Partially strict non-recursive data types

1993 ◽  
Vol 3 (2) ◽  
pp. 191-215 ◽  
Author(s):  
Eric Nöcker ◽  
Sjaak Smetsers
Keyword(s):  
Data Type ◽  
Functional Languages ◽  
Data Types ◽  
Composite Object ◽  
Space And Time ◽  
Input And Output ◽  
The Individual ◽  
Recursive Data

AbstractValues belonging to lazy data types have the advantage that sub-components can be accessed without evaluating the values as a whole: unneeded components remain unevaluated. A disadvantage is that often a large amount of space and time is required to handle lazy data types properly. Many special constructor cells are needed to ‘glue’ the individual parts of a composite object together and to store it in the heap. We present a way of representing data in functional languages which makes these special constructor cells superfluous. In some cases, no heap at all is needed to store this data. To make this possible, we introduce a new kind of data type: (partially) strict non-recursive data types. The main advantage of these types is that an efficient call-by-value mechanism can be used to pass arguments. A restrictive subclass of (partially) strict non-recursive data types, partially strict tuples, is treated more comprehensively. We also give examples of important classes of applications. In particular, we show how partially strict tuples can be used to define very efficient input and output primitives. Measurements of applications written in Concurrent Clean which exploit partially strict tuples have shown that speedups of 2 to 3 times are reasonable. Moreover, much less heap space is required when partially strict tuples are used.

scholarly journals Animal movement analysis through residence in space and time

2016 ◽  
Author(s):  
Leigh G Torres ◽  
Rachael A. Orben ◽  
Irina Tolkova ◽  
David R Thompson
Keyword(s):  
Animal Movement ◽  
Search Time ◽  
Movement Ecology ◽  
Movement Analysis ◽  
Individual Variability ◽  
Behavior Patterns ◽  
Data Types ◽  
Space And Time ◽  
Movement Data ◽  
The Individual

Identification and classification of behavior states in animal movement data can be complex, temporally biased, time-intensive, scale-dependent, and unstandardized across studies and taxa. Large movement datasets are increasingly common and there is a need for efficient methods of data exploration that adjust to the individual variability of each track. We present the Residence in Space and Time (RST) method to classify behavior patterns in movement data based on the concept that behavior states can be partitioned by the amount of space and time occupied in an area of constant scale. Using normalized values of Residence Time and Residence Distance within a constant search radius, RST is able to differentiate behavior patterns that are distance-intensive (e.g., area restricted search), time-intensive (e.g., rest), and transit (short time and distance). We use grey-headed albatross (Thalassarche chrysostoma) GPS tracks to demonstrate RST’s ability to classify behavior patterns and adjust to the inherent scale and individuality of each track. Next, we evaluate RST’s ability to discriminate between behavior states relative to other classical movement metrics. We then sub-sample albatross track data to illustrate RST’s response to less temporally resolved data. Finally, we evaluate RST’s performance using datasets from four taxa with diverse ecology, functional scales, ecosystems, and data-types. We conclude that RST is a robust, rapid, and flexible method for detailed exploratory analysis and meta-analyses of behavioral states in animal movement data based on its ability to integrate distance and time measurements into one descriptive metric of behavior groupings. Given the increasing amount of animal movement data collected, it is timely and useful to implement a consistent metric of behavior classification to enable efficient and comparative analyses. Overall, the application of RST to objectively explore and compare behavior patterns in movement data can enhance our fine- and broad- scale understanding of animal movement ecology.

scholarly journals Animal movement analysis through residence in space and time

2016 ◽  
Author(s):  
Leigh G Torres ◽  
Rachael A. Orben ◽  
Irina Tolkova ◽  
David R Thompson
Keyword(s):  
Animal Movement ◽  
Search Time ◽  
Movement Ecology ◽  
Movement Analysis ◽  
Individual Variability ◽  
Behavior Patterns ◽  
Data Types ◽  
Space And Time ◽  
Movement Data ◽  
The Individual

