143,639 research outputs found
Spatial Aggregation: Theory and Applications
Visual thinking plays an important role in scientific reasoning. Based on the
research in automating diverse reasoning tasks about dynamical systems,
nonlinear controllers, kinematic mechanisms, and fluid motion, we have
identified a style of visual thinking, imagistic reasoning. Imagistic reasoning
organizes computations around image-like, analogue representations so that
perceptual and symbolic operations can be brought to bear to infer structure
and behavior. Programs incorporating imagistic reasoning have been shown to
perform at an expert level in domains that defy current analytic or numerical
methods. We have developed a computational paradigm, spatial aggregation, to
unify the description of a class of imagistic problem solvers. A program
written in this paradigm has the following properties. It takes a continuous
field and optional objective functions as input, and produces high-level
descriptions of structure, behavior, or control actions. It computes a
multi-layer of intermediate representations, called spatial aggregates, by
forming equivalence classes and adjacency relations. It employs a small set of
generic operators such as aggregation, classification, and localization to
perform bidirectional mapping between the information-rich field and
successively more abstract spatial aggregates. It uses a data structure, the
neighborhood graph, as a common interface to modularize computations. To
illustrate our theory, we describe the computational structure of three
implemented problem solvers -- KAM, MAPS, and HIPAIR --- in terms of the
spatial aggregation generic operators by mixing and matching a library of
commonly used routines.Comment: See http://www.jair.org/ for any accompanying file
An octonion algebra originating in combinatorics
C.H. Yang discovered a polynomial version of the classical Lagrange identity
expressing the product of two sums of four squares as another sum of four
squares. He used it to give short proofs of some important theorems on
composition of delta-codes (now known as T-sequences). We investigate the
possible new versions of his polynomial Lagrange identity. Our main result
shows that all such identities are equivalent to each other.Comment: 11 pages, A simpler proof of the main theorem, due to Alberto
Elduque, is inserted. The paper will appear in the Proc. Amer. Math. So
Local False Discovery Rate Based Methods for Multiple Testing of One-Way Classified Hypotheses
This paper continues the line of research initiated in
\cite{Liu:Sarkar:Zhao:2016} on developing a novel framework for multiple
testing of hypotheses grouped in a one-way classified form using
hypothesis-specific local false discovery rates (Lfdr's). It is built on an
extension of the standard two-class mixture model from single to multiple
groups, defining hypothesis-specific Lfdr as a function of the conditional Lfdr
for the hypothesis given that it is within a significant group and the Lfdr for
the group itself and involving a new parameter that measures grouping effect.
This definition captures the underlying group structure for the hypotheses
belonging to a group more effectively than the standard two-class mixture
model. Two new Lfdr based methods, possessing meaningful optimalities, are
produced in their oracle forms. One, designed to control false discoveries
across the entire collection of hypotheses, is proposed as a powerful
alternative to simply pooling all the hypotheses into a single group and using
commonly used Lfdr based method under the standard single-group two-class
mixture model. The other is proposed as an Lfdr analog of the method of
\cite{Benjamini:Bogomolov:2014} for selective inference. It controls Lfdr based
measure of false discoveries associated with selecting groups concurrently with
controlling the average of within-group false discovery proportions across the
selected groups. Simulation studies and real-data application show that our
proposed methods are often more powerful than their relevant competitors.Comment: 26 pages, 17 figure
Genetic algorithm and neural network hybrid approach for job-shop scheduling
Copyright @ 1998 ACTA PressThis paper proposes a genetic algorithm (GA) and constraint satisfaction adaptive neural network (CSANN) hybrid approach for job-shop scheduling problems. In the hybrid approach, GA is used to iterate for searching optimal solutions, CSANN is used to obtain feasible solutions during the iteration of genetic algorithm. Simulations have shown the valid performance of the proposed hybrid approach for job-shop scheduling with respect to the quality of solutions and the speed of calculation.This research is supported by the National Nature Science Foundation and National High
-Tech Program of P. R. China
Simulating emergent urban form: desakota in China
We propose that the emergent phenomenon know as ?desakota?, the rapidurbanization of densely populated rural populations in the newlydeveloped world, particularly China, can be simulated using agent-basedmodels which combine both local and global features. We argue thatdeskota represents a surprising and unusual form of urbanization wellmatchedto processes of land development that are driven from the bottomup but moderated by the higher-level macro economy. We develop asimple logic which links local household reform to global urban reform,translating these ideas into a model structure which reflects these twoscales. Our model first determines the rate of growth of different spatialaggregates using linear statistical analysis. It then allocates this growth tothe local level using developer agents who determine the transformation ormutation of rural households to urban pursuits based on local land costs,accessibilities, and growth management practices. The model is applied todesakota development in the Suzhou region between 1990 and 2000. Weshow how the global rates of change predicted at the township level in theWuxian City region surrounding Suzhou are tempered by localtransformations of rural to urban land uses which we predict using cellularautomata rules. The model, which is implemented in the RePast 3software, is validated using a blend of data taken from remote sensing andgovernment statistical sources. It represents an example of generativesocial science that fuses plausible behavior with formalized logics matchedagainst empirical evidence, essential in showing how novel patterns ofurbanization such as desakota emerge
Discovery and Testimony of Unretained Experts: Creating a Clear and Equitable Standard to Govern Compliance With Subpoenas
Hearing impairment is known to be one of the most frequent sensory impairments. This condition is known to be a hidden disorder which is under recognised and under treated all around the world. The World Health Organisation (WHO) estimates suggest that there are over 275 million people with hearing impairment and 80% of them living in low and middle income countries. Moreover, the estimates suggest that incidence and prevalence of hearing loss and also the number of people with hearing loss accessing services varies considerably across countries. This rises the need for health promotion (or public awareness campaigns) directed to increase awareness and education of hearing loss and hearing healthcare. This paper provides brief discussion on ‘Stories and storytelling’, ‘Cross-culture and cross-cultural communication’ and ‘Health promotion and cultural sensitivity’. The central focus of this paper is to highlight the applications of storytelling in different cultural context in health promotion, particularly to hearing loss public awareness campaigns
Distributed Flow Scheduling in an Unknown Environment
Flow scheduling tends to be one of the oldest and most stubborn problems in
networking. It becomes more crucial in the next generation network, due to fast
changing link states and tremendous cost to explore the global structure. In
such situation, distributed algorithms often dominate. In this paper, we design
a distributed virtual game to solve the flow scheduling problem and then
generalize it to situations of unknown environment, where online learning
schemes are utilized. In the virtual game, we use incentives to stimulate
selfish users to reach a Nash Equilibrium Point which is valid based on the
analysis of the `Price of Anarchy'. In the unknown-environment generalization,
our ultimate goal is the minimization of cost in the long run. In order to
achieve balance between exploration of routing cost and exploitation based on
limited information, we model this problem based on Multi-armed Bandit Scenario
and combined newly proposed DSEE with the virtual game design. Armed with these
powerful tools, we find a totally distributed algorithm to ensure the
logarithmic growing of regret with time, which is optimum in classic
Multi-armed Bandit Problem. Theoretical proof and simulation results both
affirm this claim. To our knowledge, this is the first research to combine
multi-armed bandit with distributed flow scheduling.Comment: 10 pages, 3 figures, conferenc
Enhanced spin-orbit torques in MnAl/Ta films with improving chemical ordering
We report the enhancement of spin-orbit torques in MnAl/Ta films with
improving chemical ordering through annealing. The switching current density is
increased due to enhanced saturation magnetization MS and effective anisotropy
field HK after annealing. Both damplinglike effective field HD and fieldlike
effective field HF have been increased in the temperature range of 50 to 300 K.
HD varies inversely with MS in both of the films, while the HF becomes liner
dependent on 1/MS in the annealed film. We infer that the improved chemical
ordering has enhanced the interfacial spin transparency and the transmitting of
the spin current in MnAl layer
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