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Correctness of program transformations and translations in concurrent programming is the focus of our research. In this case study the relation of the synchronous pi-calculus and a core language of Concurrent Haskell (CH) with asynchronous communication is investigated. We show that CH embraces the synchronous pi-calculus. The formal foundations are contextual semantics in both languages, where may- as well as should-convergence are observed. We succeed in defining and proving smart properties of a particular translation mapping the synchronous pi-calculus into CH. This implies that pi-processes are error-free if and only if their translation is an error-free CH-program Our result shows that the chosen semantics is not only powerful, but can also be applied in concrete and technically complex situations. The developed translation uses private names. We also automatically check potentially correct translations that use global names instead of private names. As a complexity parameter we use the number of MVars introduced by the transformation, where MVars are synchronized 1-place buffers. The automated refutation of incorrect translations leads to a classification of potentially correct translations, and to the conjecture that one global MVar is insufficient.
Correctness of program transformations and translations in concurrent programming is the focus of our research. In this case study the relation of the synchronous pi-calculus and a core language of Concurrent Haskell (CH) with asynchronous communication is investigated. We show that CH embraces the synchronous pi-calculus. The formal foundations are contextual semantics in both languages, where may- as well as should-convergence are observed. We succeed in defining and proving smart properties of a particular translation mapping the synchronous pi-calculus into CH. This implies that pi-processes are error-free if and only if their translation is an error-free CH-program Our result shows that the chosen semantics is not only powerful, but can also be applied in concrete and technically complex situations. The developed translation uses private names. We also automatically check potentially correct translations that use global names instead of private names. As a complexity parameter we use the number of MVars introduced by the transformation, where MVars are synchronized 1-place buffers. The automated refutation of incorrect translations leads to a classification of potentially correct translations, and to the conjecture that one global MVar is insufficient.
Correctness of program transformations and translations in concurrent programming is the focus of our research. In this case study the relation of the synchronous pi-calculus and a core language of Concurrent Haskell (CH) with asynchronous communication is investigated. We show that CH embraces the synchronous pi-calculus. The formal foundations are contextual semantics in both languages, where may- as well as should-convergence are observed. We succeed in defining and proving smart properties of a particular translation mapping the synchronous pi-calculus into CH. This implies that pi-processes are error-free if and only if their translation is an error-free CH-program Our result shows that the chosen semantics is not only powerful, but can also be applied in concrete and technically complex situations. The developed translation uses private names. We also automatically check potentially correct translations that use global names instead of private names. As a complexity parameter we use the number of MVars introduced by the transformation, where MVars are synchronized 1-place buffers. The automated refutation of incorrect translations leads to a classification of potentially correct translations, and to the conjecture that one global MVar is insufficient.
We investigate translations from the synchronous pi-calculus
into a core language of Concurrent Haskell (CH). Synchronous messagepassing of the pi-calculus is encoded as sending messages and adding synchronization using Concurrent Haskell’s mutable shared-memory locations (MVars). Our correctness criterion for translations is invariance of may- and should-convergence. This embraces that all executions of a process are error-free if and only if this also holds for the translated program. We exhibit a particular correct translation that uses a fresh, private MVar per communication interaction and that is in addition adequate, and which is also fully abstract on closed expressions. A metaresult is that CH has the expressive power and the concurrency capabilities of the synchronous pi-calculus.
We also automatically check variants of translations of synchronous communication into an asynchronous calculus where only an a priori fixed number of MVars per channel (and not per communication interaction!) is available. We obtain non-correctness results for classes of small translations, and exemplary argue for the correctness (and adequacy) for two translations with a higher number of MVars. We introduce a classification of the potentially correct translations.
We consider matching, rewriting, critical pairs and the Knuth-Bendix confluence test on rewrite rules in a nominal setting extended by atom-variables. Computing critical pairs is done using nominal unification, and rewriting using nominal matching. We utilise atom-variables to formulate rewrite rules, which is an improvement over previous approaches, using usual nominal unification, nominal matching and nominal equivalence of expressions coupled with a freshness constraint. We determine the complexity of several problems in a quantified freshness logic. In particular we show that nominal matching is Πp2-complete. We prove that the adapted Knuth-Bendix confluence test is applicable to a nominal rewrite system with atom-variabes and thus, that there is a decidable test whether confluence of the ground instance of the abstract rewrite system holds. We apply the nominal Knuth Bendix confluence criterion to the theory of monads, and compute a convergent nominal rewrite system modulo alpha-equivalence.
A sound and complete algorithm for nominal unification of higher-order expressions with a recursive let is described, and shown to run in non-deterministic polynomial time. We also explore specializations like nominal letrec-matching for expressions, for DAGs, and for garbage-free expressions and determine their complexity. As extension a nominal unification algorithm for higher-order expressions with recursive let and atom-variables is constructed, where we show that it also runs in non-deterministic polynomial time.
In the last two decades, our understanding of human gene regulation has improved tremendously. There are plentiful computational methods which focus on integrative data analysis of humans, and model organisms, like mouse and drosophila. However, these tools are not directly employable by researchers working on non-model organisms to answer fundamental biological, and evolutionary questions. We aimed to develop new tools, and adapt existing software for the analysis of transcriptomic and epigenomic data of one such non-model organism, Paramecium tetraurelia, an unicellular eukaryote. Paramecium contains two diploid (2n) germline micronuclei (MIC) and a polyploid (800n) somatic macronuclei (MAC). The transcriptomic and epigenomic regulatory landscape of the MAC genome, which has 80% protein-coding genes and short intergenic regions, is poorly understood.
