@article{LeschKoenigKounevetal.2022, author = {Lesch, Veronika and K{\"o}nig, Maximilian and Kounev, Samuel and Stein, Anthony and Krupitzer, Christian}, title = {Tackling the rich vehicle routing problem with nature-inspired algorithms}, series = {Applied Intelligence}, volume = {52}, journal = {Applied Intelligence}, issn = {1573-7497}, doi = {10.1007/s10489-021-03035-5}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-268942}, pages = {9476-9500}, year = {2022}, abstract = {In the last decades, the classical Vehicle Routing Problem (VRP), i.e., assigning a set of orders to vehicles and planning their routes has been intensively researched. As only the assignment of order to vehicles and their routes is already an NP-complete problem, the application of these algorithms in practice often fails to take into account the constraints and restrictions that apply in real-world applications, the so called rich VRP (rVRP) and are limited to single aspects. In this work, we incorporate the main relevant real-world constraints and requirements. We propose a two-stage strategy and a Timeline algorithm for time windows and pause times, and apply a Genetic Algorithm (GA) and Ant Colony Optimization (ACO) individually to the problem to find optimal solutions. Our evaluation of eight different problem instances against four state-of-the-art algorithms shows that our approach handles all given constraints in a reasonable time.}, language = {en} } @techreport{DeutschmannHielscherGerman2022, type = {Working Paper}, author = {Deutschmann, J{\"o}rg and Hielscher, Kai-Steffen and German, Reinhard}, title = {Next-Generation Satellite Communication Networks}, series = {W{\"u}rzburg Workshop on Next-Generation Communication Networks (WueWoWas'22)}, journal = {W{\"u}rzburg Workshop on Next-Generation Communication Networks (WueWoWas'22)}, doi = {10.25972/OPUS-28073}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-280732}, pages = {4}, year = {2022}, abstract = {This paper gives an overview of our recent activities in the field of satellite communication networks, including an introduction to geostationary satellite systems and Low Earth Orbit megaconstellations. To mitigate the high latencies of geostationary satellite networks, TCP-splitting Performance Enhancing Proxies are deployed. However, these cannot be applied in the case of encrypted transport headers as it is the case for VPNs or QUIC. We summarize performance evaluation results from multiple measurement campaigns. In a recently concluded project, multipath communication was used to combine the advantages of very heterogeneous communication paths: low data rate, low latency (e.g., DSL light) and high data rate, high latency (e.g., geostationary satellite).}, subject = {Datennetz}, language = {en} } @techreport{RieglerWernerKayal2022, type = {Working Paper}, author = {Riegler, Clemens and Werner, Lennart and Kayal, Hakan}, title = {MAPLE: Marsian Autorotation Probe Lander Experiment}, doi = {10.25972/OPUS-28239}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-282390}, pages = {7}, year = {2022}, abstract = {The first step towards aerial planetary exploration has been made. Ingenuity shows extremely promising results, and new missions are already underway. Rotorcraft are capable of flight. This capability could be utilized to support the last stages of Entry, Descent, and Landing. Thus, mass and complexity could be scaled down. Autorotation is one method of descent. It describes unpowered descent and landing, typically performed by helicopters in case of an engine failure. MAPLE is suggested to test these procedures and understand autorotation on other planets. In this series of experiments, the Ingenuity helicopter is utilized. Ingenuity would autorotate a "mid-air-landing" before continuing with normal flight. Ultimately, the collected data shall help to understand autorotation on Mars and its utilization for interplanetary exploration.}, language = {en} } @article{ReinhardHelmerichBorasetal.2022, author = {Reinhard, Sebastian and Helmerich, Dominic A. and Boras, Dominik and Sauer, Markus and Kollmannsberger, Philip}, title = {ReCSAI: recursive compressed sensing artificial intelligence for confocal lifetime localization microscopy}, series = {BMC Bioinformatics}, volume = {23}, journal = {BMC Bioinformatics}, number = {1}, doi = {10.1186/s12859-022-05071-5}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-299768}, year = {2022}, abstract = {Background Localization-based super-resolution microscopy resolves macromolecular structures down to a few nanometers by computationally reconstructing fluorescent emitter coordinates from diffraction-limited spots. The most commonly used algorithms are based on fitting parametric