330 research outputs found
Recommended from our members
Learning and Representing Temporal Knowledge in Recurrent Networks
The effective integration of knowledge representation, reasoning, and learning in a robust computational model is one of the key challenges of computer science and artificial intelligence. In particular, temporal knowledge and models have been fundamental in describing the behavior of computational systems. However, knowledge acquisition of correct descriptions of a system's desired behavior is a complex task. In this paper, we present a novel neural-computation model capable of representing and learning temporal knowledge in recurrent networks. The model works in an integrated fashion. It enables the effective representation of temporal knowledge, the adaptation of temporal models given a set of desirable system properties, and effective learning from examples, which in turn can lead to temporal knowledge extraction from the corresponding trained networks. The model is sound from a theoretical standpoint, but it has also been tested on a case study in the area of model verification and adaptation. The results contained in this paper indicate that model verification and learning can be integrated within the neural computation paradigm, contributing to the development of predictive temporal knowledge-based systems and offering interpretable results that allow system researchers and engineers to improve their models and specifications. The model has been implemented and is available as part of a neural-symbolic computational toolkit
Mathematical Models and Exact Algorithms for the Colored Bin Packing Problem
This paper focuses on exact approaches for the Colored Bin Packing Problem
(CBPP), a generalization of the classical one-dimensional Bin Packing Problem
in which each item has, in addition to its length, a color, and no two items of
the same color can appear consecutively in the same bin. To simplify modeling,
we present a characterization of any feasible packing of this problem in a way
that does not depend on its ordering. Furthermore, we present four exact
algorithms for the CBPP. First, we propose a generalization of Val\'erio de
Carvalho's arc flow formulation for the CBPP using a graph with multiple
layers, each representing a color. Second, we present an improved arc flow
formulation that uses a more compact graph and has the same linear relaxation
bound as the first formulation. And finally, we design two exponential
set-partition models based on reductions to a generalized vehicle routing
problem, which are solved by a branch-cut-and-price algorithm through
VRPSolver. To compare the proposed algorithms, a varied benchmark set with 574
instances of the CBPP is presented. Results show that the best model, our
improved arc flow formulation, was able to solve over 62% of the proposed
instances to optimality, the largest of which with 500 items and 37 colors.
While being able to solve fewer instances in total, the set-partition models
exceeded their arc flow counterparts in instances with a very small number of
colors
Loss compensation in microring-based Si photonics devices via Er3+doped claddings
FAPESP - FUNDAĂĂO DE AMPARO Ă PESQUISA DO ESTADO DE SĂO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTĂFICO E TECNOLĂGICOWe propose and demonstrate a method to compensate insertion losses in Si photonics devices based on ring resonators fabricated in SOI foundries, with no additional chip area used. It consists in the employment of Er:Al2O3 as the upper cladding layer on standard Si/SiO2 rings, requiring only one simple post-processing step. The method is modeled in detail, and simulation results for single-ring configurations and photonic molecules are discussed, where the potential for loss reduction is predicted for different design choices based on the quality factor. We experimentally verify the viability of the method, obtaining an output power increase of 1 dB when a single-ring resonator is pumped. This value is increased when the method is applied to devices based on photonic molecules, where a value of 2.6 dB has been measured. This is equivalent to a loss reduction potential higher than 3 dB for a photonic molecule designed to achieve a quality factor of 50000.104113FAPESP - FUNDAĂĂO DE AMPARO Ă PESQUISA DO ESTADO DE SĂO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTĂFICO E TECNOLĂGICOFAPESP - FUNDAĂĂO DE AMPARO Ă PESQUISA DO ESTADO DE SĂO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTĂFICO E TECNOLĂGICO08/57857-22014/04748-2574017/2008-
Algorithms for the Bin Packing Problem with Scenarios
This paper presents theoretical and practical results for the bin packing
problem with scenarios, a generalization of the classical bin packing problem
which considers the presence of uncertain scenarios, of which only one is