We developed a generic automated eukaryotic short interfering RNA (siRNA) analysis tool, called RAPID. Our tool captures diverse siRNA characteristics from small RNA sequencing data and provides easily navigable visualisations. We also introduced a normalisation technique to facilitate comparison of multiple siRNA-based gene knockdown studies. Further, we developed a pipeline to characterise novel genome-wide endogenous short interfering RNAs (endo-siRNAs). In contrary to many organisms, we found that the endo-siRNAs are not acting in cis, to silence their parent mRNA. We also predicted phasing of siRNAs, which are regulated by the RNA interference (RNAi) pathway.
Further, using RAPID, we investigated the aberrations of endo-siRNAs, and their respective transcriptomic alterations caused by an RNAi pathway triggered by feeding small RNAs against a target gene. We find that the small RNA transcriptome is altered, even if a gene unrelated to RNAi pathway is targeted. This is important in the context of investigations of genetically modified organisms (GMOs). We suggest that future studies need to distinguish transcriptomic changes caused by RNAi inducing techniques and actual regulatory changes.
Subsequently, we adapted existing epigenomics analysis tools to conduct the first comprehensive epigenomic characterisation of nucleosome positioning and histone modifications of the Paramecium MAC. We identified well positioned nucleosomes shifted downstream of the transcription start site. GC content seems to dictate, in cis, the positioning of nucleosomes, histone marks (H3K4me3, H3K9ac, and H3K27me3), and Pol II in the AT-rich Paramecium genome. We employed a chromatin state segmentation approach, on nucleosomes and histone marks, which revealed genes with active, repressive, and bivalent chromatin states. Further, we constructed a regulatory association network of all the aforementioned data, using the sparse partial correlation network technique. Our analysis revealed subsets of genes, whose expression is positively associated with H3K27me3, different to the otherwise reported negative association with gene expression in many other organisms.
Further, we developed a Random Forests classifier to predict gene expression using genic (gene length, intron frequency, etc.) and epigenetic features. Our model has a test performance (PR-AUC) of 0.83. Upon evaluating different feature sets, we found that genic features are as predictive, of gene expression, as the epigenetic features. We used Shapley local feature explanation values, to suggest that high H3K4me3, high intron frequency, low gene length, high sRNA, and high GC content are the most important elements for determining gene expression status.
In this thesis, we developed novel tools, and employed several bioinformatics and machine learning methods to characterise the regulatory landscape of the Paramecium’s (epi)genome.
We empirically investigate algorithms for solving Connected Components in the external memory model. In particular, we study whether the randomized O(Sort(E)) algorithm by Karger, Klein, and Tarjan can be implemented to compete with practically promising and simpler algorithms having only slightly worse theoretical cost, namely Borůvka’s algorithm and the algorithm by Sibeyn and collaborators. For all algorithms, we develop and test a number of tuning options. Our experiments are executed on a large set of different graph classes including random graphs, grids, geometric graphs, and hyperbolic graphs. Among our findings are: The Sibeyn algorithm is a very strong contender due to its simplicity and due to an added degree of freedom in its internal workings when used in the Connected Components setting. With the right tunings, the Karger-Klein-Tarjan algorithm can be implemented to be competitive in many cases. Higher graph density seems to benefit Karger-Klein-Tarjan relative to Sibeyn. Borůvka’s algorithm is not competitive with the two others.
Dieser Arbeit war zum Ziel gesetzt, Methoden zur Simulation von neuronalen Prozessen zu entwickeln, zu implementieren, einzusetzen und zu vergleichen. Ein besonderes Augenmerk lag dabei auf der Frage, wo eine volle räumliche Auflösung der Modelle benötigt wird und wo darauf zugunsten von vereinfachenden niederdimensionalen Modellen, die wesentlich weniger Ressourcen und mathematischen Sachverstand erfordern, verzichtet werden kann. Außerdem wurde speziell bei der Beschreibung der verschiedenen Modelle für die Elektrik der Nervenzellen das Anliegen verfolgt, deren Zusammenhänge und die Natur vereinfachender Annahmen herauszuarbeiten, um deutlich zu machen, an welchen Stellen Probleme bei der Benutzung der weniger komplexen Modelle auftreten können.
In etlichen Beispielen wurde daraufhin untersucht, inwieweit die Vereinfachung auf ein eindimensionales Kabelmodell sowie der Verzicht auf die Betrachtung einzelner Ionensorten die realistische Darstellung der zellulären Elektrik beeinträchtigen können. Dabei stellte sich heraus, dass alle betrachteten Modelle für das rein elektrische Verhalten der Neuronen im Wesentlichen dieselben Ergebnisse liefern, weshalb zu dessen Simulation in den allermeisten Fällen ein 1D-Kabelmodell völlig ausreichend und angezeigt sein dürfte.
Nur wenn Größen von Interesse sind, die in diesem Modell nicht erfasst werden, etwa das Außenraumpotential oder die Ionenkonzentrationen, muss auf genauere Modelle zurückgegriffen werden. Außerdem ist in einer Konvergenzstudie exemplarisch vorgeführt worden, dass bereits eine recht grobe Darstellung der zugrundeliegenden Rechengitter genügt, um korrekte Ergebnisse bei der Simulation der rein elektrischen Signale sicherzustellen.
In scharfem Kontrast steht hierzu die Simulation von einzelnen Ionen-Dynamiken. Bereits in der Untersuchung des Poisson-Nernst-Planck-Modells für das Membranpotential erwies sich, dass für eine korrekte Simulation der diffusiven Anteile der Ionenbewegung wesentlich feinere Gitter benötigt werden.