Identification and classification of behavior states in animal movement data can be complex, temporally biased, time-intensive, scale-dependent, and unstandardized across studies and taxa. Large movement datasets are increasingly common and there is a need for efficient methods of data exploration that adjust to the individual variability of each track. We present the Residence in Space and Time (RST) method to classify behavior patterns in movement data based on the concept that behavior states can be partitioned by the amount of space and time occupied in an area of constant scale. Using normalized values of Residence Time and Residence Distance within a constant search radius, RST is able to differentiate behavior patterns that are distance-intensive (e.g., area restricted search), time-intensive (e.g., rest), and transit (short time and distance). We use grey-headed albatross (Thalassarche chrysostoma) GPS tracks to demonstrate RST’s ability to classify behavior patterns and adjust to the inherent scale and individuality of each track. Next, we evaluate RST’s ability to discriminate between behavior states relative to other classical movement metrics. We then sub-sample albatross track data to illustrate RST’s response to less temporally resolved data. Finally, we evaluate RST’s performance using datasets from four taxa with diverse ecology, functional scales, ecosystems, and data-types. We conclude that RST is a robust, rapid, and flexible method for detailed exploratory analysis and meta-analyses of behavioral states in animal movement data based on its ability to integrate distance and time measurements into one descriptive metric of behavior groupings. Given the increasing amount of animal movement data collected, it is timely and useful to implement a consistent metric of behavior classification to enable efficient and comparative analyses. Overall, the application of RST to objectively explore and compare behavior patterns in movement data can enhance our fine- and broad- scale understanding of animal movement ecology.

scholarly journals Omics in a Digital World: The Role of Bioinformatics in Providing New Insights Into Human Aging

2021 ◽  
Vol 12 ◽  
Author(s):  
Serena Dato ◽  
Paolina Crocco ◽  
Nicola Rambaldi Migliore ◽  
Francesco Lescai
Keyword(s):  
High Throughput ◽  
Aging Process ◽  
Single Cell Analysis ◽  
Physiological Age ◽  
Data Types ◽  
Aging Research ◽  
Digital World ◽  
Age Related ◽  
Critical Issues ◽  
The Individual

BackgroundAging is a complex phenotype influenced by a combination of genetic and environmental factors. Although many studies addressed its cellular and physiological age-related changes, the molecular causes of aging remain undetermined. Considering the biological complexity and heterogeneity of the aging process, it is now clear that full understanding of mechanisms underlying aging can only be achieved through the integration of different data types and sources, and with new computational methods capable to achieve such integration.Recent AdvancesIn this review, we show that an omics vision of the age-dependent changes occurring as the individual ages can provide researchers with new opportunities to understand the mechanisms of aging. Combining results from single-cell analysis with systems biology tools would allow building interaction networks and investigate how these networks are perturbed during aging and disease. The development of high-throughput technologies such as next-generation sequencing, proteomics, metabolomics, able to investigate different biological markers and to monitor them simultaneously during the aging process with high accuracy and specificity, represents a unique opportunity offered to biogerontologists today.Critical IssuesAlthough the capacity to produce big data drastically increased over the years, integration, interpretation and sharing of high-throughput data remain major challenges. In this paper we present a survey of the emerging omics approaches in aging research and provide a large collection of datasets and databases as a useful resource for the scientific community to identify causes of aging. We discuss their peculiarities, emphasizing the need for the development of methods focused on the integration of different data types.Future DirectionsWe critically review the contribution of bioinformatics into the omics of aging research, and we propose a few recommendations to boost collaborations and produce new insights. We believe that significant advancements can be achieved by following major developments in bioinformatics, investing in diversity, data sharing and community-driven portable bioinformatics methods. We also argue in favor of more engagement and participation, and we highlight the benefits of new collaborations along these lines. This review aims at being a useful resource for many researchers in the field, and a call for new partnerships in aging research.

Hydraulic tomography using joint inversion of head and flux data 

2021 ◽  
Author(s):  
Behzad Pouladiborj ◽  
Olivier Bour ◽  
Niklas Linde ◽  
Laurent Longuevergne
Keyword(s):  
Hydraulic Conductivity ◽  
Joint Inversion ◽  
Data Type ◽  
Data Types ◽  
Hydraulic Tomography ◽  
Flux Data ◽  
Planning And Design ◽  
Constant Rate ◽  
Resolution Analysis ◽  
Constant Head

<p>Hydraulic tomography is a state of the art method for inferring hydraulic conductivity fields using head data. Here, a numerical model is used to simulate a steady-state hydraulic tomography experiment by assuming a Gaussian hydraulic conductivity field (also constant storativity) and generating the head and flux data in different observation points. We employed geostatistical inversion using head and flux data individually and jointly to better understand the relative merits of each data type. For the typical case of a small number of observation points, we find that flux data provide a better resolved hydraulic conductivity field compared to head data when considering data with similar signal-to-noise ratios. In the case of a high number of observation points, we find the estimated fields to be of similar quality regardless of the data type. A resolution analysis for a small number of observations reveals that head data averages over a broader region than flux data, and flux data can better resolve the hydraulic conductivity field than head data. The inversions' performance depends on borehole boundary conditions, with the best performing setting for flux data and head data are constant head and constant rate, respectively. However, the joint inversion results of both data types are insensitive to the borehole boundary type. Considering the same number of observations, the joint inversion of head and flux data does not offer advantages over individual inversions. By increasing the hydraulic conductivity field variance, we find that the resulting increased non-linearity makes it more challenging to recover high-quality estimates of the reference hydraulic conductivity field. Our findings would be useful for future planning and design of hydraulic tomography tests comprising the flux and head data.</p>

Can we build a grammar on the basis of judgments?

