TY - JOUR A1 - Michallek, Florian A1 - Genske, Ulrich A1 - Niehues, Stefan Markus A1 - Hamm, Bernd A1 - Jahnke, Paul T1 - Deep learning reconstruction improves radiomics feature stability and discriminative power in abdominal CT imaging BT - a phantom study JF - European Radiology N2 - Objectives To compare image quality of deep learning reconstruction (AiCE) for radiomics feature extraction with filtered back projection (FBP), hybrid iterative reconstruction (AIDR 3D), and model-based iterative reconstruction (FIRST). Methods Effects of image reconstruction on radiomics features were investigated using a phantom that realistically mimicked a 65-year-old patient's abdomen with hepatic metastases. The phantom was scanned at 18 doses from 0.2 to 4 mGy, with 20 repeated scans per dose. Images were reconstructed with FBP, AIDR 3D, FIRST, and AiCE. Ninety-three radiomics features were extracted from 24 regions of interest, which were evenly distributed across three tissue classes: normal liver, metastatic core, and metastatic rim. Features were analyzed in terms of their consistent characterization of tissues within the same image (intraclass correlation coefficient >= 0.75), discriminative power (Kruskal-Wallis test p value < 0.05), and repeatability (overall concordance correlation coefficient >= 0.75). Results The median fraction of consistent features across all doses was 6%, 8%, 6%, and 22% with FBP, AIDR 3D, FIRST, and AiCE, respectively. Adequate discriminative power was achieved by 48%, 82%, 84%, and 92% of features, and 52%, 20%, 17%, and 39% of features were repeatable, respectively. Only 5% of features combined consistency, discriminative power, and repeatability with FBP, AIDR 3D, and FIRST versus 13% with AiCE at doses above 1 mGy and 17% at doses >= 3 mGy. AiCE was the only reconstruction technique that enabled extraction of higher-order features. Conclusions AiCE more than doubled the yield of radiomics features at doses typically used clinically. Inconsistent tissue characterization within CT images contributes significantly to the poor stability of radiomics features. KW - Tomography KW - X-ray computed KW - Phantoms KW - imaging KW - Liver neoplasms KW - Algorithms KW - Reproducibility of results Y1 - 2022 U6 - https://doi.org/10.1007/s00330-022-08592-y SN - 0938-7994 SN - 1432-1084 VL - 32 IS - 7 SP - 4587 EP - 4595 PB - Springer CY - New York ER - TY - JOUR A1 - Molkenthin, Christian A1 - Donner, Christian A1 - Reich, Sebastian A1 - Zöller, Gert A1 - Hainzl, Sebastian A1 - Holschneider, Matthias A1 - Opper, Manfred T1 - GP-ETAS: semiparametric Bayesian inference for the spatio-temporal epidemic type aftershock sequence model JF - Statistics and Computing N2 - The spatio-temporal epidemic type aftershock sequence (ETAS) model is widely used to describe the self-exciting nature of earthquake occurrences. While traditional inference methods provide only point estimates of the model parameters, we aim at a fully Bayesian treatment of model inference, allowing naturally to incorporate prior knowledge and uncertainty quantification of the resulting estimates. Therefore, we introduce a highly flexible, non-parametric representation for the spatially varying ETAS background intensity through a Gaussian process (GP) prior. Combined with classical triggering functions this results in a new model formulation, namely the GP-ETAS model. We enable tractable and efficient Gibbs sampling by deriving an augmented form of the GP-ETAS inference problem. This novel sampling approach allows us to assess the posterior model variables conditioned on observed earthquake catalogues, i.e., the spatial background intensity and the parameters of the triggering function. Empirical results on two synthetic data sets indicate that GP-ETAS outperforms standard models and thus demonstrate the predictive power for observed earthquake catalogues including uncertainty quantification for the estimated parameters. Finally, a case study for the l'Aquila region, Italy, with the devastating event on 6 April 2009, is presented. KW - Self-exciting point process KW - Hawkes process KW - Spatio-temporal ETAS model KW - Bayesian inference KW - Sampling KW - Earthquake modeling KW - Gaussian process KW - Data augmentation Y1 - 2022 U6 - https://doi.org/10.1007/s11222-022-10085-3 SN - 0960-3174 SN - 1573-1375 VL - 32 IS - 2 PB - Springer CY - Dordrecht ER - TY - JOUR A1 - Bandyopadhyay, Soumyadip A1 - Sarkar, Dipankar A1 - Mandal, Chittaranjan A1 - Giese, Holger T1 - Translation validation of coloured Petri net models of programs on integers JF - Acta informatica N2 - Programs are often subjected to significant optimizing and parallelizing transformations based on extensive dependence analysis. Formal validation of such transformations needs modelling paradigms which can capture both control and data dependences in the program vividly. Being value-based with an inherent scope of capturing parallelism, the untimed coloured Petri net (CPN) models, reported in the literature, fit the bill well; accordingly, they are likely to be more convenient as the intermediate representations (IRs) of both the source and the transformed codes for translation validation than strictly sequential variable-based IRs like sequential control flow graphs (CFGs). In this work, an efficient path-based equivalence checking method for CPN models of programs on integers is presented. Extensive experimentation has been carried out on several sequential and parallel examples. Complexity and correctness issues have been treated rigorously for the method. Y1 - 2022 U6 - https://doi.org/10.1007/s00236-022-00419-z SN - 0001-5903 SN - 1432-0525 VL - 59 IS - 6 SP - 725 EP - 759 PB - Springer CY - New York 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 - Chen, Junchao A1 - Lange, Thomas A1 - Andjelkovic, Marko A1 - Simevski, Aleksandar A1 - Lu, Li A1 - Krstic, Milos T1 - Solar particle event and single event upset prediction from SRAM-based monitor and supervised machine learning JF - IEEE transactions on emerging topics in computing / IEEE Computer Society, Institute of Electrical and Electronics Engineers N2 - 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. KW - Machine learning KW - Single event upsets KW - Random access memory KW - monitoring KW - machine learning algorithms KW - predictive models KW - space missions KW - solar particle event KW - single event upset KW - machine learning KW - online learning KW - hardware accelerator KW - reliability KW - self-adaptive multiprocessing system Y1 - 2022 U6 - https://doi.org/10.1109/TETC.2022.3147376 SN - 2168-6750 VL - 10 IS - 2 SP - 564 EP - 580 PB - Institute of Electrical and Electronics Engineers CY - [New York, NY] 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 VL - 31 PB - Cambridge University Press CY - Cambridge ER - TY - JOUR A1 - Marco Figuera, Ramiro A1 - Riedel, Christian A1 - Rossi, Angelo Pio A1 - Unnithan, Vikram T1 - Depth to diameter analysis on small simple craters at the lunar south pole - possible implications for ice harboring JF - Remote sensing N2 - 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. KW - craters KW - lunar exploration KW - ice harboring Y1 - 2022 U6 - https://doi.org/10.3390/rs14030450 SN - 2072-4292 VL - 14 IS - 3 PB - MDPI CY - Basel 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 - Abdelwahab, Ahmed A1 - Landwehr, Niels T1 - Deep Distributional Sequence Embeddings Based on a Wasserstein Loss JF - Neural processing letters N2 - 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. KW - Metric learning KW - Sequence embeddings KW - Deep learning Y1 - 2022 U6 - https://doi.org/10.1007/s11063-022-10784-y SN - 1370-4621 SN - 1573-773X PB - Springer CY - Dordrecht 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 -