Experimentation. (1) The Board and RAP recognize the need for experimentation and innovation in programs and techniques effecting unit members and agree to cooperate in the implementation thereof.
Experimentation. The Board and the Association recognize the need for experimentation and innovation in instructional programs and techniques and agree to cooperate in the implementation thereof.
Experimentation. A simulator that allows to control both robot and human behaviors was developed. Here, positions and velocities can be controlled. Then, it is employed to generate trajectories of robot while approaching humans. The robot is manually controlled during different approaching scenarios. A set of demonstrations was performed in this experimental platform for the learning process. The path taken by the robot in different positions with different orientations can be seen in Figure 3. This represents the path followed by the robot in the human reference frame. Figure 3: Demonstration of the robot approaching the target person.
Experimentation. Innovation involves change, new ideas, experimentation, and some risk of failure. Experiments that will help us achieve environmental goals in better ways are worth pursuing when success is clearly defined, costs are reasonable, and environmental and public health protections are maintained.
Experimentation. Expedient provides a useful graphical interface to the users for managing their experiments. Through Expedient, a user can create a project (a container for experiments) by sending a request to the administrator. Once the project is created, the creator can add other users to the project so that they can also perform experiments in that project. The user can also add aggregate managers or RMs to the project, that is, the resources that will be available for the experiments. The experiments are created as slices. AGer creating a slice, a user can select from AMs provided by that project for use in experiment. Once included, the resources of these AMs are graphically displayed in Expedient along with the links between them. Monitoring A new GUI section for the Expedient has been created in XXXXX project which provides information about the status of the components and the slice topology.
Experimentation. Adaptive management is strongly rooted in scientific experimentation. By specifically designing experiments into management actions, conclusions can be drawn that help develop better resource management decision making. Experimentation in Battle Creek is embodied in three ways, where experimentation (1) has been a component of adaptive management problem definition and solution development, (2) is embodied in the overall Adaptive Management program as envisioned in this document, and (3) may be conducted as part of individual Adaptive Management objectives considered under this plan within the established protocols.
Experimentation. Mission-Oriented innovation policy should lead to extensive experimentations of possible solutions to the problem identified. This responds both to a logic of risk management (different solutions, with different levels of risk and reward, should be tried at the same time), and to a logic of more inclusive innovation policy (the whole EU community or researchers and innovators should potentially be involved in trying to find a solution to the problem). Experimentation could follow two tracks: • Track 1: Experimenting with new technologies/business models/delivery modes, and blending funding instruments and schemes to run experiments. This could happen on a “prize” basis, or on a more top-down selection of possible paths (e.g. technology roadmap), or both. For example, the replacement of general practitioners with online, constantly available bots could be subject to experimentation with a sample of patients, carefully selected; the same could happen for the procurement of local solutions to CO2 emissions or water draught; or the application of blockchain to electoral systems or land registries. At a more basic research stage, alternative therapies for Alzheimer could be developed and tested to have a chance to speed up scientific breakthrough (e.g. Repetitive Transcranial Magnetic Stimulation, or rTMS). The expectation is that most of these attempts will fail, and a few will lead to results. In terms of instruments, the expectation is that missions will be able to tap into various sources of funding, including research funds, EIC funds, EIB, InvestEU, structural and cohesion funds, national funds made available on a voluntary basis by Member States and even non-EU countries (in the spirit of “Open to the World”), and private funds (partnerships): the ability to blend different forms of funding shall be considered as essential to the skills and activity of the mission.
