Warp Kernel Research Articles

Remote Sensing Image Rectangling with Iterative Warping Kernel Self-correction Transformer

Remote Sensing Image Rectangling with Iterative Warping Kernel Self-correction Transformer

Adaptive warped kernel estimation for nonparametric regression with circular responses

Adaptive warped kernel estimation for nonparametric regression with circular responses

Multiscale Laplacian Learning.

Machine learning has greatly influenced many fields, including science. However, despite of the tremendous accomplishments of machine learning, one of the key limitations of most existing machine learning approaches is their reliance on large labeled sets, and thus, data with limited labeled samples remains a challenge. Moreover, the performance of machine learning methods often severely hindered in case of diverse data, usually associated with smaller data sets or data associated with areas of study where the size of the data sets is constrained by high experimental cost and/or ethics. These challenges call for innovative strategies for dealing with these types of data. In this work, the aforementioned challenges are addressed by integrating graph-based frameworks, semi-supervised techniques, multiscale structures, and modified and adapted optimization procedures. This results in two innovative multiscale Laplacian learning (MLL) approaches for machine learning tasks, such as data classification, and for tackling data with limited samples, diverse data, and small data sets. The first approach, multikernel manifold learning (MML), integrates manifold learning with multikernel information and incorporates a warped kernel regularizer using multiscale graph Laplacians. The second approach, the multiscale MBO (MMBO) method, introduces multiscale Laplacians to the modification of the famous classical Merriman-Bence-Osher (MBO) scheme, and makes use of fast solvers. We demonstrate the performance of our algorithms experimentally on a variety of benchmark data sets, and compare them favorably to the state-of-art approaches.

Multivariate adaptive warped kernel estimation

We deal with the problem of nonparametric estimation of a multivariate regression function without any assumption on the compacity of the support of the random design, thanks to a warping device. An adaptive warped kernel estimator is first defined in the case of known design distribution and proved to be optimal in the oracle sense. Then, a general procedure is carried out: the marginal distributions of the design are estimated by the empirical cumulative distribution functions, and the dependence structure is built using a kernel estimation of the copula density. The copula density estimator is also proved to be optimal in the oracle and in the minimax sense. The plug-in of these estimates in the regression function estimator provides a fully data-driven estimate. A numerical study illustrates the theoretical results.

PARALLEL ALGORITHMS FOR CELLULAR MODELS SIMULATION

DEVS is a sound formal modeling and simulation (M&S) framework based on generic dynamic system concepts. Cell-DEVS is a formalism for cell-shaped models based on DEVS. This work presents a new simulation technique for execution of DEVS and Cell-DEVS models in parallel environments. These techniques are modifications to the original Time Warp mechanism offered by WARPED kernel. Time Warp functionalities are revised to include two new algorithms namely, Local Rollback Frequency Model (LRFM) and Global Rollback Frequency Model (GRFM). The resulting simulator is used as new simulation engine for CD++, an M&S toolkit that implements DEVS and Cell-DEVS theories. The results obtained allowed us to achieve considerable speedups due to the reductions that LRFM and GRFM protocols perform on number of rollbacks and anti-messages.

Parallel Environment for DEVS and Cell-DEVS Models

Discrete Event System Specification (DEVS) is a sound formalism to describe generic dynamic systems in a hierarchical and modular way. Cell-DEVS is a DEVS-based formalism intended to model compleX physical systems as cell spaces. This work presents new techniques for eXecuting DEVS and Cell-DEVS models in parallel and distributed environments based on the WARPED kernel, an implementation of the Time Warp protocol. The optimistic simulator PCD++, built as a new simulation engine for CD++, is a toolkit that implements the DEVS and Cell-DEVS formalisms. We redesign algorithms in CD++ to carry out optimistic simulations using a non-hierarchical approach that reduces the communication overhead. The message-passing organization is analyzed using a high-level abstraction referred to as wall clock time slice. We propose a two-level user-controlled state-saving mechanism to achieve efficient and fleXible state saving at runtime. Various optimization strategies are applied to PCD++ and their effects are analyzed quantitatively, including a risk-free message type-based state-saving strategy to reduce the number of states saved during the simulation significantly, and a one log file per node strategy to break the bottleneck caused by file I/O operations. It is shown that PCD++ markedly outperforms other alternatives and considerable speedups can be achieved in parallel and distributed simulations.

Toward Grid-Aware Time Warp

The authors study the adaptation of an optimistic Time Warp kernel to cross-cluster computing on the Grid. Wide-area communication, the primary source of overhead, is offloaded onto dedicated routing processes. This allows the simulation processes to run at full speed and thus significantly decreases the performance gap caused by the wide-area distribution. Further improvements are obtained by employing message aggregation on the wide-area links and using a distributed global virtual time algorithm. The authors achieve many of their objectives for a cellular automaton simulation with lazy cancellation and moderate communication. High communication rates, especially with aggressive cancellation, present a challenge. This is confirmed by the experiments with synthetic loads. Even then, a satisfactory speedup can be achieved, provided that the computational grain of events is large enough.