scholarly journals Linear-time hierarchies for a functional language machine model

1998 ◽  
Vol 32 (1-3) ◽  
pp. 109-143 ◽  
Author(s):  
Eva Rose
Keyword(s):  
Linear Time ◽  
Functional Language ◽  
Machine Model

scholarly journals On the Comparison Complexity of the String Prefix-Matching Problem

1995 ◽  
Vol 2 (46) ◽  
Author(s):  
Dany Breslauer ◽  
Livio Colussi ◽  
Laura Toniolo
Keyword(s):  
Linear Time ◽  
String Matching ◽  
Random Access ◽  
Upper Bounds ◽  
Lower And Upper Bounds ◽  
Matching Problem ◽  
Machine Model ◽  
Worst Case ◽  
On Line ◽  
Sequential Comparison

In this paper we study the exact comparison complexity of the string<br />prefix-matching problem in the deterministic sequential comparison model<br />with equality tests. We derive almost tight lower and upper bounds on<br />the number of symbol comparisons required in the worst case by on-line<br />prefix-matching algorithms for any fixed pattern and variable text. Unlike<br />previous results on the comparison complexity of string-matching and<br />prefix-matching algorithms, our bounds are almost tight for any particular pattern.<br />We also consider the special case where the pattern and the text are the<br />same string. This problem, which we call the string self-prefix problem, is<br />similar to the pattern preprocessing step of the Knuth-Morris-Pratt string-matching<br />algorithm that is used in several comparison efficient string-matching<br />and prefix-matching algorithms, including in our new algorithm.<br />We obtain roughly tight lower and upper bounds on the number of symbol<br />comparisons required in the worst case by on-line self-prefix algorithms.<br />Our algorithms can be implemented in linear time and space in the<br />standard uniform-cost random-access-machine model.

ON THE NOTION OF LINEAR TIME COMPUTABILITY

1990 ◽  
Vol 01 (03) ◽  
pp. 295-307 ◽  
Author(s):  
ERICH GRÄDEL
Keyword(s):  
Linear Time ◽  
Random Access ◽  
Rational Numbers ◽  
Turing Machines ◽  
Machine Model ◽  
Class Time ◽  
Complete Problems ◽  
Time Bounds

To capture the informal concept of linear time computability, we propose and discuss the class TIME(n1+), consisting of all functions which are computable by a Successor RAM with exponent at most one; the exponent of a function is the infimum of all rational numbers r such that the function is computable in time O(nr). This class properly contains the class NLT (“Nearly Linear Time”)—proposed by Gurevich and Shelah—which contains all functions computable by a Successor RAM in time O(n(log n)k) for some k∈N. Both classes are very robust under changes of the underlying machine model: using the same time bounds they can be defined by Random Access Computers, Storage Modification Machines, Kolmogorov algorithms, Random Access Turing machines etc.; this distinguishes them from similar classes defined by ordinary Turing machines. However, we show that TIME(n1+) can be defined e.g. by multi-dimensional Turing machines or by Turing machines which can jump; this is probably not true for NLT. Furthermore we consider two notions of completeness for TIME(n1+) and construct complete problems. These problems are based on restrictions of Gurevich’s function logic for P.

scholarly journals Word pair classification during imagined speech using direct brain recordings

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Stephanie Martin ◽  
Peter Brunner ◽  
Iñaki Iturrate ◽  
José del R. Millán ◽  
Gerwin Schalk ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Linear Time ◽  
Neural Representation ◽  
Support Vector ◽  
Imagery Task ◽  
Machine Model ◽  
Speech Stimuli ◽  
Time Alignment ◽  
Classification Framework ◽  
Overt Speech

Abstract People that cannot communicate due to neurological disorders would benefit from an internal speech decoder. Here, we showed the ability to classify individual words during imagined speech from electrocorticographic signals. In a word imagery task, we used high gamma (70–150 Hz) time features with a support vector machine model to classify individual words from a pair of words. To account for temporal irregularities during speech production, we introduced a non-linear time alignment into the SVM kernel. Classification accuracy reached 88% in a two-class classification framework (50% chance level), and average classification accuracy across fifteen word-pairs was significant across five subjects (mean = 58%; p < 0.05). We also compared classification accuracy between imagined speech, overt speech and listening. As predicted, higher classification accuracy was obtained in the listening and overt speech conditions (mean = 89% and 86%, respectively; p < 0.0001), where speech stimuli were directly presented. The results provide evidence for a neural representation for imagined words in the temporal lobe, frontal lobe and sensorimotor cortex, consistent with previous findings in speech perception and production. These data represent a proof of concept study for basic decoding of speech imagery, and delineate a number of key challenges to usage of speech imagery neural representations for clinical applications.

