Local Algorithms for Block Models with Side Information

There has been a recent interest in understanding the power of local algorithms for optimization and inference problems on sparse graphs. Gamarnik and Sudan (2014) showed that local algorithms are weaker than global algorithms for finding large independent sets in sparse random regular graphs. Montanari (2015) showed that local algorithms are suboptimal for finding a community with high connectivity in the sparse Erd\H{o}s-R\'enyi random graphs. For the symmetric planted partition problem (also named community detection for the block models) on sparse graphs, a simple observation is that local algorithms cannot have non-trivial performance. In this work we consider the effect of side information on local algorithms for community detection under the binary symmetric stochastic block model. In the block model with side information each of the $n$ vertices is labeled $+$ or $-$ independently and uniformly at random; each pair of vertices is connected independently with probability $a/n$ if both of them have the same label or $b/n$ otherwise. The goal is to estimate the underlying vertex labeling given 1) the graph structure and 2) side information in the form of a vertex labeling positively correlated with the true one. Assuming that the ratio between in and out degree $a/b$ is $\Theta(1)$ and the average degree $(a+b) / 2 = n^{o(1)}$, we characterize three different regimes under which a local algorithm, namely, belief propagation run on the local neighborhoods, maximizes the expected fraction of vertices labeled correctly. Thus, in contrast to the case of symmetric block models without side information, we show that local algorithms can achieve optimal performance for the block model with side information.Comment: Due to the limitation "The abstract field cannot be longer than 1,920 characters", the abstract here is shorter than that in the PDF fil

Indicações de gestão para um laboratório, a partir do cadastro de clientes e de serviços: estudo de caso da clínica fitossanitária Embrapa Clima Temperado.

bitstream/item/31621/1/comunicado91.pd

SE-Sync: A Certifiably Correct Algorithm for Synchronization over the Special Euclidean Group

Many important geometric estimation problems naturally take the form of synchronization over the special Euclidean group: estimate the values of a set of unknown poses given noisy measurements of a subset of their pairwise relative transforms. Examples of this class include the foundational problems of pose-graph simultaneous localization and mapping (SLAM) (in robotics), camera motion estimation (in computer vision), and sensor network localization (in distributed sensing), among others. This inference problem is typically formulated as a nonconvex maximum-likelihood estimation that is computationally hard to solve in general. Nevertheless, in this paper we present an algorithm that is able to efficiently recover certifiably globally optimal solutions of the special Euclidean synchronization problem in a non-adversarial noise regime. The crux of our approach is the development of a semidefinite relaxation of the maximum-likelihood estimation whose minimizer provides an exact MLE so long as the magnitude of the noise corrupting the available measurements falls below a certain critical threshold; furthermore, whenever exactness obtains, it is possible to verify this fact a posteriori, thereby certifying the optimality of the recovered estimate. We develop a specialized optimization scheme for solving large-scale instances of this semidefinite relaxation by exploiting its low-rank, geometric, and graph-theoretic structure to reduce it to an equivalent optimization problem defined on a low-dimensional Riemannian manifold, and then design a Riemannian truncated-Newton trust-region method to solve this reduction efficiently. Finally, we combine this fast optimization approach with a simple rounding procedure to produce our algorithm, SE-Sync. Experimental evaluation on a variety of simulated and real-world pose-graph SLAM datasets shows that SE-Sync is capable of recovering certifiably globally optimal solutions when the available measurements are corrupted by noise up to an order of magnitude greater than that typically encountered in robotics and computer vision applications, and does so more than an order of magnitude faster than the Gauss-Newton-based approach that forms the basis of current state-of-the-art techniques

A coleção de culturas de microrganismos fitopatogênicos: importante fonte de informação para a pesquisa do feijoeiro comum.

O objetivo deste trabalho foi agrupar os diversos dados relativos à coleção de culturas de microrganismos patogênicos ao feijão da Embrapa Arroz e Feijão, visando ressaltar sua complexidade e importância para o desenvolvimento de plantas resistentes a doenças.CONAFE

Sistema de gestão de clientes: Manual do usuário.

bitstream/item/33621/1/documento-179.pd

Dominance and G×E interaction effects improvegenomic prediction and genetic gain inintermediate wheatgrass (Thinopyrumintermedium)

Genomic selection (GS) based recurrent selection methods were developed to accelerate the domestication of intermediate wheatgrass [IWG, Thinopyrum intermedium (Host) Barkworth & D.R. Dewey]. A subset of the breeding population phenotyped at multiple environments is used to train GS models and then predict trait values of the breeding population. In this study, we implemented several GS models that investigated the use of additive and dominance effects and G×E interaction effects to understand how they affected trait predictions in intermediate wheatgrass. We evaluated 451 genotypes from the University of Minnesota IWG breeding program for nine agronomic and domestication traits at two Minnesota locations during 2017–2018. Genet-mean based heritabilities for these traits ranged from 0.34 to 0.77. Using fourfold cross validation, we observed the highest predictive abilities (correlation of 0.67) in models that considered G×E effects. When G×E effects were fitted in GS models, trait predictions improved by 18%, 15%, 20%, and 23% for yield, spike weight, spike length, and free threshing, respectively. Genomic selection models with dominance effects showed only modest increases of up to 3% and were trait-dependent. Crossenvironment predictions were better for high heritability traits such as spike length, shatter resistance, free threshing, grain weight, and seed length than traits with low heritability and large environmental variance such as spike weight, grain yield, and seed width. Our results confirm that GS can accelerate IWG domestication by increasing genetic gain per breeding cycle and assist in selection of genotypes with promise of better performance in diverse environments

Are internally observable vehicle data good predictors of vehicle emissions?

Scientific research has demonstrated that on-road exhaust emissions in diesel passenger vehicles (DPV) exceeds the official laboratory-test values. Increasing concern about the quantification of magnitude for these differences has meant an increasing use of Portable Emissions Monitoring System (PEMS), but the direct use of Internally Observable Variables (IOVs) can be useful to predict emissions. The motivation for this paper is to develop an empirical approach that integrates second-by-second vehicle activity and emission rates for DPV. The objectives of this research are two-fold: (1) to assess the effect of variation in acceleration-based parameters, vehicle specific power (VSP) and IOVs on carbon dioxide (CO2) and nitrogen oxides (NOx) emission rates; and (2) to examine the correlation between IOV-based predictors of engine load and VSP. Field measurements were collected from four DPV (two small, one medium and one multi-purpose) in urban, rural and highway routes using PEMS, Global Positioning System (GPS) receivers and On-board Diagnostic (OBD) scan tool, to measure real-world exhaust emissions and engine activity data. Results suggest the relative positive acceleration (RPA) and mean positive acceleration (MPA) allowed a good differentiation with respect to route trips. IOVs models based on the product of manifold absolute pressure (MAP) and engine revolutions per minute (RPM), and VSP showed to be good predictors of emission rates. Although the CO2 correlation was found to be good (R2 > 0.8), the models for NOx showed mixed results since some vehicles showed a reasonable correlation (R2 ~ 0.7) while others resulted in worst model predictions (R2 < 0.6). IOVs models have potential to be integrated into vehicle engine units and connected vehicles, for instance, to provide real-time information on emissions rates, but other parameters regarding the thermal management on after treatment system must be included in NOx prediction. This would allow for a better understanding of true physics behind NOx emissions in DPV.publishe

Sistemas de informação: manual do administrador.

bitstream/item/33585/1/documento-163.pd