488 research outputs found
G-Gaussian random fields and stochastic quantization under nonlinear expectation
We construct a G-Gaussian random field parametrized by Hilbert space, which
contains G-white noise as a special case. Using orthonormal expansion, we're
able to study the distribution of this random field. Furthermore, we study
G-spatial and G-spacetime white noise, infinite dimensional G-Brownian Motion
and their respective stochastic integrals. Based on G-white noise analysis and
Parisi-Wu's stochastic quantization, we generalize the scalar free field model
to stochastic partial differential equations (SPDEs) driven by G-Gaussian
random fields.Comment: 29 page
Design of New Oscillograph Based on FPGA
AbstractOscillograph is one of the necessary measurement instruments in modern electronic design field. A new type of Oscillograph based on FPGA is proposed and designed in this paper. It consists of oscillograph, logic analyzer and signal generator. The resolution of the oscillograph is 8 bit and the maximum value can reach 200Mbps with support of software based on windows operation system. That of the logic analyzer is 100 Msps with 16 channels. The resolution of signal generator is 140Msps with 10-bit
Design of New Oscillograph Based on FPGA
AbstractOscillograph is one of the necessary measurement instruments in modern electronic design field. A new type of Oscillograph based on FPGA is proposed and designed in this paper. It consists of oscillograph, logic analyzer and signal generator. The resolution of the oscillograph is 8 bit and the maximum value can reach 200Mbps with support of software based on windows operation system. That of the logic analyzer is 100 Msps with 16 channels. The resolution of signal generator is 140Msps with 10-bit
On the mod cohomology for : the non-semisimple case
Let be a totally real field unramified at all places above and be
a quaternion algebra which splits at either none, or exactly one, of the
infinite places. Let be a continuous irreducible
representation which, when restricted to a fixed place , is non-semisimple
and sufficiently generic. Under some mild assumptions, we prove that the
admissible smooth representations of occurring in the
corresponding Hecke eigenspaces of the mod cohomology of Shimura varieties
associated to have Gelfand-Kirillov dimension . We also
prove that any such representation can be generated as a
-representation by its subspace of invariants under the
first principal congruence subgroup. If moreover , we
prove that such representations have length , confirming a speculation of
Breuil and Pa\v{s}k\=unas.Comment: Comments welcome
The optimality of (stochastic) veto delegation
We analyze the optimal delegation problem between a principal and an agent,
assuming that the latter has state-independent preferences. Among all
incentive-compatible direct mechanisms, the veto mechanisms -- in which the
principal commits to mixing between the status quo option and another
state-dependent option -- yield the highest expected payoffs for the principal.
In the optimal veto mechanism, the principal uses veto (i.e., choosing the
status quo option) only when the state is above some threshold, and both the
veto probability and the state-dependent option increase as the state gets more
extreme. Our model captures the aspect of many real-world scenarios that the
agent only cares about the principal's final decision, and the result provides
grounds for the veto delegation pervasive in various organizations.Comment: 47 pages including Appendi
Information transmission in monopolistic credence goods markets
We study a general credence goods model with N problem types and N
treatments. Communication between the expert seller and the client is modeled
as cheap talk. We find that the expert's equilibrium payoffs admit a geometric
characterization, described by the quasiconcave envelope of his belief-based
profits function under discriminatory pricing. We establish the existence of
client-worst equilibria, apply the geometric characterization to previous
research on credence goods, and provide a necessary and sufficient condition
for when communication benefits the expert. For the binary case, we solve for
all equilibria and characterize client's possible welfare among all equilibria.Comment: 34 page
Locality Preserving Projections for Grassmann manifold
Learning on Grassmann manifold has become popular in many computer vision
tasks, with the strong capability to extract discriminative information for
imagesets and videos. However, such learning algorithms particularly on
high-dimensional Grassmann manifold always involve with significantly high
computational cost, which seriously limits the applicability of learning on
Grassmann manifold in more wide areas. In this research, we propose an
unsupervised dimensionality reduction algorithm on Grassmann manifold based on
the Locality Preserving Projections (LPP) criterion. LPP is a commonly used
dimensionality reduction algorithm for vector-valued data, aiming to preserve
local structure of data in the dimension-reduced space. The strategy is to
construct a mapping from higher dimensional Grassmann manifold into the one in
a relative low-dimensional with more discriminative capability. The proposed
method can be optimized as a basic eigenvalue problem. The performance of our
proposed method is assessed on several classification and clustering tasks and
the experimental results show its clear advantages over other Grassmann based
algorithms.Comment: Accepted by IJCAI 201
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