At universities, research data is increasingly stored in research data repositories according to a data management plan (DMP) and thus made available for further use. The challenge of reusing ...hundreds, thousands, or millions of data sets is to obtain an overview of the data in a short period of time and to search through all the data. The high variability of the formats used to store research data requires a new approach to data reusability that focuses on the visualisation and searchability of archived research data, which can also be combined with each other. In this article, we present a practical DMP that describes how information systems can be created on demand by reusing research data archived in research data repositories and how these systems can be merged into a federated information system. As a result, in our projects, information systems have been created in minutes or a couple of hours with few resources. The initial effort to create a federated system remains; however, this allows federated searches to be performed. Extending a federated system to include other information systems can then be accomplished by making a few configurations and manageable adjustments to the source code.
Given a set of software programs, each being labeled either as vulnerable or benign, deep learning technology can be used to automatically build a software vulnerability detector. A challenge in this ...context is that there are countless equivalent ways to implement a particular functionality in a program. For instance, the naming of variables is often a matter of the personal style of programmers, and thus, the detection of vulnerability patterns in programs is made difficult. Current deep learning approaches to software vulnerability detection rely on the raw text of a program and exploit general natural language processing capabilities to address the problem of dealing with different naming schemes in instances of vulnerability patterns. Relying on natural language processing, and learning how to reveal variable reference structures from the raw text, is often too high a burden, however. Thus, approaches based on deep learning still exhibit problems generating a detector with decent generalization properties due to the naming or, more generally formulated, the vocabulary explosion problem. In this work, we propose techniques to mitigate this problem by making the referential structure of variable references explicit in input representations for deep learning approaches. Evaluation results show that deep learning models based on techniques presented in this article outperform raw text approaches for vulnerability detection. In addition, the new techniques also induce a very small main memory footprint. The efficiency gain of memory usage can be up to four orders of magnitude compared to existing methods as our experiments indicate.
"Principal Component Analysis" (PCA) is an established linear technique for dimensionality reduction. It performs an orthonormal transformation to replace possibly correlated variables with a smaller ...set of linearly independent variables, the so-called principal components, which capture a large portion of the data variance. The problem of finding the optimal number of principal components has been widely studied for offline PCA. However, when working with streaming data, the optimal number changes continuously. This requires to update both the principal components and the dimensionality in every timestep. While the continuous update of the principal components is widely studied, the available algorithms for dimensionality adjustment are limited to an increment of one in neural network-based and incremental PCA. Therefore, existing approaches cannot account for abrupt changes in the presented data. The contribution of this work is to enable in neural network-based PCA the continuous dimensionality adjustment by an arbitrary number without the necessity to learn all principal components. A novel algorithm is presented that utilizes several PCA characteristics to adaptivly update the optimal number of principal components for neural network-based PCA. A precise estimation of the required dimensionality reduces the computational effort while ensuring that the desired amount of variance is kept. The computational complexity of the proposed algorithm is investigated and it is benchmarked in an experimental study against other neural network-based and incremental PCA approaches where it produces highly competitive results.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
This review focuses on the pivotal role microscopy has played in diagnosing the type(s) of microbial attacks present in waterlogged ancient wooden objects, and to understand the nature and extent of ...deterioration of such objects. The microscopic journey began with the application of light microscopy (LM) to examine the deterioration of waterlogged woods, notably foundation piles supporting historic buildings, progressing into the use of high-resolution imaging tools (SEM and TEM) and techniques. Although bacteria were implicated in the deterioration of foundation piles, confirmation that bacteria can indeed degrade wood in its native state came when decaying wood from natural environments was examined using electron microscopy, particularly TEM, which enabled bacterial association with cell wall regions undergoing degradation to be clearly resolved. The information base has been a catalyst, stimulating numerous studies in the past three decades or so to understand the nature of microbial degradation of waterlogged archaeological wood more precisely, combining LM, SEM, and TEM with high-resolution chemical analytical methods, including chemical microscopy. The emerging information is aiding targeted developments towards a more effective conservation of ancient wooden objects as they begin to be uncovered from burial and waterlogging environments.
Outdoor environments pose multiple challenges for the visual navigation of robots, like changing illumination conditions, seasonal changes, dynamic environments and non-planar terrain. Illumination ...changes are mostly caused by the movement of the Sun and by changing cloud cover. Moving clouds themselves also are a dynamic aspect of a visual scene. For visual homing algorithms, which compute the direction to a previously visited place by comparing the current view with a snapshot taken at that place, in particular, the changing cloud cover poses a problem, since cloud movements do not correspond to movements of the camera and thus constitute misleading information. We propose an edge-filtering method operating on linearly-transformed RGB channels, which reliably detects edges in the ground region of the image while suppressing edges in the sky region. To fulfill this criterion, the factors for the linear transformation of the RGB channels are optimized systematically concerning this special requirement. Furthermore, we test the proposed linear transformation on an existing visual homing algorithm (MinWarping) and show that the performance of the visual homing method is significantly improved compared to the use of edge-filtering methods on alternative color information.
