TY - JOUR A1 - Fandiño, Jorge A1 - Lifschitz, Vladimir A1 - Lühne, Patrick A1 - Schaub, Torsten H. T1 - Verifying tight logic programs with Anthem and Vampire JF - Theory and practice of logic programming N2 - This paper continues the line of research aimed at investigating the relationship between logic programs and first-order theories. We extend the definition of program completion to programs with input and output in a subset of the input language of the ASP grounder gringo, study the relationship between stable models and completion in this context, and describe preliminary experiments with the use of two software tools, anthem and vampire, for verifying the correctness of programs with input and output. Proofs of theorems are based on a lemma that relates the semantics of programs studied in this paper to stable models of first-order formulas. Y1 - 2020 U6 - https://doi.org/10.1017/S1471068420000344 SN - 1471-0684 SN - 1475-3081 VL - 20 IS - 5 SP - 735 EP - 750 PB - Cambridge Univ. Press CY - Cambridge [u.a.] ER - TY - JOUR A1 - Cabalar, Pedro A1 - Fandiño, Jorge A1 - Garea, Javier A1 - Romero, Javier A1 - Schaub, Torsten H. T1 - Eclingo BT - a solver for epistemic logic programs JF - Theory and practice of logic programming N2 - We describe eclingo, a solver for epistemic logic programs under Gelfond 1991 semantics built upon the Answer Set Programming system clingo. The input language of eclingo uses the syntax extension capabilities of clingo to define subjective literals that, as usual in epistemic logic programs, allow for checking the truth of a regular literal in all or in some of the answer sets of a program. The eclingo solving process follows a guess and check strategy. It first generates potential truth values for subjective literals and, in a second step, it checks the obtained result with respect to the cautious and brave consequences of the program. This process is implemented using the multi-shot functionalities of clingo. We have also implemented some optimisations, aiming at reducing the search space and, therefore, increasing eclingo 's efficiency in some scenarios. Finally, we compare the efficiency of eclingo with two state-of-the-art solvers for epistemic logic programs on a pair of benchmark scenarios and show that eclingo generally outperforms their obtained results. KW - Answer Set Programming KW - Epistemic Logic Programs KW - Non-Monotonic KW - Reasoning KW - Conformant Planning Y1 - 2020 U6 - https://doi.org/10.1017/S1471068420000228 SN - 1471-0684 SN - 1475-3081 VL - 20 IS - 6 SP - 834 EP - 847 PB - Cambridge Univ. Press CY - New York ER - TY - JOUR A1 - Bordihn, Henning A1 - Vaszil, György T1 - Reversible parallel communicating finite automata systems JF - Acta informatica N2 - We study the concept of reversibility in connection with parallel communicating systems of finite automata (PCFA in short). We define the notion of reversibility in the case of PCFA (also covering the non-deterministic case) and discuss the relationship of the reversibility of the systems and the reversibility of its components. We show that a system can be reversible with non-reversible components, and the other way around, the reversibility of the components does not necessarily imply the reversibility of the system as a whole. We also investigate the computational power of deterministic centralized reversible PCFA. We show that these very simple types of PCFA (returning or non-returning) can recognize regular languages which cannot be accepted by reversible (deterministic) finite automata, and that they can even accept languages that are not context-free. We also separate the deterministic and non-deterministic variants in the case of systems with non-returning communication. We show that there are languages accepted by non-deterministic centralized PCFA, which cannot be recognized by any deterministic variant of the same type. KW - Finite automata KW - Reversibility KW - Systems of parallel communicating KW - automata Y1 - 2021 U6 - https://doi.org/10.1007/s00236-021-00396-9 SN - 0001-5903 SN - 1432-0525 VL - 58 IS - 4 SP - 263 EP - 279 PB - Springer CY - Berlin ; Heidelberg ; New York, NY ER - TY - JOUR A1 - Bordihn, Henning A1 - Holzer, Markus T1 - On the number of active states in finite automata JF - Acta informatica N2 - We introduce a new measure of descriptional complexity on finite automata, called the number of active states. Roughly speaking, the number of active states of an automaton A on input w counts the number of different states visited during the most economic computation of the automaton A for the word w. This concept generalizes to finite automata and regular languages in a straightforward way. We show that the number of active states of both finite automata and regular languages is computable, even with respect to nondeterministic finite