A topology-oblivious routing protocol for NDN-VANETs

Vehicular Ad Hoc Networks (VANETs) are characterized by intermittent connectivity, which leads to failures of end-to-end paths between nodes. Named Data Networking (NDN) is a network paradigm that deals with such problems, since information is forwarded based on content and not on the location of the hosts. In this work, we propose an enhanced routing protocol of our previous topology-oblivious Multihop, Multipath, and Multichannel NDN for VANETs (MMM-VNDN) routing strategy that exploits several paths to achieve more efficient content retrieval. Our new enhanced protocol, i mproved MMM-VNDN (iMMM-VNDN), creates paths between a requester node and a provider by broadcasting Interest messages. When a provider responds with a Data message to a broadcast Interest message, we create unicast routes between nodes, by using the MAC address(es) as the distinct address(es) of each node. iMMM-VNDN extracts and thus creates routes based on the MAC addresses from the strategy layer of an NDN node. Simulation results show that our routing strategy performs better than other state of the art strategies in terms of Interest Satisfaction Rate, while keeping the latency and jitter of messages low

A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis

Automatic analysis of scanned historical documents comprises a wide range of image analysis tasks, which are often challenging for machine learning due to a lack of human-annotated learning samples. With the advent of deep neural networks, a promising way to cope with the lack of training data is to pre-train models on images from a different domain and then fine-tune them on historical documents. In the current research, a typical example of such cross-domain transfer learning is the use of neural networks that have been pre-trained on the ImageNet database for object recognition. It remains a mostly open question whether or not this pre-training helps to analyse historical documents, which have fundamentally different image properties when compared with ImageNet. In this paper, we present a comprehensive empirical survey on the effect of ImageNet pre-training for diverse historical document analysis tasks, including character recognition, style classification, manuscript dating, semantic segmentation, and content-based retrieval. While we obtain mixed results for semantic segmentation at pixel-level, we observe a clear trend across different network architectures that ImageNet pre-training has a positive effect on classification as well as content-based retrieval

Kooperationsdilemma in der Zukunftsforschung – Ein IT-basierter Lösungsansatz der Bundeswehr

Unternehmen sehen sich heute einer dynamischen Umwelt ausgesetzt. Nicht vorhergesehene Umbrüche können die Unternehmensexistenz gefährden. Zukunftsforschung ist eine Methode zur Prognose möglicher Ereignisse. Sie gewinnt an Qualität, wenn sie kooperativ durch das Wissen und die Kreativität von Menschen mit unterschiedlichsten Erfahrungen und Wissenszugängen angereichert wird. Wissen ist jedoch häufig die Basis von Wettbewerbsvorteilen und wird daher zwischen Unternehmen nur selten ausgetauscht. Dieser Beitrag beschäftigt sich mit einem IT-basierten Lösungsansatz des Kooperationsdilemmas in der Zukunftsforschung. Der Ansatz resultiert in einem technischen Konzept, welches auf integrierte Weise unternehmensübergreifendes Wissen bündelt und eine kooperative Zukunftsforschung bei gleichzeitiger Berücksichtigung von sensiblen Daten ermöglicht. Der beschriebene Ansatz wird im Dezernat Zukunftsanalyse der Bundeswehr prototypisch umgesetzt

Supramolecular behaviour and fluorescence of rhodamine-functionalised ROMP polymers

Inherently fluorescent polymers are of interest in materials and medicine. We report a ring-opening metathesis polymerisation (ROMP) platform for creation of amphiphilic block copolymers in which one block is formed from rhodamine B-containing monomers. The polymers self-assemble into well-defined micelles which are able to sequester molecular dyes and further interact with them by energy transfer. Despite incorporating a cationic dye known to bind DNA, the polymer micelles do not interact with DNA, indicating that they are potentially safe for use in bioanalytical applications

A Multihop and Multipath Routing Protocol Using NDN for VANETs

One main characteristic of Vehicular Ad Hoc Networks (VANETs) is the intermittent connectivity, which leads to failures of end to end paths between end nodes. Named Data Networking (NDN) is a network paradigm that deals with such problems, since information is forwarded based on content and not on the location of the hosts. Hence, NDN has been proposed in VANET scenarios. In this paper, we propose a multihop and multipath VANET routing algorithm that exploits several paths to achieve faster content retrieval. We create a new routing approach for NDN in VANETs, by introducing new fields to messages and to data structures. Simulation results show that our approach does not only guarantee faster data delivery, but also saves network resources, due to the lack of message retransmissions

A comprehensive study of ImageNet pre-training for historical document image analysis

Automatic analysis of scanned historical documents comprises a wide range of image analysis tasks, which are often challenging for machine learning due to a lack of humanannotated learning samples. With the advent of deep neural networks, a promising way to cope with the lack of training data is to pre-train models on images from a different domain and then fine-tune them on historical documents. In the current research, a typical example of such cross-domain transfer learning is the use of neural networks that have been pre-trained on the ImageNet database for object recognition. It remains a mostly open question whether or not this pre-training helps to analyse historical documents, which have fundamentally different image properties when compared with ImageNet. In this paper, we present a comprehensive empirical survey on the effect of ImageNet pretraining for diverse historical document analysis tasks, including character recognition, style classification, manuscript dating, semantic segmentation, and content-based retrieval. While we obtain mixed results for semantic segmentation at pixel-level, we observe a clear trend across different network architectures that ImageNet pre-training has a positive effect on classification as well as content-based retrieval

Protonated Phosphazenes: Structures and Hydrogen-Bonding Organocatalysts for Carbonyl Bond Activation

The synthesis and application of protonated phosphazeniums as hydrogen-bond donor groups was demonstrated in the solid state and in solution. In particular, their catalytic activity was shown in the activation of the carbonyl group within cyclic esters, using a benchmark reaction, that is, in the ring-opening polymerization of lactide and valerolactone, in the presence of a basic co-catalyst. The reactions proceed differently depending on monomers and/or phosphazenium salts. The impact of catalysts and reactants steric hindrance upon the outcome of the reaction is then highlighted and discussed