pinecone/core-2020-05-10-deduplication huggingface dataset:
Relic Gravitational Waves and Their Detection

The range of expected amplitudes and spectral slopes of relic (squeezed)
gravitational waves, predicted by theory and partially supported by
observations, is within the reach of sensitive gravity-wave detectors. In the
most favorable case, the detection of relic gravitational waves can be achieved
by the cross-correlation of outputs of the initial laser interferometers in
LIGO, VIRGO, GEO600. In the more realistic case, the sensitivity of advanced
ground-based and space-based laser interferometers will be needed. The specific
statistical signature of relic gravitational waves, associated with the
phenomenon of squeezing, is a potential reserve for further improvement of the
signal to noise ratio.Comment: 25 pages, 9 figures included, revtex. Based on a talk given at
  "Gyros, Clocks, and Interferometers: Testing General Relativity in Space"
  (Germany, August 99
Logic programming approaches for representing and solving constraint satisfaction problems A comparison

Many logic programming based approaches can be used to describe and solve combinatorial search problems. On the one hand there is constraint logic programming which computes a solution as an answer substitution to a query containing the variables of the constraint satisfaction problem. On the other hand there are systems based on stable model semantics, abductive systems, and first order logic model generators which compute solutions as models of some theory. This paper compares these different approaches from the point of view of knowledge representation (how declarative are the programs) and from the point of view of performance (how good are they at solving typical problems).status: publishe
Mobile transaction management in mobisnap

Lecture Notes in Computer Science, 1884To allow mobile users to continue their work while disconnected, mobile systems usually rely on optimistic replication techniques. In mobile database systems, mobile units cache subsets of the database state and allow disconnected users to perform transactions concurrently. These transactions are later integrated in the master database state. As concurrently performed transactions may conflict, it is usually impossible to determine the result of an update in the mobile unit. Moreover, this model differs from the traditional client/server model due to the fundamental fact that the user will usually not be connected to the system when the results of his transactions are finally determined - therefore, he can not immediately perform adequate alternative actions. In this paper we describe a transaction management system that takes into consideration the above-mentioned characteristics. Transactions are specified as mobile transactional programs, which allows the precise definition of operation semantics and the definition of alternative actions. Support for active user notification is also provided in the system. Finally, the system relies on a reservation mechanism to be able to guarantee the results of transactions in the mobile units
Compiling multithreaded Java bytecode for distributed execution

Distinguished PaperInternational audienceOur work combines Java compilation to native code with a run-time library that executes Java threads in a distributed-memory environment. This allows a Java programmer to view a cluster of processors as executing a single Java virtual machine. The separate processors are simply resources for executing Java threads with true concurrency and the run-time system provides the illusion of a shared memory on top of the private memories of the processors. The environment we present is available on top of several UNIX systems and can use a large variety of network protocols thanks to the high portability of its run-time system. To evaluate our approach, we compare serial C, serial Java, and multithreaded Java implementations of a branch-and-bound solution to the minimal-cost map-coloring problem. All measurements have been carried out on two platforms using two different network protocols: SISCI/SCI and MPI-BIP/Myrinet
Distance between attributed graphs and function-described graphs relaxing 2nd order restrictions

Joint IAPR International Workshop on Structural, Syntactic and Statistical Pattern Recognition (SSPR&SPR), 2000, Alicante (España)Function-Described Graphs (FDGs) have been introduced as a representation of an ensemble of Attributed Graphs (AGs) for structural pattern recognition and a distance measure using restrictions between AGs and FDGs has been reported. Nevertheless, in real applications, AGs can be distorted by some external noise, and therefore some constraints have to be relaxed. To gain more flexibility and robustness, some local costs may be added to the global cost of the labelling depending on the fulfilment of the graph element constraints of the FDG instead of applying hard binary constraints.This work was supported by the project 'Active vision systems based in automatic learning for industrial applications' ().Peer Reviewe
How to Drive a B Machine.

