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At least 253 records · Page 14

Improving ICARUS Track Reconstruction Algorithms

The ICARUS experiment is part of the Short-Baseline Neutrino (SBN) program at Fermilab. The main goal of the experiment is to investigate the possibility of sterile neutrinos in the O(1 eV) mass region and provide clarification of the anomaly detected from the Liquid Scintillator Neutrino Detector (LSND) and MiniBooNE experiments. The ICARUS-T600 detector is a Liquid Argon Time Projection Chamber (LAr-TPC), that can provide excellent 3D imaging and calorimetric reconstruction of any ionizing particles. This detection technique allows a detailed study of neutrino interactions, spanning a wide energy spectrum (from a few keV to several hundreds of GeV). The detector consists of two identical adjacent modules, filled with a total of 760 tons of ultra-pure liquid argon. Each module houses two LAr-TPCs separated by a common cathode with a maximum drift distance of 1.5 m, equivalent to about 1 ms drift time for the nominal $500$ V/m electric drift field. The anode is made of three parallel wire planes positioned 3 mm apart, where the stainless-steel wires are oriented on each plane at a different angle with respect to the horizontal direction ($+60^\degree$,$-60^\degree$,$0^\degree$). The first two planes (Induction 1 and Induction 2) provide a non-destructive charge measurement, whereas the ionization charge is fully collected by the last collection plane. In total, 53248 wires with a 3 mm pitch and length up to 9 m are installed in the detector. In the first stage of the reconstruction, segments of waveforms corresponding to physical signals (hits) are searched for in the deconvolved wire waveform with a threshold-based hit-finding algorithm. Each hit is then fitted with a Gaussian, whose area is proportional to the number of drift electrons generating the signal. In the second stage of the reconstruction, hits are passed as input to Pandora, a framework software composed of different pattern recognition algorithms, that performs a 3D reconstruction of the full image recorded in the collected event, including the identification of interaction vertices and tracks and showers inside the TPC. These are organized into a hierarchical structure (called slice) of particles generated starting from a primary interaction vertex. In some cases, related to the inefficiencies in the hit detection or excessive deflection of the particle trajectory, Pandora breaks the particle's track into two or more smaller pieces and considers each piece as an independent track. We studied this phenomenon focusing on primary muons from ν_μ CC interactions contained in a single module with a track at least 20 cm long, to exclude delta rays. The study determined that about $7-8\%$ of the muon tracks are broken. Approximately $80\%$ of the times, Pandora assigns all segments of the track to the same slice (intra-slice track split), while in the remaining $20\%$ of the cases, one of the segments is associated with another slice (extra-slice track split). To mitigate this phenomenon, we designed an algorithm that detects and stitches the tracks broken by Pandora for the intra-slice split. In Monte Carlo simulations, the algorithm showed an efficiency exceeding $80\%$ and a purity exceeding $93\%$.

Ricci, Alessandro Maria [Pisa U.; INFN, Pisa] (ORC↗

Development, construction and qualification tests of the mechanical structures of the electromagnetic calorimeter of the Mu2e experiment at Fermilab

