ppxscal/embedding_network huggingface dataset:
Heterogeneous Learning and the Targeting of Marketing Communication for New Products
Authors: Sridhar Narayanan, Puneet Manchanda
Venue: Marketing Science (2009)

New product launches are often accompanied by extensive marketing communication campaigns. Firms' allocation decisions for these marketing communication expenditures have two dimensions---across consumers and over time. This allocation problem is different relative to the problem of allocation of resources for existing products. In the case of new products, consumers are uncertain about their quality and learn about the products through marketing communication. Furthermore, different consumers may have different rates of learning about product quality; i.e., there may be heterogeneous learning. Thus, consumer responsiveness to marketing communication could vary along two dimensions. For each consumer, this responsiveness would vary over time, as she learns about product quality. Across consumers, there would be differences in responsiveness in each time period. For optimal allocation of marketing communication across both consumers and time, firms would need estimates of how consumer responsiveness varies across consumers and over time. Past studies have typically focused on one of these two dimensions in which responsiveness varies. They have either looked at heterogeneity in responsiveness across agents or the variation in responsiveness over time. In the context of new products, past research has looked at how consumer learning about product quality causes responsiveness to vary over time. In this study, we build a model that allows for heterogeneous learning rates and obtain individual-level learning parameters for each consumer. We use a novel and rich panel data set that allows us to estimate these model parameters. To obtain individual-level estimates of learning, we add a hierarchical Bayesian structure to the Bayesian learning model. We exploit the natural hierarchy in the Bayesian learning process to incorporate it in the hierarchical Bayesian model. We use data augmentation, coupled with the Metropolis-Hastings algorithm, to make inferences about individual-level parameters of learning. We conduct this analysis on a unique panel data set of physicians where we observe prescription decisions and detailing (i.e., sales-force effort) at the individual physician level for a new prescription drug category. Our results show that there is significant heterogeneity across physicians in their rates of learning about the quality of new drugs. We also find that there are asymmetries in the temporal evolution of responsiveness of physicians to detailing---physicians who are more responsive to detailing in early periods are less responsive later on and vice versa. These findings have interesting implications for the targeting of detailing across physicians and over time. We find that firms could increase their revenue if they took these temporal and cross-sectional differences in responsiveness into account while deciding on allocations of detailing.
Dynamics in argumentation with single extensions: attack refinement and the grounded extension
Authors: Guido Boella, Souhila Kaci, Leendert van der Torre
Venue: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2 (2009)

We introduce nine attack refinement principles stating whether the extension stays the same if we add a single attack, and we show that the grounded extension satisfies five of them. We define also an extension called acyclic attack refinement by adding a loop condition, and we show that the grounded extension satisfies one of the acyclic attack refinement principles. Moreover, we define an extension called conditional attack refinement taking alternative attackers into account, and we give an example of a conditional attack refinement principle satisfied by the grounded extension.
An Adaptive Optimized RTO Algorithm for Multi-homed Wireless Environments
Authors: Sheila Fallon, Paul Jacob, Yuansong Qiao, Liam Murphy
Venue: Proceedings of the 7th International Conference on Wired/Wireless Internet Communications (2009)

As a transport layer protocol SCTP uses end to end metrics, such as Retransmission Time Out (RTO), to manage mobility handover. Our investigation illustrates that Wireless LAN (WLAN) mobility causes continuously increased Round Trip Times (RTT) resulting from 802.11 MAC retransmissions, regardless of the service specified by upper layers. We present scenarios where the current understanding of SCTP switchover aggressiveness is invalid; spurious failovers together with excessive RTO result in new forms of receiver buffer blocking communication failure. Given wireless mobility performance issues, together with the ambiguity of end to end metrics, we propose an Adaptive Optimized RTO algorithm for wireless Access Networks (AORAN) which uses local as well as end to end metrics to manage mobility. AORAN measures RTT between the mobile node and Access Point (AP) to calculate wireless and Internet RTO subcomponents. We also show binary exponential backoff has negative effects on SCTP with increased wireless RTT; AORAN introduces a decision mechanism which implements backoff on RTO subcomponents only when appropriate.
Beyond the Quadruple Play: Networking, Convergence, and Customer Delivery
Authors: International Engineering Consortium
Venue: Comprehensive Report Series (2007)

Covering all topics related to strategies and experiences on quad-play service design and delivery, the guide points out that packaging together multiple services onto a single customer-friendly bundle have actually led to positive results, such as customer loyalty to service packages and a highly reduced churn rate. In addition, networks can actually deliver more efficient converging services, thus yielding a double win for providers as customer retention is increased and operating costs are better managed. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Opponent modeling in interesting adversarial environments
Authors: Maria Gini
Venue:  (2008)

We advance the field of research involving modeling opponents in interesting adversarial environments: environments in which equilibrium strategies are intractable to calculate or undesirable to use. We motivate the need for opponent models by showing how successful opponent modeling agents can exploit non-equilibrium strategies and strategies using equilibrium approximations. We examine the requirements for an opponent modeling agent, including the desiderata of a good model. We develop a new measurement which can be used to quantify how well our model can predict the opponent's behavior independently from the performance of the agent in which it resides. We show how this metric can be used to find areas of model improvement that would otherwise have remained undiscovered and demonstrate the technique for evaluating opponent model quality in the poker domain. The measurement can also be used to detect occasions when an opponent is not playing an equilibrium strategy, indicating potential opportunities for exploitation. We introduce the idea of performance bounds for classes of opponent models, present a method for calculating them, and show how these bounds are a function of only the environment and thus invariant over the set of all opponents an agent may face. We calculate the performance bounds for several classes of models in two domains: high card draw with simultaneous betting and a new simultaneous-move strategy game we developed. We describe how the performance bounds can aid selection of appropriate classes of models for a given domain as well as guide the level of effort that should be applied to developing opponent models in those domains. We expand the set of opponent modeling methods with new algorithms and study their performance empirically in several domains, including full scale Texas Hold'em poker. We explore opponent modeling improvement methods useful when the set of opponents we may face is unknown or unavailable. Using these techniques, we develop PokeMinn, an agent that learns to improve its performance by observing the opponent, even when the opponent is attempting to approximate equilibrium play. These methods also pave the way for performance optimization using genetic algorithms and efficient model queries using metareasoning.
Lifting Prediction to Alignment of RNA Pseudoknots
Authors: Mathias Möhl, Sebastian Will, Rolf Backofen
Venue: Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology (2009)

Prediction and alignment of RNA pseudoknot structures are NP-hard. Nevertheless, several efficient prediction algorithms by dynamic programming have been proposed for restricted classes of pseudoknots. We present a general scheme that yields an efficient alignment algorithm for arbitrary such classes. Moreover, we show that such an alignment algorithm benefits from the class restriction in the same way as the corresponding structure prediction algorithm does. We look at five of these classes in greater detail. The time and space complexity of the alignment algorithm is increased by only a linear factor over the respective prediction algorithm. For four of the classes, no efficient alignment algorithms were known. For the fifth, most general class, we improve the previously best complexity of O(n 5 m 5) time to O(nm 6), where n and m denote sequence lengths. Finally, we apply our fastest algorithm with O(nm 4) time and O(nm 2) space to comparative de-novo pseudoknot prediction.
Computer-Assisted Open Heart CABG: Image-Guided Navigation for All Target Vessels
Authors: Claudia Gnahm, Christine Hartung, Reinhard Friedl, Martin Hoffmann, Klaus Dietmayer
Venue: Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart (2009)

During open heart coronary artery bypass grafting (CABG), optimal placement of the bypass graft on the diseased target vessel is of utmost importance. To assist the heart surgeon in this matter, a surgical navigation system has been developed. The concept includes preprocedural planning of the optimal position for the distal anastomosis (surgical target) on the target vessel and intraoperative image-guided navigation to that position. Navigation is enabled by registration of pre- and intraoperative data. The registration mechanism was validated retrospectively. Promising results justified subsequent intraoperative live application of the system during 11 open heart CABG surgeries enabling navigation on 21 target vessels on the anterior, lateral and inferior wall of the heart. In each case, the employed registration mechanism ensured good alignment of preoperatively planned and intraoperatively identified surgical targets. The system is very well applicable for in-vivo navigation and allows for bypass grafting precisely at a preoperatively planned position.
Fault-Tolerant Embedding of Pairwise Independent Hamiltonian Paths on a Faulty Hypercube with Edge Faults
Authors: Sun-Yuan Hsieh, Yu-Fen Weng
Venue: Theory of Computing Systems (2009)

A Hamiltonian path in G is a path which contains every vertex of G exactly once. Two Hamiltonian paths P 1=&#x2329;u 1,u 2,&#x2026;,u n &#x232a; and P 2=&#x2329;v 1,v 2,&#x2026;,v n &#x232a; of G are said to be independent if u 1=v 1, u n =v n , and u i &#x2260;v i for all 1<i<n; and both are full-independent if u i &#x2260;v i for all 1&#x2264;i&#x2264;n. Moreover, P 1 and P 2 are independent starting at u 1, if u 1=v 1 and u i &#x2260;v i for all 1<i&#x2264;n. A&#x00a0;set of Hamiltonian paths {P 1,P 2,&#x2026;,P k } of G are pairwise independent (respectively, pairwise full-independent, pairwise independent starting at u 1) if any two different Hamiltonian paths in the set are independent (respectively, full-independent, independent starting at u 1). A&#x00a0;bipartite graph G is Hamiltonian-laceable if there exists a Hamiltonian path between any two vertices from different partite sets. It is well known that an n-dimensional hypercube Q n is bipartite with two partite sets of equal size. Let F be the set of faulty edges of&#x00a0;Q n . In this paper, we show the following results: When |F|&#x2264;n&#x2212;4, Q n &#x2212;F&#x2212;{x,y} remains Hamiltonian-laceable, where x and y are any two vertices from different partite sets and n&#x2265;4. When |F|&#x2264;n&#x2212;2, Q n &#x2212;F contains (n&#x2212;|F|&#x2212;1)-pairwise full-independent Hamiltonian paths between n&#x2212;|F|&#x2212;1 pairs of adjacent vertices, where n&#x2265;2. When |F|&#x2264;n&#x2212;2, Q n &#x2212;F contains (n&#x2212;|F|&#x2212;1)-pairwise independent Hamiltonian paths starting at any vertex v in a partite set to n&#x2212;|F|&#x2212;1 distinct vertices in the other partite set, where n&#x2265;2. When 1&#x2264;|F|&#x2264;n&#x2212;2, Q n &#x2212;F contains (n&#x2212;|F|&#x2212;1)-pairwise independent Hamiltonian paths between any two vertices from different partite sets, where n&#x2265;3.
A Process Model of Legal Argument with Hypotheticals
Authors: Kevin Ashley, Collin Lynch, Niels Pinkwart, Vincent Aleven
Venue: Proceeding of the 2008 conference on Legal Knowledge and Information Systems: JURIX 2008: The Twenty-First Annual Conference (2008)

This paper presents a process model of arguing with hypotheticals and uses it to explain examples of oral arguments before the U.S. Supreme Court that are like those employed in Socratic law teaching. The process model has been partially implemented in the LARGO (Legal ARgument Graph Observer) intelligent tutoring system. The program supports students in diagramming oral argument examples; its feedback on students' diagrammatic reconstructions of the examples enforces the expectations of the process model. The paper presents empirical evidence that features of the argument diagrams made with LARGO are correlated with independent measures of argumentation ability. The examples and empirical results support the model's explanatory and diagnostic utility.
Probabilistic modelling, inference and learning using logical theories
Authors: K. S. Ng, J. W. Lloyd, W. T. Uther
Venue: Annals of Mathematics and Artificial Intelligence (2008)

This paper provides a study of probabilistic modelling, inference and learning in a logic-based setting. We show how probability densities, being functions, can be represented and reasoned with naturally and directly in higher-order logic, an expressive formalism not unlike the (informal) everyday language of mathematics. We give efficient inference algorithms and illustrate the general approach with a diverse collection of applications. Some learning issues are also considered.
Interactive Programming of Reactive Systems
Authors: Louis Mandel, Florence Plateau
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2009)

ReactiveML is a synchronous reactive extension of the general purpose programming language OCaml. It allows to program reactive systems such as video games or simulators. This paper presents rmltop, the ReactiveML counterpart of the OCaml toplevel. This toplevel allows a programmer to interactively write ReactiveML programs which are type-checked, compiled and loaded on the fly. The user can then progressively run concurrent processes and observe the interactions between them. The main strength of rmltop is that all valid ReactiveML expressions are accepted in the toplevel with the same semantics as in the compiler. This allows to use the ReactiveML toplevel as a debugger. Furthermore, the interpreted code is as efficient as if it was compiled. Moreover, a toplevel interpreter being itself a reactive system, another originality of rmltop is its own implementation in ReactiveML which makes it relatively light.
Generalized regularized learning
Authors: Kwong-Sak Leung, Kin-Hong Lee
Venue:  (2007)

A classical algorithm in classification is the support vector machine (SVM) algorithm. Based on Vapnik's statistical learning theory, it tries to find a linear boundary with maximum margin to separate the given data into different classes. In non-separable case, SVM uses a kernel trick to map the data onto a feature space and finds a linear boundary in the new space. Instead of understanding SVM's behavior from Vapnik's theory, our work follows regularized learning viewpoint. In regularized learning, people try to find a solution from a function space which has small empirical error in explaining the input-output relationship for training data, yet keeping the simplicity of the solution. To provide the simplicity, the complexity of the solution is penalized, which involves all features in the function space. An equal penalty, as in standard regularized learning, is reasonable without knowing the significance of individual features. But how about if we have prior knowledge that some features are more important than others? Instead of penalizing all features, we study a generalized regularized learning framework where part of the function space is not penalized, and derive its corresponding solution. Different algorithms are derived from the framework. When the empirical error is defined by a quadratic loss, we have generalized regularized least-squares learning algorithm. When the idea is applied to SVM, we obtain semi-parametric SVM algorithm. Besides, we derive the third algorithm which generalizes the kernel logistic regression algorithm. How to choose non-regularized features? We give some empirical studies. We use dimensionality reduction techniques in text categorization, extract some non-regularized intrinsic features for the high dimensional data, and report improved results. Two generalized algorithms need to solve positive definite linear systems to get the parameters. How to solve a large-scale linear system efficiently? Different from previous work in machine learning where people generally resort to conjugate gradient method, our work proposes to use a domain decomposition approach. New interpretations and improved results are reported accordingly.
Classes and inheritance in actor-oriented design
Authors: Edward A. Lee, Xiaojun Liu, Stephen Neuendorffer
Venue: ACM Transactions on Embedded Computing Systems (TECS) (2009)

Actor-oriented components emphasize concurrency and temporal semantics and are used for modeling and designing embedded software and hardware. Actors interact with one another through ports via a messaging schema that can follow any of several concurrent semantics. Domain-specific actor-oriented languages and frameworks are common (Simulink, LabVIEW, SystemC, etc.). However, they lack many modularity and abstraction mechanisms that programmers have become accustomed to in object-oriented components, such as classes, inheritance, interfaces, and polymorphism, except as inherited from the host language. This article shows a form that such mechanisms can take in actor-oriented components, gives a formal structure, and describes a prototype implementation. The mechanisms support actor-oriented class definitions, subclassing, inheritance, and overriding. The formal structure imposes structural constraints on a model (mainly the &ldquo;derivation invariant&rdquo;) that lead to a policy to govern inheritance. In particular, the structural constraints permit a disciplined form of multiple inheritance with unambiguous inheritance and overriding behavior. The policy is based formally on a generalized ultrametric space with some remarkable properties. In this space, inheritance is favored when actors are &ldquo;closer&rdquo; (in the generalized ultrametric), and we show that when inheritance can occur from multiple sources, one source is always unambiguously closer than the other.
The excitement and challenges of space and earth exploration
Authors: Charles Etachi
Venue: Proceedings of the 2009 conference on Information Science, Technology and Applications (2009)

Over the last 50 years, the use of space platform to explore and observe our planet and the universe surrounding us has advanced in leaps and bounds. As of today there are more than hundred scientific Spacecraft monitoring all aspects of our planet environment and studying the planets in our solar system. In this talk I will discuss the advances being made and planned for the next 20 years and the role of information systems in developing the capability to better understand our planet and inform scientists, users and policy makers about the global changes which are happening. I will also show the excitement of exploration and discovery through the eyes of the teams that are exploring our solar system.
Taming coincidental correctness: Coverage refinement with context patterns to improve fault localization
Authors: Xinming Wang, S. C. Cheung, W. K. Chan, Zhenyu Zhang
Venue: Proceedings of the 2009 IEEE 31st International Conference on Software Engineering (2009)

Recent techniques for fault localization leverage code coverage to address the high cost problem of debugging. These techniques exploit the correlations between program failures and the coverage of program entities as the clue in locating faults. Experimental evidence shows that the effectiveness of these techniques can be affected adversely by coincidental correctness, which occurs when a fault is executed but no failure is detected. In this paper, we propose an approach to address this problem. We refine code coverage of test runs using control- and data-flow patterns prescribed by different fault types. We conjecture that this extra information, which we call context patterns, can strengthen the correlations between program failures and the coverage of faulty program entities, making it easier for fault localization techniques to locate the faults. To evaluate the proposed approach, we have conducted a mutation analysis on three real world programs and cross-validated the results with real faults. The experimental results consistently show that coverage refinement is effective in easing the coincidental correctness problem in fault localization techniques.
A note about the average number of real roots of a Bernstein polynomial system
Authors: Diego Armentano, Jean-Pierre Dedieu
Venue: Journal of Complexity (2009)

We prove that the real roots of normal random homogeneous polynomial systems with n+1 variables and given degrees are, in some sense, equidistributed in the projective space P(R^n^+^1). From this fact we compute the average number of real roots of normal random polynomial systems given in the Bernstein basis.
Analysis of Convergence of a Frequency-Domain LMS Adaptive Filter Implemented as a Multi-Stage Adaptive Filter
Authors: Tokunbo Ogunfunmi, Thomas Paul
Venue: Journal of Signal Processing Systems (2009)

We present an analysis of the convergence of the frequency-domain LMS adaptive filter when the DFT is computed using the LMS steepest descent algorithm. In this case, the frequency-domain adaptive filter is implemented with a cascade of two sections, each updated using the LMS algorithm. The structure requires less computations compared to using the FFT and is modular suitable for VLSI implementations. Since the structure contains two adaptive algorithms updating in parallel, an analysis of the overall system convergence needs to consider the effect of the two adaptive algorithms on each other, in addition to their individual convergence. Analysis was based on the expected mean-square coefficient error for each of the two LMS adaptive algorithms, with some simplifying approximations for the second algorithm, to describe the convergence behavior of the overall system. Simulations were used to verify the results.