ppxscal/embedding_network huggingface dataset:
Analyzing and visualizing multiagent rewards in dynamic and stochastic domains
Authors: Adrian K. Agogino, Kagan Tumer
Venue: Autonomous Agents and Multi-Agent Systems (2008)

The ability to analyze the effectiveness of agent reward structures is critical to the successful design of multiagent learning algorithms. Though final system performance is the best indicator of the suitability of a given reward structure, it is often preferable to analyze the reward properties that lead to good system behavior (i.e., properties promoting coordination among the agents and providing agents with strong signal to noise ratios). This step is particularly helpful in continuous, dynamic, stochastic domains ill-suited to simple table backup schemes commonly used in TD(¿)/Q-learning where the effectiveness of the reward structure is difficult to distinguish from the effectiveness of the chosen learning algorithm. In this paper, we present a new reward evaluation method that provides a visualization of the tradeoff between the level of coordination among the agents and the difficulty of the learning problem each agent faces. This method is independent of the learning algorithm and is only a function of the problem domain and the agents' reward structure. We use this reward property visualization method to determine an effective reward without performing extensive simulations. We then test this method in both a static and a dynamic multi-rover learning domain where the agents have continuous state spaces and take noisy actions (e.g., the agents' movement decisions are not always carried out properly). Our results show that in the more difficult dynamic domain, the reward efficiency visualization method provides a two order of magnitude speedup in selecting good rewards, compared to running a full simulation. In addition, this method facilitates the design and analysis of new rewards tailored to the observational limitations of the domain, providing rewards that combine the best properties of traditional rewards.
Modelling data dissemination in opportunistic networks
Authors: Chiara Boldrini, Marco Conti, Andrea Passarella
Venue: Proceedings of the third ACM workshop on Challenged networks (2008)

In opportunistic networks data dissemination is an important, although not widely explored, topic. Since opportunistic networks topologies are very challenged and unstable, data-centric approaches are an interesting direction to pursue. Data should be proactively and cooperatively disseminated from sources towards possibly interested receivers, as sources and receivers might not be aware of each other, and never get in touch directly. In this paper we consider a utility-based cooperative data dissemination system in which the utility of data is defined based on the social relationships between users. Specifically, we study the performance of this system through an analytical model. Our model allows us to completely characterise the data dissemination process, as it describes both its stationary and transient regimes. After validating the model, we study the system's behaviour with respect to key parameters such as the definition of the data utility function, the initial data allocation on nodes, the number of users in the system, and the data popularity.
Fast Algorithm for Order Independent Binary Homotopic Thinning
Authors: Marcin Iwanowski, Pierre Soille
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II (2007)

In this paper an efficient queue-based algorithm for order independent homotopic thinning is proposed. This generic algorithm can be applied to various thinning versions: homotopic marking, anchored skeletonisation, and the computation of the skeleton of influence zones based on local pixel characterisations. An example application of the proposed method to detect the medial axis of wide river networks from satellite imagery is also presented.
State of the art: Linux audio 2008, Part II
Authors: Dave Phillips
Venue: Linux Journal (2008)

Dave Phillips weighs in on the production side of music and sound software for Linux.
The managed motorway: real-time vehicle scheduling: a research agenda
Authors: Vinny Cahill, Aline Senart, Douglas C. Schmidt, Stefan Weber, Anthony Harrington, Barbara Hughes
Venue: Proceedings of the 9th workshop on Mobile computing systems and applications (2008)

Air pollution, traffic congestion, stress and accidents are common features of today's road transportation experience. New approaches to improving the efficiency and safety of transportation systems are therefore required. Existing work on safe high-speed motorway driving, however, either assumes that vehicles are driverless and/or is limited to local decision making or to one-lane-only reservation systems. This paper describes a novel approach to vehicle scheduling based on real-time hierarchical scheduling, local real-time coordination and real-time inter-vehicle communication. We describe a new model in which road users reserve variable-size slots on motorway lanes, which enables enforcement of timeliness guarantees and adaptive scheduling based on a combination of local and global decisions. We present the research challenges that must be tackled to ensure that this vision of managed motorways becomes a reality.
Automatic Polyphonic Music Composition Using the EMILE and ABL Grammar Inductors
Authors: David Ortega-Pacheco, Hiram Calvo
Venue: Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications (2008)

