The Uneasy Case for Patent Federalism

Nationwide uniformity is often considered an essential feature of the patent system, necessary to fulfill that system’s disclosure and incentive purposes. In the last few years, however, more than half the states have enacted laws that seek to disrupt this uniformity by making it harder for patent holders to enforce their patents. There is an easy case to be made against giving states greater authority over the patent system: doing so would threaten to disrupt the system’s balance between innovation incentives and a robust public domain and would permit rent seeking by states that disproportionately produce or consume innovation. There is, nevertheless, an uneasy case that this particular form of patent federalism may be a good thing. The federal patent system has systemic flaws that lead to low-quality patents, nuisance patent litigation, and patent trolls exploiting asymmetric bargaining power. And efforts to address these flaws have faltered, or have had limited effects, due to public-choice dynamics in the patent system, so the scope of patent protections has expanded over time without regard to the system’s purpose of encouraging innovation. States may help address some of these problems not in spite of, but because of, their own flaws. States have their own public-choice dynamics that happen to offset some of the flaws of the federal system. State anti-patent laws have been driven largely by small businesses and local small-business groups, which, unlike most patent holders, have preexisting influence in state government. And the laws they have crafted using this influence are well-targeted to affect only the most troublesome patent cases: nuisance cases, cases asserting low-quality patents, and cases targeting end users. States pushing back with anti-patent laws, then, may represent an effective second-best solution to the problem of harmful patent assertions. Moreover, recognizing the dynamics that led to these laws may provide helpful insights in designing federal patent reforms

Status of the Mountain Lion in Arkansas

Two authenticated kill records of the mountain lion, Felis concolor, in Arkansas are reported as well as numerous reliable sight records spanning an approximately 30-year period. Distribution of sightings in the state is discussed in relation to an expanding deer population. The cougar probably never was exterminated in Arkansas but it still may be considered endangered

Learning Sparse Polymatrix Games in Polynomial Time and Sample Complexity

We consider the problem of learning sparse polymatrix games from observations of strategic interactions. We show that a polynomial time method based on $\ell_{1,2}$-group regularized logistic regression recovers a game, whose Nash equilibria are the $\epsilon$-Nash equilibria of the game from which the data was generated (true game), in $\mathcal{O}(m^4 d^4 \log (pd))$ samples of strategy profiles --- where $m$ is the maximum number of pure strategies of a player, $p$ is the number of players, and $d$ is the maximum degree of the game graph. Under slightly more stringent separability conditions on the payoff matrices of the true game, we show that our method learns a game with the exact same Nash equilibria as the true game. We also show that $\Omega(d \log (pm))$ samples are necessary for any method to consistently recover a game, with the same Nash-equilibria as the true game, from observations of strategic interactions. We verify our theoretical results through simulation experiments

Fully Bayesian Penalized Regression with a Generalized Bridge Prior

We consider penalized regression models under a unified framework. The particular method is determined by the form of the penalty term, which is typically chosen by cross validation. We introduce a fully Bayesian approach that incorporates both sparse and dense settings and show how to use a type of model averaging approach to eliminate the nuisance penalty parameters and perform inference through the marginal posterior distribution of the regression coefficients. We establish tail robustness of the resulting estimator as well as conditional and marginal posterior consistency for the Bayesian model. We develop a component-wise Markov chain Monte Carlo algorithm for sampling. Numerical results show that the method tends to select the optimal penalty and performs well in both variable selection and prediction and is comparable to, and often better than alternative methods. Both simulated and real data examples are provided

Fluctuating, Lorentz-force-like coupling of Langevin equations and heat flux rectification

In a description of physical systems with Langevin equations, interacting degrees of freedom are usually coupled through symmetric parameter matrices. This coupling symmetry is a consequence of time-reversal symmetry of the involved conservative forces. If coupling parameters fluctuate randomly, the resulting noise is called multiplicative. For example, mechanical oscillators can be coupled through a fluctuating, symmetric matrix of spring "constants". Such systems exhibit well-studied instabilities. In this note, we study the complementary case of antisymmetric, time-reversal symmetry breaking coupling that can be realized with Lorentz forces or various gyrators. We consider the case that these antisymmetric couplings fluctuate. This type of multiplicative noise does not lead to instabilities in the stationary state but renormalizes the effective non-equilibrium friction. Fluctuating Lorentz-force-like couplings also allow to control and rectify heat transfer. A noteworthy property of this mechanism of producing asymmetric heat flux is that the controlling couplings do not exchange energy with the system.

Monadnocks, Divides and Ozark Physiography

Copia digital. Madrid : Ministerio de Educación, Cultura y Deporte. Subdirección General de Coordinación Bibliotecaria, 201

Toward a More realistic Evaluation of the United Nations

ITALIACopia digital. Madrid : Ministerio de Educación, Cultura y Deporte, 201

Lumping of Degree-Based Mean Field and Pair Approximation Equations for Multi-State Contact Processes

Contact processes form a large and highly interesting class of dynamic processes on networks, including epidemic and information spreading. While devising stochastic models of such processes is relatively easy, analyzing them is very challenging from a computational point of view, particularly for large networks appearing in real applications. One strategy to reduce the complexity of their analysis is to rely on approximations, often in terms of a set of differential equations capturing the evolution of a random node, distinguishing nodes with different topological contexts (i.e., different degrees of different neighborhoods), like degree-based mean field (DBMF), approximate master equation (AME), or pair approximation (PA). The number of differential equations so obtained is typically proportional to the maximum degree kmax of the network, which is much smaller than the size of the master equation of the underlying stochastic model, yet numerically solving these equations can still be problematic for large kmax. In this paper, we extend AME and PA, which has been proposed only for the binary state case, to a multi-state setting and provide an aggregation procedure that clusters together nodes having similar degrees, treating those in the same cluster as indistinguishable, thus reducing the number of equations while preserving an accurate description of global observables of interest. We also provide an automatic way to build such equations and to identify a small number of degree clusters that give accurate results. The method is tested on several case studies, where it shows a high level of compression and a reduction of computational time of several orders of magnitude for large networks, with minimal loss in accuracy.Comment: 16 pages with the Appendi

Occurrence of the Plains Harvest Mouse, Reithrondontomys montanus (Baird) in Arkansas

Copia digital. Madrid : Ministerio de Educación, Cultura y Deporte, 201

Southern expansion of the brown alga Colpomenia peregrina Sauvageau (Scytosiphonales) in the Northwest Atlantic Ocean

Blackler first recorded Colpomenia peregrina in the Northwest Atlantic based on collections from Nova Scotia, Canada. Five decades later we found large quantities of C. peregrina in Maine, USA, even though it was absent during earlier floristic studies in this region. Thus, C. peregrina has undergone a rapid southern expansion along the Northwest Atlantic coast. While the causes of such an expansion are unknown, it could have a major effect on both shellfish cultivation and native seaweeds within New England because of competitive interactions and increased drag