Machine Learning at Microsoft with ML .NET

Machine Learning is transitioning from an art and science into a technology available to every developer. In the near future, every application on every platform will incorporate trained models to encode data-based decisions that would be impossible for developers to author. This presents a significant engineering challenge, since currently data science and modeling are largely decoupled from standard software development processes. This separation makes incorporating machine learning capabilities inside applications unnecessarily costly and difficult, and furthermore discourage developers from embracing ML in first place. In this paper we present ML .NET, a framework developed at Microsoft over the last decade in response to the challenge of making it easy to ship machine learning models in large software applications. We present its architecture, and illuminate the application demands that shaped it. Specifically, we introduce DataView, the core data abstraction of ML .NET which allows it to capture full predictive pipelines efficiently and consistently across training and inference lifecycles. We close the paper with a surprisingly favorable performance study of ML .NET compared to more recent entrants, and a discussion of some lessons learned

A feature extraction software tool for agricultural object-based image analysis

A software application for automatic descriptive feature extraction from image-objects, FETEX 2.0, is presented and described in this paper. The input data include a multispectral high resolution digital image and a vector file in shapefile format containing the polygons or objects, usually extracted from a geospatial database. The design of the available descriptive features or attributes has been mainly focused on the description of agricultural parcels, providing a variety of information: spectral information from the different image bands; textural descriptors of the distribution of the intensity values based on the grey level co-occurrence matrix, the wavelet transform and a factor of edgeness; structural features describing the spatial arrangement of the elements inside the objects, based on the semivariogram curve and the Hough transform; and several descriptors of the object shape. The output file is a table that can be produced in four alternative formats, containing a vector of features for every object processed. This table of numeric values describing the objects from different points of view can be externally used as input data for any classification software. Additionally, several types of graphs and images describing the feature extraction procedure are produced, useful for interpretation and understanding the process. A test of the processing times is included, as well as an application of the program in a real parcel-based classification problem, providing some results and analyzing the applicability, the future improvement of the methodologies, and the use of additional types of data sets. This software is intended to be a dynamic tool, integrating further data and feature extraction algorithms for the progressive improvement of land use/land cover database classification and agricultural database updating processes. © 2011 Elsevier B.V.The authors appreciate the financial support provided by the Spanish Ministerio de Ciencia e Innovacion and the FEDER in the framework of the Project CGL2009-14220 and CGL2010-19591/BTE, the Spanish Institut Geografico Nacional (IGN), Institut Cartografico Valenciano (ICV), Institut Murciano de Investigacion y Desarrollo Agrario y Alimentario (IMIDA) and Banco de Terras de Galicia (Bantegal).Ruiz Fernández, LÁ.; Recio Recio, JA.; Fernández-Sarría, A.; Hermosilla, T. (2011). A feature extraction software tool for agricultural object-based image analysis. Computers and Electronics in Agriculture. 76(2):284-296. https://doi.org/10.1016/j.compag.2011.02.007S28429676

Innovative methods of community engagement: towards a low carbon climate resilient future

The proceedings of the Innovative Methods of Community Engagement: Toward a Low Carbon, Climate Resilient Future workshop have been developed by the Imagining2050 team in UCC and the Secretariat to the National Dialogue on Climate Action (NDCA). The NDCA also funded the workshop running costs. The proceedings offer a set of recommendations and insights into leveraging different community engagement approaches and methodologies in the area of climate action. They draw from interdisciplinary knowledge and experiences of researchers for identifying, mobilizing and mediating communities. The work presented below derives from a workshop held in the Environmental Research Institute in UCC on the 17th January 2019. These proceedings are complementary to an earlier workshop also funded by the NDCA and run by MaREI in UCC, titled ‘How do we Engage Communities in Climate Action? – Practical Learnings from the Coal Face’. The earlier workshop looked more closely at community development groups and other non-statutory organizations doing work in the area of climate change

Finding the engram.

Many attempts have been made to localize the physical trace of a memory, or engram, in the brain. However, until recently, engrams have remained largely elusive. In this Review, we develop four defining criteria that enable us to critically assess the recent progress that has been made towards finding the engram. Recent \u27capture\u27 studies use novel approaches to tag populations of neurons that are active during memory encoding, thereby allowing these engram-associated neurons to be manipulated at later times. We propose that findings from these capture studies represent considerable progress in allowing us to observe, erase and express the engram

Correlated long-range mixed-harmonic fluctuations measured in pp, p+Pb and low-multiplicity Pb+Pb collisions with the ATLAS detector