automata. We further compare the number of active states to related measures for regular languages. In particular, we show incomparability to the radius of regular languages and that the difference between the number of active states and the total number of states needed in finite automata for a regular language can be of exponential order. Y1 - 2021 U6 - https://doi.org/10.1007/s00236-021-00397-8 SN - 0001-5903 SN - 1432-0525 VL - 58 IS - 4 SP - 301 EP - 318 PB - Springer CY - Berlin ; Heidelberg [u.a.] ER - TY - JOUR A1 - Kreowsky, Philipp A1 - Stabernack, Christian Benno T1 - A full-featured FPGA-based pipelined architecture for SIFT extraction JF - IEEE access : practical research, open solutions / Institute of Electrical and Electronics Engineers N2 - Image feature detection is a key task in computer vision. Scale Invariant Feature Transform (SIFT) is a prevalent and well known algorithm for robust feature detection. However, it is computationally demanding and software implementations are not applicable for real-time performance. In this paper, a versatile and pipelined hardware implementation is proposed, that is capable of computing keypoints and rotation invariant descriptors on-chip. All computations are performed in single precision floating-point format which makes it possible to implement the original algorithm with little alteration. Various rotation resolutions and filter kernel sizes are supported for images of any resolution up to ultra-high definition. For full high definition images, 84 fps can be processed. Ultra high definition images can be processed at 21 fps. KW - Field programmable gate arrays KW - Convolution KW - Signal processing KW - algorithms KW - Kernel KW - Image resolution KW - Histograms KW - Feature extraction KW - Scale-invariant feature transform (SIFT) KW - field-programmable gate array KW - (FPGA) KW - image processing KW - computer vision KW - parallel processing KW - architecture KW - real-time KW - hardware architecture Y1 - 2021 U6 - https://doi.org/10.1109/ACCESS.2021.3104387 SN - 2169-3536 VL - 9 SP - 128564 EP - 128573 PB - Inst. of Electr. and Electronics Engineers CY - New York, NY ER - TY - JOUR A1 - Bauer, Chris A1 - Herwig, Ralf A1 - Lienhard, Matthias A1 - Prasse, Paul A1 - Scheffer, Tobias A1 - Schuchhardt, Johannes T1 - Large-scale literature mining to assess the relation between anti-cancer drugs and cancer types JF - Journal of translational medicine N2 - Background: There is a huge body of scientific literature describing the relation between tumor types and anti-cancer drugs. The vast amount of scientific literature makes it impossible for researchers and physicians to extract all relevant information manually. Methods: In order to cope with the large amount of literature we applied an automated text mining approach to assess the relations between 30 most frequent cancer types and 270 anti-cancer drugs. We applied two different approaches, a classical text mining based on named entity recognition and an AI-based approach employing word embeddings. The consistency of literature mining results was validated with 3 independent methods: first, using data from FDA approvals, second, using experimentally measured IC-50 cell line data and third, using clinical patient survival data. Results: We demonstrated that the automated text mining was able to successfully assess the relation between cancer types and anti-cancer drugs. All validation methods showed a good correspondence between the results from literature mining and independent confirmatory approaches. The relation between most frequent cancer types and drugs employed for their treatment were visualized in a large heatmap. All results are accessible in an interactive web-based knowledge base using the following link: . Conclusions: Our approach is able to assess the relations between compounds and cancer types in an automated manner. Both, cancer types and compounds could be grouped into different clusters. Researchers can use the interactive knowledge base to inspect the presented results and follow their own research questions, for example the identification of novel indication areas for known drugs. KW - Literature mining KW - Anti-cancer drugs KW - Tumor types KW - Word embeddings KW - Database Y1 - 2021 U6 - https://doi.org/10.1186/s12967-021-02941-z SN - 1479-5876 VL - 19 IS - 1 PB - BioMed Central CY - London ER - TY - JOUR A1 - Huang, Yizhen A1 - Richter, Eric A1 - Kleickmann, Thilo A1 - Wiepke, Axel A1 - Richter, Dirk T1 - Classroom complexity affects student teachers’ behavior in a VR classroom JF - Computers & education : an international journal N2 - Student teachers often struggle to keep track of everything that is happening in the classroom, and particularly to notice and respond when students cause disruptions. The complexity of the classroom