Study of vector boson scattering and search for new physics in events with two same-sign leptons and two jets

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Mobile vector soliton in a spin-orbit coupled spin-1 condensate

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Vector solitons in a spin-orbit-coupled spin-2 Bose-Einstein condensate

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

De novo transcriptome assembly for a non-model species, the blood-sucking bug Triatoma brasiliensis, a vector of Chagas disease

High throughput sequencing (HTS) provides new research opportunities for work on non-model organisms, such as differential expression studies between populations exposed to different environmental conditions. However, such transcriptomic studies first require the production of a reference assembly. The choice of sampling procedure, sequencing strategy and assembly workflow is crucial. To develop a reliable reference transcriptome for Triatoma brasiliensis, the major Chagas disease vector in Northeastern Brazil, different de novo assembly protocols were generated using various datasets and software. Both 454 and Illumina sequencing technologies were applied on RNA extracted from antennae and mouthparts from single or pooled individuals. The 454 library yielded 278 Mb. Fifteen Illumina libraries were constructed and yielded nearly 360 million RNA-seq single reads and 46 million RNA-seq paired-end reads for nearly 45 Gb. For the 454 reads, we used three assemblers, Newbler, CAP3 and/or MIRA and for the Illumina reads, the Trinity assembler. Ten assembly workflows were compared using these programs separately or in combination. To compare the assemblies obtained, quantitative and qualitative criteria were used, including contig length, N50, contig number and the percentage of chimeric contigs. Completeness of the assemblies was estimated using the CEGMA pipeline. The best assembly (57,657 contigs, completeness of 80 %, < 1 % chimeric contigs) was a hybrid assembly leading to recommend the use of (1) a single individual with large representation of biological tissues, (2) merging both long reads and short paired-end Illumina reads, (3) several assemblers in order to combine the specific advantages of each.

Search for vector-like T quarks decaying to top quarks and Higgs bosons in the all-hadronic channel using jet substructure

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Attribute charts for monitoring the mean vector of bivariate processes

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Pareto Frontier for the time-energy cost vector to an Earth-Moon transfer orbit using the patched-conic approximation

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Study on coinfecting vector-borne pathogens in dogs and ticks in Rio Grande do Norte, Brazil

Since dogs presenting several vector borne diseases can show none or nonspecific clinical signs depending on the phase of infection, the assessment of the particular agents involved is mandatory. The present study aimed to investigate the presence of Babesia spp., Ehrlichia spp., Anaplasma spp., Hepatozoon spp. and Leishmania spp. in blood samples and ticks, collected from two dogs from Rio Grande do Norte showing suggestive tick-borne disease by using molecular techniques. DNA of E. canis, H. canis and L. infantum were detected in blood samples and R. sanguineus ticks collected from dogs. Among all samples analyzed, two showed the presence of multiple infections with E. canis, H. canis and L. infantum chagasi. Here we highlighted the need for molecular differential diagnosis in dogs showing nonspecific clinical signs.

Learning kernels for support vector machines with polynomial powers of sigmoid

In the pattern recognition research field, Support Vector Machines (SVM) have been an effectiveness tool for classification purposes, being successively employed in many applications. The SVM input data is transformed into a high dimensional space using some kernel functions where linear separation is more likely. However, there are some computational drawbacks associated to SVM. One of them is the computational burden required to find out the more adequate parameters for the kernel mapping considering each non-linearly separable input data space, which reflects the performance of SVM. This paper introduces the Polynomial Powers of Sigmoid for SVM kernel mapping, and it shows their advantages over well-known kernel functions using real and synthetic datasets.

Epsilon(b) constrainsts on self-couplings of vector bosons

We analyze the constraints on possible anomalous contributions to the W+W-Z vertex coming from non-universal radiative corrections to the Z → bb̄ width. We parametrize these corrections in terms of ∈b and use the LEP data to establish the allowed values for the anomalous triple couplings. We examine all CP conserving effective operators that exhibit SU(2)L × U(1)Y gauge invariance and do not give any tree level contribution to the present experimental observables. For some of these operators our constraints are comparable with the bounds coming from a global fit of the oblique parameters, which evidences the increasing relevance of the precise measurement of the b-quark parameters at LEP for the search of new physics.

