Combining Voting Rules Together

We propose a simple method for combining together voting rules that performs a run-off between the different winners of each voting rule. We prove that this combinator has several good properties. For instance, even if just one of the base voting rules has a desirable property like Condorcet consistency, the combination inherits this property. In addition, we prove that combining voting rules together in this way can make finding a manipulation more computationally difficult. Finally, we study the impact of this combinator on approximation methods that find close to optimal manipulations

Quieting the Sharholders\u27 Voice: Empirical Evidence of Pervasive Bundling in Proxy Solicitations

The integrity of shareholder voting is critical to the legitimacy of corporate law. One threat to this process is proxy “bundling,” or the joinder of more than one separate item into a single proxy proposal. Bundling deprives shareholders of the right to convey their views on each separate matter being put to a vote and forces them to either reject the entire proposal or approve items they might not otherwise want implemented. In this Paper, we provide the first comprehensive evaluation of the anti-bundling rules adopted by the Securities and Exchange Commission (“SEC”) in 1992. While we find that the courts have carefully developed a framework for the proper scope and application of the rules, the SEC and proxy advisory firms have been less vigilant in defending this instrumental shareholder right. In particular, we note that the most recent SEC interpretive guidance has undercut the effectiveness of the existing rules, and that, surprisingly, proxy advisory firms do not have well-defined heuristics to discourage bundling. Building on the theoretical framework, this Article provides the first large-scale empirical study of bundling of management proposals. We develop four possible definitions of impermissible bundling and, utilizing a data set of over 1,300 management proposals, show that the frequency of bundling in our sample ranges from 6.2 percent to 28.8 percent (depending on which of the four bundling definitions is used). It is apparent that bundling occurs far more frequently than indicated by prior studies. We further examine our data to report the items that are most frequently bundled and to analyze the proxy advisors’ recommendations and the voting patterns associated with bundled proposals. This Article concludes with important implications for the SEC, proxy advisors, and institutional investors as to how each party can more effectively deter impermissible bundling and thus better protect the shareholder franchise

Enhancing the effectiveness of ligand-based virtual screening using data fusion

Data fusion is being increasingly used to combine the outputs of different types of sensor. This paper reviews the application of the approach to ligand-based virtual screening, where the sensors to be combined are functions that score molecules in a database on their likelihood of exhibiting some required biological activity. Much of the literature to date involves the combination of multiple similarity searches, although there is also increasing interest in the combination of multiple machine learning techniques. Both approaches are reviewed here, focusing on the extent to which fusion can improve the effectiveness of searching when compared with a single screening mechanism, and on the reasons that have been suggested for the observed performance enhancement

Improving Retrieval-Based Question Answering with Deep Inference Models

Question answering is one of the most important and difficult applications at the border of information retrieval and natural language processing, especially when we talk about complex science questions which require some form of inference to determine the correct answer. In this paper, we present a two-step method that combines information retrieval techniques optimized for question answering with deep learning models for natural language inference in order to tackle the multi-choice question answering in the science domain. For each question-answer pair, we use standard retrieval-based models to find relevant candidate contexts and decompose the main problem into two different sub-problems. First, assign correctness scores for each candidate answer based on the context using retrieval models from Lucene. Second, we use deep learning architectures to compute if a candidate answer can be inferred from some well-chosen context consisting of sentences retrieved from the knowledge base. In the end, all these solvers are combined using a simple neural network to predict the correct answer. This proposed two-step model outperforms the best retrieval-based solver by over 3% in absolute accuracy.Comment: 8 pages, 2 figures, 8 tables, accepted at IJCNN 201

Memory-Based Shallow Parsing

We present memory-based learning approaches to shallow parsing and apply these to five tasks: base noun phrase identification, arbitrary base phrase recognition, clause detection, noun phrase parsing and full parsing. We use feature selection techniques and system combination methods for improving the performance of the memory-based learner. Our approach is evaluated on standard data sets and the results are compared with that of other systems. This reveals that our approach works well for base phrase identification while its application towards recognizing embedded structures leaves some room for improvement