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X-ray micro-computed tomography can provide information about the composition and internal structure of materials commonly found in heritage buildings such as natural stone, mortar, brick, concrete and wood. As it is a non-destructive technique, samples can be scanned multiple times, which makes it particularly suitable for studying weathering processes and the efficacy of treatment methods.

JPI Cultural Heritage & JPI Climate, Collaboration between the two Joint Programming Initiatives “Cultural Heritage and Global Change” (JPI CH), and “Connecting Climate Knowledge for Europe” (JPI Climate) began in 2019 and led to the organisation of a joint workshop a year later. Following the recommendations in the workshop report, an expert working group was set up to scope research gaps and opportunities at the interface of cultural heritage and climate change, culminating in the publication of this White Paper. This strategic document is expected to support the two JPIs to generate policy-relevant research outcomes.

Multi-label Text Classification (MLTC) is the task of categorizing documents into one or more topics. Considering the large volumes of data and varying domains of such tasks, fully supervised learning requires manually fully annotated datasets which is costly and time-consuming. In this paper, we propose BERT-Flow-VAE (BFV), a Weakly-Supervised Multi-Label Text Classification (WSMLTC) model that reduces the need for full supervision. This new model (1) produces BERT sentence embeddings and calibrates them using a flow model, (2) generates an initial topic-document matrix by averaging results of a seeded sparse topic model and a textual entailment model which only require surface name of topics and 4-6 seed words per topic, and (3) adopts a VAE framework to reconstruct the embeddings under the guidance of the topic-document matrix. Finally, (4) it uses the means produced by the encoder model in the VAE architecture as predictions for MLTC. Experimental results on 6 multi-label datasets show that BFV can substantially outperform other baseline WSMLTC models in key metrics and achieve approximately 84% performance of a fully-supervised model., Comment: 8 pages, 4 figures

Recent conflicts have directed much academic and media attention to special operations forces (SOF). Despite the attention, the application of SOF in asymmetric conventional warfare or irregular conflict has not been fully examined in the context of just war theory. In addition, SOF have often been portrayed superficially, painting them as indiscriminate or unscrupulous warriors. This thesis argues to the contrary, that the application of SOF represents an evolution of just war theory in an era of increasingly irregular conflict. This thesis expands just war thinking to activities short of armed conflict. It contends that SOF constitute a unique extension of the ethical principles that morally bind military capabilities under just war theory. Moreover, this thesis examines the moral utility of SOF operations as a means of preventing unnecessary loss of life prior to, during, and in the wake of armed conflict. SOF can function, in both theory and practice, as a distinct moral improvement over most other forms of irregular or indirect conflict, across nearly all phases of conflict. These conclusions aim to provide insight to planners, observers, and policy-makers who seek to understand and efficiently respond to emergent or persistent challenges to global stability and national security. http://archive.org/details/justwartheoryrel1094559564 Major, United States Army Approved for public release; distribution is unlimited.