Temporal Ordered Clustering in Dynamic Networks: Unsupervised and Semi-supervised Learning Algorithms

In temporal ordered clustering, given a single snapshot of a dynamic network in which nodes arrive at distinct time instants, we aim at partitioning its nodes into $K$ ordered clusters $\mathcal{C}_1 \prec \cdots \prec \mathcal{C}_K$ such that for $i<j$, nodes in cluster $\mathcal{C}_i$ arrived before nodes in cluster $\mathcal{C}_j$, with $K$ being a data-driven parameter and not known upfront. Such a problem is of considerable significance in many applications ranging from tracking the expansion of fake news to mapping the spread of information. We first formulate our problem for a general dynamic graph, and propose an integer programming framework that finds the optimal clustering, represented as a strict partial order set, achieving the best precision (i.e., fraction of successfully ordered node pairs) for a fixed density (i.e., fraction of comparable node pairs). We then develop a sequential importance procedure and design unsupervised and semi-supervised algorithms to find temporal ordered clusters that efficiently approximate the optimal solution. To illustrate the techniques, we apply our methods to the vertex copying (duplication-divergence) model which exhibits some edge-case challenges in inferring the clusters as compared to other network models. Finally, we validate the performance of the proposed algorithms on synthetic and real-world networks.Comment: 14 pages, 9 figures, and 3 tables. This version is submitted to a journal. A shorter version of this work is published in the proceedings of IEEE International Symposium on Information Theory (ISIT), 2020. The first two authors contributed equall

\u3cem\u3eRhizobium leguminosarum\u3c/em\u3e CFN42 Genetic Regions Encoding Lipopolysaccharide Structures Essential for Complete Nodule Development on Bean Plants

Eight symbiotic mutants defective in lipopolysaccharide (LPS) synthesis were isolated from Rhizobium leguminosarum biovar phaseoli CFN42. These eight strains elicited small white nodules lacking infected cells when inoculated onto bean plants. The mutants had undetectable or greatly diminished amounts of the complete LPS (LPS I), whereas amounts of an LPS lacking the O antigen (LPS II) greatly increased. Apparent LPS bands that migrated between LPS I and LPS II on sodium dodecyl sulfate-polyacrylamide gels were detected in extracts of some of the mutants. The mutant strains were complemented to wild-type LPS I content and antigenicity by DNA from a cosmid library of the wild-type genome. Most of the mutations were clustered in two genetic regions; one mutation was located in a third region. Strains complemented by DNA from two of these regions produced healthy nitrogen-fixing nodules. Strains complemented to wild-type LPS content by the other genetic region induced nodules that exhibited little or no nitrogenase activity, although nodule development was obviously enhanced by the presence of this DNA. The results support the idea that complete LPS structures, in normal amounts, are necessary for infection thread development in bean plants

Characterization of the Lipopolysaccharide from a \u3cem\u3eRhizobium phaseoli\u3c/em\u3e Mutant that is Defective in Infection Thread Development

The lipopolysaccharide (LPS) from a Rhizobium phaseoli mutant, CE109, was isolated and compared with that of its wild-type parent, CE3. A previous report has shown that the mutant is defective in infection thread development, and sodium dodecyl sulfate-polyacrylamide gel electrophoresis shows that it has an altered LPS (K. D. Noel, K. A. VandenBosch, and B. Kulpaca, J. Bacteriol. 168:1392-1462, 1986). Mild acid hydrolysis of the CE3 LPS released a polysaccharide and an oligosaccharide, PS1 and PS2, respectively. Mild acid hydrolysis of CE109 LPS released only an oligosaccharide. Chemical and immunochemical analyses showed that CE3-PS1 is the antigenic O chain of this strain and that CE109 LPS does not contain any of the major sugar components of CE3-PS1. CE109 oligosaccharide was identical in composition to CE3-PS2. The lipid A\u27s from both strains were very similar in composition, with only minor quantitative variations. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis of CE3 and CE109 LPSs showed that CE3 LPS separated into two bands, LPS I and LPS II, while CE109 had two bands which migrated to positions similar to that of LPS II. Immunoblotting with anti-CE3 antiserum showed that LPS I contains the antigenic O chain of CE3, PS1. Anti-CE109 antiserum interacted strongly with both CE109 LPS bands and CE3 LPS II and interacted weakly with CE3 LPS I. Mild-acid hydrolysis of CE3 LPS I, extracted from the polyacrylamide gel, showed that it contained both PS1 and PS2. The results in this report showed that CE109 LPS consists of only the lipid A core and is missing the antigenic O chain

Workflow-basierte Geschäftsprozeßregelung als Konzept für das Management industrieller Produktentwicklungsprozesse

