Experiment Setup. A prototype of the architecture is underway to validate the peer based semantic processing approach. In the initial setup, two Sun Workstations W1100 running under SuSE Linux9.3, the QEMU emulation software are used to to create virtual machines support by the VDE software are used to provide virtual network for peer simulation purposes. The main objective of the initial setup is to test the communication layer between peers of P2P architecture. Next, the Prot g tool suite has been installed and tested is on some virtual machines. The goal is to test access to some sourcess of peers through HTTP. Prot g is a tool for developing common ontology and local schemas/ ontologies using OWL and several plugins. Query is executed through Query tab and SPARQL in Prot g . The rst evaluation has shown that the virtual machine and the network simulation is not very appropriate because the respond and performance is very low. An alternative is to implement a virtual web server is implemented for our experiment scenario. Experiment Results. In our evaluation scenario there are three peer providers which have di erent concepts and classi cations of classi cation of the road concept as discussed in the example above, and two other provider peers in di erent interest (business and education). A Common ontology based on the Ontology of Transportation Networks (OTN), ISO 19107, Ge- ographic Data Files (GDF), and National Road Network Canada. There are 103 classes, 37 object properties, and 89 data properties. The local ontology are developed from as a combination some sources. In the initial evaluation the contents of the ve provider peers are as follows. PP1 has 22 classes, and around 50 individuals. PP2 has 17 classes, and around 50 individuals. PP3 has 10 classes, and around 50 individuals. PP4 has 23 classes, and around 50 individuals. PP5 has 27 classes, and around 50 individuals. The result of agreement unit development gave agreement in a commu- nity is provide better agreement unit result compare to di erent community. Development of agreement unit in the experiment was for simple agreement, complex agreement need to implement by waiting maturity of tool. The result of sending query in two step method increase better query respond compare to one step method. The initial results su er from poor performance which it will require an improvement of the simulation of the underlying communication systems among peers.
Experiment Setup j xxxxx://xxxxxx.xxx/lpaparusso/ZAPP
1) Task: We consider the control task complete when the ego agent has traveled 28 m horizontally from the start e(Ge (k)), G vert to H-representation, (Ay(k), by(k)), as in (7c). Z pobs , pos y ,j obs . We apply Prop. 2 to con- 1 j 2xxxxx://xxxxxxx.xxxxxxxxxxx.xx/en/stable/ Method MATS [6] MATS [6] w/o XX XXXX w/o XX XXXX w/o Int. ZAPP (ours) G/C [%] 70.0 / 30.0 53.3 / 46.7 86.7 / 13.3 46.7 / 53.3
Experiment Setup. Datasets. We use two real datasets in our experi- ments: Brazil Census dataset (xxxxx://xxxxxxxxxxxxx.xxxxx.xxx) and US census dataset (xxxx://xxx.xxxxx.xxx). The Brazil census dataset has 188,846 records after filtering out records with missing values and eight attributes are used for the experiments: age, gender, disability, nativity, working hours per week, education, number of years residing in the current location, and annual income. We generalized the domain of income to 586. The US Census dataset has a randomly selected 100,000 records from the original 10 million records and all four attributes are used: age, oc- cupation, income and gender. Table 3.3 shows the domain sizes of the datasets. For
Experiment Setup. Datasets. We conducted our experiments with three datasets: the US census (http: //xxxxx.xxx), the Taxi-Drive trajectory data (xxxx://xxxxxxxx.xxxxxxxxx.xxx/ apps/) and the Xxxxxxxxx traffic data [30]. 4.5. EXPERIMENT 61 Table 4.2. Experiment Parameters Parameter Description Default value N Number of time points 500 d Number of data dimensions 6 n Number of tuples in Di 500K g Privacy budget 1.0 C Cutoff point 0.01 × N r Update rate 0.5 δ Deviation tolerance 0.05 x Xxxxxxxxxxxx gain 0.5 The US census dataset contains six attributes, Age, Gender, Education, Health insurance, Marital status and Income with 3M tuples and domain sizes of 96, 2, 12, 2, 2, 3. Each tuple represents an individual user. In order to avoid the sparsity of histograms, we convert Income into a categorical attribute: values smaller than 0 (mapped to 1), values between 0 and 28K (mapped to 2), and values larger than 28K (mapped to 3). 28K is a median value. Values smaller than 0 means the tuples have ages smaller than 20. The number of histogram bins are the product of the domain sizes of all attributes. We generate a series of dynamic datasets as follows. Di is the original dataset at ti. D1 has 500K tuples randomly sampled from the original 3M tuples. A public pool is initiated using the remaining tuples. Di (i ≥ 2) is obtained by deleting m tuples from Di−1 while inserting m tuples randomly selected from the public pool to simulate the user updates. m is sampled from N (µ, σ2), where µ is r×|Di−1| , and σ2 is set to 100K. Here, r is the update rate, |Di| is the data cardinality of Di and datasets at all time points have the same data cardinality. The time points are partitioned into 10 periods with different values of m to simulate varying update patterns. All experiments use US census data by default since we can generate various datasets under different parameter settings. The Taxi trajectory dataset has a one week trajectories of 10, 357 taxis during the period of Feb. 2 to Feb. 8, 2008 within Beijing. We transfer the time dimension to 168 time points with 24 × 7. The total number of points in this dataset is about 15 million and the total distance of the trajectories reaches 9 million kilometers. We
Experiment Setup. Datasets. We use two datasets from the Integrated Public Use Microdata Series1, US and Brazil, which contain 370, 000 and 190,000 cen- sus records collected in the US and Brazil, respectively. There are 13 attributes in each datasets, namely, Age, Gender, Martial Status, Education, Disability, Nativ- ity, Working Hours per Week, Number of Years Residing in the Current Location, Ownership of Dwelling, Family Size, Number of Children, Number of Automobiles, and Annual Income. Among these attributes, Marital status is the only categorical attribute whose domain contains more than 2 values, i.e., Single, Married, and Di- vorced/Widowed. Following common practice in regression analysis, we transform Marital Status into two binary attributes, Is Single and Is Married (an individual divorced or widowed would have false on both of these attributes). With this trans- formation, both of our datasets become 14 dimensional. 1Minnesota Population Center. Integrated public use microdata series-international: Version 5.0. 2009. xxxxx://xxxxxxxxxxxxx.xxxxx.xxx.