environment is a potential contributing factor that has not been empirically tested. In this experimental study, we utilized a virtual reality (VR) classroom to examine whether classroom complexity affects the likelihood of student teachers noticing disruptions and how they react after noticing. Classroom complexity was operationalized as the number of disruptions and the existence of overlapping disruptions (multidimensionality) as well as the existence of parallel teaching tasks (simultaneity). Results showed that student teachers (n = 50) were less likely to notice the scripted disruptions, and also less likely to respond to the disruptions in a comprehensive and effortful manner when facing greater complexity. These results may have implications for both teacher training and the design of VR for training or research purpose. This study contributes to the field from two aspects: 1) it revealed how features of the classroom environment can affect student teachers' noticing of and reaction to disruptions; and 2) it extends the functionality of the VR environment-from a teacher training tool to a testbed of fundamental classroom processes that are difficult to manipulate in real-life. KW - Augmented and virtual reality KW - Simulations KW - Improving classroom KW - teaching KW - Media in education KW - Pedagogical issues Y1 - 2021 U6 - https://doi.org/10.1016/j.compedu.2020.104100 SN - 0360-1315 SN - 1873-782X VL - 163 PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Hawro, Tomasz A1 - Przybylowicz, Katarzyna A1 - Spindler, Max A1 - Hawro, Marlena A1 - Steć, Michał A1 - Altrichter, Sabine A1 - Weller, Karsten A1 - Magerl, Markus A1 - Reidel, Ulrich A1 - Alarbeed, Ezzat A1 - Alraboni, Ola A1 - Maurer, Marcus A1 - Metz, Martin T1 - The characteristics and impact of pruritus in adult dermatology patients BT - a prospective, cross-sectional study JF - Journal of the American Academy of Dermatology N2 - Background: Pruritus often accompanies chronic skin diseases, exerting considerable burden on many areas of patient functioning; this burden and the features of pruritus remain insufficiently characterized. Objective: To investigate characteristics, including localization patterns, and burden of pruritus in patients with chronic dermatoses. Methods: We recruited 800 patients with active chronic skin diseases. We assessed pruritus intensity, localization, and further characteristics. We used validated questionnaires to assess quality of life, work productivity and activity impairment, anxiety, depression, and sleep quality. Results: Nine out of every 10 patients had experienced pruritus throughout their disease and 73% in the last 7 days. Pruritus often affected the entire body and was not restricted to skin lesions. Patients with moderate to severe pruritus reported significantly more impairment to their sleep quality and work productivity, and they were more depressed and anxious than control individuals and patients with mild or no pruritus. Suicidal ideations were highly prevalent in patients with chronic pruritus (18.5%) and atopic dermatitis (11.8%). Conclusions: Pruritus prevalence and intensity are very high across all dermatoses studied; intensity is linked to impairment in many areas of daily functioning. Effective treatment strategies are urgently required to treat pruritus and the underlying skin disease. ( J Am Acad Dermatol 2021;84:691-700.) KW - activity KW - anxiety KW - depression KW - pruritus KW - quality of life KW - sleep quality KW - suicidal ideations KW - work productivity Y1 - 2021 U6 - https://doi.org/10.1016/J.JAAD.2020.08.035 SN - 0190-9622 SN - 1097-6787 VL - 84 IS - 3 SP - 691 EP - 700 PB - Elsevier CY - Amsterdam [u.a.] ER - TY - JOUR A1 - Middelanis, Robin A1 - Willner, Sven N. A1 - Otto, Christian A1 - Kuhla, Kilian A1 - Quante, Lennart A1 - Levermann, Anders T1 - Wave-like global economic ripple response to Hurricane Sandy JF - Environmental research letters : ERL / Institute of Physics N2 - Tropical cyclones range among the costliest disasters on Earth. Their economic repercussions along the supply and trade network also affect remote economies that are not directly affected. We here simulate possible global repercussions on consumption for the example case of Hurricane Sandy in the US (2012) using the shock-propagation model Acclimate. The modeled shock yields a global three-phase ripple: an initial production demand reduction and associated consumption price decrease, followed by a supply shortage with increasing prices, and finally a recovery phase. Regions with strong trade relations to the US experience strong magnitudes of the ripple. A dominating demand reduction or supply shortage leads to overall consumption gains or losses of a region, respectively. While finding these repercussions in historic data is challenging due to strong volatility