Experiment Design Sample Clauses

Experiment Design. The goal is to explore how to leverage FLAME media services to enable a user to experience a branching, interactive narrative within a branching city environment as depicted in Figure 7. This section describes several of the system capabilities influencing the design of the validation experiment.
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Experiment Design. Gnome Trader involves several novel game mechanics which can benefit from a high performance underlying network. Played by users during their daily commutes, Gnome Trader requires the network to adapt to the variations of positions and densities of players. Moreover, these moving players request access to large data such as 3D models and expect to experience an uninterrupted gameplay. As a consequence, the requested content should be available close to the players’ locations, and, in some cases, even follow the players as they move in the city. Overall, there are many ways in which the Gnome Trader game mechanics depend on the performance of the network. The experiments presented in this section have a dual goal of assessing the performance of the FLAME platform, and ensuring the game requirements are met to provide the best player experience.
Sample 1Sample 2
Experiment Design. The main goal of the PMM experiment is to test an adaptive follow-me media streaming services across multiple devices and locations in the Smart City. This goal translates into investigating how the FLAME platform can impact on personalized media distribution with a specific interest in four main aspects: A detailed description of the experiment rationale, narrative storylines, stakeholders and requirements has been provided in Deliverable D3.1: FMI Vision, Use Cases and Scenarios (v1.1) [1]. Starting from that standpoint, the PMM experiment has been designed to cover incrementally up to three scenarios to be deployed in the City of Barcelona with increasing system complexity and number of involved end-users: • Scenario 1: PMM distribution in walking areas in Barcelona, i.e. my screen & preferences follow me from home to my smart hand-held devices to continue media consumption while walking in the Smart City • Scenario 2: PMM on aggregation areas of the Smart City, i.e. my media follow me also in aggregation area (e.g. shop, cafeteria, and mall), and surrogate functions for media distribution are allocated in edge nodes for more users. • Scenario 3: PMM in digital signage posts, i.e. access to media contents from large public events in the Smart City at digital signage posts and swipe them in the personal device. The planned experiment size and key demonstration steps are described in the following Error! Reference source not found. for the three scenarios. Experiment scenario # Scale Key demonstration points 1 Very Small 1-5 users “My screen follows me” from home to smart hand-held devices in the Smart city • User swipes media from a fixed video/audio device at home to personal mobile devices (e.g. tablets, smartphones) and move within the FLAME urban area • The XXX application is capable to invoke the FLAME platform APIs to instantiate content caches and media service chains to continue streaming on the move • [Complimentary] My preferences follow me: I can resume my playlist (music or video) from where I paused while I’m on the move 2 Small 10-50 users “My screen follows me” from home to public aggregation areas in the Smart City • User swipes media from a fixed video/audio device at home to personal mobile devices (e.g. tablets, smartphones) and move within the FLAME urban area • User uses public transportation or is in an aggregation area (e.g. shop, cafeteria, mall) and FLAME content caches and service chains are re-allocated to serve him/her 3 Live ...
Sample 1
Experiment Design. Definition of the goals Experimental setup
Sample 1
Experiment Design. The videos were assessed through a video HRI study. The study was designed as a 1 by 3 between-participants design with random assignment of participants. The independent variable is the sound condition which has three levels: no sound, a simple beeping sound, or the use of musical utterance. The “no sound” condition serves as a baseline to see to what extent the situational context signals the robot's intent. We also included a condition where the robot would use a simple beeping sound to assess whether the complexity of music improves emotion elicitation or intent communication.
Sample 1
Experiment Design. We designed a three-hour qualitative study aimed at designing creative and easily understandable robot movements when dealing with social errors. To achieve this, we collaborated with movement experts such as dancers and improv actors. We tasked them with portraying both the Harmony robot and hospital stakeholders, acting out three distinct social error scenarios that the robot might encounter while navigating the corridors. These scenarios included a navigation mistake, interrupting the hospital's calmness, and not meeting people's social expectations. To control the expressive range of the performers when portraying the robot's role, we imposed limitations on their modalities, such as allowing them to use only one arm or restricting changes to their shape and size. Additionally, we are also interested in looking at how the performers make use of the robot's sound modalities when communicating with the "stakeholders," as this could inspire future studies on the IDM Harmony robot's sound.
Sample 1
Experiment Design. At the specified time and date of the experiment, participants were invited to remotely log into a project PC that was connected to the university network using TeamViewer (a software application for remote control, desktop sharing). For this purpose, a Dell Alienware Aurora R8 was set up as follows: IntelⓇ Core™ i7-6700K CPU @4 GHz, 64GB RAM, 2 x GeForce RTX 2080 Super (Base clock: 1650 MHz, 8 Gb of GDDR6 Memory, and 3,072 CUDA cores), running Windows 10 Pro (1904), Visual Studio 2017 version 15.9.17, and Unity version 2018.4.12f1 (LTS). An average internet provider speed of Up = 901.41 Mbps (SD = 41.08), Down = 521.46 (SD = 11.35), and Ping = 1.6 ms (SD = 0.49) were measured at the PC before each session. The study followed a two-step scenario testing strategy. The first scenario involved creating a simulated crowd scene and the second involved retargeting the crowd from the first scene to a semantically similar one. Two crowd simulation scenes were therefore created, each serving as a problem discovery measure for