pinecone/core-2020-05-10-deduplication huggingface dataset:
SKiPPER: a skeleton-based parallel programming environment for real-time image processing applications

International audienceThis paper presents SKiPPER, a programming environment dedicated to the fast prototyping of parallel vision algorithms on MIMD- DM platforms. SKiPPER is based upon the concept of algorithmic skele- tons, i.e. higher order program constructs encapsulating recurring forms of parallel computations and hiding their low-level implementation de- tails. Each skeleton is given an architecture-independent functional (but executable) specification and a portable implementation as a generic pro- cess template. The source program is a purely functional specification of the algorithm in which all parallelism is made explicit by means of com- posing instances of selected skeletons, each instance taking as parameters the application specific sequential functions written in C. SKiPPER compiles this specification down to a process graph in which nodes cor- respond to sequential functions and/or skeleton control processes and edges to communications. This graph is then mapped onto the target topology using a third-party CAD software (SynDEx). The result is a dead-lock free, optimized (but still portable) distributed executive, which SKiPPER finally turns into executable code for the target platform. The initial specification, written in ML language, can also be executed on any sequential platform to check the correctness of the parallel algorithm. The applicability of SKiPPER concepts and tools has been demonstrated by parallelising several realistic real-time vision applications both on a multi-DSP platform and a network of workstations. It is here illustrated with a real-time vehicle detection and tracking application
Building Classes in Object-Based Languages by Automatic Clustering

The paper deals with clustering of objects described both by properties and relations. Relational attributes may make object descriptions recursively depend on themselves so that attribute values cannot be compared before objects themselves are. An approach to clustering is presented whose core element is an object dissimilarity measure. All sorts of object attributes are compared in a uniform manner with possible exploration of the existing taxonomic knowledge. Dissimilarity values for mutually dependent object couples are computed as solutions of a system of linear equations. An example of building classes on objects with self-references demonstrates the advantages of the suggested approach
Intention Reconciliation in the Context of Teamwork:  An Initial Empirical Investigation

With growing opportunities for individually motivated agents to work collaboratively to satisfy shared goals, it becomes increasingly important to design agents that can make intelligent decisions in the context of commitments to group activities. In particular, agents need to be able to reconcile their intentions to do team-related actions with other, conflicting intentions. We present the SPIRE experimental system that allows the process of intention reconciliation in team contexts to be simulated and studied. SPIRE enables us to examine the influence of team norms and environmental factors on team members faced with conflicting intentions, as well as the effectiveness of different intention-reconciliation strategies. We discuss results from pilot experiments that confirm the reasonableness of our model of the problem and illustrate some of the issues involved, and we lay the groundwork for future experiments that will allow us to derive principles for designers of collaboration-capable agents.Engineering and Applied Science
Inductive verification and validation of the KULRot RoboCup team

As in many other multi-agent applications, most RoboCup agents are complex systems, hard to construct and hard to verify whether they behave as intended. We present a technique to verify multi-agent systems based on inductive reasoning. Induction allows to derive general rules from specific examples (e.g. the inputs and outputs of software systems). Using inductive logic programming, partial declarative specifications of the software can be induced. These rules can be readily interpreted by the designers or users of the software, and can in turn result in changes to the software. The approach outlined was used to test the KULRoT RoboCup simulator team, which is briefly described.status: publishe
Bayesian models for finding and grouping junctions

In this paper, we propose two Bayesian methods for detecting and grouping junctions. Our junction detection method evolves from the Kona approach, and it is based on a competitive greedy procedure inspired in the region competition method. Then, junction grouping is accomplished by finding connecting paths between pairs of junctions. Path searching is performed by applying a Bayesian A* algorithm that has been recently proposed. Both methods are efficient and robust, and they are tested with synthetic and real images
Text Compression Using Antidictionaries

International audienceWe give a new text compression scheme based on Forbidden Words ("antidictionary"). We prove that our algorithms attain the entropy for balanced binary sources. They run in linear time. Moreover, one of the main advantages of this approach is that it produces very fast decompressors. A second advantage is a synchronization property that is helpful to search compressed data and allows parallel compression. Our algorithms can also be presented as "compilers" that create compressors dedicated to any previously fixed source. The techniques used in this paper are from Information Theory and Finite Automata
Formalizing a lazy substitution proof system for μ-calculus in the Calculus of Inductive Constructions

