Search for Resonances and New Physics

\noindent The appearance of new resonances is a most spectacular means by which physics beyond the standard model may be revealed at the LHC. Searches are conducted for bumps in mass spectra exploring a multitude of final states. Results are presented spanning from sub-GeV to multi-TeV scales.Peer Reviewe

Search and measurement of the B → μμ rare processes with LHC Run I data

The decays Bs0→μ+μ− and B0→μ+μ− are highly suppressed in the standard model constituting sensitive probes of new physics. We present the results of a search for these rare decays in proton-proton collisions using the full data sample collected by the CMS experiment during LHC Run I. Through a fit to the dimuon invariant-mass spectrum, an excess of events with respect to the background is observed, compatible with the Bs0(B0) signal with a significance of 4.3 (2.0) standard deviations ( σ ). The measured branching fractions are B(Bs0→μ+μ−)=(3.0−0.9+1.0)×10−9 and B(B0→μ+μ−)=(3.5−1.8+2.1)×10−10 . The combination of the CMS and LHCb results, using their full Run I data samples, yield an event excess compatible with the Bs0(B0) signal with a significance in excess of 6σ(3σ) . The ongoing and projected accelerator and detector upgrades will allow to establish and to carry out precision measurements of both rare decays, and explore novel observables with further sensitivity to new physics effects.Peer Reviewe

A counting multidimensional innovation index for SMEs

We developed a C ounting Multidimensional Innovation Index (MII) framework for measuring and benchmarking innovation of Small and Medium Enterprises (SMEs) , groups of SMEs, industries , regions, and countries . The methodology behind the MII is similar to the methodology behind the United Nations Multi dimensional Poverty Index and follows the innovation definitions stipulated by the OECD Oslo Manual , cover ing dime n s ions and partial indicators suggested by this Ma nual and/or adapted from the In novation Union Scoreboard (IUS) and from the Global Innovation Index (GII) . T o illustrate the MII framework , a survey was conducted among SME s of the metalworking industry in Portugal .info:eu-repo/semantics/publishedVersio

A counting multidimensional innovation index for SMEs

Purpose: The purpose of this paper is to develop a multidimensional innovation index (MII) framework for measuring and benchmarking multidimensional innovation of small and medium enterprises (SMEs) and groups of SMEs. Design/methodology/approach: A counting dual cut-off method is employed. First, output and input innovation profiles and composite scores of individual SMEs are computed. Second, a set of four composite innovation indices are generated characterizing the group of SMEs under analysis: MIIo and MIIi measure multidimensional output and input innovation, respectively; while MIIr and MIIa assess the ratio and average of MIIo and MIIi, respectively. To test the MII framework, a survey was conducted among SMEs of the metalworking industry in Portugal. Findings: In 2012, about 28.9 percent (42.2 percent) SMEs of the Portuguese metalworking industry were determined to be multidimensional output (input) innovative. The average percentage of dimensions for which output (input) innovative SMEs were innovative was 65.0 percent (66.0 percent). Thus, the industry MII vector was (MIIo; MIIi; MIIr; MIIa)¼ (0.188, 0.279, 0.674; 0.233). Significant differences were found across the industry, individual SMEs’ multidimensional output and input innovation scores, enabling the identification of groups of SMEs, which can be characterized and compared by computing the corresponding and specific MII vectors. Research limitations/implications: The research has limitations because of the small size of the sample and the benchmarking possibilities it provides. Originality/value The novelty of the MII framework lies in the counting dual cut-off method employed.info:eu-repo/semantics/acceptedVersio

Review of bottomonium measurements from CMS

We review the results on the bottomonium system from the CMS experiment at the Large Hadron Collider. Measurements have been carried out at different center-of-mass energies in proton collisions and in collisions involving heavy ions. These include precision measurements of cross sections and polarizations, shedding light on hadroproduction mechanisms, and the observation of quarkonium sequential suppression, a notable indication of quark-gluon plasma formation. The observation of the production of bottomonium pairs is also reported along with searches for new states. We close with a brief outlook of the future physics program.Comment: 32 page