the new crowd simulation tools. This also allowed a comparison of re-targeting time between the two scenes. Users were provided with descriptions for what should be achieved in each scene, along with a brief video tutorial on how to use our tools in the Unity Editor. The tasks in the steps document were a decomposition of how to create the simulated crowd scene. Participants were allowed to ask questions about how to complete these tasks. Specific information on the result they should aim to achieve was also provided. The opportunity to discuss and analyze these procedures with a project researcher post-task ensured that the participants fully understood how the tools worked and could provide an informed evaluation. Participants were allowed a 30-minute break between the creation of each scene. Figure 1: Scenario 1 - Trinity College “Metropolis” Model The first scenario uses the “Metropolis” 3D model [Figure 1]. The participants were asked to construct the scene with the following specifications: 1. Create 6 static groups of agents in the main square of the 3D model, containing 5, 4, 6, 3, 8, and 7 crowd members respectively. Each group should play an idle/chat/phone call/texting animation but not move locations throughout the simulation. 2. Create two crowd members with a walk animation that enter through the main front arch and navigate to a window in front of the “Rubric” game object. They should then trigger a “wash windows” animation. 3. ...
Sample 1
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Experiment Design. The experiment began with the assignment of Treatment and Control groups. An in- dividual would be assigned to treatment groups if he received job-related training or self-improvement in skills & knowledge within three months before the survey. The remaining individuals would be automatically assigned to control groups. Figure 3.1 presents the details of eight treatment-control pais. Figure 3.1: Observations in Control Group and Treatment Group (Propensity Score Matching Experiments) The propensity score is then estimated by the “pscore" algorithm in Stata. The logit ap- proach will be used to estimate the propensity score. According to Xxxxxxxx & Xxxxxxxx (2008), the logit approach should be more robust than the multinational probit approach and could also reduce the misspecification. In order to avoid the over-parameterisation (Xxxxxx et al. 2002), this paper followed Xxxxxxxx & Xxxxxxx’x (2001) approach to include at least three variables selected from the following three categories: Personal Information, Family Information, and Education information. Moreover, according to Xxxxxx & Xxxxxx (2002), the following two conditions must be met in order to ensure unbiased propensity score estimation: (1) the propensity score must be balanced in each block, and (2) the interval of estimated scores should be sufficient for all propen- sities. Therefore, this paper conducted propensity score estimation on each of the eight Treatment-Control Pairs to generate balanced scores. The details of propensity score estimations and subsequent results are listed in table 3.8. Sub- experiment Target Year Ethnicity Treatment Treatment- Control Pair Pre-treatment Characteristics Range of Propensity Score Pseudo-R2 Number of Blocks 1 2010 White Job-training Pair1 Age, Higher Education indicator, Family macro [0.321,0.544] 0.0156 26 2 2010 Non-white Job-training Pair2 General information macro, Family macro, higher education indicator [0.0467, 0.575] 0.0695 5 3 2010 White Self-improve Pair3 Maternity indicator; Marital status, Higher education indicator, Family macro, head of household indicator [0.1923, 0.5102] 0.0374 5 4 2010 Non-white Self-improve Pair4 Age, gender, marital Status, higher Education Indicator, Maternity leave indicator, Household Children indicator, job-training [0.2016, 0.6006] 0.0426 9 5 2016 White Job-training Pair5 Marital Status, Maternity leave indicator, higher education indicator, Household Children indicator, Skills & Knowledge Improvement [0.087, 0.6...
Sample 1
Experiment Design. Collecting a large enough sample of perceptual ratings to be able to evaluate both inter-rater agreement and human- automated reliability is challenging. For example, for lis- teners to rate 5 recordings requires making judgments of sets of individual features for 5 recordings (1, 2, 3, 4, 5), similarity for 10 different pairs (1 vs. 2, 1 vs. 3, 2 vs. 3, etc.) or 10 different triplets (1 vs. 2 vs. 3, 1 vs. 2 vs. 4, etc.), which takes approximately 30 minutes. How- ever, human judgments for only 5 recordings would not be enough to meaningfully compare with automated al- gorithms. On the other hand, increasing the sample to 10 recordings would require rating 10 sets of features, 45 pairs, and 120 triplets, which is already more than can be collected within the course of a 1-hour experiment, espe- cially when accounting for listener fatigue. If we attempt to spread out the data collection across multiple different participants by having different participants rate different recordings, we lose the ability to compare inter-rater agree- ment between participants. Unfamiliarity, use/absence of reference tracks, and order effects can also affect percep- tion of similarity. To balance the need for enough data to compare both human-human and human-automated agreement, we de- signed an experiment where we divided the set of 30 di- verse recordings previously used to evaluate inter-rater agreement into 6 sets of 5 recordings. For each set, we collected perceptual judgments of all possible features, pairs, and triplets from 10-11 participants per set (total n = 62 participants). The 62 participants were divided into 6 groups, where all members within each group rated the same 5 songs from the 30-song dataset. Each experiment lasted approximately 20-30 minutes and was divided into three blocks: feature evaluation, pair- wise evaluation and triplet (odd-one-out) evaluation. Be- fore beginning the experiment, participants are played a se- xxxx of reference tracks taken from the Cantometrics train- ing tapes in order to familiarize them with the features they would be rating and the types of recordings they would be asked to rate. Participants then evaluate a set of features for each song after listening to each song at least once, after which they performed the triplet and pairwise sim- ilarity tasks. The order of the triplet and pairwise blocks, and the order of songs/combinations within each block was randomized so as to negate order effects, but the feature evaluation...
Sample 1