The B-Method is a state-based formal method that describes behaviour in terms of MACHINES whose states change under OPERATIONS. The process algebra CSP is an event-based formalism that enables descriptions of patterns of system behaviour. We present a combination of the two views where a CSP process acts as a control executive and its events simply drive corresponding OPERATIONS. We define consistency between the two views in terms of existing semantic models. We identify proof conditions which are strong enough to ensure consistency and thus guarantee safety and liveness properties
Argumentation semantics for defeasible logics

Defeasible logic is a simple but efficient rule-based non-monotonic logic. It has powerful implementations and shows promise to be applied in the areas of legal reasoning and the modelling of business rules. So far defeasible logic has been defined only proof-theoretically. Argumentation-based semantics have become popular in the area of logic programming. In this paper we give an argumentation-based semantics for defeasible logic.  Recently it has been shown that a family of approaches can be built around defeasible logic, in which different intuitions can be followed. In this paper we present an argumentation-based semantics for an ambiguity propagating logic, too. Further defeasible logics can be characterised in a similar way.Griffith Sciences, School of Information and Communication TechnologyFull Tex
Perceptual considerations for quality of service management: An integrated architecture

The official published version can be obtained from the link below - copyright @ 2001 SpringerIn this paper, we suggest an integrated architecture that makes use of the objective-technical information provided by the  designer and the subjectiveperceptual information supplied by the user for intelligent decision making in the construction of communication protocols. Thus, this approach, based on the Analytic Hierarchy Process, incorporates not only classical Quality of Service (QoS) considerations, but, indeed, user preferences as well. Furthermore, in keeping with the task-dependent nature consistently identified in multimedia scenarios, the suggested communication protocols also take into account the type of multimedia application, which they are transporting. Lastly, our approach also opens the possibility for such protocols to dynamically adapt based on a changing operating environment
Analysing web search logs to determine session boundaries for user-oriented learning

Incremental learning approaches based on user search activities provide a means of building adaptive information retrieval systems. To develop more effective user-oriented learning techniques for the Web, we need to be able to identify a meaningful session unit from which we can learn. Without this, we run a high risk of grouping together activities that are unrelated or perhaps not from the same user. We are interested in detecting boundaries of sequences between related activities (sessions) that would group the activities for a learning purpose. Session boundaries, in Reuters transaction logs, were detected automatically. The generated boundaries were compared with human judgements. The comparison confirmed that a meaningful session threshold for establishing these session boundaries was confined to a 11-15 minute range
Can Social Interaction Skills Be Taught by a Social Agent? The Role of a Robotic Mediator in Autism Therapy

???The original publication is available at www.springerlink.com???. Copyright Springer.Increasingly socially intelligent agents (software or robotic) are used in education, rehabilitation and therapy. This paper discusses the role of inter-active, mobile robots as social mediators in the particular domain of autism therapy. This research is part of the project AURORA that studies how mobile robots can be used to teach children with autism basic interaction skills that are important in social interactions among humans. Results from a particular series of trials involving pairs of two children and a mobile robot are described. The results show that the scenario with pairs of children and a robot creates a very interesting social context which gives rise to a variety of different social and non-social interaction patterns, demonstrating the specific problems but also abilities of children with autism in social interactions. Future work will include a closer analysis of interactional structure in human-human and robot-human interaction. We outline a particular framework that we are investigating
On the (Im)possibility of Obfuscating Programs

Informally, an obfuscator O is an (efficient, probabilistic) "compiler" that takes as input a program (or circuit) P and produces a new program O(P) that has the same functionality as P yet is "unintelligible" in some sense. Obfuscators, if they exist, would have a wide variety of cryptographic and complexity-theoretic applications, ranging from software protection to homomorphic encryption to complexity-theoretic analogues of Rice's theorem. Most of these applications are based on an interpretation of the "unintelligibility" condition in obfuscation as meaning that O(P) is a "virtual black box," in the sense that anything one can efficiently compute given O(P), one could also efficiently compute given oracle access to P.

In this work, we initiate a theoretical investigation of obfuscation. Our main result is that, even under very weak formalizations of the above intuition, obfuscation is impossible. We prove this by constructing a family of programs F that are <i>unobfuscatable</i> in the sense that (a) given any efficient program Pi that computes the same function as a program P\in F, the “source code” P can be efficiently reconstructed,  yet (b) given oracle access to a (randomly selected) program P\in F, no efficient algorithm can reconstruct P (or even distinguish a certain bit in the code from random) except with negligible probability.

We extend our impossibility result in a number of ways, including even obfuscators that (a) are not necessarily computable in polynomial time, (b) only approximately preserve the functionality, and (c) only need to work for very restricted models of computation (TC_0). We also rule out several potential applications of obfuscators, by constructing “unobfuscatable” signature schemes, encryption schemes, and pseudorandom function families.Engineering and Applied Science
Specification and Verification of a Steam-Boiler with Signal-Coq