models of the point spread function (PSF) to a measured photon distribution. These algorithms make assumptions about the symmetry of the PSF and thus, do not work well with irregular, non-linear PSFs that occur for example in confocal lifetime imaging, where a laser is scanned across the sample. An alternative method for reconstructing sparse emitter sets from noisy, diffraction-limited images is compressed sensing, but due to its high computational cost it has not yet been widely adopted. Deep neural network fitters have recently emerged as a new competitive method for localization microscopy. They can learn to fit arbitrary PSFs, but require extensive simulated training data and do not generalize well. A method to efficiently fit the irregular PSFs from confocal lifetime localization microscopy combining the advantages of deep learning and compressed sensing would greatly improve the acquisition speed and throughput of this method. Results Here we introduce ReCSAI, a compressed sensing neural network to reconstruct localizations for confocal dSTORM, together with a simulation tool to generate training data. We implemented and compared different artificial network architectures, aiming to combine the advantages of compressed sensing and deep learning. We found that a U-Net with a recursive structure inspired by iterative compressed sensing showed the best results on realistic simulated datasets with noise, as well as on real experimentally measured confocal lifetime scanning data. Adding a trainable wavelet denoising layer as prior step further improved the reconstruction quality. Conclusions Our deep learning approach can reach a similar reconstruction accuracy for confocal dSTORM as frame binning with traditional fitting without requiring the acquisition of multiple frames. In addition, our work offers generic insights on the reconstruction of sparse measurements from noisy experimental data by combining compressed sensing and deep learning. We provide the trained networks, the code for network training and inference as well as the simulation tool as python code and Jupyter notebooks for easy reproducibility.}, language = {en} } @techreport{RieglerKayal2022, type = {Working Paper}, author = {Riegler, Clemens and Kayal, Hakan}, title = {VELEX: Venus Lightning Experiment}, doi = {10.25972/OPUS-28248}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-282481}, pages = {6}, year = {2022}, abstract = {Lightning has fascinated humanity since the beginning of our existence. Different types of lightning like sprites and blue jets were discovered, and many more are theorized. However, it is very likely that these phenomena are not exclusive to our home planet. Venus's dense and active atmosphere is a place where lightning is to be expected. Missions like Venera, Pioneer, and Galileo have carried instruments to measure electromagnetic activity. These measurements have indeed delivered results. However, these results are not clear. They could be explained by other effects like cosmic rays, plasma noise, or spacecraft noise. Furthermore, these lightning seem different from those we know from our home planet. In order to tackle these issues, a different approach to measurement is proposed. When multiple devices in different spacecraft or locations can measure the same atmospheric discharge, most other explanations become increasingly less likely. Thus, the suggested instrument and method of VELEX incorporates multiple spacecraft. With this approach, the question about the existence of lightning on Venus could be settled.}, language = {en} } @techreport{ElsayedRizk2022, type = {Working Paper}, author = {Elsayed, Karim and Rizk, Amr}, title = {Response Times in Time-to-Live Caching Hierarchies under Random Network Delays}, series = {W{\"u}rzburg Workshop on Next-Generation Communication Networks (WueWoWas'22)}, journal = {W{\"u}rzburg Workshop on Next-Generation Communication Networks (WueWoWas'22)}, doi = {10.25972/OPUS-28084}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-280843}, pages = {4}, year = {2022}, abstract = {Time-to-Live (TTL) caches decouple the occupancy of objects in cache through object-specific validity timers. Stateof- the art techniques provide exact methods for the calculation of object-specific hit probabilities given entire cache hierarchies with random inter-cache network delays. The system hit probability is a provider-centric metric as it relates to the origin offload, i.e., the decrease in the number of requests that are served by the content origin server. In this paper we consider a user-centric metric, i.e., the response time, which is shown to be structurally different from the system hit probability. Equipped with the state-of-theart exact modeling technique using Markov-arrival processes we derive expressions for the expected object response time and pave a way for its optimization under network delays.