A Process Theory of Competency Rallying in Engineering Projects

Firms face an environment changing at an increasingly rapid pace. Market opportunities in particular can arise and disappear in a short time. Unfortunately, the speed with which organizations can adapt their strategies and competencies to meet these opportunities remains limited. We argue that firms can address these individual limitations by cooperating with others for access to market opportunities and needed competencies. In this paper, we present a process theory of how a network of firms can reliably engineering and deliver products in the face of rapid market changes. In this theory, the success of the network is predicated on 1) identification and development of competencies, 2) identification and facing of market opportunities, 3) marshalling of competencies and 4) a short-term cooperative effort. Our theory is based on the experiences of Virtual Factory, an organized network for regional cooperation in the manufacturing industry

Parameterized Study of the Test Cover Problem

We carry out a systematic study of a natural covering problem, used for identification across several areas, in the realm of parameterized complexity. In the {\sc Test Cover} problem we are given a set $[n]=\{1,...,n\}$ of items together with a collection, $\cal T$, of distinct subsets of these items called tests. We assume that $\cal T$ is a test cover, i.e., for each pair of items there is a test in $\cal T$ containing exactly one of these items. The objective is to find a minimum size subcollection of $\cal T$, which is still a test cover. The generic parameterized version of {\sc Test Cover} is denoted by $p(k,n,|{\cal T}|)$-{\sc Test Cover}. Here, we are given $([n],\cal{T})$ and a positive integer parameter $k$ as input and the objective is to decide whether there is a test cover of size at most $p(k,n,|{\cal T}|)$. We study four parameterizations for {\sc Test Cover} and obtain the following: (a) $k$-{\sc Test Cover}, and $(n-k)$-{\sc Test Cover} are fixed-parameter tractable (FPT). (b) $(|{\cal T}|-k)$-{\sc Test Cover} and $(\log n+k)$-{\sc Test Cover} are W[1]-hard. Thus, it is unlikely that these problems are FPT

{Polynomial Kernels for λ\lambda-extendible Properties Parameterized Above the {Poljak--Turz{\'{i}}k} Bound}

Poljak and Turzik (Discrete Mathematics 1986) introduced the notion of {\lambda}-extendible properties of graphs as a generalization of the property of being bipartite. They showed that for any 0 < {\lambda} < 1 and {\lambda}-extendible property {\Pi}, any connected graph G on n vertices and m edges contains a spanning subgraph H in {\Pi} with at least {\lambda}m + (1-{\lambda})(n-1)/2 edges. The property of being bipartite is {\lambda}-extendible for {\lambda} = 1/2, and so the Poljak-Turzik bound generalizes the well-known Edwards-Erdos bound for Max-Cut. Other examples of {\lambda}-extendible properties include: being an acyclic oriented graph, a balanced signed graph, or a q-colorable graph for some integer q. Mnich et. al. (FSTTCS 2012) defined the closely related notion of strong {\lambda}-extendibility. They showed that the problem of finding a subgraph satisfying a given strongly {\lambda}-extendible property {\Pi} is fixed-parameter tractable (FPT) when parameterized above the Poljak-Turzik bound - does there exist a spanning subgraph H of a connected graph G such that H in {\Pi} and H has at least {\lambda}m + (1-{\lambda})(n-1)/2 + k edges? - subject to the condition that the problem is FPT on a certain simple class of graphs called almost-forests of cliques. In this paper we settle the kernelization complexity of nearly all problems parameterized above Poljak-Turzik bounds, in the affirmative. We show that these problems admit quadratic kernels (cubic when {\lambda} = 1/2), without using the assumption that the problem is FPT on almost-forests of cliques. Thus our results not only remove the technical condition of being FPT on almost-forests of cliques from previous results, but also unify and extend previously known kernelization results in this direction. Our results add to the select list of generic kernelization results known in the literature

Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science

(abridged for arXiv) With the first direct detection of gravitational waves, the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) has initiated a new field of astronomy by providing an alternate means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual classifier. The resulting classification and characterization should help LIGO scientists to identify causes of glitches and subsequently eliminate them from the data or the detector entirely, thereby improving the rate and accuracy of gravitational-wave observations. We demonstrate these methods using a small subset of data from LIGO's first observing run.Comment: 27 pages, 8 figures, 1 tabl

Definition of glaucoma: clinical and experimental concepts

Glaucoma is a term describing a group of ocular disorders with multi-factorial etiology united by a clinically characteristic intraocular pressure-associated optic neuropathy. It is not a single entity and is sometimes referred to in the plural as the glaucomas. All forms are potentially progressive and can lead to blindness. The diverse conditions that comprise glaucoma are united by a clinically characteristic optic neuropathy: glaucomatous optic neuropathy (GON). Evidence suggests that the primary site of neurological injury is at the optic nerve head. This fact enables the conditions to be grouped, irrespective of the causal mechanism(s). The term experimental glaucoma implies model resemblance to the human condition. We propose that 'experimental glaucoma' be restricted to animal models with demonstrable features of GON and/or evidence of a primary axonopathy at the optic nerve head. A fundamental inadequacy in this framework is any reference to the pathogenesis of GON, which remains unclear.Robert J Casson, Glyn Chidlow, John PM Wood, Jonathan G Crowston, and Ivan Goldber