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Cancer is one of the leading causes of death worldwide [183], with lung tumors being the most frequent cause of cancer deaths in men as well as one of the most common cancers diagnosed in woman [40]. As symptoms often arise in advanced stages, an early diagnosis is especially important to ensure the best and earliest possible treatment. In order to achieve this, Computed Tomography (CT) scans are frequently used for tumor detection and diagnosis. We will present examples of publicly available CT image data of lung cancer patients and discuss possible methods to realize an automatic system for automated cancer diagnosis. We will also look at the recent SPIE-AAPM Lung CT Challenge [10] data set in detail and describe possible methods and challenges for image segmentation and classification based on this data set.
Das Kernanliegen des Datenschutzes ist es, natürliche Personen vor nachteiligen Effekten der Speicherung und Verarbeitung der sie betreffenden Daten zu schützen. Aber viele Personen scheinen gar nicht geschützt werden zu wollen. Im Gegenteil, viele Endanwender willigen “freiwillig“ – bewusst oder unbewusst – in eine umfassende Verarbeitung ihrer personenbezogenen Daten ein. Warum tun Menschen dies? Es werden verschiedene Ursachen diskutiert (beispielsweise in [79]), hierzu gehören Uninformiertheit, mangelnde Sensibilität, das Gefühl der Hilflosigkeit, mangelnde Zahlungsbereitschaft und mangelnde Alternativen. Auch wenn dies in Einzelfällen zutrifft, so gibt es oft sehr wohl datenschutzfreundliche Alternativen. Beispielsweise existiert zu WhatsApp (als Instant Messaging App) die Alternative Threema. Threema gilt als EU-DS-GVO-konform und funktional durchaus mit WhatsApp vergleichbar [62]. Allerdings ist inzwischen die aktuelle Netzwerkgröße ein entscheidendes Auswahlkriterium: Im Januar 2018 hatte Threema 4,5 Millionen Nutzer [172], WhatsApp dagegen 1,5 Milliarden [171]. Dies ist ein Indiz dafür, dass WhatsApp sich quasi zum De-facto-Standard entwickelt hat und es für die einzelne Person nur schwer möglich ist, viele andere “zum Wechsel auf ein anderes Produkt zu bewegen. [. . . ] Bei Diensten mit Nutzerzahlen im Milliardenbereich kann von ’Freiwilligkeit’ nur noch bedingt gesprochen werden.“ [9]
BWL für Dummies
(2021)
The majority of biomedical knowledge is stored in structured databases or as unstructured text in scientific publications. This vast amount of information has led to numerous machine learning-based biological applications using either text through natural language processing (NLP) or structured data through knowledge graph embedding models (KGEMs). However, representations based on a single modality are inherently limited. To generate better representations of biological knowledge, we propose STonKGs, a Sophisticated Transformer trained on biomedical text and Knowledge Graphs. This multimodal Transformer uses combined input sequences of structured information from KGs and unstructured text data from biomedical literature to learn joint representations. First, we pre-trained STonKGs on a knowledge base assembled by the Integrated Network and Dynamical Reasoning Assembler (INDRA) consisting of millions of text-triple pairs extracted from biomedical literature by multiple NLP systems. Then, we benchmarked STonKGs against two baseline models trained on either one of the modalities (i.e., text or KG) across eight different classification tasks, each corresponding to a different biological application. Our results demonstrate that STonKGs outperforms both baselines, especially on the more challenging tasks with respect to the number of classes, improving upon the F1-score of the best baseline by up to 0.083. Additionally, our pre-trained model as well as the model architecture can be adapted to various other transfer learning applications. Finally, the source code and pre-trained STonKGs models are available at https://github.com/stonkgs/stonkgs and https://huggingface.co/stonkgs/stonkgs-150k.