International audienceOver the last decade, the increasing demand for the validation of safety critical systems has led to the development of domain-specific programming languages (e.g. synchronous languages) and automatic verification tools (e.g. model checkers). Conventionally, the verification of a reactive system is implemented by specifying a discrete model of the system (i.e. a finite-state machine) and then checking this model against temporal properties (e.g. using an automata-based tool). We investigate the use of a synchronous programming language, Signal, and of a proof assistant, Coq, for the specification and the verification of co-inductive properties of the well-known steam-boiler problem. By way of this large-scale case-study, the Signal-Coq formal approach, i.e. the combined use of Signal and Coq, is demonstrated to be a well-suited and practical approach for the validation of reactive systems. Indeed, the deterministic model of concurrency of Signal, for specifying systems, together with the unparalleled expressive power of the Coq proof assistant, for verifying properties, enables to disregard any compromise incurred by any limitation of either the specification and the verification tools
Near-optimum universal graphs for graphs with bounded degrees (Extended abstract)

Let H be a family of graphs. We say that G is H-universal if, for each H ∈H, the graph G contains a subgraph isomorphic to H. Let H(k, n) denote the family of graphs on n vertices with maximum degree at most k. For each fixed k and each n sufficiently large, we explicitly construct an H(k, n)-universal graph Γ(k, n) with O(n2−2/k(log n)1+8/k) edges. This is optimal up to a small polylogarithmic factor, as Ω(n2−2/k) is a lower bound for the number of edges in any such graph. En route, we use the probabilistic method in a rather unusual way. After presenting a deterministic construction of the graph Γ(k, n), we prove, using a probabilistic argument, that Γ(k, n) is H(k, n)-universal. So we use the probabilistic method to prove that an explicit construction satisfies certain properties, rather than showing the existence of a construction that satisfies these properties. © Springer-Verlag Berlin Heidelberg 200
Market-based reinforcement learning in partially observable worlds

Unlike traditional reinforcement learning (RL), market-based RL is in principle applicable to worlds described by partially observable Markov Decision Processes (POMDPs), where an agent needs to learn short-term memories of relevant previous events in order to execute optimal actions. Most previous work, however, has focused on reactive settings (MDPs) instead of POMDPs. Here we reimplement a recent approach to market-based RL and for the first time evaluate it in a toy POMDP setting.This work was supported by SNF grants 21-55409.98 and 2000-61847.0
Deferring the learning for better generalization in radial basis neural networks

Proceeding of: International Conference Artificial Neural Networks — ICANN 2001. Vienna, Austria, August 21–25, 2001The level of generalization of neural networks is heavily dependent on the quality of the training data. That is, some of the training patterns can be redundant or irrelevant. It has been shown that with careful dynamic selection of training patterns, better generalization performance may be obtained. Nevertheless, generalization is carried out independently of the novel patterns to be approximated. In this paper, we present a learning method that automatically selects the most appropriate training patterns to the new sample to be predicted. The proposed method has been applied to Radial Basis Neural Networks, whose generalization capability is usually very poor. The learning strategy slows down the response of the network in the generalisation phase. However, this does not introduces a significance limitation in the application of the method because of the fast training of Radial Basis Neural Networks
Using directional curvatures to visualize folding patterns of the GTM projection manifolds

Paper presented at ICANN 2001: Procs of the Int Conf on Artificial Neural NetworksIn data visualization, characterizing local geometric properties of non-linear projection manifolds provides the user with valuable additional information that can influence further steps in the data analysis. We take advantage of the smooth character of GTM projection manifold and analytically calculate its local directional curvatures. Curvature plots are useful for detecting regions where geometry is distorted, for changing the amount of regularization in non-linear projection manifolds, and for choosing regions of interest when constructing detailed lower-level visualization plots
Neural learning invariant to network size changes

International Conference on Artificial Neural Networks (ICANN), 2001, Viena (Austria)This paper investigates the functional invariance of neural network learning methods. By functional invariance we mean the property of producing functionally equivalent minima as the size of the network grows, when the smoothing parameters are fixed. We study three different principles on which functional invariance can be based, and try to delimit the conditions under which each of them acts. We find out that, surprisingly, some of the most popular neural learning methods, such as weight-decay and input noise addition, exhibit this interesting property.Peer Reviewe
Reconfigurable frame-grabber for real-time automated visual inspection (RT-AVI) systems

In most of the automated systems for visual inspection tasks, real time requirements constitute an important aspect to have in to account in the design of them. Often, a frame-grabber attached to a computer using MMX-optimised software libraries is not enough to satisfy the above requirements and it is necessary to use expensive specialised hardware and architectures. Reconfigurable hardware gives us the best of both worlds: the flexibility of software and the high performance of customised hardware. In this paper we present a reconfigurable frame-grabber concept to integrate complex real-time processing functions needed for high-speed line inspection applications directly on-board. This allows the efficient hardware-software co-design to achieve high-performance low-cost solutions
A Self-calibration Algorithm Based on a Unified Framework for Constraints on Multiple Views

