A Petri Net Approach to Verify and Debug Simulation Models

Verification and Simulation share many issues, one is that simulation models require validation and verification. In the context of simulation, verification is understood as the task to ensure that an executable simulation model matches its conceptual counterpart while validation is the task to ensure that a simulation model represents the system under study well enough with respect to the goals of the simulation study. Both, validation and verification, are treated in the literature at a rather high level and seem to be more an art than engineering. This paper considers discrete event simulation of stochastic models that are formulated in a process-oriented language. The ProC/B paradigm is used as a particular example of a class of simulation languages which follow the common process interaction approach and show common concepts used in performance modeling, namely a) layered systems of virtual machines that contain resources and provide services and b) concurrent processes that interact by message passing and shared memory. We describe how Petri net analysis techniques help to verify and debug a large and detailed simulation model of airport logistics. We automatically derive a Petri net that models the control flow of a Proc/B model and we make use of invariant analysis and modelchecking to shed light on the allocation of resources, constraints among entities and causes for deadlocks

Learned Cardinalities: Estimating Correlated Joins with Deep Learning

We describe a new deep learning approach to cardinality estimation. MSCN is a multi-set convolutional network, tailored to representing relational query plans, that employs set semantics to capture query features and true cardinalities. MSCN builds on sampling-based estimation, addressing its weaknesses when no sampled tuples qualify a predicate, and in capturing join-crossing correlations. Our evaluation of MSCN using a real-world dataset shows that deep learning significantly enhances the quality of cardinality estimation, which is the core problem in query optimization.Comment: CIDR 2019. https://github.com/andreaskipf/learnedcardinalitie

Reduction of Calcium Release Site Models via Fast/Slow Analysis and Iterative Aggregation/Disaggregation

Mathematical models of calcium release sites derived from Markov chain models of intracellular calcium channels exhibit collective gating reminiscent of the experimentally observed phenomenon of calcium puffs and sparks. Such models often take the form of stochastic automata networks in which the transition probabilities of each channel depend on the local calcium concentration and thus the state of the other channels. In order to overcome the state-space explosion that occurs in such compositionally defined calcium release site models, we have implemented several automated procedures for model reduction using fast/slow analysis. After categorizing rate constants in the single channel model as either fast or slow, groups of states in the expanded release site model that are connected by fast transitions are lumped, and transition rates between reduced states are chosen consistent with the conditional probability distribution among states within each group. For small problems these conditional probability distributions can be numerically calculated from the full model without approximation. For large problems the conditional probability distributions can be approximated without the construction of the full model by assuming rapid mixing of states connected by fast transitions. Alternatively, iterative aggregation/disaggregation may be employed to obtain reduced calcium release site models in a memory-efficient fashion. Benchmarking of several different iterative aggregation/disaggregation-based fast/slow reduction schemes establishes the effectiveness of automated calcium release site reduction utilizing the Koury–McAllister–Stewart method. Mathematical modeling has played an important role in understanding the relationship between single channel gating of intracellular calcium (Ca2+) channels and the stochastic dynamics of Ca2+ release events known as Ca2+ puffs and sparks. Ca2+ release site models are defined by the composition of single channel models whose transition probabilities depend on the local calcium concentration and thus the state of the other channels. Because the large state space of such models impedes computational analysis of the dynamics of Ca2+ release sites, we implement and validate the application of several automated model reduction techniques that leverage separation of time scales, a common feature of single channel models of inositol 1,4,5-trisphosphate receptors (IP3Rs) and ryanodine receptors (RyRs). The authors show for the first time that memory-efficient iterative aggregation/disaggregation (IAD)-based numerical schemes are effective for fast/slow reduction in compositionally defined Ca2+ release site models

Estimating Cardinalities with Deep Sketches

We introduce Deep Sketches, which are compact models of databases that allow us to estimate the result sizes of SQL queries. Deep Sketches are powered by a new deep learning approach to cardinality estimation that can capture correlations between columns, even across tables. Our demonstration allows users to define such sketches on the TPC-H and IMDb datasets, monitor the training process, and run ad-hoc queries against trained sketches. We also estimate query cardinalities with HyPer and PostgreSQL to visualize the gains over traditional cardinality estimators.Comment: To appear in SIGMOD'1

Genetic architecture of body size in mammals

Much of the heritability for human stature is caused by mutations of small-to-medium effect. This is because detrimental pleiotropy restricts large-effect mutations to very low frequencies

Fluorescent Calcium Imaging and Subsequent In Situ Hybridization for Neuronal Precursor Characterization in Xenopus laevis

Spontaneous intracellular calcium activity can be observed in a variety of cell types and is proposed to play critical roles in a variety of physiological processes. In particular, appropriate regulation of calcium activity patterns during embryogenesis is necessary for many aspects of vertebrate neural development, including proper neural tube closure, synaptogenesis, and neurotransmitter phenotype specification. While the observation that calcium activity patterns can differ in both frequency and amplitude suggests a compelling mechanism by which these fluxes might transmit encoded signals to downstream effectors and regulate gene expression, existing population-level approaches have lacked the precision necessary to further explore this possibility. Furthermore, these approaches limit studies of the role of cell-cell interactions by precluding the ability to assay the state of neuronal determination in the absence of cell-cell contact. Therefore, we have established an experimental workflow that pairs time-lapse calcium imaging of dissociated neuronal explants with a fluorescence in situ hybridization assay, allowing the unambiguous correlation of calcium activity pattern with molecular phenotype on a single-cell level. We were successfully able to use this approach to distinguish and characterize specific calcium activity patterns associated with differentiating neural cells and neural progenitor cells, respectively; beyond this, however, the experimental framework described in this article could be readily adapted to investigate correlations between any time-series activity profile and expression of a gene or genes of interest