Analysis of a deep neural network for missing transverse momentum reconstruction in ATLAS

The ATLAS detector is a multipurpose particle detector built to record almost all possible decay products of the high energy proton-proton collisions provided by the Large Hadron Collider. The presence and combined kinematics of unobserved particles can be inferred by the observed momentum imbalance in the transverse plane. In this work, a deep neural network was trained using supervised learning to measure this imbalance. The performance of this network was evaluated in MC simulation and in 43 fb⁻¹ of data recorded at ATLAS. The network offered superior resolution and significantly better pileup resistance than all other pre-existing algorithms in every tested topology. The network also provided the best discriminator between events that did and did not contain neutrinos. The potential gain insensitivity to new physics was demonstrated by using this network in a search for the electroweak production of supersymmetric particles. The expected sensitivity to observe the production of said particles was increased by up to 26%

Proceedings of the 2nd Conference on Production Systems and Logistics (CPSL 2021)

Proceedings of the CPSL 202

BIM in the construction industry

En las últimas décadas, el término modelado de información de construcción (BIM) se ha mencionado en una amplia gama de esfuerzos de investigación de la construcción. BIM es una nueva solución para la recesión sin precedentes en la industria de la construcción, es decir, pérdida de productividad, escasez de mano de obra, sobrecostos y competitividad severa. La tecnología BIM proporciona muchos beneficios: detección rápida de conflictos de diseño, regulación automática de diseño algoritmo de verificación, visualización de realidad virtual/aumentada y entorno de trabajo de colaboración. BIM los expertos, así como los profesionales de la industria, enfatizan la importancia de las aplicaciones BIM en el campo de construcción. Dado el rápido desarrollo y adopción de BIM en la arquitectura, ingeniería, y construcción (AEC), están surgiendo nuevas tendencias relevantes para la investigación de BIM, siendo sumamente útil no sólo para los académicos sino también para los profesionales.In recent decades, the term building information modeling (BIM) has been mentioned in a wide range of construction research endeavors. BIM is a new solution for unprecedented recession in the construction industry, i.e., productivity loss, labor shortage, cost overrun, and severe competitiveness. BIM technology provides many benefits: prompt design clash detection, automatic deign regulatory check algorithm, augmented/virtual reality visualization, and collaboration work environment. BIM experts as well as industry practitioners are stressing the importance of BIM applications in the field of construction. Given the rapid development and adoption of BIM in the architecture, engineering, and construction (AEC) industry, new trends relevant to the research of BIM are emerging, being exceedingly helpful not only for academics but also for practitioners

AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model

© 2020, The Author(s). The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods requires a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid, and it is unnecessary to execute them to find out whether they are good pipelines. To address this issue, we propose a novel method to evaluate the validity of ML pipelines using a surrogate model (AVATAR). The AVATAR enables to accelerate automatic ML pipeline composition and optimisation by quickly ignoring invalid pipelines. Our experiments show that the AVATAR is more efficient in evaluating complex pipelines in comparison with the traditional evaluation approaches requiring their execution

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp