Datasets Sample Clauses

Datasets. This Data Use Agreement (“Agreement”) is made and entered into by and between the SOUTH ALAMO REGIONAL ALLIANCE FOR THE HOMELESS (“Covered Entity”), a 501(c)(3) non-profit organization in the State of Texas, and Insert Name of Entity (“Data Recipient”), a [insert description of legal status (public / private / profit / non-profit / company incorporated in what state, LLP or LLC or other entity incorporated or registered in what state)] on (Today’s Date) for the purpose of collecting and analyzing Homeless Management Information System (HMIS) data from ▇▇▇▇▇.

Datasets. Pilot 4a consists of four datasets from the area of Milan, Italy: ● Microgrid PV power production and forecast (MicroGridPVPilot4a): consists of forecasting and modeling of Photovoltaic (PV) power. The dataset is expected to grow with more than 30K records per day, and the updates are per minute. ●Microgrid battery (MicroGridBatteryPilot4a): comprises observations of batteries described in terms of State of Charge (SOC), State of Health (SOH), Direct Current (DC), and Alternate Current (AC). Current and voltage are registered, as well as average cell temperature and average ambient temperature. This dataset grows in 86K records per day, and new observations arrive per 1 sec. ● Microgrid potable water production (MPWPPilot4a): contains relevant measurements of a plant for potable water production. The dataset collects active and reactive power values, frequency of pump rotation, feed and permeate water conductivity, concentrate and permeate water flow rate, and temperature and pressure in the hydraulic circuit. It has a growth trend of 1,440 records per day, and updates are per minute. ●Microgrid weather parameters (MicroGridWeatherStationPilot4a): consist of observations sensed by a weather station. It reports ambient temperature, wind speed, wind direction, relative humidity, rain, and irradiance. The growth trend is 65K records per day, and observations are registered every 10 seconds. ● Microgrid full skype imaging (MicroGridFSIPilot4a): comprises full-sky images in JPEG format. It grows in more than 250 records per day every 5 minutes.

Datasets. Pilot 2a focuses on integrating and deploying different PLATOON analytical services with the Institute ▇▇▇▇▇▇▇ ▇▇▇▇▇ (IMP) proprietary VIEW4 Supervisory control and data acquisition (SCADA) system deploys the energy value chain in Serbia. Energy resources related to Renewable Energy Sources (RES) in this pilot include: wind power plants and PV power Plants. Electricity production from solar and wind plants is subject to forecast errors that drive demand for balancing. These data sources are described as follows: PUPIN-RES-PROD: Historical Wind Power Production Measurements; it contains measurements of the production from the wind power plant, as well as topology data. These four data sources composed the catalog EBPM (Electricity Balance and Predictive Maintenance); they provide data in English (ENG) and Serbian (RS). More details in D2.4.

Datasets. We tested our methods using two real datasets Gowalla [1] and California [64]. Gowalla contains check-in information of users of a location-based social network in New York. The check-ins consist of time and location coordinates of users at different points of interests (POIs). We use the coordinates of 19483 points in our experiments. California dataset contains the location coordinates of 104770 points of interest in California. Table 4.1 shows the summary of databases created for each dataset along with the computational cost for retrieval of a block of data from each database (i.e. per PIR Request). The size of data points (i.e. description, images, etc) is set to 1KB in all of our experiments.

Datasets. The Parties intend to generate Datasets under the framework of open data. The Parties consider sharing Datasets necessary as it will enable the Parties to deliver expected outcomes. The purpose and the methodology for the creation of Datasets shall be described in the respective deliverables or reports. The Parties shall publicly share Datasets arising from the Project as close to real-time as possible per customary research publication norms. The Parties shall share such Datasets under the “no copyright reserved" option in the Creative Commons toolkit – CC0. If another Party’s Results or Background is to be published, such shall only occur, provided such Party has approved this beforehand.

