EMaP: Explainable AI with Manifold-based Perturbations

In the last few years, many explanation methods based on the perturbations of input data have been introduced to improve our understanding of decisions made by black-box models. The goal of this work is to introduce a novel perturbation scheme so that more faithful and robust explanations can be obtained. Our study focuses on the impact of perturbing directions on the data topology. We show that perturbing along the orthogonal directions of the input manifold better preserves the data topology, both in the worst-case analysis of the discrete Gromov-Hausdorff distance and in the average-case analysis via persistent homology. From those results, we introduce EMaP algorithm, realizing the orthogonal perturbation scheme. Our experiments show that EMaP not only improves the explainers' performance but also helps them overcome a recently-developed attack against perturbation-based methods.Comment: 29 page

Life cycle assessment of biogas production in small-scale household digesters in Vietnam

Small-scale household digesters have been promoted across Asia as a sustainable way of handling manure. The major advantages are that they produce biogas and reduce odor. However their disadvantages include the low recycling of nutrients, because digestate is dilute and therefore difficult to transport, and the loss of biogas as a result of cracks and the intentional release of excess biogas. In this study, life cycle assessment (LCA) methodology was used to assess the environmental impacts associated with biogas digesters in Vietnam. Handling 1,000 kg of liquid manure and 100 kg of solid manure in a system with a biogas digester reduced the impact potential from 4.4 kg carbon dioxide (CO2) equivalents to 3.2 kg CO2 equivalents compared with traditional manure management. However, this advantage could easily be compromised if digester construction is considered in the LCA or in situations where there is an excess of biogas which is intentionally released. A sensitivity analysis showed that biogas digesters could be a means of reducing global warming if methane emissions can be kept low. In terms of eutrophication, farms with biogas digesters had 3 to 4 times greater impacts. In order to make biogas digesters sustainable, methods for recycling digestates are urgently required

Methane emission factors from vietnamese rice production: Pooling data of 36 field sites for meta-analysis

Rice production is a significant source of greenhouse gas (GHG) emissions in the national budget of many Asian countries, but the extent of emissions varies strongly across agro-environmental zones. It is important to understand these differences in order to improve the national GHG inventory and effectively target mitigation options. This study presents a meta-analysis of CH4 database emission factors (EFs) from 36 field sites across the rice growing areas of Vietnam and covering 73 cropping seasons. The EFs were developed from field measurements using the closed chamber technique. The analysis for calculating baseline EFs in North, Central and South Vietnam in line with the Intergovernmental Panel on Climate Change (IPCC) Tier 2 methodology was specified for the three cropping seasons being early-(E), mid-(M) and late-year (L) seasons. Calculated average CH$_{4}$ EFs are given in kg ha$^{-1}$ d$^{-1}$ and reflect the distinct seasons in North (E: 2.21; L: 3.89), Central (E: 2.84; M+L: 3.13) and South Vietnam (E: 1.72; M: 2.80; L: 3.58). Derived from the available data of the edapho-hydrological zones of the Mekong River Delta, season-based EFs are more useful than zone-based EFs. In totality, these average EFs indicate an enormous variability of GHG emissions in Vietnamese rice production and represent much higher values than the IPCC default. Seasonal EFs from Vietnam exceeded IPCC defaults given for Southeast Asia corresponding to 160% (E), 240% (M) and 290% (L) of the medium value, respectively