Related to Experiment Design

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  • Research Use The Requester agrees that if access is approved, (1) the PI named in the DAR and (2) those named in the “Senior/Key Person Profile” section of the DAR, including the Information Technology Director and any trainee, employee, or contractor1 working on the proposed research project under the direct oversight of these individuals, shall become Approved Users of the requested dataset(s). Research use will occur solely in connection with the approved research project described in the DAR, which includes a 1-2 paragraph description of the proposed research (i.e., a Research Use Statement). Investigators interested in using Cloud Computing for data storage and analysis must request permission to use Cloud Computing in the DAR and identify the Cloud Service Provider (CSP) or providers and/or Private Cloud System (PCS) that they propose to use. They must also submit a Cloud Computing Use Statement as part of the DAR that describes the type of service and how it will be used to carry out the proposed research as described in the Research Use Statement. If the Approved Users plan to collaborate with investigators outside the Requester, the investigators at each external site must submit an independent DAR using the same project title and Research Use Statement, and if using the cloud, Cloud Computing Use Statement. New uses of these data outside those described in the DAR will require submission of a new DAR; modifications to the research project will require submission of an amendment to this application (e.g., adding or deleting Requester Collaborators from the Requester, adding datasets to an approved project). Access to the requested dataset(s) is granted for a period of one (1) year, with the option to renew access or close-out a project at the end of that year. Submitting Investigator(s), or their collaborators, who provided the data or samples used to generate controlled-access datasets subject to the NIH GDS Policy and who have Institutional Review Board (IRB) approval and who meet any other study specific terms of access, are exempt from the limitation on the scope of the research use as defined in the DAR.

  • Research Plan The Parties recognize that the Research Plan describes the collaborative research and development activities they will undertake and that interim research goals set forth in the Research Plan are good faith guidelines. Should events occur that require modification of these goals, then by mutual agreement the Parties can modify them through an amendment, according to Paragraph 13.6.

  • Research Primary Investigator as part of a multi-site study (25 points) • Co-Investigator as part of a multi-site study (20 points) • Primary Investigator of a facility/unit based research study (15 points) • Co-Investigator of a facility/unit based research study (10 points) • Develops a unit specific research proposal (5 points) • Conducts a literature review as part of a research study (5 points)

  • Development Work The Support Standards do not include development work either (i) on software not licensed from CentralSquare or (ii) development work for enhancements or features that are outside the documented functionality of the Solutions, except such work as may be specifically purchased and outlined in Exhibit 1. CentralSquare retains all Intellectual Property Rights in development work performed and Customer may request consulting and development work from CentralSquare as a separate billable service.