Noch viel deutlicher wurde dies in Simulationen von Calcium-Wellen in Dendriten, wo -- neben anderen Einsichten -- aufgezeigt werden konnte, dass nicht nur eine feine axiale
(und Zeit-) Auflösung der Dendritengeometrie zur Sicherstellung exakter Ergebnisse notwendig ist, sondern auch die räumliche Auflösung in die übrigen Dimensionen wichtig ist, weswegen eine eindimensionale Kabeldarstellung der Calcium-Dynamik erheblich fehlerbehaftet und
(jedenfalls im Zusammenhang mit Ryanodin-Rezeptorkanälen) von deren Nutzung dringend abzuraten ist. Auch die Darstellung von Kanälen als eine kontinuierliche Dichte in der Membran kann, wie darüber hinaus vorgeführt wurde, problematisch sein.
Ihre exaktere Modellierung, etwa durch Einbettung auch probabilistischer Einzelkanaldarstellungen in das räumliche Modell sollte in zukünftigen Arbeiten noch mehr thematisiert werden.
Mit Blick auf die Wiederverwendbarkeit bereits implementierter Funktionalität innerhalb dieser Arbeiten wurden spezielle Teile dieser Funktionalität hier in einem gesonderten
Kapitel genauer beschrieben. Als komplexes Beispiel für das, was simulationstechnisch bereits im Bereich des Machbaren
liegt, und gleichsam für eine Anwendung, die zeigt, wie möglichst viele der im Rahmen dieser Arbeit entwickelten Methoden miteinander kombiniert werden können, wurde die
Calcium-Dynamik eines kompletten Dendriten innerhalb eines großen aktiven neuronalen Netzwerks simuliert.
Co-design of a trustworthy AI system in healthcare: deep learning based skin lesion classifier
(2021)
This paper documents how an ethically aligned co-design methodology ensures trustworthiness in the early design phase of an artificial intelligence (AI) system component for healthcare. The system explains decisions made by deep learning networks analyzing images of skin lesions. The co-design of trustworthy AI developed here used a holistic approach rather than a static ethical checklist and required a multidisciplinary team of experts working with the AI designers and their managers. Ethical, legal, and technical issues potentially arising from the future use of the AI system were investigated. This paper is a first report on co-designing in the early design phase. Our results can also serve as guidance for other early-phase AI-similar tool developments.
Point-based geometry representations have become widely used in numerous contexts, ranging from particle-based simulations, over stereo image matching, to depth sensing via light detection and ranging. Our application focus is on the reconstruction of curved line structures in noisy 3D point cloud data. Respective algorithms operating on such point clouds often rely on the notion of a local neighborhood. Regarding the latter, our approach employs multi-scale neighborhoods, for which weighted covariance measures of local points are determined. Curved line structures are reconstructed via vector field tracing, using a bidirectional piecewise streamline integration. We also introduce an automatic selection of optimal starting points via multi-scale geometric measures. The pipeline development and choice of parameters was driven by an extensive, automated initial analysis process on over a million prototype test cases. The behavior of our approach is controlled by several parameters — the majority being set automatically, leaving only three to be controlled by a user. In an extensive, automated final evaluation, we cover over one hundred thousand parameter sets, including 3D test geometries with varying curvature, sharp corners, intersections, data holes, and systematically applied varying types of noise. Further, we analyzed different choices for the point of reference in the co-variance computation; using a weighted mean performed best in most cases. In addition, we compared our method to current, publicly available line reconstruction frameworks. Up to thirty times faster execution times were achieved in some cases, at comparable error measures. Finally, we also demonstrate an exemplary application on four real-world 3D light detection and ranging datasets, extracting power line cables.
High-energy physics experiments aim to deepen our understanding of the fundamental structure of matter and the governing forces. One of the most challenging aspects of the design of new experiments is data management and event selection. The search for increasingly rare and intricate physics events asks for high-statistics measurements and sophisticated event analysis. With progressively complex event signatures, traditional hardware-based trigger systems reach the limits of realizable latency and complexity. The Compressed Baryonic Matter experiment (CBM) employs a novel approach for data readout and event selection to address these challenges. Self-triggered, free-streaming detectors push all data to a central compute cluster, called First-level Event Selector (FLES), for software-based event analysis and selection. While this concept solves many issues present in classical architectures, it also sets new challenges for the design of the detector readout systems and online event selection.
This thesis presents an efficient solution to the data management challenges presented by self-triggered, free-streaming particle detectors. The FLES must receive asynchronously streamed data from a heterogeneous detector setup at rates of up to 1 TB/s. The real-time processing environment implies that all components have to deliver high performance and reliability to record as much valuable data as possible. The thesis introduces a time-based data model to partition the input streams into containers of fixed length in experiment time for efficient data management. These containers provide all necessary metadata to enable generic, detector-subsystem-agnostic data distribution across the entire cluster. An analysis shows that the introduced data overhead is well below 1 % for a wide range of system parameters.
Furthermore, a concept and the implementation of a detector data input interface for the CBM FLES, optimized for resource-efficient data transport, are presented. The central element of the architecture is an FPGA-based PCIe extension card for the FLES entry nodes. The hardware designs developed in the thesis enable interfacing with a diverse set of detector systems. A custom, high-throughput DMA design structures data in a way that enables low-overhead access and efficient software processing. The ability to share the host DMA buffers with other devices, such as an InfiniBand HCA, allows for true zero-copy data distribution between the cluster nodes. The discussed FLES input interface is fully implemented and has already proven its reliability in production operation in various physics experiments.
Ein großer Tag für das Institut für Informatik der Goethe-Universität: Es feiert sein 40-jähriges Jubiläum mit einem Festakt am Freitag, den 13. Oktober, ab 14 Uhr im Casino auf dem Campus Westend. Die Universität hat zusammen mit dem Alumni- und Förderverein hochkarätige Gastredner eingeladen, die ein Bild von der Bedeutung der Informatik und ihrer Forschung zeichnen werden.
