Polymeric compositions and their method of manufacture

Filled polymer compositions are made by dissolving the polymer binder in a suitable sublimable solvent, mixing the filler material with the polymer and its solvent, freezing the resultant mixture, and subliming the frozen solvent from the mixture from which it is then removed. The remaining composition is suitable for conventional processing such as compression molding or extruding. A particular feature of the method of manufacture is pouring the mixed solution slowly in a continuous stream into a cryogenic bath wherein frozen particles of the mixture result. The frozen individual particles are then subjected to the sublimation

Potential shift in tree species composition after interaction of fire and drought in the Central Alps

The future trajectory of forest ecosystems under climate change is heavily debated. Previous studies on the impacts of climate change on forest ecosystems have focused mainly on direct effects of altered climatic conditions, whereas interactions with disturbance events have been largely neglected. The aim of this study is to explore interactions of drought with fire disturbance and to assess their effects on tree species shifts in the European Central Alps. Tree recruitment after a stand replacing wildfire in the Rhone valley, Switzerland, was measured along an altitudinal temperature moisture gradient. Recruitment was more successful in pioneer species (Betula pendula, Populus tremula and Salix appendiculata) than in pre-fire stand forming (PFSF) species (Larix decidua, Picea abies and Pinus sylvestris). Seedling and sapling density was not related to fire intensity, but it correlated with the distance to the forest edge in PFSF species. The window of opportunity for seedling establishment was short (1-2years), and moisture deficit was the main limiting factor for tree recruitment at lower altitudes. We suggest that prolonged drought periods, as projected under continued global warming, will further aggravate tree recruitment success after fire disturbance at low altitudes of the Central Alps and may eventually lead to a shift from PFSF species to either more drought-tolerant species or to forest-free vegetatio

Quantum integrability of quadratic Killing tensors

Quantum integrability of classical integrable systems given by quadratic Killing tensors on curved configuration spaces is investigated. It is proven that, using a "minimal" quantization scheme, quantum integrability is insured for a large class of classic examples.Comment: LaTeX 2e, no figure, 35 p., references added, minor modifications. To appear in the J. Math. Phy

Registration of Multisensor Images through a Conditional Generative Adversarial Network and a Correlation-Type Similarity Measure

The automatic registration of multisensor remote sensing images is a highly challenging task due to the inherently different physical, statistical, and textural characteristics of the input data. Information-theoretic measures are often used to favor comparing local intensity distributions in the images. In this paper, a novel method based on the combination of a deep learning architecture and a correlation-type area-based functional is proposed for the registration of a multisensor pair of images, including an optical image and a synthetic aperture radar (SAR) image. The method makes use of a conditional generative adversarial network (cGAN) in order to address image-to-image translation across the optical and SAR data sources. Then, once the optical and SAR data are brought to a common domain, an area-based ℓ2 similarity measure is used together with the COBYLA constrained maximization algorithm for registration purposes. While correlation-type functionals are usually ineffective in the application to multisensor registration, exploiting the image-to-image translation capabilities of cGAN architectures allows moving the complexity of the comparison to the domain adaptation step, thus enabling the use of a simple ℓ2 similarity measure, favoring high computational efficiency, and opening the possibility to process a large amount of data at runtime. Experiments with multispectral and panchromatic optical data combined with SAR images suggest the effectiveness of this strategy and the capability of the proposed method to achieve more accurate registration as compared to state-of-the-art approaches

Hierarchical Probabilistic Graphical Models and Deep Convolutional Neural Networks for Remote Sensing Image Classification

The method presented in this paper for semantic segmentation of multiresolution remote sensing images involves convolutional neural networks (CNNs), in particular fully convolutional networks (FCNs), and hierarchical probabilistic graphical models (PGMs). These approaches are combined to overcome the limitations in classification accuracy of CNNs for small or non-exhaustive ground truth (GT) datasets. Hierarchical PGMs, e.g., hierarchical Markov random fields (MRFs), are structured output learning models that exploit information contained at different image scales. This perfectly matches the intrinsically multiscale behavior of the processes of a CNN (e.g., pooling layers). The framework consists of a hierarchical MRF on a quadtree and a planar Markov model on each layer, modeling the interactions among pixels and accounting for both the multiscale and the spatial-contextual information. The marginal posterior mode criterion is used for inference. The adopted FCN is the U-Net and the experimental validation is conducted on the ISPRS 2D Semantic Labeling Challenge Vaihingen dataset, with some modifications to approach the case of scarce GTs and to assess the classification accuracy of the proposed technique. The proposed framework attains a higher recall compared to the considered FCNs, progressively more relevant as the training set is further from the ideal case of exhaustive GTs