The “muon-to-electron conversion” (Mu2e) experiment at Fermilab will search for the Charged Lepton Flavour Violating neutrino-less coherent conversion of a muon into an electron in the field of an aluminum nucleus. The observation of this process would be the unambiguous evidence of physics beyond the Standard Model. Mu2e detectors comprise a straw-tracker, an electromagnetic calorimeter and an external veto for cosmic rays. The calorimeter provides excellent electron identification, complementary information to aid pattern recognition and track reconstruction, and a fast calorimetric online trigger. The detector has been designed as a state-of-the-art crystal calorimeter and employs 1340 pure Cesium Iodide (CsI) crystals readout by UV-extended silicon photosensors and fast front-end and digitization electronics. A design consisting of two identical annular matrices (named “disks”) positioned at the relative distance of 70 cm downstream the aluminum target along the muon beamline satisfies the Mu2e physics requirements.The hostile Mu2e operational conditions, in terms of radiation levels (total ionizing dose of 12 krad and a neutron fluence of 5x1010 n/cm2 @ 1 MeVeq (Si)/y), magnetic field intensity (1 T) and vacuum level (10$^{-4}$ Torr) have posed tight constraints on the design of the detector mechanical structures and materials choice. The support structure of the two 670 crystal matrices employs two aluminum hollow rings and parts made of open-cell vacuum-compatible carbon fiber. The photosensors and service front-end electronics for each crystal are assembled in a unique mechanical unit inserted in a machined copper holder. The 670 units are supported by a machined plate made of vacuum-compatible plastic material. The plate also integrates the cooling system made of a network of copper lines flowing a low temperature radiation-hard fluid and placed in thermal contact with the copper holders to constitute a low resistance thermal bridge. The data acquisition electronics is hosted in aluminum custom crates positioned on the external lateral surface of the two disks. The crates also integrate the electronics cooling system as lines running in parallel to the front-end system.The constraints on the calorimeter mechanical structures design, the development from the conceptual design to the specifications of all the structural components, the status of components production and the components quality assurance tests are presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

A Novel Hit-Based Method to Distinguish Tracks and Showers in ProtoDUNE Single Phase

Pandora is a pattern recognition software used in liquid argon time projection chamber (LArTPC) experiments such as MicroBooNE, DUNE, SBND, ICARUS, and ProtoDUNE Single Phase (SP). The output of a LArTPC can be considered a high-resolution 2D image and energy depositions, called hits, from particles in a LArTPC create complicated topologies that are broadly classified into tracks and showers. The event reconstruction is particularly challenging when there are multiple overlapping particles and in order to fully harness the imaging capabilities of thoseexperiments, Pandora needs to separate them. A hit-based approach to this problem is presented, which analyses small regions around each hit in events from DUNE Far Detector (FD) and from those regions it calculates local variables that are used subsequently in a machine learning approach. After this stage, it is given to each hit a probability to belong to a track or shower-like particle. Results will show the performance of separation between tracks and showers. This method is planned to be used for ProtoDUNE SP.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

SLOPE daily and 250 m gross primary productivity (GPP) for the CONUS, 2000-2019, Carbon Monitoring System (CMS)

The SatelLite Only Photosynthesis Estimation (SLOPE) GPP product is a daily, 250 m GPP dataset covering the Contiguous United States (CONUS) from 2000 to present with 1 day latency. Gross primary productivity (GPP) quantifies the amount of carbon dioxide (CO2) fixed by plants through photosynthesis. Although as a key quantity of terrestrial ecosystems, there is a lack of high-spatial-and-temporal-resolution, real-time, and observation-based GPP products. This product has been developed to address this critical gap, leveraging a number of MODIS land and atmosphere products. There are three distinct features of the SLOPE GPP production algorithm. (1) SLOPE couples machine learning models with MODIS atmosphere and land products to accurately estimate PAR. (2) SLOPE couples highly efficient and pragmatic gap-filling and filtering algorithms with surface reflectance acquired by both Terra and Aqua MODIS satellites to derive a soil-adjusted NIRv (SANIRv) dataset. (3) SLOPE couples a temporal pattern recognition approach with a long-term Crop Data Layer (CDL) product to predict dynamic C4 crop fraction. PAR, SANIRv and C4 fraction are used to drive a parsimonious model with only two slope parameters to estimate GPP along with a quantitative uncertainty on a per-pixel and daily basis. The slope GPP product has a R2 = 0.84 and a root-mean-square error (RMSE) of 1.65 gC m-2 d-1, evaluated against from 50 AmeriFlux eddy covariance sites (332 site-years). Archived data from 2000 to 2019 are publicly available at the NASA's Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC). Data from 2020 are available from the authors upon request. All data are projected in the standard MODIS Land Integerized Sinusoidal tile map projection. Each processing tile is in size of 4800 pixels by 4800 pixels, representing approximately 1200 km by 1200 km land region. In addition to the GPP product, SLOPE PAR, SANIRV, and C4 fraction, along with their uncertainties, are also released.

