Data Structures Sample Clauses

The 'Data Structures' clause defines the organization and format in which data is stored, accessed, and manipulated within a system or agreement. It typically outlines the types of data entities involved, their relationships, and any rules governing their structure, such as fields, data types, or hierarchies. For example, it may specify how user information is stored in tables or how records are linked in a database. This clause ensures consistency and clarity in data handling, reducing errors and facilitating efficient data management throughout the system or contractual relationship.
Data Structures. As observed by Xxxxx et al. [34], register automata with input and output events can be used to represent semantic interfaces of simple data structures such as stacks, queues, and FIFO-sets with fixed capacities. Since they are parametrized by their capacity, these data structures provide excellent benchmarks for model learning tools, see e.g., [3].
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Data Structures. 4.1 Overview
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Data Structures. The basic building blocks for communication across the interface are the Endpoint Descriptor (ED) and Transfer Descriptor (TD). The Host Controller Driver assigns an Endpoint Descriptor to each endpoint in the system. The Endpoint Descriptor contains the information necessary for the Host Controller to communicate with the endpoint. The fields include the maximum packet size, the endpoint address, the speed of the endpoint, and the direction of data flow. Endpoint Descriptors are linked in a list. A queue of Transfer Descriptors is linked to the Endpoint Descriptor for the specific endpoint. The Transfer Descriptor contains the information necessary to describe the data packets to be transferred. The fields include data toggle information, shared memory buffer location, and completion status codes. Each Transfer Descriptor contains information that describes one or more data packets. The data buffer for each Transfer Descriptor ranges in size from 0 to 8192 bytes with a maximum of one physical page crossing. Transfer Descriptors are linked in a queue: the first one queued is the first one processed. Each data transfer type has its own linked list of Endpoint Descriptors to be processed. Figure 3- 3, Typical List Structure, is a representation of the data structure relationships. Head Ptr ED ED ED ED TD TD TD TD TD TD TD The head pointers to the bulk and control Endpoint Descriptor lists are maintained within the operational registers in the HC. The Host Controller Driver initializes these pointers prior to the Host Controller gaining access to them. Should these pointers need to be updated, the Host Controller Driver may need to halt the Host Controller from processing the specific list, update the pointer, then re-enable the HC. The head pointers to the interrupt Endpoint Descriptor lists are maintained within the HCCA. There is no separate head pointer for isochronous transfers. The first isochronous Endpoint Descriptor simply links to the last interrupt Endpoint Descriptor. There are 32 interrupt head pointers. The head pointer used for a particular frame is determined by using the last 5 bits of the Frame Counter as an offset into the interrupt array within the HCCA. The interrupt Endpoint Descriptors are organized into a tree structure with the head pointers being the leaf nodes. The desired polling rate of an Interrupt Endpoint is achieved by scheduling the Endpoint Descriptor at the appropriate depth in the tree. The higher the polling rate, the clo...
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Data Structures. A key difference between AML and AgreementMaker is that in the former on- tologies are represented exclusively by internal data structures, whereas in the latter internal data structures are used in addition to the Jena OntModel. While building the internal data structures from the OntModel takes time, if those structures are designed with the efficiency of the matching process in mind, they will reduce the total processing time considerably. Furthermore, the internal data structures take up less memory than the OntModel, so in not keeping the latter in memory, we effectively increase the available memory for the matching process. Last but not least, this setup means that AML’s ontology matching module is not tied to Jena or any specific ontology-reading API. Thus AML can work with any ontology-reading API by simply changing the ontology loading module. Lexicon is a data structure that links each class in an ontology with its “names” (i.e., local names, labels, and synonyms) and the provenance of those names (i.e., whether they come from a local name or label, or from which type of syn- onym property statement). While an equivalent data structure already existed in AgreementMaker, it was built after the ontology loading process and only used by some matching algorithms. In AML, the Lexicon is a primary data structure used by all matching algorithms that require lexical information. A novel aspect that was incorporated in the AML Lexicon was a system of weights to reflect the reliability of each provenance. For instance, synonyms ob- tained from hasExactSynonym statements are in principle more reliable than synonyms obtained from hasRelatedSynonym statements, as they should be closer in meaning to the concept described by a class. Thus, local names were given a weight of 1.0, labels a weight of 0.95, exact synonyms a weight of 0.9 and other synonyms a weight of 0.85. These weights may be used by any matching algorithm that uses the Lexicon. The internal structure of the Lexicon consists on two MultiMaps (which are HashMaps of HashMaps) containing classes, names and provenances, with one having the class as key and the other having the name as key. Thus, the Lexicon can be queried by both class and name at virtually no computational cost. RelationshipMap is a data structure that links each class to the classes related to it through is a or part of relationships or disjoint clauses. It complements the Lexicon, and is a very efficient alternative to the node-based tre...
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Data Structures. The IG model has an absorbing barrier, and in Chapter 2 the practical illustration modelled the status of threshold sustained remission, to encounter the threshold and remain in a state of remission for the remaining duration of the assessment period. Sustained remission is a clinically plausible and relevant status; further in some scenarios it may be inappropriate to model the first fall in symptoms if the status was not maintained. However, here I focus on the formulation for the status being defined by the first time to reach the threshold. One of the key advantages of the growth curve modelling approach is that it does not have to assume an absorbing barrier. An approach that is more theoretically plausible, in the context of depression, would allow for future relapse and remission. Building on the notation used in previous chapters for the comparative survival models. The data is formed from a set of repeated measures, 𝑌𝑖𝑡 denotes the outcome measured on individual 𝑖 at time 𝑡𝑖𝑡. The index 𝑖 = 1, 2, … , 𝑁 is for patients and 𝑡 = 1, 2, . . . , 𝑛 is for observations within an individual. The failure event 𝑑 = 1 is defined as when the repeated measured response variable 𝑌 first crosses the threshold 𝜃 and for those observations that do not encounter the threshold the survival time is censored, 𝑑 = 0 . Time to remission, 𝑇, is the time to first remission and I model the probability of first remission. I assume the assessments are taken following a protocol of predefined measurement times, 𝑡𝑖𝑡 = 𝑡𝑡 , and thus independent of 𝑑𝑖 and the observed scores.
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Data Structures. Abstract data structures 1. Vector An ordered collection of numbers. The ith element of vector ˙A is denoted as Ai, 0 ≤ i < |˙A|, where |˙A| is the number of elements in vector ˙A (the size of vector ˙A).
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