Combinatorial Gradient Fields for 2D Images with Empirically Convergent Separatrices

This paper proposes an efficient probabilistic method that computes combinatorial gradient fields for two dimensional image data. In contrast to existing algorithms, this approach yields a geometric Morse-Smale complex that converges almost surely to its continuous counterpart when the image resolution is increased. This approach is motivated using basic ideas from probability theory and builds upon an algorithm from discrete Morse theory with a strong mathematical foundation. While a formal proof is only hinted at, we do provide a thorough numerical evaluation of our method and compare it to established algorithms.Comment: 17 pages, 7 figure

Cross-Company Routing Planning: Determining Value Chains in a Dynamic Production Network Through a Decentralized Approach

Demand-based, local production will gain relevance in the context of sustainability and circular economy. One way to implement local value creation is through establishing highly dynamic networks that consolidate the competencies of regional manufacturers. Consequently, the structure of the value chains needs to be determined ad hoc dependent on demand. This is a rather challenging task due to the dynamics within such networks and the flat hierarchies. Traditionally, value chains are defined and controlled in a centralized form by a lead firm or a separate stakeholder (e.g. Intermediary, Broker). However, to accommodate the dynamics of demand and the increasing complexity of products, we propose a decentralized form of coordination. The basic idea is to upscale Routing Planning, used in Process Planning, to a network level. Meaning instead of a centralized instance within a company defining the production steps, the stakeholders will collaboratively determine the cross-company Routing Plan, effectively building the value chain. Thus, the accumulated experience and knowledge of all stakeholders can be utilized to efficiently fulfil current customer demand, since the value chain will be executed by the same stakeholders that created it. But in order to coordinate the sequencing of operations by multiple stakeholders, suitable methods need to be implemented. We look at a strategy to facilitate such a collaboration between companies and demonstrate one possible technical implementation based on AI planning using Planning Domain Definition Language (PDDL)

Cinema Darkroom: A Deferred Rendering Framework for Large-Scale Datasets

This paper presents a framework that fully leverages the advantages of a deferred rendering approach for the interactive visualization of large-scale datasets. Geometry buffers (G-Buffers) are generated and stored in situ, and shading is performed post hoc in an interactive image-based rendering front end. This decoupled framework has two major advantages. First, the G-Buffers only need to be computed and stored once---which corresponds to the most expensive part of the rendering pipeline. Second, the stored G-Buffers can later be consumed in an image-based rendering front end that enables users to interactively adjust various visualization parameters---such as the applied color map or the strength of ambient occlusion---where suitable choices are often not known a priori. This paper demonstrates the use of Cinema Darkroom on several real-world datasets, highlighting CD's ability to effectively decouple the complexity and size of the dataset from its visualization

Common data elements for pediatric traumatic brain injury: Recommendations from the working group on demographics and clinical assessment

The Common Data Elements (CDEs) initiative is a National Institutes of Health (NIH) interagency effort to standardize naming, definitions, and data structure for clinical research variables. Comparisons of the results of clinical studies of neurological disorders have been hampered by variability in data coding, definitions, and procedures for sample collection. The CDE project objective is to enable comparison of future clinical trials results in major neurological disorders, including traumatic brain injury (TBI), stroke, multiple sclerosis, and epilepsy. As part of this effort, recommendations for CDEs for research on TBI were developed through a 2009 multi-agency initiative. Following the initial recommendations of the Working Group on Demographics and Clinical Assessment, a separate workgroup developed recommendations on the coding of clinical and demographic variables specific to pediatric TBI studies for subjects younger than 18 years. This article summarizes the selection of measures by the Pediatric TBI Demographics and Clinical Assessment Working Group. The variables are grouped into modules which are grouped into categories. For consistency with other CDE working groups, each variable was classified by priority (core, supplemental, and emerging). Templates were produced to summarize coding formats, guide selection of data points, and provide procedural recommendations. This proposed standardization, together with the products of the other pediatric TBI working groups in imaging, biomarkers, and outcome assessment, will facilitate multi-center studies, comparison of results across studies, and high-quality meta-analyses of individual patient data

An Analysis of Private School Closings

We add to the small literature on private school supply by exploring exits of K-12 private schools. We find that the closure of private schools is not an infrequent event, and use national survey data from the National Center for Education Statistics to study closures of private schools. We assume that the probability of an exit is a function of excess supply of private schools over the demand, as well as the school's characteristics such as age, size, and religious affiliation. Our empirical results generally support the implications of the model. Working Paper 07-0

EST-PAC a web package for EST annotation and protein sequence prediction

With the decreasing cost of DNA sequencing technology and the vast diversity of biological resources, researchers increasingly face the basic challenge of annotating a larger number of expressed sequences tags (EST) from a variety of species. This typically consists of a series of repetitive tasks, which should be automated and easy to use. The results of these annotation tasks need to be stored and organized in a consistent way. All these operations should be self-installing, platform independent, easy to customize and amenable to using distributed bioinformatics resources available on the Internet. In order to address these issues, we present EST-PAC a web oriented multi-platform software package for expressed sequences tag (EST) annotation. EST-PAC provides a solution for the administration of EST and protein sequence annotations accessible through a web interface. Three aspects of EST annotation are automated: 1) searching local or remote biological databases for sequence similarities using Blast services, 2) predicting protein coding sequence from EST data and, 3) annotating predicted protein sequences with functional domain predictions. In practice, EST-PAC integrates the BLASTALL suite, EST-Scan2 and HMMER in a relational database system accessible through a simple web interface. EST-PAC also takes advantage of the relational database to allow consistent storage, powerful queries of results and, management of the annotation process. The system allows users to customize annotation strategies and provides an open-source data-management environment for research and education in bioinformatics

Characterisations and Examples of Graph Classes with Bounded Expansion

Classes with bounded expansion, which generalise classes that exclude a topological minor, have recently been introduced by Ne\v{s}et\v{r}il and Ossona de Mendez. These classes are defined by the fact that the maximum average degree of a shallow minor of a graph in the class is bounded by a function of the depth of the shallow minor. Several linear-time algorithms are known for bounded expansion classes (such as subgraph isomorphism testing), and they allow restricted homomorphism dualities, amongst other desirable properties. In this paper we establish two new characterisations of bounded expansion classes, one in terms of so-called topological parameters, the other in terms of controlling dense parts. The latter characterisation is then used to show that the notion of bounded expansion is compatible with Erd\"os-R\'enyi model of random graphs with constant average degree. In particular, we prove that for every fixed $d>0$, there exists a class with bounded expansion, such that a random graph of order $n$ and edge probability $d/n$ asymptotically almost surely belongs to the class. We then present several new examples of classes with bounded expansion that do not exclude some topological minor, and appear naturally in the context of graph drawing or graph colouring. In particular, we prove that the following classes have bounded expansion: graphs that can be drawn in the plane with a bounded number of crossings per edge, graphs with bounded stack number, graphs with bounded queue number, and graphs with bounded non-repetitive chromatic number. We also prove that graphs with `linear' crossing number are contained in a topologically-closed class, while graphs with bounded crossing number are contained in a minor-closed class

Statements of Agreement From the Targeted Evaluation and Active Management (TEAM) Approaches to Treating Concussion Meeting Held in Pittsburgh, October 15-16, 2015

Conventional management for concussion involves prescribed rest and progressive return to activity. Recent evidence challenges this notion and suggests that active approaches may be effective for some patients. Previous concussion consensus statements provide limited guidance regarding active treatment