Jiang, Chongya↗

IRF1 Maintains Optimal Constitutive Expression of Antiviral Genes and Regulates the Early Antiviral Response

Viral defense at mucosal sites depends on interferons (IFN) and IFN stimulated genes (ISGs), either of which may be constitutively expressed to maintain an “antiviral state” (AVS). However, the mechanisms that govern the AVS are poorly defined. Using a BEAS-2B respiratory epithelial cell line deficient in IRF1, we demonstrate higher susceptibility to infection with vesicular stomatitis virus (VSV) and influenza virus. IRF1-mediated restriction of VSV is IFN-independent, as blockade of types I and III IFNs and JAK-STAT signaling before infection did not affect VSV infection of either parent or IRF1 KO cells. Transcriptome analysis revealed that IRF1 regulates constitutive expression of ~300 genes, including antiviral ISGs: OAS2, BST2, and RNASEL and knockdown of any of these IRF1-dependent genes increased VSV infection. Additionally, IRF1 enhances rapid expression of IFNβ and IFNλ after stimulation with poly I:C and also regulates ISG expression. Mechanistically, IRF1 enhances recruitment of BRD4 to promotor-enhancer regions of ISGs for rapid expression and maintains levels of histone H3K4me1 for optimal constitutive expression. Finally, IRF1 also regulates constitutive expression of TLR2 and TLR3 and promotes signaling through these pattern recognition receptors (PRR). These data reveal multiple roles for IRF1 toward effective anti-viral responses by maintaining IFN-independent constitutive expression of anti-viral ISGs and supporting early IFN-dependent responses to PRR stimulation.

60 APPLIED LIFE SCIENCES↗

Quantized Information in Spectral Cyberspace

The constant-Q Gabor atom is developed for spectral power, information, and uncertainty quantification from time–frequency representations. Stable multiresolution spectral entropy algorithms are constructed with continuous wavelet and Stockwell transforms. The recommended processing and scaling method will depend on the signature of interest, the desired information, and the acceptable levels of uncertainty of signal and noise features. Selected Lamb wave signatures and information spectra from the 2022 Tonga eruption are presented as representative case studies. Resilient transformations from physical to information metrics are provided for sensor-agnostic signal processing, pattern recognition, and machine learning applications.

74 ATOMIC AND MOLECULAR PHYSICS↗

Smart Proxy Modeling of SACROC CO2-EOR

Large CO2-enhanced oil recovery (EOR) projects usually contain an abundance of geological and good performance data. While this volume of data leads to robust models, it often results in difficult to manage, slow-running numerical flow models. To dramatically reduce the numerical run-times associated with the traditional simulation techniques, this work investigated the feasibility of using artificial intelligence and machine learning technologies to develop a smart proxy model of the Scurry Area Canyon Reef Operators Committee (SACROC) oilfield, located in the Permian Basin, TX, USA. Smart proxy models can be used to facilitate injection-production optimization for CO2-EOR projects. The use of a coupled grid-based, and well-based surrogate reservoir model (SRM) (also known as smart proxy modeling) was investigated as the base of the optimization. A fit-for-purpose coupled SRM, which executes in seconds, was built based on high-resolution numerical reservoir simulation models of the northern platform of the SACROC oilfield. This study is unique as it is the first application of coupled SRM at a large oilfield. The developed SRM was able to identify the dynamic reservoir properties (pressure, saturations, and component mole-fraction) at every grid-block, along with the production characteristics (pressure and rate) at each well. Recent attempts to use machine learning and pattern recognition to build proxy models have been simplistic, with limited predictive capabilities. The geological model used in this study is comprised of more than nine million grid blocks. The high correlation between the actual component and SRM, which can be visualized by mapping the properties, along with the fast footprint of the developed model demonstrate the successful application of this methodology.