Die Prozesse der industriellen Produktentwicklung müssen für jedes Produkt anhand dessen spezifischer Bedingungen individuell gestaltet werden und sind aufgrund der gerade am Anfang vorherrschenden unscharfen Informationssituation und der komplexen Verzahnung der Abläufe vielen unvorhersehbaren Änderungen unterworfen. Die sich daraus ergebende erhöhte Flexibilitätsanforderung an das Prozeßmanagement kann in vielen Fällen nicht bewältigt werden, da geeignete Instrumente zur Regelung nicht im voraus modellierbarer Prozesse fehlen. Mit der Workflow-basierten Geschäftsprozeßregelung wird ein Ansatz für die flexible informationstechnisch gestützte Regelung produktindividueller und situationsspezifischer Prozesse zur Verbesserung des Managements der industriellen Produktentwicklung. Ausgehend vom hohen Optimierungspotential, das mit Workflowmanagement realisiert werden kann, besteht der Ansatz in der kombinierten Anwendung von Geschäftsprozeßregelung, Workflowmanagement und Softcomputing. Dabei werden aufgabenbezogene Modellbausteine gebildet, die produktindividuell und situationsspezifisch zu einem Workflow-basierten Geschäftsprozeßregelungsmodell zusammengefügt werden. Die zur Ausübung der Geschäftsprozeßregelung notwendigen Entscheidungsfindungsprozesse werden durch Fuzzy-Logik-Ansätze unterstützt. Der Ansatz zielt auf eine flexible informationstechnische Unterstützung des Managements von industriellen Produktentwicklungsprozessen und zeigt damit eine bisher kaum berücksichtigte Anwendungsdomäne von Workflowmanagement auf.<br

Diagnostic circulating biomarkers to detect vision-threatening diabetic retinopathy: Potential screening tool of the future?

With the increasing prevalence of diabetes in developing and developed countries, the socio-economic burden of diabetic retinopathy (DR), the leading complication of diabetes, is growing. Diabetic retinopathy (DR) is currently one of the leading causes of blindness in working-age adults worldwide. Robust methodologies exist to detect and monitor DR; however, these rely on specialist imaging techniques and qualified practitioners. This makes detecting and monitoring DR expensive and time-consuming, which is particularly problematic in developing countries where many patients will be remote and have little contact with specialist medical centres. Diabetic retinopathy (DR) is largely asymptomatic until late in the pathology. Therefore, early identification and stratification of vision-threatening DR (VTDR) is highly desirable and will ameliorate the global impact of this disease. A simple, reliable and more cost-effective test would greatly assist in decreasing the burden of DR around the world. Here, we evaluate and review data on circulating protein biomarkers, which have been verified in the context of DR. We also discuss the challenges and developments necessary to translate these promising data into clinically useful assays, to detect VTDR, and their potential integration into simple point-of-care testing devices

Data-Driven Copy-Paste Imputation for Energy Time Series

A cornerstone of the worldwide transition to smart grids are smart meters. Smart meters typically collect and provide energy time series that are vital for various applications, such as grid simulations, fault-detection, load forecasting, load analysis, and load management. Unfortunately, these time series are often characterized by missing values that must be handled before the data can be used. A common approach to handle missing values in time series is imputation. However, existing imputation methods are designed for power time series and do not take into account the total energy of gaps, resulting in jumps or constant shifts when imputing energy time series. In order to overcome these issues, the present paper introduces the new Copy-Paste Imputation (CPI) method for energy time series. The CPI method copies data blocks with similar properties and pastes them into gaps of the time series while preserving the total energy of each gap. The new method is evaluated on a real-world dataset that contains six shares of artificially inserted missing values between 1 and 30%. It outperforms by far the three benchmark imputation methods selected for comparison. The comparison furthermore shows that the CPI method uses matching patterns and preserves the total energy of each gap while requiring only a moderate run-time.Comment: 8 pages, 7 figures, submitted to IEEE Transactions on Smart Grid, the first two authors equally contributed to this wor

Data-Driven Copy-Paste Imputation for Energy Time Series

A cornerstone of the worldwide transition to smart grids are smart meters. Smart meters typically collect and provide energy time series that are vital for various applications, such as grid simulations, fault-detection, load forecasting, load analysis, and load management. Unfortunately, these time series are often characterized by missing values that must be handled before the data can be used. A common approach to handle missing values in time series is imputation. However, existing imputation methods are designed for power time series and do not take into account the total energy of gaps, resulting in jumps or constant shifts when imputing energy time series. In order to overcome these issues, the present paper introduces the new Copy-Paste Imputation (CPI) method for energy time series. The CPI method copies data blocks with similar characteristics and pastes them into gaps of the time series while preserving the total energy of each gap. The new method is evaluated on a real-world dataset that contains six shares of artificially inserted missing values between 1 and 30%. It outperforms the three benchmark imputation methods selected for comparison. The comparison furthermore shows that the CPI method uses matching patterns and preserves the total energy of each gap while requiring only a moderate run-time

Ansätze für die Verbesserung von PPS-Systemen durch Fuzzy-Logik

Ziel dieses Arbeitsberichts ist es, die Teilbereiche von Produktionsplanungs- und -steuerungssystemen (PPS-Systemen) zu identifizieren, die unter Beachtung der Interdependenzen zu anderen Teilbereichen mit einem Fuzzy-Ansatz modelliert und dadurch in ihrer Effizienz gesteigert werden können. Nach einer kurzen Einführung in die Fuzzy-Logik werden zunächst Ansätze für den Einsatz der Fuzzy-Logik innerhalb der Datenstrukturen der Produktionsplanung und -steuerung dargestellt. Danach werden die Funktionen von PPS-Systemen systematisch auf diesbezügliche Potentiale untersucht, wobei zwischen originärer und derivativer Verwendung der Fuzzy-Logik unterschieden wird, und Nutzeffekte sinnvoller 'Verunschärfungen' aufgezeigt werden. Der Arbeitsbericht schließt mit einem Ausblick

Measuring streambed morphology using range imaging

River engineeringInnovative field and laboratory instrumentatio