A simple algorithm and min-max formula for the inverse arborescence problem

In 1998, Hu and Liu developed a strongly polynomial algorithm for solving the inverse arborescence problem that aims at minimally modifying a given cost-function on the edge-set of a digraph D so that an input spanning arborescence of D becomes a cheapest one. In this note, we develop a conceptually simpler algorithm along with a new min-max formula for the minimum modification of the cost-function. The approach is based on a link to a min-max theorem and a simple (two-phase greedy) algorithm by the first author from 1979 concerning the primal optimization problem of finding a cheapest subgraph of a digraph that covers an intersecting family along with the corresponding dual optimization problem, as well. (C) 2021 The Author(s). Published by Elsevier B.V

Towards a global EDGAR‐inventory of particulate matter with focus on elemental carbon

The Emissions Database for Global Atmospheric Research (EDGAR) provides technology based global anthropogenic emissions data of greenhouse gases and air pollutants by country and sector on a 0.1° x 0.1° spatial grid, on a timeline that ranges from 1970 to present days. As part of the constantly ongoing amendment and improvement of the database, a review of the available literature and emission inventory data has been conducted focusing on particulate emissions, with the aim of acquiring a comprehensive array of primary particle matter and carbonaceous particle emission factors (EF). It was found, that emission factor data from different studies show large variation for a given fuel and technology. Furthermore it is plausible that a certain literature or measurement describes emission factors better in the region where it is originating from. With this in mind, a comparison has been made between the available emission factor datasets in a number of different regions, focusing on the power generation sector. The aim of this experiment is to select the most appropriate EF dataset for a given region.JRC.H.2-Air and Climat

A multimodal deep learning architecture for smoking detection with a small data approach

Introduction: Covert tobacco advertisements often raise regulatory measures. This paper presents that artificial intelligence, particularly deep learning, has great potential for detecting hidden advertising and allows unbiased, reproducible, and fair quantification of tobacco-related media content. Methods: We propose an integrated text and image processing model based on deep learning, generative methods, and human reinforcement, which can detect smoking cases in both textual and visual formats, even with little available training data. Results: Our model can achieve 74\% accuracy for images and 98\% for text. Furthermore, our system integrates the possibility of expert intervention in the form of human reinforcement. Conclusions: Using the pre-trained multimodal, image, and text processing models available through deep learning makes it possible to detect smoking in different media even with few training data