18,393 research outputs found
Computer Aided Aroma Design. I. Molecular knowledge framework
Computer Aided Aroma Design (CAAD) is likely to become a hot issue as the REACH EC document targets many aroma compounds to require substitution. The two crucial steps in CAMD are the generation of candidate molecules and the estimation of properties, which can be difficult when complex molecular structures like odours are sought and when their odour quality are definitely subjective whereas their odour intensity are partly subjective as stated in Rossitier’s review (1996). In part I, provided that classification rules like those presented in part II exist to assess the odour quality, the CAAD methodology presented proceeds with a multilevel approach matched by a versatile and novel molecular framework. It can distinguish the infinitesimal chemical structure differences, like in isomers, that are responsible for different odour quality and intensity. Besides, its chemical graph concepts are well suited for genetic algorithm sampling techniques used for an efficient screening of large molecules such as aroma. Finally, an input/output XML format based on the aggregation of CML and ThermoML enables to store the molecular classes but also any subjective or objective property values computed during the CAAD process
Tuning the average path length of complex networks and its influence to the emergent dynamics of the majority-rule model
We show how appropriate rewiring with the aid of Metropolis Monte Carlo
computational experiments can be exploited to create network topologies
possessing prescribed values of the average path length (APL) while keeping the
same connectivity degree and clustering coefficient distributions. Using the
proposed rewiring rules we illustrate how the emergent dynamics of the
celebrated majority-rule model are shaped by the distinct impact of the APL
attesting the need for developing efficient algorithms for tuning such network
characteristics.Comment: 10 figure
Differentiable Programming Tensor Networks
Differentiable programming is a fresh programming paradigm which composes
parameterized algorithmic components and trains them using automatic
differentiation (AD). The concept emerges from deep learning but is not only
limited to training neural networks. We present theory and practice of
programming tensor network algorithms in a fully differentiable way. By
formulating the tensor network algorithm as a computation graph, one can
compute higher order derivatives of the program accurately and efficiently
using AD. We present essential techniques to differentiate through the tensor
networks contractions, including stable AD for tensor decomposition and
efficient backpropagation through fixed point iterations. As a demonstration,
we compute the specific heat of the Ising model directly by taking the second
order derivative of the free energy obtained in the tensor renormalization
group calculation. Next, we perform gradient based variational optimization of
infinite projected entangled pair states for quantum antiferromagnetic
Heisenberg model and obtain start-of-the-art variational energy and
magnetization with moderate efforts. Differentiable programming removes
laborious human efforts in deriving and implementing analytical gradients for
tensor network programs, which opens the door to more innovations in tensor
network algorithms and applications.Comment: Typos corrected, discussion and refs added; revised version accepted
for publication in PRX. Source code available at
https://github.com/wangleiphy/tensorgra
CSP design model and tool support
The CSP paradigm is known as a powerful concept for designing and analysing the architectural and behavioural parts of concurrent software. Although the theory of CSP is useful for mathematicians, the programming language occam has been derived from CSP that is useful for any engineering practice. Nowadays, the concept of occam/CSP can be used for almost every object-oriented programming language. This paper describes a tree-based description model and prototype tool that elevates the use of occam/CSP concepts at the design level and performs code generation to Java, C, C++, and machine-readable CSP for the level of implementation. The tree-based description model can be used to browse through the generated source code. The tool is a kind of browser that is able to assist modern workbenches (like Borland Builder, Microsoft Visual C++ and 20-SIM) with coding concurrency. The tool will guide the user through the design trajectory using support messages and several semantic and syntax rule checks. The machine-readable CSP can be read by FDR, enabling more advanced analysis on the design. Early experiments with the prototype tool show that the browser concept, combined with the tree-based description model, enables a user-friendly way to create a design using the CSP concepts and benefits. The design tool is available from our URL, http://www.rt.el.utwente.nl/javapp
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