Evidence for temporal population replacement and the signature of ecological adaptation in a major Neotropical malaria vector in Amazonian Peru

The major Neotropical malaria vector, Anopheles darlingi, was reintroduced into the Iquitos, Loreto, Peru area during the early 1990s, where it displaced other anophelines and caused a major malaria epidemic. Since then, case numbers in Loreto have fluctuated, but annual increases have been reported since 2012. The population genetic structure of An. darlingi sampled before and after the introduction of long-lasting insecticidal nets (LLINs) was investigated to test the hypothesis of temporal population change (2006 vs. 2012). Current samples of An. darlingi were used to test the hypothesis of ecological adaptation to human modified (highway) compared with wild (riverine) habitat, linked to forest cover. In total, 693 An. darlingi from nine localities in Loreto, Peru area were genotyped using 13 microsatellite loci. To test the hypothesis of habitat differentiation in An. darlingi biting time patterns, HBR and EIR, four collections of An. darlingi from five localities (two riverine and three highway) were analysed. Analyses of microsatellite loci from seven (2006) and nine settlements (2012-2014) in the Iquitos area detected two distinctive populations with little overlap, although it is unclear whether this population replacement event is associated with LLIN distribution or climate. Within the 2012-2014 population two admixed subpopulations, A and B, were differentiated by habitat, with B significantly overrepresented in highway, and both in near-equal proportions in riverine. Both subpopulations had a signature of expansion and there was moderate genetic differentiation between them. Habitat and forest cover level had significant effects on HBR, such that Plasmodium transmission risk, as measured by EIR, in peridomestic riverine settlements was threefold higher than in peridomestic highway settlements. HBR was directly associated with available host biomass rather than forest cover. A population replacement event occurred between 2006 and 2012-2014, concurrently with LLIN distribution and a moderate El Niño event, and prior to an increase in malaria incidence. The likely drivers of this replacement cannot be determined with current data. The present-day An. darlingi population is composed of two highly admixed subpopulations, which appear to be in an early stage of differentiation, triggered by anthropogenic alterations to local habitat.

A hybrid method for the solution of a sparse power system matrices on vector computers

This paper describes a methodology for solving a linear system of equations on vector computer. The methodology combines direct and inverse factors. The decomposition and implementation of the direct solution in a CRAY Y-MPZE/232, and the performance results are discussed.

Effects of a mixed vector-scalar kink-like potential for spinless particles in two-dimensional space time

The intrinsically relativistic problem of spinless particles subject to a general mixing of vector and scalar kink- like potentials (similar to tanh gamma x) is investigated. The problem is mapped into the exactly solvable Sturm - Liouville problem with the Rosen - Morse potential and exact bounded solutions for particles and antiparticles are found. The behavior of the spectrum is discussed in some detail. An apparent paradox concerning the uncertainty principle is solved by recurring to the concept of effective Compton wavelength.

A simulation tool for transient stability analysis on vector computers

This paper deals with transient stability analysis based on time domain simulation on vector processing. This approach requires the solution of a set of differential equations in conjunction of another set of algebraic equations. The solution of the algebraic equations has presented a scalar as sequential set of tasks, and the solution of these equations, on vector computers, has required much more investigations to speedup the simulations. Therefore, the main objective of this paper has been to present methods to solve the algebraic equations using vector processing. The results, using a GRAY computer, have shown that on-line transient stability assessment is feasible.

Managing Large Data Sets Using Support Vector Machines

Hundreds of Terabytes of CMS (Compact Muon Solenoid) data are being accumulated for storage day by day at the University of Nebraska-Lincoln, which is one of the eight US CMS Tier-2 sites. Managing this data includes retaining useful CMS data sets and clearing storage space for newly arriving data by deleting less useful data sets. This is an important task that is currently being done manually and it requires a large amount of time. The overall objective of this study was to develop a methodology to help identify the data sets to be deleted when there is a requirement for storage space. CMS data is stored using HDFS (Hadoop Distributed File System). HDFS logs give information regarding file access operations. Hadoop MapReduce was used to feed information in these logs to Support Vector Machines (SVMs), a machine learning algorithm applicable to classification and regression which is used in this Thesis to develop a classifier. Time elapsed in data set classification by this method is dependent on the size of the input HDFS log file since the algorithmic complexities of Hadoop MapReduce algorithms here are O(n). The SVM methodology produces a list of data sets for deletion along with their respective sizes. This methodology was also compared with a heuristic called Retention Cost which was calculated using size of the data set and the time since its last access to help decide how useful a data set is. Accuracies of both were compared by calculating the percentage of data sets predicted for deletion which were accessed at a later instance of time. Our methodology using SVMs proved to be more accurate than using the Retention Cost heuristic. This methodology could be used to solve similar problems involving other large data sets.