We study online secretary problems with returns in combinatorial packing domains with n candidates that arrive sequentially over time in random order. The goal is to determine a feasible packing of candidates of maximum total value. In the first variant, each candidate arrives exactly twice. All 2n arrivals occur in random order. We propose a simple 0.5‐competitive algorithm. For the online bipartite matching problem, we obtain an algorithm with ratio at least 0.5721 − o(1), and an algorithm with ratio at least 0.5459 for all n ≥ 1. We extend all algorithms and ratios to k ≥ 2 arrivals per candidate. In the second variant, there is a pool of undecided candidates. In each round, a random candidate from the pool arrives. Upon arrival a candidate can be either decided (accept/reject) or postponed. We focus on minimizing the expected number of postponements when computing an optimal solution. An expected number of Θ(n log n) is always sufficient. For bipartite matching, we can show a tight bound of O(r log n), where r is the size of the optimum matching. For matroids, we can improve this further to a tight bound of O(r′ log(n/r′)), where r′ is the minimum rank of the matroid and the dual matroid.
In this dissertation the formal abstraction and verification of analog circuit is examined. An approach is introduced that automatically abstracts a transistor level circuit with full Spice accuracy into a hybrid automaton (HA) in various output languages. The generated behavioral model exhibits a significant simulation speed-up compared to the original netlist, while maintaining an acceptable accuracy, and can be therefore used in various verification and validation routines. On top of that, the generated models can be formally verified against their Spice netlists, making the obtained models correct by construction.
The generated abstract models can be extended to enclose modeling as well as technology dependent parameter variations with little over approximations. As these models enclose the various behaviors of the sampled netlists, the obtained models are of significant importance as they can replace several simulations with just a single reachability analysis or symbolic simulation. Moreover, these models can be as well be used in different verification routines as demonstrated in this dissertation.
As the obtained models are described by HAs with linear behaviors in the locations, the abstract models can be as well compositionally linked, allowing thereby the abstraction of complex analog circuits.
Depending on the specified modeling settings, including for example the number of locations of the HA and the description of the system behavior, the accuracy, speedup, and various additional properties of the HA can be influenced. This is examined in detail in this dissertation. The underlying abstraction process is first covered in detail. Several extensions are then handled including the modeling of the HAs with parameter variations. The obtained models are then verified using various verification methodologies. The accuracy and speed-up of the abstraction methodology is finally evaluated on several transistor level circuits ranging from simple operational amplifiers up to a complex circuits.
In this talk we presented a novel technique, based on Deep Learning, to determine the impact parameter of nuclear collisions at the CBM experiment. PointNet based Deep Learning models are trained on UrQMD followed by CBMRoot simulations of Au+Au collisions at 10 AGeV to reconstruct the impact parameter of collisions from raw experimental data such as hits of the particles in the detector planes, tracks reconstructed from the hits or their combinations. The PointNet models can perform fast, accurate, event-by-event impact parameter determination in heavy ion collision experiments. They are shown to outperform a simple model which maps the track multiplicity to the impact parameter. While conventional methods for centrality classification merely provide an expected impact parameter distribution for a given centrality class, the PointNet models predict the impact parameter from 2–14 fm on an event-by-event basis with a mean error of −0.33 to 0.22 fm.
The ongoing digitalization of educational resources and the use of the internet lead to a steady increase of potentially available learning media. However, many of the media which are used for educational purposes have not been designed specifically for teaching and learning. Usually, linguistic criteria of readability and comprehensibility as well as content-related criteria are used independently to assess and compare the quality of educational media. This also holds true for educational media used in economics. This article aims to improve the analysis of textual learning media used in economic education by drawing on threshold concepts. Threshold concepts are key terms in knowledge acquisition within a domain. From a linguistic perspective, however, threshold concepts are instances of specialized vocabularies, exhibiting particular linguistic features. In three kinds of (German) resources, namely in textbooks, in newspapers, and on Wikipedia, we investigate the distributive profiles of 63 threshold concepts identified in economics education (which have been collected from threshold concept research). We looked at the threshold concepts' frequency distribution, their compound distribution, and their network structure within the three kinds of resources. The two main findings of our analysis show that firstly, the three kinds of resources can indeed be distinguished in terms of their threshold concepts' profiles. Secondly, Wikipedia definitely shows stronger associative connections between economic threshold concepts than the other sources. We discuss the findings in relation to adequate media use for teaching and learning—not only in economic education.
Learning to solve graph tasks is one of the key prerequisites of acquiring domain-specific knowledge in most study domains. Analyses of graph understanding often use eye-tracking and focus on analyzing how much time students spend gazing at particular areas of a graph—Areas of Interest (AOIs). To gain a deeper insight into students’ task-solving process, we argue that the gaze shifts between students’ fixations on different AOIs (so-termed transitions) also need to be included in holistic analyses of graph understanding that consider the importance of transitions for the task-solving process. Thus, we introduced Epistemic Network Analysis (ENA) as a novel approach to analyze eye-tracking data of 23 university students who solved eight multiple-choice graph tasks in physics and economics. ENA is a method for quantifying, visualizing, and interpreting network data allowing a weighted analysis of the gaze patterns of both correct and incorrect graph task solvers considering the interrelations between fixations and transitions. After an analysis of the differences in the number of fixations and the number of single transitions between correct and incorrect solvers, we conducted an ENA for each task. We demonstrate that an isolated analysis of fixations and transitions provides only a limited insight into graph solving behavior. In contrast, ENA identifies differences between the gaze patterns of students who solved the graph tasks correctly and incorrectly across the multiple graph tasks. For instance, incorrect solvers shifted their gaze from the graph to the x-axis and from the question to the graph comparatively more often than correct solvers. The results indicate that incorrect solvers often have problems transferring textual information into graphical information and rely more on partly irrelevant parts of a graph. Finally, we discuss how the findings can be used to design experimental studies and for innovative instructional procedures in higher education
Volatility clustering and fat tails are prominently observed in financial markets. Here, we analyze the underlying mechanisms of three agent-based models explaining these stylized facts in terms of market instabilities and compare them on empirical grounds. To this end, we first develop a general framework for detecting tail events in stock markets. In particular, we introduce Hawkes processes to automatically identify and date onsets of market turmoils which result in increased volatility. Second, we introduce three different indicators to predict those onsets. Each of the three indicators is derived from and tailored to one of the models, namely quantifying information content, critical slowing down or market risk perception. Finally, we apply our indicators to simulated and real market data. We find that all indicators reliably predict market events on simulated data and clearly distinguish the different models. In contrast, a systematic comparison on the stocks of the Forbes 500 companies shows a markedly lower performance. Overall, predicting the onset of market turmoils appears difficult, yet, over very short time horizons high or rising volatility exhibits some predictive power.
