Persistent ocean monitoring with underwater gliders: Adapting sampling resolution

Ocean processes are dynamic and complex and occur on multiple spatial and temporal scales. To obtain a synoptic view of such processes, ocean scientists collect data over long time periods. Historically, measurements were continually provided by fixed sensors, e.g., moorings, or gathered from ships. Recently, an increase in the utilization of autonomous underwater vehicles has enabled a more dynamic data acquisition approach. However, we still do not utilize the full capabilities of these vehicles. Here we present algorithms that produce persistent monitoring missions for underwater vehicles by balancing path following accuracy and sampling resolution for a given region of interest, which addresses a pressing need among ocean scientists to efficiently and effectively collect high‐value data. More specifically, this paper proposes a path planning algorithm and a speed control algorithm for underwater gliders, which together give informative trajectories for the glider to persistently monitor a patch of ocean. We optimize a cost function that blends two competing factors: maximize the information value along the path while minimizing deviation from the planned path due to ocean currents. Speed is controlled along the planned path by adjusting the pitch angle of the underwater glider, so that higher resolution samples are collected in areas of higher information value. The resulting paths are closed circuits that can be repeatedly traversed to collect long‐term ocean data in dynamic environments. The algorithms were tested during sea trials on an underwater glider operating off the coast of southern California, as well as in Monterey Bay, California. The experimental results show improvements in both data resolution and path reliability compared to previously executed sampling paths used in the respective regions. © 2011 Wiley Periodicals, Inc.     

Partitioning large networks without breaking communities

The identification of cohesive communities is a key process in social network analysis. However, the algorithms that are effective for finding communities do not scale well to very large problems, as their time complexity is worse than linear in the number of edges in the graph. This is an important issue for those interested in applying social network analysis techniques to very large networks, such as networks of mobile phone subscribers. In this respect, the contributions of this paper are twofold. First, we demonstrate these scaling issues using a prominent community-finding algorithm as a case study. Then, we show that a two-stage process, whereby the network is first decomposed into manageable subnetworks using a multilevel graph partitioning procedure, is effective in finding communities in networks with more than 10⁶ nodes.     

Efficient monitoring of parametric context-free patterns

Recent developments in runtime verification and monitoring show that parametric regular and temporal logic specifications can be efficiently monitored against large programs. However, these logics reduce to ordinary finite automata, limiting their expressivity. For example, neither can specify structured properties that refer to the call stack of the program. While context-free grammars (CFGs) are expressive and well-understood, existing techniques for monitoring CFGs generate large runtime overhead in real-life applications. This paper demonstrates that monitoring parametric CFGs is practical (with overhead on the order of 12% or lower in most cases). We present a monitor synthesis algorithm for CFGs based on an LR(1) parsing algorithm, modified to account for good prefix matching. In addition, a logic-independent mechanism is introduced to support matching against the suffixes of execution traces.     

3D face computational photography using PCA spaces

In this paper, we present a 3D face photography system based on a facial expression training dataset, composed of both facial range images (3D geometry) and facial texture (2D photography). The proposed system allows one to obtain a 3D geometry representation of a given face provided as a 2D photography, which undergoes a series of transformations through the texture and geometry spaces estimated. In the training phase of the system, the facial landmarks are obtained by an active shape model (ASM) extracted from the 2D gray-level photography. Principal components analysis (PCA) is then used to represent the face dataset, thus defining an orthonormal basis of texture and another of geometry. In the reconstruction phase, an input is given by a face image to which the ASM is matched. The extracted facial landmarks and the face image are fed to the PCA basis transform, and a 3D version of the 2D input image is built. Experimental tests using a new dataset of 70 facial expressions belonging to ten subjects as training set show rapid reconstructed 3D faces which maintain spatial coherence similar to the human perception, thus corroborating the efficiency and the applicability of the proposed system.     

