4,850 research outputs found
Mechanism Choice
This chapter reviews the literature on the selection of regulatory policy instruments, from both normative and positive perspectives. It first reviews the mechanism design literature to identify normative objectives in selecting among the menu or toolbox of policy instruments. The chapter then discusses the public choice and positive political theory literatures and the variety of models developed to attempt to predict the actual selection of alternative policy instruments. It begins with simpler early models focusing on interest group politics and proceeds to more complicated models that incorporate both supply and demand for policy, the role of policy entrepreneurs, behavioral and cognitive choice, and public perceptions and mass politics. It compares these theories to empirical experience. The chapter examines literature in law, economics, political science, and related fields, and it draws examples from US, European, and international regulation. It concludes with suggestions for future research. Document is the author\u27s manuscrip
Spatially resolved electrochemistry in ionic liquids : surface structure effects on triiodide reduction at platinum electrodes
Understanding the relationship between electrochemical activity and electrode structure is vital for improving the efficiency of dye-sensitized solar cells. Here, the reduction of triiodide to iodide in 1-butyl-3-methylimidazolium tetrafluoroborate ([BMIm][BF4]) room temperature ionic liquid (RTIL) is investigated on polycrystalline platinum using scanning electrochemical cell microscopy (SECCM) and correlated to the crystallographic orientation from electron backscatter diffraction (EBSD). Although the rate determining step in all grains was the first electron transfer, significant grain-dependent variations in activity were revealed, with grains with a dominant (110) crystallographic character exhibiting higher catalytic activity compared to those with a major (100) orientation. The SECCM technique is demonstrated to resolve heterogeneity in activity, highlighting that methods incorporating polycrystalline electrodes miss vital details for understanding and optimizing electrocatalysts. An additional advantage of the SECCM over single-crystal techniques is its ability to probe high index facets
Secret-Sharing for NP
A computational secret-sharing scheme is a method that enables a dealer, that
has a secret, to distribute this secret among a set of parties such that a
"qualified" subset of parties can efficiently reconstruct the secret while any
"unqualified" subset of parties cannot efficiently learn anything about the
secret. The collection of "qualified" subsets is defined by a Boolean function.
It has been a major open problem to understand which (monotone) functions can
be realized by a computational secret-sharing schemes. Yao suggested a method
for secret-sharing for any function that has a polynomial-size monotone circuit
(a class which is strictly smaller than the class of monotone functions in P).
Around 1990 Rudich raised the possibility of obtaining secret-sharing for all
monotone functions in NP: In order to reconstruct the secret a set of parties
must be "qualified" and provide a witness attesting to this fact.
Recently, Garg et al. (STOC 2013) put forward the concept of witness
encryption, where the goal is to encrypt a message relative to a statement "x
in L" for a language L in NP such that anyone holding a witness to the
statement can decrypt the message, however, if x is not in L, then it is
computationally hard to decrypt. Garg et al. showed how to construct several
cryptographic primitives from witness encryption and gave a candidate
construction.
One can show that computational secret-sharing implies witness encryption for
the same language. Our main result is the converse: we give a construction of a
computational secret-sharing scheme for any monotone function in NP assuming
witness encryption for NP and one-way functions. As a consequence we get a
completeness theorem for secret-sharing: computational secret-sharing scheme
for any single monotone NP-complete function implies a computational
secret-sharing scheme for every monotone function in NP
Analytic Evidence for Continuous Self Similarity of the Critical Merger Solution
The double cone, a cone over a product of a pair of spheres, is known to play
a role in the black-hole black-string phase diagram, and like all cones it is
continuously self similar (CSS). Its zero modes spectrum (in a certain sector)
is determined in detail, and it implies that the double cone is a co-dimension
1 attractor in the space of those perturbations which are smooth at the tip.
