Dimension Sample Clauses

Dimension. Dimension hereby covenants that Dimension shall not, alone or in collaboration with a Third Party, (a) during the Research Term conduct clinical development of, and (b) during the term of this Agreement Commercialize, […***…], other than the Compounds/Vectors, GT Products and Licensed GT Products in accordance with the provisions of this Agreement.

Dimension. Family and context affective-emotional. Family and affective-emotional (present) context: home, partner, family, children… Specific Inherent Needs.

Dimension. Decision-making regarding migration (autonomous/family/other); Migration history.

Dimension. The supply teacher will be expected to work a normal teaching day (unless specific agreement is in place). The supply teacher will be required to cover class teacher supervision duties where appropriate. Example day; 0800 – arrive and prepare for the day 0830 – 1030 – Teaching 1030 – 1045 – Break 1045 – 1200 – Teaching 1200 – 1300 – Lunch 1300 – 1500 – Teaching 1500 – 1530 – final marking, notes for class teacher etc Line management of the supply teacher will be the Head of Primary. Direct advice and planning will be provided by the class teacher or other members of Primary staff.

Dimension. In laying out the centre line dimensions mentioned in the drawings or deduced there from and or as directed by the Engineer-in-charge, shall be strictly followed.

Dimension. Dimension Funding, LLC, Knightscope s third-party billing administrator, that enables Client to pay for Equipment and Services monthly instead of annually in advance.

Dimension. It allows valid combinations of dimensions. • Formula. It supports complex expressions based on XPath, which can be applied to instance documents to validate your information.

Dimension. (1) with Pins Date : 2008/12/3 Rev 0 (2) without Pins

Dimension. “Education” 1. Indicator “Academic qualifications” St111_2005 - 2013 Adult education level (tertiary, % of 25-64 years-olds) Source: OECD (xxxxx://xxxx.xxxx.xxx/eduatt/adult-education-level.htm) This indicator looks at adult education level as defined by the highest level of education completed by the 25-64 year-old population. There are three levels: below upper-secondary, upper secondary and tertiary education. Upper secondary education typically follows completion of lower secondary schooling. Lower secondary education completes provision of basic education, usually in a more subject-oriented way and with more specialised teachers. The indicator is measured as a percentage of same age population. % Variable Name Variable Label Additional information Unit St112_2012 - 2013 Employment by education level (tertiary, % of 25 - 64 year-olds) Source: OECD (xxxxx://xxxx.xxxx.xxx/emp/employment-by-education- level.htm) This indicator shows the employment rates of people according to their education levels: below upper secondary, upper secondary non-tertiary, or tertiary. The employment rate refers to the number of persons in employment as a percentage of the population of working age. The employed are defined as those who work for pay or profit for at least one hour a week, or who have a job but are temporarily not at work due to illness, leave or industrial action. This indicator measures the percentage of employed 25-64 year-olds among all 25-64 year-olds. % St113_2004 - 2012 Number of students at ISCED level 5-6 (persons in 1000s) Source: EUROSTAT (xxxx://xx.xxxxxx.xx/eurostat/tgm/xxxxx.xx?tab=table&init=1&language=en &pcode=tps00062&plugin=1) This table includes the total number of persons who are enrolled in tertiary education (including university and non-university studies) in the regular education system in each country. It corresponds to the target population for policy in higher education. It provides an indication of the number of persons who had access to tertiary education and are expected to complete their studies, contributing to an increase of the educational attainment level of the population in the country in case they continue to live and work in the country at the end of their studies. For more information see Metadata: xxxx://xx.xxxxxx.xx/eurostat/cache/metadata/en/educ_uoe_h_esms.htm Persons (in 1000)

Dimension reduction in Classification of EEG data by standard deterministic methods (Inria Leading the Task, M10 – M22) / ITT 1.1 Literature review and tests with traditional methods 4. 1.2 Classify the data using the features proposed in WP3 4.1.3 Apply the classifiers on the data acquired in WP2 Task 4.2 Optimisation of the tools of task1 using GA (Inria Leading the Task, M15 – M30) / ITT UNEX 2.1 Build a GA to optimize the parameters of the deterministic methods used in task 1 4.2.2 Perform parallel coding of this GA 4.2.3 Comparison with deterministic approaches Task 4.3 Build/use new classification methods using GP (WP1) (INESC-ID Leading the Task, M22 – M36) / ITT, UNEX, Inria Task Objectives Apply GP classification methods developed in WP1 combined with the features obtained in WP3. 4.3.1 Build a GP dedicated to this problem 4.3.2 Develop a Parallel implementation of this GP 4.3.3 Performance evaluation of each problem D 4.1 Classification of EEG signals with chaos-based features M24 Inria