Differential top-antitop cross-section measurements as a function of observables constructed from final-state particles using pp collisions at s√=7 TeV in the ATLAS detector

Various differential cross-sections are measured in top-quark pair (tt¯) events produced in proton--proton collisions at a centre-of-mass energy of s√=7 TeV at the LHC with the ATLAS detector. These differential cross-sections are presented in a data set corresponding to an integrated luminosity of 4.6 fb−1. The differential cross-sections are presented in terms of kinematic variables of a top-quark proxy referred to as the pseudo-top-quark whose dependence on theoretical models is minimal. The pseudo-top-quark can be defined in terms of either reconstructed detector objects or stable particles in an analogous way. The measurements are performed on tt¯ events in the lepton+jets channel, requiring exactly one charged lepton and at least four jets with at least two of them tagged as originating from a b-quark. The hadronic and leptonic pseudo-top-quarks are defined via the leptonic or hadronic decay mode of the W boson produced by the top-quark decay in events with a single charged lepton.The cross-section is measured as a function of the transverse momentum and rapidity of both the hadronic and leptonic pseudo-top-quark as well as the transverse momentum, rapidity and invariant mass of the pseudo-top-quark pair system. The measurements are corrected for detector effects and are presented within a kinematic range that closely matches the detector acceptance. Differential cross-section measurements of the pseudo-top-quark variables are compared with several Monte Carlo models that implement next-to-leading order or leading-order multi-leg matrix-element calculations.

Measurement of the WW+WZ cross section and limits on anomalous triple gauge couplings using final states with one lepton, missing transverse momentum, and two jets with the ATLAS detector at s√=7 TeV

The production of a W boson decaying to eν or μν in association with a W or Z boson decaying to two jets is studied using 4.6 fb−1 of proton--proton collision data at s√=7 TeV recorded with the ATLAS detector at the LHC. The combined WW+WZ cross section is measured with a significance of 3.4σ and is found to be 68±7 (stat.)±19 (syst.) pb, in agreement with the Standard Model expectation of 61.1±2.2 pb. The distribution of the transverse momentum of the dijet system is used to set limits on anomalous contributions to the triple gauge coupling vertices and on parameters of an effective-field-theory model.

Performance of the ATLAS muon trigger in pp collisions at s√=8 TeV

The performance of the ATLAS muon trigger system has been evaluated with proton--proton collision data collected in 2012 at the Large Hadron Collider at a centre-of-mass energy of 8 TeV. The performance was primarily evaluated using events containing a pair of muons from the decay of Z bosons. The efficiency is measured for the single-muon trigger for a kinematic region of the transverse momentum pT between 25 and 100 GeV, with a statistical uncertainty of less than 0.01% and a systematic uncertainty of 0.6%. The performance is also compared in detail to the predictions from simulation. The efficiency was measured over a wide pT range (a few GeV to several hundred GeV) by using muons from J/ψ mesons,W bosons, and top and antitop quarks. It showed highly uniform and stable performance.

Determination of the top-quark pole mass using t(t)over-bar+1-jet events collected with the ATLAS experiment in 7 TeV pp collisions

The normalized differential cross section for top-quark pair production in association with at least one jet is studied as a function of the inverse of the invariant mass of the tt¯+1-jet system. This distribution can be used for a precise determination of the top-quark mass since gluon radiation depends on the mass of the quarks. The experimental analysis is based on proton--proton collision data collected by the ATLAS detector at the LHC with a centre-of-mass energy of 7 TeV corresponding to an integrated luminosity of 4.6 fb−1. The selected events were identified using the lepton+jets top-quark-pair decay channel, where lepton refers to either an electron or a muon. The observed distribution is compared to a theoretical prediction at next-to-leading-order accuracy in quantum chromodynamics using the pole-mass scheme. With this method, the measured value of the top-quark pole mass, mpolet, is: mpolet =173.7 ± 1.5 (stat.) ± 1.4 (syst.) +1.0−0.5 (theory) GeV. This result represents the most precise measurement of the top-quark pole mass to date.

A multi-criteria decision support system for a routing problem in waste collection

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A decision support system for a waste collection vehicle routing problem

The selective collection of municipal solid waste for recycling is a very complex and expensive process, where a major issue is to perform cost-efficient waste collection routes. Despite the abundance of commercially available software for fleet management, they often lack the capability to deal properly with sequencing problems and dynamic revision of plans and schedules during process execution. Our approach to achieve better solutions for the waste collection process is to model it as a vehicle routing problem, more specifically as a team orienteering problem where capacity constraints on the vehicles are considered, as well as time windows for the waste collection points and for the vehicles. The final model is called capacitated team orienteering problem with double time windows (CTOPdTW).We developed a genetic algorithm to solve routing problems in waste collection modelled as a CTOPdTW. The results achieved suggest possible reductions of logistic costs in selective waste collection.