of economic interactions, numerical models like ours can help to identify them by approaching the problem from an exploratory angle, isolating the effect of interest. For this, our model simulates the economic interactions of over 7000 regional economic sectors, interlinked through about 1.8 million trade relations. Under global warming, the wave-like structures of the economic response to major hurricanes like the one simulated here are likely to intensify and potentially overlap with other weather extremes. KW - supply chains KW - Hurricane Sandy KW - economic ripples KW - extreme weather KW - impacts KW - loss propagation KW - natural disasters Y1 - 2021 U6 - https://doi.org/10.1088/1748-9326/ac39c0 SN - 1748-9326 VL - 16 IS - 12 PB - IOP Publ. Ltd. CY - Bristol ER - TY - JOUR A1 - Quante, Lennart A1 - Willner, Sven N. A1 - Middelanis, Robin A1 - Levermann, Anders T1 - Regions of intensification of extreme snowfall under future warming JF - Scientific reports N2 - Due to climate change the frequency and character of precipitation are changing as the hydrological cycle intensifies. With regards to snowfall, global warming has two opposing influences; increasing humidity enables intense snowfall, whereas higher temperatures decrease the likelihood of snowfall. Here we show an intensification of extreme snowfall across large areas of the Northern Hemisphere under future warming. This is robust across an ensemble of global climate models when they are bias-corrected with observational data. While mean daily snowfall decreases, both the 99th and the 99.9th percentiles of daily snowfall increase in many regions in the next decades, especially for Northern America and Asia. Additionally, the average intensity of snowfall events exceeding these percentiles as experienced historically increases in many regions. This is likely to pose a challenge to municipalities in mid to high latitudes. Overall, extreme snowfall events are likely to become an increasingly important impact of climate change in the next decades, even if they will become rarer, but not necessarily less intense, in the second half of the century. Y1 - 2021 U6 - https://doi.org/10.1038/s41598-021-95979-4 SN - 2045-2322 VL - 11 IS - 1 PB - Macmillan Publishers Limited, part of Springer Nature CY - Berlin ER - TY - JOUR A1 - Schirrmann, Michael A1 - Landwehr, Niels A1 - Giebel, Antje A1 - Garz, Andreas A1 - Dammer, Karl-Heinz T1 - Early detection of stripe rust in winter wheat using deep residual neural networks JF - Frontiers in plant science : FPLS N2 - Stripe rust (Pst) is a major disease of wheat crops leading untreated to severe yield losses. The use of fungicides is often essential to control Pst when sudden outbreaks are imminent. Sensors capable of detecting Pst in wheat crops could optimize the use of fungicides and improve disease monitoring in high-throughput field phenotyping. Now, deep learning provides new tools for image recognition and may pave the way for new camera based sensors that can identify symptoms in early stages of a disease outbreak within the field. The aim of this study was to teach an image classifier to detect Pst symptoms in winter wheat canopies based on a deep residual neural network (ResNet). For this purpose, a large annotation database was created from images taken by a standard RGB camera that was mounted on a platform at a height of 2 m. Images were acquired while the platform was moved over a randomized field experiment with Pst-inoculated and Pst-free plots of winter wheat. The image classifier was trained with 224 x 224 px patches tiled from the original, unprocessed camera images. The image classifier was tested on different stages of the disease outbreak. At patch level the image classifier reached a total accuracy of 90%. To test the image classifier on image level, the image classifier was evaluated with a sliding window using a large striding length of 224 px allowing for fast test performance. At image level, the image classifier reached a total accuracy of 77%. Even in a stage with very low disease spreading (0.5%) at the very beginning of the Pst outbreak, a detection accuracy of 57% was obtained. Still in the initial phase of the Pst outbreak with 2 to 4% of Pst disease spreading, detection accuracy with 76% could be attained. With further optimizations, the image classifier could be implemented in embedded systems and deployed on drones, vehicles or scanning systems for fast mapping of Pst outbreaks. KW - yellow rust KW - monitoring KW - deep learning KW - wheat crops KW - image recognition KW - camera sensor KW - ResNet KW - smart farming Y1 - 2021 U6 - https://doi.org/10.3389/fpls.2021.469689 SN - 1664-462X VL - 12 PB - Frontiers Media CY - Lausanne ER - TY - JOUR A1 - Gautam, Khem Raj A1 - Zhang, Guoqiang A1 - Landwehr, Niels A1 - Adolphs, Julian T1 - Machine