Vida, Gholami↗

A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals

Endocrine disruptors such as polychlorinated biphenyls (PCBs), diethylstilbestrol (DES) and dichlorodiphenyltrichloroethane (DDT) are agents that interfere with the endocrine system and cause adverse health effects. Huge public health concern about endocrine disruptors has arisen. One of the mechanisms of endocrine disruption is through binding of endocrine disruptors with the hormone receptors in the target cells. Entrance of endocrine disruptors into target cells is the precondition of endocrine disruption. The binding capability of a chemical with proteins in the blood affects its entrance into the target cells and, thus, is very informative for the assessment of potential endocrine disruption of chemicals. α-fetoprotein is one of the major serum proteins that binds to a variety of chemicals such as estrogens. To better facilitate assessment of endocrine disruption of environmental chemicals, we developed a model for α-fetoprotein binding activity prediction using the novel pattern recognition method (Decision Forest) and the molecular descriptors calculated from two-dimensional structures by Mold2 software. The predictive capability of the model has been evaluated through internal validation using 125 training chemicals (average balanced accuracy of 69%) and external validations using 22 chemicals (balanced accuracy of 71%). Prediction confidence analysis revealed the model performed much better at high prediction confidence. Our results indicate that the model is useful (when predictions are in high confidence) in endocrine disruption risk assessment of environmental chemicals though improvement by increasing number of training chemicals is needed.

59 BASIC BIOLOGICAL SCIENCES↗

Glancing Angle Deposition in Gas Sensing: Bridging Morphological Innovations and Sensor Performances

Glancing Angle Deposition (GLAD) has emerged as a versatile and powerful nanofabrication technique for developing next-generation gas sensors by enabling precise control over nanostructure geometry, porosity, and material composition. Through dynamic substrate tilting and rotation, GLAD facilitates the fabrication of highly porous, anisotropic nanostructures, such as aligned, tilted, zigzag, helical, and multilayered nanorods, with tunable surface area and diffusion pathways optimized for gas detection. This review provides a comprehensive synthesis of recent advances in GLAD-based gas sensor design, focusing on how structural engineering and material integration converge to enhance sensor performance. Key materials strategies include the construction of heterojunctions and core–shell architectures, controlled doping, and nanoparticle decoration using noble metals or metal oxides to amplify charge transfer, catalytic activity, and redox responsiveness. GLAD-fabricated nanostructures have been effectively deployed across multiple gas sensing modalities, including resistive, capacitive, piezoelectric, and optical platforms, where their high aspect ratios, tailored porosity, and defect-rich surfaces facilitate enhanced gas adsorption kinetics and efficient signal transduction. These devices exhibit high sensitivity and selectivity toward a range of analytes, including NO2, CO, H2S, and volatile organic compounds (VOCs), with detection limits often reaching the parts-per-billion level. Emerging innovations, such as photo-assisted sensing and integration with artificial intelligence for data analysis and pattern recognition, further extend the capabilities of GLAD-based systems for multifunctional, real-time, and adaptive sensing. Finally, current challenges and future research directions are discussed, emphasizing the promise of GLAD as a scalable platform for next-generation gas sensing technologies.

Chemistry↗

Toll-like Receptor Ligands Enhance Vaccine Efficacy against a Virulent Newcastle Disease Virus Challenge in Chickens

To enhance the efficacy of the current Newcastle disease vaccine, we have selected potential adjuvants that target well-characterized pattern recognition receptors: the toll-like receptors (TLRs). Imiquimod is a small-molecule activator of TLR7, which is a sensor of dsDNA. ODN-1826 is a mimetic of CpG DNA and ligates TLR21 (a chicken homologue of TLR9 in mammals). The activation of TLRs leads to antiviral responses, including the induction of type I interferons (IFNs). In this study, birds were vaccinated intranasally with a live LaSota strain with or without imiquimod or ODN-1826 (50 µg/bird). Two weeks after vaccination, the birds were challenged with a virulent Newcastle disease virus (chicken/CA/212676/2002). Both adjuvants (imiquimod or ODN-1826) induced higher and more uniform antibody titers among vaccinated birds compared with the live vaccine-alone group. In addition, adjuvanted vaccines demonstrated greater protective efficacy in terms of the reduction in virus-shedding titer and the number of birds shedding the challenge virus at 2 and 4 days post-challenge. A differential expression of antiviral and immune-related genes was observed among groups from tissues (Harderian gland, trachea, cecal tonsil, and spleen) collected 1 and 3 days after treatment. These results demonstrate the potential of TLR-targeted adjuvants as mucosal vaccine enhancers and warrant a further characterization of immune correlates and optimization for efficacy.