This is interpreted as strong evidence for the double cone being the critical
merger solution. For the non-symmetry-breaking perturbations we proceed to
perform a fully non-linear analysis of the dynamical system. The scaling
symmetry is used to reduce the dynamical system from a 3d phase space to 2d,
and obtain the qualitative form of the phase space, including a
non-perturbative confirmation of the existence of the "smoothed cone".Comment: 25 pages, 4 figure
A Nearly Tight Sum-of-Squares Lower Bound for the Planted Clique Problem
We prove that with high probability over the choice of a random graph
from the Erd\H{o}s-R\'enyi distribution , the -time degree
Sum-of-Squares semidefinite programming relaxation for the clique problem
will give a value of at least for some constant
. This yields a nearly tight bound on the value of this
program for any degree . Moreover we introduce a new framework
that we call \emph{pseudo-calibration} to construct Sum of Squares lower
bounds. This framework is inspired by taking a computational analog of Bayesian
probability theory. It yields a general recipe for constructing good
pseudo-distributions (i.e., dual certificates for the Sum-of-Squares
semidefinite program), and sheds further light on the ways in which this
hierarchy differs from others.Comment: 55 page
ER Stress-Induced eIF2-alpha Phosphorylation Underlies Sensitivity of Striatal Neurons to Pathogenic Huntingtin
A hallmark of Huntington's disease is the pronounced sensitivity of striatal neurons to polyglutamine-expanded huntingtin expression. Here we show that cultured striatal cells and murine brain striatum have remarkably low levels of phosphorylation of translation initiation factor eIF2 alpha, a stress-induced process that interferes with general protein synthesis and also induces differential translation of pro-apoptotic factors. EIF2 alpha phosphorylation was elevated in a striatal cell line stably expressing pathogenic huntingtin, as well as in brain sections of Huntington's disease model mice. Pathogenic huntingtin caused endoplasmic reticulum (ER) stress and increased eIF2 alpha phosphorylation by increasing the activity of PKR-like ER-localized eIF2 alpha kinase (PERK). Importantly, striatal neurons exhibited special sensitivity to ER stress-inducing agents, which was potentiated by pathogenic huntingtin. We could strongly reduce huntingtin toxicity by inhibiting PERK. Therefore, alteration of protein homeostasis and eIF2 alpha phosphorylation status by pathogenic huntingtin appears to be an important cause of striatal cell death. A dephosphorylated state of eIF2 alpha has been linked to cognition, which suggests that the effect of pathogenic huntingtin might also be a source of the early cognitive impairment seen in patients
Equivalence Proofs for Multi-Layer Perceptron Classifiers and the Bayesian Discriminant Function
This paper presents a number of proofs that
equate the outputs of a Multi-Layer Perceptron
(MLP) classifier and the optimal Bayesian discriminant
function for asymptotically large sets of
statistically independent training samples. Two
broad classes of objective functions are shown to
yield Bayesian discriminant performance. The
first class are “reasonable error measures,” which
achieve Bayesian discriminant performance by
engendering classifier outputs that asymptotically
equate to a posteriori probabilities. This class includes
the mean-squared error (MSE) objective
function as well as a number of information theoretic
objective functions. The second class are
classification figures of merit (CFMmono ), which
yield a qualified approximation to Bayesian discriminant
performance by engendering classifier
outputs that asymptotically identify themaximum
a posteriori probability for a given input. Conditions
and relationships for Bayesian discriminant
functional equivalence are given for both classes
of objective functions. Differences between the
two classes are then discussed very briefly in the
context of how they might affect MLP classifier
generalization, given relatively small training
sets
Templates for stellar mass black holes falling into supermassive black holes
The spin modulated gravitational wave signals, which we shall call smirches,
emitted by stellar mass black holes tumbling and inspiralling into massive
black holes have extremely complicated shapes. Tracking these signals with the
aid of pattern matching techniques, such as Wiener filtering, is likely to be
computationally an impossible exercise. In this article we propose using a
mixture of optimal and non-optimal methods to create a search hierarchy to ease
the computational burden. Furthermore, by employing the method of principal
components (also known as singular value decomposition) we explicitly
demonstrate that the effective dimensionality of the search parameter space of
smirches is likely to be just three or four, much smaller than what has
hitherto been thought to be about nine or ten. This result, based on a limited
study of the parameter space, should be confirmed by a more exhaustive study
over the parameter space as well as Monte-Carlo simulations to test the
predictions made in this paper.Comment: 12 pages, 4 Tables, 4th LISA symposium, submitted to CQ
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