Monitoring is an indispensable tool for the operation of any large installation of grid or cluster computing, be it high energy physics or elsewhere. Usually, monitoring is configured to collect a small amount of data, just enough to enable detection of abnormal conditions. Once detected, the abnormal condition is handled by gathering all information from the affected components. This data is processed by querying it in a manner similar to a database.
This contribution shows how the metaphor of a debugger (for software applications) can be transferred to a compute cluster. The concepts of variables, assertions and breakpoints that are used in debugging can be applied to monitoring by defining variables as the quantities recorded by monitoring and breakpoints as invariants formulated via these variables. It is found that embedding fragments of a data extracting and reporting tool such as the UNIX tool awk facilitates concise notations for commonly used variables since tools like awk are designed to process large event streams (in textual representations) with bounded memory. A functional notation similar to both the pipe notation used in the UNIX shell and the point-free style used in functional programming simplify the combination of variables that commonly occur when formulating breakpoints.
We test the hypothesis that the extent to which one obtains information on a given topic through Wikipedia depends on the language in which it is consulted. Controlling the size factor, we investigate this hypothesis for a number of 25 subject areas. Since Wikipedia is a central part of the web-based information landscape, this indicates a language-related, linguistic bias. The article therefore deals with the question of whether Wikipedia exhibits this kind of linguistic relativity or not. From the perspective of educational science, the article develops a computational model of the information landscape from which multiple texts are drawn as typical input of web-based reading. For this purpose, it develops a hybrid model of intra- and intertextual similarity of different parts of the information landscape and tests this model on the example of 35 languages and corresponding Wikipedias. In the way it measures the similarities of hypertexts, the article goes beyond existing approaches by examining their structural and semantic aspects intra- and intertextually. In this way it builds a bridge between reading research, educational science, Wikipedia research and computational linguistics.
A new method of event characterization based on Deep Learning is presented. The PointNet models can be used for fast, online event-by-event impact parameter determination at the CBM experiment. For this study, UrQMD and the CBM detector simulation are used to generate Au+Au collision events at 10 AGeV which are then used to train and evaluate PointNet based architectures. The models can be trained on features like the hit position of particles in the CBM detector planes, tracks reconstructed from the hits or combinations thereof. The Deep Learning models reconstruct impact parameters from 2-14 fm with a mean error varying from -0.33 to 0.22 fm. For impact parameters in the range of 5-14 fm, a model which uses the combination of hit and track information of particles has a relative precision of 4-9% and a mean error of -0.33 to 0.13 fm. In the same range of impact parameters, a model with only track information has a relative precision of 4-10% and a mean error of -0.18 to 0.22 fm. This new method of event-classification is shown to be more accurate and less model dependent than conventional methods and can utilize the performance boost of modern GPU processor units.
The impact of columnar file formats on SQL‐on‐hadoop engine performance: a study on ORC and Parquet
(2019)
Columnar file formats provide an efficient way to store data to be queried by SQL‐on‐Hadoop engines. Related works consider the performance of processing engine and file format together, which makes it impossible to predict their individual impact. In this work, we propose an alternative approach: by executing each file format on the same processing engine, we compare the different file formats as well as their different parameter settings. We apply our strategy to two processing engines, Hive and SparkSQL, and evaluate the performance of two columnar file formats, ORC and Parquet. We use BigBench (TPCx‐BB), a standardized application‐level benchmark for Big Data scenarios. Our experiments confirm that the file format selection and its configuration significantly affect the overall performance. We show that ORC generally performs better on Hive, whereas Parquet achieves best performance with SparkSQL. Using ZLIB compression brings up to 60.2% improvement with ORC, while Parquet achieves up to 7% improvement with Snappy. Exceptions are the queries involving text processing, which do not benefit from using any compression.
Es sollte eine Simulationsumgebung mit einem Straßennetz und eine KDNA, die Autos auf diesem Straßennetz kontrolliert, implementiert werden. Für die Simulation wurde eine einfache graphische Darstellung entwickelt auf der eine variable Anzahl Autos auf einem vorprogrammierten Straßennetz fahren. Eine KDNA steuert diese Autos über Kontrolle von Gas-, Bremse- und Steuerradpositionen, wobei Geschwindigkeits- und Richtungskontrolle unabhängig stattfinden. Bei der Analyse der KDNA für mehrere Autos traten Leistungsprobleme auf, deren Quelle genauer untersucht wurde. Die Last wurde primär durch die Kommunikation zur Verwaltung der KDNA-Tasks im AHS erzeugt.