59 BASIC BIOLOGICAL SCIENCES↗

NuMI Beam Monitoring Simulation and Data Analysis †

Following the decommissioning of the Main Injector Neutrino Oscillation Search (MINOS) experiment, muon and hadron monitors have emerged as vital diagnostic tools for the NuMI Off-axis Appearance (NOvA) experiment at Fermilab. These tools are crucial for overseeing the Neutrinos at the Main Injector (NuMI) beam. This study endeavors to ensure the monitor signal quality and to correlate them with the Neutrino beam profile. Leveraging muon monitor simulations, we systematically explore the monitor responses to variations in proton-beam and lattice parameters. Through the amalgamation of individual pixel data from muon monitors, pattern-recognition algorithms, simulations, and measured data, we devise machine-learning-based models to predict muon monitor responses and Neutrino flux.

43 PARTICLE ACCELERATORS↗

Inhibition of the C1s Protease and the Classical Complement Pathway by 6-(4-Phenylpiperazin-1-yl)Pyridine-3-Carboximidamide and Chemical Analogs

Abstract The classical pathway (CP) is a potent mechanism for initiating complement activity and is a driver of pathology in many complement-mediated diseases. The CP is initiated via activation of complement component C1, which consists of the pattern recognition molecule C1q bound to a tetrameric assembly of proteases C1r and C1s. Enzymatically active C1s provides the catalytic basis for cleavage of the downstream CP components, C4 and C2, and is therefore an attractive target for therapeutic intervention in CP-driven diseases. Although an anti-C1s mAb has been Food and Drug Administration approved, identifying small-molecule C1s inhibitors remains a priority. In this study, we describe 6-(4-phenylpiperazin-1-yl)pyridine-3-carboximidamide (A1) as a selective, competitive inhibitor of C1s. A1 was identified through a virtual screen for small molecules that interact with the C1s substrate recognition site. Subsequent functional studies revealed that A1 dose-dependently inhibits CP activation by heparin-induced immune complexes, CP-driven lysis of Ab-sensitized sheep erythrocytes, CP activation in a pathway-specific ELISA, and cleavage of C2 by C1s. Biochemical experiments demonstrated that A1 binds directly to C1s with a K d of ∼9.8 μM and competitively inhibits its activity with an inhibition constant (K i) of ∼5.8 μM. A 1.8-Å-resolution crystal structure revealed the physical basis for C1s inhibition by A1 and provided information on the structure–activity relationship of the A1 scaffold, which was supported by evaluating a panel of A1 analogs. Taken together, our work identifies A1 as a new class of small-molecule C1s inhibitor and lays the foundation for development of increasingly potent and selective A1 analogs for both research and therapeutic purposes.

Immunology↗

A daily, 250 m and real-time gross primary productivity product (2000–present) covering the contiguous United States

