Learning a local-variable model of aromatic and conjugated systems

A collection of new approaches to building and training neural networks, collectively referred to as deep learning, are attracting attention in theoretical chemistry. Several groups aim to replace computationally expensive <i>ab initio</i> quantum mechanics calculations with learned estimators. This raises questions about the representability of complex quantum chemical systems with neural networks. Can local-variable models efficiently approximate nonlocal quantum chemical features? Here, we find that convolutional architectures, those that only aggregate information locally, cannot efficiently represent aromaticity and conjugation in large systems. They cannot represent long-range nonlocality known to be important in quantum chemistry. This study uses aromatic and conjugated systems computed from molecule graphs, though reproducing quantum simulations is the ultimate goal. This task, by definition, is both computable and known to be important to chemistry. The failure of convolutional architectures on this focused task calls into question their use in modeling quantum mechanics. To remedy this heretofore unrecognized deficiency, we introduce a new architecture that propagates information back and forth in waves of nonlinear computation. This architecture is still a local-variable model, and it is both computationally and representationally efficient, processing molecules in sublinear time with far fewer parameters than convolutional networks. Wave-like propagation models aromatic and conjugated systems with high accuracy, and even models the impact of small structural changes on large molecules. This new architecture demonstrates that some nonlocal features of quantum chemistry can be efficiently represented in local variable models

Accelerating Metropolis-Hastings algorithms: Delayed acceptance with prefetching

MCMC algorithms such as Metropolis-Hastings algorithms are slowed down by the computation of complex target distributions as exemplified by huge datasets. We offer in this paper an approach to reduce the computational costs of such algorithms by a simple and universal divide-and-conquer strategy. The idea behind the generic acceleration is to divide the acceptance step into several parts, aiming at a major reduction in computing time that outranks the corresponding reduction in acceptance probability. The division decomposes the "prior x likelihood" term into a product such that some of its components are much cheaper to compute than others. Each of the components can be sequentially compared with a uniform variate, the first rejection signalling that the proposed value is considered no further, This approach can in turn be accelerated as part of a prefetching algorithm taking advantage of the parallel abilities of the computer at hand. We illustrate those accelerating features on a series of toy and realistic examples.Comment: 20 pages, 12 figures, 2 tables, submitte

Inferring Interpersonal Relations in Narrative Summaries

Characterizing relationships between people is fundamental for the understanding of narratives. In this work, we address the problem of inferring the polarity of relationships between people in narrative summaries. We formulate the problem as a joint structured prediction for each narrative, and present a model that combines evidence from linguistic and semantic features, as well as features based on the structure of the social community in the text. We also provide a clustering-based approach that can exploit regularities in narrative types. e.g., learn an affinity for love-triangles in romantic stories. On a dataset of movie summaries from Wikipedia, our structured models provide more than a 30% error-reduction over a competitive baseline that considers pairs of characters in isolation

Working in the dark

Professional engineers work as experts who influence the work of others. They rarely have direct contact with the products of an enterprise. They work with analogues such as graphs, algorithms and simulations, and engage in discussions in specialized languages, which develop alongside the technological changes they promote or oppose. The engines of linguistic development are metaphors and analogies, however there is no system for creating them. Some metaphors and analogies become so familiar that they are treated as literal terms or literal explanations and become embedded in engineering language games. The field of electrical engineering offers hosts of examples. Students wishing to practice in engineering will need to become fluent in the language games of the profession. The haphazard evolution of language games offer students little help. As with acquisition of any language, repeated rehearsal is vital and practice in playing specialised language games is a primary part of engineering education

Run-time optimization of adaptive irregular applications

Compared to traditional compile-time optimization, run-time optimization could o&#64256;er signi&#64257;cant performance improvements when parallelizing and optimizing adaptive irregular applications, because it performs program analysis and adaptive optimizations during program execution. Run-time techniques can succeed where static techniques fail because they exploit the characteristics of input data, programs' dynamic behaviors, and the underneath execution environment. When optimizing adaptive irregular applications for parallel execution, a common observation is that the effectiveness of the optimizing transformations depends on programs' input data and their dynamic phases. This dissertation presents a set of run-time optimization techniques that match the characteristics of programs' dynamic memory access patterns and the appropriate optimization (parallelization) transformations. First, we present a general adaptive algorithm selection framework to automatically and adaptively select at run-time the best performing, functionally equivalent algorithm for each of its execution instances. The selection process is based on off-line automatically generated prediction models and characteristics (collected and analyzed dynamically) of the algorithm's input data, In this dissertation, we specialize this framework for automatic selection of reduction algorithms. In this research, we have identi&#64257;ed a small set of machine independent high-level characterization parameters and then we deployed an off-line, systematic experiment process to generate prediction models. These models, in turn, match the parameters to the best optimization transformations for a given machine. The technique has been evaluated thoroughly in terms of applications, platforms, and programs' dynamic behaviors. Speci&#64257;cally, for the reduction algorithm selection, the selected performance is within 2% of optimal performance and on average is 60% better than "Replicated Buffer," the default parallel reduction algorithm speci&#64257;ed by OpenMP standard. To reduce the overhead of speculative run-time parallelization, we have developed an adaptive run-time parallelization technique that dynamically chooses effcient shadow structures to record a program's dynamic memory access patterns for parallelization. This technique complements the original speculative run-time parallelization technique, the LRPD test, in parallelizing loops with sparse memory accesses. The techniques presented in this dissertation have been implemented in an optimizing research compiler and can be viewed as effective building blocks for comprehensive run-time optimization systems, e.g., feedback-directed optimization systems and dynamic compilation systems

Simple universal models capture all classical spin physics

Spin models are used in many studies of complex systems---be it condensed matter physics, neural networks, or economics---as they exhibit rich macroscopic behaviour despite their microscopic simplicity. Here we prove that all the physics of every classical spin model is reproduced in the low-energy sector of certain `universal models'. This means that (i) the low energy spectrum of the universal model reproduces the entire spectrum of the original model to any desired precision, (ii) the corresponding spin configurations of the original model are also reproduced in the universal model, (iii) the partition function is approximated to any desired precision, and (iv) the overhead in terms of number of spins and interactions is at most polynomial. This holds for classical models with discrete or continuous degrees of freedom. We prove necessary and sufficient conditions for a spin model to be universal, and show that one of the simplest and most widely studied spin models, the 2D Ising model with fields, is universal.Comment: v1: 4 pages with 2 figures (main text) + 4 pages with 3 figures (supplementary info). v2: 12 pages with 3 figures (main text) + 35 pages with 6 figures (supplementary info) (all single column). v2 contains new results and major revisions (results for spin models with continuous degrees of freedom, explicit constructions, examples...). Close to published version. v3: minor typo correcte

First principles view on chemical compound space: Gaining rigorous atomistic control of molecular properties

A well-defined notion of chemical compound space (CCS) is essential for gaining rigorous control of properties through variation of elemental composition and atomic configurations. Here, we review an atomistic first principles perspective on CCS. First, CCS is discussed in terms of variational nuclear charges in the context of conceptual density functional and molecular grand-canonical ensemble theory. Thereafter, we revisit the notion of compound pairs, related to each other via "alchemical" interpolations involving fractional nuclear chargens in the electronic Hamiltonian. We address Taylor expansions in CCS, property non-linearity, improved predictions using reference compound pairs, and the ounce-of-gold prize challenge to linearize CCS. Finally, we turn to machine learning of analytical structure property relationships in CCS. These relationships correspond to inferred, rather than derived through variational principle, solutions of the electronic Schr\"odinger equation