Das Ziel dieser Arbeit ist, einen Text automatisch darauf zu untersuchen, ob er Gebäude beschreibt, und diese gegebenenfalls zu visualisieren. Zu diesem Zweck wurde ein Prototyp entwickelt, der mithilfe von NLP-Software auf Basis einer UIMA-Pipeline einen Text auf Gebäudedaten untersucht und diese anschließend als 3D-Modelle auf einer Karte visualisiert. Um die Güte des Projekts zu bestimmen wurde eine Evaluation durchgeführt, in der die Aufgabe darin bestand, Paragraphen ihren zugehörigen 3D-Modellen zuzuordnen. Die Ergebnisse wiesen eine Erkennungsrate von 88.67\% auf. Jedoch wurden auch Schwächen im Standardisierungsverfahren der Parameter und in der einseitigen Art zu Visualisieren aufgezeigt. Zum Schluss wird vorgestellt, wie diese Schwachstellen mithilfe eines ontologischen Modells behoben werden können und wie mit dem Projekt weiterverfahren werden kann.
In diesem Beitrag untersuchen wir Entwicklungstendenzen von Infrastrukturen in den Digitalen Geisteswissenschaften. Wir argumentieren, dass infolge (1) der Verfügbarkeit von immer mehr Daten über sozial-semiotische Netzwerke, (2) der Methodeninflation in geisteswissenschaftlichen Disziplinen, (3) der zunehmend hybriden Arbeitsteilung zwischen Mensch und Maschine und (4) der explosionsartigen Vermehrung künstlicher Texte ein erheblicher Anpassungsdruck auf die Weiterentwicklung solcher Infrastrukturen entstanden ist. In diesem Zusammenhang beschreiben wir drei Informationssysteme, die sich unter anderem durch die Interaktionsmöglichkeiten unterscheiden, die sie ihren Nutzern bieten, um solchen Herausforderungen zu begegnen. Dabei skizzieren wir mit VienNA eine neuartige Architektur solcher Systeme, welche aufgrund ihrer Flexibilität die Möglichkeit bieten könnte, letztere Herausforderungen zu bewältigen.
The specific temporal evolution of bacterial and phage population sizes, in particular bacterial depletion and the emergence of a resistant bacterial population, can be seen as a kinetic fingerprint that depends on the manifold interactions of the specific phage–host pair during the course of infection. We have elaborated such a kinetic fingerprint for a human urinary tract Klebsiella pneumoniae isolate and its phage vB_KpnP_Lessing by a modeling approach based on data from in vitro co-culture. We found a faster depletion of the initially sensitive bacterial population than expected from simple mass action kinetics. A possible explanation for the rapid decline of the bacterial population is a synergistic interaction of phages which can be a favorable feature for phage therapies. In addition to this interaction characteristic, analysis of the kinetic fingerprint of this bacteria and phage combination revealed several relevant aspects of their population dynamics: A reduction of the bacterial concentration can be achieved only at high multiplicity of infection whereas bacterial extinction is hardly accomplished. Furthermore the binding affinity of the phage to bacteria is identified as one of the most crucial parameters for the reduction of the bacterial population size. Thus, kinetic fingerprinting can be used to infer phage–host interactions and to explore emergent dynamics which facilitates a rational design of phage therapies.
Software updates are a critical success factor in mobile app ecosystems. Through publishing regular updates, platform providers enhance their operating systems for the benefit of both end users and third-party developers. It is also a way of attracting new customers. However, this platform evolution poses the risk of inadvertently introducing software problems, which can severely disturb the ecosystem’s balance by compromising its foundational technologies. So far, little to no research has addressed this issue from a user-centered perspective. The thesis at hand draws on IS post-adoption literature to investigate the potential negative influences of operating system updates on mobile app users. The release of Apple’s iOS 13 update serves as research object. Based on over half a million user reviews from the AppStore, data mining techniques are applied to study the impact of the new platform version. The results show that iOS 13 caused complications with a large number of popular apps, leading to a significant decline in user ratings and an uptrend in negative sentiment. Feature requests, functional complaints, and device compatibility are identified as the three major issue categories. These issue types are compared in terms of their quantifiable negative effect on users’ continuance intention. In essence, the findings contribute to IS research on post-adoption behavior and provide guidance to ecosystem participants in dealing with update-induced platform issues.
BIOfid is a specialized information service currently being developed to mobilize biodiversity data dormant in printed historical and modern literature and to offer a platform for open access journals on the science of biodiversity. Our team of librarians, computer scientists and biologists produce high-quality text digitizations, develop new text-mining tools and generate detailed ontologies enabling semantic text analysis and semantic search by means of user-specific queries. In a pilot project we focus on German publications on the distribution and ecology of vascular plants, birds, moths and butterflies extending back to the Linnaeus period about 250 years ago. The three organism groups have been selected according to current demands of the relevant research community in Germany. The text corpus defined for this purpose comprises over 400 volumes with more than 100,000 pages to be digitized and will be complemented by journals from other digitization projects, copyright-free and project-related literature. With TextImager (Natural Language Processing & Text Visualization) and TextAnnotator (Discourse Semantic Annotation) we have already extended and launched tools that focus on the text-analytical section of our project. Furthermore, taxonomic and anatomical ontologies elaborated by us for the taxa prioritized by the project’s target group - German institutions and scientists active in biodiversity research - are constantly improved and expanded to maximize scientific data output. Our poster describes the general workflow of our project ranging from literature acquisition via software development, to data availability on the BIOfid web portal (http://biofid.de/), and the implementation into existing platforms which serve to promote global accessibility of biodiversity data.
