scholarly journals Bottom-Up Parser: Look-Ahead LR Parser

2019 ◽  
Vol 8 (3) ◽  
pp. 2406-2410
Keyword(s):  
Second Step ◽  
Minimization Algorithm ◽  
Machine Code ◽  
Top Down ◽  
State Problem ◽  
Bottom Up ◽  
Look Ahead ◽  
High Level ◽  
The Given ◽  
Lexical Analyzer

Compiler is used for the purpose of converting high level code to machine code. For doing this procedure we have six steps. On these steps the syntax analyses is the second step of compiler. The lexical analyzer produce token in the output. The tokens are used as input to syntax analyzer. Syntax analyzer performs parsing operation. The parsing can be used for deriving the string from the given grammar called as derivation. It depend upon how derivation will be performed either top down or bottom up. The bottom up parsers LR (Left-to-right), SLR (simple LR) has some conflicts. To remove these conflicts we use LALR (Look ahead LR parser). The conflicts are available if the state contains minimum two or more productions. If there is one shift operation in state and other one is reduce operation it means that shift-reduce operation at the same time. Then this state is called as inadequate state. This Inadequate state problem is solved in LALR parser. Other problem with other parsers is that they have more states as compared to LALR parser. So cost will be high. But in LALR parser minimum states used and cost will automatically be reduced. LALR is also called as Minimization algorithm of CLR (Canonical LR parser).

CSim 2

2021 ◽  
Vol 43 (1) ◽  
pp. 1-46
Author(s):  
David Sanan ◽  
Yongwang Zhao ◽  
Shang-Wei Lin ◽  
Liu Yang
Keyword(s):  
Programming Languages ◽  
Concurrent Systems ◽  
Theorem Prover ◽  
Compositional Techniques ◽  
Machine Code ◽  
Top Down ◽  
Hol Theorem Prover ◽  
High Level ◽  
High Degree

To make feasible and scalable the verification of large and complex concurrent systems, it is necessary the use of compositional techniques even at the highest abstraction layers. When focusing on the lowest software abstraction layers, such as the implementation or the machine code, the high level of detail of those layers makes the direct verification of properties very difficult and expensive. It is therefore essential to use techniques allowing to simplify the verification on these layers. One technique to tackle this challenge is top-down verification where by means of simulation properties verified on top layers (representing abstract specifications of a system) are propagated down to the lowest layers (that are an implementation of the top layers). There is no need to say that simulation of concurrent systems implies a greater level of complexity, and having compositional techniques to check simulation between layers is also desirable when seeking for both feasibility and scalability of the refinement verification. In this article, we present CSim 2 a (compositional) rely-guarantee-based framework for the top-down verification of complex concurrent systems in the Isabelle/HOL theorem prover. CSim 2 uses CSimpl, a language with a high degree of expressiveness designed for the specification of concurrent programs. Thanks to its expressibility, CSimpl is able to model many of the features found in real world programming languages like exceptions, assertions, and procedures. CSim 2 provides a framework for the verification of rely-guarantee properties to compositionally reason on CSimpl specifications. Focusing on top-down verification, CSim 2 provides a simulation-based framework for the preservation of CSimpl rely-guarantee properties from specifications to implementations. By using the simulation framework, properties proven on the top layers (abstract specifications) are compositionally propagated down to the lowest layers (source or machine code) in each concurrent component of the system. Finally, we show the usability of CSim 2 by running a case study over two CSimpl specifications of an Arinc-653 communication service. In this case study, we prove a complex property on a specification, and we use CSim 2 to preserve the property on lower abstraction layers.

scholarly journals A Congruence-Based Perspective on Finite Tree Automata

2022 ◽  
Vol 184 (1) ◽  
pp. 1-47
Author(s):  
Pierre Ganty ◽  
Elena Gutiérrez ◽  
Pedro Valero
Keyword(s):  
Expressive Power ◽  
Tree Automata ◽  
Minimization Algorithm ◽  
Top Down ◽  
Finite Tree ◽  
Bottom Up ◽  
Original Result

We provide new insights on the determinization and minimization of tree automata using congruences on trees. From this perspective, we study a Brzozowski’s style minimization algorithm for tree automata. First, we prove correct this method relying on the following fact: when the automata-based and the language-based congruences coincide, determinizing the automaton yields the minimal one. Such automata-based congruences, in the case of word automata, are defined using pre and post operators. Now we extend these operators to tree automata, a task that is particularly challenging due to the reduced expressive power of deterministic top-down (or equivalently co-deterministic bottom-up) automata. We leverage further our framework to offer an extension of the original result by Brzozowski for word automata.

