Committee Jurisdiction, Congressional Behavior and Policy Outcomes

The literature on congressional committees has largely overlooked the impact of jurisdictional fights on policy proposals and outcomes. This paper develops a theory of how legislators balance the benefits of expanded committee jurisdiction against preferred policy outcomes. It shows why a) senior members and young members in safe districts are most likely to challenge a committee’s jurisdiction; b) policy proposals may be initiated off the proposer’s ideal point in order to obtain jurisdiction; c) policy outcomes will generally be more moderate with jurisdictional fights than without these turf wars. We empirically investigate these results examining proposed Internet intellectual property protection legislation in the 106th Congress

Multicriteria sustainability evaluation of transport networks for selected European countries

As an essential economic activity, transportation has complex interactions with the environment and society. Since the concept of sustainable development has become one of the top priorities for nations, there has been a growing interest in evaluating the performance of transport systems with respect to sustainability issues. The main purpose of this study is to introduce a decision making framework to assess the sustainability of the transport networks in a multidimensional setting and a technique to identify non-compromise alternatives. We also propose an elucidation technique to identify according to which criteria a system needs to be improved and how much improvement is required to attain a certain level of sustainability. The proposed methods are applied to a set of selected European countries within a case study

Exploiting microvariation: How to make the best of your incomplete data

n this article we discuss the use of big corpuses or databases as a first step for qualitative analysis of linguistic data. We concentrate on ASIt, the Syntactic Atlas of Italy, and take into consideration the different types of dialectal data that can be collected from similar corpora and databases. We analyze all the methodological problems derived from the necessary compromise between the strict requirements imposed by a scientific inquiry and the management of big amounts of data. As a possible solution, we propose that the type of variation is per se a tool to derive meaningful generalizations. To implement this idea, we examine three different types of variation patterns that can be used in the study of morpho-syntax: the geographical distribution of properties (and their total or partial overlapping, or complementary distribution), the so-called leopard spots variation, and the lexical variation index, which can be used to determine the internal complexity of functional items

Differential expression analysis with global network adjustment

<p>Background: Large-scale chromosomal deletions or other non-specific perturbations of the transcriptome can alter the expression of hundreds or thousands of genes, and it is of biological interest to understand which genes are most profoundly affected. We present a method for predicting a gene’s expression as a function of other genes thereby accounting for the effect of transcriptional regulation that confounds the identification of genes differentially expressed relative to a regulatory network. The challenge in constructing such models is that the number of possible regulator transcripts within a global network is on the order of thousands, and the number of biological samples is typically on the order of 10. Nevertheless, there are large gene expression databases that can be used to construct networks that could be helpful in modeling transcriptional regulation in smaller experiments.</p> <p>Results: We demonstrate a type of penalized regression model that can be estimated from large gene expression databases, and then applied to smaller experiments. The ridge parameter is selected by minimizing the cross-validation error of the predictions in the independent out-sample. This tends to increase the model stability and leads to a much greater degree of parameter shrinkage, but the resulting biased estimation is mitigated by a second round of regression. Nevertheless, the proposed computationally efficient “over-shrinkage” method outperforms previously used LASSO-based techniques. In two independent datasets, we find that the median proportion of explained variability in expression is approximately 25%, and this results in a substantial increase in the signal-to-noise ratio allowing more powerful inferences on differential gene expression leading to biologically intuitive findings. We also show that a large proportion of gene dependencies are conditional on the biological state, which would be impossible with standard differential expression methods.</p> <p>Conclusions: By adjusting for the effects of the global network on individual genes, both the sensitivity and reliability of differential expression measures are greatly improved.</p&gt

Neural network ensembles: Evaluation of aggregation algorithms

Ensembles of artificial neural networks show improved generalization capabilities that outperform those of single networks. However, for aggregation to be effective, the individual networks must be as accurate and diverse as possible. An important problem is, then, how to tune the aggregate members in order to have an optimal compromise between these two conflicting conditions. We present here an extensive evaluation of several algorithms for ensemble construction, including new proposals and comparing them with standard methods in the literature. We also discuss a potential problem with sequential aggregation algorithms: the non-frequent but damaging selection through their heuristics of particularly bad ensemble members. We introduce modified algorithms that cope with this problem by allowing individual weighting of aggregate members. Our algorithms and their weighted modifications are favorably tested against other methods in the literature, producing a sensible improvement in performance on most of the standard statistical databases used as benchmarks.Comment: 35 pages, 2 figures, In press AI Journa

On the relation between Differential Privacy and Quantitative Information Flow

Differential privacy is a notion that has emerged in the community of statistical databases, as a response to the problem of protecting the privacy of the database's participants when performing statistical queries. The idea is that a randomized query satisfies differential privacy if the likelihood of obtaining a certain answer for a database $x$ is not too different from the likelihood of obtaining the same answer on adjacent databases, i.e. databases which differ from $x$ for only one individual. Information flow is an area of Security concerned with the problem of controlling the leakage of confidential information in programs and protocols. Nowadays, one of the most established approaches to quantify and to reason about leakage is based on the R\'enyi min entropy version of information theory. In this paper, we analyze critically the notion of differential privacy in light of the conceptual framework provided by the R\'enyi min information theory. We show that there is a close relation between differential privacy and leakage, due to the graph symmetries induced by the adjacency relation. Furthermore, we consider the utility of the randomized answer, which measures its expected degree of accuracy. We focus on certain kinds of utility functions called "binary", which have a close correspondence with the R\'enyi min mutual information. Again, it turns out that there can be a tight correspondence between differential privacy and utility, depending on the symmetries induced by the adjacency relation and by the query. Depending on these symmetries we can also build an optimal-utility randomization mechanism while preserving the required level of differential privacy. Our main contribution is a study of the kind of structures that can be induced by the adjacency relation and the query, and how to use them to derive bounds on the leakage and achieve the optimal utility

Prochlo: Strong Privacy for Analytics in the Crowd

The large-scale monitoring of computer users' software activities has become commonplace, e.g., for application telemetry, error reporting, or demographic profiling. This paper describes a principled systems architecture---Encode, Shuffle, Analyze (ESA)---for performing such monitoring with high utility while also protecting user privacy. The ESA design, and its Prochlo implementation, are informed by our practical experiences with an existing, large deployment of privacy-preserving software monitoring. (cont.; see the paper