Experiment Setup. We use systems for the WMT 2017 English to German news translation task for our experiment; these diGer from the WNGT shared task setting pre- viously reported. We use back-translated monolingual corpora (Xxxxxxxx et al., 2016a) and byte-pair encoding (Xxxxxxxx et al., 2016b) to preprocess Baseline 35.66 + Model Quantization 25.2 28.08 33.33 34.92 34.81 35.26 Table 2: 4-bit Transformer quantization performance for English to German translation, measured in BLEU score. We explore diGerent method to find the scaling factor, as well as skipping bias quantization and retraining. the corpus. Quality is measured with BLEU (Xxxxxxxx et al., 2002) score using sacreBLEU script (Post, 2018). We first pre-train baseline models with both Transformer and RNN ar- chitecture. Our Transformer model consists of six encoder and six decoder layers with tied embedding. Our deep RNN model consists of eight layers of bidirectional LSTM. Models were trained synchronously with a dynamic batch size of 40 GB per-batch using the Xxxxxx toolkit (Junczys-Dowmunt et al., 2018). The models are trained for 8 epochs. Models are optimized with Xxxx (Xxxxxx and Ba, 2014). The rest of the hyperparameter settings on both models are following the suggested configurations (Xxxxxxx et al., 2017; Xxxxxxxx et al., 2017).
Experiment Setup. The evaluation of mechanisms within SLARMS has been carried out entirely in CA Lab VMware Vsphere Cloud in- frastructure environment. The experimental setup consists of three types of dynamic resources: small instance (1 GB of memory, 1 CPU core, 50G of local instance storage, Windows OS); medium instance(2 GB of memory, 2 CPU core, 50G of local instance storage, Windows OS); and large instance (4 GB of memory, 4 CPU core, 50G of local instance storage, Windows OS). An enterprise application CA directory is used for experiments. SLA is defined in terms of response (a) (b) (a) (b) times. The experiment evaluation is designed based on the CA CloudMinder test strategy and plan. CloudMinder is an online application that uses CA Directory as the directory foundation. In this set of experiments the total profit, number of accepted users and number of SLA violations are evaluated as follows during the variation of request arrival rate from 20 to 200 requests per second. Up to 200 concurrent user requests are considered because 1) The test strategy provided by CA is designed using 200 user requests, which has been analysed through their customer usage data and 2) The capability of the private data centre allocated to this research work is limited, which does not allow a very large number of user requests.
Experiment Setup. Subjects received breathing interventions and performed all assessments seated upright at a table (18 cm from xiphoid process, 5 cm below xiphoid) in an adjustable chair (Figure 2). For breathing interventions, we used a hypoxic generator (Model HYP-123, Hypoxico Inc, New York, New York) to produce isocapnic oxygen mixtures of Fio2=0.09 or Fio2=0.21, as needed (Figure 2B; described previously in Trumbower et al., 2012). Oxygen reduction was balanced by an increase in percent nitrogen within the mixture. Subjects inhaled the gas mixtures through a non-rebreathing mask to prevent inhalation of room air or exhaled gas. Oxygen concentration was continuously tracked to ensure that inspired oxygen fraction was accurate (OM-25RME; Maxtec Inc.). The rAIH intervention lasted 37.5 minutes per day (fifteen 90-sec hypoxic episodes at Fio2 = 0.09, 1 minute intervals) for 5 consecutive days (D1-D5). The 5-day rSHAM intervention was identical in design, except that Fio2 was held at 0.21 (normoxia). Intervention design is depicted in Figure 3.
Experiment Setup. To test the performance of information extraction, we conduct two experiments. Experiment 1 examines the effectiveness of online learning, and experiment 2 studies the importance of adaptive vocabulary. The driving medical research is brain tumor study, in which pathology reports need to be queried based on demographic data, disease, procedure, etc, in order to locate patients with certain traits. The Human Disease Ontology, Cell Cycle Ontology and NCI Treasure were used as seed vocabulary.
Experiment Setup. Full text of radiology reports, which are complex narration style, and clinical data were extracted from the electronic medical records (Cerner Corp, Kansas City, MO) of 13,248 patients admitted to Emory University Orthopedic and Spine Hospital from 2009-2014. Patient encounters were defined as a hospital admission where both surgery (of the spine, hip, or knee) and a radiology diagnostic study for VTE were performed. A physician manually reviewed each radiology report for diagnosis of a DVT or PE. We use IDEAL- X to analyze the same radiology report under two separate modes: i) controlled vocabulary mode, where the user specifies upfront terminology and contextual information (such as relevant and irrelevant report sections) to be extracted, and ii) online machine learning mode, where all terminology and contextual information is learned incrementally. Performance was analyzed for total radiology reports, and patient encounters (multiple reports per encounter possible).
