Habsburg’s Last War: The Filmic Memory (1918 to the Present)

A divergent survey of scholarship on World War I cinema produced in succession countries of the Habsburg Empire. This untapped body of film records a contentious phase in world history, from the perspective of an often misunderstood, yet pivotal, region. The volume gathers scholarly essays exploring the intersections between the political, historical, and aesthetic, as expressed in the region’s various “moving pictures,” with sustained attention to the relationship between artistic representation and collective memory.https://scholarworks.uno.edu/hlw/1000/thumbnail.jp

Introduction to Habsburg’s Last War: The Filmic Memory (1918 to the Present)

https://scholarworks.uno.edu/hlw/1002/thumbnail.jp

Habsburg’s Last War in Austrian films, 1918 to the present

https://scholarworks.uno.edu/hlw/1003/thumbnail.jp

Habsburg’s Last War in Austrian films, 1918 to the present

https://scholarworks.uno.edu/hlw/1003/thumbnail.jp

Visualization of the Eastern Front in Austro-Hungarian World War I propaganda

Translated by Alexander Kozlov. The article was submitted on 10.12.2013.The research carried out with reference to archival Austro-Hungarian films and photographs explicates the themes, tasks and genre diversity of the visual account of World War I in the Eastern Front. The author determines how the themes and heroes of war history changed and reveals the peculiarities of the realization of state patriotism and propagandistic functions of photographs. The results obtained with reference to archival data of the Austro-Hungarian Monarchy are integrated into the international contest and compared to the visual materials created in the other countries involved in World War I.Исследование, выполненное на базе архивных австро-венгерских фильмов и фотографий, посвящено содержательным аспектам, основным задачам и значению изображения военных событий Первой мировой войны на Восточном фронте. Результаты, полученные на базе данных австро-венгерской монархии, интегрируются в международный контекст и сравниваются с другими изобразительными источниками соответствующих театрам военных действий стран, участвовавших в войне

{SCL(EQ)}: {SCL} for First-Order Logic with Equality

International audienceAbstract We propose a new calculus SCL(EQ) for first-order logic with equality that only learns non-redundant clauses. Following the idea of CDCL (Conflict Driven Clause Learning) and SCL (Clause Learning from Simple Models) a ground literal model assumption is used to guide inferences that are then guaranteed to be non-redundant. Redundancy is defined with respect to a dynamically changing ordering derived from the ground literal model assumption. We prove SCL(EQ) sound and complete and provide examples where our calculus improves on superposition

SCL(EQ): SCL for First-Order Logic with Equality

We propose a new calculus SCL(EQ) for first-order logic with equality thatonly learns non-redundant clauses. Following the idea of CDCL (Conflict DrivenClause Learning) and SCL (Clause Learning from Simple Models) a ground literalmodel assumption is used to guide inferences that are then guaranteed to benon-redundant. Redundancy is defined with respect to a dynamically changingordering derived from the ground literal model assumption. We prove SCL(EQ)sound and complete and provide examples where our calculus improves onsuperposition.<br

Robust joint and individual variance explained

Discovering the common (joint) and individual subspaces is crucial for analysis of multiple data sets, including multi-view and multi-modal data. Several statistical machine learning methods have been developed for discovering the common features across multiple data sets. The most well studied family of the methods is that of Canonical Correlation Analysis (CCA) and its variants. Even though the CCA is a powerful tool, it has several drawbacks that render its application challenging for computer vision applications. That is, it discovers only common features and not individual ones, and it is sensitive to gross errors present in visual data. Recently, efforts have been made in order to develop methods that discover individual and common components. Nevertheless, these methods are mainly applicable in two sets of data. In this paper, we investigate the use of a recently proposed statistical method, the so-called Joint and Individual Variance Explained (JIVE) method, for the recovery of joint and individual components in an arbitrary number of data sets. Since, the JIVE is not robust to gross errors, we propose alternatives, which are both robust to non-Gaussian noise of large magnitude, as well as able to automatically find the rank of the individual components. We demonstrate the effectiveness of the proposed approach to two computer vision applications, namely facial expression synthesis and face age progression in-the-wild

Robust joint and individual variance explained

Discovering the common (joint) and individual subspaces is crucial for analysis of multiple data sets, including multi-view and multi-modal data. Several statistical machine learning methods have been developed for discovering the common features across multiple data sets. The most well studied family of the methods is that of Canonical Correlation Analysis (CCA) and its variants. Even though the CCA is a powerful tool, it has several drawbacks that render its application challenging for computer vision applications. That is, it discovers only common features and not individual ones, and it is sensitive to gross errors present in visual data. Recently, efforts have been made in order to develop methods that discover individual and common components. Nevertheless, these methods are mainly applicable in two sets of data. In this paper, we investigate the use of a recently proposed statistical method, the so-called Joint and Individual Variance Explained (JIVE) method, for the recovery of joint and individual components in an arbitrary number of data sets. Since, the JIVE is not robust to gross errors, we propose alternatives, which are both robust to non-Gaussian noise of large magnitude, as well as able to automatically find the rank of the individual components. We demonstrate the effectiveness of the proposed approach to two computer vision applications, namely facial expression synthesis and face age progression in-the-wild