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At least 1,531 records · Page 85

Making Phase-Picking Neural Networks More Consistent and Interpretable

Improving the interpretability of phase-picking neural networks remains an important task to facilitate their deployment to routine, real-time seismic monitoring. The popular phase-picking neural networks published in the literature lack interpretability because their output prediction scores do not necessarily correspond with the reliability of phase picks and can even be highly inconsistent depending on how we window the waveform data. Here, we show that systematically shifting the waveforms during training and using an antialiasing filter within the neural network architecture can substantially improve the consistency of the output prediction scores and can even make them scale with the signal-to-noise ratios of the waveforms. We demonstrate the improvements by applying these approaches to a commonly used phase-picking neural network architecture and using waveform data from the 2019 Ridgecrest earthquake sequence.

58 GEOSCIENCES↗

Network construction of non-Abelian chiral spin liquids

We use a network of chiral junctions to construct a family of topological chiral spin liquids in two spatial dimensions. The chiral spin liquid phase harbors SU(2) _k k anyons, which stem from the underlying SU(2) _k k WZW models that describe the constituent spin chains of the network. The network exhibits quantized spin and thermal Hall conductances. We illustrate our construction by inspecting the topological properties of the SU(2) _2 2 model. We find that this model has emergent Ising anyons, with spinons acting as vortex excitations that bind Majorana zero modes. We also show that the ground state of this network is threefold degenerate on the torus, asserting its non-Abelian character. Our results shed new light on the stability of non-Abelian topological phases in artificial quantum materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Probing black hole microstate evolution with networks and random walks

We model black hole microstates and quantum tunneling transitions between them with networks and simulate their time evolution using well-established tools in network theory. In particular, we consider two models based on Bena-Warner three-charge multi-centered microstates and one model based on the D1-D5 system; we use network theory methods to determine how many centers (or D1-D5 string strands) we expect to see in a typical late-time state. We find three distinct possible phases in parameter space for the late-time behaviour of these networks, which we call ergodic, trapped, and amplified, depending on the relative importance and connectedness of microstates. We analyze in detail how these different phases of late-time behavior are related to the underlying physics of the black hole microstates. Our results indicate that the expected properties of microstates at late times cannot always be determined simply by entropic arguments; typicality is instead a highly non-trivial, emergent property of the full Hilbert space of microstates.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

The ITensor Software Library for Tensor Network Calculations

ITensor is a system for programming tensor network calculations with an interface modeled on tensor diagrams, allowing users to focus on the connectivity of a tensor network without manually bookkeeping tensor indices. The ITensor interface rules out common programming errors and enables rapid prototyping of algorithms. After discussing the philosophy behind the ITensor approach, we show examples of each part of the interface including Index objects, the ITensor product operator, tensor factorizations, tensor storage types, algorithms for matrix product state (MPS) and matrix product operator (MPO) tensor networks, quantum number conserving block sparse tensors, and the NDTensors library. We also review publications that have used ITensor for quantum many-body physics and for other areas where tensor networks are increasingly applied. To conclude we discuss promising features and optimizations to be added in the future.

Fishman, Matthew↗

5G Enabled Energy Innovation: Advanced Wireless Networks for Science

Digital wireless communication has become a foundational technology for the nation. The U.S. Department of Energy’s Office of Science (DOE-SC) is the Nation’s largest supporter of basic research in the physical sciences discovering new materials, designing advanced microelectronics, and understanding the physics of radio frequency signaling. The expanding national rollout of a new fifth-generation (5G) mobile network, coupled with the torrent of scientific data generated by next-generation devices such as battery-powered Internet of Things (IoT) sensors, has created an urgent need to enhance cutting-edge wireless technology. Breakthroughs in the deployment, integration, security, and operational range of wireless networking can provide new scientific capabilities for the next decade - from autonomous mobile instruments for scientific user facilities to intelligent sensors networks distributed over thousands of kilometers to study environmental processes. To realize this promise, however, we must continue to drive innovations in computing, artificial intelligence (AI), advanced materials, high-speed networking, and microelectronics. In March 2020, the DOE-SC convened a workshop to identify the potential opportunities and explore the scientific challenges of advanced wireless technologies.

