A New Index for Public Investment Management

The objective of this article is to improve the most widely used indicator of quality in public investment management (the PIMI). The methodology was to use an alternative algorithm to build a new version of the PIMI and calculate it for sixteen Latin American countries. The result is a New PIMI that appears better than the original one, as it has a positive correlation with an objective indicator of the efficiency of public investment, which is not achieved by the original PIMI. We recommend the use of the New PIMI, proposed here, as a control variable in studies on aggregated public investment and its impacts on economic growth and social welfare.One limitation of the New PIMI is that it reflects the quality of public investment management at the aggregate level and based on de jure criteria, thus not capturing key differences that occur at a more disaggregated or informal level. Even so, the relevance, originality and replicability of the New PIMI make it a significant contribution to knowledge in this field. Un nuevo índice para la gestión de la inversión públicaEl objetivo de este artículo es mejorar el indicador más utilizado sobre la calidad en la gestión de la inversión pública (Public Investment Management Index, PIMI). La metodología fue utilizar un algoritmo alternativo para construir una nueva versión del PIMI y calcularlo para dieciséis países latinoamericanos. El resultado es un Nuevo PIMI que parece mejor que el anterior, ya que tiene una correlación positiva con un indicador objetivo de la eficiencia de la inversión pública, lo que no logra el PIMI original. Recomendamos el uso del Nuevo PIMI, aquí propuesto, como variable de control en los estudios sobre la inversión pública agregada y sus impactos en el crecimiento económico y bienestar social.Una limitación del Nuevo PIMI es que refleja la calidad de la gestión de la inversión pública a nivel agregado y con base en criterios de jure, pero no permite capturar las diferencias en la eficiencia de dicha gestión que pueden ocurrir a nivel más desagregado o informal. Aun así, la originalidad y replicabilidad del Nuevo PIMI constituyen un aporte significativo al conocimiento del tema.

Signal denoising and viral particle identification in wide-field photon scattering parametric images using deep learning

Polarization parametric indirect microscopic imaging (PIMI) can obtain anisotropic nanoscale structural information of the sample by utilizing a polarization modulated illumination scheme and fitting the far-field variation of polarization states of the scattered photons. The rich scattering information of PIMI images can be exploited for identification of viral particles, aiming for early infection screening of viruses. Accurate processing and analysis of PIMI results is an important part of obtaining structural feature information of virus. Under noisy conditions, however, manually identifying viral particles in PIMI images is a very time-consuming process with a high error rate. The systematic noise degrading the image resolution and contrast are mainly due to the mechanical or electrical disturbance from the modulation of the illumination when taking raw images. To achieve efficient noise suppressing and accurate virus identification in PIMI images, we developed a neural network-based framework of algorithms. Firstly, a fairly effective denoising method particularly for PIMI imaging was proposed based on a generative network. Both the numerical and experimental results show that the developed method has the best capability of noise removal for PIMI images compared with the traditional denoising algorithms. Secondly, we use a convolutional neural network to detect and recognize viral particles in PIMI images. The experimental results show that viral particles can be identified in PIMI images with high accuracy

Gold-viral particle identification by deep learning in wide-field photon scattering parametric images

The ability to identify virus particles is important for research and clinical applications. Because of the optical diffraction limit, conventional optical microscopes are generally not suitable for virus particle detection, and higher resolution instruments such as transmission electron microscopy (TEM) and scanning electron microscopy (SEM) are required. In this paper, we propose a new method for identifying virus particles based on polarization parametric indirect microscopic imaging (PIMI) and deep learning techniques. By introducing an abrupt change of refractivity at the virus particle using antibody-conjugated gold nanoparticles (AuNPs), the strength of the photon scattering signal can be magnified. After acquiring the PIMI images, a deep learning method was applied to identify discriminating features and classify the virus particles, using electron microscopy (EM) images as the ground truth. Experimental results confirm that gold-virus particles can be identified in PIMI images with a high level of confidence

Task-Based Information Compression for Multi-Agent Communication Problems with Channel Rate Constraints

A collaborative task is assigned to a multiagent system (MAS) in which agents are allowed to communicate. The MAS runs over an underlying Markov decision process and its task is to maximize the averaged sum of discounted one-stage rewards. Although knowing the global state of the environment is necessary for the optimal action selection of the MAS, agents are limited to individual observations. The inter-agent communication can tackle the issue of local observability, however, the limited rate of the inter-agent communication prevents the agent from acquiring the precise global state information. To overcome this challenge, agents need to communicate their observations in a compact way such that the MAS compromises the minimum possible sum of rewards. We show that this problem is equivalent to a form of rate-distortion problem which we call the task-based information compression. We introduce a scheme for task-based information compression titled State aggregation for information compression (SAIC), for which a state aggregation algorithm is analytically designed. The SAIC is shown to be capable of achieving near-optimal performance in terms of the achieved sum of discounted rewards. The proposed algorithm is applied to a rendezvous problem and its performance is compared with several benchmarks. Numerical experiments confirm the superiority of the proposed algorithm.Comment: 13 pages, 9 figure

Long runs under point conditioning. The real case

This paper presents a sharp approximation of the density of long runs of a random walk conditioned on its end value or by an average of a functions of its summands as their number tends to infinity. The conditioning event is of moderate or large deviation type. The result extends the Gibbs conditional principle in the sense that it provides a description of the distribution of the random walk on long subsequences. An algorithm for the simulation of such long runs is presented, together with an algorithm determining their maximal length for which the approximation is valid up to a prescribed accuracy

Long runs under a conditional limit distribution

This paper presents a sharp approximation of the density of long runs of a random walk conditioned on its end value or by an average of a function of its summands as their number tends to infinity. In the large deviation range of the conditioning event it extends the Gibbs conditional principle in the sense that it provides a description of the distribution of the random walk on long subsequences. An approximation of the density of the runs is also obtained when the conditioning event states that the end value of the random walk belongs to a thin or a thick set with a nonempty interior. The approximations hold either in probability under the conditional distribution of the random walk, or in total variation norm between measures. An application of the approximation scheme to the evaluation of rare event probabilities through importance sampling is provided. When the conditioning event is in the range of the central limit theorem, it provides a tool for statistical inference in the sense that it produces an effective way to implement the Rao-Blackwell theorem for the improvement of estimators; it also leads to conditional inference procedures in models with nuisance parameters. An algorithm for the simulation of such long runs is presented, together with an algorithm determining the maximal length for which the approximation is valid up to a prescribed accuracy.Comment: Published in at http://dx.doi.org/10.1214/13-AAP975 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org). arXiv admin note: text overlap with arXiv:1010.361