The Pupal Parasitoid Trichopria drosophilae Is Attracted to the Same Yeast Volatiles as Its Adult Host

Journal of Chemical Ecology - There is increasing evidence that microorganisms, particularly fungi and bacteria, emit volatile compounds that mediate the foraging behaviour of insects and therefore...     

A Multicast-Enabled Delivery Framework for QoE Assurance of Over-The-Top Services in Multimedia Access Networks

Over-The-Top (OTT) video services are becoming more and more important in today’s broadband access networks. While original OTT services only offered short duration medium quality videos, more recently, premium content such as high definition full feature movies and live video are offered as well. For operators, who see the potential in providing Quality of Experience (QoE) assurance for an increased revenue, this introduces important new network management challenges. Traditional network management paradigms are often not suited for ensuring QoE guarantees as the provider does not have any control on the content’s origin. In this article, we focus on the management of an OTT-based video service. We present a loosely coupled architecture that can be seamlessly integrated into an existing OTT-based video delivery architecture. The framework has the goal of resolving the network bottleneck that might occur from high peaks in the requests for OTT video services. The proposed approach groups the existing Hypertext Transfer Protocol (HTTP) based video connections to be multicasted over an access network’s bottleneck and then splits them again to reconstruct the original HTTP connections. A prototype of this architecture is presented, which includes the caching of videos and incorporates retransmission schemes to ensure robust transmission. Furthermore, an autonomic algorithm is presented that allows to intelligently select which OTT videos need to be multicasted by making a remote assessment of the cache state to predict the future availability of content. The approach was evaluated through both simulation and large scale emulation and shows a significant gain in scalability of the prototype compared to a traditional video delivery architecture.     

Formulating the template ILP consistency problem as a constraint satisfaction problem

Inductive Logic Programming (ILP) deals with the problem of finding a hypothesis covering positive examples and excluding negative examples, where both hypotheses and examples are expressed in first-order logic. In this paper we employ constraint satisfaction techniques to model and solve a problem known as template ILP consistency, which assumes that the structure of a hypothesis is known and the task is to find unification of the contained variables. In particular, we present a constraint model with index variables accompanied by a Boolean model to strengthen inference and hence improve efficiency. The efficiency of models is demonstrated experimentally.     

An Intelligent Man-Machine Dialogue System Based on AI Planning

We describe the modular architecture of a generic dialogue system that assists a user/operator in performing a task with a tool. This coaching system is named CALLIOPE after the Greek goddess of eloquence. It aims at being an active partner in an intelligent man-machine dialogue. The intelligent dimension of the coaching system is reflected by its ability to adapt to the user and the situation at hand. The CALLIOPE system contains an explicit user model and world model to situate its dialogue actions. A plan library allows it to follow loosely predetermined dialogue scenarios.The heart of the coaching system is an AI planning module, which plans a series of dialogue actions. We present a coherent set of three dialogue or speech actions that will make up the physical form of the man-machine communication.The use of the AI planning paradigm as a basis for man-machine interaction is motivated by research in various disciplines, as e.g., AI, Cognitive Science and Social Sciences. Starting from the man-man communication metaphor, we can view the thinking before speaking of a human communication partner as constructing an underlying plan which is responsible for the purposiveness, the organisation and the relevance of the communication.CALLIOPE has been fully implemented and tested on theoretical examples. At present, also three tailored versions of CALLIOPE are in operational use in different industrial application domains: operator support for remedying tasks in chemical process industry, operator support for a combined task of planning, plan execution and process control in the area of chemical process development, and thirdly decision support in production scheduling.     

Structural Induction and Coinduction in a Fibrational Setting

We present a categorical logic formulation of induction and coinduction principles for reasoning about inductively and coinductively defined types. Our main results provide sufficient criteria for the validity of such principles: in the presence of comprehension, the induction principle for initial algebras is admissible, and dually, in the presence of quotient types, the coinduction principle for terminal coalgebras is admissible. After giving an alternative formulation of induction in terms of binary relations, we combine both principles and obtain a mixed induction/coinduction principle which allows us to reason about minimal solutionsX≅σ(X) whereXmay occur both positively and negatively in the type constructor σ. We further strengthen these logical principles to deal with contexts and prove that such strengthening is valid when the (abstract) logic we consider is contextually/functionally complete. All the main results follow from a basic result about adjunctions between “categories of algebras” (inserters).     

The Gaussian scale-space paradigm and the multiscale local jet

A representation of local image structure is proposed which takes into account both the image's spatial structure at a given location, as well as its deep structure, that is, its local behaviour as a function of scale or resolution (scale-space). This is of interest for several low-level image tasks. The proposed basis of scale-space, for example, enables a precise local study of interactions of neighbouring image intensities in the course of the blurring process. It also provides an extrapolation scheme for local image data, obtained at a given spatial location and resolution, to a finite scale-space neighbourhood. This is especially useful for the determination of sampling rates and for interpolation algorithms in a multilocal context. Another, particularly straightforward application is image enhancement or deblurring, which is an instance of data extrapolation in the high-resolution direction.A potentially interesting feature of the proposed local image parametrisation is that it captures a trade-off between spatial and scale extrapolations from a given interior point that do not exceed a given tolerance. This (rade-off suggests the possibility of a fairly coarse scale sampling at the expense of a dense spatial sampling large relative spatial overlap of scale-space kernels).The central concept developed in this paper is an equivalence class called the multiscale local jet, which is a hierarchical, local characterisation of the image in a full scale-space neighbourhood. For this local jet, a basis of fundamental polynomials is constructed that captures the scale-space paradigm at the local level up to any given order.     

Comparing notions of randomness

It is an open problem in the area of effective (algorithmic) randomness whether Kolmogorov-Loveland randomness coincides with Martin-Löf randomness. Joe Miller and André Nies suggested some variations of Kolmogorov-Loveland randomness to approach this problem and to provide a partial solution. We show that their proposed notion of injective randomness is still weaker than Martin-Löf randomness. Since in this proof some of the ideas we use are clearer, we also show the weaker theorem that permutation randomness is weaker than Martin-Löf randomness.     

Logic programming for combinatorial problems

Combinatorial problems appear in many areas in science, engineering, biomedicine, business, and operations research. This article presents a new intelligent computing approach for solving combinatorial problems, involving permutations and combinations, by incorporating logic programming. An overview of applied combinatorial problems in various domains is given. Such computationally hard and popular combinatorial problems as the traveling salesman problem are discussed to illustrate the usefulness of the logic programming approach. Detailed discussions of implementation of combinatorial problems with time complexity analyses are presented in Prolog, the standard language of logic programming. These programs can be easily integrated into other systems to implement logic programming in combinatorics.     

Automatic verification of Java programs with dynamic frames

Framing in the presence of data abstraction is a challenging and important problem in the verification of object-oriented programs Leavens et al. (Formal Aspects Comput (FACS) 19:159–189, 2007). The dynamic frames approach is a promising solution to this problem. However, the approach is formalized in the context of an idealized logical framework. In particular, it is not clear the solution is suitable for use within a program verifier for a Java-like language based on verification condition generation and automated, first-order theorem proving. In this paper, we demonstrate that the dynamic frames approach can be integrated into an automatic verifier based on verification condition generation and automated theorem proving. The approach has been proven sound and has been implemented in a verifier prototype. The prototype has been used to prove correctness of several programming patterns considered challenging in related work.