Experimentation. In order to accurately understand the role of hormone status and relative levels of progesterone-derived neurosteroids, it was important to have the mice on a normal estrous cycle. This cycle occurs on a shorter time scale than the 28- day human menstrual cycle. A typical cycle will last 4 days and pass through 4 stages, with diestrus experiencing sharp changes in hormone levels and estrus showing lower, more stable levels (Xxxxxxx et al., 1974). For the most part, when female mice are included in an experiment, their hormonal levels do not accurately mimic what they would be in nature. There is a phenomenon called the Xxx-Boot Effect, where females that are in same-sex housing will have suppressed estrous cycles (Xxx xxx Xxx, 1955). The Xxxxxxx Effect has to do with the initiation of ovulation and the regular cycling of ovarian hormones by the presence of male pheromones (Xxxxxxx et al., 1957). For this project, nest materials were taken out of a dirty male cage and placed in a cage that housed a group of females. Ovulation typically happens about a day after the cage is spiked with the male bedding. The females were allowed to naturally cycle for a week before testing began. Estrus females had their bedding spiked about 5-6 days prior to testing, whereas diestrus females had their bedding spiked about 7-8 days prior to testing. On the morning that testing was to take place, vaginal lavage samples were taken by flushing a small amount of saline into the vaginal opening to collect a sample of epithelial cells. The slides were examined to ensure the status of the females, before being allowed to dry prior to staining. Due to the stressful nature of the lavage samples, the males were also individually handled to equate the experiences of the two sexes. After the slides had dried completely, a vaginal cell staining protocol was followed from ScyTek (Papanicolaou Staining Protocol). The smears were then imaged and classified, based on cell cytology information and correlating hormone levels (XxXxxx et al., 2012). The females were then divided into two groups based on cell composition of the vaginal lavage samples and the correlating levels of hormones. Estrus females were classified if the majority of the cells in the vaginal lavage sample were cornified epithelial cells, stained pink; diestrus females were categorized by a predominance of leukocytes, stained dark brown. The first experiment that was carried out was to look at the anxiolytic effect of either o...
Experimentation. OxyChem has committed to perform a study of fugitive mercury emissions from the mercury cell building within its chlor-alkali facility. OxyChem will accomplish this study by installing a MMS Mercury Monitoring System supplied by Mercury Instruments, GmbH, of Germany. This instrument will draw samples from sixteen monitoring points. The system utilizes an Ultra Violet Photometer to detect mercury based on the UV absorption of mercury at 253.7 nm. Pilot studies conducted by OxyChem utilizing this technology have produced favorable results. Four (4) sample points will be monitored in each of the four quadrants of the cell building to provide a baseline for establishing average mercury concentrations that will be correlated with representative air flow calculations for the cell building. The monitored concentrations will be integrated into the facility’s realtime DCS that calculates air flow through the building. This is expected to result in the ability to estimate the mass of fugitive mercury emissions emitted from the cell building via the roof ridge vent system. The data from the individual monitoring points will be analyzed to localize instances of higher mercury concentrations. This localization will provide the ability to analyze what activities may be causing increased emissions of fugitive mercury. These activities can then be studied to reduce these emissions.
Experimentation. To demonstrate the effectiveness of cause and effect phrase extraction when using Causal Cue Phrases, we propose to compare accuracy of two Conditional Random Fields Relations Learning Algorithms (Xxxxxx and XxXxxxxx, 2006) trained to perform a token level labeling of cause and effect phrases. One of these CRFs will be trained given access to CCP knowledge in the form of a single additional Boolean feature. In all other respects these two algorithms will be identical. Because it is only possible to extract correct cause/effect phrases from sentences containing a causal relation, the training set for CRF will consist of all causal sentences in the corpus. Training and testing for CRF will take place using a ten-fold cross validation method. Both algorithms will perform extraction on a token level, classifying each token as one of three distinct classes: cause (part of cause phrase), effect (part of effect phrase), and non-causal (part of neither cause nor effect phrase). To evaluate the performance of each algorithm, a phrased based accuracy metric will be employed. An algorithm will be said to have correctly identified the cause phrase of a sentence if the cause phrase it identified overlaps with expected cause phrase but does not overlap with the expected effect phrase. Similarly, to correctly identify the effect phrase, it is necessary to overlap with the expected effect phrase but not the expected cause phrase. Additionally, we employ a token based accuracy metric to provide a stricter evaluation. Each token is said to be correct if matches with the expected class label. One drawback of using such a token based accuracy metric is the ability to get rather high accuracy scores simply by marking every token as Non-Causal. To combat this, we introduce a third accuracy metric, termed Focused Token Accuracy which resembles the usual token based accuracy metric only it is computed only over the accepted cause and effect phrases. This further step eliminates all Non- Causal tokens, and reveals how well each algorithm is able to perform attempting to label only cause and effect phrases. The CRF will be trained with the following feature set: Part of Speech: the part of speech of the current token Stem: the stem of the current token VP or NP: whether the current phrase (most direct parent) is a noun phrase or verb phrase Adjacent word information: the words and features of 3 adjacent tokens For the CRF trained with CCP knowledge, we add a single additional featu...
