Bright Stars or Unreliable Compasses: Navigating Patent Definiteness During the Fourth Industrial Revolution

This Article traces the evolution of the definiteness requirement over the course of two centuries. From the time of inventions relating to flour mills, the definiteness requirement evolved into the consequence for drafting uninterpretable claims. Without considering the reasons for this evolution, the Supreme Court in its Nautilus decision returned the standard for assessing definiteness to its root form. Given the consequences are the loss of patent rights, this Article grapples with the Supreme Court’s decision during an era where complex and convergent technologies are more commonplace. The Article also analyzes empirical evidence six years before and six years after the Nautilus decision to forecast its impact as we head deeper into the Fourth Industrial Revolution

Misère quotients for impartial games

AbstractWe announce misère-play solutions to several previously-unsolved combinatorial games. The solutions are described in terms of misère quotients—commutative monoids that encode the additive structure of specific misère-play games. We also introduce several advances in the structure theory of misère quotients, including a connection between the combinatorial structure of normal and misère play

Efficiently Learning Human Preferences for Robot Autonomy

Human-robot teams are invaluable for mapping unknown environments, exploring difficult-to-reach areas, and manipulating inaccessible equipment. However, guiding autonomous robots requires dealing with these dynamic domains while synthesizing a significant amount of data and balancing competing objectives. Current mission planning methods often involve manually specifying low-level parameters of the mission, such as exact waypoints or control inputs. These methods cannot perfectly cope with the changing surroundings and limited communications that come with operating in these complex conditions. To address this and reduce the burden on human operators, the field has trended towards ever-increasing levels of autonomy. Providing this long-term autonomy requires more usable, robust collaborative mission planning solutions that leverage the strengths of both the robot and the human operator. In this thesis, we propose two novel methods for improving the collaboration of human-robot teams by enabling the robot to learn an operator's preferences for mission planning. These techniques provide the robot with a rich representation of the human's goals while utilizing familiar techniques to speed learning. The first method is trained by making small-scale, iterative improvements to candidate mission plans generated by the robot, similar to the small improvements an operator would make while planning an actual mission. Using a novel coactive learning algorithm, the method learns the operator's preferences from the feature differences between the original and improved mission plans while remaining robust to errors and noise in the operator's corrections. The second proposed method simplifies the queries by asking survey-style rating and ranking questions about candidate plans. These queries are generated by a Gaussian process (GP) active learner that uses the responses to learn the most preferred region of the mission preference space. The ranking query responses provide the GP with general relational information about several points in the preference space, while the rating query responses provide a specific preference about a single point. A custom probit allows the GP to incorporate the different strengths of each query type into a single preference model. Tests in simulated lake monitoring missions show that these methods can efficiently and accurately learn an operator’s preferences. Additionally, a field trial in which an EcoMapper autonomous underwater vehicle monitors the ecology of a lake validates the use of the coactive learning method. These results demonstrate that these techniques can enable a robot to accurately learn a human operator's preferences, then autonomously plan and perform missions that apply those preferences without relying on regular intervention by the operator

Deciphering the Translation Initiation Factor 5A Modification Pathway in Halophilic Archaea

Translation initiation factor 5A (IF5A) is essential and highly conserved in Eukarya (eIF5A) and Archaea (aIF5A). The activity of IF5A requires hypusine, a posttranslational modification synthesized in Eukarya from the polyamine precursor spermidine. Intracellular polyamine analyses revealed that agmatine and cadaverine were the main polyamines produced in Haloferax volcanii in minimal medium, raising the question of how hypusine is synthesized in this halophilic Archaea. Metabolic reconstruction led to a tentative picture of polyamine metabolism and aIF5A modification in Hfx. volcanii that was experimentally tested. Analysis of aIF5A from Hfx. volcanii by LC-MS/MS revealed it was exclusively deoxyhypusinylated. Genetic studies confirmed the role of the predicted arginine decarboxylase gene (HVO 1958) in agmatine synthesis. The agmatinase-like gene (HVO 2299) was found to be essential, consistent with a role in aIF5A modification predicted by physical clustering evidence. Recombinant deoxyhypusine synthase (DHS) fromS. cerevisiae was shown to transfer 4-aminobutyl moiety from spermidine to aIF5A from Hfx. volcanii in vitro. However, at least under conditions tested, this transfer was not observed with the Hfx. volcanii DHS. Furthermore, the growth of Hfx. volcanii was not inhibited by the classical DHS inhibitor GC7. We propose a model of deoxyhypusine synthesis in Hfx. volcanii that differs from the canonical eukaryotic pathway, paving the way for further studies

A Brownian particle in a microscopic periodic potential

We study a model for a massive test particle in a microscopic periodic potential and interacting with a reservoir of light particles. In the regime considered, the fluctuations in the test particle's momentum resulting from collisions typically outweigh the shifts in momentum generated by the periodic force, and so the force is effectively a perturbative contribution. The mathematical starting point is an idealized reduced dynamics for the test particle given by a linear Boltzmann equation. In the limit that the mass ratio of a single reservoir particle to the test particle tends to zero, we show that there is convergence to the Ornstein-Uhlenbeck process under the standard normalizations for the test particle variables. Our analysis is primarily directed towards bounding the perturbative effect of the periodic potential on the particle's momentum.Comment: 60 pages. We reorganized the article and made a few simplifications of the conten

A ballistic motion disrupted by quantum reflections

I study a Lindblad dynamics modeling a quantum test particle in a Dirac comb that collides with particles from a background gas. The main result is a homogenization theorem in an adiabatic limiting regime involving large initial momentum for the test particle. Over the time interval considered, the particle would exhibit essentially ballistic motion if either the singular periodic potential or the kicks from the gas were removed. However, the particle behaves diffusively when both sources of forcing are present. The conversion of the motion from ballistic to diffusive is generated by occasional quantum reflections that result when the test particle's momentum is driven through a collision near to an element of the half-spaced reciprocal lattice of the Dirac comb.Comment: 54 pages. I rewrote the introduction and simplified some of the presentatio