Assessment of overlay masonry strengthening system under in-plane monotonic and cyclic loading using the diagonal tensile test

The development of novel strengthening techniques to address the seismic vulnerability of masonry elements is gradually leading to simpler, faster and more effective strengthening strategies. In particular, the use of fabric reinforced cementitious matrix systems is considered of great potential, given the increase of ductility achieved with simple and economic strengthening procedures. To assess the effectiveness of these strengthening systems, and considering that the seismic action is involved, one important component of the structural behaviour is the in-plane cyclic response. In this work is discussed the applicability of the diagonal tensile test for the assessment of the cyclic response of strengthened masonry. The results obtained allowed to assess the contribution of the strengthening system to the increase of the load carrying capacity of masonry elements, as well as to evaluate the damage evolution and the stiffness degradation mechanisms developing under cyclic loading.

Lightweight adjustable partition system: evaluation of resistance to horizontal loads and functional failure from impacts

This paper presents and discusses the results of the serviciability and use condition tests carried on an innovative solution for partitions, designated AdjustMembrane developed within a research project. The proposed system is a modular non-loadbearing wall, tensioned between the pavements and ceiling slabs, which are used as anchoring elements. It allows several advantages, related with the weight reduction to achieve a good sustainable performance, such as the reduction of construction costs, energy, and materials, and it is easy to recycle and to reuse, allowing self-construction. Apart from a general presentation of the partition technology, this paper presents and discusses the results of experimental tests carried out. From the results obtained, it is possible to conclude that the solution fulfils the requirements for this typology of wall in terms of resistance to horizontal loads induced by soft and hard body impacts.

An evolutionary multi-objective optimization system for earthworks

Earthworks involve the levelling or shaping of a target area through the moving or processing of the ground surface. Most construction projects require earthworks, which are heavily dependent on mechanical equipment (e.g., excavators, trucks and compactors). Often, earthworks are the most costly and time-consuming component of infrastructure constructions (e.g., road, railway and airports) and current pressure for higher productivity and safety highlights the need to optimize earthworks, which is a nontrivial task. Most previous attempts at tackling this problem focus on single-objective optimization of partial processes or aspects of earthworks, overlooking the advantages of a multi-objective and global optimization. This work describes a novel optimization system based on an evolutionary multi-objective approach, capable of globally optimizing several objectives simultaneously and dynamically. The proposed system views an earthwork construction as a production line, where the goal is to optimize resources under two crucial criteria (costs and duration) and focus the evolutionary search (non-dominated sorting genetic algorithm-II) on compaction allocation, using linear programming to distribute the remaining equipment (e.g., excavators). Several experiments were held using real-world data from a Portuguese construction site, showing that the proposed system is quite competitive when compared with current manual earthwork equipment allocation.

Hand gesture recognition system based in computer vision and machine learning

"Lecture notes in computational vision and biomechanics series, ISSN 2212-9391, vol. 19"

Generic system for human-computer gesture interaction: applications on sign language recognition and robotic soccer refereeing

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for human-computer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of visionbased interaction systems could be the same for all applications and thus facilitate the implementation. For hand posture recognition, a SVM (Support Vector Machine) model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM (Hidden Markov Model) model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications. To validate the proposed framework two applications were implemented. The first one is a real-time system able to interpret the Portuguese Sign Language. The second one is an online system able to help a robotic soccer game referee judge a game in real time.

Vision-based portuguese sign language recognition system

Vision-based hand gesture recognition is an area of active current research in computer vision and machine learning. Being a natural way of human interaction, it is an area where many researchers are working on, with the goal of making human computer interaction (HCI) easier and natural, without the need for any extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them, for example, to convey information. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. Hand gestures are a powerful human communication modality with lots of potential applications and in this context we have sign language recognition, the communication method of deaf people. Sign lan- guages are not standard and universal and the grammars differ from country to coun- try. In this paper, a real-time system able to interpret the Portuguese Sign Language is presented and described. Experiments showed that the system was able to reliably recognize the vowels in real-time, with an accuracy of 99.4% with one dataset of fea- tures and an accuracy of 99.6% with a second dataset of features. Although the im- plemented solution was only trained to recognize the vowels, it is easily extended to recognize the rest of the alphabet, being a solid foundation for the development of any vision-based sign language recognition user interface system.

Generic system for human-computer gesture interaction

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for humancomputer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of vision-based interaction systems can be the same for all applications and thus facilitate the implementation. In order to test the proposed solutions, three prototypes were implemented. For hand posture recognition, a SVM model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications.

A proactive intelligent decision support system for predicting the popularity of online news

"Lecture notes in computer science series, ISSN 0302-9743, vol. 9273"

An integrated system for human action recognition from video using hidden Markov model

In this paper, we present an integrated system for real-time automatic detection of human actions from video. The proposed approach uses the boundary of humans as the main feature for recognizing actions. Background subtraction is performed using Gaussian mixture model. Then, features are extracted from silhouettes and Vector Quantization is used to map features into symbols (bag of words approach). Finally, actions are detected using the Hidden Markov Model. The proposed system was validated using a newly collected real- world dataset. The obtained results show that the system is capable of achieving robust human detection, in both indoor and outdoor environments. Moreover, promising classification results were achieved when detecting two basic human actions: walking and sitting.