42 ENGINEERING↗

Distributed Acoustic Sensing (DAS) on Opportunistic Networks_ A Feasibility Study Utilizing Fiber Optic Infrastructure at NEON Sites for DAS

This study demonstrates the feasibility of using Distributed Acoustic Sensing (DAS) on the opportunistic optical fiber infrastructure presented by NEON (National Ecological Observatory Network) sites. The test took place at the NEON Terrestrial Site designated D10 CPER. Unlit portions of the local fiber network were successfully spliced together and optimized for length and a Silixa iDAS sensor was installed. The resulting array (composed of 12 spliced fiber runs) totaled 875m in length. Over a period of 2 weeks, we gathered a high spatial and temporal resolution data set to characterize discernable signals and understand background noise. The data set was reviewed to extract wave signatures of identifiable origin, both anthropogenic and otherwise. Continent wide distribution of these sites makes them an interesting candidate for DAS and identifying measurable signal sources informs future DAS application on opportunistic fiber networks. These observations validate the utility of both the NEON and similarly small fiber networks (<1km) for geoscience, environmental, and security DAS applications and indicate possibilities for further inquiry in methods for DAS data collection and analysis.

42 ENGINEERING↗

Ecosystems and Networks Integrated with Genes and Molecular Assemblies (ENIGMA) (Final Scientific/Technical Report, Subcontract Award No. 6953691)

The overarching objective the Baliga lab is to develop a mechanistic understanding of the field relevant metabolic processes underlying community partitioning of respiration pathways and physiological state shifts. In order to dissect how environmental perturbations influence microbial regulatory networks, we have constructed data-driven models that capture the dynamic interplay between abiotic and biotic factors during laboratory simulations. Specifically, we have developed multiple computational models (i.e., cMonkey2, EGRIN 2.0, and multiple genome-scale metabolic models) connecting environmental influence to genome-encoded regulatory programs. In addition, we have generated new tools such as Live Anaerobic Cell Sorting (LAnCS) and Fluidized Bed Reactors (FBRs) to investigate the dynamics of community partitioning of metabolic processes supporting sulfate or nitrate respiration, which are two critical activities observed from the field site at Oak Ridge National Labs (ORNL). This framework of connecting field phenomena with laboratory simulations and computational modeling has created the Environmental Simulations and Modelling (EnvSim) campaign within ENIGMA. The campaign, which spans multiple laboratories across ENIGMA and is led by the Baliga group, has established synthetic communities (SynComs) to discover, characterize, and dissect key microbial processes relevant to field observations, such as emissions of the greenhouse gas nitrous oxide (N 2 O). For example, the EnvSim campaign so far has deduced four potential mechanisms that may account for the N 2 O emissions at the FRC and are currently being investigated by labs across ENIGMA. Denitrification may be driven by complete denitrifiers, however, their NosZ enzymes, which catalyze the final step in denitrification by converting N 2 O to N 2 , may be sensitive at a lower pH. Additionally, the denitrification process could be partitioned among organisms and some may have pH-sensitive NosZ genes. Another factor contributing to variable N 2 O emissions relates to the metal co-factors involved in the denitrification pathway. For instance, excess Cu, Al, Mn, U, Ni, Co, Cu, and/or Cd may have inhibitory effects on multiple enzymatic steps during denitrification, while the enzymatic production of nitrite, the precursor of N 2 O, can be limited by the essential metal Mo. Lastly, abiotic production of N 2 O may occur as a result of chemodenitrification, in which metals like Fe, Mn, and some organic compounds can drive redox reactions that convert nitrogen cycle intermediates to N 2 O under the right conditions. Current investigations and analyses have generated multiple transcriptomic profiles aiding the refinement of new metabolic and gene regulatory network model for our denitrifying SynCom. In sum, the previous funding cycle has generated 33 peer-reviewed publications spanning predictive network biology to co-operativity of mutualistic interspecies interactions of evolved communities. Our integration of multiple omics datasets and the development of new network modelling tools has revealed important insights that can explain field observed phenomenon and generated new hypotheses to be investigated in the field.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Adversarial Perturbations Are Not So Weird: Entanglement of Robust and Non-Robust Features in Neural Network Classifiers

