@article{PrasseIversenLienhardetal.2022, author = {Prasse, Paul and Iversen, Pascal and Lienhard, Matthias and Thedinga, Kristina and Herwig, Ralf and Scheffer, Tobias}, title = {Pre-Training on In Vitro and Fine-Tuning on Patient-Derived Data Improves Deep Neural Networks for Anti-Cancer Drug-Sensitivity Prediction}, series = {MDPI}, volume = {14}, journal = {MDPI}, edition = {16}, publisher = {MDPI}, address = {Basel, Schweiz}, issn = {2072-6694}, doi = {10.3390/cancers14163950}, pages = {1 -- 14}, year = {2022}, abstract = {Large-scale databases that report the inhibitory capacities of many combinations of candidate drug compounds and cultivated cancer cell lines have driven the development of preclinical drug-sensitivity models based on machine learning. However, cultivated cell lines have devolved from human cancer cells over years or even decades under selective pressure in culture conditions. Moreover, models that have been trained on in vitro data cannot account for interactions with other types of cells. Drug-response data that are based on patient-derived cell cultures, xenografts, and organoids, on the other hand, are not available in the quantities that are needed to train high-capacity machine-learning models. We found that pre-training deep neural network models of drug sensitivity on in vitro drug-sensitivity databases before fine-tuning the model parameters on patient-derived data improves the models' accuracy and improves the biological plausibility of the features, compared to training only on patient-derived data. From our experiments, we can conclude that pre-trained models outperform models that have been trained on the target domains in the vast majority of cases.}, language = {en} } @article{Hecher2022, author = {Hecher, Markus}, title = {Treewidth-aware reductions of normal ASP to SAT}, series = {Artificial intelligence}, volume = {304}, journal = {Artificial intelligence}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0004-3702}, doi = {10.1016/j.artint.2021.103651}, pages = {24}, year = {2022}, abstract = {Answer Set Programming (ASP) is a paradigm for modeling and solving problems for knowledge representation and reasoning. There are plenty of results dedicated to studying the hardness of (fragments of) ASP. So far, these studies resulted in characterizations in terms of computational complexity as well as in fine-grained insights presented in form of dichotomy-style results, lower bounds when translating to other formalisms like propositional satisfiability (SAT), and even detailed parameterized complexity landscapes. A generic parameter in parameterized complexity originating from graph theory is the socalled treewidth, which in a sense captures structural density of a program. Recently, there was an increase in the number of treewidth-based solvers related to SAT. While there are translations from (normal) ASP to SAT, no reduction that preserves treewidth or at least keeps track of the treewidth increase is known. In this paper we propose a novel reduction from normal ASP to SAT that is aware of the treewidth, and guarantees that a slight increase of treewidth is indeed sufficient. Further, we show a new result establishing that, when considering treewidth, already the fragment of normal ASP is slightly harder than SAT (under reasonable assumptions in computational complexity). This also confirms that our reduction probably cannot be significantly improved and that the slight increase of treewidth is unavoidable. Finally, we present an empirical study of our novel reduction from normal ASP to SAT, where we compare treewidth upper bounds that are obtained via known decomposition heuristics. Overall, our reduction works better with these heuristics than existing translations. (c) 2021 Elsevier B.V. All rights reserved.}, language = {en} } @article{AlLabanRegerLucke2022, author = {Al Laban, Firas and Reger, Martin and Lucke, Ulrike}, title = {Closing the Policy Gap in the Academic Bridge}, series = {Education sciences}, volume = {12}, journal = {Education sciences}, number = {12}, publisher = {MDPI}, address = {Basel}, issn = {2227-7102}, doi = {10.3390/educsci12120930}, year = {2022}, abstract = {The highly structured nature of the educational sector demands effective policy mechanisms close to the needs of the field. That is why evidence-based policy making, endorsed by the European Commission under Erasmus+ Key Action 3, aims to make an alignment between the domains of policy and practice. Against this background, this article addresses two issues: First, that there