Solving transport network problems can be complicated by non-linear effects. In the particular case of gas transport networks, the most complex non-linear elements are compressors and their drives. They are described by a system of equations, composed of a piecewise linear ‘free’ model for the control logic and a non-linear ‘advanced’ model for calibrated characteristics of the compressor. For all element equations, certain stability criteria must be fulfilled, providing the absence of folds in associated system mapping. In this paper, we consider a transformation (warping) of a system from the space of calibration parameters to the space of transport variables, satisfying these criteria. The algorithm drastically improves stability of the network solver. Numerous tests on realistic networks show that nearly 100% convergence rate of the solver is achieved with this approach.
The deficiency of adenosine deaminase 2 (DADA2) is an autosomal recessively inherited disease that has undergone extensive phenotypic expansion since being first described in patients with fevers, recurrent strokes, livedo racemosa, and polyarteritis nodosa in 2014. It is now recognized that patients may develop multisystem disease that spans multiple medical subspecialties. Here, we describe the findings from a large single center longitudinal cohort of 60 patients, the broad phenotypic presentation, as well as highlight the cohort's experience with hematopoietic cell transplantation and COVID-19. Disease manifestations could be separated into three major phenotypes: inflammatory/vascular, immune dysregulatory, and hematologic, however, most patients presented with significant overlap between these three phenotype groups. The cardinal features of the inflammatory/vascular group included cutaneous manifestations and stroke. Evidence of immune dysregulation was commonly observed, including hypogammaglobulinemia, absent to low class-switched memory B cells, and inadequate response to vaccination. Despite these findings, infectious complications were exceedingly rare in this cohort. Hematologic findings including pure red cell aplasia (PRCA), immune-mediated neutropenia, and pancytopenia were observed in half of patients. We significantly extended our experience using anti-TNF agents, with no strokes observed in 2026 patient months on TNF inhibitors. Meanwhile, hematologic and immune features had a more varied response to anti-TNF therapy. Six patients received a total of 10 allogeneic hematopoietic cell transplant (HCT) procedures, with secondary graft failure necessitating repeat HCTs in three patients, as well as unplanned donor cell infusions to avoid graft rejection. All transplanted patients had been on anti-TNF agents prior to HCT and received varying degrees of reduced-intensity or non-myeloablative conditioning. All transplanted patients are still alive and have discontinued anti-TNF therapy. The long-term follow up afforded by this large single-center study underscores the clinical heterogeneity of DADA2 and the potential for phenotypes to evolve in any individual patient.
The lattice Boltzmann method (LBM) is an efficient simulation technique for computational fluid mechanics and beyond. It is based on a simple stream-and-collide algorithm on Cartesian grids, which is easily compatible with modern machine learning architectures. While it is becoming increasingly clear that deep learning can provide a decisive stimulus for classical simulation techniques, recent studies have not addressed possible connections between machine learning and LBM. Here, we introduce Lettuce, a PyTorch-based LBM code with a threefold aim. Lettuce enables GPU accelerated calculations with minimal source code, facilitates rapid prototyping of LBM models, and enables integrating LBM simulations with PyTorch's deep learning and automatic differentiation facility. As a proof of concept for combining machine learning with the LBM, a neural collision model is developed, trained on a doubly periodic shear layer and then transferred to a different flow, a decaying turbulence. We also exemplify the added benefit of PyTorch's automatic differentiation framework in flow control and optimization. To this end, the spectrum of a forced isotropic turbulence is maintained without further constraining the velocity field.
Open-Source Software spielt sowohl zur Ausgestaltung von Lehr- und Lernszenarien (bspw. Organisation mit Editoren und Groupware, Kollaboration und Kommunikation via Chats und Webblogs), als auch für die Umsetzung von Forschunsprojekten (zum Beispiel Auswertung großer Datenbestände, Erprobung realer Situationen in vituellen Laboren, Evaluation neuer Oberflächenentwicklungen) eine wichtige Rolle. Um eine bestmögliche Passung der Software herzustellen, erfolgt Softwareentwicklung im Hochschulbereich entweder forschungsprojektbezogen oder Disziplin- und Einrichtungsübergreifend.
Kinder – unsere Zukunft!
(2021)