Orphan Implementations of Public Policy for Conflict-Oriented Issues

2021 ◽  
Vol 21 (2) ◽  
pp. 43-63
Author(s):  
Shulamith Gertel Groome
Keyword(s):  
Public Policy ◽  
Decision Making ◽  
Public Interest ◽  
Policy Process ◽  
Frame Of Reference ◽  
Limited Resources ◽  
Top Down ◽  
Bottom Up ◽  
Field Level ◽  
High Level

This paper aims to broaden our understanding of public policy characterized by issues of non-consensus. The idea of flexible, independent administrative decision-making for a conflict-oriented policy-type is addressed in terms of chronological constructions of policy process. Distributions of limited resources are a source of public contention likely to draw ambiguous high-level policy decisions that lack practical administrative directives. Conflicting institutional, professional and stakeholder influences, at various levels of policy processes, illuminate circumstances fostering implementations incongruent with politically motivated macro-declarations. Yet, this does not necessarily represent failed policy. A reevaluation of administrative systems, by critical deconstruction of the dominant top-down discourse, provides a frame of reference for valid divergent implementations. A conceptual progression from field-level interpretation and adaptation of macro policy, initiatory orphan implementations emerge as policy itself. This revised bottom-up modality of the policy process implies a working balance of combined outputs, providing equitable outcome to serve largescale public interest.

A minimization algorithm for fuzzy top-down tree automata over lattices

2020 ◽  
pp. 1-10
Author(s):  
M. Ghorani ◽  
S. Garhwal
Keyword(s):  
Minimization Problem ◽  
Time Complexity ◽  
Tree Automaton ◽  
Tree Automata ◽  
Minimization Algorithm ◽  
Top Down ◽  
Minimal Form ◽  
Minimization Algorithms ◽  
The Given

In this paper, we study fuzzy top-down tree automata over lattices ( LTA s , for short). The purpose of this contribution is to investigate the minimization problem for LTA s . We first define the concept of statewise equivalence between two LTA s . Thereafter, we show the existence of the statewise minimal form for an LTA . To this end, we find a statewise irreducible LTA which is equivalent to a given LTA . Then, we provide an algorithm to find the statewise minimal LTA and by a theorem, we show that the output statewise minimal LTA is statewise equivalent to the given input. Moreover, we compute the time complexity of the given algorithm. The proposed algorithm can be applied to any given LTA and, unlike some minimization algorithms given in the literature, the input doesn’t need to be a complete, deterministic, or reduced lattice-valued tree automaton. Finally, we provide some examples to show the efficiency of the presented algorithm.

scholarly journals Bottom-up Retinotopic Organization Supports Top-down Mental Imagery

2013 ◽  
Vol 7 (1) ◽  
pp. 58-67 ◽  
Author(s):  
Ruey-Song Huang ◽  
Martin I. Sereno
Keyword(s):  
Premotor Cortex ◽  
Brain Regions ◽  
Retrosplenial Cortex ◽  
Wide Field ◽  
Top Down ◽  
Bottom Up ◽  
Retinotopic Maps ◽  
Upper Visual Field ◽  
Retinotopic Organization ◽  
High Level

