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At least 1,783 records · Page 99

Pairwise Association of Seismic Arrivals with Convolutional Neural Networks

Correctly determining the association of seismic phases across a network is crucial for developing accurate earthquake catalogs. Nearly all established methods use travel time information as the main criterion for determining associations, and in problems in which earthquake rates are high and many false arrivals are present, many standard techniques may fail to resolve the problem accurately. As an alternative approach, in this work we apply convolutional neural networks (CNNs) to the problem of associations; we train CNNs to read earthquake waveform arrival pairs between two stations and predict the binary classification of whether the two waveforms are from a common source or different sources. Applying the method to a large training dataset of previously cataloged earthquakes in Chile, we obtain >80% true positive prediction rates for high-frequency data (>2 Hz) and stations separated in excess of 100 km. As a secondary benefit, the output of the neural network can also be used to infer predicted phase types of arrivals. Here, the method is ideally applied in conjunction with standard travel-time-based association routines and can be adapted for arbitrary network geometries and applications, so long as sufficient training data are available.

58 GEOSCIENCES↗

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↗

Addressing concerns related to geologic hazards at the site of the proposed Transuranic Waste Facility , TA-63, Los Alamos National Laboratory: focus on the current Los Alamos Seismic Network earthquake catalog, proximity of identified seismic events to the proposed facility , and evaluation of prev

This technical paper presents the most recent and updated catalog of earthquakes measured by the Los Alamos Seismic Network at and around Los Alamos National Laboratory (LANL), with specific focus on the site of the proposed transuranic waste facility (TWF) at Technical Area 63 (TA-63). Any questions about the data presented herein, or about the Los Alamos Seismic Network, should be directed to the authors of this technical paper. LANL and the Los Alamos townsite sit atop the Pajarito Plateau, which is bounded on its western edge by the Pajarito fault system, a 35-mile-long system locally comprised of the down-to-the-east Pajarito fault (the master fault) and subsidiary down-to-the-west Rendija Canyon, Guaje Mountain, and Sawyer Canyon faults (Figure 1). This fault system forms the local active western margin of the Rio Grande rift near Los Alamos, and is potentially seismogenic (e.g., Gardner et al., 2001; Reneau et al., 2002; Lewis et al., 2009). The proposed TWF area at TA-63 is situated on an unnamed mesa in the north-central part of LANL between Twomile Canyon to the south, Ten Site Canyon to the north, and the headwaters of Canada del Buey to the east (Figure 2). The local bedrock is the Quaternary Bandelier Tuff, formed in two eruptive pulses from nearby Valles caldera, the eastern edge of which is located approximately 6.5 miles west-northwest of the technical area. The older member (Otowi Member) of the Bandelier Tuff has been dated at 1.61 Ma (Izett and Obradovich 1994). The younger member (Tshirege Member) of the Bandelier Tuff has been dated at 1.256 Ma (age from Phillips et al. 2007) and is widely exposed as the mesa-forming unit around Los Alamos. Several discrete cooling units comprise the Tshirege Member. Commonly accepted stratigraphic nomenclature for the Tshirege Member is described in detail by Broxton and Reneau (1995), Gardner et al. (2001), and Lewis et al. (2009). The Tshirege Member cooling unit exposed at the surface at TA-63 is Qbt3. Understanding the subtle differences between Tshirege Member cooling units and the nature of the contacts between cooling units is critical to identifying the presence or absence of faults associated with the Pajarito fault system on the Pajarito Plateau. The Los Alamos Seismic Network (LASN) continuously monitors local earthquake activity in the Los Alamos area in support of LANL's Seismic Hazards program. Seismic monitoring of LANL facilities is a requirement of DOE Order 420.1B (Facility Safety). LASN currently consists of nine permanent seismic instrument field stations that telemeter real-time sensitive ground motion data to a central recording facility. Four of these stations are located on LANL property, with three of those within 2.5 miles of TA-63. The other five stations are in remote locations in the Jemez Mountains, Valles Caldera, St Peters Dome, and the Caja del Rio plateau across the Rio Grande from the Los Alamos area. Local earthquakes are defined as those with locations within roughly 100 miles of Los Alamos. Plate 1 shows the current LASN station locations and all local earthquakes recorded from 1973 through 2011. During this time period, LASN has detected and recorded over 850 local earthquakes in north-central New Mexico. Over 650 of these were located within about 50 miles of Los Alamos, and roughly 60 were within 10 miles. The apparent higher density of earthquakes close to Los Alamos, relative to the rest of north-central New Mexico, is due largely to the fact that LASN is a sensitive local seismic network, recording many very small nearby events (magnitude less than 1.0) that are undetectable at greater distances.