In this paper we present a method for automatic polyphonic music composition using the ABL and EMILE grammar inductors. To evaluate the performance of the EMILE and ABL engines we use a voting classification scheme based on TF.IDF weighting--we show a novel adaptation of the n-gram concept for music classification. We performed experiments with six musical MIDI collections from a different classical music composer each (Bach, Chopin, Liszt, Schubert, Mozart and Haydn). For each composer we applied our method to obtain five new polyphonic music compositions, and then we tested the membership of the new compositions with regard to every composer. We found that new compositions have similar membership to the set of composer styles than natural compositions. We conclude that our method is capable of creating new, relatively original compositions in the same musical style of each author.
Resolving the forecasting problems of overshoot and volatility clustering using ANFIS coupling nonlinear heteroscedasticity with quantum tuning
Authors: Bao Rong Chang
Venue: Fuzzy Sets and Systems (2008)

In this paper, an approach to resolving two crucial problems of the overshoot and volatility clustering effects in time-series forecast has been proposed. In time-series prediction, big residuals round the turning-point region of a data sequence due to the overshoot phenomenon. Volatility clustering effect suggests a time series with successive disturbances being serially dependent. Both effects degrade the efficiency and effectiveness of time-series prediction and give rise to large residual errors. To overcome the overshoot and volatility clustering problems, an adaptive neuro-fuzzy inference system (ANFIS) coupling a nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH), which is adapted by quantum minimization (QM), is introduced to resolve the drawbacks of the predicted outputs with big residuals around the inflection points of a data sequence and time-varying conditional variance in residual errors. Besides, the trial of two distinct quantum amplitude amplification (QAA) techniques called by QM is also considered to show their different computational complexity. Two experiments on the financial time series were taken and a performance evaluation was made between the proposed one and several well-known alternative methods. Results show that our proposed method gains the best predictive accuracy to outperform the others. Goodness of fit of the proposed method was tested successfully by Ljung-Box Q-test. It followed that the proposed method in fact can reduce large residual errors significantly in time-series forecast because the overshoot and volatility clustering effects are simultaneously regulated to the trivial levels.
Off-Line Learning with Transductive Confidence Machines: An Empirical Evaluation
Authors: Stijn Vanderlooy, Laurens Maaten, Ida Sprinkhuizen-Kuyper
Venue: Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition (2007)

The recently introduced transductive confidence machines (TCMs) framework allows to extend classifiers such that they satisfy the calibration property. This means that the error rate can be set by the user prior to classification. An analytical proof of the calibration property was given for TCMs applied in the on-line learning setting. However, the nature of this learning setting restricts the applicability of TCMs. In this paper we provide strong empirical evidence that the calibration property also holds in the off-line learning setting. Our results extend the range of applications in which TCMs can be applied. We may conclude that TCMs are appropriate in virtually any application domain.
Meta methods for model sharing in personal information systems
Authors: Stefan Siersdorfer, Sergej Sizov
Venue: ACM Transactions on Information Systems (TOIS) (2008)

This article introduces a methodology for automatically organizing document collections into thematic categories for Personal Information Management (PIM) through collaborative sharing of machine learning models in an efficient and privacy-preserving way. Our objective is to combine multiple independently learned models from several users to construct an advanced ensemble-based decision model by taking the knowledge of multiple users into account in a decentralized manner, for example, in a peer-to-peer overlay network. High accuracy of the corresponding supervised (classification) and unsupervised (clustering) methods is achieved by restrictively leaving out uncertain documents rather than assigning them to inappropriate topics or clusters with low confidence. We introduce a formal probabilistic model for the resulting ensemble based meta methods and explain how it can be used for constructing estimators and for goal-oriented tuning. Comprehensive evaluation results on different reference data sets illustrate the viability of our approach.
A Tableau for RoBCTL
Authors: John C. Mccabe-Dansted
Venue: Proceedings of the 11th European conference on Logics in Artificial Intelligence (2008)

It can be desirable to specify polices that require a system to achieve some outcome even if a certain number of failures occur. The temporal logic of robustness RoCTL* extends CTL* with operators from Deontic logic, and a novel operator referred to as "Robustly" [7]. The only known decision procedure for RoCTL* involves a reduction to QCTL*. This paper presents a tableau for the related bundled tree logic RoBCTL*; this is the first decision procedure for RoBCTL*, and although non-elementary in degenerate cases has much better performance than the QCTL* based decision procedure for RoCTL*. The degenerate cases where this tableau performs poorly provide clues as to where we might look to prove that RoBCTL* is non-elementary.
Spectral Resolution Enhancement for SBDART
Authors: Wensheng Chen, Xiaojian Xu
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05 (2008)

