A simple mathematical definition of the 4-port model for pure Prolog is given. The model combines the intuition of ports with a compact representation of execution state. Forward and backward derivation steps are possible. The model satisfies a modularity claim, making it suitable for formal reasoning.
In contrast to the few specifications of the Byrd box, there are many more general models of pure (or even full) Prolog execution. Due to space limitations we mention here only some models, directly relevant to , and for a more comprehensive discussion see @cite_1 . Comparable to our work are the stack-based approaches. St "ark gives in @cite_3 , as a side issue, a simple operational semantics of pure logic programming. A state of execution is a stack of frame stacks, where each frame consists of a goal (ancestor) and an environment. In comparison, our state of execution consists of exactly one environment and one ancestor stack . The seminal paper of Jones and Mycroft @cite_10 was the first to present a stack-based model of execution, applicable to pure Prolog with cut added. It uses a sequence of frames. In these stack-based approaches (including our previous attempt @cite_1 ), there is no , it is not possible to abstract the execution of a subgoal.
@cite_10 @cite_1 @cite_3
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This article describes the final solution of team monkeytyping, who finished in second place in the YouTube-8M video understanding challenge. The dataset used in this challenge is a large-scale benchmark for multi-label video classification. We extend the work in [1] and propose several improvements for frame sequence modeling. We propose a network structure called Chaining that can better capture the interactions between labels. Also, we report our approaches in dealing with multi-scale information and attention pooling. In addition, We find that using the output of model ensemble as a side target in training can boost single model performance. We report our experiments in bagging, boosting, cascade, and stacking, and propose a stacking algorithm called attention weighted stacking. Our final submission is an ensemble that consists of 74 sub models, all of which are listed in the appendix. Symbolic execution extends concrete execution by allowing symbolic input data and then exploring all feasible execution paths. It has been defined and used in the context of many different programming languages and paradigms. A symbolic execution engine is at the heart of many program analysis and transformation techniques, like partial evaluation, test case generation or model checking, to name a few. Despite its relevance, traditional symbolic execution also suffers from several drawbacks. For instance, the search space is usually huge (often infinite) even for the simplest programs. Also, symbolic execution generally computes an overapproximation of the concrete execution space, so that false positives may occur. In this paper, we propose the use of a variant of symbolic execution, called concolic execution, for test case generation in Prolog. Our technique aims at full statement coverage. We argue that this technique computes an underapproximation of the concrete execution space (thus avoiding false positives) and scales up better to medium and large Prolog applications. In this paper we introduce a model for a wide class of computational systems, whose behaviour can be described by certain rewriting rules. We gathered our inspiration both from the world of term rewriting, in particular from the rewriting logic framework Mes92 , and of concurrency theory: among the others, the structured operational semantics Plo81 , the context systems LX90 and the structured transition systems CM92 approaches. Our model recollects many properties of these sources: first, it provides a compositional way to describe both the states and the sequences of transitions performed by a given system, stressing their distributed nature. Second, a suitable notion of typed proof allows to take into account also those formalisms relying on the notions of synchronization and side-effects to determine the actual behaviour of a system. Finally, an equivalence relation over sequences of transitions is defined, equipping the system under analysis with a concurrent semantics, where each equivalence class denotes a family of computationally equivalent'''' behaviours, intended to correspond to the execution of the same set of (causally unrelated) events. As a further abstraction step, our model is conveniently represented using double-categories: its operational semantics is recovered with a free construction, by means of a suitable adjunction. Recent work has demonstrated the benefits of adopting a fully probabilistic SLAM approach in sequential motion and structure estimation from an image sequence. Unlike standard Structure from Motion (SFM) methods, this 'monocular SLAM' approach is able to achieve drift-free estimation with high frame-rate real-time operation, particularly benefitting from highly efficient active feature search, map management and mismatch rejection. A consistent thread in this research on real-time monocular SLAM has been to reduce the assumptions required. In this paper we move towards the logical conclusion of this direction by implementing a fully Bayesian Interacting Multiple Models (IMM) framework which can switch automatically between parameter sets in a dimensionless formulation of monocular SLAM. Remarkably, our approach of full sequential probability propagation means that there is no need for penalty terms to achieve the Occam property of favouring simpler models - this arises automatically. We successfully tackle the known stiffness in on-the-fly monocular SLAM start up without known patterns in the scene. The search regions for matches are also reduced in size with respect to single model EKF increasing the rejection of spurious matches. We demonstrate our method with results on a complex real image sequence with varied motion.