Neural networks trained on visual data are well-known to be vulnerable to often imperceptible adversarial perturbations. The reasons for this vulnerability are still being debated in the literature. Recently Ilyas et al. (2019) showed that this vulnerability arises, in part, because neural network classifiers rely on highly predictive but brittle “non-robust” features. In this paper we extend the work of Ilyas et al. by investigating the nature of the input patterns that give rise to these features. In particular, we hypothesize that in a neural network trained in a standard way, non-robust features respond to small, “non-semantic” patterns that are typically entangled with larger, robust patterns, known to be more human-interpretable, as opposed to solely responding to statistical artifacts in a dataset. Thus, adversarial examples can be formed via minimal perturbations to these small, entangled patterns. In addition, we demonstrate a corollary of our hypothesis: robust classifiers are more effective than standard (non-robust) ones as a source for generating transferable adversarial examples in both the untargeted and targeted settings. The results we present in this paper provide new insight into the nature of the non-robust features responsible for adversarial vulnerability of neural network classifiers.

97 MATHEMATICS AND COMPUTING↗

Low Voltage Network Protection Utility Workshop (Summary and Next Steps)

Increased penetration of Distributed Energy Resources and microgrids have fundamentally changed the operation al characteristics of Low Voltage (LV) network systems. Current LV network protection philosophy and practice are due for a significant re vamp to keep up with changing operating conditions. This workshop invites four of the major LV network users in the US to discuss the challenges they face today and the new technologies they have been experimenting with in light of this workshop discussion, use cases for further hardware-in-the-loop testing efforts are proposed to evaluate new LV network protection solutions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

A technique to make an enterprise network a Darknet on the Internet, while providing required services to authorized users

In a well-designed and secure enterprise network, the hosts inside the network are not directly accessible from the Internet. The enterprise firewall blocks direct access to hosts inside the enterprise network and also blocks any attempts to probe or discover information about those hosts from the Internet. However, access to the enterprise network from the Internet is a must in today’s day and age. Hence, specialized mechanisms to allow secure access are implemented.

97 MATHEMATICS AND COMPUTING↗

ARIES Network Requirements Review

The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all of its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. On May 1, 2021, ESnet and the DOE Office of Energy Efficiency and Renewable Energy (EERE), organized an ESnet requirements review of the ARIES (Advanced Research on Integrated Energy Systems) platform. Preparation for this event included identification of key stakeholders to the process: program and facility management, research groups, technology providers, and a number of external observers. These individuals were asked to prepare formal case study documents in order to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Basic Energy Sciences Network Requirements Review (Final Report)

The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all of its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. Between March and September 2022, ESnet and the Office of Basic Energy Sciences (BES) of the DOE SC organized an ESnet requirements review of BES-supported activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about its relationship to the BES program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward. A series of pre-planning meetings better prepared case study authors for this task, along with guidance on how the review would proceed in a virtual fashion.

97 MATHEMATICS AND COMPUTING↗

High Energy Physics Network Requirements Review: One-Year Update

The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education (R&E) networking community. In April 2022, ESnet and the Office of High Energy Physics (HEP) of the DOE SC organized an ESnet requirements review of HEP-supported activities. Preparation for the review included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about the group’s relationship to the HEP program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward. A series of pre-planning meetings better prepared case study authors for this task, along with guidance on how the review would proceed in a virtual fashion.

