Network Topology Mapping from Partial Virtual Coordinates and Graph Geodesics

For many important network types (e.g., sensor networks in complex harsh environments and social networks) physical coordinate systems (e.g., Cartesian), and physical distances (e.g., Euclidean), are either difficult to discern or inapplicable. Accordingly, coordinate systems and characterizations based on hop-distance measurements, such as Topology Preserving Maps (TPMs) and Virtual-Coordinate (VC) systems are attractive alternatives to Cartesian coordinates for many network algorithms. Herein, we present an approach to recover geometric and topological properties of a network with a small set of distance measurements. In particular, our approach is a combination of shortest path (often called geodesic) recovery concepts and low-rank matrix completion, generalized to the case of hop-distances in graphs. Results for sensor networks embedded in 2-D and 3-D spaces, as well as a social networks, indicates that the method can accurately capture the network connectivity with a small set of measurements. TPM generation can now also be based on various context appropriate measurements or VC systems, as long as they characterize different nodes by distances to small sets of random nodes (instead of a set of global anchors). The proposed method is a significant generalization that allows the topology to be extracted from a random set of graph shortest paths, making it applicable in contexts such as social networks where VC generation may not be possible.Comment: 17 pages, 9 figures. arXiv admin note: substantial text overlap with arXiv:1712.1006

LISA Metrology System - Final Report

Gravitational Waves will open an entirely new window to the Universe, different from all other astronomy in that the gravitational waves will tell us about large-scale mass motions even in regions and at distances totally obscured to electromagnetic radiation. The most interesting sources are at low frequencies (mHz to Hz) inaccessible on ground due to seismic and other unavoidable disturbances. For these sources observation from space is the only option, and has been studied in detail for more than 20 years as the LISA concept. Consequently, The Gravitational Universe has been chosen as science theme for the L3 mission in ESA's Cosmic Vision program. The primary measurement in LISA and derived concepts is the observation of tiny (picometer) pathlength fluctuations between remote spacecraft using heterodyne laser interferometry. The interference of two laser beams, with MHz frequency difference, produces a MHz beat note that is converted to a photocurrent by a photodiode on the optical bench. The gravitational wave signal is encoded in the phase of this beat note. The next, and crucial, step is therefore to measure that phase with µcycle resolution in the presence of noise and other signals. This measurement is the purpose of the LISA metrology system and the subject of this report

Semantic metrics

In the context of the Semantic Web, many ontology-related operations, e.g. ontology ranking, segmentation, alignment, articulation, reuse, evaluation, can be boiled down to one fundamental operation: computing the similarity and?or dissimilarity among ontological entities, and in some cases among ontologies themselves. In this paper, we review standard metrics for computing distance measures and we propose a series of semantic metrics. We give a formal account of semantic metrics drawn from a variety of research disciplines, and enrich them with semantics based on standard Description Logic constructs. We argue that concept-based metrics can be aggregated to produce numeric distances at ontology-level and we speculate on the usability of our ideas through potential areas

Characteristics of stable flows over Southern Greenland

The main characteristic features of stable atmospheric flows over a large mountain plateau are summarised and then compared with mesoscale and synoptic scale numerical simulation, meteorological analysis, satellite imagery, and surface observations for the cases of flows over Southern Greenland for four wind directions. The detailed features are identified using the concepts and scaling of stably stratified flow over large mountains with variations in surface roughness, elevation, and heating. For westerly and easterly winds detached jets form at the southern tip, where coastal jets converge, which propagate large distances across the ocean. Near coasts katabatic winds can combine with barrier jets and wake flows generated by synoptic winds. Note how the approach flow rises/falls over southern Greenland for easterly/westerly winds, leading in both cases to more cloud on the western side. Some conclusions are drawn about the large-scale influences of these flows; detached jets in the atmosphere; air-sea interaction; formation of low pressure systems. For accurate simulations of these flows, mesoscale models are necessary with resolutions of order of 20 km or less. © Birkhäuser Verlag, Basel, 2005

Combining similarity in time and space for training set formation under concept drift

Concept drift is a challenge in supervised learning for sequential data. It describes a phenomenon when the data distributions change over time. In such a case accuracy of a classifier benefits from the selective sampling for training. We develop a method for training set selection, particularly relevant when the expected drift is gradual. Training set selection at each time step is based on the distance to the target instance. The distance function combines similarity in space and in time. The method determines an optimal training set size online at every time step using cross validation. It is a wrapper approach, it can be used plugging in different base classifiers. The proposed method shows the best accuracy in the peer group on the real and artificial drifting data. The method complexity is reasonable for the field applications

Erratum: Signal propagation in proteins and relation to equilibrium fluctuations (PLoS Computational Biology (2007) 3, 9, (e172) DOI: 10.1371/journal.pcbi.0030172))

Elastic network (EN) models have been widely used in recent years for describing protein dynamics, based on the premise that the motions naturally accessible to native structures are relevant to biological function. We posit that equilibrium motions also determine communication mechanisms inherent to the network architecture. To this end, we explore the stochastics of a discrete-time, discrete-state Markov process of information transfer across the network of residues. We measure the communication abilities of residue pairs in terms of hit and commute times, i.e., the number of steps it takes on an average to send and receive signals. Functionally active residues are found to possess enhanced communication propensities, evidenced by their short hit times. Furthermore, secondary structural elements emerge as efficient mediators of communication. The present findings provide us with insights on the topological basis of communication in proteins and design principles for efficient signal transduction. While hit/commute times are information-theoretic concepts, a central contribution of this work is to rigorously show that they have physical origins directly relevant to the equilibrium fluctuations of residues predicted by EN models

Bridging between sensor measurements and symbolic ontologies through conceptual spaces

The increasing availability of sensor data through a variety of sensor-driven devices raises the need to exploit the data observed by sensors with the help of formally specified knowledge representations, such as the ones provided by the Semantic Web. In order to facilitate such a Semantic Sensor Web, the challenge is to bridge between symbolic knowledge representations and the measured data collected by sensors. In particular, one needs to map a given set of arbitrary sensor data to a particular set of symbolic knowledge representations, e.g. ontology instances. This task is particularly challenging due to the potential infinite variety of possible sensor measurements. Conceptual Spaces (CS) provide a means to represent knowledge in geometrical vector spaces in order to enable computation of similarities between knowledge entities by means of distance metrics. We propose an ontology for CS which allows to refine symbolic concepts as CS and to ground instances to so-called prototypical members described by vectors. By computing similarities in terms of spatial distances between a given set of sensor measurements and a finite set of prototypical members, the most similar instance can be identified. In that, we provide a means to bridge between the real-world as observed by sensors and symbolic representations. We also propose an initial implementation utilizing our approach for measurement-based Semantic Web Service discovery