Datasets. To validate the proposed approach, we have consid- ered five different benchmarks, namely: Handwritten digits dataset (MNIST), ▇▇▇▇▇▇▇, ▇▇▇▇▇▇▇▇▇, Animals with At- tributes 2 (AwA2), Street View House Numbers (SVHN). Figure 3 shows some image samples from these data sources. MNIST [28] dataset consists of 70000 images with dimen- sion 28 28. The dataset has been split in 60000 and 10000 images for training and testing respectively. The dataset is a collection of greyscale images of handwritten numbers clas- sified among 10 classes. CIFAR10 [25] is a well known standard dataset for im- age recognition experimentation, it consists of 60000 im- ages from 10 classes of objects from different contexts. We maintain the dataset split in training and test suggested by the dataset authors: 50000 images in training set and 10000 images in test set. The images have dimension 32 32 and they are defined over three colour channels (RGB colour space). Model MNIST SVHN ▇▇▇▇▇▇▇ ▇▇▇▇▇▇▇▇▇ ▇▇▇▇ Baseline CapsNet AA-Caps (Ours) 99.67% (100E) 99.34% (100E) 93.23% (100E) 92.13% (100E) 68.70% 71.60% 89.56% (50E) 89.72% (50E) 12.1% (100E) 23.97% (100E) Table 1. Summary of evaluation results. The model is validated over different bechmark to prove the contribution provided respect to the original CapsNet. We present results obtained with MNIST, SVHN, ▇▇▇▇▇▇▇, ▇▇▇▇▇▇▇▇▇, and AwA2 datasets. CapsNet (Baseline) Conv - Primary Capsules - Final Capsules 8.2M 99.67% AA-Caps (Ours) Conv - Primary Capsules - Self-Attention - Conv 6.6M 99.34% Table 2. Comparison of CapsNet model with AA-Caps . The table presents a brief description layers that compose the structure of baseline CapsNet compared to the structure of AA-Caps, the number of trainable parameters, and the accuracy achieved by the model after 100 epochs on MNIST dataset. SmallNORB [29] consists of 24300 image 96 96 stereo grey-scale images defined over 2 colour channels. We re- sized the images to 48x48 and during training processed random 32x32 crops, and central 32x32 patch during test. AwA2 [51] consists of 37322 images of 50 animals classes. The images are collected from public sources, that makes the dataset challenging due to the uncontrolled images.

Datasets. Pilot 3a is about an office building equipped with a building management system (BMS) that controls HVAC and comfort in multiple zones of the building. This pilot includes LLUC 3a- 01 - Optimizing HVAC control regarding occupancy, and LLUC 3a-02 - Providing Demand Response Service through HVAC control.

Datasets. The study will analyze sets of 10-year climate retrospective forecasts, also known as decadal re-forecasts or hindcasts, which were produced as part of the EU-funded ENSEMBLES project (▇▇▇▇▇▇-▇▇▇▇▇ et al. 2010). The experimental setup is at the heart of the experimental design of the decadal prediction component of the ongoing Fifth Coupled Model Intercomparison experiment (CMIP5), which will contribute to the next IPCC Assessment Report (AR5). The use of the ENSEMBLES decadal re-forecasts allows addressing several model-dependent conclusions. Two contributions addressing the problem of model uncertainty, a multi-model and a perturbed-parameter ensemble, will be used. The ENSEMBLES multi-model re-forecasts consist in 10-year long ensemble dynamical forecasts initialized once every five years over the period 1960-2005 (i.e. 1960, 1965 …), and have three members per model and start on November 1st of each start date. The multi-model ensemble were produced by four European research centres: the European Centre of Medium-Range Weather Forecasts (ECMWF, UK), the Met Office-▇▇▇▇▇▇ Centre (UKMO, UK; with the HadGEM2 climate model), IFM- GEOMAR (Germany) and CERFACS (France). The perturbed-parameter ensemble is known as Met Office Decadal Climate Prediction System (DePreSys; ▇▇▇▇▇ et al. 2007, 2010) and was run using a nine-member ensemble of HadCM3 model variants. In order to assess the impact of initialization, two sets of decadal re-forecasts were run with and without initializing the contemporaneous state of the climate system; these re-forecasts will be referred to as DePreSys and NoAssim, respectively. On the other hand, the study will also analyze decadal re-forecasts performed at IC3 that are the corresponding contribution to the CMIP5 experiment. These decadal re-forecasts were performed with the climate model EC-EARTH (▇▇▇▇://▇▇▇▇▇▇▇.▇▇▇▇.▇▇/); the use of EC-EARTH allows the project to have a suitable tool as seamless climate prediction system (Hazeleger et al. 2010). These decadal integrations aim at exploring some indication of regional decadal predictability beyond the slow and relatively predictable warming of the planet by opening the possibility of forecasting low-frequency internal climate variability. The objective of this research is to evaluate the predictability of the low-frequency variability in the WAM rainfall.

Datasets. The proposed methods were evaluated on three language pairs: French-English (Fr-En), German- English (De-En), and Japanese-English (Ja-En). Fr-En and De-En are similar European language pairs. We used 30 million sentences from the WMT monolingual News Crawl datasets from 2007 to 2013. Ja-En is a distant languages pair and so UBWE training is much more difficult than for similar European language pairs (Søgaard et al., 2018). In addition, Japanese and English are different language families and their word orderings are quite different. As a result, the performance of Ja-En UNMT is too poor to further empirical study if only pure monolingual data are used. Therefore, we constructed simulated experiments using shuffled parallel sentences, i.e., 3.0M sentence pairs from the ASPEC corpus for Ja-En. We reported the results on WMT newstest2014 for Fr-En, WMT newstest2016 for De-En, and WAT-2018 ASPEC testset for Ja-En.