Does Domestic Intellectual Property Right Strength Affect Pharmaceutical Innovation?

From the Washington University Senior Honors Thesis Abstracts (WUSHTA), 2017. Published by the Office of Undergraduate Research. Joy Zalis Kiefer, Director of Undergraduate Research and Associate Dean in the College of Arts & Sciences; Lindsey Paunovich, Editor; Helen Human, Programs Manager and Assistant Dean in the College of Arts and Sciences Mentor: Maria Cano

Locality Sensitive Hashing for Set-Queries, Motivated by Group Recommendations

Locality Sensitive Hashing (LSH) is an effective method to index a set of points such that we can efficiently find the nearest neighbors of a query point. We extend this method to our novel Set-query LSH (SLSH), such that it can find the nearest neighbors of a set of points, given as a query. Let s(x,y) be the similarity between two points x and y. We define a similarity between a set Q and a point x by aggregating the similarities s(p,x) for all p? Q. For example, we can take s(p,x) to be the angular similarity between p and x (i.e., 1-(?(x,p)/?)), and aggregate by arithmetic or geometric averaging, or taking the lowest similarity. We develop locality sensitive hash families and data structures for a large set of such arithmetic and geometric averaging similarities, and analyze their collision probabilities. We also establish an analogous framework and hash families for distance functions. Specifically, we give a structure for the euclidean distance aggregated by either averaging or taking the maximum. We leverage SLSH to solve a geometric extension of the approximate near neighbors problem. In this version, we consider a metric for which the unit ball is an ellipsoid and its orientation is specified with the query. An important application that motivates our work is group recommendation systems. Such a system embeds movies and users in the same feature space, and the task of recommending a movie for a group to watch together, translates to a set-query Q using an appropriate similarity

Locality Sensitive Hashing for Efficient Similar Polygon Retrieval

Locality Sensitive Hashing (LSH) is an effective method of indexing a set of items to support efficient nearest neighbors queries in high-dimensional spaces. The basic idea of LSH is that similar items should produce hash collisions with higher probability than dissimilar items. We study LSH for (not necessarily convex) polygons, and use it to give efficient data structures for similar shape retrieval. Arkin et al. represent polygons by their "turning function" - a function which follows the angle between the polygon's tangent and the $x$-axis while traversing the perimeter of the polygon. They define the distance between polygons to be variations of the $L_p$ (for $p=1,2$) distance between their turning functions. This metric is invariant under translation, rotation and scaling (and the selection of the initial point on the perimeter) and therefore models well the intuitive notion of shape resemblance. We develop and analyze LSH near neighbor data structures for several variations of the $L_p$ distance for functions (for $p=1,2$). By applying our schemes to the turning functions of a collection of polygons we obtain efficient near neighbor LSH-based structures for polygons. To tune our structures to turning functions of polygons, we prove some new properties of these turning functions that may be of independent interest. As part of our analysis, we address the following problem which is of independent interest. Find the vertical translation of a function $f$ that is closest in $L_1$ distance to a function $g$. We prove tight bounds on the approximation guarantee obtained by the translation which is equal to the difference between the averages of $g$ and $f$

CPA eldercare : a practitioner\u27s resource guide;

3 1/2 disk not readable and so not included in PDFhttps://egrove.olemiss.edu/aicpa_guides/1104/thumbnail.jp

CPA eldercare : a practitioner\u27s resource guide;

https://egrove.olemiss.edu/aicpa_guides/1115/thumbnail.jp

CPA eldercare : a practitioner\u27s resource guide;

https://egrove.olemiss.edu/aicpa_guides/1114/thumbnail.jp

CPA elderCare/primePlus services : a practitioner\u27s resource guide;

CD-ROM files converted to PDF and included after main texthttps://egrove.olemiss.edu/aicpa_guides/1105/thumbnail.jp

High efficiency resonance-based spectrum filters with tunable transmission bandwidth fabricated using nanoimprint lithography

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98685/1/ApplPhysLett_99_143111.pd

Phenomenology of Electroweak Symmetry Breaking from Theory Space

Recently, a new class of realistic models for electroweak symmetry breaking have been constructed, without supersymmetry. These theories have naturally light Higgs bosons and perturbative new physics at the TeV scale. We describe these models in detail, and show that electroweak symmetry breaking can be triggered by a large top quark Yukawa coupling. A rich spectrum of particles is predicted, with a pair of light Higgs doublets accompanied by new light weak triplet and singlet scalars. The lightest of these new scalars is charged under a geometric discrete symmetry and is therefore stable, providing a new candidate for WIMP dark matter. At TeV energies, a plethora of new heavy scalars, gauge bosons and fermions are revealed, with distinctive quantum numbers and decay modes.Comment: 22 pages, latex, 6 figures. Numerical results corrected, clarifications added, conclusions unchange

The Monotonic Lagrangian Grid for Rapid Air-Traffic Evaluation

The Air Traffic Monotonic Lagrangian Grid (ATMLG) is presented as a tool to evaluate new air traffic system concepts. The model, based on an algorithm called the Monotonic Lagrangian Grid (MLG), can quickly sort, track, and update positions of many aircraft, both on the ground (at airports) and in the air. The underlying data structure is based on the MLG, which is used for sorting and ordering positions and other data needed to describe N moving bodies and their interactions. Aircraft that are close to each other in physical space are always near neighbors in the MLG data arrays, resulting in a fast nearest-neighbor interaction algorithm that scales as N. Recent upgrades to ATMLG include adding blank place-holders within the MLG data structure, which makes it possible to dynamically change the MLG size and also improves the quality of the MLG grid. Additional upgrades include adding FAA flight plan data, such as way-points and arrival and departure times from the Enhanced Traffic Management System (ETMS), and combining the MLG with the state-of-the-art strategic and tactical conflict detection and resolution algorithms from the NASA-developed Stratway software. In this paper, we present results from our early efforts to couple ATMLG with the Stratway software, and we demonstrate that it can be used to quickly simulate air traffic flow for a very large ETMS dataset