learning for improvement of thermal conditions inside a hybrid ventilated animal building JF - Computers and electronics in agriculture : COMPAG online ; an international journal N2 - In buildings with hybrid ventilation, natural ventilation opening positions (windows), mechanical ventilation rates, heating, and cooling are manipulated to maintain desired thermal conditions. The indoor temperature is regulated solely by ventilation (natural and mechanical) when the external conditions are favorable to save external heating and cooling energy. The ventilation parameters are determined by a rule-based control scheme, which is not optimal. This study proposes a methodology to enable real-time optimum control of ventilation parameters. We developed offline prediction models to estimate future thermal conditions from the data collected from building in operation. The developed offline model is then used to find the optimal controllable ventilation parameters in real-time to minimize the setpoint deviation in the building. With the proposed methodology, the experimental building's setpoint deviation improved for 87% of time, on average, by 0.53 degrees C compared to the current deviations. KW - Animal building KW - Natural ventilation KW - Automatically controlled windows KW - Machine learning KW - Optimization Y1 - 2021 U6 - https://doi.org/10.1016/j.compag.2021.106259 SN - 0168-1699 SN - 1872-7107 VL - 187 PB - Elsevier Science CY - Amsterdam [u.a.] ER - TY - JOUR A1 - Camargo, Tibor de A1 - Schirrmann, Michael A1 - Landwehr, Niels A1 - Dammer, Karl-Heinz A1 - Pflanz, Michael T1 - Optimized deep learning model as a basis for fast UAV mapping of weed species in winter wheat crops JF - Remote sensing / Molecular Diversity Preservation International (MDPI) N2 - Weed maps should be available quickly, reliably, and with high detail to be useful for site-specific management in crop protection and to promote more sustainable agriculture by reducing pesticide use. Here, the optimization of a deep residual convolutional neural network (ResNet-18) for the classification of weed and crop plants in UAV imagery is proposed. The target was to reach sufficient performance on an embedded system by maintaining the same features of the ResNet-18 model as a basis for fast UAV mapping. This would enable online recognition and subsequent mapping of weeds during UAV flying operation. Optimization was achieved mainly by avoiding redundant computations that arise when a classification model is applied on overlapping tiles in a larger input image. The model was trained and tested with imagery obtained from a UAV flight campaign at low altitude over a winter wheat field, and classification was performed on species level with the weed species Matricaria chamomilla L., Papaver rhoeas L., Veronica hederifolia L., and Viola arvensis ssp. arvensis observed in that field. The ResNet-18 model with the optimized image-level prediction pipeline reached a performance of 2.2 frames per second with an NVIDIA Jetson AGX Xavier on the full resolution UAV image, which would amount to about 1.78 ha h(-1) area output for continuous field mapping. The overall accuracy for determining crop, soil, and weed species was 94%. There were some limitations in the detection of species unknown to the model. When shifting from 16-bit to 32-bit model precision, no improvement in classification accuracy was observed, but a strong decline in speed performance, especially when a higher number of filters was used in the ResNet-18 model. Future work should be directed towards the integration of the mapping process on UAV platforms, guiding UAVs autonomously for mapping purpose, and ensuring the transferability of the models to other crop fields. KW - ResNet KW - deep residual networks KW - UAV imagery KW - embedded systems KW - crop KW - monitoring KW - image classification KW - site-specific weed management KW - real-time mapping Y1 - 2021 U6 - https://doi.org/10.3390/rs13091704 SN - 2072-4292 VL - 13 IS - 9 PB - MDPI CY - Basel ER - TY - JOUR A1 - Brede, Nuria A1 - Botta, Nicola T1 - On the correctness of monadic backward induction JF - Journal of functional programming N2 - In control theory, to solve a finite-horizon sequential decision problem (SDP) commonly means to find a list of decision rules that result in an optimal expected total reward (or cost) when taking a given number of decision steps. SDPs are routinely solved using Bellman's backward induction. Textbook authors (e.g. Bertsekas or Puterman) typically give more or less formal proofs to show that the backward induction algorithm is correct as solution method for deterministic and stochastic SDPs. Botta, Jansson and Ionescu propose a generic framework for finite horizon, monadic SDPs together with a monadic version of backward induction for solving such SDPs. In