is a vertical gap in the translation of higher-level policies to local strategies and regulations. Second, that there is a horizontal gap between educational domains regarding the policy awareness of individual players. This was analyzed in quantitative and qualitative studies with domain experts from the fields of virtual mobility and teacher training. From our findings, we argue that the combination of both gaps puts the academic bridge from secondary to tertiary education at risk, including the associated knowledge proficiency levels. We discuss the role of digitalization in the academic bridge by asking the question: which value does the involved stakeholders expect from educational policies? As a theoretical basis, we rely on the model of value co-creation for and by stakeholders. We describe the used instruments along with the obtained results and proposed benefits. Moreover, we reflect on the methodology applied, and we finally derive recommendations for future academic bridge policies.}, language = {en} } @article{LorenzClemensSchroetteretal.2022, author = {Lorenz, Claas and Clemens, Vera Elisabeth and Schr{\"o}tter, Max and Schnor, Bettina}, title = {Continuous verification of network security compliance}, series = {IEEE transactions on network and service management}, volume = {19}, journal = {IEEE transactions on network and service management}, number = {2}, publisher = {Institute of Electrical and Electronics Engineers}, address = {New York}, issn = {1932-4537}, doi = {10.1109/TNSM.2021.3130290}, pages = {1729 -- 1745}, year = {2022}, abstract = {Continuous verification of network security compliance is an accepted need. Especially, the analysis of stateful packet filters plays a central role for network security in practice. But the few existing tools which support the analysis of stateful packet filters are based on general applicable formal methods like Satifiability Modulo Theories (SMT) or theorem prover and show runtimes in the order of minutes to hours making them unsuitable for continuous compliance verification. In this work, we address these challenges and present the concept of state shell interweaving to transform a stateful firewall rule set into a stateless rule set. This allows us to reuse any fast domain specific engine from the field of data plane verification tools leveraging smart, very fast, and domain specialized data structures and algorithms including Header Space Analysis (HSA). First, we introduce the formal language FPL that enables a high-level human-understandable specification of the desired state of network security. Second, we demonstrate the instantiation of a compliance process using a verification framework that analyzes the configuration of complex networks and devices - including stateful firewalls - for compliance with FPL policies. Our evaluation results show the scalability of the presented approach for the well known Internet2 and Stanford benchmarks as well as for large firewall rule sets where it outscales state-of-the-art tools by a factor of over 41.}, language = {en} } @article{PrasseIversenLienhardetal.2022, author = {Prasse, Paul and Iversen, Pascal and Lienhard, Matthias and Thedinga, Kristina and Bauer, Christopher and Herwig, Ralf and Scheffer, Tobias}, title = {Matching anticancer compounds and tumor cell lines by neural networks with ranking loss}, series = {NAR: genomics and bioinformatics}, volume = {4}, journal = {NAR: genomics and bioinformatics}, number = {1}, publisher = {Oxford Univ. Press}, address = {Oxford}, issn = {2631-9268}, doi = {10.1093/nargab/lqab128}, pages = {10}, year = {2022}, abstract = {Computational drug sensitivity models have the potential to improve therapeutic outcomes by identifying targeted drug components that are likely to achieve the highest efficacy for a cancer cell line at hand at a therapeutic dose. State of the art drug sensitivity models use regression techniques to predict the inhibitory concentration of a drug for a tumor cell line. This regression objective is not directly aligned with either of these principal goals of drug sensitivity models: We argue that drug sensitivity modeling should be seen as a ranking problem with an optimization criterion that quantifies a drug's inhibitory capacity for the cancer cell line at hand relative to its toxicity for healthy cells. We derive an extension to the well-established drug sensitivity regression model PaccMann that employs a ranking loss and focuses on the ratio of inhibitory concentration and therapeutic dosage range. We find that the ranking extension significantly enhances the model's capability to identify the most effective anticancer drugs for unseen tumor cell profiles based in on in-vitro data.