SBDART is a software tool for plane parallel radiative transfer in the earth’s atmosphere with low to medium spectral resolution. This paper aims to make its spectral resolution reach 1nm within the spectral range from 0.4 to 16 um to form a Spectral Resolution Enhanced SBDART (SRE-SBDART) software, by introducing the code of the line file creator for LBLRTM combined with HITRAN04 database and by modifying the code of the correlated k-distribution data file creator.
Optimization of Fuzzy Membership Function Using Clonal Selection
Authors: Ayşe Merve Şakiroğlu, Ahmet Arslan
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I (2007)

A clonal selection algorithm (Clonalg) inspires from Clonal Selection Principle used to explain the basic features of an adaptive immune response to an antigenic stimulus. It takes place in various scientific applications and it can be also used to determine the membership functions in a fuzzy system. The aim of the study is to adjust the shape of membership functions and a novice aspect of the study is to determine the membership functions. Proposed method has been implemented using a developed Clonalg program for a single input and output fuzzy system. In the previous work [1], using genetic algorithm (GA) is proposed to it. In this study they are compared, too and it has been shown that using clonal selection algorithm is advantageous than using GA for finding optimum values of fuzzy membership functions.
3D Digital Breast Tomosynthesis Using Total Variation Regularization
Authors: Iason Kastanis, Simon Arridge, Alex Stewart, Spencer Gunn, Christer Ullberg, Tom Francke
Venue: Proceedings of the 9th international workshop on Digital Mammography (2008)

3D digital breast imaging promises to significantly reduce both false negatives and false positives, allowing the earlier detection of cancer while reducing the occurrence of callbacks. In this paper we have developed an iterative algorithm that improves on the quality of the reconstructed images over current algorithms like filtered back projection. Our algorithm uses Total Variation to restrain the noise while enhancing edge features. We have applied this algorithm to data obtained using the XCounter mammography system.
ASIP-eFPGA Architecture for Multioperable GNSS Receivers
Authors: Thorsten Sydow, Holger Blume, Götz Kappen, Tobias G. Noll
Venue: Proceedings of the 8th international workshop on Embedded Computer Systems: Architectures, Modeling, and Simulation (2008)

In this paper a novel flexible architecture exemplarily applied for multioperable GNSS receivers including an ASIP and an arithmetic oriented embedded FPGA is presented. The advent of next generation GNSS-systems as well as different demands in different system phases require high flexibility. The proposed architecture provides high energy and area efficiency compared to software-programmable processor while preserving flexibility. Exemplarily the mapping of the computational intensive base band processing of a Navstar GPS receiver to an ASIP-eFPGA architecture will be discussed. Results are based on a standard cell based design regarding the ASIP. A design method for physically optimized VLSI-macros has been applied for the implementation of the eFPGA. All results are acquired for a 90 nm-CMOS technology. It will be shown that the proposed heterogeneous architecture features an attractive position in the design space regarding area and energy efficiency as well as flexibility.
On the construction of polyharmonic B-splines
Authors: Milvia Rossini
Venue: Journal of Computational and Applied Mathematics (2008)

In this paper we present two constructions providing polyharmonic B-splines with a quasi-rotational invariant behavior. Their properties are discussed and it is shown that in this way we improve the decay when @?x@?->~.
A few good agents: multi-agent social learning
Authors: Jean Oh, Stephen F. Smith
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1 (2008)

In this paper, we investigate multi-agent learning (MAL) in a multi-agent resource selection problem (MARS) in which a large group of agents are competing for common resources. Since agents in such a setting are self-interested, MAL in MARS domains typically focuses on the convergence to a set of non-cooperative equilibria. As seen in the example of prisoner's dilemma, however, selfish equilibria are not necessarily optimal with respect to the natural objective function of a target problem, e.g., resource utilization in the case of MARS. Conversely, a centrally administered optimization of physically distributed agents is infeasible in many real-life applications such as transportation traffic problems. In order to explore the possibility for a middle ground solution, we analyze two types of costs for evaluating MAL algorithms in this context. The quality loss of a selfish algorithm can be quantitatively measured by the price of anarchy, i.e., the ratio of the objective function value of a selfish solution to that of an optimal solution. Analogously, we introduce the price of monarchy of a learning algorithm to quantify the practical cost of coordination in terms of communication cost. We then introduce a multi-agent social learning approach named A Few Good Agents (AFGA) that motivates self-interested agents to cooperate with one another to reduce the price of anarchy, while bounding the price of monarchy at the same time. A preliminary set of experiments on the El Farol bar problem, a simple example of MARS, show promising results.
369 Tflop/s molecular dynamics simulations on the Roadrunner general-purpose heterogeneous supercomputer
Authors: Sriram Swaminarayan, Kai Kadau, Timothy C. Germann, Gordon C. Fossum
Venue: Proceedings of the 2008 ACM/IEEE conference on Supercomputing (2008)