CANYONS↗

Defect reaction network in Si-doped GaAs: Numerical predictions

This Report characterizes the defect reaction network in silicon doped, n-type GaAs deduced from first principles density functional theory. The reaction network is deduced by following exothermic defect reactions starting with the initially mobile interstitial defects reacting with common displacement damage defects in Si-doped GaAs until culminating in immobile reaction products. The defect reactions and reaction energies are tabulated, along with the properties of all the silicon-related defects in the reaction network. This Report serves to extend the results for intrinsic defects in: P.A. Schultz and O.A. von Lilienfeld, “Simple intrinsic defects in GaAs”, Modelling Simul. Mater. Sci Eng., Vol. 17, 084007 (2009) and its numerical supplement in SAND 2012-2675, and the preliminary silicon defect network results in: P.A. Schultz, “First-principles defect chemistry for modeling irradiated GaAs and III-V semiconductors”, J. Rad. Effects, Res. and Eng. Vol. 30, p257 (2012).

Schultz, Peter Andrew↗

Defect reaction network in C-doped GaAs: Numerical predictions

This Report characterizes the defect reaction network in carbon doped, p-type GaAs deduced from first principles density functional theory. The reaction network is deduced by following exothermic defect reactions starting with the initially mobile interstitial defects reacting with common displacement damage defects in C-doped GaAs until culminating in immobile reaction products. The defect reactions and reaction energies are tabulated, along with the properties of all the carbon-related defects in the reaction network. This Report serves to extend the results for intrinsic defects in: P.A. Schultz and O.A. von Lilienfeld, “Simple intrinsic defects in GaAs”, Modelling Simul. Mater. Sci Eng., Vol. 17, 084007 (2009) and its numerical supplement in SAND 2012-2675, and the preliminary carbon defect network results in: P.A. Schultz, “First-principles defect chemistry for modeling irradiated GaAs and III-V semiconductors”, J. Rad. Effects, Res. and Eng. Vol. 30, p257 (2012).

36 MATERIALS SCIENCE↗

Use of limited data to construct Bayesian networks for probabilistic risk assessment.

Probabilistic Risk Assessment (PRA) is a fundamental part of safety/quality assurance for nuclear power and nuclear weapons. Traditional PRA very effectively models complex hardware system risks using binary probabilistic models. However, traditional PRA models are not flexible enough to accommodate non-binary soft-causal factors, such as digital instrumentation&control, passive components, aging, common cause failure, and human errors. Bayesian Networks offer the opportunity to incorporate these risks into the PRA framework. This report describes the results of an early career LDRD project titled %E2%80%9CUse of Limited Data to Construct Bayesian Networks for Probabilistic Risk Assessment%E2%80%9D. The goal of the work was to establish the capability to develop Bayesian Networks from sparse data, and to demonstrate this capability by producing a data-informed Bayesian Network for use in Human Reliability Analysis (HRA) as part of nuclear power plant Probabilistic Risk Assessment (PRA). This report summarizes the research goal and major products of the research.

Groth, Katrina M.↗

A Fundamental Investigation on the Structural Dynamics of Model Siloxane Networks under Extreme Pressure

Poly(dimethylsiloxane) elastomers are ubiquitous in their application as key components in a range of (often extreme) environments. and as such, they often experience pressures in a region where we do not understand their physical response and therefore can offer no predictive estimate of lifetime of failure . We are proposing a fundamental scientific investigation of the pressure induced morphological changes in model PDMS networks. Both NMR and BDS spectroscopies will be used to interrogate pressure induced changes and meaningful phase relations will be sought. The addition of filler to the networks will be used as an attempt to engineer the network to be more resistant to pressure changes. We have the equipment, model networks and expertise to start this project immediately; funding is sought as an investment in our time allocation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Transactional Network Platform: Applications

In FY13, Pacific Northwest National Laboratory (PNNL) with funding from the Department of Energy’s (DOE’s) Building Technologies Office (BTO) designed, prototyped and tested a transactional network platform to support energy, operational and financial transactions between any networked entities (equipment, organizations, buildings, grid, etc.). Initially, in FY13, the concept demonstrated transactions between packaged rooftop air conditioners and heat pump units (RTUs) and the electric grid using applications or "agents" that reside on the platform, on the equipment, on a local building controller or in the Cloud. The transactional network project is a multi-lab effort with Oakridge National Laboratory (ORNL) and Lawrence Berkeley National Laboratory (LBNL) also contributing to the effort. PNNL coordinated the project and also was responsible for the development of the transactional network (TN) platform and three different applications associated with RTUs. This document describes two applications or "agents" in details, and also summarizes the platform. The TN platform details are described in another companion document.

