99,811 research outputs found

    The CIFF Proof Procedure for Abductive Logic Programming with Constraints: Theory, Implementation and Experiments

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    We present the CIFF proof procedure for abductive logic programming with constraints, and we prove its correctness. CIFF is an extension of the IFF proof procedure for abductive logic programming, relaxing the original restrictions over variable quantification (allowedness conditions) and incorporating a constraint solver to deal with numerical constraints as in constraint logic programming. Finally, we describe the CIFF system, comparing it with state of the art abductive systems and answer set solvers and showing how to use it to program some applications. (To appear in Theory and Practice of Logic Programming - TPLP)

    Asynchronous Execution of Python Code on Task Based Runtime Systems

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    Despite advancements in the areas of parallel and distributed computing, the complexity of programming on High Performance Computing (HPC) resources has deterred many domain experts, especially in the areas of machine learning and artificial intelligence (AI), from utilizing performance benefits of such systems. Researchers and scientists favor high-productivity languages to avoid the inconvenience of programming in low-level languages and costs of acquiring the necessary skills required for programming at this level. In recent years, Python, with the support of linear algebra libraries like NumPy, has gained popularity despite facing limitations which prevent this code from distributed runs. Here we present a solution which maintains both high level programming abstractions as well as parallel and distributed efficiency. Phylanx, is an asynchronous array processing toolkit which transforms Python and NumPy operations into code which can be executed in parallel on HPC resources by mapping Python and NumPy functions and variables into a dependency tree executed by HPX, a general purpose, parallel, task-based runtime system written in C++. Phylanx additionally provides introspection and visualization capabilities for debugging and performance analysis. We have tested the foundations of our approach by comparing our implementation of widely used machine learning algorithms to accepted NumPy standards

    Relay: A New IR for Machine Learning Frameworks

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    Machine learning powers diverse services in industry including search, translation, recommendation systems, and security. The scale and importance of these models require that they be efficient, expressive, and portable across an array of heterogeneous hardware devices. These constraints are often at odds; in order to better accommodate them we propose a new high-level intermediate representation (IR) called Relay. Relay is being designed as a purely-functional, statically-typed language with the goal of balancing efficient compilation, expressiveness, and portability. We discuss the goals of Relay and highlight its important design constraints. Our prototype is part of the open source NNVM compiler framework, which powers Amazon's deep learning framework MxNet

    Advancing Culturally Responsive Evaluations for Boys and Men of Color

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    The field of culturally responsive evaluations (CRE) and comprehensive efforts to improve outcomes for boys and men of color (BMOC) are in their infancy. Yet attention to the development of the knowledge base and expansion of practice is needed due to the groundswell of interest in both areas in recent years. For instance, in 2014, President Obama established the My Brother's Keeper (MBK) Task Force. MBK is a coordinated federal effort with private philanthropic organizations and communities to address persistent opportunity gaps faced by BMOC and to ensure that all young people can reach their full potential. While BMOCs are the targets of many social programs and interventions, a dearth of high-quality culturally responsive evaluations exist on the effectiveness of various gender- and population-specific approaches for BMOCs to achieve measurable results
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