97 MATHEMATICS AND COMPUTING↗

Digital Signal Processing Using Deep Neural Networks

Currently there is great interest in the utility of deep neural networks (DNNs) for the physical layer of radio frequency (RF) communications. In this manuscript we describe a custom DNN specially designed to solve problems in the RF domain. Our model leverages the mechanisms of feature extraction and attention through the combination of an autoencoder convolutional network with a transformer network, to accomplish several important communications network and digital signals processing (DSP) tasks. We also present a new open dataset and physical data augmentation model that enables training of DNNs that can perform automatic modulation classification, infer, and correct transmission channel effects, and directly demodulate baseband RF signals.

42 ENGINEERING↗

Cybersecurity Anomaly Detection in SCADA-Assisted OT Networks Using Ensemble-Based State Prediction Model

The cybersecurity threats of power system gradually grow due to the increased sophisticated interactions between Information Technology (IT) and Operational Technology (OT) networks. False data injection attack (FDIA) that aims to compromise the Supervisory Control and Data Acquisition (SCADA) measurement and disturb the system operation is one of such cyber threats. Such attacks can potentially lead to significant operational issues at the control centers and substations, and hence, result in severe physical consequences. To avoid catastrophic failure across the power grid resulting from these attacks, it is essential to arm the OT network with real-time vulnerability assessment tools. To this end, this paper outlines various drawbacks of the Purdue architecture model to defend against cyberattacks in the OT network. Furthermore, a novel ensemble-based state prediction model is proposed to detect cybersecurity anomalies in SCADA assisted OT networks. The proposed model uses control center level generation and load forecasts, scheduled, and forced outages, power flow solutions, and the substation level historical data. The hypothesis of the proposed scheme relies on the fact that additional control center and substation data can hardly be accessed and compromised by attackers. One of the vital features of the proposed scheme is an hour-ahead prediction of the operational feasibility of the SCADA measurement range at the control center and substation in real time helps in detecting anomalies in measurements across both substation and the control center.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Biological and Environmental Research Network Requirements Review (Final Report)

The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education (R&E) networking community. Between August 2022 and April 2023, ESnet and the Office of Biological and Environmental Research (BER) of the DOE SC organized an ESnet requirements review of BER-supported activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about its relationship to the BER ESS program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward. A series of pre-planning meetings better prepared case study authors for this task, along with guidance on how the review would proceed in a virtual fashion.

97 MATHEMATICS AND COMPUTING↗

Nuclear Physics Network Requirements Review (Final Report)

The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. ESnet interconnects DOE national laboratories, user facilities, and major experiments so that scientists can use remote instruments and computing resources as well as share data with collaborators, transfer large datasets, and access distributed data repositories. ESnet is specifically built to provide Between July 2023 and October 2023, ESnet and the Nuclear Physics program (NP) of the DOE SC organized an ESnet requirements review of NP-supported activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about its relationship to the NP program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward.

97 MATHEMATICS AND COMPUTING↗

Integrating the Design of Desalination Technologies into Produced Water Network Optimization

The oil and gas energy sector uses billions of gallons of water for hydraulic fracturing each year to extract oil and gas. The water injected into the ground for fracturing along with naturally occurring formation water from oil wells surfaces back in the form of produced water. Produced water can contain high concentrations of total dissolved solids and is unfit for reuse outside the oil and gas industry without desalination. In semi-arid shale plays, produced water desalination for beneficial reuse could play a crucial role in alleviating water shortages and addressing extreme drought conditions. In this poster we co-optimize the design and operation of desalination technologies along with operational decisions across produced water networks. A multi-period produced water network model with simplified split-fraction-based desalination nodes is developed. Rigorous steady-state desalination mathematical models based on mechanical vapor recompression are developed and embedded at the desalination sites in the network model. An optimal common design across all periods is ensured using global capacity constraints. The solution approach is demonstrated for multi-period planning problems on networks from the PARETO open-source library. Model formulation and challenges associated with scalability are discussed.

Naik, Sakshi↗