Katipamula, Srinivas↗

Year 1 Progress Report Computational Materials and Chemical Sciences Network Administration

This document reports progress on the project “Computational Materials and Chemical Sciences Network Administration,” which is supported by DOE BES Grant DE-FG02-02ER45990 MOD 08. As stated in the original proposal, the primary goal of this project is to carry out the scientific administrative responsibilities for the Computational Materials and Chemical Sciences Network (CMCSN) of the U.S. Department of Energy, Office of Basic Energy Sciences. These responsibilities include organizing meetings, publishing and maintaining CMCSN’s website, publishing a periodic newsletter, writing original material for both the website and the newsletter, maintaining CMCSN documentation, editing scientific documents, as needed, serving as liaison for the entire Network, facilitating information exchange across the network, communicating CMCSN’s success stories to the larger community and numerous other tasks outside the purview of the scientists in the CMCSN. Given the dramatic increase in computational power, advances in computational materials science can have an enormous impact in science and technology. For many of the questions that can be addressed by computation there is a choice of theoretical techniques available, yet often there is no accepted understanding of the relative strengths and effectiveness of the competing approaches. The CMCSN fosters progress in this understanding by providing modest additional funding to research groups which engage in collaborative activities to develop, compare, and test novel computational techniques. Thus, the CMCSN provides the “glue” money which enables different groups to work together, building on their existing programs and expertise while avoiding unnecessary duplication of effort. This includes travel funding, partial postdoc salaries, and funding for periodic scientific meetings. The activities supported by this grant are briefly summarized below.

36 MATERIALS SCIENCE↗

Triangular Alignment (TAME). A Tensor-based Approach for Higher-order Network Alignment

Network alignment is an important tool with extensive applications in comparative interactomics. Traditional approaches aim to simultaneously maximize the number of conserved edges and the underlying similarity of aligned entities. We propose a novel formulation of the network alignment problem that extends topological similarity to higher-order structures and provide a new objective function that maximizes the number of aligned substructures. This objective function corresponds to an integer programming problem, which is NP-hard. Consequently, we approximate this objective function as a surrogate function whose maximization results in a tensor eigenvalue problem. Based on this formulation, we present an algorithm called Triangular AlignMEnt (TAME), which attempts to maximize the number of aligned triangles across networks. We focus on alignment of triangles because of their enrichment in complex networks; however, our formulation and resulting algorithms can be applied to general motifs. Using a case study on the NAPABench dataset, we show that TAME is capable of producing alignments with up to 99% accuracy in terms of aligned nodes. We further evaluate our method by aligning yeast and human interactomes. Our results indicate that TAME outperforms the state-of-art alignment methods both in terms of biological and topological quality of the alignments.

42 ENGINEERING↗

High Fidelity Simulations of Large-Scale Wireless Networks

The worldwide proliferation of wireless connected devices continues to accelerate. There are 10s of billions of wireless links across the planet with an additional explosion of new wireless usage anticipated as the Internet of Things develops. Wireless technologies do not only provide convenience for mobile applications, but are also extremely cost-effective to deploy. Thus, this trend towards wireless connectivity will only continue and Sandia must develop the necessary simulation technology to proactively analyze the associated emerging vulnerabilities. Wireless networks are marked by mobility and proximity-based connectivity. The de facto standard for exploratory studies of wireless networks is discrete event simulations (DES). However, the simulation of large-scale wireless networks is extremely difficult due to prohibitively large turnaround time. A path forward is to expedite simulations with parallel discrete event simulation (PDES) techniques. The mobility and distance-based connectivity associated with wireless simulations, however, typically doom PDES and fail to scale (e.g., OPNET and ns-3 simulators). We propose a PDES-based tool aimed at reducing the communication overhead between processors. The proposed solution will use light-weight processes to dynamically distribute computation workload while mitigating communication overhead associated with synchronizations. This work is vital to the analytics and validation capabilities of simulation and emulation at Sandia. We have years of experience in Sandia’s simulation and emulation projects (e.g., MINIMEGA and FIREWHEEL). Sandia’s current highly-regarded capabilities in large-scale emulations have focused on wired networks, where two assumptions prevent scalable wireless studies: (a) the connections between objects are mostly static and (b) the nodes have fixed locations.

Onunkwo, Uzoma↗

High Fidelity Simulations of Large-Scale Wireless Networks (Plus-Up)

Sandia has built a strong reputation in scalable network simulation and emulation for cyber security studies to protect our nation’s critical information infrastructures. Georgia Tech has preeminent reputation in academia for excellence in scalable discrete event simulations, with strong emphasis on simulating cyber networks. Many of the experts in this field, such as Dr. Richard Fujimoto, Dr. George Riley, and Dr. Chris Carothers, have strong affiliations with Georgia Tech. The collaborative relationship that we intend to immediately pursue is in high fidelity simulations of practical large-scale wireless networks using ns-3 simulator via Dr. George Riley. This project will have mutual benefits in bolstering both institutions’ expertise and reputation in the field of scalable simulation for cyber-security studies. This project promises to address high fidelity simulations of large-scale wireless networks. This proposed collaboration is directly in line with Georgia Tech’s goals for developing and expanding the Communications Systems Center, the Georgia Tech Broadband Institute, and Georgia Tech Information Security Center along with its yearly Emerging Cyber Threats Report. At Sandia, this work benefits the defense systems and assessment area with promise for large-scale assessment of cyber security needs and vulnerabilities of our nation’s critical cyber infrastructures exposed to wireless communications.