Semantic Segmentation of Remote-Sensing Images Through Fully Convolutional Neural Networks and Hierarchical Probabilistic Graphical Models

Deep learning (DL) is currently the dominant approach to image classification and segmentation, but the performances of DL methods are remarkably influenced by the quantity and quality of the ground truth (GT) used for training. In this article, a DL method is presented to deal with the semantic segmentation of very-high-resolution (VHR) remote-sensing data in the case of scarce GT. The main idea is to combine a specific type of deep convolutional neural networks (CNNs), namely fully convolutional networks (FCNs), with probabilistic graphical models (PGMs). Our method takes advantage of the intrinsic multiscale behavior of FCNs to deal with multiscale data representations and to connect them to a hierarchical Markov model (e.g., making use of a quadtree). As a consequence, the spatial information present in the data is better exploited, allowing a reduced sensitivity to GT incompleteness to be obtained. The marginal posterior mode (MPM) criterion is used for inference in the proposed framework. To assess the capabilities of the proposed method, the experimental validation is conducted with the ISPRS 2D Semantic Labeling Challenge datasets on the cities of Vaihingen and Potsdam, with some modifications to simulate the spatially sparse GTs that are common in real remote-sensing applications. The results are quite significant, as the proposed approach exhibits a higher producer accuracy than the standard FCNs considered and especially mitigates the impact of scarce GTs on minority classes and small spatial details

Spacetime Encodings II - Pictures of Integrability

I visually explore the features of geodesic orbits in arbitrary stationary axisymmetric vacuum (SAV) spacetimes that are constructed from a complex Ernst potential. Some of the geometric features of integrable and chaotic orbits are highlighted. The geodesic problem for these SAV spacetimes is rewritten as a two degree of freedom problem and the connection between current ideas in dynamical systems and the study of two manifolds sought. The relationship between the Hamilton-Jacobi equations, canonical transformations, constants of motion and Killing tensors are commented on. Wherever possible I illustrate the concepts by means of examples from general relativity. This investigation is designed to build the readers' intuition about how integrability arises, and to summarize some of the known facts about two degree of freedom systems. Evidence is given, in the form of orbit-crossing structure, that geodesics in SAV spacetimes might admit, a fourth constant of motion that is quartic in momentum (by contrast with Kerr spacetime, where Carter's fourth constant is quadratic).Comment: 11 pages, 10 figure

Integrated and Modular Didactic and Methodological Concept for a Learning Factory

AbstractAs today manufacturing is not only subject to a single factory, but a network of globally distributed production sites, the goal-oriented encouragement of professional capacities is the motivation for the Learning Factory on Global Production (LGP). In this context, the design of a competency-based and action-oriented didactic and methodological concept is a prerequisite for sustainable learning results and for the development of self-determined problem solving skills. The presented paper gives an overview to the didactic and methodological design approach of the LGP. The integrated modular concept of e-learning and application in the learning factory environment supports self-directed learning and implemented by structuring the teaching/ learning process according to the model of complete action

Automatic Extraction of Planetary Image Features

With the launch of several Lunar missions such as the Lunar Reconnaissance Orbiter (LRO) and Chandrayaan-1, a large amount of Lunar images will be acquired and will need to be analyzed. Although many automatic feature extraction methods have been proposed and utilized for Earth remote sensing images, these methods are not always applicable to Lunar data that often present low contrast and uneven illumination characteristics. In this paper, we propose a new method for the extraction of Lunar features (that can be generalized to other planetary images), based on the combination of several image processing techniques, a watershed segmentation and the generalized Hough Transform. This feature extraction has many applications, among which image registration

Cylindrical, periodic surface lattice — theory, dispersion analysis, and experiment

A two-dimensional surface lattice of cylindrical topology obtained via perturbing the inner surface of a cylinder is considered. Periodic perturbations of the surface lead to observation of high-impedance, dielectric-like media and resonant coupling of surface and non-propagating volume fields. This allows synthesis of tailored-for-purpose "coating" material with dispersion suitable, for instance, to mediate a Cherenkov type interaction. An analytical model of the lattice is discussed and coupled-wave equations are derived. Variations of the lattice dispersive properties with variation of parameters are shown, illustrating the tailoring of the structure's electromagnetic properties. Experimental results are presented showing agreement with the theoretical model