}, subject = {Datennetz}, language = {en} } @techreport{AlfredssonKasslerVestinetal.2022, type = {Working Paper}, author = {Alfredsson, Rebecka and Kassler, Andreas and Vestin, Jonathan and Pieska, Marcus and Amend, Markus}, title = {Accelerating a Transport Layer based 5G Multi-Access Proxy on SmartNIC}, series = {W{\"u}rzburg Workshop on Next-Generation Communication Networks (WueWoWas'22)}, journal = {W{\"u}rzburg Workshop on Next-Generation Communication Networks (WueWoWas'22)}, doi = {10.25972/OPUS-28079}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-280798}, pages = {4}, year = {2022}, abstract = {Utilizing multiple access technologies such as 5G, 4G, and Wi-Fi within a coherent framework is currently standardized by 3GPP within 5G ATSSS. Indeed, distributing packets over multiple networks can lead to increased robustness, resiliency and capacity. A key part of such a framework is the multi-access proxy, which transparently distributes packets over multiple paths. As the proxy needs to serve thousands of customers, scalability and performance are crucial for operator deployments. In this paper, we leverage recent advancements in data plane programming, implement a multi-access proxy based on the MP-DCCP tunneling approach in P4 and hardware accelerate it by deploying the pipeline on a smartNIC. This is challenging due to the complex scheduling and congestion control operations involved. We present our pipeline and data structures design for congestion control and packet scheduling state management. Initial measurements in our testbed show that packet latency is in the range of 25 μs demonstrating the feasibility of our approach.}, subject = {Datennetz}, language = {en} } @article{BencurovaShityakovSchaacketal.2022, author = {Bencurova, Elena and Shityakov, Sergey and Schaack, Dominik and Kaltdorf, Martin and Sarukhanyan, Edita and Hilgarth, Alexander and Rath, Christin and Montenegro, Sergio and Roth, G{\"u}nter and Lopez, Daniel and Dandekar, Thomas}, title = {Nanocellulose composites as smart devices with chassis, light-directed DNA Storage, engineered electronic properties, and chip integration}, series = {Frontiers in Bioengineering and Biotechnology}, volume = {10}, journal = {Frontiers in Bioengineering and Biotechnology}, issn = {2296-4185}, doi = {10.3389/fbioe.2022.869111}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-283033}, year = {2022}, abstract = {The rapid development of green and sustainable materials opens up new possibilities in the field of applied research. Such materials include nanocellulose composites that can integrate many components into composites and provide a good chassis for smart devices. In our study, we evaluate four approaches for turning a nanocellulose composite into an information storage or processing device: 1) nanocellulose can be a suitable carrier material and protect information stored in DNA. 2) Nucleotide-processing enzymes (polymerase and exonuclease) can be controlled by light after fusing them with light-gating domains; nucleotide substrate specificity can be changed by mutation or pH change (read-in and read-out of the information). 3) Semiconductors and electronic capabilities can be achieved: we show that nanocellulose is rendered electronic by iodine treatment replacing silicon including microstructures. Nanocellulose semiconductor properties are measured, and the resulting potential including single-electron transistors (SET) and their properties are modeled. Electric current can also be transported by DNA through G-quadruplex DNA molecules; these as well as classical silicon semiconductors can easily be integrated into the nanocellulose composite. 4) To elaborate upon miniaturization and integration for a smart nanocellulose chip device, we demonstrate pH-sensitive dyes in nanocellulose, nanopore creation, and kinase micropatterning on bacterial membranes as well as digital PCR micro-wells. Future application potential includes nano-3D printing and fast molecular processors (e.g., SETs) integrated with DNA storage and conventional electronics. This would also lead to environment-friendly nanocellulose chips for information processing as well as smart nanocellulose composites for biomedical applications and nano-factories.