Abstract. Gross primary productivity (GPP) quantifies the amount of carbon dioxide (CO2) fixed by plants through photosynthesis. Although as a key quantity of terrestrial ecosystems, there is a lack of high-spatial-and-temporal-resolution, real-time and observation-based GPP products. To address this critical gap, here we leverage a state-of-the-art vegetation index, near-infrared reflectance of vegetation (NIRV), along with accurate photosynthetically active radiation (PAR), to produce a SatelLite Only Photosynthesis Estimation (SLOPE) GPP product for the contiguous United States (CONUS). Compared to existing GPP products, the proposed SLOPE product is advanced in its spatial resolution (250 m versus >500 m), temporal resolution (daily versus 8 d), instantaneity (latency of 1 d versus >2 weeks) and quantitative uncertainty (on a per-pixel and daily basis versus no uncertainty information available). These characteristics are achieved because of several technical innovations employed in this study: (1) SLOPE couples machine learning models with MODIS atmosphere and land products to accurately estimate PAR. (2) SLOPE couples highly efficient and pragmatic gap-filling and filtering algorithms with surface reflectance acquired by both Terra and Aqua MODIS satellites to derive a soil-adjusted NIRV (SANIRV) dataset. (3) SLOPE couples a temporal pattern recognition approach with a long-term Cropland Data Layer (CDL) product to predict dynamic C4 crop fraction. Through developing a parsimonious model with only two slope parameters, the proposed SLOPE product explains 85 % of the spatial and temporal variations in GPP acquired from 49 AmeriFlux eddy-covariance sites (324 site years), with a root-mean-square error (RMSE) of 1.63 gC m−2 d−1. The median R2 over C3 and C4 crop sites reaches 0.87 and 0.94, respectively, indicating great potentials for monitoring crops, in particular bioenergy crops, at the field level. With such a satisfactory performance and its distinct characteristics in spatiotemporal resolution and instantaneity, the proposed SLOPE GPP product is promising for biological and environmental research, carbon cycle research, and a broad range of real-time applications at the regional scale. The archived dataset is available at https://doi.org/10.3334/ORNLDAAC/1786 (download page: https://daac.ornl.gov/daacdata/cms/SLOPE_GPP_CONUS/data/, last access: 20 January 2021) (Jiang and Guan, 2020), and the real-time dataset is available upon request.

54 ENVIRONMENTAL SCIENCES↗

Mechanism of B-box 2 domain-mediated higher-order assembly of the retroviral restriction factor TRIM5α

Restriction factors and pattern recognition receptors are important components of intrinsic cellular defenses against viral infection. Mammalian TRIM5α proteins are restriction factors and receptors that target the capsid cores of retroviruses and activate ubiquitin-dependent antiviral responses upon capsid recognition. Here, we report crystallographic and functional studies of the TRIM5α B-box 2 domain, which mediates higher-order assembly of TRIM5 proteins. The B-box can form both dimers and trimers, and the trimers can link multiple TRIM5α proteins into a hexagonal net that matches the lattice arrangement of capsid subunits and enables avid capsid binding. Two modes of conformational flexibility allow TRIM5α to accommodate the variable curvature of retroviral capsids. B-box mediated interactions also modulate TRIM5α’s E3 ubiquitin ligase activity, by stereochemically restricting how the N-terminal RING domain can dimerize. Overall, these studies define important molecular details of cellular recognition of retroviruses, and how recognition links to downstream processes to disable the virus.

59 BASIC BIOLOGICAL SCIENCES↗

ISMB Conference Funding to Support Attendance of Early Researchers and Students

ISMB Conference Funding for Students and Young Scientists Historical Description The Intelligent Systems for Molecular Biology (ISMB) conference has provided a general forum for disseminating the latest developments in bioinformatics on an annual basis for the past 22 years. ISMB is a multidisciplinary conference that brings together scientists from computer science, molecular biology, mathematics and statistics. The goal of the ISMB meeting is to bring together biologists and computational scientists in a focus on actual biological problems, i.e., not simply theoretical calculations. The combined focus on “intelligent systems” and actual biological data makes ISMB a unique and highly important meeting. 21 years of experience in holding the conference has resulted in a consistently well-organized, well attended, and highly respected annual conference. "Intelligent systems" include any software which goes beyond straightforward, closed-form algorithms or standard database technologies, and encompasses those that view data in a symbolic fashion, learn from examples, consolidate multiple levels of abstraction, or synthesize results to be cognitively tractable to a human, including the development and application of advanced computational methods for biological problems. Relevant computational techniques include, but are not limited to: machine learning, pattern recognition, knowledge representation, databases, combinatorics, stochastic modeling, string and graph algorithms, linguistic methods, robotics, constraint satisfaction, and parallel computation. Biological areas of interest include molecular structure, genomics, molecular sequence analysis, evolution and phylogenetics, molecular interactions, metabolic pathways, regulatory networks, developmental control, and molecular biology generally. Emphasis is placed on the validation of methods using real data sets, on practical applications in the biological sciences, and on development of novel computational techniques. The ISMB conferences are distinguished from many other conferences in computational biology or artificial intelligence by an insistence that the researchers work with real molecular biology data, not theoretical or toy examples; and from many other biological conferences by providing a forum for technical advances as they occur, which otherwise may be shunned until a firm experimental result is published. The resulting intellectual richness and cross-disciplinary diversity provides an important opportunity for both students and senior researchers. ISMB has become the premier conference series in this field with refereed, published proceedings, establishing an infrastructure to promote the growing body of research.