Risk evaluations for agricultural chemicals are necessary to preserve healthy populations of honey bee colonies. Field studies on whole colonies are limited in behavioural research, while results from lab studies allow only restricted conclusions on whole colony impacts. Methods for automated long-term investigations of behaviours within comb cells, such as brood care, were hitherto missing. In the present study, we demonstrate an innovative video method that enables within-cell analysis in honey bee (Apis mellifera) observation hives to detect chronic sublethal neonicotinoid effects of clothianidin (1 and 10 ppb) and thiacloprid (200 ppb) on worker behaviour and development. In May and June, colonies which were fed 10 ppb clothianidin and 200 ppb thiacloprid in syrup over three weeks showed reduced feeding visits and duration throughout various larval development days (LDDs). On LDD 6 (capping day) total feeding duration did not differ between treatments. Behavioural adaptation was exhibited by nurses in the treatment groups in response to retarded larval development by increasing the overall feeding timespan. Using our machine learning algorithm, we demonstrate a novel method for detecting behaviours in an intact hive that can be applied in a versatile manner to conduct impact analyses of chemicals, pests and other stressors.
Measurement of ϒ(1S) elliptic flow at forward rapidity in Pb-Pb collisions at √sNN = 5.02 TeV
(2019)
The first measurement of the ϒ(1S) elliptic flow coefficient (v2) is performed at forward rapidity (2.5 < y < 4) in Pb–Pb collisions at √sNN = 5.02 TeV with the ALICE detector at the LHC. The results are obtained with the scalar product method and are reported as a function of transverse momentum (pT) up to 15 GeV/c in the 5%–60% centrality interval. The measured Υ(1S)v2 is consistent with 0 and with the small positive values predicted by transport models within uncertainties. The v2 coefficient in 2 < pT < 15 GeV/c is lower than that of inclusive J/ψ mesons in the same pT interval by 2.6 standard deviations. These results, combined with earlier suppression measurements, are in agreement with a scenario in which the Υ(1S) production in Pb–Pb collisions at LHC energies is dominated by dissociation limited to the early stage of the collision, whereas in the J/ψ case there is substantial experimental evidence of an additional regeneration component.
Dancing is an activity that positively enhances the mood of people that consists of feeling the music and expressing it in rhythmic movements with the body. Learning how to dance can be challenging because it requires proper coordination and understanding of rhythm and beat. In this paper, we present the first implementation of the Dancing Coach (DC), a generic system designed to support the practice of dancing steps, which in its current state supports the practice of basic salsa dancing steps. However, the DC has been designed to allow the addition of more dance styles. We also present the first user evaluation of the DC, which consists of user tests with 25 participants. Results from the user test show that participants stated they had learned the basic salsa dancing steps, to move to the beat and body coordination in a fun way. Results also point out some direction on how to improve the future versions of the DC.
Iconographic representations on ancient artifacts are described in many existing databases and literature as human readable text. We applied Natural Language Processing (NLP) approaches in order to extract the semantics out of these textual descriptions and in this way enable semantic searches over them. This allows more sophisticated requests compared to the common existing keyword searches. As we show in our experiments based on numismatic datasets, the approach is generic in the sense that once the system is trained on one dataset, it can be applied without any further manual work also to datasets that have similar content. Of course, additional adaptions would further improve the results. Since the approach requires manual work only during the training phase, it can easily be applied to huge datasets without manual work and therefore without major extra costs. In fact, in our experience bigger datasets generate even better results because there is more data for training. Since our approach is not bound to a certain domain and the numismatic datasets are just an example, it could serve as a blueprint for many other areas. It could also help to build bridges between disciplines since textual iconographic descriptions are to be found also for pottery, sculpture and elsewhere.
This paper is a contribution to exploring and analyzing space-improvements in concurrent programming languages, in particular in the functional process-calculus CHF. Space-improvements are defined as a generalization of the corresponding notion in deterministic pure functional languages. The main part of the paper is the O(n ·logn) algorithm SPOPTN for offline space optimization of several parallel independent processes. Applications of this algorithm are: (i) affirmation of space improving transformations for particular classes of program transformations; (ii) support of an interpreter-based method for refuting space-improvements; and (iii) as a stand-alone offline-optimizer for space (or similar resources) of parallel processes.
Correction to: Scientifc Reports https://doi.org/10.1038/s41598-019-43857-5, published online 17 May 2019. In the original version of this Article, Jan-Hendrik Trösemeier was incorrectly affiliated with ‘Division of Allergology, Paul Ehrlich Institut, Langen, Germany’. Te correct afliations are listed below...
Gegenstand der hier vorgestellten Arbeit ist eine Applikation für die virtuelle Realität (VR), die in der Lage ist, die Struktur eines beliebigen Textes als begehbare, interaktive Stadt zu visualisieren. Darüber hinaus bietet das Programm eine besondere Textsuche an, die so in anderen konventionellen Textverarbeitungsprogrammen nicht vorzufinden ist. Dank der strukturellen Analyse und der Verwendung einiger außergewöhnlicher Analysetools des TextImager [2], ermöglicht text2City nicht nur die Suche nach bestimmten Textmustern, sondern zum Beispiel auch die Bestimmung der Textebene (Wort, Satz, Absatz, etc.) und einiges mehr. Ein weiteres Feature ist die Kommunikationsverbindung zwischen dem TextAnnotator-Service [1] und text2City, die dem Benutzer die Möglichkeit zum Annotieren bietet, aber auch von anderen Personen durchgeführte Annotationen sofort sichtbar machen kann. Für die Ausführung des Programms ist eine der beiden VRBrillen, Oculus Rift oder HTC Vive, ein für VR geeigneter PC, sowie die Software Unity nötig.
Though the range of invariance in recognition of novel objects is a basic aspect of human vision, its characterization has remained surprisingly elusive. Here we report tolerance to scale and position changes in one-shot learning by measuring recognition accuracy of Korean letters presented in a flash to non-Korean subjects who had no previous experience with Korean letters. We found that humans have significant scale-invariance after only a single exposure to a novel object. The range of translation-invariance is limited, depending on the size and position of presented objects. To understand the underlying brain computation associated with the invariance properties, we compared experimental data with computational modeling results. Our results suggest that to explain invariant recognition of objects by humans, neural network models should explicitly incorporate built-in scale-invariance, by encoding different scale channels as well as eccentricity-dependent representations captured by neurons’ receptive field sizes and sampling density that change with eccentricity. Our psychophysical experiments and related simulations strongly suggest that the human visual system uses a computational strategy that differs in some key aspects from current deep learning architectures, being more data efficient and relying more critically on eye-movements.