realized. For this problem, we propose an absolute approximation algorithm
whose ratio is bounded by the square root of the number of scenarios times the
approximation ratio for an algorithm for the vector bin packing problem. We
also show how an asymptotic polynomial-time approximation scheme is derived
when the number of scenarios is constant. As a practical study of the problem,
we present a branch-and-price algorithm to solve an exponential model and a
variable neighborhood search heuristic. To speed up the convergence of the
exact algorithm, we also consider lower bounds based on dual feasible
functions. Results of these algorithms show the competence of the
branch-and-price in obtaining optimal solutions for about 59% of the instances
considered, while the combined heuristic and branch-and-price optimally solved
62% of the instances considered
Synaptic Plasticity and Spike Synchronisation in Neuronal Networks
This work was possible by partial financial support from the following Brazilian government agencies: CNPq (154705/2016-0, 311467/2014-8), CAPES, Fundacao Araucaria, and Sao Paulo Research Foundation (processes FAPESP 2011/19296-1, 2015/07311-7, 2016/16148-5, 2016/23398-8, 2015/50122-0). Research supported by grant 2015/50122-0 Sao Paulo Research Foundation (FAPESP) and DFG-IRTG 1740/2.Peer reviewedPostprin
In vitro Antiplasmodial Activities of Alkaloids Isolated from Roots of Worsleya procera (Lem.) Traub (Amaryllidaceae)
A combined phytochemical, crystallographic and biological study of Worsleya procera roots was performed. Fifteen alkaloids were identified by gas chromatography mass spectrometry (GC-MS) and seven of them were isolated. The structures of the alkaloids were elucidated by spectroscopic methods, and a detailed crystallographic study of tazettine was carried out. The isolated alkaloids and the obtained extracts were tested in vitro against Plasmodium falciparum (3D7 and K1 strains) and human hepatocarcinoma cells (HepG2) to assess their antiplasmodial and cytotoxic effects, respectively. One of the isolated alkaloid derivatives, lycorine, exhibited antiplasmodial activity against both sensitive (3D7) and resistant (K1) parasite strains in the low micromolar range (half-maximal sample inhibitory concentration (IC50) values of 2.5 and 3.1 ”M, respectively) and displayed a low cytotoxicity profile, with a selectivity index greater than 100. Our findings indicate that lycorine is a hit for antimalarial drug discovery. Keywords: isoquinolinic alkaloids; Amaryllidaceae; Plasmodium falciparum; lycorine; tazettin
Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning
The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.Financial support for this investigation by National Council for
Scientific and Technological Development (CNPq), Coordination
for the Improvement of Higher Education Personnel
(CAPES), Brazilian Biosciences National Laboratory (LNBioCNPEM/MCTI),
Foundation for Support of Scientific and
Technological Research in the State of Santa Catarina
(FAPESC), and Portuguese Foundation for Science and
Technology (FCT) is acknowledged. The research fellowship
granted by CNPq to the first author is also acknowledged. The
work was partially funded by a CNPq and FCT agreement
through the PropMine grant
Determination Of Flavanones In Orange Juices Obtained From Different Sources By Hplc/dad.
Flavanones (hesperidin, naringenin, naringin, and poncirin) in industrial, hand-squeezed orange juices and from fresh-in-squeeze machines orange juices were determined by HPLC/DAD analysis using a previously described liquid-liquid extraction method. Method validation including the accuracy was performed by using recovery tests. Samples (36) collected from different Brazilian locations and brands were analyzed. Concentrations were determined using an external standard curve. The limits of detection (LOD) and the limits of quantification (LOQ) calculated were 0.0037, 1.87, 0.0147, and 0.0066âmg 100âg(-1) and 0.0089, 7.84, 0.0302, and 0.0200âmg 100âg(-1) for naringin, hesperidin, poncirin, and naringenin, respectively. The results demonstrated that hesperidin was present at the highest concentration levels, especially in the industrial orange juices. Its average content and concentration range were 69.85 and 18.80-139.00âmg 100âg(-1). The other flavanones showed the lowest concentration levels. The average contents and concentration ranges found were 0.019, 0.01-0.30, and 0.12 and 0.1-0.17, 0.13, and 0.01-0.36âmg 100âg(-1), respectively. The results were also evaluated using the principal component analysis (PCA) multivariate analysis technique which showed that poncirin, naringenin, and naringin were the principal elements that contributed to the variability in the sample concentrations.201429683
- âŠ