We present a Natural Deduction proof system for the pro- positional modal μ-calculus, and its formalization in the Calculus of In- ductive Constructions. We address several problematic issues, such as the use of higher-order abstract syntax in inductive sets in presence of recursive constructors, the encoding of modal (sequent-style) rules and of context sensitive grammars. The formalization can be used in the sy- stem Coq, providing an experimental computer-aided proof environment for the interactive development of error-free proofs in the μ-calculus. The techniques we adopt can be readily ported to other languages and proof systems featuring similar problematic issues. © Springer-Verlag Berlin Heidelberg 1999
Generalizing refinement operators to learn prenex conjunctive normal forms

Inductive Logic Programming considers almost exclusively universally quantified theories. To add expressiveness we should consider general prenex conjunctive normal forms (PCNF) with existential variables. ILP mostly uses learning with refinement operators. To extend refinement operators to PCNF, we should first extend substitutions to PCNF. If one substitutes an existential variable in a formula, one often obtains a specialization rather than a generalization. In this article we define substitutions to specialise a given PCNF and a weakly complete downward refinement operator. Based on this operator, we have implemented a simple learning system PCL on some type of PCNF.status: publishe
Instance based function learning

The principles of instance based function learning are presented. In IBFL one is given a set of positive examples of a functional predicate. These examples are true ground facts that illustrate the input output behaviour of the predicate. The purpose is then to predict the output of the predicate given a new input. Further assumptions are that there is no background theory and that the inputs and outputs of the predicate consist of structured terms. IBFL is a novel technique that addresses this problem and that combines ideas from instance based learning, first order distances and analogical or case based reasoning. We also argue that IBFL is especially useful when there is a need for handling complex and deeply nested terms. Though we present the technique in isolation, it might be more useful as a component of a larger system to deal e.g. with the logic, language and learning challenge.status: publishe
Experiments in predicting biodegradability

We present a novel application of inductive logic programming (ILP) in the area of quantitative structure-activity relationships (QSARs). The activity we want to predict is the biodegradability of chemical compounds in water. In particular, the target variable is the half-life in water for aerobic aqueous biodegradation. Structural descriptions of chemicals in terms of atoms and bonds are derived from the chemicals' SMILES encodings. Definition of substructures are used as background knowledge. Predicting biodegradability is essentially a regression problem, but we also consider a discretized version of the target variable. We thus employ a number of relational classification and regression methods on the relational representation and compare these to propositional methods applied to different propositionalisations of the problem. Some expert comments on the induced theories are also given.status: publishe
Browsing a component library using Non-functional Information

This paper highlights the role of non-functional information
when reusing from a component library. We describe a method for selecting
appropriate implementations of Ada packages taking non-functional constraints
into account; these constraints model the context of reuse. Constraints
take the form of queries using an interface description language
called NoFun, which is also used to state non-functional information in Ada
packages; query results are trees of implementations, following the import
relationships between components. We define two different situations when
reusing components, depending whether we take the library being searched
as closed or extendible. The resulting tree of implementations can be manipulated
by the user to solve ambiguities, to state default behaviours, and
by the like. As part of the proposal, we face the problem of computing from
code the non-functional information that determines the selection process
Scene Segmentation and Image Feature Extraction for Video Indexing and Retrieval

International audienceWe present a video analysis and indexing engine, that can perform fully automatic scene segmentation and feature extraction, in the context of a television archive. A template-based indexing engine is proposed, which interacts with a general-purpose video analysis server (providing spatio-temporal segmentation, feature extraction, feature clustering and feature matching services) and a template server (which provides tool settings, scripts and interpretation rules for specific program)
On the Number of Iterations for Dantzig-Wolfe Optimization and
  Packing-Covering Approximation Algorithms

We give a lower bound on the iteration complexity of a natural class of
Lagrangean-relaxation algorithms for approximately solving packing/covering
linear programs. We show that, given an input with $m$ random 0/1-constraints
on $n$ variables, with high probability, any such algorithm requires
$\Omega(\rho \log(m)/\epsilon^2)$ iterations to compute a
$(1+\epsilon)$-approximate solution, where $\rho$ is the width of the input.
The bound is tight for a range of the parameters $(m,n,\rho,\epsilon)$.
  The algorithms in the class include Dantzig-Wolfe decomposition, Benders'
decomposition, Lagrangean relaxation as developed by Held and Karp [1971] for
lower-bounding TSP, and many others (e.g. by Plotkin, Shmoys, and Tardos [1988]
and Grigoriadis and Khachiyan [1996]). To prove the bound, we use a discrepancy
argument to show an analogous lower bound on the support size of
$(1+\epsilon)$-approximate mixed strategies for random two-player zero-sum
0/1-matrix games
Communication as an Emergent Metaphor for Neuronal Operation