monadic SDPs, the monad captures a generic notion of uncertainty, while a generic measure function aggregates rewards. In the present paper, we define a notion of correctness for monadic SDPs and identify three conditions that allow us to prove a correctness result for monadic backward induction that is comparable to textbook correctness proofs for ordinary backward induction. The conditions that we impose are fairly general and can be cast in category-theoretical terms using the notion of Eilenberg-Moore algebra. They hold in familiar settings like those of deterministic or stochastic SDPs, but we also give examples in which they fail. Our results show that backward induction can safely be employed for a broader class of SDPs than usually treated in textbooks. However, they also rule out certain instances that were considered admissible in the context of Botta et al. 's generic framework. Our development is formalised in Idris as an extension of the Botta et al. framework and the sources are available as supplementary material. Y1 - 2021 U6 - https://doi.org/10.1017/S0956796821000228 SN - 1469-7653 SN - 0956-7968 VL - 31 PB - Cambridge University Press CY - Cambridge ER - TY - JOUR A1 - Andjelković, Marko A1 - Chen, Junchao A1 - Simevski, Aleksandar A1 - Schrape, Oliver A1 - Krstić, Miloš A1 - Kraemer, Rolf T1 - Monitoring of particle count rate and LET variations with pulse stretching inverters JF - IEEE transactions on nuclear science : a publication of the IEEE Nuclear and Plasma Sciences Society N2 - This study investigates the use of pulse stretching (skew-sized) inverters for monitoring the variation of count rate and linear energy transfer (LET) of energetic particles. The basic particle detector is a cascade of two pulse stretching inverters, and the required sensing area is obtained by connecting up to 12 two-inverter cells in parallel and employing the required number of parallel arrays. The incident particles are detected as single-event transients (SETs), whereby the SET count rate denotes the particle count rate, while the SET pulsewidth distribution depicts the LET variations. The advantage of the proposed solution is the possibility to sense the LET variations using fully digital processing logic. SPICE simulations conducted on IHP's 130-nm CMOS technology have shown that the SET pulsewidth varies by approximately 550 ps over the LET range from 1 to 100 MeV center dot cm(2) center dot mg(-1). The proposed detector is intended for triggering the fault-tolerant mechanisms within a self-adaptive multiprocessing system employed in space. It can be implemented as a standalone detector or integrated in the same chip with the target system. KW - Particle detector KW - pulse stretching inverters KW - single-event transient KW - (SET) count rate KW - SET pulsewidth distribution Y1 - 2021 U6 - https://doi.org/10.1109/TNS.2021.3076400 SN - 0018-9499 SN - 1558-1578 VL - 68 IS - 8 SP - 1772 EP - 1781 PB - Institute of Electrical and Electronics Engineers CY - New York, NY ER - TY - JOUR A1 - Schrape, Oliver A1 - Andjelkovic, Marko A1 - Breitenreiter, Anselm A1 - Zeidler, Steffen A1 - Balashov, Alexey A1 - Krstić, Miloš T1 - Design and evaluation of radiation-hardened standard cell flip-flops JF - IEEE transactions on circuits and systems : a publication of the IEEE Circuits and Systems Society: 1, Regular papers N2 - Use of a standard non-rad-hard digital cell library in the rad-hard design can be a cost-effective solution for space applications. In this paper we demonstrate how a standard non-rad-hard flip-flop, as one of the most vulnerable digital cells, can be converted into a rad-hard flip-flop without modifying its internal structure. We present five variants of a Triple Modular Redundancy (TMR) flip-flop: baseline TMR flip-flop, latch-based TMR flip-flop, True-Single Phase Clock (TSPC) TMR flip-flop, scannable TMR flip-flop and self-correcting TMR flipflop. For all variants, the multi-bit upsets have been addressed by applying special placement constraints, while the Single Event Transient (SET) mitigation was achieved through the usage of customized SET filters and selection of optimal inverter sizes for the clock and reset trees. The proposed flip-flop variants feature differing performance, thus enabling to choose the optimal solution for every sensitive node in the circuit, according to the predefined design constraints. Several flip-flop designs have been validated on IHP's 130nm BiCMOS process, by irradiation of custom-designed shift registers. It has been shown that the proposed TMR flip-flops are robust to soft errors with a threshold Linear Energy Transfer (LET) from (32.4 MeV.cm(2)/mg) to (62.5 MeV.cm(2)/mg), depending on the variant. KW - Single event effect KW - fault tolerance KW - triple modular redundancy KW - ASIC KW - design flow KW - radhard