Finding a path between locations is a routine task in daily life. Mental navigation is often used to plan a route to a destination that is not visible from the current location. We first used functional magnetic resonance imaging (fMRI) and surface-based averaging methods to find high-level brain regions involved in imagined navigation between locations in a building very familiar to each participant. This revealed a mental navigation network that includes the precuneus, retrosplenial cortex (RSC), parahippocampal place area (PPA), occipital place area (OPA), supplementary motor area (SMA), premotor cortex, and areas along the medial and anterior intraparietal sulcus. We then visualized retinotopic maps in the entire cortex using wide-field, natural scene stimuli in a separate set of fMRI experiments. This revealed five distinct visual streams or ‘fingers’ that extend anteriorly into middle temporal, superior parietal, medial parietal, retrosplenial and ventral occipitotemporal cortex. By using spherical morphing to overlap these two data sets, we showed that the mental navigation network primarily occupies areas that also contain retinotopic maps. Specifically, scene-selective regions RSC, PPA and OPA have a common emphasis on the far periphery of the upper visual field. These results suggest that bottom-up retinotopic organization may help to efficiently encode scene and location information in an eye-centered reference frame for top-down, internally generated mental navigation. This study pushes the border of visual cortex further anterior than was initially expected.

The Power of Weighted Regularity-Preserving Multi Bottom-Up Tree Transducers

2015 ◽  
Vol 26 (07) ◽  
pp. 987-1005 ◽  
Author(s):  
Andreas Maletti
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Expressive Power ◽  
Proof Technique ◽  
Top Down ◽  
Bottom Up ◽  
Tree Transducer ◽  
Negative Side ◽  
Look Ahead ◽  
Tree Transducers

The expressive power of regularity-preserving [Formula: see text]-free weighted linear multi bottom-up tree transducers is investigated. These models have very attractive theoretical and algorithmic properties, but (especially in the weighted setting) their expressive power is not well understood. Despite the regularity-preserving restriction, their power still exceeds that of composition chains of [Formula: see text]-free weighted linear extended top-down tree transducers with regular look-ahead. The latter devices are a natural super-class of weighted synchronous tree substitution grammars, which are commonly used in syntax-based statistical machine translation. In particular, the linguistically motivated discontinuous transformation of topicalization can be modeled by such multi bottom-up tree transducers, whereas the mentioned composition chains cannot implement it. On the negative side, the inverse of topicalization cannot be implemented by any such multi bottom-up tree transducer, which confirms their bottom-up nature (and non-closure under inverses). An interesting, promising, and widely applicable proof technique is used to prove these statements.

A bottom-up characterization of deterministic top-down tree transducers with regular look-ahead

2004 ◽  
Vol 91 (2) ◽  
pp. 57-67 ◽  
Author(s):  
Zoltán Fülöp ◽  
Armin Kühnemann ◽  
Heiko Vogler
Keyword(s):  
Top Down ◽  
Bottom Up ◽  
Look Ahead ◽  
Tree Transducers

scholarly journals Bottom-up and top-down influences at untrained conditions determine perceptual learning specificity and transfer

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Ying-Zi Xiong ◽  
Jun-Yun Zhang ◽  
Cong Yu
Keyword(s):  
Perceptual Learning ◽  
Top Down ◽  
Transfer Condition ◽  
Bottom Up ◽  
Visual Neurons ◽  
Continuous Flash Suppression ◽  
Visual Areas ◽  
Suppression Technique ◽  
High Level ◽  
Additional Exposure

Perceptual learning is often orientation and location specific, which may indicate neuronal plasticity in early visual areas. However, learning specificity diminishes with additional exposure of the transfer orientation or location via irrelevant tasks, suggesting that the specificity is related to untrained conditions, likely because neurons representing untrained conditions are neither bottom-up stimulated nor top-down attended during training. To demonstrate these top-down and bottom-up contributions, we applied a “continuous flash suppression” technique to suppress the exposure stimulus into sub-consciousness, and with additional manipulations to achieve pure bottom-up stimulation or top-down attention with the transfer condition. We found that either bottom-up or top-down influences enabled significant transfer of orientation and Vernier discrimination learning. These results suggest that learning specificity may result from under-activations of untrained visual neurons due to insufficient bottom-up stimulation and/or top-down attention during training. High-level perceptual learning thus may not functionally connect to these neurons for learning transfer.

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