}, language = {en} } @article{SteinertStabernack2022, author = {Steinert, Fritjof and Stabernack, Benno}, title = {Architecture of a low latency H.264/AVC video codec for robust ML based image classification how region of interests can minimize the impact of coding artifacts}, series = {Journal of Signal Processing Systems for Signal, Image, and Video Technology}, volume = {94}, journal = {Journal of Signal Processing Systems for Signal, Image, and Video Technology}, number = {7}, publisher = {Springer}, address = {New York}, issn = {1939-8018}, doi = {10.1007/s11265-021-01727-2}, pages = {693 -- 708}, year = {2022}, abstract = {The use of neural networks is considered as the state of the art in the field of image classification. A large number of different networks are available for this purpose, which, appropriately trained, permit a high level of classification accuracy. Typically, these networks are applied to uncompressed image data, since a corresponding training was also carried out using image data of similar high quality. However, if image data contains image errors, the classification accuracy deteriorates drastically. This applies in particular to coding artifacts which occur due to image and video compression. Typical application scenarios for video compression are narrowband transmission channels for which video coding is required but a subsequent classification is to be carried out on the receiver side. In this paper we present a special H.264/Advanced Video Codec (AVC) based video codec that allows certain regions of a picture to be coded with near constant picture quality in order to allow a reliable classification using neural networks, whereas the remaining image will be coded using constant bit rate. We have combined this feature with the ability to run with lowest latency properties, which is usually also required in remote control applications scenarios. The codec has been implemented as a fully hardwired High Definition video capable hardware architecture which is suitable for Field Programmable Gate Arrays.}, language = {en} } @article{BreitenreiterAndjelkovićSchrapeetal.2022, author = {Breitenreiter, Anselm and Andjelković, Marko and Schrape, Oliver and Krstić, Miloš}, title = {Fast error propagation probability estimates by answer set programming and approximate model counting}, series = {IEEE Access}, volume = {10}, journal = {IEEE Access}, publisher = {Inst. of Electr. and Electronics Engineers}, address = {Piscataway}, issn = {2169-3536}, doi = {10.1109/ACCESS.2022.3174564}, pages = {51814 -- 51825}, year = {2022}, abstract = {We present a method employing Answer Set Programming in combination with Approximate Model Counting for fast and accurate calculation of error propagation probabilities in digital circuits. By an efficient problem encoding, we achieve an input data format similar to a Verilog netlist so that extensive preprocessing is avoided. By a tight interconnection of our application with the underlying solver, we avoid iterating over fault sites and reduce calls to the solver. Several circuits were analyzed with varying numbers of considered cycles and different degrees of approximation. Our experiments show, that the runtime can be reduced by approximation by a factor of 91, whereas the error compared to the exact result is below 1\%.}, language = {en} } @article{MarcoFigueraRiedelRossietal.2022, author = {Marco Figuera, Ramiro and Riedel, Christian and Rossi, Angelo Pio and Unnithan, Vikram}, title = {Depth to diameter analysis on small simple craters at the lunar south pole - possible implications for ice harboring}, series = {Remote sensing}, volume = {14}, journal = {Remote sensing}, number = {3}, publisher = {MDPI}, address = {Basel}, issn = {2072-4292}, doi = {10.3390/rs14030450}, pages = {13}, year = {2022}, abstract = {In this paper, we present a study comparing the depth to diameter (d/D) ratio of small simple craters (200-1000 m) of an area between -88.5 degrees to -90 degrees latitude at the lunar south pole containing Permanent Shadowed Regions (PSRs) versus craters without PSRs. As PSRs can reach temperatures of 110 K and are capable of harboring volatiles, especially water ice, we analyzed the relationship of depth versus diameter ratios and its possible implications for harboring water ice. Variations in the d/D ratios can also be caused by other processes such as degradation, isostatic adjustment, or differences in surface properties. The conducted d/D ratio analysis suggests that a differentiation between craters containing PSRs versus craters without PSRs occurs. Thus, a possible direct relation between d/D ratio, PSRs, and water ice harboring might exist. Our results suggest that differences in the target's surface properties may explain the obtained results. The resulting d/D ratios of craters with PSRs can help to select target areas for future In-Situ Resource Utilization (ISRU) missions.