Katrina\u27s Animal Legacy: The PETS Act

This article discusses issues related to the federal Pets Evacuation and Transportation Standards Act of 2006 (PETS Act), which was signed into law in the aftermath of Hurricane Katrina. Issues discussed in this article include: Various problems concerning animal evacuations and sheltering that Hurricane Katrina brought to light; Provisions of the PETS Act and related laws and policies which were developed in response to the tragedies brought about by Hurricane Katrina; and Strengths and weaknesses of the PETS Act and recommends next steps to improve implementation of the PETS Act

The Music Downloading Deluge

Reports on the demographic characteristics and habits of the 37 million American adults and youths who have retrieved music files on the Internet

One Share - One Vote: The Theory

The impact of separating cash flow and votes depends on the ownership structure. In widely held firms, one share - one vote is in general not optimal. While it ensures an efficient outcome in bidding contests, dual-class shares mitigate the free-rider problem, thereby promoting takeovers. In the presence of a controlling shareholder, one share - one vote promotes value-increasing control transfers and deters value-decreasing control transfers more effectively than any other vote allocation. Moreover, leveraging the insider's voting power aggravates agency conflicts because it protects her from the takeover threat and provides less alignment with other shareholders. Even so, minority shareholder protection is not a compelling argument for regulatory intervention, as rational investors anticipate the insider's opportunism. Rather, the rationale for mandating one share – one vote must be to disempower controlling minority shareholders in order to promote value-increasing takeovers. As this policy tends to empower managers vis-a-vis shareholders, it is an open question whether it would improve the quality of corporate governance, notably in systems built around large active owners. The verdict in the case of depositary certificates, priority shares, voting and ownership ceilings is less I ambiguous, since they insulate managers from both takeovers and effective shareholder monitoring.Security-voting structure; market for corporate control; controlling minority shareholders

A quantum hydrodynamical description for scrambling and many-body chaos

Recent studies of out-of-time ordered thermal correlation functions (OTOC) in holographic systems and in solvable models such as the Sachdev-Ye-Kitaev (SYK) model have yielded new insights into manifestations of many-body chaos. So far the chaotic behavior has been obtained through explicit calculations in specific models. In this paper we propose a unified description of the exponential growth and ballistic butterfly spreading of OTOCs across different systems using a newly formulated "quantum hydrodynamics," which is valid at finite $\hbar$ and to all orders in derivatives. The scrambling of a generic few-body operator in a chaotic system is described as building up a "hydrodynamic cloud," and the exponential growth of the cloud arises from a shift symmetry of the hydrodynamic action. The shift symmetry also shields correlation functions of the energy density and flux, and time ordered correlation functions of generic operators from exponential growth, while leads to chaotic behavior in OTOCs. The theory also predicts an interesting phenomenon of the skipping of a pole at special values of complex frequency and momentum in two-point functions of energy density and flux. This pole-skipping phenomenon may be considered as a "smoking gun" for the hydrodynamic origin of the chaotic mode. We also discuss the possibility that such a hydrodynamic description could be a hallmark of maximally chaotic systems.Comment: 48 pages, 9 figures. v2: references added, various clarifications made including an expanded discussion of predictions in the introduction and an expanded discussion of four-point functions, v3: journal versio

Security-voting structure and bidder screening

This paper analyzes how non-voting shares affect the takeover outcome in a single-bidder model with asymmetric information and private benefit extraction. In equilibrium, the target firm’s security-voting structure influences the bidder’s participation constraint and in response the shareholders’ conditional expectations about the post-takeover share value. Therefore, the structure can be chosen to discriminate among bidder types. Typically, the socially optimal structure deviates from one share - one vote to promote all and only value-increasing bids. As target shareholders ignore takeover costs, they prefer more takeovers and hence choose a smaller fraction of voting shares than is socially optimal. In either case, the optimal fraction of voting shares decreases with the quality of shareholder protection and increases with the incumbent manager’s ability. Finally, shareholder returns are higher when a given takeover probability is implemented by (more) non-voting shares rather than by (larger) private benefits

