1,722,510 research outputs found
Stephen W. Brown, piano, Fang-Yi Shen, cello, Sunday, May 6, 2007
No full textFang-Yi Shen, cello, "In partial fulfi llment of the requirements for the degree of Doctor of Musical Arts"Stephen W. Brown, piano, "In partial fulfillment of the requirements for the degree of Master of Music
Learning and Strongly Truthful Multi-Task Peer Prediction: A Variational Approach
Get PDFPeer prediction mechanisms incentivize agents to truthfully report their signals even in the absence of verification by comparing agents' reports with those of their peers. In the detail-free multi-task setting, agents are asked to respond to multiple independent and identically distributed tasks, and the mechanism does not know the prior distribution of agents' signals. The goal is to provide an ε-strongly truthful mechanism where truth-telling rewards agents "strictly" more than any other strategy profile (with ε additive error) even for heterogeneous agents, and to do so while requiring as few tasks as possible.
We design a family of mechanisms with a scoring function that maps a pair of reports to a score. The mechanism is strongly truthful if the scoring function is "prior ideal". Moreover, the mechanism is ε-strongly truthful as long as the scoring function used is sufficiently close to the ideal scoring function. This reduces the above mechanism design problem to a learning problem - specifically learning an ideal scoring function. Because learning the prior distribution is sufficient (but not necessary) to learn the scoring function, we can apply standard learning theory techniques that leverage side information about the prior (e.g., that it is close to some parametric model). Furthermore, we derive a variational representation of an ideal scoring function and reduce the learning problem into an empirical risk minimization.
We leverage this reduction to obtain very general results for peer prediction in the multi-task setting. Specifically,
- Sample Complexity: We show how to derive good bounds on the number of tasks required for different types of priors-in some cases exponentially improving previous results. In particular, we can upper bound the required number of tasks for parametric models with bounded learning complexity. Furthermore, our reduction applies to myriad continuous signal space settings. To the best of our knowledge, this is the first peer-prediction mechanism on continuous signals designed for the multi-task setting.
- Connection to Machine Learning: We show how to turn a soft-predictor of an agent’s signals (given the other agents' signals) into a mechanism. This allows the practical use of machine learning algorithms that give good results even when many agents provide noisy information.
- Stronger Properties: In the finite setting, we obtain ε-strongly truthful mechanisms for any stochastically relevant prior. Prior works either only apply to more restrictive settings, or achieve a weaker notion of truthfulness (informed truthfulness)
EUP894059 Supplemental Material1 - Supplemental material for Delegation of committee reports in the European Parliament
No full textSupplemental material, EUP894059 Supplemental Material1 for Delegation of committee reports in the European Parliament by Fang-Yi Chiou, Silje SL Hermansen and Bjørn Høyland in European Union Politics</p
EUP894059 Supplemental Material2 - Supplemental material for Delegation of committee reports in the European Parliament
No full textSupplemental material, EUP894059 Supplemental Material2 for Delegation of committee reports in the European Parliament by Fang-Yi Chiou, Silje SL Hermansen and Bjørn Høyland in European Union Politics</p
Analytical modelling for predicting the sound field of planar acoustic metasurface
No full textAn analytical model is built to predict the acoustic fields of acoustic metasurfaces. The acoustic fields are investigated for a Gaussian sound beam incident on the acoustic metasurfaces. The Gaussian sound beam is decomposed into a set of discrete elementary plane waves. The diffraction caused by the acoustic metasurfaces can be obtained using this analytical model, which is validated with the numerical simulations for the different incident angles of the Gaussian sound beam. This model overcomes the limitation of the method based on the generalised Snell's law which can only predict the direction of a specific diffracted order. Actually, this analytical model can be also used to predict the sound fields of acoustic metasurfaces under any incident sound if its Fourier transforms exist. This conclusion is demonstrated by studying the sound field for a point sound source incident on the acoustic metasurface. The acoustic admittances of acoustic metasurfaces are required in the calculation of the analytical model. Therefore, a numerical method for obtaining the effective acoustic admittances is proposed for the structurally complex metasurfaces without the analytical expressions of material properties, such as equivalent density and sound speed.</p
Think Globally, Act Locally: On the Optimal Seeding for Nonsubmodular Influence Maximization
Get PDFWe study the r-complex contagion influence maximization problem. In the influence maximization problem, one chooses a fixed number of initial seeds in a social network to maximize the spread of their influence. In the r-complex contagion model, each uninfected vertex in the network becomes infected if it has at least r infected neighbors.
In this paper, we focus on a random graph model named the stochastic hierarchical blockmodel, which is a special case of the well-studied stochastic blockmodel. When the graph is not exceptionally sparse, in particular, when each edge appears with probability omega (n^{-(1+1/r)}), under certain mild assumptions, we prove that the optimal seeding strategy is to put all the seeds in a single community. This matches the intuition that in a nonsubmodular cascade model placing seeds near each other creates synergy. However, it sharply contrasts with the intuition for submodular cascade models (e.g., the independent cascade model and the linear threshold model) in which nearby seeds tend to erode each others' effects.
Finally, we show that this observation yields a polynomial time dynamic programming algorithm which outputs optimal seeds if each edge appears with a probability either in omega (n^{-(1+1/r)}) or in o (n^{-2})
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A question of trust: intra-party delegation in the European Parliament
Get PDFMuch of the European Parliament’s work rests on negotiations within parliamentary committees, as well as other informal negotiations that take place behind closed doors. But what determines the selection of the MEPs who participate in these negotiations? Drawing on a new study, Fang-Yi Chiou, Bjørn Høyland and Silje Synnøve Lyder Hermansen illustrate that loyalty to the leadership of the transnational parties present in Parliament is the key factor in the selection process. While knowledge about a given policy area is important, parties typically develop a group of experts from which they can select candidates rather than relying on individuals with the most expertise
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