3,143 research outputs found
A continued fraction generator for smooth pulse sequences
Digital circuit produces rational output pulse rate at fraction of continuous input pulse rate. Output pulses have average rate with least possible deviation from absolute correct time spacing. Circuit uses include frequency synthesizing, fraction generation, and approximation of irrational sequences
How to centralize and normalize quandle extensions
We show that quandle coverings in the sense of Eisermann form a (regular
epi)-reflective subcategory of the category of surjective quandle
homomorphisms, both by using arguments coming from categorical Galois theory
and by constructing concretely a centralization congruence. Moreover, we show
that a similar result holds for normal quandle extensions.Comment: 17 page
Planning in POMDPs Using Multiplicity Automata
Planning and learning in Partially Observable MDPs (POMDPs) are among the
most challenging tasks in both the AI and Operation Research communities.
Although solutions to these problems are intractable in general, there might be
special cases, such as structured POMDPs, which can be solved efficiently. A
natural and possibly efficient way to represent a POMDP is through the
predictive state representation (PSR) - a representation which recently has
been receiving increasing attention. In this work, we relate POMDPs to
multiplicity automata- showing that POMDPs can be represented by multiplicity
automata with no increase in the representation size. Furthermore, we show that
the size of the multiplicity automaton is equal to the rank of the predictive
state representation. Therefore, we relate both the predictive state
representation and POMDPs to the well-founded multiplicity automata literature.
Based on the multiplicity automata representation, we provide a planning
algorithm which is exponential only in the multiplicity automata rank rather
than the number of states of the POMDP. As a result, whenever the predictive
state representation is logarithmic in the standard POMDP representation, our
planning algorithm is efficient.Comment: Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005
An electronic model for self-assembled hybrid organic/perovskite semiconductors: reverse band edge electronic states ordering and spin-orbit coupling
Based on density functional theory, the electronic and optical properties of
hybrid organic/perovskite crystals are thoroughly investigated. We consider the
mono-crystalline 4FPEPI as material model and demonstrate the optical process
is governed by three active Bloch states at the {\Gamma} point of the reduced
Brillouin zone with a reverse ordering compared to tetrahedrally bonded
semiconductors. Giant spin-orbit coupling effects and optical activities are
subsequently inferred from symmetry analysis.Comment: 17 pages, 6 figure
Dynamic Approximate All-Pairs Shortest Paths: Breaking the O(mn) Barrier and Derandomization
We study dynamic -approximation algorithms for the all-pairs
shortest paths problem in unweighted undirected -node -edge graphs under
edge deletions. The fastest algorithm for this problem is a randomized
algorithm with a total update time of and constant
query time by Roditty and Zwick [FOCS 2004]. The fastest deterministic
algorithm is from a 1981 paper by Even and Shiloach [JACM 1981]; it has a total
update time of and constant query time. We improve these results as
follows: (1) We present an algorithm with a total update time of and constant query time that has an additive error of
in addition to the multiplicative error. This beats the previous
time when . Note that the additive
error is unavoidable since, even in the static case, an -time
(a so-called truly subcubic) combinatorial algorithm with
multiplicative error cannot have an additive error less than ,
unless we make a major breakthrough for Boolean matrix multiplication [Dor et
al. FOCS 1996] and many other long-standing problems [Vassilevska Williams and
Williams FOCS 2010]. The algorithm can also be turned into a
-approximation algorithm (without an additive error) with the
same time guarantees, improving the recent -approximation
algorithm with running
time of Bernstein and Roditty [SODA 2011] in terms of both approximation and
time guarantees. (2) We present a deterministic algorithm with a total update
time of and a query time of . The
algorithm has a multiplicative error of and gives the first
improved deterministic algorithm since 1981. It also answers an open question
raised by Bernstein [STOC 2013].Comment: A preliminary version was presented at the 2013 IEEE 54th Annual
Symposium on Foundations of Computer Science (FOCS 2013
NFAT5 genes are part of the osmotic regulatory system in Atlantic salmon (Salmo salar)
Acknowledgements This study was supported by a grant from the Biotechnology and Biological Sciences Research Council (BBSRC, BB/H008063/1), UK to DGH and SAM. Funding also came from Research Council Norway for project number 241016 for DGH and EJ. This work was carried out as part of a PhD thesis funded by the Marine Alliance of Science and Technology Scotland (MASTS).Peer reviewedPublisher PD
Electronic energy spectra and wave functions on the square Fibonacci tiling
We study the electronic energy spectra and wave functions on the square
Fibonacci tiling, using an off-diagonal tight-binding model, in order to
determine the exact nature of the transitions between different spectral
behaviors, as well as the scaling of the total bandwidth as it becomes finite.
The macroscopic degeneracy of certain energy values in the spectrum is invoked
as a possible mechanism for the emergence of extended electronic Bloch wave
functions as the dimension changes from one to two
Answer Set Programming for Non-Stationary Markov Decision Processes
Non-stationary domains, where unforeseen changes happen, present a challenge
for agents to find an optimal policy for a sequential decision making problem.
This work investigates a solution to this problem that combines Markov Decision
Processes (MDP) and Reinforcement Learning (RL) with Answer Set Programming
(ASP) in a method we call ASP(RL). In this method, Answer Set Programming is
used to find the possible trajectories of an MDP, from where Reinforcement
Learning is applied to learn the optimal policy of the problem. Results show
that ASP(RL) is capable of efficiently finding the optimal solution of an MDP
representing non-stationary domains
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