The original publication is available at www.springerlink.com . Copyright Springer --DOI : 10.1007/3-540-48834-0_19The conventional computational description of brain operations has to be understood in a metaphorical sense. In this paper arguments supporting the claim that this metaphor is too restrictive are presented. A new metaphor more accurately describing recently discovered emergent characteristics of neuron functionality is proposed and its implications are discussed. A connectionist system fitting the new paradigm is presented and its use for attention modelling briefly outlined
Distributed decision making in checkers

Proceeding of: First International Conference, CG'98, Tsukuba (Japan), November 1998The game of checkers can be played by machines running either heuristic search algorithms or complex decision making programs trained using machine learning techniques. The first approach has been used with remarkable success. The latter approach yielded encouraging results in the past, but later results were not so useful, partly because of the limitations of current machine learning algorithms. The focus of this work is the study of techniques for distributed decision making and learning by Multi-Agent DEcision Systems (MADES), by means of their application to the development of a checkers playing program. In this paper, we propose a new architecture for knowledge based systems dedicated to checkers playing. Our aim is to show how the combination of several known models for checkers playing can be integrated into a MADES, that learns how to combine individual decisions, so that the MADES plays better than any of them, without “a priori” knowledge of the quality or area of expertise of each model. In our MADES, we integrate well known search algorithms along standard machine learning algorithms. We present results that clearly show that the team as a single entity plays better than any of its components working in isolation.Publicad
On Quantum Algorithms for Noncommutative Hidden Subgroups

Quantum algorithms for factoring and discrete logarithm have previously been
generalized to finding hidden subgroups of finite Abelian groups. This paper
explores the possibility of extending this general viewpoint to finding hidden
subgroups of noncommutative groups. We present a quantum algorithm for the
special case of dihedral groups which determines the hidden subgroup in a
linear number of calls to the input function. We also explore the difficulties
of developing an algorithm to process the data to explicitly calculate a
generating set for the subgroup. A general framework for the noncommutative
hidden subgroup problem is discussed and we indicate future research
directions.Comment: 13 pages, no figures, LaTeX2
Presentation of the Fundamental Group in Digital Surfaces

International audienceAs its analogue in the continuous framework, the digital fundamental group represents a major information on the topology of discrete objects. However, the fundamental group is an abstract information and cannot directly be encoded in a computer using its deeni-tion. A classical mathematical way to encode a discrete group is to nd a presentation of this group. In this paper, we construct a presentation for the fundamental group of any subset of a digital surface. This presentation can be computed by an eecient algorithm
A wavelet toolbox for large scale image processing

Parallel Computation, volume 1557 of Lecture Notes in Computer Science, edited by P. Zinterhof, M. Vajtersic, and A. Uhl; Fourth International Conference of the Austrian Center for Parallel Computation, University of Salzburg, Austria, February 16-18, 1999The wavelet transform has proven to be a valuable tool for image processing applications, like image compression and noise reduction. In this paper we present a scheme to process very large images that do not fit in the memory of a single computer, based on the software library WAILI (Wavelets with Integer Lifting). Such images are divided into blocks that are processed quasi independently, allowing efficient parallel programming. The blocking is almost completely transparent to the user.status: publishe
CHAT: The Copy-Hybrid Approach to Tabling

The Copying Approach to Tabling, abbrv. CAT, is an alternative to SLG-WAM and based on total copying of the areas that the SLG-WAM freezes to preserve execution states of suspended computations. The disadvantage of CAT as pointed out in a previous paper is that in the worst case, CAT must copy so much that it becomes arbitrarily worse than the SLG-WAM. Remedies to this problem have been studied, but a completely satisfactory solution has not emerged. Here, a hybrid approach is presented: CHAT. Its design was guided by the requirement that for non-tabled (i.e. Prolog) execution no changes to the underlying WAM engine need to be made. CHAT combines certain features of the SLG-WAM with features of CATI but also introduces a technique for freezing WAM stacks without the use of the SLG-WAM's freeze registers that is of independent interest. Empirical results indicate that CHAT is a better choice for implementing the control of tabling than SLG-WAM or CATT. However, programs with arbitrarily worse behaviour exist.status: publishe
An AOP case with static and dynamic aspects

Aspect-oriented-programming (aop) is a promising new approach where the description of a complex system/application is enchanced with various aspects, related to communication properties, distribution, synchronization, etc. All aspects can be described separately and are brought together by using a so-called weaver. Mostly, this is performed at compile-time, what makes that aspects disappear in the final software version. We argue that in some cases aspects should remain run-time entities in order to capture the dynamic properties
of an application. We believe there is a need for dynamic aspects, e.g. strongly related to objects, which are clearly run-time entities.status: publishe
Simulation and scheduling of real-time computer vision algorithms