In pathology, tissue images are evaluated using a light microscope, relying on the expertise and experience of pathologists. There is a great need for computational methods to quantify and standardize histological observations. Computational quantification methods become more and more essential to evaluate tissue images. In particular, the distribution of tumor cells and their microenvironment are of special interest. Here, we systematically investigated tumor cell properties and their spatial neighborhood relations by a new application of statistical analysis to whole slide images of Hodgkin lymphoma, a tumor arising in lymph nodes, and inflammation of lymph nodes called lymphadenitis. We considered properties of more than 400, 000 immunohistochemically stained, CD30-positive cells in 35 whole slide images of tissue sections from subtypes of the classical Hodgkin lymphoma, nodular sclerosis and mixed cellularity, as well as from lymphadenitis. We found that cells of specific morphology exhibited significant favored and unfavored spatial neighborhood relations of cells in dependence of their morphology. This information is important to evaluate differences between Hodgkin lymph nodes infiltrated by tumor cells (Hodgkin lymphoma) and inflamed lymph nodes, concerning the neighborhood relations of cells and the sizes of cells. The quantification of neighborhood relations revealed new insights of relations of CD30-positive cells in different diagnosis cases. The approach is general and can easily be applied to whole slide image analysis of other tumor types.
The morphology of presynaptic specializations can vary greatly ranging from classical single-release-site boutons in the central nervous system to boutons of various sizes harboring multiple vesicle release sites. Multi-release-site boutons can be found in several neural contexts, for example at the neuromuscular junction (NMJ) of body wall muscles of Drosophila larvae. These NMJs are built by two motor neurons forming two types of glutamatergic multi-release-site boutons with two typical diameters. However, it is unknown why these distinct nerve terminal configurations are used on the same postsynaptic muscle fiber. To systematically dissect the biophysical properties of these boutons we developed a full three-dimensional model of such boutons, their release sites and transmitter-harboring vesicles and analyzed the local vesicle dynamics of various configurations during stimulation. Here we show that the rate of transmission of a bouton is primarily limited by diffusion-based vesicle movements and that the probability of vesicle release and the size of a bouton affect bouton-performance in distinct temporal domains allowing for an optimal transmission of the neural signals at different time scales. A comparison of our in silico simulations with in vivo recordings of the natural motor pattern of both neurons revealed that the bouton properties resemble a well-tuned cooperation of the parameters release probability and bouton size, enabling a reliable transmission of the prevailing firing-pattern at diffusion-limited boutons. Our findings indicate that the prevailing firing-pattern of a neuron may determine the physiological and morphological parameters required for its synaptic terminals.
Relying on the theory of Saward (2010) and Disch (2015), we study political representation through the lens of representative claim-making. We identify a gap between the theoretical concept of claim-making and the empirical (quantitative) assessment of representative claims made in the real world’s representative contexts. Therefore, we develop a new approach to map and quantify representative claims in order to subsequently measure the reception and validation of the claims by the audience. To test our method, we analyse all the debates of the German parliament concerned with the introduction of the gender quota in German supervisory boards from 2013 to 2017 in a two-step process. At first, we assess which constituencies the MPs claim to represent and how they justify their stance. Drawing on multiple correspondence analysis, we identify different claim patterns. Second, making use of natural language processing techniques and logistic regression on social media data, we measure if and how the asserted claims in the parliamentary debates are received and validated by the respective audience. We come to the conclusion that the constituency as ultimate judge of legitimacy has not been comprehensively conceptualized yet.
The formulation of the Partial Information Decomposition (PID) framework by Williams and Beer in 2010 attracted a significant amount of attention to the problem of defining redundant (or shared), unique and synergistic (or complementary) components of mutual information that a set of source variables provides about a target. This attention resulted in a number of measures proposed to capture these concepts, theoretical investigations into such measures, and applications to empirical data (in particular to datasets from neuroscience). In this Special Issue on “Information Decomposition of Target Effects from Multi-Source Interactions” at Entropy, we have gathered current work on such information decomposition approaches from many of the leading research groups in the field. We begin our editorial by providing the reader with a review of previous information decomposition research, including an overview of the variety of measures proposed, how they have been interpreted and applied to empirical investigations. We then introduce the articles included in the special issue one by one, providing a similar categorisation of these articles into: i. proposals of new measures; ii. theoretical investigations into properties and interpretations of such approaches, and iii. applications of these measures in empirical studies. We finish by providing an outlook on the future of the field.
This paper describes work on the morphological and syntactic annotation of Sumerian cuneiform as a model for low resource languages in general. Cuneiform texts are invaluable sources for the study of history, languages, economy, and cultures of Ancient Mesopotamia and its surrounding regions. Assyriology, the discipline dedicated to their study, has vast research potential, but lacks the modern means for computational processing and analysis. Our project, Machine Translation and Automated Analysis of Cuneiform Languages, aims to fill this gap by bringing together corpus data, lexical data, linguistic annotations and object metadata. The project’s main goal is to build a pipeline for machine translation and annotation of Sumerian Ur III administrative texts. The rich and structured data is then to be made accessible in the form of (Linguistic) Linked Open Data (LLOD), which should open them to a larger research community. Our contribution is two-fold: in terms of language technology, our work represents the first attempt to develop an integrative infrastructure for the annotation of morphology and syntax on the basis of RDF technologies and LLOD resources. With respect to Assyriology, we work towards producing the first syntactically annotated corpus of Sumerian.