A Generalization of Otsu's Method and Minimum Error Thresholding

We present Generalized Histogram Thresholding (GHT), a simple, fast, and effective technique for histogram-based image thresholding. GHT works by performing approximate maximum a posteriori estimation of a mixture of Gaussians with appropriate priors. We demonstrate that GHT subsumes three classic thresholding techniques as special cases: Otsu's method, Minimum Error Thresholding (MET), and weighted percentile thresholding. GHT thereby enables the continuous interpolation between those three algorithms, which allows thresholding accuracy to be improved significantly. GHT also provides a clarifying interpretation of the common practice of coarsening a histogram's bin width during thresholding. We show that GHT outperforms or matches the performance of all algorithms on a recent challenge for handwritten document image binarization (including deep neural networks trained to produce per-pixel binarizations), and can be implemented in a dozen lines of code or as a trivial modification to Otsu's method or MET.Comment: ECCV 202

A Methodology for the Vulnerability Analysis of the Climate Change in the Oromia Region, Ethiopia

Goal of the vulnerability research of the last years is to evaluate which community, region, or nation is more vulnerable in terms of its sensitive to damaging effects of extreme meteorological events like floods or droughts. Ethiopia is a country where it is possible to find the described conditions. Aim of this work was to develop an integrated system of early warning and response, whereas neither landmark data nor vulnerability drought analysis existed in the country. Specifically, a vulnerability index and a capacity to react index of the population of three Woredas in the Oromia Region of Ethiopia were determined and analysed. Input data concerned rainfall, water availability, physical land characteristics, agricultural and livestock dimensions, as well as population and socio-economic indices. Data were collected during a specific NGO project and thanks to a field research funded by the University of Torino. Results were analysed and specific maps were drawn. The mapping of the vulnerability indices revealed that the more isolated Woreda with less communication roads and with less water sources presented the worst data almost on all its territory. Despite not bad vulnerability indices in the other two Woredas, however, population here still encountered difficulty to adapt to sudden climatic changes, as revealed by the other index of capacity to reaction. Beyond the interpretation of each parameter, a more complete reading key was possible using the SPI (Standardized Precipitation Index) beside these indicators. In a normalized scale between 0 and 1, in this study the calculated annual SPI index was 0.83: the area is therefore considerably exposed to the drought risk, caused by an high intensity and frequency of rainfall lack

Dietary t10,c12-CLA but not c9,t11 CLA Reduces Adipocyte Size in the Absence of Changes in the Adipose Renin–Angiotensin System in fa/fa Zucker Rats

In obesity, increased activity of the local renin–angiotensin system (RAS) and enlarged adipocytes with altered adipokine production are linked to the development of obesity-related health problems and cardiovascular disease. Mixtures of conjugated linoleic acid (CLA) isomers have been shown to reduce adipocyte size and alter the production of adipokines. The objective of this study was to investigate the effects of feeding individual CLA isomers on adipocyte size and adipokines associated with the local adipose RAS. Male fa/fa Zucker rats received either (a) control, (b) cis(c)9,trans(t)11-CLA, or (c) t10,c12-CLA diet for 8 weeks. The t10,c12-CLA isomer reduced adipocyte size and increased cell number in epididymal adipose tissue. RT-PCR and Western blot analysis revealed that neither CLA isomer altered mRNA or protein levels of angiotensinogen or AngII receptors in adipose tissue. Likewise, levels of the pro-inflammatory cytokines TNF-α and IL-6 or the anti-inflammatory cytokine IL-10 were unchanged in adipose tissue. Similarly, neither CLA isomer had any effect on phosphorylation nor DNA binding of NF-κB. Our results suggest that although the t10,c12-CLA isomer had beneficial effects on reducing adipocyte size in obese rats, this did not translate into changes in the local adipose RAS or associated adipokines

Meaningful Data Sampling for a Faithful Local Explanation Method

Data sampling has an important role in the majority of local explanation methods. Generating neighborhood samples using either the Gaussian distribution or the distribution of training data is a widely-used procedure in the tabular data case. Generally, this approach has several weaknesses: first, it produces a uniform data which may not represent the actual distribution of samples; second, disregarding the interaction between features tends to create unlikely samples; and third, it may fail to define a compact and diverse locality for the sample being explained. In this paper, we propose a sampling methodology based on observation-level feature importance to derive more meaningful perturbed samples. To evaluate the efficiency of the proposed approach we applied it to the LIME explanation method. The conducted experiments demonstrate considerable improvements in terms of fidelity and explainability

Empirical Approach—Introduction

In this chapter, we will describe the fundamentals of the proposed new “empirical” approach as a systematic methodology with its nonparametric quantities derived entirely from the actual data with no subjective and/or problem-specific assumptions made. It has a potential to be a powerful extension of (and/or alternative to) the traditional probability theory, statistical learning and computational intelligence methods. The nonparametric quantities of the proposed new empirical approach include: (1) the cumulative proximity; (2) the eccentricity, and the standardized eccentricity; (3) the data density, and (4) the typicality. They can be recursively updated on a sample-by-sample basis, and they have unimodal and multimodal, discrete and continuous forms/versions. The nonparametric quantities are based on ensemble properties of the data and not limited by prior restrictive assumptions. The discrete version of the typicality resembles the unimodal probability density function, but is in a discrete form. The discrete multimodal typicality resembles the probability mass function