-of-Service (IDoS) attacks
Attacks exploiting human attentional vulnerability have posed severe threats to cybersecurity. In this work, we identify and formally define a new type of proactive attentional attacks called Informational Denial-of-Service (IDoS) attacks that generate a large volume of feint attacks to overload human operators and hide real attacks among feints. Here, we incorporate human factors (e.g., levels of expertise, stress, and efficiency) and empirical psychological results (e.g., the Yerkes-Dodson law and the sunk cost fallacy) to model the operators’ attention dynamics and their decision-making processes along with the real-time alert monitoring and inspection. To assist human operators in dismissing the feints and escalating the real attacks timely and accurately, we develop a Resilient and Adaptive Data-driven alert and Attention Management Strategy (RADAMS) that de-emphasizes alerts selectively based on the abstracted category labels of the alerts. RADAMS uses reinforcement learning to achieve a customized and transferable design for various human operators and evolving IDoS attacks. The integrated modeling and theoretical analysis lead to the Product Principle of Attention (PPoA), fundamental limits, and the tradeoff among crucial human and economic factors. Experimental results corroborate that the proposed strategy outperforms the default strategy and can reduce the IDoS risk by as much as 20%. Besides, the strategy is resilient to large variations of costs, attack frequencies, and human attention capacities. We have recognized interesting phenomena such as attentional risk equivalency, attacker’s dilemma, and the half-truth optimal attack strategy.
97 MATHEMATICS AND COMPUTING↗
A Review of Machine Learning Applications in Power System Resilience
The integration of power electronics enabled devices and the high penetration of renewable energy drastically increase the complexity of power system operation and control. Power systems are still vulnerable to large-scale blackouts caused by extreme natural events or man-made attacks. With the recent development in artificial intelligence technique, machine learning has shown a processing ability in computational, perceptual and cognitive intelligence. It is an urgent challenge to integrate the advanced machine learning technology and large amount of real-time data from wide area measurement systems and intelligent electronic devices, in order to effectively enhance power system resilience and ensure the reliable and secure operation of power systems. Therefore, this paper aims to systematically review the existing application of machine learning methods on power system resilience enhancement, to expand the interest of researchers and scholars in this topic, and to jointly promote the application of artificial intelligence in the field of power systems.
Deep Learning↗
Tonal Emergence: An agent-based model of tonal coordination
Humans have a remarkable capacity for coordination. Our ability to interact and act jointly in groups is crucial to our success as a species. Joint Action (JA) research has often concerned itself with simplistic behaviors in highly constrained laboratory tasks. But there has been a growing interest in understanding complex coordination in more open-ended contexts. In this regard, collective music improvisation has emerged as a fascinating model domain for studying basic JA mechanisms in an unconstrained and highly sophisticated setting. A number of empirical studies have begun to elucidate coordination mechanisms underlying joint musical improvisation, but these empirical findings have yet to be cached out in a working computational model. The present work fills this gap by presenting TonalEmergence, an idealized agent-based model of improvised musical coordination. TonalEmergence models the coordination of notes played by improvisers to generate harmony (i.e., tonality), by simulating agents that stochastically generate notes biased towards maximizing harmonic consonance given their partner’s previous notes. Here, the model replicates an interesting empirical result from a previous study of professional jazz pianists: feedback loops of mutual adaptation between interacting agents support the production of consonant harmony. The model is further explored to show how complex tonal dynamics, such as the production and dissolution of stable tonal centers, are supported by agents that are characterized by (i) a tendency to strive toward consonance, (ii) stochasticity, and (iii) a limited memory for previously played notes. TonalEmergence thus provides a grounded computational model to simulate and probe the coordination mechanisms underpinning one of the more remarkable feats of human cognition: collective music improvisation.
60 APPLIED LIFE SCIENCES↗
The Suisun Summit 2022: Emerging Themes and Open Questions in qEEG and Neurotherapies
The Suisun Summit 2022 was a gathering of 70 clinicians, educators, and researchers in quantitative electroencephalography and neurofeedback. During this 5-day event, several themes emerged in talks or discussion groups: EEG/qEEG Reading Skills; Medication Effects and Pharmaco-EEG; Technological Advancements; Emerging Concerns; and Growing Community Prestige through Research. Participants were asked to summarize what they believed to be the most important messages from the event to share with colleagues who were not in attendance, resulting in this review. A unifying concept for all the themes was a desire for higher quality, standardized EEG/qEEG education that provides depth as well as breadth. Models of clinical care that encourage open communication with prescribers and functional medicine specialists were strongly emphasized. Abstracts from all presentations are attached in Addendum B.
