State Weakness in Eastern Europe: concept and causes

State weakness has come to be recognized as a key problem for the transformations in a number of Eastern European countries, particularly those East and South of enlargement. This paper addresses the issue firstly by asking how state capacity can be assessed and observed empirically, and secondly, by considering the variables which may explain why some countries have developed weaker states than others. The paper is divided into three broad sections. The first section discusses the issue of how to assess state capacity. As a starting point, I distinguish between the state as a provider of solutions and the state as a source of problems. Both aspects need to be addressed in any evaluation of a state's capacity. Subsequently, the section discusses a number of qualitative and quantitative indicators which can yield useful information on the 'state of the state'. The second section turns to possible explanatory approaches. It considers the potential importance of legacies, of international integration, of the level of development, and of political consolidation. I conclude that the latter appears most directly relevant to the relative level of state capacity, while legacies and the level of development are important background conditions. International integration appears to be driven by higher capability rather than being a factor for stifling state capacity. The third and final section suggests some ways forward from situations of low state capacity. A first path is to promote learning and incremental improvements within the state. A second option is to enhance the capacity of citizens and civil society to demand more and better solutions from the state and to be able to challenge the state when it becomes a source of problems. A third option is to redesign the political systems in such a way as to increase coherence and to clearly attribute responsibility.governance; transition processes; post-Communism

Explainable digital forensics AI: Towards mitigating distrust in AI-based digital forensics analysis using interpretable models

The present level of skepticism expressed by courts, legal practitioners, and the general public over Artificial Intelligence (AI) based digital evidence extraction techniques has been observed, and understandably so. Concerns have been raised about closed-box AI models’ transparency and their suitability for use in digital evidence mining. While AI models are firmly rooted in mathematical, statistical, and computational theories, the argument has centered on their explainability and understandability, particularly in terms of how they arrive at certain conclusions. This paper examines the issues with closed-box models; the goals; and methods of explainability/interpretability. Most importantly, recommendations for interpretable AI-based digital forensics (DF) investigation are proposed

Can BL Lac emission explain the neutrinos above 0.2 PeV?

Multi-messenger astronomy can help to investigate the sources of the high-energy neutrinos observed by the high-energy neutrino telescope IceCube. We consider the hypothesis that the highest energy neutrinos are produced by BL Lacs, arguing that this is not contradicted severely by any known fact. We check the BL Lac hypothesis by searching for correlations between the through-going muon events of IceCube and the BL Lacs of the second catalog of Fermi-LAT (2FHL). We expect 10.2 $\pm$ 2.4 correlated events but we find that just 1 event has a BL Lac as counterpart. We also assess the probability of observing one multiplet from the same source, finding that the present null result is not yet of critical significance. We conclude that the hypothesis that the BL Lacs are the main emitters of the highest-energy neutrinos observed by IceCube is disfavored at 3.7{\sigma}. We discuss implications and possible ways out; for example, this could work if the angular resolution was 4{\deg}, which is much more than expected.Comment: 10 pages, 6 figures. Accepted for publication in Astronomy & Astrophysic

Greybox XAI: a Neural-Symbolic learning framework to produce interpretable predictions for image classification

Although Deep Neural Networks (DNNs) have great generalization and prediction capabilities, their functioning does not allow a detailed explanation of their behavior. Opaque deep learning models are increasingly used to make important predictions in critical environments, and the danger is that they make and use predictions that cannot be justified or legitimized. Several eXplainable Artificial Intelligence (XAI) methods that separate explanations from machine learning models have emerged, but have shortcomings in faithfulness to the model actual functioning and robustness. As a result, there is a widespread agreement on the importance of endowing Deep Learning models with explanatory capabilities so that they can themselves provide an answer to why a particular prediction was made. First, we address the problem of the lack of universal criteria for XAI by formalizing what an explanation is. We also introduced a set of axioms and definitions to clarify XAI from a mathematical perspective. Finally, we present the Greybox XAI, a framework that composes a DNN and a transparent model thanks to the use of a symbolic Knowledge Base (KB). We extract a KB from the dataset and use it to train a transparent model (i.e., a logistic regression). An encoder-decoder architecture is trained on RGB images to produce an output similar to the KB used by the transparent model. Once the two models are trained independently, they are used compositionally to form an explainable predictive model. We show how this new architecture is accurate and explainable in several datasets.French ANRT (AssociationNationale Recherche Technologie - ANRT)SEGULA TechnologiesJuan de la Cierva Incorporacion grant - MCIN/AEI by "ESF Investing in your future" I JC2019-039152-IGoogle Research Scholar ProgramDepartment of Education of the Basque Government (Consolidated Research Group MATHMODE) IT1456-2

Density-functional investigation of rhombohedral stacks of graphene: topological surface states, nonlinear dielectric response, and bulk limit

A DFT-based investigation of rhombohedral (ABC)-type graphene stacks in finite static electric fields is presented. Electronic band structures and field-induced charge densities are compared with related literature data as well as with own results on (AB) stacks. It is found, that the undoped AB-bilayer has a tiny Fermi line consisting of one electron pocket around the K-point and one hole pocket on the line K-$\Gamma$. In contrast to (AB) stacks, the breaking of translational symmetry by the surface of finite (ABC) stacks produces a gap in the bulk-like states for slabs up to a yet unknown critical thickness $N^{\rm semimet} \gg 10$, while ideal (ABC) bulk ($\beta$-graphite) is a semi-metal. Unlike in (AB) stacks, the ground state of (ABC) stacks is shown to be topologically non-trivial in the absence of external electric field. Consequently, surface states crossing the Fermi level must unavoidably exist in the case of (ABC)-type stacking, which is not the case in (AB)-type stacks. These surface states in conjunction with the mentioned gap in the bulk-like states have two major implications. First, electronic transport parallel to the slab is confined to a surface region up to the critical layer number $N^{\rm semimet}$. Related implications are expected for stacking domain walls and grain boundaries. Second, the electronic properties of (ABC) stacks are highly tunable by an external electric field. In particular, the dielectric response is found to be strongly nonlinear and can e.g. be used to discriminate slabs with different layer numbers. Thus, (ABC) stacks rather than (AB) stacks with more than two layers should be of potential interest for applications relying on the tunability by an electric field.Comment: 36 pages, 17 figure

Representation and parsing of multiword expressions

This book consists of contributions related to the definition, representation and parsing of MWEs. These reflect current trends in the representation and processing of MWEs. They cover various categories of MWEs such as verbal, adverbial and nominal MWEs, various linguistic frameworks (e.g. tree-based and unification-based grammars), various languages including English, French, Modern Greek, Hebrew, Norwegian), and various applications (namely MWE detection, parsing, automatic translation) using both symbolic and statistical approaches