FeynRules - Feynman rules made easy

In this paper we present FeynRules, a new Mathematica package that facilitates the implementation of new particle physics models. After the user implements the basic model information (e.g., particle content, parameters and Lagrangian), FeynRules derives the Feynman rules and stores them in a generic form suitable for translation to any Feynman diagram calculation program. The model can then be translated to the format specific to a particular Feynman diagram calculator via FeynRules translation interfaces. Such interfaces have been written for CalcHEP/CompHEP, FeynArts/FormCalc, MadGraph/MadEvent and Sherpa, making it possible to write a new model once and have it work in all of these programs. In this paper, we describe how to implement a new model, generate the Feynman rules, use a generic translation interface, and write a new translation interface. We also discuss the details of the FeynRules code.

Program summary

Program title: FeynRulesCatalogue identifier: AEDI_v1_0Program summary URL::http://cpc.cs.qub.ac.uk/summaries/AEDI_v1_0.htmlProgram obtainable from: CPC Program Library, Queen's University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 15 980No. of bytes in distributed program, including test data, etc.: 137 383Distribution format: tar.gzProgramming language: MathematicaComputer: Platforms on which Mathematica is availableOperating system: Operating systems on which Mathematica is availableClassification: 11.1, 11.2, 11.6Nature of problem: Automatic derivation of Feynman rules from a Lagrangian. Implementation of new models into Monte Carlo event generators and FeynArts.Solution method: FeynRules works in two steps:

1. derivation of the Feynman rules directly form the Lagrangian using canonical commutation relations among fields and creation operators.

2. implementation of the new physics model into FeynArts as well as various Monte Carlo programs via interfaces.

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Commitments and interaction norms in organisations

In an organisational setting such as an online marketplace, an entity called the ‘organisation’ or ‘institution’ defines interaction protocols, monitors agent interaction, and intervenes to enforce the interaction protocols. The organisation might be a software system that thus regulates the marketplace, for example. In this article we abstract over application-specific protocols and consider commitment lifecycles as generic interaction protocols. We model interaction protocols by explicitly-represented norms, such that we can operationalise the enforcement of protocols by means of norm enforcement, and we can analyse the protocols by a logical analysis of the norms. We adopt insights and methods from commitment-based approaches to agent interaction as well as from norm-based approaches to agent behaviour governance. First, we show how to use explicitly-represented norms to model commitment dynamics (lifecycles). Second, we introduce an operational semantics to operationalise norm enforcement. Third, we show how to logically analyse interaction protocols by means of commitment dynamics and norm enforcement. The model, semantics, and analysis are illustrated by a running example from a vehicle insurance domain.     

A new representation and associated algorithms for generalized planning

Constructing plans that can handle multiple problem instances is a longstanding open problem in AI. We present a framework for generalized planning that captures the notion of algorithm-like plans and unifies various approaches developed for addressing this problem. Using this framework, and building on the TVLA system for static analysis of programs, we develop a novel approach for computing generalizations of classical plans by identifying sequences of actions that will make measurable progress when placed in a loop. In a wide class of problems that we characterize formally in the paper, these methods allow us to find generalized plans with loops for solving problem instances of unbounded sizes and also to determine the correctness and applicability of the computed generalized plans. We demonstrate the scope and scalability of the proposed approach on a wide range of planning problems.     

Dynamic vs. Oblivious Routing in Network Design

Consider the robust network design problem of finding a minimum cost network with enough capacity to route all traffic demand matrices in a given polytope. We investigate the impact of different routing models in this robust setting: in particular, we compare oblivious routing, where the routing between each terminal pair must be fixed in advance, to dynamic routing, where routings may depend arbitrarily on the current demand. Our main result is a construction that shows that the optimal cost of such a network based on oblivious routing (fractional or integral) may be a factor of Ω(log n) more than the cost required when using dynamic routing. This is true even in the important special case of the asymmetric hose model. This answers a question in (Chekuri, SIGACT News 38(3):106–128, 2007), and is tight up to constant factors. Our proof technique builds on a connection between expander graphs and robust design for single-sink traffic patterns (Chekuri et al., Networks 50(1):50–54, 2007).