004 Datenverarbeitung; Informatik
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Background: Because most human stroke victims are elderly, studies of experimental stroke in the aged rather than the young rat model may be optimal for identifying clinically relevant cellular responses, as well for pinpointing beneficial interventions.
Methodology/Principal Findings: We employed the Affymetrix platform to analyze the whole-gene transcriptome following temporary ligation of the middle cerebral artery in aged and young rats. The correspondence, heat map, and dendrogram analyses independently suggest a differential, age-group-specific behaviour of major gene clusters after stroke. Overall, the pattern of gene expression strongly suggests that the response of the aged rat brain is qualitatively rather than quantitatively different from the young, i.e. the total number of regulated genes is comparable in the two age groups, but the aged rats had great difficulty in mounting a timely response to stroke. Our study indicates that four genes related to neuropathic syndrome, stress, anxiety disorders and depression (Acvr1c, Cort, Htr2b and Pnoc) may have impaired response to stroke in aged rats. New therapeutic options in aged rats may also include Calcrl, Cyp11b1, Prcp, Cebpa, Cfd, Gpnmb, Fcgr2b, Fcgr3a, Tnfrsf26, Adam 17 and Mmp14. An unexpected target is the enzyme 3-hydroxy-3-methylglutaryl-Coenzyme A synthase 1 in aged rats, a key enzyme in the cholesterol synthesis pathway. Post-stroke axonal growth was compromised in both age groups.
Conclusion/Significance: We suggest that a multi-stage, multimodal treatment in aged animals may be more likely to produce positive results. Such a therapeutic approach should be focused on tissue restoration but should also address other aspects of patient post-stroke therapy such as neuropathic syndrome, stress, anxiety disorders, depression, neurotransmission and blood pressure.
We attempt to identify sequences of signaling dialogs, to strengthen our understanding of the signaling behavior of IoT devices by examining a dataset containing over 270.000 distinct IoT devices whose signaling traffic has been observed over a 31-day period in a 2G network [4]. We propose a set of rules that allows the assembly of signaling dialogs into so-called sessions in order to identify common patterns and lay the foundation for future research in the areas of traffic modeling and anomaly detection.
Modern immersive multimodal technologies enable the learners to completely get immersed in various learning situations in a way that feels like experiencing an authentic learning environment. These environments also allow the collection of multimodal data, which can be used with artificial intelligence to further improve the immersion and learning outcomes. The use of artificial intelligence has been widely explored for the interpretation of multimodal data collected from multiple sensors, thus giving insights to support learners’ performance by providing personalised feedback. In this paper, we present a conceptual approach for creating immersive learning environments, integrated with multi-sensor setup to help learners improve their psychomotor skills in a remote setting.
This short letter proposes more consolidated explicit solutions for the forces and torques acting on typical rover wheels, that can be used as a method to determine their average mobility characteristics in planetary soils. The closed loop solutions stand in one of the verified methods, but at difference of the previous, observables are decoupled requiring a less amount of physical parameters to measure. As a result, we show that with knowledge of terrain properties, wheel driving performance rely in a single observable only. Because of their generality, the formulated equations established here can have further implications in autonomy and control of rovers or planetary soil characterization.
Neural networks have to capture mathematical relationships in order to learn various tasks. They approximate these relations implicitly and therefore often do not generalize well. The recently proposed Neural Arithmetic Logic Unit (NALU) is a novel neural architecture which is able to explicitly represent the mathematical relationships by the units of the network to learn operations such as summation, subtraction or multiplication. Although NALUs have been shown to perform well on various downstream tasks, an in-depth analysis reveals practical shortcomings by design, such as the inability to multiply or divide negative input values or training stability issues for deeper networks. We address these issues and propose an improved model architecture. We evaluate our model empirically in various settings from learning basic arithmetic operations to more complex functions. Our experiments indicate that our model solves stability issues and outperforms the original NALU model in means of arithmetic precision and convergence.
In this paper, we present a virtual audience simulation system for Virtual Reality (VR). The system implements an audience perception model controlling the nonverbal behaviors of virtual spectators, such as facial expressions or postures. Groups of virtual spectators are animated by a set of nonverbal behavior rules representing a particular audience attitude (e.g., indifferent or enthusiastic). Each rule specifies a nonverbal behavior category: posture, head movement, facial expression and gaze direction as well as three parameters: type, frequency and proportion. In a first user-study, we asked participants to pretend to be a speaker in VR and then create sets of nonverbal behaviour parameters to simulate different attitudes. Participants manipulated the nonverbal behaviours of single virtual spectator to match a specific levels of engagement and opinion toward them. In a second user-study, we used these parameters to design different types of virtual audiences with our nonverbal behavior rules and evaluated their perceptions. Our results demonstrate our system’s ability to create virtual audiences with three types of different perceived attitudes: indifferent, critical, enthusiastic. The analysis of the results also lead to a set of recommendations and guidelines regarding attitudes and expressions for future design of audiences for VR therapy and training applications.
Global Navigation Satellite System (GNSS) provides accurate positioning data for vehicular navigation in open outdoor environment. In an indoor environment, Light Detection and Ranging (LIDAR) Simultaneous Localization and Mapping (SLAM) establishes a two-dimensional map and provides positioning data. However, LIDAR can only provide relative positioning data and it cannot directly provide the latitude and longitude of the current position. As a consequence, GNSS/Inertial Navigation System (INS) integrated navigation could be employed in outdoors, while the indoors part makes use of INS/LIDAR integrated navigation and the corresponding switching navigation will make the indoor and outdoor positioning consistent. In addition, when the vehicle enters the garage, the GNSS signal will be blurred for a while and then disappeared. Ambiguous GNSS satellite signals will lead to the continuous distortion or overall drift of the positioning trajectory in the indoor condition. Therefore, an INS/LIDAR seamless integrated navigation algorithm and a switching algorithm based on vehicle navigation system are designed. According to the experimental data, the positioning accuracy of the INS/LIDAR navigation algorithm in the simulated environmental experiment is 50% higher than that of the Dead Reckoning (DR) algorithm. Besides, the switching algorithm developed based on the INS/LIDAR integrated navigation algorithm can achieve 80% success rate in navigation mode switching.
Innovative Software kann die Position eines Unternehmens im Wettbewerb sichern. Die Einführung innovativer Software ist aber alles andere als einfach. Denn obgleich die technischen Aspekte offensichtlicher sind, dominieren organisationale Aspekte. Zu viele Softwareprojekte schlagen fehl, da die Einführung nicht gelingt, trotz Erfüllung technischer Anforderungen. Vor diesem Hintergrund ist das Forschungsziel der Masterarbeit, Risiken und Erfolgsfaktoren für die Einführung innovativer Software in Unternehmen zu finden, eine Strategie zu formulieren und dabei die Bedeutung von Schlüsselpersonen zu bestimmen.
Inside 2003: IT-Sicherheit
(2003)