Evidence from behavioral experiments suggests that insects use the skyline as a cue for visual navigation. However, changes of lighting conditions, over hours, days or possibly seasons, significantly ...affect the appearance of the sky and ground objects. One possible solution to this problem is to extract the "skyline" by an illumination-invariant classification of the environment into two classes, ground objects and sky. In a previous study (Insect models of illumination-invariant skyline extraction from UV (ultraviolet) and green channels), we examined the idea of using two different color channels available for many insects (UV and green) to perform this segmentation. We found out that for suburban scenes in temperate zones, where the skyline is dominated by trees and artificial objects like houses, a "local" UV segmentation with adaptive thresholds applied to individual images leads to the most reliable classification. Furthermore, a "global" segmentation with fixed thresholds (trained on an image dataset recorded over several days) using UV-only information is only slightly worse compared to using both the UV and green channel. In this study, we address three issues: First, to enhance the limited range of environments covered by the dataset collected in the previous study, we gathered additional data samples of skylines consisting of minerals (stones, sand, earth) as ground objects. We could show that also for mineral-rich environments, UV-only segmentation achieves a quality comparable to multi-spectral (UV and green) segmentation. Second, we collected a wide variety of ground objects to examine their spectral characteristics under different lighting conditions. On the one hand, we found that the special case of diffusely-illuminated minerals increases the difficulty to reliably separate ground objects from the sky. On the other hand, the spectral characteristics of this collection of ground objects covers well with the data collected in the skyline databases, increasing, due to the increased variety of ground objects, the validity of our findings for novel environments. Third, we collected omnidirectional images, as often used for visual navigation tasks, of skylines using an UV-reflective hyperbolic mirror. We could show that "local" separation techniques can be adapted to the use of panoramic images by splitting the image into segments and finding individual thresholds for each segment. Contrarily, this is not possible for 'global' separation techniques.
The Low Earth Orbit (LEO) experiment Biology and Mars Experiment (BIOMEX) is an interdisciplinary and international space research project selected by ESA. The experiment will be accommodated on the ...space exposure facility EXPOSE-R2 on the International Space Station (ISS) and is foreseen to be launched in 2013. The prime objective of BIOMEX is to measure to what extent biomolecules, such as pigments and cellular components, are resistant to and able to maintain their stability under space and Mars-like conditions. The results of BIOMEX will be relevant for space proven biosignature definition and for building a biosignature data base (e.g. the proposed creation of an international Raman library). The library will be highly relevant for future space missions such as the search for life on Mars. The secondary scientific objective is to analyze to what extent terrestrial extremophiles are able to survive in space and to determine which interactions between biological samples and selected minerals (including terrestrial, Moon- and Mars analogs) can be observed under space and Mars-like conditions. In this context, the Moon will be an additional platform for performing similar experiments with negligible magnetic shielding and higher solar and galactic irradiation compared to LEO. Using the Moon as an additional astrobiological exposure platform to complement ongoing astrobiological LEO investigations could thus enhance the chances of detecting organic traces of life on Mars. We present a lunar lander mission with two related objectives: a lunar lander equipped with Raman and PanCam instruments which can analyze the lunar surface and survey an astrobiological exposure platform. This dual use of testing mission technology together with geo- and astrobiological analyses will significantly increase the science return, and support the human preparation objectives. It will provide knowledge about the Moon′s surface itself and, in addition, monitor the stability of life-markers, such as cells, cell components and pigments, in an extraterrestrial environment with much closer radiation properties to the surface of Mars. The combination of a Raman data base of these data together with data from LEO and space simulation experiments, will lead to further progress on the analysis and interpretation of data that we will obtain from future Moon and Mars exploration missions.
► We show connections between LEO investigations, Moon and Mars explorations. ► The relevance of data base collection about space resistant biosignatures. ► In situ measurements of exposed geo-bio-samples on a Lunar Lander ► BIOMEX as an example for astrobiological exposure experiments on the Moon. ► Combining Mineralogy and Biology to support the search for life on Mars.
A model of visual navigation in ants is presented which is based on a simple network predicting the changes of a visual scene under translatory movements. The model contains two behavioral ...components: the acquisition of multiple snapshots in different orientations during a learning walk, and the selection of a movement direction by a scanning behavior where the ant searches through different headings. Both components fit with observations in experiments with desert ants. The model is in most aspects biologically plausible with respect to the equivalent neural networks, and it produces reliable homing behavior in a simulated environment with a complex random surface texture. The model is closely related to the algorithmic min-warping method for visual robot navigation which shows good homing performance in real-world environments.
► We present a novel, holistic model of visual navigation in ants. ► Navigation of ants could be based on the prediction of image changes. ► Navigation behavior reduces the complexity of the required neural networks. ► Learning walks and scanning behavior can be explained by the snapshot concept. ► Holistic models are biologically more plausible than correspondence models.
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