254,330 research outputs found
Constraints on predicate invention
This chapter describes an inductive learning method that derives logic programs and invents predicates when needed. The basic idea is to form the least common anti-instance (LCA) of selected seed examples. If the LCA is too general it forms the starting poínt of a gneral-to-specific search which is guided by various constraints on argument dependencies and critical terms. A distinguishing feature of the method is its ability to introduce new predicates. Predicate invention involves three steps. First, the need for a new predicate is discovered and the arguments of the new predicate are determíned using the same constraints that guide the search. In the second step, instances of the new predicate are abductively inferred. These instances form the input for the last step where the definition of the new predicate is induced by recursively applying the method again. We also outline how such a system could be more tightly integrated with an abductive learning system
Theoretical results of research on spatial and territorial development (with examples on the european north of Russia)
This article focuses primarily on the correlation between the concepts of “spatial” and “territorial” development. It is shown that, while differing in their content, these concepts substantially complement each other when it comes to specific research studies. In this case, the topic of spatial development includes considering the general areas for the location of productive forces, geographic dimension of the specific types of economic activities, economic measurement of distances, linear communications and a network structure of the economy while. In the topic of territorial development, the author introduces the territory itself as a natural and economic capital and territorial economic management based on such capital. The study of spatial and territorial aspects of socio-economic development in the European North of Russia (ENR) showed that its immediate future is associated not so much with the large projects aimed at creating new fuel and energy, mineral and raw material, or forestry bases, as with the improvement in the existing economic systems based on scientific and technological progress and interregional integration. The progression from developed territories to new Arctic and Northern locations is associated with tremendous costs and requires time for scientific and technical preparation. The modernization of existing production facilities, territorial and production complexes is a priority in the development of productive forces in ENR. The author proposes to apply the theoretical provisions and practical recommendations formulated as a result of studying the spatial and territorial development in the elaboration of government strategic planning documents. Currently, the practice of strategic planning does not fully consider the substance of such concepts as “spatial development” and “territorial development.” This incompleteness is so significant that overcoming it should be considered as one of the key objectives pursued by the regional policy
Retrosynthetic reaction prediction using neural sequence-to-sequence models
We describe a fully data driven model that learns to perform a retrosynthetic
reaction prediction task, which is treated as a sequence-to-sequence mapping
problem. The end-to-end trained model has an encoder-decoder architecture that
consists of two recurrent neural networks, which has previously shown great
success in solving other sequence-to-sequence prediction tasks such as machine
translation. The model is trained on 50,000 experimental reaction examples from
the United States patent literature, which span 10 broad reaction types that
are commonly used by medicinal chemists. We find that our model performs
comparably with a rule-based expert system baseline model, and also overcomes
certain limitations associated with rule-based expert systems and with any
machine learning approach that contains a rule-based expert system component.
Our model provides an important first step towards solving the challenging
problem of computational retrosynthetic analysis
The Morphogenesis Of Evolutionary Developmental Biology
The early studies of evolutionary developmental biology (Evo-Devo) come from several sources. Tributaries flowing into Evo-Devo came from such disciplines as embryology, developmental genetics, evolutionary biology, ecology, paleontology, systematics, medical embryology and mathematical modeling. This essay will trace one of the major pathways, that from evolutionary embryology to Evo-Devo and it will show the interactions of this pathway with two other sources of Evo-Devo: ecological developmental biology and medical developmental biology. Together, these three fields are forming a more inclusive evolutionary developmental biology that is revitalizing and providing answers to old and important questions involving the formation of biodiversity on Earth. The phenotype of Evo-Devo is limited by internal constraints on what could be known given the methods and equipment of the time and it has been framed by external factors that include both academic and global politics
Recursive Program Optimization Through Inductive Synthesis Proof Transformation
The research described in this paper involved developing transformation techniques which increase the efficiency of the noriginal program, the source, by transforming its synthesis proof into one, the target, which yields a computationally more efficient algorithm. We describe a working proof transformation system which, by exploiting the duality between mathematical induction and recursion, employs the novel strategy of optimizing recursive programs by transforming inductive proofs. We compare and contrast this approach with the more traditional approaches to program transformation, and highlight the benefits of proof transformation with regards to search, correctness, automatability and generality
Estimating Photometric Redshifts Using Support Vector Machines
We present a new approach to obtaining photometric redshifts using a kernel
learning technique called Support Vector Machines (SVMs). Unlike traditional
spectral energy distribution fitting, this technique requires a large and
representative training set. When one is available, however, it is likely to
produce results that are comparable to the best obtained using template fitting
and artificial neural networks. Additional photometric parameters such as
morphology, size and surface brightness can be easily incorporated. The
technique is demonstrated using samples of galaxies from the Sloan Digital Sky
Survey Data Release 2 and the hybrid galaxy formation code GalICS. The RMS
error in redshift estimation is for both samples. The strengths and
limitations of the technique are assessed.Comment: 10 pages, 3 figures, to appear in the PASP, minor typos fixed to make
consistent with published versio
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