We present timing and performance numbers for a short-range parallel molecular dynamics (MD) code, SPaSM, that has been rewritten for the heterogeneous Roadrunner supercomputer. Each Roadrunner compute node consists of two AMD Opteron dualcore microprocessors and four PowerXCell 8i enhanced Cell microprocessors, so that there are four MPI ranks per node, each with one Opteron and one Cell. The interatomic forces are computed on the Cells (each with one PPU and eight SPU cores), while the Opterons are used to direct inter-rank communication and perform I/O-heavy periodic analysis, visualization, and checkpointing tasks. The performance measured for our initial implementation of a standard Lennard-Jones pair potential benchmark reached a peak of 369 Tflop/s double-precision floating-point performance on the full Roadrunner system (27.7% of peak), corresponding to 124 MFlop/Watt/s at a price of approximately 3.69 MFlops/dollar. We demonstrate an initial target application, the jetting and ejection of material from a shocked surface.
Extracting Requirements from Scenarios with ILP
Authors: Dalal Alrajeh, Oliver Ray, Alessandra Russo, Sebastian Uchitel
Venue: Inductive Logic Programming: 16th International Conference, ILP 2006, Santiago de Compostela, Spain, August 24-27, 2006, Revised Selected Papers (2007)

Requirements Engineering involves the elicitation of high-level stakeholder goals and their refinement into operational system requirements. A key difficulty is that stakeholders typically convey their goals indirectly through intuitive narrative-style scenarios of desirable and undesirable system behaviour, whereas goal refinement methods usually require goals to be expressed declaratively using, for instance, a temporal logic. Currently, the extraction of formal requirements from scenario-based descriptions is a tedious and error-prone process that would benefit from automated tool support. We present an ILP methodology for inferring requirements from a set of scenarios and an initial but incomplete requirements specification. The approach is based on translating the specification and scenarios into an event-based logic programming formalism and using a non-monotonic ILP system to learn a set of missing event preconditions. The contribution of this paper is a novel application of ILP to requirements engineering that also demonstrate the need for non-monotonic learning.
The IBM RT07 Evaluation Systems for Speaker Diarization on Lecture Meetings
Authors: Jing Huang, Etienne Marcheret, Karthik Visweswariah, Gerasimos Potamianos
Venue: Multimodal Technologies for Perception of Humans: International Evaluation Workshops CLEAR 2007 and RT 2007, Baltimore, MD, USA, May 8-11, 2007, Revised Selected Papers (2008)

We present the IBM systems for the Rich Transcription 2007 (RT07) speaker diarization evaluation task on lecture meeting data. We first overview our baseline system that was developed last year, as part of our speech-to-text system for the RT06s evaluation. We then present a number of simple schemes considered this year in our effort to improve speaker diarization performance, namely: (i) A better speech activity detection (SAD) system, a necessary pre-processing step to speaker diarization; (ii) Use of word information from a speaker-independent speech recognizer; (iii) Modifications to speaker cluster merging criteria and the underlying segment model; and (iv) Use of speaker models based on Gaussian mixture models, and their iterative refinement by frame-level re-labeling and smoothing of decision likelihoods. We report development experiments on the RT06s evaluation test set that demonstrate that these methods are effective, resulting in dramatic performance improvements over our baseline diarization system. For example, changes in the cluster segment models and cluster merging methodology result in a 24.2% relative reduction in speaker error rate, whereas use of the iterative model refinement process and word-level alignment produce a 36.0% and 9.2% speaker error relative reduction, respectively. The importance of the SAD subsystem is also shown, with SAD error reduction from 12.3% to 4.3% translating to a 20.3% relative reduction in speaker error rate. Unfortunately however, the developed diarization system heavily depends on appropriately tuning thresholds in the speaker cluster merging process. Possibly as a result of over-tuning such thresholds, performance on the RT07 evaluation test set degrades significantly compared to the one observed on development data. Nevertheless, our experiments show that the introduced techniques of cluster merging, speaker model refinement and alignment remain valuable in the RT07 evaluation.