2020 ◽  
pp. 165-188
Author(s):  
Sam Featherston
Keyword(s):  
Small Group ◽  
Data Type ◽  
Theory Building ◽  
Final Part ◽  
Data Types ◽  
Ongoing Debate ◽  
Single Person

This chapter is a contribution to the ongoing debate about the necessary quality of the database for theory building in research on syntax. In particular, the focus is upon introspective judgments as a data type or group of data types. In the first part, the chapter lays out some of the evidence for the view that the judgments of a single person or of a small group of people are much less valid than the judgments of a group. In the second part, the chapter criticizes what the author takes to be overstatements and overgeneralizations of findings by Sprouse, Almeida, and Schütze that are sometimes viewed as vindicating an “armchair method” in linguistics. The final part of the chapter attempts to sketch out a productive route forward that empirically grounded syntax could take.

Using Virtual Mobility to Alleviate Aspects of Social Exclusion

2005 ◽  
pp. 715-722 ◽  
Author(s):  
Susan Kenyon
Keyword(s):  
Social Network ◽  
Social Exclusion ◽  
Social Participation ◽  
Space And Time ◽  
Virtual Mobility ◽  
The Individual ◽  
The Right ◽  
Network Goods

People’s ability to participate in the activities that are necessary to ensure their economic, political and social participation in the society in which they live is dependent upon the accessibility of the activities. Accessibility has traditionally been perceived as a function of the space, or distance, between the origin of the individual (or community) and the destination of the activity¾the opportunity, service, social network, goods¾alongside the time that it takes to cross this space. Thus, accessibility is dependent upon the individual’s ability to overcome space and time barriers, allowing them to reach the right place or person, at the right time¾and, of course, upon the availability to them of adequate resources to do this (Couclelis, 2000)1.

Politeness in Pragmatics

2017 ◽  
Author(s):  
Dániel Z. Kádár
Keyword(s):  
Cultural Values ◽  
Interpersonal Relationships ◽  
Social Action ◽  
Social Practice ◽  
Positive Emotions ◽  
Third Wave ◽  
Data Types ◽  
The Individual ◽  
Linguistic Behavior ◽  
Second Wave

Politeness comprises linguistic and non-linguistic behavior through which people indicate that they take others’ feelings of how they should be treated into account. Politeness comes into operation through evaluative moments—the interactants’ (or other participants’) assessments of interactional behavior—and it is a key interpersonal interactional phenomenon, due to the fact that it helps people to build up and maintain interpersonal relationships. The operation of politeness involves valences: when people behave in what they perceive as polite in a given situation, they attempt to enactment shared values with others, hence triggering positive emotions. The interactants use valenced categories as a benchmark for their production and evaluation of language and behavior, and valence reflects the participants’ perceived moral order of an interactional context/event, that is, their perceptions of ‘how things should be’ in a given situation. Thus, the examination of politeness reveals information about the broader in-group, social, and cultural values that underlie the productive and evaluative interactional behavior of individuals. As politeness is a social action that consists of both linguistic and non-linguistic elements and that embodies a social practice, the research of politeness also provides insights into the social practices that surround individual language use. Pragmatics-based research on politeness started in the late 1970s and early 1980s, and has become one of the most popular areas in pragmatics. The field has undergone various methodological and theoretical changes. These include the “first wave” of politeness research, in the course of which researchers either attempted to model politeness across languages and cultures by using universal frameworks, or engaged in culture-specific criticism of such frameworks. In the “second wave” of politeness research, researchers attempted to approach politeness as an individualistic, and often idiosyncratic, interactionally co-constructed phenomenon. A key argument of the second wave is that politeness can only be studied at the micro-level of the individual, and so it may be overambitious to attempt to model this phenomenon across languages and cultures. In the “third wave” of politeness research, scholars attempt to model politeness across languages and cultures, without compromising the endeavour of examining politeness as an interactionally co-constructed phenomenon. Key phenomena studied in politeness research include, among others, impoliteness, intercultural interaction, cross-cultural similarities and differences of politeness, the gendered characteristics of politeness behavior, and convention and ritual. Politeness research is a multidisciplinary field that is engaged in the examination of a wide variety of data types.

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