60 APPLIED LIFE SCIENCES↗
Position Papers for the ASCR Workshop on the Science of Scientific-Software Development and Use
Software is an increasingly important component in the pursuit of scientific discovery. Both its development and use are essential activities for many scientific teams. At the same time, very little scientific study has been conducted to understand, characterize, and improve the development and use of software for science. Computational science teams have diversified over time to include contributions from domain scientists who provide expertise in scientific and engineering disciplines, applied mathematicians and computer scientists who provide optimal algorithms and data structures, and software and data engineers who provide methodologies and tools adapted and adopted from other software domains. These diverse contributions have enabled tremendous advances in the pursuit of scientific discovery, even as models, computer architectures, and software environments have become more complicated. With this increasing diversity, we believe the next opportunity for qualitative improvement comes from applying the scientific method to understanding, characterizing, and improving how scientific software is developed and used. We believe that this pursuit requires expertise from computational scientists themselves, and from the cognitive and social sciences as well as the software engineering research community. As we look to increase the productivity and sustainability of the scientific-software-development-and-use cycle, a more systematic application of the scientific method to understand processes for software development and use will be a valuable tool to guide future work and result in more usable and sustainable software. This workshop will bring together computer scientists, software engineering researchers, computational scientists, applied mathematicians, social scientists, cognitive scientists, and others, to explore how we can conduct such systematic investigations, what can be learned, and how doing so will benefit the scientific enterprise. The workshop will be structured around a set of breakout sessions, with every attendee expected to participate actively in the discussions. Afterward, workshop attendees — from DOE, industry, and academia — will produce a report for ASCR that summarizes the findings of the workshop.
42 ENGINEERING↗
Leveraging dendritic complexity for neuromorphic computing
Abstract Beyond-von Neumann computing approaches are necessary to sustain the growth of microelectronics and the increasing appetite for artificial intelligence/machine learning algorithms. Neuromorphic computing is an emerging paradigm that takes inspiration from the brain to provide a path forward to improve the computational efficiency and computational density of next-generation computing architectures. In nature, we observe brains performing complex computations with a much smaller energy footprint than conventional computing approaches. Current neuromorphic systems are focused primarily on scalability, namely, increasing the number of computational units (neurons) and connections between units (synapses). However, for brain-like cognition and efficiency in next-generation computing hardware, we need increased complexity in function, as well as improved connection density for scalability. Here, we present our work that aims to incorporate dendrites for ‘compute-on-wire’ in neuromorphic architectures to increase the computational complexity (e.g. number of programmable parameters, nonlinear dynamics) as well as computational efficiency (energy/compute) of artificial neural networks (ANNs). We do this by showcasing neuromorphic dendrite elements that can be leveraged for various applications. We will present examples of neuroscience-inspired direction-selective circuits and an ANN with active dendrites leveraging shunting inhibition. We also demonstrate the benefits of using dendrites in deep neural networks. To conclude, we discuss how we can utilize emerging hardware devices in these systems and design next-generation neuromorphic architectures with dendrites.
Cardwell, Suma G. (ORCID:0000000226575545)↗
Designing resilient IoT and Edge Computing with federated tinyML
The rapid growth of the Internet of Things (IoT) and Edge Computing (EC) has brought significant conveniences to modern society but has also greatly expanded the cyber attack surfaces, particularly as these technologies are being increasingly integrated into critical systems such as power grids, healthcare, and smart homes. Here, to improve IoT/EC’s cybersecurity posture, we leveraged Artificial Intelligence (AI) and Machine Learning (ML) by employing tinyML to monitor voluminous IoT data for cyber threats while addressing devices’ resource constraints, and utilizing Federated Learning (FL) to share local detection knowledge across the system while preserving privacy. Building on our three-layer architecture combining tinyML and FL to enhance autonomous cyber attack detection, this paper demonstrated that the architecture improves detection accuracy, reduces resource consumption, and enables lightweight, secure IoT device monitoring. These results were validated using the public N-BaIoT dataset as well as real IoT network traffic data collected under multiple attack scenarios from our testbeds. Additionally, we introduced an enhanced FL methodology with a novel preprocessing stage, including federated feature selection and global preprocessor construction, to address IoT/EC data heterogeneity. We developed a physical IoT testbed for attack simulations and data collection, implemented a tinyML-powered detector for realistic model validation, and also built a virtual testbed for scalable evaluations of FL models across diverse network environments.
Cognitive cyber↗