A fully integrated development tool for computer vision systems has been built in the framework of this paper.
There are many applications that helps the user in the design of such systems, using graphical interfaces and function libraries. Even in some cases, the find source code can be generated by these applications.This paper goes a step beyond;it allows the development of computer vision systems from a distributed environment. Besides, and as distinctive characteristic with regard to other similar utilities, the system is able to automatically optimize task scheduling and assignment, depending on the available hardware.This work was supported by the Spanish Government through CICYT in the
framework of project TAP-96-0629-C04-01
Brownian Motion after Einstein: Some new applications and new
  experiments

The first half of this chapter describes the development in mathematical
models of Brownian motion after Einstein's seminal papers and current
applications to optical tweezers. This instrument of choice among
single-molecule biophysicists is also an instrument of precision that requires
an understanding of Brownian motion beyond Einstein's. This is illustrated with
some applications, current and potential, and it is shown how addition of a
controlled forced motion on the nano-scale of the tweezed object's thermal
motion can improve the calibration of the instrument in general, and make it
possible also in complex surroundings. The second half of the present chapter,
starting with Sect. 9, describes the co-evolution of biological motility models
with models of Brownian motion, including very recent results for how to derive
cell-type-specific motility models from experimental cell trajectories.Comment: 18 pages, 5 figures, to appear in Proceedings of Nobel Symposium 13
The well-founded semantics is the principle of inductive definition

Existing formalisations of (transfinite) inductive definitions in constructive mathematics are reviewed and strong correspondences with LP under least model and perfect model semantics become apparent. I point to fundamental restrictions of these existing formalisations and argue that the well-founded semantics (wfs) overcomes these problems and hence, provides a superior formalisation of the principle of inductive definition. The contribution of this study for LP is that it (re-) introduces the knowledge theoretic interpretation of LP as a logic for representing definitional knowledge. I point to fundamental differences between this knowledge theoretic interpretation of LP and the more commonly known interpretations of LP as default theories or auto-epistemic theories. The relevance is that differences in knowledge theoretic interpretation have strong impact on knowledge representation methodology and on extensions of the LP formalism; for example for representing uncertainty.status: publishe
Distributed reinforcement learning in multi-agent decision systems

Proceeding of: 6th Ibero-American Conference on AI (IBERAMIA '98),Lisbon, Portugal, October 5–9, 1998Decision problems can be usually solved using systems that implement different paradigms. These systems may be integrated into a single distributed system, with the expectation of obtaining a group performance more satisfactory than individual performances. Such a distributed system is what we call a Multi Agent Decision System (MADES), a special kind of Multi Agent System, that integrates several heterogeneous autonomous decision systems (agents). A MADES must produce a single solution proposal for the problem instance it faces, despite the fact that its decision making is distributed, and every agent produces solution proposals according to its local view and to its idiosyncrasy. We present a distributed reinforcement algorithm for learning how to combine the decisions the agents make in a distributed way, into a single group decision (solution proposal).Publicad
Performance comparison of point and spatial access methods

In the past few years a large number of multidimensional point access methods, also called
multiattribute index structures, has been suggested, all of them claiming good performance. Since no
performance comparison of these structures under arbitrary (strongly correlated nonuniform, short
"ugly") data distributions and under various types of queries has been performed, database
researchers and designers were hesitant to use any of these new point access methods. As shown in
a recent paper, such point access methods are not only important in traditional database applications.
In new applications such as CAD/CIM and geographic or environmental information systems, access
methods for spatial objects are needed. As recently shown such access methods are based on point
access methods in terms of functionality and performance. Our performance comparison naturally
consists of two parts. In part I we w i l l compare multidimensional point access methods, whereas in
part I I spatial access methods for rectangles will be compared. In part I we present a survey and
classification of existing point access methods. Then we carefully select the following four methods
for implementation and performance comparison under seven different data files (distributions) and
various types of queries: the 2-level grid file, the BANG file, the hB-tree and a new scheme, called
the BUDDY hash tree. We were surprised to see one method to be the clear winner which was the
BUDDY hash tree. It exhibits an at least 20 % better average performance than its competitors and is
robust under ugly data and queries. In part I I we compare spatial access methods for rectangles.
After presenting a survey and classification of existing spatial access methods we carefully selected
the following four methods for implementation and performance comparison under six different data
files (distributions) and various types of queries: the R-tree, the BANG file, PLOP hashing and the
BUDDY hash tree. The result presented two winners: the BANG file and the BUDDY hash tree.
This comparison is a first step towards a standardized testbed or benchmark. We offer our data and
query files to each designer of a new point or spatial access method such that he can run his
implementation in our testbed.