In this paper, we propose a new self-calibration algorithm for upgrading projective space to Euclidean space. The proposed method aims to combine the most commonly used metric constraints, including zero skew and unit aspect-ratio by formulating each constraint as a cost function within a unified framework. Additional constraints, e.g., constant principal points, can also be formulated in the same framework. The cost function is very flexible and can be composed of different constraints on different views. The upgrade process is then stated as a minimization problem which may be solved by minimizing an upper bound of the cost function. This proposed method is non-iterative. Experimental results on synthetic data and real data are presented to show the performance of the proposed method and accuracy of the reconstructed scene. © 2012 The Author(s).published_or_final_versionSpringer Open Choice, 25 May 201
Pattern Matching and Neural Networks based Hybrid Forecasting System

Copyright © 2001 Springer-Verlag Berlin Heidelberg. The final publication is available at link.springer.comBook title: Advances in Pattern Recognition — ICAPR 2001Second International Conference on Advances in Pattern Recognition (ICAPR 2001), Rio de Janeiro, Brazil, March 11–14, 2001In this paper we propose a Neural Net-PMRS hybrid for forecasting time-series data. The neural network model uses the traditional MLP architecture and backpropagation method of training. Rather than using the last n lags for prediction, the input to the network is determined by the output of the PMRS (Pattern Modelling and Recognition System). PMRS matches current patterns in the time-series with historic data and generates input for the neural network that consists of both current and historic information. The results of the hybrid model are compared with those of neural networks and PMRS on their own. In general, there is no outright winner on all performance measures, however, the hybrid model is a better choice for certain types of data, or on certain error measures
Detecting temporal changes in event sequences : An application to demographic data

In this paper, we discuss an approach for discovering temporal changes in event sequences, and present first results from a study on demographic data. The data encode characteristic events in a person's life course, such as their birth date, the begin and end dates of their partnerships and marriages, and the birth dates of their children. The goal is to detect significant changes in the chronology of these events over people from different birth cohorts. To solve this problem, we encoded the temporal information in a first-order logic representation, and employed Warmr, an ILP system that discovers association rules in a multi-relational data set, to detect frequent patterns that show significant variance over different birth cohorts. As a case study in multi-relational association rule mining, this work illustrates the flexibility resulting from the use of first-order background knowledge, but also uncovers a number of important issues that hitherto received little attention.status: publishe
Towards a universal theory of artificial intelligence based on algorithmic probability and sequential decisions

Decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental probability distribution is known. Solomonoff’s theory of universal induction formally solves the problem of sequence prediction for unknown distributions. We unify both theories and give strong arguments that the resulting universal AIξ model behaves optimally in any computable environment. The major drawback of the AIξ model is that it is uncomputable. To overcome this problem, we construct a modified algorithm AIξ, which is still superior to any other time t and length l bounded agent. The computation time of AIξtl is of the order t·2 l.This work was supported by SNF grant 2000-61847.00 to Jürgen Schmidhuber
Convergence and error bounds for universal prediction of nonbinary sequences

Solomonoff’s uncomputable universal prediction scheme ξ allows to predict the next symbol x k of a sequence x 1 ...x k — 1 for any Turing computable, but otherwise unknown, probabilistic environment μ. This scheme will be generalized to arbitrary environmental classes, which, among others, allows the construction of computable universal prediction schemes ξ. Convergence of ξ to μ in a conditional mean squared sense and with μ probability 1 is proven. It is shown that the average number of prediction errors made by the universal ξ scheme rapidly converges to those made by the best possible informed μ scheme. The schemes, theorems and proofs are given for general finite alphabet, which results in additional complications as compared to the binary case. Several extensions of the presented theory and results are outlined. They include general loss functions and bounds, games of chance, infinite alphabet, partial and delayed prediction, classification, and more active systems.This work was supported by SNF grant 2000-61847.00 to Jurgen Schmidhuber
Speeding up relational reinforcement learning through the use of an incremental first order decision tree algorithm

Relational reinforcement learning (RRL) is a learning technique that combines standard reinforcement learning with inductive logic programming to enable the learning system to exploit structural knowledge about the application domain.
This paper discusses an improvement of the original RRL. We introduce a fully incremental first order decision tree learning algorithm TG and integrate this algorithm in the RRL system to form RRL-TG. We demonstrate the performance gain on similar experiments to those that were used to demonstrate the behaviour of the original RRL system.status: publishe