}, language = {en} } @article{ChenLangeAndjelkovicetal.2022, author = {Chen, Junchao and Lange, Thomas and Andjelkovic, Marko and Simevski, Aleksandar and Lu, Li and Krstic, Milos}, title = {Solar particle event and single event upset prediction from SRAM-based monitor and supervised machine learning}, series = {IEEE transactions on emerging topics in computing / IEEE Computer Society, Institute of Electrical and Electronics Engineers}, volume = {10}, journal = {IEEE transactions on emerging topics in computing / IEEE Computer Society, Institute of Electrical and Electronics Engineers}, number = {2}, publisher = {Institute of Electrical and Electronics Engineers}, address = {[New York, NY]}, issn = {2168-6750}, doi = {10.1109/TETC.2022.3147376}, pages = {564 -- 580}, year = {2022}, abstract = {The intensity of cosmic radiation may differ over five orders of magnitude within a few hours or days during the Solar Particle Events (SPEs), thus increasing for several orders of magnitude the probability of Single Event Upsets (SEUs) in space-borne electronic systems. Therefore, it is vital to enable the early detection of the SEU rate changes in order to ensure timely activation of dynamic radiation hardening measures. In this paper, an embedded approach for the prediction of SPEs and SRAM SEU rate is presented. The proposed solution combines the real-time SRAM-based SEU monitor, the offline-trained machine learning model and online learning algorithm for the prediction. With respect to the state-of-the-art, our solution brings the following benefits: (1) Use of existing on-chip data storage SRAM as a particle detector, thus minimizing the hardware and power overhead, (2) Prediction of SRAM SEU rate one hour in advance, with the fine-grained hourly tracking of SEU variations during SPEs as well as under normal conditions, (3) Online optimization of the prediction model for enhancing the prediction accuracy during run-time, (4) Negligible cost of hardware accelerator design for the implementation of selected machine learning model and online learning algorithm. The proposed design is intended for a highly dependable and self-adaptive multiprocessing system employed in space applications, allowing to trigger the radiation mitigation mechanisms before the onset of high radiation levels.}, language = {en} } @article{AbdelwahabLandwehr2022, author = {Abdelwahab, Ahmed and Landwehr, Niels}, title = {Deep Distributional Sequence Embeddings Based on a Wasserstein Loss}, series = {Neural processing letters}, journal = {Neural processing letters}, publisher = {Springer}, address = {Dordrecht}, issn = {1370-4621}, doi = {10.1007/s11063-022-10784-y}, pages = {21}, year = {2022}, abstract = {Deep metric learning employs deep neural networks to embed instances into a metric space such that distances between instances of the same class are small and distances between instances from different classes are large. In most existing deep metric learning techniques, the embedding of an instance is given by a feature vector produced by a deep neural network and Euclidean distance or cosine similarity defines distances between these vectors. This paper studies deep distributional embeddings of sequences, where the embedding of a sequence is given by the distribution of learned deep features across the sequence. The motivation for this is to better capture statistical information about the distribution of patterns within the sequence in the embedding. When embeddings are distributions rather than vectors, measuring distances between embeddings involves comparing their respective distributions. The paper therefore proposes a distance metric based on Wasserstein distances between the distributions and a corresponding loss function for metric learning, which leads to a novel end-to-end trainable embedding model. We empirically observe that distributional embeddings outperform standard vector embeddings and that training with the proposed Wasserstein metric outperforms training with other distance functions.}, language = {en} }