  • Clinical 1.1 Provides comprehensive evidence based nursing care and individual case management to a specific group of patients/clients including assessment, intervention and evaluation. 1.2 Undertakes clinical shifts at the direction of senior staff and the Nursing Director including participation on the on-call/after-hours/weekend roster if required. 1.3 Responsible and accountable for patient safety and quality of care through planning, coordinating, performing, facilitating, and evaluating the delivery of patient care relating to a particular group of patients, clients or staff in the practice setting. 1.4 Monitors, reviews and reports upon the standard of nursing practice to ensure that colleagues are working within the scope of nursing practice, following appropriate clinical pathways, policies, procedures and adopting a risk management approach in patient care delivery. 1.5 Participates in xxxx rounds/case conferences as appropriate. 1.6 Educates patients/carers in post discharge management and organises discharge summaries/referrals to other services, as appropriate. 1.7 Supports and liaises with patients, carers, colleagues, medical, nursing, allied health, support staff, external agencies and the private sector to provide coordinated multidisciplinary care. 1.8 Completes clinical documentation and undertakes other administrative/management tasks as required. 1.9 Participates in departmental and other meetings as required to meet organisational and service objectives. 1.10 Develops and seeks to implement change utilising expert clinical knowledge through research and evidence based best practice. 1.11 Monitors and maintains availability of consumable stock. 1.12 Complies with and demonstrates a positive commitment to Regulations, Acts and Policies relevant to nursing including the Code of Ethics for Nurses in Australia, the Code of Conduct for Nurses in Australia, the National Competency Standards for the Registered Nurse and the Poisons Act 2014 and Medicines and Poisons Regulations 2016. 1.13 Promotes and participates in team building and decision making. 1.14 Responsible for the clinical supervision of nurses at Level 1 and/or Enrolled Nurses/ Assistants in Nursing under their supervision.

  • Development Plan document specifying the work program, schedule, and relevant investments required for the Development and the Production of a Discovery or set of Discoveries of Oil and Gas in the Concession Area, including its abandonment.

  • Schematic Design See Section 2, Part 1, Article 2.1.4, Paragraph 2.1.4.2.

  • Study An application for leave of absence for professional study must be supported by a written statement indicating what study or research is to be undertaken, or, if applicable, what subjects are to be studied and at what institutions.

  • Development Program RWJPRI shall be [**] and have [**] in consultation with the JDAC, to select LICENSED COMPOUNDS which shall then be designated PRODUCTS for further DEVELOPMENT by RWJPRI and marketing by ORTHO and its AFFILIATES. RWJPRI shall provide KOSAN with written notice of its decision to select a LICENSED COMPOUND for DEVELOPMENT. Once a PRODUCT has been selected for further DEVELOPMENT, RWJPRI, with the advice of the JDAC, shall have the [**] right to develop the PRODUCT through STAGES O, I, II and III and shall have the [**] right to prepare and file, and shall be the owner of, all applications for MARKETING AUTHORIZATION throughout the world. During such DEVELOPMENT efforts, KOSAN will assist RWJPRI as may be mutually agreed, at RWJPRI's expense, in chemical development, formulation development, production of labeled material and production of sufficient quantities of material for STAGE O and initial STAGE I studies. RWJPRI shall exercise diligent efforts, commensurate with the efforts it would normally exercise for products with similar potential sales volume and consistent with its overall business strategy, in developing such PRODUCT in accordance with the DEVELOPMENT PLAN established by RWJPRI. In the course of such efforts RWJPRI shall, either directly or through an AFFILIATE or SUBLICENSEE to which the license shall have been extended, take appropriate steps including the following: (i) in consultation with the JDAC, select certain LICENSED COMPOUNDS for STAGE O DEVELOPMENT; and (ii) establish and maintain a program reasonably designed, funded and resourced to obtain information adequate to enable the preparation and filing with an appropriate and properly empowered national regulatory authority all necessary documentation, data and [**] CERTAIN INFORMATION IN THIS EXHIBIT HAS BEEN OMITTED AND FILED SEPARATELY WITH THE COMMISSION. CONFIDENTIAL TREATMENT HAS BEEN REQUESTED WITH RESPECT TO THE OMITTED PORTIONS. other evidence required for IND non-rejection to commence and conduct human clinical trials of such PRODUCT. (iii) proceed following IND non-rejection to commence PHASE I, II, and III clinical trials, associated studies and such other work which RWJPRI reasonably deems to be required for subsequent inclusion in filings for MARKETING AUTHORIZATION; (iv) after such submissions are filed prosecute such submissions and file all reasonably necessary, reports and respond to all reasonable requests from the pertinent regulatory, authorities for information, data, samples, tests and the like.

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