design Y1 - 2021 U6 - https://doi.org/10.1109/TCSI.2021.3109080 SN - 1549-8328 SN - 1558-0806 SN - 1057-7122 VL - 68 IS - 11 SP - 4796 EP - 4809 PB - Inst. of Electr. and Electronics Engineers CY - New York, NY ER - TY - JOUR A1 - Tavakoli, Hamad A1 - Alirezazadeh, Pendar A1 - Hedayatipour, Ava A1 - Nasib, A. H. Banijamali A1 - Landwehr, Niels T1 - Leaf image-based classification of some common bean cultivars using discriminative convolutional neural networks JF - Computers and electronics in agriculture : COMPAG online ; an international journal N2 - In recent years, many efforts have been made to apply image processing techniques for plant leaf identification. However, categorizing leaf images at the cultivar/variety level, because of the very low inter-class variability, is still a challenging task. In this research, we propose an automatic discriminative method based on convolutional neural networks (CNNs) for classifying 12 different cultivars of common beans that belong to three various species. We show that employing advanced loss functions, such as Additive Angular Margin Loss and Large Margin Cosine Loss, instead of the standard softmax loss function for the classification can yield better discrimination between classes and thereby mitigate the problem of low inter-class variability. The method was evaluated by classifying species (level I), cultivars from the same species (level II), and cultivars from different species (level III), based on images from the leaf foreside and backside. The results indicate that the performance of the classification algorithm on the leaf backside image dataset is superior. The maximum mean classification accuracies of 95.86, 91.37 and 86.87% were obtained at the levels I, II and III, respectively. The proposed method outperforms the previous relevant works and provides a reliable approach for plant cultivars identification. KW - Bean KW - Plant identification KW - Digital image analysis KW - VGG16 KW - Loss KW - functions Y1 - 2021 U6 - https://doi.org/10.1016/j.compag.2020.105935 SN - 0168-1699 SN - 1872-7107 VL - 181 PB - Elsevier CY - Amsterdam [u.a.] ER - TY - JOUR A1 - Cabalar, Pedro A1 - Fandiño, Jorge A1 - Fariñas del Cerro, Luis T1 - Splitting epistemic logic programs JF - Theory and practice of logic programming / publ. for the Association for Logic Programming N2 - Epistemic logic programs constitute an extension of the stable model semantics to deal with new constructs called subjective literals. Informally speaking, a subjective literal allows checking whether some objective literal is true in all or some stable models. As it can be imagined, the associated semantics has proved to be non-trivial, since the truth of subjective literals may interfere with the set of stable models it is supposed to query. As a consequence, no clear agreement has been reached and different semantic proposals have been made in the literature. Unfortunately, comparison among these proposals has been limited to a study of their effect on individual examples, rather than identifying general properties to be checked. In this paper, we propose an extension of the well-known splitting property for logic programs to the epistemic case. We formally define when an arbitrary semantics satisfies the epistemic splitting property and examine some of the consequences that can be derived from that, including its relation to conformant planning and to epistemic constraints. Interestingly, we prove (through counterexamples) that most of the existing approaches fail to fulfill the epistemic splitting property, except the original semantics proposed by Gelfond 1991 and a recent proposal by the authors, called Founded Autoepistemic Equilibrium Logic. KW - knowledge representation and nonmonotonic reasoning KW - logic programming methodology and applications KW - theory Y1 - 2021 U6 - https://doi.org/10.1017/S1471068420000058 SN - 1471-0684 SN - 1475-3081 VL - 21 IS - 3 SP - 296 EP - 316 PB - Cambridge Univ. Press CY - Cambridge [u.a.] ER - TY - JOUR A1 - Möring, Sebastian A1 - Leino, Olli Tapio T1 - Die neoliberale Bedingung von Computerspielen JF - Kontrollmaschinen - zur Dispositivtheorie des Computerspiels Y1 - 2022 SN - 978-3-643-14780-6 SP - 41 EP - 61 PB - LiteraturWissenschaft.de CY - Münster ER - TY - JOUR A1 - Prasse, Paul A1 - Iversen, Pascal A1 - Lienhard, Matthias A1 - Thedinga, Kristina A1 - Herwig, Ralf A1 - Scheffer, Tobias T1 - Pre-Training on In Vitro and Fine-Tuning on Patient-Derived Data Improves Deep Neural Networks for Anti-Cancer Drug-Sensitivity Prediction JF - MDPI N2 - 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. KW - deep neural networks KW - drug-sensitivity prediction KW - anti-cancer drugs Y1 - 2022 U6 - https://doi.org/10.3390/cancers14163950 SN - 2072-6694 VL - 14 SP - 1 EP - 14 PB - MDPI CY - Basel, Schweiz ET - 16 ER -