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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jss.2006.02.038 Byline: Jean Utke (a), Andrew Lyons (a), Uwe Naumann (b) Keywords: Control flow reversal; Adjoint computation; Efficient loop reversal Abstract: Numerical simulations of physical, chemical, and economical processes play an increasingly important role in modern science and engineering. The implementation of mathematical models of real-world applications on a computer facilitates both the speed and the depth of our understanding of the behavior of the respective systems. Derivative models of the computer programs are required to make the transition from pure simulation to the highly desirable optimization of the numerical models with respect to a potentially very large number of input parameters. Such models can be generated automatically from the given numerical program by a source transformation technique known as automatic differentiation. Reversal of the control flow is especially important for the generation of adjoint derivative models. We describe an approach to the control flow reversal of structured programs motivated by the significant weakness of approaches that do not exploit the result of control-flow analysis. Our approach is used to automatically generate adjoint code for numerical programs by semantic source transformation. After a short introduction to applications and the implementation tool set, we motivate the proposed approach with a simple example. We present a novel preaccumulation algorithm for local Jacobian matrices at the level of basic blocks. The main part of the paper covers the reversal of structured control flow graphs. First we show the algorithmic steps for simple branches and loops. We give a detailed algorithm for the reversal of arbitrary combinations of loops and branches based only on the structural information in a general control flow graph. Dependencies between computations and their enclosing control flow constructs can lead to inefficient adjoint code. We formulate a set of conditions that allows for considerable efficiency gains in the adjoint code while permitting a reasonably simple modification of the reversal algorithms for specially designated control flow subgraphs. We present a sensitivity computation of an oceanographic application that illustrates the benefits of this modified approach. Author Affiliation: (a) Mathematics and Computer Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60438, USA (b) Software and Tools for Computational Engineering, RWTH Aachen University, D-52056 Aachen, Germany Article History: Received 17 February 2006; Accepted 19 February 2006 Article Note: (footnote) [star] The submitted manuscript has been created by the University of Chicago as Operator of Argonne National Laboratory ("Argonne") under Contract No. W-31-109-ENG-38 with the US Department of Energy. The US Government retains for itself, and others acting on its behalf, a paid-up, nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government.

In C++, new features and semantics can be added to an existing software package without sweeping code changes by introducing a user-defined type using operator overloading. This approach is used, for example, to add capabilities such as algorithmic differentiation. However, the introduction of operator overloading can cause a multitude of compilation errors. In a previous paper, we identified code constructs that cause a violation of the C++ language standard after a type change, and a tool called OO-Lint based on the Clang compiler that identifies these code constructs with lint-like messages. In this paper, we present an extension of this work that automatically transforms such problematic code constructs in order to make an existing code base compatible with a semantic augmentation through operator overloading. We applied our tool to the CFD software OpenFOAM and detected and transformed 23 instances of problematic code constructs in 160,000 lines of code. A significant amount of these root causes are included up to 425 times in other files causing a tremendous compiler error amplification. In addition, we show the significance of our work with a case study of the evolution of the ice flow modeling software ISSM, comparing a recent version which was manually type changed with a legacy version. The recent version shows no signs of problematic code constructs. In contrast, our tool detected and transformed a remarkable amount of issues in the legacy version that previously had to be manually located and fixed. [ABSTRACT FROM AUTHOR]

Most recent machine learning research focuses on developing new classifiers for the sake of improving classification accuracy. With many well-performing state-of-the-art classifiers available, there is a growing need for understanding interpretability of a classifier necessitated by practical purposes such as to find the best diet recommendation for a diabetes patient. Inverse classification is a post modeling process to find changes in input features of samples to alter the initially predicted class. It is useful in many business applications to determine how to adjust a sample input data such that the classifier predicts it to be in a desired class. In real world applications, a budget on perturbations of samples corresponding to customers or patients is usually considered, and in this setting, the number of successfully perturbed samples is key to increase benefits. In this study, we propose a new framework to solve inverse classification that maximizes the number of perturbed samples subject to a per-feature-budget limits and favorable classification classes of the perturbed samples. We design algorithms to solve this optimization problem based on gradient methods, stochastic processes, Lagrangian relaxations, and the Gumbel trick. In experiments, we find that our algorithms based on stochastic processes exhibit an excellent performance in different budget settings and they scale well. The relative improvement of the proposed stochastic algorithms over an existing method with a traditional formulation is 15% in the real-world dataset and 21% in two public datasets on average. • A new framework to solve inverse classification is developed. • It obtains successfully perturbed samples within a per-feature budget. • Algorithms are designed based on a gradient method and the Gumbel trick. • Real data from an industrial partner and two healthcare public datasets are used. • The stochastic approaches perform great on various budget scenarios, and they scale. [ABSTRACT FROM AUTHOR]

Abstract: A dual (adjoint) model is used to explore elements of the oceanic state influencing the meridional volume and heat transports (MVT and MHT) in the sub-tropical North Atlantic so as to understand their variability and to provide the elements of useful observational program design. Focus is on the effect of temperature (and salinity) perturbations. On short timescales (months), as expected, the greatest sensitivities are to local disturbances, but as the timescales extend back to a decade and longer, the region of influence expands to occupy much of the Atlantic basin and significant areas of the global ocean, although the influence of any specific point or small area tends to be quite weak. The propagation of information in the dual solution is a clear manifestation of oceanic teleconnections. It takes place through identifiable “dual” Kelvin, Rossby, and continental shelf-waves with an interpretable physics, in particular in terms of dual expressions of barotropic and baroclinic adjustment processes. Among the notable features are the relatively fast timescales of influence (albeit weak in amplitude) between 26°N and the tropical Pacific and Indian Ocean, the absence of dominance of the sub-polar North Atlantic, significant connections to the Agulhas leakage region in the southeast Atlantic on timescales of 5–10 years, and the marked sensitivity propagation of Doppler-shifted Rossby waves in the Southern Ocean on timescales of a decade and beyond. Regional, as well as time-dependent, differences between MVT and MHT sensitivities highlight the lack of a simple correspondence between their variability. Some implications for observing systems for the purpose of climate science are discussed. [Copyright &y& Elsevier]