Many-body system with a four-parameter family of point interactions in one dimension

We consider a four-parameter family of point interactions in one dimension. This family is a generalization of the usual $\delta$-function potential. We examine a system consisting of many particles of equal masses that are interacting pairwise through such a generalized point interaction. We follow McGuire who obtained exact solutions for the system when the interaction is the $\delta$-function potential. We find exact bound states with the four-parameter family. For the scattering problem, however, we have not been so successful. This is because, as we point out, the condition of no diffraction that is crucial in McGuire's method is not satisfied except when the four-parameter family is essentially reduced to the $\delta$-function potential.Comment: 8 pages, 4 figure

Reação de Cultivares de Oliveira a Meloidogyne mayaguensis.

bitstream/item/31511/1/comunicado-224.pd

Distributing multipartite entanglement over noisy quantum networks

A quantum internet aims at harnessing networked quantum technologies, namely by distributing bipartite entanglement between distant nodes. However, multipartite entanglement between the nodes may empower the quantum internet for additional or better applications for communications, sensing, and computation. In this work, we present an algorithm for generating multipartite entanglement between different nodes of a quantum network with noisy quantum repeaters and imperfect quantum memories, where the links are entangled pairs. Our algorithm is optimal for GHZ states with 3 qubits, maximising simultaneously the final state fidelity and the rate of entanglement distribution. Furthermore, we determine the conditions yielding this simultaneous optimality for GHZ states with a higher number of qubits, and for other types of multipartite entanglement. Our algorithm is general also in the sense that it can optimize simultaneously arbitrary parameters. This work opens the way to optimally generate multipartite quantum correlations over noisy quantum networks, an important resource for distributed quantum technologies.info:eu-repo/semantics/publishedVersio

The motion of two masses coupled to a massive spring

We discuss the classical motion of a spring of arbitrary mass coupled to two arbitrary massive blocks attached at its ends. A general approach to the problem is presented and some general results are obtained. Examples for which a simple elastic function can be inferred are discussed and the normal modes and normal frequencies obtained. An approximation procedure to the evaluation of the normel frequencies in the case of uniform elastic function and mass density is also discussed.Comment: Standard Latex file plus three eps figure

The thermomechanical properties of thermally evaporated bismuth triiodide thin films

FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL E NÍVEL SUPERIORBismuth triiodide (BiI3) has been studied in recent years with the aim of developing lead-free semiconductors for photovoltaics. It has also appeared in X-ray detectors due to the high density of the Bismuth element. This material is attractive as an active layer in solar cells, or may be feasible for conversion into perovskite-like material (MA(3)Bi(2)I(9)), being also suitable for photovoltaic applications. In this study, we report on the thermomechanical properties (stress, hardness, coefficient of thermal expansion, and biaxial and reduced Young's moduli) of BiI3 thin films deposited by thermal evaporation. The stress was determined as a function of temperature, adopting the thermally induced bending technique, which allowed us to extract the coefficient of thermal expansion (31 x 10(-6) degrees C-1) and Young's biaxial modulus (19.6 GPa) for the films. Nanohardness (similar to 0.76 GPa) and a reduced Young's modulus of 27.1 GPa were determined through nanoindentation measurements.918FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL E NÍVEL SUPERIORFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL E NÍVEL SUPERIOR2012/10127-52017/11986-5147473/2014-4465423/2014-0153029/2013-7302370/2015-3306297/2017-5103203/2018-400

SiRCub - Brazilian Agricultural Crop Recognition System.

This paper presents a novel approach to classify agricultural crops using NDVI time series. The novelty lies in i) extracting a set of features from the each and every NDVI curve, and ii) using them to train a crop classification model using a Support Vector Machine (SVM). Specifically, we use the TIMESAT program package to: 1) smooth the time series, 2) decompose them into agricultural seasons?a season is the period between sowing and harvesting?, and 3) extract the features for each season.SBSR 2015

Collaborative filtering for mobile application recommendation with implicit feedback

This paper introduces a novel dataset regarding the installation of mobile applications in users devices, and benchmarks multiple well-established collaborative filtering techniques, leveraging on the user implicit feedback extracted from the data. Our experiments use 3 snapshots provided by Aptoide, one of the leading mobile application stores. These snapshots provide information about the installed applications for more than 4 million users in total. Such data allow us to infer the users activity over time, which corresponds to an implicit measure of interest in a certain application, as we consider that installs reflect a positive user opinion on an app, and, inversely, uninstalls reflect a negative user opinion. Since recommendation systems usually use explicit rating data, we have filtered and transformed the existing data into binary ratings. We have trained several recommendation models, using the Surprise Python scikit, comparing baseline algorithms to neighborhood-based and matrix factorization methods. Our evaluation shows that SVD-based and KNN-based methods achieve good performance scores while being computationally efficient, suggesting that they are suitable for recommendation in this novel dataset.info:eu-repo/semantics/acceptedVersio