Resource-Aware Protocols for Network Cost-Sharing Games

We study the extent to which decentralized cost-sharing protocols can achieve good price of anarchy (PoA) bounds in network cost-sharing games with $n$ agents. We focus on the model of resource-aware protocols, where the designer has prior access to the network structure and can also increase the total cost of an edge(overcharging), and we study classes of games with concave or convex cost functions. We first consider concave cost functions and our main result is a cost-sharing protocol for symmetric games on directed acyclic graphs that achieves a PoA of $2+\varepsilon$ for some arbitrary small positive $\varepsilon$, which improves to $1+\varepsilon$ for games with at least two players. We also achieve a PoA of 1 for series-parallel graphs and show that no protocol can achieve a PoA better than $\Omega(\sqrt{n})$ for multicast games. We then also consider convex cost functions and prove analogous results for series-parallel networks and multicast games, as well as a lower bound of $\Omega(n)$ for the PoA on directed acyclic graphs without the use of overcharging

Regression clustering for panel-data models with fixed effects

In this article, we describe the xtregcluster command, which implements the panel regression clustering approach developed by Sarafidis and Weber (2015, Oxford Bulletin of Economics and Statistics 77: 274–296). The method classifies individuals into clusters, so that within each cluster, the slope parameters are homogeneous and all intracluster heterogeneity is due to the standard two-way error-components structure. Because the clusters are heterogeneous, they do not share common parameters. The number of clusters and the optimal partition are determined by the clustering solution, which minimizes the total residual sum of squares of the model subject to a penalty function that strictly increases in the number of clusters. The method is available for linear short panel-data models and useful for exploring heterogeneity in the slope parameters when there is no a priori knowledge about parameter structures. It is also useful for empirically evaluating whether any normative classifications are justifiable from a statistical point of view

Regression clustering for panel-data models with fixed effects

In this article, we describe the xtregcluster command, which implements the panel regression clustering approach developed by Sarafidis and Weber (2015, Oxford Bulletin of Economics and Statistics 77: 274–296). The method classifies individuals into clusters, so that within each cluster, the slope parameters are homogeneous and all intracluster heterogeneity is due to the standard two-way error-components structure. Because the clusters are heterogeneous, they do not share common parameters. The number of clusters and the optimal partition are determined by the clustering solution, which minimizes the total residual sum of squares of the model subject to a penalty function that strictly increases in the number of clusters. The method is available for linear short panel-data models and useful for exploring heterogeneity in the slope parameters when there is no a priori knowledge about parameter structures. It is also useful for empirically evaluating whether any normative classifications are justifiable from a statistical point of view

Protein Secondary Structure Prediction with Bidirectional Recurrent Neural Nets: Can Weight Updating for Each Residue Enhance Performance?

International audienceSuccessful protein secondary structure prediction is an important step towards modelling protein 3D structure, with several practical applications. Even though in the last four decades several PSSP algorithms have been proposed, we are far from being accurate. The Bidirectional Recurrent Neural Network (BRNN) architecture of Baldi et al. [1] is currently considered as one of the optimal computational neural network type architectures for addressing the problem. In this paper, we implement the same BRNN architecture, but we use a modified training procedure. More specifically, our aim is to identify the effect of the contribution of local versus global information, by varying the length of the segment on which the Recurrent Neural Networks operate for each residue position considered. For training the network, the backpropagation learning algorithm with an online training procedure is used, where the weight updates occur for every amino acid, as opposed to Baldi et al. [1], where the weight updates are applied after the presentation of the entire protein. Our results with a single BRNN are better than Baldi et al. [1] by three percentage points (Q3) and comparable to results of [1] when they use an ensemble of 6 BRNNs. In addition, our results improve even further when sequence-to-structure output is filtered in a post-processing step, with a novel Hidden Markov Model-based approach