21,906 research outputs found
Structuring U.S. Innovation Policy: Creating a White House Office of Innovation Policy
This article begins with a discussion of innovationās importance to the future well-being of American society. The authors then discuss limitations of the current federal framework for making innovation policy. Specifically, the relative absence of innovation from the agenda of Congress and many relevant federal agencies manifests the confluence of two regulatory challenges: first, the tendency of political actors to focus on short-term goals and consequences; and second, political actorsā reluctance to threaten powerful incumbent actors. Courts, meanwhile, lack sufficient expertise and the ability to conduct the type of forward-looking policy planning that should be a hallmark of innovation policy.
Ultimately, their analysis leads to a proposal that President Obama (or Congress, if Congress is willing) create a White House OIP that would have the specific mission of being the āinnovation championā within these processes. The authors envision OIP as an entity that would be independent of existing federal agencies and that would have more than mere hortatory influence. It would have some authority to push agencies to act in a manner that either affirmatively promoted innovation or achieved a particular regulatory objective in a manner least damaging to innovation. We also envision OIP as an entity that would operate efficiently by drawing upon, and feeding into, existing interagency processes within OIRA and other relevant White House offices (e.g., the Office of Science and Technology Policy)
Whoās Afraid of the APA? What the Patent System Can Learn From Administrative Law
In recent years, widespread dissatisfaction with the perceived poor quality of issued patents has spurred a diverse range of groups to call for reform of administrative procedures. Strikingly, however, most calls for reform pay little attention to principles of administrative law. Similarly, judges (in particular the judges of the Federal Circuit) have treated patent law as an exception to the Administrative Procedure Act, and to administrative law more generally. In this Article, Professors Benjamin and Rai contend that this treatment is doctrinally incorrect and normatively undesirable. Standard principles of administrative law provide the appropriate approach for judicial review in the current system of patent grants and denials. As for proposed reforms, such as the institution of post-grant opposition proceedings, an administrative approach to judicial review is the best mechanism for addressing the collective action/public good problems that inevitably arise in challenges to patent validity. Finally, an administrative approach provides the doctrinally appropriate and normatively desirable institutional foundation for the determinations of economic policy that the patent system should be making
Fixing Innovation Policy: A Structural Perspective
Innovation is central to economic growth and human welfare. Government officials and commentators have recognized this reality and have called for a variety of different substantive incentives for stimulating innovation. But the question of how an innovation regulator should be structured has received little attention. Such consideration is important not only because of the significance of innovation but also because current government innovation policy is so haphazard. There is no government entity that looks at innovation broadly, and the narrower agencies that regulate aspects of innovation policy not only fail to pay systematic attention to innovation goals but often act at cross-purposes with each other. In this article, Professors Benjamin and Rai analyze how government policy on innovation should be structured. Drawing on existing theoretical and empirical work, as well as their own original empirical research, they propose the creation of an entity in the executive branch that would both analyze pending agency action and offer regulatory suggestions of its own. This entity would introduce a new, trans-agency focus on innovation while drawing upon, and feeding into, existing executive branch processes that aim to rationalize the work of disparate federal agencies. This approach, Professors Benjamin and Rai contend, offers a great improvement over existing government institutions while avoiding a costly (and politically infeasible) remaking of the administrative state
Data Assimilation: A Mathematical Introduction
These notes provide a systematic mathematical treatment of the subject of
data assimilation
Computer simulation of on-orbit manned maneuvering unit operations
Simulation of spacecraft on-orbit operations is discussed in reference to Martin Marietta's Space Operations Simulation laboratory's use of computer software models to drive a six-degree-of-freedom moving base carriage and two target gimbal systems. In particular, key simulation issues and related computer software models associated with providing real-time, man-in-the-loop simulations of the Manned Maneuvering Unit (MMU) are addressed with special attention given to how effectively these models and motion systems simulate the MMU's actual on-orbit operations. The weightless effects of the space environment require the development of entirely new devices for locomotion. Since the access to space is very limited, it is necessary to design, build, and test these new devices within the physical constraints of earth using simulators. The simulation method that is discussed here is the technique of using computer software models to drive a Moving Base Carriage (MBC) that is capable of providing simultaneous six-degree-of-freedom motions. This method, utilized at Martin Marietta's Space Operations Simulation (SOS) laboratory, provides the ability to simulate the operation of manned spacecraft, provides the pilot with proper three-dimensional visual cues, and allows training of on-orbit operations. The purpose here is to discuss significant MMU simulation issues, the related models that were developed in response to these issues and how effectively these models simulate the MMU's actual on-orbiter operations
Approximation of Bayesian inverse problems for PDEs
Inverse problems are often ill posed, with solutions that depend sensitively on data. In any numerical approach to the solution of such problems, regularization of some form is needed to counteract the resulting instability. This paper is based on an approach to regularization, employing a Bayesian formulation of the problem, which leads to a notion of well posedness for inverse problems, at the level of probability measures. The stability which results from this well posedness may be used as the basis for quantifying the approximation, in finite dimensional spaces, of inverse problems for functions. This paper contains a theory which utilizes this stability property to estimate the distance between the true and approximate posterior distributions, in the Hellinger metric, in terms of error estimates for approximation of the underlying forward problem. This is potentially useful as it allows for the transfer of estimates from the numerical analysis of forward problems into estimates for the solution of the related inverse problem. It is noteworthy that, when the prior is a Gaussian random field model, controlling differences in the Hellinger metric leads to control on the differences between expected values of polynomially bounded functions and operators, including the mean and covariance operator. The ideas are applied to some non-Gaussian inverse problems where the goal is determination of the initial condition for the Stokes or NavierāStokes equation from Lagrangian and Eulerian observations, respectively
Analysis of the 3DVAR Filter for the Partially Observed Lorenz '63 Model
The problem of effectively combining data with a mathematical model
constitutes a major challenge in applied mathematics. It is particular
challenging for high-dimensional dynamical systems where data is received
sequentially in time and the objective is to estimate the system state in an
on-line fashion; this situation arises, for example, in weather forecasting.