60 APPLIED LIFE SCIENCES↗

LLNL Partners with IBM on Brain-Like Computing Chip

Lawrence Livermore National Laboratory (LLNL) will receive a first-of-a-kind brain-inspired supercomputing platform for deep learning developed by IBM Research. Based on a breakthrough neurosynaptic computer chip called IBM TrueNorth, the scalable platform will process the equivalent of 16 million neurons and 4 billion synapses and consume the energy equivalent of a hearing aid battery – a mere 2.5 watts of power. The brain-like, neural network design of the IBM Neuromorphic System is able to infer complex cognitive tasks such as pattern recognition and integrated sensory processing far more efficiently than conventional chips.

97 MATHEMATICS AND COMPUTING↗

The Liquid Argon Software Toolkit (LArSoft): Goals, Status and Plan

LArSoft is a toolkit that provides a software infrastructure and algorithms for the simulation, reconstruction and analysis of events in Liquid Argon Time Projection Chambers (LArTPCs). It is used by the ArgoNeuT, LArIAT, MicroBooNE, DUNE (including 35ton prototype and ProtoDUNE) and SBND experiments. The LArSoft collaboration provides an environment for the development, use, and sharing of code across experiments. The ultimate goal is to develop fully automatic processes for reconstruction and analysis of LArTPC events. The toolkit is based on the art framework and has a well-defined architecture to interface to other packages, including to GEANT4 and GENIE simulation software and the Pandora software development kit for pattern recognition. It is designed to facilitate and support the evolution of algorithms including their transition to new computing platforms. The development of the toolkit is driven by the scientific stakeholders involved. The core infrastructure includes standard definitions of types and constants, means to input experiment geometries as well as meta and event- data in several formats, and relevant general utilities. Examples of algorithms experiments have contributed to date are: photon-propagation; particle identification; hit finding, track finding and fitting; electromagnetic shower identification and reconstruction. We report on the status of the toolkit and plans for future work.

Pordes, Rush↗

Funding for the 2ND IAEA technical meeting on fusion data processing, validation and analysis

The International Atomic Energy Agency (IAEA) will organize the second Technical Meeting on Fusion Da Processing, Validation and Analysis from 30 May to 02 June, 2017, in Cambridge, MA USA. The meeting w be hosted by the MIT Plasma Science and Fusion Center (PSFC). The objective of the meeting is to provide a platform where a set of topics relevant to fusion data processing, validation and analysis are discussed with the view of extrapolation needs to next step fusion devices such as ITER. The validation and analysis of experimental data obtained from diagnostics used to characterize fusion plasmas are crucial for a knowledge based understanding of the physical processes governing the dynamics of these plasmas. The meeting will aim at fostering, in particular, discussions of research and development results that set out or underline trends observed in the current major fusion confinement devices. General information on the IAEA, including its mission and organization, can be found at the IAEA websit Uncertainty quantification (UQ) Model selection, validation, and verification (V&V) Probability theory and statistical analysis Inverse problems & equilibrium reconstru ction Integrated data analysis Real time data analysis Machine learning Signal/image proc essing & pattern recognition Experimental design and synthetic diagnostics Data management

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