Abstract.A common practice to gain invariant features in object recognition models is to aggregate multiple low-level features over a small neighborhood. However, the differences between those models makes a comparison of the properties of different aggregation functions hard. Our aim is to gain insight into different functions by directly comparing them on a fixed architecture for several common object recognition tasks. Empirical results show that a maximum pooling operation significantly outperforms subsampling operations. Despite their shift-invariant properties, overlapping pooling windows are no significant improvement over non-overlapping pooling windows. By applying this knowledge, we achieve state-of-the-art error rates of 4.57% on the NORB normalized-uniform dataset and 5.6% on the NORB jittered-cluttered dataset.

No abstract

No abstract

No abstract

Interactive visualization provides valuable support for exploring, analyzing, and understanding textual documents. Certain tasks, however, require that insights derived from visual abstractions are verified by a human expert perusing the source text. So far, this problem is typically solved by offering overview-detail techniques, which present different views with different levels of abstractions. This often leads to problems with visual continuity. Focus-context techniques, on the other hand, succeed in accentuating interesting subsections of large text documents but are normally not suited for integrating visual abstractions. With VarifocalReader we present a technique that helps to solve some of these approaches' problems by combining characteristics from both. In particular, our method simplifies working with large and potentially complex text documents by simultaneously offering abstract representations of varying detail, based on the inherent structure of the document, and access to the text itself. In addition, VarifocalReader supports intra-document exploration through advanced navigation concepts and facilitates visual analysis tasks. The approach enables users to apply machine learning techniques and search mechanisms as well as to assess and adapt these techniques. This helps to extract entities, concepts and other artifacts from texts. In combination with the automatic generation of intermediate text levels through topic segmentation for thematic orientation, users can test hypotheses or develop interesting new research questions. To illustrate the advantages of our approach, we provide usage examples from literature studies.

Predicting the state of Cu under a broad range of reaction conditions (pressure and temperature with various adsorbates: CO 2 , CO, H 2 O, H*, and O*) is an important property to understand CO 2 hydrogenation catalysts. Here, unsupported copper (Cu) nanoparticles (NPs) were modeled in vacuum and under conditions relevant for CO 2 hydrogenation conditions from first principles using density functional theory calculations; such models allow precise prediction of particle shapes and surface coverage of the relevant facets of Cu NPs over a large range of conditions relevant to CO 2 hydrogenation. This model predicts that the Cu surfaces are fully reduced (in line with experimental results) and free of adsorbed oxygen (O*), H 2 O*, and CO 2 * under typical reaction conditions. Furthermore, the Cu( 111) facet is at least partially covered with hydrogen (H*) and the Cu(110) facet is partially covered with adsorbed CO* at high reverse-water−gas-shift (RWGS) conversions, while the Cu(100) and Cu(211) facet remain adsorbate-free. Overall, the particle shape of Cu NPs under CO 2 hydrogenation conditions is dominated by the (111) facet with a small area of the (100) facet being present (among all the facets considered). The final equilibrium particle shape is set during the initialization of the CO 2 hydrogenation reaction and does not change even when the WGS equilibrium is reached.

Strutinsky-type calculations indicate that the potential energy favors four channels in the nuclear fission o f 252Cf. The connection o f this finding with experimental results on the dis tribution of fragment mass, total kinetic energy, neutron multiplicities, and relative abundances is discussed. Similar calculations for 227Ac, 236U, and 258Fm show that the changing preponder ance of the four channels seems to describe striking trends in the fission o f the actinides, in particular the dip in the total kinetic energy at symmetrical fission of 236U and the enormously high average kinetic energy of the 258Fm fragments.

Abstract. In this work we propose a new information-theoretic clustering algorithm that infers cluster memberships by direct optimization of a non-parametric mutual information estimate between data distribution and cluster assignment. Although the optimization objective has a solid theoretical foundation it is hard to optimize. We propose an approximate optimization formulation that leads to an efficient algorithm with low runtime complexity. The algorithm has a single free parameter, the number of clusters to find. We demonstrate superior performance on several synthetic and real datasets.