End-to-end Neural Coreference Resolution

We introduce the first end-to-end coreference resolution model and show that it significantly outperforms all previous work without using a syntactic parser or hand-engineered mention detector. The key idea is to directly consider all spans in a document as potential mentions and learn distributions over possible antecedents for each. The model computes span embeddings that combine context-dependent boundary representations with a head-finding attention mechanism. It is trained to maximize the marginal likelihood of gold antecedent spans from coreference clusters and is factored to enable aggressive pruning of potential mentions. Experiments demonstrate state-of-the-art performance, with a gain of 1.5 F1 on the OntoNotes benchmark and by 3.1 F1 using a 5-model ensemble, despite the fact that this is the first approach to be successfully trained with no external resources.Comment: Accepted to EMNLP 201

DeLTA: GPU Performance Model for Deep Learning Applications with In-depth Memory System Traffic Analysis

Training convolutional neural networks (CNNs) requires intense compute throughput and high memory bandwidth. Especially, convolution layers account for the majority of the execution time of CNN training, and GPUs are commonly used to accelerate these layer workloads. GPU design optimization for efficient CNN training acceleration requires the accurate modeling of how their performance improves when computing and memory resources are increased. We present DeLTA, the first analytical model that accurately estimates the traffic at each GPU memory hierarchy level, while accounting for the complex reuse patterns of a parallel convolution algorithm. We demonstrate that our model is both accurate and robust for different CNNs and GPU architectures. We then show how this model can be used to carefully balance the scaling of different GPU resources for efficient CNN performance improvement

Entrepreneurship and the Barrier to Exit: How Does an Entrepreneur-Friendly Bankruptcy Law Affect Entrepreneurship Development at a Societal Level?

Does an entrepreneur-friendly bankruptcy law encourage more entrepreneurship development at a societal level? How does bankruptcy law affect entrepreneurship development around the world? Drawing on a real options perspective, we argue that if bankrupt entrepreneurs are excessively punished for failure, they may pass potentially high-return but inherently high-risk opportunities. Amassing a longitudinal, cross-country data base from 35 countries spanning ten years, we find that a lenient, entrepreneur-friendly bankruptcy law encourages entrepreneurs to take risks and thus let entrepreneurship prosper. Components of an entrepreneur-friendly bankruptcy law are: (1) the availability of a reorganization bankruptcy option, (2) the time spent on bankruptcy procedure, (3) the cost of bankruptcy procedure, (4) the opportunity to have a fresh start in liquidation bankruptcy, (5) the opportunity to have an automatic stay of assets, (6) the opportunity for managers to remain on the job after filing for bankruptcy, and (7) the protection of creditors at the time of bankruptcy.

Statistical Computational Topology and Geometry for Understanding Data

Here we describe three projects involving data analysis which focus on engaging statistics with the geometry and/or topology of the data. The first project involves the development and implementation of kernel density estimation for persistence diagrams. These kernel densities consider neighborhoods for every feature in the center diagram and gives to each feature an independent, orthogonal direction. The creation of kernel densities in this realm yields a (previously unavailable) full characterization of the (random) geometry of a dataspace or data distribution. In the second project, cohomology is used to guide a search for kidney exchange cycles within a kidney paired donation pool. The same technique also produces a score function that helps to predict a patient-donor pair\u27s a priori advantage within a donation pool. The resulting allocation of cycles is determined to be equitable according to a strict analysis of the allocation distribution. In the last project, a previously formulated metric between surfaces called continuous Procrustes distance (CPD) is applied to species discrimination in fossils. This project involves both the application and a rigorous comparison of the metric with its primary competitor, discrete Procrustes distance. Besides comparing the separation power of discrete and continuous Procrustes distances, the effect of surface resolution on CPD is investigated in this study