Shrinking lambda expressions in linear time

1997 ◽  
Vol 7 (5) ◽  
pp. 515-540 ◽  
Author(s):  
ANDREW W. APPEL ◽  
TREVOR JIM
Keyword(s):  
Blow Up ◽  
Linear Time ◽  
Functional Language ◽  
Code Size ◽  
Variable Elimination ◽  
Elimination Constant ◽  
Final Code ◽  
Code Quality ◽  
Speculative Optimizations ◽  
Rewrite System

Functional-language compilers often perform optimizations based on beta and delta reduction. To avoid speculative optimizations that can blow up the code size, we might wish to use only shrinking reduction rules guaranteed to make the program smaller: these include dead-variable elimination, constant folding, and a restricted beta rule that inlines only functions that are called just once. The restricted beta rule leads to a shrinking rewrite system that has not previously been studied. We show some efficient normalization algorithms that are immediately useful in optimizing compilers; and we give a confluence proof for our system, showing that the choice of normalization algorithm does not affect final code quality.

Implementing lazy functional languages on stock hardware: the Spineless Tagless G-machine

1992 ◽  
Vol 2 (2) ◽  
pp. 127-202 ◽  
Author(s):  
Simon L. Peyton Jones
Keyword(s):  
Higher Order ◽  
Functional Languages ◽  
Functional Language ◽  
Design Issues ◽  
Machine Code ◽  
Abstract Machine ◽  
Machine Model ◽  
C Language ◽  
C Compiler ◽  
Short Section

AbstractThe Spineless Tagless G-machine is an abstract machine designed to support non-strict higher-order functional languages. This presentation of the machine falls into three parts. Firstly, we give a general discussion of the design issues involved in implementing non-strict functional languages. Next, we present the STG language, an austere but recognizably-functional language, which as well as a denotational meaning has a well-defined operational semantics. The STG language is the ‘abstract machine code’ for the Spineless Tagless G-machine. Lastly, we discuss the mapping of the STG language onto stock hardware. The success of an abstract machine model depends largely on how efficient this mapping can be made, though this topic is often relegated to a short section. Instead, we give a detailed discussion of the design issues and the choices we have made. Our principal target is the C language, treating the C compiler as a portable assembler.

Assessing Functional Language in School-Aged Children Using Language Sample Analysis

2020 ◽  
Vol 5 (3) ◽  
pp. 622-636
Author(s):  
John Heilmann ◽  
Alexander Tucci ◽  
Elena Plante ◽  
Jon F. Miller
Keyword(s):  
Computer Hardware ◽  
School Age Children ◽  
Functional Language ◽  
School Age ◽  
Clinical Use ◽  
Sample Analysis ◽  
Validity And Reliability ◽  
Language Sample Analysis ◽  
Accuracy And Repeatability ◽  
Language Sample

Purpose The goal of this clinical focus article is to illustrate how speech-language pathologists can document the functional language of school-age children using language sample analysis (LSA). Advances in computer hardware and software are detailed making LSA more accessible for clinical use. Method This clinical focus article illustrates how documenting school-age student's communicative functioning is central to comprehensive assessment and how using LSA can meet multiple needs within this assessment. LSA can document students' meaningful participation in their daily life through assessment of their language used during everyday tasks. The many advances in computerized LSA are detailed with a primary focus on the Systematic Analysis of Language Transcripts (Miller & Iglesias, 2019). The LSA process is reviewed detailing the steps necessary for computers to calculate word, morpheme, utterance, and discourse features of functional language. Conclusion These advances in computer technology and software development have made LSA clinically feasible through standardized elicitation and transcription methods that improve accuracy and repeatability. In addition to improved accuracy, validity, and reliability of LSA, databases of typical speakers to document status and automated report writing more than justify the time required. Software now provides many innovations that make LSA simpler and more accessible for clinical use. Supplemental Material https://doi.org/10.23641/asha.12456719

A Real Time Control Architecture for Continuously Managing Patients in a Care Unit

1995 ◽  
Vol 34 (05) ◽  
pp. 475-488
Author(s):  
B. Seroussi ◽  
J. F. Boisvieux ◽  
V. Morice
Keyword(s):  
Real Time ◽  
Linear Time ◽  
Treatment Plan ◽  
Control Architecture ◽  
Real Time Control ◽  
Time Representation ◽  
Time Control ◽  
Continuous Support ◽  
Definition Of ◽  
Computerized System

Abstract:The monitoring and treatment of patients in a care unit is a complex task in which even the most experienced clinicians can make errors. A hemato-oncology department in which patients undergo chemotherapy asked for a computerized system able to provide intelligent and continuous support in this task. One issue in building such a system is the definition of a control architecture able to manage, in real time, a treatment plan containing prescriptions and protocols in which temporal constraints are expressed in various ways, that is, which supervises the treatment, including controlling the timely execution of prescriptions and suggesting modifications to the plan according to the patient’s evolving condition. The system to solve these issues, called SEPIA, has to manage the dynamic, processes involved in patient care. Its role is to generate, in real time, commands for the patient’s care (execution of tests, administration of drugs) from a plan, and to monitor the patient’s state so that it may propose actions updating the plan. The necessity of an explicit time representation is shown. We propose using a linear time structure towards the past, with precise and absolute dates, open towards the future, and with imprecise and relative dates. Temporal relative scales are introduced to facilitate knowledge representation and access.

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