The sequential particle filter is then impractical and ad hoc filters, which
employ some form of Gaussian approximation, are widely used. Prototypical of
these ad hoc filters is the 3DVAR method. The goal of this paper is to analyze
the 3DVAR method, using the Lorenz '63 model to exemplify the key ideas. The
situation where the data is partial and noisy is studied, and both discrete
time and continuous time data streams are considered. The theory demonstrates
how the widely used technique of variance inflation acts to stabilize the
filter, and hence leads to asymptotic accuracy
Cooperative Stimulation of Dendritic Cells by Cryptococcus neoformans Mannoproteins and CpG Oligodeoxynucleotides
While mannosylation targets antigens to mannose receptors on dendritic cells (DC), the resultant immune response is suboptimal. We hypothesized that the addition of toll-like receptor (TLR) ligands would enhance the DC response to mannosylated antigens. Cryptococcus neoformans mannoproteins (MP) synergized with CpG-containing oligodeoxynucleotides to stimulate enhanced production of proinflammatory cytokines and chemokines from murine conventional and plasmacytoid DC. Synergistic stimulation required the interaction of mannose residues on MP with the macrophage mannose receptor (MR), CD206. Moreover, synergy with MP was observed with other TLR ligands, including tripalmitoylated lipopeptide (Pam3CSK4), polyinosine-polycytidylic acid (pI:C), and imiquimod. Finally, CpG enhanced MP-specific MHC II-restricted CD4+ T-cell responses by a mechanism dependent upon DC expression of CD206 and TLR9. These data suggest a rationale for vaccination strategies that combine mannosylated antigens with TLR ligands and imply that immune responses to naturally mannosylated antigens on pathogens may be greatly augmented if TLR and MR are cooperatively stimulated.National Institutes of Health (RO1 AI25780, RO1 AI37532, K08 AI 53542
Well-Posedness And Accuracy Of The Ensemble Kalman Filter In Discrete And Continuous Time
The ensemble Kalman filter (EnKF) is a method for combining a dynamical model
with data in a sequential fashion. Despite its widespread use, there has been
little analysis of its theoretical properties. Many of the algorithmic
innovations associated with the filter, which are required to make a useable
algorithm in practice, are derived in an ad hoc fashion. The aim of this paper
is to initiate the development of a systematic analysis of the EnKF, in
particular to do so in the small ensemble size limit. The perspective is to
view the method as a state estimator, and not as an algorithm which
approximates the true filtering distribution. The perturbed observation version
of the algorithm is studied, without and with variance inflation. Without
variance inflation well-posedness of the filter is established; with variance
inflation accuracy of the filter, with resepct to the true signal underlying
the data, is established. The algorithm is considered in discrete time, and
also for a continuous time limit arising when observations are frequent and
subject to large noise. The underlying dynamical model, and assumptions about
it, is sufficiently general to include the Lorenz '63 and '96 models, together
with the incompressible Navier-Stokes equation on a two-dimensional torus. The
analysis is limited to the case of complete observation of the signal with
additive white noise. Numerical results are presented for the Navier-Stokes
equation on a two-dimensional torus for both complete and partial observations
of the signal with additive white noise
Stability of Filters for the Navier-Stokes Equation
Data assimilation methodologies are designed to incorporate noisy
observations of a physical system into an underlying model in order to infer
the properties of the state of the system. Filters refer to a class of data
assimilation algorithms designed to update the estimation of the state in a
on-line fashion, as data is acquired sequentially. For linear problems subject
to Gaussian noise filtering can be performed exactly using the Kalman filter.
For nonlinear systems it can be approximated in a systematic way by particle
filters. However in high dimensions these particle filtering methods can break
down. Hence, for the large nonlinear systems arising in applications such as
weather forecasting, various ad hoc filters are used, mostly based on making
Gaussian approximations. The purpose of this work is to study the properties of
these ad hoc filters, working in the context of the 2D incompressible
Navier-Stokes equation. By working in this infinite dimensional setting we
provide an analysis which is useful for understanding high dimensional
filtering, and is robust to mesh-refinement. We describe theoretical results
showing that, in the small observational noise limit, the filters can be tuned
to accurately track the signal itself (filter stability), provided the system
is observed in a sufficiently large low dimensional space; roughly speaking
this space should be large enough to contain the unstable modes of the
linearized dynamics. Numerical results are given which illustrate the theory.
In a simplified scenario we also derive, and study numerically, a stochastic
PDE which determines filter stability in the limit of frequent observations,
subject to large observational noise. The positive results herein concerning
filter stability complement recent numerical studies which demonstrate that the
ad hoc filters perform poorly in reproducing statistical variation about the
true signal
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