Algorithms for item categorization based on ordinal ranking data

We present a new method for identifying the latent categorization of items based on their rankings. Complimenting a recent work that uses a Dirichlet prior on preference vectors and variational inference, we show that this problem can be effectively dealt with using existing community detection algorithms, with the communities corresponding to item categories. In particular we convert the bipartite ranking data to a unipartite graph of item affinities, and apply community detection algorithms. In this context we modify an existing algorithm - namely the label propagation algorithm to a variant that uses the distance between the nodes for weighting the label propagation - to identify the categories. We propose and analyze a synthetic ordinal ranking model and show its relation to the recently much studied stochastic block model. We test our algorithms on synthetic data and compare performance with several popular community detection algorithms. We also test the method on real data sets of movie categorization from the Movie Lens database. In all of the cases our algorithm is able to identify the categories for a suitable choice of tuning parameter.Comment: To appear in IEEE Allerton conference on computing, communications and control, 201

Understanding Causation in Private Securities Lawsuits: Building on Amgen

With Amgen, the Supreme Court’s majority once again holds that inquiry into the alleged market impact of a misrepresentation is not required to invoke fraud on the market approach to causation so that the class can be certified. Rather than just leaving matters where they have been since the Supreme Court’s muddled encounter with causation in Basic Inc. v. Levinson, the Supreme Court’s most recent decision appears to relax some earlier-held tenets with respect to markets believed sufficiently efficient for fraud on the market to be invoked. This Article not only identifies the central flaw of Basic that has over the decades distorted applications of fraud on the market but also suggests how, building on Amgen, what the future focus should be in considering whether a suit can proceed as a class action based on fraud on the market

Preemption and Federalism in Corporate Governance: Protecting Shareholder Rights to Vote, Sell, and Sue

Thompson examines the changed roles of the state and federal governments since the enactment of the Securities Litigation Uniform Standards Act of 1998. He notes that these changes have created a greater dependence on federal law, a greater emphasis on the voting function of shareholders, and the likelihood of additional argument over traditional corporate issues

Reviving Reliance

This Article explores the misalignment between the disclosure requirements of the federal securities laws and the private causes of action available to investors to enforce those requirements. Historically, federally mandated disclosures were designed to allow investors to set an appropriate price for publicly traded securities. Today’s disclosures, however, also enable stockholders to participate in corporate governance and act as a check on managerial misbehavior. To enforce these requirements, investors’ chief option is a claim under the general antifraud statute, section 10(b) of the Securities Exchange Act of 1934. But courts are deeply suspicious of investors’ attempts to use the Act to hold corporations liable for false statements related to governance. As this Article demonstrates, judicial skepticism can be traced to the functional elimination of the element of reliance from private investors’ claims. Without the element of reliance, courts cannot discriminate between deception, which section 10(b) prohibits, and poor managerial decisionmaking, to which section 10(b) does not speak. Doctrines that courts developed to distinguish between the two now have the perverse effect of devaluing disclosures intended to facilitate shareholder participation in corporate governance. More troublingly, they enforce a normative viewpoint that shareholders do not, or should not, have interests beyond the short-term maximization of a firm’s stock price. This interpretation of shareholder preferences undermines modern regulatory initiatives that employ shareholders as a restraining force on antisocial corporate conduct. This Article proposes that courts adopt new interpretations of section 10(b) that reestablish the centrality of reliance. By doing so, courts can facilitate shareholders’ participation in the corporate governance structure and reward investors who inhabit the role of corporate monitor

A Contribution to the Defense of Liquid Democracy

Liquid democracy is a hybrid direct-representative decision making process that provides each voter with the option of either voting directly or to delegate their vote to another voter, i.e., to a representative of their choice. One of the proposed advantages of liquid democracy is that, in general, it is assumed that voters will delegate their vote to others that are better informed, which leads to more informed and better decisions. Considering an audience from various knowledge domains, we provide an accessible high-level analysis of a prominent critique of liquid democracy by Caragiannis and Micha. Caragiannis and Micha's critique contains three central topics: 1. Analysis using their $\alpha$-delegation model, which does not assume delegation to the more informed; 2. Novel delegation network structures where it is advantageous to delegate to the less informed rather than the more informed; and 3. Due to NP hardness, the implied impracticability of a social network obtaining an optimal delegation structure. We show that in the real world, Caragiannis and Micha's critique of liquid democracy has little or no relevance. Respectively, our critique is based on: 1. The identification of incorrect $\alpha$-delegation model assumptions; 2. A lack of novel delegation structures and their effect in a real-world implementation of liquid democracy, which would be guaranteed with constraints that sensibly distribute voting power; and 3. The irrelevance of an optimal delegation structure if the correct result is guaranteed regardless. We conclude that Caragiannis and Micha's critique has no significant negative relevance to the proposition of liquid democracy

Proposal Flow

Finding image correspondences remains a challenging problem in the presence of intra-class variations and large changes in scene layout.~Semantic flow methods are designed to handle images depicting different instances of the same object or scene category. We introduce a novel approach to semantic flow, dubbed proposal flow, that establishes reliable correspondences using object proposals. Unlike prevailing semantic flow approaches that operate on pixels or regularly sampled local regions, proposal flow benefits from the characteristics of modern object proposals, that exhibit high repeatability at multiple scales, and can take advantage of both local and geometric consistency constraints among proposals. We also show that proposal flow can effectively be transformed into a conventional dense flow field. We introduce a new dataset that can be used to evaluate both general semantic flow techniques and region-based approaches such as proposal flow. We use this benchmark to compare different matching algorithms, object proposals, and region features within proposal flow, to the state of the art in semantic flow. This comparison, along with experiments on standard datasets, demonstrates that proposal flow significantly outperforms existing semantic flow methods in various settings

Proposal Flow: Semantic Correspondences from Object Proposals

Finding image correspondences remains a challenging problem in the presence of intra-class variations and large changes in scene layout. Semantic flow methods are designed to handle images depicting different instances of the same object or scene category. We introduce a novel approach to semantic flow, dubbed proposal flow, that establishes reliable correspondences using object proposals. Unlike prevailing semantic flow approaches that operate on pixels or regularly sampled local regions, proposal flow benefits from the characteristics of modern object proposals, that exhibit high repeatability at multiple scales, and can take advantage of both local and geometric consistency constraints among proposals. We also show that the corresponding sparse proposal flow can effectively be transformed into a conventional dense flow field. We introduce two new challenging datasets that can be used to evaluate both general semantic flow techniques and region-based approaches such as proposal flow. We use these benchmarks to compare different matching algorithms, object proposals, and region features within proposal flow, to the state of the art in semantic flow. This comparison, along with experiments on standard datasets, demonstrates that proposal flow significantly outperforms existing semantic flow methods in various settings.Comment: arXiv admin note: text overlap with arXiv:1511.0506