Families of complete non-compact Spin(7) holonomy manifolds

We consider complete non-compact Spin(7)-manifolds which are either asymptotically locally conical (ALC) or asymptotically conical (AC). The thesis consists of two parts. In the first part we develop the deformation theory of AC Spin(7)-manifolds. We show that the moduli space of torsion-free AC Spin(7)-structures on a given 8-manifold M asymptotic to a fixed Spin(7)-cone is an orbifold for generic decay rates in the non-L² regime. Furthermore, we derive a formula for the dimension of the moduli space, which has contributions from the topology of M and from solutions of a first order PDE system on the link of the asymptotic cone. In the second part we prove existence results of cohomogeneity one Spin(7) holonomy metrics for which a generic orbit is isomorphic to the Aloff–Wallach space N(1, −1) ∼= SU(3)/U(1). The unique non-trivial rank 3 vector bundle over the 5-sphere and the universal quotient bundle of CP² each carry a 1-parameter family (up to scale) of such metrics. We show that these families share a common behaviour: a generic member of these families belongs to one of two open intervals, of which one consists of ALC Spin(7) holonomy metrics and the other one of incomplete metrics. These two intervals are separated by a distinguished parameter which gives rise to an AC Spin(7) holonomy metric. Another interesting phenomenon occurs at the other endpoint of the open interval of ALC metrics, where the family collapses to the Bryant–Salamon AC G₂ holonomy metric on Λ²_CP². Notable is the existence of the two AC spaces. These are the first examples of smooth AC Spin(7) holonomy manifolds known to exist since Bryant Salamon’s original example on S₊(S⁴) in 1989. Furthermore, they provide a Spin(7) analogue of the well-known conifold transition in the setting of Calabi–Yau 3-folds

The design and synthesis of inhibitors of Mycobacterium tuberculosis thymidylate kinase (MtTMPK)

Thymidylate kinase (TMPK) phosphorylates thymidine 5’-monophosphate (dTMP) and has been proposed as an attractive target for Mycobacterium tuberculosis (Mt).1 By mimicking the structure of the substrate (dTMP), we have previously discovered different series of nucleoside analogues with MtTMPK inhibitory activities in a micromole range.2 Based on recently reported potent piperidin-3-yl-thymine inhibitors of Gram-positive bacterial TMPK,3 we report a series of isomeric N-benzyl-substituted piperidin-4-yl-thymine analogues, some of which demonstrate potent Mt TMPK inhibitory activity. Towards this end a convenient and high-yield synthesis was developed to access 1-substitued thymine derivatives

Detection of X-ray emission from the host clusters of 3CR quasars

We report the detection of extended X-ray emission around several powerful 3CR quasars with redshifts out to 0.73. The ROSAT HRI images of the quasars have been corrected for spacecraft wobble and compared with an empirical point-spread function. All the quasars examined show excess emission at radii of 15 arcsec and more; the evidence being strong for the more distant objects and weak only for the two nearest ones, which are known from other wavelengths not to lie in strongly clustered environments. The spatial profiles of the extended component is consistent with thermal emission from the intracluster medium of moderately rich host clusters to the quasars. The total luminosities of the clusters are in the range 4x10^44 - 3x10^45 erg/s, assuming a temperature of 4keV. The inner regions of the intracluster medium are, in all cases, dense enough to be part of a cooling flow.Comment: 21 pages including 4 figures and 4 tables. To be published in MNRA

The X-ray Background and AGNs

Deep X-ray surveys have shown that the cosmic X-ray background (XRB) is largely due to the accretion onto supermassive black holes, integrated over the cosmic time. These surveys have resolved more than 80% of the 0.1-10 keV X-ray background into discrete sources. Optical spectroscopic identifications show that the sources producing the bulk of the X-ray background are a mixture of unobscured (type-1) and obscured (type-2) AGNs, as predicted by the XRB population synthesis models. A class of highly luminous type-2 AGN, so called QSO-2s, has been detected in the deepest Chandra and XMM-Newton surveys. The new Chandra AGN redshift distribution peaks at much lower redshifts (z~0.7) than that based on ROSAT data, and the new X-ray luminosity function indicates that the space density of Seyfert galaxies peaks at significantly lower redshifts than that of QSOs. It is shown here, that the low redshift peak applies both to absorbed and unabsorbed AGN and is also seen in the 0.5-2 keV band alone. Previous findings of a strong dependence of the fraction of type-2 AGN on luminosity are confirmed with better statistics here. Preliminary results from an 800 ksec XMM-Newton observation of the Lockman Hole are discussed.Comment: Proceedings of the conference: "The restless high energy universe", held in Amsterdam, May 2003. To be published in: Nucl. Physics B. Suppl. Ser., E.P.J. van den Heuvel, J.J.M. in 't Zand, and R.A.M.J. Wijers (eds.). 10 pages, 5 figure

