Wine About It: How Climate Change is Affecting International Wine Markets

The purpose of this study is to understand the social context surrounding wine and how consumers and producers will act within the existing market structures to physical changes to wine due to climate change. After defining the socially embedded structure of market values, this paper questions how they will survive with the visible and invisible changes being made to wines and wine regions. Through various case studies the paper uncovers climate events happening across the world and how they will potentially change the economic landscape of wine markets. The different lenses required to understand the market lead to competing conclusions. Economists argue that market structures will allow for producers to fill in the gaps produced by these climate events whereas sociologists see the embedded values of certain wines irreplaceable as changing climates affect the physiological makeup of valued wines and wine regions

A Full Uterus: Hematometra from Cervical Scarring

A 29-year-old female presented with abdominal pain, nausea, and vomiting. She reported no menstrual period for one year. She did report monthly episodes of severe cramping. A loop electrosurgical excision procedure was performed approximately 10 months prior. On pelvic exam, a smooth cervix with scarring over the os was visualized with no evidence of cervical opening. A pelvic ultrasound showed an enlarged uterus with contents within the endometrial cavity likely representing hemorrhage of different ages and ongoing bleeding. Gynecology was consulted and performed an incisional opening of the cervix. The patient was diagnosed with hematometra from scarred cervical os

A State Distillation Protocol to Implement Arbitrary Single-qubit Rotations

An important task required to build a scalable, fault-tolerant quantum computer is to efficiently represent an arbitrary single-qubit rotation by fault-tolerant quantum operations. Traditionally, the method for decomposing a single-qubit unitary into a discrete set of gates is Solovay-Kitaev decomposition, which in practice produces a sequence of depth O(\log^c(1/\epsilon)), where c~3.97 is the state-of-the-art. The proven lower bound is c=1, however an efficient algorithm that saturates this bound is unknown. In this paper, we present an alternative to Solovay-Kitaev decomposition employing state distillation techniques which reduces c to between 1.12 and 2.27, depending on the setting. For a given single-qubit rotation, our protocol significantly lowers the length of the approximating sequence and the number of required resource states (ancillary qubits). In addition, our protocol is robust to noise in the resource states.Comment: 10 pages, 18 figures, 5 table

Stackelberg Network Pricing Games

We study a multi-player one-round game termed Stackelberg Network Pricing Game, in which a leader can set prices for a subset of $m$ priceable edges in a graph. The other edges have a fixed cost. Based on the leader's decision one or more followers optimize a polynomial-time solvable combinatorial minimization problem and choose a minimum cost solution satisfying their requirements based on the fixed costs and the leader's prices. The leader receives as revenue the total amount of prices paid by the followers for priceable edges in their solutions, and the problem is to find revenue maximizing prices. Our model extends several known pricing problems, including single-minded and unit-demand pricing, as well as Stackelberg pricing for certain follower problems like shortest path or minimum spanning tree. Our first main result is a tight analysis of a single-price algorithm for the single follower game, which provides a $(1+\epsilon) \log m$-approximation for any $\epsilon >0$. This can be extended to provide a $(1+\epsilon)(\log k + \log m)$-approximation for the general problem and $k$ followers. The latter result is essentially best possible, as the problem is shown to be hard to approximate within \mathcal{O(\log^\epsilon k + \log^\epsilon m). If followers have demands, the single-price algorithm provides a $(1+\epsilon)m^2$-approximation, and the problem is hard to approximate within \mathcal{O(m^\epsilon) for some $\epsilon >0$. Our second main result is a polynomial time algorithm for revenue maximization in the special case of Stackelberg bipartite vertex cover, which is based on non-trivial max-flow and LP-duality techniques. Our results can be extended to provide constant-factor approximations for any constant number of followers