For abstract see published article

Performance of top-quark and W -boson tagging with ATLAS in Run 2 of the LHC

The performance of identification algorithms (“taggers”) for hadronically decaying top quarks and W bosons in pp collisions at √s=13 TeV recorded by the ATLAS experiment at the Large Hadron Collider is presented. A set of techniques based on jet shape observables are studied to determine a set of optimal cut-based taggers for use in physics analyses. The studies are extended to assess the utility of combinations of substructure observables as a multivariate tagger using boosted decision trees or deep neural networks in comparison with taggers based on two-variable combinations. In addition, for highly boosted top-quark tagging, a deep neural network based on jet constituent inputs as well as a re-optimisation of the shower deconstruction technique is presented. The performance of these taggers is studied in data collected during 2015 and 2016 corresponding to 36.1 fb −1 for the tt ¯ and γ+jet and 36.7 fb −1 −1 for the dijet event topologies

In situ calibration of large-radius jet energy and mass in 13 TeV proton–proton collisions with the ATLAS detector

The response of the ATLAS detector to largeradius jets is measured in situ using 36.2 fb−1 of √s = 13 TeV proton–proton collisions provided by the LHC and recorded by the ATLAS experiment during 2015 and 2016. The jet energy scale is measured in events where the jet recoils against a reference object, which can be either a calibrated photon, a reconstructed Z boson, or a system of well-measured small-radius jets. The jet energy resolution and a calibration of forward jets are derived using dijet balance measurements. The jet mass response is measured with two methods: using mass peaks formed by W bosons and top quarks with large transverse momenta and by comparing the jet mass measured using the energy deposited in the calorimeter with that using the momenta of charged-particle tracks. The transversemomentum and mass responses in simulations are found to be about 2–3% higher than in data. This difference is adjusted for with a correction factor. The results of the different methods are combined to yield a calibration over a large range of transverse momenta (pT). The precision of the relative jet energy scale is 1–2% for 200 GeV < pT < 2 TeV, while that of the mass scale is 2–10%. The ratio of the energy resolutions in data and simulation is measured to a precision of 10–15% over the same pT range

Measurement of prompt photon production in sNN√=8.16 TeV p+Pb collisions with ATLAS

The inclusive production rates of isolated, prompt photons in p+Pb collisions at sNN√=8.16 TeV are studied with the ATLAS detector at the Large Hadron Collider using a dataset with an integrated luminosity of 165 nb−1 recorded in 2016. The cross-section and nuclear modification factor RpPb are measured as a function of photon transverse energy from 20 GeV to 550 GeV and in three nucleon-nucleon centre-of-mass pseudorapidity regions, (-2.83,-2.02), (-1.84,0.91), and (1.09,1.90). The cross-section and RpPb values are compared with the results of a next-to-leading-order perturbative QCD calculation, with and without nuclear parton distribution function modifications, and with expectations based on a model of the energy loss of partons prior to the hard scattering. The data disfavour a large amount of energy loss and provide new constraints on the parton densities in nuclei.We acknowledge the support of ANPCyT, Argentina; YerPhI, Ar-menia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azer-baijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS, CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF and Benoziyo Center, Is-rael; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portu-gal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Fed-eration; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallen-berg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, in-dividual groups and members have received support from BCKDF, Canarie, CRC and Compute Canada, Canada; COST, ERC, ERDF, Hori-zon 2020, and Marie Skłodowska-Curie Actions, European Union; Investissements d’ Avenir Labex and Idex, ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia pro-grammes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; CERCA Programme Generalitat de Catalunya, Spain; The Royal Society and Leverhulme Trust, United Kingdom

Measurement of the azimuthal anisotropy of charged particles produced in s NN = 5.02 TeV Pb+Pb collisions with the ATLAS detector.

Measurements of the azimuthal anisotropy in lead-lead collisions at s NN = 5.02 TeV are presented using a data sample corresponding to 0.49 nb - 1 integrated luminosity collected by the ATLAS experiment at the LHC in 2015. The recorded minimum-bias sample is enhanced by triggers for "ultra-central" collisions, providing an opportunity to perform detailed study of flow harmonics in the regime where the initial state is dominated by fluctuations. The anisotropy of the charged-particle azimuthal angle distributions is characterized by the Fourier coefficients, v 2 - v 7 , which are measured using the two-particle correlation, scalar-product and event-plane methods. The goal of the paper is to provide measurements of the differential as well as integrated flow harmonics v n over wide ranges of the transverse momentum, 0.5  < p T <  60 GeV, the pseudorapidity, | η | <  2.5, and the collision centrality 0-80%. Results from different methods are compared and discussed in the context of previous and recent measurements in Pb+Pb collisions at s NN = 2.76  TeV and 5.02  TeV . In particular, the shape of the p T dependence of elliptic or triangular flow harmonics is observed to be very similar at different centralities after scaling the v n and p T values by constant factors over the centrality interval 0-60% and the p T range 0.5  < p T <  5 GeV