The productivity of innovation in Portugal

We view innovation as a productive process, with outputs and inputs. We aim at compare the productivity of innovation across the twenty seven Member States of the European Union (EU-27), having a particular focus on Portugal. The data on inputs and outputs of innovation were collected from the Innovation Union Scoreboard 2010 report and covers the EU-27 Member States, from 2006 to 2010. The Total Factor Productivity index (TFP index) was used as the technique for data analysis. The choice of this technique was mainly determined by its flexibility and by data constraints. Two types of TFP indexes were computed: i) TFPt (time), which compares the productivity of innovation in each Member State with its productivity in a base year; ii) TFPs (space), which compares the productivity of innovation in each Member State with the productivity of the EU-27 average. Results show larger TFPs differences across Member States than TFPt differences. Concerning TFPt, there is a reduction of productivity of most of the Member States during the time length, which can be explained by the recent world financial crisis. This was the case of Portugal, where average TFPt in the time length is slightly below 1. The seven Member States that did not lose any productivity are mostly from Eastern Europe, Member Sates which have entered the European Union and accede to its structural funds more recently. Concerning TFPs, Portugal presents average TFPs well above 1. The Portuguese average TFPs value is close to the one of Germany and higher than the one of Sweden. The Innovation Union Scoreboard 2010 report classifies Portugal as Moderate innovator and Germany and Sweden as innovation leaders. We conclude that productivity of innovation in Portugal is similar to the one of Germany and higher than the one of Sweden. Differences between Portugal and those Member States, such as the ones reported in the Innovation Union Scoreboard 2010, can be explained by the fact of Portugal having fewer resources allocated to innovation and thus fewer outputs from innovation than Germany or Sweden have.info:eu-repo/semantics/publishedVersio

Sampaio Bruno e Leonardo Coimbra: Olhares cruzados sobre o marxismo

info:eu-repo/semantics/publishedVersio

A Study of Generalization and Fitness Landscapes for Neuroevolution

Rodrigues, N. M., Silva, S., & Vanneschi, L. (2020). A Study of Generalization and Fitness Landscapes for Neuroevolution. IEEE Access, 8, 108216-108234. [9113453]. https://doi.org/10.1109/ACCESS.2020.3001505Fitness landscapes are a useful concept for studying the dynamics of meta-heuristics. In the last two decades, they have been successfully used for estimating the optimization capabilities of different flavors of evolutionary algorithms, including genetic algorithms and genetic programming. However, so far they have not been used for studying the performance of machine learning algorithms on unseen data, and they have not been applied to studying neuroevolution landscapes. This paper fills these gaps by applying fitness landscapes to neuroevolution, and using this concept to infer useful information about the learning and generalization ability of the machine learning method. For this task, we use a grammar-based approach to generate convolutional neural networks, and we study the dynamics of three different mutations used to evolve them. To characterize fitness landscapes, we study autocorrelation, entropic measure of ruggedness, and fitness clouds. Also, we propose the use of two additional evaluation measures: density clouds and overfitting measure. The results show that these measures are appropriate for estimating both the learning and the generalization ability of the considered neuroevolution configurations.publishersversionpublishe

A Study of Fitness Landscapes for Neuroevolution

Rodrigues, N. M., Silva, S., & Vanneschi, L. (2020). A Study of Fitness Landscapes for Neuroevolution. In 2020 IEEE Congress on Evolutionary Computation, CEC 2020: Conference Proceedings [9185783] (2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC48606.2020.9185783Fitness landscapes are a useful concept to study the dynamics of meta-heuristics. In the last two decades, they have been applied with success to estimate the optimization power of several types of evolutionary algorithms, including genetic algorithms and genetic programming. However, so far they have never been used to study the performance of machine learning algorithms on unseen data, and they have never been applied to neuroevolution. This paper aims at filling both these gaps, applying for the first time fitness landscapes to neuroevolution and using them to infer useful information about the predictive ability of the method. More specifically, we use a grammar-based approach to generate convolutional neural networks, and we study the dynamics of three different mutations to evolve them. To characterize fitness landscapes, we study autocorrelation and entropic measure of ruggedness. The results show that these measures are appropriate for estimating both the optimization power and the generalization ability of the considered neuroevolution configurations.preprintpublishe

Review of bottomonium measurements from CMS

We review the results on the bottomonium system from the CMS experiment at the Large Hadron Collider. Measurements have been carried out at different center-of-mass energies in proton collisions and in collisions involving heavy ions. These include precision measurements of cross sections and polarizations, shedding light on hadroproduction mechanisms, and the observation of quarkonium sequential suppression, a notable indication of quark–gluon plasma formation. The observation of the production of bottomonium pairs is also reported along with searches for new states. We close with a brief outlook of the future physics program.Peer Reviewe