How Workflow Engines Should Talk to Resource Managers: A Proposal for a Common Workflow Scheduling Interface

Scientific workflow management systems (SWMSs) and resource managers together ensure that tasks are scheduled on provisioned resources so that all dependencies are obeyed, and some optimization goal, such as makespan minimization, is fulfilled. In practice, however, there is no clear separation of scheduling responsibilities between an SWMS and a resource manager because there exists no agreed-upon separation of concerns between their different components. This has two consequences. First, the lack of a standardized API to exchange scheduling information between SWMSs and resource managers hinders portability. It incurs costly adaptations when a component should be replaced by another one (e.g., an SWMS with another SWMS on the same resource manager). Second, due to overlapping functionalities, current installations often actually have two schedulers, both making partial scheduling decisions under incomplete information, leading to suboptimal workflow scheduling. In this paper, we propose a simple REST interface between SWMSs and resource managers, which allows any SWMS to pass dynamic workflow information to a resource manager, enabling maximally informed scheduling decisions. We provide an exemplary implementation of this API for Nextflow as an SWMS and Kubernetes as a resource manager. Our experiments with nine real-world workflows show that this strategy reduces makespan by up to 25.1% and 10.8% on average compared to the standard Nextflow/Kubernetes configuration. Furthermore, a more widespread implementation of this API would enable leaner code bases, a simpler exchange of components of workflow systems, and a unified place to implement new scheduling algorithms.Comment: Paper accepted in: 2023 23rd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid

Lotaru: Locally Predicting Workflow Task Runtimes for Resource Management on Heterogeneous Infrastructures

Many resource management techniques for task scheduling, energy and carbon efficiency, and cost optimization in workflows rely on a-priori task runtime knowledge. Building runtime prediction models on historical data is often not feasible in practice as workflows, their input data, and the cluster infrastructure change. Online methods, on the other hand, which estimate task runtimes on specific machines while the workflow is running, have to cope with a lack of measurements during start-up. Frequently, scientific workflows are executed on heterogeneous infrastructures consisting of machines with different CPU, I/O, and memory configurations, further complicating predicting runtimes due to different task runtimes on different machine types. This paper presents Lotaru, a method for locally predicting the runtimes of scientific workflow tasks before they are executed on heterogeneous compute clusters. Crucially, our approach does not rely on historical data and copes with a lack of training data during the start-up. To this end, we use microbenchmarks, reduce the input data to quickly profile the workflow locally, and predict a task's runtime with a Bayesian linear regression based on the gathered data points from the local workflow execution and the microbenchmarks. Due to its Bayesian approach, Lotaru provides uncertainty estimates that can be used for advanced scheduling methods on distributed cluster infrastructures. In our evaluation with five real-world scientific workflows, our method outperforms two state-of-the-art runtime prediction baselines and decreases the absolute prediction error by more than 12.5%. In a second set of experiments, the prediction performance of our method, using the predicted runtimes for state-of-the-art scheduling, carbon reduction, and cost prediction, enables results close to those achieved with perfect prior knowledge of runtimes

Neural Score Matching for High-Dimensional Causal Inference

Traditional methods for matching in causal inference are impractical for high-dimensional datasets. They suffer from the curse of dimensionality: exact matching and coarsened exact matching find exponentially fewer matches as the input dimension grows, and propensity score matching may match highly unrelated units together. To overcome this problem, we develop theoretical results which motivate the use of neural networks to obtain non-trivial, multivariate balancing scores of a chosen level of coarseness, in contrast to the classical, scalar propensity score. We leverage these balancing scores to perform matching for high-dimensional causal inference and call this procedure neural score matching. We show that our method is competitive against other matching approaches on semi-synthetic high-dimensional datasets, both in terms of treatment effect estimation and reducing imbalanc