}, language = {en} } @article{KrenzerMakowskiHekaloetal.2022, author = {Krenzer, Adrian and Makowski, Kevin and Hekalo, Amar and Fitting, Daniel and Troya, Joel and Zoller, Wolfram G. and Hann, Alexander and Puppe, Frank}, title = {Fast machine learning annotation in the medical domain: a semi-automated video annotation tool for gastroenterologists}, series = {BioMedical Engineering OnLine}, volume = {21}, journal = {BioMedical Engineering OnLine}, number = {1}, doi = {10.1186/s12938-022-01001-x}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-300231}, year = {2022}, abstract = {Background Machine learning, especially deep learning, is becoming more and more relevant in research and development in the medical domain. For all the supervised deep learning applications, data is the most critical factor in securing successful implementation and sustaining the progress of the machine learning model. Especially gastroenterological data, which often involves endoscopic videos, are cumbersome to annotate. Domain experts are needed to interpret and annotate the videos. To support those domain experts, we generated a framework. With this framework, instead of annotating every frame in the video sequence, experts are just performing key annotations at the beginning and the end of sequences with pathologies, e.g., visible polyps. Subsequently, non-expert annotators supported by machine learning add the missing annotations for the frames in-between. Methods In our framework, an expert reviews the video and annotates a few video frames to verify the object's annotations for the non-expert. In a second step, a non-expert has visual confirmation of the given object and can annotate all following and preceding frames with AI assistance. After the expert has finished, relevant frames will be selected and passed on to an AI model. This information allows the AI model to detect and mark the desired object on all following and preceding frames with an annotation. Therefore, the non-expert can adjust and modify the AI predictions and export the results, which can then be used to train the AI model. Results Using this framework, we were able to reduce workload of domain experts on average by a factor of 20 on our data. This is primarily due to the structure of the framework, which is designed to minimize the workload of the domain expert. Pairing this framework with a state-of-the-art semi-automated AI model enhances the annotation speed further. Through a prospective study with 10 participants, we show that semi-automated annotation using our tool doubles the annotation speed of non-expert annotators compared to a well-known state-of-the-art annotation tool. Conclusion In summary, we introduce a framework for fast expert annotation for gastroenterologists, which reduces the workload of the domain expert considerably while maintaining a very high annotation quality. The framework incorporates a semi-automated annotation system utilizing trained object detection models. The software and framework are open-source.}, language = {en} } @article{KlemzRote2022, author = {Klemz, Boris and Rote, G{\"u}nter}, title = {Linear-Time Algorithms for Maximum-Weight Induced Matchings and Minimum Chain Covers in Convex Bipartite Graphs}, series = {Algorithmica}, volume = {84}, journal = {Algorithmica}, number = {4}, issn = {1432-0541}, doi = {10.1007/s00453-021-00904-w}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-267876}, pages = {1064-1080}, year = {2022}, abstract = {A bipartite graph G=(U,V,E) is convex if the vertices in V can be linearly ordered such that for each vertex u∈U, the neighbors of u are consecutive in the ordering of V. An induced matching H of G is a matching for which no edge of E connects endpoints of two different edges of H. We show that in a convex bipartite graph with n vertices and m weighted edges, an induced matching of maximum total weight can be computed in O(n+m) time. An unweighted convex bipartite graph has a representation of size O(n) that records for each vertex u∈U the first and last neighbor in the ordering of V. Given such a compact representation, we compute an induced matching of maximum cardinality in O(n) time. In convex bipartite graphs, maximum-cardinality induced matchings are dual to minimum chain covers. A chain cover is a covering of the edge set by chain subgraphs, that is, subgraphs that do not contain induced matchings of more than one edge. Given a compact representation, we compute a representation of a minimum chain cover in O(n) time. If no compact representation is given, the cover can be computed in O(n+m) time. All of our algorithms achieve optimal linear running time for the respective problem and model, and they improve and generalize the previous results in several ways: The best algorithms for the unweighted problem versions had a running time of O(n\(^{2}\)) (Brandst{\"a}dt et al. in Theor. Comput. Sci. 381(1-3):260-265, 2007. https://doi.org/10.1016/j.tcs.2007.04.006). The weighted case has not been considered before.}, language = {en} }