New method to test shear wave splitting: improving statistical assessment of splitting parameters, A

2016 Spring.Includes bibliographical references.Shear wave splitting has proved to be a very useful technique to probe for seismic anisotropy in the earth’s interior, and measurements of seismic anisotropy are perhaps the best way to constrain the strain history of the lithosphere and asthenosphere. However, existent methods of shear wave splitting analysis do not estimate uncertainty correctly, and do not allow for careful statistical modeling of anisotropy and uncertainty in complex scenarios. Consequently, the interpretation of shear wave splitting measurements has an undesirable subjective component. This study illustrates a new method to characterize shear wave splitting and the associated uncertainty based on the cross-convolution method [Menke and Levin, 2003]. This new method has been tested on synthetic data and benchmarked with data from the Pasadena, California seismic station (PAS). Synthetic tests show that the method can successfully obtain the splitting parameters from observed split shear waves. PAS results are very reasonable and consistent with previous studies [Liu et al., 1995; Özalaybey and Savage, 1995; Polet and Kanamori, 2002]. As presented, the Menke and Levin [2003] method does not explicitly model the errors. Our method works on noisy data without any particular need for processing, it fully accounts for correlation structures on the noise, and it models the errors with a proper bootstrapping approach. Hence, the method presented here casts the analysis of shear wave splitting into a more formal statistical context, allowing for formal hypothesis testing and more nuanced interpretation of seismic anisotropy results

Battese-coelli estimator with endogenous regressors

We provide a framework for dealing with the endogeneity problem in the Battese-Coelli estimator for productive efficiency measurement

Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Network

Fully homomorphic encryption (FHE) is one of the prospective tools for privacypreserving machine learning (PPML), and several PPML models have been proposed based on various FHE schemes and approaches. Although the FHE schemes are known as suitable tools to implement PPML models, previous PPML models on FHE encrypted data are limited to only simple and non-standard types of machine learning models. These non-standard machine learning models are not proven efficient and accurate with more practical and advanced datasets. Previous PPML schemes replace non-arithmetic activation functions with simple arithmetic functions instead of adopting approximation methods and do not use bootstrapping, which enables continuous homomorphic evaluations. Thus, they could not use standard activation functions and could not employ a large number of layers. The maximum classification accuracy of the existing PPML model with the FHE for the CIFAR-10 dataset was only 77% until now. In this work, we firstly implement the standard ResNet-20 model with the RNS-CKKS FHE with bootstrapping and verify the implemented model with the CIFAR-10 dataset and the plaintext model parameters. Instead of replacing the non-arithmetic functions with the simple arithmetic function, we use state-of-the-art approximation methods to evaluate these non-arithmetic functions, such as the ReLU, with sufficient precision [1]. Further, for the first time, we use the bootstrapping technique of the RNS-CKKS scheme in the proposed model, which enables us to evaluate a deep learning model on the encrypted data. We numerically verify that the proposed model with the CIFAR-10 dataset shows 98.67% identical results to the original ResNet-20 model with non-encrypted data. The classification accuracy of the proposed model is 90.67%, which is pretty close to that of the original ResNet-20 CNN model...Comment: 12 pages, 4 figure

Assessing Farm Efficiency Through Quantities or Revenues and Costs: Does It Matter?

We examined the effect of using input and output quantities as compared with costs and revenues when estimating farm-level efficiency scores and ranking. We used farm-level data from the 2015 Ethiopia Rural Socioeconomic Survey (ERSS) where production inputs and outputs in quantities as well as monetary units could be distinguished. Average technical efficiency scores of 72.2% and 68.6%, respectively, were found for analysis based on quantities and on costs and revenues. Efficiency ranking differed significantly. Results suggest that type of data compilation introduces bias to the efficiency assessment and that conclusions may be unclear, which complicates policy advice

The heterogeneity problem for sensitivity accounts

Offering a solution to the skeptical puzzle is a central aim of Nozick's sensitivity account of knowledge. It is well-known that this account faces serious problems. However, because of its simplicity and its explanatory power, the sensitivity principle has remained attractive and has been subject to numerous modifications, leading to a of sensitivity accounts. I will object to these accounts, arguing that sensitivity accounts of knowledge face two problems. First, they deliver a far too heterogeneous picture of higher-level beliefs about the truth or falsity of one's own beliefs. Second, this problem carries over to bootstrapping and Moorean reasoning. Some beliefs formed via bootstrapping or Moorean reasoning are insensitive, but some closely related beliefs in even stronger propositions are sensitive. These heterogeneous results regarding sensitivity do not fit with our intuitions about bootstrapping and Moorean reasoning. Thus, neither Nozick's sensitivity account of knowledge nor any of its modified versions can provide the basis for an argument that bootstrapping and Moorean reasoning are flawed or for an explanation why they seem to be flawe

Social navigation

In this chapter we present one of the pioneer approaches in supporting users in navigating the complex information spaces, social navigation support. Social navigation support is inspired by natural tendencies of individuals to follow traces of each other in exploring the world, especially when dealing with uncertainties. In this chapter, we cover details on various approaches in implementing social navigation support in the information space as we also connect the concept to supporting theories. The first part of this chapter reviews related theories and introduces the design space of social navigation support through a series of example applications. The second part of the chapter discusses the common challenges in design and implementation of social navigation support, demonstrates how these challenges have been addressed, and reviews more recent direction of social navigation support. Furthermore, as social navigation support has been an inspirational approach to various other social information access approaches we discuss how social navigation support can be integrated with those approaches. We conclude with a review of evaluation methods for social navigation support and remarks about its current state

Bootstrapping and a priori justification

El presente artículo propone una vía de solución a priori al denominado problema del bootstrapping. Para ello,  se considera en primer lugar el dilema enunciado en Cohen (2010) y la consecuencia desastrosa (CD) que de  este dilema se deriva. Para evitar la CD, se propone la noción de razonamiento derrotable (defeasible reasoning)  cuyas características principales son: (i) a prioridad y (ii) admisión de razones implícitas (RI) no sujetas al  problema justificativo a posteriori. Se argumenta que RI es consecuencia de una distinción central entre una  lectura débil y fuerte del nexo Sin duda /confíe. Por tanto, RI cumple el rol de descartar el derrotable de modo  contingente en una ocasión O.This paper proposes an a priori solution to the so-called bootstrapping problem. To do this, it is first considered  Cohen (2010) dilemma and the disastrous consequence (CD) that is derived from this dilemma. To avoid CD, the notion of defeasible reasoning is proposed, whose main characteristics are: (i) a priority and (ii) admission of  implicit reasons (RI) not subject to justificatory a posteriori problem. It is argued that RI is the result of a central  distinction between weak and strong reading of nexus No doubt/Rely on. Then, RI meets the role of discarding  the defeater contingently in occasion O