Onunkwo, Uzoma↗

Value Creation Through Integrated Networks and Convergence

Customer adoption of distributed energy resources and public policies are driving changes in the uses of the distribution system. A system originally designed and built for one-way energy flows from central generating facilities to end-use customers is now experiencing injections of energy from customers anywhere on the grid and frequent reversals in the direction of energy flow. In response, regulators and utilities are re-thinking the design and operations of the grid to create more open and transactive electric networks. This evolution has the opportunity to unlock significant value for customers and utilities. Alternatively, failure to seize this potential may instead lead to an erosion of value if customers seek to defect and disconnect from the system. This paper will discuss how current grid modernization investments may be leveraged to create open networks that increase value through the interaction of intelligent devices on the grid and prosumerization of customers. Moreover, even greater value can be realized through the synergistic effects of convergence of multiple networks. This paper will highlight examples of the emerging nexus of non-electric networks with electricity.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Concept of Operations: National Technology Test Bed Network for Water Resource Recovery

There is an urgency to advancing wastewater technologies due to aging water infrastructure and emerging regulations. A crosscutting working group proposes a conceptual design for a test bed network to understand and evaluate wastewater technologies to drive acceptance and deployment of new technologies to enhance performance. The working group includes contributors from the U.S. Department of Energy, the U.S. Environmental Protection Agency, the U.S. National Science Foundation, and the Water Research Foundation (formerly known as the Water Environment & Reuse Foundation). Upgrading today's aging wastewater treatment infrastructure to a new generation of water resource recovery facilities (WRRFs) requires the development and deployment of innovative technologies. The path to technology adoption depends on expensive, time consuming, and often repetitive cycles of testing and validation. Existing water resource recovery technology testing facilities (e.g., test beds) are underutilized and cannot, by themselves, broaden technology uptake. It has been proposed that technology adoption could be accelerated by a network linking these test facilities with innovators, manufacturers, utilities, regulators, policymakers, and educators. This document proposes a structure for a National Test bed Network (TBN) for water resource recovery. The concepts for its operation are explored herein. This document identifies drivers to launch a TBN and critical needs for successful implementation of the network. An important driver to technology implementation at WRRFs is the relationship between local utilities and state regulators. The TBN could provide access to validated performance data and facilitate deployment. To link the stakeholders and accelerate deployment, the TBN will require standards for methods, data quality, data management, and data security. These standards will enable validation of performance and will expand the “toolbox” that utilities and regulators could consider. Design, set up, and launch of this toolbox is beyond the scope of operations of utilities and regulators and will likely require external support to achieve full operation. Herein, a pathway is proposed to achieve this full operation. This document defines the function of a WRRF TBN. It also proposes a structure for the TBN to achieve its stated goals and mission. Finally, this document summarizes findings from several sources, including a series of workshops about the TBN concept, operators of test beds in ancillary industries, and discussions with regulators and permitting agencies. A description of this work was published in the peer-reviewed literature (Mihelcic 2017).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Dynamic Networks Experiment FY18 - Quality Control Focus Area Efforts

The availability of data whose quality is fittingly labeled is of tantamount importance to signal detection and all other downstream signal processing operations. Without identification, the goals of low yield nuclear monitoring cannot reach their full potential. A fundamental question that remains is the knowledge of the capability gaps that prevent us from using open network data. With these new channels, we will need better understanding of the frequency, type, severity, and impact of artifacts when compared to conventional networks. Moreover, beyond a simple quality label, thresholds for acceptable use of the data will need to be established, likely on a use case by use case basis. Finally, orders of magnitude more data with an at-best static analyst pool size means that quality artifacts must be identified with as little human interaction as possible. Automated methodologies must be developed to account for the scale of the problem when ad hoc networks are candidates for processing. At the same time that “data wealth” provides more opportunity for signal exploitation, the introduction of ad hoc networks introduces an increased risk quality issues in both sensor value and metadata issues. The artifacts shown below are examples of some sensor-based quality issues that impair downstream processing. The machine learning model used in this project was able to identify the artifacts that produce these spurious correlations, significantly improving analyst productivity.

97 MATHEMATICS AND COMPUTING↗