359,384 research outputs found
On the Estimation of Nonlinearly Aggregated Mixed Models
The article proposes an iterative algorithm for the estimation of fixed and random effects of a nonlinearly aggregated mixed model. The latter arises when an additive Gaussian model is formulated at the disaggregate level on a nonlinear transformation of the responses, but information is available in aggregate form. The nonlinear transformation breaks the linearity of the aggregate model, yielding a nonlinear tight observational constraint. The algorithm rests upon the sequential linearization of the nonlinear aggregation constraint around proposals that are iteratively updated until convergence. Likelihood inferences on the hyperparameters are also discussed. As a by product we provide a solution to the problem of disaggregating over the units of analysis the aggregate responses, enforcing the nonlinear observational constraints. Illustrations are provided with reference to the temporal disaggregation problem, concerning the distribution of annual time series flows to the quarters making up the year.Temporal and spatial disaggregation; Best linear unbiased prediction; Box-Cox transformation; Constrained nonlinear optimization.
Audit Games with Multiple Defender Resources
Modern organizations (e.g., hospitals, social networks, government agencies)
rely heavily on audit to detect and punish insiders who inappropriately access
and disclose confidential information. Recent work on audit games models the
strategic interaction between an auditor with a single audit resource and
auditees as a Stackelberg game, augmenting associated well-studied security
games with a configurable punishment parameter. We significantly generalize
this audit game model to account for multiple audit resources where each
resource is restricted to audit a subset of all potential violations, thus
enabling application to practical auditing scenarios. We provide an FPTAS that
computes an approximately optimal solution to the resulting non-convex
optimization problem. The main technical novelty is in the design and
correctness proof of an optimization transformation that enables the
construction of this FPTAS. In addition, we experimentally demonstrate that
this transformation significantly speeds up computation of solutions for a
class of audit games and security games
Searchâbased model transformations
Model transformations are an important cornerstone of modelâdriven engineering, a discipline which facilitates the abstraction of relevant information of a system as models. The success of the final system mainly depends on the optimization of these models through model transformations. Currently, the application of transformations is realized either by following the applyâasâlongâasâpossible strategy or by the provision of explicit rule orchestrations. This implies two main limitations. First, the optimization objectives are implicitly hidden in the transformation rules and their orchestration. Second, manually finding the best orchestration for a particular scenario is a major challenge due to the high number of possible combinations.
To overcome these limitations, we present a novel framework that builds on the nonâintrusive integration of optimization and model transformation technologies. In particular, we formulate the transformation orchestration task as an optimization problem, which allows for the efficient exploration of the transformation space and explication of the transformation objectives. Our generic framework provides several search algorithms and guides the user in providing a proper search configuration. We present different instantiations of our framework to demonstrate its feasibility, applicability, and benefits using several case studiesEuropean Commission ICT Policy Support Programme 317859Ministerio de Economia y Competitividad TIN2015-70560-RJunta de AndalucĂa P10-TIC-5960Junta de AndalucĂa P12-TIC-186
Sequential joint signal detection and signal-to-noise ratio estimation
The sequential analysis of the problem of joint signal detection and
signal-to-noise ratio (SNR) estimation for a linear Gaussian observation model
is considered. The problem is posed as an optimization setup where the goal is
to minimize the number of samples required to achieve the desired (i) type I
and type II error probabilities and (ii) mean squared error performance. This
optimization problem is reduced to a more tractable formulation by transforming
the observed signal and noise sequences to a single sequence of Bernoulli
random variables; joint detection and estimation is then performed on the
Bernoulli sequence. This transformation renders the problem easily solvable,
and results in a computationally simpler sufficient statistic compared to the
one based on the (untransformed) observation sequences. Experimental results
demonstrate the advantages of the proposed method, making it feasible for
applications having strict constraints on data storage and computation.Comment: 5 pages, Proceedings of IEEE International Conference on Acoustics,
Speech, and Signal Processing (ICASSP), 201
Flow Shape Design for Microfluidic Devices Using Deep Reinforcement Learning
Microfluidic devices are utilized to control and direct flow behavior in a
wide variety of applications, particularly in medical diagnostics. A
particularly popular form of microfluidics -- called inertial microfluidic flow
sculpting -- involves placing a sequence of pillars to controllably deform an
initial flow field into a desired one. Inertial flow sculpting can be formally
defined as an inverse problem, where one identifies a sequence of pillars
(chosen, with replacement, from a finite set of pillars, each of which produce
a specific transformation) whose composite transformation results in a
user-defined desired transformation. Endemic to most such problems in
engineering, inverse problems are usually quite computationally intractable,
with most traditional approaches based on search and optimization strategies.
In this paper, we pose this inverse problem as a Reinforcement Learning (RL)
problem. We train a DoubleDQN agent to learn from this environment. The results
suggest that learning is possible using a DoubleDQN model with the success
frequency reaching 90% in 200,000 episodes and the rewards converging. While
most of the results are obtained by fixing a particular target flow shape to
simplify the learning problem, we later demonstrate how to transfer the
learning of an agent based on one target shape to another, i.e. from one design
to another and thus be useful for a generic design of a flow shape.Comment: Neurips 2018 Deep RL worksho
Modeling and Optimization of Lactic Acid Synthesis by the Alkaline Degradation of Fructose in a Batch Reactor
The present work deals with the determination of the optimal operating conditions of lactic acid synthesis by the alkaline degradation of fructose. It is a complex transformation for which detailed knowledge is not available. It is carried out in a batch
or semi-batch reactor. The ââTendency Modelingââ approach, which consists of the development of an approximate stoichiometric and kinetic model, has been used.
An experimental planning method has been utilized as the database for model development.
The application of the experimental planning methodology allows comparison between the experimental and model response. The model is then used in an optimization procedure to compute the optimal process. The optimal control problem is converted into a nonlinear programming problem solved using the sequencial quadratic programming procedure coupled with the golden search method. The strategy developed allows simultaneously optimizing the different variables, which may be constrained. The validity of the methodology is illustrated by the determination
of the optimal operating conditions of lactic acid production
Non-Rigid Registration via Global to Local Transformation
Non-rigid point set and image registration are key problems in plenty of computer vision and pattern recognition tasks. Typically, the non-rigid registration can be formulated as an optimization problem. However, registration accuracy is limited by local optimum. To solve this problem, we propose a method with global to local transformation for non-rigid point sets registration and it also can be used to infrared (IR) and visible (VIS) image registration. Firstly, an objective function based on Gaussian fields is designed to make a problem of non-rigid registration transform into an optimization problem. A global transformation model, which can describe the regular pattern of non-linear deformation between point sets, is then proposed to achieve coarse registration in global scale. Finally, with the results of coarse registration as initial value, a local transformation model is employed to implement fine registration by using local feature. Meanwhile, the optimal global and local transformation models estimated from edge points of IR and VIS image pairs are used to achieve non-rigid image registration. The qualitative and quantitative comparisons demonstrate that the proposed method has good performance under various types of distortions. Moreover, our method can also produce accurate results of IR and VIS image registration
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