Truthful and Quality Conscious Query Incentive Networks

Query incentive networks capture the role of incentives in extracting information from decentralized information networks such as a social network. Several game theoretic tilt:Kids of query incentive networks have been proposed in the literature to study and characterize the dependence, of the monetary reward required to extract the answer for a query, on various factors such as the structure of the network, the level of difficulty of the query, and the required success probability.None of the existing models, however, captures the practical andimportant factor of quality of answers. In this paper, we develop a complete mechanism design based framework to incorporate the quality of answers, in the monetization of query incentive networks. First, we extend the model of Kleinberg and Raghavan [2] to allow the nodes to modulate the incentive on the basis of the quality of the answer they receive. For this qualify conscious model. we show are existence of a unique Nash equilibrium and study the impact of quality of answers on the growth rate of the initial reward, with respect to the branching factor of the network. Next, we present two mechanisms; the direct comparison mechanism and the peer prediction mechanism, for truthful elicitation of quality from the agents. These mechanisms are based on scoring rules and cover different; scenarios which may arise in query incentive networks. We show that the proposed quality elicitation mechanisms are incentive compatible and ex-ante budget balanced. We also derive conditions under which ex-post budget balance can beachieved by these mechanisms.

Network governance at local — A comparative approach

Factors influencing the effectiveness of democratic institutions and to that effect processes involved at the local governance level have been the interest in the literature, given the presence of various advocacies and networks that are context-specific. This paper is motivated to understand the adaptability issues related to governance given these complexities through a comparative analysis of diversified regions. We adopted a two-stage clustering along with regression methodology for this purpose. The results show that the formation of advocacies and networks depends on the context and institutional framework. The paper concludes by exploring different strategies and dynamics involved in network governance and insists on the importance of governing the networks for structural reformation through regional policy making.

Continuous phase transition in mixed-valent manganite Nd0.6Pb0.4MnO3

The critical behaviour has been investigated in single crystalline Nd0.6Pb0.4MnO3 near the paramagnetic to ferromagnetic transition temperature (TC) by static magnetic measurements. The values of TC and the critical exponents β, γ and δ are estimated by analysing the data in the critical region. The exponent values are very close to those expected for 3D Heisenberg ferromagnets with short-range interactions. Specific heat measurements show a broad cusp at TC (i.e., exponent α<0) being consistent with Heisenberg-like behaviour.

Mechanochemical decomposition of Gd3Fe5O12 garnet phase

Gadolinium iron garnet was milled in a high energy ball mill to study its magnetic properties in the nanocrystalline regime. XRD reveals the decomposition of the garnet phase into Gd-orthoferrite and Gd2O3 on milling. The variation of saturation magnetization and coercivity with milling is attributed to a possible shift in the compensation temperature on grain size reduction and an increase in the orthoferrite content. The Mössbauer spectrum at 16 K is characteristic of the magnetically ordered state corresponding to GdIG, GdFeO3 and α-Fe2O3 whereas at room temperature it is a superparamagnetic doublet.

Maximizing transported data in a wireless sensor network: how much can the network transport before partition?

We consider a scenario where the communication nodes in a sensor network have limited energy, and the objective is to maximize the aggregate bits transported from sources to respective destinations before network partition due to node deaths. This performance metric is novel, and captures the useful information that a network can provide over its lifetime. The optimization problem that results from our approach is nonlinear; however, we show that it can be converted to a Multicommodity Flow (MCF) problem that yields the optimal value of the metric. Subsequently, we compare the performance of a practical routing strategy, based on Node Disjoint Paths (NDPs), with the ideal corresponding to the MCF formulation. Our results indicate that the performance of NDP-based routing is within 7.5% of the optimal.

Discovering Math APIs by Mining Unit Tests

In today's API-rich world, programmer productivity depends heavily on the programmer's ability to discover the required APIs. In this paper, we present a technique and tool, called MATHFINDER, to discover APIs for mathematical computations by mining unit tests of API methods. Given a math expression, MATHFINDER synthesizes pseudo-code to compute the expression by mapping its subexpressions to API method calls. For each subexpression, MATHFINDER searches for a method such that there is a mapping between method inputs and variables of the subexpression. The subexpression, when evaluated on the test inputs of the method under this mapping, should produce results that match the method output on a large number of tests. We implemented MATHFINDER as an Eclipse plugin for discovery of third-party Java APIs and performed a user study to evaluate its effectiveness. In the study, the use of MATHFINDER resulted in a 2x improvement in programmer productivity. In 96% of the subexpressions queried for in the study, MATHFINDER retrieved the desired API methods as the top-most result. The top-most pseudo-code snippet to implement the entire expression was correct in 93% of the cases. Since the number of methods and unit tests to mine could be large in practice, we also implement MATHFINDER in a MapReduce framework and evaluate its scalability and response time.

PolyGLoT: A Polyhedral Loop Transformation Framework for a Graphical Dataflow Language

Polyhedral techniques for program transformation are now used in several proprietary and open source compilers. However, most of the research on polyhedral compilation has focused on imperative languages such as C, where the computation is specified in terms of statements with zero or more nested loops and other control structures around them. Graphical dataflow languages, where there is no notion of statements or a schedule specifying their relative execution order, have so far not been studied using a powerful transformation or optimization approach. The execution semantics and referential transparency of dataflow languages impose a different set of challenges. In this paper, we attempt to bridge this gap by presenting techniques that can be used to extract polyhedral representation from dataflow programs and to synthesize them from their equivalent polyhedral representation. We then describe PolyGLoT, a framework for automatic transformation of dataflow programs which we built using our techniques and other popular research tools such as Clan and Pluto. For the purpose of experimental evaluation, we used our tools to compile LabVIEW, one of the most widely used dataflow programming languages. Results show that dataflow programs transformed using our framework are able to outperform those compiled